| /* |
| * Copyright (c) 2017 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/core/NEON/kernels/NENormalizationLayerKernel.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/NEON/NEFixedPoint.h" |
| #include "arm_compute/core/NEON/NEMath.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| |
| using namespace arm_compute; |
| |
| NENormalizationLayerKernel::NENormalizationLayerKernel() |
| : _func(nullptr), _input(nullptr), _input_squared(nullptr), _output(nullptr), _norm_info(NormType::IN_MAP_1D), _border_size() |
| { |
| } |
| |
| BorderSize NENormalizationLayerKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void NENormalizationLayerKernel::configure(const ITensor *input, const ITensor *input_squared, ITensor *output, NormalizationLayerInfo norm_info) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32, DataType::QS8); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(output); |
| // Output tensor auto initialization if not yet initialized |
| auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position()); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, input_squared, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, input_squared, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, input_squared, output); |
| ARM_COMPUTE_ERROR_ON_MSG(!(norm_info.norm_size() % 2), "Normalization size should be odd"); |
| ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.beta(), input); |
| ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.kappa(), input); |
| ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.scale_coeff(), input); |
| |
| const unsigned int border_width = (norm_info.type() == NormType::CROSS_MAP) ? 0 : std::min(norm_info.norm_size() / 2, 3U); |
| |
| _input = input; |
| _input_squared = input_squared; |
| _output = output; |
| _norm_info = norm_info; |
| _border_size = BorderSize(0, border_width); |
| |
| unsigned int num_elems_processed_per_iteration = 16 / input->info()->element_size(); |
| |
| switch(_input->info()->data_type()) |
| { |
| case DataType::F32: |
| { |
| num_elems_processed_per_iteration = 4; |
| switch(norm_info.type()) |
| { |
| case NormType::IN_MAP_1D: |
| _func = &NENormalizationLayerKernel::normalize_float<DataType::F32, 0, false>; |
| break; |
| case NormType::IN_MAP_2D: |
| // Normalize over X and Y |
| _func = &NENormalizationLayerKernel::normalize_float<DataType::F32, 0, true>; |
| break; |
| case NormType::CROSS_MAP: |
| _func = &NENormalizationLayerKernel::normalize_float<DataType::F32, 2, false>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| break; |
| } |
| break; |
| } |
| case DataType::F16: |
| { |
| num_elems_processed_per_iteration = 8; |
| switch(norm_info.type()) |
| { |
| case NormType::IN_MAP_1D: |
| _func = &NENormalizationLayerKernel::normalize_float<DataType::F16, 0, false>; |
| break; |
| case NormType::IN_MAP_2D: |
| // Normalize over X and Y |
| _func = &NENormalizationLayerKernel::normalize_float<DataType::F16, 0, true>; |
| break; |
| case NormType::CROSS_MAP: |
| _func = &NENormalizationLayerKernel::normalize_float<DataType::F16, 2, false>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| break; |
| } |
| break; |
| } |
| case DataType::QS8: |
| { |
| num_elems_processed_per_iteration = 16; |
| switch(norm_info.type()) |
| { |
| case NormType::IN_MAP_1D: |
| _func = &NENormalizationLayerKernel::normalize_fixed_point<0, false>; |
| break; |
| case NormType::IN_MAP_2D: |
| // Normalize over X and Y |
| _func = &NENormalizationLayerKernel::normalize_fixed_point<0, true>; |
| break; |
| case NormType::CROSS_MAP: |
| _func = &NENormalizationLayerKernel::normalize_fixed_point<2, false>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| break; |
| } |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| |
| const unsigned int num_elems_read_per_iteration = num_elems_processed_per_iteration + 2 * (norm_info.norm_size() / 2); |
