{"id":51738,"date":"2025-08-11T06:19:48","date_gmt":"2025-08-10T22:19:48","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/51738.html"},"modified":"2025-08-11T06:19:48","modified_gmt":"2025-08-10T22:19:48","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%9c%bc%e5%ba%95%e5%9b%be%e5%83%8f%e5%b1%82%e6%ac%a1%e5%88%86%e5%89%b2%e7%b3%bb","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/51738.html","title":{"rendered":"\u3010\u5b8c\u6574\u6e90\u7801+\u6570\u636e\u96c6+\u90e8\u7f72\u6559\u7a0b\u3011\u773c\u5e95\u56fe\u50cf\u5c42\u6b21\u5206\u5272\u7cfb\u7edf\u6e90\u7801\u548c\u6570\u636e\u96c6\uff1a\u6539\u8fdbyolo11-DCNV3"},"content":{"rendered":"

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

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

\u773c\u5e95\u56fe\u50cf\u5206\u6790\u5728\u533b\u5b66\u5f71\u50cf\u5b66\u4e2d\u626e\u6f14\u7740\u81f3\u5173\u91cd\u8981\u7684\u89d2\u8272，\u5c24\u5176\u662f\u5728\u65e9\u671f\u8bca\u65ad\u548c\u76d1\u6d4b\u773c\u79d1\u75be\u75c5\u65b9\u9762\u3002\u968f\u7740\u4eba\u53e3\u8001\u9f84\u5316\u7684\u52a0\u5267，\u773c\u79d1\u75be\u75c5\u7684\u53d1\u75c5\u7387\u9010\u5e74\u4e0a\u5347，\u5c24\u5176\u662f\u7cd6\u5c3f\u75c5\u89c6\u7f51\u819c\u75c5\u53d8\u3001\u9ec4\u6591\u53d8\u6027\u7b49\u75be\u75c5，\u7ed9\u60a3\u8005\u7684\u89c6\u529b\u5065\u5eb7\u5e26\u6765\u4e86\u4e25\u91cd\u5a01\u80c1\u3002\u56e0\u6b64，\u5f00\u53d1\u9ad8\u6548\u3001\u51c6\u786e\u7684\u773c\u5e95\u56fe\u50cf\u5206\u6790\u7cfb\u7edf，\u5c24\u5176\u662f\u5c42\u6b21\u5206\u5272\u6280\u672f，\u663e\u5f97\u5c24\u4e3a\u91cd\u8981\u3002\u5c42\u6b21\u5206\u5272\u80fd\u591f\u5e2e\u52a9\u533b\u751f\u6e05\u6670\u5730\u8bc6\u522b\u548c\u5b9a\u4f4d\u773c\u5e95\u56fe\u50cf\u4e2d\u7684\u5404\u4e2a\u7ed3\u6784，\u8fdb\u800c\u63d0\u9ad8\u8bca\u65ad\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u3002<\/p>\n

\u672c\u7814\u7a76\u65e8\u5728\u57fa\u4e8e\u6539\u8fdb\u7684YOLOv11\u6a21\u578b，\u6784\u5efa\u4e00\u4e2a\u9ad8\u6548\u7684\u773c\u5e95\u56fe\u50cf\u5c42\u6b21\u5206\u5272\u7cfb\u7edf\u3002\u8be5\u7cfb\u7edf\u5c06\u5229\u7528oct5k_new_new\u6570\u636e\u96c6，\u8be5\u6570\u636e\u96c6\u5305\u542b4600\u5e45\u773c\u5e95\u56fe\u50cf，\u6db5\u76d6\u4e866\u4e2a\u4e0d\u540c\u7684\u7c7b\u522b，\u5305\u62ec\u5185\u754c\u819c（ILM）\u3001\u89c6\u7f51\u819c\u8272\u7d20\u4e0a\u76ae（RPE）\u3001\u5916\u5c42（OPL）\u7b49\u3002\u8fd9\u4e9b\u7c7b\u522b\u7684\u51c6\u786e\u5206\u5272\u5bf9\u4e8e\u75be\u75c5\u7684\u8bca\u65ad\u548c\u6cbb\u7597\u65b9\u6848\u7684\u5236\u5b9a\u81f3\u5173\u91cd\u8981\u3002\u901a\u8fc7\u5bf9\u8fd9\u4e9b\u56fe\u50cf\u8fdb\u884c\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3，\u7cfb\u7edf\u5c06\u80fd\u591f\u81ea\u52a8\u8bc6\u522b\u548c\u5206\u5272\u51fa\u773c\u5e95\u56fe\u50cf\u4e2d\u7684\u5173\u952e\u7ed3\u6784，\u51cf\u8f7b\u533b\u751f\u7684\u5de5\u4f5c\u8d1f\u62c5，\u63d0\u9ad8\u8bca\u65ad\u6548\u7387\u3002<\/p>\n

