diff --git a/tests/test_datasets.py b/tests/test_datasets.py
deleted file mode 100644
index 8141068..0000000
--- a/tests/test_datasets.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import unittest
-import torch
-import pywick.datasets.tnt.dataset as dataset
-import numpy as np
-import os
-import tempfile
-
-class TestDatasets(unittest.TestCase):
-    def testListDataset(self):
-        identity = lambda x: x
-        h = [0,1,2]
-        d = dataset.ListDataset(elem_list = h, load = identity)
-        self.assertEqual(len(d), 3)
-        self.assertEqual(d[0], 0)
-
-        t = torch.LongTensor([0,1,2])
-        d = dataset.ListDataset(elem_list = t, load = identity)
-        self.assertEqual(len(d), 3)
-        self.assertEqual(d[0], 0)
-
-        a = np.asarray([0,1,2])
-        d = dataset.ListDataset(elem_list = a, load = identity)
-        self.assertEqual(len(d), 3)
-        self.assertEqual(d[0], 0)
-
-    def testListDataset_path(self):
-        prefix = lambda x: 'bar/' + str(x)
-        tbl = [0,1,2]
-        d = dataset.ListDataset(tbl, prefix, 'foo')
-        self.assertEqual(len(d), 3)
-        self.assertEqual(d[2], 'bar/foo/2')
-
-    def testListDataset_file(self):
-        _, filename = tempfile.mkstemp()
-        with open(filename, 'w') as f:
-            for i in range(0,50):
-                f.write(str(i) + '\n')
-
-        identity = lambda x: x
-        d = dataset.ListDataset(filename, identity, 'foo')
-        self.assertEqual(len(d), 50)
-        self.assertEqual(d[15], 'foo/15')
-
-        os.remove(filename)
-
-    def testTensorDataset(self):
-        # dict input
-        data = {
-                # 'input': torch.range(0,7),
-                'input': np.arange(0,8),
-                'target': np.arange(0,8),
-                }
-        d = dataset.TensorDataset(data)
-        self.assertEqual(len(d), 8)
-        self.assertEqual(d[2], {'input': 2, 'target': 2})
-
-        # tensor input
-        a = torch.randn(8)
-        d = dataset.TensorDataset(a)
-        self.assertEqual(len(a), len(d))
-        self.assertEqual(a[1], d[1])
-
-        # list of tensors input
-        d = dataset.TensorDataset([a])
-        self.assertEqual(len(a), len(d))
-        self.assertEqual(a[1], d[1][0])
-
-    def testBatchDataset(self):
-        t = torch.range(0,15).long()
-        batchsize = 8
-        d = dataset.ListDataset(t, lambda x: {'input': x})
-        d = dataset.BatchDataset(d, batchsize)
-        ex = d[0]['input']
-        self.assertEqual(len(ex), batchsize)
-        self.assertEqual(ex[-1], batchsize - 1)
-
-    # def testTransformDataset(self):
-    #     d = dataset.TransformDataset(dataset.TensorDataset()
-
-    def testResampleDataset(self):
-        tbl = dataset.TensorDataset(np.asarray([0,1,2]))
-        d = dataset.ResampleDataset(tbl, lambda dataset, i: i % 2)
-        self.assertEqual(len(d), 3)
-        self.assertEqual(d[0], 0)
-        self.assertEqual(d[2], 0)
-
-    def testShuffleDataset(self):
-        tbl = dataset.TensorDataset(np.asarray([0,1,2,3,4]))
-        d = dataset.ShuffleDataset(tbl)
-        self.assertEqual(len(d), 5)
-        # TODO: every item should appear exactly once
-
-    def testSplitDataset(self):
-        h = [0, 1, 2, 3]
-        listdataset = dataset.ListDataset(elem_list=h)
-        splitdataset = dataset.MultiPartitionDataset(listdataset, {'train': 3, 'val': 1})
-
-        splitdataset.select('train')
-        self.assertEqual(len(splitdataset), 3)
-        self.assertEqual(splitdataset[2], 2)
-
-        splitdataset.select('val')
-        self.assertEqual(len(splitdataset), 1)
-        self.assertEqual(splitdataset[0], 3)
-
-        # test fluent api
-        splitdataset = listdataset.partition({'train': 3, 'val': 1})
-        splitdataset.select('train')
-        self.assertEqual(len(splitdataset), 3)
-        self.assertEqual(splitdataset[2], 2)
-
-    def testSplitDataset_fractions(self):
-        h = [0, 1, 2, 3]
-        listdataset = dataset.ListDataset(elem_list=h)
-        splitdataset = dataset.MultiPartitionDataset(listdataset, {'train': 0.75,
-                                                          'val': 0.25})
-
-        splitdataset.select('train')
-        self.assertEqual(len(splitdataset), 3)
-        self.assertEqual(splitdataset[2], 2)
-
-        splitdataset.select('val')
-        self.assertEqual(len(splitdataset), 1)
-        self.assertEqual(splitdataset[0], 3)
-
-    def testConcatDataset(self):
-        l1 = dataset.ListDataset(elem_list=[0, 1, 2, 3])
-        l2 = dataset.ListDataset(elem_list=[10, 11, 13])
-        concatdataset = dataset.ConcatDataset([l1, l2])
-
-        self.assertEqual(len(concatdataset), 7)
-        self.assertEqual(concatdataset[0], 0)
-        self.assertEqual(concatdataset[3], 3)
-        self.assertEqual(concatdataset[4], 10)
-        self.assertEqual(concatdataset[6], 13)
-
-
-
-if __name__ == '__main__':
-    unittest.main()