diff --git a/paddlets/models/forecasting/dl/PatchTST.py b/paddlets/models/forecasting/dl/PatchTST.py index 72779d8..cda1a47 100644 --- a/paddlets/models/forecasting/dl/PatchTST.py +++ b/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: diff --git a/paddlets/models/forecasting/dl/RLinear.py b/paddlets/models/forecasting/dl/RLinear.py index 9d43cd5..ece4dca 100644 --- a/paddlets/models/forecasting/dl/RLinear.py +++ b/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: diff --git a/paddlets/utils/utils.py b/paddlets/utils/utils.py index 2dec7ad..6fb890f 100644 --- a/paddlets/utils/utils.py +++ b/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: