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Spatial Group-wise Enhance (SGE) module

news 2025/7/1 2:47:54

来源：

[1905.09646] Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks

创新点：

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贡献：

提出了一种轻量级的SGE模块，能够自主增强每个语义组的特征表达并抑制噪声，设计简洁且高效。
在多种CNN架构和任务中均取得了显著的性能提升，具有广泛的适用性。
通过可视化和统计分析验证了SGE模块对语义特征分布的优化效果，为理解CNN的特征学习提供了新的视角。

代码：


# ---------------------------------------  
# 论文: “Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks.” ArXiv abs/1905.09646  
# Github地址: https://github.com/implus/PytorchInsight  
# ---------------------------------------  
import torch  
from torch import nn  class SpatialGroupEnhance(nn.Module):  def __init__(self, groups = 64):  super(SpatialGroupEnhance, self).__init__()  self.groups   = groups  self.avg_pool = nn.AdaptiveAvgPool2d(1)  self.weight   = nn.Parameter(torch.zeros(1, groups, 1, 1))  self.bias     = nn.Parameter(torch.ones(1, groups, 1, 1))  self.sig      = nn.Sigmoid()  def forward(self, x): # (b, c, h, w)  b, c, h, w = x.size()  x = x.view(b * self.groups, -1, h, w)  xn = x * self.avg_pool(x)  xn = xn.sum(dim=1, keepdim=True)  t = xn.view(b * self.groups, -1)  t = t - t.mean(dim=1, keepdim=True)  std = t.std(dim=1, keepdim=True) + 1e-5  t = t / std  t = t.view(b, self.groups, h, w)  t = t * self.weight + self.bias  t = t.view(b * self.groups, 1, h, w)  x = x * self.sig(t)  x = x.view(b, c, h, w)  return x  #   输入 N C H W,  输出 N C H Wif __name__ == '__main__':  input = torch.randn(3, 32, 64, 64)  sge = SpatialGroupEnhance(groups=8)  output = sge(input)  print(output.shape)