tests/validation/fixtures/DirectConvolution3DFixture.h - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2021, 2023 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_DIRECTCONVOLUTION3DFIXTURE_H
 #define ACL_TESTS_VALIDATION_FIXTURES_DIRECTCONVOLUTION3DFIXTURE_H

 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "tests/framework/Asserts.h" // Required for ARM_COMPUTE_ASSERT
 #include "tests/framework/Fixture.h"
 #include "tests/validation/reference/ActivationLayer.h"
 #include "tests/validation/reference/Conv3D.h"

 #include <random>

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 using namespace arm_compute::misc::shape_calculator;

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DirectConvolution3DValidationGenericFixture : public framework::Fixture
 {
 public:
     using TBias = typename std::conditional < std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int32_t, T >::type;

     void setup(const TensorShape &input_shape, int stride_x, int stride_y, int stride_z, int pad_x, int pad_y, int pad_z, unsigned int kernel_width, int kernel_height, int kernel_depth,
                unsigned int num_kernels, bool has_bias, const ActivationLayerInfo &act_info, const DataType &data_type, const DataLayout &data_layout,
                const QuantizationInfo &src_qinfo = QuantizationInfo(), const QuantizationInfo &weights_qinfo = QuantizationInfo(), const QuantizationInfo &dst_qinfo = QuantizationInfo())
     {
         ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NDHWC);

         const TensorShape weights_shape(num_kernels, input_shape[0], kernel_width, kernel_height, kernel_depth);
         const TensorShape bias_shape(num_kernels);
         const DataType    bias_data_type = is_data_type_quantized(data_type) ? DataType::S32 : data_type;
         const Conv3dInfo  conv3d_info(Size3D(stride_x, stride_y, stride_z), Padding3D(pad_x, pad_y, pad_z), act_info, Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false);
         const TensorShape output_shape = compute_conv3d_shape(input_shape, weights_shape, conv3d_info);

         _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, conv3d_info, has_bias, data_type, bias_data_type, data_layout, src_qinfo, weights_qinfo, dst_qinfo);
         _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, conv3d_info, has_bias, data_type, bias_data_type, src_qinfo, weights_qinfo, dst_qinfo);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i)
     {
         switch(tensor.data_type())
         {
             case DataType::F16:
             {
                 arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
                 library->fill(tensor, distribution, i);
                 break;
             }
             case DataType::F32:
             {
                 std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
                 library->fill(tensor, distribution, i);
                 break;
             }
             default:
                 library->fill_tensor_uniform(tensor, i);
         }
     }

     TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const Conv3dInfo &conv3d_info,
                               bool has_bias, const DataType &data_type, const DataType &bias_data_type, const DataLayout &data_layout, const QuantizationInfo &src_qinfo,
                               const QuantizationInfo &weights_qinfo, const QuantizationInfo &dst_qinfo)
     {
         // Create tensors
         TensorType src     = create_tensor<TensorType>(input_shape, data_type, 1, src_qinfo, data_layout);
         TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, weights_qinfo, data_layout);
         TensorType bias    = has_bias ? create_tensor<TensorType>(bias_shape, bias_data_type, 1, QuantizationInfo()) : TensorType();
         TensorType dst     = create_tensor<TensorType>(output_shape, data_type, 1, dst_qinfo, data_layout);

         // Create and configure function
         FunctionType conv{};
         conv.configure(&src, &weights, has_bias ? &bias : nullptr, &dst, conv3d_info);

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

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

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

         // Fill tensors
         fill(AccessorType(src), 0);
         fill(AccessorType(weights), 1);

         if(has_bias)
         {
             ARM_COMPUTE_ASSERT(bias.info()->is_resizable());
             bias.allocator()->allocate();
             ARM_COMPUTE_ASSERT(!bias.info()->is_resizable());
             fill(AccessorType(bias), 2);
         }

         // Compute Direct Convolution 3D function
         conv.run();

         return dst;
     }

     SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
                                       const Conv3dInfo &conv3d_info, bool has_bias, const DataType &data_type, const DataType &bias_data_type, const QuantizationInfo &src_qinfo,
                                       const QuantizationInfo &weights_qinfo, const QuantizationInfo &dst_qinfo)
     {
         // Create reference
         SimpleTensor<T>     src{ input_shape, data_type, 1, src_qinfo };
         SimpleTensor<T>     weights{ weights_shape, data_type, 1, weights_qinfo };
         SimpleTensor<TBias> bias{ bias_shape, bias_data_type };
         SimpleTensor<T>     dst{ output_shape, data_type, 1, dst_qinfo };

