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Implementation of Memory Efficient Video GAN.

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MeVGAN

Implementation of Memory Efficient Video GAN.

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Prepare environment

Install requirements.

pip install requirements.txt

Place ProGAN repo within MeVGAN repo.

git clone https://github.com/facebookresearch/pytorch_GAN_zoo progan

Apply patch to ProGAN repo. This will stop visdom session from running in the background, set batch size to 8 for ProGAN training, and add functionality to load ProGAN from checkpoint.

cd progan
git apply ../progan.patch

Copy model files from progan/models to MeVGAN/models to recreate original directory structure used to save ProGAN checkpoint. This is necessary to load model weigths.

cp -r models/* ../models

Prepare custom dataset. Place all your frames in one directory, following the naming convention:

<video_name>_<frame_number>.jpg

Datasets

We have used publicly available UCF-101 dataset, which can be found here: https://www.crcv.ucf.edu/data/UCF101.php

Colonoscopy data cannot be publicly shared, but we allow datasets to be available upon request. For further informaction please contact Tomasz Urbańczyk at [email protected]

Train

Train FrameSeedGenerator and VideoDiscriminator with pre-trained ProGAN.

python train.py -d <path_to_dataset>

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