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FAQ

We list some common issues faced by many users and their corresponding solutions here. Feel free to enrich the list if you find any frequent issues and have ways to help others to solve them. If the contents here do not cover your issue, please create an issue using the provided templates and make sure you fill in all required information in the template.

Installation

  • "No module named 'mmcv.ops'"; "No module named 'mmcv._ext'"

    1. Uninstall existing mmcv in the environment using pip uninstall mmcv.
    2. Install mmcv-full following the installation instruction.

  • "OSError: MoviePy Error: creation of None failed because of the following error"

    Refer to install.md

    1. For Windows users, ImageMagick will not be automatically detected by MoviePy, there is a need to modify moviepy/config_defaults.py file by providing the path to the ImageMagick binary called magick, like IMAGEMAGICK_BINARY = "C:\\Program Files\\ImageMagick_VERSION\\magick.exe"
    2. For Linux users, there is a need to modify the /etc/ImageMagick-6/policy.xml file by commenting out <policy domain="path" rights="none" pattern="@*" /> to <!-- <policy domain="path" rights="none" pattern="@*" /> -->, if ImageMagick is not detected by moviepy.

Data

  • FileNotFound like No such file or directory: xxx/xxx/img_00300.jpg

    In our repo, we set start_index=1 as default value for rawframe dataset, and start_index=0 as default value for video dataset. If users encounter FileNotFound error for the first or last frame of the data, there is a need to check the files begin with offset 0 or 1, that is xxx_00000.jpg or xxx_00001.jpg, and then change the start_index value of data pipeline in configs.

  • How should we preprocess the videos in the dataset? Resizing them to a fix size(all videos with the same height-width ratio) like 340x256(1) or resizing them so that the short edges of all videos are of the same length (256px or 320px)

    We have tried both preprocessing approaches and found (2) is a better solution in general, so we use (2) with short edge length 256px as the default preprocessing setting. We benchmarked these preprocessing approaches and you may find the results in TSN Data Benchmark and SlowOnly Data Benchmark.

  • Mismatched data pipeline items lead to errors like KeyError: 'total_frames'

    We have both pipeline for processing videos and frames.

    For videos, We should decode them on the fly in the pipeline, so pairs like DecordInit & DecordDecode, OpenCVInit & OpenCVDecode, PyAVInit & PyAVDecode should be used for this case like this example.

    For Frames, the image has been decoded offline, so pipeline item RawFrameDecode should be used for this case like this example.

    KeyError: 'total_frames' is caused by incorrectly using RawFrameDecode step for videos, since when the input is a video, it can not get the total_frame beforehand.

Training

  • How to just use trained recognizer models for backbone pre-training ?

    Refer to Use Pre-Trained Model, in order to use the pre-trained model for the whole network, the new config adds the link of pre-trained models in the load_from.

    And to use backbone for pre-training, you can change pretrained value in the backbone dict of config files to the checkpoint path / url. When training, the unexpected keys will be ignored.

  • How to visualize the training accuracy/loss curves in real-time ?

    Use TensorboardLoggerHook in log_config like

    log_config=dict(interval=20, hooks=[dict(type='TensorboardLoggerHook')])

    You can refer to tutorials/1_config.md, tutorials/7_customize_runtime.md, and this.

  • In batchnorm.py: Expected more than 1 value per channel when training

    To use batchnorm, the batch_size should be larger than 1. If drop_last is set as False when building dataloaders, sometimes the last batch of an epoch will have batch_size==1 (what a coincidence ...) and training will throw out this error. You can set drop_last as True to avoid this error:

    train_dataloader=dict(drop_last=True)

  • How to fix stages of backbone when finetuning a model ?

    You can refer to def _freeze_stages() and frozen_stages, reminding to set find_unused_parameters = True in config files for distributed training or testing.

    Actually, users can set frozen_stages to freeze stages in backbones except C3D model, since all backbones inheriting from ResNet and ResNet3D support the inner function _freeze_stages().

Testing

  • How to make predicted score normalized by softmax within [0, 1] ?

    change this in the config, make model['test_cfg'] = dict(average_clips='prob').

  • What if the model is too large and the GPU memory can not fit even only one testing sample ?

    By default, the 3d models are tested with 10clips x 3crops, which are 30 views in total. For extremely large models, the GPU memory can not fit even only one testing sample (cuz there are 30 views). To handle this, you can set max_testing_views=n in model['test_cfg'] of the config file. If so, n views will be used as a batch during forwarding to save GPU memory used.

  • How to show test results ?

    During testing, we can use the command --out xxx.json/pkl/yaml to output result files for checking. The testing output has exactly the same order as the test dataset. Besides, we provide an analysis tool for evaluating a model using the output result files in tools/analysis/eval_metric.py

Deploying

  • Why is the onnx model converted by mmaction2 throwing error when converting to other frameworks such as TensorRT?

    For now, we can only make sure that models in mmaction2 are onnx-compatible. However, some operations in onnx may be unsupported by your target framework for deployment, e.g. TensorRT in this issue. When such situation occurs, we suggest you raise an issue and ask the community to help as long as pytorch2onnx.py works well and is verified numerically.