1.解决trainer加载模型的问题; 2.删除loss与metric中对*arg的报错

fastNLP/core/losses.py

@@ -63,9 +63,9 @@ class LossBase(object):
                     f"initialization parameters, or change its signature.")
 
         # evaluate should not have varargs.
-        if func_spect.varargs:
-            raise NameError(f"Delete `*{func_spect.varargs}` in {get_func_signature(self.get_loss)}(Do not use "
-                            f"positional argument.).")
+        # if func_spect.varargs:
+        #     raise NameError(f"Delete `*{func_spect.varargs}` in {get_func_signature(self.get_loss)}(Do not use "
+        #                     f"positional argument.).")
 
     def _fast_param_map(self, pred_dict, target_dict):
         """
@@ -148,7 +148,7 @@ class LossBase(object):
                                  all_needed=check_res.all_needed,
                                  varargs=check_res.varargs)
 
-            if check_res.missing or check_res.duplicated or check_res.varargs:
+            if check_res.missing or check_res.duplicated:
                 raise CheckError(check_res=check_res,
                                  func_signature=get_func_signature(self.get_loss))
         refined_args = _build_args(self.get_loss, **mapped_pred_dict, **mapped_target_dict)

fastNLP/core/metrics.py

@@ -63,9 +63,9 @@ class MetricBase(object):
                     f"initialization parameters, or change its signature.")
 
         # evaluate should not have varargs.
-        if func_spect.varargs:
-            raise NameError(f"Delete `*{func_spect.varargs}` in {get_func_signature(self.evaluate)}(Do not use "
-                            f"positional argument.).")
+        # if func_spect.varargs:
+        #     raise NameError(f"Delete `*{func_spect.varargs}` in {get_func_signature(self.evaluate)}(Do not use "
+        #                     f"positional argument.).")
 
     def get_metric(self, reset=True):
         raise NotImplemented
@@ -165,7 +165,7 @@ class MetricBase(object):
                                  all_needed=check_res.all_needed,
                                  varargs=check_res.varargs)
 
-            if check_res.missing or check_res.duplicated or check_res.varargs:
+            if check_res.missing or check_res.duplicated:
                 raise CheckError(check_res=check_res,
                                  func_signature=get_func_signature(self.evaluate))
         refined_args = _build_args(self.evaluate, **mapped_pred_dict, **mapped_target_dict)

fastNLP/core/trainer.py

@@ -363,11 +363,11 @@ class Trainer(object):
     def _load_model(self, model, model_name, only_param=False):
         # TODO: 这个是不是有问题？
         if self.save_path is not None:
-            model_name = os.path.join(self.save_path, model_name)
+            model_path = os.path.join(self.save_path, model_name)
             if only_param:
-                states = torch.save(model.state_dict(), model_name)
+                states = torch.load(model_path)
             else:
-                states = torch.save(model, model_name).state_dict()
+                states = torch.load(model_path).state_dict()
             model.load_state_dict(states)
 
     def _better_eval_result(self, metrics):

fastNLP/core/utils.py

@@ -242,9 +242,9 @@ def _check_loss_evaluate(prev_func_signature: str, func_signature: str, check_re
     _unused_field = []
     _unused_param = []
     suggestions = []
-    if check_res.varargs:
-        errs.append(f"\tvarargs: *{check_res.varargs}")
-        suggestions.append(f"Does not support pass positional arguments, please delete *{check_res.varargs}.")
+    # if check_res.varargs:
+    #     errs.append(f"\tvarargs: *{check_res.varargs}")
+    #     suggestions.append(f"Does not support pass positional arguments, please delete *{check_res.varargs}.")
 
     if check_res.unused:
         for _unused in check_res.unused:
@@ -344,9 +344,9 @@ def _check_forward_error(forward_func, batch_x, dataset, check_level):
     suggestions = []
     _unused = []
 
-    if check_res.varargs:
-        errs.append(f"\tvarargs: {check_res.varargs}")
-        suggestions.append(f"Does not support pass positional arguments, please delete *{check_res.varargs}.")
+    # if check_res.varargs:
+    #     errs.append(f"\tvarargs: {check_res.varargs}")
+    #     suggestions.append(f"Does not support pass positional arguments, please delete *{check_res.varargs}.")
     if check_res.missing:
         errs.append(f"\tmissing param: {check_res.missing}")
         _miss_in_dataset = []