[Lint] Fix most lint automatically with autopep8

    git ls-files | grep '\.py$' | xargs -n1 -P`nproc` autopep8 -i

setup.cfg

 [bdist_wheel]
 universal=1
+
+[pep8]
+max-line-length = 120
+
+[flake8]
+max-line-length = 120
+ignore = F401,F403
+exclude = venv

test/cifar.py

@@ -14,7 +14,7 @@ print(a[3])
 
 
 dataset = dset.CIFAR10(root='cifar', download=True, transform=transforms.ToTensor())
-dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, 
+dataloader = torch.utils.data.DataLoader(dataset, batch_size=1,
                                          shuffle=True, num_workers=2)
 
 
@@ -31,10 +31,9 @@ for i, data in enumerate(dataloader, 0):
 #     except StopIteration:
 #         miter = dataloader.__iter__()
 #         return miter.next()
-    
+
 # i=0
 # while True:
 #     print(i)
 #     img, target = getBatch()
 #     i+=1
-    

test/preprocess-bench.py

@@ -20,14 +20,13 @@ parser.add_argument('--batchSize', '-b', default=256, type=int, metavar='N',
 if __name__ == "__main__":
     args = parser.parse_args()
 
-
     # Data loading code
     transform = transforms.Compose([
         transforms.RandomSizedCrop(224),
         transforms.RandomHorizontalFlip(),
         transforms.ToTensor(),
-        transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ],
-                             std = [ 0.229, 0.224, 0.225 ]),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                             std=[0.229, 0.224, 0.225]),
     ])
 
     traindir = os.path.join(args.data, 'train')
@@ -47,4 +46,3 @@ if __name__ == "__main__":
         dataset=(end_time - start_time) * (float(len(train_loader)) / batch_count / 60.0),
         batch=(end_time - start_time) / float(batch_count),
         image=(end_time - start_time) / (batch_count * args.batchSize) * 1.0e+3))
-

torchvision/datasets/cifar.py

@@ -11,36 +11,37 @@ if sys.version_info[0] == 2:
 else:
     import pickle
 
+
 class CIFAR10(data.Dataset):
     base_folder = 'cifar-10-batches-py'
     url = "http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
     filename = "cifar-10-python.tar.gz"
     tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
     train_list = [
-            ['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
-            ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
-            ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
-            ['data_batch_4', '634d18415352ddfa80567beed471001a'],
-            ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
+        ['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
+        ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
+        ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
+        ['data_batch_4', '634d18415352ddfa80567beed471001a'],
+        ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
     ]
 
     test_list = [
-            ['test_batch', '40351d587109b95175f43aff81a1287e'],
+        ['test_batch', '40351d587109b95175f43aff81a1287e'],
     ]
 
     def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
         self.root = root
         self.transform = transform
         self.target_transform = target_transform
-        self.train = train # training set or test set
-        
+        self.train = train  # training set or test set
+
         if download:
             self.download()
 
         if not self._check_integrity():
-            raise RuntimeError('Dataset not found or corrupted.' 
+            raise RuntimeError('Dataset not found or corrupted.'
                                + ' You can use download=True to download it')
-                
+
         # now load the picked numpy arrays
         if self.train:
             self.train_data = []
@@ -83,10 +84,10 @@ class CIFAR10(data.Dataset):
             img, target = self.train_data[index], self.train_labels[index]
         else:
             img, target = self.test_data[index], self.test_labels[index]
-            
+
         # doing this so that it is consistent with all other datasets
         # to return a PIL Image
-        img = Image.fromarray(np.transpose(img, (1,2,0)))
+        img = Image.fromarray(np.transpose(img, (1, 2, 0)))
 
