pytorch~改变tensor的shape

def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
    image_numpy = image_tensor.cpu().float().detach().numpy()
    if normalize:
        image_numpy = (image_numpy+1)*255.0*0.5
    else:
        image_numpy = (image_numpy+1)*255.0
    image_numpy = np.clip(image_numpy, 0, 255)    
    blank_image = np.zeros((image_tensor.shape[1],image_tensor.shape[2],image_tensor.shape[0]), np.uint8)
    if image_tensor.shape[0] == 3:  
        blank_image[:,:,0]=image_numpy[2,:,:]
        blank_image[:,:,1]=image_numpy[1,:,:]
        blank_image[:,:,2]=image_numpy[0,:,:]
    else:
        blank_image[:,:,:]=image_numpy[:,:,:]
    return blank_image
def im2tensor(image_numpy, normalize=True):
    if normalize:
        image_numpy = (image_numpy/255.0)*2.0-1.0
    else:
        image_numpy = image_numpy/255.0
    image_numpy = np.clip(image_numpy, -1, 1)    
    blank_image = np.zeros((image_numpy.shape[2],image_numpy.shape[0],image_numpy.shape[1]))
    if image_numpy.shape[2] == 3:  
        blank_image[2,:,:]=image_numpy[:,:,0]
        blank_image[1,:,:]=image_numpy[:,:,1]
        blank_image[0,:,:]=image_numpy[:,:,2]
    else:
        blank_image[:,:,:]=image_numpy[:,:,:]
    image_tensor = torch.Tensor(blank_image)
    return image_tensor

w_size = 1024
h_size = 256
input_label = torch.zeros([input_labe.shape[0],input_labe.shape[1],h_size,w_size], dtype=torch.float32,device=input_labe.device)
for i in range(input_labe.shape[0]):            
     f_label = input_labe [i,:,:,:]
     f_label_img = tensor2im(f_label)
     f_label_img = cv2.resize(f_label_img,(w_size,h_size))
     input_label[i,:,:,:] = im2tensor(f_label_img, normalize=True)