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。
61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
"""Base Model for Semantic Segmentation"""
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import torch.nn as nn
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from seg.networks.jpu import JPU
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from seg.networks.resnetv1b import resnet50_v1s, resnet101_v1s, resnet152_v1s
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__all__ = ['SegBaseModel']
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class SegBaseModel(nn.Module):
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r"""Base Model for Semantic Segmentation
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Parameters
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----------
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backbone : string
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Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
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'resnet101' or 'resnet152').
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"""
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def __init__(self, nclass, aux, backbone='resnet50', jpu=False, pretrained_base=True, **kwargs):
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super(SegBaseModel, self).__init__()
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dilated = False if jpu else True
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self.aux = aux
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self.nclass = nclass
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if backbone == 'resnet50':
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self.pretrained = resnet50_v1s(pretrained=pretrained_base, dilated=dilated, **kwargs)
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elif backbone == 'resnet101':
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self.pretrained = resnet101_v1s(pretrained=pretrained_base, dilated=dilated, **kwargs)
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elif backbone == 'resnet152':
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self.pretrained = resnet152_v1s(pretrained=pretrained_base, dilated=dilated, **kwargs)
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else:
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raise RuntimeError('unknown backbone: {}'.format(backbone))
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self.jpu = JPU([512, 1024, 2048], width=512, **kwargs) if jpu else None
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def base_forward(self, x):
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"""forwarding pre-trained network"""
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x = self.pretrained.conv1(x)
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x = self.pretrained.bn1(x)
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x = self.pretrained.relu(x)
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x = self.pretrained.maxpool(x)
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c1 = self.pretrained.layer1(x)
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c2 = self.pretrained.layer2(c1)
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c3 = self.pretrained.layer3(c2)
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c4 = self.pretrained.layer4(c3)
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if self.jpu:
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return self.jpu(c1, c2, c3, c4)
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else:
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return c1, c2, c3, c4
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def evaluate(self, x):
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"""evaluating network with inputs and targets"""
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return self.forward(x)[0]
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def demo(self, x):
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pred = self.forward(x)
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if self.aux:
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pred = pred[0]
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return pred
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