def run(self):
##### Create identifiers
myconfig = self.config
##### Set network specifics
# check if patch size isn't too large. If so make it smaller
# patchsize = myconfig["Patch size"]
patchsize = set_patch_size(myconfig)
##### Load model from source step
loc_model = myconfig["Model path"]
dataset = SingleInstanceDataset(
myconfig['Nifti paths'],
brainmask_path=myconfig['Brainmask path'],
transform=ToTensor())
dataloader = DataLoader(dataset,
batch_size=1,
num_workers=0,
shuffle=False)
logDataLoader(dataloader, self.tmpdir)
evaluator = StandardEvaluator.loadFromCheckpoint(loc_model)
sample_batched = next(iter(dataloader))
images = sample_batched["data"]
header_sources = sample_batched["header_source"]
resultpaths = [os.path.join(self.tmpdir, 'segmentation.nii.gz')]
evaluator.segmentNifti(images, header_sources, patchsize, resultpaths)
self.logger.info('Nifti image segmented for.')
self.tearDown()
def getDataloader(self):
n_workers = self.config['Num Workers']
batchsize = self.config['Batch size']
sequences = self.config["Sequences"]
loc = os.path.join(self.datadir, self.config["Nifti Source"])
# locate, organize and split the dataset
dataset = Brats18.fromFile(loc)
#Data specifics
splits = self.config['Splits']
testsplits = self.config['Testsplits']
dataset.saveSplits(self.tmpdir)
targetsize = tuple(self.config["Patch size"])
imgsize = targetsize
# initialize input transforms
transforms = [RandomCrop(output_size=imgsize), ToTensor()]
transform = Compose(transforms)
# prepare the training set loader
trainset = dataset.getDataset(splits, sequences, transform=transform)
trainset.saveListOfPatients(os.path.join(self.tmpdir, 'trainset.json'))
self.logger.info('Generating patches with input size ' + str(imgsize) +
' and outputsize ' + str(targetsize))
trainloader = DataLoader(trainset,
batch_size=batchsize,
num_workers=n_workers,
shuffle=True)
# prepare the testing set loader
if len(testsplits) > 0:
testset = dataset.getDataset(testsplits,
sequences,
transform=transform)
testloader = DataLoader(testset,
batch_size=batchsize,
num_workers=n_workers,
shuffle=True)
else:
testloader = None
# plot and save samples of a mini-batch
logDataLoader(trainloader, self.tmpdir)
return trainloader, testloader
def getDataloader(self):
batchsize = self.config['Batch size']
loc = os.path.join(self.datadir, self.config["Nifti Source"])
sequences = self.config[
"Sequences"] if "Sequences" in self.config else None
if self.config['Dataset'] == 'Brats18':
dataset = Brats18.fromFile(loc)
elif self.config['Dataset'] == 'BTD':
if self.config['Brainmask'] == False:
dataset = BTD.fromFile(loc, brainmask=False)
else:
dataset = BTD.fromFile(loc, brainmask=True)
elif self.config['Dataset'] == 'Hippocampus':
dataset = Hippocampus.fromFile(loc)
if "Splits from File" in self.config:
dataset.setSplits(self.config["Splits from File"])
elif "Crossval Splits" in self.config:
dataset.createCVSplits(self.config["Crossval Splits"])
#### Data specifics
splits = self.config['Splits']
testsplits = self.config['Testsplits']
dataset.saveSplits(self.tmpdir)
# check if patch size isn't too large. If so make it smaller
# targetsize = tuple(self.config["Patch size"])
targetsize = tuple(set_patch_size(self.config))
imgsize = targetsize
transforms = [RandomCrop(output_size=imgsize), ToTensor()]
if 'Whole Tumor' in self.config and self.config["Whole Tumor"]:
transforms = [BinarySegmentation()] + transforms
transform = Compose(transforms)
if 'Target' in self.config and self.config['Target'] == 'Brainmask':
trainset = dataset.getBrainmaskDataset(splits,
sequences,
transform=transform)
else:
trainset = dataset.getDataset(splits,
sequences,
transform=transform)