\u6b64\u5916，\u6570\u636e\u96c6\u7ecf\u8fc7\u591a\u79cd\u6570\u636e\u589e\u5f3a\u5904\u7406，\u6781\u5927\u5730\u4e30\u5bcc\u4e86\u8bad\u7ec3\u6837\u672c\u7684\u591a\u6837\u6027，\u63d0\u5347\u4e86\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u8868\u73b0，\u8fd8\u80fd\u4e3a\u540e\u7eed\u7684\u7814\u7a76\u63d0\u4f9b\u575a\u5b9e\u7684\u6570\u636e\u57fa\u7840\u3002\u968f\u7740\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u7684\u4e0d\u65ad\u8fdb\u6b65，\u57fa\u4e8eYOLOv11\u7684\u773c\u5e95\u56fe\u50cf\u5c42\u6b21\u5206\u5272\u7cfb\u7edf\u5c06\u4e3a\u773c\u79d1\u533b\u5b66\u63d0\u4f9b\u65b0\u7684\u89e3\u51b3\u65b9\u6848，\u63a8\u52a8\u773c\u5e95\u75be\u75c5\u7684\u65e9\u671f\u8bca\u65ad\u548c\u6cbb\u7597\u8fdb\u7a0b，\u5177\u6709\u91cd\u8981\u7684\u4e34\u5e8a\u5e94\u7528\u4ef7\u503c\u548c\u793e\u4f1a\u610f\u4e49\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\u6240\u4f7f\u7528\u7684\u6570\u636e\u96c6\u540d\u4e3a\u201coct5k_new_new\u201d，\u65e8\u5728\u4e3a\u6539\u8fdbYOLOv11\u7684\u773c\u5e95\u56fe\u50cf\u5c42\u6b21\u5206\u5272\u7cfb\u7edf\u63d0\u4f9b\u9ad8\u8d28\u91cf\u7684\u8bad\u7ec3\u6570\u636e\u3002\u8be5\u6570\u636e\u96c6\u4e13\u6ce8\u4e8e\u773c\u5e95\u56fe\u50cf\u7684\u5206\u5272\u4efb\u52a1，\u5305\u542b\u4e86\u516d\u4e2a\u4e3b\u8981\u7c7b\u522b，\u5206\u522b\u4e3a\u5185\u754c\u819c（ILM）\u3001\u5149\u611f\u53d7\u5668\u5c42\u4e0e\u89c6\u7f51\u819c\u8272\u7d20\u4e0a\u76ae\u5c42（IS-OS）\u3001\u4e0b\u5c42（Lower）\u3001\u5916\u6838\u5c42（OPL）\u3001\u89c6\u7f51\u819c\u8272\u7d20\u4e0a\u76ae\u5c42（RPE）\u4ee5\u53ca\u4e0a\u5c42（Upper）\u3002\u8fd9\u4e9b\u7c7b\u522b\u6db5\u76d6\u4e86\u773c\u5e95\u56fe\u50cf\u4e2d\u91cd\u8981\u7684\u89e3\u5256\u7ed3\u6784，\u5bf9\u4e8e\u773c\u79d1\u75be\u75c5\u7684\u8bca\u65ad\u548c\u6cbb\u7597\u5177\u6709\u91cd\u8981\u610f\u4e49\u3002<\/p>\n

\u5728\u6570\u636e\u96c6\u7684\u6784\u5efa\u8fc7\u7a0b\u4e2d，\u6240\u6709\u56fe\u50cf\u5747\u7ecf\u8fc7\u7cbe\u5fc3\u6807\u6ce8，\u4ee5\u786e\u4fdd\u6bcf\u4e2a\u7c7b\u522b\u7684\u5206\u5272\u8fb9\u754c\u6e05\u6670\u4e14\u51c6\u786e\u3002\u8fd9\u79cd\u9ad8\u8d28\u91cf\u7684\u6807\u6ce8\u4e0d\u4ec5\u63d0\u9ad8\u4e86\u6a21\u578b\u8bad\u7ec3\u7684\u6709\u6548\u6027，\u4e5f\u4e3a\u540e\u7eed\u7684\u9a8c\u8bc1\u548c\u6d4b\u8bd5\u63d0\u4f9b\u4e86\u53ef\u9760\u7684\u57fa\u7840\u3002\u6570\u636e\u96c6\u4e2d\u7684\u56fe\u50cf\u6837\u672c\u6765\u81ea\u591a\u79cd\u4e0d\u540c\u7684\u773c\u5e95\u626b\u63cf，\u6db5\u76d6\u4e86\u4e0d\u540c\u5e74\u9f84\u6bb5\u548c\u75c5\u7406\u72b6\u6001\u7684\u60a3\u8005，\u4ece\u800c\u589e\u5f3a\u4e86\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u548c\u9002\u5e94\u6027\u3002<\/p>\n

\u6b64\u5916，\u6570\u636e\u96c6\u7684\u591a\u6837\u6027\u4f7f\u5f97\u6a21\u578b\u80fd\u591f\u5b66\u4e60\u5230\u4e0d\u540c\u89e3\u5256\u7ed3\u6784\u5728\u4e0d\u540c\u6761\u4ef6\u4e0b\u7684\u8868\u73b0，\u8fdb\u4e00\u6b65\u63d0\u5347\u4e86\u5206\u5272\u7cbe\u5ea6\u3002\u901a\u8fc7\u4f7f\u7528\u201coct5k_new_new\u201d\u6570\u636e\u96c6，\u7814\u7a76\u4eba\u5458\u5e0c\u671b\u80fd\u591f\u63a8\u52a8\u773c\u5e95\u56fe\u50cf\u5206\u6790\u6280\u672f\u7684\u53d1\u5c55，\u5c24\u5176\u662f\u5728\u81ea\u52a8\u5316\u5206\u5272\u548c\u75be\u75c5\u68c0\u6d4b\u65b9\u9762\u7684\u5e94\u7528\u3002\u6700\u7ec8\u76ee\u6807\u662f\u5b9e\u73b0\u66f4\u4e3a\u7cbe\u51c6\u7684\u773c\u5e95\u56fe\u50cf\u5206\u6790，\u4ee5\u8f85\u52a9\u4e34\u5e8a\u533b\u751f\u8fdb\u884c\u65e9\u671f\u8bca\u65ad\u548c\u4e2a\u6027\u5316\u6cbb\u7597\u65b9\u6848\u7684\u5236\u5b9a\u3002\u6570\u636e\u96c6\u7684\u8bbe\u8ba1\u548c\u5b9e\u65bd\u4e3a\u672c\u9879\u76ee\u7684\u6210\u529f\u5960\u5b9a\u4e86\u575a\u5b9e\u7684\u57fa\u7840，\u671f\u5f85\u901a\u8fc7\u8fd9\u4e00\u7814\u7a76\u4e3a\u773c\u79d1\u9886\u57df\u5e26\u6765\u65b0\u7684\u7a81\u7834\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 torch
\nimport torch.nn as nn
\nimport torch.nn.functional as F
\nfrom timm.models.layers import SqueezeExcite<\/p>\n

