| // |
| // Copyright © 2020, 2023-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include <tensorflow/lite/c/common.h> |
| |
| #include <armnn/BackendId.hpp> |
| #include <half/half.hpp> |
| |
| #include <doctest/doctest.h> |
| |
| using Half = half_float::half; |
| |
| namespace |
| { |
| /** |
| * Based on the compilation options capture subcases for the available backends. If "onlyTheseBackends" is NOT empty |
| * then we'll ignore any backend NOT listed in it. |
| * |
| * @param onlyTheseBackends limit the number of backends considered for sub casing. If empty all are considered. |
| * @return vector of backends that have been captured for sub casing. |
| */ |
| std::vector<armnn::BackendId> CaptureAvailableBackends(const std::vector<armnn::BackendId>& onlyTheseBackends) |
| { |
| std::vector<armnn::BackendId> availableBackends; |
| #if defined(ARMNNREF_ENABLED) |
| // Careful logic here. An empty onlyTheseBackends means we always evaluate. |
| if (onlyTheseBackends.empty() || (std::find(onlyTheseBackends.begin(), onlyTheseBackends.end(), |
| armnn::Compute::CpuRef) != onlyTheseBackends.end())) |
| { |
| SUBCASE("CpuRef") |
| { |
| availableBackends.push_back({ armnn::Compute::CpuRef }); |
| } |
| } |
| #endif |
| #if defined(ARMCOMPUTENEON_ENABLED) |
| // Careful logic here. An empty onlyTheseBackends means we always evaluate. |
| if (onlyTheseBackends.empty() || (std::find(onlyTheseBackends.begin(), onlyTheseBackends.end(), |
| armnn::Compute::CpuAcc) != onlyTheseBackends.end())) |
| { |
| SUBCASE("CpuAcc") |
| { |
| availableBackends.push_back({ armnn::Compute::CpuAcc }); |
| } |
| } |
| #endif |
| #if defined(ARMCOMPUTECL_ENABLED) |
| if (onlyTheseBackends.empty() || (std::find(onlyTheseBackends.begin(), onlyTheseBackends.end(), |
| armnn::Compute::GpuAcc) != onlyTheseBackends.end())) |
| { |
| SUBCASE("GpuAcc") |
| { |
| availableBackends.push_back({ armnn::Compute::GpuAcc }); |
| } |
| } |
| #endif |
| CAPTURE(availableBackends); |
| return availableBackends; |
| } |
| |
| } // namespace |
| namespace armnnDelegate |
| { |
| |
| constexpr const char* FILE_IDENTIFIER = "TFL3"; |
| |
| /// Can be used to compare bool data coming from a tflite interpreter |
| /// Boolean types get converted to a bit representation in a vector. vector.data() returns a void pointer |
| /// instead of a pointer to bool. Therefore a special function to compare to vector of bool is required |
| void CompareData(std::vector<bool>& tensor1, std::vector<bool>& tensor2, size_t tensorSize); |
| void CompareData(bool tensor1[], bool tensor2[], size_t tensorSize); |
| |
| /// Can be used to compare float data coming from a tflite interpreter with a tolerance of limit_of_float*100 |
| void CompareData(float tensor1[], float tensor2[], size_t tensorSize); |
| |
| /// Can be used to compare float data coming from a tflite interpreter with a given percentage tolerance |
| void CompareData(float tensor1[], float tensor2[], size_t tensorSize, float percentTolerance); |
| |
| /// Can be used to compare int8_t data coming from a tflite interpreter with a tolerance of 1 |
| void CompareData(int8_t tensor1[], int8_t tensor2[], size_t tensorSize); |
| |
| /// Can be used to compare uint8_t data coming from a tflite interpreter with a tolerance of 1 |
| void CompareData(uint8_t tensor1[], uint8_t tensor2[], size_t tensorSize); |
| |
| /// Can be used to compare int16_t data coming from a tflite interpreter with a tolerance of 1 |
| void CompareData(int16_t tensor1[], int16_t tensor2[], size_t tensorSize); |
| |
| /// Can be used to compare int32_t data coming from a tflite interpreter with a tolerance of 1 |
| void CompareData(int32_t tensor1[], int32_t tensor2[], size_t tensorSize); |
| |
| /// Can be used to compare Half (Float16) data with a tolerance of limit_of_float*100 |
| void CompareData(Half tensor1[], Half tensor2[], size_t tensorSize); |
| |
| /// Can be used to compare TfLiteFloat16 data coming from a tflite interpreter |
| void CompareData(TfLiteFloat16 tensor1[], TfLiteFloat16 tensor2[], size_t tensorSize); |
| |
| /// Can be used to compare Half (Float16) data and TfLiteFloat16 data coming from a tflite interpreter |
| void CompareData(TfLiteFloat16 tensor1[], Half tensor2[], size_t tensorSize); |
| |
| /// Can be used to compare the output tensor shape |
| /// Example usage can be found in ControlTestHelper.hpp |
| void CompareOutputShape(const std::vector<int32_t>& tfLiteDelegateShape, |
| const std::vector<int32_t>& armnnDelegateShape, |
| const std::vector<int32_t>& expectedOutputShape); |
| |
| /// Can be used to compare the output tensor values |
| /// Example usage can be found in ControlTestHelper.hpp |
| template <typename T> |
| void CompareOutputData(std::vector<T>& tfLiteDelegateOutputs, |
| std::vector<T>& armnnDelegateOutputs, |
| std::vector<T>& expectedOutputValues) |
| { |
| armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputs.data(), expectedOutputValues.size()); |
| armnnDelegate::CompareData(tfLiteDelegateOutputs.data(), expectedOutputValues.data(), expectedOutputValues.size()); |
| armnnDelegate::CompareData(tfLiteDelegateOutputs.data(), armnnDelegateOutputs.data(), expectedOutputValues.size()); |
| } |
| |
| } // namespace armnnDelegate |