src/core/helpers/WindowHelpers.cpp - ml/ComputeLibrary - Gitiles

 /*
 * Copyright (c) 2020-2022 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
  * deal in the Software without restriction, including without limitation the
  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  * sell copies of the Software, and to permit persons to whom the Software is
  * furnished to do so, subject to the following conditions:
  *
  * The above copyright notice and this permission notice shall be included in all
  * copies or substantial portions of the Software.
  *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
 #include "src/core/helpers/WindowHelpers.h"

 namespace arm_compute
 {
 Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
 {
     if(!skip_border)
     {
         border_size = BorderSize(0);
     }

     const Coordinates &anchor = valid_region.anchor;
     const TensorShape &shape  = valid_region.shape;

     Window window;

     window.set(0, Window::Dimension(
                    // Skip the border left of the image
                    anchor[0] + border_size.left,
                    // Skip the border right of the image
                    // Make sure the window width is a multiple of the step size
                    anchor[0] + border_size.left + ceil_to_multiple(std::max(0, static_cast<int>(shape[0]) - static_cast<int>(border_size.left) - static_cast<int>(border_size.right)), steps[0]),
                    steps[0]));

     size_t n = 1;

     if(anchor.num_dimensions() > 1)
     {
         window.set(1, Window::Dimension(
                        // Skip the border above the image
                        anchor[1] + border_size.top,
                        // Skip the border below the image
                        anchor[1] + border_size.top + ceil_to_multiple(std::max(0, static_cast<int>(shape[1]) - static_cast<int>(border_size.top) - static_cast<int>(border_size.bottom)), steps[1]),
                        steps[1]));

         ++n;
     }

     if(anchor.num_dimensions() > 2)
     {
         window.set(2, Window::Dimension(anchor[2], std::max<size_t>(1, shape[2]), steps[2]));

         ++n;
     }

     for(; n < anchor.num_dimensions(); ++n)
     {
         window.set(n, Window::Dimension(anchor[n], std::max<size_t>(1, shape[n])));
     }

     for(; n < Coordinates::num_max_dimensions; ++n)
     {
         window.set(n, Window::Dimension(0, 1));
     }

     return window;
 }

 Window calculate_max_window(const TensorShape &shape, const Steps &steps, bool skip_border, BorderSize border_size)
 {
     if(!skip_border)
     {
         border_size = BorderSize(0);
     }

     Window window;

     window.set(0, Window::Dimension(
                    // Skip the border left of the image
                    border_size.left,
                    // Skip the border right of the image
                    // Make sure the window width is a multiple of the step size
                    border_size.left + ceil_to_multiple(std::max(0, static_cast<int>(shape[0]) - static_cast<int>(border_size.left) - static_cast<int>(border_size.right)), steps[0]),
                    steps[0]));

     size_t n = 1;

     if(shape.num_dimensions() > 1)
     {
         window.set(1, Window::Dimension(
                        // Skip the border above the image
                        border_size.top,
                        // Skip the border below the image
                        border_size.top + ceil_to_multiple(std::max(0, static_cast<int>(shape[1]) - static_cast<int>(border_size.top) - static_cast<int>(border_size.bottom)), steps[1]),
                        steps[1]));

         ++n;
     }

     if(shape.num_dimensions() > 2)
     {
         window.set(2, Window::Dimension(0, std::max<size_t>(1, shape[2]), steps[2]));

         ++n;
     }

     for(; n < shape.num_dimensions(); ++n)
     {
         window.set(n, Window::Dimension(0, std::max<size_t>(1, shape[n])));
     }

     for(; n < Coordinates::num_max_dimensions; ++n)
     {
         window.set(n, Window::Dimension(0, 1));
     }

     return window;
 }

 Window calculate_max_enlarged_window(const ValidRegion &valid_region, const Steps &steps, BorderSize border_size)
 {
     const Coordinates &anchor = valid_region.anchor;
     const TensorShape &shape  = valid_region.shape;

