Replace examples/image_retraining by a pointer to TensorFlow Hub.

https://github.com/tensorflow/hub/tree/master/examples/image_retraining
has the same tool, upgraded to use TensorFlow Hub instead of raw graph defs.

PiperOrigin-RevId: 192502469

tensorflow/examples/image_retraining/BUILD

@@ -1,51 +0,0 @@
-# Description:
-# Transfer learning example for TensorFlow.
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-load("//tensorflow:tensorflow.bzl", "py_test")
-
-py_binary(
-    name = "retrain",
-    srcs = [
-        "retrain.py",
-    ],
-    srcs_version = "PY2AND3",
-    visibility = ["//tensorflow:__subpackages__"],
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:graph_util",
-        "//tensorflow/python:platform",
-        "//tensorflow/python:util",
-        "//third_party/py/numpy",
-    ],
-)
-
-py_test(
-    name = "retrain_test",
-    size = "small",
-    srcs = [
-        "retrain.py",
-        "retrain_test.py",
-    ],
-    data = [
-        ":data/labels.txt",
-        "//tensorflow/examples/label_image:data/grace_hopper.jpg",
-    ],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":retrain",
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:graph_util",
-        "//tensorflow/python:platform",
-        "//tensorflow/python:platform_test",
-        "//tensorflow/python:tensor_shape",
-        "//tensorflow/python:util",
-        "//third_party/py/numpy",
-    ],
-)

tensorflow/examples/image_retraining/README.md

@@ -1,12 +1,15 @@
+**NOTE: This code has moved to**
+https://github.com/tensorflow/hub/tree/master/examples/image_retraining
+
 retrain.py is an example script that shows how one can adapt a pretrained
-network for other classification problems. A detailed overview of this script
+network for other classification problems (including use with TFLite and
-can be found at:
+quantization).
-https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0
-
-The script also shows how one can train layers
-with quantized weights and activations instead of taking a pre-trained floating
-point model and then quantizing weights and activations.
-The output graphdef produced by this script is compatible with the TensorFlow
-Lite Optimizing Converter and can be converted to TFLite format.
 
+As of TensorFlow 1.7, it is recommended to use a pretrained network from
+TensorFlow Hub, using the new version of this example found in the location
+above, as explained in TensorFlow's revised [image retraining
+tutorial](https://www.tensorflow.org/tutorials/image_retraining).
 
+Older versions of this example (using frozen GraphDefs instead of
+TensorFlow Hub modules) are available in the release branches of
+TensorFlow versions up to and including 1.7.

