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Diffstat (limited to 'src/core/cpu/kernels/CpuPoolingKernel.cpp')
| -rw-r--r-- | src/core/cpu/kernels/CpuPoolingKernel.cpp | 2605 |
1 files changed, 2605 insertions, 0 deletions
diff --git a/src/core/cpu/kernels/CpuPoolingKernel.cpp b/src/core/cpu/kernels/CpuPoolingKernel.cpp new file mode 100644 index 0000000000..a29aef4986 --- /dev/null +++ b/src/core/cpu/kernels/CpuPoolingKernel.cpp @@ -0,0 +1,2605 @@ +/* + * Copyright (c) 2017-2021 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/cpu/kernels/CpuPoolingKernel.h" + +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "src/core/AccessWindowStatic.h" +#include "src/core/CPP/Validate.h" +#include "src/core/NEON/NEAsymm.h" +#include "src/core/NEON/NEFixedPoint.h" +#include "src/core/NEON/NEMath.h" +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/WindowHelpers.h" +#include "support/ToolchainSupport.h" + +#include "src/core/NEON/wrapper/wrapper.h" +#include <arm_neon.h> + +namespace arm_compute +{ +namespace cpu +{ +namespace kernels +{ +using namespace misc::shape_calculator; + +namespace +{ +template <typename T> +inline typename std::enable_if<std::is_same<T, int8_t>::value, int8_t>::type +quantize(float val, const UniformQuantizationInfo &info) +{ + return quantize_qasymm8_signed(val, info); +} + +template <typename T> +inline typename std::enable_if<std::is_same<T, uint8_t>::value, uint8_t>::type +quantize(float val, const UniformQuantizationInfo &info) +{ + return quantize_qasymm8(val, info); +} + +inline float calculate_avg_scale(bool exclude_padding, DataLayout data_layout, const Coordinates &id, const int pool_size_x, const int pool_size_y, 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) +{ + const unsigned int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const unsigned int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + + int start_x = id[idx_width] * stride_x - pad_x; + int start_y = id[idx_height] * stride_y - pad_y; + + const int end_x = std::min(start_x + pool_size_x, upper_bound_w); + const int end_y = std::min(start_y + pool_size_y, upper_bound_h); + if(exclude_padding) + { + start_x = std::max(0, start_x); + start_y = std::max(0, start_y); + } + return 1.f / ((end_y - start_y) * (end_x - start_x)); +} + +template <typename T, typename TVec> +inline void scale_vector_q16x8(bool exclude_padding, TVec &v, const Coordinates &id, int id_offset, int step, + 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() + id_offset) * stride_x - pad_x; + int start_y = id.y() * stride_y - pad_y; + const int end_y = std::min(start_y + pool_size, upper_bound_h); + if(exclude_padding) + { + start_y = std::max(0, start_y); + } + + std::array<T, 8> elems = + { + { + wrapper::vgetlane(v, 0), + wrapper::vgetlane(v, 1), + wrapper::vgetlane(v, 2), + wrapper::vgetlane(v, 3), + wrapper::vgetlane(v, 4), + wrapper::vgetlane(v, 5), + wrapper::vgetlane(v, 6), + wrapper::vgetlane(v, 7), + } + }; + + for(auto &el : elems) + { + int c_start_x = start_x; + const int end_x = std::min(c_start_x + pool_size, upper_bound_w); + if(exclude_padding) + { + c_start_x = std::max(0, c_start_x); + } + float scale = 1.f / ((end_y - start_y) * (end_x - c_start_x)); + el *= scale; + start_x += step * stride_x; + } + + v = wrapper::vsetlane(elems[0], v, 0); + v = wrapper::vsetlane(elems[1], v, 1); + v = wrapper::vsetlane(elems[2], v, 2); + v = wrapper::vsetlane(elems[3], v, 3); + v = wrapper::vsetlane(elems[4], v, 4); + v = wrapper::vsetlane(elems[5], v, 5); + v = wrapper::vsetlane(elems[6], v, 6); + v = wrapper::vsetlane(elems[7], v, 7); +} + +Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, + unsigned int &pooled_w, unsigned int pooled_h, const ITensorInfo *indices, Size2D pool_size) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); + + int pool_stride_x = 0; + int pool_stride_y = 0; + PoolingType pool_type = pool_info.pool_type; + const PadStrideInfo pad_stride_info = pool_info.pad_stride_info; + std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); + + ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src); + if(indices) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32, DataType::F16); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(indices, 1, DataType::U32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_type != PoolingType::MAX, "Pooling indices only supported for MAX pooling method"); + } + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(pool_type == PoolingType::L2 && is_data_type_quantized(src->data_type())); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_quantized(src->data_type()) && !pool_info.exclude_padding && (pool_info.pool_type == PoolingType::AVG) && pool_info.pad_stride_info.has_padding() + && (src->data_layout() == DataLayout::NHWC), + "exclude_padding equal false is not supported for AVG Pooling with padding on quantized types"); + + if(dst->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst); + ARM_COMPUTE_RETURN_ERROR_ON((dst->dimension(get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH)) != pooled_w) + || (dst->dimension(get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT)) != pooled_h)); + + if(indices) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_size != Size2D(2, 2)), "Pooling indices only supported for pool size 2x2"); + ARM_COMPUTE_RETURN_ERROR_ON((indices->dimension(get_data_layout_dimension_index(indices->data_layout(), DataLayoutDimension::WIDTH)) != pooled_w) + || (indices->dimension(get_data_layout_dimension_index(indices->data_layout(), DataLayoutDimension::HEIGHT)) != pooled_h)); + } + } + + return Status{}; +} + +Status validate_arguments_pool_info(const unsigned int pool_size_x, const unsigned int pool_size_y) +{ + ARM_COMPUTE_RETURN_ERROR_ON(pool_size_x == 0); + ARM_COMPUTE_RETURN_ERROR_ON(pool_size_y == 0); + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst, ITensorInfo *indices, const PoolingLayerInfo &pool_info, + unsigned int &num_elems_processed_per_iteration, + BorderSize &border_size, + unsigned int pooled_w, unsigned int pooled_h, int pool_size_x, int pool_size_y) +{ + // dst auto inizialitation if not yet initialized + auto_init_if_empty(*dst, src->clone()->set_tensor_shape(compute_pool_shape(*src, pool_info))); + if(indices) + { + // Indices auto inizialitation if not yet initialized + auto_init_if_empty(*indices, (src->clone()->set_tensor_shape(compute_pool_shape(*src, + pool_info))) + .set_data_type(DataType::U32) /* we store the offset to the element */); + } + const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout; + unsigned int num_elems_read_per_iteration = 0; + unsigned int num_elems_horizontal_window = 0; + int pool_stride_x = 0; + int pool_stride_y = 0; + const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + const int src_width = src->dimension(idx_width); + const int src_height = src->dimension(idx_height); + const PadStrideInfo pad_stride_info = pool_info.pad_stride_info; + std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); + const int pool_pad_right = pad_stride_info.pad_right(); + const int pool_pad_top = pad_stride_info.pad_top(); + const int pool_pad_left = pad_stride_info.pad_left(); + const int pool_pad_bottom = pad_stride_info.pad_bottom(); + const bool is_square = pool_size_x == pool_size_y; + + // Check dst dimensions + std::tie(pooled_w, pooled_h) = scaled_dimensions(src->dimension(idx_width), + src->dimension(idx_height), + pool_size_x, + pool_size_y, + pad_stride_info); + + //If it's not squared and optimized will be executed the MxN + num_elems_read_per_iteration = 1; + num_elems_processed_per_iteration = 1; + num_elems_horizontal_window = 1; + + if(is_square) + { + switch(src->data_type()) + { + case DataType::QASYMM8: + case DataType::QASYMM8_SIGNED: + switch(pool_size_x) + { + case 2: + num_elems_read_per_iteration = 16; + num_elems_processed_per_iteration = (pool_stride_x == 2) ? 8 : 15; + num_elems_horizontal_window = (pool_stride_x == 2) ? 8 : 16; + break; + case 3: + num_elems_read_per_iteration = 16; + num_elems_processed_per_iteration = (pool_stride_x == 2) ? 7 : 14; + num_elems_horizontal_window = (pool_stride_x == 2) ? 8 : 16; + break; + default: + break; + } + break; +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + case DataType::F16: + switch(pool_size_x) + { + case 2: + case 3: + num_elems_read_per_iteration = 4; + num_elems_processed_per_iteration = 1; + num_elems_horizontal_window = 1; + break; + default: + break; + } + break; +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + case DataType::F32: + switch(pool_size_x) + { + case 2: + num_elems_read_per_iteration = 2; + break; + case 3: + num_elems_read_per_iteration = 4; // We use vload4 for pooling3 + break; + case 7: + num_elems_read_per_iteration = 8; // We use vload8 for pooling7 + break; + default: + break; + } + num_elems_processed_per_iteration = 1; + num_elems_horizontal_window = 1; + break; + default: + ARM_COMPUTE_ERROR("Element size not supported"); + break; + } + } + + bool window_changed = false; + Window win{}; + if(data_layout == DataLayout::NCHW) + { + // Number of iterations in X dimension + const int num_iterations_x = (pooled_w + num_elems_processed_per_iteration - 1) / num_elems_processed_per_iteration; + // Upper limit for the number of right/bottom border elements that are accessed + const int upper_bound_w = ((num_iterations_x - 1) * num_elems_processed_per_iteration * pool_stride_x - pool_pad_left + num_elems_read_per_iteration) - src_width; + const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_top + pool_size_y) - src_height; + border_size = BorderSize(pool_pad_top, pool_pad_right, pool_pad_bottom, pool_pad_left); + border_size.right = std::max(upper_bound_w, pool_pad_right); + border_size.bottom = std::max(upper_bound_h, pool_pad_bottom); + TensorShape dst_shape{ src->tensor_shape() }; + dst_shape.set(0, pooled_w); + dst_shape.set(1, pooled_h); + TensorInfo dst_info(src->clone()->set_tensor_shape(dst_shape)); + win = calculate_max_window(dst_info, Steps(num_elems_processed_per_iteration)); + AccessWindowStatic src_access(src, -pool_pad_left, -pool_pad_top, src_width + border_size.right, src_height + border_size.bottom); + AccessWindowHorizontal