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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
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