def checkIfFilesThatListFilesPerCaseAreCorrect(testConfig, testConfigFilepath) : configStruct = testConfig.configStruct print "Checking whether the given files that list channels, masks, etc per case are correctly filled..." numberOfChannels = len(configStruct[testConfig.CHANNELS]) numberOfCases = -1 print "Number of given files that list the channels per case were: ", numberOfChannels listOfListingFilesProvided = configStruct[testConfig.CHANNELS] listOfListingFilesProvided = listOfListingFilesProvided if not configStruct[testConfig.ROI_MASKS] else listOfListingFilesProvided + [configStruct[testConfig.ROI_MASKS]] listOfListingFilesProvided = listOfListingFilesProvided if not configStruct[testConfig.GT_LABELS] else listOfListingFilesProvided + [configStruct[testConfig.GT_LABELS]] for pathToListingFile_i in xrange(len(listOfListingFilesProvided)) : pathToCurrentFileListingPaths = listOfListingFilesProvided[pathToListingFile_i] absolutePathToCurrentFileListingPaths = getAbsPathEvenIfRelativeIsGiven(pathToCurrentFileListingPaths, testConfigFilepath) if not os.path.isfile(absolutePathToCurrentFileListingPaths) : print "ERROR: path provided does not correspond to a file:", absolutePathToCurrentFileListingPaths print "Exiting!" exit(1) listOfFilepathsForEachCaseInCurrentListingFile = parseAbsFileLinesInList(absolutePathToCurrentFileListingPaths) if numberOfCases == -1 : numberOfCases = len(listOfFilepathsForEachCaseInCurrentListingFile) else : checkListContainsCorrectNumberOfCasesOtherwiseExitWithError(numberOfCases, absolutePathToCurrentFileListingPaths, listOfFilepathsForEachCaseInCurrentListingFile) checkIfAllElementsOfAListAreFilesAndExitIfNot(absolutePathToCurrentFileListingPaths, listOfFilepathsForEachCaseInCurrentListingFile) #Check the list of names to give to predictions, cause it is a different case... listingFileWithNamesForPredictionsPerCase = configStruct[testConfig.NAMES_FOR_PRED_PER_CASE] absolutePathToListingFileWithPredictionNamesPerCase = getAbsPathEvenIfRelativeIsGiven(listingFileWithNamesForPredictionsPerCase, testConfigFilepath) if not os.path.isfile(absolutePathToListingFileWithPredictionNamesPerCase) : print "ERROR: path provided does not correspond to a file:", absolutePathToListingFileWithPredictionNamesPerCase print "Exiting!"; exit(1) listOfPredictionNamesForEachCaseInListingFile = parseFileLinesInList(absolutePathToListingFileWithPredictionNamesPerCase) #CAREFUL: Here we use a different parsing function! checkThatAllEntriesOfAListFollowNameConventions(listOfPredictionNamesForEachCaseInListingFile) print "Files that list the channels for each case seem fine. Thanks."
def deepMedicTestMain(testConfigFilepath, absPathToSavedModelFromCmdLine):
print("Given Test-Configuration File: ", testConfigFilepath)
#Parse the config file in this naive fashion...
testConfig = TestConfig()
#configStruct = testConfig.configStruct
exec(open(testConfigFilepath).read(), testConfig.configStruct)
configGet = testConfig.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(testConfig.FOLDER_FOR_OUTPUT), testConfigFilepath)
sessionName = configGet(testConfig.SESSION_NAME) if configGet(
testConfig.SESSION_NAME
) else TestSessionParameters.getDefaultSessionName()
[folderForLogs, folderForPredictions, folderForFeatures
] = makeFoldersNeededForTestingSession(mainOutputAbsFolder, sessionName)
loggerFileName = folderForLogs + "/" + sessionName + ".txt"
sessionLogger = myLoggerModule.MyLogger(loggerFileName)
sessionLogger.print3(
"CONFIG: The configuration file for the testing session was loaded from: "
+ str(testConfigFilepath))
#Load the CNN Model!
sessionLogger.print3(
"=========== Loading the CNN model for testing... ===============")
#If CNN-Model was specified in command line, completely override the one in the config file.
filepathToCnnModelToLoad = None
if absPathToSavedModelFromCmdLine and configGet(
testConfig.CNN_MODEL_FILEPATH):
sessionLogger.print3(
"WARN: A CNN-Model to use was specified both in the command line input and in the test-config-file! The input by the command line will be used: "
+ str(absPathToSavedModelFromCmdLine))
filepathToCnnModelToLoad = absPathToSavedModelFromCmdLine
elif absPathToSavedModelFromCmdLine:
filepathToCnnModelToLoad = absPathToSavedModelFromCmdLine
else:
filepathToCnnModelToLoad = getAbsPathEvenIfRelativeIsGiven(
configGet(testConfig.CNN_MODEL_FILEPATH), testConfigFilepath)
sessionLogger.print3(
"...Loading the network can take a few minutes if the model is big...")
cnn3dInstance = load_object_from_gzip_file(filepathToCnnModelToLoad)
sessionLogger.print3("The CNN model was loaded successfully from: " +
str(filepathToCnnModelToLoad))
#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!
