| // |
| // Copyright © 2020 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 |
| { |
| |
| 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>, 3> buffers; |
| buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); |
| |
| auto biasTensorType = ::tflite::TensorType_FLOAT32; |
| if (tensorType == ::tflite::TensorType_INT8) |
| { |
| biasTensorType = ::tflite::TensorType_INT32; |
| } |
| if (constantWeights) |
| { |
| buffers[1] = 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[2] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), |
| sizeof(int32_t) * biasData.size())); |
| |
| } |
| else |
| { |
| std::vector<float> biasData = { 10 }; |
| buffers[2] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), |
| sizeof(float) * biasData.size())); |
| } |
| } |
| else |
| { |
| buffers[1] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); |
| buffers[2] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); |
| } |
| |
| 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, |
| 0, |
| flatBufferBuilder.CreateString("input_0"), |
| quantizationParameters); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(weightsTensorShape.data(), |
| weightsTensorShape.size()), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("weights"), |
| quantizationParameters); |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), |
| biasTensorShape.size()), |
| biasTensorType, |
| 2, |
| flatBufferBuilder.CreateString("bias"), |
| quantizationParameters); |
| |
| tensors[3] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 0, |
| 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); |
| |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| template <typename T> |
| void FullyConnectedTest(std::vector<armnn::BackendId>& backends, |
| 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, |
| bool constantWeights = true, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace tflite; |
| |
| std::vector<char> modelBuffer = CreateFullyConnectedTfLiteModel(tensorType, |
| activationType, |
| inputTensorShape, |
| weightsTensorShape, |
| biasTensorShape, |
| outputTensorShape, |
| weightsData, |
| constantWeights, |
| quantScale, |
| quantOffset); |
| const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| |
| // Create TfLite Interpreters |
| 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<T>(tfLiteInterpreter, 0, inputValues); |
| armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, inputValues); |
| |
| if (!constantWeights) |
| { |
| armnnDelegate::FillInput<T>(tfLiteInterpreter, 1, weightsData); |
| armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 1, weightsData); |
| |
| if (tensorType == ::tflite::TensorType_INT8) |
| { |
| std::vector <int32_t> biasData = {10}; |
| armnnDelegate::FillInput<int32_t>(tfLiteInterpreter, 2, biasData); |
| armnnDelegate::FillInput<int32_t>(armnnDelegateInterpreter, 2, biasData); |
| } |
| else |
| { |
| std::vector<float> biasData = {10}; |
| armnnDelegate::FillInput<float>(tfLiteInterpreter, 2, biasData); |
| armnnDelegate::FillInput<float>(armnnDelegateInterpreter, 2, biasData); |
| } |
| } |
| |
| // Run EnqueWorkload |
| CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| |
| // Compare output data |
| armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, |
| armnnDelegateInterpreter, |
| outputTensorShape, |
| expectedOutputValues); |
| armnnDelegateInterpreter.reset(nullptr); |
| } |
| |
| } // anonymous namespace |