def main(opts): """Loads the data, creates checkpoint and sample directories, and starts the training loop. """ # Create train and test dataloaders for images from the two domains X and Y dataloader_X, test_dataloader_X = get_emoji_loader(emoji_type=opts.X, opts=opts) dataloader_Y, test_dataloader_Y = get_emoji_loader(emoji_type=opts.Y, opts=opts) # Create checkpoint and sample directories utils.create_dir(opts.checkpoint_dir) utils.create_dir(opts.sample_dir) # Start training training_loop(dataloader_X, dataloader_Y, test_dataloader_X, test_dataloader_Y, opts)
def main(opts): """Loads the data, creates checkpoint and sample directories, and starts the training loop. """ # Create a dataloader for the training images train_dataloader, _ = get_emoji_loader(opts.emoji, opts) # Create checkpoint and sample directories utils.create_dir(opts.checkpoint_dir) utils.create_dir(opts.sample_dir) training_loop(train_dataloader, opts)
def main(opts): """Loads the data, creates checkpoint and sample directories, and starts the training loop. """ # Create train and test dataloaders for images from the two domains X and Y dataloader_X, test_dataloader_X = get_emoji_loader(emoji_type=opts.X, opts=opts) dataloader_Y, test_dataloader_Y = get_emoji_loader(emoji_type=opts.Y, opts=opts) # Create checkpoint and sample directories create_dir(opts.checkpoint_dir) create_dir(opts.sample_dir) if torch.cuda.is_available(): device = torch.device('cuda') else: device = torch.device('cpu') # Start training training_loop(dataloader_X, dataloader_Y, test_dataloader_X, test_dataloader_Y, device, opts)
def main(opts): """Loads the data, creates checkpoint and sample directories, and starts the training loop. """ # Create a dataloader for the training images train_loader, _ = get_emoji_loader(opts.emoji, opts) # Create checkpoint and sample directories create_dir(opts.checkpoint_dir) create_dir(opts.sample_dir) if torch.cuda.is_available(): device = torch.device('cuda:0') else: device = torch.device('cpu') train(train_loader, opts, device)
def main(opts): """Loads the data, creates checkpoint and sample directories, and starts the training loop. """ # Create a dataloader for the training images train_dataloader, _ = get_emoji_loader(opts.emoji, opts) # Create checkpoint and sample directories utils.create_dir(opts.checkpoint_dir) utils.create_dir(opts.sample_dir) if opts.GAN_type == 'LSGAN': training_loop_LSGAN(train_dataloader, opts) elif opts.GAN_type == 'WGAN': training_loop_WGAN(train_dataloader, opts) elif opts.GAN_type == 'WGANGP': training_loop_WGANGP(train_dataloader, opts)