2024/01/14 17:47:47 - 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: 1688668109
    GPU 0,1,2,3,4,5,6,7: Tesla V100-SXM3-32GB
    CUDA_HOME: /usr/local/cuda
    NVCC: Cuda compilation tools, release 11.7, V11.7.99
    GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 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: 4.9.0
    MMEngine: 0.10.1

Runtime environment:
    cudnn_benchmark: True
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
    dist_cfg: {'backend': 'nccl'}
    seed: 1688668109
    Distributed launcher: pytorch
    Distributed training: True
    GPU number: 8
------------------------------------------------------------

2024/01/14 17:47:48 - mmengine - INFO - Config:
backbone_norm_cfg = dict(requires_grad=True, type='LN')
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_20220317-e9b98025.pth'
crop_size = (
    512,
    512,
)
data_preprocessor = dict(
    bgr_to_rgb=True,
    mean=[
        123.675,
        116.28,
        103.53,
    ],
    pad_val=0,
    seg_pad_val=255,
    size=(
        512,
        512,
    ),
    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 = './work_dirs/upernet_vssm_4xb4-160k_ade20k-512x512_base/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=512,
            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=128,
            drop_path_rate=0.3,
            drop_rate=0.0,
            embed_dims=128,
            init_cfg=dict(
                checkpoint=
                'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_20220317-e9b98025.pth',
                type='Pretrained'),
            mlp_ratio=4,
            norm_cfg=dict(requires_grad=True, type='LN'),
            num_heads=[
                4,
                8,
                16,
                32,
            ],
            out_indices=(
                0,
                1,
                2,
                3,
            ),
            patch_norm=True,
            patch_size=4,
            pretrain_img_size=224,
            pretrained='../../ckpts/vssmbase/ckpt_epoch_260.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=(
                512,
                512,
            ),
            std=[
                58.395,
                57.12,
                57.375,
            ],
            type='SegDataPreProcessor'),
        decode_head=dict(
            align_corners=False,
            channels=512,
            dropout_ratio=0.1,
            in_channels=[
                128,
                256,
                512,
                1024,
            ],
            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=(
        2048,
        512,
    ), 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=(
                    2048,
                    512,
                ),
                type='RandomResize'),
            dict(
                cat_max_ratio=0.75, crop_size=(
                    512,
                    512,
                ), 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=(
            2048,
            512,
        ),
        type='RandomResize'),
    dict(cat_max_ratio=0.75, crop_size=(
        512,
        512,
    ), 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=512,
            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=128,
            drop_path_rate=0.3,
            drop_rate=0.0,
            embed_dims=128,
            init_cfg=dict(
                checkpoint=
                'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_20220317-e9b98025.pth',
                type='Pretrained'),
            mlp_ratio=4,
            norm_cfg=dict(requires_grad=True, type='LN'),
            num_heads=[
                4,
                8,
                16,
                32,
            ],
            out_indices=(
                0,
                1,
                2,
                3,
            ),
            patch_norm=True,
            patch_size=4,
            pretrain_img_size=224,
            pretrained='../../ckpts/vssmbase/ckpt_epoch_260.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=(
                512,
                512,
            ),
            std=[
                58.395,
                57.12,
                57.375,
            ],
            type='SegDataPreProcessor'),
        decode_head=dict(
            align_corners=False,
            channels=512,
            dropout_ratio=0.1,
            in_channels=[
                128,
                256,
                512,
                1024,
            ],
            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=(
                2048,
                512,
            ), 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-512x512_base'

2024/01/14 17:47:58 - 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                         
 -------------------- 
2024/01/14 17:47:59 - mmengine - WARNING - The prefix is not set in metric class IoUMetric.
