feat(vla): vla框架初始化

roboimi/demos/vla_scripts/train_vla.py

@@ -0,0 +1,45 @@
+import hydra
+from omegaconf import DictConfig, OmegaConf
+from hydra.utils import instantiate
+import torch
+import os
+
+# 必须指向你的配置文件所在路径
+# config_path 是相对于当前脚本的路径，或者绝对路径
+# config_name 是不带 .yaml 后缀的主文件名
+@hydra.main(version_base=None, config_path="../../roboimi/vla/conf", config_name="config")
+def main(cfg: DictConfig):
+    print(f"Working directory : {os.getcwd()}")
+    print(f"Configuration:\n{OmegaConf.to_yaml(cfg)}")
+
+    # 1. 实例化 Agent
+    # Hydra 会自动查找 _target_ 并递归实例化 vlm_backbone 和 action_head
+    print(">>> Instantiating VLA Agent...")
+    agent = instantiate(cfg.agent)
+    
+    # 将模型移至 GPU
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    agent.to(device)
+    print(f">>> Agent created successfully. Backbone: {type(agent.vlm).__name__}")
+
+    # 2. 实例化 DataLoader (假设你也为 Data 写了 yaml)
+    # 实例化 Dataset
+    dataset = hydra.utils.instantiate(cfg.data)
+
+    # 封装进 DataLoader
+    dataloader = torch.utils.data.DataLoader(
+        dataset, 
+        batch_size=cfg.train.batch_size, 
+        shuffle=True,
+        num_workers=4
+    )
+
+    # 3. 实例化 Optimizer (Hydra 也支持 partial 实例化)
+    # optimizer = instantiate(cfg.train.optimizer, params=agent.parameters())
+
+    # 4. 模拟训练循环
+    print(f">>> Starting training with batch size: {cfg.train.batch_size}")
+    # ... training loop logic here ...
+
+if __name__ == "__main__":
+    main()