import torch from torch.utils import data import numpy as np import os from os.path import join as pjoin import random import codecs as cs from tqdm import tqdm class Text2MotionDataset(data.Dataset): """Dataset for Text2Motion generation task. """ def __init__(self, opt, mean, std, split_file, times=1, w_vectorizer=None, eval_mode=False): self.opt = opt self.max_length = 20 self.times = times self.w_vectorizer = w_vectorizer self.eval_mode = eval_mode min_motion_len = 40 if self.opt.dataset_name =='t2m' else 24 joints_num = opt.joints_num 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(opt.motion_dir, name + '.npy')) if (len(motion)) < min_motion_len or (len(motion) >= 200): continue text_data = [] flag = False with cs.open(pjoin(opt.text_dir, name + '.txt')) as f: for line in f.readlines(): text_dict = {} line_split = line.strip().split('#') caption = line_split[0] tokens = line_split[1].split(' ') f_tag = float(line_split[2]) to_tag = float(line_split[3]) f_tag = 0.0 if np.isnan(f_tag) else f_tag to_tag = 0.0 if np.isnan(to_tag) else to_tag text_dict['caption'] = caption text_dict['tokens'] = tokens if f_tag == 0.0 and to_tag == 0.0: flag = True text_data.append(text_dict) else: n_motion = motion[int(f_tag*20) : int(to_tag*20)] if (len(n_motion)) < min_motion_len or (len(n_motion) >= 200): continue new_name = random.choice('ABCDEFGHIJKLMNOPQRSTUVW') + '_' + name while new_name in data_dict: new_name = random.choice('ABCDEFGHIJKLMNOPQRSTUVW') + '_' + name data_dict[new_name] = {'motion': n_motion, 'length': len(n_motion), 'text':[text_dict]} new_name_list.append(new_name) length_list.append(len(n_motion)) if flag: data_dict[name] = {'motion': motion, 'length': len(motion), 'text':text_data} new_name_list.append(name) length_list.append(len(motion)) except: # Some motion may not exist in KIT dataset pass name_list, length_list = zip(*sorted(zip(new_name_list, length_list), key=lambda x: x[1])) if opt.is_train: # root_rot_velocity (B, seq_len, 1) std[0:1] = std[0:1] / opt.feat_bias # root_linear_velocity (B, seq_len, 2) std[1:3] = std[1:3] / opt.feat_bias # root_y (B, seq_len, 1) std[3:4] = std[3:4] / opt.feat_bias # ric_data (B, seq_len, (joint_num - 1)*3) std[4: 4 + (joints_num - 1) * 3] = std[4: 4 + (joints_num - 1) * 3] / 1.0 # rot_data (B, seq_len, (joint_num - 1)*6) std[4 + (joints_num - 1) * 3: 4 + (joints_num - 1) * 9] = std[4 + (joints_num - 1) * 3: 4 + ( joints_num - 1) * 9] / 1.0 # local_velocity (B, seq_len, joint_num*3) std[4 + (joints_num - 1) * 9: 4 + (joints_num - 1) * 9 + joints_num * 3] = std[ 4 + (joints_num - 1) * 9: 4 + ( joints_num - 1) * 9 + joints_num * 3] / 1.0 # foot contact (B, seq_len, 4) std[4 + (joints_num - 1) * 9 + joints_num * 3:] = std[ 4 + (joints_num - 1) * 9 + joints_num * 3:] / opt.feat_bias assert 4 + (joints_num - 1) * 9 + joints_num * 3 + 4 == mean.shape[-1] np.save(pjoin(opt.meta_dir, 'mean.npy'), mean) np.save(pjoin(opt.meta_dir, 'std.npy'), std) self.mean = mean self.std = std self.length_arr = np.array(length_list) self.data_dict = data_dict self.name_list = name_list def inv_transform(self, data): return data * self.std + self.mean def real_len(self): return len(self.data_dict) def __len__(self): return self.real_len() * self.times def __getitem__(self, item): idx = item % self.real_len() data = self.data_dict[self.name_list[idx]] motion, m_length, text_list = data['motion'], data['length'], data['text'] # Randomly select a caption text_data = random.choice(text_list) caption = text_data['caption'] max_motion_length = self.opt.max_motion_length if m_length >= self.opt.max_motion_length: idx = random.randint(0, len(motion) - max_motion_length) motion = motion[idx: idx + max_motion_length] else: padding_len = max_motion_length - m_length D = motion.shape[1] padding_zeros = np.zeros((padding_len, D)) motion = np.concatenate((motion, padding_zeros), axis=0) assert len(motion) == max_motion_length "Z Normalization" motion = (motion - self.mean) / self.std if self.eval_mode: tokens = text_data['tokens'] if len(tokens) < self.opt.max_text_len: # pad with "unk" tokens = ['sos/OTHER'] + tokens + ['eos/OTHER'] sent_len = len(tokens) tokens = tokens + ['unk/OTHER'] * (self.opt.max_text_len + 2 - sent_len) else: # crop tokens = tokens[:self.opt.max_text_len] tokens = ['sos/OTHER'] + tokens + ['eos/OTHER'] sent_len = len(tokens) pos_one_hots = [] word_embeddings = [] for token in tokens: word_emb, pos_oh = self.w_vectorizer[token] pos_one_hots.append(pos_oh[None, :]) word_embeddings.append(word_emb[None, :]) pos_one_hots = np.concatenate(pos_one_hots, axis=0) word_embeddings = np.concatenate(word_embeddings, axis=0) return word_embeddings, pos_one_hots, caption, sent_len, motion, m_length return caption, motion, m_length