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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=40,
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=10,
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('--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
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