import os
import sys
import mock
from sphinx.domains import Domain
from typing import Dict, List, Tuple
import sphinx_rtd_theme
class GithubURLDomain(Domain):
"""
Resolve certain links in markdown files to github source.
"""
name = "githuburl"
ROOT = "https://github.com/facebookresearch/detectron2/blob/master/"
LINKED_DOC = ["tutorials/install", "tutorials/getting_started"]
def resolve_any_xref(self, env, fromdocname, builder, target, node, contnode):
github_url = None
if not target.endswith("html") and target.startswith("../../"):
url = target.replace("../", "")
github_url = url
if fromdocname in self.LINKED_DOC:
github_url = target
if github_url is not None:
if github_url.endswith("MODEL_ZOO") or github_url.endswith("README"):
github_url += ".md"
print("Ref {} resolved to github:{}".format(target, github_url))
contnode["refuri"] = self.ROOT + github_url
return [("githuburl:any", contnode)]
else:
return []
from recommonmark.parser import CommonMarkParser
sys.path.insert(0, os.path.abspath("../"))
os.environ["DOC_BUILDING"] = "True"
DEPLOY = os.environ.get("READTHEDOCS") == "True"
try:
import torch
except ImportError:
for m in [
"torch", "torchvision", "torch.nn", "torch.nn.parallel", "torch.distributed", "torch.multiprocessing", "torch.autograd",
"torch.autograd.function", "torch.nn.modules", "torch.nn.modules.utils", "torch.utils", "torch.utils.data", "torch.onnx",
"torchvision", "torchvision.ops",
]:
sys.modules[m] = mock.Mock(name=m)
sys.modules['torch'].__version__ = "1.5"
for m in [
"cv2", "scipy", "portalocker", "detectron2._C",
"pycocotools", "pycocotools.mask", "pycocotools.coco", "pycocotools.cocoeval",
"google", "google.protobuf", "google.protobuf.internal", "onnx",
"caffe2", "caffe2.proto", "caffe2.python", "caffe2.python.utils", "caffe2.python.onnx", "caffe2.python.onnx.backend",
]:
sys.modules[m] = mock.Mock(name=m)
sys.modules["cv2"].__version__ = "3.4"
import detectron2
project = "detectron2"
copyright = "2019-2020, detectron2 contributors"
author = "detectron2 contributors"
version = detectron2.__version__
release = version
needs_sphinx = "3.0"
extensions = [
"recommonmark",
"sphinx.ext.autodoc",
"sphinx.ext.napoleon",
"sphinx.ext.intersphinx",
"sphinx.ext.todo",
"sphinx.ext.coverage",
"sphinx.ext.mathjax",
"sphinx.ext.viewcode",
"sphinx.ext.githubpages",
]
napoleon_google_docstring = True
napoleon_include_init_with_doc = True
napoleon_include_special_with_doc = True
napoleon_numpy_docstring = False
napoleon_use_rtype = False
autodoc_inherit_docstrings = False
autodoc_member_order = "bysource"
if DEPLOY:
intersphinx_timeout = 10
else:
intersphinx_timeout = 0.1
intersphinx_mapping = {
"python": ("https://docs.python.org/3.6", None),
"numpy": ("https://docs.scipy.org/doc/numpy/", None),
"torch": ("https://pytorch.org/docs/master/", None),
}
templates_path = ["_templates"]
source_suffix = [".rst", ".md"]
master_doc = "index"
language = None
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "build", "README.md", "tutorials/README.md"]
pygments_style = "sphinx"
html_theme = "sphinx_rtd_theme"
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
html_static_path = ["_static"]
html_css_files = ["css/custom.css"]
htmlhelp_basename = "detectron2doc"
latex_elements = {
}
latex_documents = [
(master_doc, "detectron2.tex", "detectron2 Documentation", "detectron2 contributors", "manual")
]
man_pages = [(master_doc, "detectron2", "detectron2 Documentation", [author], 1)]
texinfo_documents = [
(
master_doc,
"detectron2",
"detectron2 Documentation",
author,
"detectron2",
"One line description of project.",
"Miscellaneous",
)
]
todo_include_todos = True
def autodoc_skip_member(app, what, name, obj, skip, options):
if getattr(obj, "__HIDE_SPHINX_DOC__", False):
return True
HIDDEN = {
"ResNetBlockBase",
"GroupedBatchSampler",
"build_transform_gen",
"export_caffe2_model",
"export_onnx_model",
"apply_transform_gens",
}
try:
if obj.__doc__.lower().strip().startswith("deprecated") or name in HIDDEN:
print("Skipping deprecated object: {}".format(name))
return True
except:
pass
return skip
_PAPER_DATA = {
"resnet": ("1512.03385", "Deep Residual Learning for Image Recognition"),
"fpn": ("1612.03144", "Feature Pyramid Networks for Object Detection"),
"mask r-cnn": ("1703.06870", "Mask R-CNN"),
"faster r-cnn": (
"1506.01497",
"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
),
"deformconv": ("1703.06211", "Deformable Convolutional Networks"),
"deformconv2": ("1811.11168", "Deformable ConvNets v2: More Deformable, Better Results"),
"panopticfpn": ("1901.02446", "Panoptic Feature Pyramid Networks"),
"retinanet": ("1708.02002", "Focal Loss for Dense Object Detection"),
"cascade r-cnn": ("1712.00726", "Cascade R-CNN: Delving into High Quality Object Detection"),
"lvis": ("1908.03195", "LVIS: A Dataset for Large Vocabulary Instance Segmentation"),
"rrpn": ("1703.01086", "Arbitrary-Oriented Scene Text Detection via Rotation Proposals"),
"imagenet in 1h": ("1706.02677", "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour"),
}
def paper_ref_role(
typ: str,
rawtext: str,
text: str,
lineno: int,
inliner,
options: Dict = {},
content: List[str] = [],
):
"""
Parse :paper:`xxx`. Similar to the "extlinks" sphinx extension.
"""
from docutils import nodes, utils
from sphinx.util.nodes import split_explicit_title
text = utils.unescape(text)
has_explicit_title, title, link = split_explicit_title(text)
link = link.lower()
if link not in _PAPER_DATA:
inliner.reporter.warning("Cannot find paper " + link)
paper_url, paper_title = "#", link
else:
paper_url, paper_title = _PAPER_DATA[link]
if "/" not in paper_url:
paper_url = "https://arxiv.org/abs/" + paper_url
if not has_explicit_title:
title = paper_title
pnode = nodes.reference(title, title, internal=False, refuri=paper_url)
return [pnode], []
def setup(app):
from recommonmark.transform import AutoStructify
app.add_domain(GithubURLDomain)
app.connect("autodoc-skip-member", autodoc_skip_member)
app.add_role("paper", paper_ref_role)
app.add_config_value(
"recommonmark_config",
{"enable_math": True, "enable_inline_math": True, "enable_eval_rst": True},
True,
)
app.add_transform(AutoStructify)