| /* |
| * Copyright (c) 2021-2022 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. |
| */ |
| #ifndef SRC_CORE_NEON_KERNELS_QUANTIZED_H |
| #define SRC_CORE_NEON_KERNELS_QUANTIZED_H |
| |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/utils/misc/Traits.h" |
| #include "src/core/NEON/NEAsymm.h" |
| #include "src/core/NEON/NEFixedPoint.h" |
| #include "src/core/NEON/NEMath.h" |
| #include "src/core/NEON/wrapper/wrapper.h" |
| #include "src/core/helpers/PoolingHelpers.h" |
| #include <arm_neon.h> |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| template <typename T> |
| void poolingMxN_q8_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) |
| { |
| ARM_COMPUTE_UNUSED(dst1); |
| |
| const int window_start_x = window.x().start(); |
| const int window_end_x = window.x().end(); |
| const int window_step_x = 16; |
| const int window_half_step_x = window_step_x / 2; |
| |
| Window window_out = window; |
| window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator in(src, window_src); |
| Iterator out(dst0, window_out); |
| |
| using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type; |
| using q8x16_t = typename wrapper::traits::neon_vector<T, 16>::type; |
| using q16_t = typename wrapper::traits::promote_t<T>; |
| using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type; |
| using q32_t = typename wrapper::traits::promote_t<q16_t>; |
| using q32x4_t = typename wrapper::traits::neon_vector<q32_t, 4>::type; |
| |
| const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.width; |
| const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().z() : pool_info.pool_size.height; |
| const int pool_pad_right = pool_info.pad_stride_info.pad_right(); |
| const int pool_pad_top = pool_info.pad_stride_info.pad_top(); |
| const int pool_pad_left = pool_info.pad_stride_info.pad_left(); |
| const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); |
| |
| int pool_stride_x = 0; |
| int pool_stride_y = 0; |
| std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); |
| const int upper_bound_w = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = src->info()->dimension(2) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); |
| |
| const float32x4_t half_scale_v = vdupq_n_f32(0.5f); |
| const UniformQuantizationInfo src_qinfo = src->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo dst_qinfo = dst0->info()->quantization_info().uniform(); |
| |
| const float quant_rescale = dst_qinfo.scale / src_qinfo.scale; |
| // "new_offset" doesn't have to consider the "half_scale_v" in its computation |
| // With a requantization performed in a single step there won't be uncertainties introduced |
| const int32_t new_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / quant_rescale); |
| |
| const float requant_scale = dst_qinfo.scale / src_qinfo.scale; |
| const int32_t requant_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / requant_scale); |
| const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset); |
| |
| execute_window_loop(window_out, [&](const Coordinates & id) |
| { |
| const int idx_width = id.y() * pool_stride_x; |
| const int idx_height = id.z() * pool_stride_y; |
| const int pool_limit_y = pool_pad_top - idx_height; |
| const int pool_limit_x = pool_pad_left - idx_width; |
| |
| const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); |
| const int pool_end_y = std::min(pool_size_y, window_src.z().end() + pool_limit_y); |
| const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); |
| const int pool_end_x = std::min(pool_size_x, window_src.y().end() + pool_limit_x); |
| |
| int x_off = window_start_x; |
| for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) |
| { |
