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Diffstat (limited to 'delegate/src/test/PreluTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/PreluTestHelper.hpp | 195 |
1 files changed, 0 insertions, 195 deletions
diff --git a/delegate/src/test/PreluTestHelper.hpp b/delegate/src/test/PreluTestHelper.hpp deleted file mode 100644 index b50c37763f..0000000000 --- a/delegate/src/test/PreluTestHelper.hpp +++ /dev/null @@ -1,195 +0,0 @@ -// -// Copyright © 2021, 2023 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> CreatePreluTfLiteModel(tflite::BuiltinOperator preluOperatorCode, - tflite::TensorType tensorType, - const std::vector<int32_t>& inputShape, - const std::vector<int32_t>& alphaShape, - const std::vector<int32_t>& outputShape, - std::vector<float>& alphaData, - bool alphaIsConstant) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t *>(alphaData.data()), sizeof(float) * alphaData.size()))); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ 1.0f }), - flatBufferBuilder.CreateVector<int64_t>({ 0 })); - - auto inputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), - inputShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - - auto alphaTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(alphaShape.data(), - alphaShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("alpha"), - quantizationParameters); - - auto outputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), - outputShape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, alphaTensor, outputTensor }; - - const std::vector<int> operatorInputs{0, 1}; - const std::vector<int> operatorOutputs{2}; - flatbuffers::Offset <Operator> preluOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size())); - - std::vector<int> subgraphInputs{0}; - if (!alphaIsConstant) - { - subgraphInputs.push_back(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(&preluOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Prelu Operator Model"); - flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, preluOperatorCode); - - flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&opCode, 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 PreluTest(tflite::BuiltinOperator preluOperatorCode, - tflite::TensorType tensorType, - const std::vector<armnn::BackendId>& backends, - const std::vector<int32_t>& inputShape, - const std::vector<int32_t>& alphaShape, - std::vector<int32_t>& outputShape, - std::vector<float>& inputData, - std::vector<float>& alphaData, - std::vector<float>& expectedOutput, - bool alphaIsConstant) -{ - using namespace tflite; - - std::vector<char> modelBuffer = CreatePreluTfLiteModel(preluOperatorCode, - tensorType, - inputShape, - alphaShape, - outputShape, - alphaData, - alphaIsConstant); - - 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<float>(tfLiteInterpreter, 0, inputData); - armnnDelegate::FillInput<float>(armnnDelegateInterpreter, 0, inputData); - - // Set alpha data if not constant - if (!alphaIsConstant) { - armnnDelegate::FillInput<float>(tfLiteInterpreter, 1, alphaData); - armnnDelegate::FillInput<float>(armnnDelegateInterpreter, 1, alphaData); - } - - // Run EnqueueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - - auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); - - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); - - for (size_t i = 0; i < expectedOutput.size(); i++) - { - CHECK(expectedOutput[i] == armnnDelegateOutputData[i]); - CHECK(tfLiteDelegateOutputData[i] == expectedOutput[i]); - CHECK(tfLiteDelegateOutputData[i] == armnnDelegateOutputData[i]); - } -} -} // anonymous namespace
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