This repo is a simple usage of the official implementation "Video Swin Transformer".
Video Swin Transformer is initially described in "Video Swin Transformer", which advocates an inductive bias of locality in video Transformers, leading to a better speed-accuracy trade-off compared to previous approaches which compute self-attention globally even with spatial-temporal factorization. The locality of the proposed video architecture is realized by adapting the Swin Transformer designed for the image domain, while continuing to leverage the power of pre-trained image models. Our approach achieves state-of-the-art accuracy on a broad range of video recognition benchmarks, including action recognition (84.9 top-1 accuracy on Kinetics-400 and 86.1 top-1 accuracy on Kinetics-600 with ~20x less pre-training data and ~3x smaller model size) and temporal modeling (69.6 top-1 accuracy on Something-Something v2).
$ pip install -r requirements.txt
$ git clone https://github.com/haofanwang/video-swin-transformer-pytorch.git
$ cd video-swin-transformer-pytorch
$ mkdir checkpoints && cd checkpoints
$ wget https://github.com/SwinTransformer/storage/releases/download/v1.0.4/swin_base_patch244_window1677_sthv2.pth
$ cd ..
Please refer to Video-Swin-Transformer and download other checkpoints.
import torch
import torch.nn as nn
from video_swin_transformer import SwinTransformer3D
model = SwinTransformer3D()
print(model)
dummy_x = torch.rand(1, 3, 32, 224, 224)
logits = model(dummy_x)
print(logits.shape)
If you want to utilize the pre-trained checkpoints without diving into the codebase of open-mmlab, you can also do it as below.
import torch
import torch.nn as nn
from collections import OrderedDict
from video_swin_transformer import SwinTransformer3D
model = SwinTransformer3D(embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
patch_size=(2,4,4),
window_size=(16,7,7),
drop_path_rate=0.4,
patch_norm=True)
# https://github.com/SwinTransformer/Video-Swin-Transformer/blob/master/configs/recognition/swin/swin_base_patch244_window1677_sthv2.py
checkpoint = torch.load('./checkpoints/swin_base_patch244_window1677_sthv2.pth')
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
if 'backbone' in k:
name = k[9:]
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
dummy_x = torch.rand(1, 3, 32, 224, 224)
logits = model(dummy_x)
print(logits.shape)
Warning: this is an informal implementation, and there may be errors that are difficult to find. Therefore, I strongly recommend that you use the official code base to load the weights.
$ git clone https://github.com/SwinTransformer/Video-Swin-Transformer.git
$ cp *.py Video-Swin-Transformer
$ cd Video-Swin-Transformer
Then, you can load the pre-trained checkpoint.
from mmcv import Config, DictAction
from mmaction.models import build_model
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
config = './configs/recognition/swin/swin_base_patch244_window1677_sthv2.py'
checkpoint = './checkpoints/swin_base_patch244_window1677_sthv2.pth'
cfg = Config.fromfile(config)
model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
load_checkpoint(model, checkpoint, map_location='cpu')
# [batch_size, channel, temporal_dim, height, width]
dummy_x = torch.rand(1, 3, 32, 224, 224)
# SwinTransformer3D without cls_head
backbone = model.backbone
# [batch_size, hidden_dim, temporal_dim/2, height/32, width/32]
feat = backbone(dummy_x)
# alternative way
feat = model.extract_feat(dummy_x)
# mean pooling
feat = feat.mean(dim=[2,3,4]) # [batch_size, hidden_dim]
# project
batch_size, hidden_dim = feat.shape
feat_dim = 512
proj = nn.Parameter(torch.randn(hidden_dim, feat_dim))
# final output
output = feat @ proj # [batch_size, feat_dim]
The code is adapted from the official Video-Swin-Transformer repository. This project is inspired by swin-transformer-pytorch, which provides the simplest code to get started.
If you find our work useful in your research, please cite:
@article{liu2021video,
title={Video Swin Transformer},
author={Liu, Ze and Ning, Jia and Cao, Yue and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Hu, Han},
journal={arXiv preprint arXiv:2106.13230},
year={2021}
}
@article{liu2021Swin,
title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
journal={arXiv preprint arXiv:2103.14030},
year={2021}
}