feat: 添加finetune

roboimi/demos/vla_scripts/train_vla.py

@@ -211,6 +211,40 @@ def main(cfg: DictConfig):
         log.error(f"❌ Agent 初始化失败: {e}")
         raise
 
+    # =========================================================================
+    # 3.1 从预训练 checkpoint 加载权重（微调）
+    # =========================================================================
+    pretrained_ckpt = cfg.train.get('pretrained_ckpt', None)
+    if pretrained_ckpt is not None:
+        ckpt_path = Path(pretrained_ckpt)
+        if ckpt_path.exists():
+            log.info(f"🔄 [Finetune] 从预训练 checkpoint 加载权重: {ckpt_path}")
+            try:
+                checkpoint = torch.load(ckpt_path, map_location=cfg.train.device)
+
+                # 只加载模型权重（不加载 optimizer、scheduler）
+                missing_keys, unexpected_keys = agent.load_state_dict(
+                    checkpoint['model_state_dict'],
+                    strict=False  # 允许部分加载（结构不完全匹配时）
+                )
+
+                log.info(f"✅ [Finetune] 模型权重加载成功")
+
+                if missing_keys:
+                    log.warning(f"⚠️  [Finetune] 缺少的键 ({len(missing_keys)} 个): {missing_keys[:5]}...")
+                if unexpected_keys:
+                    log.warning(f"⚠️  [Finetune] 多余的键 ({len(unexpected_keys)} 个): {unexpected_keys[:5]}...")
+
+                log.info(f"📊 [Finetune] 预训练信息: 步骤={checkpoint.get('step', 'N/A')}, 损失={checkpoint.get('loss', 'N/A')}")
+                log.info(f"📈 [Finetune] 使用新的训练配置（lr={cfg.train.lr}, max_steps={cfg.train.max_steps}）")
+
+            except Exception as e:
+                log.error(f"❌ [Finetune] 加载 checkpoint 失败: {e}")
+                log.warning("⚠️  将从头开始训练")
+        else:
+            log.error(f"❌ [Finetune] Checkpoint 文件不存在: {ckpt_path}")
+            log.warning("⚠️  将从头开始训练")
+
     # =========================================================================
     # 4. 设置优化器与学习率调度器
     # =========================================================================

roboimi/vla/conf/config.yaml

@@ -32,6 +32,9 @@ train:
   weight_decay: 1e-5  # 权重衰减（L2 正则化）
   grad_clip: 1.0       # 梯度裁剪阈值
 
+  # 微调配置
+  pretrained_ckpt: null  # 预训练 checkpoint 路径（用于微调），例如: "checkpoints/vla_model_step_8000.pt"
+
 # ====================
 # 实验配置
 # ====================