Skip to content

GitLab

Commit aa58d024 authored by unknown's avatar unknown
Browse files

Initial add code.

parents
Pipeline #135 failed with stages
in 0 seconds
Hide whitespace changes
Inline Side-by-side
Showing with 1918 additions and 0 deletions
.gitignore 0 → 100644
View file @ aa58d024
# 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/_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
/data
.vscode
.idea
*.pkl
*.pkl.json
*.log.json
benchlist.txt
work_dirs/
# Pytorch
*.pth
# Profile
*.prof
# lmdb
*.mdb
# unignore some data file in tests/data
!tests/data/**/*.pkl
!tests/data/**/*.pkl.json
!tests/data/**/*.log.json
!tests/data/**/*.pth
# avoid soft links created by MIM
mmaction/configs/*
mmaction/tools/*
*.ipynb
# unignore ipython notebook files in demo
!demo/*.ipynb
mmaction/.mim
.pre-commit-config.yaml 0 → 100644
View file @ aa58d024
exclude: ^tests/data/
repos:
- repo: https://github.com/PyCQA/flake8
rev: 3.8.3
hooks:
- id: flake8
- repo: https://github.com/PyCQA/isort
rev: 5.10.1
hooks:
- id: isort
- repo: https://github.com/pre-commit/mirrors-yapf
rev: v0.30.0
hooks:
- id: yapf
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v3.1.0
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/executablebooks/mdformat
rev: 0.7.9
hooks:
- id: mdformat
args: ["--number"]
additional_dependencies:
- mdformat-openmmlab
- mdformat_frontmatter
- linkify-it-py
- 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,tools/data/hvu/label_map.json,docs_zh_CN/*", "-L", "te,nd,thre,Gool,gool"]
- repo: https://github.com/open-mmlab/pre-commit-hooks
rev: v0.2.0 # Use the ref you want to point at
hooks:
- id: check-algo-readme
- id: check-copyright
args: ["mmaction", "tests", "demo", "tools"] # these directories will be checked
.pylintrc 0 → 100644
View file @ aa58d024
[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
# 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=import-outside-toplevel
redefined-outer-name
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,
line-too-long
# 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
View file @ aa58d024
version: 2
python:
version: 3.7
install:
- requirements: requirements/docs.txt
- requirements: requirements/readthedocs.txt
CITATION.cff 0 → 100644
View file @ aa58d024
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- name: "MMAction2 Contributors"
title: "OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark"
date-released: 2020-07-21
url: "https://github.com/open-mmlab/mmaction2"
license: Apache-2.0
LICENSE 0 → 100644
View file @ aa58d024
Copyright 2018-2019 Open-MMLab. All rights reserved.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright 2018-2019 Open-MMLab.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
MANIFEST.in 0 → 100644
View file @ aa58d024
include mmaction/.mim/model-index.yml
recursive-include mmaction/.mim/configs *.py *.yml
recursive-include mmaction/.mim/tools *.sh *.py
README.md 0 → 100644
View file @ aa58d024
<div align="center">
<img src="https://github.com/open-mmlab/mmaction2/raw/master/resources/mmaction2_logo.png" width="600"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">OpenMMLab website</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b><font size="5">OpenMMLab platform</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
[![Documentation](https://readthedocs.org/projects/mmaction2/badge/?version=latest)](https://mmaction2.readthedocs.io/en/latest/)
[![actions](https://github.com/open-mmlab/mmaction2/workflows/build/badge.svg)](https://github.com/open-mmlab/mmaction2/actions)
[![codecov](https://codecov.io/gh/open-mmlab/mmaction2/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmaction2)
[![PyPI](https://img.shields.io/pypi/v/mmaction2)](https://pypi.org/project/mmaction2/)
[![LICENSE](https://img.shields.io/github/license/open-mmlab/mmaction2.svg)](https://github.com/open-mmlab/mmaction2/blob/master/LICENSE)
[![Average time to resolve an issue](https://isitmaintained.com/badge/resolution/open-mmlab/mmaction2.svg)](https://github.com/open-mmlab/mmaction2/issues)
[![Percentage of issues still open](https://isitmaintained.com/badge/open/open-mmlab/mmaction2.svg)](https://github.com/open-mmlab/mmaction2/issues)
[📘Documentation](https://mmaction2.readthedocs.io/en/latest/) |
[🛠️Installation](https://mmaction2.readthedocs.io/en/latest/install.html) |
[👀Model Zoo](https://mmaction2.readthedocs.io/en/latest/modelzoo.html) |
[🆕Update News](https://mmaction2.readthedocs.io/en/latest/changelog.html) |
[🚀Ongoing Projects](https://github.com/open-mmlab/mmaction2/projects) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmaction2/issues/new/choose)
</div>
English | [简体中文](/README_zh-CN.md)
## Introduction
MMAction2 is an open-source toolbox for video understanding based on PyTorch.
It is a part of the [OpenMMLab](http://openmmlab.org/) project.
The master branch works with **PyTorch 1.5+**.
<div align="center">
<div style="float:left;margin-right:10px;">
<img src="https://github.com/open-mmlab/mmaction2/raw/master/resources/mmaction2_overview.gif" width="380px"><br>
<p style="font-size:1.5vw;">Action Recognition Results on Kinetics-400</p>
</div>
<div style="float:right;margin-right:0px;">
<img src="https://user-images.githubusercontent.com/34324155/123989146-2ecae680-d9fb-11eb-916b-b9db5563a9e5.gif" width="380px"><br>
<p style="font-size:1.5vw;">Skeleton-base Action Recognition Results on NTU-RGB+D-120</p>
</div>
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/30782254/155710881-bb26863e-fcb4-458e-b0c4-33cd79f96901.gif" width="580px"/><br>
<p style="font-size:1.5vw;">Skeleton-based Spatio-Temporal Action Detection and Action Recognition Results on Kinetics-400</p>
</div>
<div align="center">
<img src="https://github.com/open-mmlab/mmaction2/raw/master/resources/spatio-temporal-det.gif" width="800px"/><br>
<p style="font-size:1.5vw;">Spatio-Temporal Action Detection Results on AVA-2.1</p>
</div>
## Major Features
- **Modular design**: We decompose a video understanding framework into different components. One can easily construct a customized video understanding framework by combining different modules.
- **Support four major video understanding tasks**: MMAction2 implements various algorithms for multiple video understanding tasks, including action recognition, action localization, spatio-temporal action detection, and skeleton-based action detection. We support **27** different algorithms and **20** different datasets for the four major tasks.
- **Well tested and documented**: We provide detailed documentation and API reference, as well as unit tests.
## What's New
- (2022-03-04) We support **Multigrid** on Kinetics400, achieve 76.07% Top-1 accuracy and accelerate training speed.
- (2021-11-24) We support **2s-AGCN** on NTU60 XSub, achieve 86.06% Top-1 accuracy on joint stream and 86.89% Top-1 accuracy on bone stream respectively.
- (2021-10-29) We provide a demo for skeleton-based and rgb-based spatio-temporal detection and action recognition (demo/demo_video_structuralize.py).
- (2021-10-26) We train and test **ST-GCN** on NTU60 with 3D keypoint annotations, achieve 84.61% Top-1 accuracy (higher than 81.5% in the [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17135)).
- (2021-10-25) We provide a script(tools/data/skeleton/gen_ntu_rgbd_raw.py) to convert the NTU60 and NTU120 3D raw skeleton data to our format.
- (2021-10-25) We provide a [guide](https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/posec3d/custom_dataset_training.md) on how to train PoseC3D with custom datasets, [bit-scientist](https://github.com/bit-scientist) authored this PR!
- (2021-10-16) We support **PoseC3D** on UCF101 and HMDB51, achieves 87.0% and 69.3% Top-1 accuracy with 2D skeletons only. Pre-extracted 2D skeletons are also available.
**Release**: v0.24.0 was released in 05/05/2022. Please refer to [changelog.md](docs/changelog.md) for details and release history.
## Installation
MMAction2 depends on [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv), [MMDetection](https://github.com/open-mmlab/mmdetection) (optional), and [MMPose](https://github.com/open-mmlab/mmdetection)(optional).
Below are quick steps for installation.
Please refer to [install.md](docs/install.md) for more detailed instruction.
```shell
conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
mim install mmcv-full
mim install mmdet # optional
mim install mmpose # optional
git clone https://github.com/open-mmlab/mmaction2.git
cd mmaction2
pip3 install -e .
```
## Get Started
Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMAction2.
There are also tutorials:
- [learn about configs](docs/tutorials/1_config.md)
- [finetuning models](docs/tutorials/2_finetune.md)
- [adding new dataset](docs/tutorials/3_new_dataset.md)
- [designing data pipeline](docs/tutorials/4_data_pipeline.md)
- [adding new modules](docs/tutorials/5_new_modules.md)
- [exporting model to onnx](docs/tutorials/6_export_model.md)
- [customizing runtime settings](docs/tutorials/7_customize_runtime.md)
A Colab tutorial is also provided. You may preview the notebook [here](demo/mmaction2_tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmaction2/blob/master/demo/mmaction2_tutorial.ipynb) on Colab.
