import torch
from collections import OrderedDict
from os import path as osp
from tqdm import tqdm
from basicsr.archs import build_network
from basicsr.losses import build_loss
from basicsr.metrics import calculate_metric
from basicsr.utils import get_root_logger, imwrite, tensor2img
from basicsr.utils.registry import MODEL_REGISTRY
import torch.nn.functional as F
from .sr_model import SRModel
@MODEL_REGISTRY.register()
class CodeFormerModel(SRModel):
def feed_data(self, data):
self.gt = data['gt'].to(self.device)
self.input = data['in'].to(self.device)
self.b = self.gt.shape[0]
if 'latent_gt' in data:
self.idx_gt = data['latent_gt'].to(self.device)
self.idx_gt = self.idx_gt.view(self.b, -1)
else:
self.idx_gt = None
def init_training_settings(self):
logger = get_root_logger()
train_opt = self.opt['train']
self.ema_decay = train_opt.get('ema_decay', 0)
if self.ema_decay > 0:
logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}')
# define network net_g with Exponential Moving Average (EMA)
# net_g_ema is used only for testing on one GPU and saving
# There is no need to wrap with DistributedDataParallel
self.net_g_ema = build_network(self.opt['network_g']).to(self.device)
# load pretrained model
load_path = self.opt['path'].get('pretrain_network_g', None)
if load_path is not None:
self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema')
else:
self.model_ema(0) # copy net_g weight
self.net_g_ema.eval()
if self.opt.get('network_vqgan', None) is not None and self.opt['datasets'].get('latent_gt_path') is None:
self.hq_vqgan_fix = build_network(self.opt['network_vqgan']).to(self.device)
self.hq_vqgan_fix.eval()
self.generate_idx_gt = True
for param in self.hq_vqgan_fix.parameters():
param.requires_grad = False
else:
self.generate_idx_gt = False
self.hq_feat_loss = train_opt.get('use_hq_feat_loss', True)
self.feat_loss_weight = train_opt.get('feat_loss_weight', 1.0)
self.cross_entropy_loss = train_opt.get('cross_entropy_loss', True)
self.entropy_loss_weight = train_opt.get('entropy_loss_weight', 0.5)
self.fidelity_weight = train_opt.get('fidelity_weight', 1.0)
self.scale_adaptive_gan_weight = train_opt.get('scale_adaptive_gan_weight', 0.8)
self.net_g.train()
# define network net_d
if self.fidelity_weight > 0:
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)
# load pretrained models
load_path = self.opt['path'].get('pretrain_network_d', None)
if load_path is not None:
self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True))
self.net_d.train()
# define losses
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('gan_opt'):
self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device)
self.fix_generator = train_opt.get('fix_generator', True)
logger.info(f'fix_generator: {self.fix_generator}')
self.net_g_start_iter = train_opt.get('net_g_start_iter', 0)
self.net_d_iters = train_opt.get('net_d_iters', 1)
self.net_d_start_iter = train_opt.get('net_d_start_iter', 0)
# set up optimizers and schedulers
self.setup_optimizers()
self.setup_schedulers()
def calculate_adaptive_weight(self, recon_loss, g_loss, last_layer, disc_weight_max):
recon_grads = torch.autograd.grad(recon_loss, last_layer, retain_graph=True)[0]
g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
d_weight = torch.norm(recon_grads) / (torch.norm(g_grads) + 1e-4)
d_weight = torch.clamp(d_weight, 0.0, disc_weight_max).detach()
return d_weight
def setup_optimizers(self):
train_opt = self.opt['train']
# optimizer g
optim_params_g = []
for k, v in self.net_g.named_parameters():
if v.requires_grad:
optim_params_g.append(v)
else:
logger = get_root_logger()
logger.warning(f'Params {k} will not be optimized.')
