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

 /*
  * Copyright (c) 2019-2020 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/CLArgMinMaxLayerKernel.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/TensorInfo.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "src/core/AccessWindowStatic.h"
 #include "src/core/CL/CLValidate.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"

 #include "support/StringSupport.h"

 namespace arm_compute
 {
 namespace
 {
 constexpr unsigned int vector_size = 16;

 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *prev_output, const ITensorInfo *output, unsigned int axis, ReductionOperation op)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S32, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(op != ReductionOperation::ARG_IDX_MAX && op != ReductionOperation::ARG_IDX_MIN, "Only ARG_IDX_MAX and ARG_IDX_MIN are supported");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis");

     if(output->total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32, DataType::S32);
     }
     if(prev_output != nullptr && prev_output->total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(prev_output, 1, DataType::U32, DataType::S32);
         if(output->total_size() != 0)
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(prev_output, output);
         }
     }

     return Status{};
 }

 std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *prev_output, ITensorInfo *output, unsigned int axis, ReductionOperation op)
 {
     ARM_COMPUTE_UNUSED(op);
     // Output tensor auto initialization if not yet initialized
     TensorShape output_shape{ input->tensor_shape() };
     output_shape.set(axis, 1);
     DataType output_data_type = DataType::S32;
     auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true));

     Window win            = calculate_max_window((prev_output != nullptr) ? (*prev_output) : (*input), Steps(vector_size));
     bool   window_changed = false;

     switch(axis)
     {
         case 0:
         {
             ITensorInfo           *input_tensor_access = prev_output != nullptr ? prev_output : input;
             AccessWindowStatic     input_access(input_tensor_access, 0, 0, static_cast<int>(input_tensor_access->dimension(0)), 1);
             AccessWindowHorizontal output_access(output, 0, 1);
             window_changed = update_window_and_padding(win, input_access, output_access);
             output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
         }
         break;
         case 1:
         case 2:
         case 3:
         {
             AccessWindowHorizontal input_access(input, 0, vector_size);
             AccessWindowHorizontal output_access(output, 0, vector_size);
             window_changed = update_window_and_padding(win, input_access, output_access);
             output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
         }
         break;
         default:
             ARM_COMPUTE_ERROR("Not supported");
     }

     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
     return std::make_tuple(err, win);
 }
 } // namespace

 CLArgMinMaxLayerKernel::CLArgMinMaxLayerKernel()
     : _input(nullptr), _prev_output(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::ARG_IDX_MAX)
 {
 }

 void CLArgMinMaxLayerKernel::configure(const ICLTensor *input, const ICLTensor *prev_output, ICLTensor *output, unsigned int axis, ReductionOperation op)
 {
     configure(CLKernelLibrary::get().get_compile_context(), input, prev_output, output, axis, op);
 }

 void CLArgMinMaxLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *prev_output, ICLTensor *output, unsigned int axis, ReductionOperation op)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (prev_output != nullptr) ? prev_output->info() : nullptr, output->info(), axis, op));
     auto win_config = validate_and_configure_window(input->info(), (prev_output != nullptr) ? prev_output->info() : nullptr, output->info(), axis, op);
     ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));

     _input          = input;
     _prev_output    = prev_output;
     _output         = output;
     _reduction_axis = axis;
     _op             = op;

     // Set build options
     CLBuildOptions build_opts;

     build_opts.add_option_if(_prev_output != nullptr, "-DPREV_OUTPUT");
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
     build_opts.add_option_if(is_data_type_float(input->info()->data_type()), "-DFLOAT_DATA_TYPE");
     build_opts.add_option_if_else(op == ReductionOperation::ARG_IDX_MAX, "-DARG_MAX", "-DARG_MIN");
     build_opts.add_option("-DDATA_TYPE_OUTPUT=" + get_cl_type_from_data_type(output->info()->data_type()));
     build_opts.add_option("-DDATA_TYPE_SELECT=" + get_cl_signed_type_from_element_size(input->info()->element_size()));

