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Diffstat (limited to 'delegate/test/ActivationTestHelper.hpp')
| -rw-r--r-- | delegate/test/ActivationTestHelper.hpp | 130 |
1 files changed, 130 insertions, 0 deletions
diff --git a/delegate/test/ActivationTestHelper.hpp b/delegate/test/ActivationTestHelper.hpp new file mode 100644 index 0000000000..110c684c23 --- /dev/null +++ b/delegate/test/ActivationTestHelper.hpp @@ -0,0 +1,130 @@ +// +// 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 +{ + +std::vector<char> CreateActivationTfLiteModel(tflite::BuiltinOperator activationOperatorCode, + tflite::TensorType tensorType, + const std::vector <int32_t>& tensorShape) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers; + buffers[0] = CreateBuffer(flatBufferBuilder); + + std::array<flatbuffers::Offset<Tensor>, 2> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), + tensorType); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), + tensorType); + + // create operator + const std::vector<int> operatorInputs{0}; + const std::vector<int> operatorOutputs{1}; + flatbuffers::Offset <Operator> unaryOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size())); + + 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(&unaryOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Activation Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, activationOperatorCode); + + 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()); +} + +void ActivationTest(tflite::BuiltinOperator activationOperatorCode, + std::vector<armnn::BackendId>& backends, + std::vector<float>& inputValues, + std::vector<float>& expectedOutputValues) +{ + using namespace tflite; + std::vector<int32_t> inputShape { { 4, 1, 4} }; + std::vector<char> modelBuffer = CreateActivationTfLiteModel(activationOperatorCode, + ::tflite::TensorType_FLOAT32, + inputShape); + + 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<float>(tfLiteInterpreter, 0, inputValues); + armnnDelegate::FillInput<float>(armnnDelegateInterpreter, 0, inputValues); + + // Run EnqueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + armnnDelegate::CompareOutputData<float>(tfLiteInterpreter, + armnnDelegateInterpreter, + inputShape, + expectedOutputValues); + + tfLiteInterpreter.reset(nullptr); + armnnDelegateInterpreter.reset(nullptr); +} + +} // anonymous namespace
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