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

 /*
  * Copyright (c) 2017-2019 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/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/DeconvolutionLayer.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 DeconvolutionLayerFixtureBase : public framework::Fixture
 {
 public:
     using TBias = typename std::conditional<std::is_same<typename std::decay<T>::type, uint8_t>::value, int32_t, T>::type;

 public:
     template <typename...>
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info,
                DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
     {
         _data_type         = data_type;
         _bias_data_type    = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
         _data_layout       = data_layout;
         _quantization_info = quantization_info;

         _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info);
         _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i)
     {
         switch(tensor.data_type())
         {
             case DataType::QASYMM8:
             {
                 std::pair<int, int> bounds = get_quantized_bounds(tensor.quantization_info(), -1.0f, 1.0f);
                 std::uniform_int_distribution<uint8_t> distribution(bounds.first, bounds.second);
                 library->fill(tensor, distribution, i);
                 break;
             }
             case DataType::S32:
             {
                 std::uniform_int_distribution<int32_t> distribution(-100, 100);
                 library->fill(tensor, distribution, i);
                 break;
             }
             case DataType::F16:
             case DataType::F32:
             {
                 std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
                 library->fill(tensor, distribution, i);
                 break;
             }
             default:
                 library->fill_tensor_uniform(tensor, i);
         }
     }

     TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape bias_shape, TensorShape output_shape,
                               const PadStrideInfo &info)
     {
         if(_data_layout == DataLayout::NHWC)
         {
             permute(input_shape, PermutationVector(2U, 0U, 1U));
             permute(weights_shape, PermutationVector(2U, 0U, 1U));
             permute(output_shape, PermutationVector(2U, 0U, 1U));
         }

         // Create tensors
         TensorType src     = create_tensor<TensorType>(input_shape, _data_type, 1, _quantization_info, _data_layout);
         TensorType weights = create_tensor<TensorType>(weights_shape, _data_type, 1, _quantization_info, _data_layout);
         TensorType bias    = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _quantization_info, _data_layout);
         TensorType dst     = create_tensor<TensorType>(output_shape, _data_type, 1, _quantization_info, _data_layout);

         // Create and configure function
         FunctionType conv;
         conv.configure(&src, &weights, &bias, &dst, info);

         ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

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

         ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Fill tensors
         fill(AccessorType(src), 0);
         fill(AccessorType(weights), 1);
         fill(AccessorType(bias), 2);

         // Compute DeconvolutionLayer 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 PadStrideInfo &info)
     {
         // Create reference
         SimpleTensor<T>     src{ input_shape, _data_type, 1, _quantization_info };
         SimpleTensor<T>     weights{ weights_shape, _data_type, 1, _quantization_info };
         SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _quantization_info };

         // Fill reference
         fill(src, 0);
         fill(weights, 1);
         fill(bias, 2);

         return reference::deconvolution_layer<T>(src, weights, bias, output_shape, info);
     }

     TensorType       _target{};
     SimpleTensor<T>  _reference{};
     DataType         _data_type{};
     DataType         _bias_data_type{};
     DataLayout       _data_layout{};
     QuantizationInfo _quantization_info{};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
 class DeconvolutionValidationFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
                unsigned int num_kernels, DataType data_type, DataLayout data_layout)
     {
         ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
         const TensorShape   weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
         const TensorShape   bias_shape(num_kernels);
         const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
         auto                out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, sx, sy);
         TensorInfo          input_info(input_shape, 1, data_type);
         TensorInfo          weights_info(weights_shape, 1, data_type);
         TensorShape         output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
         DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_layout, QuantizationInfo());
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
 class DeconvolutionValidationQuantizedFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
                unsigned int num_kernels, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
     {
         ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
         const TensorShape   weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
         const TensorShape   bias_shape(num_kernels);
         const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
         auto                out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, sx, sy);
         TensorInfo          input_info(input_shape, 1, data_type, quantization_info);
         TensorInfo          weights_info(weights_shape, 1, data_type, quantization_info);
         TensorShape         output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
         DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_layout, quantization_info);
     }
 };

