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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 . import BASEDataModule | |
| from .humanml import Text2MotionDatasetEval, Text2MotionDataset, Text2MotionDatasetCB, MotionDataset, MotionDatasetVQ, Text2MotionDatasetToken, Text2MotionDatasetM2T | |
| from .utils import humanml3d_collate | |
| class HumanML3DDataModule(BASEDataModule): | |
| def __init__(self, cfg, **kwargs): | |
| super().__init__(collate_fn=humanml3d_collate) | |
| self.cfg = cfg | |
| self.save_hyperparameters(logger=False) | |
| # Basic info of the dataset | |
| cfg.DATASET.JOINT_TYPE = 'humanml3d' | |
| self.name = "humanml3d" | |
| self.njoints = 22 | |
| # Path to the dataset | |
| data_root = cfg.DATASET.HUMANML3D.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 | |
| self.hparams.mean = np.load(pjoin('assets/meta', "mean.npy")) | |
| self.hparams.std = np.load(pjoin('assets/meta', "std.npy")) | |
| # Mean and std for fair evaluation | |
| self.hparams.mean_eval = np.load(pjoin('assets/meta', "mean_eval.npy")) | |
| self.hparams.std_eval = np.load(pjoin('assets/meta', "std_eval.npy")) | |
| # Length of the dataset | |
| self.hparams.max_motion_length = cfg.DATASET.HUMANML3D.MAX_MOTION_LEN | |
| self.hparams.min_motion_length = cfg.DATASET.HUMANML3D.MIN_MOTION_LEN | |
| self.hparams.max_text_len = cfg.DATASET.HUMANML3D.MAX_TEXT_LEN | |
| self.hparams.unit_length = cfg.DATASET.HUMANML3D.UNIT_LEN | |
| # Additional parameters | |
| self.hparams.debug = cfg.DEBUG | |
| self.hparams.stage = cfg.TRAIN.STAGE | |
| # Dataset switch | |
| self.DatasetEval = Text2MotionDatasetEval | |
| if cfg.TRAIN.STAGE == "vae": | |
| if cfg.model.params.motion_vae.target.split('.')[-1].lower() == "vqvae": | |
| self.hparams.win_size = 64 | |
| self.Dataset = MotionDatasetVQ | |
| else: | |
| self.Dataset = MotionDataset | |
| elif 'lm' in cfg.TRAIN.STAGE: | |
| self.hparams.code_path = cfg.DATASET.CODE_PATH | |
| self.hparams.task_path = cfg.DATASET.TASK_PATH | |
| self.hparams.std_text = cfg.DATASET.HUMANML3D.STD_TEXT | |
| self.Dataset = Text2MotionDatasetCB | |
| elif cfg.TRAIN.STAGE == "token": | |
| self.Dataset = Text2MotionDatasetToken | |
| self.DatasetEval = Text2MotionDatasetToken | |
| elif cfg.TRAIN.STAGE == "m2t": | |
| self.Dataset = Text2MotionDatasetM2T | |
| self.DatasetEval = Text2MotionDatasetM2T | |
| else: | |
| self.Dataset = Text2MotionDataset | |
| # Get additional info of the dataset | |
| self.nfeats = 263 | |
| 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] | |
| 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 denormalize(self, features): | |
| mean = torch.tensor(self.hparams.mean).to(features) | |
| std = torch.tensor(self.hparams.std).to(features) | |
| features = features * std + mean | |
| 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): | |
| 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 | |