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+        }
+    }
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/copy_agg_preds.sh b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/copy_agg_preds.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f159cc8a1c1c34cabf433bcb0e7a21322f1efba1
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/copy_agg_preds.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+
+# Define the source directories (fold_0 to fold_4)
+FOLD_DIRS=("fold_0" "fold_1" "fold_2" "fold_3" "fold_4")
+
+# Define the target directory (validation)
+TARGET_DIR="aggregated_8vx_validation"
+
+# Create the target directory if it does not exist
+if [ ! -d "$TARGET_DIR" ]; then
+  mkdir -p "$TARGET_DIR"
+  echo "Created validation directory: $TARGET_DIR"
+fi
+
+# Loop through each fold directory
+for FOLD in "${FOLD_DIRS[@]}"; do
+  # Check if the fold directory exists
+  if [ -d "$FOLD" ]; then
+    echo "Copying files from $FOLD to $TARGET_DIR..."
+    
+    # Copy all .nii.gz files from the current fold directory to the target directory
+    cp "$FOLD"/margins/8voxels/preds/*.nii.gz "$TARGET_DIR"/
+    
+    echo "Finished copying files from $FOLD."
+  else
+    echo "Directory $FOLD does not exist. Skipping..."
+  fi
+done
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/dataset.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/dataset.json
new file mode 100644
index 0000000000000000000000000000000000000000..c2586759636d74be2f1ca530616548a59ac39eac
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/dataset.json
@@ -0,0 +1,15 @@
+{
+    "channel_names": {
+        "0": "T2",
+        "1": "gradient"
+    },
+    "labels": {
+        "background": 0,
+        "GTVp": 1,
+        "GTVn": 2
+    },
+    "numTraining": 300,
+    "file_ending": ".nii.gz",
+    "name": "Dataset504_midRT_gradient",
+    "description": "Dataset 504 - midRT with Gradient Map"
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/dataset_fingerprint.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/dataset_fingerprint.json
new file mode 100644
index 0000000000000000000000000000000000000000..8df7005eedac0409f180bb78360340838d8bca81
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/dataset_fingerprint.json
@@ -0,0 +1,3027 @@
+{
+    "foreground_intensity_properties_per_channel": {
+        "0": {
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+            "mean": 349.19762434242324,
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+            "percentile_99_5": 2558.91650390625,
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+        },
+        "1": {
+            "max": 1.0,
+            "mean": 0.37307153608378946,
+            "median": 0.2966964840888977,
+            "min": 0.0,
+            "percentile_00_5": 0.01908937655389309,
+            "percentile_99_5": 1.0,
+            "std": 0.2642120030869378
+        }
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+    "network": "OptimizedModule",
+    "num_epochs": "1000",
+    "num_input_channels": "2",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0",
+    "output_folder_base": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 511.0], 'spacing': [0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetMPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 192, 192], 'median_image_size_in_voxels': [92, 310, 309], 'spacing': [1.6126957141129536, 0.826423816135876, 0.826423816135876], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': 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0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}}",
+    "preprocessed_dataset_folder": "/mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist",
+    "save_every": "50",
+    "torch_version": "2.4.0",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/2voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/2voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..cad02cd4dc209377b9a1996478f4b32a40404e27
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/2voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/2voxels/images",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/2voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/2voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/2voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..7babc7e95032b2afad4fceb1f0696a5e9ac960b5
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/2voxels/preds/summary_agg.json
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+}
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+}
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/docker_preds/summary_agg_low.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/docker_preds/summary_agg_low.json
new file mode 100644
index 0000000000000000000000000000000000000000..94fcf4244fe486e5c68f27ac6d3ae3cc350fc9e9
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/docker_preds/summary_agg_low.json
@@ -0,0 +1,512 @@
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/docker_preds/summary_agg_old.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/docker_preds/summary_agg_old.json
new file mode 100644
index 0000000000000000000000000000000000000000..ec82c1b3cf20ddbbbac597a5e0e2843fb807b8be
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/docker_preds/summary_agg_old.json
@@ -0,0 +1,512 @@
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+}
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..d35c3f7715e1c2d38365a5947ffdeecf9c5641fb
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/images",
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+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..a4c95dc94436c0022eab0da26299b0a0a4c228af
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/4voxels/preds/summary_agg.json
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+}
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/6voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/6voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..5e9de4501491798945966a701938c69530291919
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/6voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/6voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/6voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/6voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..94d4b6179ca4cbd9e61da2502f23641584398622
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/6voxels/preds/summary_agg.json
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+}
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/8voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/8voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..397f6bd590f30d18868dcd8522da1946987b8fc9
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/8voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/8voxels/images",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/8voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/8voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/8voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..cbbbefccae41115109964e1b255cdc5ed813fcb4
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/margins/8voxels/preds/summary_agg.json
@@ -0,0 +1,512 @@
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+            "vol_gt2": 2051.5,
+            "HD95_1": 4.272001872658765,
+            "HD95_2": 1.0,
+            "MSD_1": 1.2856215343817392,
+            "MSD_2": 0.3107665001420926
+        },
+        {
+            "PatientID": "195.nii.gz",
+            "TP1": 882.5,
+            "TP2": 64631.0,
+            "vol_sum1": 3727.5,
+            "vol_sum2": 136883.0,
+            "DSC1": 0.47350771294433264,
+            "DSC2": 0.9443247152677834,
+            "vol_gt1": 2205.5,
+            "vol_gt2": 66516.5,
+            "HD95_1": 9.452485052516854,
+            "HD95_2": 1.118033988749895,
+            "MSD_1": 1.9900363879546885,
+            "MSD_2": 0.45860699369806557
+        },
+        {
+            "PatientID": "31.nii.gz",
+            "TP1": 534.2342473068624,
+            "TP2": 10597.391768472726,
+            "vol_sum1": 6417.79442189551,
+            "vol_sum2": 23162.953716021497,
+            "DSC1": 0.1664853101196953,
+            "DSC2": 0.9150293955077634,
+            "vol_gt1": 1607.3583780626948,
+            "vol_gt2": 11599.517448061417,
+            "HD95_1": 22.7803777785887,
+            "HD95_2": 1.8384357739751387,
+            "MSD_1": 7.629040653482625,
+            "MSD_2": 0.7317614914962455
+        },
+        {
+            "PatientID": "83.nii.gz",
+            "TP1": 3511.5,
+            "TP2": 3134.0,
+            "vol_sum1": 10442.5,
+            "vol_sum2": 7757.5,
+            "DSC1": 0.6725401005506344,
+            "DSC2": 0.8079922655494682,
+            "vol_gt1": 4976.0,
+            "vol_gt2": 3952.0,
+            "HD95_1": 2.9154759474226504,
+            "HD95_2": 2.0,
+            "MSD_1": 1.0187153928783446,
+            "MSD_2": 0.5934941368360915
+        },
+        {
+            "PatientID": "114.nii.gz",
+            "TP1": 1680.6846473008916,
+            "TP2": 4094.6319869837507,
+            "vol_sum1": 5960.378170933425,
+            "vol_sum2": 9475.383458224764,
+            "DSC1": 0.5639523530560437,
+            "DSC2": 0.8642672890308315,
+            "vol_gt1": 3075.0476718621576,
+            "vol_gt2": 4891.9096763197,
+            "HD95_1": 5.515307321925416,
+            "HD95_2": 1.5523915512723767,
+            "MSD_1": 2.1917742358355854,
+            "MSD_2": 0.6658551746465613
+        },
+        {
+            "PatientID": "127.nii.gz",
+            "TP1": 148.1602163600985,
+            "TP2": 9296.013575077455,
+            "vol_sum1": 2986.2443608476015,
+            "vol_sum2": 28855.722138348865,
+            "DSC1": 0.0992284612087441,
+            "DSC2": 0.6443098897686694,
+            "vol_gt1": 1070.7215635872346,
+            "vol_gt2": 19115.227914191113,
+            "HD95_1": 17.118419606127212,
+            "HD95_2": 14.329286179305004,
+            "MSD_1": 4.387051985419423,
+            "MSD_2": 3.345640188156686
+        },
+        {
+            "PatientID": "146.nii.gz",
+            "TP1": 156.49999999999997,
+            "TP2": 9406.5,
+            "vol_sum1": 833.0,
+            "vol_sum2": 26460.5,
+            "DSC1": 0.37575030012004795,
+            "DSC2": 0.7109842973488785,
+            "vol_gt1": 497.5,
+            "vol_gt2": 15834.5,
+            "HD95_1": 3.5355339059327378,
+            "HD95_2": 4.716990566028302,
+            "MSD_1": 1.0438228554920257,
+            "MSD_2": 1.3979920004347797
+        },
+        {
+            "PatientID": "115.nii.gz",
+            "TP1": 1053.3376771518747,
+            "TP2": 10119.025154871159,
+            "vol_sum1": 3517.3331053623924,
+            "vol_sum2": 25903.959494687428,
+            "DSC1": 0.598941098610192,
+            "DSC2": 0.7812724658519051,
+            "vol_gt1": 2310.3594355209625,
+            "vol_gt2": 11701.941443187787,
+            "HD95_1": 27.114041363451445,
+            "HD95_2": 19.156803842368223,
+            "MSD_1": 3.244321405360708,
+            "MSD_2": 2.9082704528276824
+        }
+    ],
+    "aggregate_results": {
+        "ConventionalDsc": {
+            "GTVp": {
+                "mean": 0.4333961383497423,
+                "median": 0.5460853115536071,
+                "percentile_25": 0.11604267343648189,
+                "percentile_75": 0.6822812444666521
+            },
+            "GTVn": {
+                "mean": 0.7405166169236409,
+                "median": 0.8508019319954394,
+                "percentile_25": 0.72394109841102,
+                "percentile_75": 0.8986744034013167
+            }
+        },
+        "AggregatedDsc": {
+            "GTVp": {
+                "mean": 0.6763881639301145,
+                "median": 0.6763881639301145,
+                "percentile_25": 0.6763881639301145,
+                "percentile_75": 0.6763881639301145
+            },
+            "GTVn": {
+                "mean": 0.8714315350183938,
+                "median": 0.8714315350183938,
+                "percentile_25": 0.8714315350183938,
+                "percentile_75": 0.8714315350183938
+            }
+        },
+        "HD95": {
+            "GTVp": {
+                "mean": 10.284875296393425,
+                "median": 5.4151901141463705,
+                "percentile_25": 2.8597525614588006,
+                "percentile_75": 11.233568609608845
+            },
+            "GTVn": {
+                "mean": 4.174899987654891,
+                "median": 2.0,
+                "percentile_25": 1.118033988749895,
+                "percentile_75": 3.7241937286826405
+            }
+        },
+        "MSD": {
+            "GTVp": {
+                "mean": 3.3181603654220617,
+                "median": 1.6326770625496572,
+                "percentile_25": 0.9543223719876134,
+                "percentile_75": 2.990239821590444
+            },
+            "GTVn": {
+                "mean": 0.9919992459987141,
+                "median": 0.646165768339603,
+                "percentile_25": 0.39353941654255276,
+                "percentile_75": 1.1778315614746702
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/progress.png b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/progress.png
new file mode 100644
index 0000000000000000000000000000000000000000..ee6d90fe87a76984bc8ea4b0bf994cc10344078d
Binary files /dev/null and b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/progress.png differ
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/training_log_2024_9_17_13_53_18.txt b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/training_log_2024_9_17_13_53_18.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e4c6515fceb24d54a1962f7df382056f4c65608e
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/training_log_2024_9_17_13_53_18.txt
@@ -0,0 +1,6 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/training_log_2024_9_17_13_53_41.txt b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/training_log_2024_9_17_13_53_41.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f9e0528d9dd3624fa6260abf6f46e824011b806a
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/training_log_2024_9_17_13_53_41.txt
@@ -0,0 +1,73 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2024-09-17 13:53:44.184565: Using torch.compile... 
+2024-09-17 13:53:50.209597: Using splits from existing split file: /mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/splits_final.json 
+2024-09-17 13:53:50.211472: The split file contains 5 splits. 
+2024-09-17 13:53:50.211553: Desired fold for training: 0 
+2024-09-17 13:53:50.211613: This split has 240 training and 30 validation cases. 
+2024-09-17 13:53:50.212066: predicting 10 
+2024-09-17 13:53:50.213819: 10, shape torch.Size([2, 127, 510, 511]), rank 0 
+2024-09-17 13:54:58.993424: predicting 108 
+2024-09-17 13:54:59.024196: 108, shape torch.Size([2, 118, 509, 511]), rank 0 
+2024-09-17 13:55:44.131759: predicting 114 
+2024-09-17 13:55:44.165293: 114, shape torch.Size([2, 121, 536, 1007]), rank 0 
+2024-09-17 13:57:17.093997: predicting 115 
+2024-09-17 13:57:17.131926: 115, shape torch.Size([2, 140, 510, 1038]), rank 0 
+2024-09-17 13:58:49.124032: predicting 12 
+2024-09-17 13:58:49.182934: 12, shape torch.Size([2, 133, 509, 511]), rank 0 
+2024-09-17 13:59:41.093498: predicting 127 
+2024-09-17 13:59:41.122552: 127, shape torch.Size([2, 117, 502, 511]), rank 0 
+2024-09-17 14:00:28.632067: predicting 135 
+2024-09-17 14:00:28.661884: 135, shape torch.Size([2, 112, 512, 511]), rank 0 
+2024-09-17 14:01:15.963293: predicting 139 
+2024-09-17 14:01:15.984221: 139, shape torch.Size([2, 115, 510, 511]), rank 0 
+2024-09-17 14:02:15.923387: predicting 141 
+2024-09-17 14:02:15.953555: 141, shape torch.Size([2, 122, 512, 511]), rank 0 
+2024-09-17 14:03:09.737242: predicting 144 
+2024-09-17 14:03:09.813556: 144, shape torch.Size([2, 127, 509, 511]), rank 0 
+2024-09-17 14:04:02.909415: predicting 146 
+2024-09-17 14:04:02.953275: 146, shape torch.Size([2, 140, 510, 511]), rank 0 
+2024-09-17 14:05:00.961942: predicting 171 
+2024-09-17 14:05:01.006584: 171, shape torch.Size([2, 127, 512, 511]), rank 0 
+2024-09-17 14:05:51.166047: predicting 177 
+2024-09-17 14:05:51.232662: 177, shape torch.Size([2, 140, 534, 1040]), rank 0 
+2024-09-17 14:07:21.976022: predicting 178 
+2024-09-17 14:07:22.059680: 178, shape torch.Size([2, 125, 536, 1040]), rank 0 
+2024-09-17 14:08:50.780149: predicting 179 
+2024-09-17 14:08:50.861088: 179, shape torch.Size([2, 120, 509, 511]), rank 0 
+2024-09-17 14:10:03.320232: predicting 195 
+2024-09-17 14:10:03.362391: 195, shape torch.Size([2, 127, 512, 511]), rank 0 
+2024-09-17 14:10:42.362964: predicting 198 
+2024-09-17 14:10:42.381551: 198, shape torch.Size([2, 123, 511, 511]), rank 0 
+2024-09-17 14:11:21.766462: predicting 31 
+2024-09-17 14:11:21.803113: 31, shape torch.Size([2, 128, 536, 995]), rank 0 
+2024-09-17 14:12:40.659723: predicting 32 
+2024-09-17 14:12:40.759523: 32, shape torch.Size([2, 113, 536, 1040]), rank 0 
+2024-09-17 14:13:44.568466: predicting 36 
+2024-09-17 14:13:44.632715: 36, shape torch.Size([2, 108, 512, 510]), rank 0 
+2024-09-17 14:14:15.924064: predicting 5 
+2024-09-17 14:14:15.943929: 5, shape torch.Size([2, 135, 512, 511]), rank 0 
+2024-09-17 14:14:54.810731: predicting 52 
+2024-09-17 14:14:54.835840: 52, shape torch.Size([2, 113, 536, 1040]), rank 0 
+2024-09-17 14:15:56.078171: predicting 58 
+2024-09-17 14:15:56.184442: 58, shape torch.Size([2, 122, 512, 511]), rank 0 
+2024-09-17 14:16:34.823418: predicting 66 
+2024-09-17 14:16:34.848954: 66, shape torch.Size([2, 130, 509, 511]), rank 0 
+2024-09-17 14:17:14.225150: predicting 69 
+2024-09-17 14:17:14.263346: 69, shape torch.Size([2, 100, 512, 511]), rank 0 
+2024-09-17 14:17:45.260676: predicting 75 
+2024-09-17 14:17:45.313065: 75, shape torch.Size([2, 115, 512, 511]), rank 0 
+2024-09-17 14:18:16.417887: predicting 8 
+2024-09-17 14:18:16.435233: 8, shape torch.Size([2, 112, 512, 509]), rank 0 
+2024-09-17 14:18:48.384031: predicting 83 
+2024-09-17 14:18:48.429351: 83, shape torch.Size([2, 138, 509, 511]), rank 0 
+2024-09-17 14:19:27.931934: predicting 88 
+2024-09-17 14:19:27.958148: 88, shape torch.Size([2, 133, 494, 511]), rank 0 
+2024-09-17 14:20:06.723397: predicting 94 
+2024-09-17 14:20:06.753899: 94, shape torch.Size([2, 98, 512, 511]), rank 0 
+2024-09-17 14:20:52.563690: Validation complete 
+2024-09-17 14:20:52.563767: Mean Validation Dice:  0.6619242362440307 
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/training_log_2024_9_7_18_07_16.txt b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/training_log_2024_9_7_18_07_16.txt
new file mode 100644
index 0000000000000000000000000000000000000000..44a9c826737c2b97fc76bf7ae30b8b5b9bd271d0
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/training_log_2024_9_7_18_07_16.txt
@@ -0,0 +1,7176 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2024-09-07 18:07:16.723106: do_dummy_2d_data_aug: True 
+2024-09-07 18:07:16.724175: Using splits from existing split file: /mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/splits_final.json 
+2024-09-07 18:07:16.724368: The split file contains 5 splits. 
+2024-09-07 18:07:16.724403: Desired fold for training: 0 
+2024-09-07 18:07:16.724434: This split has 240 training and 30 validation cases. 
+2024-09-07 18:07:23.994347: Using torch.compile... 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres_bs8
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 8, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True, 'inherits_from': '3d_fullres'} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 349.19762434242324, 'median': 141.0, 'min': 0.0, 'percentile_00_5': 29.0, 'percentile_99_5': 2558.91650390625, 'std': 485.94544484039}, '1': {'max': 1.0, 'mean': 0.37307153608378946, 'median': 0.2966964840888977, 'min': 0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}} 
+ 
+2024-09-07 18:07:24.850752: unpacking dataset... 
+2024-09-07 18:07:27.432474: unpacking done... 
+2024-09-07 18:07:27.434526: Unable to plot network architecture: nnUNet_compile is enabled! 
+2024-09-07 18:07:27.444174:  
+2024-09-07 18:07:27.444393: Epoch 0 
+2024-09-07 18:07:27.444548: Current learning rate: 0.01 
+2024-09-07 18:12:53.038498: train_loss -0.0749 
+2024-09-07 18:12:53.038657: val_loss -0.2047 
+2024-09-07 18:12:53.038714: Pseudo dice [0.1821, 0.4325] 
+2024-09-07 18:12:53.038770: Epoch time: 325.6 s 
+2024-09-07 18:12:53.038815: Yayy! New best EMA pseudo Dice: 0.3073 
+2024-09-07 18:12:54.905653:  
+2024-09-07 18:12:54.905885: Epoch 1 
+2024-09-07 18:12:54.905991: Current learning rate: 0.00999 
+2024-09-07 18:17:22.434994: train_loss -0.2955 
+2024-09-07 18:17:22.435146: val_loss -0.2817 
+2024-09-07 18:17:22.435203: Pseudo dice [0.3028, 0.4491] 
+2024-09-07 18:17:22.435372: Epoch time: 267.53 s 
+2024-09-07 18:17:22.435540: Yayy! New best EMA pseudo Dice: 0.3142 
+2024-09-07 18:17:26.262090:  
+2024-09-07 18:17:26.262322: Epoch 2 
+2024-09-07 18:17:26.262424: Current learning rate: 0.00998 
+2024-09-07 18:21:40.121587: train_loss -0.3505 
+2024-09-07 18:21:40.121755: val_loss -0.3175 
+2024-09-07 18:21:40.121814: Pseudo dice [0.33, 0.5583] 
+2024-09-07 18:21:40.121871: Epoch time: 253.86 s 
+2024-09-07 18:21:40.121915: Yayy! New best EMA pseudo Dice: 0.3272 
+2024-09-07 18:21:44.037329:  
+2024-09-07 18:21:44.037590: Epoch 3 
+2024-09-07 18:21:44.037683: Current learning rate: 0.00997 
+2024-09-07 18:25:58.590497: train_loss -0.4113 
+2024-09-07 18:25:58.590689: val_loss -0.3522 
+2024-09-07 18:25:58.590763: Pseudo dice [0.3466, 0.5352] 
+2024-09-07 18:25:58.590826: Epoch time: 254.56 s 
+2024-09-07 18:25:58.590878: Yayy! New best EMA pseudo Dice: 0.3385 
+2024-09-07 18:26:02.907471:  
+2024-09-07 18:26:02.907707: Epoch 4 
+2024-09-07 18:26:02.907927: Current learning rate: 0.00996 
+2024-09-07 18:30:14.019430: train_loss -0.4743 
+2024-09-07 18:30:14.019575: val_loss -0.4225 
+2024-09-07 18:30:14.019632: Pseudo dice [0.4174, 0.6248] 
+2024-09-07 18:30:14.019688: Epoch time: 251.11 s 
+2024-09-07 18:30:14.019732: Yayy! New best EMA pseudo Dice: 0.3568 
+2024-09-07 18:30:17.893454:  
+2024-09-07 18:30:17.893719: Epoch 5 
+2024-09-07 18:30:17.893802: Current learning rate: 0.00995 
+2024-09-07 18:34:27.862021: train_loss -0.5553 
+2024-09-07 18:34:27.862188: val_loss -0.4929 
+2024-09-07 18:34:27.862284: Pseudo dice [0.4479, 0.7001] 
+2024-09-07 18:34:27.862341: Epoch time: 249.97 s 
+2024-09-07 18:34:27.862389: Yayy! New best EMA pseudo Dice: 0.3785 
+2024-09-07 18:34:32.112921:  
+2024-09-07 18:34:32.113149: Epoch 6 
+2024-09-07 18:34:32.113277: Current learning rate: 0.00995 
+2024-09-07 18:38:45.248362: train_loss -0.5529 
+2024-09-07 18:38:45.248508: val_loss -0.5229 
+2024-09-07 18:38:45.248564: Pseudo dice [0.5116, 0.7378] 
+2024-09-07 18:38:45.248619: Epoch time: 253.14 s 
+2024-09-07 18:38:45.248663: Yayy! New best EMA pseudo Dice: 0.4031 
+2024-09-07 18:38:49.092055:  
+2024-09-07 18:38:49.092245: Epoch 7 
+2024-09-07 18:38:49.092363: Current learning rate: 0.00994 
+2024-09-07 18:42:55.131970: train_loss -0.602 
+2024-09-07 18:42:55.132361: val_loss -0.5354 
+2024-09-07 18:42:55.132462: Pseudo dice [0.5323, 0.7439] 
+2024-09-07 18:42:55.132553: Epoch time: 246.04 s 
+2024-09-07 18:42:55.132627: Yayy! New best EMA pseudo Dice: 0.4266 
+2024-09-07 18:42:59.082114:  
+2024-09-07 18:42:59.082326: Epoch 8 
+2024-09-07 18:42:59.082466: Current learning rate: 0.00993 
+2024-09-07 18:47:05.006125: train_loss -0.6401 
+2024-09-07 18:47:05.006271: val_loss -0.4854 
+2024-09-07 18:47:05.006328: Pseudo dice [0.461, 0.6573] 
+2024-09-07 18:47:05.006383: Epoch time: 245.93 s 
+2024-09-07 18:47:05.006426: Yayy! New best EMA pseudo Dice: 0.4399 
+2024-09-07 18:47:08.910328:  
+2024-09-07 18:47:08.910555: Epoch 9 
+2024-09-07 18:47:08.910669: Current learning rate: 0.00992 
+2024-09-07 18:51:19.254411: train_loss -0.6267 
+2024-09-07 18:51:19.254580: val_loss -0.5787 
+2024-09-07 18:51:19.254639: Pseudo dice [0.5881, 0.7682] 
+2024-09-07 18:51:19.254699: Epoch time: 250.35 s 
+2024-09-07 18:51:19.254742: Yayy! New best EMA pseudo Dice: 0.4637 
+2024-09-07 18:51:23.074607:  
+2024-09-07 18:51:23.074826: Epoch 10 
+2024-09-07 18:51:23.074934: Current learning rate: 0.00991 
+2024-09-07 18:55:34.501070: train_loss -0.6185 
+2024-09-07 18:55:34.501216: val_loss -0.5767 
+2024-09-07 18:55:34.501273: Pseudo dice [0.5438, 0.7467] 
+2024-09-07 18:55:34.501328: Epoch time: 251.43 s 
+2024-09-07 18:55:34.501373: Yayy! New best EMA pseudo Dice: 0.4819 
+2024-09-07 18:55:38.333038:  
+2024-09-07 18:55:38.333322: Epoch 11 
+2024-09-07 18:55:38.333442: Current learning rate: 0.0099 
+2024-09-07 18:59:44.197387: train_loss -0.6401 
+2024-09-07 18:59:44.197574: val_loss -0.5693 
+2024-09-07 18:59:44.197630: Pseudo dice [0.5792, 0.7566] 
+2024-09-07 18:59:44.197685: Epoch time: 245.87 s 
+2024-09-07 18:59:44.197730: Yayy! New best EMA pseudo Dice: 0.5005 
+2024-09-07 18:59:48.039819:  
+2024-09-07 18:59:48.040068: Epoch 12 
+2024-09-07 18:59:48.040177: Current learning rate: 0.00989 
+2024-09-07 19:04:00.667041: train_loss -0.6673 
+2024-09-07 19:04:00.667192: val_loss -0.5669 
+2024-09-07 19:04:00.667249: Pseudo dice [0.55, 0.7833] 
+2024-09-07 19:04:00.667305: Epoch time: 252.63 s 
+2024-09-07 19:04:00.667349: Yayy! New best EMA pseudo Dice: 0.5171 
+2024-09-07 19:04:04.519650:  
+2024-09-07 19:04:04.519888: Epoch 13 
+2024-09-07 19:04:04.519971: Current learning rate: 0.00988 
+2024-09-07 19:08:10.017983: train_loss -0.672 
+2024-09-07 19:08:10.018149: val_loss -0.584 
+2024-09-07 19:08:10.018215: Pseudo dice [0.5873, 0.7837] 
+2024-09-07 19:08:10.018285: Epoch time: 245.5 s 
+2024-09-07 19:08:10.018329: Yayy! New best EMA pseudo Dice: 0.5339 
+2024-09-07 19:08:14.115963:  
+2024-09-07 19:08:14.116207: Epoch 14 
+2024-09-07 19:08:14.116300: Current learning rate: 0.00987 
+2024-09-07 19:12:20.107645: train_loss -0.6476 
+2024-09-07 19:12:20.107795: val_loss -0.6178 
+2024-09-07 19:12:20.107859: Pseudo dice [0.5705, 0.8112] 
+2024-09-07 19:12:20.107916: Epoch time: 245.99 s 
+2024-09-07 19:12:20.107960: Yayy! New best EMA pseudo Dice: 0.5496 
+2024-09-07 19:12:24.004301:  
+2024-09-07 19:12:24.004519: Epoch 15 
+2024-09-07 19:12:24.004606: Current learning rate: 0.00986 
+2024-09-07 19:16:29.503914: train_loss -0.6699 
+2024-09-07 19:16:29.504091: val_loss -0.6281 
+2024-09-07 19:16:29.504151: Pseudo dice [0.5863, 0.8081] 
+2024-09-07 19:16:29.504208: Epoch time: 245.5 s 
+2024-09-07 19:16:29.504253: Yayy! New best EMA pseudo Dice: 0.5644 
+2024-09-07 19:16:33.403938:  
+2024-09-07 19:16:33.404154: Epoch 16 
+2024-09-07 19:16:33.404265: Current learning rate: 0.00986 
+2024-09-07 19:20:43.575609: train_loss -0.6922 
+2024-09-07 19:20:43.575761: val_loss -0.6176 
+2024-09-07 19:20:43.575831: Pseudo dice [0.6141, 0.7867] 
+2024-09-07 19:20:43.575889: Epoch time: 250.17 s 
+2024-09-07 19:20:43.575934: Yayy! New best EMA pseudo Dice: 0.578 
+2024-09-07 19:20:47.545228:  
+2024-09-07 19:20:47.545499: Epoch 17 
+2024-09-07 19:20:47.545592: Current learning rate: 0.00985 
+2024-09-07 19:24:53.618876: train_loss -0.684 
+2024-09-07 19:24:53.619022: val_loss -0.6276 
+2024-09-07 19:24:53.619124: Pseudo dice [0.6179, 0.7852] 
+2024-09-07 19:24:53.619181: Epoch time: 246.08 s 
+2024-09-07 19:24:53.619224: Yayy! New best EMA pseudo Dice: 0.5903 
+2024-09-07 19:24:57.507120:  
+2024-09-07 19:24:57.507394: Epoch 18 
+2024-09-07 19:24:57.507535: Current learning rate: 0.00984 
+2024-09-07 19:29:03.290584: train_loss -0.6969 
+2024-09-07 19:29:03.290732: val_loss -0.6209 
+2024-09-07 19:29:03.290789: Pseudo dice [0.5848, 0.8207] 
+2024-09-07 19:29:03.290847: Epoch time: 245.79 s 
+2024-09-07 19:29:03.290893: Yayy! New best EMA pseudo Dice: 0.6016 
+2024-09-07 19:29:07.178454:  
+2024-09-07 19:29:07.178681: Epoch 19 
+2024-09-07 19:29:07.178762: Current learning rate: 0.00983 
+2024-09-07 19:33:12.898324: train_loss -0.6928 
+2024-09-07 19:33:12.898471: val_loss -0.6102 
+2024-09-07 19:33:12.898527: Pseudo dice [0.6038, 0.7889] 
+2024-09-07 19:33:12.898583: Epoch time: 245.72 s 
+2024-09-07 19:33:12.898628: Yayy! New best EMA pseudo Dice: 0.6111 
+2024-09-07 19:33:16.815444:  
+2024-09-07 19:33:16.815692: Epoch 20 
+2024-09-07 19:33:16.815799: Current learning rate: 0.00982 
+2024-09-07 19:37:22.423845: train_loss -0.7035 
+2024-09-07 19:37:22.423993: val_loss -0.6343 
+2024-09-07 19:37:22.424049: Pseudo dice [0.5839, 0.8205] 
+2024-09-07 19:37:22.424104: Epoch time: 245.61 s 
+2024-09-07 19:37:22.424148: Yayy! New best EMA pseudo Dice: 0.6202 
+2024-09-07 19:37:26.313147:  
+2024-09-07 19:37:26.313366: Epoch 21 
+2024-09-07 19:37:26.313454: Current learning rate: 0.00981 
+2024-09-07 19:41:44.694781: train_loss -0.7127 
+2024-09-07 19:41:44.694927: val_loss -0.6292 
+2024-09-07 19:41:44.694982: Pseudo dice [0.604, 0.8128] 
+2024-09-07 19:41:44.695068: Epoch time: 258.38 s 
+2024-09-07 19:41:44.695165: Yayy! New best EMA pseudo Dice: 0.629 
+2024-09-07 19:41:48.517584:  
+2024-09-07 19:41:48.517793: Epoch 22 
+2024-09-07 19:41:48.517878: Current learning rate: 0.0098 
+2024-09-07 19:46:06.577999: train_loss -0.7148 
+2024-09-07 19:46:06.578144: val_loss -0.6593 
+2024-09-07 19:46:06.578264: Pseudo dice [0.6403, 0.8277] 
+2024-09-07 19:46:06.578344: Epoch time: 258.06 s 
+2024-09-07 19:46:06.578390: Yayy! New best EMA pseudo Dice: 0.6395 
+2024-09-07 19:46:10.398769:  
+2024-09-07 19:46:10.398994: Epoch 23 
+2024-09-07 19:46:10.399089: Current learning rate: 0.00979 
+2024-09-07 19:50:16.044184: train_loss -0.718 
+2024-09-07 19:50:16.044340: val_loss -0.6583 
+2024-09-07 19:50:16.044397: Pseudo dice [0.6646, 0.8328] 
+2024-09-07 19:50:16.044517: Epoch time: 245.65 s 
+2024-09-07 19:50:16.044566: Yayy! New best EMA pseudo Dice: 0.6504 
+2024-09-07 19:50:19.862902:  
+2024-09-07 19:50:19.863098: Epoch 24 
+2024-09-07 19:50:19.863186: Current learning rate: 0.00978 
+2024-09-07 19:54:25.400080: train_loss -0.7032 
+2024-09-07 19:54:25.400225: val_loss -0.6405 
+2024-09-07 19:54:25.400281: Pseudo dice [0.6384, 0.8027] 
+2024-09-07 19:54:25.400336: Epoch time: 245.54 s 
+2024-09-07 19:54:25.400381: Yayy! New best EMA pseudo Dice: 0.6574 
+2024-09-07 19:54:29.589197:  
+2024-09-07 19:54:29.589513: Epoch 25 
+2024-09-07 19:54:29.589615: Current learning rate: 0.00977 
+2024-09-07 19:58:38.320609: train_loss -0.7231 
+2024-09-07 19:58:38.320783: val_loss -0.6733 
+2024-09-07 19:58:38.320840: Pseudo dice [0.6554, 0.8226] 
+2024-09-07 19:58:38.320894: Epoch time: 248.73 s 
+2024-09-07 19:58:38.320938: Yayy! New best EMA pseudo Dice: 0.6656 
+2024-09-07 19:58:42.177261:  
+2024-09-07 19:58:42.177501: Epoch 26 
+2024-09-07 19:58:42.177593: Current learning rate: 0.00977 
+2024-09-07 20:02:47.740563: train_loss -0.7154 
+2024-09-07 20:02:47.740725: val_loss -0.6479 
+2024-09-07 20:02:47.740781: Pseudo dice [0.6479, 0.8161] 
+2024-09-07 20:02:47.740837: Epoch time: 245.57 s 
+2024-09-07 20:02:47.740881: Yayy! New best EMA pseudo Dice: 0.6722 
+2024-09-07 20:02:51.594570:  
+2024-09-07 20:02:51.594826: Epoch 27 
+2024-09-07 20:02:51.594993: Current learning rate: 0.00976 
+2024-09-07 20:06:57.214958: train_loss -0.7213 
+2024-09-07 20:06:57.215105: val_loss -0.6095 
+2024-09-07 20:06:57.215161: Pseudo dice [0.5919, 0.8198] 
+2024-09-07 20:06:57.215218: Epoch time: 245.62 s 
+2024-09-07 20:06:57.215265: Yayy! New best EMA pseudo Dice: 0.6756 
+2024-09-07 20:07:01.001131:  
+2024-09-07 20:07:01.001391: Epoch 28 
+2024-09-07 20:07:01.001478: Current learning rate: 0.00975 
+2024-09-07 20:11:06.875842: train_loss -0.7177 
+2024-09-07 20:11:06.876029: val_loss -0.6249 
+2024-09-07 20:11:06.876087: Pseudo dice [0.6002, 0.8166] 
+2024-09-07 20:11:06.876143: Epoch time: 245.88 s 
+2024-09-07 20:11:06.876192: Yayy! New best EMA pseudo Dice: 0.6789 
+2024-09-07 20:11:11.100914:  
+2024-09-07 20:11:11.101182: Epoch 29 
+2024-09-07 20:11:11.101270: Current learning rate: 0.00974 
+2024-09-07 20:15:16.780568: train_loss -0.7288 
+2024-09-07 20:15:16.780719: val_loss -0.6455 
+2024-09-07 20:15:16.780791: Pseudo dice [0.6087, 0.8457] 
+2024-09-07 20:15:16.780858: Epoch time: 245.68 s 
+2024-09-07 20:15:16.780916: Yayy! New best EMA pseudo Dice: 0.6837 
+2024-09-07 20:15:20.654969:  
+2024-09-07 20:15:20.655192: Epoch 30 
+2024-09-07 20:15:20.655293: Current learning rate: 0.00973 
+2024-09-07 20:19:26.113811: train_loss -0.7234 
+2024-09-07 20:19:26.113959: val_loss -0.6669 
+2024-09-07 20:19:26.114016: Pseudo dice [0.6508, 0.8271] 
+2024-09-07 20:19:26.114073: Epoch time: 245.46 s 
+2024-09-07 20:19:26.114117: Yayy! New best EMA pseudo Dice: 0.6892 
+2024-09-07 20:19:30.059409:  
+2024-09-07 20:19:30.059633: Epoch 31 
+2024-09-07 20:19:30.059723: Current learning rate: 0.00972 
+2024-09-07 20:23:35.669126: train_loss -0.7357 
+2024-09-07 20:23:35.669292: val_loss -0.6601 
+2024-09-07 20:23:35.669348: Pseudo dice [0.6351, 0.8289] 
+2024-09-07 20:23:35.669403: Epoch time: 245.61 s 
+2024-09-07 20:23:35.669448: Yayy! New best EMA pseudo Dice: 0.6935 
+2024-09-07 20:23:39.525951:  
+2024-09-07 20:23:39.526191: Epoch 32 
+2024-09-07 20:23:39.526336: Current learning rate: 0.00971 
+2024-09-07 20:27:45.115938: train_loss -0.7358 
+2024-09-07 20:27:45.116183: val_loss -0.6389 
+2024-09-07 20:27:45.116240: Pseudo dice [0.6107, 0.8283] 
+2024-09-07 20:27:45.116294: Epoch time: 245.59 s 
+2024-09-07 20:27:45.116338: Yayy! New best EMA pseudo Dice: 0.6961 
+2024-09-07 20:27:48.947669:  
+2024-09-07 20:27:48.947896: Epoch 33 
+2024-09-07 20:27:48.947980: Current learning rate: 0.0097 
+2024-09-07 20:31:54.457080: train_loss -0.7291 
+2024-09-07 20:31:54.457314: val_loss -0.6726 
+2024-09-07 20:31:54.457410: Pseudo dice [0.6564, 0.8431] 
+2024-09-07 20:31:54.457592: Epoch time: 245.51 s 
+2024-09-07 20:31:54.457711: Yayy! New best EMA pseudo Dice: 0.7015 
+2024-09-07 20:31:58.684333:  
+2024-09-07 20:31:58.684581: Epoch 34 
+2024-09-07 20:31:58.684667: Current learning rate: 0.00969 
+2024-09-07 20:36:04.313495: train_loss -0.7328 
+2024-09-07 20:36:04.313669: val_loss -0.6329 
+2024-09-07 20:36:04.313814: Pseudo dice [0.6154, 0.8161] 
+2024-09-07 20:36:04.313907: Epoch time: 245.63 s 
+2024-09-07 20:36:04.313965: Yayy! New best EMA pseudo Dice: 0.7029 
+2024-09-07 20:36:08.209644:  
+2024-09-07 20:36:08.209849: Epoch 35 
+2024-09-07 20:36:08.209960: Current learning rate: 0.00968 
+2024-09-07 20:40:14.696830: train_loss -0.7343 
+2024-09-07 20:40:14.696982: val_loss -0.6567 
+2024-09-07 20:40:14.697039: Pseudo dice [0.608, 0.8373] 
+2024-09-07 20:40:14.697094: Epoch time: 246.49 s 
+2024-09-07 20:40:14.697137: Yayy! New best EMA pseudo Dice: 0.7049 
+2024-09-07 20:40:18.295457:  
+2024-09-07 20:40:18.295710: Epoch 36 
+2024-09-07 20:40:18.295794: Current learning rate: 0.00968 
+2024-09-07 20:44:24.250344: train_loss -0.7447 
+2024-09-07 20:44:24.250494: val_loss -0.6256 
+2024-09-07 20:44:24.250551: Pseudo dice [0.6327, 0.799] 
+2024-09-07 20:44:24.250606: Epoch time: 245.96 s 
+2024-09-07 20:44:24.250651: Yayy! New best EMA pseudo Dice: 0.706 
+2024-09-07 20:44:28.132174:  
+2024-09-07 20:44:28.132433: Epoch 37 
+2024-09-07 20:44:28.132532: Current learning rate: 0.00967 
+2024-09-07 20:48:34.042357: train_loss -0.7326 
+2024-09-07 20:48:34.042504: val_loss -0.6675 
+2024-09-07 20:48:34.042560: Pseudo dice [0.6594, 0.8223] 
+2024-09-07 20:48:34.042615: Epoch time: 245.91 s 
+2024-09-07 20:48:34.042659: Yayy! New best EMA pseudo Dice: 0.7095 
+2024-09-07 20:48:37.876989:  
+2024-09-07 20:48:37.877254: Epoch 38 
+2024-09-07 20:48:37.877339: Current learning rate: 0.00966 
+2024-09-07 20:52:43.833376: train_loss -0.73 
+2024-09-07 20:52:43.833517: val_loss -0.6576 
+2024-09-07 20:52:43.833572: Pseudo dice [0.6185, 0.8251] 
+2024-09-07 20:52:43.833627: Epoch time: 245.96 s 
+2024-09-07 20:52:43.833670: Yayy! New best EMA pseudo Dice: 0.7107 
+2024-09-07 20:52:47.746085:  
+2024-09-07 20:52:47.746274: Epoch 39 
+2024-09-07 20:52:47.746363: Current learning rate: 0.00965 
+2024-09-07 20:56:53.732670: train_loss -0.7301 
+2024-09-07 20:56:53.732843: val_loss -0.6515 
+2024-09-07 20:56:53.732901: Pseudo dice [0.6194, 0.829] 
+2024-09-07 20:56:53.732957: Epoch time: 245.99 s 
+2024-09-07 20:56:53.733002: Yayy! New best EMA pseudo Dice: 0.712 
+2024-09-07 20:56:57.616928:  
+2024-09-07 20:56:57.617123: Epoch 40 
+2024-09-07 20:56:57.617222: Current learning rate: 0.00964 
+2024-09-07 21:01:03.521937: train_loss -0.7379 
+2024-09-07 21:01:03.522104: val_loss -0.6259 
+2024-09-07 21:01:03.522161: Pseudo dice [0.6387, 0.8251] 
+2024-09-07 21:01:03.522219: Epoch time: 245.91 s 
+2024-09-07 21:01:03.522264: Yayy! New best EMA pseudo Dice: 0.714 
+2024-09-07 21:01:07.695020:  
+2024-09-07 21:01:07.695293: Epoch 41 
+2024-09-07 21:01:07.695434: Current learning rate: 0.00963 
+2024-09-07 21:05:13.654854: train_loss -0.7407 
+2024-09-07 21:05:13.655010: val_loss -0.6617 
+2024-09-07 21:05:13.655067: Pseudo dice [0.6298, 0.8368] 
+2024-09-07 21:05:13.655123: Epoch time: 245.96 s 
+2024-09-07 21:05:13.655169: Yayy! New best EMA pseudo Dice: 0.716 
+2024-09-07 21:05:17.514323:  
+2024-09-07 21:05:17.514560: Epoch 42 
+2024-09-07 21:05:17.514643: Current learning rate: 0.00962 
+2024-09-07 21:09:23.404157: train_loss -0.7411 
+2024-09-07 21:09:23.404302: val_loss -0.6657 
+2024-09-07 21:09:23.404358: Pseudo dice [0.6563, 0.8209] 
+2024-09-07 21:09:23.404413: Epoch time: 245.89 s 
+2024-09-07 21:09:23.404459: Yayy! New best EMA pseudo Dice: 0.7182 
+2024-09-07 21:09:27.278162:  
+2024-09-07 21:09:27.278337: Epoch 43 
+2024-09-07 21:09:27.278444: Current learning rate: 0.00961 
+2024-09-07 21:13:33.368394: train_loss -0.7329 
+2024-09-07 21:13:33.368586: val_loss -0.6544 
+2024-09-07 21:13:33.368643: Pseudo dice [0.6146, 0.8452] 
+2024-09-07 21:13:33.368701: Epoch time: 246.09 s 
+2024-09-07 21:13:33.368893: Yayy! New best EMA pseudo Dice: 0.7194 
+2024-09-07 21:13:37.603956:  
+2024-09-07 21:13:37.604198: Epoch 44 
+2024-09-07 21:13:37.604286: Current learning rate: 0.0096 
+2024-09-07 21:17:43.866737: train_loss -0.753 
+2024-09-07 21:17:43.866887: val_loss -0.6693 
+2024-09-07 21:17:43.866945: Pseudo dice [0.6752, 0.8361] 
+2024-09-07 21:17:43.867054: Epoch time: 246.26 s 
+2024-09-07 21:17:43.867100: Yayy! New best EMA pseudo Dice: 0.723 
+2024-09-07 21:17:47.758196:  
+2024-09-07 21:17:47.758393: Epoch 45 
+2024-09-07 21:17:47.758494: Current learning rate: 0.00959 
+2024-09-07 21:21:53.938000: train_loss -0.7609 
+2024-09-07 21:21:53.938145: val_loss -0.6787 
+2024-09-07 21:21:53.938202: Pseudo dice [0.6602, 0.8438] 
+2024-09-07 21:21:53.938257: Epoch time: 246.18 s 
+2024-09-07 21:21:53.938300: Yayy! New best EMA pseudo Dice: 0.7259 
+2024-09-07 21:21:57.737468:  
+2024-09-07 21:21:57.737720: Epoch 46 
+2024-09-07 21:21:57.737824: Current learning rate: 0.00959 
+2024-09-07 21:26:03.889447: train_loss -0.7588 
+2024-09-07 21:26:03.889619: val_loss -0.6748 
+2024-09-07 21:26:03.889681: Pseudo dice [0.6608, 0.8328] 
+2024-09-07 21:26:03.889750: Epoch time: 246.15 s 
+2024-09-07 21:26:03.889799: Yayy! New best EMA pseudo Dice: 0.728 
+2024-09-07 21:26:07.724824:  
+2024-09-07 21:26:07.725077: Epoch 47 
+2024-09-07 21:26:07.725162: Current learning rate: 0.00958 
+2024-09-07 21:30:13.676614: train_loss -0.7529 
+2024-09-07 21:30:13.676794: val_loss -0.6887 
+2024-09-07 21:30:13.676851: Pseudo dice [0.6634, 0.8494] 
+2024-09-07 21:30:13.676907: Epoch time: 245.95 s 
+2024-09-07 21:30:13.676952: Yayy! New best EMA pseudo Dice: 0.7308 
+2024-09-07 21:30:17.508634:  
+2024-09-07 21:30:17.508876: Epoch 48 
+2024-09-07 21:30:17.508956: Current learning rate: 0.00957 
+2024-09-07 21:34:23.474416: train_loss -0.7413 
+2024-09-07 21:34:23.474561: val_loss -0.6854 
+2024-09-07 21:34:23.474617: Pseudo dice [0.6968, 0.8441] 
+2024-09-07 21:34:23.474672: Epoch time: 245.97 s 
+2024-09-07 21:34:23.474716: Yayy! New best EMA pseudo Dice: 0.7348 
+2024-09-07 21:34:27.294543:  
+2024-09-07 21:34:27.294781: Epoch 49 
+2024-09-07 21:34:27.294868: Current learning rate: 0.00956 
+2024-09-07 21:38:33.111770: train_loss -0.7591 
+2024-09-07 21:38:33.111940: val_loss -0.6697 
+2024-09-07 21:38:33.111995: Pseudo dice [0.6422, 0.8449] 
+2024-09-07 21:38:33.112052: Epoch time: 245.82 s 
+2024-09-07 21:38:34.174497: Yayy! New best EMA pseudo Dice: 0.7357 
+2024-09-07 21:38:37.961153:  
+2024-09-07 21:38:37.961363: Epoch 50 
+2024-09-07 21:38:37.961447: Current learning rate: 0.00955 
+2024-09-07 21:42:43.879691: train_loss -0.7794 
+2024-09-07 21:42:43.879843: val_loss -0.6639 
+2024-09-07 21:42:43.879902: Pseudo dice [0.615, 0.8572] 
+2024-09-07 21:42:43.879959: Epoch time: 245.92 s 
+2024-09-07 21:42:43.880003: Yayy! New best EMA pseudo Dice: 0.7357 
+2024-09-07 21:42:47.741776:  
+2024-09-07 21:42:47.742068: Epoch 51 
+2024-09-07 21:42:47.742150: Current learning rate: 0.00954 
+2024-09-07 21:46:53.676698: train_loss -0.7659 
+2024-09-07 21:46:53.676847: val_loss -0.6695 
+2024-09-07 21:46:53.676910: Pseudo dice [0.6412, 0.8397] 
+2024-09-07 21:46:53.676967: Epoch time: 245.94 s 
+2024-09-07 21:46:53.677012: Yayy! New best EMA pseudo Dice: 0.7362 
+2024-09-07 21:46:57.524054:  
+2024-09-07 21:46:57.524287: Epoch 52 
+2024-09-07 21:46:57.524371: Current learning rate: 0.00953 
+2024-09-07 21:51:05.562957: train_loss -0.762 
+2024-09-07 21:51:05.563106: val_loss -0.6835 
+2024-09-07 21:51:05.563162: Pseudo dice [0.6754, 0.8472] 
+2024-09-07 21:51:05.563229: Epoch time: 248.04 s 
+2024-09-07 21:51:05.563274: Yayy! New best EMA pseudo Dice: 0.7387 
+2024-09-07 21:51:09.376835:  
+2024-09-07 21:51:09.377067: Epoch 53 
+2024-09-07 21:51:09.377167: Current learning rate: 0.00952 
+2024-09-07 21:55:15.402584: train_loss -0.7582 
+2024-09-07 21:55:15.402734: val_loss -0.6895 
+2024-09-07 21:55:15.402790: Pseudo dice [0.6602, 0.8692] 
+2024-09-07 21:55:15.402845: Epoch time: 246.03 s 
+2024-09-07 21:55:15.402890: Yayy! New best EMA pseudo Dice: 0.7413 
+2024-09-07 21:55:19.243060:  
+2024-09-07 21:55:19.243264: Epoch 54 
+2024-09-07 21:55:19.243364: Current learning rate: 0.00951 
+2024-09-07 21:59:25.284700: train_loss -0.7594 
+2024-09-07 21:59:25.284877: val_loss -0.683 
+2024-09-07 21:59:25.284936: Pseudo dice [0.6816, 0.8432] 
+2024-09-07 21:59:25.285035: Epoch time: 246.04 s 
+2024-09-07 21:59:25.285107: Yayy! New best EMA pseudo Dice: 0.7434 
+2024-09-07 21:59:29.142294:  
+2024-09-07 21:59:29.142518: Epoch 55 
+2024-09-07 21:59:29.142614: Current learning rate: 0.0095 
+2024-09-07 22:03:35.370389: train_loss -0.7691 
+2024-09-07 22:03:35.370530: val_loss -0.6923 
+2024-09-07 22:03:35.370586: Pseudo dice [0.6696, 0.8404] 
+2024-09-07 22:03:35.370642: Epoch time: 246.23 s 
+2024-09-07 22:03:35.370687: Yayy! New best EMA pseudo Dice: 0.7446 
+2024-09-07 22:03:39.227730:  
+2024-09-07 22:03:39.227990: Epoch 56 
+2024-09-07 22:03:39.228124: Current learning rate: 0.00949 
+2024-09-07 22:07:46.115299: train_loss -0.7792 
+2024-09-07 22:07:46.115499: val_loss -0.6968 
+2024-09-07 22:07:46.115556: Pseudo dice [0.6407, 0.8716] 
+2024-09-07 22:07:46.115611: Epoch time: 246.89 s 
+2024-09-07 22:07:46.115655: Yayy! New best EMA pseudo Dice: 0.7457 
+2024-09-07 22:07:50.227668:  
+2024-09-07 22:07:50.227980: Epoch 57 
+2024-09-07 22:07:50.228091: Current learning rate: 0.00949 
+2024-09-07 22:11:56.440711: train_loss -0.7751 
+2024-09-07 22:11:56.440942: val_loss -0.7004 
+2024-09-07 22:11:56.441004: Pseudo dice [0.6727, 0.8506] 
+2024-09-07 22:11:56.441059: Epoch time: 246.22 s 
+2024-09-07 22:11:56.441104: Yayy! New best EMA pseudo Dice: 0.7473 
+2024-09-07 22:12:00.270415:  
+2024-09-07 22:12:00.270696: Epoch 58 
+2024-09-07 22:12:00.270785: Current learning rate: 0.00948 
+2024-09-07 22:16:06.338922: train_loss -0.7735 
+2024-09-07 22:16:06.339083: val_loss -0.6699 
+2024-09-07 22:16:06.339140: Pseudo dice [0.6418, 0.8579] 
+2024-09-07 22:16:06.339196: Epoch time: 246.07 s 
+2024-09-07 22:16:06.339239: Yayy! New best EMA pseudo Dice: 0.7476 
+2024-09-07 22:16:10.203307:  
+2024-09-07 22:16:10.203606: Epoch 59 
+2024-09-07 22:16:10.203691: Current learning rate: 0.00947 
+2024-09-07 22:20:16.355472: train_loss -0.7784 
+2024-09-07 22:20:16.355731: val_loss -0.6857 
+2024-09-07 22:20:16.355852: Pseudo dice [0.6474, 0.8357] 
+2024-09-07 22:20:16.355944: Epoch time: 246.15 s 
+2024-09-07 22:20:17.788349:  
+2024-09-07 22:20:17.788609: Epoch 60 
+2024-09-07 22:20:17.788783: Current learning rate: 0.00946 
+2024-09-07 22:24:24.057576: train_loss -0.784 
+2024-09-07 22:24:24.057721: val_loss -0.6679 
+2024-09-07 22:24:24.057778: Pseudo dice [0.6046, 0.8668] 
+2024-09-07 22:24:24.057834: Epoch time: 246.27 s 
+2024-09-07 22:24:24.962437:  
+2024-09-07 22:24:24.962647: Epoch 61 
+2024-09-07 22:24:24.962747: Current learning rate: 0.00945 
+2024-09-07 22:28:34.012569: train_loss -0.7698 
+2024-09-07 22:28:34.012717: val_loss -0.6379 
+2024-09-07 22:28:34.012773: Pseudo dice [0.6057, 0.8435] 
+2024-09-07 22:28:34.012829: Epoch time: 249.05 s 
+2024-09-07 22:28:34.922555:  
+2024-09-07 22:28:34.922805: Epoch 62 
+2024-09-07 22:28:34.922889: Current learning rate: 0.00944 
+2024-09-07 22:32:45.867340: train_loss -0.7483 
+2024-09-07 22:32:45.867533: val_loss -0.7032 
+2024-09-07 22:32:45.867608: Pseudo dice [0.6855, 0.8486] 
+2024-09-07 22:32:45.867681: Epoch time: 250.95 s 
+2024-09-07 22:32:46.838183:  
+2024-09-07 22:32:46.838419: Epoch 63 
+2024-09-07 22:32:46.838506: Current learning rate: 0.00943 
+2024-09-07 22:36:53.818592: train_loss -0.7691 
+2024-09-07 22:36:53.818758: val_loss -0.6593 
+2024-09-07 22:36:53.818841: Pseudo dice [0.6537, 0.8514] 
+2024-09-07 22:36:53.818911: Epoch time: 246.98 s 
+2024-09-07 22:36:54.713025:  
+2024-09-07 22:36:54.713267: Epoch 64 
+2024-09-07 22:36:54.713350: Current learning rate: 0.00942 
+2024-09-07 22:41:00.601585: train_loss -0.7572 
+2024-09-07 22:41:00.601729: val_loss -0.6432 
+2024-09-07 22:41:00.601785: Pseudo dice [0.5964, 0.8419] 
+2024-09-07 22:41:00.601842: Epoch time: 245.89 s 
+2024-09-07 22:41:01.508961:  
+2024-09-07 22:41:01.509131: Epoch 65 
+2024-09-07 22:41:01.509245: Current learning rate: 0.00941 
+2024-09-07 22:45:07.172835: train_loss -0.7573 
+2024-09-07 22:45:07.173031: val_loss -0.6396 
+2024-09-07 22:45:07.173107: Pseudo dice [0.6092, 0.824] 
+2024-09-07 22:45:07.173182: Epoch time: 245.67 s 
+2024-09-07 22:45:08.294793:  
+2024-09-07 22:45:08.295173: Epoch 66 
+2024-09-07 22:45:08.295320: Current learning rate: 0.0094 
+2024-09-07 22:49:13.977304: train_loss -0.7649 
+2024-09-07 22:49:13.977453: val_loss -0.6892 
+2024-09-07 22:49:13.977510: Pseudo dice [0.6944, 0.8508] 
+2024-09-07 22:49:13.977569: Epoch time: 245.69 s 
+2024-09-07 22:49:14.900981:  
+2024-09-07 22:49:14.901206: Epoch 67 
+2024-09-07 22:49:14.901293: Current learning rate: 0.00939 
+2024-09-07 22:53:20.469818: train_loss -0.779 
+2024-09-07 22:53:20.469970: val_loss -0.6724 
+2024-09-07 22:53:20.470026: Pseudo dice [0.6436, 0.8408] 
+2024-09-07 22:53:20.470082: Epoch time: 245.57 s 
+2024-09-07 22:53:21.406293:  
+2024-09-07 22:53:21.406540: Epoch 68 
+2024-09-07 22:53:21.406640: Current learning rate: 0.00939 
+2024-09-07 22:57:29.075628: train_loss -0.7743 
+2024-09-07 22:57:29.075773: val_loss -0.6566 
+2024-09-07 22:57:29.075846: Pseudo dice [0.6233, 0.8504] 
+2024-09-07 22:57:29.075905: Epoch time: 247.67 s 
+2024-09-07 22:57:30.020549:  
+2024-09-07 22:57:30.020785: Epoch 69 
+2024-09-07 22:57:30.020875: Current learning rate: 0.00938 
+2024-09-07 23:01:38.843073: train_loss -0.771 
+2024-09-07 23:01:38.843206: val_loss -0.7023 
+2024-09-07 23:01:38.843263: Pseudo dice [0.6957, 0.8512] 
+2024-09-07 23:01:38.843343: Epoch time: 248.82 s 
+2024-09-07 23:01:39.785666:  
+2024-09-07 23:01:39.785917: Epoch 70 
+2024-09-07 23:01:39.786028: Current learning rate: 0.00937 
+2024-09-07 23:05:45.450160: train_loss -0.7607 
+2024-09-07 23:05:45.450303: val_loss -0.6668 
+2024-09-07 23:05:45.451089: Pseudo dice [0.6695, 0.8345] 
+2024-09-07 23:05:45.451151: Epoch time: 245.67 s 
+2024-09-07 23:05:46.390965:  
+2024-09-07 23:05:46.391176: Epoch 71 
+2024-09-07 23:05:46.391259: Current learning rate: 0.00936 
+2024-09-07 23:09:52.133509: train_loss -0.7446 
+2024-09-07 23:09:52.133654: val_loss -0.6724 
+2024-09-07 23:09:52.133710: Pseudo dice [0.6439, 0.8399] 
+2024-09-07 23:09:52.133765: Epoch time: 245.74 s 
+2024-09-07 23:09:53.071095:  
+2024-09-07 23:09:53.071346: Epoch 72 
+2024-09-07 23:09:53.071431: Current learning rate: 0.00935 
+2024-09-07 23:13:58.682845: train_loss -0.7694 
+2024-09-07 23:13:58.682997: val_loss -0.6582 
+2024-09-07 23:13:58.683052: Pseudo dice [0.6297, 0.8494] 
+2024-09-07 23:13:58.683106: Epoch time: 245.61 s 
+2024-09-07 23:13:59.611625:  
+2024-09-07 23:13:59.611863: Epoch 73 
+2024-09-07 23:13:59.611946: Current learning rate: 0.00934 
+2024-09-07 23:18:05.351788: train_loss -0.7764 
+2024-09-07 23:18:05.351937: val_loss -0.6803 
+2024-09-07 23:18:05.351993: Pseudo dice [0.671, 0.8438] 
+2024-09-07 23:18:05.352050: Epoch time: 245.74 s 
+2024-09-07 23:18:06.293152:  
+2024-09-07 23:18:06.293391: Epoch 74 
+2024-09-07 23:18:06.293510: Current learning rate: 0.00933 
+2024-09-07 23:22:12.280701: train_loss -0.7769 
+2024-09-07 23:22:12.280846: val_loss -0.6889 
+2024-09-07 23:22:12.280901: Pseudo dice [0.6508, 0.8708] 
+2024-09-07 23:22:12.280959: Epoch time: 245.99 s 
+2024-09-07 23:22:12.281003: Yayy! New best EMA pseudo Dice: 0.7483 
+2024-09-07 23:22:16.142001:  
+2024-09-07 23:22:16.142244: Epoch 75 
+2024-09-07 23:22:16.142329: Current learning rate: 0.00932 
+2024-09-07 23:26:21.937991: train_loss -0.7708 
+2024-09-07 23:26:21.938138: val_loss -0.6648 
+2024-09-07 23:26:21.938194: Pseudo dice [0.5949, 0.8398] 
+2024-09-07 23:26:21.938248: Epoch time: 245.8 s 
+2024-09-07 23:26:22.948036:  
+2024-09-07 23:26:22.948291: Epoch 76 
+2024-09-07 23:26:22.948401: Current learning rate: 0.00931 
+2024-09-07 23:30:28.940861: train_loss -0.768 
+2024-09-07 23:30:28.941013: val_loss -0.6735 
+2024-09-07 23:30:28.941069: Pseudo dice [0.6718, 0.845] 
+2024-09-07 23:30:28.941123: Epoch time: 246.0 s 
+2024-09-07 23:30:29.911986:  
+2024-09-07 23:30:29.912222: Epoch 77 
+2024-09-07 23:30:29.912309: Current learning rate: 0.0093 
+2024-09-07 23:34:36.107536: train_loss -0.7754 
+2024-09-07 23:34:36.107681: val_loss -0.6966 
+2024-09-07 23:34:36.107750: Pseudo dice [0.6644, 0.8549] 
+2024-09-07 23:34:36.107828: Epoch time: 246.2 s 
+2024-09-07 23:34:37.054454:  
+2024-09-07 23:34:37.054693: Epoch 78 
+2024-09-07 23:34:37.054776: Current learning rate: 0.0093 
+2024-09-07 23:38:42.840094: train_loss -0.7713 
+2024-09-07 23:38:42.840239: val_loss -0.685 
+2024-09-07 23:38:42.840295: Pseudo dice [0.6185, 0.8673] 
+2024-09-07 23:38:42.840402: Epoch time: 245.79 s 
+2024-09-07 23:38:44.715742:  
+2024-09-07 23:38:44.715989: Epoch 79 
+2024-09-07 23:38:44.716081: Current learning rate: 0.00929 
+2024-09-07 23:42:50.498747: train_loss -0.7848 
+2024-09-07 23:42:50.498997: val_loss -0.688 
+2024-09-07 23:42:50.499091: Pseudo dice [0.6656, 0.8564] 
+2024-09-07 23:42:50.499183: Epoch time: 245.78 s 
+2024-09-07 23:42:50.499258: Yayy! New best EMA pseudo Dice: 0.7487 
+2024-09-07 23:42:54.709528:  
+2024-09-07 23:42:54.709768: Epoch 80 
+2024-09-07 23:42:54.709895: Current learning rate: 0.00928 
+2024-09-07 23:47:00.310538: train_loss -0.7792 
+2024-09-07 23:47:00.310683: val_loss -0.677 
+2024-09-07 23:47:00.310740: Pseudo dice [0.634, 0.8521] 
+2024-09-07 23:47:00.310796: Epoch time: 245.6 s 
+2024-09-07 23:47:01.254070:  
+2024-09-07 23:47:01.254339: Epoch 81 
+2024-09-07 23:47:01.254424: Current learning rate: 0.00927 
+2024-09-07 23:51:06.833618: train_loss -0.7752 
+2024-09-07 23:51:06.833765: val_loss -0.6861 
+2024-09-07 23:51:06.833821: Pseudo dice [0.6471, 0.8599] 
+2024-09-07 23:51:06.833876: Epoch time: 245.58 s 
+2024-09-07 23:51:07.778934:  
+2024-09-07 23:51:07.779136: Epoch 82 
+2024-09-07 23:51:07.779221: Current learning rate: 0.00926 
+2024-09-07 23:55:13.239013: train_loss -0.7868 
+2024-09-07 23:55:13.239174: val_loss -0.6887 
+2024-09-07 23:55:13.239232: Pseudo dice [0.6636, 0.8577] 
+2024-09-07 23:55:13.239286: Epoch time: 245.46 s 
+2024-09-07 23:55:13.239330: Yayy! New best EMA pseudo Dice: 0.7499 
+2024-09-07 23:55:17.415963:  
+2024-09-07 23:55:17.416227: Epoch 83 
+2024-09-07 23:55:17.416327: Current learning rate: 0.00925 
+2024-09-07 23:59:23.108931: train_loss -0.7421 
+2024-09-07 23:59:23.109080: val_loss -0.6678 
+2024-09-07 23:59:23.109340: Pseudo dice [0.6369, 0.8497] 
+2024-09-07 23:59:23.109396: Epoch time: 245.7 s 
+2024-09-07 23:59:23.993483:  
+2024-09-07 23:59:23.993782: Epoch 84 
+2024-09-07 23:59:23.993867: Current learning rate: 0.00924 
+2024-09-08 00:03:29.747174: train_loss -0.7686 
+2024-09-08 00:03:29.747313: val_loss -0.7093 
+2024-09-08 00:03:29.747372: Pseudo dice [0.6815, 0.8489] 
+2024-09-08 00:03:29.747429: Epoch time: 245.76 s 
+2024-09-08 00:03:29.747473: Yayy! New best EMA pseudo Dice: 0.7508 
+2024-09-08 00:03:33.542050:  
+2024-09-08 00:03:33.542282: Epoch 85 
+2024-09-08 00:03:33.542367: Current learning rate: 0.00923 
+2024-09-08 00:07:39.323214: train_loss -0.7844 
+2024-09-08 00:07:39.323352: val_loss -0.7051 
+2024-09-08 00:07:39.323407: Pseudo dice [0.6894, 0.8518] 
+2024-09-08 00:07:39.323468: Epoch time: 245.78 s 
+2024-09-08 00:07:39.323512: Yayy! New best EMA pseudo Dice: 0.7528 
+2024-09-08 00:07:43.153067:  
+2024-09-08 00:07:43.153329: Epoch 86 
+2024-09-08 00:07:43.153413: Current learning rate: 0.00922 
+2024-09-08 00:11:49.100423: train_loss -0.7878 
+2024-09-08 00:11:49.100629: val_loss -0.6673 
+2024-09-08 00:11:49.100693: Pseudo dice [0.6587, 0.8575] 
+2024-09-08 00:11:49.100755: Epoch time: 245.95 s 
+2024-09-08 00:11:49.100805: Yayy! New best EMA pseudo Dice: 0.7533 
+2024-09-08 00:11:53.019831:  
+2024-09-08 00:11:53.020038: Epoch 87 
+2024-09-08 00:11:53.020131: Current learning rate: 0.00921 
+2024-09-08 00:15:58.884954: train_loss -0.7868 
+2024-09-08 00:15:58.885113: val_loss -0.7171 
+2024-09-08 00:15:58.885170: Pseudo dice [0.6768, 0.8649] 
+2024-09-08 00:15:58.885227: Epoch time: 245.87 s 
+2024-09-08 00:15:58.885271: Yayy! New best EMA pseudo Dice: 0.7551 
+2024-09-08 00:16:02.709262:  
+2024-09-08 00:16:02.709440: Epoch 88 
+2024-09-08 00:16:02.709544: Current learning rate: 0.0092 
+2024-09-08 00:20:08.452748: train_loss -0.7803 
+2024-09-08 00:20:08.452889: val_loss -0.6826 
+2024-09-08 00:20:08.452945: Pseudo dice [0.6695, 0.8635] 
+2024-09-08 00:20:08.453050: Epoch time: 245.75 s 
+2024-09-08 00:20:08.453114: Yayy! New best EMA pseudo Dice: 0.7562 
+2024-09-08 00:20:12.283602:  
+2024-09-08 00:20:12.283770: Epoch 89 
+2024-09-08 00:20:12.283888: Current learning rate: 0.0092 
+2024-09-08 00:24:17.912556: train_loss -0.7875 
+2024-09-08 00:24:17.912708: val_loss -0.6989 
+2024-09-08 00:24:17.912764: Pseudo dice [0.6759, 0.8408] 
+2024-09-08 00:24:17.912821: Epoch time: 245.63 s 
+2024-09-08 00:24:17.912864: Yayy! New best EMA pseudo Dice: 0.7564 
+2024-09-08 00:24:21.738801:  
+2024-09-08 00:24:21.739035: Epoch 90 
+2024-09-08 00:24:21.739155: Current learning rate: 0.00919 
+2024-09-08 00:28:27.428276: train_loss -0.7834 
+2024-09-08 00:28:27.428423: val_loss -0.6569 
+2024-09-08 00:28:27.428480: Pseudo dice [0.6143, 0.8304] 
+2024-09-08 00:28:27.428535: Epoch time: 245.69 s 
+2024-09-08 00:28:28.399879:  
+2024-09-08 00:28:28.400104: Epoch 91 
+2024-09-08 00:28:28.400189: Current learning rate: 0.00918 
+2024-09-08 00:32:34.230645: train_loss -0.7844 
+2024-09-08 00:32:34.230848: val_loss -0.6797 
+2024-09-08 00:32:34.230950: Pseudo dice [0.6499, 0.8436] 
+2024-09-08 00:32:34.231048: Epoch time: 245.83 s 
+2024-09-08 00:32:35.128206:  
+2024-09-08 00:32:35.128383: Epoch 92 
+2024-09-08 00:32:35.128544: Current learning rate: 0.00917 
+2024-09-08 00:36:40.932676: train_loss -0.7882 
+2024-09-08 00:36:40.932834: val_loss -0.7009 
+2024-09-08 00:36:40.932891: Pseudo dice [0.6666, 0.8557] 
+2024-09-08 00:36:40.932945: Epoch time: 245.81 s 
+2024-09-08 00:36:41.795494:  
+2024-09-08 00:36:41.795734: Epoch 93 
+2024-09-08 00:36:41.795829: Current learning rate: 0.00916 
+2024-09-08 00:40:47.783109: train_loss -0.7931 
+2024-09-08 00:40:47.783254: val_loss -0.6996 
+2024-09-08 00:40:47.783309: Pseudo dice [0.6576, 0.868] 
+2024-09-08 00:40:47.783365: Epoch time: 245.99 s 
+2024-09-08 00:40:48.653309:  
+2024-09-08 00:40:48.653504: Epoch 94 
+2024-09-08 00:40:48.653586: Current learning rate: 0.00915 
+2024-09-08 00:44:54.837871: train_loss -0.7838 
+2024-09-08 00:44:54.838007: val_loss -0.6316 
+2024-09-08 00:44:54.838057: Pseudo dice [0.5868, 0.8162] 
+2024-09-08 00:44:54.838107: Epoch time: 246.19 s 
+2024-09-08 00:44:55.722655:  
+2024-09-08 00:44:55.722856: Epoch 95 
+2024-09-08 00:44:55.722965: Current learning rate: 0.00914 
+2024-09-08 00:49:01.848576: train_loss -0.7865 
+2024-09-08 00:49:01.848711: val_loss -0.6221 
+2024-09-08 00:49:01.848762: Pseudo dice [0.5558, 0.7564] 
+2024-09-08 00:49:01.848813: Epoch time: 246.13 s 
+2024-09-08 00:49:02.726307:  
+2024-09-08 00:49:02.726560: Epoch 96 
+2024-09-08 00:49:02.726666: Current learning rate: 0.00913 
+2024-09-08 00:53:08.906429: train_loss -0.7869 
+2024-09-08 00:53:08.906568: val_loss -0.6826 
+2024-09-08 00:53:08.906645: Pseudo dice [0.6429, 0.8602] 
+2024-09-08 00:53:08.906699: Epoch time: 246.18 s 
+2024-09-08 00:53:09.790295:  
+2024-09-08 00:53:09.790486: Epoch 97 
+2024-09-08 00:53:09.790565: Current learning rate: 0.00912 
+2024-09-08 00:57:15.851159: train_loss -0.7963 
+2024-09-08 00:57:15.851316: val_loss -0.6699 
+2024-09-08 00:57:15.851367: Pseudo dice [0.6483, 0.8672] 
+2024-09-08 00:57:15.851418: Epoch time: 246.06 s 
+2024-09-08 00:57:16.733484:  
+2024-09-08 00:57:16.733719: Epoch 98 
+2024-09-08 00:57:16.733794: Current learning rate: 0.00911 
+2024-09-08 01:01:22.819249: train_loss -0.7967 
+2024-09-08 01:01:22.819384: val_loss -0.7033 
+2024-09-08 01:01:22.819434: Pseudo dice [0.6703, 0.8738] 
+2024-09-08 01:01:22.819484: Epoch time: 246.09 s 
+2024-09-08 01:01:23.701337:  
+2024-09-08 01:01:23.701554: Epoch 99 
+2024-09-08 01:01:23.701664: Current learning rate: 0.0091 
+2024-09-08 01:05:29.578909: train_loss -0.7556 
+2024-09-08 01:05:29.579081: val_loss -0.6551 
+2024-09-08 01:05:29.579132: Pseudo dice [0.6143, 0.8304] 
+2024-09-08 01:05:29.579184: Epoch time: 245.88 s 
+2024-09-08 01:05:33.377096:  
+2024-09-08 01:05:33.377346: Epoch 100 
+2024-09-08 01:05:33.377421: Current learning rate: 0.0091 
+2024-09-08 01:09:39.218784: train_loss -0.7666 
+2024-09-08 01:09:39.218971: val_loss -0.6664 
+2024-09-08 01:09:39.219040: Pseudo dice [0.6196, 0.8502] 
+2024-09-08 01:09:39.219100: Epoch time: 245.84 s 
+2024-09-08 01:09:40.410557:  
+2024-09-08 01:09:40.410912: Epoch 101 
+2024-09-08 01:09:40.411044: Current learning rate: 0.00909 
+2024-09-08 01:13:46.286072: train_loss -0.788 
+2024-09-08 01:13:46.286211: val_loss -0.6654 
+2024-09-08 01:13:46.286261: Pseudo dice [0.648, 0.8527] 
+2024-09-08 01:13:46.286312: Epoch time: 245.88 s 
+2024-09-08 01:13:47.194848:  
+2024-09-08 01:13:47.195092: Epoch 102 
+2024-09-08 01:13:47.195174: Current learning rate: 0.00908 
+2024-09-08 01:17:52.877752: train_loss -0.7867 
+2024-09-08 01:17:52.877891: val_loss -0.6781 
+2024-09-08 01:17:52.877990: Pseudo dice [0.6542, 0.8537] 
+2024-09-08 01:17:52.878043: Epoch time: 245.68 s 
+2024-09-08 01:17:54.713796:  
+2024-09-08 01:17:54.714073: Epoch 103 
+2024-09-08 01:17:54.714151: Current learning rate: 0.00907 
+2024-09-08 01:22:00.224659: train_loss -0.7871 
+2024-09-08 01:22:00.224802: val_loss -0.6631 
+2024-09-08 01:22:00.224856: Pseudo dice [0.6328, 0.8618] 
+2024-09-08 01:22:00.224907: Epoch time: 245.51 s 
+2024-09-08 01:22:01.118312:  
+2024-09-08 01:22:01.118553: Epoch 104 
+2024-09-08 01:22:01.118656: Current learning rate: 0.00906 
+2024-09-08 01:26:06.845425: train_loss -0.7823 
+2024-09-08 01:26:06.845592: val_loss -0.6576 
+2024-09-08 01:26:06.845652: Pseudo dice [0.5982, 0.8531] 
+2024-09-08 01:26:06.845716: Epoch time: 245.73 s 
+2024-09-08 01:26:07.748693:  
+2024-09-08 01:26:07.748952: Epoch 105 
+2024-09-08 01:26:07.749033: Current learning rate: 0.00905 
+2024-09-08 01:30:13.409340: train_loss -0.7799 
+2024-09-08 01:30:13.409491: val_loss -0.6974 
+2024-09-08 01:30:13.409544: Pseudo dice [0.6792, 0.8628] 
+2024-09-08 01:30:13.409647: Epoch time: 245.66 s 
+2024-09-08 01:30:14.309895:  
+2024-09-08 01:30:14.310140: Epoch 106 
+2024-09-08 01:30:14.310220: Current learning rate: 0.00904 
+2024-09-08 01:34:19.891002: train_loss -0.7904 
+2024-09-08 01:34:19.891138: val_loss -0.7039 
+2024-09-08 01:34:19.891188: Pseudo dice [0.6807, 0.8615] 
+2024-09-08 01:34:19.891238: Epoch time: 245.58 s 
+2024-09-08 01:34:20.800539:  
+2024-09-08 01:34:20.800796: Epoch 107 
+2024-09-08 01:34:20.800906: Current learning rate: 0.00903 
+2024-09-08 01:38:26.611891: train_loss -0.7901 
+2024-09-08 01:38:26.612158: val_loss -0.6859 
+2024-09-08 01:38:26.612247: Pseudo dice [0.6878, 0.8582] 
+2024-09-08 01:38:26.612324: Epoch time: 245.81 s 
+2024-09-08 01:38:27.704440:  
+2024-09-08 01:38:27.704791: Epoch 108 
+2024-09-08 01:38:27.704957: Current learning rate: 0.00902 
+2024-09-08 01:42:33.528797: train_loss -0.7881 
+2024-09-08 01:42:33.528928: val_loss -0.6999 
+2024-09-08 01:42:33.528983: Pseudo dice [0.7012, 0.8518] 
+2024-09-08 01:42:33.529033: Epoch time: 245.83 s 
+2024-09-08 01:42:34.438416:  
+2024-09-08 01:42:34.438686: Epoch 109 
+2024-09-08 01:42:34.438768: Current learning rate: 0.00901 
+2024-09-08 01:46:40.122380: train_loss -0.7784 
+2024-09-08 01:46:40.122515: val_loss -0.6758 
+2024-09-08 01:46:40.122565: Pseudo dice [0.6683, 0.8417] 
+2024-09-08 01:46:40.122620: Epoch time: 245.69 s 
+2024-09-08 01:46:41.019458:  
+2024-09-08 01:46:41.019726: Epoch 110 
+2024-09-08 01:46:41.019815: Current learning rate: 0.009 
+2024-09-08 01:50:46.749211: train_loss -0.7712 
+2024-09-08 01:50:46.749349: val_loss -0.6825 
+2024-09-08 01:50:46.749398: Pseudo dice [0.6749, 0.8576] 
+2024-09-08 01:50:46.749449: Epoch time: 245.73 s 
+2024-09-08 01:50:47.699068:  
+2024-09-08 01:50:47.699271: Epoch 111 
+2024-09-08 01:50:47.699408: Current learning rate: 0.009 
+2024-09-08 01:54:53.281038: train_loss -0.7878 
+2024-09-08 01:54:53.281174: val_loss -0.6862 
+2024-09-08 01:54:53.281235: Pseudo dice [0.6586, 0.8672] 
+2024-09-08 01:54:53.281286: Epoch time: 245.58 s 
+2024-09-08 01:54:54.173763:  
+2024-09-08 01:54:54.174003: Epoch 112 
+2024-09-08 01:54:54.174077: Current learning rate: 0.00899 
+2024-09-08 01:58:59.846290: train_loss -0.7969 
+2024-09-08 01:58:59.846428: val_loss -0.6761 
+2024-09-08 01:58:59.846478: Pseudo dice [0.5998, 0.8727] 
+2024-09-08 01:58:59.846532: Epoch time: 245.67 s 
+2024-09-08 01:59:00.739522:  
+2024-09-08 01:59:00.739771: Epoch 113 
+2024-09-08 01:59:00.739855: Current learning rate: 0.00898 
+2024-09-08 02:03:06.384639: train_loss -0.7927 
+2024-09-08 02:03:06.384788: val_loss -0.6841 
+2024-09-08 02:03:06.384838: Pseudo dice [0.6272, 0.87] 
+2024-09-08 02:03:06.384889: Epoch time: 245.65 s 
+2024-09-08 02:03:07.270709:  
+2024-09-08 02:03:07.270944: Epoch 114 
+2024-09-08 02:03:07.271063: Current learning rate: 0.00897 
+2024-09-08 02:07:12.849920: train_loss -0.7879 
+2024-09-08 02:07:12.850085: val_loss -0.6755 
+2024-09-08 02:07:12.850139: Pseudo dice [0.653, 0.8694] 
+2024-09-08 02:07:12.850199: Epoch time: 245.58 s 
+2024-09-08 02:07:13.858839:  
+2024-09-08 02:07:13.859068: Epoch 115 
+2024-09-08 02:07:13.859209: Current learning rate: 0.00896 
+2024-09-08 02:11:19.533088: train_loss -0.7953 
+2024-09-08 02:11:19.533226: val_loss -0.6707 
+2024-09-08 02:11:19.533276: Pseudo dice [0.6522, 0.855] 
+2024-09-08 02:11:19.533328: Epoch time: 245.68 s 
+2024-09-08 02:11:20.459629:  
+2024-09-08 02:11:20.459831: Epoch 116 
+2024-09-08 02:11:20.459982: Current learning rate: 0.00895 
+2024-09-08 02:15:26.205783: train_loss -0.8073 
+2024-09-08 02:15:26.205922: val_loss -0.6883 
+2024-09-08 02:15:26.205971: Pseudo dice [0.6363, 0.8691] 
+2024-09-08 02:15:26.206023: Epoch time: 245.75 s 
+2024-09-08 02:15:27.124271:  
+2024-09-08 02:15:27.124506: Epoch 117 
+2024-09-08 02:15:27.124590: Current learning rate: 0.00894 
+2024-09-08 02:19:33.076700: train_loss -0.7981 
+2024-09-08 02:19:33.076871: val_loss -0.6874 
+2024-09-08 02:19:33.076922: Pseudo dice [0.6891, 0.8572] 
+2024-09-08 02:19:33.076974: Epoch time: 245.95 s 
+2024-09-08 02:19:33.987441:  
+2024-09-08 02:19:33.987695: Epoch 118 
+2024-09-08 02:19:33.987771: Current learning rate: 0.00893 
+2024-09-08 02:23:40.148934: train_loss -0.8043 
+2024-09-08 02:23:40.149184: val_loss -0.6828 
+2024-09-08 02:23:40.149271: Pseudo dice [0.6313, 0.87] 
+2024-09-08 02:23:40.149356: Epoch time: 246.16 s 
+2024-09-08 02:23:41.371354:  
+2024-09-08 02:23:41.371621: Epoch 119 
+2024-09-08 02:23:41.371711: Current learning rate: 0.00892 
+2024-09-08 02:27:47.325860: train_loss -0.7961 
+2024-09-08 02:27:47.326025: val_loss -0.6998 
+2024-09-08 02:27:47.326078: Pseudo dice [0.6681, 0.8663] 
+2024-09-08 02:27:47.326131: Epoch time: 245.96 s 
+2024-09-08 02:27:48.396451:  
+2024-09-08 02:27:48.396737: Epoch 120 
+2024-09-08 02:27:48.396878: Current learning rate: 0.00891 
+2024-09-08 02:31:54.274962: train_loss -0.8009 
+2024-09-08 02:31:54.275101: val_loss -0.7027 
+2024-09-08 02:31:54.275153: Pseudo dice [0.678, 0.872] 
+2024-09-08 02:31:54.275206: Epoch time: 245.88 s 
+2024-09-08 02:31:54.275247: Yayy! New best EMA pseudo Dice: 0.7582 
+2024-09-08 02:31:58.146997:  
+2024-09-08 02:31:58.147248: Epoch 121 
+2024-09-08 02:31:58.147328: Current learning rate: 0.0089 
+2024-09-08 02:36:03.683066: train_loss -0.8027 
+2024-09-08 02:36:03.683206: val_loss -0.7232 
+2024-09-08 02:36:03.683257: Pseudo dice [0.6804, 0.8707] 
+2024-09-08 02:36:03.683309: Epoch time: 245.54 s 
+2024-09-08 02:36:03.683348: Yayy! New best EMA pseudo Dice: 0.76 
+2024-09-08 02:36:07.505799:  
+2024-09-08 02:36:07.506002: Epoch 122 
+2024-09-08 02:36:07.506101: Current learning rate: 0.00889 
+2024-09-08 02:40:13.215961: train_loss -0.8057 
+2024-09-08 02:40:13.216106: val_loss -0.6714 
+2024-09-08 02:40:13.216164: Pseudo dice [0.6364, 0.8665] 
+2024-09-08 02:40:13.216215: Epoch time: 245.71 s 
+2024-09-08 02:40:14.283055:  
+2024-09-08 02:40:14.283224: Epoch 123 
+2024-09-08 02:40:14.283309: Current learning rate: 0.00889 
+2024-09-08 02:44:19.924903: train_loss -0.7988 
+2024-09-08 02:44:19.925041: val_loss -0.5954 
+2024-09-08 02:44:19.925104: Pseudo dice [0.5509, 0.7905] 
+2024-09-08 02:44:19.925155: Epoch time: 245.64 s 
+2024-09-08 02:44:20.831736:  
+2024-09-08 02:44:20.831976: Epoch 124 
+2024-09-08 02:44:20.832055: Current learning rate: 0.00888 
+2024-09-08 02:48:26.672736: train_loss -0.7556 
+2024-09-08 02:48:26.672874: val_loss -0.692 
+2024-09-08 02:48:26.672925: Pseudo dice [0.6946, 0.8466] 
+2024-09-08 02:48:26.672976: Epoch time: 245.84 s 
+2024-09-08 02:48:27.582657:  
+2024-09-08 02:48:27.582968: Epoch 125 
+2024-09-08 02:48:27.583048: Current learning rate: 0.00887 
+2024-09-08 02:52:33.500878: train_loss -0.7715 
+2024-09-08 02:52:33.501039: val_loss -0.6535 
+2024-09-08 02:52:33.501101: Pseudo dice [0.6482, 0.8489] 
+2024-09-08 02:52:33.501171: Epoch time: 245.92 s 
+2024-09-08 02:52:34.548421:  
+2024-09-08 02:52:34.548680: Epoch 126 
+2024-09-08 02:52:34.548763: Current learning rate: 0.00886 
+2024-09-08 02:56:40.391971: train_loss -0.7784 
+2024-09-08 02:56:40.392122: val_loss -0.6975 
+2024-09-08 02:56:40.392174: Pseudo dice [0.6797, 0.8619] 
+2024-09-08 02:56:40.392227: Epoch time: 245.85 s 
+2024-09-08 02:56:42.254919:  
+2024-09-08 02:56:42.255196: Epoch 127 
+2024-09-08 02:56:42.255280: Current learning rate: 0.00885 
+2024-09-08 03:00:47.963148: train_loss -0.7906 
+2024-09-08 03:00:47.963289: val_loss -0.6656 
+2024-09-08 03:00:47.963344: Pseudo dice [0.6271, 0.8704] 
+2024-09-08 03:00:47.963398: Epoch time: 245.71 s 
+2024-09-08 03:00:48.884216:  
+2024-09-08 03:00:48.884462: Epoch 128 
+2024-09-08 03:00:48.884545: Current learning rate: 0.00884 
+2024-09-08 03:04:54.595750: train_loss -0.8 
+2024-09-08 03:04:54.595943: val_loss -0.6785 
+2024-09-08 03:04:54.596016: Pseudo dice [0.629, 0.8726] 
+2024-09-08 03:04:54.596070: Epoch time: 245.71 s 
+2024-09-08 03:04:55.777201:  
+2024-09-08 03:04:55.777541: Epoch 129 
+2024-09-08 03:04:55.777645: Current learning rate: 0.00883 
+2024-09-08 03:09:01.704265: train_loss -0.8006 
+2024-09-08 03:09:01.704405: val_loss -0.6826 
+2024-09-08 03:09:01.704506: Pseudo dice [0.6413, 0.8655] 
+2024-09-08 03:09:01.704558: Epoch time: 245.93 s 
+2024-09-08 03:09:02.637694:  
+2024-09-08 03:09:02.637951: Epoch 130 
+2024-09-08 03:09:02.638036: Current learning rate: 0.00882 
+2024-09-08 03:13:08.578834: train_loss -0.8034 
+2024-09-08 03:13:08.578973: val_loss -0.685 
+2024-09-08 03:13:08.579022: Pseudo dice [0.6625, 0.8644] 
+2024-09-08 03:13:08.579123: Epoch time: 245.94 s 
+2024-09-08 03:13:09.505550:  
+2024-09-08 03:13:09.505782: Epoch 131 
+2024-09-08 03:13:09.505892: Current learning rate: 0.00881 
+2024-09-08 03:17:15.452911: train_loss -0.7788 
+2024-09-08 03:17:15.453121: val_loss -0.6689 
+2024-09-08 03:17:15.453206: Pseudo dice [0.6363, 0.8549] 
+2024-09-08 03:17:15.453291: Epoch time: 245.95 s 
+2024-09-08 03:17:16.687659:  
+2024-09-08 03:17:16.687932: Epoch 132 
+2024-09-08 03:17:16.688056: Current learning rate: 0.0088 
+2024-09-08 03:21:22.657165: train_loss -0.7853 
+2024-09-08 03:21:22.657305: val_loss -0.6776 
+2024-09-08 03:21:22.657356: Pseudo dice [0.6789, 0.861] 
+2024-09-08 03:21:22.657409: Epoch time: 245.97 s 
+2024-09-08 03:21:23.595708:  
+2024-09-08 03:21:23.595935: Epoch 133 
+2024-09-08 03:21:23.596121: Current learning rate: 0.00879 
+2024-09-08 03:25:29.479744: train_loss -0.7851 
+2024-09-08 03:25:29.479888: val_loss -0.6857 
+2024-09-08 03:25:29.479938: Pseudo dice [0.6304, 0.8698] 
+2024-09-08 03:25:29.479989: Epoch time: 245.89 s 
+2024-09-08 03:25:30.418671:  
+2024-09-08 03:25:30.418883: Epoch 134 
+2024-09-08 03:25:30.419005: Current learning rate: 0.00879 
+2024-09-08 03:29:36.424421: train_loss -0.7983 
+2024-09-08 03:29:36.424584: val_loss -0.7152 
+2024-09-08 03:29:36.424640: Pseudo dice [0.7101, 0.869] 
+2024-09-08 03:29:36.424703: Epoch time: 246.01 s 
+2024-09-08 03:29:37.515960:  
+2024-09-08 03:29:37.516255: Epoch 135 
+2024-09-08 03:29:37.516347: Current learning rate: 0.00878 
+2024-09-08 03:33:43.372171: train_loss -0.8013 
+2024-09-08 03:33:43.372308: val_loss -0.6805 
+2024-09-08 03:33:43.372359: Pseudo dice [0.6603, 0.8406] 
+2024-09-08 03:33:43.372410: Epoch time: 245.86 s 
+2024-09-08 03:33:44.308108:  
+2024-09-08 03:33:44.308420: Epoch 136 
+2024-09-08 03:33:44.308501: Current learning rate: 0.00877 
+2024-09-08 03:37:49.938666: train_loss -0.8078 
+2024-09-08 03:37:49.938802: val_loss -0.691 
+2024-09-08 03:37:49.938852: Pseudo dice [0.6509, 0.8778] 
+2024-09-08 03:37:49.938902: Epoch time: 245.63 s 
+2024-09-08 03:37:50.870237:  
+2024-09-08 03:37:50.870419: Epoch 137 
+2024-09-08 03:37:50.870585: Current learning rate: 0.00876 
+2024-09-08 03:41:56.507546: train_loss -0.8085 
+2024-09-08 03:41:56.507697: val_loss -0.6801 
+2024-09-08 03:41:56.507748: Pseudo dice [0.6432, 0.8608] 
+2024-09-08 03:41:56.507820: Epoch time: 245.64 s 
+2024-09-08 03:41:57.571068:  
+2024-09-08 03:41:57.571354: Epoch 138 
+2024-09-08 03:41:57.571435: Current learning rate: 0.00875 
+2024-09-08 03:46:03.110302: train_loss -0.8082 
+2024-09-08 03:46:03.110440: val_loss -0.6868 
+2024-09-08 03:46:03.110491: Pseudo dice [0.6581, 0.8703] 
+2024-09-08 03:46:03.110541: Epoch time: 245.54 s 
+2024-09-08 03:46:04.028641:  
+2024-09-08 03:46:04.028889: Epoch 139 
+2024-09-08 03:46:04.028995: Current learning rate: 0.00874 
+2024-09-08 03:50:09.669229: train_loss -0.812 
+2024-09-08 03:50:09.669388: val_loss -0.6885 
+2024-09-08 03:50:09.669440: Pseudo dice [0.6446, 0.8651] 
+2024-09-08 03:50:09.669491: Epoch time: 245.64 s 
+2024-09-08 03:50:10.602233:  
+2024-09-08 03:50:10.602479: Epoch 140 
+2024-09-08 03:50:10.602556: Current learning rate: 0.00873 
+2024-09-08 03:54:16.198593: train_loss -0.7929 
+2024-09-08 03:54:16.198729: val_loss -0.6658 
+2024-09-08 03:54:16.198776: Pseudo dice [0.6337, 0.8361] 
+2024-09-08 03:54:16.198828: Epoch time: 245.6 s 
+2024-09-08 03:54:17.133298:  
+2024-09-08 03:54:17.133514: Epoch 141 
+2024-09-08 03:54:17.133634: Current learning rate: 0.00872 
+2024-09-08 03:58:22.756314: train_loss -0.7936 
+2024-09-08 03:58:22.756513: val_loss -0.6434 
+2024-09-08 03:58:22.756565: Pseudo dice [0.5582, 0.8632] 
+2024-09-08 03:58:22.756618: Epoch time: 245.63 s 
+2024-09-08 03:58:23.730883:  
+2024-09-08 03:58:23.731112: Epoch 142 
+2024-09-08 03:58:23.731190: Current learning rate: 0.00871 
+2024-09-08 04:02:29.489819: train_loss -0.8118 
+2024-09-08 04:02:29.489957: val_loss -0.6792 
+2024-09-08 04:02:29.490007: Pseudo dice [0.6507, 0.8587] 
+2024-09-08 04:02:29.490057: Epoch time: 245.76 s 
+2024-09-08 04:02:30.429126:  
+2024-09-08 04:02:30.429344: Epoch 143 
+2024-09-08 04:02:30.429424: Current learning rate: 0.0087 
+2024-09-08 04:06:36.421281: train_loss -0.7931 
+2024-09-08 04:06:36.421453: val_loss -0.6426 
+2024-09-08 04:06:36.421518: Pseudo dice [0.5913, 0.8442] 
+2024-09-08 04:06:36.421583: Epoch time: 245.99 s 
+2024-09-08 04:06:37.515188:  
+2024-09-08 04:06:37.515501: Epoch 144 
+2024-09-08 04:06:37.515597: Current learning rate: 0.00869 
+2024-09-08 04:10:43.611033: train_loss -0.7894 
+2024-09-08 04:10:43.611209: val_loss -0.6915 
+2024-09-08 04:10:43.611262: Pseudo dice [0.6708, 0.8677] 
+2024-09-08 04:10:43.611314: Epoch time: 246.1 s 
+2024-09-08 04:10:44.555409:  
+2024-09-08 04:10:44.555622: Epoch 145 
+2024-09-08 04:10:44.555705: Current learning rate: 0.00868 
+2024-09-08 04:14:50.564071: train_loss -0.8025 
+2024-09-08 04:14:50.564211: val_loss -0.6412 
+2024-09-08 04:14:50.564261: Pseudo dice [0.6302, 0.8666] 
+2024-09-08 04:14:50.564313: Epoch time: 246.01 s 
+2024-09-08 04:14:51.508326:  
+2024-09-08 04:14:51.508566: Epoch 146 
+2024-09-08 04:14:51.508649: Current learning rate: 0.00868 
+2024-09-08 04:18:57.204751: train_loss -0.8098 
+2024-09-08 04:18:57.204904: val_loss -0.7058 
+2024-09-08 04:18:57.204956: Pseudo dice [0.7175, 0.8615] 
+2024-09-08 04:18:57.205011: Epoch time: 245.7 s 
+2024-09-08 04:18:58.451529:  
+2024-09-08 04:18:58.451950: Epoch 147 
+2024-09-08 04:18:58.452093: Current learning rate: 0.00867 
+2024-09-08 04:23:04.082814: train_loss -0.8085 
+2024-09-08 04:23:04.082959: val_loss -0.6895 
+2024-09-08 04:23:04.083009: Pseudo dice [0.6567, 0.8646] 
+2024-09-08 04:23:04.083061: Epoch time: 245.63 s 
+2024-09-08 04:23:05.025642:  
+2024-09-08 04:23:05.025901: Epoch 148 
+2024-09-08 04:23:05.025979: Current learning rate: 0.00866 
+2024-09-08 04:27:10.760466: train_loss -0.7886 
+2024-09-08 04:27:10.760600: val_loss -0.684 
+2024-09-08 04:27:10.760650: Pseudo dice [0.6434, 0.8523] 
+2024-09-08 04:27:10.760700: Epoch time: 245.74 s 
+2024-09-08 04:27:11.739474:  
+2024-09-08 04:27:11.739663: Epoch 149 
+2024-09-08 04:27:11.739741: Current learning rate: 0.00865 
+2024-09-08 04:31:17.529482: train_loss -0.7805 
+2024-09-08 04:31:17.529653: val_loss -0.6752 
+2024-09-08 04:31:17.529705: Pseudo dice [0.6518, 0.8423] 
+2024-09-08 04:31:17.529757: Epoch time: 245.79 s 
+2024-09-08 04:31:22.545900:  
+2024-09-08 04:31:22.546123: Epoch 150 
+2024-09-08 04:31:22.546277: Current learning rate: 0.00864 
+2024-09-08 04:35:28.238714: train_loss -0.7807 
+2024-09-08 04:35:28.238852: val_loss -0.6495 
+2024-09-08 04:35:28.238906: Pseudo dice [0.5948, 0.8526] 
+2024-09-08 04:35:28.238958: Epoch time: 245.7 s 
+2024-09-08 04:35:29.211180:  
+2024-09-08 04:35:29.211408: Epoch 151 
+2024-09-08 04:35:29.211507: Current learning rate: 0.00863 
+2024-09-08 04:39:34.933364: train_loss -0.7932 
+2024-09-08 04:39:34.933538: val_loss -0.6551 
+2024-09-08 04:39:34.933588: Pseudo dice [0.6243, 0.8438] 
+2024-09-08 04:39:34.933639: Epoch time: 245.72 s 
+2024-09-08 04:39:35.895008:  
+2024-09-08 04:39:35.895210: Epoch 152 
+2024-09-08 04:39:35.895290: Current learning rate: 0.00862 
+2024-09-08 04:43:41.740120: train_loss -0.7968 
+2024-09-08 04:43:41.740264: val_loss -0.7286 
+2024-09-08 04:43:41.740355: Pseudo dice [0.707, 0.8736] 
+2024-09-08 04:43:41.740409: Epoch time: 245.85 s 
+2024-09-08 04:43:42.696833:  
+2024-09-08 04:43:42.697067: Epoch 153 
+2024-09-08 04:43:42.697187: Current learning rate: 0.00861 
+2024-09-08 04:47:48.396387: train_loss -0.8111 
+2024-09-08 04:47:48.396524: val_loss -0.6911 
+2024-09-08 04:47:48.396574: Pseudo dice [0.6349, 0.8559] 
+2024-09-08 04:47:48.396626: Epoch time: 245.7 s 
+2024-09-08 04:47:49.385124:  
+2024-09-08 04:47:49.385333: Epoch 154 
+2024-09-08 04:47:49.385459: Current learning rate: 0.0086 
+2024-09-08 04:51:55.312069: train_loss -0.8114 
+2024-09-08 04:51:55.312205: val_loss -0.7059 
+2024-09-08 04:51:55.312388: Pseudo dice [0.6554, 0.875] 
+2024-09-08 04:51:55.312499: Epoch time: 245.93 s 
+2024-09-08 04:51:56.309840:  
+2024-09-08 04:51:56.310071: Epoch 155 
+2024-09-08 04:51:56.310160: Current learning rate: 0.00859 
+2024-09-08 04:56:02.343367: train_loss -0.8133 
+2024-09-08 04:56:02.343506: val_loss -0.7017 
+2024-09-08 04:56:02.343579: Pseudo dice [0.6613, 0.8533] 
+2024-09-08 04:56:02.343679: Epoch time: 246.04 s 
+2024-09-08 04:56:03.320117:  
+2024-09-08 04:56:03.320284: Epoch 156 
+2024-09-08 04:56:03.320364: Current learning rate: 0.00858 
+2024-09-08 05:00:09.310615: train_loss -0.804 
+2024-09-08 05:00:09.310755: val_loss -0.6818 
+2024-09-08 05:00:09.310805: Pseudo dice [0.633, 0.8645] 
+2024-09-08 05:00:09.310855: Epoch time: 245.99 s 
+2024-09-08 05:00:10.286608:  
+2024-09-08 05:00:10.286842: Epoch 157 
+2024-09-08 05:00:10.286920: Current learning rate: 0.00858 
+2024-09-08 05:04:16.227135: train_loss -0.8086 
+2024-09-08 05:04:16.227276: val_loss -0.7008 
+2024-09-08 05:04:16.227326: Pseudo dice [0.6591, 0.8629] 
+2024-09-08 05:04:16.227377: Epoch time: 245.94 s 
+2024-09-08 05:04:17.214491:  
+2024-09-08 05:04:17.214716: Epoch 158 
+2024-09-08 05:04:17.214800: Current learning rate: 0.00857 
+2024-09-08 05:08:23.081993: train_loss -0.817 
+2024-09-08 05:08:23.082129: val_loss -0.6742 
+2024-09-08 05:08:23.082179: Pseudo dice [0.6552, 0.8586] 
+2024-09-08 05:08:23.082229: Epoch time: 245.87 s 
+2024-09-08 05:08:24.085830:  
+2024-09-08 05:08:24.085993: Epoch 159 
+2024-09-08 05:08:24.086109: Current learning rate: 0.00856 
+2024-09-08 05:12:29.944931: train_loss -0.8148 
+2024-09-08 05:12:29.945101: val_loss -0.6932 
+2024-09-08 05:12:29.945155: Pseudo dice [0.647, 0.8723] 
+2024-09-08 05:12:29.945207: Epoch time: 245.86 s 
+2024-09-08 05:12:30.920198:  
+2024-09-08 05:12:30.920369: Epoch 160 
+2024-09-08 05:12:30.920476: Current learning rate: 0.00855 
+2024-09-08 05:16:36.694449: train_loss -0.7926 
+2024-09-08 05:16:36.694590: val_loss -0.6053 
+2024-09-08 05:16:36.694641: Pseudo dice [0.5263, 0.8072] 
+2024-09-08 05:16:36.694723: Epoch time: 245.78 s 
+2024-09-08 05:16:37.767959:  
+2024-09-08 05:16:37.768233: Epoch 161 
+2024-09-08 05:16:37.768327: Current learning rate: 0.00854 
+2024-09-08 05:20:43.880070: train_loss -0.7884 
+2024-09-08 05:20:43.880206: val_loss -0.6129 
+2024-09-08 05:20:43.880257: Pseudo dice [0.5594, 0.8413] 
+2024-09-08 05:20:43.880309: Epoch time: 246.11 s 
+2024-09-08 05:20:44.888172:  
+2024-09-08 05:20:44.888372: Epoch 162 
+2024-09-08 05:20:44.888452: Current learning rate: 0.00853 
+2024-09-08 05:24:50.681520: train_loss -0.793 
+2024-09-08 05:24:50.681656: val_loss -0.6983 
+2024-09-08 05:24:50.681706: Pseudo dice [0.6765, 0.8584] 
+2024-09-08 05:24:50.681758: Epoch time: 245.8 s 
+2024-09-08 05:24:51.662942:  
+2024-09-08 05:24:51.663163: Epoch 163 
+2024-09-08 05:24:51.663268: Current learning rate: 0.00852 
+2024-09-08 05:28:57.397023: train_loss -0.8072 
+2024-09-08 05:28:57.397249: val_loss -0.6921 
+2024-09-08 05:28:57.397334: Pseudo dice [0.6392, 0.8802] 
+2024-09-08 05:28:57.397418: Epoch time: 245.74 s 
+2024-09-08 05:28:58.604556:  
+2024-09-08 05:28:58.604849: Epoch 164 
+2024-09-08 05:28:58.604939: Current learning rate: 0.00851 
+2024-09-08 05:33:04.345761: train_loss -0.809 
+2024-09-08 05:33:04.345895: val_loss -0.6952 
+2024-09-08 05:33:04.345945: Pseudo dice [0.6462, 0.8703] 
+2024-09-08 05:33:04.345997: Epoch time: 245.74 s 
+2024-09-08 05:33:05.300717:  
+2024-09-08 05:33:05.300906: Epoch 165 
+2024-09-08 05:33:05.300986: Current learning rate: 0.0085 
+2024-09-08 05:37:10.766097: train_loss -0.806 
+2024-09-08 05:37:10.766236: val_loss -0.6807 
+2024-09-08 05:37:10.766287: Pseudo dice [0.6515, 0.8596] 
+2024-09-08 05:37:10.766338: Epoch time: 245.47 s 
+2024-09-08 05:37:11.703491:  
+2024-09-08 05:37:11.703659: Epoch 166 
+2024-09-08 05:37:11.703739: Current learning rate: 0.00849 
+2024-09-08 05:41:17.250345: train_loss -0.7994 
+2024-09-08 05:41:17.250503: val_loss -0.6587 
+2024-09-08 05:41:17.250555: Pseudo dice [0.6489, 0.8426] 
+2024-09-08 05:41:17.250608: Epoch time: 245.55 s 
+2024-09-08 05:41:18.275294:  
+2024-09-08 05:41:18.275477: Epoch 167 
+2024-09-08 05:41:18.275573: Current learning rate: 0.00848 
+2024-09-08 05:45:24.050109: train_loss -0.804 
+2024-09-08 05:45:24.050247: val_loss -0.7003 
+2024-09-08 05:45:24.050297: Pseudo dice [0.6766, 0.8641] 
+2024-09-08 05:45:24.050349: Epoch time: 245.78 s 
+2024-09-08 05:45:25.013472:  
+2024-09-08 05:45:25.013628: Epoch 168 
+2024-09-08 05:45:25.013707: Current learning rate: 0.00847 
+2024-09-08 05:49:30.979896: train_loss -0.7874 
+2024-09-08 05:49:30.980061: val_loss -0.6701 
+2024-09-08 05:49:30.980112: Pseudo dice [0.657, 0.8471] 
+2024-09-08 05:49:30.980163: Epoch time: 245.97 s 
+2024-09-08 05:49:32.000903:  
+2024-09-08 05:49:32.001079: Epoch 169 
+2024-09-08 05:49:32.001160: Current learning rate: 0.00847 
+2024-09-08 05:53:37.936337: train_loss -0.7898 
+2024-09-08 05:53:37.936491: val_loss -0.6851 
+2024-09-08 05:53:37.936543: Pseudo dice [0.6397, 0.8492] 
+2024-09-08 05:53:37.936594: Epoch time: 245.94 s 
+2024-09-08 05:53:38.928193:  
+2024-09-08 05:53:38.928355: Epoch 170 
+2024-09-08 05:53:38.928438: Current learning rate: 0.00846 
+2024-09-08 05:57:44.870304: train_loss -0.8098 
+2024-09-08 05:57:44.870438: val_loss -0.6875 
+2024-09-08 05:57:44.870487: Pseudo dice [0.6543, 0.8677] 
+2024-09-08 05:57:44.870537: Epoch time: 245.94 s 
+2024-09-08 05:57:45.827393:  
+2024-09-08 05:57:45.827582: Epoch 171 
+2024-09-08 05:57:45.827659: Current learning rate: 0.00845 
+2024-09-08 06:01:51.595985: train_loss -0.8167 
+2024-09-08 06:01:51.596152: val_loss -0.6802 
+2024-09-08 06:01:51.596203: Pseudo dice [0.6039, 0.8789] 
+2024-09-08 06:01:51.596256: Epoch time: 245.77 s 
+2024-09-08 06:01:52.563918:  
+2024-09-08 06:01:52.564119: Epoch 172 
+2024-09-08 06:01:52.564212: Current learning rate: 0.00844 
+2024-09-08 06:05:58.859036: train_loss -0.7984 
+2024-09-08 06:05:58.859169: val_loss -0.666 
+2024-09-08 06:05:58.859223: Pseudo dice [0.6033, 0.8522] 
+2024-09-08 06:05:58.859274: Epoch time: 246.3 s 
+2024-09-08 06:05:59.817324:  
+2024-09-08 06:05:59.817553: Epoch 173 
+2024-09-08 06:05:59.817726: Current learning rate: 0.00843 
+2024-09-08 06:10:05.550503: train_loss -0.7799 
+2024-09-08 06:10:05.550640: val_loss -0.663 
+2024-09-08 06:10:05.550694: Pseudo dice [0.6458, 0.8625] 
+2024-09-08 06:10:05.550747: Epoch time: 245.74 s 
+2024-09-08 06:10:06.571981:  
+2024-09-08 06:10:06.572263: Epoch 174 
+2024-09-08 06:10:06.572390: Current learning rate: 0.00842 
+2024-09-08 06:14:12.116366: train_loss -0.7961 
+2024-09-08 06:14:12.116509: val_loss -0.6307 
+2024-09-08 06:14:12.116561: Pseudo dice [0.5483, 0.8385] 
+2024-09-08 06:14:12.116613: Epoch time: 245.55 s 
+2024-09-08 06:14:13.104790:  
+2024-09-08 06:14:13.105013: Epoch 175 
+2024-09-08 06:14:13.105094: Current learning rate: 0.00841 
+2024-09-08 06:18:19.529569: train_loss -0.7982 
+2024-09-08 06:18:19.529705: val_loss -0.6873 
+2024-09-08 06:18:19.529755: Pseudo dice [0.6643, 0.867] 
+2024-09-08 06:18:19.529807: Epoch time: 246.43 s 
+2024-09-08 06:18:20.495459:  
+2024-09-08 06:18:20.495646: Epoch 176 
+2024-09-08 06:18:20.495752: Current learning rate: 0.0084 
+2024-09-08 06:22:25.974083: train_loss -0.8132 
+2024-09-08 06:22:25.974227: val_loss -0.696 
+2024-09-08 06:22:25.974278: Pseudo dice [0.6812, 0.8508] 
+2024-09-08 06:22:25.974329: Epoch time: 245.48 s 
+2024-09-08 06:22:26.958171:  
+2024-09-08 06:22:26.958296: Epoch 177 
+2024-09-08 06:22:26.958372: Current learning rate: 0.00839 
+2024-09-08 06:26:32.549243: train_loss -0.8126 
+2024-09-08 06:26:32.549379: val_loss -0.6932 
+2024-09-08 06:26:32.549429: Pseudo dice [0.6379, 0.8684] 
+2024-09-08 06:26:32.549480: Epoch time: 245.59 s 
+2024-09-08 06:26:33.543028:  
+2024-09-08 06:26:33.543236: Epoch 178 
+2024-09-08 06:26:33.543318: Current learning rate: 0.00838 
+2024-09-08 06:30:39.391779: train_loss -0.8073 
+2024-09-08 06:30:39.391932: val_loss -0.6809 
+2024-09-08 06:30:39.391983: Pseudo dice [0.6132, 0.8733] 
+2024-09-08 06:30:39.392034: Epoch time: 245.85 s 
+2024-09-08 06:30:40.373961:  
+2024-09-08 06:30:40.374100: Epoch 179 
+2024-09-08 06:30:40.374178: Current learning rate: 0.00837 
+2024-09-08 06:34:46.161110: train_loss -0.8161 
+2024-09-08 06:34:46.161248: val_loss -0.6564 
+2024-09-08 06:34:46.161298: Pseudo dice [0.5939, 0.8405] 
+2024-09-08 06:34:46.161387: Epoch time: 245.79 s 
+2024-09-08 06:34:47.119436:  
+2024-09-08 06:34:47.119590: Epoch 180 
+2024-09-08 06:34:47.119668: Current learning rate: 0.00836 
+2024-09-08 06:38:52.749890: train_loss -0.8081 
+2024-09-08 06:38:52.750032: val_loss -0.699 
+2024-09-08 06:38:52.750085: Pseudo dice [0.6904, 0.8774] 
+2024-09-08 06:38:52.750136: Epoch time: 245.63 s 
+2024-09-08 06:38:53.713028:  
+2024-09-08 06:38:53.713234: Epoch 181 
+2024-09-08 06:38:53.713313: Current learning rate: 0.00836 
+2024-09-08 06:43:00.059581: train_loss -0.8076 
+2024-09-08 06:43:00.059743: val_loss -0.6915 
+2024-09-08 06:43:00.059818: Pseudo dice [0.6394, 0.8626] 
+2024-09-08 06:43:00.059880: Epoch time: 246.35 s 
+2024-09-08 06:43:01.353992:  
+2024-09-08 06:43:01.354357: Epoch 182 
+2024-09-08 06:43:01.354489: Current learning rate: 0.00835 
+2024-09-08 06:47:07.190707: train_loss -0.8147 
+2024-09-08 06:47:07.190847: val_loss -0.6931 
+2024-09-08 06:47:07.190898: Pseudo dice [0.676, 0.8752] 
+2024-09-08 06:47:07.190949: Epoch time: 245.84 s 
+2024-09-08 06:47:08.149665:  
+2024-09-08 06:47:08.149837: Epoch 183 
+2024-09-08 06:47:08.149920: Current learning rate: 0.00834 
+2024-09-08 06:51:13.695023: train_loss -0.8226 
+2024-09-08 06:51:13.695157: val_loss -0.7236 
+2024-09-08 06:51:13.695206: Pseudo dice [0.6993, 0.8836] 
+2024-09-08 06:51:13.695257: Epoch time: 245.55 s 
+2024-09-08 06:51:14.648685:  
+2024-09-08 06:51:14.648949: Epoch 184 
+2024-09-08 06:51:14.649029: Current learning rate: 0.00833 
+2024-09-08 06:55:20.240844: train_loss -0.821 
+2024-09-08 06:55:20.241000: val_loss -0.6633 
+2024-09-08 06:55:20.241051: Pseudo dice [0.6128, 0.8582] 
+2024-09-08 06:55:20.241102: Epoch time: 245.59 s 
+2024-09-08 06:55:21.194717:  
+2024-09-08 06:55:21.194876: Epoch 185 
+2024-09-08 06:55:21.194958: Current learning rate: 0.00832 
+2024-09-08 06:59:26.879701: train_loss -0.8192 
+2024-09-08 06:59:26.879850: val_loss -0.6617 
+2024-09-08 06:59:26.879913: Pseudo dice [0.5896, 0.8562] 
+2024-09-08 06:59:26.879966: Epoch time: 245.69 s 
+2024-09-08 06:59:27.850911:  
+2024-09-08 06:59:27.851114: Epoch 186 
+2024-09-08 06:59:27.851191: Current learning rate: 0.00831 
+2024-09-08 07:03:33.393676: train_loss -0.8237 
+2024-09-08 07:03:33.393815: val_loss -0.6871 
+2024-09-08 07:03:33.393865: Pseudo dice [0.6498, 0.8701] 
+2024-09-08 07:03:33.393916: Epoch time: 245.54 s 
+2024-09-08 07:03:34.370514:  
+2024-09-08 07:03:34.370623: Epoch 187 
+2024-09-08 07:03:34.370704: Current learning rate: 0.0083 
+2024-09-08 07:07:40.500534: train_loss -0.8195 
+2024-09-08 07:07:40.500686: val_loss -0.6953 
+2024-09-08 07:07:40.500739: Pseudo dice [0.6594, 0.8648] 
+2024-09-08 07:07:40.500789: Epoch time: 246.13 s 
+2024-09-08 07:07:41.556616:  
+2024-09-08 07:07:41.556814: Epoch 188 
+2024-09-08 07:07:41.556898: Current learning rate: 0.00829 
+2024-09-08 07:11:47.522710: train_loss -0.8098 
+2024-09-08 07:11:47.522848: val_loss -0.6559 
+2024-09-08 07:11:47.522898: Pseudo dice [0.6273, 0.8256] 
+2024-09-08 07:11:47.522949: Epoch time: 245.97 s 
+2024-09-08 07:11:48.487447:  
+2024-09-08 07:11:48.487607: Epoch 189 
+2024-09-08 07:11:48.487717: Current learning rate: 0.00828 
+2024-09-08 07:15:54.067254: train_loss -0.8015 
+2024-09-08 07:15:54.067393: val_loss -0.6946 
+2024-09-08 07:15:54.067442: Pseudo dice [0.6465, 0.8632] 
+2024-09-08 07:15:54.067494: Epoch time: 245.58 s 
+2024-09-08 07:15:55.037582:  
+2024-09-08 07:15:55.037743: Epoch 190 
+2024-09-08 07:15:55.037825: Current learning rate: 0.00827 
+2024-09-08 07:20:00.672658: train_loss -0.8005 
+2024-09-08 07:20:00.672797: val_loss -0.6284 
+2024-09-08 07:20:00.672848: Pseudo dice [0.5613, 0.8327] 
+2024-09-08 07:20:00.672902: Epoch time: 245.64 s 
+2024-09-08 07:20:01.640436:  
+2024-09-08 07:20:01.640550: Epoch 191 
+2024-09-08 07:20:01.640631: Current learning rate: 0.00826 
+2024-09-08 07:24:07.227292: train_loss -0.8067 
+2024-09-08 07:24:07.227492: val_loss -0.6746 
+2024-09-08 07:24:07.227545: Pseudo dice [0.6263, 0.856] 
+2024-09-08 07:24:07.227596: Epoch time: 245.59 s 
+2024-09-08 07:24:08.442117:  
+2024-09-08 07:24:08.442396: Epoch 192 
+2024-09-08 07:24:08.442501: Current learning rate: 0.00825 
+2024-09-08 07:28:14.126756: train_loss -0.816 
+2024-09-08 07:28:14.127003: val_loss -0.6651 
+2024-09-08 07:28:14.127088: Pseudo dice [0.6291, 0.8326] 
+2024-09-08 07:28:14.127175: Epoch time: 245.69 s 
+2024-09-08 07:28:15.397739:  
+2024-09-08 07:28:15.397927: Epoch 193 
+2024-09-08 07:28:15.398009: Current learning rate: 0.00824 
+2024-09-08 07:32:21.012563: train_loss -0.7819 
+2024-09-08 07:32:21.012699: val_loss -0.5883 
+2024-09-08 07:32:21.012749: Pseudo dice [0.4888, 0.8416] 
+2024-09-08 07:32:21.012801: Epoch time: 245.62 s 
+2024-09-08 07:32:21.990326:  
+2024-09-08 07:32:21.990470: Epoch 194 
+2024-09-08 07:32:21.990551: Current learning rate: 0.00824 
+2024-09-08 07:36:27.744639: train_loss -0.7819 
+2024-09-08 07:36:27.744851: val_loss -0.6732 
+2024-09-08 07:36:27.744933: Pseudo dice [0.6484, 0.8557] 
+2024-09-08 07:36:27.745014: Epoch time: 245.76 s 
+2024-09-08 07:36:30.523998:  
+2024-09-08 07:36:30.524337: Epoch 195 
+2024-09-08 07:36:30.524444: Current learning rate: 0.00823 
+2024-09-08 07:40:45.175316: train_loss -0.7957 
+2024-09-08 07:40:45.175511: val_loss -0.6818 
+2024-09-08 07:40:45.175609: Pseudo dice [0.6729, 0.8548] 
+2024-09-08 07:40:45.175701: Epoch time: 254.65 s 
+2024-09-08 07:40:46.157658:  
+2024-09-08 07:40:46.157827: Epoch 196 
+2024-09-08 07:40:46.157938: Current learning rate: 0.00822 
+2024-09-08 07:44:52.199999: train_loss -0.8081 
+2024-09-08 07:44:52.200160: val_loss -0.6873 
+2024-09-08 07:44:52.200211: Pseudo dice [0.6488, 0.8597] 
+2024-09-08 07:44:52.200262: Epoch time: 246.04 s 
+2024-09-08 07:44:53.183614:  
+2024-09-08 07:44:53.183799: Epoch 197 
+2024-09-08 07:44:53.183900: Current learning rate: 0.00821 
+2024-09-08 07:48:58.966816: train_loss -0.798 
+2024-09-08 07:48:58.966951: val_loss -0.6879 
+2024-09-08 07:48:58.967000: Pseudo dice [0.6394, 0.8729] 
+2024-09-08 07:48:58.967051: Epoch time: 245.79 s 
+2024-09-08 07:48:59.962899:  
+2024-09-08 07:48:59.963094: Epoch 198 
+2024-09-08 07:48:59.963167: Current learning rate: 0.0082 
+2024-09-08 07:53:05.907831: train_loss -0.8067 
+2024-09-08 07:53:05.908008: val_loss -0.6952 
+2024-09-08 07:53:05.908103: Pseudo dice [0.6666, 0.871] 
+2024-09-08 07:53:05.908179: Epoch time: 245.95 s 
+2024-09-08 07:53:07.117502:  
+2024-09-08 07:53:07.117756: Epoch 199 
+2024-09-08 07:53:07.117854: Current learning rate: 0.00819 
+2024-09-08 07:57:12.803171: train_loss -0.8121 
+2024-09-08 07:57:12.803326: val_loss -0.6515 
+2024-09-08 07:57:12.803377: Pseudo dice [0.5993, 0.8646] 
+2024-09-08 07:57:12.803427: Epoch time: 245.69 s 
+2024-09-08 07:57:16.684045:  
+2024-09-08 07:57:16.684277: Epoch 200 
+2024-09-08 07:57:16.684355: Current learning rate: 0.00818 
+2024-09-08 08:01:22.258212: train_loss -0.8121 
+2024-09-08 08:01:22.258366: val_loss -0.6847 
+2024-09-08 08:01:22.258420: Pseudo dice [0.6665, 0.8564] 
+2024-09-08 08:01:22.258470: Epoch time: 245.58 s 
+2024-09-08 08:01:23.234102:  
+2024-09-08 08:01:23.234282: Epoch 201 
+2024-09-08 08:01:23.234363: Current learning rate: 0.00817 
+2024-09-08 08:05:30.482593: train_loss -0.8137 
+2024-09-08 08:05:30.482730: val_loss -0.6379 
+2024-09-08 08:05:30.482779: Pseudo dice [0.5628, 0.8729] 
+2024-09-08 08:05:30.482831: Epoch time: 247.25 s 
+2024-09-08 08:05:31.494306:  
+2024-09-08 08:05:31.494601: Epoch 202 
+2024-09-08 08:05:31.494681: Current learning rate: 0.00816 
+2024-09-08 08:09:37.158287: train_loss -0.8144 
+2024-09-08 08:09:37.158426: val_loss -0.6418 
+2024-09-08 08:09:37.158503: Pseudo dice [0.5826, 0.8565] 
+2024-09-08 08:09:37.158584: Epoch time: 245.67 s 
+2024-09-08 08:09:38.159646:  
+2024-09-08 08:09:38.159856: Epoch 203 
+2024-09-08 08:09:38.159940: Current learning rate: 0.00815 
+2024-09-08 08:13:43.844826: train_loss -0.8145 
+2024-09-08 08:13:43.844961: val_loss -0.6904 
+2024-09-08 08:13:43.845010: Pseudo dice [0.6331, 0.8753] 
+2024-09-08 08:13:43.845061: Epoch time: 245.69 s 
+2024-09-08 08:13:44.850333:  
+2024-09-08 08:13:44.850512: Epoch 204 
+2024-09-08 08:13:44.850593: Current learning rate: 0.00814 
+2024-09-08 08:17:50.528644: train_loss -0.829 
+2024-09-08 08:17:50.529073: val_loss -0.6763 
+2024-09-08 08:17:50.529293: Pseudo dice [0.6094, 0.8712] 
+2024-09-08 08:17:50.529475: Epoch time: 245.68 s 
+2024-09-08 08:17:51.781223:  
+2024-09-08 08:17:51.781444: Epoch 205 
+2024-09-08 08:17:51.781551: Current learning rate: 0.00813 
+2024-09-08 08:21:57.529602: train_loss -0.8065 
+2024-09-08 08:21:57.529739: val_loss -0.7076 
+2024-09-08 08:21:57.529823: Pseudo dice [0.7023, 0.8507] 
+2024-09-08 08:21:57.529875: Epoch time: 245.75 s 
+2024-09-08 08:21:58.449527:  
+2024-09-08 08:21:58.449706: Epoch 206 
+2024-09-08 08:21:58.449785: Current learning rate: 0.00813 
+2024-09-08 08:26:04.061989: train_loss -0.8142 
+2024-09-08 08:26:04.062123: val_loss -0.6876 
+2024-09-08 08:26:04.062173: Pseudo dice [0.6439, 0.8691] 
+2024-09-08 08:26:04.062224: Epoch time: 245.61 s 
+2024-09-08 08:26:05.022566:  
+2024-09-08 08:26:05.022817: Epoch 207 
+2024-09-08 08:26:05.022910: Current learning rate: 0.00812 
+2024-09-08 08:30:10.754843: train_loss -0.8159 
+2024-09-08 08:30:10.754980: val_loss -0.6874 
+2024-09-08 08:30:10.755031: Pseudo dice [0.67, 0.8587] 
+2024-09-08 08:30:10.755082: Epoch time: 245.73 s 
+2024-09-08 08:30:11.697377:  
+2024-09-08 08:30:11.697553: Epoch 208 
+2024-09-08 08:30:11.697632: Current learning rate: 0.00811 
+2024-09-08 08:34:17.753882: train_loss -0.8166 
+2024-09-08 08:34:17.754042: val_loss -0.6915 
+2024-09-08 08:34:17.754115: Pseudo dice [0.6488, 0.8766] 
+2024-09-08 08:34:17.754166: Epoch time: 246.06 s 
+2024-09-08 08:34:18.668041:  
+2024-09-08 08:34:18.668224: Epoch 209 
+2024-09-08 08:34:18.668328: Current learning rate: 0.0081 
+2024-09-08 08:38:24.627981: train_loss -0.8133 
+2024-09-08 08:38:24.628119: val_loss -0.6784 
+2024-09-08 08:38:24.628169: Pseudo dice [0.6513, 0.8715] 
+2024-09-08 08:38:24.628219: Epoch time: 245.96 s 
+2024-09-08 08:38:25.546193:  
+2024-09-08 08:38:25.546357: Epoch 210 
+2024-09-08 08:38:25.546437: Current learning rate: 0.00809 
+2024-09-08 08:42:31.590567: train_loss -0.8175 
+2024-09-08 08:42:31.590705: val_loss -0.7133 
+2024-09-08 08:42:31.590757: Pseudo dice [0.6922, 0.8775] 
+2024-09-08 08:42:31.590808: Epoch time: 246.05 s 
+2024-09-08 08:42:32.516938:  
+2024-09-08 08:42:32.517146: Epoch 211 
+2024-09-08 08:42:32.517224: Current learning rate: 0.00808 
+2024-09-08 08:46:38.674495: train_loss -0.8057 
+2024-09-08 08:46:38.674628: val_loss -0.651 
+2024-09-08 08:46:38.674678: Pseudo dice [0.5808, 0.8291] 
+2024-09-08 08:46:38.674730: Epoch time: 246.16 s 
+2024-09-08 08:46:39.587319:  
+2024-09-08 08:46:39.587532: Epoch 212 
+2024-09-08 08:46:39.587614: Current learning rate: 0.00807 
+2024-09-08 08:50:45.479198: train_loss -0.8158 
+2024-09-08 08:50:45.479358: val_loss -0.6697 
+2024-09-08 08:50:45.479410: Pseudo dice [0.6164, 0.8572] 
+2024-09-08 08:50:45.479461: Epoch time: 245.89 s 
+2024-09-08 08:50:46.409760:  
+2024-09-08 08:50:46.409963: Epoch 213 
+2024-09-08 08:50:46.410046: Current learning rate: 0.00806 
+2024-09-08 08:54:52.309240: train_loss -0.8041 
+2024-09-08 08:54:52.309379: val_loss -0.6962 
+2024-09-08 08:54:52.309428: Pseudo dice [0.6465, 0.8748] 
+2024-09-08 08:54:52.309479: Epoch time: 245.9 s 
+2024-09-08 08:54:53.245966:  
+2024-09-08 08:54:53.246152: Epoch 214 
+2024-09-08 08:54:53.246268: Current learning rate: 0.00805 
+2024-09-08 08:58:58.964288: train_loss -0.7978 
+2024-09-08 08:58:58.964453: val_loss -0.7005 
+2024-09-08 08:58:58.964506: Pseudo dice [0.6756, 0.8669] 
+2024-09-08 08:58:58.964559: Epoch time: 245.72 s 
+2024-09-08 08:59:00.045830:  
+2024-09-08 08:59:00.046031: Epoch 215 
+2024-09-08 08:59:00.046126: Current learning rate: 0.00804 
+2024-09-08 09:03:05.579471: train_loss -0.8078 
+2024-09-08 09:03:05.579606: val_loss -0.7115 
+2024-09-08 09:03:05.579661: Pseudo dice [0.675, 0.8718] 
+2024-09-08 09:03:05.579715: Epoch time: 245.54 s 
+2024-09-08 09:03:06.494990:  
+2024-09-08 09:03:06.495186: Epoch 216 
+2024-09-08 09:03:06.495263: Current learning rate: 0.00803 
+2024-09-08 09:07:11.930741: train_loss -0.8122 
+2024-09-08 09:07:11.930930: val_loss -0.6839 
+2024-09-08 09:07:11.930980: Pseudo dice [0.6667, 0.8653] 
+2024-09-08 09:07:11.931034: Epoch time: 245.44 s 
+2024-09-08 09:07:12.875305:  
+2024-09-08 09:07:12.875488: Epoch 217 
+2024-09-08 09:07:12.875565: Current learning rate: 0.00802 
+2024-09-08 09:11:18.388308: train_loss -0.811 
+2024-09-08 09:11:18.388474: val_loss -0.6922 
+2024-09-08 09:11:18.388525: Pseudo dice [0.6754, 0.8632] 
+2024-09-08 09:11:18.388576: Epoch time: 245.51 s 
+2024-09-08 09:11:20.279648:  
+2024-09-08 09:11:20.279873: Epoch 218 
+2024-09-08 09:11:20.279954: Current learning rate: 0.00801 
+2024-09-08 09:15:26.011601: train_loss -0.8217 
+2024-09-08 09:15:26.011738: val_loss -0.6912 
+2024-09-08 09:15:26.011858: Pseudo dice [0.6685, 0.8664] 
+2024-09-08 09:15:26.011941: Epoch time: 245.73 s 
+2024-09-08 09:15:26.932775:  
+2024-09-08 09:15:26.932975: Epoch 219 
+2024-09-08 09:15:26.933076: Current learning rate: 0.00801 
+2024-09-08 09:19:32.617829: train_loss -0.8274 
+2024-09-08 09:19:32.617964: val_loss -0.6897 
+2024-09-08 09:19:32.618014: Pseudo dice [0.6373, 0.8618] 
+2024-09-08 09:19:32.618065: Epoch time: 245.69 s 
+2024-09-08 09:19:33.566856:  
+2024-09-08 09:19:33.567054: Epoch 220 
+2024-09-08 09:19:33.567178: Current learning rate: 0.008 
+2024-09-08 09:23:39.311715: train_loss -0.8139 
+2024-09-08 09:23:39.311876: val_loss -0.6709 
+2024-09-08 09:23:39.311932: Pseudo dice [0.6426, 0.8505] 
+2024-09-08 09:23:39.311987: Epoch time: 245.75 s 
+2024-09-08 09:23:40.243302:  
+2024-09-08 09:23:40.243558: Epoch 221 
+2024-09-08 09:23:40.243644: Current learning rate: 0.00799 
+2024-09-08 09:27:46.033603: train_loss -0.813 
+2024-09-08 09:27:46.033751: val_loss -0.7156 
+2024-09-08 09:27:46.033807: Pseudo dice [0.705, 0.8595] 
+2024-09-08 09:27:46.033862: Epoch time: 245.79 s 
+2024-09-08 09:27:46.957319:  
+2024-09-08 09:27:46.957505: Epoch 222 
+2024-09-08 09:27:46.957590: Current learning rate: 0.00798 
+2024-09-08 09:31:52.781177: train_loss -0.819 
+2024-09-08 09:31:52.781318: val_loss -0.6819 
+2024-09-08 09:31:52.781379: Pseudo dice [0.627, 0.864] 
+2024-09-08 09:31:52.781445: Epoch time: 245.83 s 
+2024-09-08 09:31:53.710499:  
+2024-09-08 09:31:53.710768: Epoch 223 
+2024-09-08 09:31:53.710852: Current learning rate: 0.00797 
+2024-09-08 09:35:59.688481: train_loss -0.8277 
+2024-09-08 09:35:59.688642: val_loss -0.7215 
+2024-09-08 09:35:59.688747: Pseudo dice [0.7139, 0.8654] 
+2024-09-08 09:35:59.688804: Epoch time: 245.98 s 
+2024-09-08 09:35:59.688850: Yayy! New best EMA pseudo Dice: 0.7603 
+2024-09-08 09:36:03.577357:  
+2024-09-08 09:36:03.577614: Epoch 224 
+2024-09-08 09:36:03.577700: Current learning rate: 0.00796 
+2024-09-08 09:40:09.492576: train_loss -0.8233 
+2024-09-08 09:40:09.492759: val_loss -0.6855 
+2024-09-08 09:40:09.492816: Pseudo dice [0.6497, 0.8602] 
+2024-09-08 09:40:09.492871: Epoch time: 245.92 s 
+2024-09-08 09:40:10.447344:  
+2024-09-08 09:40:10.447529: Epoch 225 
+2024-09-08 09:40:10.447614: Current learning rate: 0.00795 
+2024-09-08 09:44:16.461343: train_loss -0.8172 
+2024-09-08 09:44:16.461490: val_loss -0.6932 
+2024-09-08 09:44:16.461547: Pseudo dice [0.7039, 0.864] 
+2024-09-08 09:44:16.461603: Epoch time: 246.02 s 
+2024-09-08 09:44:16.461713: Yayy! New best EMA pseudo Dice: 0.7622 
+2024-09-08 09:44:20.333893:  
+2024-09-08 09:44:20.334125: Epoch 226 
+2024-09-08 09:44:20.334209: Current learning rate: 0.00794 
+2024-09-08 09:48:26.363231: train_loss -0.8234 
+2024-09-08 09:48:26.363386: val_loss -0.6744 
+2024-09-08 09:48:26.363452: Pseudo dice [0.6129, 0.8684] 
+2024-09-08 09:48:26.363547: Epoch time: 246.03 s 
+2024-09-08 09:48:27.380204:  
+2024-09-08 09:48:27.380433: Epoch 227 
+2024-09-08 09:48:27.380513: Current learning rate: 0.00793 
+2024-09-08 09:52:33.425875: train_loss -0.8285 
+2024-09-08 09:52:33.426021: val_loss -0.6916 
+2024-09-08 09:52:33.426077: Pseudo dice [0.6975, 0.8683] 
+2024-09-08 09:52:33.426132: Epoch time: 246.05 s 
+2024-09-08 09:52:33.426176: Yayy! New best EMA pseudo Dice: 0.7623 
+2024-09-08 09:52:37.267264:  
+2024-09-08 09:52:37.267491: Epoch 228 
+2024-09-08 09:52:37.267579: Current learning rate: 0.00792 
+2024-09-08 09:56:43.270227: train_loss -0.8258 
+2024-09-08 09:56:43.270411: val_loss -0.6499 
+2024-09-08 09:56:43.270469: Pseudo dice [0.5583, 0.8463] 
+2024-09-08 09:56:43.270524: Epoch time: 246.0 s 
+2024-09-08 09:56:44.187646:  
+2024-09-08 09:56:44.187798: Epoch 229 
+2024-09-08 09:56:44.187901: Current learning rate: 0.00791 
+2024-09-08 10:00:50.191304: train_loss -0.8185 
+2024-09-08 10:00:50.191453: val_loss -0.6954 
+2024-09-08 10:00:50.191509: Pseudo dice [0.6547, 0.8671] 
+2024-09-08 10:00:50.191566: Epoch time: 246.01 s 
+2024-09-08 10:00:51.136481:  
+2024-09-08 10:00:51.136659: Epoch 230 
+2024-09-08 10:00:51.136747: Current learning rate: 0.0079 
+2024-09-08 10:04:57.125694: train_loss -0.8303 
+2024-09-08 10:04:57.125890: val_loss -0.6632 
+2024-09-08 10:04:57.125948: Pseudo dice [0.6285, 0.8732] 
+2024-09-08 10:04:57.126004: Epoch time: 245.99 s 
+2024-09-08 10:04:58.079153:  
+2024-09-08 10:04:58.079358: Epoch 231 
+2024-09-08 10:04:58.079451: Current learning rate: 0.00789 
+2024-09-08 10:09:03.921768: train_loss -0.8318 
+2024-09-08 10:09:03.921926: val_loss -0.7058 
+2024-09-08 10:09:03.921983: Pseudo dice [0.7036, 0.8634] 
+2024-09-08 10:09:03.922089: Epoch time: 245.84 s 
+2024-09-08 10:09:04.843248:  
+2024-09-08 10:09:04.843519: Epoch 232 
+2024-09-08 10:09:04.843606: Current learning rate: 0.00789 
+2024-09-08 10:13:10.712894: train_loss -0.8306 
+2024-09-08 10:13:10.713039: val_loss -0.7039 
+2024-09-08 10:13:10.713095: Pseudo dice [0.6427, 0.8643] 
+2024-09-08 10:13:10.713151: Epoch time: 245.87 s 
+2024-09-08 10:13:11.658750:  
+2024-09-08 10:13:11.658929: Epoch 233 
+2024-09-08 10:13:11.659031: Current learning rate: 0.00788 
+2024-09-08 10:17:17.640398: train_loss -0.8294 
+2024-09-08 10:17:17.640612: val_loss -0.7059 
+2024-09-08 10:17:17.640671: Pseudo dice [0.708, 0.8745] 
+2024-09-08 10:17:17.640727: Epoch time: 245.98 s 
+2024-09-08 10:17:18.577917:  
+2024-09-08 10:17:18.578083: Epoch 234 
+2024-09-08 10:17:18.578168: Current learning rate: 0.00787 
+2024-09-08 10:21:24.555186: train_loss -0.8307 
+2024-09-08 10:21:24.555357: val_loss -0.6709 
+2024-09-08 10:21:24.555413: Pseudo dice [0.6684, 0.8593] 
+2024-09-08 10:21:24.555468: Epoch time: 245.98 s 
+2024-09-08 10:21:25.483184:  
+2024-09-08 10:21:25.483370: Epoch 235 
+2024-09-08 10:21:25.483516: Current learning rate: 0.00786 
+2024-09-08 10:25:31.694152: train_loss -0.8187 
+2024-09-08 10:25:31.694294: val_loss -0.6958 
+2024-09-08 10:25:31.694391: Pseudo dice [0.6723, 0.879] 
+2024-09-08 10:25:31.694447: Epoch time: 246.21 s 
+2024-09-08 10:25:31.694491: Yayy! New best EMA pseudo Dice: 0.7633 
+2024-09-08 10:25:35.534960:  
+2024-09-08 10:25:35.535117: Epoch 236 
+2024-09-08 10:25:35.535201: Current learning rate: 0.00785 
+2024-09-08 10:29:41.734635: train_loss -0.8224 
+2024-09-08 10:29:41.734802: val_loss -0.689 
+2024-09-08 10:29:41.734884: Pseudo dice [0.6472, 0.867] 
+2024-09-08 10:29:41.734941: Epoch time: 246.2 s 
+2024-09-08 10:29:42.653466:  
+2024-09-08 10:29:42.653661: Epoch 237 
+2024-09-08 10:29:42.653766: Current learning rate: 0.00784 
+2024-09-08 10:33:48.742382: train_loss -0.8271 
+2024-09-08 10:33:48.742535: val_loss -0.7125 
+2024-09-08 10:33:48.742592: Pseudo dice [0.6861, 0.8744] 
+2024-09-08 10:33:48.742647: Epoch time: 246.09 s 
+2024-09-08 10:33:48.742691: Yayy! New best EMA pseudo Dice: 0.7644 
+2024-09-08 10:33:52.603874:  
+2024-09-08 10:33:52.604025: Epoch 238 
+2024-09-08 10:33:52.604105: Current learning rate: 0.00783 
+2024-09-08 10:37:58.760802: train_loss -0.8323 
+2024-09-08 10:37:58.760993: val_loss -0.7291 
+2024-09-08 10:37:58.761071: Pseudo dice [0.6945, 0.8797] 
+2024-09-08 10:37:58.761150: Epoch time: 246.16 s 
+2024-09-08 10:37:58.761215: Yayy! New best EMA pseudo Dice: 0.7667 
+2024-09-08 10:38:03.038806:  
+2024-09-08 10:38:03.038991: Epoch 239 
+2024-09-08 10:38:03.039076: Current learning rate: 0.00782 
+2024-09-08 10:42:09.243670: train_loss -0.838 
+2024-09-08 10:42:09.243824: val_loss -0.6811 
+2024-09-08 10:42:09.243881: Pseudo dice [0.6262, 0.8824] 
+2024-09-08 10:42:09.243937: Epoch time: 246.21 s 
+2024-09-08 10:42:10.178530:  
+2024-09-08 10:42:10.178765: Epoch 240 
+2024-09-08 10:42:10.178854: Current learning rate: 0.00781 
+2024-09-08 10:46:16.843914: train_loss -0.8371 
+2024-09-08 10:46:16.844070: val_loss -0.6866 
+2024-09-08 10:46:16.844126: Pseudo dice [0.6291, 0.8666] 
+2024-09-08 10:46:16.844231: Epoch time: 246.67 s 
+2024-09-08 10:46:17.773643:  
+2024-09-08 10:46:17.773787: Epoch 241 
+2024-09-08 10:46:17.773883: Current learning rate: 0.0078 
+2024-09-08 10:50:23.777309: train_loss -0.8336 
+2024-09-08 10:50:23.777589: val_loss -0.6625 
+2024-09-08 10:50:23.777805: Pseudo dice [0.6133, 0.8508] 
+2024-09-08 10:50:23.777907: Epoch time: 246.01 s 
+2024-09-08 10:50:25.023746:  
+2024-09-08 10:50:25.023942: Epoch 242 
+2024-09-08 10:50:25.024058: Current learning rate: 0.00779 
+2024-09-08 10:54:31.115898: train_loss -0.8327 
+2024-09-08 10:54:31.116043: val_loss -0.7215 
+2024-09-08 10:54:31.116098: Pseudo dice [0.7, 0.8733] 
+2024-09-08 10:54:31.116153: Epoch time: 246.09 s 
+2024-09-08 10:54:32.044001:  
+2024-09-08 10:54:32.044241: Epoch 243 
+2024-09-08 10:54:32.044326: Current learning rate: 0.00778 
+2024-09-08 10:58:38.068044: train_loss -0.8318 
+2024-09-08 10:58:38.068219: val_loss -0.6743 
+2024-09-08 10:58:38.068275: Pseudo dice [0.6324, 0.8499] 
+2024-09-08 10:58:38.068330: Epoch time: 246.03 s 
+2024-09-08 10:58:39.003113:  
+2024-09-08 10:58:39.003297: Epoch 244 
+2024-09-08 10:58:39.003422: Current learning rate: 0.00777 
+2024-09-08 11:02:45.156588: train_loss -0.822 
+2024-09-08 11:02:45.156732: val_loss -0.6951 
+2024-09-08 11:02:45.156787: Pseudo dice [0.6829, 0.8638] 
+2024-09-08 11:02:45.156841: Epoch time: 246.16 s 
+2024-09-08 11:02:46.108814:  
+2024-09-08 11:02:46.109029: Epoch 245 
+2024-09-08 11:02:46.109146: Current learning rate: 0.00777 
+2024-09-08 11:06:52.462027: train_loss -0.8267 
+2024-09-08 11:06:52.462252: val_loss -0.6836 
+2024-09-08 11:06:52.462339: Pseudo dice [0.6289, 0.8653] 
+2024-09-08 11:06:52.462414: Epoch time: 246.36 s 
+2024-09-08 11:06:53.517400:  
+2024-09-08 11:06:53.517598: Epoch 246 
+2024-09-08 11:06:53.517679: Current learning rate: 0.00776 
+2024-09-08 11:10:59.503403: train_loss -0.8121 
+2024-09-08 11:10:59.503551: val_loss -0.6433 
+2024-09-08 11:10:59.503608: Pseudo dice [0.6201, 0.8529] 
+2024-09-08 11:10:59.503663: Epoch time: 245.99 s 
+2024-09-08 11:11:00.426027:  
+2024-09-08 11:11:00.426235: Epoch 247 
+2024-09-08 11:11:00.426324: Current learning rate: 0.00775 
+2024-09-08 11:15:06.279063: train_loss -0.8211 
+2024-09-08 11:15:06.279210: val_loss -0.7422 
+2024-09-08 11:15:06.279266: Pseudo dice [0.7219, 0.8822] 
+2024-09-08 11:15:06.279321: Epoch time: 245.85 s 
+2024-09-08 11:15:07.194306:  
+2024-09-08 11:15:07.194533: Epoch 248 
+2024-09-08 11:15:07.194619: Current learning rate: 0.00774 
+2024-09-08 11:19:12.966307: train_loss -0.8244 
+2024-09-08 11:19:12.966450: val_loss -0.6963 
+2024-09-08 11:19:12.966510: Pseudo dice [0.6722, 0.8739] 
+2024-09-08 11:19:12.966644: Epoch time: 245.77 s 
+2024-09-08 11:19:13.896503:  
+2024-09-08 11:19:13.896697: Epoch 249 
+2024-09-08 11:19:13.896795: Current learning rate: 0.00773 
+2024-09-08 11:23:19.804056: train_loss -0.8227 
+2024-09-08 11:23:19.804206: val_loss -0.6738 
+2024-09-08 11:23:19.804290: Pseudo dice [0.603, 0.8716] 
+2024-09-08 11:23:19.804346: Epoch time: 245.91 s 
+2024-09-08 11:23:23.657367:  
+2024-09-08 11:23:23.657582: Epoch 250 
+2024-09-08 11:23:23.657669: Current learning rate: 0.00772 
+2024-09-08 11:27:29.124799: train_loss -0.8345 
+2024-09-08 11:27:29.124956: val_loss -0.6839 
+2024-09-08 11:27:29.125011: Pseudo dice [0.6386, 0.8654] 
+2024-09-08 11:27:29.125067: Epoch time: 245.47 s 
+2024-09-08 11:27:30.056858:  
+2024-09-08 11:27:30.057028: Epoch 251 
+2024-09-08 11:27:30.057113: Current learning rate: 0.00771 
+2024-09-08 11:31:35.591482: train_loss -0.8284 
+2024-09-08 11:31:35.591630: val_loss -0.7029 
+2024-09-08 11:31:35.591687: Pseudo dice [0.6931, 0.8645] 
+2024-09-08 11:31:35.591743: Epoch time: 245.54 s 
+2024-09-08 11:31:36.527092:  
+2024-09-08 11:31:36.527267: Epoch 252 
+2024-09-08 11:31:36.527352: Current learning rate: 0.0077 
+2024-09-08 11:35:42.056572: train_loss -0.8261 
+2024-09-08 11:35:42.056728: val_loss -0.6928 
+2024-09-08 11:35:42.056785: Pseudo dice [0.6709, 0.8669] 
+2024-09-08 11:35:42.056843: Epoch time: 245.53 s 
+2024-09-08 11:35:43.006581:  
+2024-09-08 11:35:43.006700: Epoch 253 
+2024-09-08 11:35:43.006781: Current learning rate: 0.00769 
+2024-09-08 11:39:48.663713: train_loss -0.8254 
+2024-09-08 11:39:48.663916: val_loss -0.6866 
+2024-09-08 11:39:48.663977: Pseudo dice [0.6671, 0.8655] 
+2024-09-08 11:39:48.664033: Epoch time: 245.66 s 
+2024-09-08 11:39:49.597362:  
+2024-09-08 11:39:49.597595: Epoch 254 
+2024-09-08 11:39:49.597679: Current learning rate: 0.00768 
+2024-09-08 11:43:55.344797: train_loss -0.794 
+2024-09-08 11:43:55.344945: val_loss -0.7122 
+2024-09-08 11:43:55.345003: Pseudo dice [0.7175, 0.8625] 
+2024-09-08 11:43:55.345060: Epoch time: 245.75 s 
+2024-09-08 11:43:56.311232:  
+2024-09-08 11:43:56.311416: Epoch 255 
+2024-09-08 11:43:56.311506: Current learning rate: 0.00767 
+2024-09-08 11:48:01.956234: train_loss -0.8217 
+2024-09-08 11:48:01.956368: val_loss -0.6882 
+2024-09-08 11:48:01.956424: Pseudo dice [0.7019, 0.8633] 
+2024-09-08 11:48:01.956479: Epoch time: 245.65 s 
+2024-09-08 11:48:01.956523: Yayy! New best EMA pseudo Dice: 0.7674 
+2024-09-08 11:48:05.855942:  
+2024-09-08 11:48:05.856058: Epoch 256 
+2024-09-08 11:48:05.856139: Current learning rate: 0.00766 
+2024-09-08 11:52:11.314994: train_loss -0.8248 
+2024-09-08 11:52:11.315223: val_loss -0.6803 
+2024-09-08 11:52:11.315315: Pseudo dice [0.6174, 0.8632] 
+2024-09-08 11:52:11.315407: Epoch time: 245.46 s 
+2024-09-08 11:52:12.549957:  
+2024-09-08 11:52:12.550148: Epoch 257 
+2024-09-08 11:52:12.550286: Current learning rate: 0.00765 
+2024-09-08 11:56:18.098894: train_loss -0.8214 
+2024-09-08 11:56:18.099049: val_loss -0.6793 
+2024-09-08 11:56:18.099105: Pseudo dice [0.6275, 0.8718] 
+2024-09-08 11:56:18.099162: Epoch time: 245.55 s 
+2024-09-08 11:56:19.040329:  
+2024-09-08 11:56:19.040530: Epoch 258 
+2024-09-08 11:56:19.040614: Current learning rate: 0.00764 
+2024-09-08 12:00:24.679922: train_loss -0.8208 
+2024-09-08 12:00:24.680078: val_loss -0.6975 
+2024-09-08 12:00:24.680134: Pseudo dice [0.6736, 0.8677] 
+2024-09-08 12:00:24.680190: Epoch time: 245.64 s 
+2024-09-08 12:00:25.596710:  
+2024-09-08 12:00:25.596876: Epoch 259 
+2024-09-08 12:00:25.596962: Current learning rate: 0.00764 
+2024-09-08 12:04:31.400441: train_loss -0.8217 
+2024-09-08 12:04:31.400665: val_loss -0.6918 
+2024-09-08 12:04:31.400770: Pseudo dice [0.6603, 0.8595] 
+2024-09-08 12:04:31.400886: Epoch time: 245.81 s 
+2024-09-08 12:04:32.556598:  
+2024-09-08 12:04:32.556905: Epoch 260 
+2024-09-08 12:04:32.557014: Current learning rate: 0.00763 
+2024-09-08 12:08:38.494440: train_loss -0.8126 
+2024-09-08 12:08:38.494650: val_loss -0.6745 
+2024-09-08 12:08:38.494754: Pseudo dice [0.6533, 0.8396] 
+2024-09-08 12:08:38.494902: Epoch time: 245.94 s 
+2024-09-08 12:08:39.428582:  
+2024-09-08 12:08:39.428725: Epoch 261 
+2024-09-08 12:08:39.428808: Current learning rate: 0.00762 
+2024-09-08 12:12:45.375047: train_loss -0.8257 
+2024-09-08 12:12:45.375212: val_loss -0.7119 
+2024-09-08 12:12:45.375297: Pseudo dice [0.6665, 0.864] 
+2024-09-08 12:12:45.375354: Epoch time: 245.95 s 
+2024-09-08 12:12:46.329514:  
+2024-09-08 12:12:46.329696: Epoch 262 
+2024-09-08 12:12:46.329786: Current learning rate: 0.00761 
+2024-09-08 12:16:52.073075: train_loss -0.827 
+2024-09-08 12:16:52.073222: val_loss -0.6822 
+2024-09-08 12:16:52.073277: Pseudo dice [0.6825, 0.8685] 
+2024-09-08 12:16:52.073333: Epoch time: 245.75 s 
+2024-09-08 12:16:53.014733:  
+2024-09-08 12:16:53.014913: Epoch 263 
+2024-09-08 12:16:53.015000: Current learning rate: 0.0076 
+2024-09-08 12:20:58.909905: train_loss -0.81 
+2024-09-08 12:20:58.910050: val_loss -0.6796 
+2024-09-08 12:20:58.910105: Pseudo dice [0.6563, 0.8509] 
+2024-09-08 12:20:58.910159: Epoch time: 245.9 s 
+2024-09-08 12:21:00.773109:  
+2024-09-08 12:21:00.773296: Epoch 264 
+2024-09-08 12:21:00.773391: Current learning rate: 0.00759 
+2024-09-08 12:25:06.645667: train_loss -0.8046 
+2024-09-08 12:25:06.645813: val_loss -0.6693 
+2024-09-08 12:25:06.645869: Pseudo dice [0.6594, 0.8551] 
+2024-09-08 12:25:06.645924: Epoch time: 245.87 s 
+2024-09-08 12:25:07.589489:  
+2024-09-08 12:25:07.589728: Epoch 265 
+2024-09-08 12:25:07.589828: Current learning rate: 0.00758 
+2024-09-08 12:29:13.339517: train_loss -0.8135 
+2024-09-08 12:29:13.339679: val_loss -0.6409 
+2024-09-08 12:29:13.339748: Pseudo dice [0.6024, 0.8233] 
+2024-09-08 12:29:13.339837: Epoch time: 245.75 s 
+2024-09-08 12:29:14.407564:  
+2024-09-08 12:29:14.407815: Epoch 266 
+2024-09-08 12:29:14.407900: Current learning rate: 0.00757 
+2024-09-08 12:33:20.379964: train_loss -0.8081 
+2024-09-08 12:33:20.380110: val_loss -0.6914 
+2024-09-08 12:33:20.380166: Pseudo dice [0.6876, 0.8568] 
+2024-09-08 12:33:20.380222: Epoch time: 245.97 s 
+2024-09-08 12:33:21.304523:  
+2024-09-08 12:33:21.304735: Epoch 267 
+2024-09-08 12:33:21.304824: Current learning rate: 0.00756 
+2024-09-08 12:37:27.213386: train_loss -0.823 
+2024-09-08 12:37:27.213523: val_loss -0.6886 
+2024-09-08 12:37:27.213573: Pseudo dice [0.6628, 0.8618] 
+2024-09-08 12:37:27.213623: Epoch time: 245.91 s 
+2024-09-08 12:37:28.151678:  
+2024-09-08 12:37:28.151863: Epoch 268 
+2024-09-08 12:37:28.151945: Current learning rate: 0.00755 
+2024-09-08 12:41:33.997702: train_loss -0.8159 
+2024-09-08 12:41:33.997893: val_loss -0.6818 
+2024-09-08 12:41:33.997944: Pseudo dice [0.6598, 0.856] 
+2024-09-08 12:41:33.997995: Epoch time: 245.85 s 
+2024-09-08 12:41:34.932193:  
+2024-09-08 12:41:34.932421: Epoch 269 
+2024-09-08 12:41:34.932542: Current learning rate: 0.00754 
+2024-09-08 12:45:40.869082: train_loss -0.7937 
+2024-09-08 12:45:40.869307: val_loss -0.6853 
+2024-09-08 12:45:40.869358: Pseudo dice [0.6275, 0.8659] 
+2024-09-08 12:45:40.869410: Epoch time: 245.94 s 
+2024-09-08 12:45:41.819208:  
+2024-09-08 12:45:41.819404: Epoch 270 
+2024-09-08 12:45:41.819487: Current learning rate: 0.00753 
+2024-09-08 12:49:47.877873: train_loss -0.8172 
+2024-09-08 12:49:47.878027: val_loss -0.6875 
+2024-09-08 12:49:47.878110: Pseudo dice [0.6664, 0.8579] 
+2024-09-08 12:49:47.878163: Epoch time: 246.06 s 
+2024-09-08 12:49:48.817595:  
+2024-09-08 12:49:48.817818: Epoch 271 
+2024-09-08 12:49:48.817903: Current learning rate: 0.00752 
+2024-09-08 12:53:54.748285: train_loss -0.8254 
+2024-09-08 12:53:54.748513: val_loss -0.649 
+2024-09-08 12:53:54.748568: Pseudo dice [0.6002, 0.8523] 
+2024-09-08 12:53:54.748618: Epoch time: 245.93 s 
+2024-09-08 12:53:55.678591:  
+2024-09-08 12:53:55.678818: Epoch 272 
+2024-09-08 12:53:55.678899: Current learning rate: 0.00751 
+2024-09-08 12:58:01.516678: train_loss -0.802 
+2024-09-08 12:58:01.516809: val_loss -0.6691 
+2024-09-08 12:58:01.516860: Pseudo dice [0.6578, 0.8454] 
+2024-09-08 12:58:01.516913: Epoch time: 245.84 s 
+2024-09-08 12:58:02.452202:  
+2024-09-08 12:58:02.452377: Epoch 273 
+2024-09-08 12:58:02.452467: Current learning rate: 0.00751 
+2024-09-08 13:02:08.250994: train_loss -0.8151 
+2024-09-08 13:02:08.251204: val_loss -0.7047 
+2024-09-08 13:02:08.251289: Pseudo dice [0.6976, 0.8578] 
+2024-09-08 13:02:08.251373: Epoch time: 245.8 s 
+2024-09-08 13:02:09.424506:  
+2024-09-08 13:02:09.424754: Epoch 274 
+2024-09-08 13:02:09.424865: Current learning rate: 0.0075 
+2024-09-08 13:06:15.312329: train_loss -0.816 
+2024-09-08 13:06:15.312464: val_loss -0.7067 
+2024-09-08 13:06:15.312513: Pseudo dice [0.6564, 0.8675] 
+2024-09-08 13:06:15.312565: Epoch time: 245.89 s 
+2024-09-08 13:06:16.241579:  
+2024-09-08 13:06:16.241807: Epoch 275 
+2024-09-08 13:06:16.241885: Current learning rate: 0.00749 
+2024-09-08 13:10:22.048664: train_loss -0.8229 
+2024-09-08 13:10:22.048802: val_loss -0.6634 
+2024-09-08 13:10:22.048852: Pseudo dice [0.6425, 0.8506] 
+2024-09-08 13:10:22.048903: Epoch time: 245.81 s 
+2024-09-08 13:10:22.981611:  
+2024-09-08 13:10:22.981755: Epoch 276 
+2024-09-08 13:10:22.981839: Current learning rate: 0.00748 
+2024-09-08 13:14:28.766197: train_loss -0.8308 
+2024-09-08 13:14:28.766339: val_loss -0.6718 
+2024-09-08 13:14:28.766398: Pseudo dice [0.643, 0.8586] 
+2024-09-08 13:14:28.766449: Epoch time: 245.79 s 
+2024-09-08 13:14:29.892348:  
+2024-09-08 13:14:29.892556: Epoch 277 
+2024-09-08 13:14:29.892648: Current learning rate: 0.00747 
+2024-09-08 13:18:35.769429: train_loss -0.8256 
+2024-09-08 13:18:35.769564: val_loss -0.6709 
+2024-09-08 13:18:35.769614: Pseudo dice [0.6308, 0.8696] 
+2024-09-08 13:18:35.769666: Epoch time: 245.88 s 
+2024-09-08 13:18:36.718619:  
+2024-09-08 13:18:36.718814: Epoch 278 
+2024-09-08 13:18:36.718892: Current learning rate: 0.00746 
+2024-09-08 13:22:42.313392: train_loss -0.8353 
+2024-09-08 13:22:42.313523: val_loss -0.6782 
+2024-09-08 13:22:42.313575: Pseudo dice [0.655, 0.878] 
+2024-09-08 13:22:42.313627: Epoch time: 245.6 s 
+2024-09-08 13:22:43.232447:  
+2024-09-08 13:22:43.232627: Epoch 279 
+2024-09-08 13:22:43.232705: Current learning rate: 0.00745 
+2024-09-08 13:26:49.081686: train_loss -0.8227 
+2024-09-08 13:26:49.081851: val_loss -0.7113 
+2024-09-08 13:26:49.081966: Pseudo dice [0.6821, 0.8748] 
+2024-09-08 13:26:49.082019: Epoch time: 245.85 s 
+2024-09-08 13:26:50.020809:  
+2024-09-08 13:26:50.021012: Epoch 280 
+2024-09-08 13:26:50.021091: Current learning rate: 0.00744 
+2024-09-08 13:30:55.996414: train_loss -0.8237 
+2024-09-08 13:30:55.996594: val_loss -0.666 
+2024-09-08 13:30:55.996651: Pseudo dice [0.6324, 0.8599] 
+2024-09-08 13:30:55.996719: Epoch time: 245.98 s 
+2024-09-08 13:30:56.952806:  
+2024-09-08 13:30:56.953001: Epoch 281 
+2024-09-08 13:30:56.953078: Current learning rate: 0.00743 
+2024-09-08 13:35:02.747576: train_loss -0.8391 
+2024-09-08 13:35:02.747713: val_loss -0.7097 
+2024-09-08 13:35:02.747765: Pseudo dice [0.6845, 0.879] 
+2024-09-08 13:35:02.747831: Epoch time: 245.8 s 
+2024-09-08 13:35:03.700548:  
+2024-09-08 13:35:03.700763: Epoch 282 
+2024-09-08 13:35:03.700844: Current learning rate: 0.00742 
+2024-09-08 13:39:09.386761: train_loss -0.8333 
+2024-09-08 13:39:09.386896: val_loss -0.7153 
+2024-09-08 13:39:09.386946: Pseudo dice [0.6527, 0.8687] 
+2024-09-08 13:39:09.386998: Epoch time: 245.69 s 
+2024-09-08 13:39:10.329014:  
+2024-09-08 13:39:10.329188: Epoch 283 
+2024-09-08 13:39:10.329271: Current learning rate: 0.00741 
+2024-09-08 13:43:16.095506: train_loss -0.8345 
+2024-09-08 13:43:16.095644: val_loss -0.7033 
+2024-09-08 13:43:16.095695: Pseudo dice [0.6663, 0.8762] 
+2024-09-08 13:43:16.095747: Epoch time: 245.77 s 
+2024-09-08 13:43:17.024703:  
+2024-09-08 13:43:17.024922: Epoch 284 
+2024-09-08 13:43:17.025003: Current learning rate: 0.0074 
+2024-09-08 13:47:22.618962: train_loss -0.8406 
+2024-09-08 13:47:22.619099: val_loss -0.7152 
+2024-09-08 13:47:22.619150: Pseudo dice [0.6867, 0.8669] 
+2024-09-08 13:47:22.619201: Epoch time: 245.6 s 
+2024-09-08 13:47:23.558652:  
+2024-09-08 13:47:23.558867: Epoch 285 
+2024-09-08 13:47:23.558946: Current learning rate: 0.00739 
+2024-09-08 13:51:29.329376: train_loss -0.8384 
+2024-09-08 13:51:29.329512: val_loss -0.6882 
+2024-09-08 13:51:29.329563: Pseudo dice [0.7038, 0.8683] 
+2024-09-08 13:51:29.329614: Epoch time: 245.77 s 
+2024-09-08 13:51:30.265183:  
+2024-09-08 13:51:30.265369: Epoch 286 
+2024-09-08 13:51:30.265451: Current learning rate: 0.00738 
+2024-09-08 13:55:36.081558: train_loss -0.8387 
+2024-09-08 13:55:36.081699: val_loss -0.7175 
+2024-09-08 13:55:36.081752: Pseudo dice [0.6912, 0.8785] 
+2024-09-08 13:55:36.081805: Epoch time: 245.82 s 
+2024-09-08 13:55:37.049160:  
+2024-09-08 13:55:37.049296: Epoch 287 
+2024-09-08 13:55:37.049379: Current learning rate: 0.00738 
+2024-09-08 13:59:42.676794: train_loss -0.8437 
+2024-09-08 13:59:42.676934: val_loss -0.6812 
+2024-09-08 13:59:42.676987: Pseudo dice [0.6401, 0.8634] 
+2024-09-08 13:59:42.677040: Epoch time: 245.63 s 
+2024-09-08 13:59:43.638541:  
+2024-09-08 13:59:43.638723: Epoch 288 
+2024-09-08 13:59:43.638805: Current learning rate: 0.00737 
+2024-09-08 14:03:50.197922: train_loss -0.8393 
+2024-09-08 14:03:50.198085: val_loss -0.7162 
+2024-09-08 14:03:50.198135: Pseudo dice [0.7154, 0.8754] 
+2024-09-08 14:03:50.198185: Epoch time: 246.56 s 
+2024-09-08 14:03:50.198225: Yayy! New best EMA pseudo Dice: 0.7684 
+2024-09-08 14:03:54.161394:  
+2024-09-08 14:03:54.161601: Epoch 289 
+2024-09-08 14:03:54.161698: Current learning rate: 0.00736 
+2024-09-08 14:07:59.926957: train_loss -0.8416 
+2024-09-08 14:07:59.927095: val_loss -0.6846 
+2024-09-08 14:07:59.927146: Pseudo dice [0.6363, 0.8713] 
+2024-09-08 14:07:59.927197: Epoch time: 245.77 s 
+2024-09-08 14:08:00.878907:  
+2024-09-08 14:08:00.879178: Epoch 290 
+2024-09-08 14:08:00.879258: Current learning rate: 0.00735 
+2024-09-08 14:12:06.827304: train_loss -0.8489 
+2024-09-08 14:12:06.827460: val_loss -0.6847 
+2024-09-08 14:12:06.827511: Pseudo dice [0.6593, 0.8744] 
+2024-09-08 14:12:06.827562: Epoch time: 245.95 s 
+2024-09-08 14:12:07.771321:  
+2024-09-08 14:12:07.771562: Epoch 291 
+2024-09-08 14:12:07.771684: Current learning rate: 0.00734 
+2024-09-08 14:16:13.573808: train_loss -0.8396 
+2024-09-08 14:16:13.573943: val_loss -0.6951 
+2024-09-08 14:16:13.573993: Pseudo dice [0.6821, 0.8768] 
+2024-09-08 14:16:13.574044: Epoch time: 245.81 s 
+2024-09-08 14:16:14.527561:  
+2024-09-08 14:16:14.527755: Epoch 292 
+2024-09-08 14:16:14.527844: Current learning rate: 0.00733 
+2024-09-08 14:20:20.530841: train_loss -0.84 
+2024-09-08 14:20:20.530979: val_loss -0.6977 
+2024-09-08 14:20:20.531030: Pseudo dice [0.6485, 0.8778] 
+2024-09-08 14:20:20.531082: Epoch time: 246.01 s 
+2024-09-08 14:20:21.492485:  
+2024-09-08 14:20:21.492658: Epoch 293 
+2024-09-08 14:20:21.492738: Current learning rate: 0.00732 
+2024-09-08 14:24:27.341520: train_loss -0.8383 
+2024-09-08 14:24:27.341662: val_loss -0.6846 
+2024-09-08 14:24:27.341714: Pseudo dice [0.6881, 0.8438] 
+2024-09-08 14:24:27.341769: Epoch time: 245.85 s 
+2024-09-08 14:24:28.314965:  
+2024-09-08 14:24:28.315162: Epoch 294 
+2024-09-08 14:24:28.315242: Current learning rate: 0.00731 
+2024-09-08 14:28:34.317364: train_loss -0.7978 
+2024-09-08 14:28:34.317508: val_loss -0.6771 
+2024-09-08 14:28:34.317559: Pseudo dice [0.6897, 0.8503] 
+2024-09-08 14:28:34.317635: Epoch time: 246.0 s 
+2024-09-08 14:28:35.290522:  
+2024-09-08 14:28:35.290720: Epoch 295 
+2024-09-08 14:28:35.290801: Current learning rate: 0.0073 
+2024-09-08 14:32:41.431136: train_loss -0.8103 
+2024-09-08 14:32:41.431302: val_loss -0.6667 
+2024-09-08 14:32:41.431362: Pseudo dice [0.6666, 0.854] 
+2024-09-08 14:32:41.431423: Epoch time: 246.14 s 
+2024-09-08 14:32:42.515566:  
+2024-09-08 14:32:42.515856: Epoch 296 
+2024-09-08 14:32:42.515980: Current learning rate: 0.00729 
+2024-09-08 14:36:48.437829: train_loss -0.8303 
+2024-09-08 14:36:48.437965: val_loss -0.7097 
+2024-09-08 14:36:48.438016: Pseudo dice [0.6743, 0.8737] 
+2024-09-08 14:36:48.438071: Epoch time: 245.92 s 
+2024-09-08 14:36:49.388504:  
+2024-09-08 14:36:49.388630: Epoch 297 
+2024-09-08 14:36:49.388709: Current learning rate: 0.00728 
+2024-09-08 14:40:54.997778: train_loss -0.8352 
+2024-09-08 14:40:54.997914: val_loss -0.6986 
+2024-09-08 14:40:54.997965: Pseudo dice [0.65, 0.8766] 
+2024-09-08 14:40:54.998015: Epoch time: 245.61 s 
+2024-09-08 14:40:55.947121:  
+2024-09-08 14:40:55.947251: Epoch 298 
+2024-09-08 14:40:55.947330: Current learning rate: 0.00727 
+2024-09-08 14:45:01.712476: train_loss -0.8189 
+2024-09-08 14:45:01.712641: val_loss -0.6438 
+2024-09-08 14:45:01.712693: Pseudo dice [0.6217, 0.8461] 
+2024-09-08 14:45:01.712804: Epoch time: 245.77 s 
+2024-09-08 14:45:02.712632:  
+2024-09-08 14:45:02.712833: Epoch 299 
+2024-09-08 14:45:02.712917: Current learning rate: 0.00726 
+2024-09-08 14:49:08.681977: train_loss -0.8042 
+2024-09-08 14:49:08.682114: val_loss -0.68 
+2024-09-08 14:49:08.682164: Pseudo dice [0.6397, 0.8719] 
+2024-09-08 14:49:08.682213: Epoch time: 245.97 s 
+2024-09-08 14:49:12.602886:  
+2024-09-08 14:49:12.603037: Epoch 300 
+2024-09-08 14:49:12.603118: Current learning rate: 0.00725 
+2024-09-08 14:53:18.588043: train_loss -0.813 
+2024-09-08 14:53:18.588179: val_loss -0.6864 
+2024-09-08 14:53:18.588229: Pseudo dice [0.6282, 0.8696] 
+2024-09-08 14:53:18.588280: Epoch time: 245.99 s 
+2024-09-08 14:53:19.531502:  
+2024-09-08 14:53:19.531648: Epoch 301 
+2024-09-08 14:53:19.531728: Current learning rate: 0.00724 
+2024-09-08 14:57:25.625376: train_loss -0.816 
+2024-09-08 14:57:25.625526: val_loss -0.671 
+2024-09-08 14:57:25.625577: Pseudo dice [0.6626, 0.8384] 
+2024-09-08 14:57:25.625628: Epoch time: 246.1 s 
+2024-09-08 14:57:26.620551:  
+2024-09-08 14:57:26.620713: Epoch 302 
+2024-09-08 14:57:26.620793: Current learning rate: 0.00724 
+2024-09-08 15:01:32.595996: train_loss -0.8114 
+2024-09-08 15:01:32.596134: val_loss -0.6314 
+2024-09-08 15:01:32.596185: Pseudo dice [0.5576, 0.8541] 
+2024-09-08 15:01:32.596237: Epoch time: 245.98 s 
+2024-09-08 15:01:33.542745:  
+2024-09-08 15:01:33.542941: Epoch 303 
+2024-09-08 15:01:33.543025: Current learning rate: 0.00723 
+2024-09-08 15:05:39.449644: train_loss -0.8189 
+2024-09-08 15:05:39.449794: val_loss -0.6791 
+2024-09-08 15:05:39.449851: Pseudo dice [0.6672, 0.8671] 
+2024-09-08 15:05:39.449906: Epoch time: 245.91 s 
+2024-09-08 15:05:40.420946:  
+2024-09-08 15:05:40.421129: Epoch 304 
+2024-09-08 15:05:40.421215: Current learning rate: 0.00722 
+2024-09-08 15:09:46.402850: train_loss -0.814 
+2024-09-08 15:09:46.403037: val_loss -0.6059 
+2024-09-08 15:09:46.403139: Pseudo dice [0.5121, 0.8019] 
+2024-09-08 15:09:46.403208: Epoch time: 245.98 s 
+2024-09-08 15:09:47.647230:  
+2024-09-08 15:09:47.647518: Epoch 305 
+2024-09-08 15:09:47.647659: Current learning rate: 0.00721 
+2024-09-08 15:13:53.819774: train_loss -0.8271 
+2024-09-08 15:13:53.819985: val_loss -0.659 
+2024-09-08 15:13:53.820055: Pseudo dice [0.632, 0.8529] 
+2024-09-08 15:13:53.820128: Epoch time: 246.18 s 
+2024-09-08 15:13:55.029258:  
+2024-09-08 15:13:55.029557: Epoch 306 
+2024-09-08 15:13:55.029690: Current learning rate: 0.0072 
+2024-09-08 15:18:01.181211: train_loss -0.8285 
+2024-09-08 15:18:01.181357: val_loss -0.7048 
+2024-09-08 15:18:01.181415: Pseudo dice [0.6689, 0.8516] 
+2024-09-08 15:18:01.181470: Epoch time: 246.16 s 
+2024-09-08 15:18:02.152976:  
+2024-09-08 15:18:02.153144: Epoch 307 
+2024-09-08 15:18:02.153232: Current learning rate: 0.00719 
+2024-09-08 15:22:07.977268: train_loss -0.8207 
+2024-09-08 15:22:07.977419: val_loss -0.679 
+2024-09-08 15:22:07.977475: Pseudo dice [0.6097, 0.8563] 
+2024-09-08 15:22:07.977530: Epoch time: 245.83 s 
+2024-09-08 15:22:08.924840:  
+2024-09-08 15:22:08.925054: Epoch 308 
+2024-09-08 15:22:08.925182: Current learning rate: 0.00718 
+2024-09-08 15:26:14.679531: train_loss -0.8328 
+2024-09-08 15:26:14.679672: val_loss -0.6681 
+2024-09-08 15:26:14.679729: Pseudo dice [0.6556, 0.8382] 
+2024-09-08 15:26:14.679785: Epoch time: 245.76 s 
+2024-09-08 15:26:15.628327:  
+2024-09-08 15:26:15.628532: Epoch 309 
+2024-09-08 15:26:15.628615: Current learning rate: 0.00717 
+2024-09-08 15:30:21.551929: train_loss -0.8296 
+2024-09-08 15:30:21.552115: val_loss -0.6915 
+2024-09-08 15:30:21.552187: Pseudo dice [0.6478, 0.8663] 
+2024-09-08 15:30:21.552328: Epoch time: 245.93 s 
+2024-09-08 15:30:22.675976:  
+2024-09-08 15:30:22.676196: Epoch 310 
+2024-09-08 15:30:22.676292: Current learning rate: 0.00716 
+2024-09-08 15:34:28.612344: train_loss -0.8369 
+2024-09-08 15:34:28.612494: val_loss -0.6853 
+2024-09-08 15:34:28.612551: Pseudo dice [0.6556, 0.868] 
+2024-09-08 15:34:28.612665: Epoch time: 245.94 s 
+2024-09-08 15:34:30.465586:  
+2024-09-08 15:34:30.465834: Epoch 311 
+2024-09-08 15:34:30.465935: Current learning rate: 0.00715 
+2024-09-08 15:38:36.364308: train_loss -0.8192 
+2024-09-08 15:38:36.364453: val_loss -0.6545 
+2024-09-08 15:38:36.364508: Pseudo dice [0.6326, 0.8501] 
+2024-09-08 15:38:36.364564: Epoch time: 245.9 s 
+2024-09-08 15:38:37.310879:  
+2024-09-08 15:38:37.311128: Epoch 312 
+2024-09-08 15:38:37.311235: Current learning rate: 0.00714 
+2024-09-08 15:42:43.182977: train_loss -0.811 
+2024-09-08 15:42:43.183126: val_loss -0.6687 
+2024-09-08 15:42:43.183182: Pseudo dice [0.6141, 0.8575] 
+2024-09-08 15:42:43.183296: Epoch time: 245.87 s 
+2024-09-08 15:42:44.261884:  
+2024-09-08 15:42:44.262161: Epoch 313 
+2024-09-08 15:42:44.262338: Current learning rate: 0.00713 
+2024-09-08 15:46:50.108970: train_loss -0.8272 
+2024-09-08 15:46:50.109117: val_loss -0.6876 
+2024-09-08 15:46:50.109174: Pseudo dice [0.6396, 0.8615] 
+2024-09-08 15:46:50.109230: Epoch time: 245.85 s 
+2024-09-08 15:46:51.064448:  
+2024-09-08 15:46:51.064666: Epoch 314 
+2024-09-08 15:46:51.064747: Current learning rate: 0.00712 
+2024-09-08 15:50:56.904511: train_loss -0.8219 
+2024-09-08 15:50:56.904668: val_loss -0.6911 
+2024-09-08 15:50:56.904725: Pseudo dice [0.6368, 0.8756] 
+2024-09-08 15:50:56.904781: Epoch time: 245.84 s 
+2024-09-08 15:50:57.851857:  
+2024-09-08 15:50:57.852072: Epoch 315 
+2024-09-08 15:50:57.852156: Current learning rate: 0.00711 
+2024-09-08 15:55:03.731212: train_loss -0.8317 
+2024-09-08 15:55:03.731364: val_loss -0.6748 
+2024-09-08 15:55:03.731422: Pseudo dice [0.6326, 0.8759] 
+2024-09-08 15:55:03.731479: Epoch time: 245.88 s 
+2024-09-08 15:55:04.925457:  
+2024-09-08 15:55:04.925768: Epoch 316 
+2024-09-08 15:55:04.925888: Current learning rate: 0.0071 
+2024-09-08 15:59:10.993121: train_loss -0.8284 
+2024-09-08 15:59:10.993347: val_loss -0.7016 
+2024-09-08 15:59:10.993443: Pseudo dice [0.6673, 0.868] 
+2024-09-08 15:59:10.993536: Epoch time: 246.07 s 
+2024-09-08 15:59:12.242049:  
+2024-09-08 15:59:12.242328: Epoch 317 
+2024-09-08 15:59:12.242433: Current learning rate: 0.0071 
+2024-09-08 16:03:18.353646: train_loss -0.8431 
+2024-09-08 16:03:18.353809: val_loss -0.6759 
+2024-09-08 16:03:18.353878: Pseudo dice [0.6571, 0.8702] 
+2024-09-08 16:03:18.354045: Epoch time: 246.11 s 
+2024-09-08 16:03:19.502679:  
+2024-09-08 16:03:19.502978: Epoch 318 
+2024-09-08 16:03:19.503086: Current learning rate: 0.00709 
+2024-09-08 16:07:25.721010: train_loss -0.8413 
+2024-09-08 16:07:25.721148: val_loss -0.6983 
+2024-09-08 16:07:25.721205: Pseudo dice [0.6512, 0.8683] 
+2024-09-08 16:07:25.721260: Epoch time: 246.22 s 
+2024-09-08 16:07:26.685802:  
+2024-09-08 16:07:26.685978: Epoch 319 
+2024-09-08 16:07:26.686062: Current learning rate: 0.00708 
+2024-09-08 16:11:32.680327: train_loss -0.8427 
+2024-09-08 16:11:32.680478: val_loss -0.6925 
+2024-09-08 16:11:32.680534: Pseudo dice [0.6616, 0.8838] 
+2024-09-08 16:11:32.680588: Epoch time: 246.0 s 
+2024-09-08 16:11:33.625333:  
+2024-09-08 16:11:33.625516: Epoch 320 
+2024-09-08 16:11:33.625601: Current learning rate: 0.00707 
+2024-09-08 16:15:39.653347: train_loss -0.8463 
+2024-09-08 16:15:39.653494: val_loss -0.707 
+2024-09-08 16:15:39.653550: Pseudo dice [0.6711, 0.8729] 
+2024-09-08 16:15:39.653605: Epoch time: 246.03 s 
+2024-09-08 16:15:40.613891:  
+2024-09-08 16:15:40.614107: Epoch 321 
+2024-09-08 16:15:40.614192: Current learning rate: 0.00706 
+2024-09-08 16:19:46.585247: train_loss -0.8396 
+2024-09-08 16:19:46.585422: val_loss -0.6905 
+2024-09-08 16:19:46.585479: Pseudo dice [0.6403, 0.8535] 
+2024-09-08 16:19:46.585536: Epoch time: 245.97 s 
+2024-09-08 16:19:47.541514:  
+2024-09-08 16:19:47.541700: Epoch 322 
+2024-09-08 16:19:47.541785: Current learning rate: 0.00705 
+2024-09-08 16:23:53.363251: train_loss -0.8448 
+2024-09-08 16:23:53.363397: val_loss -0.668 
+2024-09-08 16:23:53.363453: Pseudo dice [0.6027, 0.8704] 
+2024-09-08 16:23:53.363509: Epoch time: 245.82 s 
+2024-09-08 16:23:54.351949:  
+2024-09-08 16:23:54.352139: Epoch 323 
+2024-09-08 16:23:54.352227: Current learning rate: 0.00704 
+2024-09-08 16:28:00.045130: train_loss -0.834 
+2024-09-08 16:28:00.045275: val_loss -0.6672 
+2024-09-08 16:28:00.045331: Pseudo dice [0.6111, 0.8613] 
+2024-09-08 16:28:00.045385: Epoch time: 245.7 s 
+2024-09-08 16:28:00.999120:  
+2024-09-08 16:28:00.999259: Epoch 324 
+2024-09-08 16:28:00.999343: Current learning rate: 0.00703 
+2024-09-08 16:32:06.890910: train_loss -0.8175 
+2024-09-08 16:32:06.891055: val_loss -0.6548 
+2024-09-08 16:32:06.891112: Pseudo dice [0.5979, 0.8599] 
+2024-09-08 16:32:06.891167: Epoch time: 245.89 s 
+2024-09-08 16:32:07.841997:  
+2024-09-08 16:32:07.842158: Epoch 325 
+2024-09-08 16:32:07.842243: Current learning rate: 0.00702 
+2024-09-08 16:36:13.868916: train_loss -0.7969 
+2024-09-08 16:36:13.869091: val_loss -0.6864 
+2024-09-08 16:36:13.869150: Pseudo dice [0.6476, 0.8331] 
+2024-09-08 16:36:13.869206: Epoch time: 246.03 s 
+2024-09-08 16:36:14.844371:  
+2024-09-08 16:36:14.844580: Epoch 326 
+2024-09-08 16:36:14.844663: Current learning rate: 0.00701 
+2024-09-08 16:40:20.839732: train_loss -0.8089 
+2024-09-08 16:40:20.839881: val_loss -0.6593 
+2024-09-08 16:40:20.839939: Pseudo dice [0.6421, 0.8615] 
+2024-09-08 16:40:20.839997: Epoch time: 246.0 s 
+2024-09-08 16:40:21.793997:  
+2024-09-08 16:40:21.794240: Epoch 327 
+2024-09-08 16:40:21.794324: Current learning rate: 0.007 
+2024-09-08 16:44:27.707416: train_loss -0.8187 
+2024-09-08 16:44:27.707566: val_loss -0.6969 
+2024-09-08 16:44:27.707625: Pseudo dice [0.6575, 0.8543] 
+2024-09-08 16:44:27.707680: Epoch time: 245.92 s 
+2024-09-08 16:44:28.658259:  
+2024-09-08 16:44:28.658399: Epoch 328 
+2024-09-08 16:44:28.658484: Current learning rate: 0.00699 
+2024-09-08 16:48:34.545371: train_loss -0.8193 
+2024-09-08 16:48:34.545521: val_loss -0.6915 
+2024-09-08 16:48:34.545579: Pseudo dice [0.7009, 0.8665] 
+2024-09-08 16:48:34.545635: Epoch time: 245.89 s 
+2024-09-08 16:48:35.494919:  
+2024-09-08 16:48:35.495111: Epoch 329 
+2024-09-08 16:48:35.495193: Current learning rate: 0.00698 
+2024-09-08 16:52:41.322636: train_loss -0.8088 
+2024-09-08 16:52:41.322786: val_loss -0.6879 
+2024-09-08 16:52:41.322845: Pseudo dice [0.6466, 0.8766] 
+2024-09-08 16:52:41.322900: Epoch time: 245.83 s 
+2024-09-08 16:52:42.268675:  
+2024-09-08 16:52:42.268881: Epoch 330 
+2024-09-08 16:52:42.268965: Current learning rate: 0.00697 
+2024-09-08 16:56:48.072192: train_loss -0.8248 
+2024-09-08 16:56:48.072363: val_loss -0.7037 
+2024-09-08 16:56:48.072423: Pseudo dice [0.6902, 0.8619] 
+2024-09-08 16:56:48.072715: Epoch time: 245.81 s 
+2024-09-08 16:56:49.066422:  
+2024-09-08 16:56:49.066605: Epoch 331 
+2024-09-08 16:56:49.066697: Current learning rate: 0.00696 
+2024-09-08 17:00:54.847782: train_loss -0.8328 
+2024-09-08 17:00:54.847943: val_loss -0.7119 
+2024-09-08 17:00:54.848001: Pseudo dice [0.7153, 0.8732] 
+2024-09-08 17:00:54.848057: Epoch time: 245.78 s 
+2024-09-08 17:00:55.810795:  
+2024-09-08 17:00:55.810951: Epoch 332 
+2024-09-08 17:00:55.811038: Current learning rate: 0.00696 
+2024-09-08 17:05:01.395097: train_loss -0.8393 
+2024-09-08 17:05:01.395266: val_loss -0.7085 
+2024-09-08 17:05:01.395325: Pseudo dice [0.6775, 0.8753] 
+2024-09-08 17:05:01.395413: Epoch time: 245.59 s 
+2024-09-08 17:05:02.350989:  
+2024-09-08 17:05:02.351177: Epoch 333 
+2024-09-08 17:05:02.351262: Current learning rate: 0.00695 
+2024-09-08 17:09:08.129110: train_loss -0.8288 
+2024-09-08 17:09:08.129284: val_loss -0.6937 
+2024-09-08 17:09:08.129340: Pseudo dice [0.6531, 0.8615] 
+2024-09-08 17:09:08.129399: Epoch time: 245.78 s 
+2024-09-08 17:09:09.082298:  
+2024-09-08 17:09:09.082449: Epoch 334 
+2024-09-08 17:09:09.082535: Current learning rate: 0.00694 
+2024-09-08 17:13:15.984636: train_loss -0.8342 
+2024-09-08 17:13:15.984785: val_loss -0.7124 
+2024-09-08 17:13:15.984842: Pseudo dice [0.7108, 0.875] 
+2024-09-08 17:13:15.984897: Epoch time: 246.9 s 
+2024-09-08 17:13:16.976016:  
+2024-09-08 17:13:16.976195: Epoch 335 
+2024-09-08 17:13:16.976291: Current learning rate: 0.00693 
+2024-09-08 17:17:22.937630: train_loss -0.8363 
+2024-09-08 17:17:22.937782: val_loss -0.7169 
+2024-09-08 17:17:22.937839: Pseudo dice [0.6934, 0.8745] 
+2024-09-08 17:17:22.937897: Epoch time: 245.96 s 
+2024-09-08 17:17:23.902711:  
+2024-09-08 17:17:23.902896: Epoch 336 
+2024-09-08 17:17:23.902997: Current learning rate: 0.00692 
+2024-09-08 17:21:29.749373: train_loss -0.8375 
+2024-09-08 17:21:29.749540: val_loss -0.6945 
+2024-09-08 17:21:29.749597: Pseudo dice [0.6468, 0.8748] 
+2024-09-08 17:21:29.749653: Epoch time: 245.85 s 
+2024-09-08 17:21:30.711567:  
+2024-09-08 17:21:30.711817: Epoch 337 
+2024-09-08 17:21:30.711906: Current learning rate: 0.00691 
+2024-09-08 17:25:36.395590: train_loss -0.8432 
+2024-09-08 17:25:36.395740: val_loss -0.7094 
+2024-09-08 17:25:36.395797: Pseudo dice [0.6801, 0.8824] 
+2024-09-08 17:25:36.395862: Epoch time: 245.69 s 
+2024-09-08 17:25:37.376512:  
+2024-09-08 17:25:37.376726: Epoch 338 
+2024-09-08 17:25:37.376807: Current learning rate: 0.0069 
+2024-09-08 17:29:43.355157: train_loss -0.842 
+2024-09-08 17:29:43.355391: val_loss -0.7007 
+2024-09-08 17:29:43.355484: Pseudo dice [0.6774, 0.8626] 
+2024-09-08 17:29:43.355575: Epoch time: 245.98 s 
+2024-09-08 17:29:44.474908:  
+2024-09-08 17:29:44.475129: Epoch 339 
+2024-09-08 17:29:44.475214: Current learning rate: 0.00689 
+2024-09-08 17:33:50.644074: train_loss -0.8465 
+2024-09-08 17:33:50.644249: val_loss -0.6669 
+2024-09-08 17:33:50.644333: Pseudo dice [0.6455, 0.8388] 
+2024-09-08 17:33:50.644392: Epoch time: 246.17 s 
+2024-09-08 17:33:51.621601:  
+2024-09-08 17:33:51.621819: Epoch 340 
+2024-09-08 17:33:51.621898: Current learning rate: 0.00688 
+2024-09-08 17:37:57.659946: train_loss -0.8482 
+2024-09-08 17:37:57.660094: val_loss -0.6908 
+2024-09-08 17:37:57.660151: Pseudo dice [0.68, 0.8817] 
+2024-09-08 17:37:57.660207: Epoch time: 246.04 s 
+2024-09-08 17:37:58.655348:  
+2024-09-08 17:37:58.655575: Epoch 341 
+2024-09-08 17:37:58.655660: Current learning rate: 0.00687 
+2024-09-08 17:42:04.594416: train_loss -0.8494 
+2024-09-08 17:42:04.594643: val_loss -0.6924 
+2024-09-08 17:42:04.594707: Pseudo dice [0.6794, 0.8662] 
+2024-09-08 17:42:04.594769: Epoch time: 245.94 s 
+2024-09-08 17:42:05.849361:  
+2024-09-08 17:42:05.849615: Epoch 342 
+2024-09-08 17:42:05.849722: Current learning rate: 0.00686 
+2024-09-08 17:46:11.593881: train_loss -0.8552 
+2024-09-08 17:46:11.594026: val_loss -0.7052 
+2024-09-08 17:46:11.594083: Pseudo dice [0.6971, 0.8661] 
+2024-09-08 17:46:11.594143: Epoch time: 245.75 s 
+2024-09-08 17:46:11.594188: Yayy! New best EMA pseudo Dice: 0.7687 
+2024-09-08 17:46:15.555421:  
+2024-09-08 17:46:15.555594: Epoch 343 
+2024-09-08 17:46:15.555683: Current learning rate: 0.00685 
+2024-09-08 17:50:21.182088: train_loss -0.8528 
+2024-09-08 17:50:21.182254: val_loss -0.708 
+2024-09-08 17:50:21.182312: Pseudo dice [0.7018, 0.8808] 
+2024-09-08 17:50:21.182369: Epoch time: 245.63 s 
+2024-09-08 17:50:21.182467: Yayy! New best EMA pseudo Dice: 0.7709 
+2024-09-08 17:50:25.102785:  
+2024-09-08 17:50:25.103006: Epoch 344 
+2024-09-08 17:50:25.103092: Current learning rate: 0.00684 
+2024-09-08 17:54:30.966129: train_loss -0.8542 
+2024-09-08 17:54:30.966290: val_loss -0.6714 
+2024-09-08 17:54:30.966358: Pseudo dice [0.6292, 0.8664] 
+2024-09-08 17:54:30.966438: Epoch time: 245.87 s 
+2024-09-08 17:54:32.334489:  
+2024-09-08 17:54:32.334767: Epoch 345 
+2024-09-08 17:54:32.334864: Current learning rate: 0.00683 
+2024-09-08 17:58:38.269523: train_loss -0.8471 
+2024-09-08 17:58:38.269673: val_loss -0.6941 
+2024-09-08 17:58:38.269731: Pseudo dice [0.669, 0.8755] 
+2024-09-08 17:58:38.269786: Epoch time: 245.94 s 
+2024-09-08 17:58:39.239883:  
+2024-09-08 17:58:39.240125: Epoch 346 
+2024-09-08 17:58:39.240234: Current learning rate: 0.00682 
+2024-09-08 18:02:45.104168: train_loss -0.8454 
+2024-09-08 18:02:45.104327: val_loss -0.6625 
+2024-09-08 18:02:45.104406: Pseudo dice [0.6146, 0.8651] 
+2024-09-08 18:02:45.104462: Epoch time: 245.87 s 
+2024-09-08 18:02:46.086578:  
+2024-09-08 18:02:46.086751: Epoch 347 
+2024-09-08 18:02:46.086838: Current learning rate: 0.00681 
+2024-09-08 18:06:51.850946: train_loss -0.8502 
+2024-09-08 18:06:51.851150: val_loss -0.7059 
+2024-09-08 18:06:51.851209: Pseudo dice [0.6996, 0.8659] 
+2024-09-08 18:06:51.851266: Epoch time: 245.77 s 
+2024-09-08 18:06:52.992643:  
+2024-09-08 18:06:52.992902: Epoch 348 
+2024-09-08 18:06:52.993035: Current learning rate: 0.0068 
+2024-09-08 18:10:58.716302: train_loss -0.8403 
+2024-09-08 18:10:58.716503: val_loss -0.697 
+2024-09-08 18:10:58.716584: Pseudo dice [0.6387, 0.8723] 
+2024-09-08 18:10:58.716663: Epoch time: 245.73 s 
+2024-09-08 18:10:59.776877:  
+2024-09-08 18:10:59.777098: Epoch 349 
+2024-09-08 18:10:59.777186: Current learning rate: 0.0068 
+2024-09-08 18:15:05.907074: train_loss -0.7958 
+2024-09-08 18:15:05.907225: val_loss -0.6973 
+2024-09-08 18:15:05.907283: Pseudo dice [0.6787, 0.8818] 
+2024-09-08 18:15:05.907340: Epoch time: 246.13 s 
+2024-09-08 18:15:09.846870:  
+2024-09-08 18:15:09.847066: Epoch 350 
+2024-09-08 18:15:09.847161: Current learning rate: 0.00679 
+2024-09-08 18:19:15.979126: train_loss -0.8113 
+2024-09-08 18:19:15.979282: val_loss -0.6712 
+2024-09-08 18:19:15.979344: Pseudo dice [0.6385, 0.8564] 
+2024-09-08 18:19:15.979402: Epoch time: 246.13 s 
+2024-09-08 18:19:16.973774:  
+2024-09-08 18:19:16.973930: Epoch 351 
+2024-09-08 18:19:16.974017: Current learning rate: 0.00678 
+2024-09-08 18:23:22.971565: train_loss -0.8288 
+2024-09-08 18:23:22.971764: val_loss -0.6293 
+2024-09-08 18:23:22.971832: Pseudo dice [0.5751, 0.8366] 
+2024-09-08 18:23:22.971895: Epoch time: 246.0 s 
+2024-09-08 18:23:24.112048:  
+2024-09-08 18:23:24.112244: Epoch 352 
+2024-09-08 18:23:24.112344: Current learning rate: 0.00677 
+2024-09-08 18:27:30.016672: train_loss -0.8167 
+2024-09-08 18:27:30.016820: val_loss -0.7101 
+2024-09-08 18:27:30.016878: Pseudo dice [0.6942, 0.8677] 
+2024-09-08 18:27:30.016936: Epoch time: 245.91 s 
+2024-09-08 18:27:30.987551:  
+2024-09-08 18:27:30.987766: Epoch 353 
+2024-09-08 18:27:30.987856: Current learning rate: 0.00676 
+2024-09-08 18:31:37.089187: train_loss -0.8335 
+2024-09-08 18:31:37.089333: val_loss -0.6927 
+2024-09-08 18:31:37.089389: Pseudo dice [0.6173, 0.8762] 
+2024-09-08 18:31:37.089450: Epoch time: 246.1 s 
+2024-09-08 18:31:38.070391:  
+2024-09-08 18:31:38.070522: Epoch 354 
+2024-09-08 18:31:38.070607: Current learning rate: 0.00675 
+2024-09-08 18:35:43.961336: train_loss -0.8547 
+2024-09-08 18:35:43.961515: val_loss -0.6389 
+2024-09-08 18:35:43.961581: Pseudo dice [0.6057, 0.8553] 
+2024-09-08 18:35:43.961645: Epoch time: 245.89 s 
+2024-09-08 18:35:45.230389:  
+2024-09-08 18:35:45.230724: Epoch 355 
+2024-09-08 18:35:45.230869: Current learning rate: 0.00674 
+2024-09-08 18:39:51.170416: train_loss -0.8511 
+2024-09-08 18:39:51.170565: val_loss -0.672 
+2024-09-08 18:39:51.170622: Pseudo dice [0.6227, 0.8598] 
+2024-09-08 18:39:51.170678: Epoch time: 245.94 s 
+2024-09-08 18:39:52.131863:  
+2024-09-08 18:39:52.132035: Epoch 356 
+2024-09-08 18:39:52.132122: Current learning rate: 0.00673 
+2024-09-08 18:43:58.715478: train_loss -0.8464 
+2024-09-08 18:43:58.715700: val_loss -0.7119 
+2024-09-08 18:43:58.715819: Pseudo dice [0.6518, 0.881] 
+2024-09-08 18:43:58.715925: Epoch time: 246.59 s 
+2024-09-08 18:43:59.683889:  
+2024-09-08 18:43:59.684072: Epoch 357 
+2024-09-08 18:43:59.684172: Current learning rate: 0.00672 
+2024-09-08 18:48:05.588231: train_loss -0.8433 
+2024-09-08 18:48:05.588401: val_loss -0.6936 
+2024-09-08 18:48:05.588466: Pseudo dice [0.7072, 0.8687] 
+2024-09-08 18:48:05.588541: Epoch time: 245.91 s 
+2024-09-08 18:48:06.725547:  
+2024-09-08 18:48:06.725872: Epoch 358 
+2024-09-08 18:48:06.725996: Current learning rate: 0.00671 
+2024-09-08 18:52:12.633077: train_loss -0.8401 
+2024-09-08 18:52:12.633268: val_loss -0.6666 
+2024-09-08 18:52:12.633328: Pseudo dice [0.6368, 0.8643] 
+2024-09-08 18:52:12.633385: Epoch time: 245.91 s 
+2024-09-08 18:52:13.612621:  
+2024-09-08 18:52:13.612811: Epoch 359 
+2024-09-08 18:52:13.612902: Current learning rate: 0.0067 
+2024-09-08 18:56:19.808400: train_loss -0.8381 
+2024-09-08 18:56:19.808550: val_loss -0.6747 
+2024-09-08 18:56:19.808606: Pseudo dice [0.6479, 0.8606] 
+2024-09-08 18:56:19.808661: Epoch time: 246.2 s 
+2024-09-08 18:56:20.790426:  
+2024-09-08 18:56:20.790611: Epoch 360 
+2024-09-08 18:56:20.790694: Current learning rate: 0.00669 
+2024-09-08 19:00:28.168678: train_loss -0.8525 
+2024-09-08 19:00:28.168826: val_loss -0.6397 
+2024-09-08 19:00:28.168996: Pseudo dice [0.5565, 0.8499] 
+2024-09-08 19:00:28.169118: Epoch time: 247.38 s 
+2024-09-08 19:00:29.142195:  
+2024-09-08 19:00:29.142341: Epoch 361 
+2024-09-08 19:00:29.142456: Current learning rate: 0.00668 
+2024-09-08 19:04:35.636619: train_loss -0.8437 
+2024-09-08 19:04:35.636769: val_loss -0.6407 
+2024-09-08 19:04:35.636830: Pseudo dice [0.5971, 0.8592] 
+2024-09-08 19:04:35.636889: Epoch time: 246.5 s 
+2024-09-08 19:04:36.610832:  
+2024-09-08 19:04:36.611050: Epoch 362 
+2024-09-08 19:04:36.611140: Current learning rate: 0.00667 
+2024-09-08 19:08:44.020813: train_loss -0.8383 
+2024-09-08 19:08:44.020962: val_loss -0.6525 
+2024-09-08 19:08:44.021017: Pseudo dice [0.6213, 0.8649] 
+2024-09-08 19:08:44.021074: Epoch time: 247.41 s 
+2024-09-08 19:08:44.997677:  
+2024-09-08 19:08:44.997831: Epoch 363 
+2024-09-08 19:08:44.997911: Current learning rate: 0.00666 
+2024-09-08 19:12:51.265713: train_loss -0.8442 
+2024-09-08 19:12:51.265899: val_loss -0.6859 
+2024-09-08 19:12:51.265958: Pseudo dice [0.6262, 0.8763] 
+2024-09-08 19:12:51.266014: Epoch time: 246.27 s 
+2024-09-08 19:12:52.233420:  
+2024-09-08 19:12:52.233565: Epoch 364 
+2024-09-08 19:12:52.233649: Current learning rate: 0.00665 
+2024-09-08 19:16:58.426474: train_loss -0.846 
+2024-09-08 19:16:58.426625: val_loss -0.6877 
+2024-09-08 19:16:58.426681: Pseudo dice [0.6623, 0.8746] 
+2024-09-08 19:16:58.426737: Epoch time: 246.2 s 
+2024-09-08 19:16:59.406816:  
+2024-09-08 19:16:59.407024: Epoch 365 
+2024-09-08 19:16:59.407110: Current learning rate: 0.00665 
+2024-09-08 19:21:05.456636: train_loss -0.846 
+2024-09-08 19:21:05.456786: val_loss -0.668 
+2024-09-08 19:21:05.456844: Pseudo dice [0.6529, 0.8425] 
+2024-09-08 19:21:05.456899: Epoch time: 246.05 s 
+2024-09-08 19:21:06.429383:  
+2024-09-08 19:21:06.429582: Epoch 366 
+2024-09-08 19:21:06.429668: Current learning rate: 0.00664 
+2024-09-08 19:25:12.435727: train_loss -0.8484 
+2024-09-08 19:25:12.435883: val_loss -0.6898 
+2024-09-08 19:25:12.435939: Pseudo dice [0.678, 0.8643] 
+2024-09-08 19:25:12.435995: Epoch time: 246.01 s 
+2024-09-08 19:25:13.412790:  
+2024-09-08 19:25:13.412977: Epoch 367 
+2024-09-08 19:25:13.413063: Current learning rate: 0.00663 
+2024-09-08 19:29:19.774989: train_loss -0.8457 
+2024-09-08 19:29:19.775142: val_loss -0.703 
+2024-09-08 19:29:19.775198: Pseudo dice [0.6714, 0.8636] 
+2024-09-08 19:29:19.775255: Epoch time: 246.36 s 
+2024-09-08 19:29:20.762954:  
+2024-09-08 19:29:20.763174: Epoch 368 
+2024-09-08 19:29:20.763260: Current learning rate: 0.00662 
+2024-09-08 19:33:26.936485: train_loss -0.8421 
+2024-09-08 19:33:26.936629: val_loss -0.6596 
+2024-09-08 19:33:26.936685: Pseudo dice [0.6014, 0.8469] 
+2024-09-08 19:33:26.936739: Epoch time: 246.18 s 
+2024-09-08 19:33:27.931297:  
+2024-09-08 19:33:27.931502: Epoch 369 
+2024-09-08 19:33:27.931586: Current learning rate: 0.00661 
+2024-09-08 19:37:34.077575: train_loss -0.8447 
+2024-09-08 19:37:34.077742: val_loss -0.6747 
+2024-09-08 19:37:34.077849: Pseudo dice [0.6518, 0.8588] 
+2024-09-08 19:37:34.077914: Epoch time: 246.15 s 
+2024-09-08 19:37:35.254279:  
+2024-09-08 19:37:35.254562: Epoch 370 
+2024-09-08 19:37:35.254667: Current learning rate: 0.0066 
+2024-09-08 19:41:41.288667: train_loss -0.8483 
+2024-09-08 19:41:41.288811: val_loss -0.6461 
+2024-09-08 19:41:41.288867: Pseudo dice [0.6515, 0.8531] 
+2024-09-08 19:41:41.288925: Epoch time: 246.04 s 
+2024-09-08 19:41:42.261242:  
+2024-09-08 19:41:42.261503: Epoch 371 
+2024-09-08 19:41:42.261587: Current learning rate: 0.00659 
+2024-09-08 19:45:48.166998: train_loss -0.8515 
+2024-09-08 19:45:48.167153: val_loss -0.6928 
+2024-09-08 19:45:48.167210: Pseudo dice [0.6921, 0.8584] 
+2024-09-08 19:45:48.167327: Epoch time: 245.91 s 
+2024-09-08 19:45:49.155416:  
+2024-09-08 19:45:49.155611: Epoch 372 
+2024-09-08 19:45:49.155695: Current learning rate: 0.00658 
+2024-09-08 19:49:54.917751: train_loss -0.8479 
+2024-09-08 19:49:54.917926: val_loss -0.7103 
+2024-09-08 19:49:54.917983: Pseudo dice [0.6833, 0.8723] 
+2024-09-08 19:49:54.918039: Epoch time: 245.76 s 
+2024-09-08 19:49:55.884204:  
+2024-09-08 19:49:55.884381: Epoch 373 
+2024-09-08 19:49:55.884468: Current learning rate: 0.00657 
+2024-09-08 19:54:01.790519: train_loss -0.8488 
+2024-09-08 19:54:01.790656: val_loss -0.6895 
+2024-09-08 19:54:01.790715: Pseudo dice [0.6652, 0.8699] 
+2024-09-08 19:54:01.790770: Epoch time: 245.91 s 
+2024-09-08 19:54:02.767360:  
+2024-09-08 19:54:02.767522: Epoch 374 
+2024-09-08 19:54:02.767604: Current learning rate: 0.00656 
+2024-09-08 19:58:08.589081: train_loss -0.8559 
+2024-09-08 19:58:08.589229: val_loss -0.7048 
+2024-09-08 19:58:08.589286: Pseudo dice [0.6789, 0.8894] 
+2024-09-08 19:58:08.589342: Epoch time: 245.82 s 
+2024-09-08 19:58:09.577818:  
+2024-09-08 19:58:09.578032: Epoch 375 
+2024-09-08 19:58:09.578118: Current learning rate: 0.00655 
+2024-09-08 20:02:15.685937: train_loss -0.8509 
+2024-09-08 20:02:15.686076: val_loss -0.729 
+2024-09-08 20:02:15.686132: Pseudo dice [0.7293, 0.8813] 
+2024-09-08 20:02:15.686186: Epoch time: 246.11 s 
+2024-09-08 20:02:16.676683:  
+2024-09-08 20:02:16.676818: Epoch 376 
+2024-09-08 20:02:16.676904: Current learning rate: 0.00654 
+2024-09-08 20:06:22.881849: train_loss -0.8513 
+2024-09-08 20:06:22.882012: val_loss -0.6967 
+2024-09-08 20:06:22.882079: Pseudo dice [0.6835, 0.8729] 
+2024-09-08 20:06:22.882148: Epoch time: 246.21 s 
+2024-09-08 20:06:23.856749:  
+2024-09-08 20:06:23.856961: Epoch 377 
+2024-09-08 20:06:23.857044: Current learning rate: 0.00653 
+2024-09-08 20:10:30.037809: train_loss -0.8502 
+2024-09-08 20:10:30.037975: val_loss -0.7002 
+2024-09-08 20:10:30.038032: Pseudo dice [0.6769, 0.8651] 
+2024-09-08 20:10:30.038089: Epoch time: 246.18 s 
+2024-09-08 20:10:31.031220:  
+2024-09-08 20:10:31.031412: Epoch 378 
+2024-09-08 20:10:31.031524: Current learning rate: 0.00652 
+2024-09-08 20:14:37.342874: train_loss -0.8411 
+2024-09-08 20:14:37.343022: val_loss -0.682 
+2024-09-08 20:14:37.343079: Pseudo dice [0.6605, 0.8726] 
+2024-09-08 20:14:37.343136: Epoch time: 246.31 s 
+2024-09-08 20:14:39.260661:  
+2024-09-08 20:14:39.260914: Epoch 379 
+2024-09-08 20:14:39.261042: Current learning rate: 0.00651 
+2024-09-08 20:18:45.630666: train_loss -0.8476 
+2024-09-08 20:18:45.630828: val_loss -0.6737 
+2024-09-08 20:18:45.630884: Pseudo dice [0.6461, 0.853] 
+2024-09-08 20:18:45.630941: Epoch time: 246.37 s 
+2024-09-08 20:18:46.638043:  
+2024-09-08 20:18:46.638307: Epoch 380 
+2024-09-08 20:18:46.638409: Current learning rate: 0.0065 
+2024-09-08 20:22:52.856624: train_loss -0.8493 
+2024-09-08 20:22:52.856891: val_loss -0.706 
+2024-09-08 20:22:52.857005: Pseudo dice [0.6901, 0.8554] 
+2024-09-08 20:22:52.857110: Epoch time: 246.22 s 
+2024-09-08 20:22:53.838525:  
+2024-09-08 20:22:53.838779: Epoch 381 
+2024-09-08 20:22:53.838866: Current learning rate: 0.00649 
+2024-09-08 20:26:59.817195: train_loss -0.8543 
+2024-09-08 20:26:59.817382: val_loss -0.6762 
+2024-09-08 20:26:59.817439: Pseudo dice [0.6501, 0.8653] 
+2024-09-08 20:26:59.817495: Epoch time: 245.98 s 
+2024-09-08 20:27:00.810934:  
+2024-09-08 20:27:00.811185: Epoch 382 
+2024-09-08 20:27:00.811271: Current learning rate: 0.00648 
+2024-09-08 20:31:06.797400: train_loss -0.852 
+2024-09-08 20:31:06.797553: val_loss -0.684 
+2024-09-08 20:31:06.797641: Pseudo dice [0.6485, 0.8569] 
+2024-09-08 20:31:06.797770: Epoch time: 245.99 s 
+2024-09-08 20:31:07.819577:  
+2024-09-08 20:31:07.819791: Epoch 383 
+2024-09-08 20:31:07.819896: Current learning rate: 0.00648 
+2024-09-08 20:35:13.676968: train_loss -0.8446 
+2024-09-08 20:35:13.677109: val_loss -0.7061 
+2024-09-08 20:35:13.677166: Pseudo dice [0.6851, 0.8809] 
+2024-09-08 20:35:13.677223: Epoch time: 245.86 s 
+2024-09-08 20:35:14.700631:  
+2024-09-08 20:35:14.700885: Epoch 384 
+2024-09-08 20:35:14.701005: Current learning rate: 0.00647 
+2024-09-08 20:39:20.318582: train_loss -0.8522 
+2024-09-08 20:39:20.318732: val_loss -0.6872 
+2024-09-08 20:39:20.318789: Pseudo dice [0.6417, 0.8643] 
+2024-09-08 20:39:20.318845: Epoch time: 245.62 s 
+2024-09-08 20:39:21.301064:  
+2024-09-08 20:39:21.301293: Epoch 385 
+2024-09-08 20:39:21.301380: Current learning rate: 0.00646 
+2024-09-08 20:43:27.154312: train_loss -0.8523 
+2024-09-08 20:43:27.154457: val_loss -0.7059 
+2024-09-08 20:43:27.154515: Pseudo dice [0.666, 0.865] 
+2024-09-08 20:43:27.154573: Epoch time: 245.86 s 
+2024-09-08 20:43:28.166198:  
+2024-09-08 20:43:28.166360: Epoch 386 
+2024-09-08 20:43:28.166441: Current learning rate: 0.00645 
+2024-09-08 20:47:34.021229: train_loss -0.8527 
+2024-09-08 20:47:34.021458: val_loss -0.7008 
+2024-09-08 20:47:34.021692: Pseudo dice [0.6831, 0.868] 
+2024-09-08 20:47:34.021797: Epoch time: 245.86 s 
+2024-09-08 20:47:35.375959:  
+2024-09-08 20:47:35.376256: Epoch 387 
+2024-09-08 20:47:35.376460: Current learning rate: 0.00644 
+2024-09-08 20:51:41.098890: train_loss -0.8567 
+2024-09-08 20:51:41.099063: val_loss -0.7009 
+2024-09-08 20:51:41.099120: Pseudo dice [0.6592, 0.8771] 
+2024-09-08 20:51:41.099176: Epoch time: 245.73 s 
+2024-09-08 20:51:42.126766:  
+2024-09-08 20:51:42.126910: Epoch 388 
+2024-09-08 20:51:42.126995: Current learning rate: 0.00643 
+2024-09-08 20:55:47.957928: train_loss -0.8404 
+2024-09-08 20:55:47.958077: val_loss -0.6538 
+2024-09-08 20:55:47.958134: Pseudo dice [0.5582, 0.8364] 
+2024-09-08 20:55:47.958193: Epoch time: 245.83 s 
+2024-09-08 20:55:48.962077:  
+2024-09-08 20:55:48.962296: Epoch 389 
+2024-09-08 20:55:48.962382: Current learning rate: 0.00642 
+2024-09-08 20:59:54.928939: train_loss -0.8291 
+2024-09-08 20:59:54.929084: val_loss -0.7085 
+2024-09-08 20:59:54.929142: Pseudo dice [0.6756, 0.8754] 
+2024-09-08 20:59:54.929201: Epoch time: 245.97 s 
+2024-09-08 20:59:55.954749:  
+2024-09-08 20:59:55.954899: Epoch 390 
+2024-09-08 20:59:55.954981: Current learning rate: 0.00641 
+2024-09-08 21:04:01.814973: train_loss -0.8423 
+2024-09-08 21:04:01.815160: val_loss -0.6809 
+2024-09-08 21:04:01.815216: Pseudo dice [0.6542, 0.8487] 
+2024-09-08 21:04:01.815273: Epoch time: 245.86 s 
+2024-09-08 21:04:02.824571:  
+2024-09-08 21:04:02.824756: Epoch 391 
+2024-09-08 21:04:02.824840: Current learning rate: 0.0064 
+2024-09-08 21:08:08.849375: train_loss -0.8425 
+2024-09-08 21:08:08.849524: val_loss -0.6844 
+2024-09-08 21:08:08.849587: Pseudo dice [0.6445, 0.8672] 
+2024-09-08 21:08:08.849644: Epoch time: 246.03 s 
+2024-09-08 21:08:09.846980:  
+2024-09-08 21:08:09.847159: Epoch 392 
+2024-09-08 21:08:09.847245: Current learning rate: 0.00639 
+2024-09-08 21:12:15.744196: train_loss -0.848 
+2024-09-08 21:12:15.744344: val_loss -0.685 
+2024-09-08 21:12:15.744402: Pseudo dice [0.6273, 0.8661] 
+2024-09-08 21:12:15.744456: Epoch time: 245.9 s 
+2024-09-08 21:12:16.737685:  
+2024-09-08 21:12:16.737932: Epoch 393 
+2024-09-08 21:12:16.738023: Current learning rate: 0.00638 
+2024-09-08 21:16:22.828024: train_loss -0.8537 
+2024-09-08 21:16:22.828174: val_loss -0.7055 
+2024-09-08 21:16:22.828231: Pseudo dice [0.677, 0.8701] 
+2024-09-08 21:16:22.828287: Epoch time: 246.09 s 
+2024-09-08 21:16:23.846506:  
+2024-09-08 21:16:23.846700: Epoch 394 
+2024-09-08 21:16:23.846796: Current learning rate: 0.00637 
+2024-09-08 21:20:29.952975: train_loss -0.8379 
+2024-09-08 21:20:29.953174: val_loss -0.63 
+2024-09-08 21:20:29.953232: Pseudo dice [0.5976, 0.8273] 
+2024-09-08 21:20:29.953287: Epoch time: 246.11 s 
+2024-09-08 21:20:30.947637:  
+2024-09-08 21:20:30.947874: Epoch 395 
+2024-09-08 21:20:30.947965: Current learning rate: 0.00636 
+2024-09-08 21:24:37.110247: train_loss -0.8323 
+2024-09-08 21:24:37.110396: val_loss -0.6885 
+2024-09-08 21:24:37.110454: Pseudo dice [0.6561, 0.8645] 
+2024-09-08 21:24:37.110509: Epoch time: 246.16 s 
+2024-09-08 21:24:38.107426:  
+2024-09-08 21:24:38.107661: Epoch 396 
+2024-09-08 21:24:38.107752: Current learning rate: 0.00635 
+2024-09-08 21:28:44.209239: train_loss -0.8408 
+2024-09-08 21:28:44.209428: val_loss -0.6771 
+2024-09-08 21:28:44.209486: Pseudo dice [0.6402, 0.8771] 
+2024-09-08 21:28:44.209542: Epoch time: 246.1 s 
+2024-09-08 21:28:45.204356:  
+2024-09-08 21:28:45.204545: Epoch 397 
+2024-09-08 21:28:45.204644: Current learning rate: 0.00634 
+2024-09-08 21:32:51.192344: train_loss -0.8501 
+2024-09-08 21:32:51.192490: val_loss -0.6882 
+2024-09-08 21:32:51.192546: Pseudo dice [0.671, 0.8595] 
+2024-09-08 21:32:51.192605: Epoch time: 245.99 s 
+2024-09-08 21:32:52.280775:  
+2024-09-08 21:32:52.280978: Epoch 398 
+2024-09-08 21:32:52.281086: Current learning rate: 0.00633 
+2024-09-08 21:36:58.283295: train_loss -0.8586 
+2024-09-08 21:36:58.283438: val_loss -0.6665 
+2024-09-08 21:36:58.283495: Pseudo dice [0.631, 0.8769] 
+2024-09-08 21:36:58.283608: Epoch time: 246.0 s 
+2024-09-08 21:36:59.285790:  
+2024-09-08 21:36:59.285920: Epoch 399 
+2024-09-08 21:36:59.286004: Current learning rate: 0.00632 
+2024-09-08 21:41:05.614292: train_loss -0.8545 
+2024-09-08 21:41:05.614439: val_loss -0.6797 
+2024-09-08 21:41:05.614496: Pseudo dice [0.6529, 0.8769] 
+2024-09-08 21:41:05.614553: Epoch time: 246.33 s 
+2024-09-08 21:41:09.574834:  
+2024-09-08 21:41:09.575008: Epoch 400 
+2024-09-08 21:41:09.575095: Current learning rate: 0.00631 
+2024-09-08 21:45:15.642572: train_loss -0.8594 
+2024-09-08 21:45:15.642793: val_loss -0.6905 
+2024-09-08 21:45:15.642851: Pseudo dice [0.6595, 0.8877] 
+2024-09-08 21:45:15.642909: Epoch time: 246.07 s 
+2024-09-08 21:45:16.641567:  
+2024-09-08 21:45:16.641737: Epoch 401 
+2024-09-08 21:45:16.641826: Current learning rate: 0.0063 
+2024-09-08 21:49:23.479967: train_loss -0.8587 
+2024-09-08 21:49:23.480128: val_loss -0.7142 
+2024-09-08 21:49:23.480232: Pseudo dice [0.6931, 0.8851] 
+2024-09-08 21:49:23.480289: Epoch time: 246.84 s 
+2024-09-08 21:49:24.473894:  
+2024-09-08 21:49:24.474237: Epoch 402 
+2024-09-08 21:49:24.474325: Current learning rate: 0.0063 
+2024-09-08 21:53:30.530174: train_loss -0.8573 
+2024-09-08 21:53:30.530416: val_loss -0.633 
+2024-09-08 21:53:30.530481: Pseudo dice [0.5858, 0.8544] 
+2024-09-08 21:53:30.530558: Epoch time: 246.06 s 
+2024-09-08 21:53:31.851551:  
+2024-09-08 21:53:31.851988: Epoch 403 
+2024-09-08 21:53:31.852226: Current learning rate: 0.00629 
+2024-09-08 21:57:37.977725: train_loss -0.8524 
+2024-09-08 21:57:37.977886: val_loss -0.6647 
+2024-09-08 21:57:37.977943: Pseudo dice [0.6472, 0.8728] 
+2024-09-08 21:57:37.977998: Epoch time: 246.13 s 
+2024-09-08 21:57:38.983872:  
+2024-09-08 21:57:38.984100: Epoch 404 
+2024-09-08 21:57:38.984188: Current learning rate: 0.00628 
+2024-09-08 22:01:44.969441: train_loss -0.8458 
+2024-09-08 22:01:44.969604: val_loss -0.6896 
+2024-09-08 22:01:44.969661: Pseudo dice [0.6388, 0.8657] 
+2024-09-08 22:01:44.969718: Epoch time: 245.99 s 
+2024-09-08 22:01:45.969057:  
+2024-09-08 22:01:45.969324: Epoch 405 
+2024-09-08 22:01:45.969475: Current learning rate: 0.00627 
+2024-09-08 22:05:51.892865: train_loss -0.8506 
+2024-09-08 22:05:51.893019: val_loss -0.6955 
+2024-09-08 22:05:51.893132: Pseudo dice [0.6666, 0.8815] 
+2024-09-08 22:05:51.893190: Epoch time: 245.93 s 
+2024-09-08 22:05:52.902313:  
+2024-09-08 22:05:52.902515: Epoch 406 
+2024-09-08 22:05:52.902626: Current learning rate: 0.00626 
+2024-09-08 22:09:58.954965: train_loss -0.8319 
+2024-09-08 22:09:58.955115: val_loss -0.6891 
+2024-09-08 22:09:58.955171: Pseudo dice [0.6676, 0.8491] 
+2024-09-08 22:09:58.955237: Epoch time: 246.05 s 
+2024-09-08 22:09:59.950611:  
+2024-09-08 22:09:59.950867: Epoch 407 
+2024-09-08 22:09:59.950954: Current learning rate: 0.00625 
+2024-09-08 22:14:05.943974: train_loss -0.8156 
+2024-09-08 22:14:05.944130: val_loss -0.6914 
+2024-09-08 22:14:05.944189: Pseudo dice [0.7006, 0.8675] 
+2024-09-08 22:14:05.944247: Epoch time: 246.0 s 
+2024-09-08 22:14:06.955562:  
+2024-09-08 22:14:06.955758: Epoch 408 
+2024-09-08 22:14:06.955887: Current learning rate: 0.00624 
+2024-09-08 22:18:13.129033: train_loss -0.8238 
+2024-09-08 22:18:13.129171: val_loss -0.6983 
+2024-09-08 22:18:13.129227: Pseudo dice [0.6825, 0.8664] 
+2024-09-08 22:18:13.129284: Epoch time: 246.18 s 
+2024-09-08 22:18:14.135782:  
+2024-09-08 22:18:14.136048: Epoch 409 
+2024-09-08 22:18:14.136132: Current learning rate: 0.00623 
+2024-09-08 22:22:20.424933: train_loss -0.8392 
+2024-09-08 22:22:20.425083: val_loss -0.6495 
+2024-09-08 22:22:20.425139: Pseudo dice [0.6113, 0.8624] 
+2024-09-08 22:22:20.425238: Epoch time: 246.29 s 
+2024-09-08 22:22:21.484406:  
+2024-09-08 22:22:21.484607: Epoch 410 
+2024-09-08 22:22:21.484691: Current learning rate: 0.00622 
+2024-09-08 22:26:27.427632: train_loss -0.83 
+2024-09-08 22:26:27.427791: val_loss -0.6989 
+2024-09-08 22:26:27.427860: Pseudo dice [0.667, 0.8635] 
+2024-09-08 22:26:27.427915: Epoch time: 245.95 s 
+2024-09-08 22:26:28.405764:  
+2024-09-08 22:26:28.405968: Epoch 411 
+2024-09-08 22:26:28.406068: Current learning rate: 0.00621 
+2024-09-08 22:30:35.208625: train_loss -0.8338 
+2024-09-08 22:30:35.208768: val_loss -0.6816 
+2024-09-08 22:30:35.208829: Pseudo dice [0.6555, 0.858] 
+2024-09-08 22:30:35.208885: Epoch time: 246.8 s 
+2024-09-08 22:30:36.161516:  
+2024-09-08 22:30:36.161696: Epoch 412 
+2024-09-08 22:30:36.161779: Current learning rate: 0.0062 
+2024-09-08 22:35:12.916552: train_loss -0.8375 
+2024-09-08 22:35:12.916772: val_loss -0.6579 
+2024-09-08 22:35:12.916861: Pseudo dice [0.5599, 0.8696] 
+2024-09-08 22:35:12.916936: Epoch time: 276.76 s 
+2024-09-08 22:35:14.038473:  
+2024-09-08 22:35:14.038693: Epoch 413 
+2024-09-08 22:35:14.038894: Current learning rate: 0.00619 
+2024-09-08 22:39:56.252102: train_loss -0.8374 
+2024-09-08 22:39:56.252262: val_loss -0.669 
+2024-09-08 22:39:56.252324: Pseudo dice [0.6439, 0.8596] 
+2024-09-08 22:39:56.252379: Epoch time: 282.22 s 
+2024-09-08 22:39:57.275034:  
+2024-09-08 22:39:57.275198: Epoch 414 
+2024-09-08 22:39:57.275290: Current learning rate: 0.00618 
+2024-09-08 22:44:08.019389: train_loss -0.8348 
+2024-09-08 22:44:08.019546: val_loss -0.7009 
+2024-09-08 22:44:08.019603: Pseudo dice [0.698, 0.8748] 
+2024-09-08 22:44:08.019660: Epoch time: 250.75 s 
+2024-09-08 22:44:08.989451:  
+2024-09-08 22:44:08.989687: Epoch 415 
+2024-09-08 22:44:08.989777: Current learning rate: 0.00617 
+2024-09-08 22:48:15.336905: train_loss -0.8427 
+2024-09-08 22:48:15.337086: val_loss -0.7138 
+2024-09-08 22:48:15.337143: Pseudo dice [0.684, 0.8802] 
+2024-09-08 22:48:15.337202: Epoch time: 246.35 s 
+2024-09-08 22:48:16.318749:  
+2024-09-08 22:48:16.318943: Epoch 416 
+2024-09-08 22:48:16.319034: Current learning rate: 0.00616 
+2024-09-08 22:52:32.060451: train_loss -0.8512 
+2024-09-08 22:52:32.060621: val_loss -0.6969 
+2024-09-08 22:52:32.060690: Pseudo dice [0.623, 0.878] 
+2024-09-08 22:52:32.060757: Epoch time: 255.74 s 
+2024-09-08 22:52:33.222622:  
+2024-09-08 22:52:33.222923: Epoch 417 
+2024-09-08 22:52:33.223023: Current learning rate: 0.00615 
+2024-09-08 22:56:50.296293: train_loss -0.8438 
+2024-09-08 22:56:50.296429: val_loss -0.6774 
+2024-09-08 22:56:50.296485: Pseudo dice [0.6393, 0.8675] 
+2024-09-08 22:56:50.296541: Epoch time: 257.08 s 
+2024-09-08 22:56:51.243044:  
+2024-09-08 22:56:51.243279: Epoch 418 
+2024-09-08 22:56:51.243401: Current learning rate: 0.00614 
+2024-09-08 23:01:25.000555: train_loss -0.8263 
+2024-09-08 23:01:25.000706: val_loss -0.657 
+2024-09-08 23:01:25.000763: Pseudo dice [0.5988, 0.8644] 
+2024-09-08 23:01:25.000824: Epoch time: 273.76 s 
+2024-09-08 23:01:25.951083:  
+2024-09-08 23:01:25.951244: Epoch 419 
+2024-09-08 23:01:25.951379: Current learning rate: 0.00613 
+2024-09-08 23:05:33.249726: train_loss -0.8372 
+2024-09-08 23:05:33.249911: val_loss -0.7047 
+2024-09-08 23:05:33.249969: Pseudo dice [0.6848, 0.853] 
+2024-09-08 23:05:33.250025: Epoch time: 247.3 s 
+2024-09-08 23:05:34.208419:  
+2024-09-08 23:05:34.208610: Epoch 420 
+2024-09-08 23:05:34.208701: Current learning rate: 0.00612 
+2024-09-08 23:09:41.232677: train_loss -0.801 
+2024-09-08 23:09:41.232863: val_loss -0.6685 
+2024-09-08 23:09:41.232935: Pseudo dice [0.584, 0.8534] 
+2024-09-08 23:09:41.233011: Epoch time: 247.03 s 
+2024-09-08 23:09:42.510200:  
+2024-09-08 23:09:42.510430: Epoch 421 
+2024-09-08 23:09:42.510529: Current learning rate: 0.00612 
+2024-09-08 23:13:56.401431: train_loss -0.7848 
+2024-09-08 23:13:56.401580: val_loss -0.6471 
+2024-09-08 23:13:56.401637: Pseudo dice [0.5992, 0.841] 
+2024-09-08 23:13:56.401693: Epoch time: 253.89 s 
+2024-09-08 23:13:57.365972:  
+2024-09-08 23:13:57.366128: Epoch 422 
+2024-09-08 23:13:57.366218: Current learning rate: 0.00611 
+2024-09-08 23:18:04.297490: train_loss -0.805 
+2024-09-08 23:18:04.297695: val_loss -0.6647 
+2024-09-08 23:18:04.297754: Pseudo dice [0.6283, 0.8561] 
+2024-09-08 23:18:04.297811: Epoch time: 246.93 s 
+2024-09-08 23:18:05.281304:  
+2024-09-08 23:18:05.281517: Epoch 423 
+2024-09-08 23:18:05.281601: Current learning rate: 0.0061 
+2024-09-08 23:22:11.538275: train_loss -0.8344 
+2024-09-08 23:22:11.538441: val_loss -0.6914 
+2024-09-08 23:22:11.538499: Pseudo dice [0.6573, 0.8635] 
+2024-09-08 23:22:11.538555: Epoch time: 246.26 s 
+2024-09-08 23:22:12.498630:  
+2024-09-08 23:22:12.498823: Epoch 424 
+2024-09-08 23:22:12.498910: Current learning rate: 0.00609 
+2024-09-08 23:26:36.268050: train_loss -0.8356 
+2024-09-08 23:26:36.268236: val_loss -0.6503 
+2024-09-08 23:26:36.268299: Pseudo dice [0.6075, 0.8481] 
+2024-09-08 23:26:36.268361: Epoch time: 263.77 s 
+2024-09-08 23:26:37.381192:  
+2024-09-08 23:26:37.381456: Epoch 425 
+2024-09-08 23:26:37.381574: Current learning rate: 0.00608 
+2024-09-08 23:30:53.512850: train_loss -0.8278 
+2024-09-08 23:30:53.512994: val_loss -0.691 
+2024-09-08 23:30:53.513053: Pseudo dice [0.6029, 0.8786] 
+2024-09-08 23:30:53.513136: Epoch time: 256.13 s 
+2024-09-08 23:30:54.462909:  
+2024-09-08 23:30:54.463140: Epoch 426 
+2024-09-08 23:30:54.463225: Current learning rate: 0.00607 
+2024-09-08 23:35:02.981522: train_loss -0.8448 
+2024-09-08 23:35:02.981776: val_loss -0.7127 
+2024-09-08 23:35:02.981906: Pseudo dice [0.69, 0.8752] 
+2024-09-08 23:35:02.982003: Epoch time: 248.52 s 
+2024-09-08 23:35:04.123905:  
+2024-09-08 23:35:04.124128: Epoch 427 
+2024-09-08 23:35:04.124210: Current learning rate: 0.00606 
+2024-09-08 23:39:23.631392: train_loss -0.845 
+2024-09-08 23:39:23.631533: val_loss -0.6861 
+2024-09-08 23:39:23.631589: Pseudo dice [0.6376, 0.8842] 
+2024-09-08 23:39:23.631648: Epoch time: 259.51 s 
+2024-09-08 23:39:24.567924:  
+2024-09-08 23:39:24.568161: Epoch 428 
+2024-09-08 23:39:24.568245: Current learning rate: 0.00605 
+2024-09-08 23:43:30.628824: train_loss -0.8412 
+2024-09-08 23:43:30.628998: val_loss -0.708 
+2024-09-08 23:43:30.629070: Pseudo dice [0.6666, 0.8799] 
+2024-09-08 23:43:30.629141: Epoch time: 246.06 s 
+2024-09-08 23:43:31.794135:  
+2024-09-08 23:43:31.794382: Epoch 429 
+2024-09-08 23:43:31.794487: Current learning rate: 0.00604 
+2024-09-08 23:47:40.919405: train_loss -0.85 
+2024-09-08 23:47:40.919559: val_loss -0.6934 
+2024-09-08 23:47:40.919623: Pseudo dice [0.638, 0.8742] 
+2024-09-08 23:47:40.919689: Epoch time: 249.13 s 
+2024-09-08 23:47:42.095722:  
+2024-09-08 23:47:42.095955: Epoch 430 
+2024-09-08 23:47:42.096060: Current learning rate: 0.00603 
+2024-09-08 23:51:48.920566: train_loss -0.8578 
+2024-09-08 23:51:48.920717: val_loss -0.7035 
+2024-09-08 23:51:48.920774: Pseudo dice [0.7019, 0.8688] 
+2024-09-08 23:51:48.920831: Epoch time: 246.83 s 
+2024-09-08 23:51:49.864456:  
+2024-09-08 23:51:49.864667: Epoch 431 
+2024-09-08 23:51:49.864786: Current learning rate: 0.00602 
+2024-09-08 23:56:02.138147: train_loss -0.8523 
+2024-09-08 23:56:02.138318: val_loss -0.666 
+2024-09-08 23:56:02.138376: Pseudo dice [0.6134, 0.8742] 
+2024-09-08 23:56:02.138430: Epoch time: 252.28 s 
+2024-09-08 23:56:03.082398:  
+2024-09-08 23:56:03.082592: Epoch 432 
+2024-09-08 23:56:03.082676: Current learning rate: 0.00601 
+2024-09-09 00:00:23.565797: train_loss -0.8586 
+2024-09-09 00:00:23.565943: val_loss -0.7172 
+2024-09-09 00:00:23.565997: Pseudo dice [0.7096, 0.8809] 
+2024-09-09 00:00:23.566054: Epoch time: 260.49 s 
+2024-09-09 00:00:24.522517:  
+2024-09-09 00:00:24.522740: Epoch 433 
+2024-09-09 00:00:24.522825: Current learning rate: 0.006 
+2024-09-09 00:04:40.295053: train_loss -0.8554 
+2024-09-09 00:04:40.295213: val_loss -0.6657 
+2024-09-09 00:04:40.295271: Pseudo dice [0.5972, 0.8679] 
+2024-09-09 00:04:40.295334: Epoch time: 255.77 s 
+2024-09-09 00:04:41.275701:  
+2024-09-09 00:04:41.275989: Epoch 434 
+2024-09-09 00:04:41.276101: Current learning rate: 0.00599 
+2024-09-09 00:09:02.289581: train_loss -0.8598 
+2024-09-09 00:09:02.289970: val_loss -0.6686 
+2024-09-09 00:09:02.290029: Pseudo dice [0.6466, 0.8604] 
+2024-09-09 00:09:02.290084: Epoch time: 261.02 s 
+2024-09-09 00:09:03.259864:  
+2024-09-09 00:09:03.260077: Epoch 435 
+2024-09-09 00:09:03.260161: Current learning rate: 0.00598 
+2024-09-09 00:13:09.594903: train_loss -0.857 
+2024-09-09 00:13:09.595108: val_loss -0.6733 
+2024-09-09 00:13:09.595166: Pseudo dice [0.6488, 0.8578] 
+2024-09-09 00:13:09.595225: Epoch time: 246.34 s 
+2024-09-09 00:13:10.556330:  
+2024-09-09 00:13:10.556563: Epoch 436 
+2024-09-09 00:13:10.556654: Current learning rate: 0.00597 
+2024-09-09 00:17:25.148539: train_loss -0.8613 
+2024-09-09 00:17:25.148708: val_loss -0.7199 
+2024-09-09 00:17:25.148770: Pseudo dice [0.6956, 0.8757] 
+2024-09-09 00:17:25.148840: Epoch time: 254.59 s 
+2024-09-09 00:17:26.450940:  
+2024-09-09 00:17:26.451195: Epoch 437 
+2024-09-09 00:17:26.451337: Current learning rate: 0.00596 
+2024-09-09 00:21:33.701038: train_loss -0.8529 
+2024-09-09 00:21:33.701194: val_loss -0.6937 
+2024-09-09 00:21:33.701250: Pseudo dice [0.6939, 0.8659] 
+2024-09-09 00:21:33.701348: Epoch time: 247.25 s 
+2024-09-09 00:21:34.711738:  
+2024-09-09 00:21:34.712003: Epoch 438 
+2024-09-09 00:21:34.712099: Current learning rate: 0.00595 
+2024-09-09 00:25:42.313720: train_loss -0.8573 
+2024-09-09 00:25:42.314115: val_loss -0.6994 
+2024-09-09 00:25:42.314180: Pseudo dice [0.6744, 0.874] 
+2024-09-09 00:25:42.314239: Epoch time: 247.6 s 
+2024-09-09 00:25:43.376817:  
+2024-09-09 00:25:43.377077: Epoch 439 
+2024-09-09 00:25:43.377161: Current learning rate: 0.00594 
+2024-09-09 00:30:00.210397: train_loss -0.8626 
+2024-09-09 00:30:00.210609: val_loss -0.6747 
+2024-09-09 00:30:00.210668: Pseudo dice [0.6437, 0.8551] 
+2024-09-09 00:30:00.210728: Epoch time: 256.84 s 
+2024-09-09 00:30:01.285630:  
+2024-09-09 00:30:01.285846: Epoch 440 
+2024-09-09 00:30:01.286000: Current learning rate: 0.00593 
+2024-09-09 00:34:18.539334: train_loss -0.8627 
+2024-09-09 00:34:18.539761: val_loss -0.6818 
+2024-09-09 00:34:18.539831: Pseudo dice [0.6345, 0.8854] 
+2024-09-09 00:34:18.539896: Epoch time: 257.26 s 
+2024-09-09 00:34:19.643459:  
+2024-09-09 00:34:19.643651: Epoch 441 
+2024-09-09 00:34:19.643779: Current learning rate: 0.00592 
+2024-09-09 00:38:32.737774: train_loss -0.8498 
+2024-09-09 00:38:32.737920: val_loss -0.6746 
+2024-09-09 00:38:32.737977: Pseudo dice [0.6359, 0.8607] 
+2024-09-09 00:38:32.738033: Epoch time: 253.1 s 
+2024-09-09 00:38:33.687279:  
+2024-09-09 00:38:33.687498: Epoch 442 
+2024-09-09 00:38:33.687583: Current learning rate: 0.00592 
+2024-09-09 00:42:39.782488: train_loss -0.8626 
+2024-09-09 00:42:39.782655: val_loss -0.7105 
+2024-09-09 00:42:39.782714: Pseudo dice [0.6649, 0.8738] 
+2024-09-09 00:42:39.782772: Epoch time: 246.1 s 
+2024-09-09 00:42:40.750753:  
+2024-09-09 00:42:40.750959: Epoch 443 
+2024-09-09 00:42:40.751041: Current learning rate: 0.00591 
+2024-09-09 00:46:47.019136: train_loss -0.86 
+2024-09-09 00:46:47.019340: val_loss -0.711 
+2024-09-09 00:46:47.019403: Pseudo dice [0.6663, 0.8797] 
+2024-09-09 00:46:47.019463: Epoch time: 246.27 s 
+2024-09-09 00:46:48.100827:  
+2024-09-09 00:46:48.101022: Epoch 444 
+2024-09-09 00:46:48.101100: Current learning rate: 0.0059 
+2024-09-09 00:50:54.470024: train_loss -0.8541 
+2024-09-09 00:50:54.470192: val_loss -0.6798 
+2024-09-09 00:50:54.470244: Pseudo dice [0.6085, 0.8778] 
+2024-09-09 00:50:54.470297: Epoch time: 246.37 s 
+2024-09-09 00:50:55.421282:  
+2024-09-09 00:50:55.421553: Epoch 445 
+2024-09-09 00:50:55.421674: Current learning rate: 0.00589 
+2024-09-09 00:55:01.785733: train_loss -0.848 
+2024-09-09 00:55:01.785871: val_loss -0.684 
+2024-09-09 00:55:01.785922: Pseudo dice [0.6691, 0.8683] 
+2024-09-09 00:55:01.785976: Epoch time: 246.37 s 
+2024-09-09 00:55:02.735582:  
+2024-09-09 00:55:02.735759: Epoch 446 
+2024-09-09 00:55:02.736064: Current learning rate: 0.00588 
+2024-09-09 00:59:09.250528: train_loss -0.8564 
+2024-09-09 00:59:09.250694: val_loss -0.6926 
+2024-09-09 00:59:09.250753: Pseudo dice [0.6583, 0.8626] 
+2024-09-09 00:59:09.250807: Epoch time: 246.52 s 
+2024-09-09 00:59:10.381445:  
+2024-09-09 00:59:10.381680: Epoch 447 
+2024-09-09 00:59:10.381765: Current learning rate: 0.00587 
+2024-09-09 01:03:16.881080: train_loss -0.8586 
+2024-09-09 01:03:16.881236: val_loss -0.7004 
+2024-09-09 01:03:16.881310: Pseudo dice [0.6533, 0.8867] 
+2024-09-09 01:03:16.881380: Epoch time: 246.5 s 
+2024-09-09 01:03:17.950503:  
+2024-09-09 01:03:17.950706: Epoch 448 
+2024-09-09 01:03:17.950799: Current learning rate: 0.00586 
+2024-09-09 01:07:24.620551: train_loss -0.8616 
+2024-09-09 01:07:24.620697: val_loss -0.6968 
+2024-09-09 01:07:24.620748: Pseudo dice [0.624, 0.8913] 
+2024-09-09 01:07:24.620800: Epoch time: 246.67 s 
+2024-09-09 01:07:25.848635:  
+2024-09-09 01:07:25.848918: Epoch 449 
+2024-09-09 01:07:25.849069: Current learning rate: 0.00585 
+2024-09-09 01:11:32.366955: train_loss -0.8622 
+2024-09-09 01:11:32.367101: val_loss -0.7079 
+2024-09-09 01:11:32.367153: Pseudo dice [0.6883, 0.8754] 
+2024-09-09 01:11:32.367204: Epoch time: 246.52 s 
+2024-09-09 01:11:36.101759:  
+2024-09-09 01:11:36.101999: Epoch 450 
+2024-09-09 01:11:36.102081: Current learning rate: 0.00584 
+2024-09-09 01:15:42.455906: train_loss -0.8604 
+2024-09-09 01:15:42.456055: val_loss -0.6931 
+2024-09-09 01:15:42.456105: Pseudo dice [0.6732, 0.8707] 
+2024-09-09 01:15:42.456158: Epoch time: 246.36 s 
+2024-09-09 01:15:43.407070:  
+2024-09-09 01:15:43.407240: Epoch 451 
+2024-09-09 01:15:43.407323: Current learning rate: 0.00583 
+2024-09-09 01:19:51.549258: train_loss -0.8417 
+2024-09-09 01:19:51.549629: val_loss -0.6803 
+2024-09-09 01:19:51.549683: Pseudo dice [0.6571, 0.8712] 
+2024-09-09 01:19:51.549734: Epoch time: 248.14 s 
+2024-09-09 01:19:52.503988:  
+2024-09-09 01:19:52.504215: Epoch 452 
+2024-09-09 01:19:52.504298: Current learning rate: 0.00582 
+2024-09-09 01:24:09.565620: train_loss -0.8201 
+2024-09-09 01:24:09.565804: val_loss -0.6742 
+2024-09-09 01:24:09.565881: Pseudo dice [0.659, 0.8623] 
+2024-09-09 01:24:09.565959: Epoch time: 257.06 s 
+2024-09-09 01:24:10.755656:  
+2024-09-09 01:24:10.755904: Epoch 453 
+2024-09-09 01:24:10.755994: Current learning rate: 0.00581 
+2024-09-09 01:28:33.243008: train_loss -0.841 
+2024-09-09 01:28:33.243170: val_loss -0.6914 
+2024-09-09 01:28:33.243223: Pseudo dice [0.6774, 0.8649] 
+2024-09-09 01:28:33.243275: Epoch time: 262.49 s 
+2024-09-09 01:28:34.202752:  
+2024-09-09 01:28:34.202935: Epoch 454 
+2024-09-09 01:28:34.203016: Current learning rate: 0.0058 
+2024-09-09 01:32:40.540335: train_loss -0.8511 
+2024-09-09 01:32:40.540472: val_loss -0.7029 
+2024-09-09 01:32:40.540521: Pseudo dice [0.6458, 0.8805] 
+2024-09-09 01:32:40.540572: Epoch time: 246.34 s 
+2024-09-09 01:32:41.475736:  
+2024-09-09 01:32:41.476001: Epoch 455 
+2024-09-09 01:32:41.476092: Current learning rate: 0.00579 
+2024-09-09 01:36:47.624951: train_loss -0.8331 
+2024-09-09 01:36:47.625090: val_loss -0.6968 
+2024-09-09 01:36:47.625140: Pseudo dice [0.6782, 0.8663] 
+2024-09-09 01:36:47.625190: Epoch time: 246.15 s 
+2024-09-09 01:36:48.567670:  
+2024-09-09 01:36:48.567824: Epoch 456 
+2024-09-09 01:36:48.567906: Current learning rate: 0.00578 
+2024-09-09 01:40:54.664269: train_loss -0.8372 
+2024-09-09 01:40:54.664451: val_loss -0.6881 
+2024-09-09 01:40:54.664519: Pseudo dice [0.6634, 0.8736] 
+2024-09-09 01:40:54.664571: Epoch time: 246.1 s 
+2024-09-09 01:40:55.596504:  
+2024-09-09 01:40:55.596737: Epoch 457 
+2024-09-09 01:40:55.596823: Current learning rate: 0.00577 
+2024-09-09 01:45:01.647523: train_loss -0.8503 
+2024-09-09 01:45:01.647697: val_loss -0.6382 
+2024-09-09 01:45:01.647749: Pseudo dice [0.5911, 0.8568] 
+2024-09-09 01:45:01.647800: Epoch time: 246.05 s 
+2024-09-09 01:45:02.584576:  
+2024-09-09 01:45:02.584784: Epoch 458 
+2024-09-09 01:45:02.584870: Current learning rate: 0.00576 
+2024-09-09 01:49:08.619833: train_loss -0.8595 
+2024-09-09 01:49:08.620001: val_loss -0.7026 
+2024-09-09 01:49:08.620052: Pseudo dice [0.6662, 0.876] 
+2024-09-09 01:49:08.620103: Epoch time: 246.04 s 
+2024-09-09 01:49:09.547731:  
+2024-09-09 01:49:09.547968: Epoch 459 
+2024-09-09 01:49:09.548048: Current learning rate: 0.00575 
+2024-09-09 01:53:15.553339: train_loss -0.8632 
+2024-09-09 01:53:15.553503: val_loss -0.6591 
+2024-09-09 01:53:15.553556: Pseudo dice [0.5907, 0.8691] 
+2024-09-09 01:53:15.553607: Epoch time: 246.01 s 
+2024-09-09 01:53:16.481678:  
+2024-09-09 01:53:16.481911: Epoch 460 
+2024-09-09 01:53:16.482030: Current learning rate: 0.00574 
+2024-09-09 01:57:22.430356: train_loss -0.8588 
+2024-09-09 01:57:22.430488: val_loss -0.6822 
+2024-09-09 01:57:22.430538: Pseudo dice [0.6316, 0.8655] 
+2024-09-09 01:57:22.430589: Epoch time: 245.95 s 
+2024-09-09 01:57:23.364996:  
+2024-09-09 01:57:23.365175: Epoch 461 
+2024-09-09 01:57:23.365256: Current learning rate: 0.00573 
+2024-09-09 02:01:29.620275: train_loss -0.8637 
+2024-09-09 02:01:29.620433: val_loss -0.6805 
+2024-09-09 02:01:29.620486: Pseudo dice [0.591, 0.8696] 
+2024-09-09 02:01:29.620547: Epoch time: 246.26 s 
+2024-09-09 02:01:30.649792:  
+2024-09-09 02:01:30.650040: Epoch 462 
+2024-09-09 02:01:30.650150: Current learning rate: 0.00572 
+2024-09-09 02:05:42.424005: train_loss -0.8611 
+2024-09-09 02:05:42.424437: val_loss -0.6772 
+2024-09-09 02:05:42.424491: Pseudo dice [0.6325, 0.8732] 
+2024-09-09 02:05:42.424544: Epoch time: 251.78 s 
+2024-09-09 02:05:43.377885:  
+2024-09-09 02:05:43.378112: Epoch 463 
+2024-09-09 02:05:43.378194: Current learning rate: 0.00571 
+2024-09-09 02:09:55.910490: train_loss -0.8687 
+2024-09-09 02:09:55.910884: val_loss -0.6699 
+2024-09-09 02:09:55.910937: Pseudo dice [0.5842, 0.8723] 
+2024-09-09 02:09:55.910988: Epoch time: 252.53 s 
+2024-09-09 02:09:56.856895:  
+2024-09-09 02:09:56.857113: Epoch 464 
+2024-09-09 02:09:56.857195: Current learning rate: 0.0057 
+2024-09-09 02:14:03.657358: train_loss -0.8749 
+2024-09-09 02:14:03.657534: val_loss -0.6794 
+2024-09-09 02:14:03.657585: Pseudo dice [0.6442, 0.8797] 
+2024-09-09 02:14:03.657640: Epoch time: 246.8 s 
+2024-09-09 02:14:04.699288:  
+2024-09-09 02:14:04.699543: Epoch 465 
+2024-09-09 02:14:04.699738: Current learning rate: 0.0057 
+2024-09-09 02:18:18.472158: train_loss -0.8667 
+2024-09-09 02:18:18.472549: val_loss -0.696 
+2024-09-09 02:18:18.472606: Pseudo dice [0.6904, 0.8725] 
+2024-09-09 02:18:18.472662: Epoch time: 253.78 s 
+2024-09-09 02:18:19.416600:  
+2024-09-09 02:18:19.416879: Epoch 466 
+2024-09-09 02:18:19.416960: Current learning rate: 0.00569 
+2024-09-09 02:22:30.323090: train_loss -0.8638 
+2024-09-09 02:22:30.323584: val_loss -0.6981 
+2024-09-09 02:22:30.323638: Pseudo dice [0.6937, 0.8608] 
+2024-09-09 02:22:30.323692: Epoch time: 250.91 s 
+2024-09-09 02:22:31.377349:  
+2024-09-09 02:22:31.377545: Epoch 467 
+2024-09-09 02:22:31.377650: Current learning rate: 0.00568 
+2024-09-09 02:26:42.651680: train_loss -0.8653 
+2024-09-09 02:26:42.652085: val_loss -0.7158 
+2024-09-09 02:26:42.652141: Pseudo dice [0.6992, 0.8696] 
+2024-09-09 02:26:42.652196: Epoch time: 251.28 s 
+2024-09-09 02:26:46.924303:  
+2024-09-09 02:26:46.924683: Epoch 468 
+2024-09-09 02:26:46.924820: Current learning rate: 0.00567 
+2024-09-09 02:31:01.452519: train_loss -0.865 
+2024-09-09 02:31:01.452936: val_loss -0.6921 
+2024-09-09 02:31:01.452992: Pseudo dice [0.6754, 0.8691] 
+2024-09-09 02:31:01.453058: Epoch time: 254.53 s 
+2024-09-09 02:31:08.618616:  
+2024-09-09 02:31:08.619308: Epoch 469 
+2024-09-09 02:31:08.619876: Current learning rate: 0.00566 
+2024-09-09 02:35:35.850878: train_loss -0.8683 
+2024-09-09 02:35:35.863594: val_loss -0.6477 
+2024-09-09 02:35:35.864596: Pseudo dice [0.55, 0.874] 
+2024-09-09 02:35:35.865909: Epoch time: 267.24 s 
+2024-09-09 02:35:41.737273:  
+2024-09-09 02:35:41.737549: Epoch 470 
+2024-09-09 02:35:41.737661: Current learning rate: 0.00565 
+2024-09-09 02:40:56.750729: train_loss -0.8596 
+2024-09-09 02:40:56.756311: val_loss -0.7056 
+2024-09-09 02:40:56.757374: Pseudo dice [0.6822, 0.8676] 
+2024-09-09 02:40:56.757755: Epoch time: 315.02 s 
+2024-09-09 02:41:03.081831:  
+2024-09-09 02:41:03.082095: Epoch 471 
+2024-09-09 02:41:03.082218: Current learning rate: 0.00564 
+2024-09-09 02:46:31.261216: train_loss -0.863 
+2024-09-09 02:46:31.261906: val_loss -0.633 
+2024-09-09 02:46:31.262014: Pseudo dice [0.5885, 0.8613] 
+2024-09-09 02:46:31.262121: Epoch time: 328.17 s 
+2024-09-09 02:46:39.661904:  
+2024-09-09 02:46:39.662108: Epoch 472 
+2024-09-09 02:46:39.662210: Current learning rate: 0.00563 
+2024-09-09 02:53:28.402771: train_loss -0.8648 
+2024-09-09 02:53:28.403198: val_loss -0.6844 
+2024-09-09 02:53:28.403252: Pseudo dice [0.6655, 0.8826] 
+2024-09-09 02:53:28.403308: Epoch time: 408.77 s 
+2024-09-09 02:53:29.791222:  
+2024-09-09 02:53:29.791484: Epoch 473 
+2024-09-09 02:53:29.791647: Current learning rate: 0.00562 
+2024-09-09 02:58:19.237005: train_loss -0.8673 
+2024-09-09 02:58:19.242264: val_loss -0.707 
+2024-09-09 02:58:19.242382: Pseudo dice [0.6637, 0.8804] 
+2024-09-09 02:58:19.242491: Epoch time: 289.45 s 
+2024-09-09 02:58:28.784186:  
+2024-09-09 02:58:28.784444: Epoch 474 
+2024-09-09 02:58:28.784566: Current learning rate: 0.00561 
+2024-09-09 03:03:40.703362: train_loss -0.8684 
+2024-09-09 03:03:40.734741: val_loss -0.7128 
+2024-09-09 03:03:40.734885: Pseudo dice [0.7106, 0.8753] 
+2024-09-09 03:03:40.734993: Epoch time: 311.91 s 
+2024-09-09 03:03:52.362385:  
+2024-09-09 03:03:52.362579: Epoch 475 
+2024-09-09 03:03:52.362699: Current learning rate: 0.0056 
+2024-09-09 03:09:50.583763: train_loss -0.8686 
+2024-09-09 03:09:50.620210: val_loss -0.7126 
+2024-09-09 03:09:50.620291: Pseudo dice [0.7016, 0.8738] 
+2024-09-09 03:09:50.632095: Epoch time: 358.23 s 
+2024-09-09 03:09:57.733713:  
+2024-09-09 03:09:57.733940: Epoch 476 
+2024-09-09 03:09:57.734025: Current learning rate: 0.00559 
+2024-09-09 03:14:16.700407: train_loss -0.8658 
+2024-09-09 03:14:16.700567: val_loss -0.6966 
+2024-09-09 03:14:16.700646: Pseudo dice [0.6569, 0.8651] 
+2024-09-09 03:14:16.700719: Epoch time: 258.97 s 
+2024-09-09 03:14:19.805949:  
+2024-09-09 03:14:19.806173: Epoch 477 
+2024-09-09 03:14:19.806258: Current learning rate: 0.00558 
+2024-09-09 03:18:33.280933: train_loss -0.8644 
+2024-09-09 03:18:33.281317: val_loss -0.6882 
+2024-09-09 03:18:33.281371: Pseudo dice [0.646, 0.8804] 
+2024-09-09 03:18:33.281422: Epoch time: 253.48 s 
+2024-09-09 03:18:36.240736:  
+2024-09-09 03:18:36.240984: Epoch 478 
+2024-09-09 03:18:36.241063: Current learning rate: 0.00557 
+2024-09-09 03:22:46.232906: train_loss -0.8661 
+2024-09-09 03:22:46.233059: val_loss -0.683 
+2024-09-09 03:22:46.233119: Pseudo dice [0.687, 0.8716] 
+2024-09-09 03:22:46.233177: Epoch time: 250.0 s 
+2024-09-09 03:22:48.866080:  
+2024-09-09 03:22:48.866283: Epoch 479 
+2024-09-09 03:22:48.866371: Current learning rate: 0.00556 
+2024-09-09 03:26:56.475047: train_loss -0.8633 
+2024-09-09 03:26:56.475199: val_loss -0.6852 
+2024-09-09 03:26:56.475251: Pseudo dice [0.6718, 0.8844] 
+2024-09-09 03:26:56.475304: Epoch time: 247.61 s 
+2024-09-09 03:26:57.641973:  
+2024-09-09 03:26:57.643019: Epoch 480 
+2024-09-09 03:26:57.643101: Current learning rate: 0.00555 
+2024-09-09 03:31:05.337078: train_loss -0.8662 
+2024-09-09 03:31:05.337220: val_loss -0.6891 
+2024-09-09 03:31:05.337286: Pseudo dice [0.6605, 0.876] 
+2024-09-09 03:31:05.337341: Epoch time: 247.7 s 
+2024-09-09 03:31:06.652638:  
+2024-09-09 03:31:06.652904: Epoch 481 
+2024-09-09 03:31:06.653074: Current learning rate: 0.00554 
+2024-09-09 03:35:13.289530: train_loss -0.8702 
+2024-09-09 03:35:13.289681: val_loss -0.7043 
+2024-09-09 03:35:13.289731: Pseudo dice [0.6742, 0.8674] 
+2024-09-09 03:35:13.289783: Epoch time: 246.64 s 
+2024-09-09 03:35:14.835488:  
+2024-09-09 03:35:14.835777: Epoch 482 
+2024-09-09 03:35:14.835867: Current learning rate: 0.00553 
+2024-09-09 03:39:21.407869: train_loss -0.8719 
+2024-09-09 03:39:21.408046: val_loss -0.707 
+2024-09-09 03:39:21.408099: Pseudo dice [0.6889, 0.8669] 
+2024-09-09 03:39:21.408151: Epoch time: 246.57 s 
+2024-09-09 03:39:23.109392:  
+2024-09-09 03:39:23.109606: Epoch 483 
+2024-09-09 03:39:23.109686: Current learning rate: 0.00552 
+2024-09-09 03:43:29.684562: train_loss -0.8651 
+2024-09-09 03:43:29.684772: val_loss -0.6772 
+2024-09-09 03:43:29.684826: Pseudo dice [0.6714, 0.8567] 
+2024-09-09 03:43:29.684884: Epoch time: 246.58 s 
+2024-09-09 03:43:31.516606:  
+2024-09-09 03:43:31.516814: Epoch 484 
+2024-09-09 03:43:31.516924: Current learning rate: 0.00551 
+2024-09-09 03:47:41.705511: train_loss -0.8676 
+2024-09-09 03:47:41.705649: val_loss -0.6998 
+2024-09-09 03:47:41.705700: Pseudo dice [0.647, 0.8821] 
+2024-09-09 03:47:41.705751: Epoch time: 250.19 s 
+2024-09-09 03:47:45.053914:  
+2024-09-09 03:47:45.054290: Epoch 485 
+2024-09-09 03:47:45.054532: Current learning rate: 0.0055 
+2024-09-09 03:51:53.128897: train_loss -0.8696 
+2024-09-09 03:51:53.129038: val_loss -0.6667 
+2024-09-09 03:51:53.129088: Pseudo dice [0.6086, 0.8671] 
+2024-09-09 03:51:53.129139: Epoch time: 248.08 s 
+2024-09-09 03:51:56.059281:  
+2024-09-09 03:51:56.059503: Epoch 486 
+2024-09-09 03:51:56.059586: Current learning rate: 0.00549 
+2024-09-09 03:56:02.416665: train_loss -0.8719 
+2024-09-09 03:56:02.416842: val_loss -0.7239 
+2024-09-09 03:56:02.416894: Pseudo dice [0.6806, 0.8847] 
+2024-09-09 03:56:02.416952: Epoch time: 246.36 s 
+2024-09-09 03:56:05.087825:  
+2024-09-09 03:56:05.088045: Epoch 487 
+2024-09-09 03:56:05.088129: Current learning rate: 0.00548 
+2024-09-09 04:00:11.315586: train_loss -0.873 
+2024-09-09 04:00:11.315728: val_loss -0.7047 
+2024-09-09 04:00:11.315778: Pseudo dice [0.6696, 0.8822] 
+2024-09-09 04:00:11.315836: Epoch time: 246.24 s 
+2024-09-09 04:00:13.800992:  
+2024-09-09 04:00:13.801164: Epoch 488 
+2024-09-09 04:00:13.801243: Current learning rate: 0.00547 
+2024-09-09 04:04:21.415092: train_loss -0.8723 
+2024-09-09 04:04:21.415327: val_loss -0.7005 
+2024-09-09 04:04:21.415391: Pseudo dice [0.6663, 0.8745] 
+2024-09-09 04:04:21.415464: Epoch time: 247.62 s 
+2024-09-09 04:04:24.176302:  
+2024-09-09 04:04:24.176676: Epoch 489 
+2024-09-09 04:04:24.176850: Current learning rate: 0.00546 
+2024-09-09 04:08:31.542149: train_loss -0.8678 
+2024-09-09 04:08:31.542289: val_loss -0.6718 
+2024-09-09 04:08:31.542341: Pseudo dice [0.6362, 0.8665] 
+2024-09-09 04:08:31.542394: Epoch time: 247.38 s 
+2024-09-09 04:08:33.364354:  
+2024-09-09 04:08:33.364597: Epoch 490 
+2024-09-09 04:08:33.364682: Current learning rate: 0.00546 
+2024-09-09 04:12:41.413062: train_loss -0.8697 
+2024-09-09 04:12:41.413229: val_loss -0.6814 
+2024-09-09 04:12:41.413280: Pseudo dice [0.6484, 0.862] 
+2024-09-09 04:12:41.413361: Epoch time: 248.05 s 
+2024-09-09 04:12:42.630578:  
+2024-09-09 04:12:42.630860: Epoch 491 
+2024-09-09 04:12:42.630984: Current learning rate: 0.00545 
+2024-09-09 04:16:50.090110: train_loss -0.8644 
+2024-09-09 04:16:50.090306: val_loss -0.6503 
+2024-09-09 04:16:50.090359: Pseudo dice [0.5823, 0.8508] 
+2024-09-09 04:16:50.090415: Epoch time: 247.47 s 
+2024-09-09 04:16:51.391392:  
+2024-09-09 04:16:51.391568: Epoch 492 
+2024-09-09 04:16:51.391648: Current learning rate: 0.00544 
+2024-09-09 04:20:58.173324: train_loss -0.8648 
+2024-09-09 04:20:58.173464: val_loss -0.7161 
+2024-09-09 04:20:58.173515: Pseudo dice [0.6867, 0.8805] 
+2024-09-09 04:20:58.173567: Epoch time: 246.79 s 
+2024-09-09 04:20:59.716886:  
+2024-09-09 04:20:59.717076: Epoch 493 
+2024-09-09 04:20:59.717200: Current learning rate: 0.00543 
+2024-09-09 04:25:07.812543: train_loss -0.8736 
+2024-09-09 04:25:07.812718: val_loss -0.7195 
+2024-09-09 04:25:07.812777: Pseudo dice [0.6875, 0.8783] 
+2024-09-09 04:25:07.812833: Epoch time: 248.1 s 
+2024-09-09 04:25:09.096885:  
+2024-09-09 04:25:09.097269: Epoch 494 
+2024-09-09 04:25:09.097476: Current learning rate: 0.00542 
+2024-09-09 04:29:16.678210: train_loss -0.8737 
+2024-09-09 04:29:16.678380: val_loss -0.6988 
+2024-09-09 04:29:16.678447: Pseudo dice [0.6538, 0.8672] 
+2024-09-09 04:29:16.678505: Epoch time: 247.59 s 
+2024-09-09 04:29:17.978669:  
+2024-09-09 04:29:17.978939: Epoch 495 
+2024-09-09 04:29:17.979058: Current learning rate: 0.00541 
+2024-09-09 04:33:25.017588: train_loss -0.8646 
+2024-09-09 04:33:25.017729: val_loss -0.6797 
+2024-09-09 04:33:25.017780: Pseudo dice [0.6706, 0.8606] 
+2024-09-09 04:33:25.017831: Epoch time: 247.04 s 
+2024-09-09 04:33:37.488294:  
+2024-09-09 04:33:37.488534: Epoch 496 
+2024-09-09 04:33:37.488669: Current learning rate: 0.0054 
+2024-09-09 04:37:44.150336: train_loss -0.8687 
+2024-09-09 04:37:44.150477: val_loss -0.6743 
+2024-09-09 04:37:44.150527: Pseudo dice [0.6339, 0.8695] 
+2024-09-09 04:37:44.150579: Epoch time: 246.66 s 
+2024-09-09 04:37:45.204555:  
+2024-09-09 04:37:45.204804: Epoch 497 
+2024-09-09 04:37:45.204890: Current learning rate: 0.00539 
+2024-09-09 04:41:51.849688: train_loss -0.8593 
+2024-09-09 04:41:51.849845: val_loss -0.6645 
+2024-09-09 04:41:51.849898: Pseudo dice [0.5815, 0.8846] 
+2024-09-09 04:41:51.849950: Epoch time: 246.65 s 
+2024-09-09 04:41:52.835165:  
+2024-09-09 04:41:52.835403: Epoch 498 
+2024-09-09 04:41:52.835482: Current learning rate: 0.00538 
+2024-09-09 04:45:59.368223: train_loss -0.8575 
+2024-09-09 04:45:59.368368: val_loss -0.7148 
+2024-09-09 04:45:59.368418: Pseudo dice [0.675, 0.8802] 
+2024-09-09 04:45:59.368469: Epoch time: 246.53 s 
+2024-09-09 04:46:00.371629:  
+2024-09-09 04:46:00.371901: Epoch 499 
+2024-09-09 04:46:00.372052: Current learning rate: 0.00537 
+2024-09-09 04:50:06.907222: train_loss -0.871 
+2024-09-09 04:50:06.907367: val_loss -0.6948 
+2024-09-09 04:50:06.907418: Pseudo dice [0.6926, 0.8681] 
+2024-09-09 04:50:06.907469: Epoch time: 246.54 s 
+2024-09-09 04:50:10.833543:  
+2024-09-09 04:50:10.833772: Epoch 500 
+2024-09-09 04:50:10.833852: Current learning rate: 0.00536 
+2024-09-09 04:54:17.380638: train_loss -0.8678 
+2024-09-09 04:54:17.380817: val_loss -0.6827 
+2024-09-09 04:54:17.380868: Pseudo dice [0.6437, 0.8841] 
+2024-09-09 04:54:17.380923: Epoch time: 246.55 s 
+2024-09-09 04:54:18.374746:  
+2024-09-09 04:54:18.374961: Epoch 501 
+2024-09-09 04:54:18.375065: Current learning rate: 0.00535 
+2024-09-09 04:58:25.129301: train_loss -0.8471 
+2024-09-09 04:58:25.129445: val_loss -0.6907 
+2024-09-09 04:58:25.129496: Pseudo dice [0.6772, 0.8707] 
+2024-09-09 04:58:25.129554: Epoch time: 246.76 s 
+2024-09-09 04:58:26.230279:  
+2024-09-09 04:58:26.230485: Epoch 502 
+2024-09-09 04:58:26.230564: Current learning rate: 0.00534 
+2024-09-09 05:02:33.098359: train_loss -0.8659 
+2024-09-09 05:02:33.098507: val_loss -0.6592 
+2024-09-09 05:02:33.098558: Pseudo dice [0.5998, 0.8591] 
+2024-09-09 05:02:33.098610: Epoch time: 246.87 s 
+2024-09-09 05:02:34.092642:  
+2024-09-09 05:02:34.092893: Epoch 503 
+2024-09-09 05:02:34.092974: Current learning rate: 0.00533 
+2024-09-09 05:06:40.882796: train_loss -0.8632 
+2024-09-09 05:06:40.882971: val_loss -0.7142 
+2024-09-09 05:06:40.883023: Pseudo dice [0.7039, 0.867] 
+2024-09-09 05:06:40.883074: Epoch time: 246.79 s 
+2024-09-09 05:06:41.862622:  
+2024-09-09 05:06:41.862804: Epoch 504 
+2024-09-09 05:06:41.862886: Current learning rate: 0.00532 
+2024-09-09 05:10:48.472862: train_loss -0.8731 
+2024-09-09 05:10:48.473005: val_loss -0.6624 
+2024-09-09 05:10:48.473055: Pseudo dice [0.6493, 0.8671] 
+2024-09-09 05:10:48.473104: Epoch time: 246.61 s 
+2024-09-09 05:10:49.449233:  
+2024-09-09 05:10:49.449444: Epoch 505 
+2024-09-09 05:10:49.449524: Current learning rate: 0.00531 
+2024-09-09 05:14:56.010331: train_loss -0.8698 
+2024-09-09 05:14:56.010463: val_loss -0.7033 
+2024-09-09 05:14:56.010515: Pseudo dice [0.6852, 0.8749] 
+2024-09-09 05:14:56.010566: Epoch time: 246.56 s 
+2024-09-09 05:14:57.049612:  
+2024-09-09 05:14:57.049858: Epoch 506 
+2024-09-09 05:14:57.049945: Current learning rate: 0.0053 
+2024-09-09 05:19:03.573426: train_loss -0.8681 
+2024-09-09 05:19:03.573708: val_loss -0.6776 
+2024-09-09 05:19:03.573760: Pseudo dice [0.6368, 0.8671] 
+2024-09-09 05:19:03.573811: Epoch time: 246.53 s 
+2024-09-09 05:19:04.557839:  
+2024-09-09 05:19:04.558066: Epoch 507 
+2024-09-09 05:19:04.558146: Current learning rate: 0.00529 
+2024-09-09 05:23:11.025655: train_loss -0.8743 
+2024-09-09 05:23:11.025823: val_loss -0.6805 
+2024-09-09 05:23:11.025877: Pseudo dice [0.6558, 0.8549] 
+2024-09-09 05:23:11.025928: Epoch time: 246.47 s 
+2024-09-09 05:23:11.998127:  
+2024-09-09 05:23:11.998327: Epoch 508 
+2024-09-09 05:23:11.998414: Current learning rate: 0.00528 
+2024-09-09 05:27:18.441285: train_loss -0.8752 
+2024-09-09 05:27:18.441425: val_loss -0.6834 
+2024-09-09 05:27:18.441629: Pseudo dice [0.6406, 0.8754] 
+2024-09-09 05:27:18.441682: Epoch time: 246.45 s 
+2024-09-09 05:27:19.441265:  
+2024-09-09 05:27:19.441463: Epoch 509 
+2024-09-09 05:27:19.441549: Current learning rate: 0.00527 
+2024-09-09 05:31:26.017646: train_loss -0.8708 
+2024-09-09 05:31:26.017798: val_loss -0.7119 
+2024-09-09 05:31:26.017850: Pseudo dice [0.7094, 0.88] 
+2024-09-09 05:31:26.017901: Epoch time: 246.58 s 
+2024-09-09 05:31:27.033777:  
+2024-09-09 05:31:27.034034: Epoch 510 
+2024-09-09 05:31:27.034115: Current learning rate: 0.00526 
+2024-09-09 05:35:33.793859: train_loss -0.8634 
+2024-09-09 05:35:33.794000: val_loss -0.6741 
+2024-09-09 05:35:33.794050: Pseudo dice [0.6754, 0.8583] 
+2024-09-09 05:35:33.794100: Epoch time: 246.76 s 
+2024-09-09 05:35:34.782422:  
+2024-09-09 05:35:34.782588: Epoch 511 
+2024-09-09 05:35:34.782670: Current learning rate: 0.00525 
+2024-09-09 05:39:41.638097: train_loss -0.8599 
+2024-09-09 05:39:41.638233: val_loss -0.6858 
+2024-09-09 05:39:41.638285: Pseudo dice [0.6334, 0.8661] 
+2024-09-09 05:39:41.638337: Epoch time: 246.86 s 
+2024-09-09 05:39:42.616882:  
+2024-09-09 05:39:42.617063: Epoch 512 
+2024-09-09 05:39:42.617147: Current learning rate: 0.00524 
+2024-09-09 05:43:50.030955: train_loss -0.8706 
+2024-09-09 05:43:50.031088: val_loss -0.6918 
+2024-09-09 05:43:50.031309: Pseudo dice [0.6597, 0.8653] 
+2024-09-09 05:43:50.031362: Epoch time: 247.42 s 
+2024-09-09 05:43:51.012779:  
+2024-09-09 05:43:51.012938: Epoch 513 
+2024-09-09 05:43:51.013049: Current learning rate: 0.00523 
+2024-09-09 05:47:57.937424: train_loss -0.859 
+2024-09-09 05:47:57.937564: val_loss -0.7006 
+2024-09-09 05:47:57.937616: Pseudo dice [0.6717, 0.8688] 
+2024-09-09 05:47:57.937669: Epoch time: 246.93 s 
+2024-09-09 05:47:58.999681:  
+2024-09-09 05:47:58.999990: Epoch 514 
+2024-09-09 05:47:59.000084: Current learning rate: 0.00522 
+2024-09-09 05:52:18.954104: train_loss -0.8603 
+2024-09-09 05:52:18.954242: val_loss -0.7223 
+2024-09-09 05:52:18.954318: Pseudo dice [0.6738, 0.8866] 
+2024-09-09 05:52:18.954405: Epoch time: 259.96 s 
+2024-09-09 05:52:19.937035:  
+2024-09-09 05:52:19.937240: Epoch 515 
+2024-09-09 05:52:19.937343: Current learning rate: 0.00521 
+2024-09-09 05:56:26.595679: train_loss -0.8712 
+2024-09-09 05:56:26.595834: val_loss -0.6779 
+2024-09-09 05:56:26.595887: Pseudo dice [0.6133, 0.8715] 
+2024-09-09 05:56:26.595937: Epoch time: 246.66 s 
+2024-09-09 05:56:27.570795:  
+2024-09-09 05:56:27.570932: Epoch 516 
+2024-09-09 05:56:27.571013: Current learning rate: 0.0052 
+2024-09-09 06:00:34.334058: train_loss -0.867 
+2024-09-09 06:00:34.334230: val_loss -0.6524 
+2024-09-09 06:00:34.334294: Pseudo dice [0.594, 0.8471] 
+2024-09-09 06:00:34.334350: Epoch time: 246.77 s 
+2024-09-09 06:00:35.415738:  
+2024-09-09 06:00:35.416020: Epoch 517 
+2024-09-09 06:00:35.416150: Current learning rate: 0.00519 
+2024-09-09 06:04:41.936864: train_loss -0.84 
+2024-09-09 06:04:41.937001: val_loss -0.6954 
+2024-09-09 06:04:41.937051: Pseudo dice [0.6401, 0.8698] 
+2024-09-09 06:04:41.937101: Epoch time: 246.52 s 
+2024-09-09 06:04:42.944049:  
+2024-09-09 06:04:42.944234: Epoch 518 
+2024-09-09 06:04:42.944344: Current learning rate: 0.00518 
+2024-09-09 06:08:49.439052: train_loss -0.8417 
+2024-09-09 06:08:49.439194: val_loss -0.6964 
+2024-09-09 06:08:49.439246: Pseudo dice [0.6767, 0.8621] 
+2024-09-09 06:08:49.439297: Epoch time: 246.5 s 
+2024-09-09 06:08:51.402698:  
+2024-09-09 06:08:51.402972: Epoch 519 
+2024-09-09 06:08:51.403070: Current learning rate: 0.00518 
+2024-09-09 06:12:57.896121: train_loss -0.8552 
+2024-09-09 06:12:57.896326: val_loss -0.6857 
+2024-09-09 06:12:57.896378: Pseudo dice [0.6483, 0.8665] 
+2024-09-09 06:12:57.896432: Epoch time: 246.5 s 
+2024-09-09 06:12:58.880935:  
+2024-09-09 06:12:58.881122: Epoch 520 
+2024-09-09 06:12:58.881221: Current learning rate: 0.00517 
+2024-09-09 06:17:05.492903: train_loss -0.8553 
+2024-09-09 06:17:05.493060: val_loss -0.7225 
+2024-09-09 06:17:05.493115: Pseudo dice [0.6882, 0.8686] 
+2024-09-09 06:17:05.493169: Epoch time: 246.61 s 
+2024-09-09 06:17:06.591638:  
+2024-09-09 06:17:06.591917: Epoch 521 
+2024-09-09 06:17:06.592030: Current learning rate: 0.00516 
+2024-09-09 06:21:13.341687: train_loss -0.8628 
+2024-09-09 06:21:13.341827: val_loss -0.6802 
+2024-09-09 06:21:13.341877: Pseudo dice [0.6431, 0.8723] 
+2024-09-09 06:21:13.341929: Epoch time: 246.75 s 
+2024-09-09 06:21:14.343477:  
+2024-09-09 06:21:14.343655: Epoch 522 
+2024-09-09 06:21:14.343736: Current learning rate: 0.00515 
+2024-09-09 06:25:21.271781: train_loss -0.8689 
+2024-09-09 06:25:21.271939: val_loss -0.6902 
+2024-09-09 06:25:21.271991: Pseudo dice [0.6297, 0.8755] 
+2024-09-09 06:25:21.272042: Epoch time: 246.93 s 
+2024-09-09 06:25:22.238537:  
+2024-09-09 06:25:22.238852: Epoch 523 
+2024-09-09 06:25:22.238946: Current learning rate: 0.00514 
+2024-09-09 06:29:29.213560: train_loss -0.8677 
+2024-09-09 06:29:29.213719: val_loss -0.6596 
+2024-09-09 06:29:29.213770: Pseudo dice [0.6299, 0.8646] 
+2024-09-09 06:29:29.213822: Epoch time: 246.98 s 
+2024-09-09 06:29:30.192432:  
+2024-09-09 06:29:30.192612: Epoch 524 
+2024-09-09 06:29:30.192727: Current learning rate: 0.00513 
+2024-09-09 06:33:36.481380: train_loss -0.8694 
+2024-09-09 06:33:36.481514: val_loss -0.6871 
+2024-09-09 06:33:36.481565: Pseudo dice [0.6542, 0.8703] 
+2024-09-09 06:33:36.481616: Epoch time: 246.29 s 
+2024-09-09 06:33:37.462027:  
+2024-09-09 06:33:37.462225: Epoch 525 
+2024-09-09 06:33:37.462332: Current learning rate: 0.00512 
+2024-09-09 06:37:43.226673: train_loss -0.8663 
+2024-09-09 06:37:43.226814: val_loss -0.691 
+2024-09-09 06:37:43.226863: Pseudo dice [0.6305, 0.8839] 
+2024-09-09 06:37:43.226913: Epoch time: 245.77 s 
+2024-09-09 06:37:44.201505:  
+2024-09-09 06:37:44.201766: Epoch 526 
+2024-09-09 06:37:44.201847: Current learning rate: 0.00511 
+2024-09-09 06:41:50.004701: train_loss -0.864 
+2024-09-09 06:41:50.004840: val_loss -0.6982 
+2024-09-09 06:41:50.004889: Pseudo dice [0.6546, 0.8736] 
+2024-09-09 06:41:50.004940: Epoch time: 245.81 s 
+2024-09-09 06:41:50.977773:  
+2024-09-09 06:41:50.977974: Epoch 527 
+2024-09-09 06:41:50.978057: Current learning rate: 0.0051 
+2024-09-09 06:45:56.677230: train_loss -0.8671 
+2024-09-09 06:45:56.677432: val_loss -0.7029 
+2024-09-09 06:45:56.677485: Pseudo dice [0.6901, 0.8572] 
+2024-09-09 06:45:56.677535: Epoch time: 245.7 s 
+2024-09-09 06:45:57.666644:  
+2024-09-09 06:45:57.666842: Epoch 528 
+2024-09-09 06:45:57.666926: Current learning rate: 0.00509 
+2024-09-09 06:50:03.413371: train_loss -0.8742 
+2024-09-09 06:50:03.413512: val_loss -0.71 
+2024-09-09 06:50:03.413561: Pseudo dice [0.6995, 0.8643] 
+2024-09-09 06:50:03.413615: Epoch time: 245.75 s 
+2024-09-09 06:50:04.378856:  
+2024-09-09 06:50:04.379112: Epoch 529 
+2024-09-09 06:50:04.379192: Current learning rate: 0.00508 
+2024-09-09 06:54:10.272594: train_loss -0.8657 
+2024-09-09 06:54:10.272748: val_loss -0.6209 
+2024-09-09 06:54:10.272798: Pseudo dice [0.5665, 0.771] 
+2024-09-09 06:54:10.272856: Epoch time: 245.9 s 
+2024-09-09 06:54:11.262473:  
+2024-09-09 06:54:11.262745: Epoch 530 
+2024-09-09 06:54:11.262826: Current learning rate: 0.00507 
+2024-09-09 06:58:16.963863: train_loss -0.8708 
+2024-09-09 06:58:16.964001: val_loss -0.6775 
+2024-09-09 06:58:16.964050: Pseudo dice [0.666, 0.8624] 
+2024-09-09 06:58:16.964110: Epoch time: 245.7 s 
+2024-09-09 06:58:17.953785:  
+2024-09-09 06:58:17.953977: Epoch 531 
+2024-09-09 06:58:17.954103: Current learning rate: 0.00506 
+2024-09-09 07:02:23.708205: train_loss -0.8731 
+2024-09-09 07:02:23.708343: val_loss -0.709 
+2024-09-09 07:02:23.708394: Pseudo dice [0.6496, 0.8899] 
+2024-09-09 07:02:23.708445: Epoch time: 245.76 s 
+2024-09-09 07:02:24.717407:  
+2024-09-09 07:02:24.717585: Epoch 532 
+2024-09-09 07:02:24.717671: Current learning rate: 0.00505 
+2024-09-09 07:06:30.527583: train_loss -0.8689 
+2024-09-09 07:06:30.527758: val_loss -0.7006 
+2024-09-09 07:06:30.527817: Pseudo dice [0.6679, 0.8728] 
+2024-09-09 07:06:30.527869: Epoch time: 245.81 s 
+2024-09-09 07:06:31.506281:  
+2024-09-09 07:06:31.506515: Epoch 533 
+2024-09-09 07:06:31.506598: Current learning rate: 0.00504 
+2024-09-09 07:10:37.138584: train_loss -0.8653 
+2024-09-09 07:10:37.138723: val_loss -0.6694 
+2024-09-09 07:10:37.138777: Pseudo dice [0.6051, 0.8767] 
+2024-09-09 07:10:37.138829: Epoch time: 245.63 s 
+2024-09-09 07:10:38.119979:  
+2024-09-09 07:10:38.120221: Epoch 534 
+2024-09-09 07:10:38.120303: Current learning rate: 0.00503 
+2024-09-09 07:14:44.161079: train_loss -0.8731 
+2024-09-09 07:14:44.161218: val_loss -0.6758 
+2024-09-09 07:14:44.161272: Pseudo dice [0.6821, 0.8631] 
+2024-09-09 07:14:44.161326: Epoch time: 246.04 s 
+2024-09-09 07:14:45.129769:  
+2024-09-09 07:14:45.129914: Epoch 535 
+2024-09-09 07:14:45.129992: Current learning rate: 0.00502 
+2024-09-09 07:18:51.069594: train_loss -0.879 
+2024-09-09 07:18:51.069734: val_loss -0.6817 
+2024-09-09 07:18:51.069784: Pseudo dice [0.655, 0.8749] 
+2024-09-09 07:18:51.069836: Epoch time: 245.94 s 
+2024-09-09 07:18:52.084215:  
+2024-09-09 07:18:52.084424: Epoch 536 
+2024-09-09 07:18:52.084509: Current learning rate: 0.00501 
+2024-09-09 07:22:57.796751: train_loss -0.8692 
+2024-09-09 07:22:57.796932: val_loss -0.6889 
+2024-09-09 07:22:57.796982: Pseudo dice [0.6442, 0.869] 
+2024-09-09 07:22:57.797032: Epoch time: 245.71 s 
+2024-09-09 07:22:58.781406:  
+2024-09-09 07:22:58.781611: Epoch 537 
+2024-09-09 07:22:58.781687: Current learning rate: 0.005 
+2024-09-09 07:27:04.712332: train_loss -0.8658 
+2024-09-09 07:27:04.712468: val_loss -0.6868 
+2024-09-09 07:27:04.712522: Pseudo dice [0.6402, 0.8683] 
+2024-09-09 07:27:04.712572: Epoch time: 245.93 s 
+2024-09-09 07:27:05.693622:  
+2024-09-09 07:27:05.693820: Epoch 538 
+2024-09-09 07:27:05.693944: Current learning rate: 0.00499 
+2024-09-09 07:31:11.399656: train_loss -0.8675 
+2024-09-09 07:31:11.399792: val_loss -0.694 
+2024-09-09 07:31:11.399851: Pseudo dice [0.6334, 0.8851] 
+2024-09-09 07:31:11.399903: Epoch time: 245.71 s 
+2024-09-09 07:31:12.403519:  
+2024-09-09 07:31:12.403727: Epoch 539 
+2024-09-09 07:31:12.403811: Current learning rate: 0.00498 
+2024-09-09 07:35:18.102760: train_loss -0.8722 
+2024-09-09 07:35:18.102901: val_loss -0.6669 
+2024-09-09 07:35:18.102951: Pseudo dice [0.5877, 0.8769] 
+2024-09-09 07:35:18.103002: Epoch time: 245.7 s 
+2024-09-09 07:35:19.092296:  
+2024-09-09 07:35:19.092548: Epoch 540 
+2024-09-09 07:35:19.092631: Current learning rate: 0.00497 
+2024-09-09 07:39:25.700240: train_loss -0.8645 
+2024-09-09 07:39:25.700380: val_loss -0.6834 
+2024-09-09 07:39:25.700431: Pseudo dice [0.6438, 0.86] 
+2024-09-09 07:39:25.700479: Epoch time: 246.61 s 
+2024-09-09 07:39:26.681124:  
+2024-09-09 07:39:26.681298: Epoch 541 
+2024-09-09 07:39:26.681382: Current learning rate: 0.00496 
+2024-09-09 07:43:34.982794: train_loss -0.8736 
+2024-09-09 07:43:34.982956: val_loss -0.7133 
+2024-09-09 07:43:34.983006: Pseudo dice [0.6832, 0.8903] 
+2024-09-09 07:43:34.983058: Epoch time: 248.3 s 
+2024-09-09 07:43:35.978672:  
+2024-09-09 07:43:35.978875: Epoch 542 
+2024-09-09 07:43:35.978955: Current learning rate: 0.00495 
+2024-09-09 07:47:41.969055: train_loss -0.8728 
+2024-09-09 07:47:41.969194: val_loss -0.6658 
+2024-09-09 07:47:41.969244: Pseudo dice [0.6162, 0.848] 
+2024-09-09 07:47:41.969294: Epoch time: 245.99 s 
+2024-09-09 07:47:43.915665:  
+2024-09-09 07:47:43.915860: Epoch 543 
+2024-09-09 07:47:43.915964: Current learning rate: 0.00494 
+2024-09-09 07:51:50.053900: train_loss -0.8524 
+2024-09-09 07:51:50.054094: val_loss -0.7156 
+2024-09-09 07:51:50.054186: Pseudo dice [0.6937, 0.8566] 
+2024-09-09 07:51:50.054241: Epoch time: 246.14 s 
+2024-09-09 07:51:51.023370:  
+2024-09-09 07:51:51.023636: Epoch 544 
+2024-09-09 07:51:51.023721: Current learning rate: 0.00493 
+2024-09-09 07:55:57.415705: train_loss -0.8561 
+2024-09-09 07:55:57.415873: val_loss -0.6675 
+2024-09-09 07:55:57.415927: Pseudo dice [0.6629, 0.8579] 
+2024-09-09 07:55:57.415979: Epoch time: 246.39 s 
+2024-09-09 07:55:58.380310:  
+2024-09-09 07:55:58.380548: Epoch 545 
+2024-09-09 07:55:58.380629: Current learning rate: 0.00492 
+2024-09-09 08:00:04.788177: train_loss -0.8671 
+2024-09-09 08:00:04.788324: val_loss -0.711 
+2024-09-09 08:00:04.788375: Pseudo dice [0.696, 0.8631] 
+2024-09-09 08:00:04.788427: Epoch time: 246.41 s 
+2024-09-09 08:00:05.753419:  
+2024-09-09 08:00:05.753694: Epoch 546 
+2024-09-09 08:00:05.753817: Current learning rate: 0.00491 
+2024-09-09 08:04:11.883118: train_loss -0.8663 
+2024-09-09 08:04:11.883255: val_loss -0.6913 
+2024-09-09 08:04:11.883305: Pseudo dice [0.6721, 0.8773] 
+2024-09-09 08:04:11.883357: Epoch time: 246.13 s 
+2024-09-09 08:04:12.859656:  
+2024-09-09 08:04:12.859847: Epoch 547 
+2024-09-09 08:04:12.859933: Current learning rate: 0.0049 
+2024-09-09 08:08:18.865356: train_loss -0.8631 
+2024-09-09 08:08:18.865506: val_loss -0.6913 
+2024-09-09 08:08:18.865556: Pseudo dice [0.6411, 0.8798] 
+2024-09-09 08:08:18.865607: Epoch time: 246.01 s 
+2024-09-09 08:08:19.830204:  
+2024-09-09 08:08:19.830394: Epoch 548 
+2024-09-09 08:08:19.830499: Current learning rate: 0.00489 
+2024-09-09 08:12:26.059468: train_loss -0.8686 
+2024-09-09 08:12:26.059622: val_loss -0.6928 
+2024-09-09 08:12:26.059673: Pseudo dice [0.6607, 0.8766] 
+2024-09-09 08:12:26.059725: Epoch time: 246.23 s 
+2024-09-09 08:12:27.030704:  
+2024-09-09 08:12:27.030887: Epoch 549 
+2024-09-09 08:12:27.031000: Current learning rate: 0.00488 
+2024-09-09 08:16:32.886340: train_loss -0.8776 
+2024-09-09 08:16:32.886510: val_loss -0.6855 
+2024-09-09 08:16:32.886562: Pseudo dice [0.6613, 0.8599] 
+2024-09-09 08:16:32.886613: Epoch time: 245.86 s 
+2024-09-09 08:16:36.816422:  
+2024-09-09 08:16:36.816667: Epoch 550 
+2024-09-09 08:16:36.816782: Current learning rate: 0.00487 
+2024-09-09 08:20:42.784585: train_loss -0.8702 
+2024-09-09 08:20:42.784764: val_loss -0.6687 
+2024-09-09 08:20:42.784815: Pseudo dice [0.6895, 0.8725] 
+2024-09-09 08:20:42.784866: Epoch time: 245.97 s 
+2024-09-09 08:20:43.746773:  
+2024-09-09 08:20:43.747011: Epoch 551 
+2024-09-09 08:20:43.747098: Current learning rate: 0.00486 
+2024-09-09 08:24:49.758281: train_loss -0.8652 
+2024-09-09 08:24:49.758422: val_loss -0.6614 
+2024-09-09 08:24:49.758473: Pseudo dice [0.6157, 0.8796] 
+2024-09-09 08:24:49.758523: Epoch time: 246.01 s 
+2024-09-09 08:24:50.736771:  
+2024-09-09 08:24:50.736944: Epoch 552 
+2024-09-09 08:24:50.737034: Current learning rate: 0.00485 
+2024-09-09 08:28:56.809707: train_loss -0.8671 
+2024-09-09 08:28:56.809845: val_loss -0.6765 
+2024-09-09 08:28:56.809895: Pseudo dice [0.6437, 0.8719] 
+2024-09-09 08:28:56.809946: Epoch time: 246.07 s 
+2024-09-09 08:28:57.787530:  
+2024-09-09 08:28:57.787740: Epoch 553 
+2024-09-09 08:28:57.787852: Current learning rate: 0.00484 
+2024-09-09 08:33:03.818058: train_loss -0.8664 
+2024-09-09 08:33:03.818198: val_loss -0.684 
+2024-09-09 08:33:03.818249: Pseudo dice [0.6671, 0.8635] 
+2024-09-09 08:33:03.818300: Epoch time: 246.03 s 
+2024-09-09 08:33:04.798404:  
+2024-09-09 08:33:04.798600: Epoch 554 
+2024-09-09 08:33:04.798681: Current learning rate: 0.00484 
+2024-09-09 08:37:10.834934: train_loss -0.8658 
+2024-09-09 08:37:10.835083: val_loss -0.7049 
+2024-09-09 08:37:10.835180: Pseudo dice [0.6746, 0.8778] 
+2024-09-09 08:37:10.835270: Epoch time: 246.04 s 
+2024-09-09 08:37:11.845498:  
+2024-09-09 08:37:11.845682: Epoch 555 
+2024-09-09 08:37:11.845764: Current learning rate: 0.00483 
+2024-09-09 08:41:17.477057: train_loss -0.8692 
+2024-09-09 08:41:17.477231: val_loss -0.6583 
+2024-09-09 08:41:17.477282: Pseudo dice [0.6061, 0.854] 
+2024-09-09 08:41:17.477333: Epoch time: 245.63 s 
+2024-09-09 08:41:18.449639:  
+2024-09-09 08:41:18.449838: Epoch 556 
+2024-09-09 08:41:18.449916: Current learning rate: 0.00482 
+2024-09-09 08:45:24.124965: train_loss -0.8742 
+2024-09-09 08:45:24.125106: val_loss -0.704 
+2024-09-09 08:45:24.125156: Pseudo dice [0.6604, 0.8719] 
+2024-09-09 08:45:24.125206: Epoch time: 245.68 s 
+2024-09-09 08:45:25.108566:  
+2024-09-09 08:45:25.108788: Epoch 557 
+2024-09-09 08:45:25.108873: Current learning rate: 0.00481 
+2024-09-09 08:49:30.776695: train_loss -0.8522 
+2024-09-09 08:49:30.776833: val_loss -0.6252 
+2024-09-09 08:49:30.776886: Pseudo dice [0.5954, 0.8325] 
+2024-09-09 08:49:30.776938: Epoch time: 245.67 s 
+2024-09-09 08:49:31.787506:  
+2024-09-09 08:49:31.787651: Epoch 558 
+2024-09-09 08:49:31.787734: Current learning rate: 0.0048 
+2024-09-09 08:53:37.665621: train_loss -0.8291 
+2024-09-09 08:53:37.665756: val_loss -0.6663 
+2024-09-09 08:53:37.665809: Pseudo dice [0.631, 0.8675] 
+2024-09-09 08:53:37.665860: Epoch time: 245.88 s 
+2024-09-09 08:53:38.627004:  
+2024-09-09 08:53:38.627178: Epoch 559 
+2024-09-09 08:53:38.627280: Current learning rate: 0.00479 
+2024-09-09 08:57:44.245306: train_loss -0.8499 
+2024-09-09 08:57:44.245444: val_loss -0.6482 
+2024-09-09 08:57:44.245495: Pseudo dice [0.6085, 0.8521] 
+2024-09-09 08:57:44.245571: Epoch time: 245.62 s 
+2024-09-09 08:57:45.221251:  
+2024-09-09 08:57:45.221400: Epoch 560 
+2024-09-09 08:57:45.221483: Current learning rate: 0.00478 
+2024-09-09 09:01:50.896770: train_loss -0.8538 
+2024-09-09 09:01:50.896935: val_loss -0.6536 
+2024-09-09 09:01:50.896984: Pseudo dice [0.6005, 0.874] 
+2024-09-09 09:01:50.897036: Epoch time: 245.68 s 
+2024-09-09 09:01:51.868181:  
+2024-09-09 09:01:51.868390: Epoch 561 
+2024-09-09 09:01:51.868470: Current learning rate: 0.00477 
+2024-09-09 09:05:57.659196: train_loss -0.851 
+2024-09-09 09:05:57.659336: val_loss -0.6663 
+2024-09-09 09:05:57.659387: Pseudo dice [0.6001, 0.8729] 
+2024-09-09 09:05:57.659438: Epoch time: 245.79 s 
+2024-09-09 09:05:58.647826:  
+2024-09-09 09:05:58.648009: Epoch 562 
+2024-09-09 09:05:58.648090: Current learning rate: 0.00476 
+2024-09-09 09:10:04.496148: train_loss -0.8529 
+2024-09-09 09:10:04.496296: val_loss -0.7348 
+2024-09-09 09:10:04.496348: Pseudo dice [0.7236, 0.8781] 
+2024-09-09 09:10:04.496399: Epoch time: 245.85 s 
+2024-09-09 09:10:05.464292:  
+2024-09-09 09:10:05.464528: Epoch 563 
+2024-09-09 09:10:05.464611: Current learning rate: 0.00475 
+2024-09-09 09:14:11.401401: train_loss -0.8546 
+2024-09-09 09:14:11.401542: val_loss -0.6665 
+2024-09-09 09:14:11.401592: Pseudo dice [0.6395, 0.8452] 
+2024-09-09 09:14:11.401643: Epoch time: 245.94 s 
+2024-09-09 09:14:12.372974:  
+2024-09-09 09:14:12.373165: Epoch 564 
+2024-09-09 09:14:12.373271: Current learning rate: 0.00474 
+2024-09-09 09:18:18.452443: train_loss -0.8473 
+2024-09-09 09:18:18.452583: val_loss -0.7006 
+2024-09-09 09:18:18.452635: Pseudo dice [0.6868, 0.8611] 
+2024-09-09 09:18:18.452686: Epoch time: 246.08 s 
+2024-09-09 09:18:19.462425:  
+2024-09-09 09:18:19.462652: Epoch 565 
+2024-09-09 09:18:19.462737: Current learning rate: 0.00473 
+2024-09-09 09:22:25.439973: train_loss -0.8535 
+2024-09-09 09:22:25.440112: val_loss -0.6812 
+2024-09-09 09:22:25.440166: Pseudo dice [0.637, 0.8721] 
+2024-09-09 09:22:25.440217: Epoch time: 245.98 s 
+2024-09-09 09:22:27.359562:  
+2024-09-09 09:22:27.359773: Epoch 566 
+2024-09-09 09:22:27.359869: Current learning rate: 0.00472 
+2024-09-09 09:26:33.238940: train_loss -0.8582 
+2024-09-09 09:26:33.239087: val_loss -0.6676 
+2024-09-09 09:26:33.239138: Pseudo dice [0.6149, 0.8461] 
+2024-09-09 09:26:33.239188: Epoch time: 245.88 s 
+2024-09-09 09:26:34.208199:  
+2024-09-09 09:26:34.208453: Epoch 567 
+2024-09-09 09:26:34.208535: Current learning rate: 0.00471 
+2024-09-09 09:30:39.945537: train_loss -0.8575 
+2024-09-09 09:30:39.945688: val_loss -0.6855 
+2024-09-09 09:30:39.945739: Pseudo dice [0.6522, 0.8727] 
+2024-09-09 09:30:39.945790: Epoch time: 245.74 s 
+2024-09-09 09:30:40.920001:  
+2024-09-09 09:30:40.920234: Epoch 568 
+2024-09-09 09:30:40.920315: Current learning rate: 0.0047 
+2024-09-09 09:34:46.704420: train_loss -0.867 
+2024-09-09 09:34:46.704564: val_loss -0.6905 
+2024-09-09 09:34:46.704618: Pseudo dice [0.6958, 0.8707] 
+2024-09-09 09:34:46.704671: Epoch time: 245.79 s 
+2024-09-09 09:34:47.678373:  
+2024-09-09 09:34:47.678630: Epoch 569 
+2024-09-09 09:34:47.678752: Current learning rate: 0.00469 
+2024-09-09 09:38:53.556410: train_loss -0.854 
+2024-09-09 09:38:53.556576: val_loss -0.6979 
+2024-09-09 09:38:53.556629: Pseudo dice [0.7161, 0.8723] 
+2024-09-09 09:38:53.556681: Epoch time: 245.88 s 
+2024-09-09 09:38:54.537281:  
+2024-09-09 09:38:54.537573: Epoch 570 
+2024-09-09 09:38:54.537656: Current learning rate: 0.00468 
+2024-09-09 09:43:00.750952: train_loss -0.8542 
+2024-09-09 09:43:00.751098: val_loss -0.6951 
+2024-09-09 09:43:00.751148: Pseudo dice [0.6781, 0.8513] 
+2024-09-09 09:43:00.751198: Epoch time: 246.22 s 
+2024-09-09 09:43:01.717493:  
+2024-09-09 09:43:01.717691: Epoch 571 
+2024-09-09 09:43:01.717772: Current learning rate: 0.00467 
+2024-09-09 09:47:07.774002: train_loss -0.8689 
+2024-09-09 09:47:07.774163: val_loss -0.7017 
+2024-09-09 09:47:07.774216: Pseudo dice [0.6854, 0.8687] 
+2024-09-09 09:47:07.774268: Epoch time: 246.06 s 
+2024-09-09 09:47:08.766845:  
+2024-09-09 09:47:08.767056: Epoch 572 
+2024-09-09 09:47:08.767136: Current learning rate: 0.00466 
+2024-09-09 09:51:14.752685: train_loss -0.8754 
+2024-09-09 09:51:14.752845: val_loss -0.6852 
+2024-09-09 09:51:14.752897: Pseudo dice [0.6571, 0.8705] 
+2024-09-09 09:51:14.752948: Epoch time: 245.99 s 
+2024-09-09 09:51:15.769901:  
+2024-09-09 09:51:15.770070: Epoch 573 
+2024-09-09 09:51:15.770180: Current learning rate: 0.00465 
+2024-09-09 09:55:21.955022: train_loss -0.8721 
+2024-09-09 09:55:21.955165: val_loss -0.6648 
+2024-09-09 09:55:21.955216: Pseudo dice [0.6794, 0.8722] 
+2024-09-09 09:55:21.955269: Epoch time: 246.19 s 
+2024-09-09 09:55:22.936624:  
+2024-09-09 09:55:22.936860: Epoch 574 
+2024-09-09 09:55:22.936938: Current learning rate: 0.00464 
+2024-09-09 09:59:28.843290: train_loss -0.8768 
+2024-09-09 09:59:28.843431: val_loss -0.6691 
+2024-09-09 09:59:28.843480: Pseudo dice [0.6429, 0.8638] 
+2024-09-09 09:59:28.843530: Epoch time: 245.91 s 
+2024-09-09 09:59:29.831441:  
+2024-09-09 09:59:29.831659: Epoch 575 
+2024-09-09 09:59:29.831743: Current learning rate: 0.00463 
+2024-09-09 10:03:35.813171: train_loss -0.8801 
+2024-09-09 10:03:35.813371: val_loss -0.6895 
+2024-09-09 10:03:35.813429: Pseudo dice [0.6784, 0.8715] 
+2024-09-09 10:03:35.813490: Epoch time: 245.98 s 
+2024-09-09 10:03:36.809179:  
+2024-09-09 10:03:36.809397: Epoch 576 
+2024-09-09 10:03:36.809502: Current learning rate: 0.00462 
+2024-09-09 10:07:42.936344: train_loss -0.8738 
+2024-09-09 10:07:42.936481: val_loss -0.6968 
+2024-09-09 10:07:42.936533: Pseudo dice [0.6694, 0.8801] 
+2024-09-09 10:07:42.936584: Epoch time: 246.13 s 
+2024-09-09 10:07:43.937530:  
+2024-09-09 10:07:43.937726: Epoch 577 
+2024-09-09 10:07:43.937808: Current learning rate: 0.00461 
+2024-09-09 10:11:49.867301: train_loss -0.8748 
+2024-09-09 10:11:49.867445: val_loss -0.6528 
+2024-09-09 10:11:49.867495: Pseudo dice [0.5653, 0.8742] 
+2024-09-09 10:11:49.867546: Epoch time: 245.93 s 
+2024-09-09 10:11:50.854386:  
+2024-09-09 10:11:50.854610: Epoch 578 
+2024-09-09 10:11:50.854726: Current learning rate: 0.0046 
+2024-09-09 10:15:56.882059: train_loss -0.8713 
+2024-09-09 10:15:56.882223: val_loss -0.6856 
+2024-09-09 10:15:56.882272: Pseudo dice [0.6583, 0.861] 
+2024-09-09 10:15:56.882404: Epoch time: 246.03 s 
+2024-09-09 10:15:57.869237:  
+2024-09-09 10:15:57.869436: Epoch 579 
+2024-09-09 10:15:57.869516: Current learning rate: 0.00459 
+2024-09-09 10:20:04.338149: train_loss -0.8686 
+2024-09-09 10:20:04.338286: val_loss -0.6903 
+2024-09-09 10:20:04.338392: Pseudo dice [0.7035, 0.8716] 
+2024-09-09 10:20:04.338445: Epoch time: 246.47 s 
+2024-09-09 10:20:05.336945:  
+2024-09-09 10:20:05.337145: Epoch 580 
+2024-09-09 10:20:05.337226: Current learning rate: 0.00458 
+2024-09-09 10:24:11.661878: train_loss -0.8697 
+2024-09-09 10:24:11.662016: val_loss -0.6703 
+2024-09-09 10:24:11.662066: Pseudo dice [0.6237, 0.8793] 
+2024-09-09 10:24:11.662117: Epoch time: 246.33 s 
+2024-09-09 10:24:12.654788:  
+2024-09-09 10:24:12.655015: Epoch 581 
+2024-09-09 10:24:12.655092: Current learning rate: 0.00457 
+2024-09-09 10:28:18.879352: train_loss -0.8761 
+2024-09-09 10:28:18.879500: val_loss -0.6985 
+2024-09-09 10:28:18.879553: Pseudo dice [0.6588, 0.8712] 
+2024-09-09 10:28:18.879611: Epoch time: 246.23 s 
+2024-09-09 10:28:20.044366:  
+2024-09-09 10:28:20.044610: Epoch 582 
+2024-09-09 10:28:20.044756: Current learning rate: 0.00456 
+2024-09-09 10:32:26.044106: train_loss -0.8732 
+2024-09-09 10:32:26.044319: val_loss -0.6872 
+2024-09-09 10:32:26.044403: Pseudo dice [0.6305, 0.8749] 
+2024-09-09 10:32:26.044486: Epoch time: 246.0 s 
+2024-09-09 10:32:27.334929:  
+2024-09-09 10:32:27.335266: Epoch 583 
+2024-09-09 10:32:27.335393: Current learning rate: 0.00455 
+2024-09-09 10:36:33.442910: train_loss -0.8698 
+2024-09-09 10:36:33.443090: val_loss -0.6791 
+2024-09-09 10:36:33.443141: Pseudo dice [0.6359, 0.8655] 
+2024-09-09 10:36:33.443204: Epoch time: 246.11 s 
+2024-09-09 10:36:34.585158:  
+2024-09-09 10:36:34.585381: Epoch 584 
+2024-09-09 10:36:34.585540: Current learning rate: 0.00454 
+2024-09-09 10:40:40.613010: train_loss -0.8691 
+2024-09-09 10:40:40.613157: val_loss -0.667 
+2024-09-09 10:40:40.613211: Pseudo dice [0.6088, 0.8508] 
+2024-09-09 10:40:40.613261: Epoch time: 246.03 s 
+2024-09-09 10:40:41.744066:  
+2024-09-09 10:40:41.744244: Epoch 585 
+2024-09-09 10:40:41.744332: Current learning rate: 0.00453 
+2024-09-09 10:44:47.708300: train_loss -0.8725 
+2024-09-09 10:44:47.708453: val_loss -0.7012 
+2024-09-09 10:44:47.708539: Pseudo dice [0.6782, 0.8766] 
+2024-09-09 10:44:47.708615: Epoch time: 245.97 s 
+2024-09-09 10:44:48.831835:  
+2024-09-09 10:44:48.832118: Epoch 586 
+2024-09-09 10:44:48.832289: Current learning rate: 0.00452 
+2024-09-09 10:48:54.932600: train_loss -0.8778 
+2024-09-09 10:48:54.932733: val_loss -0.6587 
+2024-09-09 10:48:54.932783: Pseudo dice [0.6417, 0.8722] 
+2024-09-09 10:48:54.932834: Epoch time: 246.1 s 
+2024-09-09 10:48:55.999438:  
+2024-09-09 10:48:55.999734: Epoch 587 
+2024-09-09 10:48:55.999846: Current learning rate: 0.00451 
+2024-09-09 10:53:02.057508: train_loss -0.8733 
+2024-09-09 10:53:02.057645: val_loss -0.7186 
+2024-09-09 10:53:02.057695: Pseudo dice [0.6997, 0.8714] 
+2024-09-09 10:53:02.057749: Epoch time: 246.06 s 
+2024-09-09 10:53:03.050902:  
+2024-09-09 10:53:03.051089: Epoch 588 
+2024-09-09 10:53:03.051172: Current learning rate: 0.0045 
+2024-09-09 10:57:09.580014: train_loss -0.883 
+2024-09-09 10:57:09.580171: val_loss -0.6916 
+2024-09-09 10:57:09.580222: Pseudo dice [0.6744, 0.8822] 
+2024-09-09 10:57:09.580275: Epoch time: 246.53 s 
+2024-09-09 10:57:11.518365:  
+2024-09-09 10:57:11.518650: Epoch 589 
+2024-09-09 10:57:11.518753: Current learning rate: 0.00449 
+2024-09-09 11:01:17.888844: train_loss -0.8794 
+2024-09-09 11:01:17.889049: val_loss -0.683 
+2024-09-09 11:01:17.889100: Pseudo dice [0.6466, 0.8714] 
+2024-09-09 11:01:17.889153: Epoch time: 246.37 s 
+2024-09-09 11:01:18.875676:  
+2024-09-09 11:01:18.875981: Epoch 590 
+2024-09-09 11:01:18.876064: Current learning rate: 0.00448 
+2024-09-09 11:05:25.094110: train_loss -0.883 
+2024-09-09 11:05:25.094246: val_loss -0.7027 
+2024-09-09 11:05:25.094295: Pseudo dice [0.6987, 0.8791] 
+2024-09-09 11:05:25.094347: Epoch time: 246.22 s 
+2024-09-09 11:05:26.084179:  
+2024-09-09 11:05:26.084363: Epoch 591 
+2024-09-09 11:05:26.084440: Current learning rate: 0.00447 
+2024-09-09 11:09:32.028322: train_loss -0.8807 
+2024-09-09 11:09:32.028477: val_loss -0.7149 
+2024-09-09 11:09:32.028527: Pseudo dice [0.6749, 0.8864] 
+2024-09-09 11:09:32.028578: Epoch time: 245.95 s 
+2024-09-09 11:09:33.032291:  
+2024-09-09 11:09:33.032529: Epoch 592 
+2024-09-09 11:09:33.032614: Current learning rate: 0.00446 
+2024-09-09 11:13:38.886547: train_loss -0.8793 
+2024-09-09 11:13:38.886685: val_loss -0.6931 
+2024-09-09 11:13:38.886734: Pseudo dice [0.6828, 0.8668] 
+2024-09-09 11:13:38.886796: Epoch time: 245.86 s 
+2024-09-09 11:13:39.881557:  
+2024-09-09 11:13:39.881813: Epoch 593 
+2024-09-09 11:13:39.881893: Current learning rate: 0.00445 
+2024-09-09 11:17:45.687298: train_loss -0.8816 
+2024-09-09 11:17:45.687459: val_loss -0.6632 
+2024-09-09 11:17:45.687509: Pseudo dice [0.6378, 0.8669] 
+2024-09-09 11:17:45.687560: Epoch time: 245.81 s 
+2024-09-09 11:17:46.670652:  
+2024-09-09 11:17:46.670849: Epoch 594 
+2024-09-09 11:17:46.670928: Current learning rate: 0.00444 
+2024-09-09 11:21:52.473433: train_loss -0.8814 
+2024-09-09 11:21:52.473660: val_loss -0.6843 
+2024-09-09 11:21:52.473711: Pseudo dice [0.6654, 0.8843] 
+2024-09-09 11:21:52.473763: Epoch time: 245.8 s 
+2024-09-09 11:21:53.467476:  
+2024-09-09 11:21:53.467668: Epoch 595 
+2024-09-09 11:21:53.467747: Current learning rate: 0.00443 
+2024-09-09 11:25:59.069290: train_loss -0.8805 
+2024-09-09 11:25:59.069425: val_loss -0.6819 
+2024-09-09 11:25:59.069474: Pseudo dice [0.6361, 0.8737] 
+2024-09-09 11:25:59.069524: Epoch time: 245.6 s 
+2024-09-09 11:26:00.070436:  
+2024-09-09 11:26:00.070627: Epoch 596 
+2024-09-09 11:26:00.070714: Current learning rate: 0.00442 
+2024-09-09 11:30:05.948052: train_loss -0.8797 
+2024-09-09 11:30:05.948191: val_loss -0.6962 
+2024-09-09 11:30:05.948240: Pseudo dice [0.6467, 0.8797] 
+2024-09-09 11:30:05.948339: Epoch time: 245.88 s 
+2024-09-09 11:30:06.943498:  
+2024-09-09 11:30:06.943655: Epoch 597 
+2024-09-09 11:30:06.943733: Current learning rate: 0.00441 
+2024-09-09 11:34:13.109832: train_loss -0.8763 
+2024-09-09 11:34:13.109970: val_loss -0.7022 
+2024-09-09 11:34:13.110019: Pseudo dice [0.665, 0.8815] 
+2024-09-09 11:34:13.110070: Epoch time: 246.17 s 
+2024-09-09 11:34:14.125224:  
+2024-09-09 11:34:14.125422: Epoch 598 
+2024-09-09 11:34:14.125503: Current learning rate: 0.0044 
+2024-09-09 11:38:19.847570: train_loss -0.8645 
+2024-09-09 11:38:19.847705: val_loss -0.6628 
+2024-09-09 11:38:19.847766: Pseudo dice [0.6583, 0.8577] 
+2024-09-09 11:38:19.847834: Epoch time: 245.72 s 
+2024-09-09 11:38:20.856921:  
+2024-09-09 11:38:20.857085: Epoch 599 
+2024-09-09 11:38:20.857165: Current learning rate: 0.00439 
+2024-09-09 11:42:26.864187: train_loss -0.8439 
+2024-09-09 11:42:26.864331: val_loss -0.676 
+2024-09-09 11:42:26.864380: Pseudo dice [0.6492, 0.8468] 
+2024-09-09 11:42:26.864431: Epoch time: 246.01 s 
+2024-09-09 11:42:30.805423:  
+2024-09-09 11:42:30.805620: Epoch 600 
+2024-09-09 11:42:30.805700: Current learning rate: 0.00438 
+2024-09-09 11:46:36.530714: train_loss -0.8637 
+2024-09-09 11:46:36.530853: val_loss -0.6896 
+2024-09-09 11:46:36.530904: Pseudo dice [0.6511, 0.8745] 
+2024-09-09 11:46:36.530954: Epoch time: 245.73 s 
+2024-09-09 11:46:37.527745:  
+2024-09-09 11:46:37.527968: Epoch 601 
+2024-09-09 11:46:37.528054: Current learning rate: 0.00437 
+2024-09-09 11:50:43.236500: train_loss -0.8639 
+2024-09-09 11:50:43.236639: val_loss -0.6707 
+2024-09-09 11:50:43.236692: Pseudo dice [0.6037, 0.8658] 
+2024-09-09 11:50:43.236747: Epoch time: 245.71 s 
+2024-09-09 11:50:44.206396:  
+2024-09-09 11:50:44.206610: Epoch 602 
+2024-09-09 11:50:44.206697: Current learning rate: 0.00436 
+2024-09-09 11:54:49.985659: train_loss -0.8475 
+2024-09-09 11:54:49.985814: val_loss -0.6595 
+2024-09-09 11:54:49.985864: Pseudo dice [0.6288, 0.853] 
+2024-09-09 11:54:49.985914: Epoch time: 245.78 s 
+2024-09-09 11:54:50.976105:  
+2024-09-09 11:54:50.976242: Epoch 603 
+2024-09-09 11:54:50.976353: Current learning rate: 0.00435 
+2024-09-09 11:58:56.661198: train_loss -0.8531 
+2024-09-09 11:58:56.661348: val_loss -0.667 
+2024-09-09 11:58:56.661400: Pseudo dice [0.6146, 0.8489] 
+2024-09-09 11:58:56.661453: Epoch time: 245.69 s 
+2024-09-09 11:58:57.640873:  
+2024-09-09 11:58:57.641060: Epoch 604 
+2024-09-09 11:58:57.641142: Current learning rate: 0.00434 
+2024-09-09 12:03:03.416675: train_loss -0.8688 
+2024-09-09 12:03:03.416811: val_loss -0.7021 
+2024-09-09 12:03:03.416861: Pseudo dice [0.7038, 0.8575] 
+2024-09-09 12:03:03.416914: Epoch time: 245.78 s 
+2024-09-09 12:03:04.395572:  
+2024-09-09 12:03:04.395776: Epoch 605 
+2024-09-09 12:03:04.395902: Current learning rate: 0.00433 
+2024-09-09 12:07:10.042757: train_loss -0.8752 
+2024-09-09 12:07:10.042894: val_loss -0.6823 
+2024-09-09 12:07:10.042944: Pseudo dice [0.6265, 0.8686] 
+2024-09-09 12:07:10.042993: Epoch time: 245.65 s 
+2024-09-09 12:07:11.053455:  
+2024-09-09 12:07:11.053659: Epoch 606 
+2024-09-09 12:07:11.053739: Current learning rate: 0.00432 
+2024-09-09 12:11:17.001408: train_loss -0.8757 
+2024-09-09 12:11:17.001556: val_loss -0.6679 
+2024-09-09 12:11:17.001606: Pseudo dice [0.6636, 0.8576] 
+2024-09-09 12:11:17.001657: Epoch time: 245.95 s 
+2024-09-09 12:11:17.993208:  
+2024-09-09 12:11:17.993448: Epoch 607 
+2024-09-09 12:11:17.993531: Current learning rate: 0.00431 
+2024-09-09 12:15:23.946902: train_loss -0.8746 
+2024-09-09 12:15:23.947062: val_loss -0.6688 
+2024-09-09 12:15:23.947114: Pseudo dice [0.64, 0.877] 
+2024-09-09 12:15:23.947166: Epoch time: 245.96 s 
+2024-09-09 12:15:24.956632:  
+2024-09-09 12:15:24.956803: Epoch 608 
+2024-09-09 12:15:24.956882: Current learning rate: 0.0043 
+2024-09-09 12:19:30.649022: train_loss -0.8777 
+2024-09-09 12:19:30.649225: val_loss -0.6721 
+2024-09-09 12:19:30.649319: Pseudo dice [0.6101, 0.8719] 
+2024-09-09 12:19:30.649411: Epoch time: 245.69 s 
+2024-09-09 12:19:31.637069:  
+2024-09-09 12:19:31.637269: Epoch 609 
+2024-09-09 12:19:31.637372: Current learning rate: 0.00429 
+2024-09-09 12:23:37.284125: train_loss -0.8794 
+2024-09-09 12:23:37.284323: val_loss -0.6512 
+2024-09-09 12:23:37.284374: Pseudo dice [0.6141, 0.8751] 
+2024-09-09 12:23:37.284426: Epoch time: 245.65 s 
+2024-09-09 12:23:38.275783:  
+2024-09-09 12:23:38.276014: Epoch 610 
+2024-09-09 12:23:38.276094: Current learning rate: 0.00429 
+2024-09-09 12:27:43.940273: train_loss -0.878 
+2024-09-09 12:27:43.940414: val_loss -0.683 
+2024-09-09 12:27:43.940464: Pseudo dice [0.675, 0.8752] 
+2024-09-09 12:27:43.940514: Epoch time: 245.67 s 
+2024-09-09 12:27:44.936632:  
+2024-09-09 12:27:44.936861: Epoch 611 
+2024-09-09 12:27:44.936955: Current learning rate: 0.00428 
+2024-09-09 12:31:50.789610: train_loss -0.8798 
+2024-09-09 12:31:50.789766: val_loss -0.6998 
+2024-09-09 12:31:50.789818: Pseudo dice [0.691, 0.8656] 
+2024-09-09 12:31:50.789868: Epoch time: 245.86 s 
+2024-09-09 12:31:52.735687:  
+2024-09-09 12:31:52.735907: Epoch 612 
+2024-09-09 12:31:52.736008: Current learning rate: 0.00427 
+2024-09-09 12:35:58.720216: train_loss -0.8798 
+2024-09-09 12:35:58.720377: val_loss -0.6938 
+2024-09-09 12:35:58.720436: Pseudo dice [0.7049, 0.8649] 
+2024-09-09 12:35:58.720495: Epoch time: 245.99 s 
+2024-09-09 12:35:59.800963:  
+2024-09-09 12:35:59.801231: Epoch 613 
+2024-09-09 12:35:59.801313: Current learning rate: 0.00426 
+2024-09-09 12:40:05.732026: train_loss -0.8749 
+2024-09-09 12:40:05.732219: val_loss -0.7066 
+2024-09-09 12:40:05.732311: Pseudo dice [0.6852, 0.8783] 
+2024-09-09 12:40:05.732401: Epoch time: 245.93 s 
+2024-09-09 12:40:06.721644:  
+2024-09-09 12:40:06.721865: Epoch 614 
+2024-09-09 12:40:06.721948: Current learning rate: 0.00425 
+2024-09-09 12:44:12.919714: train_loss -0.8792 
+2024-09-09 12:44:12.919868: val_loss -0.6814 
+2024-09-09 12:44:12.919920: Pseudo dice [0.633, 0.8556] 
+2024-09-09 12:44:12.919971: Epoch time: 246.2 s 
+2024-09-09 12:44:13.918236:  
+2024-09-09 12:44:13.918432: Epoch 615 
+2024-09-09 12:44:13.918512: Current learning rate: 0.00424 
+2024-09-09 12:48:20.287115: train_loss -0.8794 
+2024-09-09 12:48:20.287264: val_loss -0.6805 
+2024-09-09 12:48:20.287314: Pseudo dice [0.6276, 0.871] 
+2024-09-09 12:48:20.287371: Epoch time: 246.37 s 
+2024-09-09 12:48:21.279745:  
+2024-09-09 12:48:21.280014: Epoch 616 
+2024-09-09 12:48:21.280096: Current learning rate: 0.00423 
+2024-09-09 12:52:27.191564: train_loss -0.8814 
+2024-09-09 12:52:27.191776: val_loss -0.6863 
+2024-09-09 12:52:27.191886: Pseudo dice [0.6866, 0.8762] 
+2024-09-09 12:52:27.191970: Epoch time: 245.91 s 
+2024-09-09 12:52:28.510149:  
+2024-09-09 12:52:28.510516: Epoch 617 
+2024-09-09 12:52:28.510677: Current learning rate: 0.00422 
+2024-09-09 12:56:34.373948: train_loss -0.8819 
+2024-09-09 12:56:34.374096: val_loss -0.6878 
+2024-09-09 12:56:34.374146: Pseudo dice [0.6628, 0.8756] 
+2024-09-09 12:56:34.374196: Epoch time: 245.87 s 
+2024-09-09 12:56:35.386683:  
+2024-09-09 12:56:35.386882: Epoch 618 
+2024-09-09 12:56:35.386960: Current learning rate: 0.00421 
+2024-09-09 13:00:41.237444: train_loss -0.8858 
+2024-09-09 13:00:41.237585: val_loss -0.7326 
+2024-09-09 13:00:41.237638: Pseudo dice [0.7291, 0.872] 
+2024-09-09 13:00:41.237688: Epoch time: 245.85 s 
+2024-09-09 13:00:42.230555:  
+2024-09-09 13:00:42.230779: Epoch 619 
+2024-09-09 13:00:42.230880: Current learning rate: 0.0042 
+2024-09-09 13:04:48.409411: train_loss -0.8829 
+2024-09-09 13:04:48.409566: val_loss -0.6947 
+2024-09-09 13:04:48.409617: Pseudo dice [0.6705, 0.8668] 
+2024-09-09 13:04:48.409667: Epoch time: 246.18 s 
+2024-09-09 13:04:49.406663:  
+2024-09-09 13:04:49.406903: Epoch 620 
+2024-09-09 13:04:49.406989: Current learning rate: 0.00419 
+2024-09-09 13:08:55.552791: train_loss -0.8781 
+2024-09-09 13:08:55.552927: val_loss -0.68 
+2024-09-09 13:08:55.552979: Pseudo dice [0.6627, 0.8589] 
+2024-09-09 13:08:55.553029: Epoch time: 246.15 s 
+2024-09-09 13:08:56.567739:  
+2024-09-09 13:08:56.568034: Epoch 621 
+2024-09-09 13:08:56.568113: Current learning rate: 0.00418 
+2024-09-09 13:13:02.589014: train_loss -0.8816 
+2024-09-09 13:13:02.589154: val_loss -0.6729 
+2024-09-09 13:13:02.589205: Pseudo dice [0.6489, 0.8718] 
+2024-09-09 13:13:02.589255: Epoch time: 246.02 s 
+2024-09-09 13:13:03.580359:  
+2024-09-09 13:13:03.580540: Epoch 622 
+2024-09-09 13:13:03.580646: Current learning rate: 0.00417 
+2024-09-09 13:17:09.402452: train_loss -0.8845 
+2024-09-09 13:17:09.402587: val_loss -0.6784 
+2024-09-09 13:17:09.402639: Pseudo dice [0.6514, 0.8681] 
+2024-09-09 13:17:09.402692: Epoch time: 245.82 s 
+2024-09-09 13:17:10.388275:  
+2024-09-09 13:17:10.388481: Epoch 623 
+2024-09-09 13:17:10.388608: Current learning rate: 0.00416 
+2024-09-09 13:21:16.600709: train_loss -0.8861 
+2024-09-09 13:21:16.600848: val_loss -0.6618 
+2024-09-09 13:21:16.600898: Pseudo dice [0.6477, 0.8767] 
+2024-09-09 13:21:16.600950: Epoch time: 246.21 s 
+2024-09-09 13:21:17.590316:  
+2024-09-09 13:21:17.590459: Epoch 624 
+2024-09-09 13:21:17.590537: Current learning rate: 0.00415 
+2024-09-09 13:25:24.074167: train_loss -0.8807 
+2024-09-09 13:25:24.074325: val_loss -0.6824 
+2024-09-09 13:25:24.074376: Pseudo dice [0.6248, 0.8746] 
+2024-09-09 13:25:24.074426: Epoch time: 246.49 s 
+2024-09-09 13:25:25.063786:  
+2024-09-09 13:25:25.064005: Epoch 625 
+2024-09-09 13:25:25.064114: Current learning rate: 0.00414 
+2024-09-09 13:29:30.775223: train_loss -0.8823 
+2024-09-09 13:29:30.775390: val_loss -0.6461 
+2024-09-09 13:29:30.775456: Pseudo dice [0.6281, 0.8614] 
+2024-09-09 13:29:30.775514: Epoch time: 245.71 s 
+2024-09-09 13:29:32.053950:  
+2024-09-09 13:29:32.054277: Epoch 626 
+2024-09-09 13:29:32.054404: Current learning rate: 0.00413 
+2024-09-09 13:33:37.815232: train_loss -0.8746 
+2024-09-09 13:33:37.815471: val_loss -0.6905 
+2024-09-09 13:33:37.815558: Pseudo dice [0.6517, 0.8732] 
+2024-09-09 13:33:37.815643: Epoch time: 245.76 s 
+2024-09-09 13:33:39.079010:  
+2024-09-09 13:33:39.079267: Epoch 627 
+2024-09-09 13:33:39.079352: Current learning rate: 0.00412 
+2024-09-09 13:37:44.656176: train_loss -0.8735 
+2024-09-09 13:37:44.656316: val_loss -0.6827 
+2024-09-09 13:37:44.656366: Pseudo dice [0.6062, 0.881] 
+2024-09-09 13:37:44.656416: Epoch time: 245.58 s 
+2024-09-09 13:37:45.662563:  
+2024-09-09 13:37:45.662775: Epoch 628 
+2024-09-09 13:37:45.662857: Current learning rate: 0.00411 
+2024-09-09 13:41:50.910021: train_loss -0.8804 
+2024-09-09 13:41:50.910197: val_loss -0.6706 
+2024-09-09 13:41:50.910249: Pseudo dice [0.6464, 0.8664] 
+2024-09-09 13:41:50.910302: Epoch time: 245.25 s 
+2024-09-09 13:41:51.903123:  
+2024-09-09 13:41:51.903264: Epoch 629 
+2024-09-09 13:41:51.903340: Current learning rate: 0.0041 
+2024-09-09 13:45:57.452432: train_loss -0.877 
+2024-09-09 13:45:57.452568: val_loss -0.7075 
+2024-09-09 13:45:57.452619: Pseudo dice [0.6819, 0.8811] 
+2024-09-09 13:45:57.452669: Epoch time: 245.55 s 
+2024-09-09 13:45:58.447747:  
+2024-09-09 13:45:58.447957: Epoch 630 
+2024-09-09 13:45:58.448044: Current learning rate: 0.00409 
+2024-09-09 13:50:03.799777: train_loss -0.8793 
+2024-09-09 13:50:03.799921: val_loss -0.673 
+2024-09-09 13:50:03.799971: Pseudo dice [0.6675, 0.8582] 
+2024-09-09 13:50:03.800042: Epoch time: 245.35 s 
+2024-09-09 13:50:04.797604:  
+2024-09-09 13:50:04.797847: Epoch 631 
+2024-09-09 13:50:04.797925: Current learning rate: 0.00408 
+2024-09-09 13:54:10.092388: train_loss -0.8834 
+2024-09-09 13:54:10.092560: val_loss -0.6938 
+2024-09-09 13:54:10.092612: Pseudo dice [0.6846, 0.8718] 
+2024-09-09 13:54:10.092664: Epoch time: 245.3 s 
+2024-09-09 13:54:11.086710:  
+2024-09-09 13:54:11.086862: Epoch 632 
+2024-09-09 13:54:11.086942: Current learning rate: 0.00407 
+2024-09-09 13:58:16.698211: train_loss -0.8815 
+2024-09-09 13:58:16.698349: val_loss -0.6858 
+2024-09-09 13:58:16.698400: Pseudo dice [0.6397, 0.8614] 
+2024-09-09 13:58:16.698488: Epoch time: 245.61 s 
+2024-09-09 13:58:17.692766:  
+2024-09-09 13:58:17.692901: Epoch 633 
+2024-09-09 13:58:17.692980: Current learning rate: 0.00406 
+2024-09-09 14:02:23.443241: train_loss -0.8737 
+2024-09-09 14:02:23.443379: val_loss -0.6844 
+2024-09-09 14:02:23.443430: Pseudo dice [0.657, 0.8671] 
+2024-09-09 14:02:23.443482: Epoch time: 245.75 s 
+2024-09-09 14:02:24.451646:  
+2024-09-09 14:02:24.451842: Epoch 634 
+2024-09-09 14:02:24.451926: Current learning rate: 0.00405 
+2024-09-09 14:06:30.664705: train_loss -0.8619 
+2024-09-09 14:06:30.664840: val_loss -0.7146 
+2024-09-09 14:06:30.665084: Pseudo dice [0.6649, 0.8729] 
+2024-09-09 14:06:30.665136: Epoch time: 246.21 s 
+2024-09-09 14:06:32.590732:  
+2024-09-09 14:06:32.590995: Epoch 635 
+2024-09-09 14:06:32.591114: Current learning rate: 0.00404 
+2024-09-09 14:10:38.341125: train_loss -0.8753 
+2024-09-09 14:10:38.341282: val_loss -0.7102 
+2024-09-09 14:10:38.341352: Pseudo dice [0.6847, 0.8774] 
+2024-09-09 14:10:38.341412: Epoch time: 245.75 s 
+2024-09-09 14:10:39.528560:  
+2024-09-09 14:10:39.528873: Epoch 636 
+2024-09-09 14:10:39.528966: Current learning rate: 0.00403 
+2024-09-09 14:14:45.314125: train_loss -0.8828 
+2024-09-09 14:14:45.314270: val_loss -0.6731 
+2024-09-09 14:14:45.314327: Pseudo dice [0.6235, 0.8793] 
+2024-09-09 14:14:45.314383: Epoch time: 245.79 s 
+2024-09-09 14:14:46.296883:  
+2024-09-09 14:14:46.297132: Epoch 637 
+2024-09-09 14:14:46.297218: Current learning rate: 0.00402 
+2024-09-09 14:18:52.208252: train_loss -0.8823 
+2024-09-09 14:18:52.208423: val_loss -0.6893 
+2024-09-09 14:18:52.208482: Pseudo dice [0.6497, 0.87] 
+2024-09-09 14:18:52.208537: Epoch time: 245.91 s 
+2024-09-09 14:18:53.217818:  
+2024-09-09 14:18:53.218029: Epoch 638 
+2024-09-09 14:18:53.218115: Current learning rate: 0.00401 
+2024-09-09 14:22:59.764638: train_loss -0.8817 
+2024-09-09 14:22:59.764802: val_loss -0.6508 
+2024-09-09 14:22:59.764859: Pseudo dice [0.6416, 0.8558] 
+2024-09-09 14:22:59.764917: Epoch time: 246.55 s 
+2024-09-09 14:23:00.796663:  
+2024-09-09 14:23:00.796847: Epoch 639 
+2024-09-09 14:23:00.796934: Current learning rate: 0.004 
+2024-09-09 14:27:07.166232: train_loss -0.885 
+2024-09-09 14:27:07.166400: val_loss -0.659 
+2024-09-09 14:27:07.166456: Pseudo dice [0.5903, 0.8681] 
+2024-09-09 14:27:07.166512: Epoch time: 246.37 s 
+2024-09-09 14:27:08.193424:  
+2024-09-09 14:27:08.193606: Epoch 640 
+2024-09-09 14:27:08.193698: Current learning rate: 0.00399 
+2024-09-09 14:31:14.688568: train_loss -0.8802 
+2024-09-09 14:31:14.688714: val_loss -0.6953 
+2024-09-09 14:31:14.688770: Pseudo dice [0.6894, 0.8756] 
