How to calculate the gradient with respect to "Input" layer in Caffe?

I want to implement the algorithm proposed by paper "Generalizing to unseen domains via adversarial data augmentation" using Caffe framework. I have to compute the gradient with regard of input layer to add it onto the input blob. In PyTorch, it can be done by grad = torch.autograd.grad(loss, data)[0]. But in Caffe, there is no function to do this as I know. So how to compute the gradient of "Input" layer in Caffe. The "Input" layer means input image in semantic segmentation.

I have tried call net->input_blobs()[0]->cpu_diff() after backpropagation, but values in cpu_diff are all 0. Obviously, Caffe does not compute the gradient of input layer in default. The overall algorithm is as the image shows.enter image description here

To get what you want, try something like

for (int i=0; i<top_vec[0]->count(); i++) {
    top_vec[0]->mutable_cpu_diff()[i] = 1.0;
}

net->Backward(top_vec, propagate_down, bottom_vec);

for (int i=0; i<bottom_vec[0]->count(); i++) {
    std::cout << i << " : " << bottom_vec[0]->cpu_diff()[i] << std::endl;
}