| /* |
| * Copyright (c) 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/NEMeanStdDevNormalizationKernel.h" |
| |
| #include "arm_compute/core/CPP/Validate.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/NEON/NEMath.h" |
| #include "arm_compute/core/NEON/wrapper/wrapper.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Window.h" |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, float epsilon) |
| { |
| ARM_COMPUTE_UNUSED(epsilon); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 2, "Input tensor cannot have more than 2 dimensions"); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); |
| |
| // Checks performed when output is configured |
| if((output != nullptr) && (output->total_size() != 0)) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) |
| { |
| if(output != nullptr) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output, *input); |
| } |
| |
| // This kernel doesn't need padding. A left-over for loop on dimension X, we cannot have any read or write out of memory |
| // For this reason num_elems_processed_per_iteration is set to 1 |
| Window win = calculate_max_window(*input, Steps()); |
| if(output != nullptr) |
| { |
| output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape())); |
| } |
| |
| return std::make_pair(Status{}, win); |
| } |
| } // namespace |
| |
| template <typename ScalarType, int size> |
| void NEMeanStdDevNormalizationKernel::mean_stddev_normalization(const Window &window) |
| { |
| using ExactTagType = typename wrapper::traits::neon_vector<ScalarType, size>::tag_type; |
| |
| // Set build options |
| Window win = window; |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| const int window_step_x = size; |
| const auto window_start_x = static_cast<int>(window.x().start()); |
| const auto window_end_x = static_cast<int>(window.x().end()); |
| |
| Iterator input(_input, win); |
| Iterator output(_output, win); |
| |
| execute_window_loop(win, [&](const Coordinates &) |
| { |
| int x = window_start_x; |
| auto in_ptr = reinterpret_cast<const ScalarType *>(input.ptr()); |
| auto out_ptr = reinterpret_cast<ScalarType *>(output.ptr()); |
| |
| auto sum_vec = wrapper::vdup_n(static_cast<ScalarType>(0.f), ExactTagType{}); |
| auto sum_sq_vec = wrapper::vdup_n(static_cast<ScalarType>(0.f), ExactTagType{}); |
| |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| auto data = wrapper::vloadq(in_ptr + x); |
| sum_vec = wrapper::vadd(sum_vec, data); |
| sum_sq_vec = wrapper::vadd(sum_sq_vec, wrapper::vmul(data, data)); |
| } |
| |
| auto sum_carry_res = wrapper::vpadd(wrapper::vgethigh(sum_vec), wrapper::vgetlow(sum_vec)); |
| auto sum_sq_carry_res = wrapper::vpadd(wrapper::vgethigh(sum_sq_vec), wrapper::vgetlow(sum_sq_vec)); |
| for(int i = 0; i < size / 4; ++i) |
| { |
| sum_carry_res = wrapper::vpadd(sum_carry_res, sum_carry_res); |
| sum_sq_carry_res = wrapper::vpadd(sum_sq_carry_res, sum_sq_carry_res); |
| } |
| |
| auto sum = wrapper::vgetlane(sum_carry_res, 0); |
| auto sum_sq = wrapper::vgetlane(sum_sq_carry_res, 0); |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| ScalarType data = *(in_ptr + x); |
| sum += data; |
| sum_sq += data * data; |
| } |
| |
| ScalarType mean = sum / _input->info()->dimension(0); |
| ScalarType var = (sum_sq / _input->info()->dimension(0)) - (mean * mean); |
| ScalarType stddev_inv = 1.f / sqrt(var + _epsilon); |
| |
| auto mean_vec = wrapper::vdup_n(mean, ExactTagType{}); |
| auto stddev_inv_vec = wrapper::vdup_n(stddev_inv, ExactTagType{}); |
| for(x = window_start_x; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| auto data = wrapper::vloadq(in_ptr + x); |
| auto res = wrapper::vmul(wrapper::vsub(data, mean_vec), stddev_inv_vec); |
| // Store results |
| wrapper::vstore(out_ptr + x, res); |
| } |
| for(; x < window_end_x; ++x) |
| { |
| *(out_ptr + x) = (*(in_ptr + x) - mean) * stddev_inv; |
| } |
| }, |
| input, output); |
| } |
| |
| NEMeanStdDevNormalizationKernel::NEMeanStdDevNormalizationKernel() |
| : _input(nullptr), _output(nullptr), _epsilon(1e-8f), _func(nullptr) |
| { |
| } |
| |
| void NEMeanStdDevNormalizationKernel::configure(ITensor *input, ITensor *output, float epsilon) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(NEMeanStdDevNormalizationKernel::validate(input->info(), (output != nullptr) ? output->info() : nullptr, epsilon)); |
| |
| _input = input; |
| _output = (output == nullptr) ? input : output; |
| _epsilon = epsilon; |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), (output == nullptr) ? nullptr : output->info()); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| ICPPKernel::configure(win_config.second); |
| |
| // Configure function to run based on different data types |
| const DataType data_type = input->info()->data_type(); |
| switch(data_type) |
| { |
| case DataType::F32: |
| _func = &NEMeanStdDevNormalizationKernel::mean_stddev_normalization<float, 4>; |
| break; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = &NEMeanStdDevNormalizationKernel::mean_stddev_normalization<float16_t, 8>; |
| break; |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| default: |
| ARM_COMPUTE_ERROR("Not Supported"); |
| break; |
| } |
| } |
| |
| Status NEMeanStdDevNormalizationKernel::validate(const ITensorInfo *input, const ITensorInfo *output, float epsilon) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, epsilon)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (output != nullptr) ? output->clone().get() : nullptr).first); |
| return Status{}; |
| } |
| |
| void NEMeanStdDevNormalizationKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| (this->*_func)(window); |
| } |
| } // namespace arm_compute |