chore: 删除多余脚本

roboimi/vla/models/backbones/__init__.py

@@ -1,10 +1,4 @@
 # Backbone models
-from .siglip import SigLIPBackbone
 from .resnet import ResNetBackbone
-# from .clip import CLIPBackbone
-# from .dinov2 import DinoV2Backbone
 
-__all__ = ["SigLIPBackbone", "ResNetBackbone"]
-
-# from .debug import DebugBackbone
-# __all__ = ["DebugBackbone"]
+__all__ = ["ResNetBackbone"]

roboimi/vla/models/backbones/siglip.py

@@ -1,62 +0,0 @@
-# SigLIP Backbone 实现
-import torch
-import torch.nn as nn
-from transformers import AutoModel, AutoProcessor, SiglipVisionModel
-from typing import Dict, Optional
-from roboimi.vla.core.interfaces import VLABackbone
-
-class SigLIPBackbone(VLABackbone):
-    """
-    Wraps Google's SigLIP Vision Encoder.
-    HuggingFace ID example: "google/siglip-so400m-patch14-384"
-    """
-    def __init__(
-        self,
-        model_name: str = "google/siglip-so400m-patch14-384",
-        freeze: bool = True,
-        embed_dim: Optional[int] = None
-    ):
-        super().__init__()
-        print(f"Loading SigLIP: {model_name} ...")
-
-        # 加载视觉部分 (Vision Model only)
-        # 我们不需要 Text Tower，因为 SigLIP 是对齐好的，只用 Vision Tower 抽特征即可
-        self.vision_model = SiglipVisionModel.from_pretrained(model_name)
-
-        # 优先使用配置传入的 embed_dim，否则自动获取
-        if embed_dim is not None:
-            self._embed_dim = embed_dim
-            print(f"✓ Using configured embed_dim: {embed_dim}")
-        else:
-            # 自动获取维度 (SigLIP so400m 通常是 1152)
-            self._embed_dim = self.vision_model.config.hidden_size
-            print(f"✓ Auto-detected embed_dim: {self._embed_dim}")
-        
-        if freeze:
-            self._freeze_parameters()
-
-    def _freeze_parameters(self):
-        print("❄️ Freezing Vision Backbone parameters")
-        for param in self.vision_model.parameters():
-            param.requires_grad = False
-        self.vision_model.eval()
-
-    def forward(self, obs: Dict[str, torch.Tensor]) -> torch.Tensor:
-        """
-        Args:
-            obs['image']: (B, C, H, W) normalized tensor
-        Returns:
-            features: (B, Seq_Len, Embed_Dim)
-        """
-        images = obs['image']
-        
-        # SigLIP 期望输入是 (B, C, H, W)
-        # HuggingFace 的 VisionModel 输出是一个 BaseModelOutputWithPooling
-        # last_hidden_state shape: (B, Num_Patches, Embed_Dim)
-        outputs = self.vision_model(pixel_values=images)
-        
-        return outputs.last_hidden_state
-
-    @property
-    def embed_dim(self) -> int:
-        return self._embed_dim

roboimi/vla/models/heads/__init__.py

@@ -1,8 +1,4 @@
 # # Action Head models
 from .diffusion import ConditionalUnet1D
-# from .act import ACTHead
 
 __all__ = ["ConditionalUnet1D"]
-
-# from .debug import DebugHead
-# __all__ = ["DebugHead"]