\u5b9a\u4e49\u5377\u79ef\u5c42\u548c\u6279\u5f52\u4e00\u5316\u7684\u7ec4\u5408
\nclass Conv2d_BN(torch.nn.Sequential):
\ndef init(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, groups=1, bn_weight_init=1):
\nsuper().init()
\n# \u6dfb\u52a0\u5377\u79ef\u5c42
\nself.add_module(\u2018c\u2019, torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias=False))
\n# \u6dfb\u52a0\u6279\u5f52\u4e00\u5316\u5c42
\nself.add_module(\u2018bn\u2019, torch.nn.BatchNorm2d(out_channels))
\n# \u521d\u59cb\u5316\u6279\u5f52\u4e00\u5316\u7684\u6743\u91cd
\ntorch.nn.init.constant_(self.bn.weight, bn_weight_init)
\ntorch.nn.init.constant_(self.bn.bias, 0)<\/p>\n

@torch.no_grad()
\ndef switch_to_deploy(self):
\n # \u5c06\u8bad\u7ec3\u6a21\u5f0f\u7684\u5377\u79ef\u548c\u6279\u5f52\u4e00\u5316\u5c42\u878d\u5408\u4e3a\u4e00\u4e2a\u5377\u79ef\u5c42
\n c, bn = self._modules.values()
\n w = bn.weight \/ (bn.running_var + bn.eps)**0.5 # \u8ba1\u7b97\u65b0\u7684\u6743\u91cd
\n w = c.weight * w[:, None, None, None] # \u878d\u5408\u6743\u91cd
\n b = bn.bias – bn.running_mean * bn.weight \/ (bn.running_var + bn.eps)**0.5 # \u8ba1\u7b97\u65b0\u7684\u504f\u7f6e
\n # \u521b\u5efa\u65b0\u7684\u5377\u79ef\u5c42
\n m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups)
\n m.weight.data.copy_(w) # \u590d\u5236\u6743\u91cd
\n m.bias.data.copy_(b) # \u590d\u5236\u504f\u7f6e
\n return m<\/p>\n

\u5b9a\u4e49EfficientViT\u7684\u57fa\u672c\u6a21\u5757
\nclass EfficientViTBlock(torch.nn.Module):
\ndef init(self, embed_dim, key_dim, num_heads=8, window_resolution=7):
\nsuper().init()
\n# \u5b9a\u4e49\u5377\u79ef\u5c42\u548c\u524d\u9988\u7f51\u7edc
\nself.dw0 = Residual(Conv2d_BN(embed_dim, embed_dim, 3, 1, 1, groups=embed_dim))
\nself.ffn0 = Residual(FFN(embed_dim, int(embed_dim * 2)))
\nself.mixer = Residual(LocalWindowAttention(embed_dim, key_dim, num_heads, window_resolution=window_resolution))
\nself.dw1 = Residual(Conv2d_BN(embed_dim, embed_dim, 3, 1, 1, groups=embed_dim))
\nself.ffn1 = Residual(FFN(embed_dim, int(embed_dim * 2)))<\/p>\n

def forward(self, x):
\n # \u524d\u5411\u4f20\u64ad
\n return self.ffn1(self.dw1(self.mixer(self.ffn0(self.dw0(x)))))<\/p>\n

\u5b9a\u4e49EfficientViT\u6a21\u578b
\nclass EfficientViT(torch.nn.Module):
\ndef init(self, img_size=400, patch_size=16, embed_dim=[64, 128, 192], depth=[1, 2, 3], num_heads=[4, 4, 4], window_size=[7, 7, 7]):
\nsuper().init()
\n# \u5b9a\u4e49\u56fe\u50cf\u5d4c\u5165\u5c42
\nself.patch_embed = torch.nn.Sequential(
\nConv2d_BN(3, embed_dim[0] \/\/ 8, 3, 2, 1),
\nnn.ReLU(),
\nConv2d_BN(embed_dim[0] \/\/ 8, embed_dim[0] \/\/ 4, 3, 2, 1),
\nnn.ReLU(),
\nConv2d_BN(embed_dim[0] \/\/ 4, embed_dim[0] \/\/ 2, 3, 2, 1),
\nnn.ReLU(),
\nConv2d_BN(embed_dim[0] \/\/ 2, embed_dim[0], 3, 1, 1)
\n)<\/p>\n

