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open-mmlab / mmaction2

OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
https://mmaction2.readthedocs.io
Apache License 2.0
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[Bug] 'ActionVisualizer is not in the mmengine::visualizer registry. #2765

Open jy0x4f opened 9 months ago

jy0x4f commented 9 months ago

Branch

main branch (1.x version, such as v1.0.0, or dev-1.x branch)

Prerequisite

Environment

mmcls 0.25.0
mmcv 2.0.1 mmengine 0.10.1 opencv-contrib-python 4.8.1.78 opencv-python 4.8.1.78 python 3.10.13 pytorch 1.12.0 py3.10_cuda11.3_cudnn8.3.2_0 pytorch torchaudio 0.12.0 torchvision 0.13.0 tqdm 4.65.2

Describe the bug

我在自己3060电脑上跑着代码是没有问题的，能够出结果，但是将代码放到3090服务器上，并且配置了和本地一样的环境，提示 'ActionVisualizer is not in the mmengine::visualizer registry.我尝试很多不同版本的mmcv,mmengine等都不能解决

Reproduces the problem - code sample

No response

Reproduces the problem - command or script

CUDA_VISIBLE_DEVICES=6 nohup python tools/train.py configs/recognition/resnet_code.py >> output.out 2>&1 &

Reproduces the problem - error message

12/14 21:31:06 - 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: 1187698724 GPU 0: NVIDIA GeForce RTX 3090 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.3, V11.3.58 GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 PyTorch: 1.12.0 PyTorch compiling details: PyTorch built with:

Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 1187698724 diff_rank_seed: False deterministic: False Distributed launcher: none Distributed training: False GPU number: 1

12/14 21:31:07 - mmengine - INFO - Config: ann_file_test = '/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/dataset/Poker_Event/Poker_list/test_label.txt' ann_file_train = '/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/dataset/Poker_Event/Poker_list/train_label.txt' ann_file_val = '/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/dataset/Poker_Event/Poker_list/test_label.txt' auto_scale_lr = dict(base_batch_size=128, enable=False) batch_size = 2 data_root = '/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/dataset/Poker_Event/Poker_rgb_event/' data_root_val = '/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/dataset/Poker_Event/Poker_rgb_event/' dataset_type = 'RawframeDataset' default_hooks = dict( checkpoint=dict( interval=3, max_keep_ckpts=1, save_best='auto', type='CheckpointHook'), logger=dict(ignore_last=False, interval=500, type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), runtime_info=dict(type='RuntimeInfoHook'), sampler_seed=dict(type='DistSamplerSeedHook'), sync_buffers=dict(type='SyncBuffersHook'), timer=dict(type='IterTimerHook')) default_scope = 'mmengine' env_cfg = dict( cudnn_benchmark=False, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) file_client_args = dict(io_backend='disk') launcher = 'none' load_from = None log_level = 'INFO' log_processor = dict(by_epoch=True, type='LogProcessor', window_size=20) model = dict( backbone=dict(depth=50, type='ResNetELP'), cls_head=dict(in_channels=2048, num_classes=5, type='MutilModelHead'), type='Recognizer2D') num_workers = 4 optim_wrapper = dict( clip_grad=dict(max_norm=20, norm_type=2), constructor='TSMOptimWrapperConstructor', optimizer=dict(lr=0.001, momentum=0.9, type='SGD', weight_decay=0.0001), paramwise_cfg=dict(fc_lr5=True)) param_scheduler = [ dict( begin=0, by_epoch=True, end=30, gamma=0.1, milestones=[ 25, 30, ], type='MultiStepLR'), ] randomness = dict(deterministic=False, diff_rank_seed=False, seed=None) resume = False test_cfg = dict(type='TestLoop') test_dataloader = dict( batch_size=2, dataset=dict( ann_file= '/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/dataset/Poker_Event/Poker_list/test_label.txt', data_prefix=dict( img= '/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/dataset/Poker_Event/Poker_rgb_event/' ), pipeline=[ dict( clip_len=1, frame_interval=1, num_clips=8, test_mode=True, type='SampleFrames'), dict(type='RawFrameDecode'), dict(scale=( -1, 256, ), type='Resize'), dict(crop_size=224, type='CenterCrop'), dict(input_format='NCHW', type='FormatShape'), dict(type='PackActionInputs'), ], test_mode=True, type='RawframeDataset'), num_workers=4, persistent_workers=True, sampler=dict(shuffle=True, type='DefaultSampler')) test_evaluator = dict(type='AccMetric') test_pipeline = [ dict( clip_len=1, frame_interval=1, num_clips=8, test_mode=True, type='SampleFrames'), dict(type='RawFrameDecode'), dict(scale=( -1, 256, ), type='Resize'), dict(crop_size=224, type='CenterCrop'), dict(input_format='NCHW', type='FormatShape'), dict(type='PackActionInputs'), ] train_cfg = dict( max_epochs=100, type='EpochBasedTrainLoop', val_begin=1, val_interval=1) train_dataloader = dict( batch_size=2, dataset=dict( ann_file= '/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/dataset/Poker_Event/Poker_list/train_label.txt', data_prefix=dict( img= '/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/dataset/Poker_Event/Poker_rgb_event/' ), pipeline=[ dict( clip_len=1, frame_interval=1, num_clips=8, type='SampleFrames'), dict(type='RawFrameDecode'), dict(scale=( -1, 256, ), type='Resize'), dict( input_size=224, max_wh_scale_gap=1, num_fixed_crops=13, random_crop=False, scales=( 1, 0.875, 0.75, 0.66, ), type='MultiScaleCrop'), dict(keep_ratio=False, scale=( 224, 224, ), type='Resize'), dict(flip_ratio=0.5, type='Flip'), dict(input_format='NCHW', type='FormatShape'), dict(type='PackActionInputs'), ], type='RawframeDataset'), num_workers=4, persistent_workers=True, sampler=dict(shuffle=True, type='DefaultSampler')) train_pipeline = [ dict(clip_len=1, frame_interval=1, num_clips=8, type='SampleFrames'), dict(type='RawFrameDecode'), dict(scale=( -1, 256, ), type='Resize'), dict( input_size=224, max_wh_scale_gap=1, num_fixed_crops=13, random_crop=False, scales=( 1, 0.875, 0.75, 0.66, ), type='MultiScaleCrop'), dict(keep_ratio=False, scale=( 224, 224, ), type='Resize'), dict(flip_ratio=0.5, type='Flip'), dict(input_format='NCHW', type='FormatShape'), dict(type='PackActionInputs'), ] val_cfg = dict(type='ValLoop') val_dataloader = dict( batch_size=2, dataset=dict( ann_file= '/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/dataset/Poker_Event/Poker_list/test_label.txt', data_prefix=dict( img= '/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/dataset/Poker_Event/Poker_rgb_event/' ), pipeline=[ dict( clip_len=1, frame_interval=1, num_clips=8, test_mode=True, type='SampleFrames'), dict(type='RawFrameDecode'), dict(scale=( -1, 256, ), type='Resize'), dict(crop_size=224, type='CenterCrop'), dict(input_format='NCHW', type='FormatShape'), dict(type='PackActionInputs'), ], test_mode=True, type='RawframeDataset'), num_workers=4, persistent_workers=True, sampler=dict(shuffle=True, type='DefaultSampler')) val_evaluator = dict(type='AccMetric') val_pipeline = [ dict( clip_len=1, frame_interval=1, num_clips=8, test_mode=True, type='SampleFrames'), dict(type='RawFrameDecode'), dict(scale=( -1, 256, ), type='Resize'), dict(crop_size=224, type='CenterCrop'), dict(input_format='NCHW', type='FormatShape'), dict(type='PackActionInputs'), ] vis_backends = [ dict(type='LocalVisBackend'), ] visualizer = dict( type='ActionVisualizer', vis_backends=[ dict(type='LocalVisBackend'), ]) work_dir = './work_dirs/resnet_code'

