236 lines
9.2 KiB
C++
236 lines
9.2 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_TEST_CONVOLUTION_FIXTURE
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#define ARM_COMPUTE_TEST_CONVOLUTION_FIXTURE
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#include "arm_compute/core/TensorShape.h"
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#include "arm_compute/core/Types.h"
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#include "tests/AssetsLibrary.h"
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#include "tests/Globals.h"
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#include "tests/IAccessor.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Fixture.h"
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#include "tests/validation/reference/Convolution.h"
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#include <random>
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class ConvolutionValidationFixture : public framework::Fixture
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{
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protected:
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template <typename...>
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void setup(TensorShape shape, DataType output_data_type, BorderMode border_mode, const unsigned int width, const unsigned int height, const bool is_separable = false)
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{
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std::mt19937 gen(library->seed());
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std::uniform_int_distribution<uint8_t> distribution(0, 255);
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std::uniform_int_distribution<uint8_t> scale_distribution(1, 255);
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const uint8_t constant_border_value = distribution(gen);
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// Generate random scale value between 1 and 255.
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const uint32_t scale = scale_distribution(gen);
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ARM_COMPUTE_ERROR_ON(3 != width && 5 != width && 7 != width && 9 != width);
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ARM_COMPUTE_ERROR_ON(3 != height && 5 != height && 7 != height && 9 != height);
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std::vector<int16_t> conv(width * height);
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_width = width;
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_height = height;
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if(is_separable)
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{
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init_separable_conv(conv.data(), width, height, library->seed());
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}
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else
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{
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init_conv(conv.data(), width, height, library->seed());
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}
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_target = compute_target(shape, output_data_type, conv.data(), scale, border_mode, constant_border_value);
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_reference = compute_reference(shape, output_data_type, conv.data(), scale, border_mode, constant_border_value);
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}
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template <typename U>
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void fill(U &&tensor, int i)
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{
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library->fill_tensor_uniform(tensor, i);
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}
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SimpleTensor<T> compute_reference(const TensorShape &shape, DataType output_data_type, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value)
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{
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// Create reference
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SimpleTensor<uint8_t> src{ shape, DataType::U8 };
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// Fill reference
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fill(src, 0);
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// Compute reference
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return reference::convolution<T>(src, output_data_type, conv, scale, border_mode, constant_border_value, _width, _height);
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}
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virtual TensorType compute_target(const TensorShape &shape, DataType output_data_type, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value) = 0;
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BorderMode _border_mode{};
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TensorType _target{};
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SimpleTensor<T> _reference{};
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unsigned int _width{};
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unsigned int _height{};
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class ConvolutionSquareValidationFixture : public ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>
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{
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public:
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template <typename...>
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void setup(TensorShape shape, DataType output_data_type, BorderMode border_mode, const unsigned int width)
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{
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ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, output_data_type, border_mode, width, width);
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}
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protected:
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TensorType compute_target(const TensorShape &shape, DataType output_data_type, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value)
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{
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// Create tensors
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TensorType src = create_tensor<TensorType>(shape, DataType::U8);
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TensorType dst = create_tensor<TensorType>(shape, output_data_type);
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// Create and configure function
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FunctionType convolution;
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convolution.configure(&src, &dst, conv, scale, border_mode, constant_border_value);
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ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Allocate tensors
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src.allocator()->allocate();
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dst.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Fill tensors
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this->fill(AccessorType(src), 0);
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this->fill(AccessorType(dst), 1);
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// Compute function
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convolution.run();
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return dst;
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}
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class ConvolutionSeparableValidationFixture : public ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>
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{
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public:
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template <typename...>
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void setup(TensorShape shape, DataType output_data_type, BorderMode border_mode, const unsigned int width)
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{
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ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, output_data_type, border_mode, width, width, true);
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}
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protected:
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TensorType compute_target(const TensorShape &shape, DataType output_data_type, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value)
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{
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// Create tensors
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TensorType src = create_tensor<TensorType>(shape, DataType::U8);
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TensorType dst = create_tensor<TensorType>(shape, output_data_type);
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// Create and configure function
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FunctionType convolution;
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convolution.configure(&src, &dst, conv, scale, border_mode, constant_border_value);
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ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Allocate tensors
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src.allocator()->allocate();
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dst.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Fill tensors
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this->fill(AccessorType(src), 0);
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this->fill(AccessorType(dst), 1);
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// Compute function
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convolution.run();
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return dst;
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}
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class ConvolutionRectangleValidationFixture : public ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>
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{
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public:
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template <typename...>
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void setup(TensorShape shape, DataType output_data_type, BorderMode border_mode, const unsigned int width, const unsigned int height)
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{
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ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, output_data_type, border_mode, width, height);
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}
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protected:
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TensorType compute_target(const TensorShape &shape, DataType output_data_type, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value)
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{
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// Create tensors
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TensorType src = create_tensor<TensorType>(shape, DataType::U8);
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TensorType dst = create_tensor<TensorType>(shape, output_data_type);
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// Create and configure function
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FunctionType convolution;
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convolution.configure(&src, &dst, conv, this->_width, this->_height, scale, border_mode, constant_border_value);
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ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Allocate tensors
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src.allocator()->allocate();
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dst.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Fill tensors
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this->fill(AccessorType(src), 0);
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this->fill(AccessorType(dst), 1);
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// Compute function
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convolution.run();
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return dst;
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}
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};
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_TEST_CONVOLUTION_FIXTURE */
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