prototype conditional pipeline

stylegan2/discriminator.py

@@ -130,14 +130,9 @@ class Discriminator(tf.keras.Model):
         self.last_dense = Dense(max(self.labels_dim, 1), gain=1.0, lrmul=1.0, name='last_dense')
         self.last_bias = BiasAct(lrmul=1.0, act='linear', name='last_bias')
 
-        # conditional output
-        self.last_block_condition = DiscriminatorLastBlock(n_f0, n_f1, res, name='{:d}x{:d}'.format(res, res))
-        self.condition_dense = Dense(self.labels_dim, gain=1.0, lrmul=1.0, name='last_dense')
-        self.condition_bias = BiasAct(lrmul=1.0, act='linear', name='last_bias')
-
 
     def call(self, inputs, training=None, mask=None):
-        images, _ = inputs
+        images = inputs
 
         x = self.initial_fromrgb(images)
         for block in self.blocks:
@@ -150,13 +145,8 @@ class Discriminator(tf.keras.Model):
         x = self.last_dense(x)
         x = self.last_bias(x)
 
-        # condition output
-        cond = self.last_block_condition(features)
-        cond = self.condition_dense(cond)
-        cond = self.condition_bias(cond)
-
         scores_out = x
-        return scores_out, cond
+        return scores_out
 
     def compute_output_shape(self, input_shape):
         return input_shape[0][0], max(self.labels_dim, 1)