def deepMedicTrainMain(trainConfigFilepath, absPathToSavedModelFromCmdLine, cnnInstancePreLoaded, filenameAndPathWherePreLoadedModelWas, resetOptimizer): print("Given Training-Configuration File: ", trainConfigFilepath) #Parse the config file in this naive fashion... trainConfig = TrainConfig() exec(open(trainConfigFilepath).read(), trainConfig.configStruct) configGet = trainConfig.get #Main interface """ #Do checks. checkIfMainTestConfigIsCorrect(testConfig, testConfigFilepath, absPathToSavedModelFromCmdLine) #Checks REQUIRED fields are complete. checkIfFilesThatListFilesPerCaseAreCorrect(testConfig, testConfigFilepath) #Checks listing-files (whatever given). checkIfOptionalParametersAreGivenCorrectly(testConfig, testConfigFilepath) #At this point it was checked that all parameters (that could be checked) and filepaths are correct, pointing to files/dirs and all files/dirs exist. """ #Create Folders and Logger mainOutputAbsFolder = getAbsPathEvenIfRelativeIsGiven( configGet(trainConfig.FOLDER_FOR_OUTPUT), trainConfigFilepath) sessionName = configGet(trainConfig.SESSION_NAME) if configGet( trainConfig.SESSION_NAME ) else TrainSessionParameters.getDefaultSessionName() [ folderForSessionCnnModels, folderForLogs, folderForPredictions, folderForFeatures ] = makeFoldersNeededForTrainingSession(mainOutputAbsFolder, sessionName) loggerFileName = folderForLogs + "/" + sessionName + ".txt" sessionLogger = myLoggerModule.MyLogger(loggerFileName) sessionLogger.print3( "CONFIG: The configuration file for the training session was loaded from: " + str(trainConfigFilepath)) #Load the cnn Model if not given straight from a createCnnModel-session... cnn3dInstance = None filepathToCnnModel = None if cnnInstancePreLoaded: sessionLogger.print3( "====== CNN-Instance already loaded (from Create-New-Model procedure) =======" ) if configGet(trainConfig.CNN_MODEL_FILEPATH): sessionLogger.print3( "WARN: Any paths to cnn-models specified in the train-config file will be ommitted! Working with the pre-loaded model!" ) cnn3dInstance = cnnInstancePreLoaded filepathToCnnModel = filenameAndPathWherePreLoadedModelWas else: sessionLogger.print3( "=========== Loading the CNN model for training... ===============" ) #If CNN-Model was specified in command line, completely override the one in the config file. if absPathToSavedModelFromCmdLine and configGet( trainConfig.CNN_MODEL_FILEPATH): sessionLogger.print3( "WARN: A CNN-Model to use was specified both in the command line input and in the train-config-file! The input by the command line will be used: " + str(absPathToSavedModelFromCmdLine)) filepathToCnnModel = absPathToSavedModelFromCmdLine else: filepathToCnnModel = getAbsPathEvenIfRelativeIsGiven( configGet(trainConfig.CNN_MODEL_FILEPATH), trainConfigFilepath) sessionLogger.print3( "...Loading the network can take a few minutes if the model is big..." ) cnn3dInstance = load_object_from_gzip_file(filepathToCnnModel) sessionLogger.print3("The CNN model was loaded successfully from: " + str(filepathToCnnModel)) """ #Do final checks of the parameters. Check the ones that need check in comparison to the model's parameters! Such as: SAVE_PROBMAPS_PER_CLASS, INDICES_OF_FMS_TO_SAVE, Number of Channels! trainConfig.checkIfConfigIsCorrectForParticularCnnModel(cnn3dInstance) """ #Fill in the session's parameters. if configGet(trainConfig.CHANNELS_TR): #[[case1-ch1, ..., caseN-ch1], [case1-ch2,...,caseN-ch2]] listOfAListPerChannelWithFilepathsOfAllCasesTrain = [ parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(channelConfPath, trainConfigFilepath)) for channelConfPath in configGet(trainConfig.CHANNELS_TR) ] #[[case1-ch1, case1-ch2], ..., [caseN-ch1, caseN-ch2]] listWithAListPerCaseWithFilepathPerChannelTrain = [ list(item) for item in zip( *tuple(listOfAListPerChannelWithFilepathsOfAllCasesTrain)) ] else: listWithAListPerCaseWithFilepathPerChannelTrain = None gtLabelsFilepathsTrain = parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(configGet(trainConfig.GT_LABELS_TR), trainConfigFilepath)) if configGet( trainConfig.GT_LABELS_TR) else None roiMasksFilepathsTrain = parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(configGet(trainConfig.ROI_MASKS_TR), trainConfigFilepath)) if configGet( trainConfig.ROI_MASKS_TR) else None #~~~~~ Advanced Training Sampling~~~~~~~~~ if configGet(trainConfig.WEIGHT_MAPS_PER_CAT_FILEPATHS_TR): #[[case1-weightMap1, ..., caseN-weightMap1], [case1-weightMap2,...,caseN-weightMap2]] listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesTrain = [ parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(weightMapConfPath, trainConfigFilepath)) for weightMapConfPath in configGet( trainConfig.WEIGHT_MAPS_PER_CAT_FILEPATHS_TR) ] else: listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesTrain = None #=======VALIDATION========== if configGet(trainConfig.CHANNELS_VAL): listOfAListPerChannelWithFilepathsOfAllCasesVal = [ parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(channelConfPath, trainConfigFilepath)) for channelConfPath in configGet(trainConfig.CHANNELS_VAL) ] #[[case1-ch1, case1-ch2], ..., [caseN-ch1, caseN-ch2]] listWithAListPerCaseWithFilepathPerChannelVal = [ list(item) for item in zip( *tuple(listOfAListPerChannelWithFilepathsOfAllCasesVal)) ] else: listWithAListPerCaseWithFilepathPerChannelVal = None gtLabelsFilepathsVal = parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(configGet( trainConfig.GT_LABELS_VAL), trainConfigFilepath)) if configGet( trainConfig.GT_LABELS_VAL) else None roiMasksFilepathsVal = parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(configGet( trainConfig.ROI_MASKS_VAL), trainConfigFilepath)) if configGet( trainConfig.ROI_MASKS_VAL) else None #~~~~~Full Inference~~~~~~ namesToSavePredsAndFeatsVal = parseFileLinesInList( getAbsPathEvenIfRelativeIsGiven( configGet(trainConfig.NAMES_FOR_PRED_PER_CASE_VAL), trainConfigFilepath)) if configGet( trainConfig.NAMES_FOR_PRED_PER_CASE_VAL ) else None #CAREFUL: Here we use a different parsing function! #~~~~~Advanced Validation Sampling~~~~~~~~ if configGet(trainConfig.WEIGHT_MAPS_PER_CAT_FILEPATHS_VAL): #[[case1-weightMap1, ..., caseN-weightMap1], [case1-weightMap2,...,caseN-weightMap2]] listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesVal = [ parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(weightMapConfPath, trainConfigFilepath)) for weightMapConfPath in configGet( trainConfig.WEIGHT_MAPS_PER_CAT_FILEPATHS_VAL) ] else: listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesVal = None trainSessionParameters = TrainSessionParameters( sessionName = sessionName, sessionLogger = sessionLogger, mainOutputAbsFolder = mainOutputAbsFolder, cnn3dInstance = cnn3dInstance, cnnModelFilepath = filepathToCnnModel, #==================TRAINING==================== folderForSessionCnnModels = folderForSessionCnnModels, listWithAListPerCaseWithFilepathPerChannelTrain = listWithAListPerCaseWithFilepathPerChannelTrain, gtLabelsFilepathsTrain = gtLabelsFilepathsTrain, #[Optionals] #~~~~~~~~~Sampling~~~~~~~~~ roiMasksFilepathsTrain = roiMasksFilepathsTrain, percentOfSamplesToExtractPositTrain = configGet(trainConfig.PERC_POS_SAMPLES_TR), #~~~~~~~~~Advanced Sampling~~~~~~~ useDefaultTrainingSamplingFromGtAndRoi = configGet(trainConfig.DEFAULT_TR_SAMPLING), samplingTypeTraining = configGet(trainConfig.TYPE_OF_SAMPLING_TR), proportionOfSamplesPerCategoryTrain = configGet(trainConfig.PROP_OF_SAMPLES_PER_CAT_TR), listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesTrain = listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesTrain, #~~~~~~~~Training Cycle ~~~~~~~ numberOfEpochs = configGet(trainConfig.NUM_EPOCHS), numberOfSubepochs = configGet(trainConfig.NUM_SUBEP), numOfCasesLoadedPerSubepoch = configGet(trainConfig.NUM_CASES_LOADED_PERSUB), segmentsLoadedOnGpuPerSubepochTrain = configGet(trainConfig.NUM_TR_SEGMS_LOADED_PERSUB), #~~~~~~~ Learning Rate Schedule ~~~~~~~ #Auto requires performValidationOnSamplesThroughoutTraining and providedGtForValidationBool stable0orAuto1orPredefined2orExponential3LrSchedule = configGet(trainConfig.LR_SCH_0123), #Stable + Auto + Predefined. whenDecreasingDivideLrBy = configGet(trainConfig.DIV_LR_BY), #Stable + Auto numEpochsToWaitBeforeLoweringLr = configGet(trainConfig.NUM_EPOCHS_WAIT), #Auto: minIncreaseInValidationAccuracyThatResetsWaiting = configGet(trainConfig.AUTO_MIN_INCR_VAL_ACC), #Predefined. predefinedSchedule = configGet(trainConfig.PREDEF_SCH), #Exponential exponentialSchedForLrAndMom = configGet(trainConfig.EXPON_SCH), #~~~~~~~ Augmentation~~~~~~~~~~~~ reflectImagesPerAxis = configGet(trainConfig.REFL_AUGM_PER_AXIS), performIntAugm = configGet(trainConfig.PERF_INT_AUGM_BOOL), sampleIntAugmShiftWithMuAndStd = configGet(trainConfig.INT_AUGM_SHIF_MUSTD), sampleIntAugmMultiWithMuAndStd = configGet(trainConfig.INT_AUGM_MULT_MUSTD), #==================VALIDATION===================== performValidationOnSamplesThroughoutTraining = configGet(trainConfig.PERFORM_VAL_SAMPLES), performFullInferenceOnValidationImagesEveryFewEpochs = configGet(trainConfig.PERFORM_VAL_INFERENCE), #Required: listWithAListPerCaseWithFilepathPerChannelVal = listWithAListPerCaseWithFilepathPerChannelVal, gtLabelsFilepathsVal = gtLabelsFilepathsVal, segmentsLoadedOnGpuPerSubepochVal = configGet(trainConfig.NUM_VAL_SEGMS_LOADED_PERSUB), #[Optionals] roiMasksFilepathsVal = roiMasksFilepathsVal, #For default sampling and for fast inference. Optional. Otherwise from whole image. #~~~~~~~~Full Inference~~~~~~~~ numberOfEpochsBetweenFullInferenceOnValImages = configGet(trainConfig.NUM_EPOCHS_BETWEEN_VAL_INF), #Output namesToSavePredictionsAndFeaturesVal = namesToSavePredsAndFeatsVal, #predictions saveSegmentationVal = configGet(trainConfig.SAVE_SEGM_VAL), saveProbMapsBoolPerClassVal = configGet(trainConfig.SAVE_PROBMAPS_PER_CLASS_VAL), folderForPredictionsVal = folderForPredictions, #features: saveIndividualFmImagesVal = configGet(trainConfig.SAVE_INDIV_FMS_VAL), saveMultidimensionalImageWithAllFmsVal = configGet(trainConfig.SAVE_4DIM_FMS_VAL), indicesOfFmsToVisualisePerPathwayAndLayerVal = [configGet(trainConfig.INDICES_OF_FMS_TO_SAVE_NORMAL_VAL)] +\ [configGet(trainConfig.INDICES_OF_FMS_TO_SAVE_SUBSAMPLED_VAL)] +\ [configGet(trainConfig.INDICES_OF_FMS_TO_SAVE_FC_VAL)], folderForFeaturesVal = folderForFeatures, #~~~~~~~~ Advanced Validation Sampling ~~~~~~~~~~ useDefaultUniformValidationSampling = configGet(trainConfig.DEFAULT_VAL_SAMPLING), samplingTypeValidation = configGet(trainConfig.TYPE_OF_SAMPLING_VAL), proportionOfSamplesPerCategoryVal = configGet(trainConfig.PROP_OF_SAMPLES_PER_CAT_VAL), listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesVal = listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesVal, #====Optimization===== learningRate=configGet(trainConfig.LRATE), optimizerSgd0Adam1Rms2=configGet(trainConfig.OPTIMIZER), classicMom0Nesterov1=configGet(trainConfig.MOM_TYPE), momentumValue=configGet(trainConfig.MOM), momNonNormalized0Normalized1=configGet(trainConfig.MOM_NORM_NONNORM), #Adam b1Adam=configGet(trainConfig.B1_ADAM), b2Adam=configGet(trainConfig.B2_ADAM), eAdam=configGet(trainConfig.EPS_ADAM), #Rms rhoRms=configGet(trainConfig.RHO_RMS), eRms=configGet(trainConfig.EPS_RMS), #Regularization l1Reg=configGet(trainConfig.L1_REG), l2Reg=configGet(trainConfig.L2_REG), #~~~~~~~ Freeze Layers ~~~~~~~ layersToFreezePerPathwayType = [configGet(trainConfig.LAYERS_TO_FREEZE_NORM), configGet(trainConfig.LAYERS_TO_FREEZE_SUBS), configGet(trainConfig.LAYERS_TO_FREEZE_FC) ], #==============Generic and Preprocessing=============== padInputImagesBool = configGet(trainConfig.PAD_INPUT) ) trainSessionParameters.sessionLogger.print3( "\n=========== NEW TRAINING SESSION ===============") trainSessionParameters.printParametersOfThisSession() trainSessionParameters.sessionLogger.print3( "\n=======================================================") trainSessionParameters.sessionLogger.print3( "=========== Compiling the Training Function ===========") trainSessionParameters.sessionLogger.print3( "=======================================================") if not cnn3dInstance.checkTrainingStateAttributesInitialized( ) or resetOptimizer: trainSessionParameters.sessionLogger.print3("(Re)Initializing parameters for the optimization. " \ "Reason: Uninitialized: ["+str(not cnn3dInstance.checkTrainingStateAttributesInitialized())+"], Reset requested: ["+str(resetOptimizer)+"]" ) cnn3dInstance.initializeTrainingState( *trainSessionParameters.getTupleForInitializingTrainingState()) cnn3dInstance.compileTrainFunction( *trainSessionParameters.getTupleForCompilationOfTrainFunc()) trainSessionParameters.sessionLogger.print3( "\n=========== Compiling the Validation Function =========") cnn3dInstance.compileValidationFunction( *trainSessionParameters.getTupleForCompilationOfValFunc()) trainSessionParameters.sessionLogger.print3( "\n=========== Compiling the Testing Function ============") cnn3dInstance.compileTestAndVisualisationFunction( *trainSessionParameters.getTupleForCompilationOfTestFunc( )) # For validation with full segmentation trainSessionParameters.sessionLogger.print3( "\n=======================================================") trainSessionParameters.sessionLogger.print3( "============== Training the CNN model =================") trainSessionParameters.sessionLogger.print3( "=======================================================") do_training(*trainSessionParameters.getTupleForCnnTraining()) trainSessionParameters.sessionLogger.print3( "\n=======================================================") trainSessionParameters.sessionLogger.print3( "=========== Training session finished =================") trainSessionParameters.sessionLogger.print3( "=======================================================")
def deepMedicTrainMain(trainConfigFilepath, absPathToSavedModelFromCmdLine, cnnInstancePreLoaded, filenameAndPathWherePreLoadedModelWas, resetOptimizer) : print "Given Training-Configuration File: ", trainConfigFilepath #Parse the config file in this naive fashion... trainConfig = TrainConfig() execfile(trainConfigFilepath, trainConfig.configStruct) configGet = trainConfig.get #Main interface """ #Do checks. checkIfMainTestConfigIsCorrect(testConfig, testConfigFilepath, absPathToSavedModelFromCmdLine) #Checks REQUIRED fields are complete. checkIfFilesThatListFilesPerCaseAreCorrect(testConfig, testConfigFilepath) #Checks listing-files (whatever given). checkIfOptionalParametersAreGivenCorrectly(testConfig, testConfigFilepath) #At this point it was checked that all parameters (that could be checked) and filepaths are correct, pointing to files/dirs and all files/dirs exist. """ #Create Folders and Logger mainOutputAbsFolder = getAbsPathEvenIfRelativeIsGiven(configGet(trainConfig.FOLDER_FOR_OUTPUT), trainConfigFilepath) sessionName = configGet(trainConfig.SESSION_NAME) if configGet(trainConfig.SESSION_NAME) else TrainSessionParameters.getDefaultSessionName() [folderForSessionCnnModels, folderForLogs, folderForPredictions, folderForFeatures] = makeFoldersNeededForTrainingSession(mainOutputAbsFolder, sessionName) loggerFileName = folderForLogs + "/" + sessionName + ".txt" sessionLogger = myLoggerModule.MyLogger(loggerFileName) sessionLogger.print3("CONFIG: The configuration file for the training session was loaded from: " + str(trainConfigFilepath)) #Load the cnn Model if not given straight from a createCnnModel-session... cnn3dInstance = None filepathToCnnModel = None if cnnInstancePreLoaded : sessionLogger.print3("====== CNN-Instance already loaded (from Create-New-Model procedure) =======") if configGet(trainConfig.CNN_MODEL_FILEPATH) : sessionLogger.print3("WARN: Any paths to cnn-models specified in the train-config file will be ommitted! Working with the pre-loaded model!") cnn3dInstance = cnnInstancePreLoaded filepathToCnnModel = filenameAndPathWherePreLoadedModelWas else : sessionLogger.print3("=========== Loading the CNN model for training... ===============") #If CNN-Model was specified in command line, completely override the one in the config file. if absPathToSavedModelFromCmdLine and configGet(trainConfig.CNN_MODEL_FILEPATH) : sessionLogger.print3("WARN: A CNN-Model to use was specified both in the command line input and in the train-config-file! The input by the command line will be used: " + str(absPathToSavedModelFromCmdLine) ) filepathToCnnModel = absPathToSavedModelFromCmdLine else : filepathToCnnModel = getAbsPathEvenIfRelativeIsGiven(configGet(trainConfig.CNN_MODEL_FILEPATH), trainConfigFilepath) sessionLogger.print3("...Loading the network can take a few minutes if the model is big...") cnn3dInstance = load_object_from_gzip_file(filepathToCnnModel) sessionLogger.print3("The CNN model was loaded successfully from: " + str(filepathToCnnModel)) """ #Do final checks of the parameters. Check the ones that need check in comparison to the model's parameters! Such as: SAVE_PROBMAPS_PER_CLASS, INDICES_OF_FMS_TO_SAVE, Number of Channels! trainConfig.checkIfConfigIsCorrectForParticularCnnModel(cnn3dInstance) """ #Fill in the session's parameters. if configGet(trainConfig.CHANNELS_TR) : #[[case1-ch1, ..., caseN-ch1], [case1-ch2,...,caseN-ch2]] listOfAListPerChannelWithFilepathsOfAllCasesTrain = [parseAbsFileLinesInList(getAbsPathEvenIfRelativeIsGiven(channelConfPath, trainConfigFilepath)) for channelConfPath in configGet(trainConfig.CHANNELS_TR)] #[[case1-ch1, case1-ch2], ..., [caseN-ch1, caseN-ch2]] listWithAListPerCaseWithFilepathPerChannelTrain = [ list(item) for item in zip(*tuple(listOfAListPerChannelWithFilepathsOfAllCasesTrain)) ] else : listWithAListPerCaseWithFilepathPerChannelTrain = None gtLabelsFilepathsTrain = parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(configGet(trainConfig.GT_LABELS_TR), trainConfigFilepath) ) if configGet(trainConfig.GT_LABELS_TR) else None roiMasksFilepathsTrain = parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(configGet(trainConfig.ROI_MASKS_TR), trainConfigFilepath) ) if configGet(trainConfig.ROI_MASKS_TR) else None #~~~~~ Advanced Training Sampling~~~~~~~~~ if configGet(trainConfig.WEIGHT_MAPS_PER_CAT_FILEPATHS_TR) : #[[case1-weightMap1, ..., caseN-weightMap1], [case1-weightMap2,...,caseN-weightMap2]] listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesTrain = [parseAbsFileLinesInList(getAbsPathEvenIfRelativeIsGiven(weightMapConfPath, trainConfigFilepath)) for weightMapConfPath in configGet(trainConfig.WEIGHT_MAPS_PER_CAT_FILEPATHS_TR)] else : listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesTrain = None #=======VALIDATION========== if configGet(trainConfig.CHANNELS_VAL) : listOfAListPerChannelWithFilepathsOfAllCasesVal = [parseAbsFileLinesInList(getAbsPathEvenIfRelativeIsGiven(channelConfPath, trainConfigFilepath)) for channelConfPath in configGet(trainConfig.CHANNELS_VAL)] #[[case1-ch1, case1-ch2], ..., [caseN-ch1, caseN-ch2]] listWithAListPerCaseWithFilepathPerChannelVal = [ list(item) for item in zip(*tuple(listOfAListPerChannelWithFilepathsOfAllCasesVal)) ] else : listWithAListPerCaseWithFilepathPerChannelVal = None gtLabelsFilepathsVal = parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(configGet(trainConfig.GT_LABELS_VAL), trainConfigFilepath) ) if configGet(trainConfig.GT_LABELS_VAL) else None roiMasksFilepathsVal = parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(configGet(trainConfig.ROI_MASKS_VAL), trainConfigFilepath) ) if configGet(trainConfig.ROI_MASKS_VAL) else None #~~~~~Full Inference~~~~~~ namesToSavePredsAndFeatsVal = parseFileLinesInList( getAbsPathEvenIfRelativeIsGiven(configGet(trainConfig.NAMES_FOR_PRED_PER_CASE_VAL), trainConfigFilepath) ) if configGet(trainConfig.NAMES_FOR_PRED_PER_CASE_VAL) else None #CAREFUL: Here we use a different parsing function! #~~~~~Advanced Validation Sampling~~~~~~~~ if configGet(trainConfig.WEIGHT_MAPS_PER_CAT_FILEPATHS_VAL) : #[[case1-weightMap1, ..., caseN-weightMap1], [case1-weightMap2,...,caseN-weightMap2]] listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesVal = [parseAbsFileLinesInList(getAbsPathEvenIfRelativeIsGiven(weightMapConfPath, trainConfigFilepath)) for weightMapConfPath in configGet(trainConfig.WEIGHT_MAPS_PER_CAT_FILEPATHS_VAL)] else : listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesVal = None trainSessionParameters = TrainSessionParameters( sessionName = sessionName, sessionLogger = sessionLogger, mainOutputAbsFolder = mainOutputAbsFolder, cnn3dInstance = cnn3dInstance, cnnModelFilepath = filepathToCnnModel, #==================TRAINING==================== folderForSessionCnnModels = folderForSessionCnnModels, listWithAListPerCaseWithFilepathPerChannelTrain = listWithAListPerCaseWithFilepathPerChannelTrain, gtLabelsFilepathsTrain = gtLabelsFilepathsTrain, #[Optionals] #~~~~~~~~~Sampling~~~~~~~~~ roiMasksFilepathsTrain = roiMasksFilepathsTrain, percentOfSamplesToExtractPositTrain = configGet(trainConfig.PERC_POS_SAMPLES_TR), #~~~~~~~~~Advanced Sampling~~~~~~~ useDefaultTrainingSamplingFromGtAndRoi = configGet(trainConfig.DEFAULT_TR_SAMPLING), samplingTypeTraining = configGet(trainConfig.TYPE_OF_SAMPLING_TR), proportionOfSamplesPerCategoryTrain = configGet(trainConfig.PROP_OF_SAMPLES_PER_CAT_TR), listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesTrain = listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesTrain, #~~~~~~~~Training Cycle ~~~~~~~ numberOfEpochs = configGet(trainConfig.NUM_EPOCHS), numberOfSubepochs = configGet(trainConfig.NUM_SUBEP), numOfCasesLoadedPerSubepoch = configGet(trainConfig.NUM_CASES_LOADED_PERSUB), segmentsLoadedOnGpuPerSubepochTrain = configGet(trainConfig.NUM_TR_SEGMS_LOADED_PERSUB), #~~~~~~~ Learning Rate Schedule ~~~~~~~ #Auto requires performValidationOnSamplesThroughoutTraining and providedGtForValidationBool stable0orAuto1orPredefined2orExponential3LrSchedule = configGet(trainConfig.LR_SCH_0123), #Stable + Auto + Predefined. whenDecreasingDivideLrBy = configGet(trainConfig.DIV_LR_BY), #Stable + Auto numEpochsToWaitBeforeLoweringLr = configGet(trainConfig.NUM_EPOCHS_WAIT), #Auto: minIncreaseInValidationAccuracyThatResetsWaiting = configGet(trainConfig.AUTO_MIN_INCR_VAL_ACC), #Predefined. predefinedSchedule = configGet(trainConfig.PREDEF_SCH), #Exponential exponentialSchedForLrAndMom = configGet(trainConfig.EXPON_SCH), #~~~~~~~ Augmentation~~~~~~~~~~~~ reflectImagesPerAxis = configGet(trainConfig.REFL_AUGM_PER_AXIS), performIntAugm = configGet(trainConfig.PERF_INT_AUGM_BOOL), sampleIntAugmShiftWithMuAndStd = configGet(trainConfig.INT_AUGM_SHIF_MUSTD), sampleIntAugmMultiWithMuAndStd = configGet(trainConfig.INT_AUGM_MULT_MUSTD), #==================VALIDATION===================== performValidationOnSamplesThroughoutTraining = configGet(trainConfig.PERFORM_VAL_SAMPLES), performFullInferenceOnValidationImagesEveryFewEpochs = configGet(trainConfig.PERFORM_VAL_INFERENCE), #Required: listWithAListPerCaseWithFilepathPerChannelVal = listWithAListPerCaseWithFilepathPerChannelVal, gtLabelsFilepathsVal = gtLabelsFilepathsVal, segmentsLoadedOnGpuPerSubepochVal = configGet(trainConfig.NUM_VAL_SEGMS_LOADED_PERSUB), #[Optionals] roiMasksFilepathsVal = roiMasksFilepathsVal, #For default sampling and for fast inference. Optional. Otherwise from whole image. #~~~~~~~~Full Inference~~~~~~~~ numberOfEpochsBetweenFullInferenceOnValImages = configGet(trainConfig.NUM_EPOCHS_BETWEEN_VAL_INF), #Output namesToSavePredictionsAndFeaturesVal = namesToSavePredsAndFeatsVal, #predictions saveSegmentationVal = configGet(trainConfig.SAVE_SEGM_VAL), saveProbMapsBoolPerClassVal = configGet(trainConfig.SAVE_PROBMAPS_PER_CLASS_VAL), folderForPredictionsVal = folderForPredictions, #features: saveIndividualFmImagesVal = configGet(trainConfig.SAVE_INDIV_FMS_VAL), saveMultidimensionalImageWithAllFmsVal = configGet(trainConfig.SAVE_4DIM_FMS_VAL), indicesOfFmsToVisualisePerPathwayAndLayerVal = [configGet(trainConfig.INDICES_OF_FMS_TO_SAVE_NORMAL_VAL)] +\ [configGet(trainConfig.INDICES_OF_FMS_TO_SAVE_SUBSAMPLED_VAL)] +\ [configGet(trainConfig.INDICES_OF_FMS_TO_SAVE_FC_VAL)], folderForFeaturesVal = folderForFeatures, #~~~~~~~~ Advanced Validation Sampling ~~~~~~~~~~ useDefaultUniformValidationSampling = configGet(trainConfig.DEFAULT_VAL_SAMPLING), samplingTypeValidation = configGet(trainConfig.TYPE_OF_SAMPLING_VAL), proportionOfSamplesPerCategoryVal = configGet(trainConfig.PROP_OF_SAMPLES_PER_CAT_VAL), listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesVal = listOfAListPerWeightMapCategoryWithFilepathsOfAllCasesVal, #====Optimization===== learningRate=configGet(trainConfig.LRATE), optimizerSgd0Adam1Rms2=configGet(trainConfig.OPTIMIZER), classicMom0Nesterov1=configGet(trainConfig.MOM_TYPE), momentumValue=configGet(trainConfig.MOM), momNonNormalized0Normalized1=configGet(trainConfig.MOM_NORM_NONNORM), #Adam b1Adam=configGet(trainConfig.B1_ADAM), b2Adam=configGet(trainConfig.B2_ADAM), eAdam=configGet(trainConfig.EPS_ADAM), #Rms rhoRms=configGet(trainConfig.RHO_RMS), eRms=configGet(trainConfig.EPS_RMS), #Regularization l1Reg=configGet(trainConfig.L1_REG), l2Reg=configGet(trainConfig.L2_REG), #~~~~~~~ Freeze Layers ~~~~~~~ layersToFreezePerPathwayType = [configGet(trainConfig.LAYERS_TO_FREEZE_NORM), configGet(trainConfig.LAYERS_TO_FREEZE_SUBS), configGet(trainConfig.LAYERS_TO_FREEZE_FC) ], #==============Generic and Preprocessing=============== padInputImagesBool = configGet(trainConfig.PAD_INPUT) ) trainSessionParameters.sessionLogger.print3("\n=========== NEW TRAINING SESSION ===============") trainSessionParameters.printParametersOfThisSession() trainSessionParameters.sessionLogger.print3("\n=======================================================") trainSessionParameters.sessionLogger.print3("=========== Compiling the Training Function ===========") trainSessionParameters.sessionLogger.print3("=======================================================") if not cnn3dInstance.checkTrainingStateAttributesInitialized() or resetOptimizer : trainSessionParameters.sessionLogger.print3("(Re)Initializing parameters for the optimization. " \ "Reason: Uninitialized: ["+str(not cnn3dInstance.checkTrainingStateAttributesInitialized())+"], Reset requested: ["+str(resetOptimizer)+"]" ) cnn3dInstance.initializeTrainingState(*trainSessionParameters.getTupleForInitializingTrainingState()) cnn3dInstance.compileTrainFunction(*trainSessionParameters.getTupleForCompilationOfTrainFunc()) trainSessionParameters.sessionLogger.print3("\n=========== Compiling the Validation Function =========") cnn3dInstance.compileValidationFunction(*trainSessionParameters.getTupleForCompilationOfValFunc()) trainSessionParameters.sessionLogger.print3("\n=========== Compiling the Testing Function ============") cnn3dInstance.compileTestAndVisualisationFunction(*trainSessionParameters.getTupleForCompilationOfTestFunc()) # For validation with full segmentation trainSessionParameters.sessionLogger.print3("\n=======================================================") trainSessionParameters.sessionLogger.print3("============== Training the CNN model =================") trainSessionParameters.sessionLogger.print3("=======================================================") do_training(*trainSessionParameters.getTupleForCnnTraining()) trainSessionParameters.sessionLogger.print3("\n=======================================================") trainSessionParameters.sessionLogger.print3("=========== Training session finished =================") trainSessionParameters.sessionLogger.print3("=======================================================")