     // Create kernel
     cl::NDRange lws_hint = CLKernelLibrary::get().default_ndrange();
     std::string kernel_axis_name;
     switch(axis)
     {
         case 0:
         {
             const ICLTensor *input_for_width = prev_output != nullptr ? _prev_output : _input;
             build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(input_for_width->info()->dimension(0)));

             kernel_axis_name = "x";
             lws_hint         = create_lws_hint_parallel_implementations(input_for_width->info()->dimension(0), vector_size);
         }
         break;
         case 1:
             build_opts.add_option("-DHEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));
             kernel_axis_name = "y";
             break;
         case 2:
             build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
             kernel_axis_name = "z";
             break;
         case 3:
             build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
             build_opts.add_option("-DBATCH=" + support::cpp11::to_string(input->info()->dimension(3)));
             kernel_axis_name = "w";
             break;
         default:
             ARM_COMPUTE_ERROR("Not supported");
     }
     _kernel = create_kernel(compile_context, "arg_min_max_" + kernel_axis_name, build_opts.options());

     // Configure kernel window
     ICLKernel::configure_internal(std::get<1>(win_config), lws_hint);
 }

 Status CLArgMinMaxLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *prev_output, const ITensorInfo *output, unsigned int axis, ReductionOperation op)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, prev_output, output, axis, op));
     ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), (prev_output != nullptr) ? prev_output->clone().get() : nullptr, output->clone().get(), axis, op)));
     return Status{};
 }

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

     switch(_reduction_axis)
     {
         case 0:
         {
             // Set out window
             Window out_window(window);
             out_window.set(Window::DimX, Window::Dimension(0, 0, 0));

             // Get first input and output slices
             Window in_slice  = window.first_slice_window_2D();
             Window out_slice = out_window.first_slice_window_2D();

             // Reshape window
             const unsigned int num_tensors = _prev_output != nullptr ? 3 : 2;

             // Set local sums buffer
             unsigned int local_res_size = lws_hint()[0] * _output->info()->element_size();
             _kernel.setArg(num_arguments_per_2D_tensor() * num_tensors, local_res_size, nullptr);
             do
             {
                 unsigned int idx = 0;
                 add_2D_tensor_argument(idx, _input, in_slice);
                 if(_prev_output != nullptr)
                 {
                     add_2D_tensor_argument(idx, _prev_output, in_slice);
                 }
                 add_2D_tensor_argument(idx, _output, out_slice);
                 enqueue(queue, *this, in_slice, lws_hint());
             }
             while(window.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
         }
         break;
         case 1:
         {
             // Get first input and output slices
             Window window_in{ window };
             window_in.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), _input->info()->dimension(1)));
             Window in_slice  = window_in.first_slice_window_2D();
             Window out_slice = window.first_slice_window_2D();

             do
             {
                 unsigned int idx = 0;
                 add_2D_tensor_argument(idx, _input, in_slice);
                 add_2D_tensor_argument(idx, _output, out_slice);
                 enqueue(queue, *this, in_slice, lws_hint());
             }
             while(window_in.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
         }
         break;
         case 2:
         {
             // Get first input and output slices
             Window window_in{ window };
             window_in.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), _input->info()->dimension(2)));
             Window in_slice  = window_in.first_slice_window_3D();
             Window out_slice = window.first_slice_window_3D();

             do
             {
                 unsigned int idx = 0;
                 add_3D_tensor_argument(idx, _input, in_slice);
                 add_3D_tensor_argument(idx, _output, out_slice);
                 enqueue(queue, *this, in_slice, lws_hint());
             }
             while(window_in.slide_window_slice_3D(in_slice) && window.slide_window_slice_3D(out_slice));
         }
         break;
         case 3:
         {
             // Get first input and output slices
             Window window_in{ window };
             window_in.set(3, Window::Dimension(0, 1, 1));
             Window in_slice  = window_in.first_slice_window_4D();
             Window out_slice = window.first_slice_window_4D();

             do
             {
                 unsigned int idx = 0;
                 add_4D_tensor_argument(idx, _input, in_slice);
                 add_4D_tensor_argument(idx, _output, out_slice);
                 enqueue(queue, *this, in_slice, lws_hint());
             }
             while(window_in.slide_window_slice_4D(in_slice) && window.slide_window_slice_4D(out_slice));
         }
         break;
         default:
             ARM_COMPUTE_ERROR("Not supported");
     }
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2019-2020 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/CLArgMinMaxLayerKernel.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/TensorInfo.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "src/core/AccessWindowStatic.h"
	#include "src/core/CL/CLValidate.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"

