fix tests

fastNLP/core/callback.py

@@ -190,9 +190,6 @@ class EchoCallback(Callback):
 
     def before_batch(self, batch_x, batch_y, indices):
         print("before_batch")
-        print("batch_x:", batch_x)
-        print("batch_y:", batch_y)
-        print("indices: ", indices)
 
     def before_loss(self, batch_y, predict_y):
         print("before_loss")

fastNLP/core/trainer.py

@@ -257,7 +257,7 @@ class Trainer(object):
 
                     self._update()
                     # lr scheduler; lr_finder; one_cycle
-                    self.callback_manager.after_step()
+                    self.callback_manager.after_step(self.optimizer)
 
                     self._summary_writer.add_scalar("loss", loss.item(), global_step=self.step)
                     for name, param in self.model.named_parameters():

test/core/test_dataset.py

@@ -197,4 +197,4 @@ class TestDataSetIter(unittest.TestCase):
     def test__repr__(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})
         for iter in ds:
-            self.assertEqual(iter.__repr__(), "{'x': [1, 2, 3, 4],\n'y': [5, 6]}")
+            self.assertEqual(iter.__repr__(), "{'x': [1, 2, 3, 4] type=list,\n'y': [5, 6] type=list}")

test/core/test_metrics.py

@@ -360,7 +360,8 @@ class TestBMESF1PreRecMetric(unittest.TestCase):
 
         metric = BMESF1PreRecMetric()
         metric(pred_dict, target_dict)
-        self.assertDictEqual(metric.get_metric(), {'f1': 0.999999, 'precision': 1.0, 'recall': 1.0})
+        self.assertDictEqual(metric.get_metric(), {'f': 1.0, 'pre': 1.0, 'rec': 1.0})
+
 
 class TestUsefulFunctions(unittest.TestCase):
     # 测试metrics.py中一些看上去挺有用的函数