snapshot...

modules/deeplearning/cloud_opd_fcn_abi.py

@@ -718,14 +718,13 @@ class SRCNN:
         labels = np.concatenate(self.test_labels)
         preds = np.concatenate(self.test_preds)
         inputs = np.concatenate(self.test_input)
-        cat_cld_frac = np.concatenate(self.test_cat_cf)
 
         # labels = denormalize(labels, label_param, mean_std_dct)
         # preds = denormalize(preds, label_param, mean_std_dct)
         # labels = descale(labels, label_param, mean_std_dct)
         # preds = descale(preds, label_param, mean_std_dct)
 
-        return labels, cat_cld_frac, preds, inputs
+        return labels, preds, inputs
 
     def do_evaluate(self, inputs, ckpt_dir):
 
@@ -924,7 +923,7 @@ class SRCNN:
 
 def run_restore_static(directory, ckpt_dir, out_file=None):
     nn = SRCNN()
-    labels, cat_cld_frac, preds, inputs = nn.run_restore(directory, ckpt_dir)
+    labels, preds, inputs = nn.run_restore(directory, ckpt_dir)
     print(np.histogram(labels))
     print(np.histogram(preds))
     if out_file is not None:
@@ -937,7 +936,7 @@ def run_restore_static(directory, ckpt_dir, out_file=None):
                  descale(inputs[:, 1:y_hi, 1:x_hi, 2], 'refl_0_65um_nom', mean_std_dct),
                  inputs[:, 1:y_hi, 1:x_hi, 3],
                  descale(inputs[:, 1:y_hi, 1:x_hi, 4], label_param, mean_std_dct),
-                 cat_cld_frac[:, :, :]])
+                 inputs[:, 1:y_hi, 1:x_hi, 5]])
 
 
 def run_evaluate_static(in_file, out_file, ckpt_dir):