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0eb61f3e604ea02519bb256be2e5b844c8e0241d
change_hair_3090/hair_service_sd/faceseg/u2net.py
T
colomi 0eb61f3e60 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

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import torch
import torch.nn as nn
import torch.nn.functional as F
class REBNCONV(nn.Module):
def __init__(self,in_ch=3,out_ch=3,dirate=1):
super(REBNCONV,self).__init__()
self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate)
self.bn_s1 = nn.BatchNorm2d(out_ch)
self.relu_s1 = nn.ReLU(inplace=True)
def forward(self,x):
hx = x
xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
return xout
## upsample tensor 'src' to have the same spatial size with tensor 'tar'
def _upsample_like(src,tar):
src = F.upsample(src,size=tar.shape[2:],mode='bilinear')
return src
### RSU-7 ###
class RSU7(nn.Module):#UNet07DRES(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU7,self).__init__()
self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)
self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
def forward(self,x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
hx = self.pool4(hx4)
hx5 = self.rebnconv5(hx)
hx = self.pool5(hx5)
hx6 = self.rebnconv6(hx)
hx7 = self.rebnconv7(hx6)
hx6d = self.rebnconv6d(torch.cat((hx7,hx6),1))
hx6dup = _upsample_like(hx6d,hx5)
hx5d = self.rebnconv5d(torch.cat((hx6dup,hx5),1))
hx5dup = _upsample_like(hx5d,hx4)
hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
hx4dup = _upsample_like(hx4d,hx3)
hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
hx3dup = _upsample_like(hx3d,hx2)
hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
hx2dup = _upsample_like(hx2d,hx1)
hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
return hx1d + hxin
### RSU-6 ###
class RSU6(nn.Module):#UNet06DRES(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU6,self).__init__()
self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)
self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
def forward(self,x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
hx = self.pool4(hx4)
hx5 = self.rebnconv5(hx)
hx6 = self.rebnconv6(hx5)
hx5d = self.rebnconv5d(torch.cat((hx6,hx5),1))
hx5dup = _upsample_like(hx5d,hx4)
hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
hx4dup = _upsample_like(hx4d,hx3)
hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
hx3dup = _upsample_like(hx3d,hx2)
hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
hx2dup = _upsample_like(hx2d,hx1)
hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
return hx1d + hxin
### RSU-5 ###
class RSU5(nn.Module):#UNet05DRES(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU5,self).__init__()
self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)
self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
def forward(self,x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
hx5 = self.rebnconv5(hx4)
hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))
hx4dup = _upsample_like(hx4d,hx3)
hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
hx3dup = _upsample_like(hx3d,hx2)
hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
hx2dup = _upsample_like(hx2d,hx1)
hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
return hx1d + hxin
### RSU-4 ###
class RSU4(nn.Module):#UNet04DRES(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU4,self).__init__()
self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)
self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
def forward(self,x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx4 = self.rebnconv4(hx3)
hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
hx3dup = _upsample_like(hx3d,hx2)
hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
hx2dup = _upsample_like(hx2d,hx1)
hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
return hx1d + hxin
### RSU-4F ###
class RSU4F(nn.Module):#UNet04FRES(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU4F,self).__init__()
