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
628 changed files with 120882 additions and 0 deletions
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+import torch
+import torch.nn as nn
+import math
+import torch.optim as optim
+import sys
+import os
+
+def op_name(op_name, m):
+    m.op_name = op_name
+    return m
+
+class Flatten(nn.Module):
+    def __init__(self, axis):
+        super(Flatten, self).__init__()
+
+    def forward(self, x):
+        x = x.view(-1, 2560)
+        return x
+
+def flatten(name, axis):
+    return op_name(name, Flatten(axis))
+
+def conv_bn_relu(name, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, momentum = 0.1, track_running_stats=True):
+    return nn.Sequential(
+        op_name(name, nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, False)),
+        op_name(name + '/bn', nn.BatchNorm2d(out_channels, momentum = momentum, track_running_stats = track_running_stats)),
+        op_name(name + '/relu', nn.ReLU(inplace=True)),
+    )
+
+def linear_bn_relu(name, in_features, out_features, momentum = 0.1, track_running_stats=True):
+    return nn.Sequential(
+        op_name(name + '/FC', nn.Linear(in_features, out_features)),
+        op_name(name + '/bn', nn.BatchNorm1d(out_features, momentum = momentum, track_running_stats = track_running_stats)),
+        op_name(name + '/relu', nn.ReLU(inplace=True)),
+    )
+
+# Define a Basic resnet block
+class BasicResnetBlock(nn.Module):
+    def __init__(self, name, in_channels, out_channels, kernel_size=3, stride=2, padding=1, direct_plus=False):
+        super(BasicResnetBlock, self).__init__()
+
+        self.op_name = name
+
+        self.conv_block = nn.Sequential(
+            conv_bn_relu(name=name + '/conv1', in_channels=in_channels, out_channels=out_channels,
+                         kernel_size=kernel_size, stride=stride, padding=padding),
+
+            conv_bn_relu(name=name + '/conv2', in_channels=out_channels, out_channels=out_channels,
+                         kernel_size=3, stride=1, padding=1)
+        )
+
+        if direct_plus and (in_channels == out_channels) and (stride == 1):
+            self.sc_block = None
+        else:
+            self.sc_block = conv_bn_relu(name=name + '/sc_conv', in_channels=in_channels, out_channels=out_channels,
+                                         kernel_size=1, stride=stride, padding=0)
+
+    def forward(self, x):
+        if self.sc_block is None:
+            out = x + self.conv_block(x)
+        else:
+            out = self.conv_block(x) + self.sc_block(x)
+        return out
+
+class SuperBigResNetV2(nn.Module):
+    def __init__(self, name, in_channels, out_channels):
+        super(SuperBigResNetV2, self).__init__()
+        self.op_name = name
+
+        input_size = 256
+
+        op_list = []
+
+        stride_cnt = 4
+
+        op_list += [conv_bn_relu(name + '/first_conv', in_channels, 48, kernel_size=5, stride=4, padding=2)]
+
+        ch_num = [48, 64, 96, 128, 160]
+        for i in range(len(ch_num) - 1):
+            if ch_num[i] == ch_num[i+1]:
+                op_list += [BasicResnetBlock(name + '/stage%d'%(i + 1), ch_num[i], ch_num[i+1], kernel_size=3, stride=1, direct_plus=True)]
+            else:
+                stride_cnt = stride_cnt * 2
+                op_list += [BasicResnetBlock(name + '/stage%d'%(i + 1), ch_num[i], ch_num[i+1], kernel_size=3, stride=2)]
+
+        item_cnt = int((input_size / stride_cnt) * (input_size / stride_cnt) * ch_num[-1])
+        op_list += [flatten(name + '/flatten', 1),
+                     linear_bn_relu(name + '/FC1', item_cnt, 512),
+                     op_name(name + '/FC2', nn.Linear(512, out_channels))]
+
+        self.conv_block = nn.Sequential(*op_list)
+
+        self.model_path, _ = os.path.split(os.path.realpath(__file__))
+
+        self.weights = torch.load(os.path.join(self.model_path, 'SuperBigResNetV2_latest.pth'),
+                             map_location=lambda storage, loc: storage)
+
+    def forward(self, x):
+        return self.conv_block(x)
+
+if __name__=='__main__':
+    pass