snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -276,14 +276,14 @@ class SRCNN:
 
         # High res refectance ----------
         idx = params_i.index('refl_0_65um_nom')
-        tmp = input_label[:, idx, :, :]
+        tmp = input_label[:, idx, ::2, ::2]
         tmp = np.where(np.isnan(tmp), 0, tmp)
         tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
         data_norm.append(tmp[:, self.slc_y, self.slc_x])
 
         # High res reflectance down 2 ---------
         # idx = params_i.index('refl_0_65um_nom')
-        # tmp = input_label[:, idx, :, :]
+        # tmp = input_label[:, idx, ::2, ::2]
         # tmp = tmp.copy()
         # tmp = np.where(np.isnan(tmp), 0.0, tmp)
         # tmp = tmp[:, self.slc_y_2, self.slc_x_2]
@@ -293,7 +293,7 @@ class SRCNN:
         # tmp = normalize(tmp, label_param, mean_std_dct)
         # data_norm.append(tmp)
 
-        tmp = input_label[:, label_idx_i, :, :]
+        tmp = input_label[:, label_idx_i, ::2, ::2]
         tmp = tmp.copy()
         tmp = np.where(np.isnan(tmp), 0.0, tmp)
         tmp = tmp[:, self.slc_y_2, self.slc_x_2]
@@ -333,7 +333,7 @@ class SRCNN:
 
         # -----------------------------------------------------
         # -----------------------------------------------------
-        label = input_label[:, label_idx_i, :, :]
+        label = input_label[:, label_idx_i, ::2, ::2]
         label = label.copy()
         label = normalize(label, label_param, mean_std_dct)
         # label = scale(label, label_param, mean_std_dct)