def TestDatasets3():
print('[INFO] -------------------------------------------------')
print('[INFO] set datasets')
_, test_lips, _, test_tongue, _, test_label = load_dataset()
test_lips = test_lips[800:1200+i-1,:,:,:]
test_tongue = test_tongue[800:1200+i-1,:,:,:]
test_label = test_label[800:1200,:]
# print(test_lips.shape, test_tongue.shape, test_label.shape)
# #preprocessing
test_lips = match_image_label(test_lips)
test_tongue = match_image_label(test_tongue)
#to torch.tensor
test_lips = torch.from_numpy(test_lips).float()
test_tongue = torch.from_numpy(test_tongue).float()
test_label = torch.from_numpy(test_label).float()
#change dimension (x,64,64,1) --> (x,1,64,64)
test_lips = test_lips.permute(0,3,1,2)
test_tongue = test_tongue.permute(0,3,1,2)
#两通道
test_datasets3 = TensorDataset(test_lips, test_tongue, test_label)
test_loader3 = DataLoader(dataset=test_datasets3, batch_size=BATCH_SIZE, shuffle=False)
return test_datasets3, test_loader3
def TestDatasets2():
print('[INFO] -------------------------------------------------')
print('[INFO] set datasets')
# _, test_lips, _, test_tongue, _, test_label = load_dataset()
test_lips = np.load('../data/new_database/ssnv_train_lips.npy')
test_tongue = np.load('../data/new_database/ssnv_train_tongue.npy')
test_lips = test_lips[400:800+i-1,:,:,:]
test_tongue = test_tongue[400:800+i-1,:,:,:]
# #preprocessing
test_lips = match_image_label(test_lips)
test_tongue = match_image_label(test_tongue)
#to torch.tensor
test_lips = torch.from_numpy(test_lips).float()
test_tongue = torch.from_numpy(test_tongue).float()
#change dimension (x,64,64,1) --> (x,1,64,64)
test_lips = test_lips.permute(0,3,1,2)
test_tongue = test_tongue.permute(0,3,1,2)
#两通道
test_datasets2 = TensorDataset(test_lips, test_tongue)
test_loader2 = DataLoader(dataset=test_datasets2, batch_size=BATCH_SIZE, shuffle=False)
return test_datasets2, test_loader2