## Supported Methods
<table style="margin-left:auto;margin-right:auto;font-size:1.3vw;padding:3px 5px;text-align:center;vertical-align:center;">
<tr>
<td colspan="5" style="font-weight:bold;">Action Recognition</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/c3d/README.md">C3D</a> (CVPR'2014)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsn/README.md">TSN</a> (ECCV'2016)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/i3d/README.md">I3D</a> (CVPR'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/i3d/README.md">I3D Non-Local</a> (CVPR'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/r2plus1d/README.md">R(2+1)D</a> (CVPR'2018)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/trn/README.md">TRN</a> (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsm/README.md">TSM</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsm/README.md">TSM Non-Local</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/slowonly/README.md">SlowOnly</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/slowfast/README.md">SlowFast</a> (ICCV'2019)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/csn/README.md">CSN</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tin/README.md">TIN</a> (AAAI'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tpn/README.md">TPN</a> (CVPR'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/x3d/README.md">X3D</a> (CVPR'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/omnisource/README.md">OmniSource</a> (ECCV'2020)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition_audio/resnet/README.md">MultiModality: Audio</a> (ArXiv'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tanet/README.md">TANet</a> (ArXiv'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/timesformer/README.md">TimeSformer</a> (ICML'2021)</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="5" style="font-weight:bold;">Action Localization</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/localization/ssn/README.md">SSN</a> (ICCV'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/localization/bsn/README.md">BSN</a> (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/localization/bmn/README.md">BMN</a> (ICCV'2019)</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="5" style="font-weight:bold;">Spatio-Temporal Action Detection</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/acrn/README.md">ACRN</a> (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/ava/README.md">SlowOnly+Fast R-CNN</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/ava/README.md">SlowFast+Fast R-CNN</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/lfb/README.md">LFB</a> (CVPR'2019)</td>
<td></td>
</tr>
<tr>
<td colspan="5" style="font-weight:bold;">Skeleton-based Action Recognition</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/stgcn/README.md">ST-GCN</a> (AAAI'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/2s-agcn/README.md">2s-AGCN</a> (CVPR'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/posec3d/README.md">PoseC3D</a> (ArXiv'2021)</td>
<td></td>
<td></td>
</tr>
</table>
Results and models are available in the *README.md* of each method's config directory.
A summary can be found on the [**model zoo**](https://mmaction2.readthedocs.io/en/latest/recognition_models.html) page.
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 [Issues](https://github.com/open-mmlab/mmaction2/issues/19).
## Supported Datasets
<table style="margin-left:auto;margin-right:auto;font-size:1.3vw;padding:3px 5px;text-align:center;vertical-align:center;">
<tr>
<td colspan="4" style="font-weight:bold;">Action Recognition</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hmdb51/README.md">HMDB51</a> (<a href="https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/">Homepage</a>) (ICCV'2011)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ucf101/README.md">UCF101</a> (<a href="https://www.crcv.ucf.edu/research/data-sets/ucf101/">Homepage</a>) (CRCV-IR-12-01)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/activitynet/README.md">ActivityNet</a> (<a href="http://activity-net.org/">Homepage</a>) (CVPR'2015)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/kinetics/README.md">Kinetics-[400/600/700]</a> (<a href="https://deepmind.com/research/open-source/kinetics/">Homepage</a>) (CVPR'2017)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/sthv1/README.md">SthV1</a> (<a href="https://20bn.com/datasets/something-something/v1/">Homepage</a>) (ICCV'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/sthv2/README.md">SthV2</a> (<a href="https://20bn.com/datasets/something-something/">Homepage</a>) (ICCV'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/diving48/README.md">Diving48</a> (<a href="http://www.svcl.ucsd.edu/projects/resound/dataset.html">Homepage</a>) (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/jester/README.md">Jester</a> (<a href="https://20bn.com/datasets/jester/v1">Homepage</a>) (ICCV'2019)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/mit/README.md">Moments in Time</a> (<a href="http://moments.csail.mit.edu/">Homepage</a>) (TPAMI'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/mmit/README.md">Multi-Moments in Time</a> (<a href="http://moments.csail.mit.edu/challenge_iccv_2019.html">Homepage</a>) (ArXiv'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hvu/README.md">HVU</a> (<a href="https://github.com/holistic-video-understanding/HVU-Dataset">Homepage</a>) (ECCV'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/omnisource/README.md">OmniSource</a> (<a href="https://kennymckormick.github.io/omnisource/">Homepage</a>) (ECCV'2020)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/gym/README.md">FineGYM</a> (<a href="https://sdolivia.github.io/FineGym/">Homepage</a>) (CVPR'2020)</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="4" style="font-weight:bold;">Action Localization</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/thumos14/README.md">THUMOS14</a> (<a href="https://www.crcv.ucf.edu/THUMOS14/download.html">Homepage</a>) (THUMOS Challenge 2014)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/activitynet/README.md">ActivityNet</a> (<a href="http://activity-net.org/">Homepage</a>) (CVPR'2015)</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="4" style="font-weight:bold;">Spatio-Temporal Action Detection</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ucf101_24/README.md">UCF101-24*</a> (<a href="http://www.thumos.info/download.html">Homepage</a>) (CRCV-IR-12-01)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/jhmdb/README.md">JHMDB*</a> (<a href="http://jhmdb.is.tue.mpg.de/">Homepage</a>) (ICCV'2015)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ava/README.md">AVA</a> (<a href="https://research.google.com/ava/index.html">Homepage</a>) (CVPR'2018)</td>
<td></td>
</tr>
<tr>
<td colspan="4" style="font-weight:bold;">Skeleton-based Action Recognition</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-FineGYM</a> (<a href="https://kennymckormick.github.io/posec3d/">Homepage</a>) (ArXiv'2021)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-NTURGB+D</a> (<a href="https://kennymckormick.github.io/posec3d/">Homepage</a>) (ArXiv'2021)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-UCF101</a> (<a href="https://kennymckormick.github.io/posec3d/">Homepage</a>) (ArXiv'2021)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-HMDB51</a> (<a href="https://kennymckormick.github.io/posec3d/">Homepage</a>) (ArXiv'2021)</td>
</tr>
</table>
Datasets marked with * are not fully supported yet, but related dataset preparation steps are provided. A summary can be found on the [**Supported Datasets**](https://mmaction2.readthedocs.io/en/latest/supported_datasets.html) page.
