Skip to content

Latest commit

 

History

History
88 lines (69 loc) · 4.39 KB

README.md

File metadata and controls

88 lines (69 loc) · 4.39 KB
Raw

ViS4mer

This is an official pytorch implementation of our ECCV 2022 paper Long Movie Clip Classification with State-Space Video Models. In this repository, we provide PyTorch code for training and testing our proposed ViS4mer model. ViS4mer is an efficient video recognition model that achieves state-of-the-art results on several long-range video understanding bechmarks such as LVU, Breakfast, and COIN.

If you find ViS4mer useful in your research, please use the following BibTeX entry for citation.

@article{islam2022long,
  title={Long movie clip classification with state-space video models},
  author={Islam, Md Mohaiminul and Bertasius, Gedas},
  journal={arXiv preprint arXiv:2204.01692},
  year={2022}
}

Installation

This repository requires Python 3.8+ and Pytorch 1.9+.

  • Create a conda virtual environment and activate it.
conda create --name py38 python=3.8
conda activate py38
  • Install the package listed in requirements.txt
  • The S4 layer requires "Cauchy Kernel" and we used the CUDA version. This can be installed by following commands.
cd extensions/cauchy
python setup.py install
  • Install Pykeops by running pip install pykeops==1.5 cmake

For more details of installation regarding S4 layer, please follow this.

Demo

You can use the model as follows:

import torch
from models import ViS4mer

model = ViS4mer(d_input=1024, l_max=2048, d_output=10, d_model=1024, n_layers=3)
model.cuda()

inputs = torch.randn(32, 2048, 1024).cuda() #[batch_size, seq_len, input_dim]
outputs = model(inputs)  #[32, 10]

Run on LVU dataset

  • Dataset splits are provided data/lvu_1.0. Otherwise, you can also download here.
  • You can download videos from youtube using youtube-dl. download_videos.py provides code for downloading videos using youtube_dl. Alternatively, you can acquire the videos from here.
  • We used ImageNet21k pretrained ViT dense features from timm. Particularly, we used vit_large_patch16_224_in21k ViT model. Following provides code for extracting features for LVU dataset.
extract_features/extract_features_lvu_vit.py
  • Finally, you can run the ViS4mer model on LVU tasks using run_lvu.py. Particularly, we used 4 GPUs and the following command.
CUDA_VISIBLE_DEVICES=0,1,2,3 python run_lvu.py

Run on Breakfast dataset

  • Download the Breakfast dataset.
  • We used VideoSwin features for the Breakfast dataset. Particularly, we used swin_base_patch244_window877_kinetics600_22k prtrained model. Following files provide code for extracting features for the Breakfast dataset train and test split respectively.
extract_features/extract_features_breakfast_swin_train.py
extract_features/extract_features_breakfast_swin_test.py
  • Finally, you can run the ViS4mer model on Breakfast dataset using run_breakfast.py. Particularly, we used 4 GPUs and the following command.
CUDA_VISIBLE_DEVICES=0,1,2,3 python run_breakfast.py

Run on COIN dataset

  • Download the COIN dataset.
  • We used VideoSwin features for the COIN dataset. Particularly, we used swin_base_patch244_window877_kinetics600_22k prtrained model. Following files provide code for extracting features for the COIN dataset train and test split respectively.
extract_features/extract_features_coin_swin_train.py
extract_features/extract_features_coin_swin_test.py
  • Finally, you can run the ViS4mer model on COIN dataset using run_coin.py. Particularly, we used 4 GPUs and the following command.
CUDA_VISIBLE_DEVICES=0,1,2,3 python run_coin.py