src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.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/GLES_COMPUTE/kernels/GCPoolingLayerKernel.h"

 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/GLES_COMPUTE/GCHelpers.h"
 #include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h"
 #include "arm_compute/core/GLES_COMPUTE/IGCTensor.h"
 #include "arm_compute/core/GLES_COMPUTE/OpenGLES.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 "arm_compute/core/Window.h"

 #include <set>
 #include <string>
 #include <tuple>

 using namespace arm_compute;

 GCPoolingLayerKernel::GCPoolingLayerKernel()
     : _input(nullptr), _output(nullptr), _pool_info(), _border_size(0), _num_elems_processed_per_iteration(1)
 {
 }

 BorderSize GCPoolingLayerKernel::border_size() const
 {
     return _border_size;
 }

 void GCPoolingLayerKernel::configure(const IGCTensor *input, IGCTensor *output, const PoolingLayerInfo &pool_info)
 {
     int                 pool_pad_x      = 0;
     int                 pool_pad_y      = 0;
     int                 pool_stride_x   = 0;
     int                 pool_stride_y   = 0;
     unsigned int        pooled_w        = 0;
     unsigned int        pooled_h        = 0;
     const PoolingType   pool_type       = pool_info.pool_type();
     const int           pool_size       = pool_info.pool_size();
     const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
     std::tie(pool_pad_x, pool_pad_y)       = pad_stride_info.pad();
     std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();

     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_NULLPTR(output);
     ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size || pool_pad_y >= pool_size);
     ARM_COMPUTE_ERROR_ON(pool_size > 7 && is_data_type_fixed_point(input->info()->data_type()));

     // Check output dimensions
     std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0),
                                                      input->info()->dimension(1),
                                                      pool_size,
                                                      pool_size,
                                                      pool_info.pad_stride_info());

     // Output auto initialization if not yet initialized
     {
         TensorShape output_shape{ input->info()->tensor_shape() };
         output_shape.set(0, pooled_w);
         output_shape.set(1, pooled_h);

         auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
     }

     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
     ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h));

     const int input_width  = input->info()->dimension(0);
     const int input_height = input->info()->dimension(1);

     // Set instance variables
     _input       = input;
     _output      = output;
     _pool_info   = pool_info;
     _border_size = BorderSize(pool_pad_y, pool_pad_x);

     // Set build options
     std::set<std::string> build_opts;
     build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1));
     build_opts.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(1));
     build_opts.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(1));
     if(input->info()->data_type() == DataType::F32)
     {
         build_opts.insert("#define DATA_TYPE_FP32");
     }
     else
     {
         build_opts.insert("#define DATA_TYPE_FP16");
     }
     build_opts.emplace(("#define POOL_" + string_from_pooling_type(pool_type)));
     build_opts.emplace(("#define STRIDE_X " + support::cpp11::to_string(pool_stride_x)));
     build_opts.emplace(("#define MAX_WIDTH " + support::cpp11::to_string(input->info()->dimension(0) + pool_pad_x)));
     build_opts.emplace(("#define MAX_HEIGHT " + support::cpp11::to_string(input->info()->dimension(1) + pool_pad_y)));
     build_opts.emplace(("#define STRIDE_Y " + support::cpp11::to_string(pool_stride_y)));
     build_opts.emplace(("#define PAD_X " + support::cpp11::to_string(pool_pad_x)));
     build_opts.emplace(("#define PAD_Y " + support::cpp11::to_string(pool_pad_y)));

     // Create kernel
     if((pool_size == 2) || (pool_size == 3) || (pool_size == 7))
     {
         // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenGLES kernel where
         // each thread computes 4 output elements
         const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(input->info()->data_type());

         int num_elements_read_per_iteration = (pool_size == 7) ? 8 : pool_size;

         if(input->info()->data_type() == DataType::F32)
         {
             if(is_pool3x3_stride_le3)
             {
                 // Change the number of elements processed and number of elements read per iteration for pooling 3x3 with stride less equal than 3
                 _num_elems_processed_per_iteration = 4;
                 num_elements_read_per_iteration    = pool_size * (pool_stride_x + 1);
             }
         }
         else
         {
             num_elements_read_per_iteration = pool_size;
             if(is_pool3x3_stride_le3)
             {
                 _num_elems_processed_per_iteration = 4;
             }
             else
             {
                 _num_elems_processed_per_iteration = 2;
             }
         }

         const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elements_read_per_iteration) - input_width;
         const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;

