| // |
| // 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 |
| { |
| |
| std::vector<char> CreateResizeTfLiteModel(tflite::BuiltinOperator operatorCode, |
| tflite::TensorType inputTensorType, |
| const std::vector <int32_t>& inputTensorShape, |
| const std::vector <int32_t>& sizeTensorData, |
| const std::vector <int32_t>& sizeTensorShape, |
| const std::vector <int32_t>& outputTensorShape) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
| buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); |
| buffers.push_back(CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t*>(sizeTensorData.data()), |
| sizeof(int32_t) * sizeTensorData.size()))); |
| |
| std::array<flatbuffers::Offset<Tensor>, 3> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), |
| inputTensorType, |
| 0, |
| flatBufferBuilder.CreateString("input_tensor")); |
| |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(), |
| sizeTensorShape.size()), |
| TensorType_INT32, |
| 1, |
| flatBufferBuilder.CreateString("size_input_tensor")); |
| |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| inputTensorType, |
| 0, |
| flatBufferBuilder.CreateString("output_tensor")); |
| |
| // Create Operator |
| tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; |
| flatbuffers::Offset<void> operatorBuiltinOption = 0; |
| switch (operatorCode) |
| { |
| case BuiltinOperator_RESIZE_BILINEAR: |
| { |
| operatorBuiltinOption = CreateResizeBilinearOptions(flatBufferBuilder, false, false).Union(); |
| operatorBuiltinOptionsType = tflite::BuiltinOptions_ResizeBilinearOptions; |
| break; |
| } |
| case BuiltinOperator_RESIZE_NEAREST_NEIGHBOR: |
| { |
| operatorBuiltinOption = CreateResizeNearestNeighborOptions(flatBufferBuilder, false, false).Union(); |
| operatorBuiltinOptionsType = tflite::BuiltinOptions_ResizeNearestNeighborOptions; |
| break; |
| } |
| default: |
| break; |
| } |
| |
| const std::vector<int> operatorInputs{0, 1}; |
| const std::vector<int> operatorOutputs{2}; |
| flatbuffers::Offset <Operator> resizeOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| operatorBuiltinOptionsType, |
| operatorBuiltinOption); |
| |
| 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(&resizeOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Resize Biliniar Operator Model"); |
| flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, operatorCode); |
| |
| flatbuffers::Offset <Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&opCode, 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 ResizeFP32TestImpl(tflite::BuiltinOperator operatorCode, |
| std::vector<armnn::BackendId>& backends, |
| std::vector<float>& input1Values, |
| std::vector<int32_t> input1Shape, |
| std::vector<int32_t> input2NewShape, |
| std::vector<int32_t> input2Shape, |
| std::vector<float>& expectedOutputValues, |
| std::vector<int32_t> expectedOutputShape) |
| { |
| using namespace tflite; |
| |
| std::vector<char> modelBuffer = CreateResizeTfLiteModel(operatorCode, |
| ::tflite::TensorType_FLOAT32, |
| input1Shape, |
| input2NewShape, |
| input2Shape, |
| expectedOutputShape); |
| |
| const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| |
| // The model will be executed using tflite and using the armnn delegate so that the outputs |
| // can be compared. |
| |
| // Create TfLite Interpreter with armnn delegate |
| std::unique_ptr<Interpreter> armnnDelegateInterpreter; |
| CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| (&armnnDelegateInterpreter) == kTfLiteOk); |
| CHECK(armnnDelegateInterpreter != nullptr); |
| CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); |
| |
| // Create TfLite Interpreter without armnn delegate |
| 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 for the armnn interpreter |
| armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input1Values); |
| armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input2NewShape); |
| |
| // Set input data for the tflite interpreter |
| armnnDelegate::FillInput(tfLiteInterpreter, 0, input1Values); |
| armnnDelegate::FillInput(tfLiteInterpreter, 1, input2NewShape); |
| |
| // Run EnqueWorkload |
| CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| CHECK(tfLiteInterpreter->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 < expectedOutputValues.size(); i++) |
| { |
| CHECK(expectedOutputValues[i] == doctest::Approx(armnnDelegateOutputData[i])); |
| CHECK(armnnDelegateOutputData[i] == doctest::Approx(tfLiteDelageOutputData[i])); |
| } |
| |
| armnnDelegateInterpreter.reset(nullptr); |
| } |
| |
| } // anonymous namespace |