| // |
| // Copyright © 2020 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> CreateComparisonTfLiteModel(tflite::BuiltinOperator comparisonOperatorCode, |
| 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, flatBufferBuilder.CreateVector({}))); |
| |
| 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, |
| 0, |
| flatBufferBuilder.CreateString("input_0"), |
| quantizationParameters); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), |
| input1TensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("input_1"), |
| quantizationParameters); |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| ::tflite::TensorType_BOOL, |
| 0); |
| |
| // create operator |
| tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_EqualOptions;; |
| flatbuffers::Offset<void> operatorBuiltinOptions = CreateEqualOptions(flatBufferBuilder).Union(); |
| switch (comparisonOperatorCode) |
| { |
| case BuiltinOperator_EQUAL: |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_EqualOptions; |
| operatorBuiltinOptions = CreateEqualOptions(flatBufferBuilder).Union(); |
| break; |
| } |
| case BuiltinOperator_NOT_EQUAL: |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_NotEqualOptions; |
| operatorBuiltinOptions = CreateNotEqualOptions(flatBufferBuilder).Union(); |
| break; |
| } |
| case BuiltinOperator_GREATER: |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_GreaterOptions; |
| operatorBuiltinOptions = CreateGreaterOptions(flatBufferBuilder).Union(); |
| break; |
| } |
| case BuiltinOperator_GREATER_EQUAL: |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_GreaterEqualOptions; |
| operatorBuiltinOptions = CreateGreaterEqualOptions(flatBufferBuilder).Union(); |
| break; |
| } |
| case BuiltinOperator_LESS: |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_LessOptions; |
| operatorBuiltinOptions = CreateLessOptions(flatBufferBuilder).Union(); |
| break; |
| } |
| case BuiltinOperator_LESS_EQUAL: |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_LessEqualOptions; |
| operatorBuiltinOptions = CreateLessEqualOptions(flatBufferBuilder).Union(); |
| break; |
| } |
| default: |
| break; |
| } |
| const std::vector<int32_t> operatorInputs{0, 1}; |
| const std::vector<int32_t> operatorOutputs{2}; |
| flatbuffers::Offset <Operator> comparisonOperator = |
| 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(&comparisonOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Comparison Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, comparisonOperatorCode); |
| |
| 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 ComparisonTest(tflite::BuiltinOperator comparisonOperatorCode, |
| tflite::TensorType tensorType, |
| std::vector<armnn::BackendId>& backends, |
| std::vector<int32_t>& input0Shape, |
| std::vector<int32_t>& input1Shape, |
| std::vector<int32_t>& outputShape, |
| std::vector<T>& input0Values, |
| std::vector<T>& input1Values, |
| std::vector<bool>& expectedOutputValues, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace tflite; |
| std::vector<char> modelBuffer = CreateComparisonTfLiteModel(comparisonOperatorCode, |
| tensorType, |
| input0Shape, |
| input1Shape, |
| outputShape, |
| 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 |
| auto tfLiteDelegateInput0Id = tfLiteInterpreter->inputs()[0]; |
| auto tfLiteDelageInput0Data = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInput0Id); |
| for (unsigned int i = 0; i < input0Values.size(); ++i) |
| { |
| tfLiteDelageInput0Data[i] = input0Values[i]; |
| } |
| |
| auto tfLiteDelegateInput1Id = tfLiteInterpreter->inputs()[1]; |
| auto tfLiteDelageInput1Data = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInput1Id); |
| for (unsigned int i = 0; i < input1Values.size(); ++i) |
| { |
| tfLiteDelageInput1Data[i] = input1Values[i]; |
| } |
| |
| auto armnnDelegateInput0Id = armnnDelegateInterpreter->inputs()[0]; |
| auto armnnDelegateInput0Data = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInput0Id); |
| for (unsigned int i = 0; i < input0Values.size(); ++i) |
| { |
| armnnDelegateInput0Data[i] = input0Values[i]; |
| } |
| |
| auto armnnDelegateInput1Id = armnnDelegateInterpreter->inputs()[1]; |
| auto armnnDelegateInput1Data = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInput1Id); |
| for (unsigned int i = 0; i < input1Values.size(); ++i) |
| { |
| armnnDelegateInput1Data[i] = input1Values[i]; |
| } |
| |
| // Run EnqueWorkload |
| CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| // Compare output data |
| auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; |
| auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<bool>(tfLiteDelegateOutputId); |
| auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; |
| auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<bool>(armnnDelegateOutputId); |
| |
| armnnDelegate::CompareData(expectedOutputValues , armnnDelegateOutputData, expectedOutputValues.size()); |
| armnnDelegate::CompareData(expectedOutputValues , tfLiteDelageOutputData , expectedOutputValues.size()); |
| armnnDelegate::CompareData(tfLiteDelageOutputData, armnnDelegateOutputData, expectedOutputValues.size()); |
| } |
| |
| } // anonymous namespace |