src/runtime/NEON/functions/NEScale.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2016-2020 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/runtime/NEON/functions/NEScale.h"

 #include "arm_compute/core/Coordinates.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/ITensor.h"
 #include "arm_compute/core/PixelValue.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Window.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "arm_compute/runtime/TensorAllocator.h"
 #include "src/core/NEON/kernels/NEScaleKernel.h"

 #include "src/core/utils/ScaleUtils.h"

 #include "support/MemorySupport.h"
 #include "support/Rounding.h"

 #include <cmath>
 #include <cstddef>
 #include <utility>

 namespace arm_compute
 {
 namespace
 {
 void precompute_dx_dy_offsets(ITensor *dx, ITensor *dy, ITensor *offsets, float wr, float hr, SamplingPolicy sampling_policy, bool align_corners)
 {
     ARM_COMPUTE_ERROR_ON(nullptr == offsets);
     ARM_COMPUTE_UNUSED(sampling_policy);
     float sampling_offset = 0.0f;
     if(sampling_policy == SamplingPolicy::CENTER)
     {
         sampling_offset = 0.5f;
     }

     Window win;
     win.set(Window::DimX, Window::Dimension(0, offsets->info()->dimension(0), 1));
     win.set(Window::DimY, Window::Dimension(0, offsets->info()->dimension(1), 1));

     if(dx != nullptr && dy != nullptr)
     {
         // Pre-compute the offset and pixel's distance for BILINEAR interpolation
         Iterator offsets_it(offsets, win);
         Iterator dx_it(dx, win);
         Iterator dy_it(dy, win);

         execute_window_loop(win, [&](const Coordinates & id)
         {
             const float in_x  = (id.x() + sampling_offset) * wr - sampling_offset;
             const float in_y  = (id.y() + sampling_offset) * hr - sampling_offset;
             const int   in_xi = std::floor(in_x);
             const int   in_yi = std::floor(in_y);

             *reinterpret_cast<int32_t *>(offsets_it.ptr()) = in_xi;
             *reinterpret_cast<float *>(dx_it.ptr())        = in_x - in_xi;
             *reinterpret_cast<float *>(dy_it.ptr())        = in_y - in_yi;
         },
         offsets_it, dx_it, dy_it);
     }
     else
     {
         // Pre-compute the offset for NEAREST interpolation
         Iterator offsets_it(offsets, win);

         execute_window_loop(win, [&](const Coordinates & id)
         {
             const float float_in_xi                        = (id.x() + sampling_offset) * wr;
             const auto  in_xi                              = static_cast<size_t>(align_corners ? arm_compute::utils::rounding::round_half_away_from_zero(float_in_xi) : std::floor(float_in_xi));
             *reinterpret_cast<int32_t *>(offsets_it.ptr()) = in_xi;
         },
         offsets_it);
     }
 }
 } // namespace

 NEScale::NEScale()
     : _offsets(), _dx(), _dy()
 {
 }

 void NEScale::configure(ITensor *input, ITensor *output, const ScaleKernelInfo &info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_ERROR_THROW_ON(NEScale::validate(input->info(), output->info(), info));

     const bool is_align_corners_used = info.align_corners && arm_compute::scale_utils::is_align_corners_allowed_sampling_policy(info.sampling_policy);

     // Get data layout and width/height indices
     const DataLayout data_layout = input->info()->data_layout();
     const int        idx_width   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
     const int        idx_height  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);

     // Get the tensor shape
     TensorShape shape(output->info()->dimension(idx_width));
     shape.set(1, output->info()->dimension(idx_height), false);

     // Compute the ratio between source width/height and destination width/height
     const auto wr = arm_compute::scale_utils::calculate_resize_ratio(input->info()->dimension(idx_width), output->info()->dimension(idx_width), is_align_corners_used);
     const auto hr = arm_compute::scale_utils::calculate_resize_ratio(input->info()->dimension(idx_height), output->info()->dimension(idx_height), is_align_corners_used);

     // Area interpolation behaves as Nearest Neighbour in case of up-sampling
     const auto policy_to_use = (info.interpolation_policy == InterpolationPolicy::AREA && wr <= 1.f && hr <= 1.f) ? InterpolationPolicy::NEAREST_NEIGHBOR : info.interpolation_policy;

     auto scale_kernel = arm_compute::support::cpp14::make_unique<NEScaleKernel>();
     switch(policy_to_use)
     {
         case InterpolationPolicy::NEAREST_NEIGHBOR:
         {
             TensorInfo tensor_info_offsets(shape, Format::S32);
             _offsets.allocator()->init(tensor_info_offsets);

             scale_kernel->configure(input, nullptr, nullptr, &_offsets, output, info);

             // Allocate once the configure methods have been called
             _offsets.allocator()->allocate();

             // Pre-compute offsets for nearest interpolation
             precompute_dx_dy_offsets(nullptr, nullptr, &_offsets, wr, hr, info.sampling_policy, is_align_corners_used);
             break;
         }
         case InterpolationPolicy::BILINEAR:
         {
             TensorInfo tensor_info_offsets(shape, Format::S32);
             TensorInfo tensor_info_dxdy(shape, Format::F32);

