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def get_graph_def_from_saved_model(saved_model_dir): 
  with tf.Session() as session:
    meta_graph_def = tf.saved_model.loader.load(
    session,
    tags=[tag_constants.SERVING],
    export_dir=saved_model_dir
  ) 
  return meta_graph_def.graph_def

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def convert_graph_def_to_saved_model(export_dir, graph_filepath):
  if tf.gfile.Exists(export_dir):
    tf.gfile.DeleteRecursively(export_dir)
  graph_def = get_graph_def_from_file(graph_filepath)
  with tf.Session(graph=tf.Graph()) as session:
    tf.import_graph_def(graph_def, name='')
    tf.saved_model.simple_save(
        session,
        export_dir,
        inputs={

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from tensorflow.tools.graph_transforms import TransformGraph

def get_graph_def_from_file(graph_filepath):
  with ops.Graph().as_default():
    with tf.gfile.GFile(graph_filepath, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      return graph_def

def optimize_graph(model_dir, graph_filename, transforms, output_node):

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def freeze_model(saved_model_dir, output_node_names, output_filename):
  output_graph_filename = os.path.join(saved_model_dir, output_filename)
  initializer_nodes = ''
  freeze_graph.freeze_graph(
      input_saved_model_dir=saved_model_dir,
      output_graph=output_graph_filename,
      saved_model_tags = tag_constants.SERVING,
      output_node_names=output_node_names,
      initializer_nodes=initializer_nodes,
      input_graph=None,

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def inference_tfserving(eval_data, batch=BATCH_SIZE, 
                        repeat=1000, signature='predict'):
  url = 'http://localhost:8501/v1/models/mnist_classifier:predict'

  instances = [[float(i) for i in list(eval_data[img])] for img in range(batch)]

  request_data = {'signature_name': signature, 
                  'instances': instances}
  time_start = datetime.utcnow() 
  for i in range(repeat):

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def get_size(model_dir, model_file='saved_model.pb'):
  model_file_path = os.path.join(model_dir, model_file)
  print(model_file_path, '')
  pb_size = os.path.getsize(model_file_path)
  variables_size = 0
  if os.path.exists(
      os.path.join(model_dir,'variables/variables.data-00000-of-00001')):
    variables_size = os.path.getsize(os.path.join(
        model_dir,'variables/variables.data-00000-of-00001'))
    variables_size += os.path.getsize(os.path.join(

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def describe_graph(graph_def, show_nodes=False):
  print('Input Feature Nodes: {}'.format(
      [node.name for node in graph_def.node if node.op=='Placeholder']))
  print('')
  print('Unused Nodes: {}'.format(
      [node.name for node in graph_def.node if 'unused'  in node.name]))
  print('')
  print('Output Nodes: {}'.format( 
      [node.name for node in graph_def.node if (

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def run_experiment(hparams, train_data, train_labels, run_config, create_estimator_fn=create_estimator):
  train_spec = tf.estimator.TrainSpec(
      input_fn = tf.estimator.inputs.numpy_input_fn(
          x={'input_image': train_data},
          y=train_labels,
          batch_size=hparams.batch_size,
          num_epochs=None,
          shuffle=True),
      max_steps=hparams.max_training_steps
  )

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from __future__ import print_function

import os
import numpy as np
from datetime import datetime
import sys

import tensorflow as tf
from tensorflow import data
from tensorflow.python.saved_model import tag_constants