Initial commit
This commit is contained in:
Mido Assran
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*.swp
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*.swo
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__pycache__
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# Code of Conduct
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||||||
|
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||||||
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## Our Pledge
|
||||||
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|
||||||
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In the interest of fostering an open and welcoming environment, we as
|
||||||
|
contributors and maintainers pledge to make participation in our project and
|
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|
our community a harassment-free experience for everyone, regardless of age, body
|
||||||
|
size, disability, ethnicity, sex characteristics, gender identity and expression,
|
||||||
|
level of experience, education, socio-economic status, nationality, personal
|
||||||
|
appearance, race, religion, or sexual identity and orientation.
|
||||||
|
|
||||||
|
## Our Standards
|
||||||
|
|
||||||
|
Examples of behavior that contributes to creating a positive environment
|
||||||
|
include:
|
||||||
|
|
||||||
|
* Using welcoming and inclusive language
|
||||||
|
* Being respectful of differing viewpoints and experiences
|
||||||
|
* Gracefully accepting constructive criticism
|
||||||
|
* Focusing on what is best for the community
|
||||||
|
* Showing empathy towards other community members
|
||||||
|
|
||||||
|
Examples of unacceptable behavior by participants include:
|
||||||
|
|
||||||
|
* The use of sexualized language or imagery and unwelcome sexual attention or
|
||||||
|
advances
|
||||||
|
* Trolling, insulting/derogatory comments, and personal or political attacks
|
||||||
|
* Public or private harassment
|
||||||
|
* Publishing others' private information, such as a physical or electronic
|
||||||
|
address, without explicit permission
|
||||||
|
* Other conduct which could reasonably be considered inappropriate in a
|
||||||
|
professional setting
|
||||||
|
|
||||||
|
## Our Responsibilities
|
||||||
|
|
||||||
|
Project maintainers are responsible for clarifying the standards of acceptable
|
||||||
|
behavior and are expected to take appropriate and fair corrective action in
|
||||||
|
response to any instances of unacceptable behavior.
|
||||||
|
|
||||||
|
Project maintainers have the right and responsibility to remove, edit, or
|
||||||
|
reject comments, commits, code, wiki edits, issues, and other contributions
|
||||||
|
that are not aligned to this Code of Conduct, or to ban temporarily or
|
||||||
|
permanently any contributor for other behaviors that they deem inappropriate,
|
||||||
|
threatening, offensive, or harmful.
|
||||||
|
|
||||||
|
## Scope
|
||||||
|
|
||||||
|
This Code of Conduct applies within all project spaces, and it also applies when
|
||||||
|
an individual is representing the project or its community in public spaces.
|
||||||
|
Examples of representing a project or community include using an official
|
||||||
|
project e-mail address, posting via an official social media account, or acting
|
||||||
|
as an appointed representative at an online or offline event. Representation of
|
||||||
|
a project may be further defined and clarified by project maintainers.
|
||||||
|
|
||||||
|
This Code of Conduct also applies outside the project spaces when there is a
|
||||||
|
reasonable belief that an individual's behavior may have a negative impact on
|
||||||
|
the project or its community.
|
||||||
|
|
||||||
|
## Enforcement
|
||||||
|
|
||||||
|
Instances of abusive, harassing, or otherwise unacceptable behavior may be
|
||||||
|
reported by contacting the project team at <opensource-conduct@meta.com>. All
|
||||||
|
complaints will be reviewed and investigated and will result in a response that
|
||||||
|
is deemed necessary and appropriate to the circumstances. The project team is
|
||||||
|
obligated to maintain confidentiality with regard to the reporter of an incident.
|
||||||
|
Further details of specific enforcement policies may be posted separately.
|
||||||
|
|
||||||
|
Project maintainers who do not follow or enforce the Code of Conduct in good
|
||||||
|
faith may face temporary or permanent repercussions as determined by other
|
||||||
|
members of the project's leadership.
|
||||||
|
|
||||||
|
## Attribution
|
||||||
|
|
||||||
|
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
|
||||||
|
available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
|
||||||
|
|
||||||
|
[homepage]: https://www.contributor-covenant.org
|
||||||
|
|
||||||
|
For answers to common questions about this code of conduct, see
|
||||||
|
https://www.contributor-covenant.org/faq
|
||||||
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# Contributing to ijepa
|
||||||
|
We want to make contributing to this project as easy and transparent as
|
||||||
|
possible.
|
||||||
|
|
||||||
|
## Pull Requests
|
||||||
|
We actively welcome your pull requests.
|
||||||
|
|
||||||
|
1. Fork the repo and create your branch from `main`.
|
||||||
|
2. If you've added code that should be tested, add tests.
|
||||||
|
3. If you've changed APIs, update the documentation.
|
||||||
|
4. Ensure the test suite passes.
|
||||||
|
5. Make sure your code lints.
|
||||||
|
6. If you haven't already, complete the Contributor License Agreement ("CLA").
|
||||||
|
|
||||||
|
## Contributor License Agreement ("CLA")
|
||||||
|
In order to accept your pull request, we need you to submit a CLA. You only need
|
||||||
|
to do this once to work on any of Meta's open source projects.
|
||||||
|
|
||||||
|
Complete your CLA here: <https://code.facebook.com/cla>
|
||||||
|
|
||||||
|
## Issues
|
||||||
|
We use GitHub issues to track public bugs. Please ensure your description is
|
||||||
|
clear and has sufficient instructions to be able to reproduce the issue.
|
||||||
|
|
||||||
|
Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
|
||||||
|
disclosure of security bugs. In those cases, please go through the process
|
||||||
|
outlined on that page and do not file a public issue.
|
||||||
|
|
||||||
|
## Coding Style
|
||||||
|
* 4 spaces for indentation rather than tabs
|
||||||
|
* 80 character line length
|
||||||
|
* PEP8 formatting
|
||||||
|
|
||||||
|
## License
|
||||||
|
By contributing to this repository, you agree that your contributions will be licensed
|
||||||
|
under the LICENSE file in the root directory of this source tree.
