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Diffstat (limited to 'delegate/src/test/NormalizationTestHelper.hpp')
-rw-r--r-- | delegate/src/test/NormalizationTestHelper.hpp | 181 |
1 files changed, 181 insertions, 0 deletions
diff --git a/delegate/src/test/NormalizationTestHelper.hpp b/delegate/src/test/NormalizationTestHelper.hpp new file mode 100644 index 0000000000..26286b1c88 --- /dev/null +++ b/delegate/src/test/NormalizationTestHelper.hpp @@ -0,0 +1,181 @@ +// +// 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> + +namespace +{ + +std::vector<char> CreateNormalizationTfLiteModel(tflite::BuiltinOperator normalizationOperatorCode, + tflite::TensorType tensorType, + const std::vector<int32_t>& inputTensorShape, + const std::vector<int32_t>& outputTensorShape, + int32_t radius, + float bias, + float alpha, + float beta, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + auto inputTensor = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), + inputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + + auto outputTensor = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 1, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, outputTensor }; + + std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + + std::vector<int32_t> operatorInputs = {{ 0 }}; + std::vector<int> subgraphInputs = {{ 0 }}; + + tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_L2NormOptions; + flatbuffers::Offset<void> operatorBuiltinOptions = CreateL2NormOptions(flatBufferBuilder, + tflite::ActivationFunctionType_NONE).Union(); + + if (normalizationOperatorCode == tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION) + { + operatorBuiltinOptionsType = BuiltinOptions_LocalResponseNormalizationOptions; + operatorBuiltinOptions = + CreateLocalResponseNormalizationOptions(flatBufferBuilder, radius, bias, alpha, beta).Union(); + } + + // create operator + const std::vector<int32_t> operatorOutputs{{ 1 }}; + flatbuffers::Offset <Operator> normalizationOperator = + 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> subgraphOutputs{{ 1 }}; + 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(&normalizationOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Normalization Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, + normalizationOperatorCode); + + 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 NormalizationTest(tflite::BuiltinOperator normalizationOperatorCode, + tflite::TensorType tensorType, + const std::vector<armnn::BackendId>& backends, + const std::vector<int32_t>& inputShape, + std::vector<int32_t>& outputShape, + std::vector<T>& inputValues, + std::vector<T>& expectedOutputValues, + int32_t radius = 0, + float bias = 0.f, + float alpha = 0.f, + float beta = 0.f, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateNormalizationTfLiteModel(normalizationOperatorCode, + tensorType, + inputShape, + outputShape, + radius, + bias, + alpha, + beta, + quantScale, + quantOffset); + + const Model* tfLiteModel = GetModel(modelBuffer.data()); + CHECK(tfLiteModel != nullptr); + + 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 + armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues); +} + +} // anonymous namespace
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