def __init__(
self,
root_dir: str,
task: str,
section: str,
transform: Union[Sequence[Callable], Callable] = (),
download: bool = False,
seed: int = 0,
val_frac: float = 0.2,
cache_num: int = sys.maxsize,
cache_rate: float = 1.0,
num_workers: int = 0,
) -> None:
if not os.path.isdir(root_dir):
raise ValueError("Root directory root_dir must be a directory.")
self.section = section
self.val_frac = val_frac
self.set_random_state(seed=seed)
if task not in self.resource:
raise ValueError(
f"Unsupported task: {task}, available options are: {list(self.resource.keys())}."
)
dataset_dir = os.path.join(root_dir, task)
tarfile_name = f"{dataset_dir}.tar"
if download:
download_and_extract(self.resource[task], tarfile_name, root_dir,
self.md5[task])
if not os.path.exists(dataset_dir):
raise RuntimeError(
f"Cannot find dataset directory: {dataset_dir}, please use download=True to download it."
)
self.indices: np.ndarray = np.array([])
data = self._generate_data_list(dataset_dir)
# as `release` key has typo in Task04 config file, ignore it.
property_keys = [
"name",
"description",
"reference",
"licence",
"tensorImageSize",
"modality",
"labels",
"numTraining",
"numTest",
]
self._properties = load_decathlon_properties(
os.path.join(dataset_dir, "dataset.json"), property_keys)
if transform == ():
transform = LoadImaged(["image", "label"])
CacheDataset.__init__(self,
data,
transform,
cache_num=cache_num,
cache_rate=cache_rate,
num_workers=num_workers)
def __init__(
self,
root_dir: PathLike,
section: str,
transform: Union[Sequence[Callable], Callable] = (),
download: bool = False,
seed: int = 0,
val_frac: float = 0.1,
test_frac: float = 0.1,
cache_num: int = sys.maxsize,
cache_rate: float = 1.0,
num_workers: Optional[int] = 1,
progress: bool = True,
copy_cache: bool = True,
as_contiguous: bool = True,
) -> None:
root_dir = Path(root_dir)
if not root_dir.is_dir():
raise ValueError("Root directory root_dir must be a directory.")
self.section = section
self.val_frac = val_frac
self.test_frac = test_frac
self.set_random_state(seed=seed)
tarfile_name = root_dir / self.compressed_file_name
dataset_dir = root_dir / self.dataset_folder_name
self.num_class = 0
if download:
download_and_extract(
url=self.resource,
filepath=tarfile_name,
output_dir=root_dir,
hash_val=self.md5,
hash_type="md5",
progress=progress,
)
if not dataset_dir.is_dir():
raise RuntimeError(
f"Cannot find dataset directory: {dataset_dir}, please use download=True to download it."
)
data = self._generate_data_list(dataset_dir)
if transform == ():
transform = LoadImaged("image")
CacheDataset.__init__(
self,
data=data,
transform=transform,
cache_num=cache_num,
cache_rate=cache_rate,
num_workers=num_workers,
progress=progress,
copy_cache=copy_cache,
as_contiguous=as_contiguous,
)
def __init__(
self,
root_dir: str,
section: str,
transform: Union[Sequence[Callable], Callable] = (),
download: bool = False,
seed: int = 0,
val_frac: float = 0.1,
test_frac: float = 0.1,
cache_num: int = sys.maxsize,
cache_rate: float = 1.0,
num_workers: int = 0,
) -> None:
if not os.path.isdir(root_dir):
raise ValueError("Root directory root_dir must be a directory.")
self.section = section
self.val_frac = val_frac
self.test_frac = test_frac
self.set_random_state(seed=seed)
tarfile_name = os.path.join(root_dir, self.compressed_file_name)
dataset_dir = os.path.join(root_dir, self.dataset_folder_name)
self.num_class = 0
if download:
download_and_extract(self.resource, tarfile_name, root_dir,
self.md5)
if not os.path.exists(dataset_dir):
raise RuntimeError(
f"Cannot find dataset directory: {dataset_dir}, please use download=True to download it."
)
data = self._generate_data_list(dataset_dir)
if transform == ():
transform = LoadImaged("image")
CacheDataset.__init__(self,
data,
transform,
cache_num=cache_num,
cache_rate=cache_rate,
num_workers=num_workers)