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Diffstat (limited to 'delegate/src/test/ResizeTestHelper.hpp')
-rw-r--r-- | delegate/src/test/ResizeTestHelper.hpp | 192 |
1 files changed, 192 insertions, 0 deletions
diff --git a/delegate/src/test/ResizeTestHelper.hpp b/delegate/src/test/ResizeTestHelper.hpp new file mode 100644 index 0000000000..1e9d3bcb3b --- /dev/null +++ b/delegate/src/test/ResizeTestHelper.hpp @@ -0,0 +1,192 @@ +// +// Copyright © 2020 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 +{ + +std::vector<char> CreateResizeTfLiteModel(tflite::BuiltinOperator operatorCode, + tflite::TensorType inputTensorType, + const std::vector <int32_t>& inputTensorShape, + const std::vector <int32_t>& sizeTensorData, + const std::vector <int32_t>& sizeTensorShape, + 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({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(sizeTensorData.data()), + sizeof(int32_t) * sizeTensorData.size()))); + + std::array<flatbuffers::Offset<Tensor>, 3> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), + inputTensorType, + 0, + flatBufferBuilder.CreateString("input_tensor")); + + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(), + sizeTensorShape.size()), + TensorType_INT32, + 1, + flatBufferBuilder.CreateString("size_input_tensor")); + + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + inputTensorType, + 0, + flatBufferBuilder.CreateString("output_tensor")); + + // Create Operator + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; + flatbuffers::Offset<void> operatorBuiltinOption = 0; + switch (operatorCode) + { + case BuiltinOperator_RESIZE_BILINEAR: + { + operatorBuiltinOption = CreateResizeBilinearOptions(flatBufferBuilder, false, false).Union(); + operatorBuiltinOptionsType = tflite::BuiltinOptions_ResizeBilinearOptions; + break; + } + case BuiltinOperator_RESIZE_NEAREST_NEIGHBOR: + { + operatorBuiltinOption = CreateResizeNearestNeighborOptions(flatBufferBuilder, false, false).Union(); + operatorBuiltinOptionsType = tflite::BuiltinOptions_ResizeNearestNeighborOptions; + break; + } + default: + break; + } + + const std::vector<int> operatorInputs{{0, 1}}; + const std::vector<int> operatorOutputs{{2}}; + flatbuffers::Offset <Operator> resizeOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + operatorBuiltinOptionsType, + operatorBuiltinOption); + + 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(&resizeOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Resize Biliniar Operator Model"); + flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, operatorCode); + + flatbuffers::Offset <Model> flatbufferModel = + CreateModel(flatBufferBuilder, + TFLITE_SCHEMA_VERSION, + flatBufferBuilder.CreateVector(&opCode, 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 ResizeFP32TestImpl(tflite::BuiltinOperator operatorCode, + std::vector<armnn::BackendId>& backends, + std::vector<float>& input1Values, + std::vector<int32_t> input1Shape, + std::vector<int32_t> input2NewShape, + std::vector<int32_t> input2Shape, + std::vector<float>& expectedOutputValues, + std::vector<int32_t> expectedOutputShape) +{ + using namespace tflite; + + std::vector<char> modelBuffer = CreateResizeTfLiteModel(operatorCode, + ::tflite::TensorType_FLOAT32, + input1Shape, + input2NewShape, + input2Shape, + expectedOutputShape); + + const Model* tfLiteModel = GetModel(modelBuffer.data()); + + // The model will be executed using tflite and using the armnn delegate so that the outputs + // can be compared. + + // Create TfLite Interpreter with armnn delegate + std::unique_ptr<Interpreter> armnnDelegateInterpreter; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegateInterpreter) == kTfLiteOk); + CHECK(armnnDelegateInterpreter != nullptr); + CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); + + // Create TfLite Interpreter without armnn delegate + 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 the armnn interpreter + armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input1Values); + armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input2NewShape); + + // Set input data for the tflite interpreter + armnnDelegate::FillInput(tfLiteInterpreter, 0, input1Values); + armnnDelegate::FillInput(tfLiteInterpreter, 1, input2NewShape); + + // Run EnqueWorkload + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + CHECK(tfLiteInterpreter->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] == doctest::Approx(armnnDelegateOutputData[i])); + CHECK(armnnDelegateOutputData[i] == doctest::Approx(tfLiteDelageOutputData[i])); + } + + armnnDelegateInterpreter.reset(nullptr); +} + +} // anonymous namespace
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