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.circleci/config.yml 0 → 100644
View file @ ca8a762a
version: 2.1
# this allows you to use CircleCI's dynamic configuration feature
setup: true
# the path-filtering orb is required to continue a pipeline based on
# the path of an updated fileset
orbs:
path-filtering: circleci/path-filtering@0.1.2
workflows:
# the always-run workflow is always triggered, regardless of the pipeline parameters.
always-run:
jobs:
# the path-filtering/filter job determines which pipeline
# parameters to update.
- path-filtering/filter:
name: check-updated-files
# 3-column, whitespace-delimited mapping. One mapping per
# line:
# <regex path-to-test> <parameter-to-set> <value-of-pipeline-parameter>
mapping: |
mmpose/.* lint_only false
requirements/.* lint_only false
tests/.* lint_only false
tools/.* lint_only false
configs/.* lint_only false
.circleci/.* lint_only false
base-revision: dev-1.x
# this is the path of the configuration we should trigger once
# path filtering and pipeline parameter value updates are
# complete. In this case, we are using the parent dynamic
# configuration itself.
config-path: .circleci/test.yml
.circleci/docker/Dockerfile 0 → 100644
View file @ ca8a762a
ARG PYTORCH="1.7.1"
ARG CUDA="11.0"
ARG CUDNN="8"
FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
# To fix GPG key error when running apt-get update
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
RUN apt-get update && apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx
.circleci/scripts/get_mmcv_var.sh 0 → 100644
View file @ ca8a762a
#!/bin/bash
TORCH=$1
CUDA=$2
# 10.2 -> cu102
MMCV_CUDA="cu`echo ${CUDA} | tr -d '.'`"
# MMCV only provides pre-compiled packages for torch 1.x.0
# which works for any subversions of torch 1.x.
# We force the torch version to be 1.x.0 to ease package searching
# and avoid unnecessary rebuild during MMCV's installation.
TORCH_VER_ARR=(${TORCH//./ })
TORCH_VER_ARR[2]=0
printf -v MMCV_TORCH "%s." "${TORCH_VER_ARR[@]}"
MMCV_TORCH=${MMCV_TORCH%?} # Remove the last dot
echo "export MMCV_CUDA=${MMCV_CUDA}" >> $BASH_ENV
echo "export MMCV_TORCH=${MMCV_TORCH}" >> $BASH_ENV
.circleci/test.yml 0 → 100644
View file @ ca8a762a
version: 2.1
# the default pipeline parameters, which will be updated according to
# the results of the path-filtering orb
parameters:
lint_only:
type: boolean
default: true
jobs:
lint:
docker:
- image: cimg/python:3.7.4
steps:
- checkout
- run:
name: Install pre-commit hook
command: |
pip install pre-commit
pre-commit install
- run:
name: Linting
command: pre-commit run --all-files
- run:
name: Check docstring coverage
command: |
pip install interrogate
interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 80 mmpose
build_cpu:
parameters:
# The python version must match available image tags in
# https://circleci.com/developer/images/image/cimg/python
python:
type: string
torch:
type: string
torchvision:
type: string
docker:
- image: cimg/python:<< parameters.python >>
resource_class: large
steps:
- checkout
- run:
name: Install Libraries
command: |
sudo apt-get update
sudo apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libturbojpeg git
- run:
name: Configure Python & pip
command: |
pip install --upgrade pip
pip install wheel
- run:
name: Install PyTorch
command: |
python -V
pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
- run:
name: Install mmpose dependencies
command: |
pip install -U numpy
pip install git+https://github.com/open-mmlab/mmengine.git@main
pip install -U openmim
mim install 'mmcv >= 2.0.0'
pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
pip install -r requirements/tests.txt
pip install -r requirements/albu.txt
pip install -r requirements/poseval.txt
- run:
name: Build and install
command: |
pip install -e .
- run:
name: Run unittests
command: |
coverage run --branch --source mmpose -m pytest tests/
coverage xml
coverage report -m
build_cuda:
parameters:
torch:
type: string
cuda:
type: enum
enum: ["11.0", "11.7"]
cudnn:
type: integer
default: 8
machine:
image: ubuntu-2004-cuda-11.4:202110-01
# docker_layer_caching: true
resource_class: gpu.nvidia.small
steps:
- checkout
- run:
# Cloning repos in VM since Docker doesn't have access to the private key
name: Clone Repos
command: |
git clone -b main --depth 1 https://github.com/open-mmlab/mmengine.git /home/circleci/mmengine
git clone -b dev-3.x --depth 1 https://github.com/open-mmlab/mmdetection.git /home/circleci/mmdetection
- run:
name: Build Docker image
command: |
docker build .circleci/docker -t mmpose:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>
docker run --gpus all -t -d -v /home/circleci/project:/mmpose -v /home/circleci/mmengine:/mmengine -v /home/circleci/mmdetection:/mmdetection -w /mmpose --name mmpose mmpose:gpu
- run:
name: Install mmpose dependencies
command: |
docker exec mmpose apt install git -y
docker exec mmpose pip install -U numpy
docker exec mmpose pip install -e /mmengine
docker exec mmpose pip install -U openmim
docker exec mmpose mim install 'mmcv >= 2.0.0'
docker exec mmpose pip install -e /mmdetection
docker exec mmpose pip install -r requirements/tests.txt
docker exec mmpose pip install -r requirements/albu.txt
docker exec mmpose pip install -r requirements/poseval.txt
- run:
name: Build and install
command: |
docker exec mmpose pip install -e .
- run:
name: Run unittests
command: |
docker exec mmpose pytest tests/
workflows:
pr_stage_lint:
when: << pipeline.parameters.lint_only >>
jobs:
- lint:
name: lint
filters:
branches:
ignore:
- dev-1.x
- main
pr_stage_test:
when:
not:
<< pipeline.parameters.lint_only >>
jobs:
- lint:
name: lint
filters:
branches:
ignore:
- dev-1.x
- main
- build_cpu:
name: minimum_version_cpu
torch: 1.7.1
torchvision: 0.8.2
python: 3.7.4
requires:
- lint
- build_cpu:
name: maximum_version_cpu
torch: 2.0.0
torchvision: 0.15.1
python: 3.9.0
requires:
- minimum_version_cpu
- hold:
type: approval
requires:
- maximum_version_cpu
- build_cuda:
name: mainstream_version_gpu
torch: 1.7.1
# Use double quotation mark to explicitly specify its type
# as string instead of number
cuda: "11.0"
requires:
- hold
- build_cuda:
name: maximum_version_gpu
torch: 2.0.0
cuda: "11.7"
cudnn: 8
requires:
- hold
merge_stage_test:
when:
not:
<< pipeline.parameters.lint_only >>
jobs:
- build_cuda:
name: minimum_version_gpu
torch: 1.7.1
# Use double quotation mark to explicitly specify its type
# as string instead of number
cuda: "11.0"
filters:
branches:
only:
- dev-1.x
- main
.gitignore 0 → 100644
View file @ ca8a762a
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
**/*.pyc
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/en/_build
docs/zh_cn/_build
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
# custom
mmpose/.mim
/models
/data
.vscode
.idea
*.pkl
*.pkl.json
*.log.json
*.npy
work_dirs/
docs/**/topics/
docs/**/papers/*.md
docs/**/datasets.md
docs/**/modelzoo.md
!tests/data/**/*.pkl
!tests/data/**/*.pkl.json
!tests/data/**/*.log.json
!tests/data/**/*.pth
!tests/data/**/*.npy
# Pytorch
*.pth
*.DS_Store
.owners.yml 0 → 100644
View file @ ca8a762a
assign:
issues: enabled
pull_requests: disabled
strategy:
# random
daily-shift-based
scedule:
'*/1 * * * *'
assignees:
- Ben-Louis
- xiexinch
- Ben-Louis
- xiexinch
- Ben-Louis
- Ben-Louis
- Ben-Louis
.pre-commit-config.yaml 0 → 100644
View file @ ca8a762a
exclude: ^tests/data/
repos:
- repo: https://github.com/PyCQA/flake8
rev: 5.0.4
hooks:
- id: flake8
- repo: https://github.com/PyCQA/isort
rev: 5.11.5
hooks:
- id: isort
- repo: https://github.com/pre-commit/mirrors-yapf
rev: v0.32.0
hooks:
- id: yapf
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.3.0
hooks:
- id: trailing-whitespace
- id: check-yaml
- id: end-of-file-fixer
- id: requirements-txt-fixer
- id: double-quote-string-fixer
- id: check-merge-conflict
- id: fix-encoding-pragma
args: ["--remove"]
- id: mixed-line-ending
args: ["--fix=lf"]
- repo: https://github.com/myint/docformatter
rev: v1.3.1
hooks:
- id: docformatter
args: ["--in-place", "--wrap-descriptions", "79"]
- repo: https://github.com/codespell-project/codespell
rev: v2.1.0
hooks:
- id: codespell
args: ["--skip", "*.ipynb", "-L", "mot"]
- repo: https://github.com/executablebooks/mdformat
rev: 0.7.14
hooks:
- id: mdformat
args: ["--number", "--table-width", "200"]
additional_dependencies:
- mdformat-openmmlab
- mdformat_frontmatter
- linkify-it-py
- repo: https://github.com/open-mmlab/pre-commit-hooks
rev: v0.2.0
hooks:
- id: check-copyright
args: ["mmpose", "tests", "demo", "tools", "--excludes", "demo/mmdetection_cfg", "demo/mmtracking_cfg"]
.pylintrc 0 → 100644
View file @ ca8a762a
[MASTER]
# A comma-separated list of package or module names from where C extensions may
# be loaded. Extensions are loading into the active Python interpreter and may
# run arbitrary code.
extension-pkg-whitelist=
# Specify a score threshold to be exceeded before program exits with error.
fail-under=10.0
# Add files or directories to the blacklist. They should be base names, not
# paths.
ignore=CVS,configs
# Add files or directories matching the regex patterns to the blacklist. The
# regex matches against base names, not paths.
ignore-patterns=
# Python code to execute, usually for sys.path manipulation such as
# pygtk.require().
#init-hook=
# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
# number of processors available to use.
jobs=1
# Control the amount of potential inferred values when inferring a single
# object. This can help the performance when dealing with large functions or
# complex, nested conditions.
limit-inference-results=100
# List of plugins (as comma separated values of python module names) to load,
# usually to register additional checkers.
load-plugins=
# Pickle collected data for later comparisons.
persistent=yes
# When enabled, pylint would attempt to guess common misconfiguration and emit
# user-friendly hints instead of false-positive error messages.
suggestion-mode=yes
# Allow loading of arbitrary C extensions. Extensions are imported into the
# active Python interpreter and may run arbitrary code.
unsafe-load-any-extension=no
[MESSAGES CONTROL]
# Only show warnings with the listed confidence levels. Leave empty to show
# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED.
confidence=
# Disable the message, report, category or checker with the given id(s). You
# can either give multiple identifiers separated by comma (,) or put this
# option multiple times (only on the command line, not in the configuration
# file where it should appear only once). You can also use "--disable=all" to
# disable everything first and then reenable specific checks. For example, if
# you want to run only the similarities checker, you can use "--disable=all
# --enable=similarities". If you want to run only the classes checker, but have
# no Warning level messages displayed, use "--disable=all --enable=classes
# --disable=W".
disable=print-statement,
parameter-unpacking,
unpacking-in-except,
old-raise-syntax,
backtick,
long-suffix,
old-ne-operator,
old-octal-literal,
import-star-module-level,
non-ascii-bytes-literal,
raw-checker-failed,
bad-inline-option,
locally-disabled,
file-ignored,
suppressed-message,
useless-suppression,
deprecated-pragma,
use-symbolic-message-instead,
apply-builtin,
basestring-builtin,
buffer-builtin,
cmp-builtin,
coerce-builtin,
execfile-builtin,
file-builtin,
long-builtin,
raw_input-builtin,
reduce-builtin,
standarderror-builtin,
unicode-builtin,
xrange-builtin,
coerce-method,
delslice-method,
getslice-method,
setslice-method,
no-absolute-import,
old-division,
dict-iter-method,
dict-view-method,
next-method-called,
metaclass-assignment,
indexing-exception,
raising-string,
reload-builtin,
oct-method,
hex-method,
nonzero-method,
cmp-method,
input-builtin,
round-builtin,
intern-builtin,
unichr-builtin,
map-builtin-not-iterating,
zip-builtin-not-iterating,
range-builtin-not-iterating,
filter-builtin-not-iterating,
using-cmp-argument,
eq-without-hash,
div-method,
idiv-method,
rdiv-method,
exception-message-attribute,
invalid-str-codec,
sys-max-int,
bad-python3-import,
deprecated-string-function,
deprecated-str-translate-call,
deprecated-itertools-function,
deprecated-types-field,
next-method-defined,
dict-items-not-iterating,
dict-keys-not-iterating,
dict-values-not-iterating,
deprecated-operator-function,
deprecated-urllib-function,
xreadlines-attribute,
deprecated-sys-function,
exception-escape,
comprehension-escape,
no-member,
invalid-name,
too-many-branches,
wrong-import-order,
too-many-arguments,
missing-function-docstring,
missing-module-docstring,
too-many-locals,
too-few-public-methods,
abstract-method,
broad-except,
too-many-nested-blocks,
too-many-instance-attributes,
missing-class-docstring,
duplicate-code,
not-callable,
protected-access,
dangerous-default-value,
no-name-in-module,
logging-fstring-interpolation,
super-init-not-called,
redefined-builtin,
attribute-defined-outside-init,
arguments-differ,
cyclic-import,
bad-super-call,
too-many-statements
# Enable the message, report, category or checker with the given id(s). You can
# either give multiple identifier separated by comma (,) or put this option
# multiple time (only on the command line, not in the configuration file where
# it should appear only once). See also the "--disable" option for examples.
enable=c-extension-no-member
[REPORTS]
# Python expression which should return a score less than or equal to 10. You
# have access to the variables 'error', 'warning', 'refactor', and 'convention'
# which contain the number of messages in each category, as well as 'statement'
# which is the total number of statements analyzed. This score is used by the
# global evaluation report (RP0004).
evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
# Template used to display messages. This is a python new-style format string
# used to format the message information. See doc for all details.
#msg-template=
# Set the output format. Available formats are text, parseable, colorized, json
# and msvs (visual studio). You can also give a reporter class, e.g.
# mypackage.mymodule.MyReporterClass.
output-format=text
# Tells whether to display a full report or only the messages.
reports=no
# Activate the evaluation score.
score=yes
[REFACTORING]
# Maximum number of nested blocks for function / method body
max-nested-blocks=5
# Complete name of functions that never returns. When checking for
# inconsistent-return-statements if a never returning function is called then
# it will be considered as an explicit return statement and no message will be
# printed.
never-returning-functions=sys.exit
[TYPECHECK]
# List of decorators that produce context managers, such as
# contextlib.contextmanager. Add to this list to register other decorators that
# produce valid context managers.
contextmanager-decorators=contextlib.contextmanager
# List of members which are set dynamically and missed by pylint inference
# system, and so shouldn't trigger E1101 when accessed. Python regular
# expressions are accepted.
generated-members=
# Tells whether missing members accessed in mixin class should be ignored. A
# mixin class is detected if its name ends with "mixin" (case insensitive).
ignore-mixin-members=yes
# Tells whether to warn about missing members when the owner of the attribute
# is inferred to be None.
ignore-none=yes
# This flag controls whether pylint should warn about no-member and similar
# checks whenever an opaque object is returned when inferring. The inference
# can return multiple potential results while evaluating a Python object, but
# some branches might not be evaluated, which results in partial inference. In
# that case, it might be useful to still emit no-member and other checks for
# the rest of the inferred objects.
ignore-on-opaque-inference=yes
# List of class names for which member attributes should not be checked (useful
# for classes with dynamically set attributes). This supports the use of
# qualified names.
ignored-classes=optparse.Values,thread._local,_thread._local
# List of module names for which member attributes should not be checked
# (useful for modules/projects where namespaces are manipulated during runtime
# and thus existing member attributes cannot be deduced by static analysis). It
# supports qualified module names, as well as Unix pattern matching.
ignored-modules=
# Show a hint with possible names when a member name was not found. The aspect
# of finding the hint is based on edit distance.
missing-member-hint=yes
# The minimum edit distance a name should have in order to be considered a
# similar match for a missing member name.
missing-member-hint-distance=1
# The total number of similar names that should be taken in consideration when
# showing a hint for a missing member.
missing-member-max-choices=1
# List of decorators that change the signature of a decorated function.
signature-mutators=
[SPELLING]
# Limits count of emitted suggestions for spelling mistakes.
max-spelling-suggestions=4
# Spelling dictionary name. Available dictionaries: none. To make it work,
# install the python-enchant package.
spelling-dict=
# List of comma separated words that should not be checked.
spelling-ignore-words=
# A path to a file that contains the private dictionary; one word per line.
spelling-private-dict-file=
# Tells whether to store unknown words to the private dictionary (see the
# --spelling-private-dict-file option) instead of raising a message.
spelling-store-unknown-words=no
[LOGGING]
# The type of string formatting that logging methods do. `old` means using %
# formatting, `new` is for `{}` formatting.
logging-format-style=old
# Logging modules to check that the string format arguments are in logging
# function parameter format.
logging-modules=logging
[VARIABLES]
# List of additional names supposed to be defined in builtins. Remember that
# you should avoid defining new builtins when possible.
additional-builtins=
# Tells whether unused global variables should be treated as a violation.
allow-global-unused-variables=yes
# List of strings which can identify a callback function by name. A callback
# name must start or end with one of those strings.
callbacks=cb_,
_cb
# A regular expression matching the name of dummy variables (i.e. expected to
# not be used).
dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
# Argument names that match this expression will be ignored. Default to name
# with leading underscore.
ignored-argument-names=_.*|^ignored_|^unused_
# Tells whether we should check for unused import in __init__ files.
init-import=no
# List of qualified module names which can have objects that can redefine
# builtins.
redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
[FORMAT]
# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
expected-line-ending-format=
# Regexp for a line that is allowed to be longer than the limit.
ignore-long-lines=^\s*(# )?<?https?://\S+>?$
# Number of spaces of indent required inside a hanging or continued line.
indent-after-paren=4
# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1
# tab).
indent-string=' '
# Maximum number of characters on a single line.
max-line-length=100
# Maximum number of lines in a module.
max-module-lines=1000
# Allow the body of a class to be on the same line as the declaration if body
# contains single statement.
single-line-class-stmt=no
# Allow the body of an if to be on the same line as the test if there is no
# else.
single-line-if-stmt=no
[STRING]
# This flag controls whether inconsistent-quotes generates a warning when the
# character used as a quote delimiter is used inconsistently within a module.
check-quote-consistency=no
# This flag controls whether the implicit-str-concat should generate a warning
# on implicit string concatenation in sequences defined over several lines.
check-str-concat-over-line-jumps=no
[SIMILARITIES]
# Ignore comments when computing similarities.
ignore-comments=yes
# Ignore docstrings when computing similarities.
ignore-docstrings=yes
# Ignore imports when computing similarities.
ignore-imports=no
# Minimum lines number of a similarity.
min-similarity-lines=4
[MISCELLANEOUS]
# List of note tags to take in consideration, separated by a comma.
notes=FIXME,
XXX,
TODO
# Regular expression of note tags to take in consideration.
#notes-rgx=
[BASIC]
# Naming style matching correct argument names.
argument-naming-style=snake_case
# Regular expression matching correct argument names. Overrides argument-
# naming-style.
#argument-rgx=
# Naming style matching correct attribute names.
attr-naming-style=snake_case
# Regular expression matching correct attribute names. Overrides attr-naming-
# style.
#attr-rgx=
# Bad variable names which should always be refused, separated by a comma.
bad-names=foo,
bar,
baz,
toto,
tutu,
tata
# Bad variable names regexes, separated by a comma. If names match any regex,
# they will always be refused
bad-names-rgxs=
# Naming style matching correct class attribute names.
class-attribute-naming-style=any
# Regular expression matching correct class attribute names. Overrides class-
# attribute-naming-style.
#class-attribute-rgx=
# Naming style matching correct class names.
class-naming-style=PascalCase
# Regular expression matching correct class names. Overrides class-naming-
# style.
#class-rgx=
# Naming style matching correct constant names.
const-naming-style=UPPER_CASE
# Regular expression matching correct constant names. Overrides const-naming-
# style.
#const-rgx=
# Minimum line length for functions/classes that require docstrings, shorter
# ones are exempt.
docstring-min-length=-1
# Naming style matching correct function names.
function-naming-style=snake_case
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a,
b
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# to this list to register other decorators that produce valid properties.