|
||||||
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|
|||||||
|
Attribution-NonCommercial 4.0 International
|
||||||
|
|
||||||
|
=======================================================================
|
||||||
|
|
||||||
|
Creative Commons Corporation ("Creative Commons") is not a law firm and
|
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|
does not provide legal services or legal advice. Distribution of
|
||||||
|
Creative Commons public licenses does not create a lawyer-client or
|
||||||
|
other relationship. Creative Commons makes its licenses and related
|
||||||
|
information available on an "as-is" basis. Creative Commons gives no
|
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|
warranties regarding its licenses, any material licensed under their
|
||||||
|
terms and conditions, or any related information. Creative Commons
|
||||||
|
disclaims all liability for damages resulting from their use to the
|
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|
fullest extent possible.
|
||||||
|
|
||||||
|
Using Creative Commons Public Licenses
|
||||||
|
|
||||||
|
Creative Commons public licenses provide a standard set of terms and
|
||||||
|
conditions that creators and other rights holders may use to share
|
||||||
|
original works of authorship and other material subject to copyright
|
||||||
|
and certain other rights specified in the public license below. The
|
||||||
|
following considerations are for informational purposes only, are not
|
||||||
|
exhaustive, and do not form part of our licenses.
|
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|
|
||||||
|
Considerations for licensors: Our public licenses are
|
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intended for use by those authorized to give the public
|
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|
permission to use material in ways otherwise restricted by
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copyright and certain other rights. Our licenses are
|
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|
irrevocable. Licensors should read and understand the terms
|
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|
and conditions of the license they choose before applying it.
|
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|
Licensors should also secure all rights necessary before
|
||||||
|
applying our licenses so that the public can reuse the
|
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material as expected. Licensors should clearly mark any
|
||||||
|
material not subject to the license. This includes other CC-
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|
licensed material, or material used under an exception or
|
||||||
|
limitation to copyright. More considerations for licensors:
|
||||||
|
wiki.creativecommons.org/Considerations_for_licensors
|
||||||
|
|
||||||
|
Considerations for the public: By using one of our public
|
||||||
|
licenses, a licensor grants the public permission to use the
|
||||||
|
licensed material under specified terms and conditions. If
|
||||||
|
the licensor's permission is not necessary for any reason--for
|
||||||
|
example, because of any applicable exception or limitation to
|
||||||
|
copyright--then that use is not regulated by the license. Our
|
||||||
|
licenses grant only permissions under copyright and certain
|
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|
other rights that a licensor has authority to grant. Use of
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the licensed material may still be restricted for other
|
||||||
|
reasons, including because others have copyright or other
|
||||||
|
rights in the material. A licensor may make special requests,
|
||||||
|
such as asking that all changes be marked or described.
|
||||||
|
Although not required by our licenses, you are encouraged to
|
||||||
|
respect those requests where reasonable. More_considerations
|
||||||
|
for the public:
|
||||||
|
wiki.creativecommons.org/Considerations_for_licensees
|
||||||
|
|
||||||
|
=======================================================================
|
||||||
|
|
||||||
|
Creative Commons Attribution-NonCommercial 4.0 International Public
|
||||||
|
License
|
||||||
|
|
||||||
|
By exercising the Licensed Rights (defined below), You accept and agree
|
||||||
|
to be bound by the terms and conditions of this Creative Commons
|
||||||
|
Attribution-NonCommercial 4.0 International Public License ("Public
|
||||||
|
License"). To the extent this Public License may be interpreted as a
|
||||||
|
contract, You are granted the Licensed Rights in consideration of Your
|
||||||
|
acceptance of these terms and conditions, and the Licensor grants You
|
||||||
|
such rights in consideration of benefits the Licensor receives from
|
||||||
|
making the Licensed Material available under these terms and
|
||||||
|
conditions.
|
||||||
|
|
||||||
|
Section 1 -- Definitions.
|
||||||
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|
||||||
|
a. Adapted Material means material subject to Copyright and Similar
|
||||||
|
Rights that is derived from or based upon the Licensed Material
|
||||||
|
and in which the Licensed Material is translated, altered,
|
||||||
|
arranged, transformed, or otherwise modified in a manner requiring
|
||||||
|
permission under the Copyright and Similar Rights held by the
|
||||||
|
Licensor. For purposes of this Public License, where the Licensed
|
||||||
|
Material is a musical work, performance, or sound recording,
|
||||||
|
Adapted Material is always produced where the Licensed Material is
|
||||||
|
synched in timed relation with a moving image.
|
||||||
|
|
||||||
|
b. Adapter's License means the license You apply to Your Copyright
|
||||||
|
and Similar Rights in Your contributions to Adapted Material in
|
||||||
|
accordance with the terms and conditions of this Public License.
|
||||||
|
|
||||||
|
c. Copyright and Similar Rights means copyright and/or similar rights
|
||||||
|
closely related to copyright including, without limitation,
|
||||||
|
performance, broadcast, sound recording, and Sui Generis Database
|
||||||
|
Rights, without regard to how the rights are labeled or
|
||||||
|
categorized. For purposes of this Public License, the rights
|
||||||
|
specified in Section 2(b)(1)-(2) are not Copyright and Similar
|
||||||
|
Rights.
|
||||||
|
d. Effective Technological Measures means those measures that, in the
|
||||||
|
absence of proper authority, may not be circumvented under laws
|
||||||
|
fulfilling obligations under Article 11 of the WIPO Copyright
|
||||||
|
Treaty adopted on December 20, 1996, and/or similar international
|
||||||
|
agreements.
|
||||||
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|
||||||
|
e. Exceptions and Limitations means fair use, fair dealing, and/or
|
||||||
|
any other exception or limitation to Copyright and Similar Rights
|
||||||
|
that applies to Your use of the Licensed Material.
|
||||||
|
|
||||||
|
f. Licensed Material means the artistic or literary work, database,
|
||||||
|
or other material to which the Licensor applied this Public
|
||||||
|
License.
|
||||||
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|
||||||
|
g. Licensed Rights means the rights granted to You subject to the
|
||||||
|
terms and conditions of this Public License, which are limited to
|
||||||
|
all Copyright and Similar Rights that apply to Your use of the
|
||||||
|
Licensed Material and that the Licensor has authority to license.
