train_options.py

from .base_options import BaseOptions


class TrainOptions(BaseOptions):
    def initialize(self):
        BaseOptions.initialize(self)
        self.parser.add_argument('--pan_lambdas', nargs='+', type=float, default=[5.0, 1.0, 1.0, 1.0, 5.0], help='lambdas of PAN_loss')
        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=5000, help='frequency of saving the latest results')
        self.parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs')
        self.parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
        self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
        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('--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')
        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('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau')
        
        self.parser.add_argument('--lambda_A', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)')
        self.parser.add_argument('--lambda_B', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)')
        self.parser.add_argument('--lambda_feat_ArecA', type=float, default=1.0, help='weight for perception loss between real A and reconstructed A ')
        self.parser.add_argument('--lambda_feat_BrecB', type=float, default=1.0, help='weight for perception loss between real B and reconstruced B ')
        
        
        self.parser.add_argument('--lambda_syn_A', type=float, default=15.0, help='weight for synthesized loss between real A and fake A ')
        self.parser.add_argument('--lambda_syn_B', type=float, default=15.0, help='weight for synthesized loss between real B and fake B ')
        self.parser.add_argument('--lambda_feat_AfA', type=float, default=1.0, help='weight for perception loss between real A and fake A ')
        self.parser.add_argument('--lambda_feat_BfB', type=float, default=1.0, help='weight for perception loss between real B and fake B ')
        
              
        self.parser.add_argument('--lambda_CS_A', type=float, default=0.0, help='weight for cyclic-synthesized loss between fake A and reconstructed A ')
        self.parser.add_argument('--lambda_CS_B', type=float, default=0.0, help='weight for cyclic-synthesized loss between fake B and reconstucted B ')
        self.parser.add_argument('--lambda_feat_fArecA', type=float, default=0.0, help='weight for perception loss between fake A and reconstructed A ')
        self.parser.add_argument('--lambda_feat_fBrecB', type=float, default=0.0, help='weight for perception loss between fake B and reconstructed B ')
        
  
        self.parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images')
        self.parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
        self.isTrain = True