neural_de.external.prenet package

Submodules

neural_de.external.prenet.networks module

class neural_de.external.prenet.networks.PRN(recurrent_iter=6, use_GPU=True)

Bases: Module

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.

training: bool

class neural_de.external.prenet.networks.PRN_r(recurrent_iter=6, use_GPU=True)

Bases: Module

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.

training: bool

class neural_de.external.prenet.networks.PReNet(recurrent_iter=6, use_GPU=True)

Bases: Module

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.

training: bool

class neural_de.external.prenet.networks.PReNet_GRU(recurrent_iter=6, use_GPU=True)

Bases: Module

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.

training: bool

class neural_de.external.prenet.networks.PReNet_LSTM(recurrent_iter=6, use_GPU=True)

Bases: Module

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.

training: bool

class neural_de.external.prenet.networks.PReNet_r(recurrent_iter=6, use_GPU=True)

Bases: Module

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.

training: bool

class neural_de.external.prenet.networks.PReNet_x(recurrent_iter=6, use_GPU=True)

Bases: Module

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.

training: bool

Module contents

Prenet module used for Snow removal. Code adapted from the Prenet implementation of https://github.com/csdwren/PReNet/ .