# CSN

## 简介

<!-- [ALGORITHM] -->

```BibTeX
@inproceedings{inproceedings,
author = {Wang, Heng and Feiszli, Matt and Torresani, Lorenzo},
year = {2019},
month = {10},
pages = {5551-5560},
title = {Video Classification With Channel-Separated Convolutional Networks},
doi = {10.1109/ICCV.2019.00565}
}
```

<!-- [OTHERS] -->

```BibTeX
@inproceedings{ghadiyaram2019large,
  title={Large-scale weakly-supervised pre-training for video action recognition},
  author={Ghadiyaram, Deepti and Tran, Du and Mahajan, Dhruv},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={12046--12055},
  year={2019}
}
```

## 模型库

### Kinetics-400

| 配置文件                                                                                                                                                             |  分辨率  | GPU 数量 | 主干网络  |  预训练  | top1 准确率 | top5 准确率 | 推理时间 (video/s) | GPU 显存占用 (M) |                                                                                                                                                                                       ckpt                                                                                                                                                                                       |                                                                        log                                                                         |                                                                           json                                                                           |
| :------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :-------: | :------: | :---------: | :---------: | :----------------: | :--------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [ircsn_bnfrozen_r50_32x2x1_180e_kinetics400_rgb](/configs/recognition/csn/ircsn_bnfrozen_r50_32x2x1_180e_kinetics400_rgb.py)                                         | 短边 320 |    x     | ResNet50  |   None   |    73.6     |    91.3     |         x          |        x         |                                                                                               [ckpt](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_bnfrozen_r50_32x2x1_180e_kinetics400_rgb/ircsn_bnfrozen_r50_32x2x1_180e_kinetics400_rgb_20210618-4e29e2e8.pth)                                                                                                |         [log](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_bnfrozen_r50_32x2x1_180e_kinetics400_rgb/20210618_182414.log)          |         [json](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_bnfrozen_r50_32x2x1_180e_kinetics400_rgb/20210618_182414.log.json)          |
| [ircsn_ig65m_pretrained_bnfrozen_r50_32x2x1_58e_kinetics400_rgb](/configs/recognition/csn/ircsn_ig65m_pretrained_bnfrozen_r50_32x2x1_58e_kinetics400_rgb.py)         | 短边 320 |    x     | ResNet50  |  IG65M   |    79.0     |    94.2     |         x          |        x         |                                                                                                            [infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ircsn_ig65m_pretrained_r50_32x2x1_58e_kinetics400_rgb_20210617-86d33018.pth)                                                                                                             |                                                                         x                                                                          |                                                                            x                                                                             |
| [ircsn_bnfrozen_r152_32x2x1_180e_kinetics400_rgb](/configs/recognition/csn/ircsn_bnfrozen_r152_32x2x1_180e_kinetics400_rgb.py)                                       | 短边 320 |    x     | ResNet152 |   None   |    76.5     |    92.1     |         x          |        x         |                                                                                                             [infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ircsn_from_scratch_r152_32x2x1_180e_kinetics400_rgb_20210617-5c933ae1.pth)                                                                                                              |                                                                         x                                                                          |                                                                            x                                                                             |
| [ircsn_sports1m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb](/configs/recognition/csn/ircsn_sports1m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb.py) | 短边 320 |    x     | ResNet152 | Sports1M |    78.2     |    93.0     |         x          |        x         |                                                                                                          [infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ircsn_sports1m_pretrained_r152_32x2x1_58e_kinetics400_rgb_20210617-b9b10241.pth)                                                                                                           |                                                                         x                                                                          |                                                                            x                                                                             |
| [ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb.py](/configs/recognition/csn/ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb.py)    | 短边 320 |   8x4    | ResNet152 |  IG65M   | 82.76/82.6  | 95.68/95.3  |         x          |       8516       | [ckpt](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb/ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb_20200812-9037a758.pth)/[infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb_20210617-e63ee1bd.pth) | [log](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb/20200809_053132.log) | [json](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb/20200809_053132.log.json) |
| [ipcsn_bnfrozen_r152_32x2x1_180e_kinetics400_rgb](/configs/recognition/csn/ipcsn_bnfrozen_r152_32x2x1_180e_kinetics400_rgb.py)                                       | 短边 320 |    x     | ResNet152 |   None   |    77.8     |    92.8     |         x          |        x         |                                                                                                             [infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ipcsn_from_scratch_r152_32x2x1_180e_kinetics400_rgb_20210617-d565828d.pth)                                                                                                              |                                                                         x                                                                          |                                                                            x                                                                             |
| [ipcsn_sports1m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb](/configs/recognition/csn/ipcsn_sports1m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb.py) | 短边 320 |    x     | ResNet152 | Sports1M |    78.8     |    93.5     |         x          |        x         |                                                                                                          [infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ipcsn_sports1m_pretrained_r152_32x2x1_58e_kinetics400_rgb_20210617-3367437a.pth)                                                                                                           |                                                                         x                                                                          |                                                                            x                                                                             |
| [ipcsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb](/configs/recognition/csn/ipcsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb.py)       | 短边 320 |    x     | ResNet152 |  IG65M   |    82.5     |    95.3     |         x          |        x         |                                                                                                            [infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ipcsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb_20210617-c3be9793.pth)                                                                                                            |                                                                         x                                                                          |                                                                            x                                                                             |
| [ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb.py](/configs/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb.py)                      | 短边 320 |   8x4    | ResNet152 |  IG65M   |    80.14    |    94.93    |         x          |       8517       |                                                                                       [ckpt](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb_20200803-fc66ce8d.pth)                                                                                        |     [log](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb/20200728_031952.log)      |     [json](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb/20200728_031952.log.json)      |

注：

1. 这里的 **GPU 数量** 指的是得到模型权重文件对应的 GPU 个数。默认地，MMAction2 所提供的配置文件对应使用 8 块 GPU 进行训练的情况。
   依据 [线性缩放规则](https://arxiv.org/abs/1706.02677)，当用户使用不同数量的 GPU 或者每块 GPU 处理不同视频个数时，需要根据批大小等比例地调节学习率。
   如，lr=0.01 对应 4 GPUs x 2 video/gpu，以及 lr=0.08 对应 16 GPUs x 4 video/gpu。
2. 这里的 **推理时间** 是根据 [基准测试脚本](/tools/analysis/benchmark.py) 获得的，采用测试时的采帧策略，且只考虑模型的推理时间，
   并不包括 IO 时间以及预处理时间。对于每个配置，MMAction2 使用 1 块 GPU 并设置批大小（每块 GPU 处理的视频个数）为 1 来计算推理时间。
3. 这里使用的 Kinetics400 验证集包含 19796 个视频，用户可以从 [验证集视频](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155136485_link_cuhk_edu_hk/EbXw2WX94J1Hunyt3MWNDJUBz-nHvQYhO9pvKqm6g39PMA?e=a9QldB) 下载这些视频。同时也提供了对应的 [数据列表](https://download.openmmlab.com/mmaction/dataset/k400_val/kinetics_val_list.txt) （每行格式为：视频 ID，视频帧数目，类别序号）以及 [标签映射](https://download.openmmlab.com/mmaction/dataset/k400_val/kinetics_class2ind.txt) （类别序号到类别名称）。
4. 这里的 **infer_ckpt** 表示该模型权重文件是从 [VMZ](https://github.com/facebookresearch/VMZ) 导入的。

对于数据集准备的细节，用户可参考 [数据集准备文档](/docs_zh_CN/data_preparation.md) 中的 Kinetics400 部分。

## 如何训练

用户可以使用以下指令进行模型训练。

```shell
python tools/train.py ${CONFIG_FILE} [optional arguments]
```

例如：以一个确定性的训练方式，辅以定期的验证过程进行 CSN 模型在 Kinetics400 数据集上的训练。

```shell
python tools/train.py configs/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb.py \
    --work-dir work_dirs/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb \
    --validate --seed 0 --deterministic
```

更多训练细节，可参考 [基础教程](/docs_zh_CN/getting_started.md#%E8%AE%AD%E7%BB%83%E9%85%8D%E7%BD%AE) 中的 **训练配置** 部分。

## 如何测试

用户可以使用以下指令进行模型测试。

```shell
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
```

例如：在 Kinetics400 数据集上测试 CSN 模型，并将结果导出为一个 json 文件。

```shell
python tools/test.py configs/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb.py \
    checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \
    --out result.json --average-clips prob
```

更多测试细节，可参考 [基础教程](/docs_zh_CN/getting_started.md#%E6%B5%8B%E8%AF%95%E6%9F%90%E4%B8%AA%E6%95%B0%E6%8D%AE%E9%9B%86) 中的 **测试某个数据集** 部分。
