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| import torch | |
| from torch.utils import data | |
| import numpy as np | |
| from os.path import join as pjoin | |
| import random | |
| import codecs as cs | |
| from tqdm import tqdm | |
| class VQMotionDataset(data.Dataset): | |
| def __init__(self, dataset_name, feat_bias = 5, window_size = 64, unit_length = 8): | |
| self.window_size = window_size | |
| self.unit_length = unit_length | |
| self.feat_bias = feat_bias | |
| self.dataset_name = dataset_name | |
| min_motion_len = 40 if dataset_name =='t2m' else 24 | |
| if dataset_name == 't2m': | |
| self.data_root = './dataset/HumanML3D' | |
| self.motion_dir = pjoin(self.data_root, 'new_joint_vecs') | |
| self.text_dir = pjoin(self.data_root, 'texts') | |
| self.joints_num = 22 | |
| radius = 4 | |
| fps = 20 | |
| self.max_motion_length = 196 | |
| dim_pose = 263 | |
| self.meta_dir = 'checkpoints/t2m/VQVAEV3_CB1024_CMT_H1024_NRES3/meta' | |
| #kinematic_chain = paramUtil.t2m_kinematic_chain | |
| elif dataset_name == 'kit': | |
| self.data_root = './dataset/KIT-ML' | |
| self.motion_dir = pjoin(self.data_root, 'new_joint_vecs') | |
| self.text_dir = pjoin(self.data_root, 'texts') | |
| self.joints_num = 21 | |
| radius = 240 * 8 | |
| fps = 12.5 | |
| dim_pose = 251 | |
| self.max_motion_length = 196 | |
| self.meta_dir = 'checkpoints/kit/VQVAEV3_CB1024_CMT_H1024_NRES3/meta' | |
| #kinematic_chain = paramUtil.kit_kinematic_chain | |
| joints_num = self.joints_num | |
| mean = np.load(pjoin(self.meta_dir, 'mean.npy')) | |
| std = np.load(pjoin(self.meta_dir, 'std.npy')) | |
| split_file = pjoin(self.data_root, 'train.txt') | |
| data_dict = {} | |
| id_list = [] | |
| with cs.open(split_file, 'r') as f: | |
| for line in f.readlines(): | |
| id_list.append(line.strip()) | |
| new_name_list = [] | |
| length_list = [] | |
| for name in tqdm(id_list): | |
| try: | |
| motion = np.load(pjoin(self.motion_dir, name + '.npy')) | |
| if (len(motion)) < min_motion_len or (len(motion) >= 200): | |
| continue | |
| data_dict[name] = {'motion': motion, | |
| 'length': len(motion), | |
| 'name': name} | |
| new_name_list.append(name) | |
| length_list.append(len(motion)) | |
| except: | |
| # Some motion may not exist in KIT dataset | |
| pass | |
| self.mean = mean | |
| self.std = std | |
| self.length_arr = np.array(length_list) | |
| self.data_dict = data_dict | |
| self.name_list = new_name_list | |
| def inv_transform(self, data): | |
| return data * self.std + self.mean | |
| def __len__(self): | |
| return len(self.data_dict) | |
| def __getitem__(self, item): | |
| name = self.name_list[item] | |
| data = self.data_dict[name] | |
| motion, m_length = data['motion'], data['length'] | |
| m_length = (m_length // self.unit_length) * self.unit_length | |
| idx = random.randint(0, len(motion) - m_length) | |
| motion = motion[idx:idx+m_length] | |
| "Z Normalization" | |
| motion = (motion - self.mean) / self.std | |
| return motion, name | |
| def DATALoader(dataset_name, | |
| batch_size = 1, | |
| num_workers = 8, unit_length = 4) : | |
| train_loader = torch.utils.data.DataLoader(VQMotionDataset(dataset_name, unit_length=unit_length), | |
| batch_size, | |
| shuffle=True, | |
| num_workers=num_workers, | |
| #collate_fn=collate_fn, | |
| drop_last = True) | |
| return train_loader | |
| def cycle(iterable): | |
| while True: | |
| for x in iterable: | |
| yield x | |