#testConfig.checkIfConfigIsCorrectForParticularCnnModel(cnn3dInstance)
#Fill in the session's parameters.
#[[case1-ch1, ..., caseN-ch1], [case1-ch2,...,caseN-ch2]]
listOfAListPerChannelWithFilepathsOfAllCases = [
parseAbsFileLinesInList(
getAbsPathEvenIfRelativeIsGiven(channelConfPath,
testConfigFilepath))
for channelConfPath in configGet(testConfig.CHANNELS)
]
#[[case1-ch1, case1-ch2], ..., [caseN-ch1, caseN-ch2]]
listWithAListPerCaseWithFilepathPerChannel = [
list(item)
for item in zip(*tuple(listOfAListPerChannelWithFilepathsOfAllCases))
]
gtLabelsFilepaths = parseAbsFileLinesInList(
getAbsPathEvenIfRelativeIsGiven(configGet(testConfig.GT_LABELS),
testConfigFilepath)) if configGet(
testConfig.GT_LABELS) else None
roiMasksFilepaths = parseAbsFileLinesInList(
getAbsPathEvenIfRelativeIsGiven(configGet(testConfig.ROI_MASKS),
testConfigFilepath)) if configGet(
testConfig.ROI_MASKS) else None
namesToSavePredsAndFeats = parseFileLinesInList(
getAbsPathEvenIfRelativeIsGiven(
configGet(testConfig.NAMES_FOR_PRED_PER_CASE),
testConfigFilepath)) if configGet(
testConfig.NAMES_FOR_PRED_PER_CASE
) else None #CAREFUL: Here we use a different parsing function!
testSessionParameters = TestSessionParameters(
sessionName = sessionName,
sessionLogger = sessionLogger,
mainOutputAbsFolder = mainOutputAbsFolder,
cnn3dInstance = cnn3dInstance,
cnnModelFilepath = filepathToCnnModelToLoad,
#Input:
listWithAListPerCaseWithFilepathPerChannel = listWithAListPerCaseWithFilepathPerChannel,
gtLabelsFilepaths = gtLabelsFilepaths,
roiMasksFilepaths = roiMasksFilepaths,
#Output
namesToSavePredictionsAndFeatures = namesToSavePredsAndFeats,
#predictions
saveSegmentation = configGet(testConfig.SAVE_SEGM),
saveProbMapsBoolPerClass = configGet(testConfig.SAVE_PROBMAPS_PER_CLASS),
folderForPredictions = folderForPredictions,
#features:
saveIndividualFmImages = configGet(testConfig.SAVE_INDIV_FMS),
saveMultidimensionalImageWithAllFms = configGet(testConfig.SAVE_4DIM_FMS),
indicesOfFmsToVisualisePerPathwayAndLayer = [configGet(testConfig.INDICES_OF_FMS_TO_SAVE_NORMAL)] +\
[configGet(testConfig.INDICES_OF_FMS_TO_SAVE_SUBSAMPLED)] +\
[configGet(testConfig.INDICES_OF_FMS_TO_SAVE_FC)
],
folderForFeatures = folderForFeatures,
padInputImagesBool = configGet(testConfig.PAD_INPUT),
)
testSessionParameters.sessionLogger.print3(
"\n=========== NEW TESTING SESSION ===============")
testSessionParameters.printParametersOfThisSession()
testSessionParameters.sessionLogger.print3(
"\n=======================================================")
testSessionParameters.sessionLogger.print3(
"=========== Compiling the Testing Function ============")
testSessionParameters.sessionLogger.print3(
"=======================================================")
cnn3dInstance.compileTestAndVisualisationFunction(
*testSessionParameters.getTupleForCompilationOfTestFunc())
testSessionParameters.sessionLogger.print3(
"\n======================================================")
testSessionParameters.sessionLogger.print3(
"=========== Testing with the CNN model ===============")
testSessionParameters.sessionLogger.print3(
"======================================================")
performInferenceForTestingOnWholeVolumes(
*testSessionParameters.getTupleForCnnTesting())
testSessionParameters.sessionLogger.print3(
"\n======================================================")
testSessionParameters.sessionLogger.print3(
"=========== Testing session finished =================")
testSessionParameters.sessionLogger.print3(
"======================================================")
def checkIfMainTestConfigIsCorrect(testConfig, testConfigFilepath,
absPathToSavedModelFromCmdLine):
configStruct = testConfig.configStruct
print("Checking whether the given test-configuration file is correct...")
if not configStruct[testConfig.FOLDER_FOR_OUTPUT] or not isinstance(
configStruct[testConfig.FOLDER_FOR_OUTPUT], str):
print(
"ERROR:\tPlease provide the path to an output folder for this session, by providing an entry: ",
testConfig.FOLDER_FOR_OUTPUT,
" = \"path-to-folder\" in the configuration file (python-like-string).\n\tExiting!"
