| 2024/01/18 13:41:34 - mmengine - INFO - | |
| ------------------------------------------------------------ | |
| System environment: | |
| sys.platform: linux | |
| Python: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] | |
| CUDA available: True | |
| numpy_random_seed: 1142582054 | |
| GPU 0,1,2,3,4,5,6: NVIDIA A100-SXM4-80GB | |
| CUDA_HOME: /usr/local/cuda-11.7 | |
| NVCC: Cuda compilation tools, release 11.7, V11.7.64 | |
| GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.3) 9.4.0 | |
| PyTorch: 1.13.0 | |
| PyTorch compiling details: PyTorch built with: | |
| - GCC 9.3 | |
| - C++ Version: 201402 | |
| - Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications | |
| - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) | |
| - OpenMP 201511 (a.k.a. OpenMP 4.5) | |
| - LAPACK is enabled (usually provided by MKL) | |
| - NNPACK is enabled | |
| - CPU capability usage: AVX2 | |
| - CUDA Runtime 11.7 | |
| - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 | |
| - CuDNN 8.5 | |
| - Magma 2.6.1 | |
| - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, | |
| TorchVision: 0.14.0 | |
| OpenCV: 4.8.1 | |
| MMEngine: 0.10.1 | |
| Runtime environment: | |
| cudnn_benchmark: True | |
| mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} | |
| dist_cfg: {'backend': 'nccl'} | |
| seed: 1142582054 | |
| Distributed launcher: pytorch | |
| Distributed training: True | |
| GPU number: 4 | |
| ------------------------------------------------------------ | |
| 2024/01/18 13:41:36 - mmengine - INFO - Config: | |
| backbone_norm_cfg = dict(requires_grad=True, type='LN') | |
| checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' | |
| crop_size = ( | |
| 640, | |
| 640, | |
| ) | |
| data_preprocessor = dict( | |
| bgr_to_rgb=True, | |
| mean=[ | |
| 123.675, | |
| 116.28, | |
| 103.53, | |
| ], | |
| pad_val=0, | |
| seg_pad_val=255, | |
| size=( | |
| 640, | |
| 640, | |
| ), | |
| std=[ | |
| 58.395, | |
| 57.12, | |
| 57.375, | |
| ], | |
| type='SegDataPreProcessor') | |
| data_root = 'data/ade/ADEChallengeData2016' | |
| dataset_type = 'ADE20KDataset' | |
| default_hooks = dict( | |
| checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), | |
| logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| sampler_seed=dict(type='DistSamplerSeedHook'), | |
| timer=dict(type='IterTimerHook'), | |
| visualization=dict(type='SegVisualizationHook')) | |
| default_scope = 'mmseg' | |
| env_cfg = dict( | |
| cudnn_benchmark=True, | |
| dist_cfg=dict(backend='nccl'), | |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) | |
| img_ratios = [ | |
| 0.5, | |
| 0.75, | |
| 1.0, | |
| 1.25, | |
| 1.5, | |
| 1.75, | |
| ] | |
| launcher = 'pytorch' | |
| load_from = '/home/LiuYue/Workspace3/ckpts/segmentation/work_dirs/upernet_vssm_4xb4-160k_ade20k-640x640_small/iter_160000.pth' | |
| log_level = 'INFO' | |
| log_processor = dict(by_epoch=False) | |
| model = dict( | |
| module=dict( | |
| auxiliary_head=dict( | |
| align_corners=False, | |
| channels=256, | |
| concat_input=False, | |
| dropout_ratio=0.1, | |
| in_channels=384, | |
| in_index=2, | |
| loss_decode=dict( | |
| loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), | |
