model_deploy_test.py 文件源码

def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, '')
        self.assertDeviceEqual(v.device, 'CPU:0')