dst_access(dst, 0, num_elems_horizontal_window); + if(indices) + { + AccessWindowHorizontal indices_access(indices, 0, num_elems_horizontal_window); + window_changed = update_window_and_padding(win, src_access, dst_access, indices_access); + } + else + { + window_changed = update_window_and_padding(win, src_access, dst_access); + } + dst_access.set_valid_region(win, ValidRegion(Coordinates(), dst->tensor_shape())); + } + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} + +template <typename T> +inline T vcvtq_q32_f32(float32x4_t values); + +template <> +inline uint32x4_t vcvtq_q32_f32(float32x4_t values) +{ + return vcvtq_u32_f32(values); +} + +template <> +inline int32x4_t vcvtq_q32_f32(float32x4_t values) +{ + return vcvtq_s32_f32(values); +} + +template <typename T> +inline float32x4_t vcvtq_f32_q32(T values); + +template <> +inline float32x4_t vcvtq_f32_q32(uint32x4_t values) +{ + return vcvtq_f32_u32(values); +} + +template <> +inline float32x4_t vcvtq_f32_q32(int32x4_t values) +{ + return vcvtq_f32_s32(values); +} + +template <typename Tout> +inline Tout vrequantize_pooling_with_scale(const float32x4x4_t &acc, const float quant_rescale, const float scale_pooling, const int32_t new_offset); + +template <> +inline uint8x16_t vrequantize_pooling_with_scale(const float32x4x4_t &acc, const float quant_rescale, const float scale_pooling, const int32_t new_offset) +{ + const float new_scale = quant_rescale / scale_pooling; + return vquantize(acc, UniformQuantizationInfo(new_scale, new_offset)); +} + +template <> +inline int8x16_t vrequantize_pooling_with_scale(const float32x4x4_t &acc, const float quant_rescale, const float scale_pooling, const int32_t new_offset) +{ + const float new_scale = quant_rescale / scale_pooling; + return vquantize_signed(acc, UniformQuantizationInfo(new_scale, new_offset)); +} + +template <typename Tin, typename Tout> +inline Tout vrequantize_pooling(Tin vec1, Tin vec2, const UniformQuantizationInfo &requant_qinfo); + +template <> +inline uint8x16_t vrequantize_pooling(uint8x8_t vec1, uint8x8_t vec2, const UniformQuantizationInfo &requant_qinfo) +{ + const float32x4x4_t acc = + { + { + vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8((vec1))))), + vcvtq_f32_u32(vmovl_u16(vget_high_u16(vmovl_u8((vec1))))), + vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8((vec2))))), + vcvtq_f32_u32(vmovl_u16(vget_high_u16(vmovl_u8((vec2))))), + } + }; + return vquantize(acc, requant_qinfo); +} + +template <> +inline int8x16_t vrequantize_pooling(int8x8_t vec1, int8x8_t vec2, const UniformQuantizationInfo &requant_qinfo) +{ + const float32x4x4_t acc = + { + { + vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8((vec1))))), + vcvtq_f32_s32(vmovl_s16(vget_high_s16(vmovl_s8((vec1))))), + vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8((vec2))))), + vcvtq_f32_s32(vmovl_s16(vget_high_s16(vmovl_s8((vec2))))), + } + }; + return vquantize_signed(acc, requant_qinfo); +} + +template <typename T> +inline T vrequantize_pooling(T &vec, const UniformQuantizationInfo &requant_qinfo); + +template <> +inline uint8x8_t vrequantize_pooling(uint8x8_t &vec, const UniformQuantizationInfo &requant_qinfo) +{ + const float32x4x2_t acc = + { + { + vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8((vec))))), + vcvtq_f32_u32(vmovl_u16(vget_high_u16(vmovl_u8((vec))))), + } + }; + return vquantize(acc, requant_qinfo); +} + +template <> +inline int8x8_t vrequantize_pooling(int8x8_t &vec, const UniformQuantizationInfo &requant_qinfo) +{ + const float32x4x2_t acc = + { + { + vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8((vec))))), + vcvtq_f32_s32(vmovl_s16(vget_high_s16(vmovl_s8((vec))))), + } + }; + return vquantize_signed(acc, requant_qinfo); +} + +} // namespace + +BorderSize CpuPoolingKernel::border_size() const +{ + return _border_size; +} + +void CpuPoolingKernel::configure(ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); + const PadStrideInfo pad_stride_info = pool_info.pad_stride_info; + const bool is_global_pooling = pool_info.is_global_pooling; + const int pool_stride_x = pad_stride_info.stride().first; + + // Get data layout + const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout; + const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + + // Update pool size in case of global pooling + const Size2D pool_size( + is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width, + is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height); + + // Validate pool info before calling scaled_dimensions + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_pool_info(pool_size.x(), pool_size.y())); + + // Check dst dimensions + unsigned int pooled_w; + unsigned int pooled_h; + std::tie(pooled_w, pooled_h) = scaled_dimensions(src->dimension(idx_width), + src->dimension(idx_height), + pool_size.x(), + pool_size.y(), + pad_stride_info); + + // Perform validation step + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info, pooled_w, pooled_h, indices, pool_size)); + + // Set instance variables + _pool_info = pool_info; + _data_layout = src->data_layout(); + _is_square = (pool_size.x() == pool_size.y()); + + // Get data type + const DataType data_type = src->data_type(); + const bool is_nchw = _data_layout == DataLayout::NCHW; + + if(data_type == DataType::QASYMM8) + { + if(!is_nchw) + { + _func = &CpuPoolingKernel::poolingMxN_q8_nhwc<uint8_t>; + } + else + { + if(pool_size.x() == 2 && pool_stride_x < 3 && _is_square) + { + _func = &CpuPoolingKernel::pooling2_q8_nchw<uint8_t>; + } + else if(pool_size.x() == 3 && pool_stride_x < 3 && _is_square) + { + _func = &CpuPoolingKernel::pooling3_q8_nchw<uint8_t>; + } + else + { + _func = &CpuPoolingKernel::poolingMxN_q8_nchw<uint8_t>; + } + } + } + else if(data_type == DataType::QASYMM8_SIGNED) + { + if(!is_nchw) + { + _func = &CpuPoolingKernel::poolingMxN_q8_nhwc<int8_t>; + } + else + { + if(pool_size.x() == 2 && pool_stride_x < 3 && _is_square) + { + _func = &CpuPoolingKernel::pooling2_q8_nchw<int8_t>; + } + else if(pool_size.x() == 3 && pool_stride_x < 3 && _is_square) + { + _func = &CpuPoolingKernel::pooling3_q8_nchw<int8_t>; + } + else + { + _func = &CpuPoolingKernel::poolingMxN_q8_nchw<int8_t>; + } + } + } + else if(data_type == DataType::F16) + { + if(!is_nchw) + { + _func = &CpuPoolingKernel::poolingMxN_f16_nhwc; + } + else + { + if(_is_square) + { + switch(pool_size.x()) + { + case 2: + { + _func = &CpuPoolingKernel::pooling2_f16_nchw; + } + break; + case 3: + { + _func = &CpuPoolingKernel::pooling3_f16_nchw; + } + break; + default: + { + _func = &CpuPoolingKernel::poolingMxN_f16_nchw; + break; + } + } + } + else + { + _func = &CpuPoolingKernel::poolingMxN_f16_nchw; + } + } + } + else if(data_type == DataType::F32) + { + if(!is_nchw) + { + _func = &CpuPoolingKernel::poolingMxN_f32_nhwc; + } + else + { + if(_is_square) + { + switch(pool_size.x()) + { + case 2: + { + _func = &CpuPoolingKernel::pooling2_f32_nchw; + break; + } + case 3: + { + _func = &CpuPoolingKernel::pooling3_f32_nchw; + break; + } + case 7: + { + _func = &CpuPoolingKernel::pooling7_f32_nchw; + break; + } + default: + { + _func = &CpuPoolingKernel::poolingMxN_f32_nchw; + break; + } + } + } + else + { + _func = &CpuPoolingKernel::poolingMxN_f32_nchw; + } + } + } + + if(!is_nchw) + { + // Configure kernel window + Window win = calculate_max_window(*dst, Steps()); + Coordinates coord; + coord.set_num_dimensions(dst->num_dimensions()); + dst->set_valid_region(ValidRegion(coord, dst->tensor_shape())); + ICpuKernel::configure(win); + } + else + { + // Configure kernel window + auto win_config = validate_and_configure_window(src, dst, indices, pool_info, _num_elems_processed_per_iteration, + _border_size, pooled_w, pooled_h, pool_size.x(), pool_size.y()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICpuKernel::configure(win_config.second); + } +} + +template <typename T> +inline uint32_t offset_no_padding(uint32_t padded_offset, const Coordinates &id, const ITensorInfo &info, int pool_stride_x, int pool_stride_y) +{ + const int pad_left = info.padding().left; + const int pad_right = info.padding().right; + const int pad_top = info.padding().top; + const int pad_bottom = info.padding().bottom; + const int in_stride_y = static_cast<int>(info.strides_in_bytes().y()); + const int in_stride_w = static_cast<int>(info.strides_in_bytes()[3]); + const int pad_horiz = pad_left + pad_right; + const int pad_vert = pad_top + pad_bottom; + + if(info.data_layout() == DataLayout::NCHW) + { + const uint32_t offset_base = padded_offset + - sizeof(T) * pad_horiz * id.y() * pool_stride_y /* subtract padding elems per row */ + - pad_top * sizeof(T) /* top padding */ + - sizeof(T) * pad_horiz * info.tensor_shape()[1] * id.z() - pad_vert * in_stride_y * id.z() /* for each Z plane there are height*pad_right padding elems */ + - in_stride_w * id[3]; + + return offset_base; + } + else + { + const uint32_t offset_base = padded_offset + - sizeof(T) * pad_horiz * id.y() * pool_stride_x // subtract padding elems per row + - pad_top * sizeof(T) // top padding + - sizeof(T) * pad_horiz * info.tensor_shape()[1] * id.z() * pool_stride_y // for each Z plane there are width*pad_right padding elems + - in_stride_w * id[3]; + + return offset_base; + } +} + +template <typename T> +void CpuPoolingKernel::pooling2_q8_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding) +{ + Iterator src(_src, window_src); + Iterator dst(_dst, window); + + /** NEON vector types */ + using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type; + using q8x16_t = typename wrapper::traits::neon_vector<T, 16>::type; + using q8x8x2_t = typename std::conditional<std::is_same<T, uint8_t>::value, uint8x8x2_t, int8x8x2_t>::type; + using q16_t = typename wrapper::traits::promote_t<T>; + using q16x4_t = typename wrapper::traits::neon_vector<q16_t, 4>::type; + using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type; + using q16x8x2_t = typename wrapper::traits::neon_vector<q16_t, 16>::type; + + constexpr int pool_size = 2; + int pool_stride_x = 0; + int pool_stride_y = 0; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); + + const T *const src_top_ptr = reinterpret_cast<const T *>(_src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)))); + const T *const src_bottom_ptr = reinterpret_cast<const T *>(_src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1))); + + const int scale_step_x = (pool_stride_x == 1) ? 