     Window window;

     window.set(0, Window::Dimension(
                    // move the anchor to the start from the border
                    anchor[0] - border_size.left,
                    // move the anchor to include the right end border
                    // Make sure the window width is a multiple of the step size
                    anchor[0] - border_size.left + ceil_to_multiple(shape[0] + border_size.left + border_size.right, steps[0]),
                    steps[0]));

     size_t n = 1;

     if(anchor.num_dimensions() > 1)
     {
         window.set(1, Window::Dimension(
                        // Include the border above the image
                        anchor[1] - border_size.top,
                        // Include the border below the image
                        anchor[1] - border_size.top + ceil_to_multiple(shape[1] + border_size.top + border_size.bottom, steps[1]),
                        steps[1]));

         ++n;
     }

     if(anchor.num_dimensions() > 2)
     {
         window.set(2, Window::Dimension(0, std::max<size_t>(1, shape[n]), steps[2]));

         ++n;
     }

     for(; n < anchor.num_dimensions(); ++n)
     {
         window.set(n, Window::Dimension(anchor[n], std::max<size_t>(1, shape[n])));
     }

     for(; n < Coordinates::num_max_dimensions; ++n)
     {
         window.set(n, Window::Dimension(0, 1));
     }

     return window;
 }

 Window calculate_max_window_horizontal(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
 {
     if(skip_border)
     {
         border_size.top    = 0;
         border_size.bottom = 0;
     }
     else
     {
         border_size.left  = 0;
         border_size.right = 0;
     }

     const Coordinates &anchor = valid_region.anchor;
     const TensorShape &shape  = valid_region.shape;

     Window window;

     window.set(0, Window::Dimension(
                    // Skip the border left of the image
                    anchor[0] + border_size.left,
                    // Skip the border right of the image
                    // Make sure the window width is a multiple of the step size
                    anchor[0] + border_size.left + ceil_to_multiple(std::max(0, static_cast<int>(shape[0]) - static_cast<int>(border_size.left) - static_cast<int>(border_size.right)), steps[0]),
                    steps[0]));

     size_t n = 1;

     if(anchor.num_dimensions() > 1)
     {
         window.set(1, Window::Dimension(
                        // Skip the border above the image
                        anchor[1] - border_size.top,
                        // Skip the border below the image
                        anchor[1] + shape[1] + border_size.bottom,
                        1));

         ++n;
     }

     for(; n < anchor.num_dimensions(); ++n)
     {
         window.set(n, Window::Dimension(anchor[n], std::max<size_t>(1, shape[n])));
     }

     for(; n < Coordinates::num_max_dimensions; ++n)
     {
         window.set(n, Window::Dimension(0, 1));
     }

     return window;
 }

 std::pair<Window, size_t> calculate_squashed_or_max_window(const ITensorInfo &src0, const ITensorInfo &src1)
 {
     const auto &shape0 = src0.tensor_shape();
     const auto &shape1 = src1.tensor_shape();
     const auto &strides0 = src0.strides_in_bytes();
     const auto &strides1 = src1.strides_in_bytes();
     const auto num_dimensions = std::max(src0.num_dimensions(), src1.num_dimensions());

     Window win;
     size_t split_dimension = Window::DimY;
     size_t dim = 0;

     size_t squashed_bytes = src0.element_size();

     // Try to squash the low dimensions together.
     for(; dim < num_dimensions; ++dim)
     {
         if(shape0[dim] != shape1[dim] || strides0[dim] != squashed_bytes || strides1[dim] != squashed_bytes)
         {
             break;
         }

         squashed_bytes *= shape0[dim];
     }

     if(dim == num_dimensions)
     {
         auto squashed_elements = squashed_bytes / src0.element_size();

         split_dimension = Window::DimX;

