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options.py
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options.py
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import argparse
class TrainOptions():
def __init__(self):
self.parser = argparse.ArgumentParser()
# data loader related
self.parser.add_argument('--dataroot', type=str, required=True, help='path of data')
self.parser.add_argument('--phase1', type=str, default='train', help='phase for dataloading')
self.parser.add_argument('--phase2', type=str, default='train', help='phase for dataloading')
self.parser.add_argument('--batch_size', type=int, default=2, help='batch size')
self.parser.add_argument('--resize_size', type=int, default=256, help='resized image size for training')
self.parser.add_argument('--crop_size', type=int, default=216, help='cropped image size for training')
self.parser.add_argument('--input_dim_a', type=int, default=3, help='# of input channels for domain A')
self.parser.add_argument('--input_dim_b', type=int, default=3, help='# of input channels for domain B')
self.parser.add_argument('--nThreads', type=int, default=8, help='# of threads for data loader')
self.parser.add_argument('--no_flip', action='store_true', help='specified if no flipping')
# ouptput related
self.parser.add_argument('--name', type=str, default='trial', help='folder name to save outputs')
self.parser.add_argument('--display_dir', type=str, default='../logs', help='path for saving display results')
self.parser.add_argument('--result_dir', type=str, default='../results', help='path for saving result images and models')
self.parser.add_argument('--display_freq', type=int, default=1, help='freq (iteration) of display')
self.parser.add_argument('--img_save_freq', type=int, default=5, help='freq (epoch) of saving images')
self.parser.add_argument('--model_save_freq', type=int, default=10, help='freq (epoch) of saving models')
self.parser.add_argument('--no_display_img', action='store_true', help='specified if no dispaly')
# training related
self.parser.add_argument('--direction', type=str, default='AtoB', help='image translation direction')
self.parser.add_argument('--no_ms', action='store_true', help='disable mode seeking regularization')
self.parser.add_argument('--concat', type=int, default=1, help='concatenate attribute features for translation, set 0 for using feature-wise transform')
self.parser.add_argument('--dis_scale', type=int, default=3, help='scale of discriminator')
self.parser.add_argument('--dis_norm', type=str, default='None', help='normalization layer in discriminator [None, Instance]')
self.parser.add_argument('--dis_spectral_norm', action='store_true', help='use spectral normalization in discriminator')
self.parser.add_argument('--lr_policy', type=str, default='lambda', help='type of learn rate decay')
self.parser.add_argument('--n_ep', type=int, default=1200, help='number of epochs') # 400 * d_iter
self.parser.add_argument('--n_ep_decay', type=int, default=600, help='epoch start decay learning rate, set -1 if no decay') # 200 * d_iter
self.parser.add_argument('--resume', type=str, default=None, help='specified the dir of saved models for resume the training')
self.parser.add_argument('--d_iter', type=int, default=3, help='# of iterations for updating content discriminator')
self.parser.add_argument('--gpu', type=int, default=0, help='gpu')
def parse(self):
self.opt = self.parser.parse_args()
args = vars(self.opt)
print('\n--- load options ---')
for name, value in sorted(args.items()):
print('%s: %s' % (str(name), str(value)))
return self.opt
class TestOptions():
def __init__(self):
self.parser = argparse.ArgumentParser()
# data loader related
self.parser.add_argument('--dataroot', type=str, required=True, help='path of data')
#self.parser.add_argument('--test_phase', type=str, default='test', help='phase for dataloading')
self.parser.add_argument('--resize_size', type=int, default=256, help='resized image size for training')
self.parser.add_argument('--crop_size', type=int, default=216, help='cropped image size for training')
self.parser.add_argument('--nThreads', type=int, default=4, help='for data loader')
self.parser.add_argument('--input_dim_a', type=int, default=3, help='# of input channels for domain A')
self.parser.add_argument('--input_dim_b', type=int, default=3, help='# of input channels for domain B')
self.parser.add_argument('--a2b', type=int, default=1, help='translation direction, 1 for a2b, 0 for b2a')
self.parser.add_argument('--test_phase1', type=str, default='test', help='phase for dataloading')
self.parser.add_argument('--test_phase2', type=str, default='test', help='phase for dataloading')
# ouptput related
self.parser.add_argument('--num', type=int, default=5, help='number of outputs per image')
self.parser.add_argument('--name', type=str, default='trial', help='folder name to save outputs')
self.parser.add_argument('--direction', type=str, default='AtoB', help='transfer direction')
self.parser.add_argument('--result_dir', type=str, default='../outputs', help='path for saving result images and models')
# model related
self.parser.add_argument('--concat', type=int, default=1, help='concatenate attribute features for translation, set 0 for using feature-wise transform')
self.parser.add_argument('--no_ms', action='store_true', help='disable mode seeking regularization')
self.parser.add_argument('--resume', type=str, required=True, help='specified the dir of saved models for resume the training')
self.parser.add_argument('--gpu', type=int, default=0, help='gpu')
def parse(self):
self.opt = self.parser.parse_args()
args = vars(self.opt)
print('\n--- load options ---')
for name, value in sorted(args.items()):
print('%s: %s' % (str(name), str(value)))
# set irrelevant options
self.opt.dis_scale = 3
self.opt.dis_norm = 'None'
self.opt.dis_spectral_norm = False
return self.opt