Tensor
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.
Summary
Nested types |
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Quantization parameters that corresponds to the table, |
Public functions |
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ByteBuffer! |
Returns a read-only |
DataType! |
dataType()Returns the |
Int |
numBytes()Returns the size, in bytes, of the tensor data. |
Int |
Returns the number of dimensions (sometimes referred to as rank) of the Tensor. |
Int |
Returns the number of elements in a flattened (1-D) view of the tensor. |
Tensor.QuantizationParams! |
Returns the quantization parameters of the tensor within the owning interpreter. |
IntArray<Int>! |
shape()Returns the shape of the Tensor, i.e., the sizes of each dimension. |
IntArray<Int>! |
Returns the original shape of the Tensor, i.e., the sizes of each dimension - before any resizing was performed. |
Public functions
asReadOnlyBuffer
fun asReadOnlyBuffer(): ByteBuffer!
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 run). 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 | |
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java.lang.IllegalArgumentException |
if the tensor data has not been allocated. |
numDimensions
fun numDimensions(): Int
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.
numElements
fun numElements(): Int
Returns the number of elements in a flattened (1-D) view of the tensor.
quantizationParams
fun quantizationParams(): Tensor.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.
shape
fun shape(): IntArray<Int>!
Returns the shape of the Tensor, i.e., the sizes of each dimension.
shapeSignature
fun shapeSignature(): IntArray<Int>!
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.