snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -237,6 +237,7 @@ class SRCNN:
         data_norm.append(tmp)
         # --------
         tmp = input_data[:, label_idx, 3:131:2, 3:131:2]
+        tmp = np.where(np.isnan(tmp), 0, tmp)
         tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
         data_norm.append(tmp)
         # ---------
@@ -245,6 +246,7 @@ class SRCNN:
 
         # label = input_data[:, label_idx, 3:131:2, 3:131:2]
         label = input_data[:, label_idx, 3:131, 3:131]
+        label = np.where(np.isnan(label), 0, label)
         label = np.expand_dims(label, axis=3)
         if label_param != 'cloud_fraction':
             label = normalize(label, label_param, mean_std_dct)
@@ -650,7 +652,7 @@ class SRCNN:
         self.do_training()
 
     def run_restore(self, directory, ckpt_dir):
-        valid_data_files = glob.glob(directory + 'data_valid*.npy')
+        valid_data_files = glob.glob(directory + 'data_*.npy')
         self.num_data_samples = 1000
         self.setup_test_pipeline(valid_data_files)
         self.build_model()