| /* |
| * Copyright (c) 2017-2018 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/AccessWindowStatic.h" |
| #include "arm_compute/core/CPP/Validate.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; |
| |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *input_squared, const ITensorInfo *output, const NormalizationLayerInfo &norm_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_squared, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() == DataLayout::NHWC && norm_info.type() == NormType::IN_MAP_2D, |
| "Only Cross-map and 1D In-map normalization is supported for NHWC layout"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, input_squared); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, input_squared); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(norm_info.norm_size() % 2), "Normalization size should be odd"); |
| |
| // Checks performed when output is configured |
| if(output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *input_squared, ITensorInfo *output, const NormalizationLayerInfo &norm_info) |
| { |
| // Output tensor auto initialization if not yet initialized |
| auto_init_if_empty(*output, *input->clone()); |
| |
| const unsigned int num_elems_processed_per_iteration = 16 / input->element_size(); |
| |
| const unsigned int norm_idx = get_normalization_dimension_index(input->data_layout(), norm_info); |
| const bool is_norm_accross_width = norm_idx == 0; |
| |
| const unsigned int border_width = is_norm_accross_width ? num_elems_processed_per_iteration - 1 : 0; |
| const BorderSize border_size = BorderSize(0, border_width); |
| |
| // Configure window |
| Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); |
| bool window_changed = false; |
| |
| if(is_norm_accross_width) |
| { |
| AccessWindowStatic input_access(input, -border_size.left, 0, input->dimension(0) + border_size.right, 0); |
| AccessWindowStatic input_squared_access(input_squared, -border_size.left, 0, input->dimension(0) + border_size.right, 0); |
| window_changed = window_changed || update_window_and_padding(win, input_access, input_squared_access); |
| } |
| else |
| { |
| AccessWindowHorizontal input_access(input, -border_size.left, num_elems_processed_per_iteration); |
| AccessWindowHorizontal input_squared_access(input_squared, -border_size.left, num_elems_processed_per_iteration); |
| window_changed = window_changed || update_window_and_padding(win, input_access, input_squared_access); |
| } |
| |
| if(output->total_size() != 0) |
| { |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); |
| window_changed = window_changed || update_window_and_padding(win, output_access); |
| output_access.set_valid_region(win, input->valid_region()); |
| } |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| } // namespace |
| |
| 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_NULLPTR(input, input_squared, output); |
| // Output tensor auto initialization if not yet initialized |
| auto_init_if_empty(*output->info(), *input->info()); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), input_squared->info(), output->info(), norm_info)); |
| |
| const unsigned int num_elems_processed_per_iteration = 16 / input->info()->element_size(); |
| |
| const unsigned int norm_idx = get_normalization_dimension_index(input->info()->data_layout(), norm_info); |
| const bool is_norm_accross_width = norm_idx == 0; |
| const unsigned int border_width = is_norm_accross_width ? num_elems_processed_per_iteration - 1 : 0; |
| |
| _input = input; |
| _input_squared = input_squared; |
| _output = output; |
| _norm_info = norm_info; |
| _border_size = BorderSize(0, border_width); |
| |
| switch(_input->info()->data_type()) |
| { |
| case DataType::F32: |
| { |
| switch(norm_idx) |
| { |
| case 0: |
| { |
| if(norm_info.type() == NormType::IN_MAP_2D) |
| { |
| _func = &NENormalizationLayerKernel::normalize_float<DataType::F32, 0, true>; |
| } |
| else |
| { |
| _func = &NENormalizationLayerKernel::normalize_float<DataType::F32, 0, false>; |
| } |
| break; |
| } |
| case 2: |
| _func = &NENormalizationLayerKernel::normalize_float<DataType::F32, 2, false>; |
| break; |
| default: |
| break; |
| } |
| break; |
| } |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| { |
| switch(norm_idx) |
| { |
| case 0: |
| { |
| if(norm_info.type() == NormType::IN_MAP_2D) |
| { |
| _func = &NENormalizationLayerKernel::normalize_float<DataType::F16, 0, true>; |
| } |
| else |
| { |
| _func = &NENormalizationLayerKernel::normalize_float<DataType::F16, 0, false>; |
| } |
| break; |
| } |
| case 2: |
| _func = &NENormalizationLayerKernel::normalize_float<DataType::F16, 2, false>; |
| break; |
| default: |
| break; |
| } |
| break; |
| } |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), input_squared->info(), output->info(), norm_info); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| INEKernel::configure(win_config.second); |
| } |
| |
| 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 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 = _input->info()->dimension(dim) - 1; |
| 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, 0) : 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_FEATURE_FP16_VECTOR_ARITHMETIC |
| 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, 0) : 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_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| else |
| { |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| } |
| |
| Status NENormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *input_squared, const ITensorInfo *output, const NormalizationLayerInfo norm_info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, input_squared, output, norm_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), input_squared->clone().get(), output->clone().get(), norm_info).first); |
| |
| return Status{}; |
| } |
| |
| void NENormalizationLayerKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| // Run function |
| (this->*_func)(window); |
| } |