# \u5b9a\u4e49\u591a\u4e2aEfficientViTBlock
\n self.blocks = []
\n for i in range(len(depth)):
\n for _ in range(depth[i]):
\n self.blocks.append(EfficientViTBlock(embed_dim[i], key_dim=16, num_heads=num_heads[i], window_resolution=window_size[i]))
\n self.blocks = torch.nn.Sequential(*self.blocks)<\/p>\n

def forward(self, x):
\n # \u524d\u5411\u4f20\u64ad
\n x = self.patch_embed(x) # \u56fe\u50cf\u5d4c\u5165
\n x = self.blocks(x) # \u901a\u8fc7\u591a\u4e2aEfficientViTBlock
\n return x<\/p>\n

\u521b\u5efaEfficientViT\u6a21\u578b\u5b9e\u4f8b
\nif name == \u2018main\u2019:
\nmodel = EfficientViT(img_size=224, patch_size=16)
\ninputs = torch.randn((1, 3, 640, 640)) # \u968f\u673a\u8f93\u5165
\nres = model(inputs) # \u524d\u5411\u4f20\u64ad
\nprint(res.size()) # \u8f93\u51fa\u7ed3\u679c\u7684\u5c3a\u5bf8
\n\u4ee3\u7801\u6ce8\u91ca\u8bf4\u660e：
\nConv2d_BN\u7c7b：\u8be5\u7c7b\u5c01\u88c5\u4e86\u5377\u79ef\u5c42\u548c\u6279\u5f52\u4e00\u5316\u5c42，\u5e76\u63d0\u4f9b\u4e86\u4e00\u4e2a\u65b9\u6cd5\u6765\u5c06\u5176\u878d\u5408\u4e3a\u4e00\u4e2a\u5377\u79ef\u5c42，\u4ee5\u63d0\u9ad8\u63a8\u7406\u901f\u5ea6\u3002
\nEfficientViTBlock\u7c7b：\u8fd9\u662fEfficientViT\u7684\u57fa\u672c\u6784\u5efa\u5757，\u5305\u542b\u4e86\u5377\u79ef\u5c42\u3001\u524d\u9988\u7f51\u7edc\u548c\u5c40\u90e8\u7a97\u53e3\u6ce8\u610f\u529b\u673a\u5236\u3002
\nEfficientViT\u7c7b：\u5b9a\u4e49\u4e86\u6574\u4e2aEfficientViT\u6a21\u578b，\u5305\u62ec\u56fe\u50cf\u5d4c\u5165\u5c42\u548c\u591a\u4e2aEfficientViTBlock\u7684\u5806\u53e0\u3002
\n\u524d\u5411\u4f20\u64ad：\u6a21\u578b\u7684\u524d\u5411\u4f20\u64ad\u8fc7\u7a0b，\u9996\u5148\u5c06\u8f93\u5165\u56fe\u50cf\u5d4c\u5165，\u7136\u540e\u901a\u8fc7\u591a\u4e2a\u5757\u8fdb\u884c\u5904\u7406，\u6700\u7ec8\u8f93\u51fa\u7279\u5f81\u3002
\n\u8fd9\u4e2a\u7a0b\u5e8f\u6587\u4ef6\u5b9e\u73b0\u4e86\u4e00\u4e2a\u9ad8\u6548\u7684\u89c6\u89c9\u53d8\u6362\u5668（EfficientViT）\u6a21\u578b\u67b6\u6784，\u9002\u7528\u4e8e\u5404\u79cd\u4e0b\u6e38\u4efb\u52a1\u3002\u6587\u4ef6\u4e2d\u5305\u542b\u591a\u4e2a\u7c7b\u548c\u51fd\u6570，\u6784\u6210\u4e86\u6a21\u578b\u7684\u6574\u4f53\u7ed3\u6784\u548c\u529f\u80fd\u3002<\/p>\n

\u9996\u5148，\u6587\u4ef6\u5f15\u5165\u4e86\u5fc5\u8981\u7684\u5e93，\u5305\u62ecPyTorch\u53ca\u5176\u76f8\u5173\u6a21\u5757\u3002\u63a5\u7740\u5b9a\u4e49\u4e86\u4e00\u4e2a\u540d\u4e3aConv2d_BN\u7684\u7c7b，\u8be5\u7c7b\u662f\u4e00\u4e2a\u5305\u542b\u5377\u79ef\u5c42\u548c\u6279\u5f52\u4e00\u5316\u5c42\u7684\u987a\u5e8f\u5bb9\u5668\u3002\u8fd9\u4e2a\u7c7b\u5728\u521d\u59cb\u5316\u65f6\u8bbe\u7f6e\u4e86\u5377\u79ef\u5c42\u7684\u53c2\u6570，\u5e76\u5bf9\u6279\u5f52\u4e00\u5316\u5c42\u7684\u6743\u91cd\u548c\u504f\u7f6e\u8fdb\u884c\u4e86\u521d\u59cb\u5316\u3002\u5b83\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e2aswitch_to_deploy\u65b9\u6cd5，\u7528\u4e8e\u5728\u63a8\u7406\u9636\u6bb5\u5c06\u6279\u5f52\u4e00\u5316\u5c42\u8f6c\u6362\u4e3a\u5377\u79ef\u5c42\u3002<\/p>\n

