save

models/cvae_curv_model.py

@@ -79,16 +79,17 @@ class CVAE(tf.keras.Model):
 
         return z_cond
 
+
     def sample(self, uv_template, uv_body_posed, uv_mask, noise=None):
         if noise is None:
-            eps = tf.random.normal(shape=(uv_template.shape[0], self.latent_dim))
+            eps = tf.random.normal(shape=(uv_template.shape[0], 8, 4, self.latent_dim))
         else:
             eps = noise
 
         z_cond = self.encode_conds(uv_template, uv_body_posed, training=False)
         z = tf.concat([eps, z_cond], axis=-1)
 
-        x = self.decoder(z, training=False)
+        x = self.decoder([z, uv_mask], training=False)
 
         # x = x * uv_mask[:,:,:,tf.newaxis]
 

models/cvae_model.py

@@ -203,7 +203,7 @@ class CVAENew(tf.keras.Model):
     # @tf.function
     def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask, noise=None, add_noise=False):
         if noise is None:
-            eps = tf.random.normal(shape=(uv_template.shape[0], self.latent_dim))
+            eps = tf.random.normal(shape=(uv_template.shape[0], 8, 4, self.latent_dim))
         else:
             eps = noise
 

models/pyramid_model_optimized.py

@@ -358,8 +358,9 @@ class PyramidNew(tf.keras.Model):
             else:
                 # if self.upscale_enhancer is not None:
                     # pred = self.enhance(pred, levels_input[-1][-2], training=training)
-                pred = self.levels[-1].enhance(pred, levels_input[-1][-2], training=training)
-                pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.BILINEAR)
+                if len(self.levels) != 5:
+                    pred = self.levels[-1].enhance(pred, levels_input[-1][-2], training=training)
+                    pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.BILINEAR)
                 pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
                 out_levels.append([pred, mean, logvar])