add posed mesh, metrics, tanh, l1 with gt

stylegan2/layers/synthesis_block.py

@@ -3,7 +3,7 @@ import tensorflow as tf
 from stylegan2.layers.modulated_conv2d import ModulatedConv2D
 from stylegan2.layers.bias_act import BiasAct
 from stylegan2.layers.noise import Noise
-from stylegan2.layers.to_rgb import ToRGB
+from stylegan2.layers.to_rgb import ToRGB, ToRGBLast
 from stylegan2.layers.cuda.upfirdn_2d_v2 import upsample_2d, compute_paddings
 
 
@@ -120,12 +120,17 @@ class Synthesis(tf.keras.layers.Layer):
         prev_n_f = n_f
         self.blocks = list()
         self.torgbs = list()
-        for res, n_f in zip(self.resolutions[1:], self.featuremaps[1:]):
+        for res, n_f in zip(self.resolutions[1:-1], self.featuremaps[1:-1]):
             self.blocks.append(SynthesisBlock(in_ch=prev_n_f, fmaps=n_f, res=res,
                                               name='{:d}x{:d}/block'.format(res, res)))
             self.torgbs.append(ToRGB(in_ch=n_f, res=res * 2, name='{:d}x{:d}/ToRGB'.format(res, res)))
             prev_n_f = n_f
 
+        res, n_f = self.resolutions[-1], self.featuremaps[-1]
+        self.blocks.append(SynthesisBlock(in_ch=prev_n_f, fmaps=n_f, res=res,
+                                            name='{:d}x{:d}/block'.format(res, res)))
+        self.torgbs.append(ToRGBLast(in_ch=n_f, res=res * 2, name='{:d}x{:d}/ToRGB'.format(res, res)))
+
     def call(self, inputs, training=None, mask=None):
         w_broadcasted = inputs