| if(pool_info.pool_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_pool2d(pool_info.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 *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (src->info()->strides_in_bytes().z())) + x_off); |
| |
| const q16x8_t data_q16 = wrapper::vmovl(wrapper::vgetlow(data)); |
| const q16x8_t data2_q16 = wrapper::vmovl(wrapper::vgethigh(data)); |
| vres1 = wrapper::vadd(vres1, wrapper::vmovl(wrapper::vgetlow(data_q16))); |
| vres2 = wrapper::vadd(vres2, wrapper::vmovl(wrapper::vgethigh(data_q16))); |
| vres3 = wrapper::vadd(vres3, wrapper::vmovl(wrapper::vgetlow(data2_q16))); |
| vres4 = wrapper::vadd(vres4, wrapper::vmovl(wrapper::vgethigh(data2_q16))); |
| } |
| } |
| |
| if(src_qinfo != dst_qinfo) |
| { |
| const float32x4x4_t vres = |
| { |
| { |
| vcvtq_f32_q32(vres1), |
| vcvtq_f32_q32(vres2), |
| vcvtq_f32_q32(vres3), |
| vcvtq_f32_q32(vres4), |
| } |
| }; |
| const auto requantized_dst = vrequantize_pooling_with_scale<q8x16_t>(vres, quant_rescale, scale, new_offset); |
| // Store result |
| wrapper::vstore(reinterpret_cast<T *>(out.ptr()) + x_off, wrapper::vgetlow(requantized_dst)); |
| wrapper::vstore(reinterpret_cast<T *>(out.ptr()) + x_off + 8, wrapper::vgethigh(requantized_dst)); |
| } |
| else |
| { |
| const float32x4_t scale_v = vdupq_n_f32(scale); |
| // Divide by scale and add 0.5f to round to nearest instead of rounding towards zero |
| vres1 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres1), scale_v)); |
| vres2 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres2), scale_v)); |
| vres3 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres3), scale_v)); |
| vres4 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres4), scale_v)); |
| |
| const q8x8_t res1 = wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(vres1), wrapper::vmovn(vres2))); |
| const q8x8_t res2 = wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(vres3), wrapper::vmovn(vres4))); |
| // Store result |
| wrapper::vstore(reinterpret_cast<T *>(out.ptr()) + x_off, res1); |
| wrapper::vstore(reinterpret_cast<T *>(out.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 *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (src->info()->strides_in_bytes().z())) + x_off); |
| vres = wrapper::vmax(vres, data); |
| } |
| } |
| |
| // Store result |
| wrapper::vstore(reinterpret_cast<T *>(out.ptr()) + x_off, (src_qinfo != dst_qinfo) ? vrequantize_pooling<q8x8_t, q8x16_t>(wrapper::vgetlow(vres), wrapper::vgethigh(vres), |
| requant_qinfo) : |
| vres); |
| } |
| } |
| |
| if(pool_info.pool_type == PoolingType::MAX) |
| { |
| for(; x_off <= (window_end_x - window_half_step_x); x_off += window_half_step_x) |
| { |
| q8x8_t vres = wrapper::vdup_n(std::numeric_limits<T>::min(), wrapper::traits::vector_64_tag{}); |
| for(int y = pool_start_y; y < pool_end_y; ++y) |
| { |
| for(int x = pool_start_x; x < pool_end_x; ++x) |
| { |
| const q8x8_t data = wrapper::vload(reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (src->info()->strides_in_bytes().z())) + x_off); |
| vres = wrapper::vmax(vres, data); |
| } |
| } |
| |
| // Store result |
| wrapper::vstore(reinterpret_cast<T *>(out.ptr()) + x_off, |
| (src_qinfo != dst_qinfo) ? vrequantize_pooling<q8x8_t>(vres, requant_qinfo) : vres); |
| } |
| } |
| |
| // Left-overs loop |
| for(; x_off < window_end_x; ++x_off) |
| { |
| if(pool_info.pool_type != PoolingType::MAX) |
| { |
| q32_t res = static_cast<q32_t>(0.f); |
| |
| // Calculate scale |
| const float scale = calculate_avg_scale_pool2d(pool_info.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 *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (src->info()->strides_in_bytes().z())) + x_off); |
| res += data; |
| } |
| } |
| |
| if(src_qinfo != dst_qinfo) |
| { |
