340-automatic-code-formatting (#346)

* update contributing guides for code formatting * [MONAI] python code formatting * ENH: Provide defaults for pyflake and black formatting * excludes formatting monai/_version.py, line-length=120 * Allow edited comments to trigger /black command Co-authored-by: monai-bot <monai.miccai2019@gmail.com> Co-authored-by: Hans Johnson <hans-johnson@uiowa.edu>
Project-MONAI · May 6, 2020 · f67defa · f67defa
1 parent 9878e9a
commit f67defa
Show file tree

Hide file tree

Showing 196 changed files with 4,679 additions and 4,477 deletions.
diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
@@ -20,6 +20,7 @@ jobs:
                 "command": "black",
                 "permission": "none",
                 "issue_type": "pull-request",
+                "allow_edits": true,
                 "repository": "project-monai/monai-code-formatter"
               }
             ]
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -45,6 +45,7 @@ Ideally, the new branch should be based on the latest `master` branch.
 
 ### Coding style
 Coding style is checked by flake8, using [a flake8 configuration](./setup.cfg) similar to [PyTorch's](https://github.com/pytorch/pytorch/blob/master/.flake8).
+Python code file formatting could be done locally before submitting a pull request (e.g. using [`psf/Black`](https://github.com/psf/black)), or during the pull request review using MONAI's automatic code formatting workflow.
 