+2024-09-09 14:31:14.688827: Epoch time: 246.5 s 
+2024-09-09 14:31:15.724361:  
+2024-09-09 14:31:15.724620: Epoch 641 
+2024-09-09 14:31:15.724721: Current learning rate: 0.00398 
+2024-09-09 14:35:22.440520: train_loss -0.8818 
+2024-09-09 14:35:22.440670: val_loss -0.6635 
+2024-09-09 14:35:22.440727: Pseudo dice [0.6281, 0.8725] 
+2024-09-09 14:35:22.440783: Epoch time: 246.72 s 
+2024-09-09 14:35:23.437380:  
+2024-09-09 14:35:23.437693: Epoch 642 
+2024-09-09 14:35:23.437779: Current learning rate: 0.00397 
+2024-09-09 14:39:30.282621: train_loss -0.8782 
+2024-09-09 14:39:30.282757: val_loss -0.6914 
+2024-09-09 14:39:30.282813: Pseudo dice [0.6806, 0.8772] 
+2024-09-09 14:39:30.282868: Epoch time: 246.85 s 
+2024-09-09 14:39:31.295100:  
+2024-09-09 14:39:31.295330: Epoch 643 
+2024-09-09 14:39:31.295420: Current learning rate: 0.00396 
+2024-09-09 14:43:37.954446: train_loss -0.8803 
+2024-09-09 14:43:37.954674: val_loss -0.7052 
+2024-09-09 14:43:37.954742: Pseudo dice [0.6742, 0.8791] 
+2024-09-09 14:43:37.954808: Epoch time: 246.66 s 
+2024-09-09 14:43:39.243264:  
+2024-09-09 14:43:39.243502: Epoch 644 
+2024-09-09 14:43:39.243611: Current learning rate: 0.00395 
+2024-09-09 14:47:46.285080: train_loss -0.8881 
+2024-09-09 14:47:46.285258: val_loss -0.6756 
+2024-09-09 14:47:46.285317: Pseudo dice [0.657, 0.8645] 
+2024-09-09 14:47:46.285373: Epoch time: 247.04 s 
+2024-09-09 14:47:47.282259:  
+2024-09-09 14:47:47.282443: Epoch 645 
+2024-09-09 14:47:47.282525: Current learning rate: 0.00394 
+2024-09-09 14:51:55.991108: train_loss -0.8737 
+2024-09-09 14:51:55.991258: val_loss -0.6832 
+2024-09-09 14:51:55.991316: Pseudo dice [0.6591, 0.8628] 
+2024-09-09 14:51:55.991375: Epoch time: 248.71 s 
+2024-09-09 14:51:56.994391:  
+2024-09-09 14:51:56.994598: Epoch 646 
+2024-09-09 14:51:56.994681: Current learning rate: 0.00393 
+2024-09-09 14:56:05.209635: train_loss -0.8798 
+2024-09-09 14:56:05.209791: val_loss -0.7017 
+2024-09-09 14:56:05.209849: Pseudo dice [0.6581, 0.8792] 
+2024-09-09 14:56:05.209909: Epoch time: 248.22 s 
+2024-09-09 14:56:06.200404:  
+2024-09-09 14:56:06.200662: Epoch 647 
+2024-09-09 14:56:06.200747: Current learning rate: 0.00392 
+2024-09-09 15:00:16.303503: train_loss -0.8834 
+2024-09-09 15:00:16.303660: val_loss -0.6954 
+2024-09-09 15:00:16.303718: Pseudo dice [0.6417, 0.8894] 
+2024-09-09 15:00:16.303776: Epoch time: 250.11 s 
+2024-09-09 15:00:17.341819:  
+2024-09-09 15:00:17.342078: Epoch 648 
+2024-09-09 15:00:17.342215: Current learning rate: 0.00391 
+2024-09-09 15:04:27.243024: train_loss -0.8858 
+2024-09-09 15:04:27.243174: val_loss -0.6632 
+2024-09-09 15:04:27.243232: Pseudo dice [0.5979, 0.8629] 
+2024-09-09 15:04:27.243289: Epoch time: 249.9 s 
+2024-09-09 15:04:28.413887:  
+2024-09-09 15:04:28.414193: Epoch 649 
+2024-09-09 15:04:28.414344: Current learning rate: 0.0039 
+2024-09-09 15:08:46.982008: train_loss -0.8862 
+2024-09-09 15:08:46.982203: val_loss -0.6997 
+2024-09-09 15:08:46.982261: Pseudo dice [0.69, 0.8798] 
+2024-09-09 15:08:46.982319: Epoch time: 258.58 s 
+2024-09-09 15:08:51.146831:  
+2024-09-09 15:08:51.147077: Epoch 650 
+2024-09-09 15:08:51.147172: Current learning rate: 0.00389 
+2024-09-09 15:12:59.010154: train_loss -0.8874 
+2024-09-09 15:12:59.010582: val_loss -0.6974 
+2024-09-09 15:12:59.010642: Pseudo dice [0.6413, 0.8766] 
+2024-09-09 15:12:59.010699: Epoch time: 247.87 s 
+2024-09-09 15:13:00.023746:  
+2024-09-09 15:13:00.023924: Epoch 651 
+2024-09-09 15:13:00.024046: Current learning rate: 0.00388 
+2024-09-09 15:17:06.626719: train_loss -0.8806 
+2024-09-09 15:17:06.626911: val_loss -0.6824 
+2024-09-09 15:17:06.626977: Pseudo dice [0.6665, 0.8659] 
+2024-09-09 15:17:06.627037: Epoch time: 246.6 s 
+2024-09-09 15:17:07.685859:  
+2024-09-09 15:17:07.686043: Epoch 652 
+2024-09-09 15:17:07.686146: Current learning rate: 0.00387 
+2024-09-09 15:21:14.368192: train_loss -0.8823 
+2024-09-09 15:21:14.368365: val_loss -0.6784 
+2024-09-09 15:21:14.368423: Pseudo dice [0.6398, 0.8645] 
+2024-09-09 15:21:14.368479: Epoch time: 246.68 s 
+2024-09-09 15:21:15.377311:  
+2024-09-09 15:21:15.377455: Epoch 653 
+2024-09-09 15:21:15.377562: Current learning rate: 0.00386 
+2024-09-09 15:25:22.051879: train_loss -0.878 
+2024-09-09 15:25:22.052023: val_loss -0.6885 
+2024-09-09 15:25:22.052079: Pseudo dice [0.6593, 0.8573] 
+2024-09-09 15:25:22.052133: Epoch time: 246.68 s 
+2024-09-09 15:25:23.065195:  
+2024-09-09 15:25:23.065355: Epoch 654 
+2024-09-09 15:25:23.065440: Current learning rate: 0.00385 
+2024-09-09 15:29:29.649854: train_loss -0.8689 
+2024-09-09 15:29:29.650004: val_loss -0.6758 
+2024-09-09 15:29:29.650060: Pseudo dice [0.616, 0.8768] 
+2024-09-09 15:29:29.650125: Epoch time: 246.59 s 
+2024-09-09 15:29:30.643793:  
+2024-09-09 15:29:30.643986: Epoch 655 
+2024-09-09 15:29:30.644071: Current learning rate: 0.00384 
+2024-09-09 15:33:37.110673: train_loss -0.8717 
+2024-09-09 15:33:37.110860: val_loss -0.6261 
+2024-09-09 15:33:37.110919: Pseudo dice [0.5278, 0.867] 
+2024-09-09 15:33:37.110980: Epoch time: 246.47 s 
+2024-09-09 15:33:38.096658:  
+2024-09-09 15:33:38.096831: Epoch 656 
+2024-09-09 15:33:38.096917: Current learning rate: 0.00383 
+2024-09-09 15:37:44.775648: train_loss -0.8722 
+2024-09-09 15:37:44.775796: val_loss -0.6896 
+2024-09-09 15:37:44.775859: Pseudo dice [0.6562, 0.871] 
+2024-09-09 15:37:44.775916: Epoch time: 246.68 s 
+2024-09-09 15:37:45.768164:  
+2024-09-09 15:37:45.768358: Epoch 657 
+2024-09-09 15:37:45.768468: Current learning rate: 0.00382 
+2024-09-09 15:41:53.351141: train_loss -0.8807 
+2024-09-09 15:41:53.351325: val_loss -0.688 
+2024-09-09 15:41:53.351400: Pseudo dice [0.657, 0.8778] 
+2024-09-09 15:41:53.351474: Epoch time: 247.58 s 
+2024-09-09 15:41:54.665060:  
+2024-09-09 15:41:54.665367: Epoch 658 
+2024-09-09 15:41:54.665533: Current learning rate: 0.00381 
+2024-09-09 15:46:01.380522: train_loss -0.88 
+2024-09-09 15:46:01.380669: val_loss -0.6807 
+2024-09-09 15:46:01.380724: Pseudo dice [0.6361, 0.87] 
+2024-09-09 15:46:01.380780: Epoch time: 246.72 s 
+2024-09-09 15:46:02.363656:  
+2024-09-09 15:46:02.363941: Epoch 659 
+2024-09-09 15:46:02.364040: Current learning rate: 0.0038 
+2024-09-09 15:50:09.066415: train_loss -0.8775 
+2024-09-09 15:50:09.066590: val_loss -0.6853 
+2024-09-09 15:50:09.066651: Pseudo dice [0.6528, 0.8754] 
+2024-09-09 15:50:09.066717: Epoch time: 246.7 s 
+2024-09-09 15:50:10.182611:  
+2024-09-09 15:50:10.182865: Epoch 660 
+2024-09-09 15:50:10.182965: Current learning rate: 0.00379 
+2024-09-09 15:54:16.698892: train_loss -0.8844 
+2024-09-09 15:54:16.699060: val_loss -0.7178 
+2024-09-09 15:54:16.699117: Pseudo dice [0.6744, 0.8834] 
+2024-09-09 15:54:16.699175: Epoch time: 246.52 s 
+2024-09-09 15:54:17.694371:  
+2024-09-09 15:54:17.694636: Epoch 661 
+2024-09-09 15:54:17.694724: Current learning rate: 0.00378 
+2024-09-09 15:58:24.325613: train_loss -0.8836 
+2024-09-09 15:58:24.325769: val_loss -0.6787 
+2024-09-09 15:58:24.325827: Pseudo dice [0.6095, 0.8795] 
+2024-09-09 15:58:24.325883: Epoch time: 246.63 s 
+2024-09-09 15:58:25.335005:  
+2024-09-09 15:58:25.335213: Epoch 662 
+2024-09-09 15:58:25.335298: Current learning rate: 0.00377 
+2024-09-09 16:02:32.071653: train_loss -0.8797 
+2024-09-09 16:02:32.071814: val_loss -0.6713 
+2024-09-09 16:02:32.071873: Pseudo dice [0.6249, 0.8494] 
+2024-09-09 16:02:32.071932: Epoch time: 246.74 s 
+2024-09-09 16:02:33.208790:  
+2024-09-09 16:02:33.208992: Epoch 663 
+2024-09-09 16:02:33.209085: Current learning rate: 0.00376 
+2024-09-09 16:06:40.068028: train_loss -0.8685 
+2024-09-09 16:06:40.068176: val_loss -0.6331 
+2024-09-09 16:06:40.068234: Pseudo dice [0.5821, 0.8569] 
+2024-09-09 16:06:40.068294: Epoch time: 246.86 s 
+2024-09-09 16:06:41.075896:  
+2024-09-09 16:06:41.076072: Epoch 664 
+2024-09-09 16:06:41.076158: Current learning rate: 0.00375 
+2024-09-09 16:10:47.890445: train_loss -0.8741 
+2024-09-09 16:10:47.890597: val_loss -0.7226 
+2024-09-09 16:10:47.890655: Pseudo dice [0.7194, 0.8688] 
+2024-09-09 16:10:47.890709: Epoch time: 246.82 s 
+2024-09-09 16:10:48.907881:  
+2024-09-09 16:10:48.908109: Epoch 665 
+2024-09-09 16:10:48.908195: Current learning rate: 0.00374 
+2024-09-09 16:14:55.525120: train_loss -0.8544 
+2024-09-09 16:14:55.525266: val_loss -0.6471 
+2024-09-09 16:14:55.525322: Pseudo dice [0.5733, 0.8727] 
+2024-09-09 16:14:55.525378: Epoch time: 246.62 s 
+2024-09-09 16:14:56.518212:  
+2024-09-09 16:14:56.518424: Epoch 666 
+2024-09-09 16:14:56.518510: Current learning rate: 0.00373 
+2024-09-09 16:19:03.033573: train_loss -0.8535 
+2024-09-09 16:19:03.033794: val_loss -0.6789 
+2024-09-09 16:19:03.033889: Pseudo dice [0.6857, 0.862] 
+2024-09-09 16:19:03.033975: Epoch time: 246.52 s 
+2024-09-09 16:19:04.146507:  
+2024-09-09 16:19:04.146749: Epoch 667 
+2024-09-09 16:19:04.146839: Current learning rate: 0.00372 
+2024-09-09 16:23:10.441542: train_loss -0.8736 
+2024-09-09 16:23:10.441688: val_loss -0.6859 
+2024-09-09 16:23:10.441746: Pseudo dice [0.6444, 0.8833] 
+2024-09-09 16:23:10.441801: Epoch time: 246.3 s 
+2024-09-09 16:23:11.456134:  
+2024-09-09 16:23:11.456336: Epoch 668 
+2024-09-09 16:23:11.456425: Current learning rate: 0.00371 
+2024-09-09 16:27:17.721094: train_loss -0.8777 
+2024-09-09 16:27:17.721244: val_loss -0.6622 
+2024-09-09 16:27:17.721301: Pseudo dice [0.6429, 0.8685] 
+2024-09-09 16:27:17.721357: Epoch time: 246.27 s 
+2024-09-09 16:27:18.736639:  
+2024-09-09 16:27:18.736827: Epoch 669 
+2024-09-09 16:27:18.736927: Current learning rate: 0.0037 
+2024-09-09 16:31:25.339000: train_loss -0.86 
+2024-09-09 16:31:25.339151: val_loss -0.6911 
+2024-09-09 16:31:25.339244: Pseudo dice [0.6729, 0.8587] 
+2024-09-09 16:31:25.339303: Epoch time: 246.6 s 
+2024-09-09 16:31:26.353353:  
+2024-09-09 16:31:26.353590: Epoch 670 
+2024-09-09 16:31:26.353675: Current learning rate: 0.00369 
+2024-09-09 16:35:32.872082: train_loss -0.8694 
+2024-09-09 16:35:32.872224: val_loss -0.715 
+2024-09-09 16:35:32.872281: Pseudo dice [0.6857, 0.8828] 
+2024-09-09 16:35:32.872339: Epoch time: 246.52 s 
+2024-09-09 16:35:33.906796:  
+2024-09-09 16:35:33.907042: Epoch 671 
+2024-09-09 16:35:33.907145: Current learning rate: 0.00368 
+2024-09-09 16:39:40.525261: train_loss -0.8815 
+2024-09-09 16:39:40.525419: val_loss -0.6688 
+2024-09-09 16:39:40.525478: Pseudo dice [0.6559, 0.8732] 
+2024-09-09 16:39:40.525533: Epoch time: 246.62 s 
+2024-09-09 16:39:41.555295:  
+2024-09-09 16:39:41.555460: Epoch 672 
+2024-09-09 16:39:41.555548: Current learning rate: 0.00367 
+2024-09-09 16:43:48.165511: train_loss -0.8769 
+2024-09-09 16:43:48.165659: val_loss -0.6646 
+2024-09-09 16:43:48.165716: Pseudo dice [0.6498, 0.8757] 
+2024-09-09 16:43:48.165773: Epoch time: 246.61 s 
+2024-09-09 16:43:49.206080:  
+2024-09-09 16:43:49.206290: Epoch 673 
+2024-09-09 16:43:49.206375: Current learning rate: 0.00366 
+2024-09-09 16:47:55.817213: train_loss -0.8648 
+2024-09-09 16:47:55.817406: val_loss -0.6816 
+2024-09-09 16:47:55.817475: Pseudo dice [0.6635, 0.8712] 
+2024-09-09 16:47:55.817548: Epoch time: 246.61 s 
+2024-09-09 16:47:56.983275:  
+2024-09-09 16:47:56.983433: Epoch 674 
+2024-09-09 16:47:56.983560: Current learning rate: 0.00365 
+2024-09-09 16:52:03.656678: train_loss -0.8764 
+2024-09-09 16:52:03.656840: val_loss -0.6763 
+2024-09-09 16:52:03.656898: Pseudo dice [0.637, 0.8666] 
+2024-09-09 16:52:03.656954: Epoch time: 246.68 s 
+2024-09-09 16:52:04.665315:  
+2024-09-09 16:52:04.665544: Epoch 675 
+2024-09-09 16:52:04.665635: Current learning rate: 0.00364 
+2024-09-09 16:56:11.270190: train_loss -0.8825 
+2024-09-09 16:56:11.270337: val_loss -0.7058 
+2024-09-09 16:56:11.270394: Pseudo dice [0.6779, 0.8802] 
+2024-09-09 16:56:11.270449: Epoch time: 246.61 s 
+2024-09-09 16:56:12.283120:  
+2024-09-09 16:56:12.283371: Epoch 676 
+2024-09-09 16:56:12.283460: Current learning rate: 0.00363 
+2024-09-09 17:00:18.888506: train_loss -0.8884 
+2024-09-09 17:00:18.888733: val_loss -0.6433 
+2024-09-09 17:00:18.888827: Pseudo dice [0.5702, 0.8717] 
+2024-09-09 17:00:18.888921: Epoch time: 246.61 s 
+2024-09-09 17:00:20.005922:  
+2024-09-09 17:00:20.006100: Epoch 677 
+2024-09-09 17:00:20.006214: Current learning rate: 0.00362 
+2024-09-09 17:04:26.604775: train_loss -0.8858 
+2024-09-09 17:04:26.604917: val_loss -0.6925 
+2024-09-09 17:04:26.604973: Pseudo dice [0.6447, 0.8841] 
+2024-09-09 17:04:26.605029: Epoch time: 246.6 s 
+2024-09-09 17:04:27.613323:  
+2024-09-09 17:04:27.613497: Epoch 678 
+2024-09-09 17:04:27.613579: Current learning rate: 0.00361 
+2024-09-09 17:08:34.286260: train_loss -0.8885 
+2024-09-09 17:08:34.286410: val_loss -0.6579 
+2024-09-09 17:08:34.286467: Pseudo dice [0.6575, 0.8656] 
+2024-09-09 17:08:34.286524: Epoch time: 246.67 s 
+2024-09-09 17:08:35.294259:  
+2024-09-09 17:08:35.294432: Epoch 679 
+2024-09-09 17:08:35.294523: Current learning rate: 0.0036 
+2024-09-09 17:12:42.122852: train_loss -0.8806 
+2024-09-09 17:12:42.123007: val_loss -0.662 
+2024-09-09 17:12:42.123069: Pseudo dice [0.6056, 0.8304] 
+2024-09-09 17:12:42.123126: Epoch time: 246.83 s 
+2024-09-09 17:12:44.091133:  
+2024-09-09 17:12:44.091368: Epoch 680 
+2024-09-09 17:12:44.091470: Current learning rate: 0.00359 
+2024-09-09 17:16:50.656709: train_loss -0.8836 
+2024-09-09 17:16:50.656859: val_loss -0.7123 
+2024-09-09 17:16:50.656916: Pseudo dice [0.6772, 0.8691] 
+2024-09-09 17:16:50.656972: Epoch time: 246.57 s 
+2024-09-09 17:16:51.689531:  
+2024-09-09 17:16:51.689752: Epoch 681 
+2024-09-09 17:16:51.689835: Current learning rate: 0.00358 
+2024-09-09 17:20:58.070740: train_loss -0.8847 
+2024-09-09 17:20:58.070978: val_loss -0.6743 
+2024-09-09 17:20:58.071073: Pseudo dice [0.6672, 0.862] 
+2024-09-09 17:20:58.071170: Epoch time: 246.38 s 
+2024-09-09 17:20:59.377941:  
+2024-09-09 17:20:59.378229: Epoch 682 
+2024-09-09 17:20:59.378357: Current learning rate: 0.00357 
+2024-09-09 17:25:05.674676: train_loss -0.8818 
+2024-09-09 17:25:05.674826: val_loss -0.6697 
+2024-09-09 17:25:05.674884: Pseudo dice [0.6414, 0.8597] 
+2024-09-09 17:25:05.674940: Epoch time: 246.3 s 
+2024-09-09 17:25:06.688786:  
+2024-09-09 17:25:06.688991: Epoch 683 
+2024-09-09 17:25:06.689076: Current learning rate: 0.00356 
+2024-09-09 17:29:12.963294: train_loss -0.8803 
+2024-09-09 17:29:12.963450: val_loss -0.6697 
+2024-09-09 17:29:12.963506: Pseudo dice [0.6107, 0.8672] 
+2024-09-09 17:29:12.963562: Epoch time: 246.28 s 
+2024-09-09 17:29:14.008543:  
+2024-09-09 17:29:14.008746: Epoch 684 
+2024-09-09 17:29:14.008836: Current learning rate: 0.00355 
+2024-09-09 17:33:20.397916: train_loss -0.8803 
+2024-09-09 17:33:20.398082: val_loss -0.7073 
+2024-09-09 17:33:20.398139: Pseudo dice [0.6758, 0.88] 
+2024-09-09 17:33:20.398196: Epoch time: 246.39 s 
+2024-09-09 17:33:21.420307:  
+2024-09-09 17:33:21.420516: Epoch 685 
+2024-09-09 17:33:21.420602: Current learning rate: 0.00354 
+2024-09-09 17:37:27.965553: train_loss -0.8809 
+2024-09-09 17:37:27.965759: val_loss -0.689 
+2024-09-09 17:37:27.965826: Pseudo dice [0.6275, 0.8823] 
+2024-09-09 17:37:27.965890: Epoch time: 246.55 s 
+2024-09-09 17:37:29.100847:  
+2024-09-09 17:37:29.101068: Epoch 686 
+2024-09-09 17:37:29.101154: Current learning rate: 0.00353 
+2024-09-09 17:41:35.297139: train_loss -0.8776 
+2024-09-09 17:41:35.297304: val_loss -0.6891 
+2024-09-09 17:41:35.297363: Pseudo dice [0.6571, 0.8809] 
+2024-09-09 17:41:35.297483: Epoch time: 246.2 s 
+2024-09-09 17:41:36.291859:  
+2024-09-09 17:41:36.292086: Epoch 687 
+2024-09-09 17:41:36.292172: Current learning rate: 0.00352 
+2024-09-09 17:45:42.683178: train_loss -0.8747 
+2024-09-09 17:45:42.683327: val_loss -0.6818 
+2024-09-09 17:45:42.683384: Pseudo dice [0.6532, 0.8659] 
+2024-09-09 17:45:42.683440: Epoch time: 246.39 s 
+2024-09-09 17:45:43.715418:  
+2024-09-09 17:45:43.715664: Epoch 688 
+2024-09-09 17:45:43.715751: Current learning rate: 0.00351 
+2024-09-09 17:49:50.578764: train_loss -0.8519 
+2024-09-09 17:49:50.578924: val_loss -0.6546 
+2024-09-09 17:49:50.578981: Pseudo dice [0.6168, 0.8568] 
+2024-09-09 17:49:50.579036: Epoch time: 246.87 s 
+2024-09-09 17:49:51.584211:  
+2024-09-09 17:49:51.584459: Epoch 689 
+2024-09-09 17:49:51.584546: Current learning rate: 0.0035 
+2024-09-09 17:53:58.653648: train_loss -0.8706 
+2024-09-09 17:53:58.653802: val_loss -0.7125 
+2024-09-09 17:53:58.653862: Pseudo dice [0.6597, 0.8795] 
+2024-09-09 17:53:58.653917: Epoch time: 247.07 s 
+2024-09-09 17:53:59.668424:  
+2024-09-09 17:53:59.668650: Epoch 690 
+2024-09-09 17:53:59.668735: Current learning rate: 0.00349 
+2024-09-09 17:58:06.563040: train_loss -0.8769 
+2024-09-09 17:58:06.563289: val_loss -0.6948 
+2024-09-09 17:58:06.563406: Pseudo dice [0.6628, 0.8697] 
+2024-09-09 17:58:06.563479: Epoch time: 246.9 s 
+2024-09-09 17:58:07.731257:  
+2024-09-09 17:58:07.731472: Epoch 691 
+2024-09-09 17:58:07.731567: Current learning rate: 0.00348 
+2024-09-09 18:02:14.363236: train_loss -0.879 
+2024-09-09 18:02:14.363398: val_loss -0.7142 
+2024-09-09 18:02:14.363454: Pseudo dice [0.7045, 0.8696] 
+2024-09-09 18:02:14.363511: Epoch time: 246.63 s 
+2024-09-09 18:02:15.379465:  
+2024-09-09 18:02:15.379700: Epoch 692 
+2024-09-09 18:02:15.379787: Current learning rate: 0.00346 
+2024-09-09 18:06:22.013651: train_loss -0.8804 
+2024-09-09 18:06:22.013795: val_loss -0.7161 
+2024-09-09 18:06:22.013850: Pseudo dice [0.6835, 0.8775] 
+2024-09-09 18:06:22.013904: Epoch time: 246.64 s 
+2024-09-09 18:06:23.036580:  
+2024-09-09 18:06:23.036797: Epoch 693 
+2024-09-09 18:06:23.036880: Current learning rate: 0.00345 
+2024-09-09 18:10:29.590477: train_loss -0.8887 
+2024-09-09 18:10:29.590624: val_loss -0.6629 
+2024-09-09 18:10:29.590679: Pseudo dice [0.6497, 0.8671] 
+2024-09-09 18:10:29.590735: Epoch time: 246.56 s 
+2024-09-09 18:10:30.622990:  
+2024-09-09 18:10:30.623223: Epoch 694 
+2024-09-09 18:10:30.623314: Current learning rate: 0.00344 
+2024-09-09 18:14:37.298511: train_loss -0.8761 
+2024-09-09 18:14:37.298671: val_loss -0.6575 
+2024-09-09 18:14:37.298738: Pseudo dice [0.6302, 0.8691] 
+2024-09-09 18:14:37.298803: Epoch time: 246.68 s 
+2024-09-09 18:14:38.427959:  
+2024-09-09 18:14:38.428214: Epoch 695 
+2024-09-09 18:14:38.428302: Current learning rate: 0.00343 
+2024-09-09 18:18:45.051185: train_loss -0.8782 
+2024-09-09 18:18:45.051355: val_loss -0.6894 
+2024-09-09 18:18:45.051414: Pseudo dice [0.675, 0.8739] 
+2024-09-09 18:18:45.051472: Epoch time: 246.63 s 
+2024-09-09 18:18:46.113955:  
+2024-09-09 18:18:46.114136: Epoch 696 
+2024-09-09 18:18:46.114228: Current learning rate: 0.00342 
+2024-09-09 18:22:52.642118: train_loss -0.8808 
+2024-09-09 18:22:52.642397: val_loss -0.7065 
+2024-09-09 18:22:52.642518: Pseudo dice [0.6753, 0.8692] 
+2024-09-09 18:22:52.642621: Epoch time: 246.53 s 
+2024-09-09 18:22:53.696901:  
+2024-09-09 18:22:53.697224: Epoch 697 
+2024-09-09 18:22:53.697396: Current learning rate: 0.00341 
+2024-09-09 18:27:00.102046: train_loss -0.8902 
+2024-09-09 18:27:00.102182: val_loss -0.7029 
+2024-09-09 18:27:00.102237: Pseudo dice [0.6736, 0.8794] 
+2024-09-09 18:27:00.102293: Epoch time: 246.41 s 
+2024-09-09 18:27:01.114601:  
+2024-09-09 18:27:01.114763: Epoch 698 
+2024-09-09 18:27:01.114847: Current learning rate: 0.0034 
+2024-09-09 18:31:07.714351: train_loss -0.8828 
+2024-09-09 18:31:07.714541: val_loss -0.6794 
+2024-09-09 18:31:07.714595: Pseudo dice [0.6448, 0.8683] 
+2024-09-09 18:31:07.714649: Epoch time: 246.6 s 
+2024-09-09 18:31:08.728218:  
+2024-09-09 18:31:08.728388: Epoch 699 
+2024-09-09 18:31:08.728503: Current learning rate: 0.00339 
+2024-09-09 18:35:15.608145: train_loss -0.8916 
+2024-09-09 18:35:15.608313: val_loss -0.655 
+2024-09-09 18:35:15.608397: Pseudo dice [0.6427, 0.8558] 
+2024-09-09 18:35:15.608452: Epoch time: 246.88 s 
+2024-09-09 18:35:20.017035:  
+2024-09-09 18:35:20.017346: Epoch 700 
+2024-09-09 18:35:20.017445: Current learning rate: 0.00338 
+2024-09-09 18:39:26.392739: train_loss -0.8897 
+2024-09-09 18:39:26.392940: val_loss -0.6644 
+2024-09-09 18:39:26.392995: Pseudo dice [0.6064, 0.8708] 
+2024-09-09 18:39:26.393052: Epoch time: 246.38 s 
+2024-09-09 18:39:27.405415:  
+2024-09-09 18:39:27.405592: Epoch 701 
+2024-09-09 18:39:27.405671: Current learning rate: 0.00337 
+2024-09-09 18:43:33.781220: train_loss -0.8886 
+2024-09-09 18:43:33.781357: val_loss -0.6574 
+2024-09-09 18:43:33.781409: Pseudo dice [0.6079, 0.8531] 
+2024-09-09 18:43:33.781461: Epoch time: 246.38 s 
+2024-09-09 18:43:35.757166:  
+2024-09-09 18:43:35.757463: Epoch 702 
+2024-09-09 18:43:35.757544: Current learning rate: 0.00336 
+2024-09-09 18:47:42.287257: train_loss -0.8802 
+2024-09-09 18:47:42.287401: val_loss -0.6736 
+2024-09-09 18:47:42.287458: Pseudo dice [0.6331, 0.882] 
+2024-09-09 18:47:42.287511: Epoch time: 246.53 s 
+2024-09-09 18:47:43.287523:  
+2024-09-09 18:47:43.287771: Epoch 703 
+2024-09-09 18:47:43.287874: Current learning rate: 0.00335 
+2024-09-09 18:51:49.780679: train_loss -0.8806 
+2024-09-09 18:51:49.780825: val_loss -0.6729 
+2024-09-09 18:51:49.780879: Pseudo dice [0.6246, 0.8737] 
+2024-09-09 18:51:49.780936: Epoch time: 246.5 s 
+2024-09-09 18:51:50.927021:  
+2024-09-09 18:51:50.927334: Epoch 704 
+2024-09-09 18:51:50.927458: Current learning rate: 0.00334 
+2024-09-09 18:55:57.221080: train_loss -0.8873 
+2024-09-09 18:55:57.221222: val_loss -0.6904 
+2024-09-09 18:55:57.221274: Pseudo dice [0.6721, 0.8841] 
+2024-09-09 18:55:57.221460: Epoch time: 246.3 s 
+2024-09-09 18:55:58.233476:  
+2024-09-09 18:55:58.233711: Epoch 705 
+2024-09-09 18:55:58.233790: Current learning rate: 0.00333 
+2024-09-09 19:00:04.568923: train_loss -0.8788 
+2024-09-09 19:00:04.569063: val_loss -0.6598 
+2024-09-09 19:00:04.569113: Pseudo dice [0.6533, 0.863] 
+2024-09-09 19:00:04.569165: Epoch time: 246.34 s 
+2024-09-09 19:00:05.578054:  
+2024-09-09 19:00:05.578222: Epoch 706 
+2024-09-09 19:00:05.578323: Current learning rate: 0.00332 
+2024-09-09 19:04:11.942815: train_loss -0.8857 
+2024-09-09 19:04:11.942955: val_loss -0.7026 
+2024-09-09 19:04:11.943008: Pseudo dice [0.6916, 0.8723] 
+2024-09-09 19:04:11.943058: Epoch time: 246.37 s 
+2024-09-09 19:04:12.962317:  
+2024-09-09 19:04:12.962498: Epoch 707 
+2024-09-09 19:04:12.962581: Current learning rate: 0.00331 
+2024-09-09 19:08:19.574974: train_loss -0.8799 
+2024-09-09 19:08:19.575147: val_loss -0.6729 
+2024-09-09 19:08:19.575201: Pseudo dice [0.6713, 0.8479] 
+2024-09-09 19:08:19.575260: Epoch time: 246.61 s 
+2024-09-09 19:08:20.685084:  
+2024-09-09 19:08:20.685287: Epoch 708 
+2024-09-09 19:08:20.685371: Current learning rate: 0.0033 
+2024-09-09 19:12:27.624359: train_loss -0.8786 
+2024-09-09 19:12:27.624525: val_loss -0.6896 
+2024-09-09 19:12:27.624581: Pseudo dice [0.6665, 0.8639] 
+2024-09-09 19:12:27.624633: Epoch time: 246.94 s 
+2024-09-09 19:12:28.655478:  
+2024-09-09 19:12:28.655680: Epoch 709 
+2024-09-09 19:12:28.655761: Current learning rate: 0.00329 
+2024-09-09 19:16:35.422113: train_loss -0.8814 
+2024-09-09 19:16:35.422264: val_loss -0.6676 
+2024-09-09 19:16:35.422314: Pseudo dice [0.645, 0.866] 
+2024-09-09 19:16:35.422366: Epoch time: 246.77 s 
+2024-09-09 19:16:36.487157:  
+2024-09-09 19:16:36.487356: Epoch 710 
+2024-09-09 19:16:36.487434: Current learning rate: 0.00328 
+2024-09-09 19:20:43.195304: train_loss -0.8854 
+2024-09-09 19:20:43.195457: val_loss -0.7199 
+2024-09-09 19:20:43.195509: Pseudo dice [0.6886, 0.8817] 
+2024-09-09 19:20:43.195560: Epoch time: 246.71 s 
+2024-09-09 19:20:44.226436:  
+2024-09-09 19:20:44.226651: Epoch 711 
+2024-09-09 19:20:44.226760: Current learning rate: 0.00327 
+2024-09-09 19:24:50.750509: train_loss -0.8891 
+2024-09-09 19:24:50.750648: val_loss -0.702 
+2024-09-09 19:24:50.750699: Pseudo dice [0.6986, 0.8817] 
+2024-09-09 19:24:50.750751: Epoch time: 246.53 s 
+2024-09-09 19:24:51.762896:  
+2024-09-09 19:24:51.763083: Epoch 712 
+2024-09-09 19:24:51.763164: Current learning rate: 0.00326 
+2024-09-09 19:28:58.248055: train_loss -0.8904 
+2024-09-09 19:28:58.248194: val_loss -0.6977 
+2024-09-09 19:28:58.248245: Pseudo dice [0.6671, 0.875] 
+2024-09-09 19:28:58.248298: Epoch time: 246.49 s 
+2024-09-09 19:28:59.300752:  
+2024-09-09 19:28:59.300925: Epoch 713 
+2024-09-09 19:28:59.301007: Current learning rate: 0.00325 
+2024-09-09 19:33:05.714373: train_loss -0.8939 
+2024-09-09 19:33:05.714508: val_loss -0.6417 
+2024-09-09 19:33:05.714558: Pseudo dice [0.6425, 0.8754] 
+2024-09-09 19:33:05.714608: Epoch time: 246.42 s 
+2024-09-09 19:33:06.713102:  
+2024-09-09 19:33:06.713346: Epoch 714 
+2024-09-09 19:33:06.713449: Current learning rate: 0.00324 
+2024-09-09 19:37:13.303947: train_loss -0.8891 
+2024-09-09 19:37:13.304085: val_loss -0.6698 
+2024-09-09 19:37:13.304135: Pseudo dice [0.6225, 0.8673] 
+2024-09-09 19:37:13.304185: Epoch time: 246.59 s 
+2024-09-09 19:37:14.294343:  
+2024-09-09 19:37:14.294543: Epoch 715 
+2024-09-09 19:37:14.294624: Current learning rate: 0.00323 
+2024-09-09 19:41:20.975845: train_loss -0.8909 
+2024-09-09 19:41:20.976158: val_loss -0.7151 
+2024-09-09 19:41:20.976227: Pseudo dice [0.7095, 0.8819] 
+2024-09-09 19:41:20.976296: Epoch time: 246.68 s 
+2024-09-09 19:41:22.220021:  
+2024-09-09 19:41:22.220270: Epoch 716 
+2024-09-09 19:41:22.220385: Current learning rate: 0.00322 
+2024-09-09 19:45:29.246547: train_loss -0.8841 
+2024-09-09 19:45:29.246686: val_loss -0.6256 
+2024-09-09 19:45:29.246737: Pseudo dice [0.5955, 0.8537] 
+2024-09-09 19:45:29.246801: Epoch time: 247.03 s 
+2024-09-09 19:45:30.255419:  
+2024-09-09 19:45:30.255628: Epoch 717 
+2024-09-09 19:45:30.255743: Current learning rate: 0.00321 
+2024-09-09 19:49:37.282238: train_loss -0.8901 
+2024-09-09 19:49:37.282379: val_loss -0.6965 
+2024-09-09 19:49:37.282434: Pseudo dice [0.661, 0.8756] 
+2024-09-09 19:49:37.282486: Epoch time: 247.03 s 
+2024-09-09 19:49:38.286755:  
+2024-09-09 19:49:38.286948: Epoch 718 
+2024-09-09 19:49:38.287027: Current learning rate: 0.0032 
+2024-09-09 19:53:45.218193: train_loss -0.8934 
+2024-09-09 19:53:45.218330: val_loss -0.6675 
+2024-09-09 19:53:45.218381: Pseudo dice [0.6228, 0.8749] 
+2024-09-09 19:53:45.218432: Epoch time: 246.93 s 
+2024-09-09 19:53:46.236595:  
+2024-09-09 19:53:46.236800: Epoch 719 
+2024-09-09 19:53:46.236925: Current learning rate: 0.00319 
+2024-09-09 19:57:53.078850: train_loss -0.8916 
+2024-09-09 19:57:53.078997: val_loss -0.7076 
+2024-09-09 19:57:53.079246: Pseudo dice [0.6778, 0.8752] 
+2024-09-09 19:57:53.079302: Epoch time: 246.84 s 
+2024-09-09 19:57:54.098990:  
+2024-09-09 19:57:54.099186: Epoch 720 
+2024-09-09 19:57:54.099272: Current learning rate: 0.00318 
+2024-09-09 20:02:00.798956: train_loss -0.8942 
+2024-09-09 20:02:00.799097: val_loss -0.6447 
+2024-09-09 20:02:00.799170: Pseudo dice [0.5921, 0.8708] 
+2024-09-09 20:02:00.799263: Epoch time: 246.7 s 
+2024-09-09 20:02:01.921778:  
+2024-09-09 20:02:01.921991: Epoch 721 
+2024-09-09 20:02:01.922099: Current learning rate: 0.00317 
+2024-09-09 20:06:08.641283: train_loss -0.8956 
+2024-09-09 20:06:08.641456: val_loss -0.6853 
+2024-09-09 20:06:08.641507: Pseudo dice [0.6695, 0.8748] 
+2024-09-09 20:06:08.641561: Epoch time: 246.72 s 
+2024-09-09 20:06:09.644230:  
+2024-09-09 20:06:09.644501: Epoch 722 
+2024-09-09 20:06:09.644584: Current learning rate: 0.00316 
+2024-09-09 20:10:16.375423: train_loss -0.8916 
+2024-09-09 20:10:16.375557: val_loss -0.6842 
+2024-09-09 20:10:16.375608: Pseudo dice [0.6217, 0.8794] 
+2024-09-09 20:10:16.375660: Epoch time: 246.73 s 
+2024-09-09 20:10:17.386925:  
+2024-09-09 20:10:17.387171: Epoch 723 
+2024-09-09 20:10:17.387265: Current learning rate: 0.00315 
+2024-09-09 20:14:24.061281: train_loss -0.8968 
+2024-09-09 20:14:24.061423: val_loss -0.6815 
+2024-09-09 20:14:24.061474: Pseudo dice [0.6578, 0.8747] 
+2024-09-09 20:14:24.061524: Epoch time: 246.68 s 
+2024-09-09 20:14:25.052076:  
+2024-09-09 20:14:25.052254: Epoch 724 
+2024-09-09 20:14:25.052336: Current learning rate: 0.00314 
+2024-09-09 20:18:31.756992: train_loss -0.8976 
+2024-09-09 20:18:31.757162: val_loss -0.7023 
+2024-09-09 20:18:31.757219: Pseudo dice [0.6843, 0.8775] 
+2024-09-09 20:18:31.757274: Epoch time: 246.71 s 
+2024-09-09 20:18:34.220693:  
+2024-09-09 20:18:34.220928: Epoch 725 
+2024-09-09 20:18:34.221066: Current learning rate: 0.00313 
+2024-09-09 20:22:41.170269: train_loss -0.8962 
+2024-09-09 20:22:41.170413: val_loss -0.6637 
+2024-09-09 20:22:41.170465: Pseudo dice [0.6358, 0.8702] 
+2024-09-09 20:22:41.170516: Epoch time: 246.95 s 
+2024-09-09 20:22:42.176949:  
+2024-09-09 20:22:42.177156: Epoch 726 
+2024-09-09 20:22:42.177286: Current learning rate: 0.00312 
+2024-09-09 20:26:48.911597: train_loss -0.899 
+2024-09-09 20:26:48.911835: val_loss -0.6911 
+2024-09-09 20:26:48.911930: Pseudo dice [0.6473, 0.8699] 
+2024-09-09 20:26:48.912017: Epoch time: 246.74 s 
+2024-09-09 20:26:50.170130:  
+2024-09-09 20:26:50.170352: Epoch 727 
+2024-09-09 20:26:50.170430: Current learning rate: 0.00311 
+2024-09-09 20:30:56.748343: train_loss -0.8999 
+2024-09-09 20:30:56.748493: val_loss -0.6886 
+2024-09-09 20:30:56.748548: Pseudo dice [0.6546, 0.8844] 
+2024-09-09 20:30:56.748600: Epoch time: 246.58 s 
+2024-09-09 20:30:57.758931:  
+2024-09-09 20:30:57.759192: Epoch 728 
+2024-09-09 20:30:57.759273: Current learning rate: 0.0031 
+2024-09-09 20:35:04.304279: train_loss -0.8932 
+2024-09-09 20:35:04.304425: val_loss -0.6861 
+2024-09-09 20:35:04.304476: Pseudo dice [0.6458, 0.8551] 
+2024-09-09 20:35:04.304528: Epoch time: 246.55 s 
+2024-09-09 20:35:05.305897:  
+2024-09-09 20:35:05.306078: Epoch 729 
+2024-09-09 20:35:05.306168: Current learning rate: 0.00309 
+2024-09-09 20:39:12.142431: train_loss -0.8916 
+2024-09-09 20:39:12.142571: val_loss -0.6805 
+2024-09-09 20:39:12.142621: Pseudo dice [0.6451, 0.8736] 
+2024-09-09 20:39:12.142672: Epoch time: 246.84 s 
+2024-09-09 20:39:13.154383:  
+2024-09-09 20:39:13.154552: Epoch 730 
+2024-09-09 20:39:13.154656: Current learning rate: 0.00308 
+2024-09-09 20:43:19.651998: train_loss -0.8944 
+2024-09-09 20:43:19.652134: val_loss -0.685 
+2024-09-09 20:43:19.652185: Pseudo dice [0.6521, 0.87] 
+2024-09-09 20:43:19.652239: Epoch time: 246.5 s 
+2024-09-09 20:43:20.652212:  
+2024-09-09 20:43:20.652401: Epoch 731 
+2024-09-09 20:43:20.652480: Current learning rate: 0.00307 
+2024-09-09 20:47:27.376233: train_loss -0.8988 
+2024-09-09 20:47:27.376373: val_loss -0.6943 
+2024-09-09 20:47:27.376425: Pseudo dice [0.6739, 0.8715] 
+2024-09-09 20:47:27.376521: Epoch time: 246.73 s 
+2024-09-09 20:47:28.383209:  
+2024-09-09 20:47:28.383399: Epoch 732 
+2024-09-09 20:47:28.383481: Current learning rate: 0.00306 
+2024-09-09 20:51:35.628703: train_loss -0.8989 
+2024-09-09 20:51:35.628841: val_loss -0.676 
+2024-09-09 20:51:35.628893: Pseudo dice [0.6327, 0.8697] 
+2024-09-09 20:51:35.628947: Epoch time: 247.25 s 
+2024-09-09 20:51:36.645433:  
+2024-09-09 20:51:36.645622: Epoch 733 
+2024-09-09 20:51:36.645723: Current learning rate: 0.00305 
+2024-09-09 20:55:43.601203: train_loss -0.8948 
+2024-09-09 20:55:43.601347: val_loss -0.6742 
+2024-09-09 20:55:43.601413: Pseudo dice [0.6246, 0.8773] 
+2024-09-09 20:55:43.601466: Epoch time: 246.96 s 
+2024-09-09 20:55:44.733509:  
+2024-09-09 20:55:44.733723: Epoch 734 
+2024-09-09 20:55:44.733807: Current learning rate: 0.00304 
+2024-09-09 20:59:51.409585: train_loss -0.8938 
+2024-09-09 20:59:51.409720: val_loss -0.6952 
+2024-09-09 20:59:51.409770: Pseudo dice [0.6629, 0.8775] 
+2024-09-09 20:59:51.409821: Epoch time: 246.68 s 
+2024-09-09 20:59:52.420807:  
+2024-09-09 20:59:52.421017: Epoch 735 
+2024-09-09 20:59:52.421100: Current learning rate: 0.00303 
+2024-09-09 21:03:59.097832: train_loss -0.8968 
+2024-09-09 21:03:59.097956: val_loss -0.6887 
+2024-09-09 21:03:59.098006: Pseudo dice [0.6469, 0.8653] 
+2024-09-09 21:03:59.098057: Epoch time: 246.68 s 
+2024-09-09 21:04:00.098441:  
+2024-09-09 21:04:00.098713: Epoch 736 
+2024-09-09 21:04:00.098817: Current learning rate: 0.00302 
+2024-09-09 21:08:06.677577: train_loss -0.8989 
+2024-09-09 21:08:06.677721: val_loss -0.6849 
+2024-09-09 21:08:06.677772: Pseudo dice [0.6911, 0.8536] 
+2024-09-09 21:08:06.677860: Epoch time: 246.58 s 
+2024-09-09 21:08:07.672847:  
+2024-09-09 21:08:07.673067: Epoch 737 
+2024-09-09 21:08:07.673147: Current learning rate: 0.00301 
+2024-09-09 21:12:14.228400: train_loss -0.8946 
+2024-09-09 21:12:14.228538: val_loss -0.6914 
+2024-09-09 21:12:14.228596: Pseudo dice [0.6527, 0.8736] 
+2024-09-09 21:12:14.228658: Epoch time: 246.56 s 
+2024-09-09 21:12:15.265274:  
+2024-09-09 21:12:15.265425: Epoch 738 
+2024-09-09 21:12:15.265503: Current learning rate: 0.003 
+2024-09-09 21:16:21.810130: train_loss -0.9008 
+2024-09-09 21:16:21.810271: val_loss -0.6824 
+2024-09-09 21:16:21.810322: Pseudo dice [0.64, 0.8663] 
+2024-09-09 21:16:21.810374: Epoch time: 246.55 s 
+2024-09-09 21:16:22.852498:  
+2024-09-09 21:16:22.852717: Epoch 739 
+2024-09-09 21:16:22.852800: Current learning rate: 0.00299 
+2024-09-09 21:20:29.579580: train_loss -0.9004 
+2024-09-09 21:20:29.579723: val_loss -0.6879 
+2024-09-09 21:20:29.579774: Pseudo dice [0.6853, 0.8791] 
+2024-09-09 21:20:29.579849: Epoch time: 246.73 s 
+2024-09-09 21:20:30.596653:  
+2024-09-09 21:20:30.596838: Epoch 740 
+2024-09-09 21:20:30.596918: Current learning rate: 0.00297 
+2024-09-09 21:24:37.621528: train_loss -0.884 
+2024-09-09 21:24:37.621664: val_loss -0.7126 
+2024-09-09 21:24:37.621716: Pseudo dice [0.6941, 0.8802] 
+2024-09-09 21:24:37.621768: Epoch time: 247.03 s 
+2024-09-09 21:24:38.623450:  
+2024-09-09 21:24:38.623623: Epoch 741 
+2024-09-09 21:24:38.623718: Current learning rate: 0.00296 
+2024-09-09 21:28:45.838221: train_loss -0.8862 
+2024-09-09 21:28:45.838392: val_loss -0.6766 
+2024-09-09 21:28:45.838444: Pseudo dice [0.6354, 0.8696] 
+2024-09-09 21:28:45.838496: Epoch time: 247.22 s 
+2024-09-09 21:28:46.868174:  
+2024-09-09 21:28:46.868349: Epoch 742 
+2024-09-09 21:28:46.868435: Current learning rate: 0.00295 
+2024-09-09 21:32:53.844380: train_loss -0.8927 
+2024-09-09 21:32:53.844559: val_loss -0.663 
+2024-09-09 21:32:53.844726: Pseudo dice [0.6103, 0.8755] 
+2024-09-09 21:32:53.844858: Epoch time: 246.98 s 
+2024-09-09 21:32:55.065314:  
+2024-09-09 21:32:55.065578: Epoch 743 
+2024-09-09 21:32:55.065701: Current learning rate: 0.00294 
+2024-09-09 21:37:01.893987: train_loss -0.8923 
+2024-09-09 21:37:01.894125: val_loss -0.6906 
+2024-09-09 21:37:01.894177: Pseudo dice [0.6753, 0.8764] 
+2024-09-09 21:37:01.894229: Epoch time: 246.83 s 
+2024-09-09 21:37:02.913666:  
+2024-09-09 21:37:02.913890: Epoch 744 
+2024-09-09 21:37:02.913974: Current learning rate: 0.00293 
+2024-09-09 21:41:09.793752: train_loss -0.8911 
+2024-09-09 21:41:09.793894: val_loss -0.6919 
+2024-09-09 21:41:09.793945: Pseudo dice [0.6335, 0.8856] 
+2024-09-09 21:41:09.793996: Epoch time: 246.88 s 
+2024-09-09 21:41:10.802456:  
+2024-09-09 21:41:10.802613: Epoch 745 
+2024-09-09 21:41:10.802692: Current learning rate: 0.00292 
+2024-09-09 21:45:17.614259: train_loss -0.8941 
+2024-09-09 21:45:17.614396: val_loss -0.6748 
+2024-09-09 21:45:17.614447: Pseudo dice [0.6422, 0.8697] 
+2024-09-09 21:45:17.614498: Epoch time: 246.81 s 
+2024-09-09 21:45:18.616643:  
+2024-09-09 21:45:18.616866: Epoch 746 
+2024-09-09 21:45:18.616947: Current learning rate: 0.00291 
+2024-09-09 21:49:25.482334: train_loss -0.8965 
+2024-09-09 21:49:25.482717: val_loss -0.685 
+2024-09-09 21:49:25.482858: Pseudo dice [0.6352, 0.8654] 
+2024-09-09 21:49:25.482949: Epoch time: 246.87 s 
+2024-09-09 21:49:26.634060:  
+2024-09-09 21:49:26.634295: Epoch 747 
+2024-09-09 21:49:26.634386: Current learning rate: 0.0029 
+2024-09-09 21:53:33.264153: train_loss -0.8938 
+2024-09-09 21:53:33.264305: val_loss -0.7021 
+2024-09-09 21:53:33.264357: Pseudo dice [0.6771, 0.8837] 
+2024-09-09 21:53:33.264413: Epoch time: 246.63 s 
+2024-09-09 21:53:35.242051:  
+2024-09-09 21:53:35.242271: Epoch 748 
+2024-09-09 21:53:35.242381: Current learning rate: 0.00289 
+2024-09-09 21:57:41.904446: train_loss -0.8955 
+2024-09-09 21:57:41.904583: val_loss -0.7096 
+2024-09-09 21:57:41.904632: Pseudo dice [0.6877, 0.8688] 
+2024-09-09 21:57:41.904684: Epoch time: 246.66 s 
+2024-09-09 21:57:42.916891:  
+2024-09-09 21:57:42.917118: Epoch 749 
+2024-09-09 21:57:42.917252: Current learning rate: 0.00288 
+2024-09-09 22:01:50.045079: train_loss -0.8975 
+2024-09-09 22:01:50.045216: val_loss -0.6829 
+2024-09-09 22:01:50.045270: Pseudo dice [0.6293, 0.8687] 
+2024-09-09 22:01:50.045323: Epoch time: 247.13 s 
+2024-09-09 22:01:54.025438:  
+2024-09-09 22:01:54.025708: Epoch 750 
+2024-09-09 22:01:54.025789: Current learning rate: 0.00287 
+2024-09-09 22:06:01.280278: train_loss -0.8997 
+2024-09-09 22:06:01.280412: val_loss -0.7032 
+2024-09-09 22:06:01.280464: Pseudo dice [0.6563, 0.8805] 
+2024-09-09 22:06:01.280542: Epoch time: 247.26 s 
+2024-09-09 22:06:02.280141:  
+2024-09-09 22:06:02.280344: Epoch 751 
+2024-09-09 22:06:02.280440: Current learning rate: 0.00286 
+2024-09-09 22:10:09.412599: train_loss -0.8993 
+2024-09-09 22:10:09.412786: val_loss -0.6661 
+2024-09-09 22:10:09.412893: Pseudo dice [0.6247, 0.8601] 
+2024-09-09 22:10:09.412947: Epoch time: 247.13 s 
+2024-09-09 22:10:10.566132:  
+2024-09-09 22:10:10.566460: Epoch 752 
+2024-09-09 22:10:10.566581: Current learning rate: 0.00285 
+2024-09-09 22:14:17.659348: train_loss -0.8999 
+2024-09-09 22:14:17.659488: val_loss -0.7083 
+2024-09-09 22:14:17.659541: Pseudo dice [0.6747, 0.8719] 
+2024-09-09 22:14:17.659592: Epoch time: 247.1 s 
+2024-09-09 22:14:18.671800:  
+2024-09-09 22:14:18.671996: Epoch 753 
+2024-09-09 22:14:18.672092: Current learning rate: 0.00284 
+2024-09-09 22:18:25.463720: train_loss -0.8965 
+2024-09-09 22:18:25.463875: val_loss -0.7042 
+2024-09-09 22:18:25.463927: Pseudo dice [0.6728, 0.8731] 
+2024-09-09 22:18:25.463978: Epoch time: 246.79 s 
+2024-09-09 22:18:26.457486:  
+2024-09-09 22:18:26.457698: Epoch 754 
+2024-09-09 22:18:26.457783: Current learning rate: 0.00283 
+2024-09-09 22:22:33.078987: train_loss -0.8984 
+2024-09-09 22:22:33.079146: val_loss -0.6984 
+2024-09-09 22:22:33.079200: Pseudo dice [0.6613, 0.8706] 
+2024-09-09 22:22:33.079254: Epoch time: 246.62 s 
+2024-09-09 22:22:34.071240:  
+2024-09-09 22:22:34.071436: Epoch 755 
+2024-09-09 22:22:34.071514: Current learning rate: 0.00282 
+2024-09-09 22:26:40.698731: train_loss -0.9035 
+2024-09-09 22:26:40.698891: val_loss -0.6912 
+2024-09-09 22:26:40.698972: Pseudo dice [0.6141, 0.8713] 
+2024-09-09 22:26:40.699029: Epoch time: 246.63 s 
+2024-09-09 22:26:42.023096:  
+2024-09-09 22:26:42.023304: Epoch 756 
+2024-09-09 22:26:42.023382: Current learning rate: 0.00281 
+2024-09-09 22:30:48.868103: train_loss -0.8936 
+2024-09-09 22:30:48.868234: val_loss -0.6763 
+2024-09-09 22:30:48.868286: Pseudo dice [0.6652, 0.8704] 
+2024-09-09 22:30:48.868339: Epoch time: 246.85 s 
+2024-09-09 22:30:49.885750:  
+2024-09-09 22:30:49.885921: Epoch 757 
+2024-09-09 22:30:49.885999: Current learning rate: 0.0028 
+2024-09-09 22:34:56.841613: train_loss -0.8934 
+2024-09-09 22:34:56.841766: val_loss -0.6894 
+2024-09-09 22:34:56.841817: Pseudo dice [0.6696, 0.8643] 
+2024-09-09 22:34:56.841867: Epoch time: 246.96 s 
+2024-09-09 22:34:57.844979:  
+2024-09-09 22:34:57.845194: Epoch 758 
+2024-09-09 22:34:57.845275: Current learning rate: 0.00279 
+2024-09-09 22:39:04.676972: train_loss -0.8967 
+2024-09-09 22:39:04.677139: val_loss -0.7095 
+2024-09-09 22:39:04.677192: Pseudo dice [0.6624, 0.8792] 
+2024-09-09 22:39:04.677245: Epoch time: 246.83 s 
+2024-09-09 22:39:05.692237:  
+2024-09-09 22:39:05.692396: Epoch 759 
+2024-09-09 22:39:05.692472: Current learning rate: 0.00278 
+2024-09-09 22:43:12.436468: train_loss -0.8952 
+2024-09-09 22:43:12.436664: val_loss -0.6878 
+2024-09-09 22:43:12.436721: Pseudo dice [0.633, 0.8635] 
+2024-09-09 22:43:12.436776: Epoch time: 246.75 s 
+2024-09-09 22:43:13.623794:  
+2024-09-09 22:43:13.624046: Epoch 760 
+2024-09-09 22:43:13.624137: Current learning rate: 0.00277 
+2024-09-09 22:47:20.237324: train_loss -0.897 
+2024-09-09 22:47:20.237456: val_loss -0.6846 
+2024-09-09 22:47:20.237506: Pseudo dice [0.671, 0.8606] 
+2024-09-09 22:47:20.237558: Epoch time: 246.62 s 
+2024-09-09 22:47:21.231786:  
+2024-09-09 22:47:21.231985: Epoch 761 
+2024-09-09 22:47:21.232120: Current learning rate: 0.00276 
+2024-09-09 22:51:27.796755: train_loss -0.9014 
+2024-09-09 22:51:27.796894: val_loss -0.6891 
+2024-09-09 22:51:27.796945: Pseudo dice [0.6603, 0.8733] 
+2024-09-09 22:51:27.796996: Epoch time: 246.57 s 
+2024-09-09 22:51:28.814364:  
+2024-09-09 22:51:28.814551: Epoch 762 
+2024-09-09 22:51:28.814630: Current learning rate: 0.00275 
+2024-09-09 22:55:35.563261: train_loss -0.8967 
+2024-09-09 22:55:35.563410: val_loss -0.7087 
+2024-09-09 22:55:35.563460: Pseudo dice [0.6686, 0.8937] 
+2024-09-09 22:55:35.563512: Epoch time: 246.75 s 
+2024-09-09 22:55:36.577749:  
+2024-09-09 22:55:36.578011: Epoch 763 
+2024-09-09 22:55:36.578094: Current learning rate: 0.00274 
+2024-09-09 22:59:43.505224: train_loss -0.8908 
+2024-09-09 22:59:43.505401: val_loss -0.6942 
+2024-09-09 22:59:43.505510: Pseudo dice [0.6611, 0.873] 
+2024-09-09 22:59:43.505614: Epoch time: 246.93 s 
+2024-09-09 22:59:44.637643:  
+2024-09-09 22:59:44.637965: Epoch 764 
+2024-09-09 22:59:44.638186: Current learning rate: 0.00273 
+2024-09-09 23:03:51.653336: train_loss -0.8862 
+2024-09-09 23:03:51.653479: val_loss -0.6681 
+2024-09-09 23:03:51.653530: Pseudo dice [0.6422, 0.8682] 
+2024-09-09 23:03:51.653583: Epoch time: 247.02 s 
+2024-09-09 23:03:52.664679:  
+2024-09-09 23:03:52.664839: Epoch 765 
+2024-09-09 23:03:52.664918: Current learning rate: 0.00272 
+2024-09-09 23:07:59.770740: train_loss -0.8837 
+2024-09-09 23:07:59.770904: val_loss -0.7181 
+2024-09-09 23:07:59.770979: Pseudo dice [0.6783, 0.8872] 
+2024-09-09 23:07:59.771037: Epoch time: 247.11 s 
+2024-09-09 23:08:00.815845:  
+2024-09-09 23:08:00.816041: Epoch 766 
+2024-09-09 23:08:00.816126: Current learning rate: 0.00271 
+2024-09-09 23:12:07.557589: train_loss -0.8865 
+2024-09-09 23:12:07.557756: val_loss -0.6733 
+2024-09-09 23:12:07.557821: Pseudo dice [0.6477, 0.8706] 
+2024-09-09 23:12:07.557884: Epoch time: 246.74 s 
+2024-09-09 23:12:08.856838:  
+2024-09-09 23:12:08.857081: Epoch 767 
+2024-09-09 23:12:08.857207: Current learning rate: 0.0027 
+2024-09-09 23:16:15.554548: train_loss -0.8971 
+2024-09-09 23:16:15.554684: val_loss -0.6902 
+2024-09-09 23:16:15.554736: Pseudo dice [0.6123, 0.8737] 
+2024-09-09 23:16:15.554790: Epoch time: 246.7 s 
+2024-09-09 23:16:16.592412:  
+2024-09-09 23:16:16.592605: Epoch 768 
+2024-09-09 23:16:16.592689: Current learning rate: 0.00268 
+2024-09-09 23:20:23.330885: train_loss -0.8828 
+2024-09-09 23:20:23.331034: val_loss -0.6943 
+2024-09-09 23:20:23.331085: Pseudo dice [0.6892, 0.8704] 
+2024-09-09 23:20:23.331136: Epoch time: 246.74 s 
+2024-09-09 23:20:24.348932:  
+2024-09-09 23:20:24.349088: Epoch 769 
+2024-09-09 23:20:24.349174: Current learning rate: 0.00267 
+2024-09-09 23:24:31.001438: train_loss -0.8869 
+2024-09-09 23:24:31.001657: val_loss -0.6918 
+2024-09-09 23:24:31.001742: Pseudo dice [0.6599, 0.8769] 
+2024-09-09 23:24:31.001829: Epoch time: 246.65 s 
+2024-09-09 23:24:32.234557:  
+2024-09-09 23:24:32.234817: Epoch 770 
+2024-09-09 23:24:32.234931: Current learning rate: 0.00266 
+2024-09-09 23:28:39.676331: train_loss -0.8891 
+2024-09-09 23:28:39.676476: val_loss -0.6934 
+2024-09-09 23:28:39.676527: Pseudo dice [0.6724, 0.8745] 
+2024-09-09 23:28:39.676578: Epoch time: 247.44 s 
+2024-09-09 23:28:40.705557:  
+2024-09-09 23:28:40.705838: Epoch 771 
+2024-09-09 23:28:40.705934: Current learning rate: 0.00265 
+2024-09-09 23:32:47.399262: train_loss -0.8888 
+2024-09-09 23:32:47.399403: val_loss -0.7016 
+2024-09-09 23:32:47.399452: Pseudo dice [0.6689, 0.8576] 
+2024-09-09 23:32:47.399575: Epoch time: 246.7 s 
+2024-09-09 23:32:48.419431:  
+2024-09-09 23:32:48.419616: Epoch 772 
+2024-09-09 23:32:48.419694: Current learning rate: 0.00264 
+2024-09-09 23:36:55.226872: train_loss -0.8827 
+2024-09-09 23:36:55.227070: val_loss -0.6927 
+2024-09-09 23:36:55.227124: Pseudo dice [0.6442, 0.8759] 
+2024-09-09 23:36:55.227182: Epoch time: 246.81 s 
+2024-09-09 23:36:56.429495:  
+2024-09-09 23:36:56.429733: Epoch 773 
+2024-09-09 23:36:56.429873: Current learning rate: 0.00263 
+2024-09-09 23:41:03.369634: train_loss -0.8892 
+2024-09-09 23:41:03.369783: val_loss -0.6896 
+2024-09-09 23:41:03.369836: Pseudo dice [0.669, 0.8688] 
+2024-09-09 23:41:03.369889: Epoch time: 246.94 s 
+2024-09-09 23:41:04.383054:  
+2024-09-09 23:41:04.383239: Epoch 774 
+2024-09-09 23:41:04.383320: Current learning rate: 0.00262 
+2024-09-09 23:45:11.161467: train_loss -0.8965 
+2024-09-09 23:45:11.161630: val_loss -0.6839 
+2024-09-09 23:45:11.161682: Pseudo dice [0.6566, 0.8728] 
+2024-09-09 23:45:11.161734: Epoch time: 246.78 s 
+2024-09-09 23:45:12.188921:  
+2024-09-09 23:45:12.189198: Epoch 775 
+2024-09-09 23:45:12.189281: Current learning rate: 0.00261 
+2024-09-09 23:49:18.910259: train_loss -0.8835 
+2024-09-09 23:49:18.910395: val_loss -0.686 
+2024-09-09 23:49:18.910447: Pseudo dice [0.6612, 0.8686] 
+2024-09-09 23:49:18.910499: Epoch time: 246.72 s 
+2024-09-09 23:49:19.994406:  
+2024-09-09 23:49:19.994709: Epoch 776 
+2024-09-09 23:49:19.994795: Current learning rate: 0.0026 
+2024-09-09 23:53:26.483618: train_loss -0.8814 
+2024-09-09 23:53:26.483753: val_loss -0.6871 
+2024-09-09 23:53:26.483820: Pseudo dice [0.6781, 0.864] 
+2024-09-09 23:53:26.483875: Epoch time: 246.49 s 
+2024-09-09 23:53:27.507188:  
+2024-09-09 23:53:27.507351: Epoch 777 
+2024-09-09 23:53:27.507432: Current learning rate: 0.00259 
+2024-09-09 23:57:34.051058: train_loss -0.8966 
+2024-09-09 23:57:34.051200: val_loss -0.6918 
+2024-09-09 23:57:34.051251: Pseudo dice [0.7138, 0.8507] 
+2024-09-09 23:57:34.051303: Epoch time: 246.55 s 
+2024-09-09 23:57:35.108322:  
+2024-09-09 23:57:35.108469: Epoch 778 
+2024-09-09 23:57:35.108569: Current learning rate: 0.00258 
+2024-09-10 00:01:41.655193: train_loss -0.8952 
+2024-09-10 00:01:41.655341: val_loss -0.6865 
+2024-09-10 00:01:41.655392: Pseudo dice [0.6595, 0.8807] 
+2024-09-10 00:01:41.655455: Epoch time: 246.55 s 
+2024-09-10 00:01:42.680280:  
+2024-09-10 00:01:42.680486: Epoch 779 
+2024-09-10 00:01:42.680568: Current learning rate: 0.00257 
+2024-09-10 00:05:49.238495: train_loss -0.8985 
+2024-09-10 00:05:49.238636: val_loss -0.6631 
+2024-09-10 00:05:49.238688: Pseudo dice [0.6157, 0.8536] 
+2024-09-10 00:05:49.238746: Epoch time: 246.56 s 
+2024-09-10 00:05:50.267775:  
+2024-09-10 00:05:50.267973: Epoch 780 
+2024-09-10 00:05:50.268054: Current learning rate: 0.00256 
+2024-09-10 00:09:56.862633: train_loss -0.9044 
+2024-09-10 00:09:56.862777: val_loss -0.6925 
+2024-09-10 00:09:56.862828: Pseudo dice [0.6407, 0.8606] 
+2024-09-10 00:09:56.862879: Epoch time: 246.6 s 
+2024-09-10 00:09:57.882306:  
+2024-09-10 00:09:57.882512: Epoch 781 
+2024-09-10 00:09:57.882596: Current learning rate: 0.00255 
+2024-09-10 00:14:04.420611: train_loss -0.9035 
+2024-09-10 00:14:04.420748: val_loss -0.6777 
+2024-09-10 00:14:04.420810: Pseudo dice [0.6799, 0.8672] 
+2024-09-10 00:14:04.420958: Epoch time: 246.54 s 
+2024-09-10 00:14:05.690182:  
+2024-09-10 00:14:05.690400: Epoch 782 
+2024-09-10 00:14:05.690479: Current learning rate: 0.00254 
+2024-09-10 00:18:12.429658: train_loss -0.9035 
+2024-09-10 00:18:12.429857: val_loss -0.6738 
+2024-09-10 00:18:12.429914: Pseudo dice [0.644, 0.8747] 
+2024-09-10 00:18:12.429971: Epoch time: 246.74 s 
+2024-09-10 00:18:13.464338:  
+2024-09-10 00:18:13.464527: Epoch 783 
+2024-09-10 00:18:13.464627: Current learning rate: 0.00253 
+2024-09-10 00:22:20.340260: train_loss -0.8982 
+2024-09-10 00:22:20.340399: val_loss -0.6791 
+2024-09-10 00:22:20.340496: Pseudo dice [0.6326, 0.8646] 
+2024-09-10 00:22:20.340548: Epoch time: 246.88 s 
+2024-09-10 00:22:21.370795:  
+2024-09-10 00:22:21.371015: Epoch 784 
+2024-09-10 00:22:21.371100: Current learning rate: 0.00252 
+2024-09-10 00:26:28.463224: train_loss -0.899 
+2024-09-10 00:26:28.463370: val_loss -0.6722 
+2024-09-10 00:26:28.463421: Pseudo dice [0.6629, 0.8733] 
+2024-09-10 00:26:28.463476: Epoch time: 247.09 s 
+2024-09-10 00:26:29.499929:  
+2024-09-10 00:26:29.500107: Epoch 785 
+2024-09-10 00:26:29.500194: Current learning rate: 0.00251 
+2024-09-10 00:30:36.452471: train_loss -0.8965 
+2024-09-10 00:30:36.452636: val_loss -0.6804 
+2024-09-10 00:30:36.452694: Pseudo dice [0.6397, 0.8749] 
+2024-09-10 00:30:36.452756: Epoch time: 246.95 s 
+2024-09-10 00:30:37.743423:  
+2024-09-10 00:30:37.743703: Epoch 786 
+2024-09-10 00:30:37.743820: Current learning rate: 0.0025 
+2024-09-10 00:34:44.633043: train_loss -0.8966 
+2024-09-10 00:34:44.633224: val_loss -0.6698 
+2024-09-10 00:34:44.633282: Pseudo dice [0.648, 0.8689] 
+2024-09-10 00:34:44.633341: Epoch time: 246.89 s 
+2024-09-10 00:34:45.753389:  
+2024-09-10 00:34:45.753604: Epoch 787 
+2024-09-10 00:34:45.753689: Current learning rate: 0.00249 
+2024-09-10 00:38:52.581858: train_loss -0.8938 
+2024-09-10 00:38:52.582043: val_loss -0.7199 
+2024-09-10 00:38:52.582112: Pseudo dice [0.7026, 0.8742] 
+2024-09-10 00:38:52.582198: Epoch time: 246.83 s 
+2024-09-10 00:38:53.629941:  
+2024-09-10 00:38:53.630127: Epoch 788 
+2024-09-10 00:38:53.630229: Current learning rate: 0.00248 
+2024-09-10 00:43:00.259562: train_loss -0.8936 
+2024-09-10 00:43:00.259708: val_loss -0.6856 
+2024-09-10 00:43:00.259765: Pseudo dice [0.6432, 0.8815] 
+2024-09-10 00:43:00.259826: Epoch time: 246.63 s 
+2024-09-10 00:43:01.288203:  
+2024-09-10 00:43:01.288393: Epoch 789 
+2024-09-10 00:43:01.288478: Current learning rate: 0.00247 
+2024-09-10 00:47:08.060312: train_loss -0.9006 
+2024-09-10 00:47:08.060501: val_loss -0.6954 
+2024-09-10 00:47:08.060558: Pseudo dice [0.6596, 0.8696] 
+2024-09-10 00:47:08.060613: Epoch time: 246.77 s 
+2024-09-10 00:47:09.111150:  
+2024-09-10 00:47:09.111316: Epoch 790 
+2024-09-10 00:47:09.111400: Current learning rate: 0.00245 
+2024-09-10 00:51:47.950408: train_loss -0.901 
+2024-09-10 00:51:47.950549: val_loss -0.7003 
+2024-09-10 00:51:47.950608: Pseudo dice [0.6515, 0.881] 
+2024-09-10 00:51:47.950666: Epoch time: 278.84 s 
+2024-09-10 00:51:48.962135:  
+2024-09-10 00:51:48.962348: Epoch 791 
+2024-09-10 00:51:48.962437: Current learning rate: 0.00244 
+2024-09-10 00:57:39.543447: train_loss -0.8981 
+2024-09-10 00:57:39.543599: val_loss -0.6941 
+2024-09-10 00:57:39.543657: Pseudo dice [0.6759, 0.8653] 
+2024-09-10 00:57:39.543713: Epoch time: 350.58 s 
+2024-09-10 00:57:40.568788:  
+2024-09-10 00:57:40.568983: Epoch 792 
+2024-09-10 00:57:40.569091: Current learning rate: 0.00243 
+2024-09-10 01:01:46.702288: train_loss -0.9031 
+2024-09-10 01:01:46.702474: val_loss -0.6844 
+2024-09-10 01:01:46.702533: Pseudo dice [0.6722, 0.8761] 
+2024-09-10 01:01:46.702592: Epoch time: 246.14 s 
+2024-09-10 01:01:48.693726:  
+2024-09-10 01:01:48.693919: Epoch 793 
+2024-09-10 01:01:48.694021: Current learning rate: 0.00242 
+2024-09-10 01:05:54.974818: train_loss -0.9032 
+2024-09-10 01:05:54.974991: val_loss -0.678 
+2024-09-10 01:05:54.975049: Pseudo dice [0.6784, 0.8565] 
+2024-09-10 01:05:54.975181: Epoch time: 246.28 s 
+2024-09-10 01:05:56.009723:  
+2024-09-10 01:05:56.009922: Epoch 794 
+2024-09-10 01:05:56.010006: Current learning rate: 0.00241 
+2024-09-10 01:10:02.509107: train_loss -0.9009 
+2024-09-10 01:10:02.509258: val_loss -0.6817 
+2024-09-10 01:10:02.509317: Pseudo dice [0.6585, 0.8775] 
+2024-09-10 01:10:02.509373: Epoch time: 246.5 s 
+2024-09-10 01:10:03.546323:  
+2024-09-10 01:10:03.546589: Epoch 795 
+2024-09-10 01:10:03.546675: Current learning rate: 0.0024 
+2024-09-10 01:14:09.759707: train_loss -0.8995 
+2024-09-10 01:14:09.759864: val_loss -0.6757 
+2024-09-10 01:14:09.759922: Pseudo dice [0.6352, 0.8847] 
+2024-09-10 01:14:09.759985: Epoch time: 246.22 s 
+2024-09-10 01:14:10.779895:  
+2024-09-10 01:14:10.780128: Epoch 796 
+2024-09-10 01:14:10.780215: Current learning rate: 0.00239 
+2024-09-10 01:18:16.975893: train_loss -0.9053 
+2024-09-10 01:18:16.976059: val_loss -0.7067 
+2024-09-10 01:18:16.976115: Pseudo dice [0.6781, 0.8805] 
+2024-09-10 01:18:16.976171: Epoch time: 246.2 s 
+2024-09-10 01:18:18.017086:  
+2024-09-10 01:18:18.017257: Epoch 797 
+2024-09-10 01:18:18.017365: Current learning rate: 0.00238 
+2024-09-10 01:22:24.216648: train_loss -0.899 
+2024-09-10 01:22:24.216794: val_loss -0.6841 
+2024-09-10 01:22:24.216851: Pseudo dice [0.643, 0.8736] 
+2024-09-10 01:22:24.216908: Epoch time: 246.2 s 
+2024-09-10 01:22:25.259055:  
+2024-09-10 01:22:25.259322: Epoch 798 
+2024-09-10 01:22:25.259409: Current learning rate: 0.00237 
+2024-09-10 01:26:31.512226: train_loss -0.8986 
+2024-09-10 01:26:31.512408: val_loss -0.7095 
+2024-09-10 01:26:31.512460: Pseudo dice [0.6858, 0.878] 
+2024-09-10 01:26:31.512513: Epoch time: 246.26 s 
+2024-09-10 01:26:32.535655:  
+2024-09-10 01:26:32.535866: Epoch 799 
+2024-09-10 01:26:32.535953: Current learning rate: 0.00236 
+2024-09-10 01:30:39.025672: train_loss -0.9004 
+2024-09-10 01:30:39.025877: val_loss -0.6774 
+2024-09-10 01:30:39.025971: Pseudo dice [0.68, 0.8691] 
+2024-09-10 01:30:39.026067: Epoch time: 246.49 s 
+2024-09-10 01:30:43.011800:  
+2024-09-10 01:30:43.012007: Epoch 800 
+2024-09-10 01:30:43.012115: Current learning rate: 0.00235 
+2024-09-10 01:34:49.512799: train_loss -0.8998 
+2024-09-10 01:34:49.512939: val_loss -0.6833 
+2024-09-10 01:34:49.512991: Pseudo dice [0.6484, 0.8726] 
+2024-09-10 01:34:49.513044: Epoch time: 246.5 s 
+2024-09-10 01:34:50.561211:  
+2024-09-10 01:34:50.561422: Epoch 801 
+2024-09-10 01:34:50.561500: Current learning rate: 0.00234 
+2024-09-10 01:38:57.287138: train_loss -0.9047 
+2024-09-10 01:38:57.287303: val_loss -0.7039 
+2024-09-10 01:38:57.287356: Pseudo dice [0.6373, 0.8876] 
+2024-09-10 01:38:57.287409: Epoch time: 246.73 s 
+2024-09-10 01:38:58.322440:  
+2024-09-10 01:38:58.322669: Epoch 802 
+2024-09-10 01:38:58.322747: Current learning rate: 0.00233 
+2024-09-10 01:43:04.931898: train_loss -0.9028 
+2024-09-10 01:43:04.932036: val_loss -0.6996 
+2024-09-10 01:43:04.932087: Pseudo dice [0.6536, 0.8795] 
+2024-09-10 01:43:04.932137: Epoch time: 246.61 s 
+2024-09-10 01:43:05.949242:  
+2024-09-10 01:43:05.949447: Epoch 803 
+2024-09-10 01:43:05.949557: Current learning rate: 0.00232 
+2024-09-10 01:47:12.494942: train_loss -0.9029 
+2024-09-10 01:47:12.495091: val_loss -0.6777 
+2024-09-10 01:47:12.495142: Pseudo dice [0.6216, 0.8609] 
+2024-09-10 01:47:12.495193: Epoch time: 246.55 s 
+2024-09-10 01:47:13.522933:  
+2024-09-10 01:47:13.523153: Epoch 804 
+2024-09-10 01:47:13.523234: Current learning rate: 0.00231 
+2024-09-10 01:51:19.964030: train_loss -0.905 
+2024-09-10 01:51:19.964198: val_loss -0.7117 
+2024-09-10 01:51:19.964250: Pseudo dice [0.7163, 0.8623] 
+2024-09-10 01:51:19.964300: Epoch time: 246.44 s 
+2024-09-10 01:51:21.018288:  
+2024-09-10 01:51:21.018468: Epoch 805 
+2024-09-10 01:51:21.018548: Current learning rate: 0.0023 
+2024-09-10 01:55:27.496537: train_loss -0.8996 
+2024-09-10 01:55:27.496687: val_loss -0.7012 
+2024-09-10 01:55:27.496740: Pseudo dice [0.6682, 0.8768] 
+2024-09-10 01:55:27.496795: Epoch time: 246.48 s 
+2024-09-10 01:55:28.521501:  
+2024-09-10 01:55:28.521747: Epoch 806 
+2024-09-10 01:55:28.521827: Current learning rate: 0.00229 
+2024-09-10 01:59:35.020921: train_loss -0.9063 
+2024-09-10 01:59:35.021060: val_loss -0.6895 
+2024-09-10 01:59:35.021111: Pseudo dice [0.663, 0.8788] 
+2024-09-10 01:59:35.021163: Epoch time: 246.5 s 
+2024-09-10 01:59:36.046738:  
+2024-09-10 01:59:36.046935: Epoch 807 
+2024-09-10 01:59:36.047016: Current learning rate: 0.00228 
+2024-09-10 02:03:42.795936: train_loss -0.9076 
+2024-09-10 02:03:42.796086: val_loss -0.7017 
+2024-09-10 02:03:42.796136: Pseudo dice [0.6815, 0.8718] 
+2024-09-10 02:03:42.796187: Epoch time: 246.75 s 
+2024-09-10 02:03:43.829863:  
+2024-09-10 02:03:43.830086: Epoch 808 
+2024-09-10 02:03:43.830170: Current learning rate: 0.00226 
+2024-09-10 02:07:50.294800: train_loss -0.9052 
+2024-09-10 02:07:50.294948: val_loss -0.7244 
+2024-09-10 02:07:50.295000: Pseudo dice [0.7134, 0.8787] 
+2024-09-10 02:07:50.295055: Epoch time: 246.47 s 
+2024-09-10 02:07:50.295096: Yayy! New best EMA pseudo Dice: 0.7715 
+2024-09-10 02:07:54.142511:  
+2024-09-10 02:07:54.142748: Epoch 809 
+2024-09-10 02:07:54.142829: Current learning rate: 0.00225 
+2024-09-10 02:12:00.399948: train_loss -0.9047 
+2024-09-10 02:12:00.400105: val_loss -0.6884 
+2024-09-10 02:12:00.400161: Pseudo dice [0.6792, 0.8704] 
+2024-09-10 02:12:00.400213: Epoch time: 246.26 s 
+2024-09-10 02:12:00.400252: Yayy! New best EMA pseudo Dice: 0.7718 
+2024-09-10 02:12:04.462122:  
+2024-09-10 02:12:04.462337: Epoch 810 
+2024-09-10 02:12:04.462418: Current learning rate: 0.00224 
+2024-09-10 02:16:10.899915: train_loss -0.9069 
+2024-09-10 02:16:10.900056: val_loss -0.7039 
+2024-09-10 02:16:10.900105: Pseudo dice [0.6944, 0.8799] 
+2024-09-10 02:16:10.900159: Epoch time: 246.44 s 
+2024-09-10 02:16:10.900203: Yayy! New best EMA pseudo Dice: 0.7733 
+2024-09-10 02:16:14.905607:  
+2024-09-10 02:16:14.905786: Epoch 811 
+2024-09-10 02:16:14.905869: Current learning rate: 0.00223 
+2024-09-10 02:20:21.301765: train_loss -0.905 
+2024-09-10 02:20:21.301943: val_loss -0.703 
+2024-09-10 02:20:21.301993: Pseudo dice [0.6685, 0.8801] 
+2024-09-10 02:20:21.302044: Epoch time: 246.4 s 
+2024-09-10 02:20:21.302084: Yayy! New best EMA pseudo Dice: 0.7734 
+2024-09-10 02:20:25.300461:  
+2024-09-10 02:20:25.300673: Epoch 812 
+2024-09-10 02:20:25.300757: Current learning rate: 0.00222 
+2024-09-10 02:24:31.545526: train_loss -0.9059 
+2024-09-10 02:24:31.545662: val_loss -0.6688 
+2024-09-10 02:24:31.545713: Pseudo dice [0.6474, 0.86] 
+2024-09-10 02:24:31.545767: Epoch time: 246.25 s 
+2024-09-10 02:24:32.592127:  
+2024-09-10 02:24:32.592309: Epoch 813 
+2024-09-10 02:24:32.592390: Current learning rate: 0.00221 
+2024-09-10 02:28:39.506526: train_loss -0.9058 
+2024-09-10 02:28:39.506671: val_loss -0.6809 
+2024-09-10 02:28:39.506721: Pseudo dice [0.6543, 0.8703] 
+2024-09-10 02:28:39.506771: Epoch time: 246.92 s 
+2024-09-10 02:28:40.517819:  
+2024-09-10 02:28:40.518079: Epoch 814 
+2024-09-10 02:28:40.518175: Current learning rate: 0.0022 
+2024-09-10 02:32:46.759524: train_loss -0.9066 
+2024-09-10 02:32:46.759659: val_loss -0.6721 
+2024-09-10 02:32:46.759710: Pseudo dice [0.6653, 0.8817] 
+2024-09-10 02:32:46.759761: Epoch time: 246.24 s 
+2024-09-10 02:32:47.828095:  
+2024-09-10 02:32:47.828324: Epoch 815 
+2024-09-10 02:32:47.828428: Current learning rate: 0.00219 
+2024-09-10 02:36:53.976783: train_loss -0.9104 
+2024-09-10 02:36:53.976942: val_loss -0.6864 
+2024-09-10 02:36:53.976993: Pseudo dice [0.646, 0.8732] 
+2024-09-10 02:36:53.977044: Epoch time: 246.15 s 
+2024-09-10 02:36:55.041455:  
+2024-09-10 02:36:55.041673: Epoch 816 
+2024-09-10 02:36:55.041752: Current learning rate: 0.00218 
+2024-09-10 02:41:01.187007: train_loss -0.9052 
+2024-09-10 02:41:01.187170: val_loss -0.7155 
+2024-09-10 02:41:01.187222: Pseudo dice [0.6647, 0.8931] 
+2024-09-10 02:41:01.187278: Epoch time: 246.15 s 
+2024-09-10 02:41:02.208824:  
+2024-09-10 02:41:02.209017: Epoch 817 
+2024-09-10 02:41:02.209101: Current learning rate: 0.00217 
+2024-09-10 02:45:08.790375: train_loss -0.9001 
+2024-09-10 02:45:08.790515: val_loss -0.6858 
+2024-09-10 02:45:08.790567: Pseudo dice [0.657, 0.8658] 
+2024-09-10 02:45:08.790617: Epoch time: 246.58 s 
+2024-09-10 02:45:09.838655:  
+2024-09-10 02:45:09.838856: Epoch 818 
+2024-09-10 02:45:09.838974: Current learning rate: 0.00216 
+2024-09-10 02:49:16.553752: train_loss -0.895 
+2024-09-10 02:49:16.553893: val_loss -0.6924 
+2024-09-10 02:49:16.553957: Pseudo dice [0.6608, 0.8666] 
+2024-09-10 02:49:16.554013: Epoch time: 246.72 s 
+2024-09-10 02:49:17.641480:  
+2024-09-10 02:49:17.641662: Epoch 819 
+2024-09-10 02:49:17.641740: Current learning rate: 0.00215 
+2024-09-10 02:53:24.044377: train_loss -0.9038 
+2024-09-10 02:53:24.044514: val_loss -0.7096 
+2024-09-10 02:53:24.044564: Pseudo dice [0.6973, 0.8814] 
+2024-09-10 02:53:24.044617: Epoch time: 246.4 s 
+2024-09-10 02:53:25.016695:  
+2024-09-10 02:53:25.016926: Epoch 820 
+2024-09-10 02:53:25.017010: Current learning rate: 0.00214 
+2024-09-10 02:57:31.384842: train_loss -0.908 
+2024-09-10 02:57:31.384980: val_loss -0.6818 
+2024-09-10 02:57:31.385031: Pseudo dice [0.6454, 0.8777] 
+2024-09-10 02:57:31.385080: Epoch time: 246.37 s 
+2024-09-10 02:57:32.351194:  
+2024-09-10 02:57:32.351410: Epoch 821 
+2024-09-10 02:57:32.351494: Current learning rate: 0.00213 
+2024-09-10 03:01:38.528632: train_loss -0.9057 
+2024-09-10 03:01:38.528779: val_loss -0.7276 
+2024-09-10 03:01:38.528836: Pseudo dice [0.6958, 0.8793] 
+2024-09-10 03:01:38.528886: Epoch time: 246.18 s 
+2024-09-10 03:01:39.498280:  
+2024-09-10 03:01:39.498481: Epoch 822 
+2024-09-10 03:01:39.498560: Current learning rate: 0.00212 
+2024-09-10 03:05:45.737446: train_loss -0.906 
+2024-09-10 03:05:45.737618: val_loss -0.6802 
+2024-09-10 03:05:45.737671: Pseudo dice [0.6775, 0.8665] 
+2024-09-10 03:05:45.737762: Epoch time: 246.24 s 
+2024-09-10 03:05:46.700732:  
+2024-09-10 03:05:46.700919: Epoch 823 
+2024-09-10 03:05:46.701000: Current learning rate: 0.0021 
+2024-09-10 03:09:53.018349: train_loss -0.908 
+2024-09-10 03:09:53.018487: val_loss -0.6859 
+2024-09-10 03:09:53.018538: Pseudo dice [0.6723, 0.8682] 
+2024-09-10 03:09:53.018589: Epoch time: 246.32 s 
+2024-09-10 03:09:54.263016:  
+2024-09-10 03:09:54.263243: Epoch 824 
+2024-09-10 03:09:54.263324: Current learning rate: 0.00209 
+2024-09-10 03:14:00.482942: train_loss -0.9069 
+2024-09-10 03:14:00.483084: val_loss -0.7065 
+2024-09-10 03:14:00.483135: Pseudo dice [0.679, 0.881] 
+2024-09-10 03:14:00.483249: Epoch time: 246.22 s 
+2024-09-10 03:14:01.466353:  
+2024-09-10 03:14:01.466608: Epoch 825 
+2024-09-10 03:14:01.466687: Current learning rate: 0.00208 
+2024-09-10 03:18:07.389209: train_loss -0.9096 
+2024-09-10 03:18:07.389390: val_loss -0.7081 
+2024-09-10 03:18:07.389442: Pseudo dice [0.6796, 0.871] 
+2024-09-10 03:18:07.389493: Epoch time: 245.92 s 
+2024-09-10 03:18:08.366985:  
+2024-09-10 03:18:08.367233: Epoch 826 
+2024-09-10 03:18:08.367311: Current learning rate: 0.00207 
+2024-09-10 03:22:14.490413: train_loss -0.9063 
+2024-09-10 03:22:14.490564: val_loss -0.692 
+2024-09-10 03:22:14.490614: Pseudo dice [0.6794, 0.8704] 
+2024-09-10 03:22:14.490666: Epoch time: 246.13 s 
+2024-09-10 03:22:15.465632:  
+2024-09-10 03:22:15.465858: Epoch 827 
+2024-09-10 03:22:15.465942: Current learning rate: 0.00206 
+2024-09-10 03:26:21.980543: train_loss -0.907 
+2024-09-10 03:26:21.980702: val_loss -0.679 
+2024-09-10 03:26:21.980782: Pseudo dice [0.6257, 0.8685] 
+2024-09-10 03:26:21.980934: Epoch time: 246.52 s 
+2024-09-10 03:26:22.966486:  
+2024-09-10 03:26:22.966717: Epoch 828 
+2024-09-10 03:26:22.966801: Current learning rate: 0.00205 
+2024-09-10 03:30:29.586151: train_loss -0.9076 
+2024-09-10 03:30:29.586314: val_loss -0.6917 
+2024-09-10 03:30:29.586367: Pseudo dice [0.6561, 0.8837] 
+2024-09-10 03:30:29.586417: Epoch time: 246.62 s 
+2024-09-10 03:30:30.543172:  
+2024-09-10 03:30:30.543375: Epoch 829 
+2024-09-10 03:30:30.543455: Current learning rate: 0.00204 
+2024-09-10 03:34:36.965650: train_loss -0.9069 
+2024-09-10 03:34:36.965836: val_loss -0.6793 
+2024-09-10 03:34:36.965920: Pseudo dice [0.6357, 0.8841] 
+2024-09-10 03:34:36.965972: Epoch time: 246.42 s 
+2024-09-10 03:34:37.931507:  
+2024-09-10 03:34:37.931709: Epoch 830 
+2024-09-10 03:34:37.931793: Current learning rate: 0.00203 
+2024-09-10 03:38:44.370343: train_loss -0.9115 
+2024-09-10 03:38:44.370499: val_loss -0.6667 
+2024-09-10 03:38:44.370606: Pseudo dice [0.6547, 0.8863] 
+2024-09-10 03:38:44.370662: Epoch time: 246.44 s 
+2024-09-10 03:38:45.341789:  
+2024-09-10 03:38:45.342049: Epoch 831 
+2024-09-10 03:38:45.342131: Current learning rate: 0.00202 
+2024-09-10 03:42:51.901599: train_loss -0.9025 
+2024-09-10 03:42:51.901734: val_loss -0.7058 
+2024-09-10 03:42:51.901783: Pseudo dice [0.6579, 0.887] 
+2024-09-10 03:42:51.901834: Epoch time: 246.56 s 
+2024-09-10 03:42:52.864023:  
+2024-09-10 03:42:52.864264: Epoch 832 
+2024-09-10 03:42:52.864347: Current learning rate: 0.00201 
+2024-09-10 03:46:59.018270: train_loss -0.904 
+2024-09-10 03:46:59.018438: val_loss -0.6831 
+2024-09-10 03:46:59.018490: Pseudo dice [0.6367, 0.8694] 
+2024-09-10 03:46:59.018542: Epoch time: 246.16 s 
+2024-09-10 03:46:59.984299:  
+2024-09-10 03:46:59.984455: Epoch 833 
+2024-09-10 03:46:59.984543: Current learning rate: 0.002 
+2024-09-10 03:51:06.251884: train_loss -0.9043 
+2024-09-10 03:51:06.252053: val_loss -0.7093 
+2024-09-10 03:51:06.252103: Pseudo dice [0.7045, 0.877] 
+2024-09-10 03:51:06.252156: Epoch time: 246.27 s 
+2024-09-10 03:51:07.219478:  
+2024-09-10 03:51:07.219736: Epoch 834 
+2024-09-10 03:51:07.219828: Current learning rate: 0.00199 
+2024-09-10 03:55:13.743221: train_loss -0.9069 
+2024-09-10 03:55:13.743367: val_loss -0.7045 
+2024-09-10 03:55:13.743419: Pseudo dice [0.6583, 0.8719] 
+2024-09-10 03:55:13.743471: Epoch time: 246.53 s 
+2024-09-10 03:55:14.702730:  
+2024-09-10 03:55:14.702907: Epoch 835 
+2024-09-10 03:55:14.702995: Current learning rate: 0.00198 
+2024-09-10 03:59:21.529657: train_loss -0.9027 
+2024-09-10 03:59:21.529822: val_loss -0.6436 
+2024-09-10 03:59:21.529872: Pseudo dice [0.6099, 0.8546] 
+2024-09-10 03:59:21.529923: Epoch time: 246.83 s 
+2024-09-10 03:59:22.489877:  
+2024-09-10 03:59:22.490060: Epoch 836 
+2024-09-10 03:59:22.490143: Current learning rate: 0.00196 
+2024-09-10 04:03:29.107375: train_loss -0.9091 
+2024-09-10 04:03:29.107548: val_loss -0.6963 
+2024-09-10 04:03:29.107598: Pseudo dice [0.6543, 0.8759] 
+2024-09-10 04:03:29.107650: Epoch time: 246.62 s 
+2024-09-10 04:03:31.036173:  
+2024-09-10 04:03:31.036463: Epoch 837 
+2024-09-10 04:03:31.036564: Current learning rate: 0.00195 
+2024-09-10 04:07:37.372365: train_loss -0.9091 
+2024-09-10 04:07:37.372534: val_loss -0.6861 
+2024-09-10 04:07:37.372584: Pseudo dice [0.6573, 0.8788] 
+2024-09-10 04:07:37.372636: Epoch time: 246.34 s 
+2024-09-10 04:07:38.336297:  
+2024-09-10 04:07:38.336527: Epoch 838 
+2024-09-10 04:07:38.336614: Current learning rate: 0.00194 
+2024-09-10 04:11:44.572589: train_loss -0.9072 
+2024-09-10 04:11:44.572725: val_loss -0.6819 
+2024-09-10 04:11:44.572775: Pseudo dice [0.6504, 0.8708] 
+2024-09-10 04:11:44.572849: Epoch time: 246.24 s 
+2024-09-10 04:11:45.548453:  
+2024-09-10 04:11:45.548681: Epoch 839 
+2024-09-10 04:11:45.548766: Current learning rate: 0.00193 
+2024-09-10 04:15:51.888584: train_loss -0.9079 
+2024-09-10 04:15:51.888725: val_loss -0.7074 
+2024-09-10 04:15:51.888775: Pseudo dice [0.6621, 0.8825] 
+2024-09-10 04:15:51.888826: Epoch time: 246.34 s 
+2024-09-10 04:15:52.912874:  
+2024-09-10 04:15:52.913042: Epoch 840 
+2024-09-10 04:15:52.913126: Current learning rate: 0.00192 
+2024-09-10 04:19:59.134103: train_loss -0.9065 
+2024-09-10 04:19:59.134255: val_loss -0.7014 
+2024-09-10 04:19:59.134328: Pseudo dice [0.6828, 0.8758] 
+2024-09-10 04:19:59.134380: Epoch time: 246.22 s 
+2024-09-10 04:20:00.093074:  
+2024-09-10 04:20:00.093259: Epoch 841 
+2024-09-10 04:20:00.093347: Current learning rate: 0.00191 
+2024-09-10 04:24:06.348883: train_loss -0.9047 
+2024-09-10 04:24:06.349028: val_loss -0.7008 
+2024-09-10 04:24:06.349078: Pseudo dice [0.6679, 0.8766] 
+2024-09-10 04:24:06.349131: Epoch time: 246.26 s 
+2024-09-10 04:24:07.301703:  
+2024-09-10 04:24:07.301947: Epoch 842 
+2024-09-10 04:24:07.302035: Current learning rate: 0.0019 
+2024-09-10 04:28:13.616471: train_loss -0.9057 
+2024-09-10 04:28:13.616611: val_loss -0.6741 
+2024-09-10 04:28:13.616662: Pseudo dice [0.6504, 0.8691] 
+2024-09-10 04:28:13.616713: Epoch time: 246.32 s 
+2024-09-10 04:28:14.581427:  
+2024-09-10 04:28:14.581656: Epoch 843 
+2024-09-10 04:28:14.581737: Current learning rate: 0.00189 
+2024-09-10 04:32:20.871866: train_loss -0.9099 
+2024-09-10 04:32:20.872011: val_loss -0.6885 
+2024-09-10 04:32:20.872062: Pseudo dice [0.6478, 0.878] 
+2024-09-10 04:32:20.872115: Epoch time: 246.29 s 
+2024-09-10 04:32:21.833077:  
+2024-09-10 04:32:21.833331: Epoch 844 
+2024-09-10 04:32:21.833416: Current learning rate: 0.00188 
+2024-09-10 04:36:28.515894: train_loss -0.9096 
+2024-09-10 04:36:28.516035: val_loss -0.7051 
+2024-09-10 04:36:28.516085: Pseudo dice [0.6943, 0.8685] 
+2024-09-10 04:36:28.516137: Epoch time: 246.68 s 
+2024-09-10 04:36:29.478571:  
+2024-09-10 04:36:29.478735: Epoch 845 
+2024-09-10 04:36:29.478839: Current learning rate: 0.00187 
+2024-09-10 04:40:36.304371: train_loss -0.9096 
+2024-09-10 04:40:36.304532: val_loss -0.6775 
+2024-09-10 04:40:36.304586: Pseudo dice [0.6428, 0.8752] 
+2024-09-10 04:40:36.304640: Epoch time: 246.83 s 
+2024-09-10 04:40:37.262550:  
+2024-09-10 04:40:37.262751: Epoch 846 
+2024-09-10 04:40:37.262834: Current learning rate: 0.00186 
+2024-09-10 04:44:43.517727: train_loss -0.9136 
+2024-09-10 04:44:43.517869: val_loss -0.686 
+2024-09-10 04:44:43.517920: Pseudo dice [0.6259, 0.878] 
+2024-09-10 04:44:43.517971: Epoch time: 246.26 s 
+2024-09-10 04:44:44.494099:  
+2024-09-10 04:44:44.494334: Epoch 847 
+2024-09-10 04:44:44.494420: Current learning rate: 0.00185 
+2024-09-10 04:48:50.776321: train_loss -0.9053 
+2024-09-10 04:48:50.776461: val_loss -0.671 
+2024-09-10 04:48:50.776512: Pseudo dice [0.6353, 0.8694] 
+2024-09-10 04:48:50.776563: Epoch time: 246.28 s 
+2024-09-10 04:48:51.743596:  
+2024-09-10 04:48:51.743787: Epoch 848 
+2024-09-10 04:48:51.743876: Current learning rate: 0.00184 
+2024-09-10 04:52:58.203065: train_loss -0.9069 
+2024-09-10 04:52:58.203208: val_loss -0.6997 
+2024-09-10 04:52:58.203258: Pseudo dice [0.6744, 0.8749] 
+2024-09-10 04:52:58.203309: Epoch time: 246.46 s 
+2024-09-10 04:52:59.154607:  
+2024-09-10 04:52:59.154814: Epoch 849 
+2024-09-10 04:52:59.154894: Current learning rate: 0.00182 
+2024-09-10 04:57:05.433231: train_loss -0.903 
+2024-09-10 04:57:05.433393: val_loss -0.694 
+2024-09-10 04:57:05.433444: Pseudo dice [0.6682, 0.8758] 
+2024-09-10 04:57:05.433495: Epoch time: 246.28 s 
+2024-09-10 04:57:09.373384:  
+2024-09-10 04:57:09.373583: Epoch 850 
+2024-09-10 04:57:09.373666: Current learning rate: 0.00181 
+2024-09-10 05:01:15.872171: train_loss -0.9035 
+2024-09-10 05:01:15.872308: val_loss -0.7029 
+2024-09-10 05:01:15.872358: Pseudo dice [0.6763, 0.8808] 
+2024-09-10 05:01:15.872409: Epoch time: 246.5 s 
+2024-09-10 05:01:16.831657:  
+2024-09-10 05:01:16.831866: Epoch 851 
+2024-09-10 05:01:16.831947: Current learning rate: 0.0018 
+2024-09-10 05:05:23.336481: train_loss -0.9048 
+2024-09-10 05:05:23.336655: val_loss -0.7144 
+2024-09-10 05:05:23.336707: Pseudo dice [0.6764, 0.8879] 
+2024-09-10 05:05:23.336763: Epoch time: 246.51 s 
+2024-09-10 05:05:24.293840:  
+2024-09-10 05:05:24.294054: Epoch 852 
+2024-09-10 05:05:24.294133: Current learning rate: 0.00179 
+2024-09-10 05:09:31.227926: train_loss -0.9065 
+2024-09-10 05:09:31.228102: val_loss -0.6889 
+2024-09-10 05:09:31.228154: Pseudo dice [0.6699, 0.8677] 
+2024-09-10 05:09:31.228208: Epoch time: 246.94 s 
+2024-09-10 05:09:32.180044:  
+2024-09-10 05:09:32.180207: Epoch 853 
+2024-09-10 05:09:32.180288: Current learning rate: 0.00178 
+2024-09-10 05:13:38.792495: train_loss -0.9036 
+2024-09-10 05:13:38.792630: val_loss -0.7093 
+2024-09-10 05:13:38.792681: Pseudo dice [0.6893, 0.8761] 
+2024-09-10 05:13:38.792731: Epoch time: 246.61 s 
+2024-09-10 05:13:39.762132:  
+2024-09-10 05:13:39.762347: Epoch 854 
+2024-09-10 05:13:39.762433: Current learning rate: 0.00177 
+2024-09-10 05:17:46.343585: train_loss -0.9028 
+2024-09-10 05:17:46.343723: val_loss -0.7116 
+2024-09-10 05:17:46.343773: Pseudo dice [0.6773, 0.8736] 
+2024-09-10 05:17:46.343839: Epoch time: 246.58 s 
+2024-09-10 05:17:47.306355:  
+2024-09-10 05:17:47.306512: Epoch 855 
+2024-09-10 05:17:47.306591: Current learning rate: 0.00176 
+2024-09-10 05:21:53.681022: train_loss -0.9024 
+2024-09-10 05:21:53.681206: val_loss -0.7081 
+2024-09-10 05:21:53.681257: Pseudo dice [0.6787, 0.885] 
+2024-09-10 05:21:53.681310: Epoch time: 246.38 s 
+2024-09-10 05:21:54.631887:  
+2024-09-10 05:21:54.632117: Epoch 856 
+2024-09-10 05:21:54.632202: Current learning rate: 0.00175 
+2024-09-10 05:26:01.012763: train_loss -0.9056 
+2024-09-10 05:26:01.012897: val_loss -0.7059 
+2024-09-10 05:26:01.012952: Pseudo dice [0.7173, 0.8793] 
+2024-09-10 05:26:01.013001: Epoch time: 246.38 s 
+2024-09-10 05:26:01.013041: Yayy! New best EMA pseudo Dice: 0.7745 
+2024-09-10 05:26:04.959606:  
+2024-09-10 05:26:04.959785: Epoch 857 
+2024-09-10 05:26:04.959879: Current learning rate: 0.00174 
+2024-09-10 05:30:11.226705: train_loss -0.9084 
+2024-09-10 05:30:11.226863: val_loss -0.6794 
+2024-09-10 05:30:11.226913: Pseudo dice [0.6154, 0.8714] 
+2024-09-10 05:30:11.226997: Epoch time: 246.27 s 
+2024-09-10 05:30:12.205380:  
+2024-09-10 05:30:12.205608: Epoch 858 
+2024-09-10 05:30:12.205688: Current learning rate: 0.00173 
+2024-09-10 05:34:18.486726: train_loss -0.907 
+2024-09-10 05:34:18.486863: val_loss -0.6641 
+2024-09-10 05:34:18.486918: Pseudo dice [0.6282, 0.8701] 
+2024-09-10 05:34:18.486969: Epoch time: 246.28 s 
+2024-09-10 05:34:19.437185:  
+2024-09-10 05:34:19.437376: Epoch 859 
+2024-09-10 05:34:19.437495: Current learning rate: 0.00172 
+2024-09-10 05:38:25.778236: train_loss -0.9088 
+2024-09-10 05:38:25.778372: val_loss -0.6473 
+2024-09-10 05:38:25.778424: Pseudo dice [0.5889, 0.8759] 
+2024-09-10 05:38:25.778476: Epoch time: 246.34 s 
+2024-09-10 05:38:26.752204:  
+2024-09-10 05:38:26.752395: Epoch 860 
+2024-09-10 05:38:26.752477: Current learning rate: 0.0017 
+2024-09-10 05:42:33.252708: train_loss -0.9067 
+2024-09-10 05:42:33.252844: val_loss -0.687 
+2024-09-10 05:42:33.252894: Pseudo dice [0.6404, 0.8771] 
+2024-09-10 05:42:33.252950: Epoch time: 246.5 s 
+2024-09-10 05:42:35.154155:  
+2024-09-10 05:42:35.154376: Epoch 861 
+2024-09-10 05:42:35.154504: Current learning rate: 0.00169 
+2024-09-10 05:46:41.872279: train_loss -0.9082 
+2024-09-10 05:46:41.872496: val_loss -0.6812 
+2024-09-10 05:46:41.872550: Pseudo dice [0.671, 0.8704] 
+2024-09-10 05:46:41.872603: Epoch time: 246.72 s 
+2024-09-10 05:46:42.834212:  
+2024-09-10 05:46:42.834389: Epoch 862 
+2024-09-10 05:46:42.834540: Current learning rate: 0.00168 
+2024-09-10 05:50:49.555458: train_loss -0.9098 
+2024-09-10 05:50:49.555605: val_loss -0.6986 
+2024-09-10 05:50:49.555658: Pseudo dice [0.6778, 0.872] 
+2024-09-10 05:50:49.555710: Epoch time: 246.72 s 
+2024-09-10 05:50:50.522770:  
+2024-09-10 05:50:50.522974: Epoch 863 
+2024-09-10 05:50:50.523072: Current learning rate: 0.00167 
+2024-09-10 05:54:56.836461: train_loss -0.908 
+2024-09-10 05:54:56.836600: val_loss -0.7066 
+2024-09-10 05:54:56.836651: Pseudo dice [0.6975, 0.8811] 
+2024-09-10 05:54:56.836703: Epoch time: 246.32 s 
+2024-09-10 05:54:57.800897:  
+2024-09-10 05:54:57.801070: Epoch 864 
+2024-09-10 05:54:57.801152: Current learning rate: 0.00166 
+2024-09-10 05:59:04.194318: train_loss -0.9063 
+2024-09-10 05:59:04.194464: val_loss -0.6641 
+2024-09-10 05:59:04.194516: Pseudo dice [0.6282, 0.8738] 
+2024-09-10 05:59:04.194568: Epoch time: 246.4 s 
+2024-09-10 05:59:05.157803:  
+2024-09-10 05:59:05.158024: Epoch 865 
+2024-09-10 05:59:05.158106: Current learning rate: 0.00165 
+2024-09-10 06:03:11.440532: train_loss -0.9073 
+2024-09-10 06:03:11.440675: val_loss -0.6827 
+2024-09-10 06:03:11.440724: Pseudo dice [0.6597, 0.8703] 
+2024-09-10 06:03:11.440778: Epoch time: 246.28 s 
+2024-09-10 06:03:12.383058:  
+2024-09-10 06:03:12.383308: Epoch 866 
+2024-09-10 06:03:12.383393: Current learning rate: 0.00164 
+2024-09-10 06:07:18.460731: train_loss -0.9059 
+2024-09-10 06:07:18.460885: val_loss -0.6642 
+2024-09-10 06:07:18.460936: Pseudo dice [0.6404, 0.8774] 
+2024-09-10 06:07:18.460988: Epoch time: 246.08 s 
+2024-09-10 06:07:19.410723:  
+2024-09-10 06:07:19.410921: Epoch 867 
+2024-09-10 06:07:19.411006: Current learning rate: 0.00163 
+2024-09-10 06:11:25.676959: train_loss -0.9058 
+2024-09-10 06:11:25.677122: val_loss -0.6866 
+2024-09-10 06:11:25.677178: Pseudo dice [0.6455, 0.8809] 
+2024-09-10 06:11:25.677244: Epoch time: 246.27 s 
+2024-09-10 06:11:26.664968:  
+2024-09-10 06:11:26.665182: Epoch 868 
+2024-09-10 06:11:26.665269: Current learning rate: 0.00162 
+2024-09-10 06:15:32.780780: train_loss -0.9094 
+2024-09-10 06:15:32.780955: val_loss -0.6763 
+2024-09-10 06:15:32.781005: Pseudo dice [0.6393, 0.8736] 
+2024-09-10 06:15:32.781060: Epoch time: 246.12 s 
+2024-09-10 06:15:33.740138:  
+2024-09-10 06:15:33.740374: Epoch 869 
+2024-09-10 06:15:33.740455: Current learning rate: 0.00161 
+2024-09-10 06:19:40.111602: train_loss -0.9077 
+2024-09-10 06:19:40.111766: val_loss -0.6891 
+2024-09-10 06:19:40.111825: Pseudo dice [0.6686, 0.8799] 
+2024-09-10 06:19:40.111881: Epoch time: 246.37 s 
+2024-09-10 06:19:41.088935:  
+2024-09-10 06:19:41.089148: Epoch 870 
+2024-09-10 06:19:41.089266: Current learning rate: 0.00159 
+2024-09-10 06:23:47.611914: train_loss -0.9046 
+2024-09-10 06:23:47.612062: val_loss -0.6617 
+2024-09-10 06:23:47.612112: Pseudo dice [0.6432, 0.8836] 
+2024-09-10 06:23:47.612164: Epoch time: 246.52 s 
+2024-09-10 06:23:48.580997:  
+2024-09-10 06:23:48.581178: Epoch 871 
+2024-09-10 06:23:48.581262: Current learning rate: 0.00158 
+2024-09-10 06:27:54.757646: train_loss -0.9071 
+2024-09-10 06:27:54.757795: val_loss -0.6439 
+2024-09-10 06:27:54.757868: Pseudo dice [0.6171, 0.878] 
+2024-09-10 06:27:54.757920: Epoch time: 246.18 s 
+2024-09-10 06:27:55.710237:  
+2024-09-10 06:27:55.710495: Epoch 872 
+2024-09-10 06:27:55.710575: Current learning rate: 0.00157 
+2024-09-10 06:32:01.748482: train_loss -0.9097 
+2024-09-10 06:32:01.748617: val_loss -0.6952 
+2024-09-10 06:32:01.748666: Pseudo dice [0.6857, 0.8805] 
+2024-09-10 06:32:01.748716: Epoch time: 246.04 s 
+2024-09-10 06:32:02.700987:  
+2024-09-10 06:32:02.701209: Epoch 873 
+2024-09-10 06:32:02.701289: Current learning rate: 0.00156 
+2024-09-10 06:36:08.836259: train_loss -0.9082 
+2024-09-10 06:36:08.836399: val_loss -0.7035 
+2024-09-10 06:36:08.836450: Pseudo dice [0.6394, 0.8785] 
+2024-09-10 06:36:08.836502: Epoch time: 246.14 s 
+2024-09-10 06:36:09.786855:  
+2024-09-10 06:36:09.787068: Epoch 874 
+2024-09-10 06:36:09.787153: Current learning rate: 0.00155 
+2024-09-10 06:40:15.930133: train_loss -0.9134 
+2024-09-10 06:40:15.930293: val_loss -0.6945 
+2024-09-10 06:40:15.930342: Pseudo dice [0.657, 0.8876] 
+2024-09-10 06:40:15.930394: Epoch time: 246.15 s 
+2024-09-10 06:40:16.875996:  
+2024-09-10 06:40:16.876179: Epoch 875 
+2024-09-10 06:40:16.876260: Current learning rate: 0.00154 
+2024-09-10 06:44:23.159207: train_loss -0.9101 
+2024-09-10 06:44:23.159347: val_loss -0.6949 
+2024-09-10 06:44:23.159397: Pseudo dice [0.6806, 0.8812] 
+2024-09-10 06:44:23.159448: Epoch time: 246.29 s 
+2024-09-10 06:44:24.137829:  
+2024-09-10 06:44:24.138023: Epoch 876 
+2024-09-10 06:44:24.138103: Current learning rate: 0.00153 
+2024-09-10 06:48:30.557135: train_loss -0.9083 
+2024-09-10 06:48:30.557275: val_loss -0.6964 
+2024-09-10 06:48:30.557326: Pseudo dice [0.6817, 0.876] 
+2024-09-10 06:48:30.557378: Epoch time: 246.42 s 
+2024-09-10 06:48:31.520821:  
+2024-09-10 06:48:31.520985: Epoch 877 
+2024-09-10 06:48:31.521104: Current learning rate: 0.00152 
+2024-09-10 06:52:38.260898: train_loss -0.9125 
+2024-09-10 06:52:38.261035: val_loss -0.7016 
+2024-09-10 06:52:38.261085: Pseudo dice [0.7, 0.8941] 
+2024-09-10 06:52:38.261136: Epoch time: 246.74 s 
+2024-09-10 06:52:39.227857:  
+2024-09-10 06:52:39.228007: Epoch 878 
+2024-09-10 06:52:39.228087: Current learning rate: 0.00151 
+2024-09-10 06:56:45.693500: train_loss -0.9057 
+2024-09-10 06:56:45.693639: val_loss -0.6915 
+2024-09-10 06:56:45.693690: Pseudo dice [0.6725, 0.8784] 
+2024-09-10 06:56:45.693740: Epoch time: 246.47 s 
+2024-09-10 06:56:46.652316:  
+2024-09-10 06:56:46.652486: Epoch 879 
+2024-09-10 06:56:46.652566: Current learning rate: 0.00149 
+2024-09-10 07:00:52.933653: train_loss -0.9136 
+2024-09-10 07:00:52.933795: val_loss -0.6683 
+2024-09-10 07:00:52.933846: Pseudo dice [0.6229, 0.8837] 
+2024-09-10 07:00:52.933895: Epoch time: 246.28 s 
+2024-09-10 07:00:53.890864:  
+2024-09-10 07:00:53.891016: Epoch 880 
+2024-09-10 07:00:53.891147: Current learning rate: 0.00148 
+2024-09-10 07:05:00.173187: train_loss -0.9126 
+2024-09-10 07:05:00.173323: val_loss -0.7106 
+2024-09-10 07:05:00.173373: Pseudo dice [0.6752, 0.8635] 
+2024-09-10 07:05:00.173423: Epoch time: 246.28 s 
+2024-09-10 07:05:01.149445:  
+2024-09-10 07:05:01.149649: Epoch 881 
+2024-09-10 07:05:01.149744: Current learning rate: 0.00147 
+2024-09-10 07:09:07.477375: train_loss -0.9081 
+2024-09-10 07:09:07.477519: val_loss -0.6701 
+2024-09-10 07:09:07.477570: Pseudo dice [0.6206, 0.8909] 
+2024-09-10 07:09:07.477622: Epoch time: 246.33 s 
+2024-09-10 07:09:08.440055:  
+2024-09-10 07:09:08.440260: Epoch 882 
+2024-09-10 07:09:08.440339: Current learning rate: 0.00146 
+2024-09-10 07:13:14.884984: train_loss -0.9126 
+2024-09-10 07:13:14.885122: val_loss -0.6709 
+2024-09-10 07:13:14.885172: Pseudo dice [0.6291, 0.8609] 
+2024-09-10 07:13:14.885223: Epoch time: 246.45 s 
+2024-09-10 07:13:15.833676:  
+2024-09-10 07:13:15.833868: Epoch 883 
+2024-09-10 07:13:15.833994: Current learning rate: 0.00145 
+2024-09-10 07:17:22.332540: train_loss -0.915 
+2024-09-10 07:17:22.332720: val_loss -0.6848 
+2024-09-10 07:17:22.332772: Pseudo dice [0.6753, 0.8694] 
+2024-09-10 07:17:22.332825: Epoch time: 246.5 s 
+2024-09-10 07:17:23.289436:  
+2024-09-10 07:17:23.289634: Epoch 884 
+2024-09-10 07:17:23.289715: Current learning rate: 0.00144 
+2024-09-10 07:21:29.983655: train_loss -0.9162 
+2024-09-10 07:21:29.983820: val_loss -0.6593 
+2024-09-10 07:21:29.983880: Pseudo dice [0.6392, 0.8719] 
+2024-09-10 07:21:29.983931: Epoch time: 246.7 s 
+2024-09-10 07:21:30.966030:  
+2024-09-10 07:21:30.966177: Epoch 885 
+2024-09-10 07:21:30.966259: Current learning rate: 0.00143 
+2024-09-10 07:25:38.590143: train_loss -0.9133 
+2024-09-10 07:25:38.590284: val_loss -0.6799 
+2024-09-10 07:25:38.590333: Pseudo dice [0.6602, 0.8708] 
+2024-09-10 07:25:38.590384: Epoch time: 247.63 s 
+2024-09-10 07:25:39.576838:  
+2024-09-10 07:25:39.577062: Epoch 886 
+2024-09-10 07:25:39.577160: Current learning rate: 0.00142 
+2024-09-10 07:29:46.231071: train_loss -0.9125 
+2024-09-10 07:29:46.231219: val_loss -0.6957 
+2024-09-10 07:29:46.231271: Pseudo dice [0.6755, 0.883] 
+2024-09-10 07:29:46.231375: Epoch time: 246.66 s 
+2024-09-10 07:29:47.193114:  
+2024-09-10 07:29:47.193326: Epoch 887 
+2024-09-10 07:29:47.193408: Current learning rate: 0.00141 
+2024-09-10 07:33:53.773370: train_loss -0.9113 
+2024-09-10 07:33:53.773525: val_loss -0.7004 
+2024-09-10 07:33:53.773577: Pseudo dice [0.6657, 0.8791] 
+2024-09-10 07:33:53.773629: Epoch time: 246.58 s 
+2024-09-10 07:33:54.734763:  
+2024-09-10 07:33:54.734959: Epoch 888 
+2024-09-10 07:33:54.735061: Current learning rate: 0.00139 
+2024-09-10 07:38:01.365314: train_loss -0.9136 
+2024-09-10 07:38:01.365454: val_loss -0.676 
+2024-09-10 07:38:01.365505: Pseudo dice [0.6493, 0.8703] 
+2024-09-10 07:38:01.365556: Epoch time: 246.63 s 
+2024-09-10 07:38:02.335832:  
+2024-09-10 07:38:02.336014: Epoch 889 
+2024-09-10 07:38:02.336097: Current learning rate: 0.00138 
+2024-09-10 07:42:08.741096: train_loss -0.9106 
+2024-09-10 07:42:08.741237: val_loss -0.6793 
+2024-09-10 07:42:08.741288: Pseudo dice [0.6561, 0.8779] 
+2024-09-10 07:42:08.741341: Epoch time: 246.41 s 
+2024-09-10 07:42:09.698642:  
+2024-09-10 07:42:09.698817: Epoch 890 
+2024-09-10 07:42:09.698895: Current learning rate: 0.00137 
+2024-09-10 07:46:16.240258: train_loss -0.9122 
+2024-09-10 07:46:16.240415: val_loss -0.6728 
+2024-09-10 07:46:16.240466: Pseudo dice [0.6207, 0.8741] 
+2024-09-10 07:46:16.240516: Epoch time: 246.54 s 
+2024-09-10 07:46:17.198431:  
+2024-09-10 07:46:17.198629: Epoch 891 
+2024-09-10 07:46:17.198708: Current learning rate: 0.00136 
+2024-09-10 07:50:23.775060: train_loss -0.913 
+2024-09-10 07:50:23.775209: val_loss -0.6871 
+2024-09-10 07:50:23.775260: Pseudo dice [0.6342, 0.8728] 
+2024-09-10 07:50:23.775312: Epoch time: 246.58 s 
+2024-09-10 07:50:24.729274:  
+2024-09-10 07:50:24.729453: Epoch 892 
+2024-09-10 07:50:24.729583: Current learning rate: 0.00135 
+2024-09-10 07:54:31.322574: train_loss -0.9118 
+2024-09-10 07:54:31.322714: val_loss -0.7049 
+2024-09-10 07:54:31.322765: Pseudo dice [0.6611, 0.8802] 
+2024-09-10 07:54:31.322816: Epoch time: 246.6 s 
+2024-09-10 07:54:32.304996:  
+2024-09-10 07:54:32.305205: Epoch 893 
+2024-09-10 07:54:32.305287: Current learning rate: 0.00134 
+2024-09-10 07:58:38.828568: train_loss -0.9127 
+2024-09-10 07:58:38.828791: val_loss -0.6694 
+2024-09-10 07:58:38.828886: Pseudo dice [0.6331, 0.8874] 
+2024-09-10 07:58:38.828977: Epoch time: 246.53 s 
+2024-09-10 07:58:39.779173:  
+2024-09-10 07:58:39.779394: Epoch 894 
+2024-09-10 07:58:39.779476: Current learning rate: 0.00133 
+2024-09-10 08:02:46.315761: train_loss -0.9125 
+2024-09-10 08:02:46.315922: val_loss -0.6633 
+2024-09-10 08:02:46.315973: Pseudo dice [0.6224, 0.8814] 
+2024-09-10 08:02:46.316023: Epoch time: 246.54 s 
+2024-09-10 08:02:47.289090:  
+2024-09-10 08:02:47.289321: Epoch 895 
+2024-09-10 08:02:47.289409: Current learning rate: 0.00132 
+2024-09-10 08:06:53.775927: train_loss -0.912 
+2024-09-10 08:06:53.776091: val_loss -0.6854 
+2024-09-10 08:06:53.776141: Pseudo dice [0.649, 0.8598] 
+2024-09-10 08:06:53.776193: Epoch time: 246.49 s 
+2024-09-10 08:06:54.739488:  
+2024-09-10 08:06:54.739709: Epoch 896 
+2024-09-10 08:06:54.739790: Current learning rate: 0.0013 
+2024-09-10 08:11:01.258850: train_loss -0.9118 
+2024-09-10 08:11:01.258989: val_loss -0.6704 
+2024-09-10 08:11:01.259040: Pseudo dice [0.636, 0.8706] 
+2024-09-10 08:11:01.259090: Epoch time: 246.52 s 
+2024-09-10 08:11:02.228270:  
+2024-09-10 08:11:02.228486: Epoch 897 
+2024-09-10 08:11:02.228574: Current learning rate: 0.00129 
+2024-09-10 08:15:08.662640: train_loss -0.9157 
+2024-09-10 08:15:08.662798: val_loss -0.6906 
+2024-09-10 08:15:08.662849: Pseudo dice [0.6595, 0.8719] 
+2024-09-10 08:15:08.662902: Epoch time: 246.44 s 
+2024-09-10 08:15:09.633364:  
+2024-09-10 08:15:09.633543: Epoch 898 
+2024-09-10 08:15:09.633648: Current learning rate: 0.00128 
+2024-09-10 08:19:16.191585: train_loss -0.9159 
+2024-09-10 08:19:16.191727: val_loss -0.6924 
+2024-09-10 08:19:16.191776: Pseudo dice [0.697, 0.8579] 
+2024-09-10 08:19:16.191833: Epoch time: 246.56 s 
+2024-09-10 08:19:17.152873:  
+2024-09-10 08:19:17.153138: Epoch 899 
+2024-09-10 08:19:17.153220: Current learning rate: 0.00127 
+2024-09-10 08:23:23.839515: train_loss -0.9158 
+2024-09-10 08:23:23.839655: val_loss -0.6827 
+2024-09-10 08:23:23.839705: Pseudo dice [0.6585, 0.8821] 
+2024-09-10 08:23:23.839756: Epoch time: 246.69 s 
+2024-09-10 08:23:27.785197:  
+2024-09-10 08:23:27.785388: Epoch 900 
+2024-09-10 08:23:27.785484: Current learning rate: 0.00126 
+2024-09-10 08:27:34.686789: train_loss -0.9134 
+2024-09-10 08:27:34.686971: val_loss -0.7035 
+2024-09-10 08:27:34.687023: Pseudo dice [0.6451, 0.8876] 
+2024-09-10 08:27:34.687074: Epoch time: 246.9 s 
+2024-09-10 08:27:35.679861:  
+2024-09-10 08:27:35.680028: Epoch 901 
+2024-09-10 08:27:35.680115: Current learning rate: 0.00125 
+2024-09-10 08:31:42.260657: train_loss -0.9149 
+2024-09-10 08:31:42.260795: val_loss -0.6849 
+2024-09-10 08:31:42.260856: Pseudo dice [0.6537, 0.8782] 
+2024-09-10 08:31:42.260906: Epoch time: 246.58 s 
+2024-09-10 08:31:43.221303:  
+2024-09-10 08:31:43.221475: Epoch 902 
+2024-09-10 08:31:43.221599: Current learning rate: 0.00124 
+2024-09-10 08:35:49.618333: train_loss -0.9071 
+2024-09-10 08:35:49.618481: val_loss -0.6708 
+2024-09-10 08:35:49.618534: Pseudo dice [0.6486, 0.8761] 
+2024-09-10 08:35:49.618585: Epoch time: 246.4 s 
+2024-09-10 08:35:50.562831:  
+2024-09-10 08:35:50.563031: Epoch 903 
+2024-09-10 08:35:50.563113: Current learning rate: 0.00122 
+2024-09-10 08:39:56.974421: train_loss -0.9134 
+2024-09-10 08:39:56.974565: val_loss -0.6763 
+2024-09-10 08:39:56.974616: Pseudo dice [0.6419, 0.8667] 
+2024-09-10 08:39:56.974668: Epoch time: 246.41 s 
+2024-09-10 08:39:57.956617:  
+2024-09-10 08:39:57.956850: Epoch 904 
+2024-09-10 08:39:57.956936: Current learning rate: 0.00121 
+2024-09-10 08:44:04.212415: train_loss -0.916 
+2024-09-10 08:44:04.212549: val_loss -0.7195 
+2024-09-10 08:44:04.212599: Pseudo dice [0.6614, 0.8835] 
+2024-09-10 08:44:04.212650: Epoch time: 246.26 s 
+2024-09-10 08:44:05.157946:  
+2024-09-10 08:44:05.158097: Epoch 905 
+2024-09-10 08:44:05.158188: Current learning rate: 0.0012 
+2024-09-10 08:48:11.410996: train_loss -0.9169 
+2024-09-10 08:48:11.411170: val_loss -0.6756 
+2024-09-10 08:48:11.411222: Pseudo dice [0.6216, 0.8888] 
+2024-09-10 08:48:11.411274: Epoch time: 246.25 s 
+2024-09-10 08:48:12.381016:  
+2024-09-10 08:48:12.381231: Epoch 906 
+2024-09-10 08:48:12.381334: Current learning rate: 0.00119 
+2024-09-10 08:52:18.623821: train_loss -0.9172 
+2024-09-10 08:52:18.623980: val_loss -0.6679 
+2024-09-10 08:52:18.624032: Pseudo dice [0.6247, 0.8781] 
+2024-09-10 08:52:18.624084: Epoch time: 246.24 s 
+2024-09-10 08:52:19.574004:  
+2024-09-10 08:52:19.574170: Epoch 907 
+2024-09-10 08:52:19.574279: Current learning rate: 0.00118 
+2024-09-10 08:56:25.942595: train_loss -0.9112 
+2024-09-10 08:56:25.942733: val_loss -0.6826 
+2024-09-10 08:56:25.942785: Pseudo dice [0.6404, 0.884] 
+2024-09-10 08:56:25.942835: Epoch time: 246.37 s 
+2024-09-10 08:56:26.922632:  
+2024-09-10 08:56:26.922819: Epoch 908 
+2024-09-10 08:56:26.922937: Current learning rate: 0.00117 
+2024-09-10 09:00:33.334496: train_loss -0.9152 
+2024-09-10 09:00:33.334655: val_loss -0.6879 
+2024-09-10 09:00:33.334706: Pseudo dice [0.6609, 0.8809] 
+2024-09-10 09:00:33.334759: Epoch time: 246.41 s 
+2024-09-10 09:00:34.298842:  
+2024-09-10 09:00:34.299010: Epoch 909 
+2024-09-10 09:00:34.299090: Current learning rate: 0.00116 
+2024-09-10 09:04:41.079859: train_loss -0.9164 
+2024-09-10 09:04:41.079997: val_loss -0.6772 
+2024-09-10 09:04:41.080047: Pseudo dice [0.6756, 0.8762] 
+2024-09-10 09:04:41.080099: Epoch time: 246.78 s 
+2024-09-10 09:04:42.985332:  
+2024-09-10 09:04:42.985541: Epoch 910 
+2024-09-10 09:04:42.985651: Current learning rate: 0.00115 
+2024-09-10 09:08:49.580351: train_loss -0.9154 
+2024-09-10 09:08:49.580492: val_loss -0.6781 
+2024-09-10 09:08:49.580554: Pseudo dice [0.6612, 0.8624] 
+2024-09-10 09:08:49.580616: Epoch time: 246.6 s 
+2024-09-10 09:08:50.532077:  
+2024-09-10 09:08:50.532332: Epoch 911 
+2024-09-10 09:08:50.532420: Current learning rate: 0.00113 
+2024-09-10 09:12:57.121844: train_loss -0.9098 
+2024-09-10 09:12:57.121987: val_loss -0.6689 
+2024-09-10 09:12:57.122037: Pseudo dice [0.6558, 0.8797] 
+2024-09-10 09:12:57.122088: Epoch time: 246.59 s 
+2024-09-10 09:12:58.072441:  
+2024-09-10 09:12:58.072647: Epoch 912 
+2024-09-10 09:12:58.072727: Current learning rate: 0.00112 
+2024-09-10 09:17:04.687512: train_loss -0.9146 
+2024-09-10 09:17:04.687691: val_loss -0.6893 
+2024-09-10 09:17:04.687766: Pseudo dice [0.6309, 0.8801] 
+2024-09-10 09:17:04.687865: Epoch time: 246.62 s 
+2024-09-10 09:17:05.632741:  
+2024-09-10 09:17:05.632942: Epoch 913 
+2024-09-10 09:17:05.633020: Current learning rate: 0.00111 
+2024-09-10 09:21:12.185655: train_loss -0.9141 
+2024-09-10 09:21:12.185820: val_loss -0.7066 
+2024-09-10 09:21:12.185872: Pseudo dice [0.6937, 0.8825] 
+2024-09-10 09:21:12.185924: Epoch time: 246.55 s 
+2024-09-10 09:21:13.152465:  
+2024-09-10 09:21:13.152707: Epoch 914 
+2024-09-10 09:21:13.152789: Current learning rate: 0.0011 
+2024-09-10 09:25:19.737637: train_loss -0.9135 
+2024-09-10 09:25:19.737818: val_loss -0.6888 
+2024-09-10 09:25:19.737870: Pseudo dice [0.6713, 0.8653] 
+2024-09-10 09:25:19.737924: Epoch time: 246.59 s 
+2024-09-10 09:25:20.693165:  
+2024-09-10 09:25:20.693405: Epoch 915 
+2024-09-10 09:25:20.693488: Current learning rate: 0.00109 
+2024-09-10 09:29:27.481482: train_loss -0.9124 
+2024-09-10 09:29:27.481623: val_loss -0.6694 
+2024-09-10 09:29:27.481673: Pseudo dice [0.631, 0.8829] 
+2024-09-10 09:29:27.481725: Epoch time: 246.79 s 
+2024-09-10 09:29:28.441296:  
+2024-09-10 09:29:28.441509: Epoch 916 
+2024-09-10 09:29:28.441615: Current learning rate: 0.00108 
+2024-09-10 09:33:35.545581: train_loss -0.9161 
+2024-09-10 09:33:35.545722: val_loss -0.6831 
+2024-09-10 09:33:35.545825: Pseudo dice [0.6313, 0.8707] 
+2024-09-10 09:33:35.545880: Epoch time: 247.11 s 
+2024-09-10 09:33:36.528318:  
+2024-09-10 09:33:36.528532: Epoch 917 
+2024-09-10 09:33:36.528650: Current learning rate: 0.00106 
+2024-09-10 09:37:43.129537: train_loss -0.9153 
+2024-09-10 09:37:43.129676: val_loss -0.6631 
+2024-09-10 09:37:43.129726: Pseudo dice [0.6364, 0.8708] 
+2024-09-10 09:37:43.129778: Epoch time: 246.6 s 
+2024-09-10 09:37:44.082878:  
+2024-09-10 09:37:44.083121: Epoch 918 
+2024-09-10 09:37:44.083202: Current learning rate: 0.00105 
+2024-09-10 09:41:50.818163: train_loss -0.9159 
+2024-09-10 09:41:50.818335: val_loss -0.6842 
+2024-09-10 09:41:50.818384: Pseudo dice [0.6868, 0.8894] 
+2024-09-10 09:41:50.818437: Epoch time: 246.74 s 
+2024-09-10 09:41:51.782283:  
+2024-09-10 09:41:51.782513: Epoch 919 
+2024-09-10 09:41:51.782616: Current learning rate: 0.00104 
+2024-09-10 09:45:58.544869: train_loss -0.9203 
+2024-09-10 09:45:58.545009: val_loss -0.6966 
+2024-09-10 09:45:58.545058: Pseudo dice [0.7025, 0.8683] 
+2024-09-10 09:45:58.545110: Epoch time: 246.76 s 
+2024-09-10 09:45:59.506101:  
+2024-09-10 09:45:59.506363: Epoch 920 
+2024-09-10 09:45:59.506488: Current learning rate: 0.00103 
+2024-09-10 09:50:06.373145: train_loss -0.9158 
+2024-09-10 09:50:06.373292: val_loss -0.6841 
+2024-09-10 09:50:06.373348: Pseudo dice [0.6994, 0.8619] 
+2024-09-10 09:50:06.373403: Epoch time: 246.87 s 
+2024-09-10 09:50:07.346356:  
+2024-09-10 09:50:07.346539: Epoch 921 
+2024-09-10 09:50:07.346623: Current learning rate: 0.00102 
+2024-09-10 09:54:14.272096: train_loss -0.9197 
+2024-09-10 09:54:14.272256: val_loss -0.6778 
+2024-09-10 09:54:14.272314: Pseudo dice [0.6613, 0.8747] 
+2024-09-10 09:54:14.272369: Epoch time: 246.93 s 
+2024-09-10 09:54:15.217870:  
+2024-09-10 09:54:15.218112: Epoch 922 
+2024-09-10 09:54:15.218201: Current learning rate: 0.00101 
+2024-09-10 09:58:22.121618: train_loss -0.9201 
+2024-09-10 09:58:22.121757: val_loss -0.7018 
+2024-09-10 09:58:22.121814: Pseudo dice [0.6467, 0.8825] 
+2024-09-10 09:58:22.121870: Epoch time: 246.91 s 
+2024-09-10 09:58:23.077524:  
+2024-09-10 09:58:23.077729: Epoch 923 
+2024-09-10 09:58:23.077814: Current learning rate: 0.001 
+2024-09-10 10:02:29.722364: train_loss -0.9119 
+2024-09-10 10:02:29.722525: val_loss -0.6602 
+2024-09-10 10:02:29.722581: Pseudo dice [0.6142, 0.8777] 
+2024-09-10 10:02:29.722638: Epoch time: 246.65 s 
+2024-09-10 10:02:30.675703:  
+2024-09-10 10:02:30.675943: Epoch 924 
+2024-09-10 10:02:30.676044: Current learning rate: 0.00098 
+2024-09-10 10:06:37.193917: train_loss -0.9151 
+2024-09-10 10:06:37.194071: val_loss -0.7046 
+2024-09-10 10:06:37.194128: Pseudo dice [0.6751, 0.8765] 
+2024-09-10 10:06:37.194185: Epoch time: 246.52 s 
+2024-09-10 10:06:38.153034:  
+2024-09-10 10:06:38.153281: Epoch 925 
+2024-09-10 10:06:38.153367: Current learning rate: 0.00097 
+2024-09-10 10:10:44.455962: train_loss -0.9171 
+2024-09-10 10:10:44.456149: val_loss -0.6504 
+2024-09-10 10:10:44.456207: Pseudo dice [0.6257, 0.8762] 
+2024-09-10 10:10:44.456264: Epoch time: 246.3 s 
+2024-09-10 10:10:45.420856:  
+2024-09-10 10:10:45.421050: Epoch 926 
+2024-09-10 10:10:45.421145: Current learning rate: 0.00096 
+2024-09-10 10:14:51.922267: train_loss -0.9198 
+2024-09-10 10:14:51.922690: val_loss -0.6912 
+2024-09-10 10:14:51.922749: Pseudo dice [0.6522, 0.8734] 
+2024-09-10 10:14:51.922808: Epoch time: 246.5 s 
+2024-09-10 10:14:52.898915:  
+2024-09-10 10:14:52.899096: Epoch 927 
+2024-09-10 10:14:52.899183: Current learning rate: 0.00095 
+2024-09-10 10:19:04.943333: train_loss -0.9174 
+2024-09-10 10:19:04.943507: val_loss -0.6936 
+2024-09-10 10:19:04.943565: Pseudo dice [0.6382, 0.869] 
+2024-09-10 10:19:04.943624: Epoch time: 252.05 s 
+2024-09-10 10:19:05.956938:  
+2024-09-10 10:19:05.957113: Epoch 928 
+2024-09-10 10:19:05.957198: Current learning rate: 0.00094 
+2024-09-10 10:23:13.650816: train_loss -0.9152 
+2024-09-10 10:23:13.650980: val_loss -0.6861 
+2024-09-10 10:23:13.651037: Pseudo dice [0.6653, 0.868] 
+2024-09-10 10:23:13.651150: Epoch time: 247.7 s 
+2024-09-10 10:23:14.724176:  
+2024-09-10 10:23:14.724381: Epoch 929 
+2024-09-10 10:23:14.724468: Current learning rate: 0.00092 
+2024-09-10 10:27:21.711193: train_loss -0.9159 
+2024-09-10 10:27:21.711386: val_loss -0.6901 
+2024-09-10 10:27:21.711453: Pseudo dice [0.6784, 0.8707] 
+2024-09-10 10:27:21.711523: Epoch time: 246.99 s 
+2024-09-10 10:27:22.816304:  
+2024-09-10 10:27:22.816510: Epoch 930 
+2024-09-10 10:27:22.816606: Current learning rate: 0.00091 
+2024-09-10 10:31:29.430200: train_loss -0.9148 
+2024-09-10 10:31:29.430348: val_loss -0.6575 
+2024-09-10 10:31:29.430405: Pseudo dice [0.6134, 0.8604] 
+2024-09-10 10:31:29.430461: Epoch time: 246.62 s 
+2024-09-10 10:31:30.385312:  
+2024-09-10 10:31:30.385514: Epoch 931 
+2024-09-10 10:31:30.385603: Current learning rate: 0.0009 
+2024-09-10 10:35:36.853817: train_loss -0.9206 
+2024-09-10 10:35:36.853985: val_loss -0.6782 
+2024-09-10 10:35:36.854041: Pseudo dice [0.6623, 0.8732] 
+2024-09-10 10:35:36.854097: Epoch time: 246.47 s 
+2024-09-10 10:35:37.813293:  
+2024-09-10 10:35:37.813488: Epoch 932 
+2024-09-10 10:35:37.813604: Current learning rate: 0.00089 
+2024-09-10 10:39:44.392844: train_loss -0.9176 
+2024-09-10 10:39:44.393013: val_loss -0.6937 
+2024-09-10 10:39:44.393070: Pseudo dice [0.6823, 0.8701] 
+2024-09-10 10:39:44.393124: Epoch time: 246.58 s 
+2024-09-10 10:39:45.353084:  
+2024-09-10 10:39:45.353275: Epoch 933 
+2024-09-10 10:39:45.353406: Current learning rate: 0.00088 
+2024-09-10 10:43:52.079798: train_loss -0.9131 
+2024-09-10 10:43:52.079970: val_loss -0.7141 
+2024-09-10 10:43:52.080026: Pseudo dice [0.713, 0.8716] 
+2024-09-10 10:43:52.080081: Epoch time: 246.73 s 
+2024-09-10 10:43:53.077530:  
+2024-09-10 10:43:53.077693: Epoch 934 
+2024-09-10 10:43:53.077891: Current learning rate: 0.00087 
+2024-09-10 10:47:59.774530: train_loss -0.9205 
+2024-09-10 10:47:59.774676: val_loss -0.6809 
+2024-09-10 10:47:59.774736: Pseudo dice [0.6574, 0.8837] 
+2024-09-10 10:47:59.774793: Epoch time: 246.7 s 
+2024-09-10 10:48:01.743733:  
+2024-09-10 10:48:01.743994: Epoch 935 
+2024-09-10 10:48:01.744122: Current learning rate: 0.00085 
+2024-09-10 10:52:08.936586: train_loss -0.9185 
+2024-09-10 10:52:08.936749: val_loss -0.6687 
+2024-09-10 10:52:08.936808: Pseudo dice [0.6575, 0.8637] 
+2024-09-10 10:52:08.936865: Epoch time: 247.19 s 
+2024-09-10 10:52:09.911397:  
+2024-09-10 10:52:09.911625: Epoch 936 
+2024-09-10 10:52:09.911731: Current learning rate: 0.00084 
+2024-09-10 10:56:17.823008: train_loss -0.9205 
+2024-09-10 10:56:17.823415: val_loss -0.6737 
+2024-09-10 10:56:17.823483: Pseudo dice [0.6586, 0.8597] 
+2024-09-10 10:56:17.823546: Epoch time: 247.91 s 
+2024-09-10 10:56:18.965236:  
+2024-09-10 10:56:18.965435: Epoch 937 
+2024-09-10 10:56:18.965532: Current learning rate: 0.00083 
+2024-09-10 11:00:29.495455: train_loss -0.915 
+2024-09-10 11:00:29.495603: val_loss -0.6854 
+2024-09-10 11:00:29.495662: Pseudo dice [0.6918, 0.8795] 
+2024-09-10 11:00:29.495718: Epoch time: 250.53 s 
+2024-09-10 11:00:30.623434:  
+2024-09-10 11:00:30.623614: Epoch 938 
+2024-09-10 11:00:30.623698: Current learning rate: 0.00082 
+2024-09-10 11:04:36.998338: train_loss -0.9178 
+2024-09-10 11:04:36.998502: val_loss -0.6638 
+2024-09-10 11:04:36.998558: Pseudo dice [0.6412, 0.8637] 
+2024-09-10 11:04:36.998631: Epoch time: 246.38 s 
+2024-09-10 11:04:37.990039:  
+2024-09-10 11:04:37.990236: Epoch 939 
+2024-09-10 11:04:37.990376: Current learning rate: 0.00081 
+2024-09-10 11:08:45.045384: train_loss -0.9206 
+2024-09-10 11:08:45.045534: val_loss -0.7131 
+2024-09-10 11:08:45.045648: Pseudo dice [0.6998, 0.8685] 
+2024-09-10 11:08:45.045705: Epoch time: 247.06 s 
+2024-09-10 11:08:46.157918:  
+2024-09-10 11:08:46.158242: Epoch 940 
+2024-09-10 11:08:46.158389: Current learning rate: 0.00079 
+2024-09-10 11:12:52.404128: train_loss -0.9218 
+2024-09-10 11:12:52.404276: val_loss -0.6805 
+2024-09-10 11:12:52.404336: Pseudo dice [0.6767, 0.8732] 
+2024-09-10 11:12:52.404394: Epoch time: 246.25 s 
+2024-09-10 11:12:54.153214:  
+2024-09-10 11:12:54.153566: Epoch 941 
+2024-09-10 11:12:54.153734: Current learning rate: 0.00078 
+2024-09-10 11:17:00.204864: train_loss -0.921 
+2024-09-10 11:17:00.205013: val_loss -0.6776 
+2024-09-10 11:17:00.205070: Pseudo dice [0.6247, 0.8813] 
+2024-09-10 11:17:00.205128: Epoch time: 246.05 s 
+2024-09-10 11:17:01.496036:  
+2024-09-10 11:17:01.496245: Epoch 942 
+2024-09-10 11:17:01.496333: Current learning rate: 0.00077 
+2024-09-10 11:21:07.427545: train_loss -0.9188 
+2024-09-10 11:21:07.427706: val_loss -0.6871 
+2024-09-10 11:21:07.427763: Pseudo dice [0.6481, 0.875] 
+2024-09-10 11:21:07.427826: Epoch time: 245.93 s 
+2024-09-10 11:21:08.555606:  
+2024-09-10 11:21:08.555861: Epoch 943 
+2024-09-10 11:21:08.555995: Current learning rate: 0.00076 
+2024-09-10 11:25:14.800047: train_loss -0.9194 
+2024-09-10 11:25:14.800194: val_loss -0.7035 
+2024-09-10 11:25:14.800253: Pseudo dice [0.6525, 0.8902] 
+2024-09-10 11:25:14.800336: Epoch time: 246.25 s 
+2024-09-10 11:25:15.797053:  
+2024-09-10 11:25:15.797256: Epoch 944 
+2024-09-10 11:25:15.797359: Current learning rate: 0.00075 
+2024-09-10 11:29:26.635222: train_loss -0.9196 
+2024-09-10 11:29:26.635394: val_loss -0.7032 
+2024-09-10 11:29:26.635471: Pseudo dice [0.6801, 0.885] 
+2024-09-10 11:29:26.635530: Epoch time: 250.84 s 
+2024-09-10 11:29:27.882881:  
+2024-09-10 11:29:27.883170: Epoch 945 
+2024-09-10 11:29:27.883265: Current learning rate: 0.00074 
+2024-09-10 11:33:34.376630: train_loss -0.9175 
+2024-09-10 11:33:34.376781: val_loss -0.7017 
+2024-09-10 11:33:34.376839: Pseudo dice [0.6435, 0.8829] 
+2024-09-10 11:33:34.376897: Epoch time: 246.5 s 
+2024-09-10 11:33:35.719283:  
+2024-09-10 11:33:35.719460: Epoch 946 
+2024-09-10 11:33:35.719542: Current learning rate: 0.00072 
+2024-09-10 11:37:42.227777: train_loss -0.9182 
+2024-09-10 11:37:42.228008: val_loss -0.7105 
+2024-09-10 11:37:42.228074: Pseudo dice [0.6743, 0.882] 
+2024-09-10 11:37:42.228134: Epoch time: 246.51 s 
+2024-09-10 11:37:43.378231:  
+2024-09-10 11:37:43.378504: Epoch 947 
+2024-09-10 11:37:43.378594: Current learning rate: 0.00071 
+2024-09-10 11:41:49.799138: train_loss -0.9195 
+2024-09-10 11:41:49.799329: val_loss -0.6696 
+2024-09-10 11:41:49.799403: Pseudo dice [0.622, 0.8671] 
+2024-09-10 11:41:49.799474: Epoch time: 246.42 s 
+2024-09-10 11:41:51.008749:  
+2024-09-10 11:41:51.009039: Epoch 948 
+2024-09-10 11:41:51.009154: Current learning rate: 0.0007 
+2024-09-10 11:45:57.477324: train_loss -0.9187 
+2024-09-10 11:45:57.477483: val_loss -0.7154 
+2024-09-10 11:45:57.477540: Pseudo dice [0.7069, 0.8826] 
+2024-09-10 11:45:57.477597: Epoch time: 246.47 s 
+2024-09-10 11:45:58.692541:  
+2024-09-10 11:45:58.692699: Epoch 949 
+2024-09-10 11:45:58.692781: Current learning rate: 0.00069 
+2024-09-10 11:50:05.105442: train_loss -0.922 
+2024-09-10 11:50:05.105613: val_loss -0.6964 
+2024-09-10 11:50:05.105692: Pseudo dice [0.6628, 0.8846] 
+2024-09-10 11:50:05.105771: Epoch time: 246.42 s 
+2024-09-10 11:50:09.262161:  
+2024-09-10 11:50:09.262356: Epoch 950 
+2024-09-10 11:50:09.262440: Current learning rate: 0.00067 
+2024-09-10 11:54:15.739371: train_loss -0.9192 
+2024-09-10 11:54:15.739514: val_loss -0.7194 
+2024-09-10 11:54:15.739570: Pseudo dice [0.6804, 0.8875] 
+2024-09-10 11:54:15.739625: Epoch time: 246.48 s 
+2024-09-10 11:54:16.718266:  
+2024-09-10 11:54:16.718483: Epoch 951 
+2024-09-10 11:54:16.718568: Current learning rate: 0.00066 
+2024-09-10 11:58:23.526922: train_loss -0.9207 
+2024-09-10 11:58:23.527157: val_loss -0.6823 
+2024-09-10 11:58:23.527291: Pseudo dice [0.638, 0.866] 
+2024-09-10 11:58:23.527406: Epoch time: 246.81 s 
+2024-09-10 11:58:24.651320:  
+2024-09-10 11:58:24.651540: Epoch 952 
+2024-09-10 11:58:24.651642: Current learning rate: 0.00065 
+2024-09-10 12:02:31.200028: train_loss -0.9172 
+2024-09-10 12:02:31.200215: val_loss -0.6765 
+2024-09-10 12:02:31.200289: Pseudo dice [0.6374, 0.8828] 
+2024-09-10 12:02:31.200356: Epoch time: 246.55 s 
+2024-09-10 12:02:32.370166:  
+2024-09-10 12:02:32.370395: Epoch 953 
+2024-09-10 12:02:32.370527: Current learning rate: 0.00064 
+2024-09-10 12:06:38.861095: train_loss -0.9191 
+2024-09-10 12:06:38.861239: val_loss -0.6825 
+2024-09-10 12:06:38.861296: Pseudo dice [0.6434, 0.8597] 
+2024-09-10 12:06:38.861352: Epoch time: 246.49 s 
+2024-09-10 12:06:39.865691:  
+2024-09-10 12:06:39.865941: Epoch 954 
+2024-09-10 12:06:39.866072: Current learning rate: 0.00063 
+2024-09-10 12:10:46.492444: train_loss -0.9196 
+2024-09-10 12:10:46.492597: val_loss -0.684 
+2024-09-10 12:10:46.492655: Pseudo dice [0.628, 0.8884] 
+2024-09-10 12:10:46.492712: Epoch time: 246.63 s 
+2024-09-10 12:10:47.509875:  
+2024-09-10 12:10:47.510090: Epoch 955 
+2024-09-10 12:10:47.510176: Current learning rate: 0.00061 
+2024-09-10 12:14:54.100552: train_loss -0.919 
+2024-09-10 12:14:54.100741: val_loss -0.6772 
+2024-09-10 12:14:54.100799: Pseudo dice [0.6734, 0.865] 
+2024-09-10 12:14:54.100853: Epoch time: 246.59 s 
+2024-09-10 12:14:55.118456:  
+2024-09-10 12:14:55.118664: Epoch 956 
+2024-09-10 12:14:55.118748: Current learning rate: 0.0006 
+2024-09-10 12:19:01.727634: train_loss -0.9205 
+2024-09-10 12:19:01.727825: val_loss -0.6756 
+2024-09-10 12:19:01.727884: Pseudo dice [0.6381, 0.8838] 
+2024-09-10 12:19:01.727939: Epoch time: 246.61 s 
+2024-09-10 12:19:02.721256:  
+2024-09-10 12:19:02.721446: Epoch 957 
+2024-09-10 12:19:02.721533: Current learning rate: 0.00059 
+2024-09-10 12:23:09.443566: train_loss -0.9217 
+2024-09-10 12:23:09.443714: val_loss -0.6768 
+2024-09-10 12:23:09.443771: Pseudo dice [0.6268, 0.8813] 
+2024-09-10 12:23:09.443843: Epoch time: 246.72 s 
+2024-09-10 12:23:10.406600:  
+2024-09-10 12:23:10.406772: Epoch 958 
+2024-09-10 12:23:10.406855: Current learning rate: 0.00058 
+2024-09-10 12:27:17.466673: train_loss -0.917 
+2024-09-10 12:27:17.466854: val_loss -0.7017 
+2024-09-10 12:27:17.466927: Pseudo dice [0.6494, 0.8821] 
+2024-09-10 12:27:17.466995: Epoch time: 247.06 s 
+2024-09-10 12:27:18.661602:  
+2024-09-10 12:27:18.661822: Epoch 959 
+2024-09-10 12:27:18.661919: Current learning rate: 0.00056 
+2024-09-10 12:31:25.876952: train_loss -0.9214 
+2024-09-10 12:31:25.877199: val_loss -0.6995 
+2024-09-10 12:31:25.877294: Pseudo dice [0.6628, 0.8833] 
+2024-09-10 12:31:25.877427: Epoch time: 247.22 s 
+2024-09-10 12:31:27.011933:  
+2024-09-10 12:31:27.012185: Epoch 960 
+2024-09-10 12:31:27.012300: Current learning rate: 0.00055 
+2024-09-10 12:35:33.560976: train_loss -0.9208 
+2024-09-10 12:35:33.561126: val_loss -0.6861 
+2024-09-10 12:35:33.561186: Pseudo dice [0.6423, 0.884] 
+2024-09-10 12:35:33.561243: Epoch time: 246.55 s 
+2024-09-10 12:35:34.527005:  
+2024-09-10 12:35:34.527213: Epoch 961 
+2024-09-10 12:35:34.527330: Current learning rate: 0.00054 
+2024-09-10 12:39:41.003714: train_loss -0.9205 
+2024-09-10 12:39:41.003908: val_loss -0.696 
+2024-09-10 12:39:41.003967: Pseudo dice [0.6828, 0.8874] 
+2024-09-10 12:39:41.004022: Epoch time: 246.48 s 
+2024-09-10 12:39:42.010590:  
+2024-09-10 12:39:42.010870: Epoch 962 
+2024-09-10 12:39:42.010960: Current learning rate: 0.00053 
+2024-09-10 12:43:48.437061: train_loss -0.9239 
+2024-09-10 12:43:48.437330: val_loss -0.7014 
+2024-09-10 12:43:48.437390: Pseudo dice [0.7112, 0.8753] 
+2024-09-10 12:43:48.437447: Epoch time: 246.43 s 
+2024-09-10 12:43:49.427690:  
+2024-09-10 12:43:49.427940: Epoch 963 
+2024-09-10 12:43:49.428045: Current learning rate: 0.00051 
+2024-09-10 12:47:55.794576: train_loss -0.9223 
+2024-09-10 12:47:55.794760: val_loss -0.6796 
+2024-09-10 12:47:55.794861: Pseudo dice [0.6035, 0.883] 
+2024-09-10 12:47:55.794918: Epoch time: 246.37 s 
+2024-09-10 12:47:56.795971:  
+2024-09-10 12:47:56.796191: Epoch 964 
+2024-09-10 12:47:56.796322: Current learning rate: 0.0005 
+2024-09-10 12:52:03.169108: train_loss -0.9228 
+2024-09-10 12:52:03.169287: val_loss -0.7135 
+2024-09-10 12:52:03.169360: Pseudo dice [0.6894, 0.887] 
+2024-09-10 12:52:03.169417: Epoch time: 246.38 s 
+2024-09-10 12:52:04.142898:  
+2024-09-10 12:52:04.143150: Epoch 965 
+2024-09-10 12:52:04.143249: Current learning rate: 0.00049 
+2024-09-10 12:56:10.681453: train_loss -0.9235 
+2024-09-10 12:56:10.681601: val_loss -0.7021 
+2024-09-10 12:56:10.681657: Pseudo dice [0.7032, 0.8646] 
+2024-09-10 12:56:10.681712: Epoch time: 246.54 s 
+2024-09-10 12:56:11.814340:  
+2024-09-10 12:56:11.814583: Epoch 966 
+2024-09-10 12:56:11.814670: Current learning rate: 0.00048 
+2024-09-10 13:00:18.713840: train_loss -0.9211 
+2024-09-10 13:00:18.714052: val_loss -0.699 
+2024-09-10 13:00:18.714136: Pseudo dice [0.6625, 0.8869] 
+2024-09-10 13:00:18.714241: Epoch time: 246.9 s 
+2024-09-10 13:00:19.694079:  
+2024-09-10 13:00:19.694270: Epoch 967 
+2024-09-10 13:00:19.694356: Current learning rate: 0.00046 
+2024-09-10 13:04:25.993454: train_loss -0.9226 
+2024-09-10 13:04:25.993607: val_loss -0.6883 
+2024-09-10 13:04:25.993664: Pseudo dice [0.6967, 0.8783] 
+2024-09-10 13:04:25.993722: Epoch time: 246.3 s 
+2024-09-10 13:04:26.984492:  
+2024-09-10 13:04:26.984714: Epoch 968 
+2024-09-10 13:04:26.984799: Current learning rate: 0.00045 
+2024-09-10 13:08:33.236138: train_loss -0.9186 
+2024-09-10 13:08:33.236292: val_loss -0.7045 
+2024-09-10 13:08:33.236349: Pseudo dice [0.6962, 0.878] 
+2024-09-10 13:08:33.236405: Epoch time: 246.25 s 
+2024-09-10 13:08:34.233109:  
+2024-09-10 13:08:34.233341: Epoch 969 
+2024-09-10 13:08:34.233437: Current learning rate: 0.00044 
+2024-09-10 13:12:40.557861: train_loss -0.9211 
+2024-09-10 13:12:40.558008: val_loss -0.6997 
+2024-09-10 13:12:40.558065: Pseudo dice [0.6909, 0.8651] 
+2024-09-10 13:12:40.558120: Epoch time: 246.33 s 
+2024-09-10 13:12:40.558164: Yayy! New best EMA pseudo Dice: 0.7746 
+2024-09-10 13:12:44.385940:  
+2024-09-10 13:12:44.386200: Epoch 970 
+2024-09-10 13:12:44.386298: Current learning rate: 0.00043 
+2024-09-10 13:16:50.787793: train_loss -0.9222 
+2024-09-10 13:16:50.787971: val_loss -0.6587 
+2024-09-10 13:16:50.788028: Pseudo dice [0.6116, 0.8716] 
+2024-09-10 13:16:50.788084: Epoch time: 246.4 s 
+2024-09-10 13:16:51.758340:  
+2024-09-10 13:16:51.758528: Epoch 971 
+2024-09-10 13:16:51.758614: Current learning rate: 0.00041 
+2024-09-10 13:20:58.093073: train_loss -0.919 
+2024-09-10 13:20:58.093243: val_loss -0.6581 
+2024-09-10 13:20:58.093301: Pseudo dice [0.619, 0.8699] 
+2024-09-10 13:20:58.093360: Epoch time: 246.34 s 
+2024-09-10 13:20:59.069595:  
+2024-09-10 13:20:59.069802: Epoch 972 
+2024-09-10 13:20:59.069915: Current learning rate: 0.0004 
+2024-09-10 13:26:27.235464: train_loss -0.9245 
+2024-09-10 13:26:27.235611: val_loss -0.6967 
+2024-09-10 13:26:27.235669: Pseudo dice [0.6534, 0.8802] 
+2024-09-10 13:26:27.235726: Epoch time: 328.17 s 
+2024-09-10 13:26:28.229148:  
+2024-09-10 13:26:28.229370: Epoch 973 
+2024-09-10 13:26:28.229478: Current learning rate: 0.00039 
+2024-09-10 13:31:26.399697: train_loss -0.9182 
+2024-09-10 13:31:26.399846: val_loss -0.6344 
+2024-09-10 13:31:26.399905: Pseudo dice [0.6067, 0.8759] 
+2024-09-10 13:31:26.399961: Epoch time: 298.17 s 
+2024-09-10 13:31:27.405527:  
+2024-09-10 13:31:27.405739: Epoch 974 
+2024-09-10 13:31:27.405868: Current learning rate: 0.00037 
+2024-09-10 13:35:33.570202: train_loss -0.9221 
+2024-09-10 13:35:33.570356: val_loss -0.697 
+2024-09-10 13:35:33.570413: Pseudo dice [0.6352, 0.8728] 
+2024-09-10 13:35:33.570469: Epoch time: 246.17 s 
+2024-09-10 13:35:34.550868:  
+2024-09-10 13:35:34.551105: Epoch 975 
+2024-09-10 13:35:34.551191: Current learning rate: 0.00036 
+2024-09-10 13:39:40.602798: train_loss -0.9197 
+2024-09-10 13:39:40.602990: val_loss -0.6674 
+2024-09-10 13:39:40.603049: Pseudo dice [0.6381, 0.8732] 
+2024-09-10 13:39:40.603115: Epoch time: 246.05 s 
+2024-09-10 13:39:41.573195:  
+2024-09-10 13:39:41.573435: Epoch 976 
+2024-09-10 13:39:41.573531: Current learning rate: 0.00035 
+2024-09-10 13:43:47.798624: train_loss -0.9196 
+2024-09-10 13:43:47.798782: val_loss -0.6799 
+2024-09-10 13:43:47.798839: Pseudo dice [0.6613, 0.8662] 
+2024-09-10 13:43:47.798896: Epoch time: 246.23 s 
+2024-09-10 13:43:48.791903:  
+2024-09-10 13:43:48.792119: Epoch 977 
+2024-09-10 13:43:48.792204: Current learning rate: 0.00034 
+2024-09-10 13:47:55.008047: train_loss -0.9237 
+2024-09-10 13:47:55.008194: val_loss -0.6846 
+2024-09-10 13:47:55.008251: Pseudo dice [0.6678, 0.8672] 
+2024-09-10 13:47:55.008307: Epoch time: 246.22 s 
+2024-09-10 13:47:56.015414:  
+2024-09-10 13:47:56.015666: Epoch 978 
+2024-09-10 13:47:56.015757: Current learning rate: 0.00032 
+2024-09-10 13:52:07.300209: train_loss -0.9162 
+2024-09-10 13:52:07.300356: val_loss -0.6953 
+2024-09-10 13:52:07.300413: Pseudo dice [0.6792, 0.8814] 
+2024-09-10 13:52:07.300467: Epoch time: 251.29 s 
+2024-09-10 13:52:08.317937:  
+2024-09-10 13:52:08.318087: Epoch 979 
+2024-09-10 13:52:08.318170: Current learning rate: 0.00031 
+2024-09-10 13:56:14.473406: train_loss -0.9242 
+2024-09-10 13:56:14.473559: val_loss -0.6564 
+2024-09-10 13:56:14.473615: Pseudo dice [0.6193, 0.8767] 
+2024-09-10 13:56:14.473671: Epoch time: 246.16 s 
+2024-09-10 13:56:15.589393:  
+2024-09-10 13:56:15.589633: Epoch 980 
+2024-09-10 13:56:15.589738: Current learning rate: 0.0003 
+2024-09-10 14:00:21.839951: train_loss -0.9208 
+2024-09-10 14:00:21.840118: val_loss -0.6991 
+2024-09-10 14:00:21.840176: Pseudo dice [0.6671, 0.8787] 
+2024-09-10 14:00:21.840231: Epoch time: 246.25 s 
+2024-09-10 14:00:22.823889:  
+2024-09-10 14:00:22.824060: Epoch 981 
+2024-09-10 14:00:22.824146: Current learning rate: 0.00028 
+2024-09-10 14:04:29.099836: train_loss -0.9204 
+2024-09-10 14:04:29.099982: val_loss -0.6864 
+2024-09-10 14:04:29.100038: Pseudo dice [0.635, 0.8655] 
+2024-09-10 14:04:29.100093: Epoch time: 246.28 s 
+2024-09-10 14:04:30.072036:  
+2024-09-10 14:04:30.072208: Epoch 982 
+2024-09-10 14:04:30.072291: Current learning rate: 0.00027 
+2024-09-10 14:08:36.474076: train_loss -0.922 
+2024-09-10 14:08:36.474220: val_loss -0.6655 
+2024-09-10 14:08:36.474327: Pseudo dice [0.6565, 0.8694] 
+2024-09-10 14:08:36.474385: Epoch time: 246.4 s 
+2024-09-10 14:08:37.489527:  
+2024-09-10 14:08:37.489747: Epoch 983 
+2024-09-10 14:08:37.489844: Current learning rate: 0.00026 
+2024-09-10 14:12:44.562357: train_loss -0.9245 
+2024-09-10 14:12:44.562508: val_loss -0.6947 
+2024-09-10 14:12:44.562564: Pseudo dice [0.6778, 0.8731] 
+2024-09-10 14:12:44.562619: Epoch time: 247.07 s 
+2024-09-10 14:12:45.627072:  
+2024-09-10 14:12:45.627306: Epoch 984 
+2024-09-10 14:12:45.627407: Current learning rate: 0.00024 
+2024-09-10 14:16:51.956487: train_loss -0.924 
+2024-09-10 14:16:51.956709: val_loss -0.7076 
+2024-09-10 14:16:51.956839: Pseudo dice [0.675, 0.8877] 
+2024-09-10 14:16:51.956905: Epoch time: 246.33 s 
+2024-09-10 14:16:53.167868:  
+2024-09-10 14:16:53.168155: Epoch 985 
+2024-09-10 14:16:53.168260: Current learning rate: 0.00023 
+2024-09-10 14:20:59.586655: train_loss -0.9236 
+2024-09-10 14:20:59.586909: val_loss -0.6772 
+2024-09-10 14:20:59.587007: Pseudo dice [0.6855, 0.8817] 
+2024-09-10 14:20:59.587102: Epoch time: 246.42 s 
+2024-09-10 14:21:00.781546:  
+2024-09-10 14:21:00.781830: Epoch 986 
+2024-09-10 14:21:00.781958: Current learning rate: 0.00021 
+2024-09-10 14:25:07.197661: train_loss -0.9212 
+2024-09-10 14:25:07.197833: val_loss -0.6553 
+2024-09-10 14:25:07.197892: Pseudo dice [0.6321, 0.878] 
+2024-09-10 14:25:07.197968: Epoch time: 246.42 s 
+2024-09-10 14:25:08.327379:  
+2024-09-10 14:25:08.327677: Epoch 987 
+2024-09-10 14:25:08.327775: Current learning rate: 0.0002 
+2024-09-10 14:29:14.809956: train_loss -0.9228 
+2024-09-10 14:29:14.810122: val_loss -0.6585 
+2024-09-10 14:29:14.810179: Pseudo dice [0.6281, 0.8634] 
+2024-09-10 14:29:14.810239: Epoch time: 246.48 s 
+2024-09-10 14:29:15.884295:  
+2024-09-10 14:29:15.884497: Epoch 988 
+2024-09-10 14:29:15.884592: Current learning rate: 0.00019 
+2024-09-10 14:33:22.504815: train_loss -0.9225 
+2024-09-10 14:33:22.504958: val_loss -0.6706 
+2024-09-10 14:33:22.505015: Pseudo dice [0.646, 0.8733] 
+2024-09-10 14:33:22.505071: Epoch time: 246.62 s 
+2024-09-10 14:33:23.490029:  
+2024-09-10 14:33:23.490237: Epoch 989 
+2024-09-10 14:33:23.490324: Current learning rate: 0.00017 
+2024-09-10 14:37:30.198012: train_loss -0.9245 
+2024-09-10 14:37:30.198159: val_loss -0.6547 
+2024-09-10 14:37:30.198216: Pseudo dice [0.6033, 0.8679] 
+2024-09-10 14:37:30.198273: Epoch time: 246.71 s 
+2024-09-10 14:37:31.181286:  
+2024-09-10 14:37:31.181521: Epoch 990 
+2024-09-10 14:37:31.181609: Current learning rate: 0.00016 
+2024-09-10 14:41:37.836397: train_loss -0.9224 
+2024-09-10 14:41:37.836669: val_loss -0.6806 
+2024-09-10 14:41:37.836760: Pseudo dice [0.6538, 0.8691] 
+2024-09-10 14:41:37.836883: Epoch time: 246.66 s 
+2024-09-10 14:41:39.162442:  
+2024-09-10 14:41:39.162605: Epoch 991 
+2024-09-10 14:41:39.162784: Current learning rate: 0.00014 
+2024-09-10 14:45:45.713301: train_loss -0.9205 
+2024-09-10 14:45:45.713446: val_loss -0.6855 
+2024-09-10 14:45:45.713502: Pseudo dice [0.6663, 0.8788] 
+2024-09-10 14:45:45.713558: Epoch time: 246.55 s 
+2024-09-10 14:45:46.804615:  
+2024-09-10 14:45:46.804834: Epoch 992 
+2024-09-10 14:45:46.804917: Current learning rate: 0.00013 
+2024-09-10 14:49:53.204274: train_loss -0.9231 
+2024-09-10 14:49:53.204461: val_loss -0.703 
+2024-09-10 14:49:53.204533: Pseudo dice [0.6995, 0.8697] 
+2024-09-10 14:49:53.204602: Epoch time: 246.4 s 
+2024-09-10 14:49:54.296967:  
+2024-09-10 14:49:54.297208: Epoch 993 
+2024-09-10 14:49:54.297296: Current learning rate: 0.00011 
+2024-09-10 14:54:00.796473: train_loss -0.9242 
+2024-09-10 14:54:00.796618: val_loss -0.7058 
+2024-09-10 14:54:00.796676: Pseudo dice [0.6761, 0.8921] 
+2024-09-10 14:54:00.796731: Epoch time: 246.5 s 
+2024-09-10 14:54:01.855017:  
+2024-09-10 14:54:01.855189: Epoch 994 
+2024-09-10 14:54:01.855273: Current learning rate: 0.0001 
+2024-09-10 14:58:08.584929: train_loss -0.9212 
+2024-09-10 14:58:08.585117: val_loss -0.6818 
+2024-09-10 14:58:08.585202: Pseudo dice [0.6621, 0.8771] 
+2024-09-10 14:58:08.585263: Epoch time: 246.73 s 
+2024-09-10 14:58:09.608958:  
+2024-09-10 14:58:09.609169: Epoch 995 
+2024-09-10 14:58:09.609258: Current learning rate: 8e-05 
+2024-09-10 15:02:16.817497: train_loss -0.9208 
+2024-09-10 15:02:16.817657: val_loss -0.7012 
+2024-09-10 15:02:16.817714: Pseudo dice [0.6906, 0.8661] 
+2024-09-10 15:02:16.817770: Epoch time: 247.21 s 
+2024-09-10 15:02:17.811352:  
+2024-09-10 15:02:17.811594: Epoch 996 
+2024-09-10 15:02:17.811680: Current learning rate: 7e-05 
+2024-09-10 15:06:24.657243: train_loss -0.9219 
+2024-09-10 15:06:24.657379: val_loss -0.679 
+2024-09-10 15:06:24.657435: Pseudo dice [0.6843, 0.8573] 
+2024-09-10 15:06:24.657490: Epoch time: 246.85 s 
+2024-09-10 15:06:25.625770:  
+2024-09-10 15:06:25.625994: Epoch 997 
+2024-09-10 15:06:25.626081: Current learning rate: 5e-05 
+2024-09-10 15:10:32.239520: train_loss -0.9202 
+2024-09-10 15:10:32.239694: val_loss -0.6916 
+2024-09-10 15:10:32.239753: Pseudo dice [0.6889, 0.8827] 
+2024-09-10 15:10:32.239823: Epoch time: 246.62 s 
+2024-09-10 15:10:33.211228:  
+2024-09-10 15:10:33.211427: Epoch 998 
+2024-09-10 15:10:33.211515: Current learning rate: 4e-05 
+2024-09-10 15:14:44.479031: train_loss -0.9231 
+2024-09-10 15:14:44.479322: val_loss -0.6881 
+2024-09-10 15:14:44.479470: Pseudo dice [0.6506, 0.8809] 
+2024-09-10 15:14:44.479585: Epoch time: 251.27 s 
+2024-09-10 15:14:45.970258:  
+2024-09-10 15:14:45.970488: Epoch 999 
+2024-09-10 15:14:45.970608: Current learning rate: 2e-05 
+2024-09-10 15:19:02.949910: train_loss -0.9238 
+2024-09-10 15:19:02.950056: val_loss -0.6532 
+2024-09-10 15:19:02.950112: Pseudo dice [0.6143, 0.8662] 
+2024-09-10 15:19:02.950167: Epoch time: 256.98 s 
+2024-09-10 15:19:05.179559: Training done. 
+2024-09-10 15:19:05.271618: Using splits from existing split file: /mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/splits_final.json 
+2024-09-10 15:19:05.272260: The split file contains 5 splits. 
+2024-09-10 15:19:05.272355: Desired fold for training: 0 
+2024-09-10 15:19:05.272426: This split has 240 training and 30 validation cases. 
+2024-09-10 15:19:05.272938: predicting 10 
+2024-09-10 15:19:05.275803: 10, shape torch.Size([2, 127, 510, 511]), rank 0 
+2024-09-10 15:20:15.588951: predicting 108 
+2024-09-10 15:20:15.613252: 108, shape torch.Size([2, 118, 509, 511]), rank 0 
+2024-09-10 15:20:46.004662: predicting 114 
+2024-09-10 15:20:46.021421: 114, shape torch.Size([2, 121, 536, 1007]), rank 0 
+2024-09-10 15:22:01.931404: predicting 115 
+2024-09-10 15:22:01.977395: 115, shape torch.Size([2, 140, 510, 1038]), rank 0 
+2024-09-10 15:23:17.766799: predicting 12 
+2024-09-10 15:23:17.815667: 12, shape torch.Size([2, 133, 509, 511]), rank 0 
+2024-09-10 15:23:56.219897: predicting 127 
+2024-09-10 15:23:56.256935: 127, shape torch.Size([2, 117, 502, 511]), rank 0 
+2024-09-10 15:24:27.717075: predicting 135 
+2024-09-10 15:24:27.760670: 135, shape torch.Size([2, 112, 512, 511]), rank 0 
+2024-09-10 15:24:59.545127: predicting 139 
+2024-09-10 15:24:59.572085: 139, shape torch.Size([2, 115, 510, 511]), rank 0 
+2024-09-10 15:25:30.809906: predicting 141 
+2024-09-10 15:25:30.827697: 141, shape torch.Size([2, 122, 512, 511]), rank 0 
+2024-09-10 15:26:08.951075: predicting 144 
+2024-09-10 15:26:08.979965: 144, shape torch.Size([2, 127, 509, 511]), rank 0 
+2024-09-10 15:26:47.935579: predicting 146 
+2024-09-10 15:26:47.955092: 146, shape torch.Size([2, 140, 510, 511]), rank 0 
+2024-09-10 15:27:26.987978: predicting 171 
+2024-09-10 15:27:27.007746: 171, shape torch.Size([2, 127, 512, 511]), rank 0 
+2024-09-10 15:28:05.144684: predicting 177 
+2024-09-10 15:28:05.162736: 177, shape torch.Size([2, 140, 534, 1040]), rank 0 
+2024-09-10 15:29:21.210731: predicting 178 
+2024-09-10 15:29:21.263231: 178, shape torch.Size([2, 125, 536, 1040]), rank 0 
+2024-09-10 15:30:38.768054: predicting 179 
+2024-09-10 15:30:38.806737: 179, shape torch.Size([2, 120, 509, 511]), rank 0 
+2024-09-10 15:31:09.408890: predicting 195 
+2024-09-10 15:31:09.430307: 195, shape torch.Size([2, 127, 512, 511]), rank 0 
+2024-09-10 15:31:47.361105: predicting 198 
+2024-09-10 15:31:47.380554: 198, shape torch.Size([2, 123, 511, 511]), rank 0 
+2024-09-10 15:32:25.426425: predicting 31 
+2024-09-10 15:32:25.447533: 31, shape torch.Size([2, 128, 536, 995]), rank 0 
+2024-09-10 15:33:41.924246: predicting 32 
+2024-09-10 15:33:41.977183: 32, shape torch.Size([2, 113, 536, 1040]), rank 0 
+2024-09-10 15:34:44.427002: predicting 36 
+2024-09-10 15:34:44.461241: 36, shape torch.Size([2, 108, 512, 510]), rank 0 
+2024-09-10 15:35:15.359028: predicting 5 
+2024-09-10 15:35:15.373970: 5, shape torch.Size([2, 135, 512, 511]), rank 0 
+2024-09-10 15:35:53.854873: predicting 52 
+2024-09-10 15:35:53.873187: 52, shape torch.Size([2, 113, 536, 1040]), rank 0 
+2024-09-10 15:36:54.616394: predicting 58 
+2024-09-10 15:36:54.649964: 58, shape torch.Size([2, 122, 512, 511]), rank 0 
+2024-09-10 15:37:32.627042: predicting 66 
+2024-09-10 15:37:32.645013: 66, shape torch.Size([2, 130, 509, 511]), rank 0 
+2024-09-10 15:38:10.785763: predicting 69 
+2024-09-10 15:38:10.804167: 69, shape torch.Size([2, 100, 512, 511]), rank 0 
+2024-09-10 15:38:41.040675: predicting 75 
+2024-09-10 15:38:41.054140: 75, shape torch.Size([2, 115, 512, 511]), rank 0 
+2024-09-10 15:39:11.368077: predicting 8 
+2024-09-10 15:39:11.385877: 8, shape torch.Size([2, 112, 512, 509]), rank 0 
+2024-09-10 15:39:41.694533: predicting 83 
+2024-09-10 15:39:41.709828: 83, shape torch.Size([2, 138, 509, 511]), rank 0 
+2024-09-10 15:40:19.539569: predicting 88 
+2024-09-10 15:40:19.559940: 88, shape torch.Size([2, 133, 494, 511]), rank 0 
+2024-09-10 15:40:57.478866: predicting 94 
+2024-09-10 15:40:57.496475: 94, shape torch.Size([2, 98, 512, 511]), rank 0 
+2024-09-10 15:41:41.550096: Validation complete 
+2024-09-10 15:41:41.550177: Mean Validation Dice:  0.6619391310593138 
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/validation/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/validation/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..360f1db153611bb4ec86ed889890a02ec2e21d97
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_0/validation/summary_agg.json
@@ -0,0 +1,512 @@
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+{
+    "_best_ema": "None",
+    "batch_size": "8",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 8, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True, 'inherits_from': '3d_fullres'}",
+    "configuration_name": "3d_fullres_bs8",
+    "cudnn_version": 90100,
+    "current_epoch": "0",
+    "dataloader_train": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7f35be586390>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f35bdccd190>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "None",
+    "dataloader_val": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7f35be2a93d0>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f35be6442f0>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "None",
+    "dataset_json": "{'channel_names': {'0': 'T2', '1': 'gradient'}, 'labels': {'background': 0, 'GTVp': 1, 'GTVn': 2}, 'numTraining': 300, 'file_ending': '.nii.gz', 'name': 'Dataset504_midRT_gradient', 'description': 'Dataset 504 - midRT with Gradient Map'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
+    "fold": "1",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA RTX A6000",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f35be55e0f0>",
+    "hostname": "hermes",
+    "inference_allowed_mirroring_axes": "(0, 1, 2)",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7f35be40c260>",
+    "local_rank": "0",
+    "log_file": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/training_log_2024_9_10_15_32_21.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f35be22a300>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): OptimizedModule(\n      (_orig_mod): MemoryEfficientSoftDiceLoss()\n    )\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f35be4b0860>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 511.0], 'spacing': [0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetMPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 192, 192], 'median_image_size_in_voxels': [92, 310, 309], 'spacing': [1.6126957141129536, 0.826423816135876, 0.826423816135876], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres_bs4': {'inherits_from': '3d_fullres', 'batch_size': 4}, '3d_fullres_bs8': {'inherits_from': '3d_fullres', 'batch_size': 8}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 349.19762434242324, 'median': 141.0, 'min': 0.0, 'percentile_00_5': 29.0, 'percentile_99_5': 2558.91650390625, 'std': 485.94544484039}, '1': {'max': 1.0, 'mean': 0.37307153608378946, 'median': 0.2966964840888977, 'min': 0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}}, 'configuration': '3d_fullres_bs8', 'fold': 1, 'dataset_json': {'channel_names': {'0': 'T2', '1': 'gradient'}, 'labels': {'background': 0, 'GTVp': 1, 'GTVn': 2}, 'numTraining': 300, 'file_ending': '.nii.gz', 'name': 'Dataset504_midRT_gradient', 'description': 'Dataset 504 - midRT with Gradient Map'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "num_epochs": "1000",
+    "num_input_channels": "2",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1",
+    "output_folder_base": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 511.0], 'spacing': [0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetMPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 192, 192], 'median_image_size_in_voxels': [92, 310, 309], 'spacing': [1.6126957141129536, 0.826423816135876, 0.826423816135876], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres_bs4': {'inherits_from': '3d_fullres', 'batch_size': 4}, '3d_fullres_bs8': {'inherits_from': '3d_fullres', 'batch_size': 8}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 349.19762434242324, 'median': 141.0, 'min': 0.0, 'percentile_00_5': 29.0, 'percentile_99_5': 2558.91650390625, 'std': 485.94544484039}, '1': {'max': 1.0, 'mean': 0.37307153608378946, 'median': 0.2966964840888977, 'min': 0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}}",
+    "preprocessed_dataset_folder": "/mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist",
+    "save_every": "50",
+    "torch_version": "2.4.0",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/2voxels/images/103_0000.nii.gz b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/2voxels/images/103_0000.nii.gz
new file mode 100644
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/2voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/2voxels/preds/predict_from_raw_data_args.json
new file mode 100644
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/2voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/2voxels/preds/summary_agg.json
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/4voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/4voxels/preds/predict_from_raw_data_args.json
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/4voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/4voxels/preds/summary_agg.json
new file mode 100644
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/6voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/6voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..6a0184eb73622208bd92bdd4dd633d11ec086610
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/6voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/6voxels/images",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/6voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/6voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/6voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..ddb6758cec0beea06a6a499c5c6a94b646c5c80c
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/6voxels/preds/summary_agg.json
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/8voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/8voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..c11a288856ca7283b8ca2ae6d894fed3b361104a
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/8voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/8voxels/images",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/8voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/8voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/8voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..20e67d18d38cf11ca975266dbae95fbcc5af44e5
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/margins/8voxels/preds/summary_agg.json
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+            "vol_gt1": 2950.0,
+            "vol_gt2": 1647.0,
+            "HD95_1": 2.5,
+            "HD95_2": 3.0,
+            "MSD_1": 0.8712189894770207,
+            "MSD_2": 0.6105650619135168
+        },
+        {
+            "PatientID": "90.nii.gz",
+            "TP1": 6322.499999999999,
+            "TP2": 4796.999999999999,
+            "vol_sum1": 14979.0,
+            "vol_sum2": 10735.0,
+            "DSC1": 0.8441818545964349,
+            "DSC2": 0.8937121564974382,
+            "vol_gt1": 6723.5,
+            "vol_gt2": 5086.5,
+            "HD95_1": 1.8027756377319946,
+            "HD95_2": 1.5,
+            "MSD_1": 0.6212185943052807,
+            "MSD_2": 0.40421770039298976
+        },
+        {
+            "PatientID": "104.nii.gz",
+            "TP1": 6692.0,
+            "TP2": 2733.0,
+            "vol_sum1": 40362.0,
+            "vol_sum2": 6853.0,
+            "DSC1": 0.3315990287894554,
+            "DSC2": 0.7976068874945279,
+            "vol_gt1": 6790.0,
+            "vol_gt2": 3147.5,
+            "HD95_1": 21.434782783974434,
+            "HD95_2": 27.09058348764932,
+            "MSD_1": 6.283873313708218,
+            "MSD_2": 2.497233262886273
+        },
+        {
+            "PatientID": "96.nii.gz",
+            "TP1": 526.5,
+            "TP2": 163.99999999999997,
+            "vol_sum1": 2344.5,
+            "vol_sum2": 932.0,
+            "DSC1": 0.4491362763915547,
+            "DSC2": 0.35193133047210295,
+            "vol_gt1": 1701.5,
+            "vol_gt2": 501.5,
+            "HD95_1": 4.272001872658765,
+            "HD95_2": 6.96419413859206,
+            "MSD_1": 1.39139829776562,
+            "MSD_2": 1.9455505499150203
+        },
+        {
+            "PatientID": "6.nii.gz",
+            "TP1": 7011.0,
+            "TP2": 1414.0,
+            "vol_sum1": 15901.5,
+            "vol_sum2": 3396.5,
+            "DSC1": 0.8818036034336383,
+            "DSC2": 0.832621816575887,
+            "vol_gt1": 7880.5,
+            "vol_gt2": 1633.0,
+            "HD95_1": 1.8027756377319946,
+            "HD95_2": 1.0,
+            "MSD_1": 0.5534805251985704,
+            "MSD_2": 0.36786327480819864
+        },
+        {
+            "PatientID": "84.nii.gz",
+            "TP1": 341.02534740939143,
+            "TP2": 7397.805829809186,
+            "vol_sum1": 1657.406466590353,
+            "vol_sum2": 19643.29279258805,
+            "DSC1": 0.41151685393258425,
+            "DSC2": 0.7532144338448776,
+            "vol_gt1": 1094.0744933953172,
+            "vol_gt2": 11550.633268569285,
+            "HD95_1": 5.056578094169569,
+            "HD95_2": 19.71070499255505,
+            "MSD_1": 2.0666753070521606,
+            "MSD_2": 2.8727911892009512
+        },
+        {
+            "PatientID": "103.nii.gz",
+            "TP1": 303.0,
+            "TP2": 7207.5,
+            "vol_sum1": 3059.5,
+            "vol_sum2": 17251.0,
+            "DSC1": 0.19807158032358227,
+            "DSC2": 0.8356037331169207,
+            "vol_gt1": 820.0,
+            "vol_gt2": 7777.0,
+            "HD95_1": 6.041522986797286,
+            "HD95_2": 1.5811388300841898,
+            "MSD_1": 2.647522330624776,
+            "MSD_2": 0.501452369552553
+        },
+        {
+            "PatientID": "150.nii.gz",
+            "TP1": 0.0,
+            "TP2": 10409.5,
+            "vol_sum1": 2169.0,
+            "vol_sum2": 24585.5,
+            "DSC1": 0.0,
+            "DSC2": 0.8467999430558663,
+            "vol_gt1": 1998.0,
+            "vol_gt2": 12366.0,
+            "HD95_1": 26.123026141523354,
+            "HD95_2": 1.5,
+            "MSD_1": 8.658927011864463,
+            "MSD_2": 0.5215202325846814
+        },
+        {
+            "PatientID": "194.nii.gz",
+            "TP1": 5762.513634893847,
+            "TP2": 1973.989724253679,
+            "vol_sum1": 15632.462256162242,
+            "vol_sum2": 4826.730770330192,
+            "DSC1": 0.7372496463405553,
+            "DSC2": 0.8179406800096456,
+            "vol_gt1": 8621.074226147995,
+            "vol_gt2": 2835.2824105436093,
+            "HD95_1": 4.060634387837089,
+            "HD95_2": 2.507912636983334,
+            "MSD_1": 1.6734576815463247,
+            "MSD_2": 0.9224929591874245
+        },
+        {
+            "PatientID": "116.nii.gz",
+            "TP1": 2651.5,
+            "TP2": 771.5,
+            "vol_sum1": 7156.0,
+            "vol_sum2": 1936.0,
+            "DSC1": 0.7410564561207378,
+            "DSC2": 0.797004132231405,
+            "vol_gt1": 3768.0,
+            "vol_gt2": 801.0,
+            "HD95_1": 7.211102550927978,
+            "HD95_2": 4.031128874149275,
+            "MSD_1": 1.5418746242968364,
+            "MSD_2": 0.7291894091222134
+        },
+        {
+            "PatientID": "151.nii.gz",
+            "TP1": 1479.5000000000002,
+            "TP2": 9566.0,
+            "vol_sum1": 6365.0,
+            "vol_sum2": 22161.0,
+            "DSC1": 0.4648860958366065,
+            "DSC2": 0.8633184423085601,
+            "vol_gt1": 4878.0,
+            "vol_gt2": 11233.0,
+            "HD95_1": 28.67596434874401,
+            "HD95_2": 2.0615528128088303,
+            "MSD_1": 4.957436616547124,
+            "MSD_2": 0.6562900228956331
+        }
+    ],
+    "aggregate_results": {
+        "ConventionalDsc": {
+            "GTVp": {
+                "mean": 0.2880436521363091,
+                "median": 0.09888035243996876,
+                "percentile_25": 0.0,
+                "percentile_75": 0.6018967876442853
+            },
+            "GTVn": {
+                "mean": 0.6041235346440816,
+                "median": 0.782755033407539,
+                "percentile_25": 0.41328669531911627,
+                "percentile_75": 0.846775707656527
+            }
+        },
+        "AggregatedDsc": {
+            "GTVp": {
+                "mean": 0.4907012257497288,
+                "median": 0.4907012257497288,
+                "percentile_25": 0.4907012257497288,
+                "percentile_75": 0.4907012257497288
+            },
+            "GTVn": {
+                "mean": 0.7838872107559306,
+                "median": 0.7838872107559306,
+                "percentile_25": 0.7838872107559306,
+                "percentile_75": 0.7838872107559306
+            }
+        },
+        "HD95": {
+            "GTVp": {
+                "mean": 16.304509734089827,
+                "median": 5.549050540483428,
+                "percentile_25": 4.219160001453346,
+                "percentile_75": 26.761260693328516
+            },
+            "GTVn": {
+                "mean": 5.893464137900511,
+                "median": 3.216016194148543,
+                "percentile_25": 1.7473664358200434,
+                "percentile_75": 5.0951505008977
+            }
+        },
+        "MSD": {
+            "GTVp": {
+                "mean": 5.734232108690247,
+                "median": 1.8972611321052417,
+                "percentile_25": 1.340824286661233,
+                "percentile_75": 7.147848510485959
+            },
+            "GTVn": {
+                "mean": 1.4289152608233493,
+                "median": 0.825841184154819,
+                "percentile_25": 0.5683472358796247,
+                "percentile_75": 1.8686505089804109
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/progress.png b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/progress.png
new file mode 100644
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/training_log_2024_9_10_15_32_21.txt b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/training_log_2024_9_10_15_32_21.txt
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+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/training_log_2024_9_10_15_32_21.txt
@@ -0,0 +1,7164 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2024-09-10 15:32:21.930849: do_dummy_2d_data_aug: True 
+2024-09-10 15:32:21.931871: Using splits from existing split file: /mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/splits_final.json 
+2024-09-10 15:32:21.932053: The split file contains 5 splits. 
+2024-09-10 15:32:21.932088: Desired fold for training: 1 
+2024-09-10 15:32:21.932118: This split has 240 training and 30 validation cases. 
+2024-09-10 15:32:34.592221: Using torch.compile... 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres_bs8
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 8, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True, 'inherits_from': '3d_fullres'} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 349.19762434242324, 'median': 141.0, 'min': 0.0, 'percentile_00_5': 29.0, 'percentile_99_5': 2558.91650390625, 'std': 485.94544484039}, '1': {'max': 1.0, 'mean': 0.37307153608378946, 'median': 0.2966964840888977, 'min': 0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}} 
+ 
+2024-09-10 15:32:35.485802: unpacking dataset... 
+2024-09-10 15:32:38.088587: unpacking done... 
+2024-09-10 15:32:38.095650: Unable to plot network architecture: nnUNet_compile is enabled! 
+2024-09-10 15:32:38.113328:  
+2024-09-10 15:32:38.113579: Epoch 0 
+2024-09-10 15:32:38.113752: Current learning rate: 0.01 
+2024-09-10 15:38:10.432502: train_loss -0.0921 
+2024-09-10 15:38:10.432659: val_loss -0.1612 
+2024-09-10 15:38:10.432715: Pseudo dice [0.0804, 0.2841] 
+2024-09-10 15:38:10.432777: Epoch time: 332.32 s 
+2024-09-10 15:38:10.432822: Yayy! New best EMA pseudo Dice: 0.1822 
+2024-09-10 15:38:12.335801:  
+2024-09-10 15:38:12.336026: Epoch 1 
+2024-09-10 15:38:12.336126: Current learning rate: 0.00999 
+2024-09-10 15:42:21.354418: train_loss -0.2952 
+2024-09-10 15:42:21.354553: val_loss -0.2352 
+2024-09-10 15:42:21.354609: Pseudo dice [0.2525, 0.3254] 
+2024-09-10 15:42:21.354666: Epoch time: 249.02 s 
+2024-09-10 15:42:21.354712: Yayy! New best EMA pseudo Dice: 0.1929 
+2024-09-10 15:42:25.228964:  
+2024-09-10 15:42:25.229192: Epoch 2 
+2024-09-10 15:42:25.229281: Current learning rate: 0.00998 
+2024-09-10 15:46:31.001596: train_loss -0.3409 
+2024-09-10 15:46:31.001751: val_loss -0.2719 
+2024-09-10 15:46:31.001862: Pseudo dice [0.2545, 0.3993] 
+2024-09-10 15:46:31.001919: Epoch time: 245.77 s 
+2024-09-10 15:46:31.001966: Yayy! New best EMA pseudo Dice: 0.2063 
+2024-09-10 15:46:34.929785:  
+2024-09-10 15:46:34.929953: Epoch 3 
+2024-09-10 15:46:34.930042: Current learning rate: 0.00997 
+2024-09-10 15:50:40.727960: train_loss -0.4127 
+2024-09-10 15:50:40.728118: val_loss -0.3047 
+2024-09-10 15:50:40.728176: Pseudo dice [0.3361, 0.4293] 
+2024-09-10 15:50:40.728231: Epoch time: 245.8 s 
+2024-09-10 15:50:40.728276: Yayy! New best EMA pseudo Dice: 0.2239 
+2024-09-10 15:50:44.672200:  
+2024-09-10 15:50:44.672371: Epoch 4 
+2024-09-10 15:50:44.672498: Current learning rate: 0.00996 
+2024-09-10 15:54:50.552146: train_loss -0.5221 
+2024-09-10 15:54:50.552296: val_loss -0.4333 
+2024-09-10 15:54:50.552353: Pseudo dice [0.4505, 0.5381] 
+2024-09-10 15:54:50.552409: Epoch time: 245.88 s 
+2024-09-10 15:54:50.552454: Yayy! New best EMA pseudo Dice: 0.251 
+2024-09-10 15:54:54.477100:  
+2024-09-10 15:54:54.477281: Epoch 5 
+2024-09-10 15:54:54.477381: Current learning rate: 0.00995 
+2024-09-10 15:59:00.068258: train_loss -0.5644 
+2024-09-10 15:59:00.068456: val_loss -0.4087 
+2024-09-10 15:59:00.068534: Pseudo dice [0.3707, 0.5665] 
+2024-09-10 15:59:00.068591: Epoch time: 245.59 s 
+2024-09-10 15:59:00.068640: Yayy! New best EMA pseudo Dice: 0.2727 
+2024-09-10 15:59:03.931906:  
+2024-09-10 15:59:03.932127: Epoch 6 
+2024-09-10 15:59:03.932222: Current learning rate: 0.00995 
+2024-09-10 16:03:09.671215: train_loss -0.6056 
+2024-09-10 16:03:09.671414: val_loss -0.4919 
+2024-09-10 16:03:09.671488: Pseudo dice [0.4715, 0.6225] 
+2024-09-10 16:03:09.671544: Epoch time: 245.74 s 
+2024-09-10 16:03:09.671589: Yayy! New best EMA pseudo Dice: 0.3002 
+2024-09-10 16:03:13.567210:  
+2024-09-10 16:03:13.567381: Epoch 7 
+2024-09-10 16:03:13.567470: Current learning rate: 0.00994 
+2024-09-10 16:07:19.351251: train_loss -0.6008 
+2024-09-10 16:07:19.351402: val_loss -0.4918 
+2024-09-10 16:07:19.351458: Pseudo dice [0.4957, 0.6006] 
+2024-09-10 16:07:19.351513: Epoch time: 245.79 s 
+2024-09-10 16:07:19.351558: Yayy! New best EMA pseudo Dice: 0.325 
+2024-09-10 16:07:23.242129:  
+2024-09-10 16:07:23.242332: Epoch 8 
+2024-09-10 16:07:23.242419: Current learning rate: 0.00993 
+2024-09-10 16:11:29.323433: train_loss -0.6321 
+2024-09-10 16:11:29.323578: val_loss -0.4587 
+2024-09-10 16:11:29.323636: Pseudo dice [0.4431, 0.5791] 
+2024-09-10 16:11:29.323692: Epoch time: 246.08 s 
+2024-09-10 16:11:29.323737: Yayy! New best EMA pseudo Dice: 0.3436 
+2024-09-10 16:11:33.242879:  
+2024-09-10 16:11:33.243082: Epoch 9 
+2024-09-10 16:11:33.243173: Current learning rate: 0.00992 
+2024-09-10 16:15:39.659763: train_loss -0.6407 
+2024-09-10 16:15:39.659936: val_loss -0.5123 
+2024-09-10 16:15:39.660020: Pseudo dice [0.5077, 0.6627] 
+2024-09-10 16:15:39.660077: Epoch time: 246.42 s 
+2024-09-10 16:15:39.660122: Yayy! New best EMA pseudo Dice: 0.3677 
+2024-09-10 16:15:43.582283:  
+2024-09-10 16:15:43.582474: Epoch 10 
+2024-09-10 16:15:43.582566: Current learning rate: 0.00991 
+2024-09-10 16:19:49.433367: train_loss -0.6681 
+2024-09-10 16:19:49.433514: val_loss -0.5023 
+2024-09-10 16:19:49.433563: Pseudo dice [0.4825, 0.6241] 
+2024-09-10 16:19:49.433612: Epoch time: 245.85 s 
+2024-09-10 16:19:49.433652: Yayy! New best EMA pseudo Dice: 0.3863 
+2024-09-10 16:19:53.323547:  
+2024-09-10 16:19:53.323727: Epoch 11 
+2024-09-10 16:19:53.323860: Current learning rate: 0.0099 
+2024-09-10 16:23:59.816988: train_loss -0.6713 
+2024-09-10 16:23:59.817181: val_loss -0.5049 
+2024-09-10 16:23:59.817253: Pseudo dice [0.4411, 0.6656] 
+2024-09-10 16:23:59.817361: Epoch time: 246.5 s 
+2024-09-10 16:23:59.817410: Yayy! New best EMA pseudo Dice: 0.403 
+2024-09-10 16:24:04.807397:  
+2024-09-10 16:24:04.807612: Epoch 12 
+2024-09-10 16:24:04.807698: Current learning rate: 0.00989 
+2024-09-10 16:28:12.016811: train_loss -0.6964 
+2024-09-10 16:28:12.016980: val_loss -0.5252 
+2024-09-10 16:28:12.017046: Pseudo dice [0.4467, 0.6944] 
+2024-09-10 16:28:12.017111: Epoch time: 247.21 s 
+2024-09-10 16:28:12.017162: Yayy! New best EMA pseudo Dice: 0.4198 
+2024-09-10 16:28:16.244893:  
+2024-09-10 16:28:16.245111: Epoch 13 
+2024-09-10 16:28:16.245197: Current learning rate: 0.00988 
+2024-09-10 16:32:22.884720: train_loss -0.6645 
+2024-09-10 16:32:22.884863: val_loss -0.4813 
+2024-09-10 16:32:22.884917: Pseudo dice [0.421, 0.6399] 
+2024-09-10 16:32:22.884970: Epoch time: 246.64 s 
+2024-09-10 16:32:22.885010: Yayy! New best EMA pseudo Dice: 0.4308 
+2024-09-10 16:32:26.825421:  
+2024-09-10 16:32:26.825657: Epoch 14 
+2024-09-10 16:32:26.825747: Current learning rate: 0.00987 
+2024-09-10 16:36:32.823481: train_loss -0.6946 
+2024-09-10 16:36:32.823623: val_loss -0.5066 
+2024-09-10 16:36:32.823673: Pseudo dice [0.5077, 0.5763] 
+2024-09-10 16:36:32.823724: Epoch time: 246.0 s 
+2024-09-10 16:36:32.823765: Yayy! New best EMA pseudo Dice: 0.442 
+2024-09-10 16:36:36.765355:  
+2024-09-10 16:36:36.765510: Epoch 15 
+2024-09-10 16:36:36.765610: Current learning rate: 0.00986 
+2024-09-10 16:40:42.591457: train_loss -0.6796 
+2024-09-10 16:40:42.591611: val_loss -0.4922 
+2024-09-10 16:40:42.591663: Pseudo dice [0.4363, 0.656] 
+2024-09-10 16:40:42.591714: Epoch time: 245.83 s 
+2024-09-10 16:40:42.591754: Yayy! New best EMA pseudo Dice: 0.4524 
+2024-09-10 16:40:46.509325:  
+2024-09-10 16:40:46.509502: Epoch 16 
+2024-09-10 16:40:46.509584: Current learning rate: 0.00986 
+2024-09-10 16:44:52.449931: train_loss -0.6995 
+2024-09-10 16:44:52.450092: val_loss -0.5097 
+2024-09-10 16:44:52.450144: Pseudo dice [0.4892, 0.643] 
+2024-09-10 16:44:52.450194: Epoch time: 245.94 s 
+2024-09-10 16:44:52.450235: Yayy! New best EMA pseudo Dice: 0.4638 
+2024-09-10 16:44:56.450818:  
+2024-09-10 16:44:56.451026: Epoch 17 
+2024-09-10 16:44:56.451112: Current learning rate: 0.00985 
+2024-09-10 16:49:02.722019: train_loss -0.7008 
+2024-09-10 16:49:02.722246: val_loss -0.5341 
+2024-09-10 16:49:02.722343: Pseudo dice [0.507, 0.6808] 
+2024-09-10 16:49:02.722436: Epoch time: 246.27 s 
+2024-09-10 16:49:02.722512: Yayy! New best EMA pseudo Dice: 0.4768 
+2024-09-10 16:49:06.671763:  
+2024-09-10 16:49:06.672021: Epoch 18 
+2024-09-10 16:49:06.672107: Current learning rate: 0.00984 
+2024-09-10 16:53:12.563615: train_loss -0.7096 
+2024-09-10 16:53:12.563755: val_loss -0.5411 
+2024-09-10 16:53:12.563812: Pseudo dice [0.4436, 0.7049] 
+2024-09-10 16:53:12.563866: Epoch time: 245.89 s 
+2024-09-10 16:53:12.563905: Yayy! New best EMA pseudo Dice: 0.4865 
+2024-09-10 16:53:16.454075:  
+2024-09-10 16:53:16.454269: Epoch 19 
+2024-09-10 16:53:16.454351: Current learning rate: 0.00983 
+2024-09-10 16:57:22.391559: train_loss -0.6879 
+2024-09-10 16:57:22.391716: val_loss -0.542 
+2024-09-10 16:57:22.391766: Pseudo dice [0.5054, 0.67] 
+2024-09-10 16:57:22.391837: Epoch time: 245.94 s 
+2024-09-10 16:57:22.391881: Yayy! New best EMA pseudo Dice: 0.4966 
+2024-09-10 16:57:26.284327:  
+2024-09-10 16:57:26.284512: Epoch 20 
+2024-09-10 16:57:26.284598: Current learning rate: 0.00982 
+2024-09-10 17:01:32.102871: train_loss -0.6652 
+2024-09-10 17:01:32.103009: val_loss -0.5456 
+2024-09-10 17:01:32.103059: Pseudo dice [0.4658, 0.6977] 
+2024-09-10 17:01:32.103114: Epoch time: 245.82 s 
+2024-09-10 17:01:32.103154: Yayy! New best EMA pseudo Dice: 0.5051 
+2024-09-10 17:01:36.051205:  
+2024-09-10 17:01:36.051411: Epoch 21 
+2024-09-10 17:01:36.051495: Current learning rate: 0.00981 
+2024-09-10 17:05:42.021367: train_loss -0.7074 
+2024-09-10 17:05:42.021504: val_loss -0.5578 
+2024-09-10 17:05:42.021607: Pseudo dice [0.4965, 0.7225] 
+2024-09-10 17:05:42.021690: Epoch time: 245.97 s 
+2024-09-10 17:05:42.021732: Yayy! New best EMA pseudo Dice: 0.5156 
+2024-09-10 17:05:45.895704:  
+2024-09-10 17:05:45.895898: Epoch 22 
+2024-09-10 17:05:45.895982: Current learning rate: 0.0098 
+2024-09-10 17:09:51.863257: train_loss -0.7153 
+2024-09-10 17:09:51.863456: val_loss -0.4959 
+2024-09-10 17:09:51.863509: Pseudo dice [0.4652, 0.6071] 
+2024-09-10 17:09:51.863564: Epoch time: 245.97 s 
+2024-09-10 17:09:51.863606: Yayy! New best EMA pseudo Dice: 0.5176 
+2024-09-10 17:09:55.710477:  
+2024-09-10 17:09:55.710722: Epoch 23 
+2024-09-10 17:09:55.710803: Current learning rate: 0.00979 
+2024-09-10 17:14:01.738740: train_loss -0.6808 
+2024-09-10 17:14:01.738875: val_loss -0.5068 
+2024-09-10 17:14:01.738926: Pseudo dice [0.4463, 0.671] 
+2024-09-10 17:14:01.738978: Epoch time: 246.03 s 
+2024-09-10 17:14:01.739019: Yayy! New best EMA pseudo Dice: 0.5217 
+2024-09-10 17:14:05.636804:  
+2024-09-10 17:14:05.636999: Epoch 24 
+2024-09-10 17:14:05.637086: Current learning rate: 0.00978 
+2024-09-10 17:18:11.734300: train_loss -0.6942 
+2024-09-10 17:18:11.734492: val_loss -0.5667 
+2024-09-10 17:18:11.734545: Pseudo dice [0.4977, 0.7317] 
+2024-09-10 17:18:11.734595: Epoch time: 246.1 s 
+2024-09-10 17:18:11.734636: Yayy! New best EMA pseudo Dice: 0.531 
+2024-09-10 17:18:15.623739:  
+2024-09-10 17:18:15.623917: Epoch 25 
+2024-09-10 17:18:15.624008: Current learning rate: 0.00977 
+2024-09-10 17:22:22.101892: train_loss -0.7295 
+2024-09-10 17:22:22.102045: val_loss -0.573 
+2024-09-10 17:22:22.102093: Pseudo dice [0.5313, 0.7215] 
+2024-09-10 17:22:22.102144: Epoch time: 246.48 s 
+2024-09-10 17:22:22.102184: Yayy! New best EMA pseudo Dice: 0.5406 
+2024-09-10 17:22:26.019556:  
+2024-09-10 17:22:26.019822: Epoch 26 
+2024-09-10 17:22:26.019914: Current learning rate: 0.00977 
+2024-09-10 17:26:32.063017: train_loss -0.7199 
+2024-09-10 17:26:32.063170: val_loss -0.5242 
+2024-09-10 17:26:32.063220: Pseudo dice [0.4247, 0.7026] 
+2024-09-10 17:26:32.063273: Epoch time: 246.05 s 
+2024-09-10 17:26:32.063315: Yayy! New best EMA pseudo Dice: 0.5429 
+2024-09-10 17:26:35.948771:  
+2024-09-10 17:26:35.948974: Epoch 27 
+2024-09-10 17:26:35.949056: Current learning rate: 0.00976 
+2024-09-10 17:30:41.962624: train_loss -0.7087 
+2024-09-10 17:30:41.962818: val_loss -0.5491 
+2024-09-10 17:30:41.962913: Pseudo dice [0.4839, 0.7197] 
+2024-09-10 17:30:41.963005: Epoch time: 246.02 s 
+2024-09-10 17:30:41.963080: Yayy! New best EMA pseudo Dice: 0.5488 
+2024-09-10 17:30:45.865050:  
+2024-09-10 17:30:45.865227: Epoch 28 
+2024-09-10 17:30:45.865308: Current learning rate: 0.00975 
+2024-09-10 17:34:51.904691: train_loss -0.7368 
+2024-09-10 17:34:51.904840: val_loss -0.5858 
+2024-09-10 17:34:51.904890: Pseudo dice [0.5335, 0.7245] 
+2024-09-10 17:34:51.904944: Epoch time: 246.04 s 
+2024-09-10 17:34:51.904986: Yayy! New best EMA pseudo Dice: 0.5568 
+2024-09-10 17:34:55.764725:  
+2024-09-10 17:34:55.764919: Epoch 29 
+2024-09-10 17:34:55.765002: Current learning rate: 0.00974 
+2024-09-10 17:39:01.873666: train_loss -0.7387 
+2024-09-10 17:39:01.873844: val_loss -0.5295 
+2024-09-10 17:39:01.873896: Pseudo dice [0.51, 0.6614] 
+2024-09-10 17:39:01.873947: Epoch time: 246.11 s 
+2024-09-10 17:39:01.873987: Yayy! New best EMA pseudo Dice: 0.5597 
+2024-09-10 17:39:05.801986:  
+2024-09-10 17:39:05.802158: Epoch 30 
+2024-09-10 17:39:05.802245: Current learning rate: 0.00973 
+2024-09-10 17:43:12.104244: train_loss -0.7308 
+2024-09-10 17:43:12.104429: val_loss -0.5863 
+2024-09-10 17:43:12.104514: Pseudo dice [0.5199, 0.7621] 
+2024-09-10 17:43:12.104568: Epoch time: 246.3 s 
+2024-09-10 17:43:12.104610: Yayy! New best EMA pseudo Dice: 0.5678 
+2024-09-10 17:43:16.007445:  
+2024-09-10 17:43:16.007629: Epoch 31 
+2024-09-10 17:43:16.007736: Current learning rate: 0.00972 
+2024-09-10 17:47:22.354156: train_loss -0.7425 
+2024-09-10 17:47:22.354294: val_loss -0.5632 
+2024-09-10 17:47:22.354345: Pseudo dice [0.4915, 0.7225] 
+2024-09-10 17:47:22.354396: Epoch time: 246.35 s 
+2024-09-10 17:47:22.354436: Yayy! New best EMA pseudo Dice: 0.5717 
+2024-09-10 17:47:26.242645:  
+2024-09-10 17:47:26.242868: Epoch 32 
+2024-09-10 17:47:26.242954: Current learning rate: 0.00971 
+2024-09-10 17:51:32.816729: train_loss -0.7419 
+2024-09-10 17:51:32.816912: val_loss -0.5689 
+2024-09-10 17:51:32.816964: Pseudo dice [0.5211, 0.7152] 
+2024-09-10 17:51:32.817015: Epoch time: 246.58 s 
+2024-09-10 17:51:32.817058: Yayy! New best EMA pseudo Dice: 0.5764 
+2024-09-10 17:51:37.644470:  
+2024-09-10 17:51:37.644726: Epoch 33 
+2024-09-10 17:51:37.644834: Current learning rate: 0.0097 
+2024-09-10 17:55:43.907737: train_loss -0.7408 
+2024-09-10 17:55:43.907938: val_loss -0.5529 
+2024-09-10 17:55:43.908022: Pseudo dice [0.5035, 0.7014] 
+2024-09-10 17:55:43.908082: Epoch time: 246.27 s 
+2024-09-10 17:55:43.908123: Yayy! New best EMA pseudo Dice: 0.579 
+2024-09-10 17:55:47.898333:  
+2024-09-10 17:55:47.898492: Epoch 34 
+2024-09-10 17:55:47.898616: Current learning rate: 0.00969 
+2024-09-10 17:59:54.071033: train_loss -0.7498 
+2024-09-10 17:59:54.071216: val_loss -0.6077 
+2024-09-10 17:59:54.071271: Pseudo dice [0.5441, 0.7539] 
+2024-09-10 17:59:54.071322: Epoch time: 246.17 s 
+2024-09-10 17:59:54.071363: Yayy! New best EMA pseudo Dice: 0.586 
+2024-09-10 17:59:57.991668:  
+2024-09-10 17:59:57.991851: Epoch 35 
+2024-09-10 17:59:57.991936: Current learning rate: 0.00968 
+2024-09-10 18:04:04.047301: train_loss -0.7463 
+2024-09-10 18:04:04.047438: val_loss -0.5396 
+2024-09-10 18:04:04.047491: Pseudo dice [0.4664, 0.7076] 
+2024-09-10 18:04:04.047548: Epoch time: 246.06 s 
+2024-09-10 18:04:04.047588: Yayy! New best EMA pseudo Dice: 0.5861 
+2024-09-10 18:04:09.009954:  
+2024-09-10 18:04:09.010159: Epoch 36 
+2024-09-10 18:04:09.010245: Current learning rate: 0.00968 
+2024-09-10 18:08:15.379354: train_loss -0.7496 
+2024-09-10 18:08:15.379501: val_loss -0.5722 
+2024-09-10 18:08:15.379593: Pseudo dice [0.5113, 0.756] 
+2024-09-10 18:08:15.379648: Epoch time: 246.37 s 
+2024-09-10 18:08:15.379690: Yayy! New best EMA pseudo Dice: 0.5908 
+2024-09-10 18:08:19.331705:  
+2024-09-10 18:08:19.331899: Epoch 37 
+2024-09-10 18:08:19.331987: Current learning rate: 0.00967 
+2024-09-10 18:12:25.620100: train_loss -0.7644 
+2024-09-10 18:12:25.620239: val_loss -0.5831 
+2024-09-10 18:12:25.620290: Pseudo dice [0.546, 0.717] 
+2024-09-10 18:12:25.620340: Epoch time: 246.29 s 
+2024-09-10 18:12:25.620380: Yayy! New best EMA pseudo Dice: 0.5949 
+2024-09-10 18:12:29.554217:  
+2024-09-10 18:12:29.554368: Epoch 38 
+2024-09-10 18:12:29.554478: Current learning rate: 0.00966 
+2024-09-10 18:16:35.618045: train_loss -0.7357 
+2024-09-10 18:16:35.618186: val_loss -0.5887 
+2024-09-10 18:16:35.618238: Pseudo dice [0.5044, 0.7617] 
+2024-09-10 18:16:35.618289: Epoch time: 246.07 s 
+2024-09-10 18:16:35.618328: Yayy! New best EMA pseudo Dice: 0.5987 
+2024-09-10 18:16:39.567634:  
+2024-09-10 18:16:39.567845: Epoch 39 
+2024-09-10 18:16:39.567932: Current learning rate: 0.00965 
+2024-09-10 18:20:45.610226: train_loss -0.7572 
+2024-09-10 18:20:45.610368: val_loss -0.5702 
+2024-09-10 18:20:45.610418: Pseudo dice [0.4726, 0.7597] 
+2024-09-10 18:20:45.610470: Epoch time: 246.04 s 
+2024-09-10 18:20:45.610549: Yayy! New best EMA pseudo Dice: 0.6005 
+2024-09-10 18:20:49.575496:  
+2024-09-10 18:20:49.575789: Epoch 40 
+2024-09-10 18:20:49.575909: Current learning rate: 0.00964 
+2024-09-10 18:24:55.828011: train_loss -0.7346 
+2024-09-10 18:24:55.828190: val_loss -0.5502 
+2024-09-10 18:24:55.828241: Pseudo dice [0.4978, 0.6899] 
+2024-09-10 18:24:55.828292: Epoch time: 246.25 s 
+2024-09-10 18:24:56.806026:  
+2024-09-10 18:24:56.806196: Epoch 41 
+2024-09-10 18:24:56.806279: Current learning rate: 0.00963 
+2024-09-10 18:29:02.884846: train_loss -0.7534 
+2024-09-10 18:29:02.884988: val_loss -0.5832 
+2024-09-10 18:29:02.885040: Pseudo dice [0.5214, 0.7331] 
+2024-09-10 18:29:02.885091: Epoch time: 246.08 s 
+2024-09-10 18:29:02.885131: Yayy! New best EMA pseudo Dice: 0.6026 
+2024-09-10 18:29:06.744051:  
+2024-09-10 18:29:06.744200: Epoch 42 
+2024-09-10 18:29:06.744282: Current learning rate: 0.00962 
+2024-09-10 18:33:12.697596: train_loss -0.7656 
+2024-09-10 18:33:12.697735: val_loss -0.5833 
+2024-09-10 18:33:12.697796: Pseudo dice [0.5507, 0.7099] 
+2024-09-10 18:33:12.697846: Epoch time: 245.96 s 
+2024-09-10 18:33:12.697886: Yayy! New best EMA pseudo Dice: 0.6053 
+2024-09-10 18:33:16.598020:  
+2024-09-10 18:33:16.598236: Epoch 43 
+2024-09-10 18:33:16.598320: Current learning rate: 0.00961 
+2024-09-10 18:37:22.679668: train_loss -0.7533 
+2024-09-10 18:37:22.679814: val_loss -0.5884 
+2024-09-10 18:37:22.679866: Pseudo dice [0.5028, 0.7395] 
+2024-09-10 18:37:22.679917: Epoch time: 246.08 s 
+2024-09-10 18:37:22.679956: Yayy! New best EMA pseudo Dice: 0.6069 
+2024-09-10 18:37:26.628068:  
+2024-09-10 18:37:26.628197: Epoch 44 
+2024-09-10 18:37:26.628306: Current learning rate: 0.0096 
+2024-09-10 18:41:32.625098: train_loss -0.7518 
+2024-09-10 18:41:32.625242: val_loss -0.5737 
+2024-09-10 18:41:32.625408: Pseudo dice [0.4844, 0.7375] 
+2024-09-10 18:41:32.625532: Epoch time: 246.0 s 
+2024-09-10 18:41:32.625614: Yayy! New best EMA pseudo Dice: 0.6073 
+2024-09-10 18:41:36.464641:  
+2024-09-10 18:41:36.464867: Epoch 45 
+2024-09-10 18:41:36.464958: Current learning rate: 0.00959 
+2024-09-10 18:45:42.442475: train_loss -0.7558 
+2024-09-10 18:45:42.442623: val_loss -0.5931 
+2024-09-10 18:45:42.442727: Pseudo dice [0.504, 0.7643] 
+2024-09-10 18:45:42.442839: Epoch time: 245.98 s 
+2024-09-10 18:45:42.442917: Yayy! New best EMA pseudo Dice: 0.61 
+2024-09-10 18:45:46.350727:  
+2024-09-10 18:45:46.350879: Epoch 46 
+2024-09-10 18:45:46.350962: Current learning rate: 0.00959 
+2024-09-10 18:49:52.256892: train_loss -0.7604 
+2024-09-10 18:49:52.257031: val_loss -0.55 
+2024-09-10 18:49:52.257106: Pseudo dice [0.4772, 0.7417] 
+2024-09-10 18:49:52.257199: Epoch time: 245.91 s 
+2024-09-10 18:49:53.158767:  
+2024-09-10 18:49:53.158948: Epoch 47 
+2024-09-10 18:49:53.159071: Current learning rate: 0.00958 
+2024-09-10 18:53:59.235073: train_loss -0.761 
+2024-09-10 18:53:59.235219: val_loss -0.5725 
+2024-09-10 18:53:59.235270: Pseudo dice [0.4979, 0.745] 
+2024-09-10 18:53:59.235322: Epoch time: 246.08 s 
+2024-09-10 18:53:59.235362: Yayy! New best EMA pseudo Dice: 0.6111 
+2024-09-10 18:54:03.101593:  
+2024-09-10 18:54:03.101808: Epoch 48 
+2024-09-10 18:54:03.101950: Current learning rate: 0.00957 
+2024-09-10 18:58:09.588699: train_loss -0.7428 
+2024-09-10 18:58:09.588854: val_loss -0.5571 
+2024-09-10 18:58:09.588905: Pseudo dice [0.4525, 0.7152] 
+2024-09-10 18:58:09.588957: Epoch time: 246.49 s 
+2024-09-10 18:58:10.512026:  
+2024-09-10 18:58:10.512176: Epoch 49 
+2024-09-10 18:58:10.512261: Current learning rate: 0.00956 
+2024-09-10 19:02:16.531274: train_loss -0.7626 
+2024-09-10 19:02:16.531415: val_loss -0.5662 
+2024-09-10 19:02:16.531465: Pseudo dice [0.4779, 0.7293] 
+2024-09-10 19:02:16.531515: Epoch time: 246.02 s 
+2024-09-10 19:02:18.548378:  
+2024-09-10 19:02:18.548577: Epoch 50 
+2024-09-10 19:02:18.548683: Current learning rate: 0.00955 
+2024-09-10 19:06:24.637930: train_loss -0.7656 
+2024-09-10 19:06:24.638076: val_loss -0.5753 
+2024-09-10 19:06:24.638127: Pseudo dice [0.5081, 0.7334] 
+2024-09-10 19:06:24.638178: Epoch time: 246.09 s 
+2024-09-10 19:06:25.566818:  
+2024-09-10 19:06:25.567030: Epoch 51 
+2024-09-10 19:06:25.567115: Current learning rate: 0.00954 
+2024-09-10 19:10:31.524232: train_loss -0.7633 
+2024-09-10 19:10:31.524368: val_loss -0.5832 
+2024-09-10 19:10:31.524419: Pseudo dice [0.5035, 0.7468] 
+2024-09-10 19:10:31.524470: Epoch time: 245.96 s 
+2024-09-10 19:10:32.462106:  
+2024-09-10 19:10:32.462339: Epoch 52 
+2024-09-10 19:10:32.462423: Current learning rate: 0.00953 
+2024-09-10 19:14:38.421862: train_loss -0.7693 
+2024-09-10 19:14:38.422001: val_loss -0.5927 
+2024-09-10 19:14:38.422050: Pseudo dice [0.5121, 0.7511] 
+2024-09-10 19:14:38.422099: Epoch time: 245.96 s 
+2024-09-10 19:14:38.422139: Yayy! New best EMA pseudo Dice: 0.6129 
+2024-09-10 19:14:42.321377:  
+2024-09-10 19:14:42.321547: Epoch 53 
+2024-09-10 19:14:42.321627: Current learning rate: 0.00952 
+2024-09-10 19:18:48.450679: train_loss -0.7914 
+2024-09-10 19:18:48.450823: val_loss -0.5983 
+2024-09-10 19:18:48.450873: Pseudo dice [0.5538, 0.7333] 
+2024-09-10 19:18:48.450937: Epoch time: 246.13 s 
+2024-09-10 19:18:48.450978: Yayy! New best EMA pseudo Dice: 0.6159 
+2024-09-10 19:18:52.382243:  
+2024-09-10 19:18:52.382413: Epoch 54 
+2024-09-10 19:18:52.382507: Current learning rate: 0.00951 
+2024-09-10 19:22:59.293351: train_loss -0.7792 
+2024-09-10 19:22:59.293528: val_loss -0.5858 
+2024-09-10 19:22:59.293580: Pseudo dice [0.5227, 0.7333] 
+2024-09-10 19:22:59.293630: Epoch time: 246.91 s 
+2024-09-10 19:22:59.293740: Yayy! New best EMA pseudo Dice: 0.6171 
+2024-09-10 19:23:03.186048:  
+2024-09-10 19:23:03.186233: Epoch 55 
+2024-09-10 19:23:03.186325: Current learning rate: 0.0095 
+2024-09-10 19:27:09.598483: train_loss -0.7713 
+2024-09-10 19:27:09.598634: val_loss -0.5988 
+2024-09-10 19:27:09.598687: Pseudo dice [0.4908, 0.7838] 
+2024-09-10 19:27:09.598739: Epoch time: 246.41 s 
+2024-09-10 19:27:09.598780: Yayy! New best EMA pseudo Dice: 0.6192 
+2024-09-10 19:27:13.523056:  
+2024-09-10 19:27:13.523251: Epoch 56 
+2024-09-10 19:27:13.523335: Current learning rate: 0.00949 
+2024-09-10 19:31:19.781159: train_loss -0.7733 
+2024-09-10 19:31:19.781301: val_loss -0.6277 
+2024-09-10 19:31:19.781352: Pseudo dice [0.5414, 0.7786] 
+2024-09-10 19:31:19.781403: Epoch time: 246.26 s 
+2024-09-10 19:31:19.781443: Yayy! New best EMA pseudo Dice: 0.6232 
+2024-09-10 19:31:23.709635:  
+2024-09-10 19:31:23.709928: Epoch 57 
+2024-09-10 19:31:23.710017: Current learning rate: 0.00949 
+2024-09-10 19:35:29.861315: train_loss -0.7632 
+2024-09-10 19:35:29.861456: val_loss -0.5797 
+2024-09-10 19:35:29.861506: Pseudo dice [0.4959, 0.7621] 
+2024-09-10 19:35:29.861557: Epoch time: 246.15 s 
+2024-09-10 19:35:29.861598: Yayy! New best EMA pseudo Dice: 0.6238 
+2024-09-10 19:35:33.749120:  
+2024-09-10 19:35:33.749308: Epoch 58 
+2024-09-10 19:35:33.749394: Current learning rate: 0.00948 
+2024-09-10 19:39:39.895914: train_loss -0.7758 
+2024-09-10 19:39:39.896131: val_loss -0.6005 
+2024-09-10 19:39:39.896226: Pseudo dice [0.5175, 0.7558] 
+2024-09-10 19:39:39.896318: Epoch time: 246.15 s 
+2024-09-10 19:39:39.896394: Yayy! New best EMA pseudo Dice: 0.6251 
+2024-09-10 19:39:43.866462:  
+2024-09-10 19:39:43.866685: Epoch 59 
+2024-09-10 19:39:43.866768: Current learning rate: 0.00947 
+2024-09-10 19:43:50.048651: train_loss -0.7717 
+2024-09-10 19:43:50.048821: val_loss -0.6074 
+2024-09-10 19:43:50.048916: Pseudo dice [0.5201, 0.7704] 
+2024-09-10 19:43:50.048969: Epoch time: 246.18 s 
+2024-09-10 19:43:50.049010: Yayy! New best EMA pseudo Dice: 0.6271 
+2024-09-10 19:43:53.955647:  
+2024-09-10 19:43:53.955850: Epoch 60 
+2024-09-10 19:43:53.955933: Current learning rate: 0.00946 
+2024-09-10 19:47:59.966269: train_loss -0.7826 
+2024-09-10 19:47:59.966426: val_loss -0.5501 
+2024-09-10 19:47:59.966482: Pseudo dice [0.4481, 0.7529] 
+2024-09-10 19:47:59.966539: Epoch time: 246.01 s 
+2024-09-10 19:48:00.945486:  
+2024-09-10 19:48:00.945665: Epoch 61 
+2024-09-10 19:48:00.945748: Current learning rate: 0.00945 
+2024-09-10 19:52:06.901312: train_loss -0.7709 
+2024-09-10 19:52:06.901524: val_loss -0.5582 
+2024-09-10 19:52:06.901575: Pseudo dice [0.4649, 0.7465] 
+2024-09-10 19:52:06.901627: Epoch time: 245.96 s 
+2024-09-10 19:52:07.855093:  
+2024-09-10 19:52:07.855319: Epoch 62 
+2024-09-10 19:52:07.855410: Current learning rate: 0.00944 
+2024-09-10 19:56:13.843012: train_loss -0.7713 
+2024-09-10 19:56:13.843149: val_loss -0.5734 
+2024-09-10 19:56:13.843199: Pseudo dice [0.4894, 0.7534] 
+2024-09-10 19:56:13.843252: Epoch time: 245.99 s 
+2024-09-10 19:56:14.803792:  
+2024-09-10 19:56:14.804031: Epoch 63 
+2024-09-10 19:56:14.804114: Current learning rate: 0.00943 
+2024-09-10 20:00:20.866701: train_loss -0.7523 
+2024-09-10 20:00:20.866861: val_loss -0.549 
+2024-09-10 20:00:20.866911: Pseudo dice [0.4846, 0.6948] 
+2024-09-10 20:00:20.866965: Epoch time: 246.06 s 
+2024-09-10 20:00:21.809386:  
+2024-09-10 20:00:21.809602: Epoch 64 
+2024-09-10 20:00:21.809722: Current learning rate: 0.00942 
+2024-09-10 20:04:27.520338: train_loss -0.7645 
+2024-09-10 20:04:27.520476: val_loss -0.5825 
+2024-09-10 20:04:27.520526: Pseudo dice [0.5145, 0.7163] 
+2024-09-10 20:04:27.520587: Epoch time: 245.71 s 
+2024-09-10 20:04:28.469653:  
+2024-09-10 20:04:28.469861: Epoch 65 
+2024-09-10 20:04:28.469946: Current learning rate: 0.00941 
+2024-09-10 20:08:34.151322: train_loss -0.7632 
+2024-09-10 20:08:34.151490: val_loss -0.539 
+2024-09-10 20:08:34.151545: Pseudo dice [0.4611, 0.7257] 
+2024-09-10 20:08:34.151597: Epoch time: 245.68 s 
+2024-09-10 20:08:35.109768:  
+2024-09-10 20:08:35.109951: Epoch 66 
+2024-09-10 20:08:35.110037: Current learning rate: 0.0094 
+2024-09-10 20:12:40.659896: train_loss -0.7657 
+2024-09-10 20:12:40.660040: val_loss -0.6062 
+2024-09-10 20:12:40.660090: Pseudo dice [0.5287, 0.7574] 
+2024-09-10 20:12:40.660143: Epoch time: 245.55 s 
+2024-09-10 20:12:41.714436:  
+2024-09-10 20:12:41.714619: Epoch 67 
+2024-09-10 20:12:41.714734: Current learning rate: 0.00939 
+2024-09-10 20:16:47.410480: train_loss -0.7731 
+2024-09-10 20:16:47.410619: val_loss -0.616 
+2024-09-10 20:16:47.410670: Pseudo dice [0.5606, 0.76] 
+2024-09-10 20:16:47.410719: Epoch time: 245.7 s 
+2024-09-10 20:16:48.392968:  
+2024-09-10 20:16:48.393133: Epoch 68 
+2024-09-10 20:16:48.393242: Current learning rate: 0.00939 
+2024-09-10 20:20:54.072909: train_loss -0.776 
+2024-09-10 20:20:54.073047: val_loss -0.5972 
+2024-09-10 20:20:54.073097: Pseudo dice [0.5796, 0.7083] 
+2024-09-10 20:20:54.073148: Epoch time: 245.68 s 
+2024-09-10 20:20:55.038927:  
+2024-09-10 20:20:55.039169: Epoch 69 
+2024-09-10 20:20:55.039278: Current learning rate: 0.00938 
+2024-09-10 20:25:00.605475: train_loss -0.7742 
+2024-09-10 20:25:00.605616: val_loss -0.571 
+2024-09-10 20:25:00.605666: Pseudo dice [0.5138, 0.7348] 
+2024-09-10 20:25:00.605717: Epoch time: 245.57 s 
+2024-09-10 20:25:01.577725:  
+2024-09-10 20:25:01.577930: Epoch 70 
+2024-09-10 20:25:01.578053: Current learning rate: 0.00937 
+2024-09-10 20:29:07.153795: train_loss -0.782 
+2024-09-10 20:29:07.153936: val_loss -0.6179 
+2024-09-10 20:29:07.153987: Pseudo dice [0.5698, 0.7437] 
+2024-09-10 20:29:07.154038: Epoch time: 245.58 s 
+2024-09-10 20:29:07.154079: Yayy! New best EMA pseudo Dice: 0.6283 
+2024-09-10 20:29:11.078436:  
+2024-09-10 20:29:11.078613: Epoch 71 
+2024-09-10 20:29:11.078697: Current learning rate: 0.00936 
+2024-09-10 20:33:16.775848: train_loss -0.7857 
+2024-09-10 20:33:16.775989: val_loss -0.5931 
+2024-09-10 20:33:16.776039: Pseudo dice [0.5119, 0.7626] 
+2024-09-10 20:33:16.776090: Epoch time: 245.7 s 
+2024-09-10 20:33:16.776129: Yayy! New best EMA pseudo Dice: 0.6292 
+2024-09-10 20:33:20.696027:  
+2024-09-10 20:33:20.696278: Epoch 72 
+2024-09-10 20:33:20.696361: Current learning rate: 0.00935 
+2024-09-10 20:37:26.621811: train_loss -0.7717 
+2024-09-10 20:37:26.621964: val_loss -0.5672 
+2024-09-10 20:37:26.622015: Pseudo dice [0.4901, 0.7306] 
+2024-09-10 20:37:26.622065: Epoch time: 245.93 s 
+2024-09-10 20:37:27.599549:  
+2024-09-10 20:37:27.599760: Epoch 73 
+2024-09-10 20:37:27.599861: Current learning rate: 0.00934 
+2024-09-10 20:41:33.700847: train_loss -0.7806 
+2024-09-10 20:41:33.700982: val_loss -0.573 
+2024-09-10 20:41:33.701032: Pseudo dice [0.4934, 0.7435] 
+2024-09-10 20:41:33.701082: Epoch time: 246.1 s 
+2024-09-10 20:41:34.677166:  
+2024-09-10 20:41:34.677342: Epoch 74 
+2024-09-10 20:41:34.677425: Current learning rate: 0.00933 
+2024-09-10 20:45:40.510600: train_loss -0.7884 
+2024-09-10 20:45:40.510741: val_loss -0.6002 
+2024-09-10 20:45:40.510791: Pseudo dice [0.5444, 0.7241] 
+2024-09-10 20:45:40.510843: Epoch time: 245.84 s 
+2024-09-10 20:45:41.487218:  
+2024-09-10 20:45:41.487382: Epoch 75 
+2024-09-10 20:45:41.487472: Current learning rate: 0.00932 
+2024-09-10 20:49:47.337953: train_loss -0.7922 
+2024-09-10 20:49:47.338176: val_loss -0.582 
+2024-09-10 20:49:47.338251: Pseudo dice [0.4723, 0.7707] 
+2024-09-10 20:49:47.338328: Epoch time: 245.85 s 
+2024-09-10 20:49:48.327112:  
+2024-09-10 20:49:48.327302: Epoch 76 
+2024-09-10 20:49:48.327411: Current learning rate: 0.00931 
+2024-09-10 20:53:54.126155: train_loss -0.7904 
+2024-09-10 20:53:54.126302: val_loss -0.6192 
+2024-09-10 20:53:54.126357: Pseudo dice [0.5538, 0.768] 
+2024-09-10 20:53:54.126424: Epoch time: 245.8 s 
+2024-09-10 20:53:54.126470: Yayy! New best EMA pseudo Dice: 0.63 
+2024-09-10 20:53:59.077523:  
+2024-09-10 20:53:59.077794: Epoch 77 
+2024-09-10 20:53:59.077923: Current learning rate: 0.0093 
+2024-09-10 20:58:05.386920: train_loss -0.7834 
+2024-09-10 20:58:05.387097: val_loss -0.5698 
+2024-09-10 20:58:05.387159: Pseudo dice [0.4996, 0.7469] 
+2024-09-10 20:58:05.387216: Epoch time: 246.31 s 
+2024-09-10 20:58:06.376629:  
+2024-09-10 20:58:06.376816: Epoch 78 
+2024-09-10 20:58:06.376956: Current learning rate: 0.0093 
+2024-09-10 21:02:12.245217: train_loss -0.7842 
+2024-09-10 21:02:12.245376: val_loss -0.6213 
+2024-09-10 21:02:12.245433: Pseudo dice [0.5682, 0.764] 
+2024-09-10 21:02:12.245489: Epoch time: 245.87 s 
+2024-09-10 21:02:12.245535: Yayy! New best EMA pseudo Dice: 0.633 
+2024-09-10 21:02:16.248327:  
+2024-09-10 21:02:16.248534: Epoch 79 
+2024-09-10 21:02:16.248629: Current learning rate: 0.00929 
+2024-09-10 21:06:22.357673: train_loss -0.7847 
+2024-09-10 21:06:22.357819: val_loss -0.6106 
+2024-09-10 21:06:22.357875: Pseudo dice [0.5353, 0.7738] 
+2024-09-10 21:06:22.357930: Epoch time: 246.11 s 
+2024-09-10 21:06:22.357974: Yayy! New best EMA pseudo Dice: 0.6352 
+2024-09-10 21:06:26.320595:  
+2024-09-10 21:06:26.320860: Epoch 80 
+2024-09-10 21:06:26.320949: Current learning rate: 0.00928 
+2024-09-10 21:10:32.305787: train_loss -0.7877 
+2024-09-10 21:10:32.305936: val_loss -0.5903 
+2024-09-10 21:10:32.305993: Pseudo dice [0.5544, 0.7232] 
+2024-09-10 21:10:32.306048: Epoch time: 245.99 s 
+2024-09-10 21:10:32.306092: Yayy! New best EMA pseudo Dice: 0.6355 
+2024-09-10 21:10:36.367048:  
+2024-09-10 21:10:36.367267: Epoch 81 
+2024-09-10 21:10:36.367359: Current learning rate: 0.00927 
+2024-09-10 21:14:42.460583: train_loss -0.7914 
+2024-09-10 21:14:42.460727: val_loss -0.6162 
+2024-09-10 21:14:42.460778: Pseudo dice [0.5516, 0.761] 
+2024-09-10 21:14:42.460829: Epoch time: 246.1 s 
+2024-09-10 21:14:42.460910: Yayy! New best EMA pseudo Dice: 0.6376 
+2024-09-10 21:14:46.393930:  
+2024-09-10 21:14:46.394095: Epoch 82 
+2024-09-10 21:14:46.394240: Current learning rate: 0.00926 
+2024-09-10 21:18:52.582155: train_loss -0.7831 
+2024-09-10 21:18:52.582312: val_loss -0.5764 
+2024-09-10 21:18:52.582378: Pseudo dice [0.4781, 0.7679] 
+2024-09-10 21:18:52.582429: Epoch time: 246.19 s 
+2024-09-10 21:18:53.496700:  
+2024-09-10 21:18:53.496888: Epoch 83 
+2024-09-10 21:18:53.496973: Current learning rate: 0.00925 
+2024-09-10 21:22:59.797247: train_loss -0.7735 
+2024-09-10 21:22:59.797384: val_loss -0.5856 
+2024-09-10 21:22:59.797435: Pseudo dice [0.4844, 0.7543] 
+2024-09-10 21:22:59.797486: Epoch time: 246.3 s 
+2024-09-10 21:23:00.721609:  
+2024-09-10 21:23:00.721802: Epoch 84 
+2024-09-10 21:23:00.721884: Current learning rate: 0.00924 
+2024-09-10 21:27:06.742713: train_loss -0.7858 
+2024-09-10 21:27:06.742860: val_loss -0.5771 
+2024-09-10 21:27:06.742911: Pseudo dice [0.492, 0.7606] 
+2024-09-10 21:27:06.742961: Epoch time: 246.02 s 
+2024-09-10 21:27:07.669863:  
+2024-09-10 21:27:07.670065: Epoch 85 
+2024-09-10 21:27:07.670150: Current learning rate: 0.00923 
+2024-09-10 21:31:13.579891: train_loss -0.7868 
+2024-09-10 21:31:13.580034: val_loss -0.5723 
+2024-09-10 21:31:13.580088: Pseudo dice [0.4706, 0.7495] 
+2024-09-10 21:31:13.580139: Epoch time: 245.91 s 
+2024-09-10 21:31:14.515652:  
+2024-09-10 21:31:14.515879: Epoch 86 
+2024-09-10 21:31:14.515965: Current learning rate: 0.00922 
+2024-09-10 21:35:20.338727: train_loss -0.7985 
+2024-09-10 21:35:20.338865: val_loss -0.5805 
+2024-09-10 21:35:20.338916: Pseudo dice [0.4978, 0.7763] 
+2024-09-10 21:35:20.338968: Epoch time: 245.83 s 
+2024-09-10 21:35:21.273535:  
+2024-09-10 21:35:21.273707: Epoch 87 
+2024-09-10 21:35:21.273789: Current learning rate: 0.00921 
+2024-09-10 21:39:27.043380: train_loss -0.7952 
+2024-09-10 21:39:27.043520: val_loss -0.5771 
+2024-09-10 21:39:27.043571: Pseudo dice [0.505, 0.7614] 
+2024-09-10 21:39:27.043622: Epoch time: 245.77 s 
+2024-09-10 21:39:27.975300:  
+2024-09-10 21:39:27.975487: Epoch 88 
+2024-09-10 21:39:27.975572: Current learning rate: 0.0092 
+2024-09-10 21:43:33.791458: train_loss -0.79 
+2024-09-10 21:43:33.791593: val_loss -0.5627 
+2024-09-10 21:43:33.791676: Pseudo dice [0.4433, 0.7398] 
+2024-09-10 21:43:33.791727: Epoch time: 245.82 s 
+2024-09-10 21:43:34.778199:  
+2024-09-10 21:43:34.778361: Epoch 89 
+2024-09-10 21:43:34.778444: Current learning rate: 0.0092 
+2024-09-10 21:47:40.864584: train_loss -0.7954 
+2024-09-10 21:47:40.864716: val_loss -0.5706 
+2024-09-10 21:47:40.864765: Pseudo dice [0.4713, 0.7782] 
+2024-09-10 21:47:40.864816: Epoch time: 246.09 s 
+2024-09-10 21:47:41.805714:  
+2024-09-10 21:47:41.805848: Epoch 90 
+2024-09-10 21:47:41.805929: Current learning rate: 0.00919 
+2024-09-10 21:51:47.811411: train_loss -0.7926 
+2024-09-10 21:51:47.811548: val_loss -0.5964 
+2024-09-10 21:51:47.811598: Pseudo dice [0.5386, 0.759] 
+2024-09-10 21:51:47.811702: Epoch time: 246.01 s 
+2024-09-10 21:51:48.744880:  
+2024-09-10 21:51:48.745064: Epoch 91 
+2024-09-10 21:51:48.745147: Current learning rate: 0.00918 
+2024-09-10 21:55:54.783783: train_loss -0.7802 
+2024-09-10 21:55:54.783983: val_loss -0.5683 
+2024-09-10 21:55:54.784037: Pseudo dice [0.4658, 0.7565] 
+2024-09-10 21:55:54.784089: Epoch time: 246.04 s 
+2024-09-10 21:55:55.709927:  
+2024-09-10 21:55:55.710133: Epoch 92 
+2024-09-10 21:55:55.710217: Current learning rate: 0.00917 
+2024-09-10 22:00:01.761909: train_loss -0.8002 
+2024-09-10 22:00:01.762049: val_loss -0.6042 
+2024-09-10 22:00:01.762100: Pseudo dice [0.4959, 0.7672] 
+2024-09-10 22:00:01.762150: Epoch time: 246.05 s 
+2024-09-10 22:00:02.665617:  
+2024-09-10 22:00:02.665823: Epoch 93 
+2024-09-10 22:00:02.665907: Current learning rate: 0.00916 
+2024-09-10 22:04:08.658640: train_loss -0.788 
+2024-09-10 22:04:08.658792: val_loss -0.5356 
+2024-09-10 22:04:08.658886: Pseudo dice [0.4834, 0.6891] 
+2024-09-10 22:04:08.658957: Epoch time: 245.99 s 
+2024-09-10 22:04:09.582893:  
+2024-09-10 22:04:09.583093: Epoch 94 
+2024-09-10 22:04:09.583177: Current learning rate: 0.00915 
+2024-09-10 22:08:15.484567: train_loss -0.7876 
+2024-09-10 22:08:15.484821: val_loss -0.6141 
+2024-09-10 22:08:15.484874: Pseudo dice [0.5575, 0.7882] 
+2024-09-10 22:08:15.484930: Epoch time: 245.9 s 
+2024-09-10 22:08:16.421660:  
+2024-09-10 22:08:16.421882: Epoch 95 
+2024-09-10 22:08:16.421969: Current learning rate: 0.00914 
+2024-09-10 22:12:22.425345: train_loss -0.7838 
+2024-09-10 22:12:22.425500: val_loss -0.5939 
+2024-09-10 22:12:22.425551: Pseudo dice [0.5043, 0.7738] 
+2024-09-10 22:12:22.425601: Epoch time: 246.01 s 
+2024-09-10 22:12:23.340494:  
+2024-09-10 22:12:23.340668: Epoch 96 
+2024-09-10 22:12:23.340751: Current learning rate: 0.00913 
+2024-09-10 22:16:29.381852: train_loss -0.7961 
+2024-09-10 22:16:29.382004: val_loss -0.5435 
+2024-09-10 22:16:29.382056: Pseudo dice [0.4515, 0.7214] 
+2024-09-10 22:16:29.382107: Epoch time: 246.04 s 
+2024-09-10 22:16:30.304683:  
+2024-09-10 22:16:30.304939: Epoch 97 
+2024-09-10 22:16:30.305031: Current learning rate: 0.00912 
+2024-09-10 22:20:36.373541: train_loss -0.7942 
+2024-09-10 22:20:36.373679: val_loss -0.5519 
+2024-09-10 22:20:36.373729: Pseudo dice [0.4006, 0.7611] 
+2024-09-10 22:20:36.373781: Epoch time: 246.07 s 
+2024-09-10 22:20:37.315077:  
+2024-09-10 22:20:37.315277: Epoch 98 
+2024-09-10 22:20:37.315400: Current learning rate: 0.00911 
+2024-09-10 22:24:43.477781: train_loss -0.7902 
+2024-09-10 22:24:43.477926: val_loss -0.5906 
+2024-09-10 22:24:43.477977: Pseudo dice [0.491, 0.7704] 
+2024-09-10 22:24:43.478028: Epoch time: 246.17 s 
+2024-09-10 22:24:44.415031:  
+2024-09-10 22:24:44.415204: Epoch 99 
+2024-09-10 22:24:44.415285: Current learning rate: 0.0091 
+2024-09-10 22:28:50.708858: train_loss -0.8075 
+2024-09-10 22:28:50.709014: val_loss -0.5819 
+2024-09-10 22:28:50.709064: Pseudo dice [0.4717, 0.7559] 
+2024-09-10 22:28:50.709114: Epoch time: 246.3 s 
+2024-09-10 22:28:55.562587:  
+2024-09-10 22:28:55.562797: Epoch 100 
+2024-09-10 22:28:55.562892: Current learning rate: 0.0091 
+2024-09-10 22:33:01.968261: train_loss -0.8012 
+2024-09-10 22:33:01.968440: val_loss -0.5727 
+2024-09-10 22:33:01.968508: Pseudo dice [0.4647, 0.7569] 
+2024-09-10 22:33:01.968562: Epoch time: 246.41 s 
+2024-09-10 22:33:02.915336:  
+2024-09-10 22:33:02.915537: Epoch 101 
+2024-09-10 22:33:02.915626: Current learning rate: 0.00909 
+2024-09-10 22:37:09.064939: train_loss -0.7963 
+2024-09-10 22:37:09.065074: val_loss -0.6123 
+2024-09-10 22:37:09.065124: Pseudo dice [0.5323, 0.7711] 
+2024-09-10 22:37:09.065173: Epoch time: 246.15 s 
+2024-09-10 22:37:09.997696:  
+2024-09-10 22:37:09.997914: Epoch 102 
+2024-09-10 22:37:09.997998: Current learning rate: 0.00908 
+2024-09-10 22:41:15.994307: train_loss -0.7895 
+2024-09-10 22:41:15.994446: val_loss -0.6116 
+2024-09-10 22:41:15.994496: Pseudo dice [0.5419, 0.7622] 
+2024-09-10 22:41:15.994545: Epoch time: 246.0 s 
+2024-09-10 22:41:16.934330:  
+2024-09-10 22:41:16.934571: Epoch 103 
+2024-09-10 22:41:16.934652: Current learning rate: 0.00907 
+2024-09-10 22:45:23.007117: train_loss -0.7901 
+2024-09-10 22:45:23.007256: val_loss -0.6158 
+2024-09-10 22:45:23.007307: Pseudo dice [0.518, 0.7645] 
+2024-09-10 22:45:23.007362: Epoch time: 246.07 s 
+2024-09-10 22:45:23.965983:  
+2024-09-10 22:45:23.966209: Epoch 104 
+2024-09-10 22:45:23.966291: Current learning rate: 0.00906 
+2024-09-10 22:49:30.220775: train_loss -0.7962 
+2024-09-10 22:49:30.220913: val_loss -0.6022 
+2024-09-10 22:49:30.220963: Pseudo dice [0.5291, 0.78] 
+2024-09-10 22:49:30.221014: Epoch time: 246.26 s 
+2024-09-10 22:49:31.163195:  
+2024-09-10 22:49:31.163412: Epoch 105 
+2024-09-10 22:49:31.163495: Current learning rate: 0.00905 
+2024-09-10 22:53:37.377349: train_loss -0.8102 
+2024-09-10 22:53:37.377505: val_loss -0.6074 
+2024-09-10 22:53:37.377558: Pseudo dice [0.547, 0.7604] 
+2024-09-10 22:53:37.377609: Epoch time: 246.22 s 
+2024-09-10 22:53:38.318238:  
+2024-09-10 22:53:38.318503: Epoch 106 
+2024-09-10 22:53:38.318608: Current learning rate: 0.00904 
+2024-09-10 22:57:44.286058: train_loss -0.8011 
+2024-09-10 22:57:44.286215: val_loss -0.59 
+2024-09-10 22:57:44.286266: Pseudo dice [0.4999, 0.7572] 
+2024-09-10 22:57:44.286316: Epoch time: 245.97 s 
+2024-09-10 22:57:45.227890:  
+2024-09-10 22:57:45.228134: Epoch 107 
+2024-09-10 22:57:45.228220: Current learning rate: 0.00903 
+2024-09-10 23:01:51.273221: train_loss -0.7801 
+2024-09-10 23:01:51.273362: val_loss -0.5862 
+2024-09-10 23:01:51.273411: Pseudo dice [0.4928, 0.7535] 
+2024-09-10 23:01:51.273462: Epoch time: 246.05 s 
+2024-09-10 23:01:52.213458:  
+2024-09-10 23:01:52.213706: Epoch 108 
+2024-09-10 23:01:52.213794: Current learning rate: 0.00902 
+2024-09-10 23:05:58.450722: train_loss -0.7879 
+2024-09-10 23:05:58.450863: val_loss -0.6317 
+2024-09-10 23:05:58.450912: Pseudo dice [0.5777, 0.7823] 
+2024-09-10 23:05:58.450961: Epoch time: 246.24 s 
+2024-09-10 23:05:59.399118:  
+2024-09-10 23:05:59.399323: Epoch 109 
+2024-09-10 23:05:59.399408: Current learning rate: 0.00901 
+2024-09-10 23:10:05.347098: train_loss -0.7937 
+2024-09-10 23:10:05.347245: val_loss -0.6131 
+2024-09-10 23:10:05.347323: Pseudo dice [0.5376, 0.7748] 
+2024-09-10 23:10:05.347375: Epoch time: 245.95 s 
+2024-09-10 23:10:05.347415: Yayy! New best EMA pseudo Dice: 0.6383 
+2024-09-10 23:10:09.268343:  
+2024-09-10 23:10:09.268578: Epoch 110 
+2024-09-10 23:10:09.268689: Current learning rate: 0.009 
+2024-09-10 23:14:15.247747: train_loss -0.7996 
+2024-09-10 23:14:15.247888: val_loss -0.5829 
+2024-09-10 23:14:15.247939: Pseudo dice [0.4654, 0.7666] 
+2024-09-10 23:14:15.247990: Epoch time: 245.98 s 
+2024-09-10 23:14:16.183349:  
+2024-09-10 23:14:16.183503: Epoch 111 
+2024-09-10 23:14:16.183619: Current learning rate: 0.009 
+2024-09-10 23:18:22.040143: train_loss -0.804 
+2024-09-10 23:18:22.040294: val_loss -0.5788 
+2024-09-10 23:18:22.040344: Pseudo dice [0.4489, 0.7834] 
+2024-09-10 23:18:22.040395: Epoch time: 245.86 s 
+2024-09-10 23:18:22.997150:  
+2024-09-10 23:18:22.997367: Epoch 112 
+2024-09-10 23:18:22.997495: Current learning rate: 0.00899 
+2024-09-10 23:22:28.891982: train_loss -0.8077 
+2024-09-10 23:22:28.892137: val_loss -0.5934 
+2024-09-10 23:22:28.892189: Pseudo dice [0.5118, 0.7705] 
+2024-09-10 23:22:28.892240: Epoch time: 245.9 s 
+2024-09-10 23:22:29.836035:  
+2024-09-10 23:22:29.836267: Epoch 113 
+2024-09-10 23:22:29.836351: Current learning rate: 0.00898 
+2024-09-10 23:26:35.606647: train_loss -0.8177 
+2024-09-10 23:26:35.606929: val_loss -0.6023 
+2024-09-10 23:26:35.607038: Pseudo dice [0.5019, 0.7623] 
+2024-09-10 23:26:35.607129: Epoch time: 245.77 s 
+2024-09-10 23:26:36.553849:  
+2024-09-10 23:26:36.554042: Epoch 114 
+2024-09-10 23:26:36.554126: Current learning rate: 0.00897 
+2024-09-10 23:30:42.630605: train_loss -0.8146 
+2024-09-10 23:30:42.630750: val_loss -0.6183 
+2024-09-10 23:30:42.630799: Pseudo dice [0.5378, 0.7779] 
+2024-09-10 23:30:42.630850: Epoch time: 246.08 s 
+2024-09-10 23:30:43.571611:  
+2024-09-10 23:30:43.571777: Epoch 115 
+2024-09-10 23:30:43.571868: Current learning rate: 0.00896 
+2024-09-10 23:34:49.629150: train_loss -0.8064 
+2024-09-10 23:34:49.629296: val_loss -0.6355 
+2024-09-10 23:34:49.629346: Pseudo dice [0.5686, 0.7823] 
+2024-09-10 23:34:49.629397: Epoch time: 246.06 s 
+2024-09-10 23:34:49.629437: Yayy! New best EMA pseudo Dice: 0.6407 
+2024-09-10 23:34:53.566886:  
+2024-09-10 23:34:53.567105: Epoch 116 
+2024-09-10 23:34:53.567225: Current learning rate: 0.00895 
+2024-09-10 23:38:59.828122: train_loss -0.8085 
+2024-09-10 23:38:59.828288: val_loss -0.5601 
+2024-09-10 23:38:59.828339: Pseudo dice [0.521, 0.7184] 
+2024-09-10 23:38:59.828393: Epoch time: 246.26 s 
+2024-09-10 23:39:00.774338:  
+2024-09-10 23:39:00.774541: Epoch 117 
+2024-09-10 23:39:00.774624: Current learning rate: 0.00894 
+2024-09-10 23:43:07.095399: train_loss -0.7841 
+2024-09-10 23:43:07.095550: val_loss -0.5754 
+2024-09-10 23:43:07.095600: Pseudo dice [0.494, 0.7729] 
+2024-09-10 23:43:07.095655: Epoch time: 246.32 s 
+2024-09-10 23:43:08.086865:  
+2024-09-10 23:43:08.087013: Epoch 118 
+2024-09-10 23:43:08.087095: Current learning rate: 0.00893 
+2024-09-10 23:47:14.279325: train_loss -0.7957 
+2024-09-10 23:47:14.279473: val_loss -0.6171 
+2024-09-10 23:47:14.279524: Pseudo dice [0.5384, 0.7558] 
+2024-09-10 23:47:14.279573: Epoch time: 246.19 s 
+2024-09-10 23:47:15.235285:  
+2024-09-10 23:47:15.235473: Epoch 119 
+2024-09-10 23:47:15.235560: Current learning rate: 0.00892 
+2024-09-10 23:51:21.470359: train_loss -0.7954 
+2024-09-10 23:51:21.470499: val_loss -0.6072 
+2024-09-10 23:51:21.470550: Pseudo dice [0.5209, 0.7914] 
+2024-09-10 23:51:21.470601: Epoch time: 246.24 s 
+2024-09-10 23:51:21.470641: Yayy! New best EMA pseudo Dice: 0.6407 
+2024-09-10 23:51:25.407387:  
+2024-09-10 23:51:25.407659: Epoch 120 
+2024-09-10 23:51:25.407773: Current learning rate: 0.00891 
+2024-09-10 23:55:31.453774: train_loss -0.8077 
+2024-09-10 23:55:31.453972: val_loss -0.6168 
+2024-09-10 23:55:31.454025: Pseudo dice [0.5296, 0.7798] 
+2024-09-10 23:55:31.454076: Epoch time: 246.05 s 
+2024-09-10 23:55:31.454117: Yayy! New best EMA pseudo Dice: 0.6421 
+2024-09-10 23:55:35.326917:  
+2024-09-10 23:55:35.327141: Epoch 121 
+2024-09-10 23:55:35.327222: Current learning rate: 0.0089 
+2024-09-10 23:59:41.389663: train_loss -0.8117 
+2024-09-10 23:59:41.389800: val_loss -0.6006 
+2024-09-10 23:59:41.389850: Pseudo dice [0.5407, 0.762] 
+2024-09-10 23:59:41.389900: Epoch time: 246.06 s 
+2024-09-10 23:59:41.389944: Yayy! New best EMA pseudo Dice: 0.643 
+2024-09-10 23:59:45.288714:  
+2024-09-10 23:59:45.288913: Epoch 122 
+2024-09-10 23:59:45.288996: Current learning rate: 0.00889 
+2024-09-11 00:03:51.385612: train_loss -0.7742 
+2024-09-11 00:03:51.385795: val_loss -0.5637 
+2024-09-11 00:03:51.385847: Pseudo dice [0.4739, 0.7439] 
+2024-09-11 00:03:51.385899: Epoch time: 246.1 s 
+2024-09-11 00:03:53.287084:  
+2024-09-11 00:03:53.287344: Epoch 123 
+2024-09-11 00:03:53.287475: Current learning rate: 0.00889 
+2024-09-11 00:07:59.252274: train_loss -0.7841 
+2024-09-11 00:07:59.252415: val_loss -0.6097 
+2024-09-11 00:07:59.252466: Pseudo dice [0.5226, 0.7661] 
+2024-09-11 00:07:59.252517: Epoch time: 245.97 s 
+2024-09-11 00:08:00.214357:  
+2024-09-11 00:08:00.214535: Epoch 124 
+2024-09-11 00:08:00.214633: Current learning rate: 0.00888 
+2024-09-11 00:12:06.076037: train_loss -0.7984 
+2024-09-11 00:12:06.076187: val_loss -0.5809 
+2024-09-11 00:12:06.076237: Pseudo dice [0.483, 0.7574] 
+2024-09-11 00:12:06.076287: Epoch time: 245.86 s 
+2024-09-11 00:12:07.046623:  
+2024-09-11 00:12:07.046850: Epoch 125 
+2024-09-11 00:12:07.046962: Current learning rate: 0.00887 
+2024-09-11 00:16:13.051635: train_loss -0.8104 
+2024-09-11 00:16:13.051775: val_loss -0.5863 
+2024-09-11 00:16:13.051841: Pseudo dice [0.5, 0.7546] 
+2024-09-11 00:16:13.051906: Epoch time: 246.01 s 
+2024-09-11 00:16:14.007544:  
+2024-09-11 00:16:14.007790: Epoch 126 
+2024-09-11 00:16:14.007880: Current learning rate: 0.00886 
+2024-09-11 00:20:20.199795: train_loss -0.8117 
+2024-09-11 00:20:20.199953: val_loss -0.5994 
+2024-09-11 00:20:20.200003: Pseudo dice [0.5283, 0.7529] 
+2024-09-11 00:20:20.200054: Epoch time: 246.19 s 
+2024-09-11 00:20:21.165259:  
+2024-09-11 00:20:21.165520: Epoch 127 
+2024-09-11 00:20:21.165615: Current learning rate: 0.00885 
+2024-09-11 00:24:27.293385: train_loss -0.8057 
+2024-09-11 00:24:27.293525: val_loss -0.5923 
+2024-09-11 00:24:27.293574: Pseudo dice [0.4805, 0.7469] 
+2024-09-11 00:24:27.293624: Epoch time: 246.13 s 
+2024-09-11 00:24:28.252918:  
+2024-09-11 00:24:28.253167: Epoch 128 
+2024-09-11 00:24:28.253248: Current learning rate: 0.00884 
+2024-09-11 00:28:34.525961: train_loss -0.7996 
+2024-09-11 00:28:34.526097: val_loss -0.5643 
+2024-09-11 00:28:34.526148: Pseudo dice [0.4425, 0.7386] 
+2024-09-11 00:28:34.526199: Epoch time: 246.27 s 
+2024-09-11 00:28:35.497473:  
+2024-09-11 00:28:35.497702: Epoch 129 
+2024-09-11 00:28:35.497787: Current learning rate: 0.00883 
+2024-09-11 00:32:41.715669: train_loss -0.8043 
+2024-09-11 00:32:41.715841: val_loss -0.5882 
+2024-09-11 00:32:41.715894: Pseudo dice [0.4768, 0.7829] 
+2024-09-11 00:32:41.715945: Epoch time: 246.22 s 
+2024-09-11 00:32:42.680444:  
+2024-09-11 00:32:42.680652: Epoch 130 
+2024-09-11 00:32:42.680733: Current learning rate: 0.00882 
+2024-09-11 00:36:48.783008: train_loss -0.805 
+2024-09-11 00:36:48.783160: val_loss -0.6157 
+2024-09-11 00:36:48.783427: Pseudo dice [0.5325, 0.7545] 
+2024-09-11 00:36:48.783533: Epoch time: 246.1 s 
+2024-09-11 00:36:49.732121:  
+2024-09-11 00:36:49.732352: Epoch 131 
+2024-09-11 00:36:49.732432: Current learning rate: 0.00881 
+2024-09-11 00:40:55.687523: train_loss -0.7971 
+2024-09-11 00:40:55.687657: val_loss -0.5699 
+2024-09-11 00:40:55.687707: Pseudo dice [0.4361, 0.7667] 
+2024-09-11 00:40:55.687760: Epoch time: 245.96 s 
+2024-09-11 00:40:56.640868:  
+2024-09-11 00:40:56.641043: Epoch 132 
+2024-09-11 00:40:56.641168: Current learning rate: 0.0088 
+2024-09-11 00:45:02.575037: train_loss -0.8096 
+2024-09-11 00:45:02.575176: val_loss -0.6243 
+2024-09-11 00:45:02.575225: Pseudo dice [0.5682, 0.7772] 
+2024-09-11 00:45:02.575277: Epoch time: 245.94 s 
+2024-09-11 00:45:03.575159:  
+2024-09-11 00:45:03.575333: Epoch 133 
+2024-09-11 00:45:03.575423: Current learning rate: 0.00879 
+2024-09-11 00:49:09.505594: train_loss -0.8158 
+2024-09-11 00:49:09.505727: val_loss -0.591 
+2024-09-11 00:49:09.505779: Pseudo dice [0.51, 0.7585] 
+2024-09-11 00:49:09.505833: Epoch time: 245.93 s 
+2024-09-11 00:49:10.458621:  
+2024-09-11 00:49:10.458830: Epoch 134 
+2024-09-11 00:49:10.458918: Current learning rate: 0.00879 
+2024-09-11 00:53:16.249075: train_loss -0.8166 
+2024-09-11 00:53:16.249231: val_loss -0.5817 
+2024-09-11 00:53:16.249282: Pseudo dice [0.528, 0.7535] 
+2024-09-11 00:53:16.249334: Epoch time: 245.79 s 
+2024-09-11 00:53:17.238206:  
+2024-09-11 00:53:17.238437: Epoch 135 
+2024-09-11 00:53:17.238528: Current learning rate: 0.00878 
+2024-09-11 00:57:23.213267: train_loss -0.7969 
+2024-09-11 00:57:23.213409: val_loss -0.5864 
+2024-09-11 00:57:23.213460: Pseudo dice [0.4369, 0.7944] 
+2024-09-11 00:57:23.213511: Epoch time: 245.98 s 
+2024-09-11 00:57:24.191799:  
+2024-09-11 00:57:24.192037: Epoch 136 
+2024-09-11 00:57:24.192154: Current learning rate: 0.00877 
+2024-09-11 01:01:30.335111: train_loss -0.7858 
+2024-09-11 01:01:30.335257: val_loss -0.562 
+2024-09-11 01:01:30.335307: Pseudo dice [0.4358, 0.7769] 
+2024-09-11 01:01:30.335359: Epoch time: 246.15 s 
+2024-09-11 01:01:31.312052:  
+2024-09-11 01:01:31.312217: Epoch 137 
+2024-09-11 01:01:31.312305: Current learning rate: 0.00876 
+2024-09-11 01:05:37.310467: train_loss -0.8111 
+2024-09-11 01:05:37.310631: val_loss -0.601 
+2024-09-11 01:05:37.310682: Pseudo dice [0.5246, 0.7616] 
+2024-09-11 01:05:37.310778: Epoch time: 246.0 s 
+2024-09-11 01:05:38.285226:  
+2024-09-11 01:05:38.285388: Epoch 138 
+2024-09-11 01:05:38.285472: Current learning rate: 0.00875 
+2024-09-11 01:09:44.306928: train_loss -0.8062 
+2024-09-11 01:09:44.307094: val_loss -0.5983 
+2024-09-11 01:09:44.307145: Pseudo dice [0.4802, 0.77] 
+2024-09-11 01:09:44.307197: Epoch time: 246.02 s 
+2024-09-11 01:09:45.277694:  
+2024-09-11 01:09:45.277913: Epoch 139 
+2024-09-11 01:09:45.278004: Current learning rate: 0.00874 
+2024-09-11 01:13:51.406931: train_loss -0.8128 
+2024-09-11 01:13:51.407071: val_loss -0.5779 
+2024-09-11 01:13:51.407120: Pseudo dice [0.4808, 0.778] 
+2024-09-11 01:13:51.407171: Epoch time: 246.13 s 
+2024-09-11 01:13:52.384150:  
+2024-09-11 01:13:52.384359: Epoch 140 
+2024-09-11 01:13:52.384463: Current learning rate: 0.00873 
+2024-09-11 01:17:58.420407: train_loss -0.8009 
+2024-09-11 01:17:58.420552: val_loss -0.6047 
+2024-09-11 01:17:58.420643: Pseudo dice [0.5225, 0.7467] 
+2024-09-11 01:17:58.420722: Epoch time: 246.04 s 
+2024-09-11 01:17:59.386648:  
+2024-09-11 01:17:59.386923: Epoch 141 
+2024-09-11 01:17:59.387017: Current learning rate: 0.00872 
+2024-09-11 01:22:05.480852: train_loss -0.8153 
+2024-09-11 01:22:05.480990: val_loss -0.5921 
+2024-09-11 01:22:05.481040: Pseudo dice [0.5091, 0.7708] 
+2024-09-11 01:22:05.481091: Epoch time: 246.1 s 
+2024-09-11 01:22:06.451885:  
+2024-09-11 01:22:06.452079: Epoch 142 
+2024-09-11 01:22:06.452161: Current learning rate: 0.00871 
+2024-09-11 01:26:12.328902: train_loss -0.8172 
+2024-09-11 01:26:12.329062: val_loss -0.6252 
+2024-09-11 01:26:12.329207: Pseudo dice [0.5451, 0.7786] 
+2024-09-11 01:26:12.329261: Epoch time: 245.88 s 
+2024-09-11 01:26:13.313654:  
+2024-09-11 01:26:13.313830: Epoch 143 
+2024-09-11 01:26:13.313918: Current learning rate: 0.0087 
+2024-09-11 01:30:19.146637: train_loss -0.8085 
+2024-09-11 01:30:19.146805: val_loss -0.5752 
+2024-09-11 01:30:19.146857: Pseudo dice [0.4675, 0.758] 
+2024-09-11 01:30:19.146909: Epoch time: 245.83 s 
+2024-09-11 01:30:20.133009:  
+2024-09-11 01:30:20.133233: Epoch 144 
+2024-09-11 01:30:20.133329: Current learning rate: 0.00869 
+2024-09-11 01:34:25.994318: train_loss -0.8065 
+2024-09-11 01:34:25.994483: val_loss -0.5995 
+2024-09-11 01:34:25.994544: Pseudo dice [0.4675, 0.789] 
+2024-09-11 01:34:25.994605: Epoch time: 245.86 s 
+2024-09-11 01:34:26.975951:  
+2024-09-11 01:34:26.976151: Epoch 145 
+2024-09-11 01:34:26.976236: Current learning rate: 0.00868 
+2024-09-11 01:38:32.957909: train_loss -0.7892 
+2024-09-11 01:38:32.958092: val_loss -0.5922 
+2024-09-11 01:38:32.958143: Pseudo dice [0.513, 0.768] 
+2024-09-11 01:38:32.958194: Epoch time: 245.98 s 
+2024-09-11 01:38:33.928495:  
+2024-09-11 01:38:33.928674: Epoch 146 
+2024-09-11 01:38:33.928759: Current learning rate: 0.00868 
+2024-09-11 01:42:40.984021: train_loss -0.7897 
+2024-09-11 01:42:40.984180: val_loss -0.5716 
+2024-09-11 01:42:40.984233: Pseudo dice [0.4781, 0.7481] 
+2024-09-11 01:42:40.984286: Epoch time: 247.06 s 
+2024-09-11 01:42:42.001172:  
+2024-09-11 01:42:42.001355: Epoch 147 
+2024-09-11 01:42:42.001482: Current learning rate: 0.00867 
+2024-09-11 01:46:48.062787: train_loss -0.7734 
+2024-09-11 01:46:48.062925: val_loss -0.5914 
+2024-09-11 01:46:48.062975: Pseudo dice [0.5128, 0.7437] 
+2024-09-11 01:46:48.063025: Epoch time: 246.06 s 
+2024-09-11 01:46:49.034331:  
+2024-09-11 01:46:49.034529: Epoch 148 
+2024-09-11 01:46:49.034616: Current learning rate: 0.00866 
+2024-09-11 01:50:55.327760: train_loss -0.8025 
+2024-09-11 01:50:55.327953: val_loss -0.58 
+2024-09-11 01:50:55.328005: Pseudo dice [0.4727, 0.7654] 
+2024-09-11 01:50:55.328056: Epoch time: 246.3 s 
+2024-09-11 01:50:56.304090:  
+2024-09-11 01:50:56.304285: Epoch 149 
+2024-09-11 01:50:56.304388: Current learning rate: 0.00865 
+2024-09-11 01:55:02.477328: train_loss -0.8122 
+2024-09-11 01:55:02.477472: val_loss -0.575 
+2024-09-11 01:55:02.477522: Pseudo dice [0.4729, 0.7844] 
+2024-09-11 01:55:02.477573: Epoch time: 246.18 s 
+2024-09-11 01:55:06.395556:  
+2024-09-11 01:55:06.395803: Epoch 150 
+2024-09-11 01:55:06.395896: Current learning rate: 0.00864 
+2024-09-11 01:59:12.377610: train_loss -0.8136 
+2024-09-11 01:59:12.377843: val_loss -0.5675 
+2024-09-11 01:59:12.377938: Pseudo dice [0.4777, 0.7499] 
+2024-09-11 01:59:12.378032: Epoch time: 245.98 s 
+2024-09-11 01:59:13.349586:  
+2024-09-11 01:59:13.349834: Epoch 151 
+2024-09-11 01:59:13.349919: Current learning rate: 0.00863 
+2024-09-11 02:03:19.042433: train_loss -0.8168 
+2024-09-11 02:03:19.042572: val_loss -0.5613 
+2024-09-11 02:03:19.042622: Pseudo dice [0.524, 0.7477] 
+2024-09-11 02:03:19.042673: Epoch time: 245.69 s 
+2024-09-11 02:03:20.047983:  
+2024-09-11 02:03:20.048270: Epoch 152 
+2024-09-11 02:03:20.048366: Current learning rate: 0.00862 
+2024-09-11 02:07:25.989240: train_loss -0.8148 
+2024-09-11 02:07:25.989379: val_loss -0.5822 
+2024-09-11 02:07:25.989430: Pseudo dice [0.468, 0.7852] 
+2024-09-11 02:07:25.989482: Epoch time: 245.94 s 
+2024-09-11 02:07:26.981088:  
+2024-09-11 02:07:26.981298: Epoch 153 
+2024-09-11 02:07:26.981380: Current learning rate: 0.00861 
+2024-09-11 02:11:32.881019: train_loss -0.8161 
+2024-09-11 02:11:32.881207: val_loss -0.6088 
+2024-09-11 02:11:32.881260: Pseudo dice [0.5508, 0.7661] 
+2024-09-11 02:11:32.881313: Epoch time: 245.9 s 
+2024-09-11 02:11:33.879656:  
+2024-09-11 02:11:33.879855: Epoch 154 
+2024-09-11 02:11:33.879943: Current learning rate: 0.0086 
+2024-09-11 02:15:39.913147: train_loss -0.797 
+2024-09-11 02:15:39.913297: val_loss -0.4854 
+2024-09-11 02:15:39.913348: Pseudo dice [0.4646, 0.6257] 
+2024-09-11 02:15:39.913402: Epoch time: 246.04 s 
+2024-09-11 02:15:40.905326:  
+2024-09-11 02:15:40.905529: Epoch 155 
+2024-09-11 02:15:40.905616: Current learning rate: 0.00859 
+2024-09-11 02:19:46.804402: train_loss -0.7841 
+2024-09-11 02:19:46.804543: val_loss -0.587 
+2024-09-11 02:19:46.804592: Pseudo dice [0.4943, 0.7696] 
+2024-09-11 02:19:46.804643: Epoch time: 245.9 s 
+2024-09-11 02:19:47.821924:  
+2024-09-11 02:19:47.822095: Epoch 156 
+2024-09-11 02:19:47.822197: Current learning rate: 0.00858 
+2024-09-11 02:23:53.574861: train_loss -0.7973 
+2024-09-11 02:23:53.574999: val_loss -0.6137 
+2024-09-11 02:23:53.575049: Pseudo dice [0.4723, 0.7823] 
+2024-09-11 02:23:53.575101: Epoch time: 245.75 s 
+2024-09-11 02:23:54.580083:  
+2024-09-11 02:23:54.580277: Epoch 157 
+2024-09-11 02:23:54.580359: Current learning rate: 0.00858 
+2024-09-11 02:28:00.374333: train_loss -0.8015 
+2024-09-11 02:28:00.374473: val_loss -0.5908 
+2024-09-11 02:28:00.374524: Pseudo dice [0.4977, 0.7629] 
+2024-09-11 02:28:00.374573: Epoch time: 245.8 s 
+2024-09-11 02:28:01.359923:  
+2024-09-11 02:28:01.360074: Epoch 158 
+2024-09-11 02:28:01.360161: Current learning rate: 0.00857 
+2024-09-11 02:32:07.276777: train_loss -0.7665 
+2024-09-11 02:32:07.276934: val_loss -0.5723 
+2024-09-11 02:32:07.276985: Pseudo dice [0.5011, 0.7315] 
+2024-09-11 02:32:07.277038: Epoch time: 245.92 s 
+2024-09-11 02:32:08.260504:  
+2024-09-11 02:32:08.260672: Epoch 159 
+2024-09-11 02:32:08.260799: Current learning rate: 0.00856 
+2024-09-11 02:36:14.066337: train_loss -0.7805 
+2024-09-11 02:36:14.066491: val_loss -0.6056 
+2024-09-11 02:36:14.066541: Pseudo dice [0.5039, 0.7899] 
+2024-09-11 02:36:14.066591: Epoch time: 245.81 s 
+2024-09-11 02:36:15.048419:  
+2024-09-11 02:36:15.048577: Epoch 160 
+2024-09-11 02:36:15.048705: Current learning rate: 0.00855 
+2024-09-11 02:40:20.938546: train_loss -0.8064 
+2024-09-11 02:40:20.938693: val_loss -0.5791 
+2024-09-11 02:40:20.938744: Pseudo dice [0.4472, 0.7884] 
+2024-09-11 02:40:20.938796: Epoch time: 245.89 s 
+2024-09-11 02:40:21.942522:  
+2024-09-11 02:40:21.942686: Epoch 161 
+2024-09-11 02:40:21.942770: Current learning rate: 0.00854 
+2024-09-11 02:44:27.863640: train_loss -0.7943 
+2024-09-11 02:44:27.863780: val_loss -0.5969 
+2024-09-11 02:44:27.863837: Pseudo dice [0.5262, 0.7134] 
+2024-09-11 02:44:27.863899: Epoch time: 245.92 s 
+2024-09-11 02:44:28.872460:  
+2024-09-11 02:44:28.872629: Epoch 162 
+2024-09-11 02:44:28.872712: Current learning rate: 0.00853 
+2024-09-11 02:48:34.873167: train_loss -0.8104 
+2024-09-11 02:48:34.873363: val_loss -0.6014 
+2024-09-11 02:48:34.873416: Pseudo dice [0.5157, 0.7634] 
+2024-09-11 02:48:34.873471: Epoch time: 246.0 s 
+2024-09-11 02:48:35.857857:  
+2024-09-11 02:48:35.858023: Epoch 163 
+2024-09-11 02:48:35.858158: Current learning rate: 0.00852 
+2024-09-11 02:52:42.057934: train_loss -0.7924 
+2024-09-11 02:52:42.058072: val_loss -0.6122 
+2024-09-11 02:52:42.058122: Pseudo dice [0.511, 0.7678] 
+2024-09-11 02:52:42.058173: Epoch time: 246.2 s 
+2024-09-11 02:52:43.078498:  
+2024-09-11 02:52:43.078691: Epoch 164 
+2024-09-11 02:52:43.078773: Current learning rate: 0.00851 
+2024-09-11 02:56:49.131130: train_loss -0.79 
+2024-09-11 02:56:49.131270: val_loss -0.6001 
+2024-09-11 02:56:49.131321: Pseudo dice [0.518, 0.746] 
+2024-09-11 02:56:49.131371: Epoch time: 246.05 s 
+2024-09-11 02:56:50.107399:  
+2024-09-11 02:56:50.107590: Epoch 165 
+2024-09-11 02:56:50.107678: Current learning rate: 0.0085 
+2024-09-11 03:00:56.094915: train_loss -0.8032 
+2024-09-11 03:00:56.095055: val_loss -0.5632 
+2024-09-11 03:00:56.095106: Pseudo dice [0.5075, 0.7461] 
+2024-09-11 03:00:56.095155: Epoch time: 245.99 s 
+2024-09-11 03:00:57.069748:  
+2024-09-11 03:00:57.069939: Epoch 166 
+2024-09-11 03:00:57.070024: Current learning rate: 0.00849 
+2024-09-11 03:05:03.181190: train_loss -0.7877 
+2024-09-11 03:05:03.181399: val_loss -0.5704 
+2024-09-11 03:05:03.181452: Pseudo dice [0.4387, 0.7652] 
+2024-09-11 03:05:03.181505: Epoch time: 246.11 s 
+2024-09-11 03:05:04.149767:  
+2024-09-11 03:05:04.149941: Epoch 167 
+2024-09-11 03:05:04.150030: Current learning rate: 0.00848 
+2024-09-11 03:09:09.911430: train_loss -0.8088 
+2024-09-11 03:09:09.911639: val_loss -0.6173 
+2024-09-11 03:09:09.911733: Pseudo dice [0.5196, 0.7611] 
+2024-09-11 03:09:09.911837: Epoch time: 245.76 s 
+2024-09-11 03:09:11.071760:  
+2024-09-11 03:09:11.071993: Epoch 168 
+2024-09-11 03:09:11.072076: Current learning rate: 0.00847 
+2024-09-11 03:13:17.790384: train_loss -0.81 
+2024-09-11 03:13:17.790530: val_loss -0.5994 
+2024-09-11 03:13:17.790580: Pseudo dice [0.5374, 0.7508] 
+2024-09-11 03:13:17.790631: Epoch time: 246.72 s 
+2024-09-11 03:13:18.785395:  
+2024-09-11 03:13:18.785592: Epoch 169 
+2024-09-11 03:13:18.785713: Current learning rate: 0.00847 
+2024-09-11 03:17:24.884516: train_loss -0.8063 
+2024-09-11 03:17:24.884661: val_loss -0.5773 
+2024-09-11 03:17:24.884712: Pseudo dice [0.4983, 0.7555] 
+2024-09-11 03:17:24.884762: Epoch time: 246.1 s 
+2024-09-11 03:17:25.882149:  
+2024-09-11 03:17:25.882338: Epoch 170 
+2024-09-11 03:17:25.882419: Current learning rate: 0.00846 
+2024-09-11 03:21:31.826738: train_loss -0.8078 
+2024-09-11 03:21:31.826874: val_loss -0.5625 
+2024-09-11 03:21:31.826924: Pseudo dice [0.522, 0.7461] 
+2024-09-11 03:21:31.826976: Epoch time: 245.95 s 
+2024-09-11 03:21:32.827094:  
+2024-09-11 03:21:32.827330: Epoch 171 
+2024-09-11 03:21:32.827417: Current learning rate: 0.00845 
+2024-09-11 03:25:38.637229: train_loss -0.816 
+2024-09-11 03:25:38.637404: val_loss -0.5757 
+2024-09-11 03:25:38.637455: Pseudo dice [0.4735, 0.7826] 
+2024-09-11 03:25:38.637506: Epoch time: 245.81 s 
+2024-09-11 03:25:39.617783:  
+2024-09-11 03:25:39.618018: Epoch 172 
+2024-09-11 03:25:39.618105: Current learning rate: 0.00844 
+2024-09-11 03:29:45.673194: train_loss -0.8182 
+2024-09-11 03:29:45.673327: val_loss -0.5447 
+2024-09-11 03:29:45.673378: Pseudo dice [0.4295, 0.7365] 
+2024-09-11 03:29:45.673430: Epoch time: 246.06 s 
+2024-09-11 03:29:46.686525:  
+2024-09-11 03:29:46.686760: Epoch 173 
+2024-09-11 03:29:46.686845: Current learning rate: 0.00843 
+2024-09-11 03:33:52.795604: train_loss -0.8205 
+2024-09-11 03:33:52.795752: val_loss -0.6055 
+2024-09-11 03:33:52.795822: Pseudo dice [0.5097, 0.7972] 
+2024-09-11 03:33:52.795881: Epoch time: 246.11 s 
+2024-09-11 03:33:53.769769:  
+2024-09-11 03:33:53.769963: Epoch 174 
+2024-09-11 03:33:53.770058: Current learning rate: 0.00842 
+2024-09-11 03:37:59.930284: train_loss -0.8201 
+2024-09-11 03:37:59.930425: val_loss -0.6083 
+2024-09-11 03:37:59.930474: Pseudo dice [0.4935, 0.7876] 
+2024-09-11 03:37:59.930526: Epoch time: 246.16 s 
+2024-09-11 03:38:00.921894:  
+2024-09-11 03:38:00.922144: Epoch 175 
+2024-09-11 03:38:00.922240: Current learning rate: 0.00841 
+2024-09-11 03:42:06.811559: train_loss -0.7988 
+2024-09-11 03:42:06.811695: val_loss -0.5908 
+2024-09-11 03:42:06.811744: Pseudo dice [0.5307, 0.7428] 
+2024-09-11 03:42:06.811797: Epoch time: 245.89 s 
+2024-09-11 03:42:07.799619:  
+2024-09-11 03:42:07.799886: Epoch 176 
+2024-09-11 03:42:07.799981: Current learning rate: 0.0084 
+2024-09-11 03:46:13.898160: train_loss -0.7838 
+2024-09-11 03:46:13.898332: val_loss -0.5983 
+2024-09-11 03:46:13.898387: Pseudo dice [0.5534, 0.7374] 
+2024-09-11 03:46:13.898438: Epoch time: 246.1 s 
+2024-09-11 03:46:14.871334:  
+2024-09-11 03:46:14.871584: Epoch 177 
+2024-09-11 03:46:14.871666: Current learning rate: 0.00839 
+2024-09-11 03:50:20.736920: train_loss -0.8139 
+2024-09-11 03:50:20.737110: val_loss -0.5885 
+2024-09-11 03:50:20.737162: Pseudo dice [0.487, 0.7646] 
+2024-09-11 03:50:20.737214: Epoch time: 245.87 s 
+2024-09-11 03:50:21.723099:  
+2024-09-11 03:50:21.723344: Epoch 178 
+2024-09-11 03:50:21.723427: Current learning rate: 0.00838 
+2024-09-11 03:54:27.826068: train_loss -0.8075 
+2024-09-11 03:54:27.826203: val_loss -0.6209 
+2024-09-11 03:54:27.826258: Pseudo dice [0.5433, 0.7647] 
+2024-09-11 03:54:27.826311: Epoch time: 246.1 s 
+2024-09-11 03:54:28.803933:  
+2024-09-11 03:54:28.804121: Epoch 179 
+2024-09-11 03:54:28.804231: Current learning rate: 0.00837 
+2024-09-11 03:58:34.713146: train_loss -0.8047 
+2024-09-11 03:58:34.713289: val_loss -0.6036 
+2024-09-11 03:58:34.713339: Pseudo dice [0.5192, 0.7779] 
+2024-09-11 03:58:34.713390: Epoch time: 245.91 s 
+2024-09-11 03:58:35.689944:  
+2024-09-11 03:58:35.690182: Epoch 180 
+2024-09-11 03:58:35.690288: Current learning rate: 0.00836 
+2024-09-11 04:02:41.712315: train_loss -0.8067 
+2024-09-11 04:02:41.712454: val_loss -0.5778 
+2024-09-11 04:02:41.712505: Pseudo dice [0.5257, 0.7546] 
+2024-09-11 04:02:41.712555: Epoch time: 246.02 s 
+2024-09-11 04:02:42.698333:  
+2024-09-11 04:02:42.698502: Epoch 181 
+2024-09-11 04:02:42.698586: Current learning rate: 0.00836 
+2024-09-11 04:06:48.742274: train_loss -0.817 
+2024-09-11 04:06:48.742433: val_loss -0.5764 
+2024-09-11 04:06:48.742484: Pseudo dice [0.473, 0.78] 
+2024-09-11 04:06:48.742536: Epoch time: 246.05 s 
+2024-09-11 04:06:49.739267:  
+2024-09-11 04:06:49.739496: Epoch 182 
+2024-09-11 04:06:49.739581: Current learning rate: 0.00835 
+2024-09-11 04:10:55.768506: train_loss -0.8117 
+2024-09-11 04:10:55.768648: val_loss -0.6048 
+2024-09-11 04:10:55.768704: Pseudo dice [0.5226, 0.7667] 
+2024-09-11 04:10:55.768755: Epoch time: 246.03 s 
+2024-09-11 04:10:56.747961:  
+2024-09-11 04:10:56.748178: Epoch 183 
+2024-09-11 04:10:56.748269: Current learning rate: 0.00834 
+2024-09-11 04:15:02.799118: train_loss -0.8337 
+2024-09-11 04:15:02.799277: val_loss -0.6231 
+2024-09-11 04:15:02.799352: Pseudo dice [0.5439, 0.7877] 
+2024-09-11 04:15:02.799409: Epoch time: 246.05 s 
+2024-09-11 04:15:03.789137:  
+2024-09-11 04:15:03.789355: Epoch 184 
+2024-09-11 04:15:03.789442: Current learning rate: 0.00833 
+2024-09-11 04:19:09.611948: train_loss -0.8179 
+2024-09-11 04:19:09.612087: val_loss -0.6016 
+2024-09-11 04:19:09.612142: Pseudo dice [0.5002, 0.7815] 
+2024-09-11 04:19:09.612193: Epoch time: 245.82 s 
+2024-09-11 04:19:10.604186:  
+2024-09-11 04:19:10.604363: Epoch 185 
+2024-09-11 04:19:10.604444: Current learning rate: 0.00832 
+2024-09-11 04:23:16.344734: train_loss -0.8196 
+2024-09-11 04:23:16.344876: val_loss -0.6296 
+2024-09-11 04:23:16.344926: Pseudo dice [0.5373, 0.7919] 
+2024-09-11 04:23:16.344979: Epoch time: 245.74 s 
+2024-09-11 04:23:17.317127:  
+2024-09-11 04:23:17.317265: Epoch 186 
+2024-09-11 04:23:17.317348: Current learning rate: 0.00831 
+2024-09-11 04:27:23.348183: train_loss -0.8298 
+2024-09-11 04:27:23.348336: val_loss -0.5854 
+2024-09-11 04:27:23.348387: Pseudo dice [0.5043, 0.7388] 
+2024-09-11 04:27:23.348437: Epoch time: 246.03 s 
+2024-09-11 04:27:24.323786:  
+2024-09-11 04:27:24.323978: Epoch 187 
+2024-09-11 04:27:24.324093: Current learning rate: 0.0083 
+2024-09-11 04:31:30.271519: train_loss -0.8219 
+2024-09-11 04:31:30.271654: val_loss -0.6 
+2024-09-11 04:31:30.271703: Pseudo dice [0.5201, 0.7761] 
+2024-09-11 04:31:30.271753: Epoch time: 245.95 s 
+2024-09-11 04:31:31.259848:  
+2024-09-11 04:31:31.260011: Epoch 188 
+2024-09-11 04:31:31.260093: Current learning rate: 0.00829 
+2024-09-11 04:35:37.235061: train_loss -0.8227 
+2024-09-11 04:35:37.235250: val_loss -0.6191 
+2024-09-11 04:35:37.235304: Pseudo dice [0.5204, 0.7626] 
+2024-09-11 04:35:37.235355: Epoch time: 245.98 s 
+2024-09-11 04:35:38.216341:  
+2024-09-11 04:35:38.216526: Epoch 189 
+2024-09-11 04:35:38.216606: Current learning rate: 0.00828 
+2024-09-11 04:39:44.066407: train_loss -0.824 
+2024-09-11 04:39:44.066546: val_loss -0.5805 
+2024-09-11 04:39:44.066599: Pseudo dice [0.5017, 0.7488] 
+2024-09-11 04:39:44.066649: Epoch time: 245.85 s 
+2024-09-11 04:39:45.064999:  
+2024-09-11 04:39:45.065160: Epoch 190 
+2024-09-11 04:39:45.065244: Current learning rate: 0.00827 
+2024-09-11 04:43:50.987281: train_loss -0.822 
+2024-09-11 04:43:50.987476: val_loss -0.5938 
+2024-09-11 04:43:50.987527: Pseudo dice [0.485, 0.774] 
+2024-09-11 04:43:50.987582: Epoch time: 245.92 s 
+2024-09-11 04:43:52.926642:  
+2024-09-11 04:43:52.926813: Epoch 191 
+2024-09-11 04:43:52.926957: Current learning rate: 0.00826 
+2024-09-11 04:47:59.172004: train_loss -0.8176 
+2024-09-11 04:47:59.172147: val_loss -0.592 
+2024-09-11 04:47:59.172206: Pseudo dice [0.4723, 0.7871] 
+2024-09-11 04:47:59.172257: Epoch time: 246.25 s 
+2024-09-11 04:48:00.247884:  
+2024-09-11 04:48:00.248123: Epoch 192 
+2024-09-11 04:48:00.248208: Current learning rate: 0.00825 
+2024-09-11 04:52:06.449192: train_loss -0.823 
+2024-09-11 04:52:06.449332: val_loss -0.5837 
+2024-09-11 04:52:06.449521: Pseudo dice [0.4581, 0.7705] 
+2024-09-11 04:52:06.449632: Epoch time: 246.2 s 
+2024-09-11 04:52:07.617659:  
+2024-09-11 04:52:07.617867: Epoch 193 
+2024-09-11 04:52:07.617953: Current learning rate: 0.00824 
+2024-09-11 04:56:13.497698: train_loss -0.8273 
+2024-09-11 04:56:13.497838: val_loss -0.597 
+2024-09-11 04:56:13.497891: Pseudo dice [0.4682, 0.7632] 
+2024-09-11 04:56:13.497942: Epoch time: 245.88 s 
+2024-09-11 04:56:14.487556:  
+2024-09-11 04:56:14.487787: Epoch 194 
+2024-09-11 04:56:14.487881: Current learning rate: 0.00824 
+2024-09-11 05:00:20.292664: train_loss -0.8326 
+2024-09-11 05:00:20.292814: val_loss -0.5653 
+2024-09-11 05:00:20.292866: Pseudo dice [0.4613, 0.7625] 
+2024-09-11 05:00:20.292974: Epoch time: 245.81 s 
+2024-09-11 05:00:21.286615:  
+2024-09-11 05:00:21.286808: Epoch 195 
+2024-09-11 05:00:21.286947: Current learning rate: 0.00823 
+2024-09-11 05:04:27.164685: train_loss -0.8333 
+2024-09-11 05:04:27.164826: val_loss -0.5598 
+2024-09-11 05:04:27.164877: Pseudo dice [0.4357, 0.7705] 
+2024-09-11 05:04:27.164928: Epoch time: 245.88 s 
+2024-09-11 05:04:28.150593:  
+2024-09-11 05:04:28.150788: Epoch 196 
+2024-09-11 05:04:28.150872: Current learning rate: 0.00822 
+2024-09-11 05:08:33.920374: train_loss -0.8368 
+2024-09-11 05:08:33.920539: val_loss -0.5738 
+2024-09-11 05:08:33.920591: Pseudo dice [0.4771, 0.7431] 
+2024-09-11 05:08:33.920641: Epoch time: 245.77 s 
+2024-09-11 05:08:34.905035:  
+2024-09-11 05:08:34.905267: Epoch 197 
+2024-09-11 05:08:34.905357: Current learning rate: 0.00821 
+2024-09-11 05:12:40.712630: train_loss -0.7966 
+2024-09-11 05:12:40.712809: val_loss -0.5761 
+2024-09-11 05:12:40.712859: Pseudo dice [0.4935, 0.7301] 
+2024-09-11 05:12:40.712910: Epoch time: 245.81 s 
+2024-09-11 05:12:41.709835:  
+2024-09-11 05:12:41.710048: Epoch 198 
+2024-09-11 05:12:41.710133: Current learning rate: 0.0082 
+2024-09-11 05:16:47.694185: train_loss -0.804 
+2024-09-11 05:16:47.694323: val_loss -0.6028 
+2024-09-11 05:16:47.694374: Pseudo dice [0.512, 0.7741] 
+2024-09-11 05:16:47.694425: Epoch time: 245.99 s 
+2024-09-11 05:16:48.693803:  
+2024-09-11 05:16:48.694020: Epoch 199 
+2024-09-11 05:16:48.694130: Current learning rate: 0.00819 
+2024-09-11 05:20:54.517727: train_loss -0.8069 
+2024-09-11 05:20:54.517865: val_loss -0.5671 
+2024-09-11 05:20:54.517917: Pseudo dice [0.5034, 0.7452] 
+2024-09-11 05:20:54.517968: Epoch time: 245.83 s 
+2024-09-11 05:20:58.537540:  
+2024-09-11 05:20:58.537759: Epoch 200 
+2024-09-11 05:20:58.537865: Current learning rate: 0.00818 
+2024-09-11 05:25:04.870414: train_loss -0.8118 
+2024-09-11 05:25:04.870550: val_loss -0.6361 
+2024-09-11 05:25:04.870600: Pseudo dice [0.5326, 0.779] 
+2024-09-11 05:25:04.870651: Epoch time: 246.33 s 
+2024-09-11 05:25:05.912364:  
+2024-09-11 05:25:05.912585: Epoch 201 
+2024-09-11 05:25:05.912673: Current learning rate: 0.00817 
+2024-09-11 05:29:11.807402: train_loss -0.8244 
+2024-09-11 05:29:11.807539: val_loss -0.6113 
+2024-09-11 05:29:11.807590: Pseudo dice [0.583, 0.753] 
+2024-09-11 05:29:11.807640: Epoch time: 245.9 s 
+2024-09-11 05:29:12.827167:  
+2024-09-11 05:29:12.827321: Epoch 202 
+2024-09-11 05:29:12.827405: Current learning rate: 0.00816 
+2024-09-11 05:33:18.578631: train_loss -0.827 
+2024-09-11 05:33:18.578772: val_loss -0.606 
+2024-09-11 05:33:18.578822: Pseudo dice [0.527, 0.7754] 
+2024-09-11 05:33:18.578873: Epoch time: 245.75 s 
+2024-09-11 05:33:19.579885:  
+2024-09-11 05:33:19.580060: Epoch 203 
+2024-09-11 05:33:19.580153: Current learning rate: 0.00815 
+2024-09-11 05:37:25.328441: train_loss -0.826 
+2024-09-11 05:37:25.328651: val_loss -0.5901 
+2024-09-11 05:37:25.328702: Pseudo dice [0.4907, 0.7707] 
+2024-09-11 05:37:25.328754: Epoch time: 245.75 s 
+2024-09-11 05:37:26.356934:  
+2024-09-11 05:37:26.357166: Epoch 204 
+2024-09-11 05:37:26.357292: Current learning rate: 0.00814 
+2024-09-11 05:41:32.295551: train_loss -0.8108 
+2024-09-11 05:41:32.295721: val_loss -0.5815 
+2024-09-11 05:41:32.295812: Pseudo dice [0.5061, 0.7473] 
+2024-09-11 05:41:32.295865: Epoch time: 245.94 s 
+2024-09-11 05:41:33.291131:  
+2024-09-11 05:41:33.291292: Epoch 205 
+2024-09-11 05:41:33.291406: Current learning rate: 0.00813 
+2024-09-11 05:45:39.165895: train_loss -0.8161 
+2024-09-11 05:45:39.166037: val_loss -0.5635 
+2024-09-11 05:45:39.166086: Pseudo dice [0.472, 0.7497] 
+2024-09-11 05:45:39.166139: Epoch time: 245.88 s 
+2024-09-11 05:45:40.116906:  
+2024-09-11 05:45:40.117085: Epoch 206 
+2024-09-11 05:45:40.117167: Current learning rate: 0.00813 
+2024-09-11 05:49:46.159437: train_loss -0.8001 
+2024-09-11 05:49:46.159580: val_loss -0.5671 
+2024-09-11 05:49:46.159630: Pseudo dice [0.4719, 0.7712] 
+2024-09-11 05:49:46.159681: Epoch time: 246.04 s 
+2024-09-11 05:49:47.085467:  
+2024-09-11 05:49:47.085676: Epoch 207 
+2024-09-11 05:49:47.085759: Current learning rate: 0.00812 
+2024-09-11 05:53:52.880979: train_loss -0.8159 
+2024-09-11 05:53:52.881147: val_loss -0.6038 
+2024-09-11 05:53:52.881231: Pseudo dice [0.5491, 0.7672] 
+2024-09-11 05:53:52.881282: Epoch time: 245.8 s 
+2024-09-11 05:53:53.817468:  
+2024-09-11 05:53:53.817702: Epoch 208 
+2024-09-11 05:53:53.817829: Current learning rate: 0.00811 
+2024-09-11 05:57:59.645505: train_loss -0.8235 
+2024-09-11 05:57:59.645660: val_loss -0.5878 
+2024-09-11 05:57:59.645710: Pseudo dice [0.4986, 0.7716] 
+2024-09-11 05:57:59.645762: Epoch time: 245.83 s 
+2024-09-11 05:58:00.582651:  
+2024-09-11 05:58:00.582869: Epoch 209 
+2024-09-11 05:58:00.582949: Current learning rate: 0.0081 
+2024-09-11 06:02:06.340466: train_loss -0.8318 
+2024-09-11 06:02:06.340616: val_loss -0.6018 
+2024-09-11 06:02:06.340667: Pseudo dice [0.5472, 0.7682] 
+2024-09-11 06:02:06.340717: Epoch time: 245.76 s 
+2024-09-11 06:02:07.284655:  
+2024-09-11 06:02:07.284843: Epoch 210 
+2024-09-11 06:02:07.284925: Current learning rate: 0.00809 
+2024-09-11 06:06:13.380612: train_loss -0.8115 
+2024-09-11 06:06:13.380748: val_loss -0.6059 
+2024-09-11 06:06:13.380797: Pseudo dice [0.5014, 0.7968] 
+2024-09-11 06:06:13.380882: Epoch time: 246.1 s 
+2024-09-11 06:06:14.319919:  
+2024-09-11 06:06:14.320128: Epoch 211 
+2024-09-11 06:06:14.320210: Current learning rate: 0.00808 
+2024-09-11 06:10:20.381506: train_loss -0.7882 
+2024-09-11 06:10:20.381667: val_loss -0.6051 
+2024-09-11 06:10:20.381717: Pseudo dice [0.5595, 0.7478] 
+2024-09-11 06:10:20.381770: Epoch time: 246.06 s 
+2024-09-11 06:10:21.367262:  
+2024-09-11 06:10:21.367455: Epoch 212 
+2024-09-11 06:10:21.367542: Current learning rate: 0.00807 
+2024-09-11 06:14:27.258868: train_loss -0.7938 
+2024-09-11 06:14:27.259045: val_loss -0.5936 
+2024-09-11 06:14:27.259097: Pseudo dice [0.5105, 0.765] 
+2024-09-11 06:14:27.259147: Epoch time: 245.89 s 
+2024-09-11 06:14:28.202452:  
+2024-09-11 06:14:28.202627: Epoch 213 
+2024-09-11 06:14:28.202710: Current learning rate: 0.00806 
+2024-09-11 06:18:34.968546: train_loss -0.8166 
+2024-09-11 06:18:34.968723: val_loss -0.5959 
+2024-09-11 06:18:34.968776: Pseudo dice [0.5051, 0.7686] 
+2024-09-11 06:18:34.968826: Epoch time: 246.77 s 
+2024-09-11 06:18:35.925066:  
+2024-09-11 06:18:35.925297: Epoch 214 
+2024-09-11 06:18:35.925403: Current learning rate: 0.00805 
+2024-09-11 06:22:41.643566: train_loss -0.8126 
+2024-09-11 06:22:41.643703: val_loss -0.5791 
+2024-09-11 06:22:41.643755: Pseudo dice [0.5127, 0.7788] 
+2024-09-11 06:22:41.643846: Epoch time: 245.72 s 
+2024-09-11 06:22:42.582968:  
+2024-09-11 06:22:42.583159: Epoch 215 
+2024-09-11 06:22:42.583272: Current learning rate: 0.00804 
+2024-09-11 06:26:48.444092: train_loss -0.8231 
+2024-09-11 06:26:48.444264: val_loss -0.5929 
+2024-09-11 06:26:48.444335: Pseudo dice [0.4711, 0.7774] 
+2024-09-11 06:26:48.444407: Epoch time: 245.86 s 
+2024-09-11 06:26:49.386126:  
+2024-09-11 06:26:49.386317: Epoch 216 
+2024-09-11 06:26:49.386403: Current learning rate: 0.00803 
+2024-09-11 06:30:55.319388: train_loss -0.8214 
+2024-09-11 06:30:55.319528: val_loss -0.5617 
+2024-09-11 06:30:55.319580: Pseudo dice [0.4931, 0.703] 
+2024-09-11 06:30:55.319630: Epoch time: 245.94 s 
+2024-09-11 06:30:56.254584:  
+2024-09-11 06:30:56.254814: Epoch 217 
+2024-09-11 06:30:56.254922: Current learning rate: 0.00802 
+2024-09-11 06:35:02.296365: train_loss -0.7985 
+2024-09-11 06:35:02.296503: val_loss -0.5882 
+2024-09-11 06:35:02.296553: Pseudo dice [0.5417, 0.7406] 
+2024-09-11 06:35:02.296609: Epoch time: 246.04 s 
+2024-09-11 06:35:03.243875:  
+2024-09-11 06:35:03.244122: Epoch 218 
+2024-09-11 06:35:03.244219: Current learning rate: 0.00801 
+2024-09-11 06:39:09.159657: train_loss -0.8156 
+2024-09-11 06:39:09.159801: val_loss -0.6334 
+2024-09-11 06:39:09.159860: Pseudo dice [0.5299, 0.7829] 
+2024-09-11 06:39:09.159913: Epoch time: 245.92 s 
+2024-09-11 06:39:10.113417:  
+2024-09-11 06:39:10.113622: Epoch 219 
+2024-09-11 06:39:10.113708: Current learning rate: 0.00801 
+2024-09-11 06:43:16.150321: train_loss -0.8225 
+2024-09-11 06:43:16.150460: val_loss -0.5957 
+2024-09-11 06:43:16.150510: Pseudo dice [0.5085, 0.7508] 
+2024-09-11 06:43:16.150560: Epoch time: 246.04 s 
+2024-09-11 06:43:17.094524:  
+2024-09-11 06:43:17.094733: Epoch 220 
+2024-09-11 06:43:17.094821: Current learning rate: 0.008 
+2024-09-11 06:47:23.070105: train_loss -0.831 
+2024-09-11 06:47:23.070246: val_loss -0.6074 
+2024-09-11 06:47:23.070503: Pseudo dice [0.5131, 0.7648] 
+2024-09-11 06:47:23.070556: Epoch time: 245.98 s 
+2024-09-11 06:47:24.058207:  
+2024-09-11 06:47:24.058520: Epoch 221 
+2024-09-11 06:47:24.058613: Current learning rate: 0.00799 
+2024-09-11 06:51:30.060225: train_loss -0.8353 
+2024-09-11 06:51:30.060365: val_loss -0.5633 
+2024-09-11 06:51:30.060416: Pseudo dice [0.4791, 0.7635] 
+2024-09-11 06:51:30.060465: Epoch time: 246.0 s 
+2024-09-11 06:51:31.001126:  
+2024-09-11 06:51:31.001422: Epoch 222 
+2024-09-11 06:51:31.001514: Current learning rate: 0.00798 
+2024-09-11 06:55:37.146304: train_loss -0.8311 
+2024-09-11 06:55:37.146470: val_loss -0.573 
+2024-09-11 06:55:37.146580: Pseudo dice [0.4615, 0.7847] 
+2024-09-11 06:55:37.146634: Epoch time: 246.15 s 
+2024-09-11 06:55:38.085703:  
+2024-09-11 06:55:38.085891: Epoch 223 
+2024-09-11 06:55:38.086013: Current learning rate: 0.00797 
+2024-09-11 06:59:44.124205: train_loss -0.8295 
+2024-09-11 06:59:44.124368: val_loss -0.5963 
+2024-09-11 06:59:44.124418: Pseudo dice [0.5234, 0.7675] 
+2024-09-11 06:59:44.124483: Epoch time: 246.04 s 
+2024-09-11 06:59:45.068248:  
+2024-09-11 06:59:45.068444: Epoch 224 
+2024-09-11 06:59:45.068547: Current learning rate: 0.00796 
+2024-09-11 07:03:51.163611: train_loss -0.8226 
+2024-09-11 07:03:51.163769: val_loss -0.6136 
+2024-09-11 07:03:51.163835: Pseudo dice [0.5574, 0.7635] 
+2024-09-11 07:03:51.163889: Epoch time: 246.1 s 
+2024-09-11 07:03:52.098668:  
+2024-09-11 07:03:52.098944: Epoch 225 
+2024-09-11 07:03:52.099058: Current learning rate: 0.00795 
+2024-09-11 07:07:58.142040: train_loss -0.8147 
+2024-09-11 07:07:58.142178: val_loss -0.6153 
+2024-09-11 07:07:58.142229: Pseudo dice [0.5377, 0.7836] 
+2024-09-11 07:07:58.142279: Epoch time: 246.05 s 
+2024-09-11 07:07:59.078738:  
+2024-09-11 07:07:59.078977: Epoch 226 
+2024-09-11 07:07:59.079062: Current learning rate: 0.00794 
+2024-09-11 07:12:05.238099: train_loss -0.8267 
+2024-09-11 07:12:05.238249: val_loss -0.5314 
+2024-09-11 07:12:05.238300: Pseudo dice [0.4269, 0.7486] 
+2024-09-11 07:12:05.238353: Epoch time: 246.16 s 
+2024-09-11 07:12:06.169102:  
+2024-09-11 07:12:06.169272: Epoch 227 
+2024-09-11 07:12:06.169356: Current learning rate: 0.00793 
+2024-09-11 07:16:12.631624: train_loss -0.8279 
+2024-09-11 07:16:12.631825: val_loss -0.5907 
+2024-09-11 07:16:12.631880: Pseudo dice [0.5, 0.7783] 
+2024-09-11 07:16:12.631933: Epoch time: 246.46 s 
+2024-09-11 07:16:13.599425:  
+2024-09-11 07:16:13.599585: Epoch 228 
+2024-09-11 07:16:13.599669: Current learning rate: 0.00792 
+2024-09-11 07:20:19.839700: train_loss -0.8324 
+2024-09-11 07:20:19.839863: val_loss -0.606 
+2024-09-11 07:20:19.839915: Pseudo dice [0.5325, 0.7702] 
+2024-09-11 07:20:19.839965: Epoch time: 246.24 s 
+2024-09-11 07:20:20.775108:  
+2024-09-11 07:20:20.775314: Epoch 229 
+2024-09-11 07:20:20.775401: Current learning rate: 0.00791 
+2024-09-11 07:24:27.107582: train_loss -0.8327 
+2024-09-11 07:24:27.107735: val_loss -0.6012 
+2024-09-11 07:24:27.107785: Pseudo dice [0.4801, 0.7726] 
+2024-09-11 07:24:27.107847: Epoch time: 246.33 s 
+2024-09-11 07:24:28.044604:  
+2024-09-11 07:24:28.044852: Epoch 230 
+2024-09-11 07:24:28.044959: Current learning rate: 0.0079 
+2024-09-11 07:28:34.360275: train_loss -0.8371 
+2024-09-11 07:28:34.360439: val_loss -0.5644 
+2024-09-11 07:28:34.360515: Pseudo dice [0.4814, 0.762] 
+2024-09-11 07:28:34.360567: Epoch time: 246.32 s 
+2024-09-11 07:28:35.333367:  
+2024-09-11 07:28:35.333556: Epoch 231 
+2024-09-11 07:28:35.333642: Current learning rate: 0.00789 
+2024-09-11 07:32:41.547066: train_loss -0.8222 
+2024-09-11 07:32:41.547248: val_loss -0.58 
+2024-09-11 07:32:41.547301: Pseudo dice [0.4881, 0.7664] 
+2024-09-11 07:32:41.547353: Epoch time: 246.22 s 
+2024-09-11 07:32:42.486145:  
+2024-09-11 07:32:42.486359: Epoch 232 
+2024-09-11 07:32:42.486444: Current learning rate: 0.00789 
+2024-09-11 07:36:48.522308: train_loss -0.8277 
+2024-09-11 07:36:48.522451: val_loss -0.5936 
+2024-09-11 07:36:48.522502: Pseudo dice [0.5311, 0.7651] 
+2024-09-11 07:36:48.522556: Epoch time: 246.04 s 
+2024-09-11 07:36:49.448286:  
+2024-09-11 07:36:49.448462: Epoch 233 
+2024-09-11 07:36:49.448546: Current learning rate: 0.00788 
+2024-09-11 07:40:55.422893: train_loss -0.8337 
+2024-09-11 07:40:55.423055: val_loss -0.601 
+2024-09-11 07:40:55.423106: Pseudo dice [0.5753, 0.774] 
+2024-09-11 07:40:55.423156: Epoch time: 245.98 s 
+2024-09-11 07:40:56.350281:  
+2024-09-11 07:40:56.350465: Epoch 234 
+2024-09-11 07:40:56.350578: Current learning rate: 0.00787 
+2024-09-11 07:45:02.275141: train_loss -0.8341 
+2024-09-11 07:45:02.275310: val_loss -0.5793 
+2024-09-11 07:45:02.275361: Pseudo dice [0.5234, 0.726] 
+2024-09-11 07:45:02.275411: Epoch time: 245.93 s 
+2024-09-11 07:45:03.211171:  
+2024-09-11 07:45:03.211364: Epoch 235 
+2024-09-11 07:45:03.211483: Current learning rate: 0.00786 
+2024-09-11 07:49:09.267129: train_loss -0.8408 
+2024-09-11 07:49:09.267294: val_loss -0.5749 
+2024-09-11 07:49:09.267344: Pseudo dice [0.4952, 0.7507] 
+2024-09-11 07:49:09.267395: Epoch time: 246.06 s 
+2024-09-11 07:49:10.192712:  
+2024-09-11 07:49:10.192900: Epoch 236 
+2024-09-11 07:49:10.192990: Current learning rate: 0.00785 
+2024-09-11 07:53:16.312192: train_loss -0.834 
+2024-09-11 07:53:16.312351: val_loss -0.5868 
+2024-09-11 07:53:16.312403: Pseudo dice [0.453, 0.775] 
+2024-09-11 07:53:16.312458: Epoch time: 246.12 s 
+2024-09-11 07:53:17.244883:  
+2024-09-11 07:53:17.245106: Epoch 237 
+2024-09-11 07:53:17.245184: Current learning rate: 0.00784 
+2024-09-11 07:57:23.658521: train_loss -0.8367 
+2024-09-11 07:57:23.658662: val_loss -0.6034 
+2024-09-11 07:57:23.658713: Pseudo dice [0.5108, 0.775] 
+2024-09-11 07:57:23.658765: Epoch time: 246.42 s 
+2024-09-11 07:57:25.547502:  
+2024-09-11 07:57:25.547756: Epoch 238 
+2024-09-11 07:57:25.547871: Current learning rate: 0.00783 
+2024-09-11 08:01:31.270236: train_loss -0.8264 
+2024-09-11 08:01:31.270426: val_loss -0.5681 
+2024-09-11 08:01:31.270478: Pseudo dice [0.4697, 0.7465] 
+2024-09-11 08:01:31.270528: Epoch time: 245.72 s 
+2024-09-11 08:01:32.228946:  
+2024-09-11 08:01:32.229183: Epoch 239 
+2024-09-11 08:01:32.229288: Current learning rate: 0.00782 
+2024-09-11 08:05:38.282044: train_loss -0.8377 
+2024-09-11 08:05:38.282184: val_loss -0.5802 
+2024-09-11 08:05:38.282235: Pseudo dice [0.4753, 0.7612] 
+2024-09-11 08:05:38.282287: Epoch time: 246.05 s 
+2024-09-11 08:05:39.223027:  
+2024-09-11 08:05:39.223258: Epoch 240 
+2024-09-11 08:05:39.223342: Current learning rate: 0.00781 
+2024-09-11 08:09:45.202317: train_loss -0.8352 
+2024-09-11 08:09:45.202458: val_loss -0.5934 
+2024-09-11 08:09:45.202507: Pseudo dice [0.5406, 0.756] 
+2024-09-11 08:09:45.202559: Epoch time: 245.98 s 
+2024-09-11 08:09:46.147146:  
+2024-09-11 08:09:46.147345: Epoch 241 
+2024-09-11 08:09:46.147429: Current learning rate: 0.0078 
+2024-09-11 08:13:52.018643: train_loss -0.8343 
+2024-09-11 08:13:52.018859: val_loss -0.6038 
+2024-09-11 08:13:52.018958: Pseudo dice [0.496, 0.7694] 
+2024-09-11 08:13:52.019051: Epoch time: 245.87 s 
+2024-09-11 08:13:52.972265:  
+2024-09-11 08:13:52.972511: Epoch 242 
+2024-09-11 08:13:52.972603: Current learning rate: 0.00779 
+2024-09-11 08:17:58.977427: train_loss -0.8323 
+2024-09-11 08:17:58.977620: val_loss -0.5695 
+2024-09-11 08:17:58.977672: Pseudo dice [0.4605, 0.7617] 
+2024-09-11 08:17:58.977723: Epoch time: 246.01 s 
+2024-09-11 08:17:59.941521:  
+2024-09-11 08:17:59.941726: Epoch 243 
+2024-09-11 08:17:59.941808: Current learning rate: 0.00778 
+2024-09-11 08:22:06.048005: train_loss -0.8327 
+2024-09-11 08:22:06.048145: val_loss -0.631 
+2024-09-11 08:22:06.048195: Pseudo dice [0.5201, 0.7779] 
+2024-09-11 08:22:06.048245: Epoch time: 246.11 s 
+2024-09-11 08:22:07.025814:  
+2024-09-11 08:22:07.026009: Epoch 244 
+2024-09-11 08:22:07.026091: Current learning rate: 0.00777 
+2024-09-11 08:26:13.207438: train_loss -0.8329 
+2024-09-11 08:26:13.207728: val_loss -0.5561 
+2024-09-11 08:26:13.207801: Pseudo dice [0.4745, 0.742] 
+2024-09-11 08:26:13.207915: Epoch time: 246.18 s 
+2024-09-11 08:26:14.838451:  
+2024-09-11 08:26:14.838864: Epoch 245 
+2024-09-11 08:26:14.839063: Current learning rate: 0.00777 
+2024-09-11 08:30:21.042895: train_loss -0.8362 
+2024-09-11 08:30:21.043067: val_loss -0.5875 
+2024-09-11 08:30:21.043120: Pseudo dice [0.5224, 0.7291] 
+2024-09-11 08:30:21.043174: Epoch time: 246.21 s 
+2024-09-11 08:30:21.993872:  
+2024-09-11 08:30:21.994154: Epoch 246 
+2024-09-11 08:30:21.994240: Current learning rate: 0.00776 
+2024-09-11 08:34:28.628453: train_loss -0.8227 
+2024-09-11 08:34:28.628589: val_loss -0.5842 
+2024-09-11 08:34:28.628639: Pseudo dice [0.4817, 0.7753] 
+2024-09-11 08:34:28.628690: Epoch time: 246.64 s 
+2024-09-11 08:34:29.612680:  
+2024-09-11 08:34:29.612898: Epoch 247 
+2024-09-11 08:34:29.612980: Current learning rate: 0.00775 
+2024-09-11 08:38:35.597433: train_loss -0.8356 
+2024-09-11 08:38:35.597576: val_loss -0.5924 
+2024-09-11 08:38:35.597628: Pseudo dice [0.4627, 0.7863] 
+2024-09-11 08:38:35.597681: Epoch time: 245.99 s 
+2024-09-11 08:38:36.538249:  
+2024-09-11 08:38:36.538466: Epoch 248 
+2024-09-11 08:38:36.538563: Current learning rate: 0.00774 
+2024-09-11 08:42:42.557372: train_loss -0.8237 
+2024-09-11 08:42:42.557522: val_loss -0.5477 
+2024-09-11 08:42:42.557573: Pseudo dice [0.5206, 0.6854] 
+2024-09-11 08:42:42.557623: Epoch time: 246.02 s 
+2024-09-11 08:42:43.502459:  
+2024-09-11 08:42:43.502706: Epoch 249 
+2024-09-11 08:42:43.502799: Current learning rate: 0.00773 
+2024-09-11 08:46:49.612541: train_loss -0.7812 
+2024-09-11 08:46:49.612683: val_loss -0.5913 
+2024-09-11 08:46:49.612733: Pseudo dice [0.5316, 0.752] 
+2024-09-11 08:46:49.612785: Epoch time: 246.11 s 
+2024-09-11 08:46:53.581998:  
+2024-09-11 08:46:53.582179: Epoch 250 
+2024-09-11 08:46:53.582267: Current learning rate: 0.00772 
+2024-09-11 08:50:59.708229: train_loss -0.8 
+2024-09-11 08:50:59.708366: val_loss -0.6135 
+2024-09-11 08:50:59.708417: Pseudo dice [0.5408, 0.7773] 
+2024-09-11 08:50:59.708468: Epoch time: 246.13 s 
+2024-09-11 08:51:00.660032:  
+2024-09-11 08:51:00.660218: Epoch 251 
+2024-09-11 08:51:00.660300: Current learning rate: 0.00771 
+2024-09-11 08:55:06.843615: train_loss -0.7855 
+2024-09-11 08:55:06.843754: val_loss -0.5534 
+2024-09-11 08:55:06.843810: Pseudo dice [0.4525, 0.7342] 
+2024-09-11 08:55:06.843919: Epoch time: 246.19 s 
+2024-09-11 08:55:07.796529:  
+2024-09-11 08:55:07.796674: Epoch 252 
+2024-09-11 08:55:07.796764: Current learning rate: 0.0077 
+2024-09-11 08:59:13.620883: train_loss -0.8071 
+2024-09-11 08:59:13.621022: val_loss -0.6206 
+2024-09-11 08:59:13.621073: Pseudo dice [0.5783, 0.7615] 
+2024-09-11 08:59:13.621123: Epoch time: 245.83 s 
+2024-09-11 08:59:14.594463:  
+2024-09-11 08:59:14.594730: Epoch 253 
+2024-09-11 08:59:14.594817: Current learning rate: 0.00769 
+2024-09-11 09:03:20.575479: train_loss -0.8169 
+2024-09-11 09:03:20.575618: val_loss -0.5876 
+2024-09-11 09:03:20.575669: Pseudo dice [0.5123, 0.7903] 
+2024-09-11 09:03:20.575720: Epoch time: 245.98 s 
+2024-09-11 09:03:21.533896:  
+2024-09-11 09:03:21.534169: Epoch 254 
+2024-09-11 09:03:21.534255: Current learning rate: 0.00768 
+2024-09-11 09:07:27.332165: train_loss -0.8333 
+2024-09-11 09:07:27.332358: val_loss -0.5727 
+2024-09-11 09:07:27.332452: Pseudo dice [0.516, 0.7694] 
+2024-09-11 09:07:27.332544: Epoch time: 245.8 s 
+2024-09-11 09:07:28.290118:  
+2024-09-11 09:07:28.290273: Epoch 255 
+2024-09-11 09:07:28.290356: Current learning rate: 0.00767 
+2024-09-11 09:11:34.060100: train_loss -0.8388 
+2024-09-11 09:11:34.060243: val_loss -0.6138 
+2024-09-11 09:11:34.060294: Pseudo dice [0.5412, 0.781] 
+2024-09-11 09:11:34.060344: Epoch time: 245.77 s 
+2024-09-11 09:11:35.005547:  
+2024-09-11 09:11:35.005763: Epoch 256 
+2024-09-11 09:11:35.005852: Current learning rate: 0.00766 
+2024-09-11 09:15:40.997741: train_loss -0.8329 
+2024-09-11 09:15:40.997907: val_loss -0.5848 
+2024-09-11 09:15:40.997958: Pseudo dice [0.4905, 0.7639] 
+2024-09-11 09:15:40.998042: Epoch time: 245.99 s 
+2024-09-11 09:15:41.940939:  
+2024-09-11 09:15:41.941130: Epoch 257 
+2024-09-11 09:15:41.941212: Current learning rate: 0.00765 
+2024-09-11 09:19:47.814983: train_loss -0.8393 
+2024-09-11 09:19:47.815122: val_loss -0.5807 
+2024-09-11 09:19:47.815173: Pseudo dice [0.4769, 0.7612] 
+2024-09-11 09:19:47.815269: Epoch time: 245.88 s 
+2024-09-11 09:19:48.763256:  
+2024-09-11 09:19:48.763438: Epoch 258 
+2024-09-11 09:19:48.763524: Current learning rate: 0.00764 
+2024-09-11 09:23:54.825921: train_loss -0.8374 
+2024-09-11 09:23:54.826060: val_loss -0.6215 
+2024-09-11 09:23:54.826111: Pseudo dice [0.5246, 0.7842] 
+2024-09-11 09:23:54.826161: Epoch time: 246.06 s 
+2024-09-11 09:23:55.784542:  
+2024-09-11 09:23:55.784725: Epoch 259 
+2024-09-11 09:23:55.784810: Current learning rate: 0.00764 
+2024-09-11 09:28:01.905704: train_loss -0.8348 
+2024-09-11 09:28:01.905862: val_loss -0.575 
+2024-09-11 09:28:01.905913: Pseudo dice [0.499, 0.7613] 
+2024-09-11 09:28:01.905964: Epoch time: 246.12 s 
+2024-09-11 09:28:02.842741:  
+2024-09-11 09:28:02.842933: Epoch 260 
+2024-09-11 09:28:02.843016: Current learning rate: 0.00763 
+2024-09-11 09:32:08.823387: train_loss -0.8281 
+2024-09-11 09:32:08.823581: val_loss -0.572 
+2024-09-11 09:32:08.823656: Pseudo dice [0.4964, 0.749] 
+2024-09-11 09:32:08.823708: Epoch time: 245.98 s 
+2024-09-11 09:32:09.769288:  
+2024-09-11 09:32:09.769545: Epoch 261 
+2024-09-11 09:32:09.769631: Current learning rate: 0.00762 
+2024-09-11 09:36:16.614304: train_loss -0.8267 
+2024-09-11 09:36:16.614511: val_loss -0.5842 
+2024-09-11 09:36:16.614604: Pseudo dice [0.5138, 0.7598] 
+2024-09-11 09:36:16.614692: Epoch time: 246.85 s 
+2024-09-11 09:36:17.585994:  
+2024-09-11 09:36:17.586199: Epoch 262 
+2024-09-11 09:36:17.586298: Current learning rate: 0.00761 
+2024-09-11 09:40:23.604536: train_loss -0.8337 
+2024-09-11 09:40:23.604684: val_loss -0.5773 
+2024-09-11 09:40:23.604735: Pseudo dice [0.4625, 0.793] 
+2024-09-11 09:40:23.604787: Epoch time: 246.02 s 
+2024-09-11 09:40:24.577622:  
+2024-09-11 09:40:24.577863: Epoch 263 
+2024-09-11 09:40:24.577946: Current learning rate: 0.0076 
+2024-09-11 09:44:30.437570: train_loss -0.8282 
+2024-09-11 09:44:30.437734: val_loss -0.5883 
+2024-09-11 09:44:30.437784: Pseudo dice [0.5018, 0.7614] 
+2024-09-11 09:44:30.437835: Epoch time: 245.86 s 
+2024-09-11 09:44:31.403878:  
+2024-09-11 09:44:31.404130: Epoch 264 
+2024-09-11 09:44:31.404213: Current learning rate: 0.00759 
+2024-09-11 09:48:37.220752: train_loss -0.8218 
+2024-09-11 09:48:37.220895: val_loss -0.5947 
+2024-09-11 09:48:37.221052: Pseudo dice [0.4821, 0.7659] 
+2024-09-11 09:48:37.221112: Epoch time: 245.82 s 
+2024-09-11 09:48:38.164280:  
+2024-09-11 09:48:38.164513: Epoch 265 
+2024-09-11 09:48:38.164600: Current learning rate: 0.00758 
+2024-09-11 09:52:44.046950: train_loss -0.8269 
+2024-09-11 09:52:44.047181: val_loss -0.5717 
+2024-09-11 09:52:44.047254: Pseudo dice [0.4708, 0.7886] 
+2024-09-11 09:52:44.047312: Epoch time: 245.88 s 
+2024-09-11 09:52:44.998660:  
+2024-09-11 09:52:44.998919: Epoch 266 
+2024-09-11 09:52:44.999008: Current learning rate: 0.00757 
+2024-09-11 09:56:51.017972: train_loss -0.8297 
+2024-09-11 09:56:51.018128: val_loss -0.5879 
+2024-09-11 09:56:51.018186: Pseudo dice [0.4635, 0.7666] 
+2024-09-11 09:56:51.018243: Epoch time: 246.02 s 
+2024-09-11 09:56:51.975795:  
+2024-09-11 09:56:51.976001: Epoch 267 
+2024-09-11 09:56:51.976087: Current learning rate: 0.00756 
+2024-09-11 10:00:57.649731: train_loss -0.8297 
+2024-09-11 10:00:57.649880: val_loss -0.6094 
+2024-09-11 10:00:57.649937: Pseudo dice [0.5407, 0.7575] 
+2024-09-11 10:00:57.649992: Epoch time: 245.68 s 
+2024-09-11 10:00:58.601470:  
+2024-09-11 10:00:58.601696: Epoch 268 
+2024-09-11 10:00:58.601786: Current learning rate: 0.00755 
+2024-09-11 10:05:04.362675: train_loss -0.8446 
+2024-09-11 10:05:04.362855: val_loss -0.5876 
+2024-09-11 10:05:04.362913: Pseudo dice [0.5414, 0.7782] 
+2024-09-11 10:05:04.362970: Epoch time: 245.76 s 
+2024-09-11 10:05:05.320333:  
+2024-09-11 10:05:05.320544: Epoch 269 
+2024-09-11 10:05:05.320633: Current learning rate: 0.00754 
+2024-09-11 10:09:11.180789: train_loss -0.8395 
+2024-09-11 10:09:11.180950: val_loss -0.5772 
+2024-09-11 10:09:11.181065: Pseudo dice [0.4744, 0.7736] 
+2024-09-11 10:09:11.181129: Epoch time: 245.86 s 
+2024-09-11 10:09:12.146808:  
+2024-09-11 10:09:12.147046: Epoch 270 
+2024-09-11 10:09:12.147153: Current learning rate: 0.00753 
+2024-09-11 10:13:18.173326: train_loss -0.8317 
+2024-09-11 10:13:18.173482: val_loss -0.5765 
+2024-09-11 10:13:18.173538: Pseudo dice [0.4644, 0.7695] 
+2024-09-11 10:13:18.173593: Epoch time: 246.03 s 
+2024-09-11 10:13:19.125290:  
+2024-09-11 10:13:19.125509: Epoch 271 
+2024-09-11 10:13:19.125623: Current learning rate: 0.00752 
+2024-09-11 10:17:25.125154: train_loss -0.8421 
+2024-09-11 10:17:25.125303: val_loss -0.5739 
+2024-09-11 10:17:25.125359: Pseudo dice [0.4797, 0.7523] 
+2024-09-11 10:17:25.125413: Epoch time: 246.0 s 
+2024-09-11 10:17:26.064299:  
+2024-09-11 10:17:26.064489: Epoch 272 
+2024-09-11 10:17:26.064580: Current learning rate: 0.00751 
+2024-09-11 10:21:32.207676: train_loss -0.8392 
+2024-09-11 10:21:32.207841: val_loss -0.5905 
+2024-09-11 10:21:32.207899: Pseudo dice [0.4918, 0.768] 
+2024-09-11 10:21:32.207955: Epoch time: 246.15 s 
+2024-09-11 10:21:33.181453:  
+2024-09-11 10:21:33.181664: Epoch 273 
+2024-09-11 10:21:33.181750: Current learning rate: 0.00751 
+2024-09-11 10:25:39.239950: train_loss -0.8448 
+2024-09-11 10:25:39.240118: val_loss -0.5988 
+2024-09-11 10:25:39.240178: Pseudo dice [0.4786, 0.7614] 
+2024-09-11 10:25:39.240242: Epoch time: 246.06 s 
+2024-09-11 10:25:40.202864:  
+2024-09-11 10:25:40.203020: Epoch 274 
+2024-09-11 10:25:40.203108: Current learning rate: 0.0075 
+2024-09-11 10:29:46.157263: train_loss -0.8453 
+2024-09-11 10:29:46.157415: val_loss -0.607 
+2024-09-11 10:29:46.157473: Pseudo dice [0.55, 0.7607] 
+2024-09-11 10:29:46.157528: Epoch time: 245.96 s 
+2024-09-11 10:29:47.101581:  
+2024-09-11 10:29:47.101774: Epoch 275 
+2024-09-11 10:29:47.101864: Current learning rate: 0.00749 
+2024-09-11 10:33:53.258521: train_loss -0.8331 
+2024-09-11 10:33:53.258673: val_loss -0.5883 
+2024-09-11 10:33:53.258731: Pseudo dice [0.4873, 0.746] 
+2024-09-11 10:33:53.258790: Epoch time: 246.16 s 
+2024-09-11 10:33:54.212269:  
+2024-09-11 10:33:54.212502: Epoch 276 
+2024-09-11 10:33:54.212589: Current learning rate: 0.00748 
+2024-09-11 10:38:00.413992: train_loss -0.8459 
+2024-09-11 10:38:00.414144: val_loss -0.5723 
+2024-09-11 10:38:00.414201: Pseudo dice [0.4643, 0.7706] 
+2024-09-11 10:38:00.414256: Epoch time: 246.2 s 
+2024-09-11 10:38:01.371399:  
+2024-09-11 10:38:01.371586: Epoch 277 
+2024-09-11 10:38:01.371683: Current learning rate: 0.00747 
+2024-09-11 10:42:07.460221: train_loss -0.8395 
+2024-09-11 10:42:07.460382: val_loss -0.5809 
+2024-09-11 10:42:07.460438: Pseudo dice [0.503, 0.7817] 
+2024-09-11 10:42:07.460493: Epoch time: 246.09 s 
+2024-09-11 10:42:08.413804:  
+2024-09-11 10:42:08.413960: Epoch 278 
+2024-09-11 10:42:08.414045: Current learning rate: 0.00746 
+2024-09-11 10:46:14.654927: train_loss -0.8444 
+2024-09-11 10:46:14.655069: val_loss -0.5929 
+2024-09-11 10:46:14.655124: Pseudo dice [0.488, 0.7906] 
+2024-09-11 10:46:14.655179: Epoch time: 246.24 s 
+2024-09-11 10:46:15.604068:  
+2024-09-11 10:46:15.604272: Epoch 279 
+2024-09-11 10:46:15.604361: Current learning rate: 0.00745 
+2024-09-11 10:50:21.752362: train_loss -0.8484 
+2024-09-11 10:50:21.752524: val_loss -0.6204 
+2024-09-11 10:50:21.752574: Pseudo dice [0.5359, 0.8009] 
+2024-09-11 10:50:21.752627: Epoch time: 246.15 s 
+2024-09-11 10:50:22.697980:  
+2024-09-11 10:50:22.698221: Epoch 280 
+2024-09-11 10:50:22.698309: Current learning rate: 0.00744 
+2024-09-11 10:54:28.715779: train_loss -0.8493 
+2024-09-11 10:54:28.715939: val_loss -0.5639 
+2024-09-11 10:54:28.715989: Pseudo dice [0.4668, 0.7709] 
+2024-09-11 10:54:28.716040: Epoch time: 246.02 s 
+2024-09-11 10:54:29.664143:  
+2024-09-11 10:54:29.664312: Epoch 281 
+2024-09-11 10:54:29.664394: Current learning rate: 0.00743 
+2024-09-11 10:58:35.651220: train_loss -0.8435 
+2024-09-11 10:58:35.651362: val_loss -0.6313 
+2024-09-11 10:58:35.651412: Pseudo dice [0.5596, 0.781] 
+2024-09-11 10:58:35.651462: Epoch time: 245.99 s 
+2024-09-11 10:58:36.590836:  
+2024-09-11 10:58:36.591086: Epoch 282 
+2024-09-11 10:58:36.591174: Current learning rate: 0.00742 
+2024-09-11 11:02:42.468256: train_loss -0.8396 
+2024-09-11 11:02:42.468395: val_loss -0.577 
+2024-09-11 11:02:42.468446: Pseudo dice [0.4946, 0.7824] 
+2024-09-11 11:02:42.468499: Epoch time: 245.88 s 
+2024-09-11 11:02:43.433624:  
+2024-09-11 11:02:43.433806: Epoch 283 
+2024-09-11 11:02:43.433891: Current learning rate: 0.00741 
+2024-09-11 11:06:49.727175: train_loss -0.8132 
+2024-09-11 11:06:49.727356: val_loss -0.5888 
+2024-09-11 11:06:49.727408: Pseudo dice [0.4674, 0.7701] 
+2024-09-11 11:06:49.727461: Epoch time: 246.3 s 
+2024-09-11 11:06:50.700869:  
+2024-09-11 11:06:50.701104: Epoch 284 
+2024-09-11 11:06:50.701191: Current learning rate: 0.0074 
+2024-09-11 11:10:56.772567: train_loss -0.8125 
