| // |
| // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #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 |
| { |
| |
| std::vector<char> CreateElementwiseUnaryTfLiteModel(tflite::BuiltinOperator unaryOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector <int32_t>& tensorShape) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers; |
| buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); |
| |
| std::array<flatbuffers::Offset<Tensor>, 2> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), |
| tensorType); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), |
| tensorType); |
| |
| // create operator |
| const std::vector<int> operatorInputs{{0}}; |
| const std::vector<int> operatorOutputs{{1}}; |
| flatbuffers::Offset <Operator> unaryOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size())); |
| |
| const std::vector<int> subgraphInputs{{0}}; |
| const std::vector<int> 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(&unaryOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Elementwise Unary Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, unaryOperatorCode); |
| |
| 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()); |
| } |
| |
| void ElementwiseUnaryFP32Test(tflite::BuiltinOperator unaryOperatorCode, |
| std::vector<armnn::BackendId>& backends, |
| std::vector<float>& inputValues, |
| std::vector<float>& expectedOutputValues) |
| { |
| using namespace tflite; |
| const std::vector<int32_t> inputShape { { 3, 1, 2} }; |
| std::vector<char> modelBuffer = CreateElementwiseUnaryTfLiteModel(unaryOperatorCode, |
| ::tflite::TensorType_FLOAT32, |
| inputShape); |
| |
| 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); |
| auto armnnDelegate = TfLiteArmnnDelegateCreate(delegateOptions); |
| CHECK(armnnDelegate != nullptr); |
| // Modify armnnDelegateInterpreter to use armnnDelegate |
| CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(armnnDelegate) == kTfLiteOk); |
| |
| // Set input data |
| auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0]; |
| auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInputId); |
| for (unsigned int i = 0; i < inputValues.size(); ++i) |
| { |
| tfLiteDelageInputData[i] = inputValues[i]; |
| } |
| |
| auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; |
| auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId); |
| for (unsigned int i = 0; i < inputValues.size(); ++i) |
| { |
| armnnDelegateInputData[i] = inputValues[i]; |
| } |
| // Run EnqueWorkload |
| CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| |
| // Compare output data |
| auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; |
| auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); |
| auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; |
| auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); |
| for (size_t i = 0; i < inputValues.size(); i++) |
| { |
| CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]); |
| CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]); |
| } |
| } |
| |
| } // anonymous namespace |
| |
| |
| |
| |