| // |
| // 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> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator binaryOperatorCode, |
| tflite::ActivationFunctionType activationType, |
| tflite::TensorType tensorType, |
| const std::vector <int32_t>& input0TensorShape, |
| const std::vector <int32_t>& input1TensorShape, |
| const std::vector <int32_t>& outputTensorShape, |
| std::vector<T>& input1Values, |
| bool constantInput = false, |
| 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({}))); |
| if (constantInput) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(input1Values.data()), |
| sizeof(T) * input1Values.size()))); |
| } |
| else |
| { |
| buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); |
| } |
| 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>, 3> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), |
| input0TensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("input_0"), |
| quantizationParameters); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), |
| input1TensorShape.size()), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("input_1"), |
| quantizationParameters); |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 2, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| |
| // create operator |
| tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; |
| flatbuffers::Offset<void> operatorBuiltinOptions = 0; |
| switch (binaryOperatorCode) |
| { |
| case BuiltinOperator_ADD: |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_AddOptions; |
| operatorBuiltinOptions = CreateAddOptions(flatBufferBuilder, activationType).Union(); |
| break; |
| } |
| case BuiltinOperator_DIV: |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_DivOptions; |
| operatorBuiltinOptions = CreateDivOptions(flatBufferBuilder, activationType).Union(); |
| break; |
| } |
| case BuiltinOperator_MAXIMUM: |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions; |
| operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union(); |
| break; |
| } |
| case BuiltinOperator_MINIMUM: |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions; |
| operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union(); |
| break; |
| } |
| case BuiltinOperator_MUL: |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_MulOptions; |
| operatorBuiltinOptions = CreateMulOptions(flatBufferBuilder, activationType).Union(); |
| break; |
| } |
| case BuiltinOperator_SUB: |
| { |
| operatorBuiltinOptionsType = BuiltinOptions_SubOptions; |
| operatorBuiltinOptions = CreateSubOptions(flatBufferBuilder, activationType).Union(); |
| break; |
| } |
| case BuiltinOperator_FLOOR_DIV: |
| { |
| operatorBuiltinOptionsType = tflite::BuiltinOptions_FloorDivOptions; |
| operatorBuiltinOptions = CreateSubOptions(flatBufferBuilder, activationType).Union(); |
| break; |
| } |
| default: |
| break; |
| } |
| const std::vector<int32_t> operatorInputs{0, 1}; |
| const std::vector<int32_t> operatorOutputs{2}; |
| flatbuffers::Offset <Operator> elementwiseBinaryOperator = |
| 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}; |
| 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(&elementwiseBinaryOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Elementwise Binary Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, binaryOperatorCode); |
| |
| 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 ElementwiseBinaryTest(tflite::BuiltinOperator binaryOperatorCode, |
| tflite::ActivationFunctionType activationType, |
| tflite::TensorType tensorType, |
| std::vector<armnn::BackendId>& backends, |
| std::vector<int32_t>& input0Shape, |
| std::vector<int32_t>& input1Shape, |
| std::vector<int32_t>& outputShape, |
| std::vector<T>& input0Values, |
| std::vector<T>& input1Values, |
| std::vector<T>& expectedOutputValues, |
| float quantScale = 1.0f, |
| int quantOffset = 0, |
| bool constantInput = false) |
| { |
| using namespace tflite; |
| std::vector<char> modelBuffer = CreateElementwiseBinaryTfLiteModel<T>(binaryOperatorCode, |
| activationType, |
| tensorType, |
| input0Shape, |
| input1Shape, |
| outputShape, |
| input1Values, |
| constantInput, |
| 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, input0Values); |
| armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, input0Values); |
| if (!constantInput) |
| { |
| armnnDelegate::FillInput<T>(tfLiteInterpreter, 1, input1Values); |
| armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 1, input1Values); |
| } |
| // Run EnqueWorkload |
| CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| |
| // Compare output data |
| armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, |
| armnnDelegateInterpreter, |
| outputShape, |
| expectedOutputValues); |
| armnnDelegateInterpreter.reset(nullptr); |
| } |
| |
| } // anonymous namespace |