| const unsigned int num_rows = (norm_info.type() == NormType::IN_MAP_2D) ? norm_info.norm_size() : 1; |
| |
| // Configure window |
| Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration)); |
| |
| AccessWindowRectangle input_access(input->info(), -_border_size.left, 0, num_elems_read_per_iteration, num_rows); |
| AccessWindowRectangle input_squared_access(input_squared->info(), -_border_size.left, 0, num_elems_read_per_iteration, num_rows); |
| AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); |
| |
| update_window_and_padding(win, input_access, input_squared_access, output_access); |
| |
| output_access.set_valid_region(win, input->info()->valid_region()); |
| |
| INEKernel::configure(win); |
| } |
| |
| template <DataType dt, unsigned int dim, bool do_2D_norm> |
| void NENormalizationLayerKernel::normalize_float(const Window &window) |
| { |
| Iterator input(_input, window); |
| Iterator input_squared(_input_squared, window); |
| Iterator output(_output, window); |
| |
| const int dim_y = 1; |
| const int radius = _norm_info.norm_size() / 2; |
| const int total_size = _input->info()->dimension(dim) - 1; |
| const int input_squared_stride = _input_squared->info()->strides_in_bytes()[dim]; |
| // We account padding across X only and we iterate over rows |
| const int min_left = (dim == 2) ? 0 : -static_cast<int>(border_size().left); |
| const int max_right = (dim == 2) ? total_size : total_size + border_size().left; |
| const int min_top = 0; |
| const int max_bottom = _input->info()->dimension(dim_y) - 1; |
| |
| if(dt == DataType::F32) |
| { |
| const float32x4_t coeff_vec = vdupq_n_f32(_norm_info.scale_coeff()); |
| const float32x4_t beta_vec = vdupq_n_f32(_norm_info.beta()); |
| const float32x4_t kappa_vec = vdupq_n_f32(_norm_info.kappa()); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| // Get range to normalize |
| const int current_row = do_2D_norm ? id[dim_y] : 0; |
| const int current_slice = id[dim]; |
| const int first_row = do_2D_norm ? std::max(current_row - radius, min_top) : 0; |
| const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0; |
| const int first_slice = std::max(current_slice - radius, min_left); |
| const int last_slice = std::min(current_slice + radius, max_right); |
| |
| // Accumulate 2D In-Map values |
| float32x4_t accu = vdupq_n_f32(0.f); |
| for(int j = first_row; j <= last_row; j++) |
| { |
| // Compute row displacement |
| const int row = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y]; |
| const uint8_t *const input_squared_ptr = input_squared.ptr() + row - (current_slice * input_squared_stride); |
| for(int i = first_slice; i <= last_slice; ++i) |
| { |
| accu = vaddq_f32(accu, vld1q_f32(reinterpret_cast<const float *>(input_squared_ptr + i * input_squared_stride))); |
| } |
| } |
| |
| // Normalize |
| const float32x4_t normalized = vpowq_f32(vmlaq_f32(kappa_vec, coeff_vec, accu), beta_vec); |
| const float32x4_t normalized_pixel = vmulq_f32(vld1q_f32(reinterpret_cast<const float *>(input.ptr())), vinvq_f32(normalized)); |
| vst1q_f32(reinterpret_cast<float *>(output.ptr()), normalized_pixel); |
| }, |
| input, input_squared, output); |
| } |
| #ifdef ARM_COMPUTE_ENABLE_FP16 |
| else if(dt == DataType::F16) |
| { |
| const float16x8_t coeff_vec = vdupq_n_f16(_norm_info.scale_coeff()); |
| const float16x8_t beta_vec_f16 = vdupq_n_f16(_norm_info.beta()); |
| const float16x8_t kappa_vec = vdupq_n_f16(_norm_info.kappa()); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| // Get range to normalize |
| const int current_row = do_2D_norm ? id[dim_y] : 0; |
| const int current_slice = id[dim]; |
| const int first_row = do_2D_norm ? std::max(current_row - radius, min_top) : 0; |
| const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0; |
| const int first_slice = std::max(current_slice - radius, min_left); |
| const int last_slice = std::min(current_slice + radius, max_right); |