             _offsets.allocator()->init(tensor_info_offsets);
             _dx.allocator()->init(tensor_info_dxdy);
             _dy.allocator()->init(tensor_info_dxdy);

             scale_kernel->configure(input, &_dx, &_dy, &_offsets, output, info);

             // Allocate once the configure methods have been called
             _offsets.allocator()->allocate();
             _dx.allocator()->allocate();
             _dy.allocator()->allocate();

             // Pre-compute dx, dy and offsets for bilinear interpolation
             precompute_dx_dy_offsets(&_dx, &_dy, &_offsets, wr, hr, info.sampling_policy, is_align_corners_used);
             break;
         }
         case InterpolationPolicy::AREA:
         {
             scale_kernel->configure(input, nullptr, nullptr, nullptr, output, info);
             break;
         }
         default:
             ARM_COMPUTE_ERROR("Unsupported interpolation mode");
     }
     _kernel = std::move(scale_kernel);
 }

 Status NEScale::validate(const ITensorInfo *input, const ITensorInfo *output, const ScaleKernelInfo &info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_RETURN_ERROR_ON(info.sampling_policy != SamplingPolicy::CENTER && info.sampling_policy != SamplingPolicy::TOP_LEFT);

     ITensorInfo *offsets = nullptr;
     ITensorInfo *dx      = nullptr;
     ITensorInfo *dy      = nullptr;

     // Get data layout and width/height indices
     const DataLayout data_layout = input->data_layout();
     const int        idx_width   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
     const int        idx_height  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);

     // Get the tensor shape of auxilary buffers
     const TensorShape shape(output->dimension(idx_width), output->dimension(idx_height));

     TensorInfo tensor_info_offsets(shape, Format::S32);
     TensorInfo tensor_info_dx(shape, Format::F32);
     TensorInfo tensor_info_dy(shape, Format::F32);

     switch(info.interpolation_policy)
     {
         case InterpolationPolicy::NEAREST_NEIGHBOR:
             offsets = &tensor_info_offsets;
             break;
         case InterpolationPolicy::BILINEAR:
             offsets = &tensor_info_offsets;
             dx      = &tensor_info_dx;
             dy      = &tensor_info_dy;
             break;
         default:
             break;
     }

     ARM_COMPUTE_RETURN_ON_ERROR(NEScaleKernel::validate(input->clone().get(), dx, dy, offsets, output->clone().get(), info));
     return Status{};
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2016-2020 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/runtime/NEON/functions/NEScale.h"

	#include "arm_compute/core/Coordinates.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/ITensor.h"
	#include "arm_compute/core/PixelValue.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Window.h"
	#include "arm_compute/runtime/NEON/NEScheduler.h"
	#include "arm_compute/runtime/TensorAllocator.h"
	#include "src/core/NEON/kernels/NEScaleKernel.h"

	#include "src/core/utils/ScaleUtils.h"

	#include "support/MemorySupport.h"
	#include "support/Rounding.h"

	#include <cmath>
	#include <cstddef>
	#include <utility>

	namespace arm_compute
	{
	namespace
	{
	void precompute_dx_dy_offsets(ITensor dx, ITensor dy, ITensor *offsets, float wr, float hr, SamplingPolicy sampling_policy, bool align_corners)
	{
	ARM_COMPUTE_ERROR_ON(nullptr == offsets);
	ARM_COMPUTE_UNUSED(sampling_policy);
	float sampling_offset = 0.0f;
	if(sampling_policy == SamplingPolicy::CENTER)
	{
	sampling_offset = 0.5f;
	}

	Window win;
	win.set(Window::DimX, Window::Dimension(0, offsets->info()->dimension(0), 1));
	win.set(Window::DimY, Window::Dimension(0, offsets->info()->dimension(1), 1));

	if(dx != nullptr && dy != nullptr)
	{
	// Pre-compute the offset and pixel's distance for BILINEAR interpolation
	Iterator offsets_it(offsets, win);
	Iterator dx_it(dx, win);
	Iterator dy_it(dy, win);

	execute_window_loop(win, [&](const Coordinates & id)
	{
	const float in_x = (id.x() + sampling_offset) * wr - sampling_offset;
	const float in_y = (id.y() + sampling_offset) * hr - sampling_offset;
	const int in_xi = std::floor(in_x);
	const int in_yi = std::floor(in_y);

	reinterpret_cast<int32_t >(offsets_it.ptr()) = in_xi;
	reinterpret_cast<float >(dx_it.ptr()) = in_x - in_xi;
	reinterpret_cast<float >(dy_it.ptr()) = in_y - in_yi;
	},
	offsets_it, dx_it, dy_it);
	}
	else
	{
	// Pre-compute the offset for NEAREST interpolation
	Iterator offsets_it(offsets, win);

	execute_window_loop(win, [&](const Coordinates & id)
	{
	const float float_in_xi = (id.x() + sampling_offset) * wr;
	const auto in_xi = static_cast<size_t>(align_corners ? arm_compute::utils::rounding::round_half_away_from_zero(float_in_xi) : std::floor(float_in_xi));
	reinterpret_cast<int32_t >(offsets_it.ptr()) = in_xi;
	},
	offsets_it);
	}
	}
	} // namespace

