Tensorflow object_detection: unable to find input and output tensors - tensorflow

Question

I've successfully trained and saved a faster RCNN model for tensorflow using their object detection API. I'm now trying to run some inferences on the code, taking bits of code from this tutorial.
However, after I successfully restore the metagraph and the checkpoint, the system can't find the input and output nodes, I get the following error:
KeyError: "The name 'image_tensor:0' refers to a Tensor which does not
exist. The operation, 'image_tensor', does not exist in the graph."
The checkpoint and metagraph were created by the train.py script, on my own data, following the instructions given here.
This is my code:
OUTPUT_DIR = "my_path/models/SSD_v1/train"
CKPT_DIR = OUTPUT_DIR
LATEST_CKPT_FILENAME = "checkpoint"
LAST_CKPT_FILE = os.path.join(CKPT_DIR, LATEST_CKPT_FILENAME)
MODEL_FILENAME_PATH = os.path.join(OUTPUT_DIR, "model.ckpt.meta")
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
def test_model(images_list, path_to_ckpt=None,
meta_graph=None):
if path_to_ckpt is None:
path_to_ckpt = tf.train.latest_checkpoint(CKPT_DIR, LATEST_CKPT_FILENAME)
if meta_graph is None:
meta_graph = MODEL_FILENAME_PATH
print("test_model launched")
tf.reset_default_graph()
detection_graph = tf.Graph()
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
# Restore graph
saver = tf.train.import_meta_graph(meta_graph, clear_devices=True)
print('metagraph restored')
saver.restore(sess, path_to_ckpt)
print('graph restored')
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') # This is where the error happens
# Each box represents a part of the image where a particular object was detected.
detected_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detected_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detected_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = graph.get_tensor_by_name('num_detections:0')
print("Output tensors: ")
print(detected_boxes)
print(detected_scores)
print(detected_classes)
print('')
for i, image in enumerate(images_list):
detected_boxes, detected_scores, detected_classes, num_detect = sess.run([detected_boxes, detected_scores, detected_classes, num_detections],
feed_dict={image_tensor: image})
print(i, num_detect, detected_boxes, detected_scores, detected_classes)
def main():
directory_path = "../data/samples/"
image_files = [f for f in os.listdir(directory_path) if os.path.isfile(os.path.join(directory_path, f))]
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_list = [ np.expand_dims(load_image_into_numpy_array(Image.open(os.path.join(directory_path, f))), axis=0) for f in image_files]
test_model(images_list=image_list)
if __name__=="__main__":
main()
Full error stacktrace:
Traceback (most recent call last): File "/home/guillaumedelaboulaye/PR8210PANO/faster-rcnn/pano_faster_rcnn/src/run_faster_rcnn_inference.py", line 99, in <module>
main() File "/home/guillaumedelaboulaye/PR8210PANO/faster-rcnn/pano_faster_rcnn/src/run_faster_rcnn_inference.py", line 95, in main
test_model(images_list=image_list) File "/home/guillaumedelaboulaye/PR8210PANO/faster-rcnn/pano_faster_rcnn/src/run_faster_rcnn_inference.py", line 48, in test_model
image_tensor = graph.get_tensor_by_name('image_tensor:0') File "/home/guillaumedelaboulaye/PR8210PANO/faster-rcnn/venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2733, in get_tensor_by_name
return self.as_graph_element(name, allow_tensor=True, allow_operation=False) File "/home/guillaumedelaboulaye/PR8210PANO/faster-rcnn/venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2584, in as_graph_element
return self._as_graph_element_locked(obj, allow_tensor, allow_operation) File "/home/guillaumedelaboulaye/PR8210PANO/faster-rcnn/venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2626, in _as_graph_element_locked
"graph." % (repr(name), repr(op_name))) KeyError: "The name 'image_tensor:0' refers to a Tensor which does not exist. The operation, 'image_tensor', does not exist in the graph."

Answer 1

In the train graph, the input/output nodes are not given those names. What you will need to do is to "export" your trained model via the export_inference_graph.py tool. I believe it currently exports it to a frozen graph or a SavedModel, but in future releases, it will export to ordinary checkpoint as well.

Answer 2

If you want sample code for finding the node names of the graph, referring to the object_detection_tutorial.ipynb, after the "Load a (frozen) Tensorflow model into memory." block:
for node in od_graph_def.node:
print node.name
That should list all the node names that you can then enter in the subsequent blocks.