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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='Encoder4Editing', type=str, help='Which encoder to use') | |
| 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('--lpips_type', default='alex', type=str, help='LPIPS backbone') | |
| self.parser.add_argument('--lpips_lambda', default=0.8, type=float, help='LPIPS loss multiplier factor') | |
| self.parser.add_argument('--id_lambda', default=0.1, 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('--stylegan_weights', default=model_paths['stylegan_ffhq'], type=str, | |
| help='Path to StyleGAN model weights') | |
| self.parser.add_argument('--stylegan_size', default=1024, type=int, | |
| help='size of pretrained StyleGAN Generator') | |
| 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') | |
| # Discriminator flags | |
| self.parser.add_argument('--w_discriminator_lambda', default=0, type=float, help='Dw loss multiplier') | |
| self.parser.add_argument('--w_discriminator_lr', default=2e-5, type=float, help='Dw learning rate') | |
| self.parser.add_argument("--r1", type=float, default=10, help="weight of the r1 regularization") | |
| self.parser.add_argument("--d_reg_every", type=int, default=16, | |
| help="interval for applying r1 regularization") | |
| self.parser.add_argument('--use_w_pool', action='store_true', | |
| help='Whether to store a latnet codes pool for the discriminator\'s training') | |
| self.parser.add_argument("--w_pool_size", type=int, default=50, | |
| help="W\'s pool size, depends on --use_w_pool") | |
| # e4e specific | |
| self.parser.add_argument('--delta_norm', type=int, default=2, help="norm type of the deltas") | |
| self.parser.add_argument('--delta_norm_lambda', type=float, default=2e-4, help="lambda for delta norm loss") | |
| # Progressive training | |
| self.parser.add_argument('--progressive_steps', nargs='+', type=int, default=None, | |
| help="The training steps of training new deltas. steps[i] starts the delta_i training") | |
| self.parser.add_argument('--progressive_start', type=int, default=None, | |
| help="The training step to start training the deltas, overrides progressive_steps") | |
| self.parser.add_argument('--progressive_step_every', type=int, default=2_000, | |
| help="Amount of training steps for each progressive step") | |
| # Save additional training info to enable future training continuation from produced checkpoints | |
| self.parser.add_argument('--save_training_data', action='store_true', | |
| help='Save intermediate training data to resume training from the checkpoint') | |
| self.parser.add_argument('--sub_exp_dir', default=None, type=str, help='Name of sub experiment directory') | |
| self.parser.add_argument('--keep_optimizer', action='store_true', | |
| help='Whether to continue from the checkpoint\'s optimizer') | |
| self.parser.add_argument('--resume_training_from_ckpt', default=None, type=str, | |
| help='Path to training checkpoint, works when --save_training_data was set to True') | |
| self.parser.add_argument('--update_param_list', nargs='+', type=str, default=None, | |
| help="Name of training parameters to update the loaded training checkpoint") | |
| def parse(self): | |
| opts = self.parser.parse_args() | |
| return opts | |