From 06b184ac568dc974986bae680957c4477f8ef6ca Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Tue, 29 Aug 2017 16:05:25 +0100 Subject: COMPMID-439 - Refactored NEQuantizationLayer and NEQuantizationLayer in order to support 3D input tensors Change-Id: I03eac2108a30bed56d40dfd52e75577a35d492e0 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/85783 Tested-by: Kaizen Reviewed-by: Michele DiGiorgio Reviewed-by: Georgios Pinitas --- src/core/NEON/kernels/NEMinMaxLayerKernel.cpp | 190 ++++++++++++++++++++++++++ 1 file changed, 190 insertions(+) create mode 100644 src/core/NEON/kernels/NEMinMaxLayerKernel.cpp (limited to 'src/core/NEON/kernels/NEMinMaxLayerKernel.cpp') diff --git a/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp b/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp new file mode 100644 index 0000000000..5e6c48f4c2 --- /dev/null +++ b/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp @@ -0,0 +1,190 @@ +/* + * Copyright (c) 2017 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 +#include +#include +#include + +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.collapse_if_possible(INEKernel::window(), 3); + 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::max()); + float32x2_t carry_max = vdup_n_f32(std::numeric_limits::lowest()); + + float carry_min_scalar = std::numeric_limits::max(); + float carry_max_scalar = std::numeric_limits::lowest(); + + execute_window_loop(window_input, [&](const Coordinates & id) + { + int x = x_start; + const auto in_ptr = reinterpret_cast(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(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::max(), reset_values, 0); + reset_values = vset_lane_f32(std::numeric_limits::min(), 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(output.ptr()), reset_values); + }, + output); +} + +void NEMinMaxLayerKernel::update_min_max(float *out_ptr, float min, float max) +{ + std::lock_guard 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 \ No newline at end of file -- cgit v1.2.1