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class Trainer():
def __init__(self, model, optimizer, train_loader, val_loader, scheduler, device):
self.encoder = model.module.Encoder
self.decoder1 = model.module.Decoder1
self.decoder2 = model.module.Decoder2
self.optimizer_AE1 = optimizer
self.optimizer_AE2 = optimizer
self.train_loader = train_loader
self.val_loader = val_loader
self.scheduler = scheduler
self.device = device
# Loss Function
self.criterion = nn.L1Loss().to(self.device)
def fit(self, ):
self.encoder.to(self.device)
self.decoder1.to(self.device)
self.decoder2.to(self.device)
best_score = 0
for epoch in range(EPOCHS):
loss_AE1_list = []
loss_AE2_list = []
recon_loss_AE1_list = []
recon_loss_AE2_list = []
adv_loss_AE1_list = []
adv_loss_AE2_list = []
# Loss 가중치
if epoch <= 100:
w1=1 # Reconstruction Loss Weight
w2=0 # Adversarial Loss Weight
else:
w2 = (epoch-100)/EPOCHS
w1 = 1 -w2
# Decoder1 학습 (Generator)
for param in self.encoder.parameters():
param.requires_grad = True
for param in self.decoder1.parameters():
param.requires_grad = True
for param in self.decoder2.parameters(): # Decoder2 파라미터 고정
param.requires_grad = False
for x in iter(self.train_loader):
x = x.float().to(self.device)
self.optimizer_AE1.zero_grad()
# 1) Reconstruction Loss
x_enc = self.encoder(x)
x_dec1 = self.decoder1(x_enc)
reconstruction_loss_AE1 = self.criterion(x, x_dec1)
# 2) Adversarial Loss
x_dec1_enc = self.encoder(x_dec1)
x_dec1_dec2 = self.decoder2(x_dec1_enc)
adversarial_loss_AE1 = self.criterion(x, x_dec1_dec2)
loss_AE1 = w1*reconstruction_loss_AE1 + w2*adversarial_loss_AE1
loss_AE1.backward()
self.optimizer_AE1.step()
loss_AE1_list.append(loss_AE1.item())
recon_loss_AE1_list.append(reconstruction_loss_AE1.item())
adv_loss_AE1_list.append(adversarial_loss_AE1.item())
# Decoder 2 학습 (Discriminator)
for param in self.decoder1.parameters(): # Decoder1 파라미터 고정
param.requires_grad = False
for param in self.decoder2.parameters():
param.requires_grad = True
for x in iter(self.train_loader):
x = x.float().to(self.device)
self.optimizer_AE2.zero_grad()
# 1) Reconstruction Loss
x_enc = self.encoder(x)
x_dec2 = self.decoder2(x_enc)
reconstruction_loss_AE2 = self.criterion(x, x_dec2)
# 2) Adversarial Loss
x_dec1 = self.decoder1(x_enc)
x_dec1_enc = self.encoder(x_dec1)
x_dec1_dec2 = self.decoder2(x_dec1_enc)
adversarial_loss_AE2 = self.criterion(x, x_dec1_dec2)
loss_AE2 = w1*reconstruction_loss_AE2 - w2*adversarial_loss_AE2
loss_AE2.backward()
self.optimizer_AE2.step()
loss_AE2_list.append(loss_AE2.item())
recon_loss_AE2_list.append(reconstruction_loss_AE2.item())
adv_loss_AE2_list.append(adversarial_loss_AE2.item())
score = self.validation(self.decoder2, 0.95, 1)
recon_loss_AE1 = np.sum(recon_loss_AE1_list).round(4)
recon_loss_AE2 = np.sum(recon_loss_AE2_list).round(4)
adv_loss_AE1 = np.sum(adv_loss_AE1_list).round(4)
adv_loss_AE2 = np.sum(adv_loss_AE2_list).round(4)
print(f'Epoch : [{epoch}] Val Score : [{score}] Loss1 : [{np.mean(loss_AE1_list).round(10)}] Loss2 : [{np.mean(loss_AE2_list).round(10)}] reconAE1:[{recon_loss_AE1}] reconAE2:[{recon_loss_AE2}] advAE1:[{adv_loss_AE1}] advAE2:[{adv_loss_AE2}]')
if self.scheduler is not None:
self.scheduler.step(score)
if best_score <= score:
best_score = score
torch.save(model.module.state_dict(), '../model/best_model_autoencoder_linear_GAN5.pth', _use_new_zipfile_serialization=False)
# torch.save(model.module.state_dict(), '../content/gdrive/MyDrive/_credit/model/best_model_autoencoder_linear_GAN5.pth', _use_new_zipfile_serialization=False)
def validation(self, eval_model, thr1, thr2):
cos = nn.CosineSimilarity(dim=1, eps=1e-6)
eval_model.eval()
pred = []
true = []
with torch.no_grad():
for x, y in iter(self.val_loader):
x = x.float().to(self.device)
x_enc = self.encoder(x)
x_dec = self.decoder1(x_enc)
x_dec_enc = self.encoder(x_dec)
x_dec_dec = self.decoder2(x_dec_enc)
diff1 = cos(x, x_dec).cpu()
diff2 = cos(x_enc, x_dec_enc).cpu()
diff_mask = np.logical_and(diff1<thr1, diff2<thr2)
batch_pred = np.where(diff_mask, 1,0).tolist()
pred += batch_pred
true += y.tolist()
return f1_score(true, pred, average='macro')
model = nn.DataParallel(AutoEncoder())
model.eval()
optimizer = torch.optim.Adam(params = model.parameters(), lr = LR)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=30, threshold_mode='abs', min_lr=1e-8, verbose=True)
trainer = Trainer(model, optimizer, train_loader, val_loader, scheduler, device)
trainer.fit()
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