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[DESIGN]
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max-bool-expr=5
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max-locals=15
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max-returns=6
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max-statements=50
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min-public-methods=2
[IMPORTS]
# List of modules that can be imported at any level, not just the top level
# one.
allow-any-import-level=
# Allow wildcard imports from modules that define __all__.
allow-wildcard-with-all=no
# Analyse import fallback blocks. This can be used to support both Python 2 and
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analyse-fallback-blocks=no
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deprecated-modules=optparse,tkinter.tix
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int-import-graph=
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# compatibility libraries.
known-standard-library=
# Force import order to recognize a module as part of a third party library.
known-third-party=enchant
# Couples of modules and preferred modules, separated by a comma.
preferred-modules=
[CLASSES]
# List of method names used to declare (i.e. assign) instance attributes.
defining-attr-methods=__init__,
__new__,
setUp,
__post_init__
# List of member names, which should be excluded from the protected access
# warning.
exclude-protected=_asdict,
_fields,
_replace,
_source,
_make
# List of valid names for the first argument in a class method.
valid-classmethod-first-arg=cls
# List of valid names for the first argument in a metaclass class method.
valid-metaclass-classmethod-first-arg=cls
[EXCEPTIONS]
# Exceptions that will emit a warning when being caught. Defaults to
# "BaseException, Exception".
overgeneral-exceptions=BaseException,
Exception
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cff-version: 1.3.0
message: "If you use this software, please cite it as below."
authors:
- name: "MMPose Contributors"
title: "OpenMMLab Pose Estimation Toolbox and Benchmark"
date-released: 2020-08-31
url: "https://github.com/open-mmlab/mmpose"
license: Apache-2.0
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# Licenses for special algorithms
In this file, we list the algorithms with other licenses instead of Apache 2.0. Users should be careful about adopting these algorithms in any commercial matters.
| Algorithm | Files | License |
| :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------: |
| EDPose | [mmpose/models/heads/transformer_heads/edpose_head.py](https://github.com/open-mmlab/mmpose/blob/main/mmpose/models/heads/transformer_heads/edpose_head.py) | IDEA License 1.0 |
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include requirements/*.txt
include mmpose/.mim/model-index.yml
include mmpose/.mim/dataset-index.yml
recursive-include mmpose/.mim/configs *.py *.yml
recursive-include mmpose/.mim/tools *.py *.sh
recursive-include mmpose/.mim/demo *.py
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# RTMO
本项目的步骤适用于mmpose中的RTMO算法，库中其它姿态估计算法的使用方式以此类推。
## 论文
`RTMO: Towards High-Performance One-Stage Real-Time Multi-Person Pose Estimation`
- https://arxiv.org/pdf/2312.07526.pdf
## 模型结构
为了提高关键点预测速度，无法选择计算量较大的模型，于是，作者选择了ResNet-50以及对GPU计算更为友好的CSPDarknet，同时去掉yolo系列经典模型结构中计算量最大的最大特征图的检测头。
<div align=center>
<img src="./doc/bockbone.png"/>
</div>
## 算法原理
借鉴yolopose的基本思想，先利用onestage方法生成BBox，但此时作者提出了改进思想，不在生成BBox的同时生成Kpt，而是对BBox所在的姿态特征图进行特征变换和维度转换，利用全连接层来控制关键点的个数，对BBox的坐标进行xy分离、位置编码和维度转换操作，再将两者相乘得到特征融合的结果，对此结果的关键点进行有监督训练，经过论文中提出的损失监督，便获得更好的关键点预测结果。
<div align=center>
<img src="./doc/rtmo.png"/>
</div>
## 环境配置
```
mv mmpose_pytorch mmpose # 去框架名后缀
```
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk23.10-py38
# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：229ce1daddf6
docker run -it --shm-size=32G -v $PWD/mmpose:/home/mmpose -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name rtmo <your IMAGE ID> bash
cd mmpose
pip install -r requirements.txt
pip install mmcv-2.0.1+gite2f0eed.abi0.dtk2310.torch1.13-cp38-cp38-manylinux2014_x86_64.whl # dcu版mmcv，可从光合社区下载。
pip install -v -e . # 安装mmpose=1.3.0
# 训练时会自动下载目标检测权重到项目默认指定位置：yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth
```
### Dockerfile（方法二）
```
cd mmpose/docker
docker build --no-cache -t rtmo:latest .
docker run --shm-size=32G --name rtmo -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../mmpose:/home/mmpose -it rtmo bash
# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt。
cd mmpose
pip install mmcv-2.0.1+gite2f0eed.abi0.dtk2310.torch1.13-cp38-cp38-manylinux2014_x86_64.whl # dcu版mmcv，可从光合社区下载。
pip install -v -e . # 安装mmpose=1.3.0
# 训练时会自动下载目标检测权重到项目默认指定位置：yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
- https://developer.hpccube.com/tool/
```
DTK驱动:dtk23.10
python:python3.8
torch:1.13.1
torchvision:0.14.1
apex:0.1
mmcv:2.0.1
# cd mmpose & pip install -v -e . # 安装mmpose=1.3.0
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
2、其它非特殊库参照requirements.txt安装
```
pip install -r requirements.txt
```
## 数据集
`COCO2017`
- http://cocodataset.org/#download
项目中已提供用于试验训练的迷你数据集，[`coco.zip`](./data/coco.zip)解压即可使用，训练数据目录结构如下，用于正常训练的完整数据集请按此目录结构进行制备：
```
data/coco/
├── annotations/
│ ├── person_keypoints_train2017.json
│ ├── person_keypoints_val2017.json
│ └── ...
├── train2017/
│ ├── xxx.png
│ ├── xxx.png
│ └── ...
├── val2017/
│ ├── xxx.png
│ ├── xxx.png
│ └── ...
```
`更多资料可参考源项目的README_origin.md`
## 训练
### 单机多卡
```
cd mmpose
sh train.sh
# if bug: Timeout while waiting for Database: /root/.cache/miopen/2.15.3.0b020ba8a/gfx92678.ukdb. rc=00000005
# then: rm -rf /root/.cache/miopen/2.15.3.0b020ba8a/*
```
## 推理
```
python demo/inferencer_demo.py tests/data/coco/ --pose2d configs/body_2d_keypoint/rtmo/coco/rtmo-s_8xb32-600e_coco-640x640.py --pose2d-weights checkpoint/epoch_xxx.pth --vis-out-dir vis_results # 采用coco2017自己训练的权重推理
# 官方默认权重推理：python demo/inferencer_demo.py tests/data/coco/000000000785.jpg --pose2d rtmo --vis-out-dir vis_results
```
## result
输入人物图片：
<div align=center>
<img src="./doc/000000197388.png"/>
</div>
识别出人物关键点：
<div align=center>
<img src="./doc/000000197388_result.png"/>
</div>
### 精度
测试数据："tests/data/coco/"，测试算法为RTMO-s，其bockbone为CSPDarknet，其中V100的精度为论文中作者公开结果，推理框架：pytorch。
| device | AP | AP50 | AP75 | APM | APL | AR |
|:---------:|:----:|:----:|:----:|:----:|:----:|:----:|
| DCU Z100SM | 67.8 | 87.8 | 73.9 | 61.6 | 77.1 | 71.6 |
| GPU V100 | 66.9 | 88.8 | 73.6 | 61.1 | 75.7 | 70.9 |
## 应用场景
### 算法类别
`姿态估计`
### 热点应用行业
`制造,广媒,能源,医疗,家居,教育`
## 源码仓库及问题反馈
- http://developer.hpccube.com/codes/modelzoo/mmpose-rtmo_pytorch.git
## 参考资料
- https://github.com/open-mmlab/mmpose.git
- https://zhuanlan.zhihu.com/p/649761492
README_CN.md 0 → 100644
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<img src="resources/mmpose-logo.png" width="450"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">OpenMMLab 官网</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
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<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
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[📘文档](https://mmpose.readthedocs.io/zh_CN/latest/) |
[🛠️安装](https://mmpose.readthedocs.io/zh_CN/latest/installation.html) |
[👀模型库](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo.html) |
[📜论文库](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/algorithms.html) |
[🆕更新日志](https://mmpose.readthedocs.io/zh_CN/latest/notes/changelog.html) |
[🤔报告问题](https://github.com/open-mmlab/mmpose/issues/new/choose) |
[🔥RTMPose](/projects/rtmpose/)
</div>
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</div>
## Introduction
[English](./README.md) | 简体中文
MMPose 是一款基于 PyTorch 的姿态分析的开源工具箱，是 [OpenMMLab](https://github.com/open-mmlab) 项目的成员之一。
主分支代码目前支持 **PyTorch 1.8 以上**的版本。
https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4
<details close>
<summary><b>主要特性</b></summary>
- **支持多种人体姿态分析相关任务**
MMPose 支持当前学界广泛关注的主流姿态分析任务：主要包括 2D多人姿态估计、2D手部姿态估计、2D人脸关键点检测、133关键点的全身人体姿态估计、3D人体形状恢复、服饰关键点检测、动物关键点检测等。
具体请参考 [功能演示](demo/docs/zh_cn/)。
- **更高的精度和更快的速度**
MMPose 复现了多种学界最先进的人体姿态分析模型，包括“自顶向下”和“自底向上”两大类算法。MMPose 相比于其他主流的代码库，具有更高的模型精度和训练速度。
具体请参考 [基准测试](docs/en/notes/benchmark.md)（英文）。
- **支持多样的数据集**
MMPose 支持了很多主流数据集的准备和构建，如 COCO、 MPII 等。 具体请参考 [数据集](docs/zh_cn/dataset_zoo)。
- **模块化设计**
MMPose 将统一的人体姿态分析框架解耦成不同的模块组件，通过组合不同的模块组件，用户可以便捷地构建自定义的人体姿态分析模型。
- **详尽的单元测试和文档**
MMPose 提供了详尽的说明文档，API 接口说明，全面的单元测试，以供社区参考。
</details>
## 最新进展
- 发布了单阶段实时多人姿态估计模型 [RTMO](/projects/rtmo)。相比 RTMPose 在多人场景下性能更优
![rtmo](https://github.com/open-mmlab/mmpose/assets/26127467/54d5555a-23e5-4308-89d1-f0c82a6734c2)
- 发布了不同尺寸的 [RTMW](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw_cocktail14.md) 模型，满足不同的使用场景。模型尺寸覆盖从 RTMW-m 到 RTMW-x 的模型，输入图像尺寸包含 256x192 和 384x288
- 支持了 [PoseAnything](/projects/pose_anything) 的推理。[在线试玩](https://openxlab.org.cn/apps/detail/orhir/Pose-Anything)
- 我们支持了两个新的数据集:
- (CVPR 2023) [ExLPose](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#exlpose-dataset)
- (ICCV 2023) [H3WB](/docs/en/dataset_zoo/3d_wholebody_keypoint.md)
- 欢迎使用 [*MMPose 项目*](/projects/README.md)。在这里，您可以发现 MMPose 中的最新功能和算法，并且可以通过最快的方式与社区分享自己的创意和代码实现。向 MMPose 中添加新功能从此变得简单丝滑：
- 提供了一种简单迅捷的方式，将新的算法、功能和应用添加到 MMPose 中
- 更灵活的代码结构和风格，更少的限制，更简短的代码审核流程
- 通过独立项目的形式，利用 MMPose 的强大功能，同时不被代码框架所束缚
- 最新添加的项目包括：
- [RTMPose](/projects/rtmpose/)
- [RTMO](/projects/rtmo/)
- [PoseAnything](/projects/pose_anything/)
- [YOLOX-Pose](/projects/yolox_pose/)
- [MMPose4AIGC](/projects/mmpose4aigc/)
- [Simple Keypoints](/projects/skps/)
- [Just Dance](/projects/just_dance/)
- [Uniformer](/projects/uniformer/)
- 从简单的 [示例项目](/projects/example_project/) 开启您的 MMPose 代码贡献者之旅吧，让我们共同打造更好用的 MMPose！
<br/>
- 2024-01-04：MMPose [v1.3.0](https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0) 正式发布了，主要更新包括:
- 支持新数据集：ExLPose、H3WB
- 发布 RTMPose 系列新模型：RTMO、RTMW
- 支持新算法 PoseAnything
- 推理器 Inferencer 支持可选的进度条、提升与单阶段模型的适配性
请查看完整的 [版本说明](https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0) 以了解更多 MMPose v1.3.0 带来的更新!
