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\u8fd9\u5c01\u4fe1\u7684\u8d21\u732e\u53ef\u4ee5\u603b\u7ed3\u5982\u4e0b： 1.\u63a2\u7d22\u4e86\u7528\u4e8eHSI\u53bb\u566a\u7684\u5168\u5c40\u548c\u5c40\u90e8\u7279\u5f81\u5efa\u6a21\u7684\u6709\u524d\u9014\u4f46\u5177\u6709\u6311\u6218\u6027\u7684\u95ee\u9898\u3002\u636e\u6211\u4eec\u6240\u77e5，\u8fd9\u662f\u7b2c\u4e00\u4e2a\u5c06\u8054\u5408\u6536\u5272\u673a\u5377\u79ef\u548c\u6ce8\u610f\u529b\u673a\u5236\u7ed3\u5408\u8d77\u6765\u7528\u4e8eHSI\u53bb\u566a\u4efb\u52a1\u7684\u5de5\u4f5c\u3002 2.\u63d0\u51fa\u4e86\u591a\u5c3a\u5ea6\u524d\u9988\u7f51\u7edc，\u5728\u4e0d\u540c\u5c3a\u5ea6\u4e0a\u65e0\u7f1d\u63d0\u53d6\u7279\u5f81，\u6709\u6548\u6291\u5236\u591a\u5c3a\u5ea6\u566a\u58f0\u3002 3.\u5728\u4e24\u4e2a\u57fa\u51c6\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u4e86\u5927\u91cf\u5b9e\u9a8c，\u9a8c\u8bc1\u4e86HCANet\u7684\u5408\u7406\u6027\u548c\u6709\u6548\u6027\u3002<\/p>\n

\u7ed3\u8bba<\/h2>\n

\u6211\u4eec\u63d0\u51fa\u4e86HCANet，\u4e00\u79cd\u65b0\u7684\u7f51\u7edcHSI\u53bb\u566a\u3002\u7279\u522b\u662f，\u6211\u4eec\u63d0\u51fa\u4e86\u5377\u79ef\u548c\u6ce8\u610f\u529b\u878d\u5408\u6a21\u5757，CAFM，\u878d\u5408\u5168\u5c40\u548c\u5c40\u90e8\u7279\u5f81\u3002\u6b64\u5916，\u6211\u4eec\u63d0\u51fa\u4e86\u591a\u5c3a\u5ea6\u524d\u9988\u7f51\u7edc，MSFN\u4ece\u591a\u4e2a\u5c3a\u5ea6\u63d0\u53d6\u7279\u5f81，\u63d0\u9ad8\u53bb\u566a\u6027\u80fd\u3002\u5728\u5177\u6709\u6311\u6218\u6027\u7684HSI\u6570\u636e\u96c6\u4e0a\u7684\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e，\u4e0e\u73b0\u6709\u7684HSI\u53bb\u566a\u65b9\u6cd5\u76f8\u6bd4，\u6211\u4eec\u63d0\u51fa\u7684\u6a21\u578b\u662f\u6709\u6548\u7684\u3002\u6211\u4eec\u7684\u6a21\u578b\u5b9e\u73b0\u4e86\u663e\u7740\u7684\u53bb\u566a\u6027\u80fd\u7684\u5b9a\u91cf\u6307\u6807\u548c\u91cd\u5efa\u56fe\u50cf\u7684\u89c6\u89c9\u8d28\u91cf\u3002<\/p>\n

\u4e09\u3001\u6539\u8fdbYOLOv13，\u6253\u9020\u81ea\u5df1\u7684\u65b0\u6a21\u578b！<\/h2>\n

3.1 \u590d\u5236\u4e0b\u8ff0\u4ee3\u7801，\u65b0\u5efaMSFN.py\u6587\u4ef6\u81f3nn\u6587\u4ef6\u5939\u4e0b<\/h3>\n

import<\/span> sys
\nimport<\/span> torch
\nimport<\/span> torch.<\/span>nn as<\/span> nn
\nimport<\/span> torch.<\/span>nn.<\/span>functional as<\/span> F
\nfrom<\/span> pdb import<\/span> set_trace as<\/span> stx
\nimport<\/span> numbers
\nimport<\/span> math
\nimport<\/span> torch.<\/span>nn as<\/span> nn
\nimport<\/span> torch
\nfrom<\/span> timm.<\/span>models.<\/span>layers import<\/span> SqueezeExcite<\/p>\n

from<\/span> typing import<\/span> Optional,<\/span> Union,<\/span> Sequence<\/p>\n