2024/01/14 17:48:08 - mmengine - INFO - Load checkpoint from ./work_dirs/upernet_vssm_4xb4-160k_ade20k-512x512_base/iter_160000.pth
2024/01/14 18:02:41 - mmengine - INFO - Iter(test) [ 50/250]    eta: 0:58:11  time: 10.1193  data_time: 0.0121  memory: 20518  
2024/01/14 18:10:19 - mmengine - INFO - Iter(test) [100/250]    eta: 0:33:17  time: 8.5116  data_time: 0.0100  memory: 19429  
2024/01/14 18:15:12 - mmengine - INFO - Iter(test) [150/250]    eta: 0:18:02  time: 5.7400  data_time: 0.0111  memory: 19330  
2024/01/14 18:20:32 - mmengine - INFO - Iter(test) [200/250]    eta: 0:08:05  time: 6.0980  data_time: 0.0114  memory: 19330  
2024/01/14 18:25:01 - mmengine - INFO - Iter(test) [250/250]    eta: 0:00:00  time: 3.3462  data_time: 0.0118  memory: 18931  
2024/01/14 18:28:07 - mmengine - INFO - per class results:
2024/01/14 18:28:07 - mmengine - INFO - 
+---------------------+-------+-------+
|        Class        |  IoU  |  Acc  |
+---------------------+-------+-------+
|         wall        | 78.97 | 89.79 |
|       building      | 83.44 | 93.48 |
|         sky         | 94.33 | 97.56 |
|        floor        | 82.05 | 90.77 |
|         tree        | 74.87 | 88.67 |
|       ceiling       | 85.17 | 93.67 |
|         road        | 84.22 | 90.66 |
|         bed         | 89.05 | 96.36 |
|      windowpane     | 63.55 | 79.82 |
|        grass        | 69.99 | 84.45 |
|       cabinet       | 62.16 | 75.89 |
|       sidewalk      | 66.09 | 81.19 |
|        person       | 81.66 | 93.13 |
|        earth        | 37.51 | 48.77 |
|         door        | 52.74 | 65.76 |
|        table        | 62.93 | 78.47 |
|       mountain      | 63.34 | 78.01 |
|        plant        | 52.21 | 63.43 |
|       curtain       | 75.86 | 87.52 |
|        chair        | 61.77 | 72.89 |
|         car         | 84.12 | 90.96 |
|        water        | 53.74 | 67.62 |
|       painting      | 76.67 | 89.15 |
|         sofa        | 69.15 | 84.85 |
|        shelf        | 43.65 | 63.48 |
|        house        | 37.83 | 50.42 |
|         sea         | 63.54 | 89.14 |
|        mirror       | 69.31 | 76.99 |
|         rug         | 55.02 | 64.68 |
|        field        | 27.91 | 43.35 |
|       armchair      | 48.02 | 67.01 |
|         seat        | 63.51 | 84.09 |
|        fence        | 47.72 | 61.45 |
|         desk        | 53.92 | 72.17 |
|         rock        | 45.01 |  66.9 |
|       wardrobe      | 49.15 | 59.93 |
|         lamp        | 66.03 | 77.09 |
|       bathtub       | 80.36 | 85.58 |
|       railing       | 35.23 | 49.33 |
|       cushion       | 60.43 | 73.02 |
|         base        | 31.65 | 42.54 |
|         box         | 26.89 | 31.63 |
|        column       | 48.94 | 56.13 |
|      signboard      | 39.69 | 50.97 |
|   chest of drawers  | 48.02 | 62.14 |
|       counter       | 25.34 | 35.46 |
|         sand        | 55.67 | 73.43 |
|         sink        | 74.58 | 80.96 |
|      skyscraper     | 42.42 | 51.55 |
|      fireplace      | 80.92 | 91.73 |
|     refrigerator    | 77.76 | 85.12 |
|      grandstand     | 45.02 | 83.82 |
|         path        | 16.49 | 26.37 |
|        stairs       |  35.1 | 42.42 |
|        runway       |  72.8 |  93.9 |
|         case        | 48.01 | 62.28 |
|      pool table     | 93.33 | 97.32 |
|        pillow       | 61.87 | 72.67 |
|     screen door     | 68.21 | 77.03 |
|       stairway      |  32.7 | 38.45 |
|        river        |  11.6 | 22.81 |
|        bridge       | 38.77 | 43.57 |
|       bookcase      | 44.89 | 65.61 |
|        blind        | 46.61 | 48.95 |
|     coffee table    | 59.71 | 84.15 |
