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  • Manuel Grizonnet's avatar
    ENH: rename files with txx extension to hxx · 3a1fd1fc
    Manuel Grizonnet authored 
    OTB followed since the beginning the ITK convention and use .txx extension for all
template classes. Nevertheless, some development tools do not recognize .txx
file extension. Other tool like GitHub can't do in-browser syntax highlighting for
txx files I think.

The root problem is the use of the txx which should be changed to hxx (or hpp).

In 2011, after an in-depth discussion near April 20, 2011 on the
Insight-Developers mailing list, ITK rename all txx files to hxx (and event
prevent the push of .txx files with a pre-commit hook). It happens is major release v4.

You can find some arguments in the discussion about the change and also in other
projects related to ITK which applied the same modification, see for instance VXL:

https://github.com/vxl/vxl/issues/209

This commit apply now the same modification for OTB.

I understand that it will change some habit for developers and don't bring new
features but I think that in general it is better to stay align with ITK guidelines.

In my opinion, it always facilitate the use of OTB and ITK together if we share
when we can the same code architecture, directory organization, naming
conventions...
    3a1fd1fc
otbTrainGradientBoostedTree.hxx 5.26 KiB
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/*
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 * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
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 *
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 * This file is part of Orfeo Toolbox
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 *
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 *     https://www.orfeo-toolbox.org/
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 *
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 * Licensed under the Apache License, Version 2.0 (the "License");
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 * you may not use this file except in compliance with the License.
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 * You may obtain a copy of the License at
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 *
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 *     http://www.apache.org/licenses/LICENSE-2.0
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 *
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 * Unless required by applicable law or agreed to in writing, software
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 * distributed under the License is distributed on an "AS IS" BASIS,
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 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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 * See the License for the specific language governing permissions and
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 * limitations under the License.
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 */
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#ifndef otbTrainGradientBoostedTree_txx
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#define otbTrainGradientBoostedTree_txx
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#include "otbLearningApplicationBase.h"
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#include "otbGradientBoostedTreeMachineLearningModel.h"
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namespace otb
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{
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namespace Wrapper
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{
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template <class TInputValue, class TOutputValue>
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void
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LearningApplicationBase<TInputValue,TOutputValue>
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::InitGradientBoostedTreeParams()
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{
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// disable GBTree model with OpenCV 3 (not implemented)
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#ifndef OTB_OPENCV_3
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  AddChoice("classifier.gbt", "Gradient Boosted Tree classifier");
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  SetParameterDescription(
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      "classifier.gbt",
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      "This group of parameters allows setting Gradient Boosted Tree classifier parameters. "
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      "See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html}.");
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  if (m_RegressionFlag)
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    {
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    AddParameter(ParameterType_Choice, "classifier.gbt.t", "Loss Function Type");
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    SetParameterDescription("classifier.gbt.t","Type of loss functionused for training.");
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    AddChoice("classifier.gbt.t.sqr","Squared Loss");
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    AddChoice("classifier.gbt.t.abs","Absolute Loss");
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    AddChoice("classifier.gbt.t.hub","Huber Loss");
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    }
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  //WeakCount
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  AddParameter(ParameterType_Int, "classifier.gbt.w", "Number of boosting algorithm iterations");
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  SetParameterInt("classifier.gbt.w",200);
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  SetParameterDescription(
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      "classifier.gbt.w",
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      "Number \"w\" of boosting algorithm iterations, with w*K being the total number of trees in "
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      "the GBT model, where K is the output number of classes.");
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  //Shrinkage
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  AddParameter(ParameterType_Float, "classifier.gbt.s", "Regularization parameter");
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  SetParameterFloat("classifier.gbt.s",0.01);
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  SetParameterDescription("classifier.gbt.s", "Regularization parameter.");
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  //SubSamplePortion
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  AddParameter(ParameterType_Float, "classifier.gbt.p",
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               "Portion of the whole training set used for each algorithm iteration");
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  SetParameterFloat("classifier.gbt.p",0.8);
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  SetParameterDescription(
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"classifier.gbt.p", "Portion of the whole training set used for each algorithm iteration. The subset is generated randomly."); //MaxDepth AddParameter(ParameterType_Int, "classifier.gbt.max", "Maximum depth of the tree"); SetParameterInt("classifier.gbt.max",3); SetParameterDescription( "classifier.gbt.max", "The training algorithm attempts to split each node while its depth is smaller than the maximum " "possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or " "if the tree is pruned."); //UseSurrogates : don't need to be exposed ! //AddParameter(ParameterType_Empty, "classifier.gbt.sur", "Surrogate splits will be built"); //SetParameterDescription("classifier.gbt.sur","These splits allow working with missing data and compute variable importance correctly."); #endif } template <class TInputValue, class TOutputValue> void LearningApplicationBase<TInputValue,TOutputValue> ::TrainGradientBoostedTree(typename ListSampleType::Pointer trainingListSample, typename TargetListSampleType::Pointer trainingLabeledListSample, std::string modelPath) { #ifdef OTB_OPENCV_3 (void) trainingListSample; (void) trainingLabeledListSample; (void) modelPath; #else typedef otb::GradientBoostedTreeMachineLearningModel<InputValueType, OutputValueType> GradientBoostedTreeType; typename GradientBoostedTreeType::Pointer classifier = GradientBoostedTreeType::New(); classifier->SetRegressionMode(this->m_RegressionFlag); classifier->SetInputListSample(trainingListSample); classifier->SetTargetListSample(trainingLabeledListSample); classifier->SetWeakCount(GetParameterInt("classifier.gbt.w")); classifier->SetShrinkage(GetParameterFloat("classifier.gbt.s")); classifier->SetSubSamplePortion(GetParameterFloat("classifier.gbt.p")); classifier->SetMaxDepth(GetParameterInt("classifier.gbt.max")); if (m_RegressionFlag) { switch (GetParameterInt("classifier.gbt.t")) { case 0: // SQUARED_LOSS classifier->SetLossFunctionType(CvGBTrees::SQUARED_LOSS); break; case 1: // ABSOLUTE_LOSS classifier->SetLossFunctionType(CvGBTrees::ABSOLUTE_LOSS); break; case 2: // HUBER_LOSS classifier->SetLossFunctionType(CvGBTrees::HUBER_LOSS); break; default: classifier->SetLossFunctionType(CvGBTrees::SQUARED_LOSS); break; } } else { classifier->SetLossFunctionType(CvGBTrees::DEVIANCE_LOSS); } classifier->Train(); classifier->Save(modelPath); #endif } } //end namespace wrapper } //end namespace otb
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#endif