(self, channels, kernel_size=3)
| 39 | |
| 40 | class FReLU(nn.Module): |
| 41 | def __init__(self, channels, kernel_size=3): |
| 42 | super().__init__() |
| 43 | self.conv = nn.Conv2d(channels, channels, kernel_size, padding=kernel_size // 2, groups=channels) |
| 44 | self.bn = nn.BatchNorm2d(channels) |
| 45 | |
| 46 | def forward(self, x): |
| 47 | tx = self.bn(self.conv(x)) |