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mmedit.models.editors.stylegan1.stylegan1

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Classes

StyleGAN1

Implementation of `A Style-Based Generator Architecture for Generative

Attributes

ModelType

TrainInput

mmedit.models.editors.stylegan1.stylegan1.ModelType[source]
mmedit.models.editors.stylegan1.stylegan1.TrainInput[source]
class mmedit.models.editors.stylegan1.stylegan1.StyleGAN1(generator: ModelType, discriminator: Optional[ModelType] = None, data_preprocessor: Optional[Union[dict, mmengine.Config]] = None, style_channels: int = 512, nkimgs_per_scale: dict = {}, interp_real: Optional[dict] = None, transition_kimgs: int = 600, prev_stage: int = 0, ema_config: Optional[Dict] = None)[source]

Bases: mmedit.models.editors.pggan.ProgressiveGrowingGAN

Implementation of A Style-Based Generator Architecture for Generative Adversarial Networks.

<https://openaccess.thecvf.com/content_CVPR_2019/html/Karras_A_Style-Based_Generator_Architecture_for_Generative_Adversarial_Networks_CVPR_2019_paper.html>`_ # noqa (StyleGANv1). This class is inheriant from ProgressiveGrowingGAN to support progressive training.

Detailed architecture can be found in StyleGAN1Generator and StyleGAN1Discriminator

Parameters
  • generator (ModelType) – The config or model of the generator.

  • discriminator (Optional[ModelType]) – The config or model of the discriminator. Defaults to None.

  • data_preprocessor (Optional[Union[dict, Config]]) – The pre-process config or GenDataPreprocessor.

  • style_channels (int) – The number of channels for style code. Defaults to 128.

  • nkimgs_per_scale (dict) – The number of images need for each resolution’s training. Defaults to {}.

  • intep_real (dict, optional) – The config of interpolation method for real images. If not passed, bilinear interpolation with align_corners will be used. Defaults to None.

  • transition_kimgs (int, optional) – The number of images during used to transit from the previous torgb layer to newer torgb layer. Defaults to 600.

  • prev_stage (int, optional) – The resolution of previous stage. Used for resume training. Defaults to 0.

  • ema_config (Optional[Dict]) – The config for generator’s exponential moving average setting. Defaults to None.

disc_loss(disc_pred_fake: torch.Tensor, disc_pred_real: torch.Tensor, fake_data: torch.Tensor, real_data: torch.Tensor) Tuple[torch.Tensor, dict][source]

Get disc loss. StyleGANv1 use non-saturating gan loss and R1 gradient penalty. 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.

  • fake_data (Tensor) – Generated images, used to calculate gradient penalty.

  • real_data (Tensor) – Real images, used to calculate gradient penalty.

Returns

Loss value and a dict of log variables.

Return type

Tuple[Tensor, dict]

gen_loss(disc_pred_fake: torch.Tensor) Tuple[torch.Tensor, dict][source]

Generator loss for PGGAN. PGGAN use WGAN’s 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]

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