Open luciferasura opened 1 year ago
cir7
commented
1 year ago The error message indicates that there are too many values for each annotation line. Please check and make sure that your annotation file has the same format as follows:
some/path/000.mp4 1
some/path/001.mp4 1
...you can refer to the doc for details
luciferasura
commented
1 year ago @cir7 Is it similar to classroomvideodataset/train/001.mp4 1?,after I changed it to this, I still reported the original error
cir7
commented
1 year ago could you paste part of your annotation here?
luciferasura
commented
1 year ago Okay, please see where I went wrong. train drinking (31).mp4 1 drinking (32).mp4 1 drinking (33).mp4 1 drinking (34).mp4 1 ....... lecture (31).mp4 2 lecture (32).mp4 2 lecture (33).mp4 2 lecture (34).mp4 2 .....
train.txt classroomvideo/train/drinking (31).mp4 1 classroomvideo/train/drinking (32).mp4 1 classroomvideo/train/drinking (33).mp4 1 classroomvideo/train/drinking (34).mp4 1 ....... classroomvideo/train/lecture (31).mp4 2 classroomvideo/train/lecture (32).mp4 2 classroomvideo/train/lecture (33).mp4 2 classroomvideo/train/lecture (34).mp4 2 .....
chatGPT tells me the correct format is as follows, right?
dataset/
├── train/
│ ├── class1/
│ │ ├── video1.mp4
│ │ ├── video2.mp4
│ │ └── ...
│ ├── class2/
│ │ ├── video3.mp4
│ │ ├── video4.mp4
│ │ └── ...
│ └── ...
├── val/
│ ├── class1/
│ │ ├── video5.mp4
│ │ ├── video6.mp4
│ │ └── ...
│ ├── class2/
│ │ ├── video7.mp4
│ │ ├── video8.mp4
│ │ └── ...
│ └── ...
├── train.txt
├── val.txt
dataset/train/class1/video1.mp4 0 dataset/train/class1/video2.mp4 0 ... dataset/train/class2/video3.mp4 1 dataset/train/class2/video4.mp4 1 ...
dataset/val/class1/video5.mp4 0
dataset/val/class1/video6.mp4 0
...
dataset/val/class2/video7.mp4 1
dataset/val/class2/video8.mp4 1
...
@cir7 Thank you for your reply
cir7
commented
1 year ago There are whitespace characters in your video file name, and we default to use it as a split delimiter, which results in more values than expected. I suggest removing the whitespace in the video file name. Another way is to use , as the delimiter and modify the config file and your annotation accordingly.
luciferasura
commented
1 year ago I followed your advice and I just removed all the spaces, but after running, I always report the error of not finding the MP4 file，and the following is a new error.For example ：FileNotFoundError: [Errno 2] No such file or directory: '/mmaction2/mmaction/datasets/classvideo/train/play_phone(2).mp4'
the following is my train.txt： train/drinking(1).mp4 1 train/drinking(2).mp4 1 train/drinking(3).mp4 1 .... train/lecture(1).mp4 2 train/lecture(2).mp4 2 train/lecture(3).mp4 2 ....
and the following is a new error.
Loads checkpoint by http backend from path: https://download.pytorch.org/models/resnet50-11ad3fa6.pth
08/30 17:03:18 - mmengine - INFO - These parameters in pretrained checkpoint are not loaded: {'fc.bias', 'fc.weight'}
08/30 17:03:19 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
08/30 17:03:19 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
08/30 17:03:19 - mmengine - INFO - Checkpoints will be saved to /mmaction2/work_dirs/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_classroom.
Traceback (most recent call last):
File "tools/train.py", line 135, in
cir7
commented
1 year ago please make sure the file exist: /mmaction2/mmaction/datasets/classvideo/train/play_phone(2).mp4, maybe you forget to rename video files?
luciferasura
commented
1 year ago I renamed each file name by hand and I also rechecked the filename and removed the spaces.In addition to modifying your own config, is there anything else that needs to be modified, the official tutorial reference is not detailed, can you guide it?