|
||||||
|
|
||||||
|
h. Licensor means the individual(s) or entity(ies) granting rights
|
||||||
|
under this Public License.
|
||||||
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|
||||||
|
i. NonCommercial means not primarily intended for or directed towards
|
||||||
|
commercial advantage or monetary compensation. For purposes of
|
||||||
|
this Public License, the exchange of the Licensed Material for
|
||||||
|
other material subject to Copyright and Similar Rights by digital
|
||||||
|
file-sharing or similar means is NonCommercial provided there is
|
||||||
|
no payment of monetary compensation in connection with the
|
||||||
|
exchange.
|
||||||
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|
||||||
|
j. Share means to provide material to the public by any means or
|
||||||
|
process that requires permission under the Licensed Rights, such
|
||||||
|
as reproduction, public display, public performance, distribution,
|
||||||
|
dissemination, communication, or importation, and to make material
|
||||||
|
available to the public including in ways that members of the
|
||||||
|
public may access the material from a place and at a time
|
||||||
|
individually chosen by them.
|
||||||
|
|
||||||
|
k. Sui Generis Database Rights means rights other than copyright
|
||||||
|
resulting from Directive 96/9/EC of the European Parliament and of
|
||||||
|
the Council of 11 March 1996 on the legal protection of databases,
|
||||||
|
as amended and/or succeeded, as well as other essentially
|
||||||
|
equivalent rights anywhere in the world.
|
||||||
|
|
||||||
|
l. You means the individual or entity exercising the Licensed Rights
|
||||||
|
under this Public License. Your has a corresponding meaning.
|
||||||
|
|
||||||
|
Section 2 -- Scope.
|
||||||
|
|
||||||
|
a. License grant.
|
||||||
|
|
||||||
|
1. Subject to the terms and conditions of this Public License,
|
||||||
|
the Licensor hereby grants You a worldwide, royalty-free,
|
||||||
|
non-sublicensable, non-exclusive, irrevocable license to
|
||||||
|
exercise the Licensed Rights in the Licensed Material to:
|
||||||
|
|
||||||
|
a. reproduce and Share the Licensed Material, in whole or
|
||||||
|
in part, for NonCommercial purposes only; and
|
||||||
|
|
||||||
|
b. produce, reproduce, and Share Adapted Material for
|
||||||
|
NonCommercial purposes only.
|
||||||
|
|
||||||
|
2. Exceptions and Limitations. For the avoidance of doubt, where
|
||||||
|
Exceptions and Limitations apply to Your use, this Public
|
||||||
|
License does not apply, and You do not need to comply with
|
||||||
|
its terms and conditions.
|
||||||
|
|
||||||
|
3. Term. The term of this Public License is specified in Section
|
||||||
|
6(a).
|
||||||
|
|
||||||
|
4. Media and formats; technical modifications allowed. The
|
||||||
|
Licensor authorizes You to exercise the Licensed Rights in
|
||||||
|
all media and formats whether now known or hereafter created,
|
||||||
|
and to make technical modifications necessary to do so. The
|
||||||
|
Licensor waives and/or agrees not to assert any right or
|
||||||
|
authority to forbid You from making technical modifications
|
||||||
|
necessary to exercise the Licensed Rights, including
|
||||||
|
technical modifications necessary to circumvent Effective
|
||||||
|
Technological Measures. For purposes of this Public License,
|
||||||
|
simply making modifications authorized by this Section 2(a)
|
||||||
|
(4) never produces Adapted Material.
|
||||||
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|
||||||
|
5. Downstream recipients.
|
||||||
|
|
||||||
|
a. Offer from the Licensor -- Licensed Material. Every
|
||||||
|
recipient of the Licensed Material automatically
|
||||||
|
receives an offer from the Licensor to exercise the
|
||||||
|
Licensed Rights under the terms and conditions of this
|
||||||
|
Public License.
|
||||||
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|
||||||
|
b. No downstream restrictions. You may not offer or impose
|
||||||
|
any additional or different terms or conditions on, or
|
||||||
|
apply any Effective Technological Measures to, the
|
||||||
|
Licensed Material if doing so restricts exercise of the
|
||||||
|
Licensed Rights by any recipient of the Licensed
|
||||||
|
Material.
|
||||||
|
|
||||||
|
6. No endorsement. Nothing in this Public License constitutes or
|
||||||
|
may be construed as permission to assert or imply that You
|
||||||
|
are, or that Your use of the Licensed Material is, connected
|
||||||
|
with, or sponsored, endorsed, or granted official status by,
|
||||||
|
the Licensor or others designated to receive attribution as
|
||||||
|
provided in Section 3(a)(1)(A)(i).
|
||||||
|
|
||||||
|
b. Other rights.
|
||||||
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|
||||||
|
1. Moral rights, such as the right of integrity, are not
|
||||||
|
licensed under this Public License, nor are publicity,
|
||||||
|
privacy, and/or other similar personality rights; however, to
|
||||||
|
the extent possible, the Licensor waives and/or agrees not to
|
||||||
|
assert any such rights held by the Licensor to the limited
|
||||||
|
extent necessary to allow You to exercise the Licensed
|
||||||
|
Rights, but not otherwise.
|
||||||
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|
||||||
|
2. Patent and trademark rights are not licensed under this
|
||||||
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Public License.
|
||||||
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|
||||||
|
3. To the extent possible, the Licensor waives any right to
|
||||||
|
collect royalties from You for the exercise of the Licensed
|
||||||
|
Rights, whether directly or through a collecting society
|
||||||
|
under any voluntary or waivable statutory or compulsory
|
||||||
|
licensing scheme. In all other cases the Licensor expressly
|
||||||
|
reserves any right to collect such royalties, including when
|
||||||
|
the Licensed Material is used other than for NonCommercial
|
||||||
|
purposes.
|
||||||
|
|
||||||
|
Section 3 -- License Conditions.
|
||||||
|
|
||||||
|
Your exercise of the Licensed Rights is expressly made subject to the
|
||||||
|
following conditions.
|
||||||
|
|
||||||
|
a. Attribution.