|        toilet       |  84.8 | 90.86 |
|        flower       | 43.64 |  64.1 |
|         book        | 49.21 | 66.22 |
|         hill        | 13.48 | 21.64 |
|        bench        | 55.21 | 64.27 |
|      countertop     | 49.06 | 73.98 |
|        stove        | 77.39 | 83.56 |
|         palm        | 51.11 | 67.45 |
|    kitchen island   | 49.14 | 76.72 |
|       computer      | 69.78 | 77.84 |
|     swivel chair    | 39.71 | 56.34 |
|         boat        | 48.05 | 52.89 |
|         bar         | 26.98 |  35.7 |
|    arcade machine   | 69.15 | 76.38 |
|        hovel        | 20.92 | 30.12 |
|         bus         | 87.77 |  97.1 |
|        towel        | 67.32 | 75.99 |
|        light        | 57.87 | 64.92 |
|        truck        | 37.61 | 48.83 |
|        tower        | 35.31 | 45.43 |
|      chandelier     | 65.99 | 79.86 |
|        awning       |  31.7 | 37.19 |
|     streetlight     | 28.83 | 35.37 |
|        booth        | 52.58 | 58.07 |
| television receiver | 70.28 | 80.92 |
|       airplane      | 61.82 | 68.65 |
|      dirt track     | 13.58 | 49.33 |
|       apparel       | 40.61 | 58.04 |
|         pole        | 27.08 | 34.57 |
|         land        |  1.6  |  3.64 |
|      bannister      | 15.63 | 19.65 |
|      escalator      | 28.68 | 31.74 |
|       ottoman       |  52.2 | 63.91 |
|        bottle       | 37.11 |  60.8 |
|        buffet       | 34.32 | 38.55 |
|        poster       |  30.1 | 37.59 |
|        stage        | 19.22 | 26.17 |
|         van         | 42.28 | 60.26 |
|         ship        | 61.48 | 88.98 |
|       fountain      | 19.35 | 21.66 |
|    conveyer belt    | 86.38 | 92.34 |
|        canopy       | 31.41 | 40.68 |
|        washer       | 75.23 |  76.0 |
|      plaything      | 30.46 | 46.68 |
|    swimming pool    | 70.72 |  77.6 |
|        stool        | 44.38 | 59.55 |
|        barrel       | 60.72 | 73.06 |
|        basket       |  37.5 | 48.99 |
|      waterfall      | 64.29 | 78.71 |
|         tent        |  92.9 | 98.47 |
|         bag         |  16.7 | 19.33 |
|       minibike      | 71.87 | 86.48 |
|        cradle       | 77.59 | 96.96 |
|         oven        | 44.84 | 79.75 |
|         ball        |  33.6 | 63.33 |
|         food        | 49.67 | 60.48 |
|         step        | 11.71 | 13.09 |
|         tank        | 57.11 | 61.44 |
|      trade name     | 29.41 | 33.71 |
|      microwave      | 71.46 | 75.56 |
|         pot         | 47.52 | 56.11 |
|        animal       | 43.99 | 44.89 |
|       bicycle       | 56.79 | 78.38 |
|         lake        | 54.44 | 63.37 |
|      dishwasher     | 67.34 |  71.8 |
|        screen       | 52.35 | 69.01 |
|       blanket       |  9.74 | 11.95 |
|      sculpture      | 69.57 | 84.66 |
|         hood        |  68.9 |  73.3 |
|        sconce       | 50.94 | 60.25 |
|         vase        | 46.92 | 61.85 |
|    traffic light    | 38.47 | 57.45 |
|         tray        |  11.6 | 18.94 |
|        ashcan       | 49.51 | 59.73 |
|         fan         | 64.55 | 77.35 |
|         pier        | 43.19 | 53.55 |
|      crt screen     |  6.65 | 20.83 |
|        plate        | 56.85 |  72.0 |
|       monitor       |  6.72 |  9.36 |
|    bulletin board   | 40.76 | 47.92 |
|        shower       |  2.85 |  4.45 |
|       radiator      |  66.1 | 72.02 |
|        glass        | 14.93 | 15.65 |
|        clock        | 39.09 | 45.98 |
|         flag        | 53.01 | 56.24 |
+---------------------+-------+-------+
2024/01/14 18:28:07 - mmengine - INFO - Iter(test) [250/250]    aAcc: 83.9200  mIoU: 51.1200  mAcc: 62.5500  data_time: 0.0509  time: 8.8501