tests/validation/fixtures/dynamic_fusion/operators/ResizeFixture.h - ml/ComputeLibrary - Gitiles

 /*
 * Copyright (c) 2022-2024 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 ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H
 #define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H

 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
 #include "arm_compute/dynamic_fusion/sketch/attributes/ResizeAttributes.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"

 #include "tests/CL/CLAccessor.h"
 #include "tests/framework/Fixture.h"
 #include "tests/framework/Macros.h"
 #include "tests/SimpleTensor.h"
 #include "tests/validation/reference/Permute.h"
 #include "tests/validation/reference/Scale.h"
 #include "tests/validation/Validation.h"

 using namespace arm_compute::experimental::dynamic_fusion;

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionResizeGenericValidationFixture : public framework::Fixture
 {
 public:
     void setup(TensorShape         shape,
                DataType            data_type,
                QuantizationInfo    quantization_info,
                DataLayout          data_layout,
                InterpolationPolicy interpolation_policy,
                SamplingPolicy      sampling_policy,
                bool                align_corners,
                QuantizationInfo    output_quantization_info)
     {
         _shape                    = shape;
         _interpolation_policy     = interpolation_policy;
         _sampling_policy          = sampling_policy;
         _data_type                = data_type;
         _input_quantization_info  = quantization_info;
         _output_quantization_info = output_quantization_info;
         _align_corners            = align_corners;
         _data_layout              = data_layout;

         ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion resize supports only NHWC layout

         generate_scale(shape);

         std::mt19937                            generator(library->seed());
         std::uniform_int_distribution<uint32_t> distribution_u8(0, 255);

         _target    = compute_target(shape);
         _reference = compute_reference(shape);
     }

 protected:
     void generate_scale(const TensorShape &shape)
     {
         static constexpr float _min_scale{0.25f};
         static constexpr float _max_scale{3.f};

         constexpr float max_width{8192.0f};
         constexpr float max_height{6384.0f};
         constexpr float min_width{1.f};
         constexpr float min_height{1.f};

         std::mt19937                          generator(library->seed());
         std::uniform_real_distribution<float> distribution_float(_min_scale, _max_scale);

         auto generate = [&](size_t input_size, float min_output, float max_output) -> int
         {
             const float generated_scale = distribution_float(generator);
             const int   output_size     = static_cast<int>(
                 utility::clamp(static_cast<float>(input_size) * generated_scale, min_output, max_output));
             return output_size;
         };

         // Input shape is always given in NCHW layout. NHWC is dealt by permute in compute_target()
         const int idx_width  = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
         const int idx_height = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::HEIGHT);

         _output_width  = generate(shape[idx_width], min_width, max_width);
         _output_height = generate(shape[idx_height], min_height, max_height);
     }

     template <typename U>
     void fill(U &&tensor)
     {
         if (tensor.data_type() == DataType::F32)
         {
             std::uniform_real_distribution<float> distribution(-5.0f, 5.0f);
             library->fill(tensor, distribution, 0);
         }
         else if (tensor.data_type() == DataType::F16)
         {
             arm_compute::utils::uniform_real_distribution_16bit<half> distribution{-5.0f, 5.0f};
             library->fill(tensor, distribution, 0);
         }
         else if (is_data_type_quantized(tensor.data_type()))
         {
             std::uniform_int_distribution<> distribution(0, 100);
             library->fill(tensor, distribution, 0);
         }
         else
         {
             library->fill_tensor_uniform(tensor, 0);
         }
     }

     TensorType compute_target(TensorShape shape)
     {
         // Our test shapes are assumed in NCHW data layout, thus the permutation
         permute(shape, PermutationVector(2U, 0U, 1U));

         // Create a new workload sketch
         CLCompileContext   cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
         GpuWorkloadContext context        = GpuWorkloadContext{&cl_compile_ctx};
         GpuWorkloadSketch  sketch{&context};

         // Create sketch tensors
         ITensorInfo *src_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type, _data_layout));
         src_info->set_quantization_info(_input_quantization_info);
         ITensorInfo *dst_info = context.create_tensor_info();

         ResizeAttributes attributes;
         attributes.align_corners(_align_corners)
             .sampling_policy(_sampling_policy)
             .interpolation_policy(_interpolation_policy)
             .output_width(_output_width)
             .output_height(_output_height);

         ITensorInfo *scale_result_info = FunctionType::create_op(sketch, src_info, attributes);
         GpuOutput::create_op(sketch, scale_result_info, dst_info);

         // Configure runtime
         ClWorkloadRuntime runtime;
         runtime.configure(sketch);

         // (Important) Allocate auxiliary tensor memory if there are any
         for (auto &data : runtime.get_auxiliary_tensors())
         {
             CLTensor     *tensor      = std::get<0>(data);
             TensorInfo    info        = std::get<1>(data);
             AuxMemoryInfo aux_mem_req = std::get<2>(data);
             tensor->allocator()->init(info, aux_mem_req.alignment);
             tensor->allocator()->allocate(); // Use ACL allocated memory
         }

