`snapshot...`

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -775,7 +775,7 @@ class SRCNN:
         LEN_X = 2 * (LEN_X - 8)
 
         t0 = time.time()
-        cld_opd_sres, LEN_Y_in, LEN_X_in = self.run_inference_(bt, refl, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd, LEN_Y, LEN_X)
+        cld_opd_sres, LEN_Y_in, LEN_X_in = self.run_inference_(bt, refl, cld_opd, refl_sub_lo, refl_sub_hi, refl_sub_std, LEN_Y, LEN_X)
         t1 = time.time()
         print('inference time: ', (t1 - t0))
         print(cld_opd_sres.shape)
@@ -791,7 +791,7 @@ class SRCNN:
         else:
             return cld_opd_sres
 
-    def run_inference_(self, bt, refl, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd, LEN_Y, LEN_X):
+    def run_inference_(self, bt, refl, cld_opd, refl_sub_lo, refl_sub_hi, refl_sub_std, LEN_Y, LEN_X):
 
         self.slc_x_m = slice(1, int(LEN_X / 2) + 4)
         self.slc_y_m = slice(1, int(LEN_Y / 2) + 4)
@@ -849,7 +849,8 @@ class SRCNN:
         t1 = time.time()
         print('upsample/normalize time: ', (t1 - t0))
 
-        data = np.stack([bt_us, refl_us, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd_us], axis=3)
+        #data = np.stack([bt_us, refl_us, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd_us], axis=3)
+        data = np.stack([bt_us, refl_us, cld_opd_us, refl_sub_std], axis=3)
 
         cld_opd_sres = self.do_inference(data)
         cld_opd_sres = denormalize(cld_opd_sres, label_param, mean_std_dct)