snapshot...

modules/deeplearning/cloud_opd_srcnn_viirs.py

@@ -732,20 +732,20 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     bt = smooth_2d(bt)
     bt = normalize(bt, 'temp_11_0um_nom', mean_std_dct)
 
-    # cld_opd = np.where(np.isnan(cld_opd), 0, cld_opd)
-    # cld_opd = cld_opd[nn.slc_y_2, nn.slc_x_2]
-    # cld_opd = np.expand_dims(cld_opd, axis=0)
-    # cld_opd = nn.upsample(cld_opd)
-    # cld_opd = smooth_2d(cld_opd)
-    # cld_opd = normalize(cld_opd, label_param, mean_std_dct)
-
-    cld_opd = np.where(np.isnan(cld_opd_orig), 0, cld_opd_orig)
-    cld_opd = cld_opd[nn.slc_y_m, nn.slc_x_m]
+    cld_opd = np.where(np.isnan(cld_opd), 0, cld_opd)
+    cld_opd = cld_opd[nn.slc_y_2, nn.slc_x_2]
     cld_opd = np.expand_dims(cld_opd, axis=0)
     cld_opd = nn.upsample(cld_opd)
     cld_opd = smooth_2d(cld_opd)
     cld_opd = normalize(cld_opd, label_param, mean_std_dct)
 
+    # cld_opd = np.where(np.isnan(cld_opd_orig), 0, cld_opd_orig)
+    # cld_opd = cld_opd[nn.slc_y_m, nn.slc_x_m]
+    # cld_opd = np.expand_dims(cld_opd, axis=0)
+    # cld_opd = nn.upsample(cld_opd)
+    # cld_opd = smooth_2d(cld_opd)
+    # cld_opd = normalize(cld_opd, label_param, mean_std_dct)
+
     data = np.stack([bt, refl, cld_opd], axis=3)
     print('input data shape: ', data.shape)