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author | David Monahan <david.monahan@arm.com> | 2020-11-18 14:40:27 +0000 |
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committer | Francis Murtagh <francis.murtagh@arm.com> | 2020-11-18 17:14:50 +0000 |
commit | 1670b0c047ab56c0b3b68088a3c53f38a91355b4 (patch) | |
tree | 3205725db5950af9884807e113f4a147c882d855 /delegate/src/test/RedefineTestHelper.hpp | |
parent | 23969e8b538ce09489b108fb9efdde9af7f97a3f (diff) | |
download | armnn-1670b0c047ab56c0b3b68088a3c53f38a91355b4.tar.gz |
IVGCVSW-5397 TfLiteDelegate: Implement the redefine operators
* Adding Reshape definition to ArmNN TfLite Delegate
* Added Reshape tests and RedefineTestHelper
Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Signed-off-by: David Monahan <david.monahan@arm.com>
Change-Id: I6d3909689c820387ac0fd4fd3f7ab856ebc25f47
Diffstat (limited to 'delegate/src/test/RedefineTestHelper.hpp')
-rw-r--r-- | delegate/src/test/RedefineTestHelper.hpp | 213 |
1 files changed, 213 insertions, 0 deletions
diff --git a/delegate/src/test/RedefineTestHelper.hpp b/delegate/src/test/RedefineTestHelper.hpp new file mode 100644 index 0000000000..42fc4c878c --- /dev/null +++ b/delegate/src/test/RedefineTestHelper.hpp @@ -0,0 +1,213 @@ +// +// 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> CreateRedefineTfLiteModel( + tflite::BuiltinOperator redefineOperatorCode, + tflite::TensorType tensorType, + const std::vector<int32_t>& inputTensorShape, + const std::vector<int32_t>& outputTensorShape, + const std::vector<int32_t>& targetShape, + bool useOption = true, + 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({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + auto inputTensor = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), + inputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + + auto outputTensor = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 1, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + std::vector<flatbuffers::Offset<Tensor>> tensors; + std::vector<int32_t> operatorInputs; + std::vector<int> subgraphInputs; + flatbuffers::Offset<void> operatorBuiltinOptions; + + if (useOption) + { + tensors = { inputTensor, outputTensor}; + operatorInputs = {{0}}; + subgraphInputs = {{0}}; + operatorBuiltinOptions = CreateReshapeOptions( + flatBufferBuilder, + flatBufferBuilder.CreateVector(targetShape.data(), targetShape.size())).Union(); + } + else + { + buffers.push_back( + CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(targetShape.data()), + sizeof(int32_t) * targetShape.size()))); + int32_t size = static_cast<int32_t>(targetShape.size()); + auto shapeTensor = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>( { size } ), + tflite::TensorType_INT32, + 2, + flatBufferBuilder.CreateString("shape")); + tensors = { inputTensor, outputTensor, shapeTensor }; + operatorInputs = {{ 0, 2 }}; + subgraphInputs = {{ 0, 2 }}; + operatorBuiltinOptions = CreateReshapeOptions(flatBufferBuilder).Union(); + } + + // create operator + tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_ReshapeOptions; + + const std::vector<int32_t> operatorOutputs{{1}}; + flatbuffers::Offset <Operator> redefineOperator = + 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> subgraphOutputs{{1}}; + 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(&redefineOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Reshape Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, + redefineOperatorCode); + + 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 RedefineTest(tflite::BuiltinOperator redefineOperatorCode, + tflite::TensorType tensorType, + const std::vector<armnn::BackendId>& backends, + const std::vector<int32_t>& inputShape, + const std::vector<int32_t>& outputShape, + std::vector<T>& inputValues, + std::vector<T>& expectedOutputValues, + std::vector<int32_t>& targetShape, + bool useOption = true, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateRedefineTfLiteModel(redefineOperatorCode, + tensorType, + inputShape, + outputShape, + targetShape, + useOption, + quantScale, + quantOffset); + + const Model* tfLiteModel = GetModel(modelBuffer.data()); + CHECK(tfLiteModel != nullptr); + // 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, inputValues); + armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, inputValues); + + // Run EnqueueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); + auto tfLiteDelegateOutputTensor = tfLiteInterpreter->tensor(tfLiteDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); + auto armnnDelegateOutputTensor = armnnDelegateInterpreter->tensor(armnnDelegateOutputId); + + CHECK(outputShape.size() == tfLiteDelegateOutputTensor->dims->size); + CHECK(outputShape.size() == armnnDelegateOutputTensor->dims->size); + + for (size_t i = 0; i < static_cast<size_t>(tfLiteDelegateOutputTensor->dims->size); i++) + { + CHECK(outputShape[i] == armnnDelegateOutputTensor->dims->data[i]); + CHECK(tfLiteDelegateOutputTensor->dims->data[i] == armnnDelegateOutputTensor->dims->data[i]); + } + + for (size_t i = 0; i < expectedOutputValues.size(); i++) + { + CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]); + CHECK(tfLiteDelegateOutputData[i] == expectedOutputValues[i]); + CHECK(tfLiteDelegateOutputData[i] == armnnDelegateOutputData[i]); + } +} + +} // anonymous namespace
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