/* * Copyright (c) 2017 ARM Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "arm_compute/core/NEON/kernels/NEPoolingLayerKernel.h" #include "arm_compute/core/AccessWindowStatic.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/FixedPoint.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/NEON/NEFixedPoint.h" #include "arm_compute/core/NEON/NEMath.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" #include #include #include #include #include #include #include using namespace arm_compute; namespace { inline float calculate_avg_scale(const Coordinates &id, const int pool_size, const int upper_bound_w, const int upper_bound_h, const int pad_x, const int pad_y, const int stride_x, const int stride_y) { const int start_x = id.x() * stride_x - pad_x; const int start_y = id.y() * stride_y - pad_y; const int end_x = std::min(start_x + pool_size, upper_bound_w); const int end_y = std::min(start_y + pool_size, upper_bound_h); return 1.f / ((end_y - start_y) * (end_x - start_x)); } inline qint8_t calculate_avg_scale_q8(const Coordinates &id, int pool_size, int upper_bound_w, int upper_bound_h, int pad_x, int pad_y, int stride_x, int stride_y, int fixed_point_position) { static const std::array scale_values_q8 = { { 0x0, 0x0, 0x40, 0x2A, 0x20, 0x19, 0x15, 0x12, 0x10, 0xE } }; const int start_x = id.x() * stride_x - pad_x; const int start_y = id.y() * stride_y - pad_y; const int end_x = std::min(start_x + pool_size, upper_bound_w); const int end_y = std::min(start_y + pool_size, upper_bound_h); const int val = ((end_y - start_y) * (end_x - start_x)); return sshr_qs8(scale_values_q8[val], (7 - fixed_point_position)); } inline qint16_t calculate_avg_scale_q16(const Coordinates &id, int pool_size, int upper_bound_w, int upper_bound_h, int pad_x, int pad_y, int stride_x, int stride_y, int fixed_point_position) { static std::array scale_values_q16 = { { 0x0, 0x0, 0x4000, 0x2AAB, 0x2000, 0x199A, 0x1555, 0x1249, 0x1000, 0xE38 } }; const int start_x = id.x() * stride_x - pad_x; const int start_y = id.y() * stride_y - pad_y; const int end_x = std::min(start_x + pool_size, upper_bound_w); const int end_y = std::min(start_y + pool_size, upper_bound_h); const int val = ((end_y - start_y) * (end_x - start_x)); return sshr_qs16(scale_values_q16[val], (15 - fixed_point_position)); } } // namespace NEPoolingLayerKernel::NEPoolingLayerKernel() : _func(nullptr), _input(nullptr), _output(nullptr), _pool_info(), _num_elems_processed_per_iteration(0), _border_size(0) { } BorderSize NEPoolingLayerKernel::border_size() const { return _border_size; } void NEPoolingLayerKernel::configure(const ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info) { int pool_pad_x = 0; int pool_pad_y = 0; int pool_stride_x = 0; int pool_stride_y = 0; unsigned int pooled_w = 0; unsigned int pooled_h = 0; PoolingType pool_type = pool_info.pool_type(); int pool_size = pool_info.pool_size(); const PadStrideInfo pad_stride_info = pool_info.pad_stride_info(); std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad(); std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); static const std::set supported_pool_sizes = { 2, 3 }; ARM_COMPUTE_UNUSED(supported_pool_sizes); ARM_COMPUTE_ERROR_ON_NULLPTR(output); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON(pool_type == PoolingType::L2 && is_data_type_fixed_point(input->info()->data_type())); ARM_COMPUTE_ERROR_ON((supported_pool_sizes.find(pool_size) == supported_pool_sizes.end()) && (input->info()->data_type() != DataType::F32)); ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size || pool_pad_y >= pool_size); ARM_COMPUTE_ERROR_ON(is_data_type_fixed_point(input->info()->data_type()) && pool_stride_x > 2); // Check output dimensions std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), pool_size, pool_size, pool_info.pad_stride_info()); // Output auto initialization if not yet initialized { TensorShape output_shape{ input->info()->tensor_shape() }; output_shape.set(0, pooled_w); output_shape.set(1, pooled_h); auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position()); } ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h)); unsigned int num_elems_read_per_iteration = 0; unsigned int num_elems_processed_per_iteration = 0; unsigned int num_elems_horizontal_window = 0; // Select element size switch(input->info()->data_type()) { case DataType::QS8: num_elems_read_per_iteration = 16; switch(pool_size) { case 2: num_elems_processed_per_iteration = (pool_stride_x == 2) ? 