| // |
| // Copyright © 2022-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "TestUtils.hpp" |
| |
| #include <armnn_delegate.hpp> |
| #include <DelegateTestInterpreter.hpp> |
| |
| #include <tensorflow/lite/version.h> |
| |
| namespace |
| { |
| |
| std::vector<char> CreateStridedSliceTfLiteModel(tflite::TensorType tensorType, |
| const std::vector<int32_t>& inputTensorShape, |
| const std::vector<int32_t>& beginTensorData, |
| const std::vector<int32_t>& endTensorData, |
| const std::vector<int32_t>& strideTensorData, |
| const std::vector<int32_t>& beginTensorShape, |
| const std::vector<int32_t>& endTensorShape, |
| const std::vector<int32_t>& strideTensorShape, |
| const std::vector<int32_t>& outputTensorShape, |
| const int32_t beginMask, |
| const int32_t endMask, |
| const int32_t ellipsisMask, |
| const int32_t newAxisMask, |
| const int32_t ShrinkAxisMask, |
| const armnn::DataLayout& dataLayout) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| flatbuffers::Offset<tflite::Buffer> buffers[6] = { |
| CreateBuffer(flatBufferBuilder), |
| CreateBuffer(flatBufferBuilder), |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(beginTensorData.data()), |
| sizeof(int32_t) * beginTensorData.size())), |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(endTensorData.data()), |
| sizeof(int32_t) * endTensorData.size())), |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(strideTensorData.data()), |
| sizeof(int32_t) * strideTensorData.size())), |
| CreateBuffer(flatBufferBuilder) |
| }; |
| |
| std::array<flatbuffers::Offset<Tensor>, 5> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("input")); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(beginTensorShape.data(), |
| beginTensorShape.size()), |
| ::tflite::TensorType_INT32, |
| 2, |
| flatBufferBuilder.CreateString("begin_tensor")); |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(endTensorShape.data(), |
| endTensorShape.size()), |
| ::tflite::TensorType_INT32, |
| 3, |
| flatBufferBuilder.CreateString("end_tensor")); |
| tensors[3] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(strideTensorShape.data(), |
| strideTensorShape.size()), |
| ::tflite::TensorType_INT32, |
| 4, |
| flatBufferBuilder.CreateString("stride_tensor")); |
| tensors[4] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 5, |
| flatBufferBuilder.CreateString("output")); |
| |
| |
| // create operator |
| tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_StridedSliceOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions = CreateStridedSliceOptions(flatBufferBuilder, |
| beginMask, |
| endMask, |
| ellipsisMask, |
| newAxisMask, |
| ShrinkAxisMask).Union(); |
| |
| const std::vector<int> operatorInputs{ 0, 1, 2, 3 }; |
| const std::vector<int> operatorOutputs{ 4 }; |
| flatbuffers::Offset <Operator> sliceOperator = |
| 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, 2, 3 }; |
| const std::vector<int> subgraphOutputs{ 4 }; |
| 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(&sliceOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: StridedSlice Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, |
| BuiltinOperator_STRIDED_SLICE); |
| |
| flatbuffers::Offset <Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&operatorCode, 1), |
| flatBufferBuilder.CreateVector(&subgraph, 1), |
| modelDescription, |
| flatBufferBuilder.CreateVector(buffers, 6)); |
| |
| flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
| |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| template <typename T> |
| void StridedSliceTestImpl(std::vector<T>& inputValues, |
| std::vector<T>& expectedOutputValues, |
| std::vector<int32_t>& beginTensorData, |
| std::vector<int32_t>& endTensorData, |
| std::vector<int32_t>& strideTensorData, |
| std::vector<int32_t>& inputTensorShape, |
| std::vector<int32_t>& beginTensorShape, |
| std::vector<int32_t>& endTensorShape, |
| std::vector<int32_t>& strideTensorShape, |
| std::vector<int32_t>& outputTensorShape, |
| const std::vector<armnn::BackendId>& backends = {}, |
| const int32_t beginMask = 0, |
| const int32_t endMask = 0, |
| const int32_t ellipsisMask = 0, |
| const int32_t newAxisMask = 0, |
| const int32_t ShrinkAxisMask = 0, |
| const armnn::DataLayout& dataLayout = armnn::DataLayout::NHWC) |
| { |
| using namespace delegateTestInterpreter; |
| std::vector<char> modelBuffer = CreateStridedSliceTfLiteModel( |
| ::tflite::TensorType_FLOAT32, |
| inputTensorShape, |
| beginTensorData, |
| endTensorData, |
| strideTensorData, |
| beginTensorShape, |
| endTensorShape, |
| strideTensorShape, |
| outputTensorShape, |
| beginMask, |
| endMask, |
| ellipsisMask, |
| newAxisMask, |
| ShrinkAxisMask, |
| dataLayout); |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends)); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| |
| armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShape); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } // End of StridedSlice Test |
| |
| } // anonymous namespace |