title: Tensorflow 1.9 / Object Detection: model_main.py only evaluates one image
body: <p>I've updated to Tensorflow 1.9 &amp; the latest master of the Object Detection API.  When running a training/evaluation session that worked fine previously (I think version 1.6), the training appears to proceed as expected, but I only get evaluation &amp; metrics for one image (the first).</p>
  
  <p>In Tensorboard the image is labeled 'Detections_Left_Groundtruth_Right'.  The evaluation step itself also happens extremely quickly, which leads me to believe this isn't just a Tensorboard issue.</p>
  
  <p>Looking in model_lib.py, I see some suspicious code (near line 349):</p>
  
  <pre><code>  eval_images = (
        features[fields.InputDataFields.original_image] if use_original_images
        else features[fields.InputDataFields.image])
    eval_dict = eval_util.result_dict_for_single_example(
        eval_images[0:1],
        features[inputs.HASH_KEY][0],
        detections,
        groundtruth,
        class_agnostic=class_agnostic,
        scale_to_absolute=True)
  </code></pre>
  
  <p>This reads to me like the evaluator is always running a single evaluation on the first image.  Has anyone seen and/or fixed this?  I will update if changing the above works.</p>