mmagic.models.editors.global_local.gl_inpaintor

Module Contents

Classes

GLInpaintor

Inpaintor for global&local method.

class mmagic.models.editors.global_local.gl_inpaintor.GLInpaintor(data_preprocessor: dict, encdec: dict, disc=None, loss_gan=None, loss_gp=None, loss_disc_shift=None, loss_composed_percep=None, loss_out_percep=False, loss_l1_hole=None, loss_l1_valid=None, loss_tv=None, train_cfg=None, test_cfg=None, init_cfg: Optional[dict] = None)

Bases: mmagic.models.base_models.OneStageInpaintor

Inpaintor for global&local method.

This inpaintor is implemented according to the paper: Globally and Locally Consistent Image Completion

Importantly, this inpaintor is an example for using custom training schedule based on OneStageInpaintor.

The training pipeline of global&local is as following:

if cur_iter < iter_tc:
    update generator with only l1 loss
else:
    update discriminator
    if cur_iter > iter_td:
        update generator with l1 loss and adversarial loss

The new attribute cur_iter is added for recording current number of iteration. The train_cfg contains the setting of the training schedule:

train_cfg = dict(
    start_iter=0,
    disc_step=1,
    iter_tc=90000,
    iter_td=100000
)

iter_tc and iter_td correspond to the notation \(T_C\) and \(T_D\) of the original paper.

Parameters

• generator (dict) – Config for encoder-decoder style generator.

• disc (dict) – Config for discriminator.

• loss_gan (dict) – Config for adversarial loss.

• loss_gp (dict) – Config for gradient penalty loss.

• loss_disc_shift (dict) – Config for discriminator shift loss.

• loss_composed_percep (dict) – Config for perceptural and style loss with composed image as input.

• loss_out_percep (dict) – Config for perceptual and style loss with direct output as input.

• loss_l1_hole (dict) – Config for l1 loss in the hole.

• loss_l1_valid (dict) – Config for l1 loss in the valid region.

• loss_tv (dict) – Config for total variation loss.

• train_cfg (dict) – Configs for training scheduler. disc_step must be contained for indicates the discriminator updating steps in each training step.

• test_cfg (dict) – Configs for testing scheduler.

• init_cfg (dict, optional) – Initialization config dict. Default: None.

generator_loss(fake_res, fake_img, fake_local, gt, mask, masked_img)

Forward function in generator training step.

In this function, we mainly compute the loss items for generator with the given (fake_res, fake_img). In general, the fake_res is the direct output of the generator and the fake_img is the composition of direct output and ground-truth image.

Parameters

• fake_res (torch.Tensor) – Direct output of the generator.

• fake_img (torch.Tensor) – Composition of fake_res and ground-truth image.

• fake_local (torch.Tensor) – Local image.

• gt (torch.Tensor) – Ground-truth image.

• mask (torch.Tensor) – Mask image.

• masked_img (torch.Tensor) – Composition of mask image and ground-truth image.

Returns

A tuple containing two dictionaries. The first one is the result dict, which contains the results computed within this function for visualization. The second one is the loss dict, containing loss items computed in this function.

Return type

tuple[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 in current schedule)

3. optimize generator (if in current schedule)

If self.train_cfg.disc_step > 1, the train step will contain multiple iterations for optimizing discriminator with different input data and sonly 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