| /* |
| * Copyright (c) 2017-2022 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #ifndef ARM_COMPUTE_TEST_VALIDATION_H |
| #define ARM_COMPUTE_TEST_VALIDATION_H |
| |
| #include "arm_compute/core/IArray.h" |
| #include "arm_compute/core/Types.h" |
| #include "support/ToolchainSupport.h" |
| #include "tests/IAccessor.h" |
| #include "tests/SimpleTensor.h" |
| #include "tests/Types.h" |
| #include "tests/Utils.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Exceptions.h" |
| #include "utils/TypePrinter.h" |
| |
| #include <iomanip> |
| #include <ios> |
| #include <vector> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace |
| { |
| // Compare if 2 values are both infinities and if they are "equal" (has the same sign) |
| template <typename T> |
| inline bool are_equal_infs(T val0, T val1) |
| { |
| const auto same_sign = support::cpp11::signbit(val0) == support::cpp11::signbit(val1); |
| return (!support::cpp11::isfinite(val0)) && (!support::cpp11::isfinite(val1)) && same_sign; |
| } |
| } // namespace |
| |
| /** Class reprensenting an absolute tolerance value. */ |
| template <typename T> |
| class AbsoluteTolerance |
| { |
| public: |
| /** Underlying type. */ |
| using value_type = T; |
| |
| /* Default constructor. |
| * |
| * Initialises the tolerance to 0. |
| */ |
| AbsoluteTolerance() = default; |
| |
| /** Constructor. |
| * |
| * @param[in] value Absolute tolerance value. |
| */ |
| explicit constexpr AbsoluteTolerance(T value) |
| : _value{ value } |
| { |
| } |
| |
| /** Implicit conversion to the underlying type. |
| * |
| * @return the underlying type. |
| */ |
| constexpr operator T() const |
| { |
| return _value; |
| } |
| |
| private: |
| T _value{ std::numeric_limits<T>::epsilon() }; |
| }; |
| |
| /** Class reprensenting a relative tolerance value. */ |
| template <typename T> |
| class RelativeTolerance |
| { |
| public: |
| /** Underlying type. */ |
| using value_type = T; |
| |
| /* Default constructor. |
| * |
| * Initialises the tolerance to 0. |
| */ |
| RelativeTolerance() = default; |
| |
| /** Constructor. |
| * |
| * @param[in] value Relative tolerance value. |
| */ |
| explicit constexpr RelativeTolerance(value_type value) |
| : _value{ value } |
| { |
| } |
| |
| /** Implicit conversion to the underlying type. |
| * |
| * @return the underlying type. |
| */ |
| constexpr operator value_type() const |
| { |
| return _value; |
| } |
| |
| private: |
| value_type _value{ std::numeric_limits<T>::epsilon() }; |
| }; |
| |
| /** Print AbsoluteTolerance type. */ |
| template <typename T> |
| inline ::std::ostream &operator<<(::std::ostream &os, const AbsoluteTolerance<T> &tolerance) |
| { |
| os << static_cast<typename AbsoluteTolerance<T>::value_type>(tolerance); |
| |
| return os; |
| } |
| |
| /** Print RelativeTolerance type. */ |
| template <typename T> |
| inline ::std::ostream &operator<<(::std::ostream &os, const RelativeTolerance<T> &tolerance) |
| { |
| os << static_cast<typename RelativeTolerance<T>::value_type>(tolerance); |
| |
| return os; |
| } |
| |
| template <typename T> |
| bool compare_dimensions(const Dimensions<T> &dimensions1, const Dimensions<T> &dimensions2, const DataLayout &data_layout = DataLayout::NCHW) |
| { |
| ARM_COMPUTE_ERROR_ON(data_layout == DataLayout::UNKNOWN); |
| |
| if(data_layout != DataLayout::NHWC) |
| { |
| if(dimensions1.num_dimensions() != dimensions2.num_dimensions()) |
| { |
| return false; |
| } |
| |
| for(unsigned int i = 0; i < dimensions1.num_dimensions(); ++i) |
