tests/validation/fixtures/ROIPoolingLayerFixture.h - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2021 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.
  */
 #ifndef ARM_COMPUTE_TEST_ROIPOOLINGLAYER_FIXTURE
 #define ARM_COMPUTE_TEST_ROIPOOLINGLAYER_FIXTURE

 #include "arm_compute/core/TensorShape.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "tests/AssetsLibrary.h"
 #include "tests/Globals.h"
 #include "tests/IAccessor.h"
 #include "tests/framework/Asserts.h"
 #include "tests/framework/Fixture.h"
 #include "tests/validation/Helpers.h"
 #include "tests/validation/reference/ROIPoolingLayer.h"

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class ROIPoolingLayerGenericFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout, QuantizationInfo qinfo, QuantizationInfo output_qinfo)
     {
         _target    = compute_target(input_shape, data_type, data_layout, pool_info, rois_shape, qinfo, output_qinfo);
         _reference = compute_reference(input_shape, data_type, pool_info, rois_shape, qinfo, output_qinfo);
     }

 protected:
     template <typename U>
     void fill(U &&tensor)
     {
         library->fill_tensor_uniform(tensor, 0);
     }

     template <typename U>
     void generate_rois(U &&rois, const TensorShape &shape, const ROIPoolingLayerInfo &pool_info, TensorShape rois_shape, DataLayout data_layout = DataLayout::NCHW)
     {
         const size_t values_per_roi = rois_shape.x();
         const size_t num_rois       = rois_shape.y();

         std::mt19937 gen(library->seed());
         uint16_t    *rois_ptr = static_cast<uint16_t *>(rois.data());

         const float pool_width  = pool_info.pooled_width();
         const float pool_height = pool_info.pooled_height();
         const float roi_scale   = pool_info.spatial_scale();

         // Calculate distribution bounds
         const auto scaled_width  = static_cast<float>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)] / roi_scale) / pool_width);
         const auto scaled_height = static_cast<float>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)] / roi_scale) / pool_height);
         const auto min_width     = static_cast<float>(pool_width / roi_scale);
         const auto min_height    = static_cast<float>(pool_height / roi_scale);

         // Create distributions
         std::uniform_int_distribution<int> dist_batch(0, shape[3] - 1);
         std::uniform_int_distribution<>    dist_x1(0, scaled_width);
         std::uniform_int_distribution<>    dist_y1(0, scaled_height);
         std::uniform_int_distribution<>    dist_w(min_width, std::max(float(min_width), (pool_width - 2) * scaled_width));
         std::uniform_int_distribution<>    dist_h(min_height, std::max(float(min_height), (pool_height - 2) * scaled_height));

         for(unsigned int pw = 0; pw < num_rois; ++pw)
         {
             const auto batch_idx = dist_batch(gen);
             const auto x1        = dist_x1(gen);
             const auto y1        = dist_y1(gen);
             const auto x2        = x1 + dist_w(gen);
             const auto y2        = y1 + dist_h(gen);

             rois_ptr[values_per_roi * pw]     = batch_idx;
             rois_ptr[values_per_roi * pw + 1] = static_cast<uint16_t>(x1);
             rois_ptr[values_per_roi * pw + 2] = static_cast<uint16_t>(y1);
             rois_ptr[values_per_roi * pw + 3] = static_cast<uint16_t>(x2);
             rois_ptr[values_per_roi * pw + 4] = static_cast<uint16_t>(y2);
         }
     }

     TensorType compute_target(TensorShape                input_shape,
                               DataType                   data_type,
                               DataLayout                 data_layout,
                               const ROIPoolingLayerInfo &pool_info,
                               const TensorShape          rois_shape,
                               const QuantizationInfo    &qinfo,
                               const QuantizationInfo    &output_qinfo)
     {
         const QuantizationInfo rois_qinfo = is_data_type_quantized(data_type) ? QuantizationInfo(0.125f, 0) : QuantizationInfo();

         // Create tensors
         TensorType src         = create_tensor<TensorType>(input_shape, data_type, 1, qinfo, data_layout);
         TensorType rois_tensor = create_tensor<TensorType>(rois_shape, _rois_data_type, 1, rois_qinfo);

         // Initialise shape and declare output tensor dst
         const TensorShape dst_shape;
         TensorType        dst = create_tensor<TensorType>(dst_shape, data_type, 1, output_qinfo, data_layout);

         // Create and configure function
         FunctionType roi_pool_layer;
         roi_pool_layer.configure(&src, &rois_tensor, &dst, pool_info);

