def load_dataset(opt): if opt.dataset == 'mnist': train_data = MovingMNIST(train=True, data_root=opt.data_root, seq_len=opt.max_step, image_size=opt.image_width, num_digits=2) test_data = MovingMNIST(train=False, data_root=opt.data_root, seq_len=opt.max_step, image_size=opt.image_width, num_digits=2) elif opt.dataset == 'suncg': train_data = suncg.SUNCG(train=True, data_root=opt.data_root, seq_len=opt.max_step, image_size=opt.image_width) test_data = suncg.SUNCG(train=False, data_root=opt.data_root, seq_len=opt.max_step, image_size=opt.image_width) elif opt.dataset == 'kth': train_data = KTH(train=True, data_root=opt.data_root, seq_len=opt.max_step, image_size=opt.image_width, data_type=opt.data_type) test_data = KTH(train=False, data_root=opt.data_root, seq_len=opt.max_step, image_size=opt.image_width, data_type=opt.data_type) return train_data, test_data
def load_dataset(dataset, data_root_x, max_step, image_width, data_type): if dataset == 'moving_mnist': train_data = MovingMNIST(train=True, data_root=data_root_x, seq_len=max_step, image_size=image_width, num_digits=2) test_data = MovingMNIST(train=False, data_root=data_root_x, seq_len=max_step, image_size=image_width, num_digits=2) elif dataset == 'suncg': train_data = suncg.SUNCG(train=True, data_root=data_root_x, seq_len=max_step, image_size=image_width) test_data = suncg.SUNCG(train=False, data_root=data_root_x, seq_len=max_step, image_size=image_width) elif dataset == 'kth': train_data = KTH(train=True, data_root=data_root_x, seq_len=max_step, image_size=image_width, data_type=data_type) test_data = KTH(train=False, data_root=data_root_x, seq_len=max_step, image_size=image_width, data_type=data_type) return train_data, test_data
def load_data(opt): """ :return: raw data """ if opt.dataset == 'moving_mnist': train_data = MovingMNIST(train=True, data_root=opt.data_root, seq_len=opt.max_step, image_size=opt.image_width, num_digits=2) test_data = MovingMNIST(train=False, data_root=opt.data_root, seq_len=opt.max_step, image_size=opt.image_width, num_digits=2) elif opt.dataset == 'suncg': train_data = suncg.SUNCG(train=True, data_root=opt.data_root, seq_len=opt.max_step, image_size=opt.image_width) test_data = suncg.SUNCG(train=False, data_root=opt.data_root, seq_len=opt.max_step, image_size=opt.image_width) elif opt.dataset == 'kth': train_data = KTH(train=True, epoch_samples=opt.epoch_size, pose=opt.pose, data_root=opt.data_root, seq_len=opt.max_step, image_size=opt.image_width, data_type=opt.data_type) test_data = KTH(train=False, epoch_samples=opt.epoch_size, pose=opt.pose, data_root=opt.data_root, seq_len=opt.max_step, image_size=opt.image_width, data_type=opt.data_type) return train_data, test_data
def load_dataset(opt): if opt.data == 'moving_mnist': train_data = MovingMNIST(train=True, seq_len=opt.max_step, image_size=opt.image_width, num_digits=2) test_data = MovingMNIST(train=False, seq_len=opt.max_step, image_size=opt.image_width, num_digits=2) load_workers = 5 elif opt.data == 'suncg': train_data = suncg.SUNCG(True, opt.max_step, opt.image_width) test_data = suncg.SUNCG(False, opt.max_step, opt.image_width) load_workers = 5 elif opt.data == 'suncg_dual': train_data = suncg.DualSUNCG(opt.max_step, opt.image_width) test_data = suncg.DualSUNCG(opt.max_step, opt.image_width) load_workers = 5 elif opt.data == 'kth': train_data = KTH(True, opt.max_step, opt.image_width) test_data = KTH(False, opt.max_step, opt.image_width) load_workers = 0 return train_data, test_data, load_workers