|
||||||
|
|
||||||
|
1. If You Share the Licensed Material (including in modified
|
||||||
|
form), You must:
|
||||||
|
|
||||||
|
a. retain the following if it is supplied by the Licensor
|
||||||
|
with the Licensed Material:
|
||||||
|
|
||||||
|
i. identification of the creator(s) of the Licensed
|
||||||
|
Material and any others designated to receive
|
||||||
|
attribution, in any reasonable manner requested by
|
||||||
|
the Licensor (including by pseudonym if
|
||||||
|
designated);
|
||||||
|
|
||||||
|
ii. a copyright notice;
|
||||||
|
|
||||||
|
iii. a notice that refers to this Public License;
|
||||||
|
|
||||||
|
iv. a notice that refers to the disclaimer of
|
||||||
|
warranties;
|
||||||
|
|
||||||
|
v. a URI or hyperlink to the Licensed Material to the
|
||||||
|
extent reasonably practicable;
|
||||||
|
|
||||||
|
b. indicate if You modified the Licensed Material and
|
||||||
|
retain an indication of any previous modifications; and
|
||||||
|
|
||||||
|
c. indicate the Licensed Material is licensed under this
|
||||||
|
Public License, and include the text of, or the URI or
|
||||||
|
hyperlink to, this Public License.
|
||||||
|
|
||||||
|
2. You may satisfy the conditions in Section 3(a)(1) in any
|
||||||
|
reasonable manner based on the medium, means, and context in
|
||||||
|
which You Share the Licensed Material. For example, it may be
|
||||||
|
reasonable to satisfy the conditions by providing a URI or
|
||||||
|
hyperlink to a resource that includes the required
|
||||||
|
information.
|
||||||
|
|
||||||
|
3. If requested by the Licensor, You must remove any of the
|
||||||
|
information required by Section 3(a)(1)(A) to the extent
|
||||||
|
reasonably practicable.
|
||||||
|
|
||||||
|
4. If You Share Adapted Material You produce, the Adapter's
|
||||||
|
License You apply must not prevent recipients of the Adapted
|
||||||
|
Material from complying with this Public License.
|
||||||
|
|
||||||
|
Section 4 -- Sui Generis Database Rights.
|
||||||
|
|
||||||
|
Where the Licensed Rights include Sui Generis Database Rights that
|
||||||
|
apply to Your use of the Licensed Material:
|
||||||
|
|
||||||
|
a. for the avoidance of doubt, Section 2(a)(1) grants You the right
|
||||||
|
to extract, reuse, reproduce, and Share all or a substantial
|
||||||
|
portion of the contents of the database for NonCommercial purposes
|
||||||
|
only;
|
||||||
|
|
||||||
|
b. if You include all or a substantial portion of the database
|
||||||
|
contents in a database in which You have Sui Generis Database
|
||||||
|
Rights, then the database in which You have Sui Generis Database
|
||||||
|
Rights (but not its individual contents) is Adapted Material; and
|
||||||
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|
||||||
|
c. You must comply with the conditions in Section 3(a) if You Share
|
||||||
|
all or a substantial portion of the contents of the database.
|
||||||
|
|
||||||
|
For the avoidance of doubt, this Section 4 supplements and does not
|
||||||
|
replace Your obligations under this Public License where the Licensed
|
||||||
|
Rights include other Copyright and Similar Rights.
|
||||||
|
|
||||||
|
Section 5 -- Disclaimer of Warranties and Limitation of Liability.
|
||||||
|
|
||||||
|
a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE
|
||||||
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EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS
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||||||
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AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF
|
||||||
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ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS,
|
||||||
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IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION,
|
||||||
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WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR
|
||||||
|
PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS,
|
||||||
|
ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT
|
||||||
|
KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT
|
||||||
|
ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU.
|
||||||
|
|
||||||
|
b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE
|
||||||
|
TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION,
|
||||||
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|
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INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES,
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COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR
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||||||
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USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN
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ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR
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||||||
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DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR
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||||||
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IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.
|
||||||
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|
||||||
|
c. The disclaimer of warranties and limitation of liability provided
|
||||||
|
above shall be interpreted in a manner that, to the extent
|
||||||
|
possible, most closely approximates an absolute disclaimer and
|
||||||
|
waiver of all liability.
|
||||||
|
|
||||||
|
Section 6 -- Term and Termination.
|
||||||
|
|
||||||
|
a. This Public License applies for the term of the Copyright and
|
||||||
|
Similar Rights licensed here. However, if You fail to comply with
|
||||||
|
this Public License, then Your rights under this Public License
|
||||||
|
terminate automatically.
|
||||||
|
|
||||||
|
b. Where Your right to use the Licensed Material has terminated under
|
||||||
|
Section 6(a), it reinstates:
|
||||||
|
|
||||||
|
1. automatically as of the date the violation is cured, provided
|
||||||
|
it is cured within 30 days of Your discovery of the
|
||||||
|
violation; or
|
||||||
|
|
||||||
|
2. upon express reinstatement by the Licensor.
|
||||||
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|
||||||
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For the avoidance of doubt, this Section 6(b) does not affect any
|
||||||
|
right the Licensor may have to seek remedies for Your violations
|
||||||
|
of this Public License.
|
||||||
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|
||||||
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c. For the avoidance of doubt, the Licensor may also offer the
|
||||||
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|
||||||
|
distributing the Licensed Material at any time; however, doing so
|
||||||
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will not terminate this Public License.
|
||||||
|
|
||||||
|
d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
|
||||||
|
License.
|
||||||
|
|
||||||
|
Section 7 -- Other Terms and Conditions.
