{"id":51764,"date":"2025-08-11T06:34:52","date_gmt":"2025-08-10T22:34:52","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/51764.html"},"modified":"2025-08-11T06:34:52","modified_gmt":"2025-08-10T22:34:52","slug":"%e3%80%90%e5%ae%8c%e6%95%b4%e6%ba%90%e7%a0%81%e6%95%b0%e6%8d%ae%e9%9b%86%e9%83%a8%e7%bd%b2%e6%95%99%e7%a8%8b%e3%80%91%e7%94%b5%e6%b0%94%e8%ae%be%e5%a4%87%e9%83%a8%e4%bb%b6%e8%af%86%e5%88%ab%e7%b3%bb","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/51764.html","title":{"rendered":"\u3010\u5b8c\u6574\u6e90\u7801+\u6570\u636e\u96c6+\u90e8\u7f72\u6559\u7a0b\u3011\u7535\u6c14\u8bbe\u5907\u90e8\u4ef6\u8bc6\u522b\u7cfb\u7edf\u6e90\u7801\u548c\u6570\u636e\u96c6\uff1a\u6539\u8fdbyolo11-RevCol"},"content":{"rendered":"

\u80cc\u666f\u610f\u4e49<\/h2>\n

\u7814\u7a76\u80cc\u666f\u4e0e\u610f\u4e49<\/p>\n

\u5728\u73b0\u4ee3\u7535\u6c14\u8bbe\u5907\u7684\u8bbe\u8ba1\u4e0e\u7ef4\u62a4\u4e2d，\u90e8\u4ef6\u7684\u51c6\u786e\u8bc6\u522b\u4e0e\u5206\u7c7b\u662f\u786e\u4fdd\u7cfb\u7edf\u5b89\u5168\u6027\u548c\u53ef\u9760\u6027\u7684\u5173\u952e\u73af\u8282\u3002\u968f\u7740\u7535\u6c14\u8bbe\u5907\u7684\u590d\u6742\u6027\u4e0d\u65ad\u589e\u52a0，\u4f20\u7edf\u7684\u4eba\u5de5\u8bc6\u522b\u65b9\u6cd5\u5df2\u96be\u4ee5\u6ee1\u8db3\u9ad8\u6548\u6027\u548c\u51c6\u786e\u6027\u7684\u8981\u6c42\u3002\u56e0\u6b64，\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u6280\u672f\u9010\u6e10\u6210\u4e3a\u89e3\u51b3\u8fd9\u4e00\u95ee\u9898\u7684\u91cd\u8981\u624b\u6bb5\u3002YOLO（You Only Look Once）\u7cfb\u5217\u6a21\u578b\u56e0\u5176\u5b9e\u65f6\u6027\u548c\u9ad8\u7cbe\u5ea6\u7684\u7279\u70b9，\u5df2\u5e7f\u6cdb\u5e94\u7528\u4e8e\u76ee\u6807\u68c0\u6d4b\u548c\u8bc6\u522b\u4efb\u52a1\u4e2d\u3002\u5c24\u5176\u662fYOLOv11\u7684\u6539\u8fdb\u7248\u672c，\u51ed\u501f\u5176\u5728\u7279\u5f81\u63d0\u53d6\u548c\u5904\u7406\u901f\u5ea6\u4e0a\u7684\u4f18\u52bf，\u4e3a\u7535\u6c14\u8bbe\u5907\u90e8\u4ef6\u7684\u81ea\u52a8\u8bc6\u522b\u63d0\u4f9b\u4e86\u65b0\u7684\u53ef\u80fd\u6027\u3002<\/p>\n

\u672c\u7814\u7a76\u65e8\u5728\u57fa\u4e8e\u6539\u8fdb\u7684YOLOv11\u6a21\u578b，\u6784\u5efa\u4e00\u4e2a\u9ad8\u6548\u7684\u7535\u6c14\u8bbe\u5907\u90e8\u4ef6\u8bc6\u522b\u7cfb\u7edf\u3002\u8be5\u7cfb\u7edf\u5c06\u5229\u7528\u4e00\u4e2a\u5305\u542b1162\u5f20\u56fe\u50cf\u7684\u6570\u636e\u96c6，\u6db5\u76d6\u4e86\u516b\u7c7b\u7535\u6c14\u90e8\u4ef6，\u5305\u62ecBushing\u3001Disc\u3001Disc-Pin\u3001Isolating\u3001La\u3001Others\u3001Pin\u548cwall\u7b49\u3002\u8fd9\u4e9b\u90e8\u4ef6\u5728\u7535\u6c14\u8bbe\u5907\u7684\u8fd0\u884c\u4e2d\u626e\u6f14\u7740\u91cd\u8981\u89d2\u8272，\u51c6\u786e\u8bc6\u522b\u5b83\u4eec\u4e0d\u4ec5\u6709\u52a9\u4e8e\u8bbe\u5907\u7684\u6545\u969c\u8bca\u65ad\u548c\u7ef4\u62a4，\u8fd8\u80fd\u4e3a\u8bbe\u5907\u7684\u8bbe\u8ba1\u4f18\u5316\u63d0\u4f9b\u6570\u636e\u652f\u6301\u3002<\/p>\n

\u901a\u8fc7\u5bf9\u6570\u636e\u96c6\u7684\u6df1\u5165\u5206\u6790\u4e0e\u5904\u7406，\u7814\u7a76\u5c06\u91c7\u7528\u5b9e\u4f8b\u5206\u5272\u6280\u672f，\u4ee5\u5b9e\u73b0\u5bf9\u7535\u6c14\u90e8\u4ef6\u7684\u7cbe\u786e\u5b9a\u4f4d\u548c\u5206\u7c7b\u3002\u6570\u636e\u96c6\u4e2d\u7684\u56fe\u50cf\u7ecf\u8fc7\u9884\u5904\u7406，\u786e\u4fdd\u4e86\u5728\u4e0d\u540c\u73af\u5883\u6761\u4ef6\u4e0b\u7684\u9c81\u68d2\u6027\u3002\u5c3d\u7ba1\u672a\u91c7\u7528\u56fe\u50cf\u589e\u5f3a\u6280\u672f，\u4f46\u901a\u8fc7\u9ad8\u5206\u8fa8\u7387\u7684\u56fe\u50cf\u8f93\u5165，\u7cfb\u7edf\u4ecd\u80fd\u6709\u6548\u63d0\u53d6\u51fa\u5173\u952e\u7279\u5f81，\u63d0\u5347\u8bc6\u522b\u7684\u51c6\u786e\u6027\u3002<\/p>\n

\u672c\u7814\u7a76\u7684\u610f\u4e49\u5728\u4e8e\u63a8\u52a8\u7535\u6c14\u8bbe\u5907\u667a\u80fd\u5316\u7ba1\u7406\u7684\u53d1\u5c55，\u63d0\u5347\u8bbe\u5907\u7ef4\u62a4\u7684\u6548\u7387\u4e0e\u5b89\u5168\u6027\u3002\u901a\u8fc7\u6784\u5efa\u57fa\u4e8eYOLOv11\u7684\u8bc6\u522b\u7cfb\u7edf，\u80fd\u591f\u4e3a\u7535\u6c14\u884c\u4e1a\u63d0\u4f9b\u4e00\u79cd\u65b0\u578b\u7684\u667a\u80fd\u89e3\u51b3\u65b9\u6848，\u52a9\u529b\u4e8e\u5b9e\u73b0\u66f4\u9ad8\u6c34\u5e73\u7684\u81ea\u52a8\u5316\u4e0e\u667a\u80fd\u5316\u7ba1\u7406\u3002<\/p>\n

\u56fe\u7247\u6548\u679c<\/h2>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u6570\u636e\u96c6\u4fe1\u606f<\/h2>\n

\u672c\u9879\u76ee\u6570\u636e\u96c6\u4fe1\u606f\u4ecb\u7ecd<\/p>\n

\u672c\u9879\u76ee\u65e8\u5728\u6539\u8fdbYOLOv11\u6a21\u578b\u5728\u7535\u6c14\u8bbe\u5907\u90e8\u4ef6\u8bc6\u522b\u7cfb\u7edf\u4e2d\u7684\u5e94\u7528，\u6240\u4f7f\u7528\u7684\u6570\u636e\u96c6\u56f4\u7ed5\u201cObject_identification_Isolating\u201d\u4e3b\u9898\u6784\u5efa，\u4e13\u6ce8\u4e8e\u7535\u6c14\u8bbe\u5907\u4e2d\u7684\u5173\u952e\u90e8\u4ef6\u8bc6\u522b\u3002\u8be5\u6570\u636e\u96c6\u5305\u542b8\u4e2a\u7c7b\u522b，\u5206\u522b\u4e3a：Bushing\u3001Disc\u3001Disc-Pin\u3001Isolating\u3001La\u3001Others\u3001Pin\u548cwall\u3002\u8fd9\u4e9b\u7c7b\u522b\u6db5\u76d6\u4e86\u7535\u6c14\u8bbe\u5907\u4e2d\u5e38\u89c1\u7684\u90e8\u4ef6，\u80fd\u591f\u4e3a\u6a21\u578b\u63d0\u4f9b\u4e30\u5bcc\u7684\u8bad\u7ec3\u6837\u672c，\u4ee5\u63d0\u9ad8\u5176\u8bc6\u522b\u7cbe\u5ea6\u548c\u6548\u7387\u3002<\/p>\n

\u5728\u6570\u636e\u96c6\u7684\u6784\u5efa\u8fc7\u7a0b\u4e2d，\u6211\u4eec\u7279\u522b\u6ce8\u91cd\u6837\u672c\u7684\u591a\u6837\u6027\u548c\u4ee3\u8868\u6027，\u4ee5\u786e\u4fdd\u6a21\u578b\u80fd\u591f\u5728\u5404\u79cd\u73af\u5883\u548c\u6761\u4ef6\u4e0b\u8fdb\u884c\u6709\u6548\u8bc6\u522b\u3002\u6bcf\u4e2a\u7c7b\u522b\u7684\u6837\u672c\u5747\u7ecf\u8fc7\u7cbe\u5fc3\u6311\u9009，\u6db5\u76d6\u4e86\u4e0d\u540c\u7684\u89c6\u89d2\u3001\u5149\u7167\u6761\u4ef6\u548c\u80cc\u666f，\u4ece\u800c\u589e\u5f3a\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002\u4f8b\u5982，Bushing\u548cPin\u7c7b\u6837\u672c\u5c55\u793a\u4e86\u4e0d\u540c\u7c7b\u578b\u548c\u5c3a\u5bf8\u7684\u7edd\u7f18\u5b50\u548c\u8fde\u63a5\u4ef6，\u800cDisc\u548cDisc-Pin\u7c7b\u5219\u63d0\u4f9b\u4e86\u591a\u79cd\u5f62\u72b6\u548c\u6750\u6599\u7684\u7535\u6c14\u63a5\u89e6\u90e8\u4ef6\u3002\u8fd9\u79cd\u591a\u6837\u6027\u4e0d\u4ec5\u63d0\u5347\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\u6548\u679c，\u4e5f\u4e3a\u540e\u7eed\u7684\u5b9e\u9645\u5e94\u7528\u5960\u5b9a\u4e86\u575a\u5b9e\u7684\u57fa\u7840\u3002<\/p>\n

