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| | |
| | import os |
| | import sys |
| | from unittest 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/main/" |
| | 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" |
| |
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| |
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| | |
| |
|
| | |
| | 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__ = "1.7" |
| | HAS_TORCH = False |
| | else: |
| | try: |
| | torch.ops.detectron2 = mock.Mock(name="torch.ops.detectron2") |
| | except: |
| | pass |
| | HAS_TORCH = True |
| |
|
| | 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 |
| |
|
| | if HAS_TORCH: |
| | from detectron2.utils.env import fixup_module_metadata |
| |
|
| | fixup_module_metadata("torch.nn", torch.nn.__dict__) |
| | fixup_module_metadata("torch.utils.data", torch.utils.data.__dict__) |
| |
|
| |
|
| | project = "detectron2" |
| | copyright = "2019-2020, detectron2 contributors" |
| | author = "detectron2 contributors" |
| |
|
| | |
| | version = detectron2.__version__ |
| | |
| | release = version |
| |
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| |
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| |
|
| | |
| | |
| | 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 = 0.5 |
| | intersphinx_mapping = { |
| | "python": ("https://docs.python.org/3.7", 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" |
| |
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| |
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| | |
| |
|
| | html_theme = "sphinx_rtd_theme" |
| | html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] |
| |
|
| | |
| | |
| | |
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| |
|
| | |
| | |
| | |
| | html_static_path = ["_static"] |
| | html_css_files = ["css/custom.css"] |
| |
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| |
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| | |
| | htmlhelp_basename = "detectron2doc" |
| |
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| |
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| | |
| |
|
| | 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", |
| | "apply_transform_gens", |
| | "TransformGen", |
| | "apply_augmentations", |
| | "StandardAugInput", |
| | "build_batch_data_loader", |
| | "draw_panoptic_seg_predictions", |
| | "WarmupCosineLR", |
| | "WarmupMultiStepLR", |
| | "downgrade_config", |
| | "upgrade_config", |
| | "add_export_config", |
| | } |
| | try: |
| | if name in HIDDEN or ( |
| | hasattr(obj, "__doc__") and obj.__doc__.lower().strip().startswith("deprecated") |
| | ): |
| | 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"), |
| | "xception": ("1610.02357", "Xception: Deep Learning with Depthwise Separable Convolutions"), |
| | "mobilenet": ( |
| | "1704.04861", |
| | "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications", |
| | ), |
| | "deeplabv3+": ( |
| | "1802.02611", |
| | "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation", |
| | ), |
| | "dds": ("2003.13678", "Designing Network Design Spaces"), |
| | "scaling": ("2103.06877", "Fast and Accurate Model Scaling"), |
| | "fcos": ("2006.09214", "FCOS: A Simple and Strong Anchor-free Object Detector"), |
| | "rethinking-batchnorm": ("2105.07576", 'Rethinking "Batch" in BatchNorm'), |
| | "vitdet": ("2203.16527", "Exploring Plain Vision Transformer Backbones for Object Detection"), |
| | "mvitv2": ( |
| | "2112.01526", |
| | "MViTv2: Improved Multiscale Vision Transformers for Classification and Detection", |
| | ), |
| | "swin": ( |
| | "2103.14030", |
| | "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows", |
| | ), |
| | "omni3d": ( |
| | "2207.10660", |
| | "Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild", |
| | ), |
| | } |
| |
|
| |
|
| | 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) |
| |
|