Pytorch实现各种2d卷积示例

class ConvBNReLU(nn.Module):

 def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
  super(ConvBNReLU, self).__init__()
  self.op = nn.Sequential(
   nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False),
   nn.BatchNorm2d(C_out, eps=1e-3, affine=affine),
   nn.ReLU(inplace=False)
  )

 def forward(self, x):
  return self.op(x)

class SepConv(nn.Module):
 
 def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
  super(SepConv, self).__init__()
  self.op = nn.Sequential(
   nn.ReLU(inplace=False),
   nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=C_in, bias=False),
   nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
   nn.BatchNorm2d(C_out, eps=1e-3, affine=affine)
   )
 def forward(self, x):
  return self.op(x)

class DilConv(nn.Module):
  
 def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
  super(DilConv, self).__init__()
  self.op = nn.Sequential(
   nn.ReLU(inplace=False),
   nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=C_in, bias=False),
   nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
   nn.BatchNorm2d(C_out, eps=1e-3, affine=affine),
   )

 def forward(self, x):
  return self.op(x)

class Identity(nn.Module):

 def __init__(self):
  super(Identity, self).__init__()

 def forward(self, x):
  return x