| /* |
| * 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/NEInstanceNormalizationLayerKernel.h" |
| |
| #include "arm_compute/core/CPP/Validate.h" |
| #include "arm_compute/core/Error.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/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include <arm_neon.h> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| template <typename T> |
| void instance_normalization_nchw(ITensor *input, ITensor *output, float gamma, float beta, float epsilon, const Window &window) |
| { |
| /** NEON vector tag type. */ |
| using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>; |
| |
| // Clear X/Y dimensions on execution window as we handle the planes manually |
| Window win = window; |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| win.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| |
| constexpr int window_step_x = 16 / sizeof(T); |
| const unsigned int elements_plane = input->info()->dimension(0) * output->info()->dimension(1); |
| |
| Iterator input_it(input, win); |
| execute_window_loop(win, [&](const Coordinates & id) |
| { |
| Window win_plane = window; |
| win_plane.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| win_plane.set(Window::DimZ, Window::Dimension(id[2], id[2] + 1, 1)); |
| win_plane.set(3, Window::Dimension(id[3], id[3] + 1, 1)); |
| |
| Iterator input_plane_it(input, win_plane); |
| Iterator output_plane_it(output, win_plane); |
| |
| auto sum_h_w = static_cast<T>(0.f); |
| auto sum_squares_h_w = static_cast<T>(0.f); |
| |
| execute_window_loop(win_plane, [&](const Coordinates &) |
| { |
| const auto input_ptr = reinterpret_cast<const T *>(input_plane_it.ptr()); |
| |
| auto vec_sum_h_w = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{}); |
| auto vec_sum_squares_h_w = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{}); |
| |
| // Compute S elements per iteration |
| int x = window.x().start(); |
| for(; x <= (window.x().end() - window_step_x); x += window_step_x) |
| { |
| auto vec_input_val = wrapper::vloadq(input_ptr + x); |
| vec_sum_h_w = wrapper::vadd(vec_sum_h_w, vec_input_val); |
| vec_sum_squares_h_w = wrapper::vadd(vec_sum_squares_h_w, wrapper::vmul(vec_input_val, vec_input_val)); |
| } |
| |
| auto vec2_sum_h_w = wrapper::vpadd(wrapper::vgethigh(vec_sum_h_w), wrapper::vgetlow(vec_sum_h_w)); |
| auto vec2_sum_squares_h_w = wrapper::vpadd(wrapper::vgethigh(vec_sum_squares_h_w), wrapper::vgetlow(vec_sum_squares_h_w)); |
| for(int i = 0; i < window_step_x / 4; ++i) |
| { |
| vec2_sum_h_w = wrapper::vpadd(vec2_sum_h_w, vec2_sum_h_w); |
| vec2_sum_squares_h_w = wrapper::vpadd(vec2_sum_squares_h_w, vec2_sum_squares_h_w); |
| } |
| sum_h_w += wrapper::vgetlane(vec2_sum_h_w, 0); |
| sum_squares_h_w += wrapper::vgetlane(vec2_sum_squares_h_w, 0); |
| |
| // Compute left-over elements |
| for(; x < window.x().end(); ++x) |
| { |
| const auto value = *(input_ptr + x); |
| sum_h_w += value; |
| sum_squares_h_w += value * value; |
| } |
| }, |
| input_plane_it, output_plane_it); |
| |
| const auto mean_h_w = sum_h_w / elements_plane; |
| const auto var_h_w = sum_squares_h_w / elements_plane - mean_h_w * mean_h_w; |
| |
| const auto multip_h_w = gamma / std::sqrt(var_h_w + epsilon); |
| const auto vec_mean_h_w = wrapper::vdup_n(static_cast<T>(mean_h_w), ExactTagType{}); |
| const auto vec_multip_h_w = wrapper::vdup_n(static_cast<T>(multip_h_w), ExactTagType{}); |
| const auto vec_beta = wrapper::vdup_n(static_cast<T>(beta), ExactTagType{}); |
| |
| execute_window_loop(win_plane, [&](const Coordinates &) |
| { |
| auto input_ptr = reinterpret_cast<T *>(input_plane_it.ptr()); |
| auto output_ptr = reinterpret_cast<T *>(output_plane_it.ptr()); |
| |
| // Compute S elements per iteration |
| int x = window.x().start(); |
| auto vec_val = wrapper::vdup_n(static_cast<T>(0.0f), ExactTagType{}); |
| for(; x <= (window.x().end() - window_step_x); x += window_step_x) |
| { |
| vec_val = wrapper::vloadq(input_ptr + x); |
| vec_val = wrapper::vadd(wrapper::vmul(wrapper::vsub(vec_val, vec_mean_h_w), vec_multip_h_w), vec_beta); |
| wrapper::vstore(output_ptr + x, vec_val); |
| } |
| |
| // Compute left-over elements |
| for(; x < window.x().end(); ++x) |
| { |
| *(output_ptr + x) = ((*(input_ptr + x)) - mean_h_w) * multip_h_w + beta; |
| } |
| }, |
| input_plane_it, output_plane_it); |
| }, |
| input_it); |
| } |
| |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, float gamma, float beta, float epsilon) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); |
| ARM_COMPUTE_UNUSED(gamma); |
| ARM_COMPUTE_UNUSED(beta); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(epsilon == 0.f, "Epsilon must be different than 0"); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(input, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() == DataLayout::NHWC, "NHWC data layout is not supported by the kernel directly"); |
| |
| 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); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_channels() != output->num_channels(), "Input and output have different number of channels"); |
| } |
| return Status{}; |
| } |
| |
| std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) |
| { |
| // We handle the planes manually |
| Window win = calculate_max_window(*input, Steps(1)); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type()); |
| |
| // NEInstanceNormalizationLayerKernel doesn't need padding so update_window_and_padding() can be skipped |
| Coordinates coord; |
| coord.set_num_dimensions(output->num_dimensions()); |
| output->set_valid_region(ValidRegion(coord, output->tensor_shape())); |
| return std::make_pair(Status{}, win); |
| } |
| } // namespace |
| |
| NEInstanceNormalizationLayerKernel::NEInstanceNormalizationLayerKernel() |
| : _func(nullptr), _input(nullptr), _output(nullptr), _gamma(1), _beta(0), _epsilon(1e-12) |
| { |
| } |
| |
| void NEInstanceNormalizationLayerKernel::configure(ITensor *input, ITensor *output, float gamma, float beta, float epsilon) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input); |
| |
| _input = input; |
| _output = output == nullptr ? input : output; |
| _gamma = gamma; |
| _beta = beta; |
| _epsilon = epsilon; |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _output->info(), gamma, beta, epsilon)); |
| |
| if(_input->info()->data_type() == DataType::F32) |
| { |
| _func = &instance_normalization_nchw<float>; |
| } |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| else if(_input->info()->data_type() == DataType::F16) |
| { |
| _func = &instance_normalization_nchw<float16_t>; |
| } |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| else |
| { |
| ARM_COMPUTE_ERROR("Unsupported data type"); |
| } |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(_input->info(), _output->info()); |
| ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); |
| |
| INEKernel::configure(std::get<1>(win_config)); |
| } |
| |
| Status NEInstanceNormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, float gamma, float beta, float epsilon) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, gamma, beta, epsilon)); |
| ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), (output == nullptr ? input->clone().get() : output->clone().get())))); |
| return Status{}; |
| } |
| |
| void NEInstanceNormalizationLayerKernel::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); |
| (*_func)(_input, _output, _gamma, _beta, _epsilon, window); |
| } |
| } // namespace arm_compute |