 License information: all source code files should start with this paragraph:
 ```

diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -14,107 +14,120 @@
 import sys
 import subprocess
 
-sys.path.insert(0, os.path.abspath('..'))
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))
+sys.path.insert(0, os.path.abspath(".."))
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
 print(sys.path)
 
 import monai  # noqa: E402
 
 # -- Project information -----------------------------------------------------
-project = 'MONAI'
-copyright = '2020, MONAI Contributors'
-author = 'MONAI Contributors'
+project = "MONAI"
+copyright = "2020, MONAI Contributors"
+author = "MONAI Contributors"
 
 # The full version, including alpha/beta/rc tags
-short_version = monai.__version__.split('+')[0]
+short_version = monai.__version__.split("+")[0]
 release = short_version
 version = short_version
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['transforms', 'networks', 'metrics', 'engine', 'data',
-                    'application', 'config', 'handlers', 'losses', 'visualize', 'utils']
+exclude_patterns = [
+    "transforms",
+    "networks",
+    "metrics",
+    "engine",
+    "data",
+    "application",
+    "config",
+    "handlers",
+    "losses",
+    "visualize",
+    "utils",
+]
 
 
 def generate_apidocs(*args):
     """Generate API docs automatically by trawling the available modules"""
-    module_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'monai'))
-    output_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'apidocs'))
-    apidoc_command_path = 'sphinx-apidoc'
-    if hasattr(sys, 'real_prefix'):  # called from a virtualenv
-        apidoc_command_path = os.path.join(sys.prefix, 'bin', 'sphinx-apidoc')
+    module_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "monai"))
+    output_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "apidocs"))
+    apidoc_command_path = "sphinx-apidoc"
+    if hasattr(sys, "real_prefix"):  # called from a virtualenv
+        apidoc_command_path = os.path.join(sys.prefix, "bin", "sphinx-apidoc")
         apidoc_command_path = os.path.abspath(apidoc_command_path)
-    print('output_path {}'.format(output_path))
-    print('module_path {}'.format(module_path))
+    print("output_path {}".format(output_path))
+    print("module_path {}".format(module_path))
     subprocess.check_call(
-        [apidoc_command_path, '-f', '-e'] +
-        ['-o', output_path] +
-        [module_path] +
-        [os.path.join(module_path, p) for p in exclude_patterns])
+        [apidoc_command_path, "-f", "-e"]
+        + ["-o", output_path]
+        + [module_path]
+        + [os.path.join(module_path, p) for p in exclude_patterns]
+    )
 
 
 def setup(app):
     # Hook to allow for automatic generation of API docs
     # before doc deployment begins.
-    app.connect('builder-inited', generate_apidocs)
+    app.connect("builder-inited", generate_apidocs)
+
 
 # -- General configuration ---------------------------------------------------
 
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 source_suffix = {
-    '.rst': 'restructuredtext',
-    '.txt': 'restructuredtext',
-    '.md': 'markdown',
+    ".rst": "restructuredtext",
+    ".txt": "restructuredtext",
+    ".md": "markdown",
 }
 
 extensions = [
-    'recommonmark',
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.mathjax',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.autodoc',
-    'sphinx.ext.viewcode',
-    'sphinx.ext.autosectionlabel'
+    "recommonmark",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.mathjax",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.autosectionlabel",
 ]
 
-autoclass_content = 'both'
+autoclass_content = "both"
 add_module_names = False
 autosectionlabel_prefix_document = True
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
 # -- Options for HTML output -------------------------------------------------
 
 # 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': True,
-    'display_version': True,
-    'sticky_navigation': True,  # Set to False to disable the sticky nav while scrolling.
-    'logo_only': True,  # if we have a html_logo below, this shows /only/ the logo with no title text
-    'style_nav_header_background': '#FBFBFB',
+    "collapse_navigation": True,
+    "display_version": True,
+    "sticky_navigation": True,  # Set to False to disable the sticky nav while scrolling.
+    "logo_only": True,  # if we have a html_logo below, this shows /only/ the logo with no title text
+    "style_nav_header_background": "#FBFBFB",
 }
 html_context = {
-    'display_github': True,
-    'github_user': 'Project-MONAI',
-    'github_repo': 'MONAI',
-    'github_version': 'master',
-    'conf_py_path': '/docs/',
+    "display_github": True,
+    "github_user": "Project-MONAI",
+    "github_repo": "MONAI",
+    "github_version": "master",
+    "conf_py_path": "/docs/",
 }
 html_scaled_image_link = False
 html_show_sourcelink = True
-html_favicon = '../images/favicon.ico'
-html_logo = '../images/MONAI-logo-color.png'
+html_favicon = "../images/favicon.ico"
+html_logo = "../images/MONAI-logo-color.png"
 
 # 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 = ['../_static']
-html_css_files = ['custom.css']
+html_static_path = ["../_static"]
+html_css_files = ["custom.css"]
diff --git a/examples/classification_3d/densenet_evaluation_array.py b/examples/classification_3d/densenet_evaluation_array.py
@@ -19,55 +19,45 @@
 from monai.data import NiftiDataset, CSVSaver
 from monai.transforms import Compose, AddChannel, ScaleIntensity, Resize, ToTensor
 
+
 def main():
     monai.config.print_config()
     logging.basicConfig(stream=sys.stdout, level=logging.INFO)
 
     # IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
     images = [
-        '/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz'
+        "/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz",
     ]
     # 2 binary labels for gender classification: man and woman
-    labels = np.array([
-        0, 0, 1, 0, 1, 0, 1, 0, 1, 0
-    ])
+    labels = np.array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0])
 
     # Define transforms for image
-    val_transforms = Compose([
-        ScaleIntensity(),
-        AddChannel(),
-        Resize((96, 96, 96)),
-        ToTensor()
-    ])
+    val_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), ToTensor()])
 
     # Define nifti dataset
     val_ds = NiftiDataset(image_files=images, labels=labels, transform=val_transforms, image_only=False)
     # create a validation data loader
     val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())
 
     # Create DenseNet121
-    device = torch.device('cuda:0')
-    model = monai.networks.nets.densenet.densenet121(
-        spatial_dims=3,
-        in_channels=1,
-        out_channels=2,
-    ).to(device)
+    device = torch.device("cuda:0")
+    model = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2,).to(device)
 
-    model.load_state_dict(torch.load('best_metric_model.pth'))
+    model.load_state_dict(torch.load("best_metric_model.pth"))
     model.eval()
     with torch.no_grad():
-        num_correct = 0.
+        num_correct = 0.0
         metric_count = 0
-        saver = CSVSaver(output_dir='./output')
+        saver = CSVSaver(output_dir="./output")
         for val_data in val_loader:
             val_images, val_labels = val_data[0].to(device), val_data[1].to(device)
             val_outputs = model(val_images).argmax(dim=1)
@@ -76,8 +66,9 @@ def main():
             num_correct += value.sum().item()
             saver.save_batch(val_outputs, val_data[2])
         metric = num_correct / metric_count
-        print('evaluation metric:', metric)
+        print("evaluation metric:", metric)
         saver.finalize()
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()
diff --git a/examples/classification_3d/densenet_evaluation_dict.py b/examples/classification_3d/densenet_evaluation_dict.py
@@ -19,66 +19,64 @@
 from monai.transforms import Compose, LoadNiftid, AddChanneld, ScaleIntensityd, Resized, ToTensord
 from monai.data import CSVSaver
 
+
 def main():
     monai.config.print_config()
     logging.basicConfig(stream=sys.stdout, level=logging.INFO)
 
     # IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
     images = [
-        '/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz',
-        '/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz'
+        "/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
+        "/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz",
     ]
     # 2 binary labels for gender classification: man and woman
-    labels = np.array([
-        0, 0, 1, 0, 1, 0, 1, 0, 1, 0
-    ])
-    val_files = [{'img': img, 'label': label} for img, label in zip(images, labels)]
+    labels = np.array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0])
+    val_files = [{"img": img, "label": label} for img, label in zip(images, labels)]
 
     # Define transforms for image
-    val_transforms = Compose([
-        LoadNiftid(keys=['img']),
-        AddChanneld(keys=['img']),
-        ScaleIntensityd(keys=['img']),
-        Resized(keys=['img'], spatial_size=(96, 96, 96)),
-        ToTensord(keys=['img'])
-    ])
+    val_transforms = Compose(
+        [
+            LoadNiftid(keys=["img"]),
+            AddChanneld(keys=["img"]),
+            ScaleIntensityd(keys=["img"]),
+            Resized(keys=["img"], spatial_size=(96, 96, 96)),
+            ToTensord(keys=["img"]),
+        ]
+    )
 
     # create a validation data loader
     val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
     val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())
 
     # Create DenseNet121
-    device = torch.device('cuda:0')
-    model = monai.networks.nets.densenet.densenet121(
-        spatial_dims=3,
-        in_channels=1,
-        out_channels=2,
-    ).to(device)
+    device = torch.device("cuda:0")
+    model = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2,).to(device)
 
-    model.load_state_dict(torch.load('best_metric_model.pth'))
+    model.load_state_dict(torch.load("best_metric_model.pth"))
     model.eval()
     with torch.no_grad():
-        num_correct = 0.
+        num_correct = 0.0
         metric_count = 0
-        saver = CSVSaver(output_dir='./output')
+        saver = CSVSaver(output_dir="./output")
         for val_data in val_loader:
-            val_images, val_labels = val_data['img'].to(device), val_data['label'].to(device)
+            val_images, val_labels = val_data["img"].to(device), val_data["label"].to(device)
             val_outputs = model(val_images).argmax(dim=1)
             value = torch.eq(val_outputs, val_labels)
             metric_count += len(value)
             num_correct += value.sum().item()
-            saver.save_batch(val_outputs, {'filename_or_obj': val_data['img.filename_or_obj']})
+            saver.save_batch(val_outputs, {"filename_or_obj": val_data["img.filename_or_obj"]})
         metric = num_correct / metric_count
-        print('evaluation metric:', metric)
+        print("evaluation metric:", metric)
         saver.finalize()
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()