\u6b64\u5916，\u6570\u636e\u96c6\u4e2d\u7684\u201cIsolating\u201d\u7c7b\u522b\u4e13\u95e8\u9488\u5bf9\u7535\u6c14\u8bbe\u5907\u4e2d\u9694\u79bb\u90e8\u4ef6\u7684\u8bc6\u522b，\u5177\u6709\u91cd\u8981\u7684\u5b9e\u7528\u4ef7\u503c\u3002\u901a\u8fc7\u5bf9\u8be5\u7c7b\u522b\u7684\u6df1\u5165\u5b66\u4e60，\u6a21\u578b\u80fd\u591f\u66f4\u51c6\u786e\u5730\u8bc6\u522b\u548c\u5206\u7c7b\u9694\u79bb\u88c5\u7f6e，\u4ece\u800c\u5728\u7535\u6c14\u5b89\u5168\u548c\u7ef4\u62a4\u4e2d\u53d1\u6325\u66f4\u5927\u4f5c\u7528\u3002\u603b\u4e4b，\u672c\u9879\u76ee\u7684\u6570\u636e\u96c6\u4e3a\u6539\u8fdbYOLOv11\u6a21\u578b\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u8bad\u7ec3\u7d20\u6750，\u65e8\u5728\u63d0\u5347\u7535\u6c14\u8bbe\u5907\u90e8\u4ef6\u7684\u81ea\u52a8\u8bc6\u522b\u80fd\u529b，\u63a8\u52a8\u667a\u80fd\u7535\u6c14\u8bbe\u5907\u7684\u8fdb\u4e00\u6b65\u53d1\u5c55\u4e0e\u5e94\u7528\u3002
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u6838\u5fc3\u4ee3\u7801<\/h2>\n

\u4ee5\u4e0b\u662f\u4ee3\u7801\u4e2d\u6700\u6838\u5fc3\u7684\u90e8\u5206，\u5e76\u9644\u4e0a\u8be6\u7ec6\u7684\u4e2d\u6587\u6ce8\u91ca：<\/p>\n

import math
\nimport torch
\nimport torch.nn as nn<\/p>\n

def autopad(k, p=None, d=1): # kernel, padding, dilation
\n\u201c\u201d\u201c\u81ea\u52a8\u586b\u5145\u4ee5\u786e\u4fdd\u8f93\u51fa\u5f62\u72b6\u4e0e\u8f93\u5165\u5f62\u72b6\u76f8\u540c\u3002\u201d\u201c\u201d
\nif d > 1:
\n# \u8ba1\u7b97\u5b9e\u9645\u7684\u5377\u79ef\u6838\u5927\u5c0f
\nk = d * (k – 1) + 1 if isinstance(k, int) else [d * (x – 1) + 1 for x in k]
\nif p is None:
\n# \u5982\u679c\u6ca1\u6709\u63d0\u4f9b\u586b\u5145，\u5219\u81ea\u52a8\u8ba1\u7b97\u586b\u5145
\np = k \/\/ 2 if isinstance(k, int) else [x \/\/ 2 for x in k]
\nreturn p<\/p>\n

class Conv(nn.Module):
\n\u201c\u201d\u201c\u6807\u51c6\u5377\u79ef\u5c42，\u5305\u542b\u5377\u79ef\u3001\u6279\u5f52\u4e00\u5316\u548c\u6fc0\u6d3b\u51fd\u6570\u3002\u201d\u201c\u201d<\/p>\n

default_act = nn.SiLU() # \u9ed8\u8ba4\u6fc0\u6d3b\u51fd\u6570<\/p>\n

def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
\n """\u521d\u59cb\u5316\u5377\u79ef\u5c42，\u53c2\u6570\u5305\u62ec\u8f93\u5165\u901a\u9053\u6570\u3001\u8f93\u51fa\u901a\u9053\u6570\u3001\u5377\u79ef\u6838\u5927\u5c0f\u3001\u6b65\u5e45\u3001\u586b\u5145\u3001\u5206\u7ec4\u3001\u6269\u5f20\u548c\u6fc0\u6d3b\u51fd\u6570\u3002"""
\n super().__init__()
\n # \u5b9a\u4e49\u5377\u79ef\u5c42
\n self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
\n # \u5b9a\u4e49\u6279\u5f52\u4e00\u5316\u5c42
\n self.bn = nn.BatchNorm2d(c2)
\n # \u6839\u636e\u8f93\u5165\u53c2\u6570\u9009\u62e9\u6fc0\u6d3b\u51fd\u6570
\n self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()<\/p>\n

def forward(self, x):
\n """\u524d\u5411\u4f20\u64ad：\u5e94\u7528\u5377\u79ef\u3001\u6279\u5f52\u4e00\u5316\u548c\u6fc0\u6d3b\u51fd\u6570\u3002"""
\n return self.act(self.bn(self.conv(x)))<\/p>\n

class DWConv(Conv):
\n\u201c\u201d\u201c\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef，\u4f7f\u7528\u6df1\u5ea6\u5377\u79ef\u3002\u201d\u201c\u201d<\/p>\n

def __init__(self, c1, c2, k=1, s=1, d=1, act=True):
\n """\u521d\u59cb\u5316\u6df1\u5ea6\u5377\u79ef，\u53c2\u6570\u5305\u62ec\u8f93\u5165\u901a\u9053\u6570\u3001\u8f93\u51fa\u901a\u9053\u6570\u3001\u5377\u79ef\u6838\u5927\u5c0f\u3001\u6b65\u5e45\u3001\u6269\u5f20\u548c\u6fc0\u6d3b\u51fd\u6570\u3002"""
\n super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)<\/p>\n

class DSConv(nn.Module):
\n\u201c\u201d\u201c\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\u6a21\u5757\u3002\u201d\u201c\u201d<\/p>\n

def __init__(self, c1, c2, k=1, s=1, d=1, act=True) -> None:
\n """\u521d\u59cb\u5316\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\u6a21\u5757，\u5305\u542b\u6df1\u5ea6\u5377\u79ef\u548c\u9010\u70b9\u5377\u79ef\u3002"""
\n super().__init__()
\n self.dwconv = DWConv(c1, c1, 3) # \u6df1\u5ea6\u5377\u79ef
\n self.pwconv = Conv(c1, c2, 1) # \u9010\u70b9\u5377\u79ef<\/p>\n

def forward(self, x):
\n """\u524d\u5411\u4f20\u64ad：\u5148\u901a\u8fc7\u6df1\u5ea6\u5377\u79ef，\u518d\u901a\u8fc7\u9010\u70b9\u5377\u79ef\u3002"""
\n return self.pwconv(self.dwconv(x))<\/p>\n

