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-rw-r--r--delegate/src/test/ControlTestHelper.hpp346
1 files changed, 0 insertions, 346 deletions
diff --git a/delegate/src/test/ControlTestHelper.hpp b/delegate/src/test/ControlTestHelper.hpp
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index 3e427e60c5..0000000000
--- a/delegate/src/test/ControlTestHelper.hpp
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@@ -1,346 +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> CreateConcatTfLiteModel(tflite::BuiltinOperator controlOperatorCode,
- tflite::TensorType tensorType,
- std::vector<int32_t>& inputTensorShape,
- const std::vector <int32_t>& outputTensorShape,
- const int32_t inputTensorNum,
- int32_t 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));
- buffers.push_back(CreateBuffer(flatBufferBuilder));
- buffers.push_back(CreateBuffer(flatBufferBuilder));
-
- auto quantizationParameters =
- CreateQuantizationParameters(flatBufferBuilder,
- 0,
- 0,
- flatBufferBuilder.CreateVector<float>({ quantScale }),
- flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
-
- std::vector<int32_t> operatorInputs{};
- const std::vector<int32_t> operatorOutputs{inputTensorNum};
- std::vector<int> subgraphInputs{};
- const std::vector<int> subgraphOutputs{inputTensorNum};
-
- std::vector<flatbuffers::Offset<Tensor>> tensors(inputTensorNum + 1);
- for (int i = 0; i < inputTensorNum; ++i)
- {
- tensors[i] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
- inputTensorShape.size()),
- tensorType,
- 1,
- flatBufferBuilder.CreateString("input" + std::to_string(i)),
- quantizationParameters);
-
- // Add number of inputs to vector.
- operatorInputs.push_back(i);
- subgraphInputs.push_back(i);
- }
-
- // Create output tensor
- tensors[inputTensorNum] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
- outputTensorShape.size()),
- tensorType,
- 2,
- flatBufferBuilder.CreateString("output"),
- quantizationParameters);
-
- // create operator
- tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ConcatenationOptions;
- flatbuffers::Offset<void> operatorBuiltinOptions = CreateConcatenationOptions(flatBufferBuilder, axis).Union();
-
- 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);
-
- 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: Concatenation Operator Model");
- flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode);
-
- 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());
-}
-
-std::vector<char> CreateMeanTfLiteModel(tflite::BuiltinOperator controlOperatorCode,
- tflite::TensorType tensorType,
- std::vector<int32_t>& input0TensorShape,
- std::vector<int32_t>& input1TensorShape,
- const std::vector <int32_t>& outputTensorShape,
- std::vector<int32_t>& axisData,
- const bool keepDims,
- float quantScale = 1.0f,
- int quantOffset = 0)
-{
- using namespace tflite;
- flatbuffers::FlatBufferBuilder flatBufferBuilder;
-
- std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers;
- buffers[0] = CreateBuffer(flatBufferBuilder);
- buffers[1] = 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>, 3> tensors;
- tensors[0] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(),
- input0TensorShape.size()),
- tensorType,
- 0,
- flatBufferBuilder.CreateString("input"),
- quantizationParameters);
-
- tensors[1] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(),
- input1TensorShape.size()),
- ::tflite::TensorType_INT32,
- 1,
- flatBufferBuilder.CreateString("axis"),
- quantizationParameters);
-
- // Create output tensor
- tensors[2] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
- outputTensorShape.size()),
- tensorType,
- 0,
- flatBufferBuilder.CreateString("output"),
- quantizationParameters);
-
- // create operator. Mean uses ReducerOptions.
- tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ReducerOptions;
- flatbuffers::Offset<void> operatorBuiltinOptions = CreateReducerOptions(flatBufferBuilder, keepDims).Union();
-
- const std::vector<int> operatorInputs{ {0, 1} };
- const std::vector<int> operatorOutputs{ 2 };
- 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 };
- 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: Mean Operator Model");
- flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode);
-
- 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 ConcatenationTest(tflite::BuiltinOperator controlOperatorCode,
- tflite::TensorType tensorType,
- std::vector<armnn::BackendId>& backends,
- std::vector<int32_t>& inputShapes,
- std::vector<int32_t>& expectedOutputShape,
- std::vector<std::vector<T>>& inputValues,
- std::vector<T>& expectedOutputValues,
- int32_t axis = 0,
- float quantScale = 1.0f,
- int quantOffset = 0)
-{
- using namespace tflite;
- std::vector<char> modelBuffer = CreateConcatTfLiteModel(controlOperatorCode,
- tensorType,
- inputShapes,
- expectedOutputShape,
- inputValues.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 for all input tensors.
- for (unsigned int i = 0; i < inputValues.size(); ++i)
- {
- // Get single input tensor and assign to interpreters.
- auto inputTensorValues = inputValues[i];
- armnnDelegate::FillInput<T>(tfLiteInterpreter, i, inputTensorValues);
- armnnDelegate::FillInput<T>(armnnDelegateInterpreter, i, inputTensorValues);
- }
-
- // Run EnqueWorkload
- CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
- CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
-
- // Compare output data
- armnnDelegate::CompareOutputData<T>(tfLiteInterpreter,
- armnnDelegateInterpreter,
- expectedOutputShape,
- expectedOutputValues);
-
- armnnDelegateInterpreter.reset(nullptr);
-}
-
-template <typename T>
-void MeanTest(tflite::BuiltinOperator controlOperatorCode,
- tflite::TensorType tensorType,
- std::vector<armnn::BackendId>& backends,
- std::vector<int32_t>& input0Shape,
- std::vector<int32_t>& input1Shape,
- std::vector<int32_t>& expectedOutputShape,
- std::vector<T>& input0Values,
- std::vector<int32_t>& input1Values,
- std::vector<T>& expectedOutputValues,
- const bool keepDims,
- float quantScale = 1.0f,
- int quantOffset = 0)
-{
- using namespace tflite;
- std::vector<char> modelBuffer = CreateMeanTfLiteModel(controlOperatorCode,
- tensorType,
- input0Shape,
- input1Shape,
- expectedOutputShape,
- input1Values,
- keepDims,
- 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, input0Values);
- armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, input0Values);
-
- // Run EnqueWorkload
- CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
- CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
-
- // Compare output data
- armnnDelegate::CompareOutputData<T>(tfLiteInterpreter,
- armnnDelegateInterpreter,
- expectedOutputShape,
- expectedOutputValues);
-
- armnnDelegateInterpreter.reset(nullptr);
-}
-
-} // anonymous namespace \ No newline at end of file
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-25 20:06:41 +0000