Merge changes from github.

PiperOrigin-RevId: 177526301

1 files changed, 36 insertions, 30 deletions

diff --git a/tensorflow/contrib/lite/README.md b/tensorflow/contrib/lite/README.md
index 385ccf4680..c7464bcc9d 100644
--- a/tensorflow/contrib/lite/README.md
+++ b/tensorflow/contrib/lite/README.md
@@ -1,5 +1,5 @@
 # TensorFlow Lite
-TensorFlow Lite is TensorFlow’s lightweight solution for mobile and embedded devices. It enables low-latency inference of on-device machine learning models with a small binary size and fast performance supporting hardware acceleration.
+TensorFlow Lite is TensorFlow's lightweight solution for mobile and embedded devices. It enables low-latency inference of on-device machine learning models with a small binary size and fast performance supporting hardware acceleration.
 
 TensorFlow Lite uses many techniques for achieving low latency like optimizing the kernels for specific mobile apps, pre-fused activations, quantized kernels that allow smaller and faster (fixed-point math) models, and in the future, leverage specialized machine learning hardware to get the best possible performance for a particular model on a particular device.
 
@@ -20,18 +20,18 @@ In the demo app, inference is done using the TensorFlow Lite Java API. The demo
 The  fastest path to trying the demo, is to download the pre-built binary
 [TfLiteCameraDemo.apk](https://storage.googleapis.com/download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk)
 
-Once the apk is installed, click the app icon to start the app. The first-time the app is opened, the app asks for runtime permissions to access the device camera. The demo app opens the back-camera of the device and recognizes the objects in the camera’s field of view. At the bottom of the image (or at the left of the image if the device is in landscape mode), it shows the latency of classification and the top three objects classified.
+Once the apk is installed, click the app icon to start the app. The first-time the app is opened, the app asks for runtime permissions to access the device camera. The demo app opens the back-camera of the device and recognizes the objects in the camera's field of view. At the bottom of the image (or at the left of the image if the device is in landscape mode), it shows the latency of classification and the top three objects classified.
 
 ## Building in Android Studio using TensorFlow Lite AAR from JCenter
 The simplest way to compile the demo app, and try out changes to the project code is to use AndroidStudio.
 
  - Install the latest version of Android Studio 3 as specified [here](https://developer.android.com/studio/index.html).
  - Make sure the Android SDK version is greater than 26 and NDK version is greater than 14 (in the Android Studio Settings).
- - Import the tensorflow/contrib/lite/java/demo directory as a new Android Studio project.
+ - Import the `tensorflow/contrib/lite/java/demo` directory as a new Android Studio project.
  - Click through installing all the Gradle extensions it requests.
  - Download the quantized Mobilenet TensorFlow Lite model from [here](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip)
      - unzip and copy mobilenet_quant_v1_224.tflite to the assets directory:
-       tensorflow/contrib/lite/java/demo/app/src/main/assets/
+       `tensorflow/contrib/lite/java/demo/app/src/main/assets/`
  - Build and run the demo app
 
 ## Building TensorFlow Lite and the demo app from source
@@ -43,39 +43,45 @@ The simplest way to compile the demo app, and try out changes to the project cod
 ### Install Bazel
 If bazel is not installed on your system, install it now by following [these directions](https://bazel.build/versions/master/docs/install.html)
 
-NOTE: Bazel does not currently support building for Android on Windows. Full support for gradle/cmake builds is coming soon, but in the meantime Windows users should download the [prebuilt binary](https://storage.googleapis.com/download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk) instead.
+NOTE: Bazel does not fully support building Android on Windows yet. Full support for Gradle/CMake builds is coming soon, but in the meantime Windows users should download the [prebuilt binary](https://storage.googleapis.com/download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk) instead.
 
