Rc copy ts input (#3540)

* cherry-pick layout numclass

* add ts show (#3532)

* [cp] deepcopy fix ts show

Author: Sunting78

paddlex/inference/models/ts_classification/predictor.py

@@ -16,6 +16,7 @@
 import numpy as np
 import pandas as pd
 import os
+import copy
 
 from ....modules.ts_classification.model_list import MODELS
 from ...common.batch_sampler import TSBatchSampler
@@ -109,6 +110,7 @@ def process(self, batch_data: List[Union[str, pd.DataFrame]]) -> Dict[str, Any]:
             Dict[str, Any]: A dictionary containing the paths to the input data, the raw input time series, and the classification results.
         """
         batch_raw_ts = self.preprocessors["ReadTS"](ts_list=batch_data)
+        batch_raw_ts_ori = copy.deepcopy(batch_raw_ts)
 
         if "TSNormalize" in self.preprocessors:
             batch_ts = self.preprocessors["TSNormalize"](ts_list=batch_raw_ts)
@@ -127,7 +129,7 @@ def process(self, batch_data: List[Union[str, pd.DataFrame]]) -> Dict[str, Any]:
         return {
             "input_path": batch_data,
             "input_ts": batch_raw_ts,
-            "input_ts_data": batch_raw_ts,
+            "input_ts_data": batch_raw_ts_ori,
             "classification": batch_ts_preds,
             "target_cols": [self.config["info_params"]["target_cols"]]
         }

paddlex/inference/models/ts_forecasting/predictor.py

@@ -16,6 +16,7 @@
 import numpy as np
 import pandas as pd
 import os
+import copy
 
 from ....modules.ts_forecast.model_list import MODELS
 from ...common.batch_sampler import TSBatchSampler
@@ -122,6 +123,7 @@ def process(self, batch_data: List[Union[str, pd.DataFrame]]) -> Dict[str, Any]:
         """
 
         batch_raw_ts = self.preprocessors["ReadTS"](ts_list=batch_data)
+        batch_raw_ts_ori = copy.deepcopy(batch_raw_ts)
         batch_cutoff_ts = self.preprocessors["TSCutOff"](ts_list=batch_raw_ts)
 
         if "TSNormalize" in self.preprocessors:
@@ -152,6 +154,6 @@ def process(self, batch_data: List[Union[str, pd.DataFrame]]) -> Dict[str, Any]:
         return {
             "input_path": batch_data,
             "input_ts": batch_raw_ts,
-            "cutoff_ts": batch_raw_ts,
+            "cutoff_ts": batch_raw_ts_ori,
             "forecast": batch_ts_preds,
         }