mmagic.models.editors.lsgan.lsgan

Module Contents

Classes

LSGAN

Implementation of Least Squares Generative Adversarial Networks.

class mmagic.models.editors.lsgan.lsgan.LSGAN(generator: ModelType, discriminator: Optional[ModelType] = None, data_preprocessor: Optional[Union[dict, mmengine.Config]] = None, generator_steps: int = 1, discriminator_steps: int = 1, noise_size: Optional[int] = None, ema_config: Optional[Dict] = None, loss_config: Optional[Dict] = None)

Bases: mmagic.models.base_models.BaseGAN

Implementation of Least Squares Generative Adversarial Networks.

Paper link: https://arxiv.org/pdf/1611.04076.pdf

Detailed architecture can be found in LSGANGenerator and LSGANDiscriminator

disc_loss(disc_pred_fake: torch.Tensor, disc_pred_real: torch.Tensor) → Tuple

Get disc loss. LSGAN use the least squares loss to train the discriminator.

\[L_{D}=\left(D\left(X_{\text {data }}\right)-1\right)^{2} +(D(G(z)))^{2}\]

Parameters

• disc_pred_fake (Tensor) – Discriminator’s prediction of the fake images.

• disc_pred_real (Tensor) – Discriminator’s prediction of the real images.

Returns

Loss value and a dict of log variables.

Return type

tuple[Tensor, dict]

gen_loss(disc_pred_fake: torch.Tensor) → Tuple

Get gen loss. LSGAN use the least squares loss to train the generator.

\[L_{G}=(D(G(z))-1)^{2}\]

Parameters

disc_pred_fake (Tensor) – Discriminator’s prediction of the fake images.

Returns

Loss value and a dict of log variables.

Return type

tuple[Tensor, dict]

train_discriminator(inputs: dict, data_samples: mmagic.structures.DataSample, optimizer_wrapper: mmengine.optim.OptimWrapper) → Dict[str, torch.Tensor]

Train discriminator.

Parameters

• inputs (dict) – Inputs from dataloader.

• data_samples (DataSample) – Data samples from dataloader.

• optim_wrapper (OptimWrapper) – OptimWrapper instance used to update model parameters.

Returns

A dict of tensor for logging.

Return type

Dict[str, Tensor]

train_generator(inputs: dict, data_samples: List[mmagic.structures.DataSample], optimizer_wrapper: mmengine.optim.OptimWrapper) → Dict[str, torch.Tensor]

Train generator.

Parameters

• inputs (dict) – Inputs from dataloader.

• data_samples (List[DataSample]) – Data samples from dataloader. Do not used in generator’s training.

• optim_wrapper (OptimWrapper) – OptimWrapper instance used to update model parameters.

Returns

A dict of tensor for logging.

Return type

Dict[str, Tensor]