)
exit(1)
if not (configStruct[testConfig.CNN_MODEL_FILEPATH]
and isinstance(configStruct[testConfig.CNN_MODEL_FILEPATH],
str)) and not absPathToSavedModelFromCmdLine:
print(
"ERROR:\tPlease provide the path to an existing and trained cnn model folder for this session, by providing an entry: ",
testConfig.CNN_MODEL_FILEPATH,
" = \"path-to-saved-model\" in the configuration file, or using the corresponding option of the main deepmedic script.\n\tExiting!"
)
exit(1)
if not configStruct[testConfig.CHANNELS] or not isinstance(
configStruct[testConfig.CHANNELS], list) or not all(
isinstance(item, str)
for item in configStruct[testConfig.CHANNELS]):
print(
"ERROR:\tPlease provide a list of files that contain the paths to each case's image-channels.\n\tThis parameter should be given in the format : ",
testConfig.CHANNELS,
" = [\"path-to-file-with-paths-for-channel1-of-each-case\", ... , \"path-to-file-with-paths-for-channelN-of-each-case\"] in the configuration file (list of strings, python style).\n\tExiting!"
)
exit(1)
if not configStruct[testConfig.NAMES_FOR_PRED_PER_CASE] or not isinstance(
configStruct[testConfig.NAMES_FOR_PRED_PER_CASE], str):
print(
"ERROR:\tPlease provide the path to a file, which contains a name for each case, in order to name the inference's output correspondingly.\n\tThis parameter should be given in the format : ",
testConfig.NAMES_FOR_PRED_PER_CASE,
" = \"path-to-file-with-paths-for-channel1-for-each-patient\" in the configuration file.\n\tExiting!"
)
exit(1)
#absPathToSavedModelFromCmdLine should be absolute path already!
absPathToCnnModelGiven = absPathToSavedModelFromCmdLine if absPathToSavedModelFromCmdLine else getAbsPathEvenIfRelativeIsGiven(
configStruct[testConfig.CNN_MODEL_FILEPATH], testConfigFilepath)
if not os.path.isfile(absPathToCnnModelGiven):
print("ERROR: Specified path to a cnn-model (", absPathToCnnModelGiven,
") does not point to an existing file! Exiting!")
exit(1)
print(
"Test-configuration file seems correctly completed at first check. I hope this was not too complicated!"
)
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 deepMedicNewModelMain(modelConfigFilepath):
print "Given Model-Configuration File: ", modelConfigFilepath
#Parse the config file in this naive fashion...
modelConfig = ModelConfig()
execfile(modelConfigFilepath, modelConfig.configStruct)
configGet = modelConfig.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(modelConfig.FOLDER_FOR_OUTPUT), modelConfigFilepath)
modelName = configGet(modelConfig.MODEL_NAME) if configGet(
modelConfig.MODEL_NAME
) else CreateModelSessionParameters.getDefaultModelName()
[folderForCnnModels, folderForLogs
] = makeFoldersNeededForCreateModelSession(mainOutputAbsFolder, modelName)
loggerFileName = folderForLogs + "/" + modelName + ".txt"
sessionLogger = myLoggerModule.MyLogger(loggerFileName)
sessionLogger.print3(
"CONFIG: The configuration file for the model-creation session was loaded from: "
+ str(modelConfigFilepath))
#Fill in the session's parameters.
createModelSessionParameters = CreateModelSessionParameters(
cnnModelName=modelName,
sessionLogger=sessionLogger,
mainOutputAbsFolder=mainOutputAbsFolder,
folderForSessionCnnModels=folderForCnnModels,
#===MODEL PARAMETERS===
numberClasses=configGet(modelConfig.NUMB_CLASSES),
numberOfInputChannelsNormal=configGet(
modelConfig.NUMB_INPUT_CHANNELS_NORMAL),
#===Normal pathway===
numFMsNormal=configGet(modelConfig.N_FMS_NORM),
kernDimNormal=configGet(modelConfig.KERN_DIM_NORM),
residConnAtLayersNormal=configGet(ModelConfig.RESID_CONN_LAYERS_NORM),
lowerRankLayersNormal=configGet(ModelConfig.LOWER_RANK_LAYERS_NORM),
#==Subsampled pathway==
useSubsampledBool=configGet(modelConfig.USE_SUBSAMPLED),
numFMsSubsampled=configGet(modelConfig.N_FMS_SUBS),
kernDimSubsampled=configGet(modelConfig.KERN_DIM_SUBS),
subsampleFactor=configGet(modelConfig.SUBS_FACTOR),
residConnAtLayersSubsampled=configGet(
ModelConfig.RESID_CONN_LAYERS_SUBS),
lowerRankLayersSubsampled=configGet(
