push v0.1.3 version commit bd2ea47

docs/Makefile

+# Minimal makefile for Sphinx documentation
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/README.md

+# Read the docs:
+
+The latest documentation built from this directory is available at [detectron2.readthedocs.io](https://detectron2.readthedocs.io/).
+Documents in this directory are not meant to be read on github.
+
+# Build the docs:
+
+1. Install detectron2 according to [INSTALL.md](INSTALL.md).
+2. Install additional libraries required to build docs:
+  - docutils==0.16
+  - Sphinx==3.0.0
+  - recommonmark==0.6.0
+  - sphinx_rtd_theme
+  - mock
+
+3. Run `make html` from this directory.

docs/conf.py

+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+# flake8: noqa
+
+# Configuration file for the Sphinx documentation builder.
+#
+# This file does only contain a selection of the most common options. For a
+# full list see the documentation:
+# http://www.sphinx-doc.org/en/master/config
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+import mock
+from sphinx.domains import Domain
+from typing import Dict, List, Tuple
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+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:
+            # unresolved links in these docs are all github links
+            github_url = target
+
+        if github_url is not None:
+            if github_url.endswith("MODEL_ZOO") or github_url.endswith("README"):
+                # bug of recommonmark.
+                # https://github.com/readthedocs/recommonmark/blob/ddd56e7717e9745f11300059e4268e204138a6b1/recommonmark/parser.py#L152-L155
+                github_url += ".md"
+            print("Ref {} resolved to github:{}".format(target, github_url))
+            contnode["refuri"] = self.ROOT + github_url
+            return [("githuburl:any", contnode)]
+        else:
+            return []
+
+
+# to support markdown
+from recommonmark.parser import CommonMarkParser
+
+sys.path.insert(0, os.path.abspath("../"))
+os.environ["DOC_BUILDING"] = "True"
+DEPLOY = os.environ.get("READTHEDOCS") == "True"
+
+
+# -- Project information -----------------------------------------------------
+
+# fmt: off
+try:
+    import torch  # noqa
+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"  # fake version
+
+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)
+# fmt: on
+sys.modules["cv2"].__version__ = "3.4"
+
+import detectron2  # isort: skip
+
+
+project = "detectron2"
+copyright = "2019-2020, detectron2 contributors"
+author = "detectron2 contributors"
+
+# The short X.Y version
+version = detectron2.__version__
+# The full version, including alpha/beta/rc tags
+release = version
+
+
+# -- General configuration ---------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#
+needs_sphinx = "3.0"
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+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",
+]
+
+# -- Configurations for plugins ------------
+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:
+    # skip this when building locally
+    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),
+}
+# -------------------------
+
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+source_suffix = [".rst", ".md"]
+
+# The master toctree document.
+master_doc = "index"
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = None
+
+# 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 = ["_build", "Thumbs.db", ".DS_Store", "build", "README.md", "tutorials/README.md"]
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = "sphinx"
+
+
+# -- Options for HTML output -------------------------------------------------
+
+html_theme = "sphinx_rtd_theme"
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#
+# html_theme_options = {}
+
+# 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"]
+
+# Custom sidebar templates, must be a dictionary that maps document names
+# to template names.
+#
+# The default sidebars (for documents that don't match any pattern) are
+# defined by theme itself.  Builtin themes are using these templates by
+# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
+# 'searchbox.html']``.
+#
+# html_sidebars = {}
+
+
+# -- Options for HTMLHelp output ---------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = "detectron2doc"
+
+
+# -- Options for LaTeX output ------------------------------------------------
+
+latex_elements = {
+    # 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',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    (master_doc, "detectron2.tex", "detectron2 Documentation", "detectron2 contributors", "manual")
+]
+
+
+# -- 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, "detectron2", "detectron2 Documentation", [author], 1)]
+
+
+# -- Options for Texinfo output ----------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    (
+        master_doc,
+        "detectron2",
+        "detectron2 Documentation",
+        author,
+        "detectron2",
+        "One line description of project.",
+        "Miscellaneous",
+    )
+]
+
+
+# -- Options for todo extension ----------------------------------------------
+
+# If true, `todo` and `todoList` produce output, else they produce nothing.
+todo_include_todos = True
+
+
+_DEPRECATED_NAMES = set()
+
+
+def autodoc_skip_member(app, what, name, obj, skip, options):
+    # we hide something deliberately
+    if getattr(obj, "__HIDE_SPHINX_DOC__", False):
+        return True
+    # Hide some names that are deprecated or not intended to be used
+    if name in _DEPRECATED_NAMES:
+        return True
+    return None
+
+
+_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"),
+    "in1k1h": ("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)

