def __init__(
self,
train_transform: Optional[Union[Dict[str, Callable]]] = None,
val_transform: Optional[Union[Dict[str, Callable]]] = None,
test_transform: Optional[Union[Dict[str, Callable]]] = None,
predict_transform: Optional[Union[Dict[str, Callable]]] = None,
image_size: int = 256,
):
if val_transform:
raise_not_supported("validation")
if test_transform:
raise_not_supported("test")
if isinstance(image_size, int):
image_size = (image_size, image_size)
self.image_size = image_size
super().__init__(
train_transform=train_transform,
val_transform=val_transform,
test_transform=test_transform,
predict_transform=predict_transform,
data_sources={
DefaultDataSources.FILES: ImagePathsDataSource(),
DefaultDataSources.FOLDERS: ImagePathsDataSource(),
DefaultDataSources.NUMPY: ImageNumpyDataSource(),
DefaultDataSources.TENSORS: ImageTensorDataSource(),
DefaultDataSources.TENSORS: ImageTensorDataSource(),
},
default_data_source=DefaultDataSources.FILES,
)
def from_folders(
cls,
train_folder: Optional[Union[str, pathlib.Path]] = None,
predict_folder: Optional[Union[str, pathlib.Path]] = None,
train_transform: Optional[Union[str, Dict]] = None,
predict_transform: Optional[Union[str, Dict]] = None,
preprocess: Optional[Preprocess] = None,
**kwargs: Any,
) -> "StyleTransferData":
if any(param in kwargs for param in ("val_folder", "val_transform")):
raise_not_supported("validation")
if any(param in kwargs for param in ("test_folder", "test_transform")):
raise_not_supported("test")
preprocess = preprocess or cls.preprocess_cls(
train_transform=train_transform,
predict_transform=predict_transform,
)
return cls.from_data_source(
DefaultDataSources.FOLDERS,
train_data=train_folder,
predict_data=predict_folder,
preprocess=preprocess,
**kwargs,
)
def test_step(self, batch: Any, batch_idx: int) -> NoReturn:
raise_not_supported("test")
def validation_step(self, batch: Any, batch_idx: int) -> NoReturn:
raise_not_supported("validation")
