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)