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。
69 lines
2.7 KiB
Python
69 lines
2.7 KiB
Python
"""Joint Pyramid Upsampling"""
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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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__all__ = ['JPU']
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class SeparableConv2d(nn.Module):
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def __init__(self, inplanes, planes, kernel_size=3, stride=1, padding=1,
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dilation=1, bias=False, norm_layer=nn.BatchNorm2d):
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super(SeparableConv2d, self).__init__()
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self.conv = nn.Conv2d(inplanes, inplanes, kernel_size, stride, padding, dilation, groups=inplanes, bias=bias)
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self.bn = norm_layer(inplanes)
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self.pointwise = nn.Conv2d(inplanes, planes, 1, bias=bias)
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def forward(self, x):
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x = self.conv(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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# copy from: https://github.com/wuhuikai/FastFCN/blob/master/encoding/nn/customize.py
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class JPU(nn.Module):
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def __init__(self, in_channels, width=512, norm_layer=nn.BatchNorm2d, **kwargs):
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super(JPU, self).__init__()
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self.conv5 = nn.Sequential(
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nn.Conv2d(in_channels[-1], width, 3, padding=1, bias=False),
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norm_layer(width),
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nn.ReLU(True))
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self.conv4 = nn.Sequential(
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nn.Conv2d(in_channels[-2], width, 3, padding=1, bias=False),
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norm_layer(width),
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nn.ReLU(True))
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self.conv3 = nn.Sequential(
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nn.Conv2d(in_channels[-3], width, 3, padding=1, bias=False),
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norm_layer(width),
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nn.ReLU(True))
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self.dilation1 = nn.Sequential(
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SeparableConv2d(3 * width, width, 3, padding=1, dilation=1, bias=False),
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norm_layer(width),
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nn.ReLU(True))
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self.dilation2 = nn.Sequential(
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SeparableConv2d(3 * width, width, 3, padding=2, dilation=2, bias=False),
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norm_layer(width),
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nn.ReLU(True))
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self.dilation3 = nn.Sequential(
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SeparableConv2d(3 * width, width, 3, padding=4, dilation=4, bias=False),
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norm_layer(width),
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nn.ReLU(True))
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self.dilation4 = nn.Sequential(
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SeparableConv2d(3 * width, width, 3, padding=8, dilation=8, bias=False),
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norm_layer(width),
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nn.ReLU(True))
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def forward(self, *inputs):
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feats = [self.conv5(inputs[-1]), self.conv4(inputs[-2]), self.conv3(inputs[-3])]
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size = feats[-1].size()[2:]
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feats[-2] = F.interpolate(feats[-2], size, mode='bilinear', align_corners=True)
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feats[-3] = F.interpolate(feats[-3], size, mode='bilinear', align_corners=True)
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feat = torch.cat(feats, dim=1)
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feat = torch.cat([self.dilation1(feat), self.dilation2(feat), self.dilation3(feat), self.dilation4(feat)],
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dim=1)
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return inputs[0], inputs[1], inputs[2], feat
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