diff options
Diffstat (limited to 'delegate/src/test')
-rw-r--r-- | delegate/src/test/CastTest.cpp | 107 | ||||
-rw-r--r-- | delegate/src/test/CastTestHelper.hpp | 157 |
2 files changed, 264 insertions, 0 deletions
diff --git a/delegate/src/test/CastTest.cpp b/delegate/src/test/CastTest.cpp new file mode 100644 index 0000000000..623c045247 --- /dev/null +++ b/delegate/src/test/CastTest.cpp @@ -0,0 +1,107 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "CastTestHelper.hpp" + +#include <armnn_delegate.hpp> + +#include <flatbuffers/flatbuffers.h> +#include <tensorflow/lite/schema/schema_generated.h> + +#include <doctest/doctest.h> + +namespace armnnDelegate +{ + +void CastUint8ToFp32Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape {1, 3, 2, 3}; + + std::vector<uint8_t> inputValues { 1, 3, 1, 3, 1, 3, 1, 3, 1, + 3, 1, 3, 1, 2, 1, 3, 1, 3 }; + + std::vector<float> expectedOutputValues { 1.0f, 3.0f, 1.0f, 3.0f, 1.0f, 3.0f, 1.0f, 3.0f, 1.0f, + 3.0f, 1.0f, 3.0f, 1.0f, 2.0f, 1.0f, 3.0f, 1.0f, 3.0f }; + + CastTest<uint8_t, float>(::tflite::TensorType_UINT8, + ::tflite::TensorType_FLOAT32, + backends, + inputShape, + inputValues, + expectedOutputValues); +} + +void CastInt32ToFp32Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape {1, 3, 2, 3}; + + std::vector<int32_t> inputValues { -1, -3, -1, -3, -1, -3, -1, -3, 1, + 3, 1, 3, 1, 2, 1, 3, 1, 3 }; + + std::vector<float> expectedOutputValues { -1.0f, -3.0f, -1.0f, -3.0f, -1.0f, -3.0f, -1.0f, -3.0f, 1.0f, + 3.0f, 1.0f, 3.0f, 1.0f, 2.0f, 1.0f, 3.0f, 1.0f, 3.0f }; + + CastTest<int32_t, float>(::tflite::TensorType_INT32, + ::tflite::TensorType_FLOAT32, + backends, + inputShape, + inputValues, + expectedOutputValues); +} + +// CAST Test Suite +TEST_SUITE("CAST_CpuRefTests") +{ + +TEST_CASE ("CAST_UINT8_TO_FP32_CpuRef_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + CastUint8ToFp32Test(backends); +} + +TEST_CASE ("CAST_INT32_TO_FP32_CpuRef_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + CastInt32ToFp32Test(backends); +} + +} + +TEST_SUITE("CAST_CpuAccTests") +{ + +TEST_CASE ("CAST_UINT8_TO_FP32_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; + CastUint8ToFp32Test(backends); +} + +TEST_CASE ("CAST_INT32_TO_FP32_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; + CastInt32ToFp32Test(backends); +} + +} + +TEST_SUITE("CAST_GpuAccTests") +{ + +TEST_CASE ("CAST_UINT8_TO_FP32_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; + CastUint8ToFp32Test(backends); +} + +TEST_CASE ("CAST_INT32_TO_FP32_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; + CastInt32ToFp32Test(backends); +} + +} +// End of CAST Test Suite + +} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/CastTestHelper.hpp b/delegate/src/test/CastTestHelper.hpp new file mode 100644 index 0000000000..6b1d5ee947 --- /dev/null +++ b/delegate/src/test/CastTestHelper.hpp @@ -0,0 +1,157 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "TestUtils.hpp" + +#include <armnn_delegate.hpp> + +#include <flatbuffers/flatbuffers.h> +#include <tensorflow/lite/interpreter.h> +#include <tensorflow/lite/kernels/register.h> +#include <tensorflow/lite/model.h> +#include <tensorflow/lite/schema/schema_generated.h> +#include <tensorflow/lite/version.h> + +#include <doctest/doctest.h> + +namespace +{ +std::vector<char> CreateCastTfLiteModel(tflite::TensorType inputTensorType, + tflite::TensorType outputTensorType, + const std::vector <int32_t>& tensorShape, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({quantScale}), + flatBufferBuilder.CreateVector<int64_t>({quantOffset})); + + std::array<flatbuffers::Offset<Tensor>, 2> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), + tensorShape.size()), + inputTensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), + tensorShape.size()), + outputTensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + const std::vector<int32_t> operatorInputs({0}); + const std::vector<int32_t> operatorOutputs({1}); + + flatbuffers::Offset<Operator> castOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + BuiltinOptions_CastOptions, + CreateCastOptions(flatBufferBuilder).Union()); + + flatbuffers::Offset<flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: CAST Operator Model"); + flatbuffers::Offset<OperatorCode> operatorCode = + CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_CAST); + + const std::vector<int32_t> subgraphInputs({0}); + const std::vector<int32_t> subgraphOutputs({1}); + flatbuffers::Offset<SubGraph> subgraph = + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), + flatBufferBuilder.CreateVector(&castOperator, 1)); + + flatbuffers::Offset<Model> flatbufferModel = + CreateModel(flatBufferBuilder, + TFLITE_SCHEMA_VERSION, + flatBufferBuilder.CreateVector(&operatorCode, 1), + flatBufferBuilder.CreateVector(&subgraph, 1), + modelDescription, + flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + + flatBufferBuilder.Finish(flatbufferModel); + return std::vector<char>(flatBufferBuilder.GetBufferPointer(), + flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); +} + +template<typename T, typename K> +void CastTest(tflite::TensorType inputTensorType, + tflite::TensorType outputTensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& shape, + std::vector<T>& inputValues, + std::vector<K>& expectedOutputValues, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateCastTfLiteModel(inputTensorType, + outputTensorType, + shape, + quantScale, + quantOffset); + + const Model* tfLiteModel = GetModel(modelBuffer.data()); + + // Create TfLite Interpreters + std::unique_ptr<Interpreter> armnnDelegate; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegate) == kTfLiteOk); + CHECK(armnnDelegate != nullptr); + CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); + + std::unique_ptr<Interpreter> tfLiteDelegate; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&tfLiteDelegate) == kTfLiteOk); + CHECK(tfLiteDelegate != nullptr); + CHECK(tfLiteDelegate->AllocateTensors() == kTfLiteOk); + + // Create the ArmNN Delegate + armnnDelegate::DelegateOptions delegateOptions(backends); + std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> + theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), + armnnDelegate::TfLiteArmnnDelegateDelete); + CHECK(theArmnnDelegate != nullptr); + + // Modify armnnDelegateInterpreter to use armnnDelegate + CHECK(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); + + // Set input data + armnnDelegate::FillInput<T>(tfLiteDelegate, 0, inputValues); + armnnDelegate::FillInput<T>(armnnDelegate, 0, inputValues); + + // Run EnqueWorkload + CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); + CHECK(armnnDelegate->Invoke() == kTfLiteOk); + + // Compare output data + armnnDelegate::CompareOutputData<K>(tfLiteDelegate, + armnnDelegate, + shape, + expectedOutputValues, + 0); + + tfLiteDelegate.reset(nullptr); + armnnDelegate.reset(nullptr); +} + +} // anonymous namespace |