class ConvTranspose(nn.Module):
\n\u201c\u201d\u201c\u8f6c\u7f6e\u5377\u79ef\u5c42\u3002\u201d\u201c\u201d<\/p>\n

default_act = nn.SiLU() # \u9ed8\u8ba4\u6fc0\u6d3b\u51fd\u6570<\/p>\n

def __init__(self, c1, c2, k=2, s=2, p=0, bn=True, act=True):
\n """\u521d\u59cb\u5316\u8f6c\u7f6e\u5377\u79ef\u5c42，\u53c2\u6570\u5305\u62ec\u8f93\u5165\u901a\u9053\u6570\u3001\u8f93\u51fa\u901a\u9053\u6570\u3001\u5377\u79ef\u6838\u5927\u5c0f\u3001\u6b65\u5e45\u3001\u586b\u5145\u3001\u662f\u5426\u4f7f\u7528\u6279\u5f52\u4e00\u5316\u548c\u6fc0\u6d3b\u51fd\u6570\u3002"""
\n super().__init__()
\n self.conv_transpose = nn.ConvTranspose2d(c1, c2, k, s, p, bias=not bn)
\n self.bn = nn.BatchNorm2d(c2) if bn else nn.Identity()
\n self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()<\/p>\n

def forward(self, x):
\n """\u524d\u5411\u4f20\u64ad：\u5e94\u7528\u8f6c\u7f6e\u5377\u79ef\u3001\u6279\u5f52\u4e00\u5316\u548c\u6fc0\u6d3b\u51fd\u6570\u3002"""
\n return self.act(self.bn(self.conv_transpose(x)))<\/p>\n

class ChannelAttention(nn.Module):
\n\u201c\u201d\u201c\u901a\u9053\u6ce8\u610f\u529b\u6a21\u5757\u3002\u201d\u201c\u201d<\/p>\n

def __init__(self, channels: int) -> None:
\n """\u521d\u59cb\u5316\u901a\u9053\u6ce8\u610f\u529b\u6a21\u5757，\u8bbe\u7f6e\u57fa\u672c\u914d\u7f6e\u3002"""
\n super().__init__()
\n self.pool = nn.AdaptiveAvgPool2d(1) # \u81ea\u9002\u5e94\u5e73\u5747\u6c60\u5316
\n self.fc = nn.Conv2d(channels, channels, 1, 1, 0, bias=True) # 1×1\u5377\u79ef
\n self.act = nn.Sigmoid() # \u6fc0\u6d3b\u51fd\u6570<\/p>\n

def forward(self, x: torch.Tensor) -> torch.Tensor:
\n """\u524d\u5411\u4f20\u64ad：\u8ba1\u7b97\u901a\u9053\u6ce8\u610f\u529b\u5e76\u5e94\u7528\u4e8e\u8f93\u5165\u3002"""
\n return x * self.act(self.fc(self.pool(x)))<\/p>\n

class SpatialAttention(nn.Module):
\n\u201c\u201d\u201c\u7a7a\u95f4\u6ce8\u610f\u529b\u6a21\u5757\u3002\u201d\u201c\u201d<\/p>\n

def __init__(self, kernel_size=7):
\n """\u521d\u59cb\u5316\u7a7a\u95f4\u6ce8\u610f\u529b\u6a21\u5757，\u8bbe\u7f6e\u5377\u79ef\u6838\u5927\u5c0f\u3002"""
\n super().__init__()
\n assert kernel_size in {3, 7}, "\u5377\u79ef\u6838\u5927\u5c0f\u5fc5\u987b\u4e3a3\u62167"
\n padding = 3 if kernel_size == 7 else 1
\n self.cv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) # \u5377\u79ef\u5c42
\n self.act = nn.Sigmoid() # \u6fc0\u6d3b\u51fd\u6570<\/p>\n

def forward(self, x):
\n """\u524d\u5411\u4f20\u64ad：\u8ba1\u7b97\u7a7a\u95f4\u6ce8\u610f\u529b\u5e76\u5e94\u7528\u4e8e\u8f93\u5165\u3002"""
\n return x * self.act(self.cv1(torch.cat([torch.mean(x, 1, keepdim=True), torch.max(x, 1, keepdim=True)[0]], 1)))<\/p>\n

class CBAM(nn.Module):
\n\u201c\u201d\u201c\u5377\u79ef\u5757\u6ce8\u610f\u529b\u6a21\u5757\u3002\u201d\u201c\u201d<\/p>\n

def __init__(self, c1, kernel_size=7):
\n """\u521d\u59cb\u5316CBAM\u6a21\u5757，\u8bbe\u7f6e\u8f93\u5165\u901a\u9053\u548c\u5377\u79ef\u6838\u5927\u5c0f\u3002"""
\n super().__init__()
\n self.channel_attention = ChannelAttention(c1) # \u901a\u9053\u6ce8\u610f\u529b
\n self.spatial_attention = SpatialAttention(kernel_size) # \u7a7a\u95f4\u6ce8\u610f\u529b<\/p>\n

def forward(self, x):
\n """\u524d\u5411\u4f20\u64ad：\u4f9d\u6b21\u5e94\u7528\u901a\u9053\u6ce8\u610f\u529b\u548c\u7a7a\u95f4\u6ce8\u610f\u529b\u3002"""
\n return self.spatial_attention(self.channel_attention(x))<\/p>\n