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||||||
|
|
||||||
|
a. The Licensor shall not be bound by any additional or different
|
||||||
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terms or conditions communicated by You unless expressly agreed.
|
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|
||||||
|
b. Any arrangements, understandings, or agreements regarding the
|
||||||
|
Licensed Material not stated herein are separate from and
|
||||||
|
independent of the terms and conditions of this Public License.
|
||||||
|
|
||||||
|
Section 8 -- Interpretation.
|
||||||
|
|
||||||
|
a. For the avoidance of doubt, this Public License does not, and
|
||||||
|
shall not be interpreted to, reduce, limit, restrict, or impose
|
||||||
|
conditions on any use of the Licensed Material that could lawfully
|
||||||
|
be made without permission under this Public License.
|
||||||
|
|
||||||
|
b. To the extent possible, if any provision of this Public License is
|
||||||
|
deemed unenforceable, it shall be automatically reformed to the
|
||||||
|
minimum extent necessary to make it enforceable. If the provision
|
||||||
|
cannot be reformed, it shall be severed from this Public License
|
||||||
|
without affecting the enforceability of the remaining terms and
|
||||||
|
conditions.
|
||||||
|
|
||||||
|
c. No term or condition of this Public License will be waived and no
|
||||||
|
failure to comply consented to unless expressly agreed to by the
|
||||||
|
Licensor.
|
||||||
|
|
||||||
|
d. Nothing in this Public License constitutes or may be interpreted
|
||||||
|
as a limitation upon, or waiver of, any privileges and immunities
|
||||||
|
that apply to the Licensor or You, including from the legal
|
||||||
|
processes of any jurisdiction or authority.
|
||||||
|
|
||||||
|
=======================================================================
|
||||||
|
|
||||||
|
Creative Commons is not a party to its public
|
||||||
|
licenses. Notwithstanding, Creative Commons may elect to apply one of
|
||||||
|
its public licenses to material it publishes and in those instances
|
||||||
|
will be considered the “Licensor.” The text of the Creative Commons
|
||||||
|
public licenses is dedicated to the public domain under the CC0 Public
|
||||||
|
Domain Dedication. Except for the limited purpose of indicating that
|
||||||
|
material is shared under a Creative Commons public license or as
|
||||||
|
otherwise permitted by the Creative Commons policies published at
|
||||||
|
creativecommons.org/policies, Creative Commons does not authorize the
|
||||||
|
use of the trademark "Creative Commons" or any other trademark or logo
|
||||||
|
of Creative Commons without its prior written consent including,
|
||||||
|
without limitation, in connection with any unauthorized modifications
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||||||
|
to any of its public licenses or any other arrangements,
|
||||||
|
understandings, or agreements concerning use of licensed material. For
|
||||||
|
the avoidance of doubt, this paragraph does not form part of the
|
||||||
|
public licenses.
|
||||||
|
|
||||||
|
Creative Commons may be contacted at creativecommons.org.
|
||||||
@@ -0,0 +1,154 @@
|
|||||||
|
# I-JEPA
|
||||||
|
|
||||||
|
Official PyTorch codebase for I-JEPA (the **Image-based Joint-Embedding Predictive Architecture**) published @ CVPR-23.
|
||||||
|
[\[arXiv\]](https://arxiv.org/pdf/2301.08243.pdf) [\[JEPAs\]](https://ai.facebook.com/blog/yann-lecun-advances-in-ai-research/) [\[blogpost\]](https://ai.facebook.com/blog/yann-lecun-ai-model-i-jepa/)
|
||||||
|
|
||||||
|
## Method
|
||||||
|
I-JEPA is a method for self-supervised learning. At a high level, I-JEPA predicts the representations of part of an image from the representations of other parts of the same image. Notably, this approach learns semantic image features:
|
||||||
|
1. without relying on pre-specified invariances to hand-crafted data transformations, which tend to be biased for particular downstream tasks,
|
||||||
|
2. and without having the model fill in pixel-level details, which tend to result in learning less semantically meaningful representations.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## Visualizations
|
||||||
|
|
||||||
|
As opposed to generative methods that have a pixel decoder, I-JEPA has a predictor that makes predictions in latent space.
|
||||||
|
The predictor in I-JEPA can be seen as a primitive (and restricted) world-model that is able to model spatial uncertainty in a static image from a partially observable context.
|
||||||
|
This world model is semantic in the sense that it predicts high level information about unseen regions in the image, rather than pixel-level details.
|
||||||
|
|
||||||
|
We trained a stochastic decoder that maps the I-JEPA predicted representations back in pixel space as sketches.
|
||||||
|
The model correctly captures positional uncertainty and produces high-level object parts with the correct pose (e.g., dog’s head, wolf’s front legs).
|
||||||
|
|
||||||
|
|
||||||
|
<sub>
|
||||||
|
Caption: Illustrating how the predictor learns to model the semantics of the world. For each image, the portion outside of the blue box is encoded and given to the predictor as context. The predictor outputs a representation for what it expects to be in the region within the blue box. To visualize the prediction, we train a generative model that produces a sketch of the contents represented by the predictor output, and we show a sample output within the blue box. The predictor recognizes the semantics of what parts should be filled in (the top of the dog’s head, the bird’s leg, the wolf’s legs, the other side of the building).
|
||||||
|
</sub>
|
||||||
|
|
||||||
|
## Evaluations
|
||||||
|
|
||||||
|
I-JEPA pretraining is also computationally efficient.
|
||||||
|
It does not involve any overhead associated with applying more computationally intensive data augmentations to produce multiple views.
|
||||||
|
Only one view of the image needs to be processed by the target encoder, and only the context blocks need to be processed by the context encoder.
