TensorFlow Lite in Google Play services Java API

TensorFlow Lite in Google Play services can also be accessed using Java APIs, in addition to the Native API. In particular, TensorFlow Lite in Google Play services is available through the TensorFlow Lite Task API and the TensorFlow Lite Interpreter API. The Task Library provides optimized out-of-the-box model interfaces for common machine learning tasks using visual, audio, and text data. The TensorFlow Lite Interpreter API, provided by the TensorFlow runtime, provides a more general-purpose interface for building and running ML models.

The following sections provide instructions on how to use the Interpreter and Task Library APIs with TensorFlow Lite in Google Play services. While it is possible for an app to use both the Interpreter APIs and Task Library APIs, most apps should only use one set of APIs.

Using the Task Library APIs

The TensorFlow Lite Task API wraps the Interpreter API and provides a high-level programming interface for common machine learning tasks that use visual, audio, and text data. You should use the Task API if your application requires one of the supported tasks.

1. Add project dependencies

Your project dependency depends on your machine learning use case. The Task APIs contain the following libraries:

• Vision library: org.tensorflow:tensorflow-lite-task-vision-play-services
• Audio library: org.tensorflow:tensorflow-lite-task-audio-play-services
• Text library: org.tensorflow:tensorflow-lite-task-text-play-services

Add one of the dependencies to your app project code to access the Play services API for TensorFlow Lite. For example, use the following to implement a vision task:

dependencies {
...
    implementation 'org.tensorflow:tensorflow-lite-task-vision-play-services:0.4.2'
...
}

Caution: The TensorFlow Lite Tasks Audio library version 0.4.2 maven repository is incomplete. Use version 0.4.2.1 for this library instead: org.tensorflow:tensorflow-lite-task-audio-play-services:0.4.2.1.

2. Add initialization of TensorFlow Lite

Initialize the TensorFlow Lite component of the Google Play services API before using the TensorFlow Lite APIs. The following example initializes the vision library:

Important: Make sure the TfLite.initialize task completes before executing code that accesses TensorFlow Lite APIs.

Tip: The TensorFlow Lite modules are installed at the same time your application is installed or updated from the Play Store. You can check the availability of the modules by using ModuleInstallClient from the Google Play services APIs. For more information on checking module availability, see Ensuring API availability with ModuleInstallClient.

3. Run inferences

After initializing the TensorFlow Lite component, call the detect() method to generate inferences. The exact code within the detect() method varies depending on the library and use case. The following is for a simple object detection use case with the TfLiteVision library:

if (objectDetector == null) { setupObjectDetector() }

...

} </pre> </section> </devsite-selector>

Depending on the data format, you may also need to preprocess and convert your data within the detect() method before generating inferences. For example, image data for an object detector requires the following:

val imageProcessor = ImageProcessor.Builder().add(Rot90Op(-imageRotation / 90)).build()
val tensorImage = imageProcessor.process(TensorImage.fromBitmap(image))
val results = objectDetector?.detect(tensorImage)

Using the Interpreter APIs

The Interpreter APIs offer more control and flexibility than the Task Library APIs. You should use the Interpreter APIs if your machine learning task is not supported by the Task library, or if you require a more general-purpose interface for building and running ML models.

1. Add project dependencies

Add the following dependencies to your app project code to access the Play services API for TensorFlow Lite:

dependencies {
...
    // Tensorflow Lite dependencies for Google Play services
    implementation 'com.google.android.gms:play-services-tflite-java:16.4.0'
    // Optional: include Tensorflow Lite Support Library
    implementation 'com.google.android.gms:play-services-tflite-support:16.4.0'
...
}

2. Add initialization of TensorFlow Lite

Initialize the TensorFlow Lite component of the Google Play services API before using the TensorFlow Lite APIs:

Note: Make sure the TfLite.initialize task completes before executing code that accesses TensorFlow Lite APIs. Use the addOnSuccessListener() method, as shown in the next section.

3. Create an Interpreter and set runtime option {:#step_3_interpreter}

Create an interpreter using InterpreterApi.create() and configure it to use Google Play services runtime, by calling InterpreterApi.Options.setRuntime(), as shown in the following example code:

You should use the implementation above because it avoids blocking the Android user interface thread. If you need to manage thread execution more closely, you can add a Tasks.await() call to interpreter creation:

Warning: Do not call .await() on the foreground user interface thread because it interrupts display of user interface elements and creates a poor user experience.

4. Run inferences

Using the interpreter object you created, call the run() method to generate an inference.

Hardware acceleration {:#hardware-acceleration}

TensorFlow Lite allows you to accelerate the performance of your model using specialized hardware processors, such as graphics processing units (GPUs). You can take advantage of these specialized processors using hardware drivers called delegates. You can use the following hardware acceleration delegates with TensorFlow Lite in Google Play services:

• GPU delegate (recommended) - This delegate is provided through Google Play services and is dynamically loaded, just like the Play services versions of the Task API and Interpreter API.

• NNAPI delegate - This delegate is available as an included library dependency in your Android development project, and is bundled into your app.

For more information about hardware acceleration with TensorFlow Lite, see the TensorFlow Lite Delegates page.

Checking device compatibility

Not all devices support GPU hardware acceleration with TFLite. In order to mitigate errors and potential crashes, use the TfLiteGpu.isGpuDelegateAvailable method to check whether a device is compatible with the GPU delegate.