| norm_cfg=dict(requires_grad=True, type='SyncBN'), | |
| num_classes=150, | |
| num_convs=1, | |
| type='FCNHead'), | |
| backbone=dict( | |
| act_cfg=dict(type='GELU'), | |
| attn_drop_rate=0.0, | |
| depths=( | |
| 2, | |
| 2, | |
| 27, | |
| 2, | |
| ), | |
| dims=96, | |
| drop_path_rate=0.3, | |
| drop_rate=0.0, | |
| embed_dims=96, | |
| init_cfg=dict( | |
| checkpoint= | |
| 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth', | |
| type='Pretrained'), | |
| mlp_ratio=4, | |
| norm_cfg=dict(requires_grad=True, type='LN'), | |
| num_heads=[ | |
| 3, | |
| 6, | |
| 12, | |
| 24, | |
| ], | |
| out_indices=( | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| ), | |
| patch_norm=True, | |
| patch_size=4, | |
| pretrain_img_size=224, | |
| pretrained='../../ckpts/vssmsmall/ckpt_epoch_238.pth', | |
| qk_scale=None, | |
| qkv_bias=True, | |
| strides=( | |
| 4, | |
| 2, | |
| 2, | |
| 2, | |
| ), | |
| type='MMSEG_VSSM', | |
| use_abs_pos_embed=False, | |
| window_size=7), | |
| data_preprocessor=dict( | |
| bgr_to_rgb=True, | |
| mean=[ | |
| 123.675, | |
| 116.28, | |
| 103.53, | |
| ], | |
| pad_val=0, | |
| seg_pad_val=255, | |
| size=( | |
| 640, | |
| 640, | |
| ), | |
| std=[ | |
| 58.395, | |
| 57.12, | |
| 57.375, | |
| ], | |
| type='SegDataPreProcessor'), | |
| decode_head=dict( | |
| align_corners=False, | |
| channels=512, | |
| dropout_ratio=0.1, | |
| in_channels=[ | |
| 96, | |
| 192, | |
| 384, | |
| 768, | |
| ], | |
| in_index=[ | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| ], | |
| loss_decode=dict( | |
| loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), | |
| norm_cfg=dict(requires_grad=True, type='SyncBN'), | |
| num_classes=150, | |
| pool_scales=( | |
| 1, | |
| 2, | |
| 3, | |
| 6, | |
| ), | |
| type='UPerHead'), | |
| pretrained=None, | |
| test_cfg=dict(mode='whole'), | |
| train_cfg=dict(), | |
| type='EncoderDecoder'), | |
| type='SegTTAModel') | |
| norm_cfg = dict(requires_grad=True, type='SyncBN') | |
| optim_wrapper = dict( | |
| optimizer=dict( | |
| betas=( | |
| 0.9, | |
| 0.999, | |
| ), lr=6e-05, type='AdamW', weight_decay=0.01), | |
| paramwise_cfg=dict( | |
| custom_keys=dict( | |
| absolute_pos_embed=dict(decay_mult=0.0), | |
| norm=dict(decay_mult=0.0), | |
| relative_position_bias_table=dict(decay_mult=0.0))), | |
| type='OptimWrapper') | |
| optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) | |
| param_scheduler = [ | |
| dict( | |
| begin=0, by_epoch=False, end=1500, start_factor=1e-06, | |
| type='LinearLR'), | |
| dict( | |
| begin=1500, | |
| by_epoch=False, | |
| end=160000, | |
| eta_min=0.0, | |
| power=1.0, | |
| type='PolyLR'), | |
| ] | |
| resume = False | |
| test_cfg = dict(type='TestLoop') | |
| test_dataloader = dict( | |
| batch_size=1, | |
| dataset=dict( | |
| data_prefix=dict( | |
| img_path='images/validation', | |
| seg_map_path='annotations/validation'), | |
| data_root='data/ade/ADEChallengeData2016', | |
| pipeline=[ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict( | |
| transforms=[ | |
| [ | |
| dict(keep_ratio=True, scale_factor=0.5, type='Resize'), | |
| dict( | |
| keep_ratio=True, scale_factor=0.75, type='Resize'), | |
| dict(keep_ratio=True, scale_factor=1.0, type='Resize'), | |
| dict( | |
| keep_ratio=True, scale_factor=1.25, type='Resize'), | |
| dict(keep_ratio=True, scale_factor=1.5, type='Resize'), | |
| dict( | |
| keep_ratio=True, scale_factor=1.75, type='Resize'), | |
| ], | |
| [ | |
| dict( | |
| direction='horizontal', | |
| prob=0.0, | |
| type='RandomFlip'), | |
| dict( | |
| direction='horizontal', | |
| prob=1.0, | |
| type='RandomFlip'), | |
| ], | |
| [ | |
| dict(type='LoadAnnotations'), | |
| ], | |
| [ | |
| dict(type='PackSegInputs'), | |
| ], | |
| ], | |
| type='TestTimeAug'), | |
| ], | |
| type='ADE20KDataset'), | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(shuffle=False, type='DefaultSampler')) | |
| test_evaluator = dict( | |
| iou_metrics=[ | |
| 'mIoU', | |
| ], type='IoUMetric') | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 2560, | |
| 640, | |
| ), type='Resize'), | |
| dict(reduce_zero_label=True, type='LoadAnnotations'), | |
| dict(type='PackSegInputs'), | |
| ] | |
| train_cfg = dict( | |
| max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) | |
| train_dataloader = dict( | |
| batch_size=2, | |
| dataset=dict( | |
| data_prefix=dict( | |
| img_path='images/training', seg_map_path='annotations/training'), | |
| data_root='data/ade/ADEChallengeData2016', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(reduce_zero_label=True, type='LoadAnnotations'), | |
| dict( | |
| keep_ratio=True, | |
| ratio_range=( | |
| 0.5, | |
| 2.0, | |
| ), | |
| scale=( | |
| 2560, | |
| 640, | |
| ), | |
| type='RandomResize'), | |
| dict( | |
| cat_max_ratio=0.75, crop_size=( | |
| 640, | |
| 640, | |
| ), type='RandomCrop'), | |
| dict(prob=0.5, type='RandomFlip'), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='PackSegInputs'), | |
| ], | |
| type='ADE20KDataset'), | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(shuffle=True, type='InfiniteSampler')) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(reduce_zero_label=True, type='LoadAnnotations'), | |
| dict( | |
| keep_ratio=True, | |
| ratio_range=( | |
| 0.5, | |
| 2.0, | |
| ), | |
| scale=( | |
| 2560, | |
| 640, | |
| ), | |
| type='RandomResize'), | |
| dict(cat_max_ratio=0.75, crop_size=( | |
| 640, | |
| 640, | |
| ), type='RandomCrop'), | |
| dict(prob=0.5, type='RandomFlip'), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='PackSegInputs'), | |
| ] | |
| tta_model = dict( | |
| module=dict( | |
| auxiliary_head=dict( | |
| align_corners=False, | |
| channels=256, | |
| concat_input=False, | |
| dropout_ratio=0.1, | |
| in_channels=384, | |
| in_index=2, | |
| loss_decode=dict( | |
| loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), | |
| norm_cfg=dict(requires_grad=True, type='SyncBN'), | |
| num_classes=150, | |
| num_convs=1, | |
| type='FCNHead'), | |
| backbone=dict( | |
| act_cfg=dict(type='GELU'), | |
| attn_drop_rate=0.0, | |
| depths=( | |
| 2, | |
| 2, | |
| 27, | |
| 2, | |
| ), | |
| dims=96, | |
| drop_path_rate=0.3, | |
| drop_rate=0.0, | |
| embed_dims=96, | |
| init_cfg=dict( | |
| checkpoint= | |
| 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth', | |
| type='Pretrained'), | |