docs/index.rst

+.. detectron2 documentation master file, created by
+   sphinx-quickstart on Sat Sep 21 13:46:45 2019.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to detectron2's documentation!
+======================================
+
+.. toctree::
+   :maxdepth: 2
+
+   tutorials/index
+   notes/index
+   modules/index

docs/modules/checkpoint.rst

+detectron2.checkpoint package
+=============================
+
+.. automodule:: detectron2.checkpoint
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/config.rst

+detectron2.config package
+=========================
+
+.. automodule:: detectron2.config
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:
+
+
+Config References
+-----------------
+
+.. literalinclude:: ../../detectron2/config/defaults.py
+  :language: python
+  :linenos:
+  :lines: 4-

docs/modules/data.rst

+detectron2.data package
+=======================
+
+.. automodule:: detectron2.data
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+detectron2.data.detection\_utils module
+---------------------------------------
+
+.. automodule:: detectron2.data.detection_utils
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+detectron2.data.datasets module
+---------------------------------------
+
+.. automodule:: detectron2.data.datasets
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+detectron2.data.samplers module
+---------------------------------------
+
+.. automodule:: detectron2.data.samplers
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+detectron2.data.transforms module
+---------------------------------------
+
+.. automodule:: detectron2.data.transforms
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/engine.rst

+detectron2.engine package
+=========================
+
+
+.. automodule:: detectron2.engine
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+detectron2.engine.defaults module
+---------------------------------
+
+.. automodule:: detectron2.engine.defaults
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+detectron2.engine.hooks module
+---------------------------------
+
+.. automodule:: detectron2.engine.hooks
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/evaluation.rst

+detectron2.evaluation package
+=============================
+
+.. automodule:: detectron2.evaluation
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/export.rst

+detectron2.export package
+=========================
+
+.. automodule:: detectron2.export
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/index.rst

+API Documentation
+==================
+
+.. toctree::
+
+    checkpoint
+    config
+    data
+    engine
+    evaluation
+    layers
+    model_zoo
+    modeling
+    solver
+    structures
+    utils
+    export

docs/modules/layers.rst

+detectron2.layers package
+=========================
+
+.. automodule:: detectron2.layers
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/model_zoo.rst

+detectron2.model_zoo package
+============================
+
+.. automodule:: detectron2.model_zoo
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/modeling.rst

+detectron2.modeling package
+===========================
+
+.. automodule:: detectron2.modeling
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+detectron2.modeling.poolers module
+---------------------------------------
+
+.. automodule:: detectron2.modeling.poolers
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+detectron2.modeling.sampling module
+------------------------------------
+
+.. automodule:: detectron2.modeling.sampling
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+detectron2.modeling.box_regression module
+------------------------------------------
+
+.. automodule:: detectron2.modeling.box_regression
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Model Registries
+-----------------
+
+These are different registries provided in modeling.
+Each registry provide you the ability to replace it with your customized component,
+without having to modify detectron2's code.
+
+Note that it is impossible to allow users to customize any line of code directly.
+Even just to add one line at some place,
+you'll likely need to find out the smallest registry which contains that line,
+and register your component to that registry.
+
+
+.. autodata:: detectron2.modeling.META_ARCH_REGISTRY
+.. autodata:: detectron2.modeling.BACKBONE_REGISTRY
+.. autodata:: detectron2.modeling.PROPOSAL_GENERATOR_REGISTRY
+.. autodata:: detectron2.modeling.RPN_HEAD_REGISTRY
+.. autodata:: detectron2.modeling.ANCHOR_GENERATOR_REGISTRY
+.. autodata:: detectron2.modeling.ROI_HEADS_REGISTRY
+.. autodata:: detectron2.modeling.ROI_BOX_HEAD_REGISTRY
+.. autodata:: detectron2.modeling.ROI_MASK_HEAD_REGISTRY
+.. autodata:: detectron2.modeling.ROI_KEYPOINT_HEAD_REGISTRY

