def forward(self, z):
x = self.upscale0(z)
x = self.res0(x)
x = self.upscale1(x)
x = self.res1(x)
x = self.upscale2(x)
x = self.res2(x)
if 'd' in opts:
x = tf.nn.sigmoid(
nn.depth_to_space(
tf.concat(
(self.out_conv(x), self.out_conv1(x),
self.out_conv2(x), self.out_conv3(x)),
nn.conv2d_ch_axis), 2))
else:
x = tf.nn.sigmoid(self.out_conv(x))
m = self.upscalem0(z)
m = self.upscalem1(m)
m = self.upscalem2(m)
if 'd' in opts:
m = self.upscalem3(m)
m = tf.nn.sigmoid(self.out_convm(m))
if use_fp16:
x = tf.cast(x, tf.float32)
m = tf.cast(m, tf.float32)
return x, m
def forward(self, inp):
x = inp
for i in range(len(self.convs)):
x = self.convs[i](x)
x = self.frns[i](x)
x = self.tlus[i](x)
if self.use_upscale[i]:
x = nn.depth_to_space(x, 2)
x = self.out_conv(x)
x = tf.nn.sigmoid(x)
return x
def forward(self, inp):
z = inp
x = self.upscale0(z)
x = self.res0(x)
x = self.upscale1(x)
x = self.res1(x)
x = self.upscale2(x)
x = self.res2(x)
x = self.upscale3(x)
x = self.res3(x)
x = tf.nn.sigmoid( nn.depth_to_space(tf.concat( (self.out_conv(x),
self.out_conv1(x),
self.out_conv2(x),
self.out_conv3(x)), nn.conv2d_ch_axis), 2) )
m = self.upscalem0(z)
m = self.upscalem1(m)
m = self.upscalem2(m)
m = self.upscalem3(m)
m = self.upscalem4(m)
m = tf.nn.sigmoid(self.out_convm(m))
return x, m
def forward(self, x):
x = self.conv1(x)
x = tf.nn.leaky_relu(x, 0.1)
x = nn.depth_to_space(x, 2)
return x
def forward(self, x):
x = self.conv1(x)
x = nn.gelu(x)
x = nn.depth_to_space(x, 2)
return x
def forward(self, x):
x = nn.depth_to_space(tf.nn.leaky_relu(self.conv1(x), 0.1), 2)
return x
def forward(self, x):
x = self.conv1(x)
x = act(x, 0.1)
x = nn.depth_to_space(x, 2)
return x
