1.修复lr_schedulder的调用时机问题;2.修复replace_sampler的初始化问题

fastNLP/core/callbacks/lr_scheduler_callback.py

@@ -19,7 +19,7 @@ class LRSchedCallback(Callback):
         self.scheduler = scheduler
         self.step_on = 0 if step_on == 'batch' else 1
 
-    def on_before_optimizers_step(self, trainer, optimizers):
+    def on_after_optimizers_step(self, trainer, optimizers):
         if self.step_on == 0:
             self.scheduler.step()
 

fastNLP/core/drivers/paddle_driver/utils.py

@@ -178,19 +178,11 @@ def replace_batch_sampler(dataloader: "DataLoader", batch_sampler: "BatchSampler
     # 中寻找；VAR_KEYWORD 代表 **kwargs
     has_variadic_kwargs = any(v.kind is v.VAR_KEYWORD for k, v in init_params.items())
     if has_variadic_kwargs:
-        init_params.update(dict(inspect.signature(DataLoader.__init__).parameters))
-        del init_params["self"]
-
-    # 因为我们刚才可能用 DataLoader 的默认参数将用户定制的 dataloader 的参数覆盖掉了，因此需要重新弄一遍；
-    # 将同时在实例名和参数名中出现且不是默认值的参数收集起来
-    non_default_params = {name for name, p in init_params.items() if
-                          name in instance_attrs and p.default != instance_attrs[name]}
-    # add `dataset` as it might have been replaced with `*args`
-    non_default_params.add("dataset")
-
-    # 收集不是默认值的参数和它的值
-    reconstruct_args = {k: v for k, v in instance_attrs.items() if k in non_default_params}
-    # persistent_workers 在类中的对应成员带有下划线，因此添加进来
+        for key, value in dict(inspect.signature(DataLoader.__init__).parameters).items():
+            if key not in init_params and key != 'self':
+                init_params[key] = value
+
+    reconstruct_args = {k: v for k, v in instance_attrs.items() if k in init_params}
     reconstruct_args.update({
         "batch_sampler": batch_sampler, "shuffle": False, "drop_last": False, "batch_size": 1,
         "persistent_workers": dataloader._persistent_workers,

fastNLP/core/drivers/torch_driver/utils.py

@@ -189,16 +189,11 @@ def replace_sampler(dataloader: "DataLoader", sampler):
     # 中寻找；
     has_variadic_kwargs = any(v.kind is v.VAR_KEYWORD for k, v in init_params.items())
     if has_variadic_kwargs:
-        init_params.update(dict(inspect.signature(DataLoader.__init__).parameters))
-        del init_params["self"]
+        for key, value in dict(inspect.signature(DataLoader.__init__).parameters).items():
+            if key not in init_params and key != 'self':
+                init_params[key] = value
 
-    # 因为我们刚才可能用 DataLoader 的默认参数将用户定制的 dataloader 的参数覆盖掉了，因此需要重新弄一遍；
-    non_default_params = {name for name, p in init_params.items() if
-                          name in instance_attrs and p.default != instance_attrs[name]}
-    # add `dataset` as it might have been replaced with `*args`
-    non_default_params.add("dataset")
-
-    reconstruct_args = {k: v for k, v in instance_attrs.items() if k in non_default_params}
+    reconstruct_args = {k: v for k, v in instance_attrs.items() if k in init_params}
     reconstruct_args.update(_dataloader_init_kwargs_resolve_sampler(dataloader, sampler))
 
     required_args = {