| // |
| // Copyright © 2021, 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> 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)); |
| 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>, 2> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| inputTensorType, |
| 1, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| outputTensorType, |
| 2, |
| 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, armnnDelegate::FILE_IDENTIFIER); |
| |
| 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 delegateTestInterpreter; |
| std::vector<char> modelBuffer = CreateShapeTfLiteModel(inputTensorType, |
| outputTensorType, |
| inputShape, |
| expectedOutputShape, |
| quantScale, |
| quantOffset); |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<K> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<K>(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.Invoke() == kTfLiteOk); |
| std::vector<K> armnnOutputValues = armnnInterpreter.GetOutputResult<K>(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| |
| armnnDelegate::CompareOutputData<K>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| |
| } // anonymous namespace |