feat: 20k局/轮无限训练 + scan_wins支持存档目录

scripts/scan_wins.py

@@ -64,4 +64,16 @@ def scan(csv_dir='training_data'):
 
 if __name__ == '__main__':
     d = sys.argv[1] if len(sys.argv) > 1 else 'training_data'
+    # 如果指定了目录不存在, 可能是编号的round目录
+    if not os.path.isdir(d):
+        # 尝试 training_rounds 下最新
+        rounds = sorted(glob.glob('training_rounds/r*'))
+        if rounds:
+            d = os.path.join(rounds[-1], 'training_data')
+            if not os.path.isdir(d):
+                print(f"No training_data in {rounds[-1]}")
+                sys.exit(1)
+        else:
+            print("No data found")
+            sys.exit(1)
     scan(d)

scripts/training_orchestrator.py

@@ -1,30 +1,23 @@
 #!/usr/bin/env python3
 """
-持续训练 — 10k首轮 + 5k后续轮 → 循环到08:00
-每轮: dotnet自对弈 → V训练 → ONNX导出
+持续训练: 20k局/轮, 无限循环
+每轮: dotnet自对弈 → V训练 → ONNX导出 → 存档
+胜率趋势由 scan_wins.py 每小时 cron 独立采集
 """
-import subprocess, os, sys, time, shutil, glob, json
+import subprocess, os, sys, time, shutil, glob
 from datetime import datetime
 
 HJHA_DIR = '/home/xiaoou/projects/hjha-server'
 TRAINER_DIR = '/home/xiaoou/projects/paodekuai-trainer'
-DEADLINE = '08:00'
 LOG = os.path.join(HJHA_DIR, 'training_loop.log')
 
-def now(): return datetime.now().strftime('%H:%M:%S')
+def now(): return datetime.now().strftime('%m%d %H:%M:%S')
 def log(msg):
     line = f"[{now()}] {msg}"
     print(line, flush=True)
     with open(LOG, 'a') as f: f.write(line + '\n')
 
-def mins_left():
-    dt = datetime.now()
-    h, m = map(int, DEADLINE.split(':'))
-    dl = dt.replace(hour=h, minute=m, second=0, microsecond=0)
-    return max(0, (dl - dt).total_seconds() / 60)
-
-def run(cmd, cwd=HJHA_DIR, to=14400):
-    """返回 (ok, stdout+stderr)"""
+def run(cmd, cwd=HJHA_DIR, to=86400):
     try:
         r = subprocess.run(cmd, cwd=cwd, capture_output=True, text=True, timeout=to)
         ok = r.returncode == 0
@@ -35,7 +28,6 @@ def run(cmd, cwd=HJHA_DIR, to=14400):
         log(f"  EXCEPTION: {e}")
         return False, str(e)
 
-# ── 自对弈 ──
 def selfplay(games):
     log(f"── 自对弈 {games}局 ──")
     td = os.path.join(HJHA_DIR, 'training_data')
@@ -45,8 +37,8 @@ def selfplay(games):
     env = os.environ.copy()
     env['PATH'] = f"/usr/lib/dotnet:{env.get('PATH','')}"
     
-    ok, out = run(['/usr/lib/dotnet/dotnet', 'run', '--project', 'hjha-console', 
-                   '--', '--mix', str(games)], to=max(14400, games*2))
+    ok, out = run(['/usr/lib/dotnet/dotnet', 'run', '--project', 'hjha-console', '-c', 'Release',
+                   '--', '--mix', str(games)], to=max(36000, games*2))
     
     csv = len(glob.glob(os.path.join(td, 'game_*.csv')))
     sf = os.path.join(HJHA_DIR, 'stress_summary.txt')
@@ -55,7 +47,6 @@ def selfplay(games):
     log(f"  {csv} CSV")
     return csv
 
-# ── V训练 ──
 def train_v():
     log(f"── V训练 ──")
     
@@ -63,7 +54,6 @@ def train_v():
     import numpy as np, torch
     from models.network import PdkNet
     
-    # load data
     csv_dir = os.path.join(HJHA_DIR, 'training_data')
     states, results = [], []
     for fp in sorted(glob.glob(f'{csv_dir}/game_*.csv')):
@@ -71,6 +61,7 @@ def train_v():
             with open(fp) as f:
                 f.readline()
                 for line in f:
+                    if line.startswith('#'): continue
                     p = line.strip().split('"')
                     if len(p) < 3: continue
                     sf = [float(x) for x in p[1].split(',')]
@@ -88,15 +79,11 @@ def train_v():
     results = np.array(results, dtype=np.float32)
     log(f"  {len(states)} samples from {len(glob.glob(f'{csv_dir}/game_*.csv'))} games")
     
