AncTe/ginka-generator
1
0
Fork 0
mirror of https://github.com/unanmed/ginka-generator.git synced 2026-05-22 11:01:12 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

chore: 调整调度方式

Browse Source
This commit is contained in:
unanmed 2026-02-12 23:41:45 +08:00
parent 1352d64a50
commit a07d2cf960
1 changed files with 10 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
ginka/train_vae.py
View File

@ -56,6 +56,8 @@ from shared.image import matrix_to_image_cv
BATCH_SIZE = 128 BATCH_SIZE = 128
LATENT_DIM = 48 LATENT_DIM = 48
KL_BETA = 0.1 KL_BETA = 0.1
SELF_GATE = 0.5
GATE_EPOCH = 5
device = torch.device( device = torch.device(
"cuda:1" if torch.cuda.is_available() "cuda:1" if torch.cuda.is_available()
@ -101,6 +103,7 @@ def train():
criterion = VAELoss() criterion = VAELoss()
self_prob = 0 self_prob = 0
prob_epochs = 0
# 用于生成图片 # 用于生成图片
tile_dict = dict() tile_dict = dict()
@ -153,16 +156,21 @@ def train():
# val_loss_total = torch.Tensor([0]).to(device) # val_loss_total = torch.Tensor([0]).to(device)
# for batch in tqdm(dataloader_val, desc="Validating generator.", leave=False, disable=disable_tqdm): # for batch in tqdm(dataloader_val, desc="Validating generator.", leave=False, disable=disable_tqdm):
# target_map = batch["target_map"].to(device) # 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) # loss, reco_loss, kl_loss = criterion.vae_loss(fake_logits, target_map, mu, logvar, KL_BETA)
# val_loss_total += loss.detach() # val_loss_total += loss.detach()
# avg_loss_val = val_loss_total.item() / len(dataloader_val) # 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 # 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 self_prob < 1: if avg_loss < SELF_GATE:
gate_epochs += 1
if gate_epochs >= GATE_EPOCH and self_prob < 1:
self_prob += 0.01 self_prob += 0.01
gate_epochs = 0
scheduler_ginka.step(avg_loss, self_prob) scheduler_ginka.step(avg_loss, self_prob)