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)
if 'd' in opts:
x0 = tf.nn.sigmoid(self.out_conv(x))
x0 = nn.upsample2d(x0)
x1 = tf.nn.sigmoid(self.out_conv1(x))
x1 = nn.upsample2d(x1)
x2 = tf.nn.sigmoid(self.out_conv2(x))
x2 = nn.upsample2d(x2)
x3 = tf.nn.sigmoid(self.out_conv3(x))
x3 = nn.upsample2d(x3)
if nn.data_format == "NHWC":
tile_cfg = ( 1, resolution // 2, resolution //2, 1)
else:
tile_cfg = ( 1, 1, resolution // 2, resolution //2 )
z0 = tf.concat ( ( tf.concat ( ( tf.ones ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ),
tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ) ), axis=nn.conv2d_spatial_axes[0] )
z0 = tf.tile ( z0, tile_cfg )
z1 = tf.concat ( ( tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.ones ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ),
tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ) ), axis=nn.conv2d_spatial_axes[0] )
z1 = tf.tile ( z1, tile_cfg )
z2 = tf.concat ( ( tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ),
tf.concat ( ( tf.ones ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ) ), axis=nn.conv2d_spatial_axes[0] )
z2 = tf.tile ( z2, tile_cfg )
z3 = tf.concat ( ( tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ),
tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.ones ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ) ), axis=nn.conv2d_spatial_axes[0] )
z3 = tf.tile ( z3, tile_cfg )
x = x0*z0 + x1*z1 + x2*z2 + x3*z3
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))
return x, m
def forward(self, x):
if not self.zero_level:
x = nn.upsample2d(x)
x = tf.nn.leaky_relu(self.conv1(x), 0.2)
x = tf.nn.leaky_relu(self.conv2(x), 0.2)
return x
def forward(self, input):
up1 = self.b1(input)
low1 = tf.nn.avg_pool(input, [1,2,2,1], [1,2,2,1], 'VALID')
low1 = self.b2 (low1)
low2 = self.b2_plus(low1)
low3 = self.b3(low2)
up2 = nn.upsample2d(low3)
return up1+up2
def forward(self, stage, inp, alpha=None, inter=None):
x = inp
for level in range(stage + 1):
if level in self.to_rgbs:
if level == stage and stage > 0:
prev_level = level - 1
#prev_x, prev_xm = (inter.to_rgbs[prev_level] if inter is not None and prev_level in inter.to_rgbs else self.to_rgbs[prev_level])(x)
prev_x, prev_xm = self.to_rgbs[prev_level](x)
prev_x = nn.upsample2d(prev_x)
prev_xm = nn.upsample2d(prev_xm)
if level != 0:
x = self.dec_blocks[level](x)
if level == stage:
x, xm = self.to_rgbs[level](x)
if stage > 0:
x = x * alpha + prev_x * (1 - alpha)
xm = xm * alpha + prev_xm * (1 - alpha)
return x, xm
return x
