trainer check_code调整

fastNLP/core/trainer.py

@@ -11,9 +11,6 @@ from fastNLP.core.optimizer import Optimizer
 from fastNLP.core.sampler import RandomSampler
 from fastNLP.core.sampler import SequentialSampler
 from fastNLP.core.tester import Tester
-from fastNLP.core.utils import _build_args
-from fastNLP.core.utils import _check_arg_dict_list
-
 from fastNLP.core.utils import _check_arg_dict_list
 from fastNLP.core.utils import _build_args
 from fastNLP.core.utils import _syn_model_data
@@ -23,8 +20,7 @@ class Trainer(object):
     """Main Training Loop
 
     """
-
-   def __init__(self, train_data, model, n_epochs, batch_size, n_print=1,
+   def __init__(self, train_data, model, n_epochs=3, batch_size=32, print_every=-1,
                  dev_data=None, use_cuda=False, loss=Loss(None), save_path="./save",
                  optimizer=Optimizer("Adam", lr=0.001, weight_decay=0),
                  evaluator=Evaluator(),
@@ -210,13 +206,12 @@ IGNORE_CHECK_LEVEL = 0
 WARNING_CHECK_LEVEL = 1
 STRICT_CHECK_LEVEL = 2
 
-def _check_code(dataset, model, batch_size=DEFAULT_CHECK_BATCH_SIZE, dev_data=None, check_level=1):
+def _check_code(dataset, model, batch_size=DEFAULT_CHECK_BATCH_SIZE, dev_data=None, check_level=WARNING_CHECK_LEVEL):
     # check get_loss 方法
     model_name = model.__class__.__name__
     if not hasattr(model, 'get_loss'):
         raise AttributeError("{} has to have a 'get_loss' function.".format(model_name))
 
-    batch_size = min(DEFAULT_CHECK_BATCH_SIZE, batch_size)
     batch = Batch(dataset=dataset, batch_size=batch_size, sampler=SequentialSampler())
     for batch_count, (batch_x, batch_y) in enumerate(batch):
         if batch_count == 0:
@@ -236,8 +231,9 @@ def _check_code(dataset, model, batch_size=DEFAULT_CHECK_BATCH_SIZE, dev_data=No
 
         refined_batch_x = _build_args(model.forward, **batch_x)
         output = model(**refined_batch_x)
-
-        assert isinstance(output, dict), "The return value of {}.forward() should be dict.".format(model_name)
+        signature_str = get_func_signature(model.forward)
+        func_signature = '{}.forward(self, {})'.format(model.__class__.__name__, signature_str[1:-1])
+        assert isinstance(output, dict), "The return value of {} should be dict.".format(func_signature)
 
         # loss check
         if batch_count == 0:
@@ -287,6 +283,12 @@ def _check_code(dataset, model, batch_size=DEFAULT_CHECK_BATCH_SIZE, dev_data=No
                     break
             _check_loss_evaluate(model=model, model_func=model.evaluate, check_level=check_level,
                                  output=outputs, batch_y=truths)
+            refined_input = _build_args(model.evaluate, **outputs, **truths)
+            metrics = model.evaluate(**refined_input)
+            signature_str = get_func_signature(model.evaluate)
+            func_signature = '{}.evaluate(self, {})'.format(model.__class__.__name__, signature_str[1:-1])
+            assert isinstance(metrics, dict), "The return value of {} should be dict.". \
+                format(func_signature)
         if check_level > IGNORE_CHECK_LEVEL:
             print("Finish checking evaluate process.", flush=True)