	NEScale::NEScale()
	: _offsets(), _dx(), _dy()
	{
	}

	void NEScale::configure(ITensor input, ITensor output, const ScaleKernelInfo &info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_ERROR_THROW_ON(NEScale::validate(input->info(), output->info(), info));

	const bool is_align_corners_used = info.align_corners && arm_compute::scale_utils::is_align_corners_allowed_sampling_policy(info.sampling_policy);

	// Get data layout and width/height indices
	const DataLayout data_layout = input->info()->data_layout();
	const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
	const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);

	// Get the tensor shape
	TensorShape shape(output->info()->dimension(idx_width));
	shape.set(1, output->info()->dimension(idx_height), false);

	// Compute the ratio between source width/height and destination width/height
	const auto wr = arm_compute::scale_utils::calculate_resize_ratio(input->info()->dimension(idx_width), output->info()->dimension(idx_width), is_align_corners_used);
	const auto hr = arm_compute::scale_utils::calculate_resize_ratio(input->info()->dimension(idx_height), output->info()->dimension(idx_height), is_align_corners_used);

	// Area interpolation behaves as Nearest Neighbour in case of up-sampling
	const auto policy_to_use = (info.interpolation_policy == InterpolationPolicy::AREA && wr <= 1.f && hr <= 1.f) ? InterpolationPolicy::NEAREST_NEIGHBOR : info.interpolation_policy;

	auto scale_kernel = arm_compute::support::cpp14::make_unique<NEScaleKernel>();
	switch(policy_to_use)
	{
	case InterpolationPolicy::NEAREST_NEIGHBOR:
	{
	TensorInfo tensor_info_offsets(shape, Format::S32);
	_offsets.allocator()->init(tensor_info_offsets);

	scale_kernel->configure(input, nullptr, nullptr, &_offsets, output, info);

	// Allocate once the configure methods have been called
	_offsets.allocator()->allocate();

	// Pre-compute offsets for nearest interpolation
	precompute_dx_dy_offsets(nullptr, nullptr, &_offsets, wr, hr, info.sampling_policy, is_align_corners_used);
	break;
	}
	case InterpolationPolicy::BILINEAR:
	{
	TensorInfo tensor_info_offsets(shape, Format::S32);
	TensorInfo tensor_info_dxdy(shape, Format::F32);

	_offsets.allocator()->init(tensor_info_offsets);
	_dx.allocator()->init(tensor_info_dxdy);
	_dy.allocator()->init(tensor_info_dxdy);

	scale_kernel->configure(input, &_dx, &_dy, &_offsets, output, info);

	// Allocate once the configure methods have been called
	_offsets.allocator()->allocate();
	_dx.allocator()->allocate();
	_dy.allocator()->allocate();

	// Pre-compute dx, dy and offsets for bilinear interpolation
	precompute_dx_dy_offsets(&_dx, &_dy, &_offsets, wr, hr, info.sampling_policy, is_align_corners_used);
	break;
	}
	case InterpolationPolicy::AREA:
	{
	scale_kernel->configure(input, nullptr, nullptr, nullptr, output, info);
	break;
	}
	default:
	ARM_COMPUTE_ERROR("Unsupported interpolation mode");
	}
	_kernel = std::move(scale_kernel);
	}

	Status NEScale::validate(const ITensorInfo input, const ITensorInfo output, const ScaleKernelInfo &info)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON(info.sampling_policy != SamplingPolicy::CENTER && info.sampling_policy != SamplingPolicy::TOP_LEFT);

	ITensorInfo *offsets = nullptr;
	ITensorInfo *dx = nullptr;
	ITensorInfo *dy = nullptr;

	// Get data layout and width/height indices
	const DataLayout data_layout = input->data_layout();
	const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
	const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);

	// Get the tensor shape of auxilary buffers
	const TensorShape shape(output->dimension(idx_width), output->dimension(idx_height));

	TensorInfo tensor_info_offsets(shape, Format::S32);
	TensorInfo tensor_info_dx(shape, Format::F32);
	TensorInfo tensor_info_dy(shape, Format::F32);

	switch(info.interpolation_policy)
	{
	case InterpolationPolicy::NEAREST_NEIGHBOR:
	offsets = &tensor_info_offsets;
	break;
	case InterpolationPolicy::BILINEAR:
	offsets = &tensor_info_offsets;
	dx = &tensor_info_dx;
	dy = &tensor_info_dy;
	break;
	default:
	break;
	}

	ARM_COMPUTE_RETURN_ON_ERROR(NEScaleKernel::validate(input->clone().get(), dx, dy, offsets, output->clone().get(), info));
	return Status{};
	}
	} // namespace arm_compute