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