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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
class VQMotionDataset(data.Dataset):
def __init__(self, dataset_name, feat_bias = 5, window_size = 64, unit_length = 8):
self.window_size = window_size
self.unit_length = unit_length
self.feat_bias = feat_bias
self.dataset_name = dataset_name
min_motion_len = 40 if dataset_name =='t2m' else 24
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 = 196
dim_pose = 263
self.meta_dir = './checkpoints/t2m/VQVAEV3_CB1024_CMT_H1024_NRES3/meta'
#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 = 196
self.meta_dir = './checkpoints/kit/VQVAEV3_CB1024_CMT_H1024_NRES3/meta'
#kinematic_chain = paramUtil.kit_kinematic_chain
joints_num = self.joints_num
mean = np.load(pjoin(self.meta_dir, 'mean.npy'))
std = np.load(pjoin(self.meta_dir, 'std.npy'))
split_file = pjoin(self.data_root, 'train.txt')
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(self.motion_dir, name + '.npy'))
if (len(motion)) < min_motion_len or (len(motion) >= 200):
continue
data_dict[name] = {'motion': motion,
'length': len(motion),
'name': name}
new_name_list.append(name)
length_list.append(len(motion))
except:
# Some motion may not exist in KIT dataset
pass
self.mean = mean
self.std = std
self.length_arr = np.array(length_list)
self.data_dict = data_dict
self.name_list = new_name_list
def inv_transform(self, data):
return data * self.std + self.mean
def __len__(self):
return len(self.data_dict)
def __getitem__(self, item):
name = self.name_list[item]
data = self.data_dict[name]
motion, m_length = data['motion'], data['length']
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 motion, name
def DATALoader(dataset_name,
batch_size = 4,
num_workers = 8, unit_length = 4) :
train_loader = torch.utils.data.DataLoader(VQMotionDataset(dataset_name, unit_length=unit_length),
batch_size,
shuffle=True,
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 = 4,
# num_workers = 8, unit_length = 4) :
# dataset = VQMotionDataset(dataset_name, unit_length=unit_length)
# train_sampler = DistributedSampler(dataset)
# train_loader = torch.utils.data.DataLoader(dataset,
# batch_size=batch_size,
# shuffle=False,
# num_workers=num_workers,
# sampler=train_sampler)
# return train_loader
def cycle(iterable):
while True:
for x in iterable:
yield x
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