From c8eb955a2c9f0b432fe932e2df8445f242080e31 Mon Sep 17 00:00:00 2001 From: Matthew Sloyan Date: Thu, 26 Nov 2020 10:54:22 +0000 Subject: IVGCVSW-5381 TfLiteDelegate: Implement the Logical operators * Implemented Logical AND, NOT and OR operators. * NOT uses existing ElementwiseUnary VisitLayer function & tests. * AND/OR uses new LogicalBinary VisitLayer function & tests. Signed-off-by: Matthew Sloyan Change-Id: I5e7f1e78b30c36ac7f14c70a712b54f98d664b83 --- delegate/src/test/LogicalTestHelper.hpp | 198 ++++++++++++++++++++++++++++++++ 1 file changed, 198 insertions(+) create mode 100644 delegate/src/test/LogicalTestHelper.hpp (limited to 'delegate/src/test/LogicalTestHelper.hpp') diff --git a/delegate/src/test/LogicalTestHelper.hpp b/delegate/src/test/LogicalTestHelper.hpp new file mode 100644 index 0000000000..d08a1af388 --- /dev/null +++ b/delegate/src/test/LogicalTestHelper.hpp @@ -0,0 +1,198 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "TestUtils.hpp" + +#include + +#include +#include +#include +#include +#include +#include + +#include + +namespace +{ + +std::vector CreateLogicalBinaryTfLiteModel(tflite::BuiltinOperator logicalOperatorCode, + tflite::TensorType tensorType, + const std::vector & input0TensorShape, + const std::vector & input1TensorShape, + const std::vector & outputTensorShape, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector({ quantScale }), + flatBufferBuilder.CreateVector({ quantOffset })); + + + std::array, 3> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector(input0TensorShape.data(), + input0TensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input_0"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector(input1TensorShape.data(), + input1TensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input_1"), + quantizationParameters); + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + // create operator + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; + flatbuffers::Offset operatorBuiltinOptions = 0; + switch (logicalOperatorCode) + { + case BuiltinOperator_LOGICAL_AND: + { + operatorBuiltinOptionsType = BuiltinOptions_LogicalAndOptions; + operatorBuiltinOptions = CreateLogicalAndOptions(flatBufferBuilder).Union(); + break; + } + case BuiltinOperator_LOGICAL_OR: + { + operatorBuiltinOptionsType = BuiltinOptions_LogicalOrOptions; + operatorBuiltinOptions = CreateLogicalOrOptions(flatBufferBuilder).Union(); + break; + } + default: + break; + } + const std::vector operatorInputs{ {0, 1} }; + const std::vector operatorOutputs{ 2 }; + flatbuffers::Offset logicalBinaryOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector(operatorOutputs.data(), operatorOutputs.size()), + operatorBuiltinOptionsType, + operatorBuiltinOptions); + + const std::vector subgraphInputs{ {0, 1} }; + const std::vector subgraphOutputs{ 2 }; + flatbuffers::Offset subgraph = + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector(subgraphInputs.data(), subgraphInputs.size()), + flatBufferBuilder.CreateVector(subgraphOutputs.data(), subgraphOutputs.size()), + flatBufferBuilder.CreateVector(&logicalBinaryOperator, 1)); + + flatbuffers::Offset modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Logical Binary Operator Model"); + flatbuffers::Offset operatorCode = CreateOperatorCode(flatBufferBuilder, logicalOperatorCode); + + flatbuffers::Offset 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(flatBufferBuilder.GetBufferPointer(), + flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); +} + +template +void LogicalBinaryTest(tflite::BuiltinOperator logicalOperatorCode, + tflite::TensorType tensorType, + std::vector& backends, + std::vector& input0Shape, + std::vector& input1Shape, + std::vector& expectedOutputShape, + std::vector& input0Values, + std::vector& input1Values, + std::vector& expectedOutputValues, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector modelBuffer = CreateLogicalBinaryTfLiteModel(logicalOperatorCode, + tensorType, + input0Shape, + input1Shape, + expectedOutputShape, + quantScale, + quantOffset); + + const Model* tfLiteModel = GetModel(modelBuffer.data()); + // Create TfLite Interpreters + std::unique_ptr armnnDelegateInterpreter; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegateInterpreter) == kTfLiteOk); + CHECK(armnnDelegateInterpreter != nullptr); + CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); + + std::unique_ptr 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 + 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, input0Values); + armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input1Values); + + // Set input data for the tflite interpreter + armnnDelegate::FillInput(tfLiteInterpreter, 0, input0Values); + armnnDelegate::FillInput(tfLiteInterpreter, 1, input1Values); + + // Run EnqueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data, comparing Boolean values is handled differently and needs to call the CompareData function + // directly. This is because Boolean types get converted to a bit representation in a vector. + auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor(tfLiteDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor(armnnDelegateOutputId); + + armnnDelegate::CompareData(expectedOutputValues, armnnDelegateOutputData, expectedOutputValues.size()); + armnnDelegate::CompareData(expectedOutputValues, tfLiteDelegateOutputData, expectedOutputValues.size()); + armnnDelegate::CompareData(tfLiteDelegateOutputData, armnnDelegateOutputData, expectedOutputValues.size()); + + armnnDelegateInterpreter.reset(nullptr); + tfLiteInterpreter.reset(nullptr); +} + +} // anonymous namespace \ No newline at end of file -- cgit v1.2.1