FirebaseModelInterpreter

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(...);