| /* |
| * Copyright (c) 2017-2020 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/NEON/kernels/NEPoolingLayerKernel.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| #include "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 <algorithm> |
| #include <arm_neon.h> |
| #include <cmath> |
| #include <limits> |
| #include <set> |
| #include <string> |
| #include <tuple> |
| |
| namespace arm_compute |
| { |
| 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 *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, |
| unsigned int &pooled_w, unsigned int pooled_h, const ITensorInfo *indices, Size2D pool_size) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); |
| |
| 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(input); |
| if(indices) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 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(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON(pool_type == PoolingType::L2 && is_data_type_quantized(input->data_type())); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_quantized(input->data_type()) && !pool_info.exclude_padding && (pool_info.pool_type == PoolingType::AVG) && pool_info.pad_stride_info.has_padding() |
| && (input->data_layout() == DataLayout::NHWC), |
| "exclude_padding equal false is not supported for AVG Pooling with padding on quantized types"); |
| |
| if(output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON((output->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH)) != pooled_w) |
| || (output->dimension(get_data_layout_dimension_index(input->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 *input, ITensorInfo *output, 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) |
| { |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_pool_shape(*input, pool_info))); |
| if(indices) |
| { |
| // Indices auto inizialitation if not yet initialized |
| auto_init_if_empty(*indices, (input->clone()->set_tensor_shape(compute_pool_shape(*input, |
| pool_info))) |
| .set_data_type(DataType::U32) /* we store the offset to the element */); |
| } |
| const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->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 input_width = input->dimension(idx_width); |
| const int input_height = input->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 output dimensions |
| std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(idx_width), |
| input->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(input->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) - input_width; |
| const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_top + pool_size_y) - input_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 output_shape{ input->tensor_shape() }; |
| output_shape.set(0, pooled_w); |
| output_shape.set(1, pooled_h); |
| TensorInfo output_info(input->clone()->set_tensor_shape(output_shape)); |
| win = calculate_max_window(output_info, Steps(num_elems_processed_per_iteration)); |
| AccessWindowStatic input_access(input, -pool_pad_left, -pool_pad_top, input_width + border_size.right, input_height + border_size.bottom); |
| AccessWindowHorizontal output_access(output, 0, num_elems_horizontal_window); |
| if(indices) |
| { |
| AccessWindowHorizontal indices_access(indices, 0, num_elems_horizontal_window); |
| window_changed = update_window_and_padding(win, input_access, output_access, indices_access); |
| } |
| else |
| { |
| window_changed = update_window_and_padding(win, input_access, output_access); |
| } |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->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 |
| |
| NEPoolingLayerKernel::NEPoolingLayerKernel() |
| : _func(nullptr), _input(nullptr), _output(nullptr), _indices(nullptr), _pool_info(), _data_layout(DataLayout::UNKNOWN), _num_elems_processed_per_iteration(0), _border_size(0), _is_square(false) |
| { |
| } |
| |
| BorderSize NEPoolingLayerKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void NEPoolingLayerKernel::configure(const ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info, ITensor *indices) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| 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 ? input->info()->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 ? input->info()->dimension(idx_width) : pool_info.pool_size.width, |
| is_global_pooling ? input->info()->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 output dimensions |
| unsigned int pooled_w; |
| unsigned int pooled_h; |
| std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(idx_width), |
| input->info()->dimension(idx_height), |
| pool_size.x(), |
| pool_size.y(), |
| pad_stride_info); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info, pooled_w, pooled_h, (indices) ? indices->info() : nullptr, pool_size)); |
| |
| // Set instance variables |
| _input = input; |
| _output = output; |
| _indices = indices; |
