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Diffstat (limited to 'src/core/NEON/kernels/NEConvolutionKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEConvolutionKernel.cpp | 1623 |
1 files changed, 0 insertions, 1623 deletions
diff --git a/src/core/NEON/kernels/NEConvolutionKernel.cpp b/src/core/NEON/kernels/NEConvolutionKernel.cpp deleted file mode 100644 index b154340bee..0000000000 --- a/src/core/NEON/kernels/NEConvolutionKernel.cpp +++ /dev/null @@ -1,1623 +0,0 @@ -/* - * Copyright (c) 2016-2019 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/NEConvolutionKernel.h" - -#include "arm_compute/core/Coordinates.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/Types.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" - -#include <algorithm> -#include <arm_neon.h> -#include <array> -#include <cstdint> -#include <cstring> -#include <tuple> - -namespace arm_compute -{ -namespace -{ -const uint16x8_t max_int16 = vdupq_n_u16(INT16_MAX); - -inline void store_results(const int32x4_t &out, const int32x4_t &out2, int16_t *output) -{ - const int16x8_t s16results = vcombine_s16(vqmovn_s32(out), - vqmovn_s32(out2)); - vst1q_s16(output, s16results); -} - -inline void store_results(const int32x4_t &out, const int32x4_t &out2, uint8_t *output) -{ - const uint8x8_t u8results = vqmovn_u16(vcombine_u16(vqmovun_s32(out), - vqmovun_s32(out2))); - vst1_u8(output, u8results); -} - -inline void store_results(const uint32x4_t &out, const uint32x4_t &out2, int16_t *output) -{ - const uint16x8_t u16results = vcombine_u16(vqmovn_u32(out), vqmovn_u32(out2)); - const int16x8_t s16results = vreinterpretq_s16_u16(vminq_u16(u16results, max_int16)); - vst1q_s16(output, s16results); -} - -inline void store_results(const uint32x4_t &out, const uint32x4_t &out2, uint8_t *output) -{ - const uint8x8_t u8results = vqmovn_u16(vcombine_u16(vqmovn_u32(out), - vqmovn_u32(out2))); - vst1_u8(output, u8results); -} - -inline void store_results(const int16x8_t &out, const int16x8_t &out2, int16_t *output) -{ - vst1q_s16(output, out); - vst1q_s16(output + 8, out2); -} - -inline void store_results(const int16x8_t &out, const int16x8_t &out2, uint8_t *output) -{ - const uint8x16_t u8results = vcombine_u8(vqmovun_s16(out), - vqmovun_s16(out2)); - vst1q_u8(output, u8results); -} - -inline void store_results(const uint16x8_t &out, const uint16x8_t &out2, uint8_t *output) -{ - const uint8x16_t u8results = vcombine_u8(vqmovn_u16(out), - vqmovn_u16(out2)); - vst1q_u8(output, u8results); -} - -inline void store_results(const uint16x8_t &out, const uint16x8_t &out2, int16_t *output) -{ - vst1q_s16(output, vreinterpretq_s16_u16(vminq_u16(out, max_int16))); - vst1q_s16(output + 8, vreinterpretq_s16_u16(vminq_u16(out2, max_int16))); -} - -inline void convolve_row3x1_unrolled(int32x4_t &out, int32x4_t &out2, const uint8x16_t &row_data, const int16x4_t &mat0, const int16x4_t &mat1, const int16x4_t &mat2) -{ - // Convert to s16 and split in blocks of 4 values: - const int16x8_t s16_tmp0 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(row_data))); - const int16x8_t s16_tmp1 = vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(row_data))); - - const int16x4x3_t row = - { - { - vget_low_s16(s16_tmp0), - vget_high_s16(s16_tmp0), - vget_low_s16(s16_tmp1) - } - }; - - // Calculate row left value for pixels [0,3] - out = vmlal_s16(out, row.val[0], mat0); - // Calculate row middle value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 1), mat1); - // Calculate row right value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 2), mat2); - - // Calculate row left value for pixels [4,7] - out2 = vmlal_s16(out2, row.val[1], mat0); - // Calculate row middle value for pixels [4,7] - out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 1), mat1); - // Calculate row right value for pixels [4,7] - out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 2), mat2); -} - -inline void convolve_row3x1(int32x4_t &out, int32x4_t &out2, const uint8x16_t &row_data, const int16_t *convolution) -{ - const int16x4_t mat0 = vld1_dup_s16(convolution); - const int16x4_t mat1 = vld1_dup_s16(convolution + 1); - const int16x4_t mat2 = vld1_dup_s16(convolution + 2); - - convolve_row3x1_unrolled(out, out2, row_data, mat0, mat1, mat2); -} - -inline void convolve_row5x1(int32x4_t &out, int32x4_t &out2, const uint8x16_t &row_data, const int16_t *convolution) -{ - const int16x4_t mat0 = vld1_dup_s16(convolution); - const int16x4_t mat1 = vld1_dup_s16(convolution + 1); - const int16x4_t mat2 = vld1_dup_s16(convolution + 2); - const int16x4_t mat3 = vld1_dup_s16(convolution + 3); - const int16x4_t mat4 = vld1_dup_s16(convolution + 4); - - // Convert to s16 and split in blocks of 4 values: - const int16x8_t s16_tmp0 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(row_data))); - const int16x8_t s16_tmp1 = vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(row_data))); - - const int16x4x3_t row = - { - { - vget_low_s16(s16_tmp0), - vget_high_s16(s16_tmp0), - vget_low_s16(s16_tmp1) - } - }; - - // Calculate row left 2 value for pixels [0,3] - out = vmlal_s16(out, row.val[0], mat0); - // Calculate row left 1 value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 1), mat1); - // Calculate row middle value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 2), mat2); - // Calculate row right +1 value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 3), mat3); - // Calculate row right +2 value for pixels [0,3] - out = vmlal_s16(out, row.val[1], mat4); - - // Calculate row left 2 value for pixels [4,7] - out2 = vmlal_s16(out2, row.val[1], mat0); - // Calculate row left 1 value for pixels [4,7] - out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 1), mat1); - // Calculate row middle value for pixels [4,7] - out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 2), mat2); - // Calculate row right +1 value for pixels [4,7] - out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 3), mat3); - // Calculate row right +2 value for pixels [4,7] - out2 = vmlal_s16(out2, row.val[2], mat4); -} - -inline void convolve_row7x1(int32x4_t &out, int32x4_t &out2, const uint8x16_t &row_data, const int16_t *convolution) -{ - const int16x4_t mat0 = vld1_dup_s16(convolution); - const int16x4_t mat1 = vld1_dup_s16(convolution + 1); - const int16x4_t mat2 = vld1_dup_s16(convolution + 2); - const int16x4_t mat3 = vld1_dup_s16(convolution + 3); - const int16x4_t mat4 = vld1_dup_s16(convolution + 4); - const int16x4_t mat5 = vld1_dup_s16(convolution + 5); - const int16x4_t mat6 = vld1_dup_s16(convolution + 6); - - // Convert to s16 and split in blocks of 4 values: - const int16x8_t s16_tmp0 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(row_data))); - const int16x8_t s16_tmp1 = vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(row_data))); - - const int16x4x4_t row = - { - { - vget_low_s16(s16_tmp0), - vget_high_s16(s16_tmp0), - vget_low_s16(s16_tmp1), - vget_high_s16(s16_tmp1) - } - }; - - // Calculate row left 3 value for pixels [0,3] - out = vmlal_s16(out, row.val[0], mat0); - // Calculate row left 2 