\u4ee3\u7801\u6838\u5fc3\u90e8\u5206\u8bf4\u660e：
\nautopad：\u7528\u4e8e\u81ea\u52a8\u8ba1\u7b97\u586b\u5145，\u4ee5\u786e\u4fdd\u5377\u79ef\u64cd\u4f5c\u540e\u8f93\u51fa\u7684\u5f62\u72b6\u4e0e\u8f93\u5165\u5f62\u72b6\u76f8\u540c\u3002
\nConv：\u6807\u51c6\u5377\u79ef\u5c42，\u5305\u542b\u5377\u79ef\u3001\u6279\u5f52\u4e00\u5316\u548c\u6fc0\u6d3b\u51fd\u6570\u7684\u7ec4\u5408\u3002
\nDWConv：\u6df1\u5ea6\u5377\u79ef，\u9002\u7528\u4e8e\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\u7684\u5b9e\u73b0\u3002
\nDSConv：\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\u6a21\u5757，\u5305\u542b\u6df1\u5ea6\u5377\u79ef\u548c\u9010\u70b9\u5377\u79ef\u3002
\nConvTranspose：\u8f6c\u7f6e\u5377\u79ef\u5c42，\u901a\u5e38\u7528\u4e8e\u4e0a\u91c7\u6837\u3002
\nChannelAttention：\u901a\u9053\u6ce8\u610f\u529b\u673a\u5236，\u901a\u8fc7\u5bf9\u901a\u9053\u8fdb\u884c\u52a0\u6743\u6765\u589e\u5f3a\u7279\u5f81\u3002
\nSpatialAttention：\u7a7a\u95f4\u6ce8\u610f\u529b\u673a\u5236，\u901a\u8fc7\u5bf9\u7a7a\u95f4\u4f4d\u7f6e\u8fdb\u884c\u52a0\u6743\u6765\u589e\u5f3a\u7279\u5f81\u3002
\nCBAM：\u7ed3\u5408\u901a\u9053\u548c\u7a7a\u95f4\u6ce8\u610f\u529b\u7684\u6a21\u5757，\u63d0\u5347\u7279\u5f81\u8868\u8fbe\u80fd\u529b\u3002
\n\u8fd9\u4e2a\u7a0b\u5e8f\u6587\u4ef6 conv.py \u5b9a\u4e49\u4e86\u4e00\u7cfb\u5217\u7528\u4e8e\u5377\u79ef\u64cd\u4f5c\u7684\u6a21\u5757，\u4e3b\u8981\u7528\u4e8e\u6df1\u5ea6\u5b66\u4e60\u4e2d\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc（CNN）\u3002\u6587\u4ef6\u4e2d\u4f7f\u7528\u4e86 PyTorch \u6846\u67b6，\u5e76\u5b9e\u73b0\u4e86\u591a\u79cd\u5377\u79ef\u5c42\u548c\u6ce8\u610f\u529b\u673a\u5236\u3002\u4ee5\u4e0b\u662f\u5bf9\u4ee3\u7801\u7684\u8be6\u7ec6\u8bf4\u660e\u3002<\/p>\n

\u9996\u5148，\u6587\u4ef6\u5bfc\u5165\u4e86\u5fc5\u8981\u7684\u5e93，\u5305\u62ec math\u3001numpy \u548c torch \u4ee5\u53ca torch.nn\u3002\u63a5\u7740，\u5b9a\u4e49\u4e86\u4e00\u4e2a\u540d\u4e3a autopad \u7684\u51fd\u6570，\u8be5\u51fd\u6570\u7528\u4e8e\u81ea\u52a8\u8ba1\u7b97\u5377\u79ef\u64cd\u4f5c\u6240\u9700\u7684\u586b\u5145，\u4ee5\u786e\u4fdd\u8f93\u51fa\u7684\u5f62\u72b6\u4e0e\u8f93\u5165\u7684\u5f62\u72b6\u76f8\u540c\u3002<\/p>\n

\u63a5\u4e0b\u6765，\u5b9a\u4e49\u4e86\u591a\u4e2a\u5377\u79ef\u7c7b\u3002Conv \u7c7b\u5b9e\u73b0\u4e86\u6807\u51c6\u7684\u5377\u79ef\u64cd\u4f5c，\u5305\u542b\u5377\u79ef\u5c42\u3001\u6279\u5f52\u4e00\u5316\u5c42\u548c\u6fc0\u6d3b\u51fd\u6570\u3002\u6784\u9020\u51fd\u6570\u4e2d\u63a5\u53d7\u591a\u4e2a\u53c2\u6570，\u5982\u8f93\u5165\u901a\u9053\u6570\u3001\u8f93\u51fa\u901a\u9053\u6570\u3001\u5377\u79ef\u6838\u5927\u5c0f\u3001\u6b65\u5e45\u3001\u586b\u5145\u3001\u5206\u7ec4\u5377\u79ef\u548c\u6269\u5f20\u7b49\u3002forward \u65b9\u6cd5\u5b9e\u73b0\u4e86\u5377\u79ef\u3001\u6279\u5f52\u4e00\u5316\u548c\u6fc0\u6d3b\u7684\u987a\u5e8f\u64cd\u4f5c\u3002<\/p>\n

Conv2 \u7c7b\u662f Conv \u7c7b\u7684\u7b80\u5316\u7248\u672c，\u589e\u52a0\u4e86\u4e00\u4e2a 1×1 \u7684\u5377\u79ef\u5c42，\u4ee5\u4fbf\u5728\u524d\u5411\u4f20\u64ad\u4e2d\u878d\u5408\u4e24\u4e2a\u5377\u79ef\u7684\u8f93\u51fa\u3002LightConv \u7c7b\u5b9e\u73b0\u4e86\u4e00\u79cd\u8f7b\u91cf\u7ea7\u5377\u79ef，\u7ed3\u5408\u4e86\u6807\u51c6\u5377\u79ef\u548c\u6df1\u5ea6\u5377\u79ef（DWConv），\u7528\u4e8e\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u3002<\/p>\n

DWConv \u7c7b\u5b9e\u73b0\u4e86\u6df1\u5ea6\u5377\u79ef，\u9002\u7528\u4e8e\u6bcf\u4e2a\u8f93\u5165\u901a\u9053\u72ec\u7acb\u8fdb\u884c\u5377\u79ef\u64cd\u4f5c\u3002DSConv \u7c7b\u5219\u5b9e\u73b0\u4e86\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef，\u7ed3\u5408\u4e86\u6df1\u5ea6\u5377\u79ef\u548c\u9010\u70b9\u5377\u79ef\u7684\u7279\u70b9\u3002<\/p>\n