	#include "support/StringSupport.h"

	namespace arm_compute
	{
	namespace
	{
	constexpr unsigned int vector_size = 16;

	Status validate_arguments(const ITensorInfo input, const ITensorInfo prev_output, const ITensorInfo *output, unsigned int axis, ReductionOperation op)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S32, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(op != ReductionOperation::ARG_IDX_MAX && op != ReductionOperation::ARG_IDX_MIN, "Only ARG_IDX_MAX and ARG_IDX_MIN are supported");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis");

	if(output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32, DataType::S32);
	}
	if(prev_output != nullptr && prev_output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(prev_output, 1, DataType::U32, DataType::S32);
	if(output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(prev_output, output);
	}
	}

	return Status{};
	}

	std::tuple<Status, Window> validate_and_configure_window(ITensorInfo input, ITensorInfo prev_output, ITensorInfo *output, unsigned int axis, ReductionOperation op)
	{
	ARM_COMPUTE_UNUSED(op);
	// Output tensor auto initialization if not yet initialized
	TensorShape output_shape{ input->tensor_shape() };
	output_shape.set(axis, 1);
	DataType output_data_type = DataType::S32;
	auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true));

	Window win = calculate_max_window((prev_output != nullptr) ? (prev_output) : (input), Steps(vector_size));
	bool window_changed = false;

	switch(axis)
	{
	case 0:
	{
	ITensorInfo *input_tensor_access = prev_output != nullptr ? prev_output : input;
	AccessWindowStatic input_access(input_tensor_access, 0, 0, static_cast<int>(input_tensor_access->dimension(0)), 1);
	AccessWindowHorizontal output_access(output, 0, 1);
	window_changed = update_window_and_padding(win, input_access, output_access);
	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
	}
	break;
	case 1:
	case 2:
	case 3:
	{
	AccessWindowHorizontal input_access(input, 0, vector_size);
	AccessWindowHorizontal output_access(output, 0, vector_size);
	window_changed = update_window_and_padding(win, input_access, output_access);
	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
	}
	break;
	default:
	ARM_COMPUTE_ERROR("Not supported");
	}

	Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
	return std::make_tuple(err, win);
	}
	} // namespace

	CLArgMinMaxLayerKernel::CLArgMinMaxLayerKernel()
	: _input(nullptr), _prev_output(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::ARG_IDX_MAX)
	{
	}

	void CLArgMinMaxLayerKernel::configure(const ICLTensor input, const ICLTensor prev_output, ICLTensor *output, unsigned int axis, ReductionOperation op)
	{
	configure(CLKernelLibrary::get().get_compile_context(), input, prev_output, output, axis, op);
	}

	void CLArgMinMaxLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor input, const ICLTensor prev_output, ICLTensor *output, unsigned int axis, ReductionOperation op)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (prev_output != nullptr) ? prev_output->info() : nullptr, output->info(), axis, op));
	auto win_config = validate_and_configure_window(input->info(), (prev_output != nullptr) ? prev_output->info() : nullptr, output->info(), axis, op);
	ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));

	_input = input;
	_prev_output = prev_output;
	_output = output;
	_reduction_axis = axis;
	_op = op;

	// Set build options
	CLBuildOptions build_opts;

	build_opts.add_option_if(_prev_output != nullptr, "-DPREV_OUTPUT");
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
	build_opts.add_option_if(is_data_type_float(input->info()->data_type()), "-DFLOAT_DATA_TYPE");
	build_opts.add_option_if_else(op == ReductionOperation::ARG_IDX_MAX, "-DARG_MAX", "-DARG_MIN");
	build_opts.add_option("-DDATA_TYPE_OUTPUT=" + get_cl_type_from_data_type(output->info()->data_type()));
	build_opts.add_option("-DDATA_TYPE_SELECT=" + get_cl_signed_type_from_element_size(input->info()->element_size()));

