src/core/CL/kernels/CLIm2ColKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * 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/CLIm2ColKernel.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/CL/OpenCL.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Validate.h"

 #include <cmath>
 #include <tuple>

 using namespace arm_compute;

 CLIm2ColKernel::CLIm2ColKernel()
     : _input(nullptr), _output(nullptr), _convolved_dims(), _conv_info(), _kernel_size(0), _num_elems_processed_per_iteration(1), _run_func(nullptr)
 {
 }

 void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, std::pair<unsigned int, unsigned int> convolved_dims, const PadStrideInfo &conv_info, bool has_bias)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);

     _input  = input;
     _output = output;

     // Create kernel
     std::set<std::string> build_opts;
     build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
     build_opts.emplace((has_bias ? "-DHAS_BIAS" : ""));

     int pad_x    = 0;
     int pad_y    = 0;
     int stride_x = 0;
     int stride_y = 0;
     std::tie(pad_x, pad_y)       = conv_info.pad();
     std::tie(stride_x, stride_y) = conv_info.stride();

     const bool run_img2col_reduced = (output->info()->dimension(0) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
                                      && (std::equal(input->info()->tensor_shape().cbegin() + 3,
                                                     input->info()->tensor_shape().cend(),
                                                     output->info()->tensor_shape().cbegin() + 1))
                                      && ((stride_x == 1) && (stride_y == 1) && (pad_x == 0) && (pad_y == 0));

     if(!run_img2col_reduced)
     {
         _convolved_dims                    = convolved_dims;
         _conv_info                         = conv_info;
         _kernel_size                       = std::sqrt((output->info()->dimension(0) - (has_bias ? 1 : 0)) / input->info()->dimension(2));
         _num_elems_processed_per_iteration = output->info()->dimension(0);

         _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("im2col_generic", build_opts));

         // Create static kernel arguments
         const cl_int2 input_dims =
         {
             {
                 static_cast<cl_int>(input->info()->dimension(0)),
                 static_cast<cl_int>(input->info()->dimension(1)),
             }
         };
         const cl_int2 strides =
         {
             {
                 stride_x,
                 stride_y,
             }
         };
         const cl_int2 paddings =
         {
             {
                 pad_x,
                 pad_y,
             }
         };

         // Set static kernel arguments
         unsigned int idx = num_arguments_per_2D_tensor() + num_arguments_per_3D_tensor();
         _kernel.setArg<cl_int>(idx++, _kernel_size);
         _kernel.setArg<cl_int>(idx++, input->info()->dimension(2) /* depth */);
         _kernel.setArg<cl_int>(idx++, _convolved_dims.first /* output width */);
         _kernel.setArg<cl_int2>(idx++, input_dims);
         _kernel.setArg<cl_int2>(idx++, strides);
         _kernel.setArg<cl_int2>(idx++, paddings);

         _run_func = &CLIm2ColKernel::run_generic;
     }
     else
     {
         _num_elems_processed_per_iteration = 1;
         _kernel                            = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("im2col_reduced", build_opts));
         _run_func                          = &CLIm2ColKernel::run_reduced;
     }

     // Configure  kernel window
     Window win = calculate_max_window(*input->info(), Steps());
     // The CLIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped
     output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
     ICLKernel::configure(win);
 }

 void CLIm2ColKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON(_run_func == nullptr);
     (this->*_run_func)(window, queue);
 }

 void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);

     int pad_x    = 0;
     int pad_y    = 0;
     int stride_x = 0;
     int stride_y = 0;
     std::tie(pad_x, pad_y)       = _conv_info.pad();
     std::tie(stride_x, stride_y) = _conv_info.stride();

     // Get initial windows
     Window slice     = window.first_slice_window_3D();
     Window slice_in  = window.first_slice_window_3D();
     Window slice_out = window.first_slice_window_3D();

     // Setup slice
     slice.set(Window::DimX, Window::Dimension(0, static_cast<int>(_convolved_dims.first), 1));
     slice.set(Window::DimY, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
     slice.set(Window::DimZ, Window::Dimension(0, 1, 1));

     // Setup input slice
     // The first three dimensions of the input are increased by the inner loops
     slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
     slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
     slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));

     // Setup output slice
     slice_out.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _num_elems_processed_per_iteration));
     slice_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 1));
     slice_out.set(Window::DimZ, Window::Dimension(0, 1, 1));

     do
     {
         // Set inputs
         unsigned int idx = 0;
         add_3D_tensor_argument(idx, _input, slice_in);
         add_2D_tensor_argument(idx, _output, slice_out);
         enqueue(queue, *this, slice);
     }
     while(window.slide_window_slice_3D(slice) && window.slide_window_slice_3D(slice_out) && window.slide_window_slice_3D(slice_in));
 }

 void CLIm2ColKernel::run_reduced(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);

     Window out_window;
     out_window.use_tensor_dimensions(_output->info());

     Window out_slice = out_window.first_slice_window_1D();
     Window in_slice  = window.first_slice_window_3D();

     // Run kernel
     do
     {
         // Set arguments
         unsigned int idx = 0;
         add_3D_tensor_argument(idx, _input, in_slice);
         add_1D_tensor_argument(idx, _output, out_slice);

