title: is there a version of the inference example of the Tensorflow Object detection API that can run on batches of images simultaneously?
body: <p>I have trained a faster rcnn model using the Tensorflow object detection API and am using this inference script with my frozen graph:</p>
  
  <p><a href="https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb" rel="noreferrer">https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb</a></p>
  
  <p>I intend to use it for object tracking in videos, but inference using this script is very slow since it only processes one image at a time instead of a batch of images. Is there any way to do inference on a batch of images at once ? The relevant inference function is here, I am wondering how to modify it to work with a stack of images</p>
  
  <pre><code>def run_inference_for_single_image(image, graph):
  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['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
  </code></pre>