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| -rw-r--r-- | docs/01_library.dox | 19 |
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diff --git a/docs/01_library.dox b/docs/01_library.dox index c4edad234d..bd4b300d7d 100644 --- a/docs/01_library.dox +++ b/docs/01_library.dox @@ -293,6 +293,25 @@ Here are a couple of examples of how to use the iterators to fill / read tensors @snippet examples/neon_copy_objects.cpp Copy objects example +@subsection S4_6_5_sub_tensors Sub-tensors + +Sub-tensors are aliases to existing Tensors, as a result creating a sub-tensor does not result in any underlying memory allocation. + +Sub-tensors can be used to access a sub-set of the parent tensor, something that can be useful in case different operations need to be performed on different parts of a tensor. + +Moreover, sub-tensors can be used to perform zero copy tensor concatenation. + +The API for creating a sub-tensor is the following: +@code{.cpp} +SubTensor(ITensor *parent, const TensorShape &tensor_shape, const Coordinates &coords) +@endcode + +Where \a parent is the parent tensor which we want to create an alias for, \a tensor_shape is the shape of the sub-tensor and \a coords are the starting indexing coordinates of the sub-tensor within the parent tensor. + +@note Two sub-tensor concrete classes for different targets are currently supported : @ref CLSubTensor and @ref SubTensor + +@warning Limitation of the sub-tensor is that it cannot be extracted spatially, meaning sub-tensors should have the same width and height as the parent tensor. The main reasons for this is the fact that individual kernels might need to operate with a step size that is not a multiple of the sub-tensor spatial dimension. This could lead to elements being overwritten by different kernels operating on different sub-tensors of the same underlying tensor. + @section S4_7_memory_manager MemoryManager @ref IMemoryManager is a memory managing interface that can be used to reduce the memory requirements of a given pipeline by recycling temporary buffers. |