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。
This commit is contained in:
colomi
@@ -0,0 +1,264 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.utils.model_zoo as model_zoo
|
||||
|
||||
__all__ = ['ResNetV1b', 'resnet18_v1b', 'resnet34_v1b', 'resnet50_v1b',
|
||||
'resnet101_v1b', 'resnet152_v1b', 'resnet152_v1s', 'resnet101_v1s', 'resnet50_v1s']
|
||||
|
||||
model_urls = {
|
||||
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
|
||||
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
|
||||
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
|
||||
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
|
||||
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
|
||||
}
|
||||
|
||||
|
||||
class BasicBlockV1b(nn.Module):
|
||||
expansion = 1
|
||||
|
||||
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
|
||||
previous_dilation=1, norm_layer=nn.BatchNorm2d):
|
||||
super(BasicBlockV1b, self).__init__()
|
||||
self.conv1 = nn.Conv2d(inplanes, planes, 3, stride,
|
||||
dilation, dilation, bias=False)
|
||||
self.bn1 = norm_layer(planes)
|
||||
self.relu = nn.ReLU(True)
|
||||
self.conv2 = nn.Conv2d(planes, planes, 3, 1, previous_dilation,
|
||||
dilation=previous_dilation, bias=False)
|
||||
self.bn2 = norm_layer(planes)
|
||||
self.downsample = downsample
|
||||
self.stride = stride
|
||||
|
||||
def forward(self, x):
|
||||
identity = x
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
|
||||
if self.downsample is not None:
|
||||
identity = self.downsample(x)
|
||||
|
||||
out += identity
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class BottleneckV1b(nn.Module):
|
||||
expansion = 4
|
||||
|
||||
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
|
||||
previous_dilation=1, norm_layer=nn.BatchNorm2d):
|
||||
super(BottleneckV1b, self).__init__()
|
||||
self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
|
||||
self.bn1 = norm_layer(planes)
|
||||
self.conv2 = nn.Conv2d(planes, planes, 3, stride,
|
||||
dilation, dilation, bias=False)
|
||||
self.bn2 = norm_layer(planes)
|
||||
self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
|
||||
self.bn3 = norm_layer(planes * self.expansion)
|
||||
self.relu = nn.ReLU(True)
|
||||
self.downsample = downsample
|
||||
self.stride = stride
|
||||
|
||||
def forward(self, x):
|
||||
identity = x
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv3(out)
|
||||
out = self.bn3(out)
|
||||
|
||||
if self.downsample is not None:
|
||||
identity = self.downsample(x)
|
||||
|
||||
out += identity
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class ResNetV1b(nn.Module):
|
||||
|
||||
def __init__(self, block, layers, num_classes=1000, dilated=True, deep_stem=False,
|
||||
zero_init_residual=False, norm_layer=nn.BatchNorm2d):
|
||||
self.inplanes = 128 if deep_stem else 64
|
||||
super(ResNetV1b, self).__init__()
|
||||
if deep_stem:
|
||||
self.conv1 = nn.Sequential(
|
||||
nn.Conv2d(3, 64, 3, 2, 1, bias=False),
|
||||
norm_layer(64),
|
||||
nn.ReLU(True),
|
||||
nn.Conv2d(64, 64, 3, 1, 1, bias=False),
|
||||
norm_layer(64),
|
||||
nn.ReLU(True),
|
||||
nn.Conv2d(64, 128, 3, 1, 1, bias=False)
|
||||
)
|
||||
else:
|
||||
self.conv1 = nn.Conv2d(3, 64, 7, 2, 3, bias=False)
|
||||
self.bn1 = norm_layer(self.inplanes)
|
||||
self.relu = nn.ReLU(True)
|
||||
self.maxpool = nn.MaxPool2d(3, 2, 1)
|
||||
self.layer1 = self._make_layer(block, 64, layers[0], norm_layer=norm_layer)
|
||||
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
|
||||
if dilated:
|
||||
self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2, norm_layer=norm_layer)
|
||||
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, norm_layer=norm_layer)
|
||||
else:
|
||||
self.layer3 = self._make_layer(block, 256, layers[2], stride=2, norm_layer=norm_layer)
|
||||
self.layer4 = self._make_layer(block, 512, layers[3], stride=2, norm_layer=norm_layer)
|
||||
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
||||
self.fc = nn.Linear(512 * block.expansion, num_classes)
|
||||
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
nn.init.constant_(m.weight, 1)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
if zero_init_residual:
|
||||
for m in self.modules():
|
||||
if isinstance(m, BottleneckV1b):
|
||||
nn.init.constant_(m.bn3.weight, 0)
|
||||
elif isinstance(m, BasicBlockV1b):
|
||||
nn.init.constant_(m.bn2.weight, 0)
|
||||
|
||||
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=nn.BatchNorm2d):
|
||||
downsample = None
|
||||
if stride != 1 or self.inplanes != planes * block.expansion:
|
||||
downsample = nn.Sequential(
|
||||
nn.Conv2d(self.inplanes, planes * block.expansion, 1, stride, bias=False),
|
||||
norm_layer(planes * block.expansion),
|
||||
)
|
||||
|
||||
layers = []
|
||||
if dilation in (1, 2):
|
||||
layers.append(block(self.inplanes, planes, stride, dilation=1, downsample=downsample,
|
||||
previous_dilation=dilation, norm_layer=norm_layer))
|
||||
elif dilation == 4:
|
||||
layers.append(block(self.inplanes, planes, stride, dilation=2, downsample=downsample,
|
||||
previous_dilation=dilation, norm_layer=norm_layer))
|
||||
else:
|
||||
raise RuntimeError("=> unknown dilation size: {}".format(dilation))
|
||||
self.inplanes = planes * block.expansion
|
||||
for _ in range(1, blocks):
|
||||
layers.append(block(self.inplanes, planes, dilation=dilation,
|
||||
previous_dilation=dilation, norm_layer=norm_layer))
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
x = self.maxpool(x)
|
||||
|
||||
x = self.layer1(x)
|
||||
x = self.layer2(x)
|
||||
x = self.layer3(x)
|
||||
x = self.layer4(x)
|
||||
|
||||
x = self.avgpool(x)
|
||||
x = x.view(x.size(0), -1)
|
||||
x = self.fc(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def resnet18_v1b(pretrained=False, **kwargs):
|
||||
model = ResNetV1b(BasicBlockV1b, [2, 2, 2, 2], **kwargs)
|
||||
if pretrained:
|
||||
old_dict = model_zoo.load_url(model_urls['resnet18'])
|
||||
model_dict = model.state_dict()
|
||||
old_dict = {k: v for k, v in old_dict.items() if (k in model_dict)}
|
||||
model_dict.update(old_dict)
|
||||
model.load_state_dict(model_dict)
|
||||
return model
|
||||
|
||||
|
||||
def resnet34_v1b(pretrained=False, **kwargs):
|
||||
model = ResNetV1b(BasicBlockV1b, [3, 4, 6, 3], **kwargs)
|
||||
if pretrained:
|
||||
old_dict = model_zoo.load_url(model_urls['resnet34'])
|
||||
model_dict = model.state_dict()
|
||||
old_dict = {k: v for k, v in old_dict.items() if (k in model_dict)}
|
||||
model_dict.update(old_dict)
|
||||
model.load_state_dict(model_dict)
|
||||
return model
|
||||
|
||||
|
||||
def resnet50_v1b(pretrained=False, **kwargs):
|
||||
model = ResNetV1b(BottleneckV1b, [3, 4, 6, 3], **kwargs)
|
||||
if pretrained:
|
||||
old_dict = model_zoo.load_url(model_urls['resnet50'])
|
||||
model_dict = model.state_dict()
|
||||
old_dict = {k: v for k, v in old_dict.items() if (k in model_dict)}
|
||||
model_dict.update(old_dict)
|
||||
model.load_state_dict(model_dict)
|
||||
return model
|
||||
|
||||
|
||||
def resnet101_v1b(pretrained=False, **kwargs):
|
||||
model = ResNetV1b(BottleneckV1b, [3, 4, 23, 3], **kwargs)
|
||||
if pretrained:
|
||||
old_dict = model_zoo.load_url(model_urls['resnet101'])
|
||||
model_dict = model.state_dict()
|
||||
old_dict = {k: v for k, v in old_dict.items() if (k in model_dict)}
|
||||
model_dict.update(old_dict)
|
||||
model.load_state_dict(model_dict)
|
||||
return model
|
||||
|
||||
|
||||
def resnet152_v1b(pretrained=False, **kwargs):
|
||||
model = ResNetV1b(BottleneckV1b, [3, 8, 36, 3], **kwargs)
|
||||
if pretrained:
|
||||
old_dict = model_zoo.load_url(model_urls['resnet152'])
|
||||
model_dict = model.state_dict()
|
||||
old_dict = {k: v for k, v in old_dict.items() if (k in model_dict)}
|
||||
model_dict.update(old_dict)
|
||||
model.load_state_dict(model_dict)
|
||||
return model
|
||||
|
||||
|
||||
def resnet50_v1s(pretrained=False, root='~/.torch/models', **kwargs):
|
||||
model = ResNetV1b(BottleneckV1b, [3, 4, 6, 3], deep_stem=True, **kwargs)
|
||||
if pretrained:
|
||||
from ..model_store import get_resnet_file
|
||||
model.load_state_dict(torch.load(get_resnet_file('resnet50', root=root)), strict=False)
|
||||
return model
|
||||
|
||||
|
||||
def resnet101_v1s(pretrained=False, root='~/.torch/models', **kwargs):
|
||||
model = ResNetV1b(BottleneckV1b, [3, 4, 23, 3], deep_stem=True, **kwargs)
|
||||
if pretrained:
|
||||
from ..model_store import get_resnet_file
|
||||
model.load_state_dict(torch.load(get_resnet_file('resnet101', root=root)), strict=False)
|
||||
return model
|
||||
|
||||
|
||||
def resnet152_v1s(pretrained=False, root='~/.torch/models', **kwargs):
|
||||
model = ResNetV1b(BottleneckV1b, [3, 8, 36, 3], deep_stem=True, **kwargs)
|
||||
if pretrained:
|
||||
from ..model_store import get_resnet_file
|
||||
model.load_state_dict(torch.load(get_resnet_file('resnet152', root=root)), strict=False)
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import torch
|
||||
|
||||
img = torch.randn(4, 3, 224, 224)
|
||||
model = resnet50_v1b(True)
|
||||
output = model(img)
|
||||
Reference in New Issue
Block a user