| _pool_info = pool_info; |
| _data_layout = input->info()->data_layout(); |
| _is_square = (pool_size.x() == pool_size.y()); |
| |
| // Get data type |
| const DataType data_type = input->info()->data_type(); |
| const bool is_nchw = _data_layout == DataLayout::NCHW; |
| |
| if(data_type == DataType::QASYMM8) |
| { |
| if(!is_nchw) |
| { |
| _func = &NEPoolingLayerKernel::poolingMxN_q8_nhwc<uint8_t>; |
| } |
| else |
| { |
| if(pool_size.x() == 2 && pool_stride_x < 3 && _is_square) |
| { |
| _func = &NEPoolingLayerKernel::pooling2_q8_nchw<uint8_t>; |
| } |
| else if(pool_size.x() == 3 && pool_stride_x < 3 && _is_square) |
| { |
| _func = &NEPoolingLayerKernel::pooling3_q8_nchw<uint8_t>; |
| } |
| else |
| { |
| _func = &NEPoolingLayerKernel::poolingMxN_q8_nchw<uint8_t>; |
| } |
| } |
| } |
| else if(data_type == DataType::QASYMM8_SIGNED) |
| { |
| if(!is_nchw) |
| { |
| _func = &NEPoolingLayerKernel::poolingMxN_q8_nhwc<int8_t>; |
| } |
| else |
| { |
| if(pool_size.x() == 2 && pool_stride_x < 3 && _is_square) |
| { |
| _func = &NEPoolingLayerKernel::pooling2_q8_nchw<int8_t>; |
| } |
| else if(pool_size.x() == 3 && pool_stride_x < 3 && _is_square) |
| { |
| _func = &NEPoolingLayerKernel::pooling3_q8_nchw<int8_t>; |
| } |
| else |
| { |
| _func = &NEPoolingLayerKernel::poolingMxN_q8_nchw<int8_t>; |
| } |
| } |
| } |
| else if(data_type == DataType::F16) |
| { |
| if(!is_nchw) |
| { |
| _func = &NEPoolingLayerKernel::poolingMxN_f16_nhwc; |
| } |
| else |
| { |
| if(_is_square) |
| { |
| switch(pool_size.x()) |
| { |
| case 2: |
| { |
| _func = &NEPoolingLayerKernel::pooling2_f16_nchw; |
| } |
| break; |
| case 3: |
| { |
| _func = &NEPoolingLayerKernel::pooling3_f16_nchw; |
| } |
| break; |
| default: |
| { |
| _func = &NEPoolingLayerKernel::poolingMxN_f16_nchw; |
| break; |
| } |
| } |
| } |
| else |
| { |
| _func = &NEPoolingLayerKernel::poolingMxN_f16_nchw; |
| } |
| } |
| } |
| else if(data_type == DataType::F32) |
| { |
| if(!is_nchw) |
| { |
| _func = &NEPoolingLayerKernel::poolingMxN_f32_nhwc; |
| } |
| else |
| { |
| if(_is_square) |
| { |
| switch(pool_size.x()) |
| { |
| case 2: |
| { |
| _func = &NEPoolingLayerKernel::pooling2_f32_nchw; |
| break; |
| } |
| case 3: |
| { |
| _func = &NEPoolingLayerKernel::pooling3_f32_nchw; |
| break; |
| } |
| case 7: |
| { |
| _func = &NEPoolingLayerKernel::pooling7_f32_nchw; |
| break; |
| } |
| default: |
| { |
| _func = &NEPoolingLayerKernel::poolingMxN_f32_nchw; |
| break; |
| } |
| } |
| } |
| else |
| { |
| _func = &NEPoolingLayerKernel::poolingMxN_f32_nchw; |
| } |
| } |
| } |
| |
| if(!is_nchw) |
| { |
| // Configure kernel window |
| Window win = calculate_max_window(*output->info(), Steps()); |
| Coordinates coord; |
| coord.set_num_dimensions(output->info()->num_dimensions()); |
| output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); |
| INEKernel::configure(win); |
| } |
| else |
| { |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), output->info(), (indices) ? indices->info() : nullptr, |
| 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); |
| INEKernel::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 NEPoolingLayerKernel::pooling2_q8_nchw(const Window &window_input, const Window &window, PoolingType pooling_type, bool exclude_padding) |
| { |
| Iterator input(_input, window_input); |
| Iterator output(_output, 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 = _input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); |
| |
| const T *const input_top_ptr = reinterpret_cast<const T *>(_input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)))); |
| const T *const input_bottom_ptr = reinterpret_cast<const T *>(_input->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 input_qinfo = _input->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo output_qinfo = _output->info()->quantization_info().uniform(); |
| const bool have_different_qinfo = input_qinfo != output_qinfo; |
| |
| const float requant_scale = output_qinfo.scale / input_qinfo.scale; |
| const int32_t requant_offset = output_qinfo.offset - static_cast<int32_t>(static_cast<float>(input_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(input_top_ptr + input.offset()); |
| const auto bottom_data = wrapper::vloadq(input_bottom_ptr + input.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_output = vrequantize_pooling<q8x8_t, q8x16_t>(lower_res, upper_res, requant_qinfo); |
| lower_res = wrapper::vgetlow(requantized_output); |
| upper_res = wrapper::vgethigh(requantized_output); |
| } |
| |
| // Store result |
| if(pool_stride_x == 1) |
| { |
| const q8x8x2_t res = { { lower_res, upper_res } }; |
| wrapper::vstore(reinterpret_cast<T *>(output.ptr()), res); |
| } |
| else |
| { |
| wrapper::vstore(reinterpret_cast<T *>(output.ptr()), lower_res); |
| } |
| }, |
| input, output); |
| } |
| |