	// Create kernel
	cl::NDRange lws_hint = CLKernelLibrary::get().default_ndrange();
	std::string kernel_axis_name;
	switch(axis)
	{
	case 0:
	{
	const ICLTensor *input_for_width = prev_output != nullptr ? _prev_output : _input;
	build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(input_for_width->info()->dimension(0)));

	kernel_axis_name = "x";
	lws_hint = create_lws_hint_parallel_implementations(input_for_width->info()->dimension(0), vector_size);
	}
	break;
	case 1:
	build_opts.add_option("-DHEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));
	kernel_axis_name = "y";
	break;
	case 2:
	build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
	kernel_axis_name = "z";
	break;
	case 3:
	build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
	build_opts.add_option("-DBATCH=" + support::cpp11::to_string(input->info()->dimension(3)));
	kernel_axis_name = "w";
	break;
	default:
	ARM_COMPUTE_ERROR("Not supported");
	}
	_kernel = create_kernel(compile_context, "arg_min_max_" + kernel_axis_name, build_opts.options());

	// Configure kernel window
	ICLKernel::configure_internal(std::get<1>(win_config), lws_hint);
	}

	Status CLArgMinMaxLayerKernel::validate(const ITensorInfo input, const ITensorInfo prev_output, const ITensorInfo *output, unsigned int axis, ReductionOperation op)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, prev_output, output, axis, op));
	ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), (prev_output != nullptr) ? prev_output->clone().get() : nullptr, output->clone().get(), axis, op)));
	return Status{};
	}

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

	switch(_reduction_axis)
	{
	case 0:
	{
	// Set out window
	Window out_window(window);
	out_window.set(Window::DimX, Window::Dimension(0, 0, 0));

	// Get first input and output slices
	Window in_slice = window.first_slice_window_2D();
	Window out_slice = out_window.first_slice_window_2D();

	// Reshape window
	const unsigned int num_tensors = _prev_output != nullptr ? 3 : 2;

	// Set local sums buffer
	unsigned int local_res_size = lws_hint()[0] * _output->info()->element_size();
	_kernel.setArg(num_arguments_per_2D_tensor() * num_tensors, local_res_size, nullptr);
	do
	{
	unsigned int idx = 0;
	add_2D_tensor_argument(idx, _input, in_slice);
	if(_prev_output != nullptr)
	{
	add_2D_tensor_argument(idx, _prev_output, in_slice);
	}
	add_2D_tensor_argument(idx, _output, out_slice);
	enqueue(queue, *this, in_slice, lws_hint());
	}
	while(window.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
	}
	break;
	case 1:
	{
	// Get first input and output slices
	Window window_in{ window };
	window_in.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), _input->info()->dimension(1)));
	Window in_slice = window_in.first_slice_window_2D();
	Window out_slice = window.first_slice_window_2D();

	do
	{
	unsigned int idx = 0;
	add_2D_tensor_argument(idx, _input, in_slice);
	add_2D_tensor_argument(idx, _output, out_slice);
	enqueue(queue, *this, in_slice, lws_hint());
	}
	while(window_in.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
	}
	break;
	case 2:
	{
	// Get first input and output slices
	Window window_in{ window };
	window_in.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), _input->info()->dimension(2)));
	Window in_slice = window_in.first_slice_window_3D();
	Window out_slice = window.first_slice_window_3D();

	do
	{
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, _input, in_slice);
	add_3D_tensor_argument(idx, _output, out_slice);
	enqueue(queue, *this, in_slice, lws_hint());
	}
	while(window_in.slide_window_slice_3D(in_slice) && window.slide_window_slice_3D(out_slice));
	}
	break;
	case 3:
	{
	// Get first input and output slices
	Window window_in{ window };
	window_in.set(3, Window::Dimension(0, 1, 1));
	Window in_slice = window_in.first_slice_window_4D();
	Window out_slice = window.first_slice_window_4D();

	do
	{
	unsigned int idx = 0;
	add_4D_tensor_argument(idx, _input, in_slice);
	add_4D_tensor_argument(idx, _output, out_slice);
	enqueue(queue, *this, in_slice, lws_hint());
	}
	while(window_in.slide_window_slice_4D(in_slice) && window.slide_window_slice_4D(out_slice));
	}
	break;
	default:
	ARM_COMPUTE_ERROR("Not supported");
	}
	}
	} // namespace arm_compute