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