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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 options.option_transformer as option_trans
args = option_trans.get_args_parser()
def collate_fn(batch):
batch.sort(key=lambda x: x[3], reverse=True)
return default_collate(batch)
'''For use of training text-2-motion generative model'''
class Text2MotionDataset(data.Dataset):
def __init__(self, dataset_name, feat_bias = 5, unit_length = 4, codebook_size = 1024, tokenizer_name=None,method=None):
self.max_length = 64
self.pointer = 0
self.dataset_name = dataset_name
self.unit_length = unit_length
# self.mot_start_idx = codebook_size
self.mot_end_idx = codebook_size
self.mot_pad_idx = codebook_size + 1
self.method=method
if dataset_name == 't2m':
self.data_root = './dataset/HumanML3D'
self.motion_dir = pjoin(self.data_root, 'new_joint_vecs')
self.text_dir = pjoin(self.data_root, 'texts')
self.joints_num = 22
radius = 4
fps = 20
self.max_motion_length = 26 if unit_length == 8 else 51
dim_pose = 263
kinematic_chain = paramUtil.t2m_kinematic_chain
elif dataset_name == 'kit':
self.data_root = './dataset/KIT-ML'
self.motion_dir = pjoin(self.data_root, 'new_joint_vecs')
self.text_dir = pjoin(self.data_root, 'texts')
self.joints_num = 21
radius = 240 * 8
fps = 12.5
dim_pose = 251
self.max_motion_length = 26 if unit_length == 8 else 51
kinematic_chain = paramUtil.kit_kinematic_chain
split_file = pjoin(self.data_root, 'train.txt')
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 = {}
if self.method=='adv':
pass
for name in tqdm(id_list):
try:
m_token_list = np.load(pjoin(self.data_root, tokenizer_name, '%s.npy'%name))
# Read text
with cs.open(pjoin(self.text_dir, name + '.txt')) as f:
text_data = []
flag = False
lines = f.readlines()
for line in lines:
try:
text_dict = {}
line_split = line.strip().split('#')
caption = line_split[0]
txt_perb = line_split[-1]
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
text_dict['caption_perb'] = txt_perb
if f_tag == 0.0 and to_tag == 0.0:
flag = True
text_data.append(text_dict)
else:
m_token_list_new = [tokens[int(f_tag*fps/unit_length) : int(to_tag*fps/unit_length)] for tokens in m_token_list if 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)
except:
pass
if flag:
data_dict[name] = {'m_token_list': m_token_list,
'text':text_data}
new_name_list.append(name)
except:
pass
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 = random.choice(m_token_list)
text_data = random.choice(text_list)
caption,caption_perb= text_data['caption'], text_data['caption_perb']
coin = np.random.choice([False, False, True])
# print(len(m_tokens))
if coin:
# drop one token at the head or tail
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+1 < self.max_motion_length:
m_tokens = np.concatenate([m_tokens, np.ones((1), dtype=int) * self.mot_end_idx, np.ones((self.max_motion_length-1-m_tokens_len), dtype=int) * self.mot_pad_idx], axis=0)
else:
m_tokens = np.concatenate([m_tokens, np.ones((1), dtype=int) * self.mot_end_idx], axis=0)
return caption,caption_perb, m_tokens.reshape(-1), m_tokens_len
def DATALoader(dataset_name,
batch_size, codebook_size, tokenizer_name, unit_length=4,
num_workers = 0) :
train_loader = torch.utils.data.DataLoader(Text2MotionDataset(dataset_name, codebook_size = codebook_size, tokenizer_name = tokenizer_name, unit_length=unit_length),
batch_size,
shuffle=False,
num_workers=num_workers,
#collate_fn=collate_fn,
drop_last = True)
return train_loader
from torch.utils.data.distributed import DistributedSampler
def DATALoader_ddp(dataset_name,
batch_size, codebook_size, tokenizer_name, unit_length=4,
num_workers = 0) :
dataset = Text2MotionDataset(dataset_name, codebook_size = codebook_size, tokenizer_name = tokenizer_name, unit_length=unit_length)
train_sampler = DistributedSampler(dataset)
train_loader = torch.utils.data.DataLoader(dataset,
batch_size,
num_workers=num_workers,
#collate_fn=collate_fn,
drop_last = True,
sampler=train_sampler)
return train_loader
def cycle(iterable):
while True:
for x in iterable:
yield x
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