|
||||||
|
Empirically, I-JEPA learns strong off-the-shelf semantic representations without the use of hand-crafted view augmentations.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## Pretrained models
|
||||||
|
|
||||||
|
<table>
|
||||||
|
<tr>
|
||||||
|
<th colspan="1">arch.</th>
|
||||||
|
<th colspan="1">patch size</th>
|
||||||
|
<th colspan="1">resolution</th>
|
||||||
|
<th colspan="1">epochs</th>
|
||||||
|
<th colspan="1">data</th>
|
||||||
|
<th colspan="3">download</th>
|
||||||
|
</tr>
|
||||||
|
<tr>
|
||||||
|
<td>ViT-H</td>
|
||||||
|
<td>14x14</td>
|
||||||
|
<td>224x224</td>
|
||||||
|
<td>300</td>
|
||||||
|
<td>ImageNet-1K</td>
|
||||||
|
<td><a href="https://dl.fbaipublicfiles.com/ijepa/IN1K-vit.h.14-300e.pth.tar">full checkpoint</a></td>
|
||||||
|
<td><a href="https://dl.fbaipublicfiles.com/ijepa/IN1K-vit.h.14-logs-rank.0.csv">logs</a></td>
|
||||||
|
<td><a href="https://github.com/facebookresearch/ijepa/blob/main/configs/in1k_vith14_ep300.yaml">configs</a></td>
|
||||||
|
</tr>
|
||||||
|
<tr>
|
||||||
|
<td>ViT-H</td>
|
||||||
|
<td>16x16</td>
|
||||||
|
<td>448x448</td>
|
||||||
|
<td>300</td>
|
||||||
|
<td>ImageNet-1K</td>
|
||||||
|
<td><a href="https://dl.fbaipublicfiles.com/ijepa/IN1K-vit.h.16-448px-300e.pth.tar">full checkpoint</a></td>
|
||||||
|
<td><a href="https://dl.fbaipublicfiles.com/ijepa/IN1K-vit.h.16.448-logs-rank.0.csv">logs</a></td>
|
||||||
|
<td><a href="https://github.com/facebookresearch/ijepa/blob/main/configs/in1k_vith16-448_ep300.yaml">configs</a></td>
|
||||||
|
</tr>
|
||||||
|
<tr>
|
||||||
|
<td>ViT-H</td>
|
||||||
|
<td>14x14</td>
|
||||||
|
<td>224x224</td>
|
||||||
|
<td>66</td>
|
||||||
|
<td>ImageNet-22K</td>
|
||||||
|
<td><a href="https://dl.fbaipublicfiles.com/ijepa/IN22K-vit.h.14-900e.pth.tar">full checkpoint</a></td>
|
||||||
|
<td><a href="https://dl.fbaipublicfiles.com/ijepa/IN22K-vit.h.14-logs-rank.0.csv">logs</a></td>
|
||||||
|
<td><a href="https://github.com/facebookresearch/ijepa/blob/main/configs/in22k_vith14_ep66.yaml">configs</a></td>
|
||||||
|
</tr>
|
||||||
|
<tr>
|
||||||
|
<td>ViT-g</td>
|
||||||
|
<td>16x16</td>
|
||||||
|
<td>224x224</td>
|
||||||
|
<td>44</td>
|
||||||
|
<td>ImageNet-22K</td>
|
||||||
|
<td><a href="https://dl.fbaipublicfiles.com/ijepa/IN22K-vit.g.16-600e.pth.tar">full checkpoint</a></td>
|
||||||
|
<td><a href="https://dl.fbaipublicfiles.com/ijepa/IN22K-vit.g.16-logs-rank.0.csv">logs</a></td>
|
||||||
|
<td><a href="https://github.com/facebookresearch/ijepa/blob/main/configs/in22k_vitg16_ep44.yaml">configs</a></td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
|
||||||
|
## Code Structure
|
||||||
|
|
||||||
|
```
|
||||||
|
.
|
||||||
|
├── configs # directory in which all experiment '.yaml' configs are stored
|
||||||
|
├── src # the package
|
||||||
|
│ ├── train.py # the I-JEPA training loop
|
||||||
|
│ ├── helper.py # helper functions for init of models & opt/loading checkpoint
|
||||||
|
│ ├── transforms.py # pre-train data transforms
|
||||||
|
│ ├── datasets # datasets, data loaders, ...
|
||||||
|
│ ├── models # model definitions
|
||||||
|
│ ├── masks # mask collators, masking utilities, ...
|
||||||
|
│ └── utils # shared utilities
|
||||||
|
├── main_distributed.py # entrypoint for launch distributed I-JEPA pretraining on SLURM cluster
|
||||||
|
└── main.py # entrypoint for launch I-JEPA pretraining locally on your machine
|
||||||
|
```
|
||||||
|
|
||||||
|
**Config files:**
|
||||||
|
Note that all experiment parameters are specified in config files (as opposed to command-line-arguments). See the [configs/](configs/) directory for example config files.
|
||||||
|
|
||||||
|
## Launching I-JEPA pretraining
|
||||||
|
|
||||||
|
### Single-GPU training
|
||||||
|
This implementation starts from the [main.py](main.py), which parses the experiment config file and runs the pre-training locally on a multi-GPU (or single-GPU) machine. For example, to run I-JEPA pretraining on GPUs "0","1", and "2" on a local machine using the config [configs/in1k_vith14_ep300.yaml](configs/in1k_vith14_ep300.yaml), type the command:
|
||||||
|
```
|
||||||
|
python main.py \
|
||||||
|
--fname configs/in1k_vith14_ep300.yaml \
|
||||||
|
--devices cuda:0 cuda:1 cuda:2
|
||||||
|
```
|
||||||
|
*Note: This example is just used for illustrative purposes, as the ViT-H/14 config should be run on 16 A100 80G GPUs for an effective batch-size of 2048, in order to reproduce our results.*
|
||||||
|
|
||||||
|
### Multi-GPU training
|
||||||
|
In the multi-GPU setting, the implementation starts from [main_distributed.py](main_distributed.py), which, in addition to parsing the config file, also allows for specifying details about distributed training. For distributed training, we use the popular open-source [submitit](https://github.com/facebookincubator/submitit) tool and provide examples for a SLURM cluster.
|
||||||
|
|
||||||
|
For example, to pre-train on 16 A100 80G GPUs using the pre-training experiment configs specificed inside [configs/in1k_vith14_ep300.yaml](configs/in1k_vith14_ep300.yaml), type the command:
|
||||||
|
```
|
||||||
|
python main_distributed.py \
|
||||||
|
--fname configs/in1k_vith14_ep300.yaml \
|
||||||
|
--folder $path_to_save_submitit_logs \
|
||||||
|
--partition $slurm_partition \
|
||||||
|
--nodes 2 --tasks-per-node 8 \
|
||||||
|
--time 1000
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
### Requirements
|
||||||
|
* Python 3.8 (or newer)
|
||||||
|
* PyTorch 2.0
|
||||||
|
* torchvision
|
||||||
|
* Other dependencies: pyyaml, numpy, opencv, submitit
|
||||||
|
|
||||||
|
## License
|
||||||
|
See the [LICENSE](./LICENSE) file for details about the license under which this code is made available.