self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)
self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)
self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)
self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)
self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)
self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
def forward(self,x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx2 = self.rebnconv2(hx1)
hx3 = self.rebnconv3(hx2)
hx4 = self.rebnconv4(hx3)
hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))
return hx1d + hxin
from faceseg.tma import SequenceConv, MemoryModule
##### U^2-Net ####
class U2NET(nn.Module):
def __init__(self, in_ch=3, out_ch=1):
super(U2NET, self).__init__()
self.stage1 = RSU7(in_ch,32,64)
self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.stage2 = RSU6(64,32,128)
self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.stage3 = RSU5(128,64,256)
self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.stage4 = RSU4(256,128,512)
self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.stage5 = RSU4F(512,256,512)
self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.stage6 = RSU4F(512,256,512)
# decoder
self.stage5d = RSU4F(1024,256,512)
self.stage4d = RSU4(1024,128,256)
self.stage3d = RSU5(512,64,128)
self.stage2d = RSU6(256,32,64)
self.stage1d = RSU7(128,16,64)
self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
self.in_channels = 512
key_channels = 128
value_channels = 512
self.sequence_num = sequence_num = 2
self.memory_key_conv = nn.Sequential(
SequenceConv(self.in_channels, key_channels, 1, sequence_num),
SequenceConv(key_channels, key_channels, 3, sequence_num)
)
self.memory_value_conv = nn.Sequential(
SequenceConv(self.in_channels, value_channels, 1, sequence_num),
SequenceConv(value_channels, value_channels, 3, sequence_num)
)
self.query_key_conv = nn.Sequential(
nn.Sequential(
nn.Conv2d(self.in_channels, key_channels, 1, 1, 0, bias=False),
nn.BatchNorm2d(key_channels),
nn.ReLU()
),
nn.Sequential(
nn.Conv2d(key_channels, key_channels, 3, 1, 1, bias=False),
nn.BatchNorm2d(key_channels),
nn.ReLU()
),
)
self.query_value_conv = nn.Sequential(
nn.Sequential(
nn.Conv2d(self.in_channels, value_channels, 1, 1, 0, bias=False),
nn.BatchNorm2d(value_channels),
nn.ReLU()
),
nn.Sequential(
nn.Conv2d(value_channels, value_channels, 3, 1, 1, bias=False),
nn.BatchNorm2d(value_channels),
nn.ReLU()
),
)
self.memory_module = MemoryModule(matmul_norm=False)
self.bottleneck = nn.Sequential(
nn.Conv2d(value_channels * 2, self.in_channels, 3, 1, 1, bias=False),
nn.BatchNorm2d(value_channels),
nn.ReLU()
)
self.is_train = True
def extract_feature(self, x):
hx = x
# stage 1
hx1 = self.stage1(hx)
hx = self.pool12(hx1)
# stage 2
hx2 = self.stage2(hx)
hx = self.pool23(hx2)
# stage 3
hx3 = self.stage3(hx)
hx = self.pool34(hx3)
# stage 4
hx4 = self.stage4(hx)
hx = self.pool45(hx4)
# stage 5
hx5 = self.stage5(hx)
hx = self.pool56(hx5)
# stage 6
hx6 = self.stage6(hx)
return hx1, hx2, hx3, hx4, hx5, hx6
def decoder(self, hx1, hx2, hx3, hx4, hx5, hx6):
hx6up = _upsample_like(hx6, hx5)
# -------------------- decoder --------------------
hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
hx5dup = _upsample_like(hx5d, hx4)
hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
return hx1d, hx2d, hx3d, hx4d, hx5d
def side_output(self, hx1d, hx2d, hx3d, hx4d, hx5d, hx6):
# side output
d1 = self.side1(hx1d)
d2 = self.side2(hx2d)
d2 = _upsample_like(d2, d1)
d3 = self.side3(hx3d)
d3 = _upsample_like(d3, d1)
d4 = self.side4(hx4d)
d4 = _upsample_like(d4, d1)
d5 = self.side5(hx5d)
d5 = _upsample_like(d5, d1)
d6 = self.side6(hx6)
d6 = _upsample_like(d6, d1)
d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)
def forward(self, x, memory_sequence=None):
if self.is_train:
hx1, hx2, hx3, hx4, hx5, hx6 = self.extract_feature(x)
if memory_sequence is None:
memory_hx6 = [hx6 for _ in range(self.sequence_num)]
else:
memory_hx6 = []
