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import os |
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import sys |
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from unittest import mock |
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from sphinx.domains import Domain |
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from typing import Dict, List, Tuple |
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import sphinx_rtd_theme |
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class GithubURLDomain(Domain): |
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""" |
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Resolve certain links in markdown files to github source. |
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""" |
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name = "githuburl" |
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ROOT = "https://github.com/facebookresearch/detectron2/blob/main/" |
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LINKED_DOC = ["tutorials/install", "tutorials/getting_started"] |
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def resolve_any_xref(self, env, fromdocname, builder, target, node, contnode): |
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github_url = None |
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if not target.endswith("html") and target.startswith("../../"): |
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url = target.replace("../", "") |
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github_url = url |
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if fromdocname in self.LINKED_DOC: |
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github_url = target |
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if github_url is not None: |
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if github_url.endswith("MODEL_ZOO") or github_url.endswith("README"): |
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github_url += ".md" |
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print("Ref {} resolved to github:{}".format(target, github_url)) |
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contnode["refuri"] = self.ROOT + github_url |
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return [("githuburl:any", contnode)] |
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else: |
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return [] |
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from recommonmark.parser import CommonMarkParser |
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sys.path.insert(0, os.path.abspath("../")) |
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os.environ["_DOC_BUILDING"] = "True" |
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DEPLOY = os.environ.get("READTHEDOCS") == "True" |
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try: |
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import torch |
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except ImportError: |
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for m in [ |
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"torch", "torchvision", "torch.nn", "torch.nn.parallel", "torch.distributed", "torch.multiprocessing", "torch.autograd", |
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"torch.autograd.function", "torch.nn.modules", "torch.nn.modules.utils", "torch.utils", "torch.utils.data", "torch.onnx", |
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"torchvision", "torchvision.ops", |
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]: |
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sys.modules[m] = mock.Mock(name=m) |
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sys.modules['torch'].__version__ = "1.7" |
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HAS_TORCH = False |
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else: |
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try: |
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torch.ops.detectron2 = mock.Mock(name="torch.ops.detectron2") |
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except: |
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pass |
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HAS_TORCH = True |
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for m in [ |
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"cv2", "scipy", "portalocker", "detectron2._C", |
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"pycocotools", "pycocotools.mask", "pycocotools.coco", "pycocotools.cocoeval", |
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"google", "google.protobuf", "google.protobuf.internal", "onnx", |
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"caffe2", "caffe2.proto", "caffe2.python", "caffe2.python.utils", "caffe2.python.onnx", "caffe2.python.onnx.backend", |
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]: |
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sys.modules[m] = mock.Mock(name=m) |
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sys.modules["cv2"].__version__ = "3.4" |
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import detectron2 |
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if HAS_TORCH: |
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from detectron2.utils.env import fixup_module_metadata |
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fixup_module_metadata("torch.nn", torch.nn.__dict__) |
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fixup_module_metadata("torch.utils.data", torch.utils.data.__dict__) |
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project = "detectron2" |
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copyright = "2019-2020, detectron2 contributors" |
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author = "detectron2 contributors" |
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version = detectron2.__version__ |
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release = version |
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needs_sphinx = "3.0" |
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extensions = [ |
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"recommonmark", |
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"sphinx.ext.autodoc", |
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"sphinx.ext.napoleon", |
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"sphinx.ext.intersphinx", |
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"sphinx.ext.todo", |
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"sphinx.ext.coverage", |
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"sphinx.ext.mathjax", |
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"sphinx.ext.viewcode", |
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"sphinx.ext.githubpages", |
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] |
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napoleon_google_docstring = True |
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napoleon_include_init_with_doc = True |
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napoleon_include_special_with_doc = True |
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napoleon_numpy_docstring = False |
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napoleon_use_rtype = False |
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autodoc_inherit_docstrings = False |
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autodoc_member_order = "bysource" |
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if DEPLOY: |
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intersphinx_timeout = 10 |
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else: |
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intersphinx_timeout = 0.5 |
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intersphinx_mapping = { |
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"python": ("https://docs.python.org/3.6", None), |
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"numpy": ("https://docs.scipy.org/doc/numpy/", None), |
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"torch": ("https://pytorch.org/docs/master/", None), |
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} |
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templates_path = ["_templates"] |
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source_suffix = [".rst", ".md"] |
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master_doc = "index" |
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language = None |
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exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "build", "README.md", "tutorials/README.md"] |
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pygments_style = "sphinx" |
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html_theme = "sphinx_rtd_theme" |
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html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] |
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html_static_path = ["_static"] |
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html_css_files = ["css/custom.css"] |
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htmlhelp_basename = "detectron2doc" |
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latex_elements = { |
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} |
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latex_documents = [ |
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(master_doc, "detectron2.tex", "detectron2 Documentation", "detectron2 contributors", "manual") |
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] |
