reformat with black.

Author: Tiiiger

docs/source/conf.py

@@ -21,13 +21,15 @@
 import sys
 import shutil
 from unittest.mock import MagicMock
+
 sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..", "..")))
 
 # Mechanism to mock out modules
 class ModuleMock(object):
     def __init__(self, *args, **kwargs):
         pass
 
+
 # Putting all of our dirty hacks together
 class Mock(MagicMock):
     __metaclass__ = type
@@ -59,13 +61,18 @@ def __getattr__(cls, name):
     "torch.utils",
     "torch.utils.data",
     "torch.utils.cpp_extension",
-    "numpy"
+    "numpy",
 ]
 sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
 
 import sphinx_rtd_theme
-examples_source = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "examples", "tutorial"))
-examples_dest = os.path.abspath(os.path.join(os.path.dirname(__file__), "examples", "tutorial"))
+
+examples_source = os.path.abspath(
+    os.path.join(os.path.dirname(__file__), "..", "..", "examples", "tutorial")
+)
+examples_dest = os.path.abspath(
+    os.path.join(os.path.dirname(__file__), "examples", "tutorial")
+)
 
 if os.path.exists(examples_dest):
     shutil.rmtree(examples_dest)
@@ -91,36 +98,38 @@ def __getattr__(cls, name):
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
-extensions = ['sphinx.ext.autodoc',
-              'sphinx.ext.mathjax',
-              'sphinx.ext.viewcode',
-              'nbsphinx']
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.mathjax",
+    "sphinx.ext.viewcode",
+    "nbsphinx",
+]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['ntemplates']
+templates_path = ["ntemplates"]
 
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:
 #
 # source_suffix = ['.rst', '.md']
-source_suffix = '.rst'
+source_suffix = ".rst"
 
 # The master toctree document.
-master_doc = 'index'
+master_doc = "index"
 
 # General information about the project.
-project = 'QPyTorch'
-copyright = '2019, Tianyi Zhang, Zhiqiu Lin, Guandao Yang, Christopher De Sa'
-author = 'Tianyi Zhang, Zhiqiu Lin, Guandao Yang, Christopher De Sa'
+project = "QPyTorch"
+copyright = "2019, Tianyi Zhang, Zhiqiu Lin, Guandao Yang, Christopher De Sa"
+author = "Tianyi Zhang, Zhiqiu Lin, Guandao Yang, Christopher De Sa"
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
 # The short X.Y version.
-version = '0.0.1'
+version = "0.0.1"
 # The full version, including alpha/beta/rc tags.
-release = '0.0.1 alpha'
+release = "0.0.1 alpha"
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -135,7 +144,7 @@ def __getattr__(cls, name):
 exclude_patterns = []
 
 # The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
+pygments_style = "sphinx"
 
 # If true, `todo` and `todoList` produce output, else they produce nothing.
 todo_include_todos = False
@@ -146,7 +155,7 @@ def __getattr__(cls, name):
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'sphinx_rtd_theme'
+html_theme = "sphinx_rtd_theme"
 html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 html_theme_options = {
     "collapse_navigation": False,
@@ -162,13 +171,13 @@ def __getattr__(cls, name):
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['nstatic']
+html_static_path = ["nstatic"]
 
 
 # -- Options for HTMLHelp output ------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'QPyTorchdoc'
+htmlhelp_basename = "QPyTorchdoc"
 
 
 # -- Options for LaTeX output ---------------------------------------------
@@ -177,15 +186,12 @@ def __getattr__(cls, name):
     # The paper size ('letterpaper' or 'a4paper').
     #
     # 'papersize': 'letterpaper',
-
     # The font size ('10pt', '11pt' or '12pt').
     #
     # 'pointsize': '10pt',
-
     # Additional stuff for the LaTeX preamble.
     #
     # 'preamble': '',
-
     # Latex figure (float) alignment
     #
     # 'figure_align': 'htbp',
@@ -195,19 +201,21 @@ def __getattr__(cls, name):
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'QPyTorch.tex', 'QPyTorch Documentation',
-     'Tianyi Zhang, Zhiqiu Lin, Christopher De Sa', 'manual'),
+    (
+        master_doc,
+        "QPyTorch.tex",
+        "QPyTorch Documentation",
+        "Tianyi Zhang, Zhiqiu Lin, Christopher De Sa",
+        "manual",
+    ),
 ]
 
 autodoc_inherit_docstrings = False
 # -- Options for manual page output ---------------------------------------
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [
-    (master_doc, 'qpytorch', 'QPyTorch Documentation',
-     [author], 1)
-]
+man_pages = [(master_doc, "qpytorch", "QPyTorch Documentation", [author], 1)]
 
