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Diffstat (limited to 'delegate/test/FullyConnectedTestHelper.hpp')
-rw-r--r-- | delegate/test/FullyConnectedTestHelper.hpp | 255 |
1 files changed, 255 insertions, 0 deletions
diff --git a/delegate/test/FullyConnectedTestHelper.hpp b/delegate/test/FullyConnectedTestHelper.hpp new file mode 100644 index 0000000000..d6bbd93176 --- /dev/null +++ b/delegate/test/FullyConnectedTestHelper.hpp @@ -0,0 +1,255 @@ +// +// 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 <schema_generated.h> +#include <tensorflow/lite/version.h> + +#include <doctest/doctest.h> + +namespace +{ + +template <typename T> +std::vector<char> CreateFullyConnectedTfLiteModel(tflite::TensorType tensorType, + tflite::ActivationFunctionType activationType, + const std::vector <int32_t>& inputTensorShape, + const std::vector <int32_t>& weightsTensorShape, + const std::vector <int32_t>& biasTensorShape, + std::vector <int32_t>& outputTensorShape, + std::vector <T>& weightsData, + bool constantWeights = true, + float quantScale = 1.0f, + int quantOffset = 0, + float outputQuantScale = 2.0f, + int outputQuantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + std::array<flatbuffers::Offset<tflite::Buffer>, 5> buffers; + buffers[0] = CreateBuffer(flatBufferBuilder); + buffers[1] = CreateBuffer(flatBufferBuilder); + + auto biasTensorType = ::tflite::TensorType_FLOAT32; + if (tensorType == ::tflite::TensorType_INT8) + { + biasTensorType = ::tflite::TensorType_INT32; + } + if (constantWeights) + { + buffers[2] = CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(weightsData.data()), + sizeof(T) * weightsData.size())); + + if (tensorType == ::tflite::TensorType_INT8) + { + std::vector<int32_t> biasData = { 10 }; + buffers[3] = CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), + sizeof(int32_t) * biasData.size())); + + } + else + { + std::vector<float> biasData = { 10 }; + buffers[3] = CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), + sizeof(float) * biasData.size())); + } + } + else + { + buffers[2] = CreateBuffer(flatBufferBuilder); + buffers[3] = CreateBuffer(flatBufferBuilder); + } + buffers[4] = CreateBuffer(flatBufferBuilder); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + auto outputQuantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ outputQuantScale }), + flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset })); + + std::array<flatbuffers::Offset<Tensor>, 4> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), + inputTensorShape.size()), + tensorType, + 1, + flatBufferBuilder.CreateString("input_0"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(weightsTensorShape.data(), + weightsTensorShape.size()), + tensorType, + 2, + flatBufferBuilder.CreateString("weights"), + quantizationParameters); + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), + biasTensorShape.size()), + biasTensorType, + 3, + flatBufferBuilder.CreateString("bias"), + quantizationParameters); + + tensors[3] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 4, + flatBufferBuilder.CreateString("output"), + outputQuantizationParameters); + + + // create operator + tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_FullyConnectedOptions; + flatbuffers::Offset<void> operatorBuiltinOptions = + CreateFullyConnectedOptions(flatBufferBuilder, + activationType, + FullyConnectedOptionsWeightsFormat_DEFAULT, false).Union(); + + const std::vector<int> operatorInputs{0, 1, 2}; + const std::vector<int> operatorOutputs{3}; + flatbuffers::Offset <Operator> fullyConnectedOperator = + 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}; + 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(&fullyConnectedOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: FullyConnected Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, + tflite::BuiltinOperator_FULLY_CONNECTED); + + 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 FullyConnectedTest(std::vector<armnn::BackendId>& backends, + tflite::TensorType tensorType, + tflite::ActivationFunctionType activationType, + const std::vector <int32_t>& inputTensorShape, + const std::vector <int32_t>& weightsTensorShape, + const std::vector <int32_t>& biasTensorShape, + std::vector <int32_t>& outputTensorShape, + std::vector <T>& inputValues, + std::vector <T>& expectedOutputValues, + std::vector <T>& weightsData, + bool constantWeights = true, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + + std::vector<char> modelBuffer = CreateFullyConnectedTfLiteModel(tensorType, + activationType, + inputTensorShape, + weightsTensorShape, + biasTensorShape, + outputTensorShape, + weightsData, + constantWeights, + 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, inputValues); + armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, inputValues); + + if (!constantWeights) + { + armnnDelegate::FillInput<T>(tfLiteInterpreter, 1, weightsData); + armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 1, weightsData); + + if (tensorType == ::tflite::TensorType_INT8) + { + std::vector <int32_t> biasData = {10}; + armnnDelegate::FillInput<int32_t>(tfLiteInterpreter, 2, biasData); + armnnDelegate::FillInput<int32_t>(armnnDelegateInterpreter, 2, biasData); + } + else + { + std::vector<float> biasData = {10}; + armnnDelegate::FillInput<float>(tfLiteInterpreter, 2, biasData); + armnnDelegate::FillInput<float>(armnnDelegateInterpreter, 2, biasData); + } + } + + // Run EnqueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, + armnnDelegateInterpreter, + outputTensorShape, + expectedOutputValues); + armnnDelegateInterpreter.reset(nullptr); +} + +} // anonymous namespace
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