xsl
443cfa298f
包含： - hair_service_sd: 主服务（换发型/换发色/生发，端口8801） - photo_service: LoRA调度+训练（端口32678） - hair_grow_service: 调试测试页（端口8888，含4个测试页） - 批量训练脚本（batch_train_hairstyles.py） - 发际线mask自动识别（hairline_mask.py，4种方案） - 手绘mask换发型（hair_swap_manual.py） - 文档：README.md + LARGE_FILES.md + docs/ 大文件（模型权重200G、训练数据123G）已排除，见 LARGE_FILES.md OSS/COS密钥已脱敏为环境变量，原文件备份在本地
61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
"""Base Model for Semantic Segmentation"""
|
|
import torch.nn as nn
|
|
|
|
from seg.networks.jpu import JPU
|
|
from seg.networks.resnetv1b import resnet50_v1s, resnet101_v1s, resnet152_v1s
|
|
|
|
__all__ = ['SegBaseModel']
|
|
|
|
|
|
class SegBaseModel(nn.Module):
|
|
r"""Base Model for Semantic Segmentation
|
|
|
|
Parameters
|
|
----------
|
|
backbone : string
|
|
Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
|
|
'resnet101' or 'resnet152').
|
|
"""
|
|
|
|
def __init__(self, nclass, aux, backbone='resnet50', jpu=False, pretrained_base=True, **kwargs):
|
|
super(SegBaseModel, self).__init__()
|
|
dilated = False if jpu else True
|
|
self.aux = aux
|
|
self.nclass = nclass
|
|
if backbone == 'resnet50':
|
|
self.pretrained = resnet50_v1s(pretrained=pretrained_base, dilated=dilated, **kwargs)
|
|
elif backbone == 'resnet101':
|
|
self.pretrained = resnet101_v1s(pretrained=pretrained_base, dilated=dilated, **kwargs)
|
|
elif backbone == 'resnet152':
|
|
self.pretrained = resnet152_v1s(pretrained=pretrained_base, dilated=dilated, **kwargs)
|
|
else:
|
|
raise RuntimeError('unknown backbone: {}'.format(backbone))
|
|
|
|
self.jpu = JPU([512, 1024, 2048], width=512, **kwargs) if jpu else None
|
|
|
|
def base_forward(self, x):
|
|
"""forwarding pre-trained network"""
|
|
x = self.pretrained.conv1(x)
|
|
x = self.pretrained.bn1(x)
|
|
x = self.pretrained.relu(x)
|
|
x = self.pretrained.maxpool(x)
|
|
c1 = self.pretrained.layer1(x)
|
|
c2 = self.pretrained.layer2(c1)
|
|
c3 = self.pretrained.layer3(c2)
|
|
c4 = self.pretrained.layer4(c3)
|
|
|
|
if self.jpu:
|
|
return self.jpu(c1, c2, c3, c4)
|
|
else:
|
|
return c1, c2, c3, c4
|
|
|
|
def evaluate(self, x):
|
|
"""evaluating network with inputs and targets"""
|
|
return self.forward(x)[0]
|
|
|
|
def demo(self, x):
|
|
pred = self.forward(x)
|
|
if self.aux:
|
|
pred = pred[0]
|
|
return pred
|