DWConvTranspose2d \u7c7b\u5b9e\u73b0\u4e86\u6df1\u5ea6\u8f6c\u7f6e\u5377\u79ef，\u800c ConvTranspose \u7c7b\u5219\u5b9e\u73b0\u4e86\u6807\u51c6\u7684\u8f6c\u7f6e\u5377\u79ef\u5c42，\u652f\u6301\u6279\u5f52\u4e00\u5316\u548c\u6fc0\u6d3b\u51fd\u6570\u3002<\/p>\n

Focus \u7c7b\u7528\u4e8e\u5c06\u7a7a\u95f4\u4fe1\u606f\u805a\u7126\u5230\u901a\u9053\u7ef4\u5ea6，\u901a\u8fc7\u5bf9\u8f93\u5165\u5f20\u91cf\u8fdb\u884c\u7279\u5b9a\u7684\u5207\u7247\u548c\u62fc\u63a5，\u589e\u52a0\u4e86\u5377\u79ef\u7684\u8f93\u5165\u901a\u9053\u6570\u3002GhostConv \u7c7b\u5b9e\u73b0\u4e86 Ghost \u5377\u79ef，\u901a\u8fc7\u4e3b\u5377\u79ef\u548c\u5ec9\u4ef7\u64cd\u4f5c\u6765\u63d0\u9ad8\u7279\u5f81\u5b66\u4e60\u7684\u6548\u7387\u3002<\/p>\n

RepConv \u7c7b\u662f\u4e00\u4e2a\u57fa\u7840\u7684\u91cd\u590d\u5377\u79ef\u6a21\u5757，\u652f\u6301\u8bad\u7ec3\u548c\u63a8\u7406\u9636\u6bb5\u7684\u4e0d\u540c\u64cd\u4f5c\u3002\u5b83\u901a\u8fc7\u878d\u5408\u5377\u79ef\u548c\u6279\u5f52\u4e00\u5316\u7684\u53c2\u6570\u6765\u4f18\u5316\u6a21\u578b\u3002<\/p>\n

\u63a5\u4e0b\u6765，\u5b9a\u4e49\u4e86\u51e0\u4e2a\u6ce8\u610f\u529b\u673a\u5236\u6a21\u5757\u3002ChannelAttention \u7c7b\u5b9e\u73b0\u4e86\u901a\u9053\u6ce8\u610f\u529b\u673a\u5236，\u901a\u8fc7\u5bf9\u8f93\u5165\u7279\u5f81\u56fe\u8fdb\u884c\u5168\u5c40\u5e73\u5747\u6c60\u5316\u548c\u5168\u8fde\u63a5\u5c42\u64cd\u4f5c，\u751f\u6210\u901a\u9053\u6743\u91cd\u3002SpatialAttention \u7c7b\u5b9e\u73b0\u4e86\u7a7a\u95f4\u6ce8\u610f\u529b\u673a\u5236，\u901a\u8fc7\u5bf9\u8f93\u5165\u7279\u5f81\u56fe\u8fdb\u884c\u5e73\u5747\u548c\u6700\u5927\u6c60\u5316，\u751f\u6210\u7a7a\u95f4\u6743\u91cd\u3002<\/p>\n

CBAM \u7c7b\u7ed3\u5408\u4e86\u901a\u9053\u6ce8\u610f\u529b\u548c\u7a7a\u95f4\u6ce8\u610f\u529b，\u5f62\u6210\u4e00\u4e2a\u5b8c\u6574\u7684\u5377\u79ef\u5757\u6ce8\u610f\u529b\u6a21\u5757，\u7528\u4e8e\u7279\u5f81\u7684\u91cd\u6821\u51c6\u3002<\/p>\n

\u6700\u540e，Concat \u7c7b\u5b9e\u73b0\u4e86\u5f20\u91cf\u7684\u62fc\u63a5\u64cd\u4f5c，\u53ef\u4ee5\u5728\u6307\u5b9a\u7684\u7ef4\u5ea6\u4e0a\u5c06\u591a\u4e2a\u5f20\u91cf\u8fde\u63a5\u8d77\u6765\u3002<\/p>\n

\u6574\u4f53\u6765\u770b，\u8fd9\u4e2a\u6587\u4ef6\u5b9e\u73b0\u4e86\u591a\u79cd\u5377\u79ef\u548c\u6ce8\u610f\u529b\u673a\u5236\u6a21\u5757，\u4e3a\u6784\u5efa\u590d\u6742\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u63d0\u4f9b\u4e86\u57fa\u7840\u7ec4\u4ef6\u3002\u8fd9\u4e9b\u6a21\u5757\u53ef\u4ee5\u7075\u6d3b\u7ec4\u5408，\u9002\u7528\u4e8e\u5404\u79cd\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u3002<\/p>\n

10.4 prepbn.py
\n\u4ee5\u4e0b\u662f\u7ecf\u8fc7\u7b80\u5316\u5e76\u6ce8\u91ca\u7684\u6838\u5fc3\u4ee3\u7801\u90e8\u5206：<\/p>\n

import torch
\nimport torch.nn as nn<\/p>\n

\u81ea\u5b9a\u4e49\u7684\u6279\u91cf\u5f52\u4e00\u5316\u7c7b
\nclass RepBN(nn.Module):
\ndef init(self, channels):
\nsuper(RepBN, self).init()
\n# alpha \u662f\u4e00\u4e2a\u53ef\u5b66\u4e60\u7684\u53c2\u6570，\u521d\u59cb\u5316\u4e3a1
\nself.alpha = nn.Parameter(torch.ones(1))
\n# \u4f7f\u7528\u4e00\u7ef4\u6279\u91cf\u5f52\u4e00\u5316
\nself.bn = nn.BatchNorm1d(channels)<\/p>\n

def forward(self, x):
\n # \u5c06\u8f93\u5165\u7684\u7ef4\u5ea6\u8fdb\u884c\u8f6c\u7f6e，\u4ee5\u9002\u5e94 BatchNorm1d \u7684\u8f93\u5165\u683c\u5f0f
\n x = x.transpose(1, 2)
\n # \u8fdb\u884c\u6279\u91cf\u5f52\u4e00\u5316，\u5e76\u52a0\u4e0a alpha \u4e58\u4ee5\u539f\u59cb\u8f93\u5165
\n x = self.bn(x) + self.alpha * x
\n # \u518d\u6b21\u8f6c\u7f6e\u56de\u539f\u6765\u7684\u7ef4\u5ea6
\n x = x.transpose(1, 2)
\n return x<\/p>\n

