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import os
import torch
import torch.nn.functional as F
from tqdm import tqdm
from nets.cyclegan import compute_gradient_penalty
from utils.utils import get_lr, show_result
def fit_one_epoch(G_model_A2B_train, G_model_B2A_train, D_model_A_train, D_model_B_train, G_model_A2B, G_model_B2A, D_model_A, D_model_B, VGG_feature_model, ResNeSt_model, loss_history,
G_optimizer, D_optimizer_A, D_optimizer_B, BCE_loss, L1_loss, Face_loss, epoch, epoch_step, gen, Epoch, cuda, fp16, scaler, save_period, save_dir, photo_save_step, local_rank=0):
G_total_loss = 0
D_total_loss_A = 0
D_total_loss_B = 0
if local_rank == 0:
print('Start Train')
pbar = tqdm(total=epoch_step,desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3)
for iteration, batch in enumerate(gen):
if iteration >= epoch_step:
break
images_A, images_B = batch[0], batch[1]
batch_size = images_A.size()[0]
y_real = torch.ones(batch_size)
y_fake = torch.zeros(batch_size)
with torch.no_grad():
if cuda:
images_A, images_B, y_real, y_fake = images_A.cuda(local_rank), images_B.cuda(local_rank), y_real.cuda(local_rank), y_fake.cuda(local_rank)
if not fp16:
#---------------------------------#
# 训练生成器A2B和B2A
#---------------------------------#
G_optimizer.zero_grad()
Same_B = G_model_A2B_train(images_B)
loss_identity_B = L1_loss(Same_B, images_B)
Same_A = G_model_B2A_train(images_A)
loss_identity_A = L1_loss(Same_A, images_A)
fake_B = G_model_A2B_train(images_A)
pred_real = D_model_B_train(images_B)
pred_fake = D_model_B_train(fake_B)
pred_rf = pred_real - pred_fake.mean()
pred_fr = pred_fake - pred_real.mean()
D_train_loss_rf = BCE_loss(pred_rf, y_fake)
D_train_loss_fr = BCE_loss(pred_fr, y_real)
loss_GAN_A2B = (D_train_loss_rf + D_train_loss_fr) / 2
fake_A = G_model_B2A_train(images_B)
pred_real = D_model_A_train(images_A)
pred_fake = D_model_A_train(fake_A)
pred_rf = pred_real - pred_fake.mean()
pred_fr = pred_fake - pred_real.mean()
D_train_loss_rf = BCE_loss(pred_rf, y_fake)
D_train_loss_fr = BCE_loss(pred_fr, y_real)
loss_GAN_B2A = (D_train_loss_rf + D_train_loss_fr) / 2
recovered_A = G_model_B2A_train(fake_B)
loss_cycle_ABA = L1_loss(recovered_A, images_A)
loss_per_ABA = L1_loss(VGG_feature_model(recovered_A), VGG_feature_model(images_A))
recovered_A_face = F.interpolate(recovered_A, size=(112, 112), mode='bicubic', align_corners=True)
images_A_face = F.interpolate(images_A, size=(112, 112), mode='bicubic', align_corners=True)
loss_face_ABA = torch.mean(1. - Face_loss(ResNeSt_model(recovered_A_face), ResNeSt_model(images_A_face)))
recovered_B = G_model_A2B_train(fake_A)
loss_cycle_BAB = L1_loss(recovered_B, images_B)
loss_per_BAB = L1_loss(VGG_feature_model(recovered_B), VGG_feature_model(images_B))
recovered_B_face = F.interpolate(recovered_B, size=(112, 112), mode='bicubic', align_corners=True)
images_B_face = F.interpolate(images_B, size=(112, 112), mode='bicubic', align_corners=True)
loss_face_BAB = torch.mean(1. - Face_loss(ResNeSt_model(recovered_B_face), ResNeSt_model(images_B_face)))
G_loss = loss_identity_A * 5.0 + loss_identity_B * 5.0 + loss_GAN_A2B + loss_GAN_B2A + loss_per_ABA * 2.5 \
+ loss_per_BAB *2.5 + loss_cycle_ABA * 10.0 + loss_cycle_BAB * 10.0 + loss_face_ABA * 5 + loss_face_BAB * 5
G_loss.backward()
G_optimizer.step()
#---------------------------------#
