Code used as part of my UCL masters dissertation project.

Overview:

• datasets.py: Contains the pytorch dataloader used to load the data.
• models.py: Defines the various generator architectures used in the project.
• patchgan.py: Defines the PatchGAN discriminator architecture used.
• train.py: Trains the conditional GAN for polyp synthesis. For usage run python train.py -h.
• utils.py: Defines various util functions used in the project.

Misc folder contains an example training output folder (customB) as well as a the python notebook used to analyse a model output folder and a loading script to create the training dataset. Note that the dataset loading script requires the CVC-ClinicDB zip folder to be preprocessed to only contain JPG files, not the default TIF files. It may be easier to only provide the kvasir-seg zip folder and just train on the 1000 images from that.