+2024-09-11 11:10:56.772711: val_loss -0.6055 
+2024-09-11 11:10:56.772763: Pseudo dice [0.4917, 0.8062] 
+2024-09-11 11:10:56.772814: Epoch time: 246.07 s 
+2024-09-11 11:10:57.713703:  
+2024-09-11 11:10:57.713886: Epoch 285 
+2024-09-11 11:10:57.713991: Current learning rate: 0.00739 
+2024-09-11 11:15:04.396649: train_loss -0.8303 
+2024-09-11 11:15:04.396808: val_loss -0.5984 
+2024-09-11 11:15:04.396859: Pseudo dice [0.52, 0.7595] 
+2024-09-11 11:15:04.396920: Epoch time: 246.68 s 
+2024-09-11 11:15:05.340306:  
+2024-09-11 11:15:05.340517: Epoch 286 
+2024-09-11 11:15:05.340704: Current learning rate: 0.00738 
+2024-09-11 11:19:11.290024: train_loss -0.8227 
+2024-09-11 11:19:11.290162: val_loss -0.5905 
+2024-09-11 11:19:11.290212: Pseudo dice [0.5338, 0.7737] 
+2024-09-11 11:19:11.290263: Epoch time: 245.95 s 
+2024-09-11 11:19:12.243222:  
+2024-09-11 11:19:12.243438: Epoch 287 
+2024-09-11 11:19:12.243522: Current learning rate: 0.00738 
+2024-09-11 11:23:18.191157: train_loss -0.8139 
+2024-09-11 11:23:18.191297: val_loss -0.5917 
+2024-09-11 11:23:18.191347: Pseudo dice [0.4687, 0.7972] 
+2024-09-11 11:23:18.191399: Epoch time: 245.95 s 
+2024-09-11 11:23:19.171655:  
+2024-09-11 11:23:19.171862: Epoch 288 
+2024-09-11 11:23:19.171942: Current learning rate: 0.00737 
+2024-09-11 11:27:25.152282: train_loss -0.8373 
+2024-09-11 11:27:25.152421: val_loss -0.6034 
+2024-09-11 11:27:25.152476: Pseudo dice [0.5651, 0.7586] 
+2024-09-11 11:27:25.152531: Epoch time: 245.98 s 
+2024-09-11 11:27:26.118823:  
+2024-09-11 11:27:26.119023: Epoch 289 
+2024-09-11 11:27:26.119108: Current learning rate: 0.00736 
+2024-09-11 11:31:32.164670: train_loss -0.8082 
+2024-09-11 11:31:32.164816: val_loss -0.6095 
+2024-09-11 11:31:32.164873: Pseudo dice [0.5589, 0.7759] 
+2024-09-11 11:31:32.164928: Epoch time: 246.05 s 
+2024-09-11 11:31:32.164974: Yayy! New best EMA pseudo Dice: 0.6433 
+2024-09-11 11:31:36.131215:  
+2024-09-11 11:31:36.131509: Epoch 290 
+2024-09-11 11:31:36.131599: Current learning rate: 0.00735 
+2024-09-11 11:35:42.396818: train_loss -0.8258 
+2024-09-11 11:35:42.396970: val_loss -0.6133 
+2024-09-11 11:35:42.397026: Pseudo dice [0.5225, 0.7852] 
+2024-09-11 11:35:42.397082: Epoch time: 246.27 s 
+2024-09-11 11:35:42.397128: Yayy! New best EMA pseudo Dice: 0.6443 
+2024-09-11 11:35:46.366019:  
+2024-09-11 11:35:46.366261: Epoch 291 
+2024-09-11 11:35:46.366349: Current learning rate: 0.00734 
+2024-09-11 11:39:52.500915: train_loss -0.8262 
+2024-09-11 11:39:52.501080: val_loss -0.6055 
+2024-09-11 11:39:52.501137: Pseudo dice [0.5612, 0.7882] 
+2024-09-11 11:39:52.501194: Epoch time: 246.14 s 
+2024-09-11 11:39:52.501238: Yayy! New best EMA pseudo Dice: 0.6474 
+2024-09-11 11:39:56.447237:  
+2024-09-11 11:39:56.447443: Epoch 292 
+2024-09-11 11:39:56.447537: Current learning rate: 0.00733 
+2024-09-11 11:44:02.364301: train_loss -0.8367 
+2024-09-11 11:44:02.364454: val_loss -0.5934 
+2024-09-11 11:44:02.364512: Pseudo dice [0.4684, 0.7887] 
+2024-09-11 11:44:02.364567: Epoch time: 245.92 s 
+2024-09-11 11:44:03.338708:  
+2024-09-11 11:44:03.338918: Epoch 293 
+2024-09-11 11:44:03.339007: Current learning rate: 0.00732 
+2024-09-11 11:48:09.327220: train_loss -0.8365 
+2024-09-11 11:48:09.327377: val_loss -0.6135 
+2024-09-11 11:48:09.327435: Pseudo dice [0.5288, 0.7874] 
+2024-09-11 11:48:09.327492: Epoch time: 245.99 s 
+2024-09-11 11:48:10.291983:  
+2024-09-11 11:48:10.292157: Epoch 294 
+2024-09-11 11:48:10.292244: Current learning rate: 0.00731 
+2024-09-11 11:52:16.324666: train_loss -0.8375 
+2024-09-11 11:52:16.324817: val_loss -0.5864 
+2024-09-11 11:52:16.324876: Pseudo dice [0.4785, 0.7947] 
+2024-09-11 11:52:16.324933: Epoch time: 246.03 s 
+2024-09-11 11:52:17.304228:  
+2024-09-11 11:52:17.304458: Epoch 295 
+2024-09-11 11:52:17.304550: Current learning rate: 0.0073 
+2024-09-11 11:56:23.259058: train_loss -0.8464 
+2024-09-11 11:56:23.259236: val_loss -0.6121 
+2024-09-11 11:56:23.259295: Pseudo dice [0.5514, 0.779] 
+2024-09-11 11:56:23.259352: Epoch time: 245.96 s 
+2024-09-11 11:56:23.259398: Yayy! New best EMA pseudo Dice: 0.6477 
+2024-09-11 11:56:27.182840:  
+2024-09-11 11:56:27.183065: Epoch 296 
+2024-09-11 11:56:27.183156: Current learning rate: 0.00729 
+2024-09-11 12:00:33.358322: train_loss -0.8245 
+2024-09-11 12:00:33.358476: val_loss -0.5649 
+2024-09-11 12:00:33.358533: Pseudo dice [0.4483, 0.7872] 
+2024-09-11 12:00:33.358592: Epoch time: 246.18 s 
+2024-09-11 12:00:34.328341:  
+2024-09-11 12:00:34.328554: Epoch 297 
+2024-09-11 12:00:34.328645: Current learning rate: 0.00728 
+2024-09-11 12:04:40.008313: train_loss -0.8275 
+2024-09-11 12:04:40.008469: val_loss -0.5727 
+2024-09-11 12:04:40.008528: Pseudo dice [0.5262, 0.7418] 
+2024-09-11 12:04:40.008586: Epoch time: 245.68 s 
+2024-09-11 12:04:40.981572:  
+2024-09-11 12:04:40.981792: Epoch 298 
+2024-09-11 12:04:40.981931: Current learning rate: 0.00727 
+2024-09-11 12:08:47.009691: train_loss -0.8098 
+2024-09-11 12:08:47.009883: val_loss -0.5695 
+2024-09-11 12:08:47.009940: Pseudo dice [0.4955, 0.7413] 
+2024-09-11 12:08:47.010003: Epoch time: 246.03 s 
+2024-09-11 12:08:47.982439:  
+2024-09-11 12:08:47.982611: Epoch 299 
+2024-09-11 12:08:47.982700: Current learning rate: 0.00726 
+2024-09-11 12:12:54.075325: train_loss -0.822 
+2024-09-11 12:12:54.075475: val_loss -0.5858 
+2024-09-11 12:12:54.075531: Pseudo dice [0.4915, 0.7437] 
+2024-09-11 12:12:54.075588: Epoch time: 246.09 s 
+2024-09-11 12:12:57.937508:  
+2024-09-11 12:12:57.937697: Epoch 300 
+2024-09-11 12:12:57.937795: Current learning rate: 0.00725 
+2024-09-11 12:17:04.217349: train_loss -0.8278 
+2024-09-11 12:17:04.217533: val_loss -0.5807 
+2024-09-11 12:17:04.217592: Pseudo dice [0.4722, 0.776] 
+2024-09-11 12:17:04.217649: Epoch time: 246.28 s 
+2024-09-11 12:17:05.204856:  
+2024-09-11 12:17:05.205114: Epoch 301 
+2024-09-11 12:17:05.205204: Current learning rate: 0.00724 
+2024-09-11 12:21:10.913866: train_loss -0.8351 
+2024-09-11 12:21:10.914021: val_loss -0.597 
+2024-09-11 12:21:10.914077: Pseudo dice [0.4756, 0.7777] 
+2024-09-11 12:21:10.914133: Epoch time: 245.71 s 
+2024-09-11 12:21:11.888195:  
+2024-09-11 12:21:11.888381: Epoch 302 
+2024-09-11 12:21:11.888469: Current learning rate: 0.00724 
+2024-09-11 12:25:17.792458: train_loss -0.842 
+2024-09-11 12:25:17.792613: val_loss -0.6321 
+2024-09-11 12:25:17.792671: Pseudo dice [0.5258, 0.8068] 
+2024-09-11 12:25:17.792727: Epoch time: 245.91 s 
+2024-09-11 12:25:18.757534:  
+2024-09-11 12:25:18.757695: Epoch 303 
+2024-09-11 12:25:18.757785: Current learning rate: 0.00723 
+2024-09-11 12:29:24.475338: train_loss -0.8385 
+2024-09-11 12:29:24.475492: val_loss -0.5364 
+2024-09-11 12:29:24.475549: Pseudo dice [0.3919, 0.7887] 
+2024-09-11 12:29:24.475690: Epoch time: 245.72 s 
+2024-09-11 12:29:25.442708:  
+2024-09-11 12:29:25.442877: Epoch 304 
+2024-09-11 12:29:25.442963: Current learning rate: 0.00722 
+2024-09-11 12:33:31.214432: train_loss -0.8461 
+2024-09-11 12:33:31.214597: val_loss -0.5513 
+2024-09-11 12:33:31.214754: Pseudo dice [0.3992, 0.7738] 
+2024-09-11 12:33:31.214898: Epoch time: 245.77 s 
+2024-09-11 12:33:32.190718:  
+2024-09-11 12:33:32.190886: Epoch 305 
+2024-09-11 12:33:32.190976: Current learning rate: 0.00721 
+2024-09-11 12:37:38.117648: train_loss -0.8426 
+2024-09-11 12:37:38.117795: val_loss -0.5851 
+2024-09-11 12:37:38.117852: Pseudo dice [0.4832, 0.7558] 
+2024-09-11 12:37:38.117909: Epoch time: 245.93 s 
+2024-09-11 12:37:39.090464:  
+2024-09-11 12:37:39.090693: Epoch 306 
+2024-09-11 12:37:39.090801: Current learning rate: 0.0072 
+2024-09-11 12:41:45.333010: train_loss -0.8265 
+2024-09-11 12:41:45.333166: val_loss -0.5871 
+2024-09-11 12:41:45.333223: Pseudo dice [0.5246, 0.7665] 
+2024-09-11 12:41:45.333278: Epoch time: 246.24 s 
+2024-09-11 12:41:46.284612:  
+2024-09-11 12:41:46.284797: Epoch 307 
+2024-09-11 12:41:46.284887: Current learning rate: 0.00719 
+2024-09-11 12:45:53.258230: train_loss -0.8332 
+2024-09-11 12:45:53.258400: val_loss -0.5478 
+2024-09-11 12:45:53.258456: Pseudo dice [0.4254, 0.7478] 
+2024-09-11 12:45:53.258515: Epoch time: 246.98 s 
+2024-09-11 12:45:54.218173:  
+2024-09-11 12:45:54.218400: Epoch 308 
+2024-09-11 12:45:54.218496: Current learning rate: 0.00718 
+2024-09-11 12:50:00.195959: train_loss -0.8405 
+2024-09-11 12:50:00.196114: val_loss -0.5338 
+2024-09-11 12:50:00.196171: Pseudo dice [0.449, 0.7306] 
+2024-09-11 12:50:00.196227: Epoch time: 245.98 s 
+2024-09-11 12:50:01.166621:  
+2024-09-11 12:50:01.166854: Epoch 309 
+2024-09-11 12:50:01.166944: Current learning rate: 0.00717 
+2024-09-11 12:54:07.146275: train_loss -0.8413 
+2024-09-11 12:54:07.146437: val_loss -0.6143 
+2024-09-11 12:54:07.146494: Pseudo dice [0.5154, 0.8063] 
+2024-09-11 12:54:07.146562: Epoch time: 245.98 s 
+2024-09-11 12:54:08.121762:  
+2024-09-11 12:54:08.121966: Epoch 310 
+2024-09-11 12:54:08.122054: Current learning rate: 0.00716 
+2024-09-11 12:58:14.062966: train_loss -0.8504 
+2024-09-11 12:58:14.063126: val_loss -0.6102 
+2024-09-11 12:58:14.063184: Pseudo dice [0.4996, 0.7894] 
+2024-09-11 12:58:14.063241: Epoch time: 245.94 s 
+2024-09-11 12:58:15.067482:  
+2024-09-11 12:58:15.067740: Epoch 311 
+2024-09-11 12:58:15.067844: Current learning rate: 0.00715 
+2024-09-11 13:02:20.999261: train_loss -0.842 
+2024-09-11 13:02:20.999410: val_loss -0.5433 
+2024-09-11 13:02:20.999468: Pseudo dice [0.436, 0.7695] 
+2024-09-11 13:02:20.999527: Epoch time: 245.93 s 
+2024-09-11 13:02:21.949933:  
+2024-09-11 13:02:21.950144: Epoch 312 
+2024-09-11 13:02:21.950229: Current learning rate: 0.00714 
+2024-09-11 13:06:28.042356: train_loss -0.8325 
+2024-09-11 13:06:28.042536: val_loss -0.6159 
+2024-09-11 13:06:28.042594: Pseudo dice [0.5601, 0.7729] 
+2024-09-11 13:06:28.042651: Epoch time: 246.09 s 
+2024-09-11 13:06:29.021119:  
+2024-09-11 13:06:29.021343: Epoch 313 
+2024-09-11 13:06:29.021437: Current learning rate: 0.00713 
+2024-09-11 13:10:35.219899: train_loss -0.839 
+2024-09-11 13:10:35.220114: val_loss -0.5922 
+2024-09-11 13:10:35.220220: Pseudo dice [0.5181, 0.7321] 
+2024-09-11 13:10:35.220321: Epoch time: 246.2 s 
+2024-09-11 13:10:36.204952:  
+2024-09-11 13:10:36.205160: Epoch 314 
+2024-09-11 13:10:36.205249: Current learning rate: 0.00712 
+2024-09-11 13:14:42.291440: train_loss -0.8433 
+2024-09-11 13:14:42.291599: val_loss -0.5971 
+2024-09-11 13:14:42.291722: Pseudo dice [0.4906, 0.771] 
+2024-09-11 13:14:42.291825: Epoch time: 246.09 s 
+2024-09-11 13:14:43.269963:  
+2024-09-11 13:14:43.270175: Epoch 315 
+2024-09-11 13:14:43.270265: Current learning rate: 0.00711 
+2024-09-11 13:18:49.329328: train_loss -0.8495 
+2024-09-11 13:18:49.329484: val_loss -0.5906 
+2024-09-11 13:18:49.329540: Pseudo dice [0.4912, 0.7713] 
+2024-09-11 13:18:49.329670: Epoch time: 246.06 s 
+2024-09-11 13:18:50.292497:  
+2024-09-11 13:18:50.292727: Epoch 316 
+2024-09-11 13:18:50.292813: Current learning rate: 0.0071 
+2024-09-11 13:22:56.348093: train_loss -0.8326 
+2024-09-11 13:22:56.348244: val_loss -0.5773 
+2024-09-11 13:22:56.348300: Pseudo dice [0.4491, 0.7648] 
+2024-09-11 13:22:56.348357: Epoch time: 246.06 s 
+2024-09-11 13:22:57.326032:  
+2024-09-11 13:22:57.326267: Epoch 317 
+2024-09-11 13:22:57.326371: Current learning rate: 0.0071 
+2024-09-11 13:27:03.449842: train_loss -0.8209 
+2024-09-11 13:27:03.449995: val_loss -0.5751 
+2024-09-11 13:27:03.450052: Pseudo dice [0.4767, 0.7486] 
+2024-09-11 13:27:03.450109: Epoch time: 246.13 s 
+2024-09-11 13:27:04.410986:  
+2024-09-11 13:27:04.411196: Epoch 318 
+2024-09-11 13:27:04.411294: Current learning rate: 0.00709 
+2024-09-11 13:31:10.409501: train_loss -0.8264 
+2024-09-11 13:31:10.409652: val_loss -0.6053 
+2024-09-11 13:31:10.409707: Pseudo dice [0.5018, 0.7751] 
+2024-09-11 13:31:10.409762: Epoch time: 246.0 s 
+2024-09-11 13:31:11.376186:  
+2024-09-11 13:31:11.376441: Epoch 319 
+2024-09-11 13:31:11.376530: Current learning rate: 0.00708 
+2024-09-11 13:35:17.305809: train_loss -0.8285 
+2024-09-11 13:35:17.306005: val_loss -0.5691 
+2024-09-11 13:35:17.306067: Pseudo dice [0.4442, 0.7706] 
+2024-09-11 13:35:17.306184: Epoch time: 245.93 s 
+2024-09-11 13:35:18.298574:  
+2024-09-11 13:35:18.298822: Epoch 320 
+2024-09-11 13:35:18.298911: Current learning rate: 0.00707 
+2024-09-11 13:39:24.367132: train_loss -0.8288 
+2024-09-11 13:39:24.367297: val_loss -0.6511 
+2024-09-11 13:39:24.367354: Pseudo dice [0.5497, 0.8189] 
+2024-09-11 13:39:24.367410: Epoch time: 246.07 s 
+2024-09-11 13:39:25.332886:  
+2024-09-11 13:39:25.333100: Epoch 321 
+2024-09-11 13:39:25.333186: Current learning rate: 0.00706 
+2024-09-11 13:43:31.330052: train_loss -0.8258 
+2024-09-11 13:43:31.330377: val_loss -0.6246 
+2024-09-11 13:43:31.330498: Pseudo dice [0.5238, 0.7839] 
+2024-09-11 13:43:31.330601: Epoch time: 246.0 s 
+2024-09-11 13:43:32.345280:  
+2024-09-11 13:43:32.345461: Epoch 322 
+2024-09-11 13:43:32.345561: Current learning rate: 0.00705 
+2024-09-11 13:47:38.428969: train_loss -0.8395 
+2024-09-11 13:47:38.429121: val_loss -0.6251 
+2024-09-11 13:47:38.429177: Pseudo dice [0.5544, 0.7751] 
+2024-09-11 13:47:38.429233: Epoch time: 246.09 s 
+2024-09-11 13:47:39.422276:  
+2024-09-11 13:47:39.422448: Epoch 323 
+2024-09-11 13:47:39.422538: Current learning rate: 0.00704 
+2024-09-11 13:51:45.465813: train_loss -0.8427 
+2024-09-11 13:51:45.466158: val_loss -0.5931 
+2024-09-11 13:51:45.466280: Pseudo dice [0.4873, 0.7847] 
+2024-09-11 13:51:45.466384: Epoch time: 246.05 s 
+2024-09-11 13:51:46.451988:  
+2024-09-11 13:51:46.452200: Epoch 324 
+2024-09-11 13:51:46.452290: Current learning rate: 0.00703 
+2024-09-11 13:55:52.320450: train_loss -0.8493 
+2024-09-11 13:55:52.320606: val_loss -0.6196 
+2024-09-11 13:55:52.320662: Pseudo dice [0.5222, 0.783] 
+2024-09-11 13:55:52.320719: Epoch time: 245.87 s 
+2024-09-11 13:55:53.288827:  
+2024-09-11 13:55:53.289039: Epoch 325 
+2024-09-11 13:55:53.289129: Current learning rate: 0.00702 
+2024-09-11 13:59:59.235105: train_loss -0.8551 
+2024-09-11 13:59:59.235260: val_loss -0.5835 
+2024-09-11 13:59:59.235318: Pseudo dice [0.5374, 0.731] 
+2024-09-11 13:59:59.235376: Epoch time: 245.95 s 
+2024-09-11 14:00:00.239319:  
+2024-09-11 14:00:00.239504: Epoch 326 
+2024-09-11 14:00:00.239596: Current learning rate: 0.00701 
+2024-09-11 14:04:06.159544: train_loss -0.8533 
+2024-09-11 14:04:06.159702: val_loss -0.6183 
+2024-09-11 14:04:06.159761: Pseudo dice [0.5186, 0.7841] 
+2024-09-11 14:04:06.159828: Epoch time: 245.92 s 
+2024-09-11 14:04:07.135874:  
+2024-09-11 14:04:07.136087: Epoch 327 
+2024-09-11 14:04:07.136174: Current learning rate: 0.007 
+2024-09-11 14:08:13.231401: train_loss -0.8506 
+2024-09-11 14:08:13.231551: val_loss -0.6051 
+2024-09-11 14:08:13.231610: Pseudo dice [0.4982, 0.7998] 
+2024-09-11 14:08:13.231668: Epoch time: 246.1 s 
+2024-09-11 14:08:14.207547:  
+2024-09-11 14:08:14.207725: Epoch 328 
+2024-09-11 14:08:14.207822: Current learning rate: 0.00699 
+2024-09-11 14:12:20.133730: train_loss -0.8487 
+2024-09-11 14:12:20.133881: val_loss -0.5949 
+2024-09-11 14:12:20.133937: Pseudo dice [0.4957, 0.7728] 
+2024-09-11 14:12:20.134037: Epoch time: 245.93 s 
+2024-09-11 14:12:21.090003:  
+2024-09-11 14:12:21.090175: Epoch 329 
+2024-09-11 14:12:21.090262: Current learning rate: 0.00698 
+2024-09-11 14:16:27.054521: train_loss -0.8458 
+2024-09-11 14:16:27.054672: val_loss -0.6041 
+2024-09-11 14:16:27.054728: Pseudo dice [0.4892, 0.7844] 
+2024-09-11 14:16:27.054785: Epoch time: 245.97 s 
+2024-09-11 14:16:28.033069:  
+2024-09-11 14:16:28.033231: Epoch 330 
+2024-09-11 14:16:28.033325: Current learning rate: 0.00697 
+2024-09-11 14:20:33.864453: train_loss -0.8492 
+2024-09-11 14:20:33.864647: val_loss -0.5863 
+2024-09-11 14:20:33.864707: Pseudo dice [0.4914, 0.7711] 
+2024-09-11 14:20:33.864765: Epoch time: 245.83 s 
+2024-09-11 14:20:35.790286:  
+2024-09-11 14:20:35.790511: Epoch 331 
+2024-09-11 14:20:35.790620: Current learning rate: 0.00696 
+2024-09-11 14:24:41.862512: train_loss -0.8414 
+2024-09-11 14:24:41.862693: val_loss -0.6338 
+2024-09-11 14:24:41.862753: Pseudo dice [0.5664, 0.7893] 
+2024-09-11 14:24:41.862810: Epoch time: 246.07 s 
+2024-09-11 14:24:42.856108:  
+2024-09-11 14:24:42.856346: Epoch 332 
+2024-09-11 14:24:42.856439: Current learning rate: 0.00696 
+2024-09-11 14:28:48.915468: train_loss -0.8467 
+2024-09-11 14:28:48.915627: val_loss -0.5461 
+2024-09-11 14:28:48.915684: Pseudo dice [0.4385, 0.7571] 
+2024-09-11 14:28:48.915740: Epoch time: 246.06 s 
+2024-09-11 14:28:49.893125:  
+2024-09-11 14:28:49.893371: Epoch 333 
+2024-09-11 14:28:49.893466: Current learning rate: 0.00695 
+2024-09-11 14:32:56.082142: train_loss -0.8536 
+2024-09-11 14:32:56.082306: val_loss -0.61 
+2024-09-11 14:32:56.082364: Pseudo dice [0.523, 0.769] 
+2024-09-11 14:32:56.082423: Epoch time: 246.19 s 
+2024-09-11 14:32:57.092272:  
+2024-09-11 14:32:57.092497: Epoch 334 
+2024-09-11 14:32:57.092585: Current learning rate: 0.00694 
+2024-09-11 14:37:03.055973: train_loss -0.8531 
+2024-09-11 14:37:03.056141: val_loss -0.5628 
+2024-09-11 14:37:03.056200: Pseudo dice [0.4535, 0.7683] 
+2024-09-11 14:37:03.056258: Epoch time: 245.97 s 
+2024-09-11 14:37:04.038262:  
+2024-09-11 14:37:04.038501: Epoch 335 
+2024-09-11 14:37:04.038589: Current learning rate: 0.00693 
+2024-09-11 14:41:09.872532: train_loss -0.8577 
+2024-09-11 14:41:09.872690: val_loss -0.5852 
+2024-09-11 14:41:09.872746: Pseudo dice [0.4727, 0.7921] 
+2024-09-11 14:41:09.872801: Epoch time: 245.84 s 
+2024-09-11 14:41:11.011528:  
+2024-09-11 14:41:11.011794: Epoch 336 
+2024-09-11 14:41:11.011902: Current learning rate: 0.00692 
+2024-09-11 14:45:16.745937: train_loss -0.853 
+2024-09-11 14:45:16.746117: val_loss -0.5729 
+2024-09-11 14:45:16.746175: Pseudo dice [0.5204, 0.7759] 
+2024-09-11 14:45:16.746232: Epoch time: 245.74 s 
+2024-09-11 14:45:17.736744:  
+2024-09-11 14:45:17.736937: Epoch 337 
+2024-09-11 14:45:17.737027: Current learning rate: 0.00691 
+2024-09-11 14:49:23.728899: train_loss -0.8432 
+2024-09-11 14:49:23.729114: val_loss -0.6134 
+2024-09-11 14:49:23.729170: Pseudo dice [0.5245, 0.7945] 
+2024-09-11 14:49:23.729227: Epoch time: 245.99 s 
+2024-09-11 14:49:24.701456:  
+2024-09-11 14:49:24.701643: Epoch 338 
+2024-09-11 14:49:24.701775: Current learning rate: 0.0069 
+2024-09-11 14:53:30.682866: train_loss -0.844 
+2024-09-11 14:53:30.683042: val_loss -0.5577 
+2024-09-11 14:53:30.683100: Pseudo dice [0.4653, 0.7795] 
+2024-09-11 14:53:30.683156: Epoch time: 245.98 s 
+2024-09-11 14:53:31.684390:  
+2024-09-11 14:53:31.684597: Epoch 339 
+2024-09-11 14:53:31.684688: Current learning rate: 0.00689 
+2024-09-11 14:57:37.464944: train_loss -0.8575 
+2024-09-11 14:57:37.465098: val_loss -0.5505 
+2024-09-11 14:57:37.465154: Pseudo dice [0.4778, 0.7645] 
+2024-09-11 14:57:37.465209: Epoch time: 245.78 s 
+2024-09-11 14:57:38.447798:  
+2024-09-11 14:57:38.448001: Epoch 340 
+2024-09-11 14:57:38.448095: Current learning rate: 0.00688 
+2024-09-11 15:01:44.386892: train_loss -0.8523 
+2024-09-11 15:01:44.387034: val_loss -0.5776 
+2024-09-11 15:01:44.387090: Pseudo dice [0.4663, 0.7722] 
+2024-09-11 15:01:44.387146: Epoch time: 245.94 s 
+2024-09-11 15:01:45.382234:  
+2024-09-11 15:01:45.382449: Epoch 341 
+2024-09-11 15:01:45.382544: Current learning rate: 0.00687 
+2024-09-11 15:05:51.325247: train_loss -0.8516 
+2024-09-11 15:05:51.325396: val_loss -0.5917 
+2024-09-11 15:05:51.325453: Pseudo dice [0.4833, 0.7906] 
+2024-09-11 15:05:51.325508: Epoch time: 245.94 s 
+2024-09-11 15:05:52.309493:  
+2024-09-11 15:05:52.309721: Epoch 342 
+2024-09-11 15:05:52.309810: Current learning rate: 0.00686 
+2024-09-11 15:09:58.158263: train_loss -0.8549 
+2024-09-11 15:09:58.158412: val_loss -0.5979 
+2024-09-11 15:09:58.158527: Pseudo dice [0.4889, 0.7694] 
+2024-09-11 15:09:58.158588: Epoch time: 245.85 s 
+2024-09-11 15:09:59.145999:  
+2024-09-11 15:09:59.146198: Epoch 343 
+2024-09-11 15:09:59.146287: Current learning rate: 0.00685 
+2024-09-11 15:14:05.084826: train_loss -0.8513 
+2024-09-11 15:14:05.084997: val_loss -0.594 
+2024-09-11 15:14:05.085054: Pseudo dice [0.4761, 0.7835] 
+2024-09-11 15:14:05.085109: Epoch time: 245.94 s 
+2024-09-11 15:14:06.063043:  
+2024-09-11 15:14:06.063216: Epoch 344 
+2024-09-11 15:14:06.063307: Current learning rate: 0.00684 
+2024-09-11 15:18:12.110768: train_loss -0.8608 
+2024-09-11 15:18:12.110939: val_loss -0.5806 
+2024-09-11 15:18:12.111012: Pseudo dice [0.5138, 0.7717] 
+2024-09-11 15:18:12.111077: Epoch time: 246.05 s 
+2024-09-11 15:18:13.103883:  
+2024-09-11 15:18:13.104078: Epoch 345 
+2024-09-11 15:18:13.104181: Current learning rate: 0.00683 
+2024-09-11 15:22:19.147565: train_loss -0.8604 
+2024-09-11 15:22:19.147725: val_loss -0.5934 
+2024-09-11 15:22:19.147781: Pseudo dice [0.4753, 0.7943] 
+2024-09-11 15:22:19.147850: Epoch time: 246.05 s 
+2024-09-11 15:22:20.148000:  
+2024-09-11 15:22:20.148258: Epoch 346 
+2024-09-11 15:22:20.148350: Current learning rate: 0.00682 
+2024-09-11 15:26:26.180336: train_loss -0.8589 
+2024-09-11 15:26:26.180492: val_loss -0.5826 
+2024-09-11 15:26:26.180549: Pseudo dice [0.4141, 0.8102] 
+2024-09-11 15:26:26.180606: Epoch time: 246.03 s 
+2024-09-11 15:26:27.185017:  
+2024-09-11 15:26:27.185190: Epoch 347 
+2024-09-11 15:26:27.185285: Current learning rate: 0.00681 
+2024-09-11 15:30:32.958372: train_loss -0.8654 
+2024-09-11 15:30:32.958528: val_loss -0.6033 
+2024-09-11 15:30:32.958586: Pseudo dice [0.5067, 0.7742] 
+2024-09-11 15:30:32.958642: Epoch time: 245.78 s 
+2024-09-11 15:30:33.946575:  
+2024-09-11 15:30:33.946795: Epoch 348 
+2024-09-11 15:30:33.946884: Current learning rate: 0.0068 
+2024-09-11 15:34:39.692758: train_loss -0.8591 
+2024-09-11 15:34:39.692958: val_loss -0.6255 
+2024-09-11 15:34:39.693023: Pseudo dice [0.5221, 0.8048] 
+2024-09-11 15:34:39.693082: Epoch time: 245.75 s 
+2024-09-11 15:34:40.671357:  
+2024-09-11 15:34:40.671594: Epoch 349 
+2024-09-11 15:34:40.671714: Current learning rate: 0.0068 
+2024-09-11 15:38:46.603714: train_loss -0.855 
+2024-09-11 15:38:46.603880: val_loss -0.5785 
+2024-09-11 15:38:46.603940: Pseudo dice [0.4722, 0.794] 
+2024-09-11 15:38:46.603997: Epoch time: 245.93 s 
+2024-09-11 15:38:50.601519:  
+2024-09-11 15:38:50.601731: Epoch 350 
+2024-09-11 15:38:50.601822: Current learning rate: 0.00679 
+2024-09-11 15:42:56.543727: train_loss -0.8443 
+2024-09-11 15:42:56.543885: val_loss -0.552 
+2024-09-11 15:42:56.543943: Pseudo dice [0.4497, 0.7697] 
+2024-09-11 15:42:56.543999: Epoch time: 245.94 s 
+2024-09-11 15:42:57.559447:  
+2024-09-11 15:42:57.559641: Epoch 351 
+2024-09-11 15:42:57.559728: Current learning rate: 0.00678 
+2024-09-11 15:47:03.249391: train_loss -0.8449 
+2024-09-11 15:47:03.249544: val_loss -0.569 
+2024-09-11 15:47:03.249601: Pseudo dice [0.4626, 0.783] 
+2024-09-11 15:47:03.249657: Epoch time: 245.69 s 
+2024-09-11 15:47:04.225839:  
+2024-09-11 15:47:04.226041: Epoch 352 
+2024-09-11 15:47:04.226136: Current learning rate: 0.00677 
+2024-09-11 15:51:10.141665: train_loss -0.8202 
+2024-09-11 15:51:10.141814: val_loss -0.5726 
+2024-09-11 15:51:10.141870: Pseudo dice [0.5002, 0.7558] 
+2024-09-11 15:51:10.141926: Epoch time: 245.92 s 
+2024-09-11 15:51:11.136095:  
+2024-09-11 15:51:11.136276: Epoch 353 
+2024-09-11 15:51:11.136364: Current learning rate: 0.00676 
+2024-09-11 15:55:17.723695: train_loss -0.8316 
+2024-09-11 15:55:17.723853: val_loss -0.5615 
+2024-09-11 15:55:17.723910: Pseudo dice [0.4466, 0.7888] 
+2024-09-11 15:55:17.723969: Epoch time: 246.59 s 
+2024-09-11 15:55:18.703957:  
+2024-09-11 15:55:18.704172: Epoch 354 
+2024-09-11 15:55:18.704285: Current learning rate: 0.00675 
+2024-09-11 15:59:24.477710: train_loss -0.8498 
+2024-09-11 15:59:24.477861: val_loss -0.5495 
+2024-09-11 15:59:24.477916: Pseudo dice [0.4903, 0.74] 
+2024-09-11 15:59:24.477972: Epoch time: 245.78 s 
+2024-09-11 15:59:25.467150:  
+2024-09-11 15:59:25.467397: Epoch 355 
+2024-09-11 15:59:25.467486: Current learning rate: 0.00674 
+2024-09-11 16:03:31.464719: train_loss -0.8472 
+2024-09-11 16:03:31.464872: val_loss -0.6021 
+2024-09-11 16:03:31.464929: Pseudo dice [0.4965, 0.7869] 
+2024-09-11 16:03:31.464984: Epoch time: 246.0 s 
+2024-09-11 16:03:32.456583:  
+2024-09-11 16:03:32.456824: Epoch 356 
+2024-09-11 16:03:32.456918: Current learning rate: 0.00673 
+2024-09-11 16:07:38.530909: train_loss -0.8559 
+2024-09-11 16:07:38.531082: val_loss -0.5747 
+2024-09-11 16:07:38.531140: Pseudo dice [0.4756, 0.7638] 
+2024-09-11 16:07:38.531232: Epoch time: 246.08 s 
+2024-09-11 16:07:39.510257:  
+2024-09-11 16:07:39.510433: Epoch 357 
+2024-09-11 16:07:39.510538: Current learning rate: 0.00672 
+2024-09-11 16:11:45.499644: train_loss -0.8572 
+2024-09-11 16:11:45.499796: val_loss -0.5462 
+2024-09-11 16:11:45.499864: Pseudo dice [0.4353, 0.775] 
+2024-09-11 16:11:45.499925: Epoch time: 245.99 s 
+2024-09-11 16:11:46.496896:  
+2024-09-11 16:11:46.497121: Epoch 358 
+2024-09-11 16:11:46.497248: Current learning rate: 0.00671 
+2024-09-11 16:15:52.605493: train_loss -0.854 
+2024-09-11 16:15:52.605644: val_loss -0.542 
+2024-09-11 16:15:52.605703: Pseudo dice [0.4073, 0.7697] 
+2024-09-11 16:15:52.605759: Epoch time: 246.11 s 
+2024-09-11 16:15:53.588990:  
+2024-09-11 16:15:53.589166: Epoch 359 
+2024-09-11 16:15:53.589303: Current learning rate: 0.0067 
+2024-09-11 16:19:59.703075: train_loss -0.8555 
+2024-09-11 16:19:59.703250: val_loss -0.586 
+2024-09-11 16:19:59.703306: Pseudo dice [0.4708, 0.7849] 
+2024-09-11 16:19:59.703363: Epoch time: 246.12 s 
+2024-09-11 16:20:00.684442:  
+2024-09-11 16:20:00.684650: Epoch 360 
+2024-09-11 16:20:00.684750: Current learning rate: 0.00669 
+2024-09-11 16:24:06.682763: train_loss -0.8512 
+2024-09-11 16:24:06.682919: val_loss -0.5822 
+2024-09-11 16:24:06.682977: Pseudo dice [0.5187, 0.7556] 
+2024-09-11 16:24:06.683033: Epoch time: 246.0 s 
+2024-09-11 16:24:07.682110:  
+2024-09-11 16:24:07.682378: Epoch 361 
+2024-09-11 16:24:07.682489: Current learning rate: 0.00668 
+2024-09-11 16:28:13.754758: train_loss -0.8471 
+2024-09-11 16:28:13.754922: val_loss -0.5728 
+2024-09-11 16:28:13.754993: Pseudo dice [0.4472, 0.7718] 
+2024-09-11 16:28:13.755062: Epoch time: 246.07 s 
+2024-09-11 16:28:14.773976:  
+2024-09-11 16:28:14.774194: Epoch 362 
+2024-09-11 16:28:14.774284: Current learning rate: 0.00667 
+2024-09-11 16:32:20.801532: train_loss -0.8557 
+2024-09-11 16:32:20.801693: val_loss -0.575 
+2024-09-11 16:32:20.801750: Pseudo dice [0.4669, 0.7802] 
+2024-09-11 16:32:20.801857: Epoch time: 246.03 s 
+2024-09-11 16:32:21.792450:  
+2024-09-11 16:32:21.792658: Epoch 363 
+2024-09-11 16:32:21.792748: Current learning rate: 0.00666 
+2024-09-11 16:36:27.848593: train_loss -0.859 
+2024-09-11 16:36:27.848747: val_loss -0.5879 
+2024-09-11 16:36:27.848804: Pseudo dice [0.4784, 0.7689] 
+2024-09-11 16:36:27.848861: Epoch time: 246.06 s 
+2024-09-11 16:36:28.847340:  
+2024-09-11 16:36:28.847517: Epoch 364 
+2024-09-11 16:36:28.847608: Current learning rate: 0.00665 
+2024-09-11 16:40:34.670796: train_loss -0.8571 
+2024-09-11 16:40:34.670960: val_loss -0.5655 
+2024-09-11 16:40:34.671019: Pseudo dice [0.4674, 0.7494] 
+2024-09-11 16:40:34.671075: Epoch time: 245.83 s 
+2024-09-11 16:40:35.658796:  
+2024-09-11 16:40:35.659022: Epoch 365 
+2024-09-11 16:40:35.659116: Current learning rate: 0.00665 
+2024-09-11 16:44:41.593670: train_loss -0.8444 
+2024-09-11 16:44:41.593928: val_loss -0.5819 
+2024-09-11 16:44:41.593978: Pseudo dice [0.5102, 0.7461] 
+2024-09-11 16:44:41.594030: Epoch time: 245.94 s 
+2024-09-11 16:44:42.589523:  
+2024-09-11 16:44:42.589718: Epoch 366 
+2024-09-11 16:44:42.589803: Current learning rate: 0.00664 
+2024-09-11 16:48:48.650877: train_loss -0.8464 
+2024-09-11 16:48:48.651052: val_loss -0.514 
+2024-09-11 16:48:48.651103: Pseudo dice [0.4458, 0.6714] 
+2024-09-11 16:48:48.651155: Epoch time: 246.06 s 
+2024-09-11 16:48:49.631795:  
+2024-09-11 16:48:49.631984: Epoch 367 
+2024-09-11 16:48:49.632070: Current learning rate: 0.00663 
+2024-09-11 16:52:55.660116: train_loss -0.821 
+2024-09-11 16:52:55.660268: val_loss -0.5736 
+2024-09-11 16:52:55.660320: Pseudo dice [0.5005, 0.7285] 
+2024-09-11 16:52:55.660370: Epoch time: 246.03 s 
+2024-09-11 16:52:56.656152:  
+2024-09-11 16:52:56.656359: Epoch 368 
+2024-09-11 16:52:56.656456: Current learning rate: 0.00662 
+2024-09-11 16:57:02.526494: train_loss -0.8324 
+2024-09-11 16:57:02.526647: val_loss -0.6198 
+2024-09-11 16:57:02.526698: Pseudo dice [0.5622, 0.7854] 
+2024-09-11 16:57:02.526750: Epoch time: 245.87 s 
+2024-09-11 16:57:03.517794:  
+2024-09-11 16:57:03.517961: Epoch 369 
+2024-09-11 16:57:03.518047: Current learning rate: 0.00661 
+2024-09-11 17:01:09.230838: train_loss -0.849 
+2024-09-11 17:01:09.231028: val_loss -0.6183 
+2024-09-11 17:01:09.231087: Pseudo dice [0.5341, 0.7958] 
+2024-09-11 17:01:09.231143: Epoch time: 245.71 s 
+2024-09-11 17:01:10.230754:  
+2024-09-11 17:01:10.231006: Epoch 370 
+2024-09-11 17:01:10.231095: Current learning rate: 0.0066 
+2024-09-11 17:05:15.805954: train_loss -0.8539 
+2024-09-11 17:05:15.806114: val_loss -0.5959 
+2024-09-11 17:05:15.806174: Pseudo dice [0.4727, 0.8061] 
+2024-09-11 17:05:15.806235: Epoch time: 245.58 s 
+2024-09-11 17:05:16.824099:  
+2024-09-11 17:05:16.824323: Epoch 371 
+2024-09-11 17:05:16.824409: Current learning rate: 0.00659 
+2024-09-11 17:09:22.701811: train_loss -0.8254 
+2024-09-11 17:09:22.701959: val_loss -0.5915 
+2024-09-11 17:09:22.702070: Pseudo dice [0.4993, 0.7679] 
+2024-09-11 17:09:22.702139: Epoch time: 245.88 s 
+2024-09-11 17:09:23.696986:  
+2024-09-11 17:09:23.697188: Epoch 372 
+2024-09-11 17:09:23.697279: Current learning rate: 0.00658 
+2024-09-11 17:13:29.588130: train_loss -0.839 
+2024-09-11 17:13:29.588274: val_loss -0.5754 
+2024-09-11 17:13:29.588330: Pseudo dice [0.4741, 0.7678] 
+2024-09-11 17:13:29.588389: Epoch time: 245.89 s 
+2024-09-11 17:13:30.578316:  
+2024-09-11 17:13:30.578543: Epoch 373 
+2024-09-11 17:13:30.578632: Current learning rate: 0.00657 
+2024-09-11 17:17:36.181894: train_loss -0.8421 
+2024-09-11 17:17:36.182050: val_loss -0.5666 
+2024-09-11 17:17:36.182275: Pseudo dice [0.4748, 0.7422] 
+2024-09-11 17:17:36.182333: Epoch time: 245.61 s 
+2024-09-11 17:17:37.169312:  
+2024-09-11 17:17:37.169532: Epoch 374 
+2024-09-11 17:17:37.169624: Current learning rate: 0.00656 
+2024-09-11 17:21:42.858318: train_loss -0.8183 
+2024-09-11 17:21:42.858481: val_loss -0.6104 
+2024-09-11 17:21:42.858539: Pseudo dice [0.5526, 0.7548] 
+2024-09-11 17:21:42.858599: Epoch time: 245.69 s 
+2024-09-11 17:21:43.844117:  
+2024-09-11 17:21:43.844325: Epoch 375 
+2024-09-11 17:21:43.844425: Current learning rate: 0.00655 
+2024-09-11 17:25:49.807342: train_loss -0.8378 
+2024-09-11 17:25:49.807494: val_loss -0.6314 
+2024-09-11 17:25:49.807551: Pseudo dice [0.5327, 0.7993] 
+2024-09-11 17:25:49.807608: Epoch time: 245.97 s 
+2024-09-11 17:25:51.756536:  
+2024-09-11 17:25:51.756758: Epoch 376 
+2024-09-11 17:25:51.756873: Current learning rate: 0.00654 
+2024-09-11 17:29:57.546400: train_loss -0.8397 
+2024-09-11 17:29:57.546548: val_loss -0.5831 
+2024-09-11 17:29:57.546605: Pseudo dice [0.5166, 0.7794] 
+2024-09-11 17:29:57.546661: Epoch time: 245.79 s 
+2024-09-11 17:29:58.566363:  
+2024-09-11 17:29:58.566656: Epoch 377 
+2024-09-11 17:29:58.566745: Current learning rate: 0.00653 
+2024-09-11 17:34:04.658484: train_loss -0.8081 
+2024-09-11 17:34:04.658636: val_loss -0.5736 
+2024-09-11 17:34:04.658692: Pseudo dice [0.506, 0.7435] 
+2024-09-11 17:34:04.658750: Epoch time: 246.09 s 
+2024-09-11 17:34:05.657050:  
+2024-09-11 17:34:05.657281: Epoch 378 
+2024-09-11 17:34:05.657373: Current learning rate: 0.00652 
+2024-09-11 17:38:11.794075: train_loss -0.8117 
+2024-09-11 17:38:11.794230: val_loss -0.5753 
+2024-09-11 17:38:11.794280: Pseudo dice [0.4745, 0.7602] 
+2024-09-11 17:38:11.794330: Epoch time: 246.14 s 
+2024-09-11 17:38:12.797434:  
+2024-09-11 17:38:12.797752: Epoch 379 
+2024-09-11 17:38:12.797843: Current learning rate: 0.00651 
+2024-09-11 17:42:18.727124: train_loss -0.8286 
+2024-09-11 17:42:18.727277: val_loss -0.5935 
+2024-09-11 17:42:18.727328: Pseudo dice [0.4844, 0.77] 
+2024-09-11 17:42:18.727380: Epoch time: 245.93 s 
+2024-09-11 17:42:19.717861:  
+2024-09-11 17:42:19.718079: Epoch 380 
+2024-09-11 17:42:19.718163: Current learning rate: 0.0065 
+2024-09-11 17:46:25.351980: train_loss -0.8349 
+2024-09-11 17:46:25.352129: val_loss -0.5924 
+2024-09-11 17:46:25.352181: Pseudo dice [0.505, 0.7609] 
+2024-09-11 17:46:25.352232: Epoch time: 245.64 s 
+2024-09-11 17:46:26.343778:  
+2024-09-11 17:46:26.344031: Epoch 381 
+2024-09-11 17:46:26.344115: Current learning rate: 0.00649 
+2024-09-11 17:50:32.015158: train_loss -0.8442 
+2024-09-11 17:50:32.015332: val_loss -0.6191 
+2024-09-11 17:50:32.015383: Pseudo dice [0.481, 0.8091] 
+2024-09-11 17:50:32.015436: Epoch time: 245.67 s 
+2024-09-11 17:50:33.034108:  
+2024-09-11 17:50:33.034338: Epoch 382 
+2024-09-11 17:50:33.034420: Current learning rate: 0.00648 
+2024-09-11 17:54:38.694409: train_loss -0.8505 
+2024-09-11 17:54:38.694573: val_loss -0.5925 
+2024-09-11 17:54:38.694624: Pseudo dice [0.4727, 0.7768] 
+2024-09-11 17:54:38.694675: Epoch time: 245.66 s 
+2024-09-11 17:54:39.724754:  
+2024-09-11 17:54:39.724961: Epoch 383 
+2024-09-11 17:54:39.725046: Current learning rate: 0.00648 
+2024-09-11 17:58:45.439375: train_loss -0.8076 
+2024-09-11 17:58:45.439515: val_loss -0.5847 
+2024-09-11 17:58:45.439604: Pseudo dice [0.4754, 0.7782] 
+2024-09-11 17:58:45.439658: Epoch time: 245.72 s 
+2024-09-11 17:58:46.440522:  
+2024-09-11 17:58:46.440734: Epoch 384 
+2024-09-11 17:58:46.440825: Current learning rate: 0.00647 
+2024-09-11 18:02:52.473689: train_loss -0.7937 
+2024-09-11 18:02:52.473828: val_loss -0.5923 
+2024-09-11 18:02:52.473878: Pseudo dice [0.5034, 0.7505] 
+2024-09-11 18:02:52.473927: Epoch time: 246.04 s 
+2024-09-11 18:02:53.495811:  
+2024-09-11 18:02:53.496069: Epoch 385 
+2024-09-11 18:02:53.496155: Current learning rate: 0.00646 
+2024-09-11 18:06:59.214522: train_loss -0.8219 
+2024-09-11 18:06:59.214664: val_loss -0.5792 
+2024-09-11 18:06:59.214713: Pseudo dice [0.4668, 0.7567] 
+2024-09-11 18:06:59.214764: Epoch time: 245.72 s 
+2024-09-11 18:07:00.240825:  
+2024-09-11 18:07:00.241042: Epoch 386 
+2024-09-11 18:07:00.241125: Current learning rate: 0.00645 
+2024-09-11 18:11:05.983764: train_loss -0.8143 
+2024-09-11 18:11:05.983927: val_loss -0.5408 
+2024-09-11 18:11:05.983978: Pseudo dice [0.448, 0.7257] 
+2024-09-11 18:11:05.984029: Epoch time: 245.74 s 
+2024-09-11 18:11:06.992151:  
+2024-09-11 18:11:06.992340: Epoch 387 
+2024-09-11 18:11:06.992430: Current learning rate: 0.00644 
+2024-09-11 18:15:12.722223: train_loss -0.8251 
+2024-09-11 18:15:12.722372: val_loss -0.5913 
+2024-09-11 18:15:12.722421: Pseudo dice [0.4743, 0.7791] 
+2024-09-11 18:15:12.722470: Epoch time: 245.73 s 
+2024-09-11 18:15:13.722372:  
+2024-09-11 18:15:13.722580: Epoch 388 
+2024-09-11 18:15:13.722667: Current learning rate: 0.00643 
+2024-09-11 18:19:19.520864: train_loss -0.8048 
+2024-09-11 18:19:19.521028: val_loss -0.548 
+2024-09-11 18:19:19.521078: Pseudo dice [0.4966, 0.7536] 
+2024-09-11 18:19:19.521132: Epoch time: 245.8 s 
+2024-09-11 18:19:20.562649:  
+2024-09-11 18:19:20.562876: Epoch 389 
+2024-09-11 18:19:20.562963: Current learning rate: 0.00642 
+2024-09-11 18:23:26.525118: train_loss -0.807 
+2024-09-11 18:23:26.525263: val_loss -0.5764 
+2024-09-11 18:23:26.525315: Pseudo dice [0.4522, 0.7655] 
+2024-09-11 18:23:26.525365: Epoch time: 245.96 s 
+2024-09-11 18:23:27.524425:  
+2024-09-11 18:23:27.524585: Epoch 390 
+2024-09-11 18:23:27.524683: Current learning rate: 0.00641 
+2024-09-11 18:27:33.495681: train_loss -0.8164 
+2024-09-11 18:27:33.495833: val_loss -0.5543 
+2024-09-11 18:27:33.495885: Pseudo dice [0.4435, 0.7533] 
+2024-09-11 18:27:33.495938: Epoch time: 245.97 s 
+2024-09-11 18:27:34.524255:  
+2024-09-11 18:27:34.524477: Epoch 391 
+2024-09-11 18:27:34.524561: Current learning rate: 0.0064 
+2024-09-11 18:31:40.421210: train_loss -0.8023 
+2024-09-11 18:31:40.421354: val_loss -0.6062 
+2024-09-11 18:31:40.421403: Pseudo dice [0.4896, 0.7743] 
+2024-09-11 18:31:40.421453: Epoch time: 245.9 s 
+2024-09-11 18:31:41.431287:  
+2024-09-11 18:31:41.431491: Epoch 392 
+2024-09-11 18:31:41.431614: Current learning rate: 0.00639 
+2024-09-11 18:35:47.325944: train_loss -0.823 
+2024-09-11 18:35:47.326132: val_loss -0.5924 
+2024-09-11 18:35:47.326183: Pseudo dice [0.5365, 0.7649] 
+2024-09-11 18:35:47.326234: Epoch time: 245.9 s 
+2024-09-11 18:35:48.324466:  
+2024-09-11 18:35:48.324638: Epoch 393 
+2024-09-11 18:35:48.324729: Current learning rate: 0.00638 
+2024-09-11 18:39:54.205781: train_loss -0.834 
+2024-09-11 18:39:54.205916: val_loss -0.5952 
+2024-09-11 18:39:54.205970: Pseudo dice [0.4808, 0.7779] 
+2024-09-11 18:39:54.206021: Epoch time: 245.88 s 
+2024-09-11 18:39:55.215465:  
+2024-09-11 18:39:55.215616: Epoch 394 
+2024-09-11 18:39:55.215699: Current learning rate: 0.00637 
+2024-09-11 18:44:01.353316: train_loss -0.8397 
+2024-09-11 18:44:01.353495: val_loss -0.5977 
+2024-09-11 18:44:01.353545: Pseudo dice [0.5001, 0.7947] 
+2024-09-11 18:44:01.353596: Epoch time: 246.14 s 
+2024-09-11 18:44:02.356712:  
+2024-09-11 18:44:02.356915: Epoch 395 
+2024-09-11 18:44:02.357002: Current learning rate: 0.00636 
+2024-09-11 18:48:08.231508: train_loss -0.849 
+2024-09-11 18:48:08.231654: val_loss -0.6077 
+2024-09-11 18:48:08.231705: Pseudo dice [0.4964, 0.7856] 
+2024-09-11 18:48:08.231758: Epoch time: 245.88 s 
+2024-09-11 18:48:09.278655:  
+2024-09-11 18:48:09.278852: Epoch 396 
+2024-09-11 18:48:09.278984: Current learning rate: 0.00635 
+2024-09-11 18:52:15.408940: train_loss -0.8502 
+2024-09-11 18:52:15.409117: val_loss -0.6157 
+2024-09-11 18:52:15.409168: Pseudo dice [0.4939, 0.7913] 
+2024-09-11 18:52:15.409218: Epoch time: 246.13 s 
+2024-09-11 18:52:16.420614:  
+2024-09-11 18:52:16.420824: Epoch 397 
+2024-09-11 18:52:16.420913: Current learning rate: 0.00634 
+2024-09-11 18:56:22.341872: train_loss -0.8477 
+2024-09-11 18:56:22.342035: val_loss -0.578 
+2024-09-11 18:56:22.342085: Pseudo dice [0.5129, 0.7517] 
+2024-09-11 18:56:22.342137: Epoch time: 245.92 s 
+2024-09-11 18:56:23.351153:  
+2024-09-11 18:56:23.351403: Epoch 398 
+2024-09-11 18:56:23.351513: Current learning rate: 0.00633 
+2024-09-11 19:00:30.064241: train_loss -0.8489 
+2024-09-11 19:00:30.064384: val_loss -0.6024 
+2024-09-11 19:00:30.064433: Pseudo dice [0.476, 0.7955] 
+2024-09-11 19:00:30.064485: Epoch time: 246.72 s 
+2024-09-11 19:00:31.079179:  
+2024-09-11 19:00:31.079455: Epoch 399 
+2024-09-11 19:00:31.079561: Current learning rate: 0.00632 
+2024-09-11 19:04:36.978107: train_loss -0.8487 
+2024-09-11 19:04:36.978266: val_loss -0.6285 
+2024-09-11 19:04:36.978320: Pseudo dice [0.5571, 0.7803] 
+2024-09-11 19:04:36.978374: Epoch time: 245.9 s 
+2024-09-11 19:04:40.953998:  
+2024-09-11 19:04:40.954231: Epoch 400 
+2024-09-11 19:04:40.954318: Current learning rate: 0.00631 
+2024-09-11 19:08:46.737681: train_loss -0.8498 
+2024-09-11 19:08:46.737849: val_loss -0.5953 
+2024-09-11 19:08:46.737903: Pseudo dice [0.487, 0.7937] 
+2024-09-11 19:08:46.737955: Epoch time: 245.79 s 
+2024-09-11 19:08:47.748663:  
+2024-09-11 19:08:47.748887: Epoch 401 
+2024-09-11 19:08:47.748976: Current learning rate: 0.0063 
+2024-09-11 19:12:53.360982: train_loss -0.8454 
+2024-09-11 19:12:53.361160: val_loss -0.5867 
+2024-09-11 19:12:53.361213: Pseudo dice [0.4808, 0.7732] 
+2024-09-11 19:12:53.361268: Epoch time: 245.61 s 
+2024-09-11 19:12:54.357893:  
+2024-09-11 19:12:54.358071: Epoch 402 
+2024-09-11 19:12:54.358166: Current learning rate: 0.0063 
+2024-09-11 19:16:59.954326: train_loss -0.8202 
+2024-09-11 19:16:59.954465: val_loss -0.599 
+2024-09-11 19:16:59.954516: Pseudo dice [0.4585, 0.7955] 
+2024-09-11 19:16:59.954567: Epoch time: 245.6 s 
+2024-09-11 19:17:00.971384:  
+2024-09-11 19:17:00.971589: Epoch 403 
+2024-09-11 19:17:00.971673: Current learning rate: 0.00629 
+2024-09-11 19:21:06.705643: train_loss -0.8141 
+2024-09-11 19:21:06.705782: val_loss -0.5728 
+2024-09-11 19:21:06.705832: Pseudo dice [0.5043, 0.7414] 
+2024-09-11 19:21:06.705881: Epoch time: 245.74 s 
+2024-09-11 19:21:07.718699:  
+2024-09-11 19:21:07.718925: Epoch 404 
+2024-09-11 19:21:07.719045: Current learning rate: 0.00628 
+2024-09-11 19:25:13.652794: train_loss -0.8155 
+2024-09-11 19:25:13.652950: val_loss -0.5972 
+2024-09-11 19:25:13.653002: Pseudo dice [0.5228, 0.7885] 
+2024-09-11 19:25:13.653052: Epoch time: 245.94 s 
+2024-09-11 19:25:14.664804:  
+2024-09-11 19:25:14.665001: Epoch 405 
+2024-09-11 19:25:14.665083: Current learning rate: 0.00627 
+2024-09-11 19:29:20.436471: train_loss -0.832 
+2024-09-11 19:29:20.436613: val_loss -0.6066 
+2024-09-11 19:29:20.436664: Pseudo dice [0.5238, 0.7637] 
+2024-09-11 19:29:20.436716: Epoch time: 245.77 s 
+2024-09-11 19:29:21.533044:  
+2024-09-11 19:29:21.533255: Epoch 406 
+2024-09-11 19:29:21.533386: Current learning rate: 0.00626 
+2024-09-11 19:33:27.011326: train_loss -0.8365 
+2024-09-11 19:33:27.011463: val_loss -0.6006 
+2024-09-11 19:33:27.011514: Pseudo dice [0.4636, 0.8193] 
+2024-09-11 19:33:27.011563: Epoch time: 245.48 s 
+2024-09-11 19:33:28.019704:  
+2024-09-11 19:33:28.019960: Epoch 407 
+2024-09-11 19:33:28.020042: Current learning rate: 0.00625 
+2024-09-11 19:37:33.546451: train_loss -0.8452 
+2024-09-11 19:37:33.546588: val_loss -0.5443 
+2024-09-11 19:37:33.546639: Pseudo dice [0.4583, 0.7445] 
+2024-09-11 19:37:33.546690: Epoch time: 245.53 s 
+2024-09-11 19:37:34.547286:  
+2024-09-11 19:37:34.547469: Epoch 408 
+2024-09-11 19:37:34.547554: Current learning rate: 0.00624 
+2024-09-11 19:41:40.318160: train_loss -0.8429 
+2024-09-11 19:41:40.318318: val_loss -0.5893 
+2024-09-11 19:41:40.318368: Pseudo dice [0.4933, 0.782] 
+2024-09-11 19:41:40.318418: Epoch time: 245.77 s 
+2024-09-11 19:41:41.336316:  
+2024-09-11 19:41:41.336488: Epoch 409 
+2024-09-11 19:41:41.336570: Current learning rate: 0.00623 
+2024-09-11 19:45:46.985134: train_loss -0.8417 
+2024-09-11 19:45:46.985280: val_loss -0.5766 
+2024-09-11 19:45:46.985331: Pseudo dice [0.4421, 0.7769] 
+2024-09-11 19:45:46.985381: Epoch time: 245.65 s 
+2024-09-11 19:45:47.994509:  
+2024-09-11 19:45:47.994746: Epoch 410 
+2024-09-11 19:45:47.994840: Current learning rate: 0.00622 
+2024-09-11 19:49:53.710925: train_loss -0.8476 
+2024-09-11 19:49:53.711107: val_loss -0.5909 
+2024-09-11 19:49:53.711160: Pseudo dice [0.5158, 0.7502] 
+2024-09-11 19:49:53.711214: Epoch time: 245.72 s 
+2024-09-11 19:49:54.652295:  
+2024-09-11 19:49:54.652452: Epoch 411 
+2024-09-11 19:49:54.652586: Current learning rate: 0.00621 
+2024-09-11 19:54:00.367381: train_loss -0.8523 
+2024-09-11 19:54:00.367526: val_loss -0.5894 
+2024-09-11 19:54:00.367577: Pseudo dice [0.4742, 0.7978] 
+2024-09-11 19:54:00.367628: Epoch time: 245.72 s 
+2024-09-11 19:54:01.304503:  
+2024-09-11 19:54:01.304715: Epoch 412 
+2024-09-11 19:54:01.304801: Current learning rate: 0.0062 
+2024-09-11 19:58:07.179359: train_loss -0.8572 
+2024-09-11 19:58:07.179620: val_loss -0.6105 
+2024-09-11 19:58:07.179672: Pseudo dice [0.5374, 0.788] 
+2024-09-11 19:58:07.179733: Epoch time: 245.88 s 
+2024-09-11 19:58:08.142368:  
+2024-09-11 19:58:08.142553: Epoch 413 
+2024-09-11 19:58:08.142646: Current learning rate: 0.00619 
+2024-09-11 20:02:14.121696: train_loss -0.8559 
+2024-09-11 20:02:14.121832: val_loss -0.6155 
+2024-09-11 20:02:14.121942: Pseudo dice [0.552, 0.7654] 
+2024-09-11 20:02:14.122042: Epoch time: 245.98 s 
+2024-09-11 20:02:15.072178:  
+2024-09-11 20:02:15.072333: Epoch 414 
+2024-09-11 20:02:15.072416: Current learning rate: 0.00618 
+2024-09-11 20:06:20.751375: train_loss -0.8475 
+2024-09-11 20:06:20.751567: val_loss -0.5719 
+2024-09-11 20:06:20.751621: Pseudo dice [0.5073, 0.7742] 
+2024-09-11 20:06:20.751673: Epoch time: 245.68 s 
+2024-09-11 20:06:21.707738:  
+2024-09-11 20:06:21.707918: Epoch 415 
+2024-09-11 20:06:21.708003: Current learning rate: 0.00617 
+2024-09-11 20:10:27.629448: train_loss -0.8373 
+2024-09-11 20:10:27.629593: val_loss -0.6134 
+2024-09-11 20:10:27.629644: Pseudo dice [0.5527, 0.7797] 
+2024-09-11 20:10:27.629697: Epoch time: 245.92 s 
+2024-09-11 20:10:28.598464:  
+2024-09-11 20:10:28.598670: Epoch 416 
+2024-09-11 20:10:28.598757: Current learning rate: 0.00616 
+2024-09-11 20:14:34.505243: train_loss -0.8557 
+2024-09-11 20:14:34.505381: val_loss -0.617 
+2024-09-11 20:14:34.505431: Pseudo dice [0.5153, 0.7762] 
+2024-09-11 20:14:34.505481: Epoch time: 245.91 s 
+2024-09-11 20:14:35.474008:  
+2024-09-11 20:14:35.474180: Epoch 417 
+2024-09-11 20:14:35.474296: Current learning rate: 0.00615 
+2024-09-11 20:18:41.161931: train_loss -0.861 
+2024-09-11 20:18:41.162071: val_loss -0.6002 
+2024-09-11 20:18:41.162121: Pseudo dice [0.5125, 0.7729] 
+2024-09-11 20:18:41.162170: Epoch time: 245.69 s 
+2024-09-11 20:18:42.108969:  
+2024-09-11 20:18:42.109202: Epoch 418 
+2024-09-11 20:18:42.109319: Current learning rate: 0.00614 
+2024-09-11 20:22:48.156644: train_loss -0.8577 
+2024-09-11 20:22:48.156791: val_loss -0.6086 
+2024-09-11 20:22:48.156841: Pseudo dice [0.4987, 0.7715] 
+2024-09-11 20:22:48.156894: Epoch time: 246.05 s 
+2024-09-11 20:22:49.119477:  
+2024-09-11 20:22:49.119670: Epoch 419 
+2024-09-11 20:22:49.119756: Current learning rate: 0.00613 
+2024-09-11 20:26:55.061915: train_loss -0.8399 
+2024-09-11 20:26:55.062055: val_loss -0.6036 
+2024-09-11 20:26:55.062105: Pseudo dice [0.5312, 0.7983] 
+2024-09-11 20:26:55.062155: Epoch time: 245.94 s 
+2024-09-11 20:26:56.021084:  
+2024-09-11 20:26:56.021285: Epoch 420 
+2024-09-11 20:26:56.021372: Current learning rate: 0.00612 
+2024-09-11 20:31:01.974344: train_loss -0.8562 
+2024-09-11 20:31:01.974511: val_loss -0.5962 
+2024-09-11 20:31:01.974564: Pseudo dice [0.4915, 0.7898] 
+2024-09-11 20:31:01.974614: Epoch time: 245.96 s 
+2024-09-11 20:31:03.860079:  
+2024-09-11 20:31:03.860282: Epoch 421 
+2024-09-11 20:31:03.860388: Current learning rate: 0.00612 
+2024-09-11 20:35:09.711236: train_loss -0.8563 
+2024-09-11 20:35:09.711421: val_loss -0.5733 
+2024-09-11 20:35:09.711476: Pseudo dice [0.4974, 0.782] 
+2024-09-11 20:35:09.711527: Epoch time: 245.85 s 
+2024-09-11 20:35:10.682416:  
+2024-09-11 20:35:10.682711: Epoch 422 
+2024-09-11 20:35:10.682819: Current learning rate: 0.00611 
+2024-09-11 20:39:16.408313: train_loss -0.8463 
+2024-09-11 20:39:16.408448: val_loss -0.582 
+2024-09-11 20:39:16.408500: Pseudo dice [0.5217, 0.735] 
+2024-09-11 20:39:16.408550: Epoch time: 245.73 s 
+2024-09-11 20:39:17.353454:  
+2024-09-11 20:39:17.353715: Epoch 423 
+2024-09-11 20:39:17.353799: Current learning rate: 0.0061 
+2024-09-11 20:43:23.349394: train_loss -0.8441 
+2024-09-11 20:43:23.349534: val_loss -0.5975 
+2024-09-11 20:43:23.349587: Pseudo dice [0.4988, 0.7881] 
+2024-09-11 20:43:23.349637: Epoch time: 246.0 s 
+2024-09-11 20:43:24.318682:  
+2024-09-11 20:43:24.318913: Epoch 424 
+2024-09-11 20:43:24.318994: Current learning rate: 0.00609 
+2024-09-11 20:47:30.282344: train_loss -0.8455 
+2024-09-11 20:47:30.282494: val_loss -0.5751 
+2024-09-11 20:47:30.282572: Pseudo dice [0.4995, 0.7333] 
+2024-09-11 20:47:30.282657: Epoch time: 245.97 s 
+2024-09-11 20:47:31.248297:  
+2024-09-11 20:47:31.248532: Epoch 425 
+2024-09-11 20:47:31.248619: Current learning rate: 0.00608 
+2024-09-11 20:51:36.966651: train_loss -0.8374 
+2024-09-11 20:51:36.966791: val_loss -0.5646 
+2024-09-11 20:51:36.966842: Pseudo dice [0.4781, 0.7632] 
+2024-09-11 20:51:36.966893: Epoch time: 245.72 s 
+2024-09-11 20:51:37.923091:  
+2024-09-11 20:51:37.923326: Epoch 426 
+2024-09-11 20:51:37.923409: Current learning rate: 0.00607 
+2024-09-11 20:55:43.577280: train_loss -0.8447 
+2024-09-11 20:55:43.577439: val_loss -0.5685 
+2024-09-11 20:55:43.577489: Pseudo dice [0.4648, 0.7617] 
+2024-09-11 20:55:43.577540: Epoch time: 245.66 s 
+2024-09-11 20:55:44.527495:  
+2024-09-11 20:55:44.527698: Epoch 427 
+2024-09-11 20:55:44.527778: Current learning rate: 0.00606 
+2024-09-11 20:59:50.170758: train_loss -0.853 
+2024-09-11 20:59:50.170918: val_loss -0.596 
+2024-09-11 20:59:50.171175: Pseudo dice [0.4694, 0.789] 
+2024-09-11 20:59:50.171228: Epoch time: 245.65 s 
+2024-09-11 20:59:51.126009:  
+2024-09-11 20:59:51.126211: Epoch 428 
+2024-09-11 20:59:51.126293: Current learning rate: 0.00605 
+2024-09-11 21:03:56.896501: train_loss -0.8471 
+2024-09-11 21:03:56.896641: val_loss -0.5834 
+2024-09-11 21:03:56.896691: Pseudo dice [0.5254, 0.7498] 
+2024-09-11 21:03:56.896742: Epoch time: 245.77 s 
+2024-09-11 21:03:57.863653:  
+2024-09-11 21:03:57.863880: Epoch 429 
+2024-09-11 21:03:57.864043: Current learning rate: 0.00604 
+2024-09-11 21:08:03.696711: train_loss -0.8577 
+2024-09-11 21:08:03.696870: val_loss -0.5622 
+2024-09-11 21:08:03.696918: Pseudo dice [0.4464, 0.7868] 
+2024-09-11 21:08:03.696970: Epoch time: 245.83 s 
+2024-09-11 21:08:04.641735:  
+2024-09-11 21:08:04.641964: Epoch 430 
+2024-09-11 21:08:04.642052: Current learning rate: 0.00603 
+2024-09-11 21:12:10.679620: train_loss -0.8535 
+2024-09-11 21:12:10.679759: val_loss -0.5661 
+2024-09-11 21:12:10.679816: Pseudo dice [0.4767, 0.7952] 
+2024-09-11 21:12:10.679871: Epoch time: 246.04 s 
+2024-09-11 21:12:11.631611:  
+2024-09-11 21:12:11.631788: Epoch 431 
+2024-09-11 21:12:11.631879: Current learning rate: 0.00602 
+2024-09-11 21:16:17.719886: train_loss -0.8624 
+2024-09-11 21:16:17.720049: val_loss -0.61 
+2024-09-11 21:16:17.720101: Pseudo dice [0.5148, 0.7926] 
+2024-09-11 21:16:17.720153: Epoch time: 246.09 s 
+2024-09-11 21:16:18.683836:  
+2024-09-11 21:16:18.684027: Epoch 432 
+2024-09-11 21:16:18.684109: Current learning rate: 0.00601 
+2024-09-11 21:20:24.520110: train_loss -0.8605 
+2024-09-11 21:20:24.520247: val_loss -0.5978 
+2024-09-11 21:20:24.520298: Pseudo dice [0.5489, 0.7479] 
+2024-09-11 21:20:24.520394: Epoch time: 245.84 s 
+2024-09-11 21:20:25.486722:  
+2024-09-11 21:20:25.486943: Epoch 433 
+2024-09-11 21:20:25.487028: Current learning rate: 0.006 
+2024-09-11 21:24:31.423380: train_loss -0.8493 
+2024-09-11 21:24:31.423521: val_loss -0.5983 
+2024-09-11 21:24:31.423572: Pseudo dice [0.5496, 0.8011] 
+2024-09-11 21:24:31.423636: Epoch time: 245.94 s 
+2024-09-11 21:24:32.372602:  
+2024-09-11 21:24:32.372791: Epoch 434 
+2024-09-11 21:24:32.372904: Current learning rate: 0.00599 
+2024-09-11 21:28:38.210895: train_loss -0.8435 
+2024-09-11 21:28:38.211034: val_loss -0.6058 
+2024-09-11 21:28:38.211085: Pseudo dice [0.5146, 0.7934] 
+2024-09-11 21:28:38.211136: Epoch time: 245.84 s 
+2024-09-11 21:28:39.160021:  
+2024-09-11 21:28:39.160214: Epoch 435 
+2024-09-11 21:28:39.160317: Current learning rate: 0.00598 
+2024-09-11 21:32:44.906337: train_loss -0.8116 
+2024-09-11 21:32:44.906478: val_loss -0.6134 
+2024-09-11 21:32:44.906530: Pseudo dice [0.531, 0.776] 
+2024-09-11 21:32:44.906580: Epoch time: 245.75 s 
+2024-09-11 21:32:45.844185:  
+2024-09-11 21:32:45.844405: Epoch 436 
+2024-09-11 21:32:45.844490: Current learning rate: 0.00597 
+2024-09-11 21:36:51.819672: train_loss -0.8192 
+2024-09-11 21:36:51.819836: val_loss -0.5732 
+2024-09-11 21:36:51.819889: Pseudo dice [0.4857, 0.7723] 
+2024-09-11 21:36:51.819940: Epoch time: 245.98 s 
+2024-09-11 21:36:52.760135:  
+2024-09-11 21:36:52.760312: Epoch 437 
+2024-09-11 21:36:52.760403: Current learning rate: 0.00596 
+2024-09-11 21:40:58.562677: train_loss -0.8388 
+2024-09-11 21:40:58.562839: val_loss -0.5925 
+2024-09-11 21:40:58.562890: Pseudo dice [0.5119, 0.768] 
+2024-09-11 21:40:58.562943: Epoch time: 245.8 s 
+2024-09-11 21:40:59.502967:  
+2024-09-11 21:40:59.503135: Epoch 438 
+2024-09-11 21:40:59.503234: Current learning rate: 0.00595 
+2024-09-11 21:45:05.562660: train_loss -0.8497 
+2024-09-11 21:45:05.562798: val_loss -0.6077 
+2024-09-11 21:45:05.562848: Pseudo dice [0.5067, 0.7679] 
+2024-09-11 21:45:05.562898: Epoch time: 246.06 s 
+2024-09-11 21:45:06.509419:  
+2024-09-11 21:45:06.509607: Epoch 439 
+2024-09-11 21:45:06.509695: Current learning rate: 0.00594 
+2024-09-11 21:49:12.318530: train_loss -0.8512 
+2024-09-11 21:49:12.318693: val_loss -0.6138 
+2024-09-11 21:49:12.318755: Pseudo dice [0.4979, 0.7853] 
+2024-09-11 21:49:12.318814: Epoch time: 245.81 s 
+2024-09-11 21:49:13.286176:  
+2024-09-11 21:49:13.286350: Epoch 440 
+2024-09-11 21:49:13.286463: Current learning rate: 0.00593 
+2024-09-11 21:53:19.096869: train_loss -0.8639 
+2024-09-11 21:53:19.097033: val_loss -0.6151 
+2024-09-11 21:53:19.097083: Pseudo dice [0.4977, 0.8071] 
+2024-09-11 21:53:19.097134: Epoch time: 245.81 s 
+2024-09-11 21:53:20.047694:  
+2024-09-11 21:53:20.047917: Epoch 441 
+2024-09-11 21:53:20.048030: Current learning rate: 0.00592 
+2024-09-11 21:57:25.896622: train_loss -0.8548 
+2024-09-11 21:57:25.896766: val_loss -0.5648 
+2024-09-11 21:57:25.896817: Pseudo dice [0.4998, 0.7621] 
+2024-09-11 21:57:25.896868: Epoch time: 245.85 s 
+2024-09-11 21:57:26.864332:  
+2024-09-11 21:57:26.864483: Epoch 442 
+2024-09-11 21:57:26.864567: Current learning rate: 0.00592 
+2024-09-11 22:01:32.642017: train_loss -0.8608 
+2024-09-11 22:01:32.642169: val_loss -0.5857 
+2024-09-11 22:01:32.642221: Pseudo dice [0.4698, 0.7862] 
+2024-09-11 22:01:32.642273: Epoch time: 245.78 s 
+2024-09-11 22:01:33.586709:  
+2024-09-11 22:01:33.586958: Epoch 443 
+2024-09-11 22:01:33.587042: Current learning rate: 0.00591 
+2024-09-11 22:05:39.472008: train_loss -0.8623 
+2024-09-11 22:05:39.472165: val_loss -0.6145 
+2024-09-11 22:05:39.472214: Pseudo dice [0.5353, 0.7895] 
+2024-09-11 22:05:39.472265: Epoch time: 245.89 s 
+2024-09-11 22:05:40.425846:  
+2024-09-11 22:05:40.426051: Epoch 444 
+2024-09-11 22:05:40.426152: Current learning rate: 0.0059 
+2024-09-11 22:09:46.312229: train_loss -0.8495 
+2024-09-11 22:09:46.312367: val_loss -0.5452 
+2024-09-11 22:09:46.312418: Pseudo dice [0.4381, 0.7346] 
+2024-09-11 22:09:46.312469: Epoch time: 245.89 s 
+2024-09-11 22:09:48.243772:  
+2024-09-11 22:09:48.243968: Epoch 445 
+2024-09-11 22:09:48.244068: Current learning rate: 0.00589 
+2024-09-11 22:13:54.493344: train_loss -0.8535 
+2024-09-11 22:13:54.493491: val_loss -0.5684 
+2024-09-11 22:13:54.493589: Pseudo dice [0.4389, 0.7903] 
+2024-09-11 22:13:54.493690: Epoch time: 246.25 s 
+2024-09-11 22:13:55.447839:  
+2024-09-11 22:13:55.448099: Epoch 446 
+2024-09-11 22:13:55.448188: Current learning rate: 0.00588 
+2024-09-11 22:18:01.369986: train_loss -0.8601 
+2024-09-11 22:18:01.370123: val_loss -0.5896 
+2024-09-11 22:18:01.370172: Pseudo dice [0.4732, 0.774] 
+2024-09-11 22:18:01.370223: Epoch time: 245.92 s 
+2024-09-11 22:18:02.312969:  
+2024-09-11 22:18:02.313203: Epoch 447 
+2024-09-11 22:18:02.313284: Current learning rate: 0.00587 
+2024-09-11 22:22:08.214515: train_loss -0.8501 
+2024-09-11 22:22:08.214658: val_loss -0.5949 
+2024-09-11 22:22:08.214709: Pseudo dice [0.5392, 0.7604] 
+2024-09-11 22:22:08.214757: Epoch time: 245.9 s 
+2024-09-11 22:22:09.169608:  
+2024-09-11 22:22:09.169811: Epoch 448 
+2024-09-11 22:22:09.169897: Current learning rate: 0.00586 
+2024-09-11 22:26:14.915227: train_loss -0.846 
+2024-09-11 22:26:14.915392: val_loss -0.6131 
+2024-09-11 22:26:14.915442: Pseudo dice [0.5301, 0.7688] 
+2024-09-11 22:26:14.915494: Epoch time: 245.75 s 
+2024-09-11 22:26:15.865523:  
+2024-09-11 22:26:15.865738: Epoch 449 
+2024-09-11 22:26:15.865826: Current learning rate: 0.00585 
+2024-09-11 22:30:21.731881: train_loss -0.8521 
+2024-09-11 22:30:21.732034: val_loss -0.6115 
+2024-09-11 22:30:21.732086: Pseudo dice [0.5188, 0.7808] 
+2024-09-11 22:30:21.732140: Epoch time: 245.87 s 
+2024-09-11 22:30:25.622725:  
+2024-09-11 22:30:25.622891: Epoch 450 
+2024-09-11 22:30:25.623008: Current learning rate: 0.00584 
+2024-09-11 22:34:31.476680: train_loss -0.8526 
+2024-09-11 22:34:31.476854: val_loss -0.5782 
+2024-09-11 22:34:31.476904: Pseudo dice [0.4998, 0.7655] 
+2024-09-11 22:34:31.476955: Epoch time: 245.86 s 
+2024-09-11 22:34:32.464436:  
+2024-09-11 22:34:32.464625: Epoch 451 
+2024-09-11 22:34:32.464714: Current learning rate: 0.00583 
+2024-09-11 22:38:38.261972: train_loss -0.8655 
+2024-09-11 22:38:38.262132: val_loss -0.6183 
+2024-09-11 22:38:38.262183: Pseudo dice [0.5592, 0.7788] 
+2024-09-11 22:38:38.262236: Epoch time: 245.8 s 
+2024-09-11 22:38:39.208926:  
+2024-09-11 22:38:39.209162: Epoch 452 
+2024-09-11 22:38:39.209261: Current learning rate: 0.00582 
+2024-09-11 22:42:45.202107: train_loss -0.8619 
+2024-09-11 22:42:45.202291: val_loss -0.5856 
+2024-09-11 22:42:45.202342: Pseudo dice [0.4821, 0.7722] 
+2024-09-11 22:42:45.202393: Epoch time: 246.0 s 
+2024-09-11 22:42:46.147177:  
+2024-09-11 22:42:46.147363: Epoch 453 
+2024-09-11 22:42:46.147445: Current learning rate: 0.00581 
+2024-09-11 22:46:52.307220: train_loss -0.8658 
+2024-09-11 22:46:52.307359: val_loss -0.5733 
+2024-09-11 22:46:52.307409: Pseudo dice [0.4945, 0.7884] 
+2024-09-11 22:46:52.307460: Epoch time: 246.16 s 
+2024-09-11 22:46:53.275395:  
+2024-09-11 22:46:53.275623: Epoch 454 
+2024-09-11 22:46:53.275708: Current learning rate: 0.0058 
+2024-09-11 22:50:59.176280: train_loss -0.8653 
+2024-09-11 22:50:59.176416: val_loss -0.6084 
+2024-09-11 22:50:59.176466: Pseudo dice [0.4893, 0.8023] 
+2024-09-11 22:50:59.176516: Epoch time: 245.9 s 
+2024-09-11 22:51:00.149800:  
+2024-09-11 22:51:00.150046: Epoch 455 
+2024-09-11 22:51:00.150132: Current learning rate: 0.00579 
+2024-09-11 22:55:05.968246: train_loss -0.8366 
+2024-09-11 22:55:05.968398: val_loss -0.5827 
+2024-09-11 22:55:05.968448: Pseudo dice [0.4964, 0.7604] 
+2024-09-11 22:55:05.968499: Epoch time: 245.82 s 
+2024-09-11 22:55:06.900382:  
+2024-09-11 22:55:06.900609: Epoch 456 
+2024-09-11 22:55:06.900731: Current learning rate: 0.00578 
+2024-09-11 22:59:12.889189: train_loss -0.8503 
+2024-09-11 22:59:12.889326: val_loss -0.5788 
+2024-09-11 22:59:12.889376: Pseudo dice [0.4596, 0.7718] 
+2024-09-11 22:59:12.889428: Epoch time: 245.99 s 
+2024-09-11 22:59:13.875382:  
+2024-09-11 22:59:13.875569: Epoch 457 
+2024-09-11 22:59:13.875652: Current learning rate: 0.00577 
+2024-09-11 23:03:19.708847: train_loss -0.8562 
+2024-09-11 23:03:19.708987: val_loss -0.5951 
+2024-09-11 23:03:19.709038: Pseudo dice [0.4556, 0.8058] 
+2024-09-11 23:03:19.709090: Epoch time: 245.84 s 
+2024-09-11 23:03:20.641012:  
+2024-09-11 23:03:20.641191: Epoch 458 
+2024-09-11 23:03:20.641327: Current learning rate: 0.00576 
+2024-09-11 23:07:26.506754: train_loss -0.8625 
+2024-09-11 23:07:26.506939: val_loss -0.5936 
+2024-09-11 23:07:26.506991: Pseudo dice [0.4808, 0.7771] 
+2024-09-11 23:07:26.507042: Epoch time: 245.87 s 
+2024-09-11 23:07:27.446006:  
+2024-09-11 23:07:27.446223: Epoch 459 
+2024-09-11 23:07:27.446314: Current learning rate: 0.00575 
+2024-09-11 23:11:33.021068: train_loss -0.8682 
+2024-09-11 23:11:33.021260: val_loss -0.5871 
+2024-09-11 23:11:33.021312: Pseudo dice [0.43, 0.7958] 
+2024-09-11 23:11:33.021361: Epoch time: 245.58 s 
+2024-09-11 23:11:33.969613:  
+2024-09-11 23:11:33.969824: Epoch 460 
+2024-09-11 23:11:33.969920: Current learning rate: 0.00574 
+2024-09-11 23:15:39.444277: train_loss -0.8671 
+2024-09-11 23:15:39.444413: val_loss -0.6245 
+2024-09-11 23:15:39.444464: Pseudo dice [0.5212, 0.8094] 
+2024-09-11 23:15:39.444513: Epoch time: 245.48 s 
+2024-09-11 23:15:40.398847:  
+2024-09-11 23:15:40.399000: Epoch 461 
+2024-09-11 23:15:40.399081: Current learning rate: 0.00573 
+2024-09-11 23:19:46.075217: train_loss -0.8664 
+2024-09-11 23:19:46.075356: val_loss -0.6033 
+2024-09-11 23:19:46.075406: Pseudo dice [0.4675, 0.7848] 
+2024-09-11 23:19:46.075459: Epoch time: 245.68 s 
+2024-09-11 23:19:47.029509:  
+2024-09-11 23:19:47.029790: Epoch 462 
+2024-09-11 23:19:47.029874: Current learning rate: 0.00572 
+2024-09-11 23:23:52.827438: train_loss -0.8699 
+2024-09-11 23:23:52.827619: val_loss -0.6096 
+2024-09-11 23:23:52.827670: Pseudo dice [0.4994, 0.7903] 
+2024-09-11 23:23:52.827721: Epoch time: 245.8 s 
+2024-09-11 23:23:53.769407:  
+2024-09-11 23:23:53.769629: Epoch 463 
+2024-09-11 23:23:53.769715: Current learning rate: 0.00571 
+2024-09-11 23:28:01.165477: train_loss -0.8692 
+2024-09-11 23:28:01.165682: val_loss -0.5968 
+2024-09-11 23:28:01.165775: Pseudo dice [0.4827, 0.774] 
+2024-09-11 23:28:01.165867: Epoch time: 247.4 s 
+2024-09-11 23:28:02.143035:  
+2024-09-11 23:28:02.143227: Epoch 464 
+2024-09-11 23:28:02.143312: Current learning rate: 0.0057 
+2024-09-11 23:32:08.182295: train_loss -0.8647 
+2024-09-11 23:32:08.182441: val_loss -0.5842 
+2024-09-11 23:32:08.182491: Pseudo dice [0.4728, 0.7772] 
+2024-09-11 23:32:08.182542: Epoch time: 246.04 s 
+2024-09-11 23:32:09.164431:  
+2024-09-11 23:32:09.164592: Epoch 465 
+2024-09-11 23:32:09.164673: Current learning rate: 0.0057 
+2024-09-11 23:36:15.120643: train_loss -0.8688 
+2024-09-11 23:36:15.120815: val_loss -0.6028 
+2024-09-11 23:36:15.120871: Pseudo dice [0.5273, 0.7606] 
+2024-09-11 23:36:15.120923: Epoch time: 245.96 s 
+2024-09-11 23:36:16.079580:  
+2024-09-11 23:36:16.079705: Epoch 466 
+2024-09-11 23:36:16.079784: Current learning rate: 0.00569 
+2024-09-11 23:40:22.107121: train_loss -0.8711 
+2024-09-11 23:40:22.107258: val_loss -0.5816 
+2024-09-11 23:40:22.107308: Pseudo dice [0.5256, 0.7487] 
+2024-09-11 23:40:22.107362: Epoch time: 246.03 s 
+2024-09-11 23:40:23.052573:  
+2024-09-11 23:40:23.052816: Epoch 467 
+2024-09-11 23:40:23.052951: Current learning rate: 0.00568 
+2024-09-11 23:44:29.138113: train_loss -0.8663 
+2024-09-11 23:44:29.138294: val_loss -0.5963 
+2024-09-11 23:44:29.138345: Pseudo dice [0.471, 0.774] 
+2024-09-11 23:44:29.138395: Epoch time: 246.09 s 
+2024-09-11 23:44:30.068905:  
+2024-09-11 23:44:30.069064: Epoch 468 
+2024-09-11 23:44:30.069155: Current learning rate: 0.00567 
+2024-09-11 23:48:36.048974: train_loss -0.8692 
+2024-09-11 23:48:36.049146: val_loss -0.6186 
+2024-09-11 23:48:36.049224: Pseudo dice [0.5226, 0.7907] 
+2024-09-11 23:48:36.049276: Epoch time: 245.98 s 
+2024-09-11 23:48:37.952181:  
+2024-09-11 23:48:37.952386: Epoch 469 
+2024-09-11 23:48:37.952495: Current learning rate: 0.00566 
+2024-09-11 23:52:44.067457: train_loss -0.873 
+2024-09-11 23:52:44.067600: val_loss -0.5627 
+2024-09-11 23:52:44.067649: Pseudo dice [0.4622, 0.7709] 
+2024-09-11 23:52:44.067700: Epoch time: 246.12 s 
+2024-09-11 23:52:45.006514:  
+2024-09-11 23:52:45.006735: Epoch 470 
+2024-09-11 23:52:45.006840: Current learning rate: 0.00565 
+2024-09-11 23:56:50.887633: train_loss -0.8701 
+2024-09-11 23:56:50.887867: val_loss -0.6247 
+2024-09-11 23:56:50.887967: Pseudo dice [0.5365, 0.7817] 
+2024-09-11 23:56:50.888020: Epoch time: 245.88 s 
+2024-09-11 23:56:51.846006:  
+2024-09-11 23:56:51.846298: Epoch 471 
+2024-09-11 23:56:51.846380: Current learning rate: 0.00564 
+2024-09-12 00:00:57.515312: train_loss -0.8691 
+2024-09-12 00:00:57.515451: val_loss -0.6036 
+2024-09-12 00:00:57.515503: Pseudo dice [0.5123, 0.7784] 
+2024-09-12 00:00:57.515556: Epoch time: 245.67 s 
+2024-09-12 00:00:58.458731:  
+2024-09-12 00:00:58.458965: Epoch 472 
+2024-09-12 00:00:58.459050: Current learning rate: 0.00563 
+2024-09-12 00:05:04.180527: train_loss -0.8655 
+2024-09-12 00:05:04.180670: val_loss -0.5834 
+2024-09-12 00:05:04.180721: Pseudo dice [0.447, 0.8054] 
+2024-09-12 00:05:04.180771: Epoch time: 245.72 s 
+2024-09-12 00:05:05.126423:  
+2024-09-12 00:05:05.126655: Epoch 473 
+2024-09-12 00:05:05.126737: Current learning rate: 0.00562 
+2024-09-12 00:09:10.815468: train_loss -0.8711 
+2024-09-12 00:09:10.815612: val_loss -0.6067 
+2024-09-12 00:09:10.815662: Pseudo dice [0.5031, 0.8096] 
+2024-09-12 00:09:10.815713: Epoch time: 245.69 s 
+2024-09-12 00:09:11.760379:  
+2024-09-12 00:09:11.760588: Epoch 474 
+2024-09-12 00:09:11.760676: Current learning rate: 0.00561 
+2024-09-12 00:13:17.681985: train_loss -0.8745 
+2024-09-12 00:13:17.682213: val_loss -0.5919 
+2024-09-12 00:13:17.682308: Pseudo dice [0.5123, 0.7811] 
+2024-09-12 00:13:17.682399: Epoch time: 245.92 s 
+2024-09-12 00:13:18.618725:  
+2024-09-12 00:13:18.618904: Epoch 475 
+2024-09-12 00:13:18.618988: Current learning rate: 0.0056 
+2024-09-12 00:17:24.251237: train_loss -0.8772 
+2024-09-12 00:17:24.251398: val_loss -0.6021 
+2024-09-12 00:17:24.251450: Pseudo dice [0.4952, 0.802] 
+2024-09-12 00:17:24.251503: Epoch time: 245.63 s 
+2024-09-12 00:17:25.204006:  
+2024-09-12 00:17:25.204216: Epoch 476 
+2024-09-12 00:17:25.204310: Current learning rate: 0.00559 
+2024-09-12 00:21:30.968534: train_loss -0.8746 
+2024-09-12 00:21:30.968671: val_loss -0.629 
+2024-09-12 00:21:30.968721: Pseudo dice [0.548, 0.7871] 
+2024-09-12 00:21:30.968775: Epoch time: 245.77 s 
+2024-09-12 00:21:31.902900:  
+2024-09-12 00:21:31.903111: Epoch 477 
+2024-09-12 00:21:31.903195: Current learning rate: 0.00558 
+2024-09-12 00:25:37.499443: train_loss -0.8747 
+2024-09-12 00:25:37.499584: val_loss -0.5916 
+2024-09-12 00:25:37.499634: Pseudo dice [0.4786, 0.7855] 
+2024-09-12 00:25:37.499684: Epoch time: 245.6 s 
+2024-09-12 00:25:38.461975:  
+2024-09-12 00:25:38.462206: Epoch 478 
+2024-09-12 00:25:38.462315: Current learning rate: 0.00557 
+2024-09-12 00:29:44.188585: train_loss -0.8775 
+2024-09-12 00:29:44.188726: val_loss -0.5821 
+2024-09-12 00:29:44.188776: Pseudo dice [0.4871, 0.7838] 
+2024-09-12 00:29:44.188827: Epoch time: 245.73 s 
+2024-09-12 00:29:45.156187:  
+2024-09-12 00:29:45.156409: Epoch 479 
+2024-09-12 00:29:45.156492: Current learning rate: 0.00556 
+2024-09-12 00:33:50.963796: train_loss -0.8642 
+2024-09-12 00:33:50.963971: val_loss -0.606 
+2024-09-12 00:33:50.964023: Pseudo dice [0.5116, 0.7932] 
+2024-09-12 00:33:50.964074: Epoch time: 245.81 s 
+2024-09-12 00:33:51.929421:  
+2024-09-12 00:33:51.929596: Epoch 480 
+2024-09-12 00:33:51.929686: Current learning rate: 0.00555 
+2024-09-12 00:37:58.148351: train_loss -0.8521 
+2024-09-12 00:37:58.148490: val_loss -0.5765 
+2024-09-12 00:37:58.148542: Pseudo dice [0.4887, 0.7629] 
+2024-09-12 00:37:58.148594: Epoch time: 246.22 s 
+2024-09-12 00:37:59.130625:  
+2024-09-12 00:37:59.130794: Epoch 481 
+2024-09-12 00:37:59.130878: Current learning rate: 0.00554 
+2024-09-12 00:42:05.245516: train_loss -0.8648 
+2024-09-12 00:42:05.245675: val_loss -0.6064 
+2024-09-12 00:42:05.245725: Pseudo dice [0.4795, 0.7907] 
+2024-09-12 00:42:05.245774: Epoch time: 246.12 s 
+2024-09-12 00:42:06.212292:  
+2024-09-12 00:42:06.212478: Epoch 482 
+2024-09-12 00:42:06.212564: Current learning rate: 0.00553 
+2024-09-12 00:46:12.133011: train_loss -0.8675 
+2024-09-12 00:46:12.133158: val_loss -0.5781 
+2024-09-12 00:46:12.133208: Pseudo dice [0.457, 0.7951] 
+2024-09-12 00:46:12.133262: Epoch time: 245.92 s 
+2024-09-12 00:46:13.087464:  
+2024-09-12 00:46:13.087697: Epoch 483 
+2024-09-12 00:46:13.087783: Current learning rate: 0.00552 
+2024-09-12 00:50:18.854228: train_loss -0.8488 
+2024-09-12 00:50:18.854367: val_loss -0.6125 
+2024-09-12 00:50:18.854416: Pseudo dice [0.5257, 0.7533] 
+2024-09-12 00:50:18.854466: Epoch time: 245.77 s 
+2024-09-12 00:50:19.816947:  
+2024-09-12 00:50:19.817134: Epoch 484 
+2024-09-12 00:50:19.817215: Current learning rate: 0.00551 
+2024-09-12 00:54:25.593993: train_loss -0.8511 
+2024-09-12 00:54:25.594131: val_loss -0.5825 
+2024-09-12 00:54:25.594182: Pseudo dice [0.508, 0.7669] 
+2024-09-12 00:54:25.594234: Epoch time: 245.78 s 
+2024-09-12 00:54:26.551650:  
+2024-09-12 00:54:26.551864: Epoch 485 
+2024-09-12 00:54:26.551965: Current learning rate: 0.0055 
+2024-09-12 00:58:32.279899: train_loss -0.8608 
+2024-09-12 00:58:32.280060: val_loss -0.5847 
+2024-09-12 00:58:32.280111: Pseudo dice [0.4997, 0.7429] 
+2024-09-12 00:58:32.280162: Epoch time: 245.73 s 
+2024-09-12 00:58:33.240827:  
+2024-09-12 00:58:33.241000: Epoch 486 
+2024-09-12 00:58:33.241077: Current learning rate: 0.00549 
+2024-09-12 01:02:38.970983: train_loss -0.8558 
+2024-09-12 01:02:38.971123: val_loss -0.5945 
+2024-09-12 01:02:38.971173: Pseudo dice [0.4473, 0.7892] 
+2024-09-12 01:02:38.971224: Epoch time: 245.73 s 
+2024-09-12 01:02:39.965681:  
+2024-09-12 01:02:39.965842: Epoch 487 
+2024-09-12 01:02:39.965929: Current learning rate: 0.00548 
+2024-09-12 01:06:45.968473: train_loss -0.8624 
+2024-09-12 01:06:45.968616: val_loss -0.5526 
+2024-09-12 01:06:45.968666: Pseudo dice [0.4218, 0.7609] 
+2024-09-12 01:06:45.968716: Epoch time: 246.0 s 
+2024-09-12 01:06:46.922817:  
+2024-09-12 01:06:46.922981: Epoch 488 
+2024-09-12 01:06:46.923068: Current learning rate: 0.00547 
+2024-09-12 01:10:52.888373: train_loss -0.8743 
+2024-09-12 01:10:52.888551: val_loss -0.5886 
+2024-09-12 01:10:52.888603: Pseudo dice [0.5243, 0.7696] 
+2024-09-12 01:10:52.888653: Epoch time: 245.97 s 
+2024-09-12 01:10:53.837592:  
+2024-09-12 01:10:53.837775: Epoch 489 
+2024-09-12 01:10:53.837859: Current learning rate: 0.00546 
+2024-09-12 01:14:59.341131: train_loss -0.8751 
+2024-09-12 01:14:59.341294: val_loss -0.6154 
+2024-09-12 01:14:59.341347: Pseudo dice [0.5323, 0.7857] 
+2024-09-12 01:14:59.341398: Epoch time: 245.51 s 
+2024-09-12 01:15:00.290836:  
+2024-09-12 01:15:00.291044: Epoch 490 
+2024-09-12 01:15:00.291128: Current learning rate: 0.00546 
+2024-09-12 01:19:05.852987: train_loss -0.8755 
+2024-09-12 01:19:05.853125: val_loss -0.6125 
+2024-09-12 01:19:05.853176: Pseudo dice [0.5183, 0.798] 
+2024-09-12 01:19:05.853226: Epoch time: 245.56 s 
+2024-09-12 01:19:06.809171:  
+2024-09-12 01:19:06.809372: Epoch 491 
+2024-09-12 01:19:06.809462: Current learning rate: 0.00545 
+2024-09-12 01:23:12.482627: train_loss -0.8763 
+2024-09-12 01:23:12.482785: val_loss -0.6115 
+2024-09-12 01:23:12.482834: Pseudo dice [0.5009, 0.7967] 
+2024-09-12 01:23:12.482885: Epoch time: 245.68 s 
+2024-09-12 01:23:13.422498:  
+2024-09-12 01:23:13.422739: Epoch 492 
+2024-09-12 01:23:13.422824: Current learning rate: 0.00544 
+2024-09-12 01:27:19.210357: train_loss -0.8761 
+2024-09-12 01:27:19.210502: val_loss -0.5773 
+2024-09-12 01:27:19.210552: Pseudo dice [0.4628, 0.7976] 
+2024-09-12 01:27:19.210606: Epoch time: 245.79 s 
+2024-09-12 01:27:20.142370:  
+2024-09-12 01:27:20.142612: Epoch 493 
+2024-09-12 01:27:20.142697: Current learning rate: 0.00543 
+2024-09-12 01:31:27.146085: train_loss -0.8697 
+2024-09-12 01:31:27.146227: val_loss -0.5802 
+2024-09-12 01:31:27.146277: Pseudo dice [0.467, 0.7732] 
+2024-09-12 01:31:27.146328: Epoch time: 247.01 s 
+2024-09-12 01:31:28.082396:  
+2024-09-12 01:31:28.082602: Epoch 494 
+2024-09-12 01:31:28.082689: Current learning rate: 0.00542 
+2024-09-12 01:35:34.043745: train_loss -0.8713 
+2024-09-12 01:35:34.043942: val_loss -0.6165 
+2024-09-12 01:35:34.044000: Pseudo dice [0.5375, 0.7997] 
+2024-09-12 01:35:34.044052: Epoch time: 245.96 s 
+2024-09-12 01:35:35.010888:  
+2024-09-12 01:35:35.011130: Epoch 495 
+2024-09-12 01:35:35.011229: Current learning rate: 0.00541 
+2024-09-12 01:39:41.016024: train_loss -0.8766 
+2024-09-12 01:39:41.016189: val_loss -0.5866 
+2024-09-12 01:39:41.016240: Pseudo dice [0.4759, 0.7986] 
+2024-09-12 01:39:41.016304: Epoch time: 246.01 s 
+2024-09-12 01:39:41.967532:  
+2024-09-12 01:39:41.967833: Epoch 496 
+2024-09-12 01:39:41.967921: Current learning rate: 0.0054 
+2024-09-12 01:43:47.906156: train_loss -0.8752 
+2024-09-12 01:43:47.906295: val_loss -0.5918 
+2024-09-12 01:43:47.906455: Pseudo dice [0.5033, 0.7841] 
+2024-09-12 01:43:47.906586: Epoch time: 245.94 s 
+2024-09-12 01:43:48.875992:  
+2024-09-12 01:43:48.876243: Epoch 497 
+2024-09-12 01:43:48.876329: Current learning rate: 0.00539 
+2024-09-12 01:47:54.800706: train_loss -0.8806 
+2024-09-12 01:47:54.800852: val_loss -0.6139 
+2024-09-12 01:47:54.800903: Pseudo dice [0.5178, 0.7851] 
+2024-09-12 01:47:54.800954: Epoch time: 245.93 s 
+2024-09-12 01:47:55.752985:  
+2024-09-12 01:47:55.753177: Epoch 498 
+2024-09-12 01:47:55.753295: Current learning rate: 0.00538 
+2024-09-12 01:52:01.823627: train_loss -0.8779 
+2024-09-12 01:52:01.823768: val_loss -0.5631 
+2024-09-12 01:52:01.823835: Pseudo dice [0.471, 0.7736] 
+2024-09-12 01:52:01.823889: Epoch time: 246.07 s 
+2024-09-12 01:52:02.804110:  
+2024-09-12 01:52:02.804281: Epoch 499 
+2024-09-12 01:52:02.804366: Current learning rate: 0.00537 
+2024-09-12 01:56:08.747918: train_loss -0.8791 
+2024-09-12 01:56:08.748089: val_loss -0.6198 
+2024-09-12 01:56:08.748140: Pseudo dice [0.5363, 0.7935] 
+2024-09-12 01:56:08.748192: Epoch time: 245.95 s 
+2024-09-12 01:56:12.681946:  
+2024-09-12 01:56:12.682111: Epoch 500 
+2024-09-12 01:56:12.682191: Current learning rate: 0.00536 
+2024-09-12 02:00:19.183567: train_loss -0.8827 
+2024-09-12 02:00:19.183705: val_loss -0.5805 
+2024-09-12 02:00:19.183758: Pseudo dice [0.4558, 0.7819] 
+2024-09-12 02:00:19.183816: Epoch time: 246.5 s 
+2024-09-12 02:00:20.166928:  
+2024-09-12 02:00:20.167120: Epoch 501 
+2024-09-12 02:00:20.167204: Current learning rate: 0.00535 
+2024-09-12 02:04:26.083792: train_loss -0.8788 
+2024-09-12 02:04:26.083956: val_loss -0.6172 
+2024-09-12 02:04:26.084006: Pseudo dice [0.5419, 0.7925] 
+2024-09-12 02:04:26.084057: Epoch time: 245.92 s 
+2024-09-12 02:04:27.050824:  
+2024-09-12 02:04:27.051045: Epoch 502 
+2024-09-12 02:04:27.051130: Current learning rate: 0.00534 
+2024-09-12 02:08:32.941286: train_loss -0.8731 
+2024-09-12 02:08:32.941453: val_loss -0.6263 
+2024-09-12 02:08:32.941504: Pseudo dice [0.5811, 0.7806] 
+2024-09-12 02:08:32.941555: Epoch time: 245.89 s 
+2024-09-12 02:08:34.081284:  
+2024-09-12 02:08:34.081534: Epoch 503 
+2024-09-12 02:08:34.081616: Current learning rate: 0.00533 
+2024-09-12 02:12:40.006356: train_loss -0.8737 
+2024-09-12 02:12:40.006499: val_loss -0.5822 
+2024-09-12 02:12:40.006548: Pseudo dice [0.4981, 0.7585] 
+2024-09-12 02:12:40.006600: Epoch time: 245.93 s 
+2024-09-12 02:12:40.959766:  
+2024-09-12 02:12:40.959974: Epoch 504 
+2024-09-12 02:12:40.960067: Current learning rate: 0.00532 
+2024-09-12 02:16:46.800733: train_loss -0.8781 
+2024-09-12 02:16:46.800874: val_loss -0.597 
+2024-09-12 02:16:46.800925: Pseudo dice [0.4557, 0.7954] 
+2024-09-12 02:16:46.801052: Epoch time: 245.84 s 
+2024-09-12 02:16:47.765223:  
+2024-09-12 02:16:47.765468: Epoch 505 
+2024-09-12 02:16:47.765555: Current learning rate: 0.00531 
+2024-09-12 02:20:53.631735: train_loss -0.8692 
+2024-09-12 02:20:53.631904: val_loss -0.605 
+2024-09-12 02:20:53.631968: Pseudo dice [0.5176, 0.7862] 
+2024-09-12 02:20:53.632019: Epoch time: 245.87 s 
+2024-09-12 02:20:54.591386:  
+2024-09-12 02:20:54.591607: Epoch 506 
+2024-09-12 02:20:54.591718: Current learning rate: 0.0053 
+2024-09-12 02:25:00.590140: train_loss -0.8699 
+2024-09-12 02:25:00.590279: val_loss -0.5899 
+2024-09-12 02:25:00.590328: Pseudo dice [0.461, 0.7996] 
+2024-09-12 02:25:00.590379: Epoch time: 246.0 s 
+2024-09-12 02:25:01.540104:  
+2024-09-12 02:25:01.540286: Epoch 507 
+2024-09-12 02:25:01.540373: Current learning rate: 0.00529 
+2024-09-12 02:29:07.681927: train_loss -0.8784 
+2024-09-12 02:29:07.682064: val_loss -0.5845 
+2024-09-12 02:29:07.682115: Pseudo dice [0.4539, 0.7876] 
+2024-09-12 02:29:07.682165: Epoch time: 246.14 s 
+2024-09-12 02:29:08.646815:  
+2024-09-12 02:29:08.647008: Epoch 508 
+2024-09-12 02:29:08.647126: Current learning rate: 0.00528 
+2024-09-12 02:33:14.589356: train_loss -0.8738 
+2024-09-12 02:33:14.589506: val_loss -0.5804 
+2024-09-12 02:33:14.589557: Pseudo dice [0.4701, 0.776] 
+2024-09-12 02:33:14.589610: Epoch time: 245.94 s 
+2024-09-12 02:33:15.550204:  
+2024-09-12 02:33:15.550402: Epoch 509 
+2024-09-12 02:33:15.550491: Current learning rate: 0.00527 
+2024-09-12 02:37:21.543693: train_loss -0.8705 
+2024-09-12 02:37:21.543853: val_loss -0.602 
+2024-09-12 02:37:21.543906: Pseudo dice [0.4982, 0.7798] 
+2024-09-12 02:37:21.543957: Epoch time: 246.0 s 
+2024-09-12 02:37:22.498972:  
+2024-09-12 02:37:22.499181: Epoch 510 
+2024-09-12 02:37:22.499264: Current learning rate: 0.00526 
+2024-09-12 02:41:28.384638: train_loss -0.8663 
+2024-09-12 02:41:28.384789: val_loss -0.6095 
+2024-09-12 02:41:28.384839: Pseudo dice [0.5174, 0.7824] 
+2024-09-12 02:41:28.384889: Epoch time: 245.89 s 
+2024-09-12 02:41:29.357645:  
+2024-09-12 02:41:29.357788: Epoch 511 
+2024-09-12 02:41:29.357912: Current learning rate: 0.00525 
+2024-09-12 02:45:35.254738: train_loss -0.8762 
+2024-09-12 02:45:35.254934: val_loss -0.586 
+2024-09-12 02:45:35.254988: Pseudo dice [0.5242, 0.7813] 
+2024-09-12 02:45:35.255042: Epoch time: 245.9 s 
+2024-09-12 02:45:36.224447:  
+2024-09-12 02:45:36.224676: Epoch 512 
+2024-09-12 02:45:36.224760: Current learning rate: 0.00524 
+2024-09-12 02:49:42.044204: train_loss -0.8807 
+2024-09-12 02:49:42.044354: val_loss -0.5573 
+2024-09-12 02:49:42.044405: Pseudo dice [0.4435, 0.7528] 
+2024-09-12 02:49:42.044456: Epoch time: 245.82 s 
+2024-09-12 02:49:43.012394:  
+2024-09-12 02:49:43.012573: Epoch 513 
+2024-09-12 02:49:43.012656: Current learning rate: 0.00523 
+2024-09-12 02:53:48.878214: train_loss -0.8784 
+2024-09-12 02:53:48.878365: val_loss -0.6119 
+2024-09-12 02:53:48.878415: Pseudo dice [0.5052, 0.7852] 
+2024-09-12 02:53:48.878465: Epoch time: 245.87 s 
+2024-09-12 02:53:49.826656:  
+2024-09-12 02:53:49.826842: Epoch 514 
+2024-09-12 02:53:49.826929: Current learning rate: 0.00522 
+2024-09-12 02:57:55.734786: train_loss -0.8829 
+2024-09-12 02:57:55.734941: val_loss -0.5756 
+2024-09-12 02:57:55.734991: Pseudo dice [0.4562, 0.7885] 
+2024-09-12 02:57:55.735145: Epoch time: 245.91 s 
+2024-09-12 02:57:56.681195:  
+2024-09-12 02:57:56.681372: Epoch 515 
+2024-09-12 02:57:56.681473: Current learning rate: 0.00521 
+2024-09-12 03:02:02.767959: train_loss -0.8786 
+2024-09-12 03:02:02.768122: val_loss -0.6099 
+2024-09-12 03:02:02.768173: Pseudo dice [0.4918, 0.804] 
+2024-09-12 03:02:02.768226: Epoch time: 246.09 s 
+2024-09-12 03:02:03.750029:  
+2024-09-12 03:02:03.750208: Epoch 516 
+2024-09-12 03:02:03.750293: Current learning rate: 0.0052 
+2024-09-12 03:06:09.731585: train_loss -0.8788 
+2024-09-12 03:06:09.731724: val_loss -0.5819 
+2024-09-12 03:06:09.731775: Pseudo dice [0.4935, 0.782] 
+2024-09-12 03:06:09.731834: Epoch time: 245.98 s 
+2024-09-12 03:06:11.624708:  
+2024-09-12 03:06:11.624983: Epoch 517 
+2024-09-12 03:06:11.625093: Current learning rate: 0.00519 
+2024-09-12 03:10:17.736237: train_loss -0.879 
+2024-09-12 03:10:17.736398: val_loss -0.6296 
+2024-09-12 03:10:17.736451: Pseudo dice [0.5332, 0.8094] 
+2024-09-12 03:10:17.736501: Epoch time: 246.11 s 
+2024-09-12 03:10:18.706993:  
+2024-09-12 03:10:18.707236: Epoch 518 
+2024-09-12 03:10:18.707318: Current learning rate: 0.00518 
+2024-09-12 03:14:24.755551: train_loss -0.8794 
+2024-09-12 03:14:24.755699: val_loss -0.5647 
+2024-09-12 03:14:24.755754: Pseudo dice [0.4597, 0.7628] 
+2024-09-12 03:14:24.755818: Epoch time: 246.05 s 
+2024-09-12 03:14:25.753125:  
+2024-09-12 03:14:25.753368: Epoch 519 
+2024-09-12 03:14:25.753451: Current learning rate: 0.00518 
+2024-09-12 03:18:31.656832: train_loss -0.8781 
+2024-09-12 03:18:31.656971: val_loss -0.5665 
+2024-09-12 03:18:31.657021: Pseudo dice [0.4362, 0.765] 
+2024-09-12 03:18:31.657072: Epoch time: 245.91 s 
+2024-09-12 03:18:32.670049:  
+2024-09-12 03:18:32.670241: Epoch 520 
+2024-09-12 03:18:32.670327: Current learning rate: 0.00517 
+2024-09-12 03:22:38.662077: train_loss -0.8746 
+2024-09-12 03:22:38.662276: val_loss -0.565 
+2024-09-12 03:22:38.662327: Pseudo dice [0.4472, 0.7758] 
+2024-09-12 03:22:38.662407: Epoch time: 245.99 s 
+2024-09-12 03:22:39.642673:  
+2024-09-12 03:22:39.642905: Epoch 521 
+2024-09-12 03:22:39.642988: Current learning rate: 0.00516 
+2024-09-12 03:26:45.720585: train_loss -0.8726 
+2024-09-12 03:26:45.720726: val_loss -0.6174 
+2024-09-12 03:26:45.720777: Pseudo dice [0.5628, 0.7785] 
+2024-09-12 03:26:45.720827: Epoch time: 246.08 s 
+2024-09-12 03:26:46.689008:  
+2024-09-12 03:26:46.689209: Epoch 522 
+2024-09-12 03:26:46.689292: Current learning rate: 0.00515 
+2024-09-12 03:30:52.925118: train_loss -0.8753 
+2024-09-12 03:30:52.925294: val_loss -0.599 
+2024-09-12 03:30:52.925371: Pseudo dice [0.501, 0.7659] 
+2024-09-12 03:30:52.925451: Epoch time: 246.24 s 
+2024-09-12 03:30:53.896761:  
+2024-09-12 03:30:53.897012: Epoch 523 
+2024-09-12 03:30:53.897096: Current learning rate: 0.00514 
+2024-09-12 03:34:59.987907: train_loss -0.8743 
+2024-09-12 03:34:59.988048: val_loss -0.6355 
+2024-09-12 03:34:59.988099: Pseudo dice [0.5404, 0.7925] 
+2024-09-12 03:34:59.988148: Epoch time: 246.09 s 
+2024-09-12 03:35:00.946900:  
+2024-09-12 03:35:00.947114: Epoch 524 
+2024-09-12 03:35:00.947202: Current learning rate: 0.00513 
+2024-09-12 03:39:06.996336: train_loss -0.8747 
+2024-09-12 03:39:06.996476: val_loss -0.5621 
+2024-09-12 03:39:06.996527: Pseudo dice [0.4654, 0.7209] 
+2024-09-12 03:39:06.996578: Epoch time: 246.05 s 
+2024-09-12 03:39:07.959192:  
+2024-09-12 03:39:07.959366: Epoch 525 
+2024-09-12 03:39:07.959479: Current learning rate: 0.00512 
+2024-09-12 03:43:14.046343: train_loss -0.8743 
+2024-09-12 03:43:14.046484: val_loss -0.5829 
+2024-09-12 03:43:14.046534: Pseudo dice [0.4844, 0.7686] 
+2024-09-12 03:43:14.046584: Epoch time: 246.09 s 
+2024-09-12 03:43:15.017911:  
+2024-09-12 03:43:15.018086: Epoch 526 
+2024-09-12 03:43:15.018170: Current learning rate: 0.00511 
+2024-09-12 03:47:21.061833: train_loss -0.8781 
+2024-09-12 03:47:21.062095: val_loss -0.5756 
+2024-09-12 03:47:21.062147: Pseudo dice [0.4898, 0.776] 
+2024-09-12 03:47:21.062198: Epoch time: 246.05 s 
+2024-09-12 03:47:22.027908:  
+2024-09-12 03:47:22.028158: Epoch 527 
+2024-09-12 03:47:22.028244: Current learning rate: 0.0051 
+2024-09-12 03:51:27.730405: train_loss -0.8786 
+2024-09-12 03:51:27.730546: val_loss -0.6021 
+2024-09-12 03:51:27.730595: Pseudo dice [0.4774, 0.7841] 
+2024-09-12 03:51:27.730646: Epoch time: 245.7 s 
+2024-09-12 03:51:28.689471:  
+2024-09-12 03:51:28.689629: Epoch 528 
+2024-09-12 03:51:28.689712: Current learning rate: 0.00509 
+2024-09-12 03:55:34.589432: train_loss -0.8756 
+2024-09-12 03:55:34.589623: val_loss -0.577 
+2024-09-12 03:55:34.589674: Pseudo dice [0.4955, 0.7728] 
+2024-09-12 03:55:34.589727: Epoch time: 245.9 s 
+2024-09-12 03:55:35.558438:  
+2024-09-12 03:55:35.558599: Epoch 529 
+2024-09-12 03:55:35.558680: Current learning rate: 0.00508 
+2024-09-12 03:59:41.526877: train_loss -0.8784 
+2024-09-12 03:59:41.527019: val_loss -0.6128 
+2024-09-12 03:59:41.527069: Pseudo dice [0.5399, 0.7864] 
+2024-09-12 03:59:41.527175: Epoch time: 245.97 s 
+2024-09-12 03:59:42.491318:  
+2024-09-12 03:59:42.491544: Epoch 530 
+2024-09-12 03:59:42.491629: Current learning rate: 0.00507 
+2024-09-12 04:03:48.573470: train_loss -0.8689 
+2024-09-12 04:03:48.573658: val_loss -0.5837 
+2024-09-12 04:03:48.573710: Pseudo dice [0.4838, 0.7791] 
+2024-09-12 04:03:48.573764: Epoch time: 246.08 s 
+2024-09-12 04:03:49.547087:  
+2024-09-12 04:03:49.547274: Epoch 531 
+2024-09-12 04:03:49.547381: Current learning rate: 0.00506 
+2024-09-12 04:07:55.670429: train_loss -0.8531 
+2024-09-12 04:07:55.670615: val_loss -0.5516 
+2024-09-12 04:07:55.670668: Pseudo dice [0.4936, 0.7251] 
+2024-09-12 04:07:55.670720: Epoch time: 246.13 s 
+2024-09-12 04:07:56.640678:  
+2024-09-12 04:07:56.640867: Epoch 532 
+2024-09-12 04:07:56.641023: Current learning rate: 0.00505 
+2024-09-12 04:12:02.716464: train_loss -0.8561 
+2024-09-12 04:12:02.716600: val_loss -0.624 
+2024-09-12 04:12:02.716650: Pseudo dice [0.5466, 0.7646] 
+2024-09-12 04:12:02.716701: Epoch time: 246.08 s 
+2024-09-12 04:12:03.676579:  
+2024-09-12 04:12:03.676749: Epoch 533 
+2024-09-12 04:12:03.676865: Current learning rate: 0.00504 
+2024-09-12 04:16:09.647350: train_loss -0.8614 
+2024-09-12 04:16:09.647506: val_loss -0.6243 
+2024-09-12 04:16:09.647558: Pseudo dice [0.5232, 0.795] 
+2024-09-12 04:16:09.647611: Epoch time: 245.97 s 
+2024-09-12 04:16:10.608677:  
+2024-09-12 04:16:10.608874: Epoch 534 
+2024-09-12 04:16:10.608965: Current learning rate: 0.00503 
+2024-09-12 04:20:16.513054: train_loss -0.8665 
+2024-09-12 04:20:16.513292: val_loss -0.5818 
+2024-09-12 04:20:16.513346: Pseudo dice [0.4868, 0.791] 
+2024-09-12 04:20:16.513398: Epoch time: 245.91 s 
+2024-09-12 04:20:17.500044:  
+2024-09-12 04:20:17.500208: Epoch 535 
+2024-09-12 04:20:17.500288: Current learning rate: 0.00502 
+2024-09-12 04:24:23.544988: train_loss -0.8661 
+2024-09-12 04:24:23.545133: val_loss -0.5787 
+2024-09-12 04:24:23.545183: Pseudo dice [0.4789, 0.7675] 
+2024-09-12 04:24:23.545235: Epoch time: 246.05 s 
+2024-09-12 04:24:24.514304:  
+2024-09-12 04:24:24.514486: Epoch 536 
+2024-09-12 04:24:24.514610: Current learning rate: 0.00501 
+2024-09-12 04:28:30.678757: train_loss -0.8686 
+2024-09-12 04:28:30.678896: val_loss -0.6279 
+2024-09-12 04:28:30.678946: Pseudo dice [0.5243, 0.7845] 
+2024-09-12 04:28:30.678997: Epoch time: 246.17 s 
+2024-09-12 04:28:31.641234:  
+2024-09-12 04:28:31.641422: Epoch 537 
+2024-09-12 04:28:31.641546: Current learning rate: 0.005 
+2024-09-12 04:32:37.842327: train_loss -0.87 
+2024-09-12 04:32:37.842497: val_loss -0.6056 
+2024-09-12 04:32:37.842549: Pseudo dice [0.5157, 0.7975] 
+2024-09-12 04:32:37.842598: Epoch time: 246.2 s 
+2024-09-12 04:32:38.810496:  
+2024-09-12 04:32:38.810761: Epoch 538 
+2024-09-12 04:32:38.810842: Current learning rate: 0.00499 
+2024-09-12 04:36:44.941216: train_loss -0.8667 
+2024-09-12 04:36:44.941356: val_loss -0.5934 
+2024-09-12 04:36:44.941406: Pseudo dice [0.5083, 0.7961] 
+2024-09-12 04:36:44.941456: Epoch time: 246.13 s 
+2024-09-12 04:36:45.905909:  
+2024-09-12 04:36:45.906083: Epoch 539 
+2024-09-12 04:36:45.906171: Current learning rate: 0.00498 
+2024-09-12 04:40:51.863294: train_loss -0.8745 
+2024-09-12 04:40:51.863494: val_loss -0.5853 
+2024-09-12 04:40:51.863569: Pseudo dice [0.4766, 0.7905] 
+2024-09-12 04:40:51.863621: Epoch time: 245.96 s 
+2024-09-12 04:40:52.826339:  
+2024-09-12 04:40:52.826520: Epoch 540 
+2024-09-12 04:40:52.826611: Current learning rate: 0.00497 
+2024-09-12 04:44:58.791650: train_loss -0.8755 
+2024-09-12 04:44:58.791789: val_loss -0.6325 
+2024-09-12 04:44:58.791870: Pseudo dice [0.5503, 0.7931] 
+2024-09-12 04:44:58.791924: Epoch time: 245.97 s 
+2024-09-12 04:45:00.667012:  
+2024-09-12 04:45:00.667277: Epoch 541 
+2024-09-12 04:45:00.667432: Current learning rate: 0.00496 
+2024-09-12 04:49:06.680465: train_loss -0.878 
+2024-09-12 04:49:06.680625: val_loss -0.5573 
+2024-09-12 04:49:06.680677: Pseudo dice [0.413, 0.7906] 
+2024-09-12 04:49:06.680727: Epoch time: 246.02 s 
+2024-09-12 04:49:07.618200:  
+2024-09-12 04:49:07.618425: Epoch 542 
+2024-09-12 04:49:07.618542: Current learning rate: 0.00495 
+2024-09-12 04:53:13.461408: train_loss -0.8749 
+2024-09-12 04:53:13.461550: val_loss -0.5881 
+2024-09-12 04:53:13.461603: Pseudo dice [0.4772, 0.7895] 
+2024-09-12 04:53:13.461659: Epoch time: 245.85 s 
+2024-09-12 04:53:14.422633:  
+2024-09-12 04:53:14.422904: Epoch 543 
+2024-09-12 04:53:14.422997: Current learning rate: 0.00494 
+2024-09-12 04:57:20.450734: train_loss -0.883 
+2024-09-12 04:57:20.450875: val_loss -0.5722 
+2024-09-12 04:57:20.450926: Pseudo dice [0.4683, 0.7753] 
+2024-09-12 04:57:20.450977: Epoch time: 246.03 s 
+2024-09-12 04:57:21.410899:  
+2024-09-12 04:57:21.411080: Epoch 544 
+2024-09-12 04:57:21.411165: Current learning rate: 0.00493 
+2024-09-12 05:01:27.169768: train_loss -0.881 
+2024-09-12 05:01:27.169907: val_loss -0.5857 
+2024-09-12 05:01:27.169958: Pseudo dice [0.4878, 0.7966] 
+2024-09-12 05:01:27.170009: Epoch time: 245.76 s 
+2024-09-12 05:01:28.126722:  
+2024-09-12 05:01:28.126979: Epoch 545 
+2024-09-12 05:01:28.127094: Current learning rate: 0.00492 
+2024-09-12 05:05:34.336913: train_loss -0.8768 
+2024-09-12 05:05:34.337079: val_loss -0.6018 
+2024-09-12 05:05:34.337128: Pseudo dice [0.5267, 0.8035] 
+2024-09-12 05:05:34.337179: Epoch time: 246.21 s 
+2024-09-12 05:05:35.298386:  
+2024-09-12 05:05:35.298589: Epoch 546 
+2024-09-12 05:05:35.298671: Current learning rate: 0.00491 
+2024-09-12 05:09:41.628070: train_loss -0.8742 
+2024-09-12 05:09:41.628212: val_loss -0.5798 
+2024-09-12 05:09:41.628263: Pseudo dice [0.4293, 0.7905] 
+2024-09-12 05:09:41.628315: Epoch time: 246.33 s 
+2024-09-12 05:09:42.595970:  
+2024-09-12 05:09:42.596200: Epoch 547 
+2024-09-12 05:09:42.596286: Current learning rate: 0.0049 
+2024-09-12 05:13:48.665343: train_loss -0.8777 
+2024-09-12 05:13:48.665524: val_loss -0.6049 
+2024-09-12 05:13:48.665574: Pseudo dice [0.514, 0.8109] 
+2024-09-12 05:13:48.665627: Epoch time: 246.07 s 
+2024-09-12 05:13:49.629513:  
+2024-09-12 05:13:49.629723: Epoch 548 
+2024-09-12 05:13:49.629809: Current learning rate: 0.00489 
+2024-09-12 05:17:55.559985: train_loss -0.8769 
+2024-09-12 05:17:55.560179: val_loss -0.5882 
+2024-09-12 05:17:55.560238: Pseudo dice [0.4753, 0.7894] 
+2024-09-12 05:17:55.560299: Epoch time: 245.93 s 
+2024-09-12 05:17:56.570157:  
+2024-09-12 05:17:56.570403: Epoch 549 
+2024-09-12 05:17:56.570490: Current learning rate: 0.00488 
+2024-09-12 05:22:02.381994: train_loss -0.8793 
+2024-09-12 05:22:02.382167: val_loss -0.6146 
+2024-09-12 05:22:02.382217: Pseudo dice [0.5415, 0.7854] 
+2024-09-12 05:22:02.382270: Epoch time: 245.81 s 
+2024-09-12 05:22:06.355405:  
+2024-09-12 05:22:06.355621: Epoch 550 
+2024-09-12 05:22:06.355706: Current learning rate: 0.00487 
+2024-09-12 05:26:12.420507: train_loss -0.8822 
+2024-09-12 05:26:12.420648: val_loss -0.6054 
+2024-09-12 05:26:12.420698: Pseudo dice [0.5258, 0.7436] 
+2024-09-12 05:26:12.420747: Epoch time: 246.07 s 
+2024-09-12 05:26:13.374166:  
+2024-09-12 05:26:13.374337: Epoch 551 
+2024-09-12 05:26:13.374420: Current learning rate: 0.00486 
+2024-09-12 05:30:19.353850: train_loss -0.8794 
+2024-09-12 05:30:19.353989: val_loss -0.5197 
+2024-09-12 05:30:19.354039: Pseudo dice [0.4279, 0.7169] 
+2024-09-12 05:30:19.354091: Epoch time: 245.98 s 
+2024-09-12 05:30:20.315879:  
+2024-09-12 05:30:20.316100: Epoch 552 
+2024-09-12 05:30:20.316179: Current learning rate: 0.00485 
+2024-09-12 05:34:26.197328: train_loss -0.8659 
+2024-09-12 05:34:26.197479: val_loss -0.5937 
+2024-09-12 05:34:26.197529: Pseudo dice [0.5162, 0.775] 
+2024-09-12 05:34:26.197579: Epoch time: 245.88 s 
+2024-09-12 05:34:27.169510:  
+2024-09-12 05:34:27.169731: Epoch 553 
+2024-09-12 05:34:27.169809: Current learning rate: 0.00484 
+2024-09-12 05:38:32.990783: train_loss -0.8686 
+2024-09-12 05:38:32.990930: val_loss -0.5884 
+2024-09-12 05:38:32.990982: Pseudo dice [0.4663, 0.7925] 
+2024-09-12 05:38:32.991036: Epoch time: 245.82 s 
+2024-09-12 05:38:33.950512:  
+2024-09-12 05:38:33.950761: Epoch 554 
+2024-09-12 05:38:33.950847: Current learning rate: 0.00484 
+2024-09-12 05:42:39.740483: train_loss -0.8754 
+2024-09-12 05:42:39.740613: val_loss -0.5638 
+2024-09-12 05:42:39.740663: Pseudo dice [0.4581, 0.7651] 
+2024-09-12 05:42:39.740715: Epoch time: 245.79 s 
+2024-09-12 05:42:40.738338:  
+2024-09-12 05:42:40.738562: Epoch 555 
+2024-09-12 05:42:40.738647: Current learning rate: 0.00483 
+2024-09-12 05:46:46.956329: train_loss -0.8718 
+2024-09-12 05:46:46.956496: val_loss -0.5876 
+2024-09-12 05:46:46.956544: Pseudo dice [0.4954, 0.785] 
+2024-09-12 05:46:46.956595: Epoch time: 246.22 s 
+2024-09-12 05:46:47.924560:  
+2024-09-12 05:46:47.924767: Epoch 556 
+2024-09-12 05:46:47.924855: Current learning rate: 0.00482 
+2024-09-12 05:50:53.691345: train_loss -0.8787 
+2024-09-12 05:50:53.691513: val_loss -0.5932 
+2024-09-12 05:50:53.691598: Pseudo dice [0.5284, 0.7524] 
+2024-09-12 05:50:53.691649: Epoch time: 245.77 s 
+2024-09-12 05:50:54.665084:  
+2024-09-12 05:50:54.665271: Epoch 557 
+2024-09-12 05:50:54.665355: Current learning rate: 0.00481 
+2024-09-12 05:55:00.775085: train_loss -0.8799 
+2024-09-12 05:55:00.775223: val_loss -0.6071 
+2024-09-12 05:55:00.775274: Pseudo dice [0.485, 0.7984] 
+2024-09-12 05:55:00.775337: Epoch time: 246.11 s 
+2024-09-12 05:55:01.773984:  
+2024-09-12 05:55:01.774188: Epoch 558 
+2024-09-12 05:55:01.774277: Current learning rate: 0.0048 
+2024-09-12 05:59:07.797223: train_loss -0.877 
+2024-09-12 05:59:07.797364: val_loss -0.582 
+2024-09-12 05:59:07.797414: Pseudo dice [0.4499, 0.7761] 
+2024-09-12 05:59:07.797466: Epoch time: 246.03 s 
+2024-09-12 05:59:08.761356:  
+2024-09-12 05:59:08.761509: Epoch 559 
+2024-09-12 05:59:08.761615: Current learning rate: 0.00479 
+2024-09-12 06:03:14.668867: train_loss -0.8767 
+2024-09-12 06:03:14.669009: val_loss -0.5665 
+2024-09-12 06:03:14.669060: Pseudo dice [0.4363, 0.7993] 
+2024-09-12 06:03:14.669111: Epoch time: 245.91 s 
+2024-09-12 06:03:15.635823:  
+2024-09-12 06:03:15.635985: Epoch 560 
+2024-09-12 06:03:15.636069: Current learning rate: 0.00478 
+2024-09-12 06:07:21.731036: train_loss -0.8833 
+2024-09-12 06:07:21.731174: val_loss -0.5859 
+2024-09-12 06:07:21.731224: Pseudo dice [0.4833, 0.8027] 
+2024-09-12 06:07:21.731275: Epoch time: 246.1 s 
+2024-09-12 06:07:22.709883:  
+2024-09-12 06:07:22.710099: Epoch 561 
+2024-09-12 06:07:22.710182: Current learning rate: 0.00477 
+2024-09-12 06:11:28.634423: train_loss -0.8828 
+2024-09-12 06:11:28.634586: val_loss -0.5946 
+2024-09-12 06:11:28.634637: Pseudo dice [0.4992, 0.7936] 
+2024-09-12 06:11:28.634690: Epoch time: 245.93 s 
+2024-09-12 06:11:29.603981:  
+2024-09-12 06:11:29.604206: Epoch 562 
+2024-09-12 06:11:29.604291: Current learning rate: 0.00476 
+2024-09-12 06:15:35.714028: train_loss -0.8701 
+2024-09-12 06:15:35.714168: val_loss -0.6278 
+2024-09-12 06:15:35.714218: Pseudo dice [0.5005, 0.8039] 
+2024-09-12 06:15:35.714269: Epoch time: 246.11 s 
+2024-09-12 06:15:36.680068:  
+2024-09-12 06:15:36.680213: Epoch 563 
+2024-09-12 06:15:36.680293: Current learning rate: 0.00475 
+2024-09-12 06:19:42.516281: train_loss -0.8716 
+2024-09-12 06:19:42.516419: val_loss -0.5865 
+2024-09-12 06:19:42.516516: Pseudo dice [0.4829, 0.7823] 
+2024-09-12 06:19:42.516607: Epoch time: 245.84 s 
+2024-09-12 06:19:43.482617:  
+2024-09-12 06:19:43.482795: Epoch 564 
+2024-09-12 06:19:43.482881: Current learning rate: 0.00474 
+2024-09-12 06:23:50.008420: train_loss -0.8725 
+2024-09-12 06:23:50.008575: val_loss -0.5964 
+2024-09-12 06:23:50.008626: Pseudo dice [0.4641, 0.786] 
+2024-09-12 06:23:50.008677: Epoch time: 246.53 s 
+2024-09-12 06:23:50.950917:  
+2024-09-12 06:23:50.951188: Epoch 565 
+2024-09-12 06:23:50.951291: Current learning rate: 0.00473 
+2024-09-12 06:27:56.673360: train_loss -0.8693 
+2024-09-12 06:27:56.673501: val_loss -0.5483 
+2024-09-12 06:27:56.673550: Pseudo dice [0.4651, 0.733] 
+2024-09-12 06:27:56.673601: Epoch time: 245.72 s 
+2024-09-12 06:27:57.645492:  
+2024-09-12 06:27:57.645763: Epoch 566 
+2024-09-12 06:27:57.645849: Current learning rate: 0.00472 
+2024-09-12 06:32:03.186101: train_loss -0.8692 
+2024-09-12 06:32:03.186275: val_loss -0.6137 
+2024-09-12 06:32:03.186326: Pseudo dice [0.5254, 0.7822] 
+2024-09-12 06:32:03.186377: Epoch time: 245.54 s 
+2024-09-12 06:32:04.151984:  
+2024-09-12 06:32:04.152204: Epoch 567 
+2024-09-12 06:32:04.152288: Current learning rate: 0.00471 
+2024-09-12 06:36:09.885632: train_loss -0.8643 
+2024-09-12 06:36:09.885784: val_loss -0.6078 
+2024-09-12 06:36:09.885839: Pseudo dice [0.5185, 0.7815] 
+2024-09-12 06:36:09.885892: Epoch time: 245.74 s 
+2024-09-12 06:36:10.856145:  
+2024-09-12 06:36:10.856361: Epoch 568 
+2024-09-12 06:36:10.856443: Current learning rate: 0.0047 
+2024-09-12 06:40:16.635580: train_loss -0.8621 
+2024-09-12 06:40:16.635718: val_loss -0.6055 
+2024-09-12 06:40:16.635769: Pseudo dice [0.4981, 0.7811] 
+2024-09-12 06:40:16.635826: Epoch time: 245.78 s 
+2024-09-12 06:40:17.605856:  
+2024-09-12 06:40:17.606036: Epoch 569 
+2024-09-12 06:40:17.606119: Current learning rate: 0.00469 
+2024-09-12 06:44:23.238091: train_loss -0.8628 
+2024-09-12 06:44:23.238267: val_loss -0.5902 
+2024-09-12 06:44:23.238328: Pseudo dice [0.4659, 0.7989] 
+2024-09-12 06:44:23.238389: Epoch time: 245.63 s 
+2024-09-12 06:44:24.209416:  
+2024-09-12 06:44:24.209606: Epoch 570 
+2024-09-12 06:44:24.209710: Current learning rate: 0.00468 
+2024-09-12 06:48:30.123176: train_loss -0.8662 
+2024-09-12 06:48:30.123317: val_loss -0.5661 
+2024-09-12 06:48:30.123369: Pseudo dice [0.469, 0.7917] 
+2024-09-12 06:48:30.123422: Epoch time: 245.92 s 
+2024-09-12 06:48:31.083146:  
+2024-09-12 06:48:31.083373: Epoch 571 
+2024-09-12 06:48:31.083456: Current learning rate: 0.00467 
+2024-09-12 06:52:37.125181: train_loss -0.8464 
+2024-09-12 06:52:37.125414: val_loss -0.5925 
+2024-09-12 06:52:37.125511: Pseudo dice [0.5068, 0.7762] 
+2024-09-12 06:52:37.125606: Epoch time: 246.04 s 
+2024-09-12 06:52:38.106776:  
+2024-09-12 06:52:38.106963: Epoch 572 
+2024-09-12 06:52:38.107050: Current learning rate: 0.00466 
+2024-09-12 06:56:46.006157: train_loss -0.8302 
+2024-09-12 06:56:46.006300: val_loss -0.6031 
+2024-09-12 06:56:46.006351: Pseudo dice [0.527, 0.7862] 
+2024-09-12 06:56:46.006402: Epoch time: 247.9 s 
+2024-09-12 06:56:46.990787:  
+2024-09-12 06:56:46.990989: Epoch 573 
+2024-09-12 06:56:46.991082: Current learning rate: 0.00465 
+2024-09-12 07:03:08.550388: train_loss -0.8198 
+2024-09-12 07:03:08.550533: val_loss -0.63 
+2024-09-12 07:03:08.550586: Pseudo dice [0.5606, 0.7882] 
+2024-09-12 07:03:08.550637: Epoch time: 381.56 s 
+2024-09-12 07:03:09.547747:  
+2024-09-12 07:03:09.547960: Epoch 574 
+2024-09-12 07:03:09.548044: Current learning rate: 0.00464 
+2024-09-12 07:07:38.303079: train_loss -0.8282 
+2024-09-12 07:07:38.303263: val_loss -0.5441 
+2024-09-12 07:07:38.303318: Pseudo dice [0.4445, 0.7654] 
+2024-09-12 07:07:38.303369: Epoch time: 268.76 s 
+2024-09-12 07:07:39.315603:  
+2024-09-12 07:07:39.315830: Epoch 575 
+2024-09-12 07:07:39.315919: Current learning rate: 0.00463 
+2024-09-12 07:11:45.131490: train_loss -0.8488 
+2024-09-12 07:11:45.131629: val_loss -0.5925 
+2024-09-12 07:11:45.131680: Pseudo dice [0.4503, 0.7871] 
+2024-09-12 07:11:45.131731: Epoch time: 245.82 s 
+2024-09-12 07:11:46.140443:  
+2024-09-12 07:11:46.140702: Epoch 576 
+2024-09-12 07:11:46.140786: Current learning rate: 0.00462 
+2024-09-12 07:15:52.094961: train_loss -0.8601 
+2024-09-12 07:15:52.095127: val_loss -0.6091 
+2024-09-12 07:15:52.095179: Pseudo dice [0.4944, 0.7919] 
+2024-09-12 07:15:52.095231: Epoch time: 245.96 s 
+2024-09-12 07:15:53.082876:  
+2024-09-12 07:15:53.083062: Epoch 577 
+2024-09-12 07:15:53.083147: Current learning rate: 0.00461 
+2024-09-12 07:19:58.774527: train_loss -0.8531 
+2024-09-12 07:19:58.774673: val_loss -0.5914 
+2024-09-12 07:19:58.774725: Pseudo dice [0.4893, 0.7884] 
+2024-09-12 07:19:58.774779: Epoch time: 245.69 s 
+2024-09-12 07:19:59.756320:  
+2024-09-12 07:19:59.756543: Epoch 578 
+2024-09-12 07:19:59.756628: Current learning rate: 0.0046 
+2024-09-12 07:24:05.303015: train_loss -0.8569 
+2024-09-12 07:24:05.303169: val_loss -0.5847 
+2024-09-12 07:24:05.303220: Pseudo dice [0.481, 0.7805] 
+2024-09-12 07:24:05.303273: Epoch time: 245.55 s 
+2024-09-12 07:24:06.313862:  
+2024-09-12 07:24:06.314036: Epoch 579 
+2024-09-12 07:24:06.314123: Current learning rate: 0.00459 
+2024-09-12 07:28:11.684752: train_loss -0.862 
+2024-09-12 07:28:11.684907: val_loss -0.5876 
+2024-09-12 07:28:11.684958: Pseudo dice [0.5334, 0.7807] 
+2024-09-12 07:28:11.685009: Epoch time: 245.37 s 
+2024-09-12 07:28:12.696088:  
+2024-09-12 07:28:12.696286: Epoch 580 
+2024-09-12 07:28:12.696373: Current learning rate: 0.00458 
+2024-09-12 07:32:18.337893: train_loss -0.8643 
+2024-09-12 07:32:18.338030: val_loss -0.5911 
+2024-09-12 07:32:18.338080: Pseudo dice [0.4845, 0.7693] 
+2024-09-12 07:32:18.338131: Epoch time: 245.64 s 
+2024-09-12 07:32:19.327761:  
+2024-09-12 07:32:19.327928: Epoch 581 
+2024-09-12 07:32:19.328012: Current learning rate: 0.00457 
+2024-09-12 07:36:24.931981: train_loss -0.8767 
+2024-09-12 07:36:24.932118: val_loss -0.6278 
+2024-09-12 07:36:24.932169: Pseudo dice [0.5297, 0.8003] 
+2024-09-12 07:36:24.932224: Epoch time: 245.61 s 
+2024-09-12 07:36:25.916192:  
+2024-09-12 07:36:25.916436: Epoch 582 
+2024-09-12 07:36:25.916523: Current learning rate: 0.00456 
+2024-09-12 07:40:31.644678: train_loss -0.873 
+2024-09-12 07:40:31.644835: val_loss -0.5794 
+2024-09-12 07:40:31.644886: Pseudo dice [0.4795, 0.7533] 
+2024-09-12 07:40:31.644937: Epoch time: 245.73 s 
+2024-09-12 07:40:32.633614:  
+2024-09-12 07:40:32.633781: Epoch 583 
+2024-09-12 07:40:32.633864: Current learning rate: 0.00455 
+2024-09-12 07:44:38.291270: train_loss -0.8731 
+2024-09-12 07:44:38.291410: val_loss -0.5987 
+2024-09-12 07:44:38.291460: Pseudo dice [0.4556, 0.7868] 
+2024-09-12 07:44:38.291509: Epoch time: 245.66 s 
+2024-09-12 07:44:39.276896:  
+2024-09-12 07:44:39.277107: Epoch 584 
+2024-09-12 07:44:39.277195: Current learning rate: 0.00454 
+2024-09-12 07:48:44.850676: train_loss -0.8756 
+2024-09-12 07:48:44.850847: val_loss -0.6059 
+2024-09-12 07:48:44.850899: Pseudo dice [0.5314, 0.7873] 
+2024-09-12 07:48:44.850955: Epoch time: 245.58 s 
+2024-09-12 07:48:45.840657:  
+2024-09-12 07:48:45.840826: Epoch 585 
+2024-09-12 07:48:45.840907: Current learning rate: 0.00453 
+2024-09-12 07:52:51.660658: train_loss -0.8838 
+2024-09-12 07:52:51.660804: val_loss -0.5889 
+2024-09-12 07:52:51.660855: Pseudo dice [0.4432, 0.7941] 
+2024-09-12 07:52:51.660906: Epoch time: 245.82 s 
+2024-09-12 07:52:52.665757:  
+2024-09-12 07:52:52.665947: Epoch 586 
+2024-09-12 07:52:52.666031: Current learning rate: 0.00452 
+2024-09-12 07:56:58.414341: train_loss -0.8793 
+2024-09-12 07:56:58.414482: val_loss -0.595 
+2024-09-12 07:56:58.414546: Pseudo dice [0.4877, 0.7781] 
+2024-09-12 07:56:58.414606: Epoch time: 245.75 s 
+2024-09-12 07:56:59.406320:  
+2024-09-12 07:56:59.406482: Epoch 587 
+2024-09-12 07:56:59.406566: Current learning rate: 0.00451 
+2024-09-12 08:01:05.033616: train_loss -0.879 
+2024-09-12 08:01:05.033768: val_loss -0.5867 
+2024-09-12 08:01:05.033832: Pseudo dice [0.4804, 0.7868] 
+2024-09-12 08:01:05.033891: Epoch time: 245.63 s 
+2024-09-12 08:01:06.963857:  
+2024-09-12 08:01:06.964175: Epoch 588 
+2024-09-12 08:01:06.964308: Current learning rate: 0.0045 
+2024-09-12 08:05:12.715667: train_loss -0.8668 
+2024-09-12 08:05:12.715831: val_loss -0.5544 
+2024-09-12 08:05:12.715884: Pseudo dice [0.4493, 0.7733] 
+2024-09-12 08:05:12.715936: Epoch time: 245.75 s 
+2024-09-12 08:05:13.679452:  
+2024-09-12 08:05:13.679698: Epoch 589 
+2024-09-12 08:05:13.679785: Current learning rate: 0.00449 
+2024-09-12 08:09:19.557171: train_loss -0.8736 
+2024-09-12 08:09:19.557313: val_loss -0.6036 
+2024-09-12 08:09:19.557363: Pseudo dice [0.5116, 0.7925] 
+2024-09-12 08:09:19.557413: Epoch time: 245.88 s 
+2024-09-12 08:09:20.581852:  
+2024-09-12 08:09:20.582084: Epoch 590 
+2024-09-12 08:09:20.582170: Current learning rate: 0.00448 
+2024-09-12 08:13:26.616275: train_loss -0.8735 
+2024-09-12 08:13:26.616468: val_loss -0.5573 
+2024-09-12 08:13:26.616520: Pseudo dice [0.4404, 0.7439] 
+2024-09-12 08:13:26.616572: Epoch time: 246.04 s 
+2024-09-12 08:13:27.615675:  
+2024-09-12 08:13:27.615921: Epoch 591 
+2024-09-12 08:13:27.616024: Current learning rate: 0.00447 
+2024-09-12 08:17:33.393452: train_loss -0.8761 
+2024-09-12 08:17:33.393578: val_loss -0.604 
+2024-09-12 08:17:33.393629: Pseudo dice [0.4627, 0.7915] 
+2024-09-12 08:17:33.393681: Epoch time: 245.78 s 
+2024-09-12 08:17:34.412611:  
+2024-09-12 08:17:34.412863: Epoch 592 
+2024-09-12 08:17:34.412953: Current learning rate: 0.00446 
+2024-09-12 08:21:40.149497: train_loss -0.8685 
+2024-09-12 08:21:40.149636: val_loss -0.5749 
+2024-09-12 08:21:40.149686: Pseudo dice [0.4564, 0.7896] 
+2024-09-12 08:21:40.149737: Epoch time: 245.74 s 
+2024-09-12 08:21:41.148995:  
+2024-09-12 08:21:41.149207: Epoch 593 
+2024-09-12 08:21:41.149298: Current learning rate: 0.00445 
+2024-09-12 08:25:46.838409: train_loss -0.8758 
+2024-09-12 08:25:46.838545: val_loss -0.6004 
+2024-09-12 08:25:46.838594: Pseudo dice [0.4628, 0.7925] 
+2024-09-12 08:25:46.838644: Epoch time: 245.69 s 
+2024-09-12 08:25:47.837585:  
+2024-09-12 08:25:47.837849: Epoch 594 
+2024-09-12 08:25:47.837977: Current learning rate: 0.00444 
+2024-09-12 08:29:53.683568: train_loss -0.841 
+2024-09-12 08:29:53.683705: val_loss -0.5763 
+2024-09-12 08:29:53.683755: Pseudo dice [0.4637, 0.7654] 
+2024-09-12 08:29:53.683813: Epoch time: 245.85 s 
+2024-09-12 08:29:54.698581:  
+2024-09-12 08:29:54.698769: Epoch 595 
+2024-09-12 08:29:54.698884: Current learning rate: 0.00443 
+2024-09-12 08:34:00.474290: train_loss -0.8485 
+2024-09-12 08:34:00.474436: val_loss -0.5843 
+2024-09-12 08:34:00.474487: Pseudo dice [0.4375, 0.8092] 
+2024-09-12 08:34:00.474539: Epoch time: 245.78 s 
+2024-09-12 08:34:01.475078:  
+2024-09-12 08:34:01.475236: Epoch 596 
+2024-09-12 08:34:01.475321: Current learning rate: 0.00442 
+2024-09-12 08:38:07.529516: train_loss -0.8617 
+2024-09-12 08:38:07.529648: val_loss -0.588 
+2024-09-12 08:38:07.529703: Pseudo dice [0.4739, 0.8009] 
+2024-09-12 08:38:07.529755: Epoch time: 246.06 s 
+2024-09-12 08:38:08.524515:  
+2024-09-12 08:38:08.524755: Epoch 597 
+2024-09-12 08:38:08.524836: Current learning rate: 0.00441 
+2024-09-12 08:42:14.663960: train_loss -0.868 
+2024-09-12 08:42:14.664098: val_loss -0.6145 
+2024-09-12 08:42:14.664148: Pseudo dice [0.5146, 0.7984] 
+2024-09-12 08:42:14.664199: Epoch time: 246.14 s 
+2024-09-12 08:42:15.674063:  
+2024-09-12 08:42:15.674293: Epoch 598 
+2024-09-12 08:42:15.674377: Current learning rate: 0.0044 
+2024-09-12 08:46:21.586674: train_loss -0.872 
+2024-09-12 08:46:21.586845: val_loss -0.6104 
+2024-09-12 08:46:21.586930: Pseudo dice [0.5173, 0.7894] 
+2024-09-12 08:46:21.586983: Epoch time: 245.91 s 
+2024-09-12 08:46:22.569823:  
+2024-09-12 08:46:22.569987: Epoch 599 
+2024-09-12 08:46:22.570092: Current learning rate: 0.00439 
+2024-09-12 08:50:28.454240: train_loss -0.8653 
+2024-09-12 08:50:28.454417: val_loss -0.536 
+2024-09-12 08:50:28.454468: Pseudo dice [0.5068, 0.7227] 
+2024-09-12 08:50:28.454521: Epoch time: 245.89 s 
+2024-09-12 08:50:32.455759:  
+2024-09-12 08:50:32.456000: Epoch 600 
+2024-09-12 08:50:32.456082: Current learning rate: 0.00438 
+2024-09-12 08:54:38.462417: train_loss -0.8631 
+2024-09-12 08:54:38.462580: val_loss -0.5794 
+2024-09-12 08:54:38.462631: Pseudo dice [0.4832, 0.762] 
+2024-09-12 08:54:38.462681: Epoch time: 246.01 s 
+2024-09-12 08:54:39.459903:  
+2024-09-12 08:54:39.460092: Epoch 601 
+2024-09-12 08:54:39.460176: Current learning rate: 0.00437 
+2024-09-12 08:58:45.246192: train_loss -0.861 
+2024-09-12 08:58:45.246324: val_loss -0.6069 
+2024-09-12 08:58:45.246375: Pseudo dice [0.4952, 0.7958] 
+2024-09-12 08:58:45.246426: Epoch time: 245.79 s 
+2024-09-12 08:58:46.229719:  
+2024-09-12 08:58:46.229910: Epoch 602 
+2024-09-12 08:58:46.229994: Current learning rate: 0.00436 
+2024-09-12 09:02:52.139789: train_loss -0.8682 
+2024-09-12 09:02:52.139973: val_loss -0.5724 
+2024-09-12 09:02:52.140042: Pseudo dice [0.4834, 0.7713] 
+2024-09-12 09:02:52.140093: Epoch time: 245.91 s 
+2024-09-12 09:02:53.133808:  
+2024-09-12 09:02:53.134012: Epoch 603 
+2024-09-12 09:02:53.134114: Current learning rate: 0.00435 
+2024-09-12 09:06:59.004069: train_loss -0.8705 
+2024-09-12 09:06:59.004248: val_loss -0.604 
+2024-09-12 09:06:59.004301: Pseudo dice [0.5116, 0.8033] 
+2024-09-12 09:06:59.004353: Epoch time: 245.87 s 
+2024-09-12 09:06:59.998067:  
+2024-09-12 09:06:59.998298: Epoch 604 
+2024-09-12 09:06:59.998445: Current learning rate: 0.00434 
+2024-09-12 09:11:06.042080: train_loss -0.8743 
+2024-09-12 09:11:06.042222: val_loss -0.5797 
+2024-09-12 09:11:06.042272: Pseudo dice [0.5179, 0.7516] 
+2024-09-12 09:11:06.042322: Epoch time: 246.05 s 
+2024-09-12 09:11:07.024495:  
+2024-09-12 09:11:07.024698: Epoch 605 
+2024-09-12 09:11:07.024809: Current learning rate: 0.00433 
+2024-09-12 09:15:12.949478: train_loss -0.8698 
+2024-09-12 09:15:12.949618: val_loss -0.569 
+2024-09-12 09:15:12.949669: Pseudo dice [0.5071, 0.7349] 
+2024-09-12 09:15:12.949719: Epoch time: 245.93 s 
+2024-09-12 09:15:13.940575:  
+2024-09-12 09:15:13.940781: Epoch 606 
+2024-09-12 09:15:13.940880: Current learning rate: 0.00432 
+2024-09-12 09:19:20.099710: train_loss -0.877 
+2024-09-12 09:19:20.099869: val_loss -0.5941 
+2024-09-12 09:19:20.099925: Pseudo dice [0.4834, 0.8082] 
+2024-09-12 09:19:20.099976: Epoch time: 246.16 s 
+2024-09-12 09:19:21.088101:  
+2024-09-12 09:19:21.088277: Epoch 607 
+2024-09-12 09:19:21.088357: Current learning rate: 0.00431 
+2024-09-12 09:23:27.076910: train_loss -0.8782 
+2024-09-12 09:23:27.077046: val_loss -0.6153 
+2024-09-12 09:23:27.077100: Pseudo dice [0.5057, 0.8094] 
+2024-09-12 09:23:27.077151: Epoch time: 245.99 s 
+2024-09-12 09:23:28.063738:  
+2024-09-12 09:23:28.063997: Epoch 608 
+2024-09-12 09:23:28.064079: Current learning rate: 0.0043 
+2024-09-12 09:27:33.990282: train_loss -0.8802 
+2024-09-12 09:27:33.990425: val_loss -0.5723 
+2024-09-12 09:27:33.990475: Pseudo dice [0.4823, 0.7992] 
+2024-09-12 09:27:33.990530: Epoch time: 245.93 s 
+2024-09-12 09:27:34.995713:  
+2024-09-12 09:27:34.995942: Epoch 609 
+2024-09-12 09:27:34.996027: Current learning rate: 0.00429 
+2024-09-12 09:31:40.962011: train_loss -0.8794 
+2024-09-12 09:31:40.962154: val_loss -0.5981 
+2024-09-12 09:31:40.962205: Pseudo dice [0.5497, 0.7649] 
+2024-09-12 09:31:40.962257: Epoch time: 245.97 s 
+2024-09-12 09:31:41.957331:  
+2024-09-12 09:31:41.957498: Epoch 610 
+2024-09-12 09:31:41.957587: Current learning rate: 0.00429 
+2024-09-12 09:35:48.099072: train_loss -0.884 
+2024-09-12 09:35:48.099207: val_loss -0.598 
+2024-09-12 09:35:48.099257: Pseudo dice [0.4772, 0.7958] 
+2024-09-12 09:35:48.099435: Epoch time: 246.14 s 
+2024-09-12 09:35:50.003964:  
+2024-09-12 09:35:50.004246: Epoch 611 
+2024-09-12 09:35:50.004357: Current learning rate: 0.00428 
+2024-09-12 09:39:56.136594: train_loss -0.8803 
+2024-09-12 09:39:56.136752: val_loss -0.5951 
+2024-09-12 09:39:56.136804: Pseudo dice [0.4589, 0.7883] 
+2024-09-12 09:39:56.136856: Epoch time: 246.13 s 
+2024-09-12 09:39:57.105857:  
+2024-09-12 09:39:57.106094: Epoch 612 
+2024-09-12 09:39:57.106190: Current learning rate: 0.00427 
+2024-09-12 09:44:03.549536: train_loss -0.882 
+2024-09-12 09:44:03.549702: val_loss -0.5687 
+2024-09-12 09:44:03.549755: Pseudo dice [0.4351, 0.7995] 
+2024-09-12 09:44:03.549807: Epoch time: 246.45 s 
+2024-09-12 09:44:04.545997:  
+2024-09-12 09:44:04.546225: Epoch 613 
+2024-09-12 09:44:04.546311: Current learning rate: 0.00426 
+2024-09-12 09:48:10.834886: train_loss -0.8831 
+2024-09-12 09:48:10.835028: val_loss -0.6226 
+2024-09-12 09:48:10.835080: Pseudo dice [0.5136, 0.8052] 
+2024-09-12 09:48:10.835131: Epoch time: 246.29 s 
+2024-09-12 09:48:11.845672:  
+2024-09-12 09:48:11.845877: Epoch 614 
+2024-09-12 09:48:11.845956: Current learning rate: 0.00425 
+2024-09-12 09:52:17.898307: train_loss -0.8813 
+2024-09-12 09:52:17.898467: val_loss -0.6004 
+2024-09-12 09:52:17.898518: Pseudo dice [0.4908, 0.7886] 
+2024-09-12 09:52:17.898569: Epoch time: 246.05 s 
+2024-09-12 09:52:18.890546:  
+2024-09-12 09:52:18.890794: Epoch 615 
+2024-09-12 09:52:18.890910: Current learning rate: 0.00424 
+2024-09-12 09:56:25.109321: train_loss -0.8819 
+2024-09-12 09:56:25.109572: val_loss -0.5814 
+2024-09-12 09:56:25.109633: Pseudo dice [0.495, 0.799] 
+2024-09-12 09:56:25.109689: Epoch time: 246.22 s 
+2024-09-12 09:56:26.100366:  
+2024-09-12 09:56:26.100600: Epoch 616 
+2024-09-12 09:56:26.100699: Current learning rate: 0.00423 
+2024-09-12 10:00:32.140230: train_loss -0.8856 
+2024-09-12 10:00:32.140385: val_loss -0.598 
+2024-09-12 10:00:32.140441: Pseudo dice [0.5287, 0.7674] 
+2024-09-12 10:00:32.140497: Epoch time: 246.04 s 
+2024-09-12 10:00:33.160915:  
+2024-09-12 10:00:33.161116: Epoch 617 
+2024-09-12 10:00:33.161211: Current learning rate: 0.00422 
+2024-09-12 10:04:39.113698: train_loss -0.8839 
+2024-09-12 10:04:39.113885: val_loss -0.6176 
+2024-09-12 10:04:39.113948: Pseudo dice [0.5354, 0.8012] 
+2024-09-12 10:04:39.114006: Epoch time: 245.95 s 
+2024-09-12 10:04:40.102924:  
+2024-09-12 10:04:40.103168: Epoch 618 
+2024-09-12 10:04:40.103254: Current learning rate: 0.00421 
+2024-09-12 10:08:46.077707: train_loss -0.8886 
+2024-09-12 10:08:46.077854: val_loss -0.602 
+2024-09-12 10:08:46.077912: Pseudo dice [0.4679, 0.7983] 
+2024-09-12 10:08:46.077970: Epoch time: 245.98 s 
+2024-09-12 10:08:47.078132:  
+2024-09-12 10:08:47.078362: Epoch 619 
+2024-09-12 10:08:47.078450: Current learning rate: 0.0042 
+2024-09-12 10:12:53.024335: train_loss -0.8885 
+2024-09-12 10:12:53.024482: val_loss -0.61 
+2024-09-12 10:12:53.024537: Pseudo dice [0.4716, 0.8103] 
+2024-09-12 10:12:53.024603: Epoch time: 245.95 s 
+2024-09-12 10:12:54.003950:  
+2024-09-12 10:12:54.004212: Epoch 620 
+2024-09-12 10:12:54.004304: Current learning rate: 0.00419 
+2024-09-12 10:16:59.969283: train_loss -0.8861 
+2024-09-12 10:16:59.969516: val_loss -0.6059 
+2024-09-12 10:16:59.969574: Pseudo dice [0.4768, 0.7995] 
+2024-09-12 10:16:59.969633: Epoch time: 245.97 s 
+2024-09-12 10:17:00.987777:  
+2024-09-12 10:17:00.988015: Epoch 621 
+2024-09-12 10:17:00.988103: Current learning rate: 0.00418 
+2024-09-12 10:21:07.004832: train_loss -0.8888 
+2024-09-12 10:21:07.004996: val_loss -0.6341 
+2024-09-12 10:21:07.005063: Pseudo dice [0.5697, 0.7956] 
+2024-09-12 10:21:07.005201: Epoch time: 246.02 s 
+2024-09-12 10:21:07.999985:  
+2024-09-12 10:21:08.000171: Epoch 622 
+2024-09-12 10:21:08.000281: Current learning rate: 0.00417 
+2024-09-12 10:25:14.044363: train_loss -0.8868 
+2024-09-12 10:25:14.044521: val_loss -0.6172 
+2024-09-12 10:25:14.044578: Pseudo dice [0.5265, 0.7798] 
+2024-09-12 10:25:14.044635: Epoch time: 246.05 s 
+2024-09-12 10:25:15.026590:  
+2024-09-12 10:25:15.026786: Epoch 623 
+2024-09-12 10:25:15.026902: Current learning rate: 0.00416 
+2024-09-12 10:29:20.990039: train_loss -0.8847 
+2024-09-12 10:29:20.990186: val_loss -0.6219 
+2024-09-12 10:29:20.990242: Pseudo dice [0.5065, 0.7999] 
+2024-09-12 10:29:20.990297: Epoch time: 245.97 s 
+2024-09-12 10:29:22.003963:  
+2024-09-12 10:29:22.004128: Epoch 624 
+2024-09-12 10:29:22.004217: Current learning rate: 0.00415 
+2024-09-12 10:33:28.012597: train_loss -0.8865 
+2024-09-12 10:33:28.012761: val_loss -0.5833 
+2024-09-12 10:33:28.012819: Pseudo dice [0.4741, 0.7884] 
+2024-09-12 10:33:28.012876: Epoch time: 246.01 s 
+2024-09-12 10:33:29.030654:  
+2024-09-12 10:33:29.030829: Epoch 625 
+2024-09-12 10:33:29.030929: Current learning rate: 0.00414 
+2024-09-12 10:37:35.206637: train_loss -0.8861 
+2024-09-12 10:37:35.206788: val_loss -0.6075 
+2024-09-12 10:37:35.206846: Pseudo dice [0.5768, 0.7848] 
+2024-09-12 10:37:35.206901: Epoch time: 246.18 s 
+2024-09-12 10:37:35.206945: Yayy! New best EMA pseudo Dice: 0.6488 
+2024-09-12 10:37:39.079653:  
+2024-09-12 10:37:39.079844: Epoch 626 
+2024-09-12 10:37:39.079937: Current learning rate: 0.00413 
+2024-09-12 10:41:45.076577: train_loss -0.8806 
+2024-09-12 10:41:45.076726: val_loss -0.6076 
+2024-09-12 10:41:45.076783: Pseudo dice [0.5505, 0.7558] 
+2024-09-12 10:41:45.076837: Epoch time: 246.0 s 
+2024-09-12 10:41:45.076884: Yayy! New best EMA pseudo Dice: 0.6493 
+2024-09-12 10:41:49.050159:  
+2024-09-12 10:41:49.050330: Epoch 627 
+2024-09-12 10:41:49.050421: Current learning rate: 0.00412 
+2024-09-12 10:45:55.089789: train_loss -0.876 
+2024-09-12 10:45:55.089941: val_loss -0.5499 
+2024-09-12 10:45:55.089997: Pseudo dice [0.4367, 0.7792] 
+2024-09-12 10:45:55.090052: Epoch time: 246.04 s 
+2024-09-12 10:45:56.080873:  
+2024-09-12 10:45:56.081027: Epoch 628 
+2024-09-12 10:45:56.081115: Current learning rate: 0.00411 
+2024-09-12 10:50:01.946798: train_loss -0.8767 
+2024-09-12 10:50:01.946950: val_loss -0.6124 
+2024-09-12 10:50:01.947008: Pseudo dice [0.5215, 0.797] 
+2024-09-12 10:50:01.947064: Epoch time: 245.87 s 
+2024-09-12 10:50:02.935492:  
+2024-09-12 10:50:02.935706: Epoch 629 
+2024-09-12 10:50:02.935795: Current learning rate: 0.0041 
+2024-09-12 10:54:08.844860: train_loss -0.8796 
+2024-09-12 10:54:08.845014: val_loss -0.5954 
+2024-09-12 10:54:08.845071: Pseudo dice [0.4512, 0.8099] 
+2024-09-12 10:54:08.845126: Epoch time: 245.91 s 
+2024-09-12 10:54:09.836720:  
+2024-09-12 10:54:09.836909: Epoch 630 
+2024-09-12 10:54:09.837001: Current learning rate: 0.00409 
+2024-09-12 10:58:15.762725: train_loss -0.8764 
+2024-09-12 10:58:15.762879: val_loss -0.6569 
+2024-09-12 10:58:15.762934: Pseudo dice [0.5421, 0.8233] 
+2024-09-12 10:58:15.762990: Epoch time: 245.93 s 
+2024-09-12 10:58:16.773870:  
+2024-09-12 10:58:16.774083: Epoch 631 
+2024-09-12 10:58:16.774174: Current learning rate: 0.00408 
+2024-09-12 11:02:22.801243: train_loss -0.8842 
+2024-09-12 11:02:22.801390: val_loss -0.6184 
+2024-09-12 11:02:22.801458: Pseudo dice [0.5158, 0.7891] 
+2024-09-12 11:02:22.801514: Epoch time: 246.03 s 
+2024-09-12 11:02:23.790949:  
+2024-09-12 11:02:23.791100: Epoch 632 
+2024-09-12 11:02:23.791186: Current learning rate: 0.00407 
+2024-09-12 11:06:29.725666: train_loss -0.8834 
+2024-09-12 11:06:29.725814: val_loss -0.6007 
+2024-09-12 11:06:29.725870: Pseudo dice [0.5268, 0.7817] 
+2024-09-12 11:06:29.725926: Epoch time: 245.94 s 
+2024-09-12 11:06:29.725970: Yayy! New best EMA pseudo Dice: 0.6496 
+2024-09-12 11:06:34.639253:  
+2024-09-12 11:06:34.639574: Epoch 633 
+2024-09-12 11:06:34.639702: Current learning rate: 0.00406 
+2024-09-12 11:10:40.963313: train_loss -0.882 
+2024-09-12 11:10:40.963464: val_loss -0.5654 
+2024-09-12 11:10:40.963521: Pseudo dice [0.4656, 0.7817] 
+2024-09-12 11:10:40.963577: Epoch time: 246.33 s 
+2024-09-12 11:10:41.922187:  
+2024-09-12 11:10:41.922449: Epoch 634 
+2024-09-12 11:10:41.922536: Current learning rate: 0.00405 
+2024-09-12 11:14:47.614708: train_loss -0.8771 
+2024-09-12 11:14:47.614857: val_loss -0.6106 
+2024-09-12 11:14:47.614917: Pseudo dice [0.5381, 0.7666] 
+2024-09-12 11:14:47.614976: Epoch time: 245.69 s 
+2024-09-12 11:14:48.622873:  
+2024-09-12 11:14:48.623110: Epoch 635 
+2024-09-12 11:14:48.623198: Current learning rate: 0.00404 
+2024-09-12 11:18:54.510081: train_loss -0.8754 
+2024-09-12 11:18:54.510234: val_loss -0.5966 
+2024-09-12 11:18:54.510291: Pseudo dice [0.5088, 0.7797] 
+2024-09-12 11:18:54.510345: Epoch time: 245.89 s 
+2024-09-12 11:18:55.511480:  
+2024-09-12 11:18:55.511788: Epoch 636 
+2024-09-12 11:18:55.511896: Current learning rate: 0.00403 
+2024-09-12 11:23:01.299401: train_loss -0.8752 
+2024-09-12 11:23:01.299580: val_loss -0.5873 
+2024-09-12 11:23:01.299636: Pseudo dice [0.4867, 0.7724] 
+2024-09-12 11:23:01.299692: Epoch time: 245.79 s 
+2024-09-12 11:23:02.288724:  
+2024-09-12 11:23:02.289015: Epoch 637 
+2024-09-12 11:23:02.289104: Current learning rate: 0.00402 
+2024-09-12 11:27:08.035464: train_loss -0.8854 
+2024-09-12 11:27:08.035610: val_loss -0.6266 
+2024-09-12 11:27:08.035667: Pseudo dice [0.5441, 0.7843] 
+2024-09-12 11:27:08.035722: Epoch time: 245.75 s 
+2024-09-12 11:27:09.035422:  
+2024-09-12 11:27:09.035629: Epoch 638 
+2024-09-12 11:27:09.035738: Current learning rate: 0.00401 
+2024-09-12 11:31:14.710861: train_loss -0.8818 
+2024-09-12 11:31:14.711036: val_loss -0.6212 
+2024-09-12 11:31:14.711095: Pseudo dice [0.4893, 0.813] 
+2024-09-12 11:31:14.711158: Epoch time: 245.68 s 
+2024-09-12 11:31:15.694850:  
+2024-09-12 11:31:15.695026: Epoch 639 
+2024-09-12 11:31:15.695145: Current learning rate: 0.004 
+2024-09-12 11:35:21.556366: train_loss -0.8836 
+2024-09-12 11:35:21.556554: val_loss -0.5699 
+2024-09-12 11:35:21.556610: Pseudo dice [0.4575, 0.7642] 
+2024-09-12 11:35:21.556668: Epoch time: 245.86 s 
+2024-09-12 11:35:22.550265:  
+2024-09-12 11:35:22.550473: Epoch 640 
+2024-09-12 11:35:22.550561: Current learning rate: 0.00399 
+2024-09-12 11:39:28.507296: train_loss -0.8783 
+2024-09-12 11:39:28.507455: val_loss -0.5936 
+2024-09-12 11:39:28.507541: Pseudo dice [0.476, 0.7981] 
+2024-09-12 11:39:28.507598: Epoch time: 245.96 s 
+2024-09-12 11:39:29.495836:  
+2024-09-12 11:39:29.496042: Epoch 641 
+2024-09-12 11:39:29.496134: Current learning rate: 0.00398 
+2024-09-12 11:43:35.388247: train_loss -0.8806 
+2024-09-12 11:43:35.388388: val_loss -0.6245 
+2024-09-12 11:43:35.388447: Pseudo dice [0.5438, 0.8091] 
+2024-09-12 11:43:35.388504: Epoch time: 245.89 s 
+2024-09-12 11:43:36.412414:  
+2024-09-12 11:43:36.412627: Epoch 642 
+2024-09-12 11:43:36.412716: Current learning rate: 0.00397 
+2024-09-12 11:47:42.256520: train_loss -0.8796 
+2024-09-12 11:47:42.256652: val_loss -0.5621 
+2024-09-12 11:47:42.256708: Pseudo dice [0.4122, 0.7867] 
+2024-09-12 11:47:42.256763: Epoch time: 245.85 s 
+2024-09-12 11:47:43.254345:  
+2024-09-12 11:47:43.254534: Epoch 643 
+2024-09-12 11:47:43.254620: Current learning rate: 0.00396 
+2024-09-12 11:51:48.968180: train_loss -0.8824 
+2024-09-12 11:51:48.968347: val_loss -0.6003 
+2024-09-12 11:51:48.968404: Pseudo dice [0.4882, 0.7812] 
+2024-09-12 11:51:48.968460: Epoch time: 245.72 s 
+2024-09-12 11:51:49.957325:  
+2024-09-12 11:51:49.957556: Epoch 644 
+2024-09-12 11:51:49.957644: Current learning rate: 0.00395 
+2024-09-12 11:55:55.759991: train_loss -0.8857 
+2024-09-12 11:55:55.760144: val_loss -0.6037 
+2024-09-12 11:55:55.760257: Pseudo dice [0.4945, 0.8062] 
+2024-09-12 11:55:55.760338: Epoch time: 245.8 s 
+2024-09-12 11:55:56.778010:  
+2024-09-12 11:55:56.778322: Epoch 645 
+2024-09-12 11:55:56.778458: Current learning rate: 0.00394 
+2024-09-12 12:00:02.787586: train_loss -0.8855 
+2024-09-12 12:00:02.787740: val_loss -0.616 
+2024-09-12 12:00:02.787796: Pseudo dice [0.4882, 0.817] 
+2024-09-12 12:00:02.787859: Epoch time: 246.01 s 
+2024-09-12 12:00:03.795551:  
+2024-09-12 12:00:03.795785: Epoch 646 
+2024-09-12 12:00:03.795888: Current learning rate: 0.00393 
+2024-09-12 12:04:09.764055: train_loss -0.8645 
+2024-09-12 12:04:09.764214: val_loss -0.6191 
+2024-09-12 12:04:09.764270: Pseudo dice [0.5635, 0.7819] 
+2024-09-12 12:04:09.764326: Epoch time: 245.97 s 
+2024-09-12 12:04:10.781464:  
+2024-09-12 12:04:10.781736: Epoch 647 
+2024-09-12 12:04:10.781878: Current learning rate: 0.00392 
+2024-09-12 12:08:16.774341: train_loss -0.8524 
+2024-09-12 12:08:16.774508: val_loss -0.5701 
+2024-09-12 12:08:16.774566: Pseudo dice [0.4889, 0.7666] 
+2024-09-12 12:08:16.774667: Epoch time: 246.0 s 
+2024-09-12 12:08:17.761693:  
+2024-09-12 12:08:17.761852: Epoch 648 
+2024-09-12 12:08:17.761941: Current learning rate: 0.00391 
+2024-09-12 12:12:23.707159: train_loss -0.875 
+2024-09-12 12:12:23.707310: val_loss -0.5712 
+2024-09-12 12:12:23.707365: Pseudo dice [0.4401, 0.7866] 
+2024-09-12 12:12:23.707422: Epoch time: 245.95 s 
+2024-09-12 12:12:24.706453:  
+2024-09-12 12:12:24.706714: Epoch 649 
+2024-09-12 12:12:24.706805: Current learning rate: 0.0039 
+2024-09-12 12:16:30.679591: train_loss -0.8774 
+2024-09-12 12:16:30.679735: val_loss -0.6199 
+2024-09-12 12:16:30.679792: Pseudo dice [0.5344, 0.7988] 
+2024-09-12 12:16:30.679865: Epoch time: 245.98 s 
+2024-09-12 12:16:34.606115:  
+2024-09-12 12:16:34.606279: Epoch 650 
+2024-09-12 12:16:34.606364: Current learning rate: 0.00389 
+2024-09-12 12:20:40.569565: train_loss -0.8858 
+2024-09-12 12:20:40.569722: val_loss -0.6073 
+2024-09-12 12:20:40.569779: Pseudo dice [0.5159, 0.8054] 
+2024-09-12 12:20:40.569836: Epoch time: 245.97 s 
+2024-09-12 12:20:41.556453:  
+2024-09-12 12:20:41.556638: Epoch 651 
+2024-09-12 12:20:41.556726: Current learning rate: 0.00388 
+2024-09-12 12:24:47.264722: train_loss -0.8851 
+2024-09-12 12:24:47.264871: val_loss -0.5908 
+2024-09-12 12:24:47.264928: Pseudo dice [0.5209, 0.7831] 
+2024-09-12 12:24:47.264984: Epoch time: 245.71 s 
+2024-09-12 12:24:48.243465:  
+2024-09-12 12:24:48.243706: Epoch 652 
+2024-09-12 12:24:48.243827: Current learning rate: 0.00387 
+2024-09-12 12:28:53.919258: train_loss -0.8871 
+2024-09-12 12:28:53.919407: val_loss -0.5893 
+2024-09-12 12:28:53.919463: Pseudo dice [0.5503, 0.7711] 
+2024-09-12 12:28:53.919518: Epoch time: 245.68 s 
+2024-09-12 12:28:54.914489:  
+2024-09-12 12:28:54.914697: Epoch 653 
+2024-09-12 12:28:54.914787: Current learning rate: 0.00386 
+2024-09-12 12:33:00.742608: train_loss -0.8819 
+2024-09-12 12:33:00.742774: val_loss -0.6268 
+2024-09-12 12:33:00.742831: Pseudo dice [0.5486, 0.7937] 
+2024-09-12 12:33:00.742885: Epoch time: 245.83 s 
+2024-09-12 12:33:00.742930: Yayy! New best EMA pseudo Dice: 0.6499 
+2024-09-12 12:33:04.804835:  
+2024-09-12 12:33:04.805071: Epoch 654 
+2024-09-12 12:33:04.805160: Current learning rate: 0.00385 
+2024-09-12 12:37:10.861732: train_loss -0.882 
+2024-09-12 12:37:10.861896: val_loss -0.582 
+2024-09-12 12:37:10.861953: Pseudo dice [0.4914, 0.7991] 
+2024-09-12 12:37:10.862008: Epoch time: 246.06 s 
+2024-09-12 12:37:11.854754:  
+2024-09-12 12:37:11.854917: Epoch 655 
+2024-09-12 12:37:11.855005: Current learning rate: 0.00384 
+2024-09-12 12:41:18.625601: train_loss -0.8891 
+2024-09-12 12:41:18.625757: val_loss -0.6016 
+2024-09-12 12:41:18.625816: Pseudo dice [0.483, 0.7862] 
+2024-09-12 12:41:18.625871: Epoch time: 246.77 s 
+2024-09-12 12:41:19.583506:  
+2024-09-12 12:41:19.583770: Epoch 656 
+2024-09-12 12:41:19.583867: Current learning rate: 0.00383 
+2024-09-12 12:45:25.361844: train_loss -0.8928 
+2024-09-12 12:45:25.362001: val_loss -0.5763 
+2024-09-12 12:45:25.362058: Pseudo dice [0.4728, 0.7855] 
+2024-09-12 12:45:25.362113: Epoch time: 245.78 s 
+2024-09-12 12:45:26.354782:  
+2024-09-12 12:45:26.354989: Epoch 657 
+2024-09-12 12:45:26.355119: Current learning rate: 0.00382 
+2024-09-12 12:49:32.306098: train_loss -0.8852 
+2024-09-12 12:49:32.306246: val_loss -0.5952 
+2024-09-12 12:49:32.306347: Pseudo dice [0.4789, 0.775] 
+2024-09-12 12:49:32.306406: Epoch time: 245.95 s 
+2024-09-12 12:49:33.297850:  
+2024-09-12 12:49:33.298069: Epoch 658 
+2024-09-12 12:49:33.298162: Current learning rate: 0.00381 
+2024-09-12 12:53:39.453044: train_loss -0.8678 
+2024-09-12 12:53:39.453196: val_loss -0.5641 
+2024-09-12 12:53:39.453253: Pseudo dice [0.5238, 0.7757] 
+2024-09-12 12:53:39.453310: Epoch time: 246.16 s 
+2024-09-12 12:53:40.454665:  
+2024-09-12 12:53:40.454960: Epoch 659 
+2024-09-12 12:53:40.455053: Current learning rate: 0.0038 
+2024-09-12 12:57:46.505094: train_loss -0.8649 
+2024-09-12 12:57:46.505242: val_loss -0.6032 
+2024-09-12 12:57:46.505299: Pseudo dice [0.5301, 0.7931] 
+2024-09-12 12:57:46.505355: Epoch time: 246.05 s 
+2024-09-12 12:57:47.527881:  
+2024-09-12 12:57:47.528083: Epoch 660 
+2024-09-12 12:57:47.528222: Current learning rate: 0.00379 
+2024-09-12 13:01:53.609339: train_loss -0.8721 
+2024-09-12 13:01:53.609476: val_loss -0.5941 
+2024-09-12 13:01:53.609532: Pseudo dice [0.5304, 0.7683] 
+2024-09-12 13:01:53.609588: Epoch time: 246.08 s 
+2024-09-12 13:01:54.606417:  
+2024-09-12 13:01:54.606588: Epoch 661 
+2024-09-12 13:01:54.606718: Current learning rate: 0.00378 
+2024-09-12 13:06:00.678652: train_loss -0.8735 
+2024-09-12 13:06:00.678804: val_loss -0.578 
+2024-09-12 13:06:00.678861: Pseudo dice [0.4519, 0.7937] 
+2024-09-12 13:06:00.678916: Epoch time: 246.07 s 
+2024-09-12 13:06:01.665711:  
+2024-09-12 13:06:01.665931: Epoch 662 
+2024-09-12 13:06:01.666020: Current learning rate: 0.00377 
+2024-09-12 13:10:07.763448: train_loss -0.8819 
+2024-09-12 13:10:07.763595: val_loss -0.5736 
+2024-09-12 13:10:07.763654: Pseudo dice [0.4574, 0.7708] 
+2024-09-12 13:10:07.763711: Epoch time: 246.1 s 
+2024-09-12 13:10:08.786760:  
+2024-09-12 13:10:08.787057: Epoch 663 
+2024-09-12 13:10:08.787182: Current learning rate: 0.00376 
+2024-09-12 13:14:14.838192: train_loss -0.8869 
+2024-09-12 13:14:14.838347: val_loss -0.5783 
+2024-09-12 13:14:14.838404: Pseudo dice [0.4654, 0.7892] 
+2024-09-12 13:14:14.838465: Epoch time: 246.05 s 
+2024-09-12 13:14:15.827005:  
+2024-09-12 13:14:15.827238: Epoch 664 
+2024-09-12 13:14:15.827330: Current learning rate: 0.00375 
+2024-09-12 13:18:21.802224: train_loss -0.8857 
+2024-09-12 13:18:21.802420: val_loss -0.5953 
+2024-09-12 13:18:21.802570: Pseudo dice [0.466, 0.7788] 
+2024-09-12 13:18:21.802642: Epoch time: 245.98 s 
+2024-09-12 13:18:22.833940:  
+2024-09-12 13:18:22.834149: Epoch 665 
+2024-09-12 13:18:22.834238: Current learning rate: 0.00374 
+2024-09-12 13:22:28.851457: train_loss -0.8884 
+2024-09-12 13:22:28.851602: val_loss -0.5878 
+2024-09-12 13:22:28.851657: Pseudo dice [0.4516, 0.7748] 
+2024-09-12 13:22:28.851714: Epoch time: 246.02 s 
+2024-09-12 13:22:29.864467:  
+2024-09-12 13:22:29.864698: Epoch 666 
+2024-09-12 13:22:29.864793: Current learning rate: 0.00373 
+2024-09-12 13:26:35.969534: train_loss -0.8886 
+2024-09-12 13:26:35.969685: val_loss -0.576 
+2024-09-12 13:26:35.969742: Pseudo dice [0.5126, 0.7833] 
+2024-09-12 13:26:35.969797: Epoch time: 246.11 s 
+2024-09-12 13:26:36.979205:  
+2024-09-12 13:26:36.979452: Epoch 667 
+2024-09-12 13:26:36.979590: Current learning rate: 0.00372 
+2024-09-12 13:30:42.962378: train_loss -0.8884 
+2024-09-12 13:30:42.962527: val_loss -0.5968 
+2024-09-12 13:30:42.962584: Pseudo dice [0.4488, 0.7981] 
+2024-09-12 13:30:42.962639: Epoch time: 245.99 s 
+2024-09-12 13:30:43.992312:  
+2024-09-12 13:30:43.992465: Epoch 668 
+2024-09-12 13:30:43.992553: Current learning rate: 0.00371 
+2024-09-12 13:34:49.979233: train_loss -0.8895 
+2024-09-12 13:34:49.979388: val_loss -0.5832 
+2024-09-12 13:34:49.979445: Pseudo dice [0.4723, 0.8007] 
+2024-09-12 13:34:49.979501: Epoch time: 245.99 s 
+2024-09-12 13:34:50.982603:  
+2024-09-12 13:34:50.982796: Epoch 669 
+2024-09-12 13:34:50.982888: Current learning rate: 0.0037 
+2024-09-12 13:38:56.692158: train_loss -0.8951 
+2024-09-12 13:38:56.692312: val_loss -0.6389 
+2024-09-12 13:38:56.692369: Pseudo dice [0.5512, 0.8096] 
+2024-09-12 13:38:56.692425: Epoch time: 245.71 s 
+2024-09-12 13:38:57.713990:  
+2024-09-12 13:38:57.714194: Epoch 670 
+2024-09-12 13:38:57.714316: Current learning rate: 0.00369 
+2024-09-12 13:43:03.472556: train_loss -0.8918 
+2024-09-12 13:43:03.472707: val_loss -0.5788 
+2024-09-12 13:43:03.472763: Pseudo dice [0.4709, 0.7926] 
+2024-09-12 13:43:03.472818: Epoch time: 245.76 s 
+2024-09-12 13:43:04.482200:  
+2024-09-12 13:43:04.482382: Epoch 671 
+2024-09-12 13:43:04.482468: Current learning rate: 0.00368 
+2024-09-12 13:47:10.493535: train_loss -0.8905 
+2024-09-12 13:47:10.493689: val_loss -0.591 
+2024-09-12 13:47:10.493745: Pseudo dice [0.4697, 0.7919] 
+2024-09-12 13:47:10.493802: Epoch time: 246.01 s 
+2024-09-12 13:47:11.515556:  
+2024-09-12 13:47:11.515761: Epoch 672 
+2024-09-12 13:47:11.515863: Current learning rate: 0.00367 
+2024-09-12 13:51:17.380196: train_loss -0.8923 
+2024-09-12 13:51:17.380341: val_loss -0.5774 
+2024-09-12 13:51:17.380398: Pseudo dice [0.4606, 0.7908] 
+2024-09-12 13:51:17.380481: Epoch time: 245.87 s 
+2024-09-12 13:51:18.384595:  
+2024-09-12 13:51:18.384758: Epoch 673 
+2024-09-12 13:51:18.384848: Current learning rate: 0.00366 
+2024-09-12 13:55:24.390881: train_loss -0.8909 
+2024-09-12 13:55:24.391072: val_loss -0.5714 
+2024-09-12 13:55:24.391129: Pseudo dice [0.4606, 0.7767] 
+2024-09-12 13:55:24.391184: Epoch time: 246.01 s 
+2024-09-12 13:55:25.433762:  
+2024-09-12 13:55:25.433975: Epoch 674 
+2024-09-12 13:55:25.434062: Current learning rate: 0.00365 
+2024-09-12 13:59:31.283524: train_loss -0.8912 
+2024-09-12 13:59:31.283679: val_loss -0.5813 
+2024-09-12 13:59:31.283738: Pseudo dice [0.4838, 0.7623] 
+2024-09-12 13:59:31.283794: Epoch time: 245.85 s 
+2024-09-12 13:59:32.297711:  
+2024-09-12 13:59:32.297888: Epoch 675 
+2024-09-12 13:59:32.297979: Current learning rate: 0.00364 
+2024-09-12 14:03:38.267350: train_loss -0.8878 
+2024-09-12 14:03:38.267513: val_loss -0.6493 
+2024-09-12 14:03:38.267571: Pseudo dice [0.5511, 0.7988] 
+2024-09-12 14:03:38.267631: Epoch time: 245.97 s 
+2024-09-12 14:03:39.270491:  
+2024-09-12 14:03:39.270705: Epoch 676 
+2024-09-12 14:03:39.270793: Current learning rate: 0.00363 
+2024-09-12 14:07:45.358285: train_loss -0.8818 
+2024-09-12 14:07:45.358435: val_loss -0.5676 
+2024-09-12 14:07:45.358491: Pseudo dice [0.4803, 0.7704] 
+2024-09-12 14:07:45.358545: Epoch time: 246.09 s 
+2024-09-12 14:07:46.361859:  
+2024-09-12 14:07:46.362065: Epoch 677 
+2024-09-12 14:07:46.362199: Current learning rate: 0.00362 
+2024-09-12 14:11:52.524081: train_loss -0.891 
+2024-09-12 14:11:52.524254: val_loss -0.6041 
+2024-09-12 14:11:52.524312: Pseudo dice [0.5289, 0.7918] 
+2024-09-12 14:11:52.524367: Epoch time: 246.16 s 
+2024-09-12 14:11:54.421735:  
+2024-09-12 14:11:54.421990: Epoch 678 
+2024-09-12 14:11:54.422096: Current learning rate: 0.00361 
+2024-09-12 14:16:00.658039: train_loss -0.8871 
+2024-09-12 14:16:00.658196: val_loss -0.5982 
+2024-09-12 14:16:00.658252: Pseudo dice [0.4962, 0.7829] 
+2024-09-12 14:16:00.658309: Epoch time: 246.24 s 
+2024-09-12 14:16:01.628397:  
+2024-09-12 14:16:01.628584: Epoch 679 
+2024-09-12 14:16:01.628687: Current learning rate: 0.0036 
+2024-09-12 14:20:07.695053: train_loss -0.8885 
+2024-09-12 14:20:07.695229: val_loss -0.5826 
+2024-09-12 14:20:07.695287: Pseudo dice [0.4741, 0.802] 
+2024-09-12 14:20:07.695345: Epoch time: 246.07 s 
+2024-09-12 14:20:08.713619:  
+2024-09-12 14:20:08.713838: Epoch 680 
+2024-09-12 14:20:08.713925: Current learning rate: 0.00359 
+2024-09-12 14:24:14.823540: train_loss -0.8805 
+2024-09-12 14:24:14.823687: val_loss -0.6241 
+2024-09-12 14:24:14.823743: Pseudo dice [0.512, 0.7923] 
+2024-09-12 14:24:14.823818: Epoch time: 246.11 s 
+2024-09-12 14:24:15.864175:  
+2024-09-12 14:24:15.864360: Epoch 681 
+2024-09-12 14:24:15.864449: Current learning rate: 0.00358 
+2024-09-12 14:28:21.816751: train_loss -0.8874 
+2024-09-12 14:28:21.816896: val_loss -0.6218 
+2024-09-12 14:28:21.816952: Pseudo dice [0.5102, 0.7945] 
+2024-09-12 14:28:21.817008: Epoch time: 245.95 s 
+2024-09-12 14:28:22.824459:  
+2024-09-12 14:28:22.824664: Epoch 682 
+2024-09-12 14:28:22.824754: Current learning rate: 0.00357 
+2024-09-12 14:32:28.716119: train_loss -0.8977 
+2024-09-12 14:32:28.716285: val_loss -0.6088 
+2024-09-12 14:32:28.716343: Pseudo dice [0.5108, 0.7757] 
+2024-09-12 14:32:28.716399: Epoch time: 245.89 s 
+2024-09-12 14:32:29.719064:  
+2024-09-12 14:32:29.719252: Epoch 683 
+2024-09-12 14:32:29.719341: Current learning rate: 0.00356 
+2024-09-12 14:36:35.595341: train_loss -0.8833 
+2024-09-12 14:36:35.595490: val_loss -0.6301 
+2024-09-12 14:36:35.595547: Pseudo dice [0.5458, 0.8063] 
+2024-09-12 14:36:35.595602: Epoch time: 245.88 s 
+2024-09-12 14:36:36.634596:  
+2024-09-12 14:36:36.634827: Epoch 684 
+2024-09-12 14:36:36.634919: Current learning rate: 0.00355 
+2024-09-12 14:40:42.507499: train_loss -0.8822 
+2024-09-12 14:40:42.507649: val_loss -0.5651 
+2024-09-12 14:40:42.507704: Pseudo dice [0.4554, 0.7585] 
+2024-09-12 14:40:42.507759: Epoch time: 245.87 s 
+2024-09-12 14:40:43.521374:  
+2024-09-12 14:40:43.521577: Epoch 685 
+2024-09-12 14:40:43.521665: Current learning rate: 0.00354 
+2024-09-12 14:44:49.634910: train_loss -0.8829 
+2024-09-12 14:44:49.635095: val_loss -0.589 
+2024-09-12 14:44:49.635153: Pseudo dice [0.5026, 0.7499] 
+2024-09-12 14:44:49.635210: Epoch time: 246.12 s 
+2024-09-12 14:44:50.644915:  
+2024-09-12 14:44:50.645130: Epoch 686 
+2024-09-12 14:44:50.645239: Current learning rate: 0.00353 
+2024-09-12 14:48:56.604310: train_loss -0.8813 
+2024-09-12 14:48:56.604472: val_loss -0.5655 
+2024-09-12 14:48:56.604530: Pseudo dice [0.4842, 0.752] 
+2024-09-12 14:48:56.604586: Epoch time: 245.96 s 
+2024-09-12 14:48:57.613698:  
+2024-09-12 14:48:57.613867: Epoch 687 
+2024-09-12 14:48:57.613953: Current learning rate: 0.00352 
+2024-09-12 14:53:03.599706: train_loss -0.8872 
+2024-09-12 14:53:03.599924: val_loss -0.6051 
+2024-09-12 14:53:03.599986: Pseudo dice [0.49, 0.7864] 
+2024-09-12 14:53:03.600041: Epoch time: 245.99 s 
+2024-09-12 14:53:04.611827:  
+2024-09-12 14:53:04.612010: Epoch 688 
+2024-09-12 14:53:04.612096: Current learning rate: 0.00351 
+2024-09-12 14:57:10.828094: train_loss -0.8688 
+2024-09-12 14:57:10.828244: val_loss -0.6046 
+2024-09-12 14:57:10.828302: Pseudo dice [0.479, 0.8085] 
+2024-09-12 14:57:10.828358: Epoch time: 246.22 s 
+2024-09-12 14:57:11.832198:  
+2024-09-12 14:57:11.832376: Epoch 689 
+2024-09-12 14:57:11.832485: Current learning rate: 0.0035 
+2024-09-12 15:01:17.834399: train_loss -0.8621 
+2024-09-12 15:01:17.834810: val_loss -0.6099 
+2024-09-12 15:01:17.834869: Pseudo dice [0.4732, 0.7797] 
+2024-09-12 15:01:17.834924: Epoch time: 246.0 s 
+2024-09-12 15:01:18.854906:  
+2024-09-12 15:01:18.855167: Epoch 690 
+2024-09-12 15:01:18.855261: Current learning rate: 0.00349 
+2024-09-12 15:05:24.932936: train_loss -0.8772 
+2024-09-12 15:05:24.933083: val_loss -0.5823 
+2024-09-12 15:05:24.933141: Pseudo dice [0.4705, 0.7693] 
+2024-09-12 15:05:24.933199: Epoch time: 246.08 s 
+2024-09-12 15:05:26.024345:  
+2024-09-12 15:05:26.024542: Epoch 691 
+2024-09-12 15:05:26.024629: Current learning rate: 0.00348 
+2024-09-12 15:09:32.113719: train_loss -0.8751 
+2024-09-12 15:09:32.113869: val_loss -0.6085 
+2024-09-12 15:09:32.113958: Pseudo dice [0.4687, 0.802] 
+2024-09-12 15:09:32.114033: Epoch time: 246.09 s 
+2024-09-12 15:09:33.129484:  
+2024-09-12 15:09:33.129692: Epoch 692 
+2024-09-12 15:09:33.129784: Current learning rate: 0.00346 
+2024-09-12 15:13:39.101440: train_loss -0.8751 
+2024-09-12 15:13:39.101592: val_loss -0.5617 
+2024-09-12 15:13:39.101648: Pseudo dice [0.45, 0.774] 
+2024-09-12 15:13:39.101703: Epoch time: 245.97 s 
+2024-09-12 15:13:40.113361:  
+2024-09-12 15:13:40.113604: Epoch 693 
+2024-09-12 15:13:40.113691: Current learning rate: 0.00345 
+2024-09-12 15:17:46.071853: train_loss -0.8785 
+2024-09-12 15:17:46.072014: val_loss -0.5809 
+2024-09-12 15:17:46.072072: Pseudo dice [0.4877, 0.7504] 
+2024-09-12 15:17:46.072128: Epoch time: 245.96 s 
+2024-09-12 15:17:47.068208:  
+2024-09-12 15:17:47.068374: Epoch 694 
+2024-09-12 15:17:47.068465: Current learning rate: 0.00344 
+2024-09-12 15:21:53.035877: train_loss -0.8694 
+2024-09-12 15:21:53.036033: val_loss -0.6077 
+2024-09-12 15:21:53.036089: Pseudo dice [0.5574, 0.7775] 
+2024-09-12 15:21:53.036144: Epoch time: 245.97 s 
+2024-09-12 15:21:54.056754:  
+2024-09-12 15:21:54.056949: Epoch 695 
+2024-09-12 15:21:54.057070: Current learning rate: 0.00343 
+2024-09-12 15:25:59.915133: train_loss -0.869 
+2024-09-12 15:25:59.915282: val_loss -0.5937 
+2024-09-12 15:25:59.915351: Pseudo dice [0.5041, 0.7691] 
+2024-09-12 15:25:59.915416: Epoch time: 245.86 s 
+2024-09-12 15:26:00.937970:  
+2024-09-12 15:26:00.938221: Epoch 696 
+2024-09-12 15:26:00.938327: Current learning rate: 0.00342 
+2024-09-12 15:30:06.961742: train_loss -0.8794 
+2024-09-12 15:30:06.961892: val_loss -0.5851 
+2024-09-12 15:30:06.961949: Pseudo dice [0.4679, 0.7893] 
+2024-09-12 15:30:06.962006: Epoch time: 246.03 s 
+2024-09-12 15:30:07.984251:  
+2024-09-12 15:30:07.984472: Epoch 697 
+2024-09-12 15:30:07.984562: Current learning rate: 0.00341 
+2024-09-12 15:34:14.060449: train_loss -0.8811 
+2024-09-12 15:34:14.060618: val_loss -0.5464 
+2024-09-12 15:34:14.060676: Pseudo dice [0.4156, 0.7559] 
+2024-09-12 15:34:14.060733: Epoch time: 246.08 s 
+2024-09-12 15:34:15.087949:  
+2024-09-12 15:34:15.088436: Epoch 698 
+2024-09-12 15:34:15.088528: Current learning rate: 0.0034 
+2024-09-12 15:38:20.771688: train_loss -0.8887 
+2024-09-12 15:38:20.771851: val_loss -0.595 
+2024-09-12 15:38:20.771909: Pseudo dice [0.5676, 0.7636] 
+2024-09-12 15:38:20.771965: Epoch time: 245.69 s 
+2024-09-12 15:38:21.778878:  
+2024-09-12 15:38:21.779079: Epoch 699 
+2024-09-12 15:38:21.779172: Current learning rate: 0.00339 
+2024-09-12 15:42:27.534540: train_loss -0.8927 
+2024-09-12 15:42:27.534692: val_loss -0.5953 
+2024-09-12 15:42:27.534748: Pseudo dice [0.5104, 0.7834] 
+2024-09-12 15:42:27.534804: Epoch time: 245.76 s 
+2024-09-12 15:42:31.502101:  
+2024-09-12 15:42:31.502273: Epoch 700 
+2024-09-12 15:42:31.502362: Current learning rate: 0.00338 
+2024-09-12 15:46:38.175451: train_loss -0.885 
+2024-09-12 15:46:38.175643: val_loss -0.5684 
+2024-09-12 15:46:38.175706: Pseudo dice [0.5178, 0.7907] 
+2024-09-12 15:46:38.175765: Epoch time: 246.68 s 
+2024-09-12 15:46:39.167634:  
+2024-09-12 15:46:39.167872: Epoch 701 
+2024-09-12 15:46:39.167979: Current learning rate: 0.00337 
+2024-09-12 15:50:45.045482: train_loss -0.8526 
+2024-09-12 15:50:45.045671: val_loss -0.6085 
+2024-09-12 15:50:45.045728: Pseudo dice [0.5375, 0.7934] 
+2024-09-12 15:50:45.045788: Epoch time: 245.88 s 
+2024-09-12 15:50:46.066874:  
+2024-09-12 15:50:46.067092: Epoch 702 
+2024-09-12 15:50:46.067180: Current learning rate: 0.00336 
+2024-09-12 15:54:51.983389: train_loss -0.8526 
+2024-09-12 15:54:51.983547: val_loss -0.5927 
+2024-09-12 15:54:51.983603: Pseudo dice [0.5601, 0.7716] 
+2024-09-12 15:54:51.983660: Epoch time: 245.92 s 
+2024-09-12 15:54:52.981116:  
+2024-09-12 15:54:52.981408: Epoch 703 
+2024-09-12 15:54:52.981498: Current learning rate: 0.00335 
+2024-09-12 15:58:59.110742: train_loss -0.8663 
+2024-09-12 15:58:59.110889: val_loss -0.5855 
+2024-09-12 15:58:59.110945: Pseudo dice [0.4787, 0.8064] 
+2024-09-12 15:58:59.111004: Epoch time: 246.13 s 
+2024-09-12 15:59:00.114933:  
+2024-09-12 15:59:00.115117: Epoch 704 
+2024-09-12 15:59:00.115209: Current learning rate: 0.00334 
+2024-09-12 16:03:05.849126: train_loss -0.8725 
+2024-09-12 16:03:05.849290: val_loss -0.6016 
+2024-09-12 16:03:05.849348: Pseudo dice [0.4701, 0.802] 
+2024-09-12 16:03:05.849404: Epoch time: 245.74 s 
+2024-09-12 16:03:06.855571:  
+2024-09-12 16:03:06.855769: Epoch 705 
+2024-09-12 16:03:06.855902: Current learning rate: 0.00333 
+2024-09-12 16:07:12.661090: train_loss -0.8711 
+2024-09-12 16:07:12.661237: val_loss -0.5766 
+2024-09-12 16:07:12.661352: Pseudo dice [0.4731, 0.7996] 
+2024-09-12 16:07:12.661411: Epoch time: 245.81 s 
+2024-09-12 16:07:13.687040:  
+2024-09-12 16:07:13.687239: Epoch 706 
+2024-09-12 16:07:13.687327: Current learning rate: 0.00332 
+2024-09-12 16:11:19.553275: train_loss -0.8805 
+2024-09-12 16:11:19.553426: val_loss -0.5967 
+2024-09-12 16:11:19.553483: Pseudo dice [0.4839, 0.7914] 
+2024-09-12 16:11:19.553539: Epoch time: 245.87 s 
+2024-09-12 16:11:20.567320:  
+2024-09-12 16:11:20.567534: Epoch 707 
+2024-09-12 16:11:20.567622: Current learning rate: 0.00331 
+2024-09-12 16:15:26.260617: train_loss -0.8845 
+2024-09-12 16:15:26.260833: val_loss -0.5287 
+2024-09-12 16:15:26.260894: Pseudo dice [0.4134, 0.7631] 
+2024-09-12 16:15:26.260951: Epoch time: 245.7 s 
+2024-09-12 16:15:27.268962:  
+2024-09-12 16:15:27.269165: Epoch 708 
+2024-09-12 16:15:27.269255: Current learning rate: 0.0033 
+2024-09-12 16:19:32.961416: train_loss -0.8715 
+2024-09-12 16:19:32.961573: val_loss -0.594 
+2024-09-12 16:19:32.961630: Pseudo dice [0.4919, 0.7863] 
+2024-09-12 16:19:32.961737: Epoch time: 245.69 s 
+2024-09-12 16:19:33.973088:  
+2024-09-12 16:19:33.973301: Epoch 709 
+2024-09-12 16:19:33.973397: Current learning rate: 0.00329 
+2024-09-12 16:23:39.768057: train_loss -0.8597 
+2024-09-12 16:23:39.768231: val_loss -0.5782 
+2024-09-12 16:23:39.768287: Pseudo dice [0.4479, 0.8082] 
+2024-09-12 16:23:39.768343: Epoch time: 245.8 s 
+2024-09-12 16:23:40.780693:  
+2024-09-12 16:23:40.780897: Epoch 710 
+2024-09-12 16:23:40.780984: Current learning rate: 0.00328 
+2024-09-12 16:27:46.633900: train_loss -0.8738 
+2024-09-12 16:27:46.634049: val_loss -0.5317 
+2024-09-12 16:27:46.634108: Pseudo dice [0.4722, 0.7423] 
+2024-09-12 16:27:46.634165: Epoch time: 245.86 s 
+2024-09-12 16:27:47.654965:  
+2024-09-12 16:27:47.655168: Epoch 711 
+2024-09-12 16:27:47.655257: Current learning rate: 0.00327 
+2024-09-12 16:31:53.425831: train_loss -0.8752 
+2024-09-12 16:31:53.425977: val_loss -0.5931 
+2024-09-12 16:31:53.426028: Pseudo dice [0.4728, 0.7824] 
+2024-09-12 16:31:53.426080: Epoch time: 245.77 s 
+2024-09-12 16:31:54.461162:  
+2024-09-12 16:31:54.461365: Epoch 712 
+2024-09-12 16:31:54.461469: Current learning rate: 0.00326 
+2024-09-12 16:36:00.216559: train_loss -0.8808 
+2024-09-12 16:36:00.216717: val_loss -0.5874 
+2024-09-12 16:36:00.216767: Pseudo dice [0.4929, 0.7667] 
+2024-09-12 16:36:00.216818: Epoch time: 245.76 s 
+2024-09-12 16:36:01.254982:  
+2024-09-12 16:36:01.255220: Epoch 713 
+2024-09-12 16:36:01.255328: Current learning rate: 0.00325 
+2024-09-12 16:40:07.206055: train_loss -0.8854 
+2024-09-12 16:40:07.206190: val_loss -0.5561 
+2024-09-12 16:40:07.206243: Pseudo dice [0.4392, 0.7976] 
+2024-09-12 16:40:07.206299: Epoch time: 245.95 s 
+2024-09-12 16:40:08.210666:  
+2024-09-12 16:40:08.210885: Epoch 714 
+2024-09-12 16:40:08.210971: Current learning rate: 0.00324 
+2024-09-12 16:44:13.945688: train_loss -0.8839 
+2024-09-12 16:44:13.945826: val_loss -0.6149 
+2024-09-12 16:44:13.945877: Pseudo dice [0.4881, 0.8007] 
+2024-09-12 16:44:13.945928: Epoch time: 245.74 s 
+2024-09-12 16:44:14.967697:  
+2024-09-12 16:44:14.967955: Epoch 715 
+2024-09-12 16:44:14.968038: Current learning rate: 0.00323 
+2024-09-12 16:48:20.710004: train_loss -0.8923 
+2024-09-12 16:48:20.710143: val_loss -0.5705 
+2024-09-12 16:48:20.710193: Pseudo dice [0.4611, 0.7837] 
+2024-09-12 16:48:20.710294: Epoch time: 245.74 s 
+2024-09-12 16:48:21.713196:  
+2024-09-12 16:48:21.713400: Epoch 716 
+2024-09-12 16:48:21.713501: Current learning rate: 0.00322 
+2024-09-12 16:52:27.591610: train_loss -0.891 
+2024-09-12 16:52:27.591786: val_loss -0.5448 
+2024-09-12 16:52:27.591854: Pseudo dice [0.4727, 0.7549] 
+2024-09-12 16:52:27.591908: Epoch time: 245.88 s 
+2024-09-12 16:52:28.634383:  
+2024-09-12 16:52:28.634541: Epoch 717 
+2024-09-12 16:52:28.634631: Current learning rate: 0.00321 
+2024-09-12 16:56:34.508433: train_loss -0.8851 
+2024-09-12 16:56:34.508569: val_loss -0.6178 
+2024-09-12 16:56:34.508623: Pseudo dice [0.5532, 0.7962] 
+2024-09-12 16:56:34.508674: Epoch time: 245.88 s 
+2024-09-12 16:56:35.527797:  
+2024-09-12 16:56:35.528029: Epoch 718 
+2024-09-12 16:56:35.528112: Current learning rate: 0.0032 
+2024-09-12 17:00:41.276315: train_loss -0.8941 
+2024-09-12 17:00:41.276452: val_loss -0.5776 
+2024-09-12 17:00:41.276501: Pseudo dice [0.4867, 0.7792] 
+2024-09-12 17:00:41.276552: Epoch time: 245.75 s 
+2024-09-12 17:00:42.290359:  
+2024-09-12 17:00:42.290544: Epoch 719 
+2024-09-12 17:00:42.290630: Current learning rate: 0.00319 
+2024-09-12 17:04:47.970229: train_loss -0.8888 
+2024-09-12 17:04:47.970361: val_loss -0.5745 
+2024-09-12 17:04:47.970417: Pseudo dice [0.4784, 0.7726] 
+2024-09-12 17:04:47.970470: Epoch time: 245.68 s 
+2024-09-12 17:04:49.143283:  
+2024-09-12 17:04:49.143460: Epoch 720 
+2024-09-12 17:04:49.143545: Current learning rate: 0.00318 
+2024-09-12 17:08:54.955315: train_loss -0.874 
+2024-09-12 17:08:54.955454: val_loss -0.5901 
+2024-09-12 17:08:54.955506: Pseudo dice [0.5049, 0.8075] 
+2024-09-12 17:08:54.955556: Epoch time: 245.81 s 
+2024-09-12 17:08:55.956563:  
+2024-09-12 17:08:55.956779: Epoch 721 
+2024-09-12 17:08:55.956861: Current learning rate: 0.00317 
+2024-09-12 17:13:01.822779: train_loss -0.8825 
+2024-09-12 17:13:01.822911: val_loss -0.5772 
+2024-09-12 17:13:01.822961: Pseudo dice [0.4589, 0.7969] 
+2024-09-12 17:13:01.823012: Epoch time: 245.87 s 
+2024-09-12 17:13:02.838918:  
+2024-09-12 17:13:02.839119: Epoch 722 
+2024-09-12 17:13:02.839201: Current learning rate: 0.00316 
+2024-09-12 17:17:08.806606: train_loss -0.8898 
+2024-09-12 17:17:08.806745: val_loss -0.6106 
+2024-09-12 17:17:08.806796: Pseudo dice [0.4734, 0.8038] 
+2024-09-12 17:17:08.806847: Epoch time: 245.97 s 
+2024-09-12 17:17:10.745634:  
+2024-09-12 17:17:10.745862: Epoch 723 
+2024-09-12 17:17:10.745965: Current learning rate: 0.00315 
+2024-09-12 17:21:16.703148: train_loss -0.8909 
+2024-09-12 17:21:16.703309: val_loss -0.6041 
+2024-09-12 17:21:16.703362: Pseudo dice [0.5204, 0.7908] 
+2024-09-12 17:21:16.703411: Epoch time: 245.96 s 
+2024-09-12 17:21:17.736063:  
+2024-09-12 17:21:17.736299: Epoch 724 
+2024-09-12 17:21:17.736421: Current learning rate: 0.00314 
+2024-09-12 17:25:23.528289: train_loss -0.8819 
+2024-09-12 17:25:23.528429: val_loss -0.6348 
+2024-09-12 17:25:23.528479: Pseudo dice [0.5801, 0.793] 
+2024-09-12 17:25:23.528531: Epoch time: 245.79 s 
+2024-09-12 17:25:24.542629:  
+2024-09-12 17:25:24.542862: Epoch 725 
+2024-09-12 17:25:24.542948: Current learning rate: 0.00313 
+2024-09-12 17:29:30.338758: train_loss -0.879 
+2024-09-12 17:29:30.338899: val_loss -0.611 
+2024-09-12 17:29:30.338949: Pseudo dice [0.5012, 0.8077] 
+2024-09-12 17:29:30.339000: Epoch time: 245.8 s 
+2024-09-12 17:29:31.359164:  
+2024-09-12 17:29:31.359376: Epoch 726 
+2024-09-12 17:29:31.359461: Current learning rate: 0.00312 
+2024-09-12 17:33:37.149623: train_loss -0.8905 
+2024-09-12 17:33:37.149772: val_loss -0.5853 
+2024-09-12 17:33:37.149823: Pseudo dice [0.5005, 0.7778] 
+2024-09-12 17:33:37.149874: Epoch time: 245.79 s 
+2024-09-12 17:33:38.155441:  
+2024-09-12 17:33:38.155641: Epoch 727 
+2024-09-12 17:33:38.155725: Current learning rate: 0.00311 
+2024-09-12 17:37:44.056314: train_loss -0.8894 
+2024-09-12 17:37:44.056454: val_loss -0.5652 
+2024-09-12 17:37:44.056504: Pseudo dice [0.4672, 0.7574] 
+2024-09-12 17:37:44.056556: Epoch time: 245.9 s 
+2024-09-12 17:37:45.068402:  
+2024-09-12 17:37:45.068574: Epoch 728 
+2024-09-12 17:37:45.068657: Current learning rate: 0.0031 
+2024-09-12 17:41:51.125895: train_loss -0.8922 
+2024-09-12 17:41:51.126033: val_loss -0.6075 
+2024-09-12 17:41:51.126084: Pseudo dice [0.5099, 0.772] 
+2024-09-12 17:41:51.126134: Epoch time: 246.06 s 
+2024-09-12 17:41:52.140201:  
+2024-09-12 17:41:52.140436: Epoch 729 
+2024-09-12 17:41:52.140525: Current learning rate: 0.00309 
+2024-09-12 17:45:58.256223: train_loss -0.8928 
+2024-09-12 17:45:58.256415: val_loss -0.6263 
+2024-09-12 17:45:58.256527: Pseudo dice [0.5601, 0.7736] 
+2024-09-12 17:45:58.256580: Epoch time: 246.12 s 
+2024-09-12 17:45:59.268256:  
+2024-09-12 17:45:59.268461: Epoch 730 
+2024-09-12 17:45:59.268550: Current learning rate: 0.00308 
+2024-09-12 17:50:05.309165: train_loss -0.8911 
+2024-09-12 17:50:05.309318: val_loss -0.5532 
+2024-09-12 17:50:05.309369: Pseudo dice [0.4755, 0.7655] 
+2024-09-12 17:50:05.309418: Epoch time: 246.04 s 
+2024-09-12 17:50:06.328531:  
+2024-09-12 17:50:06.328755: Epoch 731 
+2024-09-12 17:50:06.328864: Current learning rate: 0.00307 
+2024-09-12 17:54:12.455141: train_loss -0.8906 
+2024-09-12 17:54:12.455293: val_loss -0.5918 
+2024-09-12 17:54:12.455344: Pseudo dice [0.4849, 0.7987] 
+2024-09-12 17:54:12.455395: Epoch time: 246.13 s 
+2024-09-12 17:54:13.491215:  
+2024-09-12 17:54:13.491416: Epoch 732 
+2024-09-12 17:54:13.491497: Current learning rate: 0.00306 
+2024-09-12 17:58:19.572416: train_loss -0.8927 
+2024-09-12 17:58:19.572568: val_loss -0.6142 
+2024-09-12 17:58:19.572618: Pseudo dice [0.5371, 0.7899] 
+2024-09-12 17:58:19.572668: Epoch time: 246.08 s 
+2024-09-12 17:58:20.579520:  
+2024-09-12 17:58:20.579752: Epoch 733 
+2024-09-12 17:58:20.579850: Current learning rate: 0.00305 
+2024-09-12 18:02:26.730029: train_loss -0.8898 
+2024-09-12 18:02:26.730168: val_loss -0.575 
+2024-09-12 18:02:26.730218: Pseudo dice [0.4208, 0.7869] 
+2024-09-12 18:02:26.730269: Epoch time: 246.15 s 
+2024-09-12 18:02:27.752067:  
+2024-09-12 18:02:27.752263: Epoch 734 
+2024-09-12 18:02:27.752347: Current learning rate: 0.00304 
+2024-09-12 18:06:33.830126: train_loss -0.882 
+2024-09-12 18:06:33.830276: val_loss -0.5955 
+2024-09-12 18:06:33.830327: Pseudo dice [0.4821, 0.7969] 
+2024-09-12 18:06:33.830377: Epoch time: 246.08 s 
+2024-09-12 18:06:34.838565:  
+2024-09-12 18:06:34.838807: Epoch 735 
+2024-09-12 18:06:34.838891: Current learning rate: 0.00303 
+2024-09-12 18:10:40.855186: train_loss -0.8881 
+2024-09-12 18:10:40.855331: val_loss -0.592 
+2024-09-12 18:10:40.855381: Pseudo dice [0.4672, 0.8003] 
+2024-09-12 18:10:40.855432: Epoch time: 246.02 s 
+2024-09-12 18:10:41.865696:  
+2024-09-12 18:10:41.865967: Epoch 736 
+2024-09-12 18:10:41.866054: Current learning rate: 0.00302 
+2024-09-12 18:14:48.015707: train_loss -0.889 
+2024-09-12 18:14:48.015876: val_loss -0.5916 
+2024-09-12 18:14:48.015928: Pseudo dice [0.5085, 0.7688] 
+2024-09-12 18:14:48.015978: Epoch time: 246.15 s 
+2024-09-12 18:14:49.036736:  
+2024-09-12 18:14:49.036903: Epoch 737 
+2024-09-12 18:14:49.036985: Current learning rate: 0.00301 
+2024-09-12 18:18:55.142144: train_loss -0.8906 
+2024-09-12 18:18:55.142299: val_loss -0.5712 
+2024-09-12 18:18:55.142350: Pseudo dice [0.4938, 0.7714] 
+2024-09-12 18:18:55.142403: Epoch time: 246.11 s 
+2024-09-12 18:18:56.152467:  
+2024-09-12 18:18:56.152640: Epoch 738 
+2024-09-12 18:18:56.152724: Current learning rate: 0.003 
+2024-09-12 18:23:01.988239: train_loss -0.8913 
+2024-09-12 18:23:01.988384: val_loss -0.5736 
+2024-09-12 18:23:01.988434: Pseudo dice [0.4502, 0.7917] 
+2024-09-12 18:23:01.988487: Epoch time: 245.84 s 
+2024-09-12 18:23:03.023662:  
+2024-09-12 18:23:03.023865: Epoch 739 
+2024-09-12 18:23:03.023948: Current learning rate: 0.00299 
+2024-09-12 18:27:09.026896: train_loss -0.8917 
+2024-09-12 18:27:09.027060: val_loss -0.5995 
+2024-09-12 18:27:09.027110: Pseudo dice [0.4981, 0.7911] 
+2024-09-12 18:27:09.027161: Epoch time: 246.01 s 
+2024-09-12 18:27:10.042099:  
+2024-09-12 18:27:10.042284: Epoch 740 
+2024-09-12 18:27:10.042367: Current learning rate: 0.00297 
+2024-09-12 18:31:16.071505: train_loss -0.8932 
+2024-09-12 18:31:16.071655: val_loss -0.5952 
+2024-09-12 18:31:16.071725: Pseudo dice [0.5005, 0.7675] 
+2024-09-12 18:31:16.071776: Epoch time: 246.03 s 
+2024-09-12 18:31:17.108673:  
+2024-09-12 18:31:17.108887: Epoch 741 
+2024-09-12 18:31:17.108970: Current learning rate: 0.00296 
+2024-09-12 18:35:23.161258: train_loss -0.8966 
+2024-09-12 18:35:23.161399: val_loss -0.6104 
+2024-09-12 18:35:23.161451: Pseudo dice [0.4859, 0.8114] 
+2024-09-12 18:35:23.161505: Epoch time: 246.05 s 
+2024-09-12 18:35:24.186000:  
+2024-09-12 18:35:24.186155: Epoch 742 
+2024-09-12 18:35:24.186244: Current learning rate: 0.00295 
+2024-09-12 18:39:30.233780: train_loss -0.8944 
+2024-09-12 18:39:30.233919: val_loss -0.5755 
+2024-09-12 18:39:30.233971: Pseudo dice [0.461, 0.792] 
+2024-09-12 18:39:30.234033: Epoch time: 246.05 s 
+2024-09-12 18:39:31.251122:  
+2024-09-12 18:39:31.251307: Epoch 743 
+2024-09-12 18:39:31.251391: Current learning rate: 0.00294 
+2024-09-12 18:43:37.646621: train_loss -0.8895 
+2024-09-12 18:43:37.646767: val_loss -0.5775 
+2024-09-12 18:43:37.646904: Pseudo dice [0.4738, 0.7852] 
+2024-09-12 18:43:37.646984: Epoch time: 246.4 s 
+2024-09-12 18:43:38.657562:  
+2024-09-12 18:43:38.657731: Epoch 744 
+2024-09-12 18:43:38.657818: Current learning rate: 0.00293 
+2024-09-12 18:47:44.908906: train_loss -0.8886 
+2024-09-12 18:47:44.909179: val_loss -0.5674 
+2024-09-12 18:47:44.909291: Pseudo dice [0.4607, 0.7858] 
+2024-09-12 18:47:44.909384: Epoch time: 246.25 s 
+2024-09-12 18:47:45.933541:  
+2024-09-12 18:47:45.933783: Epoch 745 
+2024-09-12 18:47:45.933867: Current learning rate: 0.00292 
+2024-09-12 18:51:52.701857: train_loss -0.8887 
+2024-09-12 18:51:52.702001: val_loss -0.5778 
+2024-09-12 18:51:52.702050: Pseudo dice [0.5088, 0.7744] 
+2024-09-12 18:51:52.702211: Epoch time: 246.77 s 
+2024-09-12 18:51:53.729363:  
+2024-09-12 18:51:53.729555: Epoch 746 
+2024-09-12 18:51:53.729665: Current learning rate: 0.00291 
+2024-09-12 18:55:59.798169: train_loss -0.8947 
+2024-09-12 18:55:59.798308: val_loss -0.5814 
+2024-09-12 18:55:59.798400: Pseudo dice [0.4471, 0.7811] 
+2024-09-12 18:55:59.798451: Epoch time: 246.07 s 
+2024-09-12 18:56:00.801694:  
+2024-09-12 18:56:00.801893: Epoch 747 
+2024-09-12 18:56:00.802002: Current learning rate: 0.0029 
+2024-09-12 19:00:06.830768: train_loss -0.8927 
+2024-09-12 19:00:06.830914: val_loss -0.6007 
+2024-09-12 19:00:06.830964: Pseudo dice [0.5056, 0.7851] 
+2024-09-12 19:00:06.831017: Epoch time: 246.03 s 
+2024-09-12 19:00:07.848048:  
+2024-09-12 19:00:07.848296: Epoch 748 
+2024-09-12 19:00:07.848398: Current learning rate: 0.00289 
+2024-09-12 19:04:13.859756: train_loss -0.8961 
+2024-09-12 19:04:13.859942: val_loss -0.5876 
+2024-09-12 19:04:13.859993: Pseudo dice [0.4739, 0.7907] 
+2024-09-12 19:04:13.860045: Epoch time: 246.01 s 
+2024-09-12 19:04:14.890990:  
+2024-09-12 19:04:14.891176: Epoch 749 
+2024-09-12 19:04:14.891276: Current learning rate: 0.00288 
+2024-09-12 19:08:20.917978: train_loss -0.8939 
+2024-09-12 19:08:20.918131: val_loss -0.5697 
+2024-09-12 19:08:20.918188: Pseudo dice [0.458, 0.7736] 
+2024-09-12 19:08:20.918244: Epoch time: 246.03 s 
+2024-09-12 19:08:24.871137:  
+2024-09-12 19:08:24.871374: Epoch 750 
+2024-09-12 19:08:24.871460: Current learning rate: 0.00287 
+2024-09-12 19:12:31.176213: train_loss -0.8972 
+2024-09-12 19:12:31.176366: val_loss -0.6012 
+2024-09-12 19:12:31.176422: Pseudo dice [0.5034, 0.7827] 
+2024-09-12 19:12:31.176479: Epoch time: 246.31 s 
+2024-09-12 19:12:32.200130:  
+2024-09-12 19:12:32.200331: Epoch 751 
+2024-09-12 19:12:32.200420: Current learning rate: 0.00286 
+2024-09-12 19:16:38.124847: train_loss -0.8993 
+2024-09-12 19:16:38.125005: val_loss -0.6151 
+2024-09-12 19:16:38.125062: Pseudo dice [0.4999, 0.7973] 
+2024-09-12 19:16:38.125119: Epoch time: 245.93 s 
+2024-09-12 19:16:39.136432:  
+2024-09-12 19:16:39.136688: Epoch 752 
+2024-09-12 19:16:39.136796: Current learning rate: 0.00285 
+2024-09-12 19:20:45.236633: train_loss -0.9005 
+2024-09-12 19:20:45.236822: val_loss -0.5828 
+2024-09-12 19:20:45.236879: Pseudo dice [0.4693, 0.8012] 
+2024-09-12 19:20:45.236935: Epoch time: 246.1 s 
+2024-09-12 19:20:46.250815:  
+2024-09-12 19:20:46.251081: Epoch 753 
+2024-09-12 19:20:46.251173: Current learning rate: 0.00284 
+2024-09-12 19:24:52.189703: train_loss -0.8963 
+2024-09-12 19:24:52.189866: val_loss -0.5878 
+2024-09-12 19:24:52.189928: Pseudo dice [0.4991, 0.7736] 
+2024-09-12 19:24:52.189985: Epoch time: 245.94 s 
+2024-09-12 19:24:53.219213:  
+2024-09-12 19:24:53.219431: Epoch 754 
+2024-09-12 19:24:53.219551: Current learning rate: 0.00283 
+2024-09-12 19:28:59.626472: train_loss -0.8909 
+2024-09-12 19:28:59.626609: val_loss -0.5841 
+2024-09-12 19:28:59.626660: Pseudo dice [0.4676, 0.7991] 
+2024-09-12 19:28:59.626717: Epoch time: 246.41 s 
+2024-09-12 19:29:00.632945:  
+2024-09-12 19:29:00.633179: Epoch 755 
+2024-09-12 19:29:00.633269: Current learning rate: 0.00282 
+2024-09-12 19:33:06.922022: train_loss -0.8986 
+2024-09-12 19:33:06.922161: val_loss -0.5716 
+2024-09-12 19:33:06.922212: Pseudo dice [0.5164, 0.7581] 
+2024-09-12 19:33:06.922262: Epoch time: 246.29 s 
+2024-09-12 19:33:07.951360:  
+2024-09-12 19:33:07.951529: Epoch 756 
+2024-09-12 19:33:07.951648: Current learning rate: 0.00281 
+2024-09-12 19:37:14.225376: train_loss -0.8993 
+2024-09-12 19:37:14.225556: val_loss -0.6432 
+2024-09-12 19:37:14.225610: Pseudo dice [0.5689, 0.7942] 
+2024-09-12 19:37:14.225662: Epoch time: 246.28 s 
+2024-09-12 19:37:15.242867:  
+2024-09-12 19:37:15.243091: Epoch 757 
+2024-09-12 19:37:15.243184: Current learning rate: 0.0028 
+2024-09-12 19:41:21.305016: train_loss -0.8988 
+2024-09-12 19:41:21.305155: val_loss -0.5475 
+2024-09-12 19:41:21.305204: Pseudo dice [0.432, 0.7717] 
+2024-09-12 19:41:21.305255: Epoch time: 246.06 s 
+2024-09-12 19:41:22.316573:  
+2024-09-12 19:41:22.316764: Epoch 758 
+2024-09-12 19:41:22.316846: Current learning rate: 0.00279 
+2024-09-12 19:45:28.154217: train_loss -0.8971 
+2024-09-12 19:45:28.154355: val_loss -0.5847 
+2024-09-12 19:45:28.154412: Pseudo dice [0.477, 0.7879] 
+2024-09-12 19:45:28.154464: Epoch time: 245.84 s 
+2024-09-12 19:45:29.182162:  
+2024-09-12 19:45:29.182458: Epoch 759 
+2024-09-12 19:45:29.182590: Current learning rate: 0.00278 
+2024-09-12 19:49:35.002468: train_loss -0.8963 
+2024-09-12 19:49:35.002619: val_loss -0.6183 
+2024-09-12 19:49:35.002669: Pseudo dice [0.5278, 0.7927] 
+2024-09-12 19:49:35.002721: Epoch time: 245.82 s 
+2024-09-12 19:49:36.045951:  
+2024-09-12 19:49:36.046137: Epoch 760 
+2024-09-12 19:49:36.046218: Current learning rate: 0.00277 
+2024-09-12 19:53:41.961737: train_loss -0.8912 
+2024-09-12 19:53:41.961895: val_loss -0.5848 
+2024-09-12 19:53:41.961947: Pseudo dice [0.4838, 0.7621] 
+2024-09-12 19:53:41.961996: Epoch time: 245.92 s 
+2024-09-12 19:53:42.982722:  
+2024-09-12 19:53:42.982875: Epoch 761 
+2024-09-12 19:53:42.982952: Current learning rate: 0.00276 
+2024-09-12 19:57:48.839450: train_loss -0.9001 
+2024-09-12 19:57:48.839586: val_loss -0.6168 
+2024-09-12 19:57:48.839637: Pseudo dice [0.5069, 0.8021] 
+2024-09-12 19:57:48.839686: Epoch time: 245.86 s 
+2024-09-12 19:57:49.865314:  
+2024-09-12 19:57:49.865511: Epoch 762 
+2024-09-12 19:57:49.865595: Current learning rate: 0.00275 
+2024-09-12 20:01:55.781327: train_loss -0.9015 
+2024-09-12 20:01:55.781485: val_loss -0.5885 
+2024-09-12 20:01:55.781535: Pseudo dice [0.5141, 0.779] 
+2024-09-12 20:01:55.781587: Epoch time: 245.92 s 
+2024-09-12 20:01:56.799818:  
+2024-09-12 20:01:56.799989: Epoch 763 
+2024-09-12 20:01:56.800080: Current learning rate: 0.00274 
+2024-09-12 20:06:02.947277: train_loss -0.8997 
+2024-09-12 20:06:02.947414: val_loss -0.5873 
+2024-09-12 20:06:02.947464: Pseudo dice [0.4706, 0.7941] 
+2024-09-12 20:06:02.947514: Epoch time: 246.15 s 
+2024-09-12 20:06:03.970994:  
+2024-09-12 20:06:03.971148: Epoch 764 
+2024-09-12 20:06:03.971231: Current learning rate: 0.00273 
+2024-09-12 20:10:10.094003: train_loss -0.8989 
+2024-09-12 20:10:10.094141: val_loss -0.5503 
+2024-09-12 20:10:10.094192: Pseudo dice [0.4984, 0.7458] 
+2024-09-12 20:10:10.094290: Epoch time: 246.12 s 
+2024-09-12 20:10:11.127434:  
+2024-09-12 20:10:11.127664: Epoch 765 
+2024-09-12 20:10:11.127759: Current learning rate: 0.00272 
+2024-09-12 20:14:17.179768: train_loss -0.9013 
+2024-09-12 20:14:17.179915: val_loss -0.6209 
+2024-09-12 20:14:17.179965: Pseudo dice [0.5564, 0.8141] 
+2024-09-12 20:14:17.180019: Epoch time: 246.05 s 
+2024-09-12 20:14:18.226850:  
+2024-09-12 20:14:18.227037: Epoch 766 
+2024-09-12 20:14:18.227120: Current learning rate: 0.00271 
+2024-09-12 20:18:24.352756: train_loss -0.9047 
+2024-09-12 20:18:24.352895: val_loss -0.586 
+2024-09-12 20:18:24.352945: Pseudo dice [0.499, 0.7884] 
+2024-09-12 20:18:24.352997: Epoch time: 246.13 s 
+2024-09-12 20:18:25.441253:  
+2024-09-12 20:18:25.441487: Epoch 767 
+2024-09-12 20:18:25.441581: Current learning rate: 0.0027 
+2024-09-12 20:22:32.298729: train_loss -0.9021 
+2024-09-12 20:22:32.298867: val_loss -0.6012 
+2024-09-12 20:22:32.298929: Pseudo dice [0.5269, 0.7922] 
+2024-09-12 20:22:32.298979: Epoch time: 246.86 s 
+2024-09-12 20:22:33.311473:  
+2024-09-12 20:22:33.311729: Epoch 768 
+2024-09-12 20:22:33.311863: Current learning rate: 0.00268 
+2024-09-12 20:26:39.429532: train_loss -0.9044 
+2024-09-12 20:26:39.429716: val_loss -0.5586 
+2024-09-12 20:26:39.429766: Pseudo dice [0.4422, 0.7599] 
+2024-09-12 20:26:39.429820: Epoch time: 246.12 s 
+2024-09-12 20:26:40.448019:  
+2024-09-12 20:26:40.448217: Epoch 769 
+2024-09-12 20:26:40.448300: Current learning rate: 0.00267 
+2024-09-12 20:30:46.626167: train_loss -0.8988 
+2024-09-12 20:30:46.626324: val_loss -0.535 
+2024-09-12 20:30:46.626376: Pseudo dice [0.4468, 0.7474] 
+2024-09-12 20:30:46.626426: Epoch time: 246.18 s 
+2024-09-12 20:30:47.657189:  
+2024-09-12 20:30:47.657466: Epoch 770 
+2024-09-12 20:30:47.657593: Current learning rate: 0.00266 
+2024-09-12 20:34:53.838545: train_loss -0.9027 
+2024-09-12 20:34:53.838708: val_loss -0.5842 
+2024-09-12 20:34:53.838764: Pseudo dice [0.4553, 0.7958] 
+2024-09-12 20:34:53.838815: Epoch time: 246.18 s 
+2024-09-12 20:34:54.860511:  
+2024-09-12 20:34:54.860701: Epoch 771 
+2024-09-12 20:34:54.860835: Current learning rate: 0.00265 
+2024-09-12 20:39:01.259642: train_loss -0.9042 
+2024-09-12 20:39:01.259800: val_loss -0.562 
+2024-09-12 20:39:01.259867: Pseudo dice [0.4566, 0.7924] 
+2024-09-12 20:39:01.259921: Epoch time: 246.4 s 
+2024-09-12 20:39:02.286154:  
+2024-09-12 20:39:02.286335: Epoch 772 
+2024-09-12 20:39:02.286439: Current learning rate: 0.00264 
+2024-09-12 20:43:08.337168: train_loss -0.9011 
+2024-09-12 20:43:08.337307: val_loss -0.5827 
+2024-09-12 20:43:08.337357: Pseudo dice [0.519, 0.7883] 
+2024-09-12 20:43:08.337406: Epoch time: 246.05 s 
+2024-09-12 20:43:09.400334:  
+2024-09-12 20:43:09.400520: Epoch 773 
+2024-09-12 20:43:09.400604: Current learning rate: 0.00263 
+2024-09-12 20:47:15.534956: train_loss -0.9003 
+2024-09-12 20:47:15.535167: val_loss -0.5908 
+2024-09-12 20:47:15.535235: Pseudo dice [0.5012, 0.7759] 
+2024-09-12 20:47:15.535286: Epoch time: 246.14 s 
+2024-09-12 20:47:16.571233:  
+2024-09-12 20:47:16.571515: Epoch 774 
+2024-09-12 20:47:16.571599: Current learning rate: 0.00262 
+2024-09-12 20:51:22.856240: train_loss -0.8886 
+2024-09-12 20:51:22.856437: val_loss -0.6103 
+2024-09-12 20:51:22.856489: Pseudo dice [0.4965, 0.8109] 
+2024-09-12 20:51:22.856541: Epoch time: 246.29 s 
+2024-09-12 20:51:23.894437:  
+2024-09-12 20:51:23.894645: Epoch 775 
+2024-09-12 20:51:23.894731: Current learning rate: 0.00261 
+2024-09-12 20:55:29.998542: train_loss -0.8928 
+2024-09-12 20:55:29.998681: val_loss -0.5749 
+2024-09-12 20:55:29.998732: Pseudo dice [0.4826, 0.7762] 
+2024-09-12 20:55:29.998783: Epoch time: 246.11 s 
+2024-09-12 20:55:31.014798:  
+2024-09-12 20:55:31.015011: Epoch 776 
+2024-09-12 20:55:31.015126: Current learning rate: 0.0026 
+2024-09-12 20:59:37.150634: train_loss -0.9005 
+2024-09-12 20:59:37.150774: val_loss -0.5642 
+2024-09-12 20:59:37.150824: Pseudo dice [0.4641, 0.785] 
+2024-09-12 20:59:37.150874: Epoch time: 246.14 s 
+2024-09-12 20:59:38.181385:  
+2024-09-12 20:59:38.181616: Epoch 777 
+2024-09-12 20:59:38.181699: Current learning rate: 0.00259 
+2024-09-12 21:03:44.383268: train_loss -0.8988 
+2024-09-12 21:03:44.383408: val_loss -0.6122 
+2024-09-12 21:03:44.383460: Pseudo dice [0.5448, 0.8053] 
+2024-09-12 21:03:44.383516: Epoch time: 246.2 s 
+2024-09-12 21:03:45.433067:  
+2024-09-12 21:03:45.433341: Epoch 778 
+2024-09-12 21:03:45.433466: Current learning rate: 0.00258 
+2024-09-12 21:07:51.324213: train_loss -0.9014 
+2024-09-12 21:07:51.324353: val_loss -0.5631 
+2024-09-12 21:07:51.324403: Pseudo dice [0.4451, 0.7893] 
+2024-09-12 21:07:51.324453: Epoch time: 245.89 s 
+2024-09-12 21:07:52.344287:  
+2024-09-12 21:07:52.344443: Epoch 779 
+2024-09-12 21:07:52.344532: Current learning rate: 0.00257 
+2024-09-12 21:11:58.191237: train_loss -0.9024 
+2024-09-12 21:11:58.191391: val_loss -0.5658 
+2024-09-12 21:11:58.191443: Pseudo dice [0.471, 0.7732] 
+2024-09-12 21:11:58.191493: Epoch time: 245.85 s 
+2024-09-12 21:11:59.217625:  
+2024-09-12 21:11:59.217842: Epoch 780 
+2024-09-12 21:11:59.217929: Current learning rate: 0.00256 
+2024-09-12 21:16:04.970735: train_loss -0.9022 
+2024-09-12 21:16:04.970875: val_loss -0.5966 
+2024-09-12 21:16:04.970924: Pseudo dice [0.5052, 0.7867] 
+2024-09-12 21:16:04.970974: Epoch time: 245.76 s 
+2024-09-12 21:16:06.013320:  
+2024-09-12 21:16:06.013528: Epoch 781 
+2024-09-12 21:16:06.013615: Current learning rate: 0.00255 
+2024-09-12 21:20:11.837755: train_loss -0.9016 
+2024-09-12 21:20:11.838037: val_loss -0.5901 
+2024-09-12 21:20:11.838091: Pseudo dice [0.4698, 0.8003] 
+2024-09-12 21:20:11.838141: Epoch time: 245.83 s 
+2024-09-12 21:20:12.897747:  
+2024-09-12 21:20:12.897919: Epoch 782 
+2024-09-12 21:20:12.898044: Current learning rate: 0.00254 
+2024-09-12 21:24:18.873641: train_loss -0.9031 
+2024-09-12 21:24:18.873861: val_loss -0.5304 
+2024-09-12 21:24:18.873945: Pseudo dice [0.4146, 0.7631] 
+2024-09-12 21:24:18.874018: Epoch time: 245.98 s 
+2024-09-12 21:24:19.894431:  
+2024-09-12 21:24:19.894637: Epoch 783 
+2024-09-12 21:24:19.894781: Current learning rate: 0.00253 
+2024-09-12 21:28:25.847342: train_loss -0.8983 
+2024-09-12 21:28:25.847497: val_loss -0.5844 
+2024-09-12 21:28:25.847549: Pseudo dice [0.4776, 0.7832] 
+2024-09-12 21:28:25.847602: Epoch time: 245.95 s 
+2024-09-12 21:28:26.877073:  
+2024-09-12 21:28:26.877296: Epoch 784 
+2024-09-12 21:28:26.877420: Current learning rate: 0.00252 
+2024-09-12 21:32:32.711468: train_loss -0.8965 
+2024-09-12 21:32:32.711607: val_loss -0.5926 
+2024-09-12 21:32:32.711662: Pseudo dice [0.4744, 0.8108] 
+2024-09-12 21:32:32.711713: Epoch time: 245.84 s 
+2024-09-12 21:32:33.746755:  
+2024-09-12 21:32:33.747052: Epoch 785 
+2024-09-12 21:32:33.747144: Current learning rate: 0.00251 
+2024-09-12 21:36:39.852754: train_loss -0.9033 
+2024-09-12 21:36:39.852894: val_loss -0.5778 
+2024-09-12 21:36:39.852943: Pseudo dice [0.4925, 0.7951] 
+2024-09-12 21:36:39.852997: Epoch time: 246.11 s 
+2024-09-12 21:36:40.874274:  
+2024-09-12 21:36:40.874438: Epoch 786 
+2024-09-12 21:36:40.874519: Current learning rate: 0.0025 
+2024-09-12 21:40:47.007589: train_loss -0.9014 
+2024-09-12 21:40:47.007733: val_loss -0.5915 
+2024-09-12 21:40:47.007784: Pseudo dice [0.483, 0.797] 
+2024-09-12 21:40:47.007869: Epoch time: 246.14 s 
+2024-09-12 21:40:48.066133:  
+2024-09-12 21:40:48.066308: Epoch 787 
+2024-09-12 21:40:48.066391: Current learning rate: 0.00249 
+2024-09-12 21:44:54.117491: train_loss -0.8984 
+2024-09-12 21:44:54.117672: val_loss -0.5232 
+2024-09-12 21:44:54.117725: Pseudo dice [0.4608, 0.7535] 
+2024-09-12 21:44:54.117777: Epoch time: 246.05 s 
+2024-09-12 21:44:55.195138:  
+2024-09-12 21:44:55.195360: Epoch 788 
+2024-09-12 21:44:55.195445: Current learning rate: 0.00248 
+2024-09-12 21:49:01.264001: train_loss -0.8999 
+2024-09-12 21:49:01.264149: val_loss -0.5749 
+2024-09-12 21:49:01.264199: Pseudo dice [0.4944, 0.7934] 
+2024-09-12 21:49:01.264252: Epoch time: 246.07 s 
+2024-09-12 21:49:02.291278:  
+2024-09-12 21:49:02.291447: Epoch 789 
+2024-09-12 21:49:02.291535: Current learning rate: 0.00247 
+2024-09-12 21:53:09.090259: train_loss -0.9041 
+2024-09-12 21:53:09.090408: val_loss -0.568 
+2024-09-12 21:53:09.090458: Pseudo dice [0.4445, 0.7861] 
+2024-09-12 21:53:09.090513: Epoch time: 246.8 s 
+2024-09-12 21:53:10.095392:  
+2024-09-12 21:53:10.095631: Epoch 790 
+2024-09-12 21:53:10.095730: Current learning rate: 0.00245 
+2024-09-12 21:57:16.012254: train_loss -0.9003 
+2024-09-12 21:57:16.012395: val_loss -0.5763 
+2024-09-12 21:57:16.012445: Pseudo dice [0.5133, 0.7752] 
+2024-09-12 21:57:16.012495: Epoch time: 245.92 s 
+2024-09-12 21:57:17.055140:  
+2024-09-12 21:57:17.055356: Epoch 791 
+2024-09-12 21:57:17.055437: Current learning rate: 0.00244 
+2024-09-12 22:01:23.164368: train_loss -0.9042 
+2024-09-12 22:01:23.164510: val_loss -0.5593 
+2024-09-12 22:01:23.164560: Pseudo dice [0.3978, 0.8039] 
+2024-09-12 22:01:23.164611: Epoch time: 246.11 s 
+2024-09-12 22:01:24.189430:  
+2024-09-12 22:01:24.189688: Epoch 792 
+2024-09-12 22:01:24.189771: Current learning rate: 0.00243 
+2024-09-12 22:05:30.352753: train_loss -0.9048 
+2024-09-12 22:05:30.352924: val_loss -0.5642 
+2024-09-12 22:05:30.352976: Pseudo dice [0.4532, 0.8027] 
+2024-09-12 22:05:30.353030: Epoch time: 246.17 s 
+2024-09-12 22:05:31.405888:  
+2024-09-12 22:05:31.406097: Epoch 793 
+2024-09-12 22:05:31.406178: Current learning rate: 0.00242 
+2024-09-12 22:09:37.559191: train_loss -0.9035 
+2024-09-12 22:09:37.559415: val_loss -0.5603 
+2024-09-12 22:09:37.559474: Pseudo dice [0.4253, 0.788] 
+2024-09-12 22:09:37.559537: Epoch time: 246.16 s 
+2024-09-12 22:09:38.587765:  
+2024-09-12 22:09:38.587978: Epoch 794 
+2024-09-12 22:09:38.588066: Current learning rate: 0.00241 
+2024-09-12 22:13:44.944677: train_loss -0.904 
+2024-09-12 22:13:44.944880: val_loss -0.5404 
+2024-09-12 22:13:44.944938: Pseudo dice [0.4713, 0.7818] 
+2024-09-12 22:13:44.944998: Epoch time: 246.36 s 
+2024-09-12 22:13:45.980798:  
+2024-09-12 22:13:45.980982: Epoch 795 
+2024-09-12 22:13:45.981073: Current learning rate: 0.0024 
+2024-09-12 22:17:52.305470: train_loss -0.9035 
+2024-09-12 22:17:52.305617: val_loss -0.5668 
+2024-09-12 22:17:52.305677: Pseudo dice [0.4648, 0.7582] 
+2024-09-12 22:17:52.305734: Epoch time: 246.33 s 
+2024-09-12 22:17:53.349674:  
+2024-09-12 22:17:53.349874: Epoch 796 
+2024-09-12 22:17:53.349963: Current learning rate: 0.00239 
+2024-09-12 22:21:59.735398: train_loss -0.9021 
+2024-09-12 22:21:59.735539: val_loss -0.666 
+2024-09-12 22:21:59.735595: Pseudo dice [0.5661, 0.8234] 
+2024-09-12 22:21:59.735652: Epoch time: 246.39 s 
+2024-09-12 22:22:00.751706:  
+2024-09-12 22:22:00.751921: Epoch 797 
+2024-09-12 22:22:00.752075: Current learning rate: 0.00238 
+2024-09-12 22:26:06.700527: train_loss -0.9032 
+2024-09-12 22:26:06.700680: val_loss -0.5916 
+2024-09-12 22:26:06.700737: Pseudo dice [0.4727, 0.7951] 
+2024-09-12 22:26:06.700794: Epoch time: 245.95 s 
+2024-09-12 22:26:07.748111:  
+2024-09-12 22:26:07.748354: Epoch 798 
+2024-09-12 22:26:07.748457: Current learning rate: 0.00237 
+2024-09-12 22:30:13.788766: train_loss -0.9035 
+2024-09-12 22:30:13.788926: val_loss -0.5901 
+2024-09-12 22:30:13.788982: Pseudo dice [0.5172, 0.7821] 
+2024-09-12 22:30:13.789040: Epoch time: 246.04 s 
+2024-09-12 22:30:14.856068:  
+2024-09-12 22:30:14.856262: Epoch 799 
+2024-09-12 22:30:14.856353: Current learning rate: 0.00236 
+2024-09-12 22:34:20.805227: train_loss -0.9047 
+2024-09-12 22:34:20.805378: val_loss -0.622 
+2024-09-12 22:34:20.805435: Pseudo dice [0.5261, 0.8034] 
+2024-09-12 22:34:20.805491: Epoch time: 245.95 s 
+2024-09-12 22:34:24.819248:  
+2024-09-12 22:34:24.819412: Epoch 800 
+2024-09-12 22:34:24.819498: Current learning rate: 0.00235 
+2024-09-12 22:38:31.004716: train_loss -0.9081 
+2024-09-12 22:38:31.004889: val_loss -0.5941 
+2024-09-12 22:38:31.004946: Pseudo dice [0.487, 0.7767] 
+2024-09-12 22:38:31.005005: Epoch time: 246.19 s 
+2024-09-12 22:38:32.050564:  
+2024-09-12 22:38:32.050781: Epoch 801 
+2024-09-12 22:38:32.050870: Current learning rate: 0.00234 
+2024-09-12 22:42:38.009213: train_loss -0.9069 
+2024-09-12 22:42:38.009382: val_loss -0.5833 
+2024-09-12 22:42:38.009441: Pseudo dice [0.477, 0.7773] 
+2024-09-12 22:42:38.009498: Epoch time: 245.96 s 
+2024-09-12 22:42:39.048623:  
+2024-09-12 22:42:39.048820: Epoch 802 
+2024-09-12 22:42:39.048914: Current learning rate: 0.00233 
+2024-09-12 22:46:45.042732: train_loss -0.9027 
+2024-09-12 22:46:45.042889: val_loss -0.5782 
+2024-09-12 22:46:45.042946: Pseudo dice [0.4772, 0.8145] 
+2024-09-12 22:46:45.043001: Epoch time: 246.0 s 
+2024-09-12 22:46:46.078564:  
+2024-09-12 22:46:46.078749: Epoch 803 
+2024-09-12 22:46:46.078838: Current learning rate: 0.00232 
+2024-09-12 22:50:51.951045: train_loss -0.9032 
+2024-09-12 22:50:51.951196: val_loss -0.5814 
+2024-09-12 22:50:51.951251: Pseudo dice [0.4796, 0.7947] 
+2024-09-12 22:50:51.951309: Epoch time: 245.87 s 
+2024-09-12 22:50:52.974537:  
+2024-09-12 22:50:52.974744: Epoch 804 
+2024-09-12 22:50:52.974839: Current learning rate: 0.00231 
+2024-09-12 22:54:58.833349: train_loss -0.9018 
+2024-09-12 22:54:58.833500: val_loss -0.6096 
+2024-09-12 22:54:58.833559: Pseudo dice [0.514, 0.7859] 
+2024-09-12 22:54:58.833629: Epoch time: 245.86 s 
+2024-09-12 22:54:59.864461:  
+2024-09-12 22:54:59.864682: Epoch 805 
+2024-09-12 22:54:59.864773: Current learning rate: 0.0023 
+2024-09-12 22:59:05.848854: train_loss -0.9028 
+2024-09-12 22:59:05.849041: val_loss -0.5731 
+2024-09-12 22:59:05.849137: Pseudo dice [0.4601, 0.7715] 
+2024-09-12 22:59:05.849199: Epoch time: 245.99 s 
+2024-09-12 22:59:06.903233:  
+2024-09-12 22:59:06.903498: Epoch 806 
+2024-09-12 22:59:06.903592: Current learning rate: 0.00229 
+2024-09-12 23:03:12.999391: train_loss -0.9044 
+2024-09-12 23:03:12.999544: val_loss -0.5802 
+2024-09-12 23:03:12.999601: Pseudo dice [0.4788, 0.796] 
+2024-09-12 23:03:12.999656: Epoch time: 246.1 s 
+2024-09-12 23:03:14.031786:  
+2024-09-12 23:03:14.031986: Epoch 807 
+2024-09-12 23:03:14.032114: Current learning rate: 0.00228 
+2024-09-12 23:07:20.244818: train_loss -0.9039 
+2024-09-12 23:07:20.244965: val_loss -0.5847 
+2024-09-12 23:07:20.245022: Pseudo dice [0.4455, 0.7885] 
+2024-09-12 23:07:20.245078: Epoch time: 246.21 s 
+2024-09-12 23:07:21.290421:  
+2024-09-12 23:07:21.290611: Epoch 808 
+2024-09-12 23:07:21.290729: Current learning rate: 0.00226 
+2024-09-12 23:11:27.328154: train_loss -0.9073 
+2024-09-12 23:11:27.328304: val_loss -0.5693 
+2024-09-12 23:11:27.328375: Pseudo dice [0.4617, 0.7933] 
+2024-09-12 23:11:27.328443: Epoch time: 246.04 s 
+2024-09-12 23:11:28.369947:  
+2024-09-12 23:11:28.370152: Epoch 809 
+2024-09-12 23:11:28.370240: Current learning rate: 0.00225 
+2024-09-12 23:15:34.456539: train_loss -0.9092 
+2024-09-12 23:15:34.456692: val_loss -0.583 
+2024-09-12 23:15:34.456750: Pseudo dice [0.4774, 0.8048] 
+2024-09-12 23:15:34.456806: Epoch time: 246.09 s 
+2024-09-12 23:15:35.492052:  
+2024-09-12 23:15:35.492208: Epoch 810 
+2024-09-12 23:15:35.492303: Current learning rate: 0.00224 
+2024-09-12 23:19:41.674512: train_loss -0.909 
+2024-09-12 23:19:41.674659: val_loss -0.5712 
+2024-09-12 23:19:41.674721: Pseudo dice [0.4635, 0.7876] 
+2024-09-12 23:19:41.674777: Epoch time: 246.18 s 
+2024-09-12 23:19:43.589216:  
+2024-09-12 23:19:43.589427: Epoch 811 
+2024-09-12 23:19:43.589535: Current learning rate: 0.00223 
+2024-09-12 23:23:49.820320: train_loss -0.9085 
+2024-09-12 23:23:49.820501: val_loss -0.5665 
+2024-09-12 23:23:49.820622: Pseudo dice [0.4629, 0.7777] 
+2024-09-12 23:23:49.820683: Epoch time: 246.23 s 
+2024-09-12 23:23:50.882090:  
+2024-09-12 23:23:50.882297: Epoch 812 
+2024-09-12 23:23:50.882376: Current learning rate: 0.00222 
+2024-09-12 23:27:57.048634: train_loss -0.9104 
+2024-09-12 23:27:57.048782: val_loss -0.6049 
+2024-09-12 23:27:57.048838: Pseudo dice [0.54, 0.7918] 
+2024-09-12 23:27:57.048893: Epoch time: 246.17 s 
+2024-09-12 23:27:58.063977:  
+2024-09-12 23:27:58.064220: Epoch 813 
+2024-09-12 23:27:58.064327: Current learning rate: 0.00221 
+2024-09-12 23:32:04.366824: train_loss -0.9052 
+2024-09-12 23:32:04.366976: val_loss -0.5718 
+2024-09-12 23:32:04.367033: Pseudo dice [0.4821, 0.7684] 
+2024-09-12 23:32:04.367089: Epoch time: 246.3 s 
+2024-09-12 23:32:05.380355:  
+2024-09-12 23:32:05.380603: Epoch 814 
+2024-09-12 23:32:05.380716: Current learning rate: 0.0022 
+2024-09-12 23:36:11.701617: train_loss -0.9062 
+2024-09-12 23:36:11.701793: val_loss -0.6184 
+2024-09-12 23:36:11.701851: Pseudo dice [0.5566, 0.7945] 
+2024-09-12 23:36:11.701911: Epoch time: 246.32 s 
+2024-09-12 23:36:12.727260:  
+2024-09-12 23:36:12.727454: Epoch 815 
+2024-09-12 23:36:12.727551: Current learning rate: 0.00219 
+2024-09-12 23:40:19.064523: train_loss -0.9069 
+2024-09-12 23:40:19.064711: val_loss -0.6066 
+2024-09-12 23:40:19.064770: Pseudo dice [0.4907, 0.8032] 
+2024-09-12 23:40:19.064827: Epoch time: 246.34 s 
+2024-09-12 23:40:20.085816:  
+2024-09-12 23:40:20.086018: Epoch 816 
+2024-09-12 23:40:20.086109: Current learning rate: 0.00218 
+2024-09-12 23:44:26.246777: train_loss -0.906 
+2024-09-12 23:44:26.246934: val_loss -0.5875 
+2024-09-12 23:44:26.246992: Pseudo dice [0.4678, 0.7932] 
+2024-09-12 23:44:26.247049: Epoch time: 246.16 s 
+2024-09-12 23:44:27.254707:  
+2024-09-12 23:44:27.254922: Epoch 817 
+2024-09-12 23:44:27.255021: Current learning rate: 0.00217 
+2024-09-12 23:48:33.452473: train_loss -0.9073 
+2024-09-12 23:48:33.452699: val_loss -0.5986 
+2024-09-12 23:48:33.452770: Pseudo dice [0.4904, 0.8016] 
+2024-09-12 23:48:33.452827: Epoch time: 246.2 s 
+2024-09-12 23:48:34.478997:  
+2024-09-12 23:48:34.479316: Epoch 818 
+2024-09-12 23:48:34.479408: Current learning rate: 0.00216 
+2024-09-12 23:52:40.657112: train_loss -0.9038 
+2024-09-12 23:52:40.657269: val_loss -0.596 
+2024-09-12 23:52:40.657350: Pseudo dice [0.4896, 0.8095] 
+2024-09-12 23:52:40.657434: Epoch time: 246.18 s 
+2024-09-12 23:52:41.680391:  
+2024-09-12 23:52:41.680595: Epoch 819 
+2024-09-12 23:52:41.680692: Current learning rate: 0.00215 
+2024-09-12 23:56:47.779166: train_loss -0.9053 
+2024-09-12 23:56:47.779318: val_loss -0.5885 
+2024-09-12 23:56:47.779426: Pseudo dice [0.485, 0.784] 
+2024-09-12 23:56:47.779485: Epoch time: 246.1 s 
+2024-09-12 23:56:48.741243:  
+2024-09-12 23:56:48.741468: Epoch 820 
+2024-09-12 23:56:48.741556: Current learning rate: 0.00214 
+2024-09-13 00:00:54.661437: train_loss -0.9088 
+2024-09-13 00:00:54.661589: val_loss -0.588 
+2024-09-13 00:00:54.661696: Pseudo dice [0.4645, 0.8025] 
+2024-09-13 00:00:54.661759: Epoch time: 245.92 s 
+2024-09-13 00:00:55.636010:  
+2024-09-13 00:00:55.636225: Epoch 821 
+2024-09-13 00:00:55.636348: Current learning rate: 0.00213 
+2024-09-13 00:05:01.590721: train_loss -0.9046 
+2024-09-13 00:05:01.590854: val_loss -0.6083 
+2024-09-13 00:05:01.590909: Pseudo dice [0.4711, 0.8029] 
+2024-09-13 00:05:01.590963: Epoch time: 245.96 s 
+2024-09-13 00:05:02.585338:  
+2024-09-13 00:05:02.585499: Epoch 822 
+2024-09-13 00:05:02.585591: Current learning rate: 0.00212 
+2024-09-13 00:09:08.515121: train_loss -0.9042 
+2024-09-13 00:09:08.515278: val_loss -0.5786 
+2024-09-13 00:09:08.515336: Pseudo dice [0.4743, 0.7938] 
+2024-09-13 00:09:08.515393: Epoch time: 245.93 s 
+2024-09-13 00:09:09.484808:  
+2024-09-13 00:09:09.485053: Epoch 823 
+2024-09-13 00:09:09.485167: Current learning rate: 0.0021 
+2024-09-13 00:13:15.597768: train_loss -0.9092 
+2024-09-13 00:13:15.597918: val_loss -0.5722 
+2024-09-13 00:13:15.597980: Pseudo dice [0.4566, 0.8017] 
+2024-09-13 00:13:15.598036: Epoch time: 246.11 s 
+2024-09-13 00:13:16.545817:  
+2024-09-13 00:13:16.546037: Epoch 824 
+2024-09-13 00:13:16.546129: Current learning rate: 0.00209 
+2024-09-13 00:17:22.584053: train_loss -0.911 
+2024-09-13 00:17:22.584203: val_loss -0.5869 
+2024-09-13 00:17:22.584260: Pseudo dice [0.5131, 0.7828] 
+2024-09-13 00:17:22.584315: Epoch time: 246.04 s 
+2024-09-13 00:17:23.551115:  
+2024-09-13 00:17:23.551325: Epoch 825 
+2024-09-13 00:17:23.551417: Current learning rate: 0.00208 
+2024-09-13 00:21:29.341215: train_loss -0.9088 
+2024-09-13 00:21:29.341367: val_loss -0.6021 
+2024-09-13 00:21:29.341424: Pseudo dice [0.447, 0.8125] 
+2024-09-13 00:21:29.341480: Epoch time: 245.79 s 
+2024-09-13 00:21:30.324766:  
+2024-09-13 00:21:30.324943: Epoch 826 
+2024-09-13 00:21:30.325027: Current learning rate: 0.00207 
+2024-09-13 00:25:36.425473: train_loss -0.906 
+2024-09-13 00:25:36.425622: val_loss -0.5893 
+2024-09-13 00:25:36.425678: Pseudo dice [0.4798, 0.7891] 
+2024-09-13 00:25:36.425734: Epoch time: 246.1 s 
+2024-09-13 00:25:37.389869:  
+2024-09-13 00:25:37.390023: Epoch 827 
+2024-09-13 00:25:37.390110: Current learning rate: 0.00206 
+2024-09-13 00:29:43.436175: train_loss -0.9063 
+2024-09-13 00:29:43.436335: val_loss -0.6268 
+2024-09-13 00:29:43.436391: Pseudo dice [0.5269, 0.8009] 
+2024-09-13 00:29:43.436446: Epoch time: 246.05 s 
+2024-09-13 00:29:44.415751:  
+2024-09-13 00:29:44.415987: Epoch 828 
+2024-09-13 00:29:44.416080: Current learning rate: 0.00205 
+2024-09-13 00:33:50.621883: train_loss -0.9076 
+2024-09-13 00:33:50.622030: val_loss -0.604 
+2024-09-13 00:33:50.622122: Pseudo dice [0.5318, 0.7959] 
+2024-09-13 00:33:50.622177: Epoch time: 246.21 s 
+2024-09-13 00:33:51.580923:  
+2024-09-13 00:33:51.581082: Epoch 829 
+2024-09-13 00:33:51.581184: Current learning rate: 0.00204 
+2024-09-13 00:37:57.704261: train_loss -0.9016 
+2024-09-13 00:37:57.704409: val_loss -0.6076 
+2024-09-13 00:37:57.704521: Pseudo dice [0.5075, 0.8125] 
+2024-09-13 00:37:57.704581: Epoch time: 246.13 s 
+2024-09-13 00:37:58.665527:  
+2024-09-13 00:37:58.665830: Epoch 830 
+2024-09-13 00:37:58.665950: Current learning rate: 0.00203 
+2024-09-13 00:42:04.816455: train_loss -0.9068 
+2024-09-13 00:42:04.816595: val_loss -0.6023 
+2024-09-13 00:42:04.816652: Pseudo dice [0.5377, 0.7764] 
+2024-09-13 00:42:04.816707: Epoch time: 246.15 s 
+2024-09-13 00:42:05.782778:  
+2024-09-13 00:42:05.783001: Epoch 831 
+2024-09-13 00:42:05.783088: Current learning rate: 0.00202 
+2024-09-13 00:46:12.006197: train_loss -0.9093 
+2024-09-13 00:46:12.006375: val_loss -0.5946 
+2024-09-13 00:46:12.006440: Pseudo dice [0.572, 0.7815] 
+2024-09-13 00:46:12.006497: Epoch time: 246.23 s 
+2024-09-13 00:46:12.980138:  
+2024-09-13 00:46:12.980347: Epoch 832 
+2024-09-13 00:46:12.980434: Current learning rate: 0.00201 
+2024-09-13 00:50:19.317696: train_loss -0.91 
+2024-09-13 00:50:19.317850: val_loss -0.566 
+2024-09-13 00:50:19.317907: Pseudo dice [0.4806, 0.7566] 
+2024-09-13 00:50:19.317962: Epoch time: 246.34 s 
+2024-09-13 00:50:20.276019:  
+2024-09-13 00:50:20.276186: Epoch 833 
+2024-09-13 00:50:20.276277: Current learning rate: 0.002 
+2024-09-13 00:54:26.366673: train_loss -0.9106 
+2024-09-13 00:54:26.366819: val_loss -0.6161 
+2024-09-13 00:54:26.366876: Pseudo dice [0.5327, 0.8193] 
+2024-09-13 00:54:26.366931: Epoch time: 246.09 s 
+2024-09-13 00:54:27.347186:  
+2024-09-13 00:54:27.347446: Epoch 834 
+2024-09-13 00:54:27.347542: Current learning rate: 0.00199 
+2024-09-13 00:58:34.219953: train_loss -0.9099 
+2024-09-13 00:58:34.220099: val_loss -0.6085 
+2024-09-13 00:58:34.220155: Pseudo dice [0.5076, 0.7915] 
+2024-09-13 00:58:34.220209: Epoch time: 246.87 s 
+2024-09-13 00:58:35.155422:  
+2024-09-13 00:58:35.155653: Epoch 835 
+2024-09-13 00:58:35.155756: Current learning rate: 0.00198 
+2024-09-13 01:02:41.275941: train_loss -0.9053 
+2024-09-13 01:02:41.276102: val_loss -0.5828 
+2024-09-13 01:02:41.276160: Pseudo dice [0.5131, 0.7727] 
+2024-09-13 01:02:41.276216: Epoch time: 246.12 s 
+2024-09-13 01:02:42.250852:  
+2024-09-13 01:02:42.251178: Epoch 836 
+2024-09-13 01:02:42.251271: Current learning rate: 0.00196 
+2024-09-13 01:06:48.683188: train_loss -0.9079 
+2024-09-13 01:06:48.683355: val_loss -0.5869 
+2024-09-13 01:06:48.683410: Pseudo dice [0.4879, 0.7694] 
+2024-09-13 01:06:48.683465: Epoch time: 246.43 s 
+2024-09-13 01:06:49.638635:  
+2024-09-13 01:06:49.638835: Epoch 837 
+2024-09-13 01:06:49.638921: Current learning rate: 0.00195 
+2024-09-13 01:10:55.786460: train_loss -0.9079 
+2024-09-13 01:10:55.786601: val_loss -0.5931 
+2024-09-13 01:10:55.786653: Pseudo dice [0.519, 0.768] 
+2024-09-13 01:10:55.786705: Epoch time: 246.15 s 
+2024-09-13 01:10:56.774987:  
+2024-09-13 01:10:56.775229: Epoch 838 
+2024-09-13 01:10:56.775321: Current learning rate: 0.00194 
+2024-09-13 01:15:02.916205: train_loss -0.9105 
+2024-09-13 01:15:02.916343: val_loss -0.6078 
+2024-09-13 01:15:02.916393: Pseudo dice [0.5013, 0.7983] 
+2024-09-13 01:15:02.916444: Epoch time: 246.14 s 
+2024-09-13 01:15:03.919000:  
+2024-09-13 01:15:03.919171: Epoch 839 
+2024-09-13 01:15:03.919269: Current learning rate: 0.00193 
+2024-09-13 01:19:09.942206: train_loss -0.9102 
+2024-09-13 01:19:09.942348: val_loss -0.5393 
+2024-09-13 01:19:09.942399: Pseudo dice [0.4227, 0.773] 
+2024-09-13 01:19:09.942452: Epoch time: 246.03 s 
+2024-09-13 01:19:10.905317:  
+2024-09-13 01:19:10.905546: Epoch 840 
+2024-09-13 01:19:10.905689: Current learning rate: 0.00192 
+2024-09-13 01:23:17.137844: train_loss -0.9091 
+2024-09-13 01:23:17.137998: val_loss -0.5823 
+2024-09-13 01:23:17.138048: Pseudo dice [0.5233, 0.7791] 
+2024-09-13 01:23:17.138099: Epoch time: 246.24 s 
+2024-09-13 01:23:18.092993:  
+2024-09-13 01:23:18.093185: Epoch 841 
+2024-09-13 01:23:18.093268: Current learning rate: 0.00191 
+2024-09-13 01:27:24.367966: train_loss -0.9101 
+2024-09-13 01:27:24.368105: val_loss -0.5793 
+2024-09-13 01:27:24.368156: Pseudo dice [0.472, 0.8087] 
+2024-09-13 01:27:24.368207: Epoch time: 246.28 s 
+2024-09-13 01:27:25.321496:  
+2024-09-13 01:27:25.321699: Epoch 842 
+2024-09-13 01:27:25.321784: Current learning rate: 0.0019 
+2024-09-13 01:31:31.443200: train_loss -0.9059 
+2024-09-13 01:31:31.443336: val_loss -0.5936 
+2024-09-13 01:31:31.443387: Pseudo dice [0.4825, 0.7857] 
+2024-09-13 01:31:31.443469: Epoch time: 246.12 s 
+2024-09-13 01:31:32.416198:  
+2024-09-13 01:31:32.416439: Epoch 843 
+2024-09-13 01:31:32.416524: Current learning rate: 0.00189 
+2024-09-13 01:35:38.699455: train_loss -0.9069 
+2024-09-13 01:35:38.699617: val_loss -0.5812 
+2024-09-13 01:35:38.699673: Pseudo dice [0.4538, 0.8006] 
+2024-09-13 01:35:38.699725: Epoch time: 246.29 s 
+2024-09-13 01:35:39.692088:  
+2024-09-13 01:35:39.692300: Epoch 844 
+2024-09-13 01:35:39.692383: Current learning rate: 0.00188 
+2024-09-13 01:39:46.050331: train_loss -0.9096 
+2024-09-13 01:39:46.050469: val_loss -0.5666 
+2024-09-13 01:39:46.050519: Pseudo dice [0.4507, 0.7837] 
+2024-09-13 01:39:46.050570: Epoch time: 246.36 s 
+2024-09-13 01:39:47.004159:  
+2024-09-13 01:39:47.004350: Epoch 845 
+2024-09-13 01:39:47.004432: Current learning rate: 0.00187 
+2024-09-13 01:43:53.246502: train_loss -0.9106 
+2024-09-13 01:43:53.246641: val_loss -0.5924 
+2024-09-13 01:43:53.246691: Pseudo dice [0.4918, 0.7871] 
+2024-09-13 01:43:53.246744: Epoch time: 246.24 s 
+2024-09-13 01:43:54.212909:  
+2024-09-13 01:43:54.213105: Epoch 846 
+2024-09-13 01:43:54.213190: Current learning rate: 0.00186 
+2024-09-13 01:48:00.334466: train_loss -0.911 
+2024-09-13 01:48:00.334606: val_loss -0.5226 
+2024-09-13 01:48:00.334657: Pseudo dice [0.3892, 0.7927] 
+2024-09-13 01:48:00.334709: Epoch time: 246.12 s 
+2024-09-13 01:48:01.307733:  
+2024-09-13 01:48:01.308019: Epoch 847 
+2024-09-13 01:48:01.308106: Current learning rate: 0.00185 
+2024-09-13 01:52:07.507050: train_loss -0.9137 
+2024-09-13 01:52:07.507195: val_loss -0.5569 
+2024-09-13 01:52:07.507246: Pseudo dice [0.4679, 0.7701] 
+2024-09-13 01:52:07.507296: Epoch time: 246.2 s 
+2024-09-13 01:52:08.509824:  
+2024-09-13 01:52:08.510065: Epoch 848 
+2024-09-13 01:52:08.510151: Current learning rate: 0.00184 
+2024-09-13 01:56:14.651004: train_loss -0.9139 
+2024-09-13 01:56:14.651156: val_loss -0.599 
+2024-09-13 01:56:14.651207: Pseudo dice [0.4758, 0.8107] 
+2024-09-13 01:56:14.651259: Epoch time: 246.14 s 
+2024-09-13 01:56:15.609711:  
+2024-09-13 01:56:15.609998: Epoch 849 
+2024-09-13 01:56:15.610082: Current learning rate: 0.00182 
+2024-09-13 02:00:21.775965: train_loss -0.9115 
+2024-09-13 02:00:21.776137: val_loss -0.5696 
+2024-09-13 02:00:21.776191: Pseudo dice [0.4736, 0.7704] 
+2024-09-13 02:00:21.776242: Epoch time: 246.17 s 
+2024-09-13 02:00:25.693787:  
+2024-09-13 02:00:25.693977: Epoch 850 
+2024-09-13 02:00:25.694059: Current learning rate: 0.00181 
+2024-09-13 02:04:31.997426: train_loss -0.9113 
+2024-09-13 02:04:31.997573: val_loss -0.6052 
+2024-09-13 02:04:31.997624: Pseudo dice [0.4988, 0.8097] 
+2024-09-13 02:04:31.997679: Epoch time: 246.31 s 
+2024-09-13 02:04:32.968631:  
+2024-09-13 02:04:32.968820: Epoch 851 
+2024-09-13 02:04:32.968903: Current learning rate: 0.0018 
+2024-09-13 02:08:39.298201: train_loss -0.9099 
+2024-09-13 02:08:39.298341: val_loss -0.6077 
+2024-09-13 02:08:39.298392: Pseudo dice [0.4948, 0.8085] 
+2024-09-13 02:08:39.298443: Epoch time: 246.33 s 
+2024-09-13 02:08:40.263394:  
+2024-09-13 02:08:40.263607: Epoch 852 
+2024-09-13 02:08:40.263693: Current learning rate: 0.00179 
+2024-09-13 02:12:46.569148: train_loss -0.9079 
+2024-09-13 02:12:46.569292: val_loss -0.6112 
+2024-09-13 02:12:46.569351: Pseudo dice [0.5068, 0.7973] 
+2024-09-13 02:12:46.569402: Epoch time: 246.31 s 
+2024-09-13 02:12:47.525515:  
+2024-09-13 02:12:47.525683: Epoch 853 
+2024-09-13 02:12:47.525767: Current learning rate: 0.00178 
+2024-09-13 02:16:53.573660: train_loss -0.9098 
+2024-09-13 02:16:53.573798: val_loss -0.5642 
+2024-09-13 02:16:53.573848: Pseudo dice [0.4683, 0.7931] 
+2024-09-13 02:16:53.573898: Epoch time: 246.05 s 
+2024-09-13 02:16:54.521262:  
+2024-09-13 02:16:54.521420: Epoch 854 
+2024-09-13 02:16:54.521539: Current learning rate: 0.00177 
+2024-09-13 02:21:00.619246: train_loss -0.9123 
+2024-09-13 02:21:00.619405: val_loss -0.5389 
+2024-09-13 02:21:00.619456: Pseudo dice [0.3922, 0.7676] 
+2024-09-13 02:21:00.619506: Epoch time: 246.1 s 
+2024-09-13 02:21:01.571880:  
+2024-09-13 02:21:01.572073: Epoch 855 
+2024-09-13 02:21:01.572158: Current learning rate: 0.00176 
+2024-09-13 02:25:07.534744: train_loss -0.9093 
+2024-09-13 02:25:07.534911: val_loss -0.5771 
+2024-09-13 02:25:07.534963: Pseudo dice [0.4559, 0.8089] 
+2024-09-13 02:25:07.535062: Epoch time: 245.96 s 
+2024-09-13 02:25:08.474580:  
+2024-09-13 02:25:08.474774: Epoch 856 
+2024-09-13 02:25:08.474862: Current learning rate: 0.00175 
+2024-09-13 02:29:14.504243: train_loss -0.9114 
+2024-09-13 02:29:14.504385: val_loss -0.5854 
+2024-09-13 02:29:14.504435: Pseudo dice [0.4482, 0.8132] 
+2024-09-13 02:29:14.504486: Epoch time: 246.03 s 
+2024-09-13 02:29:15.463981:  
+2024-09-13 02:29:15.464171: Epoch 857 
+2024-09-13 02:29:15.464282: Current learning rate: 0.00174 
+2024-09-13 02:33:21.522982: train_loss -0.9111 
+2024-09-13 02:33:21.523131: val_loss -0.6077 
+2024-09-13 02:33:21.523181: Pseudo dice [0.5459, 0.7881] 
+2024-09-13 02:33:21.523260: Epoch time: 246.06 s 
+2024-09-13 02:33:22.485850:  
+2024-09-13 02:33:22.486063: Epoch 858 
+2024-09-13 02:33:22.486176: Current learning rate: 0.00173 
+2024-09-13 02:37:28.701866: train_loss -0.9121 
+2024-09-13 02:37:28.702009: val_loss -0.617 
+2024-09-13 02:37:28.702060: Pseudo dice [0.5372, 0.8097] 
+2024-09-13 02:37:28.702111: Epoch time: 246.22 s 
+2024-09-13 02:37:30.555753:  
+2024-09-13 02:37:30.556067: Epoch 859 
+2024-09-13 02:37:30.556214: Current learning rate: 0.00172 
+2024-09-13 02:41:36.704166: train_loss -0.9136 
+2024-09-13 02:41:36.704310: val_loss -0.6068 
+2024-09-13 02:41:36.704360: Pseudo dice [0.543, 0.7862] 
+2024-09-13 02:41:36.704409: Epoch time: 246.15 s 
+2024-09-13 02:41:37.648666:  
+2024-09-13 02:41:37.648914: Epoch 860 
+2024-09-13 02:41:37.649001: Current learning rate: 0.0017 
+2024-09-13 02:45:43.785010: train_loss -0.9116 
+2024-09-13 02:45:43.785158: val_loss -0.5468 
+2024-09-13 02:45:43.785213: Pseudo dice [0.4792, 0.7472] 
+2024-09-13 02:45:43.785266: Epoch time: 246.14 s 
+2024-09-13 02:45:44.755011:  
+2024-09-13 02:45:44.755230: Epoch 861 
+2024-09-13 02:45:44.755313: Current learning rate: 0.00169 
+2024-09-13 02:49:50.854572: train_loss -0.9098 
+2024-09-13 02:49:50.854724: val_loss -0.5913 
+2024-09-13 02:49:50.854797: Pseudo dice [0.5126, 0.7776] 
+2024-09-13 02:49:50.854872: Epoch time: 246.1 s 
+2024-09-13 02:49:51.803012:  
+2024-09-13 02:49:51.803245: Epoch 862 
+2024-09-13 02:49:51.803324: Current learning rate: 0.00168 
+2024-09-13 02:53:57.862664: train_loss -0.9085 
+2024-09-13 02:53:57.862814: val_loss -0.5528 
+2024-09-13 02:53:57.862865: Pseudo dice [0.4838, 0.7624] 
+2024-09-13 02:53:57.862915: Epoch time: 246.06 s 
+2024-09-13 02:53:58.812567:  
+2024-09-13 02:53:58.812790: Epoch 863 
+2024-09-13 02:53:58.812902: Current learning rate: 0.00167 
+2024-09-13 02:58:04.966086: train_loss -0.9129 
+2024-09-13 02:58:04.966261: val_loss -0.5827 
+2024-09-13 02:58:04.966312: Pseudo dice [0.5062, 0.7806] 
+2024-09-13 02:58:04.966362: Epoch time: 246.16 s 
+2024-09-13 02:58:05.935122:  
+2024-09-13 02:58:05.935393: Epoch 864 
+2024-09-13 02:58:05.935518: Current learning rate: 0.00166 
+2024-09-13 03:02:12.244909: train_loss -0.9131 
+2024-09-13 03:02:12.245059: val_loss -0.5919 
+2024-09-13 03:02:12.245111: Pseudo dice [0.5178, 0.77] 
+2024-09-13 03:02:12.245162: Epoch time: 246.31 s 
+2024-09-13 03:02:13.204967:  
+2024-09-13 03:02:13.205226: Epoch 865 
+2024-09-13 03:02:13.205312: Current learning rate: 0.00165 
+2024-09-13 03:06:19.603224: train_loss -0.9088 
+2024-09-13 03:06:19.603391: val_loss -0.5821 
+2024-09-13 03:06:19.603443: Pseudo dice [0.4746, 0.775] 
+2024-09-13 03:06:19.603497: Epoch time: 246.4 s 
+2024-09-13 03:06:20.575013:  
+2024-09-13 03:06:20.575176: Epoch 866 
+2024-09-13 03:06:20.575300: Current learning rate: 0.00164 
+2024-09-13 03:10:26.752128: train_loss -0.9112 
+2024-09-13 03:10:26.752279: val_loss -0.5967 
+2024-09-13 03:10:26.752331: Pseudo dice [0.4576, 0.8115] 
+2024-09-13 03:10:26.752393: Epoch time: 246.18 s 
+2024-09-13 03:10:27.706340:  
+2024-09-13 03:10:27.706511: Epoch 867 
+2024-09-13 03:10:27.706596: Current learning rate: 0.00163 
+2024-09-13 03:14:33.952075: train_loss -0.9103 
+2024-09-13 03:14:33.952216: val_loss -0.5616 
+2024-09-13 03:14:33.952268: Pseudo dice [0.4743, 0.78] 
+2024-09-13 03:14:33.952319: Epoch time: 246.25 s 
+2024-09-13 03:14:34.896282:  
+2024-09-13 03:14:34.896499: Epoch 868 
+2024-09-13 03:14:34.896582: Current learning rate: 0.00162 
+2024-09-13 03:18:41.245000: train_loss -0.9105 
+2024-09-13 03:18:41.245142: val_loss -0.5875 
+2024-09-13 03:18:41.245194: Pseudo dice [0.5525, 0.7548] 
+2024-09-13 03:18:41.245244: Epoch time: 246.35 s 
+2024-09-13 03:18:42.199906:  
+2024-09-13 03:18:42.200107: Epoch 869 
+2024-09-13 03:18:42.200195: Current learning rate: 0.00161 
+2024-09-13 03:22:48.531890: train_loss -0.912 
+2024-09-13 03:22:48.532038: val_loss -0.5957 
+2024-09-13 03:22:48.532089: Pseudo dice [0.5081, 0.7992] 
+2024-09-13 03:22:48.532143: Epoch time: 246.33 s 
+2024-09-13 03:22:49.507718:  
+2024-09-13 03:22:49.507959: Epoch 870 
+2024-09-13 03:22:49.508044: Current learning rate: 0.00159 
+2024-09-13 03:26:55.877599: train_loss -0.9119 
+2024-09-13 03:26:55.877748: val_loss -0.5996 
+2024-09-13 03:26:55.877838: Pseudo dice [0.5157, 0.7947] 
+2024-09-13 03:26:55.877891: Epoch time: 246.37 s 
+2024-09-13 03:26:56.836861:  
+2024-09-13 03:26:56.837090: Epoch 871 
+2024-09-13 03:26:56.837173: Current learning rate: 0.00158 
+2024-09-13 03:31:03.322111: train_loss -0.9119 
+2024-09-13 03:31:03.322255: val_loss -0.5867 
+2024-09-13 03:31:03.322305: Pseudo dice [0.5028, 0.7957] 
+2024-09-13 03:31:03.322357: Epoch time: 246.49 s 
+2024-09-13 03:31:04.287665:  
+2024-09-13 03:31:04.287907: Epoch 872 
+2024-09-13 03:31:04.287997: Current learning rate: 0.00157 
+2024-09-13 03:35:10.611870: train_loss -0.9133 
+2024-09-13 03:35:10.612011: val_loss -0.5894 
+2024-09-13 03:35:10.612060: Pseudo dice [0.5047, 0.7894] 
+2024-09-13 03:35:10.612112: Epoch time: 246.33 s 
+2024-09-13 03:35:11.559790:  
+2024-09-13 03:35:11.559960: Epoch 873 
+2024-09-13 03:35:11.560044: Current learning rate: 0.00156 
+2024-09-13 03:39:17.833060: train_loss -0.9167 
+2024-09-13 03:39:17.833224: val_loss -0.5797 
+2024-09-13 03:39:17.833275: Pseudo dice [0.4863, 0.7754] 
+2024-09-13 03:39:17.833326: Epoch time: 246.28 s 
+2024-09-13 03:39:18.767825:  
+2024-09-13 03:39:18.768056: Epoch 874 
+2024-09-13 03:39:18.768164: Current learning rate: 0.00155 
+2024-09-13 03:43:25.184715: train_loss -0.9131 
+2024-09-13 03:43:25.184870: val_loss -0.6218 
+2024-09-13 03:43:25.185052: Pseudo dice [0.5228, 0.8295] 
+2024-09-13 03:43:25.185164: Epoch time: 246.42 s 
+2024-09-13 03:43:26.148780:  
+2024-09-13 03:43:26.148974: Epoch 875 
+2024-09-13 03:43:26.149065: Current learning rate: 0.00154 
+2024-09-13 03:47:32.725104: train_loss -0.9118 
+2024-09-13 03:47:32.725243: val_loss -0.609 
+2024-09-13 03:47:32.725294: Pseudo dice [0.5413, 0.8108] 
+2024-09-13 03:47:32.725344: Epoch time: 246.58 s 
+2024-09-13 03:47:33.680663:  
+2024-09-13 03:47:33.680847: Epoch 876 
+2024-09-13 03:47:33.680932: Current learning rate: 0.00153 
+2024-09-13 03:51:40.005914: train_loss -0.9157 
+2024-09-13 03:51:40.006054: val_loss -0.5716 
+2024-09-13 03:51:40.006104: Pseudo dice [0.4964, 0.7762] 
+2024-09-13 03:51:40.006156: Epoch time: 246.33 s 
+2024-09-13 03:51:40.969604:  
+2024-09-13 03:51:40.969786: Epoch 877 
+2024-09-13 03:51:40.969890: Current learning rate: 0.00152 
+2024-09-13 03:55:47.277152: train_loss -0.9163 
+2024-09-13 03:55:47.277304: val_loss -0.574 
+2024-09-13 03:55:47.277355: Pseudo dice [0.458, 0.8141] 
+2024-09-13 03:55:47.277409: Epoch time: 246.31 s 
+2024-09-13 03:55:48.241321:  
+2024-09-13 03:55:48.241493: Epoch 878 
+2024-09-13 03:55:48.241613: Current learning rate: 0.00151 
+2024-09-13 03:59:54.509185: train_loss -0.91 
+2024-09-13 03:59:54.509325: val_loss -0.5861 
+2024-09-13 03:59:54.509379: Pseudo dice [0.4744, 0.8084] 
+2024-09-13 03:59:54.509430: Epoch time: 246.27 s 
+2024-09-13 03:59:55.462276:  
+2024-09-13 03:59:55.462470: Epoch 879 
+2024-09-13 03:59:55.462566: Current learning rate: 0.00149 
+2024-09-13 04:04:01.730592: train_loss -0.9155 
+2024-09-13 04:04:01.730731: val_loss -0.6132 
+2024-09-13 04:04:01.730781: Pseudo dice [0.5511, 0.7831] 
+2024-09-13 04:04:01.730832: Epoch time: 246.27 s 
+2024-09-13 04:04:02.685136:  
+2024-09-13 04:04:02.685335: Epoch 880 
+2024-09-13 04:04:02.685411: Current learning rate: 0.00148 
+2024-09-13 04:08:09.040921: train_loss -0.9158 
+2024-09-13 04:08:09.041058: val_loss -0.6002 
+2024-09-13 04:08:09.041109: Pseudo dice [0.5172, 0.7934] 
+2024-09-13 04:08:09.041188: Epoch time: 246.36 s 
+2024-09-13 04:08:10.012867:  
+2024-09-13 04:08:10.013015: Epoch 881 
+2024-09-13 04:08:10.013098: Current learning rate: 0.00147 
+2024-09-13 04:12:16.466562: train_loss -0.914 
+2024-09-13 04:12:16.466718: val_loss -0.5896 
+2024-09-13 04:12:16.466769: Pseudo dice [0.4673, 0.7824] 
+2024-09-13 04:12:16.466821: Epoch time: 246.46 s 
+2024-09-13 04:12:17.436915:  
+2024-09-13 04:12:17.437101: Epoch 882 
+2024-09-13 04:12:17.437185: Current learning rate: 0.00146 
+2024-09-13 04:16:23.762840: train_loss -0.9161 
+2024-09-13 04:16:23.762991: val_loss -0.5951 
+2024-09-13 04:16:23.763041: Pseudo dice [0.5112, 0.8018] 
+2024-09-13 04:16:23.763092: Epoch time: 246.33 s 
+2024-09-13 04:16:24.718696:  
+2024-09-13 04:16:24.718876: Epoch 883 
+2024-09-13 04:16:24.718989: Current learning rate: 0.00145 
+2024-09-13 04:20:30.923281: train_loss -0.9132 
+2024-09-13 04:20:30.923438: val_loss -0.5507 
+2024-09-13 04:20:30.923490: Pseudo dice [0.4652, 0.7813] 
+2024-09-13 04:20:30.923540: Epoch time: 246.21 s 
+2024-09-13 04:20:32.796563:  
+2024-09-13 04:20:32.796736: Epoch 884 
+2024-09-13 04:20:32.796835: Current learning rate: 0.00144 
+2024-09-13 04:24:39.169437: train_loss -0.915 
+2024-09-13 04:24:39.169607: val_loss -0.5697 
+2024-09-13 04:24:39.169658: Pseudo dice [0.5123, 0.7385] 
+2024-09-13 04:24:39.169708: Epoch time: 246.37 s 
+2024-09-13 04:24:40.115839:  
+2024-09-13 04:24:40.116063: Epoch 885 
+2024-09-13 04:24:40.116187: Current learning rate: 0.00143 
+2024-09-13 04:28:46.593560: train_loss -0.9118 
+2024-09-13 04:28:46.593701: val_loss -0.5972 
+2024-09-13 04:28:46.593752: Pseudo dice [0.4558, 0.8045] 
+2024-09-13 04:28:46.593804: Epoch time: 246.48 s 
+2024-09-13 04:28:47.541582:  
+2024-09-13 04:28:47.541840: Epoch 886 
+2024-09-13 04:28:47.541927: Current learning rate: 0.00142 
+2024-09-13 04:32:53.693926: train_loss -0.9113 
+2024-09-13 04:32:53.694100: val_loss -0.579 
+2024-09-13 04:32:53.694150: Pseudo dice [0.5066, 0.7781] 
+2024-09-13 04:32:53.694201: Epoch time: 246.15 s 
+2024-09-13 04:32:54.639086:  
+2024-09-13 04:32:54.639309: Epoch 887 
+2024-09-13 04:32:54.639393: Current learning rate: 0.00141 
+2024-09-13 04:37:00.783265: train_loss -0.9141 
+2024-09-13 04:37:00.783406: val_loss -0.5873 
+2024-09-13 04:37:00.783457: Pseudo dice [0.4861, 0.7971] 
+2024-09-13 04:37:00.783509: Epoch time: 246.15 s 
+2024-09-13 04:37:01.739652:  
+2024-09-13 04:37:01.739900: Epoch 888 
+2024-09-13 04:37:01.740034: Current learning rate: 0.00139 
+2024-09-13 04:41:07.835353: train_loss -0.9109 
+2024-09-13 04:41:07.835497: val_loss -0.5842 
+2024-09-13 04:41:07.835547: Pseudo dice [0.5008, 0.7732] 
+2024-09-13 04:41:07.835598: Epoch time: 246.1 s 
+2024-09-13 04:41:08.786293:  
+2024-09-13 04:41:08.786537: Epoch 889 
+2024-09-13 04:41:08.786661: Current learning rate: 0.00138 
+2024-09-13 04:45:15.009392: train_loss -0.9155 
+2024-09-13 04:45:15.009677: val_loss -0.6018 
+2024-09-13 04:45:15.009729: Pseudo dice [0.4945, 0.807] 
+2024-09-13 04:45:15.009780: Epoch time: 246.23 s 
+2024-09-13 04:45:15.969290:  
+2024-09-13 04:45:15.969528: Epoch 890 
+2024-09-13 04:45:15.969611: Current learning rate: 0.00137 
+2024-09-13 04:49:22.290667: train_loss -0.9132 
+2024-09-13 04:49:22.290803: val_loss -0.58 
+2024-09-13 04:49:22.290852: Pseudo dice [0.4893, 0.7796] 
+2024-09-13 04:49:22.290902: Epoch time: 246.32 s 
+2024-09-13 04:49:23.241292:  
+2024-09-13 04:49:23.241533: Epoch 891 
+2024-09-13 04:49:23.241616: Current learning rate: 0.00136 
+2024-09-13 04:53:29.373977: train_loss -0.9135 
+2024-09-13 04:53:29.374116: val_loss -0.579 
+2024-09-13 04:53:29.374180: Pseudo dice [0.5092, 0.7858] 
+2024-09-13 04:53:29.374231: Epoch time: 246.13 s 
+2024-09-13 04:53:30.344973:  
+2024-09-13 04:53:30.345223: Epoch 892 
+2024-09-13 04:53:30.345309: Current learning rate: 0.00135 
+2024-09-13 04:57:36.519966: train_loss -0.9108 
+2024-09-13 04:57:36.520129: val_loss -0.5728 
+2024-09-13 04:57:36.520208: Pseudo dice [0.5279, 0.7679] 
+2024-09-13 04:57:36.520305: Epoch time: 246.18 s 
+2024-09-13 04:57:37.465312:  
+2024-09-13 04:57:37.465508: Epoch 893 
+2024-09-13 04:57:37.465594: Current learning rate: 0.00134 
+2024-09-13 05:01:43.661552: train_loss -0.9164 
+2024-09-13 05:01:43.661706: val_loss -0.5313 
+2024-09-13 05:01:43.661756: Pseudo dice [0.4667, 0.7391] 
+2024-09-13 05:01:43.661807: Epoch time: 246.2 s 
+2024-09-13 05:01:44.604128:  
+2024-09-13 05:01:44.604356: Epoch 894 
+2024-09-13 05:01:44.604440: Current learning rate: 0.00133 
+2024-09-13 05:05:50.784560: train_loss -0.9147 
+2024-09-13 05:05:50.784701: val_loss -0.5841 
+2024-09-13 05:05:50.784751: Pseudo dice [0.512, 0.7677] 
+2024-09-13 05:05:50.784802: Epoch time: 246.18 s 
+2024-09-13 05:05:51.743790:  
+2024-09-13 05:05:51.744025: Epoch 895 
+2024-09-13 05:05:51.744108: Current learning rate: 0.00132 
+2024-09-13 05:09:58.034373: train_loss -0.9142 
+2024-09-13 05:09:58.034544: val_loss -0.5739 
+2024-09-13 05:09:58.034594: Pseudo dice [0.4936, 0.7574] 
+2024-09-13 05:09:58.034647: Epoch time: 246.29 s 
+2024-09-13 05:09:58.972554:  
+2024-09-13 05:09:58.972774: Epoch 896 
+2024-09-13 05:09:58.972859: Current learning rate: 0.0013 
+2024-09-13 05:14:05.188027: train_loss -0.9162 
+2024-09-13 05:14:05.188183: val_loss -0.5919 
+2024-09-13 05:14:05.188236: Pseudo dice [0.5122, 0.7903] 
+2024-09-13 05:14:05.188286: Epoch time: 246.22 s 
+2024-09-13 05:14:06.140678:  
+2024-09-13 05:14:06.140914: Epoch 897 
+2024-09-13 05:14:06.141001: Current learning rate: 0.00129 
+2024-09-13 05:18:12.514024: train_loss -0.9118 
+2024-09-13 05:18:12.514165: val_loss -0.5681 
+2024-09-13 05:18:12.514215: Pseudo dice [0.4536, 0.7928] 
+2024-09-13 05:18:12.514266: Epoch time: 246.38 s 
+2024-09-13 05:18:13.490393:  
+2024-09-13 05:18:13.490643: Epoch 898 
+2024-09-13 05:18:13.490727: Current learning rate: 0.00128 
+2024-09-13 05:22:19.664214: train_loss -0.9093 
+2024-09-13 05:22:19.664354: val_loss -0.5598 
+2024-09-13 05:22:19.664405: Pseudo dice [0.4867, 0.7489] 
+2024-09-13 05:22:19.664455: Epoch time: 246.18 s 
+2024-09-13 05:22:20.609042:  
+2024-09-13 05:22:20.609236: Epoch 899 
+2024-09-13 05:22:20.609325: Current learning rate: 0.00127 
+2024-09-13 05:26:26.597141: train_loss -0.9116 
+2024-09-13 05:26:26.597303: val_loss -0.5999 
+2024-09-13 05:26:26.597354: Pseudo dice [0.5416, 0.797] 
+2024-09-13 05:26:26.597405: Epoch time: 245.99 s 
+2024-09-13 05:26:30.555843:  
+2024-09-13 05:26:30.556062: Epoch 900 
+2024-09-13 05:26:30.556149: Current learning rate: 0.00126 
+2024-09-13 05:30:36.593536: train_loss -0.9158 
+2024-09-13 05:30:36.593675: val_loss -0.5894 
+2024-09-13 05:30:36.593726: Pseudo dice [0.4786, 0.8149] 
+2024-09-13 05:30:36.593781: Epoch time: 246.04 s 
+2024-09-13 05:30:37.554669:  
+2024-09-13 05:30:37.554871: Epoch 901 
+2024-09-13 05:30:37.554999: Current learning rate: 0.00125 
+2024-09-13 05:34:43.417481: train_loss -0.905 
+2024-09-13 05:34:43.417620: val_loss -0.5999 
+2024-09-13 05:34:43.417669: Pseudo dice [0.4956, 0.8134] 
+2024-09-13 05:34:43.417720: Epoch time: 245.87 s 
+2024-09-13 05:34:44.376173:  
+2024-09-13 05:34:44.376340: Epoch 902 
+2024-09-13 05:34:44.376426: Current learning rate: 0.00124 
+2024-09-13 05:38:50.288936: train_loss -0.9099 
+2024-09-13 05:38:50.289074: val_loss -0.5909 
+2024-09-13 05:38:50.289180: Pseudo dice [0.4971, 0.7746] 
+2024-09-13 05:38:50.289274: Epoch time: 245.91 s 
+2024-09-13 05:38:51.255537:  
+2024-09-13 05:38:51.255702: Epoch 903 
+2024-09-13 05:38:51.255802: Current learning rate: 0.00122 
+2024-09-13 05:42:57.312643: train_loss -0.9086 
+2024-09-13 05:42:57.312778: val_loss -0.5395 
+2024-09-13 05:42:57.312830: Pseudo dice [0.4388, 0.7654] 
+2024-09-13 05:42:57.312882: Epoch time: 246.06 s 
+2024-09-13 05:42:58.302443:  
+2024-09-13 05:42:58.302603: Epoch 904 
+2024-09-13 05:42:58.302709: Current learning rate: 0.00121 
+2024-09-13 05:47:04.395496: train_loss -0.9088 
+2024-09-13 05:47:04.395637: val_loss -0.6104 
+2024-09-13 05:47:04.395688: Pseudo dice [0.548, 0.7715] 
+2024-09-13 05:47:04.395742: Epoch time: 246.09 s 
+2024-09-13 05:47:05.345741:  
+2024-09-13 05:47:05.345928: Epoch 905 
+2024-09-13 05:47:05.346021: Current learning rate: 0.0012 
+2024-09-13 05:51:11.214085: train_loss -0.9105 
+2024-09-13 05:51:11.214224: val_loss -0.586 
+2024-09-13 05:51:11.214275: Pseudo dice [0.5007, 0.735] 
+2024-09-13 05:51:11.214328: Epoch time: 245.87 s 
+2024-09-13 05:51:12.152438:  
+2024-09-13 05:51:12.152601: Epoch 906 
+2024-09-13 05:51:12.152686: Current learning rate: 0.00119 
+2024-09-13 05:55:18.013367: train_loss -0.9131 
+2024-09-13 05:55:18.013506: val_loss -0.582 
+2024-09-13 05:55:18.013556: Pseudo dice [0.4711, 0.772] 
+2024-09-13 05:55:18.013606: Epoch time: 245.86 s 
+2024-09-13 05:55:18.968405:  
+2024-09-13 05:55:18.968548: Epoch 907 
+2024-09-13 05:55:18.968630: Current learning rate: 0.00118 
+2024-09-13 05:59:24.628799: train_loss -0.9146 
+2024-09-13 05:59:24.628937: val_loss -0.5867 
+2024-09-13 05:59:24.628987: Pseudo dice [0.4973, 0.7863] 
+2024-09-13 05:59:24.629039: Epoch time: 245.66 s 
+2024-09-13 05:59:25.612505:  
+2024-09-13 05:59:25.612693: Epoch 908 
+2024-09-13 05:59:25.612777: Current learning rate: 0.00117 
+2024-09-13 06:03:31.407423: train_loss -0.9161 
+2024-09-13 06:03:31.407603: val_loss -0.6102 
+2024-09-13 06:03:31.407655: Pseudo dice [0.5261, 0.8044] 
+2024-09-13 06:03:31.407707: Epoch time: 245.8 s 
+2024-09-13 06:03:33.316329:  
+2024-09-13 06:03:33.316535: Epoch 909 
+2024-09-13 06:03:33.316657: Current learning rate: 0.00116 
+2024-09-13 06:07:39.373685: train_loss -0.9119 
+2024-09-13 06:07:39.373830: val_loss -0.5882 
+2024-09-13 06:07:39.373881: Pseudo dice [0.5046, 0.7742] 
+2024-09-13 06:07:39.373931: Epoch time: 246.06 s 
+2024-09-13 06:07:40.330059:  
+2024-09-13 06:07:40.330287: Epoch 910 
+2024-09-13 06:07:40.330403: Current learning rate: 0.00115 
+2024-09-13 06:11:46.330550: train_loss -0.9155 
+2024-09-13 06:11:46.330687: val_loss -0.5456 
+2024-09-13 06:11:46.330739: Pseudo dice [0.4472, 0.7578] 
+2024-09-13 06:11:46.330790: Epoch time: 246.0 s 
+2024-09-13 06:11:47.315448:  
+2024-09-13 06:11:47.315675: Epoch 911 
+2024-09-13 06:11:47.315799: Current learning rate: 0.00113 
+2024-09-13 06:15:53.422761: train_loss -0.9147 
+2024-09-13 06:15:53.422914: val_loss -0.5736 
+2024-09-13 06:15:53.422966: Pseudo dice [0.4841, 0.7955] 
+2024-09-13 06:15:53.423016: Epoch time: 246.11 s 
+2024-09-13 06:15:54.378445:  
+2024-09-13 06:15:54.378687: Epoch 912 
+2024-09-13 06:15:54.378772: Current learning rate: 0.00112 
+2024-09-13 06:20:00.335038: train_loss -0.9176 
+2024-09-13 06:20:00.335178: val_loss -0.5815 
+2024-09-13 06:20:00.335250: Pseudo dice [0.4794, 0.7897] 
+2024-09-13 06:20:00.335335: Epoch time: 245.96 s 
+2024-09-13 06:20:01.331501:  
+2024-09-13 06:20:01.331689: Epoch 913 
+2024-09-13 06:20:01.331774: Current learning rate: 0.00111 
+2024-09-13 06:24:07.227580: train_loss -0.915 
+2024-09-13 06:24:07.227727: val_loss -0.5892 
+2024-09-13 06:24:07.227777: Pseudo dice [0.5484, 0.7636] 
+2024-09-13 06:24:07.227840: Epoch time: 245.9 s 
+2024-09-13 06:24:08.195425:  
+2024-09-13 06:24:08.195725: Epoch 914 
+2024-09-13 06:24:08.195864: Current learning rate: 0.0011 
+2024-09-13 06:28:14.146684: train_loss -0.9164 
+2024-09-13 06:28:14.146824: val_loss -0.5716 
+2024-09-13 06:28:14.146875: Pseudo dice [0.4789, 0.7627] 
+2024-09-13 06:28:14.146925: Epoch time: 245.95 s 
+2024-09-13 06:28:15.095246:  
+2024-09-13 06:28:15.095473: Epoch 915 
+2024-09-13 06:28:15.095557: Current learning rate: 0.00109 
+2024-09-13 06:32:21.130148: train_loss -0.9173 
+2024-09-13 06:32:21.130288: val_loss -0.6174 
+2024-09-13 06:32:21.130338: Pseudo dice [0.4913, 0.7957] 
+2024-09-13 06:32:21.130389: Epoch time: 246.04 s 
+2024-09-13 06:32:22.104008:  
+2024-09-13 06:32:22.104210: Epoch 916 
+2024-09-13 06:32:22.104295: Current learning rate: 0.00108 
+2024-09-13 06:36:28.294345: train_loss -0.9162 
+2024-09-13 06:36:28.294524: val_loss -0.5625 
+2024-09-13 06:36:28.294577: Pseudo dice [0.4551, 0.7854] 
+2024-09-13 06:36:28.294632: Epoch time: 246.19 s 
+2024-09-13 06:36:29.244321:  
+2024-09-13 06:36:29.244553: Epoch 917 
+2024-09-13 06:36:29.244636: Current learning rate: 0.00106 
+2024-09-13 06:40:35.494684: train_loss -0.9148 
+2024-09-13 06:40:35.494856: val_loss -0.5891 
+2024-09-13 06:40:35.494908: Pseudo dice [0.4863, 0.7876] 
+2024-09-13 06:40:35.494960: Epoch time: 246.25 s 
+2024-09-13 06:40:36.451763:  
+2024-09-13 06:40:36.451987: Epoch 918 
+2024-09-13 06:40:36.452071: Current learning rate: 0.00105 
+2024-09-13 06:44:42.689891: train_loss -0.9161 
+2024-09-13 06:44:42.690036: val_loss -0.5465 
+2024-09-13 06:44:42.690085: Pseudo dice [0.3971, 0.7959] 
+2024-09-13 06:44:42.690136: Epoch time: 246.24 s 
+2024-09-13 06:44:43.634403:  
+2024-09-13 06:44:43.634614: Epoch 919 
+2024-09-13 06:44:43.634698: Current learning rate: 0.00104 
+2024-09-13 06:48:49.816059: train_loss -0.9166 
+2024-09-13 06:48:49.816206: val_loss -0.5878 
+2024-09-13 06:48:49.816258: Pseudo dice [0.4866, 0.7958] 
+2024-09-13 06:48:49.816356: Epoch time: 246.18 s 
+2024-09-13 06:48:50.764693:  
+2024-09-13 06:48:50.764922: Epoch 920 
+2024-09-13 06:48:50.765009: Current learning rate: 0.00103 
+2024-09-13 06:52:57.012214: train_loss -0.9176 
+2024-09-13 06:52:57.012374: val_loss -0.5758 
+2024-09-13 06:52:57.012423: Pseudo dice [0.4222, 0.7921] 
+2024-09-13 06:52:57.012473: Epoch time: 246.25 s 
+2024-09-13 06:52:57.970334:  
+2024-09-13 06:52:57.970558: Epoch 921 
+2024-09-13 06:52:57.970655: Current learning rate: 0.00102 
+2024-09-13 06:57:04.197216: train_loss -0.9167 
+2024-09-13 06:57:04.197351: val_loss -0.5817 
+2024-09-13 06:57:04.197401: Pseudo dice [0.4839, 0.7873] 
+2024-09-13 06:57:04.197454: Epoch time: 246.23 s 
+2024-09-13 06:57:05.148882:  
+2024-09-13 06:57:05.149182: Epoch 922 
+2024-09-13 06:57:05.149287: Current learning rate: 0.00101 
+2024-09-13 07:01:11.582348: train_loss -0.916 
+2024-09-13 07:01:11.582496: val_loss -0.5881 
+2024-09-13 07:01:11.582548: Pseudo dice [0.4857, 0.7866] 
+2024-09-13 07:01:11.582601: Epoch time: 246.44 s 
+2024-09-13 07:01:12.554252:  
+2024-09-13 07:01:12.554426: Epoch 923 
+2024-09-13 07:01:12.554512: Current learning rate: 0.001 
+2024-09-13 07:05:18.805271: train_loss -0.9196 
+2024-09-13 07:05:18.805415: val_loss -0.5443 
+2024-09-13 07:05:18.805465: Pseudo dice [0.4386, 0.775] 
+2024-09-13 07:05:18.805515: Epoch time: 246.25 s 
+2024-09-13 07:05:19.764333:  
+2024-09-13 07:05:19.764566: Epoch 924 
+2024-09-13 07:05:19.764657: Current learning rate: 0.00098 
+2024-09-13 07:09:26.126354: train_loss -0.9192 
+2024-09-13 07:09:26.126508: val_loss -0.5921 
+2024-09-13 07:09:26.126557: Pseudo dice [0.5019, 0.7833] 
+2024-09-13 07:09:26.126608: Epoch time: 246.36 s 
+2024-09-13 07:09:27.090707:  
+2024-09-13 07:09:27.090891: Epoch 925 
+2024-09-13 07:09:27.090978: Current learning rate: 0.00097 
+2024-09-13 07:13:33.490207: train_loss -0.9216 
+2024-09-13 07:13:33.490347: val_loss -0.5985 
+2024-09-13 07:13:33.490398: Pseudo dice [0.5246, 0.7826] 
+2024-09-13 07:13:33.490449: Epoch time: 246.4 s 
+2024-09-13 07:13:34.445309:  
+2024-09-13 07:13:34.445542: Epoch 926 
+2024-09-13 07:13:34.445634: Current learning rate: 0.00096 
+2024-09-13 07:17:40.773494: train_loss -0.9159 
+2024-09-13 07:17:40.773635: val_loss -0.6063 
+2024-09-13 07:17:40.773685: Pseudo dice [0.5659, 0.8132] 
+2024-09-13 07:17:40.773735: Epoch time: 246.33 s 
+2024-09-13 07:17:41.720352:  
+2024-09-13 07:17:41.720521: Epoch 927 
+2024-09-13 07:17:41.720626: Current learning rate: 0.00095 
+2024-09-13 07:21:48.120655: train_loss -0.9165 
+2024-09-13 07:21:48.120800: val_loss -0.595 
+2024-09-13 07:21:48.120851: Pseudo dice [0.5326, 0.8027] 
+2024-09-13 07:21:48.120902: Epoch time: 246.4 s 
+2024-09-13 07:21:49.086369:  
+2024-09-13 07:21:49.086563: Epoch 928 
+2024-09-13 07:21:49.086647: Current learning rate: 0.00094 
+2024-09-13 07:25:55.415820: train_loss -0.9186 
+2024-09-13 07:25:55.415962: val_loss -0.5701 
+2024-09-13 07:25:55.416012: Pseudo dice [0.512, 0.7611] 
+2024-09-13 07:25:55.416076: Epoch time: 246.33 s 
+2024-09-13 07:25:56.374014:  
+2024-09-13 07:25:56.374199: Epoch 929 
+2024-09-13 07:25:56.374282: Current learning rate: 0.00092 
+2024-09-13 07:30:02.763994: train_loss -0.9193 
+2024-09-13 07:30:02.764139: val_loss -0.6222 
+2024-09-13 07:30:02.764232: Pseudo dice [0.5378, 0.8084] 
+2024-09-13 07:30:02.764340: Epoch time: 246.39 s 
+2024-09-13 07:30:03.734781:  
+2024-09-13 07:30:03.735003: Epoch 930 
+2024-09-13 07:30:03.735087: Current learning rate: 0.00091 
+2024-09-13 07:34:10.124562: train_loss -0.9177 
+2024-09-13 07:34:10.124702: val_loss -0.5828 
+2024-09-13 07:34:10.124753: Pseudo dice [0.4571, 0.7886] 
+2024-09-13 07:34:10.124804: Epoch time: 246.39 s 
+2024-09-13 07:34:11.075235:  
+2024-09-13 07:34:11.075407: Epoch 931 
+2024-09-13 07:34:11.075489: Current learning rate: 0.0009 
+2024-09-13 07:38:17.558522: train_loss -0.9183 
+2024-09-13 07:38:17.558686: val_loss -0.5682 
+2024-09-13 07:38:17.558739: Pseudo dice [0.4652, 0.7713] 
+2024-09-13 07:38:17.558791: Epoch time: 246.49 s 
+2024-09-13 07:38:18.514028:  
+2024-09-13 07:38:18.514233: Epoch 932 
+2024-09-13 07:38:18.514318: Current learning rate: 0.00089 
+2024-09-13 07:42:24.978681: train_loss -0.9196 
+2024-09-13 07:42:24.978823: val_loss -0.5561 
+2024-09-13 07:42:24.978872: Pseudo dice [0.4387, 0.7975] 
+2024-09-13 07:42:24.978923: Epoch time: 246.47 s 
+2024-09-13 07:42:25.934545:  
+2024-09-13 07:42:25.934713: Epoch 933 
+2024-09-13 07:42:25.934799: Current learning rate: 0.00088 
+2024-09-13 07:46:32.243963: train_loss -0.9191 
+2024-09-13 07:46:32.244107: val_loss -0.6229 
+2024-09-13 07:46:32.244161: Pseudo dice [0.5594, 0.798] 
+2024-09-13 07:46:32.244213: Epoch time: 246.31 s 
+2024-09-13 07:46:34.108372:  
+2024-09-13 07:46:34.108627: Epoch 934 
+2024-09-13 07:46:34.108755: Current learning rate: 0.00087 
+2024-09-13 07:50:40.410596: train_loss -0.9095 
+2024-09-13 07:50:40.410737: val_loss -0.6163 
+2024-09-13 07:50:40.410787: Pseudo dice [0.5458, 0.8051] 
+2024-09-13 07:50:40.410837: Epoch time: 246.3 s 
+2024-09-13 07:50:41.345080:  
+2024-09-13 07:50:41.345271: Epoch 935 
+2024-09-13 07:50:41.345384: Current learning rate: 0.00085 
+2024-09-13 07:54:47.453367: train_loss -0.9187 
+2024-09-13 07:54:47.453546: val_loss -0.5954 
+2024-09-13 07:54:47.453598: Pseudo dice [0.5084, 0.7984] 
+2024-09-13 07:54:47.453647: Epoch time: 246.11 s 
+2024-09-13 07:54:48.402360:  
+2024-09-13 07:54:48.402572: Epoch 936 
+2024-09-13 07:54:48.402682: Current learning rate: 0.00084 
+2024-09-13 07:58:54.315231: train_loss -0.9192 
+2024-09-13 07:58:54.315370: val_loss -0.5992 
+2024-09-13 07:58:54.315421: Pseudo dice [0.4981, 0.794] 
+2024-09-13 07:58:54.315475: Epoch time: 245.91 s 
+2024-09-13 07:58:55.424910:  
+2024-09-13 07:58:55.425101: Epoch 937 
+2024-09-13 07:58:55.425218: Current learning rate: 0.00083 
+2024-09-13 08:03:01.657830: train_loss -0.9192 
+2024-09-13 08:03:01.657968: val_loss -0.5946 
+2024-09-13 08:03:01.658019: Pseudo dice [0.4921, 0.7865] 
+2024-09-13 08:03:01.658068: Epoch time: 246.23 s 
+2024-09-13 08:03:02.616920:  
+2024-09-13 08:03:02.617179: Epoch 938 
+2024-09-13 08:03:02.617264: Current learning rate: 0.00082 
+2024-09-13 08:07:08.818039: train_loss -0.9159 
+2024-09-13 08:07:08.818181: val_loss -0.5939 
+2024-09-13 08:07:08.818231: Pseudo dice [0.4748, 0.7942] 
+2024-09-13 08:07:08.818281: Epoch time: 246.2 s 
+2024-09-13 08:07:09.787024:  
+2024-09-13 08:07:09.787284: Epoch 939 
+2024-09-13 08:07:09.787369: Current learning rate: 0.00081 
+2024-09-13 08:11:15.763298: train_loss -0.919 
+2024-09-13 08:11:15.763454: val_loss -0.5716 
+2024-09-13 08:11:15.763504: Pseudo dice [0.5251, 0.7584] 
+2024-09-13 08:11:15.763556: Epoch time: 245.98 s 
+2024-09-13 08:11:16.742903:  
+2024-09-13 08:11:16.743147: Epoch 940 
+2024-09-13 08:11:16.743237: Current learning rate: 0.00079 
+2024-09-13 08:15:22.666106: train_loss -0.9192 
+2024-09-13 08:15:22.666245: val_loss -0.5396 
+2024-09-13 08:15:22.666311: Pseudo dice [0.4482, 0.7908] 
+2024-09-13 08:15:22.666368: Epoch time: 245.93 s 
+2024-09-13 08:15:23.612738:  
+2024-09-13 08:15:23.612985: Epoch 941 
+2024-09-13 08:15:23.613139: Current learning rate: 0.00078 
+2024-09-13 08:19:29.611885: train_loss -0.9149 
+2024-09-13 08:19:29.612027: val_loss -0.5794 
+2024-09-13 08:19:29.612122: Pseudo dice [0.487, 0.7798] 
+2024-09-13 08:19:29.612173: Epoch time: 246.0 s 
+2024-09-13 08:19:30.589680:  
+2024-09-13 08:19:30.589885: Epoch 942 
+2024-09-13 08:19:30.589972: Current learning rate: 0.00077 
+2024-09-13 08:23:36.648604: train_loss -0.9197 
+2024-09-13 08:23:36.648745: val_loss -0.5831 
+2024-09-13 08:23:36.648798: Pseudo dice [0.463, 0.7962] 
+2024-09-13 08:23:36.648849: Epoch time: 246.06 s 
+2024-09-13 08:23:37.601512:  
+2024-09-13 08:23:37.601704: Epoch 943 
+2024-09-13 08:23:37.601809: Current learning rate: 0.00076 
+2024-09-13 08:27:43.720251: train_loss -0.9215 
+2024-09-13 08:27:43.720391: val_loss -0.568 
+2024-09-13 08:27:43.720441: Pseudo dice [0.5287, 0.7561] 
+2024-09-13 08:27:43.720491: Epoch time: 246.12 s 
+2024-09-13 08:27:44.663869:  
+2024-09-13 08:27:44.664085: Epoch 944 
+2024-09-13 08:27:44.664168: Current learning rate: 0.00075 
+2024-09-13 08:31:50.887498: train_loss -0.9189 
+2024-09-13 08:31:50.887730: val_loss -0.6044 
+2024-09-13 08:31:50.887785: Pseudo dice [0.5387, 0.8083] 
+2024-09-13 08:31:50.887855: Epoch time: 246.23 s 
+2024-09-13 08:31:51.850002:  
+2024-09-13 08:31:51.850218: Epoch 945 
+2024-09-13 08:31:51.850302: Current learning rate: 0.00074 
+2024-09-13 08:35:57.956887: train_loss -0.921 
+2024-09-13 08:35:57.957026: val_loss -0.5659 
+2024-09-13 08:35:57.957076: Pseudo dice [0.4974, 0.7552] 
+2024-09-13 08:35:57.957127: Epoch time: 246.11 s 
+2024-09-13 08:35:58.922595:  
+2024-09-13 08:35:58.922801: Epoch 946 
+2024-09-13 08:35:58.922890: Current learning rate: 0.00072 
+2024-09-13 08:40:05.049633: train_loss -0.9179 
+2024-09-13 08:40:05.049781: val_loss -0.61 
+2024-09-13 08:40:05.049832: Pseudo dice [0.4867, 0.8004] 
+2024-09-13 08:40:05.049882: Epoch time: 246.13 s 
+2024-09-13 08:40:05.993634:  
+2024-09-13 08:40:05.993855: Epoch 947 
+2024-09-13 08:40:05.993939: Current learning rate: 0.00071 
+2024-09-13 08:44:12.185643: train_loss -0.9215 
+2024-09-13 08:44:12.185785: val_loss -0.6088 
+2024-09-13 08:44:12.185835: Pseudo dice [0.521, 0.8063] 
+2024-09-13 08:44:12.185885: Epoch time: 246.19 s 
+2024-09-13 08:44:13.131788:  
+2024-09-13 08:44:13.131991: Epoch 948 
+2024-09-13 08:44:13.132076: Current learning rate: 0.0007 
+2024-09-13 08:48:19.300286: train_loss -0.9157 
+2024-09-13 08:48:19.300433: val_loss -0.6107 
+2024-09-13 08:48:19.300484: Pseudo dice [0.4897, 0.8099] 
+2024-09-13 08:48:19.300537: Epoch time: 246.17 s 
+2024-09-13 08:48:20.242923:  
+2024-09-13 08:48:20.243146: Epoch 949 
+2024-09-13 08:48:20.243247: Current learning rate: 0.00069 
+2024-09-13 08:52:26.363262: train_loss -0.917 
+2024-09-13 08:52:26.363419: val_loss -0.5824 
+2024-09-13 08:52:26.363469: Pseudo dice [0.4528, 0.8055] 
+2024-09-13 08:52:26.363524: Epoch time: 246.12 s 
+2024-09-13 08:52:30.356428:  
+2024-09-13 08:52:30.356671: Epoch 950 
+2024-09-13 08:52:30.356763: Current learning rate: 0.00067 
+2024-09-13 08:56:36.676356: train_loss -0.9164 
+2024-09-13 08:56:36.676546: val_loss -0.5668 
+2024-09-13 08:56:36.676596: Pseudo dice [0.4969, 0.7923] 
+2024-09-13 08:56:36.676646: Epoch time: 246.32 s 
+2024-09-13 08:56:37.616801:  
+2024-09-13 08:56:37.616985: Epoch 951 
+2024-09-13 08:56:37.617068: Current learning rate: 0.00066 
+2024-09-13 09:00:43.925439: train_loss -0.9216 
+2024-09-13 09:00:43.925581: val_loss -0.6074 
+2024-09-13 09:00:43.925631: Pseudo dice [0.5184, 0.7966] 
+2024-09-13 09:00:43.925681: Epoch time: 246.31 s 
+2024-09-13 09:00:44.875075:  
+2024-09-13 09:00:44.875257: Epoch 952 
+2024-09-13 09:00:44.875371: Current learning rate: 0.00065 
+2024-09-13 09:04:51.244707: train_loss -0.9166 
+2024-09-13 09:04:51.244889: val_loss -0.5847 
+2024-09-13 09:04:51.244941: Pseudo dice [0.5319, 0.7698] 
+2024-09-13 09:04:51.244992: Epoch time: 246.37 s 
+2024-09-13 09:04:52.235574:  
+2024-09-13 09:04:52.235783: Epoch 953 
+2024-09-13 09:04:52.235876: Current learning rate: 0.00064 
+2024-09-13 09:08:58.390941: train_loss -0.9186 
+2024-09-13 09:08:58.391073: val_loss -0.5685 
+2024-09-13 09:08:58.391135: Pseudo dice [0.5129, 0.782] 
+2024-09-13 09:08:58.391191: Epoch time: 246.16 s 
+2024-09-13 09:08:59.353443:  
+2024-09-13 09:08:59.353611: Epoch 954 
+2024-09-13 09:08:59.353697: Current learning rate: 0.00063 
+2024-09-13 09:13:05.164079: train_loss -0.9171 
+2024-09-13 09:13:05.164233: val_loss -0.5989 
+2024-09-13 09:13:05.164283: Pseudo dice [0.4805, 0.8096] 
+2024-09-13 09:13:05.164335: Epoch time: 245.81 s 
+2024-09-13 09:13:06.143328:  
+2024-09-13 09:13:06.143534: Epoch 955 
+2024-09-13 09:13:06.143624: Current learning rate: 0.00061 
+2024-09-13 09:17:11.954994: train_loss -0.9225 
+2024-09-13 09:17:11.955133: val_loss -0.6092 
+2024-09-13 09:17:11.955184: Pseudo dice [0.4969, 0.8097] 
+2024-09-13 09:17:11.955235: Epoch time: 245.81 s 
+2024-09-13 09:17:12.940802:  
+2024-09-13 09:17:12.940990: Epoch 956 
+2024-09-13 09:17:12.941073: Current learning rate: 0.0006 
+2024-09-13 09:21:18.864232: train_loss -0.9206 
+2024-09-13 09:21:18.864370: val_loss -0.552 
+2024-09-13 09:21:18.864420: Pseudo dice [0.4568, 0.7704] 
+2024-09-13 09:21:18.864472: Epoch time: 245.93 s 
+2024-09-13 09:21:19.829564:  
+2024-09-13 09:21:19.829731: Epoch 957 
+2024-09-13 09:21:19.829817: Current learning rate: 0.00059 
+2024-09-13 09:25:25.719928: train_loss -0.9177 
+2024-09-13 09:25:25.720062: val_loss -0.5779 
+2024-09-13 09:25:25.720113: Pseudo dice [0.4693, 0.7971] 
+2024-09-13 09:25:25.720166: Epoch time: 245.89 s 
+2024-09-13 09:25:26.689024:  
+2024-09-13 09:25:26.689218: Epoch 958 
+2024-09-13 09:25:26.689300: Current learning rate: 0.00058 
+2024-09-13 09:29:32.527550: train_loss -0.9206 
+2024-09-13 09:29:32.527694: val_loss -0.6009 
+2024-09-13 09:29:32.527745: Pseudo dice [0.5145, 0.792] 
+2024-09-13 09:29:32.527796: Epoch time: 245.84 s 
+2024-09-13 09:29:34.432864:  
+2024-09-13 09:29:34.433074: Epoch 959 
+2024-09-13 09:29:34.433178: Current learning rate: 0.00056 
+2024-09-13 09:33:40.351917: train_loss -0.9199 
+2024-09-13 09:33:40.352056: val_loss -0.553 
+2024-09-13 09:33:40.352107: Pseudo dice [0.4302, 0.7838] 
+2024-09-13 09:33:40.352158: Epoch time: 245.92 s 
+2024-09-13 09:33:41.318042:  
+2024-09-13 09:33:41.318239: Epoch 960 
+2024-09-13 09:33:41.318339: Current learning rate: 0.00055 
+2024-09-13 09:37:47.102717: train_loss -0.9192 
+2024-09-13 09:37:47.102896: val_loss -0.5575 
+2024-09-13 09:37:47.102948: Pseudo dice [0.4671, 0.7868] 
+2024-09-13 09:37:47.103001: Epoch time: 245.79 s 
+2024-09-13 09:37:48.079283:  
+2024-09-13 09:37:48.079498: Epoch 961 
+2024-09-13 09:37:48.079581: Current learning rate: 0.00054 
+2024-09-13 09:41:53.922198: train_loss -0.9194 
+2024-09-13 09:41:53.922337: val_loss -0.6007 
+2024-09-13 09:41:53.922387: Pseudo dice [0.5398, 0.7689] 
+2024-09-13 09:41:53.922438: Epoch time: 245.84 s 
+2024-09-13 09:41:54.904536:  
+2024-09-13 09:41:54.904742: Epoch 962 
+2024-09-13 09:41:54.904826: Current learning rate: 0.00053 
+2024-09-13 09:46:00.868785: train_loss -0.922 
+2024-09-13 09:46:00.868926: val_loss -0.6023 
+2024-09-13 09:46:00.868977: Pseudo dice [0.5371, 0.8046] 
+2024-09-13 09:46:00.869067: Epoch time: 245.97 s 
+2024-09-13 09:46:01.826238:  
+2024-09-13 09:46:01.826456: Epoch 963 
+2024-09-13 09:46:01.826585: Current learning rate: 0.00051 
+2024-09-13 09:50:07.886406: train_loss -0.9215 
+2024-09-13 09:50:07.886598: val_loss -0.5454 
+2024-09-13 09:50:07.886650: Pseudo dice [0.4816, 0.7525] 
+2024-09-13 09:50:07.886702: Epoch time: 246.06 s 
+2024-09-13 09:50:08.860223:  
+2024-09-13 09:50:08.860441: Epoch 964 
+2024-09-13 09:50:08.860531: Current learning rate: 0.0005 
+2024-09-13 09:54:14.924975: train_loss -0.9228 
+2024-09-13 09:54:14.925152: val_loss -0.5928 
+2024-09-13 09:54:14.925203: Pseudo dice [0.5435, 0.7881] 
+2024-09-13 09:54:14.925253: Epoch time: 246.07 s 
+2024-09-13 09:54:15.887962:  
+2024-09-13 09:54:15.888126: Epoch 965 
+2024-09-13 09:54:15.888209: Current learning rate: 0.00049 
+2024-09-13 09:58:21.922997: train_loss -0.9225 
+2024-09-13 09:58:21.923143: val_loss -0.5575 
+2024-09-13 09:58:21.923194: Pseudo dice [0.4706, 0.7845] 
+2024-09-13 09:58:21.923245: Epoch time: 246.04 s 
+2024-09-13 09:58:22.920476:  
+2024-09-13 09:58:22.920704: Epoch 966 
+2024-09-13 09:58:22.920807: Current learning rate: 0.00048 
+2024-09-13 10:02:29.047225: train_loss -0.9213 
+2024-09-13 10:02:29.047486: val_loss -0.5765 
+2024-09-13 10:02:29.047538: Pseudo dice [0.5222, 0.7791] 
+2024-09-13 10:02:29.047590: Epoch time: 246.13 s 
+2024-09-13 10:02:30.023228:  
+2024-09-13 10:02:30.023419: Epoch 967 
+2024-09-13 10:02:30.023505: Current learning rate: 0.00046 
+2024-09-13 10:06:36.120856: train_loss -0.9213 
+2024-09-13 10:06:36.121019: val_loss -0.5906 
+2024-09-13 10:06:36.121071: Pseudo dice [0.5147, 0.8065] 
+2024-09-13 10:06:36.121122: Epoch time: 246.1 s 
+2024-09-13 10:06:37.118079:  
+2024-09-13 10:06:37.118254: Epoch 968 
+2024-09-13 10:06:37.118336: Current learning rate: 0.00045 
+2024-09-13 10:10:43.471090: train_loss -0.9193 
+2024-09-13 10:10:43.471238: val_loss -0.5739 
+2024-09-13 10:10:43.471288: Pseudo dice [0.4957, 0.7905] 
+2024-09-13 10:10:43.471339: Epoch time: 246.35 s 
+2024-09-13 10:10:44.441636:  
+2024-09-13 10:10:44.441880: Epoch 969 
+2024-09-13 10:10:44.441963: Current learning rate: 0.00044 
+2024-09-13 10:14:50.675471: train_loss -0.9196 
+2024-09-13 10:14:50.675628: val_loss -0.5968 
+2024-09-13 10:14:50.675678: Pseudo dice [0.4963, 0.7868] 
+2024-09-13 10:14:50.675730: Epoch time: 246.24 s 
+2024-09-13 10:14:51.640385:  
+2024-09-13 10:14:51.640632: Epoch 970 
+2024-09-13 10:14:51.640738: Current learning rate: 0.00043 
+2024-09-13 10:18:57.901321: train_loss -0.9191 
+2024-09-13 10:18:57.901487: val_loss -0.581 
+2024-09-13 10:18:57.901538: Pseudo dice [0.5054, 0.7907] 
+2024-09-13 10:18:57.901590: Epoch time: 246.26 s 
+2024-09-13 10:18:58.892415:  
+2024-09-13 10:18:58.892632: Epoch 971 
+2024-09-13 10:18:58.892716: Current learning rate: 0.00041 
+2024-09-13 10:23:05.150250: train_loss -0.9224 
+2024-09-13 10:23:05.150410: val_loss -0.6187 
+2024-09-13 10:23:05.150462: Pseudo dice [0.5166, 0.8192] 
+2024-09-13 10:23:05.150514: Epoch time: 246.26 s 
+2024-09-13 10:23:06.119680:  
+2024-09-13 10:23:06.119863: Epoch 972 
+2024-09-13 10:23:06.119946: Current learning rate: 0.0004 
+2024-09-13 10:27:12.393236: train_loss -0.9219 
+2024-09-13 10:27:12.393382: val_loss -0.5949 
+2024-09-13 10:27:12.393433: Pseudo dice [0.5212, 0.7768] 
+2024-09-13 10:27:12.393486: Epoch time: 246.28 s 
+2024-09-13 10:27:13.354980:  
+2024-09-13 10:27:13.355179: Epoch 973 
+2024-09-13 10:27:13.355264: Current learning rate: 0.00039 
+2024-09-13 10:31:19.770486: train_loss -0.9192 
+2024-09-13 10:31:19.770646: val_loss -0.5706 
+2024-09-13 10:31:19.770702: Pseudo dice [0.497, 0.7685] 
+2024-09-13 10:31:19.770752: Epoch time: 246.42 s 
+2024-09-13 10:31:20.748595:  
+2024-09-13 10:31:20.748755: Epoch 974 
+2024-09-13 10:31:20.748835: Current learning rate: 0.00037 
+2024-09-13 10:35:27.148748: train_loss -0.9221 
+2024-09-13 10:35:27.148962: val_loss -0.5788 
+2024-09-13 10:35:27.149018: Pseudo dice [0.5028, 0.7865] 
+2024-09-13 10:35:27.149073: Epoch time: 246.4 s 
+2024-09-13 10:35:28.141543:  
+2024-09-13 10:35:28.141757: Epoch 975 
+2024-09-13 10:35:28.141844: Current learning rate: 0.00036 
+2024-09-13 10:39:34.421265: train_loss -0.9246 
+2024-09-13 10:39:34.421408: val_loss -0.5787 
+2024-09-13 10:39:34.421458: Pseudo dice [0.5473, 0.7892] 
+2024-09-13 10:39:34.421508: Epoch time: 246.28 s 
+2024-09-13 10:39:35.415773:  
+2024-09-13 10:39:35.415939: Epoch 976 
+2024-09-13 10:39:35.416021: Current learning rate: 0.00035 
+2024-09-13 10:43:41.625622: train_loss -0.9218 
+2024-09-13 10:43:41.625779: val_loss -0.5892 
+2024-09-13 10:43:41.625830: Pseudo dice [0.4885, 0.8017] 
+2024-09-13 10:43:41.625882: Epoch time: 246.21 s 
+2024-09-13 10:43:42.597663:  
+2024-09-13 10:43:42.597839: Epoch 977 
+2024-09-13 10:43:42.597953: Current learning rate: 0.00034 
+2024-09-13 10:47:48.836696: train_loss -0.9191 
+2024-09-13 10:47:48.836835: val_loss -0.5508 
+2024-09-13 10:47:48.836884: Pseudo dice [0.4651, 0.7736] 
+2024-09-13 10:47:48.836940: Epoch time: 246.24 s 
+2024-09-13 10:47:49.802315:  
+2024-09-13 10:47:49.802572: Epoch 978 
+2024-09-13 10:47:49.802660: Current learning rate: 0.00032 
+2024-09-13 10:51:56.197223: train_loss -0.9216 
+2024-09-13 10:51:56.197361: val_loss -0.5766 
+2024-09-13 10:51:56.197412: Pseudo dice [0.4783, 0.795] 
+2024-09-13 10:51:56.197463: Epoch time: 246.4 s 
+2024-09-13 10:51:57.176282:  
+2024-09-13 10:51:57.176450: Epoch 979 
+2024-09-13 10:51:57.176551: Current learning rate: 0.00031 
+2024-09-13 10:56:03.588695: train_loss -0.9236 
+2024-09-13 10:56:03.588836: val_loss -0.578 
+2024-09-13 10:56:03.588886: Pseudo dice [0.5106, 0.7845] 
+2024-09-13 10:56:03.588937: Epoch time: 246.41 s 
+2024-09-13 10:56:04.559853:  
+2024-09-13 10:56:04.560014: Epoch 980 
+2024-09-13 10:56:04.560100: Current learning rate: 0.0003 
+2024-09-13 11:00:10.652988: train_loss -0.9223 
+2024-09-13 11:00:10.653142: val_loss -0.5932 
+2024-09-13 11:00:10.653193: Pseudo dice [0.497, 0.78] 
+2024-09-13 11:00:10.653291: Epoch time: 246.1 s 
+2024-09-13 11:00:11.622297:  
+2024-09-13 11:00:11.622464: Epoch 981 
+2024-09-13 11:00:11.622548: Current learning rate: 0.00028 
+2024-09-13 11:04:17.584217: train_loss -0.9221 
+2024-09-13 11:04:17.584379: val_loss -0.5728 
+2024-09-13 11:04:17.584431: Pseudo dice [0.5166, 0.8014] 
+2024-09-13 11:04:17.584481: Epoch time: 245.96 s 
+2024-09-13 11:04:18.563202:  
+2024-09-13 11:04:18.563441: Epoch 982 
+2024-09-13 11:04:18.563524: Current learning rate: 0.00027 
+2024-09-13 11:08:24.493025: train_loss -0.9229 
+2024-09-13 11:08:24.493200: val_loss -0.5807 
+2024-09-13 11:08:24.493289: Pseudo dice [0.4662, 0.8202] 
+2024-09-13 11:08:24.493341: Epoch time: 245.93 s 
+2024-09-13 11:08:25.459425:  
+2024-09-13 11:08:25.459552: Epoch 983 
+2024-09-13 11:08:25.459687: Current learning rate: 0.00026 
+2024-09-13 11:12:32.182481: train_loss -0.9198 
+2024-09-13 11:12:32.182649: val_loss -0.5864 
+2024-09-13 11:12:32.182725: Pseudo dice [0.5154, 0.7773] 
+2024-09-13 11:12:32.182775: Epoch time: 246.72 s 
+2024-09-13 11:12:33.129736:  
+2024-09-13 11:12:33.130011: Epoch 984 
+2024-09-13 11:12:33.130121: Current learning rate: 0.00024 
+2024-09-13 11:16:39.307236: train_loss -0.9196 
+2024-09-13 11:16:39.307377: val_loss -0.5534 
+2024-09-13 11:16:39.307427: Pseudo dice [0.4448, 0.7934] 
+2024-09-13 11:16:39.307479: Epoch time: 246.18 s 
+2024-09-13 11:16:40.271983:  
+2024-09-13 11:16:40.272230: Epoch 985 
+2024-09-13 11:16:40.272321: Current learning rate: 0.00023 
+2024-09-13 11:20:46.469990: train_loss -0.9244 
+2024-09-13 11:20:46.470138: val_loss -0.6008 
+2024-09-13 11:20:46.470189: Pseudo dice [0.5033, 0.7944] 
+2024-09-13 11:20:46.470240: Epoch time: 246.2 s 
+2024-09-13 11:20:47.441958:  
+2024-09-13 11:20:47.442208: Epoch 986 
+2024-09-13 11:20:47.442291: Current learning rate: 0.00021 
+2024-09-13 11:24:53.493303: train_loss -0.922 
+2024-09-13 11:24:53.493443: val_loss -0.5667 
+2024-09-13 11:24:53.493495: Pseudo dice [0.4693, 0.7972] 
+2024-09-13 11:24:53.493547: Epoch time: 246.05 s 
+2024-09-13 11:24:54.505531:  
+2024-09-13 11:24:54.505725: Epoch 987 
+2024-09-13 11:24:54.505806: Current learning rate: 0.0002 
+2024-09-13 11:29:00.587677: train_loss -0.922 
+2024-09-13 11:29:00.587831: val_loss -0.582 
+2024-09-13 11:29:00.587884: Pseudo dice [0.526, 0.7579] 
+2024-09-13 11:29:00.587936: Epoch time: 246.08 s 
+2024-09-13 11:29:01.563210:  
+2024-09-13 11:29:01.563462: Epoch 988 
+2024-09-13 11:29:01.563548: Current learning rate: 0.00019 
+2024-09-13 11:33:07.653626: train_loss -0.9216 
+2024-09-13 11:33:07.653776: val_loss -0.5906 
+2024-09-13 11:33:07.653826: Pseudo dice [0.485, 0.8062] 
+2024-09-13 11:33:07.653880: Epoch time: 246.09 s 
+2024-09-13 11:33:08.685133:  
+2024-09-13 11:33:08.685333: Epoch 989 
+2024-09-13 11:33:08.685414: Current learning rate: 0.00017 
+2024-09-13 11:37:14.876637: train_loss -0.9238 
+2024-09-13 11:37:14.876788: val_loss -0.6057 
+2024-09-13 11:37:14.876842: Pseudo dice [0.511, 0.7996] 
+2024-09-13 11:37:14.876892: Epoch time: 246.19 s 
+2024-09-13 11:37:15.845537:  
+2024-09-13 11:37:15.845765: Epoch 990 
+2024-09-13 11:37:15.845851: Current learning rate: 0.00016 
+2024-09-13 11:41:22.021260: train_loss -0.9227 
+2024-09-13 11:41:22.021416: val_loss -0.6299 
+2024-09-13 11:41:22.021467: Pseudo dice [0.5786, 0.7988] 
+2024-09-13 11:41:22.021530: Epoch time: 246.18 s 
+2024-09-13 11:41:23.010180:  
+2024-09-13 11:41:23.010392: Epoch 991 
+2024-09-13 11:41:23.010487: Current learning rate: 0.00014 
+2024-09-13 11:45:29.308888: train_loss -0.9231 
+2024-09-13 11:45:29.309083: val_loss -0.571 
+2024-09-13 11:45:29.309173: Pseudo dice [0.4445, 0.7963] 
+2024-09-13 11:45:29.309262: Epoch time: 246.3 s 
+2024-09-13 11:45:30.272427:  
+2024-09-13 11:45:30.272609: Epoch 992 
+2024-09-13 11:45:30.272733: Current learning rate: 0.00013 
+2024-09-13 11:49:36.628474: train_loss -0.9252 
+2024-09-13 11:49:36.628617: val_loss -0.5748 
+2024-09-13 11:49:36.628671: Pseudo dice [0.5234, 0.7666] 
+2024-09-13 11:49:36.628723: Epoch time: 246.36 s 
+2024-09-13 11:49:37.604056:  
+2024-09-13 11:49:37.604307: Epoch 993 
+2024-09-13 11:49:37.604389: Current learning rate: 0.00011 
+2024-09-13 11:53:43.834844: train_loss -0.9248 
+2024-09-13 11:53:43.834988: val_loss -0.5876 
+2024-09-13 11:53:43.835037: Pseudo dice [0.5014, 0.7996] 
+2024-09-13 11:53:43.835087: Epoch time: 246.23 s 
+2024-09-13 11:53:44.817074:  
+2024-09-13 11:53:44.817324: Epoch 994 
+2024-09-13 11:53:44.817404: Current learning rate: 0.0001 
+2024-09-13 11:57:50.968417: train_loss -0.9213 
+2024-09-13 11:57:50.968570: val_loss -0.58 
+2024-09-13 11:57:50.968623: Pseudo dice [0.4775, 0.8103] 
+2024-09-13 11:57:50.968677: Epoch time: 246.15 s 
+2024-09-13 11:57:51.935325:  
+2024-09-13 11:57:51.935534: Epoch 995 
+2024-09-13 11:57:51.935620: Current learning rate: 8e-05 
+2024-09-13 12:01:58.049318: train_loss -0.9253 
+2024-09-13 12:01:58.049460: val_loss -0.5816 
+2024-09-13 12:01:58.049512: Pseudo dice [0.501, 0.7872] 
+2024-09-13 12:01:58.049562: Epoch time: 246.12 s 
+2024-09-13 12:01:59.019705:  
+2024-09-13 12:01:59.019937: Epoch 996 
+2024-09-13 12:01:59.020024: Current learning rate: 7e-05 
+2024-09-13 12:06:05.049481: train_loss -0.9253 
+2024-09-13 12:06:05.049622: val_loss -0.6196 
+2024-09-13 12:06:05.049672: Pseudo dice [0.5177, 0.8156] 
+2024-09-13 12:06:05.049731: Epoch time: 246.03 s 
+2024-09-13 12:06:06.006308:  
+2024-09-13 12:06:06.006470: Epoch 997 
+2024-09-13 12:06:06.006554: Current learning rate: 5e-05 
+2024-09-13 12:10:12.161519: train_loss -0.9208 
+2024-09-13 12:10:12.161657: val_loss -0.5621 
+2024-09-13 12:10:12.161707: Pseudo dice [0.49, 0.7785] 
+2024-09-13 12:10:12.161757: Epoch time: 246.16 s 
+2024-09-13 12:10:13.128505:  
+2024-09-13 12:10:13.128675: Epoch 998 
+2024-09-13 12:10:13.128764: Current learning rate: 4e-05 
+2024-09-13 12:14:19.282923: train_loss -0.9253 
+2024-09-13 12:14:19.283061: val_loss -0.5754 
+2024-09-13 12:14:19.283113: Pseudo dice [0.4911, 0.7735] 
+2024-09-13 12:14:19.283164: Epoch time: 246.16 s 
+2024-09-13 12:14:20.274958:  
+2024-09-13 12:14:20.275168: Epoch 999 
+2024-09-13 12:14:20.275294: Current learning rate: 2e-05 
+2024-09-13 12:18:26.253771: train_loss -0.9211 
+2024-09-13 12:18:26.253911: val_loss -0.6122 
+2024-09-13 12:18:26.253962: Pseudo dice [0.5074, 0.8066] 
+2024-09-13 12:18:26.254014: Epoch time: 245.98 s 
+2024-09-13 12:18:28.565009: Training done. 
