| /* |
| * Copyright (c) 2016-2020, 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. |
| */ |
| namespace arm_compute |
| { |
| inline Window::Window(const Window &src) |
| : _dims(), _is_broadcasted(utility::generate_array<bool, Coordinates::num_max_dimensions, false>::value) |
| { |
| for(size_t i = 0; i < Coordinates::num_max_dimensions; ++i) |
| { |
| set(i, src[i]); |
| _is_broadcasted[i] = src.is_broadcasted(i); |
| } |
| } |
| |
| inline Window &Window::operator=(const arm_compute::Window &rhs) |
| { |
| Window tmp(rhs); |
| swap(*this, tmp); |
| return *this; |
| } |
| |
| inline constexpr const Window::Dimension &Window::operator[](size_t dimension) const |
| { |
| // Precondition: dimension < Coordinates::num_max_dimensions |
| return _dims.at(dimension); |
| } |
| |
| inline void Window::set(size_t dimension, const Window::Dimension &dim) |
| { |
| ARM_COMPUTE_ERROR_ON(dimension >= Coordinates::num_max_dimensions); |
| _dims[dimension] = dim; |
| } |
| |
| inline void Window::set_broadcasted(size_t dimension) |
| { |
| ARM_COMPUTE_ERROR_ON(dimension >= Coordinates::num_max_dimensions); |
| set(dimension, Dimension(0, 0, 0)); |
| _is_broadcasted[dimension] = true; |
| } |
| |
| inline bool Window::is_broadcasted(size_t dimension) const |
| { |
| ARM_COMPUTE_ERROR_ON(dimension >= Coordinates::num_max_dimensions); |
| return _is_broadcasted[dimension]; |
| } |
| |
| inline Window Window::collapse_if_possible(const Window &full_window, const size_t first, |
| const size_t last, bool *has_collapsed) const |
| { |
| Window collapsed(*this); |
| |
| bool is_collapsable = true; |
| int collapsed_end = _dims[first].end(); |
| |
| for(size_t d = first + 1; is_collapsable && (d < last); ++d) |
| { |
| // The _dims's dimension must match the full _dims dimension to be collapsable: |
| is_collapsable = (_dims[d].start() == 0) && (full_window[d].start() == 0) && (_dims[d].step() <= 1) |
| && (full_window[d].end() == _dims[d].end()); |
| collapsed_end *= _dims[d].end(); |
| } |
| |
| if(is_collapsable) |
| { |
| collapsed._dims.at(first).set_end(collapsed_end); |
| for(size_t d = first + 1; is_collapsable && (d < last); ++d) |
| { |
| collapsed.set(d, Dimension()); |
| } |
| } |
| |
| if(has_collapsed != nullptr) |
| { |
| *has_collapsed = is_collapsable; |
| } |
| |
| return collapsed; |
| } |
| |
| inline Window Window::shift_dimensions(unsigned int shift_value) const |
| { |
| Window shifted_window; |
| for(size_t n = 0; n < (Coordinates::num_max_dimensions - shift_value); n++) |
| { |
| shifted_window.set(n, _dims[n + shift_value]); |
| } |
| return shifted_window; |
| } |
| |
| inline Window Window::collapse(const Window &full_window, const size_t first, const size_t last) const |
| { |
| bool has_collapsed = false; |
| Window collapsed = collapse_if_possible(full_window, first, last, &has_collapsed); |
| // Make sure that the window has collapsed |
| ARM_COMPUTE_ERROR_ON(!has_collapsed); |
| return collapsed; |
| } |
| |
| inline Window Window::broadcast_if_dimension_le_one(const TensorShape &shape) const |
| { |
| Window broadcastWin(*this); |
| for(size_t d = 0; d < TensorShape::num_max_dimensions; ++d) |
| { |
| if(shape[d] <= 1) |
| { |
| broadcastWin.set_broadcasted(d); |
| } |
| } |
| return broadcastWin; |
| } |
| |
| inline void Window::shift(size_t dimension, int shift_value) |
| { |
| ARM_COMPUTE_ERROR_ON(dimension >= Coordinates::num_max_dimensions); |
| Window::Dimension &d = _dims[dimension]; |
| d = Window::Dimension(d.start() + shift_value, d.end() + shift_value, d.step()); |
| } |
| |
| inline void Window::adjust(size_t dimension, int adjust_value, bool is_at_start) |
| { |
| ARM_COMPUTE_ERROR_ON(dimension >= Coordinates::num_max_dimensions); |
| Window::Dimension &d = _dims[dimension]; |
| |
| if(is_at_start) |
| { |
| d = Window::Dimension(d.start() + adjust_value, d.end(), d.step()); |
| } |
| else |
| { |
| d = Window::Dimension(d.start(), d.end() + adjust_value, d.step()); |
| } |
| } |
| |
| inline void Window::scale(size_t dimension, float scale_value) |
| { |
| ARM_COMPUTE_ERROR_ON(dimension >= Coordinates::num_max_dimensions); |
| Window::Dimension &d = _dims[dimension]; |
| const int scaled_step = d.step() * scale_value; |
