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 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)
