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