prototype conditional pipeline

stylegan2/generator.py

@@ -53,10 +53,10 @@ class Generator(tf.keras.Model):
         self.w_avg.assign(lerp(batch_avg, self.w_avg, self.w_ema_decay))
         return
 
-    def style_mixing_regularization(self, latents1, labels, w_broadcasted1):
+    def style_mixing_regularization(self, latents1, conditions, w_broadcasted1):
         # get another w and broadcast it
         latents2 = tf.random.normal(shape=tf.shape(latents1), dtype=tf.dtypes.float32)
-        dlatents2 = self.g_mapping([latents2, labels])
+        dlatents2 = self.g_mapping([latents2, conditions])
         w_broadcasted2 = self.broadcast(dlatents2)
 
         # find mixing limit index
@@ -89,14 +89,14 @@ class Generator(tf.keras.Model):
         return truncated_w_broadcasted
 
     def call(self, inputs, ret_w_broadcasted=False, truncation_psi=1.0, truncation_cutoff=None, training=None, mask=None):
-        latents, labels = inputs
+        latents, conditions = inputs
 
-        dlatents = self.g_mapping([latents, labels])
+        dlatents = self.g_mapping([latents, conditions])
         w_broadcasted = self.broadcast(dlatents)
 
         if training:
             self.update_moving_average_of_w(w_broadcasted)
-            w_broadcasted = self.style_mixing_regularization(latents, labels, w_broadcasted)
+            w_broadcasted = self.style_mixing_regularization(latents, conditions, w_broadcasted)
 
         if not training:
             w_broadcasted = self.truncation_trick(w_broadcasted, truncation_psi, truncation_cutoff)