+2024-09-13 12:18:28.687973: Using splits from existing split file: /mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/splits_final.json 
+2024-09-13 12:18:28.688371: The split file contains 5 splits. 
+2024-09-13 12:18:28.688422: Desired fold for training: 1 
+2024-09-13 12:18:28.688466: This split has 240 training and 30 validation cases. 
+2024-09-13 12:18:28.688900: predicting 103 
+2024-09-13 12:18:28.689780: 103, shape torch.Size([2, 128, 512, 511]), rank 0 
+2024-09-13 12:19:38.098112: predicting 104 
+2024-09-13 12:19:38.115650: 104, shape torch.Size([2, 122, 512, 511]), rank 0 
+2024-09-13 12:20:16.848634: predicting 116 
+2024-09-13 12:20:16.864961: 116, shape torch.Size([2, 138, 512, 511]), rank 0 
+2024-09-13 12:20:55.344227: predicting 129 
+2024-09-13 12:20:55.362504: 129, shape torch.Size([2, 130, 512, 511]), rank 0 
+2024-09-13 12:21:33.686474: predicting 13 
+2024-09-13 12:21:33.703684: 13, shape torch.Size([2, 112, 511, 511]), rank 0 
+2024-09-13 12:22:03.993254: predicting 132 
+2024-09-13 12:22:04.008589: 132, shape torch.Size([2, 97, 536, 1001]), rank 0 
+2024-09-13 12:23:03.772034: predicting 150 
+2024-09-13 12:23:03.798741: 150, shape torch.Size([2, 128, 508, 511]), rank 0 
+2024-09-13 12:23:41.299621: predicting 151 
+2024-09-13 12:23:41.316328: 151, shape torch.Size([2, 137, 512, 511]), rank 0 
+2024-09-13 12:24:18.804636: predicting 152 
+2024-09-13 12:24:18.823250: 152, shape torch.Size([2, 144, 560, 646]), rank 0 
+2024-09-13 12:25:03.693984: predicting 158 
+2024-09-13 12:25:03.719503: 158, shape torch.Size([2, 128, 512, 510]), rank 0 
+2024-09-13 12:25:41.205409: predicting 161 
+2024-09-13 12:25:41.221928: 161, shape torch.Size([2, 115, 512, 511]), rank 0 
+2024-09-13 12:26:11.212258: predicting 170 
+2024-09-13 12:26:11.227120: 170, shape torch.Size([2, 144, 536, 1024]), rank 0 
+2024-09-13 12:27:25.997100: predicting 18 
+2024-09-13 12:27:26.035280: 18, shape torch.Size([2, 118, 511, 511]), rank 0 
+2024-09-13 12:27:56.173735: predicting 181 
+2024-09-13 12:27:56.188702: 181, shape torch.Size([2, 128, 508, 511]), rank 0 
+2024-09-13 12:28:33.642974: predicting 187 
+2024-09-13 12:28:33.659300: 187, shape torch.Size([2, 108, 512, 511]), rank 0 
+2024-09-13 12:29:03.667789: predicting 194 
+2024-09-13 12:29:03.682425: 194, shape torch.Size([2, 135, 536, 975]), rank 0 
+2024-09-13 12:30:18.291108: predicting 196 
+2024-09-13 12:30:18.325438: 196, shape torch.Size([2, 123, 512, 511]), rank 0 
+2024-09-13 12:30:55.814582: predicting 197 
+2024-09-13 12:30:55.830118: 197, shape torch.Size([2, 132, 509, 511]), rank 0 
+2024-09-13 12:31:33.349183: predicting 20 
+2024-09-13 12:31:33.366436: 20, shape torch.Size([2, 125, 512, 511]), rank 0 
+2024-09-13 12:32:10.814852: predicting 25 
+2024-09-13 12:32:10.830253: 25, shape torch.Size([2, 105, 512, 509]), rank 0 
+2024-09-13 12:32:40.868517: predicting 3 
+2024-09-13 12:32:40.882178: 3, shape torch.Size([2, 125, 560, 560]), rank 0 
+2024-09-13 12:33:18.304530: predicting 45 
+2024-09-13 12:33:18.323620: 45, shape torch.Size([2, 105, 511, 511]), rank 0 
+2024-09-13 12:33:48.371426: predicting 6 
+2024-09-13 12:33:48.384801: 6, shape torch.Size([2, 127, 510, 511]), rank 0 
+2024-09-13 12:34:25.834499: predicting 67 
+2024-09-13 12:34:25.851174: 67, shape torch.Size([2, 98, 501, 511]), rank 0 
+2024-09-13 12:34:55.881507: predicting 74 
+2024-09-13 12:34:55.893922: 74, shape torch.Size([2, 120, 511, 511]), rank 0 
+2024-09-13 12:35:25.907354: predicting 82 
+2024-09-13 12:35:25.923394: 82, shape torch.Size([2, 112, 512, 510]), rank 0 
+2024-09-13 12:35:55.978329: predicting 84 
+2024-09-13 12:35:55.992778: 84, shape torch.Size([2, 126, 607, 1040]), rank 0 
+2024-09-13 12:37:25.887795: predicting 86 
+2024-09-13 12:37:25.927074: 86, shape torch.Size([2, 112, 511, 511]), rank 0 
+2024-09-13 12:37:56.077068: predicting 90 
+2024-09-13 12:37:56.091755: 90, shape torch.Size([2, 120, 510, 510]), rank 0 
+2024-09-13 12:38:26.130302: predicting 96 
+2024-09-13 12:38:26.146116: 96, shape torch.Size([2, 125, 510, 511]), rank 0 
+2024-09-13 12:39:19.778099: Validation complete 
+2024-09-13 12:39:19.778229: Mean Validation Dice:  0.5413680677668632 
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/validation/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_1/validation/summary_agg.json
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+}
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'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetMPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 192, 192], 'median_image_size_in_voxels': [92, 310, 309], 'spacing': [1.6126957141129536, 0.826423816135876, 0.826423816135876], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres_bs4': {'inherits_from': '3d_fullres', 'batch_size': 4}, '3d_fullres_bs8': {'inherits_from': '3d_fullres', 'batch_size': 8}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 349.19762434242324, 'median': 141.0, 'min': 0.0, 'percentile_00_5': 29.0, 'percentile_99_5': 2558.91650390625, 'std': 485.94544484039}, '1': {'max': 1.0, 'mean': 0.37307153608378946, 'median': 0.2966964840888977, 'min': 0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}}, 'configuration': '3d_fullres_bs8', 'fold': 2, 'dataset_json': {'channel_names': {'0': 'T2', '1': 'gradient'}, 'labels': {'background': 0, 'GTVp': 1, 'GTVn': 2}, 'numTraining': 300, 'file_ending': '.nii.gz', 'name': 'Dataset504_midRT_gradient', 'description': 'Dataset 504 - midRT with Gradient Map'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "num_epochs": "1000",
+    "num_input_channels": "2",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.0028820821080567326\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2",
+    "output_folder_base": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 511.0], 'spacing': [0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetMPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 192, 192], 'median_image_size_in_voxels': [92, 310, 309], 'spacing': [1.6126957141129536, 0.826423816135876, 0.826423816135876], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 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0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}}",
+    "preprocessed_dataset_folder": "/mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist",
+    "save_every": "50",
+    "torch_version": "2.4.0",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}
\ No newline at end of file
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+}
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/2voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/2voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..e090bea2f51500b9d7ab73b2b486f338c0ba198c
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/2voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/2voxels/images",
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+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/2voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/2voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/2voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..caac2717750a4048e7845a502502add8f1ed1cdf
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/2voxels/preds/summary_agg.json
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/4voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/4voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..93edc75c890ac40c68f0a7757321dca95a401aa6
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/4voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "num_processes_segmentation_export": 3,
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+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/4voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/4voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..f4965c4855ff02366743e86686264185e098be23
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/4voxels/preds/summary_agg.json
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+}
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/6voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/6voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..1ba875c620499def6716f29285df34daac91c17d
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/6voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/6voxels/images",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/6voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/6voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/6voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..b93ed76cd99f309a4b0b02979bfaf435295a7f92
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/6voxels/preds/summary_agg.json
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+                    "n_conv_per_stage_decoder": [
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/8voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/8voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..4627d679bc0d3a42fbf01aac8f28d93331cca6fb
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/8voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
+    "folder_with_segs_from_prev_stage": null,
+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/8voxels/images",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/8voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/8voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/8voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..7766dbec22102a5acc4b68e6c3592908f02c4a57
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/margins/8voxels/preds/summary_agg.json
@@ -0,0 +1,512 @@
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+            "vol_sum2": 8625.729862290102,
+            "DSC1": 0.6459375,
+            "DSC2": 0.8849008231007961,
+            "vol_gt1": 2913.2643159239137,
+            "vol_gt2": 4257.579251957522,
+            "HD95_1": 14.263225257730648,
+            "HD95_2": 1.8384357739751387,
+            "MSD_1": 3.3579505925991726,
+            "MSD_2": 0.7236888052513829
+        },
+        {
+            "PatientID": "191.nii.gz",
+            "TP1": 0.0,
+            "TP2": 12265.042968750002,
+            "vol_sum1": 539.34765625,
+            "vol_sum2": 31313.19140625,
+            "DSC1": 0.0,
+            "DSC2": 0.7833786604262856,
+            "vol_gt1": 0.0,
+            "vol_gt2": 17204.9921875,
+            "HD95_1": NaN,
+            "HD95_2": 4.488265951344684,
+            "MSD_1": NaN,
+            "MSD_2": 1.1404649875623982
+        },
+        {
+            "PatientID": "95.nii.gz",
+            "TP1": 0.0,
+            "TP2": 15377.675791325695,
+            "vol_sum1": 0.0,
+            "vol_sum2": 37702.3858090993,
+            "DSC1": 0.0,
+            "DSC2": 0.8157401958161683,
+            "vol_gt1": 0.0,
+            "vol_gt2": 19785.633556524677,
+            "HD95_1": NaN,
+            "HD95_2": 3.9480402296175665,
+            "MSD_1": NaN,
+            "MSD_2": 1.4102905352324595
+        },
+        {
+            "PatientID": "64.nii.gz",
+            "TP1": 8105.377549484419,
+            "TP2": 0.0,
+            "vol_sum1": 20696.372799626082,
+            "vol_sum2": 0.0,
+            "DSC1": 0.7832655149728321,
+            "DSC2": 0.0,
+            "vol_gt1": 9138.15383402023,
+            "vol_gt2": 0.0,
+            "HD95_1": 3.742836202632038,
+            "HD95_2": NaN,
+            "MSD_1": 1.4291883358520578,
+            "MSD_2": NaN
+        }
+    ],
+    "aggregate_results": {
+        "ConventionalDsc": {
+            "GTVp": {
+                "mean": 0.3118911193460255,
+                "median": 0.35902891367252604,
+                "percentile_25": 0.0,
+                "percentile_75": 0.5635969964932976
+            },
+            "GTVn": {
+                "mean": 0.7356768263760625,
+                "median": 0.8510468255637418,
+                "percentile_25": 0.7689124824274818,
+                "percentile_75": 0.9061782873453506
+            }
+        },
+        "AggregatedDsc": {
+            "GTVp": {
+                "mean": 0.47666874543866866,
+                "median": 0.47666874543866866,
+                "percentile_25": 0.47666874543866866,
+                "percentile_75": 0.47666874543866866
+            },
+            "GTVn": {
+                "mean": 0.8703654790336479,
+                "median": 0.8703654790336479,
+                "percentile_25": 0.8703654790336479,
+                "percentile_75": 0.8703654790336479
+            }
+        },
+        "HD95": {
+            "GTVp": {
+                "mean": 14.40588026021272,
+                "median": 12.049376739593537,
+                "percentile_25": 5.220153254455275,
+                "percentile_75": 21.32397198409748
+            },
+            "GTVn": {
+                "mean": 4.249997498900151,
+                "median": 2.0,
+                "percentile_25": 1.5,
+                "percentile_75": 3.2209662339601772
+            }
+        },
+        "MSD": {
+            "GTVp": {
+                "mean": 4.127832709594843,
+                "median": 2.320592720826769,
+                "percentile_25": 1.5548892718678498,
+                "percentile_75": 6.550634914020348
+            },
+            "GTVn": {
+                "mean": 1.4089669505565159,
+                "median": 0.6546351225284931,
+                "percentile_25": 0.47455513528413396,
+                "percentile_75": 0.8738770555412144
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/progress.png b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/progress.png
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/training_log_2024_9_10_15_41_57.txt b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/training_log_2024_9_10_15_41_57.txt
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+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/training_log_2024_9_10_15_41_57.txt
@@ -0,0 +1,5368 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2024-09-10 15:41:57.367493: do_dummy_2d_data_aug: True 
+2024-09-10 15:41:57.368564: Using splits from existing split file: /mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/splits_final.json 
+2024-09-10 15:41:57.368744: The split file contains 5 splits. 
+2024-09-10 15:41:57.368780: Desired fold for training: 2 
+2024-09-10 15:41:57.368813: This split has 240 training and 30 validation cases. 
+2024-09-10 15:42:05.841461: Using torch.compile... 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres_bs8
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 8, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True, 'inherits_from': '3d_fullres'} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 349.19762434242324, 'median': 141.0, 'min': 0.0, 'percentile_00_5': 29.0, 'percentile_99_5': 2558.91650390625, 'std': 485.94544484039}, '1': {'max': 1.0, 'mean': 0.37307153608378946, 'median': 0.2966964840888977, 'min': 0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}} 
+ 
+2024-09-10 15:42:06.688460: unpacking dataset... 
+2024-09-10 15:42:09.363845: unpacking done... 
+2024-09-10 15:42:09.366026: Unable to plot network architecture: nnUNet_compile is enabled! 
+2024-09-10 15:42:09.375218:  
+2024-09-10 15:42:09.375475: Epoch 0 
+2024-09-10 15:42:09.375635: Current learning rate: 0.01 
+2024-09-10 15:47:36.612622: train_loss -0.1027 
+2024-09-10 15:47:36.612828: val_loss -0.179 
+2024-09-10 15:47:36.612889: Pseudo dice [0.0666, 0.4442] 
+2024-09-10 15:47:36.612953: Epoch time: 327.24 s 
+2024-09-10 15:47:36.613003: Yayy! New best EMA pseudo Dice: 0.2554 
+2024-09-10 15:47:38.523392:  
+2024-09-10 15:47:38.523596: Epoch 1 
+2024-09-10 15:47:38.523686: Current learning rate: 0.00999 
+2024-09-10 15:51:44.371875: train_loss -0.3112 
+2024-09-10 15:51:44.372077: val_loss -0.2228 
+2024-09-10 15:51:44.372136: Pseudo dice [0.2053, 0.3667] 
+2024-09-10 15:51:44.372197: Epoch time: 245.85 s 
+2024-09-10 15:51:44.372246: Yayy! New best EMA pseudo Dice: 0.2585 
+2024-09-10 15:51:48.203549:  
+2024-09-10 15:51:48.203710: Epoch 2 
+2024-09-10 15:51:48.203796: Current learning rate: 0.00998 
+2024-09-10 15:55:53.714976: train_loss -0.3505 
+2024-09-10 15:55:53.715122: val_loss -0.2321 
+2024-09-10 15:55:53.715179: Pseudo dice [0.2217, 0.4209] 
+2024-09-10 15:55:53.715235: Epoch time: 245.51 s 
+2024-09-10 15:55:53.715282: Yayy! New best EMA pseudo Dice: 0.2647 
+2024-09-10 15:55:57.650263:  
+2024-09-10 15:55:57.650407: Epoch 3 
+2024-09-10 15:55:57.650517: Current learning rate: 0.00997 
+2024-09-10 16:00:03.637862: train_loss -0.406 
+2024-09-10 16:00:03.638014: val_loss -0.3153 
+2024-09-10 16:00:03.638070: Pseudo dice [0.2464, 0.5708] 
+2024-09-10 16:00:03.638125: Epoch time: 245.99 s 
+2024-09-10 16:00:03.638169: Yayy! New best EMA pseudo Dice: 0.2791 
+2024-09-10 16:00:07.545377:  
+2024-09-10 16:00:07.545629: Epoch 4 
+2024-09-10 16:00:07.545726: Current learning rate: 0.00996 
+2024-09-10 16:04:13.610407: train_loss -0.4871 
+2024-09-10 16:04:13.610544: val_loss -0.387 
+2024-09-10 16:04:13.610600: Pseudo dice [0.263, 0.6307] 
+2024-09-10 16:04:13.610656: Epoch time: 246.07 s 
+2024-09-10 16:04:13.610701: Yayy! New best EMA pseudo Dice: 0.2959 
+2024-09-10 16:04:17.558174:  
+2024-09-10 16:04:17.558359: Epoch 5 
+2024-09-10 16:04:17.558444: Current learning rate: 0.00995 
+2024-09-10 16:08:23.632867: train_loss -0.5239 
+2024-09-10 16:08:23.633012: val_loss -0.3896 
+2024-09-10 16:08:23.633069: Pseudo dice [0.2904, 0.6537] 
+2024-09-10 16:08:23.633126: Epoch time: 246.08 s 
+2024-09-10 16:08:23.633171: Yayy! New best EMA pseudo Dice: 0.3135 
+2024-09-10 16:08:27.533070:  
+2024-09-10 16:08:27.533261: Epoch 6 
+2024-09-10 16:08:27.533360: Current learning rate: 0.00995 
+2024-09-10 16:12:33.483159: train_loss -0.5744 
+2024-09-10 16:12:33.483317: val_loss -0.3972 
+2024-09-10 16:12:33.483466: Pseudo dice [0.2476, 0.6663] 
+2024-09-10 16:12:33.483546: Epoch time: 245.95 s 
+2024-09-10 16:12:33.483592: Yayy! New best EMA pseudo Dice: 0.3279 
+2024-09-10 16:12:37.387964:  
+2024-09-10 16:12:37.388110: Epoch 7 
+2024-09-10 16:12:37.388197: Current learning rate: 0.00994 
+2024-09-10 16:16:43.568294: train_loss -0.6133 
+2024-09-10 16:16:43.568573: val_loss -0.4559 
+2024-09-10 16:16:43.568634: Pseudo dice [0.3415, 0.6994] 
+2024-09-10 16:16:43.568694: Epoch time: 246.18 s 
+2024-09-10 16:16:43.568741: Yayy! New best EMA pseudo Dice: 0.3471 
+2024-09-10 16:16:47.512587:  
+2024-09-10 16:16:47.512789: Epoch 8 
+2024-09-10 16:16:47.512877: Current learning rate: 0.00993 
+2024-09-10 16:20:53.800211: train_loss -0.6138 
+2024-09-10 16:20:53.800382: val_loss -0.4455 
+2024-09-10 16:20:53.800450: Pseudo dice [0.301, 0.7204] 
+2024-09-10 16:20:53.800596: Epoch time: 246.29 s 
+2024-09-10 16:20:53.800716: Yayy! New best EMA pseudo Dice: 0.3635 
+2024-09-10 16:20:57.771099:  
+2024-09-10 16:20:57.771288: Epoch 9 
+2024-09-10 16:20:57.771372: Current learning rate: 0.00992 
+2024-09-10 16:25:04.557143: train_loss -0.6197 
+2024-09-10 16:25:04.557321: val_loss -0.5013 
+2024-09-10 16:25:04.557433: Pseudo dice [0.3894, 0.7538] 
+2024-09-10 16:25:04.557496: Epoch time: 246.79 s 
+2024-09-10 16:25:04.557542: Yayy! New best EMA pseudo Dice: 0.3843 
+2024-09-10 16:25:08.799163:  
+2024-09-10 16:25:08.799311: Epoch 10 
+2024-09-10 16:25:08.799390: Current learning rate: 0.00991 
+2024-09-10 16:29:15.932308: train_loss -0.6514 
+2024-09-10 16:29:15.932452: val_loss -0.4856 
+2024-09-10 16:29:15.932508: Pseudo dice [0.3575, 0.7454] 
+2024-09-10 16:29:15.932562: Epoch time: 247.14 s 
+2024-09-10 16:29:15.932677: Yayy! New best EMA pseudo Dice: 0.401 
+2024-09-10 16:29:20.016601:  
+2024-09-10 16:29:20.016843: Epoch 11 
+2024-09-10 16:29:20.016959: Current learning rate: 0.0099 
+2024-09-10 16:33:26.328878: train_loss -0.6569 
+2024-09-10 16:33:26.329028: val_loss -0.5014 
+2024-09-10 16:33:26.329078: Pseudo dice [0.3414, 0.7734] 
+2024-09-10 16:33:26.329129: Epoch time: 246.31 s 
+2024-09-10 16:33:26.329223: Yayy! New best EMA pseudo Dice: 0.4166 
+2024-09-10 16:33:30.206880:  
+2024-09-10 16:33:30.207085: Epoch 12 
+2024-09-10 16:33:30.207169: Current learning rate: 0.00989 
+2024-09-10 16:37:36.262563: train_loss -0.6559 
+2024-09-10 16:37:36.262762: val_loss -0.4119 
+2024-09-10 16:37:36.263041: Pseudo dice [0.3181, 0.6802] 
+2024-09-10 16:37:36.263137: Epoch time: 246.06 s 
+2024-09-10 16:37:36.263273: Yayy! New best EMA pseudo Dice: 0.4249 
+2024-09-10 16:37:40.829145:  
+2024-09-10 16:37:40.829302: Epoch 13 
+2024-09-10 16:37:40.829443: Current learning rate: 0.00988 
+2024-09-10 16:41:46.808890: train_loss -0.669 
+2024-09-10 16:41:46.809065: val_loss -0.4616 
+2024-09-10 16:41:46.809115: Pseudo dice [0.3287, 0.7566] 
+2024-09-10 16:41:46.809169: Epoch time: 245.98 s 
+2024-09-10 16:41:46.809211: Yayy! New best EMA pseudo Dice: 0.4367 
+2024-09-10 16:41:50.751019:  
+2024-09-10 16:41:50.751239: Epoch 14 
+2024-09-10 16:41:50.751324: Current learning rate: 0.00987 
+2024-09-10 16:45:56.791849: train_loss -0.6777 
+2024-09-10 16:45:56.791983: val_loss -0.4894 
+2024-09-10 16:45:56.792034: Pseudo dice [0.3209, 0.7821] 
+2024-09-10 16:45:56.792089: Epoch time: 246.04 s 
+2024-09-10 16:45:56.792132: Yayy! New best EMA pseudo Dice: 0.4482 
+2024-09-10 16:46:00.718035:  
+2024-09-10 16:46:00.718233: Epoch 15 
+2024-09-10 16:46:00.718320: Current learning rate: 0.00986 
+2024-09-10 16:50:07.063071: train_loss -0.6859 
+2024-09-10 16:50:07.063255: val_loss -0.5305 
+2024-09-10 16:50:07.063308: Pseudo dice [0.3814, 0.8078] 
+2024-09-10 16:50:07.063362: Epoch time: 246.35 s 
+2024-09-10 16:50:07.063402: Yayy! New best EMA pseudo Dice: 0.4628 
+2024-09-10 16:50:11.042193:  
+2024-09-10 16:50:11.042331: Epoch 16 
+2024-09-10 16:50:11.042415: Current learning rate: 0.00986 
+2024-09-10 16:54:17.516963: train_loss -0.6915 
+2024-09-10 16:54:17.517102: val_loss -0.5273 
+2024-09-10 16:54:17.517152: Pseudo dice [0.3853, 0.7916] 
+2024-09-10 16:54:17.517201: Epoch time: 246.48 s 
+2024-09-10 16:54:17.517242: Yayy! New best EMA pseudo Dice: 0.4754 
+2024-09-10 16:54:21.423158:  
+2024-09-10 16:54:21.423351: Epoch 17 
+2024-09-10 16:54:21.423433: Current learning rate: 0.00985 
+2024-09-10 16:58:27.901893: train_loss -0.6735 
+2024-09-10 16:58:27.902083: val_loss -0.4901 
+2024-09-10 16:58:27.902179: Pseudo dice [0.3504, 0.759] 
+2024-09-10 16:58:27.902272: Epoch time: 246.48 s 
+2024-09-10 16:58:27.902347: Yayy! New best EMA pseudo Dice: 0.4833 
+2024-09-10 16:58:31.903140:  
+2024-09-10 16:58:31.903313: Epoch 18 
+2024-09-10 16:58:31.903401: Current learning rate: 0.00984 
+2024-09-10 17:02:38.002226: train_loss -0.6913 
+2024-09-10 17:02:38.002403: val_loss -0.5303 
+2024-09-10 17:02:38.002455: Pseudo dice [0.3515, 0.8172] 
+2024-09-10 17:02:38.002506: Epoch time: 246.1 s 
+2024-09-10 17:02:38.002546: Yayy! New best EMA pseudo Dice: 0.4934 
+2024-09-10 17:02:41.912463:  
+2024-09-10 17:02:41.912694: Epoch 19 
+2024-09-10 17:02:41.912817: Current learning rate: 0.00983 
+2024-09-10 17:06:47.805125: train_loss -0.7003 
+2024-09-10 17:06:47.805264: val_loss -0.5486 
+2024-09-10 17:06:47.805314: Pseudo dice [0.4142, 0.8178] 
+2024-09-10 17:06:47.805364: Epoch time: 245.9 s 
+2024-09-10 17:06:47.805404: Yayy! New best EMA pseudo Dice: 0.5057 
+2024-09-10 17:06:51.723973:  
+2024-09-10 17:06:51.724170: Epoch 20 
+2024-09-10 17:06:51.724252: Current learning rate: 0.00982 
+2024-09-10 17:10:57.697320: train_loss -0.6446 
+2024-09-10 17:10:57.697453: val_loss -0.4373 
+2024-09-10 17:10:57.697505: Pseudo dice [0.3083, 0.6913] 
+2024-09-10 17:10:57.697556: Epoch time: 245.98 s 
+2024-09-10 17:10:58.685053:  
+2024-09-10 17:10:58.685364: Epoch 21 
+2024-09-10 17:10:58.685492: Current learning rate: 0.00981 
+2024-09-10 17:15:04.286015: train_loss -0.6684 
+2024-09-10 17:15:04.286194: val_loss -0.4845 
+2024-09-10 17:15:04.286245: Pseudo dice [0.3528, 0.7975] 
+2024-09-10 17:15:04.286296: Epoch time: 245.6 s 
+2024-09-10 17:15:04.286337: Yayy! New best EMA pseudo Dice: 0.5121 
+2024-09-10 17:15:08.180372:  
+2024-09-10 17:15:08.180621: Epoch 22 
+2024-09-10 17:15:08.180702: Current learning rate: 0.0098 
+2024-09-10 17:19:13.945470: train_loss -0.711 
+2024-09-10 17:19:13.945609: val_loss -0.5539 
+2024-09-10 17:19:13.945660: Pseudo dice [0.4062, 0.7893] 
+2024-09-10 17:19:13.945713: Epoch time: 245.77 s 
+2024-09-10 17:19:13.945754: Yayy! New best EMA pseudo Dice: 0.5207 
+2024-09-10 17:19:17.901033:  
+2024-09-10 17:19:17.901205: Epoch 23 
+2024-09-10 17:19:17.901285: Current learning rate: 0.00979 
+2024-09-10 17:23:23.911213: train_loss -0.7079 
+2024-09-10 17:23:23.911386: val_loss -0.5541 
+2024-09-10 17:23:23.911437: Pseudo dice [0.4039, 0.8226] 
+2024-09-10 17:23:23.911490: Epoch time: 246.01 s 
+2024-09-10 17:23:23.911535: Yayy! New best EMA pseudo Dice: 0.5299 
+2024-09-10 17:23:27.807029:  
+2024-09-10 17:23:27.807222: Epoch 24 
+2024-09-10 17:23:27.807359: Current learning rate: 0.00978 
+2024-09-10 17:27:33.754234: train_loss -0.7274 
+2024-09-10 17:27:33.754393: val_loss -0.5472 
+2024-09-10 17:27:33.754444: Pseudo dice [0.3921, 0.8107] 
+2024-09-10 17:27:33.754494: Epoch time: 245.95 s 
+2024-09-10 17:27:33.754534: Yayy! New best EMA pseudo Dice: 0.5371 
+2024-09-10 17:27:37.646173:  
+2024-09-10 17:27:37.646366: Epoch 25 
+2024-09-10 17:27:37.646471: Current learning rate: 0.00977 
+2024-09-10 17:31:43.460034: train_loss -0.7234 
+2024-09-10 17:31:43.460173: val_loss -0.548 
+2024-09-10 17:31:43.460277: Pseudo dice [0.3896, 0.8394] 
+2024-09-10 17:31:43.460331: Epoch time: 245.82 s 
+2024-09-10 17:31:43.460372: Yayy! New best EMA pseudo Dice: 0.5448 
+2024-09-10 17:31:47.392951:  
+2024-09-10 17:31:47.393109: Epoch 26 
+2024-09-10 17:31:47.393220: Current learning rate: 0.00977 
+2024-09-10 17:35:53.017938: train_loss -0.7071 
+2024-09-10 17:35:53.018096: val_loss -0.5674 
+2024-09-10 17:35:53.018148: Pseudo dice [0.3945, 0.822] 
+2024-09-10 17:35:53.018202: Epoch time: 245.63 s 
+2024-09-10 17:35:53.018244: Yayy! New best EMA pseudo Dice: 0.5511 
+2024-09-10 17:35:56.910895:  
+2024-09-10 17:35:56.911033: Epoch 27 
+2024-09-10 17:35:56.911114: Current learning rate: 0.00976 
+2024-09-10 17:40:02.743228: train_loss -0.7118 
+2024-09-10 17:40:02.743365: val_loss -0.5323 
+2024-09-10 17:40:02.743415: Pseudo dice [0.3526, 0.8191] 
+2024-09-10 17:40:02.743520: Epoch time: 245.83 s 
+2024-09-10 17:40:02.743597: Yayy! New best EMA pseudo Dice: 0.5546 
+2024-09-10 17:40:06.620845:  
+2024-09-10 17:40:06.621024: Epoch 28 
+2024-09-10 17:40:06.621121: Current learning rate: 0.00975 
+2024-09-10 17:44:12.655388: train_loss -0.7362 
+2024-09-10 17:44:12.655528: val_loss -0.5621 
+2024-09-10 17:44:12.655578: Pseudo dice [0.4344, 0.8332] 
+2024-09-10 17:44:12.655629: Epoch time: 246.04 s 
+2024-09-10 17:44:12.655668: Yayy! New best EMA pseudo Dice: 0.5625 
+2024-09-10 17:44:16.549526:  
+2024-09-10 17:44:16.549673: Epoch 29 
+2024-09-10 17:44:16.549765: Current learning rate: 0.00974 
+2024-09-10 17:48:22.755472: train_loss -0.7468 
+2024-09-10 17:48:22.755611: val_loss -0.5631 
+2024-09-10 17:48:22.755662: Pseudo dice [0.3915, 0.8518] 
+2024-09-10 17:48:22.755713: Epoch time: 246.21 s 
+2024-09-10 17:48:22.755753: Yayy! New best EMA pseudo Dice: 0.5684 
+2024-09-10 17:48:26.681543:  
+2024-09-10 17:48:26.681781: Epoch 30 
+2024-09-10 17:48:26.681901: Current learning rate: 0.00973 
+2024-09-10 17:52:32.986442: train_loss -0.7393 
+2024-09-10 17:52:32.986593: val_loss -0.552 
+2024-09-10 17:52:32.986645: Pseudo dice [0.3941, 0.8392] 
+2024-09-10 17:52:32.986699: Epoch time: 246.31 s 
+2024-09-10 17:52:32.986740: Yayy! New best EMA pseudo Dice: 0.5733 
+2024-09-10 17:52:36.903515:  
+2024-09-10 17:52:36.903681: Epoch 31 
+2024-09-10 17:52:36.903764: Current learning rate: 0.00972 
+2024-09-10 17:56:42.883919: train_loss -0.7148 
+2024-09-10 17:56:42.884068: val_loss -0.5324 
+2024-09-10 17:56:42.884125: Pseudo dice [0.3737, 0.7946] 
+2024-09-10 17:56:42.884176: Epoch time: 245.98 s 
+2024-09-10 17:56:42.884217: Yayy! New best EMA pseudo Dice: 0.5744 
+2024-09-10 17:56:46.793433:  
+2024-09-10 17:56:46.793713: Epoch 32 
+2024-09-10 17:56:46.793806: Current learning rate: 0.00971 
+2024-09-10 18:00:52.723351: train_loss -0.7339 
+2024-09-10 18:00:52.723491: val_loss -0.5764 
+2024-09-10 18:00:52.723542: Pseudo dice [0.4176, 0.8438] 
+2024-09-10 18:00:52.723592: Epoch time: 245.93 s 
+2024-09-10 18:00:52.723632: Yayy! New best EMA pseudo Dice: 0.58 
+2024-09-10 18:00:56.675764:  
+2024-09-10 18:00:56.675984: Epoch 33 
+2024-09-10 18:00:56.676094: Current learning rate: 0.0097 
+2024-09-10 18:05:02.579366: train_loss -0.7412 
+2024-09-10 18:05:02.579520: val_loss -0.5291 
+2024-09-10 18:05:02.579572: Pseudo dice [0.314, 0.8502] 
+2024-09-10 18:05:02.579623: Epoch time: 245.91 s 
+2024-09-10 18:05:02.579663: Yayy! New best EMA pseudo Dice: 0.5802 
+2024-09-10 18:05:07.439896:  
+2024-09-10 18:05:07.440084: Epoch 34 
+2024-09-10 18:05:07.440166: Current learning rate: 0.00969 
+2024-09-10 18:09:13.360112: train_loss -0.7221 
+2024-09-10 18:09:13.360291: val_loss -0.5506 
+2024-09-10 18:09:13.360346: Pseudo dice [0.3759, 0.8258] 
+2024-09-10 18:09:13.360398: Epoch time: 245.92 s 
+2024-09-10 18:09:13.360437: Yayy! New best EMA pseudo Dice: 0.5823 
+2024-09-10 18:09:17.249533:  
+2024-09-10 18:09:17.249757: Epoch 35 
+2024-09-10 18:09:17.249838: Current learning rate: 0.00968 
+2024-09-10 18:13:23.386381: train_loss -0.7233 
+2024-09-10 18:13:23.386520: val_loss -0.5591 
+2024-09-10 18:13:23.386632: Pseudo dice [0.3914, 0.8391] 
+2024-09-10 18:13:23.386685: Epoch time: 246.14 s 
+2024-09-10 18:13:23.386726: Yayy! New best EMA pseudo Dice: 0.5856 
+2024-09-10 18:13:27.272199:  
+2024-09-10 18:13:27.272402: Epoch 36 
+2024-09-10 18:13:27.272481: Current learning rate: 0.00968 
+2024-09-10 18:17:33.388380: train_loss -0.727 
+2024-09-10 18:17:33.388516: val_loss -0.5857 
+2024-09-10 18:17:33.388567: Pseudo dice [0.4509, 0.8469] 
+2024-09-10 18:17:33.388618: Epoch time: 246.12 s 
+2024-09-10 18:17:33.388657: Yayy! New best EMA pseudo Dice: 0.5919 
+2024-09-10 18:17:37.113202:  
+2024-09-10 18:17:37.113407: Epoch 37 
+2024-09-10 18:17:37.113538: Current learning rate: 0.00967 
+2024-09-10 18:21:43.232972: train_loss -0.7425 
+2024-09-10 18:21:43.233122: val_loss -0.5745 
+2024-09-10 18:21:43.233172: Pseudo dice [0.4708, 0.8393] 
+2024-09-10 18:21:43.233224: Epoch time: 246.12 s 
+2024-09-10 18:21:43.233264: Yayy! New best EMA pseudo Dice: 0.5982 
+2024-09-10 18:21:47.173415:  
+2024-09-10 18:21:47.173608: Epoch 38 
+2024-09-10 18:21:47.173711: Current learning rate: 0.00966 
+2024-09-10 18:25:53.753411: train_loss -0.7424 
+2024-09-10 18:25:53.753590: val_loss -0.5772 
+2024-09-10 18:25:53.753688: Pseudo dice [0.4376, 0.8454] 
+2024-09-10 18:25:53.753741: Epoch time: 246.58 s 
+2024-09-10 18:25:53.753782: Yayy! New best EMA pseudo Dice: 0.6025 
+2024-09-10 18:25:57.694018:  
+2024-09-10 18:25:57.694207: Epoch 39 
+2024-09-10 18:25:57.694291: Current learning rate: 0.00965 
+2024-09-10 18:30:04.213990: train_loss -0.7575 
+2024-09-10 18:30:04.214132: val_loss -0.563 
+2024-09-10 18:30:04.214183: Pseudo dice [0.3846, 0.8506] 
+2024-09-10 18:30:04.214233: Epoch time: 246.52 s 
+2024-09-10 18:30:04.214273: Yayy! New best EMA pseudo Dice: 0.604 
+2024-09-10 18:30:08.165972:  
+2024-09-10 18:30:08.166245: Epoch 40 
+2024-09-10 18:30:08.166327: Current learning rate: 0.00964 
+2024-09-10 18:34:14.480578: train_loss -0.7434 
+2024-09-10 18:34:14.480715: val_loss -0.5665 
+2024-09-10 18:34:14.480765: Pseudo dice [0.3772, 0.856] 
+2024-09-10 18:34:14.480817: Epoch time: 246.32 s 
+2024-09-10 18:34:14.480857: Yayy! New best EMA pseudo Dice: 0.6053 
+2024-09-10 18:34:18.439024:  
+2024-09-10 18:34:18.439213: Epoch 41 
+2024-09-10 18:34:18.439313: Current learning rate: 0.00963 
+2024-09-10 18:38:24.519374: train_loss -0.7521 
+2024-09-10 18:38:24.519516: val_loss -0.5961 
+2024-09-10 18:38:24.519566: Pseudo dice [0.4345, 0.849] 
+2024-09-10 18:38:24.519617: Epoch time: 246.08 s 
+2024-09-10 18:38:24.519712: Yayy! New best EMA pseudo Dice: 0.6089 
+2024-09-10 18:38:28.434878:  
+2024-09-10 18:38:28.435051: Epoch 42 
+2024-09-10 18:38:28.435134: Current learning rate: 0.00962 
+2024-09-10 18:42:34.502122: train_loss -0.7555 
+2024-09-10 18:42:34.502281: val_loss -0.6095 
+2024-09-10 18:42:34.502344: Pseudo dice [0.4953, 0.8694] 
+2024-09-10 18:42:34.502398: Epoch time: 246.07 s 
+2024-09-10 18:42:34.502440: Yayy! New best EMA pseudo Dice: 0.6163 
+2024-09-10 18:42:38.422637:  
+2024-09-10 18:42:38.422865: Epoch 43 
+2024-09-10 18:42:38.422951: Current learning rate: 0.00961 
+2024-09-10 18:46:44.643349: train_loss -0.7447 
+2024-09-10 18:46:44.643525: val_loss -0.5815 
+2024-09-10 18:46:44.643576: Pseudo dice [0.4345, 0.8566] 
+2024-09-10 18:46:44.643627: Epoch time: 246.22 s 
+2024-09-10 18:46:44.643667: Yayy! New best EMA pseudo Dice: 0.6192 
+2024-09-10 18:46:48.500021:  
+2024-09-10 18:46:48.500203: Epoch 44 
+2024-09-10 18:46:48.500285: Current learning rate: 0.0096 
+2024-09-10 18:50:54.547700: train_loss -0.7546 
+2024-09-10 18:50:54.547883: val_loss -0.5512 
+2024-09-10 18:50:54.547952: Pseudo dice [0.3636, 0.8399] 
+2024-09-10 18:50:54.548004: Epoch time: 246.05 s 
+2024-09-10 18:50:55.474115:  
+2024-09-10 18:50:55.474272: Epoch 45 
+2024-09-10 18:50:55.474388: Current learning rate: 0.00959 
+2024-09-10 18:55:01.629798: train_loss -0.7554 
+2024-09-10 18:55:01.630014: val_loss -0.5664 
+2024-09-10 18:55:01.630089: Pseudo dice [0.4159, 0.8205] 
+2024-09-10 18:55:01.630142: Epoch time: 246.16 s 
+2024-09-10 18:55:02.553950:  
+2024-09-10 18:55:02.554223: Epoch 46 
+2024-09-10 18:55:02.554334: Current learning rate: 0.00959 
+2024-09-10 18:59:09.229681: train_loss -0.7487 
+2024-09-10 18:59:09.229824: val_loss -0.5413 
+2024-09-10 18:59:09.229874: Pseudo dice [0.3382, 0.8457] 
+2024-09-10 18:59:09.229925: Epoch time: 246.68 s 
+2024-09-10 18:59:10.191753:  
+2024-09-10 18:59:10.192011: Epoch 47 
+2024-09-10 18:59:10.192093: Current learning rate: 0.00958 
+2024-09-10 19:03:16.841831: train_loss -0.7608 
+2024-09-10 19:03:16.841969: val_loss -0.5906 
+2024-09-10 19:03:16.842020: Pseudo dice [0.4402, 0.8576] 
+2024-09-10 19:03:16.842073: Epoch time: 246.65 s 
+2024-09-10 19:03:17.768769:  
+2024-09-10 19:03:17.768931: Epoch 48 
+2024-09-10 19:03:17.769016: Current learning rate: 0.00957 
+2024-09-10 19:07:24.074706: train_loss -0.7538 
+2024-09-10 19:07:24.074848: val_loss -0.5958 
+2024-09-10 19:07:24.074901: Pseudo dice [0.4537, 0.8538] 
+2024-09-10 19:07:24.074952: Epoch time: 246.31 s 
+2024-09-10 19:07:24.074992: Yayy! New best EMA pseudo Dice: 0.6219 
+2024-09-10 19:07:27.982454:  
+2024-09-10 19:07:27.982700: Epoch 49 
+2024-09-10 19:07:27.982811: Current learning rate: 0.00956 
+2024-09-10 19:11:34.021867: train_loss -0.7419 
+2024-09-10 19:11:34.022007: val_loss -0.5412 
+2024-09-10 19:11:34.022058: Pseudo dice [0.4111, 0.8109] 
+2024-09-10 19:11:34.022109: Epoch time: 246.04 s 
+2024-09-10 19:11:36.025214:  
+2024-09-10 19:11:36.025404: Epoch 50 
+2024-09-10 19:11:36.025488: Current learning rate: 0.00955 
+2024-09-10 19:15:41.837303: train_loss -0.7372 
+2024-09-10 19:15:41.837474: val_loss -0.5955 
+2024-09-10 19:15:41.837524: Pseudo dice [0.4569, 0.8494] 
+2024-09-10 19:15:41.837574: Epoch time: 245.81 s 
+2024-09-10 19:15:41.837614: Yayy! New best EMA pseudo Dice: 0.6241 
+2024-09-10 19:15:45.734023:  
+2024-09-10 19:15:45.734196: Epoch 51 
+2024-09-10 19:15:45.734280: Current learning rate: 0.00954 
+2024-09-10 19:19:51.571280: train_loss -0.76 
+2024-09-10 19:19:51.571432: val_loss -0.5879 
+2024-09-10 19:19:51.571484: Pseudo dice [0.4253, 0.8572] 
+2024-09-10 19:19:51.571535: Epoch time: 245.84 s 
+2024-09-10 19:19:51.571580: Yayy! New best EMA pseudo Dice: 0.6258 
+2024-09-10 19:19:55.501434:  
+2024-09-10 19:19:55.501605: Epoch 52 
+2024-09-10 19:19:55.501716: Current learning rate: 0.00953 
+2024-09-10 19:24:01.403198: train_loss -0.7653 
+2024-09-10 19:24:01.403408: val_loss -0.5861 
+2024-09-10 19:24:01.403478: Pseudo dice [0.4472, 0.8614] 
+2024-09-10 19:24:01.403552: Epoch time: 245.9 s 
+2024-09-10 19:24:01.403608: Yayy! New best EMA pseudo Dice: 0.6286 
+2024-09-10 19:24:05.318063:  
+2024-09-10 19:24:05.318229: Epoch 53 
+2024-09-10 19:24:05.318311: Current learning rate: 0.00952 
+2024-09-10 19:28:11.615849: train_loss -0.749 
+2024-09-10 19:28:11.615980: val_loss -0.5787 
+2024-09-10 19:28:11.616031: Pseudo dice [0.4153, 0.8457] 
+2024-09-10 19:28:11.616095: Epoch time: 246.3 s 
+2024-09-10 19:28:11.616136: Yayy! New best EMA pseudo Dice: 0.6288 
+2024-09-10 19:28:15.500951:  
+2024-09-10 19:28:15.501120: Epoch 54 
+2024-09-10 19:28:15.501203: Current learning rate: 0.00951 
+2024-09-10 19:32:21.781956: train_loss -0.7502 
+2024-09-10 19:32:21.782093: val_loss -0.5533 
+2024-09-10 19:32:21.782144: Pseudo dice [0.3786, 0.8632] 
+2024-09-10 19:32:21.782195: Epoch time: 246.28 s 
+2024-09-10 19:32:22.713210:  
+2024-09-10 19:32:22.713420: Epoch 55 
+2024-09-10 19:32:22.713546: Current learning rate: 0.0095 
+2024-09-10 19:36:28.686797: train_loss -0.7658 
+2024-09-10 19:36:28.686931: val_loss -0.5566 
+2024-09-10 19:36:28.686981: Pseudo dice [0.3893, 0.8521] 
+2024-09-10 19:36:28.687032: Epoch time: 245.98 s 
+2024-09-10 19:36:30.546026:  
+2024-09-10 19:36:30.546234: Epoch 56 
+2024-09-10 19:36:30.546390: Current learning rate: 0.00949 
+2024-09-10 19:40:36.600777: train_loss -0.7437 
+2024-09-10 19:40:36.600926: val_loss -0.5702 
+2024-09-10 19:40:36.600981: Pseudo dice [0.3962, 0.839] 
+2024-09-10 19:40:36.601032: Epoch time: 246.06 s 
+2024-09-10 19:40:37.528699:  
+2024-09-10 19:40:37.528919: Epoch 57 
+2024-09-10 19:40:37.529006: Current learning rate: 0.00949 
+2024-09-10 19:44:43.677524: train_loss -0.7565 
+2024-09-10 19:44:43.677663: val_loss -0.5666 
+2024-09-10 19:44:43.677716: Pseudo dice [0.4046, 0.8353] 
+2024-09-10 19:44:43.677768: Epoch time: 246.15 s 
+2024-09-10 19:44:44.608580:  
+2024-09-10 19:44:44.608751: Epoch 58 
+2024-09-10 19:44:44.608871: Current learning rate: 0.00948 
+2024-09-10 19:48:50.647182: train_loss -0.7323 
+2024-09-10 19:48:50.647320: val_loss -0.5484 
+2024-09-10 19:48:50.647371: Pseudo dice [0.3775, 0.8437] 
+2024-09-10 19:48:50.647421: Epoch time: 246.04 s 
+2024-09-10 19:48:51.601689:  
+2024-09-10 19:48:51.601868: Epoch 59 
+2024-09-10 19:48:51.601981: Current learning rate: 0.00947 
+2024-09-10 19:52:57.442486: train_loss -0.7659 
+2024-09-10 19:52:57.442624: val_loss -0.5799 
+2024-09-10 19:52:57.442675: Pseudo dice [0.3784, 0.8792] 
+2024-09-10 19:52:57.442725: Epoch time: 245.84 s 
+2024-09-10 19:52:58.415962:  
+2024-09-10 19:52:58.416125: Epoch 60 
+2024-09-10 19:52:58.416207: Current learning rate: 0.00946 
+2024-09-10 19:57:04.580964: train_loss -0.7692 
+2024-09-10 19:57:04.581115: val_loss -0.5787 
+2024-09-10 19:57:04.581166: Pseudo dice [0.4079, 0.8643] 
+2024-09-10 19:57:04.581217: Epoch time: 246.17 s 
+2024-09-10 19:57:05.539958:  
+2024-09-10 19:57:05.540159: Epoch 61 
+2024-09-10 19:57:05.540241: Current learning rate: 0.00945 
+2024-09-10 20:01:11.866199: train_loss -0.7623 
+2024-09-10 20:01:11.866366: val_loss -0.5945 
+2024-09-10 20:01:11.866417: Pseudo dice [0.4447, 0.8544] 
+2024-09-10 20:01:11.866467: Epoch time: 246.33 s 
+2024-09-10 20:01:12.820413:  
+2024-09-10 20:01:12.820589: Epoch 62 
+2024-09-10 20:01:12.820671: Current learning rate: 0.00944 
+2024-09-10 20:05:18.794992: train_loss -0.7636 
+2024-09-10 20:05:18.795135: val_loss -0.5614 
+2024-09-10 20:05:18.795186: Pseudo dice [0.3957, 0.8492] 
+2024-09-10 20:05:18.795237: Epoch time: 245.98 s 
+2024-09-10 20:05:19.764510:  
+2024-09-10 20:05:19.764709: Epoch 63 
+2024-09-10 20:05:19.764789: Current learning rate: 0.00943 
+2024-09-10 20:09:25.727510: train_loss -0.7774 
+2024-09-10 20:09:25.727647: val_loss -0.5937 
+2024-09-10 20:09:25.727698: Pseudo dice [0.427, 0.865] 
+2024-09-10 20:09:25.727749: Epoch time: 245.96 s 
+2024-09-10 20:09:25.727789: Yayy! New best EMA pseudo Dice: 0.6294 
+2024-09-10 20:09:29.693463:  
+2024-09-10 20:09:29.693629: Epoch 64 
+2024-09-10 20:09:29.693749: Current learning rate: 0.00942 
+2024-09-10 20:13:35.523525: train_loss -0.7633 
+2024-09-10 20:13:35.523701: val_loss -0.5658 
+2024-09-10 20:13:35.523751: Pseudo dice [0.4243, 0.8223] 
+2024-09-10 20:13:35.523811: Epoch time: 245.83 s 
+2024-09-10 20:13:36.546632:  
+2024-09-10 20:13:36.546789: Epoch 65 
+2024-09-10 20:13:36.546890: Current learning rate: 0.00941 
+2024-09-10 20:17:42.596049: train_loss -0.753 
+2024-09-10 20:17:42.596189: val_loss -0.5852 
+2024-09-10 20:17:42.596243: Pseudo dice [0.3988, 0.8496] 
+2024-09-10 20:17:42.596307: Epoch time: 246.05 s 
+2024-09-10 20:17:43.563835:  
+2024-09-10 20:17:43.563993: Epoch 66 
+2024-09-10 20:17:43.564072: Current learning rate: 0.0094 
+2024-09-10 20:21:49.750217: train_loss -0.72 
+2024-09-10 20:21:49.750392: val_loss -0.5416 
+2024-09-10 20:21:49.750444: Pseudo dice [0.3814, 0.8338] 
+2024-09-10 20:21:49.750495: Epoch time: 246.19 s 
+2024-09-10 20:21:50.693221:  
+2024-09-10 20:21:50.693434: Epoch 67 
+2024-09-10 20:21:50.693520: Current learning rate: 0.00939 
+2024-09-10 20:25:57.093760: train_loss -0.7605 
+2024-09-10 20:25:57.093901: val_loss -0.6028 
+2024-09-10 20:25:57.093955: Pseudo dice [0.4455, 0.8674] 
+2024-09-10 20:25:57.094009: Epoch time: 246.4 s 
+2024-09-10 20:25:58.070629:  
+2024-09-10 20:25:58.070794: Epoch 68 
+2024-09-10 20:25:58.070879: Current learning rate: 0.00939 
+2024-09-10 20:30:04.181773: train_loss -0.7721 
+2024-09-10 20:30:04.181915: val_loss -0.5652 
+2024-09-10 20:30:04.181965: Pseudo dice [0.3728, 0.8496] 
+2024-09-10 20:30:04.182015: Epoch time: 246.11 s 
+2024-09-10 20:30:05.145639:  
+2024-09-10 20:30:05.145808: Epoch 69 
+2024-09-10 20:30:05.145893: Current learning rate: 0.00938 
+2024-09-10 20:34:11.250081: train_loss -0.7752 
+2024-09-10 20:34:11.250272: val_loss -0.5578 
+2024-09-10 20:34:11.250323: Pseudo dice [0.4084, 0.8677] 
+2024-09-10 20:34:11.250377: Epoch time: 246.11 s 
+2024-09-10 20:34:12.210081:  
+2024-09-10 20:34:12.210300: Epoch 70 
+2024-09-10 20:34:12.210382: Current learning rate: 0.00937 
+2024-09-10 20:38:18.439079: train_loss -0.761 
+2024-09-10 20:38:18.439229: val_loss -0.6143 
+2024-09-10 20:38:18.439280: Pseudo dice [0.457, 0.8629] 
+2024-09-10 20:38:18.439331: Epoch time: 246.23 s 
+2024-09-10 20:38:18.439371: Yayy! New best EMA pseudo Dice: 0.6317 
+2024-09-10 20:38:22.392959:  
+2024-09-10 20:38:22.393135: Epoch 71 
+2024-09-10 20:38:22.393219: Current learning rate: 0.00936 
+2024-09-10 20:42:28.848063: train_loss -0.7577 
+2024-09-10 20:42:28.848202: val_loss -0.5755 
+2024-09-10 20:42:28.848253: Pseudo dice [0.3994, 0.8636] 
+2024-09-10 20:42:28.848337: Epoch time: 246.46 s 
+2024-09-10 20:42:29.828760:  
+2024-09-10 20:42:29.828894: Epoch 72 
+2024-09-10 20:42:29.828977: Current learning rate: 0.00935 
+2024-09-10 20:46:35.729341: train_loss -0.7759 
+2024-09-10 20:46:35.729482: val_loss -0.6056 
+2024-09-10 20:46:35.729534: Pseudo dice [0.4352, 0.8691] 
+2024-09-10 20:46:35.729584: Epoch time: 245.9 s 
+2024-09-10 20:46:35.729624: Yayy! New best EMA pseudo Dice: 0.6337 
+2024-09-10 20:46:39.692533:  
+2024-09-10 20:46:39.692683: Epoch 73 
+2024-09-10 20:46:39.692764: Current learning rate: 0.00934 
+2024-09-10 20:50:45.672098: train_loss -0.7668 
+2024-09-10 20:50:45.672261: val_loss -0.5883 
+2024-09-10 20:50:45.672324: Pseudo dice [0.4411, 0.8728] 
+2024-09-10 20:50:45.672380: Epoch time: 245.98 s 
+2024-09-10 20:50:45.672426: Yayy! New best EMA pseudo Dice: 0.636 
+2024-09-10 20:50:49.588804:  
+2024-09-10 20:50:49.589033: Epoch 74 
+2024-09-10 20:50:49.589118: Current learning rate: 0.00933 
+2024-09-10 20:54:55.524826: train_loss -0.7709 
+2024-09-10 20:54:55.524971: val_loss -0.5925 
+2024-09-10 20:54:55.525024: Pseudo dice [0.4726, 0.8492] 
+2024-09-10 20:54:55.525079: Epoch time: 245.94 s 
+2024-09-10 20:54:55.525121: Yayy! New best EMA pseudo Dice: 0.6385 
+2024-09-10 20:54:59.498559:  
+2024-09-10 20:54:59.498744: Epoch 75 
+2024-09-10 20:54:59.498828: Current learning rate: 0.00932 
+2024-09-10 20:59:05.353354: train_loss -0.7684 
+2024-09-10 20:59:05.353523: val_loss -0.5979 
+2024-09-10 20:59:05.353579: Pseudo dice [0.4358, 0.8735] 
+2024-09-10 20:59:05.353633: Epoch time: 245.86 s 
+2024-09-10 20:59:05.353680: Yayy! New best EMA pseudo Dice: 0.6401 
+2024-09-10 20:59:09.330541:  
+2024-09-10 20:59:09.330724: Epoch 76 
+2024-09-10 20:59:09.330841: Current learning rate: 0.00931 
+2024-09-10 21:03:15.343704: train_loss -0.7831 
+2024-09-10 21:03:15.343878: val_loss -0.6073 
+2024-09-10 21:03:15.343936: Pseudo dice [0.4388, 0.8839] 
+2024-09-10 21:03:15.343991: Epoch time: 246.02 s 
+2024-09-10 21:03:15.344035: Yayy! New best EMA pseudo Dice: 0.6423 
+2024-09-10 21:03:19.300889:  
+2024-09-10 21:03:19.301082: Epoch 77 
+2024-09-10 21:03:19.301206: Current learning rate: 0.0093 
+2024-09-10 21:07:25.204125: train_loss -0.7759 
+2024-09-10 21:07:25.204274: val_loss -0.5604 
+2024-09-10 21:07:25.204330: Pseudo dice [0.4325, 0.8245] 
+2024-09-10 21:07:25.204386: Epoch time: 245.91 s 
+2024-09-10 21:07:26.198178:  
+2024-09-10 21:07:26.198383: Epoch 78 
+2024-09-10 21:07:26.198470: Current learning rate: 0.0093 
+2024-09-10 21:11:32.222774: train_loss -0.76 
+2024-09-10 21:11:32.222989: val_loss -0.5928 
+2024-09-10 21:11:32.223093: Pseudo dice [0.4329, 0.8488] 
+2024-09-10 21:11:32.223193: Epoch time: 246.03 s 
+2024-09-10 21:11:34.191050:  
+2024-09-10 21:11:34.191260: Epoch 79 
+2024-09-10 21:11:34.191347: Current learning rate: 0.00929 
+2024-09-10 21:15:40.316445: train_loss -0.7809 
+2024-09-10 21:15:40.316605: val_loss -0.5809 
+2024-09-10 21:15:40.316659: Pseudo dice [0.4087, 0.8381] 
+2024-09-10 21:15:40.316711: Epoch time: 246.13 s 
+2024-09-10 21:15:41.292480:  
+2024-09-10 21:15:41.292702: Epoch 80 
+2024-09-10 21:15:41.292786: Current learning rate: 0.00928 
+2024-09-10 21:19:47.583541: train_loss -0.7894 
+2024-09-10 21:19:47.583679: val_loss -0.6051 
+2024-09-10 21:19:47.583729: Pseudo dice [0.45, 0.8808] 
+2024-09-10 21:19:47.583781: Epoch time: 246.29 s 
+2024-09-10 21:19:48.577969:  
+2024-09-10 21:19:48.578168: Epoch 81 
+2024-09-10 21:19:48.578252: Current learning rate: 0.00927 
+2024-09-10 21:23:54.961526: train_loss -0.7878 
+2024-09-10 21:23:54.961664: val_loss -0.6086 
+2024-09-10 21:23:54.961719: Pseudo dice [0.4853, 0.8635] 
+2024-09-10 21:23:54.961769: Epoch time: 246.39 s 
+2024-09-10 21:23:54.961809: Yayy! New best EMA pseudo Dice: 0.645 
+2024-09-10 21:23:58.926925:  
+2024-09-10 21:23:58.927130: Epoch 82 
+2024-09-10 21:23:58.927213: Current learning rate: 0.00926 
+2024-09-10 21:28:05.183517: train_loss -0.7926 
+2024-09-10 21:28:05.183664: val_loss -0.587 
+2024-09-10 21:28:05.183714: Pseudo dice [0.4462, 0.8717] 
+2024-09-10 21:28:05.183768: Epoch time: 246.26 s 
+2024-09-10 21:28:05.183823: Yayy! New best EMA pseudo Dice: 0.6464 
+2024-09-10 21:28:09.064891:  
+2024-09-10 21:28:09.065140: Epoch 83 
+2024-09-10 21:28:09.065225: Current learning rate: 0.00925 
+2024-09-10 21:32:15.197790: train_loss -0.7792 
+2024-09-10 21:32:15.197929: val_loss -0.5835 
+2024-09-10 21:32:15.197980: Pseudo dice [0.4005, 0.8574] 
+2024-09-10 21:32:15.198031: Epoch time: 246.13 s 
+2024-09-10 21:32:16.144161:  
+2024-09-10 21:32:16.144356: Epoch 84 
+2024-09-10 21:32:16.144438: Current learning rate: 0.00924 
+2024-09-10 21:36:22.369072: train_loss -0.7727 
+2024-09-10 21:36:22.369272: val_loss -0.5973 
+2024-09-10 21:36:22.369324: Pseudo dice [0.4336, 0.8709] 
+2024-09-10 21:36:22.369379: Epoch time: 246.23 s 
+2024-09-10 21:36:23.283059:  
+2024-09-10 21:36:23.283283: Epoch 85 
+2024-09-10 21:36:23.283367: Current learning rate: 0.00923 
+2024-09-10 21:40:29.439880: train_loss -0.7782 
+2024-09-10 21:40:29.440019: val_loss -0.5872 
+2024-09-10 21:40:29.440068: Pseudo dice [0.4187, 0.8681] 
+2024-09-10 21:40:29.440119: Epoch time: 246.16 s 
+2024-09-10 21:40:30.368188:  
+2024-09-10 21:40:30.368379: Epoch 86 
+2024-09-10 21:40:30.368463: Current learning rate: 0.00922 
+2024-09-10 21:44:36.608140: train_loss -0.7821 
+2024-09-10 21:44:36.608290: val_loss -0.5661 
+2024-09-10 21:44:36.608341: Pseudo dice [0.3699, 0.8564] 
+2024-09-10 21:44:36.608393: Epoch time: 246.24 s 
+2024-09-10 21:44:37.544797:  
+2024-09-10 21:44:37.544983: Epoch 87 
+2024-09-10 21:44:37.545064: Current learning rate: 0.00921 
+2024-09-10 21:48:43.873029: train_loss -0.7844 
+2024-09-10 21:48:43.873177: val_loss -0.5637 
+2024-09-10 21:48:43.873228: Pseudo dice [0.4111, 0.8604] 
+2024-09-10 21:48:43.873279: Epoch time: 246.33 s 
+2024-09-10 21:48:44.795283:  
+2024-09-10 21:48:44.795493: Epoch 88 
+2024-09-10 21:48:44.795604: Current learning rate: 0.0092 
+2024-09-10 21:52:51.477350: train_loss -0.784 
+2024-09-10 21:52:51.477497: val_loss -0.5882 
+2024-09-10 21:52:51.477547: Pseudo dice [0.4226, 0.8594] 
+2024-09-10 21:52:51.477597: Epoch time: 246.68 s 
+2024-09-10 21:52:52.403988:  
+2024-09-10 21:52:52.404187: Epoch 89 
+2024-09-10 21:52:52.404337: Current learning rate: 0.0092 
+2024-09-10 21:56:58.643140: train_loss -0.7887 
+2024-09-10 21:56:58.643278: val_loss -0.5941 
+2024-09-10 21:56:58.643328: Pseudo dice [0.4187, 0.8589] 
+2024-09-10 21:56:58.643379: Epoch time: 246.24 s 
+2024-09-10 21:56:59.576926:  
+2024-09-10 21:56:59.577109: Epoch 90 
+2024-09-10 21:56:59.577201: Current learning rate: 0.00919 
+2024-09-10 22:01:05.812413: train_loss -0.7877 
+2024-09-10 22:01:05.812557: val_loss -0.5928 
+2024-09-10 22:01:05.812607: Pseudo dice [0.4426, 0.8521] 
+2024-09-10 22:01:05.812659: Epoch time: 246.24 s 
+2024-09-10 22:01:06.742112:  
+2024-09-10 22:01:06.742305: Epoch 91 
+2024-09-10 22:01:06.742388: Current learning rate: 0.00918 
+2024-09-10 22:05:12.873008: train_loss -0.7798 
+2024-09-10 22:05:12.873149: val_loss -0.5637 
+2024-09-10 22:05:12.873200: Pseudo dice [0.3955, 0.839] 
+2024-09-10 22:05:12.873250: Epoch time: 246.13 s 
+2024-09-10 22:05:13.785290:  
+2024-09-10 22:05:13.785490: Epoch 92 
+2024-09-10 22:05:13.785575: Current learning rate: 0.00917 
+2024-09-10 22:09:19.833433: train_loss -0.7832 
+2024-09-10 22:09:19.833629: val_loss -0.6271 
+2024-09-10 22:09:19.833681: Pseudo dice [0.4802, 0.8778] 
+2024-09-10 22:09:19.833733: Epoch time: 246.05 s 
+2024-09-10 22:09:20.746293:  
+2024-09-10 22:09:20.746485: Epoch 93 
+2024-09-10 22:09:20.746565: Current learning rate: 0.00916 
+2024-09-10 22:13:26.852890: train_loss -0.7748 
+2024-09-10 22:13:26.853028: val_loss -0.5542 
+2024-09-10 22:13:26.853079: Pseudo dice [0.3692, 0.8545] 
+2024-09-10 22:13:26.853131: Epoch time: 246.11 s 
+2024-09-10 22:13:27.788424:  
+2024-09-10 22:13:27.788603: Epoch 94 
+2024-09-10 22:13:27.788686: Current learning rate: 0.00915 
+2024-09-10 22:17:34.045442: train_loss -0.7737 
+2024-09-10 22:17:34.045576: val_loss -0.5987 
+2024-09-10 22:17:34.045626: Pseudo dice [0.4557, 0.8503] 
+2024-09-10 22:17:34.045678: Epoch time: 246.26 s 
+2024-09-10 22:17:34.959205:  
+2024-09-10 22:17:34.959408: Epoch 95 
+2024-09-10 22:17:34.959491: Current learning rate: 0.00914 
+2024-09-10 22:21:41.266492: train_loss -0.7882 
+2024-09-10 22:21:41.266706: val_loss -0.578 
+2024-09-10 22:21:41.266757: Pseudo dice [0.3916, 0.864] 
+2024-09-10 22:21:41.266809: Epoch time: 246.31 s 
+2024-09-10 22:21:42.229244:  
+2024-09-10 22:21:42.229474: Epoch 96 
+2024-09-10 22:21:42.229601: Current learning rate: 0.00913 
+2024-09-10 22:25:48.655486: train_loss -0.7554 
+2024-09-10 22:25:48.655625: val_loss -0.5709 
+2024-09-10 22:25:48.655675: Pseudo dice [0.413, 0.8579] 
+2024-09-10 22:25:48.655726: Epoch time: 246.43 s 
+2024-09-10 22:25:49.588828:  
+2024-09-10 22:25:49.588994: Epoch 97 
+2024-09-10 22:25:49.589080: Current learning rate: 0.00912 
+2024-09-10 22:29:56.280149: train_loss -0.768 
+2024-09-10 22:29:56.280348: val_loss -0.5769 
+2024-09-10 22:29:56.280443: Pseudo dice [0.4278, 0.8483] 
+2024-09-10 22:29:56.280533: Epoch time: 246.69 s 
+2024-09-10 22:29:57.210644:  
+2024-09-10 22:29:57.210856: Epoch 98 
+2024-09-10 22:29:57.210944: Current learning rate: 0.00911 
+2024-09-10 22:34:03.304502: train_loss -0.7678 
+2024-09-10 22:34:03.304638: val_loss -0.5999 
+2024-09-10 22:34:03.304693: Pseudo dice [0.4345, 0.8502] 
+2024-09-10 22:34:03.304833: Epoch time: 246.1 s 
+2024-09-10 22:34:04.261456:  
+2024-09-10 22:34:04.261623: Epoch 99 
+2024-09-10 22:34:04.261741: Current learning rate: 0.0091 
+2024-09-10 22:38:10.243589: train_loss -0.7688 
+2024-09-10 22:38:10.243748: val_loss -0.5906 
+2024-09-10 22:38:10.243801: Pseudo dice [0.4348, 0.8684] 
+2024-09-10 22:38:10.243871: Epoch time: 245.98 s 
+2024-09-10 22:38:14.150124:  
+2024-09-10 22:38:14.150290: Epoch 100 
+2024-09-10 22:38:14.150376: Current learning rate: 0.0091 
+2024-09-10 22:42:20.074344: train_loss -0.7905 
+2024-09-10 22:42:20.074486: val_loss -0.5817 
+2024-09-10 22:42:20.074537: Pseudo dice [0.4239, 0.864] 
+2024-09-10 22:42:20.074587: Epoch time: 245.93 s 
+2024-09-10 22:42:21.019628:  
+2024-09-10 22:42:21.019821: Epoch 101 
+2024-09-10 22:42:21.019904: Current learning rate: 0.00909 
+2024-09-10 22:46:26.923047: train_loss -0.7898 
+2024-09-10 22:46:26.923208: val_loss -0.6022 
+2024-09-10 22:46:26.923259: Pseudo dice [0.4569, 0.8716] 
+2024-09-10 22:46:26.923312: Epoch time: 245.91 s 
+2024-09-10 22:46:28.781592:  
+2024-09-10 22:46:28.781810: Epoch 102 
+2024-09-10 22:46:28.781901: Current learning rate: 0.00908 
+2024-09-10 22:50:34.753644: train_loss -0.7765 
+2024-09-10 22:50:34.753782: val_loss -0.5003 
+2024-09-10 22:50:34.753832: Pseudo dice [0.3286, 0.785] 
+2024-09-10 22:50:34.753881: Epoch time: 245.97 s 
+2024-09-10 22:50:35.697339:  
+2024-09-10 22:50:35.697566: Epoch 103 
+2024-09-10 22:50:35.697662: Current learning rate: 0.00907 
+2024-09-10 22:54:41.622950: train_loss -0.7706 
+2024-09-10 22:54:41.623193: val_loss -0.5783 
+2024-09-10 22:54:41.623244: Pseudo dice [0.4326, 0.8485] 
+2024-09-10 22:54:41.623294: Epoch time: 245.93 s 
+2024-09-10 22:54:42.573424:  
+2024-09-10 22:54:42.573603: Epoch 104 
+2024-09-10 22:54:42.573723: Current learning rate: 0.00906 
+2024-09-10 22:58:48.434524: train_loss -0.7838 
+2024-09-10 22:58:48.434664: val_loss -0.5753 
+2024-09-10 22:58:48.434715: Pseudo dice [0.4083, 0.8435] 
+2024-09-10 22:58:48.434766: Epoch time: 245.86 s 
+2024-09-10 22:58:49.375957:  
+2024-09-10 22:58:49.376161: Epoch 105 
+2024-09-10 22:58:49.376244: Current learning rate: 0.00905 
+2024-09-10 23:02:55.532192: train_loss -0.7907 
+2024-09-10 23:02:55.532333: val_loss -0.5844 
+2024-09-10 23:02:55.532383: Pseudo dice [0.4508, 0.8533] 
+2024-09-10 23:02:55.532435: Epoch time: 246.16 s 
+2024-09-10 23:02:56.481782:  
+2024-09-10 23:02:56.482035: Epoch 106 
+2024-09-10 23:02:56.482118: Current learning rate: 0.00904 
+2024-09-10 23:07:02.916758: train_loss -0.7945 
+2024-09-10 23:07:02.916900: val_loss -0.5902 
+2024-09-10 23:07:02.916999: Pseudo dice [0.4433, 0.8535] 
+2024-09-10 23:07:02.917050: Epoch time: 246.44 s 
+2024-09-10 23:07:03.870020:  
+2024-09-10 23:07:03.870188: Epoch 107 
+2024-09-10 23:07:03.870274: Current learning rate: 0.00903 
+2024-09-10 23:11:10.405426: train_loss -0.7946 
+2024-09-10 23:11:10.405569: val_loss -0.6103 
+2024-09-10 23:11:10.405618: Pseudo dice [0.4704, 0.8652] 
+2024-09-10 23:11:10.405668: Epoch time: 246.54 s 
+2024-09-10 23:11:11.351979:  
+2024-09-10 23:11:11.352165: Epoch 108 
+2024-09-10 23:11:11.352248: Current learning rate: 0.00902 
+2024-09-10 23:15:17.632510: train_loss -0.7966 
+2024-09-10 23:15:17.632684: val_loss -0.6035 
+2024-09-10 23:15:17.632736: Pseudo dice [0.4135, 0.8807] 
+2024-09-10 23:15:17.632787: Epoch time: 246.28 s 
+2024-09-10 23:15:18.583923:  
+2024-09-10 23:15:18.584096: Epoch 109 
+2024-09-10 23:15:18.584210: Current learning rate: 0.00901 
+2024-09-10 23:19:24.811550: train_loss -0.7957 
+2024-09-10 23:19:24.811694: val_loss -0.5756 
+2024-09-10 23:19:24.811905: Pseudo dice [0.3876, 0.8634] 
+2024-09-10 23:19:24.812020: Epoch time: 246.23 s 
+2024-09-10 23:19:25.767514:  
+2024-09-10 23:19:25.767695: Epoch 110 
+2024-09-10 23:19:25.767792: Current learning rate: 0.009 
+2024-09-10 23:23:31.835959: train_loss -0.8015 
+2024-09-10 23:23:31.836093: val_loss -0.5918 
+2024-09-10 23:23:31.836146: Pseudo dice [0.4368, 0.8604] 
+2024-09-10 23:23:31.836196: Epoch time: 246.07 s 
+2024-09-10 23:23:32.787472:  
+2024-09-10 23:23:32.787737: Epoch 111 
+2024-09-10 23:23:32.787836: Current learning rate: 0.009 
+2024-09-10 23:27:38.994550: train_loss -0.7891 
+2024-09-10 23:27:38.994689: val_loss -0.5889 
+2024-09-10 23:27:38.994740: Pseudo dice [0.4086, 0.8584] 
+2024-09-10 23:27:38.994792: Epoch time: 246.21 s 
+2024-09-10 23:27:39.967856:  
+2024-09-10 23:27:39.968037: Epoch 112 
+2024-09-10 23:27:39.968118: Current learning rate: 0.00899 
+2024-09-10 23:31:46.230825: train_loss -0.7973 
+2024-09-10 23:31:46.230965: val_loss -0.5889 
+2024-09-10 23:31:46.231016: Pseudo dice [0.4341, 0.8489] 
+2024-09-10 23:31:46.231070: Epoch time: 246.26 s 
+2024-09-10 23:31:47.175154:  
+2024-09-10 23:31:47.175387: Epoch 113 
+2024-09-10 23:31:47.175471: Current learning rate: 0.00898 
+2024-09-10 23:35:53.424011: train_loss -0.8028 
+2024-09-10 23:35:53.424139: val_loss -0.6117 
+2024-09-10 23:35:53.424189: Pseudo dice [0.4813, 0.8683] 
+2024-09-10 23:35:53.424254: Epoch time: 246.25 s 
+2024-09-10 23:35:54.354952:  
+2024-09-10 23:35:54.355135: Epoch 114 
+2024-09-10 23:35:54.355218: Current learning rate: 0.00897 
+2024-09-10 23:40:00.952719: train_loss -0.7915 
+2024-09-10 23:40:00.952870: val_loss -0.5818 
+2024-09-10 23:40:00.952922: Pseudo dice [0.3906, 0.8613] 
+2024-09-10 23:40:00.952978: Epoch time: 246.6 s 
+2024-09-10 23:40:01.913409:  
+2024-09-10 23:40:01.913652: Epoch 115 
+2024-09-10 23:40:01.913753: Current learning rate: 0.00896 
+2024-09-10 23:44:08.617425: train_loss -0.7954 
+2024-09-10 23:44:08.617562: val_loss -0.5794 
+2024-09-10 23:44:08.617611: Pseudo dice [0.3955, 0.8647] 
+2024-09-10 23:44:08.617661: Epoch time: 246.71 s 
+2024-09-10 23:44:09.576539:  
+2024-09-10 23:44:09.576745: Epoch 116 
+2024-09-10 23:44:09.576828: Current learning rate: 0.00895 
+2024-09-10 23:48:16.177402: train_loss -0.8093 
+2024-09-10 23:48:16.177556: val_loss -0.572 
+2024-09-10 23:48:16.177607: Pseudo dice [0.3936, 0.8615] 
+2024-09-10 23:48:16.177660: Epoch time: 246.6 s 
+2024-09-10 23:48:17.173395:  
+2024-09-10 23:48:17.173550: Epoch 117 
+2024-09-10 23:48:17.173636: Current learning rate: 0.00894 
+2024-09-10 23:52:23.682054: train_loss -0.7953 
+2024-09-10 23:52:23.682194: val_loss -0.5856 
+2024-09-10 23:52:23.682249: Pseudo dice [0.4302, 0.8421] 
+2024-09-10 23:52:23.682299: Epoch time: 246.51 s 
+2024-09-10 23:52:24.660217:  
+2024-09-10 23:52:24.660404: Epoch 118 
+2024-09-10 23:52:24.660486: Current learning rate: 0.00893 
+2024-09-10 23:56:30.918614: train_loss -0.7963 
+2024-09-10 23:56:30.918785: val_loss -0.5935 
+2024-09-10 23:56:30.918837: Pseudo dice [0.4417, 0.8582] 
+2024-09-10 23:56:30.918889: Epoch time: 246.26 s 
+2024-09-10 23:56:31.871276:  
+2024-09-10 23:56:31.871459: Epoch 119 
+2024-09-10 23:56:31.871545: Current learning rate: 0.00892 
+2024-09-11 00:00:38.069774: train_loss -0.799 
+2024-09-11 00:00:38.069918: val_loss -0.5892 
+2024-09-11 00:00:38.069968: Pseudo dice [0.4412, 0.8584] 
+2024-09-11 00:00:38.070018: Epoch time: 246.2 s 
+2024-09-11 00:00:39.058914:  
+2024-09-11 00:00:39.059127: Epoch 120 
+2024-09-11 00:00:39.059211: Current learning rate: 0.00891 
+2024-09-11 00:04:45.175106: train_loss -0.8006 
+2024-09-11 00:04:45.175246: val_loss -0.576 
+2024-09-11 00:04:45.175297: Pseudo dice [0.3751, 0.8718] 
+2024-09-11 00:04:45.175348: Epoch time: 246.12 s 
+2024-09-11 00:04:46.129749:  
+2024-09-11 00:04:46.129942: Epoch 121 
+2024-09-11 00:04:46.130029: Current learning rate: 0.0089 
+2024-09-11 00:08:52.107571: train_loss -0.8071 
+2024-09-11 00:08:52.107841: val_loss -0.6125 
+2024-09-11 00:08:52.107894: Pseudo dice [0.4825, 0.8828] 
+2024-09-11 00:08:52.107948: Epoch time: 245.98 s 
+2024-09-11 00:08:53.063223:  
+2024-09-11 00:08:53.063401: Epoch 122 
+2024-09-11 00:08:53.063484: Current learning rate: 0.00889 
+2024-09-11 00:12:58.987762: train_loss -0.7859 
+2024-09-11 00:12:58.987906: val_loss -0.5776 
+2024-09-11 00:12:58.987956: Pseudo dice [0.4482, 0.8233] 
+2024-09-11 00:12:58.988007: Epoch time: 245.93 s 
+2024-09-11 00:12:59.948577:  
+2024-09-11 00:12:59.948726: Epoch 123 
+2024-09-11 00:12:59.948807: Current learning rate: 0.00889 
+2024-09-11 00:17:06.232088: train_loss -0.7645 
+2024-09-11 00:17:06.232230: val_loss -0.6005 
+2024-09-11 00:17:06.232280: Pseudo dice [0.4132, 0.8578] 
+2024-09-11 00:17:06.232331: Epoch time: 246.29 s 
+2024-09-11 00:17:07.200277:  
+2024-09-11 00:17:07.200485: Epoch 124 
+2024-09-11 00:17:07.200576: Current learning rate: 0.00888 
+2024-09-11 00:21:13.689885: train_loss -0.7513 
+2024-09-11 00:21:13.690040: val_loss -0.5754 
+2024-09-11 00:21:13.690090: Pseudo dice [0.3777, 0.8704] 
+2024-09-11 00:21:13.690142: Epoch time: 246.49 s 
+2024-09-11 00:21:14.652102:  
+2024-09-11 00:21:14.652298: Epoch 125 
+2024-09-11 00:21:14.652380: Current learning rate: 0.00887 
+2024-09-11 00:25:20.826138: train_loss -0.7799 
+2024-09-11 00:25:20.826295: val_loss -0.5793 
+2024-09-11 00:25:20.826346: Pseudo dice [0.4337, 0.8643] 
+2024-09-11 00:25:20.826397: Epoch time: 246.18 s 
+2024-09-11 00:25:22.791614:  
+2024-09-11 00:25:22.791852: Epoch 126 
+2024-09-11 00:25:22.791969: Current learning rate: 0.00886 
+2024-09-11 00:29:28.848666: train_loss -0.783 
+2024-09-11 00:29:28.848820: val_loss -0.6326 
+2024-09-11 00:29:28.848873: Pseudo dice [0.5125, 0.8339] 
+2024-09-11 00:29:28.848926: Epoch time: 246.06 s 
+2024-09-11 00:29:29.836118:  
+2024-09-11 00:29:29.836294: Epoch 127 
+2024-09-11 00:29:29.836410: Current learning rate: 0.00885 
+2024-09-11 00:33:35.900705: train_loss -0.793 
+2024-09-11 00:33:35.900846: val_loss -0.6107 
+2024-09-11 00:33:35.900896: Pseudo dice [0.4604, 0.8579] 
+2024-09-11 00:33:35.900947: Epoch time: 246.07 s 
+2024-09-11 00:33:36.854712:  
+2024-09-11 00:33:36.854932: Epoch 128 
+2024-09-11 00:33:36.855023: Current learning rate: 0.00884 
+2024-09-11 00:37:42.963396: train_loss -0.7846 
+2024-09-11 00:37:42.963534: val_loss -0.5964 
+2024-09-11 00:37:42.963583: Pseudo dice [0.4353, 0.8497] 
+2024-09-11 00:37:42.963634: Epoch time: 246.11 s 
+2024-09-11 00:37:43.934893:  
+2024-09-11 00:37:43.935123: Epoch 129 
+2024-09-11 00:37:43.935206: Current learning rate: 0.00883 
+2024-09-11 00:41:50.352966: train_loss -0.7717 
+2024-09-11 00:41:50.353138: val_loss -0.5605 
+2024-09-11 00:41:50.353190: Pseudo dice [0.3725, 0.842] 
+2024-09-11 00:41:50.353248: Epoch time: 246.42 s 
+2024-09-11 00:41:51.316545:  
+2024-09-11 00:41:51.316718: Epoch 130 
+2024-09-11 00:41:51.316824: Current learning rate: 0.00882 
+2024-09-11 00:45:57.732359: train_loss -0.7915 
+2024-09-11 00:45:57.732502: val_loss -0.5973 
+2024-09-11 00:45:57.732552: Pseudo dice [0.4274, 0.861] 
+2024-09-11 00:45:57.732686: Epoch time: 246.42 s 
+2024-09-11 00:45:58.709152:  
+2024-09-11 00:45:58.709363: Epoch 131 
+2024-09-11 00:45:58.709461: Current learning rate: 0.00881 
+2024-09-11 00:50:05.040846: train_loss -0.8011 
+2024-09-11 00:50:05.040989: val_loss -0.5644 
+2024-09-11 00:50:05.041040: Pseudo dice [0.3752, 0.8572] 
+2024-09-11 00:50:05.041092: Epoch time: 246.33 s 
+2024-09-11 00:50:06.026375:  
+2024-09-11 00:50:06.026545: Epoch 132 
+2024-09-11 00:50:06.026628: Current learning rate: 0.0088 
+2024-09-11 00:54:12.244203: train_loss -0.8022 
+2024-09-11 00:54:12.244338: val_loss -0.595 
+2024-09-11 00:54:12.244388: Pseudo dice [0.4171, 0.8736] 
+2024-09-11 00:54:12.244439: Epoch time: 246.22 s 
+2024-09-11 00:54:13.196582:  
+2024-09-11 00:54:13.196826: Epoch 133 
+2024-09-11 00:54:13.196912: Current learning rate: 0.00879 
+2024-09-11 00:58:19.425428: train_loss -0.7976 
+2024-09-11 00:58:19.425587: val_loss -0.6179 
+2024-09-11 00:58:19.425645: Pseudo dice [0.4787, 0.8831] 
+2024-09-11 00:58:19.425701: Epoch time: 246.23 s 
+2024-09-11 00:58:20.385242:  
+2024-09-11 00:58:20.385443: Epoch 134 
+2024-09-11 00:58:20.385535: Current learning rate: 0.00879 
+2024-09-11 01:02:26.882307: train_loss -0.8061 
+2024-09-11 01:02:26.882502: val_loss -0.6176 
+2024-09-11 01:02:26.882556: Pseudo dice [0.4678, 0.8812] 
+2024-09-11 01:02:26.882668: Epoch time: 246.5 s 
+2024-09-11 01:02:26.882711: Yayy! New best EMA pseudo Dice: 0.6471 
+2024-09-11 01:02:30.816079:  
+2024-09-11 01:02:30.816306: Epoch 135 
+2024-09-11 01:02:30.816386: Current learning rate: 0.00878 
+2024-09-11 01:06:37.222930: train_loss -0.8018 
+2024-09-11 01:06:37.223091: val_loss -0.5839 
+2024-09-11 01:06:37.223209: Pseudo dice [0.4159, 0.8641] 
+2024-09-11 01:06:37.223262: Epoch time: 246.41 s 
+2024-09-11 01:06:38.188288:  
+2024-09-11 01:06:38.188490: Epoch 136 
+2024-09-11 01:06:38.188571: Current learning rate: 0.00877 
+2024-09-11 01:10:44.528477: train_loss -0.7994 
+2024-09-11 01:10:44.528615: val_loss -0.5906 
+2024-09-11 01:10:44.528665: Pseudo dice [0.451, 0.8563] 
+2024-09-11 01:10:44.528717: Epoch time: 246.34 s 
+2024-09-11 01:10:44.528826: Yayy! New best EMA pseudo Dice: 0.6471 
+2024-09-11 01:10:48.457872:  
+2024-09-11 01:10:48.458058: Epoch 137 
+2024-09-11 01:10:48.458172: Current learning rate: 0.00876 
+2024-09-11 01:14:54.727054: train_loss -0.8111 
+2024-09-11 01:14:54.727191: val_loss -0.6001 
+2024-09-11 01:14:54.727242: Pseudo dice [0.4332, 0.8568] 
+2024-09-11 01:14:54.727292: Epoch time: 246.27 s 
+2024-09-11 01:14:55.688265:  
+2024-09-11 01:14:55.688532: Epoch 138 
+2024-09-11 01:14:55.688617: Current learning rate: 0.00875 
+2024-09-11 01:19:01.937336: train_loss -0.8099 
+2024-09-11 01:19:01.937476: val_loss -0.5915 
+2024-09-11 01:19:01.937525: Pseudo dice [0.4524, 0.856] 
+2024-09-11 01:19:01.937576: Epoch time: 246.25 s 
+2024-09-11 01:19:01.937616: Yayy! New best EMA pseudo Dice: 0.6476 
+2024-09-11 01:19:05.924611:  
+2024-09-11 01:19:05.924784: Epoch 139 
+2024-09-11 01:19:05.924897: Current learning rate: 0.00874 
+2024-09-11 01:23:12.160189: train_loss -0.8049 
+2024-09-11 01:23:12.160326: val_loss -0.6057 
+2024-09-11 01:23:12.160376: Pseudo dice [0.4449, 0.8499] 
+2024-09-11 01:23:12.160428: Epoch time: 246.24 s 
+2024-09-11 01:23:13.133948:  
+2024-09-11 01:23:13.134182: Epoch 140 
+2024-09-11 01:23:13.134266: Current learning rate: 0.00873 
+2024-09-11 01:27:19.531512: train_loss -0.807 
+2024-09-11 01:27:19.531661: val_loss -0.6036 
+2024-09-11 01:27:19.531711: Pseudo dice [0.4451, 0.8777] 
+2024-09-11 01:27:19.531763: Epoch time: 246.4 s 
+2024-09-11 01:27:19.531811: Yayy! New best EMA pseudo Dice: 0.649 
+2024-09-11 01:27:23.425459:  
+2024-09-11 01:27:23.425616: Epoch 141 
+2024-09-11 01:27:23.425696: Current learning rate: 0.00872 
+2024-09-11 01:31:29.746126: train_loss -0.8015 
+2024-09-11 01:31:29.746266: val_loss -0.5638 
+2024-09-11 01:31:29.746317: Pseudo dice [0.3773, 0.8499] 
+2024-09-11 01:31:29.746369: Epoch time: 246.32 s 
+2024-09-11 01:31:30.746924:  
+2024-09-11 01:31:30.747128: Epoch 142 
+2024-09-11 01:31:30.747213: Current learning rate: 0.00871 
+2024-09-11 01:35:36.950022: train_loss -0.7688 
+2024-09-11 01:35:36.950201: val_loss -0.5653 
+2024-09-11 01:35:36.950253: Pseudo dice [0.4363, 0.841] 
+2024-09-11 01:35:36.950304: Epoch time: 246.2 s 
+2024-09-11 01:35:37.936193:  
+2024-09-11 01:35:37.936383: Epoch 143 
+2024-09-11 01:35:37.936465: Current learning rate: 0.0087 
+2024-09-11 01:39:44.127406: train_loss -0.7906 
+2024-09-11 01:39:44.127554: val_loss -0.5882 
+2024-09-11 01:39:44.127605: Pseudo dice [0.4702, 0.8531] 
+2024-09-11 01:39:44.127657: Epoch time: 246.19 s 
+2024-09-11 01:39:45.257087:  
+2024-09-11 01:39:45.257255: Epoch 144 
+2024-09-11 01:39:45.257347: Current learning rate: 0.00869 
+2024-09-11 01:43:51.638525: train_loss -0.7902 
+2024-09-11 01:43:51.638686: val_loss -0.6137 
+2024-09-11 01:43:51.638736: Pseudo dice [0.4879, 0.8649] 
+2024-09-11 01:43:51.638787: Epoch time: 246.38 s 
+2024-09-11 01:43:51.638826: Yayy! New best EMA pseudo Dice: 0.6495 
+2024-09-11 01:43:55.570275:  
+2024-09-11 01:43:55.570441: Epoch 145 
+2024-09-11 01:43:55.570550: Current learning rate: 0.00868 
+2024-09-11 01:48:01.811847: train_loss -0.8001 
+2024-09-11 01:48:01.812049: val_loss -0.5828 
+2024-09-11 01:48:01.812143: Pseudo dice [0.4302, 0.8583] 
+2024-09-11 01:48:01.812234: Epoch time: 246.24 s 
+2024-09-11 01:48:02.802656:  
+2024-09-11 01:48:02.802857: Epoch 146 
+2024-09-11 01:48:02.802950: Current learning rate: 0.00868 
+2024-09-11 01:52:08.916983: train_loss -0.8075 
+2024-09-11 01:52:08.917124: val_loss -0.5829 
+2024-09-11 01:52:08.917179: Pseudo dice [0.4324, 0.8496] 
+2024-09-11 01:52:08.917229: Epoch time: 246.12 s 
+2024-09-11 01:52:09.893959:  
+2024-09-11 01:52:09.894130: Epoch 147 
+2024-09-11 01:52:09.894214: Current learning rate: 0.00867 
+2024-09-11 01:56:15.877393: train_loss -0.8071 
+2024-09-11 01:56:15.877532: val_loss -0.5928 
+2024-09-11 01:56:15.877582: Pseudo dice [0.4104, 0.867] 
+2024-09-11 01:56:15.877634: Epoch time: 245.99 s 
+2024-09-11 01:56:16.851329:  
+2024-09-11 01:56:16.851532: Epoch 148 
+2024-09-11 01:56:16.851638: Current learning rate: 0.00866 
+2024-09-11 02:00:22.852601: train_loss -0.7949 
+2024-09-11 02:00:22.852753: val_loss -0.6047 
+2024-09-11 02:00:22.852806: Pseudo dice [0.4675, 0.8438] 
+2024-09-11 02:00:22.852857: Epoch time: 246.0 s 
+2024-09-11 02:00:24.818473:  
+2024-09-11 02:00:24.818663: Epoch 149 
+2024-09-11 02:00:24.818809: Current learning rate: 0.00865 
+2024-09-11 02:04:30.891545: train_loss -0.8021 
+2024-09-11 02:04:30.891695: val_loss -0.5692 
+2024-09-11 02:04:30.891745: Pseudo dice [0.3929, 0.8481] 
+2024-09-11 02:04:30.891797: Epoch time: 246.07 s 
+2024-09-11 02:04:34.850897:  
+2024-09-11 02:04:34.851081: Epoch 150 
+2024-09-11 02:04:34.851178: Current learning rate: 0.00864 
+2024-09-11 02:08:41.100065: train_loss -0.8129 
+2024-09-11 02:08:41.100202: val_loss -0.6168 
+2024-09-11 02:08:41.100253: Pseudo dice [0.4855, 0.8592] 
+2024-09-11 02:08:41.100304: Epoch time: 246.25 s 
+2024-09-11 02:08:42.080594:  
+2024-09-11 02:08:42.080846: Epoch 151 
+2024-09-11 02:08:42.080930: Current learning rate: 0.00863 
+2024-09-11 02:12:48.398120: train_loss -0.8024 
+2024-09-11 02:12:48.398263: val_loss -0.575 
+2024-09-11 02:12:48.398313: Pseudo dice [0.4083, 0.8522] 
+2024-09-11 02:12:48.398363: Epoch time: 246.32 s 
+2024-09-11 02:12:49.384445:  
+2024-09-11 02:12:49.384629: Epoch 152 
+2024-09-11 02:12:49.384710: Current learning rate: 0.00862 
+2024-09-11 02:16:55.828703: train_loss -0.8146 
+2024-09-11 02:16:55.828845: val_loss -0.6129 
+2024-09-11 02:16:55.828895: Pseudo dice [0.5075, 0.8663] 
+2024-09-11 02:16:55.828945: Epoch time: 246.45 s 
+2024-09-11 02:16:55.828985: Yayy! New best EMA pseudo Dice: 0.6503 
+2024-09-11 02:16:59.812381:  
+2024-09-11 02:16:59.812633: Epoch 153 
+2024-09-11 02:16:59.812753: Current learning rate: 0.00861 
+2024-09-11 02:21:06.444624: train_loss -0.8072 
+2024-09-11 02:21:06.444833: val_loss -0.6089 
+2024-09-11 02:21:06.444927: Pseudo dice [0.4616, 0.8667] 
+2024-09-11 02:21:06.445018: Epoch time: 246.64 s 
+2024-09-11 02:21:06.445092: Yayy! New best EMA pseudo Dice: 0.6517 
+2024-09-11 02:21:10.445493:  
+2024-09-11 02:21:10.445745: Epoch 154 
+2024-09-11 02:21:10.445830: Current learning rate: 0.0086 
+2024-09-11 02:25:16.812613: train_loss -0.8129 
+2024-09-11 02:25:16.812755: val_loss -0.611 
+2024-09-11 02:25:16.812806: Pseudo dice [0.4467, 0.8681] 
+2024-09-11 02:25:16.812858: Epoch time: 246.37 s 
+2024-09-11 02:25:16.812898: Yayy! New best EMA pseudo Dice: 0.6522 
+2024-09-11 02:25:20.738055:  
+2024-09-11 02:25:20.738280: Epoch 155 
+2024-09-11 02:25:20.738369: Current learning rate: 0.00859 
+2024-09-11 02:29:27.072757: train_loss -0.8111 
+2024-09-11 02:29:27.072896: val_loss -0.5834 
+2024-09-11 02:29:27.072993: Pseudo dice [0.4275, 0.8633] 
+2024-09-11 02:29:27.073047: Epoch time: 246.34 s 
+2024-09-11 02:29:28.072954:  
+2024-09-11 02:29:28.073163: Epoch 156 
+2024-09-11 02:29:28.073261: Current learning rate: 0.00858 
+2024-09-11 02:33:34.306078: train_loss -0.8129 
+2024-09-11 02:33:34.306236: val_loss -0.6103 
+2024-09-11 02:33:34.306287: Pseudo dice [0.4494, 0.8596] 
+2024-09-11 02:33:34.306339: Epoch time: 246.24 s 
+2024-09-11 02:33:35.290135:  
+2024-09-11 02:33:35.290392: Epoch 157 
+2024-09-11 02:33:35.290475: Current learning rate: 0.00858 
+2024-09-11 02:37:41.578906: train_loss -0.8147 
+2024-09-11 02:37:41.579045: val_loss -0.5938 
+2024-09-11 02:37:41.579096: Pseudo dice [0.4489, 0.8683] 
+2024-09-11 02:37:41.579149: Epoch time: 246.29 s 
+2024-09-11 02:37:41.579190: Yayy! New best EMA pseudo Dice: 0.6525 
+2024-09-11 02:37:45.522019:  
+2024-09-11 02:37:45.522297: Epoch 158 
+2024-09-11 02:37:45.522379: Current learning rate: 0.00857 
+2024-09-11 02:41:51.774796: train_loss -0.8116 
+2024-09-11 02:41:51.774960: val_loss -0.5811 
+2024-09-11 02:41:51.775013: Pseudo dice [0.3938, 0.854] 
+2024-09-11 02:41:51.775064: Epoch time: 246.25 s 
+2024-09-11 02:41:52.772829:  
+2024-09-11 02:41:52.773031: Epoch 159 
+2024-09-11 02:41:52.773113: Current learning rate: 0.00856 
+2024-09-11 02:45:59.135628: train_loss -0.8043 
+2024-09-11 02:45:59.135760: val_loss -0.6147 
+2024-09-11 02:45:59.135816: Pseudo dice [0.4688, 0.8827] 
+2024-09-11 02:45:59.135869: Epoch time: 246.36 s 
+2024-09-11 02:46:00.129599:  
+2024-09-11 02:46:00.129789: Epoch 160 
+2024-09-11 02:46:00.129872: Current learning rate: 0.00855 
+2024-09-11 02:50:06.488374: train_loss -0.7989 
+2024-09-11 02:50:06.488513: val_loss -0.5749 
+2024-09-11 02:50:06.488563: Pseudo dice [0.4202, 0.8502] 
+2024-09-11 02:50:06.488613: Epoch time: 246.36 s 
+2024-09-11 02:50:07.475868:  
+2024-09-11 02:50:07.476052: Epoch 161 
+2024-09-11 02:50:07.476157: Current learning rate: 0.00854 
+2024-09-11 02:54:13.823449: train_loss -0.8115 
+2024-09-11 02:54:13.823591: val_loss -0.5955 
+2024-09-11 02:54:13.823652: Pseudo dice [0.4664, 0.8467] 
+2024-09-11 02:54:13.823704: Epoch time: 246.35 s 
+2024-09-11 02:54:14.815675:  
+2024-09-11 02:54:14.815894: Epoch 162 
+2024-09-11 02:54:14.815979: Current learning rate: 0.00853 
+2024-09-11 02:58:21.214491: train_loss -0.8134 
+2024-09-11 02:58:21.214658: val_loss -0.5871 
+2024-09-11 02:58:21.214709: Pseudo dice [0.4326, 0.8645] 
+2024-09-11 02:58:21.214759: Epoch time: 246.4 s 
+2024-09-11 02:58:22.206792:  
+2024-09-11 02:58:22.207005: Epoch 163 
+2024-09-11 02:58:22.207098: Current learning rate: 0.00852 
+2024-09-11 03:02:28.560453: train_loss -0.8139 
+2024-09-11 03:02:28.560590: val_loss -0.5997 
+2024-09-11 03:02:28.560640: Pseudo dice [0.444, 0.8723] 
+2024-09-11 03:02:28.560693: Epoch time: 246.36 s 
+2024-09-11 03:02:29.548519:  
+2024-09-11 03:02:29.548667: Epoch 164 
+2024-09-11 03:02:29.548745: Current learning rate: 0.00851 
+2024-09-11 03:06:35.849358: train_loss -0.8216 
+2024-09-11 03:06:35.849500: val_loss -0.5784 
+2024-09-11 03:06:35.849550: Pseudo dice [0.4314, 0.8737] 
+2024-09-11 03:06:35.849602: Epoch time: 246.3 s 
+2024-09-11 03:06:36.814055:  
+2024-09-11 03:06:36.814203: Epoch 165 
+2024-09-11 03:06:36.814287: Current learning rate: 0.0085 
+2024-09-11 03:10:42.946883: train_loss -0.8193 
+2024-09-11 03:10:42.947044: val_loss -0.5693 
+2024-09-11 03:10:42.947093: Pseudo dice [0.4215, 0.8524] 
+2024-09-11 03:10:42.947145: Epoch time: 246.13 s 
+2024-09-11 03:10:43.905695:  
+2024-09-11 03:10:43.905881: Epoch 166 
+2024-09-11 03:10:43.905966: Current learning rate: 0.00849 
+2024-09-11 03:14:50.295969: train_loss -0.8237 
+2024-09-11 03:14:50.296108: val_loss -0.5839 
+2024-09-11 03:14:50.296157: Pseudo dice [0.444, 0.861] 
+2024-09-11 03:14:50.296208: Epoch time: 246.39 s 
+2024-09-11 03:14:51.250658:  
+2024-09-11 03:14:51.250806: Epoch 167 
+2024-09-11 03:14:51.250888: Current learning rate: 0.00848 
+2024-09-11 03:18:57.626218: train_loss -0.8134 
+2024-09-11 03:18:57.626361: val_loss -0.5888 
+2024-09-11 03:18:57.626412: Pseudo dice [0.4028, 0.8768] 
+2024-09-11 03:18:57.626463: Epoch time: 246.38 s 
+2024-09-11 03:18:58.592510:  
+2024-09-11 03:18:58.592727: Epoch 168 
+2024-09-11 03:18:58.592810: Current learning rate: 0.00847 
+2024-09-11 03:23:04.888559: train_loss -0.8135 
+2024-09-11 03:23:04.888696: val_loss -0.5749 
+2024-09-11 03:23:04.888747: Pseudo dice [0.405, 0.8681] 
+2024-09-11 03:23:04.888798: Epoch time: 246.3 s 
+2024-09-11 03:23:05.867627:  
+2024-09-11 03:23:05.867854: Epoch 169 
+2024-09-11 03:23:05.867941: Current learning rate: 0.00847 
+2024-09-11 03:27:12.038677: train_loss -0.8155 
+2024-09-11 03:27:12.038847: val_loss -0.5914 
+2024-09-11 03:27:12.038900: Pseudo dice [0.4278, 0.8825] 
+2024-09-11 03:27:12.038954: Epoch time: 246.17 s 
+2024-09-11 03:27:13.016633:  
+2024-09-11 03:27:13.016836: Epoch 170 
+2024-09-11 03:27:13.016916: Current learning rate: 0.00846 
+2024-09-11 03:31:19.388901: train_loss -0.8104 
+2024-09-11 03:31:19.389045: val_loss -0.5799 
+2024-09-11 03:31:19.389097: Pseudo dice [0.4017, 0.8631] 
+2024-09-11 03:31:19.389149: Epoch time: 246.37 s 
+2024-09-11 03:31:21.315457:  
+2024-09-11 03:31:21.315689: Epoch 171 
+2024-09-11 03:31:21.315789: Current learning rate: 0.00845 
+2024-09-11 03:35:28.057494: train_loss -0.8083 
+2024-09-11 03:35:28.057633: val_loss -0.5811 
+2024-09-11 03:35:28.057684: Pseudo dice [0.4274, 0.862] 
+2024-09-11 03:35:28.057735: Epoch time: 246.74 s 
+2024-09-11 03:35:29.048169:  
+2024-09-11 03:35:29.048391: Epoch 172 
+2024-09-11 03:35:29.048490: Current learning rate: 0.00844 
+2024-09-11 03:39:35.460556: train_loss -0.8092 
+2024-09-11 03:39:35.460703: val_loss -0.5979 
+2024-09-11 03:39:35.460754: Pseudo dice [0.4225, 0.871] 
+2024-09-11 03:39:35.460807: Epoch time: 246.41 s 
+2024-09-11 03:39:36.441470:  
+2024-09-11 03:39:36.441676: Epoch 173 
+2024-09-11 03:39:36.441762: Current learning rate: 0.00843 
+2024-09-11 03:43:42.706361: train_loss -0.8089 
+2024-09-11 03:43:42.706500: val_loss -0.6098 
+2024-09-11 03:43:42.706551: Pseudo dice [0.4939, 0.8773] 
+2024-09-11 03:43:42.706602: Epoch time: 246.27 s 
+2024-09-11 03:43:43.685256:  
+2024-09-11 03:43:43.685512: Epoch 174 
+2024-09-11 03:43:43.685597: Current learning rate: 0.00842 
+2024-09-11 03:47:49.817220: train_loss -0.8188 
+2024-09-11 03:47:49.817464: val_loss -0.6011 
+2024-09-11 03:47:49.817587: Pseudo dice [0.473, 0.8626] 
+2024-09-11 03:47:49.817682: Epoch time: 246.13 s 
+2024-09-11 03:47:50.844877:  
+2024-09-11 03:47:50.845061: Epoch 175 
+2024-09-11 03:47:50.845146: Current learning rate: 0.00841 
+2024-09-11 03:51:56.935015: train_loss -0.8154 
+2024-09-11 03:51:56.935151: val_loss -0.5673 
+2024-09-11 03:51:56.935200: Pseudo dice [0.3775, 0.8438] 
+2024-09-11 03:51:56.935251: Epoch time: 246.09 s 
+2024-09-11 03:51:57.915303:  
+2024-09-11 03:51:57.915472: Epoch 176 
+2024-09-11 03:51:57.915644: Current learning rate: 0.0084 
+2024-09-11 03:56:04.073989: train_loss -0.7993 
+2024-09-11 03:56:04.074140: val_loss -0.5699 
+2024-09-11 03:56:04.074192: Pseudo dice [0.4006, 0.8737] 
+2024-09-11 03:56:04.074244: Epoch time: 246.16 s 
+2024-09-11 03:56:05.092437:  
+2024-09-11 03:56:05.092630: Epoch 177 
+2024-09-11 03:56:05.092740: Current learning rate: 0.00839 
+2024-09-11 04:00:11.221870: train_loss -0.7864 
+2024-09-11 04:00:11.222012: val_loss -0.5757 
+2024-09-11 04:00:11.222062: Pseudo dice [0.4297, 0.8425] 
+2024-09-11 04:00:11.222170: Epoch time: 246.13 s 
+2024-09-11 04:00:12.198280:  
+2024-09-11 04:00:12.198460: Epoch 178 
+2024-09-11 04:00:12.198582: Current learning rate: 0.00838 
+2024-09-11 04:04:18.333812: train_loss -0.7854 
+2024-09-11 04:04:18.333950: val_loss -0.6033 
+2024-09-11 04:04:18.334000: Pseudo dice [0.4418, 0.8541] 
+2024-09-11 04:04:18.334052: Epoch time: 246.14 s 
+2024-09-11 04:04:19.325742:  
+2024-09-11 04:04:19.325941: Epoch 179 
+2024-09-11 04:04:19.326024: Current learning rate: 0.00837 
+2024-09-11 04:08:25.450533: train_loss -0.8035 
+2024-09-11 04:08:25.450690: val_loss -0.5733 
+2024-09-11 04:08:25.450741: Pseudo dice [0.4108, 0.8619] 
+2024-09-11 04:08:25.450793: Epoch time: 246.13 s 
+2024-09-11 04:08:26.450526:  
+2024-09-11 04:08:26.450773: Epoch 180 
+2024-09-11 04:08:26.450856: Current learning rate: 0.00836 
+2024-09-11 04:12:32.225477: train_loss -0.7882 
+2024-09-11 04:12:32.225652: val_loss -0.589 
+2024-09-11 04:12:32.225701: Pseudo dice [0.408, 0.854] 
+2024-09-11 04:12:32.225753: Epoch time: 245.78 s 
+2024-09-11 04:12:33.198897:  
+2024-09-11 04:12:33.199069: Epoch 181 
+2024-09-11 04:12:33.199188: Current learning rate: 0.00836 
+2024-09-11 04:16:39.134513: train_loss -0.7998 
+2024-09-11 04:16:39.134652: val_loss -0.5956 
+2024-09-11 04:16:39.134703: Pseudo dice [0.4209, 0.8492] 
+2024-09-11 04:16:39.134754: Epoch time: 245.94 s 
+2024-09-11 04:16:40.118304:  
+2024-09-11 04:16:40.118473: Epoch 182 
+2024-09-11 04:16:40.118580: Current learning rate: 0.00835 
+2024-09-11 04:20:45.981031: train_loss -0.8047 
+2024-09-11 04:20:45.981170: val_loss -0.6021 
+2024-09-11 04:20:45.981221: Pseudo dice [0.4633, 0.8602] 
+2024-09-11 04:20:45.981272: Epoch time: 245.86 s 
+2024-09-11 04:20:46.946216:  
+2024-09-11 04:20:46.946401: Epoch 183 
+2024-09-11 04:20:46.946481: Current learning rate: 0.00834 
+2024-09-11 04:24:52.511823: train_loss -0.8142 
+2024-09-11 04:24:52.511970: val_loss -0.5964 
+2024-09-11 04:24:52.512021: Pseudo dice [0.3893, 0.876] 
+2024-09-11 04:24:52.512073: Epoch time: 245.57 s 
+2024-09-11 04:24:53.500003:  
+2024-09-11 04:24:53.500241: Epoch 184 
+2024-09-11 04:24:53.500348: Current learning rate: 0.00833 
+2024-09-11 04:28:59.234946: train_loss -0.8171 
+2024-09-11 04:28:59.235091: val_loss -0.6088 
+2024-09-11 04:28:59.235259: Pseudo dice [0.4364, 0.8834] 
+2024-09-11 04:28:59.235378: Epoch time: 245.74 s 
+2024-09-11 04:29:00.226793:  
+2024-09-11 04:29:00.227015: Epoch 185 
+2024-09-11 04:29:00.227100: Current learning rate: 0.00832 
+2024-09-11 04:33:05.914989: train_loss -0.8109 
+2024-09-11 04:33:05.915161: val_loss -0.5815 
+2024-09-11 04:33:05.915213: Pseudo dice [0.3974, 0.8562] 
+2024-09-11 04:33:05.915266: Epoch time: 245.69 s 
+2024-09-11 04:33:06.900162:  
+2024-09-11 04:33:06.900353: Epoch 186 
+2024-09-11 04:33:06.900436: Current learning rate: 0.00831 
+2024-09-11 04:37:12.591817: train_loss -0.8121 
+2024-09-11 04:37:12.591966: val_loss -0.5628 
+2024-09-11 04:37:12.592017: Pseudo dice [0.3906, 0.8446] 
+2024-09-11 04:37:12.592067: Epoch time: 245.69 s 
+2024-09-11 04:37:13.569183:  
+2024-09-11 04:37:13.569419: Epoch 187 
+2024-09-11 04:37:13.569503: Current learning rate: 0.0083 
+2024-09-11 04:41:19.354708: train_loss -0.7956 
+2024-09-11 04:41:19.354854: val_loss -0.5622 
+2024-09-11 04:41:19.354904: Pseudo dice [0.3873, 0.8569] 
+2024-09-11 04:41:19.354955: Epoch time: 245.79 s 
+2024-09-11 04:41:20.336729:  
+2024-09-11 04:41:20.337013: Epoch 188 
+2024-09-11 04:41:20.337099: Current learning rate: 0.00829 
+2024-09-11 04:45:26.588192: train_loss -0.7938 
+2024-09-11 04:45:26.588332: val_loss -0.5952 
+2024-09-11 04:45:26.588382: Pseudo dice [0.4383, 0.8485] 
+2024-09-11 04:45:26.588435: Epoch time: 246.25 s 
+2024-09-11 04:45:27.594247:  
+2024-09-11 04:45:27.594467: Epoch 189 
+2024-09-11 04:45:27.594549: Current learning rate: 0.00828 
+2024-09-11 04:49:34.043898: train_loss -0.7794 
+2024-09-11 04:49:34.044038: val_loss -0.5917 
+2024-09-11 04:49:34.044087: Pseudo dice [0.4115, 0.8686] 
+2024-09-11 04:49:34.044138: Epoch time: 246.45 s 
+2024-09-11 04:49:35.020836:  
+2024-09-11 04:49:35.021013: Epoch 190 
+2024-09-11 04:49:35.021139: Current learning rate: 0.00827 
+2024-09-11 04:53:41.220139: train_loss -0.7812 
+2024-09-11 04:53:41.220276: val_loss -0.551 
+2024-09-11 04:53:41.220326: Pseudo dice [0.3977, 0.8407] 
+2024-09-11 04:53:41.220378: Epoch time: 246.2 s 
+2024-09-11 04:53:42.199485:  
+2024-09-11 04:53:42.199683: Epoch 191 
+2024-09-11 04:53:42.199770: Current learning rate: 0.00826 
+2024-09-11 04:57:48.136461: train_loss -0.7952 
+2024-09-11 04:57:48.136597: val_loss -0.5863 
+2024-09-11 04:57:48.136646: Pseudo dice [0.3897, 0.8592] 
+2024-09-11 04:57:48.136697: Epoch time: 245.94 s 
+2024-09-11 04:57:49.135220:  
+2024-09-11 04:57:49.135411: Epoch 192 
+2024-09-11 04:57:49.135493: Current learning rate: 0.00825 
+2024-09-11 05:01:55.004419: train_loss -0.8162 
+2024-09-11 05:01:55.004586: val_loss -0.5881 
+2024-09-11 05:01:55.004639: Pseudo dice [0.4458, 0.8617] 
+2024-09-11 05:01:55.004692: Epoch time: 245.87 s 
+2024-09-11 05:01:55.991532:  
+2024-09-11 05:01:55.991685: Epoch 193 
+2024-09-11 05:01:55.991810: Current learning rate: 0.00824 
+2024-09-11 05:06:01.946321: train_loss -0.8132 
+2024-09-11 05:06:01.946469: val_loss -0.5592 
+2024-09-11 05:06:01.946558: Pseudo dice [0.3809, 0.8604] 
+2024-09-11 05:06:01.946610: Epoch time: 245.96 s 
+2024-09-11 05:06:03.953574:  
+2024-09-11 05:06:03.953835: Epoch 194 
+2024-09-11 05:06:03.953922: Current learning rate: 0.00824 
+2024-09-11 05:10:09.868173: train_loss -0.8201 
+2024-09-11 05:10:09.868318: val_loss -0.592 
+2024-09-11 05:10:09.868368: Pseudo dice [0.395, 0.8801] 
+2024-09-11 05:10:09.868421: Epoch time: 245.92 s 
+2024-09-11 05:10:10.869845:  
+2024-09-11 05:10:10.870072: Epoch 195 
+2024-09-11 05:10:10.870157: Current learning rate: 0.00823 
+2024-09-11 05:14:16.764463: train_loss -0.8254 
+2024-09-11 05:14:16.764603: val_loss -0.5766 
+2024-09-11 05:14:16.764654: Pseudo dice [0.3973, 0.8526] 
+2024-09-11 05:14:16.764704: Epoch time: 245.9 s 
+2024-09-11 05:14:17.762638:  
+2024-09-11 05:14:17.762927: Epoch 196 
+2024-09-11 05:14:17.763010: Current learning rate: 0.00822 
+2024-09-11 05:18:24.075327: train_loss -0.8125 
+2024-09-11 05:18:24.075481: val_loss -0.6077 
+2024-09-11 05:18:24.075533: Pseudo dice [0.4721, 0.8761] 
+2024-09-11 05:18:24.075650: Epoch time: 246.31 s 
+2024-09-11 05:18:25.071067:  
+2024-09-11 05:18:25.071254: Epoch 197 
+2024-09-11 05:18:25.071335: Current learning rate: 0.00821 
+2024-09-11 05:22:31.491447: train_loss -0.8168 
+2024-09-11 05:22:31.491631: val_loss -0.5673 
+2024-09-11 05:22:31.491683: Pseudo dice [0.4085, 0.8613] 
+2024-09-11 05:22:31.491737: Epoch time: 246.42 s 
+2024-09-11 05:22:32.504380:  
+2024-09-11 05:22:32.504603: Epoch 198 
+2024-09-11 05:22:32.504713: Current learning rate: 0.0082 
+2024-09-11 05:26:38.903449: train_loss -0.8207 
+2024-09-11 05:26:38.903616: val_loss -0.5686 
+2024-09-11 05:26:38.903666: Pseudo dice [0.3801, 0.8686] 
+2024-09-11 05:26:38.903729: Epoch time: 246.4 s 
+2024-09-11 05:26:39.896158:  
+2024-09-11 05:26:39.896352: Epoch 199 
+2024-09-11 05:26:39.896473: Current learning rate: 0.00819 
+2024-09-11 05:30:46.272151: train_loss -0.7957 
+2024-09-11 05:30:46.272310: val_loss -0.5867 
+2024-09-11 05:30:46.272361: Pseudo dice [0.4199, 0.8611] 
+2024-09-11 05:30:46.272414: Epoch time: 246.38 s 
+2024-09-11 05:30:50.218464:  
+2024-09-11 05:30:50.218650: Epoch 200 
+2024-09-11 05:30:50.218735: Current learning rate: 0.00818 
+2024-09-11 05:34:56.317599: train_loss -0.7969 
+2024-09-11 05:34:56.317764: val_loss -0.6078 
+2024-09-11 05:34:56.317821: Pseudo dice [0.475, 0.8553] 
+2024-09-11 05:34:56.317872: Epoch time: 246.1 s 
+2024-09-11 05:34:57.311466:  
+2024-09-11 05:34:57.311672: Epoch 201 
+2024-09-11 05:34:57.311753: Current learning rate: 0.00817 
+2024-09-11 05:39:03.296168: train_loss -0.8094 
+2024-09-11 05:39:03.296308: val_loss -0.5724 
+2024-09-11 05:39:03.296359: Pseudo dice [0.3825, 0.8683] 
+2024-09-11 05:39:03.296412: Epoch time: 245.99 s 
+2024-09-11 05:39:04.306153:  
+2024-09-11 05:39:04.306370: Epoch 202 
+2024-09-11 05:39:04.306452: Current learning rate: 0.00816 
+2024-09-11 05:43:10.122246: train_loss -0.8193 
+2024-09-11 05:43:10.122385: val_loss -0.5975 
+2024-09-11 05:43:10.122439: Pseudo dice [0.3775, 0.8853] 
+2024-09-11 05:43:10.122493: Epoch time: 245.82 s 
+2024-09-11 05:43:11.125868:  
+2024-09-11 05:43:11.126088: Epoch 203 
+2024-09-11 05:43:11.126174: Current learning rate: 0.00815 
+2024-09-11 05:47:16.956896: train_loss -0.8251 
+2024-09-11 05:47:16.957071: val_loss -0.5839 
+2024-09-11 05:47:16.957124: Pseudo dice [0.4077, 0.8719] 
+2024-09-11 05:47:16.957175: Epoch time: 245.83 s 
+2024-09-11 05:47:17.938483:  
+2024-09-11 05:47:17.938703: Epoch 204 
+2024-09-11 05:47:17.938790: Current learning rate: 0.00814 
+2024-09-11 05:51:23.821412: train_loss -0.8156 
+2024-09-11 05:51:23.821565: val_loss -0.5674 
+2024-09-11 05:51:23.821614: Pseudo dice [0.3611, 0.8664] 
+2024-09-11 05:51:23.821666: Epoch time: 245.88 s 
+2024-09-11 05:51:24.816460:  
+2024-09-11 05:51:24.816664: Epoch 205 
+2024-09-11 05:51:24.816749: Current learning rate: 0.00813 
+2024-09-11 05:55:30.764942: train_loss -0.8038 
+2024-09-11 05:55:30.765084: val_loss -0.6358 
+2024-09-11 05:55:30.765134: Pseudo dice [0.4715, 0.8841] 
+2024-09-11 05:55:30.765184: Epoch time: 245.95 s 
+2024-09-11 05:55:31.701926:  
+2024-09-11 05:55:31.702118: Epoch 206 
+2024-09-11 05:55:31.702200: Current learning rate: 0.00813 
+2024-09-11 05:59:37.695339: train_loss -0.7942 
+2024-09-11 05:59:37.695499: val_loss -0.5919 
+2024-09-11 05:59:37.695550: Pseudo dice [0.4209, 0.864] 
+2024-09-11 05:59:37.695602: Epoch time: 246.0 s 
+2024-09-11 05:59:38.625389:  
+2024-09-11 05:59:38.625604: Epoch 207 
+2024-09-11 05:59:38.625688: Current learning rate: 0.00812 
+2024-09-11 06:03:44.739957: train_loss -0.8025 
+2024-09-11 06:03:44.740239: val_loss -0.5848 
+2024-09-11 06:03:44.740294: Pseudo dice [0.4251, 0.8625] 
+2024-09-11 06:03:44.740348: Epoch time: 246.12 s 
+2024-09-11 06:03:45.704450:  
+2024-09-11 06:03:45.704696: Epoch 208 
+2024-09-11 06:03:45.704782: Current learning rate: 0.00811 
+2024-09-11 06:07:52.007448: train_loss -0.8189 
+2024-09-11 06:07:52.007586: val_loss -0.6148 
+2024-09-11 06:07:52.007636: Pseudo dice [0.466, 0.8748] 
+2024-09-11 06:07:52.007692: Epoch time: 246.3 s 
+2024-09-11 06:07:52.945739:  
+2024-09-11 06:07:52.945849: Epoch 209 
+2024-09-11 06:07:52.945934: Current learning rate: 0.0081 
+2024-09-11 06:11:59.015120: train_loss -0.8231 
+2024-09-11 06:11:59.015255: val_loss -0.6006 
+2024-09-11 06:11:59.015306: Pseudo dice [0.4448, 0.8666] 
+2024-09-11 06:11:59.015357: Epoch time: 246.07 s 
+2024-09-11 06:11:59.988546:  
+2024-09-11 06:11:59.988763: Epoch 210 
+2024-09-11 06:11:59.988849: Current learning rate: 0.00809 
+2024-09-11 06:16:05.965707: train_loss -0.8141 
+2024-09-11 06:16:05.965847: val_loss -0.605 
+2024-09-11 06:16:05.965896: Pseudo dice [0.4331, 0.8778] 
+2024-09-11 06:16:05.965948: Epoch time: 245.98 s 
+2024-09-11 06:16:06.919905:  
+2024-09-11 06:16:06.920075: Epoch 211 
+2024-09-11 06:16:06.920174: Current learning rate: 0.00808 
+2024-09-11 06:20:12.652380: train_loss -0.8197 
+2024-09-11 06:20:12.652540: val_loss -0.6104 
+2024-09-11 06:20:12.652593: Pseudo dice [0.4587, 0.868] 
+2024-09-11 06:20:12.652645: Epoch time: 245.73 s 
+2024-09-11 06:20:13.588262:  
+2024-09-11 06:20:13.588451: Epoch 212 
+2024-09-11 06:20:13.588555: Current learning rate: 0.00807 
+2024-09-11 06:24:19.454132: train_loss -0.811 
+2024-09-11 06:24:19.454268: val_loss -0.605 
+2024-09-11 06:24:19.454319: Pseudo dice [0.4603, 0.8641] 
+2024-09-11 06:24:19.454372: Epoch time: 245.87 s 
+2024-09-11 06:24:20.399634:  
+2024-09-11 06:24:20.399788: Epoch 213 
+2024-09-11 06:24:20.399912: Current learning rate: 0.00806 
+2024-09-11 06:28:26.269640: train_loss -0.8204 
+2024-09-11 06:28:26.269780: val_loss -0.6002 
+2024-09-11 06:28:26.269830: Pseudo dice [0.4483, 0.8561] 
+2024-09-11 06:28:26.269880: Epoch time: 245.87 s 
+2024-09-11 06:28:27.217104:  
+2024-09-11 06:28:27.217282: Epoch 214 
+2024-09-11 06:28:27.217393: Current learning rate: 0.00805 
+2024-09-11 06:32:33.169728: train_loss -0.821 
+2024-09-11 06:32:33.169879: val_loss -0.5478 
+2024-09-11 06:32:33.169931: Pseudo dice [0.3618, 0.8703] 
+2024-09-11 06:32:33.169982: Epoch time: 245.95 s 
+2024-09-11 06:32:34.115554:  
+2024-09-11 06:32:34.115800: Epoch 215 
+2024-09-11 06:32:34.115896: Current learning rate: 0.00804 
+2024-09-11 06:36:40.455646: train_loss -0.82 
+2024-09-11 06:36:40.455781: val_loss -0.5972 
+2024-09-11 06:36:40.455853: Pseudo dice [0.4319, 0.879] 
+2024-09-11 06:36:40.455907: Epoch time: 246.34 s 
+2024-09-11 06:36:41.402478:  
+2024-09-11 06:36:41.402698: Epoch 216 
+2024-09-11 06:36:41.402781: Current learning rate: 0.00803 
+2024-09-11 06:40:47.713119: train_loss -0.8217 
+2024-09-11 06:40:47.713302: val_loss -0.5675 
+2024-09-11 06:40:47.713354: Pseudo dice [0.3839, 0.8622] 
+2024-09-11 06:40:47.713408: Epoch time: 246.31 s 
+2024-09-11 06:40:49.604337:  
+2024-09-11 06:40:49.604551: Epoch 217 
+2024-09-11 06:40:49.604653: Current learning rate: 0.00802 
+2024-09-11 06:44:56.295388: train_loss -0.8235 
+2024-09-11 06:44:56.295561: val_loss -0.6149 
+2024-09-11 06:44:56.295612: Pseudo dice [0.485, 0.8564] 
+2024-09-11 06:44:56.295663: Epoch time: 246.69 s 
+2024-09-11 06:44:57.243642:  
+2024-09-11 06:44:57.243837: Epoch 218 
+2024-09-11 06:44:57.243920: Current learning rate: 0.00801 
+2024-09-11 06:49:03.658520: train_loss -0.8231 
+2024-09-11 06:49:03.658658: val_loss -0.6342 
+2024-09-11 06:49:03.658710: Pseudo dice [0.4822, 0.8786] 
+2024-09-11 06:49:03.658764: Epoch time: 246.42 s 
+2024-09-11 06:49:04.610764:  
+2024-09-11 06:49:04.611045: Epoch 219 
+2024-09-11 06:49:04.611126: Current learning rate: 0.00801 
+2024-09-11 06:53:10.843083: train_loss -0.8237 
+2024-09-11 06:53:10.843237: val_loss -0.5943 
+2024-09-11 06:53:10.843292: Pseudo dice [0.4468, 0.8698] 
+2024-09-11 06:53:10.843386: Epoch time: 246.23 s 
+2024-09-11 06:53:11.789101:  
+2024-09-11 06:53:11.789303: Epoch 220 
+2024-09-11 06:53:11.789381: Current learning rate: 0.008 
+2024-09-11 06:57:18.144276: train_loss -0.8301 
+2024-09-11 06:57:18.144494: val_loss -0.6021 
+2024-09-11 06:57:18.144546: Pseudo dice [0.4538, 0.8654] 
+2024-09-11 06:57:18.144601: Epoch time: 246.36 s 
+2024-09-11 06:57:19.080303:  
+2024-09-11 06:57:19.080533: Epoch 221 
+2024-09-11 06:57:19.080617: Current learning rate: 0.00799 
+2024-09-11 07:01:25.360733: train_loss -0.8401 
+2024-09-11 07:01:25.360893: val_loss -0.6105 
+2024-09-11 07:01:25.360957: Pseudo dice [0.4722, 0.8638] 
+2024-09-11 07:01:25.361024: Epoch time: 246.28 s 
+2024-09-11 07:01:25.361072: Yayy! New best EMA pseudo Dice: 0.6537 
+2024-09-11 07:01:29.272280:  
+2024-09-11 07:01:29.272465: Epoch 222 
+2024-09-11 07:01:29.272588: Current learning rate: 0.00798 
+2024-09-11 07:05:35.441671: train_loss -0.8347 
+2024-09-11 07:05:35.441810: val_loss -0.5952 
+2024-09-11 07:05:35.441861: Pseudo dice [0.4053, 0.883] 
+2024-09-11 07:05:35.441915: Epoch time: 246.17 s 
+2024-09-11 07:05:36.377225:  
+2024-09-11 07:05:36.377444: Epoch 223 
+2024-09-11 07:05:36.377531: Current learning rate: 0.00797 
+2024-09-11 07:09:42.905809: train_loss -0.8361 
+2024-09-11 07:09:42.906011: val_loss -0.6157 
+2024-09-11 07:09:42.906067: Pseudo dice [0.4703, 0.8685] 
+2024-09-11 07:09:42.906118: Epoch time: 246.53 s 
+2024-09-11 07:09:42.906159: Yayy! New best EMA pseudo Dice: 0.6544 
+2024-09-11 07:09:46.811089:  
+2024-09-11 07:09:46.811260: Epoch 224 
+2024-09-11 07:09:46.811342: Current learning rate: 0.00796 
+2024-09-11 07:13:53.468151: train_loss -0.834 
+2024-09-11 07:13:53.468302: val_loss -0.6043 
+2024-09-11 07:13:53.468354: Pseudo dice [0.4317, 0.8784] 
+2024-09-11 07:13:53.468405: Epoch time: 246.66 s 
+2024-09-11 07:13:53.468445: Yayy! New best EMA pseudo Dice: 0.6545 
+2024-09-11 07:13:57.385012:  
+2024-09-11 07:13:57.385177: Epoch 225 
+2024-09-11 07:13:57.385258: Current learning rate: 0.00795 
+2024-09-11 07:18:04.183294: train_loss -0.8375 
+2024-09-11 07:18:04.183459: val_loss -0.5805 
+2024-09-11 07:18:04.183536: Pseudo dice [0.4284, 0.8726] 
+2024-09-11 07:18:04.183588: Epoch time: 246.8 s 
+2024-09-11 07:18:05.140233:  
+2024-09-11 07:18:05.140424: Epoch 226 
+2024-09-11 07:18:05.140543: Current learning rate: 0.00794 
+2024-09-11 07:22:11.781104: train_loss -0.8339 
+2024-09-11 07:22:11.781244: val_loss -0.5913 
+2024-09-11 07:22:11.781294: Pseudo dice [0.4538, 0.858] 
+2024-09-11 07:22:11.781347: Epoch time: 246.64 s 
+2024-09-11 07:22:12.723498:  
+2024-09-11 07:22:12.723701: Epoch 227 
+2024-09-11 07:22:12.723782: Current learning rate: 0.00793 
+2024-09-11 07:26:18.982219: train_loss -0.8295 
+2024-09-11 07:26:18.982357: val_loss -0.5934 
+2024-09-11 07:26:18.982466: Pseudo dice [0.4238, 0.8739] 
+2024-09-11 07:26:18.982547: Epoch time: 246.26 s 
+2024-09-11 07:26:19.950473:  
+2024-09-11 07:26:19.950679: Epoch 228 
+2024-09-11 07:26:19.950763: Current learning rate: 0.00792 
+2024-09-11 07:30:26.091742: train_loss -0.8124 
+2024-09-11 07:30:26.091890: val_loss -0.5812 
+2024-09-11 07:30:26.091944: Pseudo dice [0.4476, 0.8539] 
+2024-09-11 07:30:26.092011: Epoch time: 246.14 s 
+2024-09-11 07:30:27.041994:  
+2024-09-11 07:30:27.042198: Epoch 229 
+2024-09-11 07:30:27.042311: Current learning rate: 0.00791 
+2024-09-11 07:34:33.086050: train_loss -0.827 
+2024-09-11 07:34:33.086212: val_loss -0.5938 
+2024-09-11 07:34:33.086263: Pseudo dice [0.46, 0.8602] 
+2024-09-11 07:34:33.086313: Epoch time: 246.05 s 
+2024-09-11 07:34:34.016039:  
+2024-09-11 07:34:34.016268: Epoch 230 
+2024-09-11 07:34:34.016351: Current learning rate: 0.0079 
+2024-09-11 07:38:40.086954: train_loss -0.8129 
+2024-09-11 07:38:40.087095: val_loss -0.5885 
+2024-09-11 07:38:40.087147: Pseudo dice [0.4072, 0.8701] 
+2024-09-11 07:38:40.087229: Epoch time: 246.07 s 
+2024-09-11 07:38:41.010495:  
+2024-09-11 07:38:41.010700: Epoch 231 
+2024-09-11 07:38:41.010787: Current learning rate: 0.00789 
+2024-09-11 07:42:46.895368: train_loss -0.8298 
+2024-09-11 07:42:46.895552: val_loss -0.6083 
+2024-09-11 07:42:46.895606: Pseudo dice [0.5007, 0.8668] 
+2024-09-11 07:42:46.895658: Epoch time: 245.89 s 
+2024-09-11 07:42:46.895698: Yayy! New best EMA pseudo Dice: 0.6557 
+2024-09-11 07:42:50.779128:  
+2024-09-11 07:42:50.779319: Epoch 232 
+2024-09-11 07:42:50.779408: Current learning rate: 0.00789 
+2024-09-11 07:46:56.700853: train_loss -0.838 
+2024-09-11 07:46:56.700992: val_loss -0.591 
+2024-09-11 07:46:56.701239: Pseudo dice [0.4126, 0.8741] 
+2024-09-11 07:46:56.701292: Epoch time: 245.92 s 
+2024-09-11 07:46:57.622801:  
+2024-09-11 07:46:57.622981: Epoch 233 
+2024-09-11 07:46:57.623063: Current learning rate: 0.00788 
+2024-09-11 07:51:03.554268: train_loss -0.8421 
+2024-09-11 07:51:03.554423: val_loss -0.6155 
+2024-09-11 07:51:03.554473: Pseudo dice [0.4686, 0.8733] 
+2024-09-11 07:51:03.554524: Epoch time: 245.93 s 
+2024-09-11 07:51:03.554564: Yayy! New best EMA pseudo Dice: 0.6561 
+2024-09-11 07:51:07.434894:  
+2024-09-11 07:51:07.435065: Epoch 234 
+2024-09-11 07:51:07.435147: Current learning rate: 0.00787 
+2024-09-11 07:55:13.911212: train_loss -0.8133 
+2024-09-11 07:55:13.911353: val_loss -0.5959 
+2024-09-11 07:55:13.911403: Pseudo dice [0.4618, 0.8446] 
+2024-09-11 07:55:13.911454: Epoch time: 246.48 s 
+2024-09-11 07:55:14.839080:  
+2024-09-11 07:55:14.839320: Epoch 235 
+2024-09-11 07:55:14.839401: Current learning rate: 0.00786 
+2024-09-11 07:59:21.130774: train_loss -0.7955 
+2024-09-11 07:59:21.130913: val_loss -0.6121 
+2024-09-11 07:59:21.130964: Pseudo dice [0.4702, 0.8597] 
+2024-09-11 07:59:21.131018: Epoch time: 246.29 s 
+2024-09-11 07:59:21.131058: Yayy! New best EMA pseudo Dice: 0.6567 
+2024-09-11 07:59:25.035208:  
+2024-09-11 07:59:25.035394: Epoch 236 
+2024-09-11 07:59:25.035475: Current learning rate: 0.00785 
+2024-09-11 08:03:31.152128: train_loss -0.8154 
+2024-09-11 08:03:31.152270: val_loss -0.6008 
+2024-09-11 08:03:31.152319: Pseudo dice [0.429, 0.8786] 
+2024-09-11 08:03:31.152371: Epoch time: 246.12 s 
+2024-09-11 08:03:32.076540:  
+2024-09-11 08:03:32.076720: Epoch 237 
+2024-09-11 08:03:32.076802: Current learning rate: 0.00784 
+2024-09-11 08:07:38.256636: train_loss -0.8242 
+2024-09-11 08:07:38.256862: val_loss -0.6134 
+2024-09-11 08:07:38.256916: Pseudo dice [0.4767, 0.8545] 
+2024-09-11 08:07:38.256967: Epoch time: 246.18 s 
+2024-09-11 08:07:38.257008: Yayy! New best EMA pseudo Dice: 0.6573 
+2024-09-11 08:07:42.142563:  
+2024-09-11 08:07:42.142760: Epoch 238 
+2024-09-11 08:07:42.142859: Current learning rate: 0.00783 
+2024-09-11 08:11:48.509763: train_loss -0.8217 
+2024-09-11 08:11:48.509900: val_loss -0.5936 
+2024-09-11 08:11:48.509950: Pseudo dice [0.4424, 0.8562] 
+2024-09-11 08:11:48.510001: Epoch time: 246.37 s 
+2024-09-11 08:11:49.475321:  
+2024-09-11 08:11:49.475509: Epoch 239 
+2024-09-11 08:11:49.475593: Current learning rate: 0.00782 
+2024-09-11 08:15:55.681297: train_loss -0.8247 
+2024-09-11 08:15:55.681438: val_loss -0.5987 
+2024-09-11 08:15:55.681489: Pseudo dice [0.4059, 0.8739] 
+2024-09-11 08:15:55.681540: Epoch time: 246.21 s 
+2024-09-11 08:15:56.655932:  
+2024-09-11 08:15:56.656094: Epoch 240 
+2024-09-11 08:15:56.656177: Current learning rate: 0.00781 
+2024-09-11 08:20:02.866369: train_loss -0.8336 
+2024-09-11 08:20:02.866521: val_loss -0.5998 
+2024-09-11 08:20:02.866589: Pseudo dice [0.4634, 0.8643] 
+2024-09-11 08:20:02.866640: Epoch time: 246.21 s 
+2024-09-11 08:20:04.825731:  
+2024-09-11 08:20:04.825901: Epoch 241 
+2024-09-11 08:20:04.826002: Current learning rate: 0.0078 
+2024-09-11 08:24:11.450280: train_loss -0.8309 
+2024-09-11 08:24:11.450452: val_loss -0.5826 
+2024-09-11 08:24:11.450505: Pseudo dice [0.3902, 0.8731] 
+2024-09-11 08:24:11.450619: Epoch time: 246.63 s 
+2024-09-11 08:24:12.402821:  
+2024-09-11 08:24:12.403028: Epoch 242 
+2024-09-11 08:24:12.403112: Current learning rate: 0.00779 
+2024-09-11 08:28:19.151664: train_loss -0.8272 
+2024-09-11 08:28:19.151820: val_loss -0.5877 
+2024-09-11 08:28:19.151874: Pseudo dice [0.4286, 0.8669] 
+2024-09-11 08:28:19.151927: Epoch time: 246.75 s 
+2024-09-11 08:28:20.101541:  
+2024-09-11 08:28:20.101751: Epoch 243 
+2024-09-11 08:28:20.101832: Current learning rate: 0.00778 
+2024-09-11 08:32:26.309573: train_loss -0.8277 
+2024-09-11 08:32:26.309713: val_loss -0.611 
+2024-09-11 08:32:26.309764: Pseudo dice [0.4692, 0.8697] 
+2024-09-11 08:32:26.309813: Epoch time: 246.21 s 
+2024-09-11 08:32:27.266488:  
+2024-09-11 08:32:27.266815: Epoch 244 
+2024-09-11 08:32:27.266897: Current learning rate: 0.00777 
+2024-09-11 08:36:33.338559: train_loss -0.8312 
+2024-09-11 08:36:33.338708: val_loss -0.5659 
+2024-09-11 08:36:33.338773: Pseudo dice [0.3837, 0.8779] 
+2024-09-11 08:36:33.338840: Epoch time: 246.07 s 
+2024-09-11 08:36:34.313302:  
+2024-09-11 08:36:34.313496: Epoch 245 
+2024-09-11 08:36:34.313586: Current learning rate: 0.00777 
+2024-09-11 08:40:40.386730: train_loss -0.8216 
+2024-09-11 08:40:40.386866: val_loss -0.5873 
+2024-09-11 08:40:40.386916: Pseudo dice [0.4224, 0.8676] 
+2024-09-11 08:40:40.386969: Epoch time: 246.08 s 
+2024-09-11 08:40:41.322792:  
+2024-09-11 08:40:41.323017: Epoch 246 
+2024-09-11 08:40:41.323103: Current learning rate: 0.00776 
+2024-09-11 08:44:47.434368: train_loss -0.7969 
+2024-09-11 08:44:47.434520: val_loss -0.5494 
+2024-09-11 08:44:47.434619: Pseudo dice [0.3657, 0.8446] 
+2024-09-11 08:44:47.434674: Epoch time: 246.11 s 
+2024-09-11 08:44:48.389070:  
+2024-09-11 08:44:48.389335: Epoch 247 
+2024-09-11 08:44:48.389419: Current learning rate: 0.00775 
+2024-09-11 08:48:54.481875: train_loss -0.8128 
+2024-09-11 08:48:54.482052: val_loss -0.5748 
+2024-09-11 08:48:54.482104: Pseudo dice [0.4317, 0.8592] 
+2024-09-11 08:48:54.482155: Epoch time: 246.09 s 
+2024-09-11 08:48:55.437830:  
+2024-09-11 08:48:55.438059: Epoch 248 
+2024-09-11 08:48:55.438138: Current learning rate: 0.00774 
+2024-09-11 08:53:01.665028: train_loss -0.809 
+2024-09-11 08:53:01.665167: val_loss -0.5924 
+2024-09-11 08:53:01.665218: Pseudo dice [0.4442, 0.8517] 
+2024-09-11 08:53:01.665390: Epoch time: 246.23 s 
+2024-09-11 08:53:02.635843:  
+2024-09-11 08:53:02.636039: Epoch 249 
+2024-09-11 08:53:02.636139: Current learning rate: 0.00773 
+2024-09-11 08:57:09.001398: train_loss -0.8087 
+2024-09-11 08:57:09.001611: val_loss -0.5602 
+2024-09-11 08:57:09.001704: Pseudo dice [0.3984, 0.8425] 
+2024-09-11 08:57:09.001793: Epoch time: 246.37 s 
+2024-09-11 08:57:12.914494:  
+2024-09-11 08:57:12.914685: Epoch 250 
+2024-09-11 08:57:12.914818: Current learning rate: 0.00772 
+2024-09-11 09:01:19.364892: train_loss -0.7819 
+2024-09-11 09:01:19.365032: val_loss -0.5843 
+2024-09-11 09:01:19.365082: Pseudo dice [0.4537, 0.8625] 
+2024-09-11 09:01:19.365134: Epoch time: 246.45 s 
+2024-09-11 09:01:20.322832:  
+2024-09-11 09:01:20.323014: Epoch 251 
+2024-09-11 09:01:20.323101: Current learning rate: 0.00771 
+2024-09-11 09:05:26.740673: train_loss -0.8115 
+2024-09-11 09:05:26.740823: val_loss -0.5579 
+2024-09-11 09:05:26.740874: Pseudo dice [0.3622, 0.8623] 
+2024-09-11 09:05:26.740926: Epoch time: 246.42 s 
+2024-09-11 09:05:27.679702:  
+2024-09-11 09:05:27.679908: Epoch 252 
+2024-09-11 09:05:27.679990: Current learning rate: 0.0077 
+2024-09-11 09:09:34.131485: train_loss -0.8149 
+2024-09-11 09:09:34.131626: val_loss -0.5983 
+2024-09-11 09:09:34.131681: Pseudo dice [0.4341, 0.8531] 
+2024-09-11 09:09:34.131763: Epoch time: 246.45 s 
+2024-09-11 09:09:35.097253:  
+2024-09-11 09:09:35.097420: Epoch 253 
+2024-09-11 09:09:35.097513: Current learning rate: 0.00769 
+2024-09-11 09:13:41.566311: train_loss -0.8007 
+2024-09-11 09:13:41.566468: val_loss -0.6052 
+2024-09-11 09:13:41.566519: Pseudo dice [0.4278, 0.8779] 
+2024-09-11 09:13:41.566571: Epoch time: 246.47 s 
+2024-09-11 09:13:42.526101:  
+2024-09-11 09:13:42.526293: Epoch 254 
+2024-09-11 09:13:42.526376: Current learning rate: 0.00768 
+2024-09-11 09:17:48.744447: train_loss -0.8209 
+2024-09-11 09:17:48.744583: val_loss -0.57 
+2024-09-11 09:17:48.744633: Pseudo dice [0.3624, 0.8727] 
+2024-09-11 09:17:48.744684: Epoch time: 246.22 s 
+2024-09-11 09:17:49.687190:  
+2024-09-11 09:17:49.687428: Epoch 255 
+2024-09-11 09:17:49.687512: Current learning rate: 0.00767 
+2024-09-11 09:21:55.760814: train_loss -0.8227 
+2024-09-11 09:21:55.760955: val_loss -0.5595 
+2024-09-11 09:21:55.761004: Pseudo dice [0.3629, 0.8557] 
+2024-09-11 09:21:55.761057: Epoch time: 246.08 s 
+2024-09-11 09:21:56.692579:  
+2024-09-11 09:21:56.692791: Epoch 256 
+2024-09-11 09:21:56.692878: Current learning rate: 0.00766 
+2024-09-11 09:26:02.890097: train_loss -0.8279 
+2024-09-11 09:26:02.890236: val_loss -0.5872 
+2024-09-11 09:26:02.890289: Pseudo dice [0.4216, 0.8798] 
+2024-09-11 09:26:02.890340: Epoch time: 246.2 s 
+2024-09-11 09:26:03.840302:  
+2024-09-11 09:26:03.840525: Epoch 257 
+2024-09-11 09:26:03.840608: Current learning rate: 0.00765 
+2024-09-11 09:30:10.025015: train_loss -0.8188 
+2024-09-11 09:30:10.025163: val_loss -0.59 
+2024-09-11 09:30:10.025215: Pseudo dice [0.4522, 0.8787] 
+2024-09-11 09:30:10.025268: Epoch time: 246.19 s 
+2024-09-11 09:30:10.982569:  
+2024-09-11 09:30:10.982795: Epoch 258 
+2024-09-11 09:30:10.982892: Current learning rate: 0.00764 
+2024-09-11 09:34:16.805680: train_loss -0.8051 
+2024-09-11 09:34:16.805815: val_loss -0.6016 
+2024-09-11 09:34:16.805866: Pseudo dice [0.4314, 0.8813] 
+2024-09-11 09:34:16.805920: Epoch time: 245.83 s 
+2024-09-11 09:34:17.739710:  
+2024-09-11 09:34:17.739908: Epoch 259 
+2024-09-11 09:34:17.739994: Current learning rate: 0.00764 
+2024-09-11 09:38:23.561701: train_loss -0.82 
+2024-09-11 09:38:23.561913: val_loss -0.5892 
+2024-09-11 09:38:23.561969: Pseudo dice [0.4172, 0.8686] 
+2024-09-11 09:38:23.562028: Epoch time: 245.82 s 
+2024-09-11 09:38:24.707687:  
+2024-09-11 09:38:24.707863: Epoch 260 
+2024-09-11 09:38:24.707948: Current learning rate: 0.00763 
+2024-09-11 09:42:30.584980: train_loss -0.8288 
+2024-09-11 09:42:30.585154: val_loss -0.5779 
+2024-09-11 09:42:30.585205: Pseudo dice [0.3937, 0.8775] 
+2024-09-11 09:42:30.585258: Epoch time: 245.88 s 
+2024-09-11 09:42:31.528680:  
+2024-09-11 09:42:31.528910: Epoch 261 
+2024-09-11 09:42:31.529003: Current learning rate: 0.00762 
+2024-09-11 09:46:37.283006: train_loss -0.83 
+2024-09-11 09:46:37.283144: val_loss -0.6082 
+2024-09-11 09:46:37.283193: Pseudo dice [0.4932, 0.8646] 
+2024-09-11 09:46:37.283244: Epoch time: 245.76 s 
+2024-09-11 09:46:38.229068:  
+2024-09-11 09:46:38.229262: Epoch 262 
+2024-09-11 09:46:38.229347: Current learning rate: 0.00761 
+2024-09-11 09:50:44.011933: train_loss -0.8308 
+2024-09-11 09:50:44.012140: val_loss -0.6012 
+2024-09-11 09:50:44.012226: Pseudo dice [0.4671, 0.8549] 
+2024-09-11 09:50:44.012285: Epoch time: 245.78 s 
+2024-09-11 09:50:44.990539:  
+2024-09-11 09:50:44.990706: Epoch 263 
+2024-09-11 09:50:44.990797: Current learning rate: 0.0076 
+2024-09-11 09:54:51.016968: train_loss -0.8321 
+2024-09-11 09:54:51.017118: val_loss -0.6182 
+2024-09-11 09:54:51.017176: Pseudo dice [0.4495, 0.8691] 
+2024-09-11 09:54:51.017232: Epoch time: 246.03 s 
+2024-09-11 09:54:51.954711:  
+2024-09-11 09:54:51.954872: Epoch 264 
+2024-09-11 09:54:51.954959: Current learning rate: 0.00759 
+2024-09-11 09:58:57.909157: train_loss -0.8353 
+2024-09-11 09:58:57.909310: val_loss -0.599 
+2024-09-11 09:58:57.909370: Pseudo dice [0.4336, 0.862] 
+2024-09-11 09:58:57.909425: Epoch time: 245.96 s 
+2024-09-11 09:58:59.818562:  
+2024-09-11 09:58:59.818823: Epoch 265 
+2024-09-11 09:58:59.818929: Current learning rate: 0.00758 
+2024-09-11 10:03:05.651757: train_loss -0.8289 
+2024-09-11 10:03:05.651925: val_loss -0.5774 
+2024-09-11 10:03:05.651982: Pseudo dice [0.3523, 0.8708] 
+2024-09-11 10:03:05.652038: Epoch time: 245.84 s 
+2024-09-11 10:03:06.583365:  
+2024-09-11 10:03:06.583633: Epoch 266 
+2024-09-11 10:03:06.583742: Current learning rate: 0.00757 
+2024-09-11 10:07:12.620856: train_loss -0.8383 
+2024-09-11 10:07:12.621029: val_loss -0.5839 
+2024-09-11 10:07:12.621095: Pseudo dice [0.4111, 0.8706] 
+2024-09-11 10:07:12.621162: Epoch time: 246.04 s 
+2024-09-11 10:07:13.572408:  
+2024-09-11 10:07:13.572619: Epoch 267 
+2024-09-11 10:07:13.572740: Current learning rate: 0.00756 
+2024-09-11 10:11:19.685415: train_loss -0.8304 
+2024-09-11 10:11:19.685565: val_loss -0.5675 
+2024-09-11 10:11:19.685624: Pseudo dice [0.4166, 0.8533] 
+2024-09-11 10:11:19.685681: Epoch time: 246.11 s 
+2024-09-11 10:11:20.646518:  
+2024-09-11 10:11:20.646792: Epoch 268 
+2024-09-11 10:11:20.646879: Current learning rate: 0.00755 
+2024-09-11 10:15:26.820864: train_loss -0.8189 
+2024-09-11 10:15:26.821019: val_loss -0.6157 
+2024-09-11 10:15:26.821075: Pseudo dice [0.4364, 0.8891] 
+2024-09-11 10:15:26.821131: Epoch time: 246.18 s 
+2024-09-11 10:15:27.775500:  
+2024-09-11 10:15:27.775688: Epoch 269 
+2024-09-11 10:15:27.775777: Current learning rate: 0.00754 
+2024-09-11 10:19:34.102666: train_loss -0.8281 
+2024-09-11 10:19:34.102810: val_loss -0.5852 
+2024-09-11 10:19:34.102866: Pseudo dice [0.4052, 0.8706] 
+2024-09-11 10:19:34.102922: Epoch time: 246.33 s 
+2024-09-11 10:19:35.057778:  
+2024-09-11 10:19:35.058042: Epoch 270 
+2024-09-11 10:19:35.058130: Current learning rate: 0.00753 
+2024-09-11 10:23:41.126777: train_loss -0.8272 
+2024-09-11 10:23:41.126930: val_loss -0.5865 
+2024-09-11 10:23:41.126989: Pseudo dice [0.4307, 0.8594] 
+2024-09-11 10:23:41.127046: Epoch time: 246.07 s 
+2024-09-11 10:23:42.069307:  
+2024-09-11 10:23:42.069564: Epoch 271 
+2024-09-11 10:23:42.069652: Current learning rate: 0.00752 
+2024-09-11 10:27:48.225289: train_loss -0.8172 
+2024-09-11 10:27:48.225431: val_loss -0.5807 
+2024-09-11 10:27:48.225487: Pseudo dice [0.3958, 0.867] 
+2024-09-11 10:27:48.225556: Epoch time: 246.16 s 
+2024-09-11 10:27:49.176207:  
+2024-09-11 10:27:49.176397: Epoch 272 
+2024-09-11 10:27:49.176505: Current learning rate: 0.00751 
+2024-09-11 10:31:55.483568: train_loss -0.8133 
+2024-09-11 10:31:55.483708: val_loss -0.6037 
+2024-09-11 10:31:55.483764: Pseudo dice [0.4698, 0.8773] 
+2024-09-11 10:31:55.483830: Epoch time: 246.31 s 
+2024-09-11 10:31:56.423546:  
+2024-09-11 10:31:56.423782: Epoch 273 
+2024-09-11 10:31:56.423883: Current learning rate: 0.00751 
+2024-09-11 10:36:02.744152: train_loss -0.8291 
+2024-09-11 10:36:02.744299: val_loss -0.5819 
+2024-09-11 10:36:02.744357: Pseudo dice [0.3822, 0.8587] 
+2024-09-11 10:36:02.744412: Epoch time: 246.32 s 
+2024-09-11 10:36:03.707046:  
+2024-09-11 10:36:03.707233: Epoch 274 
+2024-09-11 10:36:03.707322: Current learning rate: 0.0075 
+2024-09-11 10:40:10.156382: train_loss -0.8273 
+2024-09-11 10:40:10.156557: val_loss -0.6028 
+2024-09-11 10:40:10.156614: Pseudo dice [0.4663, 0.8639] 
+2024-09-11 10:40:10.156670: Epoch time: 246.45 s 
+2024-09-11 10:40:11.117568:  
+2024-09-11 10:40:11.117765: Epoch 275 
+2024-09-11 10:40:11.117852: Current learning rate: 0.00749 
+2024-09-11 10:44:17.405142: train_loss -0.8343 
+2024-09-11 10:44:17.405289: val_loss -0.6199 
+2024-09-11 10:44:17.405345: Pseudo dice [0.4862, 0.8698] 
+2024-09-11 10:44:17.405401: Epoch time: 246.29 s 
+2024-09-11 10:44:18.376521:  
+2024-09-11 10:44:18.376742: Epoch 276 
+2024-09-11 10:44:18.376832: Current learning rate: 0.00748 
+2024-09-11 10:48:24.416654: train_loss -0.8223 
+2024-09-11 10:48:24.416804: val_loss -0.6075 
+2024-09-11 10:48:24.416861: Pseudo dice [0.4831, 0.8681] 
+2024-09-11 10:48:24.416917: Epoch time: 246.04 s 
+2024-09-11 10:48:25.382191:  
+2024-09-11 10:48:25.382405: Epoch 277 
+2024-09-11 10:48:25.382497: Current learning rate: 0.00747 
+2024-09-11 10:52:31.468127: train_loss -0.8247 
+2024-09-11 10:52:31.468296: val_loss -0.6004 
+2024-09-11 10:52:31.468347: Pseudo dice [0.434, 0.8779] 
+2024-09-11 10:52:31.468399: Epoch time: 246.09 s 
+2024-09-11 10:52:32.415533:  
+2024-09-11 10:52:32.415700: Epoch 278 
+2024-09-11 10:52:32.415782: Current learning rate: 0.00746 
+2024-09-11 10:56:38.507506: train_loss -0.8161 
+2024-09-11 10:56:38.507647: val_loss -0.5594 
+2024-09-11 10:56:38.507698: Pseudo dice [0.3615, 0.8572] 
+2024-09-11 10:56:38.507749: Epoch time: 246.09 s 
+2024-09-11 10:56:39.462876:  
+2024-09-11 10:56:39.463088: Epoch 279 
+2024-09-11 10:56:39.463171: Current learning rate: 0.00745 
+2024-09-11 11:00:45.547682: train_loss -0.83 
+2024-09-11 11:00:45.547870: val_loss -0.5675 
+2024-09-11 11:00:45.547923: Pseudo dice [0.4163, 0.857] 
+2024-09-11 11:00:45.547974: Epoch time: 246.09 s 
+2024-09-11 11:00:46.486163:  
+2024-09-11 11:00:46.486349: Epoch 280 
+2024-09-11 11:00:46.486436: Current learning rate: 0.00744 
+2024-09-11 11:04:52.513639: train_loss -0.8135 
+2024-09-11 11:04:52.513780: val_loss -0.585 
+2024-09-11 11:04:52.513830: Pseudo dice [0.436, 0.8422] 
+2024-09-11 11:04:52.513881: Epoch time: 246.03 s 
+2024-09-11 11:04:53.452367:  
+2024-09-11 11:04:53.452587: Epoch 281 
+2024-09-11 11:04:53.452670: Current learning rate: 0.00743 
+2024-09-11 11:08:59.775934: train_loss -0.8085 
+2024-09-11 11:08:59.776072: val_loss -0.63 
+2024-09-11 11:08:59.776122: Pseudo dice [0.501, 0.8875] 
+2024-09-11 11:08:59.776173: Epoch time: 246.33 s 
+2024-09-11 11:09:00.737428:  
+2024-09-11 11:09:00.737602: Epoch 282 
+2024-09-11 11:09:00.737690: Current learning rate: 0.00742 
+2024-09-11 11:13:06.818629: train_loss -0.8239 
+2024-09-11 11:13:06.818769: val_loss -0.6054 
+2024-09-11 11:13:06.818819: Pseudo dice [0.4686, 0.873] 
+2024-09-11 11:13:06.818870: Epoch time: 246.08 s 
+2024-09-11 11:13:07.779056:  
+2024-09-11 11:13:07.779265: Epoch 283 
+2024-09-11 11:13:07.779355: Current learning rate: 0.00741 
+2024-09-11 11:17:13.726006: train_loss -0.8298 
+2024-09-11 11:17:13.726205: val_loss -0.5822 
+2024-09-11 11:17:13.726361: Pseudo dice [0.4108, 0.8644] 
+2024-09-11 11:17:13.726454: Epoch time: 245.95 s 
+2024-09-11 11:17:14.697077:  
+2024-09-11 11:17:14.697357: Epoch 284 
+2024-09-11 11:17:14.697450: Current learning rate: 0.0074 
+2024-09-11 11:21:20.994226: train_loss -0.8282 
+2024-09-11 11:21:20.994365: val_loss -0.583 
+2024-09-11 11:21:20.994416: Pseudo dice [0.4286, 0.8754] 
+2024-09-11 11:21:20.994467: Epoch time: 246.3 s 
+2024-09-11 11:21:21.939128:  
+2024-09-11 11:21:21.939332: Epoch 285 
+2024-09-11 11:21:21.939416: Current learning rate: 0.00739 
+2024-09-11 11:25:28.276517: train_loss -0.8311 
+2024-09-11 11:25:28.276656: val_loss -0.5705 
+2024-09-11 11:25:28.276708: Pseudo dice [0.4014, 0.8819] 
+2024-09-11 11:25:28.276763: Epoch time: 246.34 s 
+2024-09-11 11:25:29.213105:  
+2024-09-11 11:25:29.213262: Epoch 286 
+2024-09-11 11:25:29.213346: Current learning rate: 0.00738 
+2024-09-11 11:29:35.474709: train_loss -0.8313 
+2024-09-11 11:29:35.474865: val_loss -0.6449 
+2024-09-11 11:29:35.474921: Pseudo dice [0.5198, 0.8813] 
+2024-09-11 11:29:35.474977: Epoch time: 246.26 s 
+2024-09-11 11:29:36.456234:  
+2024-09-11 11:29:36.456495: Epoch 287 
+2024-09-11 11:29:36.456591: Current learning rate: 0.00738 
+2024-09-11 11:33:42.462070: train_loss -0.8435 
+2024-09-11 11:33:42.462219: val_loss -0.6144 
+2024-09-11 11:33:42.462275: Pseudo dice [0.4298, 0.8767] 
+2024-09-11 11:33:42.462331: Epoch time: 246.01 s 
+2024-09-11 11:33:43.419497:  
+2024-09-11 11:33:43.419700: Epoch 288 
+2024-09-11 11:33:43.419795: Current learning rate: 0.00737 
+2024-09-11 11:37:49.581340: train_loss -0.8451 
+2024-09-11 11:37:49.581563: val_loss -0.5838 
+2024-09-11 11:37:49.581622: Pseudo dice [0.4476, 0.8597] 
+2024-09-11 11:37:49.581681: Epoch time: 246.16 s 
+2024-09-11 11:37:51.513692:  
+2024-09-11 11:37:51.513974: Epoch 289 
+2024-09-11 11:37:51.514079: Current learning rate: 0.00736 
+2024-09-11 11:41:57.639763: train_loss -0.8479 
+2024-09-11 11:41:57.639921: val_loss -0.5876 
+2024-09-11 11:41:57.639979: Pseudo dice [0.4232, 0.8584] 
+2024-09-11 11:41:57.640070: Epoch time: 246.13 s 
+2024-09-11 11:41:58.592973:  
+2024-09-11 11:41:58.593228: Epoch 290 
+2024-09-11 11:41:58.593323: Current learning rate: 0.00735 
+2024-09-11 11:46:04.826931: train_loss -0.8263 
+2024-09-11 11:46:04.827076: val_loss -0.6087 
+2024-09-11 11:46:04.827188: Pseudo dice [0.4521, 0.8671] 
+2024-09-11 11:46:04.827275: Epoch time: 246.24 s 
+2024-09-11 11:46:05.787056:  
+2024-09-11 11:46:05.787268: Epoch 291 
+2024-09-11 11:46:05.787355: Current learning rate: 0.00734 
+2024-09-11 11:50:11.971767: train_loss -0.8274 
+2024-09-11 11:50:11.971933: val_loss -0.6237 
+2024-09-11 11:50:11.972040: Pseudo dice [0.4996, 0.8686] 
+2024-09-11 11:50:11.972096: Epoch time: 246.19 s 
+2024-09-11 11:50:12.954445:  
+2024-09-11 11:50:12.954672: Epoch 292 
+2024-09-11 11:50:12.954759: Current learning rate: 0.00733 
+2024-09-11 11:54:18.891387: train_loss -0.8404 
+2024-09-11 11:54:18.891576: val_loss -0.6193 
+2024-09-11 11:54:18.891634: Pseudo dice [0.484, 0.8581] 
+2024-09-11 11:54:18.891690: Epoch time: 245.94 s 
+2024-09-11 11:54:18.891737: Yayy! New best EMA pseudo Dice: 0.6586 
+2024-09-11 11:54:22.837061:  
+2024-09-11 11:54:22.837268: Epoch 293 
+2024-09-11 11:54:22.837399: Current learning rate: 0.00732 
+2024-09-11 11:58:28.878897: train_loss -0.8321 
+2024-09-11 11:58:28.879041: val_loss -0.5778 
+2024-09-11 11:58:28.879098: Pseudo dice [0.3821, 0.8634] 
+2024-09-11 11:58:28.879156: Epoch time: 246.04 s 
+2024-09-11 11:58:29.840186:  
+2024-09-11 11:58:29.840447: Epoch 294 
+2024-09-11 11:58:29.840536: Current learning rate: 0.00731 
+2024-09-11 12:02:35.781252: train_loss -0.8373 
+2024-09-11 12:02:35.781407: val_loss -0.5813 
+2024-09-11 12:02:35.781464: Pseudo dice [0.4165, 0.8608] 
+2024-09-11 12:02:35.781521: Epoch time: 245.94 s 
+2024-09-11 12:02:36.739474:  
+2024-09-11 12:02:36.739727: Epoch 295 
+2024-09-11 12:02:36.739826: Current learning rate: 0.0073 
+2024-09-11 12:06:42.885097: train_loss -0.839 
+2024-09-11 12:06:42.885269: val_loss -0.5856 
+2024-09-11 12:06:42.885325: Pseudo dice [0.4336, 0.8585] 
+2024-09-11 12:06:42.885381: Epoch time: 246.15 s 
+2024-09-11 12:06:43.854586:  
+2024-09-11 12:06:43.854787: Epoch 296 
+2024-09-11 12:06:43.854876: Current learning rate: 0.00729 
+2024-09-11 12:10:50.013906: train_loss -0.8322 
+2024-09-11 12:10:50.014057: val_loss -0.6017 
+2024-09-11 12:10:50.014154: Pseudo dice [0.5016, 0.8596] 
+2024-09-11 12:10:50.014213: Epoch time: 246.16 s 
+2024-09-11 12:10:50.982424:  
+2024-09-11 12:10:50.982615: Epoch 297 
+2024-09-11 12:10:50.982707: Current learning rate: 0.00728 
+2024-09-11 12:14:57.329894: train_loss -0.8307 
+2024-09-11 12:14:57.330065: val_loss -0.6249 
+2024-09-11 12:14:57.330122: Pseudo dice [0.4799, 0.8614] 
+2024-09-11 12:14:57.330178: Epoch time: 246.35 s 
+2024-09-11 12:14:58.291240:  
+2024-09-11 12:14:58.291436: Epoch 298 
+2024-09-11 12:14:58.291523: Current learning rate: 0.00727 
+2024-09-11 12:19:04.780231: train_loss -0.8435 
+2024-09-11 12:19:04.780376: val_loss -0.5986 
+2024-09-11 12:19:04.780491: Pseudo dice [0.4393, 0.873] 
+2024-09-11 12:19:04.780550: Epoch time: 246.49 s 
+2024-09-11 12:19:05.767179:  
+2024-09-11 12:19:05.767351: Epoch 299 
+2024-09-11 12:19:05.767435: Current learning rate: 0.00726 
+2024-09-11 12:23:11.953352: train_loss -0.8482 
+2024-09-11 12:23:11.953507: val_loss -0.5939 
+2024-09-11 12:23:11.953564: Pseudo dice [0.4405, 0.8602] 
+2024-09-11 12:23:11.953619: Epoch time: 246.19 s 
+2024-09-11 12:23:15.941860:  
+2024-09-11 12:23:15.942109: Epoch 300 
+2024-09-11 12:23:15.942241: Current learning rate: 0.00725 
+2024-09-11 12:27:22.262653: train_loss -0.8422 
+2024-09-11 12:27:22.262791: val_loss -0.627 
+2024-09-11 12:27:22.262851: Pseudo dice [0.4717, 0.8873] 
+2024-09-11 12:27:22.262907: Epoch time: 246.32 s 
+2024-09-11 12:27:23.261374:  
+2024-09-11 12:27:23.261593: Epoch 301 
+2024-09-11 12:27:23.261712: Current learning rate: 0.00724 
+2024-09-11 12:31:29.528082: train_loss -0.8313 
+2024-09-11 12:31:29.528259: val_loss -0.6052 
+2024-09-11 12:31:29.528317: Pseudo dice [0.4698, 0.87] 
+2024-09-11 12:31:29.528374: Epoch time: 246.27 s 
+2024-09-11 12:31:29.528419: Yayy! New best EMA pseudo Dice: 0.6597 
+2024-09-11 12:31:33.477185:  
+2024-09-11 12:31:33.477381: Epoch 302 
+2024-09-11 12:31:33.477469: Current learning rate: 0.00724 
+2024-09-11 12:35:39.490542: train_loss -0.8381 
+2024-09-11 12:35:39.490694: val_loss -0.5933 
+2024-09-11 12:35:39.490751: Pseudo dice [0.4431, 0.8505] 
+2024-09-11 12:35:39.490807: Epoch time: 246.02 s 
+2024-09-11 12:35:40.454321:  
+2024-09-11 12:35:40.454577: Epoch 303 
+2024-09-11 12:35:40.454666: Current learning rate: 0.00723 
+2024-09-11 12:39:46.467318: train_loss -0.8322 
+2024-09-11 12:39:46.467471: val_loss -0.5926 
+2024-09-11 12:39:46.467528: Pseudo dice [0.4556, 0.8654] 
+2024-09-11 12:39:46.467585: Epoch time: 246.01 s 
+2024-09-11 12:39:47.422585:  
+2024-09-11 12:39:47.422801: Epoch 304 
+2024-09-11 12:39:47.422890: Current learning rate: 0.00722 
+2024-09-11 12:43:53.511539: train_loss -0.8482 
+2024-09-11 12:43:53.511690: val_loss -0.5917 
+2024-09-11 12:43:53.511748: Pseudo dice [0.3966, 0.8739] 
+2024-09-11 12:43:53.511821: Epoch time: 246.09 s 
+2024-09-11 12:43:54.473486:  
+2024-09-11 12:43:54.473717: Epoch 305 
+2024-09-11 12:43:54.473810: Current learning rate: 0.00721 
+2024-09-11 12:48:00.611700: train_loss -0.8465 
+2024-09-11 12:48:00.611875: val_loss -0.6204 
+2024-09-11 12:48:00.611934: Pseudo dice [0.4287, 0.8849] 
+2024-09-11 12:48:00.611990: Epoch time: 246.14 s 
+2024-09-11 12:48:01.590504:  
+2024-09-11 12:48:01.590723: Epoch 306 
+2024-09-11 12:48:01.590807: Current learning rate: 0.0072 
+2024-09-11 12:52:07.737437: train_loss -0.8488 
+2024-09-11 12:52:07.737587: val_loss -0.6264 
+2024-09-11 12:52:07.737645: Pseudo dice [0.4913, 0.8657] 
+2024-09-11 12:52:07.737704: Epoch time: 246.15 s 
+2024-09-11 12:52:08.695823:  
+2024-09-11 12:52:08.696087: Epoch 307 
+2024-09-11 12:52:08.696177: Current learning rate: 0.00719 
+2024-09-11 12:56:14.640263: train_loss -0.846 
+2024-09-11 12:56:14.640444: val_loss -0.5986 
+2024-09-11 12:56:14.640502: Pseudo dice [0.443, 0.8709] 
+2024-09-11 12:56:14.640559: Epoch time: 245.95 s 
+2024-09-11 12:56:15.628567:  
+2024-09-11 12:56:15.628746: Epoch 308 
+2024-09-11 12:56:15.628830: Current learning rate: 0.00718 
+2024-09-11 13:00:21.553726: train_loss -0.8289 
+2024-09-11 13:00:21.553903: val_loss -0.5776 
+2024-09-11 13:00:21.553962: Pseudo dice [0.4387, 0.8601] 
+2024-09-11 13:00:21.554024: Epoch time: 245.93 s 
+2024-09-11 13:00:22.509639:  
+2024-09-11 13:00:22.509870: Epoch 309 
+2024-09-11 13:00:22.509955: Current learning rate: 0.00717 
+2024-09-11 13:04:28.507154: train_loss -0.8462 
+2024-09-11 13:04:28.507346: val_loss -0.5811 
+2024-09-11 13:04:28.507444: Pseudo dice [0.4012, 0.8588] 
+2024-09-11 13:04:28.507502: Epoch time: 246.0 s 
+2024-09-11 13:04:29.482960:  
+2024-09-11 13:04:29.483129: Epoch 310 
+2024-09-11 13:04:29.483246: Current learning rate: 0.00716 
+2024-09-11 13:08:35.737581: train_loss -0.8463 
+2024-09-11 13:08:35.737735: val_loss -0.6006 
+2024-09-11 13:08:35.737793: Pseudo dice [0.4392, 0.8701] 
+2024-09-11 13:08:35.737932: Epoch time: 246.26 s 
+2024-09-11 13:08:36.732503:  
+2024-09-11 13:08:36.732716: Epoch 311 
+2024-09-11 13:08:36.732807: Current learning rate: 0.00715 
+2024-09-11 13:12:43.700370: train_loss -0.8496 
+2024-09-11 13:12:43.700516: val_loss -0.6232 
+2024-09-11 13:12:43.700573: Pseudo dice [0.4684, 0.8851] 
+2024-09-11 13:12:43.700629: Epoch time: 246.97 s 
+2024-09-11 13:12:44.664403:  
+2024-09-11 13:12:44.664626: Epoch 312 
+2024-09-11 13:12:44.664732: Current learning rate: 0.00714 
+2024-09-11 13:16:50.822031: train_loss -0.849 
+2024-09-11 13:16:50.822184: val_loss -0.5865 
+2024-09-11 13:16:50.822241: Pseudo dice [0.4043, 0.8489] 
+2024-09-11 13:16:50.822298: Epoch time: 246.16 s 
+2024-09-11 13:16:51.773958:  
+2024-09-11 13:16:51.774199: Epoch 313 
+2024-09-11 13:16:51.774287: Current learning rate: 0.00713 
+2024-09-11 13:20:58.110952: train_loss -0.8374 
+2024-09-11 13:20:58.111103: val_loss -0.6205 
+2024-09-11 13:20:58.111158: Pseudo dice [0.4788, 0.8615] 
+2024-09-11 13:20:58.111218: Epoch time: 246.34 s 
+2024-09-11 13:20:59.071214:  
+2024-09-11 13:20:59.071454: Epoch 314 
+2024-09-11 13:20:59.071542: Current learning rate: 0.00712 
+2024-09-11 13:25:05.212702: train_loss -0.8356 
+2024-09-11 13:25:05.212859: val_loss -0.5844 
+2024-09-11 13:25:05.212930: Pseudo dice [0.4155, 0.869] 
+2024-09-11 13:25:05.213001: Epoch time: 246.14 s 
+2024-09-11 13:25:06.202650:  
+2024-09-11 13:25:06.202868: Epoch 315 
+2024-09-11 13:25:06.203004: Current learning rate: 0.00711 
+2024-09-11 13:29:12.207697: train_loss -0.8444 
+2024-09-11 13:29:12.207869: val_loss -0.6087 
+2024-09-11 13:29:12.207929: Pseudo dice [0.4431, 0.8817] 
+2024-09-11 13:29:12.207988: Epoch time: 246.01 s 
+2024-09-11 13:29:13.188496:  
+2024-09-11 13:29:13.188749: Epoch 316 
+2024-09-11 13:29:13.188864: Current learning rate: 0.0071 
+2024-09-11 13:33:19.314633: train_loss -0.8439 
+2024-09-11 13:33:19.314792: val_loss -0.6128 
+2024-09-11 13:33:19.314991: Pseudo dice [0.4425, 0.8587] 
+2024-09-11 13:33:19.315115: Epoch time: 246.13 s 
+2024-09-11 13:33:20.293470:  
+2024-09-11 13:33:20.293699: Epoch 317 
+2024-09-11 13:33:20.293792: Current learning rate: 0.0071 
+2024-09-11 13:37:26.452125: train_loss -0.8393 
+2024-09-11 13:37:26.452279: val_loss -0.6194 
+2024-09-11 13:37:26.452337: Pseudo dice [0.5007, 0.874] 
+2024-09-11 13:37:26.452392: Epoch time: 246.16 s 
+2024-09-11 13:37:27.457506:  
+2024-09-11 13:37:27.457770: Epoch 318 
+2024-09-11 13:37:27.457860: Current learning rate: 0.00709 
+2024-09-11 13:41:33.643980: train_loss -0.8461 
+2024-09-11 13:41:33.644134: val_loss -0.5687 
+2024-09-11 13:41:33.644195: Pseudo dice [0.3953, 0.862] 
+2024-09-11 13:41:33.644253: Epoch time: 246.19 s 
+2024-09-11 13:41:34.624079:  
+2024-09-11 13:41:34.624281: Epoch 319 
+2024-09-11 13:41:34.624368: Current learning rate: 0.00708 
+2024-09-11 13:45:40.842870: train_loss -0.8135 
+2024-09-11 13:45:40.843011: val_loss -0.6215 
+2024-09-11 13:45:40.843069: Pseudo dice [0.4847, 0.87] 
+2024-09-11 13:45:40.843126: Epoch time: 246.22 s 
+2024-09-11 13:45:41.822143:  
+2024-09-11 13:45:41.822382: Epoch 320 
+2024-09-11 13:45:41.822493: Current learning rate: 0.00707 
+2024-09-11 13:49:47.879263: train_loss -0.8306 
+2024-09-11 13:49:47.879437: val_loss -0.6007 
+2024-09-11 13:49:47.879494: Pseudo dice [0.4225, 0.8637] 
+2024-09-11 13:49:47.879552: Epoch time: 246.06 s 
+2024-09-11 13:49:48.849719:  
+2024-09-11 13:49:48.849927: Epoch 321 
+2024-09-11 13:49:48.850015: Current learning rate: 0.00706 
+2024-09-11 13:53:54.697360: train_loss -0.8248 
+2024-09-11 13:53:54.697502: val_loss -0.6089 
+2024-09-11 13:53:54.697558: Pseudo dice [0.4499, 0.8616] 
+2024-09-11 13:53:54.697614: Epoch time: 245.85 s 
+2024-09-11 13:53:55.656228:  
+2024-09-11 13:53:55.656395: Epoch 322 
+2024-09-11 13:53:55.656480: Current learning rate: 0.00705 
+2024-09-11 13:58:01.495622: train_loss -0.8352 
+2024-09-11 13:58:01.495776: val_loss -0.6143 
+2024-09-11 13:58:01.495847: Pseudo dice [0.4765, 0.8682] 
+2024-09-11 13:58:01.495905: Epoch time: 245.84 s 
+2024-09-11 13:58:02.473190:  
+2024-09-11 13:58:02.473423: Epoch 323 
+2024-09-11 13:58:02.473545: Current learning rate: 0.00704 
+2024-09-11 14:02:08.349816: train_loss -0.8433 
+2024-09-11 14:02:08.349993: val_loss -0.5703 
+2024-09-11 14:02:08.350055: Pseudo dice [0.3501, 0.8716] 
+2024-09-11 14:02:08.350114: Epoch time: 245.88 s 
+2024-09-11 14:02:09.314496:  
+2024-09-11 14:02:09.314688: Epoch 324 
+2024-09-11 14:02:09.314774: Current learning rate: 0.00703 
+2024-09-11 14:06:15.143734: train_loss -0.8285 
+2024-09-11 14:06:15.143903: val_loss -0.5796 
+2024-09-11 14:06:15.143961: Pseudo dice [0.3855, 0.8844] 
+2024-09-11 14:06:15.144050: Epoch time: 245.83 s 
+2024-09-11 14:06:16.108942:  
+2024-09-11 14:06:16.109132: Epoch 325 
+2024-09-11 14:06:16.109254: Current learning rate: 0.00702 
+2024-09-11 14:10:22.016590: train_loss -0.8202 
+2024-09-11 14:10:22.016743: val_loss -0.5899 
+2024-09-11 14:10:22.016800: Pseudo dice [0.3995, 0.882] 
+2024-09-11 14:10:22.016858: Epoch time: 245.91 s 
+2024-09-11 14:10:22.987960:  
+2024-09-11 14:10:22.988154: Epoch 326 
+2024-09-11 14:10:22.988291: Current learning rate: 0.00701 
+2024-09-11 14:14:28.791140: train_loss -0.8266 
+2024-09-11 14:14:28.791301: val_loss -0.5637 
+2024-09-11 14:14:28.791381: Pseudo dice [0.3724, 0.8526] 
+2024-09-11 14:14:28.791441: Epoch time: 245.81 s 
+2024-09-11 14:14:29.766193:  
+2024-09-11 14:14:29.766383: Epoch 327 
+2024-09-11 14:14:29.766465: Current learning rate: 0.007 
+2024-09-11 14:18:35.655913: train_loss -0.8273 
+2024-09-11 14:18:35.656115: val_loss -0.5888 
+2024-09-11 14:18:35.656173: Pseudo dice [0.4136, 0.8628] 
+2024-09-11 14:18:35.656230: Epoch time: 245.89 s 
+2024-09-11 14:18:36.622318:  
+2024-09-11 14:18:36.622528: Epoch 328 
+2024-09-11 14:18:36.622620: Current learning rate: 0.00699 
+2024-09-11 14:22:42.541627: train_loss -0.8117 
+2024-09-11 14:22:42.541784: val_loss -0.632 
+2024-09-11 14:22:42.541844: Pseudo dice [0.4945, 0.8805] 
+2024-09-11 14:22:42.541951: Epoch time: 245.92 s 
+2024-09-11 14:22:43.522923:  
+2024-09-11 14:22:43.523099: Epoch 329 
+2024-09-11 14:22:43.523195: Current learning rate: 0.00698 
+2024-09-11 14:26:49.721836: train_loss -0.8145 
+2024-09-11 14:26:49.722008: val_loss -0.6254 
+2024-09-11 14:26:49.722077: Pseudo dice [0.4928, 0.8789] 
+2024-09-11 14:26:49.722144: Epoch time: 246.2 s 
+2024-09-11 14:26:50.698812:  
+2024-09-11 14:26:50.698975: Epoch 330 
+2024-09-11 14:26:50.699133: Current learning rate: 0.00697 
+2024-09-11 14:30:56.881059: train_loss -0.8318 
+2024-09-11 14:30:56.881213: val_loss -0.6076 
+2024-09-11 14:30:56.881269: Pseudo dice [0.472, 0.8689] 
+2024-09-11 14:30:56.881326: Epoch time: 246.18 s 
+2024-09-11 14:30:57.849805:  
+2024-09-11 14:30:57.850014: Epoch 331 
+2024-09-11 14:30:57.850102: Current learning rate: 0.00696 
+2024-09-11 14:35:04.096881: train_loss -0.8178 
+2024-09-11 14:35:04.097053: val_loss -0.6116 
+2024-09-11 14:35:04.097111: Pseudo dice [0.4936, 0.8609] 
+2024-09-11 14:35:04.097168: Epoch time: 246.25 s 
+2024-09-11 14:35:05.077725:  
+2024-09-11 14:35:05.077894: Epoch 332 
+2024-09-11 14:35:05.077983: Current learning rate: 0.00696 
+2024-09-11 14:39:11.146403: train_loss -0.8308 
+2024-09-11 14:39:11.146570: val_loss -0.5975 
+2024-09-11 14:39:11.146627: Pseudo dice [0.4542, 0.8777] 
+2024-09-11 14:39:11.146683: Epoch time: 246.07 s 
+2024-09-11 14:39:12.118079:  
+2024-09-11 14:39:12.118279: Epoch 333 
+2024-09-11 14:39:12.118366: Current learning rate: 0.00695 
+2024-09-11 14:43:18.159701: train_loss -0.836 
+2024-09-11 14:43:18.159893: val_loss -0.6253 
+2024-09-11 14:43:18.159951: Pseudo dice [0.492, 0.8711] 
+2024-09-11 14:43:18.160008: Epoch time: 246.04 s 
+2024-09-11 14:43:18.160129: Yayy! New best EMA pseudo Dice: 0.6604 
+2024-09-11 14:43:22.079776:  
+2024-09-11 14:43:22.080029: Epoch 334 
+2024-09-11 14:43:22.080124: Current learning rate: 0.00694 
+2024-09-11 14:47:28.750438: train_loss -0.8412 
+2024-09-11 14:47:28.750603: val_loss -0.6008 
+2024-09-11 14:47:28.750661: Pseudo dice [0.475, 0.8665] 
+2024-09-11 14:47:28.750716: Epoch time: 246.67 s 
+2024-09-11 14:47:28.750763: Yayy! New best EMA pseudo Dice: 0.6614 
+2024-09-11 14:47:32.723042:  
+2024-09-11 14:47:32.723319: Epoch 335 
+2024-09-11 14:47:32.723457: Current learning rate: 0.00693 
+2024-09-11 14:51:38.928822: train_loss -0.8427 
+2024-09-11 14:51:38.928977: val_loss -0.5816 
+2024-09-11 14:51:38.929034: Pseudo dice [0.4443, 0.862] 
+2024-09-11 14:51:38.929090: Epoch time: 246.21 s 
+2024-09-11 14:51:39.910315:  
+2024-09-11 14:51:39.910514: Epoch 336 
+2024-09-11 14:51:39.910603: Current learning rate: 0.00692 
+2024-09-11 14:55:46.028685: train_loss -0.8447 
+2024-09-11 14:55:46.028843: val_loss -0.6011 
+2024-09-11 14:55:46.028902: Pseudo dice [0.439, 0.878] 
+2024-09-11 14:55:46.028960: Epoch time: 246.12 s 
+2024-09-11 14:55:47.022316:  
+2024-09-11 14:55:47.022549: Epoch 337 
+2024-09-11 14:55:47.022686: Current learning rate: 0.00691 
+2024-09-11 14:59:53.253500: train_loss -0.8427 
+2024-09-11 14:59:53.253650: val_loss -0.5701 
+2024-09-11 14:59:53.253708: Pseudo dice [0.369, 0.8691] 
+2024-09-11 14:59:53.253763: Epoch time: 246.23 s 
+2024-09-11 14:59:54.239152:  
+2024-09-11 14:59:54.239401: Epoch 338 
+2024-09-11 14:59:54.239490: Current learning rate: 0.0069 
+2024-09-11 15:04:00.586742: train_loss -0.8458 
+2024-09-11 15:04:00.586904: val_loss -0.5833 
+2024-09-11 15:04:00.586976: Pseudo dice [0.3825, 0.8833] 
+2024-09-11 15:04:00.587045: Epoch time: 246.35 s 
+2024-09-11 15:04:01.559701:  
+2024-09-11 15:04:01.559991: Epoch 339 
+2024-09-11 15:04:01.560097: Current learning rate: 0.00689 
+2024-09-11 15:08:07.927975: train_loss -0.8452 
+2024-09-11 15:08:07.928119: val_loss -0.5877 
+2024-09-11 15:08:07.928176: Pseudo dice [0.4106, 0.8712] 
+2024-09-11 15:08:07.928236: Epoch time: 246.37 s 
+2024-09-11 15:08:08.912926:  
+2024-09-11 15:08:08.913128: Epoch 340 
+2024-09-11 15:08:08.913241: Current learning rate: 0.00688 
+2024-09-11 15:12:15.246647: train_loss -0.8489 
+2024-09-11 15:12:15.246797: val_loss -0.6049 
+2024-09-11 15:12:15.246854: Pseudo dice [0.45, 0.8759] 
+2024-09-11 15:12:15.246911: Epoch time: 246.34 s 
+2024-09-11 15:12:16.225986:  
+2024-09-11 15:12:16.226207: Epoch 341 
+2024-09-11 15:12:16.226301: Current learning rate: 0.00687 
+2024-09-11 15:16:22.492700: train_loss -0.8499 
+2024-09-11 15:16:22.492852: val_loss -0.601 
+2024-09-11 15:16:22.492910: Pseudo dice [0.4427, 0.8814] 
+2024-09-11 15:16:22.492968: Epoch time: 246.27 s 
+2024-09-11 15:16:23.471894:  
+2024-09-11 15:16:23.472089: Epoch 342 
+2024-09-11 15:16:23.472176: Current learning rate: 0.00686 
+2024-09-11 15:20:29.732696: train_loss -0.8546 
+2024-09-11 15:20:29.732874: val_loss -0.6038 
+2024-09-11 15:20:29.732930: Pseudo dice [0.4843, 0.8795] 
+2024-09-11 15:20:29.732985: Epoch time: 246.26 s 
+2024-09-11 15:20:30.715787:  
+2024-09-11 15:20:30.716028: Epoch 343 
+2024-09-11 15:20:30.716120: Current learning rate: 0.00685 
+2024-09-11 15:24:36.972627: train_loss -0.8484 
+2024-09-11 15:24:36.972775: val_loss -0.6489 
+2024-09-11 15:24:36.972831: Pseudo dice [0.5223, 0.8785] 
+2024-09-11 15:24:36.972888: Epoch time: 246.26 s 
+2024-09-11 15:24:36.972933: Yayy! New best EMA pseudo Dice: 0.6616 
+2024-09-11 15:24:40.955106:  
+2024-09-11 15:24:40.955291: Epoch 344 
+2024-09-11 15:24:40.955440: Current learning rate: 0.00684 
+2024-09-11 15:28:47.566181: train_loss -0.8467 
+2024-09-11 15:28:47.566378: val_loss -0.6224 
+2024-09-11 15:28:47.566435: Pseudo dice [0.4746, 0.8755] 
+2024-09-11 15:28:47.566495: Epoch time: 246.61 s 
+2024-09-11 15:28:47.566539: Yayy! New best EMA pseudo Dice: 0.6629 
+2024-09-11 15:28:51.563326:  
+2024-09-11 15:28:51.563607: Epoch 345 
+2024-09-11 15:28:51.563753: Current learning rate: 0.00683 
+2024-09-11 15:32:57.699049: train_loss -0.8244 
+2024-09-11 15:32:57.699238: val_loss -0.6089 
+2024-09-11 15:32:57.699297: Pseudo dice [0.4434, 0.8712] 
+2024-09-11 15:32:57.699354: Epoch time: 246.14 s 
+2024-09-11 15:32:58.671848:  
+2024-09-11 15:32:58.672094: Epoch 346 
+2024-09-11 15:32:58.672202: Current learning rate: 0.00682 
+2024-09-11 15:37:04.728875: train_loss -0.8097 
+2024-09-11 15:37:04.729024: val_loss -0.5642 
+2024-09-11 15:37:04.729082: Pseudo dice [0.3919, 0.8564] 
+2024-09-11 15:37:04.729194: Epoch time: 246.06 s 
+2024-09-11 15:37:05.722300:  
+2024-09-11 15:37:05.722501: Epoch 347 
+2024-09-11 15:37:05.722590: Current learning rate: 0.00681 
+2024-09-11 15:41:12.090309: train_loss -0.7976 
+2024-09-11 15:41:12.090482: val_loss -0.6185 
+2024-09-11 15:41:12.090540: Pseudo dice [0.4502, 0.8836] 
+2024-09-11 15:41:12.090596: Epoch time: 246.37 s 
+2024-09-11 15:41:13.068713:  
+2024-09-11 15:41:13.068967: Epoch 348 
+2024-09-11 15:41:13.069078: Current learning rate: 0.0068 
+2024-09-11 15:45:19.255349: train_loss -0.8322 
+2024-09-11 15:45:19.255506: val_loss -0.6061 
+2024-09-11 15:45:19.255565: Pseudo dice [0.4681, 0.8758] 
+2024-09-11 15:45:19.255625: Epoch time: 246.19 s 
+2024-09-11 15:45:20.256649:  
+2024-09-11 15:45:20.256862: Epoch 349 
+2024-09-11 15:45:20.256956: Current learning rate: 0.0068 
+2024-09-11 15:49:26.218321: train_loss -0.8447 
+2024-09-11 15:49:26.218473: val_loss -0.584 
+2024-09-11 15:49:26.218544: Pseudo dice [0.4241, 0.8645] 
+2024-09-11 15:49:26.218610: Epoch time: 245.96 s 
+2024-09-11 15:49:30.169593:  
+2024-09-11 15:49:30.169757: Epoch 350 
+2024-09-11 15:49:30.169850: Current learning rate: 0.00679 
+2024-09-11 15:53:36.233886: train_loss -0.8303 
+2024-09-11 15:53:36.234063: val_loss -0.5962 
+2024-09-11 15:53:36.234130: Pseudo dice [0.4433, 0.8735] 
+2024-09-11 15:53:36.234189: Epoch time: 246.07 s 
+2024-09-11 15:53:37.204249:  
+2024-09-11 15:53:37.204427: Epoch 351 
+2024-09-11 15:53:37.204517: Current learning rate: 0.00678 
+2024-09-11 15:57:43.376866: train_loss -0.8258 
+2024-09-11 15:57:43.377014: val_loss -0.5882 
+2024-09-11 15:57:43.377081: Pseudo dice [0.4485, 0.8735] 
+2024-09-11 15:57:43.377152: Epoch time: 246.17 s 
+2024-09-11 15:57:44.376306:  
+2024-09-11 15:57:44.376547: Epoch 352 
+2024-09-11 15:57:44.376683: Current learning rate: 0.00677 
+2024-09-11 16:01:50.463829: train_loss -0.8288 
+2024-09-11 16:01:50.463981: val_loss -0.602 
+2024-09-11 16:01:50.464041: Pseudo dice [0.4341, 0.8716] 
+2024-09-11 16:01:50.464109: Epoch time: 246.09 s 
+2024-09-11 16:01:51.466345:  
+2024-09-11 16:01:51.466580: Epoch 353 
+2024-09-11 16:01:51.466677: Current learning rate: 0.00676 
+2024-09-11 16:05:57.854214: train_loss -0.8339 
+2024-09-11 16:05:57.854389: val_loss -0.581 
+2024-09-11 16:05:57.854448: Pseudo dice [0.4117, 0.8751] 
+2024-09-11 16:05:57.854508: Epoch time: 246.39 s 
+2024-09-11 16:05:58.835009:  
+2024-09-11 16:05:58.835248: Epoch 354 
+2024-09-11 16:05:58.835340: Current learning rate: 0.00675 
+2024-09-11 16:10:04.928881: train_loss -0.8348 
+2024-09-11 16:10:04.929051: val_loss -0.5981 
+2024-09-11 16:10:04.929108: Pseudo dice [0.4307, 0.8779] 
+2024-09-11 16:10:04.929164: Epoch time: 246.1 s 
+2024-09-11 16:10:05.909622:  
+2024-09-11 16:10:05.909829: Epoch 355 
+2024-09-11 16:10:05.909922: Current learning rate: 0.00674 
+2024-09-11 16:14:12.213259: train_loss -0.805 
+2024-09-11 16:14:12.213451: val_loss -0.5733 
+2024-09-11 16:14:12.213510: Pseudo dice [0.4295, 0.859] 
+2024-09-11 16:14:12.213568: Epoch time: 246.31 s 
+2024-09-11 16:14:13.210180:  
+2024-09-11 16:14:13.210375: Epoch 356 
+2024-09-11 16:14:13.210463: Current learning rate: 0.00673 
+2024-09-11 16:18:20.345558: train_loss -0.7951 
+2024-09-11 16:18:20.345717: val_loss -0.5995 
+2024-09-11 16:18:20.345774: Pseudo dice [0.4776, 0.8502] 
+2024-09-11 16:18:20.345830: Epoch time: 247.14 s 
+2024-09-11 16:18:21.337969:  
+2024-09-11 16:18:21.338151: Epoch 357 
+2024-09-11 16:18:21.338300: Current learning rate: 0.00672 
+2024-09-11 16:22:27.616343: train_loss -0.8256 
+2024-09-11 16:22:27.616492: val_loss -0.6036 
+2024-09-11 16:22:27.616550: Pseudo dice [0.428, 0.8738] 
+2024-09-11 16:22:27.616606: Epoch time: 246.28 s 
+2024-09-11 16:22:28.619450:  
+2024-09-11 16:22:28.619711: Epoch 358 
+2024-09-11 16:22:28.619802: Current learning rate: 0.00671 
+2024-09-11 16:26:34.611168: train_loss -0.8118 
+2024-09-11 16:26:34.611318: val_loss -0.5818 
+2024-09-11 16:26:34.611374: Pseudo dice [0.4454, 0.8669] 
+2024-09-11 16:26:34.611430: Epoch time: 245.99 s 
+2024-09-11 16:26:35.613514:  
+2024-09-11 16:26:35.613791: Epoch 359 
+2024-09-11 16:26:35.613879: Current learning rate: 0.0067 
+2024-09-11 16:30:41.568645: train_loss -0.8378 
+2024-09-11 16:30:41.568800: val_loss -0.6055 
+2024-09-11 16:30:41.568925: Pseudo dice [0.4521, 0.8768] 
+2024-09-11 16:30:41.568984: Epoch time: 245.96 s 
+2024-09-11 16:30:42.582374:  
+2024-09-11 16:30:42.582577: Epoch 360 
+2024-09-11 16:30:42.582658: Current learning rate: 0.00669 
+2024-09-11 16:34:48.563665: train_loss -0.8137 
+2024-09-11 16:34:48.563817: val_loss -0.6377 
+2024-09-11 16:34:48.563879: Pseudo dice [0.4907, 0.8732] 
+2024-09-11 16:34:48.563941: Epoch time: 245.98 s 
+2024-09-11 16:34:49.559564:  
+2024-09-11 16:34:49.559788: Epoch 361 
+2024-09-11 16:34:49.559887: Current learning rate: 0.00668 
+2024-09-11 16:38:55.391634: train_loss -0.8316 
+2024-09-11 16:38:55.391784: val_loss -0.5797 
+2024-09-11 16:38:55.391855: Pseudo dice [0.4205, 0.8771] 
+2024-09-11 16:38:55.391909: Epoch time: 245.83 s 
+2024-09-11 16:38:56.370079:  
+2024-09-11 16:38:56.370274: Epoch 362 
+2024-09-11 16:38:56.370362: Current learning rate: 0.00667 
+2024-09-11 16:43:02.402451: train_loss -0.8384 
+2024-09-11 16:43:02.402602: val_loss -0.6395 
+2024-09-11 16:43:02.402655: Pseudo dice [0.5125, 0.8669] 
+2024-09-11 16:43:02.402707: Epoch time: 246.03 s 
+2024-09-11 16:43:03.412495:  
+2024-09-11 16:43:03.412677: Epoch 363 
+2024-09-11 16:43:03.412759: Current learning rate: 0.00666 
+2024-09-11 16:47:09.554040: train_loss -0.827 
+2024-09-11 16:47:09.554209: val_loss -0.561 
+2024-09-11 16:47:09.554261: Pseudo dice [0.3845, 0.8525] 
+2024-09-11 16:47:09.554314: Epoch time: 246.14 s 
+2024-09-11 16:47:10.530321:  
+2024-09-11 16:47:10.530477: Epoch 364 
+2024-09-11 16:47:10.530559: Current learning rate: 0.00665 
+2024-09-11 16:51:16.700723: train_loss -0.8387 
+2024-09-11 16:51:16.700877: val_loss -0.5465 
+2024-09-11 16:51:16.700927: Pseudo dice [0.3565, 0.8558] 
+2024-09-11 16:51:16.700979: Epoch time: 246.17 s 
+2024-09-11 16:51:17.688471:  
+2024-09-11 16:51:17.688676: Epoch 365 
+2024-09-11 16:51:17.688758: Current learning rate: 0.00665 
+2024-09-11 16:55:23.484873: train_loss -0.8406 
+2024-09-11 16:55:23.485013: val_loss -0.5903 
+2024-09-11 16:55:23.485062: Pseudo dice [0.3979, 0.8834] 
+2024-09-11 16:55:23.485112: Epoch time: 245.8 s 
+2024-09-11 16:55:24.498803:  
+2024-09-11 16:55:24.499046: Epoch 366 
+2024-09-11 16:55:24.499127: Current learning rate: 0.00664 
+2024-09-11 16:59:30.298374: train_loss -0.8468 
+2024-09-11 16:59:30.298523: val_loss -0.5722 
+2024-09-11 16:59:30.298581: Pseudo dice [0.3693, 0.8645] 
+2024-09-11 16:59:30.298637: Epoch time: 245.8 s 
+2024-09-11 16:59:31.295830:  
+2024-09-11 16:59:31.296030: Epoch 367 
+2024-09-11 16:59:31.296115: Current learning rate: 0.00663 
+2024-09-11 17:03:37.195842: train_loss -0.8534 
+2024-09-11 17:03:37.196001: val_loss -0.5725 
+2024-09-11 17:03:37.196058: Pseudo dice [0.3899, 0.8741] 
+2024-09-11 17:03:37.196113: Epoch time: 245.9 s 
+2024-09-11 17:03:38.167192:  
+2024-09-11 17:03:38.167351: Epoch 368 
+2024-09-11 17:03:38.167436: Current learning rate: 0.00662 
+2024-09-11 17:07:44.067988: train_loss -0.8459 
+2024-09-11 17:07:44.068177: val_loss -0.585 
+2024-09-11 17:07:44.068235: Pseudo dice [0.4212, 0.8826] 
+2024-09-11 17:07:44.068296: Epoch time: 245.9 s 
+2024-09-11 17:07:45.059651:  
+2024-09-11 17:07:45.059896: Epoch 369 
+2024-09-11 17:07:45.060059: Current learning rate: 0.00661 
+2024-09-11 17:11:51.079382: train_loss -0.8525 
+2024-09-11 17:11:51.079532: val_loss -0.6124 
+2024-09-11 17:11:51.079589: Pseudo dice [0.4612, 0.8773] 
+2024-09-11 17:11:51.079658: Epoch time: 246.02 s 
+2024-09-11 17:11:52.064837:  
+2024-09-11 17:11:52.065076: Epoch 370 
+2024-09-11 17:11:52.065163: Current learning rate: 0.0066 
+2024-09-11 17:15:58.043331: train_loss -0.8548 
+2024-09-11 17:15:58.043485: val_loss -0.5971 
+2024-09-11 17:15:58.043540: Pseudo dice [0.4146, 0.8714] 
+2024-09-11 17:15:58.043596: Epoch time: 245.98 s 
+2024-09-11 17:15:59.055330:  
+2024-09-11 17:15:59.055497: Epoch 371 
+2024-09-11 17:15:59.055583: Current learning rate: 0.00659 
+2024-09-11 17:20:04.829114: train_loss -0.8546 
+2024-09-11 17:20:04.829260: val_loss -0.5663 
+2024-09-11 17:20:04.829316: Pseudo dice [0.3277, 0.8855] 
+2024-09-11 17:20:04.829371: Epoch time: 245.78 s 
+2024-09-11 17:20:05.805038:  
+2024-09-11 17:20:05.805250: Epoch 372 
+2024-09-11 17:20:05.805337: Current learning rate: 0.00658 
+2024-09-11 17:24:11.704199: train_loss -0.8498 
+2024-09-11 17:24:11.704434: val_loss -0.6158 
+2024-09-11 17:24:11.704493: Pseudo dice [0.4537, 0.8815] 
+2024-09-11 17:24:11.704549: Epoch time: 245.9 s 
+2024-09-11 17:24:12.693342:  
+2024-09-11 17:24:12.693544: Epoch 373 
+2024-09-11 17:24:12.693634: Current learning rate: 0.00657 
+2024-09-11 17:28:18.832751: train_loss -0.8487 
+2024-09-11 17:28:18.832901: val_loss -0.6157 
+2024-09-11 17:28:18.832958: Pseudo dice [0.461, 0.8766] 
+2024-09-11 17:28:18.833014: Epoch time: 246.14 s 
+2024-09-11 17:28:19.829086:  
+2024-09-11 17:28:19.829254: Epoch 374 
+2024-09-11 17:28:19.829347: Current learning rate: 0.00656 
+2024-09-11 17:32:25.925389: train_loss -0.8529 
+2024-09-11 17:32:25.925541: val_loss -0.6207 
+2024-09-11 17:32:25.925598: Pseudo dice [0.4759, 0.8677] 
+2024-09-11 17:32:25.925657: Epoch time: 246.1 s 
+2024-09-11 17:32:26.928632:  
+2024-09-11 17:32:26.928795: Epoch 375 
+2024-09-11 17:32:26.928882: Current learning rate: 0.00655 
+2024-09-11 17:36:32.960017: train_loss -0.8505 
+2024-09-11 17:36:32.960219: val_loss -0.6103 
+2024-09-11 17:36:32.960277: Pseudo dice [0.4574, 0.8693] 
+2024-09-11 17:36:32.960334: Epoch time: 246.03 s 
+2024-09-11 17:36:33.947552:  
+2024-09-11 17:36:33.947738: Epoch 376 
+2024-09-11 17:36:33.947840: Current learning rate: 0.00654 
+2024-09-11 17:40:40.034549: train_loss -0.8538 
+2024-09-11 17:40:40.034685: val_loss -0.5574 
+2024-09-11 17:40:40.034737: Pseudo dice [0.3415, 0.8651] 
+2024-09-11 17:40:40.034788: Epoch time: 246.09 s 
+2024-09-11 17:40:41.036829:  
+2024-09-11 17:40:41.037084: Epoch 377 
+2024-09-11 17:40:41.037208: Current learning rate: 0.00653 
+2024-09-11 17:44:47.027280: train_loss -0.8526 
+2024-09-11 17:44:47.027425: val_loss -0.5968 
+2024-09-11 17:44:47.027475: Pseudo dice [0.4243, 0.8783] 
+2024-09-11 17:44:47.027563: Epoch time: 245.99 s 
+2024-09-11 17:44:48.006127:  
+2024-09-11 17:44:48.006317: Epoch 378 
+2024-09-11 17:44:48.006403: Current learning rate: 0.00652 
+2024-09-11 17:48:54.012879: train_loss -0.8591 
+2024-09-11 17:48:54.013053: val_loss -0.5997 
+2024-09-11 17:48:54.013104: Pseudo dice [0.4207, 0.8668] 
+2024-09-11 17:48:54.013155: Epoch time: 246.01 s 
+2024-09-11 17:48:55.010244:  
+2024-09-11 17:48:55.010456: Epoch 379 
+2024-09-11 17:48:55.010543: Current learning rate: 0.00651 
+2024-09-11 17:53:02.004367: train_loss -0.862 
+2024-09-11 17:53:02.004503: val_loss -0.6089 
+2024-09-11 17:53:02.004553: Pseudo dice [0.4437, 0.8829] 
+2024-09-11 17:53:02.004603: Epoch time: 247.0 s 
+2024-09-11 17:53:03.006046:  
+2024-09-11 17:53:03.006294: Epoch 380 
+2024-09-11 17:53:03.006389: Current learning rate: 0.0065 
+2024-09-11 17:57:09.130022: train_loss -0.8627 
+2024-09-11 17:57:09.130167: val_loss -0.5643 
+2024-09-11 17:57:09.130217: Pseudo dice [0.3855, 0.8623] 
+2024-09-11 17:57:09.130268: Epoch time: 246.13 s 
+2024-09-11 17:57:10.100804:  
+2024-09-11 17:57:10.101089: Epoch 381 
+2024-09-11 17:57:10.101173: Current learning rate: 0.00649 
+2024-09-11 18:01:16.607510: train_loss -0.86 
+2024-09-11 18:01:16.607651: val_loss -0.6092 
+2024-09-11 18:01:16.607703: Pseudo dice [0.4496, 0.8787] 
+2024-09-11 18:01:16.607753: Epoch time: 246.51 s 
+2024-09-11 18:01:17.625263:  
+2024-09-11 18:01:17.625505: Epoch 382 
+2024-09-11 18:01:17.625634: Current learning rate: 0.00648 
+2024-09-11 18:05:24.045612: train_loss -0.852 
+2024-09-11 18:05:24.045753: val_loss -0.6006 
+2024-09-11 18:05:24.045805: Pseudo dice [0.471, 0.8655] 
+2024-09-11 18:05:24.045856: Epoch time: 246.42 s 
+2024-09-11 18:05:25.052194:  
+2024-09-11 18:05:25.052424: Epoch 383 
+2024-09-11 18:05:25.052510: Current learning rate: 0.00648 
+2024-09-11 18:09:31.224221: train_loss -0.8501 
+2024-09-11 18:09:31.224361: val_loss -0.6407 
+2024-09-11 18:09:31.224411: Pseudo dice [0.5145, 0.8657] 
+2024-09-11 18:09:31.224462: Epoch time: 246.17 s 
+2024-09-11 18:09:32.230824:  
+2024-09-11 18:09:32.231091: Epoch 384 
+2024-09-11 18:09:32.231218: Current learning rate: 0.00647 
+2024-09-11 18:13:38.335540: train_loss -0.8549 
+2024-09-11 18:13:38.335691: val_loss -0.5943 
+2024-09-11 18:13:38.335745: Pseudo dice [0.3965, 0.8844] 
+2024-09-11 18:13:38.335797: Epoch time: 246.11 s 
+2024-09-11 18:13:39.344570:  
+2024-09-11 18:13:39.344795: Epoch 385 
+2024-09-11 18:13:39.344903: Current learning rate: 0.00646 
+2024-09-11 18:17:45.519683: train_loss -0.8623 
+2024-09-11 18:17:45.519844: val_loss -0.5975 
+2024-09-11 18:17:45.519897: Pseudo dice [0.4553, 0.8814] 
+2024-09-11 18:17:45.519948: Epoch time: 246.18 s 
+2024-09-11 18:17:46.521778:  
+2024-09-11 18:17:46.522079: Epoch 386 
+2024-09-11 18:17:46.522207: Current learning rate: 0.00645 
+2024-09-11 18:21:52.673218: train_loss -0.8544 
+2024-09-11 18:21:52.673361: val_loss -0.5987 
+2024-09-11 18:21:52.673411: Pseudo dice [0.4121, 0.8696] 
+2024-09-11 18:21:52.673463: Epoch time: 246.15 s 
+2024-09-11 18:21:53.669257:  
+2024-09-11 18:21:53.669446: Epoch 387 
+2024-09-11 18:21:53.669532: Current learning rate: 0.00644 
+2024-09-11 18:26:00.000463: train_loss -0.8582 
+2024-09-11 18:26:00.000601: val_loss -0.604 
+2024-09-11 18:26:00.000656: Pseudo dice [0.4374, 0.8845] 
+2024-09-11 18:26:00.000710: Epoch time: 246.33 s 
+2024-09-11 18:26:01.002528:  
+2024-09-11 18:26:01.002705: Epoch 388 
+2024-09-11 18:26:01.002810: Current learning rate: 0.00643 
+2024-09-11 18:30:07.276313: train_loss -0.8608 
+2024-09-11 18:30:07.276505: val_loss -0.6173 
+2024-09-11 18:30:07.276556: Pseudo dice [0.4977, 0.867] 
+2024-09-11 18:30:07.276608: Epoch time: 246.28 s 
+2024-09-11 18:30:08.336663:  
+2024-09-11 18:30:08.336927: Epoch 389 
+2024-09-11 18:30:08.337011: Current learning rate: 0.00642 
+2024-09-11 18:34:14.403080: train_loss -0.8618 
+2024-09-11 18:34:14.403218: val_loss -0.5845 
+2024-09-11 18:34:14.403268: Pseudo dice [0.408, 0.8706] 
+2024-09-11 18:34:14.403319: Epoch time: 246.07 s 
+2024-09-11 18:34:15.414358:  
+2024-09-11 18:34:15.414618: Epoch 390 
+2024-09-11 18:34:15.414743: Current learning rate: 0.00641 
+2024-09-11 18:38:21.577479: train_loss -0.8676 
+2024-09-11 18:38:21.577617: val_loss -0.6151 
+2024-09-11 18:38:21.577667: Pseudo dice [0.4647, 0.8763] 
+2024-09-11 18:38:21.577719: Epoch time: 246.17 s 
+2024-09-11 18:38:22.576142:  
+2024-09-11 18:38:22.576355: Epoch 391 
+2024-09-11 18:38:22.576440: Current learning rate: 0.0064 
+2024-09-11 18:42:28.854171: train_loss -0.8679 
+2024-09-11 18:42:28.854306: val_loss -0.6197 
+2024-09-11 18:42:28.854357: Pseudo dice [0.4621, 0.8745] 
+2024-09-11 18:42:28.854407: Epoch time: 246.28 s 
+2024-09-11 18:42:29.837312:  
+2024-09-11 18:42:29.837503: Epoch 392 
+2024-09-11 18:42:29.837587: Current learning rate: 0.00639 
+2024-09-11 18:46:36.238056: train_loss -0.8647 
+2024-09-11 18:46:36.238193: val_loss -0.5879 
+2024-09-11 18:46:36.238244: Pseudo dice [0.4279, 0.8813] 
+2024-09-11 18:46:36.238297: Epoch time: 246.4 s 
+2024-09-11 18:46:37.237334:  
+2024-09-11 18:46:37.237553: Epoch 393 
+2024-09-11 18:46:37.237642: Current learning rate: 0.00638 
+2024-09-11 18:50:43.747553: train_loss -0.8657 
+2024-09-11 18:50:43.747693: val_loss -0.5914 
+2024-09-11 18:50:43.747744: Pseudo dice [0.4403, 0.8763] 
+2024-09-11 18:50:43.747794: Epoch time: 246.51 s 
+2024-09-11 18:50:44.757161:  
+2024-09-11 18:50:44.757386: Epoch 394 
+2024-09-11 18:50:44.757519: Current learning rate: 0.00637 
+2024-09-11 18:54:51.341469: train_loss -0.8646 
+2024-09-11 18:54:51.341657: val_loss -0.588 
+2024-09-11 18:54:51.341710: Pseudo dice [0.405, 0.8735] 
+2024-09-11 18:54:51.341761: Epoch time: 246.59 s 
+2024-09-11 18:54:52.336391:  
+2024-09-11 18:54:52.336565: Epoch 395 
+2024-09-11 18:54:52.336649: Current learning rate: 0.00636 
+2024-09-11 18:58:58.796305: train_loss -0.867 
+2024-09-11 18:58:58.796443: val_loss -0.6072 
+2024-09-11 18:58:58.796494: Pseudo dice [0.4736, 0.8753] 
+2024-09-11 18:58:58.796545: Epoch time: 246.46 s 
+2024-09-11 18:58:59.803316:  
+2024-09-11 18:58:59.803464: Epoch 396 
+2024-09-11 18:58:59.803568: Current learning rate: 0.00635 
+2024-09-11 19:03:06.132745: train_loss -0.8596 
+2024-09-11 19:03:06.132882: val_loss -0.6258 
+2024-09-11 19:03:06.132933: Pseudo dice [0.4724, 0.8875] 
+2024-09-11 19:03:06.132982: Epoch time: 246.33 s 
+2024-09-11 19:03:07.136256:  
+2024-09-11 19:03:07.136453: Epoch 397 
+2024-09-11 19:03:07.136538: Current learning rate: 0.00634 
+2024-09-11 19:07:13.514615: train_loss -0.8633 
+2024-09-11 19:07:13.514754: val_loss -0.6118 
+2024-09-11 19:07:13.514804: Pseudo dice [0.4366, 0.8738] 
+2024-09-11 19:07:13.514855: Epoch time: 246.38 s 
+2024-09-11 19:07:14.515903:  
+2024-09-11 19:07:14.516079: Epoch 398 
+2024-09-11 19:07:14.516184: Current learning rate: 0.00633 
+2024-09-11 19:11:20.636882: train_loss -0.8637 
+2024-09-11 19:11:20.637017: val_loss -0.6053 
+2024-09-11 19:11:20.637070: Pseudo dice [0.4491, 0.8789] 
+2024-09-11 19:11:20.637125: Epoch time: 246.12 s 
+2024-09-11 19:11:21.671298:  
+2024-09-11 19:11:21.671476: Epoch 399 
+2024-09-11 19:11:21.671559: Current learning rate: 0.00632 
+2024-09-11 19:15:27.950897: train_loss -0.8567 
+2024-09-11 19:15:27.951037: val_loss -0.5984 
+2024-09-11 19:15:27.951088: Pseudo dice [0.4825, 0.8778] 
+2024-09-11 19:15:27.951138: Epoch time: 246.28 s 
+2024-09-11 19:15:31.896667:  
+2024-09-11 19:15:31.896816: Epoch 400 
+2024-09-11 19:15:31.896898: Current learning rate: 0.00631 
+2024-09-11 19:19:38.054590: train_loss -0.8634 
+2024-09-11 19:19:38.054735: val_loss -0.5851 
+2024-09-11 19:19:38.054785: Pseudo dice [0.4281, 0.869] 
+2024-09-11 19:19:38.054837: Epoch time: 246.16 s 
+2024-09-11 19:19:39.081195:  
+2024-09-11 19:19:39.081412: Epoch 401 
+2024-09-11 19:19:39.081496: Current learning rate: 0.0063 
+2024-09-11 19:23:46.131777: train_loss -0.8676 
+2024-09-11 19:23:46.131970: val_loss -0.5645 
+2024-09-11 19:23:46.132022: Pseudo dice [0.3879, 0.8665] 
+2024-09-11 19:23:46.132077: Epoch time: 247.05 s 
+2024-09-11 19:23:47.155486:  
+2024-09-11 19:23:47.155714: Epoch 402 
+2024-09-11 19:23:47.155841: Current learning rate: 0.0063 
+2024-09-11 19:27:53.522394: train_loss -0.8671 
+2024-09-11 19:27:53.522533: val_loss -0.5674 
+2024-09-11 19:27:53.522584: Pseudo dice [0.3647, 0.874] 
+2024-09-11 19:27:53.522636: Epoch time: 246.37 s 
+2024-09-11 19:27:54.557260:  
+2024-09-11 19:27:54.557513: Epoch 403 
+2024-09-11 19:27:54.557596: Current learning rate: 0.00629 
+2024-09-11 19:32:00.611656: train_loss -0.8657 
+2024-09-11 19:32:00.611794: val_loss -0.5873 
+2024-09-11 19:32:00.611859: Pseudo dice [0.4395, 0.8697] 
+2024-09-11 19:32:00.611910: Epoch time: 246.06 s 
+2024-09-11 19:32:01.615343:  
+2024-09-11 19:32:01.615540: Epoch 404 
+2024-09-11 19:32:01.615652: Current learning rate: 0.00628 
+2024-09-11 19:36:07.500008: train_loss -0.868 
+2024-09-11 19:36:07.500166: val_loss -0.6011 
+2024-09-11 19:36:07.500219: Pseudo dice [0.477, 0.8835] 
+2024-09-11 19:36:07.500270: Epoch time: 245.89 s 
+2024-09-11 19:36:08.502106:  
+2024-09-11 19:36:08.502339: Epoch 405 
+2024-09-11 19:36:08.502419: Current learning rate: 0.00627 
+2024-09-11 19:40:14.357631: train_loss -0.8629 
+2024-09-11 19:40:14.357769: val_loss -0.5711 
+2024-09-11 19:40:14.357822: Pseudo dice [0.3658, 0.8802] 
+2024-09-11 19:40:14.357960: Epoch time: 245.86 s 
+2024-09-11 19:40:15.361660:  
+2024-09-11 19:40:15.361866: Epoch 406 
+2024-09-11 19:40:15.361953: Current learning rate: 0.00626 
+2024-09-11 19:44:21.350616: train_loss -0.8625 
+2024-09-11 19:44:21.350763: val_loss -0.6042 
+2024-09-11 19:44:21.350813: Pseudo dice [0.4655, 0.8593] 
+2024-09-11 19:44:21.350865: Epoch time: 245.99 s 
+2024-09-11 19:44:22.352721:  
+2024-09-11 19:44:22.352989: Epoch 407 
+2024-09-11 19:44:22.353072: Current learning rate: 0.00625 
+2024-09-11 19:48:28.246536: train_loss -0.8649 
+2024-09-11 19:48:28.246707: val_loss -0.5841 
+2024-09-11 19:48:28.246780: Pseudo dice [0.3934, 0.8803] 
+2024-09-11 19:48:28.246851: Epoch time: 245.9 s 
+2024-09-11 19:48:29.279415:  
+2024-09-11 19:48:29.279578: Epoch 408 
+2024-09-11 19:48:29.279661: Current learning rate: 0.00624 
+2024-09-11 19:52:35.155930: train_loss -0.8609 
+2024-09-11 19:52:35.156105: val_loss -0.5793 
+2024-09-11 19:52:35.156157: Pseudo dice [0.4036, 0.8774] 
+2024-09-11 19:52:35.156209: Epoch time: 245.88 s 
+2024-09-11 19:52:36.157470:  
+2024-09-11 19:52:36.157653: Epoch 409 
+2024-09-11 19:52:36.157738: Current learning rate: 0.00623 
+2024-09-11 19:56:41.999907: train_loss -0.8627 
+2024-09-11 19:56:42.000046: val_loss -0.5818 
+2024-09-11 19:56:42.000097: Pseudo dice [0.4357, 0.8705] 
+2024-09-11 19:56:42.000147: Epoch time: 245.84 s 
+2024-09-11 19:56:43.039107:  
+2024-09-11 19:56:43.039302: Epoch 410 
+2024-09-11 19:56:43.039412: Current learning rate: 0.00622 
+2024-09-11 20:00:49.067237: train_loss -0.8603 
+2024-09-11 20:00:49.067436: val_loss -0.5874 
+2024-09-11 20:00:49.067489: Pseudo dice [0.4082, 0.8835] 
+2024-09-11 20:00:49.067543: Epoch time: 246.03 s 
+2024-09-11 20:00:49.998008:  
+2024-09-11 20:00:49.998215: Epoch 411 
+2024-09-11 20:00:49.998317: Current learning rate: 0.00621 
+2024-09-11 20:04:56.369463: train_loss -0.8634 
+2024-09-11 20:04:56.369600: val_loss -0.5908 
+2024-09-11 20:04:56.369691: Pseudo dice [0.4305, 0.8719] 
+2024-09-11 20:04:56.369791: Epoch time: 246.37 s 
+2024-09-11 20:04:57.332753:  
+2024-09-11 20:04:57.332975: Epoch 412 
+2024-09-11 20:04:57.333097: Current learning rate: 0.0062 
+2024-09-11 20:09:03.431771: train_loss -0.8684 
+2024-09-11 20:09:03.431914: val_loss -0.5809 
+2024-09-11 20:09:03.432020: Pseudo dice [0.4029, 0.8826] 
+2024-09-11 20:09:03.432073: Epoch time: 246.1 s 
+2024-09-11 20:09:04.393930:  
+2024-09-11 20:09:04.394168: Epoch 413 
+2024-09-11 20:09:04.394252: Current learning rate: 0.00619 
+2024-09-11 20:13:10.332959: train_loss -0.8609 
+2024-09-11 20:13:10.333096: val_loss -0.6033 
+2024-09-11 20:13:10.333146: Pseudo dice [0.4358, 0.8724] 
+2024-09-11 20:13:10.333198: Epoch time: 245.94 s 
+2024-09-11 20:13:11.309109:  
+2024-09-11 20:13:11.309292: Epoch 414 
+2024-09-11 20:13:11.309396: Current learning rate: 0.00618 
+2024-09-11 20:17:17.254403: train_loss -0.8637 
+2024-09-11 20:17:17.254546: val_loss -0.5722 
+2024-09-11 20:17:17.254597: Pseudo dice [0.4043, 0.8711] 
+2024-09-11 20:17:17.254649: Epoch time: 245.95 s 
+2024-09-11 20:17:18.193589:  
+2024-09-11 20:17:18.193773: Epoch 415 
+2024-09-11 20:17:18.193856: Current learning rate: 0.00617 
+2024-09-11 20:21:24.109126: train_loss -0.8546 
+2024-09-11 20:21:24.109267: val_loss -0.5736 
+2024-09-11 20:21:24.109317: Pseudo dice [0.406, 0.8724] 
+2024-09-11 20:21:24.109369: Epoch time: 245.92 s 
+2024-09-11 20:21:25.064245:  
+2024-09-11 20:21:25.064429: Epoch 416 
+2024-09-11 20:21:25.064528: Current learning rate: 0.00616 
+2024-09-11 20:25:31.248374: train_loss -0.8281 
+2024-09-11 20:25:31.248536: val_loss -0.5663 
+2024-09-11 20:25:31.248587: Pseudo dice [0.398, 0.8596] 
+2024-09-11 20:25:31.248639: Epoch time: 246.19 s 
+2024-09-11 20:25:32.191855:  
+2024-09-11 20:25:32.192006: Epoch 417 
+2024-09-11 20:25:32.192086: Current learning rate: 0.00615 
+2024-09-11 20:29:38.050398: train_loss -0.8312 
+2024-09-11 20:29:38.050570: val_loss -0.6075 
+2024-09-11 20:29:38.050621: Pseudo dice [0.4625, 0.872] 
+2024-09-11 20:29:38.050672: Epoch time: 245.86 s 
+2024-09-11 20:29:39.024607:  
+2024-09-11 20:29:39.024840: Epoch 418 
+2024-09-11 20:29:39.024932: Current learning rate: 0.00614 
+2024-09-11 20:33:44.871228: train_loss -0.8431 
+2024-09-11 20:33:44.871398: val_loss -0.5638 
+2024-09-11 20:33:44.871450: Pseudo dice [0.3772, 0.862] 
+2024-09-11 20:33:44.871501: Epoch time: 245.85 s 
+2024-09-11 20:33:45.830258:  
+2024-09-11 20:33:45.830392: Epoch 419 
+2024-09-11 20:33:45.830473: Current learning rate: 0.00613 
+2024-09-11 20:37:51.514193: train_loss -0.8376 
+2024-09-11 20:37:51.514393: val_loss -0.5732 
+2024-09-11 20:37:51.514449: Pseudo dice [0.3831, 0.8666] 
+2024-09-11 20:37:51.514506: Epoch time: 245.69 s 
+2024-09-11 20:37:52.472872:  
+2024-09-11 20:37:52.473075: Epoch 420 
+2024-09-11 20:37:52.473158: Current learning rate: 0.00612 
+2024-09-11 20:41:58.059951: train_loss -0.8484 
+2024-09-11 20:41:58.060107: val_loss -0.563 
+2024-09-11 20:41:58.060160: Pseudo dice [0.3913, 0.8705] 
+2024-09-11 20:41:58.060212: Epoch time: 245.59 s 
+2024-09-11 20:41:58.998941:  
+2024-09-11 20:41:58.999164: Epoch 421 
+2024-09-11 20:41:58.999248: Current learning rate: 0.00612 
+2024-09-11 20:46:04.594566: train_loss -0.839 
+2024-09-11 20:46:04.594763: val_loss -0.5985 
+2024-09-11 20:46:04.594815: Pseudo dice [0.4436, 0.8422] 
+2024-09-11 20:46:04.594866: Epoch time: 245.6 s 
+2024-09-11 20:46:05.595191:  
+2024-09-11 20:46:05.595400: Epoch 422 
+2024-09-11 20:46:05.595485: Current learning rate: 0.00611 
+2024-09-11 20:50:11.351477: train_loss -0.813 
+2024-09-11 20:50:11.351613: val_loss -0.5933 
+2024-09-11 20:50:11.351664: Pseudo dice [0.4109, 0.8671] 
+2024-09-11 20:50:11.351718: Epoch time: 245.76 s 
+2024-09-11 20:50:12.291681:  
+2024-09-11 20:50:12.291851: Epoch 423 
+2024-09-11 20:50:12.291934: Current learning rate: 0.0061 
+2024-09-11 20:54:17.975117: train_loss -0.8081 
+2024-09-11 20:54:17.975256: val_loss -0.6295 
+2024-09-11 20:54:17.975307: Pseudo dice [0.5026, 0.8688] 
+2024-09-11 20:54:17.975358: Epoch time: 245.69 s 
+2024-09-11 20:54:18.924256:  
+2024-09-11 20:54:18.924455: Epoch 424 
+2024-09-11 20:54:18.924537: Current learning rate: 0.00609 
+2024-09-11 20:58:24.552423: train_loss -0.7996 
+2024-09-11 20:58:24.552563: val_loss -0.5798 
+2024-09-11 20:58:24.552614: Pseudo dice [0.4495, 0.8437] 
+2024-09-11 20:58:24.552666: Epoch time: 245.63 s 
+2024-09-11 20:58:26.435705:  
+2024-09-11 20:58:26.435927: Epoch 425 
+2024-09-11 20:58:26.436078: Current learning rate: 0.00608 
+2024-09-11 21:02:32.145772: train_loss -0.8023 
+2024-09-11 21:02:32.145916: val_loss -0.5941 
+2024-09-11 21:02:32.145966: Pseudo dice [0.445, 0.8711] 
+2024-09-11 21:02:32.146018: Epoch time: 245.71 s 
+2024-09-11 21:02:33.112681:  
+2024-09-11 21:02:33.112950: Epoch 426 
+2024-09-11 21:02:33.113028: Current learning rate: 0.00607 
+2024-09-11 21:06:38.724722: train_loss -0.8259 
+2024-09-11 21:06:38.724861: val_loss -0.5709 
+2024-09-11 21:06:38.724912: Pseudo dice [0.3969, 0.8583] 
+2024-09-11 21:06:38.724962: Epoch time: 245.61 s 
+2024-09-11 21:06:39.662804:  
+2024-09-11 21:06:39.662989: Epoch 427 
+2024-09-11 21:06:39.663118: Current learning rate: 0.00606 
+2024-09-11 21:10:45.224828: train_loss -0.8221 
+2024-09-11 21:10:45.224967: val_loss -0.5625 
+2024-09-11 21:10:45.225017: Pseudo dice [0.369, 0.8747] 
+2024-09-11 21:10:45.225068: Epoch time: 245.56 s 
+2024-09-11 21:10:46.160650:  
+2024-09-11 21:10:46.160900: Epoch 428 
+2024-09-11 21:10:46.160983: Current learning rate: 0.00605 
+2024-09-11 21:14:51.994264: train_loss -0.8381 
+2024-09-11 21:14:51.994404: val_loss -0.5508 
+2024-09-11 21:14:51.994473: Pseudo dice [0.3573, 0.8602] 
+2024-09-11 21:14:51.994570: Epoch time: 245.84 s 
+2024-09-11 21:14:52.947739:  
+2024-09-11 21:14:52.947930: Epoch 429 
+2024-09-11 21:14:52.948016: Current learning rate: 0.00604 
+2024-09-11 21:18:58.953209: train_loss -0.8391 
+2024-09-11 21:18:58.953359: val_loss -0.56 
+2024-09-11 21:18:58.953410: Pseudo dice [0.375, 0.8657] 
+2024-09-11 21:18:58.953460: Epoch time: 246.01 s 
+2024-09-11 21:18:59.936982:  
+2024-09-11 21:18:59.937231: Epoch 430 
+2024-09-11 21:18:59.937319: Current learning rate: 0.00603 
+2024-09-11 21:23:05.812200: train_loss -0.8447 
+2024-09-11 21:23:05.812347: val_loss -0.5875 
+2024-09-11 21:23:05.812398: Pseudo dice [0.3853, 0.8696] 
+2024-09-11 21:23:05.812452: Epoch time: 245.88 s 
+2024-09-11 21:23:06.769542:  
+2024-09-11 21:23:06.769728: Epoch 431 
+2024-09-11 21:23:06.769811: Current learning rate: 0.00602 
+2024-09-11 21:27:12.519476: train_loss -0.8477 
+2024-09-11 21:27:12.519613: val_loss -0.6068 
+2024-09-11 21:27:12.519663: Pseudo dice [0.4411, 0.8677] 
+2024-09-11 21:27:12.519753: Epoch time: 245.75 s 
+2024-09-11 21:27:13.462917:  
+2024-09-11 21:27:13.463098: Epoch 432 
+2024-09-11 21:27:13.463221: Current learning rate: 0.00601 
+2024-09-11 21:31:19.159246: train_loss -0.8427 
+2024-09-11 21:31:19.159406: val_loss -0.576 
+2024-09-11 21:31:19.159457: Pseudo dice [0.4015, 0.8529] 
+2024-09-11 21:31:19.159510: Epoch time: 245.7 s 
+2024-09-11 21:31:20.109050:  
+2024-09-11 21:31:20.109267: Epoch 433 
+2024-09-11 21:31:20.109349: Current learning rate: 0.006 
+2024-09-11 21:35:25.824575: train_loss -0.848 
+2024-09-11 21:35:25.824721: val_loss -0.5721 
+2024-09-11 21:35:25.824771: Pseudo dice [0.3774, 0.8639] 
+2024-09-11 21:35:25.824822: Epoch time: 245.72 s 
+2024-09-11 21:35:26.790891:  
+2024-09-11 21:35:26.791063: Epoch 434 
+2024-09-11 21:35:26.791144: Current learning rate: 0.00599 
+2024-09-11 21:39:32.512651: train_loss -0.8445 
+2024-09-11 21:39:32.512794: val_loss -0.6036 
+2024-09-11 21:39:32.512844: Pseudo dice [0.4829, 0.8675] 
+2024-09-11 21:39:32.512894: Epoch time: 245.72 s 
+2024-09-11 21:39:33.461896:  
+2024-09-11 21:39:33.462068: Epoch 435 
+2024-09-11 21:39:33.462152: Current learning rate: 0.00598 
+2024-09-11 21:43:39.244163: train_loss -0.8439 
+2024-09-11 21:43:39.244303: val_loss -0.5542 
+2024-09-11 21:43:39.244354: Pseudo dice [0.3625, 0.8699] 
+2024-09-11 21:43:39.244438: Epoch time: 245.78 s 
+2024-09-11 21:43:40.194893:  
+2024-09-11 21:43:40.195082: Epoch 436 
+2024-09-11 21:43:40.195189: Current learning rate: 0.00597 
+2024-09-11 21:47:45.963300: train_loss -0.8466 
+2024-09-11 21:47:45.963442: val_loss -0.5843 
+2024-09-11 21:47:45.963493: Pseudo dice [0.429, 0.871] 
+2024-09-11 21:47:45.963545: Epoch time: 245.77 s 
+2024-09-11 21:47:46.939226:  
+2024-09-11 21:47:46.939462: Epoch 437 
+2024-09-11 21:47:46.939549: Current learning rate: 0.00596 
+2024-09-11 21:51:52.717180: train_loss -0.8416 
+2024-09-11 21:51:52.717322: val_loss -0.5887 
+2024-09-11 21:51:52.717373: Pseudo dice [0.4252, 0.8773] 
+2024-09-11 21:51:52.717424: Epoch time: 245.78 s 
+2024-09-11 21:51:53.674041:  
+2024-09-11 21:51:53.674289: Epoch 438 
+2024-09-11 21:51:53.674421: Current learning rate: 0.00595 
+2024-09-11 21:55:59.503630: train_loss -0.8269 
+2024-09-11 21:55:59.503777: val_loss -0.6108 
+2024-09-11 21:55:59.503835: Pseudo dice [0.487, 0.8728] 
+2024-09-11 21:55:59.503891: Epoch time: 245.83 s 
+2024-09-11 21:56:00.450663:  
+2024-09-11 21:56:00.450854: Epoch 439 
+2024-09-11 21:56:00.450943: Current learning rate: 0.00594 
+2024-09-11 22:00:06.386497: train_loss -0.8293 
+2024-09-11 22:00:06.386636: val_loss -0.5875 
+2024-09-11 22:00:06.386686: Pseudo dice [0.4005, 0.874] 
+2024-09-11 22:00:06.386737: Epoch time: 245.94 s 
+2024-09-11 22:00:07.333925:  
+2024-09-11 22:00:07.334188: Epoch 440 
+2024-09-11 22:00:07.334273: Current learning rate: 0.00593 
+2024-09-11 22:04:13.230689: train_loss -0.8193 
+2024-09-11 22:04:13.230837: val_loss -0.604 
+2024-09-11 22:04:13.230934: Pseudo dice [0.4282, 0.8773] 
+2024-09-11 22:04:13.230996: Epoch time: 245.9 s 
+2024-09-11 22:04:14.199938:  
+2024-09-11 22:04:14.200103: Epoch 441 
+2024-09-11 22:04:14.200186: Current learning rate: 0.00592 
+2024-09-11 22:08:20.048436: train_loss -0.8205 
+2024-09-11 22:08:20.048603: val_loss -0.5823 
+2024-09-11 22:08:20.048654: Pseudo dice [0.4013, 0.8654] 
+2024-09-11 22:08:20.048705: Epoch time: 245.85 s 
+2024-09-11 22:08:21.011586:  
+2024-09-11 22:08:21.011781: Epoch 442 
+2024-09-11 22:08:21.011879: Current learning rate: 0.00592 
+2024-09-11 22:12:26.884802: train_loss -0.821 
+2024-09-11 22:12:26.884972: val_loss -0.6128 
+2024-09-11 22:12:26.885025: Pseudo dice [0.4534, 0.8672] 
+2024-09-11 22:12:26.885076: Epoch time: 245.88 s 
+2024-09-11 22:12:27.826359:  
+2024-09-11 22:12:27.826522: Epoch 443 
+2024-09-11 22:12:27.826605: Current learning rate: 0.00591 
+2024-09-11 22:16:33.978283: train_loss -0.8237 
+2024-09-11 22:16:33.978438: val_loss -0.6045 
+2024-09-11 22:16:33.978488: Pseudo dice [0.4428, 0.8774] 
+2024-09-11 22:16:33.978541: Epoch time: 246.15 s 
+2024-09-11 22:16:34.930416:  
+2024-09-11 22:16:34.930623: Epoch 444 
+2024-09-11 22:16:34.930741: Current learning rate: 0.0059 
+2024-09-11 22:20:41.001548: train_loss -0.8383 
+2024-09-11 22:20:41.001722: val_loss -0.6045 
+2024-09-11 22:20:41.001801: Pseudo dice [0.4272, 0.8862] 
+2024-09-11 22:20:41.001852: Epoch time: 246.07 s 
+2024-09-11 22:20:41.955863:  
+2024-09-11 22:20:41.956048: Epoch 445 
+2024-09-11 22:20:41.956132: Current learning rate: 0.00589 
+2024-09-11 22:24:47.909161: train_loss -0.8508 
+2024-09-11 22:24:47.909302: val_loss -0.6046 
+2024-09-11 22:24:47.909351: Pseudo dice [0.4326, 0.8738] 
+2024-09-11 22:24:47.909403: Epoch time: 245.96 s 
+2024-09-11 22:24:48.841731:  
+2024-09-11 22:24:48.841893: Epoch 446 
+2024-09-11 22:24:48.841980: Current learning rate: 0.00588 
+2024-09-11 22:28:54.686323: train_loss -0.8524 
+2024-09-11 22:28:54.686470: val_loss -0.5588 
+2024-09-11 22:28:54.686520: Pseudo dice [0.397, 0.8743] 
+2024-09-11 22:28:54.686572: Epoch time: 245.85 s 
+2024-09-11 22:28:55.626287:  
+2024-09-11 22:28:55.626446: Epoch 447 
+2024-09-11 22:28:55.626528: Current learning rate: 0.00587 
+2024-09-11 22:33:01.546883: train_loss -0.8418 
+2024-09-11 22:33:01.547024: val_loss -0.6305 
+2024-09-11 22:33:01.547074: Pseudo dice [0.4821, 0.8819] 
+2024-09-11 22:33:01.547147: Epoch time: 245.92 s 
+2024-09-11 22:33:02.483854:  
+2024-09-11 22:33:02.484046: Epoch 448 
+2024-09-11 22:33:02.484128: Current learning rate: 0.00586 
+2024-09-11 22:37:08.402694: train_loss -0.8523 
+2024-09-11 22:37:08.402852: val_loss -0.5803 
+2024-09-11 22:37:08.402904: Pseudo dice [0.4213, 0.8616] 
+2024-09-11 22:37:08.402955: Epoch time: 245.92 s 
+2024-09-11 22:37:10.301489:  
+2024-09-11 22:37:10.301735: Epoch 449 
+2024-09-11 22:37:10.301834: Current learning rate: 0.00585 
+2024-09-11 22:41:16.169943: train_loss -0.8541 
+2024-09-11 22:41:16.170098: val_loss -0.6054 
+2024-09-11 22:41:16.170149: Pseudo dice [0.4524, 0.8673] 
+2024-09-11 22:41:16.170202: Epoch time: 245.87 s 
+2024-09-11 22:41:20.121384:  
+2024-09-11 22:41:20.121667: Epoch 450 
+2024-09-11 22:41:20.121757: Current learning rate: 0.00584 
+2024-09-11 22:45:26.143909: train_loss -0.8627 
+2024-09-11 22:45:26.144050: val_loss -0.5895 
+2024-09-11 22:45:26.144099: Pseudo dice [0.4037, 0.8885] 
+2024-09-11 22:45:26.144149: Epoch time: 246.02 s 
+2024-09-11 22:45:27.104336:  
+2024-09-11 22:45:27.104560: Epoch 451 
+2024-09-11 22:45:27.104664: Current learning rate: 0.00583 
+2024-09-11 22:49:33.302926: train_loss -0.8593 
+2024-09-11 22:49:33.303063: val_loss -0.5822 
+2024-09-11 22:49:33.303113: Pseudo dice [0.4099, 0.8814] 
+2024-09-11 22:49:33.303165: Epoch time: 246.2 s 
+2024-09-11 22:49:34.251991:  
+2024-09-11 22:49:34.252220: Epoch 452 
+2024-09-11 22:49:34.252303: Current learning rate: 0.00582 
+2024-09-11 22:53:40.592861: train_loss -0.862 
+2024-09-11 22:53:40.593033: val_loss -0.5816 
+2024-09-11 22:53:40.593085: Pseudo dice [0.3585, 0.885] 
+2024-09-11 22:53:40.593142: Epoch time: 246.34 s 
+2024-09-11 22:53:41.516515:  
+2024-09-11 22:53:41.516729: Epoch 453 
+2024-09-11 22:53:41.516812: Current learning rate: 0.00581 
+2024-09-11 22:57:47.593989: train_loss -0.8644 
+2024-09-11 22:57:47.594160: val_loss -0.5624 
+2024-09-11 22:57:47.594212: Pseudo dice [0.3789, 0.873] 
+2024-09-11 22:57:47.594264: Epoch time: 246.08 s 
+2024-09-11 22:57:48.522759:  
+2024-09-11 22:57:48.523032: Epoch 454 
+2024-09-11 22:57:48.523114: Current learning rate: 0.0058 
+2024-09-11 23:01:54.501021: train_loss -0.8617 
+2024-09-11 23:01:54.501158: val_loss -0.5671 
+2024-09-11 23:01:54.501208: Pseudo dice [0.3372, 0.88] 
+2024-09-11 23:01:54.501258: Epoch time: 245.98 s 
+2024-09-11 23:01:55.452311:  
+2024-09-11 23:01:55.452550: Epoch 455 
+2024-09-11 23:01:55.452631: Current learning rate: 0.00579 
+2024-09-11 23:06:01.443667: train_loss -0.8556 
+2024-09-11 23:06:01.443803: val_loss -0.596 
+2024-09-11 23:06:01.443873: Pseudo dice [0.435, 0.8737] 
+2024-09-11 23:06:01.443929: Epoch time: 245.99 s 
+2024-09-11 23:06:02.378567:  
+2024-09-11 23:06:02.378758: Epoch 456 
+2024-09-11 23:06:02.378845: Current learning rate: 0.00578 
+2024-09-11 23:10:08.289541: train_loss -0.8578 
+2024-09-11 23:10:08.289693: val_loss -0.5675 
+2024-09-11 23:10:08.289742: Pseudo dice [0.3719, 0.865] 
+2024-09-11 23:10:08.289793: Epoch time: 245.91 s 
+2024-09-11 23:10:09.237281:  
+2024-09-11 23:10:09.237494: Epoch 457 
+2024-09-11 23:10:09.237578: Current learning rate: 0.00577 
+2024-09-11 23:14:15.104892: train_loss -0.8605 
+2024-09-11 23:14:15.105042: val_loss -0.6175 
+2024-09-11 23:14:15.105092: Pseudo dice [0.4625, 0.8704] 
+2024-09-11 23:14:15.105143: Epoch time: 245.87 s 
+2024-09-11 23:14:16.025846:  
+2024-09-11 23:14:16.026002: Epoch 458 
+2024-09-11 23:14:16.026111: Current learning rate: 0.00576 
+2024-09-11 23:18:21.820394: train_loss -0.8651 
+2024-09-11 23:18:21.820555: val_loss -0.5986 
+2024-09-11 23:18:21.820608: Pseudo dice [0.4372, 0.8844] 
+2024-09-11 23:18:21.820658: Epoch time: 245.8 s 
+2024-09-11 23:18:22.765207:  
+2024-09-11 23:18:22.765426: Epoch 459 
+2024-09-11 23:18:22.765533: Current learning rate: 0.00575 
+2024-09-11 23:22:28.631752: train_loss -0.8669 
+2024-09-11 23:22:28.631903: val_loss -0.5858 
+2024-09-11 23:22:28.631954: Pseudo dice [0.3917, 0.8725] 
+2024-09-11 23:22:28.632004: Epoch time: 245.87 s 
+2024-09-11 23:22:29.586662:  
+2024-09-11 23:22:29.586854: Epoch 460 
+2024-09-11 23:22:29.586935: Current learning rate: 0.00574 
+2024-09-11 23:26:36.885059: train_loss -0.8704 
+2024-09-11 23:26:36.885197: val_loss -0.6115 
+2024-09-11 23:26:36.885247: Pseudo dice [0.4416, 0.8825] 
+2024-09-11 23:26:36.885296: Epoch time: 247.3 s 
+2024-09-11 23:26:37.838685:  
+2024-09-11 23:26:37.838880: Epoch 461 
+2024-09-11 23:26:37.838989: Current learning rate: 0.00573 
+2024-09-11 23:30:43.952416: train_loss -0.8646 
+2024-09-11 23:30:43.952558: val_loss -0.6216 
+2024-09-11 23:30:43.952660: Pseudo dice [0.4693, 0.873] 
+2024-09-11 23:30:43.952716: Epoch time: 246.12 s 
+2024-09-11 23:30:44.893185:  
+2024-09-11 23:30:44.893413: Epoch 462 
+2024-09-11 23:30:44.893503: Current learning rate: 0.00572 
+2024-09-11 23:34:50.919973: train_loss -0.8683 
+2024-09-11 23:34:50.920111: val_loss -0.5982 
+2024-09-11 23:34:50.920161: Pseudo dice [0.412, 0.8784] 
+2024-09-11 23:34:50.920212: Epoch time: 246.03 s 
+2024-09-11 23:34:51.857965:  
+2024-09-11 23:34:51.858151: Epoch 463 
+2024-09-11 23:34:51.858232: Current learning rate: 0.00571 
+2024-09-11 23:38:57.852761: train_loss -0.875 
+2024-09-11 23:38:57.852898: val_loss -0.5999 
+2024-09-11 23:38:57.852948: Pseudo dice [0.4772, 0.8778] 
+2024-09-11 23:38:57.852999: Epoch time: 246.0 s 
+2024-09-11 23:38:58.842101:  
+2024-09-11 23:38:58.842262: Epoch 464 
+2024-09-11 23:38:58.842340: Current learning rate: 0.0057 
+2024-09-11 23:43:04.815186: train_loss -0.868 
+2024-09-11 23:43:04.815340: val_loss -0.5716 
+2024-09-11 23:43:04.815391: Pseudo dice [0.3828, 0.8738] 
+2024-09-11 23:43:04.815445: Epoch time: 245.97 s 
+2024-09-11 23:43:05.751916:  
+2024-09-11 23:43:05.752100: Epoch 465 
+2024-09-11 23:43:05.752184: Current learning rate: 0.0057 
+2024-09-11 23:47:11.682431: train_loss -0.8672 
+2024-09-11 23:47:11.682614: val_loss -0.5738 
+2024-09-11 23:47:11.682665: Pseudo dice [0.3872, 0.8749] 
+2024-09-11 23:47:11.682717: Epoch time: 245.93 s 
+2024-09-11 23:47:12.624345:  
+2024-09-11 23:47:12.624523: Epoch 466 
+2024-09-11 23:47:12.624608: Current learning rate: 0.00569 
+2024-09-11 23:51:18.715565: train_loss -0.8658 
+2024-09-11 23:51:18.715714: val_loss -0.6073 
+2024-09-11 23:51:18.715766: Pseudo dice [0.4468, 0.8826] 
+2024-09-11 23:51:18.715836: Epoch time: 246.09 s 
+2024-09-11 23:51:19.648419:  
+2024-09-11 23:51:19.648622: Epoch 467 
+2024-09-11 23:51:19.648715: Current learning rate: 0.00568 
+2024-09-11 23:55:25.804282: train_loss -0.8613 
+2024-09-11 23:55:25.804423: val_loss -0.6027 
+2024-09-11 23:55:25.804475: Pseudo dice [0.4347, 0.8694] 
+2024-09-11 23:55:25.804527: Epoch time: 246.16 s 
+2024-09-11 23:55:26.755498:  
+2024-09-11 23:55:26.755622: Epoch 468 
+2024-09-11 23:55:26.755706: Current learning rate: 0.00567 
+2024-09-11 23:59:32.835900: train_loss -0.8558 
+2024-09-11 23:59:32.836037: val_loss -0.5594 
+2024-09-11 23:59:32.836087: Pseudo dice [0.3585, 0.8672] 
+2024-09-11 23:59:32.836138: Epoch time: 246.08 s 
+2024-09-11 23:59:33.792405:  
+2024-09-11 23:59:33.792591: Epoch 469 
+2024-09-11 23:59:33.792674: Current learning rate: 0.00566 
+2024-09-12 00:03:39.812954: train_loss -0.8464 
+2024-09-12 00:03:39.813091: val_loss -0.5841 
+2024-09-12 00:03:39.813142: Pseudo dice [0.4082, 0.8825] 
+2024-09-12 00:03:39.813192: Epoch time: 246.02 s 
+2024-09-12 00:03:40.763330:  
+2024-09-12 00:03:40.763540: Epoch 470 
+2024-09-12 00:03:40.763622: Current learning rate: 0.00565 
+2024-09-12 00:07:46.658756: train_loss -0.8267 
+2024-09-12 00:07:46.658890: val_loss -0.5777 
+2024-09-12 00:07:46.658940: Pseudo dice [0.3827, 0.8754] 
+2024-09-12 00:07:46.658993: Epoch time: 245.9 s 
+2024-09-12 00:07:47.603411:  
+2024-09-12 00:07:47.603535: Epoch 471 
+2024-09-12 00:07:47.603703: Current learning rate: 0.00564 
+2024-09-12 00:11:53.406260: train_loss -0.8389 
+2024-09-12 00:11:53.406397: val_loss -0.5823 
+2024-09-12 00:11:53.406447: Pseudo dice [0.3821, 0.8791] 
+2024-09-12 00:11:53.406500: Epoch time: 245.8 s 
+2024-09-12 00:11:54.332556:  
+2024-09-12 00:11:54.332738: Epoch 472 
+2024-09-12 00:11:54.332829: Current learning rate: 0.00563 
+2024-09-12 00:16:00.132680: train_loss -0.8528 
+2024-09-12 00:16:00.132833: val_loss -0.586 
+2024-09-12 00:16:00.132886: Pseudo dice [0.4042, 0.8734] 
+2024-09-12 00:16:00.132940: Epoch time: 245.8 s 
+2024-09-12 00:16:02.039253:  
+2024-09-12 00:16:02.039499: Epoch 473 
+2024-09-12 00:16:02.039602: Current learning rate: 0.00562 
+2024-09-12 00:20:07.670353: train_loss -0.8602 
+2024-09-12 00:20:07.670495: val_loss -0.5735 
+2024-09-12 00:20:07.670544: Pseudo dice [0.399, 0.871] 
+2024-09-12 00:20:07.670596: Epoch time: 245.63 s 
+2024-09-12 00:20:08.611644:  
+2024-09-12 00:20:08.611902: Epoch 474 
+2024-09-12 00:20:08.612017: Current learning rate: 0.00561 
+2024-09-12 00:24:14.184769: train_loss -0.8541 
+2024-09-12 00:24:14.184907: val_loss -0.5912 
+2024-09-12 00:24:14.184958: Pseudo dice [0.4106, 0.8719] 
+2024-09-12 00:24:14.185009: Epoch time: 245.57 s 
+2024-09-12 00:24:15.122486:  
+2024-09-12 00:24:15.122793: Epoch 475 
+2024-09-12 00:24:15.122876: Current learning rate: 0.0056 
+2024-09-12 00:28:20.816545: train_loss -0.8466 
+2024-09-12 00:28:20.816799: val_loss -0.5517 
+2024-09-12 00:28:20.816851: Pseudo dice [0.3965, 0.829] 
+2024-09-12 00:28:20.816905: Epoch time: 245.7 s 
+2024-09-12 00:28:21.782302:  
+2024-09-12 00:28:21.782557: Epoch 476 
+2024-09-12 00:28:21.782681: Current learning rate: 0.00559 
+2024-09-12 00:32:27.461161: train_loss -0.8411 
+2024-09-12 00:32:27.461367: val_loss -0.5923 
+2024-09-12 00:32:27.461419: Pseudo dice [0.4318, 0.869] 
+2024-09-12 00:32:27.461477: Epoch time: 245.68 s 
+2024-09-12 00:32:28.408904:  
+2024-09-12 00:32:28.409152: Epoch 477 
+2024-09-12 00:32:28.409233: Current learning rate: 0.00558 
+2024-09-12 00:36:34.201974: train_loss -0.8522 
+2024-09-12 00:36:34.202116: val_loss -0.592 
+2024-09-12 00:36:34.202165: Pseudo dice [0.4037, 0.8664] 
+2024-09-12 00:36:34.202216: Epoch time: 245.79 s 
+2024-09-12 00:36:35.168801:  
+2024-09-12 00:36:35.169100: Epoch 478 
+2024-09-12 00:36:35.169184: Current learning rate: 0.00557 
+2024-09-12 00:40:41.562890: train_loss -0.845 
+2024-09-12 00:40:41.563029: val_loss -0.6064 
+2024-09-12 00:40:41.563080: Pseudo dice [0.4338, 0.8825] 
+2024-09-12 00:40:41.563218: Epoch time: 246.4 s 
+2024-09-12 00:40:42.519224:  
+2024-09-12 00:40:42.519441: Epoch 479 
+2024-09-12 00:40:42.519530: Current learning rate: 0.00556 
+2024-09-12 00:44:48.602753: train_loss -0.8515 
+2024-09-12 00:44:48.602892: val_loss -0.5587 
+2024-09-12 00:44:48.602942: Pseudo dice [0.3842, 0.8581] 
+2024-09-12 00:44:48.602993: Epoch time: 246.09 s 
+2024-09-12 00:44:49.573146:  
+2024-09-12 00:44:49.573332: Epoch 480 
+2024-09-12 00:44:49.573410: Current learning rate: 0.00555 
+2024-09-12 00:48:55.518105: train_loss -0.8445 
+2024-09-12 00:48:55.518239: val_loss -0.5713 
+2024-09-12 00:48:55.518289: Pseudo dice [0.3875, 0.8666] 
+2024-09-12 00:48:55.518340: Epoch time: 245.95 s 
+2024-09-12 00:48:56.512048:  
+2024-09-12 00:48:56.512251: Epoch 481 
+2024-09-12 00:48:56.512379: Current learning rate: 0.00554 
+2024-09-12 00:53:02.645622: train_loss -0.8253 
+2024-09-12 00:53:02.645785: val_loss -0.588 
+2024-09-12 00:53:02.645836: Pseudo dice [0.4287, 0.8701] 
+2024-09-12 00:53:02.645887: Epoch time: 246.14 s 
+2024-09-12 00:53:03.614111:  
+2024-09-12 00:53:03.614288: Epoch 482 
+2024-09-12 00:53:03.614377: Current learning rate: 0.00553 
+2024-09-12 00:57:09.627758: train_loss -0.8415 
+2024-09-12 00:57:09.627944: val_loss -0.602 
+2024-09-12 00:57:09.627995: Pseudo dice [0.4281, 0.8783] 
+2024-09-12 00:57:09.628045: Epoch time: 246.02 s 
+2024-09-12 00:57:10.582301:  
+2024-09-12 00:57:10.582517: Epoch 483 
+2024-09-12 00:57:10.582601: Current learning rate: 0.00552 
+2024-09-12 01:01:16.489524: train_loss -0.8542 
+2024-09-12 01:01:16.489663: val_loss -0.6114 
+2024-09-12 01:01:16.489715: Pseudo dice [0.4822, 0.87] 
+2024-09-12 01:01:16.489768: Epoch time: 245.91 s 
+2024-09-12 01:01:17.446861:  
+2024-09-12 01:01:17.447042: Epoch 484 
+2024-09-12 01:01:17.447125: Current learning rate: 0.00551 
+2024-09-12 01:05:23.411901: train_loss -0.8576 
+2024-09-12 01:05:23.412050: val_loss -0.55 
+2024-09-12 01:05:23.412126: Pseudo dice [0.3612, 0.862] 
+2024-09-12 01:05:23.412230: Epoch time: 245.97 s 
+2024-09-12 01:05:24.372344:  
+2024-09-12 01:05:24.372575: Epoch 485 
+2024-09-12 01:05:24.372656: Current learning rate: 0.0055 
+2024-09-12 01:09:30.508728: train_loss -0.8602 
+2024-09-12 01:09:30.508909: val_loss -0.5773 
+2024-09-12 01:09:30.508960: Pseudo dice [0.4155, 0.8785] 
+2024-09-12 01:09:30.509013: Epoch time: 246.14 s 
+2024-09-12 01:09:31.476989:  
+2024-09-12 01:09:31.477221: Epoch 486 
+2024-09-12 01:09:31.477309: Current learning rate: 0.00549 
+2024-09-12 01:13:37.548387: train_loss -0.861 
+2024-09-12 01:13:37.548531: val_loss -0.5957 
+2024-09-12 01:13:37.548585: Pseudo dice [0.4531, 0.8729] 
+2024-09-12 01:13:37.548643: Epoch time: 246.07 s 
+2024-09-12 01:13:38.550069:  
+2024-09-12 01:13:38.550239: Epoch 487 
+2024-09-12 01:13:38.550322: Current learning rate: 0.00548 
+2024-09-12 01:17:44.600542: train_loss -0.8557 
+2024-09-12 01:17:44.600693: val_loss -0.5892 
+2024-09-12 01:17:44.600747: Pseudo dice [0.4154, 0.8797] 
+2024-09-12 01:17:44.600864: Epoch time: 246.05 s 
+2024-09-12 01:17:45.588884:  
+2024-09-12 01:17:45.589066: Epoch 488 
+2024-09-12 01:17:45.589188: Current learning rate: 0.00547 
+2024-09-12 01:21:51.623044: train_loss -0.8651 
+2024-09-12 01:21:51.623260: val_loss -0.571 
+2024-09-12 01:21:51.623353: Pseudo dice [0.4104, 0.8788] 
+2024-09-12 01:21:51.623444: Epoch time: 246.04 s 
+2024-09-12 01:21:52.577700:  
+2024-09-12 01:21:52.577882: Epoch 489 
+2024-09-12 01:21:52.577985: Current learning rate: 0.00546 
+2024-09-12 01:25:58.518481: train_loss -0.8603 
+2024-09-12 01:25:58.518629: val_loss -0.5975 
+2024-09-12 01:25:58.518679: Pseudo dice [0.4405, 0.8677] 
+2024-09-12 01:25:58.518730: Epoch time: 245.94 s 
+2024-09-12 01:25:59.485620:  
+2024-09-12 01:25:59.485892: Epoch 490 
+2024-09-12 01:25:59.486042: Current learning rate: 0.00546 
+2024-09-12 01:30:05.714374: train_loss -0.8167 
+2024-09-12 01:30:05.714516: val_loss -0.545 
+2024-09-12 01:30:05.714566: Pseudo dice [0.3534, 0.8528] 
+2024-09-12 01:30:05.714617: Epoch time: 246.23 s 
+2024-09-12 01:30:06.667507:  
+2024-09-12 01:30:06.667715: Epoch 491 
+2024-09-12 01:30:06.667825: Current learning rate: 0.00545 
+2024-09-12 01:34:12.973465: train_loss -0.8206 
+2024-09-12 01:34:12.973603: val_loss -0.5788 
+2024-09-12 01:34:12.973655: Pseudo dice [0.43, 0.8611] 
+2024-09-12 01:34:12.973706: Epoch time: 246.31 s 
+2024-09-12 01:34:13.929842:  
+2024-09-12 01:34:13.930090: Epoch 492 
+2024-09-12 01:34:13.930194: Current learning rate: 0.00544 
+2024-09-12 01:38:20.112216: train_loss -0.8161 
+2024-09-12 01:38:20.112352: val_loss -0.5626 
+2024-09-12 01:38:20.112402: Pseudo dice [0.3927, 0.8512] 
+2024-09-12 01:38:20.112453: Epoch time: 246.18 s 
+2024-09-12 01:38:21.053668:  
+2024-09-12 01:38:21.053833: Epoch 493 
+2024-09-12 01:38:21.053919: Current learning rate: 0.00543 
+2024-09-12 01:42:27.150372: train_loss -0.8373 
+2024-09-12 01:42:27.150513: val_loss -0.5988 
+2024-09-12 01:42:27.150604: Pseudo dice [0.4207, 0.8815] 
+2024-09-12 01:42:27.150671: Epoch time: 246.1 s 
+2024-09-12 01:42:28.118183:  
+2024-09-12 01:42:28.118391: Epoch 494 
+2024-09-12 01:42:28.118474: Current learning rate: 0.00542 
+2024-09-12 01:46:34.182142: train_loss -0.8456 
+2024-09-12 01:46:34.182305: val_loss -0.5912 
+2024-09-12 01:46:34.182355: Pseudo dice [0.4235, 0.8697] 
+2024-09-12 01:46:34.182406: Epoch time: 246.07 s 
+2024-09-12 01:46:35.148871:  
+2024-09-12 01:46:35.149167: Epoch 495 
+2024-09-12 01:46:35.149259: Current learning rate: 0.00541 
+2024-09-12 01:50:41.307224: train_loss -0.8418 
+2024-09-12 01:50:41.307380: val_loss -0.5817 
+2024-09-12 01:50:41.307431: Pseudo dice [0.4282, 0.8576] 
+2024-09-12 01:50:41.307483: Epoch time: 246.16 s 
+2024-09-12 01:50:42.253304:  
+2024-09-12 01:50:42.253456: Epoch 496 
+2024-09-12 01:50:42.253546: Current learning rate: 0.0054 
+2024-09-12 01:54:48.522537: train_loss -0.8551 
+2024-09-12 01:54:48.522704: val_loss -0.608 
+2024-09-12 01:54:48.522756: Pseudo dice [0.4322, 0.8783] 
+2024-09-12 01:54:48.522807: Epoch time: 246.27 s 
+2024-09-12 01:54:50.497031:  
+2024-09-12 01:54:50.497248: Epoch 497 
+2024-09-12 01:54:50.497374: Current learning rate: 0.00539 
+2024-09-12 01:58:56.859266: train_loss -0.8622 
+2024-09-12 01:58:56.859400: val_loss -0.5947 
+2024-09-12 01:58:56.859451: Pseudo dice [0.3987, 0.8784] 
+2024-09-12 01:58:56.859503: Epoch time: 246.36 s 
+2024-09-12 01:58:57.804613:  
+2024-09-12 01:58:57.804846: Epoch 498 
+2024-09-12 01:58:57.804943: Current learning rate: 0.00538 
+2024-09-12 02:03:04.145359: train_loss -0.8687 
+2024-09-12 02:03:04.145496: val_loss -0.5935 
+2024-09-12 02:03:04.145546: Pseudo dice [0.4234, 0.8763] 
+2024-09-12 02:03:04.145599: Epoch time: 246.34 s 
+2024-09-12 02:03:05.102277:  
+2024-09-12 02:03:05.102515: Epoch 499 
+2024-09-12 02:03:05.102611: Current learning rate: 0.00537 
+2024-09-12 02:07:11.235057: train_loss -0.8654 
+2024-09-12 02:07:11.235234: val_loss -0.5736 
+2024-09-12 02:07:11.235286: Pseudo dice [0.3866, 0.8723] 
+2024-09-12 02:07:11.235340: Epoch time: 246.13 s 
+2024-09-12 02:07:15.161786:  
+2024-09-12 02:07:15.162011: Epoch 500 
+2024-09-12 02:07:15.162095: Current learning rate: 0.00536 
+2024-09-12 02:11:21.342676: train_loss -0.861 
+2024-09-12 02:11:21.342832: val_loss -0.5603 
+2024-09-12 02:11:21.342883: Pseudo dice [0.3371, 0.8802] 
+2024-09-12 02:11:21.342933: Epoch time: 246.18 s 
+2024-09-12 02:11:22.323673:  
+2024-09-12 02:11:22.323983: Epoch 501 
+2024-09-12 02:11:22.324068: Current learning rate: 0.00535 
+2024-09-12 02:15:28.562298: train_loss -0.863 
+2024-09-12 02:15:28.562499: val_loss -0.599 
+2024-09-12 02:15:28.562594: Pseudo dice [0.4217, 0.8655] 
+2024-09-12 02:15:28.562686: Epoch time: 246.24 s 
+2024-09-12 02:15:29.518092:  
+2024-09-12 02:15:29.518325: Epoch 502 
+2024-09-12 02:15:29.518411: Current learning rate: 0.00534 
+2024-09-12 02:19:35.799896: train_loss -0.8674 
+2024-09-12 02:19:35.800034: val_loss -0.5881 
+2024-09-12 02:19:35.800085: Pseudo dice [0.4337, 0.888] 
+2024-09-12 02:19:35.800137: Epoch time: 246.28 s 
+2024-09-12 02:19:36.749930:  
+2024-09-12 02:19:36.750139: Epoch 503 
+2024-09-12 02:19:36.750223: Current learning rate: 0.00533 
+2024-09-12 02:23:43.230762: train_loss -0.8671 
+2024-09-12 02:23:43.230899: val_loss -0.5768 
+2024-09-12 02:23:43.230950: Pseudo dice [0.3846, 0.8742] 
+2024-09-12 02:23:43.231002: Epoch time: 246.48 s 
+2024-09-12 02:23:44.191756:  
+2024-09-12 02:23:44.192012: Epoch 504 
+2024-09-12 02:23:44.192099: Current learning rate: 0.00532 
+2024-09-12 02:27:50.688486: train_loss -0.8716 
+2024-09-12 02:27:50.688654: val_loss -0.5714 
+2024-09-12 02:27:50.688704: Pseudo dice [0.3918, 0.8619] 
+2024-09-12 02:27:50.688756: Epoch time: 246.5 s 
+2024-09-12 02:27:51.646636:  
+2024-09-12 02:27:51.646850: Epoch 505 
+2024-09-12 02:27:51.646935: Current learning rate: 0.00531 
+2024-09-12 02:31:58.412866: train_loss -0.8665 
+2024-09-12 02:31:58.413007: val_loss -0.623 
+2024-09-12 02:31:58.413059: Pseudo dice [0.4905, 0.8716] 
+2024-09-12 02:31:58.413112: Epoch time: 246.77 s 
+2024-09-12 02:31:59.388023:  
+2024-09-12 02:31:59.388253: Epoch 506 
+2024-09-12 02:31:59.388341: Current learning rate: 0.0053 
+2024-09-12 02:36:05.632622: train_loss -0.8663 
+2024-09-12 02:36:05.632758: val_loss -0.5467 
+2024-09-12 02:36:05.632808: Pseudo dice [0.3281, 0.8702] 
+2024-09-12 02:36:05.632862: Epoch time: 246.25 s 
+2024-09-12 02:36:06.597103:  
+2024-09-12 02:36:06.597289: Epoch 507 
+2024-09-12 02:36:06.597372: Current learning rate: 0.00529 
+2024-09-12 02:40:12.944783: train_loss -0.8528 
+2024-09-12 02:40:12.944926: val_loss -0.5887 
+2024-09-12 02:40:12.944976: Pseudo dice [0.4296, 0.8737] 
+2024-09-12 02:40:12.945027: Epoch time: 246.35 s 
+2024-09-12 02:40:13.905733:  
+2024-09-12 02:40:13.905979: Epoch 508 
+2024-09-12 02:40:13.906061: Current learning rate: 0.00528 
+2024-09-12 02:44:20.174112: train_loss -0.8586 
+2024-09-12 02:44:20.174267: val_loss -0.6107 
+2024-09-12 02:44:20.174317: Pseudo dice [0.471, 0.884] 
+2024-09-12 02:44:20.174369: Epoch time: 246.27 s 
+2024-09-12 02:44:21.163636:  
+2024-09-12 02:44:21.163855: Epoch 509 
+2024-09-12 02:44:21.163940: Current learning rate: 0.00527 
+2024-09-12 02:48:27.463303: train_loss -0.8629 
+2024-09-12 02:48:27.463486: val_loss -0.5933 
+2024-09-12 02:48:27.463537: Pseudo dice [0.4523, 0.8674] 
+2024-09-12 02:48:27.463591: Epoch time: 246.3 s 
+2024-09-12 02:48:28.425220:  
+2024-09-12 02:48:28.425436: Epoch 510 
+2024-09-12 02:48:28.425522: Current learning rate: 0.00526 
+2024-09-12 02:52:34.569689: train_loss -0.8649 
+2024-09-12 02:52:34.569861: val_loss -0.5712 
+2024-09-12 02:52:34.569912: Pseudo dice [0.365, 0.8693] 
+2024-09-12 02:52:34.569965: Epoch time: 246.15 s 
+2024-09-12 02:52:35.529363:  
+2024-09-12 02:52:35.529583: Epoch 511 
+2024-09-12 02:52:35.529668: Current learning rate: 0.00525 