         // Construct user tensors
         TensorType t_src{};
         TensorType t_dst{};

         // Initialize user tensors
         t_src.allocator()->init(*src_info);
         t_dst.allocator()->init(*dst_info);

         // Allocate and fill user tensors
         t_src.allocator()->allocate();
         t_dst.allocator()->allocate();

         fill(AccessorType(t_src));

         // Run runtime
         runtime.run({&t_src, &t_dst});

         return t_dst;
     }

     SimpleTensor<T> compute_reference(const TensorShape &shape)
     {
         // Create reference
         SimpleTensor<T> src{shape, _data_type, 1, _input_quantization_info};

         // Reference code is NCHW, so the input shapes are NCHW
         const int idx_width  = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
         const int idx_height = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::HEIGHT);

         const float scale_x = static_cast<float>(_output_width) / shape[idx_width];
         const float scale_y = static_cast<float>(_output_height) / shape[idx_height];

         // Fill reference
         fill(src);

         return reference::scale<T>(src, scale_x, scale_y, _interpolation_policy, BorderMode::REPLICATE,
                                    static_cast<T>(0), _sampling_policy, /* ceil_policy_scale */ false, _align_corners,
                                    _output_quantization_info);
     }

     TensorType          _target{};
     SimpleTensor<T>     _reference{};
     TensorShape         _shape{};
     InterpolationPolicy _interpolation_policy{};
     SamplingPolicy      _sampling_policy{};
     DataType            _data_type{};
     DataLayout          _data_layout{};
     QuantizationInfo    _input_quantization_info{};
     QuantizationInfo    _output_quantization_info{};
     bool                _align_corners{false};
     int                 _output_width{0};
     int                 _output_height{0};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionResizeValidationFixture
     : public DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape         shape,
                DataType            data_type,
                DataLayout          data_layout,
                InterpolationPolicy policy,
                SamplingPolicy      sampling_policy,
                bool                align_corners)
     {
         DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
             shape, data_type, QuantizationInfo(), data_layout, policy, sampling_policy, align_corners,
             QuantizationInfo());
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>
 class DynamicFusionResizeQuantizedValidationFixture
     : public DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape         shape,
                DataType            data_type,
                QuantizationInfo    quantization_info,
                DataLayout          data_layout,
                InterpolationPolicy policy,
                SamplingPolicy      sampling_policy,
                bool                align_corners)
     {
         DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
             shape, data_type, quantization_info, data_layout, policy, sampling_policy, align_corners,
             quantization_info);
     }
 };

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

 #endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H
	/*
	* Copyright (c) 2022-2024 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 ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H
	#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H

	#include "arm_compute/core/CL/CLKernelLibrary.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
	#include "arm_compute/dynamic_fusion/sketch/attributes/ResizeAttributes.h"
	#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
	#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"

	#include "tests/CL/CLAccessor.h"
	#include "tests/framework/Fixture.h"
	#include "tests/framework/Macros.h"
	#include "tests/SimpleTensor.h"
	#include "tests/validation/reference/Permute.h"
	#include "tests/validation/reference/Scale.h"
	#include "tests/validation/Validation.h"

	using namespace arm_compute::experimental::dynamic_fusion;

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DynamicFusionResizeGenericValidationFixture : public framework::Fixture
	{
	public:
	void setup(TensorShape shape,
	DataType data_type,
	QuantizationInfo quantization_info,
	DataLayout data_layout,
	InterpolationPolicy interpolation_policy,
	SamplingPolicy sampling_policy,
	bool align_corners,
	QuantizationInfo output_quantization_info)
	{
	_shape = shape;
	_interpolation_policy = interpolation_policy;
	_sampling_policy = sampling_policy;
	_data_type = data_type;
	_input_quantization_info = quantization_info;
	_output_quantization_info = output_quantization_info;
	_align_corners = align_corners;
	_data_layout = data_layout;

	ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion resize supports only NHWC layout

	generate_scale(shape);

	std::mt19937 generator(library->seed());
	std::uniform_int_distribution<uint32_t> distribution_u8(0, 255);

	_target = compute_target(shape);
	_reference = compute_reference(shape);
	}

	protected:
	void generate_scale(const TensorShape &shape)
	{
	static constexpr float _min_scale{0.25f};
	static constexpr float _max_scale{3.f};

	constexpr float max_width{8192.0f};
	constexpr float max_height{6384.0f};
	constexpr float min_width{1.f};
	constexpr float min_height{1.f};

	std::mt19937 generator(library->seed());
	std::uniform_real_distribution<float> distribution_float(_min_scale, _max_scale);

	auto generate = [&](size_t input_size, float min_output, float max_output) -> int
	{
	const float generated_scale = distribution_float(generator);
	const int output_size = static_cast<int>(
	utility::clamp(static_cast<float>(input_size) * generated_scale, min_output, max_output));
	return output_size;
	};