 
 # -- Options for Texinfo output -------------------------------------------
@@ -216,7 +224,13 @@ def __getattr__(cls, name):
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'QPyTorch', 'QPyTorch Documentation',
-     author, 'QPyTorch', 'One line description of project.',
-     'Miscellaneous'),
+    (
+        master_doc,
+        "QPyTorch",
+        "QPyTorch Documentation",
+        author,
+        "QPyTorch",
+        "One line description of project.",
+        "Miscellaneous",
+    ),
 ]

examples/IBM8/data.py

@@ -4,65 +4,85 @@
 import torchvision.datasets as datasets
 import os
 
+
 def get_data(dataset, data_path, batch_size, val_ratio, num_workers):
-    assert dataset in ["CIFAR10", "IMAGENET12"], "dataset not supported {}".format(dataset)
+    assert dataset in ["CIFAR10", "IMAGENET12"], "dataset not supported {}".format(
+        dataset
+    )
     assert val_ratio >= 0, "invalid validation ratio: {}".format(val_ratio)
-    print('Loading dataset {} from {}'.format(dataset, data_path))
-    if dataset=="CIFAR10":
+    print("Loading dataset {} from {}".format(dataset, data_path))
+    if dataset == "CIFAR10":
         ds = getattr(datasets, dataset)
         path = os.path.join(data_path, dataset.lower())
-        transform_train = transforms.Compose([
-            transforms.RandomCrop(32, padding=4),
-            transforms.RandomHorizontalFlip(),
-            transforms.ToTensor(),
-            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
-        ])
-        transform_test = transforms.Compose([
-            transforms.ToTensor(),
-            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
-        ])
+        transform_train = transforms.Compose(
+            [
+                transforms.RandomCrop(32, padding=4),
+                transforms.RandomHorizontalFlip(),
+                transforms.ToTensor(),
+                transforms.Normalize(
+                    (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
+                ),
+            ]
+        )
+        transform_test = transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Normalize(
+                    (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
+                ),
+            ]
+        )
         train_set = ds(path, train=True, download=True, transform=transform_train)
         val_set = ds(path, train=True, download=True, transform=transform_test)
         test_set = ds(path, train=False, download=True, transform=transform_test)
         train_sampler = None
         val_sampler = None
         num_classes = 10
-    elif dataset=="IMAGENET12":
-        traindir = os.path.join(data_path, dataset.lower(), 'train')
-        valdir = os.path.join(data_path, dataset.lower(), 'val')
-        normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                                         std=[0.229, 0.224, 0.225])
+    elif dataset == "IMAGENET12":
+        traindir = os.path.join(data_path, dataset.lower(), "train")
+        valdir = os.path.join(data_path, dataset.lower(), "val")
+        normalize = transforms.Normalize(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+        )
         train_set = datasets.ImageFolder(
             traindir,
-            transforms.Compose([
-                transforms.RandomResizedCrop(224),
-                transforms.RandomHorizontalFlip(),
-                transforms.ToTensor(),
-                normalize,
-            ]))
-        test_set = datasets.ImageFolder(valdir, transforms.Compose([
-            transforms.Resize(256),
-            transforms.CenterCrop(224),
-            transforms.ToTensor(),
-            normalize,
-        ]))
+            transforms.Compose(
+                [
+                    transforms.RandomResizedCrop(224),
+                    transforms.RandomHorizontalFlip(),
+                    transforms.ToTensor(),
+                    normalize,
+                ]
+            ),
+        )
+        test_set = datasets.ImageFolder(
+            valdir,
+            transforms.Compose(
+                [
+                    transforms.Resize(256),
+                    transforms.CenterCrop(224),
+                    transforms.ToTensor(),
+                    normalize,
+                ]
+            ),
+        )
         train_sampler = None
         val_sampler = None
         num_classes = 1000
     loaders = {
-        'train': torch.utils.data.DataLoader(
+        "train": torch.utils.data.DataLoader(
             train_set,
             batch_size=batch_size,
             shuffle=True,
             num_workers=num_workers,
-            pin_memory=True
+            pin_memory=True,
         ),
-        'test': torch.utils.data.DataLoader(
+        "test": torch.utils.data.DataLoader(
             test_set,
             batch_size=batch_size,
             shuffle=False,
             num_workers=num_workers,
-            pin_memory=True
-        )
+            pin_memory=True,
+        ),
     }
     return loaders