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| import numpy as np | |
| import torch | |
| from os.path import join as pjoin | |
| from .humanml.utils.word_vectorizer import WordVectorizer | |
| from .humanml.scripts.motion_process import (process_file, recover_from_ric) | |
| from .HumanML3D import HumanML3DDataModule | |
| from .humanml import Text2MotionDatasetEval, Text2MotionDataset, Text2MotionDatasetCB, MotionDataset, MotionDatasetVQ, Text2MotionDatasetToken | |
| class KitDataModule(HumanML3DDataModule): | |
| def __init__(self, cfg, **kwargs): | |
| super().__init__(cfg, **kwargs) | |
| # Basic info of the dataset | |
| self.name = "kit" | |
| self.njoints = 21 | |
| # Path to the dataset | |
| data_root = cfg.DATASET.KIT.ROOT | |
| self.hparams.data_root = data_root | |
| self.hparams.text_dir = pjoin(data_root, "texts") | |
| self.hparams.motion_dir = pjoin(data_root, 'new_joint_vecs') | |
| # Mean and std of the dataset | |
| dis_data_root = pjoin(cfg.DATASET.KIT.MEAN_STD_PATH, 'kit', | |
| "VQVAEV3_CB1024_CMT_H1024_NRES3", "meta") | |
| self.hparams.mean = np.load(pjoin(dis_data_root, "mean.npy")) | |
| self.hparams.std = np.load(pjoin(dis_data_root, "std.npy")) | |
| # Mean and std for fair evaluation | |
| dis_data_root_eval = pjoin(cfg.DATASET.KIT.MEAN_STD_PATH, 't2m', | |
| "Comp_v6_KLD005", "meta") | |
| self.hparams.mean_eval = np.load(pjoin(dis_data_root_eval, "mean.npy")) | |
| self.hparams.std_eval = np.load(pjoin(dis_data_root_eval, "std.npy")) | |
| # Length of the dataset | |
| self.hparams.max_motion_length = cfg.DATASET.KIT.MAX_MOTION_LEN | |
| self.hparams.min_motion_length = cfg.DATASET.KIT.MIN_MOTION_LEN | |
| self.hparams.max_text_len = cfg.DATASET.KIT.MAX_TEXT_LEN | |
| self.hparams.unit_length = cfg.DATASET.KIT.UNIT_LEN | |
| # Get additional info of the dataset | |
| self._sample_set = self.get_sample_set(overrides={"split": "test", "tiny": True}) | |
| self.nfeats = self._sample_set.nfeats | |
| cfg.DATASET.NFEATS = self.nfeats | |
| def feats2joints(self, features): | |
| mean = torch.tensor(self.hparams.mean).to(features) | |
| std = torch.tensor(self.hparams.std).to(features) | |
| features = features * std + mean | |
| return recover_from_ric(features, self.njoints) | |
| def joints2feats(self, features): | |
| features = process_file(features, self.njoints)[0] | |
| # mean = torch.tensor(self.hparams.mean).to(features) | |
| # std = torch.tensor(self.hparams.std).to(features) | |
| # features = (features - mean) / std | |
| return features | |
| def normalize(self, features): | |
| mean = torch.tensor(self.hparams.mean).to(features) | |
| std = torch.tensor(self.hparams.std).to(features) | |
| features = (features - mean) / std | |
| return features | |
| def renorm4t2m(self, features): | |
| # renorm to t2m norms for using t2m evaluators | |
| ori_mean = torch.tensor(self.hparams.mean).to(features) | |
| ori_std = torch.tensor(self.hparams.std).to(features) | |
| eval_mean = torch.tensor(self.hparams.mean_eval).to(features) | |
| eval_std = torch.tensor(self.hparams.std_eval).to(features) | |
| features = features * ori_std + ori_mean | |
| features = (features - eval_mean) / eval_std | |
| return features | |
| def mm_mode(self, mm_on=True): | |
| # random select samples for mm | |
| if mm_on: | |
| self.is_mm = True | |
| self.name_list = self.test_dataset.name_list | |
| self.mm_list = np.random.choice(self.name_list, | |
| self.cfg.METRIC.MM_NUM_SAMPLES, | |
| replace=False) | |
| self.test_dataset.name_list = self.mm_list | |
| else: | |
| self.is_mm = False | |
| self.test_dataset.name_list = self.name_list | |