* add tqdm in requirements.txt

* fix FieldArray type check bugs

fastNLP/core/fieldarray.py

@@ -83,12 +83,12 @@ class FieldArray(object):
                 raise RuntimeError("Cannot append a list into a 1-D FieldArray. Please provide an element.")
             if len(val) == 0:
                 raise RuntimeError("Cannot append an empty list.")
-            val_list_type = [type(_) for _ in val]  # type check
+            val_list_type = set([type(_) for _ in val])  # type check
             if len(val_list_type) == 2 and int in val_list_type and float in val_list_type:
                 # up-cast int to float
                 val_type = float
             elif len(val_list_type) == 1:
-                val_type = val_list_type[0]
+                val_type = val_list_type.pop()
             else:
                 raise RuntimeError("Cannot append a list of {}".format(val_list_type))
         else:

requirements.txt

@@ -1,3 +1,4 @@
 numpy>=1.14.2
 torch>=0.4.0
 tensorboardX
+tqdm

test/core/test_trainer.py

@@ -1,8 +1,8 @@
 import unittest
 
 import numpy as np
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from fastNLP.core.dataset import DataSet
 from fastNLP.core.instance import Instance
@@ -26,6 +26,7 @@ def prepare_fake_dataset():
                        [Instance(x=[float(item[0]), float(item[1])], y=[1.0]) for item in class_B])
     return data_set
 
+
 def prepare_fake_dataset2(*args, size=100):
     ys = np.random.randint(4, size=100)
     data = {'y': ys}
@@ -33,6 +34,7 @@ def prepare_fake_dataset2(*args, size=100):
         data[arg] = np.random.randn(size, 5)
     return DataSet(data=data)
 
+
 class TrainerTestGround(unittest.TestCase):
     def test_case(self):
         data_set = prepare_fake_dataset()
@@ -55,15 +57,20 @@ class TrainerTestGround(unittest.TestCase):
                           check_code_level=2,
                           use_tqdm=True)
         trainer.train()
+        """
+        # 应该正确运行
+        """
 
     def test_trainer_suggestion1(self):
         # 检查报错提示能否正确提醒用户。
         # 这里没有传入forward需要的数据。需要trainer提醒用户如何设置。
         dataset = prepare_fake_dataset2('x')
+
         class Model(nn.Module):
             def __init__(self):
                 super().__init__()
                 self.fc = nn.Linear(5, 4)
+
             def forward(self, x1, x2, y):
                 x1 = self.fc(x1)
                 x2 = self.fc(x2)
@@ -72,10 +79,12 @@ class TrainerTestGround(unittest.TestCase):
                 return {'loss': loss}
 
         model = Model()
-        trainer = Trainer(
-            train_data=dataset,
-            model=model
-        )
+
+        with self.assertRaises(NameError):
+            trainer = Trainer(
+                train_data=dataset,
+                model=model
+            )
         """
         # 应该获取到的报错提示
         NameError: 
@@ -91,10 +100,12 @@ class TrainerTestGround(unittest.TestCase):
         # 这里传入forward需要的数据，看是否可以运行
         dataset = prepare_fake_dataset2('x1', 'x2')
         dataset.set_input('x1', 'x2', 'y', flag=True)
+
         class Model(nn.Module):
             def __init__(self):
                 super().__init__()
                 self.fc = nn.Linear(5, 4)
+
             def forward(self, x1, x2, y):
                 x1 = self.fc(x1)
                 x2 = self.fc(x2)
@@ -119,10 +130,12 @@ class TrainerTestGround(unittest.TestCase):
         # 这里传入forward需要的数据，但是forward没有返回loss这个key
         dataset = prepare_fake_dataset2('x1', 'x2')
         dataset.set_input('x1', 'x2', 'y', flag=True)
+
         class Model(nn.Module):
             def __init__(self):
                 super().__init__()
                 self.fc = nn.Linear(5, 4)
+
             def forward(self, x1, x2, y):
                 x1 = self.fc(x1)
                 x2 = self.fc(x2)
@@ -142,7 +155,6 @@ class TrainerTestGround(unittest.TestCase):
         # 应该正确运行
         """
 
-
     def test_case2(self):
         # check metrics Wrong
         data_set = prepare_fake_dataset2('x1', 'x2')