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David Rotermund 2023-07-11 02:01:44 +02:00 committed by GitHub
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@ -146,7 +146,7 @@ For installing torch under Windows see here: https://pytorch.org/get-started/loc
remove_heartbeat_mean: bool = False, # allows us to remove a offset from the SVD heart signals (don't need that because of a bandpass filter) remove_heartbeat_mean: bool = False, # allows us to remove a offset from the SVD heart signals (don't need that because of a bandpass filter)
remove_heartbeat_linear: bool = False, # allows us to remove a linear treand from the SVD heart signals (don't need that because of a bandpass filter) remove_heartbeat_linear: bool = False, # allows us to remove a linear treand from the SVD heart signals (don't need that because of a bandpass filter)
bin_size: int = 4, # size of the kernel of the first 2d average pooling layer bin_size: int = 4, # size of the kernel of the first 2d average pooling layer
do_frame_shift: bool = True, # Do the frame shift or not. do_frame_shift: bool | None = None, # Do the frame shift or not. None = automatic mode.
half_width_frequency_window: float = 3.0, # Hz (on side ) measure_heartbeat_frequency half_width_frequency_window: float = 3.0, # Hz (on side ) measure_heartbeat_frequency
mmap_mode: bool = True, # controls the np.load mmap_mode: bool = True, # controls the np.load
initital_mask_name: str | None = None, # allows to store the map into a file (give filename here or None if you don't want to save it) initital_mask_name: str | None = None, # allows to store the map into a file (give filename here or None if you don't want to save it)
@ -156,3 +156,5 @@ For installing torch under Windows see here: https://pytorch.org/get-started/loc
gaussian_blur_sigma: float = 1.0, # parameter of a gauss blur layer: sigma gaussian_blur_sigma: float = 1.0, # parameter of a gauss blur layer: sigma
bin_size_post: int | None = None, # size of the kernel of the second 2d average pooling layer bin_size_post: int | None = None, # size of the kernel of the second 2d average pooling layer
) -> tuple[torch.Tensor, torch.Tensor | None]: ) -> tuple[torch.Tensor, torch.Tensor | None]:
This functions outputs the result(x,y,t) and mask(x,y), while the latter can be None.