snapshot...

modules/deeplearning/icing_cnn.py

@@ -1080,29 +1080,28 @@ def run_evaluate_static(h5f, ckpt_dir_s_path, flight_level=4, prob_thresh=0.5, s
     return ice_lons, ice_lats, preds_2d, lons_2d, lats_2d, x_rad, y_rad
 
 
-def run_evaluate_static_new(data_dct, num_lines, num_elems, ckpt_dir_s_path, flight_level=4, prob_thresh=0.5):
+def run_evaluate_static_new(data_dct, num_lines, num_elems, ckpt_dir_s_path, flight_levels=[0, 1, 2, 3, 4], prob_thresh=0.5):
 
     ckpt_dir_s = os.listdir(ckpt_dir_s_path)
     ckpt_dir = ckpt_dir_s[0]
 
-    nn = IcingIntensityNN()
-    nn.flight_level = flight_level
-    nn.setup_eval_pipeline(data_dct, num_lines * num_elems)
-    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)
-
     probs_2d_s = []
     preds_2d_s = []
-    for flvl in [0, 1, 2, 3, 4]:
+    for flvl in flight_levels:
+        nn = IcingIntensityNN()
         nn.flight_level = flvl
+        nn.setup_eval_pipeline(data_dct, num_lines * num_elems)
+        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)
+
         nn.do_evaluate()
-        probs = nn.test_probs
 
+        probs = nn.test_probs
         if NumClasses == 2:
             preds = np.where(probs > prob_thresh, 1, 0)
         else: