| 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 | |