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Diffstat (limited to 'delegate/src/test/UnpackTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/UnpackTestHelper.hpp | 185 |
1 files changed, 185 insertions, 0 deletions
diff --git a/delegate/src/test/UnpackTestHelper.hpp b/delegate/src/test/UnpackTestHelper.hpp new file mode 100644 index 0000000000..848713498f --- /dev/null +++ b/delegate/src/test/UnpackTestHelper.hpp @@ -0,0 +1,185 @@ +// +// Copyright © 2021 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> + +#include <string> + +namespace +{ + +std::vector<char> CreateUnpackTfLiteModel(tflite::BuiltinOperator unpackOperatorCode, + tflite::TensorType tensorType, + std::vector<int32_t>& inputTensorShape, + const std::vector <int32_t>& outputTensorShape, + const int32_t outputTensorNum, + unsigned int axis = 0, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + const std::vector<int32_t> operatorInputs{ 0 }; + std::vector<int32_t> operatorOutputs{}; + const std::vector<int> subgraphInputs{ 0 }; + std::vector<int> subgraphOutputs{}; + + std::vector<flatbuffers::Offset<Tensor>> tensors(outputTensorNum + 1); + + // Create input tensor + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), + inputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + + for (int i = 0; i < outputTensorNum; ++i) + { + tensors[i + 1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output" + std::to_string(i)), + quantizationParameters); + + operatorOutputs.push_back(i + 1); + subgraphOutputs.push_back(i + 1); + } + + // create operator + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_UnpackOptions; + flatbuffers::Offset<void> operatorBuiltinOptions = + CreateUnpackOptions(flatBufferBuilder, outputTensorNum, axis).Union(); + + flatbuffers::Offset <Operator> unpackOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + operatorBuiltinOptionsType, + operatorBuiltinOptions); + + 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(&unpackOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Unpack Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, unpackOperatorCode); + + 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()); +} + +template <typename T> +void UnpackTest(tflite::BuiltinOperator unpackOperatorCode, + tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& inputShape, + std::vector<int32_t>& expectedOutputShape, + std::vector<T>& inputValues, + std::vector<std::vector<T>>& expectedOutputValues, + unsigned int axis = 0, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateUnpackTfLiteModel(unpackOperatorCode, + tensorType, + inputShape, + expectedOutputShape, + expectedOutputValues.size(), + axis, + quantScale, + quantOffset); + + 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 + armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, inputValues); + armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, inputValues); + + + // Run EnqueueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) + { + armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, + armnnDelegateInterpreter, + expectedOutputShape, + expectedOutputValues[i], + i); + } + + armnnDelegateInterpreter.reset(nullptr); +} + +} // anonymous namespace
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