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author | Teresa Charlin <teresa.charlinreyes@arm.com> | 2023-03-14 12:10:28 +0000 |
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committer | Teresa Charlin <teresa.charlinreyes@arm.com> | 2023-03-28 11:41:55 +0100 |
commit | ad1b3d7518429e2d16a2695d9b0bbf81b6565ac9 (patch) | |
tree | a5b8e1ad68a2437f007338f0b6195ca5ed2bddc3 /delegate/src/test/ElementwiseBinaryTestHelper.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/src/test/ElementwiseBinaryTestHelper.hpp')
-rw-r--r-- | delegate/src/test/ElementwiseBinaryTestHelper.hpp | 243 |
1 files changed, 0 insertions, 243 deletions
diff --git a/delegate/src/test/ElementwiseBinaryTestHelper.hpp b/delegate/src/test/ElementwiseBinaryTestHelper.hpp deleted file mode 100644 index 09a715e7f1..0000000000 --- a/delegate/src/test/ElementwiseBinaryTestHelper.hpp +++ /dev/null @@ -1,243 +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> - -namespace -{ - -template <typename T> -std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator binaryOperatorCode, - tflite::ActivationFunctionType activationType, - tflite::TensorType tensorType, - const std::vector <int32_t>& input0TensorShape, - const std::vector <int32_t>& input1TensorShape, - const std::vector <int32_t>& outputTensorShape, - std::vector<T>& input1Values, - bool constantInput = false, - 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)); - if (constantInput) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(input1Values.data()), - sizeof(T) * input1Values.size()))); - } - else - { - 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::array<flatbuffers::Offset<Tensor>, 3> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), - input0TensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input_0"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), - input1TensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("input_1"), - quantizationParameters); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; - flatbuffers::Offset<void> operatorBuiltinOptions = 0; - switch (binaryOperatorCode) - { - case BuiltinOperator_ADD: - { - operatorBuiltinOptionsType = BuiltinOptions_AddOptions; - operatorBuiltinOptions = CreateAddOptions(flatBufferBuilder, activationType).Union(); - break; - } - case BuiltinOperator_DIV: - { - operatorBuiltinOptionsType = BuiltinOptions_DivOptions; - operatorBuiltinOptions = CreateDivOptions(flatBufferBuilder, activationType).Union(); - break; - } - case BuiltinOperator_MAXIMUM: - { - operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions; - operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union(); - break; - } - case BuiltinOperator_MINIMUM: - { - operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions; - operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union(); - break; - } - case BuiltinOperator_MUL: - { - operatorBuiltinOptionsType = BuiltinOptions_MulOptions; - operatorBuiltinOptions = CreateMulOptions(flatBufferBuilder, activationType).Union(); - break; - } - case BuiltinOperator_SUB: - { - operatorBuiltinOptionsType = BuiltinOptions_SubOptions; - operatorBuiltinOptions = CreateSubOptions(flatBufferBuilder, activationType).Union(); - break; - } - case BuiltinOperator_FLOOR_DIV: - { - operatorBuiltinOptionsType = tflite::BuiltinOptions_FloorDivOptions; - operatorBuiltinOptions = CreateSubOptions(flatBufferBuilder, activationType).Union(); - break; - } - default: - break; - } - const std::vector<int32_t> operatorInputs{0, 1}; - const std::vector<int32_t> operatorOutputs{2}; - flatbuffers::Offset <Operator> elementwiseBinaryOperator = - 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(&elementwiseBinaryOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Elementwise Binary Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, binaryOperatorCode); - - 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 ElementwiseBinaryTest(tflite::BuiltinOperator binaryOperatorCode, - tflite::ActivationFunctionType activationType, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& input0Shape, - std::vector<int32_t>& input1Shape, - std::vector<int32_t>& outputShape, - std::vector<T>& input0Values, - std::vector<T>& input1Values, - std::vector<T>& expectedOutputValues, - float quantScale = 1.0f, - int quantOffset = 0, - bool constantInput = false) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateElementwiseBinaryTfLiteModel<T>(binaryOperatorCode, - activationType, - tensorType, - input0Shape, - input1Shape, - outputShape, - input1Values, - constantInput, - 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); - if (!constantInput) - { - armnnDelegate::FillInput<T>(tfLiteInterpreter, 1, input1Values); - armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 1, input1Values); - } - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, - armnnDelegateInterpreter, - outputShape, - expectedOutputValues); - armnnDelegateInterpreter.reset(nullptr); -} - -} // anonymous namespace
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