ModelConfig.LOWER_RANK_LAYERS_SUBS),
#==FC Layers====
numFMsFc=configGet(modelConfig.N_FMS_FC),
kernelDimensionsFirstFcLayer=configGet(modelConfig.KERN_DIM_1ST_FC),
residConnAtLayersFc=configGet(ModelConfig.RESID_CONN_LAYERS_FC),
#==Size of Image Segments ==
segmDimTrain=configGet(modelConfig.SEG_DIM_TRAIN),
segmDimVal=configGet(modelConfig.SEG_DIM_VAL),
segmDimInfer=configGet(modelConfig.SEG_DIM_INFERENCE),
#== Batch Sizes ==
batchSizeTrain=configGet(modelConfig.BATCH_SIZE_TR),
batchSizeVal=configGet(modelConfig.BATCH_SIZE_VAL),
batchSizeInfer=configGet(modelConfig.BATCH_SIZE_INFER),
#===Other Architectural Parameters ===
activationFunction=configGet(modelConfig.ACTIV_FUNCTION),
#==Dropout Rates==
dropNormal=configGet(modelConfig.DROP_R_NORM),
dropSubsampled=configGet(modelConfig.DROP_R_SUBS),
dropFc=configGet(modelConfig.DROP_R_FC),
#==Regularization==
l1Reg=configGet(modelConfig.L1_REG),
l2Reg=configGet(modelConfig.L2_REG),
#== Weight Initialization==
initialMethod=configGet(modelConfig.INITIAL_METHOD),
#== Batch Normalization ==
bnRollingAverOverThatManyBatches=configGet(
modelConfig.BN_ROLL_AV_BATCHES),
#====Optimization=====
learningRate=configGet(modelConfig.LRATE),
optimizerSgd0Adam1Rms2=configGet(modelConfig.OPTIMIZER),
classicMom0Nesterov1=configGet(modelConfig.MOM_TYPE),
momentumValue=configGet(modelConfig.MOM),
momNonNormalized0Normalized1=configGet(modelConfig.MOM_NORM_NONNORM),
#Adam
b1Adam=configGet(modelConfig.B1_ADAM),
b2Adam=configGet(modelConfig.B2_ADAM),
eAdam=configGet(modelConfig.EPS_ADAM),
#Rms
rhoRms=configGet(modelConfig.RHO_RMS),
eRms=configGet(modelConfig.EPS_RMS))
createModelSessionParameters.sessionLogger.print3(
"=========== NEW CREATE-MODEL SESSION ============")
createModelSessionParameters.printParametersOfThisSession()
createModelSessionParameters.sessionLogger.print3(
"=========== Creating the CNN model ===============")
cnn3dInstance = Cnn3d()
cnn3dInstance.make_cnn_model(
*createModelSessionParameters.getTupleForCnnCreation())
cnn3dInstance.initializeTrainingState(
*createModelSessionParameters.getTupleForInitializingTrainingState())
cnn3dInstance.compileTrainFunction(
*createModelSessionParameters.getTupleForCompilationOfTrainFunc())
cnn3dInstance.compileValidationFunction(
*createModelSessionParameters.getTupleForCompilationOfValFunc())
cnn3dInstance.compileTestAndVisualisationFunction(
*createModelSessionParameters.getTupleForCompilationOfTestFunc())
filenameAndPathToSaveModel = createModelSessionParameters.getPathAndFilenameToSaveModel(
) + ".initial." + datetimeNowAsStr()
filenameAndPathWhereModelWasSaved = dump_cnn_to_gzip_file_dotSave(
cnn3dInstance, filenameAndPathToSaveModel, sessionLogger)
createModelSessionParameters.sessionLogger.print3(
"=========== Creation of the model: \"" +
str(createModelSessionParameters.cnnModelName) +
"\" finished =================")
return (cnn3dInstance, filenameAndPathWhereModelWasSaved)
def deepMedicNewModelMain(modelConfigFilepath,
absPathToPreTrainedModelGivenInCmdLine,
listOfLayersToTransfer):
print("Given Model-Configuration File: ", modelConfigFilepath)
#Parse the config file in this naive fashion...
modelConfig = ModelConfig()
exec(open(modelConfigFilepath).read(), modelConfig.configStruct)
configGet = modelConfig.get #Main interface
#Create Folders and Logger
mainOutputAbsFolder = getAbsPathEvenIfRelativeIsGiven(
configGet(modelConfig.FOLDER_FOR_OUTPUT), modelConfigFilepath)
modelName = configGet(modelConfig.MODEL_NAME) if configGet(
modelConfig.MODEL_NAME
) else CreateModelSessionParameters.getDefaultModelName()
[folderForCnnModels, folderForLogs
] = makeFoldersNeededForCreateModelSession(mainOutputAbsFolder, modelName)
loggerFileName = folderForLogs + "/" + modelName + ".txt"
sessionLogger = myLoggerModule.MyLogger(loggerFileName)
sessionLogger.print3(
"CONFIG: The configuration file for the model-creation session was loaded from: "
+ str(modelConfigFilepath))
#Fill in the session's parameters.
