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| from .base_options import BaseOptions | |
| class TrainOptions(BaseOptions): | |
| """This class includes training options. | |
| It also includes shared options defined in BaseOptions. | |
| """ | |
| def initialize(self, parser): | |
| parser = BaseOptions.initialize(self, parser) | |
| # visdom and HTML visualization parameters | |
| parser.add_argument('--display_freq', type=int, default=400, help='frequency of showing training results on screen') | |
| 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.') | |
| 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=1000, help='frequency of saving training results to html') | |
| parser.add_argument('--print_freq', type=int, default=100, 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=5, 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('--n_epochs', type=int, default=100, help='number of epochs with the initial learning rate') | |
| parser.add_argument('--n_epochs_decay', type=int, default=100, help='number of epochs to linearly decay learning rate to zero') | |
| parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') | |
| parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') | |
| parser.add_argument('--gan_mode', type=str, default='lsgan', 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='linear', help='learning rate policy. [linear | step | plateau | cosine]') | |
| parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') | |
| self.isTrain = True | |
| return parser | |