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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
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