diff options
Diffstat (limited to 'delegate/src/test/ElementwiseBinaryTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/ElementwiseBinaryTestHelper.hpp | 211 |
1 files changed, 211 insertions, 0 deletions
diff --git a/delegate/src/test/ElementwiseBinaryTestHelper.hpp b/delegate/src/test/ElementwiseBinaryTestHelper.hpp new file mode 100644 index 0000000000..72f9f850c8 --- /dev/null +++ b/delegate/src/test/ElementwiseBinaryTestHelper.hpp @@ -0,0 +1,211 @@ +// +// Copyright © 2020 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> 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) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + + std::array<flatbuffers::Offset<Tensor>, 3> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), + input0TensorShape.size()), + tensorType, 0); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), + input1TensorShape.size()), + tensorType, 0); + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType); + + // 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_MUL: + { + operatorBuiltinOptionsType = BuiltinOptions_MulOptions; + operatorBuiltinOptions = CreateMulOptions(flatBufferBuilder, activationType).Union(); + break; + } + case BuiltinOperator_SUB: + { + operatorBuiltinOptionsType = BuiltinOptions_SubOptions; + 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()); +} + +void ElementwiseBinaryFP32Test(tflite::BuiltinOperator binaryOperatorCode, + tflite::ActivationFunctionType activationType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& input0Shape, + std::vector<int32_t>& input1Shape, + std::vector<int32_t>& outputShape, + std::vector<float>& input0Values, + std::vector<float>& input1Values, + std::vector<float>& expectedOutputValues) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateElementwiseBinaryTfLiteModel(binaryOperatorCode, + activationType, + ::tflite::TensorType_FLOAT32, + input0Shape, + input1Shape, + outputShape); + + 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 tfLiteDelegateInput0Id = tfLiteInterpreter->inputs()[0]; + auto tfLiteDelageInput0Data = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInput0Id); + for (unsigned int i = 0; i < input0Values.size(); ++i) + { + tfLiteDelageInput0Data[i] = input0Values[i]; + } + + auto tfLiteDelegateInput1Id = tfLiteInterpreter->inputs()[1]; + auto tfLiteDelageInput1Data = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInput1Id); + for (unsigned int i = 0; i < input1Values.size(); ++i) + { + tfLiteDelageInput1Data[i] = input1Values[i]; + } + + auto armnnDelegateInput0Id = armnnDelegateInterpreter->inputs()[0]; + auto armnnDelegateInput0Data = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInput0Id); + for (unsigned int i = 0; i < input0Values.size(); ++i) + { + armnnDelegateInput0Data[i] = input0Values[i]; + } + + auto armnnDelegateInput1Id = armnnDelegateInterpreter->inputs()[1]; + auto armnnDelegateInput1Data = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInput1Id); + for (unsigned int i = 0; i < input1Values.size(); ++i) + { + armnnDelegateInput1Data[i] = input1Values[i]; + } + + // Run EnqueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(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]); + } + + armnnDelegateInterpreter.reset(nullptr); +} + +} // anonymous namespace + + + + |