diff --git a/fastNLP/core/sampler.py b/fastNLP/core/sampler.py
index 79dd56c0..74f67125 100644
--- a/fastNLP/core/sampler.py
+++ b/fastNLP/core/sampler.py
@@ -48,8 +48,6 @@ def simple_sort_bucketing(lengths):
     """
 
     :param lengths: list of int, the lengths of all examples.
-    :param buckets: list of int. The length of the list is the number of buckets. Each integer is the maximum length
-        threshold for each bucket (This is usually None.).
     :return data: 2-level list
             ::
 
@@ -75,6 +73,7 @@ def k_means_1d(x, k, max_iter=100):
             assignment: numpy array, 1-D, the bucket id assigned to each example.
     """
     sorted_x = sorted(list(set(x)))
+    x = np.array(x)
     if len(sorted_x) < k:
         raise ValueError("too few buckets")
     gap = len(sorted_x) / k
@@ -119,34 +118,3 @@ def k_means_bucketing(lengths, buckets):
             bucket_data[bucket_id].append(idx)
     return bucket_data
 
-
-class BucketSampler(BaseSampler):
-    """Partition all samples into multiple buckets, each of which contains sentences of approximately the same length.
-    In sampling, first random choose a bucket. Then sample data from it.
-    The number of buckets is decided dynamically by the variance of sentence lengths.
-
-    """
-
-    def __call__(self, data_set, batch_size, num_buckets):
-        return self._process(data_set, batch_size, num_buckets)
-
-    def _process(self, data_set, batch_size, num_buckets, use_kmeans=False):
-        """
-
-        :param data_set: a DataSet object
-        :param batch_size: int
-        :param num_buckets: int, number of buckets for grouping these sequences.
-        :param use_kmeans: bool, whether to use k-means to create buckets.
-
-        """
-        buckets = ([None] * num_buckets)
-        if use_kmeans is True:
-            buckets = k_means_bucketing(data_set, buckets)
-        else:
-            buckets = simple_sort_bucketing(data_set)
-        index_list = []
-        for _ in range(len(data_set) // batch_size):
-            chosen_bucket = buckets[np.random.randint(0, len(buckets))]
-            np.random.shuffle(chosen_bucket)
-            index_list += [idx for idx in chosen_bucket[:batch_size]]
-        return index_list
diff --git a/test/core/test_field.py b/test/core/test_field.py
index ccc36f49..7f1dc8c1 100644
--- a/test/core/test_field.py
+++ b/test/core/test_field.py
@@ -1,10 +1,10 @@
 import unittest
 
-from fastNLP.core.field import CharTextField
+from fastNLP.core.field import CharTextField, LabelField, SeqLabelField
 
 
 class TestField(unittest.TestCase):
-    def test_case(self):
+    def test_char_field(self):
         text = "PhD applicants must submit a Research Plan and a resume " \
                "specify your class ranking written in English and a list of research" \
                " publications if any".split()
@@ -21,3 +21,22 @@ class TestField(unittest.TestCase):
         self.assertEqual(field.contents(), text)
         tensor = field.to_tensor(50)
         self.assertEqual(tuple(tensor.shape), (50, max_word_len))
+
+    def test_label_field(self):
+        label = LabelField("A", is_target=True)
+        self.assertEqual(label.get_length(), 1)
+        self.assertEqual(label.index({"A": 10}), 10)
+
+        label = LabelField(30, is_target=True)
+        self.assertEqual(label.get_length(), 1)
+        tensor = label.to_tensor(0)
+        self.assertEqual(tensor.shape, ())
+        self.assertEqual(int(tensor), 30)
+
+    def test_seq_label_field(self):
+        seq = ["a", "b", "c", "d", "a", "c", "a", "b"]
+        field = SeqLabelField(seq)
+        vocab = {"a": 10, "b": 20, "c": 30, "d": 40}
+        self.assertEqual(field.index(vocab), [vocab[x] for x in seq])
+        tensor = field.to_tensor(10)
+        self.assertEqual(tuple(tensor.shape), (10,))
diff --git a/test/core/test_sampler.py b/test/core/test_sampler.py
index 179d20d7..cf72fe18 100644
--- a/test/core/test_sampler.py
+++ b/test/core/test_sampler.py
@@ -1,6 +1,7 @@
 import torch
 
-from fastNLP.core.sampler import convert_to_torch_tensor, SequentialSampler, RandomSampler
+from fastNLP.core.sampler import convert_to_torch_tensor, SequentialSampler, RandomSampler, \
+    k_means_1d, k_means_bucketing, simple_sort_bucketing
 
 
 def test_convert_to_torch_tensor():
@@ -26,5 +27,18 @@ def test_random_sampler():
         assert d in data
 
 
-if __name__ == "__main__":
-    test_sequential_sampler()
+def test_k_means():
+    centroids, assign = k_means_1d([21, 3, 25, 7, 9, 22, 4, 6, 28, 10], 2, max_iter=5)
+    centroids, assign = list(centroids), list(assign)
+    assert len(centroids) == 2
+    assert len(assign) == 10
+
+
+def test_k_means_bucketing():
+    res = k_means_bucketing([21, 3, 25, 7, 9, 22, 4, 6, 28, 10], [None, None])
+    assert len(res) == 2
+
+
+def test_simple_sort_bucketing():
+    _ = simple_sort_bucketing([21, 3, 25, 7, 9, 22, 4, 6, 28, 10])
+    assert len(_) == 10