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Diffstat (limited to 'delegate/src/test/QuantizationTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/QuantizationTestHelper.hpp | 200 |
1 files changed, 0 insertions, 200 deletions
diff --git a/delegate/src/test/QuantizationTestHelper.hpp b/delegate/src/test/QuantizationTestHelper.hpp deleted file mode 100644 index a8b102271a..0000000000 --- a/delegate/src/test/QuantizationTestHelper.hpp +++ /dev/null @@ -1,200 +0,0 @@ -// -// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#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> CreateQuantizationTfLiteModel(tflite::BuiltinOperator quantizationOperatorCode, - tflite::TensorType inputTensorType, - tflite::TensorType outputTensorType, - const std::vector <int32_t>& inputTensorShape, - const std::vector <int32_t>& outputTensorShape, - 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 }), - QuantizationDetails_CustomQuantization); - - std::array<flatbuffers::Offset<Tensor>, 2> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - inputTensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - outputTensorType, - 2, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; - flatbuffers::Offset<void> operatorBuiltinOptions = 0; - switch (quantizationOperatorCode) - { - case BuiltinOperator_QUANTIZE: - { - operatorBuiltinOptionsType = BuiltinOptions_QuantizeOptions; - operatorBuiltinOptions = CreateQuantizeOptions(flatBufferBuilder).Union(); - break; - } - case BuiltinOperator_DEQUANTIZE: - { - operatorBuiltinOptionsType = BuiltinOptions_DequantizeOptions; - operatorBuiltinOptions = CreateDequantizeOptions(flatBufferBuilder).Union(); - break; - } - default: - break; - } - - const std::vector<int32_t> operatorInputs{0}; - const std::vector<int32_t> operatorOutputs{1}; - flatbuffers::Offset <Operator> quantizationOperator = - 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}; - 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(&quantizationOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Quantization Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, quantizationOperatorCode); - - 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 InputT, typename OutputT> -void QuantizationTest(tflite::BuiltinOperator quantizeOperatorCode, - tflite::TensorType inputTensorType, - tflite::TensorType outputTensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& inputShape, - std::vector<int32_t>& outputShape, - std::vector<InputT>& inputValues, - std::vector<OutputT>& expectedOutputValues, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateQuantizationTfLiteModel(quantizeOperatorCode, - inputTensorType, - outputTensorType, - inputShape, - outputShape, - 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 - auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0]; - auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<InputT>(tfLiteDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - tfLiteDelageInputData[i] = inputValues[i]; - } - - auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; - auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<InputT>(armnnDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - armnnDelegateInputData[i] = inputValues[i]; - } - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<OutputT>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<OutputT>(armnnDelegateOutputId); - - for (size_t i = 0; i < expectedOutputValues.size(); i++) - { - CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]); - CHECK(tfLiteDelageOutputData[i] == expectedOutputValues[i]); - CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]); - } -} - -} // anonymous namespace
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