         // Fill reference
         fill(src, 0);
         fill(weights, 1);

         if(has_bias)
         {
             fill(bias, 2);
         }

         return reference::activation_layer(reference::conv3d<T, TBias>(src, weights, bias, dst, conv3d_info), conv3d_info.act_info);
     }

     TensorType      _target{};
     SimpleTensor<T> _reference{};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DirectConvolution3DValidationFixture : public DirectConvolution3DValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape input_shape, int stride_x, int stride_y, int stride_z, int pad_x, int pad_y, int pad_z, unsigned int kernel_width, int kernel_height, int kernel_depth,
                unsigned int num_kernels, bool has_bias, ActivationLayerInfo act_info, DataType data_type, DataLayout data_layout)
     {
         DirectConvolution3DValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, stride_z, pad_x, pad_y, pad_z, kernel_width, kernel_height,
                                                                                                       kernel_depth, num_kernels, has_bias, act_info, data_type, data_layout);
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DirectConvolution3DValidationQuantizedFixture : public DirectConvolution3DValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape input_shape, int stride_x, int stride_y, int stride_z, int pad_x, int pad_y, int pad_z, unsigned int kernel_width, int kernel_height, int kernel_depth,
                unsigned int num_kernels, bool has_bias, ActivationLayerInfo act_info, DataType data_type, DataLayout data_layout, QuantizationInfo src_qinfo, QuantizationInfo weights_qinfo,
                QuantizationInfo dst_qinfo)
     {
         DirectConvolution3DValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, stride_z, pad_x, pad_y, pad_z, kernel_width, kernel_height,
                                                                                                       kernel_depth, num_kernels, has_bias, act_info, data_type, data_layout, src_qinfo,
                                                                                                       weights_qinfo, dst_qinfo);
     }
 };
 } // namespace validation
 } // namespace test
 } // namespace arm_compute

 #endif // ACL_TESTS_VALIDATION_FIXTURES_DIRECTCONVOLUTION3DFIXTURE_H
	/*
	* Copyright (c) 2021, 2023 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_DIRECTCONVOLUTION3DFIXTURE_H
	#define ACL_TESTS_VALIDATION_FIXTURES_DIRECTCONVOLUTION3DFIXTURE_H

	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "tests/framework/Asserts.h" // Required for ARM_COMPUTE_ASSERT
	#include "tests/framework/Fixture.h"
	#include "tests/validation/reference/ActivationLayer.h"
	#include "tests/validation/reference/Conv3D.h"

	#include <random>

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	using namespace arm_compute::misc::shape_calculator;

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DirectConvolution3DValidationGenericFixture : public framework::Fixture
	{
	public:
	using TBias = typename std::conditional < std::is_same<T, uint8_t>::value \|\| std::is_same<T, int8_t>::value, int32_t, T >::type;

	void setup(const TensorShape &input_shape, int stride_x, int stride_y, int stride_z, int pad_x, int pad_y, int pad_z, unsigned int kernel_width, int kernel_height, int kernel_depth,
	unsigned int num_kernels, bool has_bias, const ActivationLayerInfo &act_info, const DataType &data_type, const DataLayout &data_layout,
	const QuantizationInfo &src_qinfo = QuantizationInfo(), const QuantizationInfo &weights_qinfo = QuantizationInfo(), const QuantizationInfo &dst_qinfo = QuantizationInfo())
	{
	ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NDHWC);

	const TensorShape weights_shape(num_kernels, input_shape[0], kernel_width, kernel_height, kernel_depth);
	const TensorShape bias_shape(num_kernels);
	const DataType bias_data_type = is_data_type_quantized(data_type) ? DataType::S32 : data_type;
	const Conv3dInfo conv3d_info(Size3D(stride_x, stride_y, stride_z), Padding3D(pad_x, pad_y, pad_z), act_info, Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false);
	const TensorShape output_shape = compute_conv3d_shape(input_shape, weights_shape, conv3d_info);

