I3D_Feature_Extraction_resnet

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I3D_Feature_Extraction_resnet

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I3D features extractor with resnet50 backbone

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# 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.
* [Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning](https://arxiv.org/pdf/2101.10030.pdf)

---

## Credits
The main resnet code and others is collected from the following repositories. 
* [pytorch-resnet3d](https://github.com/Tushar-N/pytorch-resnet3d)
* [pytorch-i3d-feature-extraction](https://github.com/Finspire13/pytorch-i3d-feature-extraction)

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
```bash
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.
```bash
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
```bash
python main.py --datasetpath=samplevideos/ --outputpath=output
```

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