| const float res_f = static_cast<float>(res); |
| const float new_scale = quant_rescale / scale; |
| const auto requantized_dst = quantize<T>(res_f, UniformQuantizationInfo(new_scale, new_offset)); |
| |
| // Store result |
| *(reinterpret_cast<T *>(out.ptr()) + x_off) = requantized_dst; |
| } |
| else |
| { |
| // Divide by scale and add 0.5f to round to nearest instead of rounding towards zero |
| res = static_cast<T>(0.5f + static_cast<float>(res) * scale); |
| |
| // Store result |
| *(reinterpret_cast<T *>(out.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 *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> |
| (src->info()->strides_in_bytes().z())) + x_off); |
| res = std::max(res, data); |
| } |
| } |
| |
| // Store result |
| if(src_qinfo != dst_qinfo) |
| { |
| const float res_f = static_cast<float>(res); |
| *(reinterpret_cast<T *>(out.ptr()) + x_off) = quantize<T>(res_f, requant_qinfo); |
| } |
| else |
| { |
| *(reinterpret_cast<T *>(out.ptr()) + x_off) = res; |
| } |
| } |
| } |
| |
| }, |
| in, out); |
| } |
| |
| #if defined(ENABLE_NCHW_KERNELS) |
| 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); |
| } |
| |
| template <typename T> |
| auto load16_boundary_aware(int srcw, int srch, int pad_l, int pad_r, int pad_t, int pad_b, int x, int y, const T *ptr, T fval) |
| { |
| ARM_COMPUTE_UNUSED(pad_b, pad_r); |
| T vec[16]; |
| //handle reading a row out of the tensor |
| const bool row_in_bounds((y >= pad_t) && (y < (srch + pad_t))); |
| for(int i = 0; i < 16; i++) |
| { |
| if(row_in_bounds && (x + i >= pad_l) && (x + i < (srcw + pad_l))) |
| { |
| vec[i] = *(ptr + i); |
| } |
| else |
| { |
| vec[i] = fval; |
| } |
| } |
| return wrapper::vloadq(vec); |
| } |
| |
| template <typename T, typename V, bool deinterleave> |
| inline void write16_boundary_aware(int x, int dst_w, const V &lower, const V &upper, T *ptr) |
| { |
| if(deinterleave) |
| { |
| for(int i = 0; i < 8 && (i * 2 + x) < dst_w; ++i) |
| { |
| *(ptr + i * 2) = lower[i]; |
| } |
| for(int i = 0; i < 8 && (i * 2 + x + 1) < dst_w; ++i) |
| { |
| *(ptr + 1 + i * 2) = upper[i]; |
| } |
| } |
| else |
| { |
| for(int i = 0; i < 8 && (i + x) < dst_w; ++i) |
| { |
| *(ptr + i) = lower[i]; |
| } |
| for(int i = 0; i < 8 && (i + x + 8) < dst_w; ++i) |
| { |
| *(ptr + i + 8) = upper[i]; |
| } |
| } |
| } |
| |
| template <typename T, typename V> |
| inline void write8_boundary_aware(int x, int dst_w, const V &v, T *ptr) |
| { |
| for(int i = 0; i < 8 && (i + x) < dst_w; ++i) |
| { |
| *(ptr + i) = v[i]; |
| } |
| } |
| |
| template <typename T> |
| void pooling2_quantized_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) |
| { |
| ARM_COMPUTE_UNUSED(dst1); |
| Iterator in(src, window_src); |
| Iterator out(dst0, window); |
| |
| /** SIMD 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 q16_t = typename wrapper::traits::promote_t<T>; |
| using q16x4_t = typename wrapper::traits::neon_vector<q16_t, 4>::type; |
| using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type; |
| using q16x8x2_t = typename wrapper::traits::neon_vector<q16_t, 16>::type; |
| |
| constexpr int pool_size = 2; |
| int pool_stride_x = 0; |
| int pool_stride_y = 0; |
| const int pool_pad_right = pool_info.pad_stride_info.pad_right(); |
| const int pool_pad_top = pool_info.pad_stride_info.pad_top(); |
| const int pool_pad_left = pool_info.pad_stride_info.pad_left(); |
| const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); |
| std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); |
| const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); |
| const T *const src_top_ptr = reinterpret_cast<const T *>(src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)))); |
