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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] use tools/misc/clip_feature_extraction.py to extract feature from long video, dataloaders do not work #2850

Closed fucker255 closed 3 months ago

fucker255 commented 4 months ago

Branch

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

Prerequisite

Environment

sys.platform: linux Python: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0] CUDA available: True MUSA available: False numpy_random_seed: 2147483648 GPU 0: NVIDIA GeForce GTX 1050 Ti CUDA_HOME: /usr/local/cuda-11.8 NVCC: Cuda compilation tools, release 11.8, V11.8.89 GCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 PyTorch: 2.1.1+cu118 PyTorch compiling details: PyTorch built with:

TorchVision: 0.16.1+cu118 OpenCV: 4.9.0 MMEngine: 0.10.4 MMAction2: 1.2.0+4d6c934 MMCV: 2.1.0 MMDetection: 3.3.0

Describe the bug

I want to use clip_feature_extraction.py to extract per clip feature from my videos, but it seems dataloader could not load data, but I looked at the dataset.load_data_list, the file_name is right.

Reproduces the problem - code sample

(https://github.com/open-mmlab/mmaction2/blob/main/tools/misc/clip_feature_extraction.py)

Reproduces the problem - command or script

python /home/liyunlong/files/mmaction2/tools/misc/clip_feature_extraction.py /home/liyunlong/files/mmaction2/configs/recognition/tsn/tsn_imagenet-pretrained-r50_8xb32-1x1x8-100e_kinetics400-rgb.py /home/liyunlong/下载/tsn_imagenet-pretrained-r50_8xb32-1x1x8-100e_kinetics400-rgb_20220906-2692d16c.pth '/home/liyunlong/files/tvseries/rgb_feature' --video-root /home/liyunlong/files/tvseries/TVSeries_Dataset_Videos/converted/ --video-list /home/liyunlong/files/MiniROAD-main/data_info/tvseries_video_list.txt --long-video-mode --clip-interval 6 --frame-interval 1

Reproduces the problem - error message

06/03 00:49:56 - mmengine - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: linux
    Python: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0]
    CUDA available: True
    MUSA available: False
    numpy_random_seed: 700832820
    GPU 0: NVIDIA GeForce GTX 1050 Ti
    CUDA_HOME: /usr/local/cuda-11.8
    NVCC: Cuda compilation tools, release 11.8, V11.8.89
    GCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
    PyTorch: 2.1.1+cu118
    PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.8
  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-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=sm_37;-gencode;arch=compute_90,code=sm_90
  - CuDNN 8.7
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -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_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

    TorchVision: 0.16.1+cu118
    OpenCV: 4.9.0
    MMEngine: 0.10.4

Runtime environment:
    cudnn_benchmark: False
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
    dist_cfg: {'backend': 'nccl'}
    seed: 700832820
    Distributed launcher: none
    Distributed training: False
    GPU number: 1
------------------------------------------------------------

06/03 00:49:57 - mmengine - INFO - Config:
ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt'
ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt'
auto_scale_lr = dict(base_batch_size=256, enable=False)
data_root = 'data/kinetics400/videos_train'
data_root_val = 'data/kinetics400/videos_val'
dataset_type = 'VideoDataset'
default_hooks = dict(
    checkpoint=dict(
        interval=3, max_keep_ckpts=3, 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 = '/home/liyunlong/下载/tsn_imagenet-pretrained-r50_8xb32-1x1x8-100e_kinetics400-rgb_20220906-2692d16c.pth'
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=None,
        backbone_name=None,
        num_segments=None,
        spatial_type='avg',
        temporal_type='avg',
        type='FeatureHead'),
    data_preprocessor=dict(
        format_shape='NCHW',
        mean=[
            123.675,
            116.28,
            103.53,
        ],
        num_frames=25,
        std=[
            58.395,
            57.12,
            57.375,
        ],
        type='LongVideoDataPreprocessor'),
    test_cfg=None,
    train_cfg=None,
    type='Recognizer2D')
optim_wrapper = dict(
    clip_grad=dict(max_norm=40, norm_type=2),
    optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0001))
param_scheduler = [
    dict(
        begin=0,
        by_epoch=True,
        end=100,
        gamma=0.1,
        milestones=[
            40,
            80,
        ],
        type='MultiStepLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file=
        '/home/liyunlong/files/MiniROAD-main/data_info/tvseries_video_list.txt',
        data_prefix=dict(
            video=
            '/home/liyunlong/files/tvseries/TVSeries_Dataset_Videos/converted/'
        ),
        pipeline=[
            dict(io_backend='disk', type='DecordInit'),
            dict(
                clip_interval=6,
                clip_len=6,
                frame_interval=1,
                type='UntrimmedSampleFrames'),
            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=8,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = [
    dict(
        out_file_path=
        '/home/liyunlong/files/tvseries/rgb_feature/total_feats.pkl',
        type='DumpResults'),
]
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=100, type='EpochBasedTrainLoop', val_begin=1, val_interval=1)
train_dataloader = dict(
    batch_size=32,
    dataset=dict(
        ann_file='data/kinetics400/kinetics400_train_list_videos.txt',
        data_prefix=dict(video='data/kinetics400/videos_train'),
        pipeline=[
            dict(io_backend='disk', type='DecordInit'),
            dict(
                clip_len=1, frame_interval=1, num_clips=8,
                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=8,
    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=8, 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='data/kinetics400/kinetics400_val_list_videos.txt',
        data_prefix=dict(video='data/kinetics400/videos_val'),
        pipeline=[
            dict(io_backend='disk', type='DecordInit'),
            dict(
                clip_len=1,
                frame_interval=1,
                num_clips=8,
                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=8,
    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=8,
        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 = '/home/liyunlong/files/tvseries/rgb_feature/work_dir'

