def get_dataset(args: argparse.Namespace) -> torch.utils.data.Dataset:
"""Returns dataset.
Returns
-------
torch.utils.data.Dataset
Dataset object.
"""
if sum([args.dataset is not None, args.path_tif is not None]) != 1:
raise ValueError('Must specify one input source type')
if args.dataset is not None:
ds_fn = str_to_object(args.dataset)
if not isinstance(ds_fn, Callable):
raise ValueError(f'{args.dataset} must be callable')
return ds_fn(**args.dataset_kwargs)
if args.path_tif is not None:
if not os.path.exists(args.path_tif):
raise ValueError(f'Path does not exists: {args.path_tif}')
paths_tif = [args.path_tif]
if os.path.isdir(args.path_tif):
paths_tif = files_from_dir(args.path_tif)
ds = TiffDataset(
dataframe=pd.DataFrame({
'path_bf': paths_tif,
'path_target': None
}),
transform_signal=[norm_around_center],
transform_target=[norm_around_center],
col_signal='path_bf',
)
return ds
raise NotImplementedError
def DummyFnetDataset(train: bool = False) -> TiffDataset:
"""Returns a dummy Fnetdataset."""
df = pd.DataFrame({
"path_signal": [os.path.join("data", "EM_low.tif")],
"path_target": [os.path.join("data", "MBP_low.tif")],
}).rename_axis("arbitrary")
if not train:
df = add_augmentations(df)
return TiffDataset(dataframe=df)