| // |
| // 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> CreateTransposeTfLiteModel(tflite::TensorType tensorType, |
| const std::vector <int32_t>& input0TensorShape, |
| const std::vector <int32_t>& inputPermVecShape, |
| const std::vector <int32_t>& outputTensorShape, |
| const std::vector<int32_t>& inputPermVec) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers; |
| buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); |
| buffers[1] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(inputPermVec.data()), |
| sizeof(int32_t) * inputPermVec.size())); |
| std::array<flatbuffers::Offset<Tensor>, 3> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), |
| input0TensorShape.size()), |
| tensorType, 0); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputPermVecShape.data(), |
| inputPermVecShape.size()), |
| tflite::TensorType_INT32, 1, |
| flatBufferBuilder.CreateString("permutation_vector")); |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType); |
| const std::vector<int32_t> operatorInputs{0, 1}; |
| const std::vector<int32_t> operatorOutputs{2}; |
| flatbuffers::Offset <Operator> transposeOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| BuiltinOptions_TransposeOptions, |
| CreateTransposeOptions(flatBufferBuilder).Union()); |
| 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(&transposeOperator, 1)); |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Transpose Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, |
| tflite::BuiltinOperator_TRANSPOSE); |
| 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 TransposeFP32Test(std::vector<armnn::BackendId>& backends) |
| { |
| using namespace tflite; |
| |
| // set test input data |
| std::vector<int32_t> input0Shape {4, 2, 3}; |
| std::vector<int32_t> inputPermVecShape {3}; |
| std::vector<int32_t> outputShape {2, 3, 4}; |
| |
| std::vector<float> input0Values = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, |
| 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}; |
| std::vector<int32_t> inputPermVec = {2, 0, 1}; |
| std::vector<float> expectedOutputValues = {0, 3, 6, 9, 12, 15, 18, 21, 1, 4, 7, 10, |
| 13, 16, 19, 22, 2, 5, 8, 11, 14, 17, 20, 23}; |
| |
| // create model |
| std::vector<char> modelBuffer = CreateTransposeTfLiteModel(::tflite::TensorType_FLOAT32, |
| input0Shape, |
| inputPermVecShape, |
| outputShape, |
| inputPermVec); |
| |
| 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 for tflite |
| auto tfLiteInterpreterInput0Id = tfLiteInterpreter->inputs()[0]; |
| auto tfLiteInterpreterInput0Data = tfLiteInterpreter->typed_tensor<float>(tfLiteInterpreterInput0Id); |
| for (unsigned int i = 0; i < input0Values.size(); ++i) |
| { |
| tfLiteInterpreterInput0Data[i] = input0Values[i]; |
| } |
| |
| auto tfLiteInterpreterInput1Id = tfLiteInterpreter->inputs()[1]; |
| auto tfLiteInterpreterInput1Data = tfLiteInterpreter->typed_tensor<int32_t>(tfLiteInterpreterInput1Id); |
| for (unsigned int i = 0; i < inputPermVec.size(); ++i) |
| { |
| tfLiteInterpreterInput1Data[i] = inputPermVec[i]; |
| } |
| |
| //Set input data for armnn delegate |
| auto armnnDelegateInput0Id = armnnDelegateInterpreter->inputs()[0]; |
| auto armnnDelegateInput0Data = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInput0Id); |
| for (unsigned int i = 0; i < input0Values.size(); ++i) |
| { |
| armnnDelegateInput0Data[i] = input0Values[i]; |
| } |
| |
| auto armnnDelegateInput1Id = armnnDelegateInterpreter->inputs()[1]; |
| auto armnnDelegateInput1Data = armnnDelegateInterpreter->typed_tensor<int32_t>(armnnDelegateInput1Id); |
| for (unsigned int i = 0; i < inputPermVec.size(); ++i) |
| { |
| armnnDelegateInput1Data[i] = inputPermVec[i]; |
| } |
| |
| // Run EnqueWorkload |
| CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| |
| // Compare output data |
| auto tfLiteInterpreterOutputId = tfLiteInterpreter->outputs()[0]; |
| auto tfLiteInterpreterOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteInterpreterOutputId); |
| auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; |
| auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); |
| for (size_t i = 0; i < expectedOutputValues.size(); ++i) |
| { |
| CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]); |
| CHECK(tfLiteInterpreterOutputData[i] == expectedOutputValues[i]); |
| CHECK(tfLiteInterpreterOutputData[i] == armnnDelegateOutputData[i]); |
| } |
| |
| armnnDelegateInterpreter.reset(nullptr); |
| } |
| } |