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man_pages = [(master_doc, "detectron2", "detectron2 Documentation", [author], 1)] |
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texinfo_documents = [ |
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( |
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master_doc, |
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"detectron2", |
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"detectron2 Documentation", |
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author, |
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"detectron2", |
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"One line description of project.", |
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"Miscellaneous", |
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) |
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] |
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todo_include_todos = True |
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def autodoc_skip_member(app, what, name, obj, skip, options): |
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if getattr(obj, "__HIDE_SPHINX_DOC__", False): |
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return True |
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HIDDEN = { |
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"ResNetBlockBase", |
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"GroupedBatchSampler", |
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"build_transform_gen", |
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"apply_transform_gens", |
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"TransformGen", |
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"apply_augmentations", |
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"StandardAugInput", |
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"build_batch_data_loader", |
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"draw_panoptic_seg_predictions", |
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"WarmupCosineLR", |
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"WarmupMultiStepLR", |
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"downgrade_config", |
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"upgrade_config", |
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"add_export_config", |
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} |
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try: |
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if name in HIDDEN or ( |
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hasattr(obj, "__doc__") and obj.__doc__.lower().strip().startswith("deprecated") |
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): |
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print("Skipping deprecated object: {}".format(name)) |
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return True |
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except: |
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pass |
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return skip |
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_PAPER_DATA = { |
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"resnet": ("1512.03385", "Deep Residual Learning for Image Recognition"), |
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"fpn": ("1612.03144", "Feature Pyramid Networks for Object Detection"), |
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"mask r-cnn": ("1703.06870", "Mask R-CNN"), |
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"faster r-cnn": ( |
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"1506.01497", |
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"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", |
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), |
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"deformconv": ("1703.06211", "Deformable Convolutional Networks"), |
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"deformconv2": ("1811.11168", "Deformable ConvNets v2: More Deformable, Better Results"), |
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"panopticfpn": ("1901.02446", "Panoptic Feature Pyramid Networks"), |
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"retinanet": ("1708.02002", "Focal Loss for Dense Object Detection"), |
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"cascade r-cnn": ("1712.00726", "Cascade R-CNN: Delving into High Quality Object Detection"), |
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"lvis": ("1908.03195", "LVIS: A Dataset for Large Vocabulary Instance Segmentation"), |
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"rrpn": ("1703.01086", "Arbitrary-Oriented Scene Text Detection via Rotation Proposals"), |
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"imagenet in 1h": ("1706.02677", "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour"), |
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"xception": ("1610.02357", "Xception: Deep Learning with Depthwise Separable Convolutions"), |
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"mobilenet": ( |
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"1704.04861", |
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"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications", |
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), |
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"deeplabv3+": ( |
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"1802.02611", |
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"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation", |
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), |
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"dds": ("2003.13678", "Designing Network Design Spaces"), |
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"scaling": ("2103.06877", "Fast and Accurate Model Scaling"), |
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"fcos": ("2006.09214", "FCOS: A Simple and Strong Anchor-free Object Detector"), |
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"rethinking-batchnorm": ("2105.07576", 'Rethinking "Batch" in BatchNorm'), |
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} |
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def paper_ref_role( |
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typ: str, |
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rawtext: str, |
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text: str, |
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lineno: int, |
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inliner, |
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options: Dict = {}, |
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content: List[str] = [], |
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): |
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""" |
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Parse :paper:`xxx`. Similar to the "extlinks" sphinx extension. |
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""" |
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from docutils import nodes, utils |
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from sphinx.util.nodes import split_explicit_title |
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|
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text = utils.unescape(text) |
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has_explicit_title, title, link = split_explicit_title(text) |
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link = link.lower() |
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if link not in _PAPER_DATA: |
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inliner.reporter.warning("Cannot find paper " + link) |
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paper_url, paper_title = "#", link |
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else: |
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paper_url, paper_title = _PAPER_DATA[link] |
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if "/" not in paper_url: |
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paper_url = "https://arxiv.org/abs/" + paper_url |
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if not has_explicit_title: |
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title = paper_title |
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pnode = nodes.reference(title, title, internal=False, refuri=paper_url) |
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return [pnode], [] |
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def setup(app): |
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from recommonmark.transform import AutoStructify |
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|
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app.add_domain(GithubURLDomain) |
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app.connect("autodoc-skip-member", autodoc_skip_member) |
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app.add_role("paper", paper_ref_role) |
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app.add_config_value( |
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"recommonmark_config", |
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{"enable_math": True, "enable_inline_math": True, "enable_eval_rst": True}, |
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True, |
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) |
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app.add_transform(AutoStructify) |
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