From 6ff3b19ee6120edf015fad8caab2991faa3070af Mon Sep 17 00:00:00 2001 From: Anthony Barbier Date: Mon, 4 Sep 2017 18:44:23 +0100 Subject: COMPMID-344 Updated doxygen Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae --- src/core/NEON/kernels/NEPoolingLayerKernel.cpp | 415 +++++++++++++++++++++++++ 1 file changed, 415 insertions(+) create mode 100644 src/core/NEON/kernels/NEPoolingLayerKernel.cpp (limited to 'src/core/NEON/kernels/NEPoolingLayerKernel.cpp') diff --git a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp new file mode 100644 index 0000000000..30b67b64b9 --- /dev/null +++ b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp @@ -0,0 +1,415 @@ +/* + * 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/NEPoolingLayerKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/Error.h" +#include "arm_compute/core/FixedPoint.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/NEON/NEFixedPoint.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" + +#include +#include +#include +#include +#include + +using namespace arm_compute; + +namespace +{ +inline float calculate_avg_scale(const Coordinates &id, const int pool_size, const int upper_bound_w, const int upper_bound_h, + const int pad_x, const int pad_y, const int stride_x, const int stride_y) +{ + int start_x = id.x() * stride_x - pad_x; + int start_y = id.y() * stride_y - pad_y; + int end_x = std::min(start_x + pool_size, upper_bound_w); + int end_y = std::min(start_y + pool_size, upper_bound_h); + return 1.f / ((end_y - start_y) * (end_x - start_x)); +} + +inline qint8_t calculate_avg_scale_q8(const Coordinates &id, int pool_size, int upper_bound_w, int upper_bound_h, + int pad_x, int pad_y, int stride_x, int stride_y, int fixed_point_position) +{ + static std::array scale_values_q8 = + { { 0x0, 0x0, 0x40, 0x2A, 0x20, 0x19, 0x15, 0x12, 0x10, 0xE } }; + const int start_x = id.x() * stride_x - pad_x; + const int start_y = id.y() * stride_y - pad_y; + const int end_x = std::min(start_x + pool_size, upper_bound_w); + const int end_y = std::min(start_y + pool_size, upper_bound_h); + const int val = ((end_y - start_y) * (end_x - start_x)); + return scale_values_q8[val] >> (7 - fixed_point_position); +} +} // namespace + +NEPoolingLayerKernel::NEPoolingLayerKernel() + : _func(nullptr), _input(nullptr), _output(nullptr), _pool_info(), _num_elems_processed_per_iteration(0), _border_size(0) +{ +} + +BorderSize NEPoolingLayerKernel::border_size() const +{ + return _border_size; +} + +void NEPoolingLayerKernel::configure(const ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info) +{ + int pool_pad_x = 0; + int pool_pad_y = 0; + int pool_stride_x = 0; + int pool_stride_y = 0; + unsigned int pooled_w = 0; + unsigned int pooled_h = 0; + PoolingType pool_type = pool_info.pool_type(); + int pool_size = pool_info.pool_size(); + const PadStrideInfo pad_stride_info = pool_info.pad_stride_info(); + DimensionRoundingType pool_round = pad_stride_info.round(); + std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad(); + std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); + + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); + ARM_COMPUTE_ERROR_ON(2 != pool_size && 3 != pool_size); + ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size || pool_pad_y >= pool_size); + ARM_COMPUTE_ERROR_ON(input->info()->data_type() == DataType::QS8 && pool_type == PoolingType::AVG && input->info()->fixed_point_position() > 6); + ARM_COMPUTE_ERROR_ON(input->info()->data_type() == DataType::QS8 && pool_stride_x > 2); + + // Check output dimensions + std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), + pool_size, pool_stride_x, pool_stride_y, + pool_pad_x, pool_pad_y, pool_round); + ARM_COMPUTE_UNUSED(pooled_w); + ARM_COMPUTE_UNUSED(pooled_h); + ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h)); + + unsigned int num_elems_read_per_iteration = 0; + unsigned int num_elems_processed_per_iteration = 0; + unsigned int num_elems_horizontal_window = 0; + + // Select element size + switch(input->info()->data_type()) + { + case DataType::QS8: + num_elems_read_per_iteration = 16; + num_elems_processed_per_iteration = (pool_size == 2) ? 8 : 7; + num_elems_horizontal_window = 8; + break; + case DataType::F32: + num_elems_read_per_iteration = (pool_size == 2) ? 