DSEC#
- class tonic.datasets.DSEC(save_to: str, split: Union[str, List[str]], data_selection: Union[str, List[str]], target_selection: Optional[Union[str, List[str]]] = None, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None)[source]#
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This is a fairly large dataset, so in order to save some disk space, event and image zips are deleted after extraction. If your download gets interrupted and you are left with a corrupted file on disk, Tonic will not be able to detect that and just proceed to download files that are not yet on disk. If you experience issues loading a particular recording, delete that folder manually and Tonic will re-download it the next time. Optical flow targets are not available for every recording, so if you select optical flow targets, only a subset of 18 training recordings will be selected.
Note
To be able to read this dataset, you will need hdf5plugin, PIL and imageio packages installed.
- Parameters:
save_to (str) – Location to save files to on disk.
split (str) – Can be ‘train’, ‘test’ or a selection of individual recordings such as ‘interlaken_00_c’ or [‘thun_00_a’, ‘zurich_city_00_a’]. Cannot mix across train/test.
data_selection (str) – Select which data to load per sample. Can be ‘events_left’, ‘events_right’, ‘images_rectified_left’, ‘images_rectified_right’, ‘image_timestamps’ or any combination thereof in a list.
target_selection (str, optional) – Select which targets to load per sample. Can be ‘disparity_events’, ‘disparity_images’, ‘disparity_timestamps’, ‘optical_flow_forward_event’, ‘optical_flow_forward_timestamps’, ‘optical_flow_backward_event’, ‘optical_flow_backward_timestamps’ or a combination thereof in a list. Note that optical flow targets are not available for every recording.
transform (callable, optional) – A callable of transforms to apply to the data.
target_transform (callable, optional) – A callable of transforms to apply to the targets/labels.
transforms (callable, optional) – A callable of transforms that is applied to both data and labels at the same time.