| // |
| // 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> CreatePreluTfLiteModel(tflite::BuiltinOperator preluOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector<int32_t>& inputShape, |
| const std::vector<int32_t>& alphaShape, |
| const std::vector<int32_t>& outputShape, |
| std::vector<float>& alphaData, |
| bool alphaIsConstant) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| 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 *>(alphaData.data()), sizeof(float) * alphaData.size()))); |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ 1.0f }), |
| flatBufferBuilder.CreateVector<int64_t>({ 0 })); |
| |
| auto inputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), |
| inputShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| |
| auto alphaTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(alphaShape.data(), |
| alphaShape.size()), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("alpha"), |
| quantizationParameters); |
| |
| auto outputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), |
| outputShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| |
| std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, alphaTensor, outputTensor }; |
| |
| const std::vector<int> operatorInputs{0, 1}; |
| const std::vector<int> operatorOutputs{2}; |
| flatbuffers::Offset <Operator> preluOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size())); |
| |
| std::vector<int> subgraphInputs{0}; |
| if (!alphaIsConstant) |
| { |
| subgraphInputs.push_back(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(&preluOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Prelu Operator Model"); |
| flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, preluOperatorCode); |
| |
| flatbuffers::Offset <Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&opCode, 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()); |
| } |
| |
| void PreluTest(tflite::BuiltinOperator preluOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector<armnn::BackendId>& backends, |
| const std::vector<int32_t>& inputShape, |
| const std::vector<int32_t>& alphaShape, |
| std::vector<int32_t>& outputShape, |
| std::vector<float>& inputData, |
| std::vector<float>& alphaData, |
| std::vector<float>& expectedOutput, |
| bool alphaIsConstant) |
| { |
| using namespace tflite; |
| |
| std::vector<char> modelBuffer = CreatePreluTfLiteModel(preluOperatorCode, |
| tensorType, |
| inputShape, |
| alphaShape, |
| outputShape, |
| alphaData, |
| alphaIsConstant); |
| |
| 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<float>(tfLiteInterpreter, 0, inputData); |
| armnnDelegate::FillInput<float>(armnnDelegateInterpreter, 0, inputData); |
| |
| // Set alpha data if not constant |
| if (!alphaIsConstant) { |
| armnnDelegate::FillInput<float>(tfLiteInterpreter, 1, alphaData); |
| armnnDelegate::FillInput<float>(armnnDelegateInterpreter, 1, alphaData); |
| } |
| |
| // Run EnqueueWorkload |
| CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| |
| // Compare output data |
| auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; |
| |
| auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); |
| |
| auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; |
| auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); |
| |
| for (size_t i = 0; i < expectedOutput.size(); i++) |
| { |
| CHECK(expectedOutput[i] == armnnDelegateOutputData[i]); |
| CHECK(tfLiteDelegateOutputData[i] == expectedOutput[i]); |
| CHECK(tfLiteDelegateOutputData[i] == armnnDelegateOutputData[i]); |
| } |
| } |
| } // anonymous namespace |