初始化换发型项目：3个微服务代码 + 部署脚本

包含： - hair_service_sd: 换发型/换发色算法服务 (端口 8801) - photo_service: LoRA 训练调度服务 (端口 32678) - stable-diffusion-webui: SD WebUI 推理服务 (端口 57860) - kohya_ss_home: 训练环境代码 - meidaojia: 监控测试脚本 - setup.sh: 一键部署脚本 (conda环境恢复 + 配置生成 + 完整性检查) - start_all_services.sh: 启动3个服务 - configure.ini.template: 路径模板化 (BASE_DIR自动推导) - conda_envs/py310.yml: py310 环境定义大文件 (weights/, models/, data/, conda_envs/*.tar.gz 等) 通过 .gitignore 排除，由网盘单独上传。
2026-07-11 18:11:49 +08:00
commit 0eb61f3e60
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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from collections import OrderedDict
+import numpy as np
+import os
+# from hairstyle_model import modelRoot
+modelRoot = "./weights"
+# modelRoot = "/home/yangchaojie/Desktop/hairstyle/hairstyle_infer/weights"
+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 = modelRoot
+
+        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(os.path.join(self.model_path, '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 = modelRoot
+
+        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(os.path.join(self.model_path, '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 = modelRoot
+
+        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(os.path.join(self.model_path, '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