Fix lint in test_dataloader.py

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17878

Reviewed By: eellison

Differential Revision: D14409933

fbshipit-source-id: 20ee8953a21e29b4557aff62b5e48dddd630eef6

diff --git a/test/test_dataloader.py b/test/test_dataloader.py
index 02a7eb2..e766c36 100644 (file)
--- a/test/test_dataloader.py
+++ b/test/test_dataloader.py
@@ -648,7 +648,7 @@ class TestDataLoader(TestCase):
         for i in range(num_processes):
             s = DistributedSampler(data_set, num_processes, i)
             d_loader = DataLoader(data_set, batch_size=int(num_batches / num_processes), drop_last=True, sampler=s)
-            for k, data in enumerate(d_loader):
+            for data in d_loader:
                 scanned_data = torch.cat((scanned_data, data), 0)
 
         self.assertEqual(scanned_data.size(), scanned_data.unique().size())
@@ -703,7 +703,7 @@ class TestDataLoader(TestCase):
             workers = loader.workers
             if pin_memory:
                 pin_memory_thread = loader.pin_memory_thread
-            for i, sample in enumerate(loader):
+            for i, _ in enumerate(loader):
                 if i == 10:
                     break
             assert i == 10
@@ -819,7 +819,7 @@ class TestDataLoader(TestCase):
         def check_len(dl, expected):
             self.assertEqual(len(dl), expected)
             n = 0
-            for sample in dl:
+            for _ in dl:
                 n += 1
             self.assertEqual(n, expected)
         check_len(self.dataset, 100)
@@ -931,7 +931,7 @@ class TestStringDataLoader(TestCase):
     @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
     def test_shuffle_pin_memory(self):
         loader = DataLoader(self.dataset, batch_size=2, shuffle=True, num_workers=4, pin_memory=True)
-        for batch_ndx, (s, n) in enumerate(loader):
+        for (s, n) in loader:
             self.assertIsInstance(s[0], str)
             self.assertTrue(n.is_pinned())
 
@@ -974,7 +974,7 @@ class TestDictDataLoader(TestCase):
     @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
     def test_pin_memory(self):
         loader = DataLoader(self.dataset, batch_size=2, pin_memory=True)
-        for batch_ndx, sample in enumerate(loader):
+        for sample in loader:
             self.assertTrue(sample['a_tensor'].is_pinned())
             self.assertTrue(sample['another_dict']['a_number'].is_pinned())
 
@@ -1031,7 +1031,7 @@ class TestCustomPinFn(TestCase):
     def test_custom_batch_pin(self):
         loader = DataLoader(self.dataset, batch_size=2, collate_fn=collate_wrapper,
                             pin_memory=True)
-        for batch_ndx, sample in enumerate(loader):
+        for sample in loader:
             self.assertTrue(sample.inp.is_pinned())
             self.assertTrue(sample.tgt.is_pinned())
 
@@ -1040,7 +1040,7 @@ class TestCustomPinFn(TestCase):
     def test_custom_batch_pin_worker(self):
         loader = DataLoader(self.dataset, batch_size=2, collate_fn=collate_wrapper,
                             pin_memory=True, num_workers=1)
-        for batch_ndx, sample in enumerate(loader):
+        for sample in loader:
             self.assertTrue(sample.inp.is_pinned())
             self.assertTrue(sample.tgt.is_pinned())