Stemblock 结构将输出的尺寸缩减为输入的 1/4,多用于轻量化网络,完成下采样操作,可以用于 YOLOv5 网络模型中原始的卷积下采样操作,减少参数量。
class StemBlock(nn.Module):
def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True):
super(StemBlock, self).__init__()
self.stem_1 = Conv(c1, c2, k, s, p, g, act)
self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0)
self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1)
self.stem_2p = nn.MaxPool2d(kernel_size=2,stride=2,ceil_mode=True)
self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0)
def forward(self, x):
stem_1_out = self.stem_1(x)
stem_2a_out = self.stem_2a(stem_1_out)
stem_2b_out = self.stem_2b(stem_2a_out)
stem_2p_out = self.stem_2p(stem_1_out)
out = self.stem_3(torch.cat((stem_2b_out,stem_2p_out),1))
return out
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