         ARM_COMPUTE_ASSERT(src.info()->is_resizable());
         ARM_COMPUTE_ASSERT(rois_tensor.info()->is_resizable());
         ARM_COMPUTE_ASSERT(dst.info()->is_resizable());

         // Allocate tensors
         src.allocator()->allocate();
         rois_tensor.allocator()->allocate();
         dst.allocator()->allocate();

         ARM_COMPUTE_ASSERT(!src.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!rois_tensor.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());

         // Fill tensors
         fill(AccessorType(src));
         generate_rois(AccessorType(rois_tensor), input_shape, pool_info, rois_shape, data_layout);

         // Compute function
         roi_pool_layer.run();

         return dst;
     }

     SimpleTensor<T> compute_reference(const TensorShape         &input_shape,
                                       DataType                   data_type,
                                       const ROIPoolingLayerInfo &pool_info,
                                       const TensorShape          rois_shape,
                                       const QuantizationInfo    &qinfo,
                                       const QuantizationInfo    &output_qinfo)
     {
         // Create reference tensor
         SimpleTensor<T>        src{ input_shape, data_type, 1, qinfo };
         const QuantizationInfo rois_qinfo = is_data_type_quantized(data_type) ? QuantizationInfo(0.125f, 0) : QuantizationInfo();
         SimpleTensor<uint16_t> rois_tensor{ rois_shape, _rois_data_type, 1, rois_qinfo };

         // Fill reference tensor
         fill(src);
         generate_rois(rois_tensor, input_shape, pool_info, rois_shape);

         return reference::roi_pool_layer(src, rois_tensor, pool_info, output_qinfo);
     }

     TensorType      _target{};
     SimpleTensor<T> _reference{};
     const DataType  _rois_data_type{ DataType::U16 };
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class ROIPoolingLayerQuantizedFixture : public ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type,
                DataLayout data_layout, QuantizationInfo qinfo, QuantizationInfo output_qinfo)
     {
         ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, pool_info, rois_shape,
                                                                                         data_type, data_layout, qinfo, output_qinfo);
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class ROIPoolingLayerFixture : public ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout)
     {
         ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, pool_info, rois_shape, data_type, data_layout,
                                                                                         QuantizationInfo(), QuantizationInfo());
     }
 };

 } // namespace validation
 } // namespace test
 } // namespace arm_compute

 #endif /* ARM_COMPUTE_TEST_ROIPOOLINGLAYER_FIXTURE */
	/*
	* Copyright (c) 2021 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.
	*/
	#ifndef ARM_COMPUTE_TEST_ROIPOOLINGLAYER_FIXTURE
	#define ARM_COMPUTE_TEST_ROIPOOLINGLAYER_FIXTURE

	#include "arm_compute/core/TensorShape.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "tests/AssetsLibrary.h"
	#include "tests/Globals.h"
	#include "tests/IAccessor.h"
	#include "tests/framework/Asserts.h"
	#include "tests/framework/Fixture.h"
	#include "tests/validation/Helpers.h"
	#include "tests/validation/reference/ROIPoolingLayer.h"

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class ROIPoolingLayerGenericFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout, QuantizationInfo qinfo, QuantizationInfo output_qinfo)
	{
	_target = compute_target(input_shape, data_type, data_layout, pool_info, rois_shape, qinfo, output_qinfo);
	_reference = compute_reference(input_shape, data_type, pool_info, rois_shape, qinfo, output_qinfo);
	}

	protected:
	template <typename U>
	void fill(U &&tensor)
	{
	library->fill_tensor_uniform(tensor, 0);
	}

	template <typename U>
	void generate_rois(U &&rois, const TensorShape &shape, const ROIPoolingLayerInfo &pool_info, TensorShape rois_shape, DataLayout data_layout = DataLayout::NCHW)
	{
	const size_t values_per_roi = rois_shape.x();
	const size_t num_rois = rois_shape.y();

	std::mt19937 gen(library->seed());
	uint16_t rois_ptr = static_cast<uint16_t >(rois.data());

	const float pool_width = pool_info.pooled_width();
	const float pool_height = pool_info.pooled_height();
	const float roi_scale = pool_info.spatial_scale();

	// Calculate distribution bounds
	const auto scaled_width = static_cast<float>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)] / roi_scale) / pool_width);
	const auto scaled_height = static_cast<float>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)] / roi_scale) / pool_height);
	const auto min_width = static_cast<float>(pool_width / roi_scale);
	const auto min_height = static_cast<float>(pool_height / roi_scale);