         // The input tensors can be interpreted as 1D array.
         win.set(0, Window::Dimension(0, squashed_elements, 1));

         for(dim = 1; dim < Coordinates::num_max_dimensions; ++dim)
         {
             win.set(dim, Window::Dimension(0, 1, 1));
         }
     }
     else
     {
         // Generates the max window.
         for(dim = 0; dim < Coordinates::num_max_dimensions; ++dim)
         {
             win.set(dim, Window::Dimension(0, std::max(shape0[dim], shape1[dim]), 1));
         }
     }

     return std::make_pair(win, split_dimension);
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2020-2022 Arm Limited.
	*
	* SPDX-License-Identifier: MIT
	*
	* Permission is hereby granted, free of charge, to any person obtaining a copy
	* of this software and associated documentation files (the "Software"), to
	* deal in the Software without restriction, including without limitation the
	* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
	* sell copies of the Software, and to permit persons to whom the Software is
	* furnished to do so, subject to the following conditions:
	*
	* The above copyright notice and this permission notice shall be included in all
	* copies or substantial portions of the Software.
	*
	* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	* SOFTWARE.
	*/
	#include "src/core/helpers/WindowHelpers.h"

	namespace arm_compute
	{
	Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
	{
	if(!skip_border)
	{
	border_size = BorderSize(0);
	}

	const Coordinates &anchor = valid_region.anchor;
	const TensorShape &shape = valid_region.shape;

	Window window;

	window.set(0, Window::Dimension(
	// Skip the border left of the image
	anchor[0] + border_size.left,
	// Skip the border right of the image
	// Make sure the window width is a multiple of the step size
	anchor[0] + border_size.left + ceil_to_multiple(std::max(0, static_cast<int>(shape[0]) - static_cast<int>(border_size.left) - static_cast<int>(border_size.right)), steps[0]),
	steps[0]));

	size_t n = 1;

	if(anchor.num_dimensions() > 1)
	{
	window.set(1, Window::Dimension(
	// Skip the border above the image
	anchor[1] + border_size.top,
	// Skip the border below the image
	anchor[1] + border_size.top + ceil_to_multiple(std::max(0, static_cast<int>(shape[1]) - static_cast<int>(border_size.top) - static_cast<int>(border_size.bottom)), steps[1]),
	steps[1]));

	++n;
	}

	if(anchor.num_dimensions() > 2)
	{
	window.set(2, Window::Dimension(anchor[2], std::max<size_t>(1, shape[2]), steps[2]));

	++n;
	}

	for(; n < anchor.num_dimensions(); ++n)
	{
	window.set(n, Window::Dimension(anchor[n], std::max<size_t>(1, shape[n])));
	}

	for(; n < Coordinates::num_max_dimensions; ++n)
	{
	window.set(n, Window::Dimension(0, 1));
	}

	return window;
	}

	Window calculate_max_window(const TensorShape &shape, const Steps &steps, bool skip_border, BorderSize border_size)
	{
	if(!skip_border)
	{
	border_size = BorderSize(0);
	}

	Window window;

	window.set(0, Window::Dimension(
	// Skip the border left of the image
	border_size.left,
	// Skip the border right of the image
	// Make sure the window width is a multiple of the step size
	border_size.left + ceil_to_multiple(std::max(0, static_cast<int>(shape[0]) - static_cast<int>(border_size.left) - static_cast<int>(border_size.right)), steps[0]),
	steps[0]));

	size_t n = 1;

	if(shape.num_dimensions() > 1)
	{
	window.set(1, Window::Dimension(
	// Skip the border above the image
	border_size.top,
	// Skip the border below the image
	border_size.top + ceil_to_multiple(std::max(0, static_cast<int>(shape[1]) - static_cast<int>(border_size.top) - static_cast<int>(border_size.bottom)), steps[1]),
	steps[1]));

	++n;
	}

	if(shape.num_dimensions() > 2)
	{
	window.set(2, Window::Dimension(0, std::max<size_t>(1, shape[2]), steps[2]));

	++n;
	}

	for(; n < shape.num_dimensions(); ++n)
	{
	window.set(n, Window::Dimension(0, std::max<size_t>(1, shape[n])));
	}

	for(; n < Coordinates::num_max_dimensions; ++n)
	{
	window.set(n, Window::Dimension(0, 1));
	}

	return window;
	}

	Window calculate_max_enlarged_window(const ValidRegion &valid_region, const Steps &steps, BorderSize border_size)
	{
	const Coordinates &anchor = valid_region.anchor;
	const TensorShape &shape = valid_region.shape;

