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 input
    2: backpropatation by the high scores of class
    """

    def __init__(self, net, layer_name):
        self.net = net
        self.layer_name = layer_name
        self.feature = None
        self.gradient = None
        self.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]: None
                                   input_grad[1]: weight
                                   input_grad[2]: bias
        :param output_grad:tuple,length