for single_memory in memory_sequence:
_, _, _, _, _, hx6 = self.extract_feature(single_memory)
memory_hx6.append(hx6)
memory_hx6 = [mhx6.unsqueeze(0) for mhx6 in memory_hx6] # T, BxCxHxW
memory_hx6 = torch.cat(memory_hx6, dim=0)
memory_keys = self.memory_key_conv(memory_hx6)
memory_values = self.memory_value_conv(memory_hx6)
query_key = self.query_key_conv(hx6)
query_value = self.query_value_conv(hx6)
merge_hx6 = self.memory_module(memory_keys, memory_values, query_key, query_value)
merge_hx6 = self.bottleneck(merge_hx6)
hx1d, hx2d, hx3d, hx4d, hx5d = self.decoder(hx1, hx2, hx3, hx4, hx5, merge_hx6)
return self.side_output(hx1d, hx2d, hx3d, hx4d, hx5d, hx6)
else:
return self.test(x)
def test(self, x):
with torch.no_grad():
hx1, hx2, hx3, hx4, hx5, hx6 = self.extract_feature(x)
memory_hx6 = [hx6 for _ in range(self.sequence_num)]
memory_hx6 = [mhx6.unsqueeze(0) for mhx6 in memory_hx6] # T, BxCxHxW
memory_hx6 = torch.cat(memory_hx6, dim=0)
memory_keys = self.memory_key_conv(memory_hx6)
memory_values = self.memory_value_conv(memory_hx6)
query_key = self.query_key_conv(hx6)
query_value = self.query_value_conv(hx6)
merge_hx6 = self.memory_module(memory_keys, memory_values, query_key, query_value)
merge_hx6 = self.bottleneck(merge_hx6)
hx1d, hx2d, hx3d, hx4d, hx5d = self.decoder(hx1, hx2, hx3, hx4, hx5, merge_hx6)
mask, _, _, _, _, _, _ = self.side_output(hx1d, hx2d, hx3d, hx4d, hx5d, hx6)
return mask
#
# ### U^2-Net small ###
# class U2NETP(nn.Module):
#
# def __init__(self,in_ch=3,out_ch=1):
# super(U2NETP,self).__init__()
#
# self.stage1 = RSU7(in_ch,16,64)
# self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
#
# self.stage2 = RSU6(64,16,64)
# self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
#
# self.stage3 = RSU5(64,16,64)
# self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
#
# self.stage4 = RSU4(64,16,64)
# self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
#
# self.stage5 = RSU4F(64,16,64)
# self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
#
# self.stage6 = RSU4F(64,16,64)
#
# # decoder
# self.stage5d = RSU4F(128,16,64)
# self.stage4d = RSU4(128,16,64)
# self.stage3d = RSU5(128,16,64)
# self.stage2d = RSU6(128,16,64)
# self.stage1d = RSU7(128,16,64)
#
# self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
# self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
# self.side3 = nn.Conv2d(64,out_ch,3,padding=1)
# self.side4 = nn.Conv2d(64,out_ch,3,padding=1)
# self.side5 = nn.Conv2d(64,out_ch,3,padding=1)
# self.side6 = nn.Conv2d(64,out_ch,3,padding=1)
#
# self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
#
# def forward(self,x):
#
# hx = x
#
# #stage 1
# hx1 = self.stage1(hx)
# hx = self.pool12(hx1)
#
# #stage 2
# hx2 = self.stage2(hx)
# hx = self.pool23(hx2)
#
# #stage 3
# hx3 = self.stage3(hx)
# hx = self.pool34(hx3)
#
# #stage 4
# hx4 = self.stage4(hx)
# hx = self.pool45(hx4)
#
# #stage 5
# hx5 = self.stage5(hx)
# hx = self.pool56(hx5)
#
# #stage 6
# hx6 = self.stage6(hx)
# hx6up = _upsample_like(hx6,hx5)
#
# #decoder
# hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
# hx5dup = _upsample_like(hx5d,hx4)
#
# hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
# hx4dup = _upsample_like(hx4d,hx3)
#
# hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
# hx3dup = _upsample_like(hx3d,hx2)
#
# hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
# hx2dup = _upsample_like(hx2d,hx1)
#
# hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
#
#
# #side output
# d1 = self.side1(hx1d)
#
# d2 = self.side2(hx2d)
# d2 = _upsample_like(d2,d1)
#
# d3 = self.side3(hx3d)
# d3 = _upsample_like(d3,d1)
#
# d4 = self.side4(hx4d)
# d4 = _upsample_like(d4,d1)
#
# d5 = self.side5(hx5d)
# d5 = _upsample_like(d5,d1)
#
# d6 = self.side6(hx6)
# d6 = _upsample_like(d6,d1)
#
# d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))
#
# return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)
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