 ### Install Android NDK and SDK
 Bazel is the primary build system for TensorFlow. Bazel and the Android NDK and SDK must be installed on your system.
  - Install the latest version of Bazel as per the instructions on the [Bazel website](https://bazel.build/versions/master/docs/install.html)
- - The Android NDK is required to build the native (C/C++) TensorFlow code. The current recommended version is 14b, which may be found [here](https://developer.android.com/tools/revisions/build-tools.html).
- - The Android SDK and build tools may be obtained [here](https://developer.android.com/tools/revisions/build-tools.html), or alternatively as part of [Android Studio](https://developer.android.com/studio/index.html). Build tools API >= 23 is required to build the TensorFlow Android demo (though it will run on API >= 21 devices).
-
  - The Android NDK is required to build the native (C/C++) TensorFlow Lite code. The current recommended version is 14b, which can be found [here](https://developer.android.com/ndk/downloads/older_releases.html#ndk-14b-downloads).
-
  - The Android SDK and build tools may be obtained [here](https://developer.android.com/tools/revisions/build-tools.html), or alternatively as part of [Android Studio](https://developer.android.com/studio/index.html). Build tools API >= 23 is required to build the TF Android demo (though it will run on API >= 21 devices).
  - In the root of the TensorFlow repository update the `WORKSPACE` file with the `api_level` and location of the SDK and NDK. If you installed it with AndroidStudio the SDK path can be found in the SDK manager, and the default NDK path is:`{SDK path}/ndk-bundle.`
 
 ```
- Android_sdk_repository (
-   name = "androidsdk",
-   api_level = 23,
-   build_tools_version = "23.0.2",
-   path = "/home/xxxx/android-sdk-linux/", )
+android_sdk_repository (
+    name = "androidsdk",
+    api_level = 23,
+    build_tools_version = "23.0.2",
+    path = "/home/xxxx/android-sdk-linux/",
+)
 
 android_ndk_repository(
-  name="androidndk",
-  path="/home/xxxx/android-ndk-r10e/",
-  api_level=19)
-
+    name = "androidndk",
+    path = "/home/xxxx/android-ndk-r10e/",
+    api_level = 19,
+)
 ```
-Additional details on building with Android can be found [here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/README.md)
+
+Additional details on building with Android can be found [here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/README.md).
 
 ### Build the  source code
 Run bazel with the following command to build the demo.
 
 Build the demo app:
-bazel build --cxxopt='--std=c++11' //tensorflow/contrib/lite/java/demo/app/src/main:TfLiteCameraDemo
+
+```
+bazel build --cxxopt=--std=c++11 //tensorflow/contrib/lite/java/demo/app/src/main:TfLiteCameraDemo
+```
+
+### Note
+
+Currently, we only support building the Android demo app within a Python 2
+environment (due to a Bazel bug).
 
 ### More about the demo
 The demo is resizing each camera image frame to (224 width * 224 height) to match the  quantized Mobilenet model being used. The resized image is converted into a ByteBuffer row by row of size 1 * 224 * 224 * 3 bytes, where 1 is the number of images in a batch 224 * 224 is the width and height of the image 3 bytes represents three colors of a pixel. This demo uses the TensorFlow Lite Java inference API for models which take a single input and provide a single output. This outputs a two-dimensional array, with the first dimension being the category index and the second dimension being the confidence of classification. The Mobilenet model has 1001 unique categories and the app sorts the probabilities of all the categories and displays the top three. The Mobilenet quantized model is bundled within the assets directory of the app.
@@ -95,7 +101,7 @@ The demo is resizing each camera image frame to (224 width * 224 height) to matc
 
 [On Device Smart Reply](https://research.googleblog.com/2017/02/on-device-machine-intelligence.html)  is an on-device model which provides one-touch replies for an incoming text message by suggesting contextually relevant messages. The model is built specifically for memory constrained devices such as watches & phones and it has been successfully used to surface [Smart Replies on Android Wear](https://research.googleblog.com/2017/02/on-device-machine-intelligence.html). Note that this model only works on Android as of now.
 
-These pre-trained models can be downloaded from [here](models.md).
+These pre-trained models can be downloaded from [here](g3doc/models.md).
 
 ### Retrain Inception-V3 or MobileNet for a custom data set
 The above pre-trained models have been trained on the ImageNet data set, which consists of 1000 predefined classes. A model will need to be re-trained if these classes are not relevant or useful for a given use case. This technique is called transfer learning, which starts with a model that has been already trained on a problem and will then be retrained on a similar problem. Deep learning from scratch can take days, but transfer learning can be done fairly quickly. In order to do this, a developer will need to generate their custom data set labeled with the relevant classes.
@@ -104,7 +110,7 @@ The [TensorFlow for Poets](https://codelabs.developers.google.com/codelabs/tenso
 
 
 ### Train a custom model
-A developer may choose to train a custom model using Tensorflow. TensorFlow documentation has [several tutorials](https://www.tensorflow.org/tutorials/) for building and training models. If the user has written a model using TensorFlow’s Slim Framework the first step is to export this to a GraphDef file. This is necessary because Slim does not store the model structure outside the code, so to communicate with other parts of the framework it needs to be exported. Documentation for the export can be found [here](https://github.com/tensorflow/models/tree/master/research/slim#Export). The output of this step will be a .pb file for the custom model.
+A developer may choose to train a custom model using Tensorflow. TensorFlow documentation has [several tutorials](https://www.tensorflow.org/tutorials/) for building and training models. If the user has written a model using TensorFlow's Slim Framework the first step is to export this to a GraphDef file. This is necessary because Slim does not store the model structure outside the code, so to communicate with other parts of the framework it needs to be exported. Documentation for the export can be found [here](https://github.com/tensorflow/models/tree/master/research/slim#Export). The output of this step will be a .pb file for the custom model.
 