8 : 15; break; case 3: num_elems_processed_per_iteration = (pool_stride_x == 2) ? 7 : 14; break; default: ARM_COMPUTE_ERROR("Pooling size not supported"); break; } num_elems_horizontal_window = (pool_stride_x == 2) ? 8 : 16; break; case DataType::QS16: num_elems_read_per_iteration = 8; switch(pool_size) { case 2: num_elems_processed_per_iteration = (pool_stride_x == 2) ? 4 : 7; break; case 3: num_elems_processed_per_iteration = (pool_stride_x == 2) ? 3 : 6; break; default: ARM_COMPUTE_ERROR("Pooling size not supported"); } num_elems_horizontal_window = (pool_stride_x == 2) ? 4 : 8; break; #ifdef ARM_COMPUTE_ENABLE_FP16 case DataType::F16: switch(pool_size) { case 2: num_elems_read_per_iteration = 16; num_elems_processed_per_iteration = 8; num_elems_horizontal_window = 8; break; case 3: num_elems_read_per_iteration = 4; num_elems_processed_per_iteration = 1; num_elems_horizontal_window = 1; break; default: ARM_COMPUTE_ERROR("Pooling size not supported"); break; } break; #endif /* ARM_COMPUTE_ENABLE_FP16 */ case DataType::F32: switch(pool_size) { 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: num_elems_read_per_iteration = 1; // We use vload4 for poolingN but with a leftover for loop break; } num_elems_processed_per_iteration = 1; num_elems_horizontal_window = 1; break; default: ARM_COMPUTE_ERROR("Element size not supported"); break; } _num_elems_processed_per_iteration = num_elems_processed_per_iteration; const int input_width = input->info()->dimension(0); const int input_height = input->info()->dimension(1); const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elems_read_per_iteration) - input_width; const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height; // Set instance variables _input = input; _output = output; _pool_info = pool_info; _border_size = BorderSize(pool_pad_y, pool_pad_x); _border_size.right = std::max(upper_bound_w, pool_pad_x); _border_size.bottom = std::max(upper_bound_h, pool_pad_y); // Select appropriate function switch(pool_size) { case 2: if(input->info()->data_type() == DataType::QS8) { switch(pool_type) { case PoolingType::AVG: _func = &NEPoolingLayerKernel::pooling2_q8; break; case PoolingType::MAX: _func = &NEPoolingLayerKernel::pooling2_q8; break; default: ARM_COMPUTE_ERROR("Unsupported pooling type!"); } } else if(input->info()->data_type() == DataType::QS16) { switch(pool_type) { case PoolingType::AVG: _func = &NEPoolingLayerKernel::pooling2_q16; break; case PoolingType::MAX: _func = &NEPoolingLayerKernel::pooling2_q16; break; default: ARM_COMPUTE_ERROR("Unsupported pooling type!"); } } else if(input->info()->data_type() == DataType::F16) { switch(pool_type) { case PoolingType::AVG: _func = &NEPoolingLayerKernel::pooling2_f16; break; case PoolingType::L2: _func = &NEPoolingLayerKernel::pooling2_f16; break; case PoolingType::MAX: _func = &NEPoolingLayerKernel::pooling2_f16; break; default: ARM_COMPUTE_ERROR("Unsupported pooling type!"); } } else if(input->info()->data_type() == DataType::F32) { switch(pool_type) { case PoolingType::AVG: _func = &NEPoolingLayerKernel::pooling2_f32; break; case PoolingType::L2: _func = &NEPoolingLayerKernel::pooling2_f32; break; case PoolingType::MAX: _func = &NEPoolingLayerKernel::pooling2_f32; break; default: ARM_COMPUTE_ERROR("Unsupported pooling type!"); } } break; case 3: if(input->info()->data_type() == DataType::QS8) { switch(pool_type) { case PoolingType::AVG: _func = &NEPoolingLayerKernel::pooling3_q8; break; case PoolingType::MAX: _func = &NEPoolingLayerKernel::pooling3_q8; break; default: ARM_COMPUTE_ERROR("Unsupported pooling type!"); } } else