mmagic.models.editors.stylegan3.stylegan3

Module Contents

Classes

StyleGAN3

Implementation of Alias-Free Generative Adversarial Networks. # noqa.

Attributes

ModelType

mmagic.models.editors.stylegan3.stylegan3.ModelType

class mmagic.models.editors.stylegan3.stylegan3.StyleGAN3(generator: ModelType, discriminator: Optional[ModelType] = None, data_preprocessor: Optional[Union[dict, mmengine.Config]] = None, generator_steps: int = 1, discriminator_steps: int = 1, forward_kwargs: Optional[Dict] = None, ema_config: Optional[Dict] = None, loss_config=dict())

Bases: mmagic.models.editors.stylegan2.StyleGAN2

Implementation of Alias-Free Generative Adversarial Networks. # noqa.

Paper link: https://nvlabs-fi-cdn.nvidia.com/stylegan3/stylegan3-paper.pdf # noqa

Detailed architecture can be found in

StyleGAN3Generator and StyleGAN2Discriminator

test_step(data: dict) → mmagic.utils.typing.SampleList

Gets the generated image of given data. Same as val_step().

Parameters

data (dict) – Data sampled from metric specific sampler. More details in Metrics and Evaluator.

Returns

A list of DataSample contain generated results.

Return type

SampleList

val_step(data: dict) → mmagic.utils.typing.SampleList

Gets the generated image of given data. Same as val_step().

Parameters

data (dict) – Data sampled from metric specific sampler. More details in Metrics and Evaluator.

Returns

A list of DataSample contain generated results.

Return type

SampleList

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]

sample_equivarience_pairs(batch_size, sample_mode='ema', eq_cfg=dict(compute_eqt_int=False, compute_eqt_frac=False, compute_eqr=False, translate_max=0.125, rotate_max=1), sample_kwargs=dict())