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Testing_Guidelines.md

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Guideline

0. Environment Preparation

  • python>=3.6
  • apex
  • pytorch=1.5.0+cu101
  • torchvision=0.6.0+cu101
  • tensorboardX
  • h5py
  • opencv
  • scikit-learn
  • yacs

1.Prepare datasets

1.1 Download dataset :

1.2.Transform the datasets into h5py form

For training speed, we translate the video datasets into a single h5py file for reducing the indexing time in Disk By keeping the compressed type as JPG, we can reduce the memory space.

Here, we give the example as translating UCF-Crime training set into a single h5py file by conducting code in utils/make_h5.py

If you do not want to make h5 file, you can modify the datasets/dataset.py to adapt the usage of raw videos.

1.3 Downlaod pretrained weights

MIST finetuned checkpoints are uploaded on OneDrive

The h5py file for ShanghaiTech and its corresponing annotations are uploaded on [BaiduYun] with multiple sub-files, you can open/unzip it with WinRAR

BaiduYun link, code：kym5

2. Path Modification

All the default paths are set in configs/constant.py. You should the file as the paths set in the file, or modify it.

3. Testing

You can test the model via the command as below:

python test.py --gpus 0,1,2,3 --MODEL SHT_C3D

The argument --MODEL is to choose the model structure and pretrained weights, which should be one of the candidate lists [SHT_C3D, SHT_I3D, UCF_C3D, UCF_I3D]

Specifically, if you wanna to make some visualization, you should choose UCF_C3D and use command below:

python test.py --gpus 0,1,2,3 --MODEL UCF_C3D --vis_UCF