value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 1), mat1); - // Calculate row left 1 value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 2), mat2); - // Calculate row middle value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 3), mat3); - // Calculate row right +1 value for pixels [0,3] - out = vmlal_s16(out, row.val[1], mat4); - // Calculate row right +2 value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[1], row.val[2], 1), mat5); - // Calculate row right +3 value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[1], row.val[2], 2), mat6); - - // Calculate row left 3 value for pixels [4,7] - out2 = vmlal_s16(out2, row.val[1], mat0); - // Calculate row left 2 value for pixels [4,7] - out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 1), mat1); - // Calculate row left 1 value for pixels [4,7] - out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 2), mat2); - // Calculate row middle value for pixels [4,7] - out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 3), mat3); - // Calculate row right +1 value for pixels [4,7] - out2 = vmlal_s16(out2, row.val[2], mat4); - // Calculate row right +2 value for pixels [4,7] - out2 = vmlal_s16(out2, vext_s16(row.val[2], row.val[3], 1), mat5); - // Calculate row right +3 value for pixels [4,7] - out2 = vmlal_s16(out2, vext_s16(row.val[2], row.val[3], 2), mat6); -} - -inline void convolve_row9x1(int32x4_t &out, int32x4_t &out2, const uint8x16_t &row_data, const int16_t *convolution) -{ - const int16x4_t mat0 = vld1_dup_s16(convolution); - const int16x4_t mat1 = vld1_dup_s16(convolution + 1); - const int16x4_t mat2 = vld1_dup_s16(convolution + 2); - const int16x4_t mat3 = vld1_dup_s16(convolution + 3); - const int16x4_t mat4 = vld1_dup_s16(convolution + 4); - const int16x4_t mat5 = vld1_dup_s16(convolution + 5); - const int16x4_t mat6 = vld1_dup_s16(convolution + 6); - const int16x4_t mat7 = vld1_dup_s16(convolution + 7); - const int16x4_t mat8 = vld1_dup_s16(convolution + 8); - - // Convert to s16 and split in blocks of 4 values: - const int16x8_t s16_tmp0 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(row_data))); - const int16x8_t s16_tmp1 = vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(row_data))); - - const int16x4x4_t row = - { - { - vget_low_s16(s16_tmp0), - vget_high_s16(s16_tmp0), - vget_low_s16(s16_tmp1), - vget_high_s16(s16_tmp1) - } - }; - - // Calculate row left 4 value for pixels [0,3] - out = vmlal_s16(out, row.val[0], mat0); - // Calculate row left 3 value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 1), mat1); - // Calculate row left 2 value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 2), mat2); - // Calculate row left 1 value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 3), mat3); - // Calculate row middle value for pixels [0,3] - out = vmlal_s16(out, row.val[1], mat4); - // Calculate row right +1 value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[1], row.val[2], 1), mat5); - // Calculate row right +2 value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[1], row.val[2], 2), mat6); - // Calculate row right +3 value for pixels [0,3] - out = vmlal_s16(out, vext_s16(row.val[1], row.val[2], 3), mat7); - // Calculate row right +4 value for pixels [0,3] - out = vmlal_s16(out, row.val[2], mat8); - - // Calculate row left 4 value for pixels [0,3] - out2 = vmlal_s16(out2, row.val[1], mat0); - // Calculate row left 3 value for pixels [0,3] - out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 1), mat1); - // Calculate row left 2 value for pixels [0,3] - out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 2), mat2); - // Calculate row left 1 value for pixels [0,3] - out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 3), mat3); - // Calculate row middle value for pixels [0,3] - out2 = vmlal_s16(out2, row.val[2], mat4); - // Calculate row right +1 value for pixels [0,3] - out2 = vmlal_s16(out2, vext_s16(row.val[2], row.val[3], 1), mat5); - // Calculate row right +2 value for pixels [0,3] - out2 = vmlal_s16(out2, vext_s16(row.val[2], row.val[3], 2), mat6); - // Calculate row right +3 value for pixels [0,3] - out2 = vmlal_s16(out2, vext_s16(row.val[2], row.val[3], 3), mat7); - // Calculate row right +4 value for pixels [0,3] - out2 = vmlal_s16(out2, row.val[3], mat8); -} -} // namespace - -/****************************************************************************************\ - * Square Convolution * -\****************************************************************************************/ - -template <unsigned int matrix_size> -NEConvolutionKernel<matrix_size>::NEConvolutionKernel() - : INESimpleKernel(), _scale(0), _convolution{ {} } -{ -} - -template <unsigned int matrix_size> -BorderSize NEConvolutionKernel<matrix_size>::border_size() const -{ - return BorderSize{ matrix_size / 2 }; -} - -template <unsigned int matrix_size> -void NEConvolutionKernel<matrix_size>::configure(const ITensor *input, ITensor *output, const int16_t *conv, uint32_t scale, bool border_undefined) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, conv); - - set_shape_if_empty(*output->info(), input->info()->tensor_shape()); - - ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16); - - _input = input; - _output = output; - - std::copy_n(conv, _convolution.size(), _convolution.begin()); - - if(scale == 0) - { - _scale = calculate_matrix_scale(_convolution.data(), matrix_size); - } - else - { - _scale = scale; - } - - // Configure kernel window - constexpr unsigned int num_elems_processed_per_iteration = 8; - constexpr unsigned int num_elems_read_per_iteration = 16; - constexpr unsigned int num_elems_written_per_iteration = 8; - - Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size()); - AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); - - update_window_and_padding(win, - AccessWindowRectangle(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, matrix_size), - output_access); - - output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size()); - - INEKernel::configure(win); -} - -template <> -template <typename OutputType> -void NEConvolutionKernel<3>::convolution(const Window &win) -{ - static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16"); - ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr); - - Iterator input(_input, win); - Iterator output(_output, win); - - // Load the matrix's coefficients into NEON registers: - const int16x4_t mat00 = vld1_dup_s16(_convolution.data()); - const int16x4_t mat01 = vld1_dup_s16(_convolution.data() + 1); - const int16x4_t mat02 = vld1_dup_s16(_convolution.data() + 2); - const int16x4_t mat10 = vld1_dup_s16(_convolution.data() + 3); - const int16x4_t mat11 = vld1_dup_s16(_convolution.data() + 4); - const int16x4_t mat12 = vld1_dup_s16(_convolution.data() + 5); - const int16x4_t mat20 = vld1_dup_s16(_convolution.data() + 6); - const int16x4_t mat21 = vld1_dup_s16(_convolution.data() + 7); - const int16x4_t mat22 = vld1_dup_s16(_convolution.data() + 8); - const float32x4_t scale_val = vdupq_n_f32(1.0f / _scale); - - const unsigned char *input_top_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-1, -1)); - const unsigned char *input_mid_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-1, 0)); - const unsigned char *input_low_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-1, 1)); - - execute_window_loop(win, [&](const Coordinates &) - { - int32x4_t out = vdupq_n_s32(0); - int32x4_t out2 = vdupq_n_s32(0); - - // Load 16 bytes from the top row: - const uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset()); - convolve_row3x1_unrolled(out, out2, top_data, mat00, mat01, mat02); - - // Load 16 bytes from the middle row: - const uint8x16_t mid_data = vld1q_u8(input_mid_ptr + input.offset()); - convolve_row3x1_unrolled(out, out2, mid_data, mat10, mat11, mat12); - - // Load 16 bytes from the middle row: - const uint8x16_t low_data = vld1q_u8(input_low_ptr + input.offset()); - convolve_row3x1_unrolled(out, out2, low_data, mat20, mat21, mat22); - - // Apply scale - if(_scale != 1) - { - // Convert to F32, scale and convert back to S32 - out = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out), scale_val)); - out2 = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out2), scale_val)); - } - - // Clamp and store as U8 or S16: - store_results(out, out2, reinterpret_cast<OutputType *>(output.ptr())); - }, - input, output); -} - -template <> -template <typename OutputType> -void NEConvolutionKernel<5>::convolution(const Window &win) -{ - static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16"); - ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr); - - Iterator input(_input, win); - Iterator output(_output, win); - - const float32x4_t scale_val = vdupq_n_f32(1.0f / _scale); - - const unsigned char *input_top2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-2, -2)); - const unsigned char *input_top1_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-2, -1)); - const unsigned char *input_mid_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-2, 0)); - const unsigned char *input_low1_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-2, 1)); - const unsigned char *input_low2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-2, 2)); - - execute_window_loop(win, [&](const Coordinates &) - { - int32x4_t out = vdupq_n_s32(0); - int32x4_t out2 = vdupq_n_s32(0); - - // Load 16 bytes from the top2 row: - const uint8x16_t data_t2 = vld1q_u8(input_top2_ptr + input.offset()); - convolve_row5x1(out, out2, data_t2, _convolution.data()); - - // Load 16 bytes from the top1 row: - const uint8x16_t data_t1 = vld1q_u8(input_top1_ptr + input.offset()); - convolve_row5x1(out, out2, data_t1, _convolution.data() + 5); - - // Load 16 bytes from the middle row: - const uint8x16_t data_m = vld1q_u8(input_mid_ptr + input.offset()); - convolve_row5x1(out, out2, data_m, _convolution.data() + 10); - - // Load 16 bytes from the low1 row: - const uint8x16_t data_b1 = vld1q_u8(input_low1_ptr + input.offset()); - convolve_row5x1(out, out2, data_b1, _convolution.data() + 15); - - // Load 16 bytes from the low2 row: - const uint8x16_t data_b2 = vld1q_u8(input_low2_ptr + input.offset()); - convolve_row5x1(out, out2, data_b2, _convolution.data() + 20); - - // Apply scale - if(_scale != 1) - { - // Convert to F32, scale and convert back to S32 - out = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out), scale_val)); - out2 = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out2), scale_val)); - } - - // Clamp and store as U8 or S16: - store_results(out, out2, reinterpret_cast<OutputType *>(output.ptr())); - }, - input, output); -} - -template <> -template <typename OutputType> -void NEConvolutionKernel<7>::convolution(const Window &win) -{ - static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16"); - ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr); - - Iterator input(_input, win); - Iterator output(_output, win); - - const float32x4_t scale_val = vdupq_n_f32(1.0f / _scale); - - const unsigned char *input_top3_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, -3)); - const unsigned char *input_top2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, -2)); - const unsigned char *input_top1_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, -1)); - const unsigned char *input_mid_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, 0)); - const unsigned char *input_low1_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, 1)); - const unsigned char *input_low2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, 2)); - const unsigned char *input_low3_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, 3)); - - execute_window_loop(win, [&](const Coordinates &) - { - int32x4_t out = vdupq_n_s32(0); - int32x4_t out2 = vdupq_n_s32(0); - - // Load 16 bytes from the top3 row: - const uint8x16_t data_t3 = vld1q_u8(input_top3_ptr + input.offset()); - convolve_row7x1(out, out2, data_t3, _convolution.data()); - - // Load 16 bytes from the top2 row: - const uint8x16_t data_t2 = vld1q_u8(input_top2_ptr + input.offset()); - convolve_row7x1(out, out2, data_t2, _convolution.data() + 7); - - // Load 16 bytes from the top1 row: - const uint8x16_t data_t1 = vld1q_u8(input_top1_ptr + input.offset()); - convolve_row7x1(out, out2, data_t1, _convolution.data() + 14); - - // Load 16 bytes from the middle row: - const uint8x16_t data_m = vld1q_u8(input_mid_ptr + input.offset()); - convolve_row7x1(out, out2, data_m, _convolution.data() + 21); - - // Load 16 bytes from the low1 row: - const uint8x16_t data_b1 = vld1q_u8(input_low1_ptr + input.offset()); - convolve_row7x1(out, out2, data_b1, _convolution.data() + 28); - - // Load 16 bytes from the low2 row: - const uint8x16_t data_b2 = vld1q_u8(input_low2_ptr + input.offset()); - convolve_row7x1(out, out2, data_b2, _convolution.data() + 35); - - // Load 16 bytes from the low3 row: - const uint8x16_t data_b3 = vld1q_u8(input_low3_ptr + input.offset()); - convolve_row7x1(out, out2, data_b3, _convolution.data() + 42); - - // Apply scale - if(_scale != 1) - { - // Convert to F32, scale and convert back to S32 - out = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out), scale_val)); - out2 = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out2), scale_val)); - } - - // Clamp and store as U8 or S16: - store_results(out, out2, reinterpret_cast<OutputType *>(output.ptr())); - }, - input, output); -} - -template <> -template <typename OutputType> -void NEConvolutionKernel<9>::convolution(const Window &win) -{ - static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16"); - ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr); - - Iterator input(_input, win); - Iterator output(_output, win); - - const float32x4_t scale_val = vdupq_n_f32(1.0f / _scale); - - const unsigned char *input_top4_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, -4)); - const unsigned char *input_top3_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, -3)); - const unsigned char *input_top2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, -2)); - const unsigned char *input_top1_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, -1)); - const unsigned char *input_mid_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, 0)); - const unsigned char *input_low1_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, 1)); - const unsigned char *input_low2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, 2)); - const unsigned char *input_low3_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, 3)); - const unsigned char *input_low4_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, 4)); - - execute_window_loop(win, [&](const Coordinates &) - { - int32x4_t out = vdupq_n_s32(0); - int32x4_t out2 = vdupq_n_s32(0); - - // Load 16 bytes from the top4 row: - const uint8x16_t data_t4 = vld1q_u8(input_top4_ptr + input.offset()); - convolve_row9x1(out, out2, data_t4, _convolution.data()); - - // Load 16 bytes from the top3 row: - const uint8x16_t data_t3 = vld1q_u8(input_top3_ptr + input.offset()); - convolve_row9x1(out, out2, data_t3, _convolution.data() + 9); - - // Load 16 bytes from the top2 row: - const uint8x16_t data_t2 = vld1q_u8(input_top2_ptr + input.offset()); - convolve_row9x1(out, out2, data_t2, _convolution.data() + 18); - - // Load 16 bytes from the top1 row: - const uint8x16_t data_t1 = vld1q_u8(input_top1_ptr + input.offset()); - convolve_row9x1(out, out2, data_t1, _convolution.data() + 27); - - // Load 16 bytes from the middle row: - const uint8x16_t data_m = vld1q_u8(input_mid_ptr + input.offset()); - convolve_row9x1(out, out2, data_m, _convolution.data() + 36); - - // Load 16 bytes from the low1 row: - const uint8x16_t data_b1 = vld1q_u8(input_low1_ptr + input.offset()); - convolve_row9x1(out, out2, data_b1, _convolution.data() + 45); - - // Load 16 bytes from the low2 row: - const uint8x16_t data_b2 = vld1q_u8(input_low2_ptr + input.offset()); - convolve_row9x1(out, out2, data_b2, _convolution.data() + 54); - - // Load 16 bytes from the low3 row: - const uint8x16_t data_b3 = vld1q_u8(input_low3_ptr + input.offset()); - convolve_row9x1(out, out2, data_b3, _convolution.data() + 63); - - // Load 16 bytes from the low4 row: - const uint8x16_t data_b4 = vld1q_u8(input_low4_ptr + input.offset()); - convolve_row9x1(out, out2, data_b4, _convolution.data() + 72); - - // Apply scale - if(_scale != 1) - { - // Convert to F32, scale and convert back to S32 - out = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out), scale_val)); - out2 = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out2), scale_val)); - } - - // Clamp and store as U8 or S16: - store_results(out, out2, reinterpret_cast<OutputType *>(output.ptr())); - }, - input, output); -} - -template <unsigned int matrix_size> -void NEConvolutionKernel<matrix_size>::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); - - switch(_output->info()->data_type()) - { - case DataType::U8: - convolution<uint8_t>(window); - break; - case DataType::S16: - convolution<int16_t>(window); - break; - default: - ARM_COMPUTE_ERROR("Not supported Data type!"); - break; - } -} - -template class arm_compute::NEConvolutionKernel<3>; -template class arm_compute::NEConvolutionKernel<5>; -template class arm_compute::NEConvolutionKernel<7>; -template class arm_compute::NEConvolutionKernel<9>; - -/****************************************************************************************\ - * Separable Square Convolution * -\****************************************************************************************/ - -template <unsigned int matrix_size> -NESeparableConvolutionHorKernel<matrix_size>::NESeparableConvolutionHorKernel() - : _conv_row{ { 0 } }, _border_size(0) -{ -} - -template <unsigned int matrix_size> -BorderSize NESeparableConvolutionHorKernel<matrix_size>::border_size() const -{ - return _border_size; -} - -template <unsigned int matrix_size> -void NESeparableConvolutionHorKernel<matrix_size>::configure(const ITensor *input, ITensor *output, const int16_t *conv_row, bool border_undefined) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, conv_row); - - set_shape_if_empty(*output->info(), input->info()->tensor_shape()); - - ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U16, DataType::S16, DataType::S32); - - _input = input; - _output = output; - std::copy_n(conv_row, _conv_row.size(), _conv_row.begin()); - _border_size = BorderSize(border_undefined ? 0 : matrix_size / 2, matrix_size / 2); - - // Configure kernel window - constexpr unsigned int num_elems_processed_per_iteration = 8; - constexpr unsigned int num_elems_read_per_iteration = 16; - constexpr unsigned int num_elems_written_per_iteration = 8; - - Window win = calculate_max_window_horizontal(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size()); - AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); - - update_window_and_padding(win, - AccessWindowHorizontal(input->info(), -border_size().left, num_elems_read_per_iteration), - output_access); - - output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size()); - - INEKernel::configure(win); -} - -template <unsigned int matrix_size> -void NESeparableConvolutionHorKernel<matrix_size>::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); - switch(_output->info()->data_type()) - { - case DataType::U16: - convolve<uint16_t>(window); - break; - case DataType::S16: - convolve<int16_t>(window); - break; - case DataType::S32: - convolve<int32_t>(window); - break; - default: - ARM_COMPUTE_ERROR("Unsupported intermediate data type!"); - break; - } -} - -template <> -template <> -inline void NESeparableConvolutionHorKernel<5>::convolve<uint16_t>(const Window &window) -{ - Window win_in(window); - win_in.shift(Window::DimX, -2); - - Iterator input(_input, win_in); - Iterator output(_output, window); - - execute_window_loop(window, [&](const Coordinates &) - { - const uint8x16_t data = vld1q_u8(input.ptr()); - - const uint16x8x2_t data_u16 = - { - { - vmovl_u8(vget_low_u8(data)), - vmovl_u8(vget_high_u8(data)) - } - }; - - uint16x8_t out = vmulq_n_u16(data_u16.val[0], _conv_row[0]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 1), _conv_row[1]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 2), _conv_row[2]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 3), _conv_row[3]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 4), _conv_row[4]); - - vst1q_u16(reinterpret_cast<uint16_t *>(output.ptr()), out); - }, - input, output); -} - -template <> -template <> -inline void NESeparableConvolutionHorKernel<5>::convolve<int16_t>(const Window &window) -{ - Window win_in(window); - win_in.shift(Window::DimX, -2); - - Iterator input(_input, win_in); - Iterator output(_output, window); - - execute_window_loop(window, [&](const Coordinates &) - { - const uint8x16_t data = vld1q_u8(input.ptr()); - - const int16x8x2_t data_s16 = - { - { - vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))), - vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data))) - } - }; - - int16x8_t out = vmulq_n_s16(data_s16.val[0], _conv_row[0]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 1), _conv_row[1]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 2), _conv_row[2]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 3), _conv_row[3]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 