Grad-CAM.py

def resize_1d(array, shape):
    res = np.zeros(shape)
    if array.shape[0] >= shape:
        ratio = array.shape[0]/shape
        for i in range(array.shape[0]):
            res[int(i/ratio)] += array[i]*(1-(i/ratio-int(i/ratio)))
            if int(i/ratio) != shape-1:
                res[int(i/ratio)+1] += array[i]*(i/ratio-int(i/ratio))
            else:
                res[int(i/ratio)] += array[i]*(i/ratio-int(i/ratio))
        res = res[::-1]
        array = array[::-1]
        for i in range(array.shape[0]):
            res[int(i/ratio)] += array[i]*(1-(i/ratio-int(i/ratio)))
            if int(i/ratio) != shape-1:
                res[int(i/ratio)+1] += array[i]*(i/ratio-int(i/ratio))
            else:
                res[int(i/ratio)] += array[i]*(i/ratio-int(i/ratio))
        res = res[::-1]/(2*ratio)
        array = array[::-1]
    else:
        ratio = shape/array.shape[0]
        left = 0
        right = 1
        for i in range(shape):
            if left < int(i/ratio):
                left += 1
                right += 1
            if right > array.shape[0]-1:
                res[i] += array[left]
            else:
                res[i] += array[right] * \
                    (i - left * ratio)/ratio+array[left]*(right*ratio-i)/ratio
        res = res[::-1]
        array = array[::-1]
        left = 0
        right = 1
        for i in range(shape):
            if left < int(i/ratio):
                left += 1
                right += 1
            if right > array.shape[0]-1:
                res[i] += array[left]
            else:
                res[i] += array[right] * \
                    (i - left * ratio)/ratio+array[left]*(right*ratio-i)/ratio
        res = res[::-1]/2
        array = array[::-1]
    return res

class ActivationsAndGradients:
    """ Class for extracting activations and
    registering gradients from targetted intermediate layers """

    def __init__(self, model, target_layer):
        self.model = model
        self.gradients = []
        self.activations = []

        target_layer.register_forward_hook(self.save_activation)
        target_layer.register_backward_hook(self.save_gradient)

    def save_activation(self, module, input, output):
        self.activations.append(output)

    def save_gradient(self, module, grad_input, grad_output):
        # Gradients are computed in reverse order
        self.gradients = [grad_output[0]] + self.gradients

    def __call__(self, x):
        self.gradients = []
        self.activations = []
        return self.model(x)


class BaseCAM:
    def __init__(self, model, target_layer, use_cuda=False):
        self.model = model.eval()
        self.target_layer = target_layer
        self.cuda = use_cuda
        if self.cuda:
            self.model = model.cuda()

        self.activations_and_grads = ActivationsAndGradients(self.model, target_layer)

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

    def get_cam_weights(self,
                        input_tensor,
                        target_category,
                        activations,
                        grads):
        raise Exception("Not Implemented")

    def get_loss(self, output, target_category):
        return output[target_category]

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

        output = self.activations_and_grads(input_tensor)

        if target_category is None:
            target_category = np.argmax(output.cpu().data.numpy())
        self.model.zero_grad()
        loss = self.get_loss(output, target_category)
        loss.backward(retain_graph=True)

        activations = self.activations_and_grads.activations[-1].cpu().data.numpy()[0, :]
        grads = self.activations_and_grads.gradients[-1].cpu().data.numpy()[0, :]
        #weights = np.mean(grads, axis=(0))
        weights = self.get_cam_weights(input_tensor, target_category, activations, grads)
        cam = np.zeros(activations.shape[1:], dtype=np.float32)
        for i, w in enumerate(weights):
            cam += w * activations[i, :]
        # cam = activations.dot(weights)
        # cam = activations.dot(weights)
        # print(input_tensor.shape[1])
        cam = resize_1d(cam, (input_tensor.shape[2]))
        cam = np.maximum(cam, 0)
        heatmap = (cam - np.min(cam)) / (np.max(cam) - np.min(cam) + 1e-10)#归一化处理
        return heatmap
class GradCAM(BaseCAM):
    def __init__(self, model, target_layer, use_cuda=False):
        super(GradCAM, self).__init__(model, target_layer, use_cuda)

    def get_cam_weights(self, input_tensor,
                              target_category,
                              activations, grads):
        return np.mean(grads, axis=1)
model = Net1()
model.load_state_dict(torch.load('./data7/parameternn.pt'))#自己保存的模型的参数
target_layer = model.p2_6#自己需要计算的模型的梯度
net = GradCAM(model, target_layer)
from settest import Test
input_tensor = Test.Data[100:101, :]#自己需要改变的数据
input_tensor = torch.tensor(input_tensor, dtype=torch.float32)
#plt.figure(figsize=(5, 1))
output = net(input_tensor)