Upload ade20k_upernet_vmamba_base_160k_512_iter160000_511.log

ade20k_upernet_vmamba_base_160k_512_iter160000_511.log

@@ -0,0 +1,770 @@
+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