\u81ea\u5b9a\u4e49\u7684\u7ebf\u6027\u5f52\u4e00\u5316\u7c7b
\nclass LinearNorm(nn.Module):
\ndef init(self, dim, norm1, norm2, warm=0, step=300000, r0=1.0):
\nsuper(LinearNorm, self).init()
\n# \u6ce8\u518c\u4e00\u4e9b\u7f13\u51b2\u533a，\u7528\u4e8e\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u72b6\u6001\u7ba1\u7406
\nself.register_buffer(\u2018warm\u2019, torch.tensor(warm)) # \u9884\u70ed\u6b65\u6570
\nself.register_buffer(\u2018iter\u2019, torch.tensor(step)) # \u5f53\u524d\u8fed\u4ee3\u6b65\u6570
\nself.register_buffer(\u2018total_step\u2019, torch.tensor(step)) # \u603b\u6b65\u6570
\nself.r0 = r0 # \u521d\u59cb\u6bd4\u4f8b
\nself.norm1 = norm1(dim) # \u7b2c\u4e00\u4e2a\u5f52\u4e00\u5316\u5c42
\nself.norm2 = norm2(dim) # \u7b2c\u4e8c\u4e2a\u5f52\u4e00\u5316\u5c42<\/p>\n

def forward(self, x):
\n if self.training: # \u8bad\u7ec3\u6a21\u5f0f
\n if self.warm > 0: # \u5982\u679c\u8fd8\u6709\u9884\u70ed\u6b65\u6570
\n self.warm.copy_(self.warm – 1) # \u51cf\u5c11\u9884\u70ed\u6b65\u6570
\n x = self.norm1(x) # \u8fdb\u884c\u7b2c\u4e00\u6b21\u5f52\u4e00\u5316
\n else:
\n # \u8ba1\u7b97\u5f53\u524d\u7684 lambda \u503c，\u7528\u4e8e\u7ebf\u6027\u63d2\u503c
\n lamda = self.r0 * self.iter \/ self.total_step
\n if self.iter > 0:
\n self.iter.copy_(self.iter – 1) # \u51cf\u5c11\u8fed\u4ee3\u6b65\u6570
\n x1 = self.norm1(x) # \u7b2c\u4e00\u6b21\u5f52\u4e00\u5316
\n x2 = self.norm2(x) # \u7b2c\u4e8c\u6b21\u5f52\u4e00\u5316
\n # \u7ebf\u6027\u63d2\u503c
\n x = lamda * x1 + (1 – lamda) * x2
\n else: # \u6d4b\u8bd5\u6a21\u5f0f
\n x = self.norm2(x) # \u53ea\u8fdb\u884c\u7b2c\u4e8c\u6b21\u5f52\u4e00\u5316
\n return x<\/p>\n

\u4ee3\u7801\u8bf4\u660e：
\nRepBN \u7c7b：<\/p>\n

\u8be5\u7c7b\u5b9e\u73b0\u4e86\u4e00\u79cd\u81ea\u5b9a\u4e49\u7684\u6279\u91cf\u5f52\u4e00\u5316\u65b9\u6cd5，\u5141\u8bb8\u901a\u8fc7\u4e00\u4e2a\u53ef\u5b66\u4e60\u7684\u53c2\u6570 alpha \u6765\u8c03\u6574\u5f52\u4e00\u5316\u7684\u8f93\u51fa\u3002
\n\u5728 forward \u65b9\u6cd5\u4e2d，\u8f93\u5165\u6570\u636e\u7684\u7ef4\u5ea6\u88ab\u8f6c\u7f6e，\u4ee5\u9002\u5e94 BatchNorm1d \u7684\u8981\u6c42，\u7ecf\u8fc7\u5f52\u4e00\u5316\u540e，\u518d\u52a0\u4e0a alpha \u4e58\u4ee5\u539f\u59cb\u8f93\u5165\u3002
\nLinearNorm \u7c7b：<\/p>\n

\u8be5\u7c7b\u5b9e\u73b0\u4e86\u4e00\u79cd\u7ebf\u6027\u5f52\u4e00\u5316\u65b9\u6cd5，\u7ed3\u5408\u4e86\u4e24\u4e2a\u4e0d\u540c\u7684\u5f52\u4e00\u5316\u5c42（norm1 \u548c norm2）\u3002
\n\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，\u4f7f\u7528\u9884\u70ed\u9636\u6bb5\u9010\u6b65\u5f15\u5165 norm1，\u7136\u540e\u6839\u636e\u5f53\u524d\u7684\u8fed\u4ee3\u6b65\u6570\u8ba1\u7b97\u4e00\u4e2a\u7ebf\u6027\u63d2\u503c\u7cfb\u6570 lambda，\u5728 norm1 \u548c norm2 \u4e4b\u95f4\u8fdb\u884c\u52a0\u6743\u3002
\n\u5728\u6d4b\u8bd5\u6a21\u5f0f\u4e0b，\u53ea\u4f7f\u7528 norm2 \u8fdb\u884c\u5f52\u4e00\u5316\u3002
\n\u8fd9\u4e2a\u7a0b\u5e8f\u6587\u4ef6\u5b9a\u4e49\u4e86\u4e24\u4e2a\u795e\u7ecf\u7f51\u7edc\u6a21\u5757，\u5206\u522b\u662f RepBN \u548c LinearNorm，\u5b83\u4eec\u90fd\u662f\u57fa\u4e8e PyTorch \u6846\u67b6\u6784\u5efa\u7684\u3002<\/p>\n

