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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from .resnet_dec import ResNet_D_Dec, BasicBlock
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from .res_shortcut_dec import ResShortCut_D_Dec
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from .res_gca_dec import ResGuidedCxtAtten_Dec
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__all__ = ['res_shortcut_decoder_22', 'res_gca_decoder_22']
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def _res_shortcut_D_dec(block, layers, **kwargs):
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model = ResShortCut_D_Dec(block, layers, **kwargs)
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return model
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def _res_gca_D_dec(block, layers, num_class, **kwargs):
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model = ResGuidedCxtAtten_Dec(block, layers, num_class, **kwargs)
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return model
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def res_shortcut_decoder_22(**kwargs):
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"""Constructs a resnet_encoder_14 model.
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"""
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return _res_shortcut_D_dec(BasicBlock, [2, 3, 3, 2], **kwargs)
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def res_gca_decoder_22(num_class=1, **kwargs):
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"""Constructs a resnet_encoder_14 model.
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"""
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return _res_gca_D_dec(BasicBlock, [2, 3, 3, 2], num_class, **kwargs)
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from matting.networks.ops import GuidedCxtAtten, SpectralNorm
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from matting.networks.decoders.res_shortcut_dec import ResShortCut_D_Dec
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class ResGuidedCxtAtten_Dec(ResShortCut_D_Dec):
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def __init__(self, block, layers, num_class=1, norm_layer=None, large_kernel=False):
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super(ResGuidedCxtAtten_Dec, self).__init__(block, layers, num_class, norm_layer, large_kernel)
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self.gca = GuidedCxtAtten(128, 128)
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self.num_class = num_class
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def forward(self, x, mid_fea):
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fea1, fea2, fea3, fea4, fea5 = mid_fea['shortcut']
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im = mid_fea['image_fea']
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x = self.layer1(x) + fea5 # N x 256 x 32 x 32
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x = self.layer2(x) + fea4 # N x 128 x 64 x 64
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x, offset = self.gca(im, x, mid_fea['unknown']) # contextual attention
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x = self.layer3(x) + fea3 # N x 64 x 128 x 128
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x = self.layer4(x) + fea2 # N x 32 x 256 x 256
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.leaky_relu(x) + fea1
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x = self.conv2(x)
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alpha = (self.tanh(x) + 1.0) / 2.0
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return alpha, {'offset_1': mid_fea['offset_1'], 'offset_2': offset}
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from matting.networks.decoders.resnet_dec import ResNet_D_Dec
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class ResShortCut_D_Dec(ResNet_D_Dec):
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def __init__(self, block, layers, num_class=1, norm_layer=None, large_kernel=False, late_downsample=False):
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super(ResShortCut_D_Dec, self).__init__(block, layers, num_class, norm_layer, large_kernel,
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late_downsample=late_downsample)
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def forward(self, x, mid_fea):
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fea1, fea2, fea3, fea4, fea5 = mid_fea['shortcut']
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x = self.layer1(x) + fea5
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x = self.layer2(x) + fea4
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x = self.layer3(x) + fea3
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x = self.layer4(x) + fea2
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.leaky_relu(x) + fea1
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x = self.conv2(x)
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alpha = (self.tanh(x) + 1.0) / 2.0
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return alpha, None
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import logging
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import torch.nn as nn
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from matting.networks.ops import SpectralNorm
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def conv5x5(in_planes, out_planes, stride=1, groups=1, dilation=1):
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"""5x5 convolution with padding"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=5, stride=stride,
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padding=2, groups=groups, bias=False, dilation=dilation)
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def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
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"""3x3 convolution with padding"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=dilation, groups=groups, bias=False, dilation=dilation)
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def conv1x1(in_planes, out_planes, stride=1):
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"""1x1 convolution"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, upsample=None, norm_layer=None, large_kernel=False):
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super(BasicBlock, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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self.stride = stride
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conv = conv5x5 if large_kernel else conv3x3
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# Both self.conv1 and self.downsample layers downsample the input when stride != 1
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if self.stride > 1:
