visualization.py 文件源码

def grad_cam(input_model, image, weights, feature_maps=None):
    #activation size of final convolition layer is 10x10"
    cam = np.ones((10, 10), dtype=np.float32)
    # Add weighted activation maps
    grads_val = weights
    for i in range(grads_val.shape[0]):
        # Added relu
        temp = (weights[i, :] * feature_maps[:, :, i])
        np.maximum(temp, 0, temp)
        cam += temp

    # resize and normalization
    del feature_maps
    cam = cv2.resize(cam, (299, 299))
    # Relu
    cam = np.maximum(cam, 0)
    cam = cam / np.max(cam)
    image = image[0, :]
    image -= np.min(image)
    image = np.minimum(image, 255)
   # print image.shape


    cam = cv2.applyColorMap(np.uint8(255 * cam), cv2.COLORMAP_JET)   
    cam = 0.5*np.float32(cam) + 0.5*np.float32(image)
    cam = 255 * cam / np.max(cam)
    return np.uint8(cam)