Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 1 | // |
Tianle Cheng | 988354d | 2023-06-28 13:20:47 +0100 | [diff] [blame] | 2 | // Copyright © 2017, 2021-2023 Arm Ltd and Contributors. All rights reserved. |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include <armnnTestUtils/PredicateResult.hpp> |
| 8 | |
| 9 | #include <armnn/Tensor.hpp> |
| 10 | #include <armnn/utility/Assert.hpp> |
| 11 | #include <armnnUtils/FloatingPointComparison.hpp> |
| 12 | |
Colm Donelan | c42a987 | 2022-02-02 16:35:09 +0000 | [diff] [blame] | 13 | #include <armnnUtils/QuantizeHelper.hpp> |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 14 | |
| 15 | #include <doctest/doctest.h> |
| 16 | |
| 17 | #include <array> |
| 18 | #include <cmath> |
| 19 | #include <random> |
| 20 | #include <vector> |
| 21 | |
| 22 | constexpr float g_FloatCloseToZeroTolerance = 1.0e-6f; |
| 23 | |
| 24 | template<typename T, bool isQuantized = true> |
| 25 | struct SelectiveComparer |
| 26 | { |
| 27 | static bool Compare(T a, T b) |
| 28 | { |
| 29 | return (std::max(a, b) - std::min(a, b)) <= 1; |
| 30 | } |
| 31 | |
| 32 | }; |
| 33 | |
| 34 | template<typename T> |
| 35 | struct SelectiveComparer<T, false> |
| 36 | { |
| 37 | static bool Compare(T a, T b) |
| 38 | { |
| 39 | // If a or b is zero, percent_tolerance does an exact match, so compare to a small, constant tolerance instead. |
| 40 | if (a == 0.0f || b == 0.0f) |
| 41 | { |
| 42 | return std::abs(a - b) <= g_FloatCloseToZeroTolerance; |
| 43 | } |
| 44 | |
| 45 | if (std::isinf(a) && a == b) |
| 46 | { |
| 47 | return true; |
| 48 | } |
| 49 | |
| 50 | if (std::isnan(a) && std::isnan(b)) |
| 51 | { |
| 52 | return true; |
| 53 | } |
| 54 | |
| 55 | // For unquantized floats we use a tolerance of 1%. |
| 56 | return armnnUtils::within_percentage_tolerance(a, b); |
| 57 | } |
| 58 | }; |
| 59 | |
| 60 | template<typename T> |
| 61 | bool SelectiveCompare(T a, T b) |
| 62 | { |
| 63 | return SelectiveComparer<T, armnn::IsQuantizedType<T>()>::Compare(a, b); |
| 64 | }; |
| 65 | |
| 66 | template<typename T> |
| 67 | bool SelectiveCompareBoolean(T a, T b) |
| 68 | { |
| 69 | return (((a == 0) && (b == 0)) || ((a != 0) && (b != 0))); |
| 70 | }; |
| 71 | |
| 72 | template <typename T> |
| 73 | armnn::PredicateResult CompareTensors(const std::vector<T>& actualData, |
| 74 | const std::vector<T>& expectedData, |
| 75 | const armnn::TensorShape& actualShape, |
| 76 | const armnn::TensorShape& expectedShape, |
| 77 | bool compareBoolean = false, |
| 78 | bool isDynamic = false) |
| 79 | { |
| 80 | if (actualData.size() != expectedData.size()) |
| 81 | { |
| 82 | armnn::PredicateResult res(false); |
| 83 | res.Message() << "Different data size [" |
| 84 | << actualData.size() |
| 85 | << "!=" |
| 86 | << expectedData.size() |
| 87 | << "]"; |
| 88 | return res; |
| 89 | } |
| 90 | |
Tianle Cheng | 988354d | 2023-06-28 13:20:47 +0100 | [diff] [blame] | 91 | // Support for comparison between empty tensors |
| 92 | if (actualData.size() == 0 && expectedData.size() == 0) |
| 93 | { |
| 94 | armnn::PredicateResult comparisonResult(true); |
| 95 | return comparisonResult; |
| 96 | } |
| 97 | |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 98 | if (actualShape.GetNumDimensions() != expectedShape.GetNumDimensions()) |
| 99 | { |
| 100 | armnn::PredicateResult res(false); |
| 101 | res.Message() << "Different number of dimensions [" |
| 102 | << actualShape.GetNumDimensions() |
| 103 | << "!=" |
| 104 | << expectedShape.GetNumDimensions() |
| 105 | << "]"; |
| 106 | return res; |
| 107 | } |
| 108 | |
| 109 | if (actualShape.GetNumElements() != expectedShape.GetNumElements()) |
| 110 | { |
| 111 | armnn::PredicateResult res(false); |
| 112 | res.Message() << "Different number of elements [" |
| 113 | << actualShape.GetNumElements() |
| 114 | << "!=" |
| 115 | << expectedShape.GetNumElements() |
