def first_cnn(self, h, resolution, channel, test=False):
with nn.parameter_scope("phase_{}".format(resolution)):
# affine is 1x1 conv with 4x4 kernel and 3x3 pad.
with nn.parameter_scope("conv1"):
h = affine(h,
channel * 4 * 4,
with_bias=not self.use_bn,
use_wscale=self.use_wscale,
use_he_backward=self.use_he_backward)
h = BN(h, use_bn=self.use_bn, test=test)
h = F.reshape(h, (h.shape[0], channel, 4, 4))
h = pixel_wise_feature_vector_normalization(
BN(h, use_bn=self.use_bn, test=test))
h = self.activation(h)
with nn.parameter_scope("conv2"):
h = conv(h,
channel,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
with_bias=not self.use_bn,
use_wscale=self.use_wscale,
use_he_backward=self.use_he_backward)
h = pixel_wise_feature_vector_normalization(
BN(h, use_bn=self.use_bn, test=test))
h = self.activation(h)
return h
def cnn(self, h, resolution, channel, test):
"""CNN block
The following operations are performed two times.
1. Upsampling
2. Conv
3. Pixel-wise normalization
4. Relu
"""
h = F.unpooling(h, kernel=(2, 2))
with nn.parameter_scope("phase_{}".format(resolution)):
with nn.parameter_scope("conv1"):
h = conv(h, channel, kernel=(3, 3), pad=(1, 1), stride=(1, 1),
with_bias=not self.use_bn,
use_wscale=self.use_wscale,
use_he_backward=self.use_he_backward)
h = pixel_wise_feature_vector_normalization(
BN(h, use_bn=self.use_bn, test=test))
h = self.activation(h)
with nn.parameter_scope("conv2"):
h = conv(h, channel, kernel=(3, 3), pad=(1, 1), stride=(1, 1),
with_bias=not self.use_bn,
use_wscale=self.use_wscale,
use_he_backward=self.use_he_backward)
h = pixel_wise_feature_vector_normalization(
BN(h, use_bn=self.use_bn, test=test))
h = self.activation(h)
return h