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| import argparse | |
| import os | |
| import torch | |
| class BaseOptions(): | |
| def __init__(self): | |
| self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) | |
| self.initialized = False | |
| def initialize(self): | |
| self.parser.add_argument('--name', type=str, default="t2m_nlayer8_nhead6_ld384_ff1024_cdp0.1_rvq6ns", help='Name of this trial') | |
| self.parser.add_argument('--vq_name', type=str, default="rvq_nq1_dc512_nc512", help='Name of the rvq model.') | |
| 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, {t2m} for humanml3d, {kit} for kit-ml') | |
| self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here.') | |
| self.parser.add_argument('--latent_dim', type=int, default=384, help='Dimension of transformer latent.') | |
| self.parser.add_argument('--n_heads', type=int, default=6, help='Number of heads.') | |
| self.parser.add_argument('--n_layers', type=int, default=8, help='Number of attention layers.') | |
| self.parser.add_argument('--ff_size', type=int, default=1024, help='FF_Size') | |
| self.parser.add_argument('--dropout', type=float, default=0.2, help='Dropout ratio in transformer') | |
| self.parser.add_argument("--max_motion_length", type=int, default=196, help="Max length of motion") | |
| self.parser.add_argument("--unit_length", type=int, default=4, help="Downscale ratio of VQ") | |
| self.parser.add_argument('--force_mask', action="store_true", help='True: mask out conditions') | |
| self.initialized = True | |
| def parse(self): | |
| if not self.initialized: | |
| self.initialize() | |
| self.opt = self.parser.parse_args() | |
| self.opt.is_train = self.is_train | |
| if self.opt.gpu_id != -1: | |
| # self.opt.gpu_id = int(self.opt.gpu_id) | |
| torch.cuda.set_device(self.opt.gpu_id) | |
| args = vars(self.opt) | |
| print('------------ Options -------------') | |
| for k, v in sorted(args.items()): | |
| print('%s: %s' % (str(k), str(v))) | |
| print('-------------- End ----------------') | |
| if self.is_train: | |
| # save to the disk | |
| expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.dataset_name, self.opt.name) | |
| if not os.path.exists(expr_dir): | |
| os.makedirs(expr_dir) | |
| file_name = os.path.join(expr_dir, 'opt.txt') | |
| with open(file_name, 'wt') as opt_file: | |
| opt_file.write('------------ Options -------------\n') | |
| for k, v in sorted(args.items()): | |
| opt_file.write('%s: %s\n' % (str(k), str(v))) | |
| opt_file.write('-------------- End ----------------\n') | |
| return self.opt |