06/03 00:49:57 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
06/03 00:49:57 - 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                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_train_epoch:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) SyncBuffersHook                    
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_val:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) SyncBuffersHook                    
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(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                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
06/03 00:49:57 - mmengine - WARNING - The prefix is not set in metric class DumpResults.
Loads checkpoint by local backend from path: /home/liyunlong/下载/tsn_imagenet-pretrained-r50_8xb32-1x1x8-100e_kinetics400-rgb_20220906-2692d16c.pth
The model and loaded state dict do not match exactly

unexpected key in source state_dict: cls_head.fc_cls.weight, cls_head.fc_cls.bias

06/03 00:49:58 - mmengine - INFO - Load checkpoint from /home/liyunlong/下载/tsn_imagenet-pretrained-r50_8xb32-1x1x8-100e_kinetics400-rgb_20220906-2692d16c.pth
06/03 00:49:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/03 00:49:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
06/03 00:49:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/03 00:49:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
06/03 00:49:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/03 00:49:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
06/03 00:49:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/03 00:49:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
06/03 00:49:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/03 00:49:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
06/03 00:49:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/03 00:49:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/03 00:49:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
06/03 00:49:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
06/03 00:49:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/03 00:49:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
terminate called after throwing an instance of 'std::bad_alloc'
  what():  std::bad_alloc
terminate called after throwing an instance of 'std::bad_alloc'
  what():  std::bad_alloc
Traceback (most recent call last):
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1132, in _try_get_data
    data = self._data_queue.get(timeout=timeout)
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/multiprocessing/queues.py", line 107, in get
    if not self._poll(timeout):
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/multiprocessing/connection.py", line 257, in poll
    return self._poll(timeout)
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/multiprocessing/connection.py", line 424, in _poll
    r = wait([self], timeout)
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/multiprocessing/connection.py", line 931, in wait
    ready = selector.select(timeout)
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/selectors.py", line 415, in select
    fd_event_list = self._selector.poll(timeout)
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
    _error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 2124181) is killed by signal: Aborted. 

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/liyunlong/files/mmaction2/tools/misc/clip_feature_extraction.py", line 266, in <module>
    main()
  File "/home/liyunlong/files/mmaction2/tools/misc/clip_feature_extraction.py", line 260, in main
    runner.test()
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1823, in test
    metrics = self.test_loop.run()  # type: ignore
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/loops.py", line 444, in run
    for idx, data_batch in enumerate(self.dataloader):
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__
    data = self._next_data()
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1328, in _next_data
    idx, data = self._get_data()
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1294, in _get_data
    success, data = self._try_get_data()
  File "/home/liyunlong/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1145, in _try_get_data
    raise RuntimeError(f'DataLoader worker (pid(s) {pids_str}) exited unexpectedly') from e
RuntimeError: DataLoader worker (pid(s) 2124181) exited unexpectedly

Additional information

I use videos in mp4 with FPS=24

fucker255 commented 3 months ago

I changed clip_len in clip_feature_extractor.py

            sample_cfgs = dict(
                type='UntrimmedSampleFrames',
                # clip_len=6,
                clip_interval=args.clip_interval,
                frame_interval=frame_interval)

and the that very problem got solved. But isn't tsn receive inputs of image clips, and my problem has not totally gone. I read the code of 'UntrimmedSampleFrames', it shows that clip_len should be as same as clip_interval, if every frame is wanted to be resolved, so I;m still confused. Wandering for any help. Sorry for my poor English.

Tommyuuu commented 3 months ago

@fucker255 Hello, Could I ask you how to obtain tvseries_video_list.txt. I am currently studying the same paper as you (MiniROAD) and am also trying to obtain features from TVSeries. If you could provide further guidance, I would be very grateful. Thank you kind attention for this problem.Hope to receive your reply.

fucker255 commented 3 months ago

@fucker255 Hello, Could I ask you how to obtain tvseries_video_list.txt. I am currently studying the same paper as you (MiniROAD) and am also trying to obtain features from TVSeries. If you could provide further guidance, I would be very grateful. Thank you kind attention for this problem.Hope to receive your reply.

You just put the filenames of tvseries dataset in a txt file in line. And about the question I mentioned above, I passed dataloader process with the setting in my first comment (which I think is correct now, after I read the paper MiniRoad again). Then I encountered another problem: CUDA out of memory, where it tried to allocate 29GB GPU memory and then another 29GB. The reason why it would happen seemed to be that it tried to process one whole video(extracted to about 10000 frames) in one batch. I've not solved this yet.