from<\/span> torch.<\/span>nn.<\/span>modules.<\/span>conv import<\/span> _ConvNd
\nfrom<\/span> torch.<\/span>nn.<\/span>modules.<\/span>utils import<\/span> _pair
\nfrom<\/span> torch.<\/span>nn.<\/span>parameter import<\/span> Parameter
\nimport<\/span> functools
\nfrom<\/span> torch.<\/span>nn.<\/span>modules.<\/span>batchnorm import<\/span> _BatchNorm
\nfrom<\/span> mmcv.<\/span>cnn import<\/span> ConvModule,<\/span> build_norm_layer
\nfrom<\/span> mmengine.<\/span>model import<\/span> BaseModule,<\/span> constant_init
\nfrom<\/span> mmengine.<\/span>model.<\/span>weight_init import<\/span> trunc_normal_init,<\/span> normal_init
\nfrom<\/span> mmengine.<\/span>logging import<\/span> MMLogger<\/p>\n

from<\/span> einops import<\/span> rearrange
\nimport<\/span> os<\/p>\n

class<\/span> MSFN<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
\n def<\/span> __init__<\/span>(<\/span>self,<\/span> dim,<\/span> ffn_expansion_factor=<\/span>2.66<\/span>,<\/span> bias=<\/span>False<\/span>)<\/span>:<\/span>
\n super<\/span>(<\/span>MSFN,<\/span> self)<\/span>.<\/span>__init__(<\/span>)<\/span><\/p>\n

hidden_features =<\/span> int<\/span>(<\/span>dim*<\/span>ffn_expansion_factor)<\/span><\/p>\n

self.<\/span>project_in =<\/span> nn.<\/span>Conv3d(<\/span>dim,<\/span> hidden_features*<\/span>3<\/span>,<\/span> kernel_size=<\/span>(<\/span>1<\/span>,<\/span>1<\/span>,<\/span>1<\/span>)<\/span>,<\/span> bias=<\/span>bias)<\/span><\/p>\n

self.<\/span>dwconv1 =<\/span> nn.<\/span>Conv3d(<\/span>hidden_features,<\/span> hidden_features,<\/span> kernel_size=<\/span>(<\/span>3<\/span>,<\/span>3<\/span>,<\/span>3<\/span>)<\/span>,<\/span> stride=<\/span>1<\/span>,<\/span> dilation=<\/span>1<\/span>,<\/span> padding=<\/span>1<\/span>,<\/span> groups=<\/span>hidden_features,<\/span> bias=<\/span>bias)<\/span>
\n # self.dwconv2 = nn.Conv3d(hidden_features, hidden_features, kernel_size=(3,3,3), stride=1, dilation=2, padding=2, groups=hidden_features, bias=bias)<\/span>
\n # self.dwconv3 = nn.Conv3d(hidden_features, hidden_features, kernel_size=(3,3,3), stride=1, dilation=3, padding=3, groups=hidden_features, bias=bias)<\/span>
\n self.<\/span>dwconv2 =<\/span> nn.<\/span>Conv2d(<\/span>hidden_features,<\/span> hidden_features,<\/span> kernel_size=<\/span>(<\/span>3<\/span>,<\/span>3<\/span>)<\/span>,<\/span> stride=<\/span>1<\/span>,<\/span> dilation=<\/span>2<\/span>,<\/span> padding=<\/span>2<\/span>,<\/span> groups=<\/span>hidden_features,<\/span> bias=<\/span>bias)<\/span>
\n self.<\/span>dwconv3 =<\/span> nn.<\/span>Conv2d(<\/span>hidden_features,<\/span> hidden_features,<\/span> kernel_size=<\/span>(<\/span>3<\/span>,<\/span>3<\/span>)<\/span>,<\/span> stride=<\/span>1<\/span>,<\/span> dilation=<\/span>3<\/span>,<\/span> padding=<\/span>3<\/span>,<\/span> groups=<\/span>hidden_features,<\/span> bias=<\/span>bias)<\/span><\/p>\n

self.<\/span>project_out =<\/span> nn.<\/span>Conv3d(<\/span>hidden_features,<\/span> dim,<\/span> kernel_size=<\/span>(<\/span>1<\/span>,<\/span>1<\/span>,<\/span>1<\/span>)<\/span>,<\/span> bias=<\/span>bias)<\/span><\/p>\n

