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import torch
from tqdm import tqdm
from .utils import get_lr, show_result
from .utils_metrics import PSNR, SSIM
def fit_one_epoch(G_model_train, D_model_train, G_model, D_model, VGG_feature_model, G_optimizer, D_optimizer, BCEWithLogits_loss, L1_loss, epoch, epoch_size, gen, Epoch, cuda, batch_size, save_interval):
G_total_loss = 0
D_total_loss = 0
G_total_PSNR = 0
G_total_SSIM = 0
with tqdm(total=epoch_size,desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar:
for iteration, batch in enumerate(gen):
if iteration >= epoch_size:
break
with torch.no_grad():
lr_images, hr_images = batch
lr_images, hr_images = torch.from_numpy(lr_images).type(torch.FloatTensor), torch.from_numpy(hr_images).type(torch.FloatTensor)
y_real, y_fake = torch.ones(batch_size), torch.zeros(batch_size)
if cuda:
lr_images, hr_images, y_real, y_fake = lr_images.cuda(), hr_images.cuda(), y_real.cuda(), y_fake.cuda()
#-------------------------------------------------#
# 训练判别器
#-------------------------------------------------#
D_optimizer.zero_grad()
D_result_r = D_model_train(hr_images)
G_result = G_model_train(lr_images)
D_result_f = D_model_train(G_result).squeeze()
D_result_rf = D_result_r - D_result_f.mean()
D_result_fr = D_result_f - D_result_r.mean()
D_train_loss_rf = BCEWithLogits_loss(D_result_rf, y_real)
D_train_loss_fr = BCEWithLogits_loss(D_result_fr, y_fake)
D_train_loss = (D_train_loss_rf + D_train_loss_fr) / 2
D_train_loss.backward()
D_optimizer.step()
#-------------------------------------------------#
# 训练生成器
#-------------------------------------------------#
G_optimizer.zero_grad()
G_result = G_model_train(lr_images)
image_loss = L1_loss(G_result, hr_images)
D_result_r = D_model_train(hr_images)
D_result_f = D_model_train(G_result).squeeze()
D_result_rf = D_result_r - D_result_f.mean()
D_result_fr = D_result_f - D_result_r.mean()
D_train_loss_rf = BCEWithLogits_loss(D_result_rf, y_fake)
D_train_loss_fr = BCEWithLogits_loss(D_result_fr, y_real)
adversarial_loss = (D_train_loss_rf + D_train_loss_fr) / 2
perception_loss = L1_loss(VGG_feature_model(G_result), VGG_feature_model(hr_images))
G_train_loss = image_loss + 1e-1 * adversarial_loss + 1e-1 * perception_loss
G_train_loss.backward()
G_optimizer.step()
G_total_loss += G_train_loss.item()
D_total_loss += D_train_loss.item()
with torch.no_grad():
G_total_PSNR += PSNR(G_result, hr_images).item()
G_total_SSIM += SSIM(G_result, hr_images).item()
pbar.set_postfix(**{'G_loss' : G_total_loss / (iteration + 1),
'D_loss' : D_total_loss / (iteration + 1),
'G_PSNR' : G_total_PSNR / (iteration + 1),
'G_SSIM' : G_total_SSIM / (iteration + 1),
'lr' : get_lr(G_optimizer)})
pbar.update(1)
if iteration % save_interval == 0:
show_result(epoch + 1, G_model_train, lr_images, hr_images)
print('Epoch:'+ str(epoch + 1) + '/' + str(Epoch))
print('G Loss: %.4f || D Loss: %.4f ' % (G_total_loss / epoch_size, D_total_loss / epoch_size))
print('Saving state, iter:', str(epoch+1))
if (epoch + 1) % 10==0:
torch.save(G_model.state_dict(), 'logs/G_Epoch%d-GLoss%.4f-DLoss%.4f.pth'%((epoch + 1), G_total_loss / epoch_size, D_total_loss / epoch_size))
torch.save(D_model.state_dict(), 'logs/D_Epoch%d-GLoss%.4f-DLoss%.4f.pth'%((epoch + 1), G_total_loss / epoch_size, D_total_loss / epoch_size))
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