src/core/CL/kernels/CLMinMaxLocationKernel.cpp - platform/external/ComputeLibrary - Git at Google

 /*
  * Copyright (c) 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/CLMinMaxLocationKernel.h"

 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"

 #include <climits>

 namespace arm_compute
 {
 inline int32_t FloatFlip(float val)
 {
     static_assert(sizeof(float) == sizeof(int32_t), "Float must be same size as int32_t");
     int32_t int_val = 0;

     memcpy(&int_val, &val, sizeof(float));
     int_val = (int_val >= 0) ? int_val : int_val ^ 0x7FFFFFFF;
     return int_val;
 }

 inline float IFloatFlip(int32_t val)
 {
     static_assert(sizeof(float) == sizeof(int32_t), "Float must be same size as int32_t");
     float flt_val = 0.f;

     val = (val >= 0) ? val : val ^ 0x7FFFFFFF;
     memcpy(&flt_val, &val, sizeof(float));
     return flt_val;
 }

 CLMinMaxKernel::CLMinMaxKernel()
     : _input(nullptr), _min_max(), _data_type_max_min()
 {
 }

 void CLMinMaxKernel::configure(const ICLImage *input, cl::Buffer *min_max)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
     ARM_COMPUTE_ERROR_ON(min_max == nullptr);

     _input                                               = input;
     _min_max                                             = min_max;
     const unsigned int num_elems_processed_per_iteration = input->info()->dimension(0);

     switch(input->info()->data_type())
     {
         case DataType::U8:
             _data_type_max_min[0] = UCHAR_MAX;
             _data_type_max_min[1] = 0;
             break;
         case DataType::S16:
             _data_type_max_min[0] = SHRT_MAX;
             _data_type_max_min[1] = SHRT_MIN;
             break;
         case DataType::F32:
             _data_type_max_min[0] = FloatFlip(std::numeric_limits<float>::max());
             _data_type_max_min[1] = FloatFlip(std::numeric_limits<float>::lowest());
             break;
         default:
             ARM_COMPUTE_ERROR("You called with the wrong image data types");
     }

     // Set kernel build options
     std::set<std::string> build_opts{ "-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()) };

     if(num_elems_processed_per_iteration % max_cl_vector_width != 0)
     {
         build_opts.emplace("-DNON_MULTIPLE_OF_16");
     }

     if(input->info()->data_type() == DataType::F32)
     {
         build_opts.emplace("-DDATA_TYPE_MAX=" + support::cpp11::to_string(std::numeric_limits<float>::max()));
         build_opts.emplace("-DDATA_TYPE_MIN=" + support::cpp11::to_string(std::numeric_limits<float>::lowest()));
         build_opts.emplace("-DIS_DATA_TYPE_FLOAT");
     }
     else
     {
         build_opts.emplace("-DDATA_TYPE_MAX=" + support::cpp11::to_string(_data_type_max_min[0]));
         build_opts.emplace("-DDATA_TYPE_MIN=" + support::cpp11::to_string(_data_type_max_min[1]));
     }

     // Create kernel
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("minmax", build_opts));

     // Set fixed arguments
     unsigned int idx = num_arguments_per_2D_tensor(); //Skip the input and output parameters
     _kernel.setArg(idx++, *_min_max);
     _kernel.setArg<cl_int>(idx++, static_cast<cl_int>(input->info()->dimension(0)));

     // Configure kernel window
     Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
     update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, ceil_to_multiple(num_elems_processed_per_iteration, 16)));
     ICLKernel::configure(win);
 }

 void CLMinMaxKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

     // Reset mininum and maximum values
     queue.enqueueWriteBuffer(*_min_max, CL_FALSE /* blocking */, 0, _data_type_max_min.size() * sizeof(int), _data_type_max_min.data());

     Window slice = window.first_slice_window_2D();
     do
     {
         unsigned int idx = 0;
         add_2D_tensor_argument(idx, _input, slice);
         enqueue(queue, *this, slice);
     }
     while(window.slide_window_slice_2D(slice));

     cl_int min = 0;
     cl_int max = 0;
     queue.enqueueReadBuffer(*_min_max, CL_TRUE /* blocking */, 0 * sizeof(cl_int), sizeof(cl_int), static_cast<int *>(&min));
     queue.enqueueReadBuffer(*_min_max, CL_TRUE /* blocking */, 1 * sizeof(cl_int), sizeof(cl_int), static_cast<int *>(&max));

     if(_input->info()->data_type() == DataType::F32)
     {
         std::array<float, 2> min_max =
         {
             {
                 IFloatFlip(min),
                 IFloatFlip(max)
             }
         };
         queue.enqueueWriteBuffer(*_min_max, CL_TRUE /* blocking */, 0, min_max.size() * sizeof(float), min_max.data());
     }
     else
     {
         std::array<int32_t, 2> min_max = { { min, max } };
         queue.enqueueWriteBuffer(*_min_max, CL_TRUE /* blocking */, 0, min_max.size() * sizeof(int32_t), min_max.data());
     }
 }