| { |
| if(dimensions1[i] != dimensions2[i]) |
| { |
| return false; |
| } |
| } |
| } |
| else |
| { |
| // In case a 1D/2D shape becomes 3D after permutation, the permuted tensor will have two/one dimension(s) more and the first (two) value(s) will be 1 |
| // clang-format off |
| const auto max_dims = std::max(dimensions1.num_dimensions(), dimensions2.num_dimensions()); |
| for(unsigned int i = 3; i < max_dims; ++i) |
| { |
| if(dimensions1[i] != dimensions2[i]) |
| { |
| return false; |
| } |
| } |
| // clang-format on |
| |
| if((dimensions1[0] != dimensions2[2]) || (dimensions1[1] != dimensions2[0]) || (dimensions1[2] != dimensions2[1])) |
| { |
| return false; |
| } |
| } |
| |
| return true; |
| } |
| |
| /** Validate valid regions. |
| * |
| * - Dimensionality has to be the same. |
| * - Anchors have to match. |
| * - Shapes have to match. |
| */ |
| void validate(const arm_compute::ValidRegion ®ion, const arm_compute::ValidRegion &reference); |
| |
| /** Validate padding. |
| * |
| * Padding on all sides has to be the same. |
| */ |
| void validate(const arm_compute::PaddingSize &padding, const arm_compute::PaddingSize &reference); |
| |
| /** Validate padding. |
| * |
| * Padding on all sides has to be the same. |
| */ |
| void validate(const arm_compute::PaddingSize &padding, const arm_compute::PaddingSize &width_reference, const arm_compute::PaddingSize &height_reference); |
| |
| /** Validate tensors. |
| * |
| * - Dimensionality has to be the same. |
| * - All values have to match. |
| * |
| * @note: wrap_range allows cases where reference tensor rounds up to the wrapping point, causing it to wrap around to |
| * zero while the test tensor stays at wrapping point to pass. This may permit true erroneous cases (difference between |
| * reference tensor and test tensor is multiple of wrap_range), but such errors would be detected by |
| * other test cases. |
| */ |
| template <typename T, typename U = AbsoluteTolerance<T>> |
| void validate(const IAccessor &tensor, const SimpleTensor<T> &reference, U tolerance_value = U(), float tolerance_number = 0.f, float absolute_tolerance_value = 0.f); |
| |
| /** Validate tensors with valid region. |
| * |
| * - Dimensionality has to be the same. |
| * - All values have to match. |
| * |
| * @note: wrap_range allows cases where reference tensor rounds up to the wrapping point, causing it to wrap around to |
| * zero while the test tensor stays at wrapping point to pass. This may permit true erroneous cases (difference between |
| * reference tensor and test tensor is multiple of wrap_range), but such errors would be detected by |
| * other test cases. |
| */ |
| template <typename T, typename U = AbsoluteTolerance<T>> |
| void validate(const IAccessor &tensor, const SimpleTensor<T> &reference, const ValidRegion &valid_region, U tolerance_value = U(), float tolerance_number = 0.f, float absolute_tolerance_value = 0.f); |
| |
| /** Validate tensors with valid mask. |
| * |
| * - Dimensionality has to be the same. |
| * - All values have to match. |
| * |
| * @note: wrap_range allows cases where reference tensor rounds up to the wrapping point, causing it to wrap around to |
| * zero while the test tensor stays at wrapping point to pass. This may permit true erroneous cases (difference between |
| * reference tensor and test tensor is multiple of wrap_range), but such errors would be detected by |
| * other test cases. |
| */ |
| template <typename T, typename U = AbsoluteTolerance<T>> |
