GANs/CycleGAN_Zhu_et_al_2017/model.py
def dk(self, in_size, out_size):
        """3x3 Conv-InstanceNorm-ReLU layer with k filters and stride 2."""
        layer = [nn.Conv2d(in_size, out_size,
                           kernel_size=3, stride=2, padding=1,
                           padding_mode='reflect',  bias=False),
                 nn.InstanceNorm2d(num_features=out_size, affine=True),
                 nn.ReLU(inplace=True)]

        return nn.Sequential(*layer)
3x3 Conv-InstanceNorm-ReLU layer with k filters and stride 2.
3x3 Conv-InstanceNorm-ReLU layer with k filters and stride 2.
3x3 Conv - InstanceNorm - ReLU layer with k filters and stride 2 .
def dk(self, in_size, out_size):
        layer = [nn.Conv2d(in_size, out_size,
                           kernel_size=3, stride=2, padding=1,
                           padding_mode='reflect',  bias=False),
                 nn.InstanceNorm2d(num_features=out_size, affine=True),
                 nn.ReLU(inplace=True)]
        return nn.Sequential(*layer)
def dk ( self , in_size , out_size ) : layer = [ nn . Conv2d ( in_size , out_size , kernel_size = 3 , stride = 2 , padding = 1 , padding_mode = 'reflect' , bias = False ) , nn . InstanceNorm2d ( num_features = out_size , affine = True ) , nn . ReLU ( inplace = True ) ] return nn . Sequential ( * layer )
3x3 Conv-InstanceNorm-ReLU layer with k filters and stride 2.
3x3 Conv - InstanceNorm - ReLU layer with k filters and stride 2 .
"""3x3 Conv-InstanceNorm-ReLU layer with k filters and stride 2."""
self
in_size
out_size