## 0.x / 1.x 迁移
MMPose v1.0.0 是一个重大更新，包括了大量的 API 和配置文件的变化。目前 v1.0.0 中已经完成了一部分算法的迁移工作，剩余的算法将在后续的版本中陆续完成，我们将在这个 [Issue 页面](https://github.com/open-mmlab/mmpose/issues/2258) 中展示迁移进度。
如果您使用的算法还没有完成迁移，您也可以继续使用访问 [0.x 分支](https://github.com/open-mmlab/mmpose/tree/0.x) 和 [旧版文档](https://mmpose.readthedocs.io/zh_CN/0.x/)
## 安装
关于安装的详细说明请参考[安装文档](https://mmpose.readthedocs.io/zh_CN/latest/installation.html)。
## 教程
我们提供了一系列简明的教程，帮助 MMPose 的新用户轻松上手使用：
1. MMPose 的基本使用方法：
- [20 分钟上手教程](https://mmpose.readthedocs.io/zh_CN/latest/guide_to_framework.html)
- [Demos](https://mmpose.readthedocs.io/zh_CN/latest/demos.html)
- [模型推理](https://mmpose.readthedocs.io/zh_CN/latest/user_guides/inference.html)
- [配置文件](https://mmpose.readthedocs.io/zh_CN/latest/user_guides/configs.html)
- [准备数据集](https://mmpose.readthedocs.io/zh_CN/latest/user_guides/prepare_datasets.html)
- [训练与测试](https://mmpose.readthedocs.io/zh_CN/latest/user_guides/train_and_test.html)
- [模型部署](https://mmpose.readthedocs.io/zh_CN/latest/user_guides/how_to_deploy.html)
- [模型分析工具](https://mmpose.readthedocs.io/zh_CN/latest/user_guides/model_analysis.html)
- [数据集标注与预处理脚本](https://mmpose.readthedocs.io/zh_CN/latest/user_guides/dataset_tools.html)
2. 对于希望基于 MMPose 进行开发的研究者和开发者：
- [编解码器](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/codecs.html)
- [数据流](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/dataflow.html)
- [实现新模型](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/implement_new_models.html)
- [自定义数据集](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/customize_datasets.html)
- [自定义数据变换](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/customize_transforms.html)
- [自定义指标](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/customize_evaluation.html)
- [自定义优化器](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/customize_optimizer.html)
- [自定义日志](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/customize_logging.html)
- [迁移指南](https://mmpose.readthedocs.io/zh_CN/latest/migration.html)
3. 对于希望加入开源社区，向 MMPose 贡献代码的研究者和开发者：
- [参与贡献代码](https://mmpose.readthedocs.io/zh_CN/latest/contribution_guide.html)
4. 对于使用过程中的常见问题：
- [FAQ](https://mmpose.readthedocs.io/zh_CN/latest/faq.html)
## 模型库
各个模型的结果和设置都可以在对应的 config（配置）目录下的 **README.md** 中查看。
整体的概况也可也在 [模型库](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo.html) 页面中查看。
<details open>
<summary><b>支持的算法</b></summary>
- [x] [DeepPose](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/algorithms.html#deeppose-cvpr-2014) (CVPR'2014)
- [x] [CPM](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#cpm-cvpr-2016) (CVPR'2016)
- [x] [Hourglass](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#hourglass-eccv-2016) (ECCV'2016)
- [x] [SimpleBaseline3D](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/algorithms.html#simplebaseline3d-iccv-2017) (ICCV'2017)
- [ ] [Associative Embedding](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/algorithms.html#associative-embedding-nips-2017) (NeurIPS'2017)
- [x] [SimpleBaseline2D](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/algorithms.html#simplebaseline2d-eccv-2018) (ECCV'2018)
- [x] [DSNT](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/algorithms.html#dsnt-2018) (ArXiv'2021)
- [x] [HRNet](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#hrnet-cvpr-2019) (CVPR'2019)
- [x] [IPR](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/algorithms.html#ipr-eccv-2018) (ECCV'2018)
- [x] [VideoPose3D](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/algorithms.html#videopose3d-cvpr-2019) (CVPR'2019)
- [x] [HRNetv2](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#hrnetv2-tpami-2019) (TPAMI'2019)
- [x] [MSPN](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#mspn-arxiv-2019) (ArXiv'2019)
- [x] [SCNet](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#scnet-cvpr-2020) (CVPR'2020)
- [ ] [HigherHRNet](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#higherhrnet-cvpr-2020) (CVPR'2020)
- [x] [RSN](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#rsn-eccv-2020) (ECCV'2020)
- [x] [InterNet](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/algorithms.html#internet-eccv-2020) (ECCV'2020)
- [ ] [VoxelPose](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/algorithms.html#voxelpose-eccv-2020) (ECCV'2020)
- [x] [LiteHRNet](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#litehrnet-cvpr-2021) (CVPR'2021)
- [x] [ViPNAS](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#vipnas-cvpr-2021) (CVPR'2021)
- [x] [Debias-IPR](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/algorithms.html#debias-ipr-iccv-2021) (ICCV'2021)
- [x] [SimCC](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/algorithms.html#simcc-eccv-2022) (ECCV'2022)
</details>
<details open>
<summary><b>支持的技术</b></summary>
- [x] [FPN](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/techniques.html#fpn-cvpr-2017) (CVPR'2017)
- [x] [FP16](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/techniques.html#fp16-arxiv-2017) (ArXiv'2017)
- [x] [Wingloss](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/techniques.html#wingloss-cvpr-2018) (CVPR'2018)
- [x] [AdaptiveWingloss](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/techniques.html#adaptivewingloss-iccv-2019) (ICCV'2019)
- [x] [DarkPose](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/techniques.html#darkpose-cvpr-2020) (CVPR'2020)
- [x] [UDP](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/techniques.html#udp-cvpr-2020) (CVPR'2020)
- [x] [Albumentations](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/techniques.html#albumentations-information-2020) (Information'2020)
- [x] [SoftWingloss](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/techniques.html#softwingloss-tip-2021) (TIP'2021)
- [x] [RLE](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/techniques.html#rle-iccv-2021) (ICCV'2021)
</details>
<details open>
<summary><b>支持的数据集</b></summary>
- [x] [AFLW](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#aflw-iccvw-2011) \[[主页](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/)\] (ICCVW'2011)
- [x] [sub-JHMDB](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#jhmdb-iccv-2013) \[[主页](http://jhmdb.is.tue.mpg.de/dataset)\] (ICCV'2013)
- [x] [COFW](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#cofw-iccv-2013) \[[主页](http://www.vision.caltech.edu/xpburgos/ICCV13/)\] (ICCV'2013)
- [x] [MPII](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#mpii-cvpr-2014) \[[主页](http://human-pose.mpi-inf.mpg.de/)\] (CVPR'2014)
- [x] [Human3.6M](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#human3-6m-tpami-2014) \[[主页](http://vision.imar.ro/human3.6m/description.php)\] (TPAMI'2014)
- [x] [COCO](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#coco-eccv-2014) \[[主页](http://cocodataset.org/)\] (ECCV'2014)
- [x] [CMU Panoptic](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#cmu-panoptic-iccv-2015) (ICCV'2015)
- [x] [DeepFashion](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#deepfashion-cvpr-2016) \[[主页](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html)\] (CVPR'2016)
- [x] [300W](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#300w-imavis-2016) \[[主页](https://ibug.doc.ic.ac.uk/resources/300-W/)\] (IMAVIS'2016)
- [x] [RHD](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#rhd-iccv-2017) \[[主页](https://lmb.informatik.uni-freiburg.de/resources/datasets/RenderedHandposeDataset.en.html)\] (ICCV'2017)
- [x] [CMU Panoptic](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#cmu-panoptic-iccv-2015) \[[主页](http://domedb.perception.cs.cmu.edu/)\] (ICCV'2015)
- [x] [AI Challenger](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#ai-challenger-arxiv-2017) \[[主页](https://github.com/AIChallenger/AI_Challenger_2017)\] (ArXiv'2017)
- [x] [MHP](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#mhp-acm-mm-2018) \[[主页](https://lv-mhp.github.io/dataset)\] (ACM MM'2018)
- [x] [WFLW](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#wflw-cvpr-2018) \[[主页](https://wywu.github.io/projects/LAB/WFLW.html)\] (CVPR'2018)
- [x] [PoseTrack18](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#posetrack18-cvpr-2018) \[[主页](https://posetrack.net/users/download.php)\] (CVPR'2018)
- [x] [OCHuman](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#ochuman-cvpr-2019) \[[主页](https://github.com/liruilong940607/OCHumanApi)\] (CVPR'2019)
- [x] [CrowdPose](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#crowdpose-cvpr-2019) \[[主页](https://github.com/Jeff-sjtu/CrowdPose)\] (CVPR'2019)
- [x] [MPII-TRB](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#mpii-trb-iccv-2019) \[[主页](https://github.com/kennymckormick/Triplet-Representation-of-human-Body)\] (ICCV'2019)
- [x] [FreiHand](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#freihand-iccv-2019) \[[主页](https://lmb.informatik.uni-freiburg.de/projects/freihand/)\] (ICCV'2019)
- [x] [Animal-Pose](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#animal-pose-iccv-2019) \[[主页](https://sites.google.com/view/animal-pose/)\] (ICCV'2019)
- [x] [OneHand10K](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#onehand10k-tcsvt-2019) \[[主页](https://www.yangangwang.com/papers/WANG-MCC-2018-10.html)\] (TCSVT'2019)
- [x] [Vinegar Fly](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#vinegar-fly-nature-methods-2019) \[[主页](https://github.com/jgraving/DeepPoseKit-Data)\] (Nature Methods'2019)
- [x] [Desert Locust](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#desert-locust-elife-2019) \[[主页](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019)
- [x] [Grévy’s Zebra](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#grevys-zebra-elife-2019) \[[主页](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019)
