#######a
加载有权重的模型
model = resnet_18_res2net(input_shape=(256, 256, 1), nclass=2)print(model.summary())model.compile(keras.optimizers.Adam(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])model_path = "train_weights/"model.load_weights(os.path.join(model_path, "resnet_18_res2net.h5"))#resnet18_se#####输入图像
image = np.array(Image.open('C:/Users/xian/Desktop/grad_CAM/2.bmp').convert("L"))#RGBimage = np.expand_dims(image, axis=0)image = np.expand_dims(image, axis=-1)image = image.astype('float32') / 255print(image.shape)###调用函数
cam,heatmap=grad_cam(model,image,1,'concatenate_5')# conv5_block16_concat concatenate_5plt.figure("cam")plt.imshow(cam)plt.show()cv2.imwrite('C:/Users/xian/Desktop/grad_CAM/2_cam.jpg', cam)####计算CAM的函数
def grad_cam(model, x, category_index, layer_name): """ Args: model: model x: image input category_index: category index layer_name: last convolution layer name """ # 取得目标分类的CNN输出值，也就是loss class_output = model.output[:, category_index] print(class_output) # 取得想要算出梯度的层的输出 convolution_output = model.get_layer(layer_name).output print(convolution_output) # 利用gradients函数，算出梯度公式 grads = K.gradients(class_output, convolution_output)[0] print(grads) # 定义计算函数（tensorflow的常见做法，与一般开发语言不同，先定义计算逻辑图，之后一起计算。） gradient_function = K.function([model.input], [convolution_output, grads]) print("开始问题") print(x.shape) # 根据实际的输入图像得出梯度张量(返回是一个tensor张量，VGG16 是一个7X7X512的三维张量) output, grads_val = gradient_function([x]) print("输出") print(output) print("梯度") print(grads_val) output, grads_val = output[0], grads_val[0] # 取得所有梯度的平均值(维度降低：7X7X512 -> 512) weights = np.mean(grads_val, axis=(0, 1)) # 把所有平面的平均梯度乘到最后卷积层(vgg16最后一层是池化层)上，得到一个影响输出的梯度权重图 cam = np.dot(output, weights) # 把梯度权重图RGB化 cam = cv2.resize(cam, (x.shape[1], x.shape[2]), cv2.INTER_LINEAR) cam = np.maximum(cam, 0) heatmap = cam / np.max(cam) # Return to BGR [0..255] from the preprocessed image image_rgb = x[0, :] image_rgb -= np.min(image_rgb) image_rgb = np.minimum(image_rgb, 255) cam = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_JET) cam = np.float32(cam) + np.float32(image_rgb) cam = 255 * cam / np.max(cam) return np.uint8(cam), heatmap
来源：https://www.cnblogs.com/love6tao/p/12214321.html