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self.conv1 = SpectralNorm(nn.ConvTranspose2d(inplanes, inplanes, kernel_size=4, stride=2, padding=1, bias=False))
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else:
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self.conv1 = SpectralNorm(conv(inplanes, inplanes))
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self.bn1 = norm_layer(inplanes)
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self.activation = nn.LeakyReLU(0.2, inplace=True)
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self.conv2 = SpectralNorm(conv(inplanes, planes))
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self.bn2 = norm_layer(planes)
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self.upsample = upsample
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def forward(self, x):
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.activation(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.upsample is not None:
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identity = self.upsample(x)
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out += identity
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out = self.activation(out)
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return out
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class ResNet_D_Dec(nn.Module):
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def __init__(self, block, layers, num_class=1, norm_layer=None, large_kernel=False, late_downsample=False):
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super(ResNet_D_Dec, self).__init__()
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self.logger = logging.getLogger("Logger")
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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self._norm_layer = norm_layer
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self.large_kernel = large_kernel
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self.kernel_size = 5 if self.large_kernel else 3
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self.inplanes = 512 if layers[0] > 0 else 256
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self.late_downsample = late_downsample
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self.midplanes = 64 if late_downsample else 32
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self.conv1 = SpectralNorm(nn.ConvTranspose2d(self.midplanes, 32, kernel_size=4, stride=2, padding=1, bias=False))
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self.bn1 = norm_layer(32)
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self.leaky_relu = nn.LeakyReLU(0.2, inplace=True)
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self.conv2 = nn.Conv2d(32, num_class, kernel_size=self.kernel_size, stride=1, padding=self.kernel_size//2)
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self.upsample = nn.UpsamplingNearest2d(scale_factor=2)
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self.tanh = nn.Tanh()
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self.layer1 = self._make_layer(block, 256, layers[0], stride=2)
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 64, layers[2], stride=2)
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self.layer4 = self._make_layer(block, self.midplanes, layers[3], stride=2)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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if hasattr(m, "weight_bar"):
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nn.init.xavier_uniform_(m.weight_bar)
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else:
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nn.init.xavier_uniform_(m.weight)
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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# Zero-initialize the last BN in each residual branch,
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# so that the residual branch starts with zeros, and each residual block behaves like an identity.
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# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
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for m in self.modules():
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if isinstance(m, BasicBlock):
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nn.init.constant_(m.bn2.weight, 0)
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self.logger.debug(self)
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def _make_layer(self, block, planes, blocks, stride=1):
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if blocks == 0:
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return nn.Sequential(nn.Identity())
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norm_layer = self._norm_layer
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upsample = None
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if stride != 1:
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upsample = nn.Sequential(
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nn.UpsamplingNearest2d(scale_factor=2),
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SpectralNorm(conv1x1(self.inplanes, planes * block.expansion)),
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norm_layer(planes * block.expansion),
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)
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elif self.inplanes != planes * block.expansion:
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upsample = nn.Sequential(
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SpectralNorm(conv1x1(self.inplanes, planes * block.expansion)),
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norm_layer(planes * block.expansion),
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)
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layers = [block(self.inplanes, planes, stride, upsample, norm_layer, self.large_kernel)]
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes, norm_layer=norm_layer, large_kernel=self.large_kernel))
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return nn.Sequential(*layers)
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def forward(self, x, mid_fea):
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x = self.layer1(x) # N x 256 x 32 x 32
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x = self.layer2(x) # N x 128 x 64 x 64
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x = self.layer3(x) # N x 64 x 128 x 128
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x = self.layer4(x) # N x 32 x 256 x 256
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.leaky_relu(x)
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x = self.conv2(x)
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alpha = (self.tanh(x) + 1.0) / 2.0
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return alpha, None
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