| 50 | class SepConv(nn.Module): |
| 51 | |
| 52 | def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True): |
| 53 | super(SepConv, self).__init__() |
| 54 | self.op = nn.Sequential( |
| 55 | nn.ReLU(inplace=False), |
| 56 | nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=C_in, bias=False), |
| 57 | nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False), |
| 58 | nn.BatchNorm2d(C_in, affine=affine), |
| 59 | nn.ReLU(inplace=False), |
| 60 | nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=1, padding=padding, groups=C_in, bias=False), |
| 61 | nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False), |
| 62 | nn.BatchNorm2d(C_out, affine=affine), |
| 63 | ) |
| 64 | |
| 65 | def forward(self, x): |
| 66 | return self.op(x) |