| void NEPoolingLayerKernel::pooling3_f16_nchw(const Window &window_input, 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 input(_input, window_input); |
| Iterator output(_output, 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 = _input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); |
| |
| const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))); |
| const unsigned char *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1)); |
| const unsigned char *const input_bottom_ptr = _input->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 *>(input_top_ptr + input.offset())); |
| float16x4_t middle_data = vld1_f16(reinterpret_cast<const float16_t *>(input_middle_ptr + input.offset())); |
| float16x4_t bottom_data = vld1_f16(reinterpret_cast<const float16_t *>(input_bottom_ptr + input.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 *>(output.ptr())) = vget_lane_f16(res, 0); |
| }, |
| input, output); |
| #else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| ARM_COMPUTE_UNUSED(window_input); |
| 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 input) |
| { |
| float32x2_t output = { static_cast<float>(vget_lane_f16(input, 0)), static_cast<float>(vget_lane_f16(input, 1)) }; |
| return output; |
| } |
| #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 input) |
| { |
| return input; |
| } |
| |
| template <typename T> |
| void NEPoolingLayerKernel::pooling2_nchw_maxpool_indices(const Window &window_input, const Window &window) |
| { |
| Iterator input(_input, window_input); |
| Iterator output(_output, 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 input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))); |
| const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1)); |
| const int pad_left = _input->info()->padding().left; |
| const int pad_right = _input->info()->padding().right; |
| const int in_stride_y = static_cast<int>(_input->info()->strides_in_bytes().y()); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| auto top_data = wrapper::vload(reinterpret_cast<const T *>(input_top_ptr + input.offset())); |
| auto bottom_data = wrapper::vload(reinterpret_cast<const T *>(input_bottom_ptr + input.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 *>(output.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>(input.offset(), id, *_input->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); |
| }, |
| input, output, indices); |
| } |
| |
| void NEPoolingLayerKernel::pooling2_f16_nchw(const Window &window_input, 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_input, window); |
| } |
| else |
| { |
| Iterator input(_input, window_input); |
| Iterator output(_output, 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 = _input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); |
| |
| const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))); |
| const unsigned char *const input_bottom_ptr = _input->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 *>(input_top_ptr + input.offset())); |
| float16x4_t bottom_data = vld1_f16(reinterpret_cast<const float16_t *>(input_bottom_ptr + input.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 *>(output.ptr())) = vget_lane_f16(res, 0); |
| }, |
| input, output); |
| } |
| #else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| ARM_COMPUTE_UNUSED(window_input); |
| 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 NEPoolingLayerKernel::pooling3_q8_nchw(const Window &window_input, const Window &window, PoolingType pooling_type, bool exclude_padding) |
| { |
| Iterator input(_input, window_input); |
| Iterator output(_output, 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 = _input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); |
| |
| const UniformQuantizationInfo &input_qinfo = _input->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo &output_qinfo = _output->info()->quantization_info().uniform(); |
| |
| const float requant_scale = output_qinfo.scale / input_qinfo.scale; |
| const int32_t requant_offset = output_qinfo.offset - static_cast<int32_t>(static_cast<float>(input_qinfo.offset) / requant_scale); |
| const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset); |
| |
| const T *const input_top_ptr = reinterpret_cast<const T *>(_input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)))); |
| const T *const input_middle_ptr = reinterpret_cast<const T *>(_input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1))); |
| const T *const input_bottom_ptr = reinterpret_cast<const T *>(_input->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(input_top_ptr + input.offset()); |
| const auto middle_data = wrapper::vloadq(input_middle_ptr + input.offset()); |
| const auto bottom_data = wrapper::vloadq(input_bottom_ptr + input.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(input_qinfo != output_qinfo) |
| { |
| fqres = vrequantize_pooling<q8x8_t, q8x16_t>(wrapper::vgetlow(fqres), wrapper::vgethigh(fqres), requant_qinfo); |
