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from data.t2m_dataset import Text2MotionDatasetEval, collate_fn # TODO
from utils.word_vectorizer import WordVectorizer
import numpy as np
from os.path import join as pjoin
from torch.utils.data import DataLoader
from utils.get_opt import get_opt
def get_dataset_motion_loader(opt_path, batch_size, fname, device):
opt = get_opt(opt_path, device)
# Configurations of T2M dataset and KIT dataset is almost the same
if opt.dataset_name == 't2m' or opt.dataset_name == 'kit':
print('Loading dataset %s ...' % opt.dataset_name)
mean = np.load(pjoin(opt.meta_dir, 'mean.npy'))
std = np.load(pjoin(opt.meta_dir, 'std.npy'))
w_vectorizer = WordVectorizer('./glove', 'our_vab')
split_file = pjoin(opt.data_root, '%s.txt'%fname)
dataset = Text2MotionDatasetEval(opt, mean, std, split_file, w_vectorizer)
dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=4, drop_last=True,
collate_fn=collate_fn, shuffle=True)
else:
raise KeyError('Dataset not Recognized !!')
print('Ground Truth Dataset Loading Completed!!!')
return dataloader, dataset |