| mlp_ratio=4, | |
| norm_cfg=dict(requires_grad=True, type='LN'), | |
| num_heads=[ | |
| 3, | |
| 6, | |
| 12, | |
| 24, | |
| ], | |
| out_indices=( | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| ), | |
| patch_norm=True, | |
| patch_size=4, | |
| pretrain_img_size=224, | |
| pretrained='../../ckpts/vssmsmall/ckpt_epoch_238.pth', | |
| qk_scale=None, | |
| qkv_bias=True, | |
| strides=( | |
| 4, | |
| 2, | |
| 2, | |
| 2, | |
| ), | |
| type='MMSEG_VSSM', | |
| use_abs_pos_embed=False, | |
| window_size=7), | |
| data_preprocessor=dict( | |
| bgr_to_rgb=True, | |
| mean=[ | |
| 123.675, | |
| 116.28, | |
| 103.53, | |
| ], | |
| pad_val=0, | |
| seg_pad_val=255, | |
| size=( | |
| 640, | |
| 640, | |
| ), | |
| std=[ | |
| 58.395, | |
| 57.12, | |
| 57.375, | |
| ], | |
| type='SegDataPreProcessor'), | |
| decode_head=dict( | |
| align_corners=False, | |
| channels=512, | |
| dropout_ratio=0.1, | |
| in_channels=[ | |
| 96, | |
| 192, | |
| 384, | |
| 768, | |
| ], | |
| in_index=[ | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| ], | |
| loss_decode=dict( | |
| loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), | |
| norm_cfg=dict(requires_grad=True, type='SyncBN'), | |
| num_classes=150, | |
| pool_scales=( | |
| 1, | |
| 2, | |
| 3, | |
| 6, | |
| ), | |
| type='UPerHead'), | |
| pretrained=None, | |
| test_cfg=dict(mode='whole'), | |
| train_cfg=dict(), | |
| type='EncoderDecoder'), | |
| type='SegTTAModel') | |
| tta_pipeline = [ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict( | |
| transforms=[ | |
| [ | |
| dict(keep_ratio=True, scale_factor=0.5, type='Resize'), | |
| dict(keep_ratio=True, scale_factor=0.75, type='Resize'), | |
| dict(keep_ratio=True, scale_factor=1.0, type='Resize'), | |
| dict(keep_ratio=True, scale_factor=1.25, type='Resize'), | |
| dict(keep_ratio=True, scale_factor=1.5, type='Resize'), | |
| dict(keep_ratio=True, scale_factor=1.75, type='Resize'), | |
| ], | |
| [ | |
| dict(direction='horizontal', prob=0.0, type='RandomFlip'), | |
| dict(direction='horizontal', prob=1.0, type='RandomFlip'), | |
| ], | |
| [ | |
| dict(type='LoadAnnotations'), | |
| ], | |
| [ | |
| dict(type='PackSegInputs'), | |
| ], | |
| ], | |
| type='TestTimeAug'), | |
| ] | |
| val_cfg = dict(type='ValLoop') | |
| val_dataloader = dict( | |
| batch_size=1, | |
| dataset=dict( | |
| data_prefix=dict( | |
| img_path='images/validation', | |
| seg_map_path='annotations/validation'), | |
| data_root='data/ade/ADEChallengeData2016', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 2560, | |
| 640, | |
| ), type='Resize'), | |
| dict(reduce_zero_label=True, type='LoadAnnotations'), | |
| dict(type='PackSegInputs'), | |
| ], | |
| type='ADE20KDataset'), | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(shuffle=False, type='DefaultSampler')) | |
| val_evaluator = dict( | |
| iou_metrics=[ | |
| 'mIoU', | |
| ], type='IoUMetric') | |
| vis_backends = [ | |
| dict(type='LocalVisBackend'), | |
| ] | |
| visualizer = dict( | |
| name='visualizer', | |
| type='SegLocalVisualizer', | |
| vis_backends=[ | |
| dict(type='LocalVisBackend'), | |
| ]) | |
| work_dir = './work_dirs/upernet_vssm_4xb4-160k_ade20k-640x640_small' | |
