CODEWITHSUNDEEP
tensorflow
Branton Davis
Branton Davis

Reputation: 433

How can I load a saved model from object detection for inference?

I'm pretty new to Tensorflow and have been running experiments with SSDs with the Tensorflow Object Detection API. I can successfully train a model, but by default, it only save the last n checkpoints. I'd like to instead save the last n checkpoints with the lowest loss (I'm assuming that's the best metric to use).

I found tf.estimator.BestExporter and it exports a saved_model.pb along with variables. However, I have yet to figure out how to load that saved model and run inference on it. After running models/research/object_detection/export_inference_graph.py on the checkpoiont, I can easily load a checkpoint and run inference on it using the object detection jupyter notebook: https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb

I've found documentation on loading saved models, and can load a graph like this:

with tf.Session(graph=tf.Graph()) as sess:
        tags = [tag_constants.SERVING]
        meta_graph = tf.saved_model.loader.load(sess, tags, PATH_TO_SAVED_MODEL)
        detection_graph = tf.get_default_graph()

However, when I use that graph with the above jupyter notebook, I get errors:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-17-9e48f0d04df2> in <module>
      7   image_np_expanded = np.expand_dims(image_np, axis=0)
      8   # Actual detection.
----> 9   output_dict = run_inference_for_single_image(image_np, detection_graph)
     10   # Visualization of the results of a detection.
     11   vis_util.visualize_boxes_and_labels_on_image_array(

<ipython-input-16-0df86999596e> in run_inference_for_single_image(image, graph)
     31             detection_masks_reframed, 0)
     32 
---> 33       image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
     34       # image_tensor = tf.get_default_graph().get_tensor_by_name('serialized_example')
     35 

~/anaconda3/envs/sb/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in get_tensor_by_name(self, name)
   3664       raise TypeError("Tensor names are strings (or similar), not %s." %
   3665                       type(name).__name__)
-> 3666     return self.as_graph_element(name, allow_tensor=True, allow_operation=False)
   3667 
   3668   def _get_tensor_by_tf_output(self, tf_output):

~/anaconda3/envs/sb/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in as_graph_element(self, obj, allow_tensor, allow_operation)
   3488 
   3489     with self._lock:
-> 3490       return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
   3491 
   3492   def _as_graph_element_locked(self, obj, allow_tensor, allow_operation):

~/anaconda3/envs/sb/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in _as_graph_element_locked(self, obj, allow_tensor, allow_operation)
   3530           raise KeyError("The name %s refers to a Tensor which does not "
   3531                          "exist. The operation, %s, does not exist in the "
-> 3532                          "graph." % (repr(name), repr(op_name)))
   3533         try:
   3534           return op.outputs[out_n]

KeyError: "The name 'image_tensor:0' refers to a Tensor which does not exist. The operation, 'image_tensor', does not exist in the graph."

Is there a better way to load the saved model or convert it to an inference graph?

Thanks!

Upvotes: 4

Views: 5579

Answers (1)

Dmitrii Rashchenko
Dmitrii Rashchenko

Reputation: 198

Tensorflow detection API supports different input formats during exporting as discribed in documentation of file export_inference_graph.py:

  • image_tensor: Accepts a uint8 4-D tensor of shape [None, None, None, 3]
  • encoded_image_string_tensor: Accepts a 1-D string tensor of shape [None] containing encoded PNG or JPEG images. Image resolutions are expected to be the same if more than 1 image is provided.
  • tf_example: Accepts a 1-D string tensor of shape [None] containing serialized TFExample protos. Image resolutions are expected to be the same if more than 1 image is provided.

So you should check that you use image_tensor input_type. The chosen input node will be named as "inputs" in exported model. So I suppose that replacing image_tensor:0 with inputs (or maybe inputs:0) will solve your problem.

Also I would like to recommend a useful tool to run exported models with several lines of code: tf.contrib.predictor.from_saved_model. Here is example of how to use it:

import tensorflow as tf
import cv2

img = cv2.imread("test.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_rgb = np.expand_dims(img, 0)

predict_fn = tf.contrib.predictor.from_saved_model("./saved_model")
output_data = predict_fn({"inputs": img_rgb})
print(output_data)  # detector output dictionary

Upvotes: 5

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