tensorflow/examples/image_retraining/data/labels.txt

@@ -1,3 +0,0 @@
-Runner-up
-Winner
-Loser

tensorflow/examples/image_retraining/retrain_test.py

@@ -1,148 +0,0 @@
-# Copyright 2015 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.
-# ==============================================================================
-# pylint: disable=g-bad-import-order,unused-import
-"""Tests the graph freezing tool."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-import os
-
-from tensorflow.examples.image_retraining import retrain
-from tensorflow.python.framework import test_util
-
-
-class ImageRetrainingTest(test_util.TensorFlowTestCase):
-
-  def dummyImageLists(self):
-    return {'label_one': {'dir': 'somedir', 'training': ['image_one.jpg',
-                                                         'image_two.jpg'],
-                          'testing': ['image_three.jpg', 'image_four.jpg'],
-                          'validation': ['image_five.jpg', 'image_six.jpg']},
-            'label_two': {'dir': 'otherdir', 'training': ['image_one.jpg',
-                                                          'image_two.jpg'],
-                          'testing': ['image_three.jpg', 'image_four.jpg'],
-                          'validation': ['image_five.jpg', 'image_six.jpg']}}
-
-  def testGetImagePath(self):
-    image_lists = self.dummyImageLists()
-    self.assertEqual('image_dir/somedir/image_one.jpg', retrain.get_image_path(
-        image_lists, 'label_one', 0, 'image_dir', 'training'))
-    self.assertEqual('image_dir/otherdir/image_four.jpg',
-                     retrain.get_image_path(image_lists, 'label_two', 1,
-                                            'image_dir', 'testing'))
-
-  def testGetBottleneckPath(self):
-    image_lists = self.dummyImageLists()
-    self.assertEqual('bottleneck_dir/somedir/image_five.jpg_imagenet_v3.txt',
-                     retrain.get_bottleneck_path(
-                         image_lists, 'label_one', 0, 'bottleneck_dir',
-                         'validation', 'imagenet_v3'))
-
-  def testShouldDistortImage(self):
-    self.assertEqual(False, retrain.should_distort_images(False, 0, 0, 0))
-    self.assertEqual(True, retrain.should_distort_images(True, 0, 0, 0))
-    self.assertEqual(True, retrain.should_distort_images(False, 10, 0, 0))
-    self.assertEqual(True, retrain.should_distort_images(False, 0, 1, 0))
-    self.assertEqual(True, retrain.should_distort_images(False, 0, 0, 50))
-
-  def testAddInputDistortions(self):
-    with tf.Graph().as_default():
-      with tf.Session() as sess:
-        retrain.add_input_distortions(True, 10, 10, 10, 299, 299, 3, 128, 128)
-        self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortJPGInput:0'))
-        self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortResult:0'))
-
-  @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01)
-  def testAddFinalRetrainOps(self, flags_mock):
-    with tf.Graph().as_default():
-      with tf.Session() as sess:
-        bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck')
-        # Test creating final training op with quantization.
-        retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, False,
-                                      False)
-        self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
-
-  @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01)
-  def testAddFinalRetrainOpsQuantized(self, flags_mock):
-    # Ensure that the training and eval graph for quantized models are correctly
-    # created.
-    with tf.Graph().as_default() as g:
-      with tf.Session() as sess:
-        bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck')
-        # Test creating final training op with quantization, set is_training to
-        # true.
-        retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, True)
-        self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
-        found_fake_quant = 0
-        for op in g.get_operations():
-          if op.type == 'FakeQuantWithMinMaxVars':
-            found_fake_quant += 1
-            # Ensure that the inputs of each FakeQuant operations has 2 Assign
-            # operations in the training graph (Assign[Min,Max]Last,
-            # Assign[Min,Max]Ema)
-            self.assertEqual(2,
-                             len([i for i in op.inputs if 'Assign' in i.name]))
-        self.assertEqual(found_fake_quant, 2)
-    with tf.Graph().as_default() as g:
-      with tf.Session() as sess:
-        bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck')
-        # Test creating final training op with quantization, set is_training to
-        # false.
-        retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, False)
-        self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
-        found_fake_quant = 0
-        for op in g.get_operations():
-          if op.type == 'FakeQuantWithMinMaxVars':
-            found_fake_quant += 1
-            for i in op.inputs:
-              # Ensure that no operations are Assign operation since this is the
-              # evaluation graph.
-              self.assertTrue('Assign' not in i.name)
-        self.assertEqual(found_fake_quant, 2)
-
-  def testAddEvaluationStep(self):
-    with tf.Graph().as_default():
-      final = tf.placeholder(tf.float32, [1], name='final')
-      gt = tf.placeholder(tf.int64, [1], name='gt')
-      self.assertIsNotNone(retrain.add_evaluation_step(final, gt))
-
-  def testAddJpegDecoding(self):
-    with tf.Graph().as_default():
-      jpeg_data, mul_image = retrain.add_jpeg_decoding(10, 10, 3, 0, 255)
-      self.assertIsNotNone(jpeg_data)
-      self.assertIsNotNone(mul_image)
-
-  def testCreateModelInfo(self):
-    did_raise_value_error = False
-    try:
-      retrain.create_model_info('no_such_model_name')
-    except ValueError:
-      did_raise_value_error = True
-    self.assertTrue(did_raise_value_error)
-    model_info = retrain.create_model_info('inception_v3')
-    self.assertIsNotNone(model_info)
-    self.assertEqual(299, model_info['input_width'])
-
-  def testCreateModelInfoQuantized(self):
-    # Test for mobilenet_quantized
-    model_info = retrain.create_model_info('mobilenet_1.0_224')
-    self.assertIsNotNone(model_info)
-    self.assertEqual(224, model_info['input_width'])
-
-
-if __name__ == '__main__':
-  tf.test.main()