def main():
args = cmd.args()
date_str = '{}'.format(
datetime.datetime.today().strftime('%Y-%m-%d_%H-%M-%S'))
sys.stdout = Logger(
osp.join(args.output_dir, 'log_{}.txt'.format(date_str)))
# save opts
with open(osp.join(args.output_dir, 'args_{}.json'.format(date_str)),
'w') as fp:
json.dump(vars(args), fp, indent=1)
generator = make_generator(args.image_size, args.use_input_pose,
args.warp_skip, args.disc_type, args.warp_agg,
args.debug)
if args.generator_checkpoint is not None:
generator.load_weights(args.generator_checkpoint)
discriminator = make_discriminator(args.image_size, args.use_input_pose,
args.warp_skip, args.disc_type,
args.warp_agg)
if args.discriminator_checkpoint is not None:
discriminator.load_weights(args.discriminator_checkpoint)
dataset = PoseHMDataset(test_phase=False, **vars(args))
gan = CGAN(generator, discriminator, **vars(args))
trainer = Trainer(dataset, gan, **vars(args))
trainer.train()
def compile_and_run(dataset, args, generator_params, discriminator_params):
additional_info = json.dumps(vars(args))
args.generator_optimizer = Adam(args.generator_lr,
beta_1=args.beta1,
beta_2=args.beta2)
args.discriminator_optimizer = Adam(args.discriminator_lr,
beta_1=args.beta1,
beta_2=args.beta2)
log_file = os.path.join(args.output_dir, 'log.txt')
at_store_checkpoint_hook = partial(compute_scores,
image_shape=args.image_shape,
log_file=log_file,
dataset=dataset,
images_inception=args.samples_inception,
images_fid=args.samples_fid,
additional_info=additional_info,
cache_file=args.fid_cache_file)
lr_decay_schedule_generator, lr_decay_schedule_discriminator = get_lr_decay_schedule(
args)
generator_checkpoint = args.generator_checkpoint
discriminator_checkpoint = args.discriminator_checkpoint
generator = make_generator(**vars(generator_params))
discriminator = make_discriminator(**vars(discriminator_params))
generator.summary()
discriminator.summary()
if generator_checkpoint is not None:
generator.load_weights(generator_checkpoint)
if discriminator_checkpoint is not None:
discriminator.load_weights(discriminator_checkpoint)
hook = partial(at_store_checkpoint_hook, generator=generator)
if args.phase == 'train':
GANS = {None: GAN, 'AC_GAN': AC_GAN, 'PROJECTIVE': ProjectiveGAN}
gan = GANS[args.gan_type](
generator=generator,
discriminator=discriminator,
lr_decay_schedule_discriminator=lr_decay_schedule_discriminator,
lr_decay_schedule_generator=lr_decay_schedule_generator,
**vars(args))
trainer = Trainer(dataset,
gan,
at_store_checkpoint_hook=hook,
**vars(args))
trainer.train()
else:
hook(0)
def main():
generator = make_generator()
discriminator = make_discriminator()
args = parser_with_default_args().parse_args()
dataset = FolderDataset(args.input_folder, args.batch_size, (128, ),
(128, 64))
gan = WGAN_GP(generator, discriminator, **vars(args))
trainer = Trainer(dataset, gan, **vars(args))
trainer.train()
def main():
parser = parser_with_default_args()
parser.add_argument("--images_folder",
default="data/photo/tx_000100000000",
help='Folder with photos')
parser.add_argument("--sketch_folder",
default="data/sketch/tx_000000000000",
help='Folder with sketches')
parser.add_argument(
"--invalid_files",
default=[
'data/info/invalid-ambiguous.txt', 'data/info/invalid-context.txt',
'data/info/invalid-error.txt', 'data/info/invalid-pose.txt'
],
help='List of files with invalid sketches, comma separated',
type=lambda x: x.split(','))
parser.add_argument("--test_set",
default='data/info/testset.txt',
help='File with test set')
parser.add_argument("--image_size",
default=(64, 64),
help='Size of the images')
parser.add_argument(
"--number_of_classes",
default=2,
help='Number of classes to train on, usefull for debugging')
parser.add_argument("--cache_dir",
default='tmp',
help='Store distance transforms to this folder.')
args = parser.parse_args()
dataset = SketchDataset(images_folder=args.images_folder,
sketch_folder=args.sketch_folder,
batch_size=args.batch_size,
invalid_images_files=args.invalid_files,
test_set=args.test_set,
number_of_classes=args.number_of_classes,
image_size=args.image_size)
generator = make_generator(image_size=args.image_size,
number_of_classes=args.number_of_classes)
discriminator = make_discriminator(
image_size=args.image_size, number_of_classes=args.number_of_classes)
generator.summary()
discriminator.summary()
gan = CGAN(generator=generator, discriminator=discriminator, **vars(args))
trainer = Trainer(dataset, gan, **vars(args))
trainer.train()
def main():
generator = make_generator()
discriminator = make_discriminator()
generator.summary()
discriminator.summary()
args = parser_with_default_args().parse_args()
dataset = MNISTDataset(args.batch_size)
gan = GDAN(generator=generator, discriminator=discriminator, **vars(args))
hook = partial(compute_scores, generator=generator, dataset=dataset, image_shape=(28, 28, 1),
compute_inception=False)
trainer = Trainer(dataset, gan, at_store_checkpoint_hook=hook, **vars(args))
trainer.train()
def main():
args = cmd.args()
generator = make_generator(args.image_size, args.use_input_pose, args.warp_skip, args.disc_type, args.warp_agg)
if args.generator_checkpoint is not None:
generator.load_weights(args.generator_checkpoint)
discriminator = make_discriminator(args.image_size, args.use_input_pose, args.warp_skip, args.disc_type, args.warp_agg)
if args.discriminator_checkpoint is not None:
discriminator.load_weights(args.discriminator_checkpoint)
dataset = PoseHMDataset(test_phase=False, **vars(args))
gan = CGAN(generator, discriminator, **vars(args))
trainer = Trainer(dataset, gan, **vars(args))
trainer.train()
def main():
generator = make_generator()
discriminator = make_discriminator()
parser = parser_with_default_args()
parser.add_argument("--input_dir", default='dataset/devian_art')
parser.add_argument("--cache_file_name", default='output/devian_art.npy')
parser.add_argument("--content_image",
default='sup-mat/cornell_cropped.jpg')
args = parser.parse_args()
dataset = StylesDataset(args.batch_size,
args.input_dir,
cache_file_name=args.cache_file_name,
content_image=args.content_image)
gan = GAN(generator, discriminator, **vars(args))
trainer = Trainer(dataset, gan, **vars(args))
trainer.train()
def main():
args = cmd.args()
generator = make_generator(args.image_size, args.use_input_pose,
args.warp_agg, args.num_landmarks,
args.num_mask)
generator.summary()
if args.generator_checkpoint is not None:
generator.load_weights(args.generator_checkpoint, by_name=True)
discriminator = make_discriminator(args.image_size, args.use_input_pose,
args.num_landmarks, args.num_mask)
if args.discriminator_checkpoint is not None:
discriminator.load_weights(args.discriminator_checkpoint)
dataset = PoseHMDataset(test_phase=False, **vars(args))
gan = CGAN(generator, discriminator, **vars(args))
trainer = Trainer(dataset, gan, **vars(args))
trainer.train()