+2024-09-12 02:56:41.619068: train_loss -0.8578 
+2024-09-12 02:56:41.619209: val_loss -0.5773 
+2024-09-12 02:56:41.619259: Pseudo dice [0.3779, 0.8949] 
+2024-09-12 02:56:41.619309: Epoch time: 246.09 s 
+2024-09-12 02:56:42.571422:  
+2024-09-12 02:56:42.571597: Epoch 512 
+2024-09-12 02:56:42.571678: Current learning rate: 0.00524 
+2024-09-12 03:00:48.625192: train_loss -0.8675 
+2024-09-12 03:00:48.625330: val_loss -0.6062 
+2024-09-12 03:00:48.625380: Pseudo dice [0.4422, 0.8855] 
+2024-09-12 03:00:48.625431: Epoch time: 246.06 s 
+2024-09-12 03:00:49.582070:  
+2024-09-12 03:00:49.582232: Epoch 513 
+2024-09-12 03:00:49.582331: Current learning rate: 0.00523 
+2024-09-12 03:04:55.608393: train_loss -0.8674 
+2024-09-12 03:04:55.608529: val_loss -0.6052 
+2024-09-12 03:04:55.608581: Pseudo dice [0.4243, 0.8689] 
+2024-09-12 03:04:55.608632: Epoch time: 246.03 s 
+2024-09-12 03:04:56.578975:  
+2024-09-12 03:04:56.579170: Epoch 514 
+2024-09-12 03:04:56.579254: Current learning rate: 0.00522 
+2024-09-12 03:09:02.646701: train_loss -0.8608 
+2024-09-12 03:09:02.646885: val_loss -0.6227 
+2024-09-12 03:09:02.646939: Pseudo dice [0.4831, 0.8755] 
+2024-09-12 03:09:02.646993: Epoch time: 246.07 s 
+2024-09-12 03:09:03.634649:  
+2024-09-12 03:09:03.634852: Epoch 515 
+2024-09-12 03:09:03.634932: Current learning rate: 0.00521 
+2024-09-12 03:13:09.523728: train_loss -0.8687 
+2024-09-12 03:13:09.523878: val_loss -0.6059 
+2024-09-12 03:13:09.523930: Pseudo dice [0.4662, 0.8677] 
+2024-09-12 03:13:09.523982: Epoch time: 245.89 s 
+2024-09-12 03:13:10.472929:  
+2024-09-12 03:13:10.473169: Epoch 516 
+2024-09-12 03:13:10.473272: Current learning rate: 0.0052 
+2024-09-12 03:17:16.262895: train_loss -0.8738 
+2024-09-12 03:17:16.263035: val_loss -0.5822 
+2024-09-12 03:17:16.263086: Pseudo dice [0.3897, 0.8734] 
+2024-09-12 03:17:16.263136: Epoch time: 245.79 s 
+2024-09-12 03:17:17.231360:  
+2024-09-12 03:17:17.231514: Epoch 517 
+2024-09-12 03:17:17.231597: Current learning rate: 0.00519 
+2024-09-12 03:21:22.984151: train_loss -0.874 
+2024-09-12 03:21:22.984305: val_loss -0.6094 
+2024-09-12 03:21:22.984355: Pseudo dice [0.4591, 0.8648] 
+2024-09-12 03:21:22.984406: Epoch time: 245.75 s 
+2024-09-12 03:21:23.957285:  
+2024-09-12 03:21:23.957516: Epoch 518 
+2024-09-12 03:21:23.957598: Current learning rate: 0.00518 
+2024-09-12 03:25:30.102503: train_loss -0.8737 
+2024-09-12 03:25:30.102649: val_loss -0.6172 
+2024-09-12 03:25:30.102710: Pseudo dice [0.4727, 0.8655] 
+2024-09-12 03:25:30.102763: Epoch time: 246.15 s 
+2024-09-12 03:25:31.066033:  
+2024-09-12 03:25:31.066252: Epoch 519 
+2024-09-12 03:25:31.066338: Current learning rate: 0.00518 
+2024-09-12 03:29:37.426295: train_loss -0.8706 
+2024-09-12 03:29:37.426431: val_loss -0.6119 
+2024-09-12 03:29:37.426482: Pseudo dice [0.4382, 0.8911] 
+2024-09-12 03:29:37.426532: Epoch time: 246.36 s 
+2024-09-12 03:29:38.381161:  
+2024-09-12 03:29:38.381315: Epoch 520 
+2024-09-12 03:29:38.381415: Current learning rate: 0.00517 
+2024-09-12 03:33:44.680531: train_loss -0.8718 
+2024-09-12 03:33:44.680677: val_loss -0.6042 
+2024-09-12 03:33:44.680727: Pseudo dice [0.4483, 0.8786] 
+2024-09-12 03:33:44.680779: Epoch time: 246.3 s 
+2024-09-12 03:33:46.608719:  
+2024-09-12 03:33:46.608959: Epoch 521 
+2024-09-12 03:33:46.609093: Current learning rate: 0.00516 
+2024-09-12 03:37:52.771336: train_loss -0.8711 
+2024-09-12 03:37:52.771476: val_loss -0.6234 
+2024-09-12 03:37:52.771528: Pseudo dice [0.4691, 0.875] 
+2024-09-12 03:37:52.771578: Epoch time: 246.16 s 
+2024-09-12 03:37:53.763129:  
+2024-09-12 03:37:53.763313: Epoch 522 
+2024-09-12 03:37:53.763448: Current learning rate: 0.00515 
+2024-09-12 03:41:59.924330: train_loss -0.8743 
+2024-09-12 03:41:59.924489: val_loss -0.5688 
+2024-09-12 03:41:59.924539: Pseudo dice [0.3814, 0.8691] 
+2024-09-12 03:41:59.924590: Epoch time: 246.16 s 
+2024-09-12 03:42:00.878919:  
+2024-09-12 03:42:00.879135: Epoch 523 
+2024-09-12 03:42:00.879243: Current learning rate: 0.00514 
+2024-09-12 03:46:07.040040: train_loss -0.8721 
+2024-09-12 03:46:07.040184: val_loss -0.5951 
+2024-09-12 03:46:07.040235: Pseudo dice [0.4283, 0.8892] 
+2024-09-12 03:46:07.040286: Epoch time: 246.16 s 
+2024-09-12 03:46:07.998493:  
+2024-09-12 03:46:07.998718: Epoch 524 
+2024-09-12 03:46:07.998800: Current learning rate: 0.00513 
+2024-09-12 03:50:14.256520: train_loss -0.8744 
+2024-09-12 03:50:14.256658: val_loss -0.5813 
+2024-09-12 03:50:14.256707: Pseudo dice [0.4139, 0.879] 
+2024-09-12 03:50:14.256764: Epoch time: 246.26 s 
+2024-09-12 03:50:15.229449:  
+2024-09-12 03:50:15.229715: Epoch 525 
+2024-09-12 03:50:15.229798: Current learning rate: 0.00512 
+2024-09-12 03:54:21.431621: train_loss -0.8782 
+2024-09-12 03:54:21.431761: val_loss -0.5511 
+2024-09-12 03:54:21.431825: Pseudo dice [0.3373, 0.868] 
+2024-09-12 03:54:21.431880: Epoch time: 246.2 s 
+2024-09-12 03:54:22.429012:  
+2024-09-12 03:54:22.429190: Epoch 526 
+2024-09-12 03:54:22.429320: Current learning rate: 0.00511 
+2024-09-12 03:58:28.700704: train_loss -0.8748 
+2024-09-12 03:58:28.700841: val_loss -0.6101 
+2024-09-12 03:58:28.700892: Pseudo dice [0.4751, 0.8791] 
+2024-09-12 03:58:28.700944: Epoch time: 246.27 s 
+2024-09-12 03:58:29.679750:  
+2024-09-12 03:58:29.680009: Epoch 527 
+2024-09-12 03:58:29.680107: Current learning rate: 0.0051 
+2024-09-12 04:02:35.844054: train_loss -0.8782 
+2024-09-12 04:02:35.844194: val_loss -0.6026 
+2024-09-12 04:02:35.844245: Pseudo dice [0.4367, 0.8874] 
+2024-09-12 04:02:35.844296: Epoch time: 246.17 s 
+2024-09-12 04:02:36.831218:  
+2024-09-12 04:02:36.831399: Epoch 528 
+2024-09-12 04:02:36.831481: Current learning rate: 0.00509 
+2024-09-12 04:06:43.284614: train_loss -0.8778 
+2024-09-12 04:06:43.284773: val_loss -0.6151 
+2024-09-12 04:06:43.284857: Pseudo dice [0.4682, 0.8876] 
+2024-09-12 04:06:43.284908: Epoch time: 246.46 s 
+2024-09-12 04:06:44.260152:  
+2024-09-12 04:06:44.260315: Epoch 529 
+2024-09-12 04:06:44.260398: Current learning rate: 0.00508 
+2024-09-12 04:10:50.532163: train_loss -0.8768 
+2024-09-12 04:10:50.532303: val_loss -0.589 
+2024-09-12 04:10:50.532352: Pseudo dice [0.4484, 0.8806] 
+2024-09-12 04:10:50.532403: Epoch time: 246.27 s 
+2024-09-12 04:10:51.489421:  
+2024-09-12 04:10:51.489663: Epoch 530 
+2024-09-12 04:10:51.489748: Current learning rate: 0.00507 
+2024-09-12 04:14:57.671005: train_loss -0.8798 
+2024-09-12 04:14:57.671151: val_loss -0.5724 
+2024-09-12 04:14:57.671201: Pseudo dice [0.3881, 0.8706] 
+2024-09-12 04:14:57.671252: Epoch time: 246.18 s 
+2024-09-12 04:14:58.628680:  
+2024-09-12 04:14:58.628890: Epoch 531 
+2024-09-12 04:14:58.629012: Current learning rate: 0.00506 
+2024-09-12 04:19:04.896901: train_loss -0.8787 
+2024-09-12 04:19:04.897051: val_loss -0.5697 
+2024-09-12 04:19:04.897101: Pseudo dice [0.4207, 0.8655] 
+2024-09-12 04:19:04.897152: Epoch time: 246.27 s 
+2024-09-12 04:19:05.861269:  
+2024-09-12 04:19:05.861419: Epoch 532 
+2024-09-12 04:19:05.861500: Current learning rate: 0.00505 
+2024-09-12 04:23:12.053036: train_loss -0.88 
+2024-09-12 04:23:12.053200: val_loss -0.5993 
+2024-09-12 04:23:12.053251: Pseudo dice [0.4405, 0.8616] 
+2024-09-12 04:23:12.053303: Epoch time: 246.19 s 
+2024-09-12 04:23:13.020863:  
+2024-09-12 04:23:13.021084: Epoch 533 
+2024-09-12 04:23:13.021174: Current learning rate: 0.00504 
+2024-09-12 04:27:19.232241: train_loss -0.8826 
+2024-09-12 04:27:19.232384: val_loss -0.5982 
+2024-09-12 04:27:19.232435: Pseudo dice [0.4398, 0.8775] 
+2024-09-12 04:27:19.232487: Epoch time: 246.21 s 
+2024-09-12 04:27:20.191451:  
+2024-09-12 04:27:20.191646: Epoch 534 
+2024-09-12 04:27:20.191768: Current learning rate: 0.00503 
+2024-09-12 04:31:26.554036: train_loss -0.8776 
+2024-09-12 04:31:26.554175: val_loss -0.6102 
+2024-09-12 04:31:26.554225: Pseudo dice [0.4381, 0.8777] 
+2024-09-12 04:31:26.554276: Epoch time: 246.36 s 
+2024-09-12 04:31:27.547546:  
+2024-09-12 04:31:27.547770: Epoch 535 
+2024-09-12 04:31:27.547906: Current learning rate: 0.00502 
+2024-09-12 04:35:34.154970: train_loss -0.8744 
+2024-09-12 04:35:34.155113: val_loss -0.6013 
+2024-09-12 04:35:34.155166: Pseudo dice [0.4785, 0.8647] 
+2024-09-12 04:35:34.155218: Epoch time: 246.61 s 
+2024-09-12 04:35:35.105454:  
+2024-09-12 04:35:35.105644: Epoch 536 
+2024-09-12 04:35:35.105761: Current learning rate: 0.00501 
+2024-09-12 04:39:41.547653: train_loss -0.8778 
+2024-09-12 04:39:41.547822: val_loss -0.6044 
+2024-09-12 04:39:41.547879: Pseudo dice [0.4412, 0.8779] 
+2024-09-12 04:39:41.547930: Epoch time: 246.44 s 
+2024-09-12 04:39:42.536708:  
+2024-09-12 04:39:42.536869: Epoch 537 
+2024-09-12 04:39:42.536959: Current learning rate: 0.005 
+2024-09-12 04:43:49.035894: train_loss -0.8752 
+2024-09-12 04:43:49.036033: val_loss -0.6072 
+2024-09-12 04:43:49.036123: Pseudo dice [0.4514, 0.8913] 
+2024-09-12 04:43:49.036199: Epoch time: 246.5 s 
+2024-09-12 04:43:49.991058:  
+2024-09-12 04:43:49.991265: Epoch 538 
+2024-09-12 04:43:49.991346: Current learning rate: 0.00499 
+2024-09-12 04:47:56.350433: train_loss -0.8806 
+2024-09-12 04:47:56.350578: val_loss -0.6063 
+2024-09-12 04:47:56.350629: Pseudo dice [0.4504, 0.8921] 
+2024-09-12 04:47:56.350682: Epoch time: 246.36 s 
+2024-09-12 04:47:57.298096:  
+2024-09-12 04:47:57.298273: Epoch 539 
+2024-09-12 04:47:57.298355: Current learning rate: 0.00498 
+2024-09-12 04:52:03.687784: train_loss -0.8803 
+2024-09-12 04:52:03.687936: val_loss -0.6099 
+2024-09-12 04:52:03.687987: Pseudo dice [0.464, 0.8824] 
+2024-09-12 04:52:03.688040: Epoch time: 246.39 s 
+2024-09-12 04:52:04.648851:  
+2024-09-12 04:52:04.649048: Epoch 540 
+2024-09-12 04:52:04.649131: Current learning rate: 0.00497 
+2024-09-12 04:56:11.024330: train_loss -0.8799 
+2024-09-12 04:56:11.024467: val_loss -0.5777 
+2024-09-12 04:56:11.024517: Pseudo dice [0.4148, 0.8679] 
+2024-09-12 04:56:11.024569: Epoch time: 246.38 s 
+2024-09-12 04:56:11.998739:  
+2024-09-12 04:56:11.998906: Epoch 541 
+2024-09-12 04:56:11.999005: Current learning rate: 0.00496 
+2024-09-12 05:00:18.431260: train_loss -0.8821 
+2024-09-12 05:00:18.431397: val_loss -0.5776 
+2024-09-12 05:00:18.431447: Pseudo dice [0.4667, 0.8673] 
+2024-09-12 05:00:18.431499: Epoch time: 246.43 s 
+2024-09-12 05:00:19.400610:  
+2024-09-12 05:00:19.400765: Epoch 542 
+2024-09-12 05:00:19.400845: Current learning rate: 0.00495 
+2024-09-12 05:04:25.682297: train_loss -0.8809 
+2024-09-12 05:04:25.682437: val_loss -0.6182 
+2024-09-12 05:04:25.682543: Pseudo dice [0.4442, 0.8815] 
+2024-09-12 05:04:25.682598: Epoch time: 246.28 s 
+2024-09-12 05:04:26.670186:  
+2024-09-12 05:04:26.670355: Epoch 543 
+2024-09-12 05:04:26.670437: Current learning rate: 0.00494 
+2024-09-12 05:08:32.924467: train_loss -0.8812 
+2024-09-12 05:08:32.924619: val_loss -0.5869 
+2024-09-12 05:08:32.924670: Pseudo dice [0.4101, 0.887] 
+2024-09-12 05:08:32.924722: Epoch time: 246.26 s 
+2024-09-12 05:08:33.896653:  
+2024-09-12 05:08:33.896839: Epoch 544 
+2024-09-12 05:08:33.896943: Current learning rate: 0.00493 
+2024-09-12 05:12:40.165402: train_loss -0.8794 
+2024-09-12 05:12:40.165543: val_loss -0.6181 
+2024-09-12 05:12:40.165594: Pseudo dice [0.4511, 0.8661] 
+2024-09-12 05:12:40.165645: Epoch time: 246.27 s 
+2024-09-12 05:12:42.096276:  
+2024-09-12 05:12:42.096485: Epoch 545 
+2024-09-12 05:12:42.096597: Current learning rate: 0.00492 
+2024-09-12 05:16:48.546880: train_loss -0.8673 
+2024-09-12 05:16:48.547023: val_loss -0.6159 
+2024-09-12 05:16:48.547075: Pseudo dice [0.5064, 0.8696] 
+2024-09-12 05:16:48.547125: Epoch time: 246.45 s 
+2024-09-12 05:16:49.505539:  
+2024-09-12 05:16:49.505753: Epoch 546 
+2024-09-12 05:16:49.505852: Current learning rate: 0.00491 
+2024-09-12 05:20:55.708903: train_loss -0.8725 
+2024-09-12 05:20:55.709048: val_loss -0.5631 
+2024-09-12 05:20:55.709103: Pseudo dice [0.4115, 0.8645] 
+2024-09-12 05:20:55.709185: Epoch time: 246.21 s 
+2024-09-12 05:20:56.666117:  
+2024-09-12 05:20:56.666345: Epoch 547 
+2024-09-12 05:20:56.666429: Current learning rate: 0.0049 
+2024-09-12 05:25:02.803228: train_loss -0.8761 
+2024-09-12 05:25:02.803380: val_loss -0.6216 
+2024-09-12 05:25:02.803429: Pseudo dice [0.4827, 0.8853] 
+2024-09-12 05:25:02.803481: Epoch time: 246.14 s 
+2024-09-12 05:25:03.750283:  
+2024-09-12 05:25:03.750481: Epoch 548 
+2024-09-12 05:25:03.750596: Current learning rate: 0.00489 
+2024-09-12 05:29:09.918018: train_loss -0.8723 
+2024-09-12 05:29:09.918157: val_loss -0.6136 
+2024-09-12 05:29:09.918207: Pseudo dice [0.4817, 0.8763] 
+2024-09-12 05:29:09.918259: Epoch time: 246.17 s 
+2024-09-12 05:29:09.918299: Yayy! New best EMA pseudo Dice: 0.6632 
+2024-09-12 05:29:13.781936:  
+2024-09-12 05:29:13.782151: Epoch 549 
+2024-09-12 05:29:13.782232: Current learning rate: 0.00488 
+2024-09-12 05:33:19.908939: train_loss -0.8721 
+2024-09-12 05:33:19.909160: val_loss -0.5618 
+2024-09-12 05:33:19.909214: Pseudo dice [0.4167, 0.8758] 
+2024-09-12 05:33:19.909268: Epoch time: 246.13 s 
+2024-09-12 05:33:23.878702:  
+2024-09-12 05:33:23.878944: Epoch 550 
+2024-09-12 05:33:23.879031: Current learning rate: 0.00487 
+2024-09-12 05:37:30.158151: train_loss -0.8761 
+2024-09-12 05:37:30.158311: val_loss -0.584 
+2024-09-12 05:37:30.158386: Pseudo dice [0.4798, 0.8778] 
+2024-09-12 05:37:30.158438: Epoch time: 246.28 s 
+2024-09-12 05:37:30.158478: Yayy! New best EMA pseudo Dice: 0.6632 
+2024-09-12 05:37:34.059240:  
+2024-09-12 05:37:34.059504: Epoch 551 
+2024-09-12 05:37:34.059589: Current learning rate: 0.00486 
+2024-09-12 05:41:40.263382: train_loss -0.8745 
+2024-09-12 05:41:40.263583: val_loss -0.6125 
+2024-09-12 05:41:40.263636: Pseudo dice [0.5087, 0.8749] 
+2024-09-12 05:41:40.263690: Epoch time: 246.21 s 
+2024-09-12 05:41:40.263731: Yayy! New best EMA pseudo Dice: 0.6661 
+2024-09-12 05:41:44.208690:  
+2024-09-12 05:41:44.208889: Epoch 552 
+2024-09-12 05:41:44.208975: Current learning rate: 0.00485 
+2024-09-12 05:45:50.434955: train_loss -0.8817 
+2024-09-12 05:45:50.435093: val_loss -0.6135 
+2024-09-12 05:45:50.435143: Pseudo dice [0.4659, 0.8582] 
+2024-09-12 05:45:50.435194: Epoch time: 246.23 s 
+2024-09-12 05:45:51.405586:  
+2024-09-12 05:45:51.405777: Epoch 553 
+2024-09-12 05:45:51.405861: Current learning rate: 0.00484 
+2024-09-12 05:49:57.737499: train_loss -0.8633 
+2024-09-12 05:49:57.737701: val_loss -0.6091 
+2024-09-12 05:49:57.737794: Pseudo dice [0.4306, 0.8804] 
+2024-09-12 05:49:57.737885: Epoch time: 246.33 s 
+2024-09-12 05:49:58.705796:  
+2024-09-12 05:49:58.706020: Epoch 554 
+2024-09-12 05:49:58.706108: Current learning rate: 0.00484 
+2024-09-12 05:54:04.742958: train_loss -0.87 
+2024-09-12 05:54:04.743137: val_loss -0.6101 
+2024-09-12 05:54:04.743188: Pseudo dice [0.446, 0.8718] 
+2024-09-12 05:54:04.743238: Epoch time: 246.04 s 
+2024-09-12 05:54:05.690992:  
+2024-09-12 05:54:05.691200: Epoch 555 
+2024-09-12 05:54:05.691291: Current learning rate: 0.00483 
+2024-09-12 05:58:11.888749: train_loss -0.8518 
+2024-09-12 05:58:11.888893: val_loss -0.5922 
+2024-09-12 05:58:11.888946: Pseudo dice [0.4554, 0.8381] 
+2024-09-12 05:58:11.889000: Epoch time: 246.2 s 
+2024-09-12 05:58:12.899814:  
+2024-09-12 05:58:12.900078: Epoch 556 
+2024-09-12 05:58:12.900172: Current learning rate: 0.00482 
+2024-09-12 06:02:18.989156: train_loss -0.836 
+2024-09-12 06:02:18.989298: val_loss -0.5667 
+2024-09-12 06:02:18.989467: Pseudo dice [0.3835, 0.8613] 
+2024-09-12 06:02:18.989589: Epoch time: 246.09 s 
+2024-09-12 06:02:19.979767:  
+2024-09-12 06:02:19.979994: Epoch 557 
+2024-09-12 06:02:19.980081: Current learning rate: 0.00481 
+2024-09-12 06:06:25.982679: train_loss -0.8622 
+2024-09-12 06:06:25.982821: val_loss -0.6102 
+2024-09-12 06:06:25.982872: Pseudo dice [0.4538, 0.8888] 
+2024-09-12 06:06:25.982922: Epoch time: 246.0 s 
+2024-09-12 06:06:26.951373:  
+2024-09-12 06:06:26.951537: Epoch 558 
+2024-09-12 06:06:26.951661: Current learning rate: 0.0048 
+2024-09-12 06:10:32.849630: train_loss -0.8639 
+2024-09-12 06:10:32.849768: val_loss -0.5975 
+2024-09-12 06:10:32.849819: Pseudo dice [0.4234, 0.8531] 
+2024-09-12 06:10:32.849869: Epoch time: 245.9 s 
+2024-09-12 06:10:33.842189:  
+2024-09-12 06:10:33.842358: Epoch 559 
+2024-09-12 06:10:33.842446: Current learning rate: 0.00479 
+2024-09-12 06:14:39.750848: train_loss -0.8447 
+2024-09-12 06:14:39.750989: val_loss -0.5639 
+2024-09-12 06:14:39.751040: Pseudo dice [0.3623, 0.8574] 
+2024-09-12 06:14:39.751091: Epoch time: 245.91 s 
+2024-09-12 06:14:40.733409:  
+2024-09-12 06:14:40.733610: Epoch 560 
+2024-09-12 06:14:40.733691: Current learning rate: 0.00478 
+2024-09-12 06:18:46.844478: train_loss -0.8477 
+2024-09-12 06:18:46.844687: val_loss -0.5702 
+2024-09-12 06:18:46.844762: Pseudo dice [0.3967, 0.876] 
+2024-09-12 06:18:46.844823: Epoch time: 246.11 s 
+2024-09-12 06:18:47.791643:  
+2024-09-12 06:18:47.791893: Epoch 561 
+2024-09-12 06:18:47.791980: Current learning rate: 0.00477 
+2024-09-12 06:22:53.496617: train_loss -0.843 
+2024-09-12 06:22:53.496755: val_loss -0.5594 
+2024-09-12 06:22:53.496806: Pseudo dice [0.3598, 0.8577] 
+2024-09-12 06:22:53.496857: Epoch time: 245.71 s 
+2024-09-12 06:22:54.471965:  
+2024-09-12 06:22:54.472182: Epoch 562 
+2024-09-12 06:22:54.472264: Current learning rate: 0.00476 
+2024-09-12 06:27:00.061909: train_loss -0.8488 
+2024-09-12 06:27:00.062056: val_loss -0.5841 
+2024-09-12 06:27:00.062108: Pseudo dice [0.4517, 0.8551] 
+2024-09-12 06:27:00.062160: Epoch time: 245.59 s 
+2024-09-12 06:27:01.024721:  
+2024-09-12 06:27:01.024889: Epoch 563 
+2024-09-12 06:27:01.024971: Current learning rate: 0.00475 
+2024-09-12 06:31:06.733613: train_loss -0.8418 
+2024-09-12 06:31:06.733747: val_loss -0.6019 
+2024-09-12 06:31:06.733797: Pseudo dice [0.4462, 0.883] 
+2024-09-12 06:31:06.733850: Epoch time: 245.71 s 
+2024-09-12 06:31:07.687813:  
+2024-09-12 06:31:07.687954: Epoch 564 
+2024-09-12 06:31:07.688035: Current learning rate: 0.00474 
+2024-09-12 06:35:13.275931: train_loss -0.8619 
+2024-09-12 06:35:13.276072: val_loss -0.5701 
+2024-09-12 06:35:13.276122: Pseudo dice [0.4105, 0.8674] 
+2024-09-12 06:35:13.276172: Epoch time: 245.59 s 
+2024-09-12 06:35:14.240644:  
+2024-09-12 06:35:14.240855: Epoch 565 
+2024-09-12 06:35:14.240942: Current learning rate: 0.00473 
+2024-09-12 06:39:19.693439: train_loss -0.8648 
+2024-09-12 06:39:19.693576: val_loss -0.6199 
+2024-09-12 06:39:19.693627: Pseudo dice [0.468, 0.8803] 
+2024-09-12 06:39:19.693678: Epoch time: 245.45 s 
+2024-09-12 06:39:20.646833:  
+2024-09-12 06:39:20.647007: Epoch 566 
+2024-09-12 06:39:20.647112: Current learning rate: 0.00472 
+2024-09-12 06:43:26.412059: train_loss -0.8384 
+2024-09-12 06:43:26.412250: val_loss -0.5811 
+2024-09-12 06:43:26.412303: Pseudo dice [0.4464, 0.8693] 
+2024-09-12 06:43:26.412354: Epoch time: 245.77 s 
+2024-09-12 06:43:27.426973:  
+2024-09-12 06:43:27.427181: Epoch 567 
+2024-09-12 06:43:27.427264: Current learning rate: 0.00471 
+2024-09-12 06:47:33.471751: train_loss -0.8322 
+2024-09-12 06:47:33.471905: val_loss -0.5969 
+2024-09-12 06:47:33.471957: Pseudo dice [0.4316, 0.8708] 
+2024-09-12 06:47:33.472013: Epoch time: 246.05 s 
+2024-09-12 06:47:35.381975:  
+2024-09-12 06:47:35.382202: Epoch 568 
+2024-09-12 06:47:35.382301: Current learning rate: 0.0047 
+2024-09-12 06:51:41.574652: train_loss -0.836 
+2024-09-12 06:51:41.574867: val_loss -0.5529 
+2024-09-12 06:51:41.574945: Pseudo dice [0.3619, 0.8637] 
+2024-09-12 06:51:41.574999: Epoch time: 246.19 s 
+2024-09-12 06:51:42.550299:  
+2024-09-12 06:51:42.550515: Epoch 569 
+2024-09-12 06:51:42.550620: Current learning rate: 0.00469 
+2024-09-12 06:55:48.637669: train_loss -0.8545 
+2024-09-12 06:55:48.637822: val_loss -0.5895 
+2024-09-12 06:55:48.637873: Pseudo dice [0.4123, 0.8828] 
+2024-09-12 06:55:48.637924: Epoch time: 246.09 s 
+2024-09-12 06:55:49.590957:  
+2024-09-12 06:55:49.591216: Epoch 570 
+2024-09-12 06:55:49.591299: Current learning rate: 0.00468 
+2024-09-12 06:59:55.566028: train_loss -0.8632 
+2024-09-12 06:59:55.566166: val_loss -0.5814 
+2024-09-12 06:59:55.566253: Pseudo dice [0.4065, 0.8679] 
+2024-09-12 06:59:55.566321: Epoch time: 245.98 s 
+2024-09-12 06:59:56.522069:  
+2024-09-12 06:59:56.522258: Epoch 571 
+2024-09-12 06:59:56.522340: Current learning rate: 0.00467 
+2024-09-12 07:04:02.507386: train_loss -0.861 
+2024-09-12 07:04:02.507525: val_loss -0.6009 
+2024-09-12 07:04:02.507575: Pseudo dice [0.4214, 0.8706] 
+2024-09-12 07:04:02.507625: Epoch time: 245.99 s 
+2024-09-12 07:04:03.469541:  
+2024-09-12 07:04:03.469719: Epoch 572 
+2024-09-12 07:04:03.469796: Current learning rate: 0.00466 
+2024-09-12 07:08:09.287659: train_loss -0.8654 
+2024-09-12 07:08:09.287796: val_loss -0.5755 
+2024-09-12 07:08:09.287855: Pseudo dice [0.3912, 0.8732] 
+2024-09-12 07:08:09.287907: Epoch time: 245.82 s 
+2024-09-12 07:08:10.278883:  
+2024-09-12 07:08:10.279081: Epoch 573 
+2024-09-12 07:08:10.279223: Current learning rate: 0.00465 
+2024-09-12 07:12:16.138637: train_loss -0.8723 
+2024-09-12 07:12:16.138799: val_loss -0.593 
+2024-09-12 07:12:16.138850: Pseudo dice [0.4115, 0.8836] 
+2024-09-12 07:12:16.138901: Epoch time: 245.86 s 
+2024-09-12 07:12:17.131985:  
+2024-09-12 07:12:17.132159: Epoch 574 
+2024-09-12 07:12:17.132278: Current learning rate: 0.00464 
+2024-09-12 07:16:23.286936: train_loss -0.8747 
+2024-09-12 07:16:23.287074: val_loss -0.5775 
+2024-09-12 07:16:23.287126: Pseudo dice [0.3795, 0.8737] 
+2024-09-12 07:16:23.287176: Epoch time: 246.16 s 
+2024-09-12 07:16:24.275909:  
+2024-09-12 07:16:24.276132: Epoch 575 
+2024-09-12 07:16:24.276217: Current learning rate: 0.00463 
+2024-09-12 07:20:30.448239: train_loss -0.8751 
+2024-09-12 07:20:30.448380: val_loss -0.6148 
+2024-09-12 07:20:30.448436: Pseudo dice [0.4651, 0.8731] 
+2024-09-12 07:20:30.448488: Epoch time: 246.17 s 
+2024-09-12 07:20:31.429772:  
+2024-09-12 07:20:31.430019: Epoch 576 
+2024-09-12 07:20:31.430110: Current learning rate: 0.00462 
+2024-09-12 07:24:37.621731: train_loss -0.873 
+2024-09-12 07:24:37.621868: val_loss -0.5966 
+2024-09-12 07:24:37.621919: Pseudo dice [0.415, 0.8731] 
+2024-09-12 07:24:37.621969: Epoch time: 246.19 s 
+2024-09-12 07:24:38.592330:  
+2024-09-12 07:24:38.592540: Epoch 577 
+2024-09-12 07:24:38.592652: Current learning rate: 0.00461 
+2024-09-12 07:28:44.731560: train_loss -0.8751 
+2024-09-12 07:28:44.731700: val_loss -0.5891 
+2024-09-12 07:28:44.731750: Pseudo dice [0.3943, 0.8681] 
+2024-09-12 07:28:44.731802: Epoch time: 246.14 s 
+2024-09-12 07:28:45.737776:  
+2024-09-12 07:28:45.737988: Epoch 578 
+2024-09-12 07:28:45.738069: Current learning rate: 0.0046 
+2024-09-12 07:32:51.925914: train_loss -0.8728 
+2024-09-12 07:32:51.926061: val_loss -0.5962 
+2024-09-12 07:32:51.926111: Pseudo dice [0.4201, 0.8827] 
+2024-09-12 07:32:51.926164: Epoch time: 246.19 s 
+2024-09-12 07:32:52.906590:  
+2024-09-12 07:32:52.906779: Epoch 579 
+2024-09-12 07:32:52.906862: Current learning rate: 0.00459 
+2024-09-12 07:36:59.234822: train_loss -0.8747 
+2024-09-12 07:36:59.235022: val_loss -0.5858 
+2024-09-12 07:36:59.235076: Pseudo dice [0.3933, 0.8762] 
+2024-09-12 07:36:59.235130: Epoch time: 246.33 s 
+2024-09-12 07:37:00.196800:  
+2024-09-12 07:37:00.197036: Epoch 580 
+2024-09-12 07:37:00.197142: Current learning rate: 0.00458 
+2024-09-12 07:41:06.485385: train_loss -0.8708 
+2024-09-12 07:41:06.485564: val_loss -0.6138 
+2024-09-12 07:41:06.485617: Pseudo dice [0.4526, 0.8851] 
+2024-09-12 07:41:06.485669: Epoch time: 246.29 s 
+2024-09-12 07:41:07.464042:  
+2024-09-12 07:41:07.464298: Epoch 581 
+2024-09-12 07:41:07.464381: Current learning rate: 0.00457 
+2024-09-12 07:45:13.537700: train_loss -0.8764 
+2024-09-12 07:45:13.537839: val_loss -0.5856 
+2024-09-12 07:45:13.537889: Pseudo dice [0.4405, 0.8636] 
+2024-09-12 07:45:13.537941: Epoch time: 246.08 s 
+2024-09-12 07:45:14.525828:  
+2024-09-12 07:45:14.526011: Epoch 582 
+2024-09-12 07:45:14.526110: Current learning rate: 0.00456 
+2024-09-12 07:49:20.419473: train_loss -0.8773 
+2024-09-12 07:49:20.419614: val_loss -0.6329 
+2024-09-12 07:49:20.419664: Pseudo dice [0.4898, 0.8859] 
+2024-09-12 07:49:20.419716: Epoch time: 245.9 s 
+2024-09-12 07:49:21.411864:  
+2024-09-12 07:49:21.412131: Epoch 583 
+2024-09-12 07:49:21.412229: Current learning rate: 0.00455 
+2024-09-12 07:53:27.302074: train_loss -0.8799 
+2024-09-12 07:53:27.302213: val_loss -0.5956 
+2024-09-12 07:53:27.302264: Pseudo dice [0.435, 0.8792] 
+2024-09-12 07:53:27.302315: Epoch time: 245.89 s 
+2024-09-12 07:53:28.292400:  
+2024-09-12 07:53:28.292558: Epoch 584 
+2024-09-12 07:53:28.292640: Current learning rate: 0.00454 
+2024-09-12 07:57:34.193959: train_loss -0.8778 
+2024-09-12 07:57:34.194099: val_loss -0.5942 
+2024-09-12 07:57:34.194151: Pseudo dice [0.4635, 0.8663] 
+2024-09-12 07:57:34.194202: Epoch time: 245.9 s 
+2024-09-12 07:57:35.178495:  
+2024-09-12 07:57:35.178730: Epoch 585 
+2024-09-12 07:57:35.178817: Current learning rate: 0.00453 
+2024-09-12 08:01:41.101216: train_loss -0.8704 
+2024-09-12 08:01:41.101376: val_loss -0.5977 
+2024-09-12 08:01:41.101427: Pseudo dice [0.4274, 0.8842] 
+2024-09-12 08:01:41.101478: Epoch time: 245.92 s 
+2024-09-12 08:01:42.065495:  
+2024-09-12 08:01:42.065651: Epoch 586 
+2024-09-12 08:01:42.065735: Current learning rate: 0.00452 
+2024-09-12 08:05:48.138819: train_loss -0.8785 
+2024-09-12 08:05:48.138967: val_loss -0.611 
+2024-09-12 08:05:48.139020: Pseudo dice [0.444, 0.8754] 
+2024-09-12 08:05:48.139074: Epoch time: 246.08 s 
+2024-09-12 08:05:49.113779:  
+2024-09-12 08:05:49.113956: Epoch 587 
+2024-09-12 08:05:49.114040: Current learning rate: 0.00451 
+2024-09-12 08:09:55.097180: train_loss -0.8788 
+2024-09-12 08:09:55.097350: val_loss -0.5862 
+2024-09-12 08:09:55.097401: Pseudo dice [0.3907, 0.8742] 
+2024-09-12 08:09:55.097458: Epoch time: 245.99 s 
+2024-09-12 08:09:56.077751:  
+2024-09-12 08:09:56.077971: Epoch 588 
+2024-09-12 08:09:56.078055: Current learning rate: 0.0045 
+2024-09-12 08:14:02.390033: train_loss -0.8271 
+2024-09-12 08:14:02.390174: val_loss -0.5958 
+2024-09-12 08:14:02.390223: Pseudo dice [0.4691, 0.8629] 
+2024-09-12 08:14:02.390274: Epoch time: 246.31 s 
+2024-09-12 08:14:03.378189:  
+2024-09-12 08:14:03.378410: Epoch 589 
+2024-09-12 08:14:03.378495: Current learning rate: 0.00449 
+2024-09-12 08:18:09.777702: train_loss -0.8517 
+2024-09-12 08:18:09.777897: val_loss -0.6192 
+2024-09-12 08:18:09.777993: Pseudo dice [0.4587, 0.8862] 
+2024-09-12 08:18:09.778087: Epoch time: 246.4 s 
+2024-09-12 08:18:10.749013:  
+2024-09-12 08:18:10.749197: Epoch 590 
+2024-09-12 08:18:10.749277: Current learning rate: 0.00448 
+2024-09-12 08:22:16.913329: train_loss -0.8482 
+2024-09-12 08:22:16.913509: val_loss -0.5741 
+2024-09-12 08:22:16.913561: Pseudo dice [0.3995, 0.8702] 
+2024-09-12 08:22:16.913610: Epoch time: 246.17 s 
+2024-09-12 08:22:17.893016:  
+2024-09-12 08:22:17.893289: Epoch 591 
+2024-09-12 08:22:17.893370: Current learning rate: 0.00447 
+2024-09-12 08:26:23.762338: train_loss -0.8662 
+2024-09-12 08:26:23.762485: val_loss -0.5967 
+2024-09-12 08:26:23.762535: Pseudo dice [0.4248, 0.8683] 
+2024-09-12 08:26:23.762586: Epoch time: 245.87 s 
+2024-09-12 08:26:25.716086:  
+2024-09-12 08:26:25.716255: Epoch 592 
+2024-09-12 08:26:25.716338: Current learning rate: 0.00446 
+2024-09-12 08:30:31.885377: train_loss -0.872 
+2024-09-12 08:30:31.885519: val_loss -0.5804 
+2024-09-12 08:30:31.885569: Pseudo dice [0.4248, 0.878] 
+2024-09-12 08:30:31.885622: Epoch time: 246.17 s 
+2024-09-12 08:30:32.872211:  
+2024-09-12 08:30:32.872449: Epoch 593 
+2024-09-12 08:30:32.872532: Current learning rate: 0.00445 
+2024-09-12 08:34:38.877635: train_loss -0.87 
+2024-09-12 08:34:38.877766: val_loss -0.5739 
+2024-09-12 08:34:38.877816: Pseudo dice [0.3932, 0.8804] 
+2024-09-12 08:34:38.877866: Epoch time: 246.01 s 
+2024-09-12 08:34:39.875649:  
+2024-09-12 08:34:39.875956: Epoch 594 
+2024-09-12 08:34:39.876048: Current learning rate: 0.00444 
+2024-09-12 08:38:45.943707: train_loss -0.8689 
+2024-09-12 08:38:45.943884: val_loss -0.6124 
+2024-09-12 08:38:45.943937: Pseudo dice [0.4434, 0.8727] 
+2024-09-12 08:38:45.943990: Epoch time: 246.07 s 
+2024-09-12 08:38:46.923585:  
+2024-09-12 08:38:46.923836: Epoch 595 
+2024-09-12 08:38:46.923933: Current learning rate: 0.00443 
+2024-09-12 08:42:53.197203: train_loss -0.8643 
+2024-09-12 08:42:53.197397: val_loss -0.5586 
+2024-09-12 08:42:53.197449: Pseudo dice [0.3626, 0.8634] 
+2024-09-12 08:42:53.197502: Epoch time: 246.28 s 
+2024-09-12 08:42:54.188669:  
+2024-09-12 08:42:54.188907: Epoch 596 
+2024-09-12 08:42:54.188998: Current learning rate: 0.00442 
+2024-09-12 08:47:00.281223: train_loss -0.8754 
+2024-09-12 08:47:00.281365: val_loss -0.6061 
+2024-09-12 08:47:00.281416: Pseudo dice [0.4529, 0.8868] 
+2024-09-12 08:47:00.281468: Epoch time: 246.09 s 
+2024-09-12 08:47:01.258109:  
+2024-09-12 08:47:01.258307: Epoch 597 
+2024-09-12 08:47:01.258390: Current learning rate: 0.00441 
+2024-09-12 08:51:07.098124: train_loss -0.8806 
+2024-09-12 08:51:07.098268: val_loss -0.587 
+2024-09-12 08:51:07.098369: Pseudo dice [0.4415, 0.8714] 
+2024-09-12 08:51:07.098421: Epoch time: 245.84 s 
+2024-09-12 08:51:08.069358:  
+2024-09-12 08:51:08.069529: Epoch 598 
+2024-09-12 08:51:08.069648: Current learning rate: 0.0044 
+2024-09-12 08:55:14.108221: train_loss -0.8777 
+2024-09-12 08:55:14.108362: val_loss -0.6023 
+2024-09-12 08:55:14.108413: Pseudo dice [0.4703, 0.8766] 
+2024-09-12 08:55:14.108463: Epoch time: 246.04 s 
+2024-09-12 08:55:15.092634:  
+2024-09-12 08:55:15.092831: Epoch 599 
+2024-09-12 08:55:15.092914: Current learning rate: 0.00439 
+2024-09-12 08:59:21.265872: train_loss -0.8815 
+2024-09-12 08:59:21.266016: val_loss -0.5793 
+2024-09-12 08:59:21.266067: Pseudo dice [0.3939, 0.8772] 
+2024-09-12 08:59:21.266120: Epoch time: 246.18 s 
+2024-09-12 08:59:25.275027:  
+2024-09-12 08:59:25.275215: Epoch 600 
+2024-09-12 08:59:25.275307: Current learning rate: 0.00438 
+2024-09-12 09:03:31.394295: train_loss -0.881 
+2024-09-12 09:03:31.394431: val_loss -0.561 
+2024-09-12 09:03:31.394481: Pseudo dice [0.382, 0.879] 
+2024-09-12 09:03:31.394531: Epoch time: 246.12 s 
+2024-09-12 09:03:32.351774:  
+2024-09-12 09:03:32.351976: Epoch 601 
+2024-09-12 09:03:32.352081: Current learning rate: 0.00437 
+2024-09-12 09:07:38.587467: train_loss -0.8817 
+2024-09-12 09:07:38.587606: val_loss -0.5826 
+2024-09-12 09:07:38.587656: Pseudo dice [0.4369, 0.864] 
+2024-09-12 09:07:38.587709: Epoch time: 246.24 s 
+2024-09-12 09:07:39.570901:  
+2024-09-12 09:07:39.571121: Epoch 602 
+2024-09-12 09:07:39.571208: Current learning rate: 0.00436 
+2024-09-12 09:11:45.838399: train_loss -0.878 
+2024-09-12 09:11:45.838593: val_loss -0.5972 
+2024-09-12 09:11:45.838649: Pseudo dice [0.3974, 0.8731] 
+2024-09-12 09:11:45.838703: Epoch time: 246.27 s 
+2024-09-12 09:11:46.819499:  
+2024-09-12 09:11:46.819729: Epoch 603 
+2024-09-12 09:11:46.819835: Current learning rate: 0.00435 
+2024-09-12 09:15:53.157893: train_loss -0.8799 
+2024-09-12 09:15:53.158033: val_loss -0.5936 
+2024-09-12 09:15:53.158296: Pseudo dice [0.4245, 0.8762] 
+2024-09-12 09:15:53.158350: Epoch time: 246.34 s 
+2024-09-12 09:15:54.129323:  
+2024-09-12 09:15:54.129555: Epoch 604 
+2024-09-12 09:15:54.129679: Current learning rate: 0.00434 
+2024-09-12 09:20:00.535167: train_loss -0.8843 
+2024-09-12 09:20:00.535316: val_loss -0.5884 
+2024-09-12 09:20:00.535371: Pseudo dice [0.4231, 0.8833] 
+2024-09-12 09:20:00.535435: Epoch time: 246.41 s 
+2024-09-12 09:20:01.525271:  
+2024-09-12 09:20:01.525423: Epoch 605 
+2024-09-12 09:20:01.525506: Current learning rate: 0.00433 
+2024-09-12 09:24:07.827921: train_loss -0.8779 
+2024-09-12 09:24:07.828083: val_loss -0.5719 
+2024-09-12 09:24:07.828155: Pseudo dice [0.3946, 0.8821] 
+2024-09-12 09:24:07.828206: Epoch time: 246.3 s 
+2024-09-12 09:24:08.816316:  
+2024-09-12 09:24:08.816509: Epoch 606 
+2024-09-12 09:24:08.816594: Current learning rate: 0.00432 
+2024-09-12 09:28:15.171708: train_loss -0.8717 
+2024-09-12 09:28:15.171852: val_loss -0.5952 
+2024-09-12 09:28:15.171906: Pseudo dice [0.4518, 0.8742] 
+2024-09-12 09:28:15.171961: Epoch time: 246.36 s 
+2024-09-12 09:28:16.154680:  
+2024-09-12 09:28:16.154875: Epoch 607 
+2024-09-12 09:28:16.154988: Current learning rate: 0.00431 
+2024-09-12 09:32:22.608560: train_loss -0.8737 
+2024-09-12 09:32:22.608699: val_loss -0.5902 
+2024-09-12 09:32:22.608751: Pseudo dice [0.4372, 0.8811] 
+2024-09-12 09:32:22.608801: Epoch time: 246.46 s 
+2024-09-12 09:32:23.586821:  
+2024-09-12 09:32:23.587021: Epoch 608 
+2024-09-12 09:32:23.587111: Current learning rate: 0.0043 
+2024-09-12 09:36:30.031666: train_loss -0.8867 
+2024-09-12 09:36:30.031821: val_loss -0.5882 
+2024-09-12 09:36:30.031874: Pseudo dice [0.4138, 0.869] 
+2024-09-12 09:36:30.031926: Epoch time: 246.45 s 
+2024-09-12 09:36:31.013111:  
+2024-09-12 09:36:31.013307: Epoch 609 
+2024-09-12 09:36:31.013397: Current learning rate: 0.00429 
+2024-09-12 09:40:37.450927: train_loss -0.8861 
+2024-09-12 09:40:37.451068: val_loss -0.6105 
+2024-09-12 09:40:37.451119: Pseudo dice [0.4419, 0.8893] 
+2024-09-12 09:40:37.451171: Epoch time: 246.44 s 
+2024-09-12 09:40:38.429249:  
+2024-09-12 09:40:38.429491: Epoch 610 
+2024-09-12 09:40:38.429578: Current learning rate: 0.00429 
+2024-09-12 09:44:44.789245: train_loss -0.8833 
+2024-09-12 09:44:44.789390: val_loss -0.5971 
+2024-09-12 09:44:44.789440: Pseudo dice [0.4481, 0.8706] 
+2024-09-12 09:44:44.789491: Epoch time: 246.36 s 
+2024-09-12 09:44:45.782175:  
+2024-09-12 09:44:45.782342: Epoch 611 
+2024-09-12 09:44:45.782426: Current learning rate: 0.00428 
+2024-09-12 09:48:52.155412: train_loss -0.8818 
+2024-09-12 09:48:52.155594: val_loss -0.5734 
+2024-09-12 09:48:52.155648: Pseudo dice [0.3886, 0.8654] 
+2024-09-12 09:48:52.155699: Epoch time: 246.38 s 
+2024-09-12 09:48:53.143664:  
+2024-09-12 09:48:53.143865: Epoch 612 
+2024-09-12 09:48:53.143990: Current learning rate: 0.00427 
+2024-09-12 09:52:59.336724: train_loss -0.8879 
+2024-09-12 09:52:59.336869: val_loss -0.5941 
+2024-09-12 09:52:59.336929: Pseudo dice [0.3782, 0.8826] 
+2024-09-12 09:52:59.336984: Epoch time: 246.2 s 
+2024-09-12 09:53:00.327861:  
+2024-09-12 09:53:00.328016: Epoch 613 
+2024-09-12 09:53:00.328100: Current learning rate: 0.00426 
+2024-09-12 09:57:06.438919: train_loss -0.8831 
+2024-09-12 09:57:06.439070: val_loss -0.612 
+2024-09-12 09:57:06.439126: Pseudo dice [0.5025, 0.8723] 
+2024-09-12 09:57:06.439182: Epoch time: 246.11 s 
+2024-09-12 09:57:07.449115:  
+2024-09-12 09:57:07.449342: Epoch 614 
+2024-09-12 09:57:07.449428: Current learning rate: 0.00425 
+2024-09-12 10:01:13.522809: train_loss -0.8701 
+2024-09-12 10:01:13.522956: val_loss -0.6096 
+2024-09-12 10:01:13.523013: Pseudo dice [0.4318, 0.8697] 
+2024-09-12 10:01:13.523101: Epoch time: 246.08 s 
+2024-09-12 10:01:15.442055:  
+2024-09-12 10:01:15.442353: Epoch 615 
+2024-09-12 10:01:15.442468: Current learning rate: 0.00424 
+2024-09-12 10:05:21.430781: train_loss -0.8761 
+2024-09-12 10:05:21.430934: val_loss -0.6224 
+2024-09-12 10:05:21.430990: Pseudo dice [0.4789, 0.8792] 
+2024-09-12 10:05:21.431045: Epoch time: 245.99 s 
+2024-09-12 10:05:22.418518:  
+2024-09-12 10:05:22.418771: Epoch 616 
+2024-09-12 10:05:22.418860: Current learning rate: 0.00423 
+2024-09-12 10:09:28.306321: train_loss -0.878 
+2024-09-12 10:09:28.306470: val_loss -0.5974 
+2024-09-12 10:09:28.306526: Pseudo dice [0.4511, 0.876] 
+2024-09-12 10:09:28.306581: Epoch time: 245.89 s 
+2024-09-12 10:09:29.292430:  
+2024-09-12 10:09:29.292680: Epoch 617 
+2024-09-12 10:09:29.292772: Current learning rate: 0.00422 
+2024-09-12 10:13:35.190236: train_loss -0.8851 
+2024-09-12 10:13:35.190408: val_loss -0.6235 
+2024-09-12 10:13:35.190465: Pseudo dice [0.4938, 0.89] 
+2024-09-12 10:13:35.190521: Epoch time: 245.9 s 
+2024-09-12 10:13:36.160792:  
+2024-09-12 10:13:36.160993: Epoch 618 
+2024-09-12 10:13:36.161083: Current learning rate: 0.00421 
+2024-09-12 10:17:42.065183: train_loss -0.8841 
+2024-09-12 10:17:42.065336: val_loss -0.6064 
+2024-09-12 10:17:42.065393: Pseudo dice [0.4803, 0.8713] 
+2024-09-12 10:17:42.065449: Epoch time: 245.91 s 
+2024-09-12 10:17:43.055230:  
+2024-09-12 10:17:43.055475: Epoch 619 
+2024-09-12 10:17:43.055564: Current learning rate: 0.0042 
+2024-09-12 10:21:48.907578: train_loss -0.8802 
+2024-09-12 10:21:48.907767: val_loss -0.6095 
+2024-09-12 10:21:48.907843: Pseudo dice [0.4864, 0.8756] 
+2024-09-12 10:21:48.907902: Epoch time: 245.85 s 
+2024-09-12 10:21:49.890576:  
+2024-09-12 10:21:49.890745: Epoch 620 
+2024-09-12 10:21:49.890836: Current learning rate: 0.00419 
+2024-09-12 10:25:55.865699: train_loss -0.8848 
+2024-09-12 10:25:55.865846: val_loss -0.6188 
+2024-09-12 10:25:55.865901: Pseudo dice [0.4637, 0.8904] 
+2024-09-12 10:25:55.865957: Epoch time: 245.98 s 
+2024-09-12 10:25:56.838225:  
+2024-09-12 10:25:56.838453: Epoch 621 
+2024-09-12 10:25:56.838540: Current learning rate: 0.00418 
+2024-09-12 10:30:02.878139: train_loss -0.8849 
+2024-09-12 10:30:02.878283: val_loss -0.5937 
+2024-09-12 10:30:02.878338: Pseudo dice [0.4387, 0.8896] 
+2024-09-12 10:30:02.878394: Epoch time: 246.04 s 
+2024-09-12 10:30:03.874417:  
+2024-09-12 10:30:03.874626: Epoch 622 
+2024-09-12 10:30:03.874714: Current learning rate: 0.00417 
+2024-09-12 10:34:09.959513: train_loss -0.8777 
+2024-09-12 10:34:09.959661: val_loss -0.623 
+2024-09-12 10:34:09.959719: Pseudo dice [0.4937, 0.8881] 
+2024-09-12 10:34:09.959775: Epoch time: 246.09 s 
+2024-09-12 10:34:09.959826: Yayy! New best EMA pseudo Dice: 0.6667 
+2024-09-12 10:34:13.929639:  
+2024-09-12 10:34:13.929872: Epoch 623 
+2024-09-12 10:34:13.929958: Current learning rate: 0.00416 
+2024-09-12 10:38:20.054464: train_loss -0.8899 
+2024-09-12 10:38:20.054599: val_loss -0.5995 
+2024-09-12 10:38:20.054655: Pseudo dice [0.4109, 0.8829] 
+2024-09-12 10:38:20.054712: Epoch time: 246.13 s 
+2024-09-12 10:38:21.065502:  
+2024-09-12 10:38:21.065707: Epoch 624 
+2024-09-12 10:38:21.065806: Current learning rate: 0.00415 
+2024-09-12 10:42:27.230673: train_loss -0.8673 
+2024-09-12 10:42:27.230843: val_loss -0.6083 
+2024-09-12 10:42:27.230901: Pseudo dice [0.4252, 0.8864] 
+2024-09-12 10:42:27.230957: Epoch time: 246.17 s 
+2024-09-12 10:42:28.217279:  
+2024-09-12 10:42:28.217484: Epoch 625 
+2024-09-12 10:42:28.217613: Current learning rate: 0.00414 
+2024-09-12 10:46:34.193682: train_loss -0.8715 
+2024-09-12 10:46:34.193835: val_loss -0.6153 
+2024-09-12 10:46:34.193890: Pseudo dice [0.4689, 0.877] 
+2024-09-12 10:46:34.193947: Epoch time: 245.98 s 
+2024-09-12 10:46:35.201569:  
+2024-09-12 10:46:35.201806: Epoch 626 
+2024-09-12 10:46:35.201902: Current learning rate: 0.00413 
+2024-09-12 10:50:41.175314: train_loss -0.8738 
+2024-09-12 10:50:41.175531: val_loss -0.586 
+2024-09-12 10:50:41.175634: Pseudo dice [0.4289, 0.8847] 
+2024-09-12 10:50:41.175734: Epoch time: 245.98 s 
+2024-09-12 10:50:42.165985:  
+2024-09-12 10:50:42.166183: Epoch 627 
+2024-09-12 10:50:42.166280: Current learning rate: 0.00412 
+2024-09-12 10:54:48.122773: train_loss -0.8761 
+2024-09-12 10:54:48.122924: val_loss -0.6245 
+2024-09-12 10:54:48.122978: Pseudo dice [0.4925, 0.8634] 
+2024-09-12 10:54:48.123034: Epoch time: 245.96 s 
+2024-09-12 10:54:49.101867:  
+2024-09-12 10:54:49.102130: Epoch 628 
+2024-09-12 10:54:49.102217: Current learning rate: 0.00411 
+2024-09-12 10:58:54.956606: train_loss -0.8584 
+2024-09-12 10:58:54.956768: val_loss -0.5723 
+2024-09-12 10:58:54.956824: Pseudo dice [0.4198, 0.8456] 
+2024-09-12 10:58:54.956882: Epoch time: 245.86 s 
+2024-09-12 10:58:55.951131:  
+2024-09-12 10:58:55.951300: Epoch 629 
+2024-09-12 10:58:55.951419: Current learning rate: 0.0041 
+2024-09-12 11:03:01.785758: train_loss -0.8617 
+2024-09-12 11:03:01.785905: val_loss -0.6182 
+2024-09-12 11:03:01.785961: Pseudo dice [0.4616, 0.8782] 
+2024-09-12 11:03:01.786017: Epoch time: 245.84 s 
+2024-09-12 11:03:02.772482:  
+2024-09-12 11:03:02.772719: Epoch 630 
+2024-09-12 11:03:02.772823: Current learning rate: 0.00409 
+2024-09-12 11:07:08.784247: train_loss -0.8695 
+2024-09-12 11:07:08.784496: val_loss -0.5985 
+2024-09-12 11:07:08.784555: Pseudo dice [0.4105, 0.8762] 
+2024-09-12 11:07:08.784612: Epoch time: 246.01 s 
+2024-09-12 11:07:09.768708:  
+2024-09-12 11:07:09.768870: Epoch 631 
+2024-09-12 11:07:09.768956: Current learning rate: 0.00408 
+2024-09-12 11:11:15.643555: train_loss -0.8775 
+2024-09-12 11:11:15.643702: val_loss -0.5712 
+2024-09-12 11:11:15.643759: Pseudo dice [0.3536, 0.8693] 
+2024-09-12 11:11:15.643825: Epoch time: 245.88 s 
+2024-09-12 11:11:16.623332:  
+2024-09-12 11:11:16.623560: Epoch 632 
+2024-09-12 11:11:16.623650: Current learning rate: 0.00407 
+2024-09-12 11:15:22.385083: train_loss -0.8724 
+2024-09-12 11:15:22.385224: val_loss -0.5999 
+2024-09-12 11:15:22.385282: Pseudo dice [0.4011, 0.8742] 
+2024-09-12 11:15:22.385338: Epoch time: 245.76 s 
+2024-09-12 11:15:23.369997:  
+2024-09-12 11:15:23.370219: Epoch 633 
+2024-09-12 11:15:23.370309: Current learning rate: 0.00406 
+2024-09-12 11:19:29.040375: train_loss -0.8701 
+2024-09-12 11:19:29.040557: val_loss -0.6239 
+2024-09-12 11:19:29.040615: Pseudo dice [0.4956, 0.8769] 
+2024-09-12 11:19:29.040671: Epoch time: 245.67 s 
+2024-09-12 11:19:30.061030:  
+2024-09-12 11:19:30.061231: Epoch 634 
+2024-09-12 11:19:30.061318: Current learning rate: 0.00405 
+2024-09-12 11:23:35.679802: train_loss -0.8733 
+2024-09-12 11:23:35.680019: val_loss -0.6081 
+2024-09-12 11:23:35.680078: Pseudo dice [0.4349, 0.8757] 
+2024-09-12 11:23:35.680135: Epoch time: 245.62 s 
+2024-09-12 11:23:36.681850:  
+2024-09-12 11:23:36.682072: Epoch 635 
+2024-09-12 11:23:36.682163: Current learning rate: 0.00404 
+2024-09-12 11:27:42.376071: train_loss -0.8824 
+2024-09-12 11:27:42.376234: val_loss -0.6019 
+2024-09-12 11:27:42.376291: Pseudo dice [0.426, 0.873] 
+2024-09-12 11:27:42.376349: Epoch time: 245.7 s 
+2024-09-12 11:27:43.380031:  
+2024-09-12 11:27:43.380212: Epoch 636 
+2024-09-12 11:27:43.380300: Current learning rate: 0.00403 
+2024-09-12 11:31:49.211641: train_loss -0.884 
+2024-09-12 11:31:49.211789: val_loss -0.5725 
+2024-09-12 11:31:49.211855: Pseudo dice [0.417, 0.8694] 
+2024-09-12 11:31:49.211912: Epoch time: 245.83 s 
+2024-09-12 11:31:50.198725:  
+2024-09-12 11:31:50.198888: Epoch 637 
+2024-09-12 11:31:50.199001: Current learning rate: 0.00402 
+2024-09-12 11:35:56.194895: train_loss -0.8845 
+2024-09-12 11:35:56.195038: val_loss -0.5976 
+2024-09-12 11:35:56.195093: Pseudo dice [0.4079, 0.8766] 
+2024-09-12 11:35:56.195148: Epoch time: 246.0 s 
+2024-09-12 11:35:58.135250:  
+2024-09-12 11:35:58.135548: Epoch 638 
+2024-09-12 11:35:58.135660: Current learning rate: 0.00401 
+2024-09-12 11:40:04.340580: train_loss -0.8749 
+2024-09-12 11:40:04.340754: val_loss -0.6118 
+2024-09-12 11:40:04.340810: Pseudo dice [0.4715, 0.8818] 
+2024-09-12 11:40:04.340865: Epoch time: 246.21 s 
+2024-09-12 11:40:05.323915:  
+2024-09-12 11:40:05.324121: Epoch 639 
+2024-09-12 11:40:05.324227: Current learning rate: 0.004 
+2024-09-12 11:44:11.526652: train_loss -0.8811 
+2024-09-12 11:44:11.526831: val_loss -0.5753 
+2024-09-12 11:44:11.526888: Pseudo dice [0.3932, 0.8684] 
+2024-09-12 11:44:11.526945: Epoch time: 246.2 s 
+2024-09-12 11:44:12.519788:  
+2024-09-12 11:44:12.520056: Epoch 640 
+2024-09-12 11:44:12.520144: Current learning rate: 0.00399 
+2024-09-12 11:48:18.813939: train_loss -0.8794 
+2024-09-12 11:48:18.814086: val_loss -0.5949 
+2024-09-12 11:48:18.814142: Pseudo dice [0.4125, 0.885] 
+2024-09-12 11:48:18.814198: Epoch time: 246.3 s 
+2024-09-12 11:48:19.784587:  
+2024-09-12 11:48:19.784840: Epoch 641 
+2024-09-12 11:48:19.784925: Current learning rate: 0.00398 
+2024-09-12 11:52:25.802037: train_loss -0.8745 
+2024-09-12 11:52:25.802212: val_loss -0.5872 
+2024-09-12 11:52:25.802270: Pseudo dice [0.4008, 0.8729] 
+2024-09-12 11:52:25.802336: Epoch time: 246.02 s 
+2024-09-12 11:52:26.764436:  
+2024-09-12 11:52:26.764607: Epoch 642 
+2024-09-12 11:52:26.764693: Current learning rate: 0.00397 
+2024-09-12 11:56:32.727879: train_loss -0.8853 
+2024-09-12 11:56:32.728021: val_loss -0.609 
+2024-09-12 11:56:32.728082: Pseudo dice [0.4574, 0.8789] 
+2024-09-12 11:56:32.728138: Epoch time: 245.97 s 
+2024-09-12 11:56:33.715091:  
+2024-09-12 11:56:33.715276: Epoch 643 
+2024-09-12 11:56:33.715422: Current learning rate: 0.00396 
+2024-09-12 12:00:39.734059: train_loss -0.8844 
+2024-09-12 12:00:39.734207: val_loss -0.6013 
+2024-09-12 12:00:39.734278: Pseudo dice [0.4334, 0.8842] 
+2024-09-12 12:00:39.734342: Epoch time: 246.02 s 
+2024-09-12 12:00:40.721140:  
+2024-09-12 12:00:40.721325: Epoch 644 
+2024-09-12 12:00:40.721414: Current learning rate: 0.00395 
+2024-09-12 12:04:46.793531: train_loss -0.8873 
+2024-09-12 12:04:46.793679: val_loss -0.5985 
+2024-09-12 12:04:46.793736: Pseudo dice [0.4335, 0.8857] 
+2024-09-12 12:04:46.793791: Epoch time: 246.07 s 
+2024-09-12 12:04:47.779371:  
+2024-09-12 12:04:47.779582: Epoch 645 
+2024-09-12 12:04:47.779674: Current learning rate: 0.00394 
+2024-09-12 12:08:54.030976: train_loss -0.8851 
+2024-09-12 12:08:54.031135: val_loss -0.6203 
+2024-09-12 12:08:54.031192: Pseudo dice [0.4703, 0.8736] 
+2024-09-12 12:08:54.031250: Epoch time: 246.25 s 
+2024-09-12 12:08:55.011574:  
+2024-09-12 12:08:55.011829: Epoch 646 
+2024-09-12 12:08:55.011916: Current learning rate: 0.00393 
+2024-09-12 12:13:01.224837: train_loss -0.8857 
+2024-09-12 12:13:01.224996: val_loss -0.5724 
+2024-09-12 12:13:01.225053: Pseudo dice [0.3657, 0.8878] 
+2024-09-12 12:13:01.225109: Epoch time: 246.22 s 
+2024-09-12 12:13:02.203254:  
+2024-09-12 12:13:02.203460: Epoch 647 
+2024-09-12 12:13:02.203548: Current learning rate: 0.00392 
+2024-09-12 12:17:08.279373: train_loss -0.8879 
+2024-09-12 12:17:08.279578: val_loss -0.5993 
+2024-09-12 12:17:08.279635: Pseudo dice [0.446, 0.8675] 
+2024-09-12 12:17:08.279691: Epoch time: 246.08 s 
+2024-09-12 12:17:09.255612:  
+2024-09-12 12:17:09.255878: Epoch 648 
+2024-09-12 12:17:09.255965: Current learning rate: 0.00391 
+2024-09-12 12:21:15.188444: train_loss -0.8875 
+2024-09-12 12:21:15.188590: val_loss -0.6243 
+2024-09-12 12:21:15.188694: Pseudo dice [0.4642, 0.8825] 
+2024-09-12 12:21:15.188752: Epoch time: 245.93 s 
+2024-09-12 12:21:16.177853:  
+2024-09-12 12:21:16.178055: Epoch 649 
+2024-09-12 12:21:16.178142: Current learning rate: 0.0039 
+2024-09-12 12:25:22.138787: train_loss -0.8792 
+2024-09-12 12:25:22.138939: val_loss -0.5833 
+2024-09-12 12:25:22.138998: Pseudo dice [0.4143, 0.8755] 
+2024-09-12 12:25:22.139053: Epoch time: 245.96 s 
+2024-09-12 12:25:26.155267:  
+2024-09-12 12:25:26.155474: Epoch 650 
+2024-09-12 12:25:26.155578: Current learning rate: 0.00389 
+2024-09-12 12:29:32.223768: train_loss -0.8772 
+2024-09-12 12:29:32.223916: val_loss -0.6164 
+2024-09-12 12:29:32.223973: Pseudo dice [0.4638, 0.8747] 
+2024-09-12 12:29:32.224029: Epoch time: 246.07 s 
+2024-09-12 12:29:33.198153:  
+2024-09-12 12:29:33.198349: Epoch 651 
+2024-09-12 12:29:33.198437: Current learning rate: 0.00388 
+2024-09-12 12:33:39.197306: train_loss -0.8832 
+2024-09-12 12:33:39.197448: val_loss -0.5831 
+2024-09-12 12:33:39.197504: Pseudo dice [0.3886, 0.877] 
+2024-09-12 12:33:39.197559: Epoch time: 246.0 s 
+2024-09-12 12:33:40.184387:  
+2024-09-12 12:33:40.184556: Epoch 652 
+2024-09-12 12:33:40.184643: Current learning rate: 0.00387 
+2024-09-12 12:37:46.151837: train_loss -0.8876 
+2024-09-12 12:37:46.152067: val_loss -0.5989 
+2024-09-12 12:37:46.152126: Pseudo dice [0.4521, 0.8834] 
+2024-09-12 12:37:46.152185: Epoch time: 245.97 s 
+2024-09-12 12:37:47.142786:  
+2024-09-12 12:37:47.142969: Epoch 653 
+2024-09-12 12:37:47.143055: Current learning rate: 0.00386 
+2024-09-12 12:41:53.288769: train_loss -0.8931 
+2024-09-12 12:41:53.288918: val_loss -0.5938 
+2024-09-12 12:41:53.288974: Pseudo dice [0.4477, 0.8799] 
+2024-09-12 12:41:53.289028: Epoch time: 246.15 s 
+2024-09-12 12:41:54.278089:  
+2024-09-12 12:41:54.278289: Epoch 654 
+2024-09-12 12:41:54.278414: Current learning rate: 0.00385 
+2024-09-12 12:46:00.282172: train_loss -0.8847 
+2024-09-12 12:46:00.282316: val_loss -0.609 
+2024-09-12 12:46:00.282373: Pseudo dice [0.4679, 0.8834] 
+2024-09-12 12:46:00.282429: Epoch time: 246.01 s 
+2024-09-12 12:46:01.260355:  
+2024-09-12 12:46:01.260510: Epoch 655 
+2024-09-12 12:46:01.260599: Current learning rate: 0.00384 
+2024-09-12 12:50:07.321479: train_loss -0.8877 
+2024-09-12 12:50:07.321651: val_loss -0.5861 
+2024-09-12 12:50:07.321708: Pseudo dice [0.4124, 0.8794] 
+2024-09-12 12:50:07.321764: Epoch time: 246.06 s 
+2024-09-12 12:50:08.308806:  
+2024-09-12 12:50:08.309020: Epoch 656 
+2024-09-12 12:50:08.309151: Current learning rate: 0.00383 
+2024-09-12 12:54:14.485670: train_loss -0.8902 
+2024-09-12 12:54:14.485815: val_loss -0.6176 
+2024-09-12 12:54:14.485873: Pseudo dice [0.4534, 0.894] 
+2024-09-12 12:54:14.485927: Epoch time: 246.18 s 
+2024-09-12 12:54:15.473490:  
+2024-09-12 12:54:15.473719: Epoch 657 
+2024-09-12 12:54:15.473806: Current learning rate: 0.00382 
+2024-09-12 12:58:21.560383: train_loss -0.8883 
+2024-09-12 12:58:21.560531: val_loss -0.5799 
+2024-09-12 12:58:21.560591: Pseudo dice [0.3814, 0.8734] 
+2024-09-12 12:58:21.560649: Epoch time: 246.09 s 
+2024-09-12 12:58:22.564316:  
+2024-09-12 12:58:22.564494: Epoch 658 
+2024-09-12 12:58:22.564584: Current learning rate: 0.00381 
+2024-09-12 13:02:28.693523: train_loss -0.8863 
+2024-09-12 13:02:28.693689: val_loss -0.6129 
+2024-09-12 13:02:28.693746: Pseudo dice [0.459, 0.8797] 
+2024-09-12 13:02:28.693802: Epoch time: 246.13 s 
+2024-09-12 13:02:29.698678:  
+2024-09-12 13:02:29.698890: Epoch 659 
+2024-09-12 13:02:29.698977: Current learning rate: 0.0038 
+2024-09-12 13:06:35.756919: train_loss -0.8908 
+2024-09-12 13:06:35.757069: val_loss -0.6119 
+2024-09-12 13:06:35.757126: Pseudo dice [0.4736, 0.8858] 
+2024-09-12 13:06:35.757182: Epoch time: 246.06 s 
+2024-09-12 13:06:36.771731:  
+2024-09-12 13:06:36.771923: Epoch 660 
+2024-09-12 13:06:36.772010: Current learning rate: 0.00379 
+2024-09-12 13:10:42.940398: train_loss -0.8901 
+2024-09-12 13:10:42.940535: val_loss -0.6115 
+2024-09-12 13:10:42.940586: Pseudo dice [0.4808, 0.8828] 
+2024-09-12 13:10:42.940637: Epoch time: 246.17 s 
+2024-09-12 13:10:43.917783:  
+2024-09-12 13:10:43.917989: Epoch 661 
+2024-09-12 13:10:43.918080: Current learning rate: 0.00378 
+2024-09-12 13:14:50.790659: train_loss -0.8903 
+2024-09-12 13:14:50.790816: val_loss -0.6188 
+2024-09-12 13:14:50.790871: Pseudo dice [0.4672, 0.8761] 
+2024-09-12 13:14:50.790926: Epoch time: 246.87 s 
+2024-09-12 13:14:51.766446:  
+2024-09-12 13:14:51.766715: Epoch 662 
+2024-09-12 13:14:51.766847: Current learning rate: 0.00377 
+2024-09-12 13:18:57.731838: train_loss -0.8912 
+2024-09-12 13:18:57.731977: val_loss -0.6058 
+2024-09-12 13:18:57.732033: Pseudo dice [0.4713, 0.8788] 
+2024-09-12 13:18:57.732088: Epoch time: 245.97 s 
+2024-09-12 13:18:58.727349:  
+2024-09-12 13:18:58.727566: Epoch 663 
+2024-09-12 13:18:58.727677: Current learning rate: 0.00376 
+2024-09-12 13:23:04.833138: train_loss -0.8947 
+2024-09-12 13:23:04.833288: val_loss -0.6041 
+2024-09-12 13:23:04.833344: Pseudo dice [0.4672, 0.891] 
+2024-09-12 13:23:04.833400: Epoch time: 246.11 s 
+2024-09-12 13:23:05.835321:  
+2024-09-12 13:23:05.835532: Epoch 664 
+2024-09-12 13:23:05.835624: Current learning rate: 0.00375 
+2024-09-12 13:27:12.105672: train_loss -0.8911 
+2024-09-12 13:27:12.105825: val_loss -0.6249 
+2024-09-12 13:27:12.105882: Pseudo dice [0.474, 0.8814] 
+2024-09-12 13:27:12.105936: Epoch time: 246.27 s 
+2024-09-12 13:27:13.085634:  
+2024-09-12 13:27:13.085904: Epoch 665 
+2024-09-12 13:27:13.086015: Current learning rate: 0.00374 
+2024-09-12 13:31:19.277643: train_loss -0.8933 
+2024-09-12 13:31:19.277827: val_loss -0.6229 
+2024-09-12 13:31:19.277885: Pseudo dice [0.4594, 0.882] 
+2024-09-12 13:31:19.277947: Epoch time: 246.19 s 
+2024-09-12 13:31:19.277992: Yayy! New best EMA pseudo Dice: 0.6668 
+2024-09-12 13:31:23.255752:  
+2024-09-12 13:31:23.255938: Epoch 666 
+2024-09-12 13:31:23.256025: Current learning rate: 0.00373 
+2024-09-12 13:35:29.349378: train_loss -0.894 
+2024-09-12 13:35:29.349530: val_loss -0.5998 
+2024-09-12 13:35:29.349588: Pseudo dice [0.3967, 0.8822] 
+2024-09-12 13:35:29.349643: Epoch time: 246.1 s 
+2024-09-12 13:35:30.315901:  
+2024-09-12 13:35:30.316079: Epoch 667 
+2024-09-12 13:35:30.316167: Current learning rate: 0.00372 
+2024-09-12 13:39:36.218039: train_loss -0.8925 
+2024-09-12 13:39:36.218219: val_loss -0.5981 
+2024-09-12 13:39:36.218276: Pseudo dice [0.4474, 0.8821] 
+2024-09-12 13:39:36.218331: Epoch time: 245.9 s 
+2024-09-12 13:39:37.207152:  
+2024-09-12 13:39:37.207417: Epoch 668 
+2024-09-12 13:39:37.207508: Current learning rate: 0.00371 
+2024-09-12 13:43:43.076663: train_loss -0.8912 
+2024-09-12 13:43:43.076825: val_loss -0.634 
+2024-09-12 13:43:43.076884: Pseudo dice [0.4987, 0.8709] 
+2024-09-12 13:43:43.076942: Epoch time: 245.87 s 
+2024-09-12 13:43:44.075787:  
+2024-09-12 13:43:44.076031: Epoch 669 
+2024-09-12 13:43:44.076133: Current learning rate: 0.0037 
+2024-09-12 13:47:50.102374: train_loss -0.8891 
+2024-09-12 13:47:50.102526: val_loss -0.6014 
+2024-09-12 13:47:50.102582: Pseudo dice [0.4108, 0.8811] 
+2024-09-12 13:47:50.102638: Epoch time: 246.03 s 
+2024-09-12 13:47:51.104677:  
+2024-09-12 13:47:51.104859: Epoch 670 
+2024-09-12 13:47:51.104948: Current learning rate: 0.00369 
+2024-09-12 13:51:57.103820: train_loss -0.8863 
+2024-09-12 13:51:57.103992: val_loss -0.6059 
+2024-09-12 13:51:57.104048: Pseudo dice [0.4811, 0.8708] 
+2024-09-12 13:51:57.104105: Epoch time: 246.0 s 
+2024-09-12 13:51:58.112170:  
+2024-09-12 13:51:58.112363: Epoch 671 
+2024-09-12 13:51:58.112447: Current learning rate: 0.00368 
+2024-09-12 13:56:03.927594: train_loss -0.885 
+2024-09-12 13:56:03.927736: val_loss -0.6151 
+2024-09-12 13:56:03.927826: Pseudo dice [0.4577, 0.8878] 
+2024-09-12 13:56:03.927907: Epoch time: 245.82 s 
+2024-09-12 13:56:04.922893:  
+2024-09-12 13:56:04.923102: Epoch 672 
+2024-09-12 13:56:04.923235: Current learning rate: 0.00367 
+2024-09-12 14:00:10.779301: train_loss -0.8877 
+2024-09-12 14:00:10.779448: val_loss -0.5921 
+2024-09-12 14:00:10.779504: Pseudo dice [0.4513, 0.8823] 
+2024-09-12 14:00:10.779561: Epoch time: 245.86 s 
+2024-09-12 14:00:11.791466:  
+2024-09-12 14:00:11.791682: Epoch 673 
+2024-09-12 14:00:11.791773: Current learning rate: 0.00366 
+2024-09-12 14:04:17.692114: train_loss -0.8872 
+2024-09-12 14:04:17.692266: val_loss -0.6123 
+2024-09-12 14:04:17.692323: Pseudo dice [0.4791, 0.8814] 
+2024-09-12 14:04:17.692380: Epoch time: 245.9 s 
+2024-09-12 14:04:17.692424: Yayy! New best EMA pseudo Dice: 0.6676 
+2024-09-12 14:04:21.682194:  
+2024-09-12 14:04:21.682423: Epoch 674 
+2024-09-12 14:04:21.682512: Current learning rate: 0.00365 
+2024-09-12 14:08:27.589879: train_loss -0.8899 
+2024-09-12 14:08:27.590026: val_loss -0.5974 
+2024-09-12 14:08:27.590083: Pseudo dice [0.4233, 0.8836] 
+2024-09-12 14:08:27.590140: Epoch time: 245.91 s 
+2024-09-12 14:08:28.584671:  
+2024-09-12 14:08:28.584876: Epoch 675 
+2024-09-12 14:08:28.584964: Current learning rate: 0.00364 
+2024-09-12 14:12:34.594012: train_loss -0.8896 
+2024-09-12 14:12:34.594223: val_loss -0.5938 
+2024-09-12 14:12:34.594283: Pseudo dice [0.4586, 0.8796] 
+2024-09-12 14:12:34.594342: Epoch time: 246.01 s 
+2024-09-12 14:12:35.594659:  
+2024-09-12 14:12:35.594853: Epoch 676 
+2024-09-12 14:12:35.594939: Current learning rate: 0.00363 
+2024-09-12 14:16:41.692422: train_loss -0.8874 
+2024-09-12 14:16:41.692568: val_loss -0.5983 
+2024-09-12 14:16:41.692627: Pseudo dice [0.4388, 0.8548] 
+2024-09-12 14:16:41.692682: Epoch time: 246.1 s 
+2024-09-12 14:16:42.693313:  
+2024-09-12 14:16:42.693513: Epoch 677 
+2024-09-12 14:16:42.693617: Current learning rate: 0.00362 
+2024-09-12 14:20:48.723798: train_loss -0.8918 
+2024-09-12 14:20:48.723955: val_loss -0.5641 
+2024-09-12 14:20:48.724012: Pseudo dice [0.3815, 0.8779] 
+2024-09-12 14:20:48.724141: Epoch time: 246.03 s 
+2024-09-12 14:20:49.715108:  
+2024-09-12 14:20:49.715310: Epoch 678 
+2024-09-12 14:20:49.715405: Current learning rate: 0.00361 
+2024-09-12 14:24:55.811201: train_loss -0.8839 
+2024-09-12 14:24:55.811373: val_loss -0.5725 
+2024-09-12 14:24:55.811431: Pseudo dice [0.4469, 0.8727] 
+2024-09-12 14:24:55.811485: Epoch time: 246.1 s 
+2024-09-12 14:24:56.794566:  
+2024-09-12 14:24:56.794732: Epoch 679 
+2024-09-12 14:24:56.794859: Current learning rate: 0.0036 
+2024-09-12 14:29:02.610479: train_loss -0.8839 
+2024-09-12 14:29:02.610664: val_loss -0.5779 
+2024-09-12 14:29:02.610723: Pseudo dice [0.4068, 0.8675] 
+2024-09-12 14:29:02.610780: Epoch time: 245.82 s 
+2024-09-12 14:29:03.617016:  
+2024-09-12 14:29:03.617180: Epoch 680 
+2024-09-12 14:29:03.617300: Current learning rate: 0.00359 
+2024-09-12 14:33:09.622088: train_loss -0.8887 
+2024-09-12 14:33:09.622250: val_loss -0.599 
+2024-09-12 14:33:09.622307: Pseudo dice [0.4378, 0.8775] 
+2024-09-12 14:33:09.622364: Epoch time: 246.01 s 
+2024-09-12 14:33:10.621441:  
+2024-09-12 14:33:10.621615: Epoch 681 
+2024-09-12 14:33:10.621705: Current learning rate: 0.00358 
+2024-09-12 14:37:16.593195: train_loss -0.8891 
+2024-09-12 14:37:16.593339: val_loss -0.6083 
+2024-09-12 14:37:16.593396: Pseudo dice [0.4327, 0.8828] 
+2024-09-12 14:37:16.593452: Epoch time: 245.97 s 
+2024-09-12 14:37:17.580729:  
+2024-09-12 14:37:17.580964: Epoch 682 
+2024-09-12 14:37:17.581106: Current learning rate: 0.00357 
+2024-09-12 14:41:23.446053: train_loss -0.8923 
+2024-09-12 14:41:23.446205: val_loss -0.5495 
+2024-09-12 14:41:23.446262: Pseudo dice [0.3536, 0.8726] 
+2024-09-12 14:41:23.446319: Epoch time: 245.87 s 
+2024-09-12 14:41:24.440374:  
+2024-09-12 14:41:24.440556: Epoch 683 
+2024-09-12 14:41:24.440641: Current learning rate: 0.00356 
+2024-09-12 14:45:31.026631: train_loss -0.8931 
+2024-09-12 14:45:31.026777: val_loss -0.6187 
+2024-09-12 14:45:31.026833: Pseudo dice [0.4669, 0.8909] 
+2024-09-12 14:45:31.026889: Epoch time: 246.59 s 
+2024-09-12 14:45:32.030021:  
+2024-09-12 14:45:32.030273: Epoch 684 
+2024-09-12 14:45:32.030420: Current learning rate: 0.00355 
+2024-09-12 14:49:37.891170: train_loss -0.8938 
+2024-09-12 14:49:37.891330: val_loss -0.6054 
+2024-09-12 14:49:37.891388: Pseudo dice [0.4572, 0.8737] 
+2024-09-12 14:49:37.891444: Epoch time: 245.86 s 
+2024-09-12 14:49:38.885604:  
+2024-09-12 14:49:38.885851: Epoch 685 
+2024-09-12 14:49:38.885941: Current learning rate: 0.00354 
+2024-09-12 14:53:44.911666: train_loss -0.8872 
+2024-09-12 14:53:44.911841: val_loss -0.5958 
+2024-09-12 14:53:44.911902: Pseudo dice [0.4259, 0.8746] 
+2024-09-12 14:53:44.911960: Epoch time: 246.03 s 
+2024-09-12 14:53:45.938672:  
+2024-09-12 14:53:45.938896: Epoch 686 
+2024-09-12 14:53:45.938986: Current learning rate: 0.00353 
+2024-09-12 14:57:52.124322: train_loss -0.887 
+2024-09-12 14:57:52.124470: val_loss -0.5768 
+2024-09-12 14:57:52.124528: Pseudo dice [0.396, 0.8797] 
+2024-09-12 14:57:52.124585: Epoch time: 246.19 s 
+2024-09-12 14:57:53.118794:  
+2024-09-12 14:57:53.119030: Epoch 687 
+2024-09-12 14:57:53.119117: Current learning rate: 0.00352 
+2024-09-12 15:01:59.324485: train_loss -0.8859 
+2024-09-12 15:01:59.324638: val_loss -0.5859 
+2024-09-12 15:01:59.324695: Pseudo dice [0.3861, 0.8862] 
+2024-09-12 15:01:59.324751: Epoch time: 246.21 s 
+2024-09-12 15:02:00.309946:  
+2024-09-12 15:02:00.310127: Epoch 688 
+2024-09-12 15:02:00.310214: Current learning rate: 0.00351 
+2024-09-12 15:06:06.549564: train_loss -0.887 
+2024-09-12 15:06:06.549741: val_loss -0.5919 
+2024-09-12 15:06:06.549797: Pseudo dice [0.4434, 0.88] 
+2024-09-12 15:06:06.549854: Epoch time: 246.24 s 
+2024-09-12 15:06:07.569342:  
+2024-09-12 15:06:07.569537: Epoch 689 
+2024-09-12 15:06:07.569642: Current learning rate: 0.0035 
+2024-09-12 15:10:13.799114: train_loss -0.8925 
+2024-09-12 15:10:13.799269: val_loss -0.587 
+2024-09-12 15:10:13.799326: Pseudo dice [0.4421, 0.8774] 
+2024-09-12 15:10:13.799382: Epoch time: 246.23 s 
+2024-09-12 15:10:14.785538:  
+2024-09-12 15:10:14.785759: Epoch 690 
+2024-09-12 15:10:14.785855: Current learning rate: 0.00349 
+2024-09-12 15:14:21.004943: train_loss -0.8918 
+2024-09-12 15:14:21.005106: val_loss -0.5906 
+2024-09-12 15:14:21.005162: Pseudo dice [0.4275, 0.8645] 
+2024-09-12 15:14:21.005219: Epoch time: 246.22 s 
+2024-09-12 15:14:22.022357:  
+2024-09-12 15:14:22.022596: Epoch 691 
+2024-09-12 15:14:22.022702: Current learning rate: 0.00348 
+2024-09-12 15:18:28.184971: train_loss -0.8889 
+2024-09-12 15:18:28.185124: val_loss -0.6075 
+2024-09-12 15:18:28.185179: Pseudo dice [0.4433, 0.8731] 
+2024-09-12 15:18:28.185275: Epoch time: 246.16 s 
+2024-09-12 15:18:29.180807:  
+2024-09-12 15:18:29.181036: Epoch 692 
+2024-09-12 15:18:29.181126: Current learning rate: 0.00346 
+2024-09-12 15:22:35.262621: train_loss -0.8949 
+2024-09-12 15:22:35.262770: val_loss -0.5819 
+2024-09-12 15:22:35.262825: Pseudo dice [0.4075, 0.8826] 
+2024-09-12 15:22:35.262881: Epoch time: 246.08 s 
+2024-09-12 15:22:36.267472:  
+2024-09-12 15:22:36.267720: Epoch 693 
+2024-09-12 15:22:36.267812: Current learning rate: 0.00345 
+2024-09-12 15:26:42.348053: train_loss -0.8972 
+2024-09-12 15:26:42.348210: val_loss -0.552 
+2024-09-12 15:26:42.348268: Pseudo dice [0.3719, 0.8653] 
+2024-09-12 15:26:42.348326: Epoch time: 246.08 s 
+2024-09-12 15:26:43.370054:  
+2024-09-12 15:26:43.370350: Epoch 694 
+2024-09-12 15:26:43.370438: Current learning rate: 0.00344 
+2024-09-12 15:30:49.438046: train_loss -0.8932 
+2024-09-12 15:30:49.438209: val_loss -0.5903 
+2024-09-12 15:30:49.438270: Pseudo dice [0.4163, 0.873] 
+2024-09-12 15:30:49.438326: Epoch time: 246.07 s 
+2024-09-12 15:30:50.441810:  
+2024-09-12 15:30:50.441982: Epoch 695 
+2024-09-12 15:30:50.442097: Current learning rate: 0.00343 
+2024-09-12 15:34:56.665042: train_loss -0.8895 
+2024-09-12 15:34:56.665217: val_loss -0.5814 
+2024-09-12 15:34:56.665274: Pseudo dice [0.4046, 0.878] 
+2024-09-12 15:34:56.665330: Epoch time: 246.23 s 
+2024-09-12 15:34:57.660044:  
+2024-09-12 15:34:57.660212: Epoch 696 
+2024-09-12 15:34:57.660299: Current learning rate: 0.00342 
+2024-09-12 15:39:03.878302: train_loss -0.8956 
+2024-09-12 15:39:03.878452: val_loss -0.5949 
+2024-09-12 15:39:03.878508: Pseudo dice [0.4624, 0.8731] 
+2024-09-12 15:39:03.878563: Epoch time: 246.22 s 
+2024-09-12 15:39:04.878742:  
+2024-09-12 15:39:04.878977: Epoch 697 
+2024-09-12 15:39:04.879074: Current learning rate: 0.00341 
+2024-09-12 15:43:10.985187: train_loss -0.8945 
+2024-09-12 15:43:10.985346: val_loss -0.599 
+2024-09-12 15:43:10.985403: Pseudo dice [0.4144, 0.8769] 
+2024-09-12 15:43:10.985457: Epoch time: 246.11 s 
+2024-09-12 15:43:12.000072:  
+2024-09-12 15:43:12.000297: Epoch 698 
+2024-09-12 15:43:12.000409: Current learning rate: 0.0034 
+2024-09-12 15:47:18.176056: train_loss -0.8979 
+2024-09-12 15:47:18.176219: val_loss -0.6012 
+2024-09-12 15:47:18.176276: Pseudo dice [0.4358, 0.8746] 
+2024-09-12 15:47:18.176334: Epoch time: 246.18 s 
+2024-09-12 15:47:19.164355:  
+2024-09-12 15:47:19.164532: Epoch 699 
+2024-09-12 15:47:19.164622: Current learning rate: 0.00339 
+2024-09-12 15:51:25.389029: train_loss -0.8924 
+2024-09-12 15:51:25.389182: val_loss -0.6127 
+2024-09-12 15:51:25.389237: Pseudo dice [0.4759, 0.8672] 
+2024-09-12 15:51:25.389294: Epoch time: 246.23 s 
+2024-09-12 15:51:29.363637:  
+2024-09-12 15:51:29.363905: Epoch 700 
+2024-09-12 15:51:29.363993: Current learning rate: 0.00338 
+2024-09-12 15:55:35.387925: train_loss -0.8868 
+2024-09-12 15:55:35.388068: val_loss -0.5375 
+2024-09-12 15:55:35.388170: Pseudo dice [0.3238, 0.8607] 
+2024-09-12 15:55:35.388279: Epoch time: 246.03 s 
+2024-09-12 15:55:36.392722:  
+2024-09-12 15:55:36.392916: Epoch 701 
+2024-09-12 15:55:36.393034: Current learning rate: 0.00337 
+2024-09-12 15:59:42.633850: train_loss -0.8858 
+2024-09-12 15:59:42.633997: val_loss -0.5929 
+2024-09-12 15:59:42.634052: Pseudo dice [0.4403, 0.8834] 
+2024-09-12 15:59:42.634109: Epoch time: 246.24 s 
+2024-09-12 15:59:43.638110:  
+2024-09-12 15:59:43.638369: Epoch 702 
+2024-09-12 15:59:43.638457: Current learning rate: 0.00336 
+2024-09-12 16:03:49.550241: train_loss -0.8758 
+2024-09-12 16:03:49.550404: val_loss -0.5691 
+2024-09-12 16:03:49.550461: Pseudo dice [0.4049, 0.8805] 
+2024-09-12 16:03:49.550516: Epoch time: 245.91 s 
+2024-09-12 16:03:50.541365:  
+2024-09-12 16:03:50.541539: Epoch 703 
+2024-09-12 16:03:50.541629: Current learning rate: 0.00335 
+2024-09-12 16:07:56.515179: train_loss -0.8878 
+2024-09-12 16:07:56.515326: val_loss -0.5983 
+2024-09-12 16:07:56.515382: Pseudo dice [0.4401, 0.871] 
+2024-09-12 16:07:56.515438: Epoch time: 245.98 s 
+2024-09-12 16:07:57.536451:  
+2024-09-12 16:07:57.536625: Epoch 704 
+2024-09-12 16:07:57.536711: Current learning rate: 0.00334 
+2024-09-12 16:12:03.587772: train_loss -0.8821 
+2024-09-12 16:12:03.587938: val_loss -0.5898 
+2024-09-12 16:12:03.587994: Pseudo dice [0.4169, 0.8848] 
+2024-09-12 16:12:03.588049: Epoch time: 246.05 s 
+2024-09-12 16:12:04.597870:  
+2024-09-12 16:12:04.598097: Epoch 705 
+2024-09-12 16:12:04.598228: Current learning rate: 0.00333 
+2024-09-12 16:16:10.405834: train_loss -0.8826 
+2024-09-12 16:16:10.405995: val_loss -0.602 
+2024-09-12 16:16:10.406053: Pseudo dice [0.4522, 0.8832] 
+2024-09-12 16:16:10.406110: Epoch time: 245.81 s 
+2024-09-12 16:16:12.354039:  
+2024-09-12 16:16:12.354284: Epoch 706 
+2024-09-12 16:16:12.354395: Current learning rate: 0.00332 
+2024-09-12 16:20:18.099276: train_loss -0.8833 
+2024-09-12 16:20:18.099426: val_loss -0.593 
+2024-09-12 16:20:18.099482: Pseudo dice [0.4147, 0.8833] 
+2024-09-12 16:20:18.099538: Epoch time: 245.75 s 
+2024-09-12 16:20:19.092234:  
+2024-09-12 16:20:19.092437: Epoch 707 
+2024-09-12 16:20:19.092569: Current learning rate: 0.00331 
+2024-09-12 16:24:24.923816: train_loss -0.8727 
+2024-09-12 16:24:24.923963: val_loss -0.6023 
+2024-09-12 16:24:24.924020: Pseudo dice [0.4511, 0.8771] 
+2024-09-12 16:24:24.924075: Epoch time: 245.83 s 
+2024-09-12 16:24:25.912933:  
+2024-09-12 16:24:25.913155: Epoch 708 
+2024-09-12 16:24:25.913242: Current learning rate: 0.0033 
+2024-09-12 16:28:31.860765: train_loss -0.8669 
+2024-09-12 16:28:31.861012: val_loss -0.5946 
+2024-09-12 16:28:31.861069: Pseudo dice [0.4307, 0.8749] 
+2024-09-12 16:28:31.861125: Epoch time: 245.95 s 
+2024-09-12 16:28:32.875115:  
+2024-09-12 16:28:32.875375: Epoch 709 
+2024-09-12 16:28:32.875505: Current learning rate: 0.00329 
+2024-09-12 16:32:38.622154: train_loss -0.8707 
+2024-09-12 16:32:38.622359: val_loss -0.5905 
+2024-09-12 16:32:38.622453: Pseudo dice [0.4338, 0.8831] 
+2024-09-12 16:32:38.622544: Epoch time: 245.75 s 
+2024-09-12 16:32:39.640648:  
+2024-09-12 16:32:39.640855: Epoch 710 
+2024-09-12 16:32:39.640937: Current learning rate: 0.00328 
+2024-09-12 16:36:45.482326: train_loss -0.8737 
+2024-09-12 16:36:45.482504: val_loss -0.5739 
+2024-09-12 16:36:45.482584: Pseudo dice [0.3941, 0.8876] 
+2024-09-12 16:36:45.482636: Epoch time: 245.84 s 
+2024-09-12 16:36:46.495312:  
+2024-09-12 16:36:46.495538: Epoch 711 
+2024-09-12 16:36:46.495619: Current learning rate: 0.00327 
+2024-09-12 16:40:52.503100: train_loss -0.8704 
+2024-09-12 16:40:52.503274: val_loss -0.5866 
+2024-09-12 16:40:52.503327: Pseudo dice [0.4179, 0.8723] 
+2024-09-12 16:40:52.503382: Epoch time: 246.01 s 
+2024-09-12 16:40:53.514410:  
+2024-09-12 16:40:53.514623: Epoch 712 
+2024-09-12 16:40:53.514711: Current learning rate: 0.00326 
+2024-09-12 16:44:59.469804: train_loss -0.8834 
+2024-09-12 16:44:59.469940: val_loss -0.5946 
+2024-09-12 16:44:59.469991: Pseudo dice [0.4201, 0.8826] 
+2024-09-12 16:44:59.470042: Epoch time: 245.96 s 
+2024-09-12 16:45:00.467446:  
+2024-09-12 16:45:00.467693: Epoch 713 
+2024-09-12 16:45:00.467774: Current learning rate: 0.00325 
+2024-09-12 16:49:06.342083: train_loss -0.886 
+2024-09-12 16:49:06.342251: val_loss -0.5942 
+2024-09-12 16:49:06.342303: Pseudo dice [0.407, 0.8854] 
+2024-09-12 16:49:06.342355: Epoch time: 245.88 s 
+2024-09-12 16:49:07.344004:  
+2024-09-12 16:49:07.344198: Epoch 714 
+2024-09-12 16:49:07.344276: Current learning rate: 0.00324 
+2024-09-12 16:53:13.212509: train_loss -0.8941 
+2024-09-12 16:53:13.212653: val_loss -0.5807 
+2024-09-12 16:53:13.212704: Pseudo dice [0.3832, 0.8889] 
+2024-09-12 16:53:13.212756: Epoch time: 245.87 s 
+2024-09-12 16:53:14.224191:  
+2024-09-12 16:53:14.224414: Epoch 715 
+2024-09-12 16:53:14.224496: Current learning rate: 0.00323 
+2024-09-12 16:57:20.086751: train_loss -0.8912 
+2024-09-12 16:57:20.086890: val_loss -0.5862 
+2024-09-12 16:57:20.086952: Pseudo dice [0.437, 0.8761] 
+2024-09-12 16:57:20.087003: Epoch time: 245.86 s 
+2024-09-12 16:57:21.080417:  
+2024-09-12 16:57:21.080665: Epoch 716 
+2024-09-12 16:57:21.080776: Current learning rate: 0.00322 
+2024-09-12 17:01:26.867173: train_loss -0.8905 
+2024-09-12 17:01:26.867312: val_loss -0.5819 
+2024-09-12 17:01:26.867362: Pseudo dice [0.3828, 0.8847] 
+2024-09-12 17:01:26.867413: Epoch time: 245.79 s 
+2024-09-12 17:01:27.894246:  
+2024-09-12 17:01:27.894462: Epoch 717 
+2024-09-12 17:01:27.894545: Current learning rate: 0.00321 
+2024-09-12 17:05:33.548634: train_loss -0.8848 
+2024-09-12 17:05:33.548793: val_loss -0.5689 
+2024-09-12 17:05:33.548842: Pseudo dice [0.3654, 0.8821] 
+2024-09-12 17:05:33.548893: Epoch time: 245.66 s 
+2024-09-12 17:05:34.544015:  
+2024-09-12 17:05:34.544219: Epoch 718 
+2024-09-12 17:05:34.544302: Current learning rate: 0.0032 
+2024-09-12 17:09:40.361927: train_loss -0.8915 
+2024-09-12 17:09:40.362072: val_loss -0.5912 
+2024-09-12 17:09:40.362123: Pseudo dice [0.4171, 0.8876] 
+2024-09-12 17:09:40.362174: Epoch time: 245.82 s 
+2024-09-12 17:09:41.401722:  
+2024-09-12 17:09:41.401983: Epoch 719 
+2024-09-12 17:09:41.402068: Current learning rate: 0.00319 
+2024-09-12 17:13:47.251082: train_loss -0.8904 
+2024-09-12 17:13:47.251229: val_loss -0.5907 
+2024-09-12 17:13:47.251279: Pseudo dice [0.4105, 0.8754] 
+2024-09-12 17:13:47.251330: Epoch time: 245.85 s 
+2024-09-12 17:13:48.258258:  
+2024-09-12 17:13:48.258484: Epoch 720 
+2024-09-12 17:13:48.258584: Current learning rate: 0.00318 
+2024-09-12 17:17:54.267859: train_loss -0.8947 
+2024-09-12 17:17:54.267998: val_loss -0.5882 
+2024-09-12 17:17:54.268048: Pseudo dice [0.4214, 0.8888] 
+2024-09-12 17:17:54.268100: Epoch time: 246.01 s 
+2024-09-12 17:17:55.260827:  
+2024-09-12 17:17:55.261052: Epoch 721 
+2024-09-12 17:17:55.261140: Current learning rate: 0.00317 
+2024-09-12 17:22:01.185185: train_loss -0.8947 
+2024-09-12 17:22:01.185335: val_loss -0.6064 
+2024-09-12 17:22:01.185386: Pseudo dice [0.4318, 0.8939] 
+2024-09-12 17:22:01.185437: Epoch time: 245.93 s 
+2024-09-12 17:22:02.208107:  
+2024-09-12 17:22:02.208315: Epoch 722 
+2024-09-12 17:22:02.208397: Current learning rate: 0.00316 
+2024-09-12 17:26:07.953248: train_loss -0.896 
+2024-09-12 17:26:07.953419: val_loss -0.6088 
+2024-09-12 17:26:07.953471: Pseudo dice [0.4535, 0.8805] 
+2024-09-12 17:26:07.953525: Epoch time: 245.75 s 
+2024-09-12 17:26:08.958232:  
+2024-09-12 17:26:08.958381: Epoch 723 
+2024-09-12 17:26:08.958469: Current learning rate: 0.00315 
+2024-09-12 17:30:14.814102: train_loss -0.8955 
+2024-09-12 17:30:14.814268: val_loss -0.5693 
+2024-09-12 17:30:14.814323: Pseudo dice [0.3935, 0.8842] 
+2024-09-12 17:30:14.814510: Epoch time: 245.86 s 
+2024-09-12 17:30:15.817859:  
+2024-09-12 17:30:15.818085: Epoch 724 
+2024-09-12 17:30:15.818190: Current learning rate: 0.00314 
+2024-09-12 17:34:21.615002: train_loss -0.8948 
+2024-09-12 17:34:21.615165: val_loss -0.5779 
+2024-09-12 17:34:21.615219: Pseudo dice [0.3885, 0.8841] 
+2024-09-12 17:34:21.615271: Epoch time: 245.8 s 
+2024-09-12 17:34:22.607898:  
+2024-09-12 17:34:22.608063: Epoch 725 
+2024-09-12 17:34:22.608147: Current learning rate: 0.00313 
+2024-09-12 17:38:28.570378: train_loss -0.8932 
+2024-09-12 17:38:28.570527: val_loss -0.5608 
+2024-09-12 17:38:28.570580: Pseudo dice [0.3769, 0.8867] 
+2024-09-12 17:38:28.570631: Epoch time: 245.96 s 
+2024-09-12 17:38:29.564482:  
+2024-09-12 17:38:29.564652: Epoch 726 
+2024-09-12 17:38:29.564737: Current learning rate: 0.00312 
+2024-09-12 17:42:35.725935: train_loss -0.8721 
+2024-09-12 17:42:35.726112: val_loss -0.5913 
+2024-09-12 17:42:35.726162: Pseudo dice [0.3989, 0.8791] 
+2024-09-12 17:42:35.726215: Epoch time: 246.16 s 
+2024-09-12 17:42:36.733470:  
+2024-09-12 17:42:36.733666: Epoch 727 
+2024-09-12 17:42:36.733749: Current learning rate: 0.00311 
+2024-09-12 17:46:42.813419: train_loss -0.8734 
+2024-09-12 17:46:42.813568: val_loss -0.5924 
+2024-09-12 17:46:42.813622: Pseudo dice [0.4171, 0.8826] 
+2024-09-12 17:46:42.813676: Epoch time: 246.08 s 
+2024-09-12 17:46:43.824248:  
+2024-09-12 17:46:43.824458: Epoch 728 
+2024-09-12 17:46:43.824546: Current learning rate: 0.0031 
+2024-09-12 17:50:50.502823: train_loss -0.8769 
+2024-09-12 17:50:50.502969: val_loss -0.5957 
+2024-09-12 17:50:50.503018: Pseudo dice [0.4424, 0.8658] 
+2024-09-12 17:50:50.503070: Epoch time: 246.68 s 
+2024-09-12 17:50:51.503734:  
+2024-09-12 17:50:51.503967: Epoch 729 
+2024-09-12 17:50:51.504087: Current learning rate: 0.00309 
+2024-09-12 17:54:57.322496: train_loss -0.8858 
+2024-09-12 17:54:57.322634: val_loss -0.5897 
+2024-09-12 17:54:57.322685: Pseudo dice [0.4105, 0.8749] 
+2024-09-12 17:54:57.322737: Epoch time: 245.82 s 
+2024-09-12 17:54:58.315643:  
+2024-09-12 17:54:58.315914: Epoch 730 
+2024-09-12 17:54:58.316019: Current learning rate: 0.00308 
+2024-09-12 17:59:04.030486: train_loss -0.8897 
+2024-09-12 17:59:04.030620: val_loss -0.6004 
+2024-09-12 17:59:04.030671: Pseudo dice [0.4722, 0.8762] 
+2024-09-12 17:59:04.030722: Epoch time: 245.72 s 
+2024-09-12 17:59:05.028928:  
+2024-09-12 17:59:05.029136: Epoch 731 
+2024-09-12 17:59:05.029231: Current learning rate: 0.00307 
+2024-09-12 18:03:10.757709: train_loss -0.8919 
+2024-09-12 18:03:10.757896: val_loss -0.555 
+2024-09-12 18:03:10.757975: Pseudo dice [0.3618, 0.8845] 
+2024-09-12 18:03:10.758029: Epoch time: 245.73 s 
+2024-09-12 18:03:11.747712:  
+2024-09-12 18:03:11.747939: Epoch 732 
+2024-09-12 18:03:11.748022: Current learning rate: 0.00306 
+2024-09-12 18:07:17.381663: train_loss -0.8796 
+2024-09-12 18:07:17.381832: val_loss -0.5858 
+2024-09-12 18:07:17.381885: Pseudo dice [0.433, 0.8758] 
+2024-09-12 18:07:17.381939: Epoch time: 245.64 s 
+2024-09-12 18:07:18.381282:  
+2024-09-12 18:07:18.381556: Epoch 733 
+2024-09-12 18:07:18.381680: Current learning rate: 0.00305 
+2024-09-12 18:11:24.085162: train_loss -0.8859 
+2024-09-12 18:11:24.085309: val_loss -0.6035 
+2024-09-12 18:11:24.085361: Pseudo dice [0.4689, 0.8813] 
+2024-09-12 18:11:24.085412: Epoch time: 245.71 s 
+2024-09-12 18:11:25.147315:  
+2024-09-12 18:11:25.147670: Epoch 734 
+2024-09-12 18:11:25.147770: Current learning rate: 0.00304 
+2024-09-12 18:15:31.004789: train_loss -0.8879 
+2024-09-12 18:15:31.004922: val_loss -0.6194 
+2024-09-12 18:15:31.004973: Pseudo dice [0.459, 0.8767] 
+2024-09-12 18:15:31.005026: Epoch time: 245.86 s 
+2024-09-12 18:15:32.006898:  
+2024-09-12 18:15:32.007084: Epoch 735 
+2024-09-12 18:15:32.007165: Current learning rate: 0.00303 
+2024-09-12 18:19:37.790087: train_loss -0.8879 
+2024-09-12 18:19:37.790226: val_loss -0.5709 
+2024-09-12 18:19:37.790276: Pseudo dice [0.3632, 0.8811] 
+2024-09-12 18:19:37.790326: Epoch time: 245.79 s 
+2024-09-12 18:19:38.802347:  
+2024-09-12 18:19:38.802593: Epoch 736 
+2024-09-12 18:19:38.802677: Current learning rate: 0.00302 
+2024-09-12 18:23:44.647168: train_loss -0.8928 
+2024-09-12 18:23:44.647320: val_loss -0.5981 
+2024-09-12 18:23:44.647370: Pseudo dice [0.4448, 0.8781] 
+2024-09-12 18:23:44.647425: Epoch time: 245.85 s 
+2024-09-12 18:23:45.660558:  
+2024-09-12 18:23:45.660781: Epoch 737 
+2024-09-12 18:23:45.660865: Current learning rate: 0.00301 
+2024-09-12 18:27:51.390488: train_loss -0.8956 
+2024-09-12 18:27:51.390626: val_loss -0.5975 
+2024-09-12 18:27:51.390676: Pseudo dice [0.4437, 0.8778] 
+2024-09-12 18:27:51.390726: Epoch time: 245.73 s 
+2024-09-12 18:27:52.380411:  
+2024-09-12 18:27:52.380601: Epoch 738 
+2024-09-12 18:27:52.380684: Current learning rate: 0.003 
+2024-09-12 18:31:58.275242: train_loss -0.8957 
+2024-09-12 18:31:58.275393: val_loss -0.5855 
+2024-09-12 18:31:58.275448: Pseudo dice [0.45, 0.872] 
+2024-09-12 18:31:58.275500: Epoch time: 245.9 s 
+2024-09-12 18:31:59.305991:  
+2024-09-12 18:31:59.306185: Epoch 739 
+2024-09-12 18:31:59.306269: Current learning rate: 0.00299 
+2024-09-12 18:36:05.235004: train_loss -0.8955 
+2024-09-12 18:36:05.235146: val_loss -0.5771 
+2024-09-12 18:36:05.235196: Pseudo dice [0.4135, 0.8874] 
+2024-09-12 18:36:05.235246: Epoch time: 245.93 s 
+2024-09-12 18:36:06.261557:  
+2024-09-12 18:36:06.261815: Epoch 740 
+2024-09-12 18:36:06.261903: Current learning rate: 0.00297 
+2024-09-12 18:40:12.165818: train_loss -0.8923 
+2024-09-12 18:40:12.165976: val_loss -0.606 
+2024-09-12 18:40:12.166026: Pseudo dice [0.462, 0.8845] 
+2024-09-12 18:40:12.166076: Epoch time: 245.91 s 
+2024-09-12 18:40:13.170660:  
+2024-09-12 18:40:13.170859: Epoch 741 
+2024-09-12 18:40:13.170943: Current learning rate: 0.00296 
+2024-09-12 18:44:19.098549: train_loss -0.8932 
+2024-09-12 18:44:19.098790: val_loss -0.5732 
+2024-09-12 18:44:19.098843: Pseudo dice [0.4177, 0.8684] 
+2024-09-12 18:44:19.098895: Epoch time: 245.93 s 
+2024-09-12 18:44:20.095833:  
+2024-09-12 18:44:20.096018: Epoch 742 
+2024-09-12 18:44:20.096100: Current learning rate: 0.00295 
+2024-09-12 18:48:26.162922: train_loss -0.8843 
+2024-09-12 18:48:26.163080: val_loss -0.5832 
+2024-09-12 18:48:26.163131: Pseudo dice [0.4102, 0.8811] 
+2024-09-12 18:48:26.163184: Epoch time: 246.07 s 
+2024-09-12 18:48:27.182176:  
+2024-09-12 18:48:27.182348: Epoch 743 
+2024-09-12 18:48:27.182429: Current learning rate: 0.00294 
+2024-09-12 18:52:33.155663: train_loss -0.8855 
+2024-09-12 18:52:33.155817: val_loss -0.6024 
+2024-09-12 18:52:33.155870: Pseudo dice [0.4497, 0.8813] 
+2024-09-12 18:52:33.155922: Epoch time: 245.98 s 
+2024-09-12 18:52:34.165791:  
+2024-09-12 18:52:34.166032: Epoch 744 
+2024-09-12 18:52:34.166137: Current learning rate: 0.00293 
+2024-09-12 18:56:40.130110: train_loss -0.8862 
+2024-09-12 18:56:40.130250: val_loss -0.5953 
+2024-09-12 18:56:40.130301: Pseudo dice [0.4065, 0.8854] 
+2024-09-12 18:56:40.130354: Epoch time: 245.97 s 
+2024-09-12 18:56:41.163520:  
+2024-09-12 18:56:41.163751: Epoch 745 
+2024-09-12 18:56:41.163848: Current learning rate: 0.00292 
+2024-09-12 19:00:47.174913: train_loss -0.8934 
+2024-09-12 19:00:47.175054: val_loss -0.5701 
+2024-09-12 19:00:47.175105: Pseudo dice [0.3904, 0.8856] 
+2024-09-12 19:00:47.175156: Epoch time: 246.01 s 
+2024-09-12 19:00:48.164951:  
+2024-09-12 19:00:48.165209: Epoch 746 
+2024-09-12 19:00:48.165338: Current learning rate: 0.00291 
+2024-09-12 19:04:54.246492: train_loss -0.8918 
+2024-09-12 19:04:54.246627: val_loss -0.5894 
+2024-09-12 19:04:54.246727: Pseudo dice [0.3822, 0.892] 
+2024-09-12 19:04:54.246793: Epoch time: 246.09 s 
+2024-09-12 19:04:55.234300:  
+2024-09-12 19:04:55.234470: Epoch 747 
+2024-09-12 19:04:55.234555: Current learning rate: 0.0029 
+2024-09-12 19:09:01.289902: train_loss -0.8925 
+2024-09-12 19:09:01.290069: val_loss -0.6071 
+2024-09-12 19:09:01.290127: Pseudo dice [0.4321, 0.8908] 
+2024-09-12 19:09:01.290182: Epoch time: 246.06 s 
+2024-09-12 19:09:02.314776:  
+2024-09-12 19:09:02.314955: Epoch 748 
+2024-09-12 19:09:02.315043: Current learning rate: 0.00289 
+2024-09-12 19:13:08.503349: train_loss -0.8878 
+2024-09-12 19:13:08.503499: val_loss -0.5922 
+2024-09-12 19:13:08.503559: Pseudo dice [0.4225, 0.8738] 
+2024-09-12 19:13:08.503615: Epoch time: 246.19 s 
+2024-09-12 19:13:09.513840:  
+2024-09-12 19:13:09.514036: Epoch 749 
+2024-09-12 19:13:09.514125: Current learning rate: 0.00288 
+2024-09-12 19:17:15.484036: train_loss -0.8926 
+2024-09-12 19:17:15.484213: val_loss -0.6068 
+2024-09-12 19:17:15.484514: Pseudo dice [0.4583, 0.8769] 
+2024-09-12 19:17:15.484572: Epoch time: 245.97 s 
+2024-09-12 19:17:19.465423:  
+2024-09-12 19:17:19.465585: Epoch 750 
+2024-09-12 19:17:19.465683: Current learning rate: 0.00287 
+2024-09-12 19:21:25.508828: train_loss -0.884 
+2024-09-12 19:21:25.508979: val_loss -0.5945 
+2024-09-12 19:21:25.509036: Pseudo dice [0.4507, 0.8653] 
+2024-09-12 19:21:25.509095: Epoch time: 246.05 s 
+2024-09-12 19:21:27.439053:  
+2024-09-12 19:21:27.439288: Epoch 751 
+2024-09-12 19:21:27.439395: Current learning rate: 0.00286 
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/training_log_2024_9_12_19_22_18.txt b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/training_log_2024_9_12_19_22_18.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ca43fe7390edbabd2bb16340a9dbd1b274b60156
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/training_log_2024_9_12_19_22_18.txt
@@ -0,0 +1,1843 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2024-09-12 19:22:21.029724: Using torch.compile... 
+2024-09-12 19:22:25.178363: do_dummy_2d_data_aug: True 
+2024-09-12 19:22:25.179428: Using splits from existing split file: /mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/splits_final.json 
+2024-09-12 19:22:25.179594: The split file contains 5 splits. 
+2024-09-12 19:22:25.179629: Desired fold for training: 2 
+2024-09-12 19:22:25.179661: This split has 240 training and 30 validation cases. 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres_bs8
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 8, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True, 'inherits_from': '3d_fullres'} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 349.19762434242324, 'median': 141.0, 'min': 0.0, 'percentile_00_5': 29.0, 'percentile_99_5': 2558.91650390625, 'std': 485.94544484039}, '1': {'max': 1.0, 'mean': 0.37307153608378946, 'median': 0.2966964840888977, 'min': 0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}} 
+ 
+2024-09-12 19:22:31.422809: unpacking dataset... 
+2024-09-12 19:22:34.302499: unpacking done... 
+2024-09-12 19:22:34.303835: Unable to plot network architecture: nnUNet_compile is enabled! 
+2024-09-12 19:22:34.315344:  
+2024-09-12 19:22:34.315591: Epoch 750 
+2024-09-12 19:22:34.315787: Current learning rate: 0.00287 
+2024-09-12 19:28:03.476404: train_loss -0.8824 
+2024-09-12 19:28:03.476557: val_loss -0.581 
+2024-09-12 19:28:03.476635: Pseudo dice [0.4377, 0.8739] 
+2024-09-12 19:28:03.476695: Epoch time: 329.17 s 
+2024-09-12 19:28:04.517063:  
+2024-09-12 19:28:04.517238: Epoch 751 
+2024-09-12 19:28:04.517320: Current learning rate: 0.00286 
+2024-09-12 19:32:10.989858: train_loss -0.8852 
+2024-09-12 19:32:10.990003: val_loss -0.5895 
+2024-09-12 19:32:10.990054: Pseudo dice [0.3856, 0.878] 
+2024-09-12 19:32:10.990105: Epoch time: 246.47 s 
+2024-09-12 19:32:12.009744:  
+2024-09-12 19:32:12.009927: Epoch 752 
+2024-09-12 19:32:12.010012: Current learning rate: 0.00285 
+2024-09-12 19:36:18.417774: train_loss -0.8885 
+2024-09-12 19:36:18.417909: val_loss -0.5907 
+2024-09-12 19:36:18.417959: Pseudo dice [0.4021, 0.881] 
+2024-09-12 19:36:18.418008: Epoch time: 246.41 s 
+2024-09-12 19:36:19.431370:  
+2024-09-12 19:36:19.431511: Epoch 753 
+2024-09-12 19:36:19.431621: Current learning rate: 0.00284 
+2024-09-12 19:40:25.751156: train_loss -0.8899 
+2024-09-12 19:40:25.751290: val_loss -0.5989 
+2024-09-12 19:40:25.751340: Pseudo dice [0.4465, 0.8878] 
+2024-09-12 19:40:25.751414: Epoch time: 246.32 s 
+2024-09-12 19:40:26.752244:  
+2024-09-12 19:40:26.752438: Epoch 754 
+2024-09-12 19:40:26.752550: Current learning rate: 0.00283 
+2024-09-12 19:44:33.273667: train_loss -0.8873 
+2024-09-12 19:44:33.273808: val_loss -0.5755 
+2024-09-12 19:44:33.273858: Pseudo dice [0.3939, 0.8921] 
+2024-09-12 19:44:33.273907: Epoch time: 246.52 s 
+2024-09-12 19:44:34.278017:  
+2024-09-12 19:44:34.278167: Epoch 755 
+2024-09-12 19:44:34.278249: Current learning rate: 0.00282 
+2024-09-12 19:48:40.884501: train_loss -0.8857 
+2024-09-12 19:48:40.884661: val_loss -0.589 
+2024-09-12 19:48:40.884713: Pseudo dice [0.4404, 0.8763] 
+2024-09-12 19:48:40.884765: Epoch time: 246.61 s 
+2024-09-12 19:48:41.895958:  
+2024-09-12 19:48:41.896140: Epoch 756 
+2024-09-12 19:48:41.896218: Current learning rate: 0.00281 
+2024-09-12 19:52:48.273633: train_loss -0.894 
+2024-09-12 19:52:48.273786: val_loss -0.5946 
+2024-09-12 19:52:48.273883: Pseudo dice [0.41, 0.8793] 
+2024-09-12 19:52:48.273938: Epoch time: 246.38 s 
+2024-09-12 19:52:49.278036:  
+2024-09-12 19:52:49.278256: Epoch 757 
+2024-09-12 19:52:49.278363: Current learning rate: 0.0028 
+2024-09-12 19:56:55.521691: train_loss -0.8964 
+2024-09-12 19:56:55.521829: val_loss -0.5983 
+2024-09-12 19:56:55.521882: Pseudo dice [0.4563, 0.878] 
+2024-09-12 19:56:55.521932: Epoch time: 246.25 s 
+2024-09-12 19:56:56.572018:  
+2024-09-12 19:56:56.572222: Epoch 758 
+2024-09-12 19:56:56.572314: Current learning rate: 0.00279 
+2024-09-12 20:01:02.883815: train_loss -0.895 
+2024-09-12 20:01:02.884015: val_loss -0.5906 
+2024-09-12 20:01:02.884091: Pseudo dice [0.4182, 0.8623] 
+2024-09-12 20:01:02.884144: Epoch time: 246.31 s 
+2024-09-12 20:01:03.896521:  
+2024-09-12 20:01:03.896672: Epoch 759 
+2024-09-12 20:01:03.896772: Current learning rate: 0.00278 
+2024-09-12 20:05:10.214894: train_loss -0.8994 
+2024-09-12 20:05:10.215033: val_loss -0.6094 
+2024-09-12 20:05:10.215083: Pseudo dice [0.4502, 0.8799] 
+2024-09-12 20:05:10.215133: Epoch time: 246.32 s 
+2024-09-12 20:05:11.228466:  
+2024-09-12 20:05:11.228627: Epoch 760 
+2024-09-12 20:05:11.228710: Current learning rate: 0.00277 
+2024-09-12 20:09:17.581526: train_loss -0.8943 
+2024-09-12 20:09:17.581666: val_loss -0.616 
+2024-09-12 20:09:17.581717: Pseudo dice [0.4356, 0.8882] 
+2024-09-12 20:09:17.581767: Epoch time: 246.35 s 
+2024-09-12 20:09:18.596597:  
+2024-09-12 20:09:18.596830: Epoch 761 
+2024-09-12 20:09:18.596914: Current learning rate: 0.00276 
+2024-09-12 20:13:24.903031: train_loss -0.8987 
+2024-09-12 20:13:24.903175: val_loss -0.6068 
+2024-09-12 20:13:24.903225: Pseudo dice [0.4418, 0.8739] 
+2024-09-12 20:13:24.903276: Epoch time: 246.31 s 
+2024-09-12 20:13:25.906019:  
+2024-09-12 20:13:25.906225: Epoch 762 
+2024-09-12 20:13:25.906309: Current learning rate: 0.00275 
+2024-09-12 20:17:32.165752: train_loss -0.8997 
+2024-09-12 20:17:32.165893: val_loss -0.5927 
+2024-09-12 20:17:32.165944: Pseudo dice [0.4582, 0.8838] 
+2024-09-12 20:17:32.165996: Epoch time: 246.26 s 
+2024-09-12 20:17:33.165566:  
+2024-09-12 20:17:33.165763: Epoch 763 
+2024-09-12 20:17:33.165847: Current learning rate: 0.00274 
+2024-09-12 20:21:39.460665: train_loss -0.8846 
+2024-09-12 20:21:39.460806: val_loss -0.5778 
+2024-09-12 20:21:39.460867: Pseudo dice [0.3868, 0.8738] 
+2024-09-12 20:21:39.460918: Epoch time: 246.3 s 
+2024-09-12 20:21:40.474522:  
+2024-09-12 20:21:40.474668: Epoch 764 
+2024-09-12 20:21:40.474751: Current learning rate: 0.00273 
+2024-09-12 20:25:46.646499: train_loss -0.8857 
+2024-09-12 20:25:46.646640: val_loss -0.6092 
+2024-09-12 20:25:46.646690: Pseudo dice [0.4229, 0.8831] 
+2024-09-12 20:25:46.646741: Epoch time: 246.17 s 
+2024-09-12 20:25:47.681670:  
+2024-09-12 20:25:47.681862: Epoch 765 
+2024-09-12 20:25:47.681942: Current learning rate: 0.00272 
+2024-09-12 20:29:53.740684: train_loss -0.8901 
+2024-09-12 20:29:53.740825: val_loss -0.5692 
+2024-09-12 20:29:53.740951: Pseudo dice [0.3741, 0.8767] 
+2024-09-12 20:29:53.741137: Epoch time: 246.06 s 
+2024-09-12 20:29:54.777516:  
+2024-09-12 20:29:54.777673: Epoch 766 
+2024-09-12 20:29:54.777756: Current learning rate: 0.00271 
+2024-09-12 20:34:00.855985: train_loss -0.9008 
+2024-09-12 20:34:00.856125: val_loss -0.5785 
+2024-09-12 20:34:00.856175: Pseudo dice [0.4468, 0.8839] 
+2024-09-12 20:34:00.856224: Epoch time: 246.08 s 
+2024-09-12 20:34:01.885689:  
+2024-09-12 20:34:01.885871: Epoch 767 
+2024-09-12 20:34:01.885954: Current learning rate: 0.0027 
+2024-09-12 20:38:08.198684: train_loss -0.8997 
+2024-09-12 20:38:08.198833: val_loss -0.5814 
+2024-09-12 20:38:08.198883: Pseudo dice [0.3959, 0.8845] 
+2024-09-12 20:38:08.198935: Epoch time: 246.31 s 
+2024-09-12 20:38:09.238184:  
+2024-09-12 20:38:09.238340: Epoch 768 
+2024-09-12 20:38:09.238425: Current learning rate: 0.00268 
+2024-09-12 20:42:15.702891: train_loss -0.896 
+2024-09-12 20:42:15.703030: val_loss -0.5957 
+2024-09-12 20:42:15.703080: Pseudo dice [0.4174, 0.8728] 
+2024-09-12 20:42:15.703131: Epoch time: 246.47 s 
+2024-09-12 20:42:16.715723:  
+2024-09-12 20:42:16.715951: Epoch 769 
+2024-09-12 20:42:16.716046: Current learning rate: 0.00267 
+2024-09-12 20:46:23.239666: train_loss -0.8971 
+2024-09-12 20:46:23.239802: val_loss -0.5956 
+2024-09-12 20:46:23.239875: Pseudo dice [0.4439, 0.8923] 
+2024-09-12 20:46:23.239926: Epoch time: 246.53 s 
+2024-09-12 20:46:25.145974:  
+2024-09-12 20:46:25.146234: Epoch 770 
+2024-09-12 20:46:25.146327: Current learning rate: 0.00266 
+2024-09-12 20:50:31.587709: train_loss -0.8967 
+2024-09-12 20:50:31.587860: val_loss -0.5964 
+2024-09-12 20:50:31.587912: Pseudo dice [0.4704, 0.8811] 
+2024-09-12 20:50:31.587961: Epoch time: 246.44 s 
+2024-09-12 20:50:32.629962:  
+2024-09-12 20:50:32.630206: Epoch 771 
+2024-09-12 20:50:32.630332: Current learning rate: 0.00265 
+2024-09-12 20:54:39.073627: train_loss -0.895 
+2024-09-12 20:54:39.073778: val_loss -0.6306 
+2024-09-12 20:54:39.073876: Pseudo dice [0.5009, 0.888] 
+2024-09-12 20:54:39.073928: Epoch time: 246.45 s 
+2024-09-12 20:54:40.084540:  
+2024-09-12 20:54:40.084824: Epoch 772 
+2024-09-12 20:54:40.084911: Current learning rate: 0.00264 
+2024-09-12 20:58:46.482172: train_loss -0.8932 
+2024-09-12 20:58:46.482309: val_loss -0.6073 
+2024-09-12 20:58:46.482360: Pseudo dice [0.4929, 0.8645] 
+2024-09-12 20:58:46.482409: Epoch time: 246.4 s 
+2024-09-12 20:58:47.503782:  
+2024-09-12 20:58:47.504035: Epoch 773 
+2024-09-12 20:58:47.504118: Current learning rate: 0.00263 
+2024-09-12 21:02:53.899262: train_loss -0.9 
+2024-09-12 21:02:53.899402: val_loss -0.6203 
+2024-09-12 21:02:53.899452: Pseudo dice [0.472, 0.8845] 
+2024-09-12 21:02:53.899502: Epoch time: 246.4 s 
+2024-09-12 21:02:54.944401:  
+2024-09-12 21:02:54.944572: Epoch 774 
+2024-09-12 21:02:54.944661: Current learning rate: 0.00262 
+2024-09-12 21:07:01.321961: train_loss -0.9035 
+2024-09-12 21:07:01.322165: val_loss -0.6083 
+2024-09-12 21:07:01.322218: Pseudo dice [0.4313, 0.891] 
+2024-09-12 21:07:01.322269: Epoch time: 246.38 s 
+2024-09-12 21:07:02.338135:  
+2024-09-12 21:07:02.338313: Epoch 775 
+2024-09-12 21:07:02.338397: Current learning rate: 0.00261 
+2024-09-12 21:11:08.660759: train_loss -0.8943 
+2024-09-12 21:11:08.660894: val_loss -0.6109 
+2024-09-12 21:11:08.660945: Pseudo dice [0.4531, 0.8752] 
+2024-09-12 21:11:08.660995: Epoch time: 246.32 s 
+2024-09-12 21:11:09.694392:  
+2024-09-12 21:11:09.694607: Epoch 776 
+2024-09-12 21:11:09.694696: Current learning rate: 0.0026 
+2024-09-12 21:15:15.845875: train_loss -0.8912 
+2024-09-12 21:15:15.846061: val_loss -0.588 
+2024-09-12 21:15:15.846113: Pseudo dice [0.482, 0.8758] 
+2024-09-12 21:15:15.846164: Epoch time: 246.15 s 
+2024-09-12 21:15:16.853066:  
+2024-09-12 21:15:16.853334: Epoch 777 
+2024-09-12 21:15:16.853420: Current learning rate: 0.00259 
+2024-09-12 21:19:22.745253: train_loss -0.8948 
+2024-09-12 21:19:22.745399: val_loss -0.5911 
+2024-09-12 21:19:22.745449: Pseudo dice [0.4081, 0.8758] 
+2024-09-12 21:19:22.745499: Epoch time: 245.89 s 
+2024-09-12 21:19:23.810056:  
+2024-09-12 21:19:23.810224: Epoch 778 
+2024-09-12 21:19:23.810346: Current learning rate: 0.00258 
+2024-09-12 21:23:29.829814: train_loss -0.8962 
+2024-09-12 21:23:29.829941: val_loss -0.592 
+2024-09-12 21:23:29.829992: Pseudo dice [0.4432, 0.8716] 
+2024-09-12 21:23:29.830043: Epoch time: 246.02 s 
+2024-09-12 21:23:30.851769:  
+2024-09-12 21:23:30.851999: Epoch 779 
+2024-09-12 21:23:30.852084: Current learning rate: 0.00257 
+2024-09-12 21:27:36.784586: train_loss -0.896 
+2024-09-12 21:27:36.784736: val_loss -0.5719 
+2024-09-12 21:27:36.784786: Pseudo dice [0.435, 0.8713] 
+2024-09-12 21:27:36.784840: Epoch time: 245.93 s 
+2024-09-12 21:27:37.798769:  
+2024-09-12 21:27:37.798927: Epoch 780 
+2024-09-12 21:27:37.799008: Current learning rate: 0.00256 
+2024-09-12 21:31:43.685342: train_loss -0.8965 
+2024-09-12 21:31:43.685477: val_loss -0.5892 
+2024-09-12 21:31:43.685529: Pseudo dice [0.4226, 0.878] 
+2024-09-12 21:31:43.685579: Epoch time: 245.89 s 
+2024-09-12 21:31:44.718726:  
+2024-09-12 21:31:44.718920: Epoch 781 
+2024-09-12 21:31:44.719005: Current learning rate: 0.00255 
+2024-09-12 21:35:50.637494: train_loss -0.9007 
+2024-09-12 21:35:50.637637: val_loss -0.581 
+2024-09-12 21:35:50.637689: Pseudo dice [0.4186, 0.8733] 
+2024-09-12 21:35:50.637739: Epoch time: 245.92 s 
+2024-09-12 21:35:51.662304:  
+2024-09-12 21:35:51.662540: Epoch 782 
+2024-09-12 21:35:51.662624: Current learning rate: 0.00254 
+2024-09-12 21:39:57.855041: train_loss -0.902 
+2024-09-12 21:39:57.855181: val_loss -0.5843 
+2024-09-12 21:39:57.855232: Pseudo dice [0.4174, 0.8684] 
+2024-09-12 21:39:57.855281: Epoch time: 246.19 s 
+2024-09-12 21:39:58.860107:  
+2024-09-12 21:39:58.860300: Epoch 783 
+2024-09-12 21:39:58.860382: Current learning rate: 0.00253 
+2024-09-12 21:44:04.889378: train_loss -0.9012 
+2024-09-12 21:44:04.889521: val_loss -0.5934 
+2024-09-12 21:44:04.889572: Pseudo dice [0.3589, 0.8841] 
+2024-09-12 21:44:04.889621: Epoch time: 246.03 s 
+2024-09-12 21:44:05.903574:  
+2024-09-12 21:44:05.903778: Epoch 784 
+2024-09-12 21:44:05.903876: Current learning rate: 0.00252 
+2024-09-12 21:48:12.122693: train_loss -0.8947 
+2024-09-12 21:48:12.122872: val_loss -0.5661 
+2024-09-12 21:48:12.122924: Pseudo dice [0.3646, 0.8747] 
+2024-09-12 21:48:12.122975: Epoch time: 246.22 s 
+2024-09-12 21:48:13.139858:  
+2024-09-12 21:48:13.140034: Epoch 785 
+2024-09-12 21:48:13.140135: Current learning rate: 0.00251 
+2024-09-12 21:52:19.449296: train_loss -0.8929 
+2024-09-12 21:52:19.449447: val_loss -0.5712 
+2024-09-12 21:52:19.449499: Pseudo dice [0.427, 0.8692] 
+2024-09-12 21:52:19.449551: Epoch time: 246.31 s 
+2024-09-12 21:52:20.475058:  
+2024-09-12 21:52:20.475205: Epoch 786 
+2024-09-12 21:52:20.475286: Current learning rate: 0.0025 
+2024-09-12 21:56:26.787520: train_loss -0.8906 
+2024-09-12 21:56:26.787663: val_loss -0.6009 
+2024-09-12 21:56:26.787714: Pseudo dice [0.4284, 0.8708] 
+2024-09-12 21:56:26.787765: Epoch time: 246.31 s 
+2024-09-12 21:56:27.810998:  
+2024-09-12 21:56:27.811178: Epoch 787 
+2024-09-12 21:56:27.811288: Current learning rate: 0.00249 
+2024-09-12 22:00:34.006418: train_loss -0.9011 
+2024-09-12 22:00:34.006582: val_loss -0.5846 
+2024-09-12 22:00:34.006634: Pseudo dice [0.437, 0.8718] 
+2024-09-12 22:00:34.006687: Epoch time: 246.2 s 
+2024-09-12 22:00:35.036664:  
+2024-09-12 22:00:35.036822: Epoch 788 
+2024-09-12 22:00:35.036928: Current learning rate: 0.00248 
+2024-09-12 22:04:41.176731: train_loss -0.8958 
+2024-09-12 22:04:41.176889: val_loss -0.5602 
+2024-09-12 22:04:41.176939: Pseudo dice [0.3617, 0.879] 
+2024-09-12 22:04:41.176992: Epoch time: 246.14 s 
+2024-09-12 22:04:42.203494:  
+2024-09-12 22:04:42.203686: Epoch 789 
+2024-09-12 22:04:42.203773: Current learning rate: 0.00247 
+2024-09-12 22:08:48.367372: train_loss -0.8905 
+2024-09-12 22:08:48.367518: val_loss -0.5866 
+2024-09-12 22:08:48.367576: Pseudo dice [0.4234, 0.8758] 
+2024-09-12 22:08:48.367634: Epoch time: 246.17 s 
+2024-09-12 22:08:49.383447:  
+2024-09-12 22:08:49.383665: Epoch 790 
+2024-09-12 22:08:49.383790: Current learning rate: 0.00245 
+2024-09-12 22:12:55.756345: train_loss -0.8975 
+2024-09-12 22:12:55.756500: val_loss -0.6202 
+2024-09-12 22:12:55.756558: Pseudo dice [0.4818, 0.8806] 
+2024-09-12 22:12:55.756618: Epoch time: 246.37 s 
+2024-09-12 22:12:56.769826:  
+2024-09-12 22:12:56.770009: Epoch 791 
+2024-09-12 22:12:56.770097: Current learning rate: 0.00244 
+2024-09-12 22:17:03.136278: train_loss -0.8984 
+2024-09-12 22:17:03.136441: val_loss -0.5672 
+2024-09-12 22:17:03.136499: Pseudo dice [0.3664, 0.8725] 
+2024-09-12 22:17:03.136554: Epoch time: 246.37 s 
+2024-09-12 22:17:04.171984:  
+2024-09-12 22:17:04.172180: Epoch 792 
+2024-09-12 22:17:04.172267: Current learning rate: 0.00243 
+2024-09-12 22:21:10.646529: train_loss -0.9026 
+2024-09-12 22:21:10.646764: val_loss -0.5765 
+2024-09-12 22:21:10.646821: Pseudo dice [0.3973, 0.886] 
+2024-09-12 22:21:10.646877: Epoch time: 246.48 s 
+2024-09-12 22:21:12.612105:  
+2024-09-12 22:21:12.612304: Epoch 793 
+2024-09-12 22:21:12.612391: Current learning rate: 0.00242 
+2024-09-12 22:25:19.169158: train_loss -0.9029 
+2024-09-12 22:25:19.169322: val_loss -0.587 
+2024-09-12 22:25:19.169379: Pseudo dice [0.3887, 0.8799] 
+2024-09-12 22:25:19.169435: Epoch time: 246.56 s 
+2024-09-12 22:25:20.206584:  
+2024-09-12 22:25:20.206782: Epoch 794 
+2024-09-12 22:25:20.206870: Current learning rate: 0.00241 
+2024-09-12 22:29:26.801767: train_loss -0.9041 
+2024-09-12 22:29:26.801911: val_loss -0.6142 
+2024-09-12 22:29:26.801967: Pseudo dice [0.4619, 0.8841] 
+2024-09-12 22:29:26.802022: Epoch time: 246.6 s 
+2024-09-12 22:29:27.830712:  
+2024-09-12 22:29:27.830963: Epoch 795 
+2024-09-12 22:29:27.831051: Current learning rate: 0.0024 
+2024-09-12 22:33:34.347830: train_loss -0.9046 
+2024-09-12 22:33:34.348016: val_loss -0.599 
+2024-09-12 22:33:34.348084: Pseudo dice [0.4273, 0.8906] 
+2024-09-12 22:33:34.348143: Epoch time: 246.52 s 
+2024-09-12 22:33:35.377420:  
+2024-09-12 22:33:35.377632: Epoch 796 
+2024-09-12 22:33:35.377731: Current learning rate: 0.00239 
+2024-09-12 22:37:41.801896: train_loss -0.8992 
+2024-09-12 22:37:41.802054: val_loss -0.5778 
+2024-09-12 22:37:41.802113: Pseudo dice [0.3871, 0.8722] 
+2024-09-12 22:37:41.802224: Epoch time: 246.43 s 
+2024-09-12 22:37:42.843063:  
+2024-09-12 22:37:42.843258: Epoch 797 
+2024-09-12 22:37:42.843358: Current learning rate: 0.00238 
+2024-09-12 22:41:49.546696: train_loss -0.9039 
+2024-09-12 22:41:49.546841: val_loss -0.5902 
+2024-09-12 22:41:49.546953: Pseudo dice [0.4299, 0.883] 
+2024-09-12 22:41:49.547011: Epoch time: 246.71 s 
+2024-09-12 22:41:50.578743:  
+2024-09-12 22:41:50.578916: Epoch 798 
+2024-09-12 22:41:50.579006: Current learning rate: 0.00237 
+2024-09-12 22:45:57.444569: train_loss -0.9013 
+2024-09-12 22:45:57.444714: val_loss -0.5605 
+2024-09-12 22:45:57.444776: Pseudo dice [0.3649, 0.8826] 
+2024-09-12 22:45:57.444834: Epoch time: 246.87 s 
+2024-09-12 22:45:58.453910:  
+2024-09-12 22:45:58.454127: Epoch 799 
+2024-09-12 22:45:58.454236: Current learning rate: 0.00236 
+2024-09-12 22:50:05.166045: train_loss -0.9021 
+2024-09-12 22:50:05.166196: val_loss -0.5899 
+2024-09-12 22:50:05.166251: Pseudo dice [0.4072, 0.8705] 
+2024-09-12 22:50:05.166305: Epoch time: 246.71 s 
+2024-09-12 22:50:09.205362:  
+2024-09-12 22:50:09.205539: Epoch 800 
+2024-09-12 22:50:09.205630: Current learning rate: 0.00235 
+2024-09-12 22:54:15.885339: train_loss -0.9034 
+2024-09-12 22:54:15.885501: val_loss -0.5982 
+2024-09-12 22:54:15.885557: Pseudo dice [0.4389, 0.8754] 
+2024-09-12 22:54:15.885612: Epoch time: 246.68 s 
+2024-09-12 22:54:16.921664:  
+2024-09-12 22:54:16.921884: Epoch 801 
+2024-09-12 22:54:16.921971: Current learning rate: 0.00234 
+2024-09-12 22:59:46.556001: train_loss -0.9038 
+2024-09-12 22:59:46.556149: val_loss -0.5983 
+2024-09-12 22:59:46.556208: Pseudo dice [0.4344, 0.8799] 
+2024-09-12 22:59:46.556265: Epoch time: 329.64 s 
+2024-09-12 22:59:47.560097:  
+2024-09-12 22:59:47.560319: Epoch 802 
+2024-09-12 22:59:47.560408: Current learning rate: 0.00233 
+2024-09-12 23:04:18.686507: train_loss -0.9055 
+2024-09-12 23:04:18.686705: val_loss -0.605 
+2024-09-12 23:04:18.686764: Pseudo dice [0.4284, 0.8863] 
+2024-09-12 23:04:18.686819: Epoch time: 271.13 s 
+2024-09-12 23:04:19.697800:  
+2024-09-12 23:04:19.697981: Epoch 803 
+2024-09-12 23:04:19.698072: Current learning rate: 0.00232 
+2024-09-12 23:08:53.996794: train_loss -0.9025 
+2024-09-12 23:08:53.996983: val_loss -0.611 
+2024-09-12 23:08:53.997043: Pseudo dice [0.4651, 0.8896] 
+2024-09-12 23:08:53.997100: Epoch time: 274.3 s 
+2024-09-12 23:08:55.028163:  
+2024-09-12 23:08:55.028347: Epoch 804 
+2024-09-12 23:08:55.028438: Current learning rate: 0.00231 
+2024-09-12 23:14:20.828726: train_loss -0.9038 
+2024-09-12 23:14:20.828875: val_loss -0.5834 
+2024-09-12 23:14:20.828932: Pseudo dice [0.4242, 0.8706] 
+2024-09-12 23:14:20.828988: Epoch time: 325.8 s 
+2024-09-12 23:14:21.866142:  
+2024-09-12 23:14:21.866347: Epoch 805 
+2024-09-12 23:14:21.866437: Current learning rate: 0.0023 
+2024-09-12 23:18:28.438399: train_loss -0.9027 
+2024-09-12 23:18:28.438546: val_loss -0.6003 
+2024-09-12 23:18:28.438603: Pseudo dice [0.4427, 0.8653] 
+2024-09-12 23:18:28.438659: Epoch time: 246.57 s 
+2024-09-12 23:18:29.444386:  
+2024-09-12 23:18:29.444556: Epoch 806 
+2024-09-12 23:18:29.444643: Current learning rate: 0.00229 
+2024-09-12 23:22:36.159841: train_loss -0.9049 
+2024-09-12 23:22:36.159992: val_loss -0.6005 
+2024-09-12 23:22:36.160048: Pseudo dice [0.4381, 0.856] 
+2024-09-12 23:22:36.160105: Epoch time: 246.72 s 
+2024-09-12 23:22:37.177127:  
+2024-09-12 23:22:37.177325: Epoch 807 
+2024-09-12 23:22:37.177414: Current learning rate: 0.00228 
+2024-09-12 23:27:06.342937: train_loss -0.9028 
+2024-09-12 23:27:06.343090: val_loss -0.6056 
+2024-09-12 23:27:06.343147: Pseudo dice [0.4433, 0.8683] 
+2024-09-12 23:27:06.343204: Epoch time: 269.17 s 
+2024-09-12 23:27:07.386836:  
+2024-09-12 23:27:07.387007: Epoch 808 
+2024-09-12 23:27:07.387094: Current learning rate: 0.00226 
+2024-09-12 23:32:16.955371: train_loss -0.9052 
+2024-09-12 23:32:16.955529: val_loss -0.5918 
+2024-09-12 23:32:16.955586: Pseudo dice [0.4254, 0.8804] 
+2024-09-12 23:32:16.955643: Epoch time: 309.57 s 
+2024-09-12 23:32:17.983084:  
+2024-09-12 23:32:17.983231: Epoch 809 
+2024-09-12 23:32:17.983322: Current learning rate: 0.00225 
+2024-09-12 23:37:08.635612: train_loss -0.9062 
+2024-09-12 23:37:08.635798: val_loss -0.5865 
+2024-09-12 23:37:08.635874: Pseudo dice [0.4431, 0.8688] 
+2024-09-12 23:37:08.635930: Epoch time: 290.65 s 
+2024-09-12 23:37:09.677933:  
+2024-09-12 23:37:09.678161: Epoch 810 
+2024-09-12 23:37:09.678269: Current learning rate: 0.00224 
+2024-09-12 23:41:16.293751: train_loss -0.9034 
+2024-09-12 23:41:16.293898: val_loss -0.6353 
+2024-09-12 23:41:16.293956: Pseudo dice [0.4928, 0.8781] 
+2024-09-12 23:41:16.294014: Epoch time: 246.62 s 
+2024-09-12 23:41:17.332357:  
+2024-09-12 23:41:17.332541: Epoch 811 
+2024-09-12 23:41:17.332628: Current learning rate: 0.00223 
+2024-09-12 23:45:23.862289: train_loss -0.9077 
+2024-09-12 23:45:23.862426: val_loss -0.6019 
+2024-09-12 23:45:23.862482: Pseudo dice [0.4653, 0.8692] 
+2024-09-12 23:45:23.862537: Epoch time: 246.53 s 
+2024-09-12 23:45:24.891721:  
+2024-09-12 23:45:24.891923: Epoch 812 
+2024-09-12 23:45:24.892010: Current learning rate: 0.00222 
+2024-09-12 23:49:31.787346: train_loss -0.9053 
+2024-09-12 23:49:31.787494: val_loss -0.6021 
+2024-09-12 23:49:31.787550: Pseudo dice [0.4472, 0.8834] 
+2024-09-12 23:49:31.787610: Epoch time: 246.9 s 
+2024-09-12 23:49:32.834617:  
+2024-09-12 23:49:32.834819: Epoch 813 
+2024-09-12 23:49:32.834909: Current learning rate: 0.00221 
+2024-09-12 23:53:39.591719: train_loss -0.9064 
+2024-09-12 23:53:39.591886: val_loss -0.6206 
+2024-09-12 23:53:39.591943: Pseudo dice [0.4605, 0.8748] 
+2024-09-12 23:53:39.591998: Epoch time: 246.76 s 
+2024-09-12 23:53:40.652353:  
+2024-09-12 23:53:40.652517: Epoch 814 
+2024-09-12 23:53:40.652624: Current learning rate: 0.0022 
+2024-09-12 23:57:47.105839: train_loss -0.9067 
+2024-09-12 23:57:47.106005: val_loss -0.6022 
+2024-09-12 23:57:47.106061: Pseudo dice [0.4511, 0.8843] 
+2024-09-12 23:57:47.106138: Epoch time: 246.46 s 
+2024-09-12 23:57:49.058749:  
+2024-09-12 23:57:49.058963: Epoch 815 
+2024-09-12 23:57:49.059067: Current learning rate: 0.00219 
+2024-09-13 00:01:55.486287: train_loss -0.9061 
+2024-09-13 00:01:55.486467: val_loss -0.6041 
+2024-09-13 00:01:55.486524: Pseudo dice [0.458, 0.8823] 
+2024-09-13 00:01:55.486580: Epoch time: 246.43 s 
+2024-09-13 00:01:56.518728:  
+2024-09-13 00:01:56.518914: Epoch 816 
+2024-09-13 00:01:56.519004: Current learning rate: 0.00218 
+2024-09-13 00:06:02.741783: train_loss -0.8985 
+2024-09-13 00:06:02.741934: val_loss -0.5817 
+2024-09-13 00:06:02.741991: Pseudo dice [0.4005, 0.8802] 
+2024-09-13 00:06:02.742047: Epoch time: 246.22 s 
+2024-09-13 00:06:03.761358:  
+2024-09-13 00:06:03.761542: Epoch 817 
+2024-09-13 00:06:03.761631: Current learning rate: 0.00217 
+2024-09-13 00:10:09.969598: train_loss -0.8982 
+2024-09-13 00:10:09.969766: val_loss -0.5844 
+2024-09-13 00:10:09.969825: Pseudo dice [0.4037, 0.878] 
+2024-09-13 00:10:09.969883: Epoch time: 246.21 s 
+2024-09-13 00:10:10.982261:  
+2024-09-13 00:10:10.982497: Epoch 818 
+2024-09-13 00:10:10.982585: Current learning rate: 0.00216 
+2024-09-13 00:14:17.450005: train_loss -0.9052 
+2024-09-13 00:14:17.450145: val_loss -0.6022 
+2024-09-13 00:14:17.450204: Pseudo dice [0.4463, 0.8805] 
+2024-09-13 00:14:17.450260: Epoch time: 246.47 s 
+2024-09-13 00:14:18.490563:  
+2024-09-13 00:14:18.490751: Epoch 819 
+2024-09-13 00:14:18.490864: Current learning rate: 0.00215 
+2024-09-13 00:18:24.874795: train_loss -0.9034 
+2024-09-13 00:18:24.874944: val_loss -0.6001 
+2024-09-13 00:18:24.875001: Pseudo dice [0.4585, 0.8659] 
+2024-09-13 00:18:24.875057: Epoch time: 246.39 s 
+2024-09-13 00:18:25.850268:  
+2024-09-13 00:18:25.850450: Epoch 820 
+2024-09-13 00:18:25.850538: Current learning rate: 0.00214 
+2024-09-13 00:22:31.806193: train_loss -0.8993 
+2024-09-13 00:22:31.806341: val_loss -0.5547 
+2024-09-13 00:22:31.806396: Pseudo dice [0.3779, 0.852] 
+2024-09-13 00:22:31.806451: Epoch time: 245.96 s 
+2024-09-13 00:22:32.800360:  
+2024-09-13 00:22:32.800539: Epoch 821 
+2024-09-13 00:22:32.800636: Current learning rate: 0.00213 
+2024-09-13 00:26:39.087139: train_loss -0.9088 
+2024-09-13 00:26:39.087295: val_loss -0.5946 
+2024-09-13 00:26:39.087405: Pseudo dice [0.4224, 0.8836] 
+2024-09-13 00:26:39.087476: Epoch time: 246.29 s 
+2024-09-13 00:26:40.058656:  
+2024-09-13 00:26:40.058856: Epoch 822 
+2024-09-13 00:26:40.058944: Current learning rate: 0.00212 
+2024-09-13 00:30:46.017402: train_loss -0.9037 
+2024-09-13 00:30:46.017549: val_loss -0.5859 
+2024-09-13 00:30:46.017605: Pseudo dice [0.4421, 0.8675] 
+2024-09-13 00:30:46.017661: Epoch time: 245.96 s 
+2024-09-13 00:30:46.984082:  
+2024-09-13 00:30:46.984283: Epoch 823 
+2024-09-13 00:30:46.984409: Current learning rate: 0.0021 
+2024-09-13 00:34:53.209634: train_loss -0.9065 
+2024-09-13 00:34:53.209794: val_loss -0.615 
+2024-09-13 00:34:53.209850: Pseudo dice [0.4715, 0.8846] 
+2024-09-13 00:34:53.209906: Epoch time: 246.23 s 
+2024-09-13 00:34:54.177432:  
+2024-09-13 00:34:54.177613: Epoch 824 
+2024-09-13 00:34:54.177703: Current learning rate: 0.00209 
+2024-09-13 00:39:00.732272: train_loss -0.9075 
+2024-09-13 00:39:00.732433: val_loss -0.6049 
+2024-09-13 00:39:00.732491: Pseudo dice [0.466, 0.8793] 
+2024-09-13 00:39:00.732581: Epoch time: 246.56 s 
+2024-09-13 00:39:01.696004:  
+2024-09-13 00:39:01.696233: Epoch 825 
+2024-09-13 00:39:01.696318: Current learning rate: 0.00208 
+2024-09-13 00:43:08.351394: train_loss -0.9088 
+2024-09-13 00:43:08.351543: val_loss -0.6094 
+2024-09-13 00:43:08.351602: Pseudo dice [0.4578, 0.8828] 
+2024-09-13 00:43:08.351669: Epoch time: 246.66 s 
+2024-09-13 00:43:09.314615:  
+2024-09-13 00:43:09.314817: Epoch 826 
+2024-09-13 00:43:09.314903: Current learning rate: 0.00207 
+2024-09-13 00:47:15.839602: train_loss -0.9083 
+2024-09-13 00:47:15.839753: val_loss -0.6178 
+2024-09-13 00:47:15.839814: Pseudo dice [0.4608, 0.8867] 
+2024-09-13 00:47:15.839872: Epoch time: 246.53 s 
+2024-09-13 00:47:16.827273:  
+2024-09-13 00:47:16.827421: Epoch 827 
+2024-09-13 00:47:16.827509: Current learning rate: 0.00206 
+2024-09-13 00:51:23.239722: train_loss -0.9052 
+2024-09-13 00:51:23.239888: val_loss -0.5765 
+2024-09-13 00:51:23.239945: Pseudo dice [0.4048, 0.8753] 
+2024-09-13 00:51:23.240002: Epoch time: 246.41 s 
+2024-09-13 00:51:24.206833:  
+2024-09-13 00:51:24.207016: Epoch 828 
+2024-09-13 00:51:24.207139: Current learning rate: 0.00205 
+2024-09-13 00:55:30.454272: train_loss -0.9056 
+2024-09-13 00:55:30.454426: val_loss -0.5976 
+2024-09-13 00:55:30.454483: Pseudo dice [0.4391, 0.8816] 
+2024-09-13 00:55:30.454539: Epoch time: 246.25 s 
+2024-09-13 00:55:31.408725:  
+2024-09-13 00:55:31.408900: Epoch 829 
+2024-09-13 00:55:31.408986: Current learning rate: 0.00204 
+2024-09-13 00:59:37.436896: train_loss -0.9028 
+2024-09-13 00:59:37.437080: val_loss -0.5926 
+2024-09-13 00:59:37.437138: Pseudo dice [0.4507, 0.8705] 
+2024-09-13 00:59:37.437193: Epoch time: 246.03 s 
+2024-09-13 00:59:38.403868:  
+2024-09-13 00:59:38.404038: Epoch 830 
+2024-09-13 00:59:38.404124: Current learning rate: 0.00203 
+2024-09-13 01:03:44.578697: train_loss -0.8983 
+2024-09-13 01:03:44.578870: val_loss -0.571 
+2024-09-13 01:03:44.578924: Pseudo dice [0.3881, 0.8706] 
+2024-09-13 01:03:44.578976: Epoch time: 246.18 s 
+2024-09-13 01:03:45.616490:  
+2024-09-13 01:03:45.616700: Epoch 831 
+2024-09-13 01:03:45.616791: Current learning rate: 0.00202 
+2024-09-13 01:07:51.974412: train_loss -0.8954 
+2024-09-13 01:07:51.974569: val_loss -0.6009 
+2024-09-13 01:07:51.974627: Pseudo dice [0.4753, 0.8692] 
+2024-09-13 01:07:51.974681: Epoch time: 246.36 s 
+2024-09-13 01:07:52.934677:  
+2024-09-13 01:07:52.934839: Epoch 832 
+2024-09-13 01:07:52.934964: Current learning rate: 0.00201 
+2024-09-13 01:11:59.187713: train_loss -0.9051 
+2024-09-13 01:11:59.187871: val_loss -0.6039 
+2024-09-13 01:11:59.187958: Pseudo dice [0.4015, 0.8835] 
+2024-09-13 01:11:59.188048: Epoch time: 246.25 s 
+2024-09-13 01:12:00.150672:  
+2024-09-13 01:12:00.150877: Epoch 833 
+2024-09-13 01:12:00.150960: Current learning rate: 0.002 
+2024-09-13 01:16:06.015157: train_loss -0.9024 
+2024-09-13 01:16:06.015298: val_loss -0.5818 
+2024-09-13 01:16:06.015347: Pseudo dice [0.3756, 0.8731] 
+2024-09-13 01:16:06.015399: Epoch time: 245.87 s 
+2024-09-13 01:16:06.997148:  
+2024-09-13 01:16:06.997343: Epoch 834 
+2024-09-13 01:16:06.997427: Current learning rate: 0.00199 
+2024-09-13 01:20:12.894717: train_loss -0.9022 
+2024-09-13 01:20:12.894872: val_loss -0.5983 
+2024-09-13 01:20:12.894923: Pseudo dice [0.44, 0.8731] 
+2024-09-13 01:20:12.894975: Epoch time: 245.9 s 
+2024-09-13 01:20:13.877466:  
+2024-09-13 01:20:13.877678: Epoch 835 
+2024-09-13 01:20:13.877767: Current learning rate: 0.00198 
+2024-09-13 01:24:19.830192: train_loss -0.889 
+2024-09-13 01:24:19.830348: val_loss -0.5665 
+2024-09-13 01:24:19.830399: Pseudo dice [0.4027, 0.8717] 
+2024-09-13 01:24:19.830450: Epoch time: 245.95 s 
+2024-09-13 01:24:20.807157:  
+2024-09-13 01:24:20.807358: Epoch 836 
+2024-09-13 01:24:20.807442: Current learning rate: 0.00196 
+2024-09-13 01:28:26.657364: train_loss -0.8959 
+2024-09-13 01:28:26.657502: val_loss -0.5943 
+2024-09-13 01:28:26.657552: Pseudo dice [0.4084, 0.8843] 
+2024-09-13 01:28:26.657605: Epoch time: 245.85 s 
+2024-09-13 01:28:27.611713:  
+2024-09-13 01:28:27.611913: Epoch 837 
+2024-09-13 01:28:27.611999: Current learning rate: 0.00195 
+2024-09-13 01:32:33.494943: train_loss -0.9028 
+2024-09-13 01:32:33.495093: val_loss -0.5913 
+2024-09-13 01:32:33.495143: Pseudo dice [0.4211, 0.8753] 
+2024-09-13 01:32:33.495196: Epoch time: 245.89 s 
+2024-09-13 01:32:35.413535:  
+2024-09-13 01:32:35.413770: Epoch 838 
+2024-09-13 01:32:35.413867: Current learning rate: 0.00194 
+2024-09-13 01:36:41.604657: train_loss -0.9021 
+2024-09-13 01:36:41.604811: val_loss -0.6054 
+2024-09-13 01:36:41.604862: Pseudo dice [0.4555, 0.8754] 
+2024-09-13 01:36:41.604917: Epoch time: 246.19 s 
+2024-09-13 01:36:42.565010:  
+2024-09-13 01:36:42.565228: Epoch 839 
+2024-09-13 01:36:42.565314: Current learning rate: 0.00193 
+2024-09-13 01:40:49.077416: train_loss -0.9034 
+2024-09-13 01:40:49.077562: val_loss -0.614 
+2024-09-13 01:40:49.077611: Pseudo dice [0.4933, 0.8773] 
+2024-09-13 01:40:49.077662: Epoch time: 246.51 s 
+2024-09-13 01:40:50.048902:  
+2024-09-13 01:40:50.049107: Epoch 840 
+2024-09-13 01:40:50.049192: Current learning rate: 0.00192 
+2024-09-13 01:44:56.285076: train_loss -0.9065 
+2024-09-13 01:44:56.285252: val_loss -0.6128 
+2024-09-13 01:44:56.285302: Pseudo dice [0.4477, 0.8714] 
+2024-09-13 01:44:56.285354: Epoch time: 246.24 s 
+2024-09-13 01:44:57.239198:  
+2024-09-13 01:44:57.239365: Epoch 841 
+2024-09-13 01:44:57.239447: Current learning rate: 0.00191 
+2024-09-13 01:49:03.516541: train_loss -0.9092 
+2024-09-13 01:49:03.516683: val_loss -0.618 
+2024-09-13 01:49:03.516734: Pseudo dice [0.4978, 0.8798] 
+2024-09-13 01:49:03.516785: Epoch time: 246.28 s 
+2024-09-13 01:49:04.486301:  
+2024-09-13 01:49:04.486524: Epoch 842 
+2024-09-13 01:49:04.486607: Current learning rate: 0.0019 
+2024-09-13 01:53:10.629608: train_loss -0.907 
+2024-09-13 01:53:10.629872: val_loss -0.594 
+2024-09-13 01:53:10.629924: Pseudo dice [0.438, 0.8876] 
+2024-09-13 01:53:10.629973: Epoch time: 246.15 s 
+2024-09-13 01:53:11.602258:  
+2024-09-13 01:53:11.602460: Epoch 843 
+2024-09-13 01:53:11.602542: Current learning rate: 0.00189 
+2024-09-13 01:57:17.737723: train_loss -0.9047 
+2024-09-13 01:57:17.737890: val_loss -0.5848 
+2024-09-13 01:57:17.737941: Pseudo dice [0.4345, 0.8732] 
+2024-09-13 01:57:17.737992: Epoch time: 246.14 s 
+2024-09-13 01:57:18.690187:  
+2024-09-13 01:57:18.690413: Epoch 844 
+2024-09-13 01:57:18.690498: Current learning rate: 0.00188 
+2024-09-13 02:01:24.879619: train_loss -0.907 
+2024-09-13 02:01:24.879775: val_loss -0.5962 
+2024-09-13 02:01:24.879842: Pseudo dice [0.4299, 0.8792] 
+2024-09-13 02:01:24.879900: Epoch time: 246.19 s 
+2024-09-13 02:01:25.847297:  
+2024-09-13 02:01:25.847448: Epoch 845 
+2024-09-13 02:01:25.847531: Current learning rate: 0.00187 
+2024-09-13 02:05:31.898287: train_loss -0.9113 
+2024-09-13 02:05:31.898478: val_loss -0.5945 
+2024-09-13 02:05:31.898556: Pseudo dice [0.4404, 0.8725] 
+2024-09-13 02:05:31.898607: Epoch time: 246.05 s 
+2024-09-13 02:05:32.881685:  
+2024-09-13 02:05:32.881874: Epoch 846 
+2024-09-13 02:05:32.881957: Current learning rate: 0.00186 
+2024-09-13 02:09:39.441839: train_loss -0.9093 
+2024-09-13 02:09:39.441977: val_loss -0.5864 
+2024-09-13 02:09:39.442028: Pseudo dice [0.434, 0.8816] 
+2024-09-13 02:09:39.442077: Epoch time: 246.56 s 
+2024-09-13 02:09:40.409443:  
+2024-09-13 02:09:40.409700: Epoch 847 
+2024-09-13 02:09:40.409785: Current learning rate: 0.00185 
+2024-09-13 02:13:47.047925: train_loss -0.9103 
+2024-09-13 02:13:47.048094: val_loss -0.5933 
+2024-09-13 02:13:47.048184: Pseudo dice [0.4212, 0.8846] 
+2024-09-13 02:13:47.048242: Epoch time: 246.64 s 
+2024-09-13 02:13:48.015103:  
+2024-09-13 02:13:48.015295: Epoch 848 
+2024-09-13 02:13:48.015378: Current learning rate: 0.00184 
+2024-09-13 02:17:54.090519: train_loss -0.908 
+2024-09-13 02:17:54.090728: val_loss -0.5759 
+2024-09-13 02:17:54.090822: Pseudo dice [0.4308, 0.8794] 
+2024-09-13 02:17:54.090914: Epoch time: 246.08 s 
+2024-09-13 02:17:55.065527:  
+2024-09-13 02:17:55.065769: Epoch 849 
+2024-09-13 02:17:55.065854: Current learning rate: 0.00182 
+2024-09-13 02:22:01.111545: train_loss -0.9083 
+2024-09-13 02:22:01.111687: val_loss -0.5747 
+2024-09-13 02:22:01.111738: Pseudo dice [0.3902, 0.8699] 
+2024-09-13 02:22:01.111789: Epoch time: 246.05 s 
+2024-09-13 02:22:05.037159:  
+2024-09-13 02:22:05.037340: Epoch 850 
+2024-09-13 02:22:05.037442: Current learning rate: 0.00181 
+2024-09-13 02:26:11.256201: train_loss -0.9071 
+2024-09-13 02:26:11.256349: val_loss -0.6001 
+2024-09-13 02:26:11.256399: Pseudo dice [0.4551, 0.8695] 
+2024-09-13 02:26:11.256449: Epoch time: 246.22 s 
+2024-09-13 02:26:12.216064:  
+2024-09-13 02:26:12.216258: Epoch 851 
+2024-09-13 02:26:12.216344: Current learning rate: 0.0018 
+2024-09-13 02:30:18.191010: train_loss -0.9106 
+2024-09-13 02:30:18.191152: val_loss -0.5871 
+2024-09-13 02:30:18.191207: Pseudo dice [0.3948, 0.8846] 
+2024-09-13 02:30:18.191262: Epoch time: 245.98 s 
+2024-09-13 02:30:19.173812:  
+2024-09-13 02:30:19.174003: Epoch 852 
+2024-09-13 02:30:19.174082: Current learning rate: 0.00179 
+2024-09-13 02:34:25.185967: train_loss -0.9041 
+2024-09-13 02:34:25.186103: val_loss -0.5919 
+2024-09-13 02:34:25.186153: Pseudo dice [0.4068, 0.8817] 
+2024-09-13 02:34:25.186204: Epoch time: 246.01 s 
+2024-09-13 02:34:26.134044:  
+2024-09-13 02:34:26.134272: Epoch 853 
+2024-09-13 02:34:26.134365: Current learning rate: 0.00178 
+2024-09-13 02:38:32.578444: train_loss -0.9069 
+2024-09-13 02:38:32.578605: val_loss -0.5904 
+2024-09-13 02:38:32.578656: Pseudo dice [0.3979, 0.8883] 
+2024-09-13 02:38:32.578708: Epoch time: 246.45 s 
+2024-09-13 02:38:33.554534:  
+2024-09-13 02:38:33.554768: Epoch 854 
+2024-09-13 02:38:33.554851: Current learning rate: 0.00177 
+2024-09-13 02:42:40.048556: train_loss -0.9091 
+2024-09-13 02:42:40.048694: val_loss -0.5708 
+2024-09-13 02:42:40.048744: Pseudo dice [0.4032, 0.8762] 
+2024-09-13 02:42:40.048794: Epoch time: 246.5 s 
+2024-09-13 02:42:41.000862:  
+2024-09-13 02:42:41.001052: Epoch 855 
+2024-09-13 02:42:41.001137: Current learning rate: 0.00176 
+2024-09-13 02:46:47.063655: train_loss -0.9078 
+2024-09-13 02:46:47.063798: val_loss -0.599 
+2024-09-13 02:46:47.063905: Pseudo dice [0.4267, 0.8842] 
+2024-09-13 02:46:47.063960: Epoch time: 246.06 s 
+2024-09-13 02:46:48.010727:  
+2024-09-13 02:46:48.010880: Epoch 856 
+2024-09-13 02:46:48.010960: Current learning rate: 0.00175 
+2024-09-13 02:50:54.341410: train_loss -0.9043 
+2024-09-13 02:50:54.341553: val_loss -0.6049 
+2024-09-13 02:50:54.341641: Pseudo dice [0.4347, 0.8748] 
+2024-09-13 02:50:54.341695: Epoch time: 246.33 s 
+2024-09-13 02:50:55.305229:  
+2024-09-13 02:50:55.305456: Epoch 857 
+2024-09-13 02:50:55.305543: Current learning rate: 0.00174 
+2024-09-13 02:55:01.533113: train_loss -0.9117 
+2024-09-13 02:55:01.533252: val_loss -0.5663 
+2024-09-13 02:55:01.533306: Pseudo dice [0.3782, 0.8906] 
+2024-09-13 02:55:01.533359: Epoch time: 246.23 s 
+2024-09-13 02:55:02.523190:  
+2024-09-13 02:55:02.523325: Epoch 858 
+2024-09-13 02:55:02.523442: Current learning rate: 0.00173 
+2024-09-13 02:59:08.785750: train_loss -0.9079 
+2024-09-13 02:59:08.785889: val_loss -0.5919 
+2024-09-13 02:59:08.785939: Pseudo dice [0.4448, 0.8756] 
+2024-09-13 02:59:08.785990: Epoch time: 246.26 s 
+2024-09-13 02:59:09.745339:  
+2024-09-13 02:59:09.745500: Epoch 859 
+2024-09-13 02:59:09.745634: Current learning rate: 0.00172 
+2024-09-13 03:03:16.123621: train_loss -0.9102 
+2024-09-13 03:03:16.123787: val_loss -0.5984 
+2024-09-13 03:03:16.123851: Pseudo dice [0.448, 0.8872] 
+2024-09-13 03:03:16.123906: Epoch time: 246.38 s 
+2024-09-13 03:03:17.086233:  
+2024-09-13 03:03:17.086412: Epoch 860 
+2024-09-13 03:03:17.086497: Current learning rate: 0.0017 
+2024-09-13 03:07:23.677041: train_loss -0.911 
+2024-09-13 03:07:23.677184: val_loss -0.6049 
+2024-09-13 03:07:23.677293: Pseudo dice [0.4839, 0.8759] 
+2024-09-13 03:07:23.677348: Epoch time: 246.59 s 
+2024-09-13 03:07:24.648177:  
+2024-09-13 03:07:24.648347: Epoch 861 
+2024-09-13 03:07:24.648435: Current learning rate: 0.00169 
+2024-09-13 03:11:31.172904: train_loss -0.912 
+2024-09-13 03:11:31.173044: val_loss -0.6151 
+2024-09-13 03:11:31.173093: Pseudo dice [0.4769, 0.8828] 
+2024-09-13 03:11:31.173144: Epoch time: 246.53 s 
+2024-09-13 03:11:32.137397:  
+2024-09-13 03:11:32.137598: Epoch 862 
+2024-09-13 03:11:32.137729: Current learning rate: 0.00168 
+2024-09-13 03:15:39.158105: train_loss -0.9121 
+2024-09-13 03:15:39.158248: val_loss -0.5881 
+2024-09-13 03:15:39.158298: Pseudo dice [0.4148, 0.8823] 
+2024-09-13 03:15:39.158348: Epoch time: 247.02 s 
+2024-09-13 03:15:40.121696:  
+2024-09-13 03:15:40.121853: Epoch 863 
+2024-09-13 03:15:40.121956: Current learning rate: 0.00167 
+2024-09-13 03:19:46.177232: train_loss -0.913 
+2024-09-13 03:19:46.177375: val_loss -0.6025 
+2024-09-13 03:19:46.177425: Pseudo dice [0.4792, 0.8807] 
+2024-09-13 03:19:46.177474: Epoch time: 246.06 s 
+2024-09-13 03:19:47.138603:  
+2024-09-13 03:19:47.138846: Epoch 864 
+2024-09-13 03:19:47.138963: Current learning rate: 0.00166 
+2024-09-13 03:23:53.344331: train_loss -0.9118 
+2024-09-13 03:23:53.344486: val_loss -0.5867 
+2024-09-13 03:23:53.344536: Pseudo dice [0.4699, 0.8653] 
+2024-09-13 03:23:53.344587: Epoch time: 246.21 s 
+2024-09-13 03:23:54.296591:  
+2024-09-13 03:23:54.296815: Epoch 865 
+2024-09-13 03:23:54.296896: Current learning rate: 0.00165 
+2024-09-13 03:28:00.230127: train_loss -0.9076 
+2024-09-13 03:28:00.230267: val_loss -0.6198 
+2024-09-13 03:28:00.230318: Pseudo dice [0.4635, 0.886] 
+2024-09-13 03:28:00.230369: Epoch time: 245.94 s 
+2024-09-13 03:28:01.183383:  
+2024-09-13 03:28:01.183613: Epoch 866 
+2024-09-13 03:28:01.183697: Current learning rate: 0.00164 
+2024-09-13 03:32:07.226242: train_loss -0.908 
+2024-09-13 03:32:07.226381: val_loss -0.5701 
+2024-09-13 03:32:07.226431: Pseudo dice [0.4061, 0.8901] 
+2024-09-13 03:32:07.226482: Epoch time: 246.04 s 
+2024-09-13 03:32:08.167298:  
+2024-09-13 03:32:08.167511: Epoch 867 
+2024-09-13 03:32:08.167594: Current learning rate: 0.00163 
+2024-09-13 03:36:14.074687: train_loss -0.9083 
+2024-09-13 03:36:14.074829: val_loss -0.6048 
+2024-09-13 03:36:14.074882: Pseudo dice [0.4556, 0.8875] 
+2024-09-13 03:36:14.074935: Epoch time: 245.91 s 
+2024-09-13 03:36:15.023144:  
+2024-09-13 03:36:15.023324: Epoch 868 
+2024-09-13 03:36:15.023439: Current learning rate: 0.00162 
+2024-09-13 03:40:21.101882: train_loss -0.9094 
+2024-09-13 03:40:21.102015: val_loss -0.5782 
+2024-09-13 03:40:21.102064: Pseudo dice [0.41, 0.8826] 
+2024-09-13 03:40:21.102113: Epoch time: 246.08 s 
+2024-09-13 03:40:22.058456:  
+2024-09-13 03:40:22.058656: Epoch 869 
+2024-09-13 03:40:22.058738: Current learning rate: 0.00161 
+2024-09-13 03:44:28.431223: train_loss -0.906 
+2024-09-13 03:44:28.431367: val_loss -0.6081 
+2024-09-13 03:44:28.431416: Pseudo dice [0.4901, 0.8747] 
+2024-09-13 03:44:28.431468: Epoch time: 246.37 s 
+2024-09-13 03:44:29.413054:  
+2024-09-13 03:44:29.413275: Epoch 870 
+2024-09-13 03:44:29.413392: Current learning rate: 0.00159 
+2024-09-13 03:48:35.955586: train_loss -0.9117 
+2024-09-13 03:48:35.955747: val_loss -0.612 
+2024-09-13 03:48:35.955798: Pseudo dice [0.4568, 0.8832] 
+2024-09-13 03:48:35.955859: Epoch time: 246.54 s 
+2024-09-13 03:48:36.937795:  
+2024-09-13 03:48:36.937953: Epoch 871 
+2024-09-13 03:48:36.938038: Current learning rate: 0.00158 
+2024-09-13 03:52:43.223132: train_loss -0.9119 
+2024-09-13 03:52:43.223278: val_loss -0.5658 
+2024-09-13 03:52:43.223328: Pseudo dice [0.3566, 0.8766] 
+2024-09-13 03:52:43.223382: Epoch time: 246.29 s 
+2024-09-13 03:52:44.164076:  
+2024-09-13 03:52:44.164250: Epoch 872 
+2024-09-13 03:52:44.164332: Current learning rate: 0.00157 
+2024-09-13 03:56:50.467561: train_loss -0.9101 
+2024-09-13 03:56:50.467701: val_loss -0.5805 
+2024-09-13 03:56:50.467752: Pseudo dice [0.4415, 0.8816] 
+2024-09-13 03:56:50.467802: Epoch time: 246.31 s 
+2024-09-13 03:56:51.439741:  
+2024-09-13 03:56:51.439952: Epoch 873 
+2024-09-13 03:56:51.440035: Current learning rate: 0.00156 
+2024-09-13 04:00:57.527724: train_loss -0.91 
+2024-09-13 04:00:57.527871: val_loss -0.581 
+2024-09-13 04:00:57.527922: Pseudo dice [0.4262, 0.8871] 
+2024-09-13 04:00:57.527973: Epoch time: 246.09 s 
+2024-09-13 04:00:58.488179:  
+2024-09-13 04:00:58.488343: Epoch 874 
+2024-09-13 04:00:58.488430: Current learning rate: 0.00155 
+2024-09-13 04:05:04.649552: train_loss -0.9156 
+2024-09-13 04:05:04.649692: val_loss -0.5934 
+2024-09-13 04:05:04.649743: Pseudo dice [0.4377, 0.8836] 
+2024-09-13 04:05:04.649797: Epoch time: 246.16 s 
+2024-09-13 04:05:05.602096:  
+2024-09-13 04:05:05.602255: Epoch 875 
+2024-09-13 04:05:05.602339: Current learning rate: 0.00154 
+2024-09-13 04:09:11.554555: train_loss -0.9114 
+2024-09-13 04:09:11.554695: val_loss -0.6207 
+2024-09-13 04:09:11.554745: Pseudo dice [0.4779, 0.8963] 
+2024-09-13 04:09:11.554795: Epoch time: 245.95 s 
+2024-09-13 04:09:12.496931:  
+2024-09-13 04:09:12.497182: Epoch 876 
+2024-09-13 04:09:12.497274: Current learning rate: 0.00153 
+2024-09-13 04:13:18.743701: train_loss -0.9132 
+2024-09-13 04:13:18.743935: val_loss -0.5948 
+2024-09-13 04:13:18.743993: Pseudo dice [0.4463, 0.8855] 
+2024-09-13 04:13:18.744048: Epoch time: 246.25 s 
+2024-09-13 04:13:19.707537:  
+2024-09-13 04:13:19.707713: Epoch 877 
+2024-09-13 04:13:19.707830: Current learning rate: 0.00152 
+2024-09-13 04:17:26.213457: train_loss -0.9117 
+2024-09-13 04:17:26.213651: val_loss -0.5816 
+2024-09-13 04:17:26.213704: Pseudo dice [0.4252, 0.8742] 
+2024-09-13 04:17:26.213756: Epoch time: 246.51 s 
+2024-09-13 04:17:27.172083:  
+2024-09-13 04:17:27.172260: Epoch 878 
+2024-09-13 04:17:27.172344: Current learning rate: 0.00151 
+2024-09-13 04:21:33.199036: train_loss -0.9154 
+2024-09-13 04:21:33.199210: val_loss -0.5892 
+2024-09-13 04:21:33.199261: Pseudo dice [0.4133, 0.8724] 
+2024-09-13 04:21:33.199314: Epoch time: 246.03 s 
+2024-09-13 04:21:34.159681:  
+2024-09-13 04:21:34.159873: Epoch 879 
+2024-09-13 04:21:34.159962: Current learning rate: 0.00149 
+2024-09-13 04:25:40.312290: train_loss -0.9148 
+2024-09-13 04:25:40.312429: val_loss -0.5977 
+2024-09-13 04:25:40.312479: Pseudo dice [0.4396, 0.877] 
+2024-09-13 04:25:40.312530: Epoch time: 246.15 s 
+2024-09-13 04:25:41.265608:  
+2024-09-13 04:25:41.265822: Epoch 880 
+2024-09-13 04:25:41.265943: Current learning rate: 0.00148 
+2024-09-13 04:29:47.209037: train_loss -0.9143 
+2024-09-13 04:29:47.209196: val_loss -0.6132 
+2024-09-13 04:29:47.209247: Pseudo dice [0.4661, 0.8837] 
+2024-09-13 04:29:47.209298: Epoch time: 245.95 s 
+2024-09-13 04:29:48.155975:  
+2024-09-13 04:29:48.156169: Epoch 881 
+2024-09-13 04:29:48.156251: Current learning rate: 0.00147 
+2024-09-13 04:33:54.329289: train_loss -0.9106 
+2024-09-13 04:33:54.329455: val_loss -0.5964 
+2024-09-13 04:33:54.329505: Pseudo dice [0.4229, 0.8948] 
+2024-09-13 04:33:54.329556: Epoch time: 246.18 s 
+2024-09-13 04:33:55.277643:  
+2024-09-13 04:33:55.277802: Epoch 882 
+2024-09-13 04:33:55.277922: Current learning rate: 0.00146 
+2024-09-13 04:38:01.442605: train_loss -0.9145 
+2024-09-13 04:38:01.442750: val_loss -0.602 
+2024-09-13 04:38:01.442799: Pseudo dice [0.4413, 0.8833] 
+2024-09-13 04:38:01.442849: Epoch time: 246.17 s 
+2024-09-13 04:38:02.395629:  
+2024-09-13 04:38:02.395834: Epoch 883 
+2024-09-13 04:38:02.395922: Current learning rate: 0.00145 
+2024-09-13 04:42:08.548727: train_loss -0.9108 
+2024-09-13 04:42:08.548878: val_loss -0.628 
+2024-09-13 04:42:08.548969: Pseudo dice [0.4789, 0.885] 
+2024-09-13 04:42:08.549023: Epoch time: 246.15 s 
+2024-09-13 04:42:09.515266:  
+2024-09-13 04:42:09.515434: Epoch 884 
+2024-09-13 04:42:09.515523: Current learning rate: 0.00144 
+2024-09-13 04:46:15.974591: train_loss -0.911 
+2024-09-13 04:46:15.974737: val_loss -0.5895 
+2024-09-13 04:46:15.974786: Pseudo dice [0.4227, 0.8819] 
+2024-09-13 04:46:15.974837: Epoch time: 246.46 s 
+2024-09-13 04:46:16.928766:  
+2024-09-13 04:46:16.928988: Epoch 885 
+2024-09-13 04:46:16.929071: Current learning rate: 0.00143 
+2024-09-13 04:50:23.563855: train_loss -0.9106 
+2024-09-13 04:50:23.564019: val_loss -0.6085 
+2024-09-13 04:50:23.564075: Pseudo dice [0.4685, 0.885] 
+2024-09-13 04:50:23.564126: Epoch time: 246.64 s 
+2024-09-13 04:50:24.530012:  
+2024-09-13 04:50:24.530241: Epoch 886 
+2024-09-13 04:50:24.530362: Current learning rate: 0.00142 
+2024-09-13 04:54:31.026611: train_loss -0.9159 
+2024-09-13 04:54:31.026748: val_loss -0.6003 
+2024-09-13 04:54:31.026798: Pseudo dice [0.4222, 0.8813] 
+2024-09-13 04:54:31.026850: Epoch time: 246.5 s 
+2024-09-13 04:54:32.925009:  
+2024-09-13 04:54:32.925158: Epoch 887 
+2024-09-13 04:54:32.925245: Current learning rate: 0.00141 
+2024-09-13 04:58:39.362629: train_loss -0.9122 
+2024-09-13 04:58:39.362775: val_loss -0.5896 
+2024-09-13 04:58:39.362837: Pseudo dice [0.4573, 0.8679] 
+2024-09-13 04:58:39.362889: Epoch time: 246.44 s 
+2024-09-13 04:58:40.334259:  
+2024-09-13 04:58:40.334457: Epoch 888 
+2024-09-13 04:58:40.334539: Current learning rate: 0.00139 
+2024-09-13 05:02:46.633216: train_loss -0.9156 
+2024-09-13 05:02:46.633355: val_loss -0.5996 
+2024-09-13 05:02:46.633405: Pseudo dice [0.4576, 0.8682] 
+2024-09-13 05:02:46.633456: Epoch time: 246.3 s 
+2024-09-13 05:02:47.580200:  
+2024-09-13 05:02:47.580402: Epoch 889 
+2024-09-13 05:02:47.580491: Current learning rate: 0.00138 
+2024-09-13 05:06:54.007985: train_loss -0.9142 
+2024-09-13 05:06:54.008131: val_loss -0.5978 
+2024-09-13 05:06:54.008181: Pseudo dice [0.4468, 0.8888] 
+2024-09-13 05:06:54.008233: Epoch time: 246.43 s 
+2024-09-13 05:06:54.967611:  
+2024-09-13 05:06:54.967789: Epoch 890 
+2024-09-13 05:06:54.967885: Current learning rate: 0.00137 
+2024-09-13 05:11:01.441213: train_loss -0.9144 
+2024-09-13 05:11:01.441351: val_loss -0.5851 
+2024-09-13 05:11:01.441400: Pseudo dice [0.4517, 0.8766] 
+2024-09-13 05:11:01.441449: Epoch time: 246.48 s 
+2024-09-13 05:11:02.402391:  
+2024-09-13 05:11:02.402586: Epoch 891 
+2024-09-13 05:11:02.402671: Current learning rate: 0.00136 
+2024-09-13 05:15:09.127157: train_loss -0.9116 
+2024-09-13 05:15:09.127295: val_loss -0.5699 
+2024-09-13 05:15:09.127346: Pseudo dice [0.3844, 0.8791] 
+2024-09-13 05:15:09.127397: Epoch time: 246.73 s 
+2024-09-13 05:15:10.069797:  
+2024-09-13 05:15:10.070074: Epoch 892 
+2024-09-13 05:15:10.070231: Current learning rate: 0.00135 
+2024-09-13 05:19:16.979573: train_loss -0.916 
+2024-09-13 05:19:16.979717: val_loss -0.5936 
+2024-09-13 05:19:16.979768: Pseudo dice [0.4388, 0.8866] 
+2024-09-13 05:19:16.979826: Epoch time: 246.91 s 
+2024-09-13 05:19:17.956228:  
+2024-09-13 05:19:17.956423: Epoch 893 
+2024-09-13 05:19:17.956506: Current learning rate: 0.00134 
+2024-09-13 05:23:24.498306: train_loss -0.913 
+2024-09-13 05:23:24.498465: val_loss -0.5903 
+2024-09-13 05:23:24.498515: Pseudo dice [0.4354, 0.8841] 
+2024-09-13 05:23:24.498571: Epoch time: 246.54 s 
+2024-09-13 05:23:25.458858:  
+2024-09-13 05:23:25.459075: Epoch 894 
+2024-09-13 05:23:25.459157: Current learning rate: 0.00133 
+2024-09-13 05:27:31.770845: train_loss -0.9156 
+2024-09-13 05:27:31.770985: val_loss -0.5911 
+2024-09-13 05:27:31.771037: Pseudo dice [0.4365, 0.8874] 
+2024-09-13 05:27:31.771089: Epoch time: 246.31 s 
+2024-09-13 05:27:32.755733:  
+2024-09-13 05:27:32.755897: Epoch 895 
+2024-09-13 05:27:32.755983: Current learning rate: 0.00132 
+2024-09-13 05:31:38.788306: train_loss -0.9167 
+2024-09-13 05:31:38.788456: val_loss -0.5916 
+2024-09-13 05:31:38.788507: Pseudo dice [0.448, 0.8849] 
+2024-09-13 05:31:38.788562: Epoch time: 246.03 s 
+2024-09-13 05:31:39.752118:  
+2024-09-13 05:31:39.752289: Epoch 896 
+2024-09-13 05:31:39.752375: Current learning rate: 0.0013 
+2024-09-13 05:35:45.729880: train_loss -0.9141 
+2024-09-13 05:35:45.730026: val_loss -0.5967 
+2024-09-13 05:35:45.730076: Pseudo dice [0.4182, 0.8801] 
+2024-09-13 05:35:45.730131: Epoch time: 245.98 s 
+2024-09-13 05:35:46.698549:  
+2024-09-13 05:35:46.698702: Epoch 897 
+2024-09-13 05:35:46.698780: Current learning rate: 0.00129 
+2024-09-13 05:39:52.754594: train_loss -0.9138 
+2024-09-13 05:39:52.754736: val_loss -0.6198 
+2024-09-13 05:39:52.754787: Pseudo dice [0.479, 0.8829] 
+2024-09-13 05:39:52.754837: Epoch time: 246.06 s 
+2024-09-13 05:39:53.699937:  
+2024-09-13 05:39:53.700136: Epoch 898 
+2024-09-13 05:39:53.700223: Current learning rate: 0.00128 
+2024-09-13 05:44:00.108526: train_loss -0.9153 
+2024-09-13 05:44:00.108695: val_loss -0.5974 
+2024-09-13 05:44:00.108748: Pseudo dice [0.4414, 0.8714] 
+2024-09-13 05:44:00.108799: Epoch time: 246.41 s 
+2024-09-13 05:44:01.058290:  
+2024-09-13 05:44:01.058475: Epoch 899 
+2024-09-13 05:44:01.058560: Current learning rate: 0.00127 
+2024-09-13 05:48:07.462038: train_loss -0.9143 
+2024-09-13 05:48:07.462179: val_loss -0.6192 
+2024-09-13 05:48:07.462229: Pseudo dice [0.5083, 0.8819] 
+2024-09-13 05:48:07.462279: Epoch time: 246.41 s 
+2024-09-13 05:48:11.429238:  
+2024-09-13 05:48:11.429520: Epoch 900 
+2024-09-13 05:48:11.429649: Current learning rate: 0.00126 
+2024-09-13 05:52:17.307182: train_loss -0.9148 
+2024-09-13 05:52:17.307325: val_loss -0.5819 
+2024-09-13 05:52:17.307375: Pseudo dice [0.438, 0.8849] 
+2024-09-13 05:52:17.307425: Epoch time: 245.88 s 
+2024-09-13 05:52:18.254015:  
+2024-09-13 05:52:18.254219: Epoch 901 
+2024-09-13 05:52:18.254306: Current learning rate: 0.00125 
+2024-09-13 05:56:24.011607: train_loss -0.9162 
+2024-09-13 05:56:24.011748: val_loss -0.572 
+2024-09-13 05:56:24.011798: Pseudo dice [0.3384, 0.8959] 
+2024-09-13 05:56:24.011858: Epoch time: 245.76 s 
+2024-09-13 05:56:24.958093:  
+2024-09-13 05:56:24.958234: Epoch 902 
+2024-09-13 05:56:24.958313: Current learning rate: 0.00124 
+2024-09-13 06:00:30.681897: train_loss -0.9102 
+2024-09-13 06:00:30.682035: val_loss -0.5786 
+2024-09-13 06:00:30.682084: Pseudo dice [0.4102, 0.8862] 
+2024-09-13 06:00:30.682133: Epoch time: 245.73 s 
+2024-09-13 06:00:31.630711:  
+2024-09-13 06:00:31.630903: Epoch 903 
+2024-09-13 06:00:31.630985: Current learning rate: 0.00122 
+2024-09-13 06:04:37.396514: train_loss -0.9135 
+2024-09-13 06:04:37.396652: val_loss -0.6012 
+2024-09-13 06:04:37.396717: Pseudo dice [0.4621, 0.8806] 
+2024-09-13 06:04:37.396777: Epoch time: 245.77 s 
+2024-09-13 06:04:38.348983:  
+2024-09-13 06:04:38.349160: Epoch 904 
+2024-09-13 06:04:38.349272: Current learning rate: 0.00121 
+2024-09-13 06:08:44.401438: train_loss -0.9121 
+2024-09-13 06:08:44.401615: val_loss -0.6121 
+2024-09-13 06:08:44.401665: Pseudo dice [0.4471, 0.8861] 
+2024-09-13 06:08:44.401722: Epoch time: 246.05 s 
+2024-09-13 06:08:45.389912:  
+2024-09-13 06:08:45.390127: Epoch 905 
+2024-09-13 06:08:45.390213: Current learning rate: 0.0012 
+2024-09-13 06:12:51.607905: train_loss -0.9165 
+2024-09-13 06:12:51.608076: val_loss -0.6063 
+2024-09-13 06:12:51.608127: Pseudo dice [0.4385, 0.8831] 
+2024-09-13 06:12:51.608178: Epoch time: 246.22 s 
+2024-09-13 06:12:52.601452:  
+2024-09-13 06:12:52.601628: Epoch 906 
+2024-09-13 06:12:52.601711: Current learning rate: 0.00119 
+2024-09-13 06:16:58.321163: train_loss -0.9113 
+2024-09-13 06:16:58.321307: val_loss -0.5892 
+2024-09-13 06:16:58.321357: Pseudo dice [0.4573, 0.8771] 
+2024-09-13 06:16:58.321409: Epoch time: 245.72 s 
+2024-09-13 06:16:59.287014:  
+2024-09-13 06:16:59.287233: Epoch 907 
+2024-09-13 06:16:59.287317: Current learning rate: 0.00118 
+2024-09-13 06:21:05.171693: train_loss -0.9176 
+2024-09-13 06:21:05.171845: val_loss -0.6014 
+2024-09-13 06:21:05.171897: Pseudo dice [0.4379, 0.879] 
+2024-09-13 06:21:05.171947: Epoch time: 245.89 s 
+2024-09-13 06:21:06.125318:  
+2024-09-13 06:21:06.125532: Epoch 908 
+2024-09-13 06:21:06.125622: Current learning rate: 0.00117 
+2024-09-13 06:25:12.172375: train_loss -0.9137 
+2024-09-13 06:25:12.172520: val_loss -0.5999 
+2024-09-13 06:25:12.172569: Pseudo dice [0.4341, 0.8941] 
+2024-09-13 06:25:12.172619: Epoch time: 246.05 s 
+2024-09-13 06:25:13.124698:  
+2024-09-13 06:25:13.124850: Epoch 909 
+2024-09-13 06:25:13.124935: Current learning rate: 0.00116 
+2024-09-13 06:29:19.043090: train_loss -0.9125 
+2024-09-13 06:29:19.043247: val_loss -0.6233 
+2024-09-13 06:29:19.043297: Pseudo dice [0.4629, 0.8866] 
+2024-09-13 06:29:19.043350: Epoch time: 245.92 s 
+2024-09-13 06:29:20.006255:  
+2024-09-13 06:29:20.006431: Epoch 910 
+2024-09-13 06:29:20.006514: Current learning rate: 0.00115 
+2024-09-13 06:33:26.123246: train_loss -0.9118 
+2024-09-13 06:33:26.123388: val_loss -0.5893 
+2024-09-13 06:33:26.123438: Pseudo dice [0.4138, 0.8866] 
+2024-09-13 06:33:26.123488: Epoch time: 246.12 s 
+2024-09-13 06:33:28.029477:  
+2024-09-13 06:33:28.029681: Epoch 911 
+2024-09-13 06:33:28.029807: Current learning rate: 0.00113 
+2024-09-13 06:37:34.525036: train_loss -0.9163 
+2024-09-13 06:37:34.525179: val_loss -0.6103 
+2024-09-13 06:37:34.525230: Pseudo dice [0.4537, 0.8955] 
+2024-09-13 06:37:34.525280: Epoch time: 246.5 s 
+2024-09-13 06:37:35.501269:  
+2024-09-13 06:37:35.501495: Epoch 912 
+2024-09-13 06:37:35.501579: Current learning rate: 0.00112 
+2024-09-13 06:41:41.731553: train_loss -0.9146 
+2024-09-13 06:41:41.731709: val_loss -0.5981 
+2024-09-13 06:41:41.731787: Pseudo dice [0.4614, 0.8768] 
+2024-09-13 06:41:41.731897: Epoch time: 246.23 s 
+2024-09-13 06:41:42.707880:  
+2024-09-13 06:41:42.708089: Epoch 913 
+2024-09-13 06:41:42.708174: Current learning rate: 0.00111 
+2024-09-13 06:45:48.788592: train_loss -0.9115 
+2024-09-13 06:45:48.788739: val_loss -0.5818 
+2024-09-13 06:45:48.788789: Pseudo dice [0.415, 0.8832] 
+2024-09-13 06:45:48.788840: Epoch time: 246.08 s 
+2024-09-13 06:45:49.740597:  
+2024-09-13 06:45:49.740765: Epoch 914 
+2024-09-13 06:45:49.740848: Current learning rate: 0.0011 
+2024-09-13 06:49:55.571092: train_loss -0.9159 
+2024-09-13 06:49:55.571232: val_loss -0.58 
+2024-09-13 06:49:55.571281: Pseudo dice [0.4235, 0.8838] 
+2024-09-13 06:49:55.571332: Epoch time: 245.83 s 
+2024-09-13 06:49:56.530069:  
+2024-09-13 06:49:56.530235: Epoch 915 
+2024-09-13 06:49:56.530319: Current learning rate: 0.00109 
+2024-09-13 06:54:02.265237: train_loss -0.9178 
+2024-09-13 06:54:02.265385: val_loss -0.5964 
+2024-09-13 06:54:02.265438: Pseudo dice [0.4195, 0.8847] 
+2024-09-13 06:54:02.265491: Epoch time: 245.74 s 
+2024-09-13 06:54:03.236747:  
+2024-09-13 06:54:03.237024: Epoch 916 
+2024-09-13 06:54:03.237138: Current learning rate: 0.00108 
+2024-09-13 06:58:09.014581: train_loss -0.9142 
+2024-09-13 06:58:09.014719: val_loss -0.5886 
+2024-09-13 06:58:09.014770: Pseudo dice [0.4435, 0.8737] 
+2024-09-13 06:58:09.014821: Epoch time: 245.78 s 
+2024-09-13 06:58:09.980810:  
+2024-09-13 06:58:09.981018: Epoch 917 
+2024-09-13 06:58:09.981125: Current learning rate: 0.00106 
+2024-09-13 07:02:15.748687: train_loss -0.9161 
+2024-09-13 07:02:15.748872: val_loss -0.605 
+2024-09-13 07:02:15.748927: Pseudo dice [0.4425, 0.8841] 
+2024-09-13 07:02:15.748980: Epoch time: 245.77 s 
+2024-09-13 07:02:16.703168:  
+2024-09-13 07:02:16.703332: Epoch 918 
+2024-09-13 07:02:16.703417: Current learning rate: 0.00105 
+2024-09-13 07:06:22.824819: train_loss -0.9156 
+2024-09-13 07:06:22.825042: val_loss -0.6273 
+2024-09-13 07:06:22.825096: Pseudo dice [0.4933, 0.8833] 
+2024-09-13 07:06:22.825150: Epoch time: 246.12 s 
+2024-09-13 07:06:23.783495:  
+2024-09-13 07:06:23.783641: Epoch 919 
+2024-09-13 07:06:23.783767: Current learning rate: 0.00104 
+2024-09-13 07:10:29.939694: train_loss -0.9158 
+2024-09-13 07:10:29.939851: val_loss -0.6004 
+2024-09-13 07:10:29.939905: Pseudo dice [0.4717, 0.8937] 
+2024-09-13 07:10:29.939955: Epoch time: 246.16 s 
+2024-09-13 07:10:30.919580:  
+2024-09-13 07:10:30.919818: Epoch 920 
+2024-09-13 07:10:30.919907: Current learning rate: 0.00103 
+2024-09-13 07:14:36.946292: train_loss -0.9183 
+2024-09-13 07:14:36.946433: val_loss -0.6079 
+2024-09-13 07:14:36.946483: Pseudo dice [0.4532, 0.8805] 
+2024-09-13 07:14:36.946533: Epoch time: 246.03 s 
+2024-09-13 07:14:37.904589:  
+2024-09-13 07:14:37.904822: Epoch 921 
+2024-09-13 07:14:37.904907: Current learning rate: 0.00102 
+2024-09-13 07:18:43.793373: train_loss -0.9161 
+2024-09-13 07:18:43.793521: val_loss -0.5883 
+2024-09-13 07:18:43.793570: Pseudo dice [0.4219, 0.8729] 
+2024-09-13 07:18:43.793622: Epoch time: 245.89 s 
+2024-09-13 07:18:44.738810:  
+2024-09-13 07:18:44.739017: Epoch 922 
+2024-09-13 07:18:44.739105: Current learning rate: 0.00101 
+2024-09-13 07:22:50.626341: train_loss -0.9172 
+2024-09-13 07:22:50.626493: val_loss -0.5846 
+2024-09-13 07:22:50.626545: Pseudo dice [0.4211, 0.8711] 
+2024-09-13 07:22:50.626598: Epoch time: 245.89 s 
+2024-09-13 07:22:51.589535:  
+2024-09-13 07:22:51.589715: Epoch 923 
+2024-09-13 07:22:51.589800: Current learning rate: 0.001 
+2024-09-13 07:26:57.473472: train_loss -0.9149 
+2024-09-13 07:26:57.473612: val_loss -0.6016 
+2024-09-13 07:26:57.473662: Pseudo dice [0.4467, 0.8929] 
+2024-09-13 07:26:57.473716: Epoch time: 245.89 s 
+2024-09-13 07:26:58.424060:  
+2024-09-13 07:26:58.424240: Epoch 924 
+2024-09-13 07:26:58.424325: Current learning rate: 0.00098 
+2024-09-13 07:31:04.126197: train_loss -0.9172 
+2024-09-13 07:31:04.126341: val_loss -0.6252 
+2024-09-13 07:31:04.126390: Pseudo dice [0.4541, 0.8861] 
+2024-09-13 07:31:04.126455: Epoch time: 245.7 s 
+2024-09-13 07:31:05.091219:  
+2024-09-13 07:31:05.091405: Epoch 925 
+2024-09-13 07:31:05.091480: Current learning rate: 0.00097 
+2024-09-13 07:35:11.275608: train_loss -0.9128 
+2024-09-13 07:35:11.275749: val_loss -0.608 
+2024-09-13 07:35:11.275800: Pseudo dice [0.4354, 0.8742] 
+2024-09-13 07:35:11.275903: Epoch time: 246.19 s 
+2024-09-13 07:35:12.254714:  
+2024-09-13 07:35:12.254929: Epoch 926 
+2024-09-13 07:35:12.255021: Current learning rate: 0.00096 
+2024-09-13 07:39:18.438764: train_loss -0.9157 
+2024-09-13 07:39:18.438947: val_loss -0.5758 
+2024-09-13 07:39:18.438998: Pseudo dice [0.4249, 0.8879] 
+2024-09-13 07:39:18.439051: Epoch time: 246.19 s 
+2024-09-13 07:39:19.400526:  
+2024-09-13 07:39:19.400784: Epoch 927 
+2024-09-13 07:39:19.400907: Current learning rate: 0.00095 
+2024-09-13 07:43:25.314350: train_loss -0.9144 
+2024-09-13 07:43:25.314487: val_loss -0.6402 
+2024-09-13 07:43:25.314537: Pseudo dice [0.5388, 0.8718] 
+2024-09-13 07:43:25.314587: Epoch time: 245.92 s 
+2024-09-13 07:43:26.273595:  
+2024-09-13 07:43:26.273767: Epoch 928 
+2024-09-13 07:43:26.273853: Current learning rate: 0.00094 
+2024-09-13 07:47:32.187901: train_loss -0.9187 
+2024-09-13 07:47:32.188045: val_loss -0.6229 
+2024-09-13 07:47:32.188097: Pseudo dice [0.4741, 0.8851] 
+2024-09-13 07:47:32.188150: Epoch time: 245.92 s 
+2024-09-13 07:47:33.146832:  
+2024-09-13 07:47:33.146997: Epoch 929 
+2024-09-13 07:47:33.147106: Current learning rate: 0.00092 
+2024-09-13 07:51:39.050673: train_loss -0.9165 
+2024-09-13 07:51:39.050864: val_loss -0.6147 
+2024-09-13 07:51:39.050917: Pseudo dice [0.4767, 0.88] 
+2024-09-13 07:51:39.050969: Epoch time: 245.91 s 
+2024-09-13 07:51:39.051011: Yayy! New best EMA pseudo Dice: 0.6686 
+2024-09-13 07:51:42.993877:  
+2024-09-13 07:51:42.994082: Epoch 930 
+2024-09-13 07:51:42.994166: Current learning rate: 0.00091 
+2024-09-13 07:55:49.227123: train_loss -0.9136 
+2024-09-13 07:55:49.227264: val_loss -0.6235 
+2024-09-13 07:55:49.227315: Pseudo dice [0.489, 0.8941] 
+2024-09-13 07:55:49.227368: Epoch time: 246.24 s 
+2024-09-13 07:55:49.227416: Yayy! New best EMA pseudo Dice: 0.6709 
+2024-09-13 07:55:53.191301:  
+2024-09-13 07:55:53.191491: Epoch 931 
+2024-09-13 07:55:53.191579: Current learning rate: 0.0009 
+2024-09-13 07:59:59.515087: train_loss -0.9184 
+2024-09-13 07:59:59.515227: val_loss -0.6129 
+2024-09-13 07:59:59.515277: Pseudo dice [0.4559, 0.8741] 
+2024-09-13 07:59:59.515328: Epoch time: 246.33 s 
+2024-09-13 08:00:00.459172:  
+2024-09-13 08:00:00.459379: Epoch 932 
+2024-09-13 08:00:00.459462: Current learning rate: 0.00089 
+2024-09-13 08:04:06.822923: train_loss -0.9194 
+2024-09-13 08:04:06.823072: val_loss -0.6056 
+2024-09-13 08:04:06.823121: Pseudo dice [0.4292, 0.8806] 
+2024-09-13 08:04:06.823171: Epoch time: 246.37 s 
+2024-09-13 08:04:07.773206:  
+2024-09-13 08:04:07.773381: Epoch 933 
+2024-09-13 08:04:07.773466: Current learning rate: 0.00088 
+2024-09-13 08:08:14.192464: train_loss -0.9133 
+2024-09-13 08:08:14.192607: val_loss -0.5742 
+2024-09-13 08:08:14.192657: Pseudo dice [0.4009, 0.881] 
+2024-09-13 08:08:14.192708: Epoch time: 246.42 s 
+2024-09-13 08:08:15.151231:  
+2024-09-13 08:08:15.151420: Epoch 934 
+2024-09-13 08:08:15.151508: Current learning rate: 0.00087 
+2024-09-13 08:12:21.214144: train_loss -0.9168 
+2024-09-13 08:12:21.214291: val_loss -0.6233 
+2024-09-13 08:12:21.214340: Pseudo dice [0.501, 0.8818] 
+2024-09-13 08:12:21.214391: Epoch time: 246.06 s 
+2024-09-13 08:12:23.117460:  
+2024-09-13 08:12:23.117729: Epoch 935 
+2024-09-13 08:12:23.117833: Current learning rate: 0.00085 
+2024-09-13 08:16:29.340495: train_loss -0.9188 
+2024-09-13 08:16:29.340679: val_loss -0.6272 
+2024-09-13 08:16:29.340729: Pseudo dice [0.498, 0.8779] 
+2024-09-13 08:16:29.340779: Epoch time: 246.22 s 
+2024-09-13 08:16:30.291723:  
+2024-09-13 08:16:30.291961: Epoch 936 
+2024-09-13 08:16:30.292045: Current learning rate: 0.00084 
+2024-09-13 08:20:36.397280: train_loss -0.9202 
+2024-09-13 08:20:36.397425: val_loss -0.6307 
+2024-09-13 08:20:36.397475: Pseudo dice [0.5072, 0.8899] 
+2024-09-13 08:20:36.397694: Epoch time: 246.11 s 
+2024-09-13 08:20:36.397739: Yayy! New best EMA pseudo Dice: 0.6733 
+2024-09-13 08:20:40.334789:  
+2024-09-13 08:20:40.335009: Epoch 937 
+2024-09-13 08:20:40.335094: Current learning rate: 0.00083 
+2024-09-13 08:24:46.581470: train_loss -0.9169 
+2024-09-13 08:24:46.581620: val_loss -0.592 
+2024-09-13 08:24:46.581669: Pseudo dice [0.4164, 0.8766] 
+2024-09-13 08:24:46.581720: Epoch time: 246.25 s 
+2024-09-13 08:24:47.533270:  
+2024-09-13 08:24:47.533480: Epoch 938 
+2024-09-13 08:24:47.533566: Current learning rate: 0.00082 
+2024-09-13 08:28:53.772981: train_loss -0.9148 
+2024-09-13 08:28:53.773124: val_loss -0.6054 
+2024-09-13 08:28:53.773172: Pseudo dice [0.4674, 0.8897] 
+2024-09-13 08:28:53.773224: Epoch time: 246.24 s 
+2024-09-13 08:28:54.733312:  
+2024-09-13 08:28:54.733554: Epoch 939 
+2024-09-13 08:28:54.733647: Current learning rate: 0.00081 
+2024-09-13 08:33:01.388085: train_loss -0.9195 
+2024-09-13 08:33:01.388244: val_loss -0.6025 
+2024-09-13 08:33:01.388295: Pseudo dice [0.4371, 0.8898] 
+2024-09-13 08:33:01.388347: Epoch time: 246.66 s 
+2024-09-13 08:33:02.341990:  
+2024-09-13 08:33:02.342173: Epoch 940 
+2024-09-13 08:33:02.342255: Current learning rate: 0.00079 
+2024-09-13 08:37:08.708952: train_loss -0.9172 
+2024-09-13 08:37:08.709093: val_loss -0.5889 
+2024-09-13 08:37:08.709144: Pseudo dice [0.4235, 0.8812] 
+2024-09-13 08:37:08.709193: Epoch time: 246.37 s 
+2024-09-13 08:37:09.660509:  
+2024-09-13 08:37:09.660685: Epoch 941 
+2024-09-13 08:37:09.660773: Current learning rate: 0.00078 
+2024-09-13 08:41:15.910018: train_loss -0.9189 
+2024-09-13 08:41:15.910183: val_loss -0.6087 
+2024-09-13 08:41:15.910233: Pseudo dice [0.4613, 0.8886] 
+2024-09-13 08:41:15.910323: Epoch time: 246.25 s 
+2024-09-13 08:41:16.864310:  
+2024-09-13 08:41:16.864472: Epoch 942 
+2024-09-13 08:41:16.864559: Current learning rate: 0.00077 
+2024-09-13 08:45:23.094132: train_loss -0.9201 
+2024-09-13 08:45:23.094282: val_loss -0.6154 
+2024-09-13 08:45:23.094332: Pseudo dice [0.4478, 0.8857] 
+2024-09-13 08:45:23.094385: Epoch time: 246.23 s 
+2024-09-13 08:45:24.058665:  
+2024-09-13 08:45:24.058839: Epoch 943 
+2024-09-13 08:45:24.058924: Current learning rate: 0.00076 
+2024-09-13 08:49:30.211956: train_loss -0.9203 
+2024-09-13 08:49:30.212092: val_loss -0.5913 
+2024-09-13 08:49:30.212142: Pseudo dice [0.435, 0.8772] 
+2024-09-13 08:49:30.212194: Epoch time: 246.16 s 
+2024-09-13 08:49:31.167985:  
+2024-09-13 08:49:31.168193: Epoch 944 
+2024-09-13 08:49:31.168278: Current learning rate: 0.00075 
+2024-09-13 08:53:37.420769: train_loss -0.9219 
+2024-09-13 08:53:37.420957: val_loss -0.5958 
+2024-09-13 08:53:37.421008: Pseudo dice [0.4147, 0.8852] 
+2024-09-13 08:53:37.421058: Epoch time: 246.25 s 
+2024-09-13 08:53:38.376979:  
+2024-09-13 08:53:38.377189: Epoch 945 
+2024-09-13 08:53:38.377275: Current learning rate: 0.00074 
+2024-09-13 08:57:44.590450: train_loss -0.9205 
+2024-09-13 08:57:44.590589: val_loss -0.5946 
+2024-09-13 08:57:44.590640: Pseudo dice [0.431, 0.8841] 
+2024-09-13 08:57:44.590692: Epoch time: 246.22 s 
+2024-09-13 08:57:45.515282:  
+2024-09-13 08:57:45.515486: Epoch 946 
+2024-09-13 08:57:45.515568: Current learning rate: 0.00072 
+2024-09-13 09:01:51.567685: train_loss -0.9213 
+2024-09-13 09:01:51.567852: val_loss -0.6066 
+2024-09-13 09:01:51.567906: Pseudo dice [0.4678, 0.8956] 
+2024-09-13 09:01:51.567959: Epoch time: 246.05 s 
+2024-09-13 09:01:52.518500:  
+2024-09-13 09:01:52.518722: Epoch 947 
+2024-09-13 09:01:52.518806: Current learning rate: 0.00071 
+2024-09-13 09:05:58.769775: train_loss -0.9181 
+2024-09-13 09:05:58.769920: val_loss -0.5956 
+2024-09-13 09:05:58.769971: Pseudo dice [0.4206, 0.8887] 
+2024-09-13 09:05:58.770022: Epoch time: 246.25 s 
+2024-09-13 09:05:59.727290:  
+2024-09-13 09:05:59.727485: Epoch 948 
+2024-09-13 09:05:59.727568: Current learning rate: 0.0007 
+2024-09-13 09:10:05.469233: train_loss -0.918 
+2024-09-13 09:10:05.469419: val_loss -0.6113 
+2024-09-13 09:10:05.469468: Pseudo dice [0.4762, 0.8742] 
+2024-09-13 09:10:05.469521: Epoch time: 245.74 s 
+2024-09-13 09:10:06.434497:  
+2024-09-13 09:10:06.434741: Epoch 949 
+2024-09-13 09:10:06.434829: Current learning rate: 0.00069 
+2024-09-13 09:14:12.133617: train_loss -0.9157 
+2024-09-13 09:14:12.133784: val_loss -0.593 
+2024-09-13 09:14:12.133835: Pseudo dice [0.423, 0.8727] 
+2024-09-13 09:14:12.133887: Epoch time: 245.7 s 
+2024-09-13 09:14:16.129418:  
+2024-09-13 09:14:16.129649: Epoch 950 
+2024-09-13 09:14:16.129749: Current learning rate: 0.00067 
+2024-09-13 09:18:22.048725: train_loss -0.9206 
+2024-09-13 09:18:22.048865: val_loss -0.5977 
+2024-09-13 09:18:22.048915: Pseudo dice [0.451, 0.8743] 
+2024-09-13 09:18:22.048966: Epoch time: 245.92 s 
+2024-09-13 09:18:22.996725:  
+2024-09-13 09:18:22.996948: Epoch 951 
+2024-09-13 09:18:22.997035: Current learning rate: 0.00066 
+2024-09-13 09:22:28.723409: train_loss -0.9194 
+2024-09-13 09:22:28.723599: val_loss -0.6002 
+2024-09-13 09:22:28.723650: Pseudo dice [0.4561, 0.8861] 
+2024-09-13 09:22:28.723699: Epoch time: 245.73 s 
+2024-09-13 09:22:29.656015:  
+2024-09-13 09:22:29.656238: Epoch 952 
+2024-09-13 09:22:29.656327: Current learning rate: 0.00065 
+2024-09-13 09:26:35.414317: train_loss -0.9173 
+2024-09-13 09:26:35.414461: val_loss -0.6239 
+2024-09-13 09:26:35.414513: Pseudo dice [0.519, 0.8896] 
+2024-09-13 09:26:35.414566: Epoch time: 245.76 s 
+2024-09-13 09:26:36.354299:  
+2024-09-13 09:26:36.354491: Epoch 953 
+2024-09-13 09:26:36.354578: Current learning rate: 0.00064 
+2024-09-13 09:30:42.112744: train_loss -0.9219 
+2024-09-13 09:30:42.112884: val_loss -0.6063 
+2024-09-13 09:30:42.112935: Pseudo dice [0.4582, 0.8913] 
+2024-09-13 09:30:42.112987: Epoch time: 245.76 s 
+2024-09-13 09:30:43.127610:  
+2024-09-13 09:30:43.127895: Epoch 954 
+2024-09-13 09:30:43.127982: Current learning rate: 0.00063 
+2024-09-13 09:34:48.873876: train_loss -0.9174 
+2024-09-13 09:34:48.874021: val_loss -0.5733 
+2024-09-13 09:34:48.874071: Pseudo dice [0.408, 0.8755] 
+2024-09-13 09:34:48.874122: Epoch time: 245.75 s 
+2024-09-13 09:34:49.826433:  
+2024-09-13 09:34:49.826601: Epoch 955 
+2024-09-13 09:34:49.826711: Current learning rate: 0.00061 
+2024-09-13 09:38:55.640366: train_loss -0.9205 
+2024-09-13 09:38:55.640509: val_loss -0.6138 
+2024-09-13 09:38:55.640560: Pseudo dice [0.4963, 0.8913] 
+2024-09-13 09:38:55.640611: Epoch time: 245.82 s 
+2024-09-13 09:38:56.594648:  
+2024-09-13 09:38:56.594819: Epoch 956 
+2024-09-13 09:38:56.594902: Current learning rate: 0.0006 
+2024-09-13 09:43:02.584551: train_loss -0.9223 
+2024-09-13 09:43:02.584689: val_loss -0.6172 
+2024-09-13 09:43:02.584740: Pseudo dice [0.5069, 0.8857] 
+2024-09-13 09:43:02.584791: Epoch time: 245.99 s 
+2024-09-13 09:43:03.545123:  
+2024-09-13 09:43:03.545347: Epoch 957 
+2024-09-13 09:43:03.545432: Current learning rate: 0.00059 
+2024-09-13 09:47:09.515783: train_loss -0.9226 
+2024-09-13 09:47:09.515974: val_loss -0.6038 
+2024-09-13 09:47:09.516027: Pseudo dice [0.447, 0.8889] 
+2024-09-13 09:47:09.516078: Epoch time: 245.97 s 
+2024-09-13 09:47:10.472429:  
+2024-09-13 09:47:10.472608: Epoch 958 
+2024-09-13 09:47:10.472690: Current learning rate: 0.00058 
+2024-09-13 09:51:16.503742: train_loss -0.9207 
+2024-09-13 09:51:16.503891: val_loss -0.622 
+2024-09-13 09:51:16.503982: Pseudo dice [0.4814, 0.8919] 
+2024-09-13 09:51:16.504035: Epoch time: 246.03 s 
+2024-09-13 09:51:17.473661:  
+2024-09-13 09:51:17.473886: Epoch 959 
+2024-09-13 09:51:17.473966: Current learning rate: 0.00056 
+2024-09-13 09:55:24.058289: train_loss -0.9225 
+2024-09-13 09:55:24.058444: val_loss -0.5973 
+2024-09-13 09:55:24.058492: Pseudo dice [0.4466, 0.8824] 
+2024-09-13 09:55:24.058542: Epoch time: 246.59 s 
+2024-09-13 09:55:25.064644:  
+2024-09-13 09:55:25.064826: Epoch 960 
+2024-09-13 09:55:25.064914: Current learning rate: 0.00055 
+2024-09-13 09:59:30.915841: train_loss -0.9186 
+2024-09-13 09:59:30.915983: val_loss -0.5999 
+2024-09-13 09:59:30.916034: Pseudo dice [0.4399, 0.8969] 
+2024-09-13 09:59:30.916084: Epoch time: 245.85 s 
+2024-09-13 09:59:31.907186:  
+2024-09-13 09:59:31.907410: Epoch 961 
+2024-09-13 09:59:31.907517: Current learning rate: 0.00054 
+2024-09-13 10:03:37.840896: train_loss -0.9162 
+2024-09-13 10:03:37.841085: val_loss -0.6133 
+2024-09-13 10:03:37.841136: Pseudo dice [0.4694, 0.8885] 
+2024-09-13 10:03:37.841190: Epoch time: 245.94 s 
+2024-09-13 10:03:38.804101:  
+2024-09-13 10:03:38.804282: Epoch 962 
+2024-09-13 10:03:38.804364: Current learning rate: 0.00053 
+2024-09-13 10:07:44.796288: train_loss -0.9207 
+2024-09-13 10:07:44.796453: val_loss -0.6112 
+2024-09-13 10:07:44.796504: Pseudo dice [0.4461, 0.8739] 
+2024-09-13 10:07:44.796557: Epoch time: 245.99 s 
+2024-09-13 10:07:45.758996:  
+2024-09-13 10:07:45.759151: Epoch 963 
+2024-09-13 10:07:45.759250: Current learning rate: 0.00051 
+2024-09-13 10:11:52.262263: train_loss -0.9202 
+2024-09-13 10:11:52.262406: val_loss -0.5946 
+2024-09-13 10:11:52.262456: Pseudo dice [0.4158, 0.8827] 
+2024-09-13 10:11:52.262506: Epoch time: 246.51 s 
+2024-09-13 10:11:53.226243:  
+2024-09-13 10:11:53.226419: Epoch 964 
+2024-09-13 10:11:53.226520: Current learning rate: 0.0005 
+2024-09-13 10:15:59.524882: train_loss -0.922 
+2024-09-13 10:15:59.525019: val_loss -0.6038 
+2024-09-13 10:15:59.525068: Pseudo dice [0.4713, 0.8843] 
+2024-09-13 10:15:59.525118: Epoch time: 246.3 s 
+2024-09-13 10:16:00.486602:  
+2024-09-13 10:16:00.486822: Epoch 965 
+2024-09-13 10:16:00.486902: Current learning rate: 0.00049 
+2024-09-13 10:20:06.798842: train_loss -0.9208 
+2024-09-13 10:20:06.798976: val_loss -0.596 
+2024-09-13 10:20:06.799026: Pseudo dice [0.4286, 0.8912] 
+2024-09-13 10:20:06.799078: Epoch time: 246.31 s 
+2024-09-13 10:20:07.747403:  
+2024-09-13 10:20:07.747613: Epoch 966 
+2024-09-13 10:20:07.747696: Current learning rate: 0.00048 
+2024-09-13 10:24:14.017751: train_loss -0.9145 
+2024-09-13 10:24:14.017892: val_loss -0.5852 
+2024-09-13 10:24:14.017942: Pseudo dice [0.4116, 0.8873] 
+2024-09-13 10:24:14.017993: Epoch time: 246.27 s 
+2024-09-13 10:24:14.986583:  
+2024-09-13 10:24:14.986785: Epoch 967 
+2024-09-13 10:24:14.986867: Current learning rate: 0.00046 
+2024-09-13 10:28:20.984094: train_loss -0.9206 
+2024-09-13 10:28:20.984219: val_loss -0.6239 
+2024-09-13 10:28:20.984272: Pseudo dice [0.4619, 0.8912] 
+2024-09-13 10:28:20.984321: Epoch time: 246.0 s 
+2024-09-13 10:28:21.950129:  
+2024-09-13 10:28:21.950339: Epoch 968 
+2024-09-13 10:28:21.950427: Current learning rate: 0.00045 
+2024-09-13 10:32:27.712489: train_loss -0.9202 
+2024-09-13 10:32:27.712657: val_loss -0.5997 
+2024-09-13 10:32:27.712707: Pseudo dice [0.4022, 0.89] 
+2024-09-13 10:32:27.712757: Epoch time: 245.76 s 
+2024-09-13 10:32:28.679471:  
+2024-09-13 10:32:28.679681: Epoch 969 
+2024-09-13 10:32:28.679765: Current learning rate: 0.00044 
+2024-09-13 10:36:34.448314: train_loss -0.9228 
+2024-09-13 10:36:34.448528: val_loss -0.6168 
+2024-09-13 10:36:34.448581: Pseudo dice [0.4705, 0.8795] 
+2024-09-13 10:36:34.448632: Epoch time: 245.77 s 
+2024-09-13 10:36:35.409821:  
+2024-09-13 10:36:35.410025: Epoch 970 
+2024-09-13 10:36:35.410109: Current learning rate: 0.00043 
+2024-09-13 10:40:41.083689: train_loss -0.9197 
+2024-09-13 10:40:41.083848: val_loss -0.6297 
+2024-09-13 10:40:41.083900: Pseudo dice [0.4847, 0.8843] 
+2024-09-13 10:40:41.083949: Epoch time: 245.68 s 
+2024-09-13 10:40:42.040633:  
+2024-09-13 10:40:42.040805: Epoch 971 
+2024-09-13 10:40:42.040891: Current learning rate: 0.00041 
+2024-09-13 10:44:47.720823: train_loss -0.9215 
+2024-09-13 10:44:47.720966: val_loss -0.5952 
+2024-09-13 10:44:47.721016: Pseudo dice [0.4216, 0.8974] 
+2024-09-13 10:44:47.721066: Epoch time: 245.68 s 
+2024-09-13 10:44:48.677920:  
+2024-09-13 10:44:48.678141: Epoch 972 
+2024-09-13 10:44:48.678267: Current learning rate: 0.0004 
+2024-09-13 10:48:54.520134: train_loss -0.9171 
+2024-09-13 10:48:54.520273: val_loss -0.6139 
+2024-09-13 10:48:54.520324: Pseudo dice [0.4811, 0.8857] 
+2024-09-13 10:48:54.520375: Epoch time: 245.85 s 
+2024-09-13 10:48:55.489594:  
+2024-09-13 10:48:55.489746: Epoch 973 
+2024-09-13 10:48:55.489832: Current learning rate: 0.00039 
+2024-09-13 10:53:01.155787: train_loss -0.9181 
+2024-09-13 10:53:01.155945: val_loss -0.5974 
+2024-09-13 10:53:01.155996: Pseudo dice [0.4244, 0.8826] 
+2024-09-13 10:53:01.156059: Epoch time: 245.67 s 
+2024-09-13 10:53:02.125122:  
+2024-09-13 10:53:02.125290: Epoch 974 
+2024-09-13 10:53:02.125422: Current learning rate: 0.00037 
+2024-09-13 10:57:07.886758: train_loss -0.9243 
+2024-09-13 10:57:07.886900: val_loss -0.5937 
+2024-09-13 10:57:07.887064: Pseudo dice [0.4618, 0.8794] 
+2024-09-13 10:57:07.887196: Epoch time: 245.76 s 
+2024-09-13 10:57:08.855721:  
+2024-09-13 10:57:08.855923: Epoch 975 
+2024-09-13 10:57:08.856011: Current learning rate: 0.00036 
+2024-09-13 11:01:14.554574: train_loss -0.9232 
+2024-09-13 11:01:14.554716: val_loss -0.5992 
+2024-09-13 11:01:14.554768: Pseudo dice [0.4258, 0.886] 
+2024-09-13 11:01:14.554820: Epoch time: 245.7 s 
+2024-09-13 11:01:15.507330:  
+2024-09-13 11:01:15.507549: Epoch 976 
+2024-09-13 11:01:15.507635: Current learning rate: 0.00035 
+2024-09-13 11:05:21.573484: train_loss -0.9207 
+2024-09-13 11:05:21.573625: val_loss -0.5852 
+2024-09-13 11:05:21.573675: Pseudo dice [0.4113, 0.8828] 
+2024-09-13 11:05:21.573726: Epoch time: 246.07 s 
+2024-09-13 11:05:22.524438:  
+2024-09-13 11:05:22.524636: Epoch 977 
+2024-09-13 11:05:22.524742: Current learning rate: 0.00034 
+2024-09-13 11:09:28.538596: train_loss -0.9222 
+2024-09-13 11:09:28.538755: val_loss -0.5661 
+2024-09-13 11:09:28.538805: Pseudo dice [0.4267, 0.8689] 
+2024-09-13 11:09:28.538855: Epoch time: 246.02 s 
+2024-09-13 11:09:29.495176:  
+2024-09-13 11:09:29.495337: Epoch 978 
+2024-09-13 11:09:29.495423: Current learning rate: 0.00032 
+2024-09-13 11:13:35.486197: train_loss -0.9217 
+2024-09-13 11:13:35.486347: val_loss -0.5948 
+2024-09-13 11:13:35.486398: Pseudo dice [0.4188, 0.8889] 
+2024-09-13 11:13:35.486451: Epoch time: 245.99 s 
+2024-09-13 11:13:36.441597:  
+2024-09-13 11:13:36.441826: Epoch 979 
+2024-09-13 11:13:36.441953: Current learning rate: 0.00031 
+2024-09-13 11:17:42.591156: train_loss -0.9227 
+2024-09-13 11:17:42.591301: val_loss -0.5994 
+2024-09-13 11:17:42.591350: Pseudo dice [0.4341, 0.8805] 
+2024-09-13 11:17:42.591400: Epoch time: 246.15 s 
+2024-09-13 11:17:43.555138:  
+2024-09-13 11:17:43.555317: Epoch 980 
+2024-09-13 11:17:43.555401: Current learning rate: 0.0003 
+2024-09-13 11:21:49.617834: train_loss -0.9264 
+2024-09-13 11:21:49.617990: val_loss -0.5965 
+2024-09-13 11:21:49.618040: Pseudo dice [0.432, 0.8854] 
+2024-09-13 11:21:49.618091: Epoch time: 246.06 s 
+2024-09-13 11:21:50.579587:  
+2024-09-13 11:21:50.579777: Epoch 981 
+2024-09-13 11:21:50.579929: Current learning rate: 0.00028 
+2024-09-13 11:25:56.591803: train_loss -0.9241 
+2024-09-13 11:25:56.591958: val_loss -0.5913 
+2024-09-13 11:25:56.592007: Pseudo dice [0.4166, 0.8857] 
+2024-09-13 11:25:56.592058: Epoch time: 246.02 s 
+2024-09-13 11:25:57.548058:  
+2024-09-13 11:25:57.548286: Epoch 982 
+2024-09-13 11:25:57.548370: Current learning rate: 0.00027 
+2024-09-13 11:30:03.663881: train_loss -0.9215 
+2024-09-13 11:30:03.664017: val_loss -0.5825 
+2024-09-13 11:30:03.664068: Pseudo dice [0.4094, 0.872] 
+2024-09-13 11:30:03.664118: Epoch time: 246.12 s 
+2024-09-13 11:30:04.624823:  
+2024-09-13 11:30:04.624955: Epoch 983 
+2024-09-13 11:30:04.625040: Current learning rate: 0.00026 
+2024-09-13 11:34:10.671735: train_loss -0.9206 
+2024-09-13 11:34:10.671898: val_loss -0.5704 
+2024-09-13 11:34:10.671947: Pseudo dice [0.3803, 0.8768] 
+2024-09-13 11:34:10.672001: Epoch time: 246.05 s 
+2024-09-13 11:34:12.596233:  
+2024-09-13 11:34:12.596426: Epoch 984 
+2024-09-13 11:34:12.596524: Current learning rate: 0.00024 
+2024-09-13 11:38:19.117772: train_loss -0.9221 
+2024-09-13 11:38:19.117948: val_loss -0.6141 
+2024-09-13 11:38:19.117998: Pseudo dice [0.4762, 0.8792] 
+2024-09-13 11:38:19.118051: Epoch time: 246.52 s 
+2024-09-13 11:38:20.090144:  
+2024-09-13 11:38:20.090330: Epoch 985 
+2024-09-13 11:38:20.090438: Current learning rate: 0.00023 
+2024-09-13 11:42:26.308504: train_loss -0.9211 
+2024-09-13 11:42:26.308643: val_loss -0.6057 
+2024-09-13 11:42:26.308692: Pseudo dice [0.4587, 0.8882] 
+2024-09-13 11:42:26.308744: Epoch time: 246.22 s 
+2024-09-13 11:42:27.281795:  
+2024-09-13 11:42:27.281979: Epoch 986 
+2024-09-13 11:42:27.282084: Current learning rate: 0.00021 
+2024-09-13 11:46:33.473241: train_loss -0.919 
+2024-09-13 11:46:33.473410: val_loss -0.5885 
+2024-09-13 11:46:33.473462: Pseudo dice [0.4451, 0.8729] 
+2024-09-13 11:46:33.473557: Epoch time: 246.19 s 
+2024-09-13 11:46:34.439341:  
+2024-09-13 11:46:34.439529: Epoch 987 
+2024-09-13 11:46:34.439618: Current learning rate: 0.0002 
+2024-09-13 11:50:40.640921: train_loss -0.9242 
+2024-09-13 11:50:40.641065: val_loss -0.6135 
+2024-09-13 11:50:40.641121: Pseudo dice [0.4508, 0.8937] 
+2024-09-13 11:50:40.641217: Epoch time: 246.2 s 
+2024-09-13 11:50:41.602006:  
+2024-09-13 11:50:41.602219: Epoch 988 
+2024-09-13 11:50:41.602305: Current learning rate: 0.00019 
+2024-09-13 11:54:47.902048: train_loss -0.9183 
+2024-09-13 11:54:47.902215: val_loss -0.6098 
+2024-09-13 11:54:47.902266: Pseudo dice [0.5033, 0.878] 
+2024-09-13 11:54:47.902317: Epoch time: 246.3 s 
+2024-09-13 11:54:48.859134:  
+2024-09-13 11:54:48.859349: Epoch 989 
+2024-09-13 11:54:48.859432: Current learning rate: 0.00017 
+2024-09-13 11:58:55.029310: train_loss -0.9228 
+2024-09-13 11:58:55.029479: val_loss -0.5751 
+2024-09-13 11:58:55.029529: Pseudo dice [0.3958, 0.8738] 
+2024-09-13 11:58:55.029583: Epoch time: 246.17 s 
+2024-09-13 11:58:55.993721:  
+2024-09-13 11:58:55.993902: Epoch 990 
+2024-09-13 11:58:55.993992: Current learning rate: 0.00016 
+2024-09-13 12:03:02.157026: train_loss -0.923 
+2024-09-13 12:03:02.157170: val_loss -0.5998 
+2024-09-13 12:03:02.157221: Pseudo dice [0.4374, 0.8828] 
+2024-09-13 12:03:02.157272: Epoch time: 246.17 s 
+2024-09-13 12:03:03.109813:  
+2024-09-13 12:03:03.109973: Epoch 991 
+2024-09-13 12:03:03.110057: Current learning rate: 0.00014 
+2024-09-13 12:07:09.317497: train_loss -0.9227 
+2024-09-13 12:07:09.317637: val_loss -0.5985 
+2024-09-13 12:07:09.317688: Pseudo dice [0.4636, 0.8743] 
+2024-09-13 12:07:09.317739: Epoch time: 246.21 s 
+2024-09-13 12:07:10.274133:  
+2024-09-13 12:07:10.274373: Epoch 992 
+2024-09-13 12:07:10.274461: Current learning rate: 0.00013 
+2024-09-13 12:11:16.448554: train_loss -0.9236 
+2024-09-13 12:11:16.448694: val_loss -0.5944 
+2024-09-13 12:11:16.448745: Pseudo dice [0.4104, 0.8666] 
+2024-09-13 12:11:16.448797: Epoch time: 246.18 s 
+2024-09-13 12:11:17.413589:  
+2024-09-13 12:11:17.413807: Epoch 993 
+2024-09-13 12:11:17.413900: Current learning rate: 0.00011 
+2024-09-13 12:15:23.542091: train_loss -0.9201 
+2024-09-13 12:15:23.542248: val_loss -0.6243 
+2024-09-13 12:15:23.542298: Pseudo dice [0.4718, 0.8809] 
+2024-09-13 12:15:23.542351: Epoch time: 246.13 s 
+2024-09-13 12:15:24.531133:  
+2024-09-13 12:15:24.531410: Epoch 994 
+2024-09-13 12:15:24.531496: Current learning rate: 0.0001 
+2024-09-13 12:19:30.459546: train_loss -0.921 
+2024-09-13 12:19:30.459722: val_loss -0.6266 
+2024-09-13 12:19:30.459772: Pseudo dice [0.5155, 0.8806] 
+2024-09-13 12:19:30.459832: Epoch time: 245.93 s 
+2024-09-13 12:19:31.436762:  
+2024-09-13 12:19:31.436962: Epoch 995 
+2024-09-13 12:19:31.437049: Current learning rate: 8e-05 
+2024-09-13 12:23:37.286340: train_loss -0.9251 
+2024-09-13 12:23:37.286477: val_loss -0.6096 
+2024-09-13 12:23:37.286539: Pseudo dice [0.479, 0.8637] 
+2024-09-13 12:23:37.286597: Epoch time: 245.85 s 
+2024-09-13 12:23:38.254164:  
+2024-09-13 12:23:38.254364: Epoch 996 
+2024-09-13 12:23:38.254459: Current learning rate: 7e-05 
+2024-09-13 12:27:44.510402: train_loss -0.9224 
+2024-09-13 12:27:44.510542: val_loss -0.5729 
+2024-09-13 12:27:44.510594: Pseudo dice [0.3607, 0.8732] 
+2024-09-13 12:27:44.510643: Epoch time: 246.26 s 
+2024-09-13 12:27:45.464516:  
+2024-09-13 12:27:45.464691: Epoch 997 
+2024-09-13 12:27:45.464778: Current learning rate: 5e-05 
+2024-09-13 12:31:51.694564: train_loss -0.9228 
+2024-09-13 12:31:51.694708: val_loss -0.6232 
+2024-09-13 12:31:51.694756: Pseudo dice [0.5218, 0.8794] 
+2024-09-13 12:31:51.694808: Epoch time: 246.23 s 
+2024-09-13 12:31:52.661946:  
+2024-09-13 12:31:52.662153: Epoch 998 
+2024-09-13 12:31:52.662240: Current learning rate: 4e-05 
+2024-09-13 12:35:59.027102: train_loss -0.9199 
+2024-09-13 12:35:59.027238: val_loss -0.6165 
+2024-09-13 12:35:59.027301: Pseudo dice [0.4672, 0.8983] 
+2024-09-13 12:35:59.027354: Epoch time: 246.37 s 
+2024-09-13 12:35:59.997469:  
+2024-09-13 12:35:59.997658: Epoch 999 
+2024-09-13 12:35:59.997749: Current learning rate: 2e-05 
+2024-09-13 12:40:06.977561: train_loss -0.9214 
+2024-09-13 12:40:06.977705: val_loss -0.6034 
+2024-09-13 12:40:06.977757: Pseudo dice [0.4915, 0.8715] 
+2024-09-13 12:40:06.977809: Epoch time: 246.98 s 
+2024-09-13 12:40:09.265634: Training done. 
+2024-09-13 12:40:09.399358: Using splits from existing split file: /mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/splits_final.json 
+2024-09-13 12:40:09.399706: The split file contains 5 splits. 
+2024-09-13 12:40:09.399765: Desired fold for training: 2 
+2024-09-13 12:40:09.399821: This split has 240 training and 30 validation cases. 
+2024-09-13 12:40:09.400265: predicting 101 
+2024-09-13 12:40:09.401117: 101, shape torch.Size([2, 122, 512, 511]), rank 0 
+2024-09-13 12:41:17.829502: predicting 11 
+2024-09-13 12:41:17.846474: 11, shape torch.Size([2, 114, 536, 1040]), rank 0 
+2024-09-13 12:42:18.077261: predicting 111 
+2024-09-13 12:42:18.108698: 111, shape torch.Size([2, 123, 512, 511]), rank 0 
+2024-09-13 12:42:55.982908: predicting 125 
+2024-09-13 12:42:55.999393: 125, shape torch.Size([2, 142, 511, 511]), rank 0 
+2024-09-13 12:43:33.969640: predicting 136 
+2024-09-13 12:43:33.988719: 136, shape torch.Size([2, 143, 509, 511]), rank 0 
+2024-09-13 12:44:11.871133: predicting 138 
+2024-09-13 12:44:11.889841: 138, shape torch.Size([2, 128, 509, 511]), rank 0 
+2024-09-13 12:44:49.735328: predicting 149 
+2024-09-13 12:44:49.752173: 149, shape torch.Size([2, 132, 512, 511]), rank 0 
+2024-09-13 12:45:27.581518: predicting 154 
+2024-09-13 12:45:27.598891: 154, shape torch.Size([2, 140, 511, 511]), rank 0 
+2024-09-13 12:46:05.481435: predicting 159 
+2024-09-13 12:46:05.499765: 159, shape torch.Size([2, 133, 512, 511]), rank 0 
+2024-09-13 12:46:43.287185: predicting 169 
+2024-09-13 12:46:43.303824: 169, shape torch.Size([2, 127, 512, 511]), rank 0 
+2024-09-13 12:47:21.095159: predicting 172 
+2024-09-13 12:47:21.111820: 172, shape torch.Size([2, 120, 505, 511]), rank 0 
+2024-09-13 12:47:51.367755: predicting 183 
+2024-09-13 12:47:51.383274: 183, shape torch.Size([2, 112, 508, 511]), rank 0 
+2024-09-13 12:48:21.628673: predicting 188 
+2024-09-13 12:48:21.643195: 188, shape torch.Size([2, 123, 512, 511]), rank 0 
+2024-09-13 12:48:59.409614: predicting 191 
+2024-09-13 12:48:59.425704: 191, shape torch.Size([2, 140, 540, 1040]), rank 0 
+2024-09-13 12:50:14.882959: predicting 2 
+2024-09-13 12:50:14.920480: 2, shape torch.Size([2, 127, 510, 511]), rank 0 
+2024-09-13 12:50:52.779691: predicting 26 
+2024-09-13 12:50:52.796937: 26, shape torch.Size([2, 137, 509, 511]), rank 0 
+2024-09-13 12:51:30.671491: predicting 34 
+2024-09-13 12:51:30.690383: 34, shape torch.Size([2, 125, 536, 1040]), rank 0 
+2024-09-13 12:52:46.098553: predicting 41 
+2024-09-13 12:52:46.132913: 41, shape torch.Size([2, 118, 512, 510]), rank 0 
+2024-09-13 12:53:16.502233: predicting 44 
+2024-09-13 12:53:16.517878: 44, shape torch.Size([2, 139, 511, 511]), rank 0 
+2024-09-13 12:53:54.408933: predicting 46 
+2024-09-13 12:53:54.426363: 46, shape torch.Size([2, 127, 509, 511]), rank 0 
+2024-09-13 12:54:32.231665: predicting 49 
+2024-09-13 12:54:32.248166: 49, shape torch.Size([2, 108, 512, 511]), rank 0 
+2024-09-13 12:55:02.500109: predicting 55 
+2024-09-13 12:55:02.513956: 55, shape torch.Size([2, 135, 509, 511]), rank 0 
+2024-09-13 12:55:40.403870: predicting 56 
+2024-09-13 12:55:40.422580: 56, shape torch.Size([2, 133, 512, 511]), rank 0 
+2024-09-13 12:56:18.225922: predicting 57 
+2024-09-13 12:56:18.243414: 57, shape torch.Size([2, 141, 536, 1024]), rank 0 
+2024-09-13 12:57:33.570153: predicting 63 
+2024-09-13 12:57:33.608815: 63, shape torch.Size([2, 117, 512, 511]), rank 0 
+2024-09-13 12:58:03.937830: predicting 64 
+2024-09-13 12:58:03.953194: 64, shape torch.Size([2, 114, 552, 1017]), rank 0 
+2024-09-13 12:59:04.093735: predicting 79 
+2024-09-13 12:59:04.126858: 79, shape torch.Size([2, 127, 536, 1040]), rank 0 
+2024-09-13 13:00:19.466941: predicting 81 
+2024-09-13 13:00:19.502514: 81, shape torch.Size([2, 115, 496, 1040]), rank 0 
+2024-09-13 13:01:19.731001: predicting 95 
+2024-09-13 13:01:19.760286: 95, shape torch.Size([2, 106, 558, 991]), rank 0 
+2024-09-13 13:02:20.012493: predicting 99 
+2024-09-13 13:02:20.041553: 99, shape torch.Size([2, 135, 512, 511]), rank 0 
+2024-09-13 13:03:14.455699: Validation complete 
+2024-09-13 13:03:14.455825: Mean Validation Dice:  0.5855573558799567 
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/validation/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_2/validation/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..7ff056990acedfc6ccbf83c1ebdc45cfe1235b77
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+}
\ No newline at end of file
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+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7fc4ab5d40b0>",
+    "local_rank": "0",
+    "log_file": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/training_log_2024_9_7_17_48_48.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7fc4abe72360>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): OptimizedModule(\n      (_orig_mod): MemoryEfficientSoftDiceLoss()\n    )\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7fc4aac8ac90>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 511.0], 'spacing': [0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetMPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 192, 192], 'median_image_size_in_voxels': [92, 310, 309], 'spacing': [1.6126957141129536, 0.826423816135876, 0.826423816135876], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres_bs4': {'inherits_from': '3d_fullres', 'batch_size': 4}, '3d_fullres_bs8': {'inherits_from': '3d_fullres', 'batch_size': 8}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 349.19762434242324, 'median': 141.0, 'min': 0.0, 'percentile_00_5': 29.0, 'percentile_99_5': 2558.91650390625, 'std': 485.94544484039}, '1': {'max': 1.0, 'mean': 0.37307153608378946, 'median': 0.2966964840888977, 'min': 0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}}, 'configuration': '3d_fullres_bs8', 'fold': 3, 'dataset_json': {'channel_names': {'0': 'T2', '1': 'gradient'}, 'labels': {'background': 0, 'GTVp': 1, 'GTVn': 2}, 'numTraining': 300, 'file_ending': '.nii.gz', 'name': 'Dataset504_midRT_gradient', 'description': 'Dataset 504 - midRT with Gradient Map'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "num_epochs": "1000",
+    "num_input_channels": "2",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3",
+    "output_folder_base": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 511.0], 'spacing': [0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetMPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 192, 192], 'median_image_size_in_voxels': [92, 310, 309], 'spacing': [1.6126957141129536, 0.826423816135876, 0.826423816135876], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 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0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}}",
+    "preprocessed_dataset_folder": "/mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist",
+    "save_every": "50",
+    "torch_version": "2.4.0",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}
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+    "plans_name": "nnUNetResEncUNetMPlans",
+    "original_median_spacing_after_transp": [
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+        0.5,
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+    "original_median_shape_after_transp": [
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+        511
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+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
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+            "patch_size": [
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+                512
+            ],
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+                511.0
+            ],
+            "spacing": [
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+                0.5
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+            "normalization_schemes": [
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+                "ZScoreNormalization"
+            ],
+            "use_mask_for_norm": [
+                false,
+                false
+            ],
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
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+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+                "arch_kwargs": {
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+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        512,
+                        512,
+                        512,
+                        512
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv2d",
+                    "kernel_sizes": [
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+                            3
+                        ],
+                        [
+                            3,
+                            3
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+                        3,
+                        4,
+                        6,
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+                        6,
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+                        6
+                    ],
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+                    }
+                },
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+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": true
+        },
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+            "preprocessor_name": "DefaultPreprocessor",
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+            "patch_size": [
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+                192,
+                192
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+                310,
+                309
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+                0.826423816135876,
+                0.826423816135876
+            ],
+            "normalization_schemes": [
+                "ZScoreNormalization",
+                "ZScoreNormalization"
+            ],
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+                false
+            ],
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
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+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
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+                "order": 1,
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+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+                "arch_kwargs": {
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+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        320,
+                        320
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv3d",
+                    "kernel_sizes": [
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
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+                            3,
+                            3
+                        ],
+                        [
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+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
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+                            1,
+                            1,
+                            1
+                        ],
+                        [
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+                            2,
+                            2
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+                            2,
+                            2
+                        ],
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+                            2,
+                            2
+                        ],
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+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ]
+                    ],
+                    "n_blocks_per_stage": [
+                        1,
+                        3,
+                        4,
+                        6,
+                        6,
+                        6
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": false,
+            "next_stage": "3d_cascade_fullres"
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                48,
+                192,
+                192
+            ],
+            "median_image_size_in_voxels": [
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+                512.0,
+                511.0
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+            "spacing": [
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+                0.5,
+                0.5
+            ],
+            "normalization_schemes": [
+                "ZScoreNormalization",
+                "ZScoreNormalization"
+            ],
+            "use_mask_for_norm": [
+                false,
+                false
+            ],
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
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+                "force_separate_z": null
+            },
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+                "arch_kwargs": {
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+                    "features_per_stage": [
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+                        64,
+                        128,
+                        256,
+                        320,
+                        320
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv3d",
+                    "kernel_sizes": [
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+                            3,
+                            3
+                        ],
+                        [
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+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
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+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
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+                            1,
+                            1
+                        ],
+                        [
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+                            2
+                        ],
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+                            2
+                        ],
+                        [
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+                            2,
+                            2
+                        ]
+                    ],
+                    "n_blocks_per_stage": [
+                        1,
+                        3,
+                        4,
+                        6,
+                        6,
+                        6
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": true
+        },
+        "3d_fullres_bs4": {
+            "inherits_from": "3d_fullres",
+            "batch_size": 4
+        },
+        "3d_fullres_bs8": {
+            "inherits_from": "3d_fullres",
+            "batch_size": 8
+        },
+        "3d_cascade_fullres": {
+            "inherits_from": "3d_fullres",
+            "previous_stage": "3d_lowres"
+        }
+    },
+    "experiment_planner_used": "nnUNetPlannerResEncM",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 4889.44140625,
+            "mean": 349.19762434242324,
+            "median": 141.0,
+            "min": 0.0,
+            "percentile_00_5": 29.0,
+            "percentile_99_5": 2558.91650390625,
+            "std": 485.94544484039
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+        "1": {
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+            "mean": 0.37307153608378946,
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+            "min": 0.0,
+            "percentile_00_5": 0.01908937655389309,
+            "percentile_99_5": 1.0,
+            "std": 0.2642120030869378
+        }
+    }
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/2voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/2voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..06a7dbc27e4d17c1c1624571dee12fb6ed8366fe
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/2voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
+    "folder_with_segs_from_prev_stage": null,
+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/2voxels/images",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/2voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/2voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/2voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..8b95504de874ff5da3b0248209a8a53fcf99ec25
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/2voxels/preds/summary_agg.json
@@ -0,0 +1,512 @@
+{
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/4voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/4voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..ea4e9bfb6b325d55a8dc7f70353ecb0c0a60d289
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/4voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/4voxels/images",
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+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/4voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/4voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/4voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..e35143689b9e4d1de361d72a89dabcdc833d121c
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/4voxels/preds/summary_agg.json
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+            "previous_stage": "3d_lowres"
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/6voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/6voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..1a7a02a643b860c514f906c3e8b24b3cacc70395
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/6voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
+    "folder_with_segs_from_prev_stage": null,
+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/6voxels/images",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/6voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/6voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/6voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..c85921f6d7942bf5cbe7dee7e10638cc6f4d4fee
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/6voxels/preds/summary_agg.json
@@ -0,0 +1,512 @@
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+}
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+                    "n_conv_per_stage_decoder": [
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+            "batch_dice": true
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+        "3d_cascade_fullres": {
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+            "previous_stage": "3d_lowres"
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+    },
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+    "label_manager": "LabelManager",
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+    }
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/8voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/8voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..9bf1b6a570a84a3505ffdc654c81ef271bb86809
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/8voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
+    "folder_with_segs_from_prev_stage": null,
+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/8voxels/images",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/8voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/8voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/8voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..58c32955738c0167a0cbb794b2fe2cc5d28ae5c5
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/margins/8voxels/preds/summary_agg.json
@@ -0,0 +1,512 @@
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+            "DSC1": 0.47639242295730844,
+            "DSC2": 0.5582087620526759,
+            "vol_gt1": 1202.5,
+            "vol_gt2": 2955.0,
+            "HD95_1": 3.8078865529319543,
+            "HD95_2": 8.41424529035081,
+            "MSD_1": 0.8532473236664792,
+            "MSD_2": 2.39063199810159
+        },
+        {
+            "PatientID": "166.nii.gz",
+            "TP1": 5443.602559159466,
+            "TP2": 17040.79218914983,
+            "vol_sum1": 16015.3883288506,
+            "vol_sum2": 37210.17186579606,
+            "DSC1": 0.679796511627907,
+            "DSC2": 0.9159211761025962,
+            "vol_gt1": 10426.297140250268,
+            "vol_gt2": 17902.08487543976,
+            "HD95_1": 6.174007601635199,
+            "HD95_2": 2.202188191969196,
+            "MSD_1": 2.2881553525368603,
+            "MSD_2": 0.7966428795923001
+        },
+        {
+            "PatientID": "14.nii.gz",
+            "TP1": 593.0000000000001,
+            "TP2": 6126.0,
+            "vol_sum1": 1961.0,
+            "vol_sum2": 15004.5,
+            "DSC1": 0.6047934727180011,
+            "DSC2": 0.8165550334899531,
+            "vol_gt1": 1228.0,
+            "vol_gt2": 8072.0,
+            "HD95_1": 2.5,
+            "HD95_2": 8.860022573334675,
+            "MSD_1": 0.7940702916662316,
+            "MSD_2": 1.3499161291421493
+        },
+        {
+            "PatientID": "53.nii.gz",
+            "TP1": 73.0,
+            "TP2": 10436.5,
+            "vol_sum1": 2102.0,
+            "vol_sum2": 22110.75,
+            "DSC1": 0.06945765937202664,
+            "DSC2": 0.944020442544916,
+            "vol_gt1": 1229.5,
+            "vol_gt2": 11321.25,
+            "HD95_1": 32.70427974641602,
+            "HD95_2": 1.224744871391589,
+            "MSD_1": 12.489630441260203,
+            "MSD_2": 0.4641502862160551
+        }
+    ],
+    "aggregate_results": {
+        "ConventionalDsc": {
+            "GTVp": {
+                "mean": 0.42953765366179597,
+                "median": 0.5408631510070999,
+                "percentile_25": 0.01736441484300666,
+                "percentile_75": 0.687174049809094
+            },
+            "GTVn": {
+                "mean": 0.6954199478226966,
+                "median": 0.8332355001698746,
+                "percentile_25": 0.5991014389119347,
+                "percentile_75": 0.8834789748213798
+            }
+        },
+        "AggregatedDsc": {
+            "GTVp": {
+                "mean": 0.6450408849328209,
+                "median": 0.6450408849328209,
+                "percentile_25": 0.6450408849328209,
+                "percentile_75": 0.6450408849328209
+            },
+            "GTVn": {
+                "mean": 0.8542482403305999,
+                "median": 0.8542482403305999,
+                "percentile_25": 0.8542482403305999,
+                "percentile_75": 0.8542482403305999
+            }
+        },
+        "HD95": {
+            "GTVp": {
+                "mean": 8.221176243858263,
+                "median": 4.2121341295694865,
+                "percentile_25": 3.7096252991956966,
+                "percentile_75": 8.092595349123666
+            },
+            "GTVn": {
+                "mean": 12.711689928206019,
+                "median": 1.9013878188659974,
+                "percentile_25": 1.5202847075210475,
+                "percentile_75": 6.136236371879429
+            }
+        },
+        "MSD": {
+            "GTVp": {
+                "mean": 2.1731030073254627,
+                "median": 1.3433881692762086,
+                "percentile_25": 1.054459275938139,
+                "percentile_75": 2.2731651372069397
+            },
+            "GTVn": {
+                "mean": 2.129698093314654,
+                "median": 0.5930804071707537,
+                "percentile_25": 0.46918568617814965,
+                "percentile_75": 1.2792928209991192
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/progress.png b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/progress.png
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/training_log_2024_9_7_17_48_48.txt b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/training_log_2024_9_7_17_48_48.txt
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+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/training_log_2024_9_7_17_48_48.txt
@@ -0,0 +1,7186 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2024-09-07 17:48:48.651864: do_dummy_2d_data_aug: True 
+2024-09-07 17:48:48.652866: Using splits from existing split file: /mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/splits_final.json 
+2024-09-07 17:48:48.653044: The split file contains 5 splits. 
+2024-09-07 17:48:48.653077: Desired fold for training: 3 
+2024-09-07 17:48:48.653105: This split has 240 training and 30 validation cases. 
+2024-09-07 17:48:57.642236: Using torch.compile... 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres_bs8
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 8, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True, 'inherits_from': '3d_fullres'} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 349.19762434242324, 'median': 141.0, 'min': 0.0, 'percentile_00_5': 29.0, 'percentile_99_5': 2558.91650390625, 'std': 485.94544484039}, '1': {'max': 1.0, 'mean': 0.37307153608378946, 'median': 0.2966964840888977, 'min': 0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}} 
+ 
+2024-09-07 17:48:58.607852: unpacking dataset... 
+2024-09-07 17:49:01.228860: unpacking done... 
+2024-09-07 17:49:01.230490: Unable to plot network architecture: nnUNet_compile is enabled! 
