From 8130296e1f1733f454108398416c3bea44201f86 Mon Sep 17 00:00:00 2001
From: unanmed <1319491857@qq.com>
Date: Sun, 6 Apr 2025 21:19:18 +0800
Subject: [PATCH] =?UTF-8?q?perf:=20=E8=B0=83=E4=BC=98=E9=83=A8=E5=88=86?=
 =?UTF-8?q?=E8=B6=85=E5=8F=82=E6=95=B0?=
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---
 ginka/model/loss.py |  2 +-
 ginka/train_wgan.py | 17 +++++++++++------
 2 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/ginka/model/loss.py b/ginka/model/loss.py
index d50842d..7bfb05c 100644
--- a/ginka/model/loss.py
+++ b/ginka/model/loss.py
@@ -311,7 +311,7 @@ def js_divergence(P, Q, epsilon=1e-10):
     return js.mean()  # 标量
 
 class WGANGinkaLoss:
-    def __init__(self, lambda_gp=10, weight=[0.7, 0.2, 0.1], diversity_lamda=0):
+    def __init__(self, lambda_gp=20, weight=[0.7, 0.2, 0.1], diversity_lamda=0):
         self.lambda_gp = lambda_gp  # 梯度惩罚系数
         self.weight = weight
         self.diversity_lamda = diversity_lamda
diff --git a/ginka/train_wgan.py b/ginka/train_wgan.py
index ac70e9c..8093946 100644
--- a/ginka/train_wgan.py
+++ b/ginka/train_wgan.py
@@ -38,7 +38,7 @@ def train():
     print(f"Using {'cuda' if torch.cuda.is_available() else 'cpu'} to train model.")
     
     c_steps = 1
-    g_steps = 3
+    g_steps = 4
     
     args = parse_arguments()
     
@@ -103,9 +103,9 @@ def train():
             for _ in range(g_steps):
                 z1 = torch.randn(batch_size, 1024, device=device)
                 z2 = torch.randn(batch_size, 1024, device=device)
-                fake_softmax1, fakse_softmax2 = ginka(z1), ginka(z2)
+                fake_softmax1, fake_softmax2 = ginka(z1), ginka(z2)
                 
-                loss_g = criterion.generator_loss(minamo, fake_softmax1, fakse_softmax2)
+                loss_g = criterion.generator_loss(minamo, fake_softmax1, fake_softmax2)
                 loss_g.backward()
                 optimizer_ginka.step()
                 
@@ -120,14 +120,19 @@ def train():
         )
         
         if avg_dis < -9:
-            g_steps = 7
+            g_steps = 21
         elif avg_dis < -6:
-            g_steps = 5
+            g_steps = 14
         elif avg_dis < -3:
-            g_steps = 3
+            g_steps = 7
         else:
             g_steps = 1
         
+        if avg_dis > 3:
+            c_steps = 3
+        else:
+            c_steps = 1
+        
         # 每五轮输出一次图片，并保存检查点
         if (epoch + 1) % 5 == 0:
             # 输出 20 张图片，每批次 4 张，一共五批