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
Use AI to generate a optimized stock portfolio

Use AI, Modern Portfolio Theory, and Monte Carlo simulation's to generate a optimized stock portfolio that minimizes risk while maximizing returns. Ho

Greg James 30 Dec 22, 2022
Advances in Neural Information Processing Systems (NeurIPS), 2020.

What is being transferred in transfer learning? This repo contains the code for the following paper: Behnam Neyshabur*, Hanie Sedghi*, Chiyuan Zhang*.

Google Research 36 Aug 26, 2022
Active Offline Policy Selection With Python

Active Offline Policy Selection This is supporting example code for NeurIPS 2021 paper Active Offline Policy Selection by Ksenia Konyushkova*, Yutian

DeepMind 27 Oct 15, 2022
RM Operation can equivalently convert ResNet to VGG, which is better for pruning; and can help RepVGG perform better when the depth is large.

RM Operation can equivalently convert ResNet to VGG, which is better for pruning; and can help RepVGG perform better when the depth is large.

184 Jan 04, 2023
YOLOv5 in PyTorch > ONNX > CoreML > TFLite

This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and e

Ultralytics 34.1k Dec 31, 2022
Multi Agent Reinforcement Learning for ROS in 2D Simulation Environments

IROS21 information To test the code and reproduce the experiments, follow the installation steps in Installation.md. Afterwards, follow the steps in E

11 Oct 29, 2022
Re-implementation of the Noise Contrastive Estimation algorithm for pyTorch, following "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models." (Gutmann and Hyvarinen, AISTATS 2010)

Noise Contrastive Estimation for pyTorch Overview This repository contains a re-implementation of the Noise Contrastive Estimation algorithm, implemen

Denis Emelin 42 Nov 24, 2022
Wenet STT Python

Wenet STT Python Beta Software Simple Python library, distributed via binary wheels with few direct dependencies, for easily using WeNet models for sp

David Zurow 33 Feb 21, 2022
Fast, Attemptable Route Planner for Navigation in Known and Unknown Environments

FAR Planner uses a dynamically updated visibility graph for fast replanning. The planner models the environment with polygons and builds a global visi

Fan Yang 346 Dec 30, 2022
Classifying audio using Wavelet transform and deep learning

Audio Classification using Wavelet Transform and Deep Learning A step-by-step tutorial to classify audio signals using continuous wavelet transform (C

Aditya Dutt 17 Nov 29, 2022
Machine learning and Deep learning models, deploy on telegram (the best social media)

Semi Intelligent BOT The project involves : Classifying fake news Classifying objects such as aeroplane, automobile, bird, cat, deer, dog, frog, horse

MohammadReza Norouzi 5 Mar 06, 2022
A PyTorch implementation of " EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks."

EfficientNet A PyTorch implementation of EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. [arxiv] [Official TF Repo] Implemen

AhnDW 298 Dec 10, 2022
NCVX (NonConVeX): A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning.

NCVX NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning. Please check https://ncvx.org for detailed instruction

SUN Group @ UMN 28 Aug 03, 2022
Hardware-accelerated DNN model inference ROS2 packages using NVIDIA Triton/TensorRT for both Jetson and x86_64 with CUDA-capable GPU

Isaac ROS DNN Inference Overview This repository provides two NVIDIA GPU-accelerated ROS2 nodes that perform deep learning inference using custom mode

NVIDIA Isaac ROS 62 Dec 14, 2022
LSTMs (Long Short Term Memory) RNN for prediction of price trends

Price Prediction with Recurrent Neural Networks LSTMs BTC-USD price prediction with deep learning algorithm. Artificial Neural Networks specifically L

5 Nov 12, 2021
Official code repository of the paper Learning Associative Inference Using Fast Weight Memory by Schlag et al.

Learning Associative Inference Using Fast Weight Memory This repository contains the offical code for the paper Learning Associative Inference Using F

Imanol Schlag 18 Oct 12, 2022
PyTorch Connectomics: segmentation toolbox for EM connectomics

Introduction The field of connectomics aims to reconstruct the wiring diagram of the brain by mapping the neural connections at the level of individua

Zudi Lin 132 Dec 26, 2022
Source for the paper "Universal Activation Function for machine learning"

Universal Activation Function Tensorflow and Pytorch source code for the paper Yuen, Brosnan, Minh Tu Hoang, Xiaodai Dong, and Tao Lu. "Universal acti

4 Dec 03, 2022
PyTorch implementation of TSception V2 using DEAP dataset

TSception This is the PyTorch implementation of TSception V2 using DEAP dataset in our paper: Yi Ding, Neethu Robinson, Su Zhang, Qiuhao Zeng, Cuntai

Yi Ding 27 Dec 15, 2022
Notebooks em Python para Métodos Eletromagnéticos

GeoSci Labs This is a repository of code used to power the notebooks and interactive examples for https://em.geosci.xyz and https://gpg.geosci.xyz. Th

Victor Cezar Tocantins 1 Nov 16, 2021