- [x] [ATRW](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#atrw-acm-mm-2020) \[[主页](https://cvwc2019.github.io/challenge.html)\] (ACM MM'2020)
- [x] [Halpe](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#halpe-cvpr-2020) \[[主页](https://github.com/Fang-Haoshu/Halpe-FullBody/)\] (CVPR'2020)
- [x] [COCO-WholeBody](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#coco-wholebody-eccv-2020) \[[主页](https://github.com/jin-s13/COCO-WholeBody/)\] (ECCV'2020)
- [x] [MacaquePose](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#macaquepose-biorxiv-2020) \[[主页](http://www.pri.kyoto-u.ac.jp/datasets/macaquepose/index.html)\] (bioRxiv'2020)
- [x] [InterHand2.6M](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#interhand2-6m-eccv-2020) \[[主页](https://mks0601.github.io/InterHand2.6M/)\] (ECCV'2020)
- [x] [AP-10K](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#ap-10k-neurips-2021) \[[主页](https://github.com/AlexTheBad/AP-10K)\] (NeurIPS'2021)
- [x] [Horse-10](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#horse-10-wacv-2021) \[[主页](http://www.mackenziemathislab.org/horse10)\] (WACV'2021)
- [x] [Human-Art](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#human-art-cvpr-2023) \[[主页](https://idea-research.github.io/HumanArt/)\] (CVPR'2023)
- [x] [LaPa](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#lapa-aaai-2020) \[[主页](https://github.com/JDAI-CV/lapa-dataset)\] (AAAI'2020)
- [x] [UBody](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#ubody-cvpr-2023) \[[主页](https://github.com/IDEA-Research/OSX)\] (CVPR'2023)
</details>
<details open>
<summary><b>支持的骨干网络</b></summary>
- [x] [AlexNet](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#alexnet-neurips-2012) (NeurIPS'2012)
- [x] [VGG](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#vgg-iclr-2015) (ICLR'2015)
- [x] [ResNet](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#resnet-cvpr-2016) (CVPR'2016)
- [x] [ResNext](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#resnext-cvpr-2017) (CVPR'2017)
- [x] [SEResNet](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#seresnet-cvpr-2018) (CVPR'2018)
- [x] [ShufflenetV1](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#shufflenetv1-cvpr-2018) (CVPR'2018)
- [x] [ShufflenetV2](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#shufflenetv2-eccv-2018) (ECCV'2018)
- [x] [MobilenetV2](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#mobilenetv2-cvpr-2018) (CVPR'2018)
- [x] [ResNetV1D](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#resnetv1d-cvpr-2019) (CVPR'2019)
- [x] [ResNeSt](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#resnest-arxiv-2020) (ArXiv'2020)
- [x] [Swin](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#swin-cvpr-2021) (CVPR'2021)
- [x] [HRFormer](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#hrformer-nips-2021) (NIPS'2021)
- [x] [PVT](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#pvt-iccv-2021) (ICCV'2021)
- [x] [PVTV2](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/backbones.html#pvtv2-cvmj-2022) (CVMJ'2022)
</details>
### 模型需求
我们将跟进学界的最新进展，并支持更多算法和框架。如果您对 MMPose 有任何功能需求，请随时在 [MMPose Roadmap](https://github.com/open-mmlab/mmpose/issues/2258) 中留言。
## 参与贡献
我们非常欢迎用户对于 MMPose 做出的任何贡献，可以参考 [贡献指南](https://mmpose.readthedocs.io/zh_CN/latest/contribution_guide.html) 文件了解更多细节。
## 致谢
MMPose 是一款由不同学校和公司共同贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。
我们希望该工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现现有算法并开发自己的新模型，从而不断为开源社区提供贡献。
## 引用
如果您觉得 MMPose 对您的研究有所帮助，请考虑引用它：
```bibtex
@misc{mmpose2020,
title={OpenMMLab Pose Estimation Toolbox and Benchmark},
author={MMPose Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmpose}},
year={2020}
}
```
## 许可证
该项目采用 [Apache 2.0 license](LICENSE) 开源协议。
## OpenMMLab的其他项目
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab 深度学习预训练工具箱
- [MMagic](https://github.com/open-mmlab/mmagic): OpenMMLab 新一代人工智能内容生成（AIGC）工具箱
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
- [MIM](https://github.com/open-mmlab/mim): OpenMMlab 项目、算法、模型的统一入口
- [Playground](https://github.com/open-mmlab/playground): 收集和展示 OpenMMLab 相关的前沿、有趣的社区项目
## 欢迎加入 OpenMMLab 社区
扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，扫描下方微信二维码添加喵喵好友，进入 MMPose 微信交流社群。【加好友申请格式：研究方向+地区+学校/公司+姓名】
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我们会在 OpenMMLab 社区为大家
- 📢 分享 AI 框架的前沿核心技术
- 💻 解读 PyTorch 常用模块源码
- 📰 发布 OpenMMLab 的相关新闻
- 🚀 介绍 OpenMMLab 开发的前沿算法
- 🏃 获取更高效的问题答疑和意见反馈
- 🔥 提供与各行各业开发者充分交流的平台
干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
README_origin.md 0 → 100644
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<sup>
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[👀Model Zoo](https://mmpose.readthedocs.io/en/latest/model_zoo.html) |
[📜Papers](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html) |
[🆕Update News](https://mmpose.readthedocs.io/en/latest/notes/changelog.html) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmpose/issues/new/choose) |
[🔥RTMPose](/projects/rtmpose/)
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</div>
## Introduction
English | [简体中文](README_CN.md)
MMPose is an open-source toolbox for pose estimation based on PyTorch.
It is a part of the [OpenMMLab project](https://github.com/open-mmlab).
The main branch works with **PyTorch 1.8+**.
https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4
<br/>
<details close>
<summary><b>Major Features</b></summary>
- **Support diverse tasks**
We support a wide spectrum of mainstream pose analysis tasks in current research community, including 2d multi-person human pose estimation, 2d hand pose estimation, 2d face landmark detection, 133 keypoint whole-body human pose estimation, 3d human mesh recovery, fashion landmark detection and animal pose estimation.
See [Demo](demo/docs/en) for more information.
- **Higher efficiency and higher accuracy**
MMPose implements multiple state-of-the-art (SOTA) deep learning models, including both top-down & bottom-up approaches. We achieve faster training speed and higher accuracy than other popular codebases, such as [HRNet](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch).
See [benchmark.md](docs/en/notes/benchmark.md) for more information.
- **Support for various datasets**
The toolbox directly supports multiple popular and representative datasets, COCO, AIC, MPII, MPII-TRB, OCHuman etc.
See [dataset_zoo](docs/en/dataset_zoo) for more information.
- **Well designed, tested and documented**
We decompose MMPose into different components and one can easily construct a customized
pose estimation framework by combining different modules.
We provide detailed documentation and API reference, as well as unittests.
</details>
## What's New
- Release [RTMO](/projects/rtmo), a state-of-the-art real-time method for multi-person pose estimation.
![rtmo](https://github.com/open-mmlab/mmpose/assets/26127467/54d5555a-23e5-4308-89d1-f0c82a6734c2)
- Release [RTMW](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw_cocktail14.md) models in various sizes ranging from RTMW-m to RTMW-x. The input sizes include `256x192` and `384x288`. This provides flexibility to select the right model for different speed and accuracy requirements.
- Support inference of [PoseAnything](/projects/pose_anything). Web demo is available [here](https://openxlab.org.cn/apps/detail/orhir/Pose-Anything).
- Support for two new datasets:
- (CVPR 2023) [ExLPose](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#exlpose-dataset)
- (ICCV 2023) [H3WB](/docs/en/dataset_zoo/3d_wholebody_keypoint.md)
- Welcome to use the [*MMPose project*](/projects/README.md). Here, you can discover the latest features and algorithms in MMPose and quickly share your ideas and code implementations with the community. Adding new features to MMPose has become smoother:
- Provides a simple and fast way to add new algorithms, features, and applications to MMPose.
- More flexible code structure and style, fewer restrictions, and a shorter code review process.
- Utilize the powerful capabilities of MMPose in the form of independent projects without being constrained by the code framework.
- Newly added projects include:
- [RTMPose](/projects/rtmpose/)
- [RTMO](/projects/rtmo/)
- [PoseAnything](/projects/pose_anything/)
- [YOLOX-Pose](/projects/yolox_pose/)
- [MMPose4AIGC](/projects/mmpose4aigc/)
- [Simple Keypoints](/projects/skps/)
- [Just Dance](/projects/just_dance/)
- [Uniformer](/projects/uniformer/)
- Start your journey as an MMPose contributor with a simple [example project](/projects/example_project/), and let's build a better MMPose together!
<br/>
- January 4, 2024: MMPose [v1.3.0](https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0) has been officially released, with major updates including:
- Support for new datasets: ExLPose, H3WB
- Release of new RTMPose series models: RTMO, RTMW
- Support for new algorithm PoseAnything
- Enhanced Inferencer with optional progress bar and improved affinity for one-stage methods
Please check the complete [release notes](https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0) for more details on the updates brought by MMPose v1.3.0!
## 0.x / 1.x Migration
MMPose v1.0.0 is a major update, including many API and config file changes. Currently, a part of the algorithms have been migrated to v1.0.0, and the remaining algorithms will be completed in subsequent versions. We will show the migration progress in this [Roadmap](https://github.com/open-mmlab/mmpose/issues/2258).
If your algorithm has not been migrated, you can continue to use the [0.x branch](https://github.com/open-mmlab/mmpose/tree/0.x) and [old documentation](https://mmpose.readthedocs.io/en/0.x/).
## Installation
Please refer to [installation.md](https://mmpose.readthedocs.io/en/latest/installation.html) for more detailed installation and dataset preparation.
## Getting Started
We provided a series of tutorials about the basic usage of MMPose for new users:
1. For the basic usage of MMPose:
- [A 20-minute Tour to MMPose](https://mmpose.readthedocs.io/en/latest/guide_to_framework.html)
- [Demos](https://mmpose.readthedocs.io/en/latest/demos.html)
- [Inference](https://mmpose.readthedocs.io/en/latest/user_guides/inference.html)
- [Configs](https://mmpose.readthedocs.io/en/latest/user_guides/configs.html)
- [Prepare Datasets](https://mmpose.readthedocs.io/en/latest/user_guides/prepare_datasets.html)
- [Train and Test](https://mmpose.readthedocs.io/en/latest/user_guides/train_and_test.html)
- [Deployment](https://mmpose.readthedocs.io/en/latest/user_guides/how_to_deploy.html)
- [Model Analysis](https://mmpose.readthedocs.io/en/latest/user_guides/model_analysis.html)
- [Dataset Annotation and Preprocessing](https://mmpose.readthedocs.io/en/latest/user_guides/dataset_tools.html)
2. For developers who wish to develop based on MMPose:
- [Learn about Codecs](https://mmpose.readthedocs.io/en/latest/advanced_guides/codecs.html)
- [Dataflow in MMPose](https://mmpose.readthedocs.io/en/latest/advanced_guides/dataflow.html)
- [Implement New Models](https://mmpose.readthedocs.io/en/latest/advanced_guides/implement_new_models.html)
- [Customize Datasets](https://mmpose.readthedocs.io/en/latest/advanced_guides/customize_datasets.html)
- [Customize Data Transforms](https://mmpose.readthedocs.io/en/latest/advanced_guides/customize_transforms.html)
- [Customize Evaluation](https://mmpose.readthedocs.io/en/latest/advanced_guides/customize_evaluation.html)
- [Customize Optimizer](https://mmpose.readthedocs.io/en/latest/advanced_guides/customize_optimizer.html)
- [Customize Logging](https://mmpose.readthedocs.io/en/latest/advanced_guides/customize_logging.html)
- [How to Deploy](https://mmpose.readthedocs.io/en/latest/user_guides/how_to_deploy.html)
- [Model Analysis](https://mmpose.readthedocs.io/en/latest/user_guides/model_analysis.html)
- [Migration Guide](https://mmpose.readthedocs.io/en/latest/migration.html)
3. For researchers and developers who are willing to contribute to MMPose:
- [Contribution Guide](https://mmpose.readthedocs.io/en/latest/contribution_guide.html)
4. For some common issues, we provide a FAQ list:
- [FAQ](https://mmpose.readthedocs.io/en/latest/faq.html)
## Model Zoo
Results and models are available in the **README.md** of each method's config directory.
A summary can be found in the [Model Zoo](https://mmpose.readthedocs.io/en/latest/model_zoo.html) page.
<details open>
<summary><b>Supported algorithms:</b></summary>
- [x] [DeepPose](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#deeppose-cvpr-2014) (CVPR'2014)
- [x] [CPM](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#cpm-cvpr-2016) (CVPR'2016)
- [x] [Hourglass](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#hourglass-eccv-2016) (ECCV'2016)
- [x] [SimpleBaseline3D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#simplebaseline3d-iccv-2017) (ICCV'2017)
- [ ] [Associative Embedding](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#associative-embedding-nips-2017) (NeurIPS'2017)
- [x] [SimpleBaseline2D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#simplebaseline2d-eccv-2018) (ECCV'2018)
- [x] [DSNT](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#dsnt-2018) (ArXiv'2021)
- [x] [HRNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#hrnet-cvpr-2019) (CVPR'2019)
- [x] [IPR](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#ipr-eccv-2018) (ECCV'2018)
- [x] [VideoPose3D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#videopose3d-cvpr-2019) (CVPR'2019)
- [x] [HRNetv2](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#hrnetv2-tpami-2019) (TPAMI'2019)
- [x] [MSPN](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#mspn-arxiv-2019) (ArXiv'2019)
- [x] [SCNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#scnet-cvpr-2020) (CVPR'2020)
- [ ] [HigherHRNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#higherhrnet-cvpr-2020) (CVPR'2020)
- [x] [RSN](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#rsn-eccv-2020) (ECCV'2020)
- [x] [InterNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#internet-eccv-2020) (ECCV'2020)
- [ ] [VoxelPose](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#voxelpose-eccv-2020) (ECCV'2020)
- [x] [LiteHRNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#litehrnet-cvpr-2021) (CVPR'2021)
- [x] [ViPNAS](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#vipnas-cvpr-2021) (CVPR'2021)
- [x] [Debias-IPR](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#debias-ipr-iccv-2021) (ICCV'2021)
- [x] [SimCC](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/algorithms.html#simcc-eccv-2022) (ECCV'2022)
</details>
<details open>
<summary><b>Supported techniques:</b></summary>
- [x] [FPN](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#fpn-cvpr-2017) (CVPR'2017)
- [x] [FP16](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#fp16-arxiv-2017) (ArXiv'2017)
- [x] [Wingloss](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#wingloss-cvpr-2018) (CVPR'2018)
- [x] [AdaptiveWingloss](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#adaptivewingloss-iccv-2019) (ICCV'2019)
- [x] [DarkPose](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#darkpose-cvpr-2020) (CVPR'2020)
- [x] [UDP](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#udp-cvpr-2020) (CVPR'2020)
- [x] [Albumentations](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#albumentations-information-2020) (Information'2020)
- [x] [SoftWingloss](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#softwingloss-tip-2021) (TIP'2021)
- [x] [RLE](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#rle-iccv-2021) (ICCV'2021)
</details>
<details open>
<summary><b>Supported datasets:</b></summary>
- [x] [AFLW](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#aflw-iccvw-2011) \[[homepage](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/)\] (ICCVW'2011)
- [x] [sub-JHMDB](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#jhmdb-iccv-2013) \[[homepage](http://jhmdb.is.tue.mpg.de/dataset)\] (ICCV'2013)
- [x] [COFW](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#cofw-iccv-2013) \[[homepage](http://www.vision.caltech.edu/xpburgos/ICCV13/)\] (ICCV'2013)
- [x] [MPII](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#mpii-cvpr-2014) \[[homepage](http://human-pose.mpi-inf.mpg.de/)\] (CVPR'2014)
- [x] [Human3.6M](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#human3-6m-tpami-2014) \[[homepage](http://vision.imar.ro/human3.6m/description.php)\] (TPAMI'2014)
- [x] [COCO](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#coco-eccv-2014) \[[homepage](http://cocodataset.org/)\] (ECCV'2014)
- [x] [CMU Panoptic](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#cmu-panoptic-iccv-2015) \[[homepage](http://domedb.perception.cs.cmu.edu/)\] (ICCV'2015)
- [x] [DeepFashion](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#deepfashion-cvpr-2016) \[[homepage](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html)\] (CVPR'2016)
- [x] [300W](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#300w-imavis-2016) \[[homepage](https://ibug.doc.ic.ac.uk/resources/300-W/)\] (IMAVIS'2016)
- [x] [RHD](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#rhd-iccv-2017) \[[homepage](https://lmb.informatik.uni-freiburg.de/resources/datasets/RenderedHandposeDataset.en.html)\] (ICCV'2017)
- [x] [CMU Panoptic HandDB](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#cmu-panoptic-handdb-cvpr-2017) \[[homepage](http://domedb.perception.cs.cmu.edu/handdb.html)\] (CVPR'2017)
- [x] [AI Challenger](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#ai-challenger-arxiv-2017) \[[homepage](https://github.com/AIChallenger/AI_Challenger_2017)\] (ArXiv'2017)
- [x] [MHP](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#mhp-acm-mm-2018) \[[homepage](https://lv-mhp.github.io/dataset)\] (ACM MM'2018)
- [x] [WFLW](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#wflw-cvpr-2018) \[[homepage](https://wywu.github.io/projects/LAB/WFLW.html)\] (CVPR'2018)
- [x] [PoseTrack18](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#posetrack18-cvpr-2018) \[[homepage](https://posetrack.net/users/download.php)\] (CVPR'2018)
- [x] [OCHuman](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#ochuman-cvpr-2019) \[[homepage](https://github.com/liruilong940607/OCHumanApi)\] (CVPR'2019)
- [x] [CrowdPose](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#crowdpose-cvpr-2019) \[[homepage](https://github.com/Jeff-sjtu/CrowdPose)\] (CVPR'2019)
- [x] [MPII-TRB](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#mpii-trb-iccv-2019) \[[homepage](https://github.com/kennymckormick/Triplet-Representation-of-human-Body)\] (ICCV'2019)
- [x] [FreiHand](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#freihand-iccv-2019) \[[homepage](https://lmb.informatik.uni-freiburg.de/projects/freihand/)\] (ICCV'2019)
- [x] [Animal-Pose](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#animal-pose-iccv-2019) \[[homepage](https://sites.google.com/view/animal-pose/)\] (ICCV'2019)
- [x] [OneHand10K](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#onehand10k-tcsvt-2019) \[[homepage](https://www.yangangwang.com/papers/WANG-MCC-2018-10.html)\] (TCSVT'2019)
- [x] [Vinegar Fly](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#vinegar-fly-nature-methods-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Nature Methods'2019)
- [x] [Desert Locust](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#desert-locust-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019)
- [x] [Grévy’s Zebra](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#grevys-zebra-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019)
- [x] [ATRW](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#atrw-acm-mm-2020) \[[homepage](https://cvwc2019.github.io/challenge.html)\] (ACM MM'2020)
- [x] [Halpe](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#halpe-cvpr-2020) \[[homepage](https://github.com/Fang-Haoshu/Halpe-FullBody/)\] (CVPR'2020)
- [x] [COCO-WholeBody](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#coco-wholebody-eccv-2020) \[[homepage](https://github.com/jin-s13/COCO-WholeBody/)\] (ECCV'2020)
- [x] [MacaquePose](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#macaquepose-biorxiv-2020) \[[homepage](http://www.pri.kyoto-u.ac.jp/datasets/macaquepose/index.html)\] (bioRxiv'2020)
- [x] [InterHand2.6M](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#interhand2-6m-eccv-2020) \[[homepage](https://mks0601.github.io/InterHand2.6M/)\] (ECCV'2020)
- [x] [AP-10K](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#ap-10k-neurips-2021) \[[homepage](https://github.com/AlexTheBad/AP-10K)\] (NeurIPS'2021)
- [x] [Horse-10](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#horse-10-wacv-2021) \[[homepage](http://www.mackenziemathislab.org/horse10)\] (WACV'2021)
- [x] [Human-Art](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#human-art-cvpr-2023) \[[homepage](https://idea-research.github.io/HumanArt/)\] (CVPR'2023)
- [x] [LaPa](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#lapa-aaai-2020) \[[homepage](https://github.com/JDAI-CV/lapa-dataset)\] (AAAI'2020)
- [x] [UBody](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#ubody-cvpr-2023) \[[homepage](https://github.com/IDEA-Research/OSX)\] (CVPR'2023)
</details>
<details open>
<summary><b>Supported backbones:</b></summary>
- [x] [AlexNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#alexnet-neurips-2012) (NeurIPS'2012)
- [x] [VGG](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#vgg-iclr-2015) (ICLR'2015)
- [x] [ResNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#resnet-cvpr-2016) (CVPR'2016)
- [x] [ResNext](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#resnext-cvpr-2017) (CVPR'2017)
- [x] [SEResNet](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#seresnet-cvpr-2018) (CVPR'2018)
- [x] [ShufflenetV1](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#shufflenetv1-cvpr-2018) (CVPR'2018)
- [x] [ShufflenetV2](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#shufflenetv2-eccv-2018) (ECCV'2018)
- [x] [MobilenetV2](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#mobilenetv2-cvpr-2018) (CVPR'2018)
- [x] [ResNetV1D](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#resnetv1d-cvpr-2019) (CVPR'2019)
- [x] [ResNeSt](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#resnest-arxiv-2020) (ArXiv'2020)
- [x] [Swin](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#swin-cvpr-2021) (CVPR'2021)
- [x] [HRFormer](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#hrformer-nips-2021) (NIPS'2021)
- [x] [PVT](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#pvt-iccv-2021) (ICCV'2021)
- [x] [PVTV2](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/backbones.html#pvtv2-cvmj-2022) (CVMJ'2022)
</details>
### Model Request
We will keep up with the latest progress of the community, and support more popular algorithms and frameworks. If you have any feature requests, please feel free to leave a comment in [MMPose Roadmap](https://github.com/open-mmlab/mmpose/issues/2258).