	// Input shape is always given in NCHW layout. NHWC is dealt by permute in compute_target()
	const int idx_width = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
	const int idx_height = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::HEIGHT);

	_output_width = generate(shape[idx_width], min_width, max_width);
	_output_height = generate(shape[idx_height], min_height, max_height);
	}

	template <typename U>
	void fill(U &&tensor)
	{
	if (tensor.data_type() == DataType::F32)
	{
	std::uniform_real_distribution<float> distribution(-5.0f, 5.0f);
	library->fill(tensor, distribution, 0);
	}
	else if (tensor.data_type() == DataType::F16)
	{
	arm_compute::utils::uniform_real_distribution_16bit<half> distribution{-5.0f, 5.0f};
	library->fill(tensor, distribution, 0);
	}
	else if (is_data_type_quantized(tensor.data_type()))
	{
	std::uniform_int_distribution<> distribution(0, 100);
	library->fill(tensor, distribution, 0);
	}
	else
	{
	library->fill_tensor_uniform(tensor, 0);
	}
	}

	TensorType compute_target(TensorShape shape)
	{
	// Our test shapes are assumed in NCHW data layout, thus the permutation
	permute(shape, PermutationVector(2U, 0U, 1U));

	// Create a new workload sketch
	CLCompileContext cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
	GpuWorkloadContext context = GpuWorkloadContext{&cl_compile_ctx};
	GpuWorkloadSketch sketch{&context};

	// Create sketch tensors
	ITensorInfo *src_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type, _data_layout));
	src_info->set_quantization_info(_input_quantization_info);
	ITensorInfo *dst_info = context.create_tensor_info();

	ResizeAttributes attributes;
	attributes.align_corners(_align_corners)
	.sampling_policy(_sampling_policy)
	.interpolation_policy(_interpolation_policy)
	.output_width(_output_width)
	.output_height(_output_height);

	ITensorInfo *scale_result_info = FunctionType::create_op(sketch, src_info, attributes);
	GpuOutput::create_op(sketch, scale_result_info, dst_info);

	// Configure runtime
	ClWorkloadRuntime runtime;
	runtime.configure(sketch);

	// (Important) Allocate auxiliary tensor memory if there are any
	for (auto &data : runtime.get_auxiliary_tensors())
	{
	CLTensor *tensor = std::get<0>(data);
	TensorInfo info = std::get<1>(data);
	AuxMemoryInfo aux_mem_req = std::get<2>(data);
	tensor->allocator()->init(info, aux_mem_req.alignment);
	tensor->allocator()->allocate(); // Use ACL allocated memory
	}

	// Construct user tensors
	TensorType t_src{};
	TensorType t_dst{};

	// Initialize user tensors
	t_src.allocator()->init(*src_info);
	t_dst.allocator()->init(*dst_info);

	// Allocate and fill user tensors
	t_src.allocator()->allocate();
	t_dst.allocator()->allocate();

	fill(AccessorType(t_src));

	// Run runtime
	runtime.run({&t_src, &t_dst});

	return t_dst;
	}

	SimpleTensor<T> compute_reference(const TensorShape &shape)
	{
	// Create reference
	SimpleTensor<T> src{shape, _data_type, 1, _input_quantization_info};

	// Reference code is NCHW, so the input shapes are NCHW
	const int idx_width = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
	const int idx_height = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::HEIGHT);

	const float scale_x = static_cast<float>(_output_width) / shape[idx_width];
	const float scale_y = static_cast<float>(_output_height) / shape[idx_height];

	// Fill reference
	fill(src);

	return reference::scale<T>(src, scale_x, scale_y, _interpolation_policy, BorderMode::REPLICATE,
	static_cast<T>(0), _sampling_policy, /* ceil_policy_scale */ false, _align_corners,
	_output_quantization_info);
	}

	TensorType _target{};
	SimpleTensor<T> _reference{};
	TensorShape _shape{};
	InterpolationPolicy _interpolation_policy{};
	SamplingPolicy _sampling_policy{};
	DataType _data_type{};
	DataLayout _data_layout{};
	QuantizationInfo _input_quantization_info{};
	QuantizationInfo _output_quantization_info{};
	bool _align_corners{false};
	int _output_width{0};
	int _output_height{0};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DynamicFusionResizeValidationFixture
	: public DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape shape,
	DataType data_type,
	DataLayout data_layout,
	InterpolationPolicy policy,
	SamplingPolicy sampling_policy,
	bool align_corners)
	{
	DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
	shape, data_type, QuantizationInfo(), data_layout, policy, sampling_policy, align_corners,
	QuantizationInfo());
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>
	class DynamicFusionResizeQuantizedValidationFixture
	: public DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape shape,
	DataType data_type,
	QuantizationInfo quantization_info,
	DataLayout data_layout,
	InterpolationPolicy policy,
	SamplingPolicy sampling_policy,
	bool align_corners)
	{
	DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
	shape, data_type, quantization_info, data_layout, policy, sampling_policy, align_corners,
	quantization_info);
	}
	};

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

	#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H