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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
import utils.paramUtil as paramUtil
from torch.utils.data._utils.collate import default_collate
import os
def collate_fn(batch):
batch.sort(key=lambda x: x[2], reverse=True)
return default_collate(batch)
'''For use of training text-2-motion generative model'''
class Text2MotionDataset(data.Dataset):
def __init__(self, dataset_name, unit_length = 4, latent_dir=None):
self.max_length = 64
self.pointer = 0
self.dataset_name = dataset_name
self.unit_length = unit_length
if dataset_name == 't2m_272':
self.data_root = './humanml3d_272'
self.text_dir = pjoin(self.data_root, 'texts')
self.joints_num = 22
fps = 30
self.max_motion_length = 78
dim_pose = 272
split_file = pjoin(self.data_root, 'split', 'train.txt')
else:
raise ValueError(f"Dataset {dataset_name} not supported")
id_list = []
with cs.open(split_file, 'r') as f:
for line in f.readlines():
id_list.append(line.strip())
new_name_list = []
data_dict = {}
for name in tqdm(id_list):
try:
m_token_list = np.load(pjoin(latent_dir, '%s.npy'%name))
except:
continue
# Read text
with cs.open(pjoin(self.text_dir, name + '.txt')) as f:
text_data = []
flag = False
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:
if int(f_tag*fps/unit_length) < int(to_tag*fps/unit_length):
m_token_list_new = [m_token_list[int(f_tag*fps/unit_length) : int(to_tag*fps/unit_length)]]
if len(m_token_list_new) == 0:
continue
new_name = '%s_%f_%f'%(name, f_tag, to_tag)
data_dict[new_name] = {'m_token_list': m_token_list_new,
'text':[text_dict]}
new_name_list.append(new_name)
if flag:
data_dict[name] = {'m_token_list': m_token_list,
'text':text_data}
new_name_list.append(name)
self.data_dict = data_dict
self.name_list = new_name_list
def __len__(self):
return len(self.data_dict)
def __getitem__(self, item):
data = self.data_dict[self.name_list[item]]
m_token_list, text_list = data['m_token_list'], data['text']
m_tokens = np.array(m_token_list)
text_data = random.choice(text_list)
caption= text_data['caption']
if len(m_tokens.shape) == 3:
m_tokens = m_tokens.squeeze(0)
coin = np.random.choice([False, False, True])
if coin:
coin2 = np.random.choice([True, False])
if coin2:
m_tokens = m_tokens[:-1]
else:
m_tokens = m_tokens[1:]
m_tokens_len = m_tokens.shape[0]
if m_tokens_len < self.max_motion_length:
m_tokens = np.concatenate([m_tokens, np.zeros((self.max_motion_length-m_tokens_len, m_tokens.shape[1]), dtype=int)], axis=0)
return caption, m_tokens, m_tokens_len
def DATALoader(dataset_name,
batch_size, latent_dir, unit_length=4,
num_workers = 8) :
train_loader = torch.utils.data.DataLoader(Text2MotionDataset(dataset_name, latent_dir = latent_dir, unit_length=unit_length),
batch_size,
shuffle=True,
num_workers=num_workers,
drop_last = True)
return train_loader
def cycle(iterable):
while True:
for x in iterable:
yield x
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