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import os
import rich
import random
import pickle
import codecs as cs
import numpy as np
from torch.utils import data
from rich.progress import track
from os.path import join as pjoin
class Text2MotionDataset(data.Dataset):
def __init__(
self,
data_root,
split,
mean,
std,
max_motion_length=196,
min_motion_length=40,
unit_length=4,
fps=20,
tmpFile=True,
tiny=False,
debug=False,
**kwargs,
):
# restrian the length of motion and text
self.max_length = 20
self.max_motion_length = max_motion_length
self.min_motion_length = min_motion_length
self.unit_length = unit_length
# Data mean and std
self.mean = mean
self.std = std
# Data path
split_file = pjoin(data_root, split + '.txt')
motion_dir = pjoin(data_root, 'new_joint_vecs')
text_dir = pjoin(data_root, 'texts')
# Data id list
self.id_list = []
with cs.open(split_file, "r") as f:
for line in f.readlines():
self.id_list.append(line.strip())
# Debug mode
if tiny or debug:
enumerator = enumerate(self.id_list)
maxdata = 100
subset = '_tiny'
else:
enumerator = enumerate(
track(
self.id_list,
f"Loading HumanML3D {split}",
))
maxdata = 1e10
subset = ''
new_name_list = []
length_list = []
data_dict = {}
# Fast loading
if os.path.exists(pjoin(data_root, f'tmp/{split}{subset}_data.pkl')):
if tiny or debug:
with open(pjoin(data_root, f'tmp/{split}{subset}_data.pkl'),
'rb') as file:
data_dict = pickle.load(file)
else:
with rich.progress.open(
pjoin(data_root, f'tmp/{split}{subset}_data.pkl'),
'rb',
description=f"Loading HumanML3D {split}") as file:
data_dict = pickle.load(file)
with open(pjoin(data_root, f'tmp/{split}{subset}_index.pkl'),
'rb') as file:
name_list = pickle.load(file)
for name in new_name_list:
length_list.append(data_dict[name]['length'])
else:
for idx, name in enumerator:
if len(new_name_list) > maxdata:
break
try:
motion = np.load(pjoin(motion_dir, name + ".npy"))
if (len(motion)) < self.min_motion_length or (len(motion)
>= 200):
continue
# Read text
text_data = []
flag = False
with cs.open(pjoin(text_dir, name + '.txt')) as f:
lines = f.readlines()
for line in lines:
text_dict = {}
line_split = line.strip().split('#')
caption = line_split[0]
t_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'] = t_tokens
if f_tag == 0.0 and to_tag == 0.0:
flag = True
text_data.append(text_dict)
else:
motion_new = motion[int(f_tag *
fps):int(to_tag * fps)]
if (len(motion_new)
) < self.min_motion_length or (
len(motion_new) >= 200):
continue
new_name = random.choice(
'ABCDEFGHIJKLMNOPQRSTUVW') + '_' + name
while new_name in new_name_list:
new_name = random.choice(
'ABCDEFGHIJKLMNOPQRSTUVW') + '_' + name
name_count = 1
while new_name in data_dict:
new_name += '_' + name_count
name_count += 1
data_dict[new_name] = {
'motion': motion_new,
"length": len(motion_new),
'text': [text_dict]
}
new_name_list.append(new_name)
length_list.append(len(motion_new))
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]))
if tmpFile:
os.makedirs(pjoin(data_root, 'tmp'), exist_ok=True)
with open(pjoin(data_root, f'tmp/{split}{subset}_data.pkl'),
'wb') as file:
pickle.dump(data_dict, file)
with open(pjoin(data_root, f'tmp/{split}{subset}_index.pkl'),
'wb') as file:
pickle.dump(name_list, file)
self.length_arr = np.array(length_list)
self.data_dict = data_dict
self.name_list = name_list
self.nfeats = data_dict[name_list[0]]['motion'].shape[1]
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 __len__(self):
return len(self.name_list) - 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 = text_data["caption"]
all_captions = [
' '.join([token.split('/')[0] for token in text_dic['tokens']])
for text_dic in text_list
]
# Crop the motions in to times of 4, and introduce small variations
if self.unit_length < 10:
coin2 = np.random.choice(["single", "single", "double"])
else:
coin2 = "single"
if coin2 == "double":
m_length = (m_length // self.unit_length - 1) * self.unit_length
elif coin2 == "single":
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 caption, motion, m_length, None, None, None, None, all_captions
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