def __init__(self,
data_split_csv,
train_preprocessings,
valid_preprocessings,
transforms,
to_tensor,
augments=None,
**kwargs):
super().__init__(**kwargs)
self.data_split_csv = data_split_csv
self.train_preprocessings = Compose.compose(train_preprocessings)
self.valid_preprocessings = Compose.compose(valid_preprocessings)
self.transforms = Compose.compose(transforms)
self.to_tensor = ToTensor()
self.augments = Compose.compose(augments)
self.data_paths = []
# Collect the data paths according to the dataset split csv.
with open(self.data_split_csv, "r") as f:
type_ = 'Training' if self.type == 'train' else 'Validation'
rows = csv.reader(f)
for _path, split_type in rows:
if split_type == type_:
self.data_paths.append(_path)
def __init__(self, data_split_file_path, preprocess, transforms, **kwargs):
super().__init__(**kwargs)
data_split_file = pd.read_csv(data_split_file_path)
self.data_paths = list(
map(Path, data_split_file[data_split_file.type == self.type].path))
self.preprocess = Compose.compose(preprocess)
self.transforms = Compose.compose(transforms)
self.to_tensor = ToTensor()
def __init__(self, data_split_file_path, preprocess, transforms, **kwargs):
super().__init__(**kwargs)
if self.type == 'train':
data_split_file = pd.read_csv(data_split_file_path)
patient_dirs = map(
Path, data_split_file[(data_split_file.type == 'train') |
(data_split_file.type == 'valid')].path)
self.data_paths = tuple(
data_path for patient_dir in patient_dirs
for data_path in sorted(patient_dir.glob('*_img.nii.gz')))
elif self.type == 'valid':
self.data_paths = tuple(['nan'])
self.preprocess = Compose.compose(preprocess)
self.transforms = Compose.compose(transforms)
self.to_tensor = ToTensor()
def __init__(self,
data_split_file_path,
transforms,
augments=None,
**kwargs):
super().__init__(**kwargs)
data_split_file = pd.read_csv(data_split_file_path)
patient_dirs = tuple(
map(Path, data_split_file[data_split_file.type == self.type].path))
self.data_paths = tuple(
data_path for patient_dir in patient_dirs for data_path in zip(
sorted(patient_dir.glob('**/*frame??.nii.gz')),
sorted(patient_dir.glob('**/*frame??_gt.nii.gz'))
# sorted(patient_dir.glob('**/*frame??.nii.gz')) # For the testing dataset.
))
self.transforms = Compose.compose(transforms)
self.augments = Compose.compose(augments)
self.to_tensor = ToTensor()
def __init__(self,
data_split_file_path,
transforms,
augments=None,
**kwargs):
super().__init__(**kwargs)
data_split_file = pd.read_csv(data_split_file_path)
self.csv_name = Path(data_split_file_path).name
patient_dirs = map(
Path, data_split_file[data_split_file.type == self.type].path)
self.data_paths = tuple(
data_path for patient_dir in patient_dirs for data_path in zip(
sorted(patient_dir.glob('**/*frame??.nii.gz')), (
sorted(patient_dir.glob('**/*frame??.nii.gz')
) if self.csv_name == 'testing.csv' else sorted(
patient_dir.glob('**/*frame??_gt.nii.gz')))))
self.transforms = Compose.compose(transforms)
self.augments = Compose.compose(augments)
self.to_tensor = ToTensor()
def __init__(self,
data_dir,
csv_name,
chosen_index,
chosen_label,
transforms,
augments=None,
**kwargs):
super().__init__(**kwargs)
self.data_dir = data_dir
self.csv_name = csv_name
self.chosen_index = chosen_index
self.chosen_label = chosen_label
self.transforms = Compose.compose(transforms)
self.augments = Compose.compose(augments)
self.to_tensor = ToTensor()
if self.type != 'Testing':
self.data_dir = self.data_dir / Path('train')
else:
self.data_dir = self.data_dir / Path('test')
self.class_folder_path = sorted(
[_dir for _dir in self.data_dir.iterdir() if _dir.is_dir()])
self.data_paths = []
for i, idx in enumerate(self.chosen_index):
folder_path = self.class_folder_path[idx]
if self.type != 'Testing':
csv_path = str(folder_path / csv_name)
with open(csv_path, 'r', newline='') as csvfile:
rows = csv.reader(csvfile)
for _path, _type in rows:
if self.type == _type:
self.data_paths.append(
(_path, self.chosen_label[i]))
else:
file_paths = list(folder_path.glob('*.npy'))
for _path in file_paths:
self.data_paths.append((_path, self.chosen_label[i]))
def __init__(self,
data_split_file_path,
transforms,
augments=None,
**kwargs):
super().__init__(**kwargs)
data_split_file = pd.read_csv(data_split_file_path)
patient_dirs = map(
Path, data_split_file[data_split_file.type == self.type].path)
self.data_paths = tuple(
(patient_dir / f'{patient_dir.name}_t1.nii.gz',
patient_dir / f'{patient_dir.name}_t1ce.nii.gz',
patient_dir / f'{patient_dir.name}_t2.nii.gz',
patient_dir / f'{patient_dir.name}_flair.nii.gz',
(patient_dir / f'{patient_dir.name}_seg.nii.gz' if self.type ==
'train' else Path(patient_dir.as_posix().
replace('brats17_resampled', 'brats17')) /
f'{patient_dir.name}_seg.nii.gz'))
for patient_dir in patient_dirs)
self.transforms = Compose.compose(transforms)
self.augments = Compose.compose(augments)
self.to_tensor = ToTensor()
def __init__(self,
data_split_file_path,
transforms,
augments=None,
**kwargs):
super().__init__(**kwargs)
data_split_file = pd.read_csv(data_split_file_path)
self.csv_name = Path(data_split_file_path).name
patient_dirs = map(
Path, data_split_file[data_split_file.type == self.type].path)
self.data_paths = tuple(
(patient_dir / f'{patient_dir.name}_img.nii.gz',
(patient_dir /
f'{patient_dir.name}_img.nii.gz' if self.csv_name ==
'testing.csv' else (
patient_dir /
f'{patient_dir.name}_label.nii.gz' if self.type ==
'train' else Path(patient_dir.as_posix().
replace('vipcup_resampled', 'vipcup')) /
f'{patient_dir.name}_label.nii.gz')))
for patient_dir in patient_dirs)
self.transforms = Compose.compose(transforms)
self.augments = Compose.compose(augments)
self.to_tensor = ToTensor()
def __init__(self, data_dir, train_data_csv, valid_data_csv, transforms=None, resize=None, augments=None, **kwargs):
super().__init__(**kwargs)
self.data_paths = []
class_type = {}
class_type['A'] = 0
class_type['B'] = 1
class_type['C'] = 2
if self.type == 'train':
csv_path = train_data_csv
elif self.type == 'valid':
csv_path = valid_data_csv
else:
raise Exception('The type of dataset is undefined!')
with open(csv_path, "r") as f:
rows = csv.reader(f)
for i, row in enumerate(rows):
if i == 0:
continue
num_img, label = row
img_path = str(Path(data_dir) / Path(num_img))
self.data_paths.append((img_path, class_type[label]))
# self.transforms = Compose.compose(transforms)
# self.augments = Compose.compose(augments)
# self.to_tensor = ToTensor()
self.transform_train = torchTransform.Compose([
# transforms.RandomRotation(15),
torchTransform.ToTensor(),
torchTransform.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.transform_valid = torchTransform.Compose([
# transforms.RandomRotation(15),
torchTransform.ToTensor(),
torchTransform.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.augments = Compose.compose(augments)