+2024-09-07 17:49:01.239726:  
+2024-09-07 17:49:01.239975: Epoch 0 
+2024-09-07 17:49:01.240111: Current learning rate: 0.01 
+2024-09-07 17:55:55.256158: train_loss -0.0672 
+2024-09-07 17:55:55.256303: val_loss -0.2149 
+2024-09-07 17:55:55.256354: Pseudo dice [0.2336, 0.3705] 
+2024-09-07 17:55:55.256410: Epoch time: 414.02 s 
+2024-09-07 17:55:55.256451: Yayy! New best EMA pseudo Dice: 0.3021 
+2024-09-07 17:55:57.112299:  
+2024-09-07 17:55:57.112496: Epoch 1 
+2024-09-07 17:55:57.112578: Current learning rate: 0.00999 
+2024-09-07 18:00:29.181616: train_loss -0.293 
+2024-09-07 18:00:29.181757: val_loss -0.2372 
+2024-09-07 18:00:29.181807: Pseudo dice [0.3095, 0.332] 
+2024-09-07 18:00:29.181858: Epoch time: 272.07 s 
+2024-09-07 18:00:29.181906: Yayy! New best EMA pseudo Dice: 0.304 
+2024-09-07 18:00:32.984094:  
+2024-09-07 18:00:32.984296: Epoch 2 
+2024-09-07 18:00:32.984376: Current learning rate: 0.00998 
+2024-09-07 18:05:13.439498: train_loss -0.3477 
+2024-09-07 18:05:13.439645: val_loss -0.2612 
+2024-09-07 18:05:13.439694: Pseudo dice [0.3478, 0.3385] 
+2024-09-07 18:05:13.439744: Epoch time: 280.46 s 
+2024-09-07 18:05:13.439785: Yayy! New best EMA pseudo Dice: 0.3079 
+2024-09-07 18:05:17.299244:  
+2024-09-07 18:05:17.299489: Epoch 3 
+2024-09-07 18:05:17.299580: Current learning rate: 0.00997 
+2024-09-07 18:09:38.623654: train_loss -0.3928 
+2024-09-07 18:09:38.623794: val_loss -0.3377 
+2024-09-07 18:09:38.623863: Pseudo dice [0.3574, 0.5026] 
+2024-09-07 18:09:38.623917: Epoch time: 261.33 s 
+2024-09-07 18:09:38.624018: Yayy! New best EMA pseudo Dice: 0.3201 
+2024-09-07 18:09:42.497005:  
+2024-09-07 18:09:42.497183: Epoch 4 
+2024-09-07 18:09:42.497265: Current learning rate: 0.00996 
+2024-09-07 18:13:46.958936: train_loss -0.4539 
+2024-09-07 18:13:46.959095: val_loss -0.4835 
+2024-09-07 18:13:46.959152: Pseudo dice [0.4619, 0.6529] 
+2024-09-07 18:13:46.959206: Epoch time: 244.46 s 
+2024-09-07 18:13:46.959248: Yayy! New best EMA pseudo Dice: 0.3438 
+2024-09-07 18:13:51.115152:  
+2024-09-07 18:13:51.115373: Epoch 5 
+2024-09-07 18:13:51.115461: Current learning rate: 0.00995 
+2024-09-07 18:18:22.213536: train_loss -0.494 
+2024-09-07 18:18:22.213675: val_loss -0.5045 
+2024-09-07 18:18:22.213753: Pseudo dice [0.5006, 0.6806] 
+2024-09-07 18:18:22.213806: Epoch time: 271.1 s 
+2024-09-07 18:18:22.213846: Yayy! New best EMA pseudo Dice: 0.3685 
+2024-09-07 18:18:26.069383:  
+2024-09-07 18:18:26.069587: Epoch 6 
+2024-09-07 18:18:26.069664: Current learning rate: 0.00995 
+2024-09-07 18:22:38.271316: train_loss -0.5496 
+2024-09-07 18:22:38.271454: val_loss -0.5523 
+2024-09-07 18:22:38.271503: Pseudo dice [0.5268, 0.7221] 
+2024-09-07 18:22:38.271554: Epoch time: 252.2 s 
+2024-09-07 18:22:38.271595: Yayy! New best EMA pseudo Dice: 0.3941 
+2024-09-07 18:22:42.175496:  
+2024-09-07 18:22:42.175666: Epoch 7 
+2024-09-07 18:22:42.175745: Current learning rate: 0.00994 
+2024-09-07 18:27:15.598233: train_loss -0.5755 
+2024-09-07 18:27:15.598620: val_loss -0.5866 
+2024-09-07 18:27:15.598671: Pseudo dice [0.5565, 0.7539] 
+2024-09-07 18:27:15.598722: Epoch time: 273.42 s 
+2024-09-07 18:27:15.598761: Yayy! New best EMA pseudo Dice: 0.4202 
+2024-09-07 18:27:19.469310:  
+2024-09-07 18:27:19.469572: Epoch 8 
+2024-09-07 18:27:19.469702: Current learning rate: 0.00993 
+2024-09-07 18:32:12.261943: train_loss -0.6182 
+2024-09-07 18:32:12.262129: val_loss -0.5778 
+2024-09-07 18:32:12.262193: Pseudo dice [0.5286, 0.764] 
+2024-09-07 18:32:12.262252: Epoch time: 292.8 s 
+2024-09-07 18:32:12.262305: Yayy! New best EMA pseudo Dice: 0.4428 
+2024-09-07 18:32:16.978452:  
+2024-09-07 18:32:16.978641: Epoch 9 
+2024-09-07 18:32:16.978725: Current learning rate: 0.00992 
+2024-09-07 18:37:09.447771: train_loss -0.6266 
+2024-09-07 18:37:09.447927: val_loss -0.5185 
+2024-09-07 18:37:09.447978: Pseudo dice [0.4888, 0.6975] 
+2024-09-07 18:37:09.448029: Epoch time: 292.47 s 
+2024-09-07 18:37:09.448069: Yayy! New best EMA pseudo Dice: 0.4578 
+2024-09-07 18:37:13.339832:  
+2024-09-07 18:37:13.339996: Epoch 10 
+2024-09-07 18:37:13.340114: Current learning rate: 0.00991 
+2024-09-07 18:41:35.562279: train_loss -0.6233 
+2024-09-07 18:41:35.562441: val_loss -0.6008 
+2024-09-07 18:41:35.562499: Pseudo dice [0.5852, 0.752] 
+2024-09-07 18:41:35.562554: Epoch time: 262.22 s 
+2024-09-07 18:41:35.562594: Yayy! New best EMA pseudo Dice: 0.4789 
+2024-09-07 18:41:39.587934:  
+2024-09-07 18:41:39.588153: Epoch 11 
+2024-09-07 18:41:39.588233: Current learning rate: 0.0099 
+2024-09-07 18:46:00.858466: train_loss -0.6409 
+2024-09-07 18:46:00.858613: val_loss -0.5517 
+2024-09-07 18:46:00.858663: Pseudo dice [0.507, 0.7618] 
+2024-09-07 18:46:00.858719: Epoch time: 261.27 s 
+2024-09-07 18:46:00.858760: Yayy! New best EMA pseudo Dice: 0.4945 
+2024-09-07 18:46:04.714181:  
+2024-09-07 18:46:04.714353: Epoch 12 
+2024-09-07 18:46:04.714438: Current learning rate: 0.00989 
+2024-09-07 18:50:26.378538: train_loss -0.6614 
+2024-09-07 18:50:26.378675: val_loss -0.5973 
+2024-09-07 18:50:26.378725: Pseudo dice [0.5686, 0.7487] 
+2024-09-07 18:50:26.378777: Epoch time: 261.67 s 
+2024-09-07 18:50:26.378818: Yayy! New best EMA pseudo Dice: 0.5109 
+2024-09-07 18:50:30.264451:  
+2024-09-07 18:50:30.264664: Epoch 13 
+2024-09-07 18:50:30.264745: Current learning rate: 0.00988 
+2024-09-07 18:54:35.631021: train_loss -0.6629 
+2024-09-07 18:54:35.631178: val_loss -0.5959 
+2024-09-07 18:54:35.631229: Pseudo dice [0.5583, 0.771] 
+2024-09-07 18:54:35.631279: Epoch time: 245.37 s 
+2024-09-07 18:54:35.631319: Yayy! New best EMA pseudo Dice: 0.5263 
+2024-09-07 18:54:39.529094:  
+2024-09-07 18:54:39.529283: Epoch 14 
+2024-09-07 18:54:39.529367: Current learning rate: 0.00987 
+2024-09-07 18:58:48.645628: train_loss -0.6338 
+2024-09-07 18:58:48.645763: val_loss -0.5747 
+2024-09-07 18:58:48.645813: Pseudo dice [0.5301, 0.7708] 
+2024-09-07 18:58:48.645864: Epoch time: 249.12 s 
+2024-09-07 18:58:48.645903: Yayy! New best EMA pseudo Dice: 0.5387 
+2024-09-07 18:58:52.570841:  
+2024-09-07 18:58:52.571063: Epoch 15 
+2024-09-07 18:58:52.571144: Current learning rate: 0.00986 
+2024-09-07 19:03:09.246872: train_loss -0.6347 
+2024-09-07 19:03:09.247026: val_loss -0.6014 
+2024-09-07 19:03:09.247085: Pseudo dice [0.5744, 0.7802] 
+2024-09-07 19:03:09.247146: Epoch time: 256.68 s 
+2024-09-07 19:03:09.247200: Yayy! New best EMA pseudo Dice: 0.5525 
+2024-09-07 19:03:13.183424:  
+2024-09-07 19:03:13.183607: Epoch 16 
+2024-09-07 19:03:13.183688: Current learning rate: 0.00986 
+2024-09-07 19:07:17.613128: train_loss -0.6584 
+2024-09-07 19:07:17.613280: val_loss -0.5602 
+2024-09-07 19:07:17.613330: Pseudo dice [0.4975, 0.7439] 
+2024-09-07 19:07:17.613382: Epoch time: 244.43 s 
+2024-09-07 19:07:17.613423: Yayy! New best EMA pseudo Dice: 0.5594 
+2024-09-07 19:07:21.525785:  
+2024-09-07 19:07:21.526027: Epoch 17 
+2024-09-07 19:07:21.526124: Current learning rate: 0.00985 
+2024-09-07 19:11:25.802580: train_loss -0.7003 
+2024-09-07 19:11:25.802721: val_loss -0.6047 
+2024-09-07 19:11:25.802771: Pseudo dice [0.5511, 0.7654] 
+2024-09-07 19:11:25.802823: Epoch time: 244.28 s 
+2024-09-07 19:11:25.802864: Yayy! New best EMA pseudo Dice: 0.5692 
+2024-09-07 19:11:29.754304:  
+2024-09-07 19:11:29.754473: Epoch 18 
+2024-09-07 19:11:29.754562: Current learning rate: 0.00984 
+2024-09-07 19:15:34.118017: train_loss -0.6944 
+2024-09-07 19:15:34.118157: val_loss -0.625 
+2024-09-07 19:15:34.118207: Pseudo dice [0.5838, 0.7749] 
+2024-09-07 19:15:34.118258: Epoch time: 244.37 s 
+2024-09-07 19:15:34.118297: Yayy! New best EMA pseudo Dice: 0.5803 
+2024-09-07 19:15:38.007132:  
+2024-09-07 19:15:38.007271: Epoch 19 
+2024-09-07 19:15:38.007368: Current learning rate: 0.00983 
+2024-09-07 19:19:42.454211: train_loss -0.6794 
+2024-09-07 19:19:42.454405: val_loss -0.612 
+2024-09-07 19:19:42.454456: Pseudo dice [0.5371, 0.7975] 
+2024-09-07 19:19:42.454508: Epoch time: 244.45 s 
+2024-09-07 19:19:42.454547: Yayy! New best EMA pseudo Dice: 0.589 
+2024-09-07 19:19:46.370104:  
+2024-09-07 19:19:46.370278: Epoch 20 
+2024-09-07 19:19:46.370380: Current learning rate: 0.00982 
+2024-09-07 19:23:59.405879: train_loss -0.6798 
+2024-09-07 19:23:59.406015: val_loss -0.6298 
+2024-09-07 19:23:59.406066: Pseudo dice [0.5703, 0.7874] 
+2024-09-07 19:23:59.406117: Epoch time: 253.04 s 
+2024-09-07 19:23:59.406157: Yayy! New best EMA pseudo Dice: 0.598 
+2024-09-07 19:24:03.316834:  
+2024-09-07 19:24:03.316998: Epoch 21 
+2024-09-07 19:24:03.317081: Current learning rate: 0.00981 
+2024-09-07 19:28:13.997288: train_loss -0.6899 
+2024-09-07 19:28:13.997423: val_loss -0.6097 
+2024-09-07 19:28:13.997473: Pseudo dice [0.5594, 0.7919] 
+2024-09-07 19:28:13.997533: Epoch time: 250.68 s 
+2024-09-07 19:28:13.997574: Yayy! New best EMA pseudo Dice: 0.6057 
+2024-09-07 19:28:17.917634:  
+2024-09-07 19:28:17.917792: Epoch 22 
+2024-09-07 19:28:17.917907: Current learning rate: 0.0098 
+2024-09-07 19:32:22.486492: train_loss -0.7022 
+2024-09-07 19:32:22.486635: val_loss -0.6099 
+2024-09-07 19:32:22.486688: Pseudo dice [0.547, 0.7825] 
+2024-09-07 19:32:22.486740: Epoch time: 244.57 s 
+2024-09-07 19:32:22.486780: Yayy! New best EMA pseudo Dice: 0.6116 
+2024-09-07 19:32:26.377789:  
+2024-09-07 19:32:26.378054: Epoch 23 
+2024-09-07 19:32:26.378179: Current learning rate: 0.00979 
+2024-09-07 19:36:31.268857: train_loss -0.7151 
+2024-09-07 19:36:31.268995: val_loss -0.591 
+2024-09-07 19:36:31.269047: Pseudo dice [0.5088, 0.7874] 
+2024-09-07 19:36:31.269099: Epoch time: 244.89 s 
+2024-09-07 19:36:31.269138: Yayy! New best EMA pseudo Dice: 0.6153 
+2024-09-07 19:36:35.158493:  
+2024-09-07 19:36:35.158685: Epoch 24 
+2024-09-07 19:36:35.158765: Current learning rate: 0.00978 
+2024-09-07 19:41:26.812030: train_loss -0.7158 
+2024-09-07 19:41:26.812371: val_loss -0.6461 
+2024-09-07 19:41:26.812482: Pseudo dice [0.5997, 0.8051] 
+2024-09-07 19:41:26.812635: Epoch time: 291.66 s 
+2024-09-07 19:41:26.812829: Yayy! New best EMA pseudo Dice: 0.624 
+2024-09-07 19:41:31.235850:  
+2024-09-07 19:41:31.235982: Epoch 25 
+2024-09-07 19:41:31.236061: Current learning rate: 0.00977 
+2024-09-07 19:45:41.121351: train_loss -0.7132 
+2024-09-07 19:45:41.121491: val_loss -0.6292 
+2024-09-07 19:45:41.121541: Pseudo dice [0.5705, 0.809] 
+2024-09-07 19:45:41.121593: Epoch time: 249.89 s 
+2024-09-07 19:45:41.121633: Yayy! New best EMA pseudo Dice: 0.6306 
+2024-09-07 19:45:44.981304:  
+2024-09-07 19:45:44.981522: Epoch 26 
+2024-09-07 19:45:44.981640: Current learning rate: 0.00977 
+2024-09-07 19:49:49.463905: train_loss -0.7296 
+2024-09-07 19:49:49.464075: val_loss -0.6187 
+2024-09-07 19:49:49.464132: Pseudo dice [0.5599, 0.7821] 
+2024-09-07 19:49:49.464184: Epoch time: 244.48 s 
+2024-09-07 19:49:49.464269: Yayy! New best EMA pseudo Dice: 0.6346 
+2024-09-07 19:49:53.334589:  
+2024-09-07 19:49:53.334749: Epoch 27 
+2024-09-07 19:49:53.334834: Current learning rate: 0.00976 
+2024-09-07 19:53:57.927679: train_loss -0.7248 
+2024-09-07 19:53:57.927817: val_loss -0.6338 
+2024-09-07 19:53:57.927869: Pseudo dice [0.5795, 0.8198] 
+2024-09-07 19:53:57.927919: Epoch time: 244.59 s 
+2024-09-07 19:53:57.927958: Yayy! New best EMA pseudo Dice: 0.6411 
+2024-09-07 19:54:01.773702:  
+2024-09-07 19:54:01.773841: Epoch 28 
+2024-09-07 19:54:01.773924: Current learning rate: 0.00975 
+2024-09-07 19:58:15.270443: train_loss -0.7225 
+2024-09-07 19:58:15.270618: val_loss -0.6354 
+2024-09-07 19:58:15.270670: Pseudo dice [0.5714, 0.8005] 
+2024-09-07 19:58:15.270722: Epoch time: 253.5 s 
+2024-09-07 19:58:15.270764: Yayy! New best EMA pseudo Dice: 0.6456 
+2024-09-07 19:58:19.127306:  
+2024-09-07 19:58:19.127510: Epoch 29 
+2024-09-07 19:58:19.127620: Current learning rate: 0.00974 
+2024-09-07 20:02:23.861836: train_loss -0.7153 
+2024-09-07 20:02:23.862020: val_loss -0.6165 
+2024-09-07 20:02:23.862070: Pseudo dice [0.5421, 0.7987] 
+2024-09-07 20:02:23.862120: Epoch time: 244.74 s 
+2024-09-07 20:02:23.862160: Yayy! New best EMA pseudo Dice: 0.6481 
+2024-09-07 20:02:27.908066:  
+2024-09-07 20:02:27.908198: Epoch 30 
+2024-09-07 20:02:27.908277: Current learning rate: 0.00973 
+2024-09-07 20:06:32.642635: train_loss -0.7006 
+2024-09-07 20:06:32.642774: val_loss -0.5883 
+2024-09-07 20:06:32.642824: Pseudo dice [0.5238, 0.7815] 
+2024-09-07 20:06:32.642875: Epoch time: 244.74 s 
+2024-09-07 20:06:32.642915: Yayy! New best EMA pseudo Dice: 0.6485 
+2024-09-07 20:06:37.408900:  
+2024-09-07 20:06:37.409064: Epoch 31 
+2024-09-07 20:06:37.409149: Current learning rate: 0.00972 
+2024-09-07 20:10:42.233476: train_loss -0.6964 
+2024-09-07 20:10:42.233616: val_loss -0.6212 
+2024-09-07 20:10:42.233665: Pseudo dice [0.5849, 0.7963] 
+2024-09-07 20:10:42.233714: Epoch time: 244.83 s 
+2024-09-07 20:10:42.233754: Yayy! New best EMA pseudo Dice: 0.6527 
+2024-09-07 20:10:46.144800:  
+2024-09-07 20:10:46.144973: Epoch 32 
+2024-09-07 20:10:46.145057: Current learning rate: 0.00971 
+2024-09-07 20:14:51.187660: train_loss -0.7128 
+2024-09-07 20:14:51.187803: val_loss -0.6418 
+2024-09-07 20:14:51.187861: Pseudo dice [0.5897, 0.7971] 
+2024-09-07 20:14:51.187912: Epoch time: 245.04 s 
+2024-09-07 20:14:51.187952: Yayy! New best EMA pseudo Dice: 0.6568 
+2024-09-07 20:14:55.130782:  
+2024-09-07 20:14:55.131001: Epoch 33 
+2024-09-07 20:14:55.131115: Current learning rate: 0.0097 
+2024-09-07 20:19:00.224738: train_loss -0.7404 
+2024-09-07 20:19:00.224873: val_loss -0.6632 
+2024-09-07 20:19:00.224923: Pseudo dice [0.6204, 0.8189] 
+2024-09-07 20:19:00.224974: Epoch time: 245.1 s 
+2024-09-07 20:19:00.225014: Yayy! New best EMA pseudo Dice: 0.6631 
+2024-09-07 20:19:04.097992:  
+2024-09-07 20:19:04.098210: Epoch 34 
+2024-09-07 20:19:04.098293: Current learning rate: 0.00969 
+2024-09-07 20:23:08.947665: train_loss -0.7503 
+2024-09-07 20:23:08.947800: val_loss -0.6305 
+2024-09-07 20:23:08.947867: Pseudo dice [0.582, 0.7955] 
+2024-09-07 20:23:08.947918: Epoch time: 244.85 s 
+2024-09-07 20:23:08.947958: Yayy! New best EMA pseudo Dice: 0.6657 
+2024-09-07 20:23:12.854159:  
+2024-09-07 20:23:12.854377: Epoch 35 
+2024-09-07 20:23:12.854499: Current learning rate: 0.00968 
+2024-09-07 20:27:17.760926: train_loss -0.728 
+2024-09-07 20:27:17.761063: val_loss -0.6526 
+2024-09-07 20:27:17.761114: Pseudo dice [0.6079, 0.7952] 
+2024-09-07 20:27:17.761164: Epoch time: 244.91 s 
+2024-09-07 20:27:17.761205: Yayy! New best EMA pseudo Dice: 0.6692 
+2024-09-07 20:27:21.805653:  
+2024-09-07 20:27:21.805834: Epoch 36 
+2024-09-07 20:27:21.805967: Current learning rate: 0.00968 
+2024-09-07 20:31:26.716051: train_loss -0.7095 
+2024-09-07 20:31:26.716192: val_loss -0.6033 
+2024-09-07 20:31:26.716246: Pseudo dice [0.5608, 0.783] 
+2024-09-07 20:31:26.716297: Epoch time: 244.91 s 
+2024-09-07 20:31:26.716337: Yayy! New best EMA pseudo Dice: 0.6695 
+2024-09-07 20:31:30.655915:  
+2024-09-07 20:31:30.656086: Epoch 37 
+2024-09-07 20:31:30.656173: Current learning rate: 0.00967 
+2024-09-07 20:35:35.526055: train_loss -0.7293 
+2024-09-07 20:35:35.526189: val_loss -0.6446 
+2024-09-07 20:35:35.526276: Pseudo dice [0.5863, 0.8048] 
+2024-09-07 20:35:35.526362: Epoch time: 244.87 s 
+2024-09-07 20:35:35.526404: Yayy! New best EMA pseudo Dice: 0.6721 
+2024-09-07 20:35:39.428981:  
+2024-09-07 20:35:39.429135: Epoch 38 
+2024-09-07 20:35:39.429214: Current learning rate: 0.00966 
+2024-09-07 20:39:44.459129: train_loss -0.7232 
+2024-09-07 20:39:44.459300: val_loss -0.649 
+2024-09-07 20:39:44.459363: Pseudo dice [0.6049, 0.8095] 
+2024-09-07 20:39:44.459435: Epoch time: 245.03 s 
+2024-09-07 20:39:44.459486: Yayy! New best EMA pseudo Dice: 0.6756 
+2024-09-07 20:39:48.567420:  
+2024-09-07 20:39:48.567560: Epoch 39 
+2024-09-07 20:39:48.567696: Current learning rate: 0.00965 
+2024-09-07 20:43:53.510856: train_loss -0.7211 
+2024-09-07 20:43:53.510995: val_loss -0.6197 
+2024-09-07 20:43:53.511045: Pseudo dice [0.5794, 0.7885] 
+2024-09-07 20:43:53.511096: Epoch time: 244.95 s 
+2024-09-07 20:43:53.511136: Yayy! New best EMA pseudo Dice: 0.6764 
+2024-09-07 20:43:57.460262:  
+2024-09-07 20:43:57.460448: Epoch 40 
+2024-09-07 20:43:57.460525: Current learning rate: 0.00964 
+2024-09-07 20:48:02.598067: train_loss -0.7247 
+2024-09-07 20:48:02.598204: val_loss -0.6533 
+2024-09-07 20:48:02.598258: Pseudo dice [0.6263, 0.809] 
+2024-09-07 20:48:02.598311: Epoch time: 245.14 s 
+2024-09-07 20:48:02.598391: Yayy! New best EMA pseudo Dice: 0.6806 
+2024-09-07 20:48:06.635416:  
+2024-09-07 20:48:06.635603: Epoch 41 
+2024-09-07 20:48:06.635686: Current learning rate: 0.00963 
+2024-09-07 20:52:11.801107: train_loss -0.7343 
+2024-09-07 20:52:11.801246: val_loss -0.6409 
+2024-09-07 20:52:11.801296: Pseudo dice [0.5686, 0.8139] 
+2024-09-07 20:52:11.801345: Epoch time: 245.17 s 
+2024-09-07 20:52:11.801385: Yayy! New best EMA pseudo Dice: 0.6816 
+2024-09-07 20:52:15.656214:  
+2024-09-07 20:52:15.656399: Epoch 42 
+2024-09-07 20:52:15.656479: Current learning rate: 0.00962 
+2024-09-07 20:56:20.531720: train_loss -0.7307 
+2024-09-07 20:56:20.531886: val_loss -0.6048 
+2024-09-07 20:56:20.531967: Pseudo dice [0.5422, 0.7849] 
+2024-09-07 20:56:20.532022: Epoch time: 244.88 s 
+2024-09-07 20:56:21.459330:  
+2024-09-07 20:56:21.459484: Epoch 43 
+2024-09-07 20:56:21.459565: Current learning rate: 0.00961 
+2024-09-07 21:00:26.236133: train_loss -0.7277 
+2024-09-07 21:00:26.236348: val_loss -0.6437 
+2024-09-07 21:00:26.236504: Pseudo dice [0.5923, 0.7928] 
+2024-09-07 21:00:26.236593: Epoch time: 244.78 s 
+2024-09-07 21:00:27.308025:  
+2024-09-07 21:00:27.308296: Epoch 44 
+2024-09-07 21:00:27.308394: Current learning rate: 0.0096 
+2024-09-07 21:04:32.281349: train_loss -0.7351 
+2024-09-07 21:04:32.281486: val_loss -0.6626 
+2024-09-07 21:04:32.281536: Pseudo dice [0.5998, 0.8286] 
+2024-09-07 21:04:32.281588: Epoch time: 244.98 s 
+2024-09-07 21:04:32.281628: Yayy! New best EMA pseudo Dice: 0.6844 
+2024-09-07 21:04:36.151026:  
+2024-09-07 21:04:36.151209: Epoch 45 
+2024-09-07 21:04:36.151329: Current learning rate: 0.00959 
+2024-09-07 21:08:41.029703: train_loss -0.7495 
+2024-09-07 21:08:41.029848: val_loss -0.6322 
+2024-09-07 21:08:41.029898: Pseudo dice [0.5578, 0.8085] 
+2024-09-07 21:08:41.029949: Epoch time: 244.88 s 
+2024-09-07 21:08:41.977710:  
+2024-09-07 21:08:41.977915: Epoch 46 
+2024-09-07 21:08:41.978001: Current learning rate: 0.00959 
+2024-09-07 21:12:46.985874: train_loss -0.7241 
+2024-09-07 21:12:46.986035: val_loss -0.6769 
+2024-09-07 21:12:46.986085: Pseudo dice [0.6278, 0.8179] 
+2024-09-07 21:12:46.986136: Epoch time: 245.01 s 
+2024-09-07 21:12:46.986175: Yayy! New best EMA pseudo Dice: 0.6881 
+2024-09-07 21:12:50.830991:  
+2024-09-07 21:12:50.831211: Epoch 47 
+2024-09-07 21:12:50.831290: Current learning rate: 0.00958 
+2024-09-07 21:16:56.065342: train_loss -0.7163 
+2024-09-07 21:16:56.065479: val_loss -0.6672 
+2024-09-07 21:16:56.065528: Pseudo dice [0.6302, 0.8326] 
+2024-09-07 21:16:56.065580: Epoch time: 245.24 s 
+2024-09-07 21:16:56.065621: Yayy! New best EMA pseudo Dice: 0.6925 
+2024-09-07 21:16:59.936123:  
+2024-09-07 21:16:59.936310: Epoch 48 
+2024-09-07 21:16:59.936388: Current learning rate: 0.00957 
+2024-09-07 21:21:05.033443: train_loss -0.754 
+2024-09-07 21:21:05.033587: val_loss -0.6884 
+2024-09-07 21:21:05.033635: Pseudo dice [0.6652, 0.8424] 
+2024-09-07 21:21:05.033688: Epoch time: 245.1 s 
+2024-09-07 21:21:05.033727: Yayy! New best EMA pseudo Dice: 0.6986 
+2024-09-07 21:21:08.888670:  
+2024-09-07 21:21:08.888860: Epoch 49 
+2024-09-07 21:21:08.888958: Current learning rate: 0.00956 
+2024-09-07 21:25:13.664640: train_loss -0.7704 
+2024-09-07 21:25:13.664779: val_loss -0.6556 
+2024-09-07 21:25:13.664883: Pseudo dice [0.5911, 0.8266] 
+2024-09-07 21:25:13.664934: Epoch time: 244.78 s 
+2024-09-07 21:25:14.734919: Yayy! New best EMA pseudo Dice: 0.6996 
+2024-09-07 21:25:18.602323:  
+2024-09-07 21:25:18.602510: Epoch 50 
+2024-09-07 21:25:18.602609: Current learning rate: 0.00955 
+2024-09-07 21:29:25.004969: train_loss -0.7487 
+2024-09-07 21:29:25.005113: val_loss -0.6642 
+2024-09-07 21:29:25.005192: Pseudo dice [0.598, 0.834] 
+2024-09-07 21:29:25.005274: Epoch time: 246.4 s 
+2024-09-07 21:29:25.005318: Yayy! New best EMA pseudo Dice: 0.7013 
+2024-09-07 21:29:28.886656:  
+2024-09-07 21:29:28.886878: Epoch 51 
+2024-09-07 21:29:28.886961: Current learning rate: 0.00954 
+2024-09-07 21:33:34.474407: train_loss -0.7236 
+2024-09-07 21:33:34.474551: val_loss -0.6188 
+2024-09-07 21:33:34.474607: Pseudo dice [0.5817, 0.8057] 
+2024-09-07 21:33:34.474662: Epoch time: 245.59 s 
+2024-09-07 21:33:35.554044:  
+2024-09-07 21:33:35.554223: Epoch 52 
+2024-09-07 21:33:35.554306: Current learning rate: 0.00953 
+2024-09-07 21:37:41.490440: train_loss -0.7121 
+2024-09-07 21:37:41.490593: val_loss -0.6549 
+2024-09-07 21:37:41.490652: Pseudo dice [0.6135, 0.8146] 
+2024-09-07 21:37:41.490708: Epoch time: 245.94 s 
+2024-09-07 21:37:41.490751: Yayy! New best EMA pseudo Dice: 0.7019 
+2024-09-07 21:37:45.331747:  
+2024-09-07 21:37:45.331935: Epoch 53 
+2024-09-07 21:37:45.332026: Current learning rate: 0.00952 
+2024-09-07 21:41:50.413466: train_loss -0.7362 
+2024-09-07 21:41:50.413615: val_loss -0.6359 
+2024-09-07 21:41:50.413671: Pseudo dice [0.5415, 0.8285] 
+2024-09-07 21:41:50.413765: Epoch time: 245.08 s 
+2024-09-07 21:41:51.351701:  
+2024-09-07 21:41:51.351869: Epoch 54 
+2024-09-07 21:41:51.351958: Current learning rate: 0.00951 
+2024-09-07 21:45:56.519053: train_loss -0.7544 
+2024-09-07 21:45:56.519242: val_loss -0.6482 
+2024-09-07 21:45:56.519299: Pseudo dice [0.6006, 0.8095] 
+2024-09-07 21:45:56.519357: Epoch time: 245.17 s 
+2024-09-07 21:45:57.469151:  
+2024-09-07 21:45:57.469311: Epoch 55 
+2024-09-07 21:45:57.469425: Current learning rate: 0.0095 
+2024-09-07 21:50:04.760624: train_loss -0.7464 
+2024-09-07 21:50:04.760810: val_loss -0.6384 
+2024-09-07 21:50:04.760875: Pseudo dice [0.5476, 0.8246] 
+2024-09-07 21:50:04.760936: Epoch time: 247.29 s 
+2024-09-07 21:50:05.903128:  
+2024-09-07 21:50:05.903311: Epoch 56 
+2024-09-07 21:50:05.903397: Current learning rate: 0.00949 
+2024-09-07 21:54:11.168737: train_loss -0.7553 
+2024-09-07 21:54:11.168881: val_loss -0.6787 
+2024-09-07 21:54:11.168937: Pseudo dice [0.6352, 0.8413] 
+2024-09-07 21:54:11.168992: Epoch time: 245.27 s 
+2024-09-07 21:54:11.169035: Yayy! New best EMA pseudo Dice: 0.7031 
+2024-09-07 21:54:15.087348:  
+2024-09-07 21:54:15.087595: Epoch 57 
+2024-09-07 21:54:15.087683: Current learning rate: 0.00949 
+2024-09-07 21:58:20.253830: train_loss -0.7549 
+2024-09-07 21:58:20.253983: val_loss -0.645 
+2024-09-07 21:58:20.254041: Pseudo dice [0.5809, 0.8232] 
+2024-09-07 21:58:20.254103: Epoch time: 245.17 s 
+2024-09-07 21:58:21.417927:  
+2024-09-07 21:58:21.418146: Epoch 58 
+2024-09-07 21:58:21.418237: Current learning rate: 0.00948 
+2024-09-07 22:02:26.706970: train_loss -0.7529 
+2024-09-07 22:02:26.707113: val_loss -0.6652 
+2024-09-07 22:02:26.707167: Pseudo dice [0.5707, 0.8442] 
+2024-09-07 22:02:26.707222: Epoch time: 245.29 s 
+2024-09-07 22:02:26.707266: Yayy! New best EMA pseudo Dice: 0.7034 
+2024-09-07 22:02:30.610629:  
+2024-09-07 22:02:30.610796: Epoch 59 
+2024-09-07 22:02:30.610880: Current learning rate: 0.00947 
+2024-09-07 22:06:44.243155: train_loss -0.7362 
+2024-09-07 22:06:44.243353: val_loss -0.6358 
+2024-09-07 22:06:44.243411: Pseudo dice [0.5786, 0.8234] 
+2024-09-07 22:06:44.243467: Epoch time: 253.63 s 
+2024-09-07 22:06:45.238169:  
+2024-09-07 22:06:45.238324: Epoch 60 
+2024-09-07 22:06:45.238409: Current learning rate: 0.00946 
+2024-09-07 22:10:50.655836: train_loss -0.7574 
+2024-09-07 22:10:50.655975: val_loss -0.6504 
+2024-09-07 22:10:50.656153: Pseudo dice [0.564, 0.8178] 
+2024-09-07 22:10:50.656211: Epoch time: 245.42 s 
+2024-09-07 22:10:51.631446:  
+2024-09-07 22:10:51.631626: Epoch 61 
+2024-09-07 22:10:51.631735: Current learning rate: 0.00945 
+2024-09-07 22:14:56.760443: train_loss -0.7627 
+2024-09-07 22:14:56.760607: val_loss -0.6703 
+2024-09-07 22:14:56.760673: Pseudo dice [0.59, 0.8423] 
+2024-09-07 22:14:56.760735: Epoch time: 245.13 s 
+2024-09-07 22:14:57.868151:  
+2024-09-07 22:14:57.868336: Epoch 62 
+2024-09-07 22:14:57.868439: Current learning rate: 0.00944 
+2024-09-07 22:19:03.097844: train_loss -0.7628 
+2024-09-07 22:19:03.097993: val_loss -0.6661 
+2024-09-07 22:19:03.098049: Pseudo dice [0.5999, 0.839] 
+2024-09-07 22:19:03.098105: Epoch time: 245.23 s 
+2024-09-07 22:19:03.098149: Yayy! New best EMA pseudo Dice: 0.705 
+2024-09-07 22:19:07.012550:  
+2024-09-07 22:19:07.012725: Epoch 63 
+2024-09-07 22:19:07.012819: Current learning rate: 0.00943 
+2024-09-07 22:23:33.758255: train_loss -0.7658 
+2024-09-07 22:23:33.758401: val_loss -0.6686 
+2024-09-07 22:23:33.758457: Pseudo dice [0.6217, 0.8407] 
+2024-09-07 22:23:33.758514: Epoch time: 266.75 s 
+2024-09-07 22:23:33.758601: Yayy! New best EMA pseudo Dice: 0.7076 
+2024-09-07 22:23:37.689618:  
+2024-09-07 22:23:37.689835: Epoch 64 
+2024-09-07 22:23:37.689967: Current learning rate: 0.00942 
+2024-09-07 22:27:45.424895: train_loss -0.7727 
+2024-09-07 22:27:45.425039: val_loss -0.6512 
+2024-09-07 22:27:45.425114: Pseudo dice [0.5601, 0.8418] 
+2024-09-07 22:27:45.425222: Epoch time: 247.74 s 
+2024-09-07 22:27:46.398525:  
+2024-09-07 22:27:46.398689: Epoch 65 
+2024-09-07 22:27:46.398827: Current learning rate: 0.00941 
+2024-09-07 22:32:07.774130: train_loss -0.756 
+2024-09-07 22:32:07.774275: val_loss -0.6674 
+2024-09-07 22:32:07.774331: Pseudo dice [0.616, 0.8154] 
+2024-09-07 22:32:07.774386: Epoch time: 261.38 s 
+2024-09-07 22:32:07.774431: Yayy! New best EMA pseudo Dice: 0.7078 
+2024-09-07 22:32:11.672509:  
+2024-09-07 22:32:11.672688: Epoch 66 
+2024-09-07 22:32:11.672781: Current learning rate: 0.0094 
+2024-09-07 22:36:28.882227: train_loss -0.7561 
+2024-09-07 22:36:28.882373: val_loss -0.6775 
+2024-09-07 22:36:28.882428: Pseudo dice [0.6203, 0.8305] 
+2024-09-07 22:36:28.882485: Epoch time: 257.21 s 
+2024-09-07 22:36:28.882530: Yayy! New best EMA pseudo Dice: 0.7096 
+2024-09-07 22:36:32.771435:  
+2024-09-07 22:36:32.771619: Epoch 67 
+2024-09-07 22:36:32.771705: Current learning rate: 0.00939 
+2024-09-07 22:40:37.789293: train_loss -0.7679 
+2024-09-07 22:40:37.789453: val_loss -0.6795 
+2024-09-07 22:40:37.789507: Pseudo dice [0.6286, 0.8536] 
+2024-09-07 22:40:37.789563: Epoch time: 245.02 s 
+2024-09-07 22:40:37.789608: Yayy! New best EMA pseudo Dice: 0.7127 
+2024-09-07 22:40:41.699847:  
+2024-09-07 22:40:41.699999: Epoch 68 
+2024-09-07 22:40:41.700083: Current learning rate: 0.00939 
+2024-09-07 22:44:46.899107: train_loss -0.7751 
+2024-09-07 22:44:46.899255: val_loss -0.6886 
+2024-09-07 22:44:46.899312: Pseudo dice [0.6162, 0.8345] 
+2024-09-07 22:44:46.899370: Epoch time: 245.2 s 
+2024-09-07 22:44:46.899415: Yayy! New best EMA pseudo Dice: 0.714 
+2024-09-07 22:44:50.788670:  
+2024-09-07 22:44:50.788825: Epoch 69 
+2024-09-07 22:44:50.788927: Current learning rate: 0.00938 
+2024-09-07 22:48:56.170267: train_loss -0.7674 
+2024-09-07 22:48:56.170444: val_loss -0.6784 
+2024-09-07 22:48:56.170501: Pseudo dice [0.6148, 0.8327] 
+2024-09-07 22:48:56.170557: Epoch time: 245.38 s 
+2024-09-07 22:48:56.170603: Yayy! New best EMA pseudo Dice: 0.715 
+2024-09-07 22:49:00.106479:  
+2024-09-07 22:49:00.106691: Epoch 70 
+2024-09-07 22:49:00.106780: Current learning rate: 0.00937 
+2024-09-07 22:53:06.051048: train_loss -0.7745 
+2024-09-07 22:53:06.051193: val_loss -0.6599 
+2024-09-07 22:53:06.051249: Pseudo dice [0.6004, 0.8259] 
+2024-09-07 22:53:06.051304: Epoch time: 245.95 s 
+2024-09-07 22:53:07.031624:  
+2024-09-07 22:53:07.031797: Epoch 71 
+2024-09-07 22:53:07.031901: Current learning rate: 0.00936 
+2024-09-07 22:57:18.196497: train_loss -0.7684 
+2024-09-07 22:57:18.196683: val_loss -0.6745 
+2024-09-07 22:57:18.196770: Pseudo dice [0.6173, 0.8306] 
+2024-09-07 22:57:18.196844: Epoch time: 251.17 s 
+2024-09-07 22:57:18.196889: Yayy! New best EMA pseudo Dice: 0.7157 
+2024-09-07 22:57:22.111226:  
+2024-09-07 22:57:22.111373: Epoch 72 
+2024-09-07 22:57:22.111459: Current learning rate: 0.00935 
+2024-09-07 23:01:30.772052: train_loss -0.7734 
+2024-09-07 23:01:30.772207: val_loss -0.6801 
+2024-09-07 23:01:30.772291: Pseudo dice [0.6469, 0.8281] 
+2024-09-07 23:01:30.772369: Epoch time: 248.66 s 
+2024-09-07 23:01:30.772414: Yayy! New best EMA pseudo Dice: 0.7179 
+2024-09-07 23:01:34.685715:  
+2024-09-07 23:01:34.685852: Epoch 73 
+2024-09-07 23:01:34.685934: Current learning rate: 0.00934 
+2024-09-07 23:05:40.235561: train_loss -0.7693 
+2024-09-07 23:05:40.235712: val_loss -0.6639 
+2024-09-07 23:05:40.235768: Pseudo dice [0.6055, 0.8319] 
+2024-09-07 23:05:40.235831: Epoch time: 245.55 s 
+2024-09-07 23:05:40.235877: Yayy! New best EMA pseudo Dice: 0.718 
+2024-09-07 23:05:44.140059:  
+2024-09-07 23:05:44.140226: Epoch 74 
+2024-09-07 23:05:44.140313: Current learning rate: 0.00933 
+2024-09-07 23:09:57.137605: train_loss -0.767 
+2024-09-07 23:09:57.137788: val_loss -0.6675 
+2024-09-07 23:09:57.138058: Pseudo dice [0.5837, 0.8381] 
+2024-09-07 23:09:57.138114: Epoch time: 253.0 s 
+2024-09-07 23:09:58.257495:  
+2024-09-07 23:09:58.257791: Epoch 75 
+2024-09-07 23:09:58.257918: Current learning rate: 0.00932 
+2024-09-07 23:14:03.629683: train_loss -0.7777 
+2024-09-07 23:14:03.629820: val_loss -0.664 
+2024-09-07 23:14:03.629876: Pseudo dice [0.5994, 0.8306] 
+2024-09-07 23:14:03.629985: Epoch time: 245.37 s 
+2024-09-07 23:14:04.614110:  
+2024-09-07 23:14:04.614305: Epoch 76 
+2024-09-07 23:14:04.614412: Current learning rate: 0.00931 
+2024-09-07 23:18:09.926061: train_loss -0.7719 
+2024-09-07 23:18:09.926219: val_loss -0.6646 
+2024-09-07 23:18:09.926278: Pseudo dice [0.6082, 0.8444] 
+2024-09-07 23:18:09.926333: Epoch time: 245.31 s 
+2024-09-07 23:18:10.897811:  
+2024-09-07 23:18:10.898037: Epoch 77 
+2024-09-07 23:18:10.898116: Current learning rate: 0.0093 
+2024-09-07 23:22:16.108215: train_loss -0.769 
+2024-09-07 23:22:16.108356: val_loss -0.6652 
+2024-09-07 23:22:16.108526: Pseudo dice [0.5929, 0.8301] 
+2024-09-07 23:22:16.108689: Epoch time: 245.21 s 
+2024-09-07 23:22:17.098964:  
+2024-09-07 23:22:17.099177: Epoch 78 
+2024-09-07 23:22:17.099268: Current learning rate: 0.0093 
+2024-09-07 23:26:22.265468: train_loss -0.756 
+2024-09-07 23:26:22.265608: val_loss -0.6772 
+2024-09-07 23:26:22.265662: Pseudo dice [0.6437, 0.8314] 
+2024-09-07 23:26:22.265718: Epoch time: 245.17 s 
+2024-09-07 23:26:22.265761: Yayy! New best EMA pseudo Dice: 0.7193 
+2024-09-07 23:26:26.546798:  
+2024-09-07 23:26:26.547070: Epoch 79 
+2024-09-07 23:26:26.547180: Current learning rate: 0.00929 
+2024-09-07 23:30:31.611424: train_loss -0.769 
+2024-09-07 23:30:31.611617: val_loss -0.6982 
+2024-09-07 23:30:31.611674: Pseudo dice [0.6434, 0.8481] 
+2024-09-07 23:30:31.611749: Epoch time: 245.07 s 
+2024-09-07 23:30:31.611824: Yayy! New best EMA pseudo Dice: 0.722 
+2024-09-07 23:30:35.520456:  
+2024-09-07 23:30:35.520705: Epoch 80 
+2024-09-07 23:30:35.520793: Current learning rate: 0.00928 
+2024-09-07 23:34:40.648671: train_loss -0.7627 
+2024-09-07 23:34:40.648831: val_loss -0.6726 
+2024-09-07 23:34:40.648889: Pseudo dice [0.6109, 0.8419] 
+2024-09-07 23:34:40.648945: Epoch time: 245.13 s 
+2024-09-07 23:34:40.648991: Yayy! New best EMA pseudo Dice: 0.7224 
+2024-09-07 23:34:44.520791:  
+2024-09-07 23:34:44.521016: Epoch 81 
+2024-09-07 23:34:44.521103: Current learning rate: 0.00927 
+2024-09-07 23:38:49.615541: train_loss -0.7575 
+2024-09-07 23:38:49.615685: val_loss -0.6616 
+2024-09-07 23:38:49.615740: Pseudo dice [0.6012, 0.8428] 
+2024-09-07 23:38:49.615794: Epoch time: 245.1 s 
+2024-09-07 23:38:50.733650:  
+2024-09-07 23:38:50.733837: Epoch 82 
+2024-09-07 23:38:50.733921: Current learning rate: 0.00926 
+2024-09-07 23:42:55.830808: train_loss -0.7518 
+2024-09-07 23:42:55.830954: val_loss -0.6303 
+2024-09-07 23:42:55.831964: Pseudo dice [0.5881, 0.8139] 
+2024-09-07 23:42:55.832025: Epoch time: 245.1 s 
+2024-09-07 23:42:56.800139:  
+2024-09-07 23:42:56.800307: Epoch 83 
+2024-09-07 23:42:56.800392: Current learning rate: 0.00925 
+2024-09-07 23:47:01.655256: train_loss -0.7658 
+2024-09-07 23:47:01.655416: val_loss -0.6628 
+2024-09-07 23:47:01.655471: Pseudo dice [0.6093, 0.8366] 
+2024-09-07 23:47:01.655526: Epoch time: 244.86 s 
+2024-09-07 23:47:02.590177:  
+2024-09-07 23:47:02.590369: Epoch 84 
+2024-09-07 23:47:02.590455: Current learning rate: 0.00924 
+2024-09-07 23:51:07.232006: train_loss -0.7257 
+2024-09-07 23:51:07.232148: val_loss -0.6647 
+2024-09-07 23:51:07.232203: Pseudo dice [0.6072, 0.8359] 
+2024-09-07 23:51:07.232257: Epoch time: 244.64 s 
+2024-09-07 23:51:08.167727:  
+2024-09-07 23:51:08.167967: Epoch 85 
+2024-09-07 23:51:08.168054: Current learning rate: 0.00923 
+2024-09-07 23:55:12.482332: train_loss -0.7561 
+2024-09-07 23:55:12.482481: val_loss -0.6711 
+2024-09-07 23:55:12.482536: Pseudo dice [0.6226, 0.8314] 
+2024-09-07 23:55:12.482591: Epoch time: 244.32 s 
+2024-09-07 23:55:13.497173:  
+2024-09-07 23:55:13.497384: Epoch 86 
+2024-09-07 23:55:13.497466: Current learning rate: 0.00922 
+2024-09-07 23:59:17.743955: train_loss -0.7611 
+2024-09-07 23:59:17.744120: val_loss -0.6481 
+2024-09-07 23:59:17.744197: Pseudo dice [0.6086, 0.8329] 
+2024-09-07 23:59:17.744277: Epoch time: 244.25 s 
+2024-09-07 23:59:18.815996:  
+2024-09-07 23:59:18.816197: Epoch 87 
+2024-09-07 23:59:18.816277: Current learning rate: 0.00921 
+2024-09-08 00:03:23.139540: train_loss -0.7608 
+2024-09-08 00:03:23.139727: val_loss -0.668 
+2024-09-08 00:03:23.139794: Pseudo dice [0.6127, 0.8311] 
+2024-09-08 00:03:23.139874: Epoch time: 244.33 s 
+2024-09-08 00:03:24.068576:  
+2024-09-08 00:03:24.068808: Epoch 88 
+2024-09-08 00:03:24.068893: Current learning rate: 0.0092 
+2024-09-08 00:07:28.447504: train_loss -0.7701 
+2024-09-08 00:07:28.447647: val_loss -0.6871 
+2024-09-08 00:07:28.447704: Pseudo dice [0.6392, 0.8177] 
+2024-09-08 00:07:28.447758: Epoch time: 244.38 s 
+2024-09-08 00:07:29.393075:  
+2024-09-08 00:07:29.393291: Epoch 89 
+2024-09-08 00:07:29.393377: Current learning rate: 0.0092 
+2024-09-08 00:11:33.803311: train_loss -0.7679 
+2024-09-08 00:11:33.803475: val_loss -0.6692 
+2024-09-08 00:11:33.803533: Pseudo dice [0.5937, 0.8332] 
+2024-09-08 00:11:33.803588: Epoch time: 244.41 s 
+2024-09-08 00:11:34.731672:  
+2024-09-08 00:11:34.731854: Epoch 90 
+2024-09-08 00:11:34.731982: Current learning rate: 0.00919 
+2024-09-08 00:15:38.938957: train_loss -0.7778 
+2024-09-08 00:15:38.939115: val_loss -0.6754 
+2024-09-08 00:15:38.939170: Pseudo dice [0.6149, 0.8429] 
+2024-09-08 00:15:38.939224: Epoch time: 244.21 s 
+2024-09-08 00:15:39.885583:  
+2024-09-08 00:15:39.885751: Epoch 91 
+2024-09-08 00:15:39.885835: Current learning rate: 0.00918 
+2024-09-08 00:19:44.213347: train_loss -0.7535 
+2024-09-08 00:19:44.213475: val_loss -0.6546 
+2024-09-08 00:19:44.213530: Pseudo dice [0.5848, 0.8313] 
+2024-09-08 00:19:44.213585: Epoch time: 244.33 s 
+2024-09-08 00:19:45.149274:  
+2024-09-08 00:19:45.149418: Epoch 92 
+2024-09-08 00:19:45.149505: Current learning rate: 0.00917 
+2024-09-08 00:23:49.597076: train_loss -0.7465 
+2024-09-08 00:23:49.597221: val_loss -0.6713 
+2024-09-08 00:23:49.597276: Pseudo dice [0.6242, 0.8306] 
+2024-09-08 00:23:49.597332: Epoch time: 244.45 s 
+2024-09-08 00:23:50.527409:  
+2024-09-08 00:23:50.527615: Epoch 93 
+2024-09-08 00:23:50.527702: Current learning rate: 0.00916 
+2024-09-08 00:27:55.168041: train_loss -0.7668 
+2024-09-08 00:27:55.168176: val_loss -0.6795 
+2024-09-08 00:27:55.168231: Pseudo dice [0.6138, 0.8491] 
+2024-09-08 00:27:55.168285: Epoch time: 244.64 s 
+2024-09-08 00:27:56.096160:  
+2024-09-08 00:27:56.096339: Epoch 94 
+2024-09-08 00:27:56.096426: Current learning rate: 0.00915 
+2024-09-08 00:32:00.493566: train_loss -0.7678 
+2024-09-08 00:32:00.493720: val_loss -0.6791 
+2024-09-08 00:32:00.493777: Pseudo dice [0.6265, 0.844] 
+2024-09-08 00:32:00.493832: Epoch time: 244.4 s 
+2024-09-08 00:32:00.493876: Yayy! New best EMA pseudo Dice: 0.7235 
+2024-09-08 00:32:04.337045:  
+2024-09-08 00:32:04.337268: Epoch 95 
+2024-09-08 00:32:04.337355: Current learning rate: 0.00914 
+2024-09-08 00:36:08.906792: train_loss -0.7687 
+2024-09-08 00:36:08.906934: val_loss -0.6762 
+2024-09-08 00:36:08.907038: Pseudo dice [0.619, 0.8441] 
+2024-09-08 00:36:08.907094: Epoch time: 244.57 s 
+2024-09-08 00:36:08.907140: Yayy! New best EMA pseudo Dice: 0.7243 
+2024-09-08 00:36:12.759151:  
+2024-09-08 00:36:12.759304: Epoch 96 
+2024-09-08 00:36:12.759420: Current learning rate: 0.00913 
+2024-09-08 00:40:17.435953: train_loss -0.7763 
+2024-09-08 00:40:17.436130: val_loss -0.6828 
+2024-09-08 00:40:17.436188: Pseudo dice [0.6213, 0.8368] 
+2024-09-08 00:40:17.436244: Epoch time: 244.68 s 
+2024-09-08 00:40:17.436287: Yayy! New best EMA pseudo Dice: 0.7248 
+2024-09-08 00:40:22.470159:  
+2024-09-08 00:40:22.470389: Epoch 97 
+2024-09-08 00:40:22.470527: Current learning rate: 0.00912 
+2024-09-08 00:44:27.179097: train_loss -0.7787 
+2024-09-08 00:44:27.179252: val_loss -0.702 
+2024-09-08 00:44:27.179302: Pseudo dice [0.6548, 0.8443] 
+2024-09-08 00:44:27.179353: Epoch time: 244.71 s 
+2024-09-08 00:44:27.179393: Yayy! New best EMA pseudo Dice: 0.7273 
+2024-09-08 00:44:31.042286:  
+2024-09-08 00:44:31.042454: Epoch 98 
+2024-09-08 00:44:31.042544: Current learning rate: 0.00911 
+2024-09-08 00:48:35.710879: train_loss -0.7822 
+2024-09-08 00:48:35.711017: val_loss -0.6908 
+2024-09-08 00:48:35.711068: Pseudo dice [0.6629, 0.8365] 
+2024-09-08 00:48:35.711118: Epoch time: 244.67 s 
+2024-09-08 00:48:35.711157: Yayy! New best EMA pseudo Dice: 0.7295 
+2024-09-08 00:48:39.587420:  
+2024-09-08 00:48:39.587662: Epoch 99 
+2024-09-08 00:48:39.587746: Current learning rate: 0.0091 
+2024-09-08 00:52:44.330945: train_loss -0.7804 
+2024-09-08 00:52:44.331083: val_loss -0.6843 
+2024-09-08 00:52:44.331136: Pseudo dice [0.6487, 0.8418] 
+2024-09-08 00:52:44.331187: Epoch time: 244.75 s 
+2024-09-08 00:52:47.252504: Yayy! New best EMA pseudo Dice: 0.7311 
+2024-09-08 00:52:51.366953:  
+2024-09-08 00:52:51.367140: Epoch 100 
+2024-09-08 00:52:51.367223: Current learning rate: 0.0091 
+2024-09-08 00:56:55.846770: train_loss -0.7759 
+2024-09-08 00:56:55.846905: val_loss -0.6525 
+2024-09-08 00:56:55.846997: Pseudo dice [0.6334, 0.8515] 
+2024-09-08 00:56:55.847049: Epoch time: 244.48 s 
+2024-09-08 00:56:55.847089: Yayy! New best EMA pseudo Dice: 0.7322 
+2024-09-08 00:56:59.722195:  
+2024-09-08 00:56:59.722385: Epoch 101 
+2024-09-08 00:56:59.722466: Current learning rate: 0.00909 
+2024-09-08 01:01:04.490941: train_loss -0.7927 
+2024-09-08 01:01:04.491080: val_loss -0.6465 
+2024-09-08 01:01:04.491129: Pseudo dice [0.5605, 0.8504] 
+2024-09-08 01:01:04.491179: Epoch time: 244.77 s 
+2024-09-08 01:01:05.450220:  
+2024-09-08 01:01:05.450436: Epoch 102 
+2024-09-08 01:01:05.450521: Current learning rate: 0.00908 
+2024-09-08 01:05:10.016430: train_loss -0.7847 
+2024-09-08 01:05:10.016666: val_loss -0.6483 
+2024-09-08 01:05:10.016738: Pseudo dice [0.6086, 0.8283] 
+2024-09-08 01:05:10.016889: Epoch time: 244.57 s 
+2024-09-08 01:05:11.154508:  
+2024-09-08 01:05:11.154716: Epoch 103 
+2024-09-08 01:05:11.154794: Current learning rate: 0.00907 
+2024-09-08 01:09:15.717995: train_loss -0.7841 
+2024-09-08 01:09:15.718144: val_loss -0.6542 
+2024-09-08 01:09:15.718194: Pseudo dice [0.5962, 0.8274] 
+2024-09-08 01:09:15.718244: Epoch time: 244.57 s 
+2024-09-08 01:09:16.660845:  
+2024-09-08 01:09:16.661046: Epoch 104 
+2024-09-08 01:09:16.661147: Current learning rate: 0.00906 
+2024-09-08 01:13:21.341685: train_loss -0.7866 
+2024-09-08 01:13:21.341856: val_loss -0.6523 
+2024-09-08 01:13:21.341906: Pseudo dice [0.5839, 0.8347] 
+2024-09-08 01:13:21.341960: Epoch time: 244.68 s 
+2024-09-08 01:13:22.301759:  
+2024-09-08 01:13:22.302005: Epoch 105 
+2024-09-08 01:13:22.302092: Current learning rate: 0.00905 
+2024-09-08 01:17:27.256997: train_loss -0.7801 
+2024-09-08 01:17:27.257131: val_loss -0.6664 
+2024-09-08 01:17:27.257181: Pseudo dice [0.6121, 0.8423] 
+2024-09-08 01:17:27.257234: Epoch time: 244.96 s 
+2024-09-08 01:17:28.212882:  
+2024-09-08 01:17:28.213072: Epoch 106 
+2024-09-08 01:17:28.213151: Current learning rate: 0.00904 
+2024-09-08 01:21:32.964603: train_loss -0.7683 
+2024-09-08 01:21:32.964742: val_loss -0.6273 
+2024-09-08 01:21:32.964792: Pseudo dice [0.6081, 0.7973] 
+2024-09-08 01:21:32.964844: Epoch time: 244.75 s 
+2024-09-08 01:21:33.906790:  
+2024-09-08 01:21:33.906995: Epoch 107 
+2024-09-08 01:21:33.907074: Current learning rate: 0.00903 
+2024-09-08 01:25:38.547638: train_loss -0.7666 
+2024-09-08 01:25:38.547771: val_loss -0.6848 
+2024-09-08 01:25:38.547837: Pseudo dice [0.6218, 0.8401] 
+2024-09-08 01:25:38.547892: Epoch time: 244.64 s 
+2024-09-08 01:25:39.505971:  
+2024-09-08 01:25:39.506110: Epoch 108 
+2024-09-08 01:25:39.506189: Current learning rate: 0.00902 
+2024-09-08 01:29:44.004444: train_loss -0.7842 
+2024-09-08 01:29:44.004585: val_loss -0.6847 
+2024-09-08 01:29:44.004674: Pseudo dice [0.6473, 0.8517] 
+2024-09-08 01:29:44.004728: Epoch time: 244.5 s 
+2024-09-08 01:29:44.968115:  
+2024-09-08 01:29:44.968277: Epoch 109 
+2024-09-08 01:29:44.968358: Current learning rate: 0.00901 
+2024-09-08 01:33:49.497848: train_loss -0.7923 
+2024-09-08 01:33:49.497984: val_loss -0.6847 
+2024-09-08 01:33:49.498034: Pseudo dice [0.636, 0.8458] 
+2024-09-08 01:33:49.498085: Epoch time: 244.53 s 
+2024-09-08 01:33:50.529368:  
+2024-09-08 01:33:50.529611: Epoch 110 
+2024-09-08 01:33:50.529704: Current learning rate: 0.009 
+2024-09-08 01:37:55.262871: train_loss -0.7839 
+2024-09-08 01:37:55.263060: val_loss -0.7053 
+2024-09-08 01:37:55.263111: Pseudo dice [0.6511, 0.8506] 
+2024-09-08 01:37:55.263163: Epoch time: 244.74 s 
+2024-09-08 01:37:56.210712:  
+2024-09-08 01:37:56.210949: Epoch 111 
+2024-09-08 01:37:56.211031: Current learning rate: 0.009 
+2024-09-08 01:42:00.983711: train_loss -0.7893 
+2024-09-08 01:42:00.983856: val_loss -0.6875 
+2024-09-08 01:42:00.983906: Pseudo dice [0.6175, 0.8297] 
+2024-09-08 01:42:00.983957: Epoch time: 244.77 s 
+2024-09-08 01:42:01.930190:  
+2024-09-08 01:42:01.930396: Epoch 112 
+2024-09-08 01:42:01.930520: Current learning rate: 0.00899 
+2024-09-08 01:46:06.718688: train_loss -0.7885 
+2024-09-08 01:46:06.718822: val_loss -0.6903 
+2024-09-08 01:46:06.718873: Pseudo dice [0.6399, 0.855] 
+2024-09-08 01:46:06.718926: Epoch time: 244.79 s 
+2024-09-08 01:46:07.662483:  
+2024-09-08 01:46:07.662701: Epoch 113 
+2024-09-08 01:46:07.662781: Current learning rate: 0.00898 
+2024-09-08 01:50:12.604283: train_loss -0.8047 
+2024-09-08 01:50:12.604461: val_loss -0.6958 
+2024-09-08 01:50:12.604528: Pseudo dice [0.6374, 0.8425] 
+2024-09-08 01:50:12.604596: Epoch time: 244.94 s 
+2024-09-08 01:50:13.794113:  
+2024-09-08 01:50:13.794365: Epoch 114 
+2024-09-08 01:50:13.794470: Current learning rate: 0.00897 
+2024-09-08 01:54:18.717214: train_loss -0.7861 
+2024-09-08 01:54:18.717352: val_loss -0.6736 
+2024-09-08 01:54:18.717402: Pseudo dice [0.5934, 0.8521] 
+2024-09-08 01:54:18.717457: Epoch time: 244.93 s 
+2024-09-08 01:54:19.664867:  
+2024-09-08 01:54:19.665044: Epoch 115 
+2024-09-08 01:54:19.665121: Current learning rate: 0.00896 
+2024-09-08 01:58:24.505028: train_loss -0.7789 
+2024-09-08 01:58:24.505164: val_loss -0.6895 
+2024-09-08 01:58:24.505214: Pseudo dice [0.6445, 0.8542] 
+2024-09-08 01:58:24.505263: Epoch time: 244.84 s 
+2024-09-08 01:58:24.505304: Yayy! New best EMA pseudo Dice: 0.733 
+2024-09-08 01:58:28.413210:  
+2024-09-08 01:58:28.413423: Epoch 116 
+2024-09-08 01:58:28.413507: Current learning rate: 0.00895 
+2024-09-08 02:02:33.184717: train_loss -0.7714 
+2024-09-08 02:02:33.184852: val_loss -0.6821 
+2024-09-08 02:02:33.184902: Pseudo dice [0.6196, 0.8472] 
+2024-09-08 02:02:33.184953: Epoch time: 244.77 s 
+2024-09-08 02:02:33.184993: Yayy! New best EMA pseudo Dice: 0.733 
+2024-09-08 02:02:37.209408:  
+2024-09-08 02:02:37.209627: Epoch 117 
+2024-09-08 02:02:37.209726: Current learning rate: 0.00894 
+2024-09-08 02:06:42.280971: train_loss -0.7908 
+2024-09-08 02:06:42.281109: val_loss -0.6803 
+2024-09-08 02:06:42.281160: Pseudo dice [0.6322, 0.8518] 
+2024-09-08 02:06:42.281211: Epoch time: 245.07 s 
+2024-09-08 02:06:42.281250: Yayy! New best EMA pseudo Dice: 0.7339 
+2024-09-08 02:06:46.191395:  
+2024-09-08 02:06:46.191568: Epoch 118 
+2024-09-08 02:06:46.191652: Current learning rate: 0.00893 
+2024-09-08 02:10:51.246062: train_loss -0.7888 
+2024-09-08 02:10:51.246248: val_loss -0.6877 
+2024-09-08 02:10:51.246366: Pseudo dice [0.6319, 0.8375] 
+2024-09-08 02:10:51.246418: Epoch time: 245.06 s 
+2024-09-08 02:10:51.246460: Yayy! New best EMA pseudo Dice: 0.734 
+2024-09-08 02:10:56.022914:  
+2024-09-08 02:10:56.023189: Epoch 119 
+2024-09-08 02:10:56.023290: Current learning rate: 0.00892 
+2024-09-08 02:15:01.022269: train_loss -0.7956 
+2024-09-08 02:15:01.022411: val_loss -0.6706 
+2024-09-08 02:15:01.022460: Pseudo dice [0.6191, 0.8469] 
+2024-09-08 02:15:01.022514: Epoch time: 245.0 s 
+2024-09-08 02:15:02.103681:  
+2024-09-08 02:15:02.103966: Epoch 120 
+2024-09-08 02:15:02.104083: Current learning rate: 0.00891 
+2024-09-08 02:19:07.156365: train_loss -0.8042 
+2024-09-08 02:19:07.156502: val_loss -0.6692 
+2024-09-08 02:19:07.156552: Pseudo dice [0.6222, 0.8477] 
+2024-09-08 02:19:07.156603: Epoch time: 245.06 s 
+2024-09-08 02:19:07.156642: Yayy! New best EMA pseudo Dice: 0.734 
+2024-09-08 02:19:11.035043:  
+2024-09-08 02:19:11.035225: Epoch 121 
+2024-09-08 02:19:11.035330: Current learning rate: 0.0089 
+2024-09-08 02:23:16.252752: train_loss -0.7907 
+2024-09-08 02:23:16.252912: val_loss -0.6574 
+2024-09-08 02:23:16.252964: Pseudo dice [0.5703, 0.8517] 
+2024-09-08 02:23:16.253015: Epoch time: 245.22 s 
+2024-09-08 02:23:17.218038:  
+2024-09-08 02:23:17.218221: Epoch 122 
+2024-09-08 02:23:17.218302: Current learning rate: 0.00889 
+2024-09-08 02:27:22.295635: train_loss -0.7775 
+2024-09-08 02:27:22.295772: val_loss -0.674 
+2024-09-08 02:27:22.295827: Pseudo dice [0.6167, 0.8332] 
+2024-09-08 02:27:22.295879: Epoch time: 245.08 s 
+2024-09-08 02:27:23.260727:  
+2024-09-08 02:27:23.260887: Epoch 123 
+2024-09-08 02:27:23.260991: Current learning rate: 0.00889 
+2024-09-08 02:31:28.238066: train_loss -0.7886 
+2024-09-08 02:31:28.238203: val_loss -0.6658 
+2024-09-08 02:31:28.238253: Pseudo dice [0.6078, 0.8418] 
+2024-09-08 02:31:28.238305: Epoch time: 244.98 s 
+2024-09-08 02:31:29.187920:  
+2024-09-08 02:31:29.188112: Epoch 124 
+2024-09-08 02:31:29.188196: Current learning rate: 0.00888 
+2024-09-08 02:35:34.137469: train_loss -0.783 
+2024-09-08 02:35:34.137615: val_loss -0.6542 
+2024-09-08 02:35:34.137694: Pseudo dice [0.5946, 0.8316] 
+2024-09-08 02:35:34.137776: Epoch time: 244.95 s 
+2024-09-08 02:35:35.301912:  
+2024-09-08 02:35:35.302176: Epoch 125 
+2024-09-08 02:35:35.302347: Current learning rate: 0.00887 
+2024-09-08 02:39:40.370968: train_loss -0.7914 
+2024-09-08 02:39:40.371160: val_loss -0.6695 
+2024-09-08 02:39:40.371253: Pseudo dice [0.6285, 0.825] 
+2024-09-08 02:39:40.371347: Epoch time: 245.07 s 
+2024-09-08 02:39:41.346232:  
+2024-09-08 02:39:41.346402: Epoch 126 
+2024-09-08 02:39:41.346483: Current learning rate: 0.00886 
+2024-09-08 02:43:46.374508: train_loss -0.7985 
+2024-09-08 02:43:46.374642: val_loss -0.6684 
+2024-09-08 02:43:46.374692: Pseudo dice [0.6044, 0.8405] 
+2024-09-08 02:43:46.374743: Epoch time: 245.03 s 
+2024-09-08 02:43:47.364067:  
+2024-09-08 02:43:47.364231: Epoch 127 
+2024-09-08 02:43:47.364340: Current learning rate: 0.00885 
+2024-09-08 02:47:52.608328: train_loss -0.7906 
+2024-09-08 02:47:52.608484: val_loss -0.6536 
+2024-09-08 02:47:52.608535: Pseudo dice [0.6098, 0.8348] 
+2024-09-08 02:47:52.608587: Epoch time: 245.25 s 
+2024-09-08 02:47:53.594683:  
+2024-09-08 02:47:53.594868: Epoch 128 
+2024-09-08 02:47:53.594949: Current learning rate: 0.00884 
+2024-09-08 02:51:58.932013: train_loss -0.7976 
+2024-09-08 02:51:58.932158: val_loss -0.6867 
+2024-09-08 02:51:58.932209: Pseudo dice [0.6297, 0.8442] 
+2024-09-08 02:51:58.932261: Epoch time: 245.34 s 
+2024-09-08 02:51:59.906518:  
+2024-09-08 02:51:59.906684: Epoch 129 
+2024-09-08 02:51:59.906765: Current learning rate: 0.00883 
+2024-09-08 02:56:05.446890: train_loss -0.7887 
+2024-09-08 02:56:05.447045: val_loss -0.6608 
+2024-09-08 02:56:05.447096: Pseudo dice [0.595, 0.826] 
+2024-09-08 02:56:05.447147: Epoch time: 245.54 s 
+2024-09-08 02:56:06.420107:  
+2024-09-08 02:56:06.420311: Epoch 130 
+2024-09-08 02:56:06.420386: Current learning rate: 0.00882 
+2024-09-08 03:00:11.885891: train_loss -0.7971 
+2024-09-08 03:00:11.886029: val_loss -0.7043 
+2024-09-08 03:00:11.886082: Pseudo dice [0.636, 0.854] 
+2024-09-08 03:00:11.886133: Epoch time: 245.47 s 
+2024-09-08 03:00:12.842565:  
+2024-09-08 03:00:12.842749: Epoch 131 
+2024-09-08 03:00:12.842842: Current learning rate: 0.00881 
+2024-09-08 03:04:18.168411: train_loss -0.8102 
+2024-09-08 03:04:18.168608: val_loss -0.648 
+2024-09-08 03:04:18.168686: Pseudo dice [0.575, 0.864] 
+2024-09-08 03:04:18.168763: Epoch time: 245.33 s 
+2024-09-08 03:04:19.349314:  
+2024-09-08 03:04:19.349546: Epoch 132 
+2024-09-08 03:04:19.349676: Current learning rate: 0.0088 
+2024-09-08 03:08:24.665513: train_loss -0.8051 
+2024-09-08 03:08:24.665650: val_loss -0.6804 
+2024-09-08 03:08:24.665701: Pseudo dice [0.6008, 0.8443] 
+2024-09-08 03:08:24.665753: Epoch time: 245.32 s 
+2024-09-08 03:08:25.619690:  
+2024-09-08 03:08:25.619897: Epoch 133 
+2024-09-08 03:08:25.619995: Current learning rate: 0.00879 
+2024-09-08 03:12:30.908721: train_loss -0.8148 
+2024-09-08 03:12:30.908881: val_loss -0.6878 
+2024-09-08 03:12:30.908931: Pseudo dice [0.6395, 0.8457] 
+2024-09-08 03:12:30.908983: Epoch time: 245.29 s 
+2024-09-08 03:12:31.898861:  
+2024-09-08 03:12:31.899070: Epoch 134 
+2024-09-08 03:12:31.899153: Current learning rate: 0.00879 
+2024-09-08 03:16:37.208447: train_loss -0.8047 
+2024-09-08 03:16:37.208593: val_loss -0.6601 
+2024-09-08 03:16:37.208644: Pseudo dice [0.6352, 0.8313] 
+2024-09-08 03:16:37.208747: Epoch time: 245.31 s 
+2024-09-08 03:16:38.385781:  
+2024-09-08 03:16:38.385987: Epoch 135 
+2024-09-08 03:16:38.386097: Current learning rate: 0.00878 
+2024-09-08 03:20:43.800705: train_loss -0.7982 
+2024-09-08 03:20:43.800842: val_loss -0.6582 
+2024-09-08 03:20:43.800891: Pseudo dice [0.5938, 0.8248] 
+2024-09-08 03:20:43.800942: Epoch time: 245.42 s 
+2024-09-08 03:20:44.789477:  
+2024-09-08 03:20:44.789715: Epoch 136 
+2024-09-08 03:20:44.789839: Current learning rate: 0.00877 
+2024-09-08 03:24:49.998500: train_loss -0.7874 
+2024-09-08 03:24:49.998638: val_loss -0.6816 
+2024-09-08 03:24:49.998687: Pseudo dice [0.6398, 0.8466] 
+2024-09-08 03:24:49.998737: Epoch time: 245.21 s 
+2024-09-08 03:24:50.994375:  
+2024-09-08 03:24:50.994594: Epoch 137 
+2024-09-08 03:24:50.994675: Current learning rate: 0.00876 
+2024-09-08 03:28:56.484325: train_loss -0.7999 
+2024-09-08 03:28:56.484481: val_loss -0.6748 
+2024-09-08 03:28:56.484531: Pseudo dice [0.5985, 0.8446] 
+2024-09-08 03:28:56.484584: Epoch time: 245.49 s 
+2024-09-08 03:28:57.639699:  
+2024-09-08 03:28:57.639951: Epoch 138 
+2024-09-08 03:28:57.640108: Current learning rate: 0.00875 
+2024-09-08 03:33:03.094213: train_loss -0.7869 
+2024-09-08 03:33:03.094351: val_loss -0.6675 
+2024-09-08 03:33:03.094455: Pseudo dice [0.6371, 0.8325] 
+2024-09-08 03:33:03.094507: Epoch time: 245.46 s 
+2024-09-08 03:33:04.063663:  
+2024-09-08 03:33:04.063833: Epoch 139 
+2024-09-08 03:33:04.063912: Current learning rate: 0.00874 
+2024-09-08 03:37:09.495991: train_loss -0.7922 
+2024-09-08 03:37:09.496165: val_loss -0.6746 
+2024-09-08 03:37:09.496215: Pseudo dice [0.5937, 0.8444] 
+2024-09-08 03:37:09.496267: Epoch time: 245.43 s 
+2024-09-08 03:37:10.469626:  
+2024-09-08 03:37:10.469800: Epoch 140 
+2024-09-08 03:37:10.469882: Current learning rate: 0.00873 
+2024-09-08 03:41:15.961332: train_loss -0.7929 
+2024-09-08 03:41:15.961464: val_loss -0.6786 
+2024-09-08 03:41:15.961514: Pseudo dice [0.5924, 0.836] 
+2024-09-08 03:41:15.961567: Epoch time: 245.49 s 
+2024-09-08 03:41:17.136714:  
+2024-09-08 03:41:17.136889: Epoch 141 
+2024-09-08 03:41:17.136972: Current learning rate: 0.00872 
+2024-09-08 03:45:22.561015: train_loss -0.7958 
+2024-09-08 03:45:22.561166: val_loss -0.6389 
+2024-09-08 03:45:22.561217: Pseudo dice [0.5832, 0.8392] 
+2024-09-08 03:45:22.561272: Epoch time: 245.43 s 
+2024-09-08 03:45:23.556984:  
+2024-09-08 03:45:23.557143: Epoch 142 
+2024-09-08 03:45:23.557221: Current learning rate: 0.00871 
+2024-09-08 03:49:29.636867: train_loss -0.7978 
+2024-09-08 03:49:29.637023: val_loss -0.6671 
+2024-09-08 03:49:29.637073: Pseudo dice [0.6003, 0.8306] 
+2024-09-08 03:49:29.637125: Epoch time: 246.08 s 
+2024-09-08 03:49:30.628882:  
+2024-09-08 03:49:30.629022: Epoch 143 
+2024-09-08 03:49:30.629114: Current learning rate: 0.0087 
+2024-09-08 03:53:35.875158: train_loss -0.7981 
+2024-09-08 03:53:35.875298: val_loss -0.6858 
+2024-09-08 03:53:35.875347: Pseudo dice [0.6199, 0.8546] 
+2024-09-08 03:53:35.875399: Epoch time: 245.25 s 
+2024-09-08 03:53:36.875520:  
+2024-09-08 03:53:36.875754: Epoch 144 
+2024-09-08 03:53:36.875845: Current learning rate: 0.00869 
+2024-09-08 03:57:42.055177: train_loss -0.7862 
+2024-09-08 03:57:42.055336: val_loss -0.6678 
+2024-09-08 03:57:42.055387: Pseudo dice [0.636, 0.841] 
+2024-09-08 03:57:42.055438: Epoch time: 245.18 s 
+2024-09-08 03:57:43.059481:  
+2024-09-08 03:57:43.059715: Epoch 145 
+2024-09-08 03:57:43.059798: Current learning rate: 0.00868 
+2024-09-08 04:01:48.166424: train_loss -0.7872 
+2024-09-08 04:01:48.166560: val_loss -0.6521 
+2024-09-08 04:01:48.166609: Pseudo dice [0.5774, 0.8318] 
+2024-09-08 04:01:48.166660: Epoch time: 245.11 s 
+2024-09-08 04:01:49.150790:  
+2024-09-08 04:01:49.151015: Epoch 146 
+2024-09-08 04:01:49.151096: Current learning rate: 0.00868 
+2024-09-08 04:05:54.342251: train_loss -0.7771 
+2024-09-08 04:05:54.342410: val_loss -0.6746 
+2024-09-08 04:05:54.342459: Pseudo dice [0.6159, 0.8458] 
+2024-09-08 04:05:54.342509: Epoch time: 245.19 s 
+2024-09-08 04:05:55.333199:  
+2024-09-08 04:05:55.333438: Epoch 147 
+2024-09-08 04:05:55.333530: Current learning rate: 0.00867 
+2024-09-08 04:10:00.648211: train_loss -0.7968 
+2024-09-08 04:10:00.648346: val_loss -0.6874 
+2024-09-08 04:10:00.648396: Pseudo dice [0.6221, 0.8472] 
+2024-09-08 04:10:00.648447: Epoch time: 245.32 s 
+2024-09-08 04:10:01.636185:  
+2024-09-08 04:10:01.636361: Epoch 148 
+2024-09-08 04:10:01.636443: Current learning rate: 0.00866 
+2024-09-08 04:14:06.769348: train_loss -0.7936 
+2024-09-08 04:14:06.769482: val_loss -0.6258 
+2024-09-08 04:14:06.769532: Pseudo dice [0.5859, 0.792] 
+2024-09-08 04:14:06.769583: Epoch time: 245.14 s 
+2024-09-08 04:14:07.744735:  
+2024-09-08 04:14:07.744984: Epoch 149 
+2024-09-08 04:14:07.745065: Current learning rate: 0.00865 
+2024-09-08 04:18:12.744869: train_loss -0.7762 
+2024-09-08 04:18:12.745007: val_loss -0.7013 
+2024-09-08 04:18:12.745056: Pseudo dice [0.64, 0.8577] 
+2024-09-08 04:18:12.745107: Epoch time: 245.0 s 
+2024-09-08 04:18:16.707278:  
+2024-09-08 04:18:16.707446: Epoch 150 
+2024-09-08 04:18:16.707547: Current learning rate: 0.00864 
+2024-09-08 04:22:21.598802: train_loss -0.8046 
+2024-09-08 04:22:21.598938: val_loss -0.6906 
+2024-09-08 04:22:21.598988: Pseudo dice [0.6199, 0.8532] 
+2024-09-08 04:22:21.599091: Epoch time: 244.89 s 
+2024-09-08 04:22:22.598099:  
+2024-09-08 04:22:22.598305: Epoch 151 
+2024-09-08 04:22:22.598408: Current learning rate: 0.00863 
+2024-09-08 04:26:27.580514: train_loss -0.7942 
+2024-09-08 04:26:27.580655: val_loss -0.6801 
+2024-09-08 04:26:27.580704: Pseudo dice [0.6283, 0.8429] 
+2024-09-08 04:26:27.580755: Epoch time: 244.98 s 
+2024-09-08 04:26:28.566572:  
+2024-09-08 04:26:28.566727: Epoch 152 
+2024-09-08 04:26:28.566806: Current learning rate: 0.00862 
+2024-09-08 04:30:33.645177: train_loss -0.7998 
+2024-09-08 04:30:33.645311: val_loss -0.6623 
+2024-09-08 04:30:33.645364: Pseudo dice [0.5921, 0.8523] 
+2024-09-08 04:30:33.645414: Epoch time: 245.08 s 
+2024-09-08 04:30:34.627130:  
+2024-09-08 04:30:34.627330: Epoch 153 
+2024-09-08 04:30:34.627434: Current learning rate: 0.00861 
+2024-09-08 04:34:39.489602: train_loss -0.7977 
+2024-09-08 04:34:39.489778: val_loss -0.6716 
+2024-09-08 04:34:39.489845: Pseudo dice [0.6189, 0.8523] 
+2024-09-08 04:34:39.489949: Epoch time: 244.86 s 
+2024-09-08 04:34:40.622141:  
+2024-09-08 04:34:40.622371: Epoch 154 
+2024-09-08 04:34:40.622491: Current learning rate: 0.0086 
+2024-09-08 04:38:45.684246: train_loss -0.7883 
+2024-09-08 04:38:45.684408: val_loss -0.6671 
+2024-09-08 04:38:45.684459: Pseudo dice [0.6302, 0.8408] 
+2024-09-08 04:38:45.684511: Epoch time: 245.06 s 
+2024-09-08 04:38:46.681530:  
+2024-09-08 04:38:46.681743: Epoch 155 
+2024-09-08 04:38:46.681818: Current learning rate: 0.00859 
+2024-09-08 04:42:51.811313: train_loss -0.806 
+2024-09-08 04:42:51.811451: val_loss -0.6755 
+2024-09-08 04:42:51.811502: Pseudo dice [0.6001, 0.8509] 
+2024-09-08 04:42:51.811553: Epoch time: 245.13 s 
+2024-09-08 04:42:52.823289:  
+2024-09-08 04:42:52.823511: Epoch 156 
+2024-09-08 04:42:52.823593: Current learning rate: 0.00858 
+2024-09-08 04:46:57.681627: train_loss -0.8014 
+2024-09-08 04:46:57.681768: val_loss -0.668 
+2024-09-08 04:46:57.681818: Pseudo dice [0.601, 0.826] 
+2024-09-08 04:46:57.681869: Epoch time: 244.86 s 
+2024-09-08 04:46:58.674327:  
+2024-09-08 04:46:58.674508: Epoch 157 
+2024-09-08 04:46:58.674589: Current learning rate: 0.00858 
+2024-09-08 04:51:03.583175: train_loss -0.8006 
+2024-09-08 04:51:03.583315: val_loss -0.6745 
+2024-09-08 04:51:03.583365: Pseudo dice [0.5596, 0.8556] 
+2024-09-08 04:51:03.583416: Epoch time: 244.91 s 
+2024-09-08 04:51:04.595082:  
+2024-09-08 04:51:04.595324: Epoch 158 
+2024-09-08 04:51:04.595402: Current learning rate: 0.00857 
+2024-09-08 04:55:09.535903: train_loss -0.8084 
+2024-09-08 04:55:09.536041: val_loss -0.7087 
+2024-09-08 04:55:09.536091: Pseudo dice [0.6278, 0.8596] 
+2024-09-08 04:55:09.536143: Epoch time: 244.94 s 
+2024-09-08 04:55:10.520747:  
+2024-09-08 04:55:10.520918: Epoch 159 
+2024-09-08 04:55:10.521003: Current learning rate: 0.00856 
+2024-09-08 04:59:15.403440: train_loss -0.8083 
+2024-09-08 04:59:15.403575: val_loss -0.6785 
+2024-09-08 04:59:15.403637: Pseudo dice [0.6184, 0.847] 
+2024-09-08 04:59:15.403687: Epoch time: 244.88 s 
+2024-09-08 04:59:16.426887:  
+2024-09-08 04:59:16.427042: Epoch 160 
+2024-09-08 04:59:16.427123: Current learning rate: 0.00855 
+2024-09-08 05:03:21.532748: train_loss -0.809 
+2024-09-08 05:03:21.532942: val_loss -0.6826 
+2024-09-08 05:03:21.532998: Pseudo dice [0.6207, 0.8433] 
+2024-09-08 05:03:21.533049: Epoch time: 245.11 s 
+2024-09-08 05:03:22.545748:  
+2024-09-08 05:03:22.545922: Epoch 161 
+2024-09-08 05:03:22.546005: Current learning rate: 0.00854 
+2024-09-08 05:07:27.749893: train_loss -0.811 
+2024-09-08 05:07:27.750138: val_loss -0.6855 
+2024-09-08 05:07:27.750204: Pseudo dice [0.6389, 0.845] 
+2024-09-08 05:07:27.750275: Epoch time: 245.21 s 
+2024-09-08 05:07:28.965625:  
+2024-09-08 05:07:28.965787: Epoch 162 
+2024-09-08 05:07:28.965868: Current learning rate: 0.00853 
+2024-09-08 05:11:34.313518: train_loss -0.8034 
+2024-09-08 05:11:34.313657: val_loss -0.7103 
+2024-09-08 05:11:34.313706: Pseudo dice [0.6545, 0.8561] 
+2024-09-08 05:11:34.313758: Epoch time: 245.35 s 
+2024-09-08 05:11:35.310757:  
+2024-09-08 05:11:35.310905: Epoch 163 
+2024-09-08 05:11:35.311016: Current learning rate: 0.00852 
+2024-09-08 05:15:40.517011: train_loss -0.8069 
+2024-09-08 05:15:40.517164: val_loss -0.6711 
+2024-09-08 05:15:40.517213: Pseudo dice [0.6019, 0.8379] 
+2024-09-08 05:15:40.517265: Epoch time: 245.21 s 
+2024-09-08 05:15:41.520301:  
+2024-09-08 05:15:41.520464: Epoch 164 
+2024-09-08 05:15:41.520544: Current learning rate: 0.00851 
+2024-09-08 05:19:46.970442: train_loss -0.8085 
+2024-09-08 05:19:46.970579: val_loss -0.7004 
+2024-09-08 05:19:46.970628: Pseudo dice [0.6303, 0.8543] 
+2024-09-08 05:19:46.970680: Epoch time: 245.45 s 
+2024-09-08 05:19:48.842416:  
+2024-09-08 05:19:48.842634: Epoch 165 
+2024-09-08 05:19:48.842731: Current learning rate: 0.0085 
+2024-09-08 05:23:54.425836: train_loss -0.8133 
+2024-09-08 05:23:54.425982: val_loss -0.6507 
+2024-09-08 05:23:54.426032: Pseudo dice [0.5534, 0.8352] 
+2024-09-08 05:23:54.426083: Epoch time: 245.59 s 
+2024-09-08 05:23:55.410573:  
+2024-09-08 05:23:55.410803: Epoch 166 
+2024-09-08 05:23:55.410886: Current learning rate: 0.00849 
+2024-09-08 05:28:00.953155: train_loss -0.8128 
+2024-09-08 05:28:00.953297: val_loss -0.6683 
+2024-09-08 05:28:00.953372: Pseudo dice [0.5953, 0.8521] 
+2024-09-08 05:28:00.953449: Epoch time: 245.54 s 
+2024-09-08 05:28:01.931185:  
+2024-09-08 05:28:01.931458: Epoch 167 
+2024-09-08 05:28:01.931535: Current learning rate: 0.00848 
+2024-09-08 05:32:07.235603: train_loss -0.8179 
+2024-09-08 05:32:07.235746: val_loss -0.7034 
+2024-09-08 05:32:07.235796: Pseudo dice [0.6494, 0.8494] 
+2024-09-08 05:32:07.235864: Epoch time: 245.31 s 
+2024-09-08 05:32:08.237580:  
+2024-09-08 05:32:08.237819: Epoch 168 
+2024-09-08 05:32:08.237898: Current learning rate: 0.00847 
+2024-09-08 05:36:13.657688: train_loss -0.7978 
+2024-09-08 05:36:13.657843: val_loss -0.7056 
+2024-09-08 05:36:13.657895: Pseudo dice [0.6478, 0.8477] 
+2024-09-08 05:36:13.657946: Epoch time: 245.42 s 
+2024-09-08 05:36:14.652139:  
+2024-09-08 05:36:14.652364: Epoch 169 
+2024-09-08 05:36:14.652446: Current learning rate: 0.00847 
+2024-09-08 05:40:19.986835: train_loss -0.7985 
+2024-09-08 05:40:19.986986: val_loss -0.6789 
+2024-09-08 05:40:19.987149: Pseudo dice [0.5914, 0.8517] 
+2024-09-08 05:40:19.987232: Epoch time: 245.34 s 
+2024-09-08 05:40:20.978002:  
+2024-09-08 05:40:20.978192: Epoch 170 
+2024-09-08 05:40:20.978295: Current learning rate: 0.00846 
+2024-09-08 05:44:26.344946: train_loss -0.8012 
+2024-09-08 05:44:26.345083: val_loss -0.6864 
+2024-09-08 05:44:26.345132: Pseudo dice [0.6185, 0.8454] 
+2024-09-08 05:44:26.345185: Epoch time: 245.37 s 
+2024-09-08 05:44:27.331104:  
+2024-09-08 05:44:27.331284: Epoch 171 
+2024-09-08 05:44:27.331371: Current learning rate: 0.00845 
+2024-09-08 05:48:32.625622: train_loss -0.8063 
+2024-09-08 05:48:32.625757: val_loss -0.6841 
+2024-09-08 05:48:32.625807: Pseudo dice [0.6119, 0.8404] 
+2024-09-08 05:48:32.625857: Epoch time: 245.3 s 
+2024-09-08 05:48:33.783789:  
+2024-09-08 05:48:33.784048: Epoch 172 
+2024-09-08 05:48:33.784166: Current learning rate: 0.00844 
+2024-09-08 05:52:39.027540: train_loss -0.7985 
+2024-09-08 05:52:39.027676: val_loss -0.7058 
+2024-09-08 05:52:39.027726: Pseudo dice [0.6566, 0.8478] 
+2024-09-08 05:52:39.027777: Epoch time: 245.25 s 
+2024-09-08 05:52:40.011425:  
+2024-09-08 05:52:40.011591: Epoch 173 
+2024-09-08 05:52:40.011671: Current learning rate: 0.00843 
+2024-09-08 05:56:45.548444: train_loss -0.7842 
+2024-09-08 05:56:45.548583: val_loss -0.6683 
+2024-09-08 05:56:45.548633: Pseudo dice [0.5968, 0.8582] 
+2024-09-08 05:56:45.548685: Epoch time: 245.54 s 
+2024-09-08 05:56:46.539713:  
+2024-09-08 05:56:46.539875: Epoch 174 
+2024-09-08 05:56:46.539988: Current learning rate: 0.00842 
+2024-09-08 06:00:52.017143: train_loss -0.7972 
+2024-09-08 06:00:52.017281: val_loss -0.6676 
+2024-09-08 06:00:52.017331: Pseudo dice [0.548, 0.8575] 
+2024-09-08 06:00:52.017381: Epoch time: 245.48 s 
+2024-09-08 06:00:53.004370:  
+2024-09-08 06:00:53.004546: Epoch 175 
+2024-09-08 06:00:53.004632: Current learning rate: 0.00841 
+2024-09-08 06:04:58.509370: train_loss -0.7858 
+2024-09-08 06:04:58.509552: val_loss -0.6634 
+2024-09-08 06:04:58.509632: Pseudo dice [0.5983, 0.8425] 
+2024-09-08 06:04:58.509710: Epoch time: 245.51 s 
+2024-09-08 06:04:59.719998:  
+2024-09-08 06:04:59.720223: Epoch 176 
+2024-09-08 06:04:59.720367: Current learning rate: 0.0084 
+2024-09-08 06:09:05.121442: train_loss -0.8093 
+2024-09-08 06:09:05.121581: val_loss -0.691 
+2024-09-08 06:09:05.121630: Pseudo dice [0.6056, 0.8548] 
+2024-09-08 06:09:05.121682: Epoch time: 245.4 s 
+2024-09-08 06:09:06.096440:  
+2024-09-08 06:09:06.096625: Epoch 177 
+2024-09-08 06:09:06.096711: Current learning rate: 0.00839 
+2024-09-08 06:13:11.427140: train_loss -0.7956 
+2024-09-08 06:13:11.427300: val_loss -0.6086 
+2024-09-08 06:13:11.427353: Pseudo dice [0.538, 0.8183] 
+2024-09-08 06:13:11.427403: Epoch time: 245.33 s 
+2024-09-08 06:13:12.419116:  
+2024-09-08 06:13:12.419271: Epoch 178 
+2024-09-08 06:13:12.419351: Current learning rate: 0.00838 
+2024-09-08 06:17:20.342915: train_loss -0.7782 
+2024-09-08 06:17:20.343156: val_loss -0.6852 
+2024-09-08 06:17:20.343267: Pseudo dice [0.6277, 0.8471] 
+2024-09-08 06:17:20.343432: Epoch time: 247.93 s 
+2024-09-08 06:17:21.412222:  
+2024-09-08 06:17:21.412454: Epoch 179 
+2024-09-08 06:17:21.412529: Current learning rate: 0.00837 
+2024-09-08 06:21:26.578440: train_loss -0.7931 
+2024-09-08 06:21:26.578581: val_loss -0.681 
+2024-09-08 06:21:26.578630: Pseudo dice [0.6417, 0.8421] 
+2024-09-08 06:21:26.578682: Epoch time: 245.17 s 
+2024-09-08 06:21:27.582480:  
+2024-09-08 06:21:27.582686: Epoch 180 
+2024-09-08 06:21:27.582770: Current learning rate: 0.00836 
+2024-09-08 06:25:32.630512: train_loss -0.7957 
+2024-09-08 06:25:32.630673: val_loss -0.6722 
+2024-09-08 06:25:32.630724: Pseudo dice [0.6143, 0.837] 
+2024-09-08 06:25:32.630777: Epoch time: 245.05 s 
+2024-09-08 06:25:33.621609:  
+2024-09-08 06:25:33.621797: Epoch 181 
+2024-09-08 06:25:33.621916: Current learning rate: 0.00836 
+2024-09-08 06:29:38.960493: train_loss -0.814 
+2024-09-08 06:29:38.960633: val_loss -0.6834 
+2024-09-08 06:29:38.960683: Pseudo dice [0.6097, 0.8432] 
+2024-09-08 06:29:38.960734: Epoch time: 245.34 s 
+2024-09-08 06:29:39.944637:  
+2024-09-08 06:29:39.944818: Epoch 182 
+2024-09-08 06:29:39.944900: Current learning rate: 0.00835 
+2024-09-08 06:33:45.371834: train_loss -0.8135 
+2024-09-08 06:33:45.371972: val_loss -0.6927 
+2024-09-08 06:33:45.372021: Pseudo dice [0.6213, 0.8497] 
+2024-09-08 06:33:45.372072: Epoch time: 245.43 s 
+2024-09-08 06:33:46.366408:  
+2024-09-08 06:33:46.366606: Epoch 183 
+2024-09-08 06:33:46.366689: Current learning rate: 0.00834 
+2024-09-08 06:37:51.715954: train_loss -0.8051 
+2024-09-08 06:37:51.716140: val_loss -0.6929 
+2024-09-08 06:37:51.716242: Pseudo dice [0.6268, 0.8389] 
+2024-09-08 06:37:51.716306: Epoch time: 245.35 s 
+2024-09-08 06:37:52.763612:  
+2024-09-08 06:37:52.763826: Epoch 184 
+2024-09-08 06:37:52.763923: Current learning rate: 0.00833 
+2024-09-08 06:41:57.889107: train_loss -0.8109 
+2024-09-08 06:41:57.889243: val_loss -0.6948 
+2024-09-08 06:41:57.889297: Pseudo dice [0.6363, 0.8565] 
+2024-09-08 06:41:57.889348: Epoch time: 245.13 s 
+2024-09-08 06:41:58.890930:  
+2024-09-08 06:41:58.891158: Epoch 185 
+2024-09-08 06:41:58.891237: Current learning rate: 0.00832 
+2024-09-08 06:46:13.934821: train_loss -0.8246 
+2024-09-08 06:46:13.934960: val_loss -0.6743 
+2024-09-08 06:46:13.935010: Pseudo dice [0.6329, 0.8477] 
+2024-09-08 06:46:13.935061: Epoch time: 255.05 s 
+2024-09-08 06:46:14.928320:  
+2024-09-08 06:46:14.928519: Epoch 186 
+2024-09-08 06:46:14.928641: Current learning rate: 0.00831 
+2024-09-08 06:50:20.454412: train_loss -0.8209 
+2024-09-08 06:50:20.454560: val_loss -0.6792 
+2024-09-08 06:50:20.454637: Pseudo dice [0.6347, 0.8433] 
+2024-09-08 06:50:20.454706: Epoch time: 245.53 s 
+2024-09-08 06:50:22.362690:  
+2024-09-08 06:50:22.362888: Epoch 187 
+2024-09-08 06:50:22.362981: Current learning rate: 0.0083 
+2024-09-08 06:54:27.589462: train_loss -0.8013 
+2024-09-08 06:54:27.589618: val_loss -0.6935 
+2024-09-08 06:54:27.589668: Pseudo dice [0.6455, 0.8262] 
+2024-09-08 06:54:27.589719: Epoch time: 245.23 s 
+2024-09-08 06:54:28.606883:  
+2024-09-08 06:54:28.607093: Epoch 188 
+2024-09-08 06:54:28.607188: Current learning rate: 0.00829 
+2024-09-08 06:58:33.775884: train_loss -0.8026 
+2024-09-08 06:58:33.776022: val_loss -0.6912 
+2024-09-08 06:58:33.776072: Pseudo dice [0.5929, 0.8533] 
+2024-09-08 06:58:33.776122: Epoch time: 245.17 s 
+2024-09-08 06:58:34.799879:  
+2024-09-08 06:58:34.800086: Epoch 189 
+2024-09-08 06:58:34.800163: Current learning rate: 0.00828 
+2024-09-08 07:02:40.058656: train_loss -0.8064 
+2024-09-08 07:02:40.058833: val_loss -0.6697 
+2024-09-08 07:02:40.058885: Pseudo dice [0.5857, 0.8467] 
+2024-09-08 07:02:40.058940: Epoch time: 245.26 s 
+2024-09-08 07:02:41.077793:  
+2024-09-08 07:02:41.077996: Epoch 190 
+2024-09-08 07:02:41.078127: Current learning rate: 0.00827 
+2024-09-08 07:06:48.537402: train_loss -0.8098 
+2024-09-08 07:06:48.537543: val_loss -0.6789 
+2024-09-08 07:06:48.537594: Pseudo dice [0.6125, 0.8497] 
+2024-09-08 07:06:48.537646: Epoch time: 247.46 s 
+2024-09-08 07:06:49.579168:  
+2024-09-08 07:06:49.579398: Epoch 191 
+2024-09-08 07:06:49.579481: Current learning rate: 0.00826 
+2024-09-08 07:10:55.266936: train_loss -0.7983 
+2024-09-08 07:10:55.267076: val_loss -0.6786 
+2024-09-08 07:10:55.267125: Pseudo dice [0.5806, 0.8625] 
+2024-09-08 07:10:55.267175: Epoch time: 245.69 s 
+2024-09-08 07:10:56.293478:  
+2024-09-08 07:10:56.293667: Epoch 192 
+2024-09-08 07:10:56.293767: Current learning rate: 0.00825 
+2024-09-08 07:15:01.414564: train_loss -0.8025 
+2024-09-08 07:15:01.414704: val_loss -0.6677 
+2024-09-08 07:15:01.414754: Pseudo dice [0.6291, 0.8473] 
+2024-09-08 07:15:01.414803: Epoch time: 245.12 s 
+2024-09-08 07:15:02.414903:  
+2024-09-08 07:15:02.415092: Epoch 193 
+2024-09-08 07:15:02.415176: Current learning rate: 0.00824 
+2024-09-08 07:19:07.194537: train_loss -0.8121 
+2024-09-08 07:19:07.194673: val_loss -0.6751 
+2024-09-08 07:19:07.194724: Pseudo dice [0.633, 0.8362] 
+2024-09-08 07:19:07.194776: Epoch time: 244.78 s 
+2024-09-08 07:19:08.206245:  
+2024-09-08 07:19:08.206475: Epoch 194 
+2024-09-08 07:19:08.206558: Current learning rate: 0.00824 
+2024-09-08 07:23:12.965504: train_loss -0.8192 
+2024-09-08 07:23:12.965644: val_loss -0.6849 
+2024-09-08 07:23:12.965696: Pseudo dice [0.6485, 0.8388] 
+2024-09-08 07:23:12.965751: Epoch time: 244.76 s 
+2024-09-08 07:23:13.955258:  
+2024-09-08 07:23:13.955480: Epoch 195 
+2024-09-08 07:23:13.955562: Current learning rate: 0.00823 
+2024-09-08 07:27:18.873284: train_loss -0.7889 
+2024-09-08 07:27:18.873459: val_loss -0.6631 
+2024-09-08 07:27:18.873512: Pseudo dice [0.6023, 0.8393] 
+2024-09-08 07:27:18.873563: Epoch time: 244.92 s 
+2024-09-08 07:27:19.881536:  
+2024-09-08 07:27:19.881788: Epoch 196 
+2024-09-08 07:27:19.881867: Current learning rate: 0.00822 
+2024-09-08 07:31:24.683973: train_loss -0.793 
+2024-09-08 07:31:24.684118: val_loss -0.6747 
+2024-09-08 07:31:24.684171: Pseudo dice [0.6079, 0.8371] 
+2024-09-08 07:31:24.684241: Epoch time: 244.8 s 
+2024-09-08 07:31:25.826140:  
+2024-09-08 07:31:25.826455: Epoch 197 
+2024-09-08 07:31:25.826567: Current learning rate: 0.00821 
+2024-09-08 07:35:30.622940: train_loss -0.8076 
+2024-09-08 07:35:30.623083: val_loss -0.7134 
+2024-09-08 07:35:30.623132: Pseudo dice [0.6467, 0.852] 
+2024-09-08 07:35:30.623183: Epoch time: 244.8 s 
+2024-09-08 07:35:31.632926:  
+2024-09-08 07:35:31.633135: Epoch 198 
+2024-09-08 07:35:31.633216: Current learning rate: 0.0082 
+2024-09-08 07:40:06.283041: train_loss -0.8147 
+2024-09-08 07:40:06.283183: val_loss -0.7137 
+2024-09-08 07:40:06.283234: Pseudo dice [0.6612, 0.8658] 
+2024-09-08 07:40:06.283287: Epoch time: 274.65 s 
+2024-09-08 07:40:06.283326: Yayy! New best EMA pseudo Dice: 0.7349 
+2024-09-08 07:40:10.272892:  
+2024-09-08 07:40:10.273073: Epoch 199 
+2024-09-08 07:40:10.273160: Current learning rate: 0.00819 
+2024-09-08 07:44:15.741610: train_loss -0.8093 
+2024-09-08 07:44:15.741743: val_loss -0.7076 
+2024-09-08 07:44:15.741794: Pseudo dice [0.6474, 0.8552] 
+2024-09-08 07:44:15.741844: Epoch time: 245.47 s 
+2024-09-08 07:44:18.732878: Yayy! New best EMA pseudo Dice: 0.7366 
+2024-09-08 07:44:22.691120:  
+2024-09-08 07:44:22.691311: Epoch 200 
+2024-09-08 07:44:22.691393: Current learning rate: 0.00818 
+2024-09-08 07:48:27.688995: train_loss -0.808 
+2024-09-08 07:48:27.689215: val_loss -0.6729 
+2024-09-08 07:48:27.689304: Pseudo dice [0.6222, 0.8436] 
+2024-09-08 07:48:27.689370: Epoch time: 245.0 s 
+2024-09-08 07:48:28.995245:  
+2024-09-08 07:48:28.995439: Epoch 201 
+2024-09-08 07:48:28.995597: Current learning rate: 0.00817 
+2024-09-08 07:52:34.278364: train_loss -0.7941 
+2024-09-08 07:52:34.278507: val_loss -0.665 
+2024-09-08 07:52:34.278557: Pseudo dice [0.6473, 0.8264] 
+2024-09-08 07:52:34.278610: Epoch time: 245.29 s 
+2024-09-08 07:52:35.274025:  
+2024-09-08 07:52:35.274215: Epoch 202 
+2024-09-08 07:52:35.274296: Current learning rate: 0.00816 
+2024-09-08 07:56:40.423127: train_loss -0.8114 
+2024-09-08 07:56:40.423304: val_loss -0.678 
+2024-09-08 07:56:40.423356: Pseudo dice [0.5956, 0.8502] 
+2024-09-08 07:56:40.423406: Epoch time: 245.15 s 
+2024-09-08 07:56:41.423198:  
+2024-09-08 07:56:41.423357: Epoch 203 
+2024-09-08 07:56:41.423433: Current learning rate: 0.00815 
+2024-09-08 08:00:46.468412: train_loss -0.7821 
+2024-09-08 08:00:46.468835: val_loss -0.6562 
+2024-09-08 08:00:46.468887: Pseudo dice [0.577, 0.8353] 
+2024-09-08 08:00:46.468940: Epoch time: 245.05 s 
+2024-09-08 08:00:47.476641:  
+2024-09-08 08:00:47.476820: Epoch 204 
+2024-09-08 08:00:47.476894: Current learning rate: 0.00814 
+2024-09-08 08:05:00.947700: train_loss -0.7905 
+2024-09-08 08:05:00.947860: val_loss -0.6695 
+2024-09-08 08:05:00.947912: Pseudo dice [0.5946, 0.8482] 
+2024-09-08 08:05:00.947961: Epoch time: 253.47 s 
+2024-09-08 08:05:01.959832:  
+2024-09-08 08:05:01.960083: Epoch 205 
+2024-09-08 08:05:01.960165: Current learning rate: 0.00813 
+2024-09-08 08:09:07.164039: train_loss -0.7954 
+2024-09-08 08:09:07.164183: val_loss -0.6767 
+2024-09-08 08:09:07.164233: Pseudo dice [0.6145, 0.842] 
+2024-09-08 08:09:07.164286: Epoch time: 245.21 s 
+2024-09-08 08:09:08.102363:  
+2024-09-08 08:09:08.102527: Epoch 206 
+2024-09-08 08:09:08.102606: Current learning rate: 0.00813 
+2024-09-08 08:13:13.473077: train_loss -0.7853 
+2024-09-08 08:13:13.473276: val_loss -0.6725 
+2024-09-08 08:13:13.473377: Pseudo dice [0.5958, 0.8217] 
+2024-09-08 08:13:13.473463: Epoch time: 245.37 s 
+2024-09-08 08:13:14.647343:  
+2024-09-08 08:13:14.647638: Epoch 207 
+2024-09-08 08:13:14.647718: Current learning rate: 0.00812 
+2024-09-08 08:17:20.162133: train_loss -0.7997 
+2024-09-08 08:17:20.162308: val_loss -0.6566 
+2024-09-08 08:17:20.162357: Pseudo dice [0.5973, 0.842] 
+2024-09-08 08:17:20.162419: Epoch time: 245.52 s 
+2024-09-08 08:17:21.101390:  
+2024-09-08 08:17:21.101554: Epoch 208 
+2024-09-08 08:17:21.101633: Current learning rate: 0.00811 
+2024-09-08 08:21:26.435501: train_loss -0.8034 
+2024-09-08 08:21:26.435636: val_loss -0.661 
+2024-09-08 08:21:26.435686: Pseudo dice [0.5885, 0.8369] 
+2024-09-08 08:21:26.435771: Epoch time: 245.34 s 
+2024-09-08 08:21:27.362296:  
+2024-09-08 08:21:27.362457: Epoch 209 
+2024-09-08 08:21:27.362537: Current learning rate: 0.0081 
+2024-09-08 08:25:33.542766: train_loss -0.8003 
+2024-09-08 08:25:33.542941: val_loss -0.6776 
+2024-09-08 08:25:33.542992: Pseudo dice [0.6406, 0.8375] 
+2024-09-08 08:25:33.543043: Epoch time: 246.18 s 
+2024-09-08 08:25:34.471024:  
+2024-09-08 08:25:34.471245: Epoch 210 
+2024-09-08 08:25:34.471337: Current learning rate: 0.00809 
+2024-09-08 08:29:39.917816: train_loss -0.8021 
+2024-09-08 08:29:39.917953: val_loss -0.6753 
+2024-09-08 08:29:39.918003: Pseudo dice [0.5932, 0.853] 
+2024-09-08 08:29:39.918053: Epoch time: 245.45 s 
+2024-09-08 08:29:40.986315:  
+2024-09-08 08:29:40.986531: Epoch 211 
+2024-09-08 08:29:40.986642: Current learning rate: 0.00808 
+2024-09-08 08:33:46.497002: train_loss -0.8061 
+2024-09-08 08:33:46.497157: val_loss -0.6731 
+2024-09-08 08:33:46.497207: Pseudo dice [0.6018, 0.8375] 
+2024-09-08 08:33:46.497259: Epoch time: 245.51 s 
+2024-09-08 08:33:47.433140:  
+2024-09-08 08:33:47.433325: Epoch 212 
+2024-09-08 08:33:47.433408: Current learning rate: 0.00807 
+2024-09-08 08:37:52.950484: train_loss -0.8026 
+2024-09-08 08:37:52.950621: val_loss -0.6834 
+2024-09-08 08:37:52.950670: Pseudo dice [0.5972, 0.8517] 
+2024-09-08 08:37:52.950721: Epoch time: 245.52 s 
+2024-09-08 08:37:53.898872:  
+2024-09-08 08:37:53.899086: Epoch 213 
+2024-09-08 08:37:53.899177: Current learning rate: 0.00806 
+2024-09-08 08:41:59.398560: train_loss -0.8093 
+2024-09-08 08:41:59.398701: val_loss -0.6594 
+2024-09-08 08:41:59.398751: Pseudo dice [0.579, 0.8523] 
+2024-09-08 08:41:59.398802: Epoch time: 245.5 s 
+2024-09-08 08:42:00.448633:  
+2024-09-08 08:42:00.448833: Epoch 214 
+2024-09-08 08:42:00.448919: Current learning rate: 0.00805 
+2024-09-08 08:46:06.044842: train_loss -0.8172 
+2024-09-08 08:46:06.044981: val_loss -0.6837 
+2024-09-08 08:46:06.045032: Pseudo dice [0.6381, 0.8518] 
+2024-09-08 08:46:06.045084: Epoch time: 245.6 s 
+2024-09-08 08:46:06.996514:  
+2024-09-08 08:46:06.996684: Epoch 215 
+2024-09-08 08:46:06.996763: Current learning rate: 0.00804 
+2024-09-08 08:50:12.563565: train_loss -0.8197 
+2024-09-08 08:50:12.563702: val_loss -0.7079 
+2024-09-08 08:50:12.563753: Pseudo dice [0.658, 0.8557] 
+2024-09-08 08:50:12.563818: Epoch time: 245.57 s 
+2024-09-08 08:50:13.551767:  
+2024-09-08 08:50:13.552007: Epoch 216 
+2024-09-08 08:50:13.552089: Current learning rate: 0.00803 
+2024-09-08 08:54:18.711906: train_loss -0.819 
+2024-09-08 08:54:18.712048: val_loss -0.691 
+2024-09-08 08:54:18.712098: Pseudo dice [0.6161, 0.8577] 
+2024-09-08 08:54:18.712150: Epoch time: 245.16 s 
+2024-09-08 08:54:19.765888:  
+2024-09-08 08:54:19.766079: Epoch 217 
+2024-09-08 08:54:19.766165: Current learning rate: 0.00802 
+2024-09-08 08:58:24.990108: train_loss -0.8219 
+2024-09-08 08:58:24.990251: val_loss -0.6944 
+2024-09-08 08:58:24.990301: Pseudo dice [0.6291, 0.854] 
+2024-09-08 08:58:24.990351: Epoch time: 245.23 s 
+2024-09-08 08:58:25.942083:  
+2024-09-08 08:58:25.942319: Epoch 218 
+2024-09-08 08:58:25.942438: Current learning rate: 0.00801 
+2024-09-08 09:02:31.443988: train_loss -0.8188 
+2024-09-08 09:02:31.444132: val_loss -0.709 
+2024-09-08 09:02:31.444182: Pseudo dice [0.6531, 0.8541] 
+2024-09-08 09:02:31.444234: Epoch time: 245.51 s 
+2024-09-08 09:02:32.384210:  
+2024-09-08 09:02:32.384414: Epoch 219 
+2024-09-08 09:02:32.384527: Current learning rate: 0.00801 
+2024-09-08 09:06:37.800194: train_loss -0.8237 
+2024-09-08 09:06:37.800331: val_loss -0.721 
+2024-09-08 09:06:37.800381: Pseudo dice [0.6566, 0.8594] 
+2024-09-08 09:06:37.800433: Epoch time: 245.42 s 
+2024-09-08 09:06:38.730534:  
+2024-09-08 09:06:38.730777: Epoch 220 
+2024-09-08 09:06:38.730855: Current learning rate: 0.008 
+2024-09-08 09:10:43.963688: train_loss -0.8241 
+2024-09-08 09:10:43.963836: val_loss -0.6713 
+2024-09-08 09:10:43.963887: Pseudo dice [0.599, 0.8452] 
+2024-09-08 09:10:43.963937: Epoch time: 245.24 s 
+2024-09-08 09:10:45.014687:  
+2024-09-08 09:10:45.014966: Epoch 221 
+2024-09-08 09:10:45.015108: Current learning rate: 0.00799 
+2024-09-08 09:14:50.422279: train_loss -0.8266 
+2024-09-08 09:14:50.422420: val_loss -0.6965 
+2024-09-08 09:14:50.422470: Pseudo dice [0.6238, 0.8592] 
+2024-09-08 09:14:50.422522: Epoch time: 245.41 s 
+2024-09-08 09:14:51.358902:  
+2024-09-08 09:14:51.359053: Epoch 222 
+2024-09-08 09:14:51.359131: Current learning rate: 0.00798 
+2024-09-08 09:18:56.658201: train_loss -0.8169 
+2024-09-08 09:18:56.658341: val_loss -0.6903 
+2024-09-08 09:18:56.658390: Pseudo dice [0.6504, 0.8395] 
+2024-09-08 09:18:56.658442: Epoch time: 245.3 s 
+2024-09-08 09:18:57.597774:  
+2024-09-08 09:18:57.597938: Epoch 223 
+2024-09-08 09:18:57.598016: Current learning rate: 0.00797 
+2024-09-08 09:23:02.765198: train_loss -0.8182 
+2024-09-08 09:23:02.765345: val_loss -0.6946 
+2024-09-08 09:23:02.765400: Pseudo dice [0.6026, 0.8666] 
+2024-09-08 09:23:02.765456: Epoch time: 245.17 s 
+2024-09-08 09:23:03.705965:  
+2024-09-08 09:23:03.706167: Epoch 224 
+2024-09-08 09:23:03.706254: Current learning rate: 0.00796 
+2024-09-08 09:27:08.878234: train_loss -0.8245 
+2024-09-08 09:27:08.878367: val_loss -0.6706 
+2024-09-08 09:27:08.878423: Pseudo dice [0.6261, 0.8533] 
+2024-09-08 09:27:08.878479: Epoch time: 245.17 s 
+2024-09-08 09:27:08.878524: Yayy! New best EMA pseudo Dice: 0.7367 
+2024-09-08 09:27:12.762524:  
+2024-09-08 09:27:12.762688: Epoch 225 
+2024-09-08 09:27:12.762772: Current learning rate: 0.00795 
+2024-09-08 09:31:17.823931: train_loss -0.8238 
+2024-09-08 09:31:17.824081: val_loss -0.6807 
+2024-09-08 09:31:17.824137: Pseudo dice [0.5976, 0.859] 
+2024-09-08 09:31:17.824197: Epoch time: 245.06 s 
+2024-09-08 09:31:18.760334:  
+2024-09-08 09:31:18.760519: Epoch 226 
+2024-09-08 09:31:18.760604: Current learning rate: 0.00794 
+2024-09-08 09:35:23.914570: train_loss -0.8205 
+2024-09-08 09:35:23.914716: val_loss -0.6781 
+2024-09-08 09:35:23.914772: Pseudo dice [0.5754, 0.8527] 
+2024-09-08 09:35:23.914827: Epoch time: 245.16 s 
+2024-09-08 09:35:24.837640:  
+2024-09-08 09:35:24.837833: Epoch 227 
+2024-09-08 09:35:24.837938: Current learning rate: 0.00793 
+2024-09-08 09:39:29.882089: train_loss -0.8243 
+2024-09-08 09:39:29.882237: val_loss -0.701 
+2024-09-08 09:39:29.882293: Pseudo dice [0.6407, 0.866] 
+2024-09-08 09:39:29.882351: Epoch time: 245.05 s 
+2024-09-08 09:39:30.815034:  
+2024-09-08 09:39:30.815212: Epoch 228 
+2024-09-08 09:39:30.815296: Current learning rate: 0.00792 
+2024-09-08 09:43:35.802439: train_loss -0.8212 
+2024-09-08 09:43:35.802587: val_loss -0.63 
+2024-09-08 09:43:35.802641: Pseudo dice [0.5, 0.852] 
+2024-09-08 09:43:35.802769: Epoch time: 244.99 s 
+2024-09-08 09:43:36.735922:  
+2024-09-08 09:43:36.736144: Epoch 229 
+2024-09-08 09:43:36.736230: Current learning rate: 0.00791 
+2024-09-08 09:47:41.820731: train_loss -0.826 
+2024-09-08 09:47:41.820875: val_loss -0.673 
+2024-09-08 09:47:41.820931: Pseudo dice [0.5589, 0.8589] 
+2024-09-08 09:47:41.820987: Epoch time: 245.09 s 
+2024-09-08 09:47:42.838612:  
+2024-09-08 09:47:42.838793: Epoch 230 
+2024-09-08 09:47:42.838892: Current learning rate: 0.0079 
+2024-09-08 09:51:48.319245: train_loss -0.824 
+2024-09-08 09:51:48.319401: val_loss -0.6757 
+2024-09-08 09:51:48.319457: Pseudo dice [0.6052, 0.8508] 
+2024-09-08 09:51:48.319513: Epoch time: 245.48 s 
+2024-09-08 09:51:49.250963:  
+2024-09-08 09:51:49.251121: Epoch 231 
+2024-09-08 09:51:49.251210: Current learning rate: 0.00789 
+2024-09-08 09:55:54.522732: train_loss -0.8279 
+2024-09-08 09:55:54.522906: val_loss -0.6899 
+2024-09-08 09:55:54.522963: Pseudo dice [0.6138, 0.8638] 
+2024-09-08 09:55:54.523017: Epoch time: 245.27 s 
+2024-09-08 09:55:55.448345:  
+2024-09-08 09:55:55.448519: Epoch 232 
+2024-09-08 09:55:55.448626: Current learning rate: 0.00789 
+2024-09-08 10:00:00.981520: train_loss -0.8086 
+2024-09-08 10:00:00.981667: val_loss -0.6475 
+2024-09-08 10:00:00.981723: Pseudo dice [0.5704, 0.8286] 
+2024-09-08 10:00:00.981778: Epoch time: 245.54 s 
+2024-09-08 10:00:02.831148:  
+2024-09-08 10:00:02.831333: Epoch 233 
+2024-09-08 10:00:02.831431: Current learning rate: 0.00788 
+2024-09-08 10:04:08.242893: train_loss -0.8214 
+2024-09-08 10:04:08.243167: val_loss -0.6777 
+2024-09-08 10:04:08.243262: Pseudo dice [0.6171, 0.8516] 
+2024-09-08 10:04:08.243355: Epoch time: 245.41 s 
+2024-09-08 10:04:09.328159:  
+2024-09-08 10:04:09.328333: Epoch 234 
+2024-09-08 10:04:09.328429: Current learning rate: 0.00787 
+2024-09-08 10:08:14.905930: train_loss -0.8051 
+2024-09-08 10:08:14.906073: val_loss -0.6589 
+2024-09-08 10:08:14.906129: Pseudo dice [0.5941, 0.8405] 
+2024-09-08 10:08:14.906188: Epoch time: 245.58 s 
+2024-09-08 10:08:15.842746:  
+2024-09-08 10:08:15.842974: Epoch 235 
+2024-09-08 10:08:15.843058: Current learning rate: 0.00786 
+2024-09-08 10:12:21.209354: train_loss -0.7997 
+2024-09-08 10:12:21.209498: val_loss -0.6913 
+2024-09-08 10:12:21.209554: Pseudo dice [0.6524, 0.8363] 
+2024-09-08 10:12:21.209609: Epoch time: 245.37 s 
+2024-09-08 10:12:22.132752:  
+2024-09-08 10:12:22.132986: Epoch 236 
+2024-09-08 10:12:22.133074: Current learning rate: 0.00785 
+2024-09-08 10:16:27.604868: train_loss -0.8155 
+2024-09-08 10:16:27.605017: val_loss -0.6589 
+2024-09-08 10:16:27.605073: Pseudo dice [0.604, 0.8458] 
+2024-09-08 10:16:27.605129: Epoch time: 245.47 s 
+2024-09-08 10:16:28.548403:  
+2024-09-08 10:16:28.548606: Epoch 237 
+2024-09-08 10:16:28.548692: Current learning rate: 0.00784 
+2024-09-08 10:20:33.936852: train_loss -0.8308 
+2024-09-08 10:20:33.936995: val_loss -0.6634 
+2024-09-08 10:20:33.937049: Pseudo dice [0.5941, 0.8437] 
+2024-09-08 10:20:33.937104: Epoch time: 245.39 s 
+2024-09-08 10:20:34.862675:  
+2024-09-08 10:20:34.862899: Epoch 238 
+2024-09-08 10:20:34.862983: Current learning rate: 0.00783 
+2024-09-08 10:24:40.426091: train_loss -0.8258 
+2024-09-08 10:24:40.426237: val_loss -0.6662 
+2024-09-08 10:24:40.426292: Pseudo dice [0.5641, 0.8542] 
+2024-09-08 10:24:40.426347: Epoch time: 245.57 s 
+2024-09-08 10:24:41.350388:  
+2024-09-08 10:24:41.350608: Epoch 239 
+2024-09-08 10:24:41.350694: Current learning rate: 0.00782 
+2024-09-08 10:28:46.717380: train_loss -0.8159 
+2024-09-08 10:28:46.717529: val_loss -0.6929 
+2024-09-08 10:28:46.717587: Pseudo dice [0.6262, 0.8466] 
+2024-09-08 10:28:46.717643: Epoch time: 245.37 s 
+2024-09-08 10:28:47.690714:  
+2024-09-08 10:28:47.690918: Epoch 240 
+2024-09-08 10:28:47.691002: Current learning rate: 0.00781 
+2024-09-08 10:32:52.975265: train_loss -0.8245 
+2024-09-08 10:32:52.975415: val_loss -0.6675 
+2024-09-08 10:32:52.975470: Pseudo dice [0.5873, 0.8496] 
+2024-09-08 10:32:52.975526: Epoch time: 245.29 s 
+2024-09-08 10:32:53.925385:  
+2024-09-08 10:32:53.925600: Epoch 241 
+2024-09-08 10:32:53.925730: Current learning rate: 0.0078 
+2024-09-08 10:36:59.107547: train_loss -0.8278 
+2024-09-08 10:36:59.107706: val_loss -0.668 
+2024-09-08 10:36:59.107762: Pseudo dice [0.6019, 0.8441] 
+2024-09-08 10:36:59.107824: Epoch time: 245.19 s 
+2024-09-08 10:37:00.049335:  
+2024-09-08 10:37:00.049564: Epoch 242 
+2024-09-08 10:37:00.049650: Current learning rate: 0.00779 
+2024-09-08 10:41:05.019025: train_loss -0.8195 
+2024-09-08 10:41:05.019173: val_loss -0.6854 
+2024-09-08 10:41:05.019230: Pseudo dice [0.6125, 0.8346] 
+2024-09-08 10:41:05.019284: Epoch time: 244.97 s 
+2024-09-08 10:41:05.975924:  
+2024-09-08 10:41:05.976105: Epoch 243 
+2024-09-08 10:41:05.976192: Current learning rate: 0.00778 
+2024-09-08 10:45:11.012342: train_loss -0.7754 
+2024-09-08 10:45:11.012492: val_loss -0.6728 
+2024-09-08 10:45:11.012549: Pseudo dice [0.6063, 0.8289] 
+2024-09-08 10:45:11.012606: Epoch time: 245.04 s 
+2024-09-08 10:45:12.110594:  
+2024-09-08 10:45:12.110860: Epoch 244 
+2024-09-08 10:45:12.110967: Current learning rate: 0.00777 
+2024-09-08 10:49:17.008859: train_loss -0.8012 
+2024-09-08 10:49:17.009011: val_loss -0.6742 
+2024-09-08 10:49:17.009066: Pseudo dice [0.6283, 0.8439] 
+2024-09-08 10:49:17.009122: Epoch time: 244.9 s 
+2024-09-08 10:49:17.950904:  
+2024-09-08 10:49:17.951082: Epoch 245 
+2024-09-08 10:49:17.951210: Current learning rate: 0.00777 
+2024-09-08 10:53:22.629034: train_loss -0.8133 
+2024-09-08 10:53:22.629181: val_loss -0.6778 
+2024-09-08 10:53:22.629283: Pseudo dice [0.622, 0.8512] 
+2024-09-08 10:53:22.629379: Epoch time: 244.68 s 
+2024-09-08 10:53:23.594280:  
+2024-09-08 10:53:23.594436: Epoch 246 
+2024-09-08 10:53:23.594517: Current learning rate: 0.00776 
+2024-09-08 10:57:28.517958: train_loss -0.8005 
+2024-09-08 10:57:28.518104: val_loss -0.665 
+2024-09-08 10:57:28.518199: Pseudo dice [0.6066, 0.8404] 
+2024-09-08 10:57:28.518256: Epoch time: 244.93 s 
+2024-09-08 10:57:29.478715:  
+2024-09-08 10:57:29.478937: Epoch 247 
+2024-09-08 10:57:29.479060: Current learning rate: 0.00775 
+2024-09-08 11:01:34.436764: train_loss -0.8039 
+2024-09-08 11:01:34.436904: val_loss -0.6558 
+2024-09-08 11:01:34.436962: Pseudo dice [0.5692, 0.847] 
+2024-09-08 11:01:34.437018: Epoch time: 244.96 s 
+2024-09-08 11:01:35.394569:  
+2024-09-08 11:01:35.394693: Epoch 248 
+2024-09-08 11:01:35.394774: Current learning rate: 0.00774 
+2024-09-08 11:05:40.568147: train_loss -0.8025 
+2024-09-08 11:05:40.568297: val_loss -0.6615 
+2024-09-08 11:05:40.568353: Pseudo dice [0.5656, 0.8562] 
+2024-09-08 11:05:40.568413: Epoch time: 245.18 s 
+2024-09-08 11:05:41.508733:  
+2024-09-08 11:05:41.508884: Epoch 249 
+2024-09-08 11:05:41.508973: Current learning rate: 0.00773 
+2024-09-08 11:09:46.633182: train_loss -0.8027 
+2024-09-08 11:09:46.633331: val_loss -0.6816 
+2024-09-08 11:09:46.633386: Pseudo dice [0.5965, 0.8432] 
+2024-09-08 11:09:46.633441: Epoch time: 245.13 s 
+2024-09-08 11:09:50.533592:  
+2024-09-08 11:09:50.533739: Epoch 250 
+2024-09-08 11:09:50.533822: Current learning rate: 0.00772 
+2024-09-08 11:13:55.654026: train_loss -0.7969 
+2024-09-08 11:13:55.654190: val_loss -0.6695 
+2024-09-08 11:13:55.654257: Pseudo dice [0.6143, 0.8382] 
+2024-09-08 11:13:55.654325: Epoch time: 245.12 s 
+2024-09-08 11:13:56.760360:  
+2024-09-08 11:13:56.760541: Epoch 251 
+2024-09-08 11:13:56.760628: Current learning rate: 0.00771 
+2024-09-08 11:18:01.856750: train_loss -0.7995 
+2024-09-08 11:18:01.856949: val_loss -0.6643 
+2024-09-08 11:18:01.857008: Pseudo dice [0.5605, 0.8511] 
+2024-09-08 11:18:01.857069: Epoch time: 245.1 s 
+2024-09-08 11:18:02.843893:  
+2024-09-08 11:18:02.844117: Epoch 252 
+2024-09-08 11:18:02.844198: Current learning rate: 0.0077 
+2024-09-08 11:22:07.782475: train_loss -0.8207 
+2024-09-08 11:22:07.782666: val_loss -0.6571 
+2024-09-08 11:22:07.782725: Pseudo dice [0.5533, 0.8471] 
+2024-09-08 11:22:07.782782: Epoch time: 244.94 s 
+2024-09-08 11:22:08.726430:  
+2024-09-08 11:22:08.726590: Epoch 253 
+2024-09-08 11:22:08.726682: Current learning rate: 0.00769 
+2024-09-08 11:26:13.713059: train_loss -0.8223 
+2024-09-08 11:26:13.713231: val_loss -0.6982 
+2024-09-08 11:26:13.713288: Pseudo dice [0.6164, 0.8651] 
+2024-09-08 11:26:13.713343: Epoch time: 244.99 s 
+2024-09-08 11:26:14.662154:  
+2024-09-08 11:26:14.662338: Epoch 254 
+2024-09-08 11:26:14.662433: Current learning rate: 0.00768 
+2024-09-08 11:30:19.662217: train_loss -0.8261 
+2024-09-08 11:30:19.662377: val_loss -0.6432 
+2024-09-08 11:30:19.662434: Pseudo dice [0.5633, 0.8271] 
+2024-09-08 11:30:19.662488: Epoch time: 245.0 s 
+2024-09-08 11:30:20.612204:  
+2024-09-08 11:30:20.612359: Epoch 255 
+2024-09-08 11:30:20.612439: Current learning rate: 0.00767 
+2024-09-08 11:34:25.528478: train_loss -0.8015 
+2024-09-08 11:34:25.528636: val_loss -0.6597 
+2024-09-08 11:34:25.528692: Pseudo dice [0.6192, 0.8452] 
+2024-09-08 11:34:25.528747: Epoch time: 244.92 s 
+2024-09-08 11:34:26.493476:  
+2024-09-08 11:34:26.493648: Epoch 256 
+2024-09-08 11:34:26.493800: Current learning rate: 0.00766 
+2024-09-08 11:38:31.486856: train_loss -0.8051 
+2024-09-08 11:38:31.487000: val_loss -0.6673 
+2024-09-08 11:38:31.487056: Pseudo dice [0.5923, 0.8378] 
+2024-09-08 11:38:31.487110: Epoch time: 245.0 s 
+2024-09-08 11:38:33.342766:  
+2024-09-08 11:38:33.342969: Epoch 257 
+2024-09-08 11:38:33.343071: Current learning rate: 0.00765 
+2024-09-08 11:42:38.307930: train_loss -0.7942 
+2024-09-08 11:42:38.308077: val_loss -0.6879 
+2024-09-08 11:42:38.308132: Pseudo dice [0.6154, 0.8387] 
+2024-09-08 11:42:38.308188: Epoch time: 244.97 s 
+2024-09-08 11:42:39.249601:  
+2024-09-08 11:42:39.249815: Epoch 258 
+2024-09-08 11:42:39.249921: Current learning rate: 0.00764 
+2024-09-08 11:46:44.175062: train_loss -0.8131 
+2024-09-08 11:46:44.175267: val_loss -0.6894 
+2024-09-08 11:46:44.175348: Pseudo dice [0.6214, 0.8591] 
+2024-09-08 11:46:44.175404: Epoch time: 244.93 s 
+2024-09-08 11:46:45.127005:  
+2024-09-08 11:46:45.127196: Epoch 259 
+2024-09-08 11:46:45.127282: Current learning rate: 0.00764 
+2024-09-08 11:50:49.919691: train_loss -0.8107 
+2024-09-08 11:50:49.919852: val_loss -0.6715 
+2024-09-08 11:50:49.919909: Pseudo dice [0.6135, 0.8383] 
+2024-09-08 11:50:49.919963: Epoch time: 244.79 s 
+2024-09-08 11:50:50.866639:  
+2024-09-08 11:50:50.866860: Epoch 260 
+2024-09-08 11:50:50.866941: Current learning rate: 0.00763 
+2024-09-08 11:54:55.715273: train_loss -0.8165 
+2024-09-08 11:54:55.715446: val_loss -0.6605 
+2024-09-08 11:54:55.715501: Pseudo dice [0.6067, 0.8347] 
+2024-09-08 11:54:55.715558: Epoch time: 244.85 s 
+2024-09-08 11:54:56.694388:  
+2024-09-08 11:54:56.694693: Epoch 261 
+2024-09-08 11:54:56.694824: Current learning rate: 0.00762 
+2024-09-08 11:59:01.568210: train_loss -0.8162 
+2024-09-08 11:59:01.568368: val_loss -0.659 
+2024-09-08 11:59:01.568423: Pseudo dice [0.5417, 0.8552] 
+2024-09-08 11:59:01.568478: Epoch time: 244.88 s 
+2024-09-08 11:59:02.516293:  
+2024-09-08 11:59:02.516495: Epoch 262 
+2024-09-08 11:59:02.516593: Current learning rate: 0.00761 
+2024-09-08 12:03:07.260120: train_loss -0.821 
+2024-09-08 12:03:07.260268: val_loss -0.6844 
+2024-09-08 12:03:07.260324: Pseudo dice [0.5919, 0.8638] 
+2024-09-08 12:03:07.260383: Epoch time: 244.75 s 
+2024-09-08 12:03:08.202908:  
+2024-09-08 12:03:08.203074: Epoch 263 
+2024-09-08 12:03:08.203160: Current learning rate: 0.0076 
+2024-09-08 12:07:13.179780: train_loss -0.825 
+2024-09-08 12:07:13.179980: val_loss -0.6772 
+2024-09-08 12:07:13.180064: Pseudo dice [0.6195, 0.8491] 
+2024-09-08 12:07:13.180121: Epoch time: 244.98 s 
+2024-09-08 12:07:14.304918:  
+2024-09-08 12:07:14.305162: Epoch 264 
+2024-09-08 12:07:14.305262: Current learning rate: 0.00759 
+2024-09-08 12:11:19.500607: train_loss -0.822 
+2024-09-08 12:11:19.500761: val_loss -0.6758 
+2024-09-08 12:11:19.500818: Pseudo dice [0.6215, 0.8488] 
+2024-09-08 12:11:19.500876: Epoch time: 245.2 s 
+2024-09-08 12:11:20.453022:  
+2024-09-08 12:11:20.453292: Epoch 265 
+2024-09-08 12:11:20.453393: Current learning rate: 0.00758 
+2024-09-08 12:15:25.644061: train_loss -0.8267 
+2024-09-08 12:15:25.644210: val_loss -0.6587 
+2024-09-08 12:15:25.644266: Pseudo dice [0.5656, 0.8473] 
+2024-09-08 12:15:25.644321: Epoch time: 245.19 s 
+2024-09-08 12:15:26.609439:  
+2024-09-08 12:15:26.609629: Epoch 266 
+2024-09-08 12:15:26.609717: Current learning rate: 0.00757 
+2024-09-08 12:19:31.758845: train_loss -0.8295 
+2024-09-08 12:19:31.759045: val_loss -0.6816 
+2024-09-08 12:19:31.759118: Pseudo dice [0.6267, 0.8553] 
+2024-09-08 12:19:31.759193: Epoch time: 245.15 s 
+2024-09-08 12:19:32.807859:  
+2024-09-08 12:19:32.808223: Epoch 267 
+2024-09-08 12:19:32.808335: Current learning rate: 0.00756 
+2024-09-08 12:23:37.825383: train_loss -0.8161 
+2024-09-08 12:23:37.825528: val_loss -0.6754 
+2024-09-08 12:23:37.825585: Pseudo dice [0.5838, 0.8547] 
+2024-09-08 12:23:37.825640: Epoch time: 245.02 s 
+2024-09-08 12:23:38.793169:  
+2024-09-08 12:23:38.793327: Epoch 268 
+2024-09-08 12:23:38.793408: Current learning rate: 0.00755 
+2024-09-08 12:27:43.772681: train_loss -0.8195 
+2024-09-08 12:27:43.772825: val_loss -0.6846 
+2024-09-08 12:27:43.772881: Pseudo dice [0.5907, 0.8604] 
+2024-09-08 12:27:43.772936: Epoch time: 244.98 s 
+2024-09-08 12:27:44.721223:  
+2024-09-08 12:27:44.721447: Epoch 269 
+2024-09-08 12:27:44.721582: Current learning rate: 0.00754 
+2024-09-08 12:31:49.599206: train_loss -0.8099 
+2024-09-08 12:31:49.599373: val_loss -0.6501 
+2024-09-08 12:31:49.599430: Pseudo dice [0.5782, 0.8544] 
+2024-09-08 12:31:49.599497: Epoch time: 244.88 s 
+2024-09-08 12:31:50.671169:  
+2024-09-08 12:31:50.671398: Epoch 270 
+2024-09-08 12:31:50.671489: Current learning rate: 0.00753 
+2024-09-08 12:35:55.384546: train_loss -0.8299 
+2024-09-08 12:35:55.384695: val_loss -0.6859 
+2024-09-08 12:35:55.384746: Pseudo dice [0.5836, 0.8579] 
+2024-09-08 12:35:55.384797: Epoch time: 244.72 s 
+2024-09-08 12:35:56.566155:  
+2024-09-08 12:35:56.566538: Epoch 271 
+2024-09-08 12:35:56.566708: Current learning rate: 0.00752 
+2024-09-08 12:40:01.305228: train_loss -0.832 
+2024-09-08 12:40:01.305382: val_loss -0.7019 
+2024-09-08 12:40:01.305432: Pseudo dice [0.6307, 0.8522] 
+2024-09-08 12:40:01.305484: Epoch time: 244.74 s 
+2024-09-08 12:40:02.260809:  
+2024-09-08 12:40:02.261040: Epoch 272 
+2024-09-08 12:40:02.261120: Current learning rate: 0.00751 
+2024-09-08 12:44:07.013273: train_loss -0.8342 
+2024-09-08 12:44:07.013413: val_loss -0.6946 
+2024-09-08 12:44:07.013519: Pseudo dice [0.6333, 0.8471] 
+2024-09-08 12:44:07.013573: Epoch time: 244.75 s 
+2024-09-08 12:44:07.964021:  
+2024-09-08 12:44:07.964240: Epoch 273 
+2024-09-08 12:44:07.964323: Current learning rate: 0.00751 
+2024-09-08 12:48:12.931561: train_loss -0.8213 
+2024-09-08 12:48:12.931698: val_loss -0.683 
+2024-09-08 12:48:12.931748: Pseudo dice [0.585, 0.85] 
+2024-09-08 12:48:12.931799: Epoch time: 244.97 s 
+2024-09-08 12:48:13.882973:  
+2024-09-08 12:48:13.883157: Epoch 274 
+2024-09-08 12:48:13.883236: Current learning rate: 0.0075 
+2024-09-08 12:52:18.804072: train_loss -0.8169 
+2024-09-08 12:52:18.804270: val_loss -0.6878 
+2024-09-08 12:52:18.804340: Pseudo dice [0.6224, 0.8531] 
+2024-09-08 12:52:18.804412: Epoch time: 244.92 s 
+2024-09-08 12:52:19.943395:  
+2024-09-08 12:52:19.943661: Epoch 275 
+2024-09-08 12:52:19.943753: Current learning rate: 0.00749 
+2024-09-08 12:56:24.867705: train_loss -0.8278 
+2024-09-08 12:56:24.867851: val_loss -0.6392 
+2024-09-08 12:56:24.867903: Pseudo dice [0.5329, 0.8499] 
+2024-09-08 12:56:24.867959: Epoch time: 244.93 s 
+2024-09-08 12:56:25.829598:  
+2024-09-08 12:56:25.829788: Epoch 276 
+2024-09-08 12:56:25.829867: Current learning rate: 0.00748 
+2024-09-08 13:00:30.786720: train_loss -0.8259 
+2024-09-08 13:00:30.786857: val_loss -0.6809 
+2024-09-08 13:00:30.786909: Pseudo dice [0.6273, 0.8504] 
+2024-09-08 13:00:30.786963: Epoch time: 244.96 s 
+2024-09-08 13:00:31.730948:  
+2024-09-08 13:00:31.731156: Epoch 277 
+2024-09-08 13:00:31.731236: Current learning rate: 0.00747 
+2024-09-08 13:04:36.782922: train_loss -0.82 
+2024-09-08 13:04:36.783082: val_loss -0.6703 
+2024-09-08 13:04:36.783155: Pseudo dice [0.5981, 0.8449] 
+2024-09-08 13:04:36.783228: Epoch time: 245.05 s 
+2024-09-08 13:04:37.739837:  
+2024-09-08 13:04:37.739993: Epoch 278 
+2024-09-08 13:04:37.740069: Current learning rate: 0.00746 
+2024-09-08 13:08:42.965531: train_loss -0.8099 
+2024-09-08 13:08:42.965683: val_loss -0.6759 
+2024-09-08 13:08:42.965733: Pseudo dice [0.5822, 0.8528] 
+2024-09-08 13:08:42.965786: Epoch time: 245.23 s 
+2024-09-08 13:08:43.914891:  
+2024-09-08 13:08:43.915045: Epoch 279 
+2024-09-08 13:08:43.915151: Current learning rate: 0.00745 
+2024-09-08 13:12:49.074894: train_loss -0.8303 
+2024-09-08 13:12:49.075063: val_loss -0.6532 
+2024-09-08 13:12:49.075118: Pseudo dice [0.5259, 0.8566] 
+2024-09-08 13:12:49.075173: Epoch time: 245.16 s 
+2024-09-08 13:12:50.032096:  
+2024-09-08 13:12:50.032304: Epoch 280 
+2024-09-08 13:12:50.032383: Current learning rate: 0.00744 
+2024-09-08 13:16:55.156910: train_loss -0.8296 
+2024-09-08 13:16:55.157048: val_loss -0.6919 
+2024-09-08 13:16:55.157098: Pseudo dice [0.6186, 0.8585] 
+2024-09-08 13:16:55.157150: Epoch time: 245.13 s 
+2024-09-08 13:16:57.016260:  
+2024-09-08 13:16:57.016476: Epoch 281 
+2024-09-08 13:16:57.016613: Current learning rate: 0.00743 
+2024-09-08 13:21:02.041269: train_loss -0.8327 
+2024-09-08 13:21:02.041413: val_loss -0.6305 
+2024-09-08 13:21:02.041467: Pseudo dice [0.5294, 0.8486] 
+2024-09-08 13:21:02.041523: Epoch time: 245.03 s 
+2024-09-08 13:21:03.102174:  
+2024-09-08 13:21:03.102422: Epoch 282 
+2024-09-08 13:21:03.102512: Current learning rate: 0.00742 
+2024-09-08 13:25:08.322938: train_loss -0.8179 
+2024-09-08 13:25:08.323076: val_loss -0.6491 
+2024-09-08 13:25:08.323126: Pseudo dice [0.5786, 0.8371] 
+2024-09-08 13:25:08.323179: Epoch time: 245.22 s 
+2024-09-08 13:25:09.266966:  
+2024-09-08 13:25:09.267153: Epoch 283 
+2024-09-08 13:25:09.267266: Current learning rate: 0.00741 
+2024-09-08 13:29:14.373407: train_loss -0.8266 
+2024-09-08 13:29:14.373548: val_loss -0.6749 
+2024-09-08 13:29:14.373603: Pseudo dice [0.6182, 0.835] 
+2024-09-08 13:29:14.373653: Epoch time: 245.11 s 
+2024-09-08 13:29:15.323183:  
+2024-09-08 13:29:15.323395: Epoch 284 
+2024-09-08 13:29:15.323477: Current learning rate: 0.0074 
+2024-09-08 13:33:20.549194: train_loss -0.8298 
+2024-09-08 13:33:20.549353: val_loss -0.6891 
+2024-09-08 13:33:20.549402: Pseudo dice [0.6244, 0.8645] 
+2024-09-08 13:33:20.549455: Epoch time: 245.23 s 
+2024-09-08 13:33:21.515727:  
+2024-09-08 13:33:21.515973: Epoch 285 
+2024-09-08 13:33:21.516052: Current learning rate: 0.00739 
+2024-09-08 13:37:26.936081: train_loss -0.8359 
+2024-09-08 13:37:26.936220: val_loss -0.7079 
+2024-09-08 13:37:26.936270: Pseudo dice [0.6192, 0.8638] 
+2024-09-08 13:37:26.936320: Epoch time: 245.42 s 
+2024-09-08 13:37:27.885151:  
+2024-09-08 13:37:27.885379: Epoch 286 
+2024-09-08 13:37:27.885466: Current learning rate: 0.00738 
+2024-09-08 13:41:33.285484: train_loss -0.8423 
+2024-09-08 13:41:33.285622: val_loss -0.6976 
+2024-09-08 13:41:33.285672: Pseudo dice [0.636, 0.8556] 
+2024-09-08 13:41:33.285722: Epoch time: 245.4 s 
+2024-09-08 13:41:34.264380:  
+2024-09-08 13:41:34.264614: Epoch 287 
+2024-09-08 13:41:34.264692: Current learning rate: 0.00738 
+2024-09-08 13:45:39.547585: train_loss -0.8393 
+2024-09-08 13:45:39.547721: val_loss -0.6352 
+2024-09-08 13:45:39.547771: Pseudo dice [0.5175, 0.861] 
+2024-09-08 13:45:39.547830: Epoch time: 245.29 s 
+2024-09-08 13:45:40.520984:  
+2024-09-08 13:45:40.521201: Epoch 288 
+2024-09-08 13:45:40.521284: Current learning rate: 0.00737 
+2024-09-08 13:49:45.956020: train_loss -0.8314 
+2024-09-08 13:49:45.956243: val_loss -0.6812 
+2024-09-08 13:49:45.956296: Pseudo dice [0.6049, 0.849] 
+2024-09-08 13:49:45.956347: Epoch time: 245.44 s 
+2024-09-08 13:49:46.960495:  
+2024-09-08 13:49:46.960723: Epoch 289 
+2024-09-08 13:49:46.960804: Current learning rate: 0.00736 
+2024-09-08 13:53:52.411541: train_loss -0.8302 
+2024-09-08 13:53:52.411699: val_loss -0.6897 
+2024-09-08 13:53:52.411749: Pseudo dice [0.6313, 0.8597] 
+2024-09-08 13:53:52.411798: Epoch time: 245.45 s 
+2024-09-08 13:53:53.391238:  
+2024-09-08 13:53:53.391426: Epoch 290 
+2024-09-08 13:53:53.391508: Current learning rate: 0.00735 
+2024-09-08 13:57:58.671016: train_loss -0.8329 
+2024-09-08 13:57:58.671154: val_loss -0.7026 
+2024-09-08 13:57:58.671205: Pseudo dice [0.6278, 0.8558] 
+2024-09-08 13:57:58.671257: Epoch time: 245.28 s 
+2024-09-08 13:57:59.637553:  
+2024-09-08 13:57:59.637705: Epoch 291 
+2024-09-08 13:57:59.637789: Current learning rate: 0.00734 
+2024-09-08 14:02:04.812143: train_loss -0.8334 
+2024-09-08 14:02:04.812396: val_loss -0.6718 
+2024-09-08 14:02:04.812458: Pseudo dice [0.5918, 0.8519] 
+2024-09-08 14:02:04.812597: Epoch time: 245.18 s 
+2024-09-08 14:02:05.979990:  
+2024-09-08 14:02:05.980188: Epoch 292 
+2024-09-08 14:02:05.980265: Current learning rate: 0.00733 
+2024-09-08 14:06:11.115636: train_loss -0.8294 
+2024-09-08 14:06:11.115775: val_loss -0.6912 
+2024-09-08 14:06:11.115866: Pseudo dice [0.6193, 0.8452] 
+2024-09-08 14:06:11.115952: Epoch time: 245.14 s 
+2024-09-08 14:06:12.109336:  
+2024-09-08 14:06:12.109496: Epoch 293 
+2024-09-08 14:06:12.109576: Current learning rate: 0.00732 
+2024-09-08 14:10:17.421966: train_loss -0.831 
+2024-09-08 14:10:17.422103: val_loss -0.6585 
+2024-09-08 14:10:17.422154: Pseudo dice [0.5971, 0.8515] 
+2024-09-08 14:10:17.422209: Epoch time: 245.31 s 
+2024-09-08 14:10:18.390479:  
+2024-09-08 14:10:18.390639: Epoch 294 
+2024-09-08 14:10:18.390722: Current learning rate: 0.00731 
+2024-09-08 14:14:23.721985: train_loss -0.8279 
+2024-09-08 14:14:23.722183: val_loss -0.6903 
+2024-09-08 14:14:23.722270: Pseudo dice [0.6178, 0.8558] 
+2024-09-08 14:14:23.722355: Epoch time: 245.33 s 
+2024-09-08 14:14:24.905735:  
+2024-09-08 14:14:24.906004: Epoch 295 
+2024-09-08 14:14:24.906118: Current learning rate: 0.0073 
+2024-09-08 14:18:30.211320: train_loss -0.8154 
+2024-09-08 14:18:30.211461: val_loss -0.6773 
+2024-09-08 14:18:30.211513: Pseudo dice [0.5997, 0.8453] 
+2024-09-08 14:18:30.211564: Epoch time: 245.31 s 
+2024-09-08 14:18:31.181687:  
+2024-09-08 14:18:31.181931: Epoch 296 
+2024-09-08 14:18:31.182013: Current learning rate: 0.00729 
+2024-09-08 14:22:36.450479: train_loss -0.8243 
+2024-09-08 14:22:36.450618: val_loss -0.6566 
+2024-09-08 14:22:36.450668: Pseudo dice [0.5733, 0.8504] 
+2024-09-08 14:22:36.450718: Epoch time: 245.27 s 
+2024-09-08 14:22:37.424010:  
+2024-09-08 14:22:37.424281: Epoch 297 
+2024-09-08 14:22:37.424363: Current learning rate: 0.00728 
+2024-09-08 14:26:42.554806: train_loss -0.8371 
+2024-09-08 14:26:42.554952: val_loss -0.6307 
+2024-09-08 14:26:42.555002: Pseudo dice [0.4934, 0.8405] 
+2024-09-08 14:26:42.555054: Epoch time: 245.13 s 
+2024-09-08 14:26:43.527793:  
+2024-09-08 14:26:43.527978: Epoch 298 
+2024-09-08 14:26:43.528058: Current learning rate: 0.00727 
+2024-09-08 14:30:48.753346: train_loss -0.8424 
+2024-09-08 14:30:48.753501: val_loss -0.6851 
+2024-09-08 14:30:48.753551: Pseudo dice [0.6196, 0.8561] 
+2024-09-08 14:30:48.753602: Epoch time: 245.23 s 
+2024-09-08 14:30:49.716439:  
+2024-09-08 14:30:49.716577: Epoch 299 
+2024-09-08 14:30:49.716659: Current learning rate: 0.00726 
+2024-09-08 14:34:55.159066: train_loss -0.8453 
+2024-09-08 14:34:55.159205: val_loss -0.7061 
+2024-09-08 14:34:55.159255: Pseudo dice [0.6331, 0.8627] 
+2024-09-08 14:34:55.159305: Epoch time: 245.44 s 
+2024-09-08 14:34:59.071362:  
+2024-09-08 14:34:59.071519: Epoch 300 
+2024-09-08 14:34:59.071599: Current learning rate: 0.00725 
+2024-09-08 14:39:04.358798: train_loss -0.845 
+2024-09-08 14:39:04.358935: val_loss -0.6868 
+2024-09-08 14:39:04.358984: Pseudo dice [0.6074, 0.8654] 
+2024-09-08 14:39:04.359035: Epoch time: 245.29 s 
+2024-09-08 14:39:05.372502:  
+2024-09-08 14:39:05.372680: Epoch 301 
+2024-09-08 14:39:05.372767: Current learning rate: 0.00724 
+2024-09-08 14:43:10.742571: train_loss -0.8373 
+2024-09-08 14:43:10.742723: val_loss -0.6733 
+2024-09-08 14:43:10.742773: Pseudo dice [0.5955, 0.8581] 
+2024-09-08 14:43:10.742823: Epoch time: 245.37 s 
+2024-09-08 14:43:11.711176:  
+2024-09-08 14:43:11.711338: Epoch 302 
+2024-09-08 14:43:11.711417: Current learning rate: 0.00724 
+2024-09-08 14:47:17.022410: train_loss -0.82 
+2024-09-08 14:47:17.022592: val_loss -0.6794 
+2024-09-08 14:47:17.022645: Pseudo dice [0.619, 0.8446] 
+2024-09-08 14:47:17.022731: Epoch time: 245.31 s 
+2024-09-08 14:47:17.994989:  
+2024-09-08 14:47:17.995181: Epoch 303 
+2024-09-08 14:47:17.995276: Current learning rate: 0.00723 
+2024-09-08 14:51:23.373544: train_loss -0.8128 
+2024-09-08 14:51:23.373677: val_loss -0.634 
+2024-09-08 14:51:23.373727: Pseudo dice [0.5371, 0.8251] 
+2024-09-08 14:51:23.373780: Epoch time: 245.38 s 
+2024-09-08 14:51:25.249990:  
+2024-09-08 14:51:25.250241: Epoch 304 
+2024-09-08 14:51:25.250359: Current learning rate: 0.00722 
+2024-09-08 14:55:30.691489: train_loss -0.8134 
+2024-09-08 14:55:30.691633: val_loss -0.6741 
+2024-09-08 14:55:30.691683: Pseudo dice [0.5792, 0.8554] 
+2024-09-08 14:55:30.691735: Epoch time: 245.44 s 
+2024-09-08 14:55:31.728902:  
+2024-09-08 14:55:31.729133: Epoch 305 
+2024-09-08 14:55:31.729252: Current learning rate: 0.00721 
+2024-09-08 14:59:37.178337: train_loss -0.7846 
+2024-09-08 14:59:37.178492: val_loss -0.6722 
+2024-09-08 14:59:37.178569: Pseudo dice [0.6101, 0.8464] 
+2024-09-08 14:59:37.178621: Epoch time: 245.45 s 
+2024-09-08 14:59:38.144643:  
+2024-09-08 14:59:38.144807: Epoch 306 
+2024-09-08 14:59:38.144942: Current learning rate: 0.0072 
+2024-09-08 15:03:43.370769: train_loss -0.7957 
+2024-09-08 15:03:43.370907: val_loss -0.6766 
+2024-09-08 15:03:43.370957: Pseudo dice [0.6174, 0.8457] 
+2024-09-08 15:03:43.371074: Epoch time: 245.23 s 
+2024-09-08 15:03:44.331718:  
+2024-09-08 15:03:44.331887: Epoch 307 
+2024-09-08 15:03:44.331967: Current learning rate: 0.00719 
+2024-09-08 15:07:49.606365: train_loss -0.8097 
+2024-09-08 15:07:49.606576: val_loss -0.6863 
+2024-09-08 15:07:49.606634: Pseudo dice [0.65, 0.8519] 
+2024-09-08 15:07:49.606703: Epoch time: 245.28 s 
+2024-09-08 15:07:50.712064:  
+2024-09-08 15:07:50.712241: Epoch 308 
+2024-09-08 15:07:50.712324: Current learning rate: 0.00718 
+2024-09-08 15:11:55.823130: train_loss -0.8238 
+2024-09-08 15:11:55.823290: val_loss -0.6925 
+2024-09-08 15:11:55.823348: Pseudo dice [0.6089, 0.8587] 
+2024-09-08 15:11:55.823404: Epoch time: 245.11 s 
+2024-09-08 15:11:56.900107:  
+2024-09-08 15:11:56.900342: Epoch 309 
+2024-09-08 15:11:56.900431: Current learning rate: 0.00717 
+2024-09-08 15:16:02.000468: train_loss -0.8181 
+2024-09-08 15:16:02.000614: val_loss -0.6451 
+2024-09-08 15:16:02.000670: Pseudo dice [0.5622, 0.8377] 
+2024-09-08 15:16:02.000725: Epoch time: 245.1 s 
+2024-09-08 15:16:02.978743:  
+2024-09-08 15:16:02.978965: Epoch 310 
+2024-09-08 15:16:02.979084: Current learning rate: 0.00716 
+2024-09-08 15:20:08.273033: train_loss -0.8196 
+2024-09-08 15:20:08.273245: val_loss -0.702 
+2024-09-08 15:20:08.273304: Pseudo dice [0.6047, 0.8641] 
+2024-09-08 15:20:08.273362: Epoch time: 245.3 s 
+2024-09-08 15:20:09.275552:  
+2024-09-08 15:20:09.275710: Epoch 311 
+2024-09-08 15:20:09.275798: Current learning rate: 0.00715 
+2024-09-08 15:24:14.425525: train_loss -0.8337 
+2024-09-08 15:24:14.425678: val_loss -0.6775 
+2024-09-08 15:24:14.425734: Pseudo dice [0.6017, 0.8518] 
+2024-09-08 15:24:14.425790: Epoch time: 245.15 s 
+2024-09-08 15:24:15.385747:  
+2024-09-08 15:24:15.385952: Epoch 312 
+2024-09-08 15:24:15.386036: Current learning rate: 0.00714 
+2024-09-08 15:28:20.396164: train_loss -0.8047 
+2024-09-08 15:28:20.396311: val_loss -0.6989 
+2024-09-08 15:28:20.396367: Pseudo dice [0.6226, 0.8524] 
+2024-09-08 15:28:20.396423: Epoch time: 245.01 s 
+2024-09-08 15:28:21.398489:  
+2024-09-08 15:28:21.398745: Epoch 313 
+2024-09-08 15:28:21.398887: Current learning rate: 0.00713 
+2024-09-08 15:32:26.581774: train_loss -0.8206 
+2024-09-08 15:32:26.581958: val_loss -0.6748 
+2024-09-08 15:32:26.582053: Pseudo dice [0.6336, 0.8463] 
+2024-09-08 15:32:26.582117: Epoch time: 245.19 s 
+2024-09-08 15:32:27.550152:  
+2024-09-08 15:32:27.550432: Epoch 314 
+2024-09-08 15:32:27.550541: Current learning rate: 0.00712 
+2024-09-08 15:36:32.641815: train_loss -0.825 
+2024-09-08 15:36:32.641995: val_loss -0.6724 
+2024-09-08 15:36:32.642075: Pseudo dice [0.5804, 0.8557] 
+2024-09-08 15:36:32.642132: Epoch time: 245.09 s 
+2024-09-08 15:36:33.608572:  
+2024-09-08 15:36:33.608749: Epoch 315 
+2024-09-08 15:36:33.608860: Current learning rate: 0.00711 
+2024-09-08 15:40:38.633711: train_loss -0.8212 
+2024-09-08 15:40:38.633864: val_loss -0.6998 
+2024-09-08 15:40:38.633920: Pseudo dice [0.6312, 0.8519] 
+2024-09-08 15:40:38.633975: Epoch time: 245.03 s 
+2024-09-08 15:40:39.620666:  
+2024-09-08 15:40:39.620864: Epoch 316 
+2024-09-08 15:40:39.620949: Current learning rate: 0.0071 
+2024-09-08 15:44:44.654631: train_loss -0.8191 
+2024-09-08 15:44:44.654818: val_loss -0.6908 
+2024-09-08 15:44:44.654878: Pseudo dice [0.6344, 0.8435] 
+2024-09-08 15:44:44.654951: Epoch time: 245.04 s 
+2024-09-08 15:44:45.848663:  
+2024-09-08 15:44:45.848929: Epoch 317 
+2024-09-08 15:44:45.849051: Current learning rate: 0.0071 
+2024-09-08 15:48:50.910123: train_loss -0.8415 
+2024-09-08 15:48:50.910268: val_loss -0.7 
+2024-09-08 15:48:50.910325: Pseudo dice [0.6226, 0.8652] 
+2024-09-08 15:48:50.910381: Epoch time: 245.06 s 
+2024-09-08 15:48:51.888345:  
+2024-09-08 15:48:51.888518: Epoch 318 
+2024-09-08 15:48:51.888603: Current learning rate: 0.00709 
+2024-09-08 15:52:56.876218: train_loss -0.84 
+2024-09-08 15:52:56.876366: val_loss -0.6949 
+2024-09-08 15:52:56.876424: Pseudo dice [0.628, 0.8567] 
+2024-09-08 15:52:56.876479: Epoch time: 244.99 s 
+2024-09-08 15:52:57.836711:  
+2024-09-08 15:52:57.836891: Epoch 319 
+2024-09-08 15:52:57.836978: Current learning rate: 0.00708 
+2024-09-08 15:57:02.883811: train_loss -0.8428 
+2024-09-08 15:57:02.883962: val_loss -0.7067 
+2024-09-08 15:57:02.884018: Pseudo dice [0.6195, 0.8604] 
+2024-09-08 15:57:02.884074: Epoch time: 245.05 s 
+2024-09-08 15:57:03.915724:  
+2024-09-08 15:57:03.915932: Epoch 320 
+2024-09-08 15:57:03.916028: Current learning rate: 0.00707 
+2024-09-08 16:01:09.178266: train_loss -0.8439 
+2024-09-08 16:01:09.178502: val_loss -0.6917 
+2024-09-08 16:01:09.178592: Pseudo dice [0.6106, 0.8517] 
+2024-09-08 16:01:09.178670: Epoch time: 245.26 s 
+2024-09-08 16:01:10.382886:  
+2024-09-08 16:01:10.383164: Epoch 321 
+2024-09-08 16:01:10.383266: Current learning rate: 0.00706 
+2024-09-08 16:05:15.759131: train_loss -0.8435 
+2024-09-08 16:05:15.759277: val_loss -0.6593 
+2024-09-08 16:05:15.759333: Pseudo dice [0.562, 0.859] 
+2024-09-08 16:05:15.759463: Epoch time: 245.38 s 
+2024-09-08 16:05:16.760568:  
+2024-09-08 16:05:16.760715: Epoch 322 
+2024-09-08 16:05:16.760846: Current learning rate: 0.00705 
+2024-09-08 16:09:22.171542: train_loss -0.8498 
+2024-09-08 16:09:22.171692: val_loss -0.6811 
+2024-09-08 16:09:22.171748: Pseudo dice [0.6043, 0.8529] 
+2024-09-08 16:09:22.171818: Epoch time: 245.41 s 
+2024-09-08 16:09:23.135175:  
+2024-09-08 16:09:23.135337: Epoch 323 
+2024-09-08 16:09:23.135422: Current learning rate: 0.00704 
+2024-09-08 16:13:28.471329: train_loss -0.8428 
+2024-09-08 16:13:28.471477: val_loss -0.6885 
+2024-09-08 16:13:28.471539: Pseudo dice [0.6078, 0.8627] 
+2024-09-08 16:13:28.471595: Epoch time: 245.34 s 
+2024-09-08 16:13:29.463106:  
+2024-09-08 16:13:29.463315: Epoch 324 
+2024-09-08 16:13:29.463403: Current learning rate: 0.00703 
+2024-09-08 16:17:34.769498: train_loss -0.8351 
+2024-09-08 16:17:34.769634: val_loss -0.6807 
+2024-09-08 16:17:34.769689: Pseudo dice [0.602, 0.849] 
+2024-09-08 16:17:34.769746: Epoch time: 245.31 s 
+2024-09-08 16:17:35.754390:  
+2024-09-08 16:17:35.754597: Epoch 325 
+2024-09-08 16:17:35.754682: Current learning rate: 0.00702 
+2024-09-08 16:21:41.121915: train_loss -0.8376 
+2024-09-08 16:21:41.122072: val_loss -0.6849 
+2024-09-08 16:21:41.122128: Pseudo dice [0.6218, 0.8649] 
+2024-09-08 16:21:41.122184: Epoch time: 245.37 s 
+2024-09-08 16:21:42.084767:  
+2024-09-08 16:21:42.084969: Epoch 326 
+2024-09-08 16:21:42.085049: Current learning rate: 0.00701 
+2024-09-08 16:25:47.390364: train_loss -0.8373 
+2024-09-08 16:25:47.390511: val_loss -0.6852 
+2024-09-08 16:25:47.390566: Pseudo dice [0.6018, 0.8675] 
+2024-09-08 16:25:47.390622: Epoch time: 245.31 s 
+2024-09-08 16:25:48.363439:  
+2024-09-08 16:25:48.363577: Epoch 327 
+2024-09-08 16:25:48.363664: Current learning rate: 0.007 
+2024-09-08 16:29:54.214671: train_loss -0.8304 
+2024-09-08 16:29:54.214813: val_loss -0.6878 
+2024-09-08 16:29:54.214868: Pseudo dice [0.602, 0.8575] 
+2024-09-08 16:29:54.214964: Epoch time: 245.85 s 
+2024-09-08 16:29:55.185482:  
+2024-09-08 16:29:55.185707: Epoch 328 
+2024-09-08 16:29:55.185809: Current learning rate: 0.00699 
+2024-09-08 16:34:00.518754: train_loss -0.8176 
+2024-09-08 16:34:00.518902: val_loss -0.6883 
+2024-09-08 16:34:00.518958: Pseudo dice [0.6191, 0.8522] 
+2024-09-08 16:34:00.519014: Epoch time: 245.34 s 
+2024-09-08 16:34:01.485165:  
+2024-09-08 16:34:01.485395: Epoch 329 
+2024-09-08 16:34:01.485481: Current learning rate: 0.00698 
+2024-09-08 16:38:06.781055: train_loss -0.8343 
+2024-09-08 16:38:06.781259: val_loss -0.6767 
+2024-09-08 16:38:06.781329: Pseudo dice [0.5834, 0.8383] 
+2024-09-08 16:38:06.781397: Epoch time: 245.3 s 
+2024-09-08 16:38:07.762815:  
+2024-09-08 16:38:07.763036: Epoch 330 
+2024-09-08 16:38:07.763126: Current learning rate: 0.00697 
+2024-09-08 16:42:12.907306: train_loss -0.833 
+2024-09-08 16:42:12.907459: val_loss -0.6837 
+2024-09-08 16:42:12.907519: Pseudo dice [0.6408, 0.8507] 
+2024-09-08 16:42:12.907623: Epoch time: 245.15 s 
+2024-09-08 16:42:13.887750:  
+2024-09-08 16:42:13.888032: Epoch 331 
+2024-09-08 16:42:13.888123: Current learning rate: 0.00696 
+2024-09-08 16:46:19.008134: train_loss -0.833 
+2024-09-08 16:46:19.008287: val_loss -0.6716 
+2024-09-08 16:46:19.008342: Pseudo dice [0.5705, 0.8612] 
+2024-09-08 16:46:19.008399: Epoch time: 245.12 s 
+2024-09-08 16:46:19.986350:  
+2024-09-08 16:46:19.986593: Epoch 332 
+2024-09-08 16:46:19.986681: Current learning rate: 0.00696 
+2024-09-08 16:50:25.259197: train_loss -0.8394 
+2024-09-08 16:50:25.259395: val_loss -0.6807 
+2024-09-08 16:50:25.259487: Pseudo dice [0.6284, 0.8548] 
+2024-09-08 16:50:25.259570: Epoch time: 245.27 s 
+2024-09-08 16:50:26.405252:  
+2024-09-08 16:50:26.405495: Epoch 333 
+2024-09-08 16:50:26.405599: Current learning rate: 0.00695 
+2024-09-08 16:54:31.551735: train_loss -0.843 
+2024-09-08 16:54:31.551919: val_loss -0.6573 
+2024-09-08 16:54:31.551977: Pseudo dice [0.5591, 0.8582] 
+2024-09-08 16:54:31.552034: Epoch time: 245.15 s 
+2024-09-08 16:54:32.513878:  
+2024-09-08 16:54:32.514060: Epoch 334 
+2024-09-08 16:54:32.514147: Current learning rate: 0.00694 
+2024-09-08 16:58:37.594152: train_loss -0.8476 
+2024-09-08 16:58:37.594401: val_loss -0.6728 
+2024-09-08 16:58:37.594459: Pseudo dice [0.6079, 0.8479] 
+2024-09-08 16:58:37.594516: Epoch time: 245.08 s 
+2024-09-08 16:58:38.569589:  
+2024-09-08 16:58:38.569788: Epoch 335 
+2024-09-08 16:58:38.569874: Current learning rate: 0.00693 
+2024-09-08 17:02:43.887309: train_loss -0.8519 
+2024-09-08 17:02:43.887462: val_loss -0.6741 
+2024-09-08 17:02:43.887521: Pseudo dice [0.6177, 0.847] 
+2024-09-08 17:02:43.887589: Epoch time: 245.32 s 
+2024-09-08 17:02:45.114680:  
+2024-09-08 17:02:45.114952: Epoch 336 
+2024-09-08 17:02:45.115079: Current learning rate: 0.00692 
+2024-09-08 17:06:50.471328: train_loss -0.8574 
+2024-09-08 17:06:50.471480: val_loss -0.7185 
+2024-09-08 17:06:50.471537: Pseudo dice [0.647, 0.8693] 
+2024-09-08 17:06:50.471599: Epoch time: 245.36 s 
+2024-09-08 17:06:51.480148:  
+2024-09-08 17:06:51.480365: Epoch 337 
+2024-09-08 17:06:51.480455: Current learning rate: 0.00691 
+2024-09-08 17:10:56.878463: train_loss -0.8561 
+2024-09-08 17:10:56.878615: val_loss -0.7043 
+2024-09-08 17:10:56.878681: Pseudo dice [0.6259, 0.8549] 
+2024-09-08 17:10:56.878746: Epoch time: 245.4 s 
+2024-09-08 17:10:57.860829:  
+2024-09-08 17:10:57.861072: Epoch 338 
+2024-09-08 17:10:57.861159: Current learning rate: 0.0069 
+2024-09-08 17:15:03.253452: train_loss -0.854 
+2024-09-08 17:15:03.253601: val_loss -0.6972 
+2024-09-08 17:15:03.253658: Pseudo dice [0.6267, 0.8659] 
+2024-09-08 17:15:03.253714: Epoch time: 245.39 s 
+2024-09-08 17:15:04.245088:  
+2024-09-08 17:15:04.245332: Epoch 339 
+2024-09-08 17:15:04.245470: Current learning rate: 0.00689 
+2024-09-08 17:19:09.765285: train_loss -0.8484 
+2024-09-08 17:19:09.765438: val_loss -0.6811 
+2024-09-08 17:19:09.765496: Pseudo dice [0.6149, 0.848] 
+2024-09-08 17:19:09.765555: Epoch time: 245.52 s 
+2024-09-08 17:19:10.751374:  
+2024-09-08 17:19:10.751551: Epoch 340 
+2024-09-08 17:19:10.751686: Current learning rate: 0.00688 
+2024-09-08 17:23:16.099693: train_loss -0.8493 
+2024-09-08 17:23:16.099868: val_loss -0.7009 
+2024-09-08 17:23:16.099928: Pseudo dice [0.6154, 0.8647] 
+2024-09-08 17:23:16.099985: Epoch time: 245.35 s 
+2024-09-08 17:23:17.092292:  
+2024-09-08 17:23:17.092525: Epoch 341 
+2024-09-08 17:23:17.092609: Current learning rate: 0.00687 
+2024-09-08 17:27:22.631080: train_loss -0.854 
+2024-09-08 17:27:22.631243: val_loss -0.7056 
+2024-09-08 17:27:22.631300: Pseudo dice [0.6264, 0.8676] 
+2024-09-08 17:27:22.631356: Epoch time: 245.54 s 
+2024-09-08 17:27:23.651798:  
+2024-09-08 17:27:23.652012: Epoch 342 
+2024-09-08 17:27:23.652095: Current learning rate: 0.00686 
+2024-09-08 17:31:28.968636: train_loss -0.8426 
+2024-09-08 17:31:28.968788: val_loss -0.688 
+2024-09-08 17:31:28.968846: Pseudo dice [0.6354, 0.8525] 
+2024-09-08 17:31:28.968902: Epoch time: 245.32 s 
+2024-09-08 17:31:29.950428:  
+2024-09-08 17:31:29.950664: Epoch 343 
+2024-09-08 17:31:29.950799: Current learning rate: 0.00685 
+2024-09-08 17:35:35.499878: train_loss -0.8504 
+2024-09-08 17:35:35.500031: val_loss -0.6923 
+2024-09-08 17:35:35.500088: Pseudo dice [0.6132, 0.8558] 
+2024-09-08 17:35:35.500146: Epoch time: 245.55 s 
+2024-09-08 17:35:36.480713:  
+2024-09-08 17:35:36.480906: Epoch 344 
+2024-09-08 17:35:36.480992: Current learning rate: 0.00684 
+2024-09-08 17:39:41.956738: train_loss -0.8479 
+2024-09-08 17:39:41.956907: val_loss -0.6962 
+2024-09-08 17:39:41.956966: Pseudo dice [0.6171, 0.8677] 
+2024-09-08 17:39:41.957026: Epoch time: 245.48 s 
+2024-09-08 17:39:41.957083: Yayy! New best EMA pseudo Dice: 0.737 
+2024-09-08 17:39:45.932479:  
+2024-09-08 17:39:45.932668: Epoch 345 
+2024-09-08 17:39:45.932756: Current learning rate: 0.00683 
+2024-09-08 17:43:51.095582: train_loss -0.8336 
+2024-09-08 17:43:51.095732: val_loss -0.6606 
+2024-09-08 17:43:51.095794: Pseudo dice [0.5742, 0.861] 
+2024-09-08 17:43:51.095861: Epoch time: 245.16 s 
+2024-09-08 17:43:52.104523:  
+2024-09-08 17:43:52.104679: Epoch 346 
+2024-09-08 17:43:52.104771: Current learning rate: 0.00682 
+2024-09-08 17:47:57.178645: train_loss -0.8217 
+2024-09-08 17:47:57.178801: val_loss -0.6558 
+2024-09-08 17:47:57.178858: Pseudo dice [0.6093, 0.8357] 
+2024-09-08 17:47:57.178914: Epoch time: 245.08 s 
+2024-09-08 17:47:58.172595:  
+2024-09-08 17:47:58.172800: Epoch 347 
+2024-09-08 17:47:58.172891: Current learning rate: 0.00681 
+2024-09-08 17:52:03.264151: train_loss -0.8149 
+2024-09-08 17:52:03.264315: val_loss -0.6642 
+2024-09-08 17:52:03.264373: Pseudo dice [0.589, 0.8486] 
+2024-09-08 17:52:03.264432: Epoch time: 245.09 s 
+2024-09-08 17:52:04.245783:  
+2024-09-08 17:52:04.245981: Epoch 348 
+2024-09-08 17:52:04.246068: Current learning rate: 0.0068 
+2024-09-08 17:56:09.255943: train_loss -0.8184 
+2024-09-08 17:56:09.256099: val_loss -0.6569 
+2024-09-08 17:56:09.256156: Pseudo dice [0.5915, 0.8355] 
+2024-09-08 17:56:09.256260: Epoch time: 245.01 s 
+2024-09-08 17:56:10.236069:  
+2024-09-08 17:56:10.236259: Epoch 349 
+2024-09-08 17:56:10.236345: Current learning rate: 0.0068 
+2024-09-08 18:00:15.142464: train_loss -0.8366 
+2024-09-08 18:00:15.142689: val_loss -0.6729 
+2024-09-08 18:00:15.142870: Pseudo dice [0.6064, 0.8493] 
+2024-09-08 18:00:15.142995: Epoch time: 244.91 s 
+2024-09-08 18:00:20.159561:  
+2024-09-08 18:00:20.159910: Epoch 350 
+2024-09-08 18:00:20.160158: Current learning rate: 0.00679 
+2024-09-08 18:04:25.070969: train_loss -0.847 
+2024-09-08 18:04:25.071122: val_loss -0.6781 
+2024-09-08 18:04:25.071178: Pseudo dice [0.5995, 0.8526] 
+2024-09-08 18:04:25.071237: Epoch time: 244.92 s 
+2024-09-08 18:04:26.063593:  
+2024-09-08 18:04:26.063817: Epoch 351 
+2024-09-08 18:04:26.063942: Current learning rate: 0.00678 
+2024-09-08 18:08:30.924374: train_loss -0.8411 
+2024-09-08 18:08:30.924539: val_loss -0.6678 
+2024-09-08 18:08:30.924598: Pseudo dice [0.5555, 0.8554] 
+2024-09-08 18:08:30.924660: Epoch time: 244.86 s 
+2024-09-08 18:08:31.908575:  
+2024-09-08 18:08:31.908787: Epoch 352 
+2024-09-08 18:08:31.908877: Current learning rate: 0.00677 
+2024-09-08 18:12:36.767802: train_loss -0.8347 
+2024-09-08 18:12:36.767973: val_loss -0.6801 
+2024-09-08 18:12:36.768057: Pseudo dice [0.6071, 0.8462] 
+2024-09-08 18:12:36.768114: Epoch time: 244.86 s 
+2024-09-08 18:12:37.781607:  
+2024-09-08 18:12:37.781838: Epoch 353 
+2024-09-08 18:12:37.781926: Current learning rate: 0.00676 
+2024-09-08 18:16:42.769965: train_loss -0.8273 
+2024-09-08 18:16:42.770113: val_loss -0.6778 
+2024-09-08 18:16:42.770170: Pseudo dice [0.6064, 0.8587] 
+2024-09-08 18:16:42.770226: Epoch time: 244.99 s 
+2024-09-08 18:16:43.784922:  
+2024-09-08 18:16:43.785147: Epoch 354 
+2024-09-08 18:16:43.785236: Current learning rate: 0.00675 
+2024-09-08 18:20:48.877423: train_loss -0.8374 
+2024-09-08 18:20:48.877599: val_loss -0.6907 
+2024-09-08 18:20:48.877663: Pseudo dice [0.6362, 0.8493] 
+2024-09-08 18:20:48.877719: Epoch time: 245.09 s 
+2024-09-08 18:20:49.920693:  
+2024-09-08 18:20:49.920885: Epoch 355 
+2024-09-08 18:20:49.920966: Current learning rate: 0.00674 
+2024-09-08 18:24:54.889135: train_loss -0.8084 
+2024-09-08 18:24:54.889298: val_loss -0.6941 
+2024-09-08 18:24:54.889361: Pseudo dice [0.6382, 0.8567] 
+2024-09-08 18:24:54.889417: Epoch time: 244.97 s 
+2024-09-08 18:24:55.878599:  
+2024-09-08 18:24:55.878770: Epoch 356 
+2024-09-08 18:24:55.878852: Current learning rate: 0.00673 
+2024-09-08 18:29:00.847599: train_loss -0.822 
+2024-09-08 18:29:00.847764: val_loss -0.7004 
+2024-09-08 18:29:00.847830: Pseudo dice [0.6274, 0.8637] 
+2024-09-08 18:29:00.847890: Epoch time: 244.97 s 
+2024-09-08 18:29:01.839685:  
+2024-09-08 18:29:01.839932: Epoch 357 
+2024-09-08 18:29:01.840076: Current learning rate: 0.00672 
+2024-09-08 18:33:06.829450: train_loss -0.8246 
+2024-09-08 18:33:06.829595: val_loss -0.6642 
+2024-09-08 18:33:06.829651: Pseudo dice [0.5777, 0.8566] 
+2024-09-08 18:33:06.829708: Epoch time: 244.99 s 
+2024-09-08 18:33:07.916348:  
+2024-09-08 18:33:07.916598: Epoch 358 
+2024-09-08 18:33:07.916708: Current learning rate: 0.00671 
+2024-09-08 18:37:12.771667: train_loss -0.8342 
+2024-09-08 18:37:12.771834: val_loss -0.6812 
+2024-09-08 18:37:12.771894: Pseudo dice [0.6024, 0.8592] 
+2024-09-08 18:37:12.771950: Epoch time: 244.86 s 
+2024-09-08 18:37:13.754793:  
+2024-09-08 18:37:13.754986: Epoch 359 
+2024-09-08 18:37:13.755096: Current learning rate: 0.0067 
+2024-09-08 18:41:18.762943: train_loss -0.7838 
+2024-09-08 18:41:18.763095: val_loss -0.6712 
+2024-09-08 18:41:18.763152: Pseudo dice [0.5942, 0.8625] 
+2024-09-08 18:41:18.763210: Epoch time: 245.01 s 
+2024-09-08 18:41:19.746079:  
+2024-09-08 18:41:19.746286: Epoch 360 
+2024-09-08 18:41:19.746366: Current learning rate: 0.00669 
+2024-09-08 18:45:24.697554: train_loss -0.7936 
+2024-09-08 18:45:24.697767: val_loss -0.6903 
+2024-09-08 18:45:24.697825: Pseudo dice [0.6185, 0.8482] 
+2024-09-08 18:45:24.697882: Epoch time: 244.95 s 
+2024-09-08 18:45:25.694782:  
+2024-09-08 18:45:25.694994: Epoch 361 
+2024-09-08 18:45:25.695085: Current learning rate: 0.00668 
+2024-09-08 18:49:30.659871: train_loss -0.7875 
+2024-09-08 18:49:30.660016: val_loss -0.6823 
+2024-09-08 18:49:30.660075: Pseudo dice [0.6019, 0.8579] 
+2024-09-08 18:49:30.660133: Epoch time: 244.97 s 
+2024-09-08 18:49:31.662794:  
+2024-09-08 18:49:31.662959: Epoch 362 
+2024-09-08 18:49:31.663057: Current learning rate: 0.00667 
+2024-09-08 18:53:36.501285: train_loss -0.8148 
+2024-09-08 18:53:36.501437: val_loss -0.6421 
+2024-09-08 18:53:36.501494: Pseudo dice [0.5717, 0.833] 
+2024-09-08 18:53:36.501551: Epoch time: 244.84 s 
+2024-09-08 18:53:37.504496:  
+2024-09-08 18:53:37.504766: Epoch 363 
+2024-09-08 18:53:37.504857: Current learning rate: 0.00666 
+2024-09-08 18:57:44.557248: train_loss -0.8095 
+2024-09-08 18:57:44.557458: val_loss -0.6757 
+2024-09-08 18:57:44.557547: Pseudo dice [0.5922, 0.8589] 
+2024-09-08 18:57:44.557612: Epoch time: 247.05 s 
+2024-09-08 18:57:45.640902:  
+2024-09-08 18:57:45.641207: Epoch 364 
+2024-09-08 18:57:45.641339: Current learning rate: 0.00665 
+2024-09-08 19:01:59.865333: train_loss -0.8139 
+2024-09-08 19:01:59.865480: val_loss -0.6664 
+2024-09-08 19:01:59.865536: Pseudo dice [0.5691, 0.8672] 
+2024-09-08 19:01:59.865591: Epoch time: 254.23 s 
+2024-09-08 19:02:00.871715:  
+2024-09-08 19:02:00.871975: Epoch 365 
+2024-09-08 19:02:00.872119: Current learning rate: 0.00665 
+2024-09-08 19:06:07.066770: train_loss -0.8215 
+2024-09-08 19:06:07.066913: val_loss -0.6761 
+2024-09-08 19:06:07.066969: Pseudo dice [0.565, 0.8644] 
+2024-09-08 19:06:07.067027: Epoch time: 246.2 s 
+2024-09-08 19:06:08.072891:  
+2024-09-08 19:06:08.073033: Epoch 366 
+2024-09-08 19:06:08.073149: Current learning rate: 0.00664 
+2024-09-08 19:10:13.952718: train_loss -0.8268 
+2024-09-08 19:10:13.952866: val_loss -0.6473 
+2024-09-08 19:10:13.952966: Pseudo dice [0.5792, 0.8266] 
+2024-09-08 19:10:13.953046: Epoch time: 245.88 s 
+2024-09-08 19:10:14.949375:  
+2024-09-08 19:10:14.949617: Epoch 367 
+2024-09-08 19:10:14.949707: Current learning rate: 0.00663 
+2024-09-08 19:14:20.083694: train_loss -0.8302 
+2024-09-08 19:14:20.083855: val_loss -0.6892 
+2024-09-08 19:14:20.083914: Pseudo dice [0.6167, 0.8576] 
+2024-09-08 19:14:20.083970: Epoch time: 245.14 s 
+2024-09-08 19:14:21.083164:  
+2024-09-08 19:14:21.083370: Epoch 368 
+2024-09-08 19:14:21.083460: Current learning rate: 0.00662 
+2024-09-08 19:18:26.231459: train_loss -0.8377 
+2024-09-08 19:18:26.231605: val_loss -0.668 
+2024-09-08 19:18:26.231663: Pseudo dice [0.586, 0.85] 
+2024-09-08 19:18:26.231718: Epoch time: 245.15 s 
+2024-09-08 19:18:27.211798:  
+2024-09-08 19:18:27.211989: Epoch 369 
+2024-09-08 19:18:27.212074: Current learning rate: 0.00661 
+2024-09-08 19:22:32.321584: train_loss -0.8351 
+2024-09-08 19:22:32.321747: val_loss -0.7052 
+2024-09-08 19:22:32.321804: Pseudo dice [0.6446, 0.8581] 
+2024-09-08 19:22:32.321861: Epoch time: 245.11 s 
+2024-09-08 19:22:33.334265:  
+2024-09-08 19:22:33.334448: Epoch 370 
+2024-09-08 19:22:33.334534: Current learning rate: 0.0066 
+2024-09-08 19:26:38.394745: train_loss -0.8258 
+2024-09-08 19:26:38.394933: val_loss -0.6586 
+2024-09-08 19:26:38.394996: Pseudo dice [0.5694, 0.8461] 
+2024-09-08 19:26:38.395059: Epoch time: 245.06 s 
+2024-09-08 19:26:39.468961:  
+2024-09-08 19:26:39.469262: Epoch 371 
+2024-09-08 19:26:39.469393: Current learning rate: 0.00659 
+2024-09-08 19:30:45.457862: train_loss -0.8389 
+2024-09-08 19:30:45.458005: val_loss -0.6586 
+2024-09-08 19:30:45.458061: Pseudo dice [0.5485, 0.8498] 
+2024-09-08 19:30:45.458117: Epoch time: 245.99 s 
+2024-09-08 19:30:46.452671:  
+2024-09-08 19:30:46.452873: Epoch 372 
+2024-09-08 19:30:46.452958: Current learning rate: 0.00658 
+2024-09-08 19:34:52.368542: train_loss -0.8392 
+2024-09-08 19:34:52.368686: val_loss -0.6764 
+2024-09-08 19:34:52.368742: Pseudo dice [0.6011, 0.8498] 
+2024-09-08 19:34:52.368805: Epoch time: 245.92 s 
+2024-09-08 19:34:54.279021:  
+2024-09-08 19:34:54.279187: Epoch 373 
+2024-09-08 19:34:54.279312: Current learning rate: 0.00657 
+2024-09-08 19:38:59.965899: train_loss -0.8434 
+2024-09-08 19:38:59.966093: val_loss -0.7054 
+2024-09-08 19:38:59.966161: Pseudo dice [0.6205, 0.8624] 
+2024-09-08 19:38:59.966234: Epoch time: 245.69 s 
+2024-09-08 19:39:01.213598:  
+2024-09-08 19:39:01.213842: Epoch 374 
+2024-09-08 19:39:01.213952: Current learning rate: 0.00656 
+2024-09-08 19:43:06.521562: train_loss -0.8402 
+2024-09-08 19:43:06.521714: val_loss -0.6747 
+2024-09-08 19:43:06.521769: Pseudo dice [0.5991, 0.849] 
+2024-09-08 19:43:06.521825: Epoch time: 245.31 s 
+2024-09-08 19:43:07.525492:  
+2024-09-08 19:43:07.525778: Epoch 375 
+2024-09-08 19:43:07.525865: Current learning rate: 0.00655 
+2024-09-08 19:47:12.871826: train_loss -0.8479 
+2024-09-08 19:47:12.871972: val_loss -0.6943 
+2024-09-08 19:47:12.872070: Pseudo dice [0.6131, 0.8596] 
+2024-09-08 19:47:12.872125: Epoch time: 245.35 s 
+2024-09-08 19:47:13.932392:  
+2024-09-08 19:47:13.932624: Epoch 376 
+2024-09-08 19:47:13.932713: Current learning rate: 0.00654 
+2024-09-08 19:51:19.160216: train_loss -0.8486 
+2024-09-08 19:51:19.160366: val_loss -0.6877 
+2024-09-08 19:51:19.160425: Pseudo dice [0.5684, 0.8625] 
+2024-09-08 19:51:19.160481: Epoch time: 245.23 s 
+2024-09-08 19:51:20.330974:  
+2024-09-08 19:51:20.331198: Epoch 377 
+2024-09-08 19:51:20.331312: Current learning rate: 0.00653 
+2024-09-08 19:55:25.444099: train_loss -0.8523 
+2024-09-08 19:55:25.444242: val_loss -0.6774 
+2024-09-08 19:55:25.444299: Pseudo dice [0.6037, 0.8497] 
+2024-09-08 19:55:25.444432: Epoch time: 245.12 s 
+2024-09-08 19:55:26.430839:  
+2024-09-08 19:55:26.431061: Epoch 378 
+2024-09-08 19:55:26.431149: Current learning rate: 0.00652 
+2024-09-08 19:59:31.507684: train_loss -0.8474 
+2024-09-08 19:59:31.507848: val_loss -0.6913 
+2024-09-08 19:59:31.507905: Pseudo dice [0.6083, 0.8517] 
+2024-09-08 19:59:31.507961: Epoch time: 245.08 s 
+2024-09-08 19:59:32.489197:  
+2024-09-08 19:59:32.489385: Epoch 379 
+2024-09-08 19:59:32.489475: Current learning rate: 0.00651 
+2024-09-08 20:03:37.619596: train_loss -0.8428 
+2024-09-08 20:03:37.619745: val_loss -0.6853 
+2024-09-08 20:03:37.619802: Pseudo dice [0.6257, 0.8557] 
+2024-09-08 20:03:37.619929: Epoch time: 245.13 s 
+2024-09-08 20:03:38.609897:  
+2024-09-08 20:03:38.610115: Epoch 380 
+2024-09-08 20:03:38.610201: Current learning rate: 0.0065 
+2024-09-08 20:07:43.946874: train_loss -0.831 
+2024-09-08 20:07:43.947053: val_loss -0.6941 
+2024-09-08 20:07:43.947110: Pseudo dice [0.6352, 0.8525] 
+2024-09-08 20:07:43.947166: Epoch time: 245.34 s 
+2024-09-08 20:07:44.969119:  
+2024-09-08 20:07:44.969285: Epoch 381 
+2024-09-08 20:07:44.969370: Current learning rate: 0.00649 
+2024-09-08 20:11:50.456490: train_loss -0.7975 
+2024-09-08 20:11:50.456648: val_loss -0.6934 
+2024-09-08 20:11:50.456704: Pseudo dice [0.6321, 0.8596] 
+2024-09-08 20:11:50.456759: Epoch time: 245.49 s 
+2024-09-08 20:11:51.501475:  
+2024-09-08 20:11:51.501670: Epoch 382 
+2024-09-08 20:11:51.501753: Current learning rate: 0.00648 
+2024-09-08 20:15:58.109968: train_loss -0.7952 
+2024-09-08 20:15:58.110192: val_loss -0.6841 
+2024-09-08 20:15:58.110284: Pseudo dice [0.6384, 0.8377] 
+2024-09-08 20:15:58.110348: Epoch time: 246.61 s 
+2024-09-08 20:15:59.266437:  
+2024-09-08 20:15:59.266705: Epoch 383 
+2024-09-08 20:15:59.266821: Current learning rate: 0.00648 
+2024-09-08 20:20:05.516635: train_loss -0.8238 
+2024-09-08 20:20:05.516788: val_loss -0.688 
+2024-09-08 20:20:05.516844: Pseudo dice [0.65, 0.8423] 
+2024-09-08 20:20:05.516901: Epoch time: 246.25 s 
+2024-09-08 20:20:06.528844:  
+2024-09-08 20:20:06.529012: Epoch 384 
+2024-09-08 20:20:06.529100: Current learning rate: 0.00647 
+2024-09-08 20:24:12.763555: train_loss -0.8338 
+2024-09-08 20:24:12.763744: val_loss -0.6937 
+2024-09-08 20:24:12.763803: Pseudo dice [0.6282, 0.869] 
+2024-09-08 20:24:12.763880: Epoch time: 246.24 s 
+2024-09-08 20:24:13.767508:  
+2024-09-08 20:24:13.767756: Epoch 385 
+2024-09-08 20:24:13.767852: Current learning rate: 0.00646 
+2024-09-08 20:28:19.946226: train_loss -0.8366 
+2024-09-08 20:28:19.946399: val_loss -0.6731 
+2024-09-08 20:28:19.946455: Pseudo dice [0.5529, 0.8642] 
+2024-09-08 20:28:19.946511: Epoch time: 246.18 s 
+2024-09-08 20:28:20.952629:  
+2024-09-08 20:28:20.952827: Epoch 386 
+2024-09-08 20:28:20.952918: Current learning rate: 0.00645 
+2024-09-08 20:32:26.964850: train_loss -0.8433 
+2024-09-08 20:32:26.964996: val_loss -0.6774 
+2024-09-08 20:32:26.965056: Pseudo dice [0.6138, 0.8603] 
+2024-09-08 20:32:26.965113: Epoch time: 246.01 s 
+2024-09-08 20:32:27.970332:  
+2024-09-08 20:32:27.970524: Epoch 387 
+2024-09-08 20:32:27.970613: Current learning rate: 0.00644 
+2024-09-08 20:36:33.791469: train_loss -0.839 
+2024-09-08 20:36:33.791645: val_loss -0.702 
+2024-09-08 20:36:33.791715: Pseudo dice [0.6244, 0.8559] 
+2024-09-08 20:36:33.791779: Epoch time: 245.82 s 
+2024-09-08 20:36:34.802400:  
+2024-09-08 20:36:34.802633: Epoch 388 
+2024-09-08 20:36:34.802721: Current learning rate: 0.00643 
+2024-09-08 20:40:40.116503: train_loss -0.8369 
+2024-09-08 20:40:40.116653: val_loss -0.6796 
+2024-09-08 20:40:40.116709: Pseudo dice [0.6161, 0.8511] 
+2024-09-08 20:40:40.116833: Epoch time: 245.32 s 
+2024-09-08 20:40:41.119350:  
+2024-09-08 20:40:41.119547: Epoch 389 
+2024-09-08 20:40:41.119699: Current learning rate: 0.00642 
+2024-09-08 20:44:48.023389: train_loss -0.8471 
+2024-09-08 20:44:48.023660: val_loss -0.6971 
+2024-09-08 20:44:48.023718: Pseudo dice [0.6178, 0.8641] 
+2024-09-08 20:44:48.023774: Epoch time: 246.91 s 
+2024-09-08 20:44:49.038081:  
+2024-09-08 20:44:49.038277: Epoch 390 
+2024-09-08 20:44:49.038363: Current learning rate: 0.00641 
+2024-09-08 20:48:54.934120: train_loss -0.8515 
+2024-09-08 20:48:54.934319: val_loss -0.7022 
+2024-09-08 20:48:54.934419: Pseudo dice [0.6434, 0.8568] 
+2024-09-08 20:48:54.934482: Epoch time: 245.9 s 
+2024-09-08 20:48:55.969826:  
+2024-09-08 20:48:55.970008: Epoch 391 
+2024-09-08 20:48:55.970140: Current learning rate: 0.0064 
+2024-09-08 20:53:01.716791: train_loss -0.8525 
+2024-09-08 20:53:01.716935: val_loss -0.7014 
+2024-09-08 20:53:01.716992: Pseudo dice [0.6185, 0.8665] 
+2024-09-08 20:53:01.717049: Epoch time: 245.75 s 
+2024-09-08 20:53:02.725583:  
+2024-09-08 20:53:02.725782: Epoch 392 
+2024-09-08 20:53:02.725901: Current learning rate: 0.00639 
+2024-09-08 20:57:08.522402: train_loss -0.8469 
+2024-09-08 20:57:08.522558: val_loss -0.6878 
+2024-09-08 20:57:08.522614: Pseudo dice [0.601, 0.8629] 
+2024-09-08 20:57:08.522675: Epoch time: 245.8 s 
+2024-09-08 20:57:09.523407:  
+2024-09-08 20:57:09.523632: Epoch 393 
+2024-09-08 20:57:09.523740: Current learning rate: 0.00638 
+2024-09-08 21:01:15.510624: train_loss -0.8503 
+2024-09-08 21:01:15.510775: val_loss -0.6856 
+2024-09-08 21:01:15.510831: Pseudo dice [0.6066, 0.8594] 
+2024-09-08 21:01:15.510888: Epoch time: 245.99 s 
+2024-09-08 21:01:16.534980:  
+2024-09-08 21:01:16.535184: Epoch 394 
+2024-09-08 21:01:16.535272: Current learning rate: 0.00637 
+2024-09-08 21:05:22.537689: train_loss -0.8582 
+2024-09-08 21:05:22.537840: val_loss -0.665 
+2024-09-08 21:05:22.537898: Pseudo dice [0.5872, 0.8465] 
+2024-09-08 21:05:22.537953: Epoch time: 246.0 s 
+2024-09-08 21:05:23.550588:  
+2024-09-08 21:05:23.550739: Epoch 395 
+2024-09-08 21:05:23.550903: Current learning rate: 0.00636 
+2024-09-08 21:09:30.554910: train_loss -0.8603 
+2024-09-08 21:09:30.555055: val_loss -0.7098 
+2024-09-08 21:09:30.555110: Pseudo dice [0.6339, 0.8699] 
+2024-09-08 21:09:30.555166: Epoch time: 247.01 s 
+2024-09-08 21:09:31.569402:  
+2024-09-08 21:09:31.569583: Epoch 396 
+2024-09-08 21:09:31.569684: Current learning rate: 0.00635 
+2024-09-08 21:13:37.732612: train_loss -0.8496 
+2024-09-08 21:13:37.732768: val_loss -0.6761 
+2024-09-08 21:13:37.732878: Pseudo dice [0.5913, 0.8546] 
+2024-09-08 21:13:37.732934: Epoch time: 246.17 s 
+2024-09-08 21:13:38.737264:  
+2024-09-08 21:13:38.737446: Epoch 397 
+2024-09-08 21:13:38.737528: Current learning rate: 0.00634 
+2024-09-08 21:17:44.281464: train_loss -0.8475 
+2024-09-08 21:17:44.281625: val_loss -0.6821 
+2024-09-08 21:17:44.281680: Pseudo dice [0.6109, 0.8696] 
+2024-09-08 21:17:44.281735: Epoch time: 245.55 s 
+2024-09-08 21:17:45.281898:  
+2024-09-08 21:17:45.282186: Epoch 398 
+2024-09-08 21:17:45.282312: Current learning rate: 0.00633 
+2024-09-08 21:21:50.854960: train_loss -0.8602 
+2024-09-08 21:21:50.855103: val_loss -0.6728 
+2024-09-08 21:21:50.855158: Pseudo dice [0.596, 0.8666] 
+2024-09-08 21:21:50.855213: Epoch time: 245.57 s 
+2024-09-08 21:21:51.875573:  
+2024-09-08 21:21:51.875770: Epoch 399 
+2024-09-08 21:21:51.875868: Current learning rate: 0.00632 
+2024-09-08 21:25:57.691252: train_loss -0.8463 
+2024-09-08 21:25:57.691399: val_loss -0.6454 
+2024-09-08 21:25:57.691456: Pseudo dice [0.5132, 0.8562] 
+2024-09-08 21:25:57.691514: Epoch time: 245.82 s 
+2024-09-08 21:26:01.701371:  
+2024-09-08 21:26:01.701545: Epoch 400 
+2024-09-08 21:26:01.701630: Current learning rate: 0.00631 
+2024-09-08 21:30:08.558583: train_loss -0.8394 
+2024-09-08 21:30:08.558732: val_loss -0.6753 
+2024-09-08 21:30:08.558789: Pseudo dice [0.5963, 0.8563] 
+2024-09-08 21:30:08.558846: Epoch time: 246.86 s 
+2024-09-08 21:30:09.604226:  
+2024-09-08 21:30:09.604492: Epoch 401 
+2024-09-08 21:30:09.604582: Current learning rate: 0.0063 
+2024-09-08 21:34:15.921169: train_loss -0.8499 
+2024-09-08 21:34:15.921352: val_loss -0.6911 
+2024-09-08 21:34:15.921454: Pseudo dice [0.5837, 0.8675] 
+2024-09-08 21:34:15.921511: Epoch time: 246.32 s 
+2024-09-08 21:34:16.939251:  
+2024-09-08 21:34:16.939476: Epoch 402 
+2024-09-08 21:34:16.939586: Current learning rate: 0.0063 
+2024-09-08 21:38:22.834142: train_loss -0.8431 
+2024-09-08 21:38:22.834305: val_loss -0.6627 
+2024-09-08 21:38:22.834361: Pseudo dice [0.5781, 0.859] 
+2024-09-08 21:38:22.834416: Epoch time: 245.9 s 
+2024-09-08 21:38:23.861198:  
+2024-09-08 21:38:23.861402: Epoch 403 
+2024-09-08 21:38:23.861491: Current learning rate: 0.00629 
+2024-09-08 21:42:29.321342: train_loss -0.8483 
+2024-09-08 21:42:29.321693: val_loss -0.6896 
+2024-09-08 21:42:29.321812: Pseudo dice [0.6015, 0.8592] 
+2024-09-08 21:42:29.321916: Epoch time: 245.46 s 
+2024-09-08 21:42:30.344029:  
+2024-09-08 21:42:30.344280: Epoch 404 
+2024-09-08 21:42:30.344368: Current learning rate: 0.00628 
+2024-09-08 21:46:36.458390: train_loss -0.8418 
+2024-09-08 21:46:36.458539: val_loss -0.6286 
+2024-09-08 21:46:36.458595: Pseudo dice [0.5115, 0.8421] 
+2024-09-08 21:46:36.458651: Epoch time: 246.12 s 
+2024-09-08 21:46:37.481081:  
+2024-09-08 21:46:37.481332: Epoch 405 
+2024-09-08 21:46:37.481423: Current learning rate: 0.00627 
+2024-09-08 21:50:44.082056: train_loss -0.8405 
+2024-09-08 21:50:44.082236: val_loss -0.6795 
+2024-09-08 21:50:44.082293: Pseudo dice [0.5865, 0.8585] 
+2024-09-08 21:50:44.082349: Epoch time: 246.6 s 
+2024-09-08 21:50:45.102349:  
+2024-09-08 21:50:45.102575: Epoch 406 
+2024-09-08 21:50:45.102660: Current learning rate: 0.00626 
+2024-09-08 21:54:51.090294: train_loss -0.835 
+2024-09-08 21:54:51.090518: val_loss -0.6639 
+2024-09-08 21:54:51.090581: Pseudo dice [0.5928, 0.8574] 
+2024-09-08 21:54:51.090671: Epoch time: 245.99 s 
+2024-09-08 21:54:52.276792:  
+2024-09-08 21:54:52.277026: Epoch 407 
+2024-09-08 21:54:52.277124: Current learning rate: 0.00625 
+2024-09-08 21:58:58.171332: train_loss -0.8335 
+2024-09-08 21:58:58.171524: val_loss -0.6209 
+2024-09-08 21:58:58.171581: Pseudo dice [0.5133, 0.8447] 
+2024-09-08 21:58:58.171637: Epoch time: 245.9 s 
+2024-09-08 21:58:59.181094:  
+2024-09-08 21:58:59.181281: Epoch 408 
+2024-09-08 21:58:59.181369: Current learning rate: 0.00624 
+2024-09-08 22:03:05.004334: train_loss -0.8381 
+2024-09-08 22:03:05.004475: val_loss -0.6385 
+2024-09-08 22:03:05.004531: Pseudo dice [0.5221, 0.8497] 
+2024-09-08 22:03:05.004588: Epoch time: 245.83 s 
+2024-09-08 22:03:06.010343:  
+2024-09-08 22:03:06.010587: Epoch 409 
+2024-09-08 22:03:06.010672: Current learning rate: 0.00623 
+2024-09-08 22:07:10.927216: train_loss -0.852 
+2024-09-08 22:07:10.927361: val_loss -0.7012 
+2024-09-08 22:07:10.927416: Pseudo dice [0.6221, 0.8624] 
+2024-09-08 22:07:10.927472: Epoch time: 244.92 s 
+2024-09-08 22:07:11.941664:  
+2024-09-08 22:07:11.941836: Epoch 410 
+2024-09-08 22:07:11.941924: Current learning rate: 0.00622 
+2024-09-08 22:11:17.014059: train_loss -0.8369 
+2024-09-08 22:11:17.014231: val_loss -0.6468 
+2024-09-08 22:11:17.014288: Pseudo dice [0.533, 0.8456] 
+2024-09-08 22:11:17.014345: Epoch time: 245.07 s 
+2024-09-08 22:11:17.975082:  
+2024-09-08 22:11:17.975273: Epoch 411 
+2024-09-08 22:11:17.975358: Current learning rate: 0.00621 
+2024-09-08 22:15:23.943113: train_loss -0.8463 
+2024-09-08 22:15:23.943264: val_loss -0.6848 
+2024-09-08 22:15:23.943319: Pseudo dice [0.5809, 0.8611] 
+2024-09-08 22:15:23.943372: Epoch time: 245.97 s 
+2024-09-08 22:15:24.883435:  
+2024-09-08 22:15:24.883618: Epoch 412 
+2024-09-08 22:15:24.883704: Current learning rate: 0.0062 
+2024-09-08 22:19:31.709437: train_loss -0.8527 
+2024-09-08 22:19:31.709587: val_loss -0.671 
+2024-09-08 22:19:31.709643: Pseudo dice [0.5472, 0.8765] 
+2024-09-08 22:19:31.709698: Epoch time: 246.83 s 
+2024-09-08 22:19:32.669298:  
+2024-09-08 22:19:32.669509: Epoch 413 
+2024-09-08 22:19:32.669640: Current learning rate: 0.00619 
+2024-09-08 22:23:38.464611: train_loss -0.8496 
+2024-09-08 22:23:38.464763: val_loss -0.6843 
+2024-09-08 22:23:38.464819: Pseudo dice [0.6137, 0.8618] 
+2024-09-08 22:23:38.464876: Epoch time: 245.8 s 
+2024-09-08 22:23:39.431233:  
+2024-09-08 22:23:39.431418: Epoch 414 
+2024-09-08 22:23:39.431509: Current learning rate: 0.00618 
+2024-09-08 22:27:45.251158: train_loss -0.8528 
+2024-09-08 22:27:45.251325: val_loss -0.6986 
+2024-09-08 22:27:45.251380: Pseudo dice [0.6093, 0.8646] 
+2024-09-08 22:27:45.251436: Epoch time: 245.82 s 
+2024-09-08 22:27:46.203178:  
+2024-09-08 22:27:46.203316: Epoch 415 
+2024-09-08 22:27:46.203399: Current learning rate: 0.00617 
+2024-09-08 22:32:00.171998: train_loss -0.8526 
+2024-09-08 22:32:00.172148: val_loss -0.6884 
+2024-09-08 22:32:00.172204: Pseudo dice [0.6067, 0.8724] 
+2024-09-08 22:32:00.172259: Epoch time: 253.97 s 
+2024-09-08 22:32:01.142455:  
+2024-09-08 22:32:01.142623: Epoch 416 
+2024-09-08 22:32:01.142758: Current learning rate: 0.00616 
+2024-09-08 22:37:22.232905: train_loss -0.8484 
+2024-09-08 22:37:22.233064: val_loss -0.6983 
+2024-09-08 22:37:22.233121: Pseudo dice [0.634, 0.862] 
+2024-09-08 22:37:22.233180: Epoch time: 321.09 s 
+2024-09-08 22:37:23.542777:  
+2024-09-08 22:37:23.543043: Epoch 417 
+2024-09-08 22:37:23.543152: Current learning rate: 0.00615 
+2024-09-08 22:42:01.525904: train_loss -0.8254 
+2024-09-08 22:42:01.526055: val_loss -0.6888 
+2024-09-08 22:42:01.526112: Pseudo dice [0.6082, 0.8623] 
+2024-09-08 22:42:01.526168: Epoch time: 277.99 s 
+2024-09-08 22:42:03.477290:  
+2024-09-08 22:42:03.477539: Epoch 418 
+2024-09-08 22:42:03.477684: Current learning rate: 0.00614 
+2024-09-08 22:46:17.959025: train_loss -0.8125 
+2024-09-08 22:46:17.959177: val_loss -0.6609 
+2024-09-08 22:46:17.959234: Pseudo dice [0.5869, 0.8449] 
+2024-09-08 22:46:17.959290: Epoch time: 254.49 s 
+2024-09-08 22:46:18.923048:  
+2024-09-08 22:46:18.923227: Epoch 419 
+2024-09-08 22:46:18.923335: Current learning rate: 0.00613 
+2024-09-08 22:50:32.173600: train_loss -0.8342 
+2024-09-08 22:50:32.173750: val_loss -0.6603 
+2024-09-08 22:50:32.173810: Pseudo dice [0.5459, 0.8636] 
+2024-09-08 22:50:32.173866: Epoch time: 253.25 s 
+2024-09-08 22:50:33.121650:  
+2024-09-08 22:50:33.121853: Epoch 420 
+2024-09-08 22:50:33.121940: Current learning rate: 0.00612 
+2024-09-08 22:54:59.759854: train_loss -0.8331 
+2024-09-08 22:54:59.760046: val_loss -0.63 
+2024-09-08 22:54:59.760118: Pseudo dice [0.5543, 0.8421] 
+2024-09-08 22:54:59.760183: Epoch time: 266.64 s 
+2024-09-08 22:55:00.727951:  
+2024-09-08 22:55:00.728153: Epoch 421 
+2024-09-08 22:55:00.728238: Current learning rate: 0.00612 
+2024-09-08 22:59:22.536492: train_loss -0.7883 
+2024-09-08 22:59:22.536639: val_loss -0.6697 
+2024-09-08 22:59:22.536699: Pseudo dice [0.6152, 0.8235] 
+2024-09-08 22:59:22.536756: Epoch time: 261.81 s 
+2024-09-08 22:59:23.510269:  
+2024-09-08 22:59:23.510560: Epoch 422 
+2024-09-08 22:59:23.510649: Current learning rate: 0.00611 
+2024-09-08 23:03:52.076063: train_loss -0.8006 
+2024-09-08 23:03:52.076217: val_loss -0.7121 
+2024-09-08 23:03:52.076274: Pseudo dice [0.6514, 0.8755] 
+2024-09-08 23:03:52.076331: Epoch time: 268.57 s 
+2024-09-08 23:03:53.034327:  
+2024-09-08 23:03:53.034526: Epoch 423 
+2024-09-08 23:03:53.034630: Current learning rate: 0.0061 
+2024-09-08 23:07:59.874866: train_loss -0.8304 
+2024-09-08 23:07:59.875013: val_loss -0.7013 
+2024-09-08 23:07:59.875068: Pseudo dice [0.6373, 0.8486] 
+2024-09-08 23:07:59.875124: Epoch time: 246.84 s 
+2024-09-08 23:08:00.840970:  
+2024-09-08 23:08:00.841149: Epoch 424 
+2024-09-08 23:08:00.841235: Current learning rate: 0.00609 
+2024-09-08 23:12:31.904247: train_loss -0.844 
+2024-09-08 23:12:31.904393: val_loss -0.7041 
+2024-09-08 23:12:31.904449: Pseudo dice [0.6433, 0.86] 
+2024-09-08 23:12:31.904505: Epoch time: 271.07 s 
+2024-09-08 23:12:32.881161:  
+2024-09-08 23:12:32.881347: Epoch 425 
+2024-09-08 23:12:32.881433: Current learning rate: 0.00608 
+2024-09-08 23:17:08.988039: train_loss -0.8531 
+2024-09-08 23:17:08.988263: val_loss -0.672 
+2024-09-08 23:17:08.988354: Pseudo dice [0.5697, 0.8649] 
+2024-09-08 23:17:08.988429: Epoch time: 276.11 s 
+2024-09-08 23:17:10.096717:  
+2024-09-08 23:17:10.096942: Epoch 426 
+2024-09-08 23:17:10.097031: Current learning rate: 0.00607 
+2024-09-08 23:21:16.450900: train_loss -0.8517 
+2024-09-08 23:21:16.451037: val_loss -0.6899 
+2024-09-08 23:21:16.451092: Pseudo dice [0.6078, 0.8592] 
+2024-09-08 23:21:16.451147: Epoch time: 246.36 s 
+2024-09-08 23:21:17.454810:  
+2024-09-08 23:21:17.454986: Epoch 427 
+2024-09-08 23:21:17.455114: Current learning rate: 0.00606 
+2024-09-08 23:26:05.929528: train_loss -0.8518 
+2024-09-08 23:26:05.929742: val_loss -0.6927 
+2024-09-08 23:26:05.929802: Pseudo dice [0.6593, 0.8657] 
+2024-09-08 23:26:05.929858: Epoch time: 288.48 s 
+2024-09-08 23:26:06.938922:  
+2024-09-08 23:26:06.939122: Epoch 428 
+2024-09-08 23:26:06.939232: Current learning rate: 0.00605 
+2024-09-08 23:31:08.522807: train_loss -0.8576 
+2024-09-08 23:31:08.522954: val_loss -0.6656 
+2024-09-08 23:31:08.523012: Pseudo dice [0.6075, 0.8591] 
+2024-09-08 23:31:08.523069: Epoch time: 301.59 s 
+2024-09-08 23:31:09.479314:  
+2024-09-08 23:31:09.479494: Epoch 429 
+2024-09-08 23:31:09.479584: Current learning rate: 0.00604 
+2024-09-08 23:35:41.856351: train_loss -0.8608 
+2024-09-08 23:35:41.856509: val_loss -0.6865 
+2024-09-08 23:35:41.856566: Pseudo dice [0.6271, 0.8528] 
+2024-09-08 23:35:41.856626: Epoch time: 272.38 s 
+2024-09-08 23:35:42.837352:  
+2024-09-08 23:35:42.837625: Epoch 430 
+2024-09-08 23:35:42.837714: Current learning rate: 0.00603 
+2024-09-08 23:40:00.159075: train_loss -0.8505 
+2024-09-08 23:40:00.159222: val_loss -0.6829 
+2024-09-08 23:40:00.159277: Pseudo dice [0.6032, 0.8584] 
+2024-09-08 23:40:00.159333: Epoch time: 257.32 s 
+2024-09-08 23:40:01.132591:  
+2024-09-08 23:40:01.132790: Epoch 431 
+2024-09-08 23:40:01.132870: Current learning rate: 0.00602 
+2024-09-08 23:44:06.927083: train_loss -0.8634 
+2024-09-08 23:44:06.927255: val_loss -0.6692 
+2024-09-08 23:44:06.927313: Pseudo dice [0.5661, 0.8572] 
+2024-09-08 23:44:06.927369: Epoch time: 245.8 s 
+2024-09-08 23:44:08.032286:  
+2024-09-08 23:44:08.032505: Epoch 432 
+2024-09-08 23:44:08.032588: Current learning rate: 0.00601 
+2024-09-08 23:48:23.996403: train_loss -0.8572 
+2024-09-08 23:48:23.996555: val_loss -0.6845 
+2024-09-08 23:48:23.996781: Pseudo dice [0.6006, 0.8702] 
+2024-09-08 23:48:23.996838: Epoch time: 255.97 s 
+2024-09-08 23:48:24.960518:  
+2024-09-08 23:48:24.960715: Epoch 433 
+2024-09-08 23:48:24.960802: Current learning rate: 0.006 
+2024-09-08 23:52:55.353642: train_loss -0.8596 
+2024-09-08 23:52:55.353788: val_loss -0.6683 
+2024-09-08 23:52:55.353848: Pseudo dice [0.5473, 0.8645] 
+2024-09-08 23:52:55.353904: Epoch time: 270.4 s 
+2024-09-08 23:52:56.318121:  
+2024-09-08 23:52:56.318307: Epoch 434 
+2024-09-08 23:52:56.318391: Current learning rate: 0.00599 
+2024-09-08 23:57:07.151846: train_loss -0.8629 
+2024-09-08 23:57:07.152012: val_loss -0.699 
+2024-09-08 23:57:07.152068: Pseudo dice [0.6295, 0.865] 
+2024-09-08 23:57:07.152124: Epoch time: 250.84 s 
+2024-09-08 23:57:08.111701:  
+2024-09-08 23:57:08.111937: Epoch 435 
+2024-09-08 23:57:08.112027: Current learning rate: 0.00598 
+2024-09-09 00:01:40.781311: train_loss -0.8586 
+2024-09-09 00:01:40.781528: val_loss -0.6855 
+2024-09-09 00:01:40.781642: Pseudo dice [0.595, 0.862] 
+2024-09-09 00:01:40.781753: Epoch time: 272.67 s 
+2024-09-09 00:01:41.745317:  
+2024-09-09 00:01:41.745481: Epoch 436 
+2024-09-09 00:01:41.745568: Current learning rate: 0.00597 
+2024-09-09 00:06:19.313323: train_loss -0.8609 
+2024-09-09 00:06:19.313491: val_loss -0.7002 
+2024-09-09 00:06:19.313547: Pseudo dice [0.6068, 0.866] 
+2024-09-09 00:06:19.313608: Epoch time: 277.57 s 
+2024-09-09 00:06:20.294966:  
+2024-09-09 00:06:20.295150: Epoch 437 
+2024-09-09 00:06:20.295236: Current learning rate: 0.00596 
+2024-09-09 00:10:55.455012: train_loss -0.8343 
+2024-09-09 00:10:55.455196: val_loss -0.6496 
+2024-09-09 00:10:55.455254: Pseudo dice [0.5508, 0.84] 
+2024-09-09 00:10:55.455311: Epoch time: 275.16 s 
+2024-09-09 00:10:56.406845:  
+2024-09-09 00:10:56.407081: Epoch 438 
+2024-09-09 00:10:56.407170: Current learning rate: 0.00595 
+2024-09-09 00:15:07.093899: train_loss -0.824 
+2024-09-09 00:15:07.094059: val_loss -0.6521 
+2024-09-09 00:15:07.094115: Pseudo dice [0.58, 0.8323] 
+2024-09-09 00:15:07.094172: Epoch time: 250.69 s 
+2024-09-09 00:15:08.053357:  
+2024-09-09 00:15:08.053529: Epoch 439 
+2024-09-09 00:15:08.053622: Current learning rate: 0.00594 
+2024-09-09 00:19:27.844485: train_loss -0.8429 
+2024-09-09 00:19:27.844645: val_loss -0.6682 
+2024-09-09 00:19:27.844701: Pseudo dice [0.5708, 0.8539] 
+2024-09-09 00:19:27.844757: Epoch time: 259.79 s 
+2024-09-09 00:19:28.832505:  
+2024-09-09 00:19:28.832680: Epoch 440 
+2024-09-09 00:19:28.832768: Current learning rate: 0.00593 
+2024-09-09 00:23:47.367543: train_loss -0.8341 
+2024-09-09 00:23:47.367929: val_loss -0.6696 
+2024-09-09 00:23:47.368029: Pseudo dice [0.5927, 0.8638] 
+2024-09-09 00:23:47.368087: Epoch time: 258.54 s 
+2024-09-09 00:23:48.337437:  
+2024-09-09 00:23:48.337678: Epoch 441 
+2024-09-09 00:23:48.337809: Current learning rate: 0.00592 
+2024-09-09 00:28:10.484097: train_loss -0.8427 
+2024-09-09 00:28:10.484295: val_loss -0.6741 
+2024-09-09 00:28:10.484361: Pseudo dice [0.6078, 0.8463] 
+2024-09-09 00:28:10.484431: Epoch time: 262.15 s 
+2024-09-09 00:28:11.539330:  
+2024-09-09 00:28:11.539528: Epoch 442 
+2024-09-09 00:28:11.539631: Current learning rate: 0.00592 
+2024-09-09 00:32:55.043250: train_loss -0.8366 
+2024-09-09 00:32:55.043449: val_loss -0.662 
+2024-09-09 00:32:55.043532: Pseudo dice [0.601, 0.8312] 
+2024-09-09 00:32:55.043600: Epoch time: 283.51 s 
+2024-09-09 00:32:56.208460:  
+2024-09-09 00:32:56.208652: Epoch 443 
+2024-09-09 00:32:56.208762: Current learning rate: 0.00591 
+2024-09-09 00:37:14.799995: train_loss -0.8228 
+2024-09-09 00:37:14.800642: val_loss -0.6604 
+2024-09-09 00:37:14.800701: Pseudo dice [0.5678, 0.853] 
+2024-09-09 00:37:14.800756: Epoch time: 258.59 s 
+2024-09-09 00:37:15.754851:  
+2024-09-09 00:37:15.755064: Epoch 444 
+2024-09-09 00:37:15.755156: Current learning rate: 0.0059 
+2024-09-09 00:41:22.020749: train_loss -0.8208 
+2024-09-09 00:41:22.020917: val_loss -0.6595 
+2024-09-09 00:41:22.020974: Pseudo dice [0.5446, 0.8377] 
+2024-09-09 00:41:22.021031: Epoch time: 246.27 s 
+2024-09-09 00:41:22.974394:  
+2024-09-09 00:41:22.974660: Epoch 445 
+2024-09-09 00:41:22.974756: Current learning rate: 0.00589 
+2024-09-09 00:45:31.331594: train_loss -0.8424 
+2024-09-09 00:45:31.331760: val_loss -0.6773 
+2024-09-09 00:45:31.331818: Pseudo dice [0.5898, 0.8465] 
+2024-09-09 00:45:31.331897: Epoch time: 248.36 s 
+2024-09-09 00:45:32.273647:  
+2024-09-09 00:45:32.273859: Epoch 446 
+2024-09-09 00:45:32.273942: Current learning rate: 0.00588 
+2024-09-09 00:49:38.807096: train_loss -0.8468 
+2024-09-09 00:49:38.807245: val_loss -0.6881 
+2024-09-09 00:49:38.807301: Pseudo dice [0.643, 0.8431] 
+2024-09-09 00:49:38.807353: Epoch time: 246.54 s 
+2024-09-09 00:49:39.771436:  
+2024-09-09 00:49:39.771705: Epoch 447 
+2024-09-09 00:49:39.771788: Current learning rate: 0.00587 
+2024-09-09 00:53:46.204944: train_loss -0.8503 
+2024-09-09 00:53:46.205102: val_loss -0.6963 
+2024-09-09 00:53:46.205152: Pseudo dice [0.604, 0.8572] 
+2024-09-09 00:53:46.205203: Epoch time: 246.44 s 
+2024-09-09 00:53:47.182459:  
+2024-09-09 00:53:47.182668: Epoch 448 
+2024-09-09 00:53:47.182753: Current learning rate: 0.00586 
+2024-09-09 00:57:53.703654: train_loss -0.8405 
+2024-09-09 00:57:53.703838: val_loss -0.6771 
+2024-09-09 00:57:53.703919: Pseudo dice [0.5824, 0.8494] 
+2024-09-09 00:57:53.703987: Epoch time: 246.52 s 
+2024-09-09 00:57:54.865364:  
+2024-09-09 00:57:54.865690: Epoch 449 
+2024-09-09 00:57:54.865818: Current learning rate: 0.00585 
+2024-09-09 01:02:01.525649: train_loss -0.8384 
+2024-09-09 01:02:01.525791: val_loss -0.7053 
+2024-09-09 01:02:01.525843: Pseudo dice [0.6254, 0.8681] 
+2024-09-09 01:02:01.525893: Epoch time: 246.66 s 
+2024-09-09 01:02:05.292338:  
+2024-09-09 01:02:05.292562: Epoch 450 
+2024-09-09 01:02:05.292649: Current learning rate: 0.00584 
+2024-09-09 01:06:11.003167: train_loss -0.841 
+2024-09-09 01:06:11.003325: val_loss -0.6821 
+2024-09-09 01:06:11.003376: Pseudo dice [0.6095, 0.8434] 
+2024-09-09 01:06:11.003432: Epoch time: 245.71 s 
+2024-09-09 01:06:11.981426:  
+2024-09-09 01:06:11.981606: Epoch 451 
+2024-09-09 01:06:11.981685: Current learning rate: 0.00583 
+2024-09-09 01:10:19.672381: train_loss -0.8399 
+2024-09-09 01:10:19.672522: val_loss -0.6836 
+2024-09-09 01:10:19.672574: Pseudo dice [0.6028, 0.8563] 
+2024-09-09 01:10:19.672625: Epoch time: 247.69 s 
+2024-09-09 01:10:20.648863:  
+2024-09-09 01:10:20.649130: Epoch 452 
+2024-09-09 01:10:20.649215: Current learning rate: 0.00582 
+2024-09-09 01:14:27.828534: train_loss -0.8239 
+2024-09-09 01:14:27.828675: val_loss -0.6776 
+2024-09-09 01:14:27.828726: Pseudo dice [0.615, 0.8553] 
+2024-09-09 01:14:27.828780: Epoch time: 247.18 s 
+2024-09-09 01:14:28.779406:  
+2024-09-09 01:14:28.779620: Epoch 453 
+2024-09-09 01:14:28.779701: Current learning rate: 0.00581 
+2024-09-09 01:18:39.065316: train_loss -0.8386 
+2024-09-09 01:18:39.065680: val_loss -0.6498 
+2024-09-09 01:18:39.065734: Pseudo dice [0.523, 0.8612] 
+2024-09-09 01:18:39.065786: Epoch time: 250.29 s 
+2024-09-09 01:18:40.026336:  
+2024-09-09 01:18:40.026549: Epoch 454 
+2024-09-09 01:18:40.026638: Current learning rate: 0.0058 
+2024-09-09 01:23:16.244740: train_loss -0.8378 
+2024-09-09 01:23:16.244880: val_loss -0.6993 
+2024-09-09 01:23:16.244930: Pseudo dice [0.6134, 0.8655] 
+2024-09-09 01:23:16.244981: Epoch time: 276.22 s 
+2024-09-09 01:23:17.230346:  
+2024-09-09 01:23:17.230581: Epoch 455 
+2024-09-09 01:23:17.230668: Current learning rate: 0.00579 
+2024-09-09 01:28:00.151458: train_loss -0.8486 
+2024-09-09 01:28:00.151733: val_loss -0.6866 
+2024-09-09 01:28:00.151871: Pseudo dice [0.5861, 0.8623] 
+2024-09-09 01:28:00.151971: Epoch time: 282.92 s 
+2024-09-09 01:28:01.104802:  
+2024-09-09 01:28:01.104980: Epoch 456 
+2024-09-09 01:28:01.105074: Current learning rate: 0.00578 
+2024-09-09 01:32:06.858847: train_loss -0.8516 
+2024-09-09 01:32:06.858981: val_loss -0.6886 
+2024-09-09 01:32:06.859032: Pseudo dice [0.5864, 0.8683] 
+2024-09-09 01:32:06.859084: Epoch time: 245.76 s 
+2024-09-09 01:32:07.808988:  
+2024-09-09 01:32:07.809155: Epoch 457 
+2024-09-09 01:32:07.809260: Current learning rate: 0.00577 
+2024-09-09 01:36:14.193558: train_loss -0.8509 
+2024-09-09 01:36:14.193699: val_loss -0.6688 
+2024-09-09 01:36:14.193749: Pseudo dice [0.5836, 0.8597] 
+2024-09-09 01:36:14.193801: Epoch time: 246.39 s 
+2024-09-09 01:36:15.152914:  
+2024-09-09 01:36:15.153111: Epoch 458 
+2024-09-09 01:36:15.153197: Current learning rate: 0.00576 
+2024-09-09 01:40:21.026995: train_loss -0.8584 
+2024-09-09 01:40:21.027131: val_loss -0.6961 
+2024-09-09 01:40:21.027182: Pseudo dice [0.6176, 0.8569] 
+2024-09-09 01:40:21.027233: Epoch time: 245.88 s 
+2024-09-09 01:40:21.969081:  
+2024-09-09 01:40:21.969216: Epoch 459 
+2024-09-09 01:40:21.969297: Current learning rate: 0.00575 
+2024-09-09 01:44:27.800260: train_loss -0.8584 
+2024-09-09 01:44:27.800402: val_loss -0.6749 
+2024-09-09 01:44:27.800452: Pseudo dice [0.5798, 0.8569] 
+2024-09-09 01:44:27.800503: Epoch time: 245.83 s 
+2024-09-09 01:44:28.743629:  
+2024-09-09 01:44:28.743801: Epoch 460 
+2024-09-09 01:44:28.743889: Current learning rate: 0.00574 
+2024-09-09 01:48:34.611037: train_loss -0.846 
+2024-09-09 01:48:34.611177: val_loss -0.6781 
+2024-09-09 01:48:34.611230: Pseudo dice [0.5846, 0.8476] 
+2024-09-09 01:48:34.611282: Epoch time: 245.87 s 
+2024-09-09 01:48:35.565524:  
+2024-09-09 01:48:35.565747: Epoch 461 
+2024-09-09 01:48:35.565828: Current learning rate: 0.00573 
+2024-09-09 01:52:41.604704: train_loss -0.8549 
+2024-09-09 01:52:41.604839: val_loss -0.6717 
+2024-09-09 01:52:41.604890: Pseudo dice [0.5793, 0.8593] 
+2024-09-09 01:52:41.604941: Epoch time: 246.04 s 
+2024-09-09 01:52:42.577705:  
+2024-09-09 01:52:42.577909: Epoch 462 
+2024-09-09 01:52:42.578031: Current learning rate: 0.00572 
+2024-09-09 01:56:48.553939: train_loss -0.8547 
+2024-09-09 01:56:48.554084: val_loss -0.6855 
+2024-09-09 01:56:48.554133: Pseudo dice [0.6137, 0.8467] 
+2024-09-09 01:56:48.554182: Epoch time: 245.98 s 
+2024-09-09 01:56:49.486784:  
+2024-09-09 01:56:49.486989: Epoch 463 
+2024-09-09 01:56:49.487089: Current learning rate: 0.00571 
+2024-09-09 02:00:55.661453: train_loss -0.8565 
+2024-09-09 02:00:55.661602: val_loss -0.67 
+2024-09-09 02:00:55.661652: Pseudo dice [0.5629, 0.8556] 
+2024-09-09 02:00:55.661702: Epoch time: 246.18 s 
+2024-09-09 02:00:56.625237:  
+2024-09-09 02:00:56.625418: Epoch 464 
+2024-09-09 02:00:56.625501: Current learning rate: 0.0057 
+2024-09-09 02:05:07.865366: train_loss -0.8574 
+2024-09-09 02:05:07.865884: val_loss -0.6862 
+2024-09-09 02:05:07.865952: Pseudo dice [0.6094, 0.8372] 
+2024-09-09 02:05:07.866032: Epoch time: 251.24 s 
+2024-09-09 02:05:10.750765:  
+2024-09-09 02:05:10.751203: Epoch 465 
+2024-09-09 02:05:10.751372: Current learning rate: 0.0057 
+2024-09-09 02:09:33.661508: train_loss -0.8481 
+2024-09-09 02:09:33.670187: val_loss -0.6793 
+2024-09-09 02:09:33.670325: Pseudo dice [0.5704, 0.8495] 
+2024-09-09 02:09:33.670443: Epoch time: 262.91 s 
+2024-09-09 02:09:34.722916:  
+2024-09-09 02:09:34.723135: Epoch 466 
+2024-09-09 02:09:34.723245: Current learning rate: 0.00569 
+2024-09-09 02:13:42.229287: train_loss -0.8568 
+2024-09-09 02:13:42.229430: val_loss -0.6619 
+2024-09-09 02:13:42.229481: Pseudo dice [0.5626, 0.8737] 
+2024-09-09 02:13:42.229531: Epoch time: 247.51 s 
+2024-09-09 02:13:43.185913:  
+2024-09-09 02:13:43.186141: Epoch 467 
+2024-09-09 02:13:43.186264: Current learning rate: 0.00568 
+2024-09-09 02:18:05.880913: train_loss -0.8614 
+2024-09-09 02:18:05.881321: val_loss -0.6888 
+2024-09-09 02:18:05.881375: Pseudo dice [0.6352, 0.8551] 
+2024-09-09 02:18:05.881427: Epoch time: 262.7 s 
+2024-09-09 02:18:06.839787:  
+2024-09-09 02:18:06.840075: Epoch 468 
+2024-09-09 02:18:06.840155: Current learning rate: 0.00567 
+2024-09-09 02:22:24.590371: train_loss -0.8669 
+2024-09-09 02:22:24.590888: val_loss -0.6647 
+2024-09-09 02:22:24.590995: Pseudo dice [0.5439, 0.8563] 
+2024-09-09 02:22:24.591081: Epoch time: 257.75 s 
+2024-09-09 02:22:25.746886:  
+2024-09-09 02:22:25.747132: Epoch 469 
+2024-09-09 02:22:25.747214: Current learning rate: 0.00566 
+2024-09-09 02:27:02.744344: train_loss -0.862 
+2024-09-09 02:27:02.744750: val_loss -0.6614 
+2024-09-09 02:27:02.744803: Pseudo dice [0.5478, 0.8524] 
+2024-09-09 02:27:02.744855: Epoch time: 277.0 s 
+2024-09-09 02:27:04.473154:  
+2024-09-09 02:27:04.473447: Epoch 470 
+2024-09-09 02:27:04.473534: Current learning rate: 0.00565 
+2024-09-09 02:31:41.821310: train_loss -0.866 
+2024-09-09 02:31:41.822685: val_loss -0.6815 
+2024-09-09 02:31:41.823559: Pseudo dice [0.6037, 0.8655] 
+2024-09-09 02:31:41.824260: Epoch time: 277.35 s 
+2024-09-09 02:31:48.585543:  
+2024-09-09 02:31:48.585810: Epoch 471 
+2024-09-09 02:31:48.585900: Current learning rate: 0.00564 
+2024-09-09 02:36:45.322553: train_loss -0.8638 
+2024-09-09 02:36:45.323082: val_loss -0.6791 
+2024-09-09 02:36:45.323202: Pseudo dice [0.5966, 0.8579] 
+2024-09-09 02:36:45.323308: Epoch time: 296.74 s 
+2024-09-09 02:36:50.842235:  
+2024-09-09 02:36:50.842475: Epoch 472 
+2024-09-09 02:36:50.842556: Current learning rate: 0.00563 
+2024-09-09 02:42:43.769434: train_loss -0.8574 
+2024-09-09 02:42:43.836169: val_loss -0.6478 
+2024-09-09 02:42:43.838094: Pseudo dice [0.5364, 0.835] 
+2024-09-09 02:42:43.849926: Epoch time: 352.91 s 
+2024-09-09 02:43:00.644475:  
+2024-09-09 02:43:00.690984: Epoch 473 
+2024-09-09 02:43:00.691123: Current learning rate: 0.00562 
+2024-09-09 02:48:15.044425: train_loss -0.8214 
+2024-09-09 02:48:15.044770: val_loss -0.6808 
+2024-09-09 02:48:15.044825: Pseudo dice [0.6114, 0.8477] 
+2024-09-09 02:48:15.044881: Epoch time: 314.4 s 
+2024-09-09 02:48:18.566137:  
+2024-09-09 02:48:18.573397: Epoch 474 
+2024-09-09 02:48:18.573527: Current learning rate: 0.00561 
+2024-09-09 02:52:43.167179: train_loss -0.8518 
+2024-09-09 02:52:43.167563: val_loss -0.6853 
+2024-09-09 02:52:43.167614: Pseudo dice [0.5722, 0.8679] 
+2024-09-09 02:52:43.167667: Epoch time: 264.61 s 
+2024-09-09 02:52:55.503304:  
+2024-09-09 02:52:55.639619: Epoch 475 
+2024-09-09 02:52:55.639768: Current learning rate: 0.0056 
+2024-09-09 02:59:58.397222: train_loss -0.8629 
+2024-09-09 02:59:58.521581: val_loss -0.6851 
+2024-09-09 02:59:58.521752: Pseudo dice [0.6333, 0.859] 
+2024-09-09 02:59:58.524669: Epoch time: 422.95 s 
+2024-09-09 03:00:19.809605:  
+2024-09-09 03:00:19.809874: Epoch 476 
+2024-09-09 03:00:19.810064: Current learning rate: 0.00559 
+2024-09-09 03:05:14.671621: train_loss -0.8461 
+2024-09-09 03:05:14.748576: val_loss -0.6776 
+2024-09-09 03:05:14.748848: Pseudo dice [0.5723, 0.8653] 
+2024-09-09 03:05:14.748920: Epoch time: 294.85 s 
+2024-09-09 03:05:26.414580:  
+2024-09-09 03:05:26.414773: Epoch 477 
+2024-09-09 03:05:26.414899: Current learning rate: 0.00558 
+2024-09-09 03:10:59.817205: train_loss -0.8493 
+2024-09-09 03:10:59.846032: val_loss -0.6602 
+2024-09-09 03:10:59.846088: Pseudo dice [0.5466, 0.8509] 
+2024-09-09 03:10:59.846147: Epoch time: 333.4 s 
+2024-09-09 03:11:03.608053:  
+2024-09-09 03:11:03.608221: Epoch 478 
+2024-09-09 03:11:03.608305: Current learning rate: 0.00557 
+2024-09-09 03:15:11.871933: train_loss -0.85 
+2024-09-09 03:15:11.872112: val_loss -0.6756 
+2024-09-09 03:15:11.872171: Pseudo dice [0.5657, 0.8577] 
+2024-09-09 03:15:11.872226: Epoch time: 248.27 s 
+2024-09-09 03:15:14.449643:  
+2024-09-09 03:15:14.449893: Epoch 479 
+2024-09-09 03:15:14.449977: Current learning rate: 0.00556 
+2024-09-09 03:19:27.011188: train_loss -0.8556 
+2024-09-09 03:19:27.011598: val_loss -0.6646 
+2024-09-09 03:19:27.011662: Pseudo dice [0.5915, 0.8645] 
+2024-09-09 03:19:27.011713: Epoch time: 252.56 s 
+2024-09-09 03:19:29.155850:  
+2024-09-09 03:19:29.156070: Epoch 480 
+2024-09-09 03:19:29.156157: Current learning rate: 0.00555 
+2024-09-09 03:23:48.925586: train_loss -0.8508 
+2024-09-09 03:23:48.925722: val_loss -0.6856 
+2024-09-09 03:23:48.925812: Pseudo dice [0.6169, 0.8614] 
+2024-09-09 03:23:48.925903: Epoch time: 259.77 s 
+2024-09-09 03:23:51.255460:  
+2024-09-09 03:23:51.255703: Epoch 481 
+2024-09-09 03:23:51.255802: Current learning rate: 0.00554 
+2024-09-09 03:28:10.511521: train_loss -0.8444 
+2024-09-09 03:28:10.511660: val_loss -0.6574 
+2024-09-09 03:28:10.511709: Pseudo dice [0.5599, 0.8531] 
+2024-09-09 03:28:10.511759: Epoch time: 259.26 s 
+2024-09-09 03:28:11.661487:  
+2024-09-09 03:28:11.661710: Epoch 482 
+2024-09-09 03:28:11.661792: Current learning rate: 0.00553 
+2024-09-09 03:32:21.716711: train_loss -0.8508 
+2024-09-09 03:32:21.716849: val_loss -0.6504 
+2024-09-09 03:32:21.716898: Pseudo dice [0.538, 0.8482] 
+2024-09-09 03:32:21.716949: Epoch time: 250.06 s 
+2024-09-09 03:32:24.495244:  
+2024-09-09 03:32:24.495457: Epoch 483 
+2024-09-09 03:32:24.495578: Current learning rate: 0.00552 
+2024-09-09 03:36:31.182970: train_loss -0.8317 
+2024-09-09 03:36:31.183220: val_loss -0.6783 
+2024-09-09 03:36:31.183272: Pseudo dice [0.621, 0.8445] 
+2024-09-09 03:36:31.183322: Epoch time: 246.7 s 
+2024-09-09 03:36:33.854647:  
+2024-09-09 03:36:33.854887: Epoch 484 
+2024-09-09 03:36:33.854969: Current learning rate: 0.00551 
+2024-09-09 03:40:40.459771: train_loss -0.8379 
+2024-09-09 03:40:40.459949: val_loss -0.6616 
+2024-09-09 03:40:40.460001: Pseudo dice [0.5701, 0.8464] 
+2024-09-09 03:40:40.460054: Epoch time: 246.61 s 
+2024-09-09 03:40:42.373161:  
+2024-09-09 03:40:42.373408: Epoch 485 
+2024-09-09 03:40:42.373519: Current learning rate: 0.0055 
+2024-09-09 03:44:52.878695: train_loss -0.8385 
+2024-09-09 03:44:52.878832: val_loss -0.6945 
+2024-09-09 03:44:52.878884: Pseudo dice [0.6216, 0.8597] 
+2024-09-09 03:44:52.878940: Epoch time: 250.51 s 
+2024-09-09 03:44:54.113252:  
+2024-09-09 03:44:54.113489: Epoch 486 
+2024-09-09 03:44:54.113618: Current learning rate: 0.00549 
+2024-09-09 03:49:04.778752: train_loss -0.8537 
+2024-09-09 03:49:04.778891: val_loss -0.6717 
+2024-09-09 03:49:04.778941: Pseudo dice [0.5805, 0.851] 
+2024-09-09 03:49:04.778993: Epoch time: 250.67 s 
+2024-09-09 03:49:06.069361:  
+2024-09-09 03:49:06.069622: Epoch 487 
+2024-09-09 03:49:06.069747: Current learning rate: 0.00548 
+2024-09-09 03:53:16.663814: train_loss -0.8503 
+2024-09-09 03:53:16.663958: val_loss -0.6783 
+2024-09-09 03:53:16.664007: Pseudo dice [0.622, 0.8513] 
+2024-09-09 03:53:16.664057: Epoch time: 250.6 s 
+2024-09-09 03:53:17.786427:  
+2024-09-09 03:53:17.786654: Epoch 488 
+2024-09-09 03:53:17.786847: Current learning rate: 0.00547 
+2024-09-09 03:57:25.106975: train_loss -0.8515 
+2024-09-09 03:57:25.107109: val_loss -0.6798 
+2024-09-09 03:57:25.107159: Pseudo dice [0.6031, 0.8495] 
+2024-09-09 03:57:25.107210: Epoch time: 247.33 s 
+2024-09-09 03:57:40.031130:  
+2024-09-09 03:57:40.031374: Epoch 489 
+2024-09-09 03:57:40.031508: Current learning rate: 0.00546 
+2024-09-09 04:01:49.608534: train_loss -0.8531 
+2024-09-09 04:01:49.608695: val_loss -0.6906 
+2024-09-09 04:01:49.608747: Pseudo dice [0.5911, 0.8632] 
+2024-09-09 04:01:49.608799: Epoch time: 249.58 s 
+2024-09-09 04:01:50.623415:  
+2024-09-09 04:01:50.623631: Epoch 490 
+2024-09-09 04:01:50.623732: Current learning rate: 0.00546 
+2024-09-09 04:06:09.739093: train_loss -0.8568 
+2024-09-09 04:06:09.739252: val_loss -0.6965 
+2024-09-09 04:06:09.739336: Pseudo dice [0.6197, 0.8638] 
+2024-09-09 04:06:09.739387: Epoch time: 259.12 s 
+2024-09-09 04:06:10.748586:  
+2024-09-09 04:06:10.748864: Epoch 491 
+2024-09-09 04:06:10.748949: Current learning rate: 0.00545 
+2024-09-09 04:10:21.601751: train_loss -0.8615 
+2024-09-09 04:10:21.601887: val_loss -0.6569 
+2024-09-09 04:10:21.601939: Pseudo dice [0.53, 0.85] 
+2024-09-09 04:10:21.601991: Epoch time: 250.86 s 
+2024-09-09 04:10:22.635611:  
+2024-09-09 04:10:22.635907: Epoch 492 
+2024-09-09 04:10:22.635991: Current learning rate: 0.00544 
+2024-09-09 04:14:29.974653: train_loss -0.865 
+2024-09-09 04:14:29.974790: val_loss -0.682 
+2024-09-09 04:14:29.974839: Pseudo dice [0.6056, 0.857] 
+2024-09-09 04:14:29.974890: Epoch time: 247.34 s 
+2024-09-09 04:14:31.125913:  
+2024-09-09 04:14:31.126147: Epoch 493 
+2024-09-09 04:14:31.126229: Current learning rate: 0.00543 
+2024-09-09 04:18:37.711767: train_loss -0.8679 
+2024-09-09 04:18:37.711917: val_loss -0.6604 
+2024-09-09 04:18:37.711979: Pseudo dice [0.5928, 0.8446] 
+2024-09-09 04:18:37.712030: Epoch time: 246.59 s 
+2024-09-09 04:18:38.749901:  
+2024-09-09 04:18:38.750148: Epoch 494 
+2024-09-09 04:18:38.750230: Current learning rate: 0.00542 
+2024-09-09 04:22:45.482240: train_loss -0.8632 
+2024-09-09 04:22:45.482407: val_loss -0.6712 
+2024-09-09 04:22:45.482458: Pseudo dice [0.5791, 0.8534] 
+2024-09-09 04:22:45.482512: Epoch time: 246.73 s 
+2024-09-09 04:22:46.517616:  
+2024-09-09 04:22:46.517825: Epoch 495 
+2024-09-09 04:22:46.517910: Current learning rate: 0.00541 
+2024-09-09 04:26:57.409897: train_loss -0.8659 
+2024-09-09 04:26:57.410077: val_loss -0.6707 
+2024-09-09 04:26:57.410128: Pseudo dice [0.5811, 0.8636] 
+2024-09-09 04:26:57.410180: Epoch time: 250.89 s 
+2024-09-09 04:26:58.434167:  
+2024-09-09 04:26:58.434384: Epoch 496 
+2024-09-09 04:26:58.434464: Current learning rate: 0.0054 
+2024-09-09 04:31:08.621835: train_loss -0.8652 
+2024-09-09 04:31:08.621974: val_loss -0.7031 
+2024-09-09 04:31:08.622025: Pseudo dice [0.6175, 0.8514] 
+2024-09-09 04:31:08.622075: Epoch time: 250.19 s 
+2024-09-09 04:31:09.710263:  
+2024-09-09 04:31:09.710470: Epoch 497 
+2024-09-09 04:31:09.710552: Current learning rate: 0.00539 
+2024-09-09 04:35:35.061964: train_loss -0.8643 
+2024-09-09 04:35:35.062103: val_loss -0.6568 
+2024-09-09 04:35:35.062154: Pseudo dice [0.5457, 0.8639] 
+2024-09-09 04:35:35.062205: Epoch time: 265.35 s 
+2024-09-09 04:35:36.161722:  
+2024-09-09 04:35:36.161932: Epoch 498 
+2024-09-09 04:35:36.162010: Current learning rate: 0.00538 
+2024-09-09 04:39:42.822753: train_loss -0.8636 
+2024-09-09 04:39:42.822933: val_loss -0.6855 
+2024-09-09 04:39:42.822984: Pseudo dice [0.6053, 0.8685] 
+2024-09-09 04:39:42.823035: Epoch time: 246.66 s 
+2024-09-09 04:39:43.929027:  
+2024-09-09 04:39:43.929256: Epoch 499 
+2024-09-09 04:39:43.929348: Current learning rate: 0.00537 
+2024-09-09 04:43:50.853756: train_loss -0.8623 
+2024-09-09 04:43:50.853919: val_loss -0.679 
+2024-09-09 04:43:50.853971: Pseudo dice [0.6181, 0.8693] 
+2024-09-09 04:43:50.854023: Epoch time: 246.93 s 
+2024-09-09 04:43:54.685038:  
+2024-09-09 04:43:54.685306: Epoch 500 
+2024-09-09 04:43:54.685406: Current learning rate: 0.00536 
+2024-09-09 04:48:01.565379: train_loss -0.8673 
+2024-09-09 04:48:01.565517: val_loss -0.687 
+2024-09-09 04:48:01.565568: Pseudo dice [0.6031, 0.8578] 
+2024-09-09 04:48:01.565619: Epoch time: 246.88 s 
+2024-09-09 04:48:02.540725:  
+2024-09-09 04:48:02.540972: Epoch 501 
+2024-09-09 04:48:02.541056: Current learning rate: 0.00535 
+2024-09-09 04:52:09.095766: train_loss -0.8692 
+2024-09-09 04:52:09.095915: val_loss -0.6936 
+2024-09-09 04:52:09.095965: Pseudo dice [0.6347, 0.8574] 
+2024-09-09 04:52:09.096017: Epoch time: 246.56 s 
+2024-09-09 04:52:10.098687:  
+2024-09-09 04:52:10.098965: Epoch 502 
+2024-09-09 04:52:10.099071: Current learning rate: 0.00534 
+2024-09-09 04:56:16.792608: train_loss -0.8602 
+2024-09-09 04:56:16.792745: val_loss -0.6979 
+2024-09-09 04:56:16.792795: Pseudo dice [0.6268, 0.8697] 
+2024-09-09 04:56:16.792846: Epoch time: 246.7 s 
+2024-09-09 04:56:17.760987:  
+2024-09-09 04:56:17.761212: Epoch 503 
+2024-09-09 04:56:17.761289: Current learning rate: 0.00533 
+2024-09-09 05:00:24.611500: train_loss -0.869 
+2024-09-09 05:00:24.611665: val_loss -0.6862 
+2024-09-09 05:00:24.611855: Pseudo dice [0.6023, 0.8612] 
+2024-09-09 05:00:24.611912: Epoch time: 246.85 s 
+2024-09-09 05:00:25.629985:  
+2024-09-09 05:00:25.630175: Epoch 504 
+2024-09-09 05:00:25.630278: Current learning rate: 0.00532 
+2024-09-09 05:04:33.529988: train_loss -0.8627 
+2024-09-09 05:04:33.530140: val_loss -0.6932 
+2024-09-09 05:04:33.530191: Pseudo dice [0.6307, 0.8658] 
+2024-09-09 05:04:33.530244: Epoch time: 247.9 s 
+2024-09-09 05:04:34.545043:  
+2024-09-09 05:04:34.545190: Epoch 505 
+2024-09-09 05:04:34.545271: Current learning rate: 0.00531 
+2024-09-09 05:08:41.147892: train_loss -0.8695 
+2024-09-09 05:08:41.148048: val_loss -0.6843 
+2024-09-09 05:08:41.148100: Pseudo dice [0.59, 0.8658] 
+2024-09-09 05:08:41.148150: Epoch time: 246.6 s 
+2024-09-09 05:08:42.158330:  
+2024-09-09 05:08:42.158523: Epoch 506 
+2024-09-09 05:08:42.158606: Current learning rate: 0.0053 
+2024-09-09 05:12:48.643022: train_loss -0.8588 
+2024-09-09 05:12:48.643161: val_loss -0.6495 
+2024-09-09 05:12:48.643210: Pseudo dice [0.4953, 0.8551] 
+2024-09-09 05:12:48.643276: Epoch time: 246.49 s 
+2024-09-09 05:12:49.626218:  
+2024-09-09 05:12:49.626442: Epoch 507 
+2024-09-09 05:12:49.626538: Current learning rate: 0.00529 
+2024-09-09 05:16:56.057546: train_loss -0.8496 
+2024-09-09 05:16:56.057726: val_loss -0.674 
+2024-09-09 05:16:56.057790: Pseudo dice [0.6072, 0.8395] 
+2024-09-09 05:16:56.057850: Epoch time: 246.43 s 
+2024-09-09 05:16:57.061659:  
+2024-09-09 05:16:57.061884: Epoch 508 
+2024-09-09 05:16:57.061980: Current learning rate: 0.00528 
+2024-09-09 05:21:03.486688: train_loss -0.8543 
+2024-09-09 05:21:03.486828: val_loss -0.69 
+2024-09-09 05:21:03.486878: Pseudo dice [0.6013, 0.8619] 
+2024-09-09 05:21:03.486929: Epoch time: 246.43 s 
+2024-09-09 05:21:04.511789:  
+2024-09-09 05:21:04.512025: Epoch 509 
+2024-09-09 05:21:04.512110: Current learning rate: 0.00527 
+2024-09-09 05:25:10.931644: train_loss -0.8609 
+2024-09-09 05:25:10.931862: val_loss -0.6611 
+2024-09-09 05:25:10.931920: Pseudo dice [0.5681, 0.856] 
+2024-09-09 05:25:10.931982: Epoch time: 246.42 s 
+2024-09-09 05:25:12.034658:  
+2024-09-09 05:25:12.034914: Epoch 510 
+2024-09-09 05:25:12.035052: Current learning rate: 0.00526 
+2024-09-09 05:29:18.387143: train_loss -0.8601 
+2024-09-09 05:29:18.387289: val_loss -0.6845 
+2024-09-09 05:29:18.387340: Pseudo dice [0.5904, 0.8596] 
+2024-09-09 05:29:18.387391: Epoch time: 246.35 s 
+2024-09-09 05:29:19.353037:  
+2024-09-09 05:29:19.353193: Epoch 511 
+2024-09-09 05:29:19.353274: Current learning rate: 0.00525 
+2024-09-09 05:33:25.719926: train_loss -0.8639 
+2024-09-09 05:33:25.720066: val_loss -0.6722 
+2024-09-09 05:33:25.720115: Pseudo dice [0.5961, 0.86] 
+2024-09-09 05:33:25.720165: Epoch time: 246.37 s 
+2024-09-09 05:33:26.720295:  
+2024-09-09 05:33:26.720499: Epoch 512 
+2024-09-09 05:33:26.720585: Current learning rate: 0.00524 
+2024-09-09 05:37:33.935813: train_loss -0.8629 
+2024-09-09 05:37:33.935950: val_loss -0.6386 
+2024-09-09 05:37:33.936000: Pseudo dice [0.5255, 0.8534] 
+2024-09-09 05:37:33.936051: Epoch time: 247.22 s 
+2024-09-09 05:37:34.884491:  
+2024-09-09 05:37:34.884725: Epoch 513 
+2024-09-09 05:37:34.884821: Current learning rate: 0.00523 
+2024-09-09 05:41:41.538448: train_loss -0.8664 
+2024-09-09 05:41:41.538586: val_loss -0.6292 
+2024-09-09 05:41:41.538635: Pseudo dice [0.4687, 0.8664] 
+2024-09-09 05:41:41.538704: Epoch time: 246.66 s 
+2024-09-09 05:41:42.525728:  
+2024-09-09 05:41:42.525985: Epoch 514 
+2024-09-09 05:41:42.526068: Current learning rate: 0.00522 
+2024-09-09 05:45:49.790709: train_loss -0.8642 
+2024-09-09 05:45:49.790847: val_loss -0.6743 
+2024-09-09 05:45:49.790896: Pseudo dice [0.5624, 0.8624] 
+2024-09-09 05:45:49.790947: Epoch time: 247.27 s 
+2024-09-09 05:45:50.773520:  
+2024-09-09 05:45:50.773772: Epoch 515 
+2024-09-09 05:45:50.773858: Current learning rate: 0.00521 
+2024-09-09 05:49:57.318139: train_loss -0.8618 
+2024-09-09 05:49:57.318305: val_loss -0.6914 
+2024-09-09 05:49:57.318373: Pseudo dice [0.594, 0.8612] 
+2024-09-09 05:49:57.318442: Epoch time: 246.55 s 
+2024-09-09 05:49:58.387895:  
+2024-09-09 05:49:58.388069: Epoch 516 
+2024-09-09 05:49:58.388148: Current learning rate: 0.0052 
+2024-09-09 05:54:05.172453: train_loss -0.8636 
+2024-09-09 05:54:05.172592: val_loss -0.6893 
+2024-09-09 05:54:05.172642: Pseudo dice [0.6071, 0.8614] 
+2024-09-09 05:54:05.172693: Epoch time: 246.79 s 
+2024-09-09 05:54:06.152942:  
+2024-09-09 05:54:06.153146: Epoch 517 
+2024-09-09 05:54:06.153231: Current learning rate: 0.00519 
+2024-09-09 05:58:12.641229: train_loss -0.867 
+2024-09-09 05:58:12.641415: val_loss -0.6989 
+2024-09-09 05:58:12.641466: Pseudo dice [0.6373, 0.8591] 
+2024-09-09 05:58:12.641516: Epoch time: 246.49 s 
+2024-09-09 05:58:13.612538:  
+2024-09-09 05:58:13.612693: Epoch 518 
+2024-09-09 05:58:13.612769: Current learning rate: 0.00518 
+2024-09-09 06:02:20.150239: train_loss -0.8678 
+2024-09-09 06:02:20.150408: val_loss -0.7001 
+2024-09-09 06:02:20.150458: Pseudo dice [0.6268, 0.8523] 
+2024-09-09 06:02:20.150509: Epoch time: 246.54 s 
+2024-09-09 06:02:21.131459:  
+2024-09-09 06:02:21.131704: Epoch 519 
+2024-09-09 06:02:21.131799: Current learning rate: 0.00518 
+2024-09-09 06:06:27.594393: train_loss -0.8685 
+2024-09-09 06:06:27.594534: val_loss -0.6728 
+2024-09-09 06:06:27.594583: Pseudo dice [0.5786, 0.8484] 
+2024-09-09 06:06:27.594634: Epoch time: 246.46 s 
+2024-09-09 06:06:28.563610:  
+2024-09-09 06:06:28.563845: Epoch 520 
+2024-09-09 06:06:28.563924: Current learning rate: 0.00517 
+2024-09-09 06:10:34.841600: train_loss -0.875 
+2024-09-09 06:10:34.841789: val_loss -0.6766 
+2024-09-09 06:10:34.841859: Pseudo dice [0.6096, 0.8576] 
+2024-09-09 06:10:34.841917: Epoch time: 246.28 s 
+2024-09-09 06:10:35.906310:  
+2024-09-09 06:10:35.906523: Epoch 521 
+2024-09-09 06:10:35.906600: Current learning rate: 0.00516 
+2024-09-09 06:14:42.284922: train_loss -0.864 
+2024-09-09 06:14:42.285065: val_loss -0.6916 
+2024-09-09 06:14:42.285114: Pseudo dice [0.6074, 0.8598] 
+2024-09-09 06:14:42.285165: Epoch time: 246.38 s 
+2024-09-09 06:14:43.261143:  
+2024-09-09 06:14:43.261339: Epoch 522 
+2024-09-09 06:14:43.261426: Current learning rate: 0.00515 
+2024-09-09 06:18:49.636112: train_loss -0.8758 
+2024-09-09 06:18:49.636252: val_loss -0.6818 
+2024-09-09 06:18:49.636302: Pseudo dice [0.5962, 0.8552] 
+2024-09-09 06:18:49.636353: Epoch time: 246.38 s 
+2024-09-09 06:18:50.624842:  
+2024-09-09 06:18:50.625063: Epoch 523 
+2024-09-09 06:18:50.625146: Current learning rate: 0.00514 
+2024-09-09 06:22:57.298840: train_loss -0.8682 
+2024-09-09 06:22:57.298976: val_loss -0.6717 
+2024-09-09 06:22:57.299026: Pseudo dice [0.6335, 0.8443] 
+2024-09-09 06:22:57.299077: Epoch time: 246.68 s 
+2024-09-09 06:22:58.364710:  
+2024-09-09 06:22:58.364890: Epoch 524 
+2024-09-09 06:22:58.364972: Current learning rate: 0.00513 
+2024-09-09 06:27:05.024642: train_loss -0.8614 
+2024-09-09 06:27:05.024817: val_loss -0.6908 
+2024-09-09 06:27:05.024878: Pseudo dice [0.6206, 0.8529] 
+2024-09-09 06:27:05.024935: Epoch time: 246.66 s 
+2024-09-09 06:27:06.108683:  
+2024-09-09 06:27:06.108859: Epoch 525 
+2024-09-09 06:27:06.108962: Current learning rate: 0.00512 
+2024-09-09 06:31:12.417185: train_loss -0.8708 
+2024-09-09 06:31:12.417325: val_loss -0.6603 
+2024-09-09 06:31:12.417376: Pseudo dice [0.5919, 0.8384] 
+2024-09-09 06:31:12.417426: Epoch time: 246.31 s 
+2024-09-09 06:31:13.463320:  
+2024-09-09 06:31:13.463485: Epoch 526 
+2024-09-09 06:31:13.463597: Current learning rate: 0.00511 
+2024-09-09 06:35:19.357359: train_loss -0.8726 
+2024-09-09 06:35:19.357525: val_loss -0.68 
+2024-09-09 06:35:19.357576: Pseudo dice [0.6083, 0.8467] 
+2024-09-09 06:35:19.357627: Epoch time: 245.9 s 
+2024-09-09 06:35:20.323821:  
+2024-09-09 06:35:20.324050: Epoch 527 
+2024-09-09 06:35:20.324132: Current learning rate: 0.0051 
+2024-09-09 06:39:26.516546: train_loss -0.8753 
+2024-09-09 06:39:26.516727: val_loss -0.6754 
+2024-09-09 06:39:26.516795: Pseudo dice [0.5717, 0.8586] 
+2024-09-09 06:39:26.516860: Epoch time: 246.19 s 
+2024-09-09 06:39:27.590988:  
+2024-09-09 06:39:27.591223: Epoch 528 
+2024-09-09 06:39:27.591303: Current learning rate: 0.00509 
+2024-09-09 06:43:33.975344: train_loss -0.8666 
+2024-09-09 06:43:33.975484: val_loss -0.6776 
+2024-09-09 06:43:33.975533: Pseudo dice [0.5786, 0.8604] 
+2024-09-09 06:43:33.975585: Epoch time: 246.39 s 
+2024-09-09 06:43:34.958505:  
+2024-09-09 06:43:34.958693: Epoch 529 
+2024-09-09 06:43:34.958777: Current learning rate: 0.00508 
+2024-09-09 06:47:41.378227: train_loss -0.8742 
+2024-09-09 06:47:41.378366: val_loss -0.6595 
+2024-09-09 06:47:41.378416: Pseudo dice [0.5768, 0.8487] 
+2024-09-09 06:47:41.378467: Epoch time: 246.42 s 
+2024-09-09 06:47:42.373087:  
+2024-09-09 06:47:42.373296: Epoch 530 
+2024-09-09 06:47:42.373380: Current learning rate: 0.00507 
+2024-09-09 06:51:48.666377: train_loss -0.8765 
+2024-09-09 06:51:48.666517: val_loss -0.7037 
+2024-09-09 06:51:48.666567: Pseudo dice [0.6407, 0.8653] 
+2024-09-09 06:51:48.666628: Epoch time: 246.3 s 
+2024-09-09 06:51:49.631940:  
+2024-09-09 06:51:49.632079: Epoch 531 
+2024-09-09 06:51:49.632158: Current learning rate: 0.00506 
+2024-09-09 06:55:55.893496: train_loss -0.8775 
+2024-09-09 06:55:55.893733: val_loss -0.644 
+2024-09-09 06:55:55.893817: Pseudo dice [0.5485, 0.8385] 
+2024-09-09 06:55:55.893904: Epoch time: 246.26 s 
+2024-09-09 06:55:56.980462:  
+2024-09-09 06:55:56.980642: Epoch 532 
+2024-09-09 06:55:56.980721: Current learning rate: 0.00505 
+2024-09-09 07:00:03.326224: train_loss -0.8731 
+2024-09-09 07:00:03.326401: val_loss -0.6616 
+2024-09-09 07:00:03.326452: Pseudo dice [0.5832, 0.8444] 
+2024-09-09 07:00:03.326504: Epoch time: 246.35 s 
+2024-09-09 07:00:04.297520:  
+2024-09-09 07:00:04.297764: Epoch 533 
+2024-09-09 07:00:04.297887: Current learning rate: 0.00504 
+2024-09-09 07:04:10.679580: train_loss -0.8715 
+2024-09-09 07:04:10.679737: val_loss -0.674 
+2024-09-09 07:04:10.679787: Pseudo dice [0.5589, 0.8596] 
+2024-09-09 07:04:10.679888: Epoch time: 246.38 s 
+2024-09-09 07:04:11.653084:  
+2024-09-09 07:04:11.653305: Epoch 534 
+2024-09-09 07:04:11.653380: Current learning rate: 0.00503 
+2024-09-09 07:08:17.989746: train_loss -0.8645 
+2024-09-09 07:08:17.989892: val_loss -0.6715 
+2024-09-09 07:08:17.989967: Pseudo dice [0.5847, 0.8493] 
+2024-09-09 07:08:17.990038: Epoch time: 246.34 s 
+2024-09-09 07:08:18.962434:  
+2024-09-09 07:08:18.962679: Epoch 535 
+2024-09-09 07:08:18.962767: Current learning rate: 0.00502 
+2024-09-09 07:12:25.253506: train_loss -0.8687 
+2024-09-09 07:12:25.253644: val_loss -0.6984 
+2024-09-09 07:12:25.253694: Pseudo dice [0.6151, 0.8624] 
+2024-09-09 07:12:25.253745: Epoch time: 246.29 s 
+2024-09-09 07:12:27.123745:  
+2024-09-09 07:12:27.124009: Epoch 536 
+2024-09-09 07:12:27.124101: Current learning rate: 0.00501 
+2024-09-09 07:16:33.660441: train_loss -0.8705 
+2024-09-09 07:16:33.660623: val_loss -0.6667 
+2024-09-09 07:16:33.660682: Pseudo dice [0.5649, 0.8584] 
+2024-09-09 07:16:33.660736: Epoch time: 246.54 s 
+2024-09-09 07:16:34.623563:  
+2024-09-09 07:16:34.623813: Epoch 537 
+2024-09-09 07:16:34.623954: Current learning rate: 0.005 
+2024-09-09 07:20:40.654416: train_loss -0.8726 
+2024-09-09 07:20:40.654552: val_loss -0.6747 
+2024-09-09 07:20:40.654640: Pseudo dice [0.5708, 0.8625] 
+2024-09-09 07:20:40.654699: Epoch time: 246.03 s 
+2024-09-09 07:20:41.625695:  
+2024-09-09 07:20:41.625903: Epoch 538 
+2024-09-09 07:20:41.625982: Current learning rate: 0.00499 
+2024-09-09 07:24:47.731872: train_loss -0.8703 
+2024-09-09 07:24:47.732007: val_loss -0.6866 
+2024-09-09 07:24:47.732057: Pseudo dice [0.6039, 0.8614] 
+2024-09-09 07:24:47.732108: Epoch time: 246.11 s 
+2024-09-09 07:24:48.689806:  
+2024-09-09 07:24:48.690012: Epoch 539 
+2024-09-09 07:24:48.690095: Current learning rate: 0.00498 
+2024-09-09 07:28:54.613045: train_loss -0.8748 
+2024-09-09 07:28:54.613193: val_loss -0.6477 
+2024-09-09 07:28:54.613282: Pseudo dice [0.5597, 0.8445] 
+2024-09-09 07:28:54.613354: Epoch time: 245.93 s 
+2024-09-09 07:28:55.760323:  
+2024-09-09 07:28:55.760550: Epoch 540 
+2024-09-09 07:28:55.760634: Current learning rate: 0.00497 
+2024-09-09 07:33:01.917266: train_loss -0.8653 
+2024-09-09 07:33:01.917411: val_loss -0.684 
+2024-09-09 07:33:01.917461: Pseudo dice [0.6253, 0.8519] 
+2024-09-09 07:33:01.917511: Epoch time: 246.16 s 
+2024-09-09 07:33:02.886282:  
+2024-09-09 07:33:02.886466: Epoch 541 
+2024-09-09 07:33:02.886544: Current learning rate: 0.00496 
+2024-09-09 07:37:09.096718: train_loss -0.8484 
+2024-09-09 07:37:09.096950: val_loss -0.6688 
+2024-09-09 07:37:09.097059: Pseudo dice [0.6035, 0.8449] 
+2024-09-09 07:37:09.097145: Epoch time: 246.21 s 
+2024-09-09 07:37:10.201639:  
+2024-09-09 07:37:10.201842: Epoch 542 
+2024-09-09 07:37:10.201922: Current learning rate: 0.00495 
+2024-09-09 07:41:22.824071: train_loss -0.8635 
+2024-09-09 07:41:22.824234: val_loss -0.6752 
+2024-09-09 07:41:22.824286: Pseudo dice [0.6049, 0.852] 
+2024-09-09 07:41:22.824344: Epoch time: 252.62 s 
+2024-09-09 07:41:23.936155:  
+2024-09-09 07:41:23.936414: Epoch 543 
+2024-09-09 07:41:23.936504: Current learning rate: 0.00494 
+2024-09-09 07:45:30.089604: train_loss -0.872 
+2024-09-09 07:45:30.089741: val_loss -0.6858 
+2024-09-09 07:45:30.089793: Pseudo dice [0.5922, 0.8532] 
+2024-09-09 07:45:30.089845: Epoch time: 246.16 s 
+2024-09-09 07:45:31.064857:  
+2024-09-09 07:45:31.065035: Epoch 544 
+2024-09-09 07:45:31.065115: Current learning rate: 0.00493 
+2024-09-09 07:49:37.149268: train_loss -0.8763 
+2024-09-09 07:49:37.149409: val_loss -0.6546 
+2024-09-09 07:49:37.149459: Pseudo dice [0.575, 0.8499] 
+2024-09-09 07:49:37.149510: Epoch time: 246.09 s 
+2024-09-09 07:49:38.145213:  
+2024-09-09 07:49:38.145416: Epoch 545 
+2024-09-09 07:49:38.145500: Current learning rate: 0.00492 
+2024-09-09 07:53:44.789630: train_loss -0.8594 
+2024-09-09 07:53:44.789768: val_loss -0.6569 
+2024-09-09 07:53:44.789818: Pseudo dice [0.526, 0.8581] 
+2024-09-09 07:53:44.789868: Epoch time: 246.65 s 
+2024-09-09 07:53:45.777152:  
+2024-09-09 07:53:45.777327: Epoch 546 
+2024-09-09 07:53:45.777407: Current learning rate: 0.00491 
+2024-09-09 07:57:52.314502: train_loss -0.8626 
+2024-09-09 07:57:52.314639: val_loss -0.6537 
+2024-09-09 07:57:52.314688: Pseudo dice [0.551, 0.8556] 
+2024-09-09 07:57:52.314739: Epoch time: 246.54 s 
+2024-09-09 07:57:53.308362:  
+2024-09-09 07:57:53.308575: Epoch 547 
+2024-09-09 07:57:53.308693: Current learning rate: 0.0049 
+2024-09-09 08:01:59.844774: train_loss -0.8677 
+2024-09-09 08:01:59.844939: val_loss -0.7138 
+2024-09-09 08:01:59.844989: Pseudo dice [0.6467, 0.8655] 
+2024-09-09 08:01:59.845040: Epoch time: 246.54 s 
+2024-09-09 08:02:00.802168:  
+2024-09-09 08:02:00.802332: Epoch 548 
+2024-09-09 08:02:00.802411: Current learning rate: 0.00489 
+2024-09-09 08:06:07.135054: train_loss -0.8678 
+2024-09-09 08:06:07.135194: val_loss -0.6882 
+2024-09-09 08:06:07.135245: Pseudo dice [0.5912, 0.8594] 
+2024-09-09 08:06:07.135297: Epoch time: 246.33 s 
+2024-09-09 08:06:08.098301:  
+2024-09-09 08:06:08.098478: Epoch 549 
+2024-09-09 08:06:08.098563: Current learning rate: 0.00488 
+2024-09-09 08:10:14.483731: train_loss -0.8677 
+2024-09-09 08:10:14.483936: val_loss -0.6894 
+2024-09-09 08:10:14.484030: Pseudo dice [0.6212, 0.8552] 
+2024-09-09 08:10:14.484123: Epoch time: 246.39 s 
+2024-09-09 08:10:18.356683:  
+2024-09-09 08:10:18.356854: Epoch 550 
+2024-09-09 08:10:18.356938: Current learning rate: 0.00487 
+2024-09-09 08:14:24.879543: train_loss -0.8723 
+2024-09-09 08:14:24.879683: val_loss -0.677 
+2024-09-09 08:14:24.879732: Pseudo dice [0.6147, 0.8485] 
+2024-09-09 08:14:24.879781: Epoch time: 246.52 s 
+2024-09-09 08:14:25.858011:  
+2024-09-09 08:14:25.858207: Epoch 551 
+2024-09-09 08:14:25.858312: Current learning rate: 0.00486 
+2024-09-09 08:18:32.144715: train_loss -0.8785 
+2024-09-09 08:18:32.144852: val_loss -0.6967 
+2024-09-09 08:18:32.144901: Pseudo dice [0.5898, 0.8706] 
+2024-09-09 08:18:32.144952: Epoch time: 246.29 s 
+2024-09-09 08:18:33.130035:  
+2024-09-09 08:18:33.130200: Epoch 552 
+2024-09-09 08:18:33.130280: Current learning rate: 0.00485 
+2024-09-09 08:22:39.482291: train_loss -0.8775 
+2024-09-09 08:22:39.482431: val_loss -0.6906 
+2024-09-09 08:22:39.482481: Pseudo dice [0.6228, 0.8532] 
+2024-09-09 08:22:39.482532: Epoch time: 246.35 s 
+2024-09-09 08:22:40.459464:  
+2024-09-09 08:22:40.459651: Epoch 553 
+2024-09-09 08:22:40.459732: Current learning rate: 0.00484 
+2024-09-09 08:26:46.957525: train_loss -0.8721 
+2024-09-09 08:26:46.957692: val_loss -0.6942 
+2024-09-09 08:26:46.957743: Pseudo dice [0.6124, 0.858] 
+2024-09-09 08:26:46.957794: Epoch time: 246.5 s 
+2024-09-09 08:26:47.944936:  
+2024-09-09 08:26:47.945098: Epoch 554 
+2024-09-09 08:26:47.945216: Current learning rate: 0.00484 
+2024-09-09 08:30:54.573114: train_loss -0.8762 
+2024-09-09 08:30:54.573254: val_loss -0.6606 
+2024-09-09 08:30:54.573307: Pseudo dice [0.6121, 0.8474] 
+2024-09-09 08:30:54.573358: Epoch time: 246.63 s 
+2024-09-09 08:30:55.544034:  
+2024-09-09 08:30:55.544197: Epoch 555 
+2024-09-09 08:30:55.544279: Current learning rate: 0.00483 
+2024-09-09 08:35:02.186879: train_loss -0.8753 
+2024-09-09 08:35:02.187017: val_loss -0.6554 
+2024-09-09 08:35:02.187077: Pseudo dice [0.5722, 0.8628] 
+2024-09-09 08:35:02.187129: Epoch time: 246.64 s 
+2024-09-09 08:35:03.156993:  
+2024-09-09 08:35:03.157154: Epoch 556 
+2024-09-09 08:35:03.157239: Current learning rate: 0.00482 
+2024-09-09 08:39:09.813668: train_loss -0.8726 
+2024-09-09 08:39:09.813826: val_loss -0.6933 
+2024-09-09 08:39:09.813886: Pseudo dice [0.6187, 0.8454] 
+2024-09-09 08:39:09.813946: Epoch time: 246.66 s 
+2024-09-09 08:39:10.794645:  
+2024-09-09 08:39:10.794856: Epoch 557 
+2024-09-09 08:39:10.794940: Current learning rate: 0.00481 
+2024-09-09 08:43:17.367070: train_loss -0.8651 
+2024-09-09 08:43:17.367238: val_loss -0.6818 
+2024-09-09 08:43:17.367299: Pseudo dice [0.5765, 0.8664] 
+2024-09-09 08:43:17.367372: Epoch time: 246.57 s 
+2024-09-09 08:43:18.527905:  
+2024-09-09 08:43:18.528097: Epoch 558 
+2024-09-09 08:43:18.528196: Current learning rate: 0.0048 
+2024-09-09 08:47:25.183315: train_loss -0.8782 
+2024-09-09 08:47:25.183500: val_loss -0.6668 
+2024-09-09 08:47:25.183566: Pseudo dice [0.6056, 0.8464] 
+2024-09-09 08:47:25.183636: Epoch time: 246.66 s 
+2024-09-09 08:47:27.069001:  
+2024-09-09 08:47:27.069228: Epoch 559 
+2024-09-09 08:47:27.069369: Current learning rate: 0.00479 
+2024-09-09 08:51:33.601562: train_loss -0.8741 
+2024-09-09 08:51:33.601697: val_loss -0.6612 
+2024-09-09 08:51:33.601784: Pseudo dice [0.5835, 0.844] 
+2024-09-09 08:51:33.601834: Epoch time: 246.53 s 
+2024-09-09 08:51:34.565200:  
+2024-09-09 08:51:34.565418: Epoch 560 
+2024-09-09 08:51:34.565513: Current learning rate: 0.00478 
+2024-09-09 08:55:40.944551: train_loss -0.8727 
+2024-09-09 08:55:40.944696: val_loss -0.6745 
+2024-09-09 08:55:40.944746: Pseudo dice [0.5944, 0.8447] 
+2024-09-09 08:55:40.944846: Epoch time: 246.38 s 
+2024-09-09 08:55:41.900439:  
+2024-09-09 08:55:41.900635: Epoch 561 
+2024-09-09 08:55:41.900717: Current learning rate: 0.00477 
+2024-09-09 08:59:48.367146: train_loss -0.8742 
+2024-09-09 08:59:48.367289: val_loss -0.6881 
+2024-09-09 08:59:48.367338: Pseudo dice [0.62, 0.8586] 
+2024-09-09 08:59:48.367396: Epoch time: 246.47 s 
+2024-09-09 08:59:49.341737:  
+2024-09-09 08:59:49.341981: Epoch 562 
+2024-09-09 08:59:49.342060: Current learning rate: 0.00476 
+2024-09-09 09:03:55.787817: train_loss -0.877 
+2024-09-09 09:03:55.787956: val_loss -0.6562 
+2024-09-09 09:03:55.788006: Pseudo dice [0.5336, 0.8589] 
+2024-09-09 09:03:55.788057: Epoch time: 246.45 s 
+2024-09-09 09:03:56.756801:  
+2024-09-09 09:03:56.757020: Epoch 563 
+2024-09-09 09:03:56.757100: Current learning rate: 0.00475 
+2024-09-09 09:08:03.208205: train_loss -0.875 
+2024-09-09 09:08:03.208343: val_loss -0.6865 
+2024-09-09 09:08:03.208392: Pseudo dice [0.6362, 0.8625] 
+2024-09-09 09:08:03.208443: Epoch time: 246.45 s 
+2024-09-09 09:08:04.182017:  
+2024-09-09 09:08:04.182214: Epoch 564 
+2024-09-09 09:08:04.182302: Current learning rate: 0.00474 
+2024-09-09 09:12:10.609708: train_loss -0.8442 
+2024-09-09 09:12:10.609848: val_loss -0.6893 
+2024-09-09 09:12:10.609903: Pseudo dice [0.6706, 0.8307] 
+2024-09-09 09:12:10.609955: Epoch time: 246.43 s 
+2024-09-09 09:12:11.577726:  
+2024-09-09 09:12:11.577919: Epoch 565 
+2024-09-09 09:12:11.578005: Current learning rate: 0.00473 
+2024-09-09 09:16:18.166661: train_loss -0.8334 
+2024-09-09 09:16:18.166797: val_loss -0.6586 
+2024-09-09 09:16:18.166847: Pseudo dice [0.5991, 0.8369] 
+2024-09-09 09:16:18.166897: Epoch time: 246.59 s 
+2024-09-09 09:16:19.143017:  
+2024-09-09 09:16:19.143220: Epoch 566 
+2024-09-09 09:16:19.143299: Current learning rate: 0.00472 
+2024-09-09 09:20:25.636713: train_loss -0.8181 
+2024-09-09 09:20:25.636852: val_loss -0.644 
+2024-09-09 09:20:25.636902: Pseudo dice [0.5744, 0.8323] 
+2024-09-09 09:20:25.636953: Epoch time: 246.5 s 
+2024-09-09 09:20:26.617800:  
+2024-09-09 09:20:26.618055: Epoch 567 
+2024-09-09 09:20:26.618141: Current learning rate: 0.00471 
+2024-09-09 09:24:32.858318: train_loss -0.7905 
+2024-09-09 09:24:32.858459: val_loss -0.6452 
+2024-09-09 09:24:32.858510: Pseudo dice [0.5759, 0.8311] 
+2024-09-09 09:24:32.858560: Epoch time: 246.24 s 
+2024-09-09 09:24:33.862592:  
+2024-09-09 09:24:33.862825: Epoch 568 
+2024-09-09 09:24:33.862904: Current learning rate: 0.0047 
+2024-09-09 09:28:40.244901: train_loss -0.8148 
+2024-09-09 09:28:40.245042: val_loss -0.654 
+2024-09-09 09:28:40.245092: Pseudo dice [0.5749, 0.8624] 
+2024-09-09 09:28:40.245143: Epoch time: 246.38 s 
+2024-09-09 09:28:41.224441:  
+2024-09-09 09:28:41.224666: Epoch 569 
+2024-09-09 09:28:41.224747: Current learning rate: 0.00469 
+2024-09-09 09:32:47.587199: train_loss -0.8484 
+2024-09-09 09:32:47.587367: val_loss -0.6798 
+2024-09-09 09:32:47.587415: Pseudo dice [0.5565, 0.8651] 
+2024-09-09 09:32:47.587525: Epoch time: 246.36 s 
+2024-09-09 09:32:48.558542:  
+2024-09-09 09:32:48.558754: Epoch 570 
+2024-09-09 09:32:48.558845: Current learning rate: 0.00468 
+2024-09-09 09:36:54.800417: train_loss -0.8572 
+2024-09-09 09:36:54.800554: val_loss -0.6774 
+2024-09-09 09:36:54.800605: Pseudo dice [0.5852, 0.8531] 
+2024-09-09 09:36:54.800656: Epoch time: 246.24 s 
+2024-09-09 09:36:55.774562:  
+2024-09-09 09:36:55.774741: Epoch 571 
+2024-09-09 09:36:55.774824: Current learning rate: 0.00467 
+2024-09-09 09:41:01.965030: train_loss -0.8607 
+2024-09-09 09:41:01.965170: val_loss -0.6762 
+2024-09-09 09:41:01.965220: Pseudo dice [0.5962, 0.8645] 
+2024-09-09 09:41:01.965271: Epoch time: 246.19 s 
+2024-09-09 09:41:02.943192:  
+2024-09-09 09:41:02.943382: Epoch 572 
+2024-09-09 09:41:02.943463: Current learning rate: 0.00466 
+2024-09-09 09:45:09.000510: train_loss -0.8585 
+2024-09-09 09:45:09.000651: val_loss -0.671 
+2024-09-09 09:45:09.000701: Pseudo dice [0.5947, 0.8492] 
+2024-09-09 09:45:09.000753: Epoch time: 246.06 s 
+2024-09-09 09:45:09.990496:  
+2024-09-09 09:45:09.990689: Epoch 573 
+2024-09-09 09:45:09.990778: Current learning rate: 0.00465 
+2024-09-09 09:49:16.157729: train_loss -0.8465 
+2024-09-09 09:49:16.157868: val_loss -0.6834 
+2024-09-09 09:49:16.157918: Pseudo dice [0.6081, 0.8546] 
+2024-09-09 09:49:16.157969: Epoch time: 246.17 s 
+2024-09-09 09:49:17.144858:  
+2024-09-09 09:49:17.145053: Epoch 574 
+2024-09-09 09:49:17.145133: Current learning rate: 0.00464 
+2024-09-09 09:53:23.375002: train_loss -0.8481 
+2024-09-09 09:53:23.375167: val_loss -0.6727 
+2024-09-09 09:53:23.375218: Pseudo dice [0.579, 0.8524] 
+2024-09-09 09:53:23.375270: Epoch time: 246.23 s 
+2024-09-09 09:53:24.354853:  
+2024-09-09 09:53:24.355037: Epoch 575 
+2024-09-09 09:53:24.355123: Current learning rate: 0.00463 
+2024-09-09 09:57:30.814417: train_loss -0.8586 
+2024-09-09 09:57:30.814559: val_loss -0.6732 
+2024-09-09 09:57:30.814609: Pseudo dice [0.5905, 0.8502] 
+2024-09-09 09:57:30.814663: Epoch time: 246.46 s 
+2024-09-09 09:57:31.794170:  
+2024-09-09 09:57:31.794343: Epoch 576 
+2024-09-09 09:57:31.794428: Current learning rate: 0.00462 
+2024-09-09 10:01:38.086344: train_loss -0.8633 
+2024-09-09 10:01:38.086483: val_loss -0.668 
+2024-09-09 10:01:38.086533: Pseudo dice [0.5769, 0.8484] 
+2024-09-09 10:01:38.086585: Epoch time: 246.29 s 
+2024-09-09 10:01:39.069365:  
+2024-09-09 10:01:39.069526: Epoch 577 
+2024-09-09 10:01:39.069607: Current learning rate: 0.00461 
+2024-09-09 10:05:45.359175: train_loss -0.857 
+2024-09-09 10:05:45.359313: val_loss -0.6656 
+2024-09-09 10:05:45.359363: Pseudo dice [0.5456, 0.8484] 
+2024-09-09 10:05:45.359413: Epoch time: 246.29 s 
+2024-09-09 10:05:46.338716:  
+2024-09-09 10:05:46.338861: Epoch 578 
+2024-09-09 10:05:46.338942: Current learning rate: 0.0046 
+2024-09-09 10:09:52.496289: train_loss -0.8565 
+2024-09-09 10:09:52.496447: val_loss -0.6658 
+2024-09-09 10:09:52.496496: Pseudo dice [0.5762, 0.8503] 
+2024-09-09 10:09:52.496547: Epoch time: 246.16 s 
+2024-09-09 10:09:53.488865:  
+2024-09-09 10:09:53.489064: Epoch 579 
+2024-09-09 10:09:53.489155: Current learning rate: 0.00459 
+2024-09-09 10:13:59.707178: train_loss -0.8655 
+2024-09-09 10:13:59.707319: val_loss -0.7029 
+2024-09-09 10:13:59.707368: Pseudo dice [0.6267, 0.8694] 
+2024-09-09 10:13:59.707417: Epoch time: 246.22 s 
+2024-09-09 10:14:00.687360:  
+2024-09-09 10:14:00.687576: Epoch 580 
+2024-09-09 10:14:00.687660: Current learning rate: 0.00458 
+2024-09-09 10:18:06.887586: train_loss -0.8691 
+2024-09-09 10:18:06.887727: val_loss -0.6973 
+2024-09-09 10:18:06.887776: Pseudo dice [0.6243, 0.8571] 
+2024-09-09 10:18:06.887832: Epoch time: 246.2 s 
+2024-09-09 10:18:07.879675:  
+2024-09-09 10:18:07.879956: Epoch 581 
+2024-09-09 10:18:07.880095: Current learning rate: 0.00457 
+2024-09-09 10:22:14.172442: train_loss -0.875 
+2024-09-09 10:22:14.172576: val_loss -0.6815 
+2024-09-09 10:22:14.172627: Pseudo dice [0.6297, 0.8575] 
+2024-09-09 10:22:14.172681: Epoch time: 246.29 s 
+2024-09-09 10:22:15.158026:  
+2024-09-09 10:22:15.158219: Epoch 582 
+2024-09-09 10:22:15.158302: Current learning rate: 0.00456 
+2024-09-09 10:26:22.059393: train_loss -0.8698 
+2024-09-09 10:26:22.059533: val_loss -0.6416 
+2024-09-09 10:26:22.059582: Pseudo dice [0.5363, 0.8527] 
+2024-09-09 10:26:22.059632: Epoch time: 246.9 s 
+2024-09-09 10:26:23.027067:  
+2024-09-09 10:26:23.027238: Epoch 583 
+2024-09-09 10:26:23.027338: Current learning rate: 0.00455 
+2024-09-09 10:30:29.375432: train_loss -0.87 
+2024-09-09 10:30:29.375571: val_loss -0.6712 
+2024-09-09 10:30:29.375621: Pseudo dice [0.5701, 0.8578] 
+2024-09-09 10:30:29.375671: Epoch time: 246.35 s 
+2024-09-09 10:30:30.353594:  
+2024-09-09 10:30:30.353834: Epoch 584 
+2024-09-09 10:30:30.353918: Current learning rate: 0.00454 
+2024-09-09 10:34:36.742175: train_loss -0.871 
+2024-09-09 10:34:36.742338: val_loss -0.7035 
+2024-09-09 10:34:36.742388: Pseudo dice [0.6196, 0.8536] 
+2024-09-09 10:34:36.742443: Epoch time: 246.39 s 
+2024-09-09 10:34:37.716680:  
+2024-09-09 10:34:37.716911: Epoch 585 
+2024-09-09 10:34:37.717008: Current learning rate: 0.00453 
+2024-09-09 10:38:43.876645: train_loss -0.874 
+2024-09-09 10:38:43.876781: val_loss -0.6746 
+2024-09-09 10:38:43.876832: Pseudo dice [0.5972, 0.8611] 
+2024-09-09 10:38:43.876883: Epoch time: 246.16 s 
+2024-09-09 10:38:44.858337:  
+2024-09-09 10:38:44.858570: Epoch 586 
+2024-09-09 10:38:44.858680: Current learning rate: 0.00452 
+2024-09-09 10:42:51.047045: train_loss -0.871 
+2024-09-09 10:42:51.047183: val_loss -0.6937 
+2024-09-09 10:42:51.047235: Pseudo dice [0.6165, 0.8602] 
+2024-09-09 10:42:51.047286: Epoch time: 246.19 s 
+2024-09-09 10:42:52.044106:  
+2024-09-09 10:42:52.044327: Epoch 587 
+2024-09-09 10:42:52.044407: Current learning rate: 0.00451 
+2024-09-09 10:46:58.260416: train_loss -0.8788 
+2024-09-09 10:46:58.260575: val_loss -0.6927 
+2024-09-09 10:46:58.260626: Pseudo dice [0.6296, 0.8525] 
+2024-09-09 10:46:58.260677: Epoch time: 246.22 s 
+2024-09-09 10:46:59.247359:  
+2024-09-09 10:46:59.247530: Epoch 588 
+2024-09-09 10:46:59.247638: Current learning rate: 0.0045 
+2024-09-09 10:51:05.802608: train_loss -0.8656 
+2024-09-09 10:51:05.802774: val_loss -0.6578 
+2024-09-09 10:51:05.802994: Pseudo dice [0.5735, 0.8469] 
+2024-09-09 10:51:05.803056: Epoch time: 246.56 s 
+2024-09-09 10:51:06.846256:  
+2024-09-09 10:51:06.846424: Epoch 589 
+2024-09-09 10:51:06.846505: Current learning rate: 0.00449 
+2024-09-09 10:55:13.483441: train_loss -0.8516 
+2024-09-09 10:55:13.483639: val_loss -0.6889 
+2024-09-09 10:55:13.483701: Pseudo dice [0.6182, 0.8482] 
+2024-09-09 10:55:13.483752: Epoch time: 246.64 s 
+2024-09-09 10:55:14.584099:  
+2024-09-09 10:55:14.584342: Epoch 590 
+2024-09-09 10:55:14.584425: Current learning rate: 0.00448 
+2024-09-09 10:59:21.031255: train_loss -0.8662 
+2024-09-09 10:59:21.031391: val_loss -0.6628 
+2024-09-09 10:59:21.031441: Pseudo dice [0.5911, 0.8484] 
+2024-09-09 10:59:21.031525: Epoch time: 246.45 s 
+2024-09-09 10:59:22.026423:  
+2024-09-09 10:59:22.026637: Epoch 591 
+2024-09-09 10:59:22.026716: Current learning rate: 0.00447 
+2024-09-09 11:03:28.364273: train_loss -0.8696 
+2024-09-09 11:03:28.364417: val_loss -0.6619 
+2024-09-09 11:03:28.364469: Pseudo dice [0.5477, 0.8521] 
+2024-09-09 11:03:28.364523: Epoch time: 246.34 s 
+2024-09-09 11:03:29.351077:  
+2024-09-09 11:03:29.351217: Epoch 592 
+2024-09-09 11:03:29.351297: Current learning rate: 0.00446 
+2024-09-09 11:07:35.775527: train_loss -0.8672 
+2024-09-09 11:07:35.775679: val_loss -0.6794 
+2024-09-09 11:07:35.775740: Pseudo dice [0.5877, 0.8739] 
+2024-09-09 11:07:35.775834: Epoch time: 246.43 s 
+2024-09-09 11:07:36.763340:  
+2024-09-09 11:07:36.763543: Epoch 593 
+2024-09-09 11:07:36.763625: Current learning rate: 0.00445 
+2024-09-09 11:11:43.002415: train_loss -0.8722 
+2024-09-09 11:11:43.002554: val_loss -0.6854 
+2024-09-09 11:11:43.002605: Pseudo dice [0.5642, 0.8659] 
+2024-09-09 11:11:43.002657: Epoch time: 246.24 s 
+2024-09-09 11:11:43.983362:  
+2024-09-09 11:11:43.983540: Epoch 594 
+2024-09-09 11:11:43.983624: Current learning rate: 0.00444 
+2024-09-09 11:15:50.335739: train_loss -0.8773 
+2024-09-09 11:15:50.335896: val_loss -0.6805 
+2024-09-09 11:15:50.335949: Pseudo dice [0.6145, 0.8576] 
+2024-09-09 11:15:50.336000: Epoch time: 246.35 s 
+2024-09-09 11:15:51.342600:  
+2024-09-09 11:15:51.342770: Epoch 595 
+2024-09-09 11:15:51.342848: Current learning rate: 0.00443 
+2024-09-09 11:19:57.635429: train_loss -0.8717 
+2024-09-09 11:19:57.635590: val_loss -0.6805 
+2024-09-09 11:19:57.635645: Pseudo dice [0.6085, 0.8559] 
+2024-09-09 11:19:57.635702: Epoch time: 246.29 s 
+2024-09-09 11:19:58.766628:  
+2024-09-09 11:19:58.766825: Epoch 596 
+2024-09-09 11:19:58.766901: Current learning rate: 0.00442 
+2024-09-09 11:24:05.098063: train_loss -0.8729 
+2024-09-09 11:24:05.098200: val_loss -0.6978 
+2024-09-09 11:24:05.098250: Pseudo dice [0.6109, 0.8626] 
+2024-09-09 11:24:05.098300: Epoch time: 246.33 s 
+2024-09-09 11:24:06.086576:  
+2024-09-09 11:24:06.086744: Epoch 597 
+2024-09-09 11:24:06.086825: Current learning rate: 0.00441 
+2024-09-09 11:28:12.518924: train_loss -0.8746 
+2024-09-09 11:28:12.519064: val_loss -0.6511 
+2024-09-09 11:28:12.519114: Pseudo dice [0.5181, 0.86] 
+2024-09-09 11:28:12.519165: Epoch time: 246.43 s 
+2024-09-09 11:28:13.502309:  
+2024-09-09 11:28:13.502511: Epoch 598 
+2024-09-09 11:28:13.502589: Current learning rate: 0.0044 
+2024-09-09 11:32:20.044056: train_loss -0.8752 
+2024-09-09 11:32:20.044194: val_loss -0.7024 
+2024-09-09 11:32:20.044244: Pseudo dice [0.6145, 0.8696] 
+2024-09-09 11:32:20.044294: Epoch time: 246.54 s 
+2024-09-09 11:32:21.052049:  
+2024-09-09 11:32:21.052245: Epoch 599 
+2024-09-09 11:32:21.052344: Current learning rate: 0.00439 
+2024-09-09 11:36:27.745095: train_loss -0.8724 
+2024-09-09 11:36:27.745232: val_loss -0.6726 
+2024-09-09 11:36:27.745283: Pseudo dice [0.5647, 0.8435] 
+2024-09-09 11:36:27.745334: Epoch time: 246.69 s 
+2024-09-09 11:36:31.950176:  
+2024-09-09 11:36:31.950446: Epoch 600 
+2024-09-09 11:36:31.950522: Current learning rate: 0.00438 
+2024-09-09 11:40:38.605601: train_loss -0.8764 
+2024-09-09 11:40:38.605765: val_loss -0.6592 
+2024-09-09 11:40:38.605827: Pseudo dice [0.5456, 0.8616] 
+2024-09-09 11:40:38.605886: Epoch time: 246.66 s 
+2024-09-09 11:40:39.780240:  
+2024-09-09 11:40:39.780417: Epoch 601 
+2024-09-09 11:40:39.780505: Current learning rate: 0.00437 
+2024-09-09 11:44:46.013721: train_loss -0.8618 
+2024-09-09 11:44:46.013884: val_loss -0.6347 
+2024-09-09 11:44:46.013947: Pseudo dice [0.539, 0.8547] 
+2024-09-09 11:44:46.014000: Epoch time: 246.24 s 
+2024-09-09 11:44:47.202965:  
+2024-09-09 11:44:47.203126: Epoch 602 
+2024-09-09 11:44:47.203226: Current learning rate: 0.00436 
+2024-09-09 11:48:53.529122: train_loss -0.8511 
+2024-09-09 11:48:53.529287: val_loss -0.656 
+2024-09-09 11:48:53.529337: Pseudo dice [0.5798, 0.8364] 
+2024-09-09 11:48:53.529388: Epoch time: 246.33 s 
+2024-09-09 11:48:54.523141:  
+2024-09-09 11:48:54.523363: Epoch 603 
+2024-09-09 11:48:54.523470: Current learning rate: 0.00435 
+2024-09-09 11:53:00.537010: train_loss -0.8596 
+2024-09-09 11:53:00.537147: val_loss -0.6576 
+2024-09-09 11:53:00.537198: Pseudo dice [0.5876, 0.8576] 
+2024-09-09 11:53:00.537250: Epoch time: 246.02 s 
+2024-09-09 11:53:01.506757:  
+2024-09-09 11:53:01.506914: Epoch 604 
+2024-09-09 11:53:01.506993: Current learning rate: 0.00434 
+2024-09-09 11:57:07.519643: train_loss -0.8361 
+2024-09-09 11:57:07.519786: val_loss -0.6654 
+2024-09-09 11:57:07.519845: Pseudo dice [0.5763, 0.8534] 
+2024-09-09 11:57:07.519897: Epoch time: 246.01 s 
+2024-09-09 11:57:09.370850:  
+2024-09-09 11:57:09.371142: Epoch 605 
+2024-09-09 11:57:09.371279: Current learning rate: 0.00433 
+2024-09-09 12:01:15.360623: train_loss -0.8515 
+2024-09-09 12:01:15.360843: val_loss -0.6685 
+2024-09-09 12:01:15.360896: Pseudo dice [0.5679, 0.8498] 
+2024-09-09 12:01:15.360971: Epoch time: 245.99 s 
+2024-09-09 12:01:16.499633:  
+2024-09-09 12:01:16.499882: Epoch 606 
+2024-09-09 12:01:16.499980: Current learning rate: 0.00432 
+2024-09-09 12:05:22.665470: train_loss -0.8463 
+2024-09-09 12:05:22.665614: val_loss -0.6603 
+2024-09-09 12:05:22.665664: Pseudo dice [0.5439, 0.8506] 
+2024-09-09 12:05:22.665715: Epoch time: 246.17 s 
+2024-09-09 12:05:23.667320:  
+2024-09-09 12:05:23.667567: Epoch 607 
+2024-09-09 12:05:23.667650: Current learning rate: 0.00431 
+2024-09-09 12:09:29.926815: train_loss -0.8513 
+2024-09-09 12:09:29.926951: val_loss -0.6842 
+2024-09-09 12:09:29.927000: Pseudo dice [0.6169, 0.8653] 
+2024-09-09 12:09:29.927166: Epoch time: 246.26 s 
+2024-09-09 12:09:30.922389:  
+2024-09-09 12:09:30.922595: Epoch 608 
+2024-09-09 12:09:30.922677: Current learning rate: 0.0043 
+2024-09-09 12:13:37.262891: train_loss -0.8647 
+2024-09-09 12:13:37.263028: val_loss -0.6709 
+2024-09-09 12:13:37.263078: Pseudo dice [0.5951, 0.8514] 
+2024-09-09 12:13:37.263128: Epoch time: 246.34 s 
+2024-09-09 12:13:38.243347:  
+2024-09-09 12:13:38.243546: Epoch 609 
+2024-09-09 12:13:38.243629: Current learning rate: 0.00429 
+2024-09-09 12:17:44.100551: train_loss -0.8637 
+2024-09-09 12:17:44.100688: val_loss -0.6653 
+2024-09-09 12:17:44.100739: Pseudo dice [0.5669, 0.8587] 
+2024-09-09 12:17:44.100792: Epoch time: 245.86 s 
+2024-09-09 12:17:45.098521:  
+2024-09-09 12:17:45.098756: Epoch 610 
+2024-09-09 12:17:45.098836: Current learning rate: 0.00429 
+2024-09-09 12:21:51.033684: train_loss -0.8695 
+2024-09-09 12:21:51.033825: val_loss -0.6584 
+2024-09-09 12:21:51.033876: Pseudo dice [0.603, 0.8531] 
+2024-09-09 12:21:51.033927: Epoch time: 245.94 s 
+2024-09-09 12:21:52.172609:  
+2024-09-09 12:21:52.172827: Epoch 611 
+2024-09-09 12:21:52.172912: Current learning rate: 0.00428 
+2024-09-09 12:25:58.345464: train_loss -0.8766 
+2024-09-09 12:25:58.345616: val_loss -0.6869 
+2024-09-09 12:25:58.345666: Pseudo dice [0.5941, 0.8625] 
+2024-09-09 12:25:58.345719: Epoch time: 246.18 s 
+2024-09-09 12:25:59.337725:  
+2024-09-09 12:25:59.337939: Epoch 612 
+2024-09-09 12:25:59.338020: Current learning rate: 0.00427 
+2024-09-09 12:30:05.522985: train_loss -0.8785 
+2024-09-09 12:30:05.523124: val_loss -0.6729 
+2024-09-09 12:30:05.523173: Pseudo dice [0.5752, 0.8611] 
+2024-09-09 12:30:05.523223: Epoch time: 246.19 s 
+2024-09-09 12:30:06.529990:  
+2024-09-09 12:30:06.530270: Epoch 613 
+2024-09-09 12:30:06.530354: Current learning rate: 0.00426 
+2024-09-09 12:34:12.654026: train_loss -0.8746 
+2024-09-09 12:34:12.654165: val_loss -0.6451 
+2024-09-09 12:34:12.654255: Pseudo dice [0.5626, 0.8522] 
+2024-09-09 12:34:12.654307: Epoch time: 246.13 s 
+2024-09-09 12:34:13.638306:  
+2024-09-09 12:34:13.638540: Epoch 614 
+2024-09-09 12:34:13.638619: Current learning rate: 0.00425 
+2024-09-09 12:38:19.669121: train_loss -0.8662 
+2024-09-09 12:38:19.669259: val_loss -0.6922 
+2024-09-09 12:38:19.669307: Pseudo dice [0.6145, 0.8596] 
+2024-09-09 12:38:19.669357: Epoch time: 246.03 s 
+2024-09-09 12:38:20.661804:  
+2024-09-09 12:38:20.661973: Epoch 615 
+2024-09-09 12:38:20.662054: Current learning rate: 0.00424 
+2024-09-09 12:42:26.862801: train_loss -0.8711 
+2024-09-09 12:42:26.862988: val_loss -0.6809 
+2024-09-09 12:42:26.863039: Pseudo dice [0.5776, 0.8602] 
+2024-09-09 12:42:26.863091: Epoch time: 246.2 s 
+2024-09-09 12:42:27.865496:  
+2024-09-09 12:42:27.865676: Epoch 616 
+2024-09-09 12:42:27.865758: Current learning rate: 0.00423 
+2024-09-09 12:46:34.129704: train_loss -0.8721 
+2024-09-09 12:46:34.129840: val_loss -0.7038 
+2024-09-09 12:46:34.129925: Pseudo dice [0.6139, 0.8622] 
+2024-09-09 12:46:34.129978: Epoch time: 246.27 s 
+2024-09-09 12:46:35.120907:  
+2024-09-09 12:46:35.121138: Epoch 617 
+2024-09-09 12:46:35.121220: Current learning rate: 0.00422 
+2024-09-09 12:50:41.247019: train_loss -0.8783 
+2024-09-09 12:50:41.247165: val_loss -0.6739 
+2024-09-09 12:50:41.247216: Pseudo dice [0.5883, 0.863] 
+2024-09-09 12:50:41.247266: Epoch time: 246.13 s 
+2024-09-09 12:50:42.228903:  
+2024-09-09 12:50:42.229158: Epoch 618 
+2024-09-09 12:50:42.229282: Current learning rate: 0.00421 
+2024-09-09 12:54:48.482570: train_loss -0.8797 
+2024-09-09 12:54:48.482716: val_loss -0.6654 
+2024-09-09 12:54:48.482765: Pseudo dice [0.581, 0.8514] 
+2024-09-09 12:54:48.482818: Epoch time: 246.26 s 
+2024-09-09 12:54:49.488165:  
+2024-09-09 12:54:49.488338: Epoch 619 
+2024-09-09 12:54:49.488418: Current learning rate: 0.0042 
+2024-09-09 12:58:55.709855: train_loss -0.8808 
+2024-09-09 12:58:55.709994: val_loss -0.7023 
+2024-09-09 12:58:55.710045: Pseudo dice [0.6403, 0.8575] 
+2024-09-09 12:58:55.710096: Epoch time: 246.22 s 
+2024-09-09 12:58:56.693744:  
+2024-09-09 12:58:56.693951: Epoch 620 
+2024-09-09 12:58:56.694030: Current learning rate: 0.00419 
+2024-09-09 13:03:02.782874: train_loss -0.8807 
+2024-09-09 13:03:02.783069: val_loss -0.6794 
+2024-09-09 13:03:02.783120: Pseudo dice [0.5856, 0.8638] 
+2024-09-09 13:03:02.783172: Epoch time: 246.09 s 
+2024-09-09 13:03:03.791955:  
+2024-09-09 13:03:03.792140: Epoch 621 
+2024-09-09 13:03:03.792224: Current learning rate: 0.00418 
+2024-09-09 13:07:10.161556: train_loss -0.8783 
+2024-09-09 13:07:10.161699: val_loss -0.6892 
+2024-09-09 13:07:10.161752: Pseudo dice [0.5948, 0.8644] 
+2024-09-09 13:07:10.161803: Epoch time: 246.37 s 
+2024-09-09 13:07:11.174155:  
+2024-09-09 13:07:11.174317: Epoch 622 
+2024-09-09 13:07:11.174398: Current learning rate: 0.00417 
+2024-09-09 13:11:17.408438: train_loss -0.8731 
+2024-09-09 13:11:17.408578: val_loss -0.6733 
+2024-09-09 13:11:17.408627: Pseudo dice [0.6231, 0.854] 
+2024-09-09 13:11:17.408677: Epoch time: 246.24 s 
+2024-09-09 13:11:18.409438:  
+2024-09-09 13:11:18.409597: Epoch 623 
+2024-09-09 13:11:18.409678: Current learning rate: 0.00416 
+2024-09-09 13:15:24.521638: train_loss -0.8817 
+2024-09-09 13:15:24.521780: val_loss -0.6724 
+2024-09-09 13:15:24.521829: Pseudo dice [0.5877, 0.8492] 
+2024-09-09 13:15:24.521882: Epoch time: 246.11 s 
+2024-09-09 13:15:25.499438:  
+2024-09-09 13:15:25.499683: Epoch 624 
+2024-09-09 13:15:25.499766: Current learning rate: 0.00415 
+2024-09-09 13:19:31.768453: train_loss -0.872 
+2024-09-09 13:19:31.768589: val_loss -0.682 
+2024-09-09 13:19:31.768639: Pseudo dice [0.5894, 0.8563] 
+2024-09-09 13:19:31.768689: Epoch time: 246.27 s 
+2024-09-09 13:19:32.763947:  
+2024-09-09 13:19:32.764120: Epoch 625 
+2024-09-09 13:19:32.764243: Current learning rate: 0.00414 
+2024-09-09 13:23:39.049354: train_loss -0.8708 
+2024-09-09 13:23:39.049501: val_loss -0.6848 
+2024-09-09 13:23:39.049551: Pseudo dice [0.6166, 0.8687] 
+2024-09-09 13:23:39.049604: Epoch time: 246.29 s 
+2024-09-09 13:23:40.036984:  
+2024-09-09 13:23:40.037162: Epoch 626 
+2024-09-09 13:23:40.037240: Current learning rate: 0.00413 
+2024-09-09 13:27:46.018878: train_loss -0.8582 
+2024-09-09 13:27:46.019033: val_loss -0.6934 
+2024-09-09 13:27:46.019083: Pseudo dice [0.6173, 0.8599] 
+2024-09-09 13:27:46.019133: Epoch time: 245.98 s 
+2024-09-09 13:27:47.016397:  
+2024-09-09 13:27:47.016572: Epoch 627 
+2024-09-09 13:27:47.016656: Current learning rate: 0.00412 
+2024-09-09 13:31:53.228239: train_loss -0.8125 
+2024-09-09 13:31:53.228374: val_loss -0.662 
+2024-09-09 13:31:53.228423: Pseudo dice [0.5935, 0.8288] 
+2024-09-09 13:31:53.228476: Epoch time: 246.21 s 
+2024-09-09 13:31:55.130599:  
+2024-09-09 13:31:55.130855: Epoch 628 
+2024-09-09 13:31:55.130949: Current learning rate: 0.00411 
+2024-09-09 13:36:01.534781: train_loss -0.8249 
+2024-09-09 13:36:01.534916: val_loss -0.6802 
+2024-09-09 13:36:01.534966: Pseudo dice [0.6079, 0.8482] 
+2024-09-09 13:36:01.535017: Epoch time: 246.41 s 
+2024-09-09 13:36:02.507367:  
+2024-09-09 13:36:02.507581: Epoch 629 
+2024-09-09 13:36:02.507679: Current learning rate: 0.0041 
+2024-09-09 13:40:08.588530: train_loss -0.8208 
+2024-09-09 13:40:08.588707: val_loss -0.62 
+2024-09-09 13:40:08.588772: Pseudo dice [0.5595, 0.8032] 
+2024-09-09 13:40:08.588842: Epoch time: 246.08 s 
+2024-09-09 13:40:09.702645:  
+2024-09-09 13:40:09.703000: Epoch 630 
+2024-09-09 13:40:09.703124: Current learning rate: 0.00409 
+2024-09-09 13:44:15.671762: train_loss -0.8133 
+2024-09-09 13:44:15.671906: val_loss -0.6851 
+2024-09-09 13:44:15.671956: Pseudo dice [0.6304, 0.8478] 
+2024-09-09 13:44:15.672008: Epoch time: 245.97 s 
+2024-09-09 13:44:16.775016:  
+2024-09-09 13:44:16.775189: Epoch 631 
+2024-09-09 13:44:16.775267: Current learning rate: 0.00408 
+2024-09-09 13:48:22.731565: train_loss -0.8376 
+2024-09-09 13:48:22.731730: val_loss -0.6619 
+2024-09-09 13:48:22.731802: Pseudo dice [0.6069, 0.8547] 
+2024-09-09 13:48:22.731890: Epoch time: 245.96 s 
+2024-09-09 13:48:23.922876:  
+2024-09-09 13:48:23.923116: Epoch 632 
+2024-09-09 13:48:23.923196: Current learning rate: 0.00407 
+2024-09-09 13:52:29.842206: train_loss -0.8527 
+2024-09-09 13:52:29.842439: val_loss -0.6927 
+2024-09-09 13:52:29.842505: Pseudo dice [0.6016, 0.8638] 
+2024-09-09 13:52:29.842562: Epoch time: 245.92 s 
+2024-09-09 13:52:31.013157:  
+2024-09-09 13:52:31.013412: Epoch 633 
+2024-09-09 13:52:31.013517: Current learning rate: 0.00406 
+2024-09-09 13:56:36.865040: train_loss -0.8463 
+2024-09-09 13:56:36.865180: val_loss -0.6687 
+2024-09-09 13:56:36.865230: Pseudo dice [0.5642, 0.8515] 
+2024-09-09 13:56:36.865281: Epoch time: 245.85 s 
+2024-09-09 13:56:37.868902:  
+2024-09-09 13:56:37.869146: Epoch 634 
+2024-09-09 13:56:37.869224: Current learning rate: 0.00405 
+2024-09-09 14:00:44.215841: train_loss -0.8497 
+2024-09-09 14:00:44.216004: val_loss -0.6836 
+2024-09-09 14:00:44.216055: Pseudo dice [0.5939, 0.8468] 
+2024-09-09 14:00:44.216106: Epoch time: 246.35 s 
+2024-09-09 14:00:45.238301:  
+2024-09-09 14:00:45.238501: Epoch 635 
+2024-09-09 14:00:45.238609: Current learning rate: 0.00404 
+2024-09-09 14:04:51.727996: train_loss -0.8351 
+2024-09-09 14:04:51.728187: val_loss -0.6755 
+2024-09-09 14:04:51.728308: Pseudo dice [0.5967, 0.848] 
+2024-09-09 14:04:51.728387: Epoch time: 246.49 s 
+2024-09-09 14:04:52.892247:  
+2024-09-09 14:04:52.892598: Epoch 636 
+2024-09-09 14:04:52.892774: Current learning rate: 0.00403 
+2024-09-09 14:08:58.918730: train_loss -0.8496 
+2024-09-09 14:08:58.918878: val_loss -0.6702 
+2024-09-09 14:08:58.918934: Pseudo dice [0.6152, 0.8519] 
+2024-09-09 14:08:58.918991: Epoch time: 246.03 s 
+2024-09-09 14:08:59.907315:  
+2024-09-09 14:08:59.907501: Epoch 637 
+2024-09-09 14:08:59.907587: Current learning rate: 0.00402 
+2024-09-09 14:13:05.686081: train_loss -0.866 
+2024-09-09 14:13:05.686247: val_loss -0.6827 
+2024-09-09 14:13:05.686303: Pseudo dice [0.6143, 0.8596] 
+2024-09-09 14:13:05.686360: Epoch time: 245.78 s 
+2024-09-09 14:13:06.704102:  
+2024-09-09 14:13:06.704325: Epoch 638 
+2024-09-09 14:13:06.704412: Current learning rate: 0.00401 
+2024-09-09 14:17:12.565119: train_loss -0.8629 
+2024-09-09 14:17:12.565280: val_loss -0.6773 
+2024-09-09 14:17:12.565337: Pseudo dice [0.6108, 0.86] 
+2024-09-09 14:17:12.565395: Epoch time: 245.86 s 
+2024-09-09 14:17:13.555368:  
+2024-09-09 14:17:13.555624: Epoch 639 
+2024-09-09 14:17:13.555707: Current learning rate: 0.004 
+2024-09-09 14:21:21.555459: train_loss -0.8622 
+2024-09-09 14:21:21.555611: val_loss -0.6643 
+2024-09-09 14:21:21.555667: Pseudo dice [0.5746, 0.8478] 
+2024-09-09 14:21:21.555722: Epoch time: 248.0 s 
+2024-09-09 14:21:22.544397:  
+2024-09-09 14:21:22.544662: Epoch 640 
+2024-09-09 14:21:22.544770: Current learning rate: 0.00399 
+2024-09-09 14:25:29.376107: train_loss -0.8738 
+2024-09-09 14:25:29.376259: val_loss -0.6602 
+2024-09-09 14:25:29.376318: Pseudo dice [0.5716, 0.8595] 
+2024-09-09 14:25:29.376379: Epoch time: 246.83 s 
+2024-09-09 14:25:30.374815:  
+2024-09-09 14:25:30.375014: Epoch 641 
+2024-09-09 14:25:30.375103: Current learning rate: 0.00398 
+2024-09-09 14:29:36.514154: train_loss -0.864 
+2024-09-09 14:29:36.514335: val_loss -0.6712 
+2024-09-09 14:29:36.514408: Pseudo dice [0.5537, 0.8554] 
+2024-09-09 14:29:36.514468: Epoch time: 246.14 s 
+2024-09-09 14:29:37.527602:  
+2024-09-09 14:29:37.527818: Epoch 642 
+2024-09-09 14:29:37.527904: Current learning rate: 0.00397 
+2024-09-09 14:33:43.888173: train_loss -0.8754 
+2024-09-09 14:33:43.888330: val_loss -0.6875 
+2024-09-09 14:33:43.888387: Pseudo dice [0.5762, 0.8608] 
+2024-09-09 14:33:43.888444: Epoch time: 246.36 s 
+2024-09-09 14:33:44.880822:  
+2024-09-09 14:33:44.881050: Epoch 643 
+2024-09-09 14:33:44.881134: Current learning rate: 0.00396 
+2024-09-09 14:37:51.383199: train_loss -0.8796 
+2024-09-09 14:37:51.383417: val_loss -0.6845 
+2024-09-09 14:37:51.383480: Pseudo dice [0.601, 0.8571] 
+2024-09-09 14:37:51.383537: Epoch time: 246.5 s 
+2024-09-09 14:37:52.383330:  
+2024-09-09 14:37:52.383519: Epoch 644 
+2024-09-09 14:37:52.383605: Current learning rate: 0.00395 
+2024-09-09 14:41:58.749251: train_loss -0.8645 
+2024-09-09 14:41:58.749413: val_loss -0.6738 
+2024-09-09 14:41:58.749470: Pseudo dice [0.6034, 0.8437] 
+2024-09-09 14:41:58.749526: Epoch time: 246.37 s 
+2024-09-09 14:41:59.749770:  
+2024-09-09 14:41:59.749984: Epoch 645 
+2024-09-09 14:41:59.750072: Current learning rate: 0.00394 
+2024-09-09 14:46:06.019522: train_loss -0.8618 
+2024-09-09 14:46:06.019694: val_loss -0.7199 
+2024-09-09 14:46:06.019766: Pseudo dice [0.6695, 0.8657] 
+2024-09-09 14:46:06.019826: Epoch time: 246.27 s 
+2024-09-09 14:46:07.110192:  
+2024-09-09 14:46:07.110449: Epoch 646 
+2024-09-09 14:46:07.110573: Current learning rate: 0.00393 
+2024-09-09 14:50:15.698612: train_loss -0.8699 
+2024-09-09 14:50:15.698775: val_loss -0.7014 
+2024-09-09 14:50:15.698831: Pseudo dice [0.6675, 0.8686] 
+2024-09-09 14:50:15.698888: Epoch time: 248.59 s 
+2024-09-09 14:50:16.724697:  
+2024-09-09 14:50:16.724859: Epoch 647 
+2024-09-09 14:50:16.724943: Current learning rate: 0.00392 
+2024-09-09 14:54:46.940759: train_loss -0.8693 
+2024-09-09 14:54:46.940997: val_loss -0.685 
+2024-09-09 14:54:46.941057: Pseudo dice [0.6167, 0.8684] 
+2024-09-09 14:54:46.941115: Epoch time: 270.22 s 
+2024-09-09 14:54:47.943767:  
+2024-09-09 14:54:47.943969: Epoch 648 
+2024-09-09 14:54:47.944052: Current learning rate: 0.00391 
+2024-09-09 14:59:05.907637: train_loss -0.8777 
+2024-09-09 14:59:05.907819: val_loss -0.7009 
+2024-09-09 14:59:05.907907: Pseudo dice [0.6203, 0.8601] 
+2024-09-09 14:59:05.907964: Epoch time: 257.97 s 
+2024-09-09 14:59:06.911854:  
+2024-09-09 14:59:06.912036: Epoch 649 
+2024-09-09 14:59:06.912123: Current learning rate: 0.0039 
+2024-09-09 15:03:27.130011: train_loss -0.8832 
+2024-09-09 15:03:27.130164: val_loss -0.7038 
+2024-09-09 15:03:27.130221: Pseudo dice [0.6058, 0.87] 
+2024-09-09 15:03:27.130279: Epoch time: 260.22 s 
+2024-09-09 15:03:31.096634:  
+2024-09-09 15:03:31.096807: Epoch 650 
+2024-09-09 15:03:31.096971: Current learning rate: 0.00389 
+2024-09-09 15:07:45.424612: train_loss -0.8807 
+2024-09-09 15:07:45.425011: val_loss -0.652 
+2024-09-09 15:07:45.425072: Pseudo dice [0.5582, 0.8705] 
+2024-09-09 15:07:45.425130: Epoch time: 254.33 s 
+2024-09-09 15:07:47.334677:  
+2024-09-09 15:07:47.334858: Epoch 651 
+2024-09-09 15:07:47.335007: Current learning rate: 0.00388 
+2024-09-09 15:11:57.313667: train_loss -0.8779 
+2024-09-09 15:11:57.313850: val_loss -0.7051 
+2024-09-09 15:11:57.313916: Pseudo dice [0.6299, 0.8527] 
+2024-09-09 15:11:57.313982: Epoch time: 249.98 s 
+2024-09-09 15:11:58.434650:  
+2024-09-09 15:11:58.434955: Epoch 652 
+2024-09-09 15:11:58.435090: Current learning rate: 0.00387 
+2024-09-09 15:16:04.924948: train_loss -0.8768 
+2024-09-09 15:16:04.925095: val_loss -0.6851 
+2024-09-09 15:16:04.925150: Pseudo dice [0.6463, 0.8538] 
+2024-09-09 15:16:04.925206: Epoch time: 246.49 s 
+2024-09-09 15:16:05.915744:  
+2024-09-09 15:16:05.915989: Epoch 653 
+2024-09-09 15:16:05.916109: Current learning rate: 0.00386 
+2024-09-09 15:20:12.448558: train_loss -0.8829 
+2024-09-09 15:20:12.448708: val_loss -0.7026 
+2024-09-09 15:20:12.448764: Pseudo dice [0.6224, 0.8559] 
+2024-09-09 15:20:12.448821: Epoch time: 246.53 s 
+2024-09-09 15:20:13.436164:  
+2024-09-09 15:20:13.436399: Epoch 654 
+2024-09-09 15:20:13.436491: Current learning rate: 0.00385 
+2024-09-09 15:24:19.975058: train_loss -0.881 
+2024-09-09 15:24:19.975224: val_loss -0.6531 
+2024-09-09 15:24:19.975280: Pseudo dice [0.5377, 0.8634] 
+2024-09-09 15:24:19.975346: Epoch time: 246.54 s 
+2024-09-09 15:24:20.973126:  
+2024-09-09 15:24:20.973395: Epoch 655 
+2024-09-09 15:24:20.973476: Current learning rate: 0.00384 
+2024-09-09 15:28:27.574795: train_loss -0.875 
+2024-09-09 15:28:27.574942: val_loss -0.6825 
+2024-09-09 15:28:27.574999: Pseudo dice [0.5862, 0.8608] 
+2024-09-09 15:28:27.575057: Epoch time: 246.6 s 
+2024-09-09 15:28:28.577485:  
+2024-09-09 15:28:28.577745: Epoch 656 
+2024-09-09 15:28:28.577836: Current learning rate: 0.00383 
+2024-09-09 15:32:35.081199: train_loss -0.8731 
+2024-09-09 15:32:35.081366: val_loss -0.675 
+2024-09-09 15:32:35.081423: Pseudo dice [0.5636, 0.8667] 
+2024-09-09 15:32:35.081479: Epoch time: 246.51 s 
+2024-09-09 15:32:36.145104:  
+2024-09-09 15:32:36.145381: Epoch 657 
+2024-09-09 15:32:36.145507: Current learning rate: 0.00382 
+2024-09-09 15:36:42.533232: train_loss -0.8706 
+2024-09-09 15:36:42.533401: val_loss -0.6927 
+2024-09-09 15:36:42.533459: Pseudo dice [0.6111, 0.8561] 
+2024-09-09 15:36:42.533519: Epoch time: 246.39 s 
+2024-09-09 15:36:43.560515:  
+2024-09-09 15:36:43.560717: Epoch 658 
+2024-09-09 15:36:43.560802: Current learning rate: 0.00381 
+2024-09-09 15:40:49.885699: train_loss -0.8774 
+2024-09-09 15:40:49.885847: val_loss -0.6974 
+2024-09-09 15:40:49.885902: Pseudo dice [0.6374, 0.8586] 
+2024-09-09 15:40:49.885964: Epoch time: 246.33 s 
+2024-09-09 15:40:50.900378:  
+2024-09-09 15:40:50.900640: Epoch 659 
+2024-09-09 15:40:50.900724: Current learning rate: 0.0038 
+2024-09-09 15:44:57.378401: train_loss -0.881 
+2024-09-09 15:44:57.378550: val_loss -0.6715 
+2024-09-09 15:44:57.378606: Pseudo dice [0.6036, 0.8587] 
+2024-09-09 15:44:57.378661: Epoch time: 246.48 s 
+2024-09-09 15:44:58.357628:  
+2024-09-09 15:44:58.357844: Epoch 660 
+2024-09-09 15:44:58.357970: Current learning rate: 0.00379 
+2024-09-09 15:49:04.735786: train_loss -0.8846 
+2024-09-09 15:49:04.735950: val_loss -0.6874 
+2024-09-09 15:49:04.736006: Pseudo dice [0.6206, 0.8558] 
+2024-09-09 15:49:04.736061: Epoch time: 246.38 s 
+2024-09-09 15:49:05.751831:  
+2024-09-09 15:49:05.752020: Epoch 661 
+2024-09-09 15:49:05.752107: Current learning rate: 0.00378 
+2024-09-09 15:53:12.328305: train_loss -0.8826 
+2024-09-09 15:53:12.328491: val_loss -0.6956 
+2024-09-09 15:53:12.328567: Pseudo dice [0.6332, 0.8654] 
+2024-09-09 15:53:12.328633: Epoch time: 246.58 s 
+2024-09-09 15:53:13.538273:  
+2024-09-09 15:53:13.538500: Epoch 662 
+2024-09-09 15:53:13.538584: Current learning rate: 0.00377 
+2024-09-09 15:57:20.161933: train_loss -0.876 
+2024-09-09 15:57:20.162077: val_loss -0.6791 
+2024-09-09 15:57:20.162133: Pseudo dice [0.6057, 0.8532] 
+2024-09-09 15:57:20.162189: Epoch time: 246.63 s 
+2024-09-09 15:57:21.154361:  
+2024-09-09 15:57:21.154532: Epoch 663 
+2024-09-09 15:57:21.154631: Current learning rate: 0.00376 
+2024-09-09 16:01:27.752095: train_loss -0.8801 
+2024-09-09 16:01:27.752246: val_loss -0.6807 
+2024-09-09 16:01:27.752302: Pseudo dice [0.6106, 0.8635] 
+2024-09-09 16:01:27.752359: Epoch time: 246.6 s 
+2024-09-09 16:01:28.746616:  
+2024-09-09 16:01:28.746825: Epoch 664 
+2024-09-09 16:01:28.746916: Current learning rate: 0.00375 
+2024-09-09 16:05:35.501980: train_loss -0.8684 
+2024-09-09 16:05:35.502156: val_loss -0.7002 
+2024-09-09 16:05:35.502212: Pseudo dice [0.6343, 0.8549] 
+2024-09-09 16:05:35.502268: Epoch time: 246.76 s 
+2024-09-09 16:05:36.514592:  
+2024-09-09 16:05:36.514823: Epoch 665 
+2024-09-09 16:05:36.514907: Current learning rate: 0.00374 
+2024-09-09 16:09:43.009509: train_loss -0.8746 
+2024-09-09 16:09:43.009705: val_loss -0.6995 
+2024-09-09 16:09:43.009776: Pseudo dice [0.6505, 0.8567] 
+2024-09-09 16:09:43.009856: Epoch time: 246.5 s 
+2024-09-09 16:09:44.199554:  
+2024-09-09 16:09:44.199802: Epoch 666 
+2024-09-09 16:09:44.199896: Current learning rate: 0.00373 
+2024-09-09 16:13:50.703357: train_loss -0.8783 
+2024-09-09 16:13:50.703504: val_loss -0.6912 
+2024-09-09 16:13:50.703561: Pseudo dice [0.6059, 0.8665] 
+2024-09-09 16:13:50.703620: Epoch time: 246.51 s 
+2024-09-09 16:13:51.712494:  
+2024-09-09 16:13:51.712697: Epoch 667 
+2024-09-09 16:13:51.712797: Current learning rate: 0.00372 
+2024-09-09 16:17:58.266362: train_loss -0.8791 
+2024-09-09 16:17:58.266529: val_loss -0.6821 
+2024-09-09 16:17:58.266586: Pseudo dice [0.6034, 0.8571] 
+2024-09-09 16:17:58.266642: Epoch time: 246.56 s 
+2024-09-09 16:17:59.261229:  
+2024-09-09 16:17:59.261412: Epoch 668 
+2024-09-09 16:17:59.261496: Current learning rate: 0.00371 
+2024-09-09 16:22:05.942227: train_loss -0.8824 
+2024-09-09 16:22:05.942374: val_loss -0.6695 
+2024-09-09 16:22:05.942434: Pseudo dice [0.6057, 0.8506] 
+2024-09-09 16:22:05.942490: Epoch time: 246.68 s 
+2024-09-09 16:22:06.970516:  
+2024-09-09 16:22:06.970714: Epoch 669 
+2024-09-09 16:22:06.970798: Current learning rate: 0.0037 
+2024-09-09 16:26:13.604014: train_loss -0.8775 
+2024-09-09 16:26:13.604158: val_loss -0.6931 
+2024-09-09 16:26:13.604215: Pseudo dice [0.6169, 0.8627] 
+2024-09-09 16:26:13.604271: Epoch time: 246.64 s 
+2024-09-09 16:26:14.693087:  
+2024-09-09 16:26:14.693366: Epoch 670 
+2024-09-09 16:26:14.693455: Current learning rate: 0.00369 
+2024-09-09 16:30:21.203227: train_loss -0.8802 
+2024-09-09 16:30:21.203377: val_loss -0.6719 
+2024-09-09 16:30:21.203434: Pseudo dice [0.5663, 0.8718] 
+2024-09-09 16:30:21.203489: Epoch time: 246.51 s 
+2024-09-09 16:30:22.200786:  
+2024-09-09 16:30:22.200975: Epoch 671 
+2024-09-09 16:30:22.201078: Current learning rate: 0.00368 
+2024-09-09 16:34:28.659415: train_loss -0.8824 
+2024-09-09 16:34:28.659572: val_loss -0.6627 
+2024-09-09 16:34:28.659628: Pseudo dice [0.5665, 0.8717] 
+2024-09-09 16:34:28.659684: Epoch time: 246.46 s 
+2024-09-09 16:34:29.685286:  
+2024-09-09 16:34:29.685497: Epoch 672 
+2024-09-09 16:34:29.685626: Current learning rate: 0.00367 
+2024-09-09 16:38:36.449124: train_loss -0.8831 
+2024-09-09 16:38:36.449287: val_loss -0.6864 
+2024-09-09 16:38:36.449367: Pseudo dice [0.6126, 0.8595] 
+2024-09-09 16:38:36.449426: Epoch time: 246.77 s 
+2024-09-09 16:38:37.470727:  
+2024-09-09 16:38:37.470951: Epoch 673 
+2024-09-09 16:38:37.471044: Current learning rate: 0.00366 
+2024-09-09 16:42:43.938361: train_loss -0.8818 
+2024-09-09 16:42:43.938521: val_loss -0.6643 
+2024-09-09 16:42:43.938589: Pseudo dice [0.5536, 0.8545] 
+2024-09-09 16:42:43.938658: Epoch time: 246.47 s 
+2024-09-09 16:42:45.862365:  
+2024-09-09 16:42:45.862612: Epoch 674 
+2024-09-09 16:42:45.862719: Current learning rate: 0.00365 
+2024-09-09 16:46:52.402204: train_loss -0.8846 
+2024-09-09 16:46:52.402349: val_loss -0.6568 
+2024-09-09 16:46:52.402405: Pseudo dice [0.584, 0.8612] 
+2024-09-09 16:46:52.402462: Epoch time: 246.54 s 
+2024-09-09 16:46:53.442597:  
+2024-09-09 16:46:53.442812: Epoch 675 
+2024-09-09 16:46:53.442911: Current learning rate: 0.00364 
+2024-09-09 16:51:00.032129: train_loss -0.8751 
+2024-09-09 16:51:00.032286: val_loss -0.7056 
+2024-09-09 16:51:00.032342: Pseudo dice [0.6262, 0.8683] 
+2024-09-09 16:51:00.032397: Epoch time: 246.59 s 
+2024-09-09 16:51:01.048654:  
+2024-09-09 16:51:01.048913: Epoch 676 
+2024-09-09 16:51:01.049045: Current learning rate: 0.00363 
+2024-09-09 16:55:07.476943: train_loss -0.8786 
+2024-09-09 16:55:07.477091: val_loss -0.7049 
+2024-09-09 16:55:07.477146: Pseudo dice [0.6226, 0.8673] 
+2024-09-09 16:55:07.477201: Epoch time: 246.43 s 
+2024-09-09 16:55:08.472291:  
+2024-09-09 16:55:08.472501: Epoch 677 
+2024-09-09 16:55:08.472586: Current learning rate: 0.00362 
+2024-09-09 16:59:14.965463: train_loss -0.8826 
+2024-09-09 16:59:14.965612: val_loss -0.6618 
+2024-09-09 16:59:14.965667: Pseudo dice [0.5624, 0.8651] 
+2024-09-09 16:59:14.965728: Epoch time: 246.5 s 
+2024-09-09 16:59:15.964391:  
+2024-09-09 16:59:15.964580: Epoch 678 
+2024-09-09 16:59:15.964664: Current learning rate: 0.00361 
+2024-09-09 17:03:22.425691: train_loss -0.8867 
+2024-09-09 17:03:22.425857: val_loss -0.6717 
+2024-09-09 17:03:22.425914: Pseudo dice [0.5511, 0.8651] 
+2024-09-09 17:03:22.425971: Epoch time: 246.46 s 
+2024-09-09 17:03:23.442651:  
+2024-09-09 17:03:23.442851: Epoch 679 
+2024-09-09 17:03:23.442937: Current learning rate: 0.0036 
+2024-09-09 17:07:30.077716: train_loss -0.8892 
+2024-09-09 17:07:30.077940: val_loss -0.6882 
+2024-09-09 17:07:30.078044: Pseudo dice [0.6238, 0.8582] 
+2024-09-09 17:07:30.078145: Epoch time: 246.64 s 
+2024-09-09 17:07:31.080545:  
+2024-09-09 17:07:31.080804: Epoch 680 
+2024-09-09 17:07:31.080886: Current learning rate: 0.00359 
+2024-09-09 17:11:37.766088: train_loss -0.879 
+2024-09-09 17:11:37.766237: val_loss -0.6943 
+2024-09-09 17:11:37.766293: Pseudo dice [0.6246, 0.8472] 
+2024-09-09 17:11:37.766348: Epoch time: 246.69 s 
+2024-09-09 17:11:38.924621:  
+2024-09-09 17:11:38.924852: Epoch 681 
+2024-09-09 17:11:38.924944: Current learning rate: 0.00358 
+2024-09-09 17:15:45.408242: train_loss -0.8843 
+2024-09-09 17:15:45.408412: val_loss -0.6879 
+2024-09-09 17:15:45.408469: Pseudo dice [0.6138, 0.867] 
+2024-09-09 17:15:45.408529: Epoch time: 246.49 s 
+2024-09-09 17:15:46.416491:  
+2024-09-09 17:15:46.416688: Epoch 682 
+2024-09-09 17:15:46.416777: Current learning rate: 0.00357 
+2024-09-09 17:19:52.838365: train_loss -0.8825 
+2024-09-09 17:19:52.838512: val_loss -0.6953 
+2024-09-09 17:19:52.838568: Pseudo dice [0.6213, 0.8589] 
+2024-09-09 17:19:52.838624: Epoch time: 246.42 s 
+2024-09-09 17:19:53.838952:  
+2024-09-09 17:19:53.839123: Epoch 683 
+2024-09-09 17:19:53.839207: Current learning rate: 0.00356 
+2024-09-09 17:24:00.169336: train_loss -0.8823 
+2024-09-09 17:24:00.169548: val_loss -0.6945 
+2024-09-09 17:24:00.169652: Pseudo dice [0.6157, 0.8555] 
+2024-09-09 17:24:00.169796: Epoch time: 246.33 s 
+2024-09-09 17:24:01.181097:  
+2024-09-09 17:24:01.181282: Epoch 684 
+2024-09-09 17:24:01.181368: Current learning rate: 0.00355 
+2024-09-09 17:28:07.553869: train_loss -0.8769 
+2024-09-09 17:28:07.554021: val_loss -0.6867 
+2024-09-09 17:28:07.554077: Pseudo dice [0.6078, 0.8631] 
+2024-09-09 17:28:07.554132: Epoch time: 246.37 s 
+2024-09-09 17:28:08.744692:  
+2024-09-09 17:28:08.744961: Epoch 685 
+2024-09-09 17:28:08.745048: Current learning rate: 0.00354 
+2024-09-09 17:32:15.048311: train_loss -0.8704 
+2024-09-09 17:32:15.048447: val_loss -0.6912 
+2024-09-09 17:32:15.048504: Pseudo dice [0.6184, 0.8619] 
+2024-09-09 17:32:15.048564: Epoch time: 246.31 s 
+2024-09-09 17:32:16.053592:  
+2024-09-09 17:32:16.053824: Epoch 686 
