This is the code implementation for: "ProGleason-GAN: Conditional Progressive Growing: GAN for prostatic cancer Gleason Grade patch synthesis"
To initiate the training, the following parameters must be set in the config.py file.
Arguments | Description |
---|---|
--START_TRAIN_AT_IMG_SIZE | Start resolution |
--CHECKPOINT_GEN | Path for generator checkpoint |
--CHECKPOINT_CRITIC | Path for discriminator checkpoint |
--RESULTS_DIR | Output directory for the recontructed slides |
--PATH_CSV_SICAP | Path with SICAPv2 partition annotations |
--PATH_IMAGES_SICAP | Path containing SICAPv2 patches |
--DEVICE | DEVICE INFO (cpu or cuda) |
--SAVE_MODEL | Flag to allow the training to save the model in the RESULTS_DIR |
--LOAD_MODEL | Flag to allow the training to load previous checkpoints |
--LEARNING_RATE_GENERATOR | Learning rate for the generator model |
--LEARNING_RATE_DISCRIMINATOR | Learning rate for the discriminator model |
--BATCH_SIZES | List of batch sizes for each resolution |
--CHANNELS_IMG | The number of channels in the input images |
--Z_DIM | Size of the input noise vector |
--IN_CHANNELS | The number of channels in the generator's input noise vector |
--LAMBDA_GP | The weight factor for the gradient penalty term used in the Wasserstein GAN (WGAN) loss |
--PROGRESSIVE_EPOCHS | List of training epochs for each resolution |
--N_CLASSES | Number of classes in the dataset |
--NUM_WORKERS | The number of parallel workers for data loading during training |
After that, you only need to call
$ python train.py