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150 lines
6.8 KiB
150 lines
6.8 KiB
//
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// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved.
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// SPDX-License-Identifier: MIT
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//
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#pragma once
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#include "TestUtils.hpp"
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#include <armnn_delegate.hpp>
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#include <DelegateTestInterpreter.hpp>
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#include <flatbuffers/flatbuffers.h>
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#include <tensorflow/lite/kernels/register.h>
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#include <tensorflow/lite/version.h>
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#include <schema_generated.h>
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#include <doctest/doctest.h>
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namespace
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{
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template <typename T>
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std::vector<char> CreateFillTfLiteModel(tflite::BuiltinOperator fillOperatorCode,
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tflite::TensorType tensorType,
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const std::vector<int32_t>& inputShape,
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const std::vector <int32_t>& tensorShape,
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const std::vector<T> fillValue)
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{
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using namespace tflite;
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flatbuffers::FlatBufferBuilder flatBufferBuilder;
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std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
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buffers.push_back(CreateBuffer(flatBufferBuilder));
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buffers.push_back(
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CreateBuffer(flatBufferBuilder,
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flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(tensorShape.data()),
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sizeof(int32_t) * tensorShape.size())));
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buffers.push_back(
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CreateBuffer(flatBufferBuilder,
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flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(fillValue.data()),
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sizeof(T) * fillValue.size())));
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buffers.push_back(CreateBuffer(flatBufferBuilder));
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std::array<flatbuffers::Offset<Tensor>, 3> tensors;
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tensors[0] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(inputShape.data(),
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inputShape.size()),
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tflite::TensorType_INT32,
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1,
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flatBufferBuilder.CreateString("dims"));
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std::vector<int32_t> fillShape = {};
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tensors[1] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(fillShape.data(),
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fillShape.size()),
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tensorType,
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2,
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flatBufferBuilder.CreateString("value"));
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tensors[2] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
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tensorShape.size()),
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tensorType,
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3,
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flatBufferBuilder.CreateString("output"));
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tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_FillOptions;
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flatbuffers::Offset<void> operatorBuiltinOptions = CreateFillOptions(flatBufferBuilder).Union();
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// create operator
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const std::vector<int> operatorInputs{ {0, 1} };
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const std::vector<int> operatorOutputs{ 2 };
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flatbuffers::Offset <Operator> fillOperator =
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CreateOperator(flatBufferBuilder,
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0,
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flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
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operatorBuiltinOptionsType,
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operatorBuiltinOptions);
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const std::vector<int> subgraphInputs{ {0, 1} };
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const std::vector<int> subgraphOutputs{ 2 };
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flatbuffers::Offset <SubGraph> subgraph =
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CreateSubGraph(flatBufferBuilder,
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flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
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flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
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flatBufferBuilder.CreateVector(&fillOperator, 1));
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flatbuffers::Offset <flatbuffers::String> modelDescription =
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flatBufferBuilder.CreateString("ArmnnDelegate: Fill Operator Model");
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flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
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fillOperatorCode);
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flatbuffers::Offset <Model> flatbufferModel =
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CreateModel(flatBufferBuilder,
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TFLITE_SCHEMA_VERSION,
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flatBufferBuilder.CreateVector(&operatorCode, 1),
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flatBufferBuilder.CreateVector(&subgraph, 1),
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modelDescription,
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flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
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flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
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return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
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flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
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}
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template <typename T>
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void FillTest(tflite::BuiltinOperator fillOperatorCode,
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tflite::TensorType tensorType,
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const std::vector<armnn::BackendId>& backends,
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std::vector<int32_t >& inputShape,
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std::vector<int32_t >& tensorShape,
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std::vector<T>& expectedOutputValues,
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T fillValue)
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{
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using namespace delegateTestInterpreter;
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std::vector<char> modelBuffer = CreateFillTfLiteModel<T>(fillOperatorCode,
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tensorType,
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inputShape,
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tensorShape,
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{fillValue});
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// Setup interpreter with just TFLite Runtime.
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auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
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CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
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CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
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std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
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std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
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// Setup interpreter with Arm NN Delegate applied.
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auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
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CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
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CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
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std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
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std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
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armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
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armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, tensorShape);
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tfLiteInterpreter.Cleanup();
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armnnInterpreter.Cleanup();
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}
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} // anonymous namespace
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