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//
// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "TestUtils.hpp"
#include <armnn_delegate.hpp>
#include <DelegateTestInterpreter.hpp>
#include <flatbuffers/flatbuffers.h>
#include <tensorflow/lite/kernels/register.h>
#include <tensorflow/lite/version.h>
#include <doctest/doctest.h>
namespace
{
std::vector<char> CreateStridedSliceTfLiteModel(tflite::TensorType tensorType,
const std::vector<int32_t>& inputTensorShape,
const std::vector<int32_t>& beginTensorData,
const std::vector<int32_t>& endTensorData,
const std::vector<int32_t>& strideTensorData,
const std::vector<int32_t>& beginTensorShape,
const std::vector<int32_t>& endTensorShape,
const std::vector<int32_t>& strideTensorShape,
const std::vector<int32_t>& outputTensorShape,
const int32_t beginMask,
const int32_t endMask,
const int32_t ellipsisMask,
const int32_t newAxisMask,
const int32_t ShrinkAxisMask,
const armnn::DataLayout& dataLayout)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
flatbuffers::Offset<tflite::Buffer> buffers[6] = {
CreateBuffer(flatBufferBuilder),
CreateBuffer(flatBufferBuilder),
CreateBuffer(flatBufferBuilder,
flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(beginTensorData.data()),
sizeof(int32_t) * beginTensorData.size())),
CreateBuffer(flatBufferBuilder,
flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(endTensorData.data()),
sizeof(int32_t) * endTensorData.size())),
CreateBuffer(flatBufferBuilder,
flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(strideTensorData.data()),
sizeof(int32_t) * strideTensorData.size())),
CreateBuffer(flatBufferBuilder)
};
std::array<flatbuffers::Offset<Tensor>, 5> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
inputTensorShape.size()),
tensorType,
1,
flatBufferBuilder.CreateString("input"));
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(beginTensorShape.data(),
beginTensorShape.size()),
::tflite::TensorType_INT32,
2,
flatBufferBuilder.CreateString("begin_tensor"));
tensors[2] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(endTensorShape.data(),
endTensorShape.size()),
::tflite::TensorType_INT32,
3,
flatBufferBuilder.CreateString("end_tensor"));
tensors[3] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(strideTensorShape.data(),
strideTensorShape.size()),
::tflite::TensorType_INT32,
4,
flatBufferBuilder.CreateString("stride_tensor"));
tensors[4] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
tensorType,
5,
flatBufferBuilder.CreateString("output"));
// create operator
tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_StridedSliceOptions;
flatbuffers::Offset<void> operatorBuiltinOptions = CreateStridedSliceOptions(flatBufferBuilder,
beginMask,
endMask,
ellipsisMask,
newAxisMask,
ShrinkAxisMask).Union();
const std::vector<int> operatorInputs{ 0, 1, 2, 3 };
const std::vector<int> operatorOutputs{ 4 };
flatbuffers::Offset <Operator> sliceOperator =
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, 2, 3 };
const std::vector<int> subgraphOutputs{ 4 };
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(&sliceOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: StridedSlice Operator Model");
flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
BuiltinOperator_STRIDED_SLICE);
flatbuffers::Offset <Model> flatbufferModel =
CreateModel(flatBufferBuilder,
TFLITE_SCHEMA_VERSION,
flatBufferBuilder.CreateVector(&operatorCode, 1),
flatBufferBuilder.CreateVector(&subgraph, 1),
modelDescription,
flatBufferBuilder.CreateVector(buffers, 6));
flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}
template <typename T>
void StridedSliceTestImpl(std::vector<armnn::BackendId>& backends,
std::vector<T>& inputValues,
std::vector<T>& expectedOutputValues,
std::vector<int32_t>& beginTensorData,
std::vector<int32_t>& endTensorData,
std::vector<int32_t>& strideTensorData,
std::vector<int32_t>& inputTensorShape,
std::vector<int32_t>& beginTensorShape,
std::vector<int32_t>& endTensorShape,
std::vector<int32_t>& strideTensorShape,
std::vector<int32_t>& outputTensorShape,
const int32_t beginMask = 0,
const int32_t endMask = 0,
const int32_t ellipsisMask = 0,
const int32_t newAxisMask = 0,
const int32_t ShrinkAxisMask = 0,
const armnn::DataLayout& dataLayout = armnn::DataLayout::NHWC)
{
using namespace delegateTestInterpreter;
std::vector<char> modelBuffer = CreateStridedSliceTfLiteModel(
::tflite::TensorType_FLOAT32,
inputTensorShape,
beginTensorData,
endTensorData,
strideTensorData,
beginTensorShape,
endTensorShape,
strideTensorShape,
outputTensorShape,
beginMask,
endMask,
ellipsisMask,
newAxisMask,
ShrinkAxisMask,
dataLayout);
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
// Setup interpreter with Arm NN Delegate applied.
auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShape);
tfLiteInterpreter.Cleanup();
armnnInterpreter.Cleanup();
} // End of StridedSlice Test
} // anonymous namespace
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