<gh_stars>1-10
import numpy as np
import torch
from torch.autograd import Function


class GuidedBackpropReLU(Function):
    @staticmethod
    def forward(self, input_img):
        positive_mask = (input_img > 0).type_as(input_img)
        output = torch.addcmul(torch.zeros(input_img.size()).type_as(input_img), input_img, positive_mask)
        self.save_for_backward(input_img, output)
        return output

    @staticmethod
    def backward(self, grad_output):
        input_img, output = self.saved_tensors
        grad_input = None

        positive_mask_1 = (input_img > 0).type_as(grad_output)
        positive_mask_2 = (grad_output > 0).type_as(grad_output)
        grad_input = torch.addcmul(torch.zeros(input_img.size()).type_as(input_img),
                                   torch.addcmul(torch.zeros(input_img.size()).type_as(input_img), grad_output,
                                                 positive_mask_1), positive_mask_2)
        return grad_input


class GuidedBackpropReLUModel:
    def __init__(self, model, use_cuda):
        self.model = model
        self.model.eval()
        self.cuda = use_cuda
        if self.cuda:
            self.model = model.cuda()

        def recursive_relu_apply(module_top):
            for idx, module in module_top._modules.items():
                recursive_relu_apply(module)
                if module.__class__.__name__ == 'ReLU':
                    module_top._modules[idx] = GuidedBackpropReLU.apply

        # replace ReLU with GuidedBackpropReLU
        recursive_relu_apply(self.model)

    def forward(self, input_img):
        return self.model(input_img)

    def __call__(self, input_img, target_category=None):
        if self.cuda:
            input_img = input_img.cuda()

        input_img = input_img.requires_grad_(True)

        output = self.forward(input_img)

        if target_category is None:
            target_category = np.argmax(output.cpu().data.numpy())

        one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)
        one_hot[0][target_category] = 1
        one_hot = torch.from_numpy(one_hot).requires_grad_(True)
        if self.cuda:
            one_hot = one_hot.cuda()

        one_hot = torch.sum(one_hot * output)
        one_hot.backward(retain_graph=True)

        output = input_img.grad.cpu().data.numpy()
        output = output[0, :, :, :]
        output = output.transpose((1, 2, 0))
        return output

import numpy as np
import torch
from torch.autograd import Function


class GuidedBackpropReLU(Function):
    @staticmethod
    def forward(self, input_img):
        positive_mask = (input_img > 0).type_as(input_img)
        output = torch.addcmul(torch.zeros(input_img.size()).type_as(input_img), input_img, positive_mask)
        self.save_for_backward(input_img, output)
        return output

    @staticmethod
    def backward(self, grad_output):
        input_img, output = self.saved_tensors
        grad_input = None

        positive_mask_1 = (input_img > 0).type_as(grad_output)
        positive_mask_2 = (grad_output > 0).type_as(grad_output)
        grad_input = torch.addcmul(torch.zeros(input_img.size()).type_as(input_img),
                                   torch.addcmul(torch.zeros(input_img.size()).type_as(input_img), grad_output,
                                                 positive_mask_1), positive_mask_2)
        return grad_input


class GuidedBackpropReLUModel:
    def __init__(self, model, use_cuda):
        self.model = model
        self.model.eval()
        self.cuda = use_cuda
        if self.cuda:
            self.model = model.cuda()

        def recursive_relu_apply(module_top):
            for idx, module in module_top._modules.items():
                recursive_relu_apply(module)
                if module.__class__.__name__ == 'ReLU':
                    module_top._modules[idx] = GuidedBackpropReLU.apply

        # replace ReLU with GuidedBackpropReLU
        recursive_relu_apply(self.model)

    def forward(self, input_img):
        return self.model(input_img)

    def __call__(self, input_img, target_category=None):
        if self.cuda:
            input_img = input_img.cuda()

        input_img = input_img.requires_grad_(True)

        output = self.forward(input_img)

        if target_category is None:
            target_category = np.argmax(output.cpu().data.numpy())

        one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)
        one_hot[0][target_category] = 1
        one_hot = torch.from_numpy(one_hot).requires_grad_(True)
        if self.cuda:
            one_hot = one_hot.cuda()

        one_hot = torch.sum(one_hot * output)
        one_hot.backward(retain_graph=True)

        output = input_img.grad.cpu().data.numpy()
        output = output[0, :, :, :]
        output = output.transpose((1, 2, 0))
        return output
# replace ReLU with GuidedBackpropReLU