 CLMinMaxLocationKernel::CLMinMaxLocationKernel()
     : _input(nullptr), _min_max_count(nullptr)
 {
 }

 void CLMinMaxLocationKernel::configure(const ICLImage *input, cl::Buffer *min_max, cl::Buffer *min_max_count, ICLCoordinates2DArray *min_loc, ICLCoordinates2DArray *max_loc)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
     ARM_COMPUTE_ERROR_ON(min_max == nullptr);
     ARM_COMPUTE_ERROR_ON(min_max_count == nullptr && min_loc == nullptr && max_loc == nullptr);

     _input         = input;
     _min_max_count = min_max_count;

     // Set kernel build options
     std::set<std::string> build_opts;
     build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
     build_opts.emplace((min_max_count != nullptr) ? "-DCOUNT_MIN_MAX" : "");
     build_opts.emplace((min_loc != nullptr) ? "-DLOCATE_MIN" : "");
     build_opts.emplace((max_loc != nullptr) ? "-DLOCATE_MAX" : "");
     if(input->info()->data_type() == DataType::F32)
     {
         build_opts.emplace("-DIS_DATA_TYPE_FLOAT");
     }

     // Create kernel
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("minmaxloc", build_opts));

     // Set static arguments
     unsigned int idx = num_arguments_per_2D_tensor(); //Skip the input and output parameters
     _kernel.setArg(idx++, *min_max);
     _kernel.setArg(idx++, *min_max_count);
     if(min_loc != nullptr)
     {
         _kernel.setArg(idx++, min_loc->cl_buffer());
         _kernel.setArg<cl_uint>(idx++, min_loc->max_num_values());
     }
     if(max_loc != nullptr)
     {
         _kernel.setArg(idx++, max_loc->cl_buffer());
         _kernel.setArg<cl_uint>(idx++, max_loc->max_num_values());
     }

     // Configure kernel window
     constexpr unsigned int num_elems_processed_per_iteration = 1;
     Window                 win                               = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
     update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration));
     ICLKernel::configure(win);
 }

 void CLMinMaxLocationKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

     static const unsigned int zero_count = 0;
     queue.enqueueWriteBuffer(*_min_max_count, CL_FALSE, 0 * sizeof(zero_count), sizeof(zero_count), &zero_count);
     queue.enqueueWriteBuffer(*_min_max_count, CL_FALSE, 1 * sizeof(zero_count), sizeof(zero_count), &zero_count);

     Window slice = window.first_slice_window_2D();
     do
     {
         unsigned int idx = 0;
         add_2D_tensor_argument(idx, _input, slice);
         enqueue(queue, *this, slice);
     }
     while(window.slide_window_slice_2D(slice));
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 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/CLMinMaxLocationKernel.h"

	#include "arm_compute/core/CL/CLHelpers.h"
	#include "arm_compute/core/CL/CLKernelLibrary.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/Window.h"

	#include <climits>

	namespace arm_compute
	{
	inline int32_t FloatFlip(float val)
	{
	static_assert(sizeof(float) == sizeof(int32_t), "Float must be same size as int32_t");
	int32_t int_val = 0;

	memcpy(&int_val, &val, sizeof(float));
	int_val = (int_val >= 0) ? int_val : int_val ^ 0x7FFFFFFF;
	return int_val;
	}

	inline float IFloatFlip(int32_t val)
	{
	static_assert(sizeof(float) == sizeof(int32_t), "Float must be same size as int32_t");
	float flt_val = 0.f;

	val = (val >= 0) ? val : val ^ 0x7FFFFFFF;
	memcpy(&flt_val, &val, sizeof(float));
	return flt_val;
	}

	CLMinMaxKernel::CLMinMaxKernel()
	: _input(nullptr), _min_max(), _data_type_max_min()
	{
	}

	void CLMinMaxKernel::configure(const ICLImage input, cl::Buffer min_max)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16, DataType::F32);
	ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
	ARM_COMPUTE_ERROR_ON(min_max == nullptr);

	_input = input;
	_min_max = min_max;
	const unsigned int num_elems_processed_per_iteration = input->info()->dimension(0);

	switch(input->info()->data_type())
	{
	case DataType::U8:
	_data_type_max_min[0] = UCHAR_MAX;
	_data_type_max_min[1] = 0;
	break;
	case DataType::S16:
	_data_type_max_min[0] = SHRT_MAX;
	_data_type_max_min[1] = SHRT_MIN;
	break;
	case DataType::F32:
	_data_type_max_min[0] = FloatFlip(std::numeric_limits<float>::max());
	_data_type_max_min[1] = FloatFlip(std::numeric_limits<float>::lowest());
	break;
	default:
	ARM_COMPUTE_ERROR("You called with the wrong image data types");
	}

	// Set kernel build options
	std::set<std::string> build_opts{ "-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()) };

	if(num_elems_processed_per_iteration % max_cl_vector_width != 0)
	{
	build_opts.emplace("-DNON_MULTIPLE_OF_16");
	}

	if(input->info()->data_type() == DataType::F32)
	{
	build_opts.emplace("-DDATA_TYPE_MAX=" + support::cpp11::to_string(std::numeric_limits<float>::max()));
	build_opts.emplace("-DDATA_TYPE_MIN=" + support::cpp11::to_string(std::numeric_limits<float>::lowest()));
	build_opts.emplace("-DIS_DATA_TYPE_FLOAT");
	}
	else
	{
	build_opts.emplace("-DDATA_TYPE_MAX=" + support::cpp11::to_string(_data_type_max_min[0]));
	build_opts.emplace("-DDATA_TYPE_MIN=" + support::cpp11::to_string(_data_type_max_min[1]));
	}

