Python 为什么我使用更快的RCNN和Tensorflows对象检测API进行预测的速度如此之慢？

我正在使用Tensorflows对象检测api，用我自己的数据集来训练我自己的对象检测。我的问题是预测每张图像大约需要25-30秒。这包括读取图像和绘制边界框。我在一个NVIDIA K80 GPU上运行，有一个内核。有什么指导可以加快这一过程吗？我基本上采用了对象检测api中的预测脚本

下面是我用来做预测的两个函数：

def predict_image(TEST_IMAGE_PATHS, PATH_TO_CKPT, category_index, save_path):
    detection_graph = load_detection_graph(PATH_TO_CKPT)
    prediction_dict = defaultdict()
    start_time = time.time()
    for image_path in TEST_IMAGE_PATHS:
        toc = time.time()
        filename = image_path.split('/')[-1]
        image = Image.open(image_path)
        # the array based representation of the image will be used later in order to prepare the
        # result image with boxes and labels on it.
        image_np = load_image_into_numpy_array(image)
        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image_np, axis=0)
        # Actual detection.
        output_dict = run_inference_for_single_image(image_np, detection_graph, filename)
        # Visualization of the results of a detection.

        vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,
          output_dict['detection_boxes'],
          output_dict['detection_classes'],
          output_dict['detection_scores'],
          category_index,
          instance_masks=output_dict.get('detection_masks'),
          use_normalized_coordinates=True,
          line_thickness=8)
        prediction_dict[filename] = output_dict

        tic = time.time()
        print('{0} saved in {1:.2f}sec'.format(filename, tic-toc))
    end_time = time.time()



def run_inference_for_single_image(image, graph, filename):
  with graph.as_default():
    with tf.Session() as sess:
      # Get handles to input and output tensors
      ops = tf.get_default_graph().get_operations()
      all_tensor_names = {output.name for op in ops for output in op.outputs}
      tensor_dict = {}
      for key in [
          'num_detections', 'detection_boxes', 'detection_scores',
          'detection_classes', 'detection_masks'
      ]:
        tensor_name = key + ':0'
        if tensor_name in all_tensor_names:
          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
              tensor_name)
      if 'detection_masks' in tensor_dict:
        # The following processing is only for single image
        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
            detection_masks, detection_boxes, image.shape[0], image.shape[1])
        detection_masks_reframed = tf.cast(
            tf.greater(detection_masks_reframed, 0.5), tf.uint8)
        # Follow the convention by adding back the batch dimension
        tensor_dict['detection_masks'] = tf.expand_dims(
            detection_masks_reframed, 0)
      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

      # Run inference
      output_dict = sess.run(tensor_dict,
                             feed_dict={image_tensor: np.expand_dims(image, 0)})

      # all outputs are float32 numpy arrays, so convert types as appropriate
      output_dict['filename'] = filename
      output_dict['num_detections'] = int(output_dict['num_detections'][0])
      output_dict['detection_classes'] = output_dict[
          'detection_classes'][0].astype(np.uint8)
      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
      output_dict['detection_scores'] = output_dict['detection_scores'][0]
      if 'detection_masks' in output_dict:
        output_dict['detection_masks'] = output_dict['detection_masks'][0]
  return output_dict

---

如果在同一个模型上进行多个预测，则可以通过加载一次检测图并仅为每个预测运行会话来缩短预测时间。第一次预测需要时间，但后续预测需要的时间较少。

---

但如果每次预测都使用不同的模型，我就找不到任何出路

您应该为每个操作计时，以找到瓶颈。我想这是开场白，我会试试的。谢谢。嗨，@dsBoulder我也面临这个问题，GPU上的预测大约需要25-30秒。你找到解决办法了吗？。如果是，你能把它作为答案发布吗？我在github页面上读到了同样的内容，似乎是图像有问题。打开I/O操作，使用opencv可以显著减少这一时间。使用tf服务器，我能够使用这个GPU在0.16秒内完成推理。