FirebaseModelInterpreter

public final class FirebaseModelInterpreter extends Object
implements Closeable

This class is deprecated.
For more information refer to the custom model implementation instructions.

Interpreter to run custom models with TensorFlow Lite (requires API level 16+)

A model interpreter is created via getInstance(FirebaseModelInterpreterOptions) Follow the steps below to specify the FirebaseCustomRemoteModel or FirebaseCustomLocalModel, create a FirebaseModelInterpreterOptions and then create a FirebaseModelInterpreter and all the way to running an inference with the model.

Firstly, create at least one of FirebaseCustomRemoteModel and FirebaseCustomLocalModel. If you want to use FirebaseCustomRemoteModel, you need to download it through FirebaseModelManager first.

The following code creates the model and the interpreterOptions:

 FirebaseCustomRemoteModel remoteModel = new FirebaseCustomRemoteModel.Builder(REMOTE_MODEL_NAME)
     .build();

 FirebaseModelInterpreterOptions interpreterOptions =
      new FirebaseModelInterpreterOptions.Builder(remoteModel).build();

 Or

 FirebaseCustomLocalModel localModel = new FirebaseCustomLocalModel.Builder()
     .setFilePath(filePath).build();

 FirebaseModelInterpreterOptions interpreterOptions =
     new FirebaseModelInterpreterOptions.Builder(localModel).build();

 

Secondly, create an instance of FirebaseModelInterpreter with the FirebaseModelInterpreterOptions

 FirebaseModelInterpreter interpreter = FirebaseModelInterpreter.getInstance(interpreterOptions);
 

Thirdly, create a FirebaseModelInputs and a FirebaseModelInputOutputOptions. You need to know the input and output data specifications of the model, including the data types and input/output dimensions. The following code is an example:

 FirebaseModelInputs modelInputs = new FirebaseModelInputs.Builder().add(...).build();

 FirebaseModelInputOutputOptions inputOutputOptions =
     new FirebaseModelInputOutputOptions.Builder()
         .setInputFormat(0, FirebaseModelDataType.FLOAT32, inputDims)
         .setOutputFormat(0, FirebaseModelDataType.FLOAT32, outputDims)
         .build();
 

Lastly, feed the inputs to run(FirebaseModelInputs, FirebaseModelInputOutputOptions). The following code is an example:

 Task<FirebaseModelOutputs> task = interpreter.run(modelInputs, inputOutputOptions);
 task.addOnSuccessListener(...).addOnFailureListener(...);
 

Public Method Summary

void	close()
Task<Integer>	getInputIndex(String name) Gets the index of an input by its name.
static FirebaseModelInterpreter	getInstance(FirebaseModelInterpreterOptions options) Gets an instance of the FirebaseModelInterpreter to support custom model specified in FirebaseModelOptions.
Task<Integer>	getOutputIndex(String name) Gets the index of an output by its name.
boolean	isStatsCollectionEnabled() Determines whether stats collection in Model Interpreter is enabled.
Task<FirebaseModelOutputs>	run(FirebaseModelInputs inputs, FirebaseModelInputOutputOptions options) Runs inference with input and data configurations.
void	setStatsCollectionEnabled(boolean enable) Enables stats collection in ML Kit model interpreter.

Inherited Method Summary

From class java.lang.Object

Object	clone()
boolean	equals(Object arg0)
void	finalize()
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()

From interface java.io.Closeable

abstract void

close()

From interface java.lang.AutoCloseable

abstract void

close()

Public Methods

public void close ()

public Task<Integer> getInputIndex (String name)

Gets the index of an input by its name.

Parameters

name	the name of the input

Returns

• a Task for the index of the input. The Task will fail with FirebaseMLException if the input name does not exist in the model.

public static FirebaseModelInterpreter getInstance (FirebaseModelInterpreterOptions options)

Gets an instance of the FirebaseModelInterpreter to support custom model specified in FirebaseModelOptions.

Parameters

options	the options for the model to use.

Returns

• an instance of FirebaseModelInterpreter. Note that the interpreter instance will be the same instance if the supplied options are the same.

Throws

IllegalArgumentException	if FirebaseModelInterpreterOptions contains no model
FirebaseMLException

public Task<Integer> getOutputIndex (String name)

Gets the index of an output by its name.

Parameters

name	the name of the output

Returns

• a Task for the index of the output name. The Task will fail with FirebaseMLException if the output name does not exist in the model.

public boolean isStatsCollectionEnabled ()

Determines whether stats collection in Model Interpreter is enabled.

Returns

• true if stats collection is enabled.

public Task<FirebaseModelOutputs> run (FirebaseModelInputs inputs, FirebaseModelInputOutputOptions options)

Runs inference with input and data configurations.

Parameters

inputs	an instance of FirebaseModelInputs containing input data
options	an instance of FirebaseModelInputOutputOptions containing types and dimensions of input and output data.

public void setStatsCollectionEnabled (boolean enable)

Enables stats collection in ML Kit model interpreter. The stats include API calls counts, errors, API call durations, options, etc. No personally identifiable information is logged.

The setting is per MlKitContext, and it is persistent together with app's private data. It means if the user uninstalls the app or clears all app data, the setting will be erased. The best practice is to set the flag in each initialization.

By default the logging is enabled. You have to specifically set it to false here to disable logging for FirebaseModelInterpreter.