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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/TensorFwd.hpp>
#include <boost/test/unit_test.hpp>
#include <boost/multi_array.hpp>
#include <vector>
#include <array>
#include <boost/assert.hpp>
#include <boost/test/tools/floating_point_comparison.hpp>
#include <boost/random/uniform_real_distribution.hpp>
#include <boost/random/mersenne_twister.hpp>
#include <boost/numeric/conversion/cast.hpp>
#include <armnn/Tensor.hpp>
#include <backends/test/QuantizeHelper.hpp>
#include <cmath>
constexpr float g_FloatCloseToZeroTolerance = 1.0e-6f;
template<typename T, bool isQuantized = true>
struct SelectiveComparer
{
static bool Compare(T a, T b)
{
return (std::max(a, b) - std::min(a, b)) <= 1;
}
};
template<typename T>
struct SelectiveComparer<T, false>
{
static bool Compare(T a, T b)
{
// If a or b is zero, percent_tolerance does an exact match, so compare to a small, constant tolerance instead.
if (a == 0.0f || b == 0.0f)
{
return std::abs(a - b) <= g_FloatCloseToZeroTolerance;
}
if (std::isinf(a) && a == b)
{
return true;
}
if (std::isnan(a) && std::isnan(b))
{
return true;
}
// For unquantized floats we use a tolerance of 1%.
boost::math::fpc::close_at_tolerance<float> comparer(boost::math::fpc::percent_tolerance(1.0f));
return comparer(a, b);
}
};
template<typename T>
bool SelectiveCompare(T a, T b)
{
return SelectiveComparer<T, armnn::IsQuantizedType<T>()>::Compare(a, b);
};
template <typename T, std::size_t n>
boost::test_tools::predicate_result CompareTensors(const boost::multi_array<T, n>& a,
const boost::multi_array<T, n>& b)
{
// Checks they are same shape.
for (unsigned int i=0; i<n; i++)
{
if (a.shape()[i] != b.shape()[i])
{
boost::test_tools::predicate_result res(false);
res.message() << "Different shapes ["
<< a.shape()[i]
<< "!="
<< b.shape()[i]
<< "]";
return res;
}
}
// Now compares element-wise.
// Fun iteration over n dimensions.
std::array<unsigned int, n> indices;
for (unsigned int i = 0; i < n; i++)
{
indices[i] = 0;
}
std::stringstream errorString;
int numFailedElements = 0;
constexpr int maxReportedDifferences = 3;
while (true)
{
bool comparison = SelectiveCompare(a(indices), b(indices));
if (!comparison)
{
++numFailedElements;
if (numFailedElements <= maxReportedDifferences)
{
if (numFailedElements >= 2)
{
errorString << ", ";
}
errorString << "[";
for (unsigned int i = 0; i < n; ++i)
{
errorString << indices[i];
if (i != n - 1)
{
errorString << ",";
}
}
errorString << "]";
errorString << " (" << +a(indices) << " != " << +b(indices) << ")";
}
}
++indices[n - 1];
for (unsigned int i=n-1; i>0; i--)
{
if (indices[i] == a.shape()[i])
{
indices[i] = 0;
++indices[i - 1];
}
}
if (indices[0] == a.shape()[0])
{
break;
}
}
boost::test_tools::predicate_result comparisonResult(true);
if (numFailedElements > 0)
{
comparisonResult = false;
comparisonResult.message() << numFailedElements << " different values at: ";
if (numFailedElements > maxReportedDifferences)
{
errorString << ", ... (and " << (numFailedElements - maxReportedDifferences) << " other differences)";
}
comparisonResult.message() << errorString.str();
}
return comparisonResult;
}
// Creates a boost::multi_array with the shape defined by the given TensorInfo.
template <typename T, std::size_t n>
boost::multi_array<T, n> MakeTensor(const armnn::TensorInfo& tensorInfo)
{
std::array<unsigned int, n> shape;
for (unsigned int i = 0; i < n; i++)
{
shape[i] = tensorInfo.GetShape()[i];
}
return boost::multi_array<T, n>(shape);
}
// Creates a boost::multi_array with the shape defined by the given TensorInfo and contents defined by the given vector.
template <typename T, std::size_t n>
boost::multi_array<T, n> MakeTensor(const armnn::TensorInfo& tensorInfo, const std::vector<T>& flat)
{
BOOST_ASSERT_MSG(flat.size() == tensorInfo.GetNumElements(), "Wrong number of components supplied to tensor");
std::array<unsigned int, n> shape;
for (unsigned int i = 0; i < n; i++)
{
shape[i] = tensorInfo.GetShape()[i];
}
boost::const_multi_array_ref<T, n> arrayRef(&flat[0], shape);
return boost::multi_array<T, n>(arrayRef);
}
template <typename T, std::size_t n>
boost::multi_array<T, n> MakeRandomTensor(const armnn::TensorInfo& tensorInfo,
unsigned int seed,
float min = -10.0f,
float max = 10.0f)
{
boost::random::mt19937 gen(seed);
boost::random::uniform_real_distribution<float> dist(min, max);
std::vector<float> init(tensorInfo.GetNumElements());
for (unsigned int i = 0; i < init.size(); i++)
{
init[i] = dist(gen);
}
float qScale = tensorInfo.GetQuantizationScale();
int32_t qOffset = tensorInfo.GetQuantizationOffset();
return MakeTensor<T, n>(tensorInfo, QuantizedVector<T>(qScale, qOffset, init));
}
template<typename T>
armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches,
unsigned int numberOfChannels,
unsigned int height,
unsigned int width,
const armnn::DataLayoutIndexed& dataLayout)
{
switch (dataLayout.GetDataLayout())
{
case armnn::DataLayout::NCHW:
return armnn::TensorInfo({numberOfBatches, numberOfChannels, height, width}, armnn::GetDataType<T>());
case armnn::DataLayout::NHWC:
return armnn::TensorInfo({numberOfBatches, height, width, numberOfChannels}, armnn::GetDataType<T>());
default:
throw armnn::InvalidArgumentException("unknown data layout ["
+ std::to_string(static_cast<int>(dataLayout.GetDataLayout())) + "]");
}
}
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