* FieldArray添加对list of np.array的支持

* 添加测试：FieldArray的初始化

fastNLP/core/fieldarray.py

@@ -112,13 +112,17 @@ class FieldArray(object):
                 2.3) 二维list  DataSet([Instance(x=[[1, 2], [3, 4]])])
                 2.4) 二维array  DataSet([Instance(x=np.array([[1, 2], [3, 4]]))])
 
-            注意：np.array必须仅在最外层，即np.array([np.array, np.array]) 和 list of np.array不考虑
             类型检查(dtype check)发生在当该field被设置为is_input或者is_target时。
 
         """
         self.name = name
         if isinstance(content, list):
-            content = content
+            # 如果DataSet使用dict初始化, content 可能是二维list/二维array/三维list
+            # 如果DataSet使用list of Instance 初始化, content可能是 [list]/[array]/[2D list]
+            if len(content) == 1 and isinstance(content[0], np.ndarray):
+                # 这是使用list of Instance 初始化时第一个样本：FieldArray(name, [field])
+                # 将[np.array] 转化为 list of list
+                content[0] = content[0].tolist()
         elif isinstance(content, np.ndarray):
             content = content.tolist()  # convert np.ndarray into 2-D list
         else:

reproduction/POS_tagging/train_pos_tag.py

@@ -144,6 +144,7 @@ if __name__ == "__main__":
     parser.add_argument("--train", type=str, help="training conll file", default="/home/zyfeng/data/sample.conllx")
     parser.add_argument("--dev", type=str, help="dev conll file", default="/home/zyfeng/data/sample.conllx")
     parser.add_argument("--test", type=str, help="test conll file", default=None)
+    parser.add_argument("--save", type=str, help="path to save", default=None)
 
     parser.add_argument("-c", "--restart", action="store_true", help="whether to continue training")
     parser.add_argument("-cp", "--checkpoint", type=str, help="checkpoint of the trained model")

test/core/test_fieldarray.py

@@ -5,8 +5,59 @@ import numpy as np
 from fastNLP.core.fieldarray import FieldArray
 
 
+class TestFieldArrayInit(unittest.TestCase):
+    """
+    1） 如果DataSet使用dict初始化，那么在add_field中会构造FieldArray：
+            1.1) 二维list  DataSet({"x": [[1, 2], [3, 4]]})
+            1.2) 二维array  DataSet({"x": np.array([[1, 2], [3, 4]])})
+            1.3) 三维list  DataSet({"x": [[[1, 2], [3, 4]], [[1, 2], [3, 4]]]})
+        2） 如果DataSet使用list of Instance 初始化,那么在append中会先对第一个样本初始化FieldArray；
+        然后后面的样本使用FieldArray.append进行添加。
+            2.1) 一维list DataSet([Instance(x=[1, 2, 3, 4])])
+            2.2) 一维array DataSet([Instance(x=np.array([1, 2, 3, 4]))])
+            2.3) 二维list  DataSet([Instance(x=[[1, 2], [3, 4]])])
+            2.4) 二维array  DataSet([Instance(x=np.array([[1, 2], [3, 4]]))])
+    """
+
+    def test_init_v1(self):
+        # 二维list
+        fa = FieldArray("x", [[1, 2], [3, 4]] * 5, is_input=True)
+
+    def test_init_v2(self):
+        # 二维array
+        fa = FieldArray("x", np.array([[1, 2], [3, 4]] * 5), is_input=True)
+
+    def test_init_v3(self):
+        # 三维list
+        fa = FieldArray("x", [[[1, 2], [3, 4]], [[1, 2], [3, 4]]], is_input=True)
+
+    def test_init_v4(self):
+        # 一维list
+        val = [1, 2, 3, 4]
+        fa = FieldArray("x", [val], is_input=True)
+        fa.append(val)
+
+    def test_init_v5(self):
+        # 一维array
+        val = np.array([1, 2, 3, 4])
+        fa = FieldArray("x", [val], is_input=True)
+        fa.append(val)
+
+    def test_init_v6(self):
+        # 二维array
+        val = [[1, 2], [3, 4]]
+        fa = FieldArray("x", [val], is_input=True)
+        fa.append(val)
+
+    def test_init_v7(self):
+        # 二维list
+        val = np.array([[1, 2], [3, 4]])
+        fa = FieldArray("x", [val], is_input=True)
+        fa.append(val)
+
+
 class TestFieldArray(unittest.TestCase):
-    def test(self):
+    def test_main(self):
         fa = FieldArray("x", [1, 2, 3, 4, 5], is_input=True)
         self.assertEqual(len(fa), 5)
         fa.append(6)

test/test_tutorials.py

@@ -408,12 +408,12 @@ class TestTutorial(unittest.TestCase):
             model=model,
             loss=CrossEntropyLoss(pred='pred', target='label'),
             metrics=AccuracyMetric(),
-            n_epochs=5,
+            n_epochs=3,
             batch_size=16,
             print_every=-1,
             validate_every=-1,
             dev_data=dev_data,
-            use_cuda=True,
+            use_cuda=False,
             optimizer=Adam(lr=1e-3, weight_decay=0),
             check_code_level=-1,
             metric_key='acc',