def last_cnn(self, h, resolution, ch_in, ch_out):
with nn.parameter_scope("phase_{}".format(resolution)):
with nn.parameter_scope("conv1"):
h = conv(h,
ch_in,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
with_bias=not self.use_ln,
use_wscale=self.use_wscale,
use_he_backward=self.use_he_backward)
h = LN(h, use_ln=self.use_ln)
h = self.activation(h)
with nn.parameter_scope("conv2"):
h = conv(h,
ch_out,
kernel=(4, 4),
pad=(0, 0),
stride=(1, 1),
with_bias=not self.use_ln,
use_wscale=self.use_wscale,
use_he_backward=self.use_he_backward)
h = LN(h, use_ln=self.use_ln)
h = self.activation(h)
with nn.parameter_scope("linear"):
h = affine(h,
1,
with_bias=True,
use_wscale=self.use_wscale,
use_he_backward=self.use_he_backward)
return h
def cnn(self, h, resolution, ch_in, ch_out):
"""CNN block
The following operations are performed two times.
1. Conv
2. Layer normalization
3. Leaky relu
"""
with nn.parameter_scope("phase_{}".format(resolution)):
with nn.parameter_scope("conv1"):
h = conv(h,
ch_in,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
with_bias=not self.use_ln,
use_wscale=self.use_wscale,
use_he_backward=self.use_he_backward)
h = LN(h, use_ln=self.use_ln)
h = self.activation(h)
with nn.parameter_scope("conv2"):
h = conv(h,
ch_out,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
with_bias=not self.use_ln,
use_wscale=self.use_wscale,
use_he_backward=self.use_he_backward)
h = LN(h, use_ln=self.use_ln)
h = self.activation(h)
h = F.average_pooling(h, kernel=(2, 2))
return h