| 2024/01/18 13:41:39 - mmengine - INFO - Hooks will be executed in the following order: | |
| before_run: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| (BELOW_NORMAL) LoggerHook | |
| -------------------- | |
| before_train: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| (NORMAL ) IterTimerHook | |
| (VERY_LOW ) CheckpointHook | |
| -------------------- | |
| before_train_epoch: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| (NORMAL ) IterTimerHook | |
| (NORMAL ) DistSamplerSeedHook | |
| -------------------- | |
| before_train_iter: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| (NORMAL ) IterTimerHook | |
| -------------------- | |
| after_train_iter: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| (NORMAL ) IterTimerHook | |
| (NORMAL ) SegVisualizationHook | |
| (BELOW_NORMAL) LoggerHook | |
| (LOW ) ParamSchedulerHook | |
| (VERY_LOW ) CheckpointHook | |
| -------------------- | |
| after_train_epoch: | |
| (NORMAL ) IterTimerHook | |
| (LOW ) ParamSchedulerHook | |
| (VERY_LOW ) CheckpointHook | |
| -------------------- | |
| before_val: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| -------------------- | |
| before_val_epoch: | |
| (NORMAL ) IterTimerHook | |
| -------------------- | |
| before_val_iter: | |
| (NORMAL ) IterTimerHook | |
| -------------------- | |
| after_val_iter: | |
| (NORMAL ) IterTimerHook | |
| (NORMAL ) SegVisualizationHook | |
| (BELOW_NORMAL) LoggerHook | |
| -------------------- | |
| after_val_epoch: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| (NORMAL ) IterTimerHook | |
| (BELOW_NORMAL) LoggerHook | |
| (LOW ) ParamSchedulerHook | |
| (VERY_LOW ) CheckpointHook | |
| -------------------- | |
| after_val: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| -------------------- | |
| after_train: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| (VERY_LOW ) CheckpointHook | |
| -------------------- | |
| before_test: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| -------------------- | |
| before_test_epoch: | |
| (NORMAL ) IterTimerHook | |
| -------------------- | |
| before_test_iter: | |
| (NORMAL ) IterTimerHook | |
| -------------------- | |
| after_test_iter: | |
| (NORMAL ) IterTimerHook | |
| (NORMAL ) SegVisualizationHook | |
| (BELOW_NORMAL) LoggerHook | |
| -------------------- | |
| after_test_epoch: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| (NORMAL ) IterTimerHook | |
| (BELOW_NORMAL) LoggerHook | |
| -------------------- | |
| after_test: | |
| (VERY_HIGH ) RuntimeInfoHook | |
| -------------------- | |
| after_run: | |
| (BELOW_NORMAL) LoggerHook | |
| -------------------- | |
| 2024/01/18 13:41:41 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. | |
| 2024/01/18 13:41:42 - mmengine - INFO - Load checkpoint from /home/LiuYue/Workspace3/ckpts/segmentation/work_dirs/upernet_vssm_4xb4-160k_ade20k-640x640_small/iter_160000.pth | |
| 2024/01/18 13:53:00 - mmengine - INFO - Iter(test) [ 50/500] eta: 1:41:38 time: 9.2342 data_time: 0.0153 memory: 53982 | |
| 2024/01/18 14:00:40 - mmengine - INFO - Iter(test) [100/500] eta: 1:15:51 time: 3.6223 data_time: 0.0136 memory: 52867 | |
