chore: 删除多余文件

roboimi/vla/models/backbones/siglip2.py

@@ -1,37 +0,0 @@
-from transformers import SiglipVisionModel
-from roboimi.vla.core.interfaces import VLABackbone
-from torchvision import transforms
-
-class SigLIP2(VLABackbone):
-    def __init__(
-            self,
-            model_name = "google/siglip2-base-patch16-384",
-            freeze: bool = True,
-    ):
-        super().__init__()
-
-        self.vision_model = SiglipVisionModel.from_pretrained(model_name)
-        self.transform = transforms.Compose([
-            transforms.Resize((384, 384), antialias=True),
-            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-        ])
-
-        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,
-            images
-    ):
-        # images: (B, C, H, W), 归一化到 [0, 1]
-        images = self.transform(images)  # 归一化到 [-1, 1]
-
-        outputs = self.vision_model(pixel_values=images)
-
-        return outputs.last_hidden_state

roboimi/vla/models/projectors/__init__.py

@@ -1,9 +0,0 @@
-# Projector models
-# from .mlp import MLPProjector
-# from .perceiver import PerceiverResampler
-
-# __all__ = ["MLPProjector", "PerceiverResampler"]
-
-from .mlp import MLPProjector
-
-__all__ = ["MLPProjector"]

roboimi/vla/models/projectors/mlp.py

@@ -1,19 +0,0 @@
-import torch
-import torch.nn as nn
-from roboimi.vla.core.interfaces import VLAProjector
-
-class MLPProjector(VLAProjector):
-    """
-    A simple Linear Projection layer.
-    First-class citizen: Adapts Backbone dim -> Head dim.
-    """
-    def __init__(self, input_dim: int, output_dim: int):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(input_dim, output_dim),
-            nn.GELU(),
-            nn.Linear(output_dim, output_dim)
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.net(x)

roboimi/vla/models/projectors/perceiver.py

@@ -1 +0,0 @@
-# Perceiver Resampler 实现