def<\/span> forward<\/span>(<\/span>self,<\/span> x)<\/span>:<\/span>
\n x =<\/span> x.<\/span>unsqueeze(<\/span>2<\/span>)<\/span>
\n x =<\/span> self.<\/span>project_in(<\/span>x)<\/span>
\n x1,<\/span>x2,<\/span>x3 =<\/span> x.<\/span>chunk(<\/span>3<\/span>,<\/span> dim=<\/span>1<\/span>)<\/span>
\n x1 =<\/span> self.<\/span>dwconv1(<\/span>x1)<\/span>.<\/span>squeeze(<\/span>2<\/span>)<\/span>
\n x2 =<\/span> self.<\/span>dwconv2(<\/span>x2.<\/span>squeeze(<\/span>2<\/span>)<\/span>)<\/span>
\n x3 =<\/span> self.<\/span>dwconv3(<\/span>x3.<\/span>squeeze(<\/span>2<\/span>)<\/span>)<\/span>
\n # x1 = self.dwconv1(x1)<\/span>
\n # x2 = self.dwconv2(x2)<\/span>
\n # x3 = self.dwconv3(x3)<\/span>
\n x =<\/span> F.<\/span>gelu(<\/span>x1)<\/span>*<\/span>x2*<\/span>x3
\n x =<\/span> x.<\/span>unsqueeze(<\/span>2<\/span>)<\/span>
\n x =<\/span> self.<\/span>project_out(<\/span>x)<\/span>
\n x =<\/span> x.<\/span>squeeze(<\/span>2<\/span>)<\/span>
\n return<\/span> x
\n #\u8be6\u7ec6\u6539\u8fdb\u6d41\u7a0b\u548c\u64cd\u4f5c，\u8bf7\u5173\u6ce8B\u7ad9\u535a\u4e3b：Ai\u5b66\u672f\u53eb\u53eb\u517d<\/span>
\n #\u8be6\u7ec6\u6539\u8fdb\u6d41\u7a0b\u548c\u64cd\u4f5c，\u8bf7\u5173\u6ce8B\u7ad9\u535a\u4e3b：Ai\u5b66\u672f\u53eb\u53eb\u517d<\/span><\/p>\n

3.2 \u5728task.py\u4e2d\u6dfb\u52a0\u4e24\u6bb5\u4ee3\u7801，\u5982\u4e0b\u56fe：<\/h3>\n

3.2.1 \u5f00\u5934\u4f4d\u7f6e\u6dfb\u52a0\u4e0a（\u8be6\u60c5\u8ddf\u7740\u6211B\u7ad9\u6559\u7a0b\u6765，\u5f88\u8be6\u7ec6\u3002）<\/h4>\n

from<\/span> ultralytics.<\/span>nn.<\/span>MSFN.<\/span>py import<\/span> MSFN<\/p>\n

3.2.2 \u627e\u5230\u6b64\u56fe\u4f4d\u7f6e\u4e2d，\u5728\u4e0b\u56fe\u4ee3\u7801\u4e0b\u65b9\u6dfb\u52a0：<\/h4>\n

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

3.3 \u65b0\u5efaMSFN.yaml\u6587\u4ef6，\u5185\u5bb9\u76f4\u63a5\u590d\u5236\u4e0b\u65b9\u5373\u53ef\u3002（\u8be6\u60c5\u8ddf\u7740\u6211B\u7ad9\u6559\u7a0b\u6765：Ai\u5b66\u672f\u53eb\u53eb\u517d，\u5f88\u8be6\u7ec6\u3002）<\/h3>\n

nc<\/span>:<\/span> 80<\/span> # number of classes<\/span>
\nscales<\/span>:<\/span> # model compound scaling constants, i.e. 'model=yolov13n.yaml' will call yolov13.yaml with scale 'n'<\/span>
\n # [depth, width, max_channels]<\/span>
\n n<\/span>:<\/span> [<\/span>0.50<\/span>,<\/span> 0.25<\/span>,<\/span> 1024<\/span>]<\/span> # Nano<\/span>
\n s<\/span>:<\/span> [<\/span>0.50<\/span>,<\/span> 0.50<\/span>,<\/span> 1024<\/span>]<\/span> # Small<\/span>
\n l<\/span>:<\/span> [<\/span>1.00<\/span>,<\/span> 1.00<\/span>,<\/span> 512<\/span>]<\/span> # Large<\/span>
\n x<\/span>:<\/span> [<\/span>1.00<\/span>,<\/span> 1.50<\/span>,<\/span> 512<\/span>]<\/span> # Extra Large<\/span><\/p>\n

