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Add doc structures and formulate requirements (open-mmlab#32)
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‎docs/Makefile

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

‎docs/changelog.md

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‎docs/conf.py

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# Configuration file for the Sphinx documentation builder.
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#
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# This file only contains a selection of the most common options. For a full
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# list see the documentation:
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# https://www.sphinx-doc.org/en/master/usage/configuration.html
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# -- Path setup --------------------------------------------------------------
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# If extensions (or modules to document with autodoc) are in another directory,
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# add these directories to sys.path here. If the directory is relative to the
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# documentation root, use os.path.abspath to make it absolute, like shown here.
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#
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import os
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import sys
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sys.path.insert(0, os.path.abspath('..'))
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# -- Project information -----------------------------------------------------
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project = 'MMTracking'
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copyright = '2018-2020, OpenMMLab'
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author = 'MMTracking Authors'
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version_file = '../mmtrack/version.py'
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def get_version():
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with open(version_file, 'r') as f:
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exec(compile(f.read(), version_file, 'exec'))
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return locals()['__version__']
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# The full version, including alpha/beta/rc tags
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release = get_version()
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# -- General configuration ---------------------------------------------------
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# Add any Sphinx extension module names here, as strings. They can be
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# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
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# ones.
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extensions = [
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'sphinx.ext.autodoc',
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'sphinx.ext.napoleon',
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'sphinx.ext.viewcode',
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'recommonmark',
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'sphinx_markdown_tables',
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]
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autodoc_mock_imports = [
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'matplotlib', 'pycocotools', 'terminaltables', 'mmtrack.version',
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'mmcv.ops'
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]
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# Add any paths that contain templates here, relative to this directory.
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templates_path = ['_templates']
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# The suffix(es) of source filenames.
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# You can specify multiple suffix as a list of string:
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#
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source_suffix = {
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'.rst': 'restructuredtext',
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'.md': 'markdown',
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}
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# The master toctree document.
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master_doc = 'index'
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# List of patterns, relative to source directory, that match files and
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# directories to ignore when looking for source files.
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# This pattern also affects html_static_path and html_extra_path.
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exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
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# -- Options for HTML output -------------------------------------------------
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# The theme to use for HTML and HTML Help pages. See the documentation for
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# a list of builtin themes.
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#
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html_theme = 'sphinx_rtd_theme'
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# Add any paths that contain custom static files (such as style sheets) here,
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# relative to this directory. They are copied after the builtin static files,
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# so a file named "default.css" will overwrite the builtin "default.css".
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html_static_path = ['_static']
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# def builder_inited_handler(app):
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# subprocess.run(['./stat.py'])
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# def setup(app):
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# app.connect('builder-inited', builder_inited_handler)