| } |
| wrapper::vstore(reinterpret_cast<T *>(output.ptr()), fqres); |
| } |
| else |
| { |
| if(input_qinfo != output_qinfo) |
| { |
| fres = vrequantize_pooling<q8x8_t>(fres, requant_qinfo); |
| } |
| wrapper::vstore(reinterpret_cast<T *>(output.ptr()), fres); |
| } |
| }, |
| input, output); |
| } |
| |
| void NEPoolingLayerKernel::poolingMxN_f16_nchw(const Window &window_input, 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 input(_input, window_input); |
| Iterator output(_output, window); |
| |
| const int pool_size_x = _pool_info.is_global_pooling ? _input->info()->tensor_shape().x() : _pool_info.pool_size.width; |
| const int pool_size_y = _pool_info.is_global_pooling ? _input->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 = _input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().x()) + |
| (y - pool_pad_top) * static_cast<int>(_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().x()) |
| + (y - pool_pad_top) * static_cast<int>(_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().x()) + |
| (y - pool_pad_top) * static_cast<int>(_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().x()) |
| + (y - pool_pad_top) * static_cast<int>(_input->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 *>(output.ptr())) = res; |
| }, |
| input, output); |
| |
| #else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| ARM_COMPUTE_UNUSED(window_input); |
| 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 NEPoolingLayerKernel::pooling2_f16_nhwc_maxpool_indices(const Window &window_input, 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 input(_input, window_input); |
| Iterator output(_output, 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 = _input->info()->padding().right; |
| const int in_stride_y = static_cast<int>(_input->info()->strides_in_bytes().y()); |
| const int in_stride_z = static_cast<int>(_input->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_input.z().start() + pool_limit_y); |
| const int pool_start_x = std::max(0, window_input.y().start() + pool_limit_x); |
| const int in_x0_offset = (pool_start_x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int> |
| (_input->info()->strides_in_bytes().z()); |
| const int in_x1_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int> |
| (_input->info()->strides_in_bytes().z()); |
| const int in_x2_offset = (pool_start_x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int> |
| (_input->info()->strides_in_bytes().z()); |
| const int in_x3_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int> |
| (_input->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 *>(input.ptr() + in_x0_offset) + x_off; |
| const auto in_x1_ptr = reinterpret_cast<const float16_t *>(input.ptr() + in_x1_offset) + x_off; |
| const auto in_x2_ptr = reinterpret_cast<const float16_t *>(input.ptr() + in_x2_offset) + x_off; |
| const auto in_x3_ptr = reinterpret_cast<const float16_t *>(input.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 *>(output.ptr()) + x_off, vres); |
| |
| const uint32_t offset_base = offset_no_padding<float16_t>(input.offset(), id, *_input->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 * _input->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 *>(input.ptr() + in_x0_offset) + x_off); |
| const auto x1 = *(reinterpret_cast<const float16_t *>(input.ptr() + in_x1_offset) + x_off); |
| const auto x2 = *(reinterpret_cast<const float16_t *>(input.ptr() + in_x2_offset) + x_off); |
| const auto x3 = *(reinterpret_cast<const float16_t *>(input.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 *>(output.ptr()) + x_off) = res; |
| |
| const uint32_t offset_base = offset_no_padding<float16_t>(input.offset(), id, *_input->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 * _input->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; |
| } |
| }, |
| input, output, indices); |
| } |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| void NEPoolingLayerKernel::poolingMxN_f16_nhwc(const Window &window_input, 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_input, 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 input(_input, window_input); |
| Iterator output(_output, window_out); |
| |
| const int pool_size_x = _pool_info.is_global_pooling ? _input->info()->tensor_shape().y() : _pool_info.pool_size.width; |
| const int pool_size_y = _pool_info.is_global_pooling ? _input->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 = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->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_input.z().start() + pool_limit_y); |
| const int pool_end_y = std::min(pool_size_y, window_input.z().end() + pool_limit_y); |
| const int pool_start_x = std::max(0, window_input.y().start() + pool_limit_x); |
| const int pool_end_x = std::min(pool_size_x, window_input.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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + |
