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Diffstat (limited to 'r_basicsr/models/stylegan2_model.py')
| -rw-r--r-- | r_basicsr/models/stylegan2_model.py | 283 |
1 files changed, 283 insertions, 0 deletions
diff --git a/r_basicsr/models/stylegan2_model.py b/r_basicsr/models/stylegan2_model.py new file mode 100644 index 0000000..58a38ae --- /dev/null +++ b/r_basicsr/models/stylegan2_model.py @@ -0,0 +1,283 @@ +import cv2
+import math
+import numpy as np
+import random
+import torch
+from collections import OrderedDict
+from os import path as osp
+
+from r_basicsr.archs import build_network
+from r_basicsr.losses import build_loss
+from r_basicsr.losses.gan_loss import g_path_regularize, r1_penalty
+from r_basicsr.utils import imwrite, tensor2img
+from r_basicsr.utils.registry import MODEL_REGISTRY
+from .base_model import BaseModel
+
+
+@MODEL_REGISTRY.register()
+class StyleGAN2Model(BaseModel):
+ """StyleGAN2 model."""
+
+ def __init__(self, opt):
+ super(StyleGAN2Model, self).__init__(opt)
+
+ # define network net_g
+ self.net_g = build_network(opt['network_g'])
+ self.net_g = self.model_to_device(self.net_g)
+ self.print_network(self.net_g)
+ # load pretrained model
+ load_path = self.opt['path'].get('pretrain_network_g', None)
+ if load_path is not None:
+ param_key = self.opt['path'].get('param_key_g', 'params')
+ self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key)
+
+ # latent dimension: self.num_style_feat
+ self.num_style_feat = opt['network_g']['num_style_feat']
+ num_val_samples = self.opt['val'].get('num_val_samples', 16)
+ self.fixed_sample = torch.randn(num_val_samples, self.num_style_feat, device=self.device)
+
+ if self.is_train:
+ self.init_training_settings()
+
+ def init_training_settings(self):
+ train_opt = self.opt['train']
+
+ # define network net_d
+ 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 model
+ load_path = self.opt['path'].get('pretrain_network_d', None)
+ if load_path is not None:
+ param_key = self.opt['path'].get('param_key_d', 'params')
+ self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key)
+
+ # define network net_g with Exponential Moving Average (EMA)
+ # net_g_ema only used for testing on one GPU and saving, do not 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.train()
+ self.net_d.train()
+ self.net_g_ema.eval()
+
+ # define losses
+ # gan loss (wgan)
+ self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device)
+ # regularization weights
+ self.r1_reg_weight = train_opt['r1_reg_weight'] # for discriminator
+ self.path_reg_weight = train_opt['path_reg_weight'] # for generator
+
+ self.net_g_reg_every = train_opt['net_g_reg_every']
+ self.net_d_reg_every = train_opt['net_d_reg_every']
+ self.mixing_prob = train_opt['mixing_prob']
+
+ self.mean_path_length = 0
+
+ # set up optimizers and schedulers
+ self.setup_optimizers()
+ self.setup_schedulers()
+
+ def setup_optimizers(self):
+ train_opt = self.opt['train']
+ # optimizer g
+ net_g_reg_ratio = self.net_g_reg_every / (self.net_g_reg_every + 1)
+ if self.opt['network_g']['type'] == 'StyleGAN2GeneratorC':
+ normal_params = []
+ style_mlp_params = []
+ modulation_conv_params = []
+ for name, param in self.net_g.named_parameters():
+ if 'modulation' in name:
+ normal_params.append(param)
+ elif 'style_mlp' in name:
+ style_mlp_params.append(param)
+ elif 'modulated_conv' in name:
+ modulation_conv_params.append(param)
+ else:
+ normal_params.append(param)
+ optim_params_g = [
+ { # add normal params first
+ 'params': normal_params,
+ 'lr': train_opt['optim_g']['lr']
+ },
+ {
+ 'params': style_mlp_params,
+ 'lr': train_opt['optim_g']['lr'] * 0.01
+ },
+ {
+ 'params': modulation_conv_params,
+ 'lr': train_opt['optim_g']['lr'] / 3
+ }
+ ]
+ else:
+ normal_params = []
+ for name, param in self.net_g.named_parameters():
+ normal_params.append(param)
+ optim_params_g = [{ # add normal params first
+ 'params': normal_params,
+ 'lr': train_opt['optim_g']['lr']
+ }]
+
+ optim_type = train_opt['optim_g'].pop('type')
+ lr = train_opt['optim_g']['lr'] * net_g_reg_ratio
+ betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio)
+ self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas)
+ self.optimizers.append(self.optimizer_g)
+
+ # optimizer d
+ net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1)
+ if self.opt['network_d']['type'] == 'StyleGAN2DiscriminatorC':
+ normal_params = []
+ linear_params = []
+ for name, param in self.net_d.named_parameters():
+ if 'final_linear' in name:
+ linear_params.append(param)
+ else:
+ normal_params.append(param)
+ optim_params_d = [
+ { # add normal params first
+ 'params': normal_params,
+ 'lr': train_opt['optim_d']['lr']
+ },
+ {
+ 'params': linear_params,
