snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -686,20 +686,20 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
 
     refl = get_grid_values_all(h5f, 'refl_0_65um_nom')
     ylen, xlen = refl.shape
-    # refl = refl[int(ylen/2):ylen, :]
+    refl = refl[int(ylen/2):ylen, :]
     LEN_Y, LEN_X = refl.shape
     print(LEN_Y, LEN_X)
 
     bt = get_grid_values_all(h5f, 'temp_11_0um_nom')
     ylen, xlen = bt.shape
-    # bt = bt[int(ylen/2):ylen, :]
+    bt = bt[int(ylen/2):ylen, :]
 
     cld_opd = get_grid_values_all(h5f, label_param)
     ylen, xlen = cld_opd.shape
-    # cld_opd = cld_opd[int(ylen/2):ylen, :]
+    cld_opd = cld_opd[int(ylen/2):ylen, :]
     cld_opd_hres = cld_opd.copy()
 
-    nn = SRCNN(LEN_Y=LEN_Y-16, LEN_X=LEN_X-16)
+    nn = SRCNN(LEN_Y=2*LEN_Y, LEN_X=2*LEN_X)
 
     refl = np.where(np.isnan(refl), 0, bt)
     refl = refl[nn.slc_y_m, nn.slc_x_m]
@@ -742,7 +742,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     _, ylen, xlen, _ = cld_opd_sres.shape
     print('OUT: ', ylen, xlen)
 
-    cld_opd_sres_out = np.zeros((LEN_Y, LEN_X), dtype=np.float32)
+    cld_opd_sres_out = np.zeros((2*LEN_Y, 2*LEN_X), dtype=np.float32)
     refl_out = np.zeros((LEN_Y, LEN_X), dtype=np.float32)
     cld_opd_out = np.zeros((LEN_Y, LEN_X), dtype=np.float32)