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aosp12/external/ComputeLibrary/tests/validation/Validation.h

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/*
* Copyright (c) 2017-2019 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
{
/** 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::NCHW)
{
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 2D shape becomes 3D after permutation, the permuted tensor will have one dimension more and the first value will be 1
if((dimensions1.num_dimensions() != dimensions2.num_dimensions()) && ((dimensions1.num_dimensions() != (dimensions2.num_dimensions() + 1)) || (dimensions1.x() != 1)))
{
return false;
}
if((dimensions1[0] != dimensions2[2]) || (dimensions1[1] != dimensions2[0]) || (dimensions1[2] != dimensions2[1]))
{
return false;
}
for(unsigned int i = 3; i < dimensions1.num_dimensions(); ++i)
{
if(dimensions1[i] != dimensions2[i])
{
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 &region, 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>());
/** Validate key points. */
template <typename T, typename U, typename V = AbsoluteTolerance<float>>
void validate_keypoints(T target_first, T target_last, U reference_first, U reference_last, V tolerance = AbsoluteTolerance<float>(),
float allowed_missing_percentage = 5.f, float allowed_mismatch_percentage = 5.f);
/** Validate detection windows. */
template <typename T, typename U, typename V = AbsoluteTolerance<float>>
void validate_detection_windows(T target_first, T target_last, U reference_first, U reference_last, V tolerance = AbsoluteTolerance<float>(),
float allowed_missing_percentage = 5.f, float allowed_mismatch_percentage = 5.f);
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(!support::cpp11::isfinite(this->_target) || !support::cpp11::isfinite(this->_reference))
{
return false;
}
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(!support::cpp11::isfinite(this->_target) || !support::cpp11::isfinite(this->_reference))
{
return false;
}
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)
{
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)
{
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(!support::cpp11::isfinite(target_value) || !support::cpp11::isfinite(reference_value))
{
equal = false;
}
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)
{
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)
{
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)
{
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);
}
}
/** Check which keypoints from [first1, last1) are missing in [first2, last2) */
template <typename T, typename U, typename V>
std::pair<int64_t, int64_t> compare_keypoints(T first1, T last1, U first2, U last2, V tolerance, bool check_mismatches = true)
{
/* Keypoint (x,y) should have similar strength (within tolerance) and other properties in both reference and target */
const auto compare_props_eq = [&](const KeyPoint & lhs, const KeyPoint & rhs)
{
return compare<V>(lhs.strength, rhs.strength, tolerance)
&& lhs.tracking_status == rhs.tracking_status
&& lhs.scale == rhs.scale
&& lhs.orientation == rhs.orientation
&& lhs.error == rhs.error;
};
/* Used to sort KeyPoints by coordinates (x, y) */
const auto compare_coords_lt = [](const KeyPoint & lhs, const KeyPoint & rhs)
{
return std::tie(lhs.x, lhs.y) < std::tie(rhs.x, rhs.y);
};
std::sort(first1, last1, compare_coords_lt);
std::sort(first2, last2, compare_coords_lt);
if(check_mismatches)
{
ARM_COMPUTE_TEST_INFO("Checking for mismatches: ref count = " << std::distance(first1, last1) << " target count = " << std::distance(first2, last2));
}
int64_t num_missing = 0;
int64_t num_mismatches = 0;
bool rest_missing = false;
while(first1 != last1)
{
if(first2 == last2)
{
rest_missing = true;
break;
}
if(compare_coords_lt(*first1, *first2))
{
++num_missing;
ARM_COMPUTE_TEST_INFO("Key point not found");
ARM_COMPUTE_TEST_INFO("keypoint1 = " << *first1++);
framework::ARM_COMPUTE_PRINT_INFO();
}
else
{
if(!compare_coords_lt(*first2, *first1)) // Equal coordinates
{
if(check_mismatches && !compare_props_eq(*first1, *first2)) // Check other properties
{
++num_mismatches;
ARM_COMPUTE_TEST_INFO("Mismatching keypoint");
ARM_COMPUTE_TEST_INFO("keypoint1 [ref] = " << *first1);
ARM_COMPUTE_TEST_INFO("keypoint2 [tgt] = " << *first2);
framework::ARM_COMPUTE_PRINT_INFO();
}
++first1;
}
++first2;
}
}
if(rest_missing)
{
while(first1 != last1)
{
++num_missing;
ARM_COMPUTE_TEST_INFO("Key point not found");
ARM_COMPUTE_TEST_INFO("keypoint1 = " << *first1++);
framework::ARM_COMPUTE_PRINT_INFO();
}
}
return std::make_pair(num_missing, num_mismatches);
}
template <typename T, typename U, typename V>
void validate_keypoints(T target_first, T target_last, U reference_first, U reference_last, V tolerance, float allowed_missing_percentage, float allowed_mismatch_percentage)
{
const int64_t num_elements_target = std::distance(target_first, target_last);
const int64_t num_elements_reference = std::distance(reference_first, reference_last);
int64_t num_missing = 0;
int64_t num_mismatches = 0;
if(num_elements_reference > 0)
