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Added upsample and downsample operators #442

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4af032c
Added upsample and downsample operators
cliffburdick Jul 5, 2023
145541b
Update operators.h
cliffburdick Jul 6, 2023
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4 changes: 2 additions & 2 deletions docs/_sources/api/signalimage/general/chirp.rst
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.. _chirp_func:

chirp
=========
=====

Creates a real chirp signal (swept-frequency cosine)

Expand All @@ -19,7 +19,7 @@ Examples


cchirp
=========
======

Creates a complex chirp signal (swept-frequency cosine)

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17 changes: 17 additions & 0 deletions docs/_sources/api/signalimage/general/downsample.rst
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.. _downsample_func:

downsample
==========

downsample a signal by dropping samples

.. doxygenfunction:: downsample

Examples
~~~~~~~~

.. literalinclude:: ../../../../test/00_operators/OperatorTests.cu
:language: cpp
:start-after: example-begin downsample-test-1
:end-before: example-end downsample-test-1
:dedent:
17 changes: 17 additions & 0 deletions docs/_sources/api/signalimage/general/upsample.rst
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.. _upsample_func:

upsample
========

Upsample a signal by stuffing zeros

.. doxygenfunction:: upsample

Examples
~~~~~~~~

.. literalinclude:: ../../../../test/00_operators/OperatorTests.cu
:language: cpp
:start-after: example-begin upsample-test-1
:end-before: example-end upsample-test-1
:dedent:
48 changes: 31 additions & 17 deletions docs/_sources/quickstart.rst
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Expand Up @@ -72,7 +72,7 @@ Tensors can also be initialized using initializer list syntax using the ``SetVal
.. code-block:: cpp

auto myT = make_tensor<float>({3, 3});
myT.SetVals({ {1,2,3}, {4,5,6}, {7,8,9} });
myT.SetVals({{1,2,3}, {4,5,6}, {7,8,9}});

In other languages it's very common to initialize a tensor with a set of values on creation (ones, zeros, ranges). This will be covered later
in the tutorial when we discuss operators, and it should become clear why we initialize this way.
Expand Down Expand Up @@ -110,16 +110,16 @@ Slicing and dicing
------------------
As the name implies, ``t`` is a view into a region of memory. When the initial view is created and memory is allocated, the tensor view is
of the entire 10x20 contiguous block of memory. Often we don't want to see the entire block of memory, but only want to view a subset of the
underlying data. To do this, we use the ``Slice`` member function of the view class:
underlying data. To do this, we use the ``slice`` operator:

.. code-block:: cpp

auto tCube = t.Slice({3, 5}, {6, 8}); // Cube of t using rows 3-5 and cols 5-7
auto tRectS = t.Slice({0, 0}, {matxEnd, matxEnd}, {2, 2}); // Rectangle with stride of 2 in both dimensions
auto tCol = t.Slice<1>({0, 4}, {matxEnd, matxDropDim}); // Create a 1D tensor with only column 5
auto tRow = t.Slice<1>({4, 0}, {matxDropDim, matxEnd}); // Create a 1D tensor with only row 5
auto tCube = slice(t, {3, 5}, {6, 8}); // Cube of t using rows 3-5 and cols 5-7
auto tRectS = slice(t, {0, 0}, {matxEnd, matxEnd}, {2, 2}); // Rectangle with stride of 2 in both dimensions
auto tCol = slice<1>(t, {0, 4}, {matxEnd, matxDropDim}); // Create a 1D tensor with only column 5
auto tRow = slice<1>(t, {4, 0}, {matxDropDim, matxEnd}); // Create a 1D tensor with only row 5

``Slice`` returns a new view of the tensor using start, stop, and optional stride parameters. Since views are simply
``slice`` returns a new view of the tensor using start, stop, and optional stride parameters. Since views are simply
light-weight views into memory, none of these variants modify the data; they return an object with new parameters describing
how the data is viewed. The resulting variables can be used exactly as the original view above:

Expand All @@ -134,27 +134,34 @@ All view functions can be used on any type of existing view:

.. code-block:: cpp

auto tCubeP = t.Slice({3, 5}, {6, 8}).Permute({1, 0});
auto tCubeP = permute(slice(t, {3, 5}, {6, 8}), {1, 0});

The above code takes the same cube as before, but permutes the cube view by swapping the two dimensions.

