| /* |
| * Copyright (c) 2017-2021 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 "src/core/NEON/kernels/NENormalizationLayerKernel.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| #include "src/core/AccessWindowStatic.h" |
| #include "src/core/CPP/Validate.h" |
| #include "src/core/NEON/NEFixedPoint.h" |
| #include "src/core/NEON/NEMath.h" |
| #include "src/core/NEON/wrapper/wrapper.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/NormalizationHelpers.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| 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_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); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| } // namespace |
| |
| NENormalizationLayerKernel::NENormalizationLayerKernel() |
| : _func(nullptr), _input(nullptr), _input_squared(nullptr), _output(nullptr), _norm_info(NormType::IN_MAP_1D) |
| { |
| } |
| |
| 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 norm_idx = get_normalization_dimension_index(input->info()->data_layout(), norm_info); |
| |
| _input = input; |
| _input_squared = input_squared; |
| _output = output; |
| _norm_info = norm_info; |
| |
| 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<float, 4, 0, true>; |
| } |
| else |
| { |
| _func = &NENormalizationLayerKernel::normalize_float<float, 4, 0, false>; |
| } |
| break; |
| } |
| case 1: |
| if(norm_info.type() == NormType::IN_MAP_2D) |
| { |
| _func = &NENormalizationLayerKernel::normalize_float<float, 4, 1, true>; |
| } |
| else |
| { |
| _func = &NENormalizationLayerKernel::normalize_float<float, 4, 1, false>; |
| } |
| break; |
| case 2: |
| _func = &NENormalizationLayerKernel::normalize_float<float, 4, 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<float16_t, 8, 0, true>; |
| } |
| else |
| { |
| _func = &NENormalizationLayerKernel::normalize_float<float16_t, 8, 0, false>; |
| } |
| break; |
| } |
| case 1: |
| if(norm_info.type() == NormType::IN_MAP_2D) |
| { |
| _func = &NENormalizationLayerKernel::normalize_float<float16_t, 8, 1, true>; |
| } |
| else |
| { |
| _func = &NENormalizationLayerKernel::normalize_float<float16_t, 8, 1, false>; |
| } |
| break; |
| case 2: |
| _func = &NENormalizationLayerKernel::normalize_float<float16_t, 8, 2, false>; |
| break; |
| default: |
| break; |
| } |
| break; |
| } |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input->info(), Steps()); |
| Coordinates coord; |
| coord.set_num_dimensions(output->info()->num_dimensions()); |
| output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); |
| INEKernel::configure(win); |
| } |
| |
| template <typename T, unsigned int S, unsigned int dim, bool do_2D_norm> |
| void NENormalizationLayerKernel::normalize_float(const Window &window) |
| { |
| /** Neon vector tag type. */ |
| using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; |
| |
| Window win(window); |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| const auto window_start_x = static_cast<int>(window.x().start()); |
| const auto window_end_x = static_cast<int>(window.x().end()); |
| const int window_step_x = S; |
| |
| Iterator input(_input, win); |
| Iterator input_squared(_input_squared, win); |
| Iterator output(_output, win); |
| |
| const int dim_y = _input->info()->data_layout() == DataLayout::NCHW ? 1 : 2; |
| const int radius = _norm_info.norm_size() / 2; |
| const int input_squared_stride_x = _input_squared->info()->strides_in_bytes()[0]; |
| const int input_squared_stride_slice = _input_squared->info()->strides_in_bytes()[dim]; |
| const int input_squared_stride_row = _input_squared->info()->strides_in_bytes()[dim_y]; |
| |
| const int max_right = _input->info()->dimension(dim) - 1; |
| const int max_bottom = _input->info()->dimension(dim_y) - 1; |
| |