backbone<\/span>:<\/span>
\n # [from, repeats, module, args]<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 1<\/span>,<\/span> Conv,<\/span> [<\/span>64<\/span>,<\/span> 3<\/span>,<\/span> 2<\/span>]<\/span>]<\/span> # 0-P1\/2<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 1<\/span>,<\/span> Conv,<\/span> [<\/span>128<\/span>,<\/span> 3<\/span>,<\/span> 2<\/span>,<\/span> 1<\/span>,<\/span> 2<\/span>]<\/span>]<\/span> # 1-P2\/4<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 2<\/span>,<\/span> DSC3k2,<\/span> [<\/span>256<\/span>,<\/span> False<\/span>,<\/span> 0.25<\/span>]<\/span>]<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 1<\/span>,<\/span> Conv,<\/span> [<\/span>256<\/span>,<\/span> 3<\/span>,<\/span> 2<\/span>,<\/span> 1<\/span>,<\/span> 4<\/span>]<\/span>]<\/span> # 3-P3\/8<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 2<\/span>,<\/span> DSC3k2,<\/span> [<\/span>512<\/span>,<\/span> False<\/span>,<\/span> 0.25<\/span>]<\/span>]<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 1<\/span>,<\/span> DSConv,<\/span> [<\/span>512<\/span>,<\/span> 3<\/span>,<\/span> 2<\/span>]<\/span>]<\/span> # 5-P4\/16<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 4<\/span>,<\/span> A2C2f,<\/span> [<\/span>512<\/span>,<\/span> True<\/span>,<\/span> 4<\/span>]<\/span>]<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 1<\/span>,<\/span> DSConv,<\/span> [<\/span>1024<\/span>,<\/span> 3<\/span>,<\/span> 2<\/span>]<\/span>]<\/span> # 7-P5\/32<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 4<\/span>,<\/span> A2C2f,<\/span> [<\/span>1024<\/span>,<\/span> True<\/span>,<\/span> 1<\/span>]<\/span>]<\/span> # 8<\/span><\/p>\n

head<\/span>:<\/span>
\n –<\/span> [<\/span>[<\/span>4<\/span>,<\/span> 6<\/span>,<\/span> 8<\/span>]<\/span>,<\/span> 2<\/span>,<\/span> HyperACE,<\/span> [<\/span>512<\/span>,<\/span> 8<\/span>,<\/span> True<\/span>,<\/span> True<\/span>,<\/span> 0.5<\/span>,<\/span> 1<\/span>,<\/span> "both"<\/span>]<\/span>]<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 1<\/span>,<\/span> nn.Upsample,<\/span> [<\/span>None,<\/span> 2<\/span>,<\/span> "nearest"<\/span>]<\/span>]<\/span>
\n –<\/span> [<\/span> 9<\/span>,<\/span> 1<\/span>,<\/span> DownsampleConv,<\/span> [<\/span>]<\/span>]<\/span>
\n –<\/span> [<\/span>[<\/span>6<\/span>,<\/span> 9<\/span>]<\/span>,<\/span> 1<\/span>,<\/span> FullPAD_Tunnel,<\/span> [<\/span>]<\/span>]<\/span> #12 <\/span>
\n –<\/span> [<\/span>[<\/span>4<\/span>,<\/span> 10<\/span>]<\/span>,<\/span> 1<\/span>,<\/span> FullPAD_Tunnel,<\/span> [<\/span>]<\/span>]<\/span> #13 <\/span>
\n –<\/span> [<\/span>[<\/span>8<\/span>,<\/span> 11<\/span>]<\/span>,<\/span> 1<\/span>,<\/span> FullPAD_Tunnel,<\/span> [<\/span>]<\/span>]<\/span> #14 <\/span><\/p>\n

–<\/span> [<\/span>-1<\/span>,<\/span> 1<\/span>,<\/span> nn.Upsample,<\/span> [<\/span>None,<\/span> 2<\/span>,<\/span> "nearest"<\/span>]<\/span>]<\/span>
\n –<\/span> [<\/span>[<\/span>-1<\/span>,<\/span> 12<\/span>]<\/span>,<\/span> 1<\/span>,<\/span> Concat,<\/span> [<\/span>1<\/span>]<\/span>]<\/span> # cat backbone P4<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 2<\/span>,<\/span> DSC3k2,<\/span> [<\/span>512<\/span>,<\/span> True<\/span>]<\/span>]<\/span> # 17<\/span>
\n –<\/span> [<\/span>[<\/span>-1<\/span>,<\/span> 9<\/span>]<\/span>,<\/span> 1<\/span>,<\/span> FullPAD_Tunnel,<\/span> [<\/span>]<\/span>]<\/span> #18<\/span><\/p>\n

