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from options.base_options import BaseOptions | |
import argparse | |
class TrainCompOptions(BaseOptions): | |
def initialize(self): | |
BaseOptions.initialize(self) | |
self.parser.add_argument('--num_layers', type=int, default=8, help='num_layers of transformer') | |
self.parser.add_argument('--latent_dim', type=int, default=512, help='latent_dim of transformer') | |
self.parser.add_argument('--diffusion_steps', type=int, default=1000, help='diffusion_steps of transformer') | |
self.parser.add_argument('--no_clip', action='store_true', help='whether use clip pretrain') | |
self.parser.add_argument('--no_eff', action='store_true', help='whether use efficient attention') | |
self.parser.add_argument('--num_epochs', type=int, default=50, help='Number of epochs') | |
self.parser.add_argument('--lr', type=float, default=2e-4, help='Learning rate') | |
self.parser.add_argument('--batch_size', type=int, default=32, help='Batch size per GPU') | |
self.parser.add_argument('--times', type=int, default=1, help='times of dataset') | |
self.parser.add_argument('--feat_bias', type=float, default=5, help='Scales for global motion features and foot contact') | |
self.parser.add_argument('--is_continue', action="store_true", help='Is this trail continued from previous trail?') | |
self.parser.add_argument('--log_every', type=int, default=50, help='Frequency of printing training progress (by iteration)') | |
self.parser.add_argument('--save_every_e', type=int, default=5, help='Frequency of saving models (by epoch)') | |
self.parser.add_argument('--eval_every_e', type=int, default=5, help='Frequency of animation results (by epoch)') | |
self.parser.add_argument('--save_latest', type=int, default=500, help='Frequency of saving models (by iteration)') | |
self.is_train = True | |