chore: 删除多余脚本

roboimi/vla/data/image_transform.py

@@ -1,75 +0,0 @@
-# 图像预处理
-import torch
-import numpy as np
-import torchvision.transforms as T
-from PIL import Image
-from typing import Union, List
-
-class VLAImageProcessor:
-    """
-    VLA 图像预处理器，专为 SigLIP/CLIP 等 ViT 架构设计。
-    功能：
-    1. Numpy (HWC) -> Tensor (CHW)
-    2. Resize (e.g., 384x384)
-    3. Normalize (SigLIP: mean=0.5, std=0.5)
-    4. Data Augmentation (训练时开启颜色抖动)
-    """
-    def __init__(
-        self, 
-        resolution: int = 384, 
-        mean: List[float] = [0.5, 0.5, 0.5], 
-        std: List[float] = [0.5, 0.5, 0.5],
-        enable_augmentation: bool = True,
-        aug_strength: float = 0.1  # 增强强度，0.1~0.2 比较安全
-    ):
-        self.resolution = resolution
-        self.enable_augmentation = enable_augmentation
-        
-        # --- 1. 基础处理 (所有模式通用) ---
-        # 注意：这里我们分步定义，因为增强通常在 PIL 阶段做比较快
-        self.resize = T.Resize((resolution, resolution), interpolation=T.InterpolationMode.BICUBIC, antialias=True)
-        self.to_tensor = T.ToTensor()
-        self.normalize = T.Normalize(mean=mean, std=std)
-
-        # --- 2. 数据增强 (仅训练用) ---
-        # 机器人学习通常不做 RandomCrop (会丢失绝对坐标信息)，主要做颜色增强
-        if enable_augmentation:
-            self.aug = T.ColorJitter(
-                brightness=aug_strength, 
-                contrast=aug_strength, 
-                saturation=aug_strength, 
-                hue=aug_strength / 2
-            )
-        else:
-            self.aug = torch.nn.Identity()
-
-    def __call__(self, img: Union[np.ndarray, Image.Image, torch.Tensor]) -> torch.Tensor:
-        """
-        Args:
-            img: (H, W, C) uint8 numpy array (from HDF5) OR PIL Image
-        Returns:
-            tensor: (C, H, W) float32, Normalized
-        """
-        # 1. 统一转为 PIL Image (方便做 Resize 和 Jitter)
-        if isinstance(img, np.ndarray):
-            img = Image.fromarray(img)
-        elif isinstance(img, torch.Tensor):
-            # 假设 Tensor 是 CHW，转回 PIL 比较麻烦，通常 HDF5 出来都是 numpy
-            pass 
-
-        # 2. 数据增强 (如果开启)
-        if self.enable_augmentation:
-            img = self.aug(img)
-
-        # 3. 调整尺寸
-        img = self.resize(img)
-
-        # 4. 转张量 & 归一化
-        # ToTensor 会把 [0, 255] -> [0.0, 1.0]
-        tensor = self.to_tensor(img)
-        tensor = self.normalize(tensor)
-
-        return tensor
-
-    def __repr__(self):
-        return f"VLAImageProcessor(res={self.resolution}, aug={self.enable_augmentation})"

roboimi/vla/data/text_processing.py

@@ -1 +0,0 @@
-# 文本 Tokenizer 包装