| 18 | from models import Generator,Discriminator,weights_init_normal |
| 19 | |
| 20 | def Parser(): |
| 21 | parser = argparse.ArgumentParser() |
| 22 | parser.add_argument('--n_epochs', type=int, default=config.NUM_EPOCHS, help='number of epochs of training') |
| 23 | parser.add_argument('--batch_size', type=int, default=config.BATCH_SIZE, help='size of the batches') |
| 24 | parser.add_argument('--lr', type=float, default=config.LEARING_RATIO, help='adam: learning rate') |
| 25 | parser.add_argument('--beta1', type=float, default=config.BETA1, help='adam: decay of first order momentum of gradient') |
| 26 | parser.add_argument('--beta2', type=float, default=config.BETA2, help='adam: decay of second order momentum of gradient') |
| 27 | parser.add_argument('--n_cpu', type=int, default=config.NUM_WORKS, help='number of cpu threads to use during batch generation') |
| 28 | parser.add_argument('--latent_dim', type=int, default=config.LATENT_DIM, help='dimensionality of the latent space') |
| 29 | parser.add_argument('--n_classes', type=int, default=config.NUM_CLASSES, help='number of classes for dataset') |
| 30 | parser.add_argument('--img_size', type=int, default=config.IMG_SIZE, help='size of each image dimension') |
| 31 | parser.add_argument('--channels', type=int, default=config.CHANNELS, help='number of image channels') |
| 32 | parser.add_argument('--sample_interval', type=int, default=config.SAMPLE_INTERVAL, help='interval between image sampling') |
| 33 | args = parser.parse_args() |
| 34 | print('args: {}'.format(args)) |
| 35 | return args |
| 36 | |
| 37 | |
| 38 | def train_fn(generator,discriminator,optimizer_G,optimizer_D,adversarial_loss,dataloader,epoch): |