2 : 1; + + const UniformQuantizationInfo src_qinfo = _src->info()->quantization_info().uniform(); + const UniformQuantizationInfo dst_qinfo = _dst->info()->quantization_info().uniform(); + const bool have_different_qinfo = src_qinfo != dst_qinfo; + + const float requant_scale = dst_qinfo.scale / src_qinfo.scale; + const int32_t requant_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / requant_scale); + const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset); + + execute_window_loop(window, [&](const Coordinates & id) + { + const auto top_data = wrapper::vloadq(src_top_ptr + src.offset()); + const auto bottom_data = wrapper::vloadq(src_bottom_ptr + src.offset()); + q8x8_t lower_res = {}; + q8x8_t upper_res = {}; + + if(pooling_type != PoolingType::MAX) + { + const q16x8x2_t top_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(top_data)), wrapper::vmovl(wrapper::vgethigh(top_data)) } }; + const q16x8x2_t bottom_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(bottom_data)), wrapper::vmovl(wrapper::vgethigh(bottom_data)) } }; + + // Add rows + const q16x8x2_t vrsum = + { + { + wrapper::vadd(top_data_q16.val[0], bottom_data_q16.val[0]), + wrapper::vadd(top_data_q16.val[1], bottom_data_q16.val[1]), + } + }; + + // Pair-wise add row data + const q16x4_t vpsum_1 = wrapper::vpadd(wrapper::vgetlow(vrsum.val[0]), wrapper::vgethigh(vrsum.val[0])); + const q16x4_t vpsum_2 = wrapper::vpadd(wrapper::vgetlow(vrsum.val[1]), wrapper::vgethigh(vrsum.val[1])); + + q16x8_t res_lower = wrapper::vcombine(vpsum_1, vpsum_2); + + // Scale lower result + scale_vector_q16x8<q16_t, q16x8_t>(exclude_padding, res_lower, id, 0, scale_step_x, + pool_size, upper_bound_w, upper_bound_h, + pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + lower_res = wrapper::vmovn(res_lower); + + // Compute upper result for stride_x == 1 + if(pool_stride_x == 1) + { + // Shifted row sum + const q16x8x2_t vrsum_shifted = + { + { + wrapper::vext_1(vrsum.val[0], vrsum.val[1]), + wrapper::vext_1(vrsum.val[1], vrsum.val[1]) + } + }; + + // Pair-wise add shifted row + q16x8_t res_upper = wrapper::vcombine( + wrapper::vpadd(wrapper::vgetlow(vrsum_shifted.val[0]), wrapper::vgethigh(vrsum_shifted.val[0])), + wrapper::vpadd(wrapper::vgetlow(vrsum_shifted.val[1]), wrapper::vgethigh(vrsum_shifted.val[1]))); + + // Scale upper result + scale_vector_q16x8<q16_t, q16x8_t>(exclude_padding, res_upper, id, 1, 2, + pool_size, upper_bound_w, upper_bound_h, + pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + upper_res = wrapper::vmovn(res_upper); + } + } + else + { + const q8x16_t max_data = wrapper::vmax(top_data, bottom_data); + lower_res = wrapper::vpmax(wrapper::vgetlow(max_data), wrapper::vgethigh(max_data)); + if(pool_stride_x == 1) + { + const q8x16_t max_data_shifted = wrapper::vext_1(max_data, max_data); + upper_res = wrapper::vpmax(wrapper::vgetlow(max_data_shifted), wrapper::vgethigh(max_data_shifted)); + } + } + + if(have_different_qinfo) + { + const auto requantized_dst = vrequantize_pooling<q8x8_t, q8x16_t>(lower_res, upper_res, requant_qinfo); + lower_res = wrapper::vgetlow(requantized_dst); + upper_res = wrapper::vgethigh(requantized_dst); + } + + // Store result + if(pool_stride_x == 1) + { + const q8x8x2_t res = { { lower_res, upper_res } }; + wrapper::vstore(reinterpret_cast<T *>(dst.ptr()), res); + } + else + { + wrapper::vstore(reinterpret_cast<T *>(dst.ptr()), lower_res); + } + }, + src, dst); +} + +void CpuPoolingKernel::pooling3_f16_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding) +{ + ARM_COMPUTE_UNUSED(pooling_type); + ARM_COMPUTE_UNUSED(exclude_padding); +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + Iterator src(_src, window_src); + Iterator dst(_dst, window); + + constexpr const int pool_size = 3; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); + + const unsigned char *const src_top_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))); + const unsigned char *const src_middle_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1)); + const unsigned char *const src_bottom_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 2)); + + execute_window_loop(window, [&](const Coordinates & id) + { + float16x4_t top_data = vld1_f16(reinterpret_cast<const float16_t *>(src_top_ptr + src.offset())); + float16x4_t middle_data = vld1_f16(reinterpret_cast<const float16_t *>(src_middle_ptr + src.offset())); + float16x4_t bottom_data = vld1_f16(reinterpret_cast<const float16_t *>(src_bottom_ptr + src.offset())); + float16x4_t res = {}; + + // Get power of 2 in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + top_data = vmul_f16(top_data, top_data); + middle_data = vmul_f16(middle_data, middle_data); + bottom_data = vmul_f16(bottom_data, bottom_data); + } + + if(pooling_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + const float16x4_t scale_v = vdup_n_f16(scale); + // Perform pooling + const float16x4_t sum_data = vadd_f16(vadd_f16(top_data, bottom_data), middle_data); + res = vpadd_f16(vset_lane_f16(0.f, sum_data, 3), sum_data); + res = vmul_f16(vpadd_f16(res, res), scale_v); + } + else + { + const float16x4_t max_data = vmax_f16(vmax_f16(top_data, bottom_data), middle_data); + res = vpmax_f16(vset_lane_f16(-std::numeric_limits<float>::max(), max_data, 3), max_data); + res = vpmax_f16(res, res); + } + + // Calculate square-root in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + res = vinv_f16(vinvsqrt_f16(res)); + } + + *(reinterpret_cast<float16_t *>(dst.ptr())) = vget_lane_f16(res, 0); + }, + src, dst); +#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + ARM_COMPUTE_UNUSED(window_src); + ARM_COMPUTE_UNUSED(window); + ARM_COMPUTE_ERROR("FP16 Not supported! Recompile the library with arch=arm64-v8.2-a"); +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +template <typename T> +inline typename std::enable_if<std::is_same<T, float16_t>::value, float32x2_t>::type +f16_to_f32(float16x4_t src) +{ + float32x2_t dst = { static_cast<float>(vget_lane_f16(src, 0)), static_cast<float>(vget_lane_f16(src, 1)) }; + return dst; +} +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + +template <typename T> +inline typename std::enable_if<std::is_same<T, float>::value, float32x2_t>::type +f16_to_f32(float32x2_t src) +{ + return src; +} + +template <typename T> +void CpuPoolingKernel::pooling2_nchw_maxpool_indices(const Window &window_src, const Window &window) +{ + Iterator src(_src, window_src); + Iterator dst(_dst, window); + Iterator indices(_indices, window); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const uint8_t *const src_top_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))); + const uint8_t *const src_bottom_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1)); + const int pad_left = _src->info()->padding().left; + const int pad_right = _src->info()->padding().right; + const int in_stride_y = static_cast<int>(_src->info()->strides_in_bytes().y()); + + execute_window_loop(window, [&](const Coordinates & id) + { + auto top_data = wrapper::vload(reinterpret_cast<const T *>(src_top_ptr + src.offset())); + auto bottom_data = wrapper::vload(reinterpret_cast<const T *>(src_bottom_ptr + src.offset())); + float32x2_t top_data_f32 = f16_to_f32<T>(top_data); + float32x2_t bottom_data_f32 = f16_to_f32<T>(bottom_data); + + // Calculate max data, compare top first, then bottom, to make sue the first max is recorded. + const float32x2_t max_data_top = vpmax_f32(top_data_f32, top_data_f32); + const float32x2_t max_data_bottom = vpmax_f32(bottom_data_f32, bottom_data_f32); + const float32x2_t max_data = vmax_f32(max_data_top, max_data_bottom); + *(reinterpret_cast<T *>(dst.ptr())) = static_cast<T>(vget_lane_f32(max_data, 0)); + + // Calculate max data indice, which will be used in max unpool. + const uint32_t offset_base = offset_no_padding<T>(src.offset(), id, *_src->info(), pool_stride_x, pool_stride_y); + const uint32_t offset_top = (uint32_t)(offset_base / sizeof(T)); + const uint32_t offset_bottom = offset_top + in_stride_y / sizeof(T) - pad_right - pad_left; + const uint32x2_t voffset_top = { offset_top, offset_top + 1u }; + const uint32x2_t voffset_bottom = { offset_bottom, offset_bottom + 1u }; + const uint32x2_t tmp_indices_top = vbsl_u32(vcge_f32(top_data_f32, vrev64_f32(top_data_f32)), voffset_top, vrev64_u32(voffset_top)); + const uint32x2_t tmp_indices_bottom = vbsl_u32(vcge_f32(bottom_data_f32, vrev64_f32(bottom_data_f32)), voffset_bottom, vrev64_u32(voffset_bottom)); + *(reinterpret_cast<int *>(indices.ptr())) = vget_lane_u32(vbsl_u32(vcge_f32(max_data_top, max_data_bottom), tmp_indices_top, tmp_indices_bottom), 0); + }, + src, dst, indices); +} + +void CpuPoolingKernel::pooling2_f16_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding) +{ + ARM_COMPUTE_UNUSED(pooling_type); + ARM_COMPUTE_UNUSED(exclude_padding); +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + if(pooling_type == PoolingType::MAX && _indices) + { + pooling2_nchw_maxpool_indices<float16_t>(window_src, window); + } + else + { + Iterator src(_src, window_src); + Iterator dst(_dst, window); + constexpr int pool_size = 2; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x, pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); + + const unsigned char *const src_top_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))); + const unsigned char *const src_bottom_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1)); + + execute_window_loop(window, [&](const Coordinates & id) + { + float16x4_t top_data = vld1_f16(reinterpret_cast<const float16_t *>(src_top_ptr + src.offset())); + float16x4_t bottom_data = vld1_f16(reinterpret_cast<const float16_t *>(src_bottom_ptr + src.offset())); + float16x4_t res = {}; + + // Get power of 2 in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + top_data = vmul_f16(top_data, top_data); + bottom_data = vmul_f16(bottom_data, bottom_data); + } + + if(pooling_type != PoolingType::MAX) + { + const float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + const float16x4_t scale_v = vdup_n_f16(scale); + + const float16x4_t sum_data = vadd_f16(top_data, bottom_data); + res = vmul_f16(vpadd_f16(sum_data, sum_data), scale_v); + } + else + { + const float16x4_t max_data = vmax_f16(top_data, bottom_data); + res = vpmax_f16(max_data, max_data); + } + + // Calculate square-root in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + res = vinv_f16(vinvsqrt_f16(res)); + } + + // Store result + *(reinterpret_cast<float16_t *>(dst.ptr())) = vget_lane_f16(res, 0); + }, + src, dst); + } +#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + ARM_COMPUTE_UNUSED(window_src); + ARM_COMPUTE_UNUSED(window); + ARM_COMPUTE_ERROR("FP16 Not supported! Recompile the library with arch=arm64-v8.2-a"); +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ +} + +template <typename T> +void CpuPoolingKernel::pooling3_q8_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding) +{ + Iterator src(_src, window_src); + Iterator dst(_dst, window); + + /** NEON vector types */ + using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type; + using q8x16_t = typename wrapper::traits::neon_vector<T, 16>::type; + using q8x8x2_t = typename std::conditional<std::is_same<T, uint8_t>::value, uint8x8x2_t, int8x8x2_t>::type; + using q16_t = typename wrapper::traits::promote_t<T>; + using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type; + using q16x8x2_t = typename wrapper::traits::neon_vector<q16_t, 16>::type; + + constexpr int pool_size = 3; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); + + const UniformQuantizationInfo &src_qinfo = _src->info()->quantization_info().uniform(); + const UniformQuantizationInfo &dst_qinfo = _dst->info()->quantization_info().uniform(); + + const float requant_scale = dst_qinfo.scale / src_qinfo.scale; + const int32_t requant_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / requant_scale); + const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset); + + const T *const src_top_ptr = reinterpret_cast<const T *>(_src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)))); + const T *const src_middle_ptr = reinterpret_cast<const T *>(_src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1))); + const T *const src_bottom_ptr = reinterpret_cast<const T *>(_src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 2))); + + execute_window_loop(window, [&](const Coordinates & id) + { + const auto top_data = wrapper::vloadq(src_top_ptr + src.offset()); + const auto middle_data = wrapper::vloadq(src_middle_ptr + src.offset()); + const auto bottom_data = wrapper::vloadq(src_bottom_ptr + src.offset()); + q8x8_t fres = {}; + q8x16_t fqres = {}; + + if(pooling_type == PoolingType::AVG) + { + // Convert data to u16 + const q16x8x2_t top_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(top_data)), wrapper::vmovl(wrapper::vgethigh(top_data)) } }; + const q16x8x2_t middle_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(middle_data)), wrapper::vmovl(wrapper::vgethigh(middle_data)) } }; + const q16x8x2_t bottom_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(bottom_data)), wrapper::vmovl(wrapper::vgethigh(bottom_data)) } }; + + // Calculate row sums + const q16x8x2_t vrsum = + { + { + wrapper::vadd(wrapper::vadd(top_data_q16.val[0], bottom_data_q16.val[0]), middle_data_q16.val[0]), + wrapper::vadd(wrapper::vadd(top_data_q16.val[1], bottom_data_q16.val[1]), middle_data_q16.val[1]), + } + }; + const q16x8x2_t vrsum_shifted_1 = + { + { + wrapper::vext_1(vrsum.val[0], vrsum.val[1]), + wrapper::vext_1(vrsum.val[1], vrsum.val[1]) + } + }; + const q16x8x2_t vrsum_shifted_2 = + { + { + wrapper::vext_2(vrsum.val[0], vrsum.val[1]), + wrapper::vext_2(vrsum.val[1], vrsum.val[1]) + } + }; + // Calculate final sum + q16x8x2_t final_sum = + { + { + wrapper::vadd(wrapper::vadd(vrsum.val[0], vrsum_shifted_1.val[0]), vrsum_shifted_2.val[0]), + wrapper::vadd(wrapper::vadd(vrsum.val[1], vrsum_shifted_1.val[1]), vrsum_shifted_2.val[1]), + } + }; + if(pool_stride_x == 2) + { + q16x8_t res = + { + wrapper::vgetlane(final_sum.val[0], 0), + wrapper::vgetlane(final_sum.val[0], 2), + wrapper::vgetlane(final_sum.val[0], 4), + wrapper::vgetlane(final_sum.val[0], 6), + wrapper::vgetlane(final_sum.val[1], 0), + wrapper::vgetlane(final_sum.val[1], 2), + wrapper::vgetlane(final_sum.val[1], 4), + wrapper::vgetlane(final_sum.val[1], 6), + }; + + scale_vector_q16x8<q16_t, q16x8_t>(exclude_padding, res, id, 0, 1, + pool_size, upper_bound_w, upper_bound_h, + pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + fres = wrapper::vmovn(res); + } + else + { + // Scale lower result + scale_vector_q16x8<q16_t, q16x8_t>(exclude_padding, final_sum.val[0], id, 0, 1, + pool_size, upper_bound_w, upper_bound_h, + pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + // Scale lower result + scale_vector_q16x8<q16_t, q16x8_t>(exclude_padding, final_sum.val[1], id, 8, 1, + pool_size, upper_bound_w, upper_bound_h, + pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + fqres = wrapper::vcombine(wrapper::vmovn(final_sum.val[0]), wrapper::vmovn(final_sum.val[1])); + } + } + else + { + const q8x16_t max_data = wrapper::vmax(wrapper::vmax(top_data, bottom_data), middle_data); + const q8x16_t max_data_shift1 = wrapper::vext_1(max_data, max_data); + const q8x16_t max_data_shift2 = wrapper::vext_2(max_data, max_data); + const q8x16_t final_max = wrapper::vmax(wrapper::vmax(max_data, max_data_shift1), max_data_shift2); + + if(pool_stride_x == 2) + { + const q8x8x2_t table = { { wrapper::vgetlow(final_max), wrapper::vgethigh(final_max) } }; + static const q8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 }; + fres = wrapper::vtbl(table, lookup_val); + } + else + { + fqres = final_max; + } + } + + // Store result + if(pool_stride_x == 1) + { + if(src_qinfo != dst_qinfo) + { + fqres = vrequantize_pooling<q8x8_t, q8x16_t>(wrapper::vgetlow(fqres), wrapper::vgethigh(fqres), requant_qinfo); + } + wrapper::vstore(reinterpret_cast<T *>(dst.ptr()), fqres); + } + else + { + if(src_qinfo != dst_qinfo) + { + fres = vrequantize_pooling<q8x8_t>(fres, requant_qinfo); + } + wrapper::vstore(reinterpret_cast<T *>(dst.ptr()), fres); + } + }, + src, dst); +} + +void CpuPoolingKernel::poolingMxN_f16_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding) +{ + ARM_COMPUTE_UNUSED(pooling_type); + ARM_COMPUTE_UNUSED(exclude_padding); +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + Iterator src(_src, window_src); + Iterator dst(_dst, window); + + const int pool_size_x = _pool_info.is_global_pooling ? _src->info()->tensor_shape().x() : _pool_info.pool_size.width; + const int pool_size_y = _pool_info.is_global_pooling ? _src->info()->tensor_shape().y() : _pool_info.pool_size.height; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); + + execute_window_loop(window, [&](const Coordinates & id) + { + float16_t res = 0.0f; + float16x8_t vres = vdupq_n_f16(0.0f); + + if(pooling_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + + // Perform pooling + + for(int y = 0; y < pool_size_y; ++y) + { + int x = 0; + for(; x <= (pool_size_x - 8); x += 8) + { + const float16x8_t data = vld1q_f16(reinterpret_cast<const float16_t *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().y()))); + + // Get power of 2 in case of l2 pooling and accumulate + if(pooling_type == PoolingType::L2) + { + vres = vaddq_f16(vres, vmulq_f16(data, data)); + } + else + { + vres = vaddq_f16(vres, data); + } + } + + // Leftover for loop + for(; x < pool_size_x; ++x) + { + float16_t data = *(reinterpret_cast<const float16_t *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + + (y - pool_pad_top) * static_cast<int>(_src->info()->strides_in_bytes().y()))); + + // Get power of 2 in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + data *= data; + } + + res += data; + } + } + + // Reduction + float16x4_t tmp = vpadd_f16(vget_high_f16(vres), vget_low_f16(vres)); + res += vget_lane_f16(tmp, 0); + res += vget_lane_f16(tmp, 1); + res += vget_lane_f16(tmp, 2); + res += vget_lane_f16(tmp, 3); + + // Divide by scale + res *= scale; + } + else + { + float16x8_t vres = vdupq_n_f16(std::numeric_limits<float>::lowest()); + res = std::numeric_limits<float>::lowest(); + + for(int y = 0; y < pool_size_y; ++y) + { + int x = 0; + for(; x <= (pool_size_x - 8); x += 8) + { + const float16x8_t data = vld1q_f16(reinterpret_cast<const float16_t *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().y()))); + vres = vmaxq_f16(vres, data); + } + + // Leftover for loop + for(; x < pool_size_x; ++x) + { + const float16_t data = *(reinterpret_cast<const float16_t *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + + (y - pool_pad_top) * static_cast<int>(_src->info()->strides_in_bytes().y()))); + res = std::max(res, data); + } + } + + float16x4_t tmp = vpmax_f16(vget_high_f16(vres), vget_low_f16(vres)); + res = std::max(res, vget_lane_f16(tmp, 0)); + res = std::max(res, vget_lane_f16(tmp, 1)); + res = std::max(res, vget_lane_f16(tmp, 2)); + res = std::max(res, vget_lane_f16(tmp, 3)); + } + + // Calculate square-root in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + res = std::sqrt(res); + } + + // Store result + *(reinterpret_cast<float16_t *>(dst.ptr())) = res; + }, + src, dst); + +#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + ARM_COMPUTE_UNUSED(window_src); + ARM_COMPUTE_UNUSED(window); + ARM_COMPUTE_ERROR("FP16 Not supported! Recompile the library with arch=arm64-v8.2-a"); +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +void CpuPoolingKernel::pooling2_f16_nhwc_maxpool_indices(const Window &window_src, const Window &window) +{ + const int window_start_x = window.x().start(); + const int window_end_x = window.x().end(); + const int window_step_x = 8; + + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator src(_src, window_src); + Iterator dst(_dst, window_out); + Iterator indices(_indices, window_out); + + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + + const int pad_right = _src->info()->padding().right; + const int in_stride_y = static_cast<int>(_src->info()->strides_in_bytes().y()); + const int in_stride_z = static_cast<int>(_src->info()->strides_in_bytes().z()); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const int idx_width = id.y() * pool_stride_x; + const int idx_height = id.z() * pool_stride_y; + const int pool_limit_y = pool_pad_top - idx_height; + const int pool_limit_x = pool_pad_left - idx_width; + + const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); + const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); + const int in_x0_offset = (pool_start_x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z()); + const int in_x1_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z()); + const int in_x2_offset = (pool_start_x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z()); + const int in_x3_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z()); + + int x_off = window_start_x; + for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) + { + const auto in_x0_ptr = reinterpret_cast<const float16_t *>(src.ptr() + in_x0_offset) + x_off; + const auto in_x1_ptr = reinterpret_cast<const float16_t *>(src.ptr() + in_x1_offset) + x_off; + const auto in_x2_ptr = reinterpret_cast<const float16_t *>(src.ptr() + in_x2_offset) + x_off; + const auto in_x3_ptr = reinterpret_cast<const float16_t *>(src.ptr() + in_x3_offset) + x_off; + const auto v_x0 = vld1q_f16(in_x0_ptr); + const auto v_x1 = vld1q_f16(in_x1_ptr); + const auto v_x2 = vld1q_f16(in_x2_ptr); + const auto v_x3 = vld1q_f16(in_x3_ptr); + float16x8_t vres = vmaxq_f16(vmaxq_f16(v_x2, v_x3), vmaxq_f16(v_x0, v_x1)); + // Store result + vst1q_f16(reinterpret_cast<float16_t *>(dst.ptr()) + x_off, vres); + + const uint32_t offset_base = offset_no_padding<float16_t>(src.offset(), id, *_src->info(), pool_stride_x, pool_stride_y); + const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float16_t) + x_off; + const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float16_t) - pad_right; + const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) - pad_right * _src->info()->tensor_shape()[1]; + const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_right; + const uint32x4_t voffset_x0_0 = { offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3 }; + const uint32x4_t voffset_x0_1 = { offset_x0 + 4, offset_x0 + 5, offset_x0 + 6, offset_x0 + 7 }; + const uint16x8_t voffset_x0 = vcombine_u16(vmovn_u32(voffset_x0_0), vmovn_u32(voffset_x0_1)); + const uint32x4_t voffset_x1_0 = { offset_x1, offset_x1 + 1, offset_x1 + 2, offset_x1 + 3 }; + const uint32x4_t voffset_x1_1 = { offset_x1 + 4, offset_x1 + 5, offset_x1 + 6, offset_x1 + 7 }; + const uint16x8_t voffset_x1 = vcombine_u16(vmovn_u32(voffset_x1_0), vmovn_u32(voffset_x1_1)); + const uint32x4_t voffset_x2_0 = { offset_x2, offset_x2 + 1, offset_x2 + 2, offset_x2 + 3 }; + const uint32x4_t voffset_x2_1 = { offset_x2 + 4, offset_x2 + 5, offset_x2 + 6, offset_x2 + 7 }; + const uint16x8_t voffset_x2 = vcombine_u16(vmovn_u32(voffset_x2_0), vmovn_u32(voffset_x2_1)); + const uint32x4_t voffset_x3_0 = { offset_x3, offset_x3 + 1, offset_x3 + 2, offset_x3 + 3 }; + const uint32x4_t voffset_x3_1 = { offset_x3 + 4, offset_x3 + 5, offset_x3 + 6, offset_x3 + 7 }; + const uint16x8_t voffset_x3 = vcombine_u16(vmovn_u32(voffset_x3_0), vmovn_u32(voffset_x3_1)); + const uint16x8_t tmp_indices0 = vbslq_u16(vcgeq_f16(v_x0, v_x1), voffset_x0, voffset_x1); + const uint16x8_t tmp_indices1 = vbslq_u16(vcgeq_f16(v_x2, v_x3), voffset_x2, voffset_x3); + const uint16x8_t tmp_indices2 = vbslq_u16(vcgeq_f16(vmaxq_f16(v_x0, v_x1), vmaxq_f16(v_x2, v_x3)), tmp_indices0, tmp_indices1); + const uint32x4_t tmp_indeces3_0 = vmovl_u16(vget_low_u16(tmp_indices2)); + const uint32x4_t tmp_indeces3_1 = vmovl_u16(vget_high_u16(tmp_indices2)); + // Store indicies + vst1q_u32(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off, tmp_indeces3_0); + vst1q_u32(reinterpret_cast<uint32_t *>(indices.ptr() + 16) + x_off, tmp_indeces3_1); + } + + // Left-overs loop + for(; x_off < window_end_x; ++x_off) + { + const auto x0 = *(reinterpret_cast<const float16_t *>(src.ptr() + in_x0_offset) + x_off); + const auto x1 = *(reinterpret_cast<const float16_t *>(src.ptr() + in_x1_offset) + x_off); + const auto x2 = *(reinterpret_cast<const float16_t *>(src.ptr() + in_x2_offset) + x_off); + const auto x3 = *(reinterpret_cast<const float16_t *>(src.ptr() + in_x3_offset) + x_off); + float16_t res = std::max(std::max(x2, x3), std::max(x0, x1)); + + // Store result + *(reinterpret_cast<float16_t *>(dst.ptr()) + x_off) = res; + + const uint32_t offset_base = offset_no_padding<float16_t>(src.offset(), id, *_src->info(), pool_stride_x, pool_stride_y); + const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float16_t) + x_off; + const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float16_t) - pad_right; + const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) - pad_right * _src->info()->tensor_shape()[1]; + const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_right; + const uint32_t tmp_idx0 = (x0 >= x1) ? offset_x0 : offset_x1; + const uint32_t tmp_idx1 = (x2 >= x3) ? offset_x2 : offset_x3; + const uint32_t tmp_idx2 = (std::max(x0, x1) >= std::max(x2, x3)) ? tmp_idx0 : tmp_idx1; + + // Store indices + *(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off) = tmp_idx2; + } + }, + src, dst, indices); +} +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + +void CpuPoolingKernel::poolingMxN_f16_nhwc(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding) +{ + ARM_COMPUTE_UNUSED(pooling_type); + ARM_COMPUTE_UNUSED(exclude_padding); +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + if(_pool_info.pool_size == Size2D(2, 2) && pooling_type == PoolingType::MAX && _indices) + { + pooling2_f16_nhwc_maxpool_indices(window_src, window); + } + const int window_start_x = window.x().start(); + const int window_end_x = window.x().end(); + const int window_step_x = 8; + + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator src(_src, window_src); + Iterator dst(_dst, window_out); + + const int pool_size_x = _pool_info.is_global_pooling ? _src->info()->tensor_shape().y() : _pool_info.pool_size.width; + const int pool_size_y = _pool_info.is_global_pooling ? _src->info()->tensor_shape().z() : _pool_info.pool_size.height; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(2) + (exclude_padding ? 0 : pool_pad_bottom); + + float16x8_t vres; + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const int idx_width = id.y() * pool_stride_x; + const int idx_height = id.z() * pool_stride_y; + const int pool_limit_y = pool_pad_top - idx_height; + const int pool_limit_x = pool_pad_left - idx_width; + + const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); + const int pool_end_y = std::min(pool_size_y, window_src.z().end() + pool_limit_y); + const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); + const int pool_end_x = std::min(pool_size_x, window_src.y().end() + pool_limit_x); + + int x_off = window_start_x; + for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) + { + if(pooling_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + const float16x8_t scale_v = vdupq_n_f16(scale); + + // Perform pooling + vres = vdupq_n_f16(0.0f); + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float16x8_t data = vld1q_f16(reinterpret_cast<const float16_t *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + + // Get power of 2 in case of l2 pooling and accumulate + if(pooling_type == PoolingType::L2) + { + vres = vaddq_f16(vres, vmulq_f16(data, data)); + } + else + { + vres = vaddq_f16(vres, data); + } + } + } + // Divide by scale + vres = vmulq_f16(vres, scale_v); + } + else + { + vres = vdupq_n_f16(std::numeric_limits<float>::lowest()); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float16x8_t data = vld1q_f16(reinterpret_cast<const float16_t *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + vres = vmaxq_f16(vres, data); + } + } + } + + // Calculate square-root in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + float16x8_t sqrt_reciprocal = vrsqrteq_f16(vres); + vres = vmulq_f16(vres, vmulq_f16(vrsqrtsq_f16(vmulq_f16(vres, sqrt_reciprocal), sqrt_reciprocal), sqrt_reciprocal)); + } + + // Store result + vst1q_f16(reinterpret_cast<float16_t *>(dst.ptr()) + x_off, vres); + } + + // Left-overs loop + for(; x_off < window_end_x; ++x_off) + { + float16_t res = 0.0f; + + if(pooling_type != PoolingType::MAX) + { + // Calculate scale + const float16_t scale = calculate_avg_scale(exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float data = *(reinterpret_cast<const float16_t *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + + // Get power of 2 in case of l2 pooling and accumulate + if(pooling_type == PoolingType::L2) + { + res += data * data; + } + else + { + res += data; + } + } + } + + // Divide by scale + res *= scale; + } + else + { + res = std::numeric_limits<float>::lowest(); + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float16_t data = *(reinterpret_cast<const float16_t *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + res = std::max(res, data); + } + } + } + + // Calculate square-root in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + res = std::sqrt(res); + } + + // Store result + *(reinterpret_cast<float16_t *>(dst.ptr()) + x_off) = res; + } + }, + src, dst); + +#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + ARM_COMPUTE_UNUSED(window_src); + ARM_COMPUTE_UNUSED(window); + ARM_COMPUTE_ERROR("FP16 Not supported! Recompile the library with arch=arm64-v8.2-a"); +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ +} + +void CpuPoolingKernel::poolingMxN_f32_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding) +{ + Iterator src(_src, window_src); + Iterator dst(_dst, window); + + const int pool_size_x = _pool_info.is_global_pooling ? _src->info()->tensor_shape().x() : _pool_info.pool_size.width; + const int pool_size_y = _pool_info.is_global_pooling ? _src->info()->tensor_shape().y() : _pool_info.pool_size.height; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); + + execute_window_loop(window, [&](const Coordinates & id) + { + float res = 0.0f; + + if(pooling_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + + // Perform pooling + float32x4_t vres = vdupq_n_f32(0.0f); + + for(int y = 0; y < pool_size_y; ++y) + { + int x = 0; + for(; x <= (pool_size_x - 4); x += 4) + { + const float32x4_t data = vld1q_f32(reinterpret_cast<const float *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().y()))); + + // Get power of 2 in case of l2 pooling and accumulate + if(pooling_type == PoolingType::L2) + { + vres = vmlaq_f32(vres, data, data); + } + else + { + vres = vaddq_f32(vres, data); + } + } + + // Leftover for loop + for(; x < pool_size_x; ++x) + { + float data = *(reinterpret_cast<const float *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().y()))); + + // Get power of 2 in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + data *= data; + } + + res += data; + } + } + +#if defined(__aarch64__) + // Reduction operation available on 64 bit architectures only + res += vaddvq_f32(vres); +#else // __aarch64__ + // Reduction + float32x2_t tmp = vpadd_f32(vget_high_f32(vres), vget_low_f32(vres)); + tmp = vpadd_f32(tmp, tmp); + + res += vget_lane_f32(tmp, 0); +#endif // __aarch64__ + // Divide by scale + res *= scale; + } + else + { + float32x4_t vres = vdupq_n_f32(std::numeric_limits<float>::lowest()); + res = std::numeric_limits<float>::lowest(); + + for(int y = 0; y < pool_size_y; ++y) + { + int x = 0; + for(; x <= (pool_size_x - 4); x += 4) + { + const float32x4_t data = vld1q_f32(reinterpret_cast<const float *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().y()))); + vres = vmaxq_f32(vres, data); + } + + // Leftover for loop + for(; x < pool_size_x; ++x) + { + const float data = *(reinterpret_cast<const float *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().y()))); + res = std::max(res, data); + } + } +#if defined(__aarch64__) + // Reduction operation available on 64 bit architectures only + res = std::max(vmaxvq_f32(vres), res); +#else // __aarch64__ + float32x2_t tmp = vpmax_f32(vget_high_f32(vres), vget_low_f32(vres)); + tmp = vpmax_f32(tmp, tmp); + + res = std::max(res, vget_lane_f32(tmp, 0)); +#endif // __aarch64__ + } + + // Calculate square-root in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + res = std::sqrt(res); + } + + // Store result + *(reinterpret_cast<float *>(dst.ptr())) = res; + }, + src, dst); +} + +void CpuPoolingKernel::pooling2_f32_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, + bool exclude_padding) +{ + if(pooling_type == PoolingType::MAX && _indices) + { + pooling2_nchw_maxpool_indices<float>(window_src, window); + } + else + { + Iterator src(_src, window_src); + Iterator dst(_dst, window); + constexpr int pool_size = 2; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); + + const uint8_t *const src_top_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))); + const uint8_t *const src_bottom_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1)); + + execute_window_loop(window, [&](const Coordinates & id) + { + const auto in_top_ptr = reinterpret_cast<const float *>(src_top_ptr + src.offset()); + const auto in_bottom_ptr = reinterpret_cast<const float *>(src_bottom_ptr + src.offset()); + float32x2_t top_data = vld1_f32(in_top_ptr); + float32x2_t bottom_data = vld1_f32(in_bottom_ptr); + float32x2_t res = {}; + float final_res = 0; + // Get power of 2 in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + top_data = vmul_f32(top_data, top_data); + bottom_data = vmul_f32(bottom_data, bottom_data); + } + + if(pooling_type != PoolingType::MAX) + { + // Calculate scale + float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, 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); + } + final_res = vget_lane_f32(res, 0); + + // Calculate square-root in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + final_res = sqrt(final_res); + } + + // Store result + *(reinterpret_cast<float *>(dst.ptr())) = final_res; + }, + src, dst); + } +} + +void CpuPoolingKernel::pooling3_f32_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding) +{ + Iterator src(_src, window_src); + Iterator dst(_dst, window); + + constexpr const int pool_size = 3; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); + + const uint8_t *const src_top_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))); + const uint8_t *const src_middle_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1)); + const uint8_t *const src_bottom_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 2)); + + execute_window_loop(window, [&](const Coordinates & id) + { + float32x4_t top_data = vld1q_f32(reinterpret_cast<const float *>(src_top_ptr + src.offset())); + float32x4_t middle_data = vld1q_f32(reinterpret_cast<const float *>(src_middle_ptr + src.offset())); + float32x4_t bottom_data = vld1q_f32(reinterpret_cast<const float *>(src_bottom_ptr + src.offset())); + float32x2_t res = {}; + float final_res = 0; + + // Get power of 2 in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + top_data = vmulq_f32(top_data, top_data); + middle_data = vmulq_f32(middle_data, middle_data); + bottom_data = vmulq_f32(bottom_data, bottom_data); + } + + if(pooling_type != PoolingType::MAX) + { + // Calculate scale + float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, 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<float>::max(), max_data, 3)), vget_low_f32(max_data)); + res = vpmax_f32(res, res); + } + final_res = vget_lane_f32(res, 0); + + // Calculate square-root in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + final_res = sqrt(final_res); + } + + // Store result + *(reinterpret_cast<float *>(dst.ptr())) = final_res; + }, + src, dst); +} + +void CpuPoolingKernel::pooling7_f32_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding) +{ + Iterator src(_src, window_src); + Iterator dst(_dst, window); + + constexpr const int pool_size = 7; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); + + std::array<const uint8_t *, pool_size> src_ptrs{ {} }; + for(int i = 0; i < pool_size; ++i) + { + src_ptrs[i] = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + i)); + } + + execute_window_loop(window, [&](const Coordinates & id) + { + float32x2_t res = {}; + float final_res = 0.f; + if(pooling_type != PoolingType::MAX) + { + // Calculate scale + float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + const float32x2_t scale_v = vdup_n_f32(scale); + + // Perform pooling + float32x4x2_t data = vld2q_f32(reinterpret_cast<const float *>(src_ptrs[0] + src.offset())); + // Get power of 2 in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + data.val[0] = vmulq_f32(data.val[0], data.val[0]); + data.val[1] = vmulq_f32(data.val[1], data.val[1]); + } + float32x4_t sum_data = vaddq_f32(data.val[0], vsetq_lane_f32(0.f, data.val[1], 3)); + for(int i = 1; i < pool_size; ++i) + { + data = vld2q_f32(reinterpret_cast<const float *>(src_ptrs[i] + src.offset())); + // Get power of 2 in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + data.val[0] = vmulq_f32(data.val[0], data.val[0]); + data.val[1] = vmulq_f32(data.val[1], data.val[1]); + } + sum_data = vaddq_f32(sum_data, data.val[0]); + sum_data = vaddq_f32(sum_data, vsetq_lane_f32(0.f, data.val[1], 3)); + } + res = vpadd_f32(vget_high_f32(sum_data), vget_low_f32(sum_data)); + res = vmul_f32(vpadd_f32(res, res), scale_v); + } + else + { + float32x4x2_t max_data = vld2q_f32(reinterpret_cast<const float *>(src_ptrs[0] + src.offset())); + for(int i = 1; i < pool_size; ++i) + { + const float32x4x2_t data = vld2q_f32(reinterpret_cast<const float *>(src_ptrs[i] + src.offset())); + max_data = vmax2q_f32(max_data, data); + } + res = vpmax_f32(vget_high_f32(vsetq_lane_f32(-std::numeric_limits<float>::max(), max_data.val[1], 3)), vget_low_f32(max_data.val[1])); + res = vpmax_f32(res, vpmax_f32(vget_high_f32(max_data.val[0]), vget_low_f32(max_data.val[0]))); + res = vpmax_f32(res, res); + } + final_res = vget_lane_f32(res, 0); + + // Calculate square-root in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + final_res = sqrt(final_res); + } + + // Store result + *(reinterpret_cast<float *>(dst.ptr())) = final_res; + }, + src, dst); +} + +void CpuPoolingKernel::poolingMxN_f32_nhwc(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding) +{ + if(_pool_info.pool_size == Size2D(2, 2) && pooling_type == PoolingType::MAX && _indices) + { + pooling2_f32_nhwc_maxpool_indices(window_src, window); + } + else + { + const int window_start_x = window.x().start(); + const int window_end_x = window.x().end(); + const int window_step_x = 4; + + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator src(_src, window_src); + Iterator dst(_dst, window_out); + + const int pool_size_x = _pool_info.is_global_pooling ? _src->info()->tensor_shape().y() : _pool_info.pool_size.width; + const int pool_size_y = _pool_info.is_global_pooling ? _src->info()->tensor_shape().z() : _pool_info.pool_size.height; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(2) + (exclude_padding ? 