 } // namespace validation
 } // namespace test
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2019 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/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/DeconvolutionLayer.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 DeconvolutionLayerFixtureBase : public framework::Fixture
	{
	public:
	using TBias = typename std::conditional<std::is_same<typename std::decay<T>::type, uint8_t>::value, int32_t, T>::type;

	public:
	template <typename...>
	void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info,
	DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
	{
	_data_type = data_type;
	_bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
	_data_layout = data_layout;
	_quantization_info = quantization_info;

	_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info);
	_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i)
	{
	switch(tensor.data_type())
	{
	case DataType::QASYMM8:
	{
	std::pair<int, int> bounds = get_quantized_bounds(tensor.quantization_info(), -1.0f, 1.0f);
	std::uniform_int_distribution<uint8_t> distribution(bounds.first, bounds.second);
	library->fill(tensor, distribution, i);
	break;
	}
	case DataType::S32:
	{
	std::uniform_int_distribution<int32_t> distribution(-100, 100);
	library->fill(tensor, distribution, i);
	break;
	}
	case DataType::F16:
	case DataType::F32:
	{
	std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
	library->fill(tensor, distribution, i);
	break;
	}
	default:
	library->fill_tensor_uniform(tensor, i);
	}
	}

	TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape bias_shape, TensorShape output_shape,
	const PadStrideInfo &info)
	{
	if(_data_layout == DataLayout::NHWC)
	{
	permute(input_shape, PermutationVector(2U, 0U, 1U));
	permute(weights_shape, PermutationVector(2U, 0U, 1U));
	permute(output_shape, PermutationVector(2U, 0U, 1U));
	}

	// Create tensors
	TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _quantization_info, _data_layout);
	TensorType weights = create_tensor<TensorType>(weights_shape, _data_type, 1, _quantization_info, _data_layout);
	TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _quantization_info, _data_layout);
	TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _quantization_info, _data_layout);

	// Create and configure function
	FunctionType conv;
	conv.configure(&src, &weights, &bias, &dst, info);

	ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

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

	ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Fill tensors
	fill(AccessorType(src), 0);
	fill(AccessorType(weights), 1);
	fill(AccessorType(bias), 2);

	// Compute DeconvolutionLayer 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 PadStrideInfo &info)
	{
	// Create reference
	SimpleTensor<T> src{ input_shape, _data_type, 1, _quantization_info };
	SimpleTensor<T> weights{ weights_shape, _data_type, 1, _quantization_info };
	SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _quantization_info };

	// Fill reference
	fill(src, 0);
	fill(weights, 1);
	fill(bias, 2);

	return reference::deconvolution_layer<T>(src, weights, bias, output_shape, info);
	}

	TensorType _target{};
	SimpleTensor<T> _reference{};
	DataType _data_type{};
	DataType _bias_data_type{};
	DataLayout _data_layout{};
	QuantizationInfo _quantization_info{};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
	class DeconvolutionValidationFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
	unsigned int num_kernels, DataType data_type, DataLayout data_layout)
	{
	ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
	const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
	const TensorShape bias_shape(num_kernels);
	const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
	auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, sx, sy);
	TensorInfo input_info(input_shape, 1, data_type);
	TensorInfo weights_info(weights_shape, 1, data_type);
	TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
	DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_layout, QuantizationInfo());
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
	class DeconvolutionValidationQuantizedFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
	unsigned int num_kernels, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
	{
	ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
	const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
	const TensorShape bias_shape(num_kernels);
	const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
	auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, sx, sy);
	TensorInfo input_info(input_shape, 1, data_type, quantization_info);
	TensorInfo weights_info(weights_shape, 1, data_type, quantization_info);
	TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
	DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_layout, quantization_info);
	}
	};

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