trainset.saveListOfPatients(os.path.join(self.tmpdir, 'trainset.json'))
self.logger.info('Generating patches with input size ' + str(imgsize) +
' and outputsize ' + str(targetsize))
n_workers = self.config['Num Workers']
trainloader = DataLoader(trainset,
batch_size=batchsize,
num_workers=n_workers,
shuffle=True)
if len(testsplits) > 0:
if 'Target' in self.config and self.config['Target'] == 'Brainmask':
testset = dataset.getBrainmaskDataset(testsplits,
sequences,
transform=transform)
else:
testset = dataset.getDataset(testsplits,
sequences,
transform=transform)
testloader = DataLoader(testset,
batch_size=batchsize,
num_workers=n_workers,
shuffle=True)
else:
testloader = None
logDataLoader(trainloader, self.tmpdir)
return trainloader, testloader
def getDataloader(self):
batchsize = self.config['Batch size']
loc = os.path.join(self.datadir, self.config["Nifti Source"])
sequences = self.config[
"Sequences"] if "Sequences" in self.config else None
# Loading the specialized config settings from file
print("normalization technique is:", self.config["technique"])
print("using otsu threshold for normalization:",
self.config["using_otsu_ROI"])
print("resampling factor is:", self.config["resampling_factor"])
if self.config['Dataset'] == 'Brats18':
dataset = Brats18.fromFile(loc)
elif self.config['Dataset'] == 'BTD':
print(self.config['Brainmask'])
if self.config['Brainmask'] == False:
dataset = BTD.fromFile(loc, brainmask=False)
else:
dataset = BTD.fromFile(loc, brainmask=True)
elif self.config['Dataset'] == 'Hippocampus':
dataset = Hippocampus.fromFile(loc)
elif self.config['Dataset'] == 'LipoData':
dataset = LipoData.fromFile(loc)
elif self.config['Dataset'] == 'LitsData':
dataset = LitsData.fromFile(loc)
elif self.config['Dataset'] == 'ErgoData':
dataset = ErgoData.fromFile(loc)
if "Splits from File" in self.config:
dataset.setSplits(self.config["Splits from File"])
elif "Crossval Splits" in self.config:
dataset.createCVSplits(self.config["Crossval Splits"])
#### Data specifics
splits = self.config['Splits']
testsplits = self.config['Testsplits']
dataset.saveSplits(self.tmpdir)
# check if patch size isn't too large. If so make it smaller
# targetsize = tuple(self.config["Patch size"])
targetsize = tuple(set_patch_size(self.config))
imgsize = targetsize
transforms = [RandomCrop(output_size=imgsize), ToTensor()]
if 'Whole Tumor' in self.config and self.config["Whole Tumor"]:
transforms = [BinarySegmentation()] + transforms
transform = Compose(transforms)
if 'Target' in self.config and self.config['Target'] == 'Brainmask':
trainset = dataset.getBrainmaskDataset(splits,
sequences,
transform=transform)
else:
trainset = dataset.getDataset(splits,
sequences,
transform=transform,
preprocess_config=self.config)
trainset.saveListOfPatients(os.path.join(self.tmpdir, 'trainset.json'))
self.logger.info('Generating patches with input size ' + str(imgsize) +
' and outputsize ' + str(targetsize))
n_workers = self.config['Num Workers']
now = datetime.now()
print('train loader is initializing', now)
trainloader = DataLoader(trainset,
batch_size=batchsize,
num_workers=n_workers,
shuffle=True)
later = datetime.now()
print('train loader is done initializing', later - now)
if len(testsplits) > 0:
if 'Target' in self.config and self.config['Target'] == 'Brainmask':
testset = dataset.getBrainmaskDataset(testsplits,
sequences,
transform=transform)
else:
testset = dataset.getDataset(testsplits,
sequences,
transform=transform,
preprocess_config=self.config)
testloader = DataLoader(testset,
batch_size=batchsize,
num_workers=n_workers,
shuffle=True)
else:
testloader = None
logDataLoader(trainloader, self.tmpdir)
return trainloader, testloader