         _border_size.right  = std::max(upper_bound_w, pool_pad_x);
         _border_size.bottom = std::max(upper_bound_h, pool_pad_y);

         std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size);
         if(is_pool3x3_stride_le3)
         {
             build_opts.insert("#define POOLING_LAYER_3_OPTIMIZED");
             _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name + "_optimized", build_opts));
         }
         else
         {
             build_opts.insert("#define POOLING_LAYER_" + support::cpp11::to_string(pool_size));
             _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name, build_opts));
         }
     }
     else // Run general case
     {
         if(input->info()->data_type() == DataType::F32)
         {
             _num_elems_processed_per_iteration = 1;
         }
         else
         {
             _num_elems_processed_per_iteration = 2;
         }
         const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + pool_size) - input_width;
         const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;

         _border_size.right  = std::max(upper_bound_w, pool_pad_x);
         _border_size.bottom = std::max(upper_bound_h, pool_pad_y);

         build_opts.emplace(("#define POOL_SIZE " + support::cpp11::to_string(pool_size)));

         build_opts.insert("#define POOLING_LAYER_N");
         _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel("pooling_layer_n", build_opts));
     }

     Window win = calculate_max_window(*output->info(), Steps(_num_elems_processed_per_iteration));

     if(input->info()->data_type() == DataType::F32)
     {
         AccessWindowStatic     input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom);
         AccessWindowHorizontal output_access(output->info(), 0, _num_elems_processed_per_iteration);
         update_window_and_padding(win, input_access, output_access);
         output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
     }
     else
     {
         // Calculate output right and bottom border
         const int output_width          = output->info()->dimension(0);
         const int output_height         = output->info()->dimension(1);
         const int output_padding_right  = ceil_to_multiple(output_width, _num_elems_processed_per_iteration) - output_width;
         const int output_padding_bottom = ceil_to_multiple(output_height, 1) - output_height;
         const int input_padding_right   = ceil_to_multiple(input_width + 2 * _border_size.right, _num_elems_processed_per_iteration) - (input_width + 2 * _border_size.right);
         const int input_padding_bottom  = ceil_to_multiple(input_height + 2 * _border_size.bottom, 1) - (input_height + 2 * _border_size.bottom);

         // Configure kernel window
         AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right + input_padding_right, input_height + _border_size.bottom + input_padding_bottom);
         AccessWindowStatic output_access(output->info(), 0, 0, output_width + output_padding_right, output_height + output_padding_bottom);
         update_window_and_padding(win, input_access, output_access);
         output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
     }

     _kernel.clear_params();
     _kernel.set_shader_params_binding_point(0);

     IGCKernel::configure(win);
 }

 void GCPoolingLayerKernel::run(const Window &window)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

     unsigned int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0;
     std::tie(pool_pad_x, pool_pad_y)       = _pool_info.pad_stride_info().pad();
     std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();

     _kernel.use();

     Window window_collapsed = window.collapse_if_possible(IGCKernel::window(), Window::DimZ);
     Window slice            = window_collapsed.first_slice_window_3D();

     do
     {
         // Upsample input by pool size
         Window in_slice(slice);
         in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - pool_pad_x, in_slice.x().end() * pool_stride_x, pool_stride_x * _num_elems_processed_per_iteration));
         in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - pool_pad_y, in_slice.y().end() * pool_stride_y, pool_stride_y));

         // Set inputs
         unsigned int idx = 0;
         add_3D_tensor_argument(idx, _input, 1, in_slice);
         add_3D_tensor_argument(idx, _output, 2, slice);