Use this method to confirm whether a device is compatible with GPU, and use CPU or the NNAPI delegate as a fallback for when GPU is not supported.

useGpuTask = TfLiteGpu.isGpuDelegateAvailable(context)

Once you have a variable like useGpuTask, you can use it to determine whether devices use the GPU delegate. The following examples show how this can be done with both the Task Library and Interpreter APIs.

With the Task Api

With the Interpreter Api

GPU with Task Library APIs

To use the GPU delegate with the Task APIs:

1. Update the project dependencies to use the GPU delegate from Play services:

   Copy

   implementation 'com.google.android.gms:play-services-tflite-gpu:16.4.0'
   

2. Initialize the GPU delegate with setEnableGpuDelegateSupport. For example, you can initialize the GPU delegate for TfLiteVision with the following:

   <div> <devsite-selector> <section> <h3>Kotlin</h3> <pre class="prettyprint"> TfLiteVision.initialize(context, TfLiteInitializationOptions.builder().setEnableGpuDelegateSupport(true).build()) </pre> </section> <section> <h3>Java</h3> <pre class="prettyprint"> TfLiteVision.initialize(context, TfLiteInitializationOptions.builder().setEnableGpuDelegateSupport(true).build()); </pre> </section> </devsite-selector> </div>

3. Enable the GPU delegate option with BaseOptions:

   <div> <devsite-selector> <section> <h3>Kotlin</h3> <pre class="prettyprint"> val baseOptions = BaseOptions.builder().useGpu().build() </pre> </section> <section> <h3>Java</h3> <pre class="prettyprint"> BaseOptions baseOptions = BaseOptions.builder().useGpu().build(); </pre> </section> </devsite-selector> </div>

4. Configure the options using .setBaseOptions. For example, you can set up GPU in ObjectDetector with the following:

   <div> <devsite-selector> <section> <h3>Kotlin</h3> <pre class="prettyprint"> val options = ObjectDetectorOptions.builder() .setBaseOptions(baseOptions) .setMaxResults(1) .build() </pre> </section> <section> <h3>Java</h3> <pre class="prettyprint"> ObjectDetectorOptions options = ObjectDetectorOptions.builder() .setBaseOptions(baseOptions) .setMaxResults(1) .build(); </pre> </section> </devsite-selector> </div>

GPU with Interpreter APIs

To use the GPU delegate with the Interpreter APIs:

1. Update the project dependencies to use the GPU delegate from Play services:

   Copy

   implementation 'com.google.android.gms:play-services-tflite-gpu:16.4.0'
   

2. Enable the GPU delegate option in the TFlite initialization:

   <div> <devsite-selector> <section> <h3>Kotlin</h3> <pre class="prettyprint"> TfLite.initialize(context, TfLiteInitializationOptions.builder() .setEnableGpuDelegateSupport(true) .build()) </pre> </section> <section> <h3>Java</h3> <pre class="prettyprint"> TfLite.initialize(context, TfLiteInitializationOptions.builder() .setEnableGpuDelegateSupport(true) .build()); </pre> </section> </devsite-selector> </div>

3. Enable GPU delegate in the interpreter options: set the delegate factory to GpuDelegateFactory by calling addDelegateFactory() withinInterpreterApi.Options()`:

   <div> <devsite-selector> <section> <h3>Kotlin</h3> <pre class="prettyprint"> val interpreterOption = InterpreterApi.Options() .setRuntime(TfLiteRuntime.FROM_SYSTEM_ONLY) .addDelegateFactory(GpuDelegateFactory()) </pre> </section> <section> <h3>Java</h3> <pre class="prettyprint"> Options interpreterOption = InterpreterApi.Options() .setRuntime(TfLiteRuntime.FROM_SYSTEM_ONLY) .addDelegateFactory(new GpuDelegateFactory()); </pre> </section> </devsite-selector> </div>

Migrating from stand-alone TensorFlow Lite {:#migrating}

If you are planning to migrate your app from stand-alone TensorFlow Lite to the Play services API, review the following additional guidance for updating your app project code:

1. Review the Limitations section of this page to ensure your use case is supported.
2. Prior to updating your code, do performance and accuracy checks for your models, particularly if you are using versions of TensorFlow Lite earlier than version 2.1, so you have a baseline to compare against the new implementation.
3. If you have migrated all of your code to use the Play services API for TensorFlow Lite, you should remove the existing TensorFlow Lite runtime library dependencies (entries with <code>org.tensorflow:tensorflow-lite:*</code>) from your build.gradle file so that you can reduce your app size.
4. Identify all occurrences of new Interpreter object creation in your code, and modify each one so that it uses the InterpreterApi.create() call. The new TfLite.initialize is asynchronous, which means in most cases it's not a drop-in replacement: you must register a listener for when the call completes. Refer to the code snippet in Step 3 code.
5. Add import org.tensorflow.lite.InterpreterApi; and import org.tensorflow.lite.InterpreterApi.Options.TfLiteRuntime; to any source files using the org.tensorflow.lite.Interpreter or org.tensorflow.lite.InterpreterApi classes.
6. If any of the resulting calls to InterpreterApi.create() have only a single argument, append new InterpreterApi.Options() to the argument list.
7. Append .setRuntime(TfLiteRuntime.FROM_SYSTEM_ONLY) to the last argument of any calls to InterpreterApi.create().
8. Replace all other occurrences of the org.tensorflow.lite.Interpreter class with org.tensorflow.lite.InterpreterApi.

If you want to use stand-alone TensorFlow Lite and the Play services API side-by-side, you must use TensorFlow Lite 2.9 (or later). TensorFlow Lite 2.8 and earlier versions are not compatible with the Play services API version.