FAQ¶

Outline¶

We list some common issues faced by many users and their corresponding solutions here.

FAQ
- Outline
- Installation
- Data
- Training
- Testing

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 to 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 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 , if ImageMagick is not detected by moviepy.
“Why I got the error message ‘Please install XXCODEBASE to use XXX’ even if I have already installed XXCODEBASE?”

You got that error message because our project failed to import a function or a class from XXCODEBASE. You can try to run the corresponding line to see what happens. One possible reason is, for some codebases in OpenMMLAB, you need to install mmcv and mmengine before you install them. You could follow this tutorial to install them.

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) (2)

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_frames beforehand.

Training¶

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

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 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 almost all backbones inheriting from ResNet and ResNet3D support the inner function _freeze_stages().
How to set memcached setting in config files?

In MMAction2, you can pass memcached kwargs to class DecordInit for video dataset or RawFrameDecode for rawframes dataset. For more details, you can refer to [class FileClient] in MMEngine for more details. Here is an example to use memcached for rawframes dataset:
```
mc_cfg = dict(server_list_cfg='server_list_cfg', client_cfg='client_cfg', sys_path='sys_path')

train_pipeline = [
  ...
  dict(type='RawFrameDecode', io_backend='memcached', **mc_cfg),
  ...
]
```
How to set load_from value in config files to finetune models?

In MMAction2, We set load_from=None as default in configs/_base_/default_runtime.py and owing to inheritance design, users can directly change it by setting load_from in their configs.
How to use RawFrameDataset for training?

In MMAction2 1.x version, most of the configs take VideoDataset as the default dataset type, which is much more friendly to file storage. If you want to use RawFrameDataset instead, there are two steps to modify:
- Dataset: modify dataset in train_dataloader/val_dataloader/test_dataloader from
```
dataset=dict(
    type=VideoDataset,
    data_prefix=dict(video=xxx),
    ...)
```
  to
```
dataset=dict(
    type=RawFrameDataset,
    data_prefix=dict(img=xxx),
    filename_tmpl='{:05}.jpg',
    ...)
```
  remaining fields of dataset don’t need to be modified. Please make sure that filename_tmpl is matching with your frame data, and you can refer to config document for more details about config file.
- Transforms: delete dict(type='DecordInit', **file_client_args), modify dict(type='DecordDecode') to dict(type='RawFrameDecode', **file_client_args) in train_pipeline/val_pipeline/test_pipeline, and please make sure that file_client_args = dict(io_backend='disk') has been defined in your config.
For more modifications about customizing datasets, please refer to prepare dataset and customize dataset.

Testing¶

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

change this in the config, make model.cls_head.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.