## Benchmark
To demonstrate the efficacy and efficiency of our framework, we compare MMAction2 with some other popular frameworks and official releases in terms of speed. Details can be found in [benchmark](docs/benchmark.md).
## Data Preparation
Please refer to [data_preparation.md](docs/data_preparation.md) for a general knowledge of data preparation.
The supported datasets are listed in [supported_datasets.md](docs/supported_datasets.md)
## FAQ
Please refer to [FAQ](docs/faq.md) for frequently asked questions.
## Projects built on MMAction2
Currently, there are many research works and projects built on MMAction2 by users from community, such as:
- Video Swin Transformer. [\[paper\]](https://arxiv.org/abs/2106.13230)[\[github\]](https://github.com/SwinTransformer/Video-Swin-Transformer)
- Evidential Deep Learning for Open Set Action Recognition, ICCV 2021 **Oral**. [\[paper\]](https://arxiv.org/abs/2107.10161)[\[github\]](https://github.com/Cogito2012/DEAR)
- Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective, ICCV 2021 **Oral**. [\[paper\]](https://arxiv.org/abs/2103.17263)[\[github\]](https://github.com/xvjiarui/VFS)
etc., check [projects.md](docs/projects.md) to see all related projects.
## Contributing
We appreciate all contributions to improve MMAction2. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) in MMCV for more details about the contributing guideline.
## Acknowledgement
MMAction2 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 and users who give valuable feedback.
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 new models.
## Citation
If you find this project useful in your research, please consider cite:
```BibTeX
@misc{2020mmaction2,
title={OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark},
author={MMAction2 Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmaction2}},
year={2020}
}
```
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Projects in OpenMMLab
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
- [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.
- [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.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression 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.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
README_zh-CN.md 0 → 100644
View file @ aa58d024
<div align="center">
<img src="https://github.com/open-mmlab/mmaction2/raw/master/resources/mmaction2_logo.png" width="600"/>
<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;
<b><font size="5">OpenMMLab 开放平台</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
[![Documentation](https://readthedocs.org/projects/mmaction2/badge/?version=latest)](https://mmaction2.readthedocs.io/zh_CN/latest/)
[![actions](https://github.com/open-mmlab/mmaction2/workflows/build/badge.svg)](https://github.com/open-mmlab/mmaction2/actions)
[![codecov](https://codecov.io/gh/open-mmlab/mmaction2/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmaction2)
[![PyPI](https://img.shields.io/pypi/v/mmaction2)](https://pypi.org/project/mmaction2/)
[![LICENSE](https://img.shields.io/github/license/open-mmlab/mmaction2.svg)](https://github.com/open-mmlab/mmaction2/blob/master/LICENSE)
[![Average time to resolve an issue](https://isitmaintained.com/badge/resolution/open-mmlab/mmaction2.svg)](https://github.com/open-mmlab/mmaction2/issues)
[![Percentage of issues still open](https://isitmaintained.com/badge/open/open-mmlab/mmaction2.svg)](https://github.com/open-mmlab/mmaction2/issues)
[📘文档](https://mmaction2.readthedocs.io/en/latest/) |
[🛠️安装指南](https://mmaction2.readthedocs.io/en/latest/install.html) |
[👀模型库](https://mmaction2.readthedocs.io/en/latest/modelzoo.html) |
[🆕更新](https://mmaction2.readthedocs.io/en/latest/changelog.html) |
[🚀进行中项目](https://github.com/open-mmlab/mmaction2/projects) |
[🤔问题反馈](https://github.com/open-mmlab/mmaction2/issues/new/choose)
</div>
[English](/README.md) | 简体中文
## 简介
MMAction2 是一款基于 PyTorch 的视频理解开源工具箱，是 [OpenMMLab](http://openmmlab.org/) 项目的成员之一
主分支代码目前支持 **PyTorch 1.5 以上**的版本
<div align="center">
<div style="float:left;margin-right:10px;">
<img src="https://github.com/open-mmlab/mmaction2/raw/master/resources/mmaction2_overview.gif" width="380px"><br>
<p style="font-size:1.5vw;">Kinetics-400 上的动作识别</p>
</div>
<div style="float:right;margin-right:0px;">
<img src="https://user-images.githubusercontent.com/34324155/123989146-2ecae680-d9fb-11eb-916b-b9db5563a9e5.gif" width="380px"><br>
