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/*
* Copyright (c) 2017-2020 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_CONVOLUTION_FIXTURE
#define ARM_COMPUTE_TEST_CONVOLUTION_FIXTURE
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.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/reference/Convolution.h"
#include <random>
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class ConvolutionValidationFixture : public framework::Fixture
{
protected:
template <typename...>
void setup(TensorShape shape, DataType output_data_type, BorderMode border_mode, const unsigned int width, const unsigned int height, const bool is_separable = false)
{
std::mt19937 gen(library->seed());
std::uniform_int_distribution<uint8_t> distribution(0, 255);
std::uniform_int_distribution<uint8_t> scale_distribution(1, 255);
const uint8_t constant_border_value = distribution(gen);
// Generate random scale value between 1 and 255.
const uint32_t scale = scale_distribution(gen);
ARM_COMPUTE_ERROR_ON(3 != width && 5 != width && 7 != width && 9 != width);
ARM_COMPUTE_ERROR_ON(3 != height && 5 != height && 7 != height && 9 != height);
std::vector<int16_t> conv(width * height);
_width = width;
_height = height;
if(is_separable)
{
init_separable_conv(conv.data(), width, height, library->seed());
}
else
{
init_conv(conv.data(), width, height, library->seed());
}
_target = compute_target(shape, output_data_type, conv.data(), scale, border_mode, constant_border_value);
_reference = compute_reference(shape, output_data_type, conv.data(), scale, border_mode, constant_border_value);
}
template <typename U>
void fill(U &&tensor, int i)
{
library->fill_tensor_uniform(tensor, i);
}
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)
{
// Create reference
SimpleTensor<uint8_t> src{ shape, DataType::U8 };
// Fill reference
fill(src, 0);
// Compute reference
return reference::convolution<T>(src, output_data_type, conv, scale, border_mode, constant_border_value, _width, _height);
}
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;
BorderMode _border_mode{};
TensorType _target{};
SimpleTensor<T> _reference{};
unsigned int _width{};
unsigned int _height{};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class ConvolutionSquareValidationFixture : public ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape shape, DataType output_data_type, BorderMode border_mode, const unsigned int width)
{
ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, output_data_type, border_mode, width, width);
}
protected:
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)
{
// Create tensors
TensorType src = create_tensor<TensorType>(shape, DataType::U8);
TensorType dst = create_tensor<TensorType>(shape, output_data_type);
// Create and configure function
FunctionType convolution;
convolution.configure(&src, &dst, conv, scale, border_mode, constant_border_value);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
src.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
this->fill(AccessorType(src), 0);
this->fill(AccessorType(dst), 1);
// Compute function
convolution.run();
return dst;
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class ConvolutionSeparableValidationFixture : public ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape shape, DataType output_data_type, BorderMode border_mode, const unsigned int width)
{
ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, output_data_type, border_mode, width, width, true);
}
protected:
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)
{
// Create tensors
TensorType src = create_tensor<TensorType>(shape, DataType::U8);
TensorType dst = create_tensor<TensorType>(shape, output_data_type);
// Create and configure function
FunctionType convolution;
convolution.configure(&src, &dst, conv, scale, border_mode, constant_border_value);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
src.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
this->fill(AccessorType(src), 0);
this->fill(AccessorType(dst), 1);
// Compute function
convolution.run();
return dst;
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class ConvolutionRectangleValidationFixture : public ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape shape, DataType output_data_type, BorderMode border_mode, const unsigned int width, const unsigned int height)
{
ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, output_data_type, border_mode, width, height);
}
protected:
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)
{
// Create tensors
TensorType src = create_tensor<TensorType>(shape, DataType::U8);
TensorType dst = create_tensor<TensorType>(shape, output_data_type);
// Create and configure function
FunctionType convolution;
convolution.configure(&src, &dst, conv, this->_width, this->_height, scale, border_mode, constant_border_value);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
src.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
this->fill(AccessorType(src), 0);
this->fill(AccessorType(dst), 1);
// Compute function
convolution.run();
return dst;
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_CONVOLUTION_FIXTURE */
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