| 2024/01/18 14:04:27 - mmengine - INFO - Iter(test) [150/500] eta: 0:53:03 time: 1.3106 data_time: 0.0160 memory: 52745 | |
| 2024/01/18 14:11:51 - mmengine - INFO - Iter(test) [200/500] eta: 0:45:12 time: 3.2742 data_time: 0.0150 memory: 52971 | |
| 2024/01/18 14:15:23 - mmengine - INFO - Iter(test) [250/500] eta: 0:33:40 time: 4.4249 data_time: 0.0168 memory: 53191 | |
| 2024/01/18 14:20:45 - mmengine - INFO - Iter(test) [300/500] eta: 0:26:01 time: 6.0236 data_time: 0.0202 memory: 56580 | |
| 2024/01/18 14:24:59 - mmengine - INFO - Iter(test) [350/500] eta: 0:18:32 time: 7.2593 data_time: 0.0146 memory: 52298 | |
| 2024/01/18 14:28:39 - mmengine - INFO - Iter(test) [400/500] eta: 0:11:44 time: 2.0090 data_time: 0.0136 memory: 53112 | |
| 2024/01/18 14:32:55 - mmengine - INFO - Iter(test) [450/500] eta: 0:05:41 time: 0.9588 data_time: 0.0158 memory: 52817 | |
| 2024/01/18 14:36:26 - mmengine - INFO - Iter(test) [500/500] eta: 0:00:00 time: 7.8064 data_time: 0.0142 memory: 52995 | |
| 2024/01/18 14:38:02 - mmengine - INFO - per class results: | |
| 2024/01/18 14:38:02 - mmengine - INFO - | |
| +---------------------+-------+-------+ | |
| | Class | IoU | Acc | | |
| +---------------------+-------+-------+ | |
| | wall | 78.75 | 89.36 | | |
| | building | 83.12 | 92.71 | | |
| | sky | 94.5 | 97.63 | | |
| | floor | 81.76 | 90.23 | | |
| | tree | 74.85 | 88.04 | | |
| | ceiling | 85.58 | 92.92 | | |
| | road | 85.53 | 91.16 | | |
| | bed | 89.56 | 95.86 | | |
| | windowpane | 64.66 | 81.12 | | |
| | grass | 65.41 | 80.54 | | |
| | cabinet | 61.71 | 73.16 | | |
| | sidewalk | 69.77 | 82.53 | | |
| | person | 80.78 | 92.72 | | |
| | earth | 39.83 | 53.66 | | |
| | door | 53.67 | 67.04 | | |
| | table | 61.54 | 79.57 | | |
| | mountain | 57.79 | 75.02 | | |
| | plant | 52.7 | 63.35 | | |
| | curtain | 74.79 | 86.97 | | |
| | chair | 59.42 | 72.69 | | |
| | car | 84.32 | 92.36 | | |
| | water | 55.89 | 69.4 | | |
| | painting | 74.79 | 87.5 | | |
| | sofa | 68.36 | 84.71 | | |
| | shelf | 44.36 | 63.6 | | |
| | house | 46.15 | 61.18 | | |
| | sea | 57.85 | 81.06 | | |
| | mirror | 69.21 | 77.51 | | |
| | rug | 61.87 | 73.64 | | |
| | field | 29.81 | 47.44 | | |
| | armchair | 46.69 | 64.08 | | |
| | seat | 62.14 | 82.15 | | |
| | fence | 47.03 | 64.8 | | |
| | desk | 53.19 | 70.23 | | |
| | rock | 46.6 | 70.86 | | |
| | wardrobe | 46.65 | 66.04 | | |
| | lamp | 66.87 | 78.03 | | |
| | bathtub | 83.11 | 86.64 | | |
| | railing | 35.37 | 49.1 | | |
| | cushion | 60.08 | 72.91 | | |
| | base | 28.85 | 42.24 | | |
| | box | 26.91 | 33.36 | | |
| | column | 46.47 | 58.22 | | |
| | signboard | 38.24 | 51.08 | | |
| | chest of drawers | 45.6 | 66.14 | | |
| | counter | 25.59 | 34.04 | | |
| | sand | 45.36 | 64.69 | | |
| | sink | 73.4 | 81.15 | | |
| | skyscraper | 49.52 | 60.23 | | |
| | fireplace | 80.08 | 90.52 | | |
| | refrigerator | 76.78 | 81.87 | | |
| | grandstand | 46.64 | 79.47 | | |
| | path | 25.75 | 36.79 | | |
| | stairs | 34.91 | 44.92 | | |
| | runway | 70.95 | 92.5 | | |
| | case | 61.74 | 76.13 | | |
| | pool table | 91.83 | 96.65 | | |
| | pillow | 60.23 | 71.02 | | |
| | screen door | 70.03 | 75.59 | | |
| | stairway | 34.92 | 41.71 | | |
