feat: 记录训练过程

data/src/topology/similarity.ts

@@ -87,8 +87,8 @@ function weisfeilerLehmanIteration(
         });
         weight *= decay;
     });
-    // 把每个节点的原始标签也加上，权重使用最远权重再衰减2次，可以认为是资源重复率
-    weight *= decay ** 2;
+    // 把每个节点的原始标签也加上，权重使用最远权重再衰减1次，可以认为是资源重复率
+    weight *= decay;
     nodes.forEach(node => {
         if (!numMap.has(node.originalLabel)) {
             numMap.set(node.originalLabel, weight);

ginka/train_gan.py

@@ -19,21 +19,26 @@ from shared.image import matrix_to_image_cv
 
 BATCH_SIZE = 32
 EPOCHS_GINKA = 30
-EPOCHS_MINAMO = 10
+EPOCHS_MINAMO = 5
 SOCKET_PATH = "./tmp/ginka_uds"
+LOSS_PATH = "result/gan/a-loss.txt"
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 os.makedirs("result", exist_ok=True)
 os.makedirs("result/ginka_checkpoint", exist_ok=True)
+os.makedirs("result/gan", exist_ok=True)
 os.makedirs("tmp", exist_ok=True)
 
+with open(LOSS_PATH, 'a', encoding='utf-8') as f:
+    f.write(f"---------- {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ----------\n")
+
 def parse_arguments():
     parser = argparse.ArgumentParser(description="training codes")
     parser.add_argument("--resume", type=bool, default=False)
     parser.add_argument("--from_state", type=str, default="result/ginka.pth")
     parser.add_argument("--train", type=str, default="ginka-dataset.json")
     parser.add_argument("--validate", type=str, default='ginka-eval.json')
-    parser.add_argument("--from_cycle", type=int, default=2)
+    parser.add_argument("--from_cycle", type=int, default=0)
     parser.add_argument("--to_cycle", type=int, default=100)
     args = parser.parse_args()
     return args
@@ -141,8 +146,8 @@ def train():
     scheduler_ginka = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer_ginka, T_0=10, T_mult=2, eta_min=1e-6)
     criterion_ginka = GinkaLoss(minamo)
     
-    optimizer_minamo = optim.AdamW(minamo.parameters(), lr=1e-3)
-    scheduler_minamo = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer_minamo, T_0=10, T_mult=2, eta_min=1e-6)
+    optimizer_minamo = optim.AdamW(minamo.parameters(), lr=1e-4)
+    scheduler_minamo = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer_minamo, T_0=5, T_mult=2, eta_min=1e-6)
     criterion_minamo = MinamoLoss()
     
     # 用于生成图片
@@ -238,6 +243,9 @@ def train():
                 gen_list = np.concatenate((gen_list, map_matrix), axis=0)
         
         tqdm.write(f"Cycle {cycle} Ginka train ended.")
+        torch.save({
+            "model_state": ginka.state_dict()
+        }, f"result/gan/ginka-{cycle}.pth")
         torch.save({
             "model_state": ginka.state_dict()
         }, f"result/ginka.pth")
@@ -256,6 +264,17 @@ def train():
         conn.sendall(buf)
         data = parse_minamo_data(conn, prob_list)
         minamo_dataset.set_data(data)
+        vis_sim = 0
+        topo_sim = 0
+        for _, _, vis, topo, _ in data:
+            vis_sim += vis
+            topo_sim += topo
+        
+        vis_sim /= len(data)
+        topo_sim /= len(data)
+        
+        with open(LOSS_PATH, 'a', encoding='utf-8') as f:
+            f.write(f'Cycle {cycle} | Ginka Vision Similarity: {vis_sim:.12f} | Ginka Topo Similarity: {topo_sim:.12f} | Ginka Loss: {avg_val_loss:.12f}')
         
         # -------------------- 训练判别器
         for epoch in tqdm(range(EPOCHS_MINAMO), leave=False, desc="Training Minamo Model"):
@@ -315,9 +334,14 @@ def train():
                 }, f"result/minamo_checkpoint/{epoch + 1}.pth")
                 
         tqdm.write(f"Cycle {cycle} Minamo train ended.")
+        torch.save({
+            "model_state": minamo.state_dict()
+        }, f"result/gan/minamo-{cycle}.pth")
         torch.save({
             "model_state": minamo.state_dict()
         }, f"result/minamo.pth")
+        with open(LOSS_PATH, 'a', encoding='utf-8') as f:
+            f.write(f' | Minamo: {avg_val_loss:.12f}\n')
         
     print("Train ended.")