Tensorflow Mobilenet_v1 inference script

label_image.py

from __future__ import division
from __future__ import absolute_import
from __future__ import print_function

import argparse
import sys
import PIL
from PIL import Image
import tensorflow as tf
import numpy as np
from numpy import array
'''
parser = argparse.ArgumentParser()
parser.add_argument(
    '--image', required=True, type=str, help='Absolute path to image file.')
parser.add_argument(
    '--num_top_predictions',
    type=int,
    default=5,
    help='Display this many predictions.')
parser.add_argument(
    '--graph',
    required=True,
    type=str,
    help='Absolute path to graph file (.pb)')
parser.add_argument(
    '--labels',
    required=True,
    type=str,
    help='Absolute path to labels file (.txt)')
parser.add_argument(
    '--output_layer',
    type=str,
    default='final_result:0',
    help='Name of the result operation')
parser.add_argument(
    '--input_layer',
    type=str,
    default='DecodeJpeg/contents:0',
    help='Name of the input operation')

'''
def load_image(filename):
  """Read in the image_data to be classified."""
  #return tf.gfile.FastGFile(filename, 'rb').read()
  

  im=Image.open(filename)
  image=np.asarray(im, dtype="float32")
  img=(image-128.0)/128.0
  
  img=array(img).reshape(1,224,224,3)
  return img

def load_labels(filename):
  """Read in labels, one label per line."""
  return [line.rstrip() for line in tf.gfile.GFile(filename)]


def load_graph(filename):
  """Unpersists graph from file as default graph."""
  with tf.gfile.FastGFile('/home/ubuntu/ML_GIT/TensorFlow/MobileNet_Multi_label/output_graph.pb', 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name='')


def run_graph(image_data, labels, input_layer_name, output_layer_name,
              num_top_predictions):


  with tf.Session() as sess:
    # Feed the image_data as input to the graph.
    #   predictions  will contain a two-dimensional array, where one
    #   dimension represents the input image count, and the other has
    #   predictions per class
    input_node = sess.graph.get_tensor_by_name('input:0')
    output_node = sess.graph.get_tensor_by_name('final_result:0')

    softmax_tensor = sess.graph.get_tensor_by_name(output_layer_name)
    predictions, = sess.run(softmax_tensor, {input_layer_name: image_data})
    
    # Sort to show labels in order of confidence
    top_k = predictions.argsort()[-num_top_predictions:][::-1]
    for node_id in top_k:
      human_string = labels[node_id]
      score = predictions[node_id]
      print('%s (score = %.5f)' % (human_string, score))

    return 0


def main(argv):
  """Runs inference on an image."""
  '''
  if argv[1:]:
    raise ValueError('Unused Command Line Args: %s' % argv[1:])

  if not tf.gfile.Exists(FLAGS.image):
    tf.logging.fatal('image file does not exist %s', FLAGS.image)

  if not tf.gfile.Exists(FLAGS.labels):
    tf.logging.fatal('labels file does not exist %s', FLAGS.labels)

  if not tf.gfile.Exists(FLAGS.graph):
    tf.logging.fatal('graph file does not exist %s', FLAGS.graph)
  '''
  input_layer='input:0'
  output_layer='final_result:0'
  num_top_predictions=5
  # load image
  image_data = load_image('path-to-image/9_56.JPG')

  # load labels
  labels = load_labels('path-to/labels.txt')

  # load graph, which is stored in the default session
  load_graph('path-to/output_graph.pb')

  run_graph(image_data, labels,input_layer,output_layer,
            num_top_predictions)


if __name__ == '__main__':
  #FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main)

This script is an update to the label_image script provide for inference at tensorflow/tensorflow/image_retraining/label_image.py.