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| from argparse import ArgumentParser | |
| from configs.paths_config import model_paths | |
| 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('--dataset_type', default='ffhq_encode', type=str, help='Type of dataset/experiment to run') | |
| self.parser.add_argument('--encoder_type', default='GradualStyleEncoder', type=str, help='Which encoder to use') | |
| self.parser.add_argument('--input_nc', default=3, type=int, help='Number of input image channels to the psp encoder') | |
| self.parser.add_argument('--label_nc', default=0, type=int, help='Number of input label channels to the psp encoder') | |
| self.parser.add_argument('--output_size', default=1024, type=int, help='Output size of generator') | |
| self.parser.add_argument('--batch_size', default=4, type=int, help='Batch size for training') | |
| self.parser.add_argument('--test_batch_size', default=2, 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=0.0001, 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('--train_decoder', default=False, type=bool, help='Whether to train the decoder model') | |
| self.parser.add_argument('--start_from_latent_avg', action='store_true', help='Whether to add average latent vector to generate codes from encoder.') | |
| self.parser.add_argument('--learn_in_w', action='store_true', help='Whether to learn in w space instead of w+') | |
| self.parser.add_argument('--lpips_lambda', default=0.8, type=float, help='LPIPS loss multiplier factor') | |
| self.parser.add_argument('--id_lambda', default=0, type=float, help='ID loss multiplier factor') | |
| self.parser.add_argument('--l2_lambda', default=1.0, type=float, help='L2 loss multiplier factor') | |
| self.parser.add_argument('--w_norm_lambda', default=0, type=float, help='W-norm loss multiplier factor') | |
| self.parser.add_argument('--lpips_lambda_crop', default=0, type=float, help='LPIPS loss multiplier factor for inner image region') | |
| self.parser.add_argument('--l2_lambda_crop', default=0, type=float, help='L2 loss multiplier factor for inner image region') | |
| self.parser.add_argument('--moco_lambda', default=0, type=float, help='Moco-based feature similarity loss multiplier factor') | |
| self.parser.add_argument('--stylegan_weights', default=model_paths['stylegan_ffhq'], type=str, help='Path to StyleGAN model weights') | |
| self.parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to pSp model checkpoint') | |
| self.parser.add_argument('--max_steps', default=500000, 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=1000, type=int, help='Validation interval') | |
| self.parser.add_argument('--save_interval', default=None, type=int, help='Model checkpoint interval') | |
| # arguments for weights & biases support | |
| self.parser.add_argument('--use_wandb', action="store_true", help='Whether to use Weights & Biases to track experiment.') | |
| # arguments for super-resolution | |
| self.parser.add_argument('--resize_factors', type=str, default=None, help='For super-res, comma-separated resize factors to use for inference.') | |
| def parse(self): | |
| opts = self.parser.parse_args() | |
| return opts | |