| 116 | << "]"; |
| 117 | return res; |
| 118 | } |
| 119 | |
| 120 | unsigned int numberOfDimensions = actualShape.GetNumDimensions(); |
| 121 | |
| 122 | if (!isDynamic) |
| 123 | { |
| 124 | // Checks they are same shape. |
| 125 | for (unsigned int i = 0; i < numberOfDimensions; ++i) |
| 126 | { |
| 127 | if (actualShape[i] != expectedShape[i]) |
| 128 | { |
| 129 | armnn::PredicateResult res(false); |
| 130 | res.Message() << "Different shapes [" |
| 131 | << actualShape[i] |
| 132 | << "!=" |
| 133 | << expectedShape[i] |
| 134 | << "]"; |
| 135 | return res; |
| 136 | } |
| 137 | } |
| 138 | } |
| 139 | |
| 140 | // Fun iteration over n dimensions. |
| 141 | std::vector<unsigned int> indices; |
| 142 | for (unsigned int i = 0; i < numberOfDimensions; i++) |
| 143 | { |
| 144 | indices.emplace_back(0); |
| 145 | } |
| 146 | |
| 147 | std::stringstream errorString; |
| 148 | int numFailedElements = 0; |
| 149 | constexpr int maxReportedDifferences = 3; |
| 150 | unsigned int index = 0; |
| 151 | |
| 152 | // Compare data element by element. |
| 153 | while (true) |
| 154 | { |
| 155 | bool comparison; |
| 156 | // As true for uint8_t is non-zero (1-255) we must have a dedicated compare for Booleans. |
| 157 | if(compareBoolean) |
| 158 | { |
| 159 | comparison = SelectiveCompareBoolean(actualData[index], expectedData[index]); |
| 160 | } |
| 161 | else |
| 162 | { |
| 163 | comparison = SelectiveCompare(actualData[index], expectedData[index]); |
| 164 | } |
| 165 | |
| 166 | if (!comparison) |
| 167 | { |
| 168 | ++numFailedElements; |
| 169 | |
| 170 | if (numFailedElements <= maxReportedDifferences) |
| 171 | { |
| 172 | if (numFailedElements >= 2) |
| 173 | { |
| 174 | errorString << ", "; |
| 175 | } |
| 176 | errorString << "["; |
| 177 | for (unsigned int i = 0; i < numberOfDimensions; ++i) |
| 178 | { |
| 179 | errorString << indices[i]; |
| 180 | if (i != numberOfDimensions - 1) |
| 181 | { |
| 182 | errorString << ","; |
| 183 | } |
| 184 | } |
| 185 | errorString << "]"; |
| 186 | |
| 187 | errorString << " (" << +actualData[index] << " != " << +expectedData[index] << ")"; |
| 188 | } |
| 189 | } |
| 190 | |
| 191 | ++indices[numberOfDimensions - 1]; |
| 192 | for (unsigned int i=numberOfDimensions-1; i>0; i--) |
| 193 | { |
| 194 | if (indices[i] == actualShape[i]) |
| 195 | { |
| 196 | indices[i] = 0; |
| 197 | ++indices[i - 1]; |
| 198 | } |
| 199 | } |
| 200 | if (indices[0] == actualShape[0]) |
| 201 | { |
| 202 | break; |
| 203 | } |
| 204 | |
| 205 | index++; |
| 206 | } |
| 207 | |
| 208 | armnn::PredicateResult comparisonResult(true); |
| 209 | if (numFailedElements > 0) |
| 210 | { |
| 211 | comparisonResult.SetResult(false); |
| 212 | comparisonResult.Message() << numFailedElements << " different values at: "; |
| 213 | if (numFailedElements > maxReportedDifferences) |
| 214 | { |
| 215 | errorString << ", ... (and " << (numFailedElements - maxReportedDifferences) << " other differences)"; |
| 216 | } |
| 217 | comparisonResult.Message() << errorString.str(); |
| 218 | } |
| 219 | |
| 220 | return comparisonResult; |
| 221 | } |
| 222 | |
| 223 | template <typename T> |
| 224 | std::vector<T> MakeRandomTensor(const armnn::TensorInfo& tensorInfo, |
| 225 | unsigned int seed, |
| 226 | float min = -10.0f, |
| 227 | float max = 10.0f) |
| 228 | { |
| 229 | std::mt19937 gen(seed); |
| 230 | std::uniform_real_distribution<float> dist(min, max); |
| 231 | |
| 232 | std::vector<float> init(tensorInfo.GetNumElements()); |
| 233 | for (unsigned int i = 0; i < init.size(); i++) |
| 234 | { |
| 235 | init[i] = dist(gen); |
| 236 | } |
| 237 | |
| 238 | const float qScale = tensorInfo.GetQuantizationScale(); |
| 239 | const int32_t qOffset = tensorInfo.GetQuantizationOffset(); |
| 240 | |
| 241 | return armnnUtils::QuantizedVector<T>(init, qScale, qOffset); |
| 242 | } |