save

models/cvae_curv_model.py

@@ -50,7 +50,7 @@ class CVAE(tf.keras.Model):
     # @tf.function
     def sample_single(self, uv_template, uv_body_posed, uv_mask, eps=None, nr_of_samples=4):
         if eps is None:
-            eps = tf.random.normal(shape=(nr_of_samples, self.latent_dim))
+            eps = tf.random.normal(shape=(nr_of_samples, 8, 4, self.latent_dim))
 
         uv_template = tf.tile(tf.transpose(uv_template, [1,2,0])[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
         uv_body_posed = tf.tile(tf.transpose(uv_body_posed, [1,2,0])[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
@@ -149,3 +149,15 @@ class CVAE(tf.keras.Model):
         # x = tf.math.softmax(x)
 
         return x, mean, logvar
+
+    def call_sample(self, inputs):
+        uv_template, uv_body_posed, uv_curv, uv_mask = inputs
+
+        z = tf.random.normal(shape=(uv_template.shape[0], 8, 4, self.latent_dim))
+
+        z_cond = self.encode_conds(uv_template, uv_body_posed, training=False)
+
+        z = tf.concat([z, z_cond], axis=-1)
+        x = self.decoder([z, uv_mask], training=False)
+
+        return x