# 训练评价器A
#---------------------------------#
D_optimizer_A.zero_grad()
pred_real = D_model_A_train(images_A)
pred_fake = D_model_A_train(fake_A.detach())
pred_rf = pred_real - pred_fake.mean()
pred_fr = pred_fake - pred_real.mean()
D_train_loss_rf = BCE_loss(pred_rf, y_real)
D_train_loss_fr = BCE_loss(pred_fr, y_fake)
gradient_penalty = compute_gradient_penalty(D_model_A_train, images_A, fake_A.detach())
D_loss_A = 10 * gradient_penalty + (D_train_loss_rf + D_train_loss_fr) / 2
D_loss_A.backward()
D_optimizer_A.step()
#---------------------------------#
# 训练评价器B
#---------------------------------#
D_optimizer_B.zero_grad()
pred_real = D_model_B_train(images_B)
pred_fake = D_model_B_train(fake_B.detach())
pred_rf = pred_real - pred_fake.mean()
pred_fr = pred_fake - pred_real.mean()
D_train_loss_rf = BCE_loss(pred_rf, y_real)
D_train_loss_fr = BCE_loss(pred_fr, y_fake)
gradient_penalty = compute_gradient_penalty(D_model_B_train, images_B, fake_B.detach())
D_loss_B = 10 * gradient_penalty + (D_train_loss_rf + D_train_loss_fr) / 2
D_loss_B.backward()
D_optimizer_B.step()
else:
from torch.cuda.amp import autocast
#---------------------------------#
# 训练生成器A2B和B2A
#---------------------------------#
with autocast():
G_optimizer.zero_grad()
Same_B = G_model_A2B_train(images_B)
loss_identity_B = L1_loss(Same_B, images_B)
Same_A = G_model_B2A_train(images_A)
loss_identity_A = L1_loss(Same_A, images_A)
fake_B = G_model_A2B_train(images_A)
pred_real = D_model_B_train(images_B)
pred_fake = D_model_B_train(fake_B)
pred_rf = pred_real - pred_fake.mean()
pred_fr = pred_fake - pred_real.mean()
D_train_loss_rf = BCE_loss(pred_rf, y_fake)
D_train_loss_fr = BCE_loss(pred_fr, y_real)
loss_GAN_A2B = (D_train_loss_rf + D_train_loss_fr) / 2
fake_A = G_model_B2A_train(images_B)
pred_real = D_model_A_train(images_A)
pred_fake = D_model_A_train(fake_A)
pred_rf = pred_real - pred_fake.mean()
pred_fr = pred_fake - pred_real.mean()
D_train_loss_rf = BCE_loss(pred_rf, y_fake)
D_train_loss_fr = BCE_loss(pred_fr, y_real)
loss_GAN_B2A = (D_train_loss_rf + D_train_loss_fr) / 2
recovered_A = G_model_B2A_train(fake_B)
loss_cycle_ABA = L1_loss(recovered_A, images_A)
recovered_A_face = F.interpolate(recovered_A, size=(112, 112), mode='bicubic', align_corners=True)
images_A_face = F.interpolate(images_A, size=(112, 112), mode='bicubic', align_corners=True)
loss_face_ABA = torch.mean(1. - Face_loss(ResNeSt_model(recovered_A_face), ResNeSt_model(images_A_face)))
recovered_B = G_model_A2B_train(fake_A)
loss_cycle_BAB = L1_loss(recovered_B, images_B)
recovered_B_face = F.interpolate(recovered_B, size=(112, 112), mode='bicubic', align_corners=True)
images_B_face = F.interpolate(images_B, size=(112, 112), mode='bicubic', align_corners=True)
loss_face_BAB = torch.mean(1. - Face_loss(ResNeSt_model(recovered_B_face), ResNeSt_model(images_B_face)))
G_loss = loss_identity_A * 5.0 + loss_identity_B * 5.0 + loss_GAN_A2B + loss_GAN_B2A \
+ loss_cycle_ABA * 10.0 + loss_cycle_BAB * 10.0 + loss_face_ABA * 5 + loss_face_BAB * 5
#----------------------#
# 反向传播
#----------------------#
scaler.scale(G_loss).backward()
scaler.step(G_optimizer)
scaler.update()
#---------------------------------#
# 训练评价器A
#---------------------------------#
with autocast():
D_optimizer_A.zero_grad()
pred_real = D_model_A_train(images_A)
