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import torch
from collections import OrderedDict
from os import path as osp
from tqdm import tqdm
from r_basicsr.archs import build_network
from r_basicsr.losses import build_loss
from r_basicsr.metrics import calculate_metric
from r_basicsr.utils import imwrite, tensor2img
from r_basicsr.utils.registry import MODEL_REGISTRY
from .sr_model import SRModel
@MODEL_REGISTRY.register()
class HiFaceGANModel(SRModel):
"""HiFaceGAN model for generic-purpose face restoration.
No prior modeling required, works for any degradations.
Currently doesn't support EMA for inference.
"""
def init_training_settings(self):
train_opt = self.opt['train']
self.ema_decay = train_opt.get('ema_decay', 0)
if self.ema_decay > 0:
raise (NotImplementedError('HiFaceGAN does not support EMA now. Pass'))
self.net_g.train()
self.net_d = build_network(self.opt['network_d'])
self.net_d = self.model_to_device(self.net_d)
self.print_network(self.net_d)
# define losses
# HiFaceGAN does not use pixel loss by default
if train_opt.get('pixel_opt'):
self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device)
else:
self.cri_pix = None
if train_opt.get('perceptual_opt'):
self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device)
else:
self.cri_perceptual = None
if train_opt.get('feature_matching_opt'):
self.cri_feat = build_loss(train_opt['feature_matching_opt']).to(self.device)
else:
self.cri_feat = None
if self.cri_pix is None and self.cri_perceptual is None:
raise ValueError('Both pixel and perceptual losses are None.')
if train_opt.get('gan_opt'):
self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device)
self.net_d_iters = train_opt.get('net_d_iters', 1)
self.net_d_init_iters = train_opt.get('net_d_init_iters', 0)
# set up optimizers and schedulers
self.setup_optimizers()
self.setup_schedulers()
def setup_optimizers(self):
train_opt = self.opt['train']
# optimizer g
optim_type = train_opt['optim_g'].pop('type')
self.optimizer_g = self.get_optimizer(optim_type, self.net_g.parameters(), **train_opt['optim_g'])
self.optimizers.append(self.optimizer_g)
# optimizer d
optim_type = train_opt['optim_d'].pop('type')
self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d'])
self.optimizers.append(self.optimizer_d)
def discriminate(self, input_lq, output, ground_truth):
"""
This is a conditional (on the input) discriminator
In Batch Normalization, the fake and real images are
recommended to be in the same batch to avoid disparate
statistics in fake and real images.
So both fake and real images are fed to D all at once.
"""
h, w = output.shape[-2:]
if output.shape[-2:] != input_lq.shape[-2:]:
lq = torch.nn.functional.interpolate(input_lq, (h, w))
real = torch.nn.functional.interpolate(ground_truth, (h, w))
fake_concat = torch.cat([lq, output], dim=1)
real_concat = torch.cat([lq, real], dim=1)
else:
fake_concat = torch.cat([input_lq, output], dim=1)
real_concat = torch.cat([input_lq, ground_truth], dim=1)
fake_and_real = torch.cat([fake_concat, real_concat], dim=0)
discriminator_out = self.net_d(fake_and_real)
pred_fake, pred_real = self._divide_pred(discriminator_out)
return pred_fake, pred_real
@staticmethod
def _divide_pred(pred):
"""
Take the prediction of fake and real images from the combined batch.
The prediction contains the intermediate outputs of multiscale GAN,
so it's usually a list
"""
if type(pred) == list:
fake = []
real = []
for p in pred:
fake.append([tensor[:tensor.size(0) // 2] for tensor in p])
real.append([tensor[tensor.size(0) // 2:] for tensor in p])
else:
fake = pred[:pred.size(0) // 2]
real = pred[pred.size(0) // 2:]
return fake, real
def optimize_parameters(self, current_iter):
# optimize net_g
for p in self.net_d.parameters():
p.requires_grad = False
self.optimizer_g.zero_grad()
self.output = self.net_g(self.lq)
l_g_total = 0
loss_dict = OrderedDict()
if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
# pixel loss
if self.cri_pix:
l_g_pix = self.cri_pix(self.output, self.gt)
l_g_total += l_g_pix
loss_dict['l_g_pix'] = l_g_pix
# perceptual loss
if self.cri_perceptual:
l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt)
if l_g_percep is not None:
l_g_total += l_g_percep
loss_dict['l_g_percep'] = l_g_percep
if l_g_style is not None:
l_g_total += l_g_style
loss_dict['l_g_style'] = l_g_style
# Requires real prediction for feature matching loss
pred_fake, pred_real = self.discriminate(self.lq, self.output, self.gt)
l_g_gan = self.cri_gan(pred_fake, True, is_disc=False)
l_g_total += l_g_gan
loss_dict['l_g_gan'] = l_g_gan
# feature matching loss
if self.cri_feat:
l_g_feat = self.cri_feat(pred_fake, pred_real)
l_g_total += l_g_feat
loss_dict['l_g_feat'] = l_g_feat
l_g_total.backward()
self.optimizer_g.step()
# optimize net_d
for p in self.net_d.parameters():
p.requires_grad = True
self.optimizer_d.zero_grad()
