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from options.base_option import BaseOptions
import argparse
class TrainT2MOptions(BaseOptions):
def initialize(self):
BaseOptions.initialize(self)
self.parser.add_argument('--batch_size', type=int, default=64, help='Batch size')
self.parser.add_argument('--max_epoch', type=int, default=500, help='Maximum number of epoch for training')
# self.parser.add_argument('--max_iters', type=int, default=150_000, help='Training iterations')
'''LR scheduler'''
self.parser.add_argument('--lr', type=float, default=2e-4, help='Learning rate')
self.parser.add_argument('--gamma', type=float, default=0.1, help='Learning rate schedule factor')
self.parser.add_argument('--milestones', default=[50_000], nargs="+", type=int,
help="learning rate schedule (iterations)")
self.parser.add_argument('--warm_up_iter', default=2000, type=int, help='number of total iterations for warmup')
'''Condition'''
self.parser.add_argument('--cond_drop_prob', type=float, default=0.1, help='Drop ratio of condition, for classifier-free guidance')
self.parser.add_argument("--seed", default=3407, type=int, help="Seed")
self.parser.add_argument('--is_continue', action="store_true", help='Is this trial continuing previous state?')
self.parser.add_argument('--gumbel_sample', action="store_true", help='Strategy for token sampling, True: Gumbel sampling, False: Categorical sampling')
self.parser.add_argument('--share_weight', action="store_true", help='Whether to share weight for projection/embedding, for residual transformer.')
self.parser.add_argument('--log_every', type=int, default=50, help='Frequency of printing training progress, (iteration)')
# self.parser.add_argument('--save_every_e', type=int, default=100, help='Frequency of printing training progress')
self.parser.add_argument('--eval_every_e', type=int, default=10, help='Frequency of animating eval results, (epoch)')
self.parser.add_argument('--save_latest', type=int, default=500, help='Frequency of saving checkpoint, (iteration)')
self.is_train = True
class TrainLenEstOptions():
def __init__(self):
self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
self.parser.add_argument('--name', type=str, default="test", help='Name of this trial')
self.parser.add_argument("--gpu_id", type=int, default=-1, help='GPU id')
self.parser.add_argument('--dataset_name', type=str, default='t2m', help='Dataset Name')
self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
self.parser.add_argument('--batch_size', type=int, default=64, help='Batch size')
self.parser.add_argument("--unit_length", type=int, default=4, help="Length of motion")
self.parser.add_argument("--max_text_len", type=int, default=20, help="Length of motion")
self.parser.add_argument('--max_epoch', type=int, default=300, help='Training iterations')
self.parser.add_argument('--lr', type=float, default=1e-4, help='Layers of GRU')
self.parser.add_argument('--is_continue', action="store_true", help='Training iterations')
self.parser.add_argument('--log_every', type=int, default=50, help='Frequency of printing training progress')
self.parser.add_argument('--save_every_e', type=int, default=5, help='Frequency of printing training progress')
self.parser.add_argument('--eval_every_e', type=int, default=3, help='Frequency of printing training progress')
self.parser.add_argument('--save_latest', type=int, default=500, help='Frequency of printing training progress')
def parse(self):
self.opt = self.parser.parse_args()
self.opt.is_train = True
# args = vars(self.opt)
return self.opt
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