| /* |
| * 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 |