mmagic.models.editors.dcgan.dcgan

Module Contents

Classes

DCGAN

Implementation of `Unsupervised Representation Learning with Deep

class mmagic.models.editors.dcgan.dcgan.DCGAN(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 Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

Paper link:

<https://arxiv.org/abs/1511.06434>`_ (DCGAN).

Detailed architecture can be found in DCGANGenerator # noqa and DCGANDiscriminator # noqa

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

Get disc loss. DCGAN use the vanilla gan loss to train the discriminator.

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. DCGAN use the vanilla gan loss to train the generator.

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: mmagic.structures.DataSample, optimizer_wrapper: mmengine.optim.OptimWrapper) → Dict[str, torch.Tensor]

Train generator.

Parameters

• inputs (dict) – Inputs from dataloader.

• data_samples (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]