	// Create distributions
	std::uniform_int_distribution<int> dist_batch(0, shape[3] - 1);
	std::uniform_int_distribution<> dist_x1(0, scaled_width);
	std::uniform_int_distribution<> dist_y1(0, scaled_height);
	std::uniform_int_distribution<> dist_w(min_width, std::max(float(min_width), (pool_width - 2) * scaled_width));
	std::uniform_int_distribution<> dist_h(min_height, std::max(float(min_height), (pool_height - 2) * scaled_height));

	for(unsigned int pw = 0; pw < num_rois; ++pw)
	{
	const auto batch_idx = dist_batch(gen);
	const auto x1 = dist_x1(gen);
	const auto y1 = dist_y1(gen);
	const auto x2 = x1 + dist_w(gen);
	const auto y2 = y1 + dist_h(gen);

	rois_ptr[values_per_roi * pw] = batch_idx;
	rois_ptr[values_per_roi * pw + 1] = static_cast<uint16_t>(x1);
	rois_ptr[values_per_roi * pw + 2] = static_cast<uint16_t>(y1);
	rois_ptr[values_per_roi * pw + 3] = static_cast<uint16_t>(x2);
	rois_ptr[values_per_roi * pw + 4] = static_cast<uint16_t>(y2);
	}
	}

	TensorType compute_target(TensorShape input_shape,
	DataType data_type,
	DataLayout data_layout,
	const ROIPoolingLayerInfo &pool_info,
	const TensorShape rois_shape,
	const QuantizationInfo &qinfo,
	const QuantizationInfo &output_qinfo)
	{
	const QuantizationInfo rois_qinfo = is_data_type_quantized(data_type) ? QuantizationInfo(0.125f, 0) : QuantizationInfo();

	// Create tensors
	TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, qinfo, data_layout);
	TensorType rois_tensor = create_tensor<TensorType>(rois_shape, _rois_data_type, 1, rois_qinfo);

	// Initialise shape and declare output tensor dst
	const TensorShape dst_shape;
	TensorType dst = create_tensor<TensorType>(dst_shape, data_type, 1, output_qinfo, data_layout);

	// Create and configure function
	FunctionType roi_pool_layer;
	roi_pool_layer.configure(&src, &rois_tensor, &dst, pool_info);

	ARM_COMPUTE_ASSERT(src.info()->is_resizable());
	ARM_COMPUTE_ASSERT(rois_tensor.info()->is_resizable());
	ARM_COMPUTE_ASSERT(dst.info()->is_resizable());

	// Allocate tensors
	src.allocator()->allocate();
	rois_tensor.allocator()->allocate();
	dst.allocator()->allocate();

	ARM_COMPUTE_ASSERT(!src.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!rois_tensor.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());

	// Fill tensors
	fill(AccessorType(src));
	generate_rois(AccessorType(rois_tensor), input_shape, pool_info, rois_shape, data_layout);

	// Compute function
	roi_pool_layer.run();

	return dst;
	}

	SimpleTensor<T> compute_reference(const TensorShape &input_shape,
	DataType data_type,
	const ROIPoolingLayerInfo &pool_info,
	const TensorShape rois_shape,
	const QuantizationInfo &qinfo,
	const QuantizationInfo &output_qinfo)
	{
	// Create reference tensor
	SimpleTensor<T> src{ input_shape, data_type, 1, qinfo };
	const QuantizationInfo rois_qinfo = is_data_type_quantized(data_type) ? QuantizationInfo(0.125f, 0) : QuantizationInfo();
	SimpleTensor<uint16_t> rois_tensor{ rois_shape, _rois_data_type, 1, rois_qinfo };

	// Fill reference tensor
	fill(src);
	generate_rois(rois_tensor, input_shape, pool_info, rois_shape);

	return reference::roi_pool_layer(src, rois_tensor, pool_info, output_qinfo);
	}

	TensorType _target{};
	SimpleTensor<T> _reference{};
	const DataType _rois_data_type{ DataType::U16 };
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class ROIPoolingLayerQuantizedFixture : public ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type,
	DataLayout data_layout, QuantizationInfo qinfo, QuantizationInfo output_qinfo)
	{
	ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, pool_info, rois_shape,
	data_type, data_layout, qinfo, output_qinfo);
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class ROIPoolingLayerFixture : public ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout)
	{
	ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, pool_info, rois_shape, data_type, data_layout,
	QuantizationInfo(), QuantizationInfo());
	}
	};

	} // namespace validation
	} // namespace test
	} // namespace arm_compute

	#endif /* ARM_COMPUTE_TEST_ROIPOOLINGLAYER_FIXTURE */