‎docs/dataset.py

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‎docs/faq.md

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‎docs/get_started.md

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## Prerequisites
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- Linux or macOS (Windows is in experimental support)
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- Python 3.6+
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- PyTorch 1.3+
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- CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
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- GCC 5+
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- [MMCV](https://mmcv.readthedocs.io/en/latest/#installation)
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The compatible MMDetection and MMCV versions are as below. Please install the correct version of MMCV to avoid installation issues.
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| MMDetection version | MMCV version |
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|:-------------------:|:-------------------:|
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| master | mmcv-full>=1.1.5, <1.3|
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| 2.7.0 | mmcv-full>=1.1.5, <1.3|
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| 2.6.0 | mmcv-full>=1.1.5, <1.3|
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| 2.5.0 | mmcv-full>=1.1.5, <1.3|
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| 2.4.0 | mmcv-full>=1.1.1, <1.3|
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| 2.3.0 | mmcv-full==1.0.5|
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| 2.3.0rc0 | mmcv-full>=1.0.2 |
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| 2.2.1 | mmcv==0.6.2 |
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| 2.2.0 | mmcv==0.6.2 |
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| 2.1.0 | mmcv>=0.5.9, <=0.6.1|
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| 2.0.0 | mmcv>=0.5.1, <=0.5.8|
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Note: You need to run `pip uninstall mmcv` first if you have mmcv installed.
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If mmcv and mmcv-full are both installed, there will be `ModuleNotFoundError`.
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## Installation
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1. Create a conda virtual environment and activate it.
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```shell
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conda create -n open-mmlab python=3.7 -y
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conda activate open-mmlab
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```
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2. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/), e.g.,
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```shell
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conda install pytorch torchvision -c pytorch
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```
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Note: Make sure that your compilation CUDA version and runtime CUDA version match.
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You can check the supported CUDA version for precompiled packages on the [PyTorch website](https://pytorch.org/).
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`E.g.1` If you have CUDA 10.1 installed under `/usr/local/cuda` and would like to install
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PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.
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```shell
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conda install pytorch cudatoolkit=10.1 torchvision -c pytorch
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```
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`E.g. 2` If you have CUDA 9.2 installed under `/usr/local/cuda` and would like to install
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PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.
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```shell
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conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch
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```
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If you build PyTorch from source instead of installing the prebuilt pacakge,
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you can use more CUDA versions such as 9.0.
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3. Install mmcv-full, we recommend you to install the pre-build package as below.
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```shell
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pip install mmcv-full==latest+torch1.6.0+cu101 -f https://download.openmmlab.com/mmcv/dist/index.html
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```
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See [here](https://github.com/open-mmlab/mmcv#install-with-pip) for different versions of MMCV compatible to different PyTorch and CUDA versions.
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Optionally you can choose to compile mmcv from source by the following command
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```shell
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git clone https://github.com/open-mmlab/mmcv.git
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cd mmcv
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MMCV_WITH_OPS=1 pip install -e . # package mmcv-full will be installed after this step
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cd ..
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```
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Or directly run
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```shell
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pip install mmcv-full
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```
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4. Clone the MMDetection repository.
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```shell
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git clone https://github.com/open-mmlab/mmdetection.git
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cd mmdetection
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```
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5. Install build requirements and then install MMDetection.
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```shell
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pip install -r requirements/build.txt
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pip install -v -e . # or "python setup.py develop"
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```
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Note:
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a. Following the above instructions, MMDetection is installed on `dev` mode
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, any local modifications made to the code will take effect without the need to reinstall it.
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b. If you would like to use `opencv-python-headless` instead of `opencv
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-python`,
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you can install it before installing MMCV.
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c. Some dependencies are optional. Simply running `pip install -v -e .` will
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only install the minimum runtime requirements. To use optional dependencies like `albumentations` and `imagecorruptions` either install them manually with `pip install -r requirements/optional.txt` or specify desired extras when calling `pip` (e.g. `pip install -v -e .[optional]`). Valid keys for the extras field are: `all`, `tests`, `build`, and `optional`.
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### Install with CPU only
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The code can be built for CPU only environment (where CUDA isn't available).
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In CPU mode you can run the demo/webcam_demo.py for example.
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However some functionality is gone in this mode:
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- Deformable Convolution
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- Deformable ROI pooling
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- CARAFE: Content-Aware ReAssembly of FEatures
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- nms_cuda
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- sigmoid_focal_loss_cuda
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So if you try to run inference with a model containing deformable convolution you will get an error.
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### Another option: Docker Image
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We provide a [Dockerfile](https://github.com/open-mmlab/mmdetection/blob/master/docker/Dockerfile) to build an image. Ensure that you are using [docker version](https://docs.docker.com/engine/install/) >=19.03.
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```shell
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# build an image with PyTorch 1.6, CUDA 10.1
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docker build -t mmdetection docker/
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```
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Run it with
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```shell
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docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection/data mmdetection
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```
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### A from-scratch setup script
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Assuming that you already have CUDA 10.1 installed, here is a full script for setting up MMDetection with conda.
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```shell
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conda create -n open-mmlab python=3.7 -y
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conda activate open-mmlab
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conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch -y
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# install the latest mmcv
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pip install mmcv-full==latest+torch1.6.0+cu101 -f https://download.openmmlab.com/mmcv/dist/index.html
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# install mmdetection
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git clone https://github.com/open-mmlab/mmdetection.git
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cd mmdetection
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pip install -r requirements/build.txt
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pip install -v -e .
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```
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### Developing with multiple MMDetection versions
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The train and test scripts already modify the `PYTHONPATH` to ensure the script use the MMDetection in the current directory.
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To use the default MMDetection installed in the environment rather than that you are working with, you can remove the following line in those scripts
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```shell
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PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
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```
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## Verification
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To verify whether MMDetection and the required environment are installed correctly, we can run sample python codes to initialize a detector and inference a demo image:
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```python
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from mmdet.apis import init_detector, inference_detector
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config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
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device = 'cuda:0'
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# init a detector
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model = init_detector(config_file, device=device)
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# inference the demo image
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inference_detector(model, 'demo/demo.jpg')
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```
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The above code is supposed to run successfully upon you finish the installation.

‎docs/index.rst

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Welcome to MMTracking's documentation!
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=======================================
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.. toctree::
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:maxdepth: 2
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:caption: Get Started
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get_started.md

‎docs/make.bat

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@ECHO OFF
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pushd %~dp0
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REM Command file for Sphinx documentation
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if "%SPHINXBUILD%" == "" (
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set SPHINXBUILD=sphinx-build
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)
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set SOURCEDIR=.
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set BUILDDIR=_build
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if "%1" == "" goto help
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%SPHINXBUILD% >NUL 2>NUL
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if errorlevel 9009 (
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echo.
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echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
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echo.installed, then set the SPHINXBUILD environment variable to point
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echo.to the full path of the 'sphinx-build' executable. Alternatively you
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echo.may add the Sphinx directory to PATH.
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echo.
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echo.If you don't have Sphinx installed, grab it from
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echo.http://sphinx-doc.org/
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exit /b 1
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)
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%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
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goto end
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:help
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%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
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:end
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popd

‎docs/model_zoo.md

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‎docs/quick_run.md

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‎docs/tools.md

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‎docs/tutorials/config.md

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‎docs/tutorials/customize_data_pipeline.md

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‎docs/tutorials/customize_dataset.md

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‎docs/tutorials/customize_models.md

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‎docs/tutorials/customize_runtime.md

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‎mmtrack/.readthedocs.yml

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version: 2
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python:
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version: 3.7
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install:
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- requirements: requirements/docs.txt
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- requirements: requirements/readthedocs.txt

‎requirements.txt

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coverage
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mmcls
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mmcv>=1.0.0
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mmdet
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motmetrics
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numpy
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torch>=1.3
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torchvision
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-r requirements/build.txt
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-r requirements/runtime.txt
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-r requirements/tests.txt

‎requirements/build.txt

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# These must be installed before building mmdetection
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cython
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numpy

‎requirements/docs.txt

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recommonmark
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sphinx
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sphinx_markdown_tables
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sphinx_rtd_theme

‎requirements/readthedocs.txt

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mmcv
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torch
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torchvision

‎requirements/runtime.txt

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matplotlib
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mmcls
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mmpycocotools
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motmetrics
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numpy
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six
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terminaltables

‎requirements/tests.txt

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asynctest
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codecov
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flake8
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interrogate
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isort==4.3.21
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# Note: used for kwarray.group_items, this may be ported to mmcv in the future.
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kwarray
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pytest
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ubelt
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xdoctest>=0.10.0
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yapf

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