<p style="font-size:1.5vw;">NTURGB+D-120 上的基于人体姿态的动作识别</p>
</div>
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/30782254/155710881-bb26863e-fcb4-458e-b0c4-33cd79f96901.gif" width="580px"/><br>
<p style="font-size:1.5vw;">Kinetics-400 上的基于 skeleton 的时空动作检测和动作识别</p>
</div>
<div align="center">
<img src="https://github.com/open-mmlab/mmaction2/raw/master/resources/spatio-temporal-det.gif" width="800px"/><br>
<p style="font-size:1.5vw;">AVA-2.1 上的时空动作检测</p>
</div>
## 主要特性
- **模块化设计**：MMAction2 将统一的视频理解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视频理解模型
- **支持多种任务和数据集**：MMAction2 支持多种视频理解任务，包括动作识别，时序动作检测，时空动作检测以及基于人体姿态的动作识别，总共支持 **27** 种算法和 **20** 种数据集
- **详尽的单元测试和文档**：MMAction2 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
## 更新记录
- (2021-11-24) 在 NTU60 XSub 上支持 **2s-AGCN**， 在 joint stream 和 bone stream 上分别达到 86.06% 和 86.89% 的识别准确率。
- (2021-10-29) 支持基于 skeleton 模态和 rgb 模态的时空动作检测和行为识别 demo (demo/demo_video_structuralize.py)。
- (2021-10-26) 在 NTU60 3d 关键点标注数据集上训练测试 **STGCN**, 可达到 84.61% (高于 [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17135) 中的 81.5%) 的识别准确率。
- (2021-10-25) 提供将 NTU60 和 NTU120 的 3d 骨骼点数据转换成我们项目的格式的脚本(tools/data/skeleton/gen_ntu_rgbd_raw.py)。
- (2021-10-25) 提供使用自定义数据集训练 PoseC3D 的 [教程](https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/posec3d/custom_dataset_training.md)，此 PR 由用户 [bit-scientist](https://github.com/bit-scientist) 完成！
- (2021-10-16) 在 UCF101, HMDB51 上支持 **PoseC3D**，仅用 2D 关键点就可分别达到 87.0% 和 69.3% 的识别准确率。两数据集的预提取骨架特征可以公开下载。
v0.24.0 版本已于 2022 年 5 月 5 日发布，可通过查阅 [更新日志](/docs/changelog.md) 了解更多细节以及发布历史
## 安装
MMAction2 依赖 [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv), [MMDetection](https://github.com/open-mmlab/mmdetection)(可选), [MMPose](https://github.com/open-mmlab/mmpose)(可选)，以下是安装的简要步骤。
更详细的安装指南请参考 [install.md](docs_zh_CN/install.md)。
```shell
conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
mim install mmcv-full
mim install mmdet # 可选
mim install mmpose # 可选
git clone https://github.com/open-mmlab/mmaction2.git
cd mmaction2
pip3 install -e .
```
## 教程
请参考 [基础教程](/docs_zh_CN/getting_started.md) 了解 MMAction2 的基本使用。MMAction2也提供了其他更详细的教程:
- [如何编写配置文件](/docs_zh_CN/tutorials/1_config.md)
- [如何微调模型](/docs_zh_CN/tutorials/2_finetune.md)
- [如何增加新数据集](/docs_zh_CN/tutorials/3_new_dataset.md)
- [如何设计数据处理流程](/docs_zh_CN/tutorials/4_data_pipeline.md)
- [如何增加新模块](/docs_zh_CN/tutorials/5_new_modules.md)
- [如何导出模型为 onnx 格式](/docs_zh_CN/tutorials/6_export_model.md)
- [如何自定义模型运行参数](/docs_zh_CN/tutorials/7_customize_runtime.md)
MMAction2 也提供了相应的中文 Colab 教程，可以点击 [这里](https://colab.research.google.com/github/open-mmlab/mmaction2/blob/master/demo/mmaction2_tutorial_zh-CN.ipynb) 进行体验！
## 模型库
<table style="margin-left:auto;margin-right:auto;font-size:1.3vw;padding:3px 5px;text-align:center;vertical-align:center;">
<tr>
<td colspan="5" style="font-weight:bold;">行为识别方法</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/c3d/README_zh-CN.md">C3D</a> (CVPR'2014)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsn/README_zh-CN.md">TSN</a> (ECCV'2016)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/i3d/README_zh-CN.md">I3D</a> (CVPR'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/i3d/README_zh-CN.md">I3D Non-Local</a> (CVPR'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/r2plus1d/README_zh-CN.md">R(2+1)D</a> (CVPR'2018)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/trn/README_zh-CN.md">TRN</a> (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsm/README_zh-CN.md">TSM</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsm/README_zh-CN.md">TSM Non-Local</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/slowonly/README_zh-CN.md">SlowOnly</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/slowfast/README_zh-CN.md">SlowFast</a> (ICCV'2019)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/csn/README_zh-CN.md">CSN</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tin/README_zh-CN.md">TIN</a> (AAAI'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tpn/README_zh-CN.md">TPN</a> (CVPR'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/x3d/README_zh-CN.md">X3D</a> (CVPR'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/omnisource/README_zh-CN.md">OmniSource</a> (ECCV'2020)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition_audio/resnet/README_zh-CN.md">MultiModality: Audio</a> (ArXiv'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tanet/README_zh-CN.md">TANet</a> (ArXiv'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/timesformer/README_zh-CN.md">TimeSformer</a> (ICML'2021)</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="5" style="font-weight:bold;">时序动作检测方法</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/localization/ssn/README_zh-CN.md">SSN</a> (ICCV'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/localization/bsn/README_zh-CN.md">BSN</a> (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/localization/bmn/README_zh-CN.md">BMN</a> (ICCV'2019)</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="5" style="font-weight:bold;">时空动作检测方法</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/acrn/README_zh-CN.md">ACRN</a> (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/ava/README_zh-CN.md">SlowOnly+Fast R-CNN</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/ava/README_zh-CN.md">SlowFast+Fast R-CNN</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/lfb/README_zh-CN.md">LFB</a> (CVPR'2019)</td>
<td></td>
</tr>
<tr>
<td colspan="5" style="font-weight:bold;">基于骨骼点的动作识别方法</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/stgcn/README.md">ST-GCN</a> (AAAI'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/2s-agcn/README.md">2s-AGCN</a> (CVPR'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/posec3d/README.md">PoseC3D</a> (ArXiv'2021)</td>
<td></td>
<td></td>
</tr>
</table>
各个模型的结果和设置都可以在对应的 config 目录下的 *README_zh-CN.md* 中查看。整体的概况也可也在 [**模型库**](https://mmaction2.readthedocs.io/zh_CN/latest/recognition_models.html) 页面中查看
MMAction2 将跟进学界的最新进展，并支持更多算法和框架。如果您对 MMAction2 有任何功能需求，请随时在 [问题](https://github.com/open-mmlab/mmaction2/issues/19) 中留言。
## 数据集
<table style="margin-left:auto;margin-right:auto;font-size:1.3vw;padding:3px 5px;text-align:center;vertical-align:center;">
<tr>
<td colspan="4" style="font-weight:bold;">动作识别数据集</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hmdb51/README_zh-CN.md">HMDB51</a> (<a href="https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/">主页</a>) (ICCV'2011)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ucf101/README_zh-CN.md">UCF101</a> (<a href="https://www.crcv.ucf.edu/research/data-sets/ucf101/">主页</a>) (CRCV-IR-12-01)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/activitynet/README_zh-CN.md">ActivityNet</a> (<a href="http://activity-net.org/">主页</a>) (CVPR'2015)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/kinetics/README_zh-CN.md">Kinetics-[400/600/700]</a> (<a href="https://deepmind.com/research/open-source/kinetics/">主页</a>) (CVPR'2017)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/sthv1/README_zh-CN.md">SthV1</a> (<a href="https://20bn.com/datasets/something-something/v1/">主页</a>) (ICCV'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/sthv2/README_zh-CN.md">SthV2</a> (<a href="https://20bn.com/datasets/something-something/">主页</a>) (ICCV'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/diving48/README_zh-CN.md">Diving48</a> (<a href="http://www.svcl.ucsd.edu/projects/resound/dataset.html">主页</a>) (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/jester/README_zh-CN.md">Jester</a> (<a href="https://20bn.com/datasets/jester/v1">主页</a>) (ICCV'2019)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/mit/README_zh-CN.md">Moments in Time</a> (<a href="http://moments.csail.mit.edu/">主页</a>) (TPAMI'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/mmit/README_zh-CN.md">Multi-Moments in Time</a> (<a href="http://moments.csail.mit.edu/challenge_iccv_2019.html">主页</a>) (ArXiv'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hvu/README_zh-CN.md">HVU</a> (<a href="https://github.com/holistic-video-understanding/HVU-Dataset">主页</a>) (ECCV'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/omnisource/README_zh-CN.md">OmniSource</a> (<a href="https://kennymckormick.github.io/omnisource/">主页</a>) (ECCV'2020)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/gym/README_zh-CN.md">FineGYM</a> (<a href="https://sdolivia.github.io/FineGym/">主页</a>) (CVPR'2020)</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="4" style="font-weight:bold;">时序动作检测数据集</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/thumos14/README_zh-CN.md">THUMOS14</a> (<a href="https://www.crcv.ucf.edu/THUMOS14/download.html">主页</a>) (THUMOS Challenge 