4), _conv_row[4]); - - vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), out); - }, - input, output); -} - -template <> -template <> -void NESeparableConvolutionHorKernel<5>::convolve<int32_t>(const Window &window) -{ - Window win_in(window); - win_in.shift(Window::DimX, -2); - - Iterator input(_input, win_in); - Iterator output(_output, window); - - execute_window_loop(window, [&](const Coordinates &) - { - const uint8x16_t data = vld1q_u8(input.ptr()); - - const int16x8x2_t data_s16 = - { - { - vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))), - vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data))) - } - }; - - const int16x8_t data_s16_l1 = vextq_s16(data_s16.val[0], data_s16.val[1], 1); - const int16x8_t data_s16_m = vextq_s16(data_s16.val[0], data_s16.val[1], 2); - const int16x8_t data_s16_r1 = vextq_s16(data_s16.val[0], data_s16.val[1], 3); - const int16x8_t data_s16_r2 = vextq_s16(data_s16.val[0], data_s16.val[1], 4); - - int32x4_t out_low = vmull_n_s16(vget_low_s16(data_s16.val[0]), _conv_row[0]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_l1), _conv_row[1]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_m), _conv_row[2]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_r1), _conv_row[3]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_r2), _conv_row[4]); - - vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()), out_low); - - int32x4_t out_high = vmull_n_s16(vget_high_s16(data_s16.val[0]), _conv_row[0]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_l1), _conv_row[1]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_m), _conv_row[2]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_r1), _conv_row[3]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_r2), _conv_row[4]); - - vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()) + 4, out_high); - }, - input, output); -} - -template <> -template <> -inline void NESeparableConvolutionHorKernel<7>::convolve<uint16_t>(const Window &window) -{ - Window win_in(window); - win_in.shift(Window::DimX, -3); - - Iterator input(_input, win_in); - Iterator output(_output, window); - - execute_window_loop(window, [&](const Coordinates &) - { - const uint8x16_t data = vld1q_u8(input.ptr()); - - const uint16x8x2_t data_u16 = - { - { - vmovl_u8(vget_low_u8(data)), - vmovl_u8(vget_high_u8(data)) - } - }; - - uint16x8_t out = vmulq_n_u16(data_u16.val[0], _conv_row[0]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 1), _conv_row[1]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 2), _conv_row[2]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 3), _conv_row[3]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 4), _conv_row[4]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 5), _conv_row[5]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 6), _conv_row[6]); - - vst1q_u16(reinterpret_cast<uint16_t *>(output.ptr()), out); - }, - input, output); -} - -template <> -template <> -inline void NESeparableConvolutionHorKernel<7>::convolve<int16_t>(const Window &window) -{ - Window win_in(window); - win_in.shift(Window::DimX, -3); - - Iterator input(_input, win_in); - Iterator output(_output, window); - - execute_window_loop(window, [&](const Coordinates &) - { - const uint8x16_t data = vld1q_u8(input.ptr()); - - const int16x8x2_t data_s16 = - { - { - vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))), - vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data))) - } - }; - - int16x8_t out = vmulq_n_s16(data_s16.val[0], _conv_row[0]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 1), _conv_row[1]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 2), _conv_row[2]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 3), _conv_row[3]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 4), _conv_row[4]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 5), _conv_row[5]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 6), _conv_row[6]); - - vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), out); - }, - input, output); -} - -template <> -template <> -void NESeparableConvolutionHorKernel<7>::convolve<int32_t>(const Window &window) -{ - Window win_in(window); - win_in.shift(Window::DimX, -3); - - Iterator input(_input, win_in); - Iterator output(_output, window); - - execute_window_loop(window, [&](const Coordinates &) - { - const uint8x16_t data = vld1q_u8(input.ptr()); - - const int16x8x2_t data_s16 = - { - { - vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))), - vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data))) - } - }; - - const int16x8_t data_s16_l2 = vextq_s16(data_s16.val[0], data_s16.val[1], 1); - const int16x8_t data_s16_l1 = vextq_s16(data_s16.val[0], data_s16.val[1], 2); - const int16x8_t data_s16_m = vextq_s16(data_s16.val[0], data_s16.val[1], 3); - const int16x8_t data_s16_r1 = vextq_s16(data_s16.val[0], data_s16.val[1], 4); - const int16x8_t data_s16_r2 = vextq_s16(data_s16.val[0], data_s16.val[1], 5); - const int16x8_t data_s16_r3 = vextq_s16(data_s16.val[0], data_s16.val[1], 6); - - int32x4_t out_low = vmull_n_s16(vget_low_s16(data_s16.val[0]), _conv_row[0]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_l2), _conv_row[1]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_l1), _conv_row[2]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_m), _conv_row[3]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_r1), _conv_row[4]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_r2), _conv_row[5]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_r3), _conv_row[6]); - - vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()), out_low); - - int32x4_t out_high = vmull_n_s16(vget_high_s16(data_s16.val[0]), _conv_row[0]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_l2), _conv_row[1]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_l1), _conv_row[2]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_m), _conv_row[3]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_r1), _conv_row[4]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_r2), _conv_row[5]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_r3), _conv_row[6]); - - vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()) + 4, out_high); - }, - input, output); -} - -template <> -template <> -inline void NESeparableConvolutionHorKernel<9>::convolve<uint16_t>(const Window &window) -{ - Window win_in(window); - win_in.shift(Window::DimX, -4); - - Iterator input(_input, win_in); - Iterator output(_output, window); - - execute_window_loop(window, [&](const Coordinates &) - { - const uint8x16_t data = vld1q_u8(input.ptr()); - - const uint16x8x2_t data_u16 = - { - { - vmovl_u8(vget_low_u8(data)), - vmovl_u8(vget_high_u8(data)) - } - }; - - uint16x8_t out = vmulq_n_u16(data_u16.val[0], _conv_row[0]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 1), _conv_row[1]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 2), _conv_row[2]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 3), _conv_row[3]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 4), _conv_row[4]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 5), _conv_row[5]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 6), _conv_row[6]); - out = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 7), _conv_row[7]); - out = vmlaq_n_u16(out, data_u16.val[1], _conv_row[8]); - - vst1q_u16(reinterpret_cast<uint16_t *>(output.ptr()), out); - }, - input, output); -} - -template <> -template <> -inline void NESeparableConvolutionHorKernel<9>::convolve<int16_t>(const Window &window) -{ - Window win_in(window); - win_in.shift(Window::DimX, -4); - - Iterator input(_input, win_in); - Iterator output(_output, window); - - execute_window_loop(window, [&](const Coordinates &) - { - const uint8x16_t data = vld1q_u8(input.ptr()); - - const int16x8x2_t data_s16 = - { - { - vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))), - vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data))) - } - }; - - int16x8_t out = vmulq_n_s16(data_s16.val[0], _conv_row[0]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 1), _conv_row[1]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 2), _conv_row[2]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 3), _conv_row[3]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 4), _conv_row[4]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 5), _conv_row[5]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 6), _conv_row[6]); - out = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 7), _conv_row[7]); - out = vmlaq_n_s16(out, data_s16.val[1], _conv_row[8]); - - vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), out); - }, - input, output); -} - -template <> -template <> -void NESeparableConvolutionHorKernel<9>::convolve<int32_t>(const Window &window) -{ - Window win_in(window); - win_in.shift(Window::DimX, -4); - - Iterator input(_input, win_in); - Iterator output(_output, window); - - execute_window_loop(window, [&](const Coordinates &) - { - const uint8x16_t data = vld1q_u8(input.ptr()); - - const int16x8x2_t data_s16 = - { - { - vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))), - vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data))) - } - }; - - const int16x8_t data_s16_l3 = vextq_s16(data_s16.val[0], data_s16.val[1], 1); - const int16x8_t data_s16_l2 = vextq_s16(data_s16.val[0], data_s16.val[1], 2); - const int16x8_t data_s16_l1 = vextq_s16(data_s16.val[0], data_s16.val[1], 3); - const int16x8_t data_s16_m = vextq_s16(data_s16.val[0], data_s16.val[1], 4); - const int16x8_t data_s16_r1 = vextq_s16(data_s16.val[0], data_s16.val[1], 5); - const int16x8_t data_s16_r2 = vextq_s16(data_s16.val[0], data_s16.val[1], 6); - const int16x8_t data_s16_r3 = vextq_s16(data_s16.val[0], data_s16.val[1], 7); - - int32x4_t out_low = vmull_n_s16(vget_low_s16(data_s16.val[0]), _conv_row[0]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_l3), _conv_row[1]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_l2), _conv_row[2]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_l1), _conv_row[3]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_m), _conv_row[4]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_r1), _conv_row[5]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_r2), _conv_row[6]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16_r3), _conv_row[7]); - out_low = vmlal_n_s16(out_low, vget_low_s16(data_s16.val[1]), _conv_row[8]); - - vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()), out_low); - - int32x4_t out_high = vmull_n_s16(vget_high_s16(data_s16.val[0]), _conv_row[0]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_l3), _conv_row[1]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_l2), _conv_row[2]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_l1), _conv_row[3]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_m), _conv_row[4]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_r1), _conv_row[5]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_r2), _conv_row[6]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16_r3), _conv_row[7]); - out_high = vmlal_n_s16(out_high, vget_high_s16(data_s16.val[1]), _conv_row[8]); - - vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()) + 4, out_high); - }, - input, output); -} - -template class arm_compute::NESeparableConvolutionHorKernel<5>; -template class arm_compute::NESeparableConvolutionHorKernel<7>; -template class arm_compute::NESeparableConvolutionHorKernel<9>; - -template <unsigned int matrix_size> -NESeparableConvolutionVertKernel<matrix_size>::NESeparableConvolutionVertKernel() - : _conv_col{ { 0 } }, _scale(0) -{ -} - -template <unsigned int matrix_size> -BorderSize NESeparableConvolutionVertKernel<matrix_size>::border_size() const -{ - return BorderSize{ matrix_size / 2, 0 }; -} - -template <unsigned int matrix_size> -void NESeparableConvolutionVertKernel<matrix_size>::configure(const ITensor *input, ITensor *output, const int16_t *conv_col, uint32_t scale, bool border_undefined) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, conv_col); - - set_shape_if_empty(*output->info(), input->info()->tensor_shape()); - - ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U16, DataType::S16, DataType::S32); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16); - ARM_COMPUTE_ERROR_ON(scale == 0); - - _input = input; - _output = output; - std::copy_n(conv_col, _conv_col.size(), _conv_col.begin()); - _scale = scale; - - // Configure kernel window - constexpr unsigned int num_elems_processed_per_iteration = 16; - constexpr unsigned int num_elems_read_per_iteration = 16; - constexpr unsigned int num_elems_written_per_iteration = 16; - - Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size()); - AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); - - update_window_and_padding(win, - AccessWindowRectangle(input->info(), 0, -border_size().top, num_elems_read_per_iteration, matrix_size), - output_access); - - output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size()); - - INEKernel::configure(win); -} - -template <unsigned int matrix_size> -void NESeparableConvolutionVertKernel<matrix_size>::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); - - switch(_input->info()->data_type()) - { - case DataType::U16: - switch(_output->info()->data_type()) - { - case DataType::U8: - convolution_u16<uint8_t>(window); - break; - case DataType::S16: - convolution_u16<int16_t>(window); - break; - default: - ARM_COMPUTE_ERROR("Not supported"); - } - break; - case DataType::S16: - switch(_output->info()->data_type()) - { - case DataType::U8: - convolution_s16<uint8_t>(window); - break; - case DataType::S16: - convolution_s16<int16_t>(window); - break; - default: - ARM_COMPUTE_ERROR("Not supported"); - } - break; - case DataType::S32: - switch(_output->info()->data_type()) - { - case DataType::U8: - convolution_s32<uint8_t>(window); - break; - case DataType::S16: - convolution_s32<int16_t>(window); - break; - default: - ARM_COMPUTE_ERROR("Not supported"); - } - break; - default: - ARM_COMPUTE_ERROR("Unsupported intermediate data type!"); - break; - } -} - -template <unsigned int matrix_size> -template <typename OutputType> -void