{
std::tie(num_missing, num_mismatches) = compare_keypoints(reference_first, reference_last, target_first, target_last, tolerance);
const float percent_missing = static_cast<float>(num_missing) / num_elements_reference * 100.f;
const float percent_mismatches = static_cast<float>(num_mismatches) / num_elements_reference * 100.f;
ARM_COMPUTE_TEST_INFO(num_missing << " keypoints (" << std::fixed << std::setprecision(2) << percent_missing << "%) in ref are missing from target");
ARM_COMPUTE_TEST_INFO("Missing (not in tgt): " << num_missing << "/" << num_elements_reference << " = " << std::fixed << std::setprecision(2) << percent_missing
<< "% \tMax allowed: " << allowed_missing_percentage << "%");
ARM_COMPUTE_EXPECT(percent_missing <= allowed_missing_percentage, framework::LogLevel::ERRORS);
ARM_COMPUTE_TEST_INFO(num_mismatches << " keypoints (" << std::fixed << std::setprecision(2) << percent_mismatches << "%) mismatched");
ARM_COMPUTE_TEST_INFO("Mismatched keypoints: " << num_mismatches << "/" << num_elements_reference << " = " << std::fixed << std::setprecision(2) << percent_mismatches
<< "% \tMax allowed: " << allowed_mismatch_percentage << "%");
ARM_COMPUTE_EXPECT(percent_mismatches <= allowed_mismatch_percentage, framework::LogLevel::ERRORS);
}
if(num_elements_target > 0)
{
// Note: no need to check for mismatches a second time (last argument is 'false')
std::tie(num_missing, num_mismatches) = compare_keypoints(target_first, target_last, reference_first, reference_last, tolerance, false);
const float percent_missing = static_cast<float>(num_missing) / num_elements_target * 100.f;
ARM_COMPUTE_TEST_INFO(num_missing << " keypoints (" << std::fixed << std::setprecision(2) << percent_missing << "%) in target are missing from ref");
ARM_COMPUTE_TEST_INFO("Missing (not in ref): " << num_missing << "/" << num_elements_target << " = " << std::fixed << std::setprecision(2) << percent_missing
<< "% \tMax allowed: " << allowed_missing_percentage << "%");
ARM_COMPUTE_EXPECT(percent_missing <= allowed_missing_percentage, framework::LogLevel::ERRORS);
}
}
/** Check which detection windows from [first1, last1) are missing in [first2, last2) */
template <typename T, typename U, typename V>
std::pair<int64_t, int64_t> compare_detection_windows(T first1, T last1, U first2, U last2, V tolerance)
{
int64_t num_missing = 0;
int64_t num_mismatches = 0;
while(first1 != last1)
{
const auto window = std::find_if(first2, last2, [&](DetectionWindow window)
{
return window.x == first1->x && window.y == first1->y && window.width == first1->width && window.height == first1->height && window.idx_class == first1->idx_class;
});
if(window == last2)
{
++num_missing;
ARM_COMPUTE_TEST_INFO("Detection window not found " << *first1)
framework::ARM_COMPUTE_PRINT_INFO();
}
else
{
if(!compare<V>(window->score, first1->score, tolerance))
{
++num_mismatches;
ARM_COMPUTE_TEST_INFO("Mismatching detection window")
ARM_COMPUTE_TEST_INFO("detection window 1= " << *first1)
ARM_COMPUTE_TEST_INFO("detection window 2= " << *window)
framework::ARM_COMPUTE_PRINT_INFO();
}
}
++first1;
}
return std::make_pair(num_missing, num_mismatches);
}
template <typename T, typename U, typename V>
void validate_detection_windows(T target_first, T target_last, U reference_first, U reference_last, V tolerance,
float allowed_missing_percentage, float allowed_mismatch_percentage)
{
const int64_t num_elements_target = std::distance(target_first, target_last);
const int64_t num_elements_reference = std::distance(reference_first, reference_last);
int64_t num_missing = 0;
int64_t num_mismatches = 0;
if(num_elements_reference > 0)
{
std::tie(num_missing, num_mismatches) = compare_detection_windows(reference_first, reference_last, target_first, target_last, tolerance);
const float percent_missing = static_cast<float>(num_missing) / num_elements_reference * 100.f;
const float percent_mismatches = static_cast<float>(num_mismatches) / num_elements_reference * 100.f;
ARM_COMPUTE_TEST_INFO(num_missing << " detection windows (" << std::fixed << std::setprecision(2) << percent_missing << "%) are missing in target");
ARM_COMPUTE_EXPECT(percent_missing <= allowed_missing_percentage, framework::LogLevel::ERRORS);
ARM_COMPUTE_TEST_INFO(num_mismatches << " detection windows (" << std::fixed << std::setprecision(2) << percent_mismatches << "%) mismatched");
ARM_COMPUTE_EXPECT(percent_mismatches <= allowed_mismatch_percentage, framework::LogLevel::ERRORS);
}
if(num_elements_target > 0)
{
std::tie(num_missing, num_mismatches) = compare_detection_windows(target_first, target_last, reference_first, reference_last, tolerance);
const float percent_missing = static_cast<float>(num_missing) / num_elements_target * 100.f;
ARM_COMPUTE_TEST_INFO(num_missing << " detection windows (" << std::fixed << std::setprecision(2) << percent_missing << "%) are not part of target");
ARM_COMPUTE_EXPECT(percent_missing <= allowed_missing_percentage, framework::LogLevel::ERRORS);
}
}
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_REFERENCE_VALIDATION_H */
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