2014)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/activitynet/README_zh-CN.md">ActivityNet</a> (<a href="http://activity-net.org/">主页</a>) (CVPR'2015)</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="4" style="font-weight:bold;">时空动作检测数据集</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ucf101_24/README_zh-CN.md">UCF101-24*</a> (<a href="http://www.thumos.info/download.html">主页</a>) (CRCV-IR-12-01)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/jhmdb/README_zh-CN.md">JHMDB*</a> (<a href="http://jhmdb.is.tue.mpg.de/">主页</a>) (ICCV'2015)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ava/README_zh-CN.md">AVA</a> (<a href="https://research.google.com/ava/index.html">主页</a>) (CVPR'2018)</td>
<td></td>
</tr>
<tr>
<td colspan="4" style="font-weight:bold;">基于骨骼点的动作识别数据集</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-FineGYM</a> (<a href="https://kennymckormick.github.io/posec3d/">主页</a>) (ArXiv'2021)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-NTURGB+D</a> (<a href="https://kennymckormick.github.io/posec3d/">主页</a>) (ArXiv'2021)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-UCF101</a> (<a href="https://kennymckormick.github.io/posec3d/">主页</a>) (ArXiv'2021)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-HMDB51</a> (<a href="https://kennymckormick.github.io/posec3d/">主页</a>) (ArXiv'2021)</td>
</tr>
</table>
标记 * 代表对应数据集并未被完全支持，但提供相应的数据准备步骤。整体的概况也可也在 [**数据集**](https://mmaction2.readthedocs.io/en/latest/supported_datasets.html) 页面中查看
## 基准测试
为了验证 MMAction2 框架的高精度和高效率，开发成员将其与当前其他主流框架进行速度对比。更多详情可见 [基准测试](/docs_zh_CN/benchmark.md)
## 数据集准备
请参考 [数据准备](/docs_zh_CN/data_preparation.md) 了解数据集准备概况。所有支持的数据集都列于 [数据集清单](/docs_zh_CN/supported_datasets.md) 中
## 常见问题
请参考 [FAQ](/docs_zh_CN/faq.md) 了解其他用户的常见问题
## 相关工作
目前有许多研究工作或工程项目基于 MMAction2 搭建，例如：
- Evidential Deep Learning for Open Set Action Recognition, ICCV 2021 **Oral**. [\[论文\]](https://arxiv.org/abs/2107.10161)[\[代码\]](https://github.com/Cogito2012/DEAR)
- Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective, ICCV 2021 **Oral**. [\[论文\]](https://arxiv.org/abs/2103.17263)[\[代码\]](https://github.com/xvjiarui/VFS)
- Video Swin Transformer. [\[论文\]](https://arxiv.org/abs/2106.13230)[\[代码\]](https://github.com/SwinTransformer/Video-Swin-Transformer)
更多详情可见 [相关工作](docs/projects.md)
## 参与贡献
我们非常欢迎用户对于 MMAction2 做出的任何贡献，可以参考 [贡献指南](/.github/CONTRIBUTING.md) 文件了解更多细节
## 致谢
MMAction2 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
如果你觉得 MMAction2 对你的研究有所帮助，可以考虑引用它：
```BibTeX
@misc{2020mmaction2,
title={OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark},
author={MMAction2 Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmaction2}},
year={2020}
}
```
## 许可
该项目开源自 [Apache 2.0 license](/LICENSE)
## OpenMMLab 的其他项目
- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
- [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
- [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箱
- [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
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
- [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
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
## 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)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3)
<div align="center">
<img src="https://github.com/open-mmlab/mmaction2/raw/master/resources/zhihu_qrcode.jpg" height="400" /> <img src="https://github.com/open-mmlab/mmaction2/raw/master/resources/qq_group_qrcode.png" height="400" />
</div>
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 👬
configs/_base_/default_runtime.py 0 → 100644
View file @ aa58d024
checkpoint_config = dict(interval=1)
log_config = dict(
interval=20,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook'),
])
# runtime settings
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
# disable opencv multithreading to avoid system being overloaded
opencv_num_threads = 0
# set multi-process start method as `fork` to speed up the training
mp_start_method = 'fork'
configs/_base_/models/audioonly_r50.py 0 → 100644
View file @ aa58d024
# model settings
model = dict(
type='AudioRecognizer',
backbone=dict(
type='ResNetAudio',
depth=50,
pretrained=None,
in_channels=1,
norm_eval=False),
cls_head=dict(
type='AudioTSNHead',
num_classes=400,
in_channels=1024,
dropout_ratio=0.5,
init_std=0.01),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob'))
configs/_base_/models/bmn_400x100.py 0 → 100644
