py.arg('--adversarial_loss_mode', default='gan', choices=['gan', 'hinge_v1', 'hinge_v2', 'lsgan', 'wgan'])
py.arg('--gradient_penalty_mode', default='none', choices=['none', 'dragan', 'wgan-gp'])
py.arg('--gradient_penalty_weight', type=float, default=10.0)
py.arg('--experiment_name', default='none')
args = py.args()
# output_dir
if args.experiment_name == 'none':
args.experiment_name = '%s_%s' % (args.dataset, args.adversarial_loss_mode)
if args.gradient_penalty_mode != 'none':
args.experiment_name += '_%s' % args.gradient_penalty_mode
output_dir = py.join('output', '%s_BN%d_DPG%d' % (args.experiment_name, args.batch_size, args.n_d ) )
py.mkdir(output_dir)
# save settings
py.args_to_yaml(py.join(output_dir, 'settings.yml'), args)
# ==============================================================================
# = data and model =
# ==============================================================================
# setup dataset
if args.dataset in ['cifar10', 'fashion_mnist', 'mnist']: # 32x32
dataset, shape, len_dataset = data.make_32x32_dataset(args.dataset, args.batch_size)
n_G_upsamplings = n_D_downsamplings = 3
elif args.dataset == 'celeba': # 64x64
img_paths = py.glob('data/img_align_celeba', '*.jpg')
dataset, shape, len_dataset = data.make_celeba_dataset(img_paths, args.batch_size)
n_G_upsamplings = n_D_downsamplings = 4
def __init__(
self,
epochs=200,
epoch_decay=100,
pool_size=50,
output_dir='output',
datasets_dir="datasets",
dataset="drawing",
image_ext="png",
crop_size=256,
load_size=286,
batch_size=0,
adversarial_loss_mode="lsgan", # ['gan', 'hinge_v1', 'hinge_v2', 'lsgan', 'wgan']
lr=0.0002,
gradient_penalty_mode='none', # ['none', 'dragan', 'wgan-gp'])
gradient_penalty_weight=10.0,
cycle_loss_weight=0.0,
identity_loss_weight=0.0,
beta_1=0.5,
color_depth=1,
progrssive=False):
logging.config.fileConfig(fname='log.conf')
self.logger = logging.getLogger('dev')
if batch_size == 0:
batch_size = 1 # later figure out what to do
epoch_decay = min(epoch_decay, epochs // 2)
self.output_dataset_dir = py.join(output_dir, dataset)
py.mkdir(self.output_dataset_dir)
py.args_to_yaml(
py.join(self.output_dataset_dir, 'settings.yml'),
Namespace(
epochs=epochs,
epoch_decay=epoch_decay,
pool_size=pool_size,
output_dir=output_dir,
datasets_dir=datasets_dir,
dataset=dataset,
image_ext=image_ext,
crop_size=crop_size,
load_size=load_size,
batch_size=batch_size,
adversarial_loss_mode=
adversarial_loss_mode, # ['gan', 'hinge_v1', 'hinge_v2', 'lsgan', 'wgan']
lr=lr,
gradient_penalty_mode=
gradient_penalty_mode, # ['none', 'dragan', 'wgan-gp'])
gradient_penalty_weight=gradient_penalty_weight,
cycle_loss_weight=cycle_loss_weight,
identity_loss_weight=identity_loss_weight,
beta_1=beta_1,
color_depth=color_depth,
progressive=progrssive))
self.sample_dir = py.join(self.output_dataset_dir, 'samples_training')
py.mkdir(self.sample_dir)
self.epochs = epochs
self.epoch_decay = epoch_decay
self.pool_size = pool_size
self.gradient_penalty_mode = gradient_penalty_mode
self.gradient_penalty_weight = gradient_penalty_weight
self.cycle_loss_weight = cycle_loss_weight
self.identity_loss_weight = identity_loss_weight
self.color_depth = color_depth
self.adversarial_loss_mode = adversarial_loss_mode
self.batch_size = batch_size
self.beta_1 = beta_1
self.color_depth = color_depth
self.dataset = dataset
self.datasets_dir = datasets_dir
self.image_ext = image_ext
self.progrssive = progrssive
self.lr = lr
self.crop_size = crop_size
self.load_size = load_size
self.A_img_paths = py.glob(py.join(datasets_dir, dataset, 'trainA'),
'*.{}'.format(image_ext))
self.B_img_paths = py.glob(py.join(datasets_dir, dataset, 'trainB'),
'*.{}'.format(image_ext))
# summary
self.train_summary_writer = tf.summary.create_file_writer(
py.join(self.output_dataset_dir, 'summaries', 'train'))
choices=["gan", "hinge_v1", "hinge_v2", "lsgan", "wgan"],
)
py.arg("--cycle_loss_weight", type=float, default=10.0)
py.arg("--identity_loss_weight", type=float, default=0.0)
py.arg("--resnet_blocks", type=int, default=9)
py.arg("--DnCNN", type=str, default=None)
py.arg("--bidirectional", type=bool, default=True)
py.arg("--pool_size", type=int, default=50) # pool size to store fake samples
args = py.args()
# output_dir
output_dir = py.join("output", args.output_dir)
py.mkdir(output_dir)
# save settings
py.args_to_yaml(py.join(output_dir, "settings.yml"), args)
# ==============================================================================
# = data =
# ==============================================================================
A_img_paths = py.glob(py.join(args.datasets_dir, args.dataset, "trainA"),
"*.jpg")
B_img_paths = py.glob(py.join(args.datasets_dir, args.dataset, "trainB"),
"*.jpg")
A_B_dataset, len_dataset = data.make_zip_dataset(
A_img_paths,
B_img_paths,
args.batch_size,
args.load_size,
args.crop_size,
py.arg('--epochs', type=int, default=225)
py.arg('--epoch_decay', type=int, default=25) # epoch to start decaying learning rate
py.arg('--lr', type=float, default=0.000001)
py.arg('--beta_1', type=float, default=0.5)
py.arg('--adversarial_loss_mode', default='gan', choices=['gan', 'lsgan'])
# py.arg('--gradient_penalty_mode', default='none', choices=['none', 'dragan', 'wgan-gp'])
py.arg('--gradient_penalty_weight', type=float, default=10.0)
py.arg('--cycle_loss_weight', type=float, default=12.0)
py.arg('--identity_loss_weight', type=float, default=0.5)
py.arg('--output_dir',default='./Results')
py.arg('--pool_size', type=int, default=50) # pool size to store fake samples
args = py.args()
if not exists(args.output_dir):
makedirs(args.output_dir)
py.args_to_yaml(join(args.output_dir, 'settings.yml'), args)
# ==============================================================================
# = data =
# ==============================================================================
A_img_paths = glob.glob(join(args.datasets_dir, 'MRI', '*.png'))
B_img_paths = glob.glob(join(args.datasets_dir, 'CT', '*.png'))
A_B_dataset, len_dataset = data.make_zip_dataset(A_img_paths, B_img_paths, args.batch_size, args.size, training=True, repeat=False)
A2B_pool = data.ItemPool(args.pool_size)
B2A_pool = data.ItemPool(args.pool_size)
A_img_paths_test = glob.glob(join(args.datasets_dir, 'MRI_test', '*.png'))