| void validate(const IAccessor &tensor, const SimpleTensor<T> &reference, const SimpleTensor<T> &valid_mask, U tolerance_value = U(), float tolerance_number = 0.f, |
| float absolute_tolerance_value = 0.f); |
| |
| /** Validate tensors against constant value. |
| * |
| * - All values have to match. |
| */ |
| void validate(const IAccessor &tensor, const void *reference_value); |
| |
| /** Validate border against a constant value. |
| * |
| * - All border values have to match the specified value if mode is CONSTANT. |
| * - All border values have to be replicated if mode is REPLICATE. |
| * - Nothing is validated for mode UNDEFINED. |
| */ |
| void validate(const IAccessor &tensor, BorderSize border_size, const BorderMode &border_mode, const void *border_value); |
| |
| /** Validate classified labels against expected ones. |
| * |
| * - All values should match |
| */ |
| void validate(std::vector<unsigned int> classified_labels, std::vector<unsigned int> expected_labels); |
| |
| /** Validate float value. |
| * |
| * - All values should match |
| */ |
| template <typename T, typename U = AbsoluteTolerance<T>> |
| bool validate(T target, T reference, U tolerance = AbsoluteTolerance<T>()); |
| |
| template <typename T> |
| struct compare_base |
| { |
| /** Construct a comparison object. |
| * |
| * @param[in] target Target value. |
| * @param[in] reference Reference value. |
| * @param[in] tolerance Allowed tolerance. |
| */ |
| compare_base(typename T::value_type target, typename T::value_type reference, T tolerance = T(0)) |
| : _target{ target }, _reference{ reference }, _tolerance{ tolerance } |
| { |
| } |
| |
| typename T::value_type _target{}; /**< Target value */ |
| typename T::value_type _reference{}; /**< Reference value */ |
| T _tolerance{}; /**< Tolerance value */ |
| }; |
| |
| template <typename T> |
| struct compare; |
| |
| /** Compare values with an absolute tolerance */ |
| template <typename U> |
| struct compare<AbsoluteTolerance<U>> : public compare_base<AbsoluteTolerance<U>> |
| { |
| using compare_base<AbsoluteTolerance<U>>::compare_base; |
| |
| /** Perform comparison */ |
| operator bool() const |
| { |
| if(are_equal_infs(this->_target, this->_reference)) |
| { |
| return true; |
| } |
| else if(this->_target == this->_reference) |
| { |
| return true; |
| } |
| |
| using comparison_type = typename std::conditional<std::is_integral<U>::value, int64_t, U>::type; |
| |
| const comparison_type abs_difference(std::abs(static_cast<comparison_type>(this->_target) - static_cast<comparison_type>(this->_reference))); |
| |
| return abs_difference <= static_cast<comparison_type>(this->_tolerance); |
| } |
| }; |
| |
| /** Compare values with a relative tolerance */ |
| template <typename U> |
| struct compare<RelativeTolerance<U>> : public compare_base<RelativeTolerance<U>> |
| { |
| using compare_base<RelativeTolerance<U>>::compare_base; |
| |
| /** Perform comparison */ |
| operator bool() const |
| { |
| if(are_equal_infs(this->_target, this->_reference)) |
| { |
| return true; |
| } |
| else if(this->_target == this->_reference) |
| { |
| return true; |
| } |
| |
| const U epsilon = (std::is_same<half, typename std::remove_cv<U>::type>::value || (this->_reference == 0)) ? static_cast<U>(0.01) : static_cast<U>(1e-05); |
| |
| if(std::abs(static_cast<double>(this->_reference) - static_cast<double>(this->_target)) <= epsilon) |
| { |
| return true; |
| } |
| else |
| { |