# TODO: Benchmark test between HiFaceGAN and SRGAN implementation:
# SRGAN use the same fake output for discriminator update
# while HiFaceGAN regenerate a new output using updated net_g
# This should not make too much difference though. Stick to SRGAN now.
# -------------------------------------------------------------------
# ---------- Below are original HiFaceGAN code snippet --------------
# -------------------------------------------------------------------
# with torch.no_grad():
# fake_image = self.net_g(self.lq)
# fake_image = fake_image.detach()
# fake_image.requires_grad_()
# pred_fake, pred_real = self.discriminate(self.lq, fake_image, self.gt)
# real
pred_fake, pred_real = self.discriminate(self.lq, self.output.detach(), self.gt)
l_d_real = self.cri_gan(pred_real, True, is_disc=True)
loss_dict['l_d_real'] = l_d_real
# fake
l_d_fake = self.cri_gan(pred_fake, False, is_disc=True)
loss_dict['l_d_fake'] = l_d_fake
l_d_total = (l_d_real + l_d_fake) / 2
l_d_total.backward()
self.optimizer_d.step()
self.log_dict = self.reduce_loss_dict(loss_dict)
if self.ema_decay > 0:
print('HiFaceGAN does not support EMA now. pass')
def validation(self, dataloader, current_iter, tb_logger, save_img=False):
"""
Warning: HiFaceGAN requires train() mode even for validation
For more info, see https://github.com/Lotayou/Face-Renovation/issues/31
Args:
dataloader (torch.utils.data.DataLoader): Validation dataloader.
current_iter (int): Current iteration.
tb_logger (tensorboard logger): Tensorboard logger.
save_img (bool): Whether to save images. Default: False.
"""
if self.opt['network_g']['type'] in ('HiFaceGAN', 'SPADEGenerator'):
self.net_g.train()
if self.opt['dist']:
self.dist_validation(dataloader, current_iter, tb_logger, save_img)
else:
print('In HiFaceGANModel: The new metrics package is under development.' +
'Using super method now (Only PSNR & SSIM are supported)')
super().nondist_validation(dataloader, current_iter, tb_logger, save_img)
def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
"""
TODO: Validation using updated metric system
The metrics are now evaluated after all images have been tested
This allows batch processing, and also allows evaluation of
distributional metrics, such as:
@ Frechet Inception Distance: FID
@ Maximum Mean Discrepancy: MMD
Warning:
Need careful batch management for different inference settings.
"""
dataset_name = dataloader.dataset.opt['name']
with_metrics = self.opt['val'].get('metrics') is not None
if with_metrics:
self.metric_results = dict() # {metric: 0 for metric in self.opt['val']['metrics'].keys()}
sr_tensors = []
gt_tensors = []
pbar = tqdm(total=len(dataloader), unit='image')
for val_data in dataloader:
img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0]
self.feed_data(val_data)
self.test()
visuals = self.get_current_visuals() # detached cpu tensor, non-squeeze
sr_tensors.append(visuals['result'])
if 'gt' in visuals:
gt_tensors.append(visuals['gt'])
del self.gt
# tentative for out of GPU memory
del self.lq
del self.output
torch.cuda.empty_cache()
if save_img:
if self.opt['is_train']:
save_img_path = osp.join(self.opt['path']['visualization'], img_name,
f'{img_name}_{current_iter}.png')
else:
if self.opt['val']['suffix']:
save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
f'{img_name}_{self.opt["val"]["suffix"]}.png')
else:
save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
f'{img_name}_{self.opt["name"]}.png')
imwrite(tensor2img(visuals['result']), save_img_path)
pbar.update(1)
pbar.set_description(f'Test {img_name}')
pbar.close()
if with_metrics:
sr_pack = torch.cat(sr_tensors, dim=0)
gt_pack = torch.cat(gt_tensors, dim=0)
# calculate metrics
for name, opt_ in self.opt['val']['metrics'].items():
# The new metric caller automatically returns mean value
# FIXME: ERROR: calculate_metric only supports two arguments. Now the codes cannot be successfully run
self.metric_results[name] = calculate_metric(dict(sr_pack=sr_pack, gt_pack=gt_pack), opt_)
self._log_validation_metric_values(current_iter, dataset_name, tb_logger)
def save(self, epoch, current_iter):
if hasattr(self, 'net_g_ema'):
print('HiFaceGAN does not support EMA now. Fallback to normal mode.')
self.save_network(self.net_g, 'net_g', current_iter)
self.save_network(self.net_d, 'net_d', current_iter)
self.save_training_state(epoch, current_iter)
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