| const auto coeff_vec = wrapper::vdup_n(static_cast<T>(_norm_info.scale_coeff()), ExactTagType{}); |
| const auto beta_vec = wrapper::vdup_n(static_cast<T>(_norm_info.beta()), ExactTagType{}); |
| const auto kappa_vec = wrapper::vdup_n(static_cast<T>(_norm_info.kappa()), ExactTagType{}); |
| |
| auto sequential_normalization = [&](const int x, const Coordinates & id, const int current_row, const int first_row, const int last_row, const T * input_ptr, const uint8_t *input_squared_start_ptr, |
| T * output_ptr) |
| { |
| const int current_slice = dim == 0 ? x : id[dim]; |
| const int first_slice = std::max(current_slice - radius, 0); |
| const int last_slice = std::min(current_slice + radius, max_right); |
| |
| const uint8_t *const input_squared_x_ptr = input_squared_start_ptr + x * input_squared_stride_x; |
| // Accumulate 2D In-Map values |
| auto accu = static_cast<T>(0.f); |
| for(int j = first_row; j <= last_row; ++j) |
| { |
| // Compute row displacement |
| const uint8_t *const input_squared_ptr = input_squared_x_ptr + (j - current_row) * input_squared_stride_row; |
| for(int i = first_slice; i <= last_slice; ++i) |
| { |
| accu += *reinterpret_cast<const T *>(input_squared_ptr + (i - current_slice) * input_squared_stride_slice); |
| } |
| } |
| |
| // Normalize |
| const auto normalized = std::pow(accu * static_cast<T>(_norm_info.scale_coeff()) + static_cast<T>(_norm_info.kappa()), _norm_info.beta()); |
| const auto normalized_pixel = (*(input_ptr + x)) / normalized; |
| *(output_ptr + x) = normalized_pixel; |
| }; |
| |
| execute_window_loop(win, [&](const Coordinates & id) |
| { |
| const auto input_ptr = reinterpret_cast<const T *>(input.ptr()); |
| auto output_ptr = reinterpret_cast<T *>(output.ptr()); |
| |
| // Get range to normalize |
| const int current_row = do_2D_norm ? id[dim_y] : 0; |
| 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; |
| |
| int x = window_start_x; |
| // Compute serially starting elements for the case x dimension is width |
| for(; x < radius && x < window_end_x && dim == 0; ++x) |
| { |
| sequential_normalization(x, id, current_row, first_row, last_row, input_ptr, input_squared.ptr(), output_ptr); |
| } |
| |
| // Compute vectorized |
| for(; x <= window_end_x - window_step_x - radius; x += window_step_x) |
| { |
| const int current_slice = dim == 0 ? x : id[dim]; |
| const int first_slice = std::max(current_slice - radius, 0); |
| const int last_slice = std::min(current_slice + radius, max_right); |
| |
| const uint8_t *const input_squared_x_ptr = input_squared.ptr() + x * input_squared_stride_x; |
| // Accumulate 2D In-Map values |
| auto accu = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{}); |
| for(int j = first_row; j <= last_row; ++j) |
| { |
| // Compute row displacement |
| const uint8_t *const input_squared_ptr = input_squared_x_ptr + (j - current_row) * input_squared_stride_row; |
| for(int i = first_slice; i <= last_slice; ++i) |
| { |
| accu = wrapper::vadd(accu, wrapper::vloadq(reinterpret_cast<const T *>(input_squared_ptr + (i - current_slice) * input_squared_stride_slice))); |
| } |
| } |
| |
| // Normalize |
| const auto normalized = wrapper::vpow(wrapper::vmla(kappa_vec, coeff_vec, accu), beta_vec); |
| const auto normalized_pixel = wrapper::vmul(wrapper::vloadq(input_ptr + x), wrapper::vinv(normalized)); |
| wrapper::vstore(reinterpret_cast<T *>(output_ptr + x), normalized_pixel); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| sequential_normalization(x, id, current_row, first_row, last_row, input_ptr, input_squared.ptr(), output_ptr); |
| } |
| }, |
| input, input_squared, output); |
| } |
| |
| 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)); |
| |
| 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); |
| } |
| } // namespace arm_compute |