colomi
0eb61f3e60
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。
106 lines
3.7 KiB
Python
106 lines
3.7 KiB
Python
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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from collections import OrderedDict
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import numpy as np
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import os
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class Flatten(nn.Module):
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def __init__(self):
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super(Flatten, self).__init__()
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def forward(self, x):
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x = x.transpose(3, 2).contiguous()
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return x.view(x.size(0), -1)
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class PNet(nn.Module):
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def __init__(self):
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super(PNet, self).__init__()
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self.model_path,_ = os.path.split(os.path.realpath(__file__))
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self.features = nn.Sequential(OrderedDict([
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('conv1', nn.Conv2d(3, 10, 3, 1)),
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('prelu1', nn.PReLU(10)),
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('pool1', nn.MaxPool2d(2, 2, ceil_mode=True)),
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('conv2', nn.Conv2d(10, 16, 3, 1)),
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('prelu2', nn.PReLU(16)),
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('conv3', nn.Conv2d(16, 32, 3, 1)),
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('prelu3', nn.PReLU(32))
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]))
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self.conv4_1 = nn.Conv2d(32, 2, 1, 1)
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self.conv4_2 = nn.Conv2d(32, 4, 1, 1)
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weights = np.load('./weights/pnet.npy', allow_pickle=True)[()]
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for n, p in self.named_parameters():
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p.data = torch.FloatTensor(weights[n])
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def forward(self, x):
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x = self.features(x)
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a = self.conv4_1(x)
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b = self.conv4_2(x)
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a = F.softmax(a, dim=1)
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return b, a
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class RNet(nn.Module):
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def __init__(self):
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super(RNet, self).__init__()
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self.model_path,_ = os.path.split(os.path.realpath(__file__))
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self.features = nn.Sequential(OrderedDict([
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('conv1', nn.Conv2d(3, 28, 3, 1)),
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('prelu1', nn.PReLU(28)),
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('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)),
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('conv2', nn.Conv2d(28, 48, 3, 1)),
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('prelu2', nn.PReLU(48)),
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('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)),
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('conv3', nn.Conv2d(48, 64, 2, 1)),
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('prelu3', nn.PReLU(64)),
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('flatten', Flatten()),
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('conv4', nn.Linear(576, 128)),
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('prelu4', nn.PReLU(128))
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]))
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self.conv5_1 = nn.Linear(128, 2)
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self.conv5_2 = nn.Linear(128, 4)
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weights = np.load('./weights/rnet.npy', allow_pickle=True)[()]
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for n, p in self.named_parameters():
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p.data = torch.FloatTensor(weights[n])
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def forward(self, x):
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x = self.features(x)
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a = self.conv5_1(x)
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b = self.conv5_2(x)
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a = F.softmax(a, dim=1)
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return b, a
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class ONet(nn.Module):
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def __init__(self):
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super(ONet, self).__init__()
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self.model_path,_ = os.path.split(os.path.realpath(__file__))
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self.features = nn.Sequential(OrderedDict([
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('conv1', nn.Conv2d(3, 32, 3, 1)),
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('prelu1', nn.PReLU(32)),
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('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)),
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('conv2', nn.Conv2d(32, 64, 3, 1)),
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('prelu2', nn.PReLU(64)),
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('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)),
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('conv3', nn.Conv2d(64, 64, 3, 1)),
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('prelu3', nn.PReLU(64)),
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('pool3', nn.MaxPool2d(2, 2, ceil_mode=True)),
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('conv4', nn.Conv2d(64, 128, 2, 1)),
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('prelu4', nn.PReLU(128)),
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('flatten', Flatten()),
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('conv5', nn.Linear(1152, 256)),
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('drop5', nn.Dropout(0.25)),
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('prelu5', nn.PReLU(256)),
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]))
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self.conv6_1 = nn.Linear(256, 2)
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self.conv6_2 = nn.Linear(256, 4)
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self.conv6_3 = nn.Linear(256, 10)
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weights = np.load('./weights/onet.npy', allow_pickle=True)[()]
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for n, p in self.named_parameters():
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p.data = torch.FloatTensor(weights[n])
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def forward(self, x):
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x = self.features(x)
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a = self.conv6_1(x)
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b = self.conv6_2(x)
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c = self.conv6_3(x)
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a = F.softmax(a, dim=1)
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return c, b, a
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