| // |
| // Copyright © 2020 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 |