\u63a5\u4e0b\u6765，replace_batchnorm\u51fd\u6570\u7528\u4e8e\u904d\u5386\u7f51\u7edc\u4e2d\u7684\u6240\u6709\u5b50\u6a21\u5757，\u5c06\u6279\u5f52\u4e00\u5316\u5c42\u66ff\u6362\u4e3a\u6052\u7b49\u6620\u5c04，\u4ee5\u4fbf\u5728\u63a8\u7406\u65f6\u63d0\u9ad8\u6548\u7387\u3002<\/p>\n

PatchMerging\u7c7b\u5b9e\u73b0\u4e86\u5bf9\u8f93\u5165\u7279\u5f81\u56fe\u7684\u5408\u5e76\u64cd\u4f5c，\u4f7f\u7528\u591a\u4e2a\u5377\u79ef\u5c42\u548c\u6fc0\u6d3b\u51fd\u6570\u6765\u5904\u7406\u8f93\u5165\u6570\u636e\u3002Residual\u7c7b\u5219\u5b9e\u73b0\u4e86\u6b8b\u5dee\u8fde\u63a5\u7684\u529f\u80fd，\u53ef\u4ee5\u5728\u8bad\u7ec3\u65f6\u968f\u673a\u4e22\u5f03\u90e8\u5206\u8f93\u5165，\u4ee5\u589e\u5f3a\u6a21\u578b\u7684\u9c81\u68d2\u6027\u3002<\/p>\n

FFN\u7c7b\u5b9e\u73b0\u4e86\u524d\u9988\u795e\u7ecf\u7f51\u7edc，\u5305\u542b\u4e24\u4e2a\u5377\u79ef\u5c42\u548c\u4e00\u4e2aReLU\u6fc0\u6d3b\u51fd\u6570\u3002CascadedGroupAttention\u548cLocalWindowAttention\u7c7b\u5b9e\u73b0\u4e86\u4e0d\u540c\u7c7b\u578b\u7684\u6ce8\u610f\u529b\u673a\u5236，\u7528\u4e8e\u5904\u7406\u8f93\u5165\u7279\u5f81\u56fe\u4e2d\u7684\u4fe1\u606f\u3002\u5b83\u4eec\u901a\u8fc7\u5206\u7ec4\u5377\u79ef\u548c\u6ce8\u610f\u529b\u673a\u5236\u6765\u63d0\u53d6\u7279\u5f81\u3002<\/p>\n

EfficientViTBlock\u7c7b\u662f\u9ad8\u6548\u89c6\u89c9\u53d8\u6362\u5668\u7684\u57fa\u672c\u6784\u5efa\u5757，\u7ed3\u5408\u4e86\u5377\u79ef\u5c42\u3001\u524d\u9988\u7f51\u7edc\u548c\u6ce8\u610f\u529b\u673a\u5236\u3002EfficientViT\u7c7b\u5219\u662f\u6574\u4e2a\u6a21\u578b\u7684\u4e3b\u7c7b，\u8d1f\u8d23\u6784\u5efa\u6a21\u578b\u7684\u4e0d\u540c\u9636\u6bb5，\u5305\u62ec\u56fe\u50cf\u5d4c\u5165\u3001\u591a\u4e2aEfficientViT\u5757\u7684\u5806\u53e0\u7b49\u3002<\/p>\n

\u5728\u6a21\u578b\u7684\u521d\u59cb\u5316\u8fc7\u7a0b\u4e2d，\u7528\u6237\u53ef\u4ee5\u8bbe\u7f6e\u56fe\u50cf\u5927\u5c0f\u3001\u8865\u4e01\u5927\u5c0f\u3001\u5d4c\u5165\u7ef4\u5ea6\u3001\u6df1\u5ea6\u3001\u6ce8\u610f\u529b\u5934\u6570\u7b49\u53c2\u6570\u3002\u6a21\u578b\u7684\u524d\u5411\u4f20\u64ad\u65b9\u6cd5\u8fd4\u56de\u4e86\u591a\u4e2a\u9636\u6bb5\u7684\u8f93\u51fa，\u4fbf\u4e8e\u4e0b\u6e38\u4efb\u52a1\u7684\u4f7f\u7528\u3002<\/p>\n

\u6700\u540e，\u6587\u4ef6\u5b9a\u4e49\u4e86\u4e00\u4e9b\u4e0d\u540c\u914d\u7f6e\u7684EfficientViT\u6a21\u578b（\u5982EfficientViT_m0\u5230EfficientViT_m5），\u5e76\u63d0\u4f9b\u4e86\u76f8\u5e94\u7684\u6784\u5efa\u51fd\u6570\u3002\u8fd9\u4e9b\u51fd\u6570\u5141\u8bb8\u7528\u6237\u52a0\u8f7d\u9884\u8bad\u7ec3\u6743\u91cd，\u5e76\u9009\u62e9\u662f\u5426\u878d\u5408\u6279\u5f52\u4e00\u5316\u5c42\u3002<\/p>\n

\u5728__main__\u90e8\u5206，\u793a\u4f8b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5b9e\u4f8b\u5316\u4e00\u4e2aEfficientViT\u6a21\u578b，\u5e76\u5bf9\u968f\u673a\u8f93\u5165\u8fdb\u884c\u524d\u5411\u4f20\u64ad，\u8f93\u51fa\u5404\u4e2a\u9636\u6bb5\u7684\u7279\u5f81\u56fe\u5927\u5c0f\u3002\u6574\u4f53\u4e0a，\u8fd9\u4e2a\u6587\u4ef6\u5b9e\u73b0\u4e86\u4e00\u4e2a\u7075\u6d3b\u4e14\u9ad8\u6548\u7684\u89c6\u89c9\u53d8\u6362\u5668\u67b6\u6784，\u9002\u7528\u4e8e\u591a\u79cd\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u3002<\/p>\n