0 : pool_pad_bottom); + + float32x4_t vres; + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const int idx_width = id.y() * pool_stride_x; + const int idx_height = id.z() * pool_stride_y; + const int pool_limit_y = pool_pad_top - idx_height; + const int pool_limit_x = pool_pad_left - idx_width; + + const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); + const int pool_end_y = std::min(pool_size_y, window_src.z().end() + pool_limit_y); + const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); + const int pool_end_x = std::min(pool_size_x, window_src.y().end() + pool_limit_x); + + int x_off = window_start_x; + for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) + { + if(pooling_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + const float32x4_t scale_v = vdupq_n_f32(scale); + + // Perform pooling + vres = vdupq_n_f32(0.0f); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float32x4_t data = vld1q_f32(reinterpret_cast<const float *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + + // Get power of 2 in case of l2 pooling and accumulate + if(pooling_type == PoolingType::L2) + { + vres = vmlaq_f32(vres, data, data); + } + else + { + vres = vaddq_f32(vres, data); + } + } + } + // Divide by scale + vres = vmulq_f32(vres, scale_v); + } + else + { + vres = vdupq_n_f32(std::numeric_limits<float>::lowest()); + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float32x4_t data = vld1q_f32(reinterpret_cast<const float *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + vres = vmaxq_f32(vres, data); + } + } + } + + // Calculate square-root in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + float32x4_t l2_res = { static_cast<float>(sqrt(vgetq_lane_f32(vres, 0))), + static_cast<float>(sqrt(vgetq_lane_f32(vres, 1))), + static_cast<float>(sqrt(vgetq_lane_f32(vres, 2))), + static_cast<float>(sqrt(vgetq_lane_f32(vres, 3))) + }; + vres = l2_res; + } + + // Store result + vst1q_f32(reinterpret_cast<float *>(dst.ptr()) + x_off, vres); + } + + // Left-overs loop + for(; x_off < window_end_x; ++x_off) + { + float res = 0.0f; + + if(pooling_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float data = *(reinterpret_cast<const float *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + + // Get power of 2 in case of l2 pooling and accumulate + if(pooling_type == PoolingType::L2) + { + res += data * data; + } + else + { + res += data; + } + } + } + + // Divide by scale + res *= scale; + } + else + { + res = std::numeric_limits<float>::lowest(); + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float data = *(reinterpret_cast<const float *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + res = std::max(res, data); + } + } + } + + // Calculate square-root in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + res = std::sqrt(res); + } + + // Store result + *(reinterpret_cast<float *>(dst.ptr()) + x_off) = res; + } + }, + src, dst); + } +} + +void CpuPoolingKernel::pooling2_f32_nhwc_maxpool_indices(const Window &window_src, const Window &window) +{ + const int window_start_x = window.x().start(); + const int window_end_x = window.x().end(); + const int window_step_x = 4; + + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator src(_src, window_src); + Iterator dst(_dst, window_out); + Iterator indices(_indices, window_out); + + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + + float32x4_t vres; + float res; + + const int pad_right = _src->info()->padding().right; + const int in_stride_y = static_cast<int>(_src->info()->strides_in_bytes().y()); + const int in_stride_z = static_cast<int>(_src->info()->strides_in_bytes().z()); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const int idx_width = id.y() * pool_stride_x; + const int idx_height = id.z() * pool_stride_y; + const int pool_limit_y = pool_pad_top - idx_height; + const int pool_limit_x = pool_pad_left - idx_width; + + const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); + const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); + + const int in_x0_offset = (pool_start_x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z()); + const int in_x1_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z()); + const int in_x2_offset = (pool_start_x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z()); + const int in_x3_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z()); + + int x_off = window_start_x; + for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) + { + const auto in_x0_ptr = reinterpret_cast<const float *>(src.ptr() + in_x0_offset); + const auto in_x1_ptr = reinterpret_cast<const float *>(src.ptr() + in_x1_offset); + const auto in_x2_ptr = reinterpret_cast<const float *>(src.ptr() + in_x2_offset); + const auto in_x3_ptr = reinterpret_cast<const float *>(src.ptr() + in_x3_offset); + const auto v_x0 = vld1q_f32(in_x0_ptr + x_off); + const auto v_x1 = vld1q_f32(in_x1_ptr + x_off); + const auto v_x2 = vld1q_f32(in_x2_ptr + x_off); + const auto v_x3 = vld1q_f32(in_x3_ptr + x_off); + vres = vmaxq_f32(vmaxq_f32(v_x2, v_x3), vmaxq_f32(v_x0, v_x1)); + // Store result + vst1q_f32(reinterpret_cast<float *>(dst.ptr()) + x_off, vres); + + const uint32_t offset_base = offset_no_padding<float>(src.offset(), id, *_src->info(), pool_stride_x, pool_stride_y); + const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float) + x_off; + const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float) - pad_right; + const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float) - pad_right * _src->info()->tensor_shape()[1]; + const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float) - pad_right; + const uint32x4_t voffset_x0 = { offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3 }; + const uint32x4_t voffset_x1 = { offset_x1, offset_x1 + 1, offset_x1 + 2, offset_x1 + 3 }; + const uint32x4_t voffset_x2 = { offset_x2, offset_x2 + 1, offset_x2 + 2, offset_x2 + 3 }; + const uint32x4_t voffset_x3 = { offset_x3, offset_x3 + 1, offset_x3 + 2, offset_x3 + 3 }; + const uint32x4_t tmp_indices0 = vbslq_u32(vcgeq_f32(v_x0, v_x1), voffset_x0, voffset_x1); + const uint32x4_t tmp_indices1 = vbslq_u32(vcgeq_f32(v_x2, v_x3), voffset_x2, voffset_x3); + const uint32x4_t tmp_indices2 = vbslq_u32(vcgeq_f32(vmaxq_f32(v_x0, v_x1), vmaxq_f32(v_x2, v_x3)), tmp_indices0, tmp_indices1); + + // Store indices + vst1q_u32(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off, tmp_indices2); + } + + // Left-overs loop + for(; x_off < window_end_x; ++x_off) + { + const auto x0 = *(reinterpret_cast<const float *>(src.ptr() + in_x0_offset) + x_off); + const auto x1 = *(reinterpret_cast<const float *>(src.ptr() + in_x1_offset) + x_off); + const auto x2 = *(reinterpret_cast<const float *>(src.ptr() + in_x2_offset) + x_off); + const auto x3 = *(reinterpret_cast<const float *>(src.ptr() + in_x3_offset) + x_off); + res = std::max(std::max(x2, x3), std::max(x0, x1)); + + // Store result + *(reinterpret_cast<float *>(dst.ptr()) + x_off) = res; + + const uint32_t offset_base = offset_no_padding<float>(src.offset(), id, *_src->info(), pool_stride_x, pool_stride_y); + const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float) + x_off; + const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float) - pad_right; + const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float) - pad_right * _src->info()->tensor_shape()[1]; + const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float) - pad_right; + const uint32_t tmp_idx0 = (x0 >= x1) ? offset_x0 : offset_x1; + const uint32_t tmp_idx1 = (x2 >= x3) ? offset_x2 : offset_x3; + const uint32_t tmp_idx2 = (std::max(x0, x1) >= std::max(x2, x3)) ? tmp_idx0 : tmp_idx1; + + // Store indices + *(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off) = tmp_idx2; + } + }, + src, dst, indices); +} + +template <typename T> +void CpuPoolingKernel::poolingMxN_q8_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding) +{ + Iterator src(_src, window_src); + Iterator dst(_dst, window); + + /** NEON vector types */ + using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type; + using q16_t = typename wrapper::traits::promote_t<T>; + using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type; + using q32_t = typename wrapper::traits::promote_t<q16_t>; + using q32x4_t = typename wrapper::traits::neon_vector<q32_t, 4>::type; + + const int pool_size_x = _pool_info.is_global_pooling ? _src->info()->tensor_shape().x() : _pool_info.pool_size.width; + const int pool_size_y = _pool_info.is_global_pooling ? _src->info()->tensor_shape().y() : _pool_info.pool_size.height; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); + + const UniformQuantizationInfo &src_qinfo = _src->info()->quantization_info().uniform(); + const UniformQuantizationInfo &dst_qinfo = _dst->info()->quantization_info().uniform(); + + execute_window_loop(window, [&](const Coordinates & id) + { + T res = std::numeric_limits<T>::min(); + + if(pooling_type != PoolingType::MAX) + { + q32x4_t vres = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{}); + q32_t sres = 0; + + // Calculate scale + const float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + + // Perform pooling + for(int y = 0; y < pool_size_y; ++y) + { + int x = 0; + for(; x <= (pool_size_x - 8); x += 8) + { + const q8x8_t data = wrapper::vload(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().y()))); + + const q16x8_t data_q16 = wrapper::vmovl(data); + vres = wrapper::vadd(vres, wrapper::vaddl(wrapper::vgethigh(data_q16), wrapper::vgetlow(data_q16))); + } + + // Leftover for loop + for(; x < pool_size_x; ++x) + { + T data = *(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().y()))); + sres += data; + } + } + + // Reduction + const auto tmp = wrapper::vpadd(wrapper::vgethigh(vres), wrapper::vgetlow(vres)); + sres += wrapper::vgetlane(tmp, 0) + wrapper::vgetlane(tmp, 1); + + // Divide by scale + res = static_cast<T>(support::cpp11::round(sres * scale)); + } + else + { + q8x8_t vres = wrapper::vdup_n(std::numeric_limits<T>::min(), wrapper::traits::vector_64_tag{}); + + for(int y = 0; y < pool_size_y; ++y) + { + int x = 0; + for(; x <= (pool_size_x - 8); x += 8) + { + const q8x8_t data = wrapper::vload(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().y()))); + vres = wrapper::vmax(vres, data); + } + // Leftover for loop + for(; x < pool_size_x; ++x) + { + const T data = *(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().y()))); + res = std::max(res, data); + } + } + + // Reduce max + vres = wrapper::vpmax(vres, vres); + vres = wrapper::vpmax(vres, vres); + vres = wrapper::vpmax(vres, vres); + + // Get max value + res = std::max(res, wrapper::vgetlane(vres, 0)); + } + // Store result + res = (src_qinfo != dst_qinfo) ? Qasymm8QuantizationHelper<T>::quantize(Qasymm8QuantizationHelper<T>::dequantize(res, src_qinfo), dst_qinfo) : res; + *(reinterpret_cast<T *>(dst.ptr())) = res; + }, + src, dst); +} + +template <typename T> +void CpuPoolingKernel::poolingMxN_q8_nhwc(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding) +{ + const int window_start_x = window.x().start(); + const int window_end_x = window.x().end(); + const int window_step_x = 16; + const int window_half_step_x = window_step_x / 2; + + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator src(_src, window_src); + Iterator dst(_dst, window_out); + + using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type; + using q8x16_t = typename wrapper::traits::neon_vector<T, 16>::type; + using q16_t = typename wrapper::traits::promote_t<T>; + using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type; + using q32_t = typename wrapper::traits::promote_t<q16_t>; + using q32x4_t = typename wrapper::traits::neon_vector<q32_t, 4>::type; + + const int pool_size_x = _pool_info.is_global_pooling ? _src->info()->tensor_shape().y() : _pool_info.pool_size.width; + const int pool_size_y = _pool_info.is_global_pooling ? _src->info()->tensor_shape().z() : _pool_info.pool_size.height; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _src->info()->dimension(2) + (exclude_padding ? 