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
76 lines
2.0 KiB
Python
76 lines
2.0 KiB
Python
import numpy as np
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import torch
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from torch import nn
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def get_norm(norm, out_channels=None):
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"""
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Args:
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norm (str or callable):
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Returns:
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nn.Module or None: the normalization layer
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"""
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if isinstance(norm, str):
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if len(norm) == 0:
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return None
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norm = {
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"BN": nn.BatchNorm2d,
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"IN": nn.InstanceNorm2d,
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"GN": lambda channels: nn.GroupNorm(32, channels),
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"nnSyncBN": nn.SyncBatchNorm, # keep for debugging
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}[norm]
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if out_channels is not None:
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return norm(out_channels)
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else:
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return norm
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class Conv2d(torch.nn.Conv2d):
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"""
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A wrapper around :class:`torch.nn.Conv2d` to support zero-size tensor and more features.
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"""
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def __init__(self, *args, **kwargs):
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"""
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Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`:
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Args:
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norm (nn.Module, optional): a normalization layer
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activation (callable(Tensor) -> Tensor): a callable activation function
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It assumes that norm layer is used before activation.
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"""
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norm = kwargs.pop("norm", None)
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activation = kwargs.pop("activation", None)
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super().__init__(*args, **kwargs)
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self.norm = norm
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self.activation = activation
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def forward(self, x):
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x = super().forward(x)
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if self.norm is not None:
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x = self.norm(x)
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if self.activation is not None:
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x = self.activation(x)
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return x
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class Backbone(nn.Module):
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"""
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Abstract base class for network backbones.
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"""
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def __init__(self):
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"""
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The `__init__` method of any subclass can specify its own set of arguments.
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"""
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super().__init__()
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def forward(self):
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"""
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Subclasses must override this method, but adhere to the same return type.
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Returns:
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dict[str: Tensor]: mapping from feature name (e.g., "res2") to tensor
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"""
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pass
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