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v1.0

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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
# Regular expression matching correct function names. Overrides function-
# naming-style.
#function-rgx=
# Good variable names which should always be accepted, separated by a comma.
good-names=i,
j,
k,
ex,
Run,
_,
x,
y,
w,
h,
a,
b
# Good variable names regexes, separated by a comma. If names match any regex,
# they will always be accepted
good-names-rgxs=
# Include a hint for the correct naming format with invalid-name.
include-naming-hint=no
# Naming style matching correct inline iteration names.
inlinevar-naming-style=any
# Regular expression matching correct inline iteration names. Overrides
# inlinevar-naming-style.
#inlinevar-rgx=
# Naming style matching correct method names.
method-naming-style=snake_case
# Regular expression matching correct method names. Overrides method-naming-
# style.
#method-rgx=
# Naming style matching correct module names.
module-naming-style=snake_case
# Regular expression matching correct module names. Overrides module-naming-
# style.
#module-rgx=
# Colon-delimited sets of names that determine each other's naming style when
# the name regexes allow several styles.
name-group=
# Regular expression which should only match function or class names that do
# not require a docstring.
no-docstring-rgx=^_
# List of decorators that produce properties, such as abc.abstractproperty. Add
# to this list to register other decorators that produce valid properties.
# These decorators are taken in consideration only for invalid-name.
property-classes=abc.abstractproperty
# Naming style matching correct variable names.
variable-naming-style=snake_case
# Regular expression matching correct variable names. Overrides variable-
# naming-style.
#variable-rgx=
[DESIGN]
# Maximum number of arguments for function / method.
max-args=5
# Maximum number of attributes for a class (see R0902).
max-attributes=7
# Maximum number of boolean expressions in an if statement (see R0916).
max-bool-expr=5
# Maximum number of branch for function / method body.
max-branches=12
# Maximum number of locals for function / method body.
max-locals=15
# Maximum number of parents for a class (see R0901).
max-parents=7
# Maximum number of public methods for a class (see R0904).
max-public-methods=20
# Maximum number of return / yield for function / method body.
max-returns=6
# Maximum number of statements in function / method body.
max-statements=50
# Minimum number of public methods for a class (see R0903).
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
# 3 compatible code, which means that the block might have code that exists
# only in one or another interpreter, leading to false positives when analysed.
analyse-fallback-blocks=no
# Deprecated modules which should not be used, separated by a comma.
deprecated-modules=optparse,tkinter.tix
# Create a graph of external dependencies in the given file (report RP0402 must
# not be disabled).
ext-import-graph=
# Create a graph of every (i.e. internal and external) dependencies in the
# given file (report RP0402 must not be disabled).
import-graph=
# Create a graph of internal dependencies in the given file (report RP0402 must
# not be disabled).
int-import-graph=
# Force import order to recognize a module as part of the standard
# 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
.readthedocs.yml 0 → 100644
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version: 2
formats:
- epub
build:
os: ubuntu-22.04
tools:
python: "3.8"
python:
install:
- requirements: requirements/docs.txt
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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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Copyright 2018-2020 Open-MMLab. All rights reserved.
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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
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configs/_base_/datasets/300w.py 0 → 100644
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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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