docs/modules/solver.rst

+detectron2.solver package
+=========================
+
+.. automodule:: detectron2.solver
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/structures.rst

+detectron2.structures package
+=============================
+
+.. automodule:: detectron2.structures
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/utils.rst

+detectron2.utils package
+========================
+
+detectron2.utils.colormap module
+--------------------------------
+
+.. automodule:: detectron2.utils.colormap
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+detectron2.utils.comm module
+----------------------------
+
+.. automodule:: detectron2.utils.comm
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+detectron2.utils.events module
+------------------------------
+
+.. automodule:: detectron2.utils.events
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+detectron2.utils.logger module
+------------------------------
+
+.. automodule:: detectron2.utils.logger
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+detectron2.utils.registry module
+--------------------------------
+
+.. automodule:: detectron2.utils.registry
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+detectron2.utils.memory module
+----------------------------------
+
+.. automodule:: detectron2.utils.memory
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+detectron2.utils.analysis module
+----------------------------------
+
+.. automodule:: detectron2.utils.analysis
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+detectron2.utils.visualizer module
+----------------------------------
+
+.. automodule:: detectron2.utils.visualizer
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+detectron2.utils.video\_visualizer module
+-----------------------------------------
+
+.. automodule:: detectron2.utils.video_visualizer
+    :members:
+    :undoc-members:
+    :show-inheritance:
+