10.4 cfpt.py
\n\u4ee5\u4e0b\u662f\u4fdd\u7559\u7684\u6838\u5fc3\u4ee3\u7801\u90e8\u5206，\u5e76\u6dfb\u52a0\u4e86\u8be6\u7ec6\u7684\u4e2d\u6587\u6ce8\u91ca：<\/p>\n

import torch
\nimport torch.nn as nn
\nimport torch.nn.functional as F
\nfrom timm.layers import to_2tuple, trunc_normal_<\/p>\n

class CrossLayerPosEmbedding3D(nn.Module):
\ndef init(self, num_heads=4, window_size=(5, 3, 1), spatial=True):
\nsuper(CrossLayerPosEmbedding3D, self).init()
\nself.spatial = spatial # \u662f\u5426\u4f7f\u7528\u7a7a\u95f4\u4f4d\u7f6e\u5d4c\u5165
\nself.num_heads = num_heads # \u6ce8\u610f\u529b\u5934\u7684\u6570\u91cf
\nself.layer_num = len(window_size) # \u5c42\u6570
\n# \u521d\u59cb\u5316\u76f8\u5bf9\u4f4d\u7f6e\u504f\u7f6e\u8868
\nself.relative_position_bias_table = nn.Parameter(
\ntorch.zeros((2 * window_size[0] – 1) * (2 * window_size[0] – 1), num_heads))
\n)
\n# \u8ba1\u7b97\u76f8\u5bf9\u4f4d\u7f6e\u7d22\u5f15
\nself.register_buffer(\u201crelative_position_index\u201d, self.calculate_relative_position_index(window_size))
\ntrunc_normal_(self.relative_position_bias_table, std=.02) # \u521d\u59cb\u5316\u76f8\u5bf9\u4f4d\u7f6e\u504f\u7f6e<\/p>\n

# \u521d\u59cb\u5316\u7edd\u5bf9\u4f4d\u7f6e\u504f\u7f6e
\n self.absolute_position_bias = nn.Parameter(torch.zeros(len(window_size), num_heads, 1, 1, 1))
\n trunc_normal_(self.absolute_position_bias, std=.02)<\/p>\n

def calculate_relative_position_index(self, window_size):
\n # \u8ba1\u7b97\u76f8\u5bf9\u4f4d\u7f6e\u7d22\u5f15
\n coords_h = [torch.arange(ws) – ws \/\/ 2 for ws in window_size]
\n coords_w = [torch.arange(ws) – ws \/\/ 2 for ws in window_size]
\n coords = [torch.stack(torch.meshgrid([coord_h, coord_w])) for coord_h, coord_w in zip(coords_h, coords_w)]
\n coords_flatten = torch.cat([torch.flatten(coord, 1) for coord in coords], dim=-1)
\n relative_coords = coords_flatten[:, :, None] – coords_flatten[:, None, :]
\n relative_coords = relative_coords.permute(1, 2, 0).contiguous()
\n relative_coords[:, :, 0] += window_size[0] – 1
\n relative_coords[:, :, 1] += window_size[0] – 1
\n relative_coords[:, :, 0] *= 2 * window_size[0] – 1
\n return relative_coords.sum(-1)<\/p>\n

def forward(self):
\n # \u8ba1\u7b97\u4f4d\u7f6e\u5d4c\u5165
\n pos_indicies = self.relative_position_index.view(-1)
\n pos_indicies_floor = torch.floor(pos_indicies).long()
\n pos_indicies_ceil = torch.ceil(pos_indicies).long()
\n value_floor = self.relative_position_bias_table[pos_indicies_floor]
\n value_ceil = self.relative_position_bias_table[pos_indicies_ceil]
\n weights_ceil = pos_indicies – pos_indicies_floor.float()
\n weights_floor = 1.0 – weights_ceil<\/p>\n

pos_embed = weights_floor.unsqueeze(-1) * value_floor + weights_ceil.unsqueeze(-1) * value_ceil
\n pos_embed = pos_embed.reshape(1, 1, self.num_token, -1, self.num_heads).permute(0, 4, 1, 2, 3)<\/p>\n

return pos_embed + self.absolute_position_bias<\/p>\n

class CrossLayerSpatialAttention(nn.Module):
\ndef init(self, in_dim, layer_num=3, num_heads=4):
\nsuper(CrossLayerSpatialAttention, self).init()
\nself.num_heads = num_heads # \u6ce8\u610f\u529b\u5934\u7684\u6570\u91cf
\nself.hidden_dim = in_dim \/\/ 4 # \u9690\u85cf\u7ef4\u5ea6
\nself.qkv = nn.Conv2d(in_dim, self.hidden_dim * 3, kernel_size=1) # \u7ebf\u6027\u53d8\u6362
\nself.softmax = nn.Softmax(dim=-1) # softmax\u5c42
\nself.pos_embed = CrossLayerPosEmbedding3D(num_heads=num_heads) # \u4f4d\u7f6e\u5d4c\u5165<\/p>\n

def forward(self, x_list):
\n q_list, k_list, v_list = [], [], []<\/p>\n

for x in x_list:
\n qkv = self.qkv(x) # \u8ba1\u7b97Q, K, V
\n q, k, v = qkv.chunk(3, dim=1) # \u5206\u5272Q, K, V
\n q_list.append(q)
\n k_list.append(k)
\n v_list.append(v)<\/p>\n

