Update the Select Tensorflow ops guide
PiperOrigin-RevId: 327724719 Change-Id: Ie3bc87072bf8dbb11588259da6d593b9be86a391
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Thai Nguyen
TensorFlower Gardener
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@ -21,6 +21,10 @@ TensorFlow ops when TFLite builtin ops are not sufficient.
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Models converted with TensorFlow ops will require a TensorFlow Lite interpreter
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that has a larger binary size than the interpreter with only TFLite builtin ops.
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For Android, It is possible to reduce binary size by selectively linking only
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required Tensorflow ops. For the details, please see the
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[Reduce TensorFlow Lite binary size](../guide/reduce_binary_size.md) section.
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Additionally, performance optimizations will not be available for any TensorFlow
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ops in the TensorFlow Lite model.
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@ -66,7 +70,7 @@ open("converted_model.tflite", "wb").write(tflite_model)
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```
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The following example shows how to use this feature in the
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[`tflite_convert`](../convert/cmdline_examples.md) command line tool using the
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[`tflite_convert`](../convert/cmdline.md) command line tool using the
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command line flag `target_ops`.
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```sh
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@ -98,8 +102,10 @@ includes the necessary library of TensorFlow ops.
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### Android AAR
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For Android, we recommend using the prebuilt [AAR with TensorFlow ops hosted at
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JCenter](https://bintray.com/google/tensorflow/tensorflow-lite-select-tf-ops).
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To reduce the binary size, please build your own custom AAR files as guided in
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the [next section](#building-the-android-aar). If the binary size is not a
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considerable concern, we recommend using the prebuilt
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[AAR with TensorFlow ops hosted at JCenter](https://bintray.com/google/tensorflow/tensorflow-lite-select-tf-ops).
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You can specify this in your `build.gradle` dependencies by adding it alongside
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the standard TensorFlow Lite AAR as follows:
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@ -112,9 +118,9 @@ dependencies {
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}
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```
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Once you've added the dependency, the necessary delegate for handling
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the graph's TensorFlow ops should be automatically installed for
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graphs that require them.
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Once you've added the dependency, the necessary delegate for handling the
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graph's TensorFlow ops should be automatically installed for graphs that require
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them.
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*Note*: The TensorFlow ops dependency is relatively large, so you'll probably
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want to filter out unnecessary x86 ABIs in your `.gradle` file by setting up
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@ -132,23 +138,32 @@ android {
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#### Building the Android AAR
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For more advanced cases, you can also build the library manually. Assuming a
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<a href="android.md">working TensorFlow Lite build environment</a>, build the
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Android AAR with select TensorFlow ops as follows:
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For reducing the binary size or other advanced cases, you can also build the
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library manually. Assuming a <a href="android.md">working TensorFlow Lite build
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environment</a>, build the Android AAR with select TensorFlow ops as follows:
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```sh
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bazel build --cxxopt='--std=c++14' -c opt \
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--config=android_arm --config=monolithic \
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//tensorflow/lite/java:tensorflow-lite-select-tf-ops
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sh tensorflow/lite/tools/build_aar.sh \
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--input_models=/a/b/model_one.tflite,/c/d/model_two.tflite \
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--target_archs=x86,x86_64,arm64-v8a,armeabi-v7a
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```
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This will generate an AAR file in `bazel-bin/tensorflow/lite/java/`. From there,
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you can either import the AAR directly into your project, or publish the custom
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AAR to your local Maven repository:
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This will generate the AAR file `bazel-bin/tmp/tensorflow-lite.aar` for
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TensorFlow Lite built-in and custom ops; and generate the AAR file
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`bazel-bin/tmp/tensorflow-lite-select-tf-ops.aar` for TensorFlow ops. If you
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don't have a working build environment, You can also
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[build above files with docker](../guide/reduce_binary_size.md#selectively_build_tensorflow_lite_with_docker).
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From there, you can either import the AAR files directly into your project, or
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publish the custom AAR files to your local Maven repository:
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```sh
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mvn install:install-file \
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-Dfile=bazel-bin/tensorflow/lite/java/tensorflow-lite-select-tf-ops.aar \
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-Dfile=bazel-bin/tmp/tensorflow-lite.aar \
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-DgroupId=org.tensorflow \
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-DartifactId=tensorflow-lite -Dversion=0.1.100 -Dpackaging=aar
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mvn install:install-file \
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-Dfile=bazel-bin/tmp/tensorflow-lite-select-tf-ops.aar \
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-DgroupId=org.tensorflow \
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-DartifactId=tensorflow-lite-select-tf-ops -Dversion=0.1.100 -Dpackaging=aar
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```
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@ -166,7 +181,8 @@ allprojects {
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}
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dependencies {
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implementation 'org.tensorflow:tensorflow-lite-with-select-tf-ops:0.1.100'
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implementation 'org.tensorflow:tensorflow-lite:0.1.100'
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implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:0.1.100'
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}
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```
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@ -285,10 +301,16 @@ Using only TF ops (`SELECT_TF_OPS`) | 264.5
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The following table describes the binary size of TensorFlow Lite for each build.
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These targets were built for Android using `--config=android_arm -c opt`.
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Build | C++ Binary Size | Android APK Size
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--------------------- | --------------- | ----------------
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Only built-in ops | 796 KB | 561 KB
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Built-in ops + TF ops | 23.0 MB | 8.0 MB
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Build | C++ Binary Size | Android APK Size
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------------------------- | --------------- | ----------------
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Only built-in ops | 796 KB | 561 KB
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Built-in ops + TF ops | 23.0 MB | 8.0 MB
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Built-in ops + TF ops (1) | 4.1 MB | 1.8 MB
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(1) These libraries are selectively built for
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[i3d-kinetics-400 model](https://tfhub.dev/deepmind/i3d-kinetics-400/1) with 8
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TFLite builtin ops and 3 Tensorflow ops. For more details, please see the
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[Reduce TensorFlow Lite binary size](../guide/reduce_binary_size.md) section.
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## Known limitations
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@ -309,10 +331,6 @@ The following is a list of some of the known limitations:
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The following is a list of improvements to this pipeline that are in progress:
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* *Selective registration* - There is work being done to make it simple to
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generate TFLite interpreter binaries that only contain the TensorFlow ops
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required for a particular set of models.
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* *Improved usability* - The conversion process will be simplified to only
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require a single pass through the converter.
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* *Improved performance* - Work is being done to ensure TensorFlow Lite with
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TensorFlow ops has performance parity to TensorFlow Mobile.
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TensorFlow ops nicely cooperates with hardware accelerated delegates, for
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example, NNAPI and GPU delegates.
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