`snapshot...`

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -759,11 +759,11 @@ class SRCNN:
         cld_opd = get_grid_values_all(h5f, 'cld_opd_dcomp')
         cld_opd = cld_opd[s_y, s_x]
 
-        # refl_sub_lo = get_grid_values_all(h5f, 'refl_0_65um_nom_min_sub')
-        # refl_sub_lo = refl_sub_lo[s_y, s_x]
-        #
-        # refl_sub_hi = get_grid_values_all(h5f, 'refl_0_65um_nom_max_sub')
-        # refl_sub_hi = refl_sub_hi[s_y, s_x]
+        refl_sub_lo = get_grid_values_all(h5f, 'refl_0_65um_nom_min_sub')
+        refl_sub_lo = refl_sub_lo[s_y, s_x]
+
+        refl_sub_hi = get_grid_values_all(h5f, 'refl_0_65um_nom_max_sub')
+        refl_sub_hi = refl_sub_hi[s_y, s_x]
 
         refl_sub_std = get_grid_values_all(h5f, 'refl_0_65um_nom_stddev_sub')
         refl_sub_std = refl_sub_std[s_y, s_x]
@@ -777,8 +777,7 @@ class SRCNN:
         LEN_X = 2 * (LEN_X - 8)
 
         t0 = time.time()
-        # cld_opd_sres, LEN_Y_in, LEN_X_in = self.run_inference_(bt, refl, cld_opd, refl_sub_lo, refl_sub_hi, refl_sub_std, LEN_Y, LEN_X)
-        cld_opd_sres, LEN_Y_in, LEN_X_in = self.run_inference_(bt, refl, cld_opd, None, None, refl_sub_std, LEN_Y, LEN_X)
+        cld_opd_sres, LEN_Y_in, LEN_X_in = self.run_inference_(bt, refl, cld_opd, refl_sub_lo, refl_sub_hi, refl_sub_std, LEN_Y, LEN_X)
         t1 = time.time()
         print('inference time: ', (t1 - t0))
         print(cld_opd_sres.shape)
@@ -854,6 +853,7 @@ class SRCNN:
 
         # data = np.stack([bt_us, refl_us, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd_us], axis=3)
         data = np.stack([bt_us, refl_us, cld_opd_us, refl_sub_std], axis=3)
+        print('input data shape: ', data.shape)
 
         cld_opd_sres = self.do_inference(data)
         cld_opd_sres = denormalize(cld_opd_sres, label_param, mean_std_dct)