| /* |
| * Copyright (c) 2017-2018 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 "arm_compute/core/NEON/kernels/NEMinMaxLayerKernel.h" |
| |
| #include "arm_compute/core/Coordinates.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/IAccessWindow.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include <algorithm> |
| #include <arm_neon.h> |
| #include <climits> |
| #include <cstddef> |
| |
| namespace arm_compute |
| { |
| NEMinMaxLayerKernel::NEMinMaxLayerKernel() |
| : _input(nullptr), _output(nullptr), _mtx() |
| { |
| } |
| |
| void NEMinMaxLayerKernel::configure(const ITensor *input, ITensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() < 3); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| TensorShape output_shape{ input->info()->tensor_shape() }; |
| output_shape.set(Window::DimX, 2); |
| output_shape.remove_dimension(1); |
| output_shape.remove_dimension(1); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position()); |
| |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); |
| |
| _input = input; |
| _output = output; |
| |
| // Configure kernel window |
| constexpr unsigned int num_elems_processed_per_iteration = 1; |
| |
| Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration)); |
| AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration); |
| AccessWindowHorizontal output_access(output->info(), 0, 2); |
| |
| update_window_and_padding(win, input_access, output_access); |
| |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| INEKernel::configure(win); |
| } |
| |
| void NEMinMaxLayerKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| |
| const int x_start = window.x().start(); |
| const int x_end = window.x().end(); |
| |
| Window window_output; |
| window_output.use_tensor_dimensions(_output->info()->tensor_shape()); |
| window_output.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| // Handle X dimension manually to split into two loops |
| // First one will use vector operations, second one processes the left over pixels |
| Window window_input(window); |
| window_input.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| window_input.set(3, Window::Dimension(0, 1, 1)); |
| |
| Iterator input(_input, window_input); |
| Iterator output(_output, window_output); |
| |
| execute_window_loop(window_output, [&](const Coordinates & id_batch) |
| { |
| float32x2_t carry_min = vdup_n_f32(std::numeric_limits<float>::max()); |
| float32x2_t carry_max = vdup_n_f32(std::numeric_limits<float>::lowest()); |
| |
| float carry_min_scalar = std::numeric_limits<float>::max(); |
| float carry_max_scalar = std::numeric_limits<float>::lowest(); |
| |
| execute_window_loop(window_input, [&](const Coordinates & id) |
| { |
| int x = x_start; |
| const auto in_ptr = reinterpret_cast<const float *const>(input.ptr() + id_batch[1] * _input->info()->strides_in_bytes()[3]); |
| |
| // Vector loop |
| for(; x <= x_end - 8; x += 8) |
| { |
| const float32x4x2_t pixels = vld2q_f32(in_ptr + x); |
| const float32x4_t tmp_min1 = vminq_f32(pixels.val[0], pixels.val[1]); |
| const float32x4_t tmp_max1 = vmaxq_f32(pixels.val[0], pixels.val[1]); |
| const float32x2_t tmp_min2 = vmin_f32(vget_high_f32(tmp_min1), vget_low_f32(tmp_min1)); |
| const float32x2_t tmp_max2 = vmax_f32(vget_high_f32(tmp_max1), vget_low_f32(tmp_max1)); |
| carry_min = vmin_f32(tmp_min2, carry_min); |
| carry_max = vmax_f32(tmp_max2, carry_max); |
| } |
| |
| // Process leftover pixels |
| for(; x < x_end; ++x) |
| { |
| const float pixel = in_ptr[x]; |
| carry_min_scalar = std::min(pixel, carry_min_scalar); |
| carry_max_scalar = std::max(pixel, carry_max_scalar); |
| } |
| }, |
| input); |
| |
| // Reduce result |
| carry_min = vpmin_f32(carry_min, carry_min); |
| carry_max = vpmax_f32(carry_max, carry_max); |
| carry_min = vpmin_f32(carry_min, carry_min); |
| carry_max = vpmax_f32(carry_max, carry_max); |
| |
| // Extract max/min values |
| const float min_i = std::min(vget_lane_f32(carry_min, 0), carry_min_scalar); |
| const float max_i = std::max(vget_lane_f32(carry_max, 0), carry_max_scalar); |
| |
| auto out_ptr = reinterpret_cast<float *const>(output.ptr()); |
| |
| // Perform reduction of local min/max values |
| update_min_max(out_ptr, min_i, max_i); |
| }, |
| output); |
| } |
| |
| void NEMinMaxLayerKernel::reset() |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| |
| float32x2_t reset_values = vdup_n_f32(0.0f); |
| reset_values = vset_lane_f32(std::numeric_limits<float>::max(), reset_values, 0); |
| reset_values = vset_lane_f32(std::numeric_limits<float>::lowest(), reset_values, 1); |
| |
| Window window_output; |
| window_output.use_tensor_dimensions(_output->info()->tensor_shape()); |
| window_output.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator output(_output, window_output); |
| |
| execute_window_loop(window_output, [&](const Coordinates & id) |
| { |
| vst1_f32(reinterpret_cast<float *const>(output.ptr()), reset_values); |
| }, |
| output); |
| } |
| |
| void NEMinMaxLayerKernel::update_min_max(float *out_ptr, float min, float max) |
| { |
| std::lock_guard<Mutex> lock(_mtx); |
| |
| const float32x2_t old_min = vld1_dup_f32(out_ptr); |
| const float32x2_t old_max = vld1_dup_f32(out_ptr + 1); |
| const float32x2_t new_min = vmin_f32(vdup_n_f32(min), old_min); |
| const float32x2_t new_max = vmax_f32(vdup_n_f32(max), old_max); |
| |
| vst1_f32(out_ptr, vzip_f32(new_min, new_max).val[0]); |
| } |
| } // namespace arm_compute |