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Tensor

public interface Tensor

A typed multi-dimensional array used in Tensorflow Lite.

The native handle of a Tensor is managed by NativeInterpreterWrapper, and does not needed to be closed by the client. However, once the NativeInterpreterWrapper has been closed, the tensor handle will be invalidated.

Nested Classes

class Tensor.QuantizationParams Quantization parameters that corresponds to the table, QuantizationParameters, in the TFLite Model schema file. 

Public Methods

abstract ByteBuffer
asReadOnlyBuffer()
Returns a read-only ByteBuffer view of the tensor data.
abstract DataType
dataType()
Returns the DataType of elements stored in the Tensor.
abstract int
numBytes()
Returns the size, in bytes, of the tensor data.
abstract int
numDimensions()
Returns the number of dimensions (sometimes referred to as rank) of the Tensor.
abstract int
numElements()
Returns the number of elements in a flattened (1-D) view of the tensor.
abstract Tensor.QuantizationParams
quantizationParams()
Returns the quantization parameters of the tensor within the owning Interpreter.
abstract int[]
shape()
Returns the shape of the Tensor, i.e., the sizes of each dimension.
abstract int[]
shapeSignature()
Returns the original shape of the Tensor, i.e., the sizes of each dimension - before any resizing was performed.

Public Methods

public abstract ByteBuffer asReadOnlyBuffer ()

Returns a read-only ByteBuffer view of the tensor data.

In general, this method is most useful for obtaining a read-only view of output tensor data, *after* inference has been executed (e.g., via Interpreter.run(Object, Object)). In particular, some graphs have dynamically shaped outputs, which can make feeding a predefined output buffer to the Interpreter awkward. Example usage:

interpreter.run(input, null);
 ByteBuffer outputBuffer = interpreter.getOutputTensor(0).asReadOnlyBuffer();
 // Copy or read from outputBuffer.
 

WARNING: If the tensor has not yet been allocated, e.g., before inference has been executed, the result is undefined. Note that the underlying tensor pointer may also change when the tensor is invalidated in any way (e.g., if inference is executed, or the graph is resized), so it is *not* safe to hold a reference to the returned buffer beyond immediate use directly following inference. Example *bad* usage:

ByteBuffer outputBuffer = interpreter.getOutputTensor(0).asReadOnlyBuffer();
 interpreter.run(input, null);
 // Copy or read from outputBuffer (which may now be invalid).
 

Throws
IllegalArgumentException if the tensor data has not been allocated.

public abstract DataType dataType ()

Returns the DataType of elements stored in the Tensor.

public abstract int numBytes ()

Returns the size, in bytes, of the tensor data.

public abstract int numDimensions ()

Returns the number of dimensions (sometimes referred to as rank) of the Tensor.

Will be 0 for a scalar, 1 for a vector, 2 for a matrix, 3 for a 3-dimensional tensor etc.

public abstract int numElements ()

Returns the number of elements in a flattened (1-D) view of the tensor.

public abstract Tensor.QuantizationParams quantizationParams ()

Returns the quantization parameters of the tensor within the owning Interpreter.

Only quantized tensors have valid QuantizationParameters. For tensor that are not quantized, the values of scale and zero_point are both 0.

public abstract int[] shape ()

Returns the shape of the Tensor, i.e., the sizes of each dimension.

Returns
  • an array where the i-th element is the size of the i-th dimension of the tensor.

public abstract int[] shapeSignature ()

Returns the original shape of the Tensor, i.e., the sizes of each dimension - before any resizing was performed. Unknown dimensions are designated with a value of -1.

Returns
  • an array where the i-th element is the size of the i-th dimension of the tensor.