| if(static_cast<double>(this->_reference) == 0.0f) // We have checked whether _reference and _target is closing. If _reference is 0 but not closed to _target, it should return false |
| { |
| return false; |
| } |
| |
| const double relative_change = std::abs((static_cast<double>(this->_target) - static_cast<double>(this->_reference)) / this->_reference); |
| |
| return relative_change <= static_cast<U>(this->_tolerance); |
| } |
| } |
| }; |
| |
| template <typename T, typename U> |
| void validate(const IAccessor &tensor, const SimpleTensor<T> &reference, U tolerance_value, float tolerance_number, float absolute_tolerance_value) |
| { |
| // Validate with valid region covering the entire shape |
| validate(tensor, reference, shape_to_valid_region(reference.shape()), tolerance_value, tolerance_number, absolute_tolerance_value); |
| } |
| |
| template <typename T, typename U, typename = typename std::enable_if<std::is_integral<T>::value>::type> |
| void validate_wrap(const IAccessor &tensor, const SimpleTensor<T> &reference, U tolerance_value, float tolerance_number) |
| { |
| // Validate with valid region covering the entire shape |
| validate_wrap(tensor, reference, shape_to_valid_region(reference.shape()), tolerance_value, tolerance_number); |
| } |
| |
| template <typename T, typename U> |
| void validate(const IAccessor &tensor, const SimpleTensor<T> &reference, const ValidRegion &valid_region, U tolerance_value, float tolerance_number, float absolute_tolerance_value) |
| { |
| if(framework::Framework::get().configure_only() && framework::Framework::get().new_fixture_call()) |
| { |
| return; |
| } |
| |
| uint64_t num_mismatches = 0; |
| uint64_t num_elements = 0; |
| |
| ARM_COMPUTE_EXPECT_EQUAL(tensor.element_size(), reference.element_size(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT_EQUAL(tensor.data_type(), reference.data_type(), framework::LogLevel::ERRORS); |
| |
| if(reference.format() != Format::UNKNOWN) |
| { |
| ARM_COMPUTE_EXPECT_EQUAL(tensor.format(), reference.format(), framework::LogLevel::ERRORS); |
| } |
| |
| ARM_COMPUTE_EXPECT_EQUAL(tensor.num_channels(), reference.num_channels(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(compare_dimensions(tensor.shape(), reference.shape(), tensor.data_layout()), framework::LogLevel::ERRORS); |
| |
| const int min_elements = std::min(tensor.num_elements(), reference.num_elements()); |
| const int min_channels = std::min(tensor.num_channels(), reference.num_channels()); |
| |
| // Iterate over all elements within valid region, e.g. U8, S16, RGB888, ... |
| for(int element_idx = 0; element_idx < min_elements; ++element_idx) |
| { |
| const Coordinates id = index2coord(reference.shape(), element_idx); |
| |
| Coordinates target_id(id); |
| if(tensor.data_layout() == DataLayout::NHWC) |
| { |
| permute(target_id, PermutationVector(2U, 0U, 1U)); |
| } |
| |
| if(is_in_valid_region(valid_region, id)) |
| { |
| // Iterate over all channels within one element |
| for(int c = 0; c < min_channels; ++c) |
| { |
| const T &target_value = reinterpret_cast<const T *>(tensor(target_id))[c]; |
| const T &reference_value = reinterpret_cast<const T *>(reference(id))[c]; |
| |
| if(!compare<U>(target_value, reference_value, tolerance_value)) |
| { |
| if(absolute_tolerance_value != 0.f) |
| { |
| const AbsoluteTolerance<float> abs_tolerance(absolute_tolerance_value); |
| if(compare<AbsoluteTolerance<float>>(target_value, reference_value, abs_tolerance)) |
| { |
| continue; |
| } |
| } |
| ARM_COMPUTE_TEST_INFO("id = " << id); |