Branch
main branch (1.x version, such as
v1.0.0, ordev-1.xbranch)Prerequisite
Environment
System environment: sys.platform: linux Python: 3.8.17 (default, Jul 5 2023, 21:04:15) [GCC 11.2.0] CUDA available: True numpy_random_seed: 738065409 GPU 0: NVIDIA GeForce RTX 3090 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.0, V11.0.221 GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0 PyTorch: 1.7.0 PyTorch compiling details: PyTorch built with:
Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -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 -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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,
TorchVision: 0.8.1 OpenCV: 4.8.0 MMEngine: 0.8.3
Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 738065409 diff_rank_seed: False deterministic: False Distributed launcher: none Distributed training: False GPU number: 1
Describe the bug
Traceback (most recent call last): File "tools/train.py", line 135, in
main()
File "tools/train.py", line 131, in main
runner.train()
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1703, in train
self._train_loop = self.build_train_loop(
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1495, in build_train_loop
loop = LOOPS.build(
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build
return self.build_func(cfg, args, kwargs, registry=self)
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg
obj = obj_cls(args) # type: ignore
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/runner/loops.py", line 44, in init
super().init(runner, dataloader)
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/runner/base_loop.py", line 26, in init
self.dataloader = runner.build_dataloader(
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1353, in build_dataloader
dataset = DATASETS.build(dataset_cfg)
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build
return self.build_func(cfg, args, kwargs, registry=self)
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg
obj = obj_cls(args) # type: ignore
File "/mmaction2/mmaction/datasets/video_dataset.py", line 67, in init
super().init(
File "/mmaction2/mmaction/datasets/base.py", line 48, in init
super().init(
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/dataset/base_dataset.py", line 245, in init
self.full_init()
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/dataset/base_dataset.py", line 296, in full_init
self.data_list = self.load_data_list()
File "/mmaction2/mmaction/datasets/video_dataset.py", line 93, in load_data_list
filename, label = line_split
ValueError: too many values to unpack (expected 2)
Reproduces the problem - code sample
The following is the complete config code： ann_file_train = '/mmaction2/classroomactionvideo/train.txt' ann_file_val = '/mmaction2/classroomactionvideo/val.txt' auto_scale_lr = dict(base_batch_size=256, enable=False) data_root = '/mmaction2/classroomactionvideo/train/' data_root_val = '/mmaction2/classroomactionvideo/val/' dataset_type = 'VideoDataset' default_hooks = dict( checkpoint=dict(interval=1, save_best='auto', type='CheckpointHook'), logger=dict(ignore_last=False, interval=20, 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 = 'mmaction' 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, norm_eval=False, pretrained='https://download.pytorch.org/models/resnet50-11ad3fa6.pth', type='ResNet'), cls_head=dict( average_clips='prob', consensus=dict(dim=1, type='AvgConsensus'), dropout_ratio=0.4, in_channels=2048, init_std=0.01, num_classes=7, spatial_type='avg', type='TSNHead'), data_preprocessor=dict( format_shape='NCHW', mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], type='ActionDataPreprocessor'), test_cfg=None, train_cfg=None, type='Recognizer2D') optim_wrapper = dict( clip_grad=dict(max_norm=40, norm_type=2), optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0001)) param_scheduler = [ dict( begin=0, by_epoch=True, end=50, gamma=0.1, milestones=[ 20, 40, ], type='MultiStepLR'), ] randomness = dict(deterministic=False, diff_rank_seed=False, seed=None) resume = False test_cfg = dict(type='TestLoop') test_dataloader = dict( batch_size=1, dataset=dict( ann_file='/mmaction2/classroomactionvideo/val.txt', data_prefix=dict(video='/mmaction2/classroomactionvideo/val/'), pipeline=[ dict(io_backend='disk', type='DecordInit'), dict( clip_len=1, frame_interval=1, num_clips=25, test_mode=True, type='SampleFrames'), dict(type='DecordDecode'), dict(scale=( -1, 256, ), type='Resize'), dict(crop_size=224, type='TenCrop'), dict(input_format='NCHW', type='FormatShape'), dict(type='PackActionInputs'), ], test_mode=True, type='VideoDataset'), num_workers=2, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) test_evaluator = dict(type='AccMetric') test_pipeline = [ dict(io_backend='disk', type='DecordInit'), dict( clip_len=1, frame_interval=1, num_clips=25, test_mode=True, type='SampleFrames'), dict(type='DecordDecode'), dict(scale=( -1, 256, ), type='Resize'), dict(crop_size=224, type='TenCrop'), dict(input_format='NCHW', type='FormatShape'), dict(type='PackActionInputs'), ] train_cfg = dict( max_epochs=50, type='EpochBasedTrainLoop', val_begin=1, val_interval=1) train_dataloader = dict( batch_size=32, dataset=dict( ann_file='/mmaction2/classroomactionvideo/train.txt', data_prefix=dict(video='/mmaction2/classroomactionvideo/train/'), pipeline=[ dict(io_backend='disk', type='DecordInit'), dict( clip_len=1, frame_interval=1, num_clips=3, type='SampleFrames'), dict(type='DecordDecode'), dict(scale=( -1, 256, ), type='Resize'), dict( input_size=224, max_wh_scale_gap=1, 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='VideoDataset'), num_workers=2, persistent_workers=True, sampler=dict(shuffle=True, type='DefaultSampler')) train_pipeline = [ dict(io_backend='disk', type='DecordInit'), dict(clip_len=1, frame_interval=1, num_clips=3, type='SampleFrames'), dict(type='DecordDecode'), dict(scale=( -1, 256, ), type='Resize'), dict( input_size=224, max_wh_scale_gap=1, 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=32, dataset=dict( ann_file='/mmaction2/classroomactionvideo/val.txt', data_prefix=dict(video='/mmaction2/classroomactionvideo/val/'), pipeline=[ dict(io_backend='disk', type='DecordInit'), dict( clip_len=1, frame_interval=1, num_clips=3, test_mode=True, type='SampleFrames'), dict(type='DecordDecode'), 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='VideoDataset'), num_workers=2, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) val_evaluator = dict(type='AccMetric') val_pipeline = [ dict(io_backend='disk', type='DecordInit'), dict( clip_len=1, frame_interval=1, num_clips=3, test_mode=True, type='SampleFrames'), dict(type='DecordDecode'), 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/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_classroom'
Reproduces the problem - command or script
python tools/train.py checkpiont/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_classroom.py
Reproduces the problem - error message
Traceback (most recent call last): File "tools/train.py", line 135, in
main()
File "tools/train.py", line 131, in main
runner.train()
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1703, in train
self._train_loop = self.build_train_loop(
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1495, in build_train_loop
loop = LOOPS.build(
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build
return self.build_func(cfg, args, kwargs, registry=self)
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg
obj = obj_cls(args) # type: ignore
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/runner/loops.py", line 44, in init
super().init(runner, dataloader)
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/runner/base_loop.py", line 26, in init
self.dataloader = runner.build_dataloader(
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1353, in build_dataloader
dataset = DATASETS.build(dataset_cfg)
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build
return self.build_func(cfg, args, kwargs, registry=self)
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg
obj = obj_cls(args) # type: ignore
File "/mmaction2/mmaction/datasets/video_dataset.py", line 67, in init
super().init(
File "/mmaction2/mmaction/datasets/base.py", line 48, in init
super().init(
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/dataset/base_dataset.py", line 245, in init
self.full_init()
File "/usr/local/miniconda3/envs/mmlab/lib/python3.8/site-packages/mmengine/dataset/base_dataset.py", line 296, in full_init
self.data_list = self.load_data_list()
File "/mmaction2/mmaction/datasets/video_dataset.py", line 93, in load_data_list
filename, label = line_split
ValueError: too many values to unpack (expected 2)
Additional information
dataset path --classroomactionvideo --train --val --train.txt --val.txt
train.txt and val.txt typography here is similar to the multi-line drinking (1).mp4 1.I am a beginner and am now learning the model fine-tuning of the user guide in MMAction2, in which I want to train action recognition with my own dataset videodataset, and report an error after running ValueError: too many values to unpack (expected 2).I would like to ask what is wrong and How to correct thanks.(我是一个入门小白，现在在学习MMAction2中的用户指南的模型微调，其中我用自己的数据集videodataset想训练一下动作识别，运行之后报错ValueError: too many values to unpack (expected 2)，请教一下哪里错了，如何改正谢谢)