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