|
||||||
|
|
||||||
|
## Citation
|
||||||
|
If you find this repository useful in your research, please consider giving a star :star: and a citation
|
||||||
|
```
|
||||||
|
@article{assran2023self,
|
||||||
|
title={Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture},
|
||||||
|
author={Assran, Mahmoud and Duval, Quentin and Misra, Ishan and Bojanowski, Piotr and Vincent, Pascal and Rabbat, Michael and LeCun, Yann and Ballas, Nicolas},
|
||||||
|
journal={arXiv preprint arXiv:2301.08243},
|
||||||
|
year={2023}
|
||||||
|
}
|
||||||
@@ -0,0 +1,52 @@
|
|||||||
|
data:
|
||||||
|
batch_size: 128
|
||||||
|
color_jitter_strength: 0.0
|
||||||
|
crop_scale:
|
||||||
|
- 0.3
|
||||||
|
- 1.0
|
||||||
|
crop_size: 224
|
||||||
|
image_folder: imagenet_full_size/061417/
|
||||||
|
num_workers: 10
|
||||||
|
pin_mem: true
|
||||||
|
root_path: $replace_this_with_absolute_path_to_your_datasets_directory
|
||||||
|
use_color_distortion: false
|
||||||
|
use_gaussian_blur: false
|
||||||
|
use_horizontal_flip: false
|
||||||
|
logging:
|
||||||
|
folder: $replace_this_with_path_for_experiment_logs/vith14.224-bs.2048-ep.300/
|
||||||
|
write_tag: jepa
|
||||||
|
mask:
|
||||||
|
allow_overlap: false
|
||||||
|
aspect_ratio:
|
||||||
|
- 0.75
|
||||||
|
- 1.5
|
||||||
|
enc_mask_scale:
|
||||||
|
- 0.85
|
||||||
|
- 1.0
|
||||||
|
min_keep: 10
|
||||||
|
num_enc_masks: 1
|
||||||
|
num_pred_masks: 4
|
||||||
|
patch_size: 14
|
||||||
|
pred_mask_scale:
|
||||||
|
- 0.15
|
||||||
|
- 0.2
|
||||||
|
meta:
|
||||||
|
copy_data: false
|
||||||
|
load_checkpoint: false
|
||||||
|
model_name: vit_huge
|
||||||
|
pred_depth: 12
|
||||||
|
pred_emb_dim: 384
|
||||||
|
read_checkpoint: null
|
||||||
|
use_bfloat16: true
|
||||||
|
optimization:
|
||||||
|
ema:
|
||||||
|
- 0.996
|
||||||
|
- 1.0
|
||||||
|
epochs: 300
|
||||||
|
final_lr: 1.0e-06
|
||||||
|
final_weight_decay: 0.4
|
||||||
|
ipe_scale: 1.0
|
||||||
|
lr: 0.001
|
||||||
|
start_lr: 0.0002
|
||||||
|
warmup: 40
|
||||||
|
weight_decay: 0.04
|
||||||
@@ -0,0 +1,52 @@
|
|||||||
|
data:
|
||||||
|
batch_size: 16
|
||||||
|
color_jitter_strength: 0.0
|
||||||
|
crop_scale:
|
||||||
|
- 0.3
|
||||||
|
- 1.0
|
||||||
|
crop_size: 448
|
||||||
|
image_folder: imagenet_full_size/061417/
|
||||||
|
num_workers: 10
|
||||||
|
pin_mem: true
|
||||||
|
root_path: $replace_this_with_absolute_path_to_your_datasets_directory
|
||||||
|
use_color_distortion: false
|
||||||
|
use_gaussian_blur: false
|
||||||
|
use_horizontal_flip: false
|
||||||
|
logging:
|
||||||
|
folder: $replace_this_with_path_for_experiment_logs/vith16.448-bs.2048-ep.300/
|
||||||
|
write_tag: jepa
|
||||||
|
mask:
|
||||||
|
allow_overlap: false
|
||||||
|
aspect_ratio:
|
||||||
|
- 0.75
|
||||||
|
- 1.5
|
||||||
|
enc_mask_scale:
|
||||||
|
- 0.85
|
||||||
|
- 1.0
|
||||||
|
min_keep: 10
|
||||||
|
num_enc_masks: 1
|
||||||
|
num_pred_masks: 4
|
||||||
|
patch_size: 16
|
||||||
|
pred_mask_scale:
|
||||||
|
- 0.15
|
||||||
|
- 0.2
|
||||||
|
meta:
|
||||||
|
copy_data: false
|
||||||
|
load_checkpoint: false
|
||||||
|
model_name: vit_huge
|
||||||
|
pred_depth: 12
|
||||||
|
pred_emb_dim: 384
|
||||||
|
read_checkpoint: null
|
||||||
|
use_bfloat16: true
|
||||||
|
optimization:
|
||||||
|
ema:
