GANs/CycleGAN_Zhu_et_al_2017/model.py
def c7s1_k(self, in_size, out_size):
        """7x7 Conv-InstanceNorm-ReLU layer with k filters and stride 1."""
        layer = [nn.Conv2d(in_size, out_size,
                           kernel_size=7, stride=1, padding=3,
                           padding_mode='reflect',  bias=False),
                 nn.InstanceNorm2d(num_features=out_size, affine=True),
                 nn.ReLU(inplace=True)]

        return nn.Sequential(*layer)
7x7 Conv-InstanceNorm-ReLU layer with k filters and stride 1.
7x7 Conv-InstanceNorm-ReLU layer with k filters and stride 1.
7x7 Conv - InstanceNorm - ReLU layer with k filters and stride 1 .
def c7s1_k(self, in_size, out_size):
        layer = [nn.Conv2d(in_size, out_size,
                           kernel_size=7, stride=1, padding=3,
                           padding_mode='reflect',  bias=False),
                 nn.InstanceNorm2d(num_features=out_size, affine=True),
                 nn.ReLU(inplace=True)]
        return nn.Sequential(*layer)
def c7s1_k ( self , in_size , out_size ) : layer = [ nn . Conv2d ( in_size , out_size , kernel_size = 7 , stride = 1 , padding = 3 , padding_mode = 'reflect' , bias = False ) , nn . InstanceNorm2d ( num_features = out_size , affine = True ) , nn . ReLU ( inplace = True ) ] return nn . Sequential ( * layer )
7x7 Conv-InstanceNorm-ReLU layer with k filters and stride 1.
7x7 Conv - InstanceNorm - ReLU layer with k filters and stride 1 .
"""7x7 Conv-InstanceNorm-ReLU layer with k filters and stride 1."""
self
in_size
out_size