 TensorFlow Lite currently supports a subset of TensorFlow operators. Please refer to [this document](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md) for details of supported operators and their usage. This
 set will continue to expand in future releases of Tensorflow Lite.
@@ -128,9 +134,9 @@ Since we employ several formats, the following definitions may be useful:
  - TensorFlow lite model (.lite) - a serialized flatbuffer, containing TensorFlow lite operators and Tensors for the TensorFlow lite interpreter. This is most analogous to TensorFlow frozen GraphDefs.
 
 ### Freeze Graph
-To use this .pb GraphDef file within TensorFlow Lite, the application developer will need checkpoints containing trained weight parameters. The .pb contains only the structure of the graph. The process of merging the checkpoint values with the graph structure is known as “freezing” the graph.
+To use this .pb GraphDef file within TensorFlow Lite, the application developer will need checkpoints containing trained weight parameters. The .pb contains only the structure of the graph. The process of merging the checkpoint values with the graph structure is known as "freezing" the graph.
 
-The developer should know where the checkpoints folder is present or checkpoints can also be downloaded for a pre-trained model (Example: Here is a link to the [MobileNets](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md)
+The developer should know where the checkpoints folder is present or checkpoints can also be downloaded for a pre-trained model (Example: Here is a link to the [MobileNets](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md)).
 
 Graph freezing can be done using the command below (and modifying the arguments appropriately)
 
@@ -155,7 +161,7 @@ Here is a sample command line to convert the frozen Graphdef to '.lite' format f
 bazel build tensorflow/contrib/lite/toco:toco
 
 bazel-bin/tensorflow/contrib/lite/toco/toco -- \
-  --input_file=(pwd)/mobilenet_v1_1.0_224/frozen_graph.pb \
+  --input_file=$(pwd)/mobilenet_v1_1.0_224/frozen_graph.pb \
   --input_format=TENSORFLOW_GRAPHDEF  --output_format=TFLITE \
   --output_file=/tmp/mobilenet_v1_1.0_224.lite --inference_type=FLOAT \
   --input_type=FLOAT --input_arrays=input \
@@ -183,18 +189,18 @@ with tf.Session() as sess:
 ```
 For detailed instructions on how to use the Tensorflow Optimizing Converter, please see [here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md).
 
-You may refer to the [Ops compatibility guide](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tf_ops_compatibility.md) for troubleshooting help. If that doesn’t help, please file an [issue](https://github.com/tensorflow/tensorflow/issues).
+You may refer to the [Ops compatibility guide](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md) for troubleshooting help. If that doesn't help, please file an [issue](https://github.com/tensorflow/tensorflow/issues).
 
 ## Step 3. Use the TensorFlow Lite model for inference in a mobile app
 
 After completion of Step 2 the developer should have a .lite model.
 
 ### For Android
-Because Android apps need to be written in Java, and core TensorFlow is in C++, a JNI library is provided to interface between the two. Its interface is aimed only at inference, so it provides the ability to load a graph, set up inputs, and run the model to calculate particular outputs. The full documentation for the set of methods can be seen [here](https://github.com/TensorFlow/TensorFlow/blob/master/TensorFlow/contrib/lite/g3doc/). The demo app is also open sourced on [github](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/app).
+Because Android apps need to be written in Java, and core TensorFlow is in C++, a JNI library is provided to interface between the two. Its interface is aimed only at inference, so it provides the ability to load a graph, set up inputs, and run the model to calculate particular outputs. The full documentation for the set of methods can be seen [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/g3doc/). The demo app is also open sourced on [github](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/app).
 
-The [demo app] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/app)  uses this interface, so it’s a good place to look for example usage. You can also download the prebuilt binary [here](http://download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk).
+The [demo app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/app)  uses this interface, so it's a good place to look for example usage. You can also download the prebuilt binary [here](http://download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk).
 
-Note that you’d need to follow instructions for installing TensorFlow on Android, setting up bazel and Android Studio outlined [here](https://www.tensorflow.org/mobile/android_build).
+Note that you'd need to follow instructions for installing TensorFlow on Android, setting up bazel and Android Studio outlined [here](https://www.tensorflow.org/mobile/android_build).
 
 ### For iOS
-Follow the documentation [here](https://github.com/TensorFlow/TensorFlow/blob/master/TensorFlow/contrib/lite/g3doc/ios.md) to get integrate a TFLite model into your app.
+Follow the documentation [here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/g3doc/ios.md) to get integrate a TFLite model into your app.