         if self.transform is not None:
             img = self.transform(img)
@@ -134,7 +135,7 @@ class CIFAR10(data.Dataset):
         if self._check_integrity():
             print('Files already downloaded and verified')
             return
-        
+
         # downloads file
         if os.path.isfile(fpath) and \
            hashlib.md5(open(fpath, 'rb').read()).hexdigest() == self.tgz_md5:
@@ -147,7 +148,7 @@ class CIFAR10(data.Dataset):
         cwd = os.getcwd()
         print('Extracting tar file')
         tar = tarfile.open(fpath, "r:gz")
-        os.chdir(root)        
+        os.chdir(root)
         tar.extractall()
         tar.close()
         os.chdir(cwd)
@@ -160,10 +161,9 @@ class CIFAR100(CIFAR10):
     filename = "cifar-100-python.tar.gz"
     tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
     train_list = [
-            ['train', '16019d7e3df5f24257cddd939b257f8d'],
+        ['train', '16019d7e3df5f24257cddd939b257f8d'],
     ]
 
     test_list = [
-            ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
+        ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
     ]
-

torchvision/datasets/coco.py

@@ -3,7 +3,9 @@ from PIL import Image
 import os
 import os.path
 
+
 class CocoCaptions(data.Dataset):
+
     def __init__(self, root, annFile, transform=None, target_transform=None):
         from pycocotools.coco import COCO
         self.root = root
@@ -15,7 +17,7 @@ class CocoCaptions(data.Dataset):
     def __getitem__(self, index):
         coco = self.coco
         img_id = self.ids[index]
-        ann_ids = coco.getAnnIds(imgIds = img_id)
+        ann_ids = coco.getAnnIds(imgIds=img_id)
         anns = coco.loadAnns(ann_ids)
         target = [ann['caption'] for ann in anns]
 
@@ -33,7 +35,9 @@ class CocoCaptions(data.Dataset):
     def __len__(self):
         return len(self.ids)
 
+
 class CocoDetection(data.Dataset):
+
     def __init__(self, root, annFile, transform=None, target_transform=None):
         from pycocotools.coco import COCO
         self.root = root
@@ -45,7 +49,7 @@ class CocoDetection(data.Dataset):
     def __getitem__(self, index):
         coco = self.coco
         img_id = self.ids[index]
-        ann_ids = coco.getAnnIds(imgIds = img_id)
+        ann_ids = coco.getAnnIds(imgIds=img_id)
         target = coco.loadAnns(ann_ids)
 
         path = coco.loadImgs(img_id)[0]['file_name']

torchvision/datasets/folder.py

@@ -9,15 +9,18 @@ IMG_EXTENSIONS = [
     '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
 ]
 
+
 def is_image_file(filename):
     return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
 
+
 def find_classes(dir):
     classes = os.listdir(dir)
     classes.sort()
     class_to_idx = {classes[i]: i for i in range(len(classes))}
     return classes, class_to_idx
 
+
 def make_dataset(dir, class_to_idx):
     images = []
     for target in os.listdir(dir):
@@ -39,6 +42,7 @@ def default_loader(path):
 
 
 class ImageFolder(data.Dataset):
+
     def __init__(self, root, transform=None, target_transform=None,
                  loader=default_loader):
         classes, class_to_idx = find_classes(root)

torchvision/datasets/lsun.py

@@ -10,7 +10,9 @@ if sys.version_info[0] == 2:
 else:
     import pickle
 
+
 class LSUNClass(data.Dataset):
+
     def __init__(self, db_path, transform=None, target_transform=None):
         import lmdb
         self.db_path = db_path
@@ -20,11 +22,11 @@ class LSUNClass(data.Dataset):
             self.length = txn.stat()['entries']
         cache_file = '_cache_' + db_path.replace('/', '_')
         if os.path.isfile(cache_file):
-            self.keys = pickle.load( open( cache_file, "rb" ) )
+            self.keys = pickle.load(open(cache_file, "rb"))
         else:
             with self.env.begin(write=False) as txn:
-                self.keys = [ key for key, _ in txn.cursor() ]
-            pickle.dump( self.keys, open( cache_file, "wb" ) )
+                self.keys = [key for key, _ in txn.cursor()]
+            pickle.dump(self.keys, open(cache_file, "wb"))
         self.transform = transform
         self.target_transform = target_transform
 
@@ -53,11 +55,13 @@ class LSUNClass(data.Dataset):
     def __repr__(self):
         return self.__class__.__name__ + ' (' + self.db_path + ')'
 