–<\/span> [<\/span>17<\/span>,<\/span> 1<\/span>,<\/span> nn.Upsample,<\/span> [<\/span>None,<\/span> 2<\/span>,<\/span> "nearest"<\/span>]<\/span>]<\/span>
\n –<\/span> [<\/span>[<\/span>-1<\/span>,<\/span> 13<\/span>]<\/span>,<\/span> 1<\/span>,<\/span> Concat,<\/span> [<\/span>1<\/span>]<\/span>]<\/span> # cat backbone P3<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 2<\/span>,<\/span> DSC3k2,<\/span> [<\/span>256<\/span>,<\/span> True<\/span>]<\/span>]<\/span> # 21<\/span>
\n –<\/span> [<\/span>10<\/span>,<\/span> 1<\/span>,<\/span> Conv,<\/span> [<\/span>256<\/span>,<\/span> 1<\/span>,<\/span> 1<\/span>]<\/span>]<\/span>
\n –<\/span> [<\/span>[<\/span>21<\/span>,<\/span> 22<\/span>]<\/span>,<\/span> 1<\/span>,<\/span> FullPAD_Tunnel,<\/span> [<\/span>]<\/span>]<\/span> #23<\/span><\/p>\n

–<\/span> [<\/span>-1<\/span>,<\/span> 1<\/span>,<\/span> Conv,<\/span> [<\/span>256<\/span>,<\/span> 3<\/span>,<\/span> 2<\/span>]<\/span>]<\/span>
\n –<\/span> [<\/span>[<\/span>-1<\/span>,<\/span> 18<\/span>]<\/span>,<\/span> 1<\/span>,<\/span> Concat,<\/span> [<\/span>1<\/span>]<\/span>]<\/span> # cat head P4<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 2<\/span>,<\/span> DSC3k2,<\/span> [<\/span>512<\/span>,<\/span> True<\/span>]<\/span>]<\/span> # 26<\/span>
\n –<\/span> [<\/span>[<\/span>-1<\/span>,<\/span> 9<\/span>]<\/span>,<\/span> 1<\/span>,<\/span> FullPAD_Tunnel,<\/span> [<\/span>]<\/span>]<\/span> <\/p>\n

–<\/span> [<\/span>26<\/span>,<\/span> 1<\/span>,<\/span> Conv,<\/span> [<\/span>512<\/span>,<\/span> 3<\/span>,<\/span> 2<\/span>]<\/span>]<\/span>
\n –<\/span> [<\/span>[<\/span>-1<\/span>,<\/span> 14<\/span>]<\/span>,<\/span> 1<\/span>,<\/span> Concat,<\/span> [<\/span>1<\/span>]<\/span>]<\/span> # cat head P5<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 2<\/span>,<\/span> DSC3k2,<\/span> [<\/span>1024<\/span>,<\/span>True<\/span>]<\/span>]<\/span> # 30 (P5\/32-large)<\/span>
\n –<\/span> [<\/span>[<\/span>-1<\/span>,<\/span> 11<\/span>]<\/span>,<\/span> 1<\/span>,<\/span> FullPAD_Tunnel,<\/span> [<\/span>]<\/span>]<\/span>
\n –<\/span> [<\/span>-1<\/span>,<\/span> 1<\/span>,<\/span> MSFN,<\/span> [<\/span>1024<\/span>]<\/span>]<\/span><\/p>\n

–<\/span> [<\/span>[<\/span>23<\/span>,<\/span> 27<\/span>,<\/span> 32<\/span>]<\/span>,<\/span> 1<\/span>,<\/span> Detect,<\/span> [<\/span>nc]<\/span>]<\/span> # Detect(P3, P4, P5)<\/span><\/p>\n

\u5199\u5728\u6700\u540e<\/h2>\n

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\u795d\u6240\u6709\u79d1\u7814\u5de5\u4f5c\u8005\u90fd\u80fd\u591f\u5728\u81ea\u5df1\u7684\u9886\u57df\u4e0a\u66f4\u4e0a\u4e00\u5c42\u697c！！！<\/p>\n

\u6240\u6709\u79d1\u7814\u53c2\u8003\u8d44\u6599\u5747\u53ef\u70b9\u51fb\u6b64\u94fe\u63a5，\u5408\u9002\u7684\u624d\u662f\u6700\u597d\u7684，\u5e0c\u671b\u6211\u7684\u80fd\u529b\u914d\u4e0a\u4f60\u7684\u52aa\u529b\u521a\u597d\u5408\u9002！<\/p>\n

\"\u8bf7\u6dfb\u52a0\u56fe\u7247\u63cf\u8ff0\"<\/p>\n","protected":false},"excerpt":{"rendered":"

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