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import cv2
import math
import numpy as np
import random
import torch
from collections import OrderedDict
from os import path as osp
from basicsr.archs import build_network
from basicsr.losses import build_loss
from basicsr.losses.losses import g_path_regularize, r1_penalty
from basicsr.utils import imwrite, tensor2img
from 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:
self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True))
# 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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