| const T *const src_bottom_ptr = reinterpret_cast<const T *>(src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1))); |
| const int scale_step_x = (pool_stride_x == 1) ? 2 : 1; |
| const UniformQuantizationInfo src_qinfo = src->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo dst_qinfo = dst0->info()->quantization_info().uniform(); |
| const bool have_different_qinfo = src_qinfo != dst_qinfo; |
| |
| const float requant_scale = dst_qinfo.scale / src_qinfo.scale; |
| const int32_t requant_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / requant_scale); |
| const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset); |
| const int src_w = src->info()->dimension(0); |
| const int src_h = src->info()->dimension(1); |
| const int dst_w = dst0->info()->dimension(0); |
| |
| const T fill_value = (pool_info.pool_type == PoolingType::MAX) ? std::numeric_limits<T>::min() : T(0); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const auto x_val = id.x() * pool_stride_x; |
| const auto y_val_0 = id.y() * pool_stride_y; |
| const auto y_val_1 = (id.y() * pool_stride_y) + 1; |
| |
| auto top_data = load16_boundary_aware(src_w, src_h, pool_pad_left, pool_pad_right, pool_pad_top, pool_pad_bottom, |
| x_val, y_val_0, reinterpret_cast<const T *>(src_top_ptr + in.offset()), fill_value); |
| auto bottom_data = load16_boundary_aware(src_w, src_h, pool_pad_left, pool_pad_right, pool_pad_top, pool_pad_bottom, |
| x_val, y_val_1, reinterpret_cast<const T *>(src_bottom_ptr + in.offset()), fill_value); |
| |
| q8x8_t lower_res = {}; |
| q8x8_t upper_res = {}; |
| |
| if(pool_info.pool_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>(pool_info.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>(pool_info.exclude_padding, res_upper, id, 1, 2, |
| pool_size, upper_bound_w, upper_bound_h, |
| pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); |
| upper_res = wrapper::vmovn(res_upper); |
| } |
| } |
| else |
| { |
| const q8x16_t max_data = wrapper::vmax(top_data, bottom_data); |
| lower_res = wrapper::vpmax(wrapper::vgetlow(max_data), wrapper::vgethigh(max_data)); |
| if(pool_stride_x == 1) |
| { |
| const q8x16_t max_data_shifted = wrapper::vext_1(max_data, max_data); |
| upper_res = wrapper::vpmax(wrapper::vgetlow(max_data_shifted), wrapper::vgethigh(max_data_shifted)); |
| } |
| } |
| |
| if(have_different_qinfo) |
| { |
| const auto requantized_dst = vrequantize_pooling<q8x8_t, q8x16_t>(lower_res, upper_res, requant_qinfo); |
| lower_res = wrapper::vgetlow(requantized_dst); |
| upper_res = wrapper::vgethigh(requantized_dst); |
| } |
| auto out_ptr = reinterpret_cast<T *>(out.ptr()); |
| // Store result |
| if(pool_stride_x == 1) |
| { |
| write16_boundary_aware<T, q8x8_t, true>(id.x(), dst_w, lower_res, upper_res, out_ptr); |
| } |
| else |
| { |
| write8_boundary_aware<T, q8x8_t>(id.x(), dst_w, lower_res, out_ptr); |
| } |
| }, |
| in, out); |
| } |
| |
| template <typename T> |
| void pooling3_quantized_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) |
| { |
| ARM_COMPUTE_UNUSED(dst1); |
| Iterator in(src, window_src); |
| Iterator out(dst0, window); |
| |
| /** SIMD vector types */ |
| using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type; |
| using q8x16_t = typename wrapper::traits::neon_vector<T, 16>::type; |
| using q8x8x2_t = typename std::conditional<std::is_same<T, uint8_t>::value, uint8x8x2_t, int8x8x2_t>::type; |
| using q16_t = typename wrapper::traits::promote_t<T>; |
| using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type; |
| using q16x8x2_t = typename wrapper::traits::neon_vector<q16_t, 16>::type; |
| |
| constexpr int pool_size = 3; |
| const int pool_pad_right = pool_info.pad_stride_info.pad_right(); |
| const int pool_pad_top = pool_info.pad_stride_info.pad_top(); |
