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from .base_options import BaseOptions
class TrainOptions(BaseOptions):
def initialize(self):
BaseOptions.initialize(self)
# for displays
self.parser.add_argument('--display_freq', type=int, default=100,
help='frequency of showing training results on screen')
self.parser.add_argument('--print_freq', type=int, default=100,
help='frequency of showing training results on console')
self.parser.add_argument('--save_latest_freq', type=int,
default=1000, help='frequency of saving the latest results')
self.parser.add_argument('--save_epoch_freq', type=int, default=10,
help='frequency of saving checkpoints at the end of epochs')
self.parser.add_argument('--no_html', action='store_true',
help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
self.parser.add_argument('--debug', action='store_true',
help='only do one epoch and displays at each iteration')
# for training
self.parser.add_argument('--load_pretrain', type=str, default='./checkpoints/label2city',
help='load the pretrained model from the specified location')
self.parser.add_argument('--which_epoch', type=str, default='latest',
help='which epoch to load? set to latest to use latest cached model')
self.parser.add_argument(
'--phase', type=str, default='test', help='train, val, test, etc')
self.parser.add_argument('--serial_batches', action='store_true',
help='if true, takes images in order to make batches, otherwise takes them randomly')
self.parser.add_argument(
'--niter', type=int, default=100, help='# of iter at starting learning rate')
self.parser.add_argument('--niter_decay', type=int, default=100,
help='# of iter to linearly decay learning rate to zero')
self.parser.add_argument(
'--beta1', type=float, default=0.5, help='momentum term of adam')
self.parser.add_argument(
'--lr', type=float, default=0.0002, help='initial learning rate for adam')
# for discriminators
self.parser.add_argument(
'--num_D', type=int, default=2, help='number of discriminators to use')
self.parser.add_argument(
'--n_layers_D', type=int, default=3, help='only used if which_model_netD==n_layers')
self.parser.add_argument(
'--ndf', type=int, default=64, help='# of discrim filters in first conv layer')
self.parser.add_argument(
'--lambda_feat', type=float, default=10.0, help='weight for feature matching loss')
self.parser.add_argument('--no_ganFeat_loss', action='store_true',
help='if specified, do *not* use discriminator feature matching loss')
self.parser.add_argument('--no_vgg_loss', action='store_true',
help='if specified, do *not* use VGG feature matching loss')
self.parser.add_argument('--no_lsgan', action='store_true',
help='do *not* use least square GAN, if false, use vanilla GAN')
self.parser.add_argument('--pool_size', type=int, default=0,
help='the size of image buffer that stores previously generated images')
self.isTrain = True
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