1. trainer中losser修改为loss

fastNLP/core/trainer.py

@@ -28,7 +28,7 @@ class Trainer(object):
     """Main Training Loop
 
     """
-    def __init__(self, train_data, model, losser=None, metrics=None, n_epochs=3, batch_size=32, print_every=50,
+    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, use_cuda=False, save_path=None,
                  optimizer=Adam(lr=0.01, weight_decay=0), check_code_level=0,
                  metric_key=None, sampler=RandomSampler(), use_tqdm=True):
@@ -36,7 +36,7 @@ class Trainer(object):
 
         :param DataSet train_data: the training data
         :param torch.nn.modules.module model: a PyTorch model
-        :param LossBase losser: a loss object
+        :param LossBase loss: a loss object
         :param MetricBase or List[MetricBase] metrics: a metric object or a list of metrics
         :param int n_epochs: the number of training epochs
         :param int batch_size: batch size for training and validation
@@ -88,7 +88,7 @@ class Trainer(object):
             self.metric_key = None
 
         # prepare loss
-        losser = _prepare_losser(losser)
+        losser = _prepare_losser(loss)
 
         # sampler check
         if not isinstance(sampler, BaseSampler):

fastNLP/core/utils.py

@@ -7,7 +7,6 @@ from collections import namedtuple
 
 import numpy as np
 import torch
-from tqdm import tqdm
 
 CheckRes = namedtuple('CheckRes', ['missing', 'unused', 'duplicated', 'required', 'all_needed',
                                    'varargs'], verbose=False)

test/core/test_tester.py

@@ -42,7 +42,6 @@ def prepare_fake_dataset2(*args, size=100):
 class TestTester(unittest.TestCase):
     def test_case_1(self):
         # 检查报错提示能否正确提醒用户
-        # 这里传入多余参数，让其duplicate
         dataset = prepare_fake_dataset2('x1', 'x_unused')
         dataset.rename_field('x_unused', 'x2')
         dataset.set_input('x1', 'x2')
@@ -60,8 +59,9 @@ class TestTester(unittest.TestCase):
                 return {'preds': x}
 
         model = Model()
-        tester = Tester(
-            data=dataset,
-            model=model,
-            metrics=AccuracyMetric())
-        tester.test()
+        with self.assertRaises(NameError):
+            tester = Tester(
+                data=dataset,
+                model=model,
+                metrics=AccuracyMetric())
+            tester.test()

test/core/test_trainer.py

@@ -48,7 +48,7 @@ class TrainerTestGround(unittest.TestCase):
         model = NaiveClassifier(2, 1)
 
         trainer = Trainer(train_set, model,
-                          losser=BCELoss(pred="predict", target="y"),
+                          loss=BCELoss(pred="predict", target="y"),
                           metrics=AccuracyMetric(pred="predict", target="y"),
                           n_epochs=10,
                           batch_size=32,
@@ -227,14 +227,15 @@ class TrainerTestGround(unittest.TestCase):
                 return {'preds': x}
 
         model = Model()
-        trainer = Trainer(
-            train_data=dataset,
-            model=model,
-            dev_data=dataset,
-            losser=CrossEntropyLoss(),
-            metrics=AccuracyMetric(),
-            use_tqdm=False,
-            print_every=2)
+        with self.assertRaises(NameError):
+            trainer = Trainer(
+                train_data=dataset,
+                model=model,
+                dev_data=dataset,
+                loss=CrossEntropyLoss(),
+                metrics=AccuracyMetric(),
+                use_tqdm=False,
+                print_every=2)
 
     def test_case2(self):
         # check metrics Wrong