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InterpreterApi

public interface InterpreterApi
Known Indirect Subclasses

Interface to TensorFlow Lite model interpreter, excluding experimental methods.

An InterpreterApi instance 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 (InterpreterApi 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<Integer, Object> 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 (InterpreterApi 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 (InterpreterApi interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
   interpreter.runForMultipleInputsOutputs(input, output);
 }
 

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:InterpreterApi instances are not thread-safe.

WARNING:An InterpreterApi instance owns resources that must be explicitly freed by invoking AutoCloseable.close()

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

Nested Classes

class InterpreterApi.Options An options class for controlling runtime interpreter behavior. 

Public Methods

abstract void
allocateTensors()
Explicitly updates allocations for all tensors, if necessary.
abstract int
getInputIndex(String opName)
Gets index of an input given the op name of the input.
abstract Tensor
getInputTensor(int inputIndex)
Gets the Tensor associated with the provdied input index.
abstract int
getInputTensorCount()
Gets the number of input tensors.
abstract Long
getLastNativeInferenceDurationNanoseconds()
Returns native inference timing.
abstract int
getOutputIndex(String opName)
Gets index of an output given the op name of the output.
abstract Tensor
getOutputTensor(int outputIndex)
Gets the Tensor associated with the provdied output index.
abstract int
getOutputTensorCount()
Gets the number of output Tensors.
abstract void
resizeInput(int idx, int[] dims)
Resizes idx-th input of the native model to the given dims.
abstract void
resizeInput(int idx, int[] dims, boolean strict)
Resizes idx-th input of the native model to the given dims.
abstract void
run(Object input, Object output)
Runs model inference if the model takes only one input, and provides only one output.
abstract void
runForMultipleInputsOutputs(Object[] inputs, Map<IntegerObject> outputs)
Runs model inference if the model takes multiple inputs, or returns multiple outputs.

Inherited Methods

Public Methods

public abstract void allocateTensors ()

Explicitly updates allocations for all tensors, if necessary.

This will propagate shapes and memory allocations for dependent tensors using the input tensor shape(s) as given.

Note: This call is *purely optional*. Tensor allocation will occur automatically during execution if any input tensors have been resized. This call is most useful in determining the shapes for any output tensors before executing the graph, e.g.,

interpreter.resizeInput(0, new int[]{1, 4, 4, 3}));
 interpreter.allocateTensors();
 FloatBuffer input = FloatBuffer.allocate(interpreter.getInputTensor(0).numElements());
 // Populate inputs...
 FloatBuffer output = FloatBuffer.allocate(interpreter.getOutputTensor(0).numElements());
 interpreter.run(input, output)
 // Process outputs...
 

Note: Some graphs have dynamically shaped outputs, in which case the output shape may not fully propagate until inference is executed.

Throws
IllegalStateException if the graph's tensors could not be successfully allocated.

public abstract int getInputIndex (String opName)

Gets index of an input given the op name of the input.

Parameters
opName
Throws
IllegalArgumentException if opName does not match any input in the model used to initialize the Interpreter.

public abstract Tensor getInputTensor (int inputIndex)

Gets the Tensor associated with the provdied input index.

Parameters
inputIndex
Throws
IllegalArgumentException if inputIndex is negtive or is not smaller than the number of model inputs.

public abstract int getInputTensorCount ()

Gets the number of input tensors.

public abstract Long getLastNativeInferenceDurationNanoseconds ()

Returns native inference timing.

Throws
IllegalArgumentException if the model is not initialized by the Interpreter.

public abstract int getOutputIndex (String opName)

Gets index of an output given the op name of the output.

Parameters
opName
Throws
IllegalArgumentException if opName does not match any output in the model used to initialize the Interpreter.

public abstract Tensor getOutputTensor (int outputIndex)

Gets the Tensor associated with the provdied output index.

Note: Output tensor details (e.g., shape) may not be fully populated until after inference is executed. If you need updated details *before* running inference (e.g., after resizing an input tensor, which may invalidate output tensor shapes), use allocateTensors() to explicitly trigger allocation and shape propagation. Note that, for graphs with output shapes that are dependent on input *values*, the output shape may not be fully determined until running inference.

Parameters
outputIndex
Throws
IllegalArgumentException if outputIndex is negtive or is not smaller than the number of model outputs.

public abstract int getOutputTensorCount ()

Gets the number of output Tensors.

public abstract void resizeInput (int idx, int[] dims)

Resizes idx-th input of the native model to the given dims.