+ 'lr': train_opt['optim_d']['lr'] * (1 / math.sqrt(512))
+ }
+ ]
+ else:
+ normal_params = []
+ for name, param in self.net_d.named_parameters():
+ normal_params.append(param)
+ optim_params_d = [{ # add normal params first
+ 'params': normal_params,
+ 'lr': train_opt['optim_d']['lr']
+ }]
+
+ optim_type = train_opt['optim_d'].pop('type')
+ lr = train_opt['optim_d']['lr'] * net_d_reg_ratio
+ betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio)
+ self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas)
+ self.optimizers.append(self.optimizer_d)
+
+ def feed_data(self, data):
+ self.real_img = data['gt'].to(self.device)
+
+ def make_noise(self, batch, num_noise):
+ if num_noise == 1:
+ noises = torch.randn(batch, self.num_style_feat, device=self.device)
+ else:
+ noises = torch.randn(num_noise, batch, self.num_style_feat, device=self.device).unbind(0)
+ return noises
+
+ def mixing_noise(self, batch, prob):
+ if random.random() < prob:
+ return self.make_noise(batch, 2)
+ else:
+ return [self.make_noise(batch, 1)]
+
+ def optimize_parameters(self, current_iter):
+ loss_dict = OrderedDict()
+
+ # optimize net_d
+ for p in self.net_d.parameters():
+ p.requires_grad = True
+ self.optimizer_d.zero_grad()
+
+ batch = self.real_img.size(0)
+ noise = self.mixing_noise(batch, self.mixing_prob)
+ fake_img, _ = self.net_g(noise)
+ fake_pred = self.net_d(fake_img.detach())
+
+ real_pred = self.net_d(self.real_img)
+ # wgan loss with softplus (logistic loss) for discriminator
+ l_d = self.cri_gan(real_pred, True, is_disc=True) + self.cri_gan(fake_pred, False, is_disc=True)
+ loss_dict['l_d'] = l_d
+ # In wgan, real_score should be positive and fake_score should be
+ # negative
+ loss_dict['real_score'] = real_pred.detach().mean()
+ loss_dict['fake_score'] = fake_pred.detach().mean()
+ l_d.backward()
+
+ if current_iter % self.net_d_reg_every == 0:
+ self.real_img.requires_grad = True
+ real_pred = self.net_d(self.real_img)
+ l_d_r1 = r1_penalty(real_pred, self.real_img)
+ l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0])
+ # TODO: why do we need to add 0 * real_pred, otherwise, a runtime
+ # error will arise: RuntimeError: Expected to have finished
+ # reduction in the prior iteration before starting a new one.
+ # This error indicates that your module has parameters that were
+ # not used in producing loss.
+ loss_dict['l_d_r1'] = l_d_r1.detach().mean()
+ l_d_r1.backward()
+
+ self.optimizer_d.step()
+
+ # optimize net_g
+ for p in self.net_d.parameters():
+ p.requires_grad = False
+ self.optimizer_g.zero_grad()
+
+ noise = self.mixing_noise(batch, self.mixing_prob)
+ fake_img, _ = self.net_g(noise)
+ fake_pred = self.net_d(fake_img)
+
+ # wgan loss with softplus (non-saturating loss) for generator
+ l_g = self.cri_gan(fake_pred, True, is_disc=False)
+ loss_dict['l_g'] = l_g
+ l_g.backward()
+
+ if current_iter % self.net_g_reg_every == 0:
+ path_batch_size = max(1, batch // self.opt['train']['path_batch_shrink'])
+ noise = self.mixing_noise(path_batch_size, self.mixing_prob)
+ fake_img, latents = self.net_g(noise, return_latents=True)
+ l_g_path, path_lengths, self.mean_path_length = g_path_regularize(fake_img, latents, self.mean_path_length)
+
+ l_g_path = (self.path_reg_weight * self.net_g_reg_every * l_g_path + 0 * fake_img[0, 0, 0, 0])
+ # TODO: why do we need to add 0 * fake_img[0, 0, 0, 0]
+ l_g_path.backward()
+ loss_dict['l_g_path'] = l_g_path.detach().mean()
+ loss_dict['path_length'] = path_lengths
+
+ self.optimizer_g.step()
+
+ self.log_dict = self.reduce_loss_dict(loss_dict)
+
+ # EMA
+ self.model_ema(decay=0.5**(32 / (10 * 1000)))
+
+ def test(self):
+ with torch.no_grad():
+ self.net_g_ema.eval()
+ self.output, _ = self.net_g_ema([self.fixed_sample])
+
+ 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):
+ assert dataloader is None, 'Validation dataloader should be None.'
+ self.test()
+ result = tensor2img(self.output, min_max=(-1, 1))
+ if self.opt['is_train']:
+ save_img_path = osp.join(self.opt['path']['visualization'], 'train', f'train_{current_iter}.png')
+ else:
+ save_img_path = osp.join(self.opt['path']['visualization'], 'test', f'test_{self.opt["name"]}.png')
+ imwrite(result, save_img_path)
+ # add sample images to tb_logger
+ result = (result / 255.).astype(np.float32)
+ result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
+ if tb_logger is not None:
+ tb_logger.add_image('samples', result, global_step=current_iter, dataformats='HWC')
+
+ def save(self, epoch, current_iter):
+ self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
+ self.save_network(self.net_d, 'net_d', current_iter)
+ self.save_training_state(epoch, current_iter)
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