# 原理：对局部卷积后的输出与原始数据进行一个max的比对
class FReLU(nn.Module):
    def __init__(self, c1, k=3):  # ch_in, kernel
        super().__init__()
        # 可分离卷积，不改变hw与channels
        self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)  
        self.bn = nn.BatchNorm2d(c1)
 
    def forward(self, x):
        # 卷积处理后的特征图与原来的x是相同的shape的
        return torch.max(x, self.bn(self.conv(x)))   

class Conv(nn.Module):
    # Standard convolution
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super(Conv, self).__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = FReLU(c2) if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
 
    def forward(self, x):
        return self.act(self.bn(self.conv(x)))
 
    def fuseforward(self, x):
        return self.act(self.conv(x))