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
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A. Unique TensorFlower
TensorFlower Gardener
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# Description:
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# Transfer learning example for TensorFlow.
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licenses(["notice"]) # Apache 2.0
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exports_files(["LICENSE"])
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load("//tensorflow:tensorflow.bzl", "py_test")
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py_binary(
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name = "retrain",
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srcs = [
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"retrain.py",
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],
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srcs_version = "PY2AND3",
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visibility = ["//tensorflow:__subpackages__"],
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deps = [
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"//tensorflow:tensorflow_py",
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"//tensorflow/python:framework",
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"//tensorflow/python:framework_for_generated_wrappers",
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"//tensorflow/python:graph_util",
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"//tensorflow/python:platform",
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"//tensorflow/python:util",
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"//third_party/py/numpy",
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],
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)
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py_test(
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name = "retrain_test",
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size = "small",
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srcs = [
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"retrain.py",
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"retrain_test.py",
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],
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data = [
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":data/labels.txt",
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"//tensorflow/examples/label_image:data/grace_hopper.jpg",
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],
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srcs_version = "PY2AND3",
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deps = [
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":retrain",
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"//tensorflow:tensorflow_py",
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"//tensorflow/python:framework_test_lib",
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"//tensorflow/python:graph_util",
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"//tensorflow/python:platform",
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"//tensorflow/python:platform_test",
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"//tensorflow/python:tensor_shape",
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"//tensorflow/python:util",
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"//third_party/py/numpy",
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],
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)
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@ -1,12 +1,15 @@
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**NOTE: This code has moved to**
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https://github.com/tensorflow/hub/tree/master/examples/image_retraining
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retrain.py is an example script that shows how one can adapt a pretrained
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network for other classification problems. A detailed overview of this script
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can be found at:
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https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0
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The script also shows how one can train layers
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with quantized weights and activations instead of taking a pre-trained floating
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point model and then quantizing weights and activations.
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The output graphdef produced by this script is compatible with the TensorFlow
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Lite Optimizing Converter and can be converted to TFLite format.
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network for other classification problems (including use with TFLite and
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quantization).
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As of TensorFlow 1.7, it is recommended to use a pretrained network from
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TensorFlow Hub, using the new version of this example found in the location
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above, as explained in TensorFlow's revised [image retraining
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tutorial](https://www.tensorflow.org/tutorials/image_retraining).
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Older versions of this example (using frozen GraphDefs instead of
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TensorFlow Hub modules) are available in the release branches of
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TensorFlow versions up to and including 1.7.
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Runner-up
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Winner
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Loser
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File diff suppressed because it is too large
Load Diff
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# pylint: disable=g-bad-import-order,unused-import
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"""Tests the graph freezing tool."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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import os
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from tensorflow.examples.image_retraining import retrain
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from tensorflow.python.framework import test_util
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class ImageRetrainingTest(test_util.TensorFlowTestCase):
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def dummyImageLists(self):
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return {'label_one': {'dir': 'somedir', 'training': ['image_one.jpg',
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'image_two.jpg'],
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'testing': ['image_three.jpg', 'image_four.jpg'],
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'validation': ['image_five.jpg', 'image_six.jpg']},
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'label_two': {'dir': 'otherdir', 'training': ['image_one.jpg',
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'image_two.jpg'],
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'testing': ['image_three.jpg', 'image_four.jpg'],
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'validation': ['image_five.jpg', 'image_six.jpg']}}
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def testGetImagePath(self):
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image_lists = self.dummyImageLists()
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self.assertEqual('image_dir/somedir/image_one.jpg', retrain.get_image_path(
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image_lists, 'label_one', 0, 'image_dir', 'training'))
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self.assertEqual('image_dir/otherdir/image_four.jpg',
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retrain.get_image_path(image_lists, 'label_two', 1,
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'image_dir', 'testing'))
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def testGetBottleneckPath(self):
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image_lists = self.dummyImageLists()
