snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -709,21 +709,29 @@ def run_restore_static(directory, ckpt_dir):
 
 
 def run_evaluate_static(in_file, out_file, ckpt_dir):
+    N = 4
+    sub_y, sub_x = (N+1) * 128, (N+1) * 128
+    y_0, x_0, = 2500 - int(sub_y/2), 2500 - int(sub_x/2)
+
+    slc_y_2, slc_x_2 = slice(1, 128*N + 6, 2), slice(1, 128*N + 6, 2)
+    y_2, x_2 = np.arange((128*N)/2 + 3), np.arange((128*N)/2 + 3)
+    t, s = np.arange(1, (128*N)/2 + 2, 0.5), np.arange(1, (128*N)/2 + 2, 0.5)
+
     h5f = h5py.File(in_file, 'r')
     grd_a = get_grid_values_all(h5f, 'temp_11_0um_nom')
-    grd_a = grd_a[2432:2944, 2432:2944]
+    grd_a = grd_a[y_0:y_0+sub_y, x_0:x_0+sub_x]
     grd_a = grd_a[slc_y_2, slc_x_2]
     grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
     grd_a = resample_2d_linear_one(x_2, y_2, grd_a, t, s)
 
     grd_b = get_grid_values_all(h5f, 'refl_0_65um_nom')
-    grd_b = grd_b[2432:2944, 2432:2944]
+    grd_b = grd_b[y_0:y_0+sub_y, x_0:x_0+sub_x]
     grd_b = grd_b[slc_y_2, slc_x_2]
     grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
     grd_b = resample_2d_linear_one(x_2, y_2, grd_b, t, s)
 
     grd_c = get_grid_values_all(h5f, label_param)
-    grd_c = grd_c[2432:2944, 2432:2944]
+    grd_c = grd_c[y_0:y_0+sub_y, x_0:x_0+sub_x]
     grd_c = grd_c[slc_y_2, slc_x_2]
     if label_param != 'cloud_fraction':
         grd_c = normalize(grd_c, label_param, mean_std_dct)