snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -702,14 +702,15 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     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]
+    #refl = refl[nn.slc_y_m, nn.slc_x_m]
     refl = np.expand_dims(refl, axis=0)
     refl = nn.upsample(refl)
+    print(refl.shape)
     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 = 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)
@@ -727,7 +728,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     # refl_avg = np.squeeze(refl_avg)
 
     cld_opd = np.where(np.isnan(cld_opd), 0, cld_opd)
-    cld_opd = cld_opd[nn.slc_y_m, nn.slc_x_m]
+    #cld_opd = cld_opd[nn.slc_y_m, nn.slc_x_m]
     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)