Method initialize

options/train_options.py:10–46

(self, parser)

    """

    def initialize(self, parser):
        parser = BaseOptions.initialize(self, parser)
        
        parser.add_argument('--display_freq', type=int, default=500, help='frequency of showing training results on screen')
        parser.add_argument('--display_id', type=int, default=1, help='window id of the web display')
        parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display')
        parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is "main")')
        parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display')
        parser.add_argument('--update_html_freq', type=int, default=500, help='frequency of saving training results to html')
        parser.add_argument('--print_freq', type=int, default=300, help='frequency of showing training results on console')
        parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
        # network saving and loading parameters
        parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results')
        parser.add_argument('--save_epoch_freq', type=int, default=1, help='frequency of saving checkpoints at the end of epochs')
        parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration')
        parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
        parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>, ...')
        parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
        # training parameters
        parser.add_argument('--niter', type=int, default=120, help='# of iter at starting learning rate')
        parser.add_argument('--niter_decay', type=int, default=0, help='# of iter to linearly decay learning rate to zero')
        parser.add_argument('--beta1', type=float, default=0.9, help='momentum term of adam')
        parser.add_argument('--lr', type=float, default=0.001, help='initial learning rate for adam')
        parser.add_argument('--g_lr_ratio', type=float, default=1.0, help='a ratio for changing learning rate of generator') #mia
        parser.add_argument('--d_lr_ratio', type=float, default=1.0, help='a ratio for changing learning rate of discriminator') #mia
        parser.add_argument('--gan_mode', type=str, default='wgangp', help='the type of GAN objective. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
        parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images')
        parser.add_argument('--lr_policy', type=str, default='target_decay', help='learning rate policy. [linear | step | plateau | cosine]')
        parser.add_argument('--lr_decay_iters', type=int, default=100, help='multiply by a gamma every lr_decay_iters iterations')
        parser.set_defaults(pool_size=0, gan_mode='vanilla')

        parser.add_argument('--lambda_L1', type=float, default=1.0, help='weight for L1 loss')
        parser.add_argument('--gp_ratio', type=float, default=1.0, help='weight for gradient_penalty')
        parser.add_argument('--lambda_a', type=float, default=1.0, help='weight for adversarial loss')
        parser.add_argument('--lambda_v', type=float, default=1.0, help='weight for verification loss')
        self.isTrain = True
        return parser