def __init__(self,
in_channels,
out_channels,
kernel_size,
downsample=False,
resample_kernel=(1, 3, 3, 1),
bias=True,
activate=True):
layers = []
# downsample
if downsample:
layers.append(
UpFirDnSmooth(resample_kernel,
upsample_factor=1,
downsample_factor=2,
kernel_size=kernel_size))
stride = 2
self.padding = 0
else:
stride = 1
self.padding = kernel_size // 2
# conv
layers.append(
EqualConv2d(in_channels,
out_channels,
kernel_size,
stride=stride,
padding=self.padding,
bias=bias and not activate))
# activation
if activate:
if bias:
layers.append(FusedLeakyReLU(out_channels))
else:
layers.append(ScaledLeakyReLU(0.2))
super(ConvLayer, self).__init__(*layers)