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deepMedicRun
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deepMedicRun
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#!/usr/bin/env python
# Copyright (c) 2016, Konstantinos Kamnitsas
# All rights reserved.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the BSD license. See the accompanying LICENSE file
# or read the terms at https://opensource.org/licenses/BSD-3-Clause.
import sys
import os
import argparse
sys.setrecursionlimit(20000)
from deepmedic.frontEndModules.frontEndHelpers.parsingFilesHelpers import getAbsPathEvenIfRelativeIsGiven
def strIsInt(s):
try:
int(s)
return True
except ValueError:
return False
def setupArgParser() :
parser = argparse.ArgumentParser( prog='DeepMedic', formatter_class=argparse.RawTextHelpFormatter,
description='''\nThis software allows creation and supervised training of 3D, multi-scale CNN models for segmentation of structures in biomedical NIFTI volumes.\nThe project is hosted at: https://github.com/Kamnitsask/deepmedic \nSee the documentation for details on its use.\nThis software accompanies the research presented in:\nKamnitsas et al, "Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation", arXiv:1603.05959, 2016.\nWe hope our work aids you in your endeavours.\nFor questions and feedback contact: konstantinos.kamnitsas12@ic.ac.uk''')
parser.add_argument("-newModel", dest='model_conf', type=str, help="Create a new CNN model with model parameters at given config file [MODEL_CONF].")
parser.add_argument("-train", dest='training_conf', type=str, help="Train a CNN model with training parameters at given config file [TRAINING_CONF].\nThis option can follow a [-newModel MODEL_CONF] option, to create a new model and start training it immediately.\nOtherwise, existing model can be specified in the training-config file or by the additional option [-model].")
parser.add_argument("-test", dest='testing_conf', type=str, help="Test with an existing CNN model. The testing session's parameters should be given in config file [TESTING_CONF].\nExisting model can be specified in the given training-config file or by the additional option [-model].\nThis option cannot be used in combination with [-newModel] or [-train].")
parser.add_argument("-model", dest='saved_model', type=str, help="The path to a saved existing cnn model, to train or test with.\nThis option can follow a [-train] or [-test] option. Not in combination with a [-newModel].\nIf given, this option will overide any \"model\" parameters given in the train or testing configuration file.")
parser.add_argument("-dev", default = "cpu", dest='device', type=str, help="Specify the device to run the process on. Values: [cpu] or [gpu] (default = cpu).\nIn the case of multiple GPUs, specify a particular GPU device with a number, in the format: gpu2.\nNOTE: For GPU processing, CUDA libraries must be first added in your environment's PATH and LD_LIBRARY_PATH. See accompanying documentation.")
return parser
def checkDevicePassedCorrectly(devArg) :
if devArg <> "cpu" and devArg <> "gpu" and not (devArg.startswith("gpu") and strIsInt(devArg[3:]) ) :
print("ERROR: Value for the [-dev] option was not specified correctly. Specify the device to run the process on. Values: [cpu] or [gpu] (Default = cpu).\nIn the case of multiple GPUs, specify a particular GPU device with a number, in the format: gpu2. Exiting."); exit(1)
def setEnvironment(deviceString) :
#This adds the necessary libraries to the path/ld_library_path, if given in the accompanying file: ./environment.txt
#Finally, sets the necessary theano flags.
pathToEnvironmentFile = os.getcwd() + "/environment.txt"
if os.path.isfile(pathToEnvironmentFile) :
envGivenInFile = {}
execfile(pathToEnvironmentFile, envGivenInFile)
if "path_to_theano" in envGivenInFile :
sys.path.insert(1, envGivenInFile["path_to_theano"])
if "path_to_nose" in envGivenInFile :
sys.path.insert(1, envGivenInFile["path_to_nose"])
if "path_to_nibabel" in envGivenInFile :
sys.path.insert(1, envGivenInFile["path_to_nibabel"])
if "path_to_parallelPython" in envGivenInFile :
sys.path.insert(1, envGivenInFile["path_to_parallelPython"])
os.environ['THEANO_FLAGS']='on_unused_input=warn, mode=FAST_RUN,device=' + deviceString + ',floatX=float32,force_device=True'
#################################################
# MAIN #
#################################################
if __name__ == '__main__':
cwd = os.getcwd()
parser = setupArgParser()
args = parser.parse_args()
if len(sys.argv) == 1:
print("For help on the usage of this program, please use the option -h."); exit(1)
if not (args.model_conf or args.training_conf or args.testing_conf) :
print("ERROR: One of the options must be specified:\n\t[-newModel] to create a new model.\n\t[-train] to start a training session on a model.\n\t[-test] to test with an existing model.\nPlease try [-h] for more information. Exiting."); exit(1)
#Preliminary checks:
if args.model_conf and args.saved_model :
print("ERROR:\t[-newModel] and [-model] options cannot be used together.\n\tUse [-newModel] to create a new cnn model.\n\t[-model] can follow a [-train] or [-test] option, to specify which saved model to use.\n\tPlease try -h for more information. Exiting."); exit(1)
if args.testing_conf and (args.model_conf or args.training_conf) :
print("ERROR: [-test] cannot be used in conjuction with [-newModel] or [-train]. To test with an existing network, please just specify a configuration file for the testing process, which will include a path to a trained model to use for the inference. Exiting."); exit(1)
checkDevicePassedCorrectly(args.device)
#Set the environment for Theano.
setEnvironment(args.device)
cnnInstanceLoaded = None
filenameAndPathWhereNewlyCreatedModelWasSaved = None
#Find out what procedure we are being asked to perform:
if args.model_conf :#and not args.training_conf and not args.testing_conf:
#Just create and save a new CNN model.
from deepmedic.frontEndModules import deepMedicNewModel
absPathToModelConf = getAbsPathEvenIfRelativeIsGiven(args.model_conf, cwd)
(cnnInstanceLoaded, filenameAndPathWhereNewlyCreatedModelWasSaved) = deepMedicNewModel.deepMedicNewModelMain(absPathToModelConf)
if args.training_conf : #and not args.model_conf and not args.testing_conf: #Second part was checked already.
#Run training
from deepmedic.frontEndModules import deepMedicTrain
absPathToTrainConf = getAbsPathEvenIfRelativeIsGiven(args.training_conf, cwd)
absPathToCnnModelGivenInCmdLine = getAbsPathEvenIfRelativeIsGiven(args.saved_model, cwd) if args.saved_model else None
deepMedicTrain.deepMedicTrainMain(absPathToTrainConf, absPathToCnnModelGivenInCmdLine, cnnInstanceLoaded, filenameAndPathWhereNewlyCreatedModelWasSaved)
if args.testing_conf : #and not args.model_conf and not args.training_conf : #Second part was checked already.
#Test with an existing model.
from deepmedic.frontEndModules import deepMedicTest
absPathToTestConf = getAbsPathEvenIfRelativeIsGiven(args.testing_conf, cwd)
absPathToCnnModelGivenInCmdLine = getAbsPathEvenIfRelativeIsGiven(args.saved_model, cwd) if args.saved_model else None
deepMedicTest.deepMedicTestMain(absPathToTestConf, absPathToCnnModelGivenInCmdLine)