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author | Matthew Sloyan <matthew.sloyan@arm.com> | 2020-11-13 09:47:35 +0000 |
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committer | Francis Murtagh <francis.murtagh@arm.com> | 2020-11-17 12:50:19 +0000 |
commit | 91c4171421633b3ff9764bd586f43137aef0ff1a (patch) | |
tree | 5462dfa28832b6da848a3acb5ba471f0813ec52b /delegate/src/test/ControlTestHelper.hpp | |
parent | 145c88f851d12d2cadc2f080d232c1d5963d6e47 (diff) | |
download | armnn-91c4171421633b3ff9764bd586f43137aef0ff1a.tar.gz |
IVGCVSW-5486 TfLiteDelegate: Implement Concat and Mean operators
* Implemented Concatenation & Mean operator.
* Added unit tests for Concatenation & Mean operator.
* Added CompareOutputData function to TestUtils.hpp.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I31b7b1517a9ce041c3269f69f16a419f967d0fb0
Diffstat (limited to 'delegate/src/test/ControlTestHelper.hpp')
-rw-r--r-- | delegate/src/test/ControlTestHelper.hpp | 344 |
1 files changed, 344 insertions, 0 deletions
diff --git a/delegate/src/test/ControlTestHelper.hpp b/delegate/src/test/ControlTestHelper.hpp new file mode 100644 index 0000000000..0c9796170d --- /dev/null +++ b/delegate/src/test/ControlTestHelper.hpp @@ -0,0 +1,344 @@ +// +// 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> CreateConcatTfLiteModel(tflite::BuiltinOperator controlOperatorCode, + tflite::TensorType tensorType, + std::vector<int32_t>& inputTensorShape, + const std::vector <int32_t>& outputTensorShape, + const int32_t inputTensorNum, + int32_t axis = 0, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + std::vector<int32_t> operatorInputs{}; + const std::vector<int32_t> operatorOutputs{inputTensorNum}; + std::vector<int> subgraphInputs{}; + const std::vector<int> subgraphOutputs{inputTensorNum}; + + std::vector<flatbuffers::Offset<Tensor>> tensors(inputTensorNum + 1); + for (int i = 0; i < inputTensorNum; ++i) + { + tensors[i] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), + inputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input" + std::to_string(i)), + quantizationParameters); + + // Add number of inputs to vector. + operatorInputs.push_back(i); + subgraphInputs.push_back(i); + } + + // Create output tensor + tensors[inputTensorNum] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + // create operator + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ConcatenationOptions; + flatbuffers::Offset<void> operatorBuiltinOptions = CreateConcatenationOptions(flatBufferBuilder, axis).Union(); + + 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); + + 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: Concatenation Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode); + + 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()); +} + +std::vector<char> CreateMeanTfLiteModel(tflite::BuiltinOperator controlOperatorCode, + tflite::TensorType tensorType, + std::vector<int32_t>& input0TensorShape, + std::vector<int32_t>& input1TensorShape, + const std::vector <int32_t>& outputTensorShape, + std::vector<int32_t>& axisData, + const bool keepDims, + 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>, 3> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), + input0TensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), + input1TensorShape.size()), + ::tflite::TensorType_INT32, + 1, + flatBufferBuilder.CreateString("axis"), + quantizationParameters); + + // Create output tensor + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + // create operator. Mean uses ReducerOptions. + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ReducerOptions; + flatbuffers::Offset<void> operatorBuiltinOptions = CreateReducerOptions(flatBufferBuilder, keepDims).Union(); + + const std::vector<int> operatorInputs{ {0, 1} }; + const std::vector<int> operatorOutputs{ 2 }; + 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 }; + 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: Mean Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode); + + 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 ConcatenationTest(tflite::BuiltinOperator controlOperatorCode, + tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& inputShapes, + std::vector<int32_t>& expectedOutputShape, + std::vector<std::vector<T>>& inputValues, + std::vector<T>& expectedOutputValues, + int32_t axis = 0, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateConcatTfLiteModel(controlOperatorCode, + tensorType, + inputShapes, + expectedOutputShape, + inputValues.size(), + axis, + quantScale, + quantOffset); + + 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 all input tensors. + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + // Get single input tensor and assign to interpreters. + auto inputTensorValues = inputValues[i]; + armnnDelegate::FillInput<T>(tfLiteInterpreter, i, inputTensorValues); + armnnDelegate::FillInput<T>(armnnDelegateInterpreter, i, inputTensorValues); + } + + // Run EnqueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, + armnnDelegateInterpreter, + expectedOutputShape, + expectedOutputValues); + + armnnDelegateInterpreter.reset(nullptr); +} + +template <typename T> +void MeanTest(tflite::BuiltinOperator controlOperatorCode, + tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& input0Shape, + std::vector<int32_t>& input1Shape, + std::vector<int32_t>& expectedOutputShape, + std::vector<T>& input0Values, + std::vector<int32_t>& input1Values, + std::vector<T>& expectedOutputValues, + const bool keepDims, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateMeanTfLiteModel(controlOperatorCode, + tensorType, + input0Shape, + input1Shape, + expectedOutputShape, + input1Values, + keepDims, + quantScale, + quantOffset); + + 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 + armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, input0Values); + armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, input0Values); + + // Run EnqueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, + armnnDelegateInterpreter, + expectedOutputShape, + expectedOutputValues); + + armnnDelegateInterpreter.reset(nullptr); +} + +} // anonymous namespace
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