| // |
| // Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "TestUtils.hpp" |
| |
| #include <armnn_delegate.hpp> |
| #include <DelegateTestInterpreter.hpp> |
| |
| #include <flatbuffers/flatbuffers.h> |
| #include <tensorflow/lite/kernels/register.h> |
| #include <tensorflow/lite/version.h> |
| |
| #include <schema_generated.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, armnnDelegate::FILE_IDENTIFIER); |
| |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| 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<bool>& input0Values, |
| std::vector<bool>& input1Values, |
| std::vector<bool>& expectedOutputValues, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace delegateTestInterpreter; |
| std::vector<char> modelBuffer = CreateLogicalBinaryTfLiteModel(logicalOperatorCode, |
| tensorType, |
| input0Shape, |
| input1Shape, |
| expectedOutputShape, |
| quantScale, |
| quantOffset); |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor(input0Values, 0) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor(input1Values, 1) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<bool> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult(0); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor(input0Values, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor(input1Values, 1) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<bool> armnnOutputValues = armnnInterpreter.GetOutputResult(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape); |
| |
| armnnDelegate::CompareData(expectedOutputValues, armnnOutputValues, expectedOutputValues.size()); |
| armnnDelegate::CompareData(expectedOutputValues, tfLiteOutputValues, expectedOutputValues.size()); |
| armnnDelegate::CompareData(tfLiteOutputValues, armnnOutputValues, expectedOutputValues.size()); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| |
| } // anonymous namespace |