``slice`` is not limited to only tensors; it can be used on any operator as input:

.. code-block:: cpp

slice(eye(t.Shape()), {3, 5}, {6, 8});

Permuting
---------
Permuting a tensor is done using the ``Permute`` member function of a view:
Permuting a tensor is done using the ``permute`` function:

.. code-block:: cpp

auto t = make_tensor<float>({10, 20});
auto tp = t.Permute({1,0});
auto tp = permute(t, {1,0});

``tp`` is now a view into ``t`` where the rows and columns are swapped (transpose). ``Permute`` is not limited to matrices, though:
``tp`` is now a view into ``t`` where the rows and columns are swapped (transpose). ``permute`` is not limited to matrices, though:

.. code-block:: cpp

auto t4 = make_tensor<float>({10, 20, 5, 2});
auto tp4 = t.Permute({1,3,2,0});
auto tp4 = permute(t, {1,3,2,0});

``t4p`` is now a permuted view of the original 4D tensor, but with the dimensions swapped as ordered in the initializer list.
``t4p`` is now a permuted view of the original 4D tensor, but with the dimensions swapped as ordered in the initializer list. Just like
with ``slice``, ``permute`` works on operators as well.

Note that since no data is moved, permuting a tensor can be detrimental to performance, depending on the context. Permuting usually
changes the strides of dimensions such that the memory access patterns are no longer optimal, and accessing the permuted view
Expand All @@ -166,7 +173,13 @@ a new tensor so that the new layout is contiguous. Using the variables from abov
auto t4pc = make_tensor<float>(tp4.Shape());
copy(t4pc, t4p);

``t4pc`` will now contain the permuted data, but in contiguous memory.
``t4pc`` will now contain the permuted data, but in contiguous memory. Copying a tensor (or operator) can also be done by the assignment
operator:

.. code-block:: cpp

(t4pc = t4p).run();


Reshaping
---------
Expand All @@ -192,7 +205,7 @@ all rows in a matrix, you can clone the tensor to a higher rank to match the oth
auto t2 = make_tensor<float>({16, 16});
// ... Initialize tensors

auto t1c = t1.Clone<2>({16, matxKeepDim});
auto t1c = clone<2>(t1, {16, matxKeepDim});

``t1c`` is now a new tensor view where each row is a replica of the tensor ``t1``. Again, this is just a view and no data was modified or
allocated, so modifying a row/column in either of these tensors will affect the other.
Expand All @@ -201,9 +214,9 @@ The keyword ``matxKeepDim`` tells MatX which dimensions should be kept from the
In this example we used it in the columns place of the shape, but we also could have used ``{matxKeepDim, 16}`` and we would have a 2D
view where all columns of ``t1c`` matches ``t1``.

Note in some cases MatX's *broadcasting* feature can be used instead of ``Clone``. This allows an implicit expansion of ranks during an
Note in some cases MatX's *broadcasting* feature can be used instead of ``clone``. This allows an implicit expansion of ranks during an
element-wise operation. For example, adding a 4D tensor to a 1D tensor will work as long as the outer dimension of the 4D tensor matches
that of the 1D tensor. Broadcasting is covered in the documentation. ``Clone`` is much more powerful since it gives more control over which
that of the 1D tensor. Broadcasting is covered in the documentation. ``clone`` is much more powerful since it gives more control over which
dimensions are cloned instead of assuming the outer dimensions.

Creating a view from an existing pointer
Expand Down Expand Up @@ -319,6 +332,7 @@ device, at which point only *one* of these assignments will occur.

Initialization of operators and generators
##########################################

As mentioned above, it's common in high-level languages to initialize a tensor/array with a known set of values. For example, generating a range of linearly-
spaced values, all ones, or a diagonal matrix. These are all operations that do not need to be generated and stored in memory before using since they are
all generated from a formula. MatX calls these types of operators a *generator*, indicating that they generate data without storage.
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