Traceback (most recent call last): File "/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/jinyu/TSM_Former-main/tools/train.py", line 136, in main() File "/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/jinyu/TSM_Former-main/tools/train.py", line 128, in main runner = Runner.from_cfg(cfg) File "/home/jiny/.conda/envs/mmaction/lib/python3.10/site-packages/mmengine/runner/runner.py", line 462, in from_cfg runner = cls( File "/home/jiny/.conda/envs/mmaction/lib/python3.10/site-packages/mmengine/runner/runner.py", line 416, in init self.visualizer = self.build_visualizer(visualizer) File "/home/jiny/.conda/envs/mmaction/lib/python3.10/site-packages/mmengine/runner/runner.py", line 803, in build_visualizer return VISUALIZERS.build(visualizer) File "/home/jiny/.conda/envs/mmaction/lib/python3.10/site-packages/mmengine/registry/registry.py", line 570, in build return self.build_func(cfg, *args, **kwargs, registry=self) File "/home/jiny/.conda/envs/mmaction/lib/python3.10/site-packages/mmengine/registry/build_functions.py", line 100, in build_from_cfg raise KeyError( KeyError: 'ActionVisualizer is not in the mmengine::visualizer registry. Please check whether the value of ActionVisualizer is correct or it was registered as expected. More details can be found at https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#import-the-custom-module'

Additional information

No response

Dilmperis commented 7 months ago

@jy0x4f did you find a solution, because i have the same problem with DatasetZelda. I get the same error

wuyiqii commented 3 months ago

Have you solved this problem? Because I had the same problem

jy0x4f commented 2 months ago

I just deleted the conda env and created the new one, and solved. I don't know what the problem was, but it really worked