View file @ aa58d024
# model settings
model = dict(
type='BMN',
temporal_dim=100,
boundary_ratio=0.5,
num_samples=32,
num_samples_per_bin=3,
feat_dim=400,
soft_nms_alpha=0.4,
soft_nms_low_threshold=0.5,
soft_nms_high_threshold=0.9,
post_process_top_k=100)
configs/_base_/models/bsn_pem.py 0 → 100644
View file @ aa58d024
# model settings
model = dict(
type='PEM',
pem_feat_dim=32,
pem_hidden_dim=256,
pem_u_ratio_m=1,
pem_u_ratio_l=2,
pem_high_temporal_iou_threshold=0.6,
pem_low_temporal_iou_threshold=0.2,
soft_nms_alpha=0.75,
soft_nms_low_threshold=0.65,
soft_nms_high_threshold=0.9,
post_process_top_k=100)
configs/_base_/models/bsn_tem.py 0 → 100644
View file @ aa58d024
# model settings
model = dict(
type='TEM',
temporal_dim=100,
boundary_ratio=0.1,
tem_feat_dim=400,
tem_hidden_dim=512,
tem_match_threshold=0.5)
configs/_base_/models/c3d_sports1m_pretrained.py 0 → 100644
View file @ aa58d024
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='C3D',
pretrained= # noqa: E251
'https://download.openmmlab.com/mmaction/recognition/c3d/c3d_sports1m_pretrain_20201016-dcc47ddc.pth', # noqa: E501
style='pytorch',
conv_cfg=dict(type='Conv3d'),
norm_cfg=None,
act_cfg=dict(type='ReLU'),
dropout_ratio=0.5,
init_std=0.005),
cls_head=dict(
type='I3DHead',
num_classes=101,
in_channels=4096,
spatial_type=None,
dropout_ratio=0.5,
init_std=0.01),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='score'))
configs/_base_/models/i3d_r50.py 0 → 100644
View file @ aa58d024
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3d',
pretrained2d=True,
pretrained='torchvision://resnet50',
depth=50,
conv1_kernel=(5, 7, 7),
conv1_stride_t=2,
pool1_stride_t=2,
conv_cfg=dict(type='Conv3d'),
norm_eval=False,
inflate=((1, 1, 1), (1, 0, 1, 0), (1, 0, 1, 0, 1, 0), (0, 1, 0)),
zero_init_residual=False),
cls_head=dict(
type='I3DHead',
num_classes=400,
in_channels=2048,
spatial_type='avg',
dropout_ratio=0.5,
init_std=0.01),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob'))
# This setting refers to https://github.com/open-mmlab/mmaction/blob/master/mmaction/models/tenons/backbones/resnet_i3d.py#L329-L332 # noqa: E501
configs/_base_/models/ircsn_r152.py 0 → 100644
View file @ aa58d024
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dCSN',
pretrained2d=False,
pretrained=None,
depth=152,
with_pool2=False,
bottleneck_mode='ir',
norm_eval=False,
zero_init_residual=False),
cls_head=dict(
type='I3DHead',
num_classes=400,
in_channels=2048,
spatial_type='avg',
dropout_ratio=0.5,
init_std=0.01),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob', max_testing_views=10))
configs/_base_/models/r2plus1d_r34.py 0 → 100644
View file @ aa58d024
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet2Plus1d',
depth=34,
pretrained=None,
pretrained2d=False,
norm_eval=False,
conv_cfg=dict(type='Conv2plus1d'),
norm_cfg=dict(type='SyncBN', requires_grad=True, eps=1e-3),
conv1_kernel=(3, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(1, 1, 1, 1),
spatial_strides=(1, 2, 2, 2),
temporal_strides=(1, 2, 2, 2),
zero_init_residual=False),
cls_head=dict(
type='I3DHead',
num_classes=400,
in_channels=512,
spatial_type='avg',
dropout_ratio=0.5,
init_std=0.01),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob'))
configs/_base_/models/slowfast_r50.py 0 → 100644
View file @ aa58d024
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowFast',
pretrained=None,
resample_rate=8, # tau
speed_ratio=8, # alpha
channel_ratio=8, # beta_inv
slow_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=True,
conv1_kernel=(1, 7, 7),
dilations=(1, 1, 1, 1),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
fast_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=False,
base_channels=8,
conv1_kernel=(5, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
norm_eval=False)),
cls_head=dict(
type='SlowFastHead',
in_channels=2304, # 2048+256
num_classes=400,
spatial_type='avg',
dropout_ratio=0.5),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob'))
configs/_base_/models/slowonly_r50.py 0 → 100644
View file @ aa58d024
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained='torchvision://resnet50',
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
cls_head=dict(
type='I3DHead',
in_channels=2048,
num_classes=400,
spatial_type='avg',
dropout_ratio=0.5),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob'))
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment