How to keep tensorflow session open between predictions? Loading from SavedModel

Question

I trained a tensorflow model that i'd like to run predictions on from numpy arrays. This is for image processing within videos. I will pass the images to the model as they happen. Not every frame is passed.

I reload my SavedModel within a session like so

def run(self):                
    with tf.Session(graph=tf.Graph()) as sess:
        tf.saved_model.loader.load(sess,
                    [tf.saved_model.tag_constants.SERVING], "model")

My code works perfectly if I pass a list of images (self.tfimages) to the prediction. Condensed to:

    softmax_tensor = sess.graph.get_tensor_by_name('final_ops/softmax:0')
    predictions = sess.run(softmax_tensor, {'Placeholder:0': self.tfimages})

But i won't have all the images at once. Do I really have to reload the model from file each time (takes 2+ minutes).

I thought to do something like this

class tensorflow_model:
def __init__(self):                
    with tf.Session(graph=tf.Graph()) as self.sess:
        tf.saved_model.loader.load(self.sess,
                    [tf.saved_model.tag_constants.SERVING], "model")
def predict(self):

        # Feed the image_data as input to the graph and get first prediction
        softmax_tensor = self.sess.graph.get_tensor_by_name('final_ops/softmax:0')

        predictions = self.sess.run(softmax_tensor, {'Placeholder:0': self.tfimages})

but that yields

builtins.RuntimeError: Attempted to use a closed Session

Is there a way to keep a session open, or perhaps load SavedModel independent of a session?

EDIT I tried the first answer to create a session in two steps:

sess=tf.Session(graph=tf.Graph())
sess
<tensorflow.python.client.session.Session object at 0x0000021ACBB62EF0>
tf.saved_model.loader.load(sess,[tf.saved_model.tag_constants.SERVING], "model")
Traceback (most recent call last):
  Debug Probe, prompt 138, line 1
  File "C:\Program Files\Python35\Lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 222, in load
    saver.restore(sess, variables_path)
  File "C:\Program Files\Python35\Lib\site-packages\tensorflow\python\training\saver.py", line 1428, in restore
    {self.saver_def.filename_tensor_name: save_path})
  File "C:\Program Files\Python35\Lib\site-packages\tensorflow\python\client\session.py", line 774, in run
    run_metadata_ptr)
  File "C:\Program Files\Python35\Lib\site-packages\tensorflow\python\client\session.py", line 905, in _run
    raise RuntimeError('The Session graph is empty.  Add operations to the '
builtins.RuntimeError: The Session graph is empty.  Add operations to the graph before calling run().

Whereas

with tf.Session(graph=tf.Graph()) as sess:
    tf.saved_model.loader.load(sess,[tf.saved_model.tag_constants.SERVING], "model")

executes without error.

As for the second idea of passing sess as a variable to class, which is a good one. This works:

with tf.Session(graph=tf.Graph()) as sess:
    tf.saved_model.loader.load(sess,[tf.saved_model.tag_constants.SERVING], "model")
    tensorflow_instance=tensorflow(read_from="file")
    tensorflow_instance.predict(sess)

But this doesn't

sess=tf.Session(graph=tf.Graph())
tf.saved_model.loader.load(sess,[tf.saved_model.tag_constants.SERVING], "model")
tensorflow_instance=tensorflow(read_from="file")
tensorflow_instance.predict(sess)

It would be pretty awkward to wrap my program into the with as sess statement.

Full code:

import tensorflow as tf
import sys
from google.protobuf import text_format
from tensorflow.core.framework import graph_pb2
import os
import glob

class tensorflow:    

def __init__(self,read_from):

    #frames to be analyzed
    self.tfimages=[]    

    find_photos=glob.glob("*.jpg")

    # Read in the image_data
    if read_from=="file":
        for x in find_photos:
            image_data = tf.gfile.FastGFile(x, 'rb').read()    
            self.tfimages.append(image_data)

    # Loads label file, strips off carriage return
    self.label_lines = [line.rstrip() for line in tf.gfile.GFile("dict.txt")]

def predict(self,sess):

    # Feed the image_data as input to the graph and get first prediction
    softmax_tensor = sess.graph.get_tensor_by_name('final_ops/softmax:0')

    predictions = sess.run(softmax_tensor, {'Placeholder:0': self.tfimages})
    for prediction in predictions:
        # Sort to show labels of first prediction in order of confidence
        top_k = prediction.argsort()[-len(prediction):][::-1]

        for node_id in top_k:
            human_string = self.label_lines[node_id]
            score = prediction[node_id]
            print('%s (score = %.5f)' % (human_string, score))
        return(human_string)

if __name__ == "__main__":
    with tf.Session(graph=tf.Graph()) as sess:
        tf.saved_model.loader.load(sess,[tf.saved_model.tag_constants.SERVING], "model")
        tensorflow_instance=tensorflow(read_from="file")
        tensorflow_instance.predict(sess)

    sess=tf.Session(graph=tf.Graph())
    tf.saved_model.loader.load(sess,[tf.saved_model.tag_constants.SERVING], "model")
    tensorflow_instance=tensorflow(read_from="file")
    tensorflow_instance.predict(sess)

Answer 1

Others have explained why you can't put your session in a with statement in the constructor.

The reason you see different behavior when using the context manager vs. not is because tf.saved_model.loader.load has some weird interactions between the default graph and the graph that is part of the session.

The solution is simple; don't pass a graph to session if you're not using it in a with block:

sess=tf.Session()
tf.saved_model.loader.load(sess,[tf.saved_model.tag_constants.SERVING], "model")

Here's some example code for a class to do predictions:

class Model(object):

  def __init__(self, model_path):
    # Note, if you don't want to leak this, you'll want to turn Model into
    # a context manager. In practice, you probably don't have to worry
    # about it.
    self.session = tf.Session()

    tf.saved_model.loader.load(
        self.session,
        [tf.saved_model.tag_constants.SERVING],
        model_path)

    self.softmax_tensor = self.session.graph.get_tensor_by_name('final_ops/softmax:0')

  def predict(self, images):
    predictions = self.session.run(self.softmax, {'Placeholder:0': images})
    # TODO: convert to human-friendly labels
    return predictions


images = [tf.gfile.FastGFile(f, 'rb').read() for f in glob.glob("*.jpg")]
model = Model('model_path')
print(model.predict(images))

# Alternatively (uses less memory, but has lower throughput):
for f in glob.glob("*.jpg"):
  print(model.predict([tf.gfile.FastGFile(f, 'rb').read()]))

Answer 2

Your code creates a scope which is exited after it leaves init.

def __init__(self): 
  with tf.Session(graph=tf.Graph()) as self.sess:
    tf.saved_model.loader.load(self.sess[tf.saved_model.tag_constants.SERVING], "model")

The following should work for you if you have everything else working properly.

def __init__(self):   
  self.sess=tf.Session(graph=tf.Graph())
  tf.saved_model.loader.load(self.sess[tf.saved_model.tag_constants.SERVING], "model")

When I do something like this I also usually create the option of passing the session to the class by a parameter, then when I call the class I pass in a session create by with