GPM-T

The code for GPM-T and provide a demo of GPM-T+DCAN. We will upload the code as soon as possible.

Environment

We use Miniconda3 to manage our python environments.

conda create -n dcan python=3.7
conda activate dcan
conda install matplotlib tqdm joblib h5py
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch

Data preparation

ActivityNet v1.3: We use the TSN feature extracted by the two-stream network. The frame interval is set to 16. Using linear interpolation, each video feature sequence is rescaled to L = 100 snippets.

THUMOS-14: We use the TSN feature extracted by the TSN network. The FPS of the feature is consistent with the original videos. This feature is stored through HDF5 for rapid reading.

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The TSN features for both datasets are available on the website. We also provide our download links to long-term support.

Baidu Netdisk:

ActivityNet v1.3 and THUMOS-14, code: uvpk

Zenodo:

ActivityNet v1.3 , THUMOS-14

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In order to use the downloaded video features, the feature_path attributes in the two opt.py need to be modified respectively.

Training and Testing

For example on THUMOS-14.

cd anet_thumos

Training model using 4 GPUS (ids=0,1,2,3).

Python train.py --gpus 0,1,2,3

Testing model on a sepific epoch.

python test.py --checkpoint_path ./save/xxx-xx/ --test_epoch 4

The 'xxx-xx' is the training work directory in the 'save' directory named by a timestamp, such as "./save/20220116-1417".

For ActivityNet v1.3, the training and testing procedure is similar to THUMOS-14.