Interpreter

Driver class to drive model inference with TensorFlow Lite.

Note: If you don't need access to any of the "experimental" API features below, prefer to use InterpreterApi and InterpreterFactory rather than using Interpreter directly.

A Interpreter encapsulates a pre-trained TensorFlow Lite model, in which operations are executed for model inference.

For example, if a model takes only one input and returns only one output:

try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
   interpreter.run(input, output);
 }
 

If a model takes multiple inputs or outputs:

Object[] inputs = {input0, input1, ...};
 Map map_of_indices_to_outputs = new HashMap<>();
 FloatBuffer ith_output = FloatBuffer.allocateDirect(3 * 2 * 4);  // Float tensor, shape 3x2x4.
 ith_output.order(ByteOrder.nativeOrder());
 map_of_indices_to_outputs.put(i, ith_output);
 try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
   interpreter.runForMultipleInputsOutputs(inputs, map_of_indices_to_outputs);
 }
 

If a model takes or produces string tensors:

String[] input = {"foo", "bar"};  // Input tensor shape is [2].
 String[][] output = new String[3][2];  // Output tensor shape is [3, 2].
 try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
   interpreter.runForMultipleInputsOutputs(input, output);
 }
 

Note that there's a distinction between shape [] and shape[1]. For scalar string tensor outputs:

String[] input = {"foo"};  // Input tensor shape is [1].
 ByteBuffer outputBuffer = ByteBuffer.allocate(OUTPUT_BYTES_SIZE);  // Output tensor shape is [].
 try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
   interpreter.runForMultipleInputsOutputs(input, outputBuffer);
 }
 byte[] outputBytes = new byte[outputBuffer.remaining()];
 outputBuffer.get(outputBytes);
 // Below, the `charset` can be StandardCharsets.UTF_8.
 String output = new String(outputBytes, charset);
 

Orders of inputs and outputs are determined when converting TensorFlow model to TensorFlowLite model with Toco, as are the default shapes of the inputs.

When inputs are provided as (multi-dimensional) arrays, the corresponding input tensor(s) will be implicitly resized according to that array's shape. When inputs are provided as Buffer types, no implicit resizing is done; the caller must ensure that the Buffer byte size either matches that of the corresponding tensor, or that they first resize the tensor via resizeInput(int, int[]). Tensor shape and type information can be obtained via the Tensor class, available via getInputTensor(int) and getOutputTensor(int).

WARNING:Interpreter instances are not thread-safe. A Interpreter owns resources that must be explicitly freed by invoking close()

The TFLite library is built against NDK API 19. It may work for Android API levels below 19, but is not guaranteed.