| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // See LICENSE file in the project root for full license information. |
| // |
| #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; |
| } |
| // 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)); |
| } |