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self.assertEqual('bottleneck_dir/somedir/image_five.jpg_imagenet_v3.txt',
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retrain.get_bottleneck_path(
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image_lists, 'label_one', 0, 'bottleneck_dir',
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'validation', 'imagenet_v3'))
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def testShouldDistortImage(self):
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self.assertEqual(False, retrain.should_distort_images(False, 0, 0, 0))
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self.assertEqual(True, retrain.should_distort_images(True, 0, 0, 0))
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self.assertEqual(True, retrain.should_distort_images(False, 10, 0, 0))
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self.assertEqual(True, retrain.should_distort_images(False, 0, 1, 0))
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self.assertEqual(True, retrain.should_distort_images(False, 0, 0, 50))
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def testAddInputDistortions(self):
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with tf.Graph().as_default():
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with tf.Session() as sess:
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retrain.add_input_distortions(True, 10, 10, 10, 299, 299, 3, 128, 128)
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self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortJPGInput:0'))
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self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortResult:0'))
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@tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01)
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def testAddFinalRetrainOps(self, flags_mock):
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with tf.Graph().as_default():
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with tf.Session() as sess:
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bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck')
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# Test creating final training op with quantization.
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retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, False,
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False)
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self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
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@tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01)
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def testAddFinalRetrainOpsQuantized(self, flags_mock):
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# Ensure that the training and eval graph for quantized models are correctly
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# created.
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with tf.Graph().as_default() as g:
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with tf.Session() as sess:
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bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck')
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# Test creating final training op with quantization, set is_training to
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# true.
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retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, True)
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self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
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found_fake_quant = 0
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for op in g.get_operations():
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if op.type == 'FakeQuantWithMinMaxVars':
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found_fake_quant += 1
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# Ensure that the inputs of each FakeQuant operations has 2 Assign
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# operations in the training graph (Assign[Min,Max]Last,
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# Assign[Min,Max]Ema)
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self.assertEqual(2,
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len([i for i in op.inputs if 'Assign' in i.name]))
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self.assertEqual(found_fake_quant, 2)
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with tf.Graph().as_default() as g:
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with tf.Session() as sess:
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bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck')
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# Test creating final training op with quantization, set is_training to
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# false.
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retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, False)
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self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
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found_fake_quant = 0
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for op in g.get_operations():
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if op.type == 'FakeQuantWithMinMaxVars':
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found_fake_quant += 1
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for i in op.inputs:
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# Ensure that no operations are Assign operation since this is the
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# evaluation graph.
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self.assertTrue('Assign' not in i.name)
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self.assertEqual(found_fake_quant, 2)
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def testAddEvaluationStep(self):
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with tf.Graph().as_default():
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final = tf.placeholder(tf.float32, [1], name='final')
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gt = tf.placeholder(tf.int64, [1], name='gt')
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self.assertIsNotNone(retrain.add_evaluation_step(final, gt))
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def testAddJpegDecoding(self):
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with tf.Graph().as_default():
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jpeg_data, mul_image = retrain.add_jpeg_decoding(10, 10, 3, 0, 255)
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self.assertIsNotNone(jpeg_data)
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self.assertIsNotNone(mul_image)
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def testCreateModelInfo(self):
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did_raise_value_error = False
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try:
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retrain.create_model_info('no_such_model_name')
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except ValueError:
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did_raise_value_error = True
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self.assertTrue(did_raise_value_error)
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model_info = retrain.create_model_info('inception_v3')
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self.assertIsNotNone(model_info)
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self.assertEqual(299, model_info['input_width'])
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def testCreateModelInfoQuantized(self):
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# Test for mobilenet_quantized
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model_info = retrain.create_model_info('mobilenet_1.0_224')
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self.assertIsNotNone(model_info)
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self.assertEqual(224, model_info['input_width'])
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if __name__ == '__main__':
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tf.test.main()
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