Update training.py

Signed-off-by: Anchor Yu <[email protected]>

deepmd/pt/train/training.py

@@ -185,6 +185,7 @@ def get_dataloader_and_buffer(_data, _params):
                     if dist.is_available()
                     else 0,  # setting to 0 diverges the behavior of its iterator; should be >=1
                     drop_last=False,
+                    collate_fn=lambda batch: batch,  # prevent extra conversion
                     pin_memory=True,
                 )
                 with torch.device("cpu"):
@@ -284,10 +285,6 @@ def get_lr(lr_params):
             }
 
         # Model
-        dp_random.seed(training_params["seed"])
-        if training_params["seed"] is not None:
-            torch.manual_seed(training_params["seed"])
-
         self.model = get_model_for_wrapper(model_params, loss_param_tmp)
 
         # Loss
@@ -312,7 +309,6 @@ def get_lr(lr_params):
                 )
 
         # Data
-        dp_random.seed(training_params["seed"])
         if not self.multi_task:
             self.get_sample_func = single_model_stat(
                 self.model,
@@ -1116,7 +1112,7 @@ def get_data(self, is_train=True, task_key="Default"):
                     batch_data = next(iter(self.validation_data[task_key]))
 
         for key in batch_data.keys():
-            if key == "sid" or key == "fid" or key == "box":
+            if key == "sid" or key == "fid" or key == "box" or "find_" in key:
                 continue
             elif not isinstance(batch_data[key], list):
                 if batch_data[key] is not None: