ml4gw.nn.norm

Classes

`GroupNorm1D`(num_channels[, num_groups, eps])	Custom implementation of GroupNorm which is faster than the out-of-the-box PyTorch version at inference time.
`GroupNorm1DGetter`([groups])	Utility for making a NormLayer Callable that maps from an integer number of channels to a torch Module.
`GroupNorm2DGetter`([groups])	Utility for making a NormLayer Callable that maps from an integer number of channels to a torch Module.

class ml4gw.nn.norm.GroupNorm1D(num_channels, num_groups=None, eps=1e-05)

Bases: Module

Custom implementation of GroupNorm which is faster than the out-of-the-box PyTorch version at inference time.

Parameters:

num_channels (int)
num_groups (int | None)
eps (float)

forward(x)

Return type:: Float[Tensor, 'batch channel length']
Parameters:: x (Float[Tensor, 'batch channel length'])

class ml4gw.nn.norm.GroupNorm1DGetter(groups=None)

Bases: object

Utility for making a NormLayer Callable that maps from an integer number of channels to a torch Module. Useful for command-line parameterization with jsonargparse.

Parameters:: groups (int | None)

class ml4gw.nn.norm.GroupNorm2DGetter(groups=None)

Bases: object

Utility for making a NormLayer Callable that maps from an integer number of channels to a torch Module. Useful for command-line parameterization with jsonargparse.

Parameters:: groups (int | None)