NESeparableConvolutionVertKernel<matrix_size>::convolution_u16(const Window &win) -{ - static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16"); - - Window win_in(win); - win_in.set_dimension_step(Window::DimX, 8); - - Iterator in(_input, win_in); - Iterator out(_output, win); - - std::array<unsigned char *, matrix_size> input_ptrs{ {} }; - const float32x4_t oneoverscale = vdupq_n_f32(1.0f / _scale); - const int k_half = matrix_size / 2; - - // Set row pointers - for(int i = -k_half; i <= k_half; ++i) - { - input_ptrs[k_half + i] = _input->ptr_to_element(Coordinates(0, i)); - } - - execute_window_loop(win, [&](const Coordinates &) - { - uint16x8_t out0 = vdupq_n_u16(0); - uint16x8_t out1 = vdupq_n_u16(0); - - // First half - for(unsigned int r = 0; r < matrix_size; ++r) - { - const uint16x8_t data = vld1q_u16(reinterpret_cast<const uint16_t *>(input_ptrs[r] + in.offset())); - out0 = vmlaq_n_u16(out0, data, _conv_col[r]); - } - - in.increment(Window::DimX); - - // Second half - for(unsigned int r = 0; r < matrix_size; ++r) - { - const uint16x8_t data = vld1q_u16(reinterpret_cast<const uint16_t *>(input_ptrs[r] + in.offset())); - out1 = vmlaq_n_u16(out1, data, _conv_col[r]); - } - - //scale the result if needed - if(_scale != 1) - { - float32x4_t out0_f32_high = vcvtq_f32_u32(vmovl_u16(vget_high_u16(out0))); - float32x4_t out0_f32_low = vcvtq_f32_u32(vmovl_u16(vget_low_u16(out0))); - out0_f32_high = vmulq_f32(out0_f32_high, oneoverscale); - out0_f32_low = vmulq_f32(out0_f32_low, oneoverscale); - store_results(vcvtq_u32_f32(out0_f32_low), vcvtq_u32_f32(out0_f32_high), reinterpret_cast<OutputType *>(out.ptr())); - - float32x4_t out1_f32_high = vcvtq_f32_u32(vmovl_u16(vget_high_u16(out1))); - float32x4_t out1_f32_low = vcvtq_f32_u32(vmovl_u16(vget_low_u16(out1))); - out1_f32_high = vmulq_f32(out1_f32_high, oneoverscale); - out1_f32_low = vmulq_f32(out1_f32_low, oneoverscale); - store_results(vcvtq_u32_f32(out1_f32_low), vcvtq_u32_f32(out1_f32_high), reinterpret_cast<OutputType *>(out.ptr()) + 8); - } - else - { - store_results(out0, out1, reinterpret_cast<OutputType *>(out.ptr())); - } - }, - in, out); -} - -template <unsigned int matrix_size> -template <typename OutputType> -void NESeparableConvolutionVertKernel<matrix_size>::convolution_s16(const Window &win) -{ - static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16"); - - Window win_in(win); - win_in.set_dimension_step(Window::DimX, 8); - - Iterator in(_input, win_in); - Iterator out(_output, win); - - std::array<unsigned char *, matrix_size> input_ptrs{ {} }; - const float32x4_t oneoverscale = vdupq_n_f32(1.0f / _scale); - const int k_half = matrix_size / 2; - - // Set row pointers - for(int i = -k_half; i <= k_half; ++i) - { - input_ptrs[k_half + i] = _input->ptr_to_element(Coordinates(0, i)); - } - - execute_window_loop(win, [&](const Coordinates &) - { - int16x8_t out0 = vdupq_n_s16(0); - int16x8_t out1 = vdupq_n_s16(0); - - // First half - for(unsigned int r = 0; r < matrix_size; ++r) - { - const int16x8_t data = vld1q_s16(reinterpret_cast<const int16_t *>(input_ptrs[r] + in.offset())); - out0 = vmlaq_n_s16(out0, data, _conv_col[r]); - } - - in.increment(Window::DimX); - - // Second half - for(unsigned int r = 0; r < matrix_size; ++r) - { - const int16x8_t data = vld1q_s16(reinterpret_cast<const int16_t *>(input_ptrs[r] + in.offset())); - out1 = vmlaq_n_s16(out1, data, _conv_col[r]); - } - - //scale the result if needed - if(_scale != 1) - { - float32x4_t out0_f32_high = vcvtq_f32_s32(vmovl_s16(vget_high_s16(out0))); - float32x4_t out0_f32_low = vcvtq_f32_s32(vmovl_s16(vget_low_s16(out0))); - out0_f32_high = vmulq_f32(out0_f32_high, oneoverscale); - out0_f32_low = vmulq_f32(out0_f32_low, oneoverscale); - store_results(vcvtq_s32_f32(out0_f32_low), vcvtq_s32_f32(out0_f32_high), reinterpret_cast<OutputType *>(out.ptr())); - - float32x4_t out1_f32_high = vcvtq_f32_s32(vmovl_s16(vget_high_s16(out1))); - float32x4_t out1_f32_low = vcvtq_f32_s32(vmovl_s16(vget_low_s16(out1))); - out1_f32_high = vmulq_f32(out1_f32_high, oneoverscale); - out1_f32_low = vmulq_f32(out1_f32_low, oneoverscale); - store_results(vcvtq_s32_f32(out1_f32_low), vcvtq_s32_f32(out1_f32_high), reinterpret_cast<OutputType *>(out.ptr()) + 8); - } - else - { - store_results(out0, out1, reinterpret_cast<OutputType *>(out.ptr())); - } - }, - in, out); -} - -template <unsigned int matrix_size> -template <typename OutputType> -void NESeparableConvolutionVertKernel<matrix_size>::convolution_s32(const Window &win) -{ - static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16"); - - Window win_in(win); - win_in.set_dimension_step(Window::DimX, 8); - - Iterator in(_input, win_in); - Iterator out(_output, win); - - std::array<unsigned char *, matrix_size> input_ptrs{ {} }; - const float32x4_t oneoverscale = vdupq_n_f32(1.0f / _scale); - const int k_half = matrix_size / 2; - - // Set row pointers - for(int i = -k_half; i <= k_half; ++i) - { - input_ptrs[k_half + i] = _input->ptr_to_element(Coordinates(0, i)); - } - - const int32x4_t zero = vdupq_n_s32(0); - - execute_window_loop(win, [&](const Coordinates &) - { - int32x4x2_t out0 = - { - { - zero, - zero - } - }; - - int32x4x2_t out1 = - { - { - zero, - zero - } - }; - - // First half - for(unsigned int r = 0; r < matrix_size; ++r) - { - const int32x4x2_t data = vld2q_s32(reinterpret_cast<const int32_t *>(input_ptrs[r] + in.offset())); - out0.val[0] = vmlaq_n_s32(out0.val[0], data.val[0], _conv_col[r]); - out0.val[1] = vmlaq_n_s32(out0.val[1], data.val[1], _conv_col[r]); - } - - in.increment(Window::DimX); - - // Second half - for(unsigned int r = 0; r < matrix_size; ++r) - { - const int32x4x2_t data = vld2q_s32(reinterpret_cast<const int32_t *>(input_ptrs[r] + in.offset())); - out1.val[0] = vmlaq_n_s32(out1.val[0], data.val[0], _conv_col[r]); - out1.val[1] = vmlaq_n_s32(out1.val[1], data.val[1], _conv_col[r]); - } - - //scale the result if needed - if(_scale != 1) - { - float32x4_t out0_f32_odd = vcvtq_f32_s32(out0.val[0]); - float32x4_t out0_f32_even = vcvtq_f32_s32(out0.val[1]); - out0_f32_odd = vmulq_f32(out0_f32_odd, oneoverscale); - out0_f32_even = vmulq_f32(out0_f32_even, oneoverscale); - out0.val[0] = vcvtq_s32_f32(out0_f32_odd); - out0.val[1] = vcvtq_s32_f32(out0_f32_even); - - float32x4_t out1_f32_odd = vcvtq_f32_s32(out1.val[0]); - float32x4_t out1_f32_even = vcvtq_f32_s32(out1.val[1]); - out1_f32_odd = vmulq_f32(out1_f32_odd, oneoverscale); - out1_f32_even = vmulq_f32(out1_f32_even, oneoverscale); - out1.val[0] = vcvtq_s32_f32(out1_f32_odd); - out1.val[1] = vcvtq_s32_f32(out1_f32_even); - } - - const int32x4x2_t out0_s32 = vzipq_s32(out0.val[0], out0.val[1]); - store_results(out0_s32.val[0], out0_s32.val[1], reinterpret_cast<OutputType *>(out.ptr())); - - const int32x4x2_t out1_s32 = vzipq_s32(out1.val[0], out1.val[1]); - store_results(out1_s32.val[0], out1_s32.val[1], reinterpret_cast<OutputType *>(out.ptr()) + 8); - }, - in, out); -} - -template class arm_compute::NESeparableConvolutionVertKernel<5>; -template class arm_compute::NESeparableConvolutionVertKernel<7>; -template