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//
// Copyright © 2017, 2021-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnnTestUtils/PredicateResult.hpp>
#include <armnn/Tensor.hpp>
#include <armnn/utility/Assert.hpp>
#include <armnnUtils/FloatingPointComparison.hpp>
#include <armnnUtils/QuantizeHelper.hpp>
#include <doctest/doctest.h>
#include <array>
#include <cmath>
#include <random>
#include <vector>
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%.
return armnnUtils::within_percentage_tolerance(a, b);
}
};
template<typename T>
bool SelectiveCompare(T a, T b)
{
return SelectiveComparer<T, armnn::IsQuantizedType<T>()>::Compare(a, b);
};
template<typename T>
bool SelectiveCompareBoolean(T a, T b)
{
return (((a == 0) && (b == 0)) || ((a != 0) && (b != 0)));
};
template <typename T>
armnn::PredicateResult CompareTensors(const std::vector<T>& actualData,
const std::vector<T>& expectedData,
const armnn::TensorShape& actualShape,
const armnn::TensorShape& expectedShape,
bool compareBoolean = false,
bool isDynamic = false)
{
if (actualData.size() != expectedData.size())
{
armnn::PredicateResult res(false);
res.Message() << "Different data size ["
<< actualData.size()
<< "!="
<< expectedData.size()
<< "]";
return res;
}
// Support for comparison between empty tensors
if (actualData.size() == 0 && expectedData.size() == 0)
{
armnn::PredicateResult comparisonResult(true);
return comparisonResult;
}
if (actualShape.GetNumDimensions() != expectedShape.GetNumDimensions())
{
armnn::PredicateResult res(false);
res.Message() << "Different number of dimensions ["
<< actualShape.GetNumDimensions()
<< "!="
<< expectedShape.GetNumDimensions()
<< "]";
return res;
}
if (actualShape.GetNumElements() != expectedShape.GetNumElements())
{
armnn::PredicateResult res(false);
res.Message() << "Different number of elements ["
<< actualShape.GetNumElements()
<< "!="
<< expectedShape.GetNumElements()
<< "]";
return res;
}
unsigned int numberOfDimensions = actualShape.GetNumDimensions();
if (!isDynamic)
{
// Checks they are same shape.
for (unsigned int i = 0; i < numberOfDimensions; ++i)
{
if (actualShape[i] != expectedShape[i])
{
armnn::PredicateResult res(false);
res.Message() << "Different shapes ["
<< actualShape[i]
<< "!="
<< expectedShape[i]
<< "]";
return res;
}
}
}
// Fun iteration over n dimensions.
std::vector<unsigned int> indices;
for (unsigned int i = 0; i < numberOfDimensions; i++)
{
indices.emplace_back(0);
}
std::stringstream errorString;
int numFailedElements = 0;
constexpr int maxReportedDifferences = 3;
unsigned int index = 0;
// Compare data element by element.
while (true)
{
bool comparison;
// As true for uint8_t is non-zero (1-255) we must have a dedicated compare for Booleans.
if(compareBoolean)
{
comparison = SelectiveCompareBoolean(actualData[index], expectedData[index]);
}
else
{
comparison = SelectiveCompare(actualData[index], expectedData[index]);
}
if (!comparison)
{
++numFailedElements;
if (numFailedElements <= maxReportedDifferences)
{
if (numFailedElements >= 2)
{
errorString << ", ";
}
errorString << "[";
for (unsigned int i = 0; i < numberOfDimensions; ++i)
{
errorString << indices[i];
if (i != numberOfDimensions - 1)
{
errorString << ",";
}
}
errorString << "]";
errorString << " (" << +actualData[index] << " != " << +expectedData[index] << ")";
}
}
++indices[numberOfDimensions - 1];
for (unsigned int i=numberOfDimensions-1; i>0; i--)
{
if (indices[i] == actualShape[i])
{
indices[i] = 0;
++indices[i - 1];
}
}
if (indices[0] == actualShape[0])
{
break;
}
index++;
}
armnn::PredicateResult comparisonResult(true);
if (numFailedElements > 0)
{
comparisonResult.SetResult(false);
comparisonResult.Message() << numFailedElements << " different values at: ";
if (numFailedElements > maxReportedDifferences)
{
errorString << ", ... (and " << (numFailedElements - maxReportedDifferences) << " other differences)";
}
comparisonResult.Message() << errorString.str();
}
return comparisonResult;
}
template <typename T>
std::vector<T> MakeRandomTensor(const armnn::TensorInfo& tensorInfo,
unsigned int seed,
float min = -10.0f,
float max = 10.0f)
{
std::mt19937 gen(seed);
std::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);
}
const float qScale = tensorInfo.GetQuantizationScale();
const int32_t qOffset = tensorInfo.GetQuantizationOffset();
return armnnUtils::QuantizedVector<T>(init, qScale, qOffset);
}
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