Tensor

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 InterpreterApi.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).

Returns the DataType of elements stored in the Tensor.

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

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.

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

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.

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

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.