| /* |
| * Copyright (c) 2016, 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/CL/kernels/CLConvolutionKernel.h" |
| |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/ICLKernel.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/CL/OpenCL.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| |
| #include <set> |
| #include <sstream> |
| #include <string> |
| |
| using namespace arm_compute; |
| |
| #define MAX_MATRIX_SIZE 81 |
| |
| /****************************************************************************************\ |
| * Square Convolution * |
| \****************************************************************************************/ |
| |
| template <unsigned int matrix_size> |
| BorderSize CLConvolutionKernel<matrix_size>::border_size() const |
| { |
| return BorderSize(matrix_size / 2); |
| } |
| |
| template <unsigned int matrix_size> |
| void CLConvolutionKernel<matrix_size>::configure(const ICLTensor *input, ICLTensor *output, const int16_t *conv, uint32_t scale, bool border_undefined) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16); |
| ARM_COMPUTE_ERROR_ON(conv == nullptr); |
| |
| _input = input; |
| _output = output; |
| |
| std::stringstream kernel_name; |
| std::set<std::string> options; |
| kernel_name << "convolution" << matrix_size << "x" << matrix_size << "_static"; |
| |
| if(scale == 0) |
| { |
| scale = calculate_matrix_scale(conv, matrix_size); |
| } |
| |
| for(unsigned int i = 0; i < matrix_size * matrix_size; i++) |
| { |
| std::stringstream mat_str; |
| mat_str << "-DMAT" << i << "=" << conv[i]; |
| options.insert(mat_str.str()); |
| } |
| |
| options.insert("-DSCALE=" + support::cpp11::to_string(scale)); |
| |
| DataType data_type = data_type_for_convolution_matrix(conv, matrix_size * matrix_size); |
| options.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); |
| |
| std::stringstream out_type; |
| out_type << "-DDATA_TYPE_OUT=" << get_cl_type_from_data_type(output->info()->data_type()); |
| options.insert(out_type.str()); |
| |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), options)); |
| |
| // Configure kernel window |
| constexpr unsigned int num_elems_processed_per_iteration = 8; |
| constexpr unsigned int num_elems_written_per_iteration = 8; |
| constexpr unsigned int num_elems_read_per_iteration = 16; |
| constexpr unsigned int num_rows_read_per_iteration = matrix_size; |
| |
| Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size()); |
| |
| AccessWindowRectangle input_access(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration); |
| AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); |
| |
| update_window_and_padding(win, input_access, output_access); |
| |
| output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size()); |
| |
| ICLKernel::configure(win); |
| } |
| |
| /****************************************************************************************\ |
| * Separable Convolution * |
| \****************************************************************************************/ |
| template <unsigned int matrix_size> |
| CLSeparableConvolutionHorKernel<matrix_size>::CLSeparableConvolutionHorKernel() |
| : _border_size(0) |
| { |
| } |
| |
| template <unsigned int matrix_size> |
| BorderSize CLSeparableConvolutionHorKernel<matrix_size>::border_size() const |
| { |
| return _border_size; |
| } |
| |
| template <unsigned int matrix_size> |
| void CLSeparableConvolutionHorKernel<matrix_size>::configure(const ICLTensor *input, ICLTensor *output, const int16_t *conv, bool border_undefined) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U16, DataType::S16, DataType::S32); |
| |
| ARM_COMPUTE_ERROR_ON((matrix_size != 5) && (matrix_size != 7) && (matrix_size != 9)); |
| |
| _input = input; |
| _output = output; |
| _border_size = BorderSize(border_undefined ? 0 : matrix_size / 2, matrix_size / 2); |
| |
| // Set build options |
| std::set<std::string> build_opts; |
| |
| int16_t mat[matrix_size * matrix_size] = { 0 }; |
| memcpy(mat, conv, matrix_size * sizeof(int16_t)); |
| |
| for(unsigned int j = 0; j < matrix_size * matrix_size; j++) |
| { |
| build_opts.insert("-DMAT" + support::cpp11::to_string(j) + "=" + support::cpp11::to_string(mat[j])); |
| } |
| |
| build_opts.insert("-DSCALE=0"); |
| |
| build_opts.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(output->info()->data_type())); |
| |
| // Create kernel |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("convolution_separable1x" + support::cpp11::to_string(matrix_size) + "_static", build_opts)); |
| |
| // Configure kernel window |
| constexpr unsigned int num_elems_processed_per_iteration = 8; |
| constexpr unsigned int num_elems_read_per_iteration = 16; |
| constexpr unsigned int num_elems_written_per_iteration = 8; |
| |
| Window win = calculate_max_window_horizontal(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size()); |
| |
| AccessWindowHorizontal input_access(input->info(), -border_size().left, num_elems_read_per_iteration); |
| AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); |
| |
| update_window_and_padding(win, input_access, output_access); |
| |
| output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size()); |
| |
| ICLKernel::configure(win); |
| } |
| |
| template <unsigned int matrix_size> |
| BorderSize CLSeparableConvolutionVertKernel<matrix_size>::border_size() const |
| { |
| return BorderSize(matrix_size / 2, 0); |
| } |
| |
| template <unsigned int matrix_size> |
| void CLSeparableConvolutionVertKernel<matrix_size>::configure(const ICLTensor *input, ICLTensor *output, |
| const int16_t *conv, uint32_t scale, bool border_undefined, DataType data_type) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U16, DataType::S16, DataType::S32); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16); |