+2024-09-09 17:32:16.053926: Current learning rate: 0.00353 
+2024-09-09 17:36:22.452085: train_loss -0.8795 
+2024-09-09 17:36:22.452230: val_loss -0.6558 
+2024-09-09 17:36:22.452287: Pseudo dice [0.54, 0.8596] 
+2024-09-09 17:36:22.452347: Epoch time: 246.4 s 
+2024-09-09 17:36:23.441170:  
+2024-09-09 17:36:23.441402: Epoch 687 
+2024-09-09 17:36:23.441483: Current learning rate: 0.00352 
+2024-09-09 17:40:29.756750: train_loss -0.8784 
+2024-09-09 17:40:29.756959: val_loss -0.688 
+2024-09-09 17:40:29.757015: Pseudo dice [0.6241, 0.8568] 
+2024-09-09 17:40:29.757074: Epoch time: 246.32 s 
+2024-09-09 17:40:30.764003:  
+2024-09-09 17:40:30.764182: Epoch 688 
+2024-09-09 17:40:30.764297: Current learning rate: 0.00351 
+2024-09-09 17:44:37.188749: train_loss -0.885 
+2024-09-09 17:44:37.188969: val_loss -0.6732 
+2024-09-09 17:44:37.189134: Pseudo dice [0.5848, 0.8627] 
+2024-09-09 17:44:37.189283: Epoch time: 246.43 s 
+2024-09-09 17:44:38.313723:  
+2024-09-09 17:44:38.313954: Epoch 689 
+2024-09-09 17:44:38.314037: Current learning rate: 0.0035 
+2024-09-09 17:48:44.915027: train_loss -0.8831 
+2024-09-09 17:48:44.915175: val_loss -0.6813 
+2024-09-09 17:48:44.915231: Pseudo dice [0.6007, 0.8626] 
+2024-09-09 17:48:44.915287: Epoch time: 246.6 s 
+2024-09-09 17:48:45.918880:  
+2024-09-09 17:48:45.919058: Epoch 690 
+2024-09-09 17:48:45.919145: Current learning rate: 0.00349 
+2024-09-09 17:52:52.673768: train_loss -0.8776 
+2024-09-09 17:52:52.673920: val_loss -0.6653 
+2024-09-09 17:52:52.674038: Pseudo dice [0.5858, 0.8593] 
+2024-09-09 17:52:52.674096: Epoch time: 246.76 s 
+2024-09-09 17:52:53.676832:  
+2024-09-09 17:52:53.677080: Epoch 691 
+2024-09-09 17:52:53.677166: Current learning rate: 0.00348 
+2024-09-09 17:57:00.018703: train_loss -0.8584 
+2024-09-09 17:57:00.018843: val_loss -0.6633 
+2024-09-09 17:57:00.018899: Pseudo dice [0.5814, 0.8473] 
+2024-09-09 17:57:00.018956: Epoch time: 246.34 s 
+2024-09-09 17:57:01.021858:  
+2024-09-09 17:57:01.022047: Epoch 692 
+2024-09-09 17:57:01.022135: Current learning rate: 0.00346 
+2024-09-09 18:01:07.361667: train_loss -0.8655 
+2024-09-09 18:01:07.361814: val_loss -0.6769 
+2024-09-09 18:01:07.361870: Pseudo dice [0.5837, 0.8548] 
+2024-09-09 18:01:07.361924: Epoch time: 246.34 s 
+2024-09-09 18:01:08.374861:  
+2024-09-09 18:01:08.375115: Epoch 693 
+2024-09-09 18:01:08.375264: Current learning rate: 0.00345 
+2024-09-09 18:05:14.637134: train_loss -0.8759 
+2024-09-09 18:05:14.637384: val_loss -0.661 
+2024-09-09 18:05:14.637442: Pseudo dice [0.5699, 0.8567] 
+2024-09-09 18:05:14.637497: Epoch time: 246.26 s 
+2024-09-09 18:05:15.786005:  
+2024-09-09 18:05:15.786178: Epoch 694 
+2024-09-09 18:05:15.786290: Current learning rate: 0.00344 
+2024-09-09 18:09:22.014679: train_loss -0.8813 
+2024-09-09 18:09:22.014873: val_loss -0.685 
+2024-09-09 18:09:22.014929: Pseudo dice [0.6068, 0.8635] 
+2024-09-09 18:09:22.014995: Epoch time: 246.23 s 
+2024-09-09 18:09:23.002507:  
+2024-09-09 18:09:23.002717: Epoch 695 
+2024-09-09 18:09:23.002805: Current learning rate: 0.00343 
+2024-09-09 18:13:29.088624: train_loss -0.8806 
+2024-09-09 18:13:29.088776: val_loss -0.6919 
+2024-09-09 18:13:29.088833: Pseudo dice [0.6209, 0.8629] 
+2024-09-09 18:13:29.088888: Epoch time: 246.09 s 
+2024-09-09 18:13:30.108038:  
+2024-09-09 18:13:30.108216: Epoch 696 
+2024-09-09 18:13:30.108300: Current learning rate: 0.00342 
+2024-09-09 18:17:36.386258: train_loss -0.8878 
+2024-09-09 18:17:36.386431: val_loss -0.6729 
+2024-09-09 18:17:36.386488: Pseudo dice [0.5973, 0.8756] 
+2024-09-09 18:17:36.386543: Epoch time: 246.28 s 
+2024-09-09 18:17:38.272991:  
+2024-09-09 18:17:38.273247: Epoch 697 
+2024-09-09 18:17:38.273360: Current learning rate: 0.00341 
+2024-09-09 18:21:44.426713: train_loss -0.8845 
+2024-09-09 18:21:44.426861: val_loss -0.6829 
+2024-09-09 18:21:44.426916: Pseudo dice [0.5977, 0.8554] 
+2024-09-09 18:21:44.426972: Epoch time: 246.16 s 
+2024-09-09 18:21:45.416856:  
+2024-09-09 18:21:45.417093: Epoch 698 
+2024-09-09 18:21:45.417193: Current learning rate: 0.0034 
+2024-09-09 18:25:51.696579: train_loss -0.8836 
+2024-09-09 18:25:51.696749: val_loss -0.6918 
+2024-09-09 18:25:51.696804: Pseudo dice [0.6026, 0.8703] 
+2024-09-09 18:25:51.696860: Epoch time: 246.28 s 
+2024-09-09 18:25:52.695423:  
+2024-09-09 18:25:52.695657: Epoch 699 
+2024-09-09 18:25:52.695750: Current learning rate: 0.00339 
+2024-09-09 18:29:59.261381: train_loss -0.8807 
+2024-09-09 18:29:59.261522: val_loss -0.6722 
+2024-09-09 18:29:59.261572: Pseudo dice [0.5763, 0.8554] 
+2024-09-09 18:29:59.261625: Epoch time: 246.57 s 
+2024-09-09 18:30:03.230505:  
+2024-09-09 18:30:03.230703: Epoch 700 
+2024-09-09 18:30:03.230783: Current learning rate: 0.00338 
+2024-09-09 18:34:10.423630: train_loss -0.8832 
+2024-09-09 18:34:10.423776: val_loss -0.6632 
+2024-09-09 18:34:10.423844: Pseudo dice [0.537, 0.864] 
+2024-09-09 18:34:10.423920: Epoch time: 247.2 s 
+2024-09-09 18:34:11.434215:  
+2024-09-09 18:34:11.434374: Epoch 701 
+2024-09-09 18:34:11.434489: Current learning rate: 0.00337 
+2024-09-09 18:38:18.103089: train_loss -0.8877 
+2024-09-09 18:38:18.103229: val_loss -0.6797 
+2024-09-09 18:38:18.103279: Pseudo dice [0.5877, 0.8649] 
+2024-09-09 18:38:18.103331: Epoch time: 246.67 s 
+2024-09-09 18:38:19.105492:  
+2024-09-09 18:38:19.105695: Epoch 702 
+2024-09-09 18:38:19.105778: Current learning rate: 0.00336 
+2024-09-09 18:42:27.281109: train_loss -0.8883 
+2024-09-09 18:42:27.281277: val_loss -0.6774 
+2024-09-09 18:42:27.281328: Pseudo dice [0.6071, 0.8648] 
+2024-09-09 18:42:27.281398: Epoch time: 248.18 s 
+2024-09-09 18:42:28.475331:  
+2024-09-09 18:42:28.475582: Epoch 703 
+2024-09-09 18:42:28.475666: Current learning rate: 0.00335 
+2024-09-09 18:46:34.928879: train_loss -0.8913 
+2024-09-09 18:46:34.929044: val_loss -0.6577 
+2024-09-09 18:46:34.929134: Pseudo dice [0.5527, 0.8529] 
+2024-09-09 18:46:34.929187: Epoch time: 246.46 s 
+2024-09-09 18:46:35.926722:  
+2024-09-09 18:46:35.926899: Epoch 704 
+2024-09-09 18:46:35.926976: Current learning rate: 0.00334 
+2024-09-09 18:50:42.351667: train_loss -0.8852 
+2024-09-09 18:50:42.351896: val_loss -0.6784 
+2024-09-09 18:50:42.351995: Pseudo dice [0.6063, 0.8625] 
+2024-09-09 18:50:42.352087: Epoch time: 246.43 s 
+2024-09-09 18:50:43.358119:  
+2024-09-09 18:50:43.358337: Epoch 705 
+2024-09-09 18:50:43.358419: Current learning rate: 0.00333 
+2024-09-09 18:54:49.739902: train_loss -0.8864 
+2024-09-09 18:54:49.740043: val_loss -0.6737 
+2024-09-09 18:54:49.740151: Pseudo dice [0.5992, 0.8579] 
+2024-09-09 18:54:49.740228: Epoch time: 246.38 s 
+2024-09-09 18:54:50.732842:  
+2024-09-09 18:54:50.733073: Epoch 706 
+2024-09-09 18:54:50.733155: Current learning rate: 0.00332 
+2024-09-09 18:58:57.139174: train_loss -0.8846 
+2024-09-09 18:58:57.139316: val_loss -0.6838 
+2024-09-09 18:58:57.139366: Pseudo dice [0.6153, 0.8588] 
+2024-09-09 18:58:57.139418: Epoch time: 246.41 s 
+2024-09-09 18:58:58.314842:  
+2024-09-09 18:58:58.315031: Epoch 707 
+2024-09-09 18:58:58.315174: Current learning rate: 0.00331 
+2024-09-09 19:03:04.777601: train_loss -0.8813 
+2024-09-09 19:03:04.777760: val_loss -0.6848 
+2024-09-09 19:03:04.777875: Pseudo dice [0.6403, 0.8481] 
+2024-09-09 19:03:04.777957: Epoch time: 246.47 s 
+2024-09-09 19:03:05.764880:  
+2024-09-09 19:03:05.765040: Epoch 708 
+2024-09-09 19:03:05.765137: Current learning rate: 0.0033 
+2024-09-09 19:07:12.448495: train_loss -0.8858 
+2024-09-09 19:07:12.448636: val_loss -0.6864 
+2024-09-09 19:07:12.448687: Pseudo dice [0.5851, 0.8655] 
+2024-09-09 19:07:12.448739: Epoch time: 246.69 s 
+2024-09-09 19:07:13.474656:  
+2024-09-09 19:07:13.474882: Epoch 709 
+2024-09-09 19:07:13.474965: Current learning rate: 0.00329 
+2024-09-09 19:11:20.388566: train_loss -0.8834 
+2024-09-09 19:11:20.388710: val_loss -0.6709 
+2024-09-09 19:11:20.388760: Pseudo dice [0.6091, 0.857] 
+2024-09-09 19:11:20.388812: Epoch time: 246.92 s 
+2024-09-09 19:11:21.411823:  
+2024-09-09 19:11:21.412045: Epoch 710 
+2024-09-09 19:11:21.412125: Current learning rate: 0.00328 
+2024-09-09 19:15:27.933358: train_loss -0.8894 
+2024-09-09 19:15:27.933501: val_loss -0.6709 
+2024-09-09 19:15:27.933550: Pseudo dice [0.5942, 0.8615] 
+2024-09-09 19:15:27.933601: Epoch time: 246.52 s 
+2024-09-09 19:15:29.121010:  
+2024-09-09 19:15:29.121255: Epoch 711 
+2024-09-09 19:15:29.121333: Current learning rate: 0.00327 
+2024-09-09 19:19:35.677907: train_loss -0.8821 
+2024-09-09 19:19:35.678049: val_loss -0.6828 
+2024-09-09 19:19:35.678099: Pseudo dice [0.5971, 0.8526] 
+2024-09-09 19:19:35.678151: Epoch time: 246.56 s 
+2024-09-09 19:19:36.680331:  
+2024-09-09 19:19:36.680551: Epoch 712 
+2024-09-09 19:19:36.680633: Current learning rate: 0.00326 
+2024-09-09 19:23:43.229127: train_loss -0.8842 
+2024-09-09 19:23:43.229287: val_loss -0.6916 
+2024-09-09 19:23:43.229338: Pseudo dice [0.605, 0.8689] 
+2024-09-09 19:23:43.229390: Epoch time: 246.55 s 
+2024-09-09 19:23:44.237616:  
+2024-09-09 19:23:44.237804: Epoch 713 
+2024-09-09 19:23:44.237888: Current learning rate: 0.00325 
+2024-09-09 19:27:50.811735: train_loss -0.8785 
+2024-09-09 19:27:50.811917: val_loss -0.6357 
+2024-09-09 19:27:50.811976: Pseudo dice [0.4799, 0.861] 
+2024-09-09 19:27:50.812045: Epoch time: 246.58 s 
+2024-09-09 19:27:51.853483:  
+2024-09-09 19:27:51.853626: Epoch 714 
+2024-09-09 19:27:51.853711: Current learning rate: 0.00324 
+2024-09-09 19:31:58.151997: train_loss -0.8799 
+2024-09-09 19:31:58.152144: val_loss -0.6583 
+2024-09-09 19:31:58.152194: Pseudo dice [0.5727, 0.8673] 
+2024-09-09 19:31:58.152245: Epoch time: 246.3 s 
+2024-09-09 19:31:59.211083:  
+2024-09-09 19:31:59.211254: Epoch 715 
+2024-09-09 19:31:59.211406: Current learning rate: 0.00323 
+2024-09-09 19:36:05.678158: train_loss -0.8824 
+2024-09-09 19:36:05.678298: val_loss -0.6579 
+2024-09-09 19:36:05.678348: Pseudo dice [0.5484, 0.8665] 
+2024-09-09 19:36:05.678399: Epoch time: 246.47 s 
+2024-09-09 19:36:06.680730:  
+2024-09-09 19:36:06.680912: Epoch 716 
+2024-09-09 19:36:06.681027: Current learning rate: 0.00322 
+2024-09-09 19:40:13.181981: train_loss -0.8913 
+2024-09-09 19:40:13.182119: val_loss -0.6941 
+2024-09-09 19:40:13.182168: Pseudo dice [0.6115, 0.8624] 
+2024-09-09 19:40:13.182233: Epoch time: 246.5 s 
+2024-09-09 19:40:14.176266:  
+2024-09-09 19:40:14.176528: Epoch 717 
+2024-09-09 19:40:14.176629: Current learning rate: 0.00321 
+2024-09-09 19:44:20.940878: train_loss -0.8907 
+2024-09-09 19:44:20.941016: val_loss -0.673 
+2024-09-09 19:44:20.941066: Pseudo dice [0.6001, 0.8539] 
+2024-09-09 19:44:20.941122: Epoch time: 246.77 s 
+2024-09-09 19:44:21.932506:  
+2024-09-09 19:44:21.932657: Epoch 718 
+2024-09-09 19:44:21.932737: Current learning rate: 0.0032 
+2024-09-09 19:48:28.633192: train_loss -0.8784 
+2024-09-09 19:48:28.633383: val_loss -0.6866 
+2024-09-09 19:48:28.633461: Pseudo dice [0.6259, 0.8649] 
+2024-09-09 19:48:28.633514: Epoch time: 246.7 s 
+2024-09-09 19:48:29.783639:  
+2024-09-09 19:48:29.783947: Epoch 719 
+2024-09-09 19:48:29.784062: Current learning rate: 0.00319 
+2024-09-09 19:52:36.290453: train_loss -0.8781 
+2024-09-09 19:52:36.290700: val_loss -0.6977 
+2024-09-09 19:52:36.290801: Pseudo dice [0.6356, 0.8528] 
+2024-09-09 19:52:36.290899: Epoch time: 246.51 s 
+2024-09-09 19:52:38.223930:  
+2024-09-09 19:52:38.224083: Epoch 720 
+2024-09-09 19:52:38.224198: Current learning rate: 0.00318 
+2024-09-09 19:56:44.642152: train_loss -0.8797 
+2024-09-09 19:56:44.642291: val_loss -0.6634 
+2024-09-09 19:56:44.642340: Pseudo dice [0.6053, 0.8517] 
+2024-09-09 19:56:44.642391: Epoch time: 246.42 s 
+2024-09-09 19:56:45.622370:  
+2024-09-09 19:56:45.622598: Epoch 721 
+2024-09-09 19:56:45.622708: Current learning rate: 0.00317 
+2024-09-09 20:00:52.173146: train_loss -0.8767 
+2024-09-09 20:00:52.173285: val_loss -0.6813 
+2024-09-09 20:00:52.173336: Pseudo dice [0.6205, 0.865] 
+2024-09-09 20:00:52.173387: Epoch time: 246.55 s 
+2024-09-09 20:00:53.184871:  
+2024-09-09 20:00:53.185140: Epoch 722 
+2024-09-09 20:00:53.185230: Current learning rate: 0.00316 
+2024-09-09 20:04:59.747936: train_loss -0.8767 
+2024-09-09 20:04:59.748144: val_loss -0.659 
+2024-09-09 20:04:59.748229: Pseudo dice [0.6111, 0.8522] 
+2024-09-09 20:04:59.748300: Epoch time: 246.57 s 
+2024-09-09 20:05:00.944164:  
+2024-09-09 20:05:00.944392: Epoch 723 
+2024-09-09 20:05:00.944477: Current learning rate: 0.00315 
+2024-09-09 20:09:07.494622: train_loss -0.8762 
+2024-09-09 20:09:07.494770: val_loss -0.671 
+2024-09-09 20:09:07.494821: Pseudo dice [0.5976, 0.8512] 
+2024-09-09 20:09:07.494875: Epoch time: 246.55 s 
+2024-09-09 20:09:08.494210:  
+2024-09-09 20:09:08.494407: Epoch 724 
+2024-09-09 20:09:08.494491: Current learning rate: 0.00314 
+2024-09-09 20:13:14.924289: train_loss -0.8763 
+2024-09-09 20:13:14.924448: val_loss -0.6898 
+2024-09-09 20:13:14.924499: Pseudo dice [0.5913, 0.8663] 
+2024-09-09 20:13:14.924554: Epoch time: 246.43 s 
+2024-09-09 20:13:15.930072:  
+2024-09-09 20:13:15.930281: Epoch 725 
+2024-09-09 20:13:15.930365: Current learning rate: 0.00313 
+2024-09-09 20:17:22.607607: train_loss -0.869 
+2024-09-09 20:17:22.607755: val_loss -0.6877 
+2024-09-09 20:17:22.607813: Pseudo dice [0.6253, 0.8488] 
+2024-09-09 20:17:22.607871: Epoch time: 246.68 s 
+2024-09-09 20:17:23.612424:  
+2024-09-09 20:17:23.612594: Epoch 726 
+2024-09-09 20:17:23.612677: Current learning rate: 0.00312 
+2024-09-09 20:21:30.533121: train_loss -0.8731 
+2024-09-09 20:21:30.533263: val_loss -0.6681 
+2024-09-09 20:21:30.533314: Pseudo dice [0.593, 0.8526] 
+2024-09-09 20:21:30.533365: Epoch time: 246.92 s 
+2024-09-09 20:21:31.536259:  
+2024-09-09 20:21:31.536433: Epoch 727 
+2024-09-09 20:21:31.536516: Current learning rate: 0.00311 
+2024-09-09 20:25:37.829999: train_loss -0.8796 
+2024-09-09 20:25:37.830184: val_loss -0.6778 
+2024-09-09 20:25:37.830235: Pseudo dice [0.5767, 0.8561] 
+2024-09-09 20:25:37.830288: Epoch time: 246.3 s 
+2024-09-09 20:25:38.823731:  
+2024-09-09 20:25:38.824008: Epoch 728 
+2024-09-09 20:25:38.824091: Current learning rate: 0.0031 
+2024-09-09 20:29:45.052453: train_loss -0.8772 
+2024-09-09 20:29:45.052592: val_loss -0.6849 
+2024-09-09 20:29:45.052642: Pseudo dice [0.6242, 0.8591] 
+2024-09-09 20:29:45.052693: Epoch time: 246.23 s 
+2024-09-09 20:29:46.218772:  
+2024-09-09 20:29:46.219004: Epoch 729 
+2024-09-09 20:29:46.219089: Current learning rate: 0.00309 
+2024-09-09 20:33:52.288956: train_loss -0.8785 
+2024-09-09 20:33:52.289143: val_loss -0.6428 
+2024-09-09 20:33:52.289267: Pseudo dice [0.5484, 0.8568] 
+2024-09-09 20:33:52.289318: Epoch time: 246.07 s 
+2024-09-09 20:33:53.298618:  
+2024-09-09 20:33:53.298801: Epoch 730 
+2024-09-09 20:33:53.298883: Current learning rate: 0.00308 
+2024-09-09 20:37:59.398209: train_loss -0.8819 
+2024-09-09 20:37:59.398352: val_loss -0.6751 
+2024-09-09 20:37:59.398402: Pseudo dice [0.5784, 0.8637] 
+2024-09-09 20:37:59.398453: Epoch time: 246.1 s 
+2024-09-09 20:38:00.392916:  
+2024-09-09 20:38:00.393139: Epoch 731 
+2024-09-09 20:38:00.393222: Current learning rate: 0.00307 
+2024-09-09 20:42:06.549726: train_loss -0.8877 
+2024-09-09 20:42:06.549869: val_loss -0.6636 
+2024-09-09 20:42:06.549919: Pseudo dice [0.5396, 0.8544] 
+2024-09-09 20:42:06.549971: Epoch time: 246.16 s 
+2024-09-09 20:42:07.713208:  
+2024-09-09 20:42:07.713529: Epoch 732 
+2024-09-09 20:42:07.713647: Current learning rate: 0.00306 
+2024-09-09 20:46:13.969286: train_loss -0.8891 
+2024-09-09 20:46:13.969446: val_loss -0.6821 
+2024-09-09 20:46:13.969499: Pseudo dice [0.6049, 0.8582] 
+2024-09-09 20:46:13.969555: Epoch time: 246.26 s 
+2024-09-09 20:46:14.986840:  
+2024-09-09 20:46:14.987065: Epoch 733 
+2024-09-09 20:46:14.987144: Current learning rate: 0.00305 
+2024-09-09 20:50:21.607945: train_loss -0.8896 
+2024-09-09 20:50:21.608125: val_loss -0.6566 
+2024-09-09 20:50:21.608177: Pseudo dice [0.5707, 0.8647] 
+2024-09-09 20:50:21.608229: Epoch time: 246.62 s 
+2024-09-09 20:50:22.618812:  
+2024-09-09 20:50:22.618983: Epoch 734 
+2024-09-09 20:50:22.619063: Current learning rate: 0.00304 
+2024-09-09 20:54:29.033834: train_loss -0.8895 
+2024-09-09 20:54:29.033993: val_loss -0.6689 
+2024-09-09 20:54:29.034045: Pseudo dice [0.5942, 0.8604] 
+2024-09-09 20:54:29.034097: Epoch time: 246.42 s 
+2024-09-09 20:54:30.050216:  
+2024-09-09 20:54:30.050402: Epoch 735 
+2024-09-09 20:54:30.050487: Current learning rate: 0.00303 
+2024-09-09 20:58:36.299191: train_loss -0.8888 
+2024-09-09 20:58:36.299442: val_loss -0.6997 
+2024-09-09 20:58:36.299527: Pseudo dice [0.6155, 0.8477] 
+2024-09-09 20:58:36.299614: Epoch time: 246.25 s 
+2024-09-09 20:58:37.387874:  
+2024-09-09 20:58:37.388042: Epoch 736 
+2024-09-09 20:58:37.388171: Current learning rate: 0.00302 
+2024-09-09 21:02:43.539605: train_loss -0.8825 
+2024-09-09 21:02:43.539741: val_loss -0.6841 
+2024-09-09 21:02:43.539791: Pseudo dice [0.5927, 0.858] 
+2024-09-09 21:02:43.539850: Epoch time: 246.15 s 
+2024-09-09 21:02:44.535009:  
+2024-09-09 21:02:44.535208: Epoch 737 
+2024-09-09 21:02:44.535288: Current learning rate: 0.00301 
+2024-09-09 21:06:50.794912: train_loss -0.8874 
+2024-09-09 21:06:50.795055: val_loss -0.6837 
+2024-09-09 21:06:50.795108: Pseudo dice [0.6088, 0.8625] 
+2024-09-09 21:06:50.795159: Epoch time: 246.26 s 
+2024-09-09 21:06:51.792302:  
+2024-09-09 21:06:51.792528: Epoch 738 
+2024-09-09 21:06:51.792613: Current learning rate: 0.003 
+2024-09-09 21:10:58.064971: train_loss -0.8898 
+2024-09-09 21:10:58.065108: val_loss -0.6676 
+2024-09-09 21:10:58.065157: Pseudo dice [0.5791, 0.8564] 
+2024-09-09 21:10:58.065208: Epoch time: 246.27 s 
+2024-09-09 21:10:59.090209:  
+2024-09-09 21:10:59.090420: Epoch 739 
+2024-09-09 21:10:59.090533: Current learning rate: 0.00299 
+2024-09-09 21:15:05.200432: train_loss -0.8906 
+2024-09-09 21:15:05.200625: val_loss -0.6598 
+2024-09-09 21:15:05.200708: Pseudo dice [0.5713, 0.8643] 
+2024-09-09 21:15:05.200760: Epoch time: 246.11 s 
+2024-09-09 21:15:06.363728:  
+2024-09-09 21:15:06.363908: Epoch 740 
+2024-09-09 21:15:06.363991: Current learning rate: 0.00297 
+2024-09-09 21:19:12.530699: train_loss -0.8957 
+2024-09-09 21:19:12.530860: val_loss -0.6758 
+2024-09-09 21:19:12.530911: Pseudo dice [0.5657, 0.8684] 
+2024-09-09 21:19:12.530962: Epoch time: 246.17 s 
+2024-09-09 21:19:13.537936:  
+2024-09-09 21:19:13.538100: Epoch 741 
+2024-09-09 21:19:13.538182: Current learning rate: 0.00296 
+2024-09-09 21:23:19.963017: train_loss -0.8916 
+2024-09-09 21:23:19.963164: val_loss -0.6866 
+2024-09-09 21:23:19.963214: Pseudo dice [0.6259, 0.8625] 
+2024-09-09 21:23:19.963268: Epoch time: 246.43 s 
+2024-09-09 21:23:20.952762:  
+2024-09-09 21:23:20.952960: Epoch 742 
+2024-09-09 21:23:20.953040: Current learning rate: 0.00295 
+2024-09-09 21:27:27.614510: train_loss -0.8867 
+2024-09-09 21:27:27.614679: val_loss -0.6779 
+2024-09-09 21:27:27.614729: Pseudo dice [0.6117, 0.861] 
+2024-09-09 21:27:27.614780: Epoch time: 246.66 s 
+2024-09-09 21:27:28.649330:  
+2024-09-09 21:27:28.649504: Epoch 743 
+2024-09-09 21:27:28.649586: Current learning rate: 0.00294 
+2024-09-09 21:31:35.939879: train_loss -0.892 
+2024-09-09 21:31:35.940098: val_loss -0.6845 
+2024-09-09 21:31:35.940172: Pseudo dice [0.5898, 0.8654] 
+2024-09-09 21:31:35.940249: Epoch time: 247.29 s 
+2024-09-09 21:31:36.995307:  
+2024-09-09 21:31:36.995528: Epoch 744 
+2024-09-09 21:31:36.995626: Current learning rate: 0.00293 
+2024-09-09 21:35:43.207057: train_loss -0.8888 
+2024-09-09 21:35:43.207192: val_loss -0.6646 
+2024-09-09 21:35:43.207242: Pseudo dice [0.5681, 0.8629] 
+2024-09-09 21:35:43.207292: Epoch time: 246.21 s 
+2024-09-09 21:35:44.214894:  
+2024-09-09 21:35:44.215234: Epoch 745 
+2024-09-09 21:35:44.215328: Current learning rate: 0.00292 
+2024-09-09 21:39:50.720914: train_loss -0.8955 
+2024-09-09 21:39:50.721090: val_loss -0.7029 
+2024-09-09 21:39:50.721142: Pseudo dice [0.6333, 0.8669] 
+2024-09-09 21:39:50.721194: Epoch time: 246.51 s 
+2024-09-09 21:39:51.728641:  
+2024-09-09 21:39:51.728895: Epoch 746 
+2024-09-09 21:39:51.728979: Current learning rate: 0.00291 
+2024-09-09 21:43:58.059065: train_loss -0.8947 
+2024-09-09 21:43:58.059230: val_loss -0.6863 
+2024-09-09 21:43:58.059280: Pseudo dice [0.5678, 0.8626] 
+2024-09-09 21:43:58.059332: Epoch time: 246.33 s 
+2024-09-09 21:43:59.048307:  
+2024-09-09 21:43:59.048486: Epoch 747 
+2024-09-09 21:43:59.048565: Current learning rate: 0.0029 
+2024-09-09 21:48:05.437660: train_loss -0.8942 
+2024-09-09 21:48:05.437806: val_loss -0.6588 
+2024-09-09 21:48:05.437856: Pseudo dice [0.5871, 0.8505] 
+2024-09-09 21:48:05.437908: Epoch time: 246.39 s 
+2024-09-09 21:48:06.436773:  
+2024-09-09 21:48:06.436944: Epoch 748 
+2024-09-09 21:48:06.437025: Current learning rate: 0.00289 
+2024-09-09 21:52:12.782642: train_loss -0.8925 
+2024-09-09 21:52:12.782782: val_loss -0.656 
+2024-09-09 21:52:12.782832: Pseudo dice [0.5761, 0.8563] 
+2024-09-09 21:52:12.782883: Epoch time: 246.35 s 
+2024-09-09 21:52:13.777910:  
+2024-09-09 21:52:13.778117: Epoch 749 
+2024-09-09 21:52:13.778235: Current learning rate: 0.00288 
+2024-09-09 21:56:20.216869: train_loss -0.8928 
+2024-09-09 21:56:20.217036: val_loss -0.6905 
+2024-09-09 21:56:20.217085: Pseudo dice [0.6137, 0.8633] 
+2024-09-09 21:56:20.217137: Epoch time: 246.44 s 
+2024-09-09 21:56:24.196792:  
+2024-09-09 21:56:24.196975: Epoch 750 
+2024-09-09 21:56:24.197057: Current learning rate: 0.00287 
+2024-09-09 22:00:31.111868: train_loss -0.8903 
+2024-09-09 22:00:31.112044: val_loss -0.6548 
+2024-09-09 22:00:31.112095: Pseudo dice [0.5834, 0.8574] 
+2024-09-09 22:00:31.112146: Epoch time: 246.92 s 
+2024-09-09 22:00:32.107861:  
+2024-09-09 22:00:32.108078: Epoch 751 
+2024-09-09 22:00:32.108166: Current learning rate: 0.00286 
+2024-09-09 22:04:38.836650: train_loss -0.8955 
+2024-09-09 22:04:38.836792: val_loss -0.6964 
+2024-09-09 22:04:38.836843: Pseudo dice [0.63, 0.8577] 
+2024-09-09 22:04:38.836895: Epoch time: 246.73 s 
+2024-09-09 22:04:39.856556:  
+2024-09-09 22:04:39.856717: Epoch 752 
+2024-09-09 22:04:39.856799: Current learning rate: 0.00285 
+2024-09-09 22:08:46.297611: train_loss -0.8877 
+2024-09-09 22:08:46.297752: val_loss -0.6853 
+2024-09-09 22:08:46.297803: Pseudo dice [0.602, 0.8524] 
+2024-09-09 22:08:46.297879: Epoch time: 246.44 s 
+2024-09-09 22:08:47.383583:  
+2024-09-09 22:08:47.383771: Epoch 753 
+2024-09-09 22:08:47.383861: Current learning rate: 0.00284 
+2024-09-09 22:12:53.986418: train_loss -0.8895 
+2024-09-09 22:12:53.986560: val_loss -0.6669 
+2024-09-09 22:12:53.986611: Pseudo dice [0.5899, 0.8582] 
+2024-09-09 22:12:53.986664: Epoch time: 246.6 s 
+2024-09-09 22:12:54.981485:  
+2024-09-09 22:12:54.981684: Epoch 754 
+2024-09-09 22:12:54.981765: Current learning rate: 0.00283 
+2024-09-09 22:17:01.524901: train_loss -0.8929 
+2024-09-09 22:17:01.525080: val_loss -0.6776 
+2024-09-09 22:17:01.525135: Pseudo dice [0.6442, 0.8636] 
+2024-09-09 22:17:01.525186: Epoch time: 246.55 s 
+2024-09-09 22:17:02.540533:  
+2024-09-09 22:17:02.540725: Epoch 755 
+2024-09-09 22:17:02.540805: Current learning rate: 0.00282 
+2024-09-09 22:21:08.934421: train_loss -0.8903 
+2024-09-09 22:21:08.934566: val_loss -0.6987 
+2024-09-09 22:21:08.934616: Pseudo dice [0.6327, 0.8639] 
+2024-09-09 22:21:08.934666: Epoch time: 246.4 s 
+2024-09-09 22:21:09.933495:  
+2024-09-09 22:21:09.933739: Epoch 756 
+2024-09-09 22:21:09.933823: Current learning rate: 0.00281 
+2024-09-09 22:25:16.390466: train_loss -0.8933 
+2024-09-09 22:25:16.390614: val_loss -0.7015 
+2024-09-09 22:25:16.390759: Pseudo dice [0.6316, 0.8703] 
+2024-09-09 22:25:16.390890: Epoch time: 246.46 s 
+2024-09-09 22:25:17.568001:  
+2024-09-09 22:25:17.568310: Epoch 757 
+2024-09-09 22:25:17.568483: Current learning rate: 0.0028 
+2024-09-09 22:29:24.177108: train_loss -0.8781 
+2024-09-09 22:29:24.177258: val_loss -0.6833 
+2024-09-09 22:29:24.177308: Pseudo dice [0.6181, 0.8629] 
+2024-09-09 22:29:24.177361: Epoch time: 246.61 s 
+2024-09-09 22:29:25.175189:  
+2024-09-09 22:29:25.175354: Epoch 758 
+2024-09-09 22:29:25.175431: Current learning rate: 0.00279 
+2024-09-09 22:33:31.794165: train_loss -0.8803 
+2024-09-09 22:33:31.794307: val_loss -0.6504 
+2024-09-09 22:33:31.794357: Pseudo dice [0.596, 0.8391] 
+2024-09-09 22:33:31.794409: Epoch time: 246.62 s 
+2024-09-09 22:33:32.807930:  
+2024-09-09 22:33:32.808168: Epoch 759 
+2024-09-09 22:33:32.808254: Current learning rate: 0.00278 
+2024-09-09 22:37:39.254304: train_loss -0.8837 
+2024-09-09 22:37:39.254441: val_loss -0.7083 
+2024-09-09 22:37:39.254491: Pseudo dice [0.6408, 0.856] 
+2024-09-09 22:37:39.254542: Epoch time: 246.45 s 
+2024-09-09 22:37:40.267737:  
+2024-09-09 22:37:40.267926: Epoch 760 
+2024-09-09 22:37:40.268011: Current learning rate: 0.00277 
+2024-09-09 22:41:46.648828: train_loss -0.8884 
+2024-09-09 22:41:46.648960: val_loss -0.6769 
+2024-09-09 22:41:46.649010: Pseudo dice [0.6099, 0.8536] 
+2024-09-09 22:41:46.649063: Epoch time: 246.38 s 
+2024-09-09 22:41:47.829237:  
+2024-09-09 22:41:47.829490: Epoch 761 
+2024-09-09 22:41:47.829581: Current learning rate: 0.00276 
+2024-09-09 22:45:54.192065: train_loss -0.8883 
+2024-09-09 22:45:54.192209: val_loss -0.691 
+2024-09-09 22:45:54.192259: Pseudo dice [0.6298, 0.8615] 
+2024-09-09 22:45:54.192310: Epoch time: 246.37 s 
+2024-09-09 22:45:55.180911:  
+2024-09-09 22:45:55.181116: Epoch 762 
+2024-09-09 22:45:55.181206: Current learning rate: 0.00275 
+2024-09-09 22:50:01.632383: train_loss -0.8914 
+2024-09-09 22:50:01.632518: val_loss -0.711 
+2024-09-09 22:50:01.632568: Pseudo dice [0.6292, 0.8642] 
+2024-09-09 22:50:01.632619: Epoch time: 246.45 s 
+2024-09-09 22:50:02.644579:  
+2024-09-09 22:50:02.644852: Epoch 763 
+2024-09-09 22:50:02.644976: Current learning rate: 0.00274 
+2024-09-09 22:54:09.216129: train_loss -0.897 
+2024-09-09 22:54:09.216269: val_loss -0.6522 
+2024-09-09 22:54:09.216325: Pseudo dice [0.5896, 0.8519] 
+2024-09-09 22:54:09.216375: Epoch time: 246.58 s 
+2024-09-09 22:54:10.257715:  
+2024-09-09 22:54:10.257899: Epoch 764 
+2024-09-09 22:54:10.257982: Current learning rate: 0.00273 
+2024-09-09 22:58:16.661027: train_loss -0.8918 
+2024-09-09 22:58:16.661256: val_loss -0.6671 
+2024-09-09 22:58:16.661322: Pseudo dice [0.5855, 0.8586] 
+2024-09-09 22:58:16.661384: Epoch time: 246.41 s 
+2024-09-09 22:58:17.854625:  
+2024-09-09 22:58:17.854800: Epoch 765 
+2024-09-09 22:58:17.854880: Current learning rate: 0.00272 
+2024-09-09 23:02:24.254523: train_loss -0.894 
+2024-09-09 23:02:24.254657: val_loss -0.6635 
+2024-09-09 23:02:24.254707: Pseudo dice [0.5441, 0.8641] 
+2024-09-09 23:02:24.254757: Epoch time: 246.4 s 
+2024-09-09 23:02:26.139735:  
+2024-09-09 23:02:26.139987: Epoch 766 
+2024-09-09 23:02:26.140087: Current learning rate: 0.00271 
+2024-09-09 23:06:33.012556: train_loss -0.8951 
+2024-09-09 23:06:33.012697: val_loss -0.6818 
+2024-09-09 23:06:33.012747: Pseudo dice [0.6195, 0.8512] 
+2024-09-09 23:06:33.012798: Epoch time: 246.87 s 
+2024-09-09 23:06:34.049178:  
+2024-09-09 23:06:34.049396: Epoch 767 
+2024-09-09 23:06:34.049512: Current learning rate: 0.0027 
+2024-09-09 23:10:40.574544: train_loss -0.8932 
+2024-09-09 23:10:40.574687: val_loss -0.6667 
+2024-09-09 23:10:40.574739: Pseudo dice [0.6062, 0.8615] 
+2024-09-09 23:10:40.574792: Epoch time: 246.53 s 
+2024-09-09 23:10:41.724069:  
+2024-09-09 23:10:41.724284: Epoch 768 
+2024-09-09 23:10:41.724384: Current learning rate: 0.00268 
+2024-09-09 23:14:48.148456: train_loss -0.8976 
+2024-09-09 23:14:48.148586: val_loss -0.6743 
+2024-09-09 23:14:48.148674: Pseudo dice [0.6027, 0.8647] 
+2024-09-09 23:14:48.148744: Epoch time: 246.43 s 
+2024-09-09 23:14:49.199480:  
+2024-09-09 23:14:49.199659: Epoch 769 
+2024-09-09 23:14:49.199745: Current learning rate: 0.00267 
+2024-09-09 23:18:55.618412: train_loss -0.8966 
+2024-09-09 23:18:55.618649: val_loss -0.6874 
+2024-09-09 23:18:55.618713: Pseudo dice [0.5845, 0.8626] 
+2024-09-09 23:18:55.618769: Epoch time: 246.42 s 
+2024-09-09 23:18:56.619915:  
+2024-09-09 23:18:56.620168: Epoch 770 
+2024-09-09 23:18:56.620249: Current learning rate: 0.00266 
+2024-09-09 23:23:02.911553: train_loss -0.892 
+2024-09-09 23:23:02.911715: val_loss -0.6837 
+2024-09-09 23:23:02.911771: Pseudo dice [0.5917, 0.8607] 
+2024-09-09 23:23:02.911844: Epoch time: 246.29 s 
+2024-09-09 23:23:04.077817:  
+2024-09-09 23:23:04.078031: Epoch 771 
+2024-09-09 23:23:04.078125: Current learning rate: 0.00265 
+2024-09-09 23:27:10.391127: train_loss -0.8922 
+2024-09-09 23:27:10.391272: val_loss -0.6826 
+2024-09-09 23:27:10.391323: Pseudo dice [0.5833, 0.8597] 
+2024-09-09 23:27:10.391375: Epoch time: 246.32 s 
+2024-09-09 23:27:11.421275:  
+2024-09-09 23:27:11.421493: Epoch 772 
+2024-09-09 23:27:11.421635: Current learning rate: 0.00264 
+2024-09-09 23:31:17.901211: train_loss -0.895 
+2024-09-09 23:31:17.901384: val_loss -0.6946 
+2024-09-09 23:31:17.901458: Pseudo dice [0.5989, 0.8629] 
+2024-09-09 23:31:17.901509: Epoch time: 246.48 s 
+2024-09-09 23:31:18.931902:  
+2024-09-09 23:31:18.932124: Epoch 773 
+2024-09-09 23:31:18.932203: Current learning rate: 0.00263 
+2024-09-09 23:35:25.533835: train_loss -0.893 
+2024-09-09 23:35:25.534000: val_loss -0.686 
+2024-09-09 23:35:25.534053: Pseudo dice [0.6131, 0.8632] 
+2024-09-09 23:35:25.534104: Epoch time: 246.6 s 
+2024-09-09 23:35:26.547849:  
+2024-09-09 23:35:26.548058: Epoch 774 
+2024-09-09 23:35:26.548137: Current learning rate: 0.00262 
+2024-09-09 23:39:33.458148: train_loss -0.8916 
+2024-09-09 23:39:33.458290: val_loss -0.6499 
+2024-09-09 23:39:33.458340: Pseudo dice [0.5981, 0.847] 
+2024-09-09 23:39:33.458394: Epoch time: 246.91 s 
+2024-09-09 23:39:34.509970:  
+2024-09-09 23:39:34.510256: Epoch 775 
+2024-09-09 23:39:34.510341: Current learning rate: 0.00261 
+2024-09-09 23:43:41.295992: train_loss -0.8897 
+2024-09-09 23:43:41.296133: val_loss -0.6857 
+2024-09-09 23:43:41.296182: Pseudo dice [0.6251, 0.8493] 
+2024-09-09 23:43:41.296235: Epoch time: 246.79 s 
+2024-09-09 23:43:42.316856:  
+2024-09-09 23:43:42.317099: Epoch 776 
+2024-09-09 23:43:42.317180: Current learning rate: 0.0026 
+2024-09-09 23:47:49.017135: train_loss -0.8904 
+2024-09-09 23:47:49.017354: val_loss -0.6363 
+2024-09-09 23:47:49.017409: Pseudo dice [0.4911, 0.8594] 
+2024-09-09 23:47:49.017488: Epoch time: 246.7 s 
+2024-09-09 23:47:50.184161:  
+2024-09-09 23:47:50.184371: Epoch 777 
+2024-09-09 23:47:50.184451: Current learning rate: 0.00259 
+2024-09-09 23:51:56.710403: train_loss -0.8926 
+2024-09-09 23:51:56.710555: val_loss -0.7001 
+2024-09-09 23:51:56.710605: Pseudo dice [0.6132, 0.868] 
+2024-09-09 23:51:56.710655: Epoch time: 246.53 s 
+2024-09-09 23:51:57.709073:  
+2024-09-09 23:51:57.709326: Epoch 778 
+2024-09-09 23:51:57.709406: Current learning rate: 0.00258 
+2024-09-09 23:56:04.234745: train_loss -0.8936 
+2024-09-09 23:56:04.234901: val_loss -0.7022 
+2024-09-09 23:56:04.234976: Pseudo dice [0.6459, 0.8649] 
+2024-09-09 23:56:04.235040: Epoch time: 246.53 s 
+2024-09-09 23:56:05.264094:  
+2024-09-09 23:56:05.264291: Epoch 779 
+2024-09-09 23:56:05.264377: Current learning rate: 0.00257 
+2024-09-10 00:00:11.843857: train_loss -0.8949 
+2024-09-10 00:00:11.844031: val_loss -0.703 
+2024-09-10 00:00:11.844082: Pseudo dice [0.6311, 0.85] 
+2024-09-10 00:00:11.844139: Epoch time: 246.58 s 
+2024-09-10 00:00:12.898620:  
+2024-09-10 00:00:12.898851: Epoch 780 
+2024-09-10 00:00:12.898960: Current learning rate: 0.00256 
+2024-09-10 00:04:19.671451: train_loss -0.8965 
+2024-09-10 00:04:19.671593: val_loss -0.6816 
+2024-09-10 00:04:19.671644: Pseudo dice [0.5973, 0.8683] 
+2024-09-10 00:04:19.671694: Epoch time: 246.77 s 
+2024-09-10 00:04:20.690689:  
+2024-09-10 00:04:20.690916: Epoch 781 
+2024-09-10 00:04:20.690997: Current learning rate: 0.00255 
+2024-09-10 00:08:27.458662: train_loss -0.8967 
+2024-09-10 00:08:27.458811: val_loss -0.6904 
+2024-09-10 00:08:27.458861: Pseudo dice [0.6173, 0.8694] 
+2024-09-10 00:08:27.458913: Epoch time: 246.77 s 
+2024-09-10 00:08:28.461754:  
+2024-09-10 00:08:28.461935: Epoch 782 
+2024-09-10 00:08:28.462015: Current learning rate: 0.00254 
+2024-09-10 00:12:35.037299: train_loss -0.8979 
+2024-09-10 00:12:35.037440: val_loss -0.663 
+2024-09-10 00:12:35.037489: Pseudo dice [0.5875, 0.8461] 
+2024-09-10 00:12:35.037539: Epoch time: 246.58 s 
+2024-09-10 00:12:36.127385:  
+2024-09-10 00:12:36.127660: Epoch 783 
+2024-09-10 00:12:36.127763: Current learning rate: 0.00253 
+2024-09-10 00:16:42.769272: train_loss -0.897 
+2024-09-10 00:16:42.769418: val_loss -0.6799 
+2024-09-10 00:16:42.769467: Pseudo dice [0.5972, 0.8627] 
+2024-09-10 00:16:42.769518: Epoch time: 246.64 s 
+2024-09-10 00:16:43.797708:  
+2024-09-10 00:16:43.797875: Epoch 784 
+2024-09-10 00:16:43.797952: Current learning rate: 0.00252 
+2024-09-10 00:20:50.487492: train_loss -0.8992 
+2024-09-10 00:20:50.487656: val_loss -0.6916 
+2024-09-10 00:20:50.487707: Pseudo dice [0.5982, 0.8706] 
+2024-09-10 00:20:50.487762: Epoch time: 246.69 s 
+2024-09-10 00:20:51.518492:  
+2024-09-10 00:20:51.518666: Epoch 785 
+2024-09-10 00:20:51.518776: Current learning rate: 0.00251 
+2024-09-10 00:24:58.293165: train_loss -0.9017 
+2024-09-10 00:24:58.293308: val_loss -0.6797 
+2024-09-10 00:24:58.293358: Pseudo dice [0.5869, 0.8673] 
+2024-09-10 00:24:58.293408: Epoch time: 246.78 s 
+2024-09-10 00:24:59.306986:  
+2024-09-10 00:24:59.307200: Epoch 786 
+2024-09-10 00:24:59.307281: Current learning rate: 0.0025 
+2024-09-10 00:29:05.638103: train_loss -0.89 
+2024-09-10 00:29:05.638258: val_loss -0.6712 
+2024-09-10 00:29:05.638344: Pseudo dice [0.5925, 0.8498] 
+2024-09-10 00:29:05.638416: Epoch time: 246.33 s 
+2024-09-10 00:29:06.757438:  
+2024-09-10 00:29:06.757749: Epoch 787 
+2024-09-10 00:29:06.757881: Current learning rate: 0.00249 
+2024-09-10 00:33:13.087496: train_loss -0.8811 
+2024-09-10 00:33:13.087643: val_loss -0.6834 
+2024-09-10 00:33:13.087703: Pseudo dice [0.5946, 0.8613] 
+2024-09-10 00:33:13.087760: Epoch time: 246.33 s 
+2024-09-10 00:33:14.095105:  
+2024-09-10 00:33:14.095318: Epoch 788 
+2024-09-10 00:33:14.095409: Current learning rate: 0.00248 
+2024-09-10 00:37:20.619001: train_loss -0.8893 
+2024-09-10 00:37:20.619165: val_loss -0.6576 
+2024-09-10 00:37:20.619231: Pseudo dice [0.6097, 0.8474] 
+2024-09-10 00:37:20.619296: Epoch time: 246.53 s 
+2024-09-10 00:37:22.744268:  
+2024-09-10 00:37:22.744664: Epoch 789 
+2024-09-10 00:37:22.744807: Current learning rate: 0.00247 
+2024-09-10 00:41:29.415442: train_loss -0.8813 
+2024-09-10 00:41:29.415668: val_loss -0.6854 
+2024-09-10 00:41:29.415728: Pseudo dice [0.6294, 0.8591] 
+2024-09-10 00:41:29.415783: Epoch time: 246.67 s 
+2024-09-10 00:41:30.594363:  
+2024-09-10 00:41:30.594646: Epoch 790 
+2024-09-10 00:41:30.594760: Current learning rate: 0.00245 
+2024-09-10 00:45:37.192595: train_loss -0.869 
+2024-09-10 00:45:37.192741: val_loss -0.6649 
+2024-09-10 00:45:37.192798: Pseudo dice [0.5678, 0.8469] 
+2024-09-10 00:45:37.192855: Epoch time: 246.6 s 
+2024-09-10 00:45:38.304683:  
+2024-09-10 00:45:38.304952: Epoch 791 
+2024-09-10 00:45:38.305061: Current learning rate: 0.00244 
+2024-09-10 00:49:47.241183: train_loss -0.8767 
+2024-09-10 00:49:47.241472: val_loss -0.7051 
+2024-09-10 00:49:47.241583: Pseudo dice [0.6293, 0.8705] 
+2024-09-10 00:49:47.241746: Epoch time: 248.94 s 
+2024-09-10 00:49:48.921830:  
+2024-09-10 00:49:48.922060: Epoch 792 
+2024-09-10 00:49:48.922180: Current learning rate: 0.00243 
+2024-09-10 00:54:04.948680: train_loss -0.8523 
+2024-09-10 00:54:04.948823: val_loss -0.6953 
+2024-09-10 00:54:04.948883: Pseudo dice [0.6308, 0.8639] 
+2024-09-10 00:54:04.948990: Epoch time: 256.03 s 
+2024-09-10 00:54:05.954782:  
+2024-09-10 00:54:05.955029: Epoch 793 
+2024-09-10 00:54:05.955119: Current learning rate: 0.00242 
+2024-09-10 00:58:12.380354: train_loss -0.8643 
+2024-09-10 00:58:12.380497: val_loss -0.7032 
+2024-09-10 00:58:12.380553: Pseudo dice [0.6302, 0.8655] 
+2024-09-10 00:58:12.380607: Epoch time: 246.43 s 
+2024-09-10 00:58:13.401281:  
+2024-09-10 00:58:13.401512: Epoch 794 
+2024-09-10 00:58:13.401601: Current learning rate: 0.00241 
+2024-09-10 01:02:19.588093: train_loss -0.8822 
+2024-09-10 01:02:19.588255: val_loss -0.6833 
+2024-09-10 01:02:19.588325: Pseudo dice [0.6145, 0.8488] 
+2024-09-10 01:02:19.588388: Epoch time: 246.19 s 
+2024-09-10 01:02:20.614007:  
+2024-09-10 01:02:20.614187: Epoch 795 
+2024-09-10 01:02:20.614280: Current learning rate: 0.0024 
+2024-09-10 01:06:27.004378: train_loss -0.8871 
+2024-09-10 01:06:27.004529: val_loss -0.6814 
+2024-09-10 01:06:27.004584: Pseudo dice [0.6308, 0.8529] 
+2024-09-10 01:06:27.004640: Epoch time: 246.39 s 
+2024-09-10 01:06:28.040993:  
+2024-09-10 01:06:28.041185: Epoch 796 
+2024-09-10 01:06:28.041302: Current learning rate: 0.00239 
+2024-09-10 01:10:34.209830: train_loss -0.8902 
+2024-09-10 01:10:34.210000: val_loss -0.6685 
+2024-09-10 01:10:34.210057: Pseudo dice [0.5847, 0.8464] 
+2024-09-10 01:10:34.210117: Epoch time: 246.17 s 
+2024-09-10 01:10:35.235367:  
+2024-09-10 01:10:35.235547: Epoch 797 
+2024-09-10 01:10:35.235639: Current learning rate: 0.00238 
+2024-09-10 01:14:41.372585: train_loss -0.8907 
+2024-09-10 01:14:41.372745: val_loss -0.6808 
+2024-09-10 01:14:41.372800: Pseudo dice [0.6133, 0.853] 
+2024-09-10 01:14:41.372857: Epoch time: 246.14 s 
+2024-09-10 01:14:42.416821:  
+2024-09-10 01:14:42.417021: Epoch 798 
+2024-09-10 01:14:42.417134: Current learning rate: 0.00237 
+2024-09-10 01:18:48.593126: train_loss -0.8914 
+2024-09-10 01:18:48.593274: val_loss -0.6878 
+2024-09-10 01:18:48.593330: Pseudo dice [0.624, 0.865] 
+2024-09-10 01:18:48.593386: Epoch time: 246.18 s 
+2024-09-10 01:18:49.605811:  
+2024-09-10 01:18:49.606020: Epoch 799 
+2024-09-10 01:18:49.606107: Current learning rate: 0.00236 
+2024-09-10 01:22:55.820637: train_loss -0.8958 
+2024-09-10 01:22:55.820795: val_loss -0.6985 
+2024-09-10 01:22:55.820853: Pseudo dice [0.6512, 0.8617] 
+2024-09-10 01:22:55.820912: Epoch time: 246.22 s 
+2024-09-10 01:22:59.793037:  
+2024-09-10 01:22:59.793222: Epoch 800 
+2024-09-10 01:22:59.793309: Current learning rate: 0.00235 
+2024-09-10 01:27:06.211327: train_loss -0.9005 
+2024-09-10 01:27:06.211467: val_loss -0.7014 
+2024-09-10 01:27:06.211517: Pseudo dice [0.6294, 0.8652] 
+2024-09-10 01:27:06.211568: Epoch time: 246.42 s 
+2024-09-10 01:27:06.211608: Yayy! New best EMA pseudo Dice: 0.7372 
+2024-09-10 01:27:10.150847:  
+2024-09-10 01:27:10.151075: Epoch 801 
+2024-09-10 01:27:10.151161: Current learning rate: 0.00234 
+2024-09-10 01:31:16.691749: train_loss -0.8959 
+2024-09-10 01:31:16.691958: val_loss -0.6545 
+2024-09-10 01:31:16.692052: Pseudo dice [0.5726, 0.8574] 
+2024-09-10 01:31:16.692144: Epoch time: 246.54 s 
+2024-09-10 01:31:17.739643:  
+2024-09-10 01:31:17.739878: Epoch 802 
+2024-09-10 01:31:17.739967: Current learning rate: 0.00233 
+2024-09-10 01:35:24.353940: train_loss -0.8976 
+2024-09-10 01:35:24.354107: val_loss -0.689 
+2024-09-10 01:35:24.354159: Pseudo dice [0.5951, 0.8586] 
+2024-09-10 01:35:24.354213: Epoch time: 246.62 s 
+2024-09-10 01:35:25.410240:  
+2024-09-10 01:35:25.410438: Epoch 803 
+2024-09-10 01:35:25.410521: Current learning rate: 0.00232 
+2024-09-10 01:39:31.964382: train_loss -0.8987 
+2024-09-10 01:39:31.964520: val_loss -0.6746 
+2024-09-10 01:39:31.964570: Pseudo dice [0.5911, 0.8544] 
+2024-09-10 01:39:31.964621: Epoch time: 246.56 s 
+2024-09-10 01:39:33.007188:  
+2024-09-10 01:39:33.007375: Epoch 804 
+2024-09-10 01:39:33.007479: Current learning rate: 0.00231 
+2024-09-10 01:43:39.332208: train_loss -0.8941 
+2024-09-10 01:43:39.332348: val_loss -0.7103 
+2024-09-10 01:43:39.332397: Pseudo dice [0.649, 0.862] 
+2024-09-10 01:43:39.332449: Epoch time: 246.33 s 
+2024-09-10 01:43:40.355140:  
+2024-09-10 01:43:40.355382: Epoch 805 
+2024-09-10 01:43:40.355463: Current learning rate: 0.0023 
+2024-09-10 01:47:46.838697: train_loss -0.8971 
+2024-09-10 01:47:46.838834: val_loss -0.6717 
+2024-09-10 01:47:46.838883: Pseudo dice [0.6174, 0.8548] 
+2024-09-10 01:47:46.838933: Epoch time: 246.49 s 
+2024-09-10 01:47:47.880411:  
+2024-09-10 01:47:47.880620: Epoch 806 
+2024-09-10 01:47:47.880703: Current learning rate: 0.00229 
+2024-09-10 01:51:54.335954: train_loss -0.8977 
+2024-09-10 01:51:54.336100: val_loss -0.6783 
+2024-09-10 01:51:54.336150: Pseudo dice [0.6148, 0.8614] 
+2024-09-10 01:51:54.336205: Epoch time: 246.46 s 
+2024-09-10 01:51:55.353616:  
+2024-09-10 01:51:55.353886: Epoch 807 
+2024-09-10 01:51:55.353980: Current learning rate: 0.00228 
+2024-09-10 01:56:01.834382: train_loss -0.8954 
+2024-09-10 01:56:01.834520: val_loss -0.7045 
+2024-09-10 01:56:01.834570: Pseudo dice [0.6262, 0.8607] 
+2024-09-10 01:56:01.834624: Epoch time: 246.48 s 
+2024-09-10 01:56:02.883259:  
+2024-09-10 01:56:02.883525: Epoch 808 
+2024-09-10 01:56:02.883611: Current learning rate: 0.00226 
+2024-09-10 02:00:09.353778: train_loss -0.9028 
+2024-09-10 02:00:09.353940: val_loss -0.6746 
+2024-09-10 02:00:09.353991: Pseudo dice [0.5868, 0.8603] 
+2024-09-10 02:00:09.354041: Epoch time: 246.47 s 
+2024-09-10 02:00:10.370296:  
+2024-09-10 02:00:10.370479: Epoch 809 
+2024-09-10 02:00:10.370594: Current learning rate: 0.00225 
+2024-09-10 02:04:16.760406: train_loss -0.8964 
+2024-09-10 02:04:16.760556: val_loss -0.675 
+2024-09-10 02:04:16.760605: Pseudo dice [0.5896, 0.8525] 
+2024-09-10 02:04:16.760658: Epoch time: 246.39 s 
+2024-09-10 02:04:17.770234:  
+2024-09-10 02:04:17.770449: Epoch 810 
+2024-09-10 02:04:17.770568: Current learning rate: 0.00224 
+2024-09-10 02:08:24.245048: train_loss -0.8987 
+2024-09-10 02:08:24.245187: val_loss -0.6824 
+2024-09-10 02:08:24.245236: Pseudo dice [0.5858, 0.8662] 
+2024-09-10 02:08:24.245288: Epoch time: 246.48 s 
+2024-09-10 02:08:26.175984:  
+2024-09-10 02:08:26.176253: Epoch 811 
+2024-09-10 02:08:26.176353: Current learning rate: 0.00223 
+2024-09-10 02:12:32.722399: train_loss -0.8977 
+2024-09-10 02:12:32.722537: val_loss -0.6751 
+2024-09-10 02:12:32.722588: Pseudo dice [0.5749, 0.8627] 
+2024-09-10 02:12:32.722639: Epoch time: 246.55 s 
+2024-09-10 02:12:33.765530:  
+2024-09-10 02:12:33.765721: Epoch 812 
+2024-09-10 02:12:33.765821: Current learning rate: 0.00222 
+2024-09-10 02:16:40.458987: train_loss -0.9008 
+2024-09-10 02:16:40.459151: val_loss -0.6905 
+2024-09-10 02:16:40.459202: Pseudo dice [0.6202, 0.8642] 
+2024-09-10 02:16:40.459255: Epoch time: 246.7 s 
+2024-09-10 02:16:41.476530:  
+2024-09-10 02:16:41.476743: Epoch 813 
+2024-09-10 02:16:41.476865: Current learning rate: 0.00221 
+2024-09-10 02:20:47.771615: train_loss -0.9 
+2024-09-10 02:20:47.771791: val_loss -0.7075 
+2024-09-10 02:20:47.771870: Pseudo dice [0.6587, 0.8651] 
+2024-09-10 02:20:47.771997: Epoch time: 246.3 s 
+2024-09-10 02:20:48.794879:  
+2024-09-10 02:20:48.795133: Epoch 814 
+2024-09-10 02:20:48.795239: Current learning rate: 0.0022 
+2024-09-10 02:24:55.080656: train_loss -0.8925 
+2024-09-10 02:24:55.080798: val_loss -0.6459 
+2024-09-10 02:24:55.080849: Pseudo dice [0.5203, 0.8548] 
+2024-09-10 02:24:55.080901: Epoch time: 246.29 s 
+2024-09-10 02:24:56.103013:  
+2024-09-10 02:24:56.103237: Epoch 815 
+2024-09-10 02:24:56.103318: Current learning rate: 0.00219 
+2024-09-10 02:29:02.430600: train_loss -0.8943 
+2024-09-10 02:29:02.430747: val_loss -0.672 
+2024-09-10 02:29:02.430797: Pseudo dice [0.5848, 0.8629] 
+2024-09-10 02:29:02.430863: Epoch time: 246.33 s 
+2024-09-10 02:29:03.482214:  
+2024-09-10 02:29:03.482413: Epoch 816 
+2024-09-10 02:29:03.482524: Current learning rate: 0.00218 
+2024-09-10 02:33:09.708819: train_loss -0.8969 
+2024-09-10 02:33:09.708956: val_loss -0.6786 
+2024-09-10 02:33:09.709006: Pseudo dice [0.58, 0.8526] 
+2024-09-10 02:33:09.709057: Epoch time: 246.23 s 
+2024-09-10 02:33:10.754998:  
+2024-09-10 02:33:10.755180: Epoch 817 
+2024-09-10 02:33:10.755292: Current learning rate: 0.00217 
+2024-09-10 02:37:16.979159: train_loss -0.896 
+2024-09-10 02:37:16.979299: val_loss -0.6835 
+2024-09-10 02:37:16.979386: Pseudo dice [0.5852, 0.8598] 
+2024-09-10 02:37:16.979473: Epoch time: 246.23 s 
+2024-09-10 02:37:17.988722:  
+2024-09-10 02:37:17.988913: Epoch 818 
+2024-09-10 02:37:17.988997: Current learning rate: 0.00216 
+2024-09-10 02:41:24.199028: train_loss -0.8946 
+2024-09-10 02:41:24.199231: val_loss -0.6726 
+2024-09-10 02:41:24.199302: Pseudo dice [0.6104, 0.8692] 
+2024-09-10 02:41:24.199355: Epoch time: 246.21 s 
+2024-09-10 02:41:25.227539:  
+2024-09-10 02:41:25.227781: Epoch 819 
+2024-09-10 02:41:25.227914: Current learning rate: 0.00215 
+2024-09-10 02:45:31.584834: train_loss -0.8966 
+2024-09-10 02:45:31.584972: val_loss -0.6965 
+2024-09-10 02:45:31.585023: Pseudo dice [0.6414, 0.8579] 
+2024-09-10 02:45:31.585075: Epoch time: 246.36 s 
+2024-09-10 02:45:32.562817:  
+2024-09-10 02:45:32.563051: Epoch 820 
+2024-09-10 02:45:32.563135: Current learning rate: 0.00214 
+2024-09-10 02:49:39.047530: train_loss -0.8957 
+2024-09-10 02:49:39.047669: val_loss -0.702 
+2024-09-10 02:49:39.047720: Pseudo dice [0.6433, 0.8691] 
+2024-09-10 02:49:39.047772: Epoch time: 246.49 s 
+2024-09-10 02:49:40.004856:  
+2024-09-10 02:49:40.005020: Epoch 821 
+2024-09-10 02:49:40.005102: Current learning rate: 0.00213 
+2024-09-10 02:53:46.525093: train_loss -0.9004 
+2024-09-10 02:53:46.525239: val_loss -0.7047 
+2024-09-10 02:53:46.525288: Pseudo dice [0.6318, 0.8706] 
+2024-09-10 02:53:46.525340: Epoch time: 246.52 s 
+2024-09-10 02:53:47.504726:  
+2024-09-10 02:53:47.504944: Epoch 822 
+2024-09-10 02:53:47.505032: Current learning rate: 0.00212 
+2024-09-10 02:57:53.974383: train_loss -0.9001 
+2024-09-10 02:57:53.974528: val_loss -0.6923 
+2024-09-10 02:57:53.974600: Pseudo dice [0.621, 0.8593] 
+2024-09-10 02:57:53.974672: Epoch time: 246.47 s 
+2024-09-10 02:57:54.932733:  
+2024-09-10 02:57:54.932903: Epoch 823 
+2024-09-10 02:57:54.932981: Current learning rate: 0.0021 
+2024-09-10 03:02:01.373843: train_loss -0.9007 
+2024-09-10 03:02:01.373979: val_loss -0.7026 
+2024-09-10 03:02:01.374029: Pseudo dice [0.6283, 0.8663] 
+2024-09-10 03:02:01.374082: Epoch time: 246.44 s 
+2024-09-10 03:02:02.333499:  
+2024-09-10 03:02:02.333718: Epoch 824 
+2024-09-10 03:02:02.333814: Current learning rate: 0.00209 
+2024-09-10 03:06:08.826351: train_loss -0.9008 
+2024-09-10 03:06:08.826489: val_loss -0.715 
+2024-09-10 03:06:08.826539: Pseudo dice [0.6639, 0.8587] 
+2024-09-10 03:06:08.826589: Epoch time: 246.5 s 
+2024-09-10 03:06:08.826629: Yayy! New best EMA pseudo Dice: 0.7395 
+2024-09-10 03:06:12.755413:  
+2024-09-10 03:06:12.755625: Epoch 825 
+2024-09-10 03:06:12.755706: Current learning rate: 0.00208 
+2024-09-10 03:10:19.333588: train_loss -0.901 
+2024-09-10 03:10:19.333728: val_loss -0.6774 
+2024-09-10 03:10:19.333778: Pseudo dice [0.6152, 0.8537] 
+2024-09-10 03:10:19.333829: Epoch time: 246.58 s 
+2024-09-10 03:10:20.297229:  
+2024-09-10 03:10:20.297458: Epoch 826 
+2024-09-10 03:10:20.297540: Current learning rate: 0.00207 
+2024-09-10 03:14:26.660774: train_loss -0.9045 
+2024-09-10 03:14:26.660911: val_loss -0.6971 
+2024-09-10 03:14:26.660964: Pseudo dice [0.6106, 0.8718] 
+2024-09-10 03:14:26.661015: Epoch time: 246.37 s 
+2024-09-10 03:14:27.626975:  
+2024-09-10 03:14:27.627153: Epoch 827 
+2024-09-10 03:14:27.627232: Current learning rate: 0.00206 
+2024-09-10 03:18:34.105369: train_loss -0.9032 
+2024-09-10 03:18:34.105523: val_loss -0.6725 
+2024-09-10 03:18:34.105573: Pseudo dice [0.5896, 0.8607] 
+2024-09-10 03:18:34.105624: Epoch time: 246.48 s 
+2024-09-10 03:18:35.062877:  
+2024-09-10 03:18:35.063037: Epoch 828 
+2024-09-10 03:18:35.063175: Current learning rate: 0.00205 
+2024-09-10 03:22:41.415689: train_loss -0.9046 
+2024-09-10 03:22:41.415846: val_loss -0.6872 
+2024-09-10 03:22:41.415897: Pseudo dice [0.6101, 0.8639] 
+2024-09-10 03:22:41.415948: Epoch time: 246.35 s 
+2024-09-10 03:22:42.374292:  
+2024-09-10 03:22:42.374535: Epoch 829 
+2024-09-10 03:22:42.374618: Current learning rate: 0.00204 
+2024-09-10 03:26:49.164365: train_loss -0.9084 
+2024-09-10 03:26:49.164548: val_loss -0.7002 
+2024-09-10 03:26:49.164600: Pseudo dice [0.6281, 0.8557] 
+2024-09-10 03:26:49.164652: Epoch time: 246.79 s 
+2024-09-10 03:26:50.134417:  
+2024-09-10 03:26:50.134585: Epoch 830 
+2024-09-10 03:26:50.134666: Current learning rate: 0.00203 
+2024-09-10 03:30:56.772236: train_loss -0.906 
+2024-09-10 03:30:56.772375: val_loss -0.6986 
+2024-09-10 03:30:56.772424: Pseudo dice [0.6234, 0.8575] 
+2024-09-10 03:30:56.772475: Epoch time: 246.64 s 
+2024-09-10 03:30:57.763930:  
+2024-09-10 03:30:57.764124: Epoch 831 
+2024-09-10 03:30:57.764205: Current learning rate: 0.00202 
+2024-09-10 03:35:04.210352: train_loss -0.9069 
+2024-09-10 03:35:04.210490: val_loss -0.7057 
+2024-09-10 03:35:04.210540: Pseudo dice [0.6378, 0.8684] 
+2024-09-10 03:35:04.210591: Epoch time: 246.45 s 
+2024-09-10 03:35:04.210632: Yayy! New best EMA pseudo Dice: 0.7399 
+2024-09-10 03:35:08.840825:  
+2024-09-10 03:35:08.840975: Epoch 832 
+2024-09-10 03:35:08.841089: Current learning rate: 0.00201 
+2024-09-10 03:39:15.495191: train_loss -0.9065 
+2024-09-10 03:39:15.495342: val_loss -0.6973 
+2024-09-10 03:39:15.495392: Pseudo dice [0.6168, 0.8599] 
+2024-09-10 03:39:15.495443: Epoch time: 246.66 s 
+2024-09-10 03:39:16.464150:  
+2024-09-10 03:39:16.464354: Epoch 833 
+2024-09-10 03:39:16.464444: Current learning rate: 0.002 
+2024-09-10 03:43:22.819170: train_loss -0.8994 
+2024-09-10 03:43:22.819308: val_loss -0.6883 
+2024-09-10 03:43:22.819359: Pseudo dice [0.6033, 0.8527] 
+2024-09-10 03:43:22.819409: Epoch time: 246.36 s 
+2024-09-10 03:43:23.775495:  
+2024-09-10 03:43:23.775718: Epoch 834 
+2024-09-10 03:43:23.775820: Current learning rate: 0.00199 
+2024-09-10 03:47:30.854371: train_loss -0.9013 
+2024-09-10 03:47:30.854510: val_loss -0.6565 
+2024-09-10 03:47:30.854559: Pseudo dice [0.5342, 0.8482] 
+2024-09-10 03:47:30.854613: Epoch time: 247.08 s 
+2024-09-10 03:47:31.797776:  
+2024-09-10 03:47:31.797961: Epoch 835 
+2024-09-10 03:47:31.798057: Current learning rate: 0.00198 
+2024-09-10 03:51:38.397944: train_loss -0.8944 
+2024-09-10 03:51:38.398108: val_loss -0.6602 
+2024-09-10 03:51:38.398160: Pseudo dice [0.5865, 0.8554] 
+2024-09-10 03:51:38.398211: Epoch time: 246.6 s 
+2024-09-10 03:51:39.358413:  
+2024-09-10 03:51:39.358690: Epoch 836 
+2024-09-10 03:51:39.358799: Current learning rate: 0.00196 
+2024-09-10 03:55:46.066922: train_loss -0.8986 
+2024-09-10 03:55:46.067076: val_loss -0.6797 
+2024-09-10 03:55:46.067127: Pseudo dice [0.5893, 0.8609] 
+2024-09-10 03:55:46.067179: Epoch time: 246.71 s 
+2024-09-10 03:55:47.034926:  
+2024-09-10 03:55:47.035160: Epoch 837 
+2024-09-10 03:55:47.035243: Current learning rate: 0.00195 
+2024-09-10 03:59:53.657347: train_loss -0.903 
+2024-09-10 03:59:53.657550: val_loss -0.6907 
+2024-09-10 03:59:53.657603: Pseudo dice [0.6052, 0.8566] 
+2024-09-10 03:59:53.657655: Epoch time: 246.62 s 
+2024-09-10 03:59:54.621502:  
+2024-09-10 03:59:54.621733: Epoch 838 
+2024-09-10 03:59:54.621812: Current learning rate: 0.00194 
+2024-09-10 04:04:01.196171: train_loss -0.8953 
+2024-09-10 04:04:01.196361: val_loss -0.7077 
+2024-09-10 04:04:01.196414: Pseudo dice [0.659, 0.8631] 
+2024-09-10 04:04:01.196465: Epoch time: 246.58 s 
+2024-09-10 04:04:02.148650:  
+2024-09-10 04:04:02.148827: Epoch 839 
+2024-09-10 04:04:02.148909: Current learning rate: 0.00193 
+2024-09-10 04:08:08.452451: train_loss -0.8837 
+2024-09-10 04:08:08.452611: val_loss -0.6612 
+2024-09-10 04:08:08.452667: Pseudo dice [0.6042, 0.8438] 
+2024-09-10 04:08:08.452720: Epoch time: 246.31 s 
+2024-09-10 04:08:09.411627:  
+2024-09-10 04:08:09.411870: Epoch 840 
+2024-09-10 04:08:09.411951: Current learning rate: 0.00192 
+2024-09-10 04:12:15.692956: train_loss -0.8774 
+2024-09-10 04:12:15.693099: val_loss -0.6661 
+2024-09-10 04:12:15.693149: Pseudo dice [0.5688, 0.8657] 
+2024-09-10 04:12:15.693204: Epoch time: 246.28 s 
+2024-09-10 04:12:16.652533:  
+2024-09-10 04:12:16.652774: Epoch 841 
+2024-09-10 04:12:16.652857: Current learning rate: 0.00191 
+2024-09-10 04:16:22.916593: train_loss -0.8961 
+2024-09-10 04:16:22.916734: val_loss -0.6779 
+2024-09-10 04:16:22.916784: Pseudo dice [0.6149, 0.8557] 
+2024-09-10 04:16:22.916835: Epoch time: 246.27 s 
+2024-09-10 04:16:23.875078:  
+2024-09-10 04:16:23.875287: Epoch 842 
+2024-09-10 04:16:23.875372: Current learning rate: 0.0019 
+2024-09-10 04:20:30.207711: train_loss -0.8929 
+2024-09-10 04:20:30.207858: val_loss -0.6491 
+2024-09-10 04:20:30.207911: Pseudo dice [0.5435, 0.8515] 
+2024-09-10 04:20:30.207978: Epoch time: 246.33 s 
+2024-09-10 04:20:31.167094:  
+2024-09-10 04:20:31.167312: Epoch 843 
+2024-09-10 04:20:31.167392: Current learning rate: 0.00189 
+2024-09-10 04:24:37.488417: train_loss -0.8975 
+2024-09-10 04:24:37.488572: val_loss -0.6859 
+2024-09-10 04:24:37.488622: Pseudo dice [0.6255, 0.8512] 
+2024-09-10 04:24:37.488672: Epoch time: 246.32 s 
+2024-09-10 04:24:38.443128:  
+2024-09-10 04:24:38.443342: Epoch 844 
+2024-09-10 04:24:38.443438: Current learning rate: 0.00188 
+2024-09-10 04:28:44.872411: train_loss -0.8997 
+2024-09-10 04:28:44.872554: val_loss -0.6493 
+2024-09-10 04:28:44.872604: Pseudo dice [0.5352, 0.8552] 
+2024-09-10 04:28:44.872656: Epoch time: 246.43 s 
+2024-09-10 04:28:45.824441:  
+2024-09-10 04:28:45.824669: Epoch 845 
+2024-09-10 04:28:45.824749: Current learning rate: 0.00187 
+2024-09-10 04:32:52.200848: train_loss -0.9006 
+2024-09-10 04:32:52.200990: val_loss -0.6994 
+2024-09-10 04:32:52.201041: Pseudo dice [0.6462, 0.8551] 
+2024-09-10 04:32:52.201142: Epoch time: 246.38 s 
+2024-09-10 04:32:53.179396:  
+2024-09-10 04:32:53.179582: Epoch 846 
+2024-09-10 04:32:53.179679: Current learning rate: 0.00186 
+2024-09-10 04:36:59.762994: train_loss -0.9056 
+2024-09-10 04:36:59.763134: val_loss -0.6945 
+2024-09-10 04:36:59.763185: Pseudo dice [0.6146, 0.8672] 
+2024-09-10 04:36:59.763239: Epoch time: 246.59 s 
+2024-09-10 04:37:00.756385:  
+2024-09-10 04:37:00.756597: Epoch 847 
+2024-09-10 04:37:00.756675: Current learning rate: 0.00185 
+2024-09-10 04:41:07.362168: train_loss -0.9036 
+2024-09-10 04:41:07.362309: val_loss -0.6974 
+2024-09-10 04:41:07.362359: Pseudo dice [0.6244, 0.8662] 
+2024-09-10 04:41:07.362411: Epoch time: 246.61 s 
+2024-09-10 04:41:08.329456:  
+2024-09-10 04:41:08.329651: Epoch 848 
+2024-09-10 04:41:08.329757: Current learning rate: 0.00184 
+2024-09-10 04:45:14.700643: train_loss -0.902 
+2024-09-10 04:45:14.700781: val_loss -0.6841 
+2024-09-10 04:45:14.700830: Pseudo dice [0.5701, 0.8656] 
+2024-09-10 04:45:14.700883: Epoch time: 246.37 s 
+2024-09-10 04:45:15.662277:  
+2024-09-10 04:45:15.662454: Epoch 849 
+2024-09-10 04:45:15.662537: Current learning rate: 0.00182 
+2024-09-10 04:49:22.038772: train_loss -0.8984 
+2024-09-10 04:49:22.038925: val_loss -0.6906 
+2024-09-10 04:49:22.038976: Pseudo dice [0.6014, 0.8665] 
+2024-09-10 04:49:22.039028: Epoch time: 246.38 s 
+2024-09-10 04:49:25.980407:  
+2024-09-10 04:49:25.980554: Epoch 850 
+2024-09-10 04:49:25.980631: Current learning rate: 0.00181 
+2024-09-10 04:53:32.474285: train_loss -0.8985 
+2024-09-10 04:53:32.474422: val_loss -0.6932 
+2024-09-10 04:53:32.474472: Pseudo dice [0.5948, 0.8608] 
+2024-09-10 04:53:32.474523: Epoch time: 246.5 s 
+2024-09-10 04:53:33.434350:  
+2024-09-10 04:53:33.434508: Epoch 851 
+2024-09-10 04:53:33.434593: Current learning rate: 0.0018 
+2024-09-10 04:57:39.888393: train_loss -0.9 
+2024-09-10 04:57:39.888708: val_loss -0.6869 
+2024-09-10 04:57:39.888762: Pseudo dice [0.5956, 0.863] 
+2024-09-10 04:57:39.888819: Epoch time: 246.46 s 
+2024-09-10 04:57:40.837165:  
+2024-09-10 04:57:40.837352: Epoch 852 
+2024-09-10 04:57:40.837435: Current learning rate: 0.00179 
+2024-09-10 05:01:47.421094: train_loss -0.9014 
+2024-09-10 05:01:47.421266: val_loss -0.686 
+2024-09-10 05:01:47.421316: Pseudo dice [0.5772, 0.8743] 
+2024-09-10 05:01:47.421369: Epoch time: 246.59 s 
+2024-09-10 05:01:48.388522:  
+2024-09-10 05:01:48.388736: Epoch 853 
+2024-09-10 05:01:48.388817: Current learning rate: 0.00178 
+2024-09-10 05:05:55.131600: train_loss -0.9028 
+2024-09-10 05:05:55.131782: val_loss -0.6499 
+2024-09-10 05:05:55.131852: Pseudo dice [0.5256, 0.8542] 
+2024-09-10 05:05:55.131907: Epoch time: 246.75 s 
+2024-09-10 05:05:56.088007:  
+2024-09-10 05:05:56.088210: Epoch 854 
+2024-09-10 05:05:56.088291: Current learning rate: 0.00177 
+2024-09-10 05:10:02.689236: train_loss -0.9042 
+2024-09-10 05:10:02.689375: val_loss -0.6685 
+2024-09-10 05:10:02.689425: Pseudo dice [0.5666, 0.8687] 
+2024-09-10 05:10:02.689476: Epoch time: 246.6 s 
+2024-09-10 05:10:03.640984:  
+2024-09-10 05:10:03.641185: Epoch 855 
+2024-09-10 05:10:03.641268: Current learning rate: 0.00176 
+2024-09-10 05:14:10.032446: train_loss -0.9022 
+2024-09-10 05:14:10.032584: val_loss -0.6823 
+2024-09-10 05:14:10.032635: Pseudo dice [0.5992, 0.8717] 
+2024-09-10 05:14:10.032686: Epoch time: 246.39 s 
+2024-09-10 05:14:10.986633:  
+2024-09-10 05:14:10.986851: Epoch 856 
+2024-09-10 05:14:10.986933: Current learning rate: 0.00175 
+2024-09-10 05:18:17.595134: train_loss -0.8984 
+2024-09-10 05:18:17.595271: val_loss -0.6626 
+2024-09-10 05:18:17.595321: Pseudo dice [0.5786, 0.8509] 
+2024-09-10 05:18:17.595373: Epoch time: 246.61 s 
+2024-09-10 05:18:18.538778:  
+2024-09-10 05:18:18.538945: Epoch 857 
+2024-09-10 05:18:18.539029: Current learning rate: 0.00174 
+2024-09-10 05:22:25.024130: train_loss -0.9017 
+2024-09-10 05:22:25.024269: val_loss -0.67 
+2024-09-10 05:22:25.024320: Pseudo dice [0.582, 0.8664] 
+2024-09-10 05:22:25.024371: Epoch time: 246.49 s 
+2024-09-10 05:22:25.994080:  
+2024-09-10 05:22:25.994290: Epoch 858 
+2024-09-10 05:22:25.994370: Current learning rate: 0.00173 
+2024-09-10 05:26:33.124521: train_loss -0.8996 
+2024-09-10 05:26:33.124681: val_loss -0.6778 
+2024-09-10 05:26:33.124731: Pseudo dice [0.5765, 0.8677] 
+2024-09-10 05:26:33.124785: Epoch time: 247.13 s 
+2024-09-10 05:26:34.047759:  
+2024-09-10 05:26:34.047929: Epoch 859 
+2024-09-10 05:26:34.048023: Current learning rate: 0.00172 
+2024-09-10 05:30:40.594257: train_loss -0.9049 
+2024-09-10 05:30:40.594404: val_loss -0.6525 
+2024-09-10 05:30:40.594454: Pseudo dice [0.5409, 0.8633] 
+2024-09-10 05:30:40.594504: Epoch time: 246.55 s 
+2024-09-10 05:30:41.580157:  
+2024-09-10 05:30:41.580354: Epoch 860 
+2024-09-10 05:30:41.580460: Current learning rate: 0.0017 
+2024-09-10 05:34:48.041364: train_loss -0.9062 
+2024-09-10 05:34:48.041512: val_loss -0.6793 
+2024-09-10 05:34:48.041562: Pseudo dice [0.603, 0.8563] 
+2024-09-10 05:34:48.041613: Epoch time: 246.46 s 
+2024-09-10 05:34:48.995366:  
+2024-09-10 05:34:48.995568: Epoch 861 
+2024-09-10 05:34:48.995666: Current learning rate: 0.00169 
+2024-09-10 05:38:55.545736: train_loss -0.9053 
+2024-09-10 05:38:55.545877: val_loss -0.7005 
+2024-09-10 05:38:55.545927: Pseudo dice [0.6434, 0.8578] 
+2024-09-10 05:38:55.545978: Epoch time: 246.55 s 
+2024-09-10 05:38:56.501025:  
+2024-09-10 05:38:56.501275: Epoch 862 
+2024-09-10 05:38:56.501355: Current learning rate: 0.00168 
+2024-09-10 05:43:03.135929: train_loss -0.9033 
+2024-09-10 05:43:03.136065: val_loss -0.6839 
+2024-09-10 05:43:03.136115: Pseudo dice [0.6051, 0.8697] 
+2024-09-10 05:43:03.136166: Epoch time: 246.64 s 
+2024-09-10 05:43:04.081423:  
+2024-09-10 05:43:04.081598: Epoch 863 
+2024-09-10 05:43:04.081683: Current learning rate: 0.00167 
+2024-09-10 05:47:10.635010: train_loss -0.8989 
+2024-09-10 05:47:10.635148: val_loss -0.6901 
+2024-09-10 05:47:10.635200: Pseudo dice [0.5804, 0.8724] 
+2024-09-10 05:47:10.635255: Epoch time: 246.56 s 
+2024-09-10 05:47:11.600002:  
+2024-09-10 05:47:11.600281: Epoch 864 
+2024-09-10 05:47:11.600382: Current learning rate: 0.00166 
+2024-09-10 05:51:17.852618: train_loss -0.9038 
+2024-09-10 05:51:17.852757: val_loss -0.6891 
+2024-09-10 05:51:17.852807: Pseudo dice [0.5919, 0.8634] 
+2024-09-10 05:51:17.852858: Epoch time: 246.25 s 
+2024-09-10 05:51:18.821568:  
+2024-09-10 05:51:18.821716: Epoch 865 
+2024-09-10 05:51:18.821846: Current learning rate: 0.00165 
+2024-09-10 05:55:25.092046: train_loss -0.8953 
+2024-09-10 05:55:25.092185: val_loss -0.6971 
+2024-09-10 05:55:25.092235: Pseudo dice [0.6096, 0.8721] 
+2024-09-10 05:55:25.092286: Epoch time: 246.27 s 
+2024-09-10 05:55:26.041674:  
+2024-09-10 05:55:26.041874: Epoch 866 
+2024-09-10 05:55:26.041957: Current learning rate: 0.00164 
+2024-09-10 05:59:32.430719: train_loss -0.8962 
+2024-09-10 05:59:32.430858: val_loss -0.6612 
+2024-09-10 05:59:32.430908: Pseudo dice [0.6169, 0.8632] 
+2024-09-10 05:59:32.430961: Epoch time: 246.39 s 
+2024-09-10 05:59:33.394377:  
+2024-09-10 05:59:33.394542: Epoch 867 
+2024-09-10 05:59:33.394649: Current learning rate: 0.00163 
+2024-09-10 06:03:39.763571: train_loss -0.8967 
+2024-09-10 06:03:39.763712: val_loss -0.697 
+2024-09-10 06:03:39.763762: Pseudo dice [0.6488, 0.8561] 
+2024-09-10 06:03:39.763836: Epoch time: 246.37 s 
+2024-09-10 06:03:40.723150:  
+2024-09-10 06:03:40.723358: Epoch 868 
+2024-09-10 06:03:40.723442: Current learning rate: 0.00162 
+2024-09-10 06:07:47.080110: train_loss -0.8938 
+2024-09-10 06:07:47.080256: val_loss -0.7046 
+2024-09-10 06:07:47.080307: Pseudo dice [0.6392, 0.864] 
+2024-09-10 06:07:47.080360: Epoch time: 246.36 s 
+2024-09-10 06:07:48.040227:  
+2024-09-10 06:07:48.040449: Epoch 869 
+2024-09-10 06:07:48.040529: Current learning rate: 0.00161 
+2024-09-10 06:11:54.372394: train_loss -0.8926 
+2024-09-10 06:11:54.372550: val_loss -0.6681 
+2024-09-10 06:11:54.372601: Pseudo dice [0.6075, 0.8531] 
+2024-09-10 06:11:54.372652: Epoch time: 246.33 s 
+2024-09-10 06:11:55.314609:  
+2024-09-10 06:11:55.314826: Epoch 870 
+2024-09-10 06:11:55.314907: Current learning rate: 0.00159 
+2024-09-10 06:16:01.865824: train_loss -0.8913 
+2024-09-10 06:16:01.865971: val_loss -0.6755 
+2024-09-10 06:16:01.866021: Pseudo dice [0.5986, 0.8649] 
+2024-09-10 06:16:01.866071: Epoch time: 246.55 s 
+2024-09-10 06:16:02.840259:  
+2024-09-10 06:16:02.840429: Epoch 871 
+2024-09-10 06:16:02.840517: Current learning rate: 0.00158 
+2024-09-10 06:20:09.617100: train_loss -0.8994 
+2024-09-10 06:20:09.617247: val_loss -0.689 
+2024-09-10 06:20:09.617297: Pseudo dice [0.5794, 0.8701] 
+2024-09-10 06:20:09.617348: Epoch time: 246.78 s 
+2024-09-10 06:20:10.584330:  
+2024-09-10 06:20:10.584566: Epoch 872 
+2024-09-10 06:20:10.584652: Current learning rate: 0.00157 
+2024-09-10 06:24:17.120837: train_loss -0.9058 
+2024-09-10 06:24:17.121016: val_loss -0.6915 
+2024-09-10 06:24:17.121068: Pseudo dice [0.6027, 0.8561] 
+2024-09-10 06:24:17.121118: Epoch time: 246.54 s 
+2024-09-10 06:24:18.079316:  
+2024-09-10 06:24:18.079552: Epoch 873 
+2024-09-10 06:24:18.079636: Current learning rate: 0.00156 
+2024-09-10 06:28:24.657307: train_loss -0.9036 
+2024-09-10 06:28:24.657449: val_loss -0.6965 
+2024-09-10 06:28:24.657499: Pseudo dice [0.6098, 0.8623] 
+2024-09-10 06:28:24.657551: Epoch time: 246.58 s 
+2024-09-10 06:28:25.625012:  
+2024-09-10 06:28:25.625241: Epoch 874 
+2024-09-10 06:28:25.625319: Current learning rate: 0.00155 
+2024-09-10 06:32:32.061749: train_loss -0.9052 
+2024-09-10 06:32:32.061930: val_loss -0.6718 
+2024-09-10 06:32:32.062076: Pseudo dice [0.5741, 0.8624] 
+2024-09-10 06:32:32.062147: Epoch time: 246.44 s 
+2024-09-10 06:32:33.039698:  
+2024-09-10 06:32:33.039879: Epoch 875 
+2024-09-10 06:32:33.039961: Current learning rate: 0.00154 
+2024-09-10 06:36:39.366090: train_loss -0.9025 
+2024-09-10 06:36:39.366250: val_loss -0.7034 
+2024-09-10 06:36:39.366300: Pseudo dice [0.6427, 0.8583] 
+2024-09-10 06:36:39.366350: Epoch time: 246.33 s 
+2024-09-10 06:36:40.315083:  
+2024-09-10 06:36:40.315297: Epoch 876 
+2024-09-10 06:36:40.315396: Current learning rate: 0.00153 
+2024-09-10 06:40:46.724719: train_loss -0.9037 
+2024-09-10 06:40:46.724860: val_loss -0.6788 
+2024-09-10 06:40:46.724908: Pseudo dice [0.6126, 0.8607] 
+2024-09-10 06:40:46.724959: Epoch time: 246.41 s 
+2024-09-10 06:40:47.681628:  
+2024-09-10 06:40:47.681796: Epoch 877 
+2024-09-10 06:40:47.681878: Current learning rate: 0.00152 
+2024-09-10 06:44:54.028366: train_loss -0.9053 
+2024-09-10 06:44:54.028548: val_loss -0.6983 
+2024-09-10 06:44:54.028598: Pseudo dice [0.6322, 0.8628] 
+2024-09-10 06:44:54.028654: Epoch time: 246.35 s 
+2024-09-10 06:44:54.968790:  
+2024-09-10 06:44:54.968974: Epoch 878 
+2024-09-10 06:44:54.969058: Current learning rate: 0.00151 
+2024-09-10 06:49:01.401597: train_loss -0.9036 
+2024-09-10 06:49:01.401736: val_loss -0.6968 
+2024-09-10 06:49:01.401785: Pseudo dice [0.6378, 0.8654] 
+2024-09-10 06:49:01.401835: Epoch time: 246.43 s 
+2024-09-10 06:49:02.378091:  
+2024-09-10 06:49:02.378249: Epoch 879 
+2024-09-10 06:49:02.378331: Current learning rate: 0.00149 
+2024-09-10 06:53:08.843241: train_loss -0.9042 
+2024-09-10 06:53:08.843384: val_loss -0.6809 
+2024-09-10 06:53:08.843435: Pseudo dice [0.5911, 0.8655] 
+2024-09-10 06:53:08.843487: Epoch time: 246.47 s 
+2024-09-10 06:53:09.805822:  
+2024-09-10 06:53:09.806061: Epoch 880 
+2024-09-10 06:53:09.806161: Current learning rate: 0.00148 
+2024-09-10 06:57:16.078683: train_loss -0.9029 
+2024-09-10 06:57:16.078819: val_loss -0.6761 
+2024-09-10 06:57:16.078869: Pseudo dice [0.6, 0.8599] 
+2024-09-10 06:57:16.078918: Epoch time: 246.28 s 
+2024-09-10 06:57:17.042611:  
+2024-09-10 06:57:17.042840: Epoch 881 
+2024-09-10 06:57:17.042925: Current learning rate: 0.00147 
+2024-09-10 07:01:23.248336: train_loss -0.9012 
+2024-09-10 07:01:23.248503: val_loss -0.6834 
+2024-09-10 07:01:23.248554: Pseudo dice [0.6283, 0.8575] 
+2024-09-10 07:01:23.248607: Epoch time: 246.21 s 
+2024-09-10 07:01:24.208521:  
+2024-09-10 07:01:24.208718: Epoch 882 
+2024-09-10 07:01:24.208828: Current learning rate: 0.00146 
+2024-09-10 07:05:30.607345: train_loss -0.9051 
+2024-09-10 07:05:30.607481: val_loss -0.6943 
+2024-09-10 07:05:30.607530: Pseudo dice [0.6383, 0.864] 
+2024-09-10 07:05:30.607580: Epoch time: 246.4 s 
+2024-09-10 07:05:31.552250:  
+2024-09-10 07:05:31.552437: Epoch 883 
+2024-09-10 07:05:31.552516: Current learning rate: 0.00145 
+2024-09-10 07:09:38.832309: train_loss -0.9035 
+2024-09-10 07:09:38.832453: val_loss -0.6902 
+2024-09-10 07:09:38.832503: Pseudo dice [0.617, 0.8638] 
+2024-09-10 07:09:38.832608: Epoch time: 247.28 s 
+2024-09-10 07:09:39.803847:  
+2024-09-10 07:09:39.804088: Epoch 884 
+2024-09-10 07:09:39.804185: Current learning rate: 0.00144 
+2024-09-10 07:13:46.233895: train_loss -0.9083 
+2024-09-10 07:13:46.234035: val_loss -0.6719 
+2024-09-10 07:13:46.234084: Pseudo dice [0.6138, 0.8572] 
+2024-09-10 07:13:46.234135: Epoch time: 246.43 s 
+2024-09-10 07:13:47.190876:  
+2024-09-10 07:13:47.191097: Epoch 885 
+2024-09-10 07:13:47.191205: Current learning rate: 0.00143 
+2024-09-10 07:17:53.583526: train_loss -0.9065 
+2024-09-10 07:17:53.583676: val_loss -0.6974 
+2024-09-10 07:17:53.583726: Pseudo dice [0.6252, 0.8597] 
+2024-09-10 07:17:53.583780: Epoch time: 246.39 s 
+2024-09-10 07:17:54.552326:  
+2024-09-10 07:17:54.552562: Epoch 886 
+2024-09-10 07:17:54.552661: Current learning rate: 0.00142 
+2024-09-10 07:22:01.255133: train_loss -0.908 
+2024-09-10 07:22:01.255276: val_loss -0.7072 
+2024-09-10 07:22:01.255330: Pseudo dice [0.6322, 0.8704] 
+2024-09-10 07:22:01.255380: Epoch time: 246.7 s 
+2024-09-10 07:22:02.200637:  
+2024-09-10 07:22:02.200911: Epoch 887 
+2024-09-10 07:22:02.200989: Current learning rate: 0.00141 
+2024-09-10 07:26:08.866811: train_loss -0.9041 
+2024-09-10 07:26:08.866976: val_loss -0.6341 
+2024-09-10 07:26:08.867026: Pseudo dice [0.5079, 0.8643] 
+2024-09-10 07:26:08.867078: Epoch time: 246.67 s 
+2024-09-10 07:26:09.829013:  
+2024-09-10 07:26:09.829280: Epoch 888 
+2024-09-10 07:26:09.829360: Current learning rate: 0.00139 
+2024-09-10 07:30:16.409561: train_loss -0.9038 
+2024-09-10 07:30:16.409700: val_loss -0.6736 
+2024-09-10 07:30:16.409751: Pseudo dice [0.5911, 0.8646] 
+2024-09-10 07:30:16.409803: Epoch time: 246.58 s 
+2024-09-10 07:30:17.392833:  
+2024-09-10 07:30:17.393039: Epoch 889 
+2024-09-10 07:30:17.393120: Current learning rate: 0.00138 
+2024-09-10 07:34:23.960490: train_loss -0.8994 
+2024-09-10 07:34:23.960626: val_loss -0.6999 
+2024-09-10 07:34:23.960688: Pseudo dice [0.6491, 0.8575] 
+2024-09-10 07:34:23.960740: Epoch time: 246.57 s 
+2024-09-10 07:34:24.910394:  
+2024-09-10 07:34:24.910638: Epoch 890 
+2024-09-10 07:34:24.910717: Current learning rate: 0.00137 
+2024-09-10 07:38:31.399407: train_loss -0.9026 
+2024-09-10 07:38:31.399551: val_loss -0.6977 
+2024-09-10 07:38:31.399602: Pseudo dice [0.6292, 0.8642] 
+2024-09-10 07:38:31.399653: Epoch time: 246.49 s 
+2024-09-10 07:38:32.360895:  
+2024-09-10 07:38:32.361047: Epoch 891 
+2024-09-10 07:38:32.361125: Current learning rate: 0.00136 
+2024-09-10 07:42:38.686136: train_loss -0.9057 
+2024-09-10 07:42:38.686282: val_loss -0.6985 
+2024-09-10 07:42:38.686333: Pseudo dice [0.6032, 0.865] 
+2024-09-10 07:42:38.686384: Epoch time: 246.33 s 
+2024-09-10 07:42:39.635741:  
+2024-09-10 07:42:39.635959: Epoch 892 
+2024-09-10 07:42:39.636041: Current learning rate: 0.00135 
+2024-09-10 07:46:46.043849: train_loss -0.9092 
+2024-09-10 07:46:46.043994: val_loss -0.6955 
+2024-09-10 07:46:46.044048: Pseudo dice [0.6095, 0.8696] 
+2024-09-10 07:46:46.044100: Epoch time: 246.41 s 
+2024-09-10 07:46:47.026701:  
+2024-09-10 07:46:47.026913: Epoch 893 
+2024-09-10 07:46:47.027042: Current learning rate: 0.00134 
+2024-09-10 07:50:53.587212: train_loss -0.9057 
+2024-09-10 07:50:53.587353: val_loss -0.6867 
+2024-09-10 07:50:53.587402: Pseudo dice [0.6291, 0.8573] 
+2024-09-10 07:50:53.587452: Epoch time: 246.56 s 
+2024-09-10 07:50:54.555855:  
+2024-09-10 07:50:54.556071: Epoch 894 
+2024-09-10 07:50:54.556158: Current learning rate: 0.00133 
+2024-09-10 07:55:01.263341: train_loss -0.9052 
+2024-09-10 07:55:01.263545: val_loss -0.6812 
+2024-09-10 07:55:01.263642: Pseudo dice [0.6016, 0.8579] 
+2024-09-10 07:55:01.263732: Epoch time: 246.71 s 
+2024-09-10 07:55:02.238075:  
+2024-09-10 07:55:02.238305: Epoch 895 
+2024-09-10 07:55:02.238389: Current learning rate: 0.00132 
+2024-09-10 07:59:08.637242: train_loss -0.9068 
+2024-09-10 07:59:08.637405: val_loss -0.6974 
+2024-09-10 07:59:08.637458: Pseudo dice [0.6371, 0.8521] 
+2024-09-10 07:59:08.637509: Epoch time: 246.4 s 
+2024-09-10 07:59:09.611287:  
+2024-09-10 07:59:09.611488: Epoch 896 
+2024-09-10 07:59:09.611596: Current learning rate: 0.0013 
+2024-09-10 08:03:16.100373: train_loss -0.9056 
+2024-09-10 08:03:16.100528: val_loss -0.6716 
+2024-09-10 08:03:16.100578: Pseudo dice [0.6033, 0.8518] 
+2024-09-10 08:03:16.100631: Epoch time: 246.49 s 
+2024-09-10 08:03:17.050898:  
+2024-09-10 08:03:17.051090: Epoch 897 
+2024-09-10 08:03:17.051172: Current learning rate: 0.00129 
+2024-09-10 08:07:23.538390: train_loss -0.9086 
+2024-09-10 08:07:23.538610: val_loss -0.6831 
+2024-09-10 08:07:23.538703: Pseudo dice [0.5873, 0.8609] 
+2024-09-10 08:07:23.538794: Epoch time: 246.49 s 
+2024-09-10 08:07:24.509759:  
+2024-09-10 08:07:24.509995: Epoch 898 
+2024-09-10 08:07:24.510095: Current learning rate: 0.00128 
+2024-09-10 08:11:31.022558: train_loss -0.9089 
+2024-09-10 08:11:31.022699: val_loss -0.6929 
+2024-09-10 08:11:31.022751: Pseudo dice [0.6256, 0.8664] 
+2024-09-10 08:11:31.022803: Epoch time: 246.51 s 
+2024-09-10 08:11:31.973221:  
+2024-09-10 08:11:31.973404: Epoch 899 
+2024-09-10 08:11:31.973488: Current learning rate: 0.00127 
+2024-09-10 08:15:38.589587: train_loss -0.9043 
+2024-09-10 08:15:38.589763: val_loss -0.6916 
+2024-09-10 08:15:38.589838: Pseudo dice [0.6215, 0.8635] 
+2024-09-10 08:15:38.589890: Epoch time: 246.62 s 
+2024-09-10 08:15:42.530288:  
+2024-09-10 08:15:42.530449: Epoch 900 
+2024-09-10 08:15:42.530531: Current learning rate: 0.00126 
+2024-09-10 08:19:49.217950: train_loss -0.9103 
+2024-09-10 08:19:49.218088: val_loss -0.6848 
+2024-09-10 08:19:49.218138: Pseudo dice [0.6434, 0.8604] 
+2024-09-10 08:19:49.218232: Epoch time: 246.69 s 
+2024-09-10 08:19:50.169819:  
+2024-09-10 08:19:50.170025: Epoch 901 
+2024-09-10 08:19:50.170150: Current learning rate: 0.00125 
+2024-09-10 08:23:56.747997: train_loss -0.9092 
+2024-09-10 08:23:56.748151: val_loss -0.7117 
+2024-09-10 08:23:56.748200: Pseudo dice [0.6617, 0.8606] 
+2024-09-10 08:23:56.748253: Epoch time: 246.58 s 
+2024-09-10 08:23:56.748294: Yayy! New best EMA pseudo Dice: 0.7406 
+2024-09-10 08:24:00.701342:  
+2024-09-10 08:24:00.701517: Epoch 902 
+2024-09-10 08:24:00.701594: Current learning rate: 0.00124 
+2024-09-10 08:28:07.621416: train_loss -0.9031 
+2024-09-10 08:28:07.621554: val_loss -0.6724 
+2024-09-10 08:28:07.621605: Pseudo dice [0.5857, 0.8715] 
+2024-09-10 08:28:07.621657: Epoch time: 246.92 s 
+2024-09-10 08:28:08.579827:  
+2024-09-10 08:28:08.580001: Epoch 903 
+2024-09-10 08:28:08.580086: Current learning rate: 0.00122 
+2024-09-10 08:32:14.973273: train_loss -0.9083 
+2024-09-10 08:32:14.973477: val_loss -0.6983 
+2024-09-10 08:32:14.973570: Pseudo dice [0.6133, 0.8679] 
+2024-09-10 08:32:14.973706: Epoch time: 246.4 s 
+2024-09-10 08:32:15.960003:  
+2024-09-10 08:32:15.960237: Epoch 904 
+2024-09-10 08:32:15.960324: Current learning rate: 0.00121 
+2024-09-10 08:36:22.386135: train_loss -0.9067 
+2024-09-10 08:36:22.386277: val_loss -0.6786 
+2024-09-10 08:36:22.386328: Pseudo dice [0.5877, 0.8613] 
+2024-09-10 08:36:22.386379: Epoch time: 246.43 s 
+2024-09-10 08:36:23.337275:  
+2024-09-10 08:36:23.337443: Epoch 905 
+2024-09-10 08:36:23.337528: Current learning rate: 0.0012 
+2024-09-10 08:40:29.706334: train_loss -0.907 
+2024-09-10 08:40:29.706474: val_loss -0.688 
+2024-09-10 08:40:29.706523: Pseudo dice [0.6231, 0.859] 
+2024-09-10 08:40:29.706573: Epoch time: 246.37 s 
+2024-09-10 08:40:30.654749:  
+2024-09-10 08:40:30.654905: Epoch 906 
+2024-09-10 08:40:30.655021: Current learning rate: 0.00119 
+2024-09-10 08:44:37.009568: train_loss -0.9068 
+2024-09-10 08:44:37.009726: val_loss -0.6962 
+2024-09-10 08:44:37.009777: Pseudo dice [0.6191, 0.8643] 
+2024-09-10 08:44:37.009829: Epoch time: 246.36 s 
+2024-09-10 08:44:37.991114:  
+2024-09-10 08:44:37.991321: Epoch 907 
+2024-09-10 08:44:37.991424: Current learning rate: 0.00118 
+2024-09-10 08:48:45.215321: train_loss -0.9075 
+2024-09-10 08:48:45.215463: val_loss -0.6903 
+2024-09-10 08:48:45.215513: Pseudo dice [0.6375, 0.8579] 
+2024-09-10 08:48:45.215564: Epoch time: 247.23 s 
+2024-09-10 08:48:46.151618:  
+2024-09-10 08:48:46.151856: Epoch 908 
+2024-09-10 08:48:46.151958: Current learning rate: 0.00117 
+2024-09-10 08:52:52.732014: train_loss -0.908 
+2024-09-10 08:52:52.732175: val_loss -0.6888 
+2024-09-10 08:52:52.732225: Pseudo dice [0.6162, 0.8676] 
+2024-09-10 08:52:52.732276: Epoch time: 246.58 s 
+2024-09-10 08:52:53.688268:  
+2024-09-10 08:52:53.688504: Epoch 909 
+2024-09-10 08:52:53.688587: Current learning rate: 0.00116 
+2024-09-10 08:57:00.184285: train_loss -0.9117 
+2024-09-10 08:57:00.184451: val_loss -0.6963 
+2024-09-10 08:57:00.184503: Pseudo dice [0.6343, 0.8688] 
+2024-09-10 08:57:00.184555: Epoch time: 246.5 s 
+2024-09-10 08:57:00.184595: Yayy! New best EMA pseudo Dice: 0.741 
+2024-09-10 08:57:04.098818:  
+2024-09-10 08:57:04.099030: Epoch 910 
+2024-09-10 08:57:04.099111: Current learning rate: 0.00115 
+2024-09-10 09:01:11.129158: train_loss -0.9088 
+2024-09-10 09:01:11.129299: val_loss -0.6723 
+2024-09-10 09:01:11.129349: Pseudo dice [0.5845, 0.8613] 
+2024-09-10 09:01:11.129404: Epoch time: 247.03 s 
+2024-09-10 09:01:12.078258:  
+2024-09-10 09:01:12.078479: Epoch 911 
+2024-09-10 09:01:12.078558: Current learning rate: 0.00113 
+2024-09-10 09:05:18.686906: train_loss -0.9071 
+2024-09-10 09:05:18.687049: val_loss -0.688 
+2024-09-10 09:05:18.687102: Pseudo dice [0.6384, 0.8519] 
+2024-09-10 09:05:18.687155: Epoch time: 246.61 s 
+2024-09-10 09:05:19.634725:  
+2024-09-10 09:05:19.634931: Epoch 912 
+2024-09-10 09:05:19.635016: Current learning rate: 0.00112 
+2024-09-10 09:09:26.181747: train_loss -0.9022 
+2024-09-10 09:09:26.181887: val_loss -0.7034 
+2024-09-10 09:09:26.181938: Pseudo dice [0.6341, 0.8586] 
+2024-09-10 09:09:26.181990: Epoch time: 246.55 s 
+2024-09-10 09:09:27.152889:  
+2024-09-10 09:09:27.153214: Epoch 913 
+2024-09-10 09:09:27.153341: Current learning rate: 0.00111 
+2024-09-10 09:13:33.638732: train_loss -0.9096 
+2024-09-10 09:13:33.638909: val_loss -0.6862 
+2024-09-10 09:13:33.638963: Pseudo dice [0.5929, 0.8657] 
+2024-09-10 09:13:33.639014: Epoch time: 246.49 s 
+2024-09-10 09:13:34.586887:  
+2024-09-10 09:13:34.587077: Epoch 914 
+2024-09-10 09:13:34.587159: Current learning rate: 0.0011 
+2024-09-10 09:17:41.032458: train_loss -0.9098 
+2024-09-10 09:17:41.032594: val_loss -0.6926 
+2024-09-10 09:17:41.032643: Pseudo dice [0.5961, 0.8567] 
+2024-09-10 09:17:41.032693: Epoch time: 246.45 s 
+2024-09-10 09:17:41.983167:  
+2024-09-10 09:17:41.983406: Epoch 915 
+2024-09-10 09:17:41.983506: Current learning rate: 0.00109 
+2024-09-10 09:21:48.461036: train_loss -0.9053 
+2024-09-10 09:21:48.461222: val_loss -0.6974 
+2024-09-10 09:21:48.461298: Pseudo dice [0.6138, 0.8581] 
+2024-09-10 09:21:48.461350: Epoch time: 246.48 s 
+2024-09-10 09:21:49.421708:  
+2024-09-10 09:21:49.421953: Epoch 916 
+2024-09-10 09:21:49.422039: Current learning rate: 0.00108 
+2024-09-10 09:25:55.901401: train_loss -0.9067 
+2024-09-10 09:25:55.901543: val_loss -0.6966 
+2024-09-10 09:25:55.901594: Pseudo dice [0.64, 0.8587] 
+2024-09-10 09:25:55.901645: Epoch time: 246.48 s 
+2024-09-10 09:25:56.880273:  
+2024-09-10 09:25:56.880479: Epoch 917 
+2024-09-10 09:25:56.880567: Current learning rate: 0.00106 
+2024-09-10 09:30:03.365684: train_loss -0.9114 
+2024-09-10 09:30:03.365822: val_loss -0.7122 
+2024-09-10 09:30:03.365872: Pseudo dice [0.6456, 0.8619] 
+2024-09-10 09:30:03.365924: Epoch time: 246.49 s 
+2024-09-10 09:30:04.324443:  
+2024-09-10 09:30:04.324645: Epoch 918 
+2024-09-10 09:30:04.324723: Current learning rate: 0.00105 
+2024-09-10 09:34:10.711955: train_loss -0.9088 
+2024-09-10 09:34:10.712090: val_loss -0.6753 
+2024-09-10 09:34:10.712140: Pseudo dice [0.6132, 0.8575] 
+2024-09-10 09:34:10.712192: Epoch time: 246.39 s 
+2024-09-10 09:34:11.670750:  
+2024-09-10 09:34:11.671014: Epoch 919 
+2024-09-10 09:34:11.671100: Current learning rate: 0.00104 
+2024-09-10 09:38:18.048431: train_loss -0.9102 
+2024-09-10 09:38:18.048571: val_loss -0.7048 
+2024-09-10 09:38:18.048621: Pseudo dice [0.6595, 0.8562] 
+2024-09-10 09:38:18.048673: Epoch time: 246.38 s 
+2024-09-10 09:38:18.048715: Yayy! New best EMA pseudo Dice: 0.7417 
+2024-09-10 09:38:21.984887:  
+2024-09-10 09:38:21.985054: Epoch 920 
+2024-09-10 09:38:21.985135: Current learning rate: 0.00103 
+2024-09-10 09:42:28.670133: train_loss -0.9098 
+2024-09-10 09:42:28.670271: val_loss -0.6998 
+2024-09-10 09:42:28.670321: Pseudo dice [0.65, 0.8545] 
+2024-09-10 09:42:28.670372: Epoch time: 246.69 s 
+2024-09-10 09:42:28.670412: Yayy! New best EMA pseudo Dice: 0.7428 
+2024-09-10 09:42:32.563526:  
+2024-09-10 09:42:32.563738: Epoch 921 
+2024-09-10 09:42:32.563830: Current learning rate: 0.00102 
+2024-09-10 09:46:39.199553: train_loss -0.9099 
+2024-09-10 09:46:39.199744: val_loss -0.6927 
+2024-09-10 09:46:39.199820: Pseudo dice [0.6551, 0.855] 
+2024-09-10 09:46:39.199895: Epoch time: 246.64 s 
+2024-09-10 09:46:39.199936: Yayy! New best EMA pseudo Dice: 0.744 
+2024-09-10 09:46:43.119920:  
+2024-09-10 09:46:43.120086: Epoch 922 
+2024-09-10 09:46:43.120181: Current learning rate: 0.00101 
+2024-09-10 09:50:49.876645: train_loss -0.9097 
+2024-09-10 09:50:49.876799: val_loss -0.7014 
+2024-09-10 09:50:49.876856: Pseudo dice [0.6467, 0.8486] 
+2024-09-10 09:50:49.876912: Epoch time: 246.76 s 
+2024-09-10 09:50:49.876958: Yayy! New best EMA pseudo Dice: 0.7444 
+2024-09-10 09:50:53.782548:  
+2024-09-10 09:50:53.782791: Epoch 923 
+2024-09-10 09:50:53.782877: Current learning rate: 0.001 
+2024-09-10 09:55:00.447293: train_loss -0.9101 
+2024-09-10 09:55:00.447523: val_loss -0.6812 
+2024-09-10 09:55:00.447581: Pseudo dice [0.6138, 0.8579] 
+2024-09-10 09:55:00.447638: Epoch time: 246.67 s 
+2024-09-10 09:55:01.406446:  
+2024-09-10 09:55:01.406631: Epoch 924 
+2024-09-10 09:55:01.406718: Current learning rate: 0.00098 
+2024-09-10 09:59:07.882065: train_loss -0.9094 
+2024-09-10 09:59:07.882225: val_loss -0.6762 
+2024-09-10 09:59:07.882282: Pseudo dice [0.6185, 0.8502] 
+2024-09-10 09:59:07.882339: Epoch time: 246.48 s 
+2024-09-10 09:59:08.851904:  
+2024-09-10 09:59:08.852082: Epoch 925 
+2024-09-10 09:59:08.852170: Current learning rate: 0.00097 
+2024-09-10 10:03:15.293046: train_loss -0.914 
+2024-09-10 10:03:15.293202: val_loss -0.6966 
+2024-09-10 10:03:15.293362: Pseudo dice [0.6204, 0.8638] 
+2024-09-10 10:03:15.293508: Epoch time: 246.44 s 
+2024-09-10 10:03:16.253685:  
+2024-09-10 10:03:16.253862: Epoch 926 
+2024-09-10 10:03:16.253952: Current learning rate: 0.00096 
+2024-09-10 10:07:22.741294: train_loss -0.9073 
+2024-09-10 10:07:22.741511: val_loss -0.6896 
+2024-09-10 10:07:22.741614: Pseudo dice [0.6295, 0.8641] 
+2024-09-10 10:07:22.741714: Epoch time: 246.49 s 
+2024-09-10 10:07:23.702014:  
+2024-09-10 10:07:23.702194: Epoch 927 
+2024-09-10 10:07:23.702288: Current learning rate: 0.00095 
+2024-09-10 10:11:30.081024: train_loss -0.9146 
+2024-09-10 10:11:30.081165: val_loss -0.6921 
+2024-09-10 10:11:30.081215: Pseudo dice [0.6101, 0.8636] 
+2024-09-10 10:11:30.081265: Epoch time: 246.38 s 
+2024-09-10 10:11:31.050964:  
+2024-09-10 10:11:31.051119: Epoch 928 
+2024-09-10 10:11:31.051245: Current learning rate: 0.00094 
+2024-09-10 10:15:39.970250: train_loss -0.9077 
+2024-09-10 10:15:39.970674: val_loss -0.6876 
+2024-09-10 10:15:39.970733: Pseudo dice [0.6264, 0.8637] 
+2024-09-10 10:15:39.970791: Epoch time: 248.92 s 
+2024-09-10 10:15:40.936017:  
+2024-09-10 10:15:40.936217: Epoch 929 
+2024-09-10 10:15:40.936301: Current learning rate: 0.00092 
+2024-09-10 10:19:54.707409: train_loss -0.9145 
+2024-09-10 10:19:54.707556: val_loss -0.6855 
+2024-09-10 10:19:54.707613: Pseudo dice [0.6335, 0.8596] 
+2024-09-10 10:19:54.707673: Epoch time: 253.77 s 
+2024-09-10 10:19:55.666130:  
+2024-09-10 10:19:55.666350: Epoch 930 
+2024-09-10 10:19:55.666435: Current learning rate: 0.00091 
+2024-09-10 10:24:06.724312: train_loss -0.9148 
+2024-09-10 10:24:06.724457: val_loss -0.6918 
+2024-09-10 10:24:06.724515: Pseudo dice [0.6251, 0.8499] 
+2024-09-10 10:24:06.724574: Epoch time: 251.06 s 
+2024-09-10 10:24:07.672170:  
+2024-09-10 10:24:07.672425: Epoch 931 
+2024-09-10 10:24:07.672600: Current learning rate: 0.0009 
+2024-09-10 10:28:19.786497: train_loss -0.9148 
+2024-09-10 10:28:19.786685: val_loss -0.6846 
+2024-09-10 10:28:19.786769: Pseudo dice [0.6288, 0.861] 
+2024-09-10 10:28:19.786862: Epoch time: 252.12 s 
+2024-09-10 10:28:20.906944:  
+2024-09-10 10:28:20.907219: Epoch 932 
+2024-09-10 10:28:20.907341: Current learning rate: 0.00089 
+2024-09-10 10:32:27.779656: train_loss -0.9088 
+2024-09-10 10:32:27.779819: val_loss -0.6803 
+2024-09-10 10:32:27.779876: Pseudo dice [0.6066, 0.8574] 
+2024-09-10 10:32:27.779933: Epoch time: 246.87 s 
+2024-09-10 10:32:28.813449:  
+2024-09-10 10:32:28.813713: Epoch 933 
+2024-09-10 10:32:28.813813: Current learning rate: 0.00088 
+2024-09-10 10:36:35.531525: train_loss -0.9109 
+2024-09-10 10:36:35.531682: val_loss -0.6846 
+2024-09-10 10:36:35.531794: Pseudo dice [0.624, 0.8706] 
+2024-09-10 10:36:35.531864: Epoch time: 246.72 s 
+2024-09-10 10:36:36.644628:  
+2024-09-10 10:36:36.644870: Epoch 934 
+2024-09-10 10:36:36.644973: Current learning rate: 0.00087 
+2024-09-10 10:40:43.176934: train_loss -0.9123 
+2024-09-10 10:40:43.177095: val_loss -0.7005 
+2024-09-10 10:40:43.177151: Pseudo dice [0.6502, 0.8575] 
+2024-09-10 10:40:43.177207: Epoch time: 246.53 s 
+2024-09-10 10:40:44.262291:  
+2024-09-10 10:40:44.262564: Epoch 935 
+2024-09-10 10:40:44.262655: Current learning rate: 0.00085 
+2024-09-10 10:44:50.841022: train_loss -0.9067 
+2024-09-10 10:44:50.841206: val_loss -0.6984 
+2024-09-10 10:44:50.841264: Pseudo dice [0.6184, 0.8576] 
+2024-09-10 10:44:50.841322: Epoch time: 246.58 s 
+2024-09-10 10:44:51.811987:  
+2024-09-10 10:44:51.812198: Epoch 936 
+2024-09-10 10:44:51.812282: Current learning rate: 0.00084 
+2024-09-10 10:48:58.357506: train_loss -0.9102 
+2024-09-10 10:48:58.357674: val_loss -0.7019 
+2024-09-10 10:48:58.357732: Pseudo dice [0.6186, 0.87] 
+2024-09-10 10:48:58.357787: Epoch time: 246.55 s 
+2024-09-10 10:48:59.313435:  
+2024-09-10 10:48:59.313609: Epoch 937 
+2024-09-10 10:48:59.313696: Current learning rate: 0.00083 
+2024-09-10 10:53:06.055254: train_loss -0.9106 
+2024-09-10 10:53:06.055639: val_loss -0.6961 
+2024-09-10 10:53:06.055698: Pseudo dice [0.6171, 0.8637] 
+2024-09-10 10:53:06.055754: Epoch time: 246.74 s 
+2024-09-10 10:53:07.115825:  
+2024-09-10 10:53:07.116004: Epoch 938 
+2024-09-10 10:53:07.116093: Current learning rate: 0.00082 
+2024-09-10 10:57:17.872190: train_loss -0.9115 
+2024-09-10 10:57:17.872333: val_loss -0.6738 
+2024-09-10 10:57:17.872390: Pseudo dice [0.5842, 0.8596] 
+2024-09-10 10:57:17.872448: Epoch time: 250.76 s 
+2024-09-10 10:57:19.085799:  
+2024-09-10 10:57:19.086052: Epoch 939 
+2024-09-10 10:57:19.086156: Current learning rate: 0.00081 
+2024-09-10 11:01:28.131256: train_loss -0.9144 
+2024-09-10 11:01:28.131407: val_loss -0.7081 
+2024-09-10 11:01:28.131528: Pseudo dice [0.642, 0.8575] 
+2024-09-10 11:01:28.131587: Epoch time: 249.05 s 
+2024-09-10 11:01:29.115106:  
+2024-09-10 11:01:29.115308: Epoch 940 
+2024-09-10 11:01:29.115394: Current learning rate: 0.00079 
+2024-09-10 11:05:36.898847: train_loss -0.9131 
+2024-09-10 11:05:36.899258: val_loss -0.6834 
+2024-09-10 11:05:36.899318: Pseudo dice [0.6087, 0.8649] 
+2024-09-10 11:05:36.899376: Epoch time: 247.79 s 
+2024-09-10 11:05:38.117365:  
+2024-09-10 11:05:38.117573: Epoch 941 
+2024-09-10 11:05:38.117659: Current learning rate: 0.00078 
+2024-09-10 11:09:46.566310: train_loss -0.9102 
+2024-09-10 11:09:46.566460: val_loss -0.6861 
+2024-09-10 11:09:46.566517: Pseudo dice [0.5994, 0.8707] 
+2024-09-10 11:09:46.566572: Epoch time: 248.45 s 
+2024-09-10 11:09:47.678436:  
+2024-09-10 11:09:47.678698: Epoch 942 
+2024-09-10 11:09:47.678794: Current learning rate: 0.00077 
+2024-09-10 11:13:54.357581: train_loss -0.9122 
+2024-09-10 11:13:54.357756: val_loss -0.6915 
+2024-09-10 11:13:54.357814: Pseudo dice [0.6101, 0.8588] 
+2024-09-10 11:13:54.357870: Epoch time: 246.68 s 
+2024-09-10 11:13:55.363607:  
+2024-09-10 11:13:55.363835: Epoch 943 
+2024-09-10 11:13:55.363965: Current learning rate: 0.00076 
+2024-09-10 11:18:01.922438: train_loss -0.9136 
+2024-09-10 11:18:01.922611: val_loss -0.6998 
+2024-09-10 11:18:01.922668: Pseudo dice [0.6295, 0.8679] 
+2024-09-10 11:18:01.922723: Epoch time: 246.56 s 
+2024-09-10 11:18:02.922690:  
+2024-09-10 11:18:02.922948: Epoch 944 
+2024-09-10 11:18:02.923037: Current learning rate: 0.00075 
+2024-09-10 11:22:09.251222: train_loss -0.9152 
+2024-09-10 11:22:09.251408: val_loss -0.6932 
+2024-09-10 11:22:09.251466: Pseudo dice [0.6335, 0.8558] 
+2024-09-10 11:22:09.251525: Epoch time: 246.33 s 
+2024-09-10 11:22:10.353930:  
+2024-09-10 11:22:10.354121: Epoch 945 
+2024-09-10 11:22:10.354207: Current learning rate: 0.00074 
+2024-09-10 11:26:16.837911: train_loss -0.9118 
+2024-09-10 11:26:16.838059: val_loss -0.6928 
+2024-09-10 11:26:16.838116: Pseudo dice [0.6129, 0.8608] 
+2024-09-10 11:26:16.838172: Epoch time: 246.49 s 
+2024-09-10 11:26:17.843449:  
+2024-09-10 11:26:17.843647: Epoch 946 
+2024-09-10 11:26:17.843734: Current learning rate: 0.00072 
+2024-09-10 11:30:31.252443: train_loss -0.9143 
+2024-09-10 11:30:31.252592: val_loss -0.6904 
+2024-09-10 11:30:31.252650: Pseudo dice [0.6208, 0.8683] 
+2024-09-10 11:30:31.252707: Epoch time: 253.41 s 
+2024-09-10 11:30:32.272630:  
+2024-09-10 11:30:32.272839: Epoch 947 
+2024-09-10 11:30:32.272949: Current learning rate: 0.00071 
+2024-09-10 11:34:42.823643: train_loss -0.9141 
+2024-09-10 11:34:42.823829: val_loss -0.6977 
+2024-09-10 11:34:42.823934: Pseudo dice [0.6203, 0.8645] 
+2024-09-10 11:34:42.823996: Epoch time: 250.55 s 
+2024-09-10 11:34:43.940122:  
+2024-09-10 11:34:43.940370: Epoch 948 
+2024-09-10 11:34:43.940481: Current learning rate: 0.0007 
+2024-09-10 11:38:50.523585: train_loss -0.9142 
+2024-09-10 11:38:50.523748: val_loss -0.6699 
+2024-09-10 11:38:50.523813: Pseudo dice [0.5924, 0.8651] 
+2024-09-10 11:38:50.523875: Epoch time: 246.59 s 
+2024-09-10 11:38:51.652297:  
+2024-09-10 11:38:51.652470: Epoch 949 
+2024-09-10 11:38:51.652567: Current learning rate: 0.00069 
+2024-09-10 11:42:58.097942: train_loss -0.9138 
+2024-09-10 11:42:58.098088: val_loss -0.6867 
+2024-09-10 11:42:58.098145: Pseudo dice [0.614, 0.8637] 
+2024-09-10 11:42:58.098201: Epoch time: 246.45 s 
+2024-09-10 11:43:01.994980:  
+2024-09-10 11:43:01.995219: Epoch 950 
+2024-09-10 11:43:01.995307: Current learning rate: 0.00067 
+2024-09-10 11:47:08.639883: train_loss -0.9162 
+2024-09-10 11:47:08.640043: val_loss -0.6777 
+2024-09-10 11:47:08.640100: Pseudo dice [0.6125, 0.8673] 
+2024-09-10 11:47:08.640155: Epoch time: 246.65 s 
+2024-09-10 11:47:09.593256:  
+2024-09-10 11:47:09.593432: Epoch 951 
+2024-09-10 11:47:09.593524: Current learning rate: 0.00066 
+2024-09-10 11:51:16.123417: train_loss -0.9125 
+2024-09-10 11:51:16.123586: val_loss -0.7044 
+2024-09-10 11:51:16.123644: Pseudo dice [0.6183, 0.8717] 
+2024-09-10 11:51:16.123698: Epoch time: 246.53 s 
+2024-09-10 11:51:17.086121:  
+2024-09-10 11:51:17.086318: Epoch 952 
+2024-09-10 11:51:17.086416: Current learning rate: 0.00065 
+2024-09-10 11:55:23.767319: train_loss -0.9103 
+2024-09-10 11:55:23.767462: val_loss -0.6862 
+2024-09-10 11:55:23.767519: Pseudo dice [0.6127, 0.8491] 
+2024-09-10 11:55:23.767574: Epoch time: 246.68 s 
+2024-09-10 11:55:24.749248:  
+2024-09-10 11:55:24.749423: Epoch 953 
+2024-09-10 11:55:24.749506: Current learning rate: 0.00064 
+2024-09-10 11:59:31.514267: train_loss -0.9137 
+2024-09-10 11:59:31.514421: val_loss -0.6901 
+2024-09-10 11:59:31.514478: Pseudo dice [0.6138, 0.8568] 
+2024-09-10 11:59:31.514534: Epoch time: 246.77 s 
+2024-09-10 11:59:32.566024:  
+2024-09-10 11:59:32.566221: Epoch 954 
+2024-09-10 11:59:32.566311: Current learning rate: 0.00063 
+2024-09-10 12:03:39.229001: train_loss -0.9133 
+2024-09-10 12:03:39.229152: val_loss -0.6829 
+2024-09-10 12:03:39.229210: Pseudo dice [0.6305, 0.8583] 
+2024-09-10 12:03:39.229267: Epoch time: 246.66 s 
+2024-09-10 12:03:41.090371:  
+2024-09-10 12:03:41.090556: Epoch 955 
+2024-09-10 12:03:41.090662: Current learning rate: 0.00061 
+2024-09-10 12:07:47.762575: train_loss -0.9161 
+2024-09-10 12:07:47.762783: val_loss -0.6975 
+2024-09-10 12:07:47.762840: Pseudo dice [0.6249, 0.8685] 
+2024-09-10 12:07:47.762896: Epoch time: 246.67 s 
+2024-09-10 12:07:48.744664:  
+2024-09-10 12:07:48.744874: Epoch 956 
+2024-09-10 12:07:48.744974: Current learning rate: 0.0006 
+2024-09-10 12:11:55.369132: train_loss -0.9168 
+2024-09-10 12:11:55.369277: val_loss -0.6961 
+2024-09-10 12:11:55.369333: Pseudo dice [0.6215, 0.8587] 
+2024-09-10 12:11:55.369389: Epoch time: 246.63 s 
+2024-09-10 12:11:56.348733:  
+2024-09-10 12:11:56.348912: Epoch 957 
+2024-09-10 12:11:56.349003: Current learning rate: 0.00059 
+2024-09-10 12:16:03.111956: train_loss -0.9174 
+2024-09-10 12:16:03.112120: val_loss -0.6979 
+2024-09-10 12:16:03.112178: Pseudo dice [0.6246, 0.8638] 
+2024-09-10 12:16:03.112237: Epoch time: 246.77 s 
+2024-09-10 12:16:04.090104:  
+2024-09-10 12:16:04.090278: Epoch 958 
+2024-09-10 12:16:04.090364: Current learning rate: 0.00058 
+2024-09-10 12:20:10.885841: train_loss -0.9111 
+2024-09-10 12:20:10.885989: val_loss -0.6829 
+2024-09-10 12:20:10.886045: Pseudo dice [0.6099, 0.8526] 
+2024-09-10 12:20:10.886101: Epoch time: 246.8 s 
+2024-09-10 12:20:11.849830:  
+2024-09-10 12:20:11.850075: Epoch 959 
+2024-09-10 12:20:11.850162: Current learning rate: 0.00056 
+2024-09-10 12:24:18.743998: train_loss -0.9135 
+2024-09-10 12:24:18.744148: val_loss -0.7029 
+2024-09-10 12:24:18.744401: Pseudo dice [0.6469, 0.8596] 
+2024-09-10 12:24:18.744508: Epoch time: 246.9 s 
+2024-09-10 12:24:19.731311:  
+2024-09-10 12:24:19.731519: Epoch 960 
+2024-09-10 12:24:19.731605: Current learning rate: 0.00055 
+2024-09-10 12:28:26.572484: train_loss -0.9156 
+2024-09-10 12:28:26.572636: val_loss -0.6824 
+2024-09-10 12:28:26.572694: Pseudo dice [0.6093, 0.8533] 
+2024-09-10 12:28:26.572750: Epoch time: 246.84 s 
+2024-09-10 12:28:27.545497:  
+2024-09-10 12:28:27.545686: Epoch 961 
+2024-09-10 12:28:27.545837: Current learning rate: 0.00054 
+2024-09-10 12:32:34.176594: train_loss -0.9123 
+2024-09-10 12:32:34.176738: val_loss -0.6883 
+2024-09-10 12:32:34.176792: Pseudo dice [0.6253, 0.8646] 
+2024-09-10 12:32:34.176847: Epoch time: 246.63 s 
+2024-09-10 12:32:35.160377:  
+2024-09-10 12:32:35.160573: Epoch 962 
+2024-09-10 12:32:35.160665: Current learning rate: 0.00053 
+2024-09-10 12:36:41.692085: train_loss -0.9127 
+2024-09-10 12:36:41.692235: val_loss -0.706 
+2024-09-10 12:36:41.692291: Pseudo dice [0.6269, 0.8717] 
+2024-09-10 12:36:41.692357: Epoch time: 246.53 s 
+2024-09-10 12:36:42.666075:  
+2024-09-10 12:36:42.666251: Epoch 963 
+2024-09-10 12:36:42.666340: Current learning rate: 0.00051 
+2024-09-10 12:40:49.011948: train_loss -0.9187 
+2024-09-10 12:40:49.012167: val_loss -0.6895 
+2024-09-10 12:40:49.012235: Pseudo dice [0.6183, 0.861] 
+2024-09-10 12:40:49.012301: Epoch time: 246.35 s 
+2024-09-10 12:40:50.080415:  
+2024-09-10 12:40:50.080630: Epoch 964 
+2024-09-10 12:40:50.080709: Current learning rate: 0.0005 
+2024-09-10 12:44:56.544754: train_loss -0.9164 
+2024-09-10 12:44:56.544982: val_loss -0.6962 
+2024-09-10 12:44:56.545047: Pseudo dice [0.651, 0.8549] 
+2024-09-10 12:44:56.545109: Epoch time: 246.47 s 
+2024-09-10 12:44:57.607168:  
+2024-09-10 12:44:57.607432: Epoch 965 
+2024-09-10 12:44:57.607518: Current learning rate: 0.00049 
+2024-09-10 12:49:03.959702: train_loss -0.9172 
+2024-09-10 12:49:03.959859: val_loss -0.7078 
+2024-09-10 12:49:03.959947: Pseudo dice [0.6599, 0.8479] 
+2024-09-10 12:49:03.960029: Epoch time: 246.35 s 
+2024-09-10 12:49:05.062221:  
+2024-09-10 12:49:05.062409: Epoch 966 
+2024-09-10 12:49:05.062517: Current learning rate: 0.00048 
+2024-09-10 12:53:11.451791: train_loss -0.9162 
+2024-09-10 12:53:11.452028: val_loss -0.683 
+2024-09-10 12:53:11.452099: Pseudo dice [0.624, 0.8628] 
+2024-09-10 12:53:11.452186: Epoch time: 246.39 s 
+2024-09-10 12:53:12.589339:  
+2024-09-10 12:53:12.589539: Epoch 967 
+2024-09-10 12:53:12.589622: Current learning rate: 0.00046 
+2024-09-10 12:57:19.205538: train_loss -0.9168 
+2024-09-10 12:57:19.205687: val_loss -0.7045 
+2024-09-10 12:57:19.205747: Pseudo dice [0.6369, 0.8512] 
+2024-09-10 12:57:19.205805: Epoch time: 246.62 s 
+2024-09-10 12:57:20.266763:  
+2024-09-10 12:57:20.267062: Epoch 968 
+2024-09-10 12:57:20.267244: Current learning rate: 0.00045 
+2024-09-10 13:01:26.709712: train_loss -0.917 
+2024-09-10 13:01:26.709905: val_loss -0.6916 
+2024-09-10 13:01:26.709964: Pseudo dice [0.6086, 0.8554] 
+2024-09-10 13:01:26.710098: Epoch time: 246.45 s 
+2024-09-10 13:01:27.838776:  
+2024-09-10 13:01:27.839046: Epoch 969 
+2024-09-10 13:01:27.839172: Current learning rate: 0.00044 
+2024-09-10 13:05:34.385066: train_loss -0.9142 
+2024-09-10 13:05:34.385283: val_loss -0.7129 
+2024-09-10 13:05:34.385355: Pseudo dice [0.6313, 0.8797] 
+2024-09-10 13:05:34.385419: Epoch time: 246.55 s 
+2024-09-10 13:05:35.548977:  
+2024-09-10 13:05:35.549194: Epoch 970 
+2024-09-10 13:05:35.549285: Current learning rate: 0.00043 
+2024-09-10 13:09:42.120068: train_loss -0.913 
+2024-09-10 13:09:42.120215: val_loss -0.694 
+2024-09-10 13:09:42.120271: Pseudo dice [0.6327, 0.8663] 
+2024-09-10 13:09:42.120327: Epoch time: 246.57 s 
+2024-09-10 13:09:43.098812:  
+2024-09-10 13:09:43.099040: Epoch 971 
+2024-09-10 13:09:43.099128: Current learning rate: 0.00041 
+2024-09-10 13:13:49.772223: train_loss -0.9189 
+2024-09-10 13:13:49.772372: val_loss -0.6977 
+2024-09-10 13:13:49.772431: Pseudo dice [0.6408, 0.8649] 
+2024-09-10 13:13:49.772488: Epoch time: 246.68 s 
+2024-09-10 13:13:49.772532: Yayy! New best EMA pseudo Dice: 0.7452 
+2024-09-10 13:13:53.588040:  
+2024-09-10 13:13:53.588223: Epoch 972 
+2024-09-10 13:13:53.588309: Current learning rate: 0.0004 
+2024-09-10 13:18:00.387192: train_loss -0.9144 
+2024-09-10 13:18:00.387368: val_loss -0.6996 
+2024-09-10 13:18:00.387424: Pseudo dice [0.6206, 0.869] 
+2024-09-10 13:18:00.387489: Epoch time: 246.8 s 
+2024-09-10 13:18:01.370993:  
+2024-09-10 13:18:01.371191: Epoch 973 
+2024-09-10 13:18:01.371301: Current learning rate: 0.00039 
+2024-09-10 13:22:08.007789: train_loss -0.9177 
+2024-09-10 13:22:08.007972: val_loss -0.6909 
+2024-09-10 13:22:08.008083: Pseudo dice [0.6422, 0.85] 
+2024-09-10 13:22:08.008139: Epoch time: 246.64 s 
+2024-09-10 13:22:08.008184: Yayy! New best EMA pseudo Dice: 0.7452 
+2024-09-10 13:22:11.946441:  
+2024-09-10 13:22:11.946672: Epoch 974 
+2024-09-10 13:22:11.946789: Current learning rate: 0.00037 
+2024-09-10 13:26:28.653389: train_loss -0.9161 
+2024-09-10 13:26:28.653533: val_loss -0.706 
+2024-09-10 13:26:28.653593: Pseudo dice [0.649, 0.8615] 
+2024-09-10 13:26:28.653692: Epoch time: 256.71 s 
+2024-09-10 13:26:28.653772: Yayy! New best EMA pseudo Dice: 0.7462 
+2024-09-10 13:26:32.599348:  
+2024-09-10 13:26:32.599510: Epoch 975 
+2024-09-10 13:26:32.599598: Current learning rate: 0.00036 
+2024-09-10 13:30:47.220021: train_loss -0.9154 
+2024-09-10 13:30:47.220172: val_loss -0.6956 
+2024-09-10 13:30:47.220230: Pseudo dice [0.6284, 0.8636] 
+2024-09-10 13:30:47.220287: Epoch time: 254.62 s 
+2024-09-10 13:30:48.209887:  
+2024-09-10 13:30:48.210102: Epoch 976 
+2024-09-10 13:30:48.210237: Current learning rate: 0.00035 
+2024-09-10 13:34:54.531068: train_loss -0.9194 
+2024-09-10 13:34:54.531246: val_loss -0.7005 
+2024-09-10 13:34:54.531303: Pseudo dice [0.6354, 0.8588] 
+2024-09-10 13:34:54.531362: Epoch time: 246.32 s 
+2024-09-10 13:34:54.531406: Yayy! New best EMA pseudo Dice: 0.7463 
+2024-09-10 13:34:58.485880:  
+2024-09-10 13:34:58.486044: Epoch 977 
+2024-09-10 13:34:58.486131: Current learning rate: 0.00034 
+2024-09-10 13:39:05.132645: train_loss -0.9188 
+2024-09-10 13:39:05.132788: val_loss -0.707 
+2024-09-10 13:39:05.132846: Pseudo dice [0.6435, 0.8678] 
+2024-09-10 13:39:05.132904: Epoch time: 246.65 s 
+2024-09-10 13:39:05.132950: Yayy! New best EMA pseudo Dice: 0.7472 
+2024-09-10 13:39:09.940829:  
+2024-09-10 13:39:09.941074: Epoch 978 
+2024-09-10 13:39:09.941174: Current learning rate: 0.00032 
+2024-09-10 13:43:16.584512: train_loss -0.9176 
+2024-09-10 13:43:16.584659: val_loss -0.6987 
+2024-09-10 13:43:16.584714: Pseudo dice [0.6277, 0.8654] 
+2024-09-10 13:43:16.584768: Epoch time: 246.65 s 
+2024-09-10 13:43:17.553658:  
+2024-09-10 13:43:17.553920: Epoch 979 
+2024-09-10 13:43:17.554028: Current learning rate: 0.00031 
+2024-09-10 13:47:24.150260: train_loss -0.9182 
+2024-09-10 13:47:24.150395: val_loss -0.6988 
+2024-09-10 13:47:24.150452: Pseudo dice [0.6324, 0.8606] 
+2024-09-10 13:47:24.150507: Epoch time: 246.6 s 
+2024-09-10 13:47:25.142036:  
+2024-09-10 13:47:25.142280: Epoch 980 
+2024-09-10 13:47:25.142368: Current learning rate: 0.0003 
+2024-09-10 13:51:35.061591: train_loss -0.9178 
+2024-09-10 13:51:35.061740: val_loss -0.6923 
+2024-09-10 13:51:35.061797: Pseudo dice [0.6098, 0.8669] 
+2024-09-10 13:51:35.061853: Epoch time: 249.92 s 
+2024-09-10 13:51:36.028828:  
+2024-09-10 13:51:36.029051: Epoch 981 
+2024-09-10 13:51:36.029137: Current learning rate: 0.00028 
+2024-09-10 13:55:42.805263: train_loss -0.9164 
+2024-09-10 13:55:42.805412: val_loss -0.6683 
+2024-09-10 13:55:42.805469: Pseudo dice [0.6036, 0.8537] 
+2024-09-10 13:55:42.805525: Epoch time: 246.78 s 
+2024-09-10 13:55:43.785331:  
+2024-09-10 13:55:43.785555: Epoch 982 
+2024-09-10 13:55:43.785640: Current learning rate: 0.00027 
+2024-09-10 13:59:50.692677: train_loss -0.9178 
+2024-09-10 13:59:50.692846: val_loss -0.6786 
+2024-09-10 13:59:50.692966: Pseudo dice [0.5854, 0.8628] 
+2024-09-10 13:59:50.693135: Epoch time: 246.91 s 
+2024-09-10 13:59:51.831553:  
+2024-09-10 13:59:51.831799: Epoch 983 
+2024-09-10 13:59:51.831903: Current learning rate: 0.00026 
+2024-09-10 14:03:58.782159: train_loss -0.9175 
+2024-09-10 14:03:58.782310: val_loss -0.6967 
+2024-09-10 14:03:58.782366: Pseudo dice [0.6343, 0.8635] 
+2024-09-10 14:03:58.782423: Epoch time: 246.95 s 
+2024-09-10 14:03:59.762937:  
+2024-09-10 14:03:59.763118: Epoch 984 
+2024-09-10 14:03:59.763232: Current learning rate: 0.00024 
+2024-09-10 14:08:06.633819: train_loss -0.9178 
+2024-09-10 14:08:06.633967: val_loss -0.7071 
+2024-09-10 14:08:06.634024: Pseudo dice [0.6441, 0.865] 
+2024-09-10 14:08:06.634117: Epoch time: 246.87 s 
+2024-09-10 14:08:07.611020:  
+2024-09-10 14:08:07.611240: Epoch 985 
+2024-09-10 14:08:07.611332: Current learning rate: 0.00023 
+2024-09-10 14:12:14.402889: train_loss -0.9164 
+2024-09-10 14:12:14.403047: val_loss -0.7022 
+2024-09-10 14:12:14.403106: Pseudo dice [0.6322, 0.8591] 
+2024-09-10 14:12:14.403163: Epoch time: 246.79 s 
+2024-09-10 14:12:15.409420:  
+2024-09-10 14:12:15.409649: Epoch 986 
+2024-09-10 14:12:15.409738: Current learning rate: 0.00021 
+2024-09-10 14:16:22.172558: train_loss -0.9205 
+2024-09-10 14:16:22.172722: val_loss -0.6977 
+2024-09-10 14:16:22.172792: Pseudo dice [0.6292, 0.866] 
+2024-09-10 14:16:22.172849: Epoch time: 246.77 s 
+2024-09-10 14:16:23.145610:  
+2024-09-10 14:16:23.145781: Epoch 987 
+2024-09-10 14:16:23.145867: Current learning rate: 0.0002 
+2024-09-10 14:20:29.923031: train_loss -0.9171 
+2024-09-10 14:20:29.923208: val_loss -0.6952 
+2024-09-10 14:20:29.923264: Pseudo dice [0.6408, 0.857] 
+2024-09-10 14:20:29.923320: Epoch time: 246.78 s 
+2024-09-10 14:20:30.907301:  
+2024-09-10 14:20:30.907531: Epoch 988 
+2024-09-10 14:20:30.907620: Current learning rate: 0.00019 
+2024-09-10 14:24:37.723314: train_loss -0.9168 
+2024-09-10 14:24:37.723462: val_loss -0.7049 
+2024-09-10 14:24:37.723537: Pseudo dice [0.6219, 0.8642] 
+2024-09-10 14:24:37.723643: Epoch time: 246.82 s 
+2024-09-10 14:24:38.729388:  
+2024-09-10 14:24:38.729606: Epoch 989 
+2024-09-10 14:24:38.729694: Current learning rate: 0.00017 
+2024-09-10 14:28:45.633926: train_loss -0.9175 
+2024-09-10 14:28:45.634092: val_loss -0.7025 
+2024-09-10 14:28:45.634148: Pseudo dice [0.6468, 0.8664] 
+2024-09-10 14:28:45.634206: Epoch time: 246.91 s 
+2024-09-10 14:28:46.615128:  
+2024-09-10 14:28:46.615360: Epoch 990 
+2024-09-10 14:28:46.615444: Current learning rate: 0.00016 
+2024-09-10 14:32:53.450239: train_loss -0.9133 
+2024-09-10 14:32:53.450412: val_loss -0.6953 
+2024-09-10 14:32:53.450491: Pseudo dice [0.64, 0.8648] 
+2024-09-10 14:32:53.450547: Epoch time: 246.84 s 
+2024-09-10 14:32:54.426935:  
+2024-09-10 14:32:54.427100: Epoch 991 
+2024-09-10 14:32:54.427187: Current learning rate: 0.00014 
+2024-09-10 14:37:01.095144: train_loss -0.9145 
+2024-09-10 14:37:01.095294: val_loss -0.7027 
+2024-09-10 14:37:01.095350: Pseudo dice [0.654, 0.8578] 
+2024-09-10 14:37:01.095406: Epoch time: 246.67 s 
+2024-09-10 14:37:01.095450: Yayy! New best EMA pseudo Dice: 0.7476 
+2024-09-10 14:37:05.004646:  
+2024-09-10 14:37:05.004830: Epoch 992 
+2024-09-10 14:37:05.004919: Current learning rate: 0.00013 
+2024-09-10 14:41:11.678965: train_loss -0.9192 
+2024-09-10 14:41:11.679149: val_loss -0.6813 
+2024-09-10 14:41:11.679207: Pseudo dice [0.6385, 0.856] 
+2024-09-10 14:41:11.679262: Epoch time: 246.68 s 
+2024-09-10 14:41:12.655317:  
+2024-09-10 14:41:12.655530: Epoch 993 
+2024-09-10 14:41:12.655615: Current learning rate: 0.00011 
+2024-09-10 14:45:19.138681: train_loss -0.919 
+2024-09-10 14:45:19.138850: val_loss -0.7013 
+2024-09-10 14:45:19.138908: Pseudo dice [0.6201, 0.8727] 
+2024-09-10 14:45:19.138966: Epoch time: 246.49 s 
+2024-09-10 14:45:20.116180:  
+2024-09-10 14:45:20.116414: Epoch 994 
+2024-09-10 14:45:20.116520: Current learning rate: 0.0001 
+2024-09-10 14:49:26.678865: train_loss -0.9177 
+2024-09-10 14:49:26.679054: val_loss -0.6981 
+2024-09-10 14:49:26.679113: Pseudo dice [0.6253, 0.8734] 
+2024-09-10 14:49:26.679173: Epoch time: 246.56 s 
+2024-09-10 14:49:26.679220: Yayy! New best EMA pseudo Dice: 0.7477 
+2024-09-10 14:49:30.646563:  
+2024-09-10 14:49:30.646750: Epoch 995 
+2024-09-10 14:49:30.646838: Current learning rate: 8e-05 
+2024-09-10 14:53:37.391361: train_loss -0.9161 
+2024-09-10 14:53:37.391554: val_loss -0.6862 
+2024-09-10 14:53:37.391639: Pseudo dice [0.6095, 0.8592] 
+2024-09-10 14:53:37.391696: Epoch time: 246.75 s 
+2024-09-10 14:53:38.366202:  
+2024-09-10 14:53:38.366387: Epoch 996 
+2024-09-10 14:53:38.366474: Current learning rate: 7e-05 
+2024-09-10 14:57:45.019513: train_loss -0.9205 
+2024-09-10 14:57:45.019657: val_loss -0.7 
+2024-09-10 14:57:45.019713: Pseudo dice [0.617, 0.8702] 
+2024-09-10 14:57:45.019768: Epoch time: 246.66 s 
+2024-09-10 14:57:45.982435:  
+2024-09-10 14:57:45.982584: Epoch 997 
+2024-09-10 14:57:45.982668: Current learning rate: 5e-05 
+2024-09-10 15:01:52.678772: train_loss -0.9174 
+2024-09-10 15:01:52.678922: val_loss -0.6899 
+2024-09-10 15:01:52.679168: Pseudo dice [0.6412, 0.8615] 
+2024-09-10 15:01:52.679224: Epoch time: 246.7 s 
+2024-09-10 15:01:53.661122:  
+2024-09-10 15:01:53.661324: Epoch 998 
+2024-09-10 15:01:53.661463: Current learning rate: 4e-05 
+2024-09-10 15:06:00.281387: train_loss -0.9187 
+2024-09-10 15:06:00.281559: val_loss -0.6983 
+2024-09-10 15:06:00.281628: Pseudo dice [0.6413, 0.8584] 
+2024-09-10 15:06:00.281684: Epoch time: 246.62 s 
+2024-09-10 15:06:01.275288:  
+2024-09-10 15:06:01.275487: Epoch 999 
+2024-09-10 15:06:01.275577: Current learning rate: 2e-05 
+2024-09-10 15:10:07.917750: train_loss -0.9175 
+2024-09-10 15:10:07.917954: val_loss -0.6819 
+2024-09-10 15:10:07.918012: Pseudo dice [0.6044, 0.8524] 
+2024-09-10 15:10:07.918074: Epoch time: 246.64 s 
+2024-09-10 15:10:10.203624: Training done. 
+2024-09-10 15:10:10.315346: Using splits from existing split file: /mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/splits_final.json 
+2024-09-10 15:10:10.318618: The split file contains 5 splits. 
+2024-09-10 15:10:10.318813: Desired fold for training: 3 
+2024-09-10 15:10:10.318918: This split has 240 training and 30 validation cases. 
+2024-09-10 15:10:10.319791: predicting 105 
+2024-09-10 15:10:10.323159: 105, shape torch.Size([2, 129, 536, 1016]), rank 0 
+2024-09-10 15:12:09.117879: predicting 109 
+2024-09-10 15:12:09.153480: 109, shape torch.Size([2, 110, 509, 511]), rank 0 
+2024-09-10 15:12:40.206540: predicting 110 
+2024-09-10 15:12:40.242275: 110, shape torch.Size([2, 130, 510, 511]), rank 0 
+2024-09-10 15:13:18.867403: predicting 112 
+2024-09-10 15:13:18.885036: 112, shape torch.Size([2, 132, 510, 511]), rank 0 
+2024-09-10 15:13:56.972374: predicting 118 
+2024-09-10 15:13:56.989862: 118, shape torch.Size([2, 102, 510, 509]), rank 0 
+2024-09-10 15:14:27.200588: predicting 131 
+2024-09-10 15:14:27.216347: 131, shape torch.Size([2, 123, 512, 511]), rank 0 
+2024-09-10 15:15:04.749287: predicting 14 
+2024-09-10 15:15:04.775370: 14, shape torch.Size([2, 138, 510, 511]), rank 0 
+2024-09-10 15:15:42.294673: predicting 148 
+2024-09-10 15:15:42.317139: 148, shape torch.Size([2, 115, 534, 971]), rank 0 
+2024-09-10 15:16:41.963207: predicting 153 
+2024-09-10 15:16:41.995325: 153, shape torch.Size([2, 113, 509, 510]), rank 0 
+2024-09-10 15:17:11.999531: predicting 164 
+2024-09-10 15:17:12.014059: 164, shape torch.Size([2, 127, 512, 511]), rank 0 
+2024-09-10 15:17:49.401419: predicting 166 
+2024-09-10 15:17:49.418159: 166, shape torch.Size([2, 137, 536, 1040]), rank 0 
+2024-09-10 15:19:04.180099: predicting 17 
+2024-09-10 15:19:04.217230: 17, shape torch.Size([2, 121, 536, 940]), rank 0 
+2024-09-10 15:20:11.521473: predicting 173 
+2024-09-10 15:20:11.550866: 173, shape torch.Size([2, 127, 512, 511]), rank 0 
+2024-09-10 15:20:49.075809: predicting 175 
+2024-09-10 15:20:49.094977: 175, shape torch.Size([2, 105, 508, 509]), rank 0 
+2024-09-10 15:21:19.182017: predicting 190 
+2024-09-10 15:21:19.195970: 190, shape torch.Size([2, 105, 510, 510]), rank 0 
+2024-09-10 15:21:49.254047: predicting 193 
+2024-09-10 15:21:49.277049: 193, shape torch.Size([2, 98, 512, 509]), rank 0 
+2024-09-10 15:22:19.444827: predicting 23 
+2024-09-10 15:22:19.468061: 23, shape torch.Size([2, 113, 512, 510]), rank 0 
+2024-09-10 15:22:49.700073: predicting 27 
+2024-09-10 15:22:49.744275: 27, shape torch.Size([2, 100, 512, 509]), rank 0 
+2024-09-10 15:23:20.195096: predicting 30 
+2024-09-10 15:23:20.238302: 30, shape torch.Size([2, 117, 510, 511]), rank 0 
+2024-09-10 15:23:53.088875: predicting 37 
+2024-09-10 15:23:53.128417: 37, shape torch.Size([2, 142, 510, 511]), rank 0 
+2024-09-10 15:24:39.764765: predicting 39 
+2024-09-10 15:24:39.783267: 39, shape torch.Size([2, 123, 536, 1040]), rank 0 
+2024-09-10 15:25:59.062425: predicting 4 
+2024-09-10 15:25:59.116444: 4, shape torch.Size([2, 128, 512, 511]), rank 0 
+2024-09-10 15:26:38.625649: predicting 42 
+2024-09-10 15:26:38.650484: 42, shape torch.Size([2, 128, 498, 511]), rank 0 
+2024-09-10 15:27:17.705896: predicting 47 
+2024-09-10 15:27:17.730564: 47, shape torch.Size([2, 140, 476, 511]), rank 0 
+2024-09-10 15:27:48.342735: predicting 53 
+2024-09-10 15:27:48.373507: 53, shape torch.Size([2, 121, 478, 511]), rank 0 
+2024-09-10 15:28:18.750153: predicting 61 
+2024-09-10 15:28:18.784343: 61, shape torch.Size([2, 123, 510, 510]), rank 0 
+2024-09-10 15:28:56.582433: predicting 70 
+2024-09-10 15:28:56.604830: 70, shape torch.Size([2, 122, 512, 511]), rank 0 
+2024-09-10 15:29:34.945601: predicting 71 
+2024-09-10 15:29:34.978992: 71, shape torch.Size([2, 120, 511, 511]), rank 0 
+2024-09-10 15:30:06.029972: predicting 80 
+2024-09-10 15:30:06.052803: 80, shape torch.Size([2, 125, 512, 510]), rank 0 
+2024-09-10 15:30:43.756965: predicting 91 
+2024-09-10 15:30:43.805393: 91, shape torch.Size([2, 117, 758, 800]), rank 0 
+2024-09-10 15:32:06.392670: Validation complete 
+2024-09-10 15:32:06.392753: Mean Validation Dice:  0.6572741026462359 
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/validation/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_3/validation/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..6afd8549f18c35525d5f7dd2d9650e80490fb619
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+}
\ No newline at end of file
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'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetMPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 192, 192], 'median_image_size_in_voxels': [92, 310, 309], 'spacing': [1.6126957141129536, 0.826423816135876, 0.826423816135876], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres_bs4': {'inherits_from': '3d_fullres', 'batch_size': 4}, '3d_fullres_bs8': {'inherits_from': '3d_fullres', 'batch_size': 8}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 349.19762434242324, 'median': 141.0, 'min': 0.0, 'percentile_00_5': 29.0, 'percentile_99_5': 2558.91650390625, 'std': 485.94544484039}, '1': {'max': 1.0, 'mean': 0.37307153608378946, 'median': 0.2966964840888977, 'min': 0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}}, 'configuration': '3d_fullres_bs8', 'fold': 4, 'dataset_json': {'channel_names': {'0': 'T2', '1': 'gradient'}, 'labels': {'background': 0, 'GTVp': 1, 'GTVn': 2}, 'numTraining': 300, 'file_ending': '.nii.gz', 'name': 'Dataset504_midRT_gradient', 'description': 'Dataset 504 - midRT with Gradient Map'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "num_epochs": "1000",
+    "num_input_channels": "2",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4",
+    "output_folder_base": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 511.0], 'spacing': [0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetMPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 192, 192], 'median_image_size_in_voxels': [92, 310, 309], 'spacing': [1.6126957141129536, 0.826423816135876, 0.826423816135876], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres_bs4': {'inherits_from': '3d_fullres', 'batch_size': 4}, '3d_fullres_bs8': {'inherits_from': '3d_fullres', 'batch_size': 8}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 349.19762434242324, 'median': 141.0, 'min': 0.0, 'percentile_00_5': 29.0, 'percentile_99_5': 2558.91650390625, 'std': 485.94544484039}, '1': {'max': 1.0, 'mean': 0.37307153608378946, 'median': 0.2966964840888977, 'min': 0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}}",
+    "preprocessed_dataset_folder": "/mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist",
+    "save_every": "50",
+    "torch_version": "2.4.0",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}
\ No newline at end of file
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/2voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/2voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..86922e32bc9392e90b7fc620a2e196283493eb29
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/2voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/2voxels/images",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/2voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/2voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/2voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..6c039435bfcfca2e830bf34f72b0befcf20ea307
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/2voxels/preds/summary_agg.json
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/4voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/4voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..1010c69cfa9901037528579d01e69dacf7d319d3
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/4voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/4voxels/images",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/4voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/4voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/4voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..d2af68cf2feb6abefb7a435a5006427c177050bd
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/4voxels/preds/summary_agg.json
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+        "3d_cascade_fullres": {
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+            "previous_stage": "3d_lowres"
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+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/6voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/6voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..4bf0bf85656c175f4464f3aa083e6507699f914e
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/6voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
+    "folder_with_segs_from_prev_stage": null,
+    "list_of_lists_or_source_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/6voxels/images",
+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/6voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/6voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/6voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..4b83f6e70810f0ad7400d566188824ce6d0ed504
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/6voxels/preds/summary_agg.json
@@ -0,0 +1,512 @@
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\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/8voxels/preds/predict_from_raw_data_args.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/8voxels/preds/predict_from_raw_data_args.json
new file mode 100644
index 0000000000000000000000000000000000000000..9fa9918712f4ac6f348c42ed5d468eed80f785ab
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/8voxels/preds/predict_from_raw_data_args.json
@@ -0,0 +1,11 @@
+{
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+    "num_parts": 1,
+    "num_processes_preprocessing": 3,
+    "num_processes_segmentation_export": 3,
+    "output_folder_or_list_of_truncated_output_files": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/8voxels/preds",
+    "overwrite": true,
+    "part_id": 0,
+    "save_probabilities": false
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/8voxels/preds/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/8voxels/preds/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..b9ee64045ffffc00a29836332e2bccd00376639b
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/margins/8voxels/preds/summary_agg.json
@@ -0,0 +1,512 @@
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+            "vol_gt2": 3026.5600901985176,
+            "HD95_1": 4.019950249041444,
+            "HD95_2": 0.8944272043279197,
+            "MSD_1": 1.0750213907562527,
+            "MSD_2": 0.3552627519269162
+        },
+        {
+            "PatientID": "157.nii.gz",
+            "TP1": 1964.7509368658064,
+            "TP2": 4168.001987457275,
+            "vol_sum1": 5032.502399682999,
+            "vol_sum2": 9121.25434935093,
+            "DSC1": 0.7808246398410332,
+            "DSC2": 0.9139098259558722,
+            "vol_gt1": 2645.7512615919113,
+            "vol_gt2": 4673.502228498459,
+            "HD95_1": 3.0,
+            "HD95_2": 1.118033988749895,
+            "MSD_1": 0.8413326283583603,
+            "MSD_2": 0.470923213253012
+        }
+    ],
+    "aggregate_results": {
+        "ConventionalDsc": {
+            "GTVp": {
+                "mean": 0.47362301395745143,
+                "median": 0.6020142870299412,
+                "percentile_25": 0.11683621024579616,
+                "percentile_75": 0.7547717719592247
+            },
+            "GTVn": {
+                "mean": 0.6573180926464417,
+                "median": 0.7555141690123437,
+                "percentile_25": 0.5956633234393429,
+                "percentile_75": 0.8789842703666688
+            }
+        },
+        "AggregatedDsc": {
+            "GTVp": {
+                "mean": 0.7049552388545155,
+                "median": 0.7049552388545155,
+                "percentile_25": 0.7049552388545155,
+                "percentile_75": 0.7049552388545155
+            },
+            "GTVn": {
+                "mean": 0.8264876050850773,
+                "median": 0.8264876050850773,
+                "percentile_25": 0.8264876050850773,
+                "percentile_75": 0.8264876050850773
+            }
+        },
+        "HD95": {
+            "GTVp": {
+                "mean": 7.962835038131392,
+                "median": 4.025539561595359,
+                "percentile_25": 3.129971288801179,
+                "percentile_75": 9.067485570298675
+            },
+            "GTVn": {
+                "mean": 6.459019058971462,
+                "median": 2.0615528128088303,
+                "percentile_25": 1.4142135623730951,
+                "percentile_75": 6.103277807866851
+            }
+        },
+        "MSD": {
+            "GTVp": {
+                "mean": 2.368107577911101,
+                "median": 1.2279557770383773,
+                "percentile_25": 0.9804103129544147,
+                "percentile_75": 2.757614273128158
+            },
+            "GTVn": {
+                "mean": 1.3485964494159808,
+                "median": 0.609042182799669,
+                "percentile_25": 0.470923213253012,
+                "percentile_75": 1.357804846464776
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/progress.png b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/progress.png
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/training_log_2024_9_13_12_39_26.txt b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/training_log_2024_9_13_12_39_26.txt
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+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/training_log_2024_9_13_12_39_26.txt
@@ -0,0 +1,7188 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2024-09-13 12:39:26.945572: do_dummy_2d_data_aug: True 
+2024-09-13 12:39:26.946631: Using splits from existing split file: /mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/splits_final.json 
+2024-09-13 12:39:26.946842: The split file contains 5 splits. 
+2024-09-13 12:39:26.946879: Desired fold for training: 4 
+2024-09-13 12:39:26.946908: This split has 240 training and 30 validation cases. 
+2024-09-13 12:39:35.858285: Using torch.compile... 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres_bs8
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 8, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True, 'inherits_from': '3d_fullres'} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 349.19762434242324, 'median': 141.0, 'min': 0.0, 'percentile_00_5': 29.0, 'percentile_99_5': 2558.91650390625, 'std': 485.94544484039}, '1': {'max': 1.0, 'mean': 0.37307153608378946, 'median': 0.2966964840888977, 'min': 0.0, 'percentile_00_5': 0.01908937655389309, 'percentile_99_5': 1.0, 'std': 0.2642120030869378}}} 
+ 
+2024-09-13 12:39:36.743000: unpacking dataset... 
+2024-09-13 12:39:39.458817: unpacking done... 
+2024-09-13 12:39:39.460685: Unable to plot network architecture: nnUNet_compile is enabled! 
+2024-09-13 12:39:39.468060:  
+2024-09-13 12:39:39.468261: Epoch 0 
+2024-09-13 12:39:39.468401: Current learning rate: 0.01 
+2024-09-13 12:45:11.337336: train_loss -0.0761 
+2024-09-13 12:45:11.337492: val_loss -0.2022 
+2024-09-13 12:45:11.337552: Pseudo dice [0.2071, 0.3216] 
+2024-09-13 12:45:11.337605: Epoch time: 331.87 s 
+2024-09-13 12:45:11.337646: Yayy! New best EMA pseudo Dice: 0.2644 
+2024-09-13 12:45:13.205618:  
+2024-09-13 12:45:13.205790: Epoch 1 
+2024-09-13 12:45:13.205920: Current learning rate: 0.00999 
+2024-09-13 12:49:22.176651: train_loss -0.2853 
+2024-09-13 12:49:22.176808: val_loss -0.2683 
+2024-09-13 12:49:22.176916: Pseudo dice [0.3325, 0.3696] 
+2024-09-13 12:49:22.176970: Epoch time: 248.97 s 
+2024-09-13 12:49:22.177016: Yayy! New best EMA pseudo Dice: 0.273 
+2024-09-13 12:49:26.019927:  
+2024-09-13 12:49:26.020143: Epoch 2 
+2024-09-13 12:49:26.020271: Current learning rate: 0.00998 
+2024-09-13 12:53:31.785894: train_loss -0.3265 
+2024-09-13 12:53:31.786033: val_loss -0.2954 
+2024-09-13 12:53:31.786215: Pseudo dice [0.4161, 0.3458] 
+2024-09-13 12:53:31.786337: Epoch time: 245.77 s 
+2024-09-13 12:53:31.786419: Yayy! New best EMA pseudo Dice: 0.2838 
+2024-09-13 12:53:35.721537:  
+2024-09-13 12:53:35.721721: Epoch 3 
+2024-09-13 12:53:35.721806: Current learning rate: 0.00997 
+2024-09-13 12:57:41.467606: train_loss -0.3927 
+2024-09-13 12:57:41.467785: val_loss -0.3737 
+2024-09-13 12:57:41.467844: Pseudo dice [0.4366, 0.5161] 
+2024-09-13 12:57:41.467899: Epoch time: 245.75 s 
+2024-09-13 12:57:41.467985: Yayy! New best EMA pseudo Dice: 0.3031 
+2024-09-13 12:57:45.417187:  
+2024-09-13 12:57:45.417366: Epoch 4 
+2024-09-13 12:57:45.417447: Current learning rate: 0.00996 
+2024-09-13 13:01:51.110029: train_loss -0.468 
+2024-09-13 13:01:51.110165: val_loss -0.4551 
+2024-09-13 13:01:51.110215: Pseudo dice [0.496, 0.6185] 
+2024-09-13 13:01:51.110265: Epoch time: 245.69 s 
+2024-09-13 13:01:51.110310: Yayy! New best EMA pseudo Dice: 0.3285 
+2024-09-13 13:01:55.003213:  
+2024-09-13 13:01:55.003450: Epoch 5 
+2024-09-13 13:01:55.003533: Current learning rate: 0.00995 
+2024-09-13 13:06:00.999839: train_loss -0.5268 
+2024-09-13 13:06:00.999981: val_loss -0.4835 
+2024-09-13 13:06:01.000033: Pseudo dice [0.5416, 0.6289] 
+2024-09-13 13:06:01.000084: Epoch time: 246.0 s 
+2024-09-13 13:06:01.000124: Yayy! New best EMA pseudo Dice: 0.3542 
+2024-09-13 13:06:04.890921:  
+2024-09-13 13:06:04.891113: Epoch 6 
+2024-09-13 13:06:04.891216: Current learning rate: 0.00995 
+2024-09-13 13:10:10.908024: train_loss -0.581 
+2024-09-13 13:10:10.908203: val_loss -0.453 
+2024-09-13 13:10:10.908257: Pseudo dice [0.4711, 0.5873] 
+2024-09-13 13:10:10.908309: Epoch time: 246.02 s 
+2024-09-13 13:10:10.908350: Yayy! New best EMA pseudo Dice: 0.3717 
+2024-09-13 13:10:14.849824:  
+2024-09-13 13:10:14.850024: Epoch 7 
+2024-09-13 13:10:14.850107: Current learning rate: 0.00994 
+2024-09-13 13:14:20.837850: train_loss -0.5768 
+2024-09-13 13:14:20.837989: val_loss -0.5174 
+2024-09-13 13:14:20.838040: Pseudo dice [0.5374, 0.6409] 
+2024-09-13 13:14:20.838093: Epoch time: 245.99 s 
+2024-09-13 13:14:20.838132: Yayy! New best EMA pseudo Dice: 0.3934 
+2024-09-13 13:14:24.739228:  
+2024-09-13 13:14:24.739384: Epoch 8 
+2024-09-13 13:14:24.739501: Current learning rate: 0.00993 
+2024-09-13 13:18:30.772094: train_loss -0.624 
+2024-09-13 13:18:30.772233: val_loss -0.5446 
+2024-09-13 13:18:30.772285: Pseudo dice [0.605, 0.6956] 
+2024-09-13 13:18:30.772336: Epoch time: 246.03 s 
+2024-09-13 13:18:30.772378: Yayy! New best EMA pseudo Dice: 0.4191 
+2024-09-13 13:18:34.718731:  
+2024-09-13 13:18:34.718883: Epoch 9 
+2024-09-13 13:18:34.718987: Current learning rate: 0.00992 
+2024-09-13 13:22:40.665331: train_loss -0.6226 
+2024-09-13 13:22:40.665471: val_loss -0.5954 
+2024-09-13 13:22:40.665524: Pseudo dice [0.6083, 0.7267] 
+2024-09-13 13:22:40.665574: Epoch time: 245.95 s 
+2024-09-13 13:22:40.665617: Yayy! New best EMA pseudo Dice: 0.4439 
+2024-09-13 13:22:44.561617:  
+2024-09-13 13:22:44.561821: Epoch 10 
+2024-09-13 13:22:44.561930: Current learning rate: 0.00991 
+2024-09-13 13:26:50.608802: train_loss -0.6404 
+2024-09-13 13:26:50.608968: val_loss -0.5816 
+2024-09-13 13:26:50.609020: Pseudo dice [0.6146, 0.7271] 
+2024-09-13 13:26:50.609069: Epoch time: 246.05 s 
+2024-09-13 13:26:50.609108: Yayy! New best EMA pseudo Dice: 0.4666 
+2024-09-13 13:26:54.520486:  
+2024-09-13 13:26:54.520702: Epoch 11 
+2024-09-13 13:26:54.520787: Current learning rate: 0.0099 
+2024-09-13 13:31:00.587583: train_loss -0.6527 
+2024-09-13 13:31:00.587731: val_loss -0.6026 
+2024-09-13 13:31:00.587782: Pseudo dice [0.6147, 0.76] 
+2024-09-13 13:31:00.587843: Epoch time: 246.07 s 
+2024-09-13 13:31:00.587885: Yayy! New best EMA pseudo Dice: 0.4887 
+2024-09-13 13:31:04.509758:  
+2024-09-13 13:31:04.509959: Epoch 12 
+2024-09-13 13:31:04.510067: Current learning rate: 0.00989 
+2024-09-13 13:35:10.576925: train_loss -0.6359 
+2024-09-13 13:35:10.577065: val_loss -0.5853 
+2024-09-13 13:35:10.577116: Pseudo dice [0.6172, 0.7244] 
+2024-09-13 13:35:10.577167: Epoch time: 246.07 s 
+2024-09-13 13:35:10.577207: Yayy! New best EMA pseudo Dice: 0.5069 
+2024-09-13 13:35:15.223697:  
+2024-09-13 13:35:15.223875: Epoch 13 
+2024-09-13 13:35:15.223959: Current learning rate: 0.00988 
+2024-09-13 13:39:21.434878: train_loss -0.6826 
+2024-09-13 13:39:21.435022: val_loss -0.557 
+2024-09-13 13:39:21.435074: Pseudo dice [0.5697, 0.7396] 
+2024-09-13 13:39:21.435125: Epoch time: 246.21 s 
+2024-09-13 13:39:21.435165: Yayy! New best EMA pseudo Dice: 0.5217 
+2024-09-13 13:39:25.334819:  
+2024-09-13 13:39:25.334976: Epoch 14 
+2024-09-13 13:39:25.335099: Current learning rate: 0.00987 
+2024-09-13 13:43:31.478983: train_loss -0.6795 
+2024-09-13 13:43:31.479124: val_loss -0.6169 
+2024-09-13 13:43:31.479175: Pseudo dice [0.6356, 0.7546] 
+2024-09-13 13:43:31.479229: Epoch time: 246.15 s 
+2024-09-13 13:43:31.479269: Yayy! New best EMA pseudo Dice: 0.539 
+2024-09-13 13:43:35.392965:  
+2024-09-13 13:43:35.393203: Epoch 15 
+2024-09-13 13:43:35.393294: Current learning rate: 0.00986 
+2024-09-13 13:47:41.565697: train_loss -0.6739 
+2024-09-13 13:47:41.565836: val_loss -0.6206 
+2024-09-13 13:47:41.565887: Pseudo dice [0.62, 0.7687] 
+2024-09-13 13:47:41.565938: Epoch time: 246.17 s 
+2024-09-13 13:47:41.565978: Yayy! New best EMA pseudo Dice: 0.5546 
+2024-09-13 13:47:45.487579:  
+2024-09-13 13:47:45.487779: Epoch 16 
+2024-09-13 13:47:45.487876: Current learning rate: 0.00986 
+2024-09-13 13:51:51.704892: train_loss -0.68 
+2024-09-13 13:51:51.705016: val_loss -0.6134 
+2024-09-13 13:51:51.705066: Pseudo dice [0.6379, 0.7676] 
+2024-09-13 13:51:51.705117: Epoch time: 246.22 s 
+2024-09-13 13:51:51.705157: Yayy! New best EMA pseudo Dice: 0.5694 
+2024-09-13 13:51:55.639494:  
+2024-09-13 13:51:55.639683: Epoch 17 
+2024-09-13 13:51:55.639767: Current learning rate: 0.00985 
+2024-09-13 13:56:01.926195: train_loss -0.679 
+2024-09-13 13:56:01.926369: val_loss -0.5832 
+2024-09-13 13:56:01.926421: Pseudo dice [0.5988, 0.7587] 
+2024-09-13 13:56:01.926472: Epoch time: 246.29 s 
+2024-09-13 13:56:01.926511: Yayy! New best EMA pseudo Dice: 0.5803 
+2024-09-13 13:56:05.838231:  
+2024-09-13 13:56:05.838412: Epoch 18 
+2024-09-13 13:56:05.838496: Current learning rate: 0.00984 
+2024-09-13 14:00:12.075077: train_loss -0.6806 
+2024-09-13 14:00:12.075215: val_loss -0.6128 
+2024-09-13 14:00:12.075266: Pseudo dice [0.6243, 0.7722] 
+2024-09-13 14:00:12.075317: Epoch time: 246.24 s 
+2024-09-13 14:00:12.075357: Yayy! New best EMA pseudo Dice: 0.5921 
+2024-09-13 14:00:15.938188:  
+2024-09-13 14:00:15.938387: Epoch 19 
+2024-09-13 14:00:15.938474: Current learning rate: 0.00983 
+2024-09-13 14:04:22.079304: train_loss -0.7211 
+2024-09-13 14:04:22.079468: val_loss -0.65 
+2024-09-13 14:04:22.079524: Pseudo dice [0.6755, 0.788] 
+2024-09-13 14:04:22.079574: Epoch time: 246.14 s 
+2024-09-13 14:04:22.079614: Yayy! New best EMA pseudo Dice: 0.6061 
+2024-09-13 14:04:25.966471:  
+2024-09-13 14:04:25.966757: Epoch 20 
+2024-09-13 14:04:25.966851: Current learning rate: 0.00982 
+2024-09-13 14:08:32.198705: train_loss -0.7131 
+2024-09-13 14:08:32.198867: val_loss -0.639 
+2024-09-13 14:08:32.198920: Pseudo dice [0.6514, 0.7652] 
+2024-09-13 14:08:32.198973: Epoch time: 246.23 s 
+2024-09-13 14:08:32.199014: Yayy! New best EMA pseudo Dice: 0.6163 
+2024-09-13 14:08:36.111459:  
+2024-09-13 14:08:36.111607: Epoch 21 
+2024-09-13 14:08:36.111726: Current learning rate: 0.00981 
+2024-09-13 14:12:42.372878: train_loss -0.7202 
+2024-09-13 14:12:42.373061: val_loss -0.6244 
+2024-09-13 14:12:42.373113: Pseudo dice [0.5959, 0.7866] 
+2024-09-13 14:12:42.373164: Epoch time: 246.26 s 
+2024-09-13 14:12:42.373216: Yayy! New best EMA pseudo Dice: 0.6238 
+2024-09-13 14:12:46.228412:  
+2024-09-13 14:12:46.228634: Epoch 22 
+2024-09-13 14:12:46.228719: Current learning rate: 0.0098 
+2024-09-13 14:16:52.227554: train_loss -0.7121 
+2024-09-13 14:16:52.227692: val_loss -0.6123 
+2024-09-13 14:16:52.227742: Pseudo dice [0.6038, 0.7546] 
+2024-09-13 14:16:52.227793: Epoch time: 246.0 s 
+2024-09-13 14:16:52.227843: Yayy! New best EMA pseudo Dice: 0.6293 
+2024-09-13 14:16:55.962942:  
+2024-09-13 14:16:55.963161: Epoch 23 
+2024-09-13 14:16:55.963247: Current learning rate: 0.00979 
+2024-09-13 14:21:02.082356: train_loss -0.7044 
+2024-09-13 14:21:02.082495: val_loss -0.6082 
+2024-09-13 14:21:02.082546: Pseudo dice [0.626, 0.7664] 
+2024-09-13 14:21:02.082597: Epoch time: 246.12 s 
+2024-09-13 14:21:02.082637: Yayy! New best EMA pseudo Dice: 0.636 
+2024-09-13 14:21:05.951484:  
+2024-09-13 14:21:05.951774: Epoch 24 
+2024-09-13 14:21:05.951881: Current learning rate: 0.00978 
+2024-09-13 14:25:12.140765: train_loss -0.7071 
+2024-09-13 14:25:12.141034: val_loss -0.6352 
+2024-09-13 14:25:12.141086: Pseudo dice [0.6615, 0.7583] 
+2024-09-13 14:25:12.141136: Epoch time: 246.19 s 
+2024-09-13 14:25:12.141175: Yayy! New best EMA pseudo Dice: 0.6434 
+2024-09-13 14:25:15.992175:  
+2024-09-13 14:25:15.992406: Epoch 25 
+2024-09-13 14:25:15.992497: Current learning rate: 0.00977 
+2024-09-13 14:29:22.035185: train_loss -0.7101 
+2024-09-13 14:29:22.035326: val_loss -0.625 
+2024-09-13 14:29:22.035377: Pseudo dice [0.6314, 0.7719] 
+2024-09-13 14:29:22.035427: Epoch time: 246.04 s 
+2024-09-13 14:29:22.035466: Yayy! New best EMA pseudo Dice: 0.6492 
+2024-09-13 14:29:25.896574:  
+2024-09-13 14:29:25.896732: Epoch 26 
+2024-09-13 14:29:25.896849: Current learning rate: 0.00977 
+2024-09-13 14:33:32.081787: train_loss -0.7249 
+2024-09-13 14:33:32.081926: val_loss -0.6361 
+2024-09-13 14:33:32.081976: Pseudo dice [0.6454, 0.789] 
+2024-09-13 14:33:32.082026: Epoch time: 246.19 s 
+2024-09-13 14:33:32.082095: Yayy! New best EMA pseudo Dice: 0.656 
+2024-09-13 14:33:35.952415:  
+2024-09-13 14:33:35.952637: Epoch 27 
+2024-09-13 14:33:35.952745: Current learning rate: 0.00976 
+2024-09-13 14:37:42.110548: train_loss -0.73 
+2024-09-13 14:37:42.110715: val_loss -0.6685 
+2024-09-13 14:37:42.110767: Pseudo dice [0.6739, 0.7919] 
+2024-09-13 14:37:42.110818: Epoch time: 246.16 s 
+2024-09-13 14:37:42.110858: Yayy! New best EMA pseudo Dice: 0.6637 
+2024-09-13 14:37:45.985900:  
+2024-09-13 14:37:45.986111: Epoch 28 
+2024-09-13 14:37:45.986206: Current learning rate: 0.00975 
+2024-09-13 14:41:52.188165: train_loss -0.7324 
+2024-09-13 14:41:52.188316: val_loss -0.6559 
+2024-09-13 14:41:52.188368: Pseudo dice [0.6718, 0.7921] 
+2024-09-13 14:41:52.188419: Epoch time: 246.2 s 
+2024-09-13 14:41:52.188459: Yayy! New best EMA pseudo Dice: 0.6705 
+2024-09-13 14:41:56.028835:  
+2024-09-13 14:41:56.029001: Epoch 29 
+2024-09-13 14:41:56.029083: Current learning rate: 0.00974 
+2024-09-13 14:46:02.158590: train_loss -0.7229 
+2024-09-13 14:46:02.158744: val_loss -0.6439 
+2024-09-13 14:46:02.158801: Pseudo dice [0.6912, 0.7963] 
+2024-09-13 14:46:02.158855: Epoch time: 246.13 s 
+2024-09-13 14:46:02.158899: Yayy! New best EMA pseudo Dice: 0.6779 
+2024-09-13 14:46:06.064169:  
+2024-09-13 14:46:06.064340: Epoch 30 
+2024-09-13 14:46:06.064435: Current learning rate: 0.00973 
+2024-09-13 14:50:12.277682: train_loss -0.7296 
+2024-09-13 14:50:12.277824: val_loss -0.6271 
+2024-09-13 14:50:12.277881: Pseudo dice [0.6416, 0.7658] 
+2024-09-13 14:50:12.277935: Epoch time: 246.22 s 
+2024-09-13 14:50:12.277978: Yayy! New best EMA pseudo Dice: 0.6804 
+2024-09-13 14:50:16.168456:  
+2024-09-13 14:50:16.168643: Epoch 31 
+2024-09-13 14:50:16.168733: Current learning rate: 0.00972 
+2024-09-13 14:54:22.392602: train_loss -0.7109 
+2024-09-13 14:54:22.392754: val_loss -0.6674 
+2024-09-13 14:54:22.392811: Pseudo dice [0.6776, 0.813] 
+2024-09-13 14:54:22.392867: Epoch time: 246.23 s 
+2024-09-13 14:54:22.392911: Yayy! New best EMA pseudo Dice: 0.6869 
+2024-09-13 14:54:26.132993:  
+2024-09-13 14:54:26.133205: Epoch 32 
+2024-09-13 14:54:26.133292: Current learning rate: 0.00971 
+2024-09-13 14:58:32.283468: train_loss -0.7374 
+2024-09-13 14:58:32.283614: val_loss -0.6467 
+2024-09-13 14:58:32.283671: Pseudo dice [0.64, 0.8007] 
+2024-09-13 14:58:32.283727: Epoch time: 246.15 s 
+2024-09-13 14:58:32.283772: Yayy! New best EMA pseudo Dice: 0.6903 
+2024-09-13 14:59:05.211833:  
+2024-09-13 14:59:05.212076: Epoch 33 
+2024-09-13 14:59:05.212197: Current learning rate: 0.0097 
+2024-09-13 15:03:56.277027: train_loss -0.7361 
+2024-09-13 15:03:56.277463: val_loss -0.6493 
+2024-09-13 15:03:56.277527: Pseudo dice [0.6725, 0.7894] 
+2024-09-13 15:03:56.277589: Epoch time: 291.07 s 
+2024-09-13 15:03:56.277647: Yayy! New best EMA pseudo Dice: 0.6943 
+2024-09-13 15:04:01.549327:  
+2024-09-13 15:04:01.549794: Epoch 34 
+2024-09-13 15:04:01.550002: Current learning rate: 0.00969 
+2024-09-13 15:08:18.153325: train_loss -0.7284 
+2024-09-13 15:08:18.153484: val_loss -0.6544 
+2024-09-13 15:08:18.153543: Pseudo dice [0.6575, 0.8059] 
+2024-09-13 15:08:18.153598: Epoch time: 256.61 s 
+2024-09-13 15:08:18.153641: Yayy! New best EMA pseudo Dice: 0.6981 
+2024-09-13 15:08:22.072228:  
+2024-09-13 15:08:22.072409: Epoch 35 
+2024-09-13 15:08:22.072496: Current learning rate: 0.00968 
+2024-09-13 15:12:31.046273: train_loss -0.7364 
+2024-09-13 15:12:31.046464: val_loss -0.6734 
+2024-09-13 15:12:31.046523: Pseudo dice [0.7071, 0.8118] 
+2024-09-13 15:12:31.046577: Epoch time: 248.98 s 
+2024-09-13 15:12:31.046621: Yayy! New best EMA pseudo Dice: 0.7042 
+2024-09-13 15:12:35.506349:  
+2024-09-13 15:12:35.506569: Epoch 36 
+2024-09-13 15:12:35.506679: Current learning rate: 0.00968 
+2024-09-13 15:16:41.870563: train_loss -0.7312 
+2024-09-13 15:16:41.870729: val_loss -0.6611 
+2024-09-13 15:16:41.870785: Pseudo dice [0.6864, 0.7879] 
+2024-09-13 15:16:41.870840: Epoch time: 246.37 s 
+2024-09-13 15:16:41.870884: Yayy! New best EMA pseudo Dice: 0.7075 
+2024-09-13 15:16:46.108989:  
+2024-09-13 15:16:46.109152: Epoch 37 
+2024-09-13 15:16:46.109238: Current learning rate: 0.00967 
+2024-09-13 15:20:52.324430: train_loss -0.759 
+2024-09-13 15:20:52.324610: val_loss -0.6609 
+2024-09-13 15:20:52.324668: Pseudo dice [0.6609, 0.8082] 
+2024-09-13 15:20:52.324725: Epoch time: 246.22 s 
+2024-09-13 15:20:52.324770: Yayy! New best EMA pseudo Dice: 0.7102 
+2024-09-13 15:20:56.243261:  
+2024-09-13 15:20:56.243459: Epoch 38 
+2024-09-13 15:20:56.243548: Current learning rate: 0.00966 
+2024-09-13 15:25:02.411981: train_loss -0.7407 
+2024-09-13 15:25:02.412125: val_loss -0.6518 
+2024-09-13 15:25:02.412183: Pseudo dice [0.6552, 0.792] 
+2024-09-13 15:25:02.412240: Epoch time: 246.17 s 
+2024-09-13 15:25:02.412285: Yayy! New best EMA pseudo Dice: 0.7116 
+2024-09-13 15:25:06.326192:  
+2024-09-13 15:25:06.326403: Epoch 39 
+2024-09-13 15:25:06.326494: Current learning rate: 0.00965 
+2024-09-13 15:29:12.451446: train_loss -0.6798 
+2024-09-13 15:29:12.451596: val_loss -0.644 
+2024-09-13 15:29:12.451653: Pseudo dice [0.6017, 0.7977] 
+2024-09-13 15:29:12.451707: Epoch time: 246.13 s 
+2024-09-13 15:29:13.449015:  
+2024-09-13 15:29:13.449197: Epoch 40 
+2024-09-13 15:29:13.449292: Current learning rate: 0.00964 
+2024-09-13 15:33:19.441903: train_loss -0.7228 
+2024-09-13 15:33:19.442045: val_loss -0.6873 
+2024-09-13 15:33:19.442124: Pseudo dice [0.6812, 0.8051] 
+2024-09-13 15:33:19.442211: Epoch time: 245.99 s 
+2024-09-13 15:33:19.442253: Yayy! New best EMA pseudo Dice: 0.7136 
+2024-09-13 15:33:23.381862:  
+2024-09-13 15:33:23.382014: Epoch 41 
+2024-09-13 15:33:23.382105: Current learning rate: 0.00963 
+2024-09-13 15:37:29.619963: train_loss -0.729 
+2024-09-13 15:37:29.620108: val_loss -0.6431 
+2024-09-13 15:37:29.620159: Pseudo dice [0.6966, 0.7779] 
+2024-09-13 15:37:29.620248: Epoch time: 246.24 s 
+2024-09-13 15:37:29.620290: Yayy! New best EMA pseudo Dice: 0.716 
+2024-09-13 15:37:33.528434:  
+2024-09-13 15:37:33.528627: Epoch 42 
+2024-09-13 15:37:33.528747: Current learning rate: 0.00962 
+2024-09-13 15:41:39.639815: train_loss -0.7391 
+2024-09-13 15:41:39.639988: val_loss -0.6367 
+2024-09-13 15:41:39.640082: Pseudo dice [0.6596, 0.8011] 
+2024-09-13 15:41:39.640141: Epoch time: 246.11 s 
+2024-09-13 15:41:39.640189: Yayy! New best EMA pseudo Dice: 0.7174 
+2024-09-13 15:41:43.518829:  
+2024-09-13 15:41:43.518997: Epoch 43 
+2024-09-13 15:41:43.519088: Current learning rate: 0.00961 
+2024-09-13 15:45:49.859551: train_loss -0.7266 
+2024-09-13 15:45:49.859724: val_loss -0.6964 
+2024-09-13 15:45:49.859776: Pseudo dice [0.7043, 0.8043] 
+2024-09-13 15:45:49.859840: Epoch time: 246.34 s 
+2024-09-13 15:45:49.859883: Yayy! New best EMA pseudo Dice: 0.7211 
+2024-09-13 15:45:53.737799:  
+2024-09-13 15:45:53.737991: Epoch 44 
+2024-09-13 15:45:53.738075: Current learning rate: 0.0096 
+2024-09-13 15:49:59.880758: train_loss -0.7316 
+2024-09-13 15:49:59.880901: val_loss -0.6221 
+2024-09-13 15:49:59.880952: Pseudo dice [0.6261, 0.7843] 
+2024-09-13 15:49:59.881004: Epoch time: 246.14 s 
+2024-09-13 15:50:00.796391:  
+2024-09-13 15:50:00.796587: Epoch 45 
+2024-09-13 15:50:00.796670: Current learning rate: 0.00959 
+2024-09-13 15:54:06.658713: train_loss -0.7282 
+2024-09-13 15:54:06.658856: val_loss -0.6758 
+2024-09-13 15:54:06.658907: Pseudo dice [0.6565, 0.8096] 
+2024-09-13 15:54:06.658958: Epoch time: 245.86 s 
+2024-09-13 15:54:07.589676:  
+2024-09-13 15:54:07.589832: Epoch 46 
+2024-09-13 15:54:07.589915: Current learning rate: 0.00959 
+2024-09-13 15:58:13.463010: train_loss -0.7468 
+2024-09-13 15:58:13.463143: val_loss -0.6674 
+2024-09-13 15:58:13.463194: Pseudo dice [0.6673, 0.8013] 
+2024-09-13 15:58:13.463245: Epoch time: 245.88 s 
+2024-09-13 15:58:13.463284: Yayy! New best EMA pseudo Dice: 0.7222 
+2024-09-13 15:58:17.313138:  
+2024-09-13 15:58:17.313336: Epoch 47 
+2024-09-13 15:58:17.313479: Current learning rate: 0.00958 
+2024-09-13 16:02:23.364050: train_loss -0.744 
+2024-09-13 16:02:23.364186: val_loss -0.6701 
+2024-09-13 16:02:23.364236: Pseudo dice [0.674, 0.7849] 
+2024-09-13 16:02:23.364286: Epoch time: 246.05 s 
+2024-09-13 16:02:23.364401: Yayy! New best EMA pseudo Dice: 0.723 
+2024-09-13 16:02:27.228950:  
+2024-09-13 16:02:27.229091: Epoch 48 
+2024-09-13 16:02:27.229170: Current learning rate: 0.00957 
+2024-09-13 16:06:33.355617: train_loss -0.7368 
+2024-09-13 16:06:33.355757: val_loss -0.6435 
+2024-09-13 16:06:33.355813: Pseudo dice [0.6398, 0.804] 
+2024-09-13 16:06:33.355866: Epoch time: 246.13 s 
+2024-09-13 16:06:34.302715:  
+2024-09-13 16:06:34.302924: Epoch 49 
+2024-09-13 16:06:34.303008: Current learning rate: 0.00956 
+2024-09-13 16:10:40.259018: train_loss -0.7496 
+2024-09-13 16:10:40.259202: val_loss -0.6632 
+2024-09-13 16:10:40.259298: Pseudo dice [0.6686, 0.8128] 
+2024-09-13 16:10:40.259352: Epoch time: 245.96 s 
+2024-09-13 16:10:41.326168: Yayy! New best EMA pseudo Dice: 0.7246 
+2024-09-13 16:10:45.208009:  
+2024-09-13 16:10:45.208155: Epoch 50 
+2024-09-13 16:10:45.208246: Current learning rate: 0.00955 
+2024-09-13 16:14:51.441721: train_loss -0.7535 
+2024-09-13 16:14:51.441875: val_loss -0.6467 
+2024-09-13 16:14:51.441966: Pseudo dice [0.6622, 0.8075] 
+2024-09-13 16:14:51.442056: Epoch time: 246.24 s 
+2024-09-13 16:14:51.442147: Yayy! New best EMA pseudo Dice: 0.7257 
+2024-09-13 16:14:55.304460:  
+2024-09-13 16:14:55.304688: Epoch 51 
+2024-09-13 16:14:55.304776: Current learning rate: 0.00954 
+2024-09-13 16:19:01.559931: train_loss -0.7553 
+2024-09-13 16:19:01.560056: val_loss -0.6457 
+2024-09-13 16:19:01.560107: Pseudo dice [0.6886, 0.7891] 
+2024-09-13 16:19:01.560158: Epoch time: 246.26 s 
+2024-09-13 16:19:01.560198: Yayy! New best EMA pseudo Dice: 0.727 
+2024-09-13 16:19:05.410579:  
+2024-09-13 16:19:05.410791: Epoch 52 
+2024-09-13 16:19:05.410878: Current learning rate: 0.00953 
+2024-09-13 16:23:11.876958: train_loss -0.7537 
+2024-09-13 16:23:11.877127: val_loss -0.646 
+2024-09-13 16:23:11.877180: Pseudo dice [0.6798, 0.7774] 
+2024-09-13 16:23:11.877230: Epoch time: 246.47 s 
+2024-09-13 16:23:11.877269: Yayy! New best EMA pseudo Dice: 0.7271 
+2024-09-13 16:23:15.756601:  
+2024-09-13 16:23:15.756748: Epoch 53 
+2024-09-13 16:23:15.756829: Current learning rate: 0.00952 
+2024-09-13 16:27:22.079472: train_loss -0.7565 
+2024-09-13 16:27:22.079632: val_loss -0.6417 
+2024-09-13 16:27:22.079684: Pseudo dice [0.6589, 0.8006] 
+2024-09-13 16:27:22.079734: Epoch time: 246.32 s 
+2024-09-13 16:27:22.079774: Yayy! New best EMA pseudo Dice: 0.7274 
+2024-09-13 16:27:25.973577:  
+2024-09-13 16:27:25.973709: Epoch 54 
+2024-09-13 16:27:25.973799: Current learning rate: 0.00951 
+2024-09-13 16:31:32.241739: train_loss -0.7525 
+2024-09-13 16:31:32.241914: val_loss -0.6258 
+2024-09-13 16:31:32.241971: Pseudo dice [0.5965, 0.7614] 
+2024-09-13 16:31:32.242021: Epoch time: 246.27 s 
+2024-09-13 16:31:33.206453:  
+2024-09-13 16:31:33.206640: Epoch 55 
+2024-09-13 16:31:33.206722: Current learning rate: 0.0095 
+2024-09-13 16:35:39.404079: train_loss -0.7413 
+2024-09-13 16:35:39.404242: val_loss -0.6769 
+2024-09-13 16:35:39.404293: Pseudo dice [0.6837, 0.8185] 
+2024-09-13 16:35:39.404343: Epoch time: 246.2 s 
+2024-09-13 16:35:40.374108:  
+2024-09-13 16:35:40.374362: Epoch 56 
+2024-09-13 16:35:40.374449: Current learning rate: 0.00949 
+2024-09-13 16:39:47.408322: train_loss -0.7715 
+2024-09-13 16:39:47.408477: val_loss -0.6401 
+2024-09-13 16:39:47.408534: Pseudo dice [0.6683, 0.7891] 
+2024-09-13 16:39:47.408589: Epoch time: 247.04 s 
+2024-09-13 16:39:48.378777:  
+2024-09-13 16:39:48.379002: Epoch 57 
+2024-09-13 16:39:48.379092: Current learning rate: 0.00949 
+2024-09-13 16:43:54.416223: train_loss -0.7618 
+2024-09-13 16:43:54.416385: val_loss -0.6842 
+2024-09-13 16:43:54.416483: Pseudo dice [0.6561, 0.824] 
+2024-09-13 16:43:54.416539: Epoch time: 246.04 s 
+2024-09-13 16:43:55.357695:  
+2024-09-13 16:43:55.357921: Epoch 58 
+2024-09-13 16:43:55.358010: Current learning rate: 0.00948 
+2024-09-13 16:48:01.589386: train_loss -0.7651 
+2024-09-13 16:48:01.589534: val_loss -0.6593 
+2024-09-13 16:48:01.589589: Pseudo dice [0.6888, 0.7955] 
+2024-09-13 16:48:01.589644: Epoch time: 246.23 s 
+2024-09-13 16:48:01.589688: Yayy! New best EMA pseudo Dice: 0.7287 
+2024-09-13 16:48:05.487893:  
+2024-09-13 16:48:05.488108: Epoch 59 
+2024-09-13 16:48:05.488194: Current learning rate: 0.00947 
+2024-09-13 16:52:11.646479: train_loss -0.7553 
+2024-09-13 16:52:11.646646: val_loss -0.6295 
+2024-09-13 16:52:11.646702: Pseudo dice [0.642, 0.7599] 
+2024-09-13 16:52:11.646757: Epoch time: 246.16 s 
+2024-09-13 16:52:12.600183:  
+2024-09-13 16:52:12.600387: Epoch 60 
+2024-09-13 16:52:12.600501: Current learning rate: 0.00946 
+2024-09-13 16:56:18.558010: train_loss -0.7413 
+2024-09-13 16:56:18.558151: val_loss -0.6453 
+2024-09-13 16:56:18.558209: Pseudo dice [0.6333, 0.8022] 
+2024-09-13 16:56:18.558263: Epoch time: 245.96 s 
+2024-09-13 16:56:19.501915:  
+2024-09-13 16:56:19.502070: Epoch 61 
+2024-09-13 16:56:19.502156: Current learning rate: 0.00945 
+2024-09-13 17:00:25.417714: train_loss -0.7499 
+2024-09-13 17:00:25.417879: val_loss -0.6775 
+2024-09-13 17:00:25.417940: Pseudo dice [0.6785, 0.7983] 
+2024-09-13 17:00:25.417995: Epoch time: 245.92 s 
+2024-09-13 17:00:26.375674:  
+2024-09-13 17:00:26.375852: Epoch 62 
+2024-09-13 17:00:26.375971: Current learning rate: 0.00944 
+2024-09-13 17:04:32.009327: train_loss -0.7444 
+2024-09-13 17:04:32.009479: val_loss -0.643 
+2024-09-13 17:04:32.009535: Pseudo dice [0.6465, 0.8022] 
+2024-09-13 17:04:32.009591: Epoch time: 245.64 s 
+2024-09-13 17:04:32.960735:  
+2024-09-13 17:04:32.960956: Epoch 63 
+2024-09-13 17:04:32.961049: Current learning rate: 0.00943 
+2024-09-13 17:08:38.692736: train_loss -0.7533 
+2024-09-13 17:08:38.692880: val_loss -0.65 
+2024-09-13 17:08:38.692935: Pseudo dice [0.6607, 0.805] 
+2024-09-13 17:08:38.692990: Epoch time: 245.73 s 
+2024-09-13 17:08:39.639744:  
+2024-09-13 17:08:39.639935: Epoch 64 
+2024-09-13 17:08:39.640025: Current learning rate: 0.00942 
+2024-09-13 17:12:45.246132: train_loss -0.7608 
+2024-09-13 17:12:45.246297: val_loss -0.6476 
+2024-09-13 17:12:45.246355: Pseudo dice [0.6022, 0.8068] 
+2024-09-13 17:12:45.246413: Epoch time: 245.61 s 
+2024-09-13 17:12:46.203789:  
+2024-09-13 17:12:46.204007: Epoch 65 
+2024-09-13 17:12:46.204098: Current learning rate: 0.00941 
+2024-09-13 17:16:51.865465: train_loss -0.7515 
+2024-09-13 17:16:51.865633: val_loss -0.6744 
+2024-09-13 17:16:51.865690: Pseudo dice [0.676, 0.8074] 
+2024-09-13 17:16:51.865746: Epoch time: 245.66 s 
+2024-09-13 17:16:52.833493:  
+2024-09-13 17:16:52.833699: Epoch 66 
+2024-09-13 17:16:52.833797: Current learning rate: 0.0094 
+2024-09-13 17:20:58.391681: train_loss -0.7526 
+2024-09-13 17:20:58.391833: val_loss -0.6784 
+2024-09-13 17:20:58.391890: Pseudo dice [0.6775, 0.8053] 
+2024-09-13 17:20:58.391945: Epoch time: 245.56 s 
+2024-09-13 17:20:59.341750:  
+2024-09-13 17:20:59.341907: Epoch 67 
+2024-09-13 17:20:59.341993: Current learning rate: 0.00939 
+2024-09-13 17:25:05.122039: train_loss -0.7635 
+2024-09-13 17:25:05.122189: val_loss -0.6662 
+2024-09-13 17:25:05.122248: Pseudo dice [0.6746, 0.7794] 
+2024-09-13 17:25:05.122304: Epoch time: 245.78 s 
+2024-09-13 17:25:06.099280:  
+2024-09-13 17:25:06.099440: Epoch 68 
+2024-09-13 17:25:06.099528: Current learning rate: 0.00939 
+2024-09-13 17:29:11.809295: train_loss -0.752 
+2024-09-13 17:29:11.809445: val_loss -0.6947 
+2024-09-13 17:29:11.809502: Pseudo dice [0.6759, 0.8238] 
+2024-09-13 17:29:11.809557: Epoch time: 245.71 s 
+2024-09-13 17:29:11.809602: Yayy! New best EMA pseudo Dice: 0.73 
+2024-09-13 17:29:15.730895:  
+2024-09-13 17:29:15.731065: Epoch 69 
+2024-09-13 17:29:15.731155: Current learning rate: 0.00938 
+2024-09-13 17:33:21.689239: train_loss -0.7672 
+2024-09-13 17:33:21.689483: val_loss -0.6826 
+2024-09-13 17:33:21.689540: Pseudo dice [0.6665, 0.8109] 
+2024-09-13 17:33:21.689594: Epoch time: 245.96 s 
+2024-09-13 17:33:21.689637: Yayy! New best EMA pseudo Dice: 0.7308 
+2024-09-13 17:33:25.562066:  
+2024-09-13 17:33:25.562281: Epoch 70 
+2024-09-13 17:33:25.562374: Current learning rate: 0.00937 
+2024-09-13 17:37:31.654631: train_loss -0.772 
+2024-09-13 17:37:31.654776: val_loss -0.6355 
+2024-09-13 17:37:31.654832: Pseudo dice [0.673, 0.8067] 
+2024-09-13 17:37:31.654886: Epoch time: 246.09 s 
+2024-09-13 17:37:31.654929: Yayy! New best EMA pseudo Dice: 0.7317 
+2024-09-13 17:37:35.587680:  
+2024-09-13 17:37:35.587913: Epoch 71 
+2024-09-13 17:37:35.588008: Current learning rate: 0.00936 
+2024-09-13 17:41:41.549457: train_loss -0.7727 
+2024-09-13 17:41:41.549603: val_loss -0.6895 
+2024-09-13 17:41:41.549660: Pseudo dice [0.7013, 0.8096] 
+2024-09-13 17:41:41.549715: Epoch time: 245.96 s 
+2024-09-13 17:41:41.549758: Yayy! New best EMA pseudo Dice: 0.7341 
+2024-09-13 17:41:45.436213:  
+2024-09-13 17:41:45.436436: Epoch 72 
+2024-09-13 17:41:45.436527: Current learning rate: 0.00935 
+2024-09-13 17:45:51.483202: train_loss -0.7692 
+2024-09-13 17:45:51.483356: val_loss -0.6942 
+2024-09-13 17:45:51.483467: Pseudo dice [0.6708, 0.831] 
+2024-09-13 17:45:51.483523: Epoch time: 246.05 s 
+2024-09-13 17:45:51.483567: Yayy! New best EMA pseudo Dice: 0.7358 
+2024-09-13 17:45:55.398470:  
+2024-09-13 17:45:55.398701: Epoch 73 
+2024-09-13 17:45:55.398810: Current learning rate: 0.00934 
+2024-09-13 17:50:01.424746: train_loss -0.7795 
+2024-09-13 17:50:01.424894: val_loss -0.655 
+2024-09-13 17:50:01.424949: Pseudo dice [0.6686, 0.8099] 
+2024-09-13 17:50:01.425004: Epoch time: 246.03 s 
+2024-09-13 17:50:01.425049: Yayy! New best EMA pseudo Dice: 0.7361 
+2024-09-13 17:50:05.341799:  
+2024-09-13 17:50:05.341973: Epoch 74 
+2024-09-13 17:50:05.342062: Current learning rate: 0.00933 
+2024-09-13 17:54:11.204747: train_loss -0.7706 
+2024-09-13 17:54:11.204905: val_loss -0.658 
+2024-09-13 17:54:11.204997: Pseudo dice [0.635, 0.8105] 
+2024-09-13 17:54:11.205056: Epoch time: 245.86 s 
+2024-09-13 17:54:12.159842:  
+2024-09-13 17:54:12.160003: Epoch 75 
+2024-09-13 17:54:12.160098: Current learning rate: 0.00932 
+2024-09-13 17:58:17.790596: train_loss -0.7774 
+2024-09-13 17:58:17.790807: val_loss -0.6738 
+2024-09-13 17:58:17.790910: Pseudo dice [0.7129, 0.8099] 
+2024-09-13 17:58:17.791008: Epoch time: 245.63 s 
+2024-09-13 17:58:17.791092: Yayy! New best EMA pseudo Dice: 0.7375 
+2024-09-13 17:58:21.675711:  
+2024-09-13 17:58:21.675920: Epoch 76 
+2024-09-13 17:58:21.676007: Current learning rate: 0.00931 
+2024-09-13 18:02:27.795683: train_loss -0.7788 
+2024-09-13 18:02:27.795833: val_loss -0.6672 
+2024-09-13 18:02:27.795892: Pseudo dice [0.6826, 0.809] 
+2024-09-13 18:02:27.795947: Epoch time: 246.12 s 
+2024-09-13 18:02:27.795991: Yayy! New best EMA pseudo Dice: 0.7383 
+2024-09-13 18:02:31.740657:  
+2024-09-13 18:02:31.740836: Epoch 77 
+2024-09-13 18:02:31.740938: Current learning rate: 0.0093 
+2024-09-13 18:06:37.752451: train_loss -0.7862 
+2024-09-13 18:06:37.752596: val_loss -0.7215 
+2024-09-13 18:06:37.752652: Pseudo dice [0.697, 0.8328] 
+2024-09-13 18:06:37.752707: Epoch time: 246.01 s 
+2024-09-13 18:06:37.752752: Yayy! New best EMA pseudo Dice: 0.741 
+2024-09-13 18:06:41.697372:  
+2024-09-13 18:06:41.697572: Epoch 78 
+2024-09-13 18:06:41.697662: Current learning rate: 0.0093 
+2024-09-13 18:10:48.420782: train_loss -0.7781 
+2024-09-13 18:10:48.420934: val_loss -0.6517 
+2024-09-13 18:10:48.420990: Pseudo dice [0.6355, 0.817] 
+2024-09-13 18:10:48.421046: Epoch time: 246.73 s 
+2024-09-13 18:10:49.434756:  
+2024-09-13 18:10:49.434977: Epoch 79 
+2024-09-13 18:10:49.435072: Current learning rate: 0.00929 
+2024-09-13 18:14:55.147728: train_loss -0.7749 
+2024-09-13 18:14:55.147896: val_loss -0.6227 
+2024-09-13 18:14:55.147954: Pseudo dice [0.6798, 0.7709] 
+2024-09-13 18:14:55.148008: Epoch time: 245.71 s 
+2024-09-13 18:14:56.128841:  
+2024-09-13 18:14:56.129046: Epoch 80 
+2024-09-13 18:14:56.129137: Current learning rate: 0.00928 
+2024-09-13 18:19:01.943264: train_loss -0.7672 
+2024-09-13 18:19:01.943413: val_loss -0.6734 
+2024-09-13 18:19:01.943469: Pseudo dice [0.6954, 0.7966] 
+2024-09-13 18:19:01.943524: Epoch time: 245.82 s 
+2024-09-13 18:19:02.924344:  
+2024-09-13 18:19:02.924535: Epoch 81 
+2024-09-13 18:19:02.924620: Current learning rate: 0.00927 
+2024-09-13 18:23:08.759943: train_loss -0.765 
+2024-09-13 18:23:08.760110: val_loss -0.6659 
+2024-09-13 18:23:08.760192: Pseudo dice [0.691, 0.8124] 
+2024-09-13 18:23:08.760281: Epoch time: 245.84 s 
+2024-09-13 18:23:09.745984:  
+2024-09-13 18:23:09.746189: Epoch 82 
+2024-09-13 18:23:09.746280: Current learning rate: 0.00926 
+2024-09-13 18:27:15.617688: train_loss -0.7516 
+2024-09-13 18:27:15.617922: val_loss -0.6581 
+2024-09-13 18:27:15.618062: Pseudo dice [0.6792, 0.7909] 
+2024-09-13 18:27:15.618167: Epoch time: 245.87 s 
+2024-09-13 18:27:16.574769:  
+2024-09-13 18:27:16.574990: Epoch 83 
+2024-09-13 18:27:16.575079: Current learning rate: 0.00925 
+2024-09-13 18:31:22.333070: train_loss -0.7784 
+2024-09-13 18:31:22.333219: val_loss -0.6953 
+2024-09-13 18:31:22.333274: Pseudo dice [0.6928, 0.8267] 
+2024-09-13 18:31:22.333329: Epoch time: 245.76 s 
+2024-09-13 18:31:22.333373: Yayy! New best EMA pseudo Dice: 0.7417 
+2024-09-13 18:31:26.199539:  
+2024-09-13 18:31:26.199725: Epoch 84 
+2024-09-13 18:31:26.199827: Current learning rate: 0.00924 
+2024-09-13 18:35:31.732280: train_loss -0.7674 
+2024-09-13 18:35:31.732444: val_loss -0.6663 
+2024-09-13 18:35:31.732500: Pseudo dice [0.6737, 0.8067] 
+2024-09-13 18:35:31.732555: Epoch time: 245.53 s 
+2024-09-13 18:35:32.669939:  
+2024-09-13 18:35:32.670131: Epoch 85 
+2024-09-13 18:35:32.670217: Current learning rate: 0.00923 
+2024-09-13 18:39:38.156959: train_loss -0.7689 
+2024-09-13 18:39:38.157145: val_loss -0.656 
+2024-09-13 18:39:38.157201: Pseudo dice [0.6324, 0.8107] 
+2024-09-13 18:39:38.157257: Epoch time: 245.49 s 
+2024-09-13 18:39:39.090740:  
+2024-09-13 18:39:39.090954: Epoch 86 
+2024-09-13 18:39:39.091044: Current learning rate: 0.00922 
+2024-09-13 18:43:44.557815: train_loss -0.7477 
+2024-09-13 18:43:44.557961: val_loss -0.6856 
+2024-09-13 18:43:44.558017: Pseudo dice [0.7158, 0.8118] 
+2024-09-13 18:43:44.558073: Epoch time: 245.47 s 
+2024-09-13 18:43:44.558117: Yayy! New best EMA pseudo Dice: 0.7419 
+2024-09-13 18:43:48.444590:  
+2024-09-13 18:43:48.444852: Epoch 87 
+2024-09-13 18:43:48.444943: Current learning rate: 0.00921 
+2024-09-13 18:47:54.131324: train_loss -0.7527 
+2024-09-13 18:47:54.131476: val_loss -0.6665 
+2024-09-13 18:47:54.131534: Pseudo dice [0.6834, 0.8078] 
+2024-09-13 18:47:54.131592: Epoch time: 245.69 s 
+2024-09-13 18:47:54.131637: Yayy! New best EMA pseudo Dice: 0.7423 
+2024-09-13 18:47:58.140532:  
+2024-09-13 18:47:58.140754: Epoch 88 
+2024-09-13 18:47:58.140843: Current learning rate: 0.0092 
+2024-09-13 18:52:03.982112: train_loss -0.7708 
+2024-09-13 18:52:03.982264: val_loss -0.6545 
+2024-09-13 18:52:03.982352: Pseudo dice [0.6941, 0.8061] 
+2024-09-13 18:52:03.982439: Epoch time: 245.84 s 
+2024-09-13 18:52:03.982485: Yayy! New best EMA pseudo Dice: 0.7431 
+2024-09-13 18:52:08.009031:  
+2024-09-13 18:52:08.009243: Epoch 89 
+2024-09-13 18:52:08.009329: Current learning rate: 0.0092 
+2024-09-13 18:56:14.080226: train_loss -0.7826 
+2024-09-13 18:56:14.080371: val_loss -0.6639 
+2024-09-13 18:56:14.080426: Pseudo dice [0.6462, 0.8008] 
+2024-09-13 18:56:14.080481: Epoch time: 246.07 s 
+2024-09-13 18:56:15.026124:  
+2024-09-13 18:56:15.026311: Epoch 90 
+2024-09-13 18:56:15.026401: Current learning rate: 0.00919 
+2024-09-13 19:00:20.669979: train_loss -0.7795 
+2024-09-13 19:00:20.670135: val_loss -0.6929 
+2024-09-13 19:00:20.670192: Pseudo dice [0.6881, 0.8385] 
+2024-09-13 19:00:20.670249: Epoch time: 245.65 s 
+2024-09-13 19:00:20.670295: Yayy! New best EMA pseudo Dice: 0.7433 
+2024-09-13 19:00:24.556416:  
+2024-09-13 19:00:24.556591: Epoch 91 
+2024-09-13 19:00:24.556688: Current learning rate: 0.00918 
+2024-09-13 19:04:30.574434: train_loss -0.7845 
+2024-09-13 19:04:30.574579: val_loss -0.6889 
+2024-09-13 19:04:30.574671: Pseudo dice [0.6799, 0.8045] 
+2024-09-13 19:04:30.574729: Epoch time: 246.02 s 
+2024-09-13 19:04:31.507846:  
+2024-09-13 19:04:31.508004: Epoch 92 
+2024-09-13 19:04:31.508089: Current learning rate: 0.00917 
+2024-09-13 19:08:37.042736: train_loss -0.7801 
+2024-09-13 19:08:37.042893: val_loss -0.6577 
+2024-09-13 19:08:37.043008: Pseudo dice [0.7012, 0.7901] 
+2024-09-13 19:08:37.043065: Epoch time: 245.54 s 
+2024-09-13 19:08:37.043109: Yayy! New best EMA pseudo Dice: 0.7435 
+2024-09-13 19:08:40.957007:  
+2024-09-13 19:08:40.957224: Epoch 93 
+2024-09-13 19:08:40.957311: Current learning rate: 0.00916 
+2024-09-13 19:12:46.835679: train_loss -0.7883 
+2024-09-13 19:12:46.835829: val_loss -0.683 
+2024-09-13 19:12:46.835885: Pseudo dice [0.6927, 0.8036] 
+2024-09-13 19:12:46.835940: Epoch time: 245.88 s 
+2024-09-13 19:12:46.835983: Yayy! New best EMA pseudo Dice: 0.7439 
+2024-09-13 19:12:50.698124:  
+2024-09-13 19:12:50.698281: Epoch 94 
+2024-09-13 19:12:50.698417: Current learning rate: 0.00915 
+2024-09-13 19:17:36.905814: train_loss -0.7849 
+2024-09-13 19:17:36.906116: val_loss -0.6565 
+2024-09-13 19:17:36.906232: Pseudo dice [0.6625, 0.8206] 
+2024-09-13 19:17:36.906342: Epoch time: 286.21 s 
+2024-09-13 19:17:38.532876:  
+2024-09-13 19:17:38.533073: Epoch 95 
+2024-09-13 19:17:38.533167: Current learning rate: 0.00914 
+2024-09-13 19:21:58.640890: train_loss -0.7778 
+2024-09-13 19:21:58.646454: val_loss -0.6726 
+2024-09-13 19:21:58.646522: Pseudo dice [0.6777, 0.8247] 
+2024-09-13 19:21:58.646585: Epoch time: 260.11 s 
+2024-09-13 19:21:58.646698: Yayy! New best EMA pseudo Dice: 0.7445 
+2024-09-13 19:22:19.177196:  
+2024-09-13 19:22:19.177506: Epoch 96 
+2024-09-13 19:22:19.177603: Current learning rate: 0.00913 
+2024-09-13 19:27:11.332996: train_loss -0.7761 
+2024-09-13 19:27:11.333380: val_loss -0.6618 
+2024-09-13 19:27:11.333439: Pseudo dice [0.6443, 0.7897] 
+2024-09-13 19:27:11.333494: Epoch time: 292.16 s 
+2024-09-13 19:27:12.292739:  
+2024-09-13 19:27:12.292962: Epoch 97 
+2024-09-13 19:27:12.293047: Current learning rate: 0.00912 
+2024-09-13 19:31:20.908834: train_loss -0.7677 
+2024-09-13 19:31:20.908977: val_loss -0.6389 
+2024-09-13 19:31:20.909034: Pseudo dice [0.6251, 0.8099] 
+2024-09-13 19:31:20.909136: Epoch time: 248.62 s 
+2024-09-13 19:31:21.849290:  
+2024-09-13 19:31:21.849455: Epoch 98 
+2024-09-13 19:31:21.849546: Current learning rate: 0.00911 
+2024-09-13 19:35:44.726854: train_loss -0.778 
+2024-09-13 19:35:44.726993: val_loss -0.6557 
+2024-09-13 19:35:44.727048: Pseudo dice [0.6597, 0.8155] 
+2024-09-13 19:35:44.727103: Epoch time: 262.88 s 
+2024-09-13 19:35:45.676488:  
+2024-09-13 19:35:45.676686: Epoch 99 
+2024-09-13 19:35:45.676780: Current learning rate: 0.0091 
+2024-09-13 19:39:52.053627: train_loss -0.7726 
+2024-09-13 19:39:52.053789: val_loss -0.667 
+2024-09-13 19:39:52.053851: Pseudo dice [0.6647, 0.8206] 
+2024-09-13 19:39:52.053910: Epoch time: 246.38 s 
+2024-09-13 19:39:55.883290:  
+2024-09-13 19:39:55.883481: Epoch 100 
+2024-09-13 19:39:55.883571: Current learning rate: 0.0091 
+2024-09-13 19:44:01.965476: train_loss -0.7788 
+2024-09-13 19:44:01.965634: val_loss -0.6755 
+2024-09-13 19:44:01.965696: Pseudo dice [0.6932, 0.8048] 
+2024-09-13 19:44:01.965757: Epoch time: 246.08 s 
+2024-09-13 19:44:02.913274:  
+2024-09-13 19:44:02.913465: Epoch 101 
+2024-09-13 19:44:02.913554: Current learning rate: 0.00909 
+2024-09-13 19:48:09.423707: train_loss -0.7704 
+2024-09-13 19:48:09.423910: val_loss -0.6734 
+2024-09-13 19:48:09.423970: Pseudo dice [0.6502, 0.8167] 
+2024-09-13 19:48:09.424027: Epoch time: 246.51 s 
+2024-09-13 19:48:10.375076:  
+2024-09-13 19:48:10.375289: Epoch 102 
+2024-09-13 19:48:10.375379: Current learning rate: 0.00908 
+2024-09-13 19:52:16.078517: train_loss -0.7858 
+2024-09-13 19:52:16.078665: val_loss -0.7002 
+2024-09-13 19:52:16.078725: Pseudo dice [0.7013, 0.8171] 
+2024-09-13 19:52:16.078780: Epoch time: 245.71 s 
+2024-09-13 19:52:17.017464:  
+2024-09-13 19:52:17.017663: Epoch 103 
+2024-09-13 19:52:17.017751: Current learning rate: 0.00907 
+2024-09-13 19:56:26.170186: train_loss -0.7934 
+2024-09-13 19:56:26.170346: val_loss -0.6881 
+2024-09-13 19:56:26.170406: Pseudo dice [0.678, 0.8363] 
+2024-09-13 19:56:26.170469: Epoch time: 249.15 s 
+2024-09-13 19:56:27.677399:  
+2024-09-13 19:56:27.677654: Epoch 104 
+2024-09-13 19:56:27.677774: Current learning rate: 0.00906 
+2024-09-13 20:01:31.411031: train_loss -0.7866 
+2024-09-13 20:01:31.411424: val_loss -0.6695 
+2024-09-13 20:01:31.411488: Pseudo dice [0.6682, 0.8245] 
+2024-09-13 20:01:31.411551: Epoch time: 303.74 s 
+2024-09-13 20:01:32.940249:  
+2024-09-13 20:01:32.940647: Epoch 105 
+2024-09-13 20:01:32.940823: Current learning rate: 0.00905 
+2024-09-13 20:05:59.205036: train_loss -0.7875 
+2024-09-13 20:05:59.205503: val_loss -0.6778 
+2024-09-13 20:05:59.205720: Pseudo dice [0.6471, 0.8169] 
+2024-09-13 20:05:59.205880: Epoch time: 266.27 s 
+2024-09-13 20:06:01.017785:  
+2024-09-13 20:06:01.018201: Epoch 106 
+2024-09-13 20:06:01.018365: Current learning rate: 0.00904 
+2024-09-13 20:10:48.649167: train_loss -0.7928 
+2024-09-13 20:10:48.649339: val_loss -0.6714 
+2024-09-13 20:10:48.649478: Pseudo dice [0.7131, 0.8125] 
+2024-09-13 20:10:48.649546: Epoch time: 287.64 s 
+2024-09-13 20:10:49.955852:  
+2024-09-13 20:10:49.956243: Epoch 107 
+2024-09-13 20:10:49.956405: Current learning rate: 0.00903 
+2024-09-13 20:15:23.001183: train_loss -0.7908 
+2024-09-13 20:15:23.001638: val_loss -0.6763 
+2024-09-13 20:15:23.001730: Pseudo dice [0.6556, 0.8296] 
+2024-09-13 20:15:23.001798: Epoch time: 273.05 s 
+2024-09-13 20:15:24.274876:  
+2024-09-13 20:15:24.275157: Epoch 108 
+2024-09-13 20:15:24.275343: Current learning rate: 0.00902 
+2024-09-13 20:20:24.848324: train_loss -0.8065 
+2024-09-13 20:20:24.848715: val_loss -0.6644 
+2024-09-13 20:20:24.848779: Pseudo dice [0.6833, 0.8236] 
+2024-09-13 20:20:24.848852: Epoch time: 300.58 s 
+2024-09-13 20:20:24.848905: Yayy! New best EMA pseudo Dice: 0.7452 
+2024-09-13 20:20:32.989801:  
+2024-09-13 20:20:32.990200: Epoch 109 
+2024-09-13 20:20:32.990435: Current learning rate: 0.00901 
+2024-09-13 20:25:16.214203: train_loss -0.7971 
+2024-09-13 20:25:16.215583: val_loss -0.6576 
+2024-09-13 20:25:16.216504: Pseudo dice [0.6895, 0.8167] 
+2024-09-13 20:25:16.216888: Epoch time: 283.23 s 
+2024-09-13 20:25:16.217178: Yayy! New best EMA pseudo Dice: 0.746 
+2024-09-13 20:25:22.837816:  
+2024-09-13 20:25:22.838121: Epoch 110 
+2024-09-13 20:25:22.838251: Current learning rate: 0.009 
+2024-09-13 20:30:03.025576: train_loss -0.7975 
+2024-09-13 20:30:03.026011: val_loss -0.6658 
+2024-09-13 20:30:03.026092: Pseudo dice [0.6905, 0.7963] 
+2024-09-13 20:30:03.026163: Epoch time: 280.19 s 
+2024-09-13 20:30:04.257764:  
+2024-09-13 20:30:04.258005: Epoch 111 
+2024-09-13 20:30:04.258196: Current learning rate: 0.009 
+2024-09-13 20:34:54.182675: train_loss -0.7899 
+2024-09-13 20:34:54.182878: val_loss -0.67 
+2024-09-13 20:34:54.182958: Pseudo dice [0.6399, 0.8129] 
+2024-09-13 20:34:54.183036: Epoch time: 289.93 s 
+2024-09-13 20:34:55.505308:  
+2024-09-13 20:34:55.505512: Epoch 112 
+2024-09-13 20:34:55.505680: Current learning rate: 0.00899 
+2024-09-13 20:39:41.574229: train_loss -0.7967 
+2024-09-13 20:39:41.574694: val_loss -0.6883 
+2024-09-13 20:39:41.574775: Pseudo dice [0.6911, 0.8228] 
+2024-09-13 20:39:41.574844: Epoch time: 286.07 s 
+2024-09-13 20:39:42.927948:  
+2024-09-13 20:39:42.928281: Epoch 113 
+2024-09-13 20:39:42.928442: Current learning rate: 0.00898 
+2024-09-13 20:44:12.192836: train_loss -0.8081 
+2024-09-13 20:44:12.193012: val_loss -0.6401 
+2024-09-13 20:44:12.193069: Pseudo dice [0.6488, 0.8003] 
+2024-09-13 20:44:12.193127: Epoch time: 269.27 s 
+2024-09-13 20:44:13.304273:  
+2024-09-13 20:44:13.304536: Epoch 114 
+2024-09-13 20:44:13.304633: Current learning rate: 0.00897 
+2024-09-13 20:48:19.043531: train_loss -0.782 
+2024-09-13 20:48:19.043707: val_loss -0.6639 
+2024-09-13 20:48:19.043765: Pseudo dice [0.6729, 0.8149] 
+2024-09-13 20:48:19.043829: Epoch time: 245.74 s 
+2024-09-13 20:48:20.897574:  
+2024-09-13 20:48:20.897804: Epoch 115 
+2024-09-13 20:48:20.897916: Current learning rate: 0.00896 
+2024-09-13 20:52:27.183249: train_loss -0.8036 
+2024-09-13 20:52:27.183398: val_loss -0.6762 
+2024-09-13 20:52:27.183456: Pseudo dice [0.669, 0.8189] 
+2024-09-13 20:52:27.183512: Epoch time: 246.29 s 
+2024-09-13 20:52:28.823896:  
+2024-09-13 20:52:28.824067: Epoch 116 
+2024-09-13 20:52:28.824153: Current learning rate: 0.00895 
+2024-09-13 20:56:34.798944: train_loss -0.8046 
+2024-09-13 20:56:34.799092: val_loss -0.6535 
+2024-09-13 20:56:34.799148: Pseudo dice [0.6645, 0.8097] 
+2024-09-13 20:56:34.799202: Epoch time: 245.98 s 
+2024-09-13 20:56:36.331388:  
+2024-09-13 20:56:36.331597: Epoch 117 
+2024-09-13 20:56:36.331727: Current learning rate: 0.00894 
+2024-09-13 21:00:42.338361: train_loss -0.7884 
+2024-09-13 21:00:42.338531: val_loss -0.6797 
+2024-09-13 21:00:42.338593: Pseudo dice [0.6976, 0.8099] 
+2024-09-13 21:00:42.338650: Epoch time: 246.04 s 
+2024-09-13 21:00:43.866622:  
+2024-09-13 21:00:43.866781: Epoch 118 
+2024-09-13 21:00:43.866868: Current learning rate: 0.00893 
+2024-09-13 21:04:49.815402: train_loss -0.7922 
+2024-09-13 21:04:49.815548: val_loss -0.6214 
+2024-09-13 21:04:49.815608: Pseudo dice [0.6373, 0.7841] 
+2024-09-13 21:04:49.815663: Epoch time: 245.95 s 
+2024-09-13 21:04:50.913285:  
+2024-09-13 21:04:50.913523: Epoch 119 
+2024-09-13 21:04:50.913634: Current learning rate: 0.00892 
+2024-09-13 21:08:56.790710: train_loss -0.7798 
+2024-09-13 21:08:56.790934: val_loss -0.6574 
+2024-09-13 21:08:56.791044: Pseudo dice [0.6574, 0.8099] 
+2024-09-13 21:08:56.791125: Epoch time: 245.88 s 
+2024-09-13 21:08:57.834990:  
+2024-09-13 21:08:57.835158: Epoch 120 
+2024-09-13 21:08:57.835246: Current learning rate: 0.00891 
+2024-09-13 21:13:03.446254: train_loss -0.7992 
+2024-09-13 21:13:03.446409: val_loss -0.7104 
+2024-09-13 21:13:03.446465: Pseudo dice [0.6979, 0.8373] 
+2024-09-13 21:13:03.446521: Epoch time: 245.61 s 
+2024-09-13 21:13:04.477730:  
+2024-09-13 21:13:04.477993: Epoch 121 
+2024-09-13 21:13:04.478112: Current learning rate: 0.0089 
+2024-09-13 21:17:10.115602: train_loss -0.7969 
+2024-09-13 21:17:10.115752: val_loss -0.6847 
+2024-09-13 21:17:10.115816: Pseudo dice [0.6944, 0.8129] 
+2024-09-13 21:17:10.115874: Epoch time: 245.64 s 
+2024-09-13 21:17:11.432030:  
+2024-09-13 21:17:11.432197: Epoch 122 
+2024-09-13 21:17:11.432283: Current learning rate: 0.00889 
+2024-09-13 21:21:17.024961: train_loss -0.8042 
+2024-09-13 21:21:17.025110: val_loss -0.6632 
+2024-09-13 21:21:17.025168: Pseudo dice [0.6481, 0.8256] 
+2024-09-13 21:21:17.025221: Epoch time: 245.59 s 
+2024-09-13 21:21:18.056229:  
+2024-09-13 21:21:18.056431: Epoch 123 
+2024-09-13 21:21:18.056518: Current learning rate: 0.00889 
+2024-09-13 21:25:23.598850: train_loss -0.7977 
+2024-09-13 21:25:23.599010: val_loss -0.6839 
+2024-09-13 21:25:23.599079: Pseudo dice [0.6653, 0.8298] 
+2024-09-13 21:25:23.599137: Epoch time: 245.54 s 
+2024-09-13 21:25:24.600220:  
+2024-09-13 21:25:24.600423: Epoch 124 
+2024-09-13 21:25:24.600522: Current learning rate: 0.00888 
+2024-09-13 21:29:30.383276: train_loss -0.782 
+2024-09-13 21:29:30.383420: val_loss -0.6741 
+2024-09-13 21:29:30.383474: Pseudo dice [0.665, 0.7985] 
+2024-09-13 21:29:30.383530: Epoch time: 245.78 s 
+2024-09-13 21:29:31.423600:  
+2024-09-13 21:29:31.423799: Epoch 125 
+2024-09-13 21:29:31.423893: Current learning rate: 0.00887 
+2024-09-13 21:33:45.733005: train_loss -0.7828 
+2024-09-13 21:33:45.733182: val_loss -0.6464 
+2024-09-13 21:33:45.733254: Pseudo dice [0.6302, 0.8089] 
+2024-09-13 21:33:45.733310: Epoch time: 254.31 s 
+2024-09-13 21:33:46.685350:  
+2024-09-13 21:33:46.685611: Epoch 126 
+2024-09-13 21:33:46.685703: Current learning rate: 0.00886 
+2024-09-13 21:37:52.325722: train_loss -0.7823 
+2024-09-13 21:37:52.325888: val_loss -0.6731 
+2024-09-13 21:37:52.326090: Pseudo dice [0.6721, 0.8216] 
+2024-09-13 21:37:52.326209: Epoch time: 245.64 s 
+2024-09-13 21:37:53.292776:  
+2024-09-13 21:37:53.292971: Epoch 127 
+2024-09-13 21:37:53.293079: Current learning rate: 0.00885 
+2024-09-13 21:41:59.071715: train_loss -0.7585 
+2024-09-13 21:41:59.071883: val_loss -0.6116 
+2024-09-13 21:41:59.071940: Pseudo dice [0.6109, 0.7997] 
+2024-09-13 21:41:59.071995: Epoch time: 245.78 s 
+2024-09-13 21:42:00.038897:  
+2024-09-13 21:42:00.039161: Epoch 128 
+2024-09-13 21:42:00.039252: Current learning rate: 0.00884 
+2024-09-13 21:46:05.684554: train_loss -0.7635 
+2024-09-13 21:46:05.684700: val_loss -0.6291 
+2024-09-13 21:46:05.684756: Pseudo dice [0.6231, 0.8025] 
+2024-09-13 21:46:05.684809: Epoch time: 245.65 s 
+2024-09-13 21:46:06.670660:  
+2024-09-13 21:46:06.670856: Epoch 129 
+2024-09-13 21:46:06.670949: Current learning rate: 0.00883 
+2024-09-13 21:50:12.279344: train_loss -0.7761 
+2024-09-13 21:50:12.279491: val_loss -0.6587 
+2024-09-13 21:50:12.279548: Pseudo dice [0.6423, 0.8037] 
+2024-09-13 21:50:12.279604: Epoch time: 245.61 s 
+2024-09-13 21:50:13.248144:  
+2024-09-13 21:50:13.248362: Epoch 130 
+2024-09-13 21:50:13.248448: Current learning rate: 0.00882 
+2024-09-13 21:54:19.084002: train_loss -0.781 
+2024-09-13 21:54:19.084151: val_loss -0.6492 
+2024-09-13 21:54:19.084207: Pseudo dice [0.6365, 0.8054] 
+2024-09-13 21:54:19.084262: Epoch time: 245.84 s 
+2024-09-13 21:54:20.051532:  
+2024-09-13 21:54:20.051763: Epoch 131 
+2024-09-13 21:54:20.051860: Current learning rate: 0.00881 
+2024-09-13 21:58:25.966409: train_loss -0.7576 
+2024-09-13 21:58:25.966556: val_loss -0.6885 
+2024-09-13 21:58:25.966612: Pseudo dice [0.6952, 0.8238] 
+2024-09-13 21:58:25.966667: Epoch time: 245.92 s 
+2024-09-13 21:58:27.029537:  
+2024-09-13 21:58:27.029712: Epoch 132 
+2024-09-13 21:58:27.029797: Current learning rate: 0.0088 
+2024-09-13 22:02:32.846454: train_loss -0.778 
+2024-09-13 22:02:32.846634: val_loss -0.6738 
+2024-09-13 22:02:32.846699: Pseudo dice [0.6728, 0.8206] 
+2024-09-13 22:02:32.846755: Epoch time: 245.82 s 
+2024-09-13 22:02:33.809752:  
+2024-09-13 22:02:33.809991: Epoch 133 
+2024-09-13 22:02:33.810091: Current learning rate: 0.00879 
+2024-09-13 22:06:39.551768: train_loss -0.7918 
+2024-09-13 22:06:39.551951: val_loss -0.701 
+2024-09-13 22:06:39.552008: Pseudo dice [0.7073, 0.8223] 
+2024-09-13 22:06:39.552143: Epoch time: 245.74 s 
+2024-09-13 22:06:40.512671:  
+2024-09-13 22:06:40.512856: Epoch 134 
+2024-09-13 22:06:40.512990: Current learning rate: 0.00879 
+2024-09-13 22:10:46.335790: train_loss -0.796 
+2024-09-13 22:10:46.335963: val_loss -0.6614 
+2024-09-13 22:10:46.336020: Pseudo dice [0.6533, 0.8133] 
+2024-09-13 22:10:46.336076: Epoch time: 245.83 s 
+2024-09-13 22:10:47.337340:  
+2024-09-13 22:10:47.337596: Epoch 135 
+2024-09-13 22:10:47.337682: Current learning rate: 0.00878 
+2024-09-13 22:14:53.114916: train_loss -0.7967 
+2024-09-13 22:14:53.115062: val_loss -0.6828 
+2024-09-13 22:14:53.115117: Pseudo dice [0.6875, 0.8112] 
+2024-09-13 22:14:53.115171: Epoch time: 245.78 s 
+2024-09-13 22:14:54.134683:  
+2024-09-13 22:14:54.134871: Epoch 136 
+2024-09-13 22:14:54.134960: Current learning rate: 0.00877 
+2024-09-13 22:18:59.820405: train_loss -0.7923 
+2024-09-13 22:18:59.820565: val_loss -0.6798 
+2024-09-13 22:18:59.820621: Pseudo dice [0.6867, 0.8128] 
+2024-09-13 22:18:59.820680: Epoch time: 245.69 s 
+2024-09-13 22:19:00.810724:  
+2024-09-13 22:19:00.810966: Epoch 137 
+2024-09-13 22:19:00.811060: Current learning rate: 0.00876 
+2024-09-13 22:23:06.530544: train_loss -0.8008 
+2024-09-13 22:23:06.530705: val_loss -0.6465 
+2024-09-13 22:23:06.530762: Pseudo dice [0.6164, 0.8107] 
+2024-09-13 22:23:06.530817: Epoch time: 245.72 s 
+2024-09-13 22:23:07.521153:  
+2024-09-13 22:23:07.521361: Epoch 138 
+2024-09-13 22:23:07.521446: Current learning rate: 0.00875 
+2024-09-13 22:27:13.323713: train_loss -0.7967 
+2024-09-13 22:27:13.323882: val_loss -0.6896 
+2024-09-13 22:27:13.323941: Pseudo dice [0.6705, 0.8228] 
+2024-09-13 22:27:13.323996: Epoch time: 245.8 s 
+2024-09-13 22:27:14.306173:  
+2024-09-13 22:27:14.306399: Epoch 139 
+2024-09-13 22:27:14.306486: Current learning rate: 0.00874 
+2024-09-13 22:31:20.108843: train_loss -0.8102 
+2024-09-13 22:31:20.108989: val_loss -0.6701 
+2024-09-13 22:31:20.109048: Pseudo dice [0.671, 0.8199] 
+2024-09-13 22:31:20.109108: Epoch time: 245.8 s 
+2024-09-13 22:31:21.093166:  
+2024-09-13 22:31:21.093375: Epoch 140 
+2024-09-13 22:31:21.093464: Current learning rate: 0.00873 
+2024-09-13 22:35:26.849194: train_loss -0.8202 
+2024-09-13 22:35:26.849343: val_loss -0.7051 
+2024-09-13 22:35:26.849399: Pseudo dice [0.7049, 0.82] 
+2024-09-13 22:35:26.849454: Epoch time: 245.76 s 
+2024-09-13 22:35:27.823165:  
+2024-09-13 22:35:27.823342: Epoch 141 
+2024-09-13 22:35:27.823429: Current learning rate: 0.00872 
+2024-09-13 22:39:33.616409: train_loss -0.8079 
+2024-09-13 22:39:33.616556: val_loss -0.6825 
+2024-09-13 22:39:33.616612: Pseudo dice [0.6851, 0.8036] 
+2024-09-13 22:39:33.616668: Epoch time: 245.8 s 
+2024-09-13 22:39:34.601037:  
+2024-09-13 22:39:34.601256: Epoch 142 
+2024-09-13 22:39:34.601343: Current learning rate: 0.00871 
+2024-09-13 22:43:40.492096: train_loss -0.7994 
+2024-09-13 22:43:40.492243: val_loss -0.6465 
+2024-09-13 22:43:40.492300: Pseudo dice [0.6188, 0.7883] 
+2024-09-13 22:43:40.492355: Epoch time: 245.89 s 
+2024-09-13 22:43:41.487016:  
+2024-09-13 22:43:41.487214: Epoch 143 
+2024-09-13 22:43:41.487306: Current learning rate: 0.0087 
+2024-09-13 22:47:47.969478: train_loss -0.8057 
+2024-09-13 22:47:47.969628: val_loss -0.6658 
+2024-09-13 22:47:47.969684: Pseudo dice [0.6694, 0.8094] 
+2024-09-13 22:47:47.969740: Epoch time: 246.48 s 
+2024-09-13 22:47:48.992335:  
+2024-09-13 22:47:48.992503: Epoch 144 
+2024-09-13 22:47:48.992592: Current learning rate: 0.00869 
+2024-09-13 22:51:54.915616: train_loss -0.8036 
+2024-09-13 22:51:54.915763: val_loss -0.6579 
+2024-09-13 22:51:54.915827: Pseudo dice [0.6491, 0.8233] 
+2024-09-13 22:51:54.915884: Epoch time: 245.93 s 
+2024-09-13 22:51:55.888459:  
+2024-09-13 22:51:55.888697: Epoch 145 
+2024-09-13 22:51:55.888781: Current learning rate: 0.00868 
+2024-09-13 22:56:01.666133: train_loss -0.7967 
+2024-09-13 22:56:01.666282: val_loss -0.6532 
+2024-09-13 22:56:01.666340: Pseudo dice [0.6423, 0.8028] 
+2024-09-13 22:56:01.666395: Epoch time: 245.78 s 
+2024-09-13 22:56:02.630242:  
+2024-09-13 22:56:02.630410: Epoch 146 
+2024-09-13 22:56:02.630498: Current learning rate: 0.00868 
+2024-09-13 23:00:08.279477: train_loss -0.7965 
+2024-09-13 23:00:08.279640: val_loss -0.657 
+2024-09-13 23:00:08.279699: Pseudo dice [0.6799, 0.7992] 
+2024-09-13 23:00:08.279754: Epoch time: 245.65 s 
+2024-09-13 23:00:09.422873:  
+2024-09-13 23:00:09.423180: Epoch 147 
+2024-09-13 23:00:09.423326: Current learning rate: 0.00867 
+2024-09-13 23:04:15.097615: train_loss -0.8011 
+2024-09-13 23:04:15.097763: val_loss -0.7064 
+2024-09-13 23:04:15.097820: Pseudo dice [0.7161, 0.8156] 
+2024-09-13 23:04:15.097875: Epoch time: 245.68 s 
+2024-09-13 23:04:16.089914:  
+2024-09-13 23:04:16.090106: Epoch 148 
+2024-09-13 23:04:16.090194: Current learning rate: 0.00866 
+2024-09-13 23:08:22.615649: train_loss -0.807 
+2024-09-13 23:08:22.615821: val_loss -0.667 
+2024-09-13 23:08:22.615881: Pseudo dice [0.6804, 0.8099] 
+2024-09-13 23:08:22.615935: Epoch time: 246.53 s 
+2024-09-13 23:08:23.596623:  
+2024-09-13 23:08:23.596868: Epoch 149 
+2024-09-13 23:08:23.596988: Current learning rate: 0.00865 
+2024-09-13 23:12:29.455975: train_loss -0.8108 
+2024-09-13 23:12:29.456120: val_loss -0.695 
+2024-09-13 23:12:29.456176: Pseudo dice [0.6875, 0.8191] 
+2024-09-13 23:12:29.456229: Epoch time: 245.86 s 
+2024-09-13 23:12:33.264081:  
+2024-09-13 23:12:33.264312: Epoch 150 
+2024-09-13 23:12:33.264406: Current learning rate: 0.00864 
+2024-09-13 23:16:39.400123: train_loss -0.7969 
+2024-09-13 23:16:39.400270: val_loss -0.6866 
+2024-09-13 23:16:39.400325: Pseudo dice [0.6842, 0.8212] 
+2024-09-13 23:16:39.400380: Epoch time: 246.14 s 
+2024-09-13 23:16:40.386159:  
+2024-09-13 23:16:40.386491: Epoch 151 
+2024-09-13 23:16:40.386642: Current learning rate: 0.00863 
+2024-09-13 23:20:46.099158: train_loss -0.8034 
+2024-09-13 23:20:46.099304: val_loss -0.6606 
+2024-09-13 23:20:46.099360: Pseudo dice [0.6206, 0.8289] 
+2024-09-13 23:20:46.099416: Epoch time: 245.71 s 
+2024-09-13 23:20:47.082792:  
+2024-09-13 23:20:47.083046: Epoch 152 
+2024-09-13 23:20:47.083146: Current learning rate: 0.00862 
+2024-09-13 23:24:52.725595: train_loss -0.7999 
+2024-09-13 23:24:52.725721: val_loss -0.652 
+2024-09-13 23:24:52.725771: Pseudo dice [0.6579, 0.8095] 
+2024-09-13 23:24:52.725822: Epoch time: 245.64 s 
+2024-09-13 23:24:53.716341:  
+2024-09-13 23:24:53.716560: Epoch 153 
+2024-09-13 23:24:53.716648: Current learning rate: 0.00861 
+2024-09-13 23:28:59.449907: train_loss -0.8083 
+2024-09-13 23:28:59.450046: val_loss -0.685 
+2024-09-13 23:28:59.450096: Pseudo dice [0.7003, 0.821] 
+2024-09-13 23:28:59.450146: Epoch time: 245.74 s 
+2024-09-13 23:29:00.459443:  
+2024-09-13 23:29:00.459671: Epoch 154 
+2024-09-13 23:29:00.459758: Current learning rate: 0.0086 
+2024-09-13 23:33:06.208787: train_loss -0.7881 
+2024-09-13 23:33:06.208980: val_loss -0.6427 
+2024-09-13 23:33:06.209038: Pseudo dice [0.6598, 0.7659] 
+2024-09-13 23:33:06.209100: Epoch time: 245.75 s 
+2024-09-13 23:33:07.236731:  
+2024-09-13 23:33:07.236976: Epoch 155 
+2024-09-13 23:33:07.237059: Current learning rate: 0.00859 
+2024-09-13 23:37:12.989412: train_loss -0.7863 
+2024-09-13 23:37:12.989552: val_loss -0.6356 
+2024-09-13 23:37:12.989603: Pseudo dice [0.631, 0.7956] 
+2024-09-13 23:37:12.989654: Epoch time: 245.75 s 
+2024-09-13 23:37:13.975235:  
+2024-09-13 23:37:13.975451: Epoch 156 
+2024-09-13 23:37:13.975537: Current learning rate: 0.00858 
+2024-09-13 23:41:19.679394: train_loss -0.78 
+2024-09-13 23:41:19.679533: val_loss -0.6689 
+2024-09-13 23:41:19.679584: Pseudo dice [0.6794, 0.8088] 
+2024-09-13 23:41:19.679634: Epoch time: 245.71 s 
+2024-09-13 23:41:20.685236:  
+2024-09-13 23:41:20.685487: Epoch 157 
+2024-09-13 23:41:20.685574: Current learning rate: 0.00858 
+2024-09-13 23:45:26.546369: train_loss -0.7921 
+2024-09-13 23:45:26.546508: val_loss -0.6446 
+2024-09-13 23:45:26.546558: Pseudo dice [0.6462, 0.8266] 
+2024-09-13 23:45:26.546609: Epoch time: 245.86 s 
+2024-09-13 23:45:27.533844:  
+2024-09-13 23:45:27.534076: Epoch 158 
+2024-09-13 23:45:27.534161: Current learning rate: 0.00857 
+2024-09-13 23:49:33.330933: train_loss -0.8009 
+2024-09-13 23:49:33.331073: val_loss -0.6887 
+2024-09-13 23:49:33.331126: Pseudo dice [0.6936, 0.8229] 
+2024-09-13 23:49:33.331179: Epoch time: 245.8 s 
+2024-09-13 23:49:34.313243:  
+2024-09-13 23:49:34.313420: Epoch 159 
+2024-09-13 23:49:34.313503: Current learning rate: 0.00856 
+2024-09-13 23:53:40.055091: train_loss -0.8044 
+2024-09-13 23:53:40.055231: val_loss -0.7014 
+2024-09-13 23:53:40.055281: Pseudo dice [0.7122, 0.8079] 
+2024-09-13 23:53:40.055331: Epoch time: 245.74 s 
+2024-09-13 23:53:41.065465:  
+2024-09-13 23:53:41.065669: Epoch 160 
+2024-09-13 23:53:41.065753: Current learning rate: 0.00855 
+2024-09-13 23:57:46.754605: train_loss -0.7989 
+2024-09-13 23:57:46.754750: val_loss -0.6673 
+2024-09-13 23:57:46.754800: Pseudo dice [0.6624, 0.8107] 
+2024-09-13 23:57:46.754861: Epoch time: 245.69 s 
+2024-09-13 23:57:47.738248:  
+2024-09-13 23:57:47.738413: Epoch 161 
+2024-09-13 23:57:47.738505: Current learning rate: 0.00854 
+2024-09-14 00:01:53.626320: train_loss -0.807 
+2024-09-14 00:01:53.626479: val_loss -0.7117 
+2024-09-14 00:01:53.626545: Pseudo dice [0.7031, 0.8278] 
+2024-09-14 00:01:53.626596: Epoch time: 245.89 s 
+2024-09-14 00:01:54.632102:  
+2024-09-14 00:01:54.632262: Epoch 162 
+2024-09-14 00:01:54.632389: Current learning rate: 0.00853 
+2024-09-14 00:06:00.666615: train_loss -0.8102 
+2024-09-14 00:06:00.666753: val_loss -0.6625 
+2024-09-14 00:06:00.666803: Pseudo dice [0.68, 0.8149] 
+2024-09-14 00:06:00.666854: Epoch time: 246.04 s 
+2024-09-14 00:06:01.684023:  
+2024-09-14 00:06:01.684266: Epoch 163 
+2024-09-14 00:06:01.684353: Current learning rate: 0.00852 
+2024-09-14 00:10:07.509730: train_loss -0.8154 
+2024-09-14 00:10:07.509884: val_loss -0.6859 
+2024-09-14 00:10:07.509935: Pseudo dice [0.6978, 0.8281] 
+2024-09-14 00:10:07.509987: Epoch time: 245.83 s 
+2024-09-14 00:10:08.494689:  
+2024-09-14 00:10:08.494876: Epoch 164 
+2024-09-14 00:10:08.494965: Current learning rate: 0.00851 
+2024-09-14 00:14:14.282132: train_loss -0.8178 
+2024-09-14 00:14:14.282267: val_loss -0.6849 
+2024-09-14 00:14:14.282318: Pseudo dice [0.6987, 0.8268] 
+2024-09-14 00:14:14.282372: Epoch time: 245.79 s 
+2024-09-14 00:14:14.282413: Yayy! New best EMA pseudo Dice: 0.7474 
+2024-09-14 00:14:17.994952:  
+2024-09-14 00:14:17.995115: Epoch 165 
+2024-09-14 00:14:17.995199: Current learning rate: 0.0085 
+2024-09-14 00:18:24.029086: train_loss -0.8056 
+2024-09-14 00:18:24.029224: val_loss -0.6814 
+2024-09-14 00:18:24.029275: Pseudo dice [0.678, 0.8237] 
+2024-09-14 00:18:24.029325: Epoch time: 246.04 s 
+2024-09-14 00:18:24.029366: Yayy! New best EMA pseudo Dice: 0.7478 
+2024-09-14 00:18:27.940630:  
+2024-09-14 00:18:27.940859: Epoch 166 
+2024-09-14 00:18:27.940965: Current learning rate: 0.00849 
+2024-09-14 00:22:33.953324: train_loss -0.808 
+2024-09-14 00:22:33.953521: val_loss -0.6756 
+2024-09-14 00:22:33.953612: Pseudo dice [0.6661, 0.8131] 
+2024-09-14 00:22:33.953699: Epoch time: 246.01 s 
+2024-09-14 00:22:34.915976:  
+2024-09-14 00:22:34.916191: Epoch 167 
+2024-09-14 00:22:34.916272: Current learning rate: 0.00848 
+2024-09-14 00:26:40.666917: train_loss -0.8187 
+2024-09-14 00:26:40.667120: val_loss -0.6947 
+2024-09-14 00:26:40.667214: Pseudo dice [0.6912, 0.8165] 
+2024-09-14 00:26:40.667307: Epoch time: 245.75 s 
+2024-09-14 00:26:41.667035:  
+2024-09-14 00:26:41.667218: Epoch 168 
+2024-09-14 00:26:41.667304: Current learning rate: 0.00847 
+2024-09-14 00:30:47.532965: train_loss -0.8078 
+2024-09-14 00:30:47.533099: val_loss -0.672 
+2024-09-14 00:30:47.533151: Pseudo dice [0.6915, 0.8116] 
+2024-09-14 00:30:47.533201: Epoch time: 245.87 s 
+2024-09-14 00:30:47.533241: Yayy! New best EMA pseudo Dice: 0.748 
+2024-09-14 00:30:51.452639:  
+2024-09-14 00:30:51.452818: Epoch 169 
+2024-09-14 00:30:51.452902: Current learning rate: 0.00847 
+2024-09-14 00:34:57.717589: train_loss -0.8204 
+2024-09-14 00:34:57.717721: val_loss -0.6836 
+2024-09-14 00:34:57.717772: Pseudo dice [0.6743, 0.8347] 
+2024-09-14 00:34:57.717823: Epoch time: 246.27 s 
+2024-09-14 00:34:57.717864: Yayy! New best EMA pseudo Dice: 0.7487 
+2024-09-14 00:35:01.704363:  
+2024-09-14 00:35:01.704583: Epoch 170 
+2024-09-14 00:35:01.704668: Current learning rate: 0.00846 
+2024-09-14 00:39:08.616254: train_loss -0.8293 
+2024-09-14 00:39:08.616393: val_loss -0.6904 
+2024-09-14 00:39:08.616443: Pseudo dice [0.6949, 0.8173] 
+2024-09-14 00:39:08.616493: Epoch time: 246.91 s 
+2024-09-14 00:39:08.616533: Yayy! New best EMA pseudo Dice: 0.7494 
+2024-09-14 00:39:12.574319:  
+2024-09-14 00:39:12.574556: Epoch 171 
+2024-09-14 00:39:12.574665: Current learning rate: 0.00845 
+2024-09-14 00:43:18.788135: train_loss -0.8272 
+2024-09-14 00:43:18.788315: val_loss -0.6895 
+2024-09-14 00:43:18.788366: Pseudo dice [0.6768, 0.8252] 
+2024-09-14 00:43:18.788418: Epoch time: 246.22 s 
+2024-09-14 00:43:18.788457: Yayy! New best EMA pseudo Dice: 0.7496 
+2024-09-14 00:43:22.723497:  
+2024-09-14 00:43:22.723739: Epoch 172 
+2024-09-14 00:43:22.723866: Current learning rate: 0.00844 
+2024-09-14 00:47:28.911482: train_loss -0.8248 
+2024-09-14 00:47:28.911618: val_loss -0.6964 
+2024-09-14 00:47:28.911667: Pseudo dice [0.6995, 0.8134] 
+2024-09-14 00:47:28.911717: Epoch time: 246.19 s 
+2024-09-14 00:47:28.911757: Yayy! New best EMA pseudo Dice: 0.7503 
+2024-09-14 00:47:32.833103:  
+2024-09-14 00:47:32.833346: Epoch 173 
+2024-09-14 00:47:32.833471: Current learning rate: 0.00843 
+2024-09-14 00:51:39.082890: train_loss -0.814 
+2024-09-14 00:51:39.083031: val_loss -0.7087 
+2024-09-14 00:51:39.083124: Pseudo dice [0.7143, 0.8116] 
+2024-09-14 00:51:39.083177: Epoch time: 246.25 s 
+2024-09-14 00:51:39.083217: Yayy! New best EMA pseudo Dice: 0.7515 
+2024-09-14 00:51:43.011072:  
+2024-09-14 00:51:43.011324: Epoch 174 
+2024-09-14 00:51:43.011407: Current learning rate: 0.00842 
+2024-09-14 00:55:49.286299: train_loss -0.7982 
+2024-09-14 00:55:49.286438: val_loss -0.6818 
+2024-09-14 00:55:49.286489: Pseudo dice [0.6699, 0.8153] 
+2024-09-14 00:55:49.286540: Epoch time: 246.28 s 
+2024-09-14 00:55:50.296626:  
+2024-09-14 00:55:50.296822: Epoch 175 
+2024-09-14 00:55:50.296956: Current learning rate: 0.00841 
+2024-09-14 00:59:56.366398: train_loss -0.8001 
+2024-09-14 00:59:56.366535: val_loss -0.6716 
+2024-09-14 00:59:56.366585: Pseudo dice [0.6884, 0.8261] 
+2024-09-14 00:59:56.366635: Epoch time: 246.07 s 
+2024-09-14 00:59:57.389299:  
+2024-09-14 00:59:57.389524: Epoch 176 
+2024-09-14 00:59:57.389608: Current learning rate: 0.0084 
+2024-09-14 01:04:03.603683: train_loss -0.811 
+2024-09-14 01:04:03.603843: val_loss -0.6961 
+2024-09-14 01:04:03.603896: Pseudo dice [0.7044, 0.8124] 
+2024-09-14 01:04:03.603947: Epoch time: 246.22 s 
+2024-09-14 01:04:03.603988: Yayy! New best EMA pseudo Dice: 0.752 
+2024-09-14 01:04:07.531254:  
+2024-09-14 01:04:07.531467: Epoch 177 
+2024-09-14 01:04:07.531552: Current learning rate: 0.00839 
+2024-09-14 01:08:13.901148: train_loss -0.7984 
+2024-09-14 01:08:13.901284: val_loss -0.68 
+2024-09-14 01:08:13.901333: Pseudo dice [0.7029, 0.8124] 
+2024-09-14 01:08:13.901384: Epoch time: 246.37 s 
+2024-09-14 01:08:13.901423: Yayy! New best EMA pseudo Dice: 0.7526 
+2024-09-14 01:08:17.812428:  
+2024-09-14 01:08:17.812625: Epoch 178 
+2024-09-14 01:08:17.812712: Current learning rate: 0.00838 
+2024-09-14 01:12:24.012895: train_loss -0.811 
+2024-09-14 01:12:24.013054: val_loss -0.6794 
+2024-09-14 01:12:24.013106: Pseudo dice [0.6912, 0.8162] 
+2024-09-14 01:12:24.013158: Epoch time: 246.2 s 
+2024-09-14 01:12:24.013198: Yayy! New best EMA pseudo Dice: 0.7527 
+2024-09-14 01:12:27.947096:  
+2024-09-14 01:12:27.947287: Epoch 179 
+2024-09-14 01:12:27.947381: Current learning rate: 0.00837 
+2024-09-14 01:16:34.274059: train_loss -0.8204 
+2024-09-14 01:16:34.274198: val_loss -0.6768 
+2024-09-14 01:16:34.274254: Pseudo dice [0.7031, 0.8232] 
+2024-09-14 01:16:34.274351: Epoch time: 246.33 s 
+2024-09-14 01:16:34.274398: Yayy! New best EMA pseudo Dice: 0.7537 
+2024-09-14 01:16:38.167973:  
+2024-09-14 01:16:38.168185: Epoch 180 
+2024-09-14 01:16:38.168267: Current learning rate: 0.00836 
+2024-09-14 01:20:44.402768: train_loss -0.8171 
+2024-09-14 01:20:44.402908: val_loss -0.6803 
+2024-09-14 01:20:44.402960: Pseudo dice [0.6842, 0.8234] 
+2024-09-14 01:20:44.403011: Epoch time: 246.24 s 
+2024-09-14 01:20:44.403050: Yayy! New best EMA pseudo Dice: 0.7537 
+2024-09-14 01:20:48.327441:  
+2024-09-14 01:20:48.327620: Epoch 181 
+2024-09-14 01:20:48.327704: Current learning rate: 0.00836 
+2024-09-14 01:24:54.565762: train_loss -0.8154 
+2024-09-14 01:24:54.565898: val_loss -0.6953 
+2024-09-14 01:24:54.565949: Pseudo dice [0.7082, 0.8288] 
+2024-09-14 01:24:54.566003: Epoch time: 246.24 s 
+2024-09-14 01:24:54.566043: Yayy! New best EMA pseudo Dice: 0.7552 
+2024-09-14 01:24:58.458613:  
+2024-09-14 01:24:58.458786: Epoch 182 
+2024-09-14 01:24:58.458894: Current learning rate: 0.00835 
+2024-09-14 01:29:04.753116: train_loss -0.8149 
+2024-09-14 01:29:04.753255: val_loss -0.6555 
+2024-09-14 01:29:04.753307: Pseudo dice [0.6496, 0.8149] 
+2024-09-14 01:29:04.753369: Epoch time: 246.3 s 
+2024-09-14 01:29:05.717616:  
+2024-09-14 01:29:05.717833: Epoch 183 
+2024-09-14 01:29:05.717916: Current learning rate: 0.00834 
+2024-09-14 01:33:11.745123: train_loss -0.8126 
+2024-09-14 01:33:11.745266: val_loss -0.6561 
+2024-09-14 01:33:11.745319: Pseudo dice [0.6772, 0.8066] 
+2024-09-14 01:33:11.745371: Epoch time: 246.03 s 
+2024-09-14 01:33:12.711307:  
+2024-09-14 01:33:12.711503: Epoch 184 
+2024-09-14 01:33:12.711583: Current learning rate: 0.00833 
+2024-09-14 01:37:18.707823: train_loss -0.7985 
+2024-09-14 01:37:18.707958: val_loss -0.6646 
+2024-09-14 01:37:18.708009: Pseudo dice [0.6869, 0.8034] 
+2024-09-14 01:37:18.708059: Epoch time: 246.0 s 
+2024-09-14 01:37:19.687827:  
+2024-09-14 01:37:19.688020: Epoch 185 
+2024-09-14 01:37:19.688104: Current learning rate: 0.00832 
+2024-09-14 01:41:25.755693: train_loss -0.7988 
+2024-09-14 01:41:25.755840: val_loss -0.6771 
+2024-09-14 01:41:25.755893: Pseudo dice [0.6824, 0.8301] 
+2024-09-14 01:41:25.755944: Epoch time: 246.07 s 
+2024-09-14 01:41:26.725683:  
+2024-09-14 01:41:26.725912: Epoch 186 
+2024-09-14 01:41:26.726018: Current learning rate: 0.00831 
+2024-09-14 01:45:32.746229: train_loss -0.7775 
+2024-09-14 01:45:32.746370: val_loss -0.6586 
+2024-09-14 01:45:32.746426: Pseudo dice [0.6771, 0.8123] 
+2024-09-14 01:45:32.746478: Epoch time: 246.02 s 
+2024-09-14 01:45:33.718812:  
+2024-09-14 01:45:33.718989: Epoch 187 
+2024-09-14 01:45:33.719095: Current learning rate: 0.0083 
+2024-09-14 01:49:39.644181: train_loss -0.7909 
+2024-09-14 01:49:39.644319: val_loss -0.6645 
+2024-09-14 01:49:39.644370: Pseudo dice [0.6892, 0.7967] 
+2024-09-14 01:49:39.644421: Epoch time: 245.93 s 
+2024-09-14 01:49:40.666434:  
+2024-09-14 01:49:40.666622: Epoch 188 
+2024-09-14 01:49:40.666715: Current learning rate: 0.00829 
+2024-09-14 01:53:46.671234: train_loss -0.7788 
+2024-09-14 01:53:46.671374: val_loss -0.6767 
+2024-09-14 01:53:46.671424: Pseudo dice [0.6832, 0.8206] 
+2024-09-14 01:53:46.671482: Epoch time: 246.01 s 
+2024-09-14 01:53:47.669438:  
+2024-09-14 01:53:47.669663: Epoch 189 
+2024-09-14 01:53:47.669744: Current learning rate: 0.00828 
+2024-09-14 01:57:53.495650: train_loss -0.7993 
+2024-09-14 01:57:53.495785: val_loss -0.6995 
+2024-09-14 01:57:53.495891: Pseudo dice [0.6992, 0.8258] 
+2024-09-14 01:57:53.495983: Epoch time: 245.83 s 
+2024-09-14 01:57:54.485701:  
+2024-09-14 01:57:54.485854: Epoch 190 
+2024-09-14 01:57:54.485935: Current learning rate: 0.00827 
+2024-09-14 02:02:00.236059: train_loss -0.8094 
+2024-09-14 02:02:00.236195: val_loss -0.6817 
+2024-09-14 02:02:00.236244: Pseudo dice [0.6797, 0.8031] 
+2024-09-14 02:02:00.236294: Epoch time: 245.75 s 
+2024-09-14 02:02:01.251093:  
+2024-09-14 02:02:01.251261: Epoch 191 
+2024-09-14 02:02:01.251343: Current learning rate: 0.00826 
+2024-09-14 02:06:07.108864: train_loss -0.8019 
+2024-09-14 02:06:07.109003: val_loss -0.6776 
+2024-09-14 02:06:07.109056: Pseudo dice [0.6602, 0.8135] 
+2024-09-14 02:06:07.109108: Epoch time: 245.86 s 
+2024-09-14 02:06:08.998880:  
+2024-09-14 02:06:08.999123: Epoch 192 
+2024-09-14 02:06:08.999205: Current learning rate: 0.00825 
+2024-09-14 02:10:14.950159: train_loss -0.7966 
+2024-09-14 02:10:14.950345: val_loss -0.6967 
+2024-09-14 02:10:14.950398: Pseudo dice [0.695, 0.8124] 
+2024-09-14 02:10:14.950449: Epoch time: 245.95 s 
+2024-09-14 02:10:15.946295:  
+2024-09-14 02:10:15.946550: Epoch 193 
+2024-09-14 02:10:15.946635: Current learning rate: 0.00824 
+2024-09-14 02:14:21.836436: train_loss -0.8164 
+2024-09-14 02:14:21.836603: val_loss -0.7089 
+2024-09-14 02:14:21.836656: Pseudo dice [0.7206, 0.8029] 
+2024-09-14 02:14:21.836707: Epoch time: 245.89 s 
+2024-09-14 02:14:22.852637:  
+2024-09-14 02:14:22.852848: Epoch 194 
+2024-09-14 02:14:22.852933: Current learning rate: 0.00824 
+2024-09-14 02:18:28.839189: train_loss -0.8189 
+2024-09-14 02:18:28.839326: val_loss -0.6981 
+2024-09-14 02:18:28.839376: Pseudo dice [0.6968, 0.8203] 
+2024-09-14 02:18:28.839427: Epoch time: 245.99 s 
+2024-09-14 02:18:29.823291:  
+2024-09-14 02:18:29.823517: Epoch 195 
+2024-09-14 02:18:29.823612: Current learning rate: 0.00823 
+2024-09-14 02:22:35.881063: train_loss -0.8231 
+2024-09-14 02:22:35.881200: val_loss -0.7076 
+2024-09-14 02:22:35.881251: Pseudo dice [0.6882, 0.8261] 
+2024-09-14 02:22:35.881301: Epoch time: 246.06 s 
+2024-09-14 02:22:36.866002:  
+2024-09-14 02:22:36.866257: Epoch 196 
+2024-09-14 02:22:36.866362: Current learning rate: 0.00822 
+2024-09-14 02:26:42.679024: train_loss -0.8123 
+2024-09-14 02:26:42.679165: val_loss -0.6906 
+2024-09-14 02:26:42.679216: Pseudo dice [0.6794, 0.8276] 
+2024-09-14 02:26:42.679266: Epoch time: 245.81 s 
+2024-09-14 02:26:43.665414:  
+2024-09-14 02:26:43.665608: Epoch 197 
+2024-09-14 02:26:43.665695: Current learning rate: 0.00821 
+2024-09-14 02:30:49.400548: train_loss -0.8124 
+2024-09-14 02:30:49.400688: val_loss -0.6871 
+2024-09-14 02:30:49.400738: Pseudo dice [0.6913, 0.7965] 
+2024-09-14 02:30:49.400789: Epoch time: 245.74 s 
+2024-09-14 02:30:50.398501:  
+2024-09-14 02:30:50.398701: Epoch 198 
+2024-09-14 02:30:50.398792: Current learning rate: 0.0082 
+2024-09-14 02:34:56.112560: train_loss -0.8049 
+2024-09-14 02:34:56.112700: val_loss -0.6753 
+2024-09-14 02:34:56.112751: Pseudo dice [0.6996, 0.8126] 
+2024-09-14 02:34:56.112801: Epoch time: 245.72 s 
+2024-09-14 02:34:57.111211:  
+2024-09-14 02:34:57.111445: Epoch 199 
+2024-09-14 02:34:57.111530: Current learning rate: 0.00819 
+2024-09-14 02:39:02.854437: train_loss -0.8138 
+2024-09-14 02:39:02.854583: val_loss -0.6802 
+2024-09-14 02:39:02.854633: Pseudo dice [0.6724, 0.8271] 
+2024-09-14 02:39:02.854685: Epoch time: 245.75 s 
+2024-09-14 02:39:06.820740:  
+2024-09-14 02:39:06.820930: Epoch 200 
+2024-09-14 02:39:06.821015: Current learning rate: 0.00818 
+2024-09-14 02:43:12.819295: train_loss -0.7987 
+2024-09-14 02:43:12.819437: val_loss -0.6425 
+2024-09-14 02:43:12.819487: Pseudo dice [0.6608, 0.7819] 
+2024-09-14 02:43:12.819542: Epoch time: 246.0 s 
+2024-09-14 02:43:13.824211:  
+2024-09-14 02:43:13.824374: Epoch 201 
+2024-09-14 02:43:13.824459: Current learning rate: 0.00817 
+2024-09-14 02:47:19.535505: train_loss -0.7883 
+2024-09-14 02:47:19.535652: val_loss -0.6028 
+2024-09-14 02:47:19.535749: Pseudo dice [0.5427, 0.7563] 
+2024-09-14 02:47:19.535841: Epoch time: 245.71 s 
+2024-09-14 02:47:20.543900:  
+2024-09-14 02:47:20.544073: Epoch 202 
+2024-09-14 02:47:20.544167: Current learning rate: 0.00816 
+2024-09-14 02:51:26.471768: train_loss -0.7567 
+2024-09-14 02:51:26.471944: val_loss -0.6812 
+2024-09-14 02:51:26.472041: Pseudo dice [0.7079, 0.8124] 
+2024-09-14 02:51:26.472093: Epoch time: 245.93 s 
+2024-09-14 02:51:27.479464:  
+2024-09-14 02:51:27.479680: Epoch 203 
+2024-09-14 02:51:27.479761: Current learning rate: 0.00815 
+2024-09-14 02:55:33.195248: train_loss -0.7972 
+2024-09-14 02:55:33.195502: val_loss -0.6518 
+2024-09-14 02:55:33.195555: Pseudo dice [0.6724, 0.8071] 
+2024-09-14 02:55:33.195605: Epoch time: 245.72 s 
+2024-09-14 02:55:34.191780:  
+2024-09-14 02:55:34.192019: Epoch 204 
+2024-09-14 02:55:34.192102: Current learning rate: 0.00814 
+2024-09-14 02:59:39.835447: train_loss -0.7903 
+2024-09-14 02:59:39.835584: val_loss -0.6714 
+2024-09-14 02:59:39.835640: Pseudo dice [0.6658, 0.8045] 
+2024-09-14 02:59:39.835694: Epoch time: 245.65 s 
+2024-09-14 02:59:40.842086:  
+2024-09-14 02:59:40.842275: Epoch 205 
+2024-09-14 02:59:40.842357: Current learning rate: 0.00813 
+2024-09-14 03:03:46.501031: train_loss -0.7906 
+2024-09-14 03:03:46.501173: val_loss -0.6693 
+2024-09-14 03:03:46.501225: Pseudo dice [0.6726, 0.8319] 
+2024-09-14 03:03:46.501276: Epoch time: 245.66 s 
+2024-09-14 03:03:47.441789:  
+2024-09-14 03:03:47.441997: Epoch 206 
+2024-09-14 03:03:47.442082: Current learning rate: 0.00813 
+2024-09-14 03:07:53.212420: train_loss -0.8058 
+2024-09-14 03:07:53.212557: val_loss -0.7061 
+2024-09-14 03:07:53.212609: Pseudo dice [0.7047, 0.8295] 
+2024-09-14 03:07:53.212788: Epoch time: 245.77 s 
+2024-09-14 03:07:54.142991:  
+2024-09-14 03:07:54.143294: Epoch 207 
+2024-09-14 03:07:54.143436: Current learning rate: 0.00812 
+2024-09-14 03:11:59.807319: train_loss -0.8039 
+2024-09-14 03:11:59.807477: val_loss -0.6574 
+2024-09-14 03:11:59.807529: Pseudo dice [0.67, 0.7857] 
+2024-09-14 03:11:59.807580: Epoch time: 245.67 s 
+2024-09-14 03:12:00.740055:  
+2024-09-14 03:12:00.740286: Epoch 208 
+2024-09-14 03:12:00.740372: Current learning rate: 0.00811 
+2024-09-14 03:16:06.380329: train_loss -0.8118 
+2024-09-14 03:16:06.380502: val_loss -0.6517 
+2024-09-14 03:16:06.380553: Pseudo dice [0.6142, 0.8156] 
+2024-09-14 03:16:06.380603: Epoch time: 245.64 s 
+2024-09-14 03:16:07.305837:  
+2024-09-14 03:16:07.306039: Epoch 209 
+2024-09-14 03:16:07.306125: Current learning rate: 0.0081 
+2024-09-14 03:20:12.901168: train_loss -0.8191 
+2024-09-14 03:20:12.901385: val_loss -0.7064 
+2024-09-14 03:20:12.901438: Pseudo dice [0.7091, 0.8247] 
+2024-09-14 03:20:12.901489: Epoch time: 245.6 s 
+2024-09-14 03:20:13.844375:  
+2024-09-14 03:20:13.844559: Epoch 210 
+2024-09-14 03:20:13.844640: Current learning rate: 0.00809 
+2024-09-14 03:24:19.538721: train_loss -0.8239 
+2024-09-14 03:24:19.538857: val_loss -0.6844 
+2024-09-14 03:24:19.538907: Pseudo dice [0.6728, 0.8286] 
+2024-09-14 03:24:19.538957: Epoch time: 245.7 s 
+2024-09-14 03:24:20.487557:  
+2024-09-14 03:24:20.487768: Epoch 211 
+2024-09-14 03:24:20.487873: Current learning rate: 0.00808 
+2024-09-14 03:28:26.232580: train_loss -0.8224 
+2024-09-14 03:28:26.232718: val_loss -0.6788 
+2024-09-14 03:28:26.232768: Pseudo dice [0.7037, 0.8125] 
+2024-09-14 03:28:26.232819: Epoch time: 245.75 s 
+2024-09-14 03:28:27.171556:  
+2024-09-14 03:28:27.171745: Epoch 212 
+2024-09-14 03:28:27.171836: Current learning rate: 0.00807 
+2024-09-14 03:32:32.871884: train_loss -0.8291 
+2024-09-14 03:32:32.872020: val_loss -0.689 
+2024-09-14 03:32:32.872070: Pseudo dice [0.6809, 0.8189] 
+2024-09-14 03:32:32.872120: Epoch time: 245.7 s 
+2024-09-14 03:32:33.800828:  
+2024-09-14 03:32:33.801059: Epoch 213 
+2024-09-14 03:32:33.801142: Current learning rate: 0.00806 
+2024-09-14 03:36:39.608406: train_loss -0.83 
+2024-09-14 03:36:39.608548: val_loss -0.6898 
+2024-09-14 03:36:39.608601: Pseudo dice [0.6867, 0.8119] 
+2024-09-14 03:36:39.608653: Epoch time: 245.81 s 
+2024-09-14 03:36:40.540483:  
+2024-09-14 03:36:40.540665: Epoch 214 
+2024-09-14 03:36:40.540753: Current learning rate: 0.00805 
+2024-09-14 03:40:46.405959: train_loss -0.8141 
+2024-09-14 03:40:46.406099: val_loss -0.7043 
+2024-09-14 03:40:46.406149: Pseudo dice [0.7157, 0.8236] 
+2024-09-14 03:40:46.406199: Epoch time: 245.87 s 
+2024-09-14 03:40:48.263077:  
+2024-09-14 03:40:48.263305: Epoch 215 
+2024-09-14 03:40:48.263405: Current learning rate: 0.00804 
+2024-09-14 03:44:54.352112: train_loss -0.814 
+2024-09-14 03:44:54.352251: val_loss -0.6794 
+2024-09-14 03:44:54.352340: Pseudo dice [0.6698, 0.8235] 
+2024-09-14 03:44:54.352423: Epoch time: 246.09 s 
+2024-09-14 03:44:55.268662:  
+2024-09-14 03:44:55.268920: Epoch 216 
+2024-09-14 03:44:55.269002: Current learning rate: 0.00803 
+2024-09-14 03:49:01.250030: train_loss -0.8101 
+2024-09-14 03:49:01.250168: val_loss -0.686 
+2024-09-14 03:49:01.250264: Pseudo dice [0.6933, 0.8152] 
+2024-09-14 03:49:01.250355: Epoch time: 245.98 s 
+2024-09-14 03:49:02.176529:  
+2024-09-14 03:49:02.176747: Epoch 217 
+2024-09-14 03:49:02.176834: Current learning rate: 0.00802 
+2024-09-14 03:53:08.101865: train_loss -0.8292 
+2024-09-14 03:53:08.102038: val_loss -0.6762 
+2024-09-14 03:53:08.102091: Pseudo dice [0.6941, 0.8161] 
+2024-09-14 03:53:08.102141: Epoch time: 245.93 s 
+2024-09-14 03:53:09.048183:  
+2024-09-14 03:53:09.048417: Epoch 218 
+2024-09-14 03:53:09.048499: Current learning rate: 0.00801 
+2024-09-14 03:57:14.957842: train_loss -0.8299 
+2024-09-14 03:57:14.957978: val_loss -0.6953 
+2024-09-14 03:57:14.958028: Pseudo dice [0.6838, 0.827] 
+2024-09-14 03:57:14.958078: Epoch time: 245.91 s 
+2024-09-14 03:57:15.886451:  
+2024-09-14 03:57:15.886683: Epoch 219 
+2024-09-14 03:57:15.886796: Current learning rate: 0.00801 
+2024-09-14 04:01:21.925909: train_loss -0.8322 
+2024-09-14 04:01:21.926044: val_loss -0.6867 
+2024-09-14 04:01:21.926202: Pseudo dice [0.7056, 0.8135] 
+2024-09-14 04:01:21.926332: Epoch time: 246.04 s 
+2024-09-14 04:01:22.880313:  
+2024-09-14 04:01:22.880530: Epoch 220 
+2024-09-14 04:01:22.880610: Current learning rate: 0.008 
+2024-09-14 04:05:28.931664: train_loss -0.833 
+2024-09-14 04:05:28.931798: val_loss -0.6689 
+2024-09-14 04:05:28.931911: Pseudo dice [0.6307, 0.8089] 
+2024-09-14 04:05:28.931981: Epoch time: 246.05 s 
+2024-09-14 04:05:29.877231:  
+2024-09-14 04:05:29.877457: Epoch 221 
+2024-09-14 04:05:29.877541: Current learning rate: 0.00799 
+2024-09-14 04:09:35.736214: train_loss -0.8261 
+2024-09-14 04:09:35.736376: val_loss -0.7058 
+2024-09-14 04:09:35.736428: Pseudo dice [0.6938, 0.8228] 
+2024-09-14 04:09:35.736481: Epoch time: 245.86 s 
+2024-09-14 04:09:36.676438:  
+2024-09-14 04:09:36.676673: Epoch 222 
+2024-09-14 04:09:36.676801: Current learning rate: 0.00798 
+2024-09-14 04:13:42.354548: train_loss -0.8275 
+2024-09-14 04:13:42.354706: val_loss -0.6842 
+2024-09-14 04:13:42.354758: Pseudo dice [0.6825, 0.8082] 
+2024-09-14 04:13:42.354808: Epoch time: 245.68 s 
+2024-09-14 04:13:43.290301:  
+2024-09-14 04:13:43.290509: Epoch 223 
+2024-09-14 04:13:43.290595: Current learning rate: 0.00797 
+2024-09-14 04:17:49.001198: train_loss -0.8295 
+2024-09-14 04:17:49.001354: val_loss -0.658 
+2024-09-14 04:17:49.001405: Pseudo dice [0.6631, 0.816] 
+2024-09-14 04:17:49.001457: Epoch time: 245.71 s 
+2024-09-14 04:17:49.938264:  
+2024-09-14 04:17:49.938535: Epoch 224 
+2024-09-14 04:17:49.938619: Current learning rate: 0.00796 
+2024-09-14 04:21:55.726722: train_loss -0.8315 
+2024-09-14 04:21:55.726858: val_loss -0.7068 
+2024-09-14 04:21:55.726910: Pseudo dice [0.7036, 0.8153] 
+2024-09-14 04:21:55.726963: Epoch time: 245.79 s 
+2024-09-14 04:21:56.666122:  
+2024-09-14 04:21:56.666390: Epoch 225 
+2024-09-14 04:21:56.666479: Current learning rate: 0.00795 
+2024-09-14 04:26:02.462286: train_loss -0.8196 
+2024-09-14 04:26:02.462435: val_loss -0.6683 
+2024-09-14 04:26:02.462485: Pseudo dice [0.6871, 0.8162] 
+2024-09-14 04:26:02.462538: Epoch time: 245.8 s 
+2024-09-14 04:26:03.378750:  
+2024-09-14 04:26:03.378955: Epoch 226 
+2024-09-14 04:26:03.379065: Current learning rate: 0.00794 
+2024-09-14 04:30:09.039383: train_loss -0.8259 
+2024-09-14 04:30:09.039520: val_loss -0.6816 
+2024-09-14 04:30:09.039572: Pseudo dice [0.6937, 0.8169] 
+2024-09-14 04:30:09.039623: Epoch time: 245.66 s 
+2024-09-14 04:30:09.981100:  
+2024-09-14 04:30:09.981321: Epoch 227 
+2024-09-14 04:30:09.981403: Current learning rate: 0.00793 
+2024-09-14 04:34:15.735980: train_loss -0.8407 
+2024-09-14 04:34:15.736135: val_loss -0.6817 
+2024-09-14 04:34:15.736186: Pseudo dice [0.7042, 0.826] 
+2024-09-14 04:34:15.736279: Epoch time: 245.76 s 
+2024-09-14 04:34:16.653851:  
+2024-09-14 04:34:16.654042: Epoch 228 
+2024-09-14 04:34:16.654122: Current learning rate: 0.00792 
+2024-09-14 04:38:22.467815: train_loss -0.8318 
+2024-09-14 04:38:22.468093: val_loss -0.6857 
+2024-09-14 04:38:22.468148: Pseudo dice [0.6631, 0.8304] 
+2024-09-14 04:38:22.468200: Epoch time: 245.82 s 
+2024-09-14 04:38:23.417917:  
+2024-09-14 04:38:23.418108: Epoch 229 
+2024-09-14 04:38:23.418192: Current learning rate: 0.00791 
+2024-09-14 04:42:29.253918: train_loss -0.8353 
+2024-09-14 04:42:29.254057: val_loss -0.6753 
+2024-09-14 04:42:29.254108: Pseudo dice [0.6673, 0.8277] 
+2024-09-14 04:42:29.254158: Epoch time: 245.84 s 
+2024-09-14 04:42:30.190220:  
+2024-09-14 04:42:30.190389: Epoch 230 
+2024-09-14 04:42:30.190472: Current learning rate: 0.0079 
+2024-09-14 04:46:36.029532: train_loss -0.8403 
+2024-09-14 04:46:36.029670: val_loss -0.702 
+2024-09-14 04:46:36.029720: Pseudo dice [0.6934, 0.8287] 
+2024-09-14 04:46:36.029770: Epoch time: 245.84 s 
+2024-09-14 04:46:36.947174:  
+2024-09-14 04:46:36.947342: Epoch 231 
+2024-09-14 04:46:36.947426: Current learning rate: 0.00789 
+2024-09-14 04:50:43.048753: train_loss -0.8365 
+2024-09-14 04:50:43.048959: val_loss -0.7004 
+2024-09-14 04:50:43.049011: Pseudo dice [0.7245, 0.8316] 
+2024-09-14 04:50:43.049064: Epoch time: 246.1 s 
+2024-09-14 04:50:43.984117:  
+2024-09-14 04:50:43.984339: Epoch 232 
+2024-09-14 04:50:43.984421: Current learning rate: 0.00789 
+2024-09-14 04:54:50.007257: train_loss -0.8435 
+2024-09-14 04:54:50.007404: val_loss -0.6861 
+2024-09-14 04:54:50.007458: Pseudo dice [0.6994, 0.8075] 
+2024-09-14 04:54:50.007511: Epoch time: 246.03 s 
+2024-09-14 04:54:50.919650:  
+2024-09-14 04:54:50.919884: Epoch 233 
+2024-09-14 04:54:50.919970: Current learning rate: 0.00788 
+2024-09-14 04:58:56.942150: train_loss -0.8411 
+2024-09-14 04:58:56.942285: val_loss -0.6812 
+2024-09-14 04:58:56.942340: Pseudo dice [0.6745, 0.8383] 
+2024-09-14 04:58:56.942390: Epoch time: 246.02 s 
+2024-09-14 04:58:57.871226:  
+2024-09-14 04:58:57.871441: Epoch 234 
+2024-09-14 04:58:57.871527: Current learning rate: 0.00787 
+2024-09-14 05:03:03.779686: train_loss -0.8402 
+2024-09-14 05:03:03.779830: val_loss -0.6861 
+2024-09-14 05:03:03.779882: Pseudo dice [0.702, 0.8403] 
+2024-09-14 05:03:03.779933: Epoch time: 245.91 s 
+2024-09-14 05:03:03.779973: Yayy! New best EMA pseudo Dice: 0.756 
+2024-09-14 05:03:07.667392:  
+2024-09-14 05:03:07.667586: Epoch 235 
+2024-09-14 05:03:07.667680: Current learning rate: 0.00786 
+2024-09-14 05:07:14.006791: train_loss -0.8471 
+2024-09-14 05:07:14.006932: val_loss -0.6934 
+2024-09-14 05:07:14.006982: Pseudo dice [0.681, 0.8235] 
+2024-09-14 05:07:14.007031: Epoch time: 246.34 s 
+2024-09-14 05:07:14.956389:  
+2024-09-14 05:07:14.956569: Epoch 236 
+2024-09-14 05:07:14.956657: Current learning rate: 0.00785 
+2024-09-14 05:11:20.967872: train_loss -0.8468 
+2024-09-14 05:11:20.968008: val_loss -0.7085 
+2024-09-14 05:11:20.968061: Pseudo dice [0.7253, 0.8135] 
+2024-09-14 05:11:20.968115: Epoch time: 246.01 s 
+2024-09-14 05:11:20.968207: Yayy! New best EMA pseudo Dice: 0.757 
+2024-09-14 05:11:24.829809:  
+2024-09-14 05:11:24.829969: Epoch 237 
+2024-09-14 05:11:24.830051: Current learning rate: 0.00784 
+2024-09-14 05:15:31.106509: train_loss -0.8453 
+2024-09-14 05:15:31.106656: val_loss -0.6866 
+2024-09-14 05:15:31.106707: Pseudo dice [0.6976, 0.8198] 
+2024-09-14 05:15:31.106758: Epoch time: 246.28 s 
+2024-09-14 05:15:31.106797: Yayy! New best EMA pseudo Dice: 0.7572 
+2024-09-14 05:15:35.002216:  
+2024-09-14 05:15:35.002382: Epoch 238 
+2024-09-14 05:15:35.002472: Current learning rate: 0.00783 
+2024-09-14 05:19:41.355913: train_loss -0.8388 
+2024-09-14 05:19:41.356051: val_loss -0.6884 
+2024-09-14 05:19:41.356102: Pseudo dice [0.7007, 0.8149] 
+2024-09-14 05:19:41.356153: Epoch time: 246.36 s 
+2024-09-14 05:19:41.356193: Yayy! New best EMA pseudo Dice: 0.7572 
+2024-09-14 05:19:46.153208:  
+2024-09-14 05:19:46.153413: Epoch 239 
+2024-09-14 05:19:46.153527: Current learning rate: 0.00782 
+2024-09-14 05:23:52.655486: train_loss -0.8104 
+2024-09-14 05:23:52.655628: val_loss -0.6787 
+2024-09-14 05:23:52.655723: Pseudo dice [0.671, 0.8231] 
+2024-09-14 05:23:52.655777: Epoch time: 246.5 s 
+2024-09-14 05:23:53.598386:  
+2024-09-14 05:23:53.598687: Epoch 240 
+2024-09-14 05:23:53.598826: Current learning rate: 0.00781 
+2024-09-14 05:27:59.808038: train_loss -0.8169 
+2024-09-14 05:27:59.808175: val_loss -0.6663 
+2024-09-14 05:27:59.808226: Pseudo dice [0.6258, 0.8368] 
+2024-09-14 05:27:59.808276: Epoch time: 246.21 s 
+2024-09-14 05:28:00.761004:  
+2024-09-14 05:28:00.761245: Epoch 241 
+2024-09-14 05:28:00.761329: Current learning rate: 0.0078 
+2024-09-14 05:32:06.741055: train_loss -0.8265 
+2024-09-14 05:32:06.741194: val_loss -0.6815 
+2024-09-14 05:32:06.741250: Pseudo dice [0.7052, 0.8014] 
+2024-09-14 05:32:06.741300: Epoch time: 245.98 s 
+2024-09-14 05:32:07.674222:  
+2024-09-14 05:32:07.674485: Epoch 242 
+2024-09-14 05:32:07.674570: Current learning rate: 0.00779 
+2024-09-14 05:36:13.448412: train_loss -0.8243 
+2024-09-14 05:36:13.448648: val_loss -0.6738 
+2024-09-14 05:36:13.448701: Pseudo dice [0.677, 0.8263] 
+2024-09-14 05:36:13.448752: Epoch time: 245.78 s 
+2024-09-14 05:36:14.402691:  
+2024-09-14 05:36:14.402911: Epoch 243 
+2024-09-14 05:36:14.403009: Current learning rate: 0.00778 
+2024-09-14 05:40:20.231704: train_loss -0.8059 
+2024-09-14 05:40:20.231844: val_loss -0.6386 
+2024-09-14 05:40:20.231895: Pseudo dice [0.6241, 0.8111] 
+2024-09-14 05:40:20.231946: Epoch time: 245.83 s 
+2024-09-14 05:40:21.172278:  
+2024-09-14 05:40:21.172469: Epoch 244 
+2024-09-14 05:40:21.172551: Current learning rate: 0.00777 
+2024-09-14 05:44:27.137446: train_loss -0.8184 
+2024-09-14 05:44:27.137587: val_loss -0.6744 
+2024-09-14 05:44:27.137638: Pseudo dice [0.6734, 0.8135] 
+2024-09-14 05:44:27.137688: Epoch time: 245.97 s 
+2024-09-14 05:44:28.080658:  
+2024-09-14 05:44:28.080915: Epoch 245 
+2024-09-14 05:44:28.081000: Current learning rate: 0.00777 
+2024-09-14 05:48:33.993492: train_loss -0.8259 
+2024-09-14 05:48:33.993633: val_loss -0.6648 
+2024-09-14 05:48:33.993684: Pseudo dice [0.6685, 0.8019] 
+2024-09-14 05:48:33.993734: Epoch time: 245.91 s 
+2024-09-14 05:48:34.949312:  
+2024-09-14 05:48:34.949562: Epoch 246 
+2024-09-14 05:48:34.949669: Current learning rate: 0.00776 
+2024-09-14 05:52:40.783389: train_loss -0.8312 
+2024-09-14 05:52:40.783529: val_loss -0.6865 
+2024-09-14 05:52:40.783580: Pseudo dice [0.693, 0.8246] 
+2024-09-14 05:52:40.783630: Epoch time: 245.84 s 
+2024-09-14 05:52:41.738449:  
+2024-09-14 05:52:41.738635: Epoch 247 
+2024-09-14 05:52:41.738734: Current learning rate: 0.00775 
+2024-09-14 05:56:47.500790: train_loss -0.8274 
+2024-09-14 05:56:47.500951: val_loss -0.6805 
+2024-09-14 05:56:47.501003: Pseudo dice [0.6702, 0.8079] 
+2024-09-14 05:56:47.501054: Epoch time: 245.76 s 
+2024-09-14 05:56:48.443468:  
+2024-09-14 05:56:48.443715: Epoch 248 
+2024-09-14 05:56:48.443799: Current learning rate: 0.00774 
+2024-09-14 06:00:54.257885: train_loss -0.8264 
+2024-09-14 06:00:54.258042: val_loss -0.6676 
+2024-09-14 06:00:54.258093: Pseudo dice [0.6624, 0.8155] 
+2024-09-14 06:00:54.258144: Epoch time: 245.82 s 
+2024-09-14 06:00:55.200981:  
+2024-09-14 06:00:55.201163: Epoch 249 
+2024-09-14 06:00:55.201265: Current learning rate: 0.00773 
+2024-09-14 06:05:00.996424: train_loss -0.8364 
+2024-09-14 06:05:00.996578: val_loss -0.7195 
+2024-09-14 06:05:00.996631: Pseudo dice [0.7144, 0.8373] 
+2024-09-14 06:05:00.996682: Epoch time: 245.8 s 
+2024-09-14 06:05:04.886938:  
+2024-09-14 06:05:04.887115: Epoch 250 
+2024-09-14 06:05:04.887253: Current learning rate: 0.00772 
+2024-09-14 06:09:11.070493: train_loss -0.8413 
+2024-09-14 06:09:11.070634: val_loss -0.6613 
+2024-09-14 06:09:11.070684: Pseudo dice [0.6338, 0.816] 
+2024-09-14 06:09:11.070734: Epoch time: 246.19 s 
+2024-09-14 06:09:12.025666:  
+2024-09-14 06:09:12.025911: Epoch 251 
+2024-09-14 06:09:12.026006: Current learning rate: 0.00771 
+2024-09-14 06:13:17.879668: train_loss -0.8385 
+2024-09-14 06:13:17.879817: val_loss -0.6942 
+2024-09-14 06:13:17.879872: Pseudo dice [0.7043, 0.8301] 
+2024-09-14 06:13:17.879922: Epoch time: 245.86 s 
+2024-09-14 06:13:18.824493:  
+2024-09-14 06:13:18.824714: Epoch 252 
+2024-09-14 06:13:18.824814: Current learning rate: 0.0077 
+2024-09-14 06:17:24.770250: train_loss -0.8341 
+2024-09-14 06:17:24.770388: val_loss -0.7054 
+2024-09-14 06:17:24.770443: Pseudo dice [0.7234, 0.8187] 
+2024-09-14 06:17:24.770494: Epoch time: 245.95 s 
+2024-09-14 06:17:25.711330:  
+2024-09-14 06:17:25.711547: Epoch 253 
+2024-09-14 06:17:25.711632: Current learning rate: 0.00769 
+2024-09-14 06:21:31.511296: train_loss -0.8354 
+2024-09-14 06:21:31.511441: val_loss -0.7003 
+2024-09-14 06:21:31.511492: Pseudo dice [0.683, 0.8329] 
+2024-09-14 06:21:31.511543: Epoch time: 245.8 s 
+2024-09-14 06:21:32.467680:  
+2024-09-14 06:21:32.467926: Epoch 254 
+2024-09-14 06:21:32.468044: Current learning rate: 0.00768 
+2024-09-14 06:25:38.340134: train_loss -0.8284 
+2024-09-14 06:25:38.340295: val_loss -0.6737 
+2024-09-14 06:25:38.340346: Pseudo dice [0.6765, 0.8182] 
+2024-09-14 06:25:38.340398: Epoch time: 245.87 s 
+2024-09-14 06:25:39.277495:  
+2024-09-14 06:25:39.277648: Epoch 255 
+2024-09-14 06:25:39.277732: Current learning rate: 0.00767 
+2024-09-14 06:29:45.242313: train_loss -0.7947 
+2024-09-14 06:29:45.242452: val_loss -0.6331 
+2024-09-14 06:29:45.242503: Pseudo dice [0.6583, 0.8073] 
+2024-09-14 06:29:45.242582: Epoch time: 245.97 s 
+2024-09-14 06:29:46.185081:  
+2024-09-14 06:29:46.185276: Epoch 256 
+2024-09-14 06:29:46.185361: Current learning rate: 0.00766 
+2024-09-14 06:33:52.077113: train_loss -0.8212 
+2024-09-14 06:33:52.077252: val_loss -0.6733 
+2024-09-14 06:33:52.077303: Pseudo dice [0.6484, 0.8265] 
+2024-09-14 06:33:52.077353: Epoch time: 245.89 s 
+2024-09-14 06:33:53.054187:  
+2024-09-14 06:33:53.054342: Epoch 257 
+2024-09-14 06:33:53.054460: Current learning rate: 0.00765 
+2024-09-14 06:37:58.993306: train_loss -0.8266 
+2024-09-14 06:37:58.993439: val_loss -0.6707 
+2024-09-14 06:37:58.993489: Pseudo dice [0.6647, 0.8172] 
+2024-09-14 06:37:58.993538: Epoch time: 245.94 s 
+2024-09-14 06:37:59.936208:  
+2024-09-14 06:37:59.936402: Epoch 258 
+2024-09-14 06:37:59.936487: Current learning rate: 0.00764 
+2024-09-14 06:42:05.933662: train_loss -0.8307 
+2024-09-14 06:42:05.933829: val_loss -0.6906 
+2024-09-14 06:42:05.933880: Pseudo dice [0.6813, 0.8096] 
+2024-09-14 06:42:05.933933: Epoch time: 246.0 s 
+2024-09-14 06:42:06.867947:  
+2024-09-14 06:42:06.868158: Epoch 259 
+2024-09-14 06:42:06.868242: Current learning rate: 0.00764 
+2024-09-14 06:46:13.039833: train_loss -0.8267 
+2024-09-14 06:46:13.040027: val_loss -0.6688 
+2024-09-14 06:46:13.040080: Pseudo dice [0.6591, 0.8039] 
+2024-09-14 06:46:13.040131: Epoch time: 246.17 s 
+2024-09-14 06:46:13.982745:  
+2024-09-14 06:46:13.982908: Epoch 260 
+2024-09-14 06:46:13.982989: Current learning rate: 0.00763 
+2024-09-14 06:50:20.151802: train_loss -0.8174 
+2024-09-14 06:50:20.151947: val_loss -0.6805 
+2024-09-14 06:50:20.151996: Pseudo dice [0.6711, 0.8188] 
+2024-09-14 06:50:20.152046: Epoch time: 246.17 s 
+2024-09-14 06:50:21.103515:  
+2024-09-14 06:50:21.103647: Epoch 261 
+2024-09-14 06:50:21.103726: Current learning rate: 0.00762 
+2024-09-14 06:54:27.127042: train_loss -0.8146 
+2024-09-14 06:54:27.127179: val_loss -0.6757 
+2024-09-14 06:54:27.127230: Pseudo dice [0.6783, 0.7892] 
+2024-09-14 06:54:27.127281: Epoch time: 246.03 s 
+2024-09-14 06:54:28.062905:  
+2024-09-14 06:54:28.063060: Epoch 262 
+2024-09-14 06:54:28.063141: Current learning rate: 0.00761 
+2024-09-14 06:58:34.025803: train_loss -0.8279 
+2024-09-14 06:58:34.025985: val_loss -0.6894 
+2024-09-14 06:58:34.026064: Pseudo dice [0.6656, 0.8285] 
+2024-09-14 06:58:34.026117: Epoch time: 245.96 s 
+2024-09-14 06:58:35.899300:  
+2024-09-14 06:58:35.899553: Epoch 263 
+2024-09-14 06:58:35.899657: Current learning rate: 0.0076 
+2024-09-14 07:02:42.026397: train_loss -0.819 
+2024-09-14 07:02:42.026540: val_loss -0.6824 
+2024-09-14 07:02:42.026700: Pseudo dice [0.661, 0.8245] 
+2024-09-14 07:02:42.026825: Epoch time: 246.13 s 
+2024-09-14 07:02:42.979306:  
+2024-09-14 07:02:42.979548: Epoch 264 
+2024-09-14 07:02:42.979639: Current learning rate: 0.00759 
+2024-09-14 07:06:48.996807: train_loss -0.8243 
+2024-09-14 07:06:48.996947: val_loss -0.6703 
+2024-09-14 07:06:48.996997: Pseudo dice [0.6787, 0.8121] 
+2024-09-14 07:06:48.997101: Epoch time: 246.02 s 
+2024-09-14 07:06:49.942744:  
+2024-09-14 07:06:49.942949: Epoch 265 
+2024-09-14 07:06:49.943057: Current learning rate: 0.00758 
+2024-09-14 07:10:55.923503: train_loss -0.8257 
+2024-09-14 07:10:55.923653: val_loss -0.693 
+2024-09-14 07:10:55.923704: Pseudo dice [0.699, 0.8164] 
+2024-09-14 07:10:55.923754: Epoch time: 245.98 s 
+2024-09-14 07:10:56.877542:  
+2024-09-14 07:10:56.877743: Epoch 266 
+2024-09-14 07:10:56.877825: Current learning rate: 0.00757 
+2024-09-14 07:15:02.939479: train_loss -0.8329 
+2024-09-14 07:15:02.939630: val_loss -0.6775 
+2024-09-14 07:15:02.939682: Pseudo dice [0.6454, 0.8295] 
+2024-09-14 07:15:02.939733: Epoch time: 246.06 s 
+2024-09-14 07:15:03.885025:  
+2024-09-14 07:15:03.885245: Epoch 267 
+2024-09-14 07:15:03.885373: Current learning rate: 0.00756 
+2024-09-14 07:19:09.998467: train_loss -0.8318 
+2024-09-14 07:19:09.998629: val_loss -0.688 
+2024-09-14 07:19:09.998680: Pseudo dice [0.7015, 0.8146] 
+2024-09-14 07:19:09.998730: Epoch time: 246.12 s 
+2024-09-14 07:19:10.949949:  
+2024-09-14 07:19:10.950181: Epoch 268 
+2024-09-14 07:19:10.950269: Current learning rate: 0.00755 
+2024-09-14 07:23:17.103648: train_loss -0.8253 
+2024-09-14 07:23:17.103790: val_loss -0.6654 
+2024-09-14 07:23:17.103849: Pseudo dice [0.6688, 0.8156] 
+2024-09-14 07:23:17.103912: Epoch time: 246.16 s 
+2024-09-14 07:23:18.053406:  
+2024-09-14 07:23:18.053608: Epoch 269 
+2024-09-14 07:23:18.053694: Current learning rate: 0.00754 
+2024-09-14 07:27:24.116771: train_loss -0.8274 
+2024-09-14 07:27:24.116911: val_loss -0.6754 
+2024-09-14 07:27:24.116961: Pseudo dice [0.6721, 0.8182] 
+2024-09-14 07:27:24.117014: Epoch time: 246.07 s 
+2024-09-14 07:27:25.069728:  
+2024-09-14 07:27:25.069918: Epoch 270 
+2024-09-14 07:27:25.070002: Current learning rate: 0.00753 
+2024-09-14 07:31:31.203915: train_loss -0.8203 
+2024-09-14 07:31:31.204080: val_loss -0.6704 
+2024-09-14 07:31:31.204132: Pseudo dice [0.667, 0.803] 
+2024-09-14 07:31:31.204185: Epoch time: 246.14 s 
+2024-09-14 07:31:32.140940:  
+2024-09-14 07:31:32.141138: Epoch 271 
+2024-09-14 07:31:32.141222: Current learning rate: 0.00752 
+2024-09-14 07:35:38.243336: train_loss -0.8376 
+2024-09-14 07:35:38.243477: val_loss -0.6963 
+2024-09-14 07:35:38.243527: Pseudo dice [0.7062, 0.8299] 
+2024-09-14 07:35:38.243578: Epoch time: 246.1 s 
+2024-09-14 07:35:39.193536:  
+2024-09-14 07:35:39.193742: Epoch 272 
+2024-09-14 07:35:39.193823: Current learning rate: 0.00751 
+2024-09-14 07:39:45.162568: train_loss -0.8201 
+2024-09-14 07:39:45.162703: val_loss -0.6733 
+2024-09-14 07:39:45.162753: Pseudo dice [0.679, 0.8216] 
+2024-09-14 07:39:45.162802: Epoch time: 245.97 s 
+2024-09-14 07:39:46.112512:  
+2024-09-14 07:39:46.112761: Epoch 273 
+2024-09-14 07:39:46.112845: Current learning rate: 0.00751 
+2024-09-14 07:43:52.031006: train_loss -0.8261 
+2024-09-14 07:43:52.031147: val_loss -0.6546 
+2024-09-14 07:43:52.031198: Pseudo dice [0.6487, 0.796] 
+2024-09-14 07:43:52.031254: Epoch time: 245.92 s 
+2024-09-14 07:43:53.004884:  
+2024-09-14 07:43:53.005155: Epoch 274 
+2024-09-14 07:43:53.005296: Current learning rate: 0.0075 
+2024-09-14 07:47:59.003342: train_loss -0.8144 
+2024-09-14 07:47:59.003485: val_loss -0.6988 
+2024-09-14 07:47:59.003536: Pseudo dice [0.727, 0.8148] 
+2024-09-14 07:47:59.003625: Epoch time: 246.0 s 
+2024-09-14 07:47:59.961482:  
+2024-09-14 07:47:59.961689: Epoch 275 
+2024-09-14 07:47:59.961787: Current learning rate: 0.00749 
+2024-09-14 07:52:05.981221: train_loss -0.818 
+2024-09-14 07:52:05.981363: val_loss -0.6683 
+2024-09-14 07:52:05.981640: Pseudo dice [0.6564, 0.8286] 
+2024-09-14 07:52:05.981692: Epoch time: 246.02 s 
+2024-09-14 07:52:06.959005:  
+2024-09-14 07:52:06.959206: Epoch 276 
+2024-09-14 07:52:06.959293: Current learning rate: 0.00748 
+2024-09-14 07:56:12.796699: train_loss -0.821 
+2024-09-14 07:56:12.796835: val_loss -0.6869 
+2024-09-14 07:56:12.796886: Pseudo dice [0.6623, 0.833] 
+2024-09-14 07:56:12.796940: Epoch time: 245.84 s 
+2024-09-14 07:56:13.732480:  
+2024-09-14 07:56:13.732652: Epoch 277 
+2024-09-14 07:56:13.732738: Current learning rate: 0.00747 
+2024-09-14 08:00:19.696144: train_loss -0.8079 
+2024-09-14 08:00:19.696317: val_loss -0.6866 
+2024-09-14 08:00:19.696371: Pseudo dice [0.6783, 0.8157] 
+2024-09-14 08:00:19.696424: Epoch time: 245.97 s 
+2024-09-14 08:00:20.633139:  
+2024-09-14 08:00:20.633279: Epoch 278 
+2024-09-14 08:00:20.633360: Current learning rate: 0.00746 
+2024-09-14 08:04:26.527662: train_loss -0.822 
+2024-09-14 08:04:26.527829: val_loss -0.6555 
+2024-09-14 08:04:26.527883: Pseudo dice [0.6334, 0.8342] 
+2024-09-14 08:04:26.527935: Epoch time: 245.9 s 
+2024-09-14 08:04:27.483593:  
+2024-09-14 08:04:27.483823: Epoch 279 
+2024-09-14 08:04:27.483917: Current learning rate: 0.00745 
+2024-09-14 08:08:33.368976: train_loss -0.8255 
+2024-09-14 08:08:33.369209: val_loss -0.6885 
+2024-09-14 08:08:33.369263: Pseudo dice [0.6788, 0.8261] 
+2024-09-14 08:08:33.369314: Epoch time: 245.89 s 
+2024-09-14 08:08:34.308590:  
+2024-09-14 08:08:34.308782: Epoch 280 
+2024-09-14 08:08:34.308866: Current learning rate: 0.00744 
+2024-09-14 08:12:40.317656: train_loss -0.8291 
+2024-09-14 08:12:40.317802: val_loss -0.6894 
+2024-09-14 08:12:40.317853: Pseudo dice [0.6926, 0.8296] 
+2024-09-14 08:12:40.317904: Epoch time: 246.01 s 
+2024-09-14 08:12:41.262894:  
+2024-09-14 08:12:41.263063: Epoch 281 
+2024-09-14 08:12:41.263175: Current learning rate: 0.00743 
+2024-09-14 08:16:47.028154: train_loss -0.827 
+2024-09-14 08:16:47.028296: val_loss -0.6863 
+2024-09-14 08:16:47.028346: Pseudo dice [0.6436, 0.8321] 
+2024-09-14 08:16:47.028399: Epoch time: 245.77 s 
+2024-09-14 08:16:47.971774:  
+2024-09-14 08:16:47.972023: Epoch 282 
+2024-09-14 08:16:47.972108: Current learning rate: 0.00742 
+2024-09-14 08:20:53.977294: train_loss -0.8274 
+2024-09-14 08:20:53.977435: val_loss -0.6958 
+2024-09-14 08:20:53.977486: Pseudo dice [0.6874, 0.8333] 
+2024-09-14 08:20:53.977536: Epoch time: 246.01 s 
+2024-09-14 08:20:54.930664:  
+2024-09-14 08:20:54.930840: Epoch 283 
+2024-09-14 08:20:54.930923: Current learning rate: 0.00741 
+2024-09-14 08:25:00.845865: train_loss -0.8353 
+2024-09-14 08:25:00.846029: val_loss -0.6924 
+2024-09-14 08:25:00.846080: Pseudo dice [0.6529, 0.837] 
+2024-09-14 08:25:00.846130: Epoch time: 245.92 s 
+2024-09-14 08:25:01.792800:  
+2024-09-14 08:25:01.792981: Epoch 284 
+2024-09-14 08:25:01.793062: Current learning rate: 0.0074 
+2024-09-14 08:29:07.524271: train_loss -0.8324 
+2024-09-14 08:29:07.524534: val_loss -0.6956 
+2024-09-14 08:29:07.524645: Pseudo dice [0.7089, 0.8106] 
+2024-09-14 08:29:07.524738: Epoch time: 245.73 s 
+2024-09-14 08:29:08.480039:  
+2024-09-14 08:29:08.480232: Epoch 285 
+2024-09-14 08:29:08.480317: Current learning rate: 0.00739 
+2024-09-14 08:33:14.334592: train_loss -0.8202 
+2024-09-14 08:33:14.334773: val_loss -0.6783 
+2024-09-14 08:33:14.334824: Pseudo dice [0.675, 0.8161] 
+2024-09-14 08:33:14.334874: Epoch time: 245.86 s 
+2024-09-14 08:33:16.200189:  
+2024-09-14 08:33:16.200455: Epoch 286 
+2024-09-14 08:33:16.200559: Current learning rate: 0.00738 
+2024-09-14 08:37:22.371230: train_loss -0.7972 
+2024-09-14 08:37:22.371369: val_loss -0.657 
+2024-09-14 08:37:22.371421: Pseudo dice [0.7062, 0.7832] 
+2024-09-14 08:37:22.371472: Epoch time: 246.17 s 
+2024-09-14 08:37:23.326530:  
+2024-09-14 08:37:23.326701: Epoch 287 
+2024-09-14 08:37:23.326808: Current learning rate: 0.00738 
+2024-09-14 08:41:29.402286: train_loss -0.7829 
+2024-09-14 08:41:29.402424: val_loss -0.6483 
+2024-09-14 08:41:29.402474: Pseudo dice [0.6301, 0.805] 
+2024-09-14 08:41:29.402525: Epoch time: 246.08 s 
+2024-09-14 08:41:30.363083:  
+2024-09-14 08:41:30.363319: Epoch 288 
+2024-09-14 08:41:30.363441: Current learning rate: 0.00737 
+2024-09-14 08:45:36.463027: train_loss -0.803 
+2024-09-14 08:45:36.463231: val_loss -0.6733 
+2024-09-14 08:45:36.463329: Pseudo dice [0.6666, 0.8035] 
+2024-09-14 08:45:36.463381: Epoch time: 246.1 s 
+2024-09-14 08:45:37.420765:  
+2024-09-14 08:45:37.421030: Epoch 289 
+2024-09-14 08:45:37.421118: Current learning rate: 0.00736 
+2024-09-14 08:49:43.479199: train_loss -0.8068 
+2024-09-14 08:49:43.479393: val_loss -0.6928 
+2024-09-14 08:49:43.479446: Pseudo dice [0.7021, 0.822] 
+2024-09-14 08:49:43.479497: Epoch time: 246.06 s 
+2024-09-14 08:49:44.436864:  
+2024-09-14 08:49:44.437026: Epoch 290 
+2024-09-14 08:49:44.437106: Current learning rate: 0.00735 
+2024-09-14 08:53:50.473221: train_loss -0.8155 
+2024-09-14 08:53:50.473368: val_loss -0.6809 
+2024-09-14 08:53:50.473418: Pseudo dice [0.695, 0.8196] 
+2024-09-14 08:53:50.473468: Epoch time: 246.04 s 
+2024-09-14 08:53:51.438400:  
+2024-09-14 08:53:51.438620: Epoch 291 
+2024-09-14 08:53:51.438717: Current learning rate: 0.00734 
+2024-09-14 08:57:57.523689: train_loss -0.8286 
+2024-09-14 08:57:57.523834: val_loss -0.6816 
+2024-09-14 08:57:57.523885: Pseudo dice [0.6855, 0.8302] 
+2024-09-14 08:57:57.523936: Epoch time: 246.09 s 
+2024-09-14 08:57:58.488340:  
+2024-09-14 08:57:58.488564: Epoch 292 
+2024-09-14 08:57:58.488648: Current learning rate: 0.00733 
+2024-09-14 09:02:04.637955: train_loss -0.8318 
+2024-09-14 09:02:04.638094: val_loss -0.669 
+2024-09-14 09:02:04.638144: Pseudo dice [0.7028, 0.8193] 
+2024-09-14 09:02:04.638193: Epoch time: 246.15 s 
+2024-09-14 09:02:05.589404:  
+2024-09-14 09:02:05.589617: Epoch 293 
+2024-09-14 09:02:05.589728: Current learning rate: 0.00732 
+2024-09-14 09:06:10.975688: train_loss -0.8024 
+2024-09-14 09:06:10.975844: val_loss -0.6537 
+2024-09-14 09:06:10.975895: Pseudo dice [0.651, 0.8103] 
+2024-09-14 09:06:10.975945: Epoch time: 245.39 s 
+2024-09-14 09:06:11.960506:  
+2024-09-14 09:06:11.960688: Epoch 294 
+2024-09-14 09:06:11.960770: Current learning rate: 0.00731 
+2024-09-14 09:10:17.385011: train_loss -0.8204 
+2024-09-14 09:10:17.385187: val_loss -0.6768 
+2024-09-14 09:10:17.385239: Pseudo dice [0.6995, 0.8165] 
+2024-09-14 09:10:17.385289: Epoch time: 245.43 s 
+2024-09-14 09:10:18.331672:  
+2024-09-14 09:10:18.331905: Epoch 295 
+2024-09-14 09:10:18.331994: Current learning rate: 0.0073 
+2024-09-14 09:14:23.874355: train_loss -0.8057 
+2024-09-14 09:14:23.874493: val_loss -0.6725 
+2024-09-14 09:14:23.874544: Pseudo dice [0.6787, 0.8095] 
+2024-09-14 09:14:23.874594: Epoch time: 245.54 s 
+2024-09-14 09:14:24.823760:  
+2024-09-14 09:14:24.824013: Epoch 296 
+2024-09-14 09:14:24.824097: Current learning rate: 0.00729 
+2024-09-14 09:18:30.410832: train_loss -0.8021 
+2024-09-14 09:18:30.411036: val_loss -0.7018 
+2024-09-14 09:18:30.411088: Pseudo dice [0.6818, 0.833] 
+2024-09-14 09:18:30.411139: Epoch time: 245.59 s 
+2024-09-14 09:18:31.364846:  
+2024-09-14 09:18:31.365096: Epoch 297 
+2024-09-14 09:18:31.365183: Current learning rate: 0.00728 
+2024-09-14 09:22:36.909631: train_loss -0.814 
+2024-09-14 09:22:36.909770: val_loss -0.6639 
+2024-09-14 09:22:36.909819: Pseudo dice [0.6765, 0.8195] 
+2024-09-14 09:22:36.909868: Epoch time: 245.55 s 
+2024-09-14 09:22:37.878660:  
+2024-09-14 09:22:37.878856: Epoch 298 
+2024-09-14 09:22:37.878942: Current learning rate: 0.00727 
+2024-09-14 09:26:43.419379: train_loss -0.8277 
+2024-09-14 09:26:43.419519: val_loss -0.6794 
+2024-09-14 09:26:43.419569: Pseudo dice [0.6833, 0.801] 
+2024-09-14 09:26:43.419621: Epoch time: 245.54 s 
+2024-09-14 09:26:44.374677:  
+2024-09-14 09:26:44.374840: Epoch 299 
+2024-09-14 09:26:44.374923: Current learning rate: 0.00726 
+2024-09-14 09:30:50.038820: train_loss -0.8292 
+2024-09-14 09:30:50.038980: val_loss -0.6848 
+2024-09-14 09:30:50.039030: Pseudo dice [0.677, 0.8272] 
+2024-09-14 09:30:50.039080: Epoch time: 245.67 s 
+2024-09-14 09:30:53.924678:  
+2024-09-14 09:30:53.924877: Epoch 300 
+2024-09-14 09:30:53.924960: Current learning rate: 0.00725 
+2024-09-14 09:35:00.034908: train_loss -0.8365 
+2024-09-14 09:35:00.035045: val_loss -0.6833 
+2024-09-14 09:35:00.035095: Pseudo dice [0.6871, 0.8286] 
+2024-09-14 09:35:00.035148: Epoch time: 246.11 s 
+2024-09-14 09:35:01.018564:  
+2024-09-14 09:35:01.018732: Epoch 301 
+2024-09-14 09:35:01.018842: Current learning rate: 0.00724 
+2024-09-14 09:39:06.591000: train_loss -0.7928 
+2024-09-14 09:39:06.591136: val_loss -0.6335 
+2024-09-14 09:39:06.591187: Pseudo dice [0.6107, 0.8054] 
+2024-09-14 09:39:06.591238: Epoch time: 245.57 s 
+2024-09-14 09:39:07.566759:  
+2024-09-14 09:39:07.566969: Epoch 302 
+2024-09-14 09:39:07.567086: Current learning rate: 0.00724 
+2024-09-14 09:43:13.485753: train_loss -0.8032 
+2024-09-14 09:43:13.485898: val_loss -0.6942 
+2024-09-14 09:43:13.485948: Pseudo dice [0.6979, 0.8325] 
+2024-09-14 09:43:13.485999: Epoch time: 245.92 s 
+2024-09-14 09:43:14.448066:  
+2024-09-14 09:43:14.448242: Epoch 303 
+2024-09-14 09:43:14.448347: Current learning rate: 0.00723 
+2024-09-14 09:47:20.332095: train_loss -0.8158 
+2024-09-14 09:47:20.332233: val_loss -0.6846 
+2024-09-14 09:47:20.332289: Pseudo dice [0.6999, 0.8207] 
+2024-09-14 09:47:20.332340: Epoch time: 245.89 s 
+2024-09-14 09:47:21.287050:  
+2024-09-14 09:47:21.287233: Epoch 304 
+2024-09-14 09:47:21.287322: Current learning rate: 0.00722 
+2024-09-14 09:51:27.033322: train_loss -0.8224 
+2024-09-14 09:51:27.033458: val_loss -0.6853 
+2024-09-14 09:51:27.033508: Pseudo dice [0.6806, 0.8147] 
+2024-09-14 09:51:27.033558: Epoch time: 245.75 s 
+2024-09-14 09:51:27.994393:  
+2024-09-14 09:51:27.994647: Epoch 305 
+2024-09-14 09:51:27.994729: Current learning rate: 0.00721 
+2024-09-14 09:55:33.731410: train_loss -0.8183 
+2024-09-14 09:55:33.731546: val_loss -0.6456 
+2024-09-14 09:55:33.731597: Pseudo dice [0.5886, 0.8317] 
+2024-09-14 09:55:33.731647: Epoch time: 245.74 s 
+2024-09-14 09:55:34.701927:  
+2024-09-14 09:55:34.702125: Epoch 306 
+2024-09-14 09:55:34.702211: Current learning rate: 0.0072 
+2024-09-14 09:59:40.568762: train_loss -0.8214 
+2024-09-14 09:59:40.568900: val_loss -0.6781 
+2024-09-14 09:59:40.568951: Pseudo dice [0.6832, 0.8279] 
+2024-09-14 09:59:40.569003: Epoch time: 245.87 s 
+2024-09-14 09:59:41.526972:  
+2024-09-14 09:59:41.527133: Epoch 307 
+2024-09-14 09:59:41.527216: Current learning rate: 0.00719 
+2024-09-14 10:03:47.310300: train_loss -0.8258 
+2024-09-14 10:03:47.310438: val_loss -0.6991 
+2024-09-14 10:03:47.310489: Pseudo dice [0.6859, 0.816] 
+2024-09-14 10:03:47.310540: Epoch time: 245.79 s 
+2024-09-14 10:03:48.296289:  
+2024-09-14 10:03:48.296458: Epoch 308 
+2024-09-14 10:03:48.296541: Current learning rate: 0.00718 
+2024-09-14 10:07:54.124583: train_loss -0.8188 
+2024-09-14 10:07:54.124718: val_loss -0.7004 
+2024-09-14 10:07:54.124771: Pseudo dice [0.6985, 0.8291] 
+2024-09-14 10:07:54.124821: Epoch time: 245.83 s 
+2024-09-14 10:07:55.083084:  
+2024-09-14 10:07:55.083259: Epoch 309 
+2024-09-14 10:07:55.083341: Current learning rate: 0.00717 
+2024-09-14 10:12:01.573937: train_loss -0.837 
+2024-09-14 10:12:01.574072: val_loss -0.6698 
+2024-09-14 10:12:01.574125: Pseudo dice [0.644, 0.8186] 
+2024-09-14 10:12:01.574174: Epoch time: 246.49 s 
+2024-09-14 10:12:02.543793:  
+2024-09-14 10:12:02.544055: Epoch 310 
+2024-09-14 10:12:02.544140: Current learning rate: 0.00716 
+2024-09-14 10:16:08.375419: train_loss -0.8415 
+2024-09-14 10:16:08.375584: val_loss -0.6981 
+2024-09-14 10:16:08.375641: Pseudo dice [0.6918, 0.8381] 
+2024-09-14 10:16:08.375700: Epoch time: 245.83 s 
+2024-09-14 10:16:09.373671:  
+2024-09-14 10:16:09.373904: Epoch 311 
+2024-09-14 10:16:09.374010: Current learning rate: 0.00715 
+2024-09-14 10:20:15.255677: train_loss -0.8434 
+2024-09-14 10:20:15.255844: val_loss -0.7176 
+2024-09-14 10:20:15.255896: Pseudo dice [0.7286, 0.8306] 
+2024-09-14 10:20:15.255947: Epoch time: 245.88 s 
+2024-09-14 10:20:16.224833:  
+2024-09-14 10:20:16.225075: Epoch 312 
+2024-09-14 10:20:16.225159: Current learning rate: 0.00714 
+2024-09-14 10:24:22.164244: train_loss -0.8437 
+2024-09-14 10:24:22.164406: val_loss -0.6756 
+2024-09-14 10:24:22.164458: Pseudo dice [0.699, 0.8224] 
+2024-09-14 10:24:22.164509: Epoch time: 245.94 s 
+2024-09-14 10:24:23.127710:  
+2024-09-14 10:24:23.127973: Epoch 313 
+2024-09-14 10:24:23.128078: Current learning rate: 0.00713 
+2024-09-14 10:28:29.130603: train_loss -0.85 
+2024-09-14 10:28:29.130794: val_loss -0.6881 
+2024-09-14 10:28:29.130854: Pseudo dice [0.6769, 0.823] 
+2024-09-14 10:28:29.130904: Epoch time: 246.0 s 
+2024-09-14 10:28:30.096887:  
+2024-09-14 10:28:30.097142: Epoch 314 
+2024-09-14 10:28:30.097230: Current learning rate: 0.00712 
+2024-09-14 10:32:35.895510: train_loss -0.8439 
+2024-09-14 10:32:35.895646: val_loss -0.6877 
+2024-09-14 10:32:35.895695: Pseudo dice [0.6751, 0.8019] 
+2024-09-14 10:32:35.895746: Epoch time: 245.8 s 
+2024-09-14 10:32:36.873681:  
+2024-09-14 10:32:36.873928: Epoch 315 
+2024-09-14 10:32:36.874013: Current learning rate: 0.00711 
+2024-09-14 10:36:42.458262: train_loss -0.8354 
+2024-09-14 10:36:42.458438: val_loss -0.7025 
+2024-09-14 10:36:42.458488: Pseudo dice [0.7076, 0.8285] 
+2024-09-14 10:36:42.458540: Epoch time: 245.59 s 
+2024-09-14 10:36:43.442521:  
+2024-09-14 10:36:43.442716: Epoch 316 
+2024-09-14 10:36:43.442799: Current learning rate: 0.0071 
+2024-09-14 10:40:48.982558: train_loss -0.8435 
+2024-09-14 10:40:48.982698: val_loss -0.6678 
+2024-09-14 10:40:48.982892: Pseudo dice [0.6618, 0.82] 
+2024-09-14 10:40:48.983058: Epoch time: 245.54 s 
+2024-09-14 10:40:49.944135:  
+2024-09-14 10:40:49.944350: Epoch 317 
+2024-09-14 10:40:49.944438: Current learning rate: 0.0071 
+2024-09-14 10:44:55.633540: train_loss -0.843 
+2024-09-14 10:44:55.633676: val_loss -0.6602 
+2024-09-14 10:44:55.633727: Pseudo dice [0.6331, 0.8182] 
+2024-09-14 10:44:55.633778: Epoch time: 245.69 s 
+2024-09-14 10:44:56.599538:  
+2024-09-14 10:44:56.599776: Epoch 318 
+2024-09-14 10:44:56.599872: Current learning rate: 0.00709 
+2024-09-14 10:49:02.467314: train_loss -0.8445 
+2024-09-14 10:49:02.467465: val_loss -0.6874 
+2024-09-14 10:49:02.467517: Pseudo dice [0.6953, 0.8234] 
+2024-09-14 10:49:02.467566: Epoch time: 245.87 s 
+2024-09-14 10:49:03.452318:  
+2024-09-14 10:49:03.452490: Epoch 319 
+2024-09-14 10:49:03.452575: Current learning rate: 0.00708 
+2024-09-14 10:53:09.356377: train_loss -0.8449 
+2024-09-14 10:53:09.356519: val_loss -0.6926 
+2024-09-14 10:53:09.356571: Pseudo dice [0.6761, 0.8285] 
+2024-09-14 10:53:09.356622: Epoch time: 245.91 s 
+2024-09-14 10:53:10.340117:  
+2024-09-14 10:53:10.340317: Epoch 320 
+2024-09-14 10:53:10.340421: Current learning rate: 0.00707 
+2024-09-14 10:57:16.232687: train_loss -0.8368 
+2024-09-14 10:57:16.232828: val_loss -0.7004 
+2024-09-14 10:57:16.232879: Pseudo dice [0.678, 0.8304] 
+2024-09-14 10:57:16.232929: Epoch time: 245.89 s 
+2024-09-14 10:57:17.211628:  
+2024-09-14 10:57:17.211817: Epoch 321 
+2024-09-14 10:57:17.211900: Current learning rate: 0.00706 
+2024-09-14 11:01:22.878685: train_loss -0.8472 
+2024-09-14 11:01:22.878826: val_loss -0.6994 
+2024-09-14 11:01:22.878879: Pseudo dice [0.6826, 0.8311] 
+2024-09-14 11:01:22.878930: Epoch time: 245.67 s 
+2024-09-14 11:01:23.872218:  
+2024-09-14 11:01:23.872430: Epoch 322 
+2024-09-14 11:01:23.872513: Current learning rate: 0.00705 
+2024-09-14 11:05:29.659747: train_loss -0.8468 
+2024-09-14 11:05:29.659907: val_loss -0.6741 
