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author | Sadik Armagan <sadik.armagan@arm.com> | 2020-11-27 12:40:52 +0000 |
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committer | TeresaARM <teresa.charlinreyes@arm.com> | 2020-11-30 15:12:22 +0000 |
commit | 34fa1bd7994af9abf52dbcc4aa808d0fa5f14aa3 (patch) | |
tree | 8ff6041b92da57cd56ccbcd3b746bdc0fb29078a /delegate/src/test/SplitTestHelper.hpp | |
parent | b7fa5104715e097bf3778d2485d759a4612460cc (diff) | |
download | armnn-34fa1bd7994af9abf52dbcc4aa808d0fa5f14aa3.tar.gz |
IVGCVSW-5393 'TfLiteDelegate: Implement the split operators'
* Added SPLIT and SPLIT_V support to armnn_delegate
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: I2def9b8be783b25ef17a997e521c6027553035d3
Diffstat (limited to 'delegate/src/test/SplitTestHelper.hpp')
-rw-r--r-- | delegate/src/test/SplitTestHelper.hpp | 368 |
1 files changed, 368 insertions, 0 deletions
diff --git a/delegate/src/test/SplitTestHelper.hpp b/delegate/src/test/SplitTestHelper.hpp new file mode 100644 index 0000000000..31fc7d5e46 --- /dev/null +++ b/delegate/src/test/SplitTestHelper.hpp @@ -0,0 +1,368 @@ +// +// 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> + +#include <string> + +namespace +{ + +std::vector<char> CreateSplitTfLiteModel(tflite::TensorType tensorType, + std::vector<int32_t>& axisTensorShape, + std::vector<int32_t>& inputTensorShape, + const std::vector<std::vector<int32_t>>& outputTensorShapes, + std::vector<int32_t>& axisData, + const int32_t numSplits, + float quantScale = 1.0f, + int quantOffset = 0) +{ + 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*>(axisData.data()), + sizeof(int32_t) * axisData.size())); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + std::array<flatbuffers::Offset<Tensor>, 4> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), + axisTensorShape.size()), + ::tflite::TensorType_INT32, + 1, + flatBufferBuilder.CreateString("axis"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), + inputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + + // Create output tensor + for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) + { + tensors[i + 2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), + outputTensorShapes[i].size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + } + + // create operator. Mean uses ReducerOptions. + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitOptions; + flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitOptions(flatBufferBuilder, numSplits).Union(); + + const std::vector<int> operatorInputs{ {0, 1} }; + const std::vector<int> operatorOutputs{ {2, 3} }; + flatbuffers::Offset <Operator> controlOperator = + 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, 3} }; + 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(&controlOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT); + + 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()); +} + +template <typename T> +void SplitTest(tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& axisTensorShape, + std::vector<int32_t>& inputTensorShape, + std::vector<std::vector<int32_t>>& outputTensorShapes, + std::vector<int32_t>& axisData, + std::vector<T>& inputValues, + std::vector<std::vector<T>>& expectedOutputValues, + const int32_t numSplits, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateSplitTfLiteModel(tensorType, + axisTensorShape, + inputTensorShape, + outputTensorShapes, + axisData, + numSplits, + quantScale, + quantOffset); + const Model* tfLiteModel = GetModel(modelBuffer.data()); + + // Create TfLite Interpreters + std::unique_ptr<Interpreter> armnnDelegate; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegate) == kTfLiteOk); + CHECK(armnnDelegate != nullptr); + CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); + + std::unique_ptr<Interpreter> tfLiteDelegate; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&tfLiteDelegate) == kTfLiteOk); + CHECK(tfLiteDelegate != nullptr); + CHECK(tfLiteDelegate->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(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); + + // Set input data + armnnDelegate::FillInput<T>(tfLiteDelegate, 1, inputValues); + armnnDelegate::FillInput<T>(armnnDelegate, 1, inputValues); + + // Run EnqueWorkload + CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); + CHECK(armnnDelegate->Invoke() == kTfLiteOk); + + // Compare output data + for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) + { + armnnDelegate::CompareOutputData<T>(tfLiteDelegate, + armnnDelegate, + outputTensorShapes[i], + expectedOutputValues[i], + i); + } + + tfLiteDelegate.reset(nullptr); + armnnDelegate.reset(nullptr); +} // End of SPLIT Test + +std::vector<char> CreateSplitVTfLiteModel(tflite::TensorType tensorType, + std::vector<int32_t>& inputTensorShape, + std::vector<int32_t>& splitsTensorShape, + std::vector<int32_t>& axisTensorShape, + const std::vector<std::vector<int32_t>>& outputTensorShapes, + std::vector<int32_t>& splitsData, + std::vector<int32_t>& axisData, + const int32_t numSplits, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; + buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); + buffers[1] = CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(splitsData.data()), + sizeof(int32_t) * splitsData.size())); + buffers[2] = CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), + sizeof(int32_t) * axisData.size())); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + std::array<flatbuffers::Offset<Tensor>, 5> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), + inputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(splitsTensorShape.data(), + splitsTensorShape.size()), + ::tflite::TensorType_INT32, + 1, + flatBufferBuilder.CreateString("splits"), + quantizationParameters); + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), + axisTensorShape.size()), + ::tflite::TensorType_INT32, + 2, + flatBufferBuilder.CreateString("axis"), + quantizationParameters); + + // Create output tensor + for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) + { + tensors[i + 3] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), + outputTensorShapes[i].size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + } + + // create operator. Mean uses ReducerOptions. + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitVOptions; + flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitVOptions(flatBufferBuilder, numSplits).Union(); + + const std::vector<int> operatorInputs{ {0, 1, 2} }; + const std::vector<int> operatorOutputs{ {3, 4} }; + flatbuffers::Offset <Operator> controlOperator = + 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} }; + const std::vector<int> subgraphOutputs{ {3, 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(&controlOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT_V Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT_V); + + 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()); +} + +template <typename T> +void SplitVTest(tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& inputTensorShape, + std::vector<int32_t>& splitsTensorShape, + std::vector<int32_t>& axisTensorShape, + std::vector<std::vector<int32_t>>& outputTensorShapes, + std::vector<T>& inputValues, + std::vector<int32_t>& splitsData, + std::vector<int32_t>& axisData, + std::vector<std::vector<T>>& expectedOutputValues, + const int32_t numSplits, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateSplitVTfLiteModel(tensorType, + inputTensorShape, + splitsTensorShape, + axisTensorShape, + outputTensorShapes, + splitsData, + axisData, + numSplits, + quantScale, + quantOffset); + const Model* tfLiteModel = GetModel(modelBuffer.data()); + + // Create TfLite Interpreters + std::unique_ptr<Interpreter> armnnDelegate; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegate) == kTfLiteOk); + CHECK(armnnDelegate != nullptr); + CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); + + std::unique_ptr<Interpreter> tfLiteDelegate; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&tfLiteDelegate) == kTfLiteOk); + CHECK(tfLiteDelegate != nullptr); + CHECK(tfLiteDelegate->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(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); + + // Set input data + armnnDelegate::FillInput<T>(tfLiteDelegate, 0, inputValues); + armnnDelegate::FillInput<T>(armnnDelegate, 0, inputValues); + + // Run EnqueWorkload + CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); + CHECK(armnnDelegate->Invoke() == kTfLiteOk); + + // Compare output data + for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) + { + armnnDelegate::CompareOutputData<T>(tfLiteDelegate, + armnnDelegate, + outputTensorShapes[i], + expectedOutputValues[i], + i); + } + + tfLiteDelegate.reset(nullptr); + armnnDelegate.reset(nullptr); +} // End of SPLIT_V Test + +} // anonymous namespace
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