| // |
| // Copyright © 2017 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; |
| } |
| |
| 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); |
| } |