CNN visualization using autograd


I am trying to write a code to visualize the maximum feature response of a fine-tuned vgg16 network. However, I have the following error while using the autograd function:

TypeError: expected tuple, but hook returned 'Tensor'

Here is a shortened version of the code:

def relu_backward_deconv_hook ( module, grad_input, grad_output):
    return F.relu(grad_output[0])

def equip_model_deconv (model):
    for m in model.modules():
        if isinstance(m, nn.ReLU):

def grad_view(model, image_name):
    to_tensor = transforms.ToTensor()
    img = to_tensor(
    img = 0.5 + 0.5 * (img - img.mean()) / img.std()
    if torch.cuda.is_available():
        img = img.cuda()
    input = Variable(img.view(1, img. size(0), img.size(1), img.size(2)), requires_grad=True)
    output = model(input)
    result = torch.autograd.grad(output.max(), input)
    result = / + 0.5
    return result

model = model_fine_tuned
model = model.features

result = grad_view(model, image)

It would be really nice of you to help me in solving this issue.

Thanks in advance,
best wishes.