docs/notes/benchmarks.md

+
+# Benchmarks
+
+Here we benchmark the training speed of a Mask R-CNN in detectron2,
+with some other popular open source Mask R-CNN implementations.
+
+
+### Settings
+
+* Hardware: 8 NVIDIA V100s with NVLink.
+* Software: Python 3.7, CUDA 10.1, cuDNN 7.6.5, PyTorch 1.5,
+  TensorFlow 1.15.0rc2, Keras 2.2.5, MxNet 1.6.0b20190820.
+* Model: an end-to-end R-50-FPN Mask-RCNN model, using the same hyperparameter as the
+  [Detectron baseline config](https://github.com/facebookresearch/Detectron/blob/master/configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml)
+	(it does no have scale augmentation).
+* Metrics: We use the average throughput in iterations 100-500 to skip GPU warmup time.
+  Note that for R-CNN-style models, the throughput of a model typically changes during training, because
+  it depends on the predictions of the model. Therefore this metric is not directly comparable with
+  "train speed" in model zoo, which is the average speed of the entire training run.
+
+
+### Main Results
+
+```eval_rst
++-------------------------------+--------------------+
+| Implementation                | Throughput (img/s) |
++===============================+====================+
+| |D2| |PT|                     | 62                 |
++-------------------------------+--------------------+
+| mmdetection_  |PT|            | 53                 |
++-------------------------------+--------------------+
+| maskrcnn-benchmark_  |PT|     | 53                 |
++-------------------------------+--------------------+
+| tensorpack_ |TF|              | 50                 |
++-------------------------------+--------------------+
+| simpledet_ |mxnet|            | 39                 |
++-------------------------------+--------------------+
+| Detectron_  |C2|              | 19                 |
++-------------------------------+--------------------+
+| `matterport/Mask_RCNN`__ |TF| | 14                 |
++-------------------------------+--------------------+
+
+.. _maskrcnn-benchmark: https://github.com/facebookresearch/maskrcnn-benchmark/
+.. _tensorpack: https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN
+.. _mmdetection: https://github.com/open-mmlab/mmdetection/
+.. _simpledet: https://github.com/TuSimple/simpledet/
+.. _Detectron: https://github.com/facebookresearch/Detectron
+__ https://github.com/matterport/Mask_RCNN/
+
+.. |D2| image:: https://github.com/facebookresearch/detectron2/raw/master/.github/Detectron2-Logo-Horz.svg?sanitize=true
+   :height: 15pt
+   :target: https://github.com/facebookresearch/detectron2/
+.. |PT| image:: https://pytorch.org/assets/images/logo-icon.svg
+   :width: 15pt
+   :height: 15pt
+   :target: https://pytorch.org
+.. |TF| image:: https://static.nvidiagrid.net/ngc/containers/tensorflow.png
+   :width: 15pt
+   :height: 15pt
+   :target: https://tensorflow.org
+.. |mxnet| image:: https://github.com/dmlc/web-data/raw/master/mxnet/image/mxnet_favicon.png
+   :width: 15pt
+   :height: 15pt
+   :target: https://mxnet.apache.org/
+.. |C2| image:: https://caffe2.ai/static/logo.svg
+   :width: 15pt
+   :height: 15pt
+   :target: https://caffe2.ai
+```
+
+
+Details for each implementation:
+
+* __Detectron2__: with release v0.1.2, run:
+  ```
+  python tools/train_net.py  --config-file configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml --num-gpus 8
+  ```
+
+* __mmdetection__: at commit `b0d845f`, run
+  ```
+  ./tools/dist_train.sh configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py 8
+  ```
+
+* __maskrcnn-benchmark__: use commit `0ce8f6f` with `sed -i ‘s/torch.uint8/torch.bool/g’ **/*.py; sed -i 's/AT_CHECK/TORCH_CHECK/g' **/*.cu`
+	to make it compatible with PyTorch 1.5. Then, run training with
+  ```
+  python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
+  ```
+  The speed we observed is faster than its model zoo, likely due to different software versions.
+
+* __tensorpack__: at commit `caafda`, `export TF_CUDNN_USE_AUTOTUNE=0`, then run
+  ```
+  mpirun -np 8 ./train.py --config DATA.BASEDIR=/data/coco TRAINER=horovod BACKBONE.STRIDE_1X1=True TRAIN.STEPS_PER_EPOCH=50 --load ImageNet-R50-AlignPadding.npz
+  ```
+
+* __SimpleDet__: at commit `9187a1`, run
+  ```
+  python detection_train.py --config config/mask_r50v1_fpn_1x.py
+  ```
+
+* __Detectron__: run
+  ```
+  python tools/train_net.py --cfg configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml
+  ```
+  Note that many of its ops run on CPUs, therefore the performance is limited.
+
+* __matterport/Mask_RCNN__: at commit `3deaec`, apply the following diff, `export TF_CUDNN_USE_AUTOTUNE=0`, then run
+  ```
+  python coco.py train --dataset=/data/coco/ --model=imagenet
+  ```
+  Note that many small details in this implementation might be different
+  from Detectron's standards.
+
+  <details>
+  <summary>
+  (diff to make it use the same hyperparameters - click to expand)
+  </summary>
+
+  ```diff
+  diff --git i/mrcnn/model.py w/mrcnn/model.py
+  index 62cb2b0..61d7779 100644
+  --- i/mrcnn/model.py
+  +++ w/mrcnn/model.py
+  @@ -2367,8 +2367,8 @@ class MaskRCNN():
+        epochs=epochs,
+        steps_per_epoch=self.config.STEPS_PER_EPOCH,
+        callbacks=callbacks,
+  -            validation_data=val_generator,
+  -            validation_steps=self.config.VALIDATION_STEPS,
+  +            #validation_data=val_generator,
+  +            #validation_steps=self.config.VALIDATION_STEPS,
+        max_queue_size=100,
+        workers=workers,
+        use_multiprocessing=True,
+  diff --git i/mrcnn/parallel_model.py w/mrcnn/parallel_model.py
+  index d2bf53b..060172a 100644
+  --- i/mrcnn/parallel_model.py
+  +++ w/mrcnn/parallel_model.py
+  @@ -32,6 +32,7 @@ class ParallelModel(KM.Model):
+      keras_model: The Keras model to parallelize
+      gpu_count: Number of GPUs. Must be > 1
+      """
+  +        super().__init__()
+      self.inner_model = keras_model
+      self.gpu_count = gpu_count
+      merged_outputs = self.make_parallel()
+  diff --git i/samples/coco/coco.py w/samples/coco/coco.py
+  index 5d172b5..239ed75 100644
+  --- i/samples/coco/coco.py
+  +++ w/samples/coco/coco.py
+  @@ -81,7 +81,10 @@ class CocoConfig(Config):
+    IMAGES_PER_GPU = 2
+
+    # Uncomment to train on 8 GPUs (default is 1)
+  -    # GPU_COUNT = 8
+  +    GPU_COUNT = 8
+  +    BACKBONE = "resnet50"
+  +    STEPS_PER_EPOCH = 50
+  +    TRAIN_ROIS_PER_IMAGE = 512
+
+    # Number of classes (including background)
+    NUM_CLASSES = 1 + 80  # COCO has 80 classes
+  @@ -496,29 +499,10 @@ if __name__ == '__main__':
+      # *** This training schedule is an example. Update to your needs ***
+
+      # Training - Stage 1
+  -        print("Training network heads")
+      model.train(dataset_train, dataset_val,
+            learning_rate=config.LEARNING_RATE,
+            epochs=40,
+  -                    layers='heads',
+  -                    augmentation=augmentation)
+  -
+  -        # Training - Stage 2
+  -        # Finetune layers from ResNet stage 4 and up
+  -        print("Fine tune Resnet stage 4 and up")
+  -        model.train(dataset_train, dataset_val,
+  -                    learning_rate=config.LEARNING_RATE,
+  -                    epochs=120,
+  -                    layers='4+',
+  -                    augmentation=augmentation)
+  -
+  -        # Training - Stage 3
+  -        # Fine tune all layers
+  -        print("Fine tune all layers")
+  -        model.train(dataset_train, dataset_val,
+  -                    learning_rate=config.LEARNING_RATE / 10,
+  -                    epochs=160,
+  -                    layers='all',
+  +                    layers='3+',
+            augmentation=augmentation)
+
+    elif args.command == "evaluate":
+  ```
+
+  </details>