# \u5c06Q, K, V\u5806\u53e0\u5728\u4e00\u8d77
\n q_stack = torch.cat(q_list, dim=1)
\n k_stack = torch.cat(k_list, dim=1)
\n v_stack = torch.cat(v_list, dim=1)<\/p>\n

# \u8ba1\u7b97\u6ce8\u610f\u529b
\n attn = F.normalize(q_stack, dim=-1) @ F.normalize(k_stack, dim=-1).transpose(-1, -2)
\n attn = attn + self.pos_embed() # \u52a0\u5165\u4f4d\u7f6e\u5d4c\u5165
\n attn = self.softmax(attn) # \u5f52\u4e00\u5316<\/p>\n

out = attn @ v_stack # \u8ba1\u7b97\u8f93\u51fa
\n return out # \u8fd4\u56de\u8f93\u51fa<\/p>\n

\u4ee3\u7801\u6838\u5fc3\u90e8\u5206\u8bf4\u660e：
\nCrossLayerPosEmbedding3D: \u8be5\u7c7b\u7528\u4e8e\u8ba1\u7b97\u8de8\u5c42\u76843D\u4f4d\u7f6e\u5d4c\u5165，\u5305\u62ec\u76f8\u5bf9\u4f4d\u7f6e\u504f\u7f6e\u548c\u7edd\u5bf9\u4f4d\u7f6e\u504f\u7f6e\u7684\u521d\u59cb\u5316\u548c\u8ba1\u7b97\u3002
\nCrossLayerSpatialAttention: \u8be5\u7c7b\u5b9e\u73b0\u4e86\u8de8\u5c42\u7a7a\u95f4\u6ce8\u610f\u529b\u673a\u5236，\u5305\u542bQ\u3001K\u3001V\u7684\u8ba1\u7b97\u548c\u6ce8\u610f\u529b\u7684\u8ba1\u7b97\u903b\u8f91\u3002
\nforward\u65b9\u6cd5: \u5728\u8fd9\u4e24\u4e2a\u7c7b\u4e2d，forward\u65b9\u6cd5\u5b9a\u4e49\u4e86\u5982\u4f55\u5904\u7406\u8f93\u5165\u6570\u636e\u5e76\u8ba1\u7b97\u8f93\u51fa\u3002\u5bf9\u4e8eCrossLayerSpatialAttention，\u5b83\u5c06\u8f93\u5165\u5206\u4e3aQ\u3001K\u3001V，\u5e76\u8ba1\u7b97\u6ce8\u610f\u529b\u8f93\u51fa\u3002
\n\u4ee5\u4e0a\u4ee3\u7801\u5c55\u793a\u4e86\u8de8\u5c42\u6ce8\u610f\u529b\u673a\u5236\u7684\u6838\u5fc3\u903b\u8f91，\u6ce8\u91ca\u8be6\u7ec6\u89e3\u91ca\u4e86\u6bcf\u4e2a\u90e8\u5206\u7684\u529f\u80fd\u548c\u4f5c\u7528\u3002<\/p>\n

\u8fd9\u4e2a\u7a0b\u5e8f\u6587\u4ef6 cfpt.py \u5b9e\u73b0\u4e86\u4e24\u4e2a\u4e3b\u8981\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u5757：CrossLayerChannelAttention \u548c CrossLayerSpatialAttention，\u5b83\u4eec\u90fd\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6 PyTorch\u3002\u6587\u4ef6\u4e2d\u8fd8\u5b9a\u4e49\u4e86\u4e00\u4e9b\u8f85\u52a9\u7c7b\u548c\u51fd\u6570，\u7528\u4e8e\u5b9e\u73b0\u7279\u5b9a\u7684\u529f\u80fd，\u5982\u4f4d\u7f6e\u7f16\u7801\u3001\u5377\u79ef\u64cd\u4f5c\u548c\u7a97\u53e3\u5212\u5206\u7b49\u3002<\/p>\n

\u9996\u5148，\u6587\u4ef6\u5bfc\u5165\u4e86\u5fc5\u8981\u7684\u5e93，\u5305\u62ec PyTorch\u3001\u6570\u5b66\u5e93\u3001einops（\u7528\u4e8e\u5f20\u91cf\u91cd\u6392）\u3001\u4ee5\u53ca\u4e00\u4e9b PyTorch \u7684\u6a21\u5757\u548c\u529f\u80fd\u3002\u63a5\u7740，\u5b9a\u4e49\u4e86\u4e00\u4e2a LayerNormProxy \u7c7b，\u8be5\u7c7b\u5c01\u88c5\u4e86 PyTorch \u7684\u5c42\u5f52\u4e00\u5316\u529f\u80fd，\u5e76\u5728\u524d\u5411\u4f20\u64ad\u4e2d\u8c03\u6574\u8f93\u5165\u5f20\u91cf\u7684\u7ef4\u5ea6\u3002<\/p>\n