| ARM_COMPUTE_ERROR_ON((matrix_size != 5) && (matrix_size != 7) && (matrix_size != 9)); |
| ARM_COMPUTE_ERROR_ON(scale == 0); |
| |
| _input = input; |
| _output = output; |
| |
| std::set<std::string> build_opts; |
| |
| int16_t mat[matrix_size * matrix_size] = { 0 }; |
| memcpy(mat + matrix_size, conv, matrix_size * sizeof(int16_t)); |
| |
| for(unsigned int j = 0; j < matrix_size * matrix_size; j++) |
| { |
| build_opts.insert("-DMAT" + support::cpp11::to_string(j) + "=" + support::cpp11::to_string(mat[j])); |
| } |
| |
| build_opts.insert("-DSCALE=" + support::cpp11::to_string(scale)); |
| |
| build_opts.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); |
| |
| build_opts.insert("-DCOMPUTE_TYPE=" + get_cl_type_from_data_type(data_type)); |
| |
| std::stringstream out_type; |
| out_type << "-DDATA_TYPE_OUT=" << get_cl_type_from_data_type(output->info()->data_type()); |
| build_opts.insert(out_type.str()); |
| |
| // Create kernel |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("convolution_separable" + support::cpp11::to_string(matrix_size) + "x1_static", build_opts)); |
| |
| // Configure kernel window |
| constexpr unsigned int num_elems_processed_per_iteration = 8; |
| constexpr unsigned int num_elems_written_per_iteration = 8; |
| constexpr unsigned int num_elems_read_per_iteration = 8; |
| constexpr unsigned int num_rows_read_per_iteration = matrix_size; |
| |
| Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size()); |
| |
| AccessWindowRectangle input_access(input->info(), 0, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration); |
| AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); |
| |
| update_window_and_padding(win, input_access, output_access); |
| |
| output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size()); |
| |
| ICLKernel::configure(win); |
| } |
| |
| /****************************************************************************************\ |
| * Rectangle Convolution * |
| \****************************************************************************************/ |
| |
| CLConvolutionRectangleKernel::CLConvolutionRectangleKernel() |
| : _border_size(0), _input(nullptr), _output(nullptr) |
| { |
| } |
| |
| BorderSize CLConvolutionRectangleKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void CLConvolutionRectangleKernel::configure(const ICLTensor *input, ICLTensor *output, const int16_t *conv, uint32_t width, uint32_t height, uint32_t scale, bool border_undefined) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16); |
| ARM_COMPUTE_ERROR_ON(nullptr == conv); |
| ARM_COMPUTE_ERROR_ON(3 != width && 5 != width && 7 != width && 9 != width); |
| ARM_COMPUTE_ERROR_ON(3 != height && 5 != height && 7 != height && 9 != height); |
| ARM_COMPUTE_ERROR_ON(0 == scale); |
| |
| _input = input; |
| _output = output; |
| _border_size = BorderSize(height / 2, width / 2); |
| |
| std::set<std::string> options; |
| |
| std::stringstream output_type; |
| output_type << "-DDATA_TYPE_OUT=" << get_cl_type_from_data_type(output->info()->data_type()); |
| options.insert(output_type.str()); |
| |
| uint32_t matrix_size = width * height; |
| |
| int16_t mat[MAX_MATRIX_SIZE] = { 0 }; |
| |
| memcpy(mat, conv, matrix_size * sizeof(int16_t)); |
| |
| for(unsigned int j = 0; j < MAX_MATRIX_SIZE; j++) |
| { |
| options.insert("-DMAT" + support::cpp11::to_string(j) + "=" + support::cpp11::to_string(mat[j])); |
| } |
| |
| options.insert("-DSCALE=" + support::cpp11::to_string(scale)); |
| |
| DataType data_type = data_type_for_convolution_matrix(conv, matrix_size); |
| options.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); |
| |
| options.insert("-DMATRIX_WIDTH=" + support::cpp11::to_string(width)); |
| options.insert("-DMATRIX_HEIGHT=" + support::cpp11::to_string(height)); |
| |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("convolution_rectangle", options)); |
| |
| // Configure kernel window |
| constexpr unsigned int num_elems_processed_per_iteration = 8; |
| constexpr unsigned int num_elems_read_per_iteration = 16; |
| constexpr unsigned int num_elems_written_per_iteration = 8; |
| const unsigned int num_rows_read_per_iteration = height; |
| |
| Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size()); |
| |
| AccessWindowRectangle input_access(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration); |
| AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); |
| |
| update_window_and_padding(win, input_access, output_access); |
| |
| output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size()); |
| |
| ICLKernel::configure(win); |
| } |
| |
| void CLConvolutionRectangleKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| Window slice = window.first_slice_window_2D(); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_2D_tensor_argument(idx, _input, slice); |
| add_2D_tensor_argument(idx, _output, slice); |
| enqueue(queue, *this, slice); |
| } |
| while(window.slide_window_slice_2D(slice)); |
| } |
| |
| template class arm_compute::CLConvolutionKernel<3>; |
| template class arm_compute::CLConvolutionKernel<5>; |
| template class arm_compute::CLConvolutionKernel<7>; |
| template class arm_compute::CLConvolutionKernel<9>; |
| template class arm_compute::CLSeparableConvolutionVertKernel<5>; |
| template class arm_compute::CLSeparableConvolutionVertKernel<7>; |
| template class arm_compute::CLSeparableConvolutionVertKernel<9>; |
| template class arm_compute::CLSeparableConvolutionHorKernel<5>; |
| template class arm_compute::CLSeparableConvolutionHorKernel<7>; |
| template class arm_compute::CLSeparableConvolutionHorKernel<9>; |