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

Related tags

Deep Learningdeep-learningtransformersartificial-intelligenceattention-mechanismvideo-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
GitHub Repository
Pytorch implementation of 'Fingerprint Presentation Attack Detector Using Global-Local Model'

RTK-PAD This is an official pytorch implementation of 'Fingerprint Presentation Attack Detector Using Global-Local Model', which is accepted by IEEE T

6 Aug 01, 2022
Cross-modal Retrieval using Transformer Encoder Reasoning Networks (TERN). With use of Metric Learning and FAISS for fast similarity search on GPU

Cross-modal Retrieval using Transformer Encoder Reasoning Networks This project reimplements the idea from "Transformer Reasoning Network for Image-Te

Minh-Khoi Pham 5 Nov 05, 2022
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

AlphaZero-Gomoku This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) f

Junxiao Song 2.8k Dec 26, 2022
CVPR 2020 oral paper: Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax.

Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax ⚠️ Latest: Current repo is a complete version. But we delet

FishYuLi 341 Dec 23, 2022
This repository contains the code for "Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP".

Self-Diagnosis and Self-Debiasing This repository contains the source code for Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based

Timo Schick 62 Dec 12, 2022
Code for Towards Streaming Perception (ECCV 2020) :car:

sAP — Code for Towards Streaming Perception ECCV Best Paper Honorable Mention Award Feb 2021: Announcing the Streaming Perception Challenge (CVPR 2021

Martin Li 85 Dec 22, 2022
PyTorch implementation of SwAV (Swapping Assignments between Views)

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments This code provides a PyTorch implementation and pretrained models for SwAV

Meta Research 1.7k Jan 04, 2023
Opinionated code formatter, just like Python's black code formatter but for Beancount

beancount-black Opinionated code formatter, just like Python's black code formatter but for Beancount Try it out online here Features MIT licensed - b

Launch Platform 16 Oct 11, 2022
Benchmarking Pipeline for Prediction of Protein-Protein Interactions

B4PPI Benchmarking Pipeline for the Prediction of Protein-Protein Interactions How this benchmarking pipeline has been built, and how to use it, is de

Loïc Lannelongue 4 Jun 27, 2022
CompilerGym is a library of easy to use and performant reinforcement learning environments for compiler tasks

CompilerGym is a library of easy to use and performant reinforcement learning environments for compiler tasks

Facebook Research 721 Jan 03, 2023
Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.

PAWS-TF 🐾 Implementation of Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples (PAWS)

Sayak Paul 43 Jan 08, 2023
Pytorch implementation of “Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement”

Graph-to-Graph Transformers Self-attention models, such as Transformer, have been hugely successful in a wide range of natural language processing (NL

Idiap Research Institute 40 Aug 14, 2022
Python Actor concurrency library

Thespian Actor Library This library provides the framework of an Actor model for use by applications implementing Actors. Thespian Site with Documenta

Kevin Quick 177 Dec 11, 2022
Official PyTorch implementation of "Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics".

Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics This repository is the official PyTorch implementation of "Physics-aware Differ

USC-Melady 46 Nov 20, 2022
Code for our ICASSP 2021 paper: SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

SA-Net: Shuffle Attention for Deep Convolutional Neural Networks (paper) By Qing-Long Zhang and Yu-Bin Yang [State Key Laboratory for Novel Software T

Qing-Long Zhang 199 Jan 08, 2023
[WWW 2022] Zero-Shot Stance Detection via Contrastive Learning

PT-HCL for Zero-Shot Stance Detection The code of this repository is constantly being updated... Please look forward to it! Introduction This reposito

Akuchi 12 Dec 21, 2022
Animation of solving the traveling salesman problem to optimality using mixed-integer programming and iteratively eliminating sub tours

tsp-streamlit Animation of solving the traveling salesman problem to optimality using mixed-integer programming and iteratively eliminating sub tours.

4 Nov 05, 2022
This repository is for our paper Exploiting Scene Graphs for Human-Object Interaction Detection accepted by ICCV 2021.

SG2HOI This repository is for our paper Exploiting Scene Graphs for Human-Object Interaction Detection accepted by ICCV 2021. Installation Pytorch 1.7

HT 10 Dec 20, 2022
A lightweight deep network for fast and accurate optical flow estimation.

FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation The official PyTorch implementation of FastFlowNet (ICRA 2021). Authors: Lingtong

Tone 161 Jan 03, 2023
NLP made easy

GluonNLP: Your Choice of Deep Learning for NLP GluonNLP is a toolkit that helps you solve NLP problems. It provides easy-to-use tools that helps you l

Distributed (Deep) Machine Learning Community 2.5k Jan 04, 2023