| // |
| // Copyright © 2020, 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 |
| { |
| |
| template <typename T> |
| std::vector<char> CreatePadTfLiteModel( |
| tflite::BuiltinOperator padOperatorCode, |
| tflite::TensorType tensorType, |
| tflite::MirrorPadMode paddingMode, |
| const std::vector<int32_t>& inputTensorShape, |
| const std::vector<int32_t>& paddingTensorShape, |
| const std::vector<int32_t>& outputTensorShape, |
| const std::vector<int32_t>& paddingDim, |
| const std::vector<T> paddingValue, |
| 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 paddingTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(paddingTensorShape.data(), |
| paddingTensorShape.size()), |
| tflite::TensorType_INT32, |
| 1, |
| flatBufferBuilder.CreateString("padding")); |
| |
| auto outputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 2, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| |
| std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, paddingTensor, outputTensor}; |
| |
| std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingDim.data()), |
| sizeof(int32_t) * paddingDim.size()))); |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| |
| std::vector<int32_t> operatorInputs; |
| std::vector<int> subgraphInputs; |
| |
| tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_PadOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions; |
| |
| if (padOperatorCode == tflite::BuiltinOperator_PAD) |
| { |
| operatorInputs = {{ 0, 1 }}; |
| subgraphInputs = {{ 0, 1 }}; |
| operatorBuiltinOptions = CreatePadOptions(flatBufferBuilder).Union(); |
| } |
| else if(padOperatorCode == tflite::BuiltinOperator_MIRROR_PAD) |
| { |
| operatorInputs = {{ 0, 1 }}; |
| subgraphInputs = {{ 0, 1 }}; |
| |
| operatorBuiltinOptionsType = BuiltinOptions_MirrorPadOptions; |
| operatorBuiltinOptions = CreateMirrorPadOptions(flatBufferBuilder, paddingMode).Union(); |
| } |
| else if (padOperatorCode == tflite::BuiltinOperator_PADV2) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingValue.data()), |
| sizeof(T)))); |
| |
| const std::vector<int32_t> shape = { 1 }; |
| auto padValueTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(shape.data(), |
| shape.size()), |
| tensorType, |
| 3, |
| flatBufferBuilder.CreateString("paddingValue"), |
| quantizationParameters); |
| |
| tensors.push_back(padValueTensor); |
| |
| operatorInputs = {{ 0, 1, 3 }}; |
| subgraphInputs = {{ 0, 1, 3 }}; |
| |
| operatorBuiltinOptionsType = BuiltinOptions_PadV2Options; |
| operatorBuiltinOptions = CreatePadV2Options(flatBufferBuilder).Union(); |
| } |
| |
| // create operator |
| const std::vector<int32_t> operatorOutputs{ 2 }; |
| flatbuffers::Offset <Operator> paddingOperator = |
| 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(&paddingOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Pad Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, |
| padOperatorCode); |
| |
| 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> |
| void PadTest(tflite::BuiltinOperator padOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector<armnn::BackendId>& backends, |
| const std::vector<int32_t>& inputShape, |
| const std::vector<int32_t>& paddingShape, |
| std::vector<int32_t>& outputShape, |
| std::vector<T>& inputValues, |
| std::vector<int32_t>& paddingDim, |
| std::vector<T>& expectedOutputValues, |
| T paddingValue, |
| float quantScale = 1.0f, |
| int quantOffset = 0, |
| tflite::MirrorPadMode paddingMode = tflite::MirrorPadMode_SYMMETRIC) |
| { |
| using namespace delegateTestInterpreter; |
| std::vector<char> modelBuffer = CreatePadTfLiteModel<T>(padOperatorCode, |
| tensorType, |
| paddingMode, |
| inputShape, |
| paddingShape, |
| outputShape, |
| paddingDim, |
| {paddingValue}, |
| quantScale, |
| quantOffset); |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(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.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| |
| armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| |
| } // anonymous namespace |