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| import argparse | |
| import os | |
| import torch | |
| from mmcv.runner import get_dist_info | |
| import torch.distributed as dist | |
| 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="test", help='Name of this trial') | |
| self.parser.add_argument('--decomp_name', type=str, default="Decomp_SP001_SM001_H512", help='Name of autoencoder model') | |
| self.parser.add_argument("--gpu_id", type=int, default=-1, help='GPU id') | |
| self.parser.add_argument("--distributed", action="store_true", help='Weather to use DDP training') | |
| 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("--unit_length", type=int, default=4, help="Motions are cropped to the maximum times of unit_length") | |
| self.parser.add_argument("--max_text_len", type=int, default=20, help="Maximum length of text description") | |
| self.parser.add_argument('--text_enc_mod', type=str, default='bigru') | |
| self.parser.add_argument('--estimator_mod', type=str, default='bigru') | |
| self.parser.add_argument('--dim_text_hidden', type=int, default=512, help='Dimension of hidden unit in text encoder') | |
| self.parser.add_argument('--dim_att_vec', type=int, default=512, help='Dimension of attention vector') | |
| self.parser.add_argument('--dim_z', type=int, default=128, help='Dimension of latent Gaussian vector') | |
| self.parser.add_argument('--n_layers_pri', type=int, default=1, help='Number of layers in prior network') | |
| self.parser.add_argument('--n_layers_pos', type=int, default=1, help='Number of layers in posterior network') | |
| self.parser.add_argument('--n_layers_dec', type=int, default=1, help='Number of layers in generator') | |
| self.parser.add_argument('--dim_pri_hidden', type=int, default=1024, help='Dimension of hidden unit in prior network') | |
| self.parser.add_argument('--dim_pos_hidden', type=int, default=1024, help='Dimension of hidden unit in posterior network') | |
| self.parser.add_argument('--dim_dec_hidden', type=int, default=1024, help='Dimension of hidden unit in generator') | |
| self.parser.add_argument('--dim_movement_enc_hidden', type=int, default=512, | |
| help='Dimension of hidden in AutoEncoder(encoder)') | |
| self.parser.add_argument('--dim_movement_dec_hidden', type=int, default=512, | |
| help='Dimension of hidden in AutoEncoder(decoder)') | |
| self.parser.add_argument('--dim_movement_latent', type=int, default=512, help='Dimension of motion snippet') | |
| 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) | |
| if args["distributed"]: | |
| init_dist('slurm') | |
| rank, world_size = get_dist_info() | |
| if rank == 0: | |
| 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') | |
| if world_size > 1: | |
| dist.barrier() | |
| return self.opt | |