if(input->info()->data_type() == DataType::QS16) { switch(pool_type) { case PoolingType::AVG: _func = &NEPoolingLayerKernel::pooling3_q16; break; case PoolingType::MAX: _func = &NEPoolingLayerKernel::pooling3_q16; break; default: ARM_COMPUTE_ERROR("Unsupported pooling type!"); } } else if(input->info()->data_type() == DataType::F16) { switch(pool_type) { case PoolingType::AVG: _func = &NEPoolingLayerKernel::pooling3_f16; break; case PoolingType::L2: _func = &NEPoolingLayerKernel::pooling3_f16; break; case PoolingType::MAX: _func = &NEPoolingLayerKernel::pooling3_f16; break; default: ARM_COMPUTE_ERROR("Unsupported pooling type!"); } } else if(input->info()->data_type() == DataType::F32) { switch(pool_type) { case PoolingType::AVG: _func = &NEPoolingLayerKernel::pooling3_f32; break; case PoolingType::L2: _func = &NEPoolingLayerKernel::pooling3_f32; break; case PoolingType::MAX: _func = &NEPoolingLayerKernel::pooling3_f32; break; default: ARM_COMPUTE_ERROR("Unsupported pooling type!"); } } break; case 7: switch(pool_type) { case PoolingType::AVG: _func = &NEPoolingLayerKernel::pooling7_f32; break; case PoolingType::L2: _func = &NEPoolingLayerKernel::pooling7_f32; break; case PoolingType::MAX: _func = &NEPoolingLayerKernel::pooling7_f32; break; default: ARM_COMPUTE_ERROR("Unsupported pooling type!"); } break; default: switch(pool_type) { case PoolingType::AVG: _func = &NEPoolingLayerKernel::poolingN_f32; break; case PoolingType::L2: _func = &NEPoolingLayerKernel::poolingN_f32; break; case PoolingType::MAX: _func = &NEPoolingLayerKernel::poolingN_f32; break; default: ARM_COMPUTE_ERROR("Unsupported pooling type!"); } break; } // Configure kernel window Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration)); AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom); AccessWindowHorizontal output_access(output->info(), 0, num_elems_horizontal_window); update_window_and_padding(win, input_access, output_access); output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); INEKernel::configure(win); } template void NEPoolingLayerKernel::pooling2_q8(const Window &window_input, const Window &window) { Iterator input(_input, window_input); Iterator output(_output, window); const int fixed_point_position = _input->info()->fixed_point_position(); constexpr int pool_size = 2; int pool_pad_x = 0; int pool_pad_y = 0; int pool_stride_x = 0; int pool_stride_y = 0; std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y))); const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 1)); execute_window_loop(window, [&](const Coordinates & id) { const auto top_data = vld1q_qs8(reinterpret_cast(input_top_ptr + input.offset())); const auto bottom_data = vld1q_qs8(reinterpret_cast(input_bottom_ptr + input.offset())); qint8x8_t lower_res = {}; qint8x8_t upper_res = {}; if(pooling_type == PoolingType::AVG) { // Calculate scale const qint8_t scale = calculate_avg_scale_q8(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y, fixed_point_position); const qint8x8_t scale_vec = vdup_n_qs8(scale); // Perform pooling const qint8x16_t sum_data = vqaddq_qs8(top_data, bottom_data); lower_res = vqmul_qs8(vpadd_s8(vget_low_s8(sum_data), vget_high_s8(sum_data)), scale_vec, fixed_point_position); if(pool_stride_x == 1) { const qint8x16_t sum_data_shifted = vextq_s8(sum_data, sum_data, 1); upper_res = vqmul_qs8(vpadd_s8(vget_low_s8(sum_data_shifted), vget_high_s8(sum_data_shifted)), scale_vec, fixed_point_position); } } else { const qint8x16_t max_data = vmaxq_s8(top_data, bottom_data); lower_res = vpmax_s8(vget_low_s8(max_data), vget_high_s8(max_data)); if(pool_stride_x == 1) { const qint8x16_t max_data_shifted = vextq_s8(max_data, max_data, 1); upper_res = vpmax_s8(vget_low_s8(max_data_shifted), vget_high_s8(max_data_shifted)); } } if(pool_stride_x == 1) { const qint8x8x2_t res = { { lower_res, upper_res } }; vst2_s8(reinterpret_cast(output.ptr()), res); } else { vst1_qs8(reinterpret_cast(output.ptr()), lower_res); } }, input, output); } template void NEPoolingLayerKernel::pooling2_q16(const Window &window_input, const