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Diffstat (limited to 'delegate/test/TestUtils.cpp')
| -rw-r--r-- | delegate/test/TestUtils.cpp | 152 |
1 files changed, 152 insertions, 0 deletions
diff --git a/delegate/test/TestUtils.cpp b/delegate/test/TestUtils.cpp new file mode 100644 index 0000000000..2689c2eaa3 --- /dev/null +++ b/delegate/test/TestUtils.cpp @@ -0,0 +1,152 @@ +// +// 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, 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); + } +} + +template <> +void CompareOutputData(std::unique_ptr<tflite::Interpreter>& tfLiteInterpreter, + std::unique_ptr<tflite::Interpreter>& armnnDelegateInterpreter, + std::vector<int32_t>& expectedOutputShape, + std::vector<Half>& expectedOutputValues, + unsigned int outputIndex) +{ + auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[outputIndex]; + auto tfLiteDelegateOutputTensor = tfLiteInterpreter->tensor(tfLiteDelegateOutputId); + auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<TfLiteFloat16>(tfLiteDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[outputIndex]; + auto armnnDelegateOutputTensor = armnnDelegateInterpreter->tensor(armnnDelegateOutputId); + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<TfLiteFloat16>(armnnDelegateOutputId); + + CHECK(expectedOutputShape.size() == tfLiteDelegateOutputTensor->dims->size); + CHECK(expectedOutputShape.size() == armnnDelegateOutputTensor->dims->size); + + for (size_t i = 0; i < expectedOutputShape.size(); i++) + { + CHECK(armnnDelegateOutputTensor->dims->data[i] == expectedOutputShape[i]); + CHECK(tfLiteDelegateOutputTensor->dims->data[i] == expectedOutputShape[i]); + CHECK(tfLiteDelegateOutputTensor->dims->data[i] == armnnDelegateOutputTensor->dims->data[i]); + } + + armnnDelegate::CompareData(armnnDelegateOutputData, expectedOutputValues.data(), expectedOutputValues.size()); + armnnDelegate::CompareData(tfLiteDelegateOutputData, expectedOutputValues.data(), expectedOutputValues.size()); + armnnDelegate::CompareData(tfLiteDelegateOutputData, armnnDelegateOutputData, expectedOutputValues.size()); +} + +template <> +void FillInput<Half>(std::unique_ptr<tflite::Interpreter>& interpreter, int inputIndex, std::vector<Half>& inputValues) +{ + auto tfLiteDelegateInputId = interpreter->inputs()[inputIndex]; + auto tfLiteDelageInputData = interpreter->typed_tensor<TfLiteFloat16>(tfLiteDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + tfLiteDelageInputData[i].data = half_float::detail::float2half<std::round_indeterminate, float>(inputValues[i]); + + } +} + +} // namespace armnnDelegate
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