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"""This script contains the training options for Deep3DFaceRecon_pytorch
"""
from util import util
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)
# 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
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