| ARM_COMPUTE_TEST_INFO("channel = " << c); |
| ARM_COMPUTE_TEST_INFO("target = " << std::setprecision(5) << framework::make_printable(target_value)); |
| ARM_COMPUTE_TEST_INFO("reference = " << std::setprecision(5) << framework::make_printable(reference_value)); |
| ARM_COMPUTE_TEST_INFO("tolerance = " << std::setprecision(5) << framework::make_printable(static_cast<typename U::value_type>(tolerance_value))); |
| framework::ARM_COMPUTE_PRINT_INFO(); |
| |
| ++num_mismatches; |
| } |
| |
| ++num_elements; |
| } |
| } |
| } |
| |
| if(num_elements != 0) |
| { |
| const uint64_t absolute_tolerance_number = tolerance_number * num_elements; |
| const float percent_mismatches = static_cast<float>(num_mismatches) / num_elements * 100.f; |
| |
| ARM_COMPUTE_TEST_INFO(num_mismatches << " values (" << std::fixed << std::setprecision(2) << percent_mismatches |
| << "%) mismatched (maximum tolerated " << std::setprecision(2) << tolerance_number * 100 << "%)"); |
| ARM_COMPUTE_EXPECT(num_mismatches <= absolute_tolerance_number, framework::LogLevel::ERRORS); |
| } |
| } |
| |
| template <typename T, typename U, typename = typename std::enable_if<std::is_integral<T>::value>::type> |
| void validate_wrap(const IAccessor &tensor, const SimpleTensor<T> &reference, const ValidRegion &valid_region, U tolerance_value, float tolerance_number) |
| { |
| if(framework::Framework::get().configure_only() && framework::Framework::get().new_fixture_call()) |
| { |
| return; |
| } |
| |
| uint64_t num_mismatches = 0; |
| uint64_t num_elements = 0; |
| |
| ARM_COMPUTE_EXPECT_EQUAL(tensor.element_size(), reference.element_size(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT_EQUAL(tensor.data_type(), reference.data_type(), framework::LogLevel::ERRORS); |
| |
| if(reference.format() != Format::UNKNOWN) |
| { |
| ARM_COMPUTE_EXPECT_EQUAL(tensor.format(), reference.format(), framework::LogLevel::ERRORS); |
| } |
| |
| ARM_COMPUTE_EXPECT_EQUAL(tensor.num_channels(), reference.num_channels(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(compare_dimensions(tensor.shape(), reference.shape(), tensor.data_layout()), framework::LogLevel::ERRORS); |
| |
| const int min_elements = std::min(tensor.num_elements(), reference.num_elements()); |
| const int min_channels = std::min(tensor.num_channels(), reference.num_channels()); |
| |
| // Iterate over all elements within valid region, e.g. U8, S16, RGB888, ... |
| for(int element_idx = 0; element_idx < min_elements; ++element_idx) |
| { |
| const Coordinates id = index2coord(reference.shape(), element_idx); |
| |
| Coordinates target_id(id); |
| if(tensor.data_layout() == DataLayout::NHWC) |
| { |
| permute(target_id, PermutationVector(2U, 0U, 1U)); |
| } |
| |
| if(is_in_valid_region(valid_region, id)) |
| { |
| // Iterate over all channels within one element |
| for(int c = 0; c < min_channels; ++c) |
| { |
| const T &target_value = reinterpret_cast<const T *>(tensor(target_id))[c]; |
| const T &reference_value = reinterpret_cast<const T *>(reference(id))[c]; |
| |
| bool equal = compare<U>(target_value, reference_value, tolerance_value); |
| |
| // check for wrapping |
| if(!equal) |
| { |
| if(are_equal_infs(target_value, reference_value)) |
| { |
| equal = true; |
| } |
| else |
| { |
| using limits_type = typename std::make_unsigned<T>::type; |
| |
| uint64_t max = std::numeric_limits<limits_type>::max(); |
| uint64_t abs_sum = std::abs(static_cast<int64_t>(target_value)) + std::abs(static_cast<int64_t>(reference_value)); |
