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from options.base_option import BaseOptions
class EvalT2MOptions(BaseOptions):
def initialize(self):
BaseOptions.initialize(self)
self.parser.add_argument('--which_epoch', type=str, default="latest", help='Checkpoint you want to use, {latest, net_best_fid, etc}')
self.parser.add_argument('--batch_size', type=int, default=32, help='Batch size')
self.parser.add_argument('--ext', type=str, default='text2motion', help='Extension of the result file or folder')
self.parser.add_argument("--num_batch", default=2, type=int,
help="Number of batch for generation")
self.parser.add_argument("--repeat_times", default=1, type=int,
help="Number of repetitions, per sample text prompt")
self.parser.add_argument("--cond_scale", default=4, type=float,
help="For classifier-free sampling - specifies the s parameter, as defined in the paper.")
self.parser.add_argument("--temperature", default=1., type=float,
help="Sampling Temperature.")
self.parser.add_argument("--topkr", default=0.9, type=float,
help="Filter out percentil low prop entries.")
self.parser.add_argument("--time_steps", default=18, type=int,
help="Mask Generate steps.")
self.parser.add_argument("--seed", default=10107, type=int)
self.parser.add_argument('--gumbel_sample', action="store_true", help='True: gumbel sampling, False: categorical sampling.')
self.parser.add_argument('--use_res_model', action="store_true", help='Whether to use residual transformer.')
# self.parser.add_argument('--est_length', action="store_true", help='Training iterations')
self.parser.add_argument('--res_name', type=str, default='tres_nlayer8_ld384_ff1024_rvq6ns_cdp0.2_sw', help='Model name of residual transformer')
self.parser.add_argument('--text_path', type=str, default="", help='Text prompt file')
self.parser.add_argument('-msec', '--mask_edit_section', nargs='*', type=str, help='Indicate sections for editing, use comma to separate the start and end of a section'
'type int will specify the token frame, type float will specify the ratio of seq_len')
self.parser.add_argument('--text_prompt', default='', type=str, help="A text prompt to be generated. If empty, will take text prompts from dataset.")
self.parser.add_argument('--source_motion', default='example_data/000612.npy', type=str, help="Source motion path for editing. (new_joint_vecs format .npy file)")
self.parser.add_argument("--motion_length", default=0, type=int,
help="Motion length for generation, only applicable with single text prompt.")
self.is_train = False |