Switch line orders in trainer so that restore_map is called after moving...

Switch line orders in trainer so that restore_map is called after moving average variables are created.

Moving averages are now properly loaded during fine-tuning, instead of being recreated.

PiperOrigin-RevId: 190496046

research/object_detection/trainer.py

@@ -264,29 +264,6 @@ def train(create_tensor_dict_fn, create_model_fn, train_config, master, task,
           total_num_replicas=worker_replicas)
       sync_optimizer = training_optimizer
 
-    # Create ops required to initialize the model from a given checkpoint.
-    init_fn = None
-    if train_config.fine_tune_checkpoint:
-      if not train_config.fine_tune_checkpoint_type:
-        # train_config.from_detection_checkpoint field is deprecated. For
-        # backward compatibility, fine_tune_checkpoint_type is set based on
-        # from_detection_checkpoint.
-        if train_config.from_detection_checkpoint:
-          train_config.fine_tune_checkpoint_type = 'detection'
-        else:
-          train_config.fine_tune_checkpoint_type = 'classification'
-      var_map = detection_model.restore_map(
-          fine_tune_checkpoint_type=train_config.fine_tune_checkpoint_type,
-          load_all_detection_checkpoint_vars=(
-              train_config.load_all_detection_checkpoint_vars))
-      available_var_map = (variables_helper.
-                           get_variables_available_in_checkpoint(
-                               var_map, train_config.fine_tune_checkpoint))
-      init_saver = tf.train.Saver(available_var_map)
-      def initializer_fn(sess):
-        init_saver.restore(sess, train_config.fine_tune_checkpoint)
-      init_fn = initializer_fn
-
     with tf.device(deploy_config.optimizer_device()):
       regularization_losses = (None if train_config.add_regularization_loss
                                else [])
@@ -354,6 +331,29 @@ def train(create_tensor_dict_fn, create_model_fn, train_config, master, task,
     saver = tf.train.Saver(
         keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
 
+    # Create ops required to initialize the model from a given checkpoint.
+    init_fn = None
+    if train_config.fine_tune_checkpoint:
+      if not train_config.fine_tune_checkpoint_type:
+        # train_config.from_detection_checkpoint field is deprecated. For
+        # backward compatibility, fine_tune_checkpoint_type is set based on
+        # from_detection_checkpoint.
+        if train_config.from_detection_checkpoint:
+          train_config.fine_tune_checkpoint_type = 'detection'
+        else:
+          train_config.fine_tune_checkpoint_type = 'classification'
+      var_map = detection_model.restore_map(
+          fine_tune_checkpoint_type=train_config.fine_tune_checkpoint_type,
+          load_all_detection_checkpoint_vars=(
+              train_config.load_all_detection_checkpoint_vars))
+      available_var_map = (variables_helper.
+                           get_variables_available_in_checkpoint(
+                               var_map, train_config.fine_tune_checkpoint))
+      init_saver = tf.train.Saver(available_var_map)
+      def initializer_fn(sess):
+        init_saver.restore(sess, train_config.fine_tune_checkpoint)
+      init_fn = initializer_fn
+
     slim.learning.train(
         train_tensor,
         logdir=train_dir,