Remove python2 compability code (#2033)

* remove sys.version_info == 2

* remove sys.version_info < 3

* remove from __future__ imports

references/classification/train.py

-from __future__ import print_function
 import datetime
 import os
 import time
-import sys
 
 import torch
 import torch.utils.data
@@ -141,12 +139,9 @@ def load_data(traindir, valdir, cache_dataset, distributed):
 
 
 def main(args):
-    if args.apex:
-        if sys.version_info < (3, 0):
-            raise RuntimeError("Apex currently only supports Python 3. Aborting.")
-        if amp is None:
-            raise RuntimeError("Failed to import apex. Please install apex from https://www.github.com/nvidia/apex "
-                               "to enable mixed-precision training.")
+    if args.apex and amp is None:
+        raise RuntimeError("Failed to import apex. Please install apex from https://www.github.com/nvidia/apex "
+                           "to enable mixed-precision training.")
 
     if args.output_dir:
         utils.mkdir(args.output_dir)

references/classification/train_quantization.py

-from __future__ import print_function
 import datetime
 import os
 import time

references/classification/utils.py

-from __future__ import print_function
 from collections import defaultdict, deque
 import datetime
 import time

references/detection/utils.py

-from __future__ import print_function
-
 from collections import defaultdict, deque
 import datetime
 import pickle

references/segmentation/utils.py

-from __future__ import print_function
 from collections import defaultdict, deque
 import datetime
 import math

references/video_classification/train.py

-from __future__ import print_function
 import datetime
 import os
 import time
-import sys
-
 import torch
 import torch.utils.data
 from torch.utils.data.dataloader import default_collate
@@ -95,12 +92,9 @@ def collate_fn(batch):
 
 
 def main(args):
-    if args.apex:
-        if sys.version_info < (3, 0):
-            raise RuntimeError("Apex currently only supports Python 3. Aborting.")
-        if amp is None:
-            raise RuntimeError("Failed to import apex. Please install apex from https://www.github.com/nvidia/apex "
-                               "to enable mixed-precision training.")
+    if args.apex and amp is None:
+        raise RuntimeError("Failed to import apex. Please install apex from https://www.github.com/nvidia/apex "
+                           "to enable mixed-precision training.")
 
     if args.output_dir:
         utils.mkdir(args.output_dir)

references/video_classification/utils.py

-from __future__ import print_function
 from collections import defaultdict, deque
 import datetime
 import time

setup.py

-from __future__ import print_function
 import os
 import io
 import re

test/fakedata_generation.py

 import os
-import sys
 import contextlib
 import tarfile
 import json
@@ -7,12 +6,7 @@ import numpy as np
 import PIL
 import torch
 from common_utils import get_tmp_dir
-
-PYTHON2 = sys.version_info[0] == 2
-if PYTHON2:
-    import cPickle as pickle
-else:
-    import pickle
+import pickle
 
 
 @contextlib.contextmanager

test/test_functional_tensor.py

-from __future__ import division
 import torch
 from torch import Tensor
 import torchvision.transforms as transforms

test/test_ops.py

-from __future__ import division
 import math
 import unittest
 

test/test_transforms.py

-from __future__ import division
 import os
 import mock
 import torch

test/test_transforms_video.py

-from __future__ import division
 import torch
 import torchvision.transforms._transforms_video as transforms
 from torchvision.transforms import Compose

test/test_video_reader.py

 import collections
 import math
 import os
-import sys
 import time
 import unittest
 from fractions import Fraction
@@ -22,10 +21,7 @@ except ImportError:
     av = None
 
 
-if sys.version_info < (3,):
-    from urllib2 import URLError
-else:
-    from urllib.error import URLError
+from urllib.error import URLError
 
 
 VIDEO_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "videos")

torchvision/datasets/caltech.py

-from __future__ import print_function
 from PIL import Image
 import os
 import os.path

torchvision/datasets/cifar.py

-from __future__ import print_function
 from PIL import Image
 import os
 import os.path
 import numpy as np
-import sys
-
-if sys.version_info[0] == 2:
-    import cPickle as pickle
-else:
-    import pickle
+import pickle
 
 from .vision import VisionDataset
 from .utils import check_integrity, download_and_extract_archive
@@ -79,10 +73,7 @@ class CIFAR10(VisionDataset):
         for file_name, checksum in downloaded_list:
             file_path = os.path.join(self.root, self.base_folder, file_name)
             with open(file_path, 'rb') as f:
-                if sys.version_info[0] == 2:
-                    entry = pickle.load(f)
-                else:
-                    entry = pickle.load(f, encoding='latin1')
+                entry = pickle.load(f, encoding='latin1')
                 self.data.append(entry['data'])
                 if 'labels' in entry:
                     self.targets.extend(entry['labels'])
@@ -100,10 +91,7 @@ class CIFAR10(VisionDataset):
             raise RuntimeError('Dataset metadata file not found or corrupted.' +
                                ' You can use download=True to download it')
         with open(path, 'rb') as infile:
-            if sys.version_info[0] == 2:
-                data = pickle.load(infile)
-            else:
-                data = pickle.load(infile, encoding='latin1')
+            data = pickle.load(infile, encoding='latin1')
             self.classes = data[self.meta['key']]
         self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)}
 

torchvision/datasets/lsun.py

@@ -11,11 +11,7 @@ if sys.version_info < (3, 3):
 else:
     from collections.abc import Iterable
 
-if sys.version_info[0] == 2:
-    import cPickle as pickle
-else:
-    import pickle
-
+import pickle
 from .utils import verify_str_arg, iterable_to_str
 
 

torchvision/datasets/mnist.py

-from __future__ import print_function
 from .vision import VisionDataset
 import warnings
 from PIL import Image

torchvision/datasets/omniglot.py

-from __future__ import print_function
 from PIL import Image
 from os.path import join
 import os

torchvision/datasets/semeion.py

-from __future__ import print_function
 from PIL import Image
 import os
 import os.path