	_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, conv3d_info, has_bias, data_type, bias_data_type, data_layout, src_qinfo, weights_qinfo, dst_qinfo);
	_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, conv3d_info, has_bias, data_type, bias_data_type, src_qinfo, weights_qinfo, dst_qinfo);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i)
	{
	switch(tensor.data_type())
	{
	case DataType::F16:
	{
	arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
	library->fill(tensor, distribution, i);
	break;
	}
	case DataType::F32:
	{
	std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
	library->fill(tensor, distribution, i);
	break;
	}
	default:
	library->fill_tensor_uniform(tensor, i);
	}
	}

	TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const Conv3dInfo &conv3d_info,
	bool has_bias, const DataType &data_type, const DataType &bias_data_type, const DataLayout &data_layout, const QuantizationInfo &src_qinfo,
	const QuantizationInfo &weights_qinfo, const QuantizationInfo &dst_qinfo)
	{
	// Create tensors
	TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, src_qinfo, data_layout);
	TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, weights_qinfo, data_layout);
	TensorType bias = has_bias ? create_tensor<TensorType>(bias_shape, bias_data_type, 1, QuantizationInfo()) : TensorType();
	TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, dst_qinfo, data_layout);

	// Create and configure function
	FunctionType conv{};
	conv.configure(&src, &weights, has_bias ? &bias : nullptr, &dst, conv3d_info);

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

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

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

	// Fill tensors
	fill(AccessorType(src), 0);
	fill(AccessorType(weights), 1);

	if(has_bias)
	{
	ARM_COMPUTE_ASSERT(bias.info()->is_resizable());
	bias.allocator()->allocate();
	ARM_COMPUTE_ASSERT(!bias.info()->is_resizable());
	fill(AccessorType(bias), 2);
	}

	// Compute Direct Convolution 3D function
	conv.run();

	return dst;
	}

	SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
	const Conv3dInfo &conv3d_info, bool has_bias, const DataType &data_type, const DataType &bias_data_type, const QuantizationInfo &src_qinfo,
	const QuantizationInfo &weights_qinfo, const QuantizationInfo &dst_qinfo)
	{
	// Create reference
	SimpleTensor<T> src{ input_shape, data_type, 1, src_qinfo };
	SimpleTensor<T> weights{ weights_shape, data_type, 1, weights_qinfo };
	SimpleTensor<TBias> bias{ bias_shape, bias_data_type };
	SimpleTensor<T> dst{ output_shape, data_type, 1, dst_qinfo };

	// Fill reference
	fill(src, 0);
	fill(weights, 1);

	if(has_bias)
	{
	fill(bias, 2);
	}

	return reference::activation_layer(reference::conv3d<T, TBias>(src, weights, bias, dst, conv3d_info), conv3d_info.act_info);
	}

	TensorType _target{};
	SimpleTensor<T> _reference{};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DirectConvolution3DValidationFixture : public DirectConvolution3DValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape input_shape, int stride_x, int stride_y, int stride_z, int pad_x, int pad_y, int pad_z, unsigned int kernel_width, int kernel_height, int kernel_depth,
	unsigned int num_kernels, bool has_bias, ActivationLayerInfo act_info, DataType data_type, DataLayout data_layout)
	{
	DirectConvolution3DValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, stride_z, pad_x, pad_y, pad_z, kernel_width, kernel_height,
	kernel_depth, num_kernels, has_bias, act_info, data_type, data_layout);
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DirectConvolution3DValidationQuantizedFixture : public DirectConvolution3DValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape input_shape, int stride_x, int stride_y, int stride_z, int pad_x, int pad_y, int pad_z, unsigned int kernel_width, int kernel_height, int kernel_depth,
	unsigned int num_kernels, bool has_bias, ActivationLayerInfo act_info, DataType data_type, DataLayout data_layout, QuantizationInfo src_qinfo, QuantizationInfo weights_qinfo,
	QuantizationInfo dst_qinfo)
	{
	DirectConvolution3DValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, stride_z, pad_x, pad_y, pad_z, kernel_width, kernel_height,
	kernel_depth, num_kernels, has_bias, act_info, data_type, data_layout, src_qinfo,
	weights_qinfo, dst_qinfo);
	}
	};
	} // namespace validation
	} // namespace test
	} // namespace arm_compute

	#endif // ACL_TESTS_VALIDATION_FIXTURES_DIRECTCONVOLUTION3DFIXTURE_H