2 : 4; // We use vload4 for pooling3 + num_elems_processed_per_iteration = 1; + num_elems_horizontal_window = 1; + break; + default: + ARM_COMPUTE_ERROR("Element size not supported"); + break; + } + + _num_elems_processed_per_iteration = num_elems_processed_per_iteration; + const int input_width = input->info()->dimension(0); + const int input_height = input->info()->dimension(1); + const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elems_read_per_iteration) - input_width; + const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height; + + // Set instance variables + _input = input; + _output = output; + _pool_info = pool_info; + _border_size = BorderSize(pool_pad_y, pool_pad_x); + _border_size.right = std::max(upper_bound_w, pool_pad_x); + _border_size.bottom = std::max(upper_bound_h, pool_pad_y); + + // Select appropriate function + switch(pool_size) + { + case 2: + if(input->info()->data_type() == DataType::QS8) + { + _func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling2_q8 : &NEPoolingLayerKernel::pooling2_q8; + } + else if(input->info()->data_type() == DataType::F32) + { + _func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling2_f32 : &NEPoolingLayerKernel::pooling2_f32; + } + break; + case 3: + if(input->info()->data_type() == DataType::QS8) + { + _func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling3_q8 : &NEPoolingLayerKernel::pooling3_q8; + } + else if(input->info()->data_type() == DataType::F32) + { + _func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling3_f32 : &NEPoolingLayerKernel::pooling3_f32; + } + break; + default: + ARM_COMPUTE_ERROR("Unsupported pooling size"); + break; + } + + // Configure kernel window + Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration)); + AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom); + AccessWindowHorizontal output_access(output->info(), 0, num_elems_horizontal_window); + update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); + INEKernel::configure(win); +} + +template +void NEPoolingLayerKernel::pooling2_q8(const Window &window_input, const Window &window) +{ + Iterator input(_input, window_input); + Iterator output(_output, window); + + const int fixed_point_position = _input->info()->fixed_point_position(); + constexpr int pool_size = 2; + int pool_pad_x = 0; + int pool_pad_y = 0; + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); + const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; + const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; + + const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y))); + const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 1)); + + execute_window_loop(window, [&](const Coordinates & id) + { + const auto top_data = vld1q_qs8(reinterpret_cast(input_top_ptr + input.offset())); + const auto bottom_data = vld1q_qs8(reinterpret_cast(input_bottom_ptr + input.offset())); + qint8x8_t res = {}; + if(pooling_type == PoolingType::AVG) + { + // Calculate scale + const qint8_t scale = calculate_avg_scale_q8(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y, fixed_point_position); + const qint8x8_t scale_vec = vdup_n_qs8(scale); + + // Perform pooling + const qint8x16_t sum_data = vqaddq_qs8(top_data, bottom_data); + res = vqmul_qs8(vpadd_s8(vget_low_s8(sum_data), vget_high_s8(sum_data)), scale_vec, fixed_point_position); + } + else + { + const qint8x16_t max_data = vmaxq_s8(top_data, bottom_data); + res = vpmax_s8(vget_low_s8(max_data), vget_high_s8(max_data)); + } + vst1_qs8(reinterpret_cast(output.ptr()), res); + }, + input, output); +} + +template +void NEPoolingLayerKernel::pooling2_f32(const Window &window_input, const Window &window) +{ + Iterator input(_input, window_input); + Iterator output(_output, window); + + constexpr int pool_size = 2; + int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0; + std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); + const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; + const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; + + const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y))); + const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 1)); + + execute_window_loop(window, [&](const Coordinates & id) + { + const float32x2_t top_data = vld1_f32(reinterpret_cast(input_top_ptr + input.offset())); + const float32x2_t bottom_data = vld1_f32(reinterpret_cast(input_bottom_ptr + input.offset())); + float32x2_t res = {}; + if(pooling_type == PoolingType::AVG) + { + // Calculate scale + float scale = calculate_avg_scale(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y); + const float32x2_t scale_v = vdup_n_f32(scale); + + // Perform pooling + const float32x2_t sum_data = vadd_f32(top_data, bottom_data); + res = vmul_f32(vpadd_f32(sum_data, sum_data), scale_v); + } + else + { + const float32x2_t max_data = vmax_f32(top_data, bottom_data); + res = vpmax_f32(max_data, max_data); + } + *(reinterpret_cast(output.ptr())) = vget_lane_f32(res, 0); + }, + input, output); +} + +template +void NEPoolingLayerKernel::pooling3_q8(const Window &window_input, const Window &window) +{ + Iterator input(_input, window_input); + Iterator output(_output, window); + + const int fixed_point_position = _input->info()->fixed_point_position(); + constexpr int pool_size = 3; + int pool_pad_x = 0; + int pool_pad_y = 0; + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); + const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; + const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; + + const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y))); + const