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。
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colomi
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.model_zoo as model_zoo
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from bodyseg.backbone.backbone import Backbone
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def fixed_padding(inputs, kernel_size, dilation):
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kernel_size_effective = kernel_size + (kernel_size - 1) * (dilation - 1)
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pad_total = kernel_size_effective - 1
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pad_beg = pad_total // 2
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pad_end = pad_total - pad_beg
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padded_inputs = F.pad(inputs, (pad_beg, pad_end, pad_beg, pad_end))
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return padded_inputs
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class SeparableConv2d(nn.Module):
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def __init__(self, inplanes, planes, kernel_size=3, stride=1, dilation=1, bias=False, BatchNorm=None):
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super(SeparableConv2d, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, inplanes, kernel_size, stride, 0, dilation,
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groups=inplanes, bias=bias)
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self.bn = BatchNorm(inplanes)
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self.pointwise = nn.Conv2d(inplanes, planes, 1, 1, 0, 1, 1, bias=bias)
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def forward(self, x):
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x = fixed_padding(x, self.conv1.kernel_size[0], dilation=self.conv1.dilation[0])
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x = self.conv1(x)
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x = self.bn(x)
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x = self.pointwise(x)
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return x
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class Block(nn.Module):
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def __init__(self, inplanes, planes, reps, stride=1, dilation=1, BatchNorm=None,
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start_with_relu=True, grow_first=True, is_last=False):
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super(Block, self).__init__()
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if planes != inplanes or stride != 1:
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self.skip = nn.Conv2d(inplanes, planes, 1, stride=stride, bias=False)
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self.skipbn = BatchNorm(planes)
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else:
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self.skip = None
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self.relu = nn.ReLU(inplace=True)
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rep = []
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filters = inplanes
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if grow_first:
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rep.append(self.relu)
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rep.append(SeparableConv2d(inplanes, planes, 3, 1, dilation, BatchNorm=BatchNorm))
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rep.append(BatchNorm(planes))
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filters = planes
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for i in range(reps - 1):
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rep.append(self.relu)
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rep.append(SeparableConv2d(filters, filters, 3, 1, dilation, BatchNorm=BatchNorm))
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rep.append(BatchNorm(filters))
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if not grow_first:
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rep.append(self.relu)
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rep.append(SeparableConv2d(inplanes, planes, 3, 1, dilation, BatchNorm=BatchNorm))
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rep.append(BatchNorm(planes))
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if stride != 1:
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rep.append(self.relu)
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rep.append(SeparableConv2d(planes, planes, 3, 2, BatchNorm=BatchNorm))
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rep.append(BatchNorm(planes))
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if stride == 1 and is_last:
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rep.append(self.relu)
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rep.append(SeparableConv2d(planes, planes, 3, 1, BatchNorm=BatchNorm))
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rep.append(BatchNorm(planes))
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if not start_with_relu:
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rep = rep[1:]
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self.rep = nn.Sequential(*rep)
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def forward(self, inp):
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x = self.rep(inp)
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if self.skip is not None:
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skip = self.skip(inp)
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skip = self.skipbn(skip)
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else:
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skip = inp
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x = x + skip
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return x
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class AlignedXception(Backbone):
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"""
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Modified Alighed Xception
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"""
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def __init__(self, output_stride, BatchNorm):
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super(AlignedXception, self).__init__()
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if output_stride == 16:
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entry_block3_stride = 2
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middle_block_dilation = 1
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exit_block_dilations = (1, 2)
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elif output_stride == 8:
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entry_block3_stride = 1
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middle_block_dilation = 2
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exit_block_dilations = (2, 4)
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else:
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raise NotImplementedError
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# Entry flow
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self.conv1 = nn.Conv2d(3, 32, 3, stride=2, padding=1, bias=False)
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self.bn1 = BatchNorm(32)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1, bias=False)
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self.bn2 = BatchNorm(64)
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self.block1 = Block(64, 128, reps=2, stride=2, BatchNorm=BatchNorm, start_with_relu=False)
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self.block2 = Block(128, 256, reps=2, stride=2, BatchNorm=BatchNorm, start_with_relu=False,
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grow_first=True)
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self.block3 = Block(256, 728, reps=2, stride=entry_block3_stride, BatchNorm=BatchNorm,
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start_with_relu=True, grow_first=True, is_last=True)
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# Middle flow
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self.block4 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block5 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block6 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block7 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block8 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block9 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block10 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block11 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block12 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block13 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block14 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block15 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block16 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block17 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block18 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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self.block19 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
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# Exit flow
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self.block20 = Block(728, 1024, reps=2, stride=1, dilation=exit_block_dilations[0],
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BatchNorm=BatchNorm, start_with_relu=True, grow_first=False, is_last=True)
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self.conv3 = SeparableConv2d(1024, 1536, 3, stride=1, dilation=exit_block_dilations[1], BatchNorm=BatchNorm)
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self.bn3 = BatchNorm(1536)
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self.conv4 = SeparableConv2d(1536, 1536, 3, stride=1, dilation=exit_block_dilations[1], BatchNorm=BatchNorm)
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self.bn4 = BatchNorm(1536)
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self.conv5 = SeparableConv2d(1536, 2048, 3, stride=1, dilation=exit_block_dilations[1], BatchNorm=BatchNorm)
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self.bn5 = BatchNorm(2048)
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# Init weights
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self._init_weight()
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def forward(self, x):
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# Entry flow
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.conv2(x)
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x = self.bn2(x)
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x = self.relu(x)
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x = self.block1(x)
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# add relu here
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x = self.relu(x)
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low_level_feat = x
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x = self.block2(x)
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x = self.block3(x)
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# Middle flow
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x = self.block4(x)
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x = self.block5(x)
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x = self.block6(x)
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x = self.block7(x)
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x = self.block8(x)
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x = self.block9(x)
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x = self.block10(x)
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x = self.block11(x)
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x = self.block12(x)
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x = self.block13(x)
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x = self.block14(x)
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x = self.block15(x)
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x = self.block16(x)
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x = self.block17(x)
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x = self.block18(x)
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x = self.block19(x)
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# Exit flow
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x = self.block20(x)
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x = self.relu(x)
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x = self.conv3(x)
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x = self.bn3(x)
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x = self.relu(x)
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x = self.conv4(x)
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x = self.bn4(x)
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x = self.relu(x)
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x = self.conv5(x)
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x = self.bn5(x)
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x = self.relu(x)
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return x, low_level_feat
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def _init_weight(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, nn.SyncBatchNorm):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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if __name__ == "__main__":
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import torch
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model = AlignedXception(BatchNorm=nn.BatchNorm2d, output_stride=16)
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input = torch.rand(1, 3, 512, 512)
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output, low_level_feat = model(input)
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print(output.size())
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print(low_level_feat.size())
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