[Fix] Remove padding data at the end of solver.predict (#669)

* fix(test=document_fix)

* remove redundant padding data of input_data in solver.predict

* remove padding data in solver.predict after prediction

Author: HydrogenSulfate

docs/zh/examples/heat_pinn.md

@@ -14,7 +14,7 @@
 
 | 预训练模型  | 指标 |
 |:--| :--|
-| [heat_pinn_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/heat_pinn/heat_pinn_pretrained.pdparams) | norm MSE loss between the FDM and PINN is 0.0013415 |
+| [heat_pinn_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/heat_pinn/heat_pinn_pretrained.pdparams) | norm MSE loss between the FDM and PINN is 1.30174e-03 |
 
 ## 1. 背景简介
 

examples/heat_pinn/heat_pinn.py

@@ -139,8 +139,8 @@ def train(cfg: DictConfig):
         N_EVAL, N_EVAL
     )
     fdm_output = fdm.solve(N_EVAL, 1).T
-    mes_loss = np.mean(np.square(pinn_output - (fdm_output / 75.0)))
-    logger.info(f"The norm MSE loss between the FDM and PINN is {mes_loss}")
+    mse_loss = np.mean(np.square(pinn_output - (fdm_output / 75.0)))
+    logger.info(f"The norm MSE loss between the FDM and PINN is {mse_loss}")
 
     x = input_data["x"].reshape(N_EVAL, N_EVAL)
     y = input_data["y"].reshape(N_EVAL, N_EVAL)
@@ -237,8 +237,8 @@ def evaluate(cfg: DictConfig):
         "u"
     ].reshape(N_EVAL, N_EVAL)
     fdm_output = fdm.solve(N_EVAL, 1).T
-    mes_loss = np.mean(np.square(pinn_output - (fdm_output / 75.0)))
-    logger.info(f"The norm MSE loss between the FDM and PINN is {mes_loss}")
+    mse_loss = np.mean(np.square(pinn_output - (fdm_output / 75.0)))
+    logger.info(f"The norm MSE loss between the FDM and PINN is {mse_loss:.5e}")
 
     x = input_data["x"].reshape(N_EVAL, N_EVAL)
     y = input_data["y"].reshape(N_EVAL, N_EVAL)

ppsci/solver/solver.py

@@ -234,7 +234,8 @@ def __init__(
             logger.warning(
                 f"Detected 'world_size'({self.world_size}) > 1, it is recommended to "
                 "scale up the learning rate and reduce the 'epochs' or "
-                "'iters_per_epoch' according to the 'world_size' both linearly."
+                "'iters_per_epoch' according to the 'world_size' both linearly if you "
+                "are training model."
             )
 
         # load pretrained model, usually used for transfer learning
@@ -511,8 +512,6 @@ def predict(
         # pad with last element if `num_samples` is not divisible by `world_size`
         # ensuring every device get same number of data.
         if num_pad > 0:
-            # NOTE: This will modify input_dict inplace by appending padding data at the
-            # end if num_pad > 0.
             for k, v in input_dict.items():
                 repeat_times = (num_pad, *(1 for _ in range(v.ndim - 1)))
                 if isinstance(v, np.ndarray):
@@ -596,6 +595,9 @@ def predict(
                     pred_dict = {
                         key: value[:num_samples] for key, value in pred_dict.items()
                     }
+                    # NOTE: Discard padding data in input_dict for consistency
+                    for k in input_dict:
+                        input_dict[k] = input_dict[k][:num_samples]
 
         # convert to numpy ndarray if specified
         if return_numpy: