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import torch.nn as nn | |
class AGGNet(nn.Module): | |
def __init__(self) -> None: | |
super().__init__() | |
self.stage1=nn.Sequential( | |
nn.Conv2d(in_channels=3,out_channels=64,kernel_size=3,padding=1,bias=False), | |
nn.ReLU() | |
) | |
self.stage2=nn.Sequential( | |
nn.ConvTranspose2d(in_channels=64,out_channels=3,kernel_size=3,padding=1,bias=False), | |
) | |
def forward(self, x): | |
x1 = self.stage1(x) | |
x2 = self.stage2(x1) | |
return x + x2 |