Version 22.0.2 of the firebase-ml-model-interpreter library introduces a new getLatestModelFile() method, which gets the location on the device of custom models. You can use this method to directly instantiate a TensorFlow Lite Interpreter object, which you can use instead of the FirebaseModelInterpreter wrapper.
Going forward, this is the preferred approach. Because the TensorFlow Lite interpreter version is no longer coupled with the Firebase library version, you have more flexibility to upgrade to new versions of TensorFlow Lite when you want, or more easily use custom TensorFlow Lite builds.
This page shows how you can migrate from using FirebaseModelInterpreter to the TensorFlow Lite Interpreter.
1. Update project dependencies
Update your project's dependencies to include version 22.0.2 of the firebase-ml-model-interpreter library (or newer) and the tensorflow-lite library:
Before
implementation("com.google.firebase:firebase-ml-model-interpreter:22.0.1") After
implementation("com.google.firebase:firebase-ml-model-interpreter:22.0.2") implementation("org.tensorflow:tensorflow-lite:2.0.0") 2. Create a TensorFlow Lite interpreter instead of a FirebaseModelInterpreter
Instead of creating a FirebaseModelInterpreter, get the model's location on device with getLatestModelFile() and use it to create a TensorFlow Lite Interpreter.
Before
Kotlin
val remoteModel = FirebaseCustomRemoteModel.Builder("your_model").build() val options = FirebaseModelInterpreterOptions.Builder(remoteModel).build() val interpreter = FirebaseModelInterpreter.getInstance(options) Java
FirebaseCustomRemoteModel remoteModel = new FirebaseCustomRemoteModel.Builder("your_model").build(); FirebaseModelInterpreterOptions options = new FirebaseModelInterpreterOptions.Builder(remoteModel).build(); FirebaseModelInterpreter interpreter = FirebaseModelInterpreter.getInstance(options); After
Kotlin
val remoteModel = FirebaseCustomRemoteModel.Builder("your_model").build() FirebaseModelManager.getInstance().getLatestModelFile(remoteModel) .addOnCompleteListener { task -> val modelFile = task.getResult() if (modelFile != null) { // Instantiate an org.tensorflow.lite.Interpreter object. interpreter = Interpreter(modelFile) } } Java
FirebaseCustomRemoteModel remoteModel = new FirebaseCustomRemoteModel.Builder("your_model").build(); FirebaseModelManager.getInstance().getLatestModelFile(remoteModel) .addOnCompleteListener(new OnCompleteListener<File>() { @Override public void onComplete(@NonNull Task<File> task) { File modelFile = task.getResult(); if (modelFile != null) { // Instantiate an org.tensorflow.lite.Interpreter object. Interpreter interpreter = new Interpreter(modelFile); } } }); 3. Update input and output preparation code
With FirebaseModelInterpreter, you specify the model's input and output shapes by passing a FirebaseModelInputOutputOptions object to the interpreter when you run it.
For the TensorFlow Lite interpreter, you instead allocate ByteBuffer objects with the right size for your model's input and output.
For example, if your model has an input shape of [1 224 224 3] float values and an output shape of [1 1000] float values, make these changes:
Before
Kotlin
val inputOutputOptions = FirebaseModelInputOutputOptions.Builder() .setInputFormat(0, FirebaseModelDataType.FLOAT32, intArrayOf(1, 224, 224, 3)) .setOutputFormat(0, FirebaseModelDataType.FLOAT32, intArrayOf(1, 1000)) .build() val input = ByteBuffer.allocateDirect(224*224*3*4).order(ByteOrder.nativeOrder()) // Then populate with input data. val inputs = FirebaseModelInputs.Builder() .add(input) .build() interpreter.run(inputs, inputOutputOptions) .addOnSuccessListener { outputs -> // ... } .addOnFailureListener { // Task failed with an exception. // ... } Java
FirebaseModelInputOutputOptions inputOutputOptions = new FirebaseModelInputOutputOptions.Builder() .setInputFormat(0, FirebaseModelDataType.FLOAT32, new int[]{1, 224, 224, 3}) .setOutputFormat(0, FirebaseModelDataType.FLOAT32, new int[]{1, 1000}) .build(); float[][][][] input = new float[1][224][224][3]; // Then populate with input data. FirebaseModelInputs inputs = new FirebaseModelInputs.Builder() .add(input) .build(); interpreter.run(inputs, inputOutputOptions) .addOnSuccessListener( new OnSuccessListener<FirebaseModelOutputs>() { @Override public void onSuccess(FirebaseModelOutputs result) { // ... } }) .addOnFailureListener( new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { // Task failed with an exception // ... } }); After
Kotlin
val inBufferSize = 1 * 224 * 224 * 3 * java.lang.Float.SIZE / java.lang.Byte.SIZE val inputBuffer = ByteBuffer.allocateDirect(inBufferSize).order(ByteOrder.nativeOrder()) // Then populate with input data. val outBufferSize = 1 * 1000 * java.lang.Float.SIZE / java.lang.Byte.SIZE val outputBuffer = ByteBuffer.allocateDirect(outBufferSize).order(ByteOrder.nativeOrder()) interpreter.run(inputBuffer, outputBuffer) Java
int inBufferSize = 1 * 224 * 224 * 3 * java.lang.Float.SIZE / java.lang.Byte.SIZE; ByteBuffer inputBuffer = ByteBuffer.allocateDirect(inBufferSize).order(ByteOrder.nativeOrder()); // Then populate with input data. int outBufferSize = 1 * 1000 * java.lang.Float.SIZE / java.lang.Byte.SIZE; ByteBuffer outputBuffer = ByteBuffer.allocateDirect(outBufferSize).order(ByteOrder.nativeOrder()); interpreter.run(inputBuffer, outputBuffer); 4. Update output handling code
Finally, instead of getting the model's output with the FirebaseModelOutputs object's getOutput() method, convert the ByteBuffer output to whatever structure is convenient for your use case.
For example, if you're doing classification, you might make changes like the following:
Before
Kotlin
val output = result.getOutput(0) val probabilities = output[0] try { val reader = BufferedReader(InputStreamReader(assets.open("custom_labels.txt"))) for (probability in probabilities) { val label: String = reader.readLine() println("$label: $probability") } } catch (e: IOException) { // File not found? } Java
float[][] output = result.getOutput(0); float[] probabilities = output[0]; try { BufferedReader reader = new BufferedReader( new InputStreamReader(getAssets().open("custom_labels.txt"))); for (float probability : probabilities) { String label = reader.readLine(); Log.i(TAG, String.format("%s: %1.4f", label, probability)); } } catch (IOException e) { // File not found? } After
Kotlin
modelOutput.rewind() val probabilities = modelOutput.asFloatBuffer() try { val reader = BufferedReader( InputStreamReader(assets.open("custom_labels.txt"))) for (i in probabilities.capacity()) { val label: String = reader.readLine() val probability = probabilities.get(i) println("$label: $probability") } } catch (e: IOException) { // File not found? } Java
modelOutput.rewind(); FloatBuffer probabilities = modelOutput.asFloatBuffer(); try { BufferedReader reader = new BufferedReader( new InputStreamReader(getAssets().open("custom_labels.txt"))); for (int i = 0; i < probabilities.capacity(); i++) { String label = reader.readLine(); float probability = probabilities.get(i); Log.i(TAG, String.format("%s: %1.4f", label, probability)); } } catch (IOException e) { // File not found? }