| // |
| // 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 |
| { |
| |
| template <typename InputT, typename OutputT> |
| std::vector<char> CreateArgMinMaxTfLiteModel(tflite::BuiltinOperator argMinMaxOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector<int32_t>& inputTensorShape, |
| const std::vector<int32_t>& axisTensorShape, |
| const std::vector<int32_t>& outputTensorShape, |
| const std::vector<OutputT> axisValue, |
| tflite::TensorType outputType, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| |
| auto inputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| |
| auto axisTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), |
| axisTensorShape.size()), |
| tflite::TensorType_INT32, |
| 1, |
| flatBufferBuilder.CreateString("axis")); |
| |
| auto outputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| outputType, |
| 2, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| |
| std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, axisTensor, outputTensor }; |
| |
| std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
| buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisValue.data()), |
| sizeof(OutputT)))); |
| buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); |
| |
| std::vector<int32_t> operatorInputs = {{ 0, 1 }}; |
| std::vector<int> subgraphInputs = {{ 0, 1 }}; |
| |
| tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_ArgMaxOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions = CreateArgMaxOptions(flatBufferBuilder, outputType).Union(); |
| |
| if (argMinMaxOperatorCode == tflite::BuiltinOperator_ARG_MIN) |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_ArgMinOptions; |
| operatorBuiltinOptions = CreateArgMinOptions(flatBufferBuilder, outputType).Union(); |
| } |
| |
| // create operator |
| const std::vector<int32_t> operatorOutputs{ 2 }; |
| flatbuffers::Offset <Operator> argMinMaxOperator = |
| 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> 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(&argMinMaxOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: ArgMinMax Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, |
| argMinMaxOperatorCode); |
| |
| 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 InputT, typename OutputT> |
| void ArgMinMaxTest(tflite::BuiltinOperator argMinMaxOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector<armnn::BackendId>& backends, |
| const std::vector<int32_t>& inputShape, |
| const std::vector<int32_t>& axisShape, |
| std::vector<int32_t>& outputShape, |
| std::vector<InputT>& inputValues, |
| std::vector<OutputT>& expectedOutputValues, |
| OutputT axisValue, |
| tflite::TensorType outputType, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace tflite; |
| std::vector<char> modelBuffer = CreateArgMinMaxTfLiteModel<InputT, OutputT>(argMinMaxOperatorCode, |
| tensorType, |
| inputShape, |
| axisShape, |
| outputShape, |
| {axisValue}, |
| outputType, |
| quantScale, |
| quantOffset); |
| |
| const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| CHECK(tfLiteModel != nullptr); |
| |
| 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 |
| armnnDelegate::FillInput<InputT>(tfLiteInterpreter, 0, inputValues); |
| armnnDelegate::FillInput<InputT>(armnnDelegateInterpreter, 0, inputValues); |
| |
| // Run EnqueueWorkload |
| CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| |
| // Compare output data |
| auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; |
| auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<OutputT>(tfLiteDelegateOutputId); |
| auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; |
| auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<OutputT>(armnnDelegateOutputId); |
| |
| for (size_t i = 0; i < expectedOutputValues.size(); i++) |
| { |
| CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]); |
| CHECK(tfLiteDelageOutputData[i] == expectedOutputValues[i]); |
| CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]); |
| } |
| } |
| |
| } // anonymous namespace |