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//
// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "TestUtils.hpp"
namespace armnnDelegate
{
void CompareData(bool tensor1[], bool tensor2[], size_t tensorSize)
{
auto compareBool = [](auto a, auto b) {return (((a == 0) && (b == 0)) || ((a != 0) && (b != 0)));};
for (size_t i = 0; i < tensorSize; i++)
{
CHECK(compareBool(tensor1[i], tensor2[i]));
}
}
void CompareData(std::vector<bool>& tensor1, std::vector<bool>& tensor2, size_t tensorSize)
{
auto compareBool = [](auto a, auto b) {return (((a == 0) && (b == 0)) || ((a != 0) && (b != 0)));};
for (size_t i = 0; i < tensorSize; i++)
{
CHECK(compareBool(tensor1[i], tensor2[i]));
}
}
void CompareData(float tensor1[], float tensor2[], size_t tensorSize)
{
for (size_t i = 0; i < tensorSize; i++)
{
CHECK(tensor1[i] == doctest::Approx( tensor2[i] ));
}
}
void CompareData(float tensor1[], float tensor2[], size_t tensorSize, float percentTolerance)
{
for (size_t i = 0; i < tensorSize; i++)
{
CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <=
std::abs(tensor1[i]*percentTolerance/100));
}
}
void CompareData(uint8_t tensor1[], uint8_t tensor2[], size_t tensorSize)
{
uint8_t tolerance = 1;
for (size_t i = 0; i < tensorSize; i++)
{
CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance);
}
}
void CompareData(int16_t tensor1[], int16_t tensor2[], size_t tensorSize)
{
int16_t tolerance = 1;
for (size_t i = 0; i < tensorSize; i++)
{
CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance);
}
}
void CompareData(int32_t tensor1[], int32_t tensor2[], size_t tensorSize)
{
int32_t tolerance = 1;
for (size_t i = 0; i < tensorSize; i++)
{
CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance);
}
}
void CompareData(int8_t tensor1[], int8_t tensor2[], size_t tensorSize)
{
int8_t tolerance = 1;
for (size_t i = 0; i < tensorSize; i++)
{
CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance);
}
}
void CompareData(Half tensor1[], Half tensor2[], size_t tensorSize)
{
for (size_t i = 0; i < tensorSize; i++)
{
CHECK(tensor1[i] == doctest::Approx( tensor2[i] ));
}
}
void CompareData(TfLiteFloat16 tensor1[], TfLiteFloat16 tensor2[], size_t tensorSize)
{
uint16_t tolerance = 1;
for (size_t i = 0; i < tensorSize; i++)
{
uint16_t tensor1Data = tensor1[i].data;
uint16_t tensor2Data = tensor2[i].data;
CHECK(std::max(tensor1Data, tensor2Data) - std::min(tensor1Data, tensor2Data) <= tolerance);
}
}
void CompareData(TfLiteFloat16 tensor1[], Half tensor2[], size_t tensorSize) {
uint16_t tolerance = 1;
for (size_t i = 0; i < tensorSize; i++)
{
uint16_t tensor1Data = tensor1[i].data;
uint16_t tensor2Data = half_float::detail::float2half<std::round_indeterminate, float>(tensor2[i]);
CHECK(std::max(tensor1Data, tensor2Data) - std::min(tensor1Data, tensor2Data) <= tolerance);
}
}
void CompareOutputShape(const std::vector<int32_t>& tfLiteDelegateShape,
const std::vector<int32_t>& armnnDelegateShape,
const std::vector<int32_t>& expectedOutputShape)
{
CHECK(expectedOutputShape.size() == tfLiteDelegateShape.size());
CHECK(expectedOutputShape.size() == armnnDelegateShape.size());
for (size_t i = 0; i < expectedOutputShape.size(); i++)
{
CHECK(expectedOutputShape[i] == armnnDelegateShape[i]);
CHECK(tfLiteDelegateShape[i] == expectedOutputShape[i]);
CHECK(tfLiteDelegateShape[i] == armnnDelegateShape[i]);
}
}
} // namespace armnnDelegate
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