Awesome
I3D_Feature_Extraction_resnet
This repo contains code to extract I3D features with resnet50 backbone given a folder of videos
This code can be used for the below paper. Use at your own risk since this is still untested.
Credits
The main resnet code and others is collected from the following repositories.
I modified and combined them and also added features to make it suitable for the given task.
Overview
This code takes a folder of videos as input and for each video it saves I3D feature numpy file of dimension 1*n/16*2048 where n is the no.of frames in the video
Usage
Setup
Download pretrained weights for I3D from the nonlocal repo
wget https://dl.fbaipublicfiles.com/video-nonlocal/i3d_baseline_32x2_IN_pretrain_400k.pkl -P pretrained/
Convert these weights from caffe2 to pytorch. This is just a simple renaming of the blobs to match the pytorch model.
python -m utils.convert_weights pretrained/i3d_baseline_32x2_IN_pretrain_400k.pkl pretrained/i3d_r50_kinetics.pth
Parameters
<pre> --datasetpath: folder of input videos (contains videos or subdirectories of videos) --outputpath: folder of extracted features --frequency: how many frames between adjacent snippet --batch_size: batch size for snippets </pre>Run
python main.py --datasetpath=samplevideos/ --outputpath=output