\u63a5\u4e0b\u6765，CrossLayerPosEmbedding3D \u7c7b\u7528\u4e8e\u751f\u6210\u8de8\u5c42\u4f4d\u7f6e\u5d4c\u5165\u3002\u5b83\u6839\u636e\u7ed9\u5b9a\u7684\u7a97\u53e3\u5927\u5c0f\u548c\u5934\u6570\u521d\u59cb\u5316\u76f8\u5bf9\u4f4d\u7f6e\u504f\u7f6e\u8868，\u5e76\u8ba1\u7b97\u76f8\u5bf9\u4f4d\u7f6e\u7d22\u5f15\u3002\u8be5\u7c7b\u7684\u524d\u5411\u65b9\u6cd5\u751f\u6210\u4f4d\u7f6e\u5d4c\u5165，\u7528\u4e8e\u540e\u7eed\u7684\u6ce8\u610f\u529b\u8ba1\u7b97\u3002<\/p>\n

ConvPosEnc \u7c7b\u5b9e\u73b0\u4e86\u5377\u79ef\u4f4d\u7f6e\u7f16\u7801，\u4f7f\u7528\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\u6765\u589e\u5f3a\u7279\u5f81\u56fe，\u5e76\u53ef\u9009\u62e9\u6027\u5730\u6dfb\u52a0\u6fc0\u6d3b\u51fd\u6570\u3002DWConv \u7c7b\u5219\u5b9e\u73b0\u4e86\u6df1\u5ea6\u5377\u79ef\u64cd\u4f5c，\u7528\u4e8e\u5904\u7406\u8f93\u5165\u7279\u5f81\u56fe\u3002<\/p>\n

Mlp \u7c7b\u5b9e\u73b0\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u591a\u5c42\u611f\u77e5\u673a（MLP），\u5305\u542b\u4e24\u4e2a\u7ebf\u6027\u5c42\u548c\u4e00\u4e2a\u6fc0\u6d3b\u51fd\u6570\u3002<\/p>\n

\u63a5\u4e0b\u6765\u7684\u51e0\u4e2a\u51fd\u6570\u5b9e\u73b0\u4e86\u7a97\u53e3\u5212\u5206\u548c\u9006\u64cd\u4f5c，\u4e3b\u8981\u7528\u4e8e\u5904\u7406\u8f93\u5165\u7279\u5f81\u56fe\u7684\u91cd\u7ec4\u548c\u6062\u590d，\u652f\u6301\u91cd\u53e0\u7a97\u53e3\u7684\u5904\u7406\u3002<\/p>\n

CrossLayerSpatialAttention \u7c7b\u5b9e\u73b0\u4e86\u7a7a\u95f4\u6ce8\u610f\u529b\u673a\u5236\u3002\u5b83\u901a\u8fc7\u591a\u5c42\u5377\u79ef\u3001\u5f52\u4e00\u5316\u548c\u6ce8\u610f\u529b\u8ba1\u7b97\u6765\u5904\u7406\u8f93\u5165\u7279\u5f81\u56fe\u3002\u8be5\u7c7b\u7684\u524d\u5411\u65b9\u6cd5\u63a5\u6536\u591a\u4e2a\u8f93\u5165\u7279\u5f81\u56fe，\u8ba1\u7b97\u67e5\u8be2\u3001\u952e\u3001\u503c，\u5e76\u901a\u8fc7\u6ce8\u610f\u529b\u673a\u5236\u751f\u6210\u8f93\u51fa\u7279\u5f81\u56fe\u3002\u5b83\u8fd8\u4f7f\u7528\u4e86\u4e4b\u524d\u5b9a\u4e49\u7684\u5377\u79ef\u4f4d\u7f6e\u7f16\u7801\u548c\u4f4d\u7f6e\u5d4c\u5165\u3002<\/p>\n

CrossLayerChannelAttention \u7c7b\u5b9e\u73b0\u4e86\u901a\u9053\u6ce8\u610f\u529b\u673a\u5236，\u7ed3\u6784\u4e0e\u7a7a\u95f4\u6ce8\u610f\u529b\u7c7b\u4f3c，\u4f46\u5904\u7406\u65b9\u5f0f\u4e0d\u540c\u3002\u5b83\u4f7f\u7528\u901a\u9053\u5212\u5206\u548c\u9006\u64cd\u4f5c\u6765\u5b9e\u73b0\u5bf9\u8f93\u5165\u7279\u5f81\u56fe\u7684\u5904\u7406\u3002\u8be5\u7c7b\u540c\u6837\u5728\u524d\u5411\u65b9\u6cd5\u4e2d\u63a5\u6536\u591a\u4e2a\u8f93\u5165\u7279\u5f81\u56fe，\u8ba1\u7b97\u6ce8\u610f\u529b\u5e76\u751f\u6210\u8f93\u51fa\u3002<\/p>\n

\u6574\u4f53\u6765\u770b，\u8fd9\u4e2a\u6587\u4ef6\u5b9e\u73b0\u4e86\u4e00\u4e2a\u590d\u6742\u7684\u6ce8\u610f\u529b\u673a\u5236，\u65e8\u5728\u63d0\u9ad8\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u5728\u5904\u7406\u56fe\u50cf\u6216\u5176\u4ed6\u9ad8\u7ef4\u6570\u636e\u65f6\u7684\u6027\u80fd\u3002\u901a\u8fc7\u8de8\u5c42\u7684\u901a\u9053\u548c\u7a7a\u95f4\u6ce8\u610f\u529b，\u6a21\u578b\u80fd\u591f\u66f4\u597d\u5730\u6355\u6349\u7279\u5f81\u4e4b\u95f4\u7684\u5173\u7cfb，\u4ece\u800c\u63d0\u5347\u7279\u5f81\u8868\u793a\u7684\u80fd\u529b\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

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