| const int pool_pad_left = pool_info.pad_stride_info.pad_left(); |
| const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); |
| int pool_stride_x = 0; |
| int pool_stride_y = 0; |
| std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); |
| const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); |
| |
| const UniformQuantizationInfo &src_qinfo = src->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo &dst_qinfo = dst0->info()->quantization_info().uniform(); |
| |
| const float requant_scale = dst_qinfo.scale / src_qinfo.scale; |
| const int32_t requant_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / requant_scale); |
| const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset); |
| |
| const T *const src_top_ptr = reinterpret_cast<const T *>(src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)))); |
| const T *const src_middle_ptr = reinterpret_cast<const T *>(src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1))); |
| const T *const src_bottom_ptr = reinterpret_cast<const T *>(src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 2))); |
| |
| const int src_w = src->info()->dimension(0); |
| const int src_h = src->info()->dimension(1); |
| const T fill_value = (pool_info.pool_type == PoolingType::AVG) ? T(0) : std::numeric_limits<T>::min(); |
| const int dst_w = dst0->info()->dimension(0); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const auto x_val = id.x() * pool_stride_x; |
| const auto y_val_0 = id.y() * pool_stride_y; |
| const auto y_val_1 = (id.y() * pool_stride_y) + 1; |
| const auto y_val_2 = (id.y() * pool_stride_y) + 2; |
| |
| auto top_data = load16_boundary_aware(src_w, src_h, pool_pad_left, pool_pad_right, pool_pad_top, pool_pad_bottom, |
| x_val, y_val_0, reinterpret_cast<const T *>(src_top_ptr + in.offset()), fill_value); |
| auto middle_data = load16_boundary_aware(src_w, src_h, pool_pad_left, pool_pad_right, pool_pad_top, pool_pad_bottom, |
| x_val, y_val_1, reinterpret_cast<const T *>(src_middle_ptr + in.offset()), fill_value); |
| auto bottom_data = load16_boundary_aware(src_w, src_h, pool_pad_left, pool_pad_right, pool_pad_top, pool_pad_bottom, |
| x_val, y_val_2, reinterpret_cast<const T *>(src_bottom_ptr + in.offset()), fill_value); |
| |
| q8x8_t fres = {}; |
| q8x16_t fqres = {}; |
| |
| if(pool_info.pool_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>(pool_info.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>(pool_info.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>(pool_info.exclude_padding, final_sum.val[1], id, 8, 1, |
| pool_size, upper_bound_w, upper_bound_h, |
| pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); |
| fqres = wrapper::vcombine(wrapper::vmovn(final_sum.val[0]), wrapper::vmovn(final_sum.val[1])); |
| } |
| } |
| else |
| { |
| const q8x16_t max_data = wrapper::vmax(wrapper::vmax(top_data, bottom_data), middle_data); |
| const q8x16_t max_data_shift1 = wrapper::vext_1(max_data, max_data); |
| const q8x16_t max_data_shift2 = wrapper::vext_2(max_data, max_data); |
| const q8x16_t final_max = wrapper::vmax(wrapper::vmax(max_data, max_data_shift1), max_data_shift2); |
| |
| if(pool_stride_x == 2) |
| { |
| const q8x8x2_t table = { { wrapper::vgetlow(final_max), wrapper::vgethigh(final_max) } }; |
| static const q8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 }; |
| fres = wrapper::vtbl(table, lookup_val); |
| } |
| else |
| { |
| fqres = final_max; |
| } |
| } |
| |
| // Store result |
| if(pool_stride_x == 1) |
| { |
| if(src_qinfo != dst_qinfo) |
| { |
| fqres = vrequantize_pooling<q8x8_t, q8x16_t>(wrapper::vgetlow(fqres), wrapper::vgethigh(fqres), requant_qinfo); |
| } |
| write16_boundary_aware<T, q8x8_t, false>(id.x(), dst_w, wrapper::vgetlow(fqres), wrapper::vgethigh(fqres), reinterpret_cast<T *>(out.ptr())); |
