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Diffstat (limited to 'delegate/src/test/TransposeTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/TransposeTestHelper.hpp | 174 |
1 files changed, 174 insertions, 0 deletions
diff --git a/delegate/src/test/TransposeTestHelper.hpp b/delegate/src/test/TransposeTestHelper.hpp new file mode 100644 index 0000000000..d63a854fbf --- /dev/null +++ b/delegate/src/test/TransposeTestHelper.hpp @@ -0,0 +1,174 @@ +// +// 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> CreateTransposeTfLiteModel(tflite::TensorType tensorType, + const std::vector <int32_t>& input0TensorShape, + const std::vector <int32_t>& inputPermVecShape, + const std::vector <int32_t>& outputTensorShape, + const std::vector<int32_t>& inputPermVec) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers; + buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); + buffers[1] = CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(inputPermVec.data()), + sizeof(int32_t) * inputPermVec.size())); + 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>(inputPermVecShape.data(), + inputPermVecShape.size()), + tflite::TensorType_INT32, 1, + flatBufferBuilder.CreateString("permutation_vector")); + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType); + const std::vector<int32_t> operatorInputs{ {0, 1} }; + const std::vector<int32_t> operatorOutputs{{2}}; + flatbuffers::Offset <Operator> transposeOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + BuiltinOptions_TransposeOptions, + CreateTransposeOptions(flatBufferBuilder).Union()); + 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(&transposeOperator, 1)); + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Transpose Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, + tflite::BuiltinOperator_TRANSPOSE); + 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 TransposeFP32Test(std::vector<armnn::BackendId>& backends) +{ + using namespace tflite; + + // set test input data + std::vector<int32_t> input0Shape {4, 2, 3}; + std::vector<int32_t> inputPermVecShape {3}; + std::vector<int32_t> outputShape {2, 3, 4}; + + std::vector<float> input0Values = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, + 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}; + std::vector<int32_t> inputPermVec = {2, 0, 1}; + std::vector<float> expectedOutputValues = {0, 3, 6, 9, 12, 15, 18, 21, 1, 4, 7, 10, + 13, 16, 19, 22, 2, 5, 8, 11, 14, 17, 20, 23}; + + // create model + std::vector<char> modelBuffer = CreateTransposeTfLiteModel(::tflite::TensorType_FLOAT32, + input0Shape, + inputPermVecShape, + outputShape, + inputPermVec); + + 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 for tflite + auto tfLiteInterpreterInput0Id = tfLiteInterpreter->inputs()[0]; + auto tfLiteInterpreterInput0Data = tfLiteInterpreter->typed_tensor<float>(tfLiteInterpreterInput0Id); + for (unsigned int i = 0; i < input0Values.size(); ++i) + { + tfLiteInterpreterInput0Data[i] = input0Values[i]; + } + + auto tfLiteInterpreterInput1Id = tfLiteInterpreter->inputs()[1]; + auto tfLiteInterpreterInput1Data = tfLiteInterpreter->typed_tensor<int32_t>(tfLiteInterpreterInput1Id); + for (unsigned int i = 0; i < inputPermVec.size(); ++i) + { + tfLiteInterpreterInput1Data[i] = inputPermVec[i]; + } + + //Set input data for armnn delegate + 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<int32_t>(armnnDelegateInput1Id); + for (unsigned int i = 0; i < inputPermVec.size(); ++i) + { + armnnDelegateInput1Data[i] = inputPermVec[i]; + } + + // Run EnqueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + auto tfLiteInterpreterOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteInterpreterOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteInterpreterOutputId); + 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(tfLiteInterpreterOutputData[i] == expectedOutputValues[i]); + CHECK(tfLiteInterpreterOutputData[i] == armnnDelegateOutputData[i]); + } + + armnnDelegateInterpreter.reset(nullptr); +} +} |