snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -755,6 +755,8 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     hr_grd_a = grd_a.copy()
     hr_grd_a = hr_grd_a[y_128, x_128]
     grd_a = grd_a[slc_y_2, slc_x_2]
+    grd_a = resample_2d_linear_one(x_2, y_2, grd_a, t, s)
+    grd_a = grd_a[:, y_k, x_k]
     grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
     #
     # grd_b = get_grid_values_all(h5f, 'refl_0_65um_nom')
@@ -769,10 +771,10 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     hr_grd_c = grd_c.copy()
     hr_grd_c = hr_grd_c[y_128, x_128]
     grd_c = grd_c[slc_y_2, slc_x_2]
-    if label_param != 'cloud_probability':
-        grd_c = normalize(grd_c, label_param, mean_std_dct)
     grd_c = resample_2d_linear_one(x_2, y_2, grd_c, t, s)
     grd_c = grd_c[y_k, x_k]
+    if label_param != 'cloud_probability':
+        grd_c = normalize(grd_c, label_param, mean_std_dct)
 
     # data = np.stack([grd_a, grd_b, grd_c], axis=2)
     data = np.stack([grd_a, grd_c], axis=2)