Window &window) { Iterator input(_input, window_input); Iterator output(_output, window); const int fixed_point_position = _input->info()->fixed_point_position(); constexpr int pool_size = 2; int pool_pad_x = 0; int pool_pad_y = 0; int pool_stride_x = 0; int pool_stride_y = 0; std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y))); const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 1)); execute_window_loop(window, [&](const Coordinates & id) { const auto top_data = vld1q_qs16(reinterpret_cast(input_top_ptr + input.offset())); const auto bottom_data = vld1q_qs16(reinterpret_cast(input_bottom_ptr + input.offset())); qint16x4_t lower_res = {}; qint16x4_t upper_res = {}; if(pooling_type == PoolingType::AVG) { // Calculate scale const qint16_t scale = calculate_avg_scale_q16(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y, fixed_point_position); const qint16x4_t scale_vec = vdup_n_qs16(scale); // Perform pooling const qint16x8_t sum_data = vqaddq_qs16(top_data, bottom_data); lower_res = vqmul_qs16(vpadd_s16(vget_low_s16(sum_data), vget_high_s16(sum_data)), scale_vec, fixed_point_position); if(pool_stride_x == 1) { const qint16x8_t sum_data_shifted = vextq_s16(sum_data, sum_data, 1); upper_res = vqmul_qs16(vpadd_s16(vget_low_s16(sum_data_shifted), vget_high_s16(sum_data_shifted)), scale_vec, fixed_point_position); } } else { const qint16x8_t max_data = vmaxq_s16(top_data, bottom_data); lower_res = vpmax_s16(vget_low_s16(max_data), vget_high_s16(max_data)); if(pool_stride_x == 1) { const qint16x8_t max_data_shifted = vextq_s16(max_data, max_data, 1); upper_res = vpmax_s16(vget_low_s16(max_data_shifted), vget_high_s16(max_data_shifted)); } } if(pool_stride_x == 1) { const qint16x4x2_t res = { { lower_res, upper_res } }; vst2_s16(reinterpret_cast(output.ptr()), res); } else { vst1_qs16(reinterpret_cast(output.ptr()), lower_res); } }, input, output); } template void NEPoolingLayerKernel::pooling3_f16(const Window &window_input, const Window &window) { #ifdef ARM_COMPUTE_ENABLE_FP16 Iterator input(_input, window_input); Iterator output(_output, window); constexpr const int pool_size = 3; int pool_pad_x = 0; int pool_pad_y = 0; int pool_stride_x = 0; int pool_stride_y = 0; std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y))); const unsigned char *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 1)); const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 2)); execute_window_loop(window, [&](const Coordinates & id) { float16x4_t top_data = vld1_f16(reinterpret_cast(input_top_ptr + input.offset())); float16x4_t middle_data = vld1_f16(reinterpret_cast(input_middle_ptr + input.offset())); float16x4_t bottom_data = vld1_f16(reinterpret_cast(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(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, 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::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(output.ptr())) = vget_lane_f16(res, 0); }, input, output); #else /* ARM_COMPUTE_ENABLE_FP16 */ 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_COMPUTE_ENABLE_FP16 */ } template void NEPoolingLayerKernel::pooling2_f16(const Window &window_input, const Window &window) { #ifdef ARM_COMPUTE_ENABLE_FP16 Iterator input(_input, window_input); Iterator output(_output, window); constexpr int pool_size = 2; int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0; std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y))); const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 1)); execute_window_loop(window, [&](const Coordinates & id) { auto top_data = vld2q_f16(reinterpret_cast(input_top_ptr + input.offset())); auto bottom_data = vld2q_f16(reinterpret_cast(input_bottom_ptr + input.offset())); float16x8_t res = {}; // Get power of 2 in case of l2 pooling if(pooling_type == PoolingType::L2) { top_data.val[0] = vmulq_f16(top_data.val[0], top_data.val[0]); top_data.val[1] = vmulq_f16(top_data.val[1], top_data.val[1]); bottom_data.val[0] = vmulq_f16(bottom_data.val[0], bottom_data.val[0]); bottom_data.val[1] = vmulq_f16(bottom_data.val[1], bottom_data.val[1]); } if(pooling_type != PoolingType::MAX) { const float scale = calculate_avg_scale(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y); const float16x8_t scale_v = vdupq_n_f16(scale); res = vmulq_f16(scale_v, vaddq_f16(bottom_data.val[1], vaddq_f16(bottom_data.val[0], vaddq_f16(top_data.val[0], top_data.val[1])))); } else { res = vmaxq_f16(bottom_data.val[1], vmaxq_f16(bottom_data.val[0], vmaxq_f16(top_data.val[0], top_data.val[1]))); } // Calculate square-root in case of l2 pooling if(pooling_type == PoolingType::L2) { res = vinvq_f16(vinvsqrtq_f16(res)); } // Store result vst1q_f16(reinterpret_cast(output.ptr()), res); }, input, output); #else /* ARM_COMPUTE_ENABLE_FP16 */ 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_COMPUTE_ENABLE_FP16 */ } template void NEPoolingLayerKernel::pooling2_f32(const Window &window_input, const Window &window) { Iterator input(_input, window_input); Iterator output(_output, window); constexpr int pool_size = 2; int pool_pad_x = 0; int pool_pad_y = 0; int pool_stride_x = 0; int pool_stride_y = 0; std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y))); const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 1)); execute_window_loop(window, [&](const Coordinates & id) { float32x2_t top_data = vld1_f32(reinterpret_cast(input_top_ptr + input.offset())); float32x2_t bottom_data = vld1_f32(reinterpret_cast(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 = 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(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y); const float32x2_t scale_v = vdup_n_f32(scale); // Perform pooling const float32x2_t sum_data = vadd_f32(top_data, bottom_data); res = vmul_f32(vpadd_f32(sum_data, sum_data), scale_v); } else { const float32x2_t max_data = vmax_f32(top_data, bottom_data); res = vpmax_f32(max_data, max_data); } 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(output.ptr())) = final_res; }, input, output); } template void NEPoolingLayerKernel::pooling3_q8(const Window &window_input, const Window &window) { Iterator input(_input, window_input); Iterator output(_output, window); const int fixed_point_position = _input->info()->fixed_point_position(); constexpr int pool_size = 3; int pool_pad_x = 0; int pool_pad_y = 0; int pool_stride_x = 0; int pool_stride_y = 0; std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y))); const uint8_t *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 1)); const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 2)); execute_window_loop(window, [&](const Coordinates & id) { const auto top_data = vld1q_qs8(reinterpret_cast(input_top_ptr + input.offset())); const auto middle_data = vld1q_qs8(reinterpret_cast(input_middle_ptr + input.offset())); const auto bottom_data = vld1q_qs8(reinterpret_cast(input_bottom_ptr + input.offset())); qint8x8_t res = {}; if(pooling_type == PoolingType::AVG) { // Calculate scale const qint8_t scale = calculate_avg_scale_q8(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y, fixed_point_position); // Perform pooling for stride 2 const qint8x16_t sum_data = vqaddq_qs8(vqaddq_qs8(top_data, bottom_data), middle_data); const qint8x16_t sum_data2 = vextq_s8(sum_data, sum_data, 1); const qint8x16_t sum_data3 = vextq_s8(sum_data, sum_data, 2); const qint8x16_t final_sum = vqaddq_qs8(vqaddq_qs8(sum_data, sum_data2), sum_data3); if(pool_stride_x == 2) { const qint8x8x2_t table = { { vget_low_s8(final_sum), vget_high_s8(final_sum) } }; static const qint8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 }; const qint8x8_t scale_vec = vdup_n_qs8(scale); res = vtbl2_s8(table, lookup_val); res = vqmul_qs8(res, scale_vec, fixed_point_position); vst1_qs8(reinterpret_cast(output.ptr()), res); } else { const qint8x16_t scale_vec = vdupq_n_qs8(scale); vst1q_qs8(reinterpret_cast(output.ptr()), vqmulq_qs8(final_sum, scale_vec, fixed_point_position)); } } else { const qint8x16_t max_data = vmaxq_s8(vmaxq_s8(top_data, bottom_data), middle_data); const qint8x16_t max_data2 = vextq_s8(max_data, max_data, 1); const qint8x16_t max_data3 = vextq_s8(max_data, max_data, 2); const qint8x16_t final_max = vmaxq_s8(vmaxq_s8(max_data, max_data2), max_data3); if(pool_stride_x == 2) { const qint8x8x2_t table = { { vget_low_s8(final_max), vget_high_s8(final_max) } }; static const qint8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 }; res = vtbl2_s8(table, lookup_val); vst1_qs8(reinterpret_cast(output.ptr()), res); } else { vst1q_qs8(reinterpret_cast(output.ptr()), final_max); } } }, input, output); } template void NEPoolingLayerKernel::pooling3_q16(const Window &window_input, const Window &window) { Iterator input(_input, window_input); Iterator output(_output, window); const int fixed_point_position = _input->info()->fixed_point_position(); constexpr int pool_size = 3; int pool_pad_x = 0; int pool_pad_y = 0; int pool_stride_x = 0; int pool_stride_y = 0; std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y))); const unsigned char *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 1)); const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 2)); execute_window_loop(window, [&](const Coordinates & id) { const auto top_data = vld1q_qs16(reinterpret_cast(input_top_ptr + input.offset())); const auto middle_data = vld1q_qs16(reinterpret_cast(input_middle_ptr + input.offset())); const auto bottom_data = vld1q_qs16(reinterpret_cast(input_bottom_ptr + input.offset())); if(pooling_type == PoolingType::AVG) { // Calculate scale const qint16_t scale = calculate_avg_scale_q16(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y, fixed_point_position); // Perform pooling for stride 2 const qint16x8_t sum_data = vqaddq_qs16(vqaddq_qs16(top_data, bottom_data), middle_data); const qint16x8_t sum_data2 = vextq_s16(sum_data, sum_data, 1); const qint16x8_t sum_data3 = vextq_s16(sum_data, sum_data, 2); const qint16x8_t final_sum = vqaddq_qs16(vqaddq_qs16(sum_data, sum_data2), sum_data3); if(pool_stride_x == 2) { const qint16x4_t tmp = { vgetq_lane_s16(final_sum, 0), vgetq_lane_s16(final_sum, 2), vgetq_lane_s16(final_sum, 4), vgetq_lane_s16(final_sum, 6) }; const qint16x4_t scale_vec = vdup_n_qs16(scale); vst1_qs16(reinterpret_cast(output.ptr()), vqmul_qs16(tmp, scale_vec, fixed_point_position)); } else { const qint16x8_t scale_vec = vdupq_n_qs16(scale); vst1q_qs16(reinterpret_cast(output.ptr()), vqmulq_qs16(final_sum, scale_vec, fixed_point_position)); } } else { const qint16x8_t max_data = vmaxq_s16(vmaxq_s16(top_data, bottom_data), middle_data); const qint16x8_t max_data2 = vextq_s16(max_data, max_data, 1); const qint16x8_t max_data3 = vextq_s16(max_data, max_data, 2); const qint16x8_t final_max = vmaxq_s16(vmaxq_s16(max_data, max_data2), max_data3); if(pool_stride_x == 2) { const qint16x4_t tmp = { vgetq_lane_s16(final_max, 0), vgetq_lane_s16(final_max, 2), vgetq_lane_s16(final_max, 4), vgetq_lane_s16(final_max, 6) }; vst1_qs16(reinterpret_cast(output.ptr()), tmp); } else { vst1q_qs16(reinterpret_cast(output.ptr()), final_max); } } }, input, output); } template void NEPoolingLayerKernel::pooling3_f32(const Window &window_input, const Window &window) { Iterator input(_input, window_input); Iterator output(_output, window); constexpr const int pool_size = 3; int pool_pad_x = 0; int pool_pad_y = 0; int pool_stride_x = 0; int pool_stride_y = 0; std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y))); const uint8_t *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 1)); const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 2)); execute_window_loop(window, [&](const Coordinates & id) { float32x4_t top_data = vld1q_f32(reinterpret_cast(input_top_ptr + input.offset())); float32x4_t middle_data = vld1q_f32(reinterpret_cast(input_middle_ptr + input.offset())); float32x4_t bottom_data = vld1q_f32(reinterpret_cast(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(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y); const float32x2_t scale_v = vdup_n_f32(scale); // Perform pooling const float32x4_t sum_data = vaddq_f32(vaddq_f32(top_data, bottom_data), middle_data); res = vpadd_f32(vget_high_f32(vsetq_lane_f32(0.f, sum_data, 3)), vget_low_f32(sum_data)); res = vmul_f32(vpadd_f32(res, res), scale_v); } else { const float32x4_t max_data = vmaxq_f32(vmaxq_f32(top_data, bottom_data), middle_data); res = vpmax_f32(vget_high_f32(vsetq_lane_f32(-std::numeric_limits::max(), max_data, 3)), vget_low_f32(max_data)); res = vpmax_f32(res, res); } 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(output.ptr())) = final_res; }, input, output); } template void NEPoolingLayerKernel::pooling7_f32(const Window &window_input, const Window &window) { Iterator input(_input, window_input); Iterator output(_output, window); constexpr const int pool_size = 7; int pool_pad_x = 0; int pool_pad_y = 0; int pool_stride_x = 0; int pool_stride_y = 0; std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; std::array input_ptrs{ {} }; for(int i = 0; i < pool_size; ++i) { input_ptrs[i] = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_x), -static_cast(pool_pad_y) + 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(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y); const float32x2_t scale_v = vdup_n_f32(scale); // Perform pooling float32x4x2_t data = vld2q_f32(reinterpret_cast(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(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(input_ptrs[0] + input.offset())); for(int i = 1; i < pool_size; ++i) { const float32x4x2_t data = vld2q_f32(reinterpret_cast(input_ptrs[i] + input.offset())); max_data = vmax2q_f32(max_data, data); } res = vpmax_f32(vget_high_f32(vsetq_lane_f32(-std::numeric_limits::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(output.ptr())) = final_res; }, input, output); } template void NEPoolingLayerKernel::poolingN_f32(const Window &window_input, const Window &window) { Iterator input(_input, window_input); Iterator output(_output, window); const int pool_size = _pool_info.pool_size(); int pool_pad_x = 0; int pool_pad_y = 0; int pool_stride_x = 0; int pool_stride_y = 0; std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x; const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y; execute_window_loop(window, [&](const Coordinates & id) { float res = 0.0f; if(pooling_type != PoolingType::MAX) { // Calculate scale const float scale = calculate_avg_scale(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y); // Perform pooling float32x4_t vres = vdupq_n_f32(0.0f); for(int y = 0; y < pool_size; ++y) { int x = 0; for(; x <= (pool_size - 4); x += 4) { const float32x4_t data = vld1q_f32(reinterpret_cast(input.ptr() + (x - pool_pad_x) * _input->info()->strides_in_bytes().x() + (y - pool_pad_y) * _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) { float data = *(reinterpret_cast(input.ptr() + (x - pool_pad_x) * _input->info()->strides_in_bytes().x() + (y - pool_pad_y) * _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::min()); res = std::numeric_limits::min(); for(int y = 0; y < pool_size; ++y) { int x = 0; for(; x <= (pool_size - 4); x += 4) { const float32x4_t data = vld1q_f32(reinterpret_cast(input.ptr() + (x - pool_pad_x) * _input->info()->strides_in_bytes().x() + (y - pool_pad_y) * _input->info()->strides_in_bytes().y())); vres = vmaxq_f32(vres, data); } // Leftover for loop for(; x < pool_size; ++x) { const float data = *(reinterpret_cast(input.ptr() + (x - pool_pad_x) * _input->info()->strides_in_bytes().x() + (y - pool_pad_y) * _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(output.ptr())) = res; }, input, output); } 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; // Set step for input in x and y direction for the input Window window_input(window); unsigned int window_x_inc = 0; switch(_input->info()->data_type()) { case DataType::QS8: case DataType::QS16: case DataType::F16: { window_x_inc = (pool_stride_x == 2) ? _num_elems_processed_per_iteration * 2 : _num_elems_processed_per_iteration; break; } 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)); // Run function (this->*_func)(window_input, window); }