simulation.utils.machine_learning.models.wasserstein_critic module

Summary

Classes:

WassersteinCritic

Reference

class WassersteinCritic(input_nc: int, n_blocks: int = 3, norm: str = 'instance', ndf=32, height=256, width=256, use_dropout: bool = False, padding_type: str = 'reflect', conv_layers_in_block: int = 2, dilations: Optional[List[int]] = None)

Bases: torch.nn.modules.module.Module, simulation.utils.basics.init_options.InitOptions

forward(input: torch.Tensor)

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.

_clip_weights(bounds: Tuple[float, float] = (- 0.01, 0.01))

Clip weights to given bounds.

perform_optimization_step(generator: torch.nn.modules.module.Module, optimizer: torch.optim.optimizer.Optimizer, batch_critic: torch.Tensor, batch_generator: torch.Tensor, weight_clips: Optional[Tuple[float, float]] = None) → float

Do one iteration to update the parameters.

Parameters

• generator – Generation network

• optimizer – Optimizer for the critic’s weights

• batch_critic – A batch of inputs for the critic

• batch_generator – A batch of inputs for the generator

• weight_clips – Optional weight bounds for the critic’s weights

Returns

Current wasserstein distance estimated by critic.

training: bool