| // |
| // 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> CreateShapeTfLiteModel(tflite::TensorType inputTensorType, |
| tflite::TensorType outputTensorType, |
| const std::vector<int32_t>& inputTensorShape, |
| 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>, 2> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| inputTensorType, |
| 0, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| outputTensorType, |
| 0, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| |
| const std::vector<int32_t> operatorInputs({ 0 }); |
| const std::vector<int32_t> operatorOutputs({ 1 }); |
| |
| flatbuffers::Offset<Operator> shapeOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), |
| operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), |
| operatorOutputs.size()), |
| BuiltinOptions_ShapeOptions, |
| CreateShapeOptions(flatBufferBuilder, outputTensorType).Union()); |
| |
| flatbuffers::Offset<flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: SHAPE Operator Model"); |
| |
| flatbuffers::Offset<OperatorCode> operatorCode = |
| CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_SHAPE); |
| |
| const std::vector<int32_t> subgraphInputs({ 0 }); |
| const std::vector<int32_t> subgraphOutputs({ 1 }); |
| |
| 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(&shapeOperator, 1)); |
| |
| 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, typename K> |
| void ShapeTest(tflite::TensorType inputTensorType, |
| tflite::TensorType outputTensorType, |
| std::vector<armnn::BackendId>& backends, |
| std::vector<int32_t>& inputShape, |
| std::vector<T>& inputValues, |
| std::vector<K>& expectedOutputValues, |
| std::vector<int32_t>& expectedOutputShape, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace tflite; |
| std::vector<char> modelBuffer = CreateShapeTfLiteModel(inputTensorType, |
| outputTensorType, |
| inputShape, |
| expectedOutputShape, |
| quantScale, |
| quantOffset); |
| |
| const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| |
| // Create TfLite Interpreters |
| std::unique_ptr<Interpreter> armnnDelegate; |
| |
| CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| (&armnnDelegate) == kTfLiteOk); |
| CHECK(armnnDelegate != nullptr); |
| CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); |
| |
| std::unique_ptr<Interpreter> tfLiteDelegate; |
| |
| CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| (&tfLiteDelegate) == kTfLiteOk); |
| CHECK(tfLiteDelegate != nullptr); |
| CHECK(tfLiteDelegate->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(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); |
| |
| // Set input data |
| armnnDelegate::FillInput<T>(tfLiteDelegate, 0, inputValues); |
| armnnDelegate::FillInput<T>(armnnDelegate, 0, inputValues); |
| |
| // Run EnqueWorkload |
| CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); |
| CHECK(armnnDelegate->Invoke() == kTfLiteOk); |
| |
| // Compare output data |
| armnnDelegate::CompareOutputData<K>(tfLiteDelegate, |
| armnnDelegate, |
| expectedOutputShape, |
| expectedOutputValues, |
| 0); |
| |
| tfLiteDelegate.reset(nullptr); |
| armnnDelegate.reset(nullptr); |
| } |
| |
| } // anonymous namespace |