| (y - pool_pad_top) * static_cast<int>(_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + |
| (y - pool_pad_top) * static_cast<int>(_input->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 *>(output.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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(output.ptr()) + x_off) = res; |
| } |
| }, |
| input, output); |
| |
| #else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| ARM_COMPUTE_UNUSED(window_input); |
| 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 NEPoolingLayerKernel::poolingMxN_f32_nchw(const Window &window_input, const Window &window, PoolingType pooling_type, bool exclude_padding) |
| { |
| Iterator input(_input, window_input); |
| Iterator output(_output, window); |
| |
| const int pool_size_x = _pool_info.is_global_pooling ? _input->info()->tensor_shape().x() : _pool_info.pool_size.width; |
| const int pool_size_y = _pool_info.is_global_pooling ? _input->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 = _input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(output.ptr())) = res; |
| }, |
| input, output); |
| } |
| |
| void NEPoolingLayerKernel::pooling2_f32_nchw(const Window &window_input, const Window &window, PoolingType pooling_type, |
| bool exclude_padding) |
| { |
| if(pooling_type == PoolingType::MAX && _indices) |
| { |
| pooling2_nchw_maxpool_indices<float>(window_input, window); |
| } |
| else |
| { |
| Iterator input(_input, window_input); |
| Iterator output(_output, 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 = _input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); |
| |
| const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))); |
| const uint8_t *const input_bottom_ptr = _input->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 *>(input_top_ptr + input.offset()); |
| const auto in_bottom_ptr = reinterpret_cast<const float *>(input_bottom_ptr + input.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 *>(output.ptr())) = final_res; |
| }, |
| input, output); |
| } |
| } |
| |
| void NEPoolingLayerKernel::pooling3_f32_nchw(const Window &window_input, const Window &window, PoolingType pooling_type, bool exclude_padding) |
| { |
| Iterator input(_input, window_input); |
| Iterator output(_output, 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 = _input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); |
| |
| const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))); |
| const uint8_t *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1)); |
| const uint8_t *const input_bottom_ptr = _input->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 *>(input_top_ptr + input.offset())); |
| float32x4_t middle_data = vld1q_f32(reinterpret_cast<const float *>(input_middle_ptr + input.offset())); |
| float32x4_t bottom_data = vld1q_f32(reinterpret_cast<const float *>(input_bottom_ptr + input.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 *>(output.ptr())) = final_res; |
| }, |
| input, output); |
| } |
| |
| void NEPoolingLayerKernel::pooling7_f32_nchw(const Window &window_input, const Window &window, PoolingType pooling_type, bool exclude_padding) |
| { |
| Iterator input(_input, window_input); |
| Iterator output(_output, 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 = _input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); |
| |
| std::array<const uint8_t *, pool_size> input_ptrs{ {} }; |
| for(int i = 0; i < pool_size; ++i) |
| { |
| input_ptrs[i] = _input->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 *>(input_ptrs[0] + input.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 *>(input_ptrs[i] + input.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 *>(input_ptrs[0] + input.offset())); |
| for(int i = 1; i < pool_size; ++i) |
| { |
| const float32x4x2_t data = vld2q_f32(reinterpret_cast<const float *>(input_ptrs[i] + input.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 *>(output.ptr())) = final_res; |
| }, |
| input, output); |
| } |
| |
| void NEPoolingLayerKernel::poolingMxN_f32_nhwc(const Window &window_input, 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_input, 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 input(_input, window_input); |
| Iterator output(_output, window_out); |
| |
| const int pool_size_x = _pool_info.is_global_pooling ? _input->info()->tensor_shape().y() : _pool_info.pool_size.width; |
| const int pool_size_y = _pool_info.is_global_pooling ? _input->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 = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->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_input.z().start() + pool_limit_y); |
| const int pool_end_y = std::min(pool_size_y, window_input.z().end() + pool_limit_y); |
| const int pool_start_x = std::max(0, window_input.y().start() + pool_limit_x); |