-    # load or init model — NOTE: old model.pt may be Q-network (14-output)
-    # V-training needs 1-output. Always start fresh for V-training.
-    mp = os.path.join(TRAINER_DIR, 'data/model.pt')
     model = PdkNet()
-    log("  新V模型 (从头训练)")
-    
     opt = torch.optim.Adam(model.parameters(), lr=0.001)
     loss_fn = torch.nn.MSELoss()
     n = len(states)
+    t0 = datetime.now()
     
     for ep in range(20):
         perm = np.random.permutation(n)
@@ -105,18 +92,19 @@ def train_v():
             idx = perm[i:i+128]
             x = torch.from_numpy(states[idx]).float()
             y = torch.from_numpy(results[idx]).float().unsqueeze(1)
-            model.train()
-            pred = model(x)
+            model.train(); pred = model(x)
             loss = loss_fn(pred, y)
-            opt.zero_grad()
-            loss.backward()
-            opt.step()
+            opt.zero_grad(); loss.backward(); opt.step()
             losses.append(loss.item())
         if ep % 5 == 0:
-            log(f"  ep{ep+1}/20 loss={np.mean(losses):.4f}")
-    log(f"  done. final loss={np.mean(losses):.4f}")
+            dt = (datetime.now() - t0).total_seconds()
+            log(f"  ep{ep+1}/20 loss={np.mean(losses):.4f} ({dt:.0f}s)")
     
-    # save pytorch
+    elapsed = (datetime.now() - t0).total_seconds()
+    log(f"  done {elapsed:.0f}s, final loss={np.mean(losses):.4f}")
+    
+    # save pt
+    mp = os.path.join(TRAINER_DIR, 'data/model.pt')
     model.save(mp)
     
     # export ONNX
@@ -129,14 +117,12 @@ def train_v():
             dynamic_axes={'state': {0: 'batch'}, 'v': {0: 'batch'}},
             opset_version=15)
         
-        # copy to hjha-server (include .data if external storage)
         dest = os.path.join(HJHA_DIR, 'model.onnx')
         shutil.copy(onnx_path, dest)
         data_src = onnx_path + '.data'
         if os.path.exists(data_src):
             shutil.copy(data_src, dest + '.data')
         
-        # archive
         tag = datetime.now().strftime('%m%d_%H%M')
         rd = os.path.join(HJHA_DIR, f'training_rounds/r{tag}')
         os.makedirs(rd, exist_ok=True)
@@ -148,60 +134,36 @@ def train_v():
             shutil.move(csv_dir, os.path.join(rd, 'training_data'))
         os.makedirs(csv_dir, exist_ok=True)
         
-        sz = os.path.getsize(dest) / 1024
-        log(f"  ONNX → {dest} ({sz:.0f}KB), 存档 → {rd}")
+        sz = os.path.getsize(dest) + os.path.getsize(dest + '.data') if os.path.exists(dest + '.data') else os.path.getsize(dest)
+        log(f"  ONNX → {dest} ({sz//1024}KB), 存档 → {rd}")
     except Exception as e:
-        log(f"  ONNX失败: {e}")
+        log(f"  ONNX failed: {e}")
         return False
     
     return True
 
-# ── 主循环 ──
 def main():
     log("=" * 50)
-    log(f"持续训练 启动 | 截止 {DEADLINE}")
-    log("策略: 首轮10k → 后续5k/轮")
+    log("持续训练: 20k局/轮 | 无限循环")
     log("=" * 50)
     
     rn, total = 0, 0
     while True:
-        ml = mins_left()
-        if ml < 3:
-            log(f"只剩 {ml:.0f}min, 停止")
-            break
-        
         rn += 1
-        games = 10000 if rn == 1 else 5000
-        est = games/60 + 15  # estimate minutes
-        
-        if est > ml - 3:
-            # shrink
-            games = max(500, int((ml - 18) * 60))
-            if games < 500:
-                log(f"不够时间跑完整轮 ({ml:.0f}min), 停止")
-                break
-            log(f"缩减为 {games} 局 (剩{ml:.0f}min)")
-        
-        log(f"\n── 第{rn}轮 {games}局 (剩{ml:.0f}min) ──")
+        games = 20000
+        log(f"\n── 第{rn}轮: {games}局 ──")
         
         csv = selfplay(games)
         if csv == 0:
-            log("自对弈失败! 跳过训练, 继续下一轮")
+            log("自对弈失败! 跳过")
             continue
         total += games
         
         if not train_v():
-            log("训练失败! 继续下一轮")
+            log("训练失败! 跳过")
             continue
-    
-    log(f"\n{'='*40}")
-    log(f"结束: {rn}轮, {total}局")
-    mp = os.path.join(HJHA_DIR, 'model.onnx')
-    if os.path.exists(mp):
-        log(f"最终模型: {mp} ({os.path.getsize(mp)/1024:.0f}KB)")
-    rd = os.path.join(HJHA_DIR, 'training_rounds')
-    if os.path.exists(rd):
-        log(f"存档: {len(os.listdir(rd))} 轮 → {rd}")
+        
+        log(f"累计: {rn}轮, {total}局")
 
 if __name__ == '__main__':
     main()