- [On Multiple GPUs with Slurm](#on-multiple-gpus-with-slurm-1)
- [Results](#results)
- [Citation](#citation)
- [Checklist](#checklist)
## Description
<!-- Share any information you would like others to know. For example:
Author: @xxx.
This is an implementation of \[XXX\]. -->
Author: @fangyixiao18
This is the implementation of **MaskFeat** with video dataset, like Kinetics400.
## Usage
<!-- For a typical model, this section should contain the commands for dataset prepareation, pre-training, downstream tasks. You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`. -->
Besides, to process the video data, we apply transforms in MMAction2. The instruction to install MMAction2 can be found in [Get Started documentation](https://mmaction2.readthedocs.io/en/1.x/get_started.html).
### Data Preparation
You can refer to the [documentation](https://mmaction2.readthedocs.io/en/1.x/user_guides/2_data_prepare.html) in MMAction2.
### Pre-training Commands
At first, you need to add the current folder to `PYTHONPATH`, so that Python can find your model files. In `projects/maskfeat_video/` root directory, please run command below to add it.
```shell
export PYTHONPATH=`pwd`:$PYTHONPATH
```
Then run the following commands to train the model:
- CONFIG: the config files under the directory `configs/`
- WORK_DIR: the working directory to save configs, logs, and checkpoints
- PARTITION: the slurm partition you are using
- CHECKPOINT: the pretrained checkpoint of MMPretrain saved in working directory, like `$WORK_DIR/epoch_300.pth`
- PY_ARGS: other optional args
## Results
<!-- You should claim whether this is based on the pre-trained weights, which are converted from the official release; or it's a reproduced result obtained from retraining the model in this project. -->
The Fine-tuning results are based on Kinetics400(K400) dataset.
Due to the version of K400 dataset, our pretraining, fine-tuning and the final test results are based on MMAction2 version, which is a little different from PySlowFast version.
- We converted the pretrained model from PySlowFast and run fine-tuning with MMAction2, based on MMAction2 version of K400, we got `81.5` test accuracy. The pretrained model from MMPretrain got `81.8`, as provided above.
- We also tested our model on [other version](https://github.com/facebookresearch/video-nonlocal-net/blob/main/DATASET.md) of K400, we got `82.1` test accuracy.
- Some other details can be found in [MMAction2 MViT page](https://github.com/open-mmlab/mmaction2/tree/dev-1.x/configs/recognition/mvit).
## Citation
```bibtex
@InProceedings{wei2022masked,
author = {Wei, Chen and Fan, Haoqi and Xie, Saining and Wu, Chao-Yuan and Yuille, Alan and Feichtenhofer, Christoph},
title = {Masked Feature Prediction for Self-Supervised Visual Pre-Training},
booktitle = {CVPR},
year = {2022},
}
```
## Checklist
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<!--The PIC (person in charge) or contributors of this project should check all the items that they believe have been finished, which will further be verified by codebase maintainers via a PR.
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- [x] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.
- [x] Finish the code
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- [x] Basic docstrings & proper citation
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- [x] Inference correctness
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- [x] A full README
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- [x] Milestone 2: Indicates a successful model implementation.
- [x] Training-time correctness
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- [ ] Milestone 3: Good to be a part of our core package!
- [ ] Type hints and docstrings
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