| const int pool_end_x = std::min(pool_size_x, window_input.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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(output.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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(output.ptr()) + x_off) = res; |
| } |
| }, |
| input, output); |
| } |
| } |
| |
| void NEPoolingLayerKernel::pooling2_f32_nhwc_maxpool_indices(const Window &window_input, 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 input(_input, window_input); |
| Iterator output(_output, 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 = _input->info()->padding().right; |
| const int in_stride_y = static_cast<int>(_input->info()->strides_in_bytes().y()); |
| const int in_stride_z = static_cast<int>(_input->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_input.z().start() + pool_limit_y); |
| const int pool_start_x = std::max(0, window_input.y().start() + pool_limit_x); |
| |
| const int in_x0_offset = (pool_start_x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int> |
| (_input->info()->strides_in_bytes().z()); |
| const int in_x1_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int> |
| (_input->info()->strides_in_bytes().z()); |
| const int in_x2_offset = (pool_start_x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int> |
| (_input->info()->strides_in_bytes().z()); |
| const int in_x3_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int> |
| (_input->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 *>(input.ptr() + in_x0_offset); |
| const auto in_x1_ptr = reinterpret_cast<const float *>(input.ptr() + in_x1_offset); |
| const auto in_x2_ptr = reinterpret_cast<const float *>(input.ptr() + in_x2_offset); |
| const auto in_x3_ptr = reinterpret_cast<const float *>(input.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 *>(output.ptr()) + x_off, vres); |
| |
| const uint32_t offset_base = offset_no_padding<float>(input.offset(), id, *_input->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 * _input->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 *>(input.ptr() + in_x0_offset) + x_off); |
| const auto x1 = *(reinterpret_cast<const float *>(input.ptr() + in_x1_offset) + x_off); |
| const auto x2 = *(reinterpret_cast<const float *>(input.ptr() + in_x2_offset) + x_off); |
| const auto x3 = *(reinterpret_cast<const float *>(input.ptr() + in_x3_offset) + x_off); |
| res = std::max(std::max(x2, x3), std::max(x0, x1)); |
| |
| // Store result |
| *(reinterpret_cast<float *>(output.ptr()) + x_off) = res; |
| |
| const uint32_t offset_base = offset_no_padding<float>(input.offset(), id, *_input->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 * _input->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; |
| } |
| }, |
| input, output, indices); |
| } |
| |
| template <typename T> |
| void NEPoolingLayerKernel::poolingMxN_q8_nchw(const Window &window_input, const Window &window, PoolingType pooling_type, bool exclude_padding) |
| { |
| Iterator input(_input, window_input); |
| Iterator output(_output, 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 ? _input->info()->tensor_shape().x() : _pool_info.pool_size.width; |
| const int pool_size_y = _pool_info.is_global_pooling ? _input->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 = _input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); |
| |
| const UniformQuantizationInfo &input_qinfo = _input->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo &output_qinfo = _output->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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 = (input_qinfo != output_qinfo) ? Qasymm8QuantizationHelper<T>::quantize(Qasymm8QuantizationHelper<T>::dequantize(res, input_qinfo), output_qinfo) : res; |
| *(reinterpret_cast<T *>(output.ptr())) = res; |
| }, |
| input, output); |
| } |
| |
| template <typename T> |
| void NEPoolingLayerKernel::poolingMxN_q8_nhwc(const Window &window_input, 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; |
| |
| Window window_out = window; |
| window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator input(_input, window_input); |
| Iterator output(_output, 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 ? _input->info()->tensor_shape().y() : _pool_info.pool_size.width; |
| const int pool_size_y = _pool_info.is_global_pooling ? _input->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 = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = _input->info()->dimension(2) + (exclude_padding ? 0 : pool_pad_bottom); |
| |
| const float32x4_t half_scale_v = vdupq_n_f32(0.5f); |
| const UniformQuantizationInfo input_qinfo = _input->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo output_qinfo = _output->info()->quantization_info().uniform(); |
| |
| const float quant_rescale = output_qinfo.scale / input_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 = output_qinfo.offset - static_cast<int32_t>(static_cast<float>(input_qinfo.offset) / quant_rescale); |