Parameters
idx
dims
Throws
IllegalArgumentException if idx is negtive or is not smaller than the number of model inputs; or if error occurs when resizing the idx-th input.

public abstract void resizeInput (int idx, int[] dims, boolean strict)

Resizes idx-th input of the native model to the given dims.

When `strict` is True, only unknown dimensions can be resized. Unknown dimensions are indicated as `-1` in the array returned by `Tensor.shapeSignature()`.

Parameters
idx
dims
strict
Throws
IllegalArgumentException if idx is negtive or is not smaller than the number of model inputs; or if error occurs when resizing the idx-th input. Additionally, the error occurs when attempting to resize a tensor with fixed dimensions when `struct` is True.

public abstract void run (Object input, Object output)

Runs model inference if the model takes only one input, and provides only one output.

Warning: The API is more efficient if a Buffer (preferably direct, but not required) is used as the input/output data type. Please consider using Buffer to feed and fetch primitive data for better performance. The following concrete Buffer types are supported:

  • ByteBuffer - compatible with any underlying primitive Tensor type.
  • FloatBuffer - compatible with float Tensors.
  • IntBuffer - compatible with int32 Tensors.
  • LongBuffer - compatible with int64 Tensors.
Note that boolean types are only supported as arrays, not Buffers, or as scalar inputs.

Parameters
input an array or multidimensional array, or a Buffer of primitive types including int, float, long, and byte. Buffer is the preferred way to pass large input data for primitive types, whereas string types require using the (multi-dimensional) array input path. When a Buffer is used, its content should remain unchanged until model inference is done, and the caller must ensure that the Buffer is at the appropriate read position. A null value is allowed only if the caller is using a Delegate that allows buffer handle interop, and such a buffer has been bound to the input Tensor.
output a multidimensional array of output data, or a Buffer of primitive types including int, float, long, and byte. When a Buffer is used, the caller must ensure that it is set the appropriate write position. A null value is allowed, and is useful for certain cases, e.g., if the caller is using a Delegate that allows buffer handle interop, and such a buffer has been bound to the output Tensor (see also Interpreter.Options.setAllowBufferHandleOutput(boolean)), or if the graph has dynamically shaped outputs and the caller must query the output Tensor shape after inference has been invoked, fetching the data directly from the output tensor (via Tensor.asReadOnlyBuffer()).
Throws
IllegalArgumentException if input is null or empty, or if an error occurs when running inference.
IllegalArgumentException (EXPERIMENTAL, subject to change) if the inference is interrupted by setCancelled(true).

public abstract void runForMultipleInputsOutputs (Object[] inputs, Map<IntegerObject> outputs)

Runs model inference if the model takes multiple inputs, or returns multiple outputs.

Warning: The API is more efficient if Buffers (preferably direct, but not required) are used as the input/output data types. Please consider using Buffer to feed and fetch primitive data for better performance. The following concrete Buffer types are supported:

  • ByteBuffer - compatible with any underlying primitive Tensor type.
  • FloatBuffer - compatible with float Tensors.
  • IntBuffer - compatible with int32 Tensors.
  • LongBuffer - compatible with int64 Tensors.
Note that boolean types are only supported as arrays, not Buffers, or as scalar inputs.

Note: null values for invididual elements of inputs and outputs is allowed only if the caller is using a Delegate that allows buffer handle interop, and such a buffer has been bound to the corresponding input or output Tensor(s).

Parameters
inputs an array of input data. The inputs should be in the same order as inputs of the model. Each input can be an array or multidimensional array, or a Buffer of primitive types including int, float, long, and byte. Buffer is the preferred way to pass large input data, whereas string types require using the (multi-dimensional) array input path. When Buffer is used, its content should remain unchanged until model inference is done, and the caller must ensure that the Buffer is at the appropriate read position.
outputs a map mapping output indices to multidimensional arrays of output data or Buffers of primitive types including int, float, long, and byte. It only needs to keep entries for the outputs to be used. When a Buffer is used, the caller must ensure that it is set the appropriate write position. The map may be empty for cases where either buffer handles are used for output tensor data (see Interpreter.Options.setAllowBufferHandleOutput(boolean)), or cases where the outputs are dynamically shaped and the caller must query the output Tensor shape after inference has been invoked, fetching the data directly from the output tensor (via Tensor.asReadOnlyBuffer()).
Throws
IllegalArgumentException if inputs is null or empty, if outputs is null, or if an error occurs when running inference.