         _kernel.setArg<cl_uint>(idx++, _input->info()->dimension(0));
         _kernel.setArg<cl_uint>(idx++, _input->info()->dimension(1));
         enqueue(queue, *this, in_slice);
     }
     while(window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice));
 }
	/*
	* 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/CLIm2ColKernel.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/CL/OpenCL.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Validate.h"

	#include <cmath>
	#include <tuple>

	using namespace arm_compute;

	CLIm2ColKernel::CLIm2ColKernel()
	: _input(nullptr), _output(nullptr), _convolved_dims(), _conv_info(), _kernel_size(0), _num_elems_processed_per_iteration(1), _run_func(nullptr)
	{
	}

	void CLIm2ColKernel::configure(const ICLTensor input, ICLTensor output, std::pair<unsigned int, unsigned int> convolved_dims, const PadStrideInfo &conv_info, bool has_bias)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);

	_input = input;
	_output = output;

	// Create kernel
	std::set<std::string> build_opts;
	build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
	build_opts.emplace((has_bias ? "-DHAS_BIAS" : ""));

	int pad_x = 0;
	int pad_y = 0;
	int stride_x = 0;
	int stride_y = 0;
	std::tie(pad_x, pad_y) = conv_info.pad();
	std::tie(stride_x, stride_y) = conv_info.stride();

	const bool run_img2col_reduced = (output->info()->dimension(0) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
	&& (std::equal(input->info()->tensor_shape().cbegin() + 3,
	input->info()->tensor_shape().cend(),
	output->info()->tensor_shape().cbegin() + 1))
	&& ((stride_x == 1) && (stride_y == 1) && (pad_x == 0) && (pad_y == 0));

	if(!run_img2col_reduced)
	{
	_convolved_dims = convolved_dims;
	_conv_info = conv_info;
	_kernel_size = std::sqrt((output->info()->dimension(0) - (has_bias ? 1 : 0)) / input->info()->dimension(2));
	_num_elems_processed_per_iteration = output->info()->dimension(0);

	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("im2col_generic", build_opts));

	// Create static kernel arguments
	const cl_int2 input_dims =
	{
	{
	static_cast<cl_int>(input->info()->dimension(0)),
	static_cast<cl_int>(input->info()->dimension(1)),
	}
	};
	const cl_int2 strides =
	{
	{
	stride_x,
	stride_y,
	}
	};
	const cl_int2 paddings =
	{
	{
	pad_x,
	pad_y,
	}
	};

	// Set static kernel arguments
	unsigned int idx = num_arguments_per_2D_tensor() + num_arguments_per_3D_tensor();
	_kernel.setArg<cl_int>(idx++, _kernel_size);
	_kernel.setArg<cl_int>(idx++, input->info()->dimension(2) /* depth */);
	_kernel.setArg<cl_int>(idx++, _convolved_dims.first /* output width */);
	_kernel.setArg<cl_int2>(idx++, input_dims);
	_kernel.setArg<cl_int2>(idx++, strides);
	_kernel.setArg<cl_int2>(idx++, paddings);

	_run_func = &CLIm2ColKernel::run_generic;
	}
	else
	{
	_num_elems_processed_per_iteration = 1;
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("im2col_reduced", build_opts));
	_run_func = &CLIm2ColKernel::run_reduced;
	}

	// Configure kernel window
	Window win = calculate_max_window(*input->info(), Steps());
	// The CLIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped
	output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
	ICLKernel::configure(win);
	}

	void CLIm2ColKernel::run(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON(_run_func == nullptr);
	(this->*_run_func)(window, queue);
	}

	void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);

	int pad_x = 0;
	int pad_y = 0;
	int stride_x = 0;
	int stride_y = 0;
	std::tie(pad_x, pad_y) = _conv_info.pad();
	std::tie(stride_x, stride_y) = _conv_info.stride();

	// Get initial windows
	Window slice = window.first_slice_window_3D();
	Window slice_in = window.first_slice_window_3D();
	Window slice_out = window.first_slice_window_3D();

	// Setup slice
	slice.set(Window::DimX, Window::Dimension(0, static_cast<int>(_convolved_dims.first), 1));
	slice.set(Window::DimY, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
	slice.set(Window::DimZ, Window::Dimension(0, 1, 1));

	// Setup input slice
	// The first three dimensions of the input are increased by the inner loops
	slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
	slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
	slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));

	// Setup output slice
	slice_out.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _num_elems_processed_per_iteration));
	slice_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 1));
	slice_out.set(Window::DimZ, Window::Dimension(0, 1, 1));

	do
	{
	// Set inputs
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, _input, slice_in);
	add_2D_tensor_argument(idx, _output, slice_out);
	enqueue(queue, *this, slice);
	}
	while(window.slide_window_slice_3D(slice) && window.slide_window_slice_3D(slice_out) && window.slide_window_slice_3D(slice_in));
	}

	void CLIm2ColKernel::run_reduced(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);

	Window out_window;
	out_window.use_tensor_dimensions(_output->info());

	Window out_slice = out_window.first_slice_window_1D();
	Window in_slice = window.first_slice_window_3D();

	// Run kernel
	do
	{
	// Set arguments
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, _input, in_slice);
	add_1D_tensor_argument(idx, _output, out_slice);

	_kernel.setArg<cl_uint>(idx++, _input->info()->dimension(0));
	_kernel.setArg<cl_uint>(idx++, _input->info()->dimension(1));
	enqueue(queue, *this, in_slice);
	}
	while(window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice));
	}