Spaces:
Runtime error
Runtime error
| import logging | |
| import torch | |
| import torch.nn.functional as F | |
| from omegaconf import OmegaConf | |
| from saicinpainting.training.data.datasets import make_constant_area_crop_params | |
| from saicinpainting.training.losses.distance_weighting import make_mask_distance_weighter | |
| from saicinpainting.training.losses.feature_matching import feature_matching_loss, masked_l1_loss | |
| from saicinpainting.training.modules.fake_fakes import FakeFakesGenerator | |
| from saicinpainting.training.trainers.base import BaseInpaintingTrainingModule, make_multiscale_noise | |
| from saicinpainting.utils import add_prefix_to_keys, get_ramp | |
| LOGGER = logging.getLogger(__name__) | |
| def make_constant_area_crop_batch(batch, **kwargs): | |
| crop_y, crop_x, crop_height, crop_width = make_constant_area_crop_params(img_height=batch['image'].shape[2], | |
| img_width=batch['image'].shape[3], | |
| **kwargs) | |
| batch['image'] = batch['image'][:, :, crop_y : crop_y + crop_height, crop_x : crop_x + crop_width] | |
| batch['mask'] = batch['mask'][:, :, crop_y: crop_y + crop_height, crop_x: crop_x + crop_width] | |
| return batch | |
| class DefaultInpaintingTrainingModule(BaseInpaintingTrainingModule): | |
| def __init__(self, *args, concat_mask=True, rescale_scheduler_kwargs=None, image_to_discriminator='predicted_image', | |
| add_noise_kwargs=None, noise_fill_hole=False, const_area_crop_kwargs=None, | |
| distance_weighter_kwargs=None, distance_weighted_mask_for_discr=False, | |
| fake_fakes_proba=0, fake_fakes_generator_kwargs=None, | |
| **kwargs): | |
| super().__init__(*args, **kwargs) | |
| self.concat_mask = concat_mask | |
| self.rescale_size_getter = get_ramp(**rescale_scheduler_kwargs) if rescale_scheduler_kwargs is not None else None | |
| self.image_to_discriminator = image_to_discriminator | |
| self.add_noise_kwargs = add_noise_kwargs | |
| self.noise_fill_hole = noise_fill_hole | |
| self.const_area_crop_kwargs = const_area_crop_kwargs | |
| self.refine_mask_for_losses = make_mask_distance_weighter(**distance_weighter_kwargs) \ | |
| if distance_weighter_kwargs is not None else None | |
| self.distance_weighted_mask_for_discr = distance_weighted_mask_for_discr | |
| self.fake_fakes_proba = fake_fakes_proba | |
| if self.fake_fakes_proba > 1e-3: | |
| self.fake_fakes_gen = FakeFakesGenerator(**(fake_fakes_generator_kwargs or {})) | |
| def forward(self, batch): | |
| if self.training and self.rescale_size_getter is not None: | |
| cur_size = self.rescale_size_getter(self.global_step) | |
| batch['image'] = F.interpolate(batch['image'], size=cur_size, mode='bilinear', align_corners=False) | |
| batch['mask'] = F.interpolate(batch['mask'], size=cur_size, mode='nearest') | |
| if self.training and self.const_area_crop_kwargs is not None: | |
| batch = make_constant_area_crop_batch(batch, **self.const_area_crop_kwargs) | |
| img = batch['image'] | |
| mask = batch['mask'] | |
| masked_img = img * (1 - mask) | |
| if self.add_noise_kwargs is not None: | |
| noise = make_multiscale_noise(masked_img, **self.add_noise_kwargs) | |
| if self.noise_fill_hole: | |
| masked_img = masked_img + mask * noise[:, :masked_img.shape[1]] | |
| masked_img = torch.cat([masked_img, noise], dim=1) | |
| if self.concat_mask: | |
| masked_img = torch.cat([masked_img, mask], dim=1) | |
| batch['predicted_image'] = self.generator(masked_img) | |
| batch['inpainted'] = mask * batch['predicted_image'] + (1 - mask) * batch['image'] | |
| if self.fake_fakes_proba > 1e-3: | |
| if self.training and torch.rand(1).item() < self.fake_fakes_proba: | |
| batch['fake_fakes'], batch['fake_fakes_masks'] = self.fake_fakes_gen(img, mask) | |
| batch['use_fake_fakes'] = True | |
| else: | |
| batch['fake_fakes'] = torch.zeros_like(img) | |
| batch['fake_fakes_masks'] = torch.zeros_like(mask) | |
| batch['use_fake_fakes'] = False | |
| batch['mask_for_losses'] = self.refine_mask_for_losses(img, batch['predicted_image'], mask) \ | |