| } |
| else |
| { |
| if(src_qinfo != dst_qinfo) |
| { |
| fres = vrequantize_pooling<q8x8_t>(fres, requant_qinfo); |
| } |
| write8_boundary_aware<T, q8x8_t>(id.x(), dst_w, fres, reinterpret_cast<T *>(out.ptr())); |
| } |
| }, |
| in, out); |
| } |
| |
| template <typename T> |
| void poolingMxN_quantized_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) |
| { |
| ARM_COMPUTE_UNUSED(dst1); |
| Iterator in(src, window_src); |
| Iterator out(dst0, window); |
| |
| /** SIMD vector types */ |
| using q16_t = typename wrapper::traits::promote_t<T>; |
| using q32_t = typename wrapper::traits::promote_t<q16_t>; |
| |
| const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().x() : pool_info.pool_size.width; |
| const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.height; |
| const int pool_pad_right = pool_info.pad_stride_info.pad_right(); |
| const int pool_pad_top = pool_info.pad_stride_info.pad_top(); |
| const int pool_pad_left = pool_info.pad_stride_info.pad_left(); |
| const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); |
| int pool_stride_x = 0; |
| int pool_stride_y = 0; |
| std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); |
| const int upper_bound_w = src->info()->dimension(0) + (pool_info.exclude_padding ? 0 : pool_pad_right); |
| const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); |
| |
| const UniformQuantizationInfo &src_qinfo = src->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo &dst_qinfo = dst0->info()->quantization_info().uniform(); |
| const int src_w = src->info()->dimension(0); |
| const int src_h = src->info()->dimension(1); |
| const T fill_value = (pool_info.pool_type == PoolingType::AVG) ? T(0) : std::numeric_limits<T>::min(); |
| const int stridex_in_bytes = static_cast<int>(src->info()->strides_in_bytes().x()); |
| const int stridey_in_bytes = static_cast<int>(src->info()->strides_in_bytes().y()); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| T res = std::numeric_limits<T>::min(); |
| |
| if(pool_info.pool_type != PoolingType::MAX) |
| { |
| q32_t sres = 0; |
| |
| // Calculate scale |
| const float scale = calculate_avg_scale_pool2d(pool_info.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) |
| { |
| for(int x = 0; x < pool_size_x; ++x) |
| { |
| const auto in_ptr = reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * stridex_in_bytes + (y - pool_pad_top) * stridey_in_bytes); |
| |
| const int idx = x + id.x() * pool_stride_x - pool_pad_left; |
| const int idy = y + id.y() * pool_stride_y - pool_pad_top; |
| const T data = (idx < 0 || idy < 0 || idx >= src_w || idy >= src_h) ? fill_value : *in_ptr; |
| sres += data; |
| } |
| } |
| // Divide by scale |
| res = static_cast<T>(support::cpp11::round(sres * scale)); |
| } |
| else |
| { |
| for(int y = 0; y < pool_size_y; ++y) |
| { |
| for(int x = 0; x < pool_size_x; ++x) |
| { |
| const auto in_ptr = reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * stridex_in_bytes + (y - pool_pad_top) * stridey_in_bytes); |
| |
| const int idx = x + id.x() * pool_stride_x - pool_pad_left; |
| const int idy = y + id.y() * pool_stride_y - pool_pad_top; |
| const T data = (idx < 0 || idy < 0 || idx >= src_w || idy >= src_h) ? fill_value : *in_ptr; |
| res = std::max(res, data); |
| } |
| } |
| } |
| // Store result |
| res = (src_qinfo != dst_qinfo) ? Qasymm8QuantizationHelper<T>::quantize(Qasymm8QuantizationHelper<T>::dequantize(res, src_qinfo), dst_qinfo) : res; |
| *(reinterpret_cast<T *>(out.ptr())) = res; |
| }, |
| in, out); |
| } |
| #endif /* defined(ENABLE_NCHW_KERNELS) */ |
| } // namespace cpu |
| } // namespace arm_compute |
| |
| #endif // SRC_CORE_NEON_KERNELS_QUANTIZED_H |