fnet.data package

Submodules

fnet.data.bufferedpatchdataset module

class fnet.data.bufferedpatchdataset.BufferedPatchDataset(dataset: collections.abc.Sequence, patch_shape: Sequence[int] = (32, 64, 64), buffer_size: int = 1, buffer_switch_interval: int = -1, shuffle_images: bool = True)[source]

Bases: object

Provides patches from items of a dataset.

Parameters

  • dataset – Dataset object.

  • patch_shape – Shape of patch to be extracted from dataset items.

  • buffer_size – Size of buffer.

  • buffer_switch_interval – Number of patches provided between buffer item exchanges. Set to -1 to disable exchanges.

  • shuffle_images – Set to randomize order of dataset item insertion into buffer.

get_batch(batch_size: int) → Sequence[torch.Tensor][source]

Returns a batch of patches.

Parameters

batch_size – Number of patches in batch.

Returns

Batch of patches.

Return type

Sequence[torch.Tensor]

get_buffer_history() → List[int][source]

Returns a list of indices of dataset elements inserted into the buffer.

Returns

Indices of dataset elements.

Return type

List[int]

get_random_patch() → List[Union[numpy.ndarray, torch.Tensor]][source]

Samples random patch from an item in the buffer.

Let nd be the number of dimensions of the patch. If the item has more dimensions than the patch, then sampling will be from the last nd dimensions of the item.

Returns

Random patch sampled from a dataset item.

Return type

List[ArrayLike]

insert_new_element_into_buffer() → None[source]

Inserts new dataset item into buffer.

Returns

Return type

None

fnet.data.czidataset module

fnet.data.czireader module

fnet.data.dummydataset module

fnet.data.dummydataset.DummyCustomFnetDataset(train: bool = False) → fnet.data.tiffdataset.TiffDataset[source]

Returns a dummy custom dataset.

fnet.data.dummydataset.DummyFnetDataset(train: bool = False) → fnet.data.tiffdataset.TiffDataset[source]

Returns a dummy Fnetdataset.

fnet.data.fnetdataset module

class fnet.data.fnetdataset.FnetDataset(dataframe: Optional[pandas.core.frame.DataFrame] = None, path_csv: Optional[str] = None, transform_signal: Optional[list] = None, transform_target: Optional[list] = None)[source]

Bases: torch.utils.data.dataset.Dataset

Abstract class for fnet datasets.

Parameters

  • dataframe – DataFrame where rows are dataset elements. Overrides path_csv.

  • path_csv – Path to csv from which to create DataFrame.

  • transform_signal – List of transforms to apply to signal image.

  • transform_target – List of transforms to apply to target image.

get_information(index) → Union[dict, str][source]

Returns information to identify dataset element specified by index.

property metadata

Returns metadata about the dataset.

fnet.data.multichtiffdataset module

class fnet.data.multichtiffdataset.MultiChTiffDataset(dataframe: pandas.core.frame.DataFrame = None, path_csv: str = None, transform_signal=None, transform_target=None)[source]

Bases: fnet.data.fnetdataset.FnetDataset

Dataset for multi-channel tiff files.

get_information(index: int) → dict[source]

Returns information to identify dataset element specified by index.

fnet.data.tiffdataset module

class fnet.data.tiffdataset.TiffDataset(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)[source]

Bases: fnet.data.fnetdataset.FnetDataset

Dataset where each row is a signal-target pairing from TIFF files.

Dataset items will be 2-item or 3-item tuples:

(signal image, target image) or (signal image, target image, cost map)

Parameters

augment – Set to augment dataset with flips about the x and/or y axis.

get_information(idx: int) → dict[source]

Returns information about the dataset item.

Parameters

idx – Index of dataset item for which to retrieve information.

Returns

Information about dataset item.

Return type

dict

Module contents

class fnet.data.BufferedPatchDataset(dataset: collections.abc.Sequence, patch_shape: Sequence[int] = (32, 64, 64), buffer_size: int = 1, buffer_switch_interval: int = -1, shuffle_images: bool = True)[source]

Bases: object

Provides patches from items of a dataset.

Parameters

  • dataset – Dataset object.

  • patch_shape – Shape of patch to be extracted from dataset items.

  • buffer_size – Size of buffer.

  • buffer_switch_interval – Number of patches provided between buffer item exchanges. Set to -1 to disable exchanges.

  • shuffle_images – Set to randomize order of dataset item insertion into buffer.

get_batch(batch_size: int) → Sequence[torch.Tensor][source]

Returns a batch of patches.

Parameters

batch_size – Number of patches in batch.

Returns

Batch of patches.

Return type

Sequence[torch.Tensor]

get_buffer_history() → List[int][source]

Returns a list of indices of dataset elements inserted into the buffer.

Returns

Indices of dataset elements.

Return type

List[int]

get_random_patch() → List[Union[numpy.ndarray, torch.Tensor]][source]

Samples random patch from an item in the buffer.

Let nd be the number of dimensions of the patch. If the item has more dimensions than the patch, then sampling will be from the last nd dimensions of the item.

Returns

Random patch sampled from a dataset item.

Return type

List[ArrayLike]

insert_new_element_into_buffer() → None[source]

Inserts new dataset item into buffer.

Returns

Return type

None

class fnet.data.FnetDataset(dataframe: Optional[pandas.core.frame.DataFrame] = None, path_csv: Optional[str] = None, transform_signal: Optional[list] = None, transform_target: Optional[list] = None)[source]

Bases: torch.utils.data.dataset.Dataset

Abstract class for fnet datasets.

Parameters

  • dataframe – DataFrame where rows are dataset elements. Overrides path_csv.

  • path_csv – Path to csv from which to create DataFrame.

  • transform_signal – List of transforms to apply to signal image.

  • transform_target – List of transforms to apply to target image.

get_information(index) → Union[dict, str][source]

Returns information to identify dataset element specified by index.

property metadata

Returns metadata about the dataset.

class fnet.data.TiffDataset(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)[source]

Bases: fnet.data.fnetdataset.FnetDataset

Dataset where each row is a signal-target pairing from TIFF files.

Dataset items will be 2-item or 3-item tuples:

(signal image, target image) or (signal image, target image, cost map)

Parameters

augment – Set to augment dataset with flips about the x and/or y axis.

get_information(idx: int) → dict[source]

Returns information about the dataset item.

Parameters

idx – Index of dataset item for which to retrieve information.

Returns

Information about dataset item.

Return type

dict

class fnet.data.MultiChTiffDataset(dataframe: pandas.core.frame.DataFrame = None, path_csv: str = None, transform_signal=None, transform_target=None)[source]

Bases: fnet.data.fnetdataset.FnetDataset

Dataset for multi-channel tiff files.

get_information(index: int) → dict[source]

Returns information to identify dataset element specified by index.

fnet.data.DummyFnetDataset(train: bool = False) → fnet.data.tiffdataset.TiffDataset[source]

Returns a dummy Fnetdataset.

fnet.data.DummyCustomFnetDataset(train: bool = False) → fnet.data.tiffdataset.TiffDataset[source]

Returns a dummy custom dataset.