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)