| const int scaled_start = d.start() * scale_value; |
| const int scaled_diff = (d.end() - d.start()) * scale_value; |
| const int scaled_end = scaled_start + ceil_to_multiple(scaled_diff, scaled_step); |
| |
| d = Window::Dimension(scaled_start, scaled_end, scaled_step); |
| } |
| |
| inline void Window::set_dimension_step(size_t dimension, int step) |
| { |
| ARM_COMPUTE_ERROR_ON(dimension >= Coordinates::num_max_dimensions); |
| _dims[dimension].set_step(step); |
| } |
| |
| inline void Window::validate() const |
| { |
| for(size_t i = 0; i < Coordinates::num_max_dimensions; ++i) |
| { |
| ARM_COMPUTE_ERROR_ON(_dims[i].end() < _dims[i].start()); |
| ARM_COMPUTE_ERROR_ON((_dims[i].step() != 0) && (((_dims[i].end() - _dims[i].start()) % _dims[i].step()) != 0)); |
| } |
| } |
| |
| inline constexpr size_t Window::num_iterations(size_t dimension) const |
| { |
| // Precondition: dimension < Coordinates::num_max_dimensions |
| // Precondition: (end - start) % step == 0 |
| return (_dims.at(dimension).end() - _dims.at(dimension).start()) / _dims.at(dimension).step(); |
| } |
| |
| inline Window Window::split_window(size_t dimension, size_t id, size_t total) const |
| { |
| ARM_COMPUTE_ERROR_ON(id >= total); |
| ARM_COMPUTE_ERROR_ON(dimension >= Coordinates::num_max_dimensions); |
| |
| Window out; |
| |
| for(size_t d = 0; d < Coordinates::num_max_dimensions; ++d) |
| { |
| if(d == dimension) |
| { |
| int start = _dims[d].start(); |
| int end = _dims[d].end(); |
| const int step = _dims[d].step(); |
| |
| const int num_it = num_iterations(d); |
| const int rem = num_it % total; |
| int work = num_it / total; |
| |
| int it_start = work * id; |
| |
| if(int(id) < rem) |
| { |
| ++work; |
| it_start += id; |
| } |
| else |
| { |
| it_start += rem; |
| } |
| |
| start += it_start * step; |
| end = std::min(end, start + work * step); |
| |
| out.set(d, Dimension(start, end, step)); |
| } |
| else |
| { |
| out.set(d, _dims[d]); |
| } |
| } |
| |
| return out; |
| } |
| |
| template <unsigned int window_dimension> |
| inline bool Window::slide_window_slice(Window &slice) const |
| { |
| for(unsigned int n = window_dimension; n < Coordinates::num_max_dimensions; ++n) |
| { |
| // Did we reach the end of this dimension? |
| const int v = slice._dims[n].start() + 1; |
| |
| if(v < _dims[n].end()) |
| { |
| // No: increment |
| slice._dims[n] = Dimension(v, v + 1, 1); |
| |
| // Reset lower dimensions: |
| for(unsigned int lower = window_dimension; lower < n; ++lower) |
| { |
| slice._dims[lower] = Dimension(_dims[lower].start(), _dims[lower].start() + 1, 1); |
| } |
| return true; |
| } |
| } |
| |
| // It was the last slice |
| return false; // Iteration over |
| } |
| |
| template <unsigned int window_dimension> |
| inline Window Window::first_slice_window() const |
| { |
| Window slice; |
| |
| std::copy_n(_dims.begin(), window_dimension, slice._dims.begin()); |
| |
| //Initialise higher dimensions to be the first slice. |
| for(unsigned int n = window_dimension; n < Coordinates::num_max_dimensions; ++n) |
| { |
| slice._dims[n] = Dimension(_dims[n].start(), _dims[n].start() + 1, 1); |
| } |
| |
| return slice; |
| } |
| |
| inline void Window::use_tensor_dimensions(const TensorShape &shape, size_t first_dimension) |
| { |
| for(unsigned int n = first_dimension; n < shape.num_dimensions(); ++n) |
| { |
| set(n, Window::Dimension(0, std::max(shape[n], static_cast<size_t>(1)))); |
| } |
| } |
| |
| inline TensorShape Window::shape() const |
| { |
| TensorShape shape; |
| for(size_t d = 0; d < TensorShape::num_max_dimensions; ++d) |
| { |
| shape.set(d, (_dims[d].end() - _dims[d].start()) / _dims[d].step()); |
| } |
| return shape; |
| } |
| |
| inline size_t Window::num_iterations_total() const |
| { |
| size_t total = 1; |
| for(size_t d = 0; d < Coordinates::num_max_dimensions; ++d) |
| { |
| total *= num_iterations(d); |
| } |
| return total; |
| } |
| |
| inline void swap(Window &lhs, Window &rhs) |
| { |
| lhs._dims.swap(rhs._dims); |
| } |
| |
| inline bool operator==(const Window &lhs, const Window &rhs) |
| { |
| return (lhs._dims == rhs._dims) && (lhs._is_broadcasted == rhs._is_broadcasted); |
| } |
| } // namespace arm_compute |