title: Tensorflow object detection API tutorial error
body: <p>After struggling with compatibility issues between Tensorflow 2.00 and the object detection API, I downgraded to Tensorflow 1.15 to be able to train my own model. after completing the training I modified the jupyter notebook included in the <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb" rel="nofollow noreferrer">Tensorflow object detection API</a> repo to test on my own images but I keep getting this error:</p>
  
  <p><code>Traceback (most recent call last):
    File "object_detection_tutorial_converted.py", line 254, in &lt;module&gt;
      show_inference(detection_model, image_path)
    File "object_detection_tutorial_converted.py", line 235, in show_inference
      output_dict = run_inference_for_single_image(model, image_np)
    File "object_detection_tutorial_converted.py", line 203, in run_inference_for_single_image
      num_detections = int(output_dict.pop('num_detections'))
  TypeError: int() argument must be a string, a bytes-like object or a number, not 'Tensor'</code></p>
  
  <p>Here's my modified jupyter notebook</p>
  
  <pre><code>import os
  import pathlib
  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):
    model_dir = pathlib.Path(model_name)/"saved_model"
    model = model = tf.compat.v2.saved_model.load(str(model_dir), None)
    model = model.signatures['serving_default']
    return model
  
  
  # List of the strings that is used to add correct label for each box.
  PATH_TO_LABELS = 'training/label_map.pbtxt'
  category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
  
  
  TEST_IMAGE_PATHS.
  PATH_TO_TEST_IMAGES_DIR = pathlib.Path('test_images')
  TEST_IMAGE_PATHS = sorted(list(PATH_TO_TEST_IMAGES_DIR.glob("*.jpg")))
  TEST_IMAGE_PATHS
  
  
  
  model_name = 'devices_graph'
  detection_model = load_model(model_name)
  
  
  print(detection_model.inputs)
  
  
  detection_model.output_dtypes
  
  
  detection_model.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
    output_dict = model(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
  
  
  # Run it on each test image and show the results:
  
  
  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)
  </code></pre>