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Diffstat (limited to 'delegate/test/LogicalTestHelper.hpp')
-rw-r--r-- | delegate/test/LogicalTestHelper.hpp | 201 |
1 files changed, 201 insertions, 0 deletions
diff --git a/delegate/test/LogicalTestHelper.hpp b/delegate/test/LogicalTestHelper.hpp new file mode 100644 index 0000000000..2f2ae7bf40 --- /dev/null +++ b/delegate/test/LogicalTestHelper.hpp @@ -0,0 +1,201 @@ +// +// Copyright © 2020, 2023 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 <schema_generated.h> +#include <tensorflow/lite/version.h> + +#include <doctest/doctest.h> + +namespace +{ + +std::vector<char> CreateLogicalBinaryTfLiteModel(tflite::BuiltinOperator logicalOperatorCode, + tflite::TensorType tensorType, + const std::vector <int32_t>& input0TensorShape, + const std::vector <int32_t>& input1TensorShape, + const std::vector <int32_t>& outputTensorShape, + 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)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + + std::array<flatbuffers::Offset<Tensor>, 3> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), + input0TensorShape.size()), + tensorType, + 1, + flatBufferBuilder.CreateString("input_0"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), + input1TensorShape.size()), + tensorType, + 2, + flatBufferBuilder.CreateString("input_1"), + quantizationParameters); + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 3, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + // create operator + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; + flatbuffers::Offset<void> operatorBuiltinOptions = 0; + switch (logicalOperatorCode) + { + case BuiltinOperator_LOGICAL_AND: + { + operatorBuiltinOptionsType = BuiltinOptions_LogicalAndOptions; + operatorBuiltinOptions = CreateLogicalAndOptions(flatBufferBuilder).Union(); + break; + } + case BuiltinOperator_LOGICAL_OR: + { + operatorBuiltinOptionsType = BuiltinOptions_LogicalOrOptions; + operatorBuiltinOptions = CreateLogicalOrOptions(flatBufferBuilder).Union(); + break; + } + default: + break; + } + const std::vector<int32_t> operatorInputs{ {0, 1} }; + const std::vector<int32_t> operatorOutputs{ 2 }; + flatbuffers::Offset <Operator> logicalBinaryOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + operatorBuiltinOptionsType, + operatorBuiltinOptions); + + const std::vector<int> subgraphInputs{ {0, 1} }; + const std::vector<int> subgraphOutputs{ 2 }; + 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(&logicalBinaryOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Logical Binary Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, logicalOperatorCode); + + 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> +void LogicalBinaryTest(tflite::BuiltinOperator logicalOperatorCode, + tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& input0Shape, + std::vector<int32_t>& input1Shape, + std::vector<int32_t>& expectedOutputShape, + std::vector<T>& input0Values, + std::vector<T>& input1Values, + std::vector<T>& expectedOutputValues, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateLogicalBinaryTfLiteModel(logicalOperatorCode, + tensorType, + input0Shape, + input1Shape, + expectedOutputShape, + quantScale, + quantOffset); + + const Model* tfLiteModel = GetModel(modelBuffer.data()); + // Create TfLite Interpreters + std::unique_ptr<Interpreter> armnnDelegateInterpreter; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegateInterpreter) == kTfLiteOk); + CHECK(armnnDelegateInterpreter != nullptr); + CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); + + std::unique_ptr<Interpreter> tfLiteInterpreter; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&tfLiteInterpreter) == kTfLiteOk); + CHECK(tfLiteInterpreter != nullptr); + CHECK(tfLiteInterpreter->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(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); + + // Set input data for the armnn interpreter + armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input0Values); + armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input1Values); + + // Set input data for the tflite interpreter + armnnDelegate::FillInput(tfLiteInterpreter, 0, input0Values); + armnnDelegate::FillInput(tfLiteInterpreter, 1, input1Values); + + // Run EnqueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data, comparing Boolean values is handled differently and needs to call the CompareData function + // directly. This is because Boolean types get converted to a bit representation in a vector. + auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); + + armnnDelegate::CompareData(expectedOutputValues, armnnDelegateOutputData, expectedOutputValues.size()); + armnnDelegate::CompareData(expectedOutputValues, tfLiteDelegateOutputData, expectedOutputValues.size()); + armnnDelegate::CompareData(tfLiteDelegateOutputData, armnnDelegateOutputData, expectedOutputValues.size()); + + armnnDelegateInterpreter.reset(nullptr); + tfLiteInterpreter.reset(nullptr); +} + +} // anonymous namespace
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