diff options
author | Teresa Charlin <teresa.charlinreyes@arm.com> | 2023-03-14 12:10:28 +0000 |
---|---|---|
committer | Teresa Charlin <teresa.charlinreyes@arm.com> | 2023-03-28 11:41:55 +0100 |
commit | ad1b3d7518429e2d16a2695d9b0bbf81b6565ac9 (patch) | |
tree | a5b8e1ad68a2437f007338f0b6195ca5ed2bddc3 /delegate/test/ReduceTestHelper.hpp | |
parent | 9cb3466b677a1048b8abb24661e92c4c83fdda04 (diff) | |
download | armnn-ad1b3d7518429e2d16a2695d9b0bbf81b6565ac9.tar.gz |
IVGCVSW-7555 Restructure Delegate
* New folders created:
* common is for common code where TfLite API is not used
* classic is for existing delegate implementations
* opaque is for new opaque delegate implementation,
* tests is for shared between existing Delegate and Opaque Delegate which have test utils to work which delegate to use.
* Existing delegate is built to libarmnnDelegate.so and opaque delegate is built as libarmnnOpaqueDelegate.so
* Opaque structure is introduced but no API is added yet.
* CmakeList.txt and delegate/CMakeList.txt have been modified and 2 new CmakeList.txt added
* Rename BUILD_ARMNN_TFLITE_DELEGATE as BUILD_CLASSIC_DELEGATE
* Rename BUILD_ARMNN_TFLITE_OPAQUE_DELEGATE as BUILD_OPAQUE_DELEGATE
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: Ib682b9ad0ac8d8acdc4ec6d9099bb0008a9fe8ed
Diffstat (limited to 'delegate/test/ReduceTestHelper.hpp')
-rw-r--r-- | delegate/test/ReduceTestHelper.hpp | 228 |
1 files changed, 228 insertions, 0 deletions
diff --git a/delegate/test/ReduceTestHelper.hpp b/delegate/test/ReduceTestHelper.hpp new file mode 100644 index 0000000000..fedf7ee150 --- /dev/null +++ b/delegate/test/ReduceTestHelper.hpp @@ -0,0 +1,228 @@ +// +// Copyright © 2021, 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> + +#include <string> + +namespace +{ + +std::vector<char> CreateReduceTfLiteModel(tflite::BuiltinOperator reduceOperatorCode, + 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, + bool kTfLiteNoQuantizationForQuantized = false) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + flatbuffers::Offset<tflite::Buffer> buffers[4] = { + CreateBuffer(flatBufferBuilder), + CreateBuffer(flatBufferBuilder), + CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), + sizeof(int32_t) * axisData.size())), + CreateBuffer(flatBufferBuilder) + }; + + flatbuffers::Offset<tflite::QuantizationParameters> quantizationParametersAxis + = CreateQuantizationParameters(flatBufferBuilder); + + flatbuffers::Offset<tflite::QuantizationParameters> quantizationParameters; + + if (kTfLiteNoQuantizationForQuantized) + { + if ((quantScale == 1 || quantScale == 0) && quantOffset == 0) + { + // Creates quantization parameter with quantization.type = kTfLiteNoQuantization + quantizationParameters = CreateQuantizationParameters(flatBufferBuilder); + } + else + { + // Creates quantization parameter with quantization.type != kTfLiteNoQuantization + quantizationParameters = CreateQuantizationParameters( + flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({quantScale}), + flatBufferBuilder.CreateVector<int64_t>({quantOffset})); + } + } + else + { + 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, + 1, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), + input1TensorShape.size()), + ::tflite::TensorType_INT32, + 2, + flatBufferBuilder.CreateString("axis"), + quantizationParametersAxis); + + // Create output tensor + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 3, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + // Create operator. Reduce operations MIN, MAX, SUM, MEAN, PROD 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> reduceOperator = + 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(&reduceOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Reduce Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, reduceOperatorCode); + + flatbuffers::Offset <Model> flatbufferModel = + CreateModel(flatBufferBuilder, + TFLITE_SCHEMA_VERSION, + flatBufferBuilder.CreateVector(&operatorCode, 1), + flatBufferBuilder.CreateVector(&subgraph, 1), + modelDescription, + flatBufferBuilder.CreateVector(buffers, 4)); + + flatBufferBuilder.Finish(flatbufferModel); + + return std::vector<char>(flatBufferBuilder.GetBufferPointer(), + flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); +} + +template <typename T> +void ReduceTest(tflite::BuiltinOperator reduceOperatorCode, + 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> modelBufferArmNN = CreateReduceTfLiteModel(reduceOperatorCode, + tensorType, + input0Shape, + input1Shape, + expectedOutputShape, + input1Values, + keepDims, + quantScale, + quantOffset, + false); + std::vector<char> modelBufferTFLite = CreateReduceTfLiteModel(reduceOperatorCode, + tensorType, + input0Shape, + input1Shape, + expectedOutputShape, + input1Values, + keepDims, + quantScale, + quantOffset, + true); + + const Model* tfLiteModelArmNN = GetModel(modelBufferArmNN.data()); + const Model* tfLiteModelTFLite = GetModel(modelBufferTFLite.data()); + + // Create TfLite Interpreters + std::unique_ptr<Interpreter> armnnDelegateInterpreter; + CHECK(InterpreterBuilder(tfLiteModelArmNN, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegateInterpreter) == kTfLiteOk); + CHECK(armnnDelegateInterpreter != nullptr); + CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); + + std::unique_ptr<Interpreter> tfLiteInterpreter; + CHECK(InterpreterBuilder(tfLiteModelTFLite, ::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 |