mmagic.models.editors.pconv.pconv_inpaintor

Module Contents

Classes

PConvInpaintor

Inpaintor for Partial Convolution method.

class mmagic.models.editors.pconv.pconv_inpaintor.PConvInpaintor(data_preprocessor: Union[dict, mmengine.config.Config], encdec: dict, disc: Optional[dict] = None, loss_gan: Optional[dict] = None, loss_gp: Optional[dict] = None, loss_disc_shift: Optional[dict] = None, loss_composed_percep: Optional[dict] = None, loss_out_percep: bool = False, loss_l1_hole: Optional[dict] = None, loss_l1_valid: Optional[dict] = None, loss_tv: Optional[dict] = None, train_cfg: Optional[dict] = None, test_cfg: Optional[dict] = None, init_cfg: Optional[dict] = None)

Bases: mmagic.models.base_models.OneStageInpaintor

Inpaintor for Partial Convolution method.

This inpaintor is implemented according to the paper: Image inpainting for irregular holes using partial convolutions

forward_tensor(inputs, data_samples)

Forward function in tensor mode.

Parameters

• inputs (torch.Tensor) – Input tensor.

• data_sample (dict) – Dict contains data sample.

Returns

Dict contains output results.

Return type

dict

train_step(data: List[dict], optim_wrapper)

Train step function.

In this function, the inpaintor will finish the train step following the pipeline:

1. get fake res/image

2. optimize discriminator (if have)

3. optimize generator

If self.train_cfg.disc_step > 1, the train step will contain multiple iterations for optimizing discriminator with different input data and only one iteration for optimizing generator after disc_step iterations for discriminator.

Parameters

• data (List[dict]) – Batch of data as input.

• optim_wrapper (dict[torch.optim.Optimizer]) – Dict with optimizers for generator and discriminator (if have).

Returns

Dict with loss, information for logger, the number of samples and results for visualization.

Return type

dict