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/models/model.py

@@ -0,0 +1,105 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from collections import OrderedDict
+import numpy as np
+import os
+
+class Flatten(nn.Module):
+    def __init__(self):
+        super(Flatten, self).__init__()
+    def forward(self, x):
+        x = x.transpose(3, 2).contiguous()
+        return x.view(x.size(0), -1)
+
+class PNet(nn.Module):
+    def __init__(self):
+        super(PNet, self).__init__()
+        self.model_path,_ = os.path.split(os.path.realpath(__file__))
+        self.features = nn.Sequential(OrderedDict([
+            ('conv1', nn.Conv2d(3, 10, 3, 1)),
+            ('prelu1', nn.PReLU(10)),
+            ('pool1', nn.MaxPool2d(2, 2, ceil_mode=True)),
+            ('conv2', nn.Conv2d(10, 16, 3, 1)),
+            ('prelu2', nn.PReLU(16)),
+            ('conv3', nn.Conv2d(16, 32, 3, 1)),
+            ('prelu3', nn.PReLU(32))
+        ]))
+        self.conv4_1 = nn.Conv2d(32, 2, 1, 1)
+        self.conv4_2 = nn.Conv2d(32, 4, 1, 1)
+        weights = np.load('./weights/pnet.npy', allow_pickle=True)[()]
+        for n, p in self.named_parameters():
+            p.data = torch.FloatTensor(weights[n])
+
+    def forward(self, x):
+        x = self.features(x)
+        a = self.conv4_1(x)
+        b = self.conv4_2(x)
+        a = F.softmax(a, dim=1)
+        return b, a
+
+class RNet(nn.Module):
+    def __init__(self):
+        super(RNet, self).__init__()
+        self.model_path,_ = os.path.split(os.path.realpath(__file__))
+        self.features = nn.Sequential(OrderedDict([
+            ('conv1', nn.Conv2d(3, 28, 3, 1)),
+            ('prelu1', nn.PReLU(28)),
+            ('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)),
+            ('conv2', nn.Conv2d(28, 48, 3, 1)),
+            ('prelu2', nn.PReLU(48)),
+            ('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)),
+            ('conv3', nn.Conv2d(48, 64, 2, 1)),
+            ('prelu3', nn.PReLU(64)),
+            ('flatten', Flatten()),
+            ('conv4', nn.Linear(576, 128)),
+            ('prelu4', nn.PReLU(128))
+        ]))
+        self.conv5_1 = nn.Linear(128, 2)
+        self.conv5_2 = nn.Linear(128, 4)
+        weights = np.load('./weights/rnet.npy', allow_pickle=True)[()]
+        for n, p in self.named_parameters():
+            p.data = torch.FloatTensor(weights[n])
+
+    def forward(self, x):
+        x = self.features(x)
+        a = self.conv5_1(x)
+        b = self.conv5_2(x)
+        a = F.softmax(a, dim=1)
+        return b, a
+
+class ONet(nn.Module):
+    def __init__(self):
+        super(ONet, self).__init__()
+        self.model_path,_ = os.path.split(os.path.realpath(__file__))
+        self.features = nn.Sequential(OrderedDict([
+            ('conv1', nn.Conv2d(3, 32, 3, 1)),
+            ('prelu1', nn.PReLU(32)),
+            ('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)),
+            ('conv2', nn.Conv2d(32, 64, 3, 1)),
+            ('prelu2', nn.PReLU(64)),
+            ('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)),
+            ('conv3', nn.Conv2d(64, 64, 3, 1)),
+            ('prelu3', nn.PReLU(64)),
+            ('pool3', nn.MaxPool2d(2, 2, ceil_mode=True)),
+            ('conv4', nn.Conv2d(64, 128, 2, 1)),
+            ('prelu4', nn.PReLU(128)),
+            ('flatten', Flatten()),
+            ('conv5', nn.Linear(1152, 256)),
+            ('drop5', nn.Dropout(0.25)),
+            ('prelu5', nn.PReLU(256)),
+        ]))
+        self.conv6_1 = nn.Linear(256, 2)
+        self.conv6_2 = nn.Linear(256, 4)
+        self.conv6_3 = nn.Linear(256, 10)
+        weights = np.load('./weights/onet.npy', allow_pickle=True)[()]
+        for n, p in self.named_parameters():
+            p.data = torch.FloatTensor(weights[n])
+
+    def forward(self, x):
+        x = self.features(x)
+        a = self.conv6_1(x)
+        b = self.conv6_2(x)
+        c = self.conv6_3(x)
+        a = F.softmax(a, dim=1)
+        return c, b, a