diff options
Diffstat (limited to 'delegate/src/test/Pooling3dTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/Pooling3dTestHelper.hpp | 295 |
1 files changed, 295 insertions, 0 deletions
diff --git a/delegate/src/test/Pooling3dTestHelper.hpp b/delegate/src/test/Pooling3dTestHelper.hpp new file mode 100644 index 0000000000..f5f5cc3809 --- /dev/null +++ b/delegate/src/test/Pooling3dTestHelper.hpp @@ -0,0 +1,295 @@ +// +// Copyright © 2022 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 <flatbuffers/flexbuffers.h> +#include <tensorflow/lite/interpreter.h> +#include <tensorflow/lite/kernels/custom_ops_register.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 +{ +#if defined(ARMNN_POST_TFLITE_2_5) + +std::vector<uint8_t> CreateCustomOptions(int, int, int, int, int, int, TfLitePadding); + +std::vector<char> CreatePooling3dTfLiteModel( + std::string poolType, + tflite::TensorType tensorType, + const std::vector<int32_t>& inputTensorShape, + const std::vector<int32_t>& outputTensorShape, + TfLitePadding padding = kTfLitePaddingSame, + int32_t strideWidth = 0, + int32_t strideHeight = 0, + int32_t strideDepth = 0, + int32_t filterWidth = 0, + int32_t filterHeight = 0, + int32_t filterDepth = 0, + tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE, + 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 })); + + // Create the input and output tensors + std::array<flatbuffers::Offset<Tensor>, 2> 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>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + // Create the custom options from the function below + std::vector<uint8_t> customOperatorOptions = CreateCustomOptions(strideHeight, strideWidth, strideDepth, + filterHeight, filterWidth, filterDepth, padding); + // opCodeIndex is created as a uint8_t to avoid map lookup + uint8_t opCodeIndex = 0; + // Set the operator name based on the PoolType passed in from the test case + std::string opName = ""; + if (poolType == "kMax") + { + opName = "MaxPool3D"; + } + else + { + opName = "AveragePool3D"; + } + // To create a custom operator code you pass in the builtin code for custom operators and the name of the custom op + flatbuffers::Offset<OperatorCode> operatorCode = CreateOperatorCodeDirect(flatBufferBuilder, + tflite::BuiltinOperator_CUSTOM, + opName.c_str()); + + // Create the Operator using the opCodeIndex and custom options. Also sets builtin options to none. + const std::vector<int32_t> operatorInputs{ 0 }; + const std::vector<int32_t> operatorOutputs{ 1 }; + flatbuffers::Offset<Operator> poolingOperator = + CreateOperator(flatBufferBuilder, + opCodeIndex, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + tflite::BuiltinOptions_NONE, + 0, + flatBufferBuilder.CreateVector<uint8_t>(customOperatorOptions), + tflite::CustomOptionsFormat_FLEXBUFFERS); + + // Create the subgraph using the operator created above. + const std::vector<int> subgraphInputs{ 0 }; + 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(&poolingOperator, 1)); + + flatbuffers::Offset<flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Pooling3d Operator Model"); + + // Create the model using operatorCode and the subgraph. + 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 Pooling3dTest(std::string poolType, + tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& inputShape, + std::vector<int32_t>& outputShape, + std::vector<T>& inputValues, + std::vector<T>& expectedOutputValues, + TfLitePadding padding = kTfLitePaddingSame, + int32_t strideWidth = 0, + int32_t strideHeight = 0, + int32_t strideDepth = 0, + int32_t filterWidth = 0, + int32_t filterHeight = 0, + int32_t filterDepth = 0, + tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + // Create the single op model buffer + std::vector<char> modelBuffer = CreatePooling3dTfLiteModel(poolType, + tensorType, + inputShape, + outputShape, + padding, + strideWidth, + strideHeight, + strideDepth, + filterWidth, + filterHeight, + filterDepth, + fusedActivation, + quantScale, + quantOffset); + + const Model* tfLiteModel = GetModel(modelBuffer.data()); + CHECK(tfLiteModel != nullptr); + // Create TfLite Interpreters + std::unique_ptr<Interpreter> armnnDelegateInterpreter; + + // Custom ops need to be added to the BuiltinOp resolver before the interpreter is created + // Based on the poolType from the test case add the custom operator using the name and the tflite + // registration function + tflite::ops::builtin::BuiltinOpResolver armnn_op_resolver; + if (poolType == "kMax") + { + armnn_op_resolver.AddCustom("MaxPool3D", tflite::ops::custom::Register_MAX_POOL_3D()); + } + else + { + armnn_op_resolver.AddCustom("AveragePool3D", tflite::ops::custom::Register_AVG_POOL_3D()); + } + + CHECK(InterpreterBuilder(tfLiteModel, armnn_op_resolver) + (&armnnDelegateInterpreter) == kTfLiteOk); + CHECK(armnnDelegateInterpreter != nullptr); + CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); + + std::unique_ptr<Interpreter> tfLiteInterpreter; + + // Custom ops need to be added to the BuiltinOp resolver before the interpreter is created + // Based on the poolType from the test case add the custom operator using the name and the tflite + // registration function + tflite::ops::builtin::BuiltinOpResolver tflite_op_resolver; + if (poolType == "kMax") + { + tflite_op_resolver.AddCustom("MaxPool3D", tflite::ops::custom::Register_MAX_POOL_3D()); + } + else + { + tflite_op_resolver.AddCustom("AveragePool3D", tflite::ops::custom::Register_AVG_POOL_3D()); + } + + CHECK(InterpreterBuilder(tfLiteModel, tflite_op_resolver) + (&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 + auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0]; + auto tfLiteDelegateInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + tfLiteDelegateInputData[i] = inputValues[i]; + } + + auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; + auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + armnnDelegateInputData[i] = inputValues[i]; + } + + // Run EnqueueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues); +} + +// Function to create the flexbuffer custom options for the custom pooling3d operator. +std::vector<uint8_t> CreateCustomOptions(int strideHeight, int strideWidth, int strideDepth, + int filterHeight, int filterWidth, int filterDepth, TfLitePadding padding) +{ + auto flex_builder = std::make_unique<flexbuffers::Builder>(); + size_t map_start = flex_builder->StartMap(); + flex_builder->String("data_format", "NDHWC"); + // Padding is created as a key and padding type. Only VALID and SAME supported + if (padding == kTfLitePaddingValid) + { + flex_builder->String("padding", "VALID"); + } + else + { + flex_builder->String("padding", "SAME"); + } + + // Vector of filter dimensions in order ( 1, Depth, Height, Width, 1 ) + auto start = flex_builder->StartVector("ksize"); + flex_builder->Add(1); + flex_builder->Add(filterDepth); + flex_builder->Add(filterHeight); + flex_builder->Add(filterWidth); + flex_builder->Add(1); + // EndVector( start, bool typed, bool fixed) + flex_builder->EndVector(start, true, false); + + // Vector of stride dimensions in order ( 1, Depth, Height, Width, 1 ) + auto stridesStart = flex_builder->StartVector("strides"); + flex_builder->Add(1); + flex_builder->Add(strideDepth); + flex_builder->Add(strideHeight); + flex_builder->Add(strideWidth); + flex_builder->Add(1); + // EndVector( stridesStart, bool typed, bool fixed) + flex_builder->EndVector(stridesStart, true, false); + + flex_builder->EndMap(map_start); + flex_builder->Finish(); + + return flex_builder->GetBuffer(); +} +#endif +} // anonymous namespace + + + + |