from os.path import join as pjoin
import torch
from torch.utils import data
import numpy as np
from tqdm import tqdm
from torch.utils.data._utils.collate import default_collate
import random
import codecs as cs
def collate_fn(batch):
batch.sort(key=lambda x: x[3], reverse=True)
return default_collate(batch)
class MotionDataset(data.Dataset):
def __init__(self, opt, mean, std, split_file):
self.opt = opt
joints_num = opt.joints_num
self.data = []
self.lengths = []
id_list = []
with open(split_file, 'r') as f:
for line in f.readlines():
id_list.append(line.strip())
for name in tqdm(id_list):
try:
motion = np.load(pjoin(opt.motion_dir, name + '.npy'))
if motion.shape[0] < opt.window_size:
continue
self.lengths.append(motion.shape[0] - opt.window_size)
self.data.append(motion)
except Exception as e:
# Some motion may not exist in KIT dataset
print(e)
pass
self.cumsum = np.cumsum([0] + self.lengths)
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
print("Total number of motions {}, snippets {}".format(len(self.data), self.cumsum[-1]))
def inv_transform(self, data):
return data * self.std + self.mean
def __len__(self):
return self.cumsum[-1]
def __getitem__(self, item):
if item != 0:
motion_id = np.searchsorted(self.cumsum, item) - 1
idx = item - self.cumsum[motion_id] - 1
else:
motion_id = 0
idx = 0
motion = self.data[motion_id][idx:idx + self.opt.window_size]
"Z Normalization"
motion = (motion - self.mean) / self.std
return motion
class Text2MotionDatasetEval(data.Dataset):
def __init__(self, opt, mean, std, split_file, w_vectorizer):
self.opt = opt
self.w_vectorizer = w_vectorizer
self.max_length = 20
self.pointer = 0
self.max_motion_length = opt.max_motion_length
min_motion_len = 40 if self.opt.dataset_name =='t2m' else 24
data_dict = {}
id_list = []
with cs.open(split_file, 'r') as f:
for line in f.readlines():
id_list.append(line.strip())
# id_list = id_list[:250]
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:
try:
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))
except:
print(line_split)
print(line_split[2], line_split[3], f_tag, to_tag, name)
# break
if flag:
data_dict[name] = {'motion': motion,
'length': len(motion),
'text': text_data}
new_name_list.append(name)
length_list.append(len(motion))
except:
pass
name_list, length_list = zip(*sorted(zip(new_name_list, length_list), key=lambda x: x[1]))
self.mean = mean
self.std = std
self.length_arr = np.array(length_list)
self.data_dict = data_dict
self.name_list = name_list
self.reset_max_len(self.max_length)
def reset_max_len(self, length):
assert length <= self.max_motion_length
self.pointer = np.searchsorted(self.length_arr, length)
print("Pointer Pointing at %d"%self.pointer)
self.max_length = length
def inv_transform(self, data):
return data * self.std + self.mean
def __len__(self):
return len(self.data_dict) - self.pointer
def __getitem__(self, item):
idx = self.pointer + item
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, tokens = text_data['caption'], 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)
if self.opt.unit_length < 10:
coin2 = np.random.choice(['single', 'single', 'double'])
else:
coin2 = 'single'
if coin2 == 'double':
m_length = (m_length // self.opt.unit_length - 1) * self.opt.unit_length
elif coin2 == 'single':
m_length = (m_length // self.opt.unit_length) * self.opt.unit_length
idx = random.randint(0, len(motion) - m_length)
motion = motion[idx:idx+m_length]
"Z Normalization"
motion = (motion - self.mean) / self.std
if m_length < self.max_motion_length:
motion = np.concatenate([motion,
np.zeros((self.max_motion_length - m_length, motion.shape[1]))
], axis=0)
# print(word_embeddings.shape, motion.shape)
# print(tokens)
return word_embeddings, pos_one_hots, caption, sent_len, motion, m_length, '_'.join(tokens)
class Text2MotionDataset(data.Dataset):
def __init__(self, opt, mean, std, split_file):
self.opt = opt
self.max_length = 20
self.pointer = 0
self.max_motion_length = opt.max_motion_length
min_motion_len = 40 if self.opt.dataset_name =='t2m' else 24
data_dict = {}
id_list = []
with cs.open(split_file, 'r') as f:
for line in f.readlines():
id_list.append(line.strip())
# id_list = id_list[:250]
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('#')
# print(line)
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:
try:
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))
except:
print(line_split)
print(line_split[2], line_split[3], f_tag, to_tag, name)
# break
if flag:
data_dict[name] = {'motion': motion,
'length': len(motion),
'text': text_data}
new_name_list.append(name)
length_list.append(len(motion))
except Exception as e:
# print(e)
pass
# name_list, length_list = zip(*sorted(zip(new_name_list, length_list), key=lambda x: x[1]))
name_list, length_list = new_name_list, length_list
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 __len__(self):
return len(self.data_dict) - self.pointer
def __getitem__(self, item):
idx = self.pointer + item
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, tokens = text_data['caption'], text_data['tokens']
if self.opt.unit_length < 10:
coin2 = np.random.choice(['single', 'single', 'double'])
else:
coin2 = 'single'
if coin2 == 'double':
m_length = (m_length // self.opt.unit_length - 1) * self.opt.unit_length
elif coin2 == 'single':
m_length = (m_length // self.opt.unit_length) * self.opt.unit_length
idx = random.randint(0, len(motion) - m_length)
motion = motion[idx:idx+m_length]
"Z Normalization"
motion = (motion - self.mean) / self.std
if m_length < self.max_motion_length:
motion = np.concatenate([motion,
np.zeros((self.max_motion_length - m_length, motion.shape[1]))
], axis=0)
# print(word_embeddings.shape, motion.shape)
# print(tokens)
return caption, motion, m_length
def reset_min_len(self, length):
assert length <= self.max_motion_length
self.pointer = np.searchsorted(self.length_arr, length)
print("Pointer Pointing at %d" % self.pointer)