docs/notes/changelog.md

+# Change Log
+
+### Releases
+See release log at
+[https://github.com/facebookresearch/detectron2/releases](https://github.com/facebookresearch/detectron2/releases).
+
+### Notable Backward Incompatible Changes:
+
+* 03/30/2020: Custom box head's `output_size` changed to `output_shape`.
+* 02/14/2020,02/18/2020: Mask head and keypoint head now include logic for losses & inference. Custom heads
+	should overwrite the feature computation by `layers()` method.
+* 11/11/2019: `detectron2.data.detection_utils.read_image` transposes images with exif information.
+
+### Config Version Change Log
+
+* v1: Rename `RPN_HEAD.NAME` to `RPN.HEAD_NAME`.
+* v2: A batch of rename of many configurations before release.
+
+### Silent Regression in Historical Versions:
+
+We list a few silent regressions since they may silently produce incorrect results and will be hard to debug.
+
+* 04/01/2020 - 05/11/2020: Bad accuracy if `TRAIN_ON_PRED_BOXES` is set to True.
+* 03/30/2020 - 04/01/2020: ResNets are not correctly built.
+* 12/19/2019 - 12/26/2019: Using aspect ratio grouping causes a drop in accuracy.
+* release - 11/9/2019: Test time augmentation does not predict the last category.

