fix save train result bug

Author: zhangyubo0722

paddlets/models/forecasting/dl/PatchTST.py

@@ -367,12 +367,6 @@ def _update_fit_params(
             "known_cov_dim": 0,
             "observed_cov_dim": 0
         }
-        if train_tsdataset[0].get_known_cov() is not None:
-            fit_params["known_cov_dim"] = train_tsdataset[0].get_known_cov(
-            ).data.shape[1]
-        if train_tsdataset[0].get_observed_cov() is not None:
-            fit_params["observed_cov_dim"] = train_tsdataset[
-                0].get_observed_cov().data.shape[1]
         return fit_params
 
     def _init_network(self) -> paddle.nn.Layer:

paddlets/models/forecasting/dl/RLinear.py

@@ -221,12 +221,12 @@ def _update_fit_params(
             "known_cov_dim": 0,
             "observed_cov_dim": 0
         }
-        if train_tsdataset[0].get_known_cov() is not None:
-            fit_params["known_cov_dim"] = train_tsdataset[0].get_known_cov(
-            ).data.shape[1]
-        if train_tsdataset[0].get_observed_cov() is not None:
-            fit_params["observed_cov_dim"] = train_tsdataset[
-                0].get_observed_cov().data.shape[1]
+        #if train_tsdataset[0].get_known_cov() is not None:
+        #    fit_params["known_cov_dim"] = train_tsdataset[0].get_known_cov(
+        #    ).data.shape[1]
+        #if train_tsdataset[0].get_observed_cov() is not None:
+        #    fit_params["observed_cov_dim"] = train_tsdataset[
+        #        0].get_observed_cov().data.shape[1]
         return fit_params
 
     def _init_network(self) -> paddle.nn.Layer:

paddlets/utils/utils.py

@@ -535,12 +535,12 @@ def update_train_results(save_path, score, model_name="", done_flag=True):
     train_results["models"]["best"]["score"] = score
     for tag in save_model_tag:
         train_results["models"]["best"][
-            tag] = "" if tag != "pdparams" else os.path.join("best_model",
-                                                             "model.pdparams")
+            tag] = "" if tag != "pdparams" else "best_accuracy.pdparams.tar"
     for tag in save_inference_tag:
         train_results["models"]["best"][tag] = os.path.join(
             "inference", f"inference.{tag}"
             if tag != "inference_config" else "inference.yml")
+    train_results["models"]["best"]["pdiparams"] = "best_accuracy.pdparams.tar"
 
     train_results = convert_and_remove_types(train_results)
     with open(train_results_path, "w") as fp: