grad-cam.py 文件源码

def grad_cam(input_model, image, category_index, layer_name):
    model = Sequential()
    model.add(input_model)

    nb_classes = 1000
    target_layer = lambda x: target_category_loss(x, category_index, nb_classes)
    model.add(Lambda(target_layer,
                     output_shape = target_category_loss_output_shape))

    loss = K.sum(model.layers[-1].output)
    conv_output =  [l for l in model.layers[0].layers if l.name is layer_name][0].output
    grads = normalize(K.gradients(loss, conv_output)[0])
    gradient_function = K.function([model.layers[0].input], [conv_output, grads])

    output, grads_val = gradient_function([image])
    output, grads_val = output[0, :], grads_val[0, :, :, :]

    weights = np.mean(grads_val, axis = (0, 1))
    cam = np.ones(output.shape[0 : 2], dtype = np.float32)

    for i, w in enumerate(weights):
        cam += w * output[:, :, i]

    cam = cv2.resize(cam, (224, 224))
    cam = np.maximum(cam, 0)
    heatmap = cam / np.max(cam)

    #Return to BGR [0..255] from the preprocessed image
    image = image[0, :]
    image -= np.min(image)
    image = np.minimum(image, 255)

    cam = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET)
    cam = np.float32(cam) + np.float32(image)
    cam = 255 * cam / np.max(cam)
    return np.uint8(cam), heatmap