| if self.refine_mask_for_losses is not None and self.training \ | |
| else mask | |
| return batch | |
| def generator_loss(self, batch): | |
| img = batch['image'] | |
| predicted_img = batch[self.image_to_discriminator] | |
| original_mask = batch['mask'] | |
| supervised_mask = batch['mask_for_losses'] | |
| # L1 | |
| l1_value = masked_l1_loss(predicted_img, img, supervised_mask, | |
| self.config.losses.l1.weight_known, | |
| self.config.losses.l1.weight_missing) | |
| total_loss = l1_value | |
| metrics = dict(gen_l1=l1_value) | |
| # vgg-based perceptual loss | |
| if self.config.losses.perceptual.weight > 0: | |
| pl_value = self.loss_pl(predicted_img, img, mask=supervised_mask).sum() * self.config.losses.perceptual.weight | |
| total_loss = total_loss + pl_value | |
| metrics['gen_pl'] = pl_value | |
| # discriminator | |
| # adversarial_loss calls backward by itself | |
| mask_for_discr = supervised_mask if self.distance_weighted_mask_for_discr else original_mask | |
| self.adversarial_loss.pre_generator_step(real_batch=img, fake_batch=predicted_img, | |
| generator=self.generator, discriminator=self.discriminator) | |
| discr_real_pred, discr_real_features = self.discriminator(img) | |
| discr_fake_pred, discr_fake_features = self.discriminator(predicted_img) | |
| adv_gen_loss, adv_metrics = self.adversarial_loss.generator_loss(real_batch=img, | |
| fake_batch=predicted_img, | |
| discr_real_pred=discr_real_pred, | |
| discr_fake_pred=discr_fake_pred, | |
| mask=mask_for_discr) | |
| total_loss = total_loss + adv_gen_loss | |
| metrics['gen_adv'] = adv_gen_loss | |
| metrics.update(add_prefix_to_keys(adv_metrics, 'adv_')) | |
| # feature matching | |
| if self.config.losses.feature_matching.weight > 0: | |
| need_mask_in_fm = OmegaConf.to_container(self.config.losses.feature_matching).get('pass_mask', False) | |
| mask_for_fm = supervised_mask if need_mask_in_fm else None | |
| fm_value = feature_matching_loss(discr_fake_features, discr_real_features, | |
| mask=mask_for_fm) * self.config.losses.feature_matching.weight | |
| total_loss = total_loss + fm_value | |
| metrics['gen_fm'] = fm_value | |
| if self.loss_resnet_pl is not None: | |
| resnet_pl_value = self.loss_resnet_pl(predicted_img, img) | |
| total_loss = total_loss + resnet_pl_value | |
| metrics['gen_resnet_pl'] = resnet_pl_value | |
| return total_loss, metrics | |
| def discriminator_loss(self, batch): | |
| total_loss = 0 | |
| metrics = {} | |
| predicted_img = batch[self.image_to_discriminator].detach() | |
| self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=predicted_img, | |
| generator=self.generator, discriminator=self.discriminator) | |
| discr_real_pred, discr_real_features = self.discriminator(batch['image']) | |
| discr_fake_pred, discr_fake_features = self.discriminator(predicted_img) | |
| adv_discr_loss, adv_metrics = self.adversarial_loss.discriminator_loss(real_batch=batch['image'], | |
| fake_batch=predicted_img, | |
| discr_real_pred=discr_real_pred, | |
| discr_fake_pred=discr_fake_pred, | |
| mask=batch['mask']) | |
| total_loss = total_loss + adv_discr_loss | |
| metrics['discr_adv'] = adv_discr_loss | |
| metrics.update(add_prefix_to_keys(adv_metrics, 'adv_')) | |
| if batch.get('use_fake_fakes', False): | |
| fake_fakes = batch['fake_fakes'] | |
| self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=fake_fakes, | |
| generator=self.generator, discriminator=self.discriminator) | |
| discr_fake_fakes_pred, _ = self.discriminator(fake_fakes) | |
| fake_fakes_adv_discr_loss, fake_fakes_adv_metrics = self.adversarial_loss.discriminator_loss( | |
| real_batch=batch['image'], | |
| fake_batch=fake_fakes, | |
| discr_real_pred=discr_real_pred, | |
| discr_fake_pred=discr_fake_fakes_pred, | |
| mask=batch['mask'] | |
| ) | |
| total_loss = total_loss + fake_fakes_adv_discr_loss | |
| metrics['discr_adv_fake_fakes'] = fake_fakes_adv_discr_loss | |
| metrics.update(add_prefix_to_keys(fake_fakes_adv_metrics, 'adv_')) | |
| return total_loss, metrics | |