[Feature] Add RandAugment_T to pipelines

[sttaseen]

Motivation

While torchvision.transforms.RandAugment works effectively for spatial transformations, it does not cover any temporally varying transformations needed for video clips. T. Kim et al. proposed RandAug_T in their paper, Learning Temporally Invariant and Localizable Features via Data Augmentations, which is an extension of torchvision.transforms.RandAugment that, linearly interpolates a random transformation between two magnitudes from the first frame to the last frame in a video clip.

Modification

Added randaugment_utils.py under mmaction/datasets/pipelines.
Modified __init__.py and augmentations.py under mmaction/datasets/pipelines to add new data augmentation, RandAugment_T.

Use cases (Optional)

Sample Use:

train_pipeline = [
    dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
    dict(type='RawFrameDecode'),
    dict(type='Resize', scale=(224, 224), keep_ratio=False),
    dict(type='RandAugment_T'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs', 'label'])

Checklist

1. Pre-commit or other linting tools should be used to fix the potential lint issues.
2. The modification should be covered by complete unit tests. If not, please add more unit tests to ensure the correctness.
3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMCls.
4. The documentation should be modified accordingly, like docstring or example tutorials.

[CLAassistant]

Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.
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[ly015]

Thank you very much for contributing to MMAction2. Could you please sign the CLA so we can accept your contribution?

And there seem to be lint issues in the code. Would you mind fixing them by with pre-commit hooks following our documentation?

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