+2024-09-14 11:05:29.659957: Pseudo dice [0.6619, 0.8249] 
+2024-09-14 11:05:29.660007: Epoch time: 245.79 s 
+2024-09-14 11:05:30.617939:  
+2024-09-14 11:05:30.618138: Epoch 323 
+2024-09-14 11:05:30.618224: Current learning rate: 0.00704 
+2024-09-14 11:09:36.536451: train_loss -0.8467 
+2024-09-14 11:09:36.536589: val_loss -0.674 
+2024-09-14 11:09:36.536639: Pseudo dice [0.6495, 0.8351] 
+2024-09-14 11:09:36.536688: Epoch time: 245.92 s 
+2024-09-14 11:09:37.501089:  
+2024-09-14 11:09:37.501262: Epoch 324 
+2024-09-14 11:09:37.501351: Current learning rate: 0.00703 
+2024-09-14 11:13:43.370163: train_loss -0.86 
+2024-09-14 11:13:43.370301: val_loss -0.7029 
+2024-09-14 11:13:43.370351: Pseudo dice [0.6964, 0.8474] 
+2024-09-14 11:13:43.370402: Epoch time: 245.87 s 
+2024-09-14 11:13:44.325473:  
+2024-09-14 11:13:44.325673: Epoch 325 
+2024-09-14 11:13:44.325754: Current learning rate: 0.00702 
+2024-09-14 11:17:50.141043: train_loss -0.8569 
+2024-09-14 11:17:50.141179: val_loss -0.7257 
+2024-09-14 11:17:50.141228: Pseudo dice [0.7276, 0.838] 
+2024-09-14 11:17:50.141279: Epoch time: 245.82 s 
+2024-09-14 11:17:51.108089:  
+2024-09-14 11:17:51.108267: Epoch 326 
+2024-09-14 11:17:51.108352: Current learning rate: 0.00701 
+2024-09-14 11:21:57.027967: train_loss -0.8548 
+2024-09-14 11:21:57.028104: val_loss -0.6864 
+2024-09-14 11:21:57.028154: Pseudo dice [0.6886, 0.8172] 
+2024-09-14 11:21:57.028203: Epoch time: 245.92 s 
+2024-09-14 11:21:57.992942:  
+2024-09-14 11:21:57.993136: Epoch 327 
+2024-09-14 11:21:57.993225: Current learning rate: 0.007 
+2024-09-14 11:26:04.118820: train_loss -0.8415 
+2024-09-14 11:26:04.118955: val_loss -0.6621 
+2024-09-14 11:26:04.119004: Pseudo dice [0.6426, 0.8303] 
+2024-09-14 11:26:04.119056: Epoch time: 246.13 s 
+2024-09-14 11:26:05.071953:  
+2024-09-14 11:26:05.072119: Epoch 328 
+2024-09-14 11:26:05.072213: Current learning rate: 0.00699 
+2024-09-14 11:30:10.943158: train_loss -0.8512 
+2024-09-14 11:30:10.943303: val_loss -0.6856 
+2024-09-14 11:30:10.943353: Pseudo dice [0.6639, 0.8334] 
+2024-09-14 11:30:10.943404: Epoch time: 245.87 s 
+2024-09-14 11:30:11.903534:  
+2024-09-14 11:30:11.903709: Epoch 329 
+2024-09-14 11:30:11.903794: Current learning rate: 0.00698 
+2024-09-14 11:34:17.747908: train_loss -0.8527 
+2024-09-14 11:34:17.748045: val_loss -0.6837 
+2024-09-14 11:34:17.748095: Pseudo dice [0.7077, 0.8129] 
+2024-09-14 11:34:17.748148: Epoch time: 245.85 s 
+2024-09-14 11:34:18.708450:  
+2024-09-14 11:34:18.708631: Epoch 330 
+2024-09-14 11:34:18.708755: Current learning rate: 0.00697 
+2024-09-14 11:38:24.600517: train_loss -0.8541 
+2024-09-14 11:38:24.600654: val_loss -0.6812 
+2024-09-14 11:38:24.600703: Pseudo dice [0.6722, 0.8259] 
+2024-09-14 11:38:24.600767: Epoch time: 245.89 s 
+2024-09-14 11:38:25.571553:  
+2024-09-14 11:38:25.571765: Epoch 331 
+2024-09-14 11:38:25.571916: Current learning rate: 0.00696 
+2024-09-14 11:42:31.429292: train_loss -0.8578 
+2024-09-14 11:42:31.429435: val_loss -0.7077 
+2024-09-14 11:42:31.429504: Pseudo dice [0.6943, 0.8334] 
+2024-09-14 11:42:31.429556: Epoch time: 245.86 s 
+2024-09-14 11:42:32.390808:  
+2024-09-14 11:42:32.391011: Epoch 332 
+2024-09-14 11:42:32.391097: Current learning rate: 0.00696 
+2024-09-14 11:46:38.305744: train_loss -0.8549 
+2024-09-14 11:46:38.305892: val_loss -0.6645 
+2024-09-14 11:46:38.305944: Pseudo dice [0.6551, 0.8237] 
+2024-09-14 11:46:38.305994: Epoch time: 245.92 s 
+2024-09-14 11:46:40.219366:  
+2024-09-14 11:46:40.219580: Epoch 333 
+2024-09-14 11:46:40.219676: Current learning rate: 0.00695 
+2024-09-14 11:50:46.236553: train_loss -0.8563 
+2024-09-14 11:50:46.236692: val_loss -0.7024 
+2024-09-14 11:50:46.236744: Pseudo dice [0.6738, 0.8411] 
+2024-09-14 11:50:46.236794: Epoch time: 246.02 s 
+2024-09-14 11:50:47.188330:  
+2024-09-14 11:50:47.188580: Epoch 334 
+2024-09-14 11:50:47.188666: Current learning rate: 0.00694 
+2024-09-14 11:54:53.136641: train_loss -0.8444 
+2024-09-14 11:54:53.136783: val_loss -0.6847 
+2024-09-14 11:54:53.136832: Pseudo dice [0.6599, 0.8032] 
+2024-09-14 11:54:53.136881: Epoch time: 245.95 s 
+2024-09-14 11:54:54.108983:  
+2024-09-14 11:54:54.109191: Epoch 335 
+2024-09-14 11:54:54.109274: Current learning rate: 0.00693 
+2024-09-14 11:58:59.670439: train_loss -0.8433 
+2024-09-14 11:58:59.670578: val_loss -0.6849 
+2024-09-14 11:58:59.670627: Pseudo dice [0.6476, 0.8345] 
+2024-09-14 11:58:59.670676: Epoch time: 245.56 s 
+2024-09-14 11:59:00.652759:  
+2024-09-14 11:59:00.652985: Epoch 336 
+2024-09-14 11:59:00.653067: Current learning rate: 0.00692 
+2024-09-14 12:03:06.396852: train_loss -0.8421 
+2024-09-14 12:03:06.396992: val_loss -0.704 
+2024-09-14 12:03:06.397042: Pseudo dice [0.7084, 0.8101] 
+2024-09-14 12:03:06.397092: Epoch time: 245.75 s 
+2024-09-14 12:03:07.363978:  
+2024-09-14 12:03:07.364192: Epoch 337 
+2024-09-14 12:03:07.364280: Current learning rate: 0.00691 
+2024-09-14 12:07:13.165241: train_loss -0.8508 
+2024-09-14 12:07:13.165393: val_loss -0.6777 
+2024-09-14 12:07:13.165444: Pseudo dice [0.6764, 0.8201] 
+2024-09-14 12:07:13.165494: Epoch time: 245.8 s 
+2024-09-14 12:07:14.161578:  
+2024-09-14 12:07:14.161783: Epoch 338 
+2024-09-14 12:07:14.161867: Current learning rate: 0.0069 
+2024-09-14 12:11:19.963520: train_loss -0.8305 
+2024-09-14 12:11:19.963660: val_loss -0.6889 
+2024-09-14 12:11:19.963709: Pseudo dice [0.7105, 0.8317] 
+2024-09-14 12:11:19.963759: Epoch time: 245.8 s 
+2024-09-14 12:11:20.961628:  
+2024-09-14 12:11:20.961871: Epoch 339 
+2024-09-14 12:11:20.961954: Current learning rate: 0.00689 
+2024-09-14 12:15:26.769245: train_loss -0.8347 
+2024-09-14 12:15:26.769410: val_loss -0.6818 
+2024-09-14 12:15:26.769463: Pseudo dice [0.6448, 0.8283] 
+2024-09-14 12:15:26.769513: Epoch time: 245.81 s 
+2024-09-14 12:15:27.746732:  
+2024-09-14 12:15:27.746905: Epoch 340 
+2024-09-14 12:15:27.746992: Current learning rate: 0.00688 
+2024-09-14 12:19:33.527935: train_loss -0.8238 
+2024-09-14 12:19:33.528079: val_loss -0.6651 
+2024-09-14 12:19:33.528129: Pseudo dice [0.6848, 0.8076] 
+2024-09-14 12:19:33.528180: Epoch time: 245.78 s 
+2024-09-14 12:19:34.518915:  
+2024-09-14 12:19:34.519097: Epoch 341 
+2024-09-14 12:19:34.519179: Current learning rate: 0.00687 
+2024-09-14 12:23:40.334531: train_loss -0.8271 
+2024-09-14 12:23:40.334666: val_loss -0.6664 
+2024-09-14 12:23:40.334715: Pseudo dice [0.671, 0.8083] 
+2024-09-14 12:23:40.334764: Epoch time: 245.82 s 
+2024-09-14 12:23:41.333845:  
+2024-09-14 12:23:41.334075: Epoch 342 
+2024-09-14 12:23:41.334211: Current learning rate: 0.00686 
+2024-09-14 12:27:47.123081: train_loss -0.8315 
+2024-09-14 12:27:47.123267: val_loss -0.7053 
+2024-09-14 12:27:47.123318: Pseudo dice [0.7171, 0.8214] 
+2024-09-14 12:27:47.123367: Epoch time: 245.79 s 
+2024-09-14 12:27:48.134277:  
+2024-09-14 12:27:48.134475: Epoch 343 
+2024-09-14 12:27:48.134570: Current learning rate: 0.00685 
+2024-09-14 12:31:53.974206: train_loss -0.8429 
+2024-09-14 12:31:53.974347: val_loss -0.7223 
+2024-09-14 12:31:53.974398: Pseudo dice [0.7278, 0.8331] 
+2024-09-14 12:31:53.974450: Epoch time: 245.84 s 
+2024-09-14 12:31:54.983844:  
+2024-09-14 12:31:54.984076: Epoch 344 
+2024-09-14 12:31:54.984193: Current learning rate: 0.00684 
+2024-09-14 12:36:00.792979: train_loss -0.8479 
+2024-09-14 12:36:00.793118: val_loss -0.6726 
+2024-09-14 12:36:00.793169: Pseudo dice [0.6702, 0.822] 
+2024-09-14 12:36:00.793219: Epoch time: 245.81 s 
+2024-09-14 12:36:01.811049:  
+2024-09-14 12:36:01.811213: Epoch 345 
+2024-09-14 12:36:01.811297: Current learning rate: 0.00683 
+2024-09-14 12:40:07.537859: train_loss -0.8446 
+2024-09-14 12:40:07.537998: val_loss -0.6863 
+2024-09-14 12:40:07.538048: Pseudo dice [0.701, 0.8174] 
+2024-09-14 12:40:07.538098: Epoch time: 245.73 s 
+2024-09-14 12:40:08.509230:  
+2024-09-14 12:40:08.509400: Epoch 346 
+2024-09-14 12:40:08.509483: Current learning rate: 0.00682 
+2024-09-14 12:44:14.304374: train_loss -0.8345 
+2024-09-14 12:44:14.304513: val_loss -0.6639 
+2024-09-14 12:44:14.304595: Pseudo dice [0.6528, 0.8332] 
+2024-09-14 12:44:14.304664: Epoch time: 245.8 s 
+2024-09-14 12:44:15.293641:  
+2024-09-14 12:44:15.293853: Epoch 347 
+2024-09-14 12:44:15.293938: Current learning rate: 0.00681 
+2024-09-14 12:48:21.019901: train_loss -0.8341 
+2024-09-14 12:48:21.020096: val_loss -0.6984 
+2024-09-14 12:48:21.020172: Pseudo dice [0.6817, 0.8274] 
+2024-09-14 12:48:21.020253: Epoch time: 245.73 s 
+2024-09-14 12:48:22.005503:  
+2024-09-14 12:48:22.005733: Epoch 348 
+2024-09-14 12:48:22.005814: Current learning rate: 0.0068 
+2024-09-14 12:52:27.310427: train_loss -0.8406 
+2024-09-14 12:52:27.310599: val_loss -0.6834 
+2024-09-14 12:52:27.310663: Pseudo dice [0.6918, 0.8015] 
+2024-09-14 12:52:27.310727: Epoch time: 245.31 s 
+2024-09-14 12:52:28.293480:  
+2024-09-14 12:52:28.293695: Epoch 349 
+2024-09-14 12:52:28.293793: Current learning rate: 0.0068 
+2024-09-14 12:56:33.656433: train_loss -0.8304 
+2024-09-14 12:56:33.656571: val_loss -0.6944 
+2024-09-14 12:56:33.656620: Pseudo dice [0.6577, 0.8296] 
+2024-09-14 12:56:33.656670: Epoch time: 245.36 s 
+2024-09-14 12:56:37.570108:  
+2024-09-14 12:56:37.570307: Epoch 350 
+2024-09-14 12:56:37.570406: Current learning rate: 0.00679 
+2024-09-14 13:00:42.970102: train_loss -0.8445 
+2024-09-14 13:00:42.970245: val_loss -0.6762 
+2024-09-14 13:00:42.970295: Pseudo dice [0.6909, 0.8141] 
+2024-09-14 13:00:42.970345: Epoch time: 245.4 s 
+2024-09-14 13:00:43.969331:  
+2024-09-14 13:00:43.969551: Epoch 351 
+2024-09-14 13:00:43.969631: Current learning rate: 0.00678 
+2024-09-14 13:04:49.266040: train_loss -0.8426 
+2024-09-14 13:04:49.266211: val_loss -0.706 
+2024-09-14 13:04:49.266260: Pseudo dice [0.6993, 0.8324] 
+2024-09-14 13:04:49.266309: Epoch time: 245.3 s 
+2024-09-14 13:04:50.260574:  
+2024-09-14 13:04:50.260757: Epoch 352 
+2024-09-14 13:04:50.260842: Current learning rate: 0.00677 
+2024-09-14 13:08:55.451892: train_loss -0.8275 
+2024-09-14 13:08:55.452029: val_loss -0.686 
+2024-09-14 13:08:55.452079: Pseudo dice [0.6741, 0.8294] 
+2024-09-14 13:08:55.452130: Epoch time: 245.19 s 
+2024-09-14 13:08:56.435373:  
+2024-09-14 13:08:56.435584: Epoch 353 
+2024-09-14 13:08:56.435668: Current learning rate: 0.00676 
+2024-09-14 13:13:01.606133: train_loss -0.8303 
+2024-09-14 13:13:01.606271: val_loss -0.6957 
+2024-09-14 13:13:01.606320: Pseudo dice [0.7137, 0.8221] 
+2024-09-14 13:13:01.606369: Epoch time: 245.17 s 
+2024-09-14 13:13:02.592300:  
+2024-09-14 13:13:02.592535: Epoch 354 
+2024-09-14 13:13:02.592619: Current learning rate: 0.00675 
+2024-09-14 13:17:07.738487: train_loss -0.8467 
+2024-09-14 13:17:07.738624: val_loss -0.7068 
+2024-09-14 13:17:07.738674: Pseudo dice [0.733, 0.8352] 
+2024-09-14 13:17:07.738724: Epoch time: 245.15 s 
+2024-09-14 13:17:07.738768: Yayy! New best EMA pseudo Dice: 0.7574 
+2024-09-14 13:17:12.547720:  
+2024-09-14 13:17:12.547947: Epoch 355 
+2024-09-14 13:17:12.548050: Current learning rate: 0.00674 
+2024-09-14 13:21:17.786793: train_loss -0.848 
+2024-09-14 13:21:17.786928: val_loss -0.6995 
+2024-09-14 13:21:17.786979: Pseudo dice [0.7202, 0.83] 
+2024-09-14 13:21:17.787031: Epoch time: 245.24 s 
+2024-09-14 13:21:17.787071: Yayy! New best EMA pseudo Dice: 0.7592 
+2024-09-14 13:21:21.654240:  
+2024-09-14 13:21:21.654448: Epoch 356 
+2024-09-14 13:21:21.654530: Current learning rate: 0.00673 
+2024-09-14 13:25:27.178150: train_loss -0.8557 
+2024-09-14 13:25:27.178295: val_loss -0.7107 
+2024-09-14 13:25:27.178345: Pseudo dice [0.7156, 0.8423] 
+2024-09-14 13:25:27.178394: Epoch time: 245.53 s 
+2024-09-14 13:25:27.178433: Yayy! New best EMA pseudo Dice: 0.7612 
+2024-09-14 13:25:31.080914:  
+2024-09-14 13:25:31.081123: Epoch 357 
+2024-09-14 13:25:31.081205: Current learning rate: 0.00672 
+2024-09-14 13:29:36.637851: train_loss -0.8575 
+2024-09-14 13:29:36.637989: val_loss -0.6746 
+2024-09-14 13:29:36.638038: Pseudo dice [0.6488, 0.822] 
+2024-09-14 13:29:36.638089: Epoch time: 245.56 s 
+2024-09-14 13:29:37.618184:  
+2024-09-14 13:29:37.618412: Epoch 358 
+2024-09-14 13:29:37.618496: Current learning rate: 0.00671 
+2024-09-14 13:33:43.331020: train_loss -0.8381 
+2024-09-14 13:33:43.331155: val_loss -0.6703 
+2024-09-14 13:33:43.331205: Pseudo dice [0.7007, 0.8078] 
+2024-09-14 13:33:43.331254: Epoch time: 245.71 s 
+2024-09-14 13:33:44.311132:  
+2024-09-14 13:33:44.311406: Epoch 359 
+2024-09-14 13:33:44.311553: Current learning rate: 0.0067 
+2024-09-14 13:37:49.870066: train_loss -0.8455 
+2024-09-14 13:37:49.870207: val_loss -0.6812 
+2024-09-14 13:37:49.870257: Pseudo dice [0.6955, 0.8137] 
+2024-09-14 13:37:49.870308: Epoch time: 245.56 s 
+2024-09-14 13:37:50.846675:  
+2024-09-14 13:37:50.846872: Epoch 360 
+2024-09-14 13:37:50.846954: Current learning rate: 0.00669 
+2024-09-14 13:41:56.752084: train_loss -0.8461 
+2024-09-14 13:41:56.752223: val_loss -0.6945 
+2024-09-14 13:41:56.752272: Pseudo dice [0.6746, 0.8175] 
+2024-09-14 13:41:56.752323: Epoch time: 245.91 s 
+2024-09-14 13:41:57.733978:  
+2024-09-14 13:41:57.734224: Epoch 361 
+2024-09-14 13:41:57.734307: Current learning rate: 0.00668 
+2024-09-14 13:46:03.303829: train_loss -0.8431 
+2024-09-14 13:46:03.303996: val_loss -0.6662 
+2024-09-14 13:46:03.304071: Pseudo dice [0.6684, 0.8033] 
+2024-09-14 13:46:03.304121: Epoch time: 245.57 s 
+2024-09-14 13:46:04.294581:  
+2024-09-14 13:46:04.294810: Epoch 362 
+2024-09-14 13:46:04.294894: Current learning rate: 0.00667 
+2024-09-14 13:50:09.903653: train_loss -0.8367 
+2024-09-14 13:50:09.903787: val_loss -0.6954 
+2024-09-14 13:50:09.903855: Pseudo dice [0.6961, 0.8274] 
+2024-09-14 13:50:09.903907: Epoch time: 245.61 s 
+2024-09-14 13:50:10.903760:  
+2024-09-14 13:50:10.903950: Epoch 363 
+2024-09-14 13:50:10.904034: Current learning rate: 0.00666 
+2024-09-14 13:54:16.459546: train_loss -0.8424 
+2024-09-14 13:54:16.459698: val_loss -0.7064 
+2024-09-14 13:54:16.459749: Pseudo dice [0.7219, 0.8129] 
+2024-09-14 13:54:16.459798: Epoch time: 245.56 s 
+2024-09-14 13:54:17.446986:  
+2024-09-14 13:54:17.447245: Epoch 364 
+2024-09-14 13:54:17.447329: Current learning rate: 0.00665 
+2024-09-14 13:58:22.936856: train_loss -0.8219 
+2024-09-14 13:58:22.937025: val_loss -0.6771 
+2024-09-14 13:58:22.937075: Pseudo dice [0.6928, 0.8151] 
+2024-09-14 13:58:22.937126: Epoch time: 245.49 s 
+2024-09-14 13:58:23.921635:  
+2024-09-14 13:58:23.921847: Epoch 365 
+2024-09-14 13:58:23.921932: Current learning rate: 0.00665 
+2024-09-14 14:02:29.392443: train_loss -0.8241 
+2024-09-14 14:02:29.392582: val_loss -0.7008 
+2024-09-14 14:02:29.392632: Pseudo dice [0.689, 0.8187] 
+2024-09-14 14:02:29.392681: Epoch time: 245.47 s 
+2024-09-14 14:02:30.381970:  
+2024-09-14 14:02:30.382232: Epoch 366 
+2024-09-14 14:02:30.382317: Current learning rate: 0.00664 
+2024-09-14 14:06:35.674205: train_loss -0.8387 
+2024-09-14 14:06:35.674365: val_loss -0.6796 
+2024-09-14 14:06:35.674415: Pseudo dice [0.6532, 0.8179] 
+2024-09-14 14:06:35.674463: Epoch time: 245.29 s 
+2024-09-14 14:06:36.658697:  
+2024-09-14 14:06:36.658886: Epoch 367 
+2024-09-14 14:06:36.658972: Current learning rate: 0.00663 
+2024-09-14 14:10:41.934607: train_loss -0.8355 
+2024-09-14 14:10:41.934757: val_loss -0.7016 
+2024-09-14 14:10:41.934806: Pseudo dice [0.6912, 0.8233] 
+2024-09-14 14:10:41.934856: Epoch time: 245.28 s 
+2024-09-14 14:10:42.930517:  
+2024-09-14 14:10:42.930703: Epoch 368 
+2024-09-14 14:10:42.930784: Current learning rate: 0.00662 
+2024-09-14 14:14:48.213891: train_loss -0.8408 
+2024-09-14 14:14:48.214027: val_loss -0.697 
+2024-09-14 14:14:48.214078: Pseudo dice [0.6695, 0.8372] 
+2024-09-14 14:14:48.214130: Epoch time: 245.29 s 
+2024-09-14 14:14:49.245479:  
+2024-09-14 14:14:49.245733: Epoch 369 
+2024-09-14 14:14:49.245817: Current learning rate: 0.00661 
+2024-09-14 14:18:54.452183: train_loss -0.8507 
+2024-09-14 14:18:54.452328: val_loss -0.6983 
+2024-09-14 14:18:54.452377: Pseudo dice [0.7024, 0.8261] 
+2024-09-14 14:18:54.452428: Epoch time: 245.21 s 
+2024-09-14 14:18:55.443717:  
+2024-09-14 14:18:55.443981: Epoch 370 
+2024-09-14 14:18:55.444067: Current learning rate: 0.0066 
+2024-09-14 14:23:00.552111: train_loss -0.8441 
+2024-09-14 14:23:00.552291: val_loss -0.6862 
+2024-09-14 14:23:00.552343: Pseudo dice [0.7203, 0.8164] 
+2024-09-14 14:23:00.552393: Epoch time: 245.11 s 
+2024-09-14 14:23:01.549912:  
+2024-09-14 14:23:01.550136: Epoch 371 
+2024-09-14 14:23:01.550220: Current learning rate: 0.00659 
+2024-09-14 14:27:06.722901: train_loss -0.8446 
+2024-09-14 14:27:06.723094: val_loss -0.7117 
+2024-09-14 14:27:06.723187: Pseudo dice [0.6841, 0.843] 
+2024-09-14 14:27:06.723278: Epoch time: 245.17 s 
+2024-09-14 14:27:07.702728:  
+2024-09-14 14:27:07.702934: Epoch 372 
+2024-09-14 14:27:07.703033: Current learning rate: 0.00658 
+2024-09-14 14:31:13.253944: train_loss -0.831 
+2024-09-14 14:31:13.254085: val_loss -0.6833 
+2024-09-14 14:31:13.254134: Pseudo dice [0.6704, 0.8335] 
+2024-09-14 14:31:13.254187: Epoch time: 245.55 s 
+2024-09-14 14:31:14.260752:  
+2024-09-14 14:31:14.260911: Epoch 373 
+2024-09-14 14:31:14.260993: Current learning rate: 0.00657 
+2024-09-14 14:35:19.657887: train_loss -0.8353 
+2024-09-14 14:35:19.658024: val_loss -0.7106 
+2024-09-14 14:35:19.658074: Pseudo dice [0.7195, 0.8238] 
+2024-09-14 14:35:19.658127: Epoch time: 245.4 s 
+2024-09-14 14:35:20.648369:  
+2024-09-14 14:35:20.648602: Epoch 374 
+2024-09-14 14:35:20.648683: Current learning rate: 0.00656 
+2024-09-14 14:39:26.213749: train_loss -0.8288 
+2024-09-14 14:39:26.213888: val_loss -0.6761 
+2024-09-14 14:39:26.213939: Pseudo dice [0.6894, 0.8014] 
+2024-09-14 14:39:26.213989: Epoch time: 245.57 s 
+2024-09-14 14:39:27.203161:  
+2024-09-14 14:39:27.203399: Epoch 375 
+2024-09-14 14:39:27.203486: Current learning rate: 0.00655 
+2024-09-14 14:43:32.769241: train_loss -0.832 
+2024-09-14 14:43:32.769383: val_loss -0.6982 
+2024-09-14 14:43:32.769433: Pseudo dice [0.6873, 0.8357] 
+2024-09-14 14:43:32.769483: Epoch time: 245.57 s 
+2024-09-14 14:43:33.772463:  
+2024-09-14 14:43:33.772683: Epoch 376 
+2024-09-14 14:43:33.772768: Current learning rate: 0.00654 
+2024-09-14 14:47:39.331639: train_loss -0.8175 
+2024-09-14 14:47:39.331776: val_loss -0.6461 
+2024-09-14 14:47:39.331834: Pseudo dice [0.6098, 0.8187] 
+2024-09-14 14:47:39.331885: Epoch time: 245.56 s 
+2024-09-14 14:47:40.329814:  
+2024-09-14 14:47:40.330013: Epoch 377 
+2024-09-14 14:47:40.330121: Current learning rate: 0.00653 
+2024-09-14 14:51:46.648318: train_loss -0.8222 
+2024-09-14 14:51:46.648459: val_loss -0.6803 
+2024-09-14 14:51:46.648509: Pseudo dice [0.7026, 0.8093] 
+2024-09-14 14:51:46.648558: Epoch time: 246.32 s 
+2024-09-14 14:51:47.623822:  
+2024-09-14 14:51:47.624019: Epoch 378 
+2024-09-14 14:51:47.624115: Current learning rate: 0.00652 
+2024-09-14 14:55:53.053780: train_loss -0.8291 
+2024-09-14 14:55:53.053952: val_loss -0.7001 
+2024-09-14 14:55:53.054004: Pseudo dice [0.7238, 0.8219] 
+2024-09-14 14:55:53.054054: Epoch time: 245.43 s 
+2024-09-14 14:55:54.041691:  
+2024-09-14 14:55:54.041966: Epoch 379 
+2024-09-14 14:55:54.042049: Current learning rate: 0.00651 
+2024-09-14 14:59:59.624864: train_loss -0.8346 
+2024-09-14 14:59:59.625001: val_loss -0.6916 
+2024-09-14 14:59:59.625050: Pseudo dice [0.6916, 0.8225] 
+2024-09-14 14:59:59.625110: Epoch time: 245.59 s 
+2024-09-14 15:00:00.598748:  
+2024-09-14 15:00:00.599028: Epoch 380 
+2024-09-14 15:00:00.599111: Current learning rate: 0.0065 
+2024-09-14 15:04:06.291130: train_loss -0.8342 
+2024-09-14 15:04:06.291269: val_loss -0.6809 
+2024-09-14 15:04:06.291318: Pseudo dice [0.6537, 0.8261] 
+2024-09-14 15:04:06.291368: Epoch time: 245.69 s 
+2024-09-14 15:04:07.262956:  
+2024-09-14 15:04:07.263203: Epoch 381 
+2024-09-14 15:04:07.263288: Current learning rate: 0.00649 
+2024-09-14 15:08:12.804362: train_loss -0.8396 
+2024-09-14 15:08:12.804527: val_loss -0.6785 
+2024-09-14 15:08:12.804577: Pseudo dice [0.6972, 0.8063] 
+2024-09-14 15:08:12.804628: Epoch time: 245.54 s 
+2024-09-14 15:08:13.795368:  
+2024-09-14 15:08:13.795612: Epoch 382 
+2024-09-14 15:08:13.795697: Current learning rate: 0.00648 
+2024-09-14 15:12:19.273284: train_loss -0.8451 
+2024-09-14 15:12:19.273422: val_loss -0.7005 
+2024-09-14 15:12:19.273472: Pseudo dice [0.6929, 0.8416] 
+2024-09-14 15:12:19.273570: Epoch time: 245.48 s 
+2024-09-14 15:12:20.279289:  
+2024-09-14 15:12:20.279486: Epoch 383 
+2024-09-14 15:12:20.279570: Current learning rate: 0.00648 
+2024-09-14 15:16:25.793159: train_loss -0.85 
+2024-09-14 15:16:25.793294: val_loss -0.6769 
+2024-09-14 15:16:25.793345: Pseudo dice [0.6766, 0.8332] 
+2024-09-14 15:16:25.793397: Epoch time: 245.52 s 
+2024-09-14 15:16:26.795934:  
+2024-09-14 15:16:26.796126: Epoch 384 
+2024-09-14 15:16:26.796209: Current learning rate: 0.00647 
+2024-09-14 15:20:32.417658: train_loss -0.8473 
+2024-09-14 15:20:32.417795: val_loss -0.6897 
+2024-09-14 15:20:32.417845: Pseudo dice [0.6829, 0.8335] 
+2024-09-14 15:20:32.417894: Epoch time: 245.62 s 
+2024-09-14 15:20:33.413725:  
+2024-09-14 15:20:33.413944: Epoch 385 
+2024-09-14 15:20:33.414024: Current learning rate: 0.00646 
+2024-09-14 15:24:39.044269: train_loss -0.8472 
+2024-09-14 15:24:39.044411: val_loss -0.695 
+2024-09-14 15:24:39.044461: Pseudo dice [0.6919, 0.813] 
+2024-09-14 15:24:39.044510: Epoch time: 245.63 s 
+2024-09-14 15:24:40.037862:  
+2024-09-14 15:24:40.038132: Epoch 386 
+2024-09-14 15:24:40.038272: Current learning rate: 0.00645 
+2024-09-14 15:28:45.494518: train_loss -0.8446 
+2024-09-14 15:28:45.494658: val_loss -0.6763 
+2024-09-14 15:28:45.494713: Pseudo dice [0.6667, 0.8109] 
+2024-09-14 15:28:45.494794: Epoch time: 245.46 s 
+2024-09-14 15:28:46.485686:  
+2024-09-14 15:28:46.485913: Epoch 387 
+2024-09-14 15:28:46.486016: Current learning rate: 0.00644 
+2024-09-14 15:32:51.914206: train_loss -0.8274 
+2024-09-14 15:32:51.914343: val_loss -0.6613 
+2024-09-14 15:32:51.914392: Pseudo dice [0.6374, 0.8254] 
+2024-09-14 15:32:51.914442: Epoch time: 245.43 s 
+2024-09-14 15:32:52.922541:  
+2024-09-14 15:32:52.922728: Epoch 388 
+2024-09-14 15:32:52.922809: Current learning rate: 0.00643 
+2024-09-14 15:36:58.180181: train_loss -0.8224 
+2024-09-14 15:36:58.180330: val_loss -0.63 
+2024-09-14 15:36:58.180379: Pseudo dice [0.6176, 0.811] 
+2024-09-14 15:36:58.180429: Epoch time: 245.26 s 
+2024-09-14 15:36:59.163040:  
+2024-09-14 15:36:59.163234: Epoch 389 
+2024-09-14 15:36:59.163316: Current learning rate: 0.00642 
+2024-09-14 15:41:04.443940: train_loss -0.8251 
+2024-09-14 15:41:04.444125: val_loss -0.6617 
+2024-09-14 15:41:04.444177: Pseudo dice [0.668, 0.8158] 
+2024-09-14 15:41:04.444227: Epoch time: 245.28 s 
+2024-09-14 15:41:05.428944:  
+2024-09-14 15:41:05.429108: Epoch 390 
+2024-09-14 15:41:05.429190: Current learning rate: 0.00641 
+2024-09-14 15:45:10.682910: train_loss -0.8417 
+2024-09-14 15:45:10.683046: val_loss -0.7156 
+2024-09-14 15:45:10.683095: Pseudo dice [0.7109, 0.8222] 
+2024-09-14 15:45:10.683144: Epoch time: 245.26 s 
+2024-09-14 15:45:11.689339:  
+2024-09-14 15:45:11.689587: Epoch 391 
+2024-09-14 15:45:11.689674: Current learning rate: 0.0064 
+2024-09-14 15:49:16.906514: train_loss -0.8452 
+2024-09-14 15:49:16.906656: val_loss -0.7067 
+2024-09-14 15:49:16.906765: Pseudo dice [0.6974, 0.8196] 
+2024-09-14 15:49:16.906838: Epoch time: 245.22 s 
+2024-09-14 15:49:17.896819:  
+2024-09-14 15:49:17.896967: Epoch 392 
+2024-09-14 15:49:17.897057: Current learning rate: 0.00639 
+2024-09-14 15:53:23.237871: train_loss -0.8488 
+2024-09-14 15:53:23.238143: val_loss -0.67 
+2024-09-14 15:53:23.238254: Pseudo dice [0.6494, 0.8314] 
+2024-09-14 15:53:23.238344: Epoch time: 245.34 s 
+2024-09-14 15:53:24.248183:  
+2024-09-14 15:53:24.248377: Epoch 393 
+2024-09-14 15:53:24.248463: Current learning rate: 0.00638 
+2024-09-14 15:57:29.394781: train_loss -0.8565 
+2024-09-14 15:57:29.394946: val_loss -0.6977 
+2024-09-14 15:57:29.394999: Pseudo dice [0.6914, 0.8216] 
+2024-09-14 15:57:29.395048: Epoch time: 245.15 s 
+2024-09-14 15:57:30.390831:  
+2024-09-14 15:57:30.391025: Epoch 394 
+2024-09-14 15:57:30.391109: Current learning rate: 0.00637 
+2024-09-14 16:01:35.531528: train_loss -0.8554 
+2024-09-14 16:01:35.531668: val_loss -0.6808 
+2024-09-14 16:01:35.531719: Pseudo dice [0.6887, 0.8197] 
+2024-09-14 16:01:35.531768: Epoch time: 245.14 s 
+2024-09-14 16:01:36.525913:  
+2024-09-14 16:01:36.526105: Epoch 395 
+2024-09-14 16:01:36.526196: Current learning rate: 0.00636 
+2024-09-14 16:05:41.747885: train_loss -0.8585 
+2024-09-14 16:05:41.748021: val_loss -0.6966 
+2024-09-14 16:05:41.748071: Pseudo dice [0.6915, 0.8344] 
+2024-09-14 16:05:41.748121: Epoch time: 245.22 s 
+2024-09-14 16:05:42.737577:  
+2024-09-14 16:05:42.737800: Epoch 396 
+2024-09-14 16:05:42.737887: Current learning rate: 0.00635 
+2024-09-14 16:09:48.139681: train_loss -0.8567 
+2024-09-14 16:09:48.139825: val_loss -0.6685 
+2024-09-14 16:09:48.139877: Pseudo dice [0.6672, 0.8206] 
+2024-09-14 16:09:48.139926: Epoch time: 245.4 s 
+2024-09-14 16:09:49.151731:  
+2024-09-14 16:09:49.151888: Epoch 397 
+2024-09-14 16:09:49.151971: Current learning rate: 0.00634 
+2024-09-14 16:13:54.544441: train_loss -0.8605 
+2024-09-14 16:13:54.544577: val_loss -0.6833 
+2024-09-14 16:13:54.544627: Pseudo dice [0.6801, 0.8214] 
+2024-09-14 16:13:54.544676: Epoch time: 245.39 s 
+2024-09-14 16:13:55.536236:  
+2024-09-14 16:13:55.536401: Epoch 398 
+2024-09-14 16:13:55.536483: Current learning rate: 0.00633 
+2024-09-14 16:18:01.015260: train_loss -0.865 
+2024-09-14 16:18:01.015398: val_loss -0.7007 
+2024-09-14 16:18:01.015449: Pseudo dice [0.7195, 0.8139] 
+2024-09-14 16:18:01.015499: Epoch time: 245.48 s 
+2024-09-14 16:18:02.012140:  
+2024-09-14 16:18:02.012347: Epoch 399 
+2024-09-14 16:18:02.012425: Current learning rate: 0.00632 
+2024-09-14 16:22:07.383968: train_loss -0.8663 
+2024-09-14 16:22:07.384104: val_loss -0.6756 
+2024-09-14 16:22:07.384155: Pseudo dice [0.6876, 0.814] 
+2024-09-14 16:22:07.384207: Epoch time: 245.37 s 
+2024-09-14 16:22:12.233549:  
+2024-09-14 16:22:12.233791: Epoch 400 
+2024-09-14 16:22:12.233878: Current learning rate: 0.00631 
+2024-09-14 16:26:17.859405: train_loss -0.8607 
+2024-09-14 16:26:17.859552: val_loss -0.6872 
+2024-09-14 16:26:17.859602: Pseudo dice [0.6969, 0.8316] 
+2024-09-14 16:26:17.859651: Epoch time: 245.63 s 
+2024-09-14 16:26:18.858688:  
+2024-09-14 16:26:18.858889: Epoch 401 
+2024-09-14 16:26:18.858976: Current learning rate: 0.0063 
+2024-09-14 16:30:24.491922: train_loss -0.8581 
+2024-09-14 16:30:24.492059: val_loss -0.7002 
+2024-09-14 16:30:24.492109: Pseudo dice [0.6976, 0.8351] 
+2024-09-14 16:30:24.492159: Epoch time: 245.64 s 
+2024-09-14 16:30:25.489045:  
+2024-09-14 16:30:25.489309: Epoch 402 
+2024-09-14 16:30:25.489393: Current learning rate: 0.0063 
+2024-09-14 16:34:31.153233: train_loss -0.8665 
+2024-09-14 16:34:31.153430: val_loss -0.7189 
+2024-09-14 16:34:31.153482: Pseudo dice [0.7239, 0.8315] 
+2024-09-14 16:34:31.153532: Epoch time: 245.67 s 
+2024-09-14 16:34:32.167448:  
+2024-09-14 16:34:32.167698: Epoch 403 
+2024-09-14 16:34:32.167780: Current learning rate: 0.00629 
+2024-09-14 16:38:37.861478: train_loss -0.8593 
+2024-09-14 16:38:37.861633: val_loss -0.6948 
+2024-09-14 16:38:37.861684: Pseudo dice [0.6825, 0.8302] 
+2024-09-14 16:38:37.861735: Epoch time: 245.7 s 
+2024-09-14 16:38:38.864350:  
+2024-09-14 16:38:38.864540: Epoch 404 
+2024-09-14 16:38:38.864623: Current learning rate: 0.00628 
+2024-09-14 16:42:44.699742: train_loss -0.8652 
+2024-09-14 16:42:44.699927: val_loss -0.6941 
+2024-09-14 16:42:44.699979: Pseudo dice [0.6928, 0.8366] 
+2024-09-14 16:42:44.700053: Epoch time: 245.84 s 
+2024-09-14 16:42:45.694431:  
+2024-09-14 16:42:45.694658: Epoch 405 
+2024-09-14 16:42:45.694761: Current learning rate: 0.00627 
+2024-09-14 16:46:51.441597: train_loss -0.8647 
+2024-09-14 16:46:51.441737: val_loss -0.6906 
+2024-09-14 16:46:51.441786: Pseudo dice [0.6769, 0.8371] 
+2024-09-14 16:46:51.441838: Epoch time: 245.75 s 
+2024-09-14 16:46:52.452158:  
+2024-09-14 16:46:52.452359: Epoch 406 
+2024-09-14 16:46:52.452445: Current learning rate: 0.00626 
+2024-09-14 16:50:58.294153: train_loss -0.8642 
+2024-09-14 16:50:58.294290: val_loss -0.6935 
+2024-09-14 16:50:58.294340: Pseudo dice [0.69, 0.8201] 
+2024-09-14 16:50:58.294389: Epoch time: 245.84 s 
+2024-09-14 16:50:59.294047:  
+2024-09-14 16:50:59.294235: Epoch 407 
+2024-09-14 16:50:59.294341: Current learning rate: 0.00625 
+2024-09-14 16:55:05.151407: train_loss -0.8655 
+2024-09-14 16:55:05.151548: val_loss -0.6978 
+2024-09-14 16:55:05.151600: Pseudo dice [0.7027, 0.8466] 
+2024-09-14 16:55:05.151651: Epoch time: 245.86 s 
+2024-09-14 16:55:06.156816:  
+2024-09-14 16:55:06.156998: Epoch 408 
+2024-09-14 16:55:06.157081: Current learning rate: 0.00624 
+2024-09-14 16:59:12.003465: train_loss -0.8523 
+2024-09-14 16:59:12.003625: val_loss -0.7156 
+2024-09-14 16:59:12.003677: Pseudo dice [0.7111, 0.8254] 
+2024-09-14 16:59:12.003727: Epoch time: 245.85 s 
+2024-09-14 16:59:12.999684:  
+2024-09-14 16:59:12.999873: Epoch 409 
+2024-09-14 16:59:12.999974: Current learning rate: 0.00623 
+2024-09-14 17:03:18.720042: train_loss -0.8522 
+2024-09-14 17:03:18.720179: val_loss -0.6592 
+2024-09-14 17:03:18.720294: Pseudo dice [0.6395, 0.803] 
+2024-09-14 17:03:18.720363: Epoch time: 245.72 s 
+2024-09-14 17:03:19.715578:  
+2024-09-14 17:03:19.715776: Epoch 410 
+2024-09-14 17:03:19.715917: Current learning rate: 0.00622 
+2024-09-14 17:07:25.361298: train_loss -0.8531 
+2024-09-14 17:07:25.361439: val_loss -0.694 
+2024-09-14 17:07:25.361488: Pseudo dice [0.7086, 0.8188] 
+2024-09-14 17:07:25.361538: Epoch time: 245.65 s 
+2024-09-14 17:07:26.304777:  
+2024-09-14 17:07:26.305008: Epoch 411 
+2024-09-14 17:07:26.305091: Current learning rate: 0.00621 
+2024-09-14 17:11:31.897239: train_loss -0.8629 
+2024-09-14 17:11:31.897397: val_loss -0.6906 
+2024-09-14 17:11:31.897448: Pseudo dice [0.6755, 0.8322] 
+2024-09-14 17:11:31.897499: Epoch time: 245.59 s 
+2024-09-14 17:11:32.853861:  
+2024-09-14 17:11:32.854081: Epoch 412 
+2024-09-14 17:11:32.854167: Current learning rate: 0.0062 
+2024-09-14 17:15:38.372652: train_loss -0.8594 
+2024-09-14 17:15:38.372794: val_loss -0.6903 
+2024-09-14 17:15:38.372844: Pseudo dice [0.6666, 0.8229] 
+2024-09-14 17:15:38.372895: Epoch time: 245.52 s 
+2024-09-14 17:15:39.310969:  
+2024-09-14 17:15:39.311136: Epoch 413 
+2024-09-14 17:15:39.311261: Current learning rate: 0.00619 
+2024-09-14 17:19:44.891944: train_loss -0.8511 
+2024-09-14 17:19:44.892073: val_loss -0.716 
+2024-09-14 17:19:44.892123: Pseudo dice [0.7051, 0.8307] 
+2024-09-14 17:19:44.892173: Epoch time: 245.58 s 
+2024-09-14 17:19:45.812807:  
+2024-09-14 17:19:45.812997: Epoch 414 
+2024-09-14 17:19:45.813082: Current learning rate: 0.00618 
+2024-09-14 17:23:51.459775: train_loss -0.8521 
+2024-09-14 17:23:51.459918: val_loss -0.6992 
+2024-09-14 17:23:51.459969: Pseudo dice [0.7225, 0.8303] 
+2024-09-14 17:23:51.460019: Epoch time: 245.65 s 
+2024-09-14 17:23:52.404805:  
+2024-09-14 17:23:52.404987: Epoch 415 
+2024-09-14 17:23:52.405068: Current learning rate: 0.00617 
+2024-09-14 17:27:58.140153: train_loss -0.818 
+2024-09-14 17:27:58.140293: val_loss -0.6954 
+2024-09-14 17:27:58.140345: Pseudo dice [0.7087, 0.8131] 
+2024-09-14 17:27:58.140395: Epoch time: 245.74 s 
+2024-09-14 17:27:59.164423:  
+2024-09-14 17:27:59.164633: Epoch 416 
+2024-09-14 17:27:59.164717: Current learning rate: 0.00616 
+2024-09-14 17:32:04.582193: train_loss -0.814 
+2024-09-14 17:32:04.582343: val_loss -0.6409 
+2024-09-14 17:32:04.582393: Pseudo dice [0.6325, 0.8055] 
+2024-09-14 17:32:04.582443: Epoch time: 245.42 s 
+2024-09-14 17:32:05.521924:  
+2024-09-14 17:32:05.522131: Epoch 417 
+2024-09-14 17:32:05.522239: Current learning rate: 0.00615 
+2024-09-14 17:36:11.020769: train_loss -0.8314 
+2024-09-14 17:36:11.020960: val_loss -0.6926 
+2024-09-14 17:36:11.021011: Pseudo dice [0.6794, 0.809] 
+2024-09-14 17:36:11.021062: Epoch time: 245.5 s 
+2024-09-14 17:36:11.964334:  
+2024-09-14 17:36:11.964495: Epoch 418 
+2024-09-14 17:36:11.964577: Current learning rate: 0.00614 
+2024-09-14 17:40:17.268110: train_loss -0.8375 
+2024-09-14 17:40:17.268246: val_loss -0.703 
+2024-09-14 17:40:17.268297: Pseudo dice [0.6869, 0.8223] 
+2024-09-14 17:40:17.268347: Epoch time: 245.31 s 
+2024-09-14 17:40:18.238922:  
+2024-09-14 17:40:18.239136: Epoch 419 
+2024-09-14 17:40:18.239217: Current learning rate: 0.00613 
+2024-09-14 17:44:23.496289: train_loss -0.8392 
+2024-09-14 17:44:23.496427: val_loss -0.6809 
+2024-09-14 17:44:23.496478: Pseudo dice [0.6935, 0.8129] 
+2024-09-14 17:44:23.496528: Epoch time: 245.26 s 
+2024-09-14 17:44:24.436804:  
+2024-09-14 17:44:24.436985: Epoch 420 
+2024-09-14 17:44:24.437068: Current learning rate: 0.00612 
+2024-09-14 17:48:29.630228: train_loss -0.8491 
+2024-09-14 17:48:29.630363: val_loss -0.6731 
+2024-09-14 17:48:29.630413: Pseudo dice [0.6877, 0.8034] 
+2024-09-14 17:48:29.630463: Epoch time: 245.2 s 
+2024-09-14 17:48:30.580257:  
+2024-09-14 17:48:30.580492: Epoch 421 
+2024-09-14 17:48:30.580590: Current learning rate: 0.00612 
+2024-09-14 17:52:35.958443: train_loss -0.8404 
+2024-09-14 17:52:35.958675: val_loss -0.673 
+2024-09-14 17:52:35.958727: Pseudo dice [0.663, 0.8185] 
+2024-09-14 17:52:35.958778: Epoch time: 245.38 s 
+2024-09-14 17:52:36.914616:  
+2024-09-14 17:52:36.914865: Epoch 422 
+2024-09-14 17:52:36.915006: Current learning rate: 0.00611 
+2024-09-14 17:56:43.105801: train_loss -0.8298 
+2024-09-14 17:56:43.105941: val_loss -0.6816 
+2024-09-14 17:56:43.105995: Pseudo dice [0.7185, 0.8195] 
+2024-09-14 17:56:43.106048: Epoch time: 246.19 s 
+2024-09-14 17:56:44.041277:  
+2024-09-14 17:56:44.041562: Epoch 423 
+2024-09-14 17:56:44.041702: Current learning rate: 0.0061 
+2024-09-14 18:00:49.450547: train_loss -0.844 
+2024-09-14 18:00:49.450684: val_loss -0.7084 
+2024-09-14 18:00:49.450734: Pseudo dice [0.6901, 0.8441] 
+2024-09-14 18:00:49.450783: Epoch time: 245.41 s 
+2024-09-14 18:00:50.391294:  
+2024-09-14 18:00:50.391549: Epoch 424 
+2024-09-14 18:00:50.391649: Current learning rate: 0.00609 
+2024-09-14 18:04:56.006770: train_loss -0.8484 
+2024-09-14 18:04:56.006909: val_loss -0.6849 
+2024-09-14 18:04:56.006960: Pseudo dice [0.6741, 0.8368] 
+2024-09-14 18:04:56.007010: Epoch time: 245.62 s 
+2024-09-14 18:04:56.952283:  
+2024-09-14 18:04:56.952555: Epoch 425 
+2024-09-14 18:04:56.952658: Current learning rate: 0.00608 
+2024-09-14 18:09:02.530988: train_loss -0.8473 
+2024-09-14 18:09:02.531127: val_loss -0.7082 
+2024-09-14 18:09:02.531209: Pseudo dice [0.7185, 0.8339] 
+2024-09-14 18:09:02.531259: Epoch time: 245.58 s 
+2024-09-14 18:09:03.473419:  
+2024-09-14 18:09:03.473628: Epoch 426 
+2024-09-14 18:09:03.473712: Current learning rate: 0.00607 
+2024-09-14 18:13:08.983023: train_loss -0.8517 
+2024-09-14 18:13:08.983178: val_loss -0.6909 
+2024-09-14 18:13:08.983228: Pseudo dice [0.7173, 0.812] 
+2024-09-14 18:13:08.983277: Epoch time: 245.51 s 
+2024-09-14 18:13:09.946877:  
+2024-09-14 18:13:09.947079: Epoch 427 
+2024-09-14 18:13:09.947203: Current learning rate: 0.00606 
+2024-09-14 18:17:15.544415: train_loss -0.8422 
+2024-09-14 18:17:15.544559: val_loss -0.7034 
+2024-09-14 18:17:15.544610: Pseudo dice [0.6894, 0.8294] 
+2024-09-14 18:17:15.544659: Epoch time: 245.6 s 
+2024-09-14 18:17:16.539561:  
+2024-09-14 18:17:16.539831: Epoch 428 
+2024-09-14 18:17:16.539916: Current learning rate: 0.00605 
+2024-09-14 18:21:21.978679: train_loss -0.8397 
+2024-09-14 18:21:21.978837: val_loss -0.6758 
+2024-09-14 18:21:21.978887: Pseudo dice [0.6839, 0.8118] 
+2024-09-14 18:21:21.978937: Epoch time: 245.44 s 
+2024-09-14 18:21:22.907403:  
+2024-09-14 18:21:22.907578: Epoch 429 
+2024-09-14 18:21:22.907660: Current learning rate: 0.00604 
+2024-09-14 18:25:28.644228: train_loss -0.83 
+2024-09-14 18:25:28.644372: val_loss -0.6784 
+2024-09-14 18:25:28.644422: Pseudo dice [0.6786, 0.8041] 
+2024-09-14 18:25:28.644471: Epoch time: 245.74 s 
+2024-09-14 18:25:29.590039:  
+2024-09-14 18:25:29.590261: Epoch 430 
+2024-09-14 18:25:29.590343: Current learning rate: 0.00603 
+2024-09-14 18:29:35.158176: train_loss -0.8411 
+2024-09-14 18:29:35.158314: val_loss -0.6534 
+2024-09-14 18:29:35.158363: Pseudo dice [0.625, 0.8102] 
+2024-09-14 18:29:35.158413: Epoch time: 245.57 s 
+2024-09-14 18:29:36.091846:  
+2024-09-14 18:29:36.092021: Epoch 431 
+2024-09-14 18:29:36.092102: Current learning rate: 0.00602 
+2024-09-14 18:33:41.726646: train_loss -0.842 
+2024-09-14 18:33:41.726787: val_loss -0.687 
+2024-09-14 18:33:41.726838: Pseudo dice [0.6704, 0.8367] 
+2024-09-14 18:33:41.726889: Epoch time: 245.64 s 
+2024-09-14 18:33:42.655477:  
+2024-09-14 18:33:42.655637: Epoch 432 
+2024-09-14 18:33:42.655718: Current learning rate: 0.00601 
+2024-09-14 18:37:48.336807: train_loss -0.8384 
+2024-09-14 18:37:48.336951: val_loss -0.6817 
+2024-09-14 18:37:48.337003: Pseudo dice [0.6746, 0.8328] 
+2024-09-14 18:37:48.337054: Epoch time: 245.68 s 
+2024-09-14 18:37:49.281262:  
+2024-09-14 18:37:49.281450: Epoch 433 
+2024-09-14 18:37:49.281533: Current learning rate: 0.006 
+2024-09-14 18:41:55.001944: train_loss -0.8486 
+2024-09-14 18:41:55.002080: val_loss -0.6884 
+2024-09-14 18:41:55.002129: Pseudo dice [0.6654, 0.8305] 
+2024-09-14 18:41:55.002180: Epoch time: 245.72 s 
+2024-09-14 18:41:55.926560:  
+2024-09-14 18:41:55.926744: Epoch 434 
+2024-09-14 18:41:55.926827: Current learning rate: 0.00599 
+2024-09-14 18:46:01.631828: train_loss -0.8531 
+2024-09-14 18:46:01.631979: val_loss -0.6947 
+2024-09-14 18:46:01.632028: Pseudo dice [0.7214, 0.8228] 
+2024-09-14 18:46:01.632078: Epoch time: 245.71 s 
+2024-09-14 18:46:02.574004:  
+2024-09-14 18:46:02.574195: Epoch 435 
+2024-09-14 18:46:02.574280: Current learning rate: 0.00598 
+2024-09-14 18:50:08.254816: train_loss -0.8618 
+2024-09-14 18:50:08.254995: val_loss -0.687 
+2024-09-14 18:50:08.255047: Pseudo dice [0.6713, 0.8381] 
+2024-09-14 18:50:08.255098: Epoch time: 245.68 s 
+2024-09-14 18:50:09.193719:  
+2024-09-14 18:50:09.193881: Epoch 436 
+2024-09-14 18:50:09.193968: Current learning rate: 0.00597 
+2024-09-14 18:54:14.604881: train_loss -0.8616 
+2024-09-14 18:54:14.605033: val_loss -0.6909 
+2024-09-14 18:54:14.605083: Pseudo dice [0.6761, 0.8056] 
+2024-09-14 18:54:14.605134: Epoch time: 245.41 s 
+2024-09-14 18:54:15.542976:  
+2024-09-14 18:54:15.543196: Epoch 437 
+2024-09-14 18:54:15.543277: Current learning rate: 0.00596 
+2024-09-14 18:58:20.890616: train_loss -0.8543 
+2024-09-14 18:58:20.890771: val_loss -0.6664 
+2024-09-14 18:58:20.890865: Pseudo dice [0.6939, 0.8107] 
+2024-09-14 18:58:20.890952: Epoch time: 245.35 s 
+2024-09-14 18:58:21.822890:  
+2024-09-14 18:58:21.823074: Epoch 438 
+2024-09-14 18:58:21.823155: Current learning rate: 0.00595 
+2024-09-14 19:02:27.253519: train_loss -0.8631 
+2024-09-14 19:02:27.253722: val_loss -0.6933 
+2024-09-14 19:02:27.253772: Pseudo dice [0.6769, 0.8345] 
+2024-09-14 19:02:27.253821: Epoch time: 245.43 s 
+2024-09-14 19:02:28.213523:  
+2024-09-14 19:02:28.213709: Epoch 439 
+2024-09-14 19:02:28.213801: Current learning rate: 0.00594 
+2024-09-14 19:06:33.645942: train_loss -0.861 
+2024-09-14 19:06:33.646084: val_loss -0.6676 
+2024-09-14 19:06:33.646134: Pseudo dice [0.6728, 0.82] 
+2024-09-14 19:06:33.646184: Epoch time: 245.43 s 
+2024-09-14 19:06:34.593522:  
+2024-09-14 19:06:34.593732: Epoch 440 
+2024-09-14 19:06:34.593820: Current learning rate: 0.00593 
+2024-09-14 19:10:40.024234: train_loss -0.8647 
+2024-09-14 19:10:40.024374: val_loss -0.7059 
+2024-09-14 19:10:40.024469: Pseudo dice [0.7022, 0.8166] 
+2024-09-14 19:10:40.024541: Epoch time: 245.43 s 
+2024-09-14 19:10:40.976699:  
+2024-09-14 19:10:40.976864: Epoch 441 
+2024-09-14 19:10:40.976958: Current learning rate: 0.00592 
+2024-09-14 19:14:46.329901: train_loss -0.8673 
+2024-09-14 19:14:46.330045: val_loss -0.6797 
+2024-09-14 19:14:46.330094: Pseudo dice [0.6877, 0.8246] 
+2024-09-14 19:14:46.330154: Epoch time: 245.36 s 
+2024-09-14 19:14:47.260130:  
+2024-09-14 19:14:47.260350: Epoch 442 
+2024-09-14 19:14:47.260434: Current learning rate: 0.00592 
+2024-09-14 19:18:52.781631: train_loss -0.866 
+2024-09-14 19:18:52.781773: val_loss -0.6933 
+2024-09-14 19:18:52.781824: Pseudo dice [0.6733, 0.838] 
+2024-09-14 19:18:52.781875: Epoch time: 245.52 s 
+2024-09-14 19:18:53.731926:  
+2024-09-14 19:18:53.732121: Epoch 443 
+2024-09-14 19:18:53.732206: Current learning rate: 0.00591 
+2024-09-14 19:22:59.204308: train_loss -0.8612 
+2024-09-14 19:22:59.204448: val_loss -0.6675 
+2024-09-14 19:22:59.204497: Pseudo dice [0.6569, 0.8103] 
+2024-09-14 19:22:59.204546: Epoch time: 245.47 s 
+2024-09-14 19:23:00.125412:  
+2024-09-14 19:23:00.125625: Epoch 444 
+2024-09-14 19:23:00.125725: Current learning rate: 0.0059 
+2024-09-14 19:27:05.767385: train_loss -0.8683 
+2024-09-14 19:27:05.767524: val_loss -0.6815 
+2024-09-14 19:27:05.767574: Pseudo dice [0.7081, 0.8203] 
+2024-09-14 19:27:05.767623: Epoch time: 245.64 s 
+2024-09-14 19:27:06.705645:  
+2024-09-14 19:27:06.705868: Epoch 445 
+2024-09-14 19:27:06.705950: Current learning rate: 0.00589 
+2024-09-14 19:31:12.393435: train_loss -0.8677 
+2024-09-14 19:31:12.393568: val_loss -0.7207 
+2024-09-14 19:31:12.393619: Pseudo dice [0.6923, 0.8351] 
+2024-09-14 19:31:12.393671: Epoch time: 245.69 s 
+2024-09-14 19:31:13.321015:  
+2024-09-14 19:31:13.321202: Epoch 446 
+2024-09-14 19:31:13.321285: Current learning rate: 0.00588 
+2024-09-14 19:35:19.618555: train_loss -0.8647 
+2024-09-14 19:35:19.618695: val_loss -0.6788 
+2024-09-14 19:35:19.618743: Pseudo dice [0.6509, 0.8369] 
+2024-09-14 19:35:19.618792: Epoch time: 246.3 s 
+2024-09-14 19:35:20.568890:  
+2024-09-14 19:35:20.569056: Epoch 447 
+2024-09-14 19:35:20.569154: Current learning rate: 0.00587 
+2024-09-14 19:39:26.339925: train_loss -0.8522 
+2024-09-14 19:39:26.340077: val_loss -0.7134 
+2024-09-14 19:39:26.340126: Pseudo dice [0.7019, 0.8263] 
+2024-09-14 19:39:26.340175: Epoch time: 245.77 s 
+2024-09-14 19:39:27.257461:  
+2024-09-14 19:39:27.257701: Epoch 448 
+2024-09-14 19:39:27.257800: Current learning rate: 0.00586 
+2024-09-14 19:43:32.905949: train_loss -0.8553 
+2024-09-14 19:43:32.906139: val_loss -0.6954 
+2024-09-14 19:43:32.906192: Pseudo dice [0.6728, 0.8328] 
+2024-09-14 19:43:32.906243: Epoch time: 245.65 s 
+2024-09-14 19:43:33.844373:  
+2024-09-14 19:43:33.844577: Epoch 449 
+2024-09-14 19:43:33.844658: Current learning rate: 0.00585 
+2024-09-14 19:47:39.353802: train_loss -0.8614 
+2024-09-14 19:47:39.353944: val_loss -0.6622 
+2024-09-14 19:47:39.353995: Pseudo dice [0.681, 0.8279] 
+2024-09-14 19:47:39.354045: Epoch time: 245.51 s 
+2024-09-14 19:47:43.250027:  
+2024-09-14 19:47:43.250257: Epoch 450 
+2024-09-14 19:47:43.250338: Current learning rate: 0.00584 
+2024-09-14 19:51:48.873902: train_loss -0.8605 
+2024-09-14 19:51:48.874044: val_loss -0.6778 
+2024-09-14 19:51:48.874094: Pseudo dice [0.6821, 0.8224] 
+2024-09-14 19:51:48.874145: Epoch time: 245.63 s 
+2024-09-14 19:51:49.809953:  
+2024-09-14 19:51:49.810141: Epoch 451 
+2024-09-14 19:51:49.810221: Current learning rate: 0.00583 
+2024-09-14 19:55:55.354386: train_loss -0.8605 
+2024-09-14 19:55:55.354524: val_loss -0.6792 
+2024-09-14 19:55:55.354573: Pseudo dice [0.6935, 0.8216] 
+2024-09-14 19:55:55.354624: Epoch time: 245.55 s 
+2024-09-14 19:55:56.270397:  
+2024-09-14 19:55:56.270593: Epoch 452 
+2024-09-14 19:55:56.270675: Current learning rate: 0.00582 
+2024-09-14 20:00:01.729624: train_loss -0.8572 
+2024-09-14 20:00:01.729761: val_loss -0.6797 
+2024-09-14 20:00:01.729811: Pseudo dice [0.6875, 0.8105] 
+2024-09-14 20:00:01.729866: Epoch time: 245.46 s 
+2024-09-14 20:00:02.650031:  
+2024-09-14 20:00:02.650209: Epoch 453 
+2024-09-14 20:00:02.650288: Current learning rate: 0.00581 
+2024-09-14 20:04:08.239824: train_loss -0.8478 
+2024-09-14 20:04:08.239976: val_loss -0.669 
+2024-09-14 20:04:08.240027: Pseudo dice [0.6659, 0.8275] 
+2024-09-14 20:04:08.240077: Epoch time: 245.59 s 
+2024-09-14 20:04:09.168544:  
+2024-09-14 20:04:09.168729: Epoch 454 
+2024-09-14 20:04:09.168820: Current learning rate: 0.0058 
+2024-09-14 20:08:14.705612: train_loss -0.856 
+2024-09-14 20:08:14.705748: val_loss -0.7048 
+2024-09-14 20:08:14.705797: Pseudo dice [0.6905, 0.8349] 
+2024-09-14 20:08:14.705887: Epoch time: 245.54 s 
+2024-09-14 20:08:15.627162:  
+2024-09-14 20:08:15.627334: Epoch 455 
+2024-09-14 20:08:15.627417: Current learning rate: 0.00579 
+2024-09-14 20:12:21.086287: train_loss -0.8585 
+2024-09-14 20:12:21.086428: val_loss -0.6937 
+2024-09-14 20:12:21.086479: Pseudo dice [0.6943, 0.8248] 
+2024-09-14 20:12:21.086529: Epoch time: 245.46 s 
+2024-09-14 20:12:22.024227:  
+2024-09-14 20:12:22.024422: Epoch 456 
+2024-09-14 20:12:22.024504: Current learning rate: 0.00578 
+2024-09-14 20:16:27.761412: train_loss -0.8682 
+2024-09-14 20:16:27.761546: val_loss -0.6914 
+2024-09-14 20:16:27.761595: Pseudo dice [0.6688, 0.8377] 
+2024-09-14 20:16:27.761645: Epoch time: 245.74 s 
+2024-09-14 20:16:28.693396:  
+2024-09-14 20:16:28.693609: Epoch 457 
+2024-09-14 20:16:28.693696: Current learning rate: 0.00577 
+2024-09-14 20:20:34.330831: train_loss -0.8668 
+2024-09-14 20:20:34.330970: val_loss -0.7129 
+2024-09-14 20:20:34.331020: Pseudo dice [0.7193, 0.8392] 
+2024-09-14 20:20:34.331069: Epoch time: 245.64 s 
+2024-09-14 20:20:35.277272:  
+2024-09-14 20:20:35.277449: Epoch 458 
+2024-09-14 20:20:35.277530: Current learning rate: 0.00576 
+2024-09-14 20:24:40.895533: train_loss -0.8695 
+2024-09-14 20:24:40.895672: val_loss -0.7158 
+2024-09-14 20:24:40.895721: Pseudo dice [0.728, 0.8313] 
+2024-09-14 20:24:40.895771: Epoch time: 245.62 s 
+2024-09-14 20:24:41.841246:  
+2024-09-14 20:24:41.841457: Epoch 459 
+2024-09-14 20:24:41.841602: Current learning rate: 0.00575 
+2024-09-14 20:28:47.521158: train_loss -0.8743 
+2024-09-14 20:28:47.521301: val_loss -0.6779 
+2024-09-14 20:28:47.521350: Pseudo dice [0.679, 0.8284] 
+2024-09-14 20:28:47.521400: Epoch time: 245.68 s 
+2024-09-14 20:28:48.440014:  
+2024-09-14 20:28:48.440192: Epoch 460 
+2024-09-14 20:28:48.440313: Current learning rate: 0.00574 
+2024-09-14 20:32:53.978803: train_loss -0.8698 
+2024-09-14 20:32:53.978939: val_loss -0.7045 
+2024-09-14 20:32:53.979044: Pseudo dice [0.7051, 0.8236] 
+2024-09-14 20:32:53.979127: Epoch time: 245.54 s 
+2024-09-14 20:32:54.922337:  
+2024-09-14 20:32:54.922513: Epoch 461 
+2024-09-14 20:32:54.922595: Current learning rate: 0.00573 
+2024-09-14 20:37:00.410180: train_loss -0.8714 
+2024-09-14 20:37:00.410317: val_loss -0.6822 
+2024-09-14 20:37:00.410366: Pseudo dice [0.6692, 0.8372] 
+2024-09-14 20:37:00.410417: Epoch time: 245.49 s 
+2024-09-14 20:37:01.339882:  
+2024-09-14 20:37:01.340149: Epoch 462 
+2024-09-14 20:37:01.340233: Current learning rate: 0.00572 
+2024-09-14 20:41:06.885090: train_loss -0.864 
+2024-09-14 20:41:06.885245: val_loss -0.7205 
+2024-09-14 20:41:06.885297: Pseudo dice [0.6959, 0.8334] 
+2024-09-14 20:41:06.885347: Epoch time: 245.55 s 
+2024-09-14 20:41:07.807865:  
+2024-09-14 20:41:07.808048: Epoch 463 
+2024-09-14 20:41:07.808133: Current learning rate: 0.00571 
+2024-09-14 20:45:13.369790: train_loss -0.8665 
+2024-09-14 20:45:13.369947: val_loss -0.7077 
+2024-09-14 20:45:13.369998: Pseudo dice [0.7177, 0.8293] 
+2024-09-14 20:45:13.370049: Epoch time: 245.56 s 
+2024-09-14 20:45:14.287044:  
+2024-09-14 20:45:14.287213: Epoch 464 
+2024-09-14 20:45:14.287301: Current learning rate: 0.0057 
+2024-09-14 20:49:19.958322: train_loss -0.8632 
+2024-09-14 20:49:19.958459: val_loss -0.7009 
+2024-09-14 20:49:19.958508: Pseudo dice [0.7186, 0.8312] 
+2024-09-14 20:49:19.958559: Epoch time: 245.67 s 
+2024-09-14 20:49:19.958598: Yayy! New best EMA pseudo Dice: 0.762 
+2024-09-14 20:49:23.806019:  
+2024-09-14 20:49:23.806220: Epoch 465 
+2024-09-14 20:49:23.806301: Current learning rate: 0.0057 
+2024-09-14 20:53:29.648413: train_loss -0.8433 
+2024-09-14 20:53:29.648547: val_loss -0.6854 
+2024-09-14 20:53:29.648597: Pseudo dice [0.6834, 0.8193] 
+2024-09-14 20:53:29.648647: Epoch time: 245.84 s 
+2024-09-14 20:53:30.562605:  
+2024-09-14 20:53:30.562738: Epoch 466 
+2024-09-14 20:53:30.562818: Current learning rate: 0.00569 
+2024-09-14 20:57:36.251525: train_loss -0.8485 
+2024-09-14 20:57:36.251665: val_loss -0.6987 
+2024-09-14 20:57:36.251715: Pseudo dice [0.687, 0.8151] 
+2024-09-14 20:57:36.251767: Epoch time: 245.69 s 
+2024-09-14 20:57:37.174366:  
+2024-09-14 20:57:37.174515: Epoch 467 
+2024-09-14 20:57:37.174595: Current learning rate: 0.00568 
+2024-09-14 21:01:42.817506: train_loss -0.8556 
+2024-09-14 21:01:42.817646: val_loss -0.7089 
+2024-09-14 21:01:42.817695: Pseudo dice [0.7088, 0.837] 
+2024-09-14 21:01:42.817746: Epoch time: 245.65 s 
+2024-09-14 21:01:43.743051:  
+2024-09-14 21:01:43.743248: Epoch 468 
+2024-09-14 21:01:43.743353: Current learning rate: 0.00567 
+2024-09-14 21:05:49.253666: train_loss -0.8599 
+2024-09-14 21:05:49.253802: val_loss -0.7073 
+2024-09-14 21:05:49.253851: Pseudo dice [0.6903, 0.8426] 
+2024-09-14 21:05:49.253902: Epoch time: 245.51 s 
+2024-09-14 21:05:50.179291:  
+2024-09-14 21:05:50.179453: Epoch 469 
+2024-09-14 21:05:50.179535: Current learning rate: 0.00566 
+2024-09-14 21:09:55.636412: train_loss -0.8519 
+2024-09-14 21:09:55.636680: val_loss -0.6808 
+2024-09-14 21:09:55.636790: Pseudo dice [0.6639, 0.7957] 
+2024-09-14 21:09:55.636884: Epoch time: 245.46 s 
+2024-09-14 21:09:57.451411:  
+2024-09-14 21:09:57.451647: Epoch 470 
+2024-09-14 21:09:57.451755: Current learning rate: 0.00565 
+2024-09-14 21:14:02.866024: train_loss -0.8508 
+2024-09-14 21:14:02.866170: val_loss -0.719 
+2024-09-14 21:14:02.866221: Pseudo dice [0.7214, 0.8352] 
+2024-09-14 21:14:02.866271: Epoch time: 245.42 s 
+2024-09-14 21:14:03.789128:  
+2024-09-14 21:14:03.789377: Epoch 471 
+2024-09-14 21:14:03.789480: Current learning rate: 0.00564 
+2024-09-14 21:18:09.192572: train_loss -0.857 
+2024-09-14 21:18:09.192709: val_loss -0.6957 
+2024-09-14 21:18:09.192758: Pseudo dice [0.716, 0.8307] 
+2024-09-14 21:18:09.192807: Epoch time: 245.41 s 
+2024-09-14 21:18:10.127537:  
+2024-09-14 21:18:10.127785: Epoch 472 
+2024-09-14 21:18:10.127876: Current learning rate: 0.00563 
+2024-09-14 21:22:15.520108: train_loss -0.8536 
+2024-09-14 21:22:15.520245: val_loss -0.6992 
+2024-09-14 21:22:15.520295: Pseudo dice [0.7108, 0.8208] 
+2024-09-14 21:22:15.520345: Epoch time: 245.39 s 
+2024-09-14 21:22:15.520385: Yayy! New best EMA pseudo Dice: 0.7622 
+2024-09-14 21:22:19.329489:  
+2024-09-14 21:22:19.329654: Epoch 473 
+2024-09-14 21:22:19.329767: Current learning rate: 0.00562 
+2024-09-14 21:26:24.780430: train_loss -0.8493 
+2024-09-14 21:26:24.780560: val_loss -0.6851 
+2024-09-14 21:26:24.780609: Pseudo dice [0.6917, 0.8149] 
+2024-09-14 21:26:24.780659: Epoch time: 245.45 s 
+2024-09-14 21:26:25.697750:  
+2024-09-14 21:26:25.698000: Epoch 474 
+2024-09-14 21:26:25.698090: Current learning rate: 0.00561 
+2024-09-14 21:30:31.264972: train_loss -0.8077 
+2024-09-14 21:30:31.265141: val_loss -0.6495 
+2024-09-14 21:30:31.265199: Pseudo dice [0.5969, 0.8139] 
+2024-09-14 21:30:31.265249: Epoch time: 245.57 s 
+2024-09-14 21:30:32.194703:  
+2024-09-14 21:30:32.194927: Epoch 475 
+2024-09-14 21:30:32.195007: Current learning rate: 0.0056 
+2024-09-14 21:34:37.698120: train_loss -0.8212 
+2024-09-14 21:34:37.698258: val_loss -0.6657 
+2024-09-14 21:34:37.698307: Pseudo dice [0.6462, 0.8123] 
+2024-09-14 21:34:37.698356: Epoch time: 245.51 s 
+2024-09-14 21:34:38.625420:  
+2024-09-14 21:34:38.625644: Epoch 476 
+2024-09-14 21:34:38.625725: Current learning rate: 0.00559 
+2024-09-14 21:38:44.146906: train_loss -0.8244 
+2024-09-14 21:38:44.147044: val_loss -0.6634 
+2024-09-14 21:38:44.147093: Pseudo dice [0.6563, 0.8101] 
+2024-09-14 21:38:44.147143: Epoch time: 245.52 s 
+2024-09-14 21:38:45.243268:  
+2024-09-14 21:38:45.243481: Epoch 477 
+2024-09-14 21:38:45.243565: Current learning rate: 0.00558 
+2024-09-14 21:42:50.754934: train_loss -0.8376 
+2024-09-14 21:42:50.755092: val_loss -0.6675 
+2024-09-14 21:42:50.755142: Pseudo dice [0.6552, 0.8155] 
+2024-09-14 21:42:50.755194: Epoch time: 245.51 s 
+2024-09-14 21:42:51.697773:  
+2024-09-14 21:42:51.697940: Epoch 478 
+2024-09-14 21:42:51.698052: Current learning rate: 0.00557 
+2024-09-14 21:46:57.331115: train_loss -0.8369 
+2024-09-14 21:46:57.331264: val_loss -0.6994 
+2024-09-14 21:46:57.331313: Pseudo dice [0.6881, 0.8046] 
+2024-09-14 21:46:57.331362: Epoch time: 245.64 s 
+2024-09-14 21:46:58.280010:  
+2024-09-14 21:46:58.280201: Epoch 479 
+2024-09-14 21:46:58.280284: Current learning rate: 0.00556 
+2024-09-14 21:51:03.770125: train_loss -0.8503 
+2024-09-14 21:51:03.770296: val_loss -0.6917 
+2024-09-14 21:51:03.770375: Pseudo dice [0.6824, 0.8353] 
+2024-09-14 21:51:03.770425: Epoch time: 245.49 s 
+2024-09-14 21:51:04.698663:  
+2024-09-14 21:51:04.698851: Epoch 480 
+2024-09-14 21:51:04.698927: Current learning rate: 0.00555 
+2024-09-14 21:55:10.026135: train_loss -0.8591 
+2024-09-14 21:55:10.026272: val_loss -0.6728 
+2024-09-14 21:55:10.026322: Pseudo dice [0.6299, 0.8277] 
+2024-09-14 21:55:10.026372: Epoch time: 245.33 s 
+2024-09-14 21:55:10.992851:  
+2024-09-14 21:55:10.993042: Epoch 481 
+2024-09-14 21:55:10.993122: Current learning rate: 0.00554 
+2024-09-14 21:59:16.388514: train_loss -0.8585 
+2024-09-14 21:59:16.388655: val_loss -0.6921 
+2024-09-14 21:59:16.388705: Pseudo dice [0.6839, 0.8119] 
+2024-09-14 21:59:16.388754: Epoch time: 245.4 s 
+2024-09-14 21:59:17.346012:  
+2024-09-14 21:59:17.346242: Epoch 482 
+2024-09-14 21:59:17.346365: Current learning rate: 0.00553 
+2024-09-14 22:03:22.785194: train_loss -0.86 
+2024-09-14 22:03:22.785334: val_loss -0.7017 
+2024-09-14 22:03:22.785386: Pseudo dice [0.7107, 0.8351] 
+2024-09-14 22:03:22.785435: Epoch time: 245.44 s 
+2024-09-14 22:03:23.729990:  
+2024-09-14 22:03:23.730192: Epoch 483 
+2024-09-14 22:03:23.730273: Current learning rate: 0.00552 
+2024-09-14 22:07:29.216682: train_loss -0.8578 
+2024-09-14 22:07:29.216819: val_loss -0.7247 
+2024-09-14 22:07:29.216868: Pseudo dice [0.7277, 0.8294] 
+2024-09-14 22:07:29.216918: Epoch time: 245.49 s 
+2024-09-14 22:07:30.156087:  
+2024-09-14 22:07:30.156277: Epoch 484 
+2024-09-14 22:07:30.156359: Current learning rate: 0.00551 
+2024-09-14 22:11:35.580868: train_loss -0.8613 
+2024-09-14 22:11:35.581006: val_loss -0.7214 
+2024-09-14 22:11:35.581059: Pseudo dice [0.7199, 0.8406] 
+2024-09-14 22:11:35.581151: Epoch time: 245.43 s 
+2024-09-14 22:11:36.522184:  
+2024-09-14 22:11:36.522362: Epoch 485 
+2024-09-14 22:11:36.522465: Current learning rate: 0.0055 
+2024-09-14 22:15:42.011902: train_loss -0.8572 
+2024-09-14 22:15:42.012049: val_loss -0.7031 
+2024-09-14 22:15:42.012140: Pseudo dice [0.7071, 0.8302] 
+2024-09-14 22:15:42.012216: Epoch time: 245.49 s 
+2024-09-14 22:15:42.945550:  
+2024-09-14 22:15:42.945711: Epoch 486 
+2024-09-14 22:15:42.945793: Current learning rate: 0.00549 
+2024-09-14 22:19:48.334485: train_loss -0.8672 
+2024-09-14 22:19:48.334623: val_loss -0.667 
+2024-09-14 22:19:48.334673: Pseudo dice [0.6975, 0.8161] 
+2024-09-14 22:19:48.334723: Epoch time: 245.39 s 
+2024-09-14 22:19:49.293998:  
+2024-09-14 22:19:49.294178: Epoch 487 
+2024-09-14 22:19:49.294280: Current learning rate: 0.00548 
+2024-09-14 22:23:54.719779: train_loss -0.8494 
+2024-09-14 22:23:54.719946: val_loss -0.6749 
+2024-09-14 22:23:54.719996: Pseudo dice [0.711, 0.8152] 
+2024-09-14 22:23:54.720046: Epoch time: 245.43 s 
+2024-09-14 22:23:55.659237:  
+2024-09-14 22:23:55.659375: Epoch 488 
+2024-09-14 22:23:55.659456: Current learning rate: 0.00547 
+2024-09-14 22:28:01.161474: train_loss -0.855 
+2024-09-14 22:28:01.161612: val_loss -0.7013 
+2024-09-14 22:28:01.161664: Pseudo dice [0.6889, 0.8294] 
+2024-09-14 22:28:01.161714: Epoch time: 245.5 s 
+2024-09-14 22:28:02.101320:  
+2024-09-14 22:28:02.101473: Epoch 489 
+2024-09-14 22:28:02.101558: Current learning rate: 0.00546 
+2024-09-14 22:32:07.657814: train_loss -0.8564 
+2024-09-14 22:32:07.657977: val_loss -0.6839 
+2024-09-14 22:32:07.658028: Pseudo dice [0.7041, 0.8189] 
+2024-09-14 22:32:07.658078: Epoch time: 245.56 s 
+2024-09-14 22:32:08.614790:  
+2024-09-14 22:32:08.615021: Epoch 490 
+2024-09-14 22:32:08.615104: Current learning rate: 0.00546 
+2024-09-14 22:36:14.006794: train_loss -0.8638 
+2024-09-14 22:36:14.006931: val_loss -0.687 
+2024-09-14 22:36:14.006981: Pseudo dice [0.6644, 0.838] 
+2024-09-14 22:36:14.007034: Epoch time: 245.39 s 
+2024-09-14 22:36:14.933241:  
+2024-09-14 22:36:14.933392: Epoch 491 
+2024-09-14 22:36:14.933476: Current learning rate: 0.00545 
+2024-09-14 22:40:20.466757: train_loss -0.8629 
+2024-09-14 22:40:20.466895: val_loss -0.6669 
+2024-09-14 22:40:20.466947: Pseudo dice [0.6808, 0.8242] 
+2024-09-14 22:40:20.466997: Epoch time: 245.54 s 
+2024-09-14 22:40:21.429460:  
+2024-09-14 22:40:21.429644: Epoch 492 
+2024-09-14 22:40:21.429726: Current learning rate: 0.00544 
+2024-09-14 22:44:26.873902: train_loss -0.8607 
+2024-09-14 22:44:26.874071: val_loss -0.7122 
+2024-09-14 22:44:26.874121: Pseudo dice [0.7175, 0.8204] 
+2024-09-14 22:44:26.874170: Epoch time: 245.45 s 
+2024-09-14 22:44:27.812925:  
+2024-09-14 22:44:27.813128: Epoch 493 
+2024-09-14 22:44:27.813211: Current learning rate: 0.00543 
+2024-09-14 22:48:33.348040: train_loss -0.8629 
+2024-09-14 22:48:33.348177: val_loss -0.6636 
+2024-09-14 22:48:33.348227: Pseudo dice [0.6483, 0.8247] 
+2024-09-14 22:48:33.348303: Epoch time: 245.54 s 
+2024-09-14 22:48:35.199064:  
+2024-09-14 22:48:35.199284: Epoch 494 
+2024-09-14 22:48:35.199379: Current learning rate: 0.00542 
+2024-09-14 22:52:40.838235: train_loss -0.866 
+2024-09-14 22:52:40.838374: val_loss -0.6854 
+2024-09-14 22:52:40.838424: Pseudo dice [0.6796, 0.8258] 
+2024-09-14 22:52:40.838474: Epoch time: 245.64 s 
+2024-09-14 22:52:41.773826:  
+2024-09-14 22:52:41.774034: Epoch 495 
+2024-09-14 22:52:41.774109: Current learning rate: 0.00541 
+2024-09-14 22:56:47.380681: train_loss -0.8583 
+2024-09-14 22:56:47.380830: val_loss -0.7108 
+2024-09-14 22:56:47.380879: Pseudo dice [0.7147, 0.8195] 
+2024-09-14 22:56:47.380931: Epoch time: 245.61 s 
+2024-09-14 22:56:48.322789:  
+2024-09-14 22:56:48.322998: Epoch 496 
+2024-09-14 22:56:48.323104: Current learning rate: 0.0054 
+2024-09-14 23:00:53.694034: train_loss -0.865 
+2024-09-14 23:00:53.694175: val_loss -0.6873 
+2024-09-14 23:00:53.694225: Pseudo dice [0.6941, 0.8218] 
+2024-09-14 23:00:53.694276: Epoch time: 245.37 s 
+2024-09-14 23:00:54.635091:  
+2024-09-14 23:00:54.635293: Epoch 497 
+2024-09-14 23:00:54.635395: Current learning rate: 0.00539 
+2024-09-14 23:04:59.948887: train_loss -0.8512 
+2024-09-14 23:04:59.949048: val_loss -0.7068 
+2024-09-14 23:04:59.949099: Pseudo dice [0.7123, 0.8188] 
+2024-09-14 23:04:59.949149: Epoch time: 245.32 s 
+2024-09-14 23:05:00.905194:  
+2024-09-14 23:05:00.905406: Epoch 498 
+2024-09-14 23:05:00.905489: Current learning rate: 0.00538 
+2024-09-14 23:09:06.236134: train_loss -0.8684 
+2024-09-14 23:09:06.236331: val_loss -0.7023 
+2024-09-14 23:09:06.236423: Pseudo dice [0.7019, 0.8401] 
+2024-09-14 23:09:06.236513: Epoch time: 245.33 s 
+2024-09-14 23:09:07.182175:  
+2024-09-14 23:09:07.182390: Epoch 499 
+2024-09-14 23:09:07.182473: Current learning rate: 0.00537 
+2024-09-14 23:13:12.564390: train_loss -0.8713 
+2024-09-14 23:13:12.564530: val_loss -0.7044 
+2024-09-14 23:13:12.564580: Pseudo dice [0.7012, 0.8153] 
+2024-09-14 23:13:12.564630: Epoch time: 245.38 s 
+2024-09-14 23:13:16.452426:  
+2024-09-14 23:13:16.452622: Epoch 500 
+2024-09-14 23:13:16.452711: Current learning rate: 0.00536 
+2024-09-14 23:17:21.783107: train_loss -0.872 
+2024-09-14 23:17:21.783246: val_loss -0.6948 
+2024-09-14 23:17:21.783295: Pseudo dice [0.7109, 0.8169] 
+2024-09-14 23:17:21.783345: Epoch time: 245.33 s 
+2024-09-14 23:17:22.713980:  
+2024-09-14 23:17:22.714162: Epoch 501 
+2024-09-14 23:17:22.714267: Current learning rate: 0.00535 
+2024-09-14 23:21:28.027592: train_loss -0.8658 
+2024-09-14 23:21:28.027731: val_loss -0.6847 
+2024-09-14 23:21:28.027781: Pseudo dice [0.7048, 0.8158] 
+2024-09-14 23:21:28.027848: Epoch time: 245.32 s 
+2024-09-14 23:21:28.958066:  
+2024-09-14 23:21:28.958303: Epoch 502 
+2024-09-14 23:21:28.958385: Current learning rate: 0.00534 
+2024-09-14 23:25:34.291370: train_loss -0.8634 
+2024-09-14 23:25:34.291543: val_loss -0.6612 
+2024-09-14 23:25:34.291593: Pseudo dice [0.6703, 0.803] 
+2024-09-14 23:25:34.291644: Epoch time: 245.34 s 
+2024-09-14 23:25:35.245991:  
+2024-09-14 23:25:35.246209: Epoch 503 
+2024-09-14 23:25:35.246293: Current learning rate: 0.00533 
+2024-09-14 23:29:40.798292: train_loss -0.871 
+2024-09-14 23:29:40.798458: val_loss -0.7169 
+2024-09-14 23:29:40.798545: Pseudo dice [0.7291, 0.836] 
+2024-09-14 23:29:40.798597: Epoch time: 245.55 s 
+2024-09-14 23:29:41.762368:  
+2024-09-14 23:29:41.762566: Epoch 504 
+2024-09-14 23:29:41.762647: Current learning rate: 0.00532 
+2024-09-14 23:33:47.508750: train_loss -0.8719 
+2024-09-14 23:33:47.508894: val_loss -0.6857 
+2024-09-14 23:33:47.508945: Pseudo dice [0.6662, 0.8334] 
+2024-09-14 23:33:47.508995: Epoch time: 245.75 s 
+2024-09-14 23:33:48.449117:  
+2024-09-14 23:33:48.449327: Epoch 505 
+2024-09-14 23:33:48.449408: Current learning rate: 0.00531 
+2024-09-14 23:37:54.106593: train_loss -0.8733 
+2024-09-14 23:37:54.106730: val_loss -0.6961 
+2024-09-14 23:37:54.106779: Pseudo dice [0.7142, 0.8185] 
+2024-09-14 23:37:54.106831: Epoch time: 245.66 s 
+2024-09-14 23:37:55.061097:  
+2024-09-14 23:37:55.061286: Epoch 506 
+2024-09-14 23:37:55.061367: Current learning rate: 0.0053 
+2024-09-14 23:42:00.513133: train_loss -0.8568 
+2024-09-14 23:42:00.513271: val_loss -0.7225 
+2024-09-14 23:42:00.513320: Pseudo dice [0.704, 0.8341] 
+2024-09-14 23:42:00.513369: Epoch time: 245.45 s 
+2024-09-14 23:42:01.463631:  
+2024-09-14 23:42:01.463768: Epoch 507 
+2024-09-14 23:42:01.463865: Current learning rate: 0.00529 
+2024-09-14 23:46:06.815910: train_loss -0.8647 
+2024-09-14 23:46:06.816050: val_loss -0.6821 
+2024-09-14 23:46:06.816098: Pseudo dice [0.6875, 0.8255] 
+2024-09-14 23:46:06.816148: Epoch time: 245.35 s 
+2024-09-14 23:46:07.767782:  
+2024-09-14 23:46:07.767985: Epoch 508 
+2024-09-14 23:46:07.768065: Current learning rate: 0.00528 
+2024-09-14 23:50:13.286030: train_loss -0.8707 
+2024-09-14 23:50:13.286173: val_loss -0.708 
+2024-09-14 23:50:13.286227: Pseudo dice [0.7134, 0.834] 
+2024-09-14 23:50:13.286278: Epoch time: 245.52 s 
+2024-09-14 23:50:14.235530:  
+2024-09-14 23:50:14.235743: Epoch 509 
+2024-09-14 23:50:14.235854: Current learning rate: 0.00527 
+2024-09-14 23:54:19.674841: train_loss -0.8773 
+2024-09-14 23:54:19.675026: val_loss -0.7055 
+2024-09-14 23:54:19.675100: Pseudo dice [0.7055, 0.8238] 
+2024-09-14 23:54:19.675194: Epoch time: 245.44 s 
+2024-09-14 23:54:20.621773:  
+2024-09-14 23:54:20.621949: Epoch 510 
+2024-09-14 23:54:20.622052: Current learning rate: 0.00526 
+2024-09-14 23:58:26.047338: train_loss -0.8693 
+2024-09-14 23:58:26.047491: val_loss -0.6945 
+2024-09-14 23:58:26.047544: Pseudo dice [0.6945, 0.8333] 
+2024-09-14 23:58:26.047616: Epoch time: 245.43 s 
+2024-09-14 23:58:27.002945:  
+2024-09-14 23:58:27.003222: Epoch 511 
+2024-09-14 23:58:27.003306: Current learning rate: 0.00525 
+2024-09-15 00:02:32.507971: train_loss -0.867 
+2024-09-15 00:02:32.508122: val_loss -0.713 
+2024-09-15 00:02:32.508172: Pseudo dice [0.7149, 0.8387] 
+2024-09-15 00:02:32.508221: Epoch time: 245.51 s 
+2024-09-15 00:02:32.508261: Yayy! New best EMA pseudo Dice: 0.7635 
+2024-09-15 00:02:36.377919:  
+2024-09-15 00:02:36.378081: Epoch 512 
+2024-09-15 00:02:36.378166: Current learning rate: 0.00524 
+2024-09-15 00:06:41.831327: train_loss -0.8623 
+2024-09-15 00:06:41.831466: val_loss -0.6875 
+2024-09-15 00:06:41.831515: Pseudo dice [0.6888, 0.8231] 
+2024-09-15 00:06:41.831566: Epoch time: 245.46 s 
+2024-09-15 00:06:42.801197:  
+2024-09-15 00:06:42.801418: Epoch 513 
+2024-09-15 00:06:42.801501: Current learning rate: 0.00523 
+2024-09-15 00:10:48.144009: train_loss -0.8768 
+2024-09-15 00:10:48.144186: val_loss -0.6942 
+2024-09-15 00:10:48.144236: Pseudo dice [0.6994, 0.8317] 
+2024-09-15 00:10:48.144286: Epoch time: 245.34 s 
+2024-09-15 00:10:49.091209:  
+2024-09-15 00:10:49.091413: Epoch 514 
+2024-09-15 00:10:49.091494: Current learning rate: 0.00522 
+2024-09-15 00:14:54.656046: train_loss -0.876 
+2024-09-15 00:14:54.656183: val_loss -0.7172 
+2024-09-15 00:14:54.656233: Pseudo dice [0.7095, 0.8467] 
+2024-09-15 00:14:54.656283: Epoch time: 245.57 s 
+2024-09-15 00:14:54.656323: Yayy! New best EMA pseudo Dice: 0.7645 
+2024-09-15 00:14:58.541238:  
+2024-09-15 00:14:58.541412: Epoch 515 
+2024-09-15 00:14:58.541518: Current learning rate: 0.00521 
+2024-09-15 00:19:04.155370: train_loss -0.8769 
+2024-09-15 00:19:04.155516: val_loss -0.7353 
+2024-09-15 00:19:04.155566: Pseudo dice [0.7409, 0.8383] 
+2024-09-15 00:19:04.155615: Epoch time: 245.62 s 
+2024-09-15 00:19:04.155654: Yayy! New best EMA pseudo Dice: 0.767 
+2024-09-15 00:19:08.038049:  
+2024-09-15 00:19:08.038201: Epoch 516 
+2024-09-15 00:19:08.038283: Current learning rate: 0.0052 
+2024-09-15 00:23:14.267037: train_loss -0.8792 
+2024-09-15 00:23:14.267196: val_loss -0.7102 
+2024-09-15 00:23:14.267250: Pseudo dice [0.6988, 0.8431] 
+2024-09-15 00:23:14.267299: Epoch time: 246.23 s 
+2024-09-15 00:23:14.267338: Yayy! New best EMA pseudo Dice: 0.7674 
+2024-09-15 00:23:18.153165:  
+2024-09-15 00:23:18.153375: Epoch 517 
+2024-09-15 00:23:18.153458: Current learning rate: 0.00519 
+2024-09-15 00:27:23.752861: train_loss -0.8815 
+2024-09-15 00:27:23.753022: val_loss -0.6949 
+2024-09-15 00:27:23.753077: Pseudo dice [0.6861, 0.821] 
+2024-09-15 00:27:23.753178: Epoch time: 245.6 s 
+2024-09-15 00:27:24.690024:  
+2024-09-15 00:27:24.690264: Epoch 518 
+2024-09-15 00:27:24.690347: Current learning rate: 0.00518 
+2024-09-15 00:31:30.359296: train_loss -0.8792 
+2024-09-15 00:31:30.359434: val_loss -0.7029 
+2024-09-15 00:31:30.359484: Pseudo dice [0.6941, 0.8288] 
+2024-09-15 00:31:30.359533: Epoch time: 245.67 s 
+2024-09-15 00:31:31.315245:  
+2024-09-15 00:31:31.315468: Epoch 519 
+2024-09-15 00:31:31.315555: Current learning rate: 0.00518 
+2024-09-15 00:35:37.005900: train_loss -0.8773 
+2024-09-15 00:35:37.006038: val_loss -0.6929 
+2024-09-15 00:35:37.006137: Pseudo dice [0.7085, 0.8221] 
+2024-09-15 00:35:37.006188: Epoch time: 245.69 s 
+2024-09-15 00:35:37.958219:  
+2024-09-15 00:35:37.958418: Epoch 520 
+2024-09-15 00:35:37.958501: Current learning rate: 0.00517 
+2024-09-15 00:39:43.679071: train_loss -0.8767 
+2024-09-15 00:39:43.679251: val_loss -0.7042 
+2024-09-15 00:39:43.679306: Pseudo dice [0.7047, 0.8228] 
+2024-09-15 00:39:43.679355: Epoch time: 245.72 s 
+2024-09-15 00:39:44.618936:  
+2024-09-15 00:39:44.619143: Epoch 521 
+2024-09-15 00:39:44.619223: Current learning rate: 0.00516 
+2024-09-15 00:43:50.267509: train_loss -0.8761 
+2024-09-15 00:43:50.267649: val_loss -0.6919 
+2024-09-15 00:43:50.267700: Pseudo dice [0.6851, 0.8113] 
+2024-09-15 00:43:50.267752: Epoch time: 245.65 s 
+2024-09-15 00:43:51.255919:  
+2024-09-15 00:43:51.256105: Epoch 522 
+2024-09-15 00:43:51.256187: Current learning rate: 0.00515 
+2024-09-15 00:47:57.165981: train_loss -0.869 
+2024-09-15 00:47:57.166116: val_loss -0.7131 
+2024-09-15 00:47:57.166167: Pseudo dice [0.7099, 0.8408] 
+2024-09-15 00:47:57.166218: Epoch time: 245.91 s 
+2024-09-15 00:47:58.112418:  
+2024-09-15 00:47:58.112622: Epoch 523 
+2024-09-15 00:47:58.112706: Current learning rate: 0.00514 
+2024-09-15 00:52:03.750890: train_loss -0.8781 
+2024-09-15 00:52:03.751088: val_loss -0.7053 
+2024-09-15 00:52:03.751181: Pseudo dice [0.7054, 0.8204] 
+2024-09-15 00:52:03.751271: Epoch time: 245.64 s 
+2024-09-15 00:52:04.705148:  
+2024-09-15 00:52:04.705346: Epoch 524 
+2024-09-15 00:52:04.705427: Current learning rate: 0.00513 
+2024-09-15 00:56:10.306734: train_loss -0.8753 
+2024-09-15 00:56:10.306871: val_loss -0.6808 
+2024-09-15 00:56:10.306924: Pseudo dice [0.6816, 0.8314] 
+2024-09-15 00:56:10.306976: Epoch time: 245.6 s 
+2024-09-15 00:56:11.268338:  
+2024-09-15 00:56:11.268525: Epoch 525 
+2024-09-15 00:56:11.268607: Current learning rate: 0.00512 
+2024-09-15 01:00:16.881833: train_loss -0.8763 
+2024-09-15 01:00:16.882006: val_loss -0.7218 
+2024-09-15 01:00:16.882059: Pseudo dice [0.7282, 0.8443] 
+2024-09-15 01:00:16.882110: Epoch time: 245.62 s 
+2024-09-15 01:00:17.827318:  
+2024-09-15 01:00:17.827515: Epoch 526 
+2024-09-15 01:00:17.827599: Current learning rate: 0.00511 
+2024-09-15 01:04:23.398992: train_loss -0.8815 
+2024-09-15 01:04:23.399130: val_loss -0.6928 
+2024-09-15 01:04:23.399201: Pseudo dice [0.6877, 0.8333] 
+2024-09-15 01:04:23.399279: Epoch time: 245.57 s 
+2024-09-15 01:04:24.353125:  
+2024-09-15 01:04:24.353350: Epoch 527 
+2024-09-15 01:04:24.353433: Current learning rate: 0.0051 
+2024-09-15 01:08:29.939710: train_loss -0.8786 
+2024-09-15 01:08:29.939854: val_loss -0.7179 
+2024-09-15 01:08:29.939905: Pseudo dice [0.7199, 0.8362] 
+2024-09-15 01:08:29.939955: Epoch time: 245.59 s 
+2024-09-15 01:08:30.912632:  
+2024-09-15 01:08:30.912823: Epoch 528 
+2024-09-15 01:08:30.912905: Current learning rate: 0.00509 
+2024-09-15 01:12:36.206358: train_loss -0.884 
+2024-09-15 01:12:36.206492: val_loss -0.6936 
+2024-09-15 01:12:36.206542: Pseudo dice [0.7247, 0.8183] 
+2024-09-15 01:12:36.206593: Epoch time: 245.3 s 
+2024-09-15 01:12:37.179166:  
+2024-09-15 01:12:37.179347: Epoch 529 
+2024-09-15 01:12:37.179430: Current learning rate: 0.00508 
+2024-09-15 01:16:42.511575: train_loss -0.8779 
+2024-09-15 01:16:42.511766: val_loss -0.7037 
+2024-09-15 01:16:42.511854: Pseudo dice [0.6936, 0.8367] 
+2024-09-15 01:16:42.511905: Epoch time: 245.33 s 
+2024-09-15 01:16:43.461850:  
+2024-09-15 01:16:43.462072: Epoch 530 
+2024-09-15 01:16:43.462174: Current learning rate: 0.00507 
+2024-09-15 01:20:48.836336: train_loss -0.8777 
+2024-09-15 01:20:48.836478: val_loss -0.7092 
+2024-09-15 01:20:48.836529: Pseudo dice [0.7194, 0.8137] 
+2024-09-15 01:20:48.836579: Epoch time: 245.38 s 
+2024-09-15 01:20:49.777623:  
+2024-09-15 01:20:49.777837: Epoch 531 
+2024-09-15 01:20:49.777942: Current learning rate: 0.00506 
+2024-09-15 01:24:55.135643: train_loss -0.8763 
+2024-09-15 01:24:55.135801: val_loss -0.6952 
+2024-09-15 01:24:55.135860: Pseudo dice [0.7162, 0.8242] 
+2024-09-15 01:24:55.135909: Epoch time: 245.36 s 
+2024-09-15 01:24:56.080973:  
+2024-09-15 01:24:56.081224: Epoch 532 
+2024-09-15 01:24:56.081327: Current learning rate: 0.00505 
+2024-09-15 01:29:01.505101: train_loss -0.8699 
+2024-09-15 01:29:01.505241: val_loss -0.6895 
+2024-09-15 01:29:01.505290: Pseudo dice [0.6931, 0.8149] 
+2024-09-15 01:29:01.505340: Epoch time: 245.43 s 
+2024-09-15 01:29:02.473726:  
+2024-09-15 01:29:02.473864: Epoch 533 
+2024-09-15 01:29:02.473946: Current learning rate: 0.00504 
+2024-09-15 01:33:08.082987: train_loss -0.8421 
+2024-09-15 01:33:08.083123: val_loss -0.6987 
+2024-09-15 01:33:08.083260: Pseudo dice [0.6782, 0.8334] 
+2024-09-15 01:33:08.083316: Epoch time: 245.61 s 
+2024-09-15 01:33:09.041013:  
+2024-09-15 01:33:09.041225: Epoch 534 
+2024-09-15 01:33:09.041365: Current learning rate: 0.00503 
+2024-09-15 01:37:14.586445: train_loss -0.8466 
+2024-09-15 01:37:14.586582: val_loss -0.6907 
+2024-09-15 01:37:14.586632: Pseudo dice [0.6894, 0.8272] 
+2024-09-15 01:37:14.586681: Epoch time: 245.55 s 
+2024-09-15 01:37:15.561743:  
+2024-09-15 01:37:15.561911: Epoch 535 
+2024-09-15 01:37:15.561993: Current learning rate: 0.00502 
+2024-09-15 01:41:21.038902: train_loss -0.8571 
+2024-09-15 01:41:21.039042: val_loss -0.6872 
+2024-09-15 01:41:21.039093: Pseudo dice [0.695, 0.8231] 
+2024-09-15 01:41:21.039144: Epoch time: 245.48 s 
+2024-09-15 01:41:22.000278:  
+2024-09-15 01:41:22.000474: Epoch 536 
+2024-09-15 01:41:22.000556: Current learning rate: 0.00501 
+2024-09-15 01:45:27.691406: train_loss -0.862 
+2024-09-15 01:45:27.691559: val_loss -0.693 
+2024-09-15 01:45:27.691640: Pseudo dice [0.7069, 0.827] 
+2024-09-15 01:45:27.691735: Epoch time: 245.69 s 
+2024-09-15 01:45:28.639234:  
+2024-09-15 01:45:28.639504: Epoch 537 
+2024-09-15 01:45:28.639611: Current learning rate: 0.005 
+2024-09-15 01:49:34.143717: train_loss -0.8675 
+2024-09-15 01:49:34.143862: val_loss -0.7075 
+2024-09-15 01:49:34.143913: Pseudo dice [0.6828, 0.839] 
+2024-09-15 01:49:34.143963: Epoch time: 245.51 s 
+2024-09-15 01:49:35.096166:  
+2024-09-15 01:49:35.096351: Epoch 538 
+2024-09-15 01:49:35.096432: Current learning rate: 0.00499 
+2024-09-15 01:53:40.598344: train_loss -0.8717 
+2024-09-15 01:53:40.598489: val_loss -0.6734 
+2024-09-15 01:53:40.598540: Pseudo dice [0.6978, 0.8263] 
+2024-09-15 01:53:40.598592: Epoch time: 245.5 s 
+2024-09-15 01:53:41.542140:  
+2024-09-15 01:53:41.542353: Epoch 539 
+2024-09-15 01:53:41.542435: Current learning rate: 0.00498 
+2024-09-15 01:57:47.692334: train_loss -0.8692 
+2024-09-15 01:57:47.692477: val_loss -0.7031 
+2024-09-15 01:57:47.692538: Pseudo dice [0.7032, 0.8377] 
+2024-09-15 01:57:47.692617: Epoch time: 246.15 s 
+2024-09-15 01:57:48.657794:  
+2024-09-15 01:57:48.658025: Epoch 540 
+2024-09-15 01:57:48.658105: Current learning rate: 0.00497 
+2024-09-15 02:01:54.143795: train_loss -0.8756 
+2024-09-15 02:01:54.143941: val_loss -0.7108 
+2024-09-15 02:01:54.144029: Pseudo dice [0.7114, 0.8275] 
+2024-09-15 02:01:54.144081: Epoch time: 245.49 s 
+2024-09-15 02:01:55.086859:  
+2024-09-15 02:01:55.087143: Epoch 541 
+2024-09-15 02:01:55.087278: Current learning rate: 0.00496 
+2024-09-15 02:06:00.576658: train_loss -0.8652 
+2024-09-15 02:06:00.576797: val_loss -0.6729 
+2024-09-15 02:06:00.576846: Pseudo dice [0.6515, 0.8315] 
+2024-09-15 02:06:00.576897: Epoch time: 245.49 s 
+2024-09-15 02:06:01.513151:  
+2024-09-15 02:06:01.513356: Epoch 542 
+2024-09-15 02:06:01.513454: Current learning rate: 0.00495 
+2024-09-15 02:10:07.098543: train_loss -0.8745 
+2024-09-15 02:10:07.098680: val_loss -0.6932 
+2024-09-15 02:10:07.098730: Pseudo dice [0.6836, 0.8345] 
+2024-09-15 02:10:07.098780: Epoch time: 245.59 s 
+2024-09-15 02:10:08.046059:  
+2024-09-15 02:10:08.046280: Epoch 543 
+2024-09-15 02:10:08.046363: Current learning rate: 0.00494 
+2024-09-15 02:14:13.557360: train_loss -0.8686 
+2024-09-15 02:14:13.557499: val_loss -0.6813 
+2024-09-15 02:14:13.557549: Pseudo dice [0.65, 0.8293] 
+2024-09-15 02:14:13.557599: Epoch time: 245.51 s 
+2024-09-15 02:14:14.502858:  
+2024-09-15 02:14:14.503087: Epoch 544 
+2024-09-15 02:14:14.503168: Current learning rate: 0.00493 
+2024-09-15 02:18:19.945361: train_loss -0.8716 
+2024-09-15 02:18:19.945502: val_loss -0.7319 
+2024-09-15 02:18:19.945552: Pseudo dice [0.7209, 0.8394] 
+2024-09-15 02:18:19.945603: Epoch time: 245.44 s 
+2024-09-15 02:18:20.888297:  
+2024-09-15 02:18:20.888511: Epoch 545 
+2024-09-15 02:18:20.888595: Current learning rate: 0.00492 
+2024-09-15 02:22:26.548874: train_loss -0.8763 
+2024-09-15 02:22:26.549010: val_loss -0.6958 
+2024-09-15 02:22:26.549060: Pseudo dice [0.683, 0.8259] 
+2024-09-15 02:22:26.549109: Epoch time: 245.66 s 
+2024-09-15 02:22:27.504836:  
+2024-09-15 02:22:27.504994: Epoch 546 
+2024-09-15 02:22:27.505073: Current learning rate: 0.00491 
+2024-09-15 02:26:32.846011: train_loss -0.8734 
+2024-09-15 02:26:32.846150: val_loss -0.7258 
+2024-09-15 02:26:32.846200: Pseudo dice [0.7116, 0.824] 
+2024-09-15 02:26:32.846250: Epoch time: 245.34 s 
+2024-09-15 02:26:33.813462:  
+2024-09-15 02:26:33.813656: Epoch 547 
+2024-09-15 02:26:33.813748: Current learning rate: 0.0049 
+2024-09-15 02:30:39.355536: train_loss -0.8491 
+2024-09-15 02:30:39.355675: val_loss -0.6734 
+2024-09-15 02:30:39.355726: Pseudo dice [0.6811, 0.8205] 
+2024-09-15 02:30:39.355800: Epoch time: 245.54 s 
+2024-09-15 02:30:40.304056:  
+2024-09-15 02:30:40.304231: Epoch 548 
+2024-09-15 02:30:40.304312: Current learning rate: 0.00489 
+2024-09-15 02:34:45.696048: train_loss -0.8652 
+2024-09-15 02:34:45.696186: val_loss -0.7047 
+2024-09-15 02:34:45.696237: Pseudo dice [0.725, 0.82] 
+2024-09-15 02:34:45.696286: Epoch time: 245.39 s 
+2024-09-15 02:34:46.642678:  
+2024-09-15 02:34:46.642862: Epoch 549 
+2024-09-15 02:34:46.642944: Current learning rate: 0.00488 
+2024-09-15 02:38:52.140029: train_loss -0.858 
+2024-09-15 02:38:52.140188: val_loss -0.6938 
+2024-09-15 02:38:52.140238: Pseudo dice [0.6927, 0.8201] 
+2024-09-15 02:38:52.140288: Epoch time: 245.5 s 
+2024-09-15 02:38:55.995280:  
+2024-09-15 02:38:55.995494: Epoch 550 
+2024-09-15 02:38:55.995577: Current learning rate: 0.00487 
+2024-09-15 02:43:01.355003: train_loss -0.8651 
+2024-09-15 02:43:01.355155: val_loss -0.6877 
+2024-09-15 02:43:01.355232: Pseudo dice [0.6957, 0.8239] 
+2024-09-15 02:43:01.355340: Epoch time: 245.36 s 
+2024-09-15 02:43:02.306810:  
+2024-09-15 02:43:02.307008: Epoch 551 
+2024-09-15 02:43:02.307092: Current learning rate: 0.00486 
+2024-09-15 02:47:07.802003: train_loss -0.8598 
+2024-09-15 02:47:07.802142: val_loss -0.6803 
+2024-09-15 02:47:07.802192: Pseudo dice [0.6842, 0.8365] 
+2024-09-15 02:47:07.802242: Epoch time: 245.5 s 
+2024-09-15 02:47:08.737159:  
+2024-09-15 02:47:08.737332: Epoch 552 
+2024-09-15 02:47:08.737415: Current learning rate: 0.00485 
+2024-09-15 02:51:14.152121: train_loss -0.8561 
+2024-09-15 02:51:14.152270: val_loss -0.7135 
+2024-09-15 02:51:14.152318: Pseudo dice [0.7035, 0.8421] 
+2024-09-15 02:51:14.152368: Epoch time: 245.42 s 
+2024-09-15 02:51:15.095320:  
+2024-09-15 02:51:15.095538: Epoch 553 
+2024-09-15 02:51:15.095662: Current learning rate: 0.00484 
+2024-09-15 02:55:20.399107: train_loss -0.8679 
+2024-09-15 02:55:20.399246: val_loss -0.6576 
+2024-09-15 02:55:20.399295: Pseudo dice [0.6758, 0.8196] 
+2024-09-15 02:55:20.399345: Epoch time: 245.31 s 
+2024-09-15 02:55:21.330369:  
+2024-09-15 02:55:21.330544: Epoch 554 
+2024-09-15 02:55:21.330630: Current learning rate: 0.00484 
+2024-09-15 02:59:26.919803: train_loss -0.8717 
+2024-09-15 02:59:26.919970: val_loss -0.7119 
+2024-09-15 02:59:26.920021: Pseudo dice [0.7075, 0.8235] 
+2024-09-15 02:59:26.920072: Epoch time: 245.59 s 
+2024-09-15 02:59:27.877664:  
+2024-09-15 02:59:27.877914: Epoch 555 
+2024-09-15 02:59:27.878039: Current learning rate: 0.00483 
+2024-09-15 03:03:33.295945: train_loss -0.8639 
+2024-09-15 03:03:33.296083: val_loss -0.6897 
+2024-09-15 03:03:33.296132: Pseudo dice [0.6942, 0.8164] 
+2024-09-15 03:03:33.296183: Epoch time: 245.42 s 
+2024-09-15 03:03:34.232744:  
+2024-09-15 03:03:34.232940: Epoch 556 
+2024-09-15 03:03:34.233024: Current learning rate: 0.00482 
+2024-09-15 03:07:39.056997: train_loss -0.8561 
+2024-09-15 03:07:39.057135: val_loss -0.6648 
+2024-09-15 03:07:39.057184: Pseudo dice [0.6754, 0.8199] 
+2024-09-15 03:07:39.057233: Epoch time: 244.83 s 
+2024-09-15 03:07:39.993393:  
+2024-09-15 03:07:39.993534: Epoch 557 
+2024-09-15 03:07:39.993615: Current learning rate: 0.00481 
+2024-09-15 03:11:44.954799: train_loss -0.8565 
+2024-09-15 03:11:44.954952: val_loss -0.6807 
+2024-09-15 03:11:44.955001: Pseudo dice [0.6654, 0.81] 
+2024-09-15 03:11:44.955051: Epoch time: 244.96 s 
+2024-09-15 03:11:45.882524:  
+2024-09-15 03:11:45.882703: Epoch 558 
+2024-09-15 03:11:45.882786: Current learning rate: 0.0048 
+2024-09-15 03:15:50.851506: train_loss -0.8677 
+2024-09-15 03:15:50.851656: val_loss -0.6913 
+2024-09-15 03:15:50.851706: Pseudo dice [0.7175, 0.835] 
+2024-09-15 03:15:50.851756: Epoch time: 244.97 s 
+2024-09-15 03:15:51.798947:  
+2024-09-15 03:15:51.799164: Epoch 559 
+2024-09-15 03:15:51.799247: Current learning rate: 0.00479 
+2024-09-15 03:19:56.693265: train_loss -0.8692 
+2024-09-15 03:19:56.693402: val_loss -0.7121 
+2024-09-15 03:19:56.693452: Pseudo dice [0.7144, 0.8158] 
+2024-09-15 03:19:56.693502: Epoch time: 244.9 s 
+2024-09-15 03:19:57.638345:  
+2024-09-15 03:19:57.638584: Epoch 560 
+2024-09-15 03:19:57.638665: Current learning rate: 0.00478 
+2024-09-15 03:24:02.558166: train_loss -0.8757 
+2024-09-15 03:24:02.558324: val_loss -0.6849 
+2024-09-15 03:24:02.558374: Pseudo dice [0.6915, 0.8204] 
+2024-09-15 03:24:02.558486: Epoch time: 244.92 s 
+2024-09-15 03:24:03.503676:  
+2024-09-15 03:24:03.503835: Epoch 561 
+2024-09-15 03:24:03.503919: Current learning rate: 0.00477 
+2024-09-15 03:28:08.446143: train_loss -0.8762 
+2024-09-15 03:28:08.446277: val_loss -0.7069 
+2024-09-15 03:28:08.446327: Pseudo dice [0.7045, 0.8321] 
+2024-09-15 03:28:08.446377: Epoch time: 244.94 s 
+2024-09-15 03:28:09.386307:  
+2024-09-15 03:28:09.386482: Epoch 562 
+2024-09-15 03:28:09.386578: Current learning rate: 0.00476 
+2024-09-15 03:32:14.566738: train_loss -0.8791 
+2024-09-15 03:32:14.566901: val_loss -0.7157 
+2024-09-15 03:32:14.566950: Pseudo dice [0.6969, 0.8387] 
+2024-09-15 03:32:14.567001: Epoch time: 245.18 s 
+2024-09-15 03:32:16.390169:  
+2024-09-15 03:32:16.390396: Epoch 563 
+2024-09-15 03:32:16.390493: Current learning rate: 0.00475 
+2024-09-15 03:36:21.586877: train_loss -0.8724 
+2024-09-15 03:36:21.587018: val_loss -0.6879 
+2024-09-15 03:36:21.587072: Pseudo dice [0.6645, 0.8273] 
+2024-09-15 03:36:21.587123: Epoch time: 245.2 s 
+2024-09-15 03:36:22.560961:  
+2024-09-15 03:36:22.561144: Epoch 564 
+2024-09-15 03:36:22.561227: Current learning rate: 0.00474 
+2024-09-15 03:40:27.794358: train_loss -0.8727 
+2024-09-15 03:40:27.794507: val_loss -0.7045 
+2024-09-15 03:40:27.794556: Pseudo dice [0.7112, 0.8221] 
+2024-09-15 03:40:27.794606: Epoch time: 245.24 s 
+2024-09-15 03:40:28.741575:  
+2024-09-15 03:40:28.741793: Epoch 565 
+2024-09-15 03:40:28.741873: Current learning rate: 0.00473 
+2024-09-15 03:44:33.887509: train_loss -0.8759 
+2024-09-15 03:44:33.887659: val_loss -0.6941 
+2024-09-15 03:44:33.887715: Pseudo dice [0.7033, 0.8258] 
+2024-09-15 03:44:33.887764: Epoch time: 245.15 s 
+2024-09-15 03:44:34.836054:  
+2024-09-15 03:44:34.836278: Epoch 566 
+2024-09-15 03:44:34.836360: Current learning rate: 0.00472 
+2024-09-15 03:48:39.780891: train_loss -0.8816 
+2024-09-15 03:48:39.781031: val_loss -0.7123 
+2024-09-15 03:48:39.781081: Pseudo dice [0.6975, 0.8456] 
+2024-09-15 03:48:39.781131: Epoch time: 244.95 s 
+2024-09-15 03:48:40.726096:  
+2024-09-15 03:48:40.726310: Epoch 567 
+2024-09-15 03:48:40.726395: Current learning rate: 0.00471 
+2024-09-15 03:52:45.572580: train_loss -0.8824 
+2024-09-15 03:52:45.572716: val_loss -0.6711 
+2024-09-15 03:52:45.572765: Pseudo dice [0.6798, 0.8142] 
+2024-09-15 03:52:45.572816: Epoch time: 244.85 s 
+2024-09-15 03:52:46.517369:  
+2024-09-15 03:52:46.517573: Epoch 568 
+2024-09-15 03:52:46.517657: Current learning rate: 0.0047 
+2024-09-15 03:56:51.343992: train_loss -0.8794 
+2024-09-15 03:56:51.344129: val_loss -0.6895 
+2024-09-15 03:56:51.344178: Pseudo dice [0.7075, 0.8353] 
+2024-09-15 03:56:51.344229: Epoch time: 244.83 s 
+2024-09-15 03:56:52.296450:  
+2024-09-15 03:56:52.296646: Epoch 569 
+2024-09-15 03:56:52.296730: Current learning rate: 0.00469 
+2024-09-15 04:00:57.033693: train_loss -0.8808 
+2024-09-15 04:00:57.033832: val_loss -0.6888 
+2024-09-15 04:00:57.033881: Pseudo dice [0.6925, 0.8122] 
+2024-09-15 04:00:57.033930: Epoch time: 244.74 s 
+2024-09-15 04:00:57.969481:  
+2024-09-15 04:00:57.969663: Epoch 570 
+2024-09-15 04:00:57.969745: Current learning rate: 0.00468 
+2024-09-15 04:05:02.822924: train_loss -0.8726 
+2024-09-15 04:05:02.823060: val_loss -0.682 
+2024-09-15 04:05:02.823110: Pseudo dice [0.6777, 0.8328] 
+2024-09-15 04:05:02.823162: Epoch time: 244.86 s 
+2024-09-15 04:05:03.760246:  
+2024-09-15 04:05:03.760439: Epoch 571 
+2024-09-15 04:05:03.760542: Current learning rate: 0.00467 
+2024-09-15 04:09:08.409125: train_loss -0.8621 
+2024-09-15 04:09:08.409289: val_loss -0.7128 
+2024-09-15 04:09:08.409375: Pseudo dice [0.7246, 0.8416] 
+2024-09-15 04:09:08.409442: Epoch time: 244.65 s 
+2024-09-15 04:09:09.352488:  
+2024-09-15 04:09:09.352679: Epoch 572 
+2024-09-15 04:09:09.352759: Current learning rate: 0.00466 
+2024-09-15 04:13:14.109896: train_loss -0.8702 
+2024-09-15 04:13:14.110035: val_loss -0.7162 
+2024-09-15 04:13:14.110085: Pseudo dice [0.7385, 0.833] 
+2024-09-15 04:13:14.110134: Epoch time: 244.76 s 
+2024-09-15 04:13:15.070500:  
+2024-09-15 04:13:15.070653: Epoch 573 
+2024-09-15 04:13:15.070735: Current learning rate: 0.00465 
+2024-09-15 04:17:19.799973: train_loss -0.8742 
+2024-09-15 04:17:19.800108: val_loss -0.6994 
+2024-09-15 04:17:19.800158: Pseudo dice [0.682, 0.8232] 
+2024-09-15 04:17:19.800206: Epoch time: 244.73 s 
+2024-09-15 04:17:20.767838:  
+2024-09-15 04:17:20.768016: Epoch 574 
+2024-09-15 04:17:20.768128: Current learning rate: 0.00464 
+2024-09-15 04:21:25.498544: train_loss -0.8724 
+2024-09-15 04:21:25.498699: val_loss -0.6991 
+2024-09-15 04:21:25.498765: Pseudo dice [0.7014, 0.8238] 
+2024-09-15 04:21:25.498815: Epoch time: 244.73 s 
+2024-09-15 04:21:26.473289:  
+2024-09-15 04:21:26.473482: Epoch 575 
+2024-09-15 04:21:26.473565: Current learning rate: 0.00463 
+2024-09-15 04:25:31.242980: train_loss -0.8767 
+2024-09-15 04:25:31.243118: val_loss -0.6991 
+2024-09-15 04:25:31.243167: Pseudo dice [0.6968, 0.8079] 
+2024-09-15 04:25:31.243215: Epoch time: 244.77 s 
+2024-09-15 04:25:32.204639:  
+2024-09-15 04:25:32.204856: Epoch 576 
+2024-09-15 04:25:32.204935: Current learning rate: 0.00462 
+2024-09-15 04:29:37.016272: train_loss -0.8653 
+2024-09-15 04:29:37.016423: val_loss -0.6934 
+2024-09-15 04:29:37.016474: Pseudo dice [0.6864, 0.8187] 
+2024-09-15 04:29:37.016535: Epoch time: 244.81 s 
+2024-09-15 04:29:37.970656:  
+2024-09-15 04:29:37.970811: Epoch 577 
+2024-09-15 04:29:37.970892: Current learning rate: 0.00461 
+2024-09-15 04:33:42.738057: train_loss -0.8752 
+2024-09-15 04:33:42.738194: val_loss -0.6922 
+2024-09-15 04:33:42.738244: Pseudo dice [0.6783, 0.8187] 
+2024-09-15 04:33:42.738293: Epoch time: 244.77 s 
+2024-09-15 04:33:43.706393:  
+2024-09-15 04:33:43.706597: Epoch 578 
+2024-09-15 04:33:43.706700: Current learning rate: 0.0046 
+2024-09-15 04:37:48.463459: train_loss -0.877 
+2024-09-15 04:37:48.463598: val_loss -0.7185 
+2024-09-15 04:37:48.463648: Pseudo dice [0.7342, 0.8298] 
+2024-09-15 04:37:48.463699: Epoch time: 244.76 s 
+2024-09-15 04:37:49.438265:  
+2024-09-15 04:37:49.438427: Epoch 579 
+2024-09-15 04:37:49.438508: Current learning rate: 0.00459 
+2024-09-15 04:41:54.349682: train_loss -0.8675 
+2024-09-15 04:41:54.349818: val_loss -0.7136 
+2024-09-15 04:41:54.349872: Pseudo dice [0.7223, 0.837] 
+2024-09-15 04:41:54.349921: Epoch time: 244.91 s 
+2024-09-15 04:41:55.288000:  
+2024-09-15 04:41:55.288195: Epoch 580 
+2024-09-15 04:41:55.288276: Current learning rate: 0.00458 
+2024-09-15 04:46:00.147492: train_loss -0.8694 
+2024-09-15 04:46:00.147627: val_loss -0.7206 
+2024-09-15 04:46:00.147676: Pseudo dice [0.7312, 0.8361] 
+2024-09-15 04:46:00.147725: Epoch time: 244.86 s 
+2024-09-15 04:46:01.108455:  
+2024-09-15 04:46:01.108671: Epoch 581 
+2024-09-15 04:46:01.108749: Current learning rate: 0.00457 
+2024-09-15 04:50:05.852841: train_loss -0.8594 
+2024-09-15 04:50:05.853005: val_loss -0.7108 
+2024-09-15 04:50:05.853093: Pseudo dice [0.6961, 0.8359] 
+2024-09-15 04:50:05.853145: Epoch time: 244.75 s 
+2024-09-15 04:50:06.817355:  
+2024-09-15 04:50:06.817501: Epoch 582 
+2024-09-15 04:50:06.817582: Current learning rate: 0.00456 
+2024-09-15 04:54:11.695097: train_loss -0.8641 
+2024-09-15 04:54:11.695233: val_loss -0.7092 
+2024-09-15 04:54:11.695395: Pseudo dice [0.6969, 0.8382] 
+2024-09-15 04:54:11.695513: Epoch time: 244.88 s 
+2024-09-15 04:54:12.645369:  
+2024-09-15 04:54:12.645601: Epoch 583 
+2024-09-15 04:54:12.645688: Current learning rate: 0.00455 
+2024-09-15 04:58:17.370693: train_loss -0.8735 
+2024-09-15 04:58:17.370831: val_loss -0.7153 
+2024-09-15 04:58:17.370879: Pseudo dice [0.7015, 0.841] 
+2024-09-15 04:58:17.370929: Epoch time: 244.73 s 
+2024-09-15 04:58:18.320441:  
+2024-09-15 04:58:18.320626: Epoch 584 
+2024-09-15 04:58:18.320711: Current learning rate: 0.00454 
+2024-09-15 05:02:23.089924: train_loss -0.8702 
+2024-09-15 05:02:23.090062: val_loss -0.7086 
+2024-09-15 05:02:23.090111: Pseudo dice [0.6989, 0.8253] 
+2024-09-15 05:02:23.090161: Epoch time: 244.77 s 
+2024-09-15 05:02:24.041455:  
+2024-09-15 05:02:24.041707: Epoch 585 
+2024-09-15 05:02:24.041792: Current learning rate: 0.00453 
+2024-09-15 05:06:28.870920: train_loss -0.8715 
+2024-09-15 05:06:28.871058: val_loss -0.6921 
+2024-09-15 05:06:28.871108: Pseudo dice [0.6724, 0.8291] 
+2024-09-15 05:06:28.871158: Epoch time: 244.83 s 
+2024-09-15 05:06:29.836854:  
+2024-09-15 05:06:29.837026: Epoch 586 
+2024-09-15 05:06:29.837147: Current learning rate: 0.00452 
+2024-09-15 05:10:35.222199: train_loss -0.87 
+2024-09-15 05:10:35.222351: val_loss -0.7104 
+2024-09-15 05:10:35.222405: Pseudo dice [0.7172, 0.8306] 
+2024-09-15 05:10:35.222455: Epoch time: 245.39 s 
+2024-09-15 05:10:36.177824:  
+2024-09-15 05:10:36.178051: Epoch 587 
+2024-09-15 05:10:36.178133: Current learning rate: 0.00451 
+2024-09-15 05:14:40.882407: train_loss -0.8727 
+2024-09-15 05:14:40.882562: val_loss -0.7016 
+2024-09-15 05:14:40.882616: Pseudo dice [0.714, 0.8205] 
+2024-09-15 05:14:40.882668: Epoch time: 244.71 s 
+2024-09-15 05:14:41.837247:  
+2024-09-15 05:14:41.837461: Epoch 588 
+2024-09-15 05:14:41.837542: Current learning rate: 0.0045 
+2024-09-15 05:18:46.648473: train_loss -0.8744 
+2024-09-15 05:18:46.648611: val_loss -0.702 
+2024-09-15 05:18:46.648661: Pseudo dice [0.6916, 0.8218] 
+2024-09-15 05:18:46.648710: Epoch time: 244.81 s 
+2024-09-15 05:18:47.611460:  
+2024-09-15 05:18:47.611627: Epoch 589 
+2024-09-15 05:18:47.611708: Current learning rate: 0.00449 
+2024-09-15 05:22:52.571780: train_loss -0.8642 
+2024-09-15 05:22:52.571925: val_loss -0.7206 
+2024-09-15 05:22:52.571975: Pseudo dice [0.7007, 0.8393] 
+2024-09-15 05:22:52.572024: Epoch time: 244.96 s 
+2024-09-15 05:22:53.530952:  
+2024-09-15 05:22:53.531168: Epoch 590 
+2024-09-15 05:22:53.531250: Current learning rate: 0.00448 
+2024-09-15 05:26:58.472422: train_loss -0.874 
+2024-09-15 05:26:58.472582: val_loss -0.7013 
+2024-09-15 05:26:58.472633: Pseudo dice [0.6963, 0.8265] 
+2024-09-15 05:26:58.472682: Epoch time: 244.94 s 
+2024-09-15 05:26:59.420074:  
+2024-09-15 05:26:59.420281: Epoch 591 
+2024-09-15 05:26:59.420365: Current learning rate: 0.00447 
+2024-09-15 05:31:04.365436: train_loss -0.8758 
+2024-09-15 05:31:04.365576: val_loss -0.6624 
+2024-09-15 05:31:04.365631: Pseudo dice [0.6264, 0.8353] 
+2024-09-15 05:31:04.365681: Epoch time: 244.95 s 
+2024-09-15 05:31:05.355454:  
+2024-09-15 05:31:05.355639: Epoch 592 
+2024-09-15 05:31:05.355723: Current learning rate: 0.00446 
+2024-09-15 05:35:10.227593: train_loss -0.8711 
+2024-09-15 05:35:10.227727: val_loss -0.7211 
+2024-09-15 05:35:10.227777: Pseudo dice [0.7201, 0.8355] 
+2024-09-15 05:35:10.227834: Epoch time: 244.87 s 
+2024-09-15 05:35:11.182983:  
+2024-09-15 05:35:11.183160: Epoch 593 
+2024-09-15 05:35:11.183240: Current learning rate: 0.00445 
+2024-09-15 05:39:16.083025: train_loss -0.8784 
+2024-09-15 05:39:16.083161: val_loss -0.6996 
+2024-09-15 05:39:16.083210: Pseudo dice [0.6892, 0.8503] 
+2024-09-15 05:39:16.083261: Epoch time: 244.9 s 
+2024-09-15 05:39:17.030817:  
+2024-09-15 05:39:17.031065: Epoch 594 
+2024-09-15 05:39:17.031148: Current learning rate: 0.00444 
+2024-09-15 05:43:21.723715: train_loss -0.8795 
+2024-09-15 05:43:21.723861: val_loss -0.7202 
+2024-09-15 05:43:21.723912: Pseudo dice [0.709, 0.846] 
+2024-09-15 05:43:21.723960: Epoch time: 244.69 s 
+2024-09-15 05:43:22.670922:  
+2024-09-15 05:43:22.671111: Epoch 595 
+2024-09-15 05:43:22.671193: Current learning rate: 0.00443 
+2024-09-15 05:47:27.127962: train_loss -0.8773 
+2024-09-15 05:47:27.128119: val_loss -0.7235 
+2024-09-15 05:47:27.128169: Pseudo dice [0.7068, 0.8316] 
+2024-09-15 05:47:27.128220: Epoch time: 244.46 s 
+2024-09-15 05:47:28.086196:  
+2024-09-15 05:47:28.086338: Epoch 596 
+2024-09-15 05:47:28.086417: Current learning rate: 0.00442 
+2024-09-15 05:51:32.413321: train_loss -0.8829 
+2024-09-15 05:51:32.413457: val_loss -0.7099 
+2024-09-15 05:51:32.413507: Pseudo dice [0.6892, 0.8401] 
+2024-09-15 05:51:32.413556: Epoch time: 244.33 s 
+2024-09-15 05:51:33.362817:  
+2024-09-15 05:51:33.362992: Epoch 597 
+2024-09-15 05:51:33.363076: Current learning rate: 0.00441 
+2024-09-15 05:55:37.800725: train_loss -0.8758 
+2024-09-15 05:55:37.800864: val_loss -0.702 
+2024-09-15 05:55:37.800913: Pseudo dice [0.7012, 0.8155] 
+2024-09-15 05:55:37.800963: Epoch time: 244.44 s 
+2024-09-15 05:55:38.755475:  
+2024-09-15 05:55:38.755702: Epoch 598 
+2024-09-15 05:55:38.755785: Current learning rate: 0.0044 
+2024-09-15 05:59:43.272487: train_loss -0.8727 
+2024-09-15 05:59:43.272627: val_loss -0.7203 
+2024-09-15 05:59:43.272677: Pseudo dice [0.7197, 0.8297] 
+2024-09-15 05:59:43.272727: Epoch time: 244.52 s 
+2024-09-15 05:59:44.216877:  
+2024-09-15 05:59:44.217101: Epoch 599 
+2024-09-15 05:59:44.217186: Current learning rate: 0.00439 
+2024-09-15 06:03:48.632398: train_loss -0.8787 
+2024-09-15 06:03:48.632571: val_loss -0.7257 
+2024-09-15 06:03:48.632641: Pseudo dice [0.7158, 0.836] 
+2024-09-15 06:03:48.632715: Epoch time: 244.42 s 
+2024-09-15 06:03:52.599897:  
+2024-09-15 06:03:52.600055: Epoch 600 
+2024-09-15 06:03:52.600135: Current learning rate: 0.00438 
+2024-09-15 06:07:56.951029: train_loss -0.8778 
+2024-09-15 06:07:56.951167: val_loss -0.6918 
+2024-09-15 06:07:56.951216: Pseudo dice [0.6786, 0.8144] 
+2024-09-15 06:07:56.951267: Epoch time: 244.35 s 
+2024-09-15 06:07:57.903291:  
+2024-09-15 06:07:57.903453: Epoch 601 
+2024-09-15 06:07:57.903532: Current learning rate: 0.00437 
+2024-09-15 06:12:02.319175: train_loss -0.874 
+2024-09-15 06:12:02.319316: val_loss -0.6653 
+2024-09-15 06:12:02.319380: Pseudo dice [0.6618, 0.8171] 
+2024-09-15 06:12:02.319429: Epoch time: 244.42 s 
+2024-09-15 06:12:03.285328:  
+2024-09-15 06:12:03.285515: Epoch 602 
+2024-09-15 06:12:03.285595: Current learning rate: 0.00436 
+2024-09-15 06:16:07.953961: train_loss -0.8449 
+2024-09-15 06:16:07.954103: val_loss -0.698 
+2024-09-15 06:16:07.954151: Pseudo dice [0.6878, 0.8323] 
+2024-09-15 06:16:07.954201: Epoch time: 244.67 s 
+2024-09-15 06:16:08.897454:  
+2024-09-15 06:16:08.897613: Epoch 603 
+2024-09-15 06:16:08.897695: Current learning rate: 0.00435 
+2024-09-15 06:20:13.580056: train_loss -0.8536 
+2024-09-15 06:20:13.580192: val_loss -0.7209 
+2024-09-15 06:20:13.580241: Pseudo dice [0.7282, 0.8327] 
+2024-09-15 06:20:13.580291: Epoch time: 244.68 s 
+2024-09-15 06:20:14.553518:  
+2024-09-15 06:20:14.553668: Epoch 604 
+2024-09-15 06:20:14.553749: Current learning rate: 0.00434 
+2024-09-15 06:24:19.355474: train_loss -0.8597 
+2024-09-15 06:24:19.355610: val_loss -0.6849 
+2024-09-15 06:24:19.355658: Pseudo dice [0.6492, 0.8214] 
+2024-09-15 06:24:19.355708: Epoch time: 244.8 s 
+2024-09-15 06:24:20.301060:  
+2024-09-15 06:24:20.301222: Epoch 605 
+2024-09-15 06:24:20.301306: Current learning rate: 0.00433 
+2024-09-15 06:28:24.874786: train_loss -0.8663 
+2024-09-15 06:28:24.874926: val_loss -0.6913 
+2024-09-15 06:28:24.874975: Pseudo dice [0.664, 0.8404] 
+2024-09-15 06:28:24.875026: Epoch time: 244.58 s 
+2024-09-15 06:28:25.824289:  
+2024-09-15 06:28:25.824473: Epoch 606 
+2024-09-15 06:28:25.824555: Current learning rate: 0.00432 
+2024-09-15 06:32:30.356787: train_loss -0.8664 
+2024-09-15 06:32:30.356921: val_loss -0.6646 
+2024-09-15 06:32:30.356971: Pseudo dice [0.7007, 0.8266] 
+2024-09-15 06:32:30.357020: Epoch time: 244.53 s 
+2024-09-15 06:32:31.320349:  
+2024-09-15 06:32:31.320537: Epoch 607 
+2024-09-15 06:32:31.320621: Current learning rate: 0.00431 
+2024-09-15 06:36:35.727513: train_loss -0.865 
+2024-09-15 06:36:35.727667: val_loss -0.6808 
+2024-09-15 06:36:35.727717: Pseudo dice [0.6874, 0.8159] 
+2024-09-15 06:36:35.727768: Epoch time: 244.41 s 
+2024-09-15 06:36:36.686146:  
+2024-09-15 06:36:36.686340: Epoch 608 
+2024-09-15 06:36:36.686422: Current learning rate: 0.0043 
+2024-09-15 06:40:41.243942: train_loss -0.8661 
+2024-09-15 06:40:41.244080: val_loss -0.6826 
+2024-09-15 06:40:41.244178: Pseudo dice [0.6875, 0.8235] 
+2024-09-15 06:40:41.244230: Epoch time: 244.56 s 
+2024-09-15 06:40:42.202662:  
+2024-09-15 06:40:42.202826: Epoch 609 
+2024-09-15 06:40:42.202909: Current learning rate: 0.00429 
+2024-09-15 06:44:47.434252: train_loss -0.8645 
+2024-09-15 06:44:47.434390: val_loss -0.6849 
+2024-09-15 06:44:47.434438: Pseudo dice [0.6596, 0.8371] 
+2024-09-15 06:44:47.434486: Epoch time: 245.23 s 
+2024-09-15 06:44:48.388544:  
+2024-09-15 06:44:48.388698: Epoch 610 
+2024-09-15 06:44:48.388778: Current learning rate: 0.00429 
+2024-09-15 06:48:53.013488: train_loss -0.8628 
+2024-09-15 06:48:53.013626: val_loss -0.6818 
+2024-09-15 06:48:53.013675: Pseudo dice [0.6906, 0.8124] 
+2024-09-15 06:48:53.013724: Epoch time: 244.63 s 
+2024-09-15 06:48:53.964462:  
+2024-09-15 06:48:53.964667: Epoch 611 
+2024-09-15 06:48:53.964767: Current learning rate: 0.00428 
+2024-09-15 06:52:58.518080: train_loss -0.8603 
+2024-09-15 06:52:58.518215: val_loss -0.674 
+2024-09-15 06:52:58.518264: Pseudo dice [0.6745, 0.8207] 
+2024-09-15 06:52:58.518313: Epoch time: 244.56 s 
+2024-09-15 06:52:59.477153:  
+2024-09-15 06:52:59.477427: Epoch 612 
+2024-09-15 06:52:59.477549: Current learning rate: 0.00427 
+2024-09-15 06:57:04.039504: train_loss -0.8526 
+2024-09-15 06:57:04.039638: val_loss -0.6884 
+2024-09-15 06:57:04.039687: Pseudo dice [0.6912, 0.819] 
+2024-09-15 06:57:04.039739: Epoch time: 244.57 s 
+2024-09-15 06:57:05.018604:  
+2024-09-15 06:57:05.018863: Epoch 613 
+2024-09-15 06:57:05.018947: Current learning rate: 0.00426 
+2024-09-15 07:01:09.565595: train_loss -0.8643 
+2024-09-15 07:01:09.565749: val_loss -0.6754 
+2024-09-15 07:01:09.565800: Pseudo dice [0.6664, 0.8187] 
+2024-09-15 07:01:09.565850: Epoch time: 244.55 s 
+2024-09-15 07:01:10.525492:  
+2024-09-15 07:01:10.525680: Epoch 614 
+2024-09-15 07:01:10.525761: Current learning rate: 0.00425 
+2024-09-15 07:05:15.220908: train_loss -0.8605 
+2024-09-15 07:05:15.221042: val_loss -0.6686 
+2024-09-15 07:05:15.221123: Pseudo dice [0.6898, 0.8139] 
+2024-09-15 07:05:15.221230: Epoch time: 244.7 s 
+2024-09-15 07:05:16.164737:  
+2024-09-15 07:05:16.164954: Epoch 615 
+2024-09-15 07:05:16.165037: Current learning rate: 0.00424 
+2024-09-15 07:09:20.817154: train_loss -0.8623 
+2024-09-15 07:09:20.817287: val_loss -0.688 
+2024-09-15 07:09:20.817336: Pseudo dice [0.6924, 0.8233] 
+2024-09-15 07:09:20.817438: Epoch time: 244.65 s 
+2024-09-15 07:09:21.770664:  
+2024-09-15 07:09:21.770904: Epoch 616 
+2024-09-15 07:09:21.770990: Current learning rate: 0.00423 
+2024-09-15 07:13:26.411800: train_loss -0.8636 
+2024-09-15 07:13:26.411943: val_loss -0.6699 
+2024-09-15 07:13:26.411994: Pseudo dice [0.6613, 0.8227] 
+2024-09-15 07:13:26.412043: Epoch time: 244.64 s 
+2024-09-15 07:13:27.389198:  
+2024-09-15 07:13:27.389414: Epoch 617 
+2024-09-15 07:13:27.389541: Current learning rate: 0.00422 
+2024-09-15 07:17:32.094271: train_loss -0.8695 
+2024-09-15 07:17:32.094408: val_loss -0.6894 
+2024-09-15 07:17:32.094458: Pseudo dice [0.6825, 0.8268] 
+2024-09-15 07:17:32.094507: Epoch time: 244.71 s 
+2024-09-15 07:17:33.069611:  
+2024-09-15 07:17:33.069811: Epoch 618 
+2024-09-15 07:17:33.069892: Current learning rate: 0.00421 
+2024-09-15 07:21:37.760371: train_loss -0.8714 
+2024-09-15 07:21:37.760509: val_loss -0.6737 
+2024-09-15 07:21:37.760558: Pseudo dice [0.698, 0.8168] 
+2024-09-15 07:21:37.760608: Epoch time: 244.69 s 
+2024-09-15 07:21:38.727100:  
+2024-09-15 07:21:38.727294: Epoch 619 
+2024-09-15 07:21:38.727379: Current learning rate: 0.0042 
+2024-09-15 07:25:43.451144: train_loss -0.8804 
+2024-09-15 07:25:43.451283: val_loss -0.6802 
+2024-09-15 07:25:43.451331: Pseudo dice [0.6671, 0.8079] 
+2024-09-15 07:25:43.451383: Epoch time: 244.73 s 
+2024-09-15 07:25:44.415022:  
+2024-09-15 07:25:44.415275: Epoch 620 
+2024-09-15 07:25:44.415357: Current learning rate: 0.00419 
+2024-09-15 07:29:49.149728: train_loss -0.8731 
+2024-09-15 07:29:49.149865: val_loss -0.7007 
+2024-09-15 07:29:49.149914: Pseudo dice [0.7091, 0.8224] 
+2024-09-15 07:29:49.149964: Epoch time: 244.74 s 
+2024-09-15 07:29:50.169726:  
+2024-09-15 07:29:50.169924: Epoch 621 
+2024-09-15 07:29:50.170031: Current learning rate: 0.00418 
+2024-09-15 07:33:54.854171: train_loss -0.8723 
+2024-09-15 07:33:54.854358: val_loss -0.6598 
+2024-09-15 07:33:54.854427: Pseudo dice [0.6465, 0.8243] 
+2024-09-15 07:33:54.854506: Epoch time: 244.69 s 
+2024-09-15 07:33:55.801212:  
+2024-09-15 07:33:55.801404: Epoch 622 
+2024-09-15 07:33:55.801486: Current learning rate: 0.00417 
+2024-09-15 07:38:00.739824: train_loss -0.8725 
+2024-09-15 07:38:00.739963: val_loss -0.6826 
+2024-09-15 07:38:00.740012: Pseudo dice [0.6738, 0.8339] 
+2024-09-15 07:38:00.740062: Epoch time: 244.94 s 
+2024-09-15 07:38:01.708874:  
+2024-09-15 07:38:01.709073: Epoch 623 
+2024-09-15 07:38:01.709156: Current learning rate: 0.00416 
+2024-09-15 07:42:06.517382: train_loss -0.8746 
+2024-09-15 07:42:06.517516: val_loss -0.7281 
+2024-09-15 07:42:06.517565: Pseudo dice [0.7205, 0.8394] 
+2024-09-15 07:42:06.517613: Epoch time: 244.81 s 
+2024-09-15 07:42:07.467376:  
+2024-09-15 07:42:07.467554: Epoch 624 
+2024-09-15 07:42:07.467638: Current learning rate: 0.00415 
+2024-09-15 07:46:12.228089: train_loss -0.8818 
+2024-09-15 07:46:12.228229: val_loss -0.7158 
+2024-09-15 07:46:12.228278: Pseudo dice [0.6866, 0.846] 
+2024-09-15 07:46:12.228327: Epoch time: 244.76 s 
+2024-09-15 07:46:13.203147:  
+2024-09-15 07:46:13.203286: Epoch 625 
+2024-09-15 07:46:13.203366: Current learning rate: 0.00414 
+2024-09-15 07:50:17.958973: train_loss -0.8689 
+2024-09-15 07:50:17.959120: val_loss -0.7067 
+2024-09-15 07:50:17.959221: Pseudo dice [0.689, 0.8465] 
+2024-09-15 07:50:17.959271: Epoch time: 244.76 s 
+2024-09-15 07:50:18.926013:  
+2024-09-15 07:50:18.926190: Epoch 626 
+2024-09-15 07:50:18.926300: Current learning rate: 0.00413 
+2024-09-15 07:54:23.577117: train_loss -0.8801 
+2024-09-15 07:54:23.577262: val_loss -0.7001 
+2024-09-15 07:54:23.577311: Pseudo dice [0.673, 0.8438] 
+2024-09-15 07:54:23.577359: Epoch time: 244.65 s 
+2024-09-15 07:54:24.530158:  
+2024-09-15 07:54:24.530327: Epoch 627 
+2024-09-15 07:54:24.530406: Current learning rate: 0.00412 
+2024-09-15 07:58:29.202064: train_loss -0.8819 
+2024-09-15 07:58:29.202319: val_loss -0.7057 
+2024-09-15 07:58:29.202371: Pseudo dice [0.7145, 0.8364] 
+2024-09-15 07:58:29.202421: Epoch time: 244.67 s 
+2024-09-15 07:58:30.151672:  
+2024-09-15 07:58:30.151836: Epoch 628 
+2024-09-15 07:58:30.151922: Current learning rate: 0.00411 
+2024-09-15 08:02:34.731043: train_loss -0.8845 
+2024-09-15 08:02:34.731182: val_loss -0.7053 
+2024-09-15 08:02:34.731231: Pseudo dice [0.7035, 0.8446] 
+2024-09-15 08:02:34.731280: Epoch time: 244.58 s 
+2024-09-15 08:02:35.686318:  
+2024-09-15 08:02:35.686482: Epoch 629 
+2024-09-15 08:02:35.686563: Current learning rate: 0.0041 
+2024-09-15 08:06:40.340359: train_loss -0.8841 
+2024-09-15 08:06:40.340496: val_loss -0.6967 
+2024-09-15 08:06:40.340548: Pseudo dice [0.7052, 0.8213] 
+2024-09-15 08:06:40.340598: Epoch time: 244.66 s 
+2024-09-15 08:06:41.305350:  
+2024-09-15 08:06:41.305537: Epoch 630 
+2024-09-15 08:06:41.305620: Current learning rate: 0.00409 
+2024-09-15 08:10:45.812660: train_loss -0.885 
+2024-09-15 08:10:45.812796: val_loss -0.6877 
+2024-09-15 08:10:45.812847: Pseudo dice [0.6582, 0.8186] 
+2024-09-15 08:10:45.812896: Epoch time: 244.51 s 
+2024-09-15 08:10:46.771311:  
+2024-09-15 08:10:46.771495: Epoch 631 
+2024-09-15 08:10:46.771580: Current learning rate: 0.00408 
+2024-09-15 08:14:51.513652: train_loss -0.8794 
+2024-09-15 08:14:51.513804: val_loss -0.7226 
+2024-09-15 08:14:51.513855: Pseudo dice [0.7147, 0.844] 
+2024-09-15 08:14:51.513904: Epoch time: 244.74 s 
+2024-09-15 08:14:52.480100:  
+2024-09-15 08:14:52.480247: Epoch 632 
+2024-09-15 08:14:52.480327: Current learning rate: 0.00407 
+2024-09-15 08:18:57.917302: train_loss -0.8799 
+2024-09-15 08:18:57.917443: val_loss -0.6674 
+2024-09-15 08:18:57.917491: Pseudo dice [0.666, 0.819] 
+2024-09-15 08:18:57.917542: Epoch time: 245.44 s 
+2024-09-15 08:18:58.873262:  
+2024-09-15 08:18:58.873503: Epoch 633 
+2024-09-15 08:18:58.873600: Current learning rate: 0.00406 
+2024-09-15 08:23:03.794452: train_loss -0.8854 
+2024-09-15 08:23:03.794615: val_loss -0.6912 
+2024-09-15 08:23:03.794668: Pseudo dice [0.7141, 0.8268] 
+2024-09-15 08:23:03.794719: Epoch time: 244.92 s 
+2024-09-15 08:23:04.755208:  
+2024-09-15 08:23:04.755381: Epoch 634 
+2024-09-15 08:23:04.755497: Current learning rate: 0.00405 
+2024-09-15 08:27:09.667998: train_loss -0.8821 
+2024-09-15 08:27:09.668136: val_loss -0.711 
+2024-09-15 08:27:09.668185: Pseudo dice [0.7024, 0.8378] 
+2024-09-15 08:27:09.668234: Epoch time: 244.91 s 
+2024-09-15 08:27:10.626595:  
+2024-09-15 08:27:10.626838: Epoch 635 
+2024-09-15 08:27:10.626920: Current learning rate: 0.00404 
+2024-09-15 08:31:15.449744: train_loss -0.8672 
+2024-09-15 08:31:15.449916: val_loss -0.6916 
+2024-09-15 08:31:15.449965: Pseudo dice [0.7067, 0.8332] 
+2024-09-15 08:31:15.450016: Epoch time: 244.82 s 
+2024-09-15 08:31:16.406289:  
+2024-09-15 08:31:16.406504: Epoch 636 
+2024-09-15 08:31:16.406587: Current learning rate: 0.00403 
+2024-09-15 08:35:21.121325: train_loss -0.8529 
+2024-09-15 08:35:21.121467: val_loss -0.6926 
+2024-09-15 08:35:21.121516: Pseudo dice [0.6946, 0.8328] 
+2024-09-15 08:35:21.121569: Epoch time: 244.72 s 
+2024-09-15 08:35:22.060600:  
+2024-09-15 08:35:22.060795: Epoch 637 
+2024-09-15 08:35:22.060877: Current learning rate: 0.00402 
+2024-09-15 08:39:26.723209: train_loss -0.8649 
+2024-09-15 08:39:26.723341: val_loss -0.6586 
+2024-09-15 08:39:26.723390: Pseudo dice [0.6494, 0.8268] 
+2024-09-15 08:39:26.723440: Epoch time: 244.66 s 
+2024-09-15 08:39:27.698406:  
+2024-09-15 08:39:27.698618: Epoch 638 
+2024-09-15 08:39:27.698699: Current learning rate: 0.00401 
+2024-09-15 08:43:32.223481: train_loss -0.8635 
+2024-09-15 08:43:32.223650: val_loss -0.698 
+2024-09-15 08:43:32.223700: Pseudo dice [0.6964, 0.836] 
+2024-09-15 08:43:32.223761: Epoch time: 244.53 s 
+2024-09-15 08:43:33.169410:  
+2024-09-15 08:43:33.169590: Epoch 639 
+2024-09-15 08:43:33.169665: Current learning rate: 0.004 
+2024-09-15 08:47:37.606715: train_loss -0.8677 
+2024-09-15 08:47:37.606869: val_loss -0.6254 
+2024-09-15 08:47:37.606919: Pseudo dice [0.5764, 0.8321] 
+2024-09-15 08:47:37.606968: Epoch time: 244.44 s 
+2024-09-15 08:47:38.570991:  
+2024-09-15 08:47:38.571181: Epoch 640 
+2024-09-15 08:47:38.571278: Current learning rate: 0.00399 
+2024-09-15 08:51:43.102123: train_loss -0.8582 
+2024-09-15 08:51:43.102318: val_loss -0.6941 
+2024-09-15 08:51:43.102410: Pseudo dice [0.7091, 0.8183] 
+2024-09-15 08:51:43.102499: Epoch time: 244.53 s 
+2024-09-15 08:51:44.064130:  
+2024-09-15 08:51:44.064341: Epoch 641 
+2024-09-15 08:51:44.064424: Current learning rate: 0.00398 
+2024-09-15 08:55:48.727705: train_loss -0.839 
+2024-09-15 08:55:48.727861: val_loss -0.6871 
+2024-09-15 08:55:48.727912: Pseudo dice [0.6888, 0.8194] 
+2024-09-15 08:55:48.727961: Epoch time: 244.67 s 
+2024-09-15 08:55:49.678811:  
+2024-09-15 08:55:49.679053: Epoch 642 
+2024-09-15 08:55:49.679147: Current learning rate: 0.00397 
+2024-09-15 08:59:54.178080: train_loss -0.8583 
+2024-09-15 08:59:54.178219: val_loss -0.6728 
+2024-09-15 08:59:54.178268: Pseudo dice [0.6967, 0.8233] 
+2024-09-15 08:59:54.178351: Epoch time: 244.5 s 
+2024-09-15 08:59:55.155592:  
+2024-09-15 08:59:55.155790: Epoch 643 
+2024-09-15 08:59:55.155923: Current learning rate: 0.00396 
+2024-09-15 09:03:59.522194: train_loss -0.8649 
+2024-09-15 09:03:59.522351: val_loss -0.6835 
+2024-09-15 09:03:59.522401: Pseudo dice [0.7074, 0.8287] 
+2024-09-15 09:03:59.522450: Epoch time: 244.37 s 
+2024-09-15 09:04:00.472209:  
+2024-09-15 09:04:00.472380: Epoch 644 
+2024-09-15 09:04:00.472461: Current learning rate: 0.00395 
+2024-09-15 09:08:04.763482: train_loss -0.8668 
+2024-09-15 09:08:04.763618: val_loss -0.6744 
+2024-09-15 09:08:04.763668: Pseudo dice [0.6882, 0.8233] 
+2024-09-15 09:08:04.763716: Epoch time: 244.29 s 
+2024-09-15 09:08:05.752656:  
+2024-09-15 09:08:05.752885: Epoch 645 
+2024-09-15 09:08:05.752970: Current learning rate: 0.00394 
+2024-09-15 09:12:10.020569: train_loss -0.8687 
+2024-09-15 09:12:10.020705: val_loss -0.6975 
+2024-09-15 09:12:10.020755: Pseudo dice [0.6842, 0.8447] 
+2024-09-15 09:12:10.020804: Epoch time: 244.27 s 
+2024-09-15 09:12:10.962712:  
+2024-09-15 09:12:10.962885: Epoch 646 
+2024-09-15 09:12:10.962968: Current learning rate: 0.00393 
+2024-09-15 09:16:15.170826: train_loss -0.8727 
+2024-09-15 09:16:15.170963: val_loss -0.6906 
+2024-09-15 09:16:15.171013: Pseudo dice [0.6986, 0.8061] 
+2024-09-15 09:16:15.171062: Epoch time: 244.21 s 
+2024-09-15 09:16:16.123661:  
+2024-09-15 09:16:16.123852: Epoch 647 
+2024-09-15 09:16:16.123931: Current learning rate: 0.00392 
+2024-09-15 09:20:20.360057: train_loss -0.8693 
+2024-09-15 09:20:20.360217: val_loss -0.6744 
+2024-09-15 09:20:20.360271: Pseudo dice [0.6971, 0.8181] 
+2024-09-15 09:20:20.360321: Epoch time: 244.24 s 
+2024-09-15 09:20:21.309593:  
+2024-09-15 09:20:21.309747: Epoch 648 
+2024-09-15 09:20:21.309830: Current learning rate: 0.00391 
+2024-09-15 09:24:25.376519: train_loss -0.8757 
+2024-09-15 09:24:25.376688: val_loss -0.6987 
+2024-09-15 09:24:25.376740: Pseudo dice [0.7229, 0.8184] 
+2024-09-15 09:24:25.376788: Epoch time: 244.07 s 
+2024-09-15 09:24:26.325656:  
+2024-09-15 09:24:26.325841: Epoch 649 
+2024-09-15 09:24:26.325922: Current learning rate: 0.0039 
+2024-09-15 09:28:30.426183: train_loss -0.8757 
+2024-09-15 09:28:30.426319: val_loss -0.687 
+2024-09-15 09:28:30.426368: Pseudo dice [0.7034, 0.8097] 
+2024-09-15 09:28:30.426417: Epoch time: 244.1 s 
+2024-09-15 09:28:34.377510:  
+2024-09-15 09:28:34.377714: Epoch 650 
+2024-09-15 09:28:34.377815: Current learning rate: 0.00389 
+2024-09-15 09:32:38.614457: train_loss -0.8546 
+2024-09-15 09:32:38.614623: val_loss -0.6971 
+2024-09-15 09:32:38.614674: Pseudo dice [0.6869, 0.8205] 
+2024-09-15 09:32:38.614727: Epoch time: 244.24 s 
+2024-09-15 09:32:39.576094:  
+2024-09-15 09:32:39.576277: Epoch 651 
+2024-09-15 09:32:39.576358: Current learning rate: 0.00388 
+2024-09-15 09:36:44.153662: train_loss -0.8602 
+2024-09-15 09:36:44.153800: val_loss -0.6715 
+2024-09-15 09:36:44.153850: Pseudo dice [0.6851, 0.8111] 
+2024-09-15 09:36:44.153898: Epoch time: 244.58 s 
+2024-09-15 09:36:45.117217:  
+2024-09-15 09:36:45.117462: Epoch 652 
+2024-09-15 09:36:45.117547: Current learning rate: 0.00387 
+2024-09-15 09:40:49.644078: train_loss -0.8525 
+2024-09-15 09:40:49.644248: val_loss -0.6997 
+2024-09-15 09:40:49.644298: Pseudo dice [0.7077, 0.822] 
+2024-09-15 09:40:49.644348: Epoch time: 244.53 s 
+2024-09-15 09:40:50.604386:  
+2024-09-15 09:40:50.604552: Epoch 653 
+2024-09-15 09:40:50.604674: Current learning rate: 0.00386 
+2024-09-15 09:44:55.129303: train_loss -0.8475 
+2024-09-15 09:44:55.129458: val_loss -0.6342 
+2024-09-15 09:44:55.129534: Pseudo dice [0.6337, 0.8091] 
+2024-09-15 09:44:55.129584: Epoch time: 244.53 s 
+2024-09-15 09:44:56.094594:  
+2024-09-15 09:44:56.094768: Epoch 654 
+2024-09-15 09:44:56.094847: Current learning rate: 0.00385 
+2024-09-15 09:49:00.538198: train_loss -0.8667 
+2024-09-15 09:49:00.538334: val_loss -0.6248 
+2024-09-15 09:49:00.538385: Pseudo dice [0.6093, 0.8085] 
+2024-09-15 09:49:00.538437: Epoch time: 244.45 s 
+2024-09-15 09:49:02.367326:  
+2024-09-15 09:49:02.367543: Epoch 655 
+2024-09-15 09:49:02.367624: Current learning rate: 0.00384 
+2024-09-15 09:53:06.833097: train_loss -0.8717 
+2024-09-15 09:53:06.833239: val_loss -0.7094 
+2024-09-15 09:53:06.833291: Pseudo dice [0.7437, 0.8268] 
+2024-09-15 09:53:06.833341: Epoch time: 244.47 s 
+2024-09-15 09:53:07.774765:  
+2024-09-15 09:53:07.775009: Epoch 656 
+2024-09-15 09:53:07.775092: Current learning rate: 0.00383 
+2024-09-15 09:57:12.212084: train_loss -0.8752 
+2024-09-15 09:57:12.212222: val_loss -0.6658 
+2024-09-15 09:57:12.212274: Pseudo dice [0.6261, 0.8422] 
+2024-09-15 09:57:12.212324: Epoch time: 244.44 s 
+2024-09-15 09:57:13.210518:  
+2024-09-15 09:57:13.210725: Epoch 657 
+2024-09-15 09:57:13.210811: Current learning rate: 0.00382 
+2024-09-15 10:01:17.626531: train_loss -0.8682 
+2024-09-15 10:01:17.626669: val_loss -0.6748 
+2024-09-15 10:01:17.626719: Pseudo dice [0.6552, 0.836] 
+2024-09-15 10:01:17.626768: Epoch time: 244.42 s 
+2024-09-15 10:01:18.604002:  
+2024-09-15 10:01:18.604223: Epoch 658 
+2024-09-15 10:01:18.604303: Current learning rate: 0.00381 
+2024-09-15 10:05:23.022884: train_loss -0.876 
+2024-09-15 10:05:23.023023: val_loss -0.6308 
+2024-09-15 10:05:23.023077: Pseudo dice [0.5396, 0.8163] 
+2024-09-15 10:05:23.023129: Epoch time: 244.42 s 
+2024-09-15 10:05:23.980223:  
+2024-09-15 10:05:23.980473: Epoch 659 
+2024-09-15 10:05:23.980557: Current learning rate: 0.0038 
+2024-09-15 10:09:28.759866: train_loss -0.8459 
+2024-09-15 10:09:28.760000: val_loss -0.6928 
+2024-09-15 10:09:28.760049: Pseudo dice [0.6829, 0.8268] 
+2024-09-15 10:09:28.760098: Epoch time: 244.78 s 
+2024-09-15 10:09:29.720701:  
+2024-09-15 10:09:29.720862: Epoch 660 
+2024-09-15 10:09:29.720941: Current learning rate: 0.00379 
+2024-09-15 10:13:34.529838: train_loss -0.8554 
+2024-09-15 10:13:34.529973: val_loss -0.699 
+2024-09-15 10:13:34.530021: Pseudo dice [0.669, 0.84] 
+2024-09-15 10:13:34.530070: Epoch time: 244.81 s 
+2024-09-15 10:13:35.499930:  
+2024-09-15 10:13:35.500099: Epoch 661 
+2024-09-15 10:13:35.500180: Current learning rate: 0.00378 
+2024-09-15 10:17:40.161327: train_loss -0.8723 
+2024-09-15 10:17:40.161464: val_loss -0.6955 
+2024-09-15 10:17:40.161514: Pseudo dice [0.6843, 0.823] 
+2024-09-15 10:17:40.161563: Epoch time: 244.66 s 
+2024-09-15 10:17:41.131768:  
+2024-09-15 10:17:41.131969: Epoch 662 
+2024-09-15 10:17:41.132045: Current learning rate: 0.00377 
+2024-09-15 10:21:45.735668: train_loss -0.8775 
+2024-09-15 10:21:45.735813: val_loss -0.7134 
+2024-09-15 10:21:45.735865: Pseudo dice [0.7141, 0.8206] 
+2024-09-15 10:21:45.735913: Epoch time: 244.61 s 
+2024-09-15 10:21:46.706719:  
+2024-09-15 10:21:46.706926: Epoch 663 
+2024-09-15 10:21:46.707008: Current learning rate: 0.00376 
+2024-09-15 10:25:51.368232: train_loss -0.8735 
+2024-09-15 10:25:51.368371: val_loss -0.6886 
+2024-09-15 10:25:51.368434: Pseudo dice [0.7015, 0.8291] 
+2024-09-15 10:25:51.368487: Epoch time: 244.66 s 
+2024-09-15 10:25:52.328982:  
+2024-09-15 10:25:52.329165: Epoch 664 
+2024-09-15 10:25:52.329246: Current learning rate: 0.00375 
+2024-09-15 10:29:57.026497: train_loss -0.8826 
+2024-09-15 10:29:57.026653: val_loss -0.6845 
+2024-09-15 10:29:57.026702: Pseudo dice [0.6763, 0.8274] 
+2024-09-15 10:29:57.026751: Epoch time: 244.7 s 
+2024-09-15 10:29:57.979132:  
+2024-09-15 10:29:57.979312: Epoch 665 
+2024-09-15 10:29:57.979428: Current learning rate: 0.00374 
+2024-09-15 10:34:02.652761: train_loss -0.885 
+2024-09-15 10:34:02.652896: val_loss -0.7006 
+2024-09-15 10:34:02.652946: Pseudo dice [0.6855, 0.829] 
+2024-09-15 10:34:02.653075: Epoch time: 244.68 s 
+2024-09-15 10:34:03.594824:  
+2024-09-15 10:34:03.595037: Epoch 666 
+2024-09-15 10:34:03.595120: Current learning rate: 0.00373 
+2024-09-15 10:38:08.216238: train_loss -0.8839 
+2024-09-15 10:38:08.216387: val_loss -0.6888 
+2024-09-15 10:38:08.216436: Pseudo dice [0.7006, 0.8189] 
+2024-09-15 10:38:08.216484: Epoch time: 244.62 s 
+2024-09-15 10:38:09.168478:  
+2024-09-15 10:38:09.168673: Epoch 667 
+2024-09-15 10:38:09.168751: Current learning rate: 0.00372 
+2024-09-15 10:42:14.097622: train_loss -0.8804 
+2024-09-15 10:42:14.097759: val_loss -0.6983 
+2024-09-15 10:42:14.097813: Pseudo dice [0.6773, 0.8358] 
+2024-09-15 10:42:14.097861: Epoch time: 244.93 s 
+2024-09-15 10:42:15.092980:  
+2024-09-15 10:42:15.093175: Epoch 668 
+2024-09-15 10:42:15.093255: Current learning rate: 0.00371 
+2024-09-15 10:46:19.838199: train_loss -0.8751 
+2024-09-15 10:46:19.838347: val_loss -0.68 
+2024-09-15 10:46:19.838396: Pseudo dice [0.6838, 0.8059] 
+2024-09-15 10:46:19.838445: Epoch time: 244.75 s 
+2024-09-15 10:46:20.799089:  
+2024-09-15 10:46:20.799238: Epoch 669 
+2024-09-15 10:46:20.799317: Current learning rate: 0.0037 
+2024-09-15 10:50:25.426127: train_loss -0.8613 
+2024-09-15 10:50:25.426263: val_loss -0.6868 
+2024-09-15 10:50:25.426311: Pseudo dice [0.6845, 0.8267] 
+2024-09-15 10:50:25.426360: Epoch time: 244.63 s 
+2024-09-15 10:50:26.399890:  
+2024-09-15 10:50:26.400117: Epoch 670 
+2024-09-15 10:50:26.400198: Current learning rate: 0.00369 
+2024-09-15 10:54:30.833684: train_loss -0.8744 
+2024-09-15 10:54:30.833835: val_loss -0.6977 
+2024-09-15 10:54:30.833884: Pseudo dice [0.6977, 0.8193] 
+2024-09-15 10:54:30.833934: Epoch time: 244.44 s 
+2024-09-15 10:54:31.793778:  
+2024-09-15 10:54:31.793987: Epoch 671 
+2024-09-15 10:54:31.794069: Current learning rate: 0.00368 
+2024-09-15 10:58:36.182583: train_loss -0.8805 
+2024-09-15 10:58:36.182717: val_loss -0.6822 
+2024-09-15 10:58:36.182768: Pseudo dice [0.6893, 0.8186] 
+2024-09-15 10:58:36.182820: Epoch time: 244.39 s 
+2024-09-15 10:58:37.143448:  
+2024-09-15 10:58:37.143635: Epoch 672 
+2024-09-15 10:58:37.143717: Current learning rate: 0.00367 
+2024-09-15 11:02:41.438778: train_loss -0.8844 
+2024-09-15 11:02:41.438949: val_loss -0.7168 
+2024-09-15 11:02:41.439005: Pseudo dice [0.6967, 0.8347] 
+2024-09-15 11:02:41.439060: Epoch time: 244.3 s 
+2024-09-15 11:02:42.423104:  
+2024-09-15 11:02:42.423323: Epoch 673 
+2024-09-15 11:02:42.423413: Current learning rate: 0.00366 
+2024-09-15 11:06:46.721900: train_loss -0.8806 
+2024-09-15 11:06:46.722045: val_loss -0.6874 
+2024-09-15 11:06:46.722100: Pseudo dice [0.7023, 0.8245] 
+2024-09-15 11:06:46.722153: Epoch time: 244.3 s 
+2024-09-15 11:06:47.704155:  
+2024-09-15 11:06:47.704302: Epoch 674 
+2024-09-15 11:06:47.704388: Current learning rate: 0.00365 
+2024-09-15 11:10:52.116473: train_loss -0.8833 
+2024-09-15 11:10:52.116621: val_loss -0.7023 
+2024-09-15 11:10:52.116675: Pseudo dice [0.6952, 0.8383] 
+2024-09-15 11:10:52.116728: Epoch time: 244.41 s 
+2024-09-15 11:10:53.083685:  
+2024-09-15 11:10:53.083936: Epoch 675 
+2024-09-15 11:10:53.084048: Current learning rate: 0.00364 
+2024-09-15 11:14:57.532902: train_loss -0.8846 
+2024-09-15 11:14:57.533046: val_loss -0.6917 
+2024-09-15 11:14:57.533101: Pseudo dice [0.7014, 0.8331] 
+2024-09-15 11:14:57.533155: Epoch time: 244.45 s 
+2024-09-15 11:14:58.497350:  
+2024-09-15 11:14:58.497496: Epoch 676 
+2024-09-15 11:14:58.497583: Current learning rate: 0.00363 
+2024-09-15 11:19:02.846166: train_loss -0.8846 
+2024-09-15 11:19:02.846339: val_loss -0.6871 
+2024-09-15 11:19:02.846396: Pseudo dice [0.6643, 0.835] 
+2024-09-15 11:19:02.846450: Epoch time: 244.35 s 
+2024-09-15 11:19:03.831712:  
+2024-09-15 11:19:03.831987: Epoch 677 
+2024-09-15 11:19:03.832077: Current learning rate: 0.00362 
+2024-09-15 11:23:08.366039: train_loss -0.8867 
+2024-09-15 11:23:08.366195: val_loss -0.702 
+2024-09-15 11:23:08.366250: Pseudo dice [0.7085, 0.8394] 
+2024-09-15 11:23:08.366304: Epoch time: 244.54 s 
+2024-09-15 11:23:09.348283:  
+2024-09-15 11:23:09.348515: Epoch 678 
+2024-09-15 11:23:09.348622: Current learning rate: 0.00361 
+2024-09-15 11:27:14.775957: train_loss -0.8814 
+2024-09-15 11:27:14.776126: val_loss -0.7176 
+2024-09-15 11:27:14.776184: Pseudo dice [0.724, 0.841] 
+2024-09-15 11:27:14.776238: Epoch time: 245.43 s 
+2024-09-15 11:27:15.751723:  
+2024-09-15 11:27:15.751985: Epoch 679 
+2024-09-15 11:27:15.752069: Current learning rate: 0.0036 
+2024-09-15 11:31:20.560975: train_loss -0.8808 
+2024-09-15 11:31:20.561126: val_loss -0.6975 
+2024-09-15 11:31:20.561184: Pseudo dice [0.7018, 0.8312] 
+2024-09-15 11:31:20.561239: Epoch time: 244.81 s 
+2024-09-15 11:31:21.541314:  
+2024-09-15 11:31:21.541553: Epoch 680 
+2024-09-15 11:31:21.541663: Current learning rate: 0.00359 
+2024-09-15 11:35:26.303844: train_loss -0.8811 
+2024-09-15 11:35:26.303988: val_loss -0.7012 
+2024-09-15 11:35:26.304044: Pseudo dice [0.7009, 0.8373] 
+2024-09-15 11:35:26.304097: Epoch time: 244.76 s 
+2024-09-15 11:35:27.268561:  
+2024-09-15 11:35:27.268746: Epoch 681 
+2024-09-15 11:35:27.268829: Current learning rate: 0.00358 
+2024-09-15 11:39:32.002578: train_loss -0.8804 
+2024-09-15 11:39:32.002760: val_loss -0.6884 
+2024-09-15 11:39:32.002817: Pseudo dice [0.6863, 0.8337] 
+2024-09-15 11:39:32.002871: Epoch time: 244.74 s 
+2024-09-15 11:39:32.979863:  
+2024-09-15 11:39:32.980045: Epoch 682 
+2024-09-15 11:39:32.980131: Current learning rate: 0.00357 
+2024-09-15 11:43:37.635065: train_loss -0.887 
+2024-09-15 11:43:37.635210: val_loss -0.6996 
+2024-09-15 11:43:37.635267: Pseudo dice [0.6844, 0.8284] 
+2024-09-15 11:43:37.635321: Epoch time: 244.66 s 
+2024-09-15 11:43:38.624612:  
+2024-09-15 11:43:38.624796: Epoch 683 
+2024-09-15 11:43:38.624901: Current learning rate: 0.00356 
+2024-09-15 11:47:43.246902: train_loss -0.8875 
+2024-09-15 11:47:43.247050: val_loss -0.7093 
+2024-09-15 11:47:43.247106: Pseudo dice [0.7064, 0.8351] 
+2024-09-15 11:47:43.247160: Epoch time: 244.62 s 
+2024-09-15 11:47:44.255077:  
+2024-09-15 11:47:44.255223: Epoch 684 
+2024-09-15 11:47:44.255342: Current learning rate: 0.00355 
+2024-09-15 11:51:48.929052: train_loss -0.8886 
+2024-09-15 11:51:48.929196: val_loss -0.7195 
+2024-09-15 11:51:48.929252: Pseudo dice [0.7199, 0.8406] 
+2024-09-15 11:51:48.929307: Epoch time: 244.68 s 
+2024-09-15 11:51:49.906204:  
+2024-09-15 11:51:49.906423: Epoch 685 
+2024-09-15 11:51:49.906507: Current learning rate: 0.00354 
+2024-09-15 11:55:54.730856: train_loss -0.889 
+2024-09-15 11:55:54.731004: val_loss -0.7024 
+2024-09-15 11:55:54.731062: Pseudo dice [0.7034, 0.8323] 
+2024-09-15 11:55:54.731117: Epoch time: 244.83 s 
+2024-09-15 11:55:55.711890:  
+2024-09-15 11:55:55.712141: Epoch 686 
+2024-09-15 11:55:55.712254: Current learning rate: 0.00353 
+2024-09-15 12:00:00.629223: train_loss -0.8835 
+2024-09-15 12:00:00.629430: val_loss -0.6813 
+2024-09-15 12:00:00.629530: Pseudo dice [0.6731, 0.8217] 
+2024-09-15 12:00:00.629627: Epoch time: 244.92 s 
+2024-09-15 12:00:01.614642:  
+2024-09-15 12:00:01.614841: Epoch 687 
+2024-09-15 12:00:01.614930: Current learning rate: 0.00352 
+2024-09-15 12:04:06.497235: train_loss -0.8882 
+2024-09-15 12:04:06.497386: val_loss -0.6832 
+2024-09-15 12:04:06.497440: Pseudo dice [0.6976, 0.8188] 
+2024-09-15 12:04:06.497494: Epoch time: 244.88 s 
+2024-09-15 12:04:07.485023:  
+2024-09-15 12:04:07.485196: Epoch 688 
+2024-09-15 12:04:07.485329: Current learning rate: 0.00351 
+2024-09-15 12:08:12.240043: train_loss -0.8916 
+2024-09-15 12:08:12.240215: val_loss -0.6864 
+2024-09-15 12:08:12.240272: Pseudo dice [0.6905, 0.8137] 
+2024-09-15 12:08:12.240327: Epoch time: 244.76 s 
+2024-09-15 12:08:13.243406:  
+2024-09-15 12:08:13.243611: Epoch 689 
+2024-09-15 12:08:13.243697: Current learning rate: 0.0035 
+2024-09-15 12:12:17.809104: train_loss -0.8872 
+2024-09-15 12:12:17.809249: val_loss -0.7011 
+2024-09-15 12:12:17.809304: Pseudo dice [0.7077, 0.8395] 
+2024-09-15 12:12:17.809358: Epoch time: 244.57 s 
+2024-09-15 12:12:18.811330:  
+2024-09-15 12:12:18.811555: Epoch 690 
+2024-09-15 12:12:18.811640: Current learning rate: 0.00349 
+2024-09-15 12:16:23.351435: train_loss -0.8883 
+2024-09-15 12:16:23.351581: val_loss -0.6722 
+2024-09-15 12:16:23.351636: Pseudo dice [0.6576, 0.8296] 
+2024-09-15 12:16:23.351688: Epoch time: 244.54 s 
+2024-09-15 12:16:24.331384:  
+2024-09-15 12:16:24.331558: Epoch 691 
+2024-09-15 12:16:24.331667: Current learning rate: 0.00348 
+2024-09-15 12:20:29.023707: train_loss -0.8842 
+2024-09-15 12:20:29.023867: val_loss -0.6668 
+2024-09-15 12:20:29.023925: Pseudo dice [0.6646, 0.8141] 
+2024-09-15 12:20:29.023980: Epoch time: 244.69 s 
+2024-09-15 12:20:29.994359:  
+2024-09-15 12:20:29.994580: Epoch 692 
+2024-09-15 12:20:29.994661: Current learning rate: 0.00346 
+2024-09-15 12:24:34.717193: train_loss -0.8899 
+2024-09-15 12:24:34.717403: val_loss -0.689 
+2024-09-15 12:24:34.717458: Pseudo dice [0.6866, 0.8246] 
+2024-09-15 12:24:34.717511: Epoch time: 244.72 s 
+2024-09-15 12:24:35.673571:  
+2024-09-15 12:24:35.673749: Epoch 693 
+2024-09-15 12:24:35.673836: Current learning rate: 0.00345 
+2024-09-15 12:28:40.429814: train_loss -0.8898 
+2024-09-15 12:28:40.429957: val_loss -0.6657 
+2024-09-15 12:28:40.430012: Pseudo dice [0.6668, 0.8311] 
+2024-09-15 12:28:40.430065: Epoch time: 244.76 s 
+2024-09-15 12:28:41.409536:  
+2024-09-15 12:28:41.409743: Epoch 694 
+2024-09-15 12:28:41.409833: Current learning rate: 0.00344 
+2024-09-15 12:32:46.179452: train_loss -0.8916 
+2024-09-15 12:32:46.179588: val_loss -0.7043 
+2024-09-15 12:32:46.179641: Pseudo dice [0.7029, 0.8352] 
+2024-09-15 12:32:46.179695: Epoch time: 244.77 s 
+2024-09-15 12:32:47.153032:  
+2024-09-15 12:32:47.153277: Epoch 695 
+2024-09-15 12:32:47.153364: Current learning rate: 0.00343 
+2024-09-15 12:36:51.888203: train_loss -0.8888 
+2024-09-15 12:36:51.888348: val_loss -0.705 
+2024-09-15 12:36:51.888404: Pseudo dice [0.7142, 0.8305] 
+2024-09-15 12:36:51.888458: Epoch time: 244.74 s 
+2024-09-15 12:36:52.871992:  
+2024-09-15 12:36:52.872157: Epoch 696 
+2024-09-15 12:36:52.872242: Current learning rate: 0.00342 
+2024-09-15 12:40:57.605582: train_loss -0.8896 
+2024-09-15 12:40:57.605727: val_loss -0.6709 
+2024-09-15 12:40:57.605782: Pseudo dice [0.7001, 0.8206] 
+2024-09-15 12:40:57.605835: Epoch time: 244.74 s 
+2024-09-15 12:40:58.580325:  
+2024-09-15 12:40:58.580497: Epoch 697 
+2024-09-15 12:40:58.580582: Current learning rate: 0.00341 
+2024-09-15 12:45:03.339640: train_loss -0.8921 
+2024-09-15 12:45:03.339786: val_loss -0.6903 
+2024-09-15 12:45:03.339849: Pseudo dice [0.7065, 0.8154] 
+2024-09-15 12:45:03.339904: Epoch time: 244.76 s 
+2024-09-15 12:45:04.322897:  
+2024-09-15 12:45:04.323105: Epoch 698 
+2024-09-15 12:45:04.323188: Current learning rate: 0.0034 
+2024-09-15 12:49:08.975921: train_loss -0.8928 
+2024-09-15 12:49:08.976067: val_loss -0.6976 
+2024-09-15 12:49:08.976122: Pseudo dice [0.7052, 0.8336] 
+2024-09-15 12:49:08.976177: Epoch time: 244.65 s 
+2024-09-15 12:49:09.959535:  
+2024-09-15 12:49:09.959683: Epoch 699 
+2024-09-15 12:49:09.959793: Current learning rate: 0.00339 
+2024-09-15 12:53:14.663273: train_loss -0.8933 
+2024-09-15 12:53:14.663423: val_loss -0.694 
+2024-09-15 12:53:14.663504: Pseudo dice [0.6952, 0.8195] 
+2024-09-15 12:53:14.663559: Epoch time: 244.71 s 
+2024-09-15 12:53:18.620287:  
+2024-09-15 12:53:18.620444: Epoch 700 
+2024-09-15 12:53:18.620531: Current learning rate: 0.00338 
+2024-09-15 12:57:23.248259: train_loss -0.894 
+2024-09-15 12:57:23.248410: val_loss -0.711 
+2024-09-15 12:57:23.248464: Pseudo dice [0.7115, 0.8287] 
+2024-09-15 12:57:23.248528: Epoch time: 244.63 s 
+2024-09-15 12:57:25.117232:  
+2024-09-15 12:57:25.117440: Epoch 701 
+2024-09-15 12:57:25.117526: Current learning rate: 0.00337 
+2024-09-15 13:01:29.803798: train_loss -0.8911 
+2024-09-15 13:01:29.803963: val_loss -0.7048 
+2024-09-15 13:01:29.804023: Pseudo dice [0.707, 0.8324] 
+2024-09-15 13:01:29.804078: Epoch time: 244.69 s 
+2024-09-15 13:01:30.777912:  
+2024-09-15 13:01:30.778133: Epoch 702 
+2024-09-15 13:01:30.778216: Current learning rate: 0.00336 
+2024-09-15 13:05:35.572789: train_loss -0.8954 
+2024-09-15 13:05:35.572932: val_loss -0.7105 
+2024-09-15 13:05:35.572988: Pseudo dice [0.7292, 0.8372] 
+2024-09-15 13:05:35.573042: Epoch time: 244.8 s 
+2024-09-15 13:05:36.546511:  
+2024-09-15 13:05:36.546761: Epoch 703 
+2024-09-15 13:05:36.546849: Current learning rate: 0.00335 
+2024-09-15 13:09:41.314324: train_loss -0.8945 
+2024-09-15 13:09:41.314473: val_loss -0.7093 
+2024-09-15 13:09:41.314528: Pseudo dice [0.7275, 0.8299] 
+2024-09-15 13:09:41.314582: Epoch time: 244.77 s 
+2024-09-15 13:09:42.275649:  
+2024-09-15 13:09:42.275932: Epoch 704 
+2024-09-15 13:09:42.276022: Current learning rate: 0.00334 
+2024-09-15 13:13:47.090216: train_loss -0.8921 
+2024-09-15 13:13:47.090366: val_loss -0.6868 
+2024-09-15 13:13:47.090421: Pseudo dice [0.7166, 0.8356] 
+2024-09-15 13:13:47.090475: Epoch time: 244.82 s 
+2024-09-15 13:13:48.051922:  
+2024-09-15 13:13:48.052112: Epoch 705 
+2024-09-15 13:13:48.052199: Current learning rate: 0.00333 
+2024-09-15 13:17:53.055352: train_loss -0.8898 
+2024-09-15 13:17:53.055562: val_loss -0.6906 
+2024-09-15 13:17:53.055663: Pseudo dice [0.6827, 0.8216] 
+2024-09-15 13:17:53.055760: Epoch time: 245.01 s 
+2024-09-15 13:17:54.024421:  
+2024-09-15 13:17:54.024622: Epoch 706 
+2024-09-15 13:17:54.024709: Current learning rate: 0.00332 
+2024-09-15 13:21:58.961520: train_loss -0.8905 
+2024-09-15 13:21:58.961664: val_loss -0.6792 
+2024-09-15 13:21:58.961720: Pseudo dice [0.6753, 0.8247] 
+2024-09-15 13:21:58.961776: Epoch time: 244.94 s 
+2024-09-15 13:21:59.916034:  
+2024-09-15 13:21:59.916255: Epoch 707 
+2024-09-15 13:21:59.916341: Current learning rate: 0.00331 
+2024-09-15 13:26:04.868719: train_loss -0.8898 
+2024-09-15 13:26:04.868868: val_loss -0.6954 
+2024-09-15 13:26:04.868922: Pseudo dice [0.6751, 0.8254] 
+2024-09-15 13:26:04.868976: Epoch time: 244.95 s 
+2024-09-15 13:26:05.833592:  
+2024-09-15 13:26:05.833750: Epoch 708 
+2024-09-15 13:26:05.833838: Current learning rate: 0.0033 
+2024-09-15 13:30:10.900687: train_loss -0.8913 
+2024-09-15 13:30:10.900832: val_loss -0.7021 
+2024-09-15 13:30:10.900887: Pseudo dice [0.6806, 0.8341] 
+2024-09-15 13:30:10.900941: Epoch time: 245.07 s 
+2024-09-15 13:30:11.882644:  
+2024-09-15 13:30:11.882862: Epoch 709 
+2024-09-15 13:30:11.882948: Current learning rate: 0.00329 
+2024-09-15 13:34:16.928024: train_loss -0.8961 
+2024-09-15 13:34:16.928169: val_loss -0.7042 
+2024-09-15 13:34:16.928225: Pseudo dice [0.6993, 0.8401] 
+2024-09-15 13:34:16.928278: Epoch time: 245.05 s 
+2024-09-15 13:34:17.904396:  
+2024-09-15 13:34:17.904586: Epoch 710 
+2024-09-15 13:34:17.904673: Current learning rate: 0.00328 
+2024-09-15 13:38:22.847192: train_loss -0.895 
+2024-09-15 13:38:22.847344: val_loss -0.6579 
+2024-09-15 13:38:22.847399: Pseudo dice [0.6496, 0.8281] 
+2024-09-15 13:38:22.847453: Epoch time: 244.94 s 
+2024-09-15 13:38:23.805413:  
+2024-09-15 13:38:23.805578: Epoch 711 
+2024-09-15 13:38:23.805699: Current learning rate: 0.00327 
+2024-09-15 13:42:28.550296: train_loss -0.8945 
+2024-09-15 13:42:28.550461: val_loss -0.7204 
+2024-09-15 13:42:28.550552: Pseudo dice [0.7295, 0.8362] 
+2024-09-15 13:42:28.550607: Epoch time: 244.75 s 
+2024-09-15 13:42:29.524616:  
+2024-09-15 13:42:29.524795: Epoch 712 
+2024-09-15 13:42:29.524882: Current learning rate: 0.00326 
+2024-09-15 13:46:34.324444: train_loss -0.8953 
+2024-09-15 13:46:34.324592: val_loss -0.6874 
+2024-09-15 13:46:34.324647: Pseudo dice [0.6988, 0.8314] 
+2024-09-15 13:46:34.324701: Epoch time: 244.8 s 
+2024-09-15 13:46:35.299147:  
+2024-09-15 13:46:35.299354: Epoch 713 
+2024-09-15 13:46:35.299445: Current learning rate: 0.00325 
+2024-09-15 13:50:40.110552: train_loss -0.895 
+2024-09-15 13:50:40.110728: val_loss -0.6808 
+2024-09-15 13:50:40.110785: Pseudo dice [0.6677, 0.8127] 
+2024-09-15 13:50:40.110837: Epoch time: 244.81 s 
+2024-09-15 13:50:41.078672:  
+2024-09-15 13:50:41.078825: Epoch 714 
+2024-09-15 13:50:41.078908: Current learning rate: 0.00324 
+2024-09-15 13:54:45.878642: train_loss -0.8903 
+2024-09-15 13:54:45.878788: val_loss -0.6955 
+2024-09-15 13:54:45.878842: Pseudo dice [0.6797, 0.8427] 
+2024-09-15 13:54:45.878895: Epoch time: 244.8 s 
+2024-09-15 13:54:46.850590:  
+2024-09-15 13:54:46.850747: Epoch 715 
+2024-09-15 13:54:46.850834: Current learning rate: 0.00323 
+2024-09-15 13:58:51.597685: train_loss -0.8955 
+2024-09-15 13:58:51.597844: val_loss -0.7038 
+2024-09-15 13:58:51.597900: Pseudo dice [0.6731, 0.8389] 
+2024-09-15 13:58:51.597958: Epoch time: 244.75 s 
+2024-09-15 13:58:52.562369:  
+2024-09-15 13:58:52.562566: Epoch 716 
+2024-09-15 13:58:52.562654: Current learning rate: 0.00322 
+2024-09-15 14:02:57.278162: train_loss -0.8959 
+2024-09-15 14:02:57.278307: val_loss -0.7185 
+2024-09-15 14:02:57.278362: Pseudo dice [0.7251, 0.8481] 
+2024-09-15 14:02:57.278452: Epoch time: 244.72 s 
+2024-09-15 14:02:58.236056:  
+2024-09-15 14:02:58.236239: Epoch 717 
+2024-09-15 14:02:58.236324: Current learning rate: 0.00321 
+2024-09-15 14:07:02.948226: train_loss -0.8928 
+2024-09-15 14:07:02.948372: val_loss -0.7214 
+2024-09-15 14:07:02.948427: Pseudo dice [0.7219, 0.8297] 
+2024-09-15 14:07:02.948482: Epoch time: 244.71 s 
+2024-09-15 14:07:03.918725:  
+2024-09-15 14:07:03.918894: Epoch 718 
+2024-09-15 14:07:03.918980: Current learning rate: 0.0032 
+2024-09-15 14:11:08.801083: train_loss -0.8818 
+2024-09-15 14:11:08.801229: val_loss -0.704 
+2024-09-15 14:11:08.801289: Pseudo dice [0.709, 0.8202] 
+2024-09-15 14:11:08.801342: Epoch time: 244.88 s 
+2024-09-15 14:11:09.758337:  
+2024-09-15 14:11:09.758534: Epoch 719 
+2024-09-15 14:11:09.758621: Current learning rate: 0.00319 
+2024-09-15 14:15:14.595376: train_loss -0.8793 
+2024-09-15 14:15:14.595521: val_loss -0.7025 
+2024-09-15 14:15:14.595576: Pseudo dice [0.716, 0.8216] 
+2024-09-15 14:15:14.595631: Epoch time: 244.84 s 
+2024-09-15 14:15:15.549743:  
+2024-09-15 14:15:15.549932: Epoch 720 
+2024-09-15 14:15:15.550020: Current learning rate: 0.00318 
+2024-09-15 14:19:20.257670: train_loss -0.8807 
+2024-09-15 14:19:20.257823: val_loss -0.701 
+2024-09-15 14:19:20.257879: Pseudo dice [0.7155, 0.8297] 
+2024-09-15 14:19:20.257933: Epoch time: 244.71 s 
+2024-09-15 14:19:21.216046:  
+2024-09-15 14:19:21.216216: Epoch 721 
+2024-09-15 14:19:21.216303: Current learning rate: 0.00317 
+2024-09-15 14:23:26.054388: train_loss -0.8721 
+2024-09-15 14:23:26.054537: val_loss -0.6575 
+2024-09-15 14:23:26.054593: Pseudo dice [0.6403, 0.816] 
+2024-09-15 14:23:26.054647: Epoch time: 244.84 s 
+2024-09-15 14:23:27.034486:  
+2024-09-15 14:23:27.034678: Epoch 722 
+2024-09-15 14:23:27.034764: Current learning rate: 0.00316 
+2024-09-15 14:27:31.769175: train_loss -0.8837 
+2024-09-15 14:27:31.769321: val_loss -0.6734 
+2024-09-15 14:27:31.769377: Pseudo dice [0.6567, 0.8267] 
+2024-09-15 14:27:31.769430: Epoch time: 244.74 s 
+2024-09-15 14:27:32.726025:  
+2024-09-15 14:27:32.726207: Epoch 723 
+2024-09-15 14:27:32.726295: Current learning rate: 0.00315 
+2024-09-15 14:31:37.161782: train_loss -0.8924 
+2024-09-15 14:31:37.161945: val_loss -0.6845 
+2024-09-15 14:31:37.162031: Pseudo dice [0.6566, 0.8231] 
+2024-09-15 14:31:37.162087: Epoch time: 244.44 s 
+2024-09-15 14:31:39.010718:  
+2024-09-15 14:31:39.010947: Epoch 724 
+2024-09-15 14:31:39.011033: Current learning rate: 0.00314 
+2024-09-15 14:35:43.381924: train_loss -0.894 
+2024-09-15 14:35:43.382105: val_loss -0.7127 
+2024-09-15 14:35:43.382160: Pseudo dice [0.691, 0.8438] 
+2024-09-15 14:35:43.382214: Epoch time: 244.37 s 
+2024-09-15 14:35:44.521276:  
+2024-09-15 14:35:44.521513: Epoch 725 
+2024-09-15 14:35:44.521600: Current learning rate: 0.00313 
+2024-09-15 14:39:49.002820: train_loss -0.893 
+2024-09-15 14:39:49.003030: val_loss -0.6922 
+2024-09-15 14:39:49.003149: Pseudo dice [0.6759, 0.8225] 
+2024-09-15 14:39:49.003246: Epoch time: 244.48 s 
+2024-09-15 14:39:49.966298:  
+2024-09-15 14:39:49.966497: Epoch 726 
+2024-09-15 14:39:49.966579: Current learning rate: 0.00312 
+2024-09-15 14:43:54.369972: train_loss -0.8972 
+2024-09-15 14:43:54.370119: val_loss -0.6987 
+2024-09-15 14:43:54.370174: Pseudo dice [0.686, 0.8309] 
+2024-09-15 14:43:54.370277: Epoch time: 244.41 s 
+2024-09-15 14:43:55.350374:  
+2024-09-15 14:43:55.350590: Epoch 727 
+2024-09-15 14:43:55.350675: Current learning rate: 0.00311 
+2024-09-15 14:47:59.763155: train_loss -0.8985 
+2024-09-15 14:47:59.763308: val_loss -0.6766 
+2024-09-15 14:47:59.763363: Pseudo dice [0.6731, 0.8126] 
+2024-09-15 14:47:59.763421: Epoch time: 244.41 s 
+2024-09-15 14:48:00.734346:  
+2024-09-15 14:48:00.734543: Epoch 728 
+2024-09-15 14:48:00.734631: Current learning rate: 0.0031 
+2024-09-15 14:52:05.172208: train_loss -0.8957 
+2024-09-15 14:52:05.172357: val_loss -0.709 
+2024-09-15 14:52:05.172414: Pseudo dice [0.7015, 0.8289] 
+2024-09-15 14:52:05.172467: Epoch time: 244.44 s 
+2024-09-15 14:52:06.173500:  
+2024-09-15 14:52:06.173728: Epoch 729 
+2024-09-15 14:52:06.173814: Current learning rate: 0.00309 
+2024-09-15 14:56:10.609222: train_loss -0.8968 
+2024-09-15 14:56:10.609373: val_loss -0.7069 
+2024-09-15 14:56:10.609431: Pseudo dice [0.6865, 0.8307] 
+2024-09-15 14:56:10.609485: Epoch time: 244.44 s 
+2024-09-15 14:56:11.580610:  
+2024-09-15 14:56:11.580835: Epoch 730 
+2024-09-15 14:56:11.580928: Current learning rate: 0.00308 
+2024-09-15 15:00:16.003648: train_loss -0.9006 
+2024-09-15 15:00:16.003797: val_loss -0.7061 
+2024-09-15 15:00:16.003931: Pseudo dice [0.7041, 0.8396] 
+2024-09-15 15:00:16.003986: Epoch time: 244.42 s 
+2024-09-15 15:00:16.961929:  
+2024-09-15 15:00:16.962087: Epoch 731 
+2024-09-15 15:00:16.962173: Current learning rate: 0.00307 
+2024-09-15 15:04:21.483735: train_loss -0.8984 
+2024-09-15 15:04:21.483887: val_loss -0.7112 
+2024-09-15 15:04:21.483942: Pseudo dice [0.7142, 0.8403] 
+2024-09-15 15:04:21.483995: Epoch time: 244.52 s 
+2024-09-15 15:04:22.438078:  
+2024-09-15 15:04:22.438230: Epoch 732 
+2024-09-15 15:04:22.438318: Current learning rate: 0.00306 
+2024-09-15 15:08:27.020488: train_loss -0.8983 
+2024-09-15 15:08:27.020661: val_loss -0.6981 
+2024-09-15 15:08:27.020717: Pseudo dice [0.6902, 0.8278] 
+2024-09-15 15:08:27.020771: Epoch time: 244.58 s 
+2024-09-15 15:08:27.977746:  
+2024-09-15 15:08:27.977937: Epoch 733 
+2024-09-15 15:08:27.978028: Current learning rate: 0.00305 
+2024-09-15 15:12:32.543669: train_loss -0.8984 
+2024-09-15 15:12:32.543821: val_loss -0.684 
+2024-09-15 15:12:32.543892: Pseudo dice [0.6532, 0.8239] 
+2024-09-15 15:12:32.543957: Epoch time: 244.57 s 
+2024-09-15 15:12:33.511487:  
+2024-09-15 15:12:33.511690: Epoch 734 
+2024-09-15 15:12:33.511791: Current learning rate: 0.00304 
+2024-09-15 15:16:37.969372: train_loss -0.901 
+2024-09-15 15:16:37.969543: val_loss -0.7255 
+2024-09-15 15:16:37.969612: Pseudo dice [0.7202, 0.8504] 
+2024-09-15 15:16:37.969666: Epoch time: 244.46 s 
+2024-09-15 15:16:38.944041:  
+2024-09-15 15:16:38.944257: Epoch 735 
+2024-09-15 15:16:38.944355: Current learning rate: 0.00303 
+2024-09-15 15:20:43.385637: train_loss -0.8994 
+2024-09-15 15:20:43.385798: val_loss -0.6839 
+2024-09-15 15:20:43.385857: Pseudo dice [0.6953, 0.8361] 
+2024-09-15 15:20:43.385910: Epoch time: 244.44 s 
+2024-09-15 15:20:44.355582:  
+2024-09-15 15:20:44.355774: Epoch 736 
+2024-09-15 15:20:44.355868: Current learning rate: 0.00302 
+2024-09-15 15:24:48.844429: train_loss -0.9001 
+2024-09-15 15:24:48.844600: val_loss -0.6863 
+2024-09-15 15:24:48.844657: Pseudo dice [0.6774, 0.8133] 
+2024-09-15 15:24:48.844713: Epoch time: 244.49 s 
+2024-09-15 15:24:49.813557:  
+2024-09-15 15:24:49.813761: Epoch 737 
+2024-09-15 15:24:49.813846: Current learning rate: 0.00301 
+2024-09-15 15:28:54.275069: train_loss -0.8992 
+2024-09-15 15:28:54.275208: val_loss -0.6801 
+2024-09-15 15:28:54.275260: Pseudo dice [0.6767, 0.8407] 
+2024-09-15 15:28:54.275313: Epoch time: 244.46 s 
+2024-09-15 15:28:55.237484:  
+2024-09-15 15:28:55.237670: Epoch 738 
+2024-09-15 15:28:55.237756: Current learning rate: 0.003 
+2024-09-15 15:32:59.646956: train_loss -0.9007 
+2024-09-15 15:32:59.647100: val_loss -0.7168 
+2024-09-15 15:32:59.647156: Pseudo dice [0.7171, 0.8279] 
+2024-09-15 15:32:59.647208: Epoch time: 244.41 s 
+2024-09-15 15:33:00.608996:  
+2024-09-15 15:33:00.609174: Epoch 739 
+2024-09-15 15:33:00.609259: Current learning rate: 0.00299 
+2024-09-15 15:37:05.040394: train_loss -0.8987 
+2024-09-15 15:37:05.040576: val_loss -0.6875 
+2024-09-15 15:37:05.040657: Pseudo dice [0.6821, 0.8301] 
+2024-09-15 15:37:05.040710: Epoch time: 244.43 s 
+2024-09-15 15:37:05.996941:  
+2024-09-15 15:37:05.997082: Epoch 740 
+2024-09-15 15:37:05.997166: Current learning rate: 0.00297 
+2024-09-15 15:41:10.435952: train_loss -0.9021 
+2024-09-15 15:41:10.436098: val_loss -0.6785 
+2024-09-15 15:41:10.436182: Pseudo dice [0.6746, 0.8239] 
+2024-09-15 15:41:10.436280: Epoch time: 244.44 s 
+2024-09-15 15:41:11.386953:  
+2024-09-15 15:41:11.387127: Epoch 741 
+2024-09-15 15:41:11.387212: Current learning rate: 0.00296 
+2024-09-15 15:45:15.820835: train_loss -0.8961 
+2024-09-15 15:45:15.821054: val_loss -0.6726 
+2024-09-15 15:45:15.821112: Pseudo dice [0.6585, 0.8344] 
+2024-09-15 15:45:15.821167: Epoch time: 244.44 s 
+2024-09-15 15:45:16.784356:  
+2024-09-15 15:45:16.784515: Epoch 742 
+2024-09-15 15:45:16.784602: Current learning rate: 0.00295 
+2024-09-15 15:49:21.348144: train_loss -0.9002 
+2024-09-15 15:49:21.348292: val_loss -0.692 
+2024-09-15 15:49:21.348350: Pseudo dice [0.688, 0.8258] 
+2024-09-15 15:49:21.348403: Epoch time: 244.57 s 
+2024-09-15 15:49:22.305818:  
+2024-09-15 15:49:22.305989: Epoch 743 
+2024-09-15 15:49:22.306074: Current learning rate: 0.00294 
+2024-09-15 15:53:26.920327: train_loss -0.9001 
+2024-09-15 15:53:26.920473: val_loss -0.692 
+2024-09-15 15:53:26.920528: Pseudo dice [0.7018, 0.8214] 
+2024-09-15 15:53:26.920582: Epoch time: 244.62 s 
+2024-09-15 15:53:27.872533:  
+2024-09-15 15:53:27.872688: Epoch 744 
+2024-09-15 15:53:27.872779: Current learning rate: 0.00293 
+2024-09-15 15:57:32.451727: train_loss -0.8974 
+2024-09-15 15:57:32.451883: val_loss -0.7207 
+2024-09-15 15:57:32.451939: Pseudo dice [0.7072, 0.8329] 
+2024-09-15 15:57:32.451992: Epoch time: 244.58 s 
+2024-09-15 15:57:33.413887:  
+2024-09-15 15:57:33.414093: Epoch 745 
+2024-09-15 15:57:33.414203: Current learning rate: 0.00292 
+2024-09-15 16:01:38.020716: train_loss -0.8992 
+2024-09-15 16:01:38.020871: val_loss -0.7051 
+2024-09-15 16:01:38.020927: Pseudo dice [0.6802, 0.8342] 
+2024-09-15 16:01:38.020982: Epoch time: 244.61 s 
+2024-09-15 16:01:38.983823:  
+2024-09-15 16:01:38.983984: Epoch 746 
+2024-09-15 16:01:38.984071: Current learning rate: 0.00291 
+2024-09-15 16:05:43.608493: train_loss -0.902 
+2024-09-15 16:05:43.608644: val_loss -0.6879 
+2024-09-15 16:05:43.608699: Pseudo dice [0.668, 0.8249] 
+2024-09-15 16:05:43.608752: Epoch time: 244.63 s 
+2024-09-15 16:05:45.514519:  
+2024-09-15 16:05:45.514747: Epoch 747 
+2024-09-15 16:05:45.514834: Current learning rate: 0.0029 
+2024-09-15 16:09:50.218782: train_loss -0.8926 
+2024-09-15 16:09:50.218935: val_loss -0.6597 
+2024-09-15 16:09:50.218990: Pseudo dice [0.6426, 0.8241] 
+2024-09-15 16:09:50.219043: Epoch time: 244.71 s 
+2024-09-15 16:09:51.170168:  
+2024-09-15 16:09:51.170427: Epoch 748 
+2024-09-15 16:09:51.170531: Current learning rate: 0.00289 
+2024-09-15 16:13:55.808027: train_loss -0.8976 
+2024-09-15 16:13:55.808172: val_loss -0.6978 
+2024-09-15 16:13:55.808228: Pseudo dice [0.6764, 0.8305] 
+2024-09-15 16:13:55.808282: Epoch time: 244.64 s 
+2024-09-15 16:13:56.789608:  
+2024-09-15 16:13:56.789835: Epoch 749 
+2024-09-15 16:13:56.789921: Current learning rate: 0.00288 
+2024-09-15 16:18:01.633803: train_loss -0.897 
+2024-09-15 16:18:01.633947: val_loss -0.7013 
+2024-09-15 16:18:01.634003: Pseudo dice [0.6933, 0.832] 
+2024-09-15 16:18:01.634056: Epoch time: 244.85 s 
+2024-09-15 16:18:05.554277:  
+2024-09-15 16:18:05.554506: Epoch 750 
+2024-09-15 16:18:05.554588: Current learning rate: 0.00287 
+2024-09-15 16:22:10.390191: train_loss -0.8979 
+2024-09-15 16:22:10.390347: val_loss -0.7074 
+2024-09-15 16:22:10.390475: Pseudo dice [0.6991, 0.8307] 
+2024-09-15 16:22:10.390533: Epoch time: 244.84 s 
+2024-09-15 16:22:11.359526:  
+2024-09-15 16:22:11.359716: Epoch 751 
+2024-09-15 16:22:11.359810: Current learning rate: 0.00286 
+2024-09-15 16:26:16.249527: train_loss -0.8916 
+2024-09-15 16:26:16.249686: val_loss -0.7037 
+2024-09-15 16:26:16.249742: Pseudo dice [0.7079, 0.8254] 
+2024-09-15 16:26:16.249796: Epoch time: 244.89 s 
+2024-09-15 16:26:17.205101:  
+2024-09-15 16:26:17.205292: Epoch 752 
+2024-09-15 16:26:17.205378: Current learning rate: 0.00285 
+2024-09-15 16:30:22.114679: train_loss -0.8973 
+2024-09-15 16:30:22.114825: val_loss -0.6712 
+2024-09-15 16:30:22.114880: Pseudo dice [0.6463, 0.8289] 
+2024-09-15 16:30:22.114933: Epoch time: 244.91 s 
+2024-09-15 16:30:23.077120:  
+2024-09-15 16:30:23.077325: Epoch 753 
+2024-09-15 16:30:23.077409: Current learning rate: 0.00284 
+2024-09-15 16:34:28.019391: train_loss -0.9006 
+2024-09-15 16:34:28.019534: val_loss -0.6998 
+2024-09-15 16:34:28.019589: Pseudo dice [0.6768, 0.8344] 
+2024-09-15 16:34:28.019642: Epoch time: 244.94 s 
+2024-09-15 16:34:28.977371:  
+2024-09-15 16:34:28.977585: Epoch 754 
+2024-09-15 16:34:28.977672: Current learning rate: 0.00283 
+2024-09-15 16:38:33.890946: train_loss -0.8994 
+2024-09-15 16:38:33.891114: val_loss -0.7104 
+2024-09-15 16:38:33.891170: Pseudo dice [0.6876, 0.8395] 
+2024-09-15 16:38:33.891224: Epoch time: 244.92 s 
+2024-09-15 16:38:34.871320:  
+2024-09-15 16:38:34.871545: Epoch 755 
+2024-09-15 16:38:34.871631: Current learning rate: 0.00282 
+2024-09-15 16:42:39.743221: train_loss -0.9019 
+2024-09-15 16:42:39.743367: val_loss -0.6807 
+2024-09-15 16:42:39.743422: Pseudo dice [0.6671, 0.8224] 
+2024-09-15 16:42:39.743475: Epoch time: 244.87 s 
+2024-09-15 16:42:40.713570:  
+2024-09-15 16:42:40.713777: Epoch 756 
+2024-09-15 16:42:40.713862: Current learning rate: 0.00281 
+2024-09-15 16:46:45.548923: train_loss -0.9037 
+2024-09-15 16:46:45.549067: val_loss -0.7056 
+2024-09-15 16:46:45.549121: Pseudo dice [0.6948, 0.83] 
+2024-09-15 16:46:45.549175: Epoch time: 244.84 s 
+2024-09-15 16:46:46.513056:  
+2024-09-15 16:46:46.513237: Epoch 757 
+2024-09-15 16:46:46.513323: Current learning rate: 0.0028 
+2024-09-15 16:50:51.548889: train_loss -0.9037 
+2024-09-15 16:50:51.549035: val_loss -0.6792 
+2024-09-15 16:50:51.549089: Pseudo dice [0.6756, 0.828] 
+2024-09-15 16:50:51.549142: Epoch time: 245.04 s 
+2024-09-15 16:50:52.515934:  
+2024-09-15 16:50:52.516128: Epoch 758 
+2024-09-15 16:50:52.516212: Current learning rate: 0.00279 
+2024-09-15 16:54:57.350558: train_loss -0.9008 
+2024-09-15 16:54:57.350703: val_loss -0.6761 
+2024-09-15 16:54:57.350759: Pseudo dice [0.6423, 0.8174] 
+2024-09-15 16:54:57.350812: Epoch time: 244.84 s 
+2024-09-15 16:54:58.305029:  
+2024-09-15 16:54:58.305248: Epoch 759 
+2024-09-15 16:54:58.305332: Current learning rate: 0.00278 
+2024-09-15 16:59:02.881707: train_loss -0.9037 
+2024-09-15 16:59:02.881858: val_loss -0.6763 
+2024-09-15 16:59:02.881913: Pseudo dice [0.6392, 0.8345] 
+2024-09-15 16:59:02.881967: Epoch time: 244.58 s 
+2024-09-15 16:59:03.841799:  
+2024-09-15 16:59:03.841975: Epoch 760 
+2024-09-15 16:59:03.842063: Current learning rate: 0.00277 
+2024-09-15 17:03:08.396574: train_loss -0.8999 
+2024-09-15 17:03:08.396708: val_loss -0.6979 
+2024-09-15 17:03:08.396757: Pseudo dice [0.6951, 0.843] 
+2024-09-15 17:03:08.396807: Epoch time: 244.56 s 
+2024-09-15 17:03:09.360800:  
+2024-09-15 17:03:09.360963: Epoch 761 
+2024-09-15 17:03:09.361046: Current learning rate: 0.00276 
+2024-09-15 17:07:14.007737: train_loss -0.8997 
+2024-09-15 17:07:14.007881: val_loss -0.6742 
+2024-09-15 17:07:14.007929: Pseudo dice [0.6873, 0.8265] 
+2024-09-15 17:07:14.007979: Epoch time: 244.65 s 
+2024-09-15 17:07:15.013494:  
+2024-09-15 17:07:15.013696: Epoch 762 
+2024-09-15 17:07:15.013780: Current learning rate: 0.00275 
+2024-09-15 17:11:19.587409: train_loss -0.9035 
+2024-09-15 17:11:19.587547: val_loss -0.6679 
+2024-09-15 17:11:19.587601: Pseudo dice [0.6755, 0.8194] 
+2024-09-15 17:11:19.587664: Epoch time: 244.58 s 
+2024-09-15 17:11:20.559279:  
+2024-09-15 17:11:20.559480: Epoch 763 
+2024-09-15 17:11:20.559562: Current learning rate: 0.00274 
+2024-09-15 17:15:25.154211: train_loss -0.9033 
+2024-09-15 17:15:25.154346: val_loss -0.7046 
+2024-09-15 17:15:25.154396: Pseudo dice [0.6838, 0.8459] 
+2024-09-15 17:15:25.154445: Epoch time: 244.6 s 
+2024-09-15 17:15:26.127306:  
+2024-09-15 17:15:26.127496: Epoch 764 
+2024-09-15 17:15:26.127581: Current learning rate: 0.00273 
+2024-09-15 17:19:30.706006: train_loss -0.9034 
+2024-09-15 17:19:30.706139: val_loss -0.7148 
+2024-09-15 17:19:30.706187: Pseudo dice [0.696, 0.8374] 
+2024-09-15 17:19:30.706236: Epoch time: 244.58 s 
+2024-09-15 17:19:31.700469:  
+2024-09-15 17:19:31.700649: Epoch 765 
+2024-09-15 17:19:31.700734: Current learning rate: 0.00272 
+2024-09-15 17:23:36.287503: train_loss -0.9052 
+2024-09-15 17:23:36.287640: val_loss -0.702 
+2024-09-15 17:23:36.287688: Pseudo dice [0.7071, 0.8274] 
+2024-09-15 17:23:36.287737: Epoch time: 244.59 s 
+2024-09-15 17:23:37.288501:  
+2024-09-15 17:23:37.288666: Epoch 766 
+2024-09-15 17:23:37.288749: Current learning rate: 0.00271 
+2024-09-15 17:27:42.045350: train_loss -0.9042 
+2024-09-15 17:27:42.045485: val_loss -0.7023 
+2024-09-15 17:27:42.045535: Pseudo dice [0.6995, 0.8292] 
+2024-09-15 17:27:42.045585: Epoch time: 244.76 s 
+2024-09-15 17:27:43.014808:  
+2024-09-15 17:27:43.014955: Epoch 767 
+2024-09-15 17:27:43.015074: Current learning rate: 0.0027 
+2024-09-15 17:31:48.024525: train_loss -0.9019 
+2024-09-15 17:31:48.024662: val_loss -0.6914 
+2024-09-15 17:31:48.024712: Pseudo dice [0.6864, 0.8236] 
+2024-09-15 17:31:48.024761: Epoch time: 245.01 s 
+2024-09-15 17:31:49.006541:  
+2024-09-15 17:31:49.006706: Epoch 768 
+2024-09-15 17:31:49.006812: Current learning rate: 0.00268 
+2024-09-15 17:35:53.941613: train_loss -0.8988 
+2024-09-15 17:35:53.941805: val_loss -0.7076 
+2024-09-15 17:35:53.941895: Pseudo dice [0.6913, 0.8462] 
+2024-09-15 17:35:53.941983: Epoch time: 244.94 s 
+2024-09-15 17:35:54.937124:  
+2024-09-15 17:35:54.937285: Epoch 769 
+2024-09-15 17:35:54.937366: Current learning rate: 0.00267 
+2024-09-15 17:39:59.967479: train_loss -0.9009 
+2024-09-15 17:39:59.967617: val_loss -0.6702 
+2024-09-15 17:39:59.967667: Pseudo dice [0.6835, 0.8318] 
+2024-09-15 17:39:59.967718: Epoch time: 245.03 s 
+2024-09-15 17:40:00.955533:  
+2024-09-15 17:40:00.955710: Epoch 770 
+2024-09-15 17:40:00.955793: Current learning rate: 0.00266 
+2024-09-15 17:44:06.674157: train_loss -0.8939 
+2024-09-15 17:44:06.674296: val_loss -0.7032 
+2024-09-15 17:44:06.674346: Pseudo dice [0.6819, 0.8348] 
+2024-09-15 17:44:06.674395: Epoch time: 245.72 s 
+2024-09-15 17:44:07.665529:  
+2024-09-15 17:44:07.665766: Epoch 771 
+2024-09-15 17:44:07.665847: Current learning rate: 0.00265 
+2024-09-15 17:48:12.676756: train_loss -0.9 
+2024-09-15 17:48:12.676909: val_loss -0.6831 
+2024-09-15 17:48:12.676959: Pseudo dice [0.696, 0.8298] 
+2024-09-15 17:48:12.677009: Epoch time: 245.01 s 
+2024-09-15 17:48:13.643847:  
+2024-09-15 17:48:13.644106: Epoch 772 
+2024-09-15 17:48:13.644187: Current learning rate: 0.00264 
+2024-09-15 17:52:18.536942: train_loss -0.9005 
+2024-09-15 17:52:18.537078: val_loss -0.6985 
+2024-09-15 17:52:18.537128: Pseudo dice [0.711, 0.8202] 
+2024-09-15 17:52:18.537179: Epoch time: 244.89 s 
+2024-09-15 17:52:19.541873:  
+2024-09-15 17:52:19.542099: Epoch 773 
+2024-09-15 17:52:19.542183: Current learning rate: 0.00263 
+2024-09-15 17:56:24.635844: train_loss -0.8899 
+2024-09-15 17:56:24.635988: val_loss -0.6659 
+2024-09-15 17:56:24.636040: Pseudo dice [0.6653, 0.8322] 
+2024-09-15 17:56:24.636090: Epoch time: 245.1 s 
+2024-09-15 17:56:25.609763:  
+2024-09-15 17:56:25.609950: Epoch 774 
+2024-09-15 17:56:25.610027: Current learning rate: 0.00262 
+2024-09-15 18:00:30.428639: train_loss -0.8899 
+2024-09-15 18:00:30.428805: val_loss -0.6867 
+2024-09-15 18:00:30.428878: Pseudo dice [0.6747, 0.8259] 
+2024-09-15 18:00:30.428928: Epoch time: 244.82 s 
+2024-09-15 18:00:31.407243:  
+2024-09-15 18:00:31.407444: Epoch 775 
+2024-09-15 18:00:31.407557: Current learning rate: 0.00261 
+2024-09-15 18:04:36.259310: train_loss -0.8999 
+2024-09-15 18:04:36.259506: val_loss -0.7229 
+2024-09-15 18:04:36.259597: Pseudo dice [0.7421, 0.8409] 
+2024-09-15 18:04:36.259687: Epoch time: 244.85 s 
+2024-09-15 18:04:37.243923:  
+2024-09-15 18:04:37.244131: Epoch 776 
+2024-09-15 18:04:37.244211: Current learning rate: 0.0026 
+2024-09-15 18:08:42.118620: train_loss -0.8897 
+2024-09-15 18:08:42.118760: val_loss -0.6809 
+2024-09-15 18:08:42.118810: Pseudo dice [0.6787, 0.8396] 
+2024-09-15 18:08:42.118859: Epoch time: 244.88 s 
+2024-09-15 18:08:43.103763:  
+2024-09-15 18:08:43.104054: Epoch 777 
+2024-09-15 18:08:43.104162: Current learning rate: 0.00259 
+2024-09-15 18:12:47.956747: train_loss -0.8961 
+2024-09-15 18:12:47.956882: val_loss -0.7001 
+2024-09-15 18:12:47.956931: Pseudo dice [0.7169, 0.8288] 
+2024-09-15 18:12:47.956981: Epoch time: 244.85 s 
+2024-09-15 18:12:48.965427:  
+2024-09-15 18:12:48.965601: Epoch 778 
+2024-09-15 18:12:48.965684: Current learning rate: 0.00258 
+2024-09-15 18:16:53.709430: train_loss -0.8975 
+2024-09-15 18:16:53.709566: val_loss -0.6626 
+2024-09-15 18:16:53.709747: Pseudo dice [0.6519, 0.8277] 
+2024-09-15 18:16:53.709860: Epoch time: 244.75 s 
+2024-09-15 18:16:54.701715:  
+2024-09-15 18:16:54.701915: Epoch 779 
+2024-09-15 18:16:54.701996: Current learning rate: 0.00257 
+2024-09-15 18:20:59.461414: train_loss -0.8982 
+2024-09-15 18:20:59.461587: val_loss -0.6692 
+2024-09-15 18:20:59.461638: Pseudo dice [0.674, 0.8274] 
+2024-09-15 18:20:59.461688: Epoch time: 244.76 s 
+2024-09-15 18:21:00.452914:  
+2024-09-15 18:21:00.453133: Epoch 780 
+2024-09-15 18:21:00.453229: Current learning rate: 0.00256 
+2024-09-15 18:25:05.248703: train_loss -0.9023 
+2024-09-15 18:25:05.248840: val_loss -0.6712 
+2024-09-15 18:25:05.248891: Pseudo dice [0.6487, 0.8289] 
+2024-09-15 18:25:05.248940: Epoch time: 244.8 s 
+2024-09-15 18:25:06.234122:  
+2024-09-15 18:25:06.234300: Epoch 781 
+2024-09-15 18:25:06.234383: Current learning rate: 0.00255 
+2024-09-15 18:29:11.007085: train_loss -0.8982 
+2024-09-15 18:29:11.007246: val_loss -0.703 
+2024-09-15 18:29:11.007301: Pseudo dice [0.6998, 0.8296] 
+2024-09-15 18:29:11.007350: Epoch time: 244.77 s 
+2024-09-15 18:29:11.981881:  
+2024-09-15 18:29:11.982080: Epoch 782 
+2024-09-15 18:29:11.982177: Current learning rate: 0.00254 
+2024-09-15 18:33:16.974636: train_loss -0.8971 
+2024-09-15 18:33:16.974776: val_loss -0.6711 
+2024-09-15 18:33:16.974825: Pseudo dice [0.6763, 0.8161] 
+2024-09-15 18:33:16.974874: Epoch time: 244.99 s 
+2024-09-15 18:33:17.948374:  
+2024-09-15 18:33:17.948604: Epoch 783 
+2024-09-15 18:33:17.948689: Current learning rate: 0.00253 
+2024-09-15 18:37:22.930203: train_loss -0.9003 
+2024-09-15 18:37:22.930342: val_loss -0.6749 
+2024-09-15 18:37:22.930392: Pseudo dice [0.6473, 0.8218] 
+2024-09-15 18:37:22.930442: Epoch time: 244.98 s 
+2024-09-15 18:37:23.914342:  
+2024-09-15 18:37:23.914517: Epoch 784 
+2024-09-15 18:37:23.914599: Current learning rate: 0.00252 
+2024-09-15 18:41:28.907712: train_loss -0.9017 
+2024-09-15 18:41:28.907860: val_loss -0.6792 
+2024-09-15 18:41:28.907911: Pseudo dice [0.656, 0.8229] 
+2024-09-15 18:41:28.907960: Epoch time: 245.0 s 
+2024-09-15 18:41:29.891009:  
+2024-09-15 18:41:29.891167: Epoch 785 
+2024-09-15 18:41:29.891250: Current learning rate: 0.00251 
+2024-09-15 18:45:34.866956: train_loss -0.9037 
+2024-09-15 18:45:34.867105: val_loss -0.6693 
+2024-09-15 18:45:34.867154: Pseudo dice [0.6687, 0.8291] 
+2024-09-15 18:45:34.867204: Epoch time: 244.98 s 
+2024-09-15 18:45:35.878540:  
+2024-09-15 18:45:35.878738: Epoch 786 
+2024-09-15 18:45:35.878819: Current learning rate: 0.0025 
+2024-09-15 18:49:40.793624: train_loss -0.9074 
+2024-09-15 18:49:40.793760: val_loss -0.667 
+2024-09-15 18:49:40.793809: Pseudo dice [0.679, 0.8364] 
+2024-09-15 18:49:40.793858: Epoch time: 244.92 s 
+2024-09-15 18:49:41.759483:  
+2024-09-15 18:49:41.759645: Epoch 787 
+2024-09-15 18:49:41.759726: Current learning rate: 0.00249 
+2024-09-15 18:53:46.802360: train_loss -0.9013 
+2024-09-15 18:53:46.802498: val_loss -0.6957 
+2024-09-15 18:53:46.802560: Pseudo dice [0.7162, 0.8215] 
+2024-09-15 18:53:46.802649: Epoch time: 245.04 s 
+2024-09-15 18:53:47.770328:  
+2024-09-15 18:53:47.770477: Epoch 788 
+2024-09-15 18:53:47.770559: Current learning rate: 0.00248 
+2024-09-15 18:57:52.695550: train_loss -0.9034 
+2024-09-15 18:57:52.695688: val_loss -0.7133 
+2024-09-15 18:57:52.695737: Pseudo dice [0.7059, 0.8438] 
+2024-09-15 18:57:52.695786: Epoch time: 244.93 s 
+2024-09-15 18:57:53.676380:  
+2024-09-15 18:57:53.676564: Epoch 789 
+2024-09-15 18:57:53.676651: Current learning rate: 0.00247 
+2024-09-15 19:01:58.485775: train_loss -0.9028 
+2024-09-15 19:01:58.485913: val_loss -0.6992 
+2024-09-15 19:01:58.485964: Pseudo dice [0.6802, 0.8328] 
+2024-09-15 19:01:58.486013: Epoch time: 244.81 s 
+2024-09-15 19:01:59.482770:  
+2024-09-15 19:01:59.482945: Epoch 790 
+2024-09-15 19:01:59.483055: Current learning rate: 0.00245 
+2024-09-15 19:06:04.300810: train_loss -0.9011 
+2024-09-15 19:06:04.300962: val_loss -0.6901 
+2024-09-15 19:06:04.301055: Pseudo dice [0.7046, 0.8181] 
+2024-09-15 19:06:04.301107: Epoch time: 244.82 s 
+2024-09-15 19:06:05.287527:  
+2024-09-15 19:06:05.287772: Epoch 791 
+2024-09-15 19:06:05.287863: Current learning rate: 0.00244 
+2024-09-15 19:10:10.214866: train_loss -0.8959 
+2024-09-15 19:10:10.215025: val_loss -0.6872 
+2024-09-15 19:10:10.215076: Pseudo dice [0.6761, 0.8322] 
+2024-09-15 19:10:10.215126: Epoch time: 244.93 s 
+2024-09-15 19:10:11.195688:  
+2024-09-15 19:10:11.195847: Epoch 792 
+2024-09-15 19:10:11.195930: Current learning rate: 0.00243 
+2024-09-15 19:14:16.062543: train_loss -0.8971 
+2024-09-15 19:14:16.062697: val_loss -0.6722 
+2024-09-15 19:14:16.062747: Pseudo dice [0.6695, 0.8266] 
+2024-09-15 19:14:16.062796: Epoch time: 244.87 s 
+2024-09-15 19:14:17.951663:  
+2024-09-15 19:14:17.951946: Epoch 793 
+2024-09-15 19:14:17.952030: Current learning rate: 0.00242 
+2024-09-15 19:18:22.988314: train_loss -0.8817 
+2024-09-15 19:18:22.988452: val_loss -0.6985 
+2024-09-15 19:18:22.988506: Pseudo dice [0.6858, 0.827] 
+2024-09-15 19:18:22.988555: Epoch time: 245.04 s 
+2024-09-15 19:18:23.966339:  
+2024-09-15 19:18:23.966603: Epoch 794 
+2024-09-15 19:18:23.966727: Current learning rate: 0.00241 
+2024-09-15 19:22:28.919535: train_loss -0.8905 
+2024-09-15 19:22:28.919673: val_loss -0.6994 
+2024-09-15 19:22:28.919726: Pseudo dice [0.6888, 0.831] 
+2024-09-15 19:22:28.919776: Epoch time: 244.96 s 
+2024-09-15 19:22:29.908232:  
+2024-09-15 19:22:29.908426: Epoch 795 
+2024-09-15 19:22:29.908505: Current learning rate: 0.0024 
+2024-09-15 19:26:34.764967: train_loss -0.8966 
+2024-09-15 19:26:34.765104: val_loss -0.6867 
+2024-09-15 19:26:34.765153: Pseudo dice [0.6632, 0.8238] 
+2024-09-15 19:26:34.765202: Epoch time: 244.86 s 
+2024-09-15 19:26:35.747624:  
+2024-09-15 19:26:35.747835: Epoch 796 
+2024-09-15 19:26:35.747936: Current learning rate: 0.00239 
+2024-09-15 19:30:40.662216: train_loss -0.8948 
+2024-09-15 19:30:40.662354: val_loss -0.6915 
+2024-09-15 19:30:40.662404: Pseudo dice [0.6713, 0.8348] 
+2024-09-15 19:30:40.662454: Epoch time: 244.92 s 
+2024-09-15 19:30:41.656785:  
+2024-09-15 19:30:41.656979: Epoch 797 
+2024-09-15 19:30:41.657062: Current learning rate: 0.00238 
+2024-09-15 19:34:46.466859: train_loss -0.8962 
+2024-09-15 19:34:46.467016: val_loss -0.7048 
+2024-09-15 19:34:46.467066: Pseudo dice [0.6768, 0.8342] 
+2024-09-15 19:34:46.467115: Epoch time: 244.81 s 
+2024-09-15 19:34:47.445705:  
+2024-09-15 19:34:47.445850: Epoch 798 
+2024-09-15 19:34:47.445930: Current learning rate: 0.00237 
+2024-09-15 19:38:52.252692: train_loss -0.8983 
+2024-09-15 19:38:52.252851: val_loss -0.7164 
+2024-09-15 19:38:52.252901: Pseudo dice [0.699, 0.8407] 
+2024-09-15 19:38:52.252951: Epoch time: 244.81 s 
+2024-09-15 19:38:53.234662:  
+2024-09-15 19:38:53.234822: Epoch 799 
+2024-09-15 19:38:53.234901: Current learning rate: 0.00236 
+2024-09-15 19:42:58.069068: train_loss -0.9049 
+2024-09-15 19:42:58.069205: val_loss -0.6558 
+2024-09-15 19:42:58.069255: Pseudo dice [0.6371, 0.8305] 
+2024-09-15 19:42:58.069305: Epoch time: 244.84 s 
+2024-09-15 19:43:02.073453:  
+2024-09-15 19:43:02.073643: Epoch 800 
+2024-09-15 19:43:02.073729: Current learning rate: 0.00235 
+2024-09-15 19:47:06.779462: train_loss -0.9035 
+2024-09-15 19:47:06.779601: val_loss -0.6813 
+2024-09-15 19:47:06.779649: Pseudo dice [0.7052, 0.8253] 
+2024-09-15 19:47:06.779755: Epoch time: 244.71 s 
+2024-09-15 19:47:07.765051:  
+2024-09-15 19:47:07.765239: Epoch 801 
+2024-09-15 19:47:07.765320: Current learning rate: 0.00234 
+2024-09-15 19:51:12.439411: train_loss -0.9014 
+2024-09-15 19:51:12.439548: val_loss -0.6971 
+2024-09-15 19:51:12.439598: Pseudo dice [0.7062, 0.8469] 
+2024-09-15 19:51:12.439648: Epoch time: 244.68 s 
+2024-09-15 19:51:13.435698:  
+2024-09-15 19:51:13.435896: Epoch 802 
+2024-09-15 19:51:13.435980: Current learning rate: 0.00233 
+2024-09-15 19:55:18.236612: train_loss -0.9028 
+2024-09-15 19:55:18.236748: val_loss -0.7095 
+2024-09-15 19:55:18.236798: Pseudo dice [0.7091, 0.8378] 
+2024-09-15 19:55:18.236849: Epoch time: 244.8 s 
+2024-09-15 19:55:19.217268:  
+2024-09-15 19:55:19.217472: Epoch 803 
+2024-09-15 19:55:19.217560: Current learning rate: 0.00232 
+2024-09-15 19:59:23.877494: train_loss -0.9062 
+2024-09-15 19:59:23.877640: val_loss -0.706 
+2024-09-15 19:59:23.877694: Pseudo dice [0.7047, 0.8359] 
+2024-09-15 19:59:23.877753: Epoch time: 244.66 s 
+2024-09-15 19:59:24.856096:  
+2024-09-15 19:59:24.856370: Epoch 804 
+2024-09-15 19:59:24.856499: Current learning rate: 0.00231 
+2024-09-15 20:03:29.541443: train_loss -0.9062 
+2024-09-15 20:03:29.541580: val_loss -0.6915 
+2024-09-15 20:03:29.541666: Pseudo dice [0.6882, 0.8234] 
+2024-09-15 20:03:29.541720: Epoch time: 244.69 s 
+2024-09-15 20:03:30.521394:  
+2024-09-15 20:03:30.521651: Epoch 805 
+2024-09-15 20:03:30.521735: Current learning rate: 0.0023 
+2024-09-15 20:07:35.359032: train_loss -0.9063 
+2024-09-15 20:07:35.359171: val_loss -0.6909 
+2024-09-15 20:07:35.359221: Pseudo dice [0.7109, 0.8309] 
+2024-09-15 20:07:35.359270: Epoch time: 244.84 s 
+2024-09-15 20:07:36.340509:  
+2024-09-15 20:07:36.340640: Epoch 806 
+2024-09-15 20:07:36.340721: Current learning rate: 0.00229 
+2024-09-15 20:11:41.241135: train_loss -0.9067 
+2024-09-15 20:11:41.241267: val_loss -0.6978 
+2024-09-15 20:11:41.241317: Pseudo dice [0.6866, 0.8305] 
+2024-09-15 20:11:41.241368: Epoch time: 244.9 s 
+2024-09-15 20:11:42.221719:  
+2024-09-15 20:11:42.221945: Epoch 807 
+2024-09-15 20:11:42.222052: Current learning rate: 0.00228 
+2024-09-15 20:15:47.214598: train_loss -0.9089 
+2024-09-15 20:15:47.214736: val_loss -0.6964 
+2024-09-15 20:15:47.214785: Pseudo dice [0.702, 0.8293] 
+2024-09-15 20:15:47.214835: Epoch time: 244.99 s 
+2024-09-15 20:15:48.224135:  
+2024-09-15 20:15:48.224292: Epoch 808 
+2024-09-15 20:15:48.224376: Current learning rate: 0.00226 
+2024-09-15 20:19:53.080814: train_loss -0.905 
+2024-09-15 20:19:53.080955: val_loss -0.7018 
+2024-09-15 20:19:53.081003: Pseudo dice [0.6971, 0.8287] 
+2024-09-15 20:19:53.081053: Epoch time: 244.86 s 
+2024-09-15 20:19:54.072038:  
+2024-09-15 20:19:54.072196: Epoch 809 
+2024-09-15 20:19:54.072277: Current learning rate: 0.00225 
+2024-09-15 20:23:59.138761: train_loss -0.9015 
+2024-09-15 20:23:59.138934: val_loss -0.6636 
+2024-09-15 20:23:59.138988: Pseudo dice [0.6451, 0.8256] 
+2024-09-15 20:23:59.139037: Epoch time: 245.07 s 
+2024-09-15 20:24:00.153378:  
+2024-09-15 20:24:00.153535: Epoch 810 
+2024-09-15 20:24:00.153615: Current learning rate: 0.00224 
+2024-09-15 20:28:05.150450: train_loss -0.9079 
+2024-09-15 20:28:05.150634: val_loss -0.6922 
+2024-09-15 20:28:05.150725: Pseudo dice [0.675, 0.837] 
+2024-09-15 20:28:05.150776: Epoch time: 245.0 s 
+2024-09-15 20:28:06.143297:  
+2024-09-15 20:28:06.143446: Epoch 811 
+2024-09-15 20:28:06.143529: Current learning rate: 0.00223 
+2024-09-15 20:32:11.127047: train_loss -0.9079 
+2024-09-15 20:32:11.127186: val_loss -0.6986 
+2024-09-15 20:32:11.127235: Pseudo dice [0.7025, 0.8305] 
+2024-09-15 20:32:11.127285: Epoch time: 244.99 s 
+2024-09-15 20:32:12.107119:  
+2024-09-15 20:32:12.107301: Epoch 812 
+2024-09-15 20:32:12.107387: Current learning rate: 0.00222 
+2024-09-15 20:36:17.143836: train_loss -0.9023 
+2024-09-15 20:36:17.144001: val_loss -0.6891 
+2024-09-15 20:36:17.144057: Pseudo dice [0.6928, 0.8259] 
+2024-09-15 20:36:17.144117: Epoch time: 245.04 s 
+2024-09-15 20:36:18.133056:  
+2024-09-15 20:36:18.133214: Epoch 813 
+2024-09-15 20:36:18.133300: Current learning rate: 0.00221 
+2024-09-15 20:40:22.994687: train_loss -0.9033 
+2024-09-15 20:40:22.994821: val_loss -0.6873 
+2024-09-15 20:40:22.994872: Pseudo dice [0.7042, 0.8224] 
+2024-09-15 20:40:22.994921: Epoch time: 244.86 s 
+2024-09-15 20:40:23.987231:  
+2024-09-15 20:40:23.987436: Epoch 814 
+2024-09-15 20:40:23.987517: Current learning rate: 0.0022 
+2024-09-15 20:44:28.757025: train_loss -0.8999 
+2024-09-15 20:44:28.757162: val_loss -0.6645 
+2024-09-15 20:44:28.757211: Pseudo dice [0.6777, 0.8137] 
+2024-09-15 20:44:28.757261: Epoch time: 244.77 s 
+2024-09-15 20:44:29.750326:  
+2024-09-15 20:44:29.750473: Epoch 815 
+2024-09-15 20:44:29.750553: Current learning rate: 0.00219 
+2024-09-15 20:48:34.521129: train_loss -0.9066 
+2024-09-15 20:48:34.521265: val_loss -0.6896 
+2024-09-15 20:48:34.521313: Pseudo dice [0.6857, 0.8332] 
+2024-09-15 20:48:34.521363: Epoch time: 244.77 s 
+2024-09-15 20:48:36.428630:  
+2024-09-15 20:48:36.428881: Epoch 816 
+2024-09-15 20:48:36.428981: Current learning rate: 0.00218 
+2024-09-15 20:52:41.252175: train_loss -0.9023 
+2024-09-15 20:52:41.252318: val_loss -0.6847 
+2024-09-15 20:52:41.252372: Pseudo dice [0.6878, 0.8241] 
+2024-09-15 20:52:41.252421: Epoch time: 244.83 s 
+2024-09-15 20:52:42.270940:  
+2024-09-15 20:52:42.271153: Epoch 817 
+2024-09-15 20:52:42.271243: Current learning rate: 0.00217 
+2024-09-15 20:56:47.040718: train_loss -0.8985 
+2024-09-15 20:56:47.040856: val_loss -0.691 
+2024-09-15 20:56:47.040905: Pseudo dice [0.6832, 0.8167] 
+2024-09-15 20:56:47.040954: Epoch time: 244.77 s 
+2024-09-15 20:56:48.015219:  
+2024-09-15 20:56:48.015455: Epoch 818 
+2024-09-15 20:56:48.015539: Current learning rate: 0.00216 
+2024-09-15 21:00:52.701718: train_loss -0.9012 
+2024-09-15 21:00:52.701889: val_loss -0.664 
+2024-09-15 21:00:52.701946: Pseudo dice [0.663, 0.8248] 
+2024-09-15 21:00:52.701995: Epoch time: 244.69 s 
+2024-09-15 21:00:53.687495:  
+2024-09-15 21:00:53.687712: Epoch 819 
+2024-09-15 21:00:53.687794: Current learning rate: 0.00215 
+2024-09-15 21:04:58.535450: train_loss -0.893 
+2024-09-15 21:04:58.535600: val_loss -0.6953 
+2024-09-15 21:04:58.535650: Pseudo dice [0.6923, 0.8364] 
+2024-09-15 21:04:58.535700: Epoch time: 244.85 s 
+2024-09-15 21:04:59.467697:  
+2024-09-15 21:04:59.467927: Epoch 820 
+2024-09-15 21:04:59.468009: Current learning rate: 0.00214 
+2024-09-15 21:09:04.220035: train_loss -0.8985 
+2024-09-15 21:09:04.220173: val_loss -0.6898 
+2024-09-15 21:09:04.220222: Pseudo dice [0.6908, 0.8297] 
+2024-09-15 21:09:04.220275: Epoch time: 244.75 s 
+2024-09-15 21:09:05.143214:  
+2024-09-15 21:09:05.143446: Epoch 821 
+2024-09-15 21:09:05.143532: Current learning rate: 0.00213 
+2024-09-15 21:13:09.846817: train_loss -0.902 
+2024-09-15 21:13:09.846953: val_loss -0.6841 
+2024-09-15 21:13:09.847008: Pseudo dice [0.6946, 0.8175] 
+2024-09-15 21:13:09.847057: Epoch time: 244.71 s 
+2024-09-15 21:13:10.756043:  
+2024-09-15 21:13:10.756231: Epoch 822 
+2024-09-15 21:13:10.756310: Current learning rate: 0.00212 
+2024-09-15 21:17:15.522474: train_loss -0.9008 
+2024-09-15 21:17:15.522613: val_loss -0.6967 
+2024-09-15 21:17:15.522662: Pseudo dice [0.6799, 0.833] 
+2024-09-15 21:17:15.522713: Epoch time: 244.77 s 
+2024-09-15 21:17:16.450229:  
+2024-09-15 21:17:16.450436: Epoch 823 
+2024-09-15 21:17:16.450519: Current learning rate: 0.0021 
+2024-09-15 21:21:21.249482: train_loss -0.8923 
+2024-09-15 21:21:21.249674: val_loss -0.6959 
+2024-09-15 21:21:21.249724: Pseudo dice [0.6809, 0.8441] 
+2024-09-15 21:21:21.249775: Epoch time: 244.8 s 
+2024-09-15 21:21:22.178219:  
+2024-09-15 21:21:22.178415: Epoch 824 
+2024-09-15 21:21:22.178495: Current learning rate: 0.00209 
+2024-09-15 21:25:26.980173: train_loss -0.8935 
+2024-09-15 21:25:26.980328: val_loss -0.6894 
+2024-09-15 21:25:26.980378: Pseudo dice [0.6748, 0.8384] 
+2024-09-15 21:25:26.980440: Epoch time: 244.8 s 
+2024-09-15 21:25:27.898987:  
+2024-09-15 21:25:27.899168: Epoch 825 
+2024-09-15 21:25:27.899255: Current learning rate: 0.00208 
+2024-09-15 21:29:32.865076: train_loss -0.9027 
+2024-09-15 21:29:32.865213: val_loss -0.6905 
+2024-09-15 21:29:32.865262: Pseudo dice [0.6978, 0.8416] 
+2024-09-15 21:29:32.865312: Epoch time: 244.97 s 
+2024-09-15 21:29:33.791673:  
+2024-09-15 21:29:33.791853: Epoch 826 
+2024-09-15 21:29:33.791990: Current learning rate: 0.00207 
+2024-09-15 21:33:38.727253: train_loss -0.897 
+2024-09-15 21:33:38.727392: val_loss -0.6783 
+2024-09-15 21:33:38.727442: Pseudo dice [0.686, 0.8264] 
+2024-09-15 21:33:38.727491: Epoch time: 244.94 s 
+2024-09-15 21:33:39.647120:  
+2024-09-15 21:33:39.647337: Epoch 827 
+2024-09-15 21:33:39.647423: Current learning rate: 0.00206 
+2024-09-15 21:37:44.619683: train_loss -0.899 
+2024-09-15 21:37:44.619828: val_loss -0.7179 
+2024-09-15 21:37:44.619879: Pseudo dice [0.7008, 0.8325] 
+2024-09-15 21:37:44.619928: Epoch time: 244.97 s 
+2024-09-15 21:37:45.552074:  
+2024-09-15 21:37:45.552289: Epoch 828 
+2024-09-15 21:37:45.552371: Current learning rate: 0.00205 
+2024-09-15 21:41:50.563787: train_loss -0.9006 
+2024-09-15 21:41:50.563931: val_loss -0.6722 
+2024-09-15 21:41:50.563980: Pseudo dice [0.6679, 0.8259] 
+2024-09-15 21:41:50.564032: Epoch time: 245.01 s 
+2024-09-15 21:41:51.495604:  
+2024-09-15 21:41:51.495780: Epoch 829 
+2024-09-15 21:41:51.495894: Current learning rate: 0.00204 
+2024-09-15 21:45:56.407341: train_loss -0.904 
+2024-09-15 21:45:56.407514: val_loss -0.6936 
+2024-09-15 21:45:56.407565: Pseudo dice [0.6888, 0.8296] 
+2024-09-15 21:45:56.407615: Epoch time: 244.91 s 
+2024-09-15 21:45:57.320043:  
+2024-09-15 21:45:57.320218: Epoch 830 
+2024-09-15 21:45:57.320297: Current learning rate: 0.00203 
+2024-09-15 21:50:02.086035: train_loss -0.905 
+2024-09-15 21:50:02.086172: val_loss -0.7007 
+2024-09-15 21:50:02.086222: Pseudo dice [0.6835, 0.846] 
+2024-09-15 21:50:02.086272: Epoch time: 244.77 s 
+2024-09-15 21:50:03.003204:  
+2024-09-15 21:50:03.003386: Epoch 831 
+2024-09-15 21:50:03.003469: Current learning rate: 0.00202 
+2024-09-15 21:54:07.724673: train_loss -0.901 
+2024-09-15 21:54:07.724811: val_loss -0.7214 
+2024-09-15 21:54:07.724861: Pseudo dice [0.711, 0.8458] 
+2024-09-15 21:54:07.724916: Epoch time: 244.72 s 
+2024-09-15 21:54:08.646454:  
+2024-09-15 21:54:08.646618: Epoch 832 
+2024-09-15 21:54:08.646720: Current learning rate: 0.00201 
+2024-09-15 21:58:13.368509: train_loss -0.9083 
+2024-09-15 21:58:13.368646: val_loss -0.7054 
+2024-09-15 21:58:13.368696: Pseudo dice [0.6854, 0.8276] 
+2024-09-15 21:58:13.368745: Epoch time: 244.72 s 
+2024-09-15 21:58:14.306341:  
+2024-09-15 21:58:14.306615: Epoch 833 
+2024-09-15 21:58:14.306698: Current learning rate: 0.002 
+2024-09-15 22:02:19.199342: train_loss -0.9074 
+2024-09-15 22:02:19.199534: val_loss -0.7086 
+2024-09-15 22:02:19.199627: Pseudo dice [0.7157, 0.822] 
+2024-09-15 22:02:19.199717: Epoch time: 244.89 s 
+2024-09-15 22:02:20.115257:  
+2024-09-15 22:02:20.115492: Epoch 834 
+2024-09-15 22:02:20.115576: Current learning rate: 0.00199 
+2024-09-15 22:06:25.059282: train_loss -0.9035 
+2024-09-15 22:06:25.059440: val_loss -0.7227 
+2024-09-15 22:06:25.059492: Pseudo dice [0.7248, 0.8397] 
+2024-09-15 22:06:25.059541: Epoch time: 244.95 s 
+2024-09-15 22:06:25.980151:  
+2024-09-15 22:06:25.980345: Epoch 835 
+2024-09-15 22:06:25.980449: Current learning rate: 0.00198 
+2024-09-15 22:10:30.842846: train_loss -0.9042 
+2024-09-15 22:10:30.842987: val_loss -0.6946 
+2024-09-15 22:10:30.843037: Pseudo dice [0.6999, 0.8277] 
+2024-09-15 22:10:30.843086: Epoch time: 244.86 s 
+2024-09-15 22:10:31.768206:  
+2024-09-15 22:10:31.768411: Epoch 836 
+2024-09-15 22:10:31.768494: Current learning rate: 0.00196 
+2024-09-15 22:14:36.540694: train_loss -0.9012 
+2024-09-15 22:14:36.540864: val_loss -0.6913 
+2024-09-15 22:14:36.540916: Pseudo dice [0.707, 0.8407] 
+2024-09-15 22:14:36.540969: Epoch time: 244.77 s 
+2024-09-15 22:14:37.460370:  
+2024-09-15 22:14:37.460517: Epoch 837 
+2024-09-15 22:14:37.460599: Current learning rate: 0.00195 
+2024-09-15 22:18:42.332348: train_loss -0.8972 
+2024-09-15 22:18:42.332486: val_loss -0.7082 
+2024-09-15 22:18:42.332536: Pseudo dice [0.7087, 0.8286] 
+2024-09-15 22:18:42.332585: Epoch time: 244.87 s 
+2024-09-15 22:18:43.260558:  
+2024-09-15 22:18:43.260736: Epoch 838 
+2024-09-15 22:18:43.260818: Current learning rate: 0.00194 
+2024-09-15 22:22:48.001405: train_loss -0.8963 
+2024-09-15 22:22:48.001541: val_loss -0.7289 
+2024-09-15 22:22:48.001590: Pseudo dice [0.7142, 0.839] 
+2024-09-15 22:22:48.001641: Epoch time: 244.74 s 
+2024-09-15 22:22:48.917871:  
+2024-09-15 22:22:48.918050: Epoch 839 
+2024-09-15 22:22:48.918150: Current learning rate: 0.00193 
+2024-09-15 22:26:53.750088: train_loss -0.9056 
+2024-09-15 22:26:53.750221: val_loss -0.6747 
+2024-09-15 22:26:53.750270: Pseudo dice [0.6974, 0.812] 
+2024-09-15 22:26:53.750322: Epoch time: 244.83 s 
+2024-09-15 22:26:55.591861:  
+2024-09-15 22:26:55.592114: Epoch 840 
+2024-09-15 22:26:55.592196: Current learning rate: 0.00192 
+2024-09-15 22:31:00.482401: train_loss -0.9079 
+2024-09-15 22:31:00.482566: val_loss -0.7005 
+2024-09-15 22:31:00.482616: Pseudo dice [0.7029, 0.8313] 
+2024-09-15 22:31:00.482666: Epoch time: 244.89 s 
+2024-09-15 22:31:01.406356:  
+2024-09-15 22:31:01.406545: Epoch 841 
+2024-09-15 22:31:01.406643: Current learning rate: 0.00191 
+2024-09-15 22:35:06.295398: train_loss -0.9097 
+2024-09-15 22:35:06.295536: val_loss -0.6816 
+2024-09-15 22:35:06.295586: Pseudo dice [0.6887, 0.823] 
+2024-09-15 22:35:06.295640: Epoch time: 244.89 s 
+2024-09-15 22:35:07.208217:  
+2024-09-15 22:35:07.208452: Epoch 842 
+2024-09-15 22:35:07.208532: Current learning rate: 0.0019 
+2024-09-15 22:39:12.120639: train_loss -0.8974 
+2024-09-15 22:39:12.120804: val_loss -0.6957 
+2024-09-15 22:39:12.120854: Pseudo dice [0.6938, 0.8192] 
+2024-09-15 22:39:12.120905: Epoch time: 244.91 s 
+2024-09-15 22:39:13.031859:  
+2024-09-15 22:39:13.032071: Epoch 843 
+2024-09-15 22:39:13.032159: Current learning rate: 0.00189 
+2024-09-15 22:43:17.855679: train_loss -0.9049 
+2024-09-15 22:43:17.855833: val_loss -0.6743 
+2024-09-15 22:43:17.855886: Pseudo dice [0.6726, 0.8292] 
+2024-09-15 22:43:17.855935: Epoch time: 244.83 s 
+2024-09-15 22:43:18.775571:  
+2024-09-15 22:43:18.775830: Epoch 844 
+2024-09-15 22:43:18.775911: Current learning rate: 0.00188 
+2024-09-15 22:47:23.752060: train_loss -0.9041 
+2024-09-15 22:47:23.752197: val_loss -0.7094 
+2024-09-15 22:47:23.752247: Pseudo dice [0.7074, 0.8366] 
+2024-09-15 22:47:23.752295: Epoch time: 244.98 s 
+2024-09-15 22:47:24.686727:  
+2024-09-15 22:47:24.686944: Epoch 845 
+2024-09-15 22:47:24.687025: Current learning rate: 0.00187 
+2024-09-15 22:51:29.724429: train_loss -0.9043 
+2024-09-15 22:51:29.724569: val_loss -0.6942 
+2024-09-15 22:51:29.724622: Pseudo dice [0.6998, 0.8216] 
+2024-09-15 22:51:29.724671: Epoch time: 245.04 s 
+2024-09-15 22:51:30.633784:  
+2024-09-15 22:51:30.633975: Epoch 846 
+2024-09-15 22:51:30.634058: Current learning rate: 0.00186 
+2024-09-15 22:55:35.674874: train_loss -0.909 
+2024-09-15 22:55:35.675011: val_loss -0.7289 
+2024-09-15 22:55:35.675061: Pseudo dice [0.7229, 0.8357] 
+2024-09-15 22:55:35.675120: Epoch time: 245.04 s 
+2024-09-15 22:55:36.584710:  
+2024-09-15 22:55:36.584903: Epoch 847 
+2024-09-15 22:55:36.585017: Current learning rate: 0.00185 
+2024-09-15 22:59:41.584740: train_loss -0.9081 
+2024-09-15 22:59:41.584879: val_loss -0.6961 
+2024-09-15 22:59:41.584928: Pseudo dice [0.6973, 0.8348] 
+2024-09-15 22:59:41.584976: Epoch time: 245.0 s 
+2024-09-15 22:59:42.513531:  
+2024-09-15 22:59:42.513761: Epoch 848 
+2024-09-15 22:59:42.513845: Current learning rate: 0.00184 
+2024-09-15 23:03:47.480022: train_loss -0.9091 
+2024-09-15 23:03:47.480160: val_loss -0.7121 
+2024-09-15 23:03:47.480210: Pseudo dice [0.7261, 0.8261] 
+2024-09-15 23:03:47.480261: Epoch time: 244.97 s 
+2024-09-15 23:03:48.439609:  
+2024-09-15 23:03:48.439810: Epoch 849 
+2024-09-15 23:03:48.439893: Current learning rate: 0.00182 
+2024-09-15 23:07:53.527298: train_loss -0.9099 
+2024-09-15 23:07:53.527435: val_loss -0.6756 
+2024-09-15 23:07:53.527497: Pseudo dice [0.6742, 0.8257] 
+2024-09-15 23:07:53.527547: Epoch time: 245.09 s 
+2024-09-15 23:07:57.435401:  
+2024-09-15 23:07:57.435572: Epoch 850 
+2024-09-15 23:07:57.435655: Current learning rate: 0.00181 
+2024-09-15 23:12:02.477473: train_loss -0.9109 
+2024-09-15 23:12:02.477627: val_loss -0.6948 
+2024-09-15 23:12:02.477677: Pseudo dice [0.6976, 0.8222] 
+2024-09-15 23:12:02.477727: Epoch time: 245.04 s 
+2024-09-15 23:12:03.397559:  
+2024-09-15 23:12:03.397760: Epoch 851 
+2024-09-15 23:12:03.397844: Current learning rate: 0.0018 
+2024-09-15 23:16:08.511213: train_loss -0.9058 
+2024-09-15 23:16:08.511353: val_loss -0.6825 
+2024-09-15 23:16:08.511403: Pseudo dice [0.6813, 0.823] 
+2024-09-15 23:16:08.511453: Epoch time: 245.12 s 
+2024-09-15 23:16:09.442460:  
+2024-09-15 23:16:09.442649: Epoch 852 
+2024-09-15 23:16:09.442748: Current learning rate: 0.00179 
+2024-09-15 23:20:14.824998: train_loss -0.9063 
+2024-09-15 23:20:14.825131: val_loss -0.7044 
+2024-09-15 23:20:14.825182: Pseudo dice [0.7216, 0.8128] 
+2024-09-15 23:20:14.825232: Epoch time: 245.38 s 
+2024-09-15 23:20:15.752821:  
+2024-09-15 23:20:15.753025: Epoch 853 
+2024-09-15 23:20:15.753107: Current learning rate: 0.00178 
+2024-09-15 23:24:21.106030: train_loss -0.9046 
+2024-09-15 23:24:21.106168: val_loss -0.7137 
+2024-09-15 23:24:21.106217: Pseudo dice [0.7126, 0.8383] 
+2024-09-15 23:24:21.106266: Epoch time: 245.36 s 
+2024-09-15 23:24:22.021652:  
+2024-09-15 23:24:22.021871: Epoch 854 
+2024-09-15 23:24:22.021998: Current learning rate: 0.00177 
+2024-09-15 23:28:27.214351: train_loss -0.9065 
+2024-09-15 23:28:27.214488: val_loss -0.6948 
+2024-09-15 23:28:27.214538: Pseudo dice [0.7014, 0.8188] 
+2024-09-15 23:28:27.214593: Epoch time: 245.2 s 
+2024-09-15 23:28:28.147514:  
+2024-09-15 23:28:28.147705: Epoch 855 
+2024-09-15 23:28:28.147785: Current learning rate: 0.00176 
+2024-09-15 23:32:33.152544: train_loss -0.906 
+2024-09-15 23:32:33.152682: val_loss -0.6878 
+2024-09-15 23:32:33.152753: Pseudo dice [0.6937, 0.826] 
+2024-09-15 23:32:33.152830: Epoch time: 245.01 s 
+2024-09-15 23:32:34.072000:  
+2024-09-15 23:32:34.072201: Epoch 856 
+2024-09-15 23:32:34.072284: Current learning rate: 0.00175 
+2024-09-15 23:36:39.150070: train_loss -0.9096 
+2024-09-15 23:36:39.150205: val_loss -0.6675 
+2024-09-15 23:36:39.150254: Pseudo dice [0.681, 0.8255] 
+2024-09-15 23:36:39.150303: Epoch time: 245.08 s 
+2024-09-15 23:36:40.057790:  
+2024-09-15 23:36:40.058004: Epoch 857 
+2024-09-15 23:36:40.058084: Current learning rate: 0.00174 
+2024-09-15 23:40:45.292645: train_loss -0.9099 
+2024-09-15 23:40:45.292783: val_loss -0.7212 
+2024-09-15 23:40:45.292833: Pseudo dice [0.7193, 0.8395] 
+2024-09-15 23:40:45.292883: Epoch time: 245.24 s 
+2024-09-15 23:40:46.205638:  
+2024-09-15 23:40:46.205777: Epoch 858 
+2024-09-15 23:40:46.205860: Current learning rate: 0.00173 
+2024-09-15 23:44:51.314873: train_loss -0.9104 
+2024-09-15 23:44:51.315015: val_loss -0.7057 
+2024-09-15 23:44:51.315065: Pseudo dice [0.7013, 0.8411] 
+2024-09-15 23:44:51.315114: Epoch time: 245.11 s 
+2024-09-15 23:44:52.232601:  
+2024-09-15 23:44:52.232784: Epoch 859 
+2024-09-15 23:44:52.232866: Current learning rate: 0.00172 
+2024-09-15 23:48:57.958135: train_loss -0.9099 
+2024-09-15 23:48:57.958345: val_loss -0.6991 
+2024-09-15 23:48:57.958439: Pseudo dice [0.7048, 0.8339] 
+2024-09-15 23:48:57.958529: Epoch time: 245.73 s 
+2024-09-15 23:48:58.895294:  
+2024-09-15 23:48:58.895492: Epoch 860 
+2024-09-15 23:48:58.895576: Current learning rate: 0.0017 
+2024-09-15 23:53:05.173874: train_loss -0.9082 
+2024-09-15 23:53:05.174284: val_loss -0.697 
+2024-09-15 23:53:05.174335: Pseudo dice [0.6921, 0.8416] 
+2024-09-15 23:53:05.174386: Epoch time: 246.28 s 
+2024-09-15 23:53:06.087777:  
+2024-09-15 23:53:06.087924: Epoch 861 
+2024-09-15 23:53:06.088007: Current learning rate: 0.00169 
+2024-09-15 23:57:13.488280: train_loss -0.9069 
+2024-09-15 23:57:13.488419: val_loss -0.7056 
+2024-09-15 23:57:13.488469: Pseudo dice [0.7063, 0.829] 
+2024-09-15 23:57:13.488519: Epoch time: 247.4 s 
+2024-09-15 23:57:14.400271:  
+2024-09-15 23:57:14.400477: Epoch 862 
+2024-09-15 23:57:14.400573: Current learning rate: 0.00168 
+2024-09-16 00:01:24.457989: train_loss -0.9095 
+2024-09-16 00:01:24.458150: val_loss -0.6996 
+2024-09-16 00:01:24.458201: Pseudo dice [0.7177, 0.834] 
+2024-09-16 00:01:24.458251: Epoch time: 250.06 s 
+2024-09-16 00:01:25.381799:  
+2024-09-16 00:01:25.381962: Epoch 863 
+2024-09-16 00:01:25.382049: Current learning rate: 0.00167 
+2024-09-16 00:05:31.242252: train_loss -0.9047 
+2024-09-16 00:05:31.242402: val_loss -0.6994 
+2024-09-16 00:05:31.242466: Pseudo dice [0.7145, 0.8336] 
+2024-09-16 00:05:31.242530: Epoch time: 245.86 s 
+2024-09-16 00:05:31.242576: Yayy! New best EMA pseudo Dice: 0.7674 
+2024-09-16 00:05:35.054489:  
+2024-09-16 00:05:35.054615: Epoch 864 
+2024-09-16 00:05:35.054704: Current learning rate: 0.00166 
+2024-09-16 00:09:44.500667: train_loss -0.9 
+2024-09-16 00:09:44.500813: val_loss -0.6946 
+2024-09-16 00:09:44.500870: Pseudo dice [0.694, 0.8268] 
+2024-09-16 00:09:44.500921: Epoch time: 249.45 s 
+2024-09-16 00:09:45.436319:  
+2024-09-16 00:09:45.436533: Epoch 865 
+2024-09-16 00:09:45.436617: Current learning rate: 0.00165 
+2024-09-16 00:13:51.750237: train_loss -0.9003 
+2024-09-16 00:13:51.750404: val_loss -0.6601 
+2024-09-16 00:13:51.750453: Pseudo dice [0.6589, 0.8329] 
+2024-09-16 00:13:51.750506: Epoch time: 246.32 s 
+2024-09-16 00:13:52.727537:  
+2024-09-16 00:13:52.727850: Epoch 866 
+2024-09-16 00:13:52.727944: Current learning rate: 0.00164 
+2024-09-16 00:17:59.606459: train_loss -0.9002 
+2024-09-16 00:17:59.606855: val_loss -0.6674 
+2024-09-16 00:17:59.606907: Pseudo dice [0.6581, 0.8153] 
+2024-09-16 00:17:59.606961: Epoch time: 246.88 s 
+2024-09-16 00:18:00.552348:  
+2024-09-16 00:18:00.552614: Epoch 867 
+2024-09-16 00:18:00.552699: Current learning rate: 0.00163 
+2024-09-16 00:22:06.245586: train_loss -0.9046 
+2024-09-16 00:22:06.245728: val_loss -0.6933 
+2024-09-16 00:22:06.245830: Pseudo dice [0.6884, 0.8286] 
+2024-09-16 00:22:06.245883: Epoch time: 245.7 s 
+2024-09-16 00:22:07.171516:  
+2024-09-16 00:22:07.171705: Epoch 868 
+2024-09-16 00:22:07.171783: Current learning rate: 0.00162 
+2024-09-16 00:26:12.863903: train_loss -0.911 
+2024-09-16 00:26:12.864044: val_loss -0.7043 
+2024-09-16 00:26:12.864094: Pseudo dice [0.7087, 0.8364] 
+2024-09-16 00:26:12.864146: Epoch time: 245.69 s 
+2024-09-16 00:26:13.806754:  
+2024-09-16 00:26:13.806961: Epoch 869 
+2024-09-16 00:26:13.807045: Current learning rate: 0.00161 
+2024-09-16 00:30:19.284944: train_loss -0.911 
+2024-09-16 00:30:19.285083: val_loss -0.7134 
+2024-09-16 00:30:19.285134: Pseudo dice [0.6988, 0.8342] 
+2024-09-16 00:30:19.285184: Epoch time: 245.48 s 
+2024-09-16 00:30:20.230214:  
+2024-09-16 00:30:20.230488: Epoch 870 
+2024-09-16 00:30:20.230576: Current learning rate: 0.00159 
+2024-09-16 00:34:25.680297: train_loss -0.9072 
+2024-09-16 00:34:25.680458: val_loss -0.6845 
+2024-09-16 00:34:25.680510: Pseudo dice [0.689, 0.8243] 
+2024-09-16 00:34:25.680561: Epoch time: 245.45 s 
+2024-09-16 00:34:26.609666:  
+2024-09-16 00:34:26.609847: Epoch 871 
+2024-09-16 00:34:26.609933: Current learning rate: 0.00158 
+2024-09-16 00:38:32.082563: train_loss -0.9066 
+2024-09-16 00:38:32.082733: val_loss -0.709 
+2024-09-16 00:38:32.082783: Pseudo dice [0.6924, 0.8392] 
+2024-09-16 00:38:32.082835: Epoch time: 245.47 s 
+2024-09-16 00:38:33.024648:  
+2024-09-16 00:38:33.024858: Epoch 872 
+2024-09-16 00:38:33.024944: Current learning rate: 0.00157 
+2024-09-16 00:42:38.668957: train_loss -0.9033 
+2024-09-16 00:42:38.669144: val_loss -0.6988 
+2024-09-16 00:42:38.669195: Pseudo dice [0.6907, 0.8337] 
+2024-09-16 00:42:38.669248: Epoch time: 245.65 s 
+2024-09-16 00:42:39.601526:  
+2024-09-16 00:42:39.601742: Epoch 873 
+2024-09-16 00:42:39.601854: Current learning rate: 0.00156 
+2024-09-16 00:46:45.634193: train_loss -0.9023 
+2024-09-16 00:46:45.634362: val_loss -0.6975 
+2024-09-16 00:46:45.634413: Pseudo dice [0.6751, 0.8379] 
+2024-09-16 00:46:45.634467: Epoch time: 246.03 s 
+2024-09-16 00:46:46.588309:  
+2024-09-16 00:46:46.588493: Epoch 874 
+2024-09-16 00:46:46.588603: Current learning rate: 0.00155 
+2024-09-16 00:50:52.288568: train_loss -0.9039 
+2024-09-16 00:50:52.288703: val_loss -0.6932 
+2024-09-16 00:50:52.288753: Pseudo dice [0.7002, 0.8246] 
+2024-09-16 00:50:52.288803: Epoch time: 245.7 s 
+2024-09-16 00:50:53.222601:  
+2024-09-16 00:50:53.222820: Epoch 875 
+2024-09-16 00:50:53.222906: Current learning rate: 0.00154 
+2024-09-16 00:54:58.849994: train_loss -0.9022 
+2024-09-16 00:54:58.850134: val_loss -0.7111 
+2024-09-16 00:54:58.850183: Pseudo dice [0.7083, 0.8433] 
+2024-09-16 00:54:58.850234: Epoch time: 245.63 s 
+2024-09-16 00:54:59.785063:  
+2024-09-16 00:54:59.785281: Epoch 876 
+2024-09-16 00:54:59.785365: Current learning rate: 0.00153 
+2024-09-16 00:59:05.461128: train_loss -0.907 
+2024-09-16 00:59:05.461278: val_loss -0.6913 
+2024-09-16 00:59:05.461328: Pseudo dice [0.6921, 0.8393] 
+2024-09-16 00:59:05.461378: Epoch time: 245.68 s 
+2024-09-16 00:59:06.392622:  
+2024-09-16 00:59:06.392784: Epoch 877 
+2024-09-16 00:59:06.392870: Current learning rate: 0.00152 
+2024-09-16 01:03:11.923764: train_loss -0.898 
+2024-09-16 01:03:11.923943: val_loss -0.695 
+2024-09-16 01:03:11.923992: Pseudo dice [0.699, 0.8377] 
+2024-09-16 01:03:11.924043: Epoch time: 245.53 s 
+2024-09-16 01:03:12.849671:  
+2024-09-16 01:03:12.849822: Epoch 878 
+2024-09-16 01:03:12.849930: Current learning rate: 0.00151 
+2024-09-16 01:07:18.339127: train_loss -0.9048 
+2024-09-16 01:07:18.339265: val_loss -0.6997 
+2024-09-16 01:07:18.339315: Pseudo dice [0.715, 0.8146] 
+2024-09-16 01:07:18.339366: Epoch time: 245.49 s 
+2024-09-16 01:07:19.422597:  
+2024-09-16 01:07:19.422811: Epoch 879 
+2024-09-16 01:07:19.422894: Current learning rate: 0.00149 
+2024-09-16 01:11:24.875927: train_loss -0.9052 
+2024-09-16 01:11:24.876065: val_loss -0.7241 
+2024-09-16 01:11:24.876132: Pseudo dice [0.7123, 0.8443] 
+2024-09-16 01:11:24.876243: Epoch time: 245.46 s 
+2024-09-16 01:11:25.794344:  
+2024-09-16 01:11:25.794516: Epoch 880 
+2024-09-16 01:11:25.794614: Current learning rate: 0.00148 
+2024-09-16 01:15:31.462758: train_loss -0.9088 
+2024-09-16 01:15:31.462897: val_loss -0.7012 
+2024-09-16 01:15:31.462948: Pseudo dice [0.6924, 0.8275] 
+2024-09-16 01:15:31.463000: Epoch time: 245.67 s 
+2024-09-16 01:15:32.391935:  
+2024-09-16 01:15:32.392142: Epoch 881 
+2024-09-16 01:15:32.392226: Current learning rate: 0.00147 
+2024-09-16 01:19:38.228233: train_loss -0.9144 
+2024-09-16 01:19:38.228369: val_loss -0.678 
+2024-09-16 01:19:38.228418: Pseudo dice [0.6867, 0.8241] 
+2024-09-16 01:19:38.228470: Epoch time: 245.84 s 
+2024-09-16 01:19:39.148933:  
+2024-09-16 01:19:39.149060: Epoch 882 
+2024-09-16 01:19:39.149142: Current learning rate: 0.00146 
+2024-09-16 01:23:44.955360: train_loss -0.9135 
+2024-09-16 01:23:44.955575: val_loss -0.7236 
+2024-09-16 01:23:44.955629: Pseudo dice [0.7277, 0.8289] 
+2024-09-16 01:23:44.955680: Epoch time: 245.81 s 
+2024-09-16 01:23:45.891839:  
+2024-09-16 01:23:45.892154: Epoch 883 
+2024-09-16 01:23:45.892237: Current learning rate: 0.00145 
+2024-09-16 01:27:51.661800: train_loss -0.9121 
+2024-09-16 01:27:51.662001: val_loss -0.6651 
+2024-09-16 01:27:51.662094: Pseudo dice [0.6575, 0.8149] 
+2024-09-16 01:27:51.662183: Epoch time: 245.77 s 
+2024-09-16 01:27:52.593762:  
+2024-09-16 01:27:52.593968: Epoch 884 
+2024-09-16 01:27:52.594056: Current learning rate: 0.00144 
+2024-09-16 01:31:58.091831: train_loss -0.9123 
+2024-09-16 01:31:58.091975: val_loss -0.6775 
+2024-09-16 01:31:58.092024: Pseudo dice [0.6785, 0.8141] 
+2024-09-16 01:31:58.092076: Epoch time: 245.5 s 
+2024-09-16 01:31:59.048740:  
+2024-09-16 01:31:59.048990: Epoch 885 
+2024-09-16 01:31:59.049076: Current learning rate: 0.00143 
+2024-09-16 01:36:04.587317: train_loss -0.9125 
+2024-09-16 01:36:04.587456: val_loss -0.7154 
+2024-09-16 01:36:04.587507: Pseudo dice [0.7056, 0.8445] 
+2024-09-16 01:36:04.587557: Epoch time: 245.54 s 
+2024-09-16 01:36:05.515909:  
+2024-09-16 01:36:05.516067: Epoch 886 
+2024-09-16 01:36:05.516147: Current learning rate: 0.00142 
+2024-09-16 01:40:11.065646: train_loss -0.9141 
+2024-09-16 01:40:11.065784: val_loss -0.6576 
+2024-09-16 01:40:11.065875: Pseudo dice [0.6899, 0.8268] 
+2024-09-16 01:40:11.065987: Epoch time: 245.55 s 
+2024-09-16 01:40:11.998415:  
+2024-09-16 01:40:11.998621: Epoch 887 
+2024-09-16 01:40:11.998704: Current learning rate: 0.00141 
+2024-09-16 01:44:17.465625: train_loss -0.9157 
+2024-09-16 01:44:17.465760: val_loss -0.6956 
+2024-09-16 01:44:17.465811: Pseudo dice [0.7098, 0.8405] 
+2024-09-16 01:44:17.465861: Epoch time: 245.47 s 
+2024-09-16 01:44:18.399976:  
+2024-09-16 01:44:18.400161: Epoch 888 
+2024-09-16 01:44:18.400243: Current learning rate: 0.00139 
+2024-09-16 01:48:24.047228: train_loss -0.9128 
+2024-09-16 01:48:24.047367: val_loss -0.6726 
+2024-09-16 01:48:24.047416: Pseudo dice [0.6758, 0.8294] 
+2024-09-16 01:48:24.047467: Epoch time: 245.65 s 
+2024-09-16 01:48:24.971094:  
+2024-09-16 01:48:24.971266: Epoch 889 
+2024-09-16 01:48:24.971345: Current learning rate: 0.00138 
+2024-09-16 01:52:30.595304: train_loss -0.9093 
+2024-09-16 01:52:30.595483: val_loss -0.6525 
+2024-09-16 01:52:30.595537: Pseudo dice [0.6275, 0.8291] 
+2024-09-16 01:52:30.595593: Epoch time: 245.63 s 
+2024-09-16 01:52:32.435339:  
+2024-09-16 01:52:32.435566: Epoch 890 
+2024-09-16 01:52:32.435648: Current learning rate: 0.00137 
+2024-09-16 01:56:38.108361: train_loss -0.9136 
+2024-09-16 01:56:38.108511: val_loss -0.7023 
+2024-09-16 01:56:38.108564: Pseudo dice [0.7111, 0.8289] 
+2024-09-16 01:56:38.108615: Epoch time: 245.67 s 
+2024-09-16 01:56:39.047875:  
+2024-09-16 01:56:39.048095: Epoch 891 
+2024-09-16 01:56:39.048178: Current learning rate: 0.00136 
+2024-09-16 02:00:44.802781: train_loss -0.9108 
+2024-09-16 02:00:44.802920: val_loss -0.7003 
+2024-09-16 02:00:44.803012: Pseudo dice [0.6935, 0.8327] 
+2024-09-16 02:00:44.803065: Epoch time: 245.76 s 
+2024-09-16 02:00:45.746965:  
+2024-09-16 02:00:45.747169: Epoch 892 
+2024-09-16 02:00:45.747291: Current learning rate: 0.00135 
+2024-09-16 02:04:51.674537: train_loss -0.9101 
+2024-09-16 02:04:51.674675: val_loss -0.6829 
+2024-09-16 02:04:51.674724: Pseudo dice [0.6832, 0.8093] 
+2024-09-16 02:04:51.674775: Epoch time: 245.93 s 
+2024-09-16 02:04:52.619463:  
+2024-09-16 02:04:52.619757: Epoch 893 
+2024-09-16 02:04:52.619853: Current learning rate: 0.00134 
+2024-09-16 02:08:58.522861: train_loss -0.9146 
+2024-09-16 02:08:58.523000: val_loss -0.6991 
+2024-09-16 02:08:58.523050: Pseudo dice [0.6792, 0.8409] 
+2024-09-16 02:08:58.523100: Epoch time: 245.91 s 
+2024-09-16 02:08:59.447485:  
+2024-09-16 02:08:59.447692: Epoch 894 
+2024-09-16 02:08:59.447776: Current learning rate: 0.00133 
+2024-09-16 02:13:05.302684: train_loss -0.9097 
+2024-09-16 02:13:05.302820: val_loss -0.647 
+2024-09-16 02:13:05.302869: Pseudo dice [0.6346, 0.825] 
+2024-09-16 02:13:05.302919: Epoch time: 245.86 s 
+2024-09-16 02:13:06.228569:  
+2024-09-16 02:13:06.228737: Epoch 895 
+2024-09-16 02:13:06.228814: Current learning rate: 0.00132 
+2024-09-16 02:17:12.070521: train_loss -0.9134 
+2024-09-16 02:17:12.070657: val_loss -0.6665 
+2024-09-16 02:17:12.070707: Pseudo dice [0.6656, 0.8355] 
+2024-09-16 02:17:12.070756: Epoch time: 245.84 s 
+2024-09-16 02:17:12.992252:  
+2024-09-16 02:17:12.992456: Epoch 896 
+2024-09-16 02:17:12.992569: Current learning rate: 0.0013 
+2024-09-16 02:21:18.863253: train_loss -0.9107 
+2024-09-16 02:21:18.863391: val_loss -0.7018 
+2024-09-16 02:21:18.863505: Pseudo dice [0.6909, 0.8435] 
+2024-09-16 02:21:18.863559: Epoch time: 245.87 s 
+2024-09-16 02:21:19.792089:  
+2024-09-16 02:21:19.792270: Epoch 897 
+2024-09-16 02:21:19.792351: Current learning rate: 0.00129 
+2024-09-16 02:25:25.553674: train_loss -0.9108 
+2024-09-16 02:25:25.553843: val_loss -0.7016 
+2024-09-16 02:25:25.553894: Pseudo dice [0.6747, 0.8393] 
+2024-09-16 02:25:25.553948: Epoch time: 245.76 s 
+2024-09-16 02:25:26.486515:  
+2024-09-16 02:25:26.486790: Epoch 898 
+2024-09-16 02:25:26.486904: Current learning rate: 0.00128 
+2024-09-16 02:29:32.055304: train_loss -0.9098 
+2024-09-16 02:29:32.055441: val_loss -0.6701 
+2024-09-16 02:29:32.055490: Pseudo dice [0.6702, 0.8218] 
+2024-09-16 02:29:32.055541: Epoch time: 245.57 s 
+2024-09-16 02:29:32.973636:  
+2024-09-16 02:29:32.973820: Epoch 899 
+2024-09-16 02:29:32.973902: Current learning rate: 0.00127 
+2024-09-16 02:33:38.570789: train_loss -0.9117 
+2024-09-16 02:33:38.570928: val_loss -0.6781 
+2024-09-16 02:33:38.570978: Pseudo dice [0.6823, 0.823] 
+2024-09-16 02:33:38.571029: Epoch time: 245.6 s 
+2024-09-16 02:33:42.306600:  
+2024-09-16 02:33:42.306798: Epoch 900 
+2024-09-16 02:33:42.306926: Current learning rate: 0.00126 
+2024-09-16 02:37:47.658017: train_loss -0.9082 
+2024-09-16 02:37:47.658163: val_loss -0.6809 
+2024-09-16 02:37:47.658220: Pseudo dice [0.6815, 0.8304] 
+2024-09-16 02:37:47.658284: Epoch time: 245.35 s 
+2024-09-16 02:37:48.588796:  
+2024-09-16 02:37:48.589019: Epoch 901 
+2024-09-16 02:37:48.589102: Current learning rate: 0.00125 
+2024-09-16 02:41:53.909815: train_loss -0.9093 
+2024-09-16 02:41:53.909956: val_loss -0.6772 
+2024-09-16 02:41:53.910006: Pseudo dice [0.6742, 0.8329] 
+2024-09-16 02:41:53.910057: Epoch time: 245.32 s 
+2024-09-16 02:41:54.845877:  
+2024-09-16 02:41:54.846091: Epoch 902 
+2024-09-16 02:41:54.846174: Current learning rate: 0.00124 
+2024-09-16 02:46:00.229814: train_loss -0.9116 
+2024-09-16 02:46:00.229954: val_loss -0.6775 
+2024-09-16 02:46:00.230004: Pseudo dice [0.6724, 0.8304] 
+2024-09-16 02:46:00.230054: Epoch time: 245.39 s 
+2024-09-16 02:46:01.160263:  
+2024-09-16 02:46:01.160493: Epoch 903 
+2024-09-16 02:46:01.160581: Current learning rate: 0.00122 
+2024-09-16 02:50:06.417756: train_loss -0.9117 
+2024-09-16 02:50:06.417892: val_loss -0.6834 
+2024-09-16 02:50:06.417943: Pseudo dice [0.6749, 0.8426] 
+2024-09-16 02:50:06.417996: Epoch time: 245.26 s 
+2024-09-16 02:50:07.369731:  
+2024-09-16 02:50:07.369965: Epoch 904 
+2024-09-16 02:50:07.370049: Current learning rate: 0.00121 
+2024-09-16 02:54:12.692445: train_loss -0.9092 
+2024-09-16 02:54:12.692581: val_loss -0.6722 
+2024-09-16 02:54:12.692631: Pseudo dice [0.6576, 0.8244] 
+2024-09-16 02:54:12.692681: Epoch time: 245.32 s 
+2024-09-16 02:54:13.633719:  
+2024-09-16 02:54:13.633878: Epoch 905 
+2024-09-16 02:54:13.633980: Current learning rate: 0.0012 
+2024-09-16 02:58:18.875052: train_loss -0.917 
+2024-09-16 02:58:18.875190: val_loss -0.6697 
+2024-09-16 02:58:18.875240: Pseudo dice [0.6726, 0.8211] 
+2024-09-16 02:58:18.875291: Epoch time: 245.24 s 
+2024-09-16 02:58:19.804946:  
+2024-09-16 02:58:19.805180: Epoch 906 
+2024-09-16 02:58:19.805290: Current learning rate: 0.00119 
+2024-09-16 03:02:25.113642: train_loss -0.916 
+2024-09-16 03:02:25.113778: val_loss -0.6974 
+2024-09-16 03:02:25.113829: Pseudo dice [0.6905, 0.8495] 
+2024-09-16 03:02:25.113879: Epoch time: 245.31 s 
+2024-09-16 03:02:26.036485:  
+2024-09-16 03:02:26.036631: Epoch 907 
+2024-09-16 03:02:26.036729: Current learning rate: 0.00118 
+2024-09-16 03:06:31.411062: train_loss -0.913 
+2024-09-16 03:06:31.411223: val_loss -0.6745 
+2024-09-16 03:06:31.411280: Pseudo dice [0.6485, 0.8236] 
+2024-09-16 03:06:31.411330: Epoch time: 245.38 s 
+2024-09-16 03:06:32.340996:  
+2024-09-16 03:06:32.341163: Epoch 908 
+2024-09-16 03:06:32.341249: Current learning rate: 0.00117 
+2024-09-16 03:10:38.035157: train_loss -0.9073 
+2024-09-16 03:10:38.035293: val_loss -0.6952 
+2024-09-16 03:10:38.035454: Pseudo dice [0.6882, 0.8453] 
+2024-09-16 03:10:38.035573: Epoch time: 245.7 s 
+2024-09-16 03:10:38.962785:  
+2024-09-16 03:10:38.962988: Epoch 909 
+2024-09-16 03:10:38.963069: Current learning rate: 0.00116 
+2024-09-16 03:14:44.538038: train_loss -0.9082 
+2024-09-16 03:14:44.538176: val_loss -0.6726 
+2024-09-16 03:14:44.538226: Pseudo dice [0.6671, 0.8217] 
+2024-09-16 03:14:44.538276: Epoch time: 245.58 s 
+2024-09-16 03:14:45.468750:  
+2024-09-16 03:14:45.469034: Epoch 910 
+2024-09-16 03:14:45.469116: Current learning rate: 0.00115 
+2024-09-16 03:18:51.147682: train_loss -0.9109 
+2024-09-16 03:18:51.147827: val_loss -0.7073 
+2024-09-16 03:18:51.147880: Pseudo dice [0.7039, 0.8346] 
+2024-09-16 03:18:51.147930: Epoch time: 245.68 s 
+2024-09-16 03:18:52.103978:  
+2024-09-16 03:18:52.104189: Epoch 911 
+2024-09-16 03:18:52.104300: Current learning rate: 0.00113 
+2024-09-16 03:22:57.762915: train_loss -0.9144 
+2024-09-16 03:22:57.763056: val_loss -0.6883 
+2024-09-16 03:22:57.763105: Pseudo dice [0.6784, 0.825] 
+2024-09-16 03:22:57.763156: Epoch time: 245.66 s 
+2024-09-16 03:22:58.696228:  
+2024-09-16 03:22:58.696368: Epoch 912 
+2024-09-16 03:22:58.696450: Current learning rate: 0.00112 
+2024-09-16 03:27:04.365666: train_loss -0.9075 
+2024-09-16 03:27:04.365810: val_loss -0.6676 
+2024-09-16 03:27:04.365859: Pseudo dice [0.6982, 0.8216] 
+2024-09-16 03:27:04.365909: Epoch time: 245.67 s 
+2024-09-16 03:27:05.287997:  
+2024-09-16 03:27:05.288283: Epoch 913 
+2024-09-16 03:27:05.288369: Current learning rate: 0.00111 
+2024-09-16 03:31:11.119217: train_loss -0.9057 
+2024-09-16 03:31:11.119354: val_loss -0.7009 
+2024-09-16 03:31:11.119405: Pseudo dice [0.6942, 0.8117] 
+2024-09-16 03:31:11.119454: Epoch time: 245.83 s 
+2024-09-16 03:31:12.043056:  
+2024-09-16 03:31:12.043231: Epoch 914 
+2024-09-16 03:31:12.043313: Current learning rate: 0.0011 
+2024-09-16 03:35:17.692035: train_loss -0.9103 
+2024-09-16 03:35:17.692172: val_loss -0.6711 
+2024-09-16 03:35:17.692221: Pseudo dice [0.6742, 0.8293] 
+2024-09-16 03:35:17.692271: Epoch time: 245.65 s 
+2024-09-16 03:35:19.527981:  
+2024-09-16 03:35:19.528241: Epoch 915 
+2024-09-16 03:35:19.528327: Current learning rate: 0.00109 
+2024-09-16 03:39:25.281411: train_loss -0.9136 
+2024-09-16 03:39:25.281539: val_loss -0.6865 
+2024-09-16 03:39:25.281589: Pseudo dice [0.6961, 0.823] 
+2024-09-16 03:39:25.281641: Epoch time: 245.76 s 
+2024-09-16 03:39:26.208912:  
+2024-09-16 03:39:26.209147: Epoch 916 
+2024-09-16 03:39:26.209230: Current learning rate: 0.00108 
+2024-09-16 03:43:32.074479: train_loss -0.9176 
+2024-09-16 03:43:32.074615: val_loss -0.7101 
+2024-09-16 03:43:32.074665: Pseudo dice [0.7224, 0.8388] 
+2024-09-16 03:43:32.074716: Epoch time: 245.87 s 
+2024-09-16 03:43:32.992594:  
+2024-09-16 03:43:32.992802: Epoch 917 
+2024-09-16 03:43:32.992885: Current learning rate: 0.00106 
+2024-09-16 03:47:38.882227: train_loss -0.9136 
+2024-09-16 03:47:38.882368: val_loss -0.7189 
+2024-09-16 03:47:38.882417: Pseudo dice [0.7141, 0.841] 
+2024-09-16 03:47:38.882471: Epoch time: 245.89 s 
+2024-09-16 03:47:39.815517:  
+2024-09-16 03:47:39.815695: Epoch 918 
+2024-09-16 03:47:39.815776: Current learning rate: 0.00105 
+2024-09-16 03:51:45.804832: train_loss -0.9152 
+2024-09-16 03:51:45.804990: val_loss -0.7071 
+2024-09-16 03:51:45.805041: Pseudo dice [0.7105, 0.8275] 
+2024-09-16 03:51:45.805091: Epoch time: 245.99 s 
+2024-09-16 03:51:46.731868:  
+2024-09-16 03:51:46.732104: Epoch 919 
+2024-09-16 03:51:46.732208: Current learning rate: 0.00104 
+2024-09-16 03:55:52.720893: train_loss -0.9106 
+2024-09-16 03:55:52.721057: val_loss -0.6866 
+2024-09-16 03:55:52.721107: Pseudo dice [0.6921, 0.8267] 
+2024-09-16 03:55:52.721156: Epoch time: 245.99 s 
+2024-09-16 03:55:53.648168:  
+2024-09-16 03:55:53.648379: Epoch 920 
+2024-09-16 03:55:53.648468: Current learning rate: 0.00103 
+2024-09-16 03:59:59.539363: train_loss -0.9149 
+2024-09-16 03:59:59.539500: val_loss -0.7021 
+2024-09-16 03:59:59.539549: Pseudo dice [0.6973, 0.8366] 
+2024-09-16 03:59:59.539598: Epoch time: 245.89 s 
+2024-09-16 04:00:00.468047:  
+2024-09-16 04:00:00.468215: Epoch 921 
+2024-09-16 04:00:00.468336: Current learning rate: 0.00102 
+2024-09-16 04:04:06.289926: train_loss -0.916 
+2024-09-16 04:04:06.290066: val_loss -0.6732 
+2024-09-16 04:04:06.290116: Pseudo dice [0.6575, 0.8104] 
+2024-09-16 04:04:06.290167: Epoch time: 245.82 s 
+2024-09-16 04:04:07.220121:  
+2024-09-16 04:04:07.220307: Epoch 922 
+2024-09-16 04:04:07.220404: Current learning rate: 0.00101 
+2024-09-16 04:08:13.152799: train_loss -0.9143 
+2024-09-16 04:08:13.152953: val_loss -0.6838 
+2024-09-16 04:08:13.153003: Pseudo dice [0.6707, 0.8363] 
+2024-09-16 04:08:13.153053: Epoch time: 245.93 s 
+2024-09-16 04:08:14.072095:  
+2024-09-16 04:08:14.072292: Epoch 923 
+2024-09-16 04:08:14.072379: Current learning rate: 0.001 
+2024-09-16 04:12:19.885039: train_loss -0.9166 
+2024-09-16 04:12:19.885176: val_loss -0.6805 
+2024-09-16 04:12:19.885226: Pseudo dice [0.6769, 0.8248] 
+2024-09-16 04:12:19.885322: Epoch time: 245.81 s 
+2024-09-16 04:12:20.816217:  
+2024-09-16 04:12:20.816417: Epoch 924 
+2024-09-16 04:12:20.816496: Current learning rate: 0.00098 
+2024-09-16 04:16:26.725774: train_loss -0.9144 
+2024-09-16 04:16:26.725909: val_loss -0.68 
+2024-09-16 04:16:26.725959: Pseudo dice [0.6848, 0.8274] 
+2024-09-16 04:16:26.726012: Epoch time: 245.91 s 
+2024-09-16 04:16:27.649448:  
+2024-09-16 04:16:27.649615: Epoch 925 
+2024-09-16 04:16:27.649696: Current learning rate: 0.00097 
+2024-09-16 04:20:33.453845: train_loss -0.9149 
+2024-09-16 04:20:33.454032: val_loss -0.695 
+2024-09-16 04:20:33.454083: Pseudo dice [0.7155, 0.8359] 
+2024-09-16 04:20:33.454134: Epoch time: 245.81 s 
+2024-09-16 04:20:34.396657:  
+2024-09-16 04:20:34.396856: Epoch 926 
+2024-09-16 04:20:34.396939: Current learning rate: 0.00096 
+2024-09-16 04:24:40.330333: train_loss -0.9127 
+2024-09-16 04:24:40.330484: val_loss -0.7184 
+2024-09-16 04:24:40.330534: Pseudo dice [0.7203, 0.8312] 
+2024-09-16 04:24:40.330585: Epoch time: 245.94 s 
+2024-09-16 04:24:41.256718:  
+2024-09-16 04:24:41.256900: Epoch 927 
+2024-09-16 04:24:41.256988: Current learning rate: 0.00095 
+2024-09-16 04:28:47.194870: train_loss -0.9184 
+2024-09-16 04:28:47.195017: val_loss -0.7001 
+2024-09-16 04:28:47.195067: Pseudo dice [0.7034, 0.8351] 
+2024-09-16 04:28:47.195117: Epoch time: 245.94 s 
+2024-09-16 04:28:48.131109:  
+2024-09-16 04:28:48.131281: Epoch 928 
+2024-09-16 04:28:48.131361: Current learning rate: 0.00094 
+2024-09-16 04:32:53.960115: train_loss -0.9102 
+2024-09-16 04:32:53.960264: val_loss -0.6666 
+2024-09-16 04:32:53.960314: Pseudo dice [0.6806, 0.8324] 
+2024-09-16 04:32:53.960365: Epoch time: 245.83 s 
+2024-09-16 04:32:54.878820:  
+2024-09-16 04:32:54.878989: Epoch 929 
+2024-09-16 04:32:54.879072: Current learning rate: 0.00092 
+2024-09-16 04:37:00.686438: train_loss -0.9146 
+2024-09-16 04:37:00.686575: val_loss -0.6936 
+2024-09-16 04:37:00.686624: Pseudo dice [0.6764, 0.8338] 
+2024-09-16 04:37:00.686675: Epoch time: 245.81 s 
+2024-09-16 04:37:01.634121:  
+2024-09-16 04:37:01.634336: Epoch 930 
+2024-09-16 04:37:01.634421: Current learning rate: 0.00091 
+2024-09-16 04:41:07.487021: train_loss -0.9104 
+2024-09-16 04:41:07.487191: val_loss -0.7016 
+2024-09-16 04:41:07.487243: Pseudo dice [0.6928, 0.8332] 
+2024-09-16 04:41:07.487293: Epoch time: 245.85 s 
+2024-09-16 04:41:08.403587:  
+2024-09-16 04:41:08.403760: Epoch 931 
+2024-09-16 04:41:08.403873: Current learning rate: 0.0009 
+2024-09-16 04:45:14.218710: train_loss -0.9122 
+2024-09-16 04:45:14.218851: val_loss -0.6632 
+2024-09-16 04:45:14.218900: Pseudo dice [0.6565, 0.8083] 
+2024-09-16 04:45:14.218951: Epoch time: 245.82 s 
+2024-09-16 04:45:15.147410:  
+2024-09-16 04:45:15.147591: Epoch 932 
+2024-09-16 04:45:15.147673: Current learning rate: 0.00089 
+2024-09-16 04:49:20.834671: train_loss -0.9166 
+2024-09-16 04:49:20.834911: val_loss -0.7097 
+2024-09-16 04:49:20.834962: Pseudo dice [0.7234, 0.8358] 
+2024-09-16 04:49:20.835013: Epoch time: 245.69 s 
+2024-09-16 04:49:21.762780:  
+2024-09-16 04:49:21.762991: Epoch 933 
+2024-09-16 04:49:21.763076: Current learning rate: 0.00088 
+2024-09-16 04:53:27.459270: train_loss -0.9189 
+2024-09-16 04:53:27.459409: val_loss -0.6843 
+2024-09-16 04:53:27.459458: Pseudo dice [0.6847, 0.8142] 
+2024-09-16 04:53:27.459508: Epoch time: 245.7 s 
+2024-09-16 04:53:28.407670:  
+2024-09-16 04:53:28.407888: Epoch 934 
+2024-09-16 04:53:28.407971: Current learning rate: 0.00087 
+2024-09-16 04:57:34.143008: train_loss -0.918 
+2024-09-16 04:57:34.143155: val_loss -0.6855 
+2024-09-16 04:57:34.143212: Pseudo dice [0.6694, 0.8331] 
+2024-09-16 04:57:34.143323: Epoch time: 245.74 s 
+2024-09-16 04:57:35.065529:  
+2024-09-16 04:57:35.065700: Epoch 935 
+2024-09-16 04:57:35.065805: Current learning rate: 0.00085 
+2024-09-16 05:01:40.816694: train_loss -0.9172 
+2024-09-16 05:01:40.816963: val_loss -0.7124 
+2024-09-16 05:01:40.817015: Pseudo dice [0.6987, 0.8514] 
+2024-09-16 05:01:40.817066: Epoch time: 245.75 s 
+2024-09-16 05:01:41.758165:  
+2024-09-16 05:01:41.758352: Epoch 936 
+2024-09-16 05:01:41.758443: Current learning rate: 0.00084 
+2024-09-16 05:05:47.579617: train_loss -0.9195 
+2024-09-16 05:05:47.579767: val_loss -0.6688 
+2024-09-16 05:05:47.579825: Pseudo dice [0.6829, 0.8226] 
+2024-09-16 05:05:47.579877: Epoch time: 245.82 s 
+2024-09-16 05:05:48.526502:  
+2024-09-16 05:05:48.526672: Epoch 937 
+2024-09-16 05:05:48.526756: Current learning rate: 0.00083 
+2024-09-16 05:09:54.309707: train_loss -0.9149 
+2024-09-16 05:09:54.309846: val_loss -0.685 
+2024-09-16 05:09:54.309896: Pseudo dice [0.6784, 0.8339] 
+2024-09-16 05:09:54.309945: Epoch time: 245.79 s 
+2024-09-16 05:09:55.226171:  
+2024-09-16 05:09:55.226383: Epoch 938 
+2024-09-16 05:09:55.226469: Current learning rate: 0.00082 
+2024-09-16 05:14:00.936393: train_loss -0.9197 
+2024-09-16 05:14:00.936528: val_loss -0.7054 
+2024-09-16 05:14:00.936578: Pseudo dice [0.7138, 0.8324] 
+2024-09-16 05:14:00.936628: Epoch time: 245.71 s 
+2024-09-16 05:14:01.865643:  
+2024-09-16 05:14:01.865800: Epoch 939 
+2024-09-16 05:14:01.865916: Current learning rate: 0.00081 
+2024-09-16 05:18:07.661215: train_loss -0.9202 
+2024-09-16 05:18:07.661355: val_loss -0.7046 
+2024-09-16 05:18:07.661404: Pseudo dice [0.7253, 0.8333] 
+2024-09-16 05:18:07.661454: Epoch time: 245.8 s 
+2024-09-16 05:18:08.596046:  
+2024-09-16 05:18:08.596211: Epoch 940 
+2024-09-16 05:18:08.596319: Current learning rate: 0.00079 
+2024-09-16 05:22:15.189159: train_loss -0.9177 
+2024-09-16 05:22:15.189325: val_loss -0.697 
+2024-09-16 05:22:15.189381: Pseudo dice [0.7214, 0.8374] 
+2024-09-16 05:22:15.189430: Epoch time: 246.6 s 
+2024-09-16 05:22:16.111337:  
+2024-09-16 05:22:16.111543: Epoch 941 
+2024-09-16 05:22:16.111624: Current learning rate: 0.00078 
+2024-09-16 05:26:22.136095: train_loss -0.9176 
+2024-09-16 05:26:22.136234: val_loss -0.6746 
+2024-09-16 05:26:22.136289: Pseudo dice [0.6719, 0.8383] 
+2024-09-16 05:26:22.136340: Epoch time: 246.03 s 
+2024-09-16 05:26:23.055846:  
+2024-09-16 05:26:23.056055: Epoch 942 
+2024-09-16 05:26:23.056163: Current learning rate: 0.00077 
+2024-09-16 05:30:29.062816: train_loss -0.9151 
+2024-09-16 05:30:29.062971: val_loss -0.6965 
+2024-09-16 05:30:29.063022: Pseudo dice [0.7178, 0.8258] 
+2024-09-16 05:30:29.063072: Epoch time: 246.01 s 
+2024-09-16 05:30:29.978688:  
+2024-09-16 05:30:29.978851: Epoch 943 
+2024-09-16 05:30:29.978935: Current learning rate: 0.00076 
+2024-09-16 05:34:35.946105: train_loss -0.9219 
+2024-09-16 05:34:35.946240: val_loss -0.7059 
+2024-09-16 05:34:35.946290: Pseudo dice [0.7048, 0.8432] 
+2024-09-16 05:34:35.946339: Epoch time: 245.97 s 
+2024-09-16 05:34:36.892276:  
+2024-09-16 05:34:36.892459: Epoch 944 
+2024-09-16 05:34:36.892543: Current learning rate: 0.00075 
+2024-09-16 05:38:42.645409: train_loss -0.9169 
+2024-09-16 05:38:42.645549: val_loss -0.6994 
+2024-09-16 05:38:42.645598: Pseudo dice [0.6992, 0.8355] 
+2024-09-16 05:38:42.645649: Epoch time: 245.76 s 
+2024-09-16 05:38:43.580204:  
+2024-09-16 05:38:43.580446: Epoch 945 
+2024-09-16 05:38:43.580528: Current learning rate: 0.00074 
+2024-09-16 05:42:49.269884: train_loss -0.9185 
+2024-09-16 05:42:49.270025: val_loss -0.6898 
+2024-09-16 05:42:49.270077: Pseudo dice [0.6827, 0.8328] 
+2024-09-16 05:42:49.270126: Epoch time: 245.69 s 
+2024-09-16 05:42:50.193264:  
+2024-09-16 05:42:50.193449: Epoch 946 
+2024-09-16 05:42:50.193538: Current learning rate: 0.00072 
+2024-09-16 05:46:56.037052: train_loss -0.9197 
+2024-09-16 05:46:56.037212: val_loss -0.717 
+2024-09-16 05:46:56.037263: Pseudo dice [0.6993, 0.8442] 
+2024-09-16 05:46:56.037313: Epoch time: 245.85 s 
+2024-09-16 05:46:56.949452:  
+2024-09-16 05:46:56.949630: Epoch 947 
+2024-09-16 05:46:56.949713: Current learning rate: 0.00071 
+2024-09-16 05:51:02.657941: train_loss -0.914 
+2024-09-16 05:51:02.658158: val_loss -0.6835 
+2024-09-16 05:51:02.658252: Pseudo dice [0.692, 0.8231] 
+2024-09-16 05:51:02.658342: Epoch time: 245.71 s 
+2024-09-16 05:51:03.584602:  
+2024-09-16 05:51:03.584825: Epoch 948 
+2024-09-16 05:51:03.584906: Current learning rate: 0.0007 
+2024-09-16 05:55:09.418003: train_loss -0.9188 
+2024-09-16 05:55:09.418175: val_loss -0.7003 
+2024-09-16 05:55:09.418236: Pseudo dice [0.6841, 0.8419] 
+2024-09-16 05:55:09.418288: Epoch time: 245.84 s 
+2024-09-16 05:55:10.341534:  
+2024-09-16 05:55:10.341727: Epoch 949 
+2024-09-16 05:55:10.341808: Current learning rate: 0.00069 
+2024-09-16 05:59:15.978868: train_loss -0.9196 
+2024-09-16 05:59:15.979007: val_loss -0.6957 
+2024-09-16 05:59:15.979058: Pseudo dice [0.7183, 0.8226] 
+2024-09-16 05:59:15.979108: Epoch time: 245.64 s 
+2024-09-16 05:59:19.859421:  
+2024-09-16 05:59:19.859646: Epoch 950 
+2024-09-16 05:59:19.859740: Current learning rate: 0.00067 
+2024-09-16 06:03:25.469634: train_loss -0.9211 
+2024-09-16 06:03:25.469772: val_loss -0.6884 
+2024-09-16 06:03:25.469822: Pseudo dice [0.6948, 0.8166] 
+2024-09-16 06:03:25.469874: Epoch time: 245.61 s 
+2024-09-16 06:03:26.397360:  
+2024-09-16 06:03:26.397538: Epoch 951 
+2024-09-16 06:03:26.397658: Current learning rate: 0.00066 
+2024-09-16 06:07:32.033263: train_loss -0.9194 
+2024-09-16 06:07:32.033405: val_loss -0.6756 
+2024-09-16 06:07:32.033455: Pseudo dice [0.6814, 0.8329] 
+2024-09-16 06:07:32.033507: Epoch time: 245.64 s 
+2024-09-16 06:07:32.950853:  
+2024-09-16 06:07:32.951031: Epoch 952 
+2024-09-16 06:07:32.951114: Current learning rate: 0.00065 
+2024-09-16 06:11:38.501887: train_loss -0.921 
+2024-09-16 06:11:38.502028: val_loss -0.6755 
+2024-09-16 06:11:38.502079: Pseudo dice [0.6872, 0.8191] 
+2024-09-16 06:11:38.502129: Epoch time: 245.55 s 
+2024-09-16 06:11:39.422350:  
+2024-09-16 06:11:39.422549: Epoch 953 
+2024-09-16 06:11:39.422632: Current learning rate: 0.00064 
+2024-09-16 06:15:45.053190: train_loss -0.9182 
+2024-09-16 06:15:45.053328: val_loss -0.6535 
+2024-09-16 06:15:45.053378: Pseudo dice [0.6486, 0.821] 
+2024-09-16 06:15:45.053427: Epoch time: 245.63 s 
+2024-09-16 06:15:45.996379:  
+2024-09-16 06:15:45.996520: Epoch 954 
+2024-09-16 06:15:45.996601: Current learning rate: 0.00063 
+2024-09-16 06:19:51.614069: train_loss -0.9192 
+2024-09-16 06:19:51.614208: val_loss -0.7023 
+2024-09-16 06:19:51.614259: Pseudo dice [0.6893, 0.8402] 
+2024-09-16 06:19:51.614310: Epoch time: 245.62 s 
+2024-09-16 06:19:52.560260:  
+2024-09-16 06:19:52.560421: Epoch 955 
+2024-09-16 06:19:52.560504: Current learning rate: 0.00061 
+2024-09-16 06:23:58.076615: train_loss -0.9145 
+2024-09-16 06:23:58.076753: val_loss -0.7078 
+2024-09-16 06:23:58.076802: Pseudo dice [0.7167, 0.8459] 
+2024-09-16 06:23:58.076915: Epoch time: 245.52 s 
+2024-09-16 06:23:59.023752:  
+2024-09-16 06:23:59.023972: Epoch 956 
+2024-09-16 06:23:59.024051: Current learning rate: 0.0006 
+2024-09-16 06:28:04.600929: train_loss -0.9169 
+2024-09-16 06:28:04.601092: val_loss -0.6796 
+2024-09-16 06:28:04.601182: Pseudo dice [0.7001, 0.8334] 
+2024-09-16 06:28:04.601234: Epoch time: 245.58 s 
+2024-09-16 06:28:05.549922:  
+2024-09-16 06:28:05.550070: Epoch 957 
+2024-09-16 06:28:05.550153: Current learning rate: 0.00059 
+2024-09-16 06:32:11.217049: train_loss -0.9215 
+2024-09-16 06:32:11.217188: val_loss -0.7132 
+2024-09-16 06:32:11.217238: Pseudo dice [0.72, 0.8287] 
+2024-09-16 06:32:11.217339: Epoch time: 245.67 s 
+2024-09-16 06:32:12.176536:  
+2024-09-16 06:32:12.176728: Epoch 958 
+2024-09-16 06:32:12.176813: Current learning rate: 0.00058 
+2024-09-16 06:36:17.973892: train_loss -0.9148 
+2024-09-16 06:36:17.974092: val_loss -0.6805 
+2024-09-16 06:36:17.974184: Pseudo dice [0.6832, 0.8219] 
+2024-09-16 06:36:17.974273: Epoch time: 245.8 s 
+2024-09-16 06:36:18.909769:  
+2024-09-16 06:36:18.909962: Epoch 959 
+2024-09-16 06:36:18.910046: Current learning rate: 0.00056 
+2024-09-16 06:40:24.676135: train_loss -0.9199 
+2024-09-16 06:40:24.676262: val_loss -0.6707 
+2024-09-16 06:40:24.676313: Pseudo dice [0.6598, 0.8291] 
+2024-09-16 06:40:24.676363: Epoch time: 245.77 s 
+2024-09-16 06:40:25.606646:  
+2024-09-16 06:40:25.606817: Epoch 960 
+2024-09-16 06:40:25.606926: Current learning rate: 0.00055 
+2024-09-16 06:44:31.332333: train_loss -0.9183 
+2024-09-16 06:44:31.332467: val_loss -0.7047 
+2024-09-16 06:44:31.332516: Pseudo dice [0.7129, 0.8275] 
+2024-09-16 06:44:31.332566: Epoch time: 245.73 s 
+2024-09-16 06:44:32.267864:  
+2024-09-16 06:44:32.268094: Epoch 961 
+2024-09-16 06:44:32.268178: Current learning rate: 0.00054 
+2024-09-16 06:48:38.021506: train_loss -0.9179 
+2024-09-16 06:48:38.021647: val_loss -0.6643 
+2024-09-16 06:48:38.021697: Pseudo dice [0.6686, 0.8347] 
+2024-09-16 06:48:38.021749: Epoch time: 245.76 s 
+2024-09-16 06:48:38.994092:  
+2024-09-16 06:48:38.994363: Epoch 962 
+2024-09-16 06:48:38.994443: Current learning rate: 0.00053 
+2024-09-16 06:52:44.737462: train_loss -0.9162 
+2024-09-16 06:52:44.737599: val_loss -0.6571 
+2024-09-16 06:52:44.737648: Pseudo dice [0.6835, 0.824] 
+2024-09-16 06:52:44.737699: Epoch time: 245.75 s 
+2024-09-16 06:52:45.677187:  
+2024-09-16 06:52:45.677384: Epoch 963 
+2024-09-16 06:52:45.677464: Current learning rate: 0.00051 
+2024-09-16 06:56:51.424474: train_loss -0.9141 
+2024-09-16 06:56:51.424611: val_loss -0.6769 
+2024-09-16 06:56:51.424662: Pseudo dice [0.691, 0.8378] 
+2024-09-16 06:56:51.424712: Epoch time: 245.75 s 
+2024-09-16 06:56:52.373418:  
+2024-09-16 06:56:52.373604: Epoch 964 
+2024-09-16 06:56:52.373685: Current learning rate: 0.0005 
+2024-09-16 07:00:58.232184: train_loss -0.9183 
+2024-09-16 07:00:58.232446: val_loss -0.7013 
+2024-09-16 07:00:58.232497: Pseudo dice [0.6748, 0.8429] 
+2024-09-16 07:00:58.232546: Epoch time: 245.86 s 
+2024-09-16 07:01:00.072255:  
+2024-09-16 07:01:00.072500: Epoch 965 
+2024-09-16 07:01:00.072599: Current learning rate: 0.00049 
+2024-09-16 07:05:06.140429: train_loss -0.9214 
+2024-09-16 07:05:06.140646: val_loss -0.6915 
+2024-09-16 07:05:06.140710: Pseudo dice [0.6888, 0.8267] 
+2024-09-16 07:05:06.140760: Epoch time: 246.07 s 
+2024-09-16 07:05:07.088161:  
+2024-09-16 07:05:07.088402: Epoch 966 
+2024-09-16 07:05:07.088500: Current learning rate: 0.00048 
+2024-09-16 07:09:13.169953: train_loss -0.9179 
+2024-09-16 07:09:13.170091: val_loss -0.6812 
+2024-09-16 07:09:13.170145: Pseudo dice [0.6949, 0.8252] 
+2024-09-16 07:09:13.170199: Epoch time: 246.08 s 
+2024-09-16 07:09:14.122173:  
+2024-09-16 07:09:14.122363: Epoch 967 
+2024-09-16 07:09:14.122447: Current learning rate: 0.00046 
+2024-09-16 07:13:20.040087: train_loss -0.9211 
+2024-09-16 07:13:20.040227: val_loss -0.7004 
+2024-09-16 07:13:20.040277: Pseudo dice [0.6875, 0.8498] 
+2024-09-16 07:13:20.040326: Epoch time: 245.92 s 
+2024-09-16 07:13:20.990174:  
+2024-09-16 07:13:20.990389: Epoch 968 
+2024-09-16 07:13:20.990472: Current learning rate: 0.00045 
+2024-09-16 07:17:26.825041: train_loss -0.9201 
+2024-09-16 07:17:26.825182: val_loss -0.7032 
+2024-09-16 07:17:26.825232: Pseudo dice [0.7144, 0.8271] 
+2024-09-16 07:17:26.825313: Epoch time: 245.84 s 
+2024-09-16 07:17:27.775944:  
+2024-09-16 07:17:27.776206: Epoch 969 
+2024-09-16 07:17:27.776320: Current learning rate: 0.00044 
+2024-09-16 07:21:33.304761: train_loss -0.9206 
+2024-09-16 07:21:33.304901: val_loss -0.6653 
+2024-09-16 07:21:33.304950: Pseudo dice [0.6636, 0.8208] 
+2024-09-16 07:21:33.305001: Epoch time: 245.53 s 
+2024-09-16 07:21:34.254199:  
+2024-09-16 07:21:34.254423: Epoch 970 
+2024-09-16 07:21:34.254540: Current learning rate: 0.00043 
+2024-09-16 07:25:39.830925: train_loss -0.9195 
+2024-09-16 07:25:39.831059: val_loss -0.6829 
+2024-09-16 07:25:39.831168: Pseudo dice [0.6674, 0.8238] 
+2024-09-16 07:25:39.831221: Epoch time: 245.58 s 
+2024-09-16 07:25:40.780107:  
+2024-09-16 07:25:40.780304: Epoch 971 
+2024-09-16 07:25:40.780386: Current learning rate: 0.00041 
+2024-09-16 07:29:46.268083: train_loss -0.9205 
+2024-09-16 07:29:46.268223: val_loss -0.6666 
+2024-09-16 07:29:46.268275: Pseudo dice [0.6648, 0.8234] 
+2024-09-16 07:29:46.268327: Epoch time: 245.49 s 
+2024-09-16 07:29:47.230365:  
+2024-09-16 07:29:47.230500: Epoch 972 
+2024-09-16 07:29:47.230581: Current learning rate: 0.0004 
+2024-09-16 07:33:52.723098: train_loss -0.9212 
+2024-09-16 07:33:52.723236: val_loss -0.7203 
+2024-09-16 07:33:52.723480: Pseudo dice [0.7024, 0.8465] 
+2024-09-16 07:33:52.723532: Epoch time: 245.49 s 
+2024-09-16 07:33:53.666152:  
+2024-09-16 07:33:53.666352: Epoch 973 
+2024-09-16 07:33:53.666436: Current learning rate: 0.00039 
+2024-09-16 07:37:59.294587: train_loss -0.9191 
+2024-09-16 07:37:59.294727: val_loss -0.7071 
+2024-09-16 07:37:59.294777: Pseudo dice [0.7072, 0.8398] 
+2024-09-16 07:37:59.294827: Epoch time: 245.63 s 
+2024-09-16 07:38:00.236005:  
+2024-09-16 07:38:00.236199: Epoch 974 
+2024-09-16 07:38:00.236306: Current learning rate: 0.00037 
+2024-09-16 07:42:05.868154: train_loss -0.9214 
+2024-09-16 07:42:05.868293: val_loss -0.6998 
+2024-09-16 07:42:05.868344: Pseudo dice [0.6868, 0.8456] 
+2024-09-16 07:42:05.868395: Epoch time: 245.63 s 
+2024-09-16 07:42:06.829804:  
+2024-09-16 07:42:06.830083: Epoch 975 
+2024-09-16 07:42:06.830169: Current learning rate: 0.00036 
+2024-09-16 07:46:12.482755: train_loss -0.9196 
+2024-09-16 07:46:12.482917: val_loss -0.7145 
+2024-09-16 07:46:12.482968: Pseudo dice [0.6976, 0.8322] 
+2024-09-16 07:46:12.483018: Epoch time: 245.65 s 
+2024-09-16 07:46:13.416428:  
+2024-09-16 07:46:13.416609: Epoch 976 
+2024-09-16 07:46:13.416690: Current learning rate: 0.00035 
+2024-09-16 07:50:19.040409: train_loss -0.9149 
+2024-09-16 07:50:19.040572: val_loss -0.6878 
+2024-09-16 07:50:19.040622: Pseudo dice [0.6925, 0.8289] 
+2024-09-16 07:50:19.040674: Epoch time: 245.63 s 
+2024-09-16 07:50:19.993022:  
+2024-09-16 07:50:19.993221: Epoch 977 
+2024-09-16 07:50:19.993320: Current learning rate: 0.00034 
+2024-09-16 07:54:25.753237: train_loss -0.9205 
+2024-09-16 07:54:25.753376: val_loss -0.682 
+2024-09-16 07:54:25.753426: Pseudo dice [0.6573, 0.8286] 
+2024-09-16 07:54:25.753477: Epoch time: 245.76 s 
+2024-09-16 07:54:26.698524:  
+2024-09-16 07:54:26.698769: Epoch 978 
+2024-09-16 07:54:26.698850: Current learning rate: 0.00032 
+2024-09-16 07:58:32.528605: train_loss -0.917 
+2024-09-16 07:58:32.528742: val_loss -0.6951 
+2024-09-16 07:58:32.528792: Pseudo dice [0.7028, 0.8435] 
+2024-09-16 07:58:32.528841: Epoch time: 245.83 s 
+2024-09-16 07:58:33.468222:  
+2024-09-16 07:58:33.468443: Epoch 979 
+2024-09-16 07:58:33.468547: Current learning rate: 0.00031 
+2024-09-16 08:02:39.309166: train_loss -0.9201 
+2024-09-16 08:02:39.309305: val_loss -0.6859 
+2024-09-16 08:02:39.309354: Pseudo dice [0.681, 0.8308] 
+2024-09-16 08:02:39.309403: Epoch time: 245.84 s 
+2024-09-16 08:02:40.407509:  
+2024-09-16 08:02:40.407726: Epoch 980 
+2024-09-16 08:02:40.407823: Current learning rate: 0.0003 
+2024-09-16 08:06:46.203625: train_loss -0.9213 
+2024-09-16 08:06:46.203766: val_loss -0.6994 
+2024-09-16 08:06:46.203821: Pseudo dice [0.689, 0.8403] 
+2024-09-16 08:06:46.203874: Epoch time: 245.8 s 
+2024-09-16 08:06:47.188754:  
+2024-09-16 08:06:47.188923: Epoch 981 
+2024-09-16 08:06:47.189007: Current learning rate: 0.00028 
+2024-09-16 08:10:53.127465: train_loss -0.9187 
+2024-09-16 08:10:53.127615: val_loss -0.6777 
+2024-09-16 08:10:53.127664: Pseudo dice [0.6838, 0.8128] 
+2024-09-16 08:10:53.127713: Epoch time: 245.94 s 
+2024-09-16 08:10:54.053894:  
+2024-09-16 08:10:54.054082: Epoch 982 
+2024-09-16 08:10:54.054168: Current learning rate: 0.00027 
+2024-09-16 08:14:59.940601: train_loss -0.9222 
+2024-09-16 08:14:59.940741: val_loss -0.7118 
+2024-09-16 08:14:59.940791: Pseudo dice [0.7075, 0.8331] 
+2024-09-16 08:14:59.940841: Epoch time: 245.89 s 
+2024-09-16 08:15:00.898801:  
+2024-09-16 08:15:00.899109: Epoch 983 
+2024-09-16 08:15:00.899201: Current learning rate: 0.00026 
+2024-09-16 08:19:06.665395: train_loss -0.9176 
+2024-09-16 08:19:06.665537: val_loss -0.7156 
+2024-09-16 08:19:06.665586: Pseudo dice [0.7099, 0.844] 
+2024-09-16 08:19:06.665637: Epoch time: 245.77 s 
+2024-09-16 08:19:07.611504:  
+2024-09-16 08:19:07.611715: Epoch 984 
+2024-09-16 08:19:07.611838: Current learning rate: 0.00024 
+2024-09-16 08:23:13.351687: train_loss -0.9246 
+2024-09-16 08:23:13.351860: val_loss -0.6917 
+2024-09-16 08:23:13.351911: Pseudo dice [0.6926, 0.8306] 
+2024-09-16 08:23:13.351963: Epoch time: 245.74 s 
+2024-09-16 08:23:14.285612:  
+2024-09-16 08:23:14.285799: Epoch 985 
+2024-09-16 08:23:14.285930: Current learning rate: 0.00023 
+2024-09-16 08:27:20.140063: train_loss -0.9201 
+2024-09-16 08:27:20.140242: val_loss -0.6781 
+2024-09-16 08:27:20.140292: Pseudo dice [0.7024, 0.8256] 
+2024-09-16 08:27:20.140354: Epoch time: 245.86 s 
+2024-09-16 08:27:21.097704:  
+2024-09-16 08:27:21.097896: Epoch 986 
+2024-09-16 08:27:21.097978: Current learning rate: 0.00021 
+2024-09-16 08:31:26.881946: train_loss -0.9234 
+2024-09-16 08:31:26.882125: val_loss -0.7217 
+2024-09-16 08:31:26.882219: Pseudo dice [0.7117, 0.844] 
+2024-09-16 08:31:26.882269: Epoch time: 245.79 s 
+2024-09-16 08:31:27.835325:  
+2024-09-16 08:31:27.835482: Epoch 987 
+2024-09-16 08:31:27.835565: Current learning rate: 0.0002 
+2024-09-16 08:35:33.587205: train_loss -0.9226 
+2024-09-16 08:35:33.587347: val_loss -0.6855 
+2024-09-16 08:35:33.587395: Pseudo dice [0.6542, 0.8302] 
+2024-09-16 08:35:33.587445: Epoch time: 245.75 s 
+2024-09-16 08:35:34.524678:  
+2024-09-16 08:35:34.524908: Epoch 988 
+2024-09-16 08:35:34.524992: Current learning rate: 0.00019 
+2024-09-16 08:39:40.354963: train_loss -0.9202 
+2024-09-16 08:39:40.355102: val_loss -0.7029 
+2024-09-16 08:39:40.355152: Pseudo dice [0.6898, 0.8319] 
+2024-09-16 08:39:40.355202: Epoch time: 245.83 s 
+2024-09-16 08:39:41.287537:  
+2024-09-16 08:39:41.287706: Epoch 989 
+2024-09-16 08:39:41.287791: Current learning rate: 0.00017 
+2024-09-16 08:43:47.038839: train_loss -0.9218 
+2024-09-16 08:43:47.038976: val_loss -0.6997 
+2024-09-16 08:43:47.039025: Pseudo dice [0.702, 0.8292] 
+2024-09-16 08:43:47.039080: Epoch time: 245.75 s 
+2024-09-16 08:43:48.928802:  
+2024-09-16 08:43:48.929078: Epoch 990 
+2024-09-16 08:43:48.929203: Current learning rate: 0.00016 
+2024-09-16 08:47:54.672821: train_loss -0.9234 
+2024-09-16 08:47:54.672960: val_loss -0.6785 
+2024-09-16 08:47:54.673009: Pseudo dice [0.6782, 0.8274] 
+2024-09-16 08:47:54.673059: Epoch time: 245.75 s 
+2024-09-16 08:47:55.615495:  
+2024-09-16 08:47:55.615703: Epoch 991 
+2024-09-16 08:47:55.615779: Current learning rate: 0.00014 
+2024-09-16 08:52:01.466056: train_loss -0.9243 
+2024-09-16 08:52:01.466191: val_loss -0.6876 
+2024-09-16 08:52:01.466240: Pseudo dice [0.6984, 0.8315] 
+2024-09-16 08:52:01.466325: Epoch time: 245.85 s 
+2024-09-16 08:52:02.405464:  
+2024-09-16 08:52:02.405693: Epoch 992 
+2024-09-16 08:52:02.405775: Current learning rate: 0.00013 
+2024-09-16 08:56:08.173720: train_loss -0.9224 
+2024-09-16 08:56:08.173860: val_loss -0.708 
+2024-09-16 08:56:08.173910: Pseudo dice [0.728, 0.8323] 
+2024-09-16 08:56:08.173965: Epoch time: 245.77 s 
+2024-09-16 08:56:09.110630:  
+2024-09-16 08:56:09.110858: Epoch 993 
+2024-09-16 08:56:09.110942: Current learning rate: 0.00011 
+2024-09-16 09:00:14.646956: train_loss -0.9185 
+2024-09-16 09:00:14.647108: val_loss -0.6782 
+2024-09-16 09:00:14.647159: Pseudo dice [0.6911, 0.8205] 
+2024-09-16 09:00:14.647210: Epoch time: 245.54 s 
+2024-09-16 09:00:15.587169:  
+2024-09-16 09:00:15.587375: Epoch 994 
+2024-09-16 09:00:15.587452: Current learning rate: 0.0001 
+2024-09-16 09:04:20.979185: train_loss -0.9231 
+2024-09-16 09:04:20.979320: val_loss -0.6839 
+2024-09-16 09:04:20.979375: Pseudo dice [0.6959, 0.8342] 
+2024-09-16 09:04:20.979424: Epoch time: 245.39 s 
+2024-09-16 09:04:21.928206:  
+2024-09-16 09:04:21.928395: Epoch 995 
+2024-09-16 09:04:21.928478: Current learning rate: 8e-05 
+2024-09-16 09:08:27.398605: train_loss -0.9238 
+2024-09-16 09:08:27.398745: val_loss -0.688 
+2024-09-16 09:08:27.398797: Pseudo dice [0.6919, 0.8206] 
+2024-09-16 09:08:27.398847: Epoch time: 245.47 s 
+2024-09-16 09:08:28.344188:  
+2024-09-16 09:08:28.344371: Epoch 996 
+2024-09-16 09:08:28.344454: Current learning rate: 7e-05 
+2024-09-16 09:12:33.774061: train_loss -0.9212 
+2024-09-16 09:12:33.774202: val_loss -0.6773 
+2024-09-16 09:12:33.774252: Pseudo dice [0.6764, 0.834] 
+2024-09-16 09:12:33.774302: Epoch time: 245.43 s 
+2024-09-16 09:12:34.709664:  
+2024-09-16 09:12:34.709897: Epoch 997 
+2024-09-16 09:12:34.710001: Current learning rate: 5e-05 
+2024-09-16 09:16:40.139036: train_loss -0.9183 
+2024-09-16 09:16:40.139185: val_loss -0.7067 
+2024-09-16 09:16:40.139241: Pseudo dice [0.6999, 0.8424] 
+2024-09-16 09:16:40.139300: Epoch time: 245.43 s 
+2024-09-16 09:16:41.090374:  
+2024-09-16 09:16:41.090553: Epoch 998 
+2024-09-16 09:16:41.090648: Current learning rate: 4e-05 
+2024-09-16 09:20:46.376193: train_loss -0.9238 
+2024-09-16 09:20:46.376341: val_loss -0.6857 
+2024-09-16 09:20:46.376396: Pseudo dice [0.6705, 0.8307] 
+2024-09-16 09:20:46.376450: Epoch time: 245.29 s 
+2024-09-16 09:20:47.336569:  
+2024-09-16 09:20:47.336796: Epoch 999 
+2024-09-16 09:20:47.336885: Current learning rate: 2e-05 
+2024-09-16 09:24:53.003784: train_loss -0.9225 
+2024-09-16 09:24:53.003952: val_loss -0.6981 
+2024-09-16 09:24:53.004009: Pseudo dice [0.6824, 0.8383] 
+2024-09-16 09:24:53.004125: Epoch time: 245.67 s 
+2024-09-16 09:24:55.431071: Training done. 
+2024-09-16 09:24:55.598651: Using splits from existing split file: /mnt/processing/jintao/nnUNet_preprocessed/Dataset504_midRT_geodist/splits_final.json 
+2024-09-16 09:24:55.600126: The split file contains 5 splits. 
+2024-09-16 09:24:55.600199: Desired fold for training: 4 
+2024-09-16 09:24:55.600256: This split has 240 training and 30 validation cases. 
+2024-09-16 09:24:55.600751: predicting 107 
+2024-09-16 09:24:55.606470: 107, shape torch.Size([2, 113, 512, 510]), rank 0 
+2024-09-16 09:26:06.125925: predicting 113 
+2024-09-16 09:26:06.148117: 113, shape torch.Size([2, 130, 512, 511]), rank 0 
+2024-09-16 09:26:43.774328: predicting 119 
+2024-09-16 09:26:43.791491: 119, shape torch.Size([2, 130, 512, 511]), rank 0 
+2024-09-16 09:27:21.501875: predicting 122 
+2024-09-16 09:27:21.518633: 122, shape torch.Size([2, 108, 511, 511]), rank 0 
+2024-09-16 09:27:51.723785: predicting 130 
+2024-09-16 09:27:51.737578: 130, shape torch.Size([2, 140, 512, 511]), rank 0 
+2024-09-16 09:28:29.346692: predicting 133 
+2024-09-16 09:28:29.364844: 133, shape torch.Size([2, 127, 502, 511]), rank 0 
+2024-09-16 09:29:06.862479: predicting 142 
+2024-09-16 09:29:06.878582: 142, shape torch.Size([2, 133, 536, 1034]), rank 0 
+2024-09-16 09:30:21.760489: predicting 145 
+2024-09-16 09:30:21.796253: 145, shape torch.Size([2, 128, 512, 511]), rank 0 
+2024-09-16 09:30:59.311007: predicting 155 
+2024-09-16 09:30:59.327553: 155, shape torch.Size([2, 118, 512, 511]), rank 0 
+2024-09-16 09:31:29.325294: predicting 156 
+2024-09-16 09:31:29.339898: 156, shape torch.Size([2, 121, 559, 1040]), rank 0 
+2024-09-16 09:32:43.882509: predicting 157 
+2024-09-16 09:32:43.915712: 157, shape torch.Size([2, 103, 507, 511]), rank 0 
+2024-09-16 09:33:13.913007: predicting 163 
+2024-09-16 09:33:13.925916: 163, shape torch.Size([2, 113, 512, 511]), rank 0 
+2024-09-16 09:33:43.872350: predicting 165 
+2024-09-16 09:33:43.886312: 165, shape torch.Size([2, 112, 536, 1040]), rank 0 
+2024-09-16 09:34:43.635058: predicting 174 
+2024-09-16 09:34:43.665352: 174, shape torch.Size([2, 150, 510, 511]), rank 0 
+2024-09-16 09:35:28.707812: predicting 176 
+2024-09-16 09:35:28.726899: 176, shape torch.Size([2, 110, 512, 511]), rank 0 
+2024-09-16 09:35:58.734415: predicting 180 
+2024-09-16 09:35:58.748294: 180, shape torch.Size([2, 123, 512, 511]), rank 0 
+2024-09-16 09:36:36.149503: predicting 184 
+2024-09-16 09:36:36.165093: 184, shape torch.Size([2, 116, 536, 985]), rank 0 
+2024-09-16 09:37:35.828985: predicting 185 
+2024-09-16 09:37:35.857172: 185, shape torch.Size([2, 95, 512, 511]), rank 0 
+2024-09-16 09:37:58.422279: predicting 201 
+2024-09-16 09:37:58.434250: 201, shape torch.Size([2, 138, 536, 1040]), rank 0 
+2024-09-16 09:39:13.201180: predicting 21 
+2024-09-16 09:39:13.238091: 21, shape torch.Size([2, 112, 505, 511]), rank 0 
+2024-09-16 09:39:43.331836: predicting 22 
+2024-09-16 09:39:43.346081: 22, shape torch.Size([2, 110, 512, 511]), rank 0 
+2024-09-16 09:40:13.354430: predicting 24 
+2024-09-16 09:40:13.368187: 24, shape torch.Size([2, 113, 508, 509]), rank 0 
+2024-09-16 09:40:43.371160: predicting 29 
+2024-09-16 09:40:43.385014: 29, shape torch.Size([2, 127, 538, 1040]), rank 0 
+2024-09-16 09:41:57.996545: predicting 33 
+2024-09-16 09:41:58.029368: 33, shape torch.Size([2, 125, 512, 511]), rank 0 
+2024-09-16 09:42:35.530607: predicting 48 
+2024-09-16 09:42:35.546649: 48, shape torch.Size([2, 112, 511, 510]), rank 0 
+2024-09-16 09:43:05.594379: predicting 50 
+2024-09-16 09:43:05.608764: 50, shape torch.Size([2, 113, 512, 510]), rank 0 
+2024-09-16 09:43:35.632919: predicting 60 
+2024-09-16 09:43:35.647133: 60, shape torch.Size([2, 133, 512, 511]), rank 0 
+2024-09-16 09:44:13.089806: predicting 77 
+2024-09-16 09:44:13.106857: 77, shape torch.Size([2, 104, 536, 1032]), rank 0 
+2024-09-16 09:45:12.818029: predicting 78 
+2024-09-16 09:45:12.845912: 78, shape torch.Size([2, 150, 512, 511]), rank 0 
+2024-09-16 09:45:57.804376: predicting 93 
+2024-09-16 09:45:57.823329: 93, shape torch.Size([2, 133, 512, 511]), rank 0 
+2024-09-16 09:46:49.714916: Validation complete 
+2024-09-16 09:46:49.715016: Mean Validation Dice:  0.6574180195881955 
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diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/validation/summary_agg.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_4/validation/summary_agg.json
new file mode 100644
index 0000000000000000000000000000000000000000..4e950994e87c57c9b7cd6085f48bfd542a9fb5ca
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+        }
+    }
+}
\ No newline at end of file
diff --git a/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/plans.json b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/plans.json
new file mode 100644
index 0000000000000000000000000000000000000000..eb12729e215590446fda5109b6b5cf7826a62ea4
--- /dev/null
+++ b/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres_bs8/plans.json
@@ -0,0 +1,555 @@
+{
+    "dataset_name": "Dataset504_midRT_geodist",
+    "plans_name": "nnUNetResEncUNetMPlans",
+    "original_median_spacing_after_transp": [
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+        0.5,
+        0.5
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+        511,
+        511
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+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
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+    "transpose_backward": [
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+        2
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+    "configurations": {
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+            },
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+                "order": 1,
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+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
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+                "force_separate_z": null
+            },
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+                        256,
+                        512,
+                        512,
+                        512,
+                        512
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+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ]
+                    ],
+                    "n_blocks_per_stage": [
+                        1,
+                        3,
+                        4,
+                        6,
+                        6,
+                        6
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": true
+        },
+        "3d_fullres_bs4": {
+            "inherits_from": "3d_fullres",
+            "batch_size": 4
+        },
+        "3d_fullres_bs8": {
+            "inherits_from": "3d_fullres",
+            "batch_size": 8
+        },
+        "3d_cascade_fullres": {
+            "inherits_from": "3d_fullres",
+            "previous_stage": "3d_lowres"
+        }
+    },
+    "experiment_planner_used": "nnUNetPlannerResEncM",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 4889.44140625,
+            "mean": 349.19762434242324,
+            "median": 141.0,
+            "min": 0.0,
+            "percentile_00_5": 29.0,
+            "percentile_99_5": 2558.91650390625,
+            "std": 485.94544484039
+        },
+        "1": {
+            "max": 1.0,
+            "mean": 0.37307153608378946,
+            "median": 0.2966964840888977,
+            "min": 0.0,
+            "percentile_00_5": 0.01908937655389309,
+            "percentile_99_5": 1.0,
+            "std": 0.2642120030869378
+        }
+    }
+}
\ No newline at end of file