snapshot...don't unnecessarily reload chkpt files

modules/deeplearning/icing_cnn.py

@@ -967,10 +967,10 @@ class IcingIntensityNN:
 
     def do_evaluate(self, ckpt_dir=None, prob_thresh=0.5):
 
-        if ckpt_dir is not None:  # if is None, this has been done already
-            ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=self.model)
-            ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
-            ckpt.restore(ckpt_manager.latest_checkpoint)
+        # if ckpt_dir is not None:  # if is None, this has been done already
+        #     ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=self.model)
+        #     ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
+        #     ckpt.restore(ckpt_manager.latest_checkpoint)
 
         self.reset_test_metrics()
 
@@ -1115,13 +1115,19 @@ def run_evaluate_static(data_dct, num_tiles, ckpt_dir_s_path, day_night='DAY', l
     probs_dct = {flvl: None for flvl in flight_levels}
     preds_dct = {flvl: None for flvl in flight_levels}
 
-    for flvl in flight_levels:
     nn = IcingIntensityNN(day_night=day_night, l1b_or_l2=l1b_or_l2, use_flight_altitude=use_flight_altitude)
-        nn.flight_level = flvl
-        nn.setup_eval_pipeline(data_dct, num_tiles)
+    nn.num_data_samples = num_tiles
     nn.build_model()
     nn.build_training()
     nn.build_evaluation()
+
+    ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=nn.model)
+    ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
+    ckpt.restore(ckpt_manager.latest_checkpoint)
+
+    for flvl in flight_levels:
+        nn.flight_level = flvl
+        nn.setup_eval_pipeline(data_dct, num_tiles)
         nn.do_evaluate(ckpt_dir)
 
         probs = nn.test_probs