| |
| const float requant_scale = output_qinfo.scale / input_qinfo.scale; |
| const int32_t requant_offset = output_qinfo.offset - static_cast<int32_t>(static_cast<float>(input_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_input.z().start() + pool_limit_y); |
| const int pool_end_y = std::min(pool_size_y, window_input.z().end() + pool_limit_y); |
| const int pool_start_x = std::max(0, window_input.y().start() + pool_limit_x); |
| const int pool_end_x = std::min(pool_size_x, window_input.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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (_input->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(input_qinfo != output_qinfo) |
| { |
| const float32x4x4_t vres = |
| { |
| { |
| vcvtq_f32_q32(vres1), |
| vcvtq_f32_q32(vres2), |
| vcvtq_f32_q32(vres3), |
| vcvtq_f32_q32(vres4), |
| } |
| }; |
| const auto requantized_output = vrequantize_pooling_with_scale<q8x16_t>(vres, quant_rescale, scale, new_offset); |
| // Store result |
| wrapper::vstore(reinterpret_cast<T *>(output.ptr()) + x_off, wrapper::vgetlow(requantized_output)); |
| wrapper::vstore(reinterpret_cast<T *>(output.ptr()) + x_off + 8, wrapper::vgethigh(requantized_output)); |
| } |
| 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 *>(output.ptr()) + x_off, res1); |
| wrapper::vstore(reinterpret_cast<T *>(output.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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (_input->info()->strides_in_bytes().z())) + x_off); |
| vres = wrapper::vmax(vres, data); |
| } |
| } |
| |
| // Store result |
| wrapper::vstore(reinterpret_cast<T *>(output.ptr()) + x_off, (input_qinfo != output_qinfo) ? vrequantize_pooling<q8x8_t, q8x16_t>(wrapper::vgetlow(vres), wrapper::vgethigh(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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (_input->info()->strides_in_bytes().z())) + x_off); |
| res += data; |
| } |
| } |
| |
| if(input_qinfo != output_qinfo) |
| { |
| const float res_f = static_cast<float>(res); |
| const float new_scale = quant_rescale / scale; |
| const auto requantized_output = quantize<T>(res_f, UniformQuantizationInfo(new_scale, new_offset)); |
| |
| // Store result |
| *(reinterpret_cast<T *>(output.ptr()) + x_off) = requantized_output; |
| } |
| 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 *>(output.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 *>(input.ptr() + (x - pool_pad_left) * static_cast<int>(_input->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (_input->info()->strides_in_bytes().z())) + x_off); |
| res = std::max(res, data); |
| } |
| } |
| |
| // Store result |
| if(input_qinfo != output_qinfo) |
| { |
| const float res_f = static_cast<float>(res); |
| *(reinterpret_cast<T *>(output.ptr()) + x_off) = quantize<T>(res_f, requant_qinfo); |
| } |
| else |
| { |
| *(reinterpret_cast<T *>(output.ptr()) + x_off) = res; |
| } |
| } |
| } |
| |
| }, |
| input, output); |
| } |
| |
| Status NEPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, const ITensorInfo *indices) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); |
| |
| 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 ? input->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 ? input->dimension(idx_width) : pool_info.pool_size.width; |
| pool_size_y = is_global_pooling ? input->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 output dimensions |
| std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(idx_width), |
| input->dimension(idx_height), |
| pool_size_x, |
| pool_size_y, |
| pool_info.pad_stride_info); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info, pooled_w, pooled_h, indices, Size2D(pool_size_x, pool_size_y))); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->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 NEPoolingLayerKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| 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_input(window); |
| if(_data_layout == DataLayout::NCHW) |
| { |
| // Set step for input in x and y direction for the input |
| unsigned int window_x_inc = 0; |
| switch(_input->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_input.set(Window::DimX, Window::Dimension(window.x().start() * pool_stride_x, window.x().end() * pool_stride_x, window_x_inc)); |
| window_input.set(Window::DimY, Window::Dimension(window.y().start() * pool_stride_y, window.y().end() * pool_stride_y, pool_stride_y)); |
| } |
| else |
| { |
| window_input.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| window_input.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), pool_stride_x)); |
| window_input.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), pool_stride_y)); |
| } |
| |
| // Run function |
| (this->*_func)(window_input, window, _pool_info.pool_type, exclude_padding); |
| } |
| } // namespace arm_compute |