diff --git a/modules/deeplearning/cloud_opd_srcnn_abi.py b/modules/deeplearning/cloud_opd_srcnn_abi.py
index 56b816f24b2c5b3867d0134e2ab409c1c6535f30..5d8887121fd7dfaaa4fad8d342bafe28138c4448 100644
--- a/modules/deeplearning/cloud_opd_srcnn_abi.py
+++ b/modules/deeplearning/cloud_opd_srcnn_abi.py
@@ -141,10 +141,10 @@ def get_min_max_std(grd_k):
     return lo, hi, std, avg
 
 
-# def upsample_static(grd, x_2, y_2, t, s, y_k, x_k):
-#     grd = resample_2d_linear(x_2, y_2, grd, t, s, y_k, x_k)
-#     grd = grd[:, y_k, x_k]
-#     return grd
+def upsample_static(grd, x_2, y_2, t, s, y_k, x_k):
+    grd = resample_2d_linear(x_2, y_2, grd, t, s)
+    # grd = grd[:, y_k, x_k]
+    return grd
 
 
 class SRCNN:
@@ -705,12 +705,19 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     cld_opd = cld_opd[int(ylen/2):ylen, :]
     cld_opd_hres = cld_opd.copy()
 
-    nn = SRCNN(LEN_Y=2*LEN_Y, LEN_X=2*LEN_X)
+    nn = SRCNN()
+
+    slc_x = slice(0, (LEN_X - 16) + 4)
+    slc_y = slice(0, (LEN_Y - 16) + 4)
+    x_2 = np.arange((LEN_X - 16) + 4)
+    y_2 = np.arange((LEN_Y - 16) + 4)
+    t = np.arange(0, (LEN_X - 16) + 4, 0.5)
+    s = np.arange(0, (LEN_Y - 16) + 4, 0.5)
 
     refl = np.where(np.isnan(refl), 0, bt)
-    refl = refl[nn.slc_y, nn.slc_x]
+    refl = refl[slc_y, slc_x]
     refl = np.expand_dims(refl, axis=0)
-    refl = nn.upsample(refl)
+    refl = upsample_static(refl, x_2, y_2, t, s)
     print(refl.shape)
     refl = normalize(refl, 'refl_0_65um_nom', mean_std_dct)
     print('REFL done')
@@ -718,7 +725,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     bt = np.where(np.isnan(bt), 0, bt)
     bt = bt[nn.slc_y, nn.slc_x]
     bt = np.expand_dims(bt, axis=0)
-    bt = nn.upsample(bt)
+    bt = upsample_static(bt, x_2, y_2, t, s)
     bt = normalize(bt, 'temp_11_0um_nom', mean_std_dct)
     print('BT done')
 
@@ -736,7 +743,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     cld_opd = np.where(np.isnan(cld_opd), 0, cld_opd)
     cld_opd = cld_opd[nn.slc_y, nn.slc_x]
     cld_opd = np.expand_dims(cld_opd, axis=0)
-    cld_opd = nn.upsample(cld_opd)
+    cld_opd = upsample_static(cld_opd, x_2, y_2, t, s)
     cld_opd = normalize(cld_opd, label_param, mean_std_dct)
     print('OPD done')