From 77f09930226ea2f49567ca1a497e3d8e7d7c01bf Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 11 Aug 2021 14:16:39 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_cnn.py | 80 ++++++++++++++++++++-----------
1 file changed, 51 insertions(+), 29 deletions(-)
diff --git a/modules/deeplearning/icing_cnn.py b/modules/deeplearning/icing_cnn.py
index 5221e041..3a910e5d 100644
--- a/modules/deeplearning/icing_cnn.py
+++ b/modules/deeplearning/icing_cnn.py
@@ -651,7 +651,6 @@ class IcingIntensityNN:
best_test_f1 = 0
best_test_mcc = 0
-
if EARLY_STOP:
es = EarlyStop()
@@ -801,9 +800,7 @@ class IcingIntensityNN:
self.test_preds = preds
- self.h5f_tst.close()
-
- def do_evaluate(self, ckpt_dir, ll, cc, prob_thresh=0.5):
+ def do_evaluate(self, ckpt_dir, prob_thresh=0.5):
ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=self.model)
ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
@@ -819,32 +816,14 @@ class IcingIntensityNN:
pred_s.append(pred)
preds = np.concatenate(pred_s)
+ preds = preds[:,0]
+ self.test_probs = preds
if NumClasses == 2:
preds = np.where(preds > prob_thresh, 1, 0)
else:
preds = np.argmax(preds, axis=1)
- print(preds.shape[0], np.sum(preds == 1))
- preds = preds[:,0]
-
- cc = np.array(cc)
- ll = np.array(ll)
- ice_mask = preds == 1
- print(cc.shape, ll.shape, ice_mask.shape)
- ice_cc = cc[ice_mask]
- ice_ll = ll[ice_mask]
-
- nav = GEOSNavigation(sub_lon=-75.0, CFAC=5.6E-05, COFF=-0.101332, LFAC=-5.6E-05, LOFF=0.128212, num_elems=2500,
- num_lines=1500)
-
- ice_lons = []
- ice_lats = []
- for k in range(ice_cc.shape[0]):
- lon, lat = nav.lc_to_earth(ice_cc[k], ice_ll[k])
- ice_lons.append(lon)
- ice_lats.append(lat)
-
- return ice_lons, ice_lats
+ self.test_preds = preds
def run(self, filename_trn, filename_tst):
with tf.device('/device:GPU:'+str(self.gpu_device)):
@@ -860,6 +839,7 @@ class IcingIntensityNN:
self.build_training()
self.build_evaluation()
self.restore(ckpt_dir)
+ self.h5f_tst.close()
def run_evaluate(self, filename, ckpt_dir):
data_dct, ll, cc = make_for_full_domain_predict(filename, name_list=train_params)
@@ -867,8 +847,7 @@ class IcingIntensityNN:
self.build_model()
self.build_training()
self.build_evaluation()
- ice_lons, ice_lats = self.do_evaluate(ckpt_dir, ll, cc)
- return filename, ice_lons, ice_lats
+ self.do_evaluate(ckpt_dir)
def run_restore_static(filename_tst, ckpt_dir_s_path):
@@ -890,8 +869,51 @@ def run_restore_static(filename_tst, ckpt_dir_s_path):
return cm_avg
-def run_evaluate_static(filename, ckpt_dir_s):
- nn = IcingIntensityNN()
+def run_evaluate_static(filename, ckpt_dir_s_path, prob_thresh=0.5):
+ data_dct, ll, cc = make_for_full_domain_predict(filename, name_list=train_params)
+ ckpt_dir_s = os.listdir(ckpt_dir_s_path)
+ prob_s = []
+ for ckpt in ckpt_dir_s:
+ ckpt_dir = ckpt_dir_s_path + ckpt
+ if not os.path.isdir(ckpt_dir):
+ continue
+ nn = IcingIntensityNN()
+ nn.setup_eval_pipeline(data_dct, len(ll))
+ nn.build_model()
+ nn.build_training()
+ nn.build_evaluation()
+ nn.do_evaluate(ckpt_dir, ll, cc)
+ prob_s.append(nn.test_probs)
+ num = len(prob_s)
+ prob_avg = prob_s[0]
+ for k in range(num-1):
+ prob_avg += prob_s[k+1]
+ prob_avg /= num
+ probs = prob_avg
+
+ if NumClasses == 2:
+ preds = np.where(probs > prob_thresh, 1, 0)
+ else:
+ preds = np.argmax(probs, axis=1)
+
+ cc = np.array(cc)
+ ll = np.array(ll)
+ ice_mask = preds == 1
+ print(cc.shape, ll.shape, ice_mask.shape)
+ ice_cc = cc[ice_mask]
+ ice_ll = ll[ice_mask]
+
+ nav = GEOSNavigation(sub_lon=-75.0, CFAC=5.6E-05, COFF=-0.101332, LFAC=-5.6E-05, LOFF=0.128212, num_elems=2500,
+ num_lines=1500)
+
+ ice_lons = []
+ ice_lats = []
+ for k in range(ice_cc.shape[0]):
+ lon, lat = nav.lc_to_earth(ice_cc[k], ice_ll[k])
+ ice_lons.append(lon)
+ ice_lats.append(lat)
+
+ return filename, ice_lons, ice_lats
if __name__ == "__main__":
--
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