After some discussion on the PR [https://github.com/apache/arrow/pull/37166]
we've altered the proposed type by removing the ndim parameter and
adding ragged_dimensions one.
If there is no further feedback I'd like to call for a vote early next
week. Proposed language now reads:
Variable shape tensor
=====================
* Extension name: `arrow.variable_shape_tensor`.
* The storage type of the extension is: ``StructArray`` where struct
is composed of **data** and **shape** fields describing a single
tensor per row:
* **data** is a ``List`` holding tensor elements of a single tensor.
Data type of the list elements is uniform across the entire column
and also provided in metadata.
* **shape** is a ``FixedSizeList<uint32>[ndim]`` of the tensor shape where
the size of the list ``ndim`` is equal to the number of dimensions of
the
tensor.
* Extension type parameters:
* **value_type** = the Arrow data type of individual tensor elements.
Optional parameters describing the logical layout:
* **dim_names** = explicit names to tensor dimensions
as an array. The length of it should be equal to the shape
length and equal to the number of dimensions.
``dim_names`` can be used if the dimensions have well-known
names and they map to the physical layout (row-major).
* **permutation** = indices of the desired ordering of the
original dimensions, defined as an array.
The indices contain a permutation of the values [0, 1, .., N-1] where
N is the number of dimensions. The permutation indicates which
dimension of the logical layout corresponds to which dimension of the
physical tensor (the i-th dimension of the logical view corresponds
to the dimension with number ``permutations[i]`` of the physical
tensor).
Permutation can be useful in case the logical order of
the tensor is a permutation of the physical order (row-major).
When logical and physical layout are equal, the permutation will always
be ([0, 1, .., N-1]) and can therefore be left out.
* **ragged_dimensions** = indices of ragged dimensions whose sizes may
differ. Dimensions where all elements have the same size are called
uniform dimensions. Indices are a subset of all possible dimension
indices ([0, 1, .., N-1]).
Ragged dimensions list can be left out. In that case all dimensions
are assumed ragged.
* Description of the serialization:
The metadata must be a valid JSON object including number of
dimensions of the contained tensors as an integer with key **"ndim"**
plus optional dimension names with keys **"dim_names"** and ordering of
the dimensions with key **"permutation"**.
- Example with ``dim_names`` metadata for NCHW ordered data:
``{ "dim_names": ["C", "H", "W"] }``
- Example with ``ragged_dimensions`` metadata for a set of color images
with variable width:
``{ "dim_names": ["H", "W", "C"], "ragged_dimensions": [1] }``
- Example of permuted 3-dimensional tensor:
``{ "permutation": [2, 0, 1] }``
This is the physical layout shape and the shape of the logical
layout would given an individual tensor of shape [100, 200, 500]
be ``[500, 100, 200]``.
.. note::
Elements in a variable shape tensor extension array are stored
in row-major/C-contiguous order.
Rok
