Python slowfast설치 windows환경

 

https://github.com/facebookresearch/SlowFast

GitHub - facebookresearch/SlowFast: PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video mo

 

Requirements(필요 패키지, 사전설치)

• Python >= 3.8
• Numpy
• PyTorch >= 1.3
• fvcore: pip install 'git+https://github.com/facebookresearch/fvcore'
• torchvision that matches the PyTorch installation. You can install them together at pytorch.org to make sure of this.
• simplejson: pip install simplejson
• GCC >= 4.9
• PyAV: conda install av -c conda-forge
• ffmpeg (4.0 is prefereed, will be installed along with PyAV)
• PyYaml: (will be installed along with fvcore)
• tqdm: (will be installed along with fvcore)
• iopath: pip install -U iopath or conda install -c iopath iopath
• psutil: pip install psutil
• OpenCV: pip install opencv-python
• torchvision: pip install torchvision or conda install torchvision -c pytorch
• tensorboard: pip install tensorboard
• moviepy: (optional, for visualizing video on tensorboard) conda install -c conda-forge moviepy or pip install moviepy
• PyTorchVideo: pip install pytorchvideo
• Detectron2:
• FairScale: pip install 'git+https://github.com/facebookresearch/fairscale'

 

필수 패키지들 설치 명령어입니다.

터미널에 한줄씩 입력해서 설치하시면 됩니다.

pip install 'git+https://github.com/facebookresearch/fvcore'
pip install simplejson

pip install -U iopath
pip install psutil
pip install opencv-python
pip install torchvision
pip install tensorboard
pip install moviepy

pip install cython

git clone https://github.com/facebookresearch/slowfast

pip install -U torch torchvision cython
pip install -U 'git+https://github.com/facebookresearch/fvcore.git' 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
git clone https://github.com/facebookresearch/detectron2 detectron2_repo
pip install -e detectron2_repo

 

 

 

PySlowFast

PySlowFast is an open source video understanding codebase from FAIR that provides state-of-the-art video classification models with efficient training. This repository includes implementations of the following methods:

• SlowFast Networks for Video Recognition
• Non-local Neural Networks
• A Multigrid Method for Efficiently Training Video Models
• X3D: Progressive Network Expansion for Efficient Video Recognition
• Multiscale Vision Transformers
• A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning
• MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
• Masked Feature Prediction for Self-Supervised Visual Pre-Training
• Masked Autoencoders As Spatiotemporal Learners
• Reversible Vision Transformers

Introduction

The goal of PySlowFast is to provide a high-performance, light-weight pytorch codebase provides state-of-the-art video backbones for video understanding research on different tasks (classification, detection, and etc). It is designed in order to support rapid implementation and evaluation of novel video research ideas. PySlowFast includes implementations of the following backbone network architectures:

• SlowFast
• Slow
• C2D
• I3D
• Non-local Network
• X3D
• MViTv1 and MViTv2
• Rev-ViT and Rev-MViT