https://github.com/IGLICT/PRS-Net_stamp
Tip revision: 42710b64de733f1c45aeb9b011585f159cf3ac75 authored by Lingxiao Zhang on 22 February 2021, 04:22:16 UTC
Update README.MD
Update README.MD
Tip revision: 42710b6
train_options.py
### Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
from .base_options import BaseOptions
class TrainOptions(BaseOptions):
def initialize(self):
BaseOptions.initialize(self)
# for displays
self.parser.add_argument('--display_freq', type=int, default=1, 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=1000, help='frequency of saving the latest results')
self.parser.add_argument('--save_epoch_freq', type=int, default=10, help='frequency of saving checkpoints at the end of epochs')
self.parser.add_argument('--debug', action='store_true', help='only do one epoch and displays at each iteration')
# for training
self.parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
self.parser.add_argument('--load_pretrain', type=str, default='', help='load the pretrained model from the specified location')
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('--phase', type=str, default='train', help='train, val, test, etc')
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=0.9, help='momentum term of adam')
self.parser.add_argument('--lr', type=float, default=0.001, help='initial learning rate for adam')
self.parser.add_argument('--weight', type=float, default=25, help='weight of regularization loss')
self.isTrain = True