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from .base_options import BaseOptions |
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class TrainOptions(BaseOptions): |
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"""This class includes training options. |
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It also includes shared options defined in BaseOptions. |
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""" |
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def initialize(self, parser): |
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parser = BaseOptions.initialize(self, parser) |
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parser.add_argument('--display_freq', type=int, default=400, help='frequency of showing training results on screen') |
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parser.add_argument('--display_ncols', type=int, default=4, help='if positive, display all images in a single visdom web panel with certain number of images per row.') |
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parser.add_argument('--display_id', type=int, default=1, help='window id of the web display') |
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parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display') |
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parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is "main")') |
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parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display') |
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parser.add_argument('--update_html_freq', type=int, default=1000, help='frequency of saving training results to html') |
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parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console') |
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parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') |
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parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results') |
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parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs') |
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parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration') |
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parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') |
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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>, ...') |
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parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') |
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parser.add_argument('--n_epochs', type=int, default=200, help='number of epochs with the initial learning rate') |
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parser.add_argument('--n_epochs_decay', type=int, default=800, help='number of epochs to linearly decay learning rate to zero') |
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parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') |
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parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') |
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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.') |
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parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images') |
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parser.add_argument('--lr_policy', type=str, default='linear', help='learning rate policy. [linear | step | plateau | cosine]') |
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parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') |
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parser.add_argument('--vert_class', type=str, default="normal", help='input vert is normal or fructured') |
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self.isTrain = True |
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return parser |
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