snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -687,6 +687,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     ylen, xlen = refl.shape
     # 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
@@ -704,12 +705,14 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     refl = np.expand_dims(refl, axis=0)
     refl = nn.upsample(refl)
     refl = normalize(refl, 'refl_0_65um_nom', mean_std_dct)
+    print('REFL done')
 
     bt = np.where(np.isnan(bt), 0, bt)
     bt = bt[nn.slc_y_m, nn.slc_x_m]
     bt = np.expand_dims(bt, axis=0)
     bt = nn.upsample(bt)
     bt = normalize(bt, 'temp_11_0um_nom', mean_std_dct)
+    print('BT done')
 
     # refl = get_grid_values_all(h5f, 'super/refl_0_65um')
     # refl = np.where(np.isnan(refl), 0, refl)
@@ -727,6 +730,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     cld_opd = np.expand_dims(cld_opd, axis=0)
     cld_opd = nn.upsample(cld_opd)
     cld_opd = normalize(cld_opd, label_param, mean_std_dct)
+    print('OPD done')
 
     data = np.stack([bt, refl, cld_opd], axis=3)
 
@@ -735,6 +739,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     cld_opd_sres = nn.run_evaluate(data, ckpt_dir)
     cld_opd_sres = denormalize(cld_opd_sres, label_param, mean_std_dct)
     _, ylen, xlen, _ = cld_opd_sres.shape
+    print('OUT: ', ylen, xlen)
 
     cld_opd_sres_out = np.zeros((LEN_Y, LEN_X), dtype=np.float32)
     refl_out = np.zeros((LEN_Y, LEN_X), dtype=np.float32)