初始化：换发型/换发色/训练发型服务

包含：
- hair_service_sd: 主服务（换发型/换发色/生发，端口8801）
- photo_service: LoRA调度+训练（端口32678）
- hair_grow_service: 调试测试页（端口8888，含4个测试页）
- 批量训练脚本（batch_train_hairstyles.py）
- 发际线mask自动识别（hairline_mask.py，4种方案）
- 手绘mask换发型（hair_swap_manual.py）
- 文档：README.md + LARGE_FILES.md + docs/

大文件（模型权重200G、训练数据123G）已排除，见 LARGE_FILES.md
OSS/COS密钥已脱敏为环境变量，原文件备份在本地

project/hair_service_sd/bodyseg/backbone/backbone.py

@@ -0,0 +1,75 @@
+import numpy as np
+import torch
+from torch import nn
+
+def get_norm(norm, out_channels=None):
+    """
+    Args:
+        norm (str or callable):
+
+    Returns:
+        nn.Module or None: the normalization layer
+    """
+    if isinstance(norm, str):
+        if len(norm) == 0:
+            return None
+        norm = {
+            "BN": nn.BatchNorm2d,
+            "IN": nn.InstanceNorm2d,
+            "GN": lambda channels: nn.GroupNorm(32, channels),
+            "nnSyncBN": nn.SyncBatchNorm,  # keep for debugging
+        }[norm]
+    if out_channels is not None:
+        return norm(out_channels)
+    else:
+        return norm
+
+class Conv2d(torch.nn.Conv2d):
+    """
+    A wrapper around :class:`torch.nn.Conv2d` to support zero-size tensor and more features.
+    """
+
+    def __init__(self, *args, **kwargs):
+        """
+        Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`:
+
+        Args:
+            norm (nn.Module, optional): a normalization layer
+            activation (callable(Tensor) -> Tensor): a callable activation function
+
+        It assumes that norm layer is used before activation.
+        """
+        norm = kwargs.pop("norm", None)
+        activation = kwargs.pop("activation", None)
+        super().__init__(*args, **kwargs)
+
+        self.norm = norm
+        self.activation = activation
+
+    def forward(self, x):
+        x = super().forward(x)
+        if self.norm is not None:
+            x = self.norm(x)
+        if self.activation is not None:
+            x = self.activation(x)
+        return x
+
+class Backbone(nn.Module):
+    """
+    Abstract base class for network backbones.
+    """
+
+    def __init__(self):
+        """
+        The `__init__` method of any subclass can specify its own set of arguments.
+        """
+        super().__init__()
+
+    def forward(self):
+        """
+        Subclasses must override this method, but adhere to the same return type.
+
+        Returns:
+            dict[str: Tensor]: mapping from feature name (e.g., "res2") to tensor
+        """
+        pass