Grad-CAM (CNN可视化) Python示例

论文：ICCV 2017《Grad-CAM:Visual Explanations from Deep Networks via Gradient-based Localization》

代码：https://github.com/yizt/Grad-CAM.pytorch/blob/master/main.py
https://github.com/jacobgil/pytorch-grad-cam/blob/master/grad-cam.py

1、首先定义并训练好CNN网络，原网络结构不用调整。假设网路训练好，得到一个best_net。

class GradCAM(object):"""1: gradients update when input2: backpropatation by the high scores of class"""def __init__(self, net, layer_name):self.net = netself.layer_name = layer_nameself.feature = Noneself.gradient = Noneself.net.eval()self.handlers = []self._register_hook()def _get_features_hook(self, module, input, output):self.feature = output#print("feature shape:{}".format(output.size()))def _get_grads_hook(self, module, input_grad, output_grad):""":param input_grad: tuple, input_grad[0]: Noneinput_grad[1]: weightinput_grad[2]: bias:param output_grad:tuple,length = 1:return:"""self.gradient = output_grad[0]def _register_hook(self):for (name, module) in self.net.named_modules():if name == self.layer_name:self.handlers.append(module.register_forward_hook(self._get_features_hook))self.handlers.append(module.register_backward_hook(self._get_grads_hook))def remove_handlers(self):for handle in self.handlers:handle.remove()def __call__(self, inputs, index=None):""":param inputs: [1,3,H,W]:param index: class id:return:"""self.net.zero_grad()output = self.net(inputs)  # [1,num_classes]if index is None:index = np.argmax(output.cpu().data.numpy())target = output[0][index]target.backward()gradient = self.gradient[0].cpu().data.numpy()  # [C,H,W]weight = np.mean(gradient, axis=(1, 2))  # [C]feature = self.feature[0].cpu().data.numpy()  # [C,H,W]cam = feature * weight[:, np.newaxis, np.newaxis]  # [C,H,W]cam = np.sum(cam, axis=0)  # [H,W]cam = np.maximum(cam, 0)  # ReLU# nomalizationcam -= np.min(cam)cam /= np.max(cam)# resize to 256*256cam = cv2.resize(cam, (256, 256))return camclass GradCamPlusPlus(GradCAM):def __init__(self, net, layer_name):super(GradCamPlusPlus, self).__init__(net, layer_name)def __call__(self, inputs, index=None):""":param inputs: [1,3,H,W]:param index: class id:return:"""self.net.zero_grad()output = self.net(inputs)  # [1,num_classes]if index is None:index = np.argmax(output.cpu().data.numpy())target = output[0][index]target.backward()gradient = self.gradient[0].cpu().data.numpy()  # [C,H,W]gradient = np.maximum(gradient, 0.)  # ReLUindicate = np.where(gradient > 0, 1., 0.)  # 示性函数norm_factor = np.sum(gradient, axis=(1, 2))  # [C]归一化for i in range(len(norm_factor)):norm_factor[i] = 1. / norm_factor[i] if norm_factor[i] > 0. else 0.  # 避免除零alpha = indicate * norm_factor[:, np.newaxis, np.newaxis]  # [C,H,W]weight = np.sum(gradient * alpha, axis=(1, 2))  # [C]  alpha*ReLU(gradient)feature = self.feature[0].cpu().data.numpy()  # [C,H,W]cam = feature * weight[:, np.newaxis, np.newaxis]  # [C,H,W]cam = np.sum(cam, axis=0)  # [H,W]# cam = np.maximum(cam, 0)  # ReLU# nomalizationcam -= np.min(cam)cam /= np.max(cam)# resize cam = cv2.resize(cam, (256, 256))return camclass GuidedBackPropagation(object):def __init__(self, net):self.net = netfor (name, module) in self.net.named_modules():if isinstance(module, nn.ReLU):module.register_backward_hook(self.backward_hook)self.net.eval()@classmethoddef backward_hook(cls, module, grad_in, grad_out):""":param module::param grad_in: tuple,length=1:param grad_out: tuple,length=1:return: tuple(new_grad_in,)"""return torch.clamp(grad_in[0], min=0.0),def __call__(self, inputs, index=None):""":param inputs: [1,3,H,W]:param index: class_id:return:"""self.net.zero_grad()output = self.net(inputs)  # [1,num_classes]if index is None:index = np.argmax(output.cpu().data.numpy())target = output[0][index]target.backward()return inputs.grad[0]  # [3,H,W]

def show_cam_on_image(img, mask):heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET)heatmap = np.float32(heatmap) / 255cam = heatmap + np.float32(img)cam = cam / np.max(cam)cv2.imwrite("cam.jpg", np.uint8(255 * cam))root='/data/fjsdata/qtsys/img/sz.002509-20200325.png'
img_list = []
img_list.append( cv2.resize(cv2.imread(root).astype(np.float32), (256, 256)))#(256, 256) is the model input size
inputs = torch.from_numpy(np.array(img_list)).type(torch.FloatTensor).cuda()
# Grad-CAM
#grad_cam = GradCAM(net=best_net, layer_name='conv3')
#mask = grad_cam(inputs.permute(0, 3, 1, 2))  # cam mask
#show_cam_on_image(img_list[0], mask)
#grad_cam.remove_handlers()# Grad-CAM++
#grad_cam_plus_plus = GradCamPlusPlus(net=best_net, layer_name='conv3')
#mask_plus_plus = grad_cam_plus_plus(inputs.permute(0, 3, 1, 2))  # cam mask
#show_cam_on_image(img_list[0], mask)
#grad_cam_plus_plus.remove_handlers()# GuidedBackPropagation
gbp = GuidedBackPropagation(best_net)
inputs = inputs.requires_grad_(True)
inputs.grad.zero_()
grad = gbp(inputs.permute(0, 3, 1, 2))
print(grad)

最后GuidedBackPropagation没完全调通，详细阅读论文后再处理。前面Grad-CAM 和Grad-CAM++可以。

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