snapshot...

modules/deeplearning/icing_fcn.py

@@ -572,7 +572,7 @@ class IcingIntensityFCN:
 
     def setup_eval_pipeline(self, data_dct, num_tiles=1):
         self.data_dct = data_dct
-        self.cth_max = data_dct.get('cth_high_avg', None)
+        # self.cth_max = data_dct.get('cth_high_avg', None)
         idxs = np.arange(num_tiles)
         self.num_data_samples = idxs.shape[0]
 
@@ -1160,9 +1160,12 @@ def run_evaluate_static(data_dct, num_tiles, ckpt_dir_s_path, day_night='DAY', l
 def run_evaluate_static_2(model, data_dct, num_tiles, prob_thresh=0.5, flight_levels=[0, 1, 2, 3, 4],
                           use_flight_altitude=False, use_max_cth_level=False):
 
-    if not use_flight_altitude or use_max_cth_level:
+    if not use_flight_altitude:
         flight_levels = [0]
 
+    if use_max_cth_level:
+        flight_levels.append(5)
+
     probs_dct = {flvl: None for flvl in flight_levels}
     preds_dct = {flvl: None for flvl in flight_levels}
 
@@ -1174,6 +1177,18 @@ def run_evaluate_static_2(model, data_dct, num_tiles, prob_thresh=0.5, flight_le
         probs_dct[flvl] = model.test_probs.flatten()
         preds_dct[flvl] = model.test_preds.flatten()
 
+    if use_max_cth_level:
+        flvl = 5
+        model.cth_max = data_dct.get('cth_high_avg', None)
+        model.flight_level = flvl
+        model.setup_eval_pipeline(data_dct, num_tiles)
+        model.do_evaluate(prob_thresh=prob_thresh)
+
+        probs_dct[flvl] = model.test_probs.flatten()
+        preds_dct[flvl] = model.test_preds.flatten()
+
+
+
     return preds_dct, probs_dct