createModelSessionParameters = CreateModelSessionParameters(
cnnModelName=modelName,
sessionLogger=sessionLogger,
mainOutputAbsFolder=mainOutputAbsFolder,
folderForSessionCnnModels=folderForCnnModels,
#===MODEL PARAMETERS===
numberClasses=configGet(modelConfig.NUMB_CLASSES),
numberOfInputChannelsNormal=configGet(
modelConfig.NUMB_INPUT_CHANNELS_NORMAL),
#===Normal pathway===
numFMsNormal=configGet(modelConfig.N_FMS_NORM),
kernDimNormal=configGet(modelConfig.KERN_DIM_NORM),
residConnAtLayersNormal=configGet(ModelConfig.RESID_CONN_LAYERS_NORM),
lowerRankLayersNormal=configGet(ModelConfig.LOWER_RANK_LAYERS_NORM),
#==Subsampled pathway==
useSubsampledBool=configGet(modelConfig.USE_SUBSAMPLED),
numFMsSubsampled=configGet(modelConfig.N_FMS_SUBS),
kernDimSubsampled=configGet(modelConfig.KERN_DIM_SUBS),
subsampleFactor=configGet(modelConfig.SUBS_FACTOR),
residConnAtLayersSubsampled=configGet(
ModelConfig.RESID_CONN_LAYERS_SUBS),
lowerRankLayersSubsampled=configGet(
ModelConfig.LOWER_RANK_LAYERS_SUBS),
#==FC Layers====
numFMsFc=configGet(modelConfig.N_FMS_FC),
kernelDimensionsFirstFcLayer=configGet(modelConfig.KERN_DIM_1ST_FC),
residConnAtLayersFc=configGet(ModelConfig.RESID_CONN_LAYERS_FC),
#==Size of Image Segments ==
segmDimTrain=configGet(modelConfig.SEG_DIM_TRAIN),
segmDimVal=configGet(modelConfig.SEG_DIM_VAL),
segmDimInfer=configGet(modelConfig.SEG_DIM_INFERENCE),
#== Batch Sizes ==
batchSizeTrain=configGet(modelConfig.BATCH_SIZE_TR),
batchSizeVal=configGet(modelConfig.BATCH_SIZE_VAL),
batchSizeInfer=configGet(modelConfig.BATCH_SIZE_INFER),
#===Other Architectural Parameters ===
activationFunction=configGet(modelConfig.ACTIV_FUNCTION),
#==Dropout Rates==
dropNormal=configGet(modelConfig.DROP_R_NORM),
dropSubsampled=configGet(modelConfig.DROP_R_SUBS),
dropFc=configGet(modelConfig.DROP_R_FC),
#== Weight Initialization==
initialMethod=configGet(modelConfig.INITIAL_METHOD),
#== Batch Normalization ==
bnRollingAverOverThatManyBatches=configGet(
modelConfig.BN_ROLL_AV_BATCHES),
)
createModelSessionParameters.sessionLogger.print3(
"\n=========== NEW CREATE-MODEL SESSION ============")
createModelSessionParameters.printParametersOfThisSession()
createModelSessionParameters.sessionLogger.print3(
"\n=========== Creating the CNN model ===============")
cnn3dInstance = Cnn3d()
cnn3dInstance.make_cnn_model(
*createModelSessionParameters.getTupleForCnnCreation())
if absPathToPreTrainedModelGivenInCmdLine != None: # Transfer parameters from a previously trained model to the new one.
createModelSessionParameters.sessionLogger.print3(
"\n=========== Pre-training the new model ===============")
sessionLogger.print3(
"...Loading the pre-trained network. This can take a few minutes if the model is big..."
)
cnnPretrainedInstance = load_object_from_gzip_file(
absPathToPreTrainedModelGivenInCmdLine)
sessionLogger.print3(
"The pre-trained model was loaded successfully from: " +
str(absPathToPreTrainedModelGivenInCmdLine))
from deepmedic import cnnTransferParameters
cnn3dInstance = cnnTransferParameters.transferParametersBetweenModels(
sessionLogger, cnn3dInstance, cnnPretrainedInstance,
listOfLayersToTransfer)
createModelSessionParameters.sessionLogger.print3(
"\n=========== Saving the model ===============")
if absPathToPreTrainedModelGivenInCmdLine != None:
filenameAndPathToSaveModel = createModelSessionParameters.getPathAndFilenameToSaveModel(
) + ".initial.pretrained." + datetimeNowAsStr()
else:
filenameAndPathToSaveModel = createModelSessionParameters.getPathAndFilenameToSaveModel(
) + ".initial." + datetimeNowAsStr()
filenameAndPathWhereModelWasSaved = dump_cnn_to_gzip_file_dotSave(
cnn3dInstance, filenameAndPathToSaveModel, sessionLogger)
createModelSessionParameters.sessionLogger.print3(
"=========== Creation of the model: \"" +
str(createModelSessionParameters.cnnModelName) +
"\" finished =================")
return (cnn3dInstance, filenameAndPathWhereModelWasSaved)
def deepMedicNewModelMain(modelConfigFilepath) :
print "Given Model-Configuration File: ", modelConfigFilepath
#Parse the config file in this naive fashion...
