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/*
* Copyright (c) 2018-2019 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.
*/
#ifndef ARM_COMPUTE_TEST_RANGE_FIXTURE
#define ARM_COMPUTE_TEST_RANGE_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/Helpers.h"
#include "tests/validation/reference/Range.h"
#include <algorithm>
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace
{
size_t num_of_elements_in_range(float start, float end, float step)
{
ARM_COMPUTE_ERROR_ON(step == 0);
return size_t(std::ceil((end - start) / step));
}
}
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class RangeFixture : public framework::Fixture
{
public:
template <typename...>
void setup(const DataType data_type0, float start, float step, const QuantizationInfo qinfo0 = QuantizationInfo())
{
_target = compute_target(data_type0, qinfo0, start, step);
_reference = compute_reference(data_type0, qinfo0, start, step);
}
protected:
float get_random_end(const DataType output_data_type, const QuantizationInfo qinfo_out, float start, float step)
{
std::uniform_real_distribution<> distribution(1, 100);
std::mt19937 gen(library->seed());
float end = start;
switch(output_data_type)
{
case DataType::U8:
end += std::max((uint8_t)1, static_cast<uint8_t>(distribution(gen))) * step;
return utility::clamp<float, uint8_t>(end);
case DataType::U16:
end += std::max((uint16_t)1, static_cast<uint16_t>(distribution(gen))) * step;
return utility::clamp<float, uint16_t>(end);
case DataType::U32:
end += std::max((uint32_t)1, static_cast<uint32_t>(distribution(gen))) * step;
return utility::clamp<float, uint32_t>(end);
case DataType::S8:
end += std::max((int8_t)1, static_cast<int8_t>(distribution(gen))) * step;
return utility::clamp<float, int8_t>(end);
case DataType::S16:
end += std::max((int16_t)1, static_cast<int16_t>(distribution(gen))) * step;
return utility::clamp<float, int16_t>(end);
case DataType::S32:
end += std::max((int32_t)1, static_cast<int32_t>(distribution(gen))) * step;
return utility::clamp<float, int32_t>(end);
case DataType::F32:
end += std::max(1.0f, static_cast<float>(distribution(gen))) * step;
return end;
case DataType::F16:
end += std::max(half(1.0f), static_cast<half>(distribution(gen))) * step;
return utility::clamp<float, half>(end);
case DataType::QASYMM8:
return utility::clamp<float, uint8_t>(end + (float)distribution(gen) * step,
dequantize_qasymm8(0, qinfo_out.uniform()),
dequantize_qasymm8(std::numeric_limits<uint8_t>::max(), qinfo_out.uniform()));
default:
return 0;
}
}
TensorType compute_target(const DataType output_data_type, const QuantizationInfo qinfo_out, float start, float step)
{
float end = get_random_end(output_data_type, qinfo_out, start, step);
size_t num_of_elements = num_of_elements_in_range(start, end, step);
// Create tensor
TensorType dst = create_tensor<TensorType>(TensorShape(num_of_elements), output_data_type, 1, qinfo_out);
// Create and configure function
FunctionType range_func;
range_func.configure(&dst, start, end, step);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Compute function
range_func.run();
return dst;
}
SimpleTensor<T> compute_reference(const DataType output_data_type, const QuantizationInfo qinfo_out, float start, float step)
{
// Create tensor
const float end = get_random_end(output_data_type, qinfo_out, start, step);
size_t num_of_elements = num_of_elements_in_range(start, end, step);
SimpleTensor<T> ref_dst{ TensorShape(num_of_elements ? num_of_elements : 1), output_data_type, 1, qinfo_out };
return reference::range<T>(ref_dst, start, num_of_elements, step);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_RANGE_FIXTURE */
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