Generate model interfaces using metadata

Using TensorFlow Lite Metadata, developers can generate wrapper code to enable integration on Android. For most developers, the graphical interface of Android Studio ML Model Binding is the easiest to use. If you require more customisation or are using command line tooling, the TensorFlow Lite Codegen is also available.

Use Android Studio ML Model Binding

For TensorFlow Lite models enhanced with metadata, developers can use Android Studio ML Model Binding to automatically configure settings for the project and generate wrapper classes based on the model metadata. The wrapper code removes the need to interact directly with ByteBuffer. Instead, developers can interact with the TensorFlow Lite model with typed objects such as Bitmap and Rect.

Import a TensorFlow Lite model in Android Studio

  1. Right-click on the module you would like to use the TFLite model or click on File, then New > Other > TensorFlow Lite Model Right-click menus to access the TensorFlow Lite import functionality

  2. Select the location of your TFLite file. Note that the tooling will configure the module's dependency on your behalf with ML Model binding and all dependencies automatically inserted into your Android module's build.gradle file.

    Optional: Select the second checkbox for importing TensorFlow GPU if you want to use GPU acceleration. Import dialog for TFLite model

  3. Click Finish.

  4. The following screen will appear after the import is successful. To start using the model, select Kotlin or Java, copy and paste the code under the Sample Code section. You can get back to this screen by double clicking the TFLite model under the ml directory in Android Studio. Model details page in Android Studio

Accelerating model inference

ML Model Binding provides a way for developers to accelerate their code through the use of delegates and the number of threads.

Step 1. Check the module build.gradle file that it contains the following dependency:

    dependencies {
        ...
        // TFLite GPU delegate 2.3.0 or above is required.
        implementation 'org.tensorflow:tensorflow-lite-gpu:2.3.0'
    }

Step 2. Detect if GPU running on the device is compatible with TensorFlow GPU delegate, if not run the model using multiple CPU threads:

Kotlin

    import org.tensorflow.lite.gpu.CompatibilityList
    import org.tensorflow.lite.gpu.GpuDelegate

    val compatList = CompatibilityList()

    val options = if(compatList.isDelegateSupportedOnThisDevice) {
        // if the device has a supported GPU, add the GPU delegate
        Model.Options.Builder().setDevice(Model.Device.GPU).build()
    } else {
        // if the GPU is not supported, run on 4 threads
        Model.Options.Builder().setNumThreads(4).build()
    }

    // Initialize the model as usual feeding in the options object
    val myModel = MyModel.newInstance(context, options)

    // Run inference per sample code
      

Java

    import org.tensorflow.lite.support.model.Model
    import org.tensorflow.lite.gpu.CompatibilityList;
    import org.tensorflow.lite.gpu.GpuDelegate;

    // Initialize interpreter with GPU delegate
    Model.Options options;
    CompatibilityList compatList = CompatibilityList();

    if(compatList.isDelegateSupportedOnThisDevice()){
        // if the device has a supported GPU, add the GPU delegate
        options = Model.Options.Builder().setDevice(Model.Device.GPU).build();
    } else {
        // if the GPU is not supported, run on 4 threads
        options = Model.Options.Builder().setNumThreads(4).build();
    }

    MyModel myModel = new MyModel.newInstance(context, options);

    // Run inference per sample code
      

Generate model interfaces with TensorFlow Lite code generator

For TensorFlow Lite model enhanced with metadata, developers can use the TensorFlow Lite Android wrapper code generator to create platform specific wrapper code. The wrapper code removes the need to interact directly with ByteBuffer. Instead, developers can interact with the TensorFlow Lite model with typed objects such as Bitmap and Rect.

The usefulness of the code generator depend on the completeness of the TensorFlow Lite model's metadata entry. Refer to the <Codegen usage> section under relevant fields in metadata_schema.fbs, to see how the codegen tool parses each field.

Generate wrapper Code

You will need to install the following tooling in your terminal:

pip install tflite-support

Once completed, the code generator can be used using the following syntax:

tflite_codegen --model=./model_with_metadata/mobilenet_v1_0.75_160_quantized.tflite \
    --package_name=org.tensorflow.lite.classify \
    --model_class_name=MyClassifierModel \
    --destination=./classify_wrapper

The resulting code will be located in the destination directory. If you are using Google Colab or other remote environment, it maybe easier to zip up the result in a zip archive and download it to your Android Studio project:

# Zip up the generated code
!zip -r classify_wrapper.zip classify_wrapper/

# Download the archive
from google.colab import files
files.download('classify_wrapper.zip')

Using the generated code

Step 1: Import the generated code

Unzip the generated code if necessary into a directory structure. The root of the generated code is assumed to be SRC_ROOT.

Open the Android Studio project where you would like to use the TensorFlow lite model and import the generated module by: And File -> New -> Import Module -> select SRC_ROOT

Using the above example, the directory and the module imported would be called classify_wrapper.

Step 2: Update the app's build.gradle file

In the app module that will be consuming the generated library module:

Under the android section, add the following:

aaptOptions {
   noCompress "tflite"
}

Under the dependencies section, add the following:

implementation project(":classify_wrapper")

Step 3: Using the model

// 1. Initialize the model
MyClassifierModel myImageClassifier = null;

try {
    myImageClassifier = new MyClassifierModel(this);
} catch (IOException io){
    // Error reading the model
}

if(null != myImageClassifier) {

    // 2. Set the input with a Bitmap called inputBitmap
    MyClassifierModel.Inputs inputs = myImageClassifier.createInputs();
    inputs.loadImage(inputBitmap));

    // 3. Run the model
    MyClassifierModel.Outputs outputs = myImageClassifier.run(inputs);

    // 4. Retrieve the result
    Map<String, Float> labeledProbability = outputs.getProbability();
}

Accelerating model inference

The generated code provides a way for developers to accelerate their code through the use of delegates and the number of threads. These can be set when initiatizing the model object as it takes three parameters:

  • Context: Context from the Android Activity or Service
  • (Optional) Device: TFLite acceleration delegate for example GPUDelegate or NNAPIDelegate
  • (Optional) numThreads: Number of threads used to run the model - default is one.

For example, to use a NNAPI delegate and up to three threads, you can initialize the model like this:

try {
    myImageClassifier = new MyClassifierModel(this, Model.Device.NNAPI, 3);
} catch (IOException io){
    // Error reading the model
}

Troubleshooting

If you get a 'java.io.FileNotFoundException: This file can not be opened as a file descriptor; it is probably compressed' error, insert the following lines under the android section of the app module that will uses the library module:

aaptOptions {
   noCompress "tflite"
}