| |
| // Accumulate 2D In-Map values |
| float16x8_t accu = vdupq_n_f16(0.f); |
| for(int j = first_row; j <= last_row; j++) |
| { |
| // Compute row displacement |
| const int row = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y]; |
| const uint8_t *const input_squared_ptr = input_squared.ptr() + row - (current_slice * input_squared_stride); |
| for(int i = first_slice; i <= last_slice; ++i) |
| { |
| accu = vaddq_f16(accu, vld1q_f16(reinterpret_cast<const float16_t *>(input_squared_ptr + i * input_squared_stride))); |
| } |
| } |
| |
| const float16x8_t norm_f16 = vpowq_f16(vaddq_f16(kappa_vec, vmulq_f16(coeff_vec, accu)), beta_vec_f16); |
| const float16x8_t normalized_pixel = vmulq_f16(vld1q_f16(reinterpret_cast<const float16_t *>(input.ptr())), vinvq_f16(norm_f16)); |
| vst1q_f16(reinterpret_cast<float16_t *>(output.ptr()), normalized_pixel); |
| }, |
| input, input_squared, output); |
| } |
| #endif /* ARM_COMPUTE_ENABLE_FP16 */ |
| else |
| { |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| } |
| |
| template <unsigned int dim, bool do_2D_norm> |
| void NENormalizationLayerKernel::normalize_fixed_point(const Window &window) |
| { |
| Iterator input(_input, window); |
| Iterator input_squared(_input_squared, window); |
| Iterator output(_output, window); |
| |
| const int dim_y = 1; |
| const int radius = _norm_info.norm_size() / 2; |
| const int total_size = _input->info()->dimension(dim) - 1; |
| const int input_squared_stride = _input_squared->info()->strides_in_bytes()[dim]; |
| // We account padding across X only and we iterate over rows |
| const int min_left = (dim == 2) ? 0 : -static_cast<int>(border_size().left); |
| const int max_right = (dim == 2) ? total_size : total_size + border_size().left; |
| const int min_top = 0; |
| const int max_bottom = _input->info()->dimension(dim_y) - 1; |
| |
| const int fixed_point_position = _input->info()->fixed_point_position(); |
| |
| const qint8x16_t coeff_vec = vdupq_n_qs8_f32(_norm_info.scale_coeff(), fixed_point_position); |
| const qint8x16_t beta_vec = vdupq_n_qs8_f32(_norm_info.beta(), fixed_point_position); |
| const qint8x16_t kappa_vec = vdupq_n_qs8_f32(_norm_info.kappa(), fixed_point_position); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| // Get range to normalize |
| const int current_row = do_2D_norm ? id[dim_y] : 0; |
| const int current_slice = id[dim]; |
| const int first_row = do_2D_norm ? std::max(current_row - radius, min_top) : 0; |
| const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0; |
| const int first_slice = std::max(current_slice - radius, min_left); |
| const int last_slice = std::min(current_slice + radius, max_right); |
| |
| // Accumulate 2D In-Map values |
| qint8x16_t accu = vdupq_n_qs8(0); |
| for(int j = first_row; j <= last_row; ++j) |
| { |
| // Compute row displacement |
| const int row = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y]; |
| const uint8_t *const input_squared_ptr = input_squared.ptr() + row - (current_slice * input_squared_stride); |
| for(int i = first_slice; i <= last_slice; ++i) |
| { |
| accu = vqaddq_qs8(accu, vld1q_qs8(reinterpret_cast<const qint8_t *>(input_squared_ptr + i * input_squared_stride))); |
| } |
| } |
| |
| // Normalize |
| const qint8x16_t accu_scale = vqmlaq_qs8(kappa_vec, coeff_vec, accu, fixed_point_position); |
| const qint8x16_t normalized = vqpowq_qs8(accu_scale, beta_vec, fixed_point_position); |
| const qint8x16_t normalized_pixel = vdivq_qs8(vld1q_qs8(reinterpret_cast<const qint8_t *>(input.ptr())), normalized, fixed_point_position); |
| vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), normalized_pixel); |
| }, |
| input, input_squared, output); |
| } |
| |
| void NENormalizationLayerKernel::run(const Window &window) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| // Run function |
| (this->*_func)(window); |
| } |