| uint64_t wrap_difference = max - abs_sum; |
| |
| equal = wrap_difference < static_cast<uint64_t>(tolerance_value); |
| } |
| } |
| |
| if(!equal) |
| { |
| ARM_COMPUTE_TEST_INFO("id = " << id); |
| ARM_COMPUTE_TEST_INFO("channel = " << c); |
| ARM_COMPUTE_TEST_INFO("target = " << std::setprecision(5) << framework::make_printable(target_value)); |
| ARM_COMPUTE_TEST_INFO("reference = " << std::setprecision(5) << framework::make_printable(reference_value)); |
| ARM_COMPUTE_TEST_INFO("wrap_tolerance = " << std::setprecision(5) << framework::make_printable(static_cast<typename U::value_type>(tolerance_value))); |
| framework::ARM_COMPUTE_PRINT_INFO(); |
| |
| ++num_mismatches; |
| } |
| |
| ++num_elements; |
| } |
| } |
| } |
| |
| if(num_elements != 0) |
| { |
| const uint64_t absolute_tolerance_number = tolerance_number * num_elements; |
| const float percent_mismatches = static_cast<float>(num_mismatches) / num_elements * 100.f; |
| |
| ARM_COMPUTE_TEST_INFO(num_mismatches << " values (" << std::fixed << std::setprecision(2) << percent_mismatches |
| << "%) mismatched (maximum tolerated " << std::setprecision(2) << tolerance_number * 100 << "%)"); |
| ARM_COMPUTE_EXPECT(num_mismatches <= absolute_tolerance_number, framework::LogLevel::ERRORS); |
| } |
| } |
| |
| template <typename T, typename U> |
| void validate(const IAccessor &tensor, const SimpleTensor<T> &reference, const SimpleTensor<T> &valid_mask, U tolerance_value, float tolerance_number, float absolute_tolerance_value) |
| { |
| if(framework::Framework::get().configure_only() && framework::Framework::get().new_fixture_call()) |
| { |
| return; |
| } |
| |
| uint64_t num_mismatches = 0; |
| uint64_t num_elements = 0; |
| |
| ARM_COMPUTE_EXPECT_EQUAL(tensor.element_size(), reference.element_size(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT_EQUAL(tensor.data_type(), reference.data_type(), framework::LogLevel::ERRORS); |
| |
| if(reference.format() != Format::UNKNOWN) |
| { |
| ARM_COMPUTE_EXPECT_EQUAL(tensor.format(), reference.format(), framework::LogLevel::ERRORS); |
| } |
| |
| ARM_COMPUTE_EXPECT_EQUAL(tensor.num_channels(), reference.num_channels(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(compare_dimensions(tensor.shape(), reference.shape(), tensor.data_layout()), framework::LogLevel::ERRORS); |
| |
| const int min_elements = std::min(tensor.num_elements(), reference.num_elements()); |
| const int min_channels = std::min(tensor.num_channels(), reference.num_channels()); |
| |
| // Iterate over all elements within valid region, e.g. U8, S16, RGB888, ... |
| for(int element_idx = 0; element_idx < min_elements; ++element_idx) |
| { |
| const Coordinates id = index2coord(reference.shape(), element_idx); |
| |
| Coordinates target_id(id); |
| if(tensor.data_layout() == DataLayout::NHWC) |
| { |
| permute(target_id, PermutationVector(2U, 0U, 1U)); |
| } |
| |
| if(valid_mask[element_idx] == 1) |
| { |
| // Iterate over all channels within one element |
| for(int c = 0; c < min_channels; ++c) |
| { |
| const T &target_value = reinterpret_cast<const T *>(tensor(target_id))[c]; |
| const T &reference_value = reinterpret_cast<const T *>(reference(id))[c]; |
| |
| if(!compare<U>(target_value, reference_value, tolerance_value)) |
| { |
| if(absolute_tolerance_value != 0.f) |
| { |
| const AbsoluteTolerance<float> abs_tolerance(absolute_tolerance_value); |
| if(compare<AbsoluteTolerance<float>>(target_value, reference_value, abs_tolerance)) |