+
 class LSUN(data.Dataset):
     """
     db_path = root directory for the database files
     classes = 'train' | 'val' | 'test' | ['bedroom_train', 'church_train', ...]
     """
+
     def __init__(self, db_path, classes='train',
                  transform=None, target_transform=None):
         categories = ['bedroom', 'bridge', 'church_outdoor', 'classroom',
@@ -73,13 +77,13 @@ class LSUN(data.Dataset):
                 c_short.pop(len(c_short) - 1)
                 c_short = '_'.join(c_short)
                 if c_short not in categories:
-                    raise(ValueError('Unknown LSUN class: ' + c_short + '.'\
-                          'Options are: ' + str(categories)))
+                    raise(ValueError('Unknown LSUN class: ' + c_short + '.'
+                                     'Options are: ' + str(categories)))
                 c_short = c.split('_')
                 c_short = c_short.pop(len(c_short) - 1)
                 if c_short not in dset_opts:
-                    raise(ValueError('Unknown postfix: ' + c_short + '.'\
-                          'Options are: ' + str(dset_opts)))
+                    raise(ValueError('Unknown postfix: ' + c_short + '.'
+                                     'Options are: ' + str(dset_opts)))
         else:
             raise(ValueError('Unknown option for classes'))
         self.classes = classes
@@ -88,8 +92,8 @@ class LSUN(data.Dataset):
         self.dbs = []
         for c in self.classes:
             self.dbs.append(LSUNClass(
-                db_path = db_path + '/' + c + '_lmdb',
-                transform = transform))
+                db_path=db_path + '/' + c + '_lmdb',
+                transform=transform))
 
         self.indices = []
         count = 0
@@ -128,9 +132,9 @@ if __name__ == '__main__':
     #lsun = LSUNClass(db_path='/home/soumith/local/lsun/train/bedroom_train_lmdb')
     #a = lsun[0]
     lsun = LSUN(db_path='/home/soumith/local/lsun/train',
-                       classes=['bedroom_train', 'church_outdoor_train'])
+                classes=['bedroom_train', 'church_outdoor_train'])
     print(lsun.classes)
     print(lsun.dbs)
-    a, t = lsun[len(lsun)-1]
+    a, t = lsun[len(lsun) - 1]
     print(a)
     print(t)

torchvision/datasets/mnist.py

@@ -9,6 +9,7 @@ import json
 import codecs
 import numpy as np
 
+
 class MNIST(data.Dataset):
     urls = [
         'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
@@ -25,7 +26,7 @@ class MNIST(data.Dataset):
         self.root = root
         self.transform = transform
         self.target_transform = target_transform
-        self.train = train # training set or test set
+        self.train = train  # training set or test set
 
         if download:
             self.download()
@@ -35,7 +36,8 @@ class MNIST(data.Dataset):
                                + ' You can use download=True to download it')
 
         if self.train:
-            self.train_data, self.train_labels = torch.load(os.path.join(root, self.processed_folder, self.training_file))
+            self.train_data, self.train_labels = torch.load(
+                os.path.join(root, self.processed_folder, self.training_file))
         else:
             self.test_data, self.test_labels = torch.load(os.path.join(root, self.processed_folder, self.test_file))
 
@@ -65,7 +67,7 @@ class MNIST(data.Dataset):
 
     def _check_exists(self):
         return os.path.exists(os.path.join(self.root, self.processed_folder, self.training_file)) and \
-               os.path.exists(os.path.join(self.root, self.processed_folder, self.test_file))
+            os.path.exists(os.path.join(self.root, self.processed_folder, self.test_file))
 
     def download(self):
         from six.moves import urllib
@@ -92,7 +94,7 @@ class MNIST(data.Dataset):
             with open(file_path, 'wb') as f:
                 f.write(data.read())
             with open(file_path.replace('.gz', ''), 'wb') as out_f, \
-                 gzip.GzipFile(file_path) as zip_f:
+                    gzip.GzipFile(file_path) as zip_f:
                 out_f.write(zip_f.read())
             os.unlink(file_path)
 
@@ -114,14 +116,17 @@ class MNIST(data.Dataset):
 
         print('Done!')
 
+
 def get_int(b):
     return int(codecs.encode(b, 'hex'), 16)
 
+
 def parse_byte(b):
     if isinstance(b, str):
         return ord(b)
     return b
 
+
 def read_label_file(path):
     with open(path, 'rb') as f:
         data = f.read()
@@ -131,6 +136,7 @@ def read_label_file(path):
         assert len(labels) == length
         return torch.LongTensor(labels)
 
+
 def read_image_file(path):
     with open(path, 'rb') as f:
         data = f.read()

torchvision/models/alexnet.py

@@ -11,6 +11,7 @@ model_urls = {
 
 
 class AlexNet(nn.Module):
+
     def __init__(self, num_classes=1000):
         super(AlexNet, self).__init__()
         self.features = nn.Sequential(

torchvision/models/resnet.py

@@ -94,6 +94,7 @@ class Bottleneck(nn.Module):
 
 
 class ResNet(nn.Module):
+
     def __init__(self, block, layers, num_classes=1000):
         self.inplanes = 64
         super(ResNet, self).__init__()

torchvision/models/squeezenet.py

@@ -14,8 +14,9 @@ model_urls = {
 
 
 class Fire(nn.Module):
+
     def __init__(self, inplanes, squeeze_planes,
-            expand1x1_planes, expand3x3_planes):
+                 expand1x1_planes, expand3x3_planes):
         super(Fire, self).__init__()
         self.inplanes = inplanes
         self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
@@ -36,6 +37,7 @@ class Fire(nn.Module):
 
 
 class SqueezeNet(nn.Module):
+
     def __init__(self, version=1.0, num_classes=1000):
         super(SqueezeNet, self).__init__()
         if version not in [1.0, 1.1]:

torchvision/models/vgg.py

@@ -18,6 +18,7 @@ model_urls = {
 
 
 class VGG(nn.Module):
+
     def __init__(self, features):
         super(VGG, self).__init__()
         self.features = features

torchvision/transforms.py

@@ -7,6 +7,7 @@ import numpy as np
 import numbers
 import types
 
+
 class Compose(object):
     """Composes several transforms together.
 
@@ -19,6 +20,7 @@ class Compose(object):
         >>>     transforms.ToTensor(),
         >>> ])
     """
+
     def __init__(self, transforms):
         self.transforms = transforms
 
@@ -32,6 +34,7 @@ class ToTensor(object):
     """Converts a PIL.Image or numpy.ndarray (H x W x C) in the range
     [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].
     """
+
     def __call__(self, pic):
         if isinstance(pic, np.ndarray):
             # handle numpy array
@@ -56,12 +59,13 @@ class ToPILImage(object):
     or numpy ndarray of dtype=uint8, range[0, 255] and shape H x W x C
     to a PIL.Image of range [0, 255]
     """
+
     def __call__(self, pic):
         npimg = pic
         mode = None
         if not isinstance(npimg, np.ndarray):
             npimg = pic.mul(255).byte().numpy()
-            npimg = np.transpose(npimg, (1,2,0))
+            npimg = np.transpose(npimg, (1, 2, 0))
 
         if npimg.shape[2] == 1:
             npimg = npimg[:, :, 0]
@@ -75,6 +79,7 @@ class Normalize(object):
     will normalize each channel of the torch.*Tensor, i.e.
     channel = (channel - mean) / std
     """
+
     def __init__(self, mean, std):
         self.mean = mean
         self.std = std
@@ -94,6 +99,7 @@ class Scale(object):
     size: size of the smaller edge
     interpolation: Default: PIL.Image.BILINEAR
     """
+
     def __init__(self, size, interpolation=Image.BILINEAR):
         self.size = size
         self.interpolation = interpolation
@@ -117,6 +123,7 @@ class CenterCrop(object):
     the given size. size can be a tuple (target_height, target_width)
     or an integer, in which case the target will be of a square shape (size, size)
     """
+
     def __init__(self, size):
         if isinstance(size, numbers.Number):
             self.size = (int(size), int(size))
@@ -133,6 +140,7 @@ class CenterCrop(object):
 
 class Pad(object):
     """Pads the given PIL.Image on all sides with the given "pad" value"""
+
     def __init__(self, padding, fill=0):
         assert isinstance(padding, numbers.Number)
         assert isinstance(fill, numbers.Number) or isinstance(fill, str) or isinstance(fill, tuple)
@@ -142,8 +150,10 @@ class Pad(object):
     def __call__(self, img):
         return ImageOps.expand(img, border=self.padding, fill=self.fill)
 
+
 class Lambda(object):
     """Applies a lambda as a transform."""
+
     def __init__(self, lambd):
         assert type(lambd) is types.LambdaType
         self.lambd = lambd
@@ -157,6 +167,7 @@ class RandomCrop(object):
     the given size. size can be a tuple (target_height, target_width)
     or an integer, in which case the target will be of a square shape (size, size)
     """
+
     def __init__(self, size, padding=0):
         if isinstance(size, numbers.Number):
             self.size = (int(size), int(size))
@@ -181,6 +192,7 @@ class RandomCrop(object):
 class RandomHorizontalFlip(object):
     """Randomly horizontally flips the given PIL.Image with a probability of 0.5
     """
+
     def __call__(self, img):
         if random.random() < 0.5:
             return img.transpose(Image.FLIP_LEFT_RIGHT)
@@ -194,6 +206,7 @@ class RandomSizedCrop(object):
     size: size of the smaller edge
     interpolation: Default: PIL.Image.BILINEAR
     """
+
     def __init__(self, size, interpolation=Image.BILINEAR):
         self.size = size
         self.interpolation = interpolation

torchvision/utils.py

 import torch
 import math
 
+
 def make_grid(tensor, nrow=8, padding=2):
     """
     Given a 4D mini-batch Tensor of shape (B x C x H x W),
@@ -15,13 +16,13 @@ def make_grid(tensor, nrow=8, padding=2):
         tensor = tensorlist[0].new(size)
         for i in range(numImages):
             tensor[i].copy_(tensorlist[i])
-    if tensor.dim() == 2: # single image H x W
+    if tensor.dim() == 2:  # single image H x W
         tensor = tensor.view(1, tensor.size(0), tensor.size(1))
-    if tensor.dim() == 3: # single image
+    if tensor.dim() == 3:  # single image
         if tensor.size(0) == 1:
             tensor = torch.cat((tensor, tensor, tensor), 0)
         return tensor
-    if tensor.dim() == 4 and tensor.size(1) == 1: # single-channel images
+    if tensor.dim() == 4 and tensor.size(1) == 1:  # single-channel images
         tensor = torch.cat((tensor, tensor, tensor), 1)
     # make the mini-batch of images into a grid
     nmaps = tensor.size(0)
@@ -34,8 +35,8 @@ def make_grid(tensor, nrow=8, padding=2):
         for x in range(xmaps):
             if k >= nmaps:
                 break
-            grid.narrow(1, y*height+1+padding//2,height-padding)\
-                .narrow(2, x*width+1+padding//2, width-padding)\
+            grid.narrow(1, y * height + 1 + padding // 2, height - padding)\
+                .narrow(2, x * width + 1 + padding // 2, width - padding)\
                 .copy_(tensor[k])
             k = k + 1
     return grid
@@ -49,6 +50,6 @@ def save_image(tensor, filename, nrow=8, padding=2):
     from PIL import Image
     tensor = tensor.cpu()
     grid = make_grid(tensor, nrow=nrow, padding=padding)
-    ndarr = grid.mul(0.5).add(0.5).mul(255).byte().transpose(0,2).transpose(0,1).numpy()
+    ndarr = grid.mul(0.5).add(0.5).mul(255).byte().transpose(0, 2).transpose(0, 1).numpy()
     im = Image.fromarray(ndarr)
     im.save(filename)