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from options.base_option import BaseOptions |
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import argparse |
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class TrainT2MOptions(BaseOptions): |
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def initialize(self): |
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BaseOptions.initialize(self) |
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self.parser.add_argument('--batch_size', type=int, default=64, help='Batch size') |
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self.parser.add_argument('--max_epoch', type=int, default=500, help='Maximum number of epoch for training') |
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'''LR scheduler''' |
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self.parser.add_argument('--lr', type=float, default=2e-4, help='Learning rate') |
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self.parser.add_argument('--gamma', type=float, default=0.1, help='Learning rate schedule factor') |
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self.parser.add_argument('--milestones', default=[50_000], nargs="+", type=int, |
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help="learning rate schedule (iterations)") |
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self.parser.add_argument('--warm_up_iter', default=2000, type=int, help='number of total iterations for warmup') |
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'''Condition''' |
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self.parser.add_argument('--cond_drop_prob', type=float, default=0.1, help='Drop ratio of condition, for classifier-free guidance') |
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self.parser.add_argument("--seed", default=3407, type=int, help="Seed") |
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self.parser.add_argument('--is_continue', action="store_true", help='Is this trial continuing previous state?') |
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self.parser.add_argument('--gumbel_sample', action="store_true", help='Strategy for token sampling, True: Gumbel sampling, False: Categorical sampling') |
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self.parser.add_argument('--share_weight', action="store_true", help='Whether to share weight for projection/embedding, for residual transformer.') |
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self.parser.add_argument('--log_every', type=int, default=50, help='Frequency of printing training progress, (iteration)') |
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self.parser.add_argument('--eval_every_e', type=int, default=10, help='Frequency of animating eval results, (epoch)') |
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self.parser.add_argument('--save_latest', type=int, default=500, help='Frequency of saving checkpoint, (iteration)') |
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self.is_train = True |
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class TrainLenEstOptions(): |
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def __init__(self): |
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self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) |
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self.parser.add_argument('--name', type=str, default="test", help='Name of this trial') |
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self.parser.add_argument("--gpu_id", type=int, default=-1, help='GPU id') |
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self.parser.add_argument('--dataset_name', type=str, default='t2m', help='Dataset Name') |
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self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') |
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self.parser.add_argument('--batch_size', type=int, default=64, help='Batch size') |
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self.parser.add_argument("--unit_length", type=int, default=4, help="Length of motion") |
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self.parser.add_argument("--max_text_len", type=int, default=20, help="Length of motion") |
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self.parser.add_argument('--max_epoch', type=int, default=300, help='Training iterations') |
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self.parser.add_argument('--lr', type=float, default=1e-4, help='Layers of GRU') |
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self.parser.add_argument('--is_continue', action="store_true", help='Training iterations') |
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self.parser.add_argument('--log_every', type=int, default=50, help='Frequency of printing training progress') |
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self.parser.add_argument('--save_every_e', type=int, default=5, help='Frequency of printing training progress') |
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self.parser.add_argument('--eval_every_e', type=int, default=3, help='Frequency of printing training progress') |
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self.parser.add_argument('--save_latest', type=int, default=500, help='Frequency of printing training progress') |
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def parse(self): |
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self.opt = self.parser.parse_args() |
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self.opt.is_train = True |
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return self.opt |
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