	Window window;

	window.set(0, Window::Dimension(
	// move the anchor to the start from the border
	anchor[0] - border_size.left,
	// move the anchor to include the right end border
	// Make sure the window width is a multiple of the step size
	anchor[0] - border_size.left + ceil_to_multiple(shape[0] + border_size.left + border_size.right, steps[0]),
	steps[0]));

	size_t n = 1;

	if(anchor.num_dimensions() > 1)
	{
	window.set(1, Window::Dimension(
	// Include the border above the image
	anchor[1] - border_size.top,
	// Include the border below the image
	anchor[1] - border_size.top + ceil_to_multiple(shape[1] + border_size.top + border_size.bottom, steps[1]),
	steps[1]));

	++n;
	}

	if(anchor.num_dimensions() > 2)
	{
	window.set(2, Window::Dimension(0, std::max<size_t>(1, shape[n]), steps[2]));

	++n;
	}

	for(; n < anchor.num_dimensions(); ++n)
	{
	window.set(n, Window::Dimension(anchor[n], std::max<size_t>(1, shape[n])));
	}

	for(; n < Coordinates::num_max_dimensions; ++n)
	{
	window.set(n, Window::Dimension(0, 1));
	}

	return window;
	}

	Window calculate_max_window_horizontal(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
	{
	if(skip_border)
	{
	border_size.top = 0;
	border_size.bottom = 0;
	}
	else
	{
	border_size.left = 0;
	border_size.right = 0;
	}

	const Coordinates &anchor = valid_region.anchor;
	const TensorShape &shape = valid_region.shape;

	Window window;

	window.set(0, Window::Dimension(
	// Skip the border left of the image
	anchor[0] + border_size.left,
	// Skip the border right of the image
	// Make sure the window width is a multiple of the step size
	anchor[0] + border_size.left + ceil_to_multiple(std::max(0, static_cast<int>(shape[0]) - static_cast<int>(border_size.left) - static_cast<int>(border_size.right)), steps[0]),
	steps[0]));

	size_t n = 1;

	if(anchor.num_dimensions() > 1)
	{
	window.set(1, Window::Dimension(
	// Skip the border above the image
	anchor[1] - border_size.top,
	// Skip the border below the image
	anchor[1] + shape[1] + border_size.bottom,
	1));

	++n;
	}

	for(; n < anchor.num_dimensions(); ++n)
	{
	window.set(n, Window::Dimension(anchor[n], std::max<size_t>(1, shape[n])));
	}

	for(; n < Coordinates::num_max_dimensions; ++n)
	{
	window.set(n, Window::Dimension(0, 1));
	}

	return window;
	}

	std::pair<Window, size_t> calculate_squashed_or_max_window(const ITensorInfo &src0, const ITensorInfo &src1)
	{
	const auto &shape0 = src0.tensor_shape();
	const auto &shape1 = src1.tensor_shape();
	const auto &strides0 = src0.strides_in_bytes();
	const auto &strides1 = src1.strides_in_bytes();
	const auto num_dimensions = std::max(src0.num_dimensions(), src1.num_dimensions());

	Window win;
	size_t split_dimension = Window::DimY;
	size_t dim = 0;

	size_t squashed_bytes = src0.element_size();

	// Try to squash the low dimensions together.
	for(; dim < num_dimensions; ++dim)
	{
	if(shape0[dim] != shape1[dim] \|\| strides0[dim] != squashed_bytes \|\| strides1[dim] != squashed_bytes)
	{
	break;
	}

	squashed_bytes *= shape0[dim];
	}

	if(dim == num_dimensions)
	{
	auto squashed_elements = squashed_bytes / src0.element_size();

	split_dimension = Window::DimX;

	// The input tensors can be interpreted as 1D array.
	win.set(0, Window::Dimension(0, squashed_elements, 1));

	for(dim = 1; dim < Coordinates::num_max_dimensions; ++dim)
	{
	win.set(dim, Window::Dimension(0, 1, 1));
	}
	}
	else
	{
	// Generates the max window.
	for(dim = 0; dim < Coordinates::num_max_dimensions; ++dim)
	{
	win.set(dim, Window::Dimension(0, std::max(shape0[dim], shape1[dim]), 1));
	}
	}

	return std::make_pair(win, split_dimension);
	}
	} // namespace arm_compute