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//
// 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);
}
}
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