def __init__(
self,
col_index: Optional[str] = None,
col_signal: str = "path_signal",
col_target: str = "path_target",
col_weight_map: str = "path_weight_map",
augment: bool = False,
**kwargs,
):
super().__init__(**kwargs)
self.col_index = col_index
self.col_signal = col_signal
self.col_target = col_target
self.col_weight_map = col_weight_map
self.augment = augment
if self.col_index is not None:
self.df = self.df.set_index(self.col_index)
if self.augment:
self.df = add_augmentations(self.df)
if self.col_weight_map not in self.df.columns:
self.col_weight_map = None
for col in [self.col_signal, self.col_target, self.col_weight_map]:
if col is not None and col not in self.df.columns:
raise ValueError(f"{col} not a dataset DataFrame column")
def __init__(self, augment: bool = False, **kwargs):
super().__init__(**kwargs)
assert all(col in self.df.columns
for col in ['path_signal', 'path_target'])
self.augment = augment
if self.augment:
self.df = add_augmentations(self.df)
def DummyCustomFnetDataset(train: bool = False) -> TiffDataset:
"""Returns a dummy custom dataset."""
df = pd.DataFrame({
"path_signal": [os.path.join("data", "EM_low.tif")],
"path_target": [os.path.join("data", "MBP_low.tif")],
})
if not train:
df = add_augmentations(df)
return _CustomDataset(df)
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)
def dummy_custom_dataset(train: bool = False) -> TiffDataset:
"""Returns a dummy custom dataset."""
df = pd.DataFrame({
'path_signal': [os.path.join('data', 'EM_low.tif')],
'path_target': [os.path.join('data', 'MBP_low.tif')],
})
if not train:
df = add_augmentations(df)
return _CustomDataset(df)
def dummy_fnet_dataset(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)
def testdataset(train: bool) -> TiffDataset:
"""Dummy dataset for testing."""
path_data_dir = os.path.join(os.path.dirname(fnet.__file__), os.pardir,
'data')
df = pd.DataFrame({
'path_signal': [os.path.join(path_data_dir, 'EM_low.tif')],
'path_target': [os.path.join(path_data_dir, 'MBP_low.tif')],
})
if not train:
df = add_augmentations(df)
df = df.iloc[1:, :].reset_index(drop=True)
return TiffDataset(dataframe=df)
def __init__(
self,
col_index: Optional[str] = None,
col_signal: str = 'path_signal',
col_target: str = 'path_target',
augment: bool = False,
**kwargs,
):
super().__init__(**kwargs)
assert col_signal in self.df.columns
assert col_target in self.df.columns
self.col_index = col_index
self.col_signal = col_signal
self.col_target = col_target
self.augment = augment
if self.col_index is not None:
self.df = self.df.set_index(self.col_index)
if self.augment:
self.df = add_augmentations(self.df)