nidn.training.utils package

Submodules

nidn.training.utils.abs_layer module

class nidn.training.utils.abs_layer.AbsLayer

Bases: Module

Very simple activation layer to allow different abs layer activations of the siren.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

nidn.training.utils.nerf module

class nidn.training.utils.nerf.NERF(in_features, out_features, skip=[], n_neurons=100, activation=Sigmoid(), hidden_layers=8)

Bases: Module

NERF architecture as described by Mildenhall et al. (2020)

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class nidn.training.utils.nerf.NERFLayer(in_features, out_features, bias=True, activation=ReLU())

Bases: Module

Layer as used in NERF.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

init_weights()

nidn.training.utils.siren module

class nidn.training.utils.siren.SineLayer(in_features, out_features, bias=True, is_first=False, omega_0=30)

Bases: Module

Sinuosidal layer for the SIREN model.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

forward_with_intermediate(input)

init_weights()

class nidn.training.utils.siren.Siren(in_features, hidden_features, hidden_layers, out_features, outermost_linear=False, outermost_activation=AbsLayer(), first_omega_0=30, hidden_omega_0=30.0)

Bases: Module

SIREN model as described in the paper by Sitzmann et al. (2020).

forward(coords)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

forward_with_activations(coords, retain_grad=False)

Returns not only model output, but also intermediate activations. Only used for visualizing activations later!

nidn.training.utils.validate_config module

Module contents