## Contributing
We appreciate all contributions to improve MMPose. Please refer to [CONTRIBUTING.md](https://mmpose.readthedocs.io/en/latest/contribution_guide.html) for the contributing guideline.
## Acknowledgement
MMPose is an open source project that is contributed by researchers and engineers from various colleges and companies.
We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new models.
## Citation
If you find this project useful in your research, please consider cite:
```bibtex
@misc{mmpose2020,
title={OpenMMLab Pose Estimation Toolbox and Benchmark},
author={MMPose Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmpose}},
year={2020}
}
```
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Projects in OpenMMLab
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark.
- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab Model Deployment Framework.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.
configs/_base_/datasets/300w.py 0 → 100644
View file @ ca8a762a
dataset_info = dict(
dataset_name='300w',
paper_info=dict(
author='Sagonas, Christos and Antonakos, Epameinondas '
'and Tzimiropoulos, Georgios and Zafeiriou, Stefanos '
'and Pantic, Maja',
title='300 faces in-the-wild challenge: '
'Database and results',
container='Image and vision computing',
year='2016',
homepage='https://ibug.doc.ic.ac.uk/resources/300-W/',
),
keypoint_info={
0: dict(name='kpt-0', id=0, color=[255, 0, 0], type='', swap='kpt-16'),
1: dict(name='kpt-1', id=1, color=[255, 0, 0], type='', swap='kpt-15'),
2: dict(name='kpt-2', id=2, color=[255, 0, 0], type='', swap='kpt-14'),
3: dict(name='kpt-3', id=3, color=[255, 0, 0], type='', swap='kpt-13'),
4: dict(name='kpt-4', id=4, color=[255, 0, 0], type='', swap='kpt-12'),
5: dict(name='kpt-5', id=5, color=[255, 0, 0], type='', swap='kpt-11'),
6: dict(name='kpt-6', id=6, color=[255, 0, 0], type='', swap='kpt-10'),
7: dict(name='kpt-7', id=7, color=[255, 0, 0], type='', swap='kpt-9'),
8: dict(name='kpt-8', id=8, color=[255, 0, 0], type='', swap=''),
9: dict(name='kpt-9', id=9, color=[255, 0, 0], type='', swap='kpt-7'),
10:
dict(name='kpt-10', id=10, color=[255, 0, 0], type='', swap='kpt-6'),
11:
dict(name='kpt-11', id=11, color=[255, 0, 0], type='', swap='kpt-5'),
12:
dict(name='kpt-12', id=12, color=[255, 0, 0], type='', swap='kpt-4'),
13:
dict(name='kpt-13', id=13, color=[255, 0, 0], type='', swap='kpt-3'),
14:
dict(name='kpt-14', id=14, color=[255, 0, 0], type='', swap='kpt-2'),
15:
dict(name='kpt-15', id=15, color=[255, 0, 0], type='', swap='kpt-1'),
16:
dict(name='kpt-16', id=16, color=[255, 0, 0], type='', swap='kpt-0'),
17:
dict(name='kpt-17', id=17, color=[255, 0, 0], type='', swap='kpt-26'),
18:
dict(name='kpt-18', id=18, color=[255, 0, 0], type='', swap='kpt-25'),
19:
dict(name='kpt-19', id=19, color=[255, 0, 0], type='', swap='kpt-24'),
20:
dict(name='kpt-20', id=20, color=[255, 0, 0], type='', swap='kpt-23'),
21:
dict(name='kpt-21', id=21, color=[255, 0, 0], type='', swap='kpt-22'),
22:
dict(name='kpt-22', id=22, color=[255, 0, 0], type='', swap='kpt-21'),
23:
dict(name='kpt-23', id=23, color=[255, 0, 0], type='', swap='kpt-20'),
24:
dict(name='kpt-24', id=24, color=[255, 0, 0], type='', swap='kpt-19'),
25:
dict(name='kpt-25', id=25, color=[255, 0, 0], type='', swap='kpt-18'),
26:
dict(name='kpt-26', id=26, color=[255, 0, 0], type='', swap='kpt-17'),
27: dict(name='kpt-27', id=27, color=[255, 0, 0], type='', swap=''),
28: dict(name='kpt-28', id=28, color=[255, 0, 0], type='', swap=''),
29: dict(name='kpt-29', id=29, color=[255, 0, 0], type='', swap=''),
30: dict(name='kpt-30', id=30, color=[255, 0, 0], type='', swap=''),
31:
dict(name='kpt-31', id=31, color=[255, 0, 0], type='', swap='kpt-35'),
32:
dict(name='kpt-32', id=32, color=[255, 0, 0], type='', swap='kpt-34'),
33: dict(name='kpt-33', id=33, color=[255, 0, 0], type='', swap=''),
34:
dict(name='kpt-34', id=34, color=[255, 0, 0], type='', swap='kpt-32'),
35:
dict(name='kpt-35', id=35, color=[255, 0, 0], type='', swap='kpt-31'),
36:
dict(name='kpt-36', id=36, color=[255, 0, 0], type='', swap='kpt-45'),
37:
dict(name='kpt-37', id=37, color=[255, 0, 0], type='', swap='kpt-44'),
38:
dict(name='kpt-38', id=38, color=[255, 0, 0], type='', swap='kpt-43'),
39:
dict(name='kpt-39', id=39, color=[255, 0, 0], type='', swap='kpt-42'),
40:
dict(name='kpt-40', id=40, color=[255, 0, 0], type='', swap='kpt-47'),
41: dict(
name='kpt-41', id=41, color=[255, 0, 0], type='', swap='kpt-46'),
42: dict(
name='kpt-42', id=42, color=[255, 0, 0], type='', swap='kpt-39'),
43: dict(
name='kpt-43', id=43, color=[255, 0, 0], type='', swap='kpt-38'),
44: dict(
name='kpt-44', id=44, color=[255, 0, 0], type='', swap='kpt-37'),
45: dict(
name='kpt-45', id=45, color=[255, 0, 0], type='', swap='kpt-36'),
46: dict(
name='kpt-46', id=46, color=[255, 0, 0], type='', swap='kpt-41'),
47: dict(
name='kpt-47', id=47, color=[255, 0, 0], type='', swap='kpt-40'),
48: dict(
name='kpt-48', id=48, color=[255, 0, 0], type='', swap='kpt-54'),
49: dict(
name='kpt-49', id=49, color=[255, 0, 0], type='', swap='kpt-53'),
50: dict(
name='kpt-50', id=50, color=[255, 0, 0], type='', swap='kpt-52'),
51: dict(name='kpt-51', id=51, color=[255, 0, 0], type='', swap=''),
52: dict(
name='kpt-52', id=52, color=[255, 0, 0], type='', swap='kpt-50'),
53: dict(
name='kpt-53', id=53, color=[255, 0, 0], type='', swap='kpt-49'),
54: dict(
name='kpt-54', id=54, color=[255, 0, 0], type='', swap='kpt-48'),
55: dict(
name='kpt-55', id=55, color=[255, 0, 0], type='', swap='kpt-59'),
56: dict(
name='kpt-56', id=56, color=[255, 0, 0], type='', swap='kpt-58'),
57: dict(name='kpt-57', id=57, color=[255, 0, 0], type='', swap=''),
58: dict(
name='kpt-58', id=58, color=[255, 0, 0], type='', swap='kpt-56'),
59: dict(
name='kpt-59', id=59, color=[255, 0, 0], type='', swap='kpt-55'),
60: dict(
name='kpt-60', id=60, color=[255, 0, 0], type='', swap='kpt-64'),
61: dict(
name='kpt-61', id=61, color=[255, 0, 0], type='', swap='kpt-63'),
62: dict(name='kpt-62', id=62, color=[255, 0, 0], type='', swap=''),
63: dict(
name='kpt-63', id=63, color=[255, 0, 0], type='', swap='kpt-61'),
64: dict(
name='kpt-64', id=64, color=[255, 0, 0], type='', swap='kpt-60'),
65: dict(
name='kpt-65', id=65, color=[255, 0, 0], type='', swap='kpt-67'),
66: dict(name='kpt-66', id=66, color=[255, 0, 0], type='', swap=''),
67: dict(
name='kpt-67', id=67, color=[255, 0, 0], type='', swap='kpt-65'),
},
skeleton_info={},
joint_weights=[1.] * 68,
sigmas=[])
configs/_base_/datasets/300wlp.py 0 → 100644
View file @ ca8a762a
dataset_info = dict(
dataset_name='300wlp',
paper_info=dict(
author='Xiangyu Zhu1, and Zhen Lei1 '
'and Xiaoming Liu2, and Hailin Shi1 '
'and Stan Z. Li1',
title='300 faces in-the-wild challenge: '
'Database and results',
container='Image and vision computing',
year='2016',
homepage='http://www.cbsr.ia.ac.cn/users/xiangyuzhu/'
'projects/3DDFA/main.htm',
),
keypoint_info={
0: dict(name='kpt-0', id=0, color=[255, 0, 0], type='', swap=''),
1: dict(name='kpt-1', id=1, color=[255, 0, 0], type='', swap=''),
2: dict(name='kpt-2', id=2, color=[255, 0, 0], type='', swap=''),
3: dict(name='kpt-3', id=3, color=[255, 0, 0], type='', swap=''),
4: dict(name='kpt-4', id=4, color=[255, 0, 0], type='', swap=''),
5: dict(name='kpt-5', id=5, color=[255, 0, 0], type='', swap=''),
6: dict(name='kpt-6', id=6, color=[255, 0, 0], type='', swap=''),
7: dict(name='kpt-7', id=7, color=[255, 0, 0], type='', swap=''),
8: dict(name='kpt-8', id=8, color=[255, 0, 0], type='', swap=''),
9: dict(name='kpt-9', id=9, color=[255, 0, 0], type='', swap=''),
10: dict(name='kpt-10', id=10, color=[255, 0, 0], type='', swap=''),
11: dict(name='kpt-11', id=11, color=[255, 0, 0], type='', swap=''),
12: dict(name='kpt-12', id=12, color=[255, 0, 0], type='', swap=''),
13: dict(name='kpt-13', id=13, color=[255, 0, 0], type='', swap=''),
14: dict(name='kpt-14', id=14, color=[255, 0, 0], type='', swap=''),
15: dict(name='kpt-15', id=15, color=[255, 0, 0], type='', swap=''),
16: dict(name='kpt-16', id=16, color=[255, 0, 0], type='', swap=''),
17: dict(name='kpt-17', id=17, color=[255, 0, 0], type='', swap=''),
18: dict(name='kpt-18', id=18, color=[255, 0, 0], type='', swap=''),
19: dict(name='kpt-19', id=19, color=[255, 0, 0], type='', swap=''),
20: dict(name='kpt-20', id=20, color=[255, 0, 0], type='', swap=''),
21: dict(name='kpt-21', id=21, color=[255, 0, 0], type='', swap=''),