modelConfig = ModelConfig()
execfile(modelConfigFilepath, modelConfig.configStruct)
configGet = modelConfig.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(modelConfig.FOLDER_FOR_OUTPUT), modelConfigFilepath)
modelName = configGet(modelConfig.MODEL_NAME) if configGet(modelConfig.MODEL_NAME) else CreateModelSessionParameters.getDefaultModelName()
[folderForCnnModels,
folderForLogs] = makeFoldersNeededForCreateModelSession(mainOutputAbsFolder, modelName)
loggerFileName = folderForLogs + "/" + modelName + ".txt"
sessionLogger = myLoggerModule.MyLogger(loggerFileName)
sessionLogger.print3("CONFIG: The configuration file for the model-creation session was loaded from: " + str(modelConfigFilepath))
#Fill in the session's parameters.
createModelSessionParameters = CreateModelSessionParameters(
cnnModelName=modelName,
sessionLogger=sessionLogger,
mainOutputAbsFolder=mainOutputAbsFolder,
folderForSessionCnnModels=folderForCnnModels,
#===MODEL PARAMETERS===
numberClasses=configGet(modelConfig.NUMB_CLASSES),
numberOfInputChannelsNormal=configGet(modelConfig.NUMB_INPUT_CHANNELS_NORMAL),
#===Normal pathway===
numFMsNormal=configGet(modelConfig.N_FMS_NORM),
kernDimNormal=configGet(modelConfig.KERN_DIM_NORM),
residConnAtLayersNormal=configGet(ModelConfig.RESID_CONN_LAYERS_NORM),
lowerRankLayersNormal=configGet(ModelConfig.LOWER_RANK_LAYERS_NORM),
#==Subsampled pathway==
useSubsampledBool=configGet(modelConfig.USE_SUBSAMPLED),
numFMsSubsampled=configGet(modelConfig.N_FMS_SUBS),
kernDimSubsampled=configGet(modelConfig.KERN_DIM_SUBS),
subsampleFactor=configGet(modelConfig.SUBS_FACTOR),
residConnAtLayersSubsampled=configGet(ModelConfig.RESID_CONN_LAYERS_SUBS),
lowerRankLayersSubsampled=configGet(ModelConfig.LOWER_RANK_LAYERS_SUBS),
#==FC Layers====
numFMsFc=configGet(modelConfig.N_FMS_FC),
kernelDimensionsFirstFcLayer=configGet(modelConfig.KERN_DIM_1ST_FC),
residConnAtLayersFc=configGet(ModelConfig.RESID_CONN_LAYERS_FC),
#==Size of Image Segments ==
segmDimTrain=configGet(modelConfig.SEG_DIM_TRAIN),
segmDimVal=configGet(modelConfig.SEG_DIM_VAL),
segmDimInfer=configGet(modelConfig.SEG_DIM_INFERENCE),
#== Batch Sizes ==
batchSizeTrain=configGet(modelConfig.BATCH_SIZE_TR),
batchSizeVal=configGet(modelConfig.BATCH_SIZE_VAL),
batchSizeInfer=configGet(modelConfig.BATCH_SIZE_INFER),
#===Other Architectural Parameters ===
activationFunction=configGet(modelConfig.ACTIV_FUNCTION),
#==Dropout Rates==
dropNormal=configGet(modelConfig.DROP_R_NORM),
dropSubsampled=configGet(modelConfig.DROP_R_SUBS),
dropFc=configGet(modelConfig.DROP_R_FC),
#==Regularization==
l1Reg=configGet(modelConfig.L1_REG),
l2Reg=configGet(modelConfig.L2_REG),
#== Weight Initialization==
initialMethod=configGet(modelConfig.INITIAL_METHOD),
#== Batch Normalization ==
bnRollingAverOverThatManyBatches=configGet(modelConfig.BN_ROLL_AV_BATCHES),
#====Optimization=====
learningRate=configGet(modelConfig.LRATE),
optimizerSgd0Adam1Rms2=configGet(modelConfig.OPTIMIZER),
classicMom0Nesterov1=configGet(modelConfig.MOM_TYPE),
momentumValue=configGet(modelConfig.MOM),
momNonNormalized0Normalized1=configGet(modelConfig.MOM_NORM_NONNORM),
#Adam
b1Adam=configGet(modelConfig.B1_ADAM),
b2Adam=configGet(modelConfig.B2_ADAM),
eAdam=configGet(modelConfig.EPS_ADAM),
#Rms
rhoRms=configGet(modelConfig.RHO_RMS),
eRms=configGet(modelConfig.EPS_RMS)
)
createModelSessionParameters.sessionLogger.print3("=========== NEW CREATE-MODEL SESSION ============")
createModelSessionParameters.printParametersOfThisSession()
createModelSessionParameters.sessionLogger.print3("=========== Creating the CNN model ===============")
cnn3dInstance = Cnn3d()
cnn3dInstance.make_cnn_model(*createModelSessionParameters.getTupleForCnnCreation())
cnn3dInstance.initializeTrainingState(*createModelSessionParameters.getTupleForInitializingTrainingState())
cnn3dInstance.compileTrainFunction(*createModelSessionParameters.getTupleForCompilationOfTrainFunc())
cnn3dInstance.compileValidationFunction(*createModelSessionParameters.getTupleForCompilationOfValFunc())
cnn3dInstance.compileTestAndVisualisationFunction(*createModelSessionParameters.getTupleForCompilationOfTestFunc())
filenameAndPathToSaveModel = createModelSessionParameters.getPathAndFilenameToSaveModel() + ".initial." + datetimeNowAsStr()
filenameAndPathWhereModelWasSaved = dump_cnn_to_gzip_file_dotSave(cnn3dInstance, filenameAndPathToSaveModel, sessionLogger)
createModelSessionParameters.sessionLogger.print3("=========== Creation of the model: \"" + str(createModelSessionParameters.cnnModelName) +"\" finished =================")
return (cnn3dInstance, filenameAndPathWhereModelWasSaved)
def checkIfMainTestConfigIsCorrect(testConfig, testConfigFilepath, absPathToSavedModelFromCmdLine) :
configStruct = testConfig.configStruct
print "Checking whether the given test-configuration file is correct..."