	// Create kernel
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("minmax", build_opts));

	// Set fixed arguments
	unsigned int idx = num_arguments_per_2D_tensor(); //Skip the input and output parameters
	_kernel.setArg(idx++, *_min_max);
	_kernel.setArg<cl_int>(idx++, static_cast<cl_int>(input->info()->dimension(0)));

	// Configure kernel window
	Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
	update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, ceil_to_multiple(num_elems_processed_per_iteration, 16)));
	ICLKernel::configure(win);
	}

	void CLMinMaxKernel::run(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

	// Reset mininum and maximum values
	queue.enqueueWriteBuffer(_min_max, CL_FALSE / blocking /, 0, _data_type_max_min.size() sizeof(int), _data_type_max_min.data());

	Window slice = window.first_slice_window_2D();
	do
	{
	unsigned int idx = 0;
	add_2D_tensor_argument(idx, _input, slice);
	enqueue(queue, *this, slice);
	}
	while(window.slide_window_slice_2D(slice));

	cl_int min = 0;
	cl_int max = 0;
	queue.enqueueReadBuffer(_min_max, CL_TRUE / blocking /, 0 sizeof(cl_int), sizeof(cl_int), static_cast<int *>(&min));
	queue.enqueueReadBuffer(_min_max, CL_TRUE / blocking /, 1 sizeof(cl_int), sizeof(cl_int), static_cast<int *>(&max));

	if(_input->info()->data_type() == DataType::F32)
	{
	std::array<float, 2> min_max =
	{
	{
	IFloatFlip(min),
	IFloatFlip(max)
	}
	};
	queue.enqueueWriteBuffer(_min_max, CL_TRUE / blocking /, 0, min_max.size() sizeof(float), min_max.data());
	}
	else
	{
	std::array<int32_t, 2> min_max = { { min, max } };
	queue.enqueueWriteBuffer(_min_max, CL_TRUE / blocking /, 0, min_max.size() sizeof(int32_t), min_max.data());
	}
	}

	CLMinMaxLocationKernel::CLMinMaxLocationKernel()
	: _input(nullptr), _min_max_count(nullptr)
	{
	}

	void CLMinMaxLocationKernel::configure(const ICLImage input, cl::Buffer min_max, cl::Buffer min_max_count, ICLCoordinates2DArray min_loc, ICLCoordinates2DArray *max_loc)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16, DataType::F32);
	ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
	ARM_COMPUTE_ERROR_ON(min_max == nullptr);
	ARM_COMPUTE_ERROR_ON(min_max_count == nullptr && min_loc == nullptr && max_loc == nullptr);

	_input = input;
	_min_max_count = min_max_count;

	// Set kernel build options
	std::set<std::string> build_opts;
	build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
	build_opts.emplace((min_max_count != nullptr) ? "-DCOUNT_MIN_MAX" : "");
	build_opts.emplace((min_loc != nullptr) ? "-DLOCATE_MIN" : "");
	build_opts.emplace((max_loc != nullptr) ? "-DLOCATE_MAX" : "");
	if(input->info()->data_type() == DataType::F32)
	{
	build_opts.emplace("-DIS_DATA_TYPE_FLOAT");
	}

	// Create kernel
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("minmaxloc", build_opts));

	// Set static arguments
	unsigned int idx = num_arguments_per_2D_tensor(); //Skip the input and output parameters
	_kernel.setArg(idx++, *min_max);
	_kernel.setArg(idx++, *min_max_count);
	if(min_loc != nullptr)
	{
	_kernel.setArg(idx++, min_loc->cl_buffer());
	_kernel.setArg<cl_uint>(idx++, min_loc->max_num_values());
	}
	if(max_loc != nullptr)
	{
	_kernel.setArg(idx++, max_loc->cl_buffer());
	_kernel.setArg<cl_uint>(idx++, max_loc->max_num_values());
	}

	// Configure kernel window
	constexpr unsigned int num_elems_processed_per_iteration = 1;
	Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
	update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration));
	ICLKernel::configure(win);
	}

	void CLMinMaxLocationKernel::run(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

	static const unsigned int zero_count = 0;
	queue.enqueueWriteBuffer(_min_max_count, CL_FALSE, 0 sizeof(zero_count), sizeof(zero_count), &zero_count);
	queue.enqueueWriteBuffer(_min_max_count, CL_FALSE, 1 sizeof(zero_count), sizeof(zero_count), &zero_count);

	Window slice = window.first_slice_window_2D();
	do
	{
	unsigned int idx = 0;
	add_2D_tensor_argument(idx, _input, slice);
	enqueue(queue, *this, slice);
	}
	while(window.slide_window_slice_2D(slice));
	}
	} // namespace arm_compute