22: dict(name='kpt-22', id=22, color=[255, 0, 0], type='', swap=''),
23: dict(name='kpt-23', id=23, color=[255, 0, 0], type='', swap=''),
24: dict(name='kpt-24', id=24, color=[255, 0, 0], type='', swap=''),
25: dict(name='kpt-25', id=25, color=[255, 0, 0], type='', swap=''),
26: dict(name='kpt-26', id=26, color=[255, 0, 0], type='', swap=''),
27: dict(name='kpt-27', id=27, color=[255, 0, 0], type='', swap=''),
28: dict(name='kpt-28', id=28, color=[255, 0, 0], type='', swap=''),
29: dict(name='kpt-29', id=29, color=[255, 0, 0], type='', swap=''),
30: dict(name='kpt-30', id=30, color=[255, 0, 0], type='', swap=''),
31: dict(name='kpt-31', id=31, color=[255, 0, 0], type='', swap=''),
32: dict(name='kpt-32', id=32, color=[255, 0, 0], type='', swap=''),
33: dict(name='kpt-33', id=33, color=[255, 0, 0], type='', swap=''),
34: dict(name='kpt-34', id=34, color=[255, 0, 0], type='', swap=''),
35: dict(name='kpt-35', id=35, color=[255, 0, 0], type='', swap=''),
36: dict(name='kpt-36', id=36, color=[255, 0, 0], type='', swap=''),
37: dict(name='kpt-37', id=37, color=[255, 0, 0], type='', swap=''),
38: dict(name='kpt-38', id=38, color=[255, 0, 0], type='', swap=''),
39: dict(name='kpt-39', id=39, color=[255, 0, 0], type='', swap=''),
40: dict(name='kpt-40', id=40, color=[255, 0, 0], type='', swap=''),
41: dict(name='kpt-41', id=41, color=[255, 0, 0], type='', swap=''),
42: dict(name='kpt-42', id=42, color=[255, 0, 0], type='', swap=''),
43: dict(name='kpt-43', id=43, color=[255, 0, 0], type='', swap=''),
44: dict(name='kpt-44', id=44, color=[255, 0, 0], type='', swap=''),
45: dict(name='kpt-45', id=45, color=[255, 0, 0], type='', swap=''),
46: dict(name='kpt-46', id=46, color=[255, 0, 0], type='', swap=''),
47: dict(name='kpt-47', id=47, color=[255, 0, 0], type='', swap=''),
48: dict(name='kpt-48', id=48, color=[255, 0, 0], type='', swap=''),
49: dict(name='kpt-49', id=49, color=[255, 0, 0], type='', swap=''),
50: dict(name='kpt-50', id=50, color=[255, 0, 0], type='', swap=''),
51: dict(name='kpt-51', id=51, color=[255, 0, 0], type='', swap=''),
52: dict(name='kpt-52', id=52, color=[255, 0, 0], type='', swap=''),
53: dict(name='kpt-53', id=53, color=[255, 0, 0], type='', swap=''),
54: dict(name='kpt-54', id=54, color=[255, 0, 0], type='', swap=''),
55: dict(name='kpt-55', id=55, color=[255, 0, 0], type='', swap=''),
56: dict(name='kpt-56', id=56, color=[255, 0, 0], type='', swap=''),
57: dict(name='kpt-57', id=57, color=[255, 0, 0], type='', swap=''),
58: dict(name='kpt-58', id=58, color=[255, 0, 0], type='', swap=''),
59: dict(name='kpt-59', id=59, color=[255, 0, 0], type='', swap=''),
60: dict(name='kpt-60', id=60, color=[255, 0, 0], type='', swap=''),
61: dict(name='kpt-61', id=61, color=[255, 0, 0], type='', swap=''),
62: dict(name='kpt-62', id=62, color=[255, 0, 0], type='', swap=''),
63: dict(name='kpt-63', id=63, color=[255, 0, 0], type='', swap=''),
64: dict(name='kpt-64', id=64, color=[255, 0, 0], type='', swap=''),
65: dict(name='kpt-65', id=65, color=[255, 0, 0], type='', swap=''),
66: dict(name='kpt-66', id=66, color=[255, 0, 0], type='', swap=''),
67: dict(name='kpt-67', id=67, color=[255, 0, 0], type='', swap=''),
},
skeleton_info={},
joint_weights=[1.] * 68,
sigmas=[])
configs/_base_/datasets/aflw.py 0 → 100644
View file @ ca8a762a
dataset_info = dict(
dataset_name='aflw',
paper_info=dict(
author='Koestinger, Martin and Wohlhart, Paul and '
'Roth, Peter M and Bischof, Horst',
title='Annotated facial landmarks in the wild: '
'A large-scale, real-world database for facial '
'landmark localization',
container='2011 IEEE international conference on computer '
'vision workshops (ICCV workshops)',
year='2011',
homepage='https://www.tugraz.at/institute/icg/research/'
'team-bischof/lrs/downloads/aflw/',
),
keypoint_info={
0: dict(name='kpt-0', id=0, color=[255, 0, 0], type='', swap='kpt-5'),
1: dict(name='kpt-1', id=1, color=[255, 0, 0], type='', swap='kpt-4'),
2: dict(name='kpt-2', id=2, color=[255, 0, 0], type='', swap='kpt-3'),
3: dict(name='kpt-3', id=3, color=[255, 0, 0], type='', swap='kpt-2'),
4: dict(name='kpt-4', id=4, color=[255, 0, 0], type='', swap='kpt-1'),
5: dict(name='kpt-5', id=5, color=[255, 0, 0], type='', swap='kpt-0'),
6: dict(name='kpt-6', id=6, color=[255, 0, 0], type='', swap='kpt-11'),
7: dict(name='kpt-7', id=7, color=[255, 0, 0], type='', swap='kpt-10'),
8: dict(name='kpt-8', id=8, color=[255, 0, 0], type='', swap='kpt-9'),
9: dict(name='kpt-9', id=9, color=[255, 0, 0], type='', swap='kpt-8'),
10:
dict(name='kpt-10', id=10, color=[255, 0, 0], type='', swap='kpt-7'),
11:
dict(name='kpt-11', id=11, color=[255, 0, 0], type='', swap='kpt-6'),
12:
dict(name='kpt-12', id=12, color=[255, 0, 0], type='', swap='kpt-14'),
13: dict(name='kpt-13', id=13, color=[255, 0, 0], type='', swap=''),
14:
dict(name='kpt-14', id=14, color=[255, 0, 0], type='', swap='kpt-12'),
15:
dict(name='kpt-15', id=15, color=[255, 0, 0], type='', swap='kpt-17'),
16: dict(name='kpt-16', id=16, color=[255, 0, 0], type='', swap=''),
17:
dict(name='kpt-17', id=17, color=[255, 0, 0], type='', swap='kpt-15'),
18: dict(name='kpt-18', id=18, color=[255, 0, 0], type='', swap='')
},
skeleton_info={},
joint_weights=[1.] * 19,
sigmas=[])
configs/_base_/datasets/aic.py 0 → 100644
View file @ ca8a762a
dataset_info = dict(
dataset_name='aic',
paper_info=dict(
author='Wu, Jiahong and Zheng, He and Zhao, Bo and '
'Li, Yixin and Yan, Baoming and Liang, Rui and '
'Wang, Wenjia and Zhou, Shipei and Lin, Guosen and '
'Fu, Yanwei and others',
title='Ai challenger: A large-scale dataset for going '
'deeper in image understanding',
container='arXiv',
year='2017',
homepage='https://github.com/AIChallenger/AI_Challenger_2017',
),
keypoint_info={
0:
dict(
name='right_shoulder',
id=0,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
1:
dict(
name='right_elbow',
id=1,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
2:
dict(
name='right_wrist',
id=2,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
3:
dict(
name='left_shoulder',
id=3,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
4:
dict(
name='left_elbow',
id=4,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
5:
dict(
name='left_wrist',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
6:
dict(
name='right_hip',
id=6,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
7:
dict(
name='right_knee',
id=7,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
8:
dict(
name='right_ankle',
id=8,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
9:
dict(
name='left_hip',
id=9,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
10:
dict(
name='left_knee',
id=10,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
11:
dict(
name='left_ankle',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
12:
dict(
name='head_top',
id=12,
color=[51, 153, 255],
type='upper',
swap=''),
13:
dict(name='neck', id=13, color=[51, 153, 255], type='upper', swap='')
},
skeleton_info={
0:
dict(link=('right_wrist', 'right_elbow'), id=0, color=[255, 128, 0]),
1: dict(
link=('right_elbow', 'right_shoulder'), id=1, color=[255, 128, 0]),
2: dict(link=('right_shoulder', 'neck'), id=2, color=[51, 153, 255]),
3: dict(link=('neck', 'left_shoulder'), id=3, color=[51, 153, 255]),
4: dict(link=('left_shoulder', 'left_elbow'), id=4, color=[0, 255, 0]),
5: dict(link=('left_elbow', 'left_wrist'), id=5, color=[0, 255, 0]),
6: dict(link=('right_ankle', 'right_knee'), id=6, color=[255, 128, 0]),
7: dict(link=('right_knee', 'right_hip'), id=7, color=[255, 128, 0]),
8: dict(link=('right_hip', 'left_hip'), id=8, color=[51, 153, 255]),
9: dict(link=('left_hip', 'left_knee'), id=9, color=[0, 255, 0]),
10: dict(link=('left_knee', 'left_ankle'), id=10, color=[0, 255, 0]),
11: dict(link=('head_top', 'neck'), id=11, color=[51, 153, 255]),
12: dict(
link=('right_shoulder', 'right_hip'), id=12, color=[51, 153, 255]),
13:
dict(link=('left_shoulder', 'left_hip'), id=13, color=[51, 153, 255])
},
joint_weights=[
1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.
],
# 'https://github.com/AIChallenger/AI_Challenger_2017/blob/master/'
# 'Evaluation/keypoint_eval/keypoint_eval.py#L50'
# delta = 2 x sigma
sigmas=[
0.01388152, 0.01515228, 0.01057665, 0.01417709, 0.01497891, 0.01402144,
0.03909642, 0.03686941, 0.01981803, 0.03843971, 0.03412318, 0.02415081,
0.01291456, 0.01236173
])
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