ViViT: A Video Vision Transformer
Unofficial implementation of ViViT: A Video Vision Transformer.
Notes:
- This is in WIP.
- Model 2 is implemented, Model 3 and Model 4 isn't.
Usage:
img = torch.ones([1, 16, 3, 224, 224])
model = ViViT(224, 16, 100, 16)
parameters = filter(lambda p: p.requires_grad, model.parameters())
parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
print('Trainable Parameters: %.3fM' % parameters)
out = model(img)
print("Shape of out :", out.shape) # [B, num_classes]
Citation:
@misc{arnab2021vivit,
title={ViViT: A Video Vision Transformer},
author={Anurag Arnab and Mostafa Dehghani and Georg Heigold and Chen Sun and Mario Lučić and Cordelia Schmid},
year={2021},
eprint={2103.15691},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Acknowledgement:
- Base ViT code is borrowed from @lucidrains repo : https://github.com/lucidrains/vit-pytorch
- Some logic for Model 2 ViViT is from : https://github.com/lucidrains/STAM-pytorch
