change two default init arguments of Trainer into None

fastNLP/core/trainer.py

@@ -32,8 +32,8 @@ from fastNLP.core.utils import get_func_signature
 
 class Trainer(object):
     def __init__(self, train_data, model, loss=None, metrics=None, n_epochs=3, batch_size=32, print_every=50,
-                 validate_every=-1, dev_data=None, save_path=None, optimizer=Adam(lr=0.01, weight_decay=0),
-                 check_code_level=0, metric_key=None, sampler=RandomSampler(), prefetch=False, use_tqdm=True,
+                 validate_every=-1, dev_data=None, save_path=None, optimizer=None,
+                 check_code_level=0, metric_key=None, sampler=None, prefetch=False, use_tqdm=True,
                  use_cuda=False, callbacks=None):
         """
         :param DataSet train_data: the training data
@@ -96,7 +96,7 @@ class Trainer(object):
         losser = _prepare_losser(loss)
 
         # sampler check
-        if not isinstance(sampler, BaseSampler):
+        if sampler is not None and not isinstance(sampler, BaseSampler):
             raise ValueError("The type of sampler should be fastNLP.BaseSampler, got {}.".format(type(sampler)))
 
         if check_code_level > -1:
@@ -119,13 +119,15 @@ class Trainer(object):
         self.best_dev_epoch = None
         self.best_dev_step = None
         self.best_dev_perf = None
-        self.sampler = sampler
+        self.sampler = sampler if sampler is not None else RandomSampler()
         self.prefetch = prefetch
         self.callback_manager = CallbackManager(env={"trainer": self}, callbacks=callbacks)
 
         if isinstance(optimizer, torch.optim.Optimizer):
             self.optimizer = optimizer
         else:
+            if optimizer is None:
+                optimizer = Adam(lr=0.01, weight_decay=0)
             self.optimizer = optimizer.construct_from_pytorch(self.model.parameters())
 
         self.use_tqdm = use_tqdm