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Diffstat (limited to 'delegate/src/test/SplitTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/SplitTestHelper.hpp | 370 |
1 files changed, 0 insertions, 370 deletions
diff --git a/delegate/src/test/SplitTestHelper.hpp b/delegate/src/test/SplitTestHelper.hpp deleted file mode 100644 index 3c5f50ffac..0000000000 --- a/delegate/src/test/SplitTestHelper.hpp +++ /dev/null @@ -1,370 +0,0 @@ -// -// Copyright © 2020, 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> - -#include <string> - -namespace -{ - -std::vector<char> CreateSplitTfLiteModel(tflite::TensorType tensorType, - std::vector<int32_t>& axisTensorShape, - std::vector<int32_t>& inputTensorShape, - const std::vector<std::vector<int32_t>>& outputTensorShapes, - std::vector<int32_t>& axisData, - const int32_t numSplits, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), - sizeof(int32_t) * axisData.size()))); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - std::array<flatbuffers::Offset<Tensor>, 4> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), - axisTensorShape.size()), - ::tflite::TensorType_INT32, - 2, - flatBufferBuilder.CreateString("axis"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - - // Create output tensor - for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) - { - buffers.push_back(CreateBuffer(flatBufferBuilder)); - tensors[i + 2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), - outputTensorShapes[i].size()), - tensorType, - (i+3), - flatBufferBuilder.CreateString("output"), - quantizationParameters); - } - - // create operator. Mean uses ReducerOptions. - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitOptions(flatBufferBuilder, numSplits).Union(); - - const std::vector<int> operatorInputs{ {0, 1} }; - const std::vector<int> operatorOutputs{ {2, 3} }; - flatbuffers::Offset <Operator> controlOperator = - 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} }; - const std::vector<int> subgraphOutputs{ {2, 3} }; - 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(&controlOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT); - - flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers)); - - flatBufferBuilder.Finish(flatbufferModel); - - return std::vector<char>(flatBufferBuilder.GetBufferPointer(), - flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); -} - -template <typename T> -void SplitTest(tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& axisTensorShape, - std::vector<int32_t>& inputTensorShape, - std::vector<std::vector<int32_t>>& outputTensorShapes, - std::vector<int32_t>& axisData, - std::vector<T>& inputValues, - std::vector<std::vector<T>>& expectedOutputValues, - const int32_t numSplits, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateSplitTfLiteModel(tensorType, - axisTensorShape, - inputTensorShape, - outputTensorShapes, - axisData, - numSplits, - quantScale, - quantOffset); - const Model* tfLiteModel = GetModel(modelBuffer.data()); - - // Create TfLite Interpreters - std::unique_ptr<Interpreter> armnnDelegate; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegate) == kTfLiteOk); - CHECK(armnnDelegate != nullptr); - CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); - - std::unique_ptr<Interpreter> tfLiteDelegate; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&tfLiteDelegate) == kTfLiteOk); - CHECK(tfLiteDelegate != nullptr); - CHECK(tfLiteDelegate->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(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); - - // Set input data - armnnDelegate::FillInput<T>(tfLiteDelegate, 1, inputValues); - armnnDelegate::FillInput<T>(armnnDelegate, 1, inputValues); - - // Run EnqueWorkload - CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); - CHECK(armnnDelegate->Invoke() == kTfLiteOk); - - // Compare output data - for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) - { - armnnDelegate::CompareOutputData<T>(tfLiteDelegate, - armnnDelegate, - outputTensorShapes[i], - expectedOutputValues[i], - i); - } - - tfLiteDelegate.reset(nullptr); - armnnDelegate.reset(nullptr); -} // End of SPLIT Test - -std::vector<char> CreateSplitVTfLiteModel(tflite::TensorType tensorType, - std::vector<int32_t>& inputTensorShape, - std::vector<int32_t>& splitsTensorShape, - std::vector<int32_t>& axisTensorShape, - const std::vector<std::vector<int32_t>>& outputTensorShapes, - std::vector<int32_t>& splitsData, - std::vector<int32_t>& axisData, - const int32_t numSplits, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); - buffers[1] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(splitsData.data()), - sizeof(int32_t) * splitsData.size())); - buffers[2] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), - sizeof(int32_t) * axisData.size())); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - std::array<flatbuffers::Offset<Tensor>, 5> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 0, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(splitsTensorShape.data(), - splitsTensorShape.size()), - ::tflite::TensorType_INT32, - 1, - flatBufferBuilder.CreateString("splits"), - quantizationParameters); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), - axisTensorShape.size()), - ::tflite::TensorType_INT32, - 2, - flatBufferBuilder.CreateString("axis"), - quantizationParameters); - - // Create output tensor - for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) - { - tensors[i + 3] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), - outputTensorShapes[i].size()), - tensorType, - 0, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - } - - // create operator. Mean uses ReducerOptions. - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitVOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitVOptions(flatBufferBuilder, numSplits).Union(); - - const std::vector<int> operatorInputs{ {0, 1, 2} }; - const std::vector<int> operatorOutputs{ {3, 4} }; - flatbuffers::Offset <Operator> controlOperator = - 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} }; - const std::vector<int> subgraphOutputs{ {3, 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(&controlOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT_V Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT_V); - - 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 SplitVTest(tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& inputTensorShape, - std::vector<int32_t>& splitsTensorShape, - std::vector<int32_t>& axisTensorShape, - std::vector<std::vector<int32_t>>& outputTensorShapes, - std::vector<T>& inputValues, - std::vector<int32_t>& splitsData, - std::vector<int32_t>& axisData, - std::vector<std::vector<T>>& expectedOutputValues, - const int32_t numSplits, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateSplitVTfLiteModel(tensorType, - inputTensorShape, - splitsTensorShape, - axisTensorShape, - outputTensorShapes, - splitsData, - axisData, - numSplits, - quantScale, - quantOffset); - const Model* tfLiteModel = GetModel(modelBuffer.data()); - - // Create TfLite Interpreters - std::unique_ptr<Interpreter> armnnDelegate; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegate) == kTfLiteOk); - CHECK(armnnDelegate != nullptr); - CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); - - std::unique_ptr<Interpreter> tfLiteDelegate; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&tfLiteDelegate) == kTfLiteOk); - CHECK(tfLiteDelegate != nullptr); - CHECK(tfLiteDelegate->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(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); - - // Set input data - armnnDelegate::FillInput<T>(tfLiteDelegate, 0, inputValues); - armnnDelegate::FillInput<T>(armnnDelegate, 0, inputValues); - - // Run EnqueWorkload - CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); - CHECK(armnnDelegate->Invoke() == kTfLiteOk); - - // Compare output data - for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) - { - armnnDelegate::CompareOutputData<T>(tfLiteDelegate, - armnnDelegate, - outputTensorShapes[i], - expectedOutputValues[i], - i); - } - - tfLiteDelegate.reset(nullptr); - armnnDelegate.reset(nullptr); -} // End of SPLIT_V Test - -} // anonymous namespace
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