Delete duplicate label_image script.

The version in examples/label_image is more complete (with image size and normalization options), so it can be used with `mobilenets`.

Also: removed bazel from main tutorial instructions.
PiperOrigin-RevId: 174212674

3 files changed, 0 insertions, 192 deletions

diff --git a/tensorflow/examples/image_retraining/BUILD b/tensorflow/examples/image_retraining/BUILD
index c8c136ac14..9f9244a74c 100644
--- a/tensorflow/examples/image_retraining/BUILD
+++ b/tensorflow/examples/image_retraining/BUILD
@@ -25,23 +25,10 @@ py_binary(
     ],
 )
 
-py_binary(
-    name = "label_image",
-    srcs = [
-        "label_image.py",
-    ],
-    srcs_version = "PY2AND3",
-    visibility = ["//tensorflow:__subpackages__"],
-    deps = [
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
 py_test(
     name = "retrain_test",
     size = "small",
     srcs = [
-        "label_image.py",
         "retrain.py",
         "retrain_test.py",
     ],
@@ -51,7 +38,6 @@ py_test(
     ],
     srcs_version = "PY2AND3",
     deps = [
-        ":label_image",
         ":retrain",
         "//tensorflow:tensorflow_py",
         "//tensorflow/python:framework_test_lib",
diff --git a/tensorflow/examples/image_retraining/label_image.py b/tensorflow/examples/image_retraining/label_image.py
deleted file mode 100644
index de2713fc10..0000000000
--- a/tensorflow/examples/image_retraining/label_image.py
+++ /dev/null
@@ -1,147 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Simple image classification with Inception.
-
-Run image classification with your model.
-
-This script is usually used with retrain.py found in this same
-directory.
-
-This program creates a graph from a saved GraphDef protocol buffer,
-and runs inference on an input JPEG image. You are required
-to pass in the graph file and the txt file.
-
-It outputs human readable strings of the top 5 predictions along with
-their probabilities.
-
-Change the --image_file argument to any jpg image to compute a
-classification of that image.
-
-Example usage:
-python label_image.py --graph=retrained_graph.pb
-  --labels=retrained_labels.txt
-  --image=flower_photos/daisy/54377391_15648e8d18.jpg
-
-NOTE: To learn to use this file and retrain.py, please see:
-
-https://codelabs.developers.google.com/codelabs/tensorflow-for-poets
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import sys
-
-import tensorflow as tf
-
-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()
-
-
-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(filename, '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
-    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)
-
-  # load image
-  image_data = load_image(FLAGS.image)
-
-  # load labels
-  labels = load_labels(FLAGS.labels)
-
-  # load graph, which is stored in the default session
-  load_graph(FLAGS.graph)
-
-  run_graph(image_data, labels, FLAGS.input_layer, FLAGS.output_layer,
-            FLAGS.num_top_predictions)
-
-
-if __name__ == '__main__':
-  FLAGS, unparsed = parser.parse_known_args()
-  tf.app.run(main=main, argv=sys.argv[:1]+unparsed)
diff --git a/tensorflow/examples/image_retraining/retrain_test.py b/tensorflow/examples/image_retraining/retrain_test.py
index 467c15d0de..c342a17dd8 100644
--- a/tensorflow/examples/image_retraining/retrain_test.py
+++ b/tensorflow/examples/image_retraining/retrain_test.py
@@ -21,7 +21,6 @@ from __future__ import print_function
 import tensorflow as tf
 import os
 
-from tensorflow.examples.image_retraining import label_image
 from tensorflow.examples.image_retraining import retrain
 from tensorflow.python.framework import test_util
 
@@ -83,36 +82,6 @@ class ImageRetrainingTest(test_util.TensorFlowTestCase):
       gt = tf.placeholder(tf.float32, [1], name='gt')
       self.assertIsNotNone(retrain.add_evaluation_step(final, gt))
 
-  def testLabelImage(self):
-
-    image_filename = ('../label_image/data/grace_hopper.jpg')
-
-    # Load some default data
-    label_path = os.path.join(tf.resource_loader.get_data_files_path(),
-                              'data/labels.txt')
-    labels = label_image.load_labels(label_path)
-    self.assertEqual(len(labels), 3)
-
-    image_path = os.path.join(tf.resource_loader.get_data_files_path(),
-                              image_filename)
-
-    image = label_image.load_image(image_path)
-    self.assertEqual(len(image), 61306)
-
-    # Create trivial graph; note that the two nodes don't meet
-    with tf.Graph().as_default():
-      jpeg = tf.constant(image)
-      # Input node that doesn't lead anywhere.
-      tf.image.decode_jpeg(jpeg, name='DecodeJpeg')
-
-      # Output node, that always outputs a constant.
-      tf.constant([[10, 30, 5]], name='final')
-
-      # As label_image outputs via print, we assume that
-      # if it returns, everything is OK.
-      result = label_image.run_graph(image, labels, jpeg, 'final:0', 3)
-      self.assertEqual(result, 0)
-
   def testAddJpegDecoding(self):
     with tf.Graph().as_default():
       jpeg_data, mul_image = retrain.add_jpeg_decoding(10, 10, 3, 0, 255)