optim_type = train_opt['optim_g'].pop('type')
self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, **train_opt['optim_g'])
self.optimizers.append(self.optimizer_g)
# optimizer d
if self.fidelity_weight > 0:
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 gray_resize_for_identity(self, out, size=128):
out_gray = (0.2989 * out[:, 0, :, :] + 0.5870 * out[:, 1, :, :] + 0.1140 * out[:, 2, :, :])
out_gray = out_gray.unsqueeze(1)
out_gray = F.interpolate(out_gray, (size, size), mode='bilinear', align_corners=False)
return out_gray
def optimize_parameters(self, current_iter):
logger = get_root_logger()
# optimize net_g
for p in self.net_d.parameters():
p.requires_grad = False
self.optimizer_g.zero_grad()
if self.generate_idx_gt:
x = self.hq_vqgan_fix.encoder(self.gt)
output, _, quant_stats = self.hq_vqgan_fix.quantize(x)
min_encoding_indices = quant_stats['min_encoding_indices']
self.idx_gt = min_encoding_indices.view(self.b, -1)
if self.fidelity_weight > 0:
self.output, logits, lq_feat = self.net_g(self.input, w=self.fidelity_weight, detach_16=True)
else:
logits, lq_feat = self.net_g(self.input, w=0, code_only=True)
if self.hq_feat_loss:
# quant_feats
quant_feat_gt = self.net_g.module.quantize.get_codebook_feat(self.idx_gt, shape=[self.b,16,16,256])
l_g_total = 0
loss_dict = OrderedDict()
if current_iter % self.net_d_iters == 0 and current_iter > self.net_g_start_iter:
# hq_feat_loss
if self.hq_feat_loss: # codebook loss
l_feat_encoder = torch.mean((quant_feat_gt.detach()-lq_feat)**2) * self.feat_loss_weight
l_g_total += l_feat_encoder
loss_dict['l_feat_encoder'] = l_feat_encoder
# cross_entropy_loss
if self.cross_entropy_loss:
# b(hw)n -> bn(hw)
cross_entropy_loss = F.cross_entropy(logits.permute(0, 2, 1), self.idx_gt) * self.entropy_loss_weight
l_g_total += cross_entropy_loss
loss_dict['cross_entropy_loss'] = cross_entropy_loss
if self.fidelity_weight > 0: # when fidelity_weight == 0 don't need image-level loss
# 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 = self.cri_perceptual(self.output, self.gt)
l_g_total += l_g_percep
loss_dict['l_g_percep'] = l_g_percep
# gan loss
if current_iter > self.net_d_start_iter:
fake_g_pred = self.net_d(self.output)
l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
recon_loss = l_g_pix + l_g_percep
if not self.fix_generator:
last_layer = self.net_g.module.generator.blocks[-1].weight
d_weight = self.calculate_adaptive_weight(recon_loss, l_g_gan, last_layer, disc_weight_max=1.0)
else:
largest_fuse_size = self.opt['network_g']['connect_list'][-1]
last_layer = self.net_g.module.fuse_convs_dict[largest_fuse_size].shift[-1].weight
d_weight = self.calculate_adaptive_weight(recon_loss, l_g_gan, last_layer, disc_weight_max=1.0)
d_weight *= self.scale_adaptive_gan_weight # 0.8
loss_dict['d_weight'] = d_weight
l_g_total += d_weight * l_g_gan
loss_dict['l_g_gan'] = d_weight * l_g_gan
l_g_total.backward()
self.optimizer_g.step()
if self.ema_decay > 0:
self.model_ema(decay=self.ema_decay)
# optimize net_d
if current_iter > self.net_d_start_iter and self.fidelity_weight > 0:
for p in self.net_d.parameters():
p.requires_grad = True
self.optimizer_d.zero_grad()
# real
real_d_pred = self.net_d(self.gt)
l_d_real = self.cri_gan(real_d_pred, True, is_disc=True)
loss_dict['l_d_real'] = l_d_real
loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
l_d_real.backward()
# fake
fake_d_pred = self.net_d(self.output.detach())
l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True)
loss_dict['l_d_fake'] = l_d_fake
loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
l_d_fake.backward()
self.optimizer_d.step()
self.log_dict = self.reduce_loss_dict(loss_dict)
def test(self):
with torch.no_grad():
if hasattr(self, 'net_g_ema'):
self.net_g_ema.eval()
self.output, _, _ = self.net_g_ema(self.input, w=self.fidelity_weight)
else:
logger = get_root_logger()
logger.warning('Do not have self.net_g_ema, use self.net_g.')
self.net_g.eval()
self.output, _, _ = self.net_g(self.input, w=self.fidelity_weight)
self.net_g.train()
def dist_validation(self, dataloader, current_iter, tb_logger, save_img):
if self.opt['rank'] == 0:
self.nondist_validation(dataloader, current_iter, tb_logger, save_img)
def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
dataset_name = dataloader.dataset.opt['name']
with_metrics = self.opt['val'].get('metrics') is not None
if with_metrics:
self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()}
pbar = tqdm(total=len(dataloader), unit='image')
for idx, val_data in enumerate(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()
sr_img = tensor2img([visuals['result']])
if 'gt' in visuals:
gt_img = tensor2img([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(sr_img, save_img_path)
if with_metrics:
# calculate metrics
for name, opt_ in self.opt['val']['metrics'].items():
metric_data = dict(img1=sr_img, img2=gt_img)
self.metric_results[name] += calculate_metric(metric_data, opt_)
pbar.update(1)
pbar.set_description(f'Test {img_name}')
pbar.close()
if with_metrics:
for metric in self.metric_results.keys():
self.metric_results[metric] /= (idx + 1)
self._log_validation_metric_values(current_iter, dataset_name, tb_logger)
def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger):
log_str = f'Validation {dataset_name}\n'
for metric, value in self.metric_results.items():
log_str += f'\t # {metric}: {value:.4f}\n'
logger = get_root_logger()
logger.info(log_str)
if tb_logger:
for metric, value in self.metric_results.items():
tb_logger.add_scalar(f'metrics/{metric}', value, current_iter)
def get_current_visuals(self):
out_dict = OrderedDict()
out_dict['gt'] = self.gt.detach().cpu()
out_dict['result'] = self.output.detach().cpu()
return out_dict
def save(self, epoch, current_iter):
if self.ema_decay > 0:
self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
else:
self.save_network(self.net_g, 'net_g', current_iter)
if self.fidelity_weight > 0:
self.save_network(self.net_d, 'net_d', current_iter)
self.save_training_state(epoch, current_iter)