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+/*
+* 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);
+}
+
+std::pair<Window, size_t> calculate_squashed_or_max_window(const ITensorInfo &src)
+{
+ const auto &shape = src.tensor_shape();
+ const auto &strides = src.strides_in_bytes();
+ const auto num_dimensions = src.num_dimensions();
+
+ Window win;
+ size_t split_dimension = Window::DimY;
+ size_t dim = 0;
+ size_t squashed_bytes = src.element_size();
+
+ // Try to squash the low dimensions together.
+ for (; dim < num_dimensions; ++dim)
+ {
+ if (strides[dim] != squashed_bytes)
+ {
+ break;
+ }
+ squashed_bytes *= shape[dim];
+ }
+ if (dim == num_dimensions)
+ {
+ const auto squashed_elements = squashed_bytes / src.element_size();
+ split_dimension = Window::DimX;
+ // The input tensor 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
+ {
+ // Generate the max window.
+ for (dim = 0; dim < Coordinates::num_max_dimensions; ++dim)
+ {
+ win.set(dim, Window::Dimension(0, shape[dim], 1));
+ }
+ }
+ return std::make_pair(win, split_dimension);
+}
+
+} // namespace arm_compute
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