| | river | 9.03 | 17.44 | | |
| | bridge | 67.13 | 78.16 | | |
| | bookcase | 44.09 | 68.9 | | |
| | blind | 46.02 | 50.39 | | |
| | coffee table | 59.14 | 82.97 | | |
| | toilet | 85.59 | 90.78 | | |
| | flower | 37.12 | 51.46 | | |
| | book | 46.03 | 62.65 | | |
| | hill | 12.8 | 20.47 | | |
| | bench | 40.19 | 46.67 | | |
| | countertop | 56.79 | 74.35 | | |
| | stove | 78.19 | 85.06 | | |
| | palm | 51.92 | 70.76 | | |
| | kitchen island | 49.25 | 77.56 | | |
| | computer | 76.69 | 89.25 | | |
| | swivel chair | 46.97 | 64.54 | | |
| | boat | 39.55 | 56.75 | | |
| | bar | 40.71 | 53.86 | | |
| | arcade machine | 85.72 | 94.08 | | |
| | hovel | 33.09 | 39.0 | | |
| | bus | 93.28 | 97.04 | | |
| | towel | 66.95 | 78.09 | | |
| | light | 57.36 | 64.37 | | |
| | truck | 43.92 | 56.1 | | |
| | tower | 17.34 | 27.06 | | |
| | chandelier | 70.27 | 85.27 | | |
| | awning | 25.15 | 30.83 | | |
| | streetlight | 27.76 | 33.84 | | |
| | booth | 34.47 | 38.09 | | |
| | television receiver | 70.57 | 77.57 | | |
| | airplane | 60.13 | 67.32 | | |
| | dirt track | 1.29 | 2.65 | | |
| | apparel | 30.46 | 48.93 | | |
| | pole | 22.16 | 29.32 | | |
| | land | 2.43 | 3.38 | | |
| | bannister | 12.98 | 17.41 | | |
| | escalator | 35.52 | 51.31 | | |
| | ottoman | 49.75 | 64.2 | | |
| | bottle | 36.52 | 57.03 | | |
| | buffet | 45.18 | 59.91 | | |
| | poster | 26.96 | 30.14 | | |
| | stage | 15.07 | 19.98 | | |
| | van | 40.79 | 58.46 | | |
| | ship | 58.61 | 93.2 | | |
| | fountain | 37.13 | 37.62 | | |
| | conveyer belt | 73.14 | 91.11 | | |
| | canopy | 16.41 | 21.48 | | |
| | washer | 70.64 | 72.56 | | |
| | plaything | 26.95 | 40.15 | | |
| | swimming pool | 46.85 | 49.67 | | |
| | stool | 43.95 | 55.94 | | |
| | barrel | 43.46 | 68.28 | | |
| | basket | 28.25 | 40.89 | | |
| | waterfall | 52.45 | 64.56 | | |
| | tent | 88.84 | 98.38 | | |
| | bag | 16.38 | 20.77 | | |
| | minibike | 74.94 | 87.17 | | |
| | cradle | 76.09 | 97.44 | | |
| | oven | 56.13 | 67.6 | | |
| | ball | 48.07 | 61.55 | | |
| | food | 47.43 | 54.79 | | |
| | step | 11.71 | 13.34 | | |
| | tank | 49.12 | 52.75 | | |
| | trade name | 25.88 | 29.71 | | |
| | microwave | 85.51 | 93.72 | | |
| | pot | 45.76 | 52.34 | | |
| | animal | 55.15 | 57.0 | | |
| | bicycle | 57.35 | 80.39 | | |
| | lake | 47.5 | 63.73 | | |
| | dishwasher | 70.78 | 80.28 | | |
| | screen | 66.71 | 81.93 | | |
| | blanket | 11.9 | 13.84 | | |
| | sculpture | 64.88 | 77.88 | | |
| | hood | 58.27 | 69.63 | | |
| | sconce | 49.96 | 61.09 | | |
| | vase | 44.7 | 55.6 | | |
| | traffic light | 37.16 | 53.95 | | |
| | tray | 7.6 | 10.69 | | |
| | ashcan | 42.42 | 56.09 | | |
| | fan | 62.16 | 76.81 | | |
| | pier | 47.99 | 56.02 | | |
| | crt screen | 6.95 | 19.0 | | |
| | plate | 53.63 | 67.7 | | |
| | monitor | 4.75 | 5.08 | | |
| | bulletin board | 54.16 | 62.52 | | |
| | shower | 0.0 | 0.0 | | |
| | radiator | 62.46 | 71.64 | | |
| | glass | 13.45 | 14.01 | | |
| | clock | 40.9 | 46.31 | | |
| | flag | 50.72 | 53.6 | | |
| +---------------------+-------+-------+ | |
| 2024/01/18 14:38:02 - mmengine - INFO - Iter(test) [500/500] aAcc: 83.8800 mIoU: 50.7800 mAcc: 62.2700 data_time: 0.0226 time: 6.5675 | |