docs/notes/compatibility.md

+# Compatibility with Other Libraries
+
+## Compatibility with Detectron (and maskrcnn-benchmark)
+
+Detectron2 addresses some legacy issues left in Detectron. As a result, their models
+are not compatible:
+running inference with the same model weights will produce different results in the two code bases.
+
+The major differences regarding inference are:
+
+- The height and width of a box with corners (x1, y1) and (x2, y2) is now computed more naturally as
+  width = x2 - x1 and height = y2 - y1;
+  In Detectron, a "+ 1" was added both height and width.
+
+  Note that the relevant ops in Caffe2 have [adopted this change of convention](https://github.com/pytorch/pytorch/pull/20550)
+  with an extra option.
+  So it is still possible to run inference with a Detectron2-trained model in Caffe2.
+
+  The change in height/width calculations most notably changes:
+  - encoding/decoding in bounding box regression.
+  - non-maximum suppression. The effect here is very negligible, though.
+
+- RPN now uses simpler anchors with fewer quantization artifacts.
+
+  In Detectron, the anchors were quantized and
+  [do not have accurate areas](https://github.com/facebookresearch/Detectron/issues/227).
+  In Detectron2, the anchors are center-aligned to feature grid points and not quantized.
+
+- Classification layers have a different ordering of class labels.
+
+  This involves any trainable parameter with shape (..., num_categories + 1, ...).
+  In Detectron2, integer labels [0, K-1] correspond to the K = num_categories object categories
+  and the label "K" corresponds to the special "background" category.
+  In Detectron, label "0" means background, and labels [1, K] correspond to the K categories.
+
+- ROIAlign is implemented differently. The new implementation is [available in Caffe2](https://github.com/pytorch/pytorch/pull/23706).
+
+  1. All the ROIs are shifted by half a pixel compared to Detectron in order to create better image-feature-map alignment.
+     See `layers/roi_align.py` for details.
+     To enable the old behavior, use `ROIAlign(aligned=False)`, or `POOLER_TYPE=ROIAlign` instead of
+     `ROIAlignV2` (the default).
+
+  1. The ROIs are not required to have a minimum size of 1.
+     This will lead to tiny differences in the output, but should be negligible.
+
+- Mask inference function is different.
+
+  In Detectron2, the "paste_mask" function is different and should be more accurate than in Detectron. This change
+  can improve mask AP on COCO by ~0.5% absolute.
+
+There are some other differences in training as well, but they won't affect
+model-level compatibility. The major ones are:
+
+- We fixed a [bug](https://github.com/facebookresearch/Detectron/issues/459) in
+  Detectron, by making `RPN.POST_NMS_TOPK_TRAIN` per-image, rather than per-batch.
+  The fix may lead to a small accuracy drop for a few models (e.g. keypoint
+  detection) and will require some parameter tuning to match the Detectron results.
+- For simplicity, we change the default loss in bounding box regression to L1 loss, instead of smooth L1 loss.
+  We have observed that this tends to slightly decrease box AP50 while improving box AP for higher
+  overlap thresholds (and leading to a slight overall improvement in box AP).
+- We interpret the coordinates in COCO bounding box and segmentation annotations
+  as coordinates in range `[0, width]` or `[0, height]`. The coordinates in
+  COCO keypoint annotations are interpreted as pixel indices in range `[0, width - 1]` or `[0, height - 1]`.
+  Note that this affects how flip augmentation is implemented.
+
+
+We will later share more details and rationale behind the above mentioned issues
+about pixels, coordinates, and "+1"s.
+
+
+## Compatibility with Caffe2
+
+As mentioned above, despite the incompatibilities with Detectron, the relevant
+ops have been implemented in Caffe2.
+Therefore, models trained with detectron2 can be converted in Caffe2.
+See [Deployment](../tutorials/deployment.md) for the tutorial.
+
+## Compatibility with TensorFlow
+
+Most ops are available in TensorFlow, although some tiny differences in
+the implementation of resize / ROIAlign / padding need to be addressed.
+A working conversion script is provided by [tensorpack FasterRCNN](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN/convert_d2)
+to run a standard detectron2 model in TensorFlow.