| // |
| // Copyright © 2020, 2023-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "TestUtils.hpp" |
| |
| #include <armnn_delegate.hpp> |
| #include <DelegateTestInterpreter.hpp> |
| |
| #include <tensorflow/lite/version.h> |
| |
| namespace |
| { |
| |
| template <typename T> |
| std::vector<char> CreateFullyConnectedTfLiteModel(tflite::TensorType tensorType, |
| tflite::ActivationFunctionType activationType, |
| const std::vector <int32_t>& inputTensorShape, |
| const std::vector <int32_t>& weightsTensorShape, |
| const std::vector <int32_t>& biasTensorShape, |
| std::vector <int32_t>& outputTensorShape, |
| std::vector <T>& weightsData, |
| bool constantWeights = true, |
| float quantScale = 1.0f, |
| int quantOffset = 0, |
| float outputQuantScale = 2.0f, |
| int outputQuantOffset = 0) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| std::array<flatbuffers::Offset<tflite::Buffer>, 5> buffers; |
| buffers[0] = CreateBuffer(flatBufferBuilder); |
| buffers[1] = CreateBuffer(flatBufferBuilder); |
| |
| auto biasTensorType = ::tflite::TensorType_FLOAT32; |
| if (tensorType == ::tflite::TensorType_INT8) |
| { |
| biasTensorType = ::tflite::TensorType_INT32; |
| } |
| if (constantWeights) |
| { |
| buffers[2] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(weightsData.data()), |
| sizeof(T) * weightsData.size())); |
| |
| if (tensorType == ::tflite::TensorType_INT8) |
| { |
| std::vector<int32_t> biasData = { 10 }; |
| buffers[3] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), |
| sizeof(int32_t) * biasData.size())); |
| |
| } |
| else |
| { |
| std::vector<float> biasData = { 10 }; |
| buffers[3] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), |
| sizeof(float) * biasData.size())); |
| } |
| } |
| else |
| { |
| buffers[2] = CreateBuffer(flatBufferBuilder); |
| buffers[3] = CreateBuffer(flatBufferBuilder); |
| } |
| buffers[4] = CreateBuffer(flatBufferBuilder); |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| |
| auto outputQuantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ outputQuantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset })); |
| |
| std::array<flatbuffers::Offset<Tensor>, 4> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("input_0"), |
| quantizationParameters); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(weightsTensorShape.data(), |
| weightsTensorShape.size()), |
| tensorType, |
| 2, |
| flatBufferBuilder.CreateString("weights"), |
| quantizationParameters); |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), |
| biasTensorShape.size()), |
| biasTensorType, |
| 3, |
| flatBufferBuilder.CreateString("bias"), |
| quantizationParameters); |
| |
| tensors[3] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 4, |
| flatBufferBuilder.CreateString("output"), |
| outputQuantizationParameters); |
| |
| |
| // create operator |
| tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_FullyConnectedOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions = |
| CreateFullyConnectedOptions(flatBufferBuilder, |
| activationType, |
| FullyConnectedOptionsWeightsFormat_DEFAULT, false).Union(); |
| |
| const std::vector<int> operatorInputs{0, 1, 2}; |
| const std::vector<int> operatorOutputs{3}; |
| flatbuffers::Offset <Operator> fullyConnectedOperator = |
| 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> subgraphInputs{0, 1, 2}; |
| const std::vector<int> subgraphOutputs{3}; |
| 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(&fullyConnectedOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: FullyConnected Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, |
| tflite::BuiltinOperator_FULLY_CONNECTED); |
| |
| 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 FullyConnectedTest(tflite::TensorType tensorType, |
| tflite::ActivationFunctionType activationType, |
| const std::vector <int32_t>& inputTensorShape, |
| const std::vector <int32_t>& weightsTensorShape, |
| const std::vector <int32_t>& biasTensorShape, |
| std::vector <int32_t>& outputTensorShape, |
| std::vector <T>& inputValues, |
| std::vector <T>& expectedOutputValues, |
| std::vector <T>& weightsData, |
| const std::vector<armnn::BackendId>& backends = {}, |
| bool constantWeights = true, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace delegateTestInterpreter; |
| |
| std::vector<char> modelBuffer = CreateFullyConnectedTfLiteModel(tensorType, |
| activationType, |
| inputTensorShape, |
| weightsTensorShape, |
| biasTensorShape, |
| outputTensorShape, |
| weightsData, |
| constantWeights, |
| quantScale, |
| quantOffset); |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends)); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| |
| if (!constantWeights) |
| { |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(weightsData, 1) == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(weightsData, 1) == kTfLiteOk); |
| |
| if (tensorType == ::tflite::TensorType_INT8) |
| { |
| std::vector <int32_t> biasData = {10}; |
| CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(biasData, 2) == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<int32_t>(biasData, 2) == kTfLiteOk); |
| } |
| else |
| { |
| std::vector<float> biasData = {10}; |
| CHECK(tfLiteInterpreter.FillInputTensor<float>(biasData, 2) == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<float>(biasData, 2) == kTfLiteOk); |
| } |
| } |
| |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| |
| 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, outputTensorShape); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| |
| } // anonymous namespace |