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| """This script contains the training options for Deep3DFaceRecon_pytorch | |
| """ | |
| from .base_options import BaseOptions | |
| from util import util | |
| 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) | |
| # dataset parameters | |
| # for train | |
| parser.add_argument('--data_root', type=str, default='./', help='dataset root') | |
| parser.add_argument('--flist', type=str, default='datalist/train/masks.txt', help='list of mask names of training set') | |
| parser.add_argument('--batch_size', type=int, default=32) | |
| parser.add_argument('--dataset_mode', type=str, default='flist', help='chooses how datasets are loaded. [None | flist]') | |
| parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') | |
| parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data') | |
| parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.') | |
| parser.add_argument('--preprocess', type=str, default='shift_scale_rot_flip', help='scaling and cropping of images at load time [shift_scale_rot_flip | shift_scale | shift | shift_rot_flip ]') | |
| parser.add_argument('--use_aug', type=util.str2bool, nargs='?', const=True, default=True, help='whether use data augmentation') | |
| # for val | |
| parser.add_argument('--flist_val', type=str, default='datalist/val/masks.txt', help='list of mask names of val set') | |
| parser.add_argument('--batch_size_val', type=int, default=32) | |
| # visualization parameters | |
| parser.add_argument('--display_freq', type=int, default=1000, help='frequency of showing training results on screen') | |
| parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console') | |
| # 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('--evaluation_freq', type=int, default=5000, help='evaluation freq') | |
| 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') | |
| parser.add_argument('--pretrained_name', type=str, default=None, help='resume training from another checkpoint') | |
| # training parameters | |
| parser.add_argument('--n_epochs', type=int, default=20, help='number of epochs with the initial learning rate') | |
| parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate for adam') | |
| parser.add_argument('--lr_policy', type=str, default='step', help='learning rate policy. [linear | step | plateau | cosine]') | |
| parser.add_argument('--lr_decay_epochs', type=int, default=10, help='multiply by a gamma every lr_decay_epochs epoches') | |
| self.isTrain = True | |
| return parser | |