if not configStruct[testConfig.FOLDER_FOR_OUTPUT] or not isinstance(configStruct[testConfig.FOLDER_FOR_OUTPUT], str) :
print "ERROR:\tPlease provide the path to an output folder for this session, by providing an entry: ", testConfig.FOLDER_FOR_OUTPUT, " = \"path-to-folder\" in the configuration file (python-like-string).\n\tExiting!"
exit(1)
if not (configStruct[testConfig.CNN_MODEL_FILEPATH] and isinstance(configStruct[testConfig.CNN_MODEL_FILEPATH], str) ) and not absPathToSavedModelFromCmdLine :
print "ERROR:\tPlease provide the path to an existing and trained cnn model folder for this session, by providing an entry: ", testConfig.CNN_MODEL_FILEPATH, " = \"path-to-saved-model\" in the configuration file, or using the corresponding option of the main deepmedic script.\n\tExiting!"
exit(1)
if not configStruct[testConfig.CHANNELS] or not isinstance(configStruct[testConfig.CHANNELS], list) or not all(isinstance(item, str) for item in configStruct[testConfig.CHANNELS]) :
print "ERROR:\tPlease provide a list of files that contain the paths to each case's image-channels.\n\tThis parameter should be given in the format : ", testConfig.CHANNELS, " = [\"path-to-file-with-paths-for-channel1-of-each-case\", ... , \"path-to-file-with-paths-for-channelN-of-each-case\"] in the configuration file (list of strings, python style).\n\tExiting!"
exit(1)
if not configStruct[testConfig.NAMES_FOR_PRED_PER_CASE] or not isinstance(configStruct[testConfig.NAMES_FOR_PRED_PER_CASE], str) :
print "ERROR:\tPlease provide the path to a file, which contains a name for each case, in order to name the inference's output correspondingly.\n\tThis parameter should be given in the format : ", testConfig.NAMES_FOR_PRED_PER_CASE, " = \"path-to-file-with-paths-for-channel1-for-each-patient\" in the configuration file.\n\tExiting!"
exit(1)
#absPathToSavedModelFromCmdLine should be absolute path already!
absPathToCnnModelGiven = absPathToSavedModelFromCmdLine if absPathToSavedModelFromCmdLine else getAbsPathEvenIfRelativeIsGiven(configStruct[testConfig.CNN_MODEL_FILEPATH], testConfigFilepath)
if not os.path.isfile(absPathToCnnModelGiven) :
print "ERROR: Specified path to a cnn-model (", absPathToCnnModelGiven, ") does not point to an existing file! Exiting!"
exit(1)
print "Test-configuration file seems correctly completed at first check. I hope this was not too complicated!"
def deepMedicTestMain(testConfigFilepath, absPathToSavedModelFromCmdLine) :
print "Given Test-Configuration File: ", testConfigFilepath
#Parse the config file in this naive fashion...
testConfig = TestConfig()
#configStruct = testConfig.configStruct
execfile(testConfigFilepath, testConfig.configStruct)
configGet = testConfig.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(testConfig.FOLDER_FOR_OUTPUT), testConfigFilepath)
sessionName = configGet(testConfig.SESSION_NAME) if configGet(testConfig.SESSION_NAME) else TestSessionParameters.getDefaultSessionName()
[folderForLogs,
folderForPredictions,
folderForFeatures] = makeFoldersNeededForTestingSession(mainOutputAbsFolder, sessionName)
loggerFileName = folderForLogs + "/" + sessionName + ".txt"
sessionLogger = myLoggerModule.MyLogger(loggerFileName)
sessionLogger.print3("CONFIG: The configuration file for the testing session was loaded from: " + str(testConfigFilepath))
#Load the CNN Model!
