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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, window_size = 64, unit_length = 4):
self.window_size = window_size
self.unit_length = unit_length
self.dataset_name = dataset_name
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
self.max_motion_length = 196
self.meta_dir = 'checkpoints/t2m/VQVAEV3_CB1024_CMT_H1024_NRES3/meta'
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
self.max_motion_length = 196
self.meta_dir = 'checkpoints/kit/VQVAEV3_CB1024_CMT_H1024_NRES3/meta'
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')
self.data = []
self.lengths = []
id_list = []
with cs.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(self.motion_dir, name + '.npy'))
if motion.shape[0] < self.window_size:
continue
self.lengths.append(motion.shape[0] - self.window_size)
self.data.append(motion)
except:
# Some motion may not exist in KIT dataset
pass
self.mean = mean
self.std = std
print("Total number of motions {}".format(len(self.data)))
def inv_transform(self, data):
return data * self.std + self.mean
def compute_sampling_prob(self) :
prob = np.array(self.lengths, dtype=np.float32)
prob /= np.sum(prob)
return prob
def __len__(self):
return len(self.data)
def __getitem__(self, item):
motion = self.data[item]
idx = random.randint(0, len(motion) - self.window_size)
motion = motion[idx:idx+self.window_size]
"Z Normalization"
motion = (motion - self.mean) / self.std
return motion
def DATALoader(dataset_name,
batch_size,
num_workers = 8,
window_size = 64,
unit_length = 4):
trainSet = VQMotionDataset(dataset_name, window_size=window_size, unit_length=unit_length)
prob = trainSet.compute_sampling_prob()
sampler = torch.utils.data.WeightedRandomSampler(prob, num_samples = len(trainSet) * 1000, replacement=True)
train_loader = torch.utils.data.DataLoader(trainSet,
batch_size,
shuffle=True,
#sampler=sampler,
num_workers=num_workers,
#collate_fn=collate_fn,
drop_last = True)
return train_loader
def cycle(iterable):
while True:
for x in iterable:
yield x
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