         _kernel.update_shader_params();
         enqueue(*this, slice);
     }
     while(window_collapsed.slide_window_slice_3D(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/GLES_COMPUTE/kernels/GCPoolingLayerKernel.h"

	#include "arm_compute/core/AccessWindowStatic.h"
	#include "arm_compute/core/GLES_COMPUTE/GCHelpers.h"
	#include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h"
	#include "arm_compute/core/GLES_COMPUTE/IGCTensor.h"
	#include "arm_compute/core/GLES_COMPUTE/OpenGLES.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 "arm_compute/core/Window.h"

	#include <set>
	#include <string>
	#include <tuple>

	using namespace arm_compute;

	GCPoolingLayerKernel::GCPoolingLayerKernel()
	: _input(nullptr), _output(nullptr), _pool_info(), _border_size(0), _num_elems_processed_per_iteration(1)
	{
	}

	BorderSize GCPoolingLayerKernel::border_size() const
	{
	return _border_size;
	}

	void GCPoolingLayerKernel::configure(const IGCTensor input, IGCTensor output, const PoolingLayerInfo &pool_info)
	{
	int pool_pad_x = 0;
	int pool_pad_y = 0;
	int pool_stride_x = 0;
	int pool_stride_y = 0;
	unsigned int pooled_w = 0;
	unsigned int pooled_h = 0;
	const PoolingType pool_type = pool_info.pool_type();
	const int pool_size = pool_info.pool_size();
	const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
	std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad();
	std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();

	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
	ARM_COMPUTE_ERROR_ON_NULLPTR(output);
	ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size \|\| pool_pad_y >= pool_size);
	ARM_COMPUTE_ERROR_ON(pool_size > 7 && is_data_type_fixed_point(input->info()->data_type()));

	// Check output dimensions
	std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0),
	input->info()->dimension(1),
	pool_size,
	pool_size,
	pool_info.pad_stride_info());

	// Output auto initialization if not yet initialized
	{
	TensorShape output_shape{ input->info()->tensor_shape() };
	output_shape.set(0, pooled_w);
	output_shape.set(1, pooled_h);

	auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
	}

	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) \|\| (output->info()->dimension(1) != pooled_h));

	const int input_width = input->info()->dimension(0);
	const int input_height = input->info()->dimension(1);

	// Set instance variables
	_input = input;
	_output = output;
	_pool_info = pool_info;
	_border_size = BorderSize(pool_pad_y, pool_pad_x);

	// Set build options
	std::set<std::string> build_opts;
	build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1));
	build_opts.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(1));
	build_opts.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(1));
	if(input->info()->data_type() == DataType::F32)
	{
	build_opts.insert("#define DATA_TYPE_FP32");
	}
	else
	{
	build_opts.insert("#define DATA_TYPE_FP16");
	}
	build_opts.emplace(("#define POOL_" + string_from_pooling_type(pool_type)));
	build_opts.emplace(("#define STRIDE_X " + support::cpp11::to_string(pool_stride_x)));
	build_opts.emplace(("#define MAX_WIDTH " + support::cpp11::to_string(input->info()->dimension(0) + pool_pad_x)));
	build_opts.emplace(("#define MAX_HEIGHT " + support::cpp11::to_string(input->info()->dimension(1) + pool_pad_y)));
	build_opts.emplace(("#define STRIDE_Y " + support::cpp11::to_string(pool_stride_y)));
	build_opts.emplace(("#define PAD_X " + support::cpp11::to_string(pool_pad_x)));
	build_opts.emplace(("#define PAD_Y " + support::cpp11::to_string(pool_pad_y)));

	// Create kernel
	if((pool_size == 2) \|\| (pool_size == 3) \|\| (pool_size == 7))
	{
	// Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenGLES kernel where
	// each thread computes 4 output elements
	const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(input->info()->data_type());

	int num_elements_read_per_iteration = (pool_size == 7) ? 8 : pool_size;

	if(input->info()->data_type() == DataType::F32)
	{
	if(is_pool3x3_stride_le3)
	{
	// Change the number of elements processed and number of elements read per iteration for pooling 3x3 with stride less equal than 3
	_num_elems_processed_per_iteration = 4;
	num_elements_read_per_iteration = pool_size * (pool_stride_x + 1);
	}
	}
	else
	{
	num_elements_read_per_iteration = pool_size;
	if(is_pool3x3_stride_le3)
	{
	_num_elems_processed_per_iteration = 4;
	}
	else
	{
	_num_elems_processed_per_iteration = 2;
	}
	}

	const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elements_read_per_iteration) - input_width;
	const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;

	_border_size.right = std::max(upper_bound_w, pool_pad_x);
	_border_size.bottom = std::max(upper_bound_h, pool_pad_y);

	std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size);
	if(is_pool3x3_stride_le3)
	{
	build_opts.insert("#define POOLING_LAYER_3_OPTIMIZED");
	_kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name + "_optimized", build_opts));
	}
	else
	{
	build_opts.insert("#define POOLING_LAYER_" + support::cpp11::to_string(pool_size));
	_kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name, build_opts));
	}
	}
	else // Run general case
	{
	if(input->info()->data_type() == DataType::F32)
	{
	_num_elems_processed_per_iteration = 1;
	}
	else
	{
	_num_elems_processed_per_iteration = 2;
	}
	const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + pool_size) - input_width;
	const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;

	_border_size.right = std::max(upper_bound_w, pool_pad_x);
	_border_size.bottom = std::max(upper_bound_h, pool_pad_y);

	build_opts.emplace(("#define POOL_SIZE " + support::cpp11::to_string(pool_size)));

	build_opts.insert("#define POOLING_LAYER_N");
	_kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel("pooling_layer_n", build_opts));
	}

	Window win = calculate_max_window(*output->info(), Steps(_num_elems_processed_per_iteration));

	if(input->info()->data_type() == DataType::F32)
	{
	AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom);
	AccessWindowHorizontal output_access(output->info(), 0, _num_elems_processed_per_iteration);
	update_window_and_padding(win, input_access, output_access);
	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
	}
	else
	{
	// Calculate output right and bottom border
	const int output_width = output->info()->dimension(0);
	const int output_height = output->info()->dimension(1);
	const int output_padding_right = ceil_to_multiple(output_width, _num_elems_processed_per_iteration) - output_width;
	const int output_padding_bottom = ceil_to_multiple(output_height, 1) - output_height;
	const int input_padding_right = ceil_to_multiple(input_width + 2 * _border_size.right, _num_elems_processed_per_iteration) - (input_width + 2 * _border_size.right);
	const int input_padding_bottom = ceil_to_multiple(input_height + 2 * _border_size.bottom, 1) - (input_height + 2 * _border_size.bottom);

	// Configure kernel window
	AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right + input_padding_right, input_height + _border_size.bottom + input_padding_bottom);
	AccessWindowStatic output_access(output->info(), 0, 0, output_width + output_padding_right, output_height + output_padding_bottom);
	update_window_and_padding(win, input_access, output_access);
	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
	}

	_kernel.clear_params();
	_kernel.set_shader_params_binding_point(0);

	IGCKernel::configure(win);
	}

	void GCPoolingLayerKernel::run(const Window &window)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

	unsigned int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0;
	std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad();
	std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();

	_kernel.use();

	Window window_collapsed = window.collapse_if_possible(IGCKernel::window(), Window::DimZ);
	Window slice = window_collapsed.first_slice_window_3D();

	do
	{
	// Upsample input by pool size
	Window in_slice(slice);
	in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - pool_pad_x, in_slice.x().end() * pool_stride_x, pool_stride_x * _num_elems_processed_per_iteration));
	in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - pool_pad_y, in_slice.y().end() * pool_stride_y, pool_stride_y));

	// Set inputs
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, _input, 1, in_slice);
	add_3D_tensor_argument(idx, _output, 2, slice);

	_kernel.update_shader_params();
	enqueue(*this, slice);
	}
	while(window_collapsed.slide_window_slice_3D(slice));
	}