0 : pool_pad_bottom); + + const float32x4_t half_scale_v = vdupq_n_f32(0.5f); + const UniformQuantizationInfo src_qinfo = _src->info()->quantization_info().uniform(); + const UniformQuantizationInfo dst_qinfo = _dst->info()->quantization_info().uniform(); + + const float quant_rescale = dst_qinfo.scale / src_qinfo.scale; + // "new_offset" doesn't have to consider the "half_scale_v" in its computation + // With a requantization performed in a single step there won't be uncertainties introduced + const int32_t new_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / quant_rescale); + + const float requant_scale = dst_qinfo.scale / src_qinfo.scale; + const int32_t requant_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / requant_scale); + const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const int idx_width = id.y() * pool_stride_x; + const int idx_height = id.z() * pool_stride_y; + const int pool_limit_y = pool_pad_top - idx_height; + const int pool_limit_x = pool_pad_left - idx_width; + + const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); + const int pool_end_y = std::min(pool_size_y, window_src.z().end() + pool_limit_y); + const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); + const int pool_end_x = std::min(pool_size_x, window_src.y().end() + pool_limit_x); + + int x_off = window_start_x; + for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) + { + if(pooling_type != PoolingType::MAX) + { + q32x4_t vres1 = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{}); + q32x4_t vres2 = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{}); + q32x4_t vres3 = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{}); + q32x4_t vres4 = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{}); + + // Calculate scale + const float scale = calculate_avg_scale(exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + + // Perform pooling + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const q8x16_t data = wrapper::vloadq(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + + const q16x8_t data_q16 = wrapper::vmovl(wrapper::vgetlow(data)); + const q16x8_t data2_q16 = wrapper::vmovl(wrapper::vgethigh(data)); + vres1 = wrapper::vadd(vres1, wrapper::vmovl(wrapper::vgetlow(data_q16))); + vres2 = wrapper::vadd(vres2, wrapper::vmovl(wrapper::vgethigh(data_q16))); + vres3 = wrapper::vadd(vres3, wrapper::vmovl(wrapper::vgetlow(data2_q16))); + vres4 = wrapper::vadd(vres4, wrapper::vmovl(wrapper::vgethigh(data2_q16))); + } + } + + if(src_qinfo != dst_qinfo) + { + const float32x4x4_t vres = + { + { + vcvtq_f32_q32(vres1), + vcvtq_f32_q32(vres2), + vcvtq_f32_q32(vres3), + vcvtq_f32_q32(vres4), + } + }; + const auto requantized_dst = vrequantize_pooling_with_scale<q8x16_t>(vres, quant_rescale, scale, new_offset); + // Store result + wrapper::vstore(reinterpret_cast<T *>(dst.ptr()) + x_off, wrapper::vgetlow(requantized_dst)); + wrapper::vstore(reinterpret_cast<T *>(dst.ptr()) + x_off + 8, wrapper::vgethigh(requantized_dst)); + } + else + { + const float32x4_t scale_v = vdupq_n_f32(scale); + // Divide by scale and add 0.5f to round to nearest instead of rounding towards zero + vres1 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres1), scale_v)); + vres2 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres2), scale_v)); + vres3 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres3), scale_v)); + vres4 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres4), scale_v)); + + const q8x8_t res1 = wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(vres1), wrapper::vmovn(vres2))); + const q8x8_t res2 = wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(vres3), wrapper::vmovn(vres4))); + // Store result + wrapper::vstore(reinterpret_cast<T *>(dst.ptr()) + x_off, res1); + wrapper::vstore(reinterpret_cast<T *>(dst.ptr()) + x_off + 8, res2); + } + } + else + { + q8x16_t vres = wrapper::vdup_n(std::numeric_limits<T>::min(), wrapper::traits::vector_128_tag{}); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const q8x16_t data = wrapper::vloadq(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + vres = wrapper::vmax(vres, data); + } + } + + // Store result + wrapper::vstore(reinterpret_cast<T *>(dst.ptr()) + x_off, (src_qinfo != dst_qinfo) ? vrequantize_pooling<q8x8_t, q8x16_t>(wrapper::vgetlow(vres), wrapper::vgethigh(vres), + requant_qinfo) : + vres); + } + } + + if(pooling_type == PoolingType::MAX) + { + for(; x_off <= (window_end_x - window_half_step_x); x_off += window_half_step_x) + { + q8x8_t vres = wrapper::vdup_n(std::numeric_limits<T>::min(), wrapper::traits::vector_64_tag{}); + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const q8x8_t data = wrapper::vload(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + vres = wrapper::vmax(vres, data); + } + } + + // Store result + wrapper::vstore(reinterpret_cast<T *>(dst.ptr()) + x_off, + (src_qinfo != dst_qinfo) ? vrequantize_pooling<q8x8_t>(vres, requant_qinfo) : vres); + } + } + + // Left-overs loop + for(; x_off < window_end_x; ++x_off) + { + if(pooling_type != PoolingType::MAX) + { + q32_t res = static_cast<q32_t>(0.f); + + // Calculate scale + const float scale = calculate_avg_scale(exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + + // Perform pooling + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const T data = *(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + res += data; + } + } + + if(src_qinfo != dst_qinfo) + { + const float res_f = static_cast<float>(res); + const float new_scale = quant_rescale / scale; + const auto requantized_dst = quantize<T>(res_f, UniformQuantizationInfo(new_scale, new_offset)); + + // Store result + *(reinterpret_cast<T *>(dst.ptr()) + x_off) = requantized_dst; + } + else + { + // Divide by scale and add 0.5f to round to nearest instead of rounding towards zero + res = static_cast<T>(0.5f + static_cast<float>(res) * scale); + + // Store result + *(reinterpret_cast<T *>(dst.ptr()) + x_off) = res; + } + } + else + { + T res = std::numeric_limits<T>::min(); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const T data = *(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (_src->info()->strides_in_bytes().z())) + x_off); + res = std::max(res, data); + } + } + + // Store result + if(src_qinfo != dst_qinfo) + { + const float res_f = static_cast<float>(res); + *(reinterpret_cast<T *>(dst.ptr()) + x_off) = quantize<T>(res_f, requant_qinfo); + } + else + { + *(reinterpret_cast<T *>(dst.ptr()) + x_off) = res; + } + } + } + + }, + src, dst); +} + +Status CpuPoolingKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src); + + unsigned int pooled_w = 0; + unsigned int pooled_h = 0; + unsigned int num_elems_processed_per_iteration = 0; + BorderSize border_size(0); + + const bool is_global_pooling = pool_info.is_global_pooling; + unsigned int pool_size_x = 0; + unsigned int pool_size_y = 0; + + // Get data layout + const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout; + const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + + pool_size_x = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width; + pool_size_y = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height; + + // Validate pool info before calling scaled_dimensions + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_pool_info(pool_size_x, pool_size_y)); + + // Check dst dimensions + std::tie(pooled_w, pooled_h) = scaled_dimensions(src->dimension(idx_width), + src->dimension(idx_height), + pool_size_x, + pool_size_y, + pool_info.pad_stride_info); + + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info, pooled_w, pooled_h, indices, Size2D(pool_size_x, pool_size_y))); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), + (indices) ? indices->clone().get() : nullptr, pool_info, num_elems_processed_per_iteration, border_size, pooled_w, pooled_h, + pool_size_x, pool_size_y) + .first); + + return Status{}; +} + +void CpuPoolingKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); + ARM_COMPUTE_ERROR_ON(_func == nullptr); + + _src = tensors.get_const_tensor(TensorType::ACL_SRC_0); + _dst = tensors.get_tensor(TensorType::ACL_DST_0); + _indices = tensors.get_tensor(TensorType::ACL_DST_1); + + const unsigned int pool_stride_x = _pool_info.pad_stride_info.stride().first; + const unsigned int pool_stride_y = _pool_info.pad_stride_info.stride().second; + const unsigned int pool_size = _pool_info.pool_size.width; + const bool exclude_padding = _pool_info.exclude_padding; + + Window window_src(window); + if(_data_layout == DataLayout::NCHW) + { + // Set step for src in x and y direction for the src + unsigned int window_x_inc = 0; + switch(_src->info()->data_type()) + { + case DataType::QASYMM8: + case DataType::QASYMM8_SIGNED: + { + window_x_inc = pool_stride_x; + if((pool_size == 2 || pool_size == 3) && pool_stride_x < 3) + { + window_x_inc = (pool_stride_x == 2) ? _num_elems_processed_per_iteration * 2 : _num_elems_processed_per_iteration; + } + break; + } + + case DataType::F16: + case DataType::F32: + { + window_x_inc = pool_stride_x; + break; + } + default: + { + ARM_COMPUTE_ERROR("Not supported"); + } + } + window_src.set(Window::DimX, Window::Dimension(window.x().start() * pool_stride_x, window.x().end() * pool_stride_x, window_x_inc)); + window_src.set(Window::DimY, Window::Dimension(window.y().start() * pool_stride_y, window.y().end() * pool_stride_y, pool_stride_y)); + } + else + { + window_src.set(Window::DimX, Window::Dimension(0, 1, 1)); + window_src.set(Window::DimY, Window::Dimension(0, _src->info()->dimension(1), pool_stride_x)); + window_src.set(Window::DimZ, Window::Dimension(0, _src->info()->dimension(2), pool_stride_y)); + } + + // Run function + (this->*_func)(window_src, window, _pool_info.pool_type, exclude_padding); +} +} // namespace kernels +} // namespace cpu +} // namespace arm_compute |