uint8_t *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 1)); + const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 2)); + + execute_window_loop(window, [&](const Coordinates & id) + { + const auto top_data = vld1q_qs8(reinterpret_cast(input_top_ptr + input.offset())); + const auto middle_data = vld1q_qs8(reinterpret_cast(input_middle_ptr + input.offset())); + const auto bottom_data = vld1q_qs8(reinterpret_cast(input_bottom_ptr + input.offset())); + qint8x8_t res = {}; + if(pooling_type == PoolingType::AVG) + { + // Calculate scale + const qint8_t scale = calculate_avg_scale_q8(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y, fixed_point_position); + const qint8x8_t scale_vec = vdup_n_qs8(scale); + + // Perform pooling for stride 2 + const qint8x16_t sum_data = vqaddq_qs8(vqaddq_qs8(top_data, bottom_data), middle_data); + const qint8x16_t sum_data2 = vextq_s8(sum_data, sum_data, 1); + const qint8x16_t sum_data3 = vextq_s8(sum_data, sum_data, 2); + const qint8x16_t final_sum = vqaddq_qs8(vqaddq_qs8(sum_data, sum_data2), sum_data3); + if(pool_stride_x == 2) + { + const qint8x8x2_t table = { { vget_low_s8(final_sum), vget_high_s8(final_sum) } }; + static const qint8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 }; + res = vtbl2_s8(table, lookup_val); + } + else + { + res = vget_low_s8(final_sum); + } + res = vqmul_qs8(res, scale_vec, fixed_point_position); + } + else + { + const qint8x16_t max_data = vmaxq_s8(vmaxq_s8(top_data, bottom_data), middle_data); + const qint8x16_t max_data2 = vextq_s8(max_data, max_data, 1); + const qint8x16_t max_data3 = vextq_s8(max_data, max_data, 2); + const qint8x16_t final_max = vmaxq_s8(vmaxq_s8(max_data, max_data2), max_data3); + + if(pool_stride_x == 2) + { + const qint8x8x2_t table = { { vget_low_s8(final_max), vget_high_s8(final_max) } }; + static const qint8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 }; + res = vtbl2_s8(table, lookup_val); + } + else + { + res = vget_low_s8(final_max); + } + } + vst1_qs8(reinterpret_cast(output.ptr()), res); + }, + input, output); +} + +template +void NEPoolingLayerKernel::pooling3_f32(const Window &window_input, const Window &window) +{ + Iterator input(_input, window_input); + Iterator output(_output, window); + + constexpr const int pool_size = 3; + int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0; + std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); + const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; + const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; + + const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y))); + const unsigned char *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 1)); + const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 2)); + + execute_window_loop(window, [&](const Coordinates & id) + { + const float32x4_t top_data = vld1q_f32(reinterpret_cast(input_top_ptr + input.offset())); + const float32x4_t middle_data = vld1q_f32(reinterpret_cast(input_middle_ptr + input.offset())); + const float32x4_t bottom_data = vld1q_f32(reinterpret_cast(input_bottom_ptr + input.offset())); + float32x2_t res = {}; + if(pooling_type == PoolingType::AVG) + { + // Calculate scale + float scale = calculate_avg_scale(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y); + const float32x2_t scale_v = vdup_n_f32(scale); + + // Perform pooling + const float32x4_t sum_data = vaddq_f32(vaddq_f32(top_data, bottom_data), middle_data); + res = vpadd_f32(vget_high_f32(vsetq_lane_f32(0.f, sum_data, 3)), vget_low_f32(sum_data)); + res = vmul_f32(vpadd_f32(res, res), scale_v); + } + else + { + const float32x4_t max_data = vmaxq_f32(vmaxq_f32(top_data, bottom_data), middle_data); + res = vpmax_f32(vget_high_f32(vsetq_lane_f32(-std::numeric_limits::max(), max_data, 3)), vget_low_f32(max_data)); + res = vpmax_f32(res, res); + } + *(reinterpret_cast(output.ptr())) = vget_lane_f32(res, 0); + }, + input, output); +} + +void NEPoolingLayerKernel::run(const Window &window) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); + ARM_COMPUTE_ERROR_ON(_func == nullptr); + + unsigned int pool_stride_x, pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); + + // Set step for input in x and y direction for the input + Window window_input(window); + unsigned int window_x_inc = 0; + if(_input->info()->data_type() == DataType::QS8) + { + window_x_inc = (pool_stride_x == 2) ? _num_elems_processed_per_iteration * 2 : _num_elems_processed_per_iteration; + } + else + { + window_x_inc = pool_stride_x; + } + window_input.set(Window::DimX, Window::Dimension(window.x().start() * pool_stride_x, window.x().end() * pool_stride_x, window_x_inc)); + window_input.set(Window::DimY, Window::Dimension(window.y().start() * pool_stride_y, window.y().end() * pool_stride_y, pool_stride_y)); + + // Run function + (this->*_func)(window_input, window); +} -- cgit v1.2.1