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chore: 调整kl参数

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unanmed 2026-02-06 23:54:59 +08:00
parent dd9d8a3713
commit 45cfa3b611
1 changed files with 12 additions and 11 deletions
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23
ginka/train_vae.py
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@ -54,7 +54,7 @@ from shared.image import matrix_to_image_cv
BATCH_SIZE = 128
LATENT_DIM = 48
KL_BETA = 0.05
KL_BETA = 0.1
device = torch.device(
"cuda:1" if torch.cuda.is_available()
@ -145,21 +145,22 @@ def train():
)
# 验证集
with torch.no_grad():
val_loss_total = torch.Tensor([0]).to(device)
for batch in tqdm(dataloader_val, desc="Validating generator.", leave=False, disable=disable_tqdm):
target_map = batch["target_map"].to(device)
# with torch.no_grad():
# val_loss_total = torch.Tensor([0]).to(device)
# for batch in tqdm(dataloader_val, desc="Validating generator.", leave=False, disable=disable_tqdm):
# target_map = batch["target_map"].to(device)
fake_logits, mu, logvar = vae(target_map, 1 - gt_prob)
# fake_logits, mu, logvar = vae(target_map, 1 - gt_prob)
loss, reco_loss, kl_loss = criterion.vae_loss(fake_logits, target_map, mu, logvar, KL_BETA)
val_loss_total += loss.detach()
# loss, reco_loss, kl_loss = criterion.vae_loss(fake_logits, target_map, mu, logvar, KL_BETA)
# val_loss_total += loss.detach()
avg_loss_val = val_loss_total.item() / len(dataloader_val)
if avg_loss_val < 0.5 and gt_prob > 0:
# avg_loss_val = val_loss_total.item() / len(dataloader_val)
# 先使用训练集的损失值，因为过拟合比较严重，后续再想办法
if avg_loss < 0.5 and gt_prob > 0:
gt_prob -= 0.01
scheduler_ginka.step(avg_loss_val)
scheduler_ginka.step(avg_loss)
# 每若干轮输出一次图片，并保存检查点
if (epoch + 1) % args.checkpoint == 0: