(self, cfg, **kwargs)
| 9 | |
| 10 | class KitDataModule(HumanML3DDataModule): |
| 11 | def __init__(self, cfg, **kwargs): |
| 12 | |
| 13 | super().__init__(cfg, **kwargs) |
| 14 | |
| 15 | # Basic info of the dataset |
| 16 | self.name = "kit" |
| 17 | self.njoints = 21 |
| 18 | |
| 19 | # Path to the dataset |
| 20 | data_root = cfg.DATASET.KIT.ROOT |
| 21 | self.hparams.data_root = data_root |
| 22 | self.hparams.text_dir = pjoin(data_root, "texts") |
| 23 | self.hparams.motion_dir = pjoin(data_root, 'new_joint_vecs') |
| 24 | |
| 25 | # Mean and std of the dataset |
| 26 | dis_data_root = pjoin(cfg.DATASET.KIT.MEAN_STD_PATH, 'kit', |
| 27 | "VQVAEV3_CB1024_CMT_H1024_NRES3", "meta") |
| 28 | self.hparams.mean = np.load(pjoin(dis_data_root, "mean.npy")) |
| 29 | self.hparams.std = np.load(pjoin(dis_data_root, "std.npy")) |
| 30 | |
| 31 | # Mean and std for fair evaluation |
| 32 | dis_data_root_eval = pjoin(cfg.DATASET.KIT.MEAN_STD_PATH, 't2m', |
| 33 | "Comp_v6_KLD005", "meta") |
| 34 | self.hparams.mean_eval = np.load(pjoin(dis_data_root_eval, "mean.npy")) |
| 35 | self.hparams.std_eval = np.load(pjoin(dis_data_root_eval, "std.npy")) |
| 36 | |
| 37 | # Length of the dataset |
| 38 | self.hparams.max_motion_length = cfg.DATASET.KIT.MAX_MOTION_LEN |
| 39 | self.hparams.min_motion_length = cfg.DATASET.KIT.MIN_MOTION_LEN |
| 40 | self.hparams.max_text_len = cfg.DATASET.KIT.MAX_TEXT_LEN |
| 41 | self.hparams.unit_length = cfg.DATASET.KIT.UNIT_LEN |
| 42 | |
| 43 | # Get additional info of the dataset |
| 44 | self._sample_set = self.get_sample_set(overrides={"split": "test", "tiny": True}) |
| 45 | self.nfeats = self._sample_set.nfeats |
| 46 | cfg.DATASET.NFEATS = self.nfeats |
| 47 | |
| 48 | def feats2joints(self, features): |
| 49 | mean = torch.tensor(self.hparams.mean).to(features) |
nothing calls this directly
no test coverage detected