title: 'AutoTrackable' object has no attribute 'output_shapes'
body: <p>I use python3.6 and tensorflow 2.3.0</p>
  <p>I want playing object_detection DEMO</p>
  <p>but last &quot;masking_model.output_shapes&quot; is make error
  I don't know
  How to change python code...?</p>
  <pre><code>!pip3 install -U --pre tensorflow==&quot;2.*&quot;
  !pip3 install tf_slim
  !pip install pycocotools
  
  import os
  import pathlib
  
  if &quot;models&quot; in pathlib.Path.cwd().parts:
    while &quot;models&quot; in pathlib.Path.cwd().parts:
      os.chdir('..')
  elif not pathlib.Path('models').exists():
    !git clone --depth 1 https://github.com/tensorflow/models
  
  %%bash
  cd models/research/
  protoc object_detection/protos/*.proto --python_out=.
  
  %%bash 
  cd models/researchls
  pip install .
  
  import numpy as np
  import os
  import six.moves.urllib as urllib
  import sys
  import tarfile
  import tensorflow as tf
  import zipfile
  
  from collections import defaultdict
  from io import StringIO
  from matplotlib import pyplot as plt
  from PIL import Image
  from IPython.display import display
  
  from object_detection.utils import ops as utils_ops
  from object_detection.utils import label_map_util
  from object_detection.utils import visualization_utils as vis_util
  
  # patch tf1 into `utils.ops`
  utils_ops.tf = tf.compat.v1
  
  # Patch the location of gfile
  tf.gfile = tf.io.gfile
  
  def load_model(model_name):
    base_url = 'http://download.tensorflow.org/models/object_detection/'
    model_file = model_name + '.tar.gz'
    model_dir = tf.keras.utils.get_file(
      fname=model_name, 
      origin=base_url + model_file,
      untar=True)
  
    model_dir = pathlib.Path(model_dir)/&quot;saved_model&quot;
  
    model = tf.saved_model.load(str(model_dir))
  
    return model
  
  # List of the strings that is used to add correct label for each box.
  PATH_TO_LABELS = 'models/research/object_detection/data/mscoco_label_map.pbtxt'
  category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
  
  # If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
  PATH_TO_TEST_IMAGES_DIR = pathlib.Path('models/research/object_detection/test_images')
  TEST_IMAGE_PATHS = sorted(list(PATH_TO_TEST_IMAGES_DIR.glob(&quot;*.jpg&quot;)))
  TEST_IMAGE_PATHS
  
  model_name = 'ssd_mobilenet_v1_coco_2017_11_17'
  detection_model = load_model(model_name)
  
  print(detection_model.signatures['serving_default'].inputs)
  
  detection_model.signatures['serving_default'].output_dtypes
  
  detection_model.signatures['serving_default'].output_shapes
  def run_inference_for_single_image(model, image):
    image = np.asarray(image)
    # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
    input_tensor = tf.convert_to_tensor(image)
    # The model expects a batch of images, so add an axis with `tf.newaxis`.
    input_tensor = input_tensor[tf.newaxis,...]
  
    # Run inference
    model_fn = model.signatures['serving_default']
    output_dict = model_fn(input_tensor)
  
    # All outputs are batches tensors.
    # Convert to numpy arrays, and take index [0] to remove the batch dimension.
    # We're only interested in the first num_detections.
    num_detections = int(output_dict.pop('num_detections'))
    output_dict = {key:value[0, :num_detections].numpy() 
                   for key,value in output_dict.items()}
    output_dict['num_detections'] = num_detections
  
    # detection_classes should be ints.
    output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64)
     
    # Handle models with masks:
    if 'detection_masks' in output_dict:
      # Reframe the the bbox mask to the image size.
      detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
                output_dict['detection_masks'], output_dict['detection_boxes'],
                 image.shape[0], image.shape[1])      
      detection_masks_reframed = tf.cast(detection_masks_reframed &gt; 0.5,
                                         tf.uint8)
      output_dict['detection_masks_reframed'] = detection_masks_reframed.numpy()
      
    return output_dict
  
  def show_inference(model, 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 = np.array(Image.open(image_path))
    # Actual detection.
    output_dict = run_inference_for_single_image(model, image_np)
    # 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_reframed', None),
        use_normalized_coordinates=True,
        line_thickness=8)
  
    display(Image.fromarray(image_np))
  
  for image_path in TEST_IMAGE_PATHS:
    show_inference(detection_model, image_path)
  
  model_name = &quot;mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28&quot;
  masking_model = load_model(model_name)
  </code></pre>
  <h1>It went so well so far.</h1>
  <p>masking_model.output_shape</p>
  <hr />
  <p><strong>AttributeError</strong>                            Traceback (most recent call last)
   in 
  ----&gt; 1 masking_model.output_shapes</p>
  <p>AttributeError: 'AutoTrackable' object has no attribute 'output_shapes'</p>
  <p>#I don't know what the'AutoTrackable' object has no attribute'output_shapes' should fix.
  #plz help me....</p>