| from argparse import ArgumentParser | |
| class TrainOptions: | |
| def __init__(self): | |
| self.parser = ArgumentParser() | |
| self.initialize() | |
| def initialize(self): | |
| self.parser.add_argument('--exp_dir', type=str, help='Path to experiment output directory') | |
| self.parser.add_argument('--mapper_type', default='LevelsMapper', type=str, help='Which mapper to use') | |
| self.parser.add_argument('--no_coarse_mapper', default=False, action="store_true") | |
| self.parser.add_argument('--no_medium_mapper', default=False, action="store_true") | |
| self.parser.add_argument('--no_fine_mapper', default=False, action="store_true") | |
| self.parser.add_argument('--latents_train_path', default="train_faces.pt", type=str, help="The latents for the training") | |
| self.parser.add_argument('--latents_test_path', default="test_faces.pt", type=str, help="The latents for the validation") | |
| self.parser.add_argument('--train_dataset_size', default=5000, type=int, help="Will be used only if no latents are given") | |
| self.parser.add_argument('--test_dataset_size', default=1000, type=int, help="Will be used only if no latents are given") | |
| self.parser.add_argument('--batch_size', default=2, type=int, help='Batch size for training') | |
| self.parser.add_argument('--test_batch_size', default=1, type=int, help='Batch size for testing and inference') | |
| self.parser.add_argument('--workers', default=4, type=int, help='Number of train dataloader workers') | |
| self.parser.add_argument('--test_workers', default=2, type=int, help='Number of test/inference dataloader workers') | |
| self.parser.add_argument('--learning_rate', default=0.5, type=float, help='Optimizer learning rate') | |
| self.parser.add_argument('--optim_name', default='ranger', type=str, help='Which optimizer to use') | |
| self.parser.add_argument('--id_lambda', default=0.1, type=float, help='ID loss multiplier factor') | |
| self.parser.add_argument('--clip_lambda', default=1.0, type=float, help='CLIP loss multiplier factor') | |
| self.parser.add_argument('--latent_l2_lambda', default=0.8, type=float, help='Latent L2 loss multiplier factor') | |
| self.parser.add_argument('--stylegan_weights', default='../pretrained_models/stylegan2-ffhq-config-f.pt', type=str, help='Path to StyleGAN model weights') | |
| self.parser.add_argument('--stylegan_size', default=1024, type=int) | |
| self.parser.add_argument('--ir_se50_weights', default='../pretrained_models/model_ir_se50.pth', type=str, help="Path to facial recognition network used in ID loss") | |
| self.parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to StyleCLIPModel model checkpoint') | |
| self.parser.add_argument('--max_steps', default=50000, type=int, help='Maximum number of training steps') | |
| self.parser.add_argument('--image_interval', default=100, type=int, help='Interval for logging train images during training') | |
| self.parser.add_argument('--board_interval', default=50, type=int, help='Interval for logging metrics to tensorboard') | |
| self.parser.add_argument('--val_interval', default=2000, type=int, help='Validation interval') | |
| self.parser.add_argument('--save_interval', default=2000, type=int, help='Model checkpoint interval') | |
| self.parser.add_argument('--description', required=True, type=str, help='Driving text prompt') | |
| def parse(self): | |
| opts = self.parser.parse_args() | |
| return opts |