| { |
| continue; |
| } |
| } |
| ARM_COMPUTE_TEST_INFO("id = " << id); |
| ARM_COMPUTE_TEST_INFO("channel = " << c); |
| ARM_COMPUTE_TEST_INFO("target = " << std::setprecision(5) << framework::make_printable(target_value)); |
| ARM_COMPUTE_TEST_INFO("reference = " << std::setprecision(5) << framework::make_printable(reference_value)); |
| ARM_COMPUTE_TEST_INFO("tolerance = " << std::setprecision(5) << framework::make_printable(static_cast<typename U::value_type>(tolerance_value))); |
| framework::ARM_COMPUTE_PRINT_INFO(); |
| |
| ++num_mismatches; |
| } |
| |
| ++num_elements; |
| } |
| } |
| else |
| { |
| ++num_elements; |
| } |
| } |
| |
| if(num_elements != 0) |
| { |
| const uint64_t absolute_tolerance_number = tolerance_number * num_elements; |
| const float percent_mismatches = static_cast<float>(num_mismatches) / num_elements * 100.f; |
| |
| ARM_COMPUTE_TEST_INFO(num_mismatches << " values (" << std::fixed << std::setprecision(2) << percent_mismatches |
| << "%) mismatched (maximum tolerated " << std::setprecision(2) << tolerance_number * 100 << "%)"); |
| ARM_COMPUTE_EXPECT(num_mismatches <= absolute_tolerance_number, framework::LogLevel::ERRORS); |
| } |
| } |
| |
| template <typename T, typename U> |
| bool validate(T target, T reference, U tolerance) |
| { |
| if(framework::Framework::get().configure_only() && framework::Framework::get().new_fixture_call()) |
| { |
| return true; |
| } |
| |
| ARM_COMPUTE_TEST_INFO("reference = " << std::setprecision(5) << framework::make_printable(reference)); |
| ARM_COMPUTE_TEST_INFO("target = " << std::setprecision(5) << framework::make_printable(target)); |
| ARM_COMPUTE_TEST_INFO("tolerance = " << std::setprecision(5) << framework::make_printable(static_cast<typename U::value_type>(tolerance))); |
| |
| const bool equal = compare<U>(target, reference, tolerance); |
| |
| ARM_COMPUTE_EXPECT(equal, framework::LogLevel::ERRORS); |
| |
| return equal; |
| } |
| |
| template <typename T, typename U> |
| void validate_min_max_loc(const MinMaxLocationValues<T> &target, const MinMaxLocationValues<U> &reference) |
| { |
| if(framework::Framework::get().configure_only() && framework::Framework::get().new_fixture_call()) |
| { |
| return; |
| } |
| |
| ARM_COMPUTE_EXPECT_EQUAL(target.min, reference.min, framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT_EQUAL(target.max, reference.max, framework::LogLevel::ERRORS); |
| |
| ARM_COMPUTE_EXPECT_EQUAL(target.min_loc.size(), reference.min_loc.size(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT_EQUAL(target.max_loc.size(), reference.max_loc.size(), framework::LogLevel::ERRORS); |
| |
| for(uint32_t i = 0; i < target.min_loc.size(); ++i) |
| { |
| const auto same_coords = std::find_if(reference.min_loc.begin(), reference.min_loc.end(), [&target, i](Coordinates2D coord) |
| { |
| return coord.x == target.min_loc.at(i).x && coord.y == target.min_loc.at(i).y; |
| }); |
| |
| ARM_COMPUTE_EXPECT(same_coords != reference.min_loc.end(), framework::LogLevel::ERRORS); |
| } |
| |
| for(uint32_t i = 0; i < target.max_loc.size(); ++i) |
| { |
| const auto same_coords = std::find_if(reference.max_loc.begin(), reference.max_loc.end(), [&target, i](Coordinates2D coord) |
| { |
| return coord.x == target.max_loc.at(i).x && coord.y == target.max_loc.at(i).y; |
| }); |
| |
| ARM_COMPUTE_EXPECT(same_coords != reference.max_loc.end(), framework::LogLevel::ERRORS); |
| } |
| } |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_REFERENCE_VALIDATION_H */ |
