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/seg/networks/segbase.py

@@ -0,0 +1,60 @@
+"""Base Model for Semantic Segmentation"""
+import torch.nn as nn
+
+from seg.networks.jpu import JPU
+from seg.networks.resnetv1b import resnet50_v1s, resnet101_v1s, resnet152_v1s
+
+__all__ = ['SegBaseModel']
+
+
+class SegBaseModel(nn.Module):
+    r"""Base Model for Semantic Segmentation
+
+    Parameters
+    ----------
+    backbone : string
+        Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
+        'resnet101' or 'resnet152').
+    """
+
+    def __init__(self, nclass, aux, backbone='resnet50', jpu=False, pretrained_base=True, **kwargs):
+        super(SegBaseModel, self).__init__()
+        dilated = False if jpu else True
+        self.aux = aux
+        self.nclass = nclass
+        if backbone == 'resnet50':
+            self.pretrained = resnet50_v1s(pretrained=pretrained_base, dilated=dilated, **kwargs)
+        elif backbone == 'resnet101':
+            self.pretrained = resnet101_v1s(pretrained=pretrained_base, dilated=dilated, **kwargs)
+        elif backbone == 'resnet152':
+            self.pretrained = resnet152_v1s(pretrained=pretrained_base, dilated=dilated, **kwargs)
+        else:
+            raise RuntimeError('unknown backbone: {}'.format(backbone))
+
+        self.jpu = JPU([512, 1024, 2048], width=512, **kwargs) if jpu else None
+
+    def base_forward(self, x):
+        """forwarding pre-trained network"""
+        x = self.pretrained.conv1(x)
+        x = self.pretrained.bn1(x)
+        x = self.pretrained.relu(x)
+        x = self.pretrained.maxpool(x)
+        c1 = self.pretrained.layer1(x)
+        c2 = self.pretrained.layer2(c1)
+        c3 = self.pretrained.layer3(c2)
+        c4 = self.pretrained.layer4(c3)
+
+        if self.jpu:
+            return self.jpu(c1, c2, c3, c4)
+        else:
+            return c1, c2, c3, c4
+
+    def evaluate(self, x):
+        """evaluating network with inputs and targets"""
+        return self.forward(x)[0]
+
+    def demo(self, x):
+        pred = self.forward(x)
+        if self.aux:
+            pred = pred[0]
+        return pred