pred_fake = D_model_A_train(fake_A.detach())
pred_rf = pred_real - pred_fake.mean()
pred_fr = pred_fake - pred_real.mean()
D_train_loss_rf = BCE_loss(pred_rf, y_real)
D_train_loss_fr = BCE_loss(pred_fr, y_fake)
gradient_penalty = compute_gradient_penalty(D_model_A_train, images_A, fake_A.detach())
D_loss_A = 10 * gradient_penalty + (D_train_loss_rf + D_train_loss_fr) / 2
#----------------------#
# 反向传播
#----------------------#
scaler.scale(D_loss_A).backward()
scaler.step(D_optimizer_A)
scaler.update()
#---------------------------------#
# 训练评价器B
#---------------------------------#
with autocast():
D_optimizer_B.zero_grad()
pred_real = D_model_B_train(images_B)
pred_fake = D_model_B_train(fake_B.detach())
pred_rf = pred_real - pred_fake.mean()
pred_fr = pred_fake - pred_real.mean()
D_train_loss_rf = BCE_loss(pred_rf, y_real)
D_train_loss_fr = BCE_loss(pred_fr, y_fake)
gradient_penalty = compute_gradient_penalty(D_model_B_train, images_B, fake_B.detach())
D_loss_B = 10 * gradient_penalty + (D_train_loss_rf + D_train_loss_fr) / 2
#----------------------#
# 反向传播
#----------------------#
scaler.scale(D_loss_B).backward()
scaler.step(D_optimizer_B)
scaler.update()
G_total_loss += G_loss.item()
D_total_loss_A += D_loss_A.item()
D_total_loss_B += D_loss_B.item()
if local_rank == 0:
pbar.set_postfix(**{'G_loss' : G_total_loss / (iteration + 1),
'D_loss_A' : D_total_loss_A / (iteration + 1),
'D_loss_B' : D_total_loss_B / (iteration + 1),
'lr' : get_lr(G_optimizer)})
pbar.update(1)
if iteration % photo_save_step == 0:
show_result(epoch + 1, G_model_A2B, G_model_B2A, images_A, images_B)
G_total_loss = G_total_loss / epoch_step
D_total_loss_A = D_total_loss_A / epoch_step
D_total_loss_B = D_total_loss_B / epoch_step
if local_rank == 0:
pbar.close()
print('Epoch:'+ str(epoch + 1) + '/' + str(Epoch))
print('G Loss: %.4f || D Loss A: %.4f || D Loss B: %.4f ' % (G_total_loss, D_total_loss_A, D_total_loss_B))
loss_history.append_loss(epoch + 1, G_total_loss = G_total_loss, D_total_loss_A = D_total_loss_A, D_total_loss_B = D_total_loss_B)
#-----------------------------------------------#
# 保存权值
#-----------------------------------------------#
if (epoch + 1) % save_period == 0 or epoch + 1 == Epoch:
torch.save(G_model_A2B.state_dict(), os.path.join(save_dir, 'G_model_A2B_Epoch%d-GLoss%.4f-DALoss%.4f-DBLoss%.4f.pth'%(epoch + 1, G_total_loss, D_total_loss_A, D_total_loss_B)))
torch.save(G_model_B2A.state_dict(), os.path.join(save_dir, 'G_model_B2A_Epoch%d-GLoss%.4f-DALoss%.4f-DBLoss%.4f.pth'%(epoch + 1, G_total_loss, D_total_loss_A, D_total_loss_B)))
torch.save(D_model_A.state_dict(), os.path.join(save_dir, 'D_model_A_Epoch%d-GLoss%.4f-DALoss%.4f-DBLoss%.4f.pth'%(epoch + 1, G_total_loss, D_total_loss_A, D_total_loss_B)))
torch.save(D_model_B.state_dict(), os.path.join(save_dir, 'D_model_B_Epoch%d-GLoss%.4f-DALoss%.4f-DBLoss%.4f.pth'%(epoch + 1, G_total_loss, D_total_loss_A, D_total_loss_B)))
torch.save(G_model_A2B.state_dict(), os.path.join(save_dir, "G_model_A2B_last_epoch_weights.pth"))
torch.save(G_model_B2A.state_dict(), os.path.join(save_dir, "G_model_B2A_last_epoch_weights.pth"))
torch.save(D_model_A.state_dict(), os.path.join(save_dir, "D_model_A_last_epoch_weights.pth"))
torch.save(D_model_B.state_dict(), os.path.join(save_dir, "D_model_B_last_epoch_weights.pth")) |