Implementation of TimeSformer, a pure attention-based solution for video classification

Related tags

Deep Learning deep-learning transformers artificial-intelligence attention-mechanism video-classification
Overview

TimeSformer - Pytorch

Implementation of TimeSformer, a pure and simple attention-based solution for reaching SOTA on video classification. This repository will only house the best performing variant, 'Divided Space-Time Attention', which is nothing more than attention along the time axis before the spatial.

Install

$ pip install timesformer-pytorch

Usage

import torch
from timesformer_pytorch import TimeSformer

model = TimeSformer(
    dim = 512,
    image_size = 224,
    patch_size = 16,
    num_frames = 8,
    num_classes = 10,
    depth = 12,
    heads = 8,
    dim_head =  64,
    attn_dropout = 0.1,
    ff_dropout = 0.1
)

video = torch.randn(2, 8, 3, 224, 224) # (batch x frames x channels x height x width)
pred = model(video) # (2, 10)

Citations

@misc{bertasius2021spacetime,
    title   = {Is Space-Time Attention All You Need for Video Understanding?}, 
    author  = {Gedas Bertasius and Heng Wang and Lorenzo Torresani},
    year    = {2021},
    eprint  = {2102.05095},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
Comments
  • How to deal with varying length video? Thanks

    How to deal with varying length video? Thanks

    Dear all, I am wondering if TimeSformer can handle different videos with diverse lengths? Is it possible to use mask as the original Transformer? Any ideas, thanks a lot.

    opened by junyongyou 2
  • fix runtime error in SpaceTime Attention

    fix runtime error in SpaceTime Attention

    There is a shape mismatch error in Attention. When we splice out the classification token from the first token of each sequence in q, k and v, the shape becomes (batch_size * num_heads, num_frames * num_patches - 1, head_dim). Then we try to reshape the tensor by taking out a factor of num_frames or num_patches (depending on whether it is space or time attention) from dimension 1. That doesn't work because we subtracted out the classification token.

    I found that performing the rearrange operation before splicing the token fixes the issue.

    I recreate the problem and illustrate the solution in this notebook: https://colab.research.google.com/drive/1lHFcn_vgSDJNSqxHy7rtqhMVxe0nUCMS?usp=sharing.

    By the way, thank you to @lucidrains; all of your implementations on attention-based models are helping me more than you know.

    opened by adam-mehdi 1
  • Update timesformer_pytorch.py

    Update timesformer_pytorch.py

    fixing issue for scaling

    File "/home/aarti9/.local/lib/python3.6/site-packages/timesformer_pytorch/timesformer_pytorch.py", line 82, in forward q *= self.scale

    RuntimeError: Output 0 of ViewBackward is a view and is being modified inplace. This view is an output of a function that returns multiple views. Inplace operators on such views is forbidden. You should replace the inplace operation by an out-of-place one.

    opened by aarti9 0
  • Fine-tune with new datasets

    Fine-tune with new datasets

    Thank you so much for your great effort. I can predict the images using the given .py files. But, I couldn't find train.py files, so how to fine-tune the network with new datasets? where should i define the image samples of the new dataset ?

    opened by Jeba-create 0
  • problem in timesformer_pytorch.py

    problem in timesformer_pytorch.py

    start from line 182 video = rearrange(video, 'b f c (h p1) (w p2) -> b (f h w) (p1 p2 c)', p1 = p, p2 = p) i think this should be video = rearrange(video, 'b f c (hp p1) (wp p2) -> b (f hp wp) (p1 p2 c)', p1 = p, p2 = p)

    opened by Weizhongjin 2
  • Imagenet Pretrained Weights

    Imagenet Pretrained Weights

    Thanks for the work! In their paper they say For all our experiments, we adopt the “Base” ViT model architecture (Dosovitskiy et al., 2020) pretrained on ImageNet.

    I know that you said the official weights trained on kinetics and such are not officially released yet. However, I am not interested in those but am actually in need of the initial weights of the network just based on ViT Imagenet pretraining. I need to train this implementation of yours starting from those. From what it looks like, you don't have weights for this implementation that come from imagenet pretraining, do you?

    opened by RaivoKoot 5
Owner
Phil Wang
Working with Attention. It's all we need.
Phil Wang
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