RepBN \u7c7b\u662f\u4e00\u4e2a\u81ea\u5b9a\u4e49\u7684\u6279\u91cf\u5f52\u4e00\u5316\u5c42，\u7ee7\u627f\u81ea nn.Module\u3002\u5728\u521d\u59cb\u5316\u65b9\u6cd5\u4e2d，\u5b83\u63a5\u53d7\u4e00\u4e2a\u53c2\u6570 channels，\u7528\u4e8e\u6307\u5b9a\u8f93\u5165\u6570\u636e\u7684\u901a\u9053\u6570\u3002\u8be5\u7c7b\u5b9a\u4e49\u4e86\u4e00\u4e2a\u53ef\u5b66\u4e60\u7684\u53c2\u6570 alpha，\u521d\u59cb\u503c\u4e3a 1，\u5e76\u521b\u5efa\u4e86\u4e00\u4e2a\u6807\u51c6\u7684\u6279\u91cf\u5f52\u4e00\u5316\u5c42 bn\u3002\u5728\u524d\u5411\u4f20\u64ad\u65b9\u6cd5 forward \u4e2d，\u8f93\u5165\u5f20\u91cf x \u9996\u5148\u8fdb\u884c\u7ef4\u5ea6\u8f6c\u7f6e，\u5c06\u901a\u9053\u7ef4\u79fb\u5230\u6700\u540e，\u7136\u540e\u901a\u8fc7\u6279\u91cf\u5f52\u4e00\u5316\u5c42\u8fdb\u884c\u5904\u7406\u3002\u5904\u7406\u540e，\u7ed3\u679c\u4e0e alpha \u4e58\u4ee5\u539f\u59cb\u8f93\u5165 x \u76f8\u52a0，\u6700\u540e\u518d\u8fdb\u884c\u4e00\u6b21\u7ef4\u5ea6\u8f6c\u7f6e\u4ee5\u6062\u590d\u539f\u6765\u7684\u5f62\u72b6\u3002\u8fd9\u4e2a\u6a21\u5757\u7684\u8bbe\u8ba1\u76ee\u7684\u662f\u5728\u6807\u51c6\u7684\u6279\u91cf\u5f52\u4e00\u5316\u57fa\u7840\u4e0a\u5f15\u5165\u4e00\u4e2a\u53ef\u8c03\u7684\u52a0\u6743\u9879，\u4ee5\u589e\u5f3a\u6a21\u578b\u7684\u8868\u8fbe\u80fd\u529b\u3002<\/p>\n

LinearNorm \u7c7b\u540c\u6837\u7ee7\u627f\u81ea nn.Module，\u5b83\u7684\u6784\u9020\u51fd\u6570\u63a5\u53d7\u591a\u4e2a\u53c2\u6570，\u5305\u62ec dim（\u8f93\u5165\u7684\u7ef4\u5ea6）\u3001norm1 \u548c norm2（\u4e24\u4e2a\u5f52\u4e00\u5316\u51fd\u6570）\u3001warm（\u9884\u70ed\u6b65\u6570）\u3001step（\u5f53\u524d\u6b65\u6570）\u548c r0（\u521d\u59cb\u6743\u91cd）\u3002\u5728\u521d\u59cb\u5316\u8fc7\u7a0b\u4e2d，\u4f7f\u7528 register_buffer \u65b9\u6cd5\u6ce8\u518c\u4e86\u4e00\u4e9b\u7f13\u51b2\u533a\u53d8\u91cf，\u8fd9\u4e9b\u53d8\u91cf\u5728\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4f1a\u4fdd\u6301\u5176\u72b6\u6001\u3002\u524d\u5411\u4f20\u64ad\u65b9\u6cd5 forward \u7684\u903b\u8f91\u8f83\u4e3a\u590d\u6742：\u5982\u679c\u6a21\u578b\u5904\u4e8e\u8bad\u7ec3\u72b6\u6001\u4e14\u9884\u70ed\u6b65\u6570\u5927\u4e8e 0，\u5219\u6267\u884c\u7b2c\u4e00\u6b21\u5f52\u4e00\u5316 norm1，\u5e76\u5c06\u9884\u70ed\u6b65\u6570\u51cf\u4e00；\u5426\u5219，\u8ba1\u7b97\u4e00\u4e2a\u6743\u91cd lamda，\u5b83\u4e0e\u5f53\u524d\u8fed\u4ee3\u6b65\u6570\u548c\u603b\u6b65\u6570\u6709\u5173\u3002\u63a5\u7740，\u5206\u522b\u5bf9\u8f93\u5165 x \u5e94\u7528\u4e24\u4e2a\u5f52\u4e00\u5316\u64cd\u4f5c norm1 \u548c norm2，\u5e76\u6839\u636e\u8ba1\u7b97\u51fa\u7684\u6743\u91cd\u5c06\u8fd9\u4e24\u4e2a\u7ed3\u679c\u8fdb\u884c\u7ebf\u6027\u7ec4\u5408\u3002\u82e5\u6a21\u578b\u4e0d\u5728\u8bad\u7ec3\u72b6\u6001，\u5219\u76f4\u63a5\u4f7f\u7528 norm2 \u5bf9\u8f93\u5165\u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406\u3002<\/p>\n

\u603b\u4f53\u800c\u8a00，\u8fd9\u4e24\u4e2a\u6a21\u5757\u901a\u8fc7\u81ea\u5b9a\u4e49\u7684\u5f52\u4e00\u5316\u65b9\u6cd5\u548c\u52a8\u6001\u8c03\u6574\u7684\u6743\u91cd，\u65e8\u5728\u63d0\u9ad8\u795e\u7ecf\u7f51\u7edc\u7684\u8bad\u7ec3\u6548\u679c\u548c\u7a33\u5b9a\u6027\u3002<\/p>\n

\u6e90\u7801\u6587\u4ef6<\/h2>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u6e90\u7801\u83b7\u53d6<\/h2>\n

\u6b22\u8fce\u5927\u5bb6\u70b9\u8d5e\u3001\u6536\u85cf\u3001\u5173\u6ce8\u3001\u8bc4\u8bba\u5566 \u3001\u67e5\u770b👇🏻\u83b7\u53d6\u8054\u7cfb\u65b9\u5f0f<\/p>\n","protected":false},"excerpt":{"rendered":"

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