sessionLogger.print3("=========== Loading the CNN model for testing... ===============")
#If CNN-Model was specified in command line, completely override the one in the config file.
filepathToCnnModelToLoad = None
if absPathToSavedModelFromCmdLine and configGet(testConfig.CNN_MODEL_FILEPATH) :
sessionLogger.print3("WARN: A CNN-Model to use was specified both in the command line input and in the test-config-file! The input by the command line will be used: " + str(absPathToSavedModelFromCmdLine) )
filepathToCnnModelToLoad = absPathToSavedModelFromCmdLine
elif absPathToSavedModelFromCmdLine :
filepathToCnnModelToLoad = absPathToSavedModelFromCmdLine
else :
filepathToCnnModelToLoad = getAbsPathEvenIfRelativeIsGiven(configGet(testConfig.CNN_MODEL_FILEPATH), testConfigFilepath)
sessionLogger.print3("...Loading the network can take a few minutes if the model is big...")
cnn3dInstance = load_object_from_gzip_file(filepathToCnnModelToLoad)
sessionLogger.print3("The CNN model was loaded successfully from: " + str(filepathToCnnModelToLoad))
#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!
#testConfig.checkIfConfigIsCorrectForParticularCnnModel(cnn3dInstance)
#Fill in the session's parameters.
#[[case1-ch1, ..., caseN-ch1], [case1-ch2,...,caseN-ch2]]
listOfAListPerChannelWithFilepathsOfAllCases = [parseAbsFileLinesInList(getAbsPathEvenIfRelativeIsGiven(channelConfPath, testConfigFilepath)) for channelConfPath in configGet(testConfig.CHANNELS)]
#[[case1-ch1, case1-ch2], ..., [caseN-ch1, caseN-ch2]]
listWithAListPerCaseWithFilepathPerChannel = [ list(item) for item in zip(*tuple(listOfAListPerChannelWithFilepathsOfAllCases)) ]
gtLabelsFilepaths = parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(configGet(testConfig.GT_LABELS), testConfigFilepath) ) if configGet(testConfig.GT_LABELS) else None
roiMasksFilepaths = parseAbsFileLinesInList( getAbsPathEvenIfRelativeIsGiven(configGet(testConfig.ROI_MASKS), testConfigFilepath) ) if configGet(testConfig.ROI_MASKS) else None
namesToSavePredsAndFeats = parseFileLinesInList( getAbsPathEvenIfRelativeIsGiven(configGet(testConfig.NAMES_FOR_PRED_PER_CASE), testConfigFilepath) ) if configGet(testConfig.NAMES_FOR_PRED_PER_CASE) else None #CAREFUL: Here we use a different parsing function!
testSessionParameters = TestSessionParameters(
sessionName = sessionName,
sessionLogger = sessionLogger,
mainOutputAbsFolder = mainOutputAbsFolder,
cnn3dInstance = cnn3dInstance,
cnnModelFilepath = filepathToCnnModelToLoad,
#Input:
listWithAListPerCaseWithFilepathPerChannel = listWithAListPerCaseWithFilepathPerChannel,
gtLabelsFilepaths = gtLabelsFilepaths,
roiMasksFilepaths = roiMasksFilepaths,
#Output
namesToSavePredictionsAndFeatures = namesToSavePredsAndFeats,
#predictions
saveSegmentation = configGet(testConfig.SAVE_SEGM),
saveProbMapsBoolPerClass = configGet(testConfig.SAVE_PROBMAPS_PER_CLASS),
folderForPredictions = folderForPredictions,
#features:
saveIndividualFmImages = configGet(testConfig.SAVE_INDIV_FMS),
saveMultidimensionalImageWithAllFms = configGet(testConfig.SAVE_4DIM_FMS),
indicesOfFmsToVisualisePerPathwayAndLayer = [ configGet(testConfig.INDICES_OF_FMS_TO_SAVE_NORMAL),
configGet(testConfig.INDICES_OF_FMS_TO_SAVE_SUBSAMPLED),
configGet(testConfig.INDICES_OF_FMS_TO_SAVE_FC)
],
folderForFeatures = folderForFeatures,
padInputImagesBool = configGet(testConfig.PAD_INPUT),
)
testSessionParameters.sessionLogger.print3("=========== NEW TESTING SESSION ===============")
testSessionParameters.printParametersOfThisSession()
testSessionParameters.sessionLogger.print3("======================================================")
testSessionParameters.sessionLogger.print3("=========== Testing with the CNN model ===============")
testSessionParameters.sessionLogger.print3("======================================================")
performInferenceForTestingOnWholeVolumes(*testSessionParameters.getTupleForCnnTesting())
testSessionParameters.sessionLogger.print3("======================================================")
testSessionParameters.sessionLogger.print3("=========== Testing session finished =================")
testSessionParameters.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("=======================================================")
