def subsampled(inputs, reuse=False):
# Less border effect
inputs = Layers.pad(inputs)
with tf.variable_scope('subsampled', reuse=reuse):
conv1 = Layers.conv2d(inputs, 3, 32, 9, 1, 'SAME', 'conv1')
norm1 = Layers.instance_norm(conv1)
relu1 = Layers.relu(norm1)
conv2 = Layers.conv2d(relu1, 32, 64, 3, 2, 'SAME', 'conv2')
norm2 = Layers.instance_norm(conv2)
relu2 = Layers.relu(norm2)
conv3 = Layers.conv2d(relu2, 64, 128, 3, 2, 'SAME', 'conv3')
norm3 = Layers.instance_norm(conv3)
relu3 = Layers.relu(norm3)
return relu3
def upsampling(inputs, reuse=False):
with tf.variable_scope('upsampling', reuse=reuse):
deconv1 = Layers.resize_conv2d(inputs, 128, 64, 3, 2, 'SAME', 'deconv1')
denorm1 = Layers.instance_norm(deconv1)
derelu1 = Layers.relu(denorm1)
deconv2 = Layers.resize_conv2d(derelu1, 64, 32, 3, 2, 'SAME', 'deconv2')
denorm2 = Layers.instance_norm(deconv2)
derelu2 = Layers.relu(denorm2)
deconv3 = Layers.resize_conv2d(derelu2, 32, 3, 9, 1, 'SAME', 'deconv3')
denorm3 = Layers.instance_norm(deconv3)
detanh3 = tf.nn.tanh(denorm3)
y = (detanh3 + 1) * 127.5
# Remove the border effect
y = Layers.remove_pad(y)
return y