class arm_compute::NESeparableConvolutionVertKernel<9>; - -/****************************************************************************************\ - * Rectangle Convolution * -\****************************************************************************************/ - -NEConvolutionRectangleKernel::NEConvolutionRectangleKernel() - : _input(nullptr), _output(nullptr), _scale(0), _convolution(), _border_size(), _func_idx(0) -{ -} - -BorderSize NEConvolutionRectangleKernel::border_size() const -{ - return _border_size; -} - -void NEConvolutionRectangleKernel::configure(const ITensor *input, ITensor *output, const int16_t *conv, uint32_t width, uint32_t height, uint32_t scale, bool border_undefined) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, conv); - - set_shape_if_empty(*output->info(), input->info()->tensor_shape()); - - ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16); - ARM_COMPUTE_ERROR_ON(width != 3 && width != 5 && width != 7 && width != 9); - ARM_COMPUTE_ERROR_ON(height != 3 && height != 5 && height != 7 && height != 9); - ARM_COMPUTE_ERROR_ON(0 == scale); - - _input = input; - _output = output; - _scale = scale; - _border_size = BorderSize(height / 2, width / 2); - - // Setup the convolution matrix - const uint32_t nr_elements = width * height; - _convolution.resize(nr_elements); - std::copy_n(conv, nr_elements, _convolution.begin()); - - // Set function index to help choose appropriate function in run() - _func_idx = get_index(height) * 4 + get_index(width); - ARM_COMPUTE_ERROR_ON(_func_idx > (_nr_supported_sizes * _nr_supported_sizes)); - - // Configure kernel window - constexpr unsigned int num_elems_processed_per_iteration = 8; - constexpr unsigned int num_elems_read_per_iteration = 16; - constexpr unsigned int num_elems_written_per_iteration = 8; - - Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, _border_size); - AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); - - update_window_and_padding(win, - AccessWindowRectangle(input->info(), -_border_size.left, -_border_size.top, num_elems_read_per_iteration, height), - output_access); - - output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, _border_size); - - INEKernel::configure(win); -} - -void NEConvolutionRectangleKernel::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); - - using ConvolutionRectangleFunction = void (NEConvolutionRectangleKernel::*)(const Window & window); - - // uint8_t function table - static const std::array<ConvolutionRectangleFunction, 16> func_table_u8 = - { - { - &NEConvolutionRectangleKernel::convolution<uint8_t, 3, 3>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 3, 5>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 3, 7>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 3, 9>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 5, 3>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 5, 5>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 5, 7>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 5, 9>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 7, 3>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 7, 5>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 7, 7>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 7, 9>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 9, 3>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 9, 5>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 9, 7>, - &NEConvolutionRectangleKernel::convolution<uint8_t, 9, 9> - } - }; - // int16_t function table - static const std::array<ConvolutionRectangleFunction, 16> func_table_s16 = - { - { - &NEConvolutionRectangleKernel::convolution<int16_t, 3, 3>, - &NEConvolutionRectangleKernel::convolution<int16_t, 3, 5>, - &NEConvolutionRectangleKernel::convolution<int16_t, 3, 7>, - &NEConvolutionRectangleKernel::convolution<int16_t, 3, 9>, - &NEConvolutionRectangleKernel::convolution<int16_t, 5, 3>, - &NEConvolutionRectangleKernel::convolution<int16_t, 5, 5>, - &NEConvolutionRectangleKernel::convolution<int16_t, 5, 7>, - &NEConvolutionRectangleKernel::convolution<int16_t, 5, 9>, - &NEConvolutionRectangleKernel::convolution<int16_t, 7, 3>, - &NEConvolutionRectangleKernel::convolution<int16_t, 7, 5>, - &NEConvolutionRectangleKernel::convolution<int16_t, 7, 7>, - &NEConvolutionRectangleKernel::convolution<int16_t, 7, 9>, - &NEConvolutionRectangleKernel::convolution<int16_t, 9, 3>, - &NEConvolutionRectangleKernel::convolution<int16_t, 9, 5>, - &NEConvolutionRectangleKernel::convolution<int16_t, 9, 7>, - &NEConvolutionRectangleKernel::convolution<int16_t, 9, 9> - } - }; - - // Run appropriate function - switch(_output->info()->data_type()) - { - case DataType::U8: - ARM_COMPUTE_ERROR_ON(_func_idx >= func_table_u8.size()); - (this->*func_table_u8[_func_idx])(window); - break; - case DataType::S16: - ARM_COMPUTE_ERROR_ON(_func_idx >= func_table_s16.size()); - (this->*func_table_s16[_func_idx])(window); - break; - default: - ARM_COMPUTE_ERROR("Not supported"); - } -} - -unsigned int NEConvolutionRectangleKernel::get_index(uint32_t val) -{ - switch(val) - { - case 3: - return 0; - case 5: - return 1; - case 7: - return 2; - case 9: - return 3; - default: - ARM_COMPUTE_ERROR("Not supported dimension size"); - return 0; - } -} - -template <typename OutputType, unsigned int rows, unsigned int cols> -void NEConvolutionRectangleKernel::convolution(const Window &win) -{ - static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16"); - ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr); - - Iterator input(_input, win); - Iterator output(_output, win); - - std::array<unsigned char *, rows> input_ptrs{ {} }; - const int16_t *conv = _convolution.data(); - const float32x4_t scale_val = vdupq_n_f32(1.0f / _scale); - const int k_row_half = rows / 2; - const int k_col_half = cols / 2; - - // Set row pointers - for(int i = -k_row_half; i <= k_row_half; ++i) - { - input_ptrs[k_row_half + i] = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-k_col_half, i)); - } - - execute_window_loop(win, [&](const Coordinates &) - { - int32x4_t out = vdupq_n_s32(0); - int32x4_t out2 = vdupq_n_s32(0); - - // Perform appropriate convolution - for(unsigned int r = 0; r < rows; ++r) - { - const uint8x16_t data = vld1q_u8(input_ptrs[r] + input.offset()); - if(3 == cols) - { - convolve_row3x1(out, out2, data, conv + r * cols); - } - else if(5 == cols) - { - convolve_row5x1(out, out2, data, conv + r * cols); - } - else if(7 == cols) - { - convolve_row7x1(out, out2, data, conv + r * cols); - } - else if(9 == cols) - { - convolve_row9x1(out, out2, data, conv + r * cols); - } - else - { - ARM_COMPUTE_ERROR("Unsupported number of columns"); - } - } - - // Apply scale - if(_scale != 1) - { - // Convert to F32, scale and convert back to S32 - out = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out), scale_val)); - out2 = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out2), scale_val)); - } - - // Clamp and store as U8 or S16: - store_results(out, out2, reinterpret_cast<OutputType *>(output.ptr())); - }, - input, output); -} -} // namespace arm_compute |