|
||||||
|
- 0.996
|
||||||
|
- 1.0
|
||||||
|
epochs: 300
|
||||||
|
final_lr: 1.0e-06
|
||||||
|
final_weight_decay: 0.4
|
||||||
|
ipe_scale: 1.0
|
||||||
|
lr: 0.001
|
||||||
|
start_lr: 0.0002
|
||||||
|
warmup: 40
|
||||||
|
weight_decay: 0.04
|
||||||
@@ -0,0 +1,56 @@
|
|||||||
|
# NOTE: ImageNet-22K (IN22k) dataloader is not implemented
|
||||||
|
# please implement IN22k data loader based on your data
|
||||||
|
# storage format, and update the paths in your config
|
||||||
|
# to load from your IN22k dataset.
|
||||||
|
data:
|
||||||
|
batch_size: 16
|
||||||
|
color_jitter_strength: 0.0
|
||||||
|
crop_scale:
|
||||||
|
- 0.3
|
||||||
|
- 1.0
|
||||||
|
crop_size: 224
|
||||||
|
image_folder: imagenet_full_size/061417/
|
||||||
|
num_workers: 10
|
||||||
|
pin_mem: true
|
||||||
|
root_path: $replace_this_with_absolute_path_to_your_datasets_directory
|
||||||
|
use_color_distortion: false
|
||||||
|
use_gaussian_blur: false
|
||||||
|
use_horizontal_flip: false
|
||||||
|
logging:
|
||||||
|
folder: $replace_this_with_path_for_experiment_logs/in2kk_vith16.224-bs.2048-ep.44/
|
||||||
|
write_tag: jepa
|
||||||
|
mask:
|
||||||
|
allow_overlap: false
|
||||||
|
aspect_ratio:
|
||||||
|
- 0.75
|
||||||
|
- 1.5
|
||||||
|
enc_mask_scale:
|
||||||
|
- 0.85
|
||||||
|
- 1.0
|
||||||
|
min_keep: 10
|
||||||
|
num_enc_masks: 1
|
||||||
|
num_pred_masks: 4
|
||||||
|
patch_size: 16
|
||||||
|
pred_mask_scale:
|
||||||
|
- 0.15
|
||||||
|
- 0.2
|
||||||
|
meta:
|
||||||
|
copy_data: false
|
||||||
|
load_checkpoint: false
|
||||||
|
model_name: vit_giant
|
||||||
|
pred_depth: 16
|
||||||
|
pred_emb_dim: 384
|
||||||
|
read_checkpoint: null
|
||||||
|
use_bfloat16: true
|
||||||
|
optimization:
|
||||||
|
ema:
|
||||||
|
- 0.996
|
||||||
|
- 1.0
|
||||||
|
epochs: 44
|
||||||
|
final_lr: 1.0e-06
|
||||||
|
final_weight_decay: 0.4
|
||||||
|
ipe_scale: 1.0
|
||||||
|
lr: 0.001
|
||||||
|
start_lr: 0.0002
|
||||||
|
warmup: 3
|
||||||
|
weight_decay: 0.04
|
||||||
@@ -0,0 +1,56 @@
|
|||||||
|
# NOTE: ImageNet-22K (IN22k) dataloader is not implemented
|
||||||
|
# please implement IN22k data loader based on your data
|
||||||
|
# storage format, and update the paths in your config
|
||||||
|
# to load from your IN22k dataset.
|
||||||
|
data:
|
||||||
|
batch_size: 32
|
||||||
|
color_jitter_strength: 0.0
|
||||||
|
crop_scale:
|
||||||
|
- 0.3
|
||||||
|
- 1.0
|
||||||
|
crop_size: 224
|
||||||
|
image_folder: imagenet_full_size/061417/
|
||||||
|
num_workers: 10
|
||||||
|
pin_mem: true
|
||||||
|
root_path: $replace_this_with_absolute_path_to_your_datasets_directory
|
||||||
|
use_color_distortion: false
|
||||||
|
use_gaussian_blur: false
|
||||||
|
use_horizontal_flip: false
|
||||||
|
logging:
|
||||||
|
folder: $replace_this_with_path_for_experiment_logs/in2kk_vith14.224-bs.2048-ep.66/
|
||||||
|
write_tag: jepa
|
||||||
|
mask:
|
||||||
|
allow_overlap: false
|
||||||
|
aspect_ratio:
|
||||||
|
- 0.75
|
||||||
|
- 1.5
|
||||||
|
enc_mask_scale:
|
||||||
|
- 0.85
|
||||||
|
- 1.0
|
||||||
|
min_keep: 10
|
||||||
|
num_enc_masks: 1
|
||||||
|
num_pred_masks: 4
|
||||||
|
patch_size: 14
|
||||||
|
pred_mask_scale:
|
||||||
|
- 0.15
|
||||||
|
- 0.2
|
||||||
|
meta:
|
||||||
|
copy_data: false
|
||||||
|
load_checkpoint: false
|
||||||
|
model_name: vit_huge
|
||||||
|
pred_depth: 12
|
||||||
|
pred_emb_dim: 384
|
||||||
|
read_checkpoint: null
|
||||||
|
use_bfloat16: true
|
||||||
|
optimization:
|
||||||
|
ema:
|
||||||
|
- 0.996
|
||||||
|
- 1.0
|
||||||
|
epochs: 66
|
||||||
|
final_lr: 1.0e-06
|
||||||
|
final_weight_decay: 0.4
|
||||||
|
ipe_scale: 1.0
|
||||||
|
lr: 0.001
|
||||||
|
start_lr: 0.0002
|
||||||
|
warmup: 3
|
||||||
|
weight_decay: 0.04
|
||||||
@@ -0,0 +1,65 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
|
||||||
|
import multiprocessing as mp
|
||||||
|
|
||||||
|
import pprint
|
||||||
|
import yaml
|
||||||
|
|
||||||
|
from src.utils.distributed import init_distributed
|
||||||
|
from src.train import main as app_main
|
||||||
|
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument(
|
||||||
|
'--fname', type=str,
|
||||||
|
help='name of config file to load',
|
||||||
|
default='configs.yaml')
|
||||||
|
parser.add_argument(
|
||||||
|
'--devices', type=str, nargs='+', default=['cuda:0'],
|
||||||
|
help='which devices to use on local machine')
|
||||||
|
|
||||||
|
|
||||||
|
def process_main(rank, fname, world_size, devices):
|
||||||
|
import os
|
||||||
|
os.environ['CUDA_VISIBLE_DEVICES'] = str(devices[rank].split(':')[-1])
|
||||||
|
|
||||||
|
import logging
|
||||||
|
logging.basicConfig()
|
||||||
|
logger = logging.getLogger()
|
||||||
|
if rank == 0:
|
||||||
|
logger.setLevel(logging.INFO)
|
||||||
|
else:
|
||||||
|
logger.setLevel(logging.ERROR)
|
||||||
|
|
||||||
|
logger.info(f'called-params {fname}')
|
||||||
|
|
||||||
|
# -- load script params
|
||||||
|
params = None
|
||||||
|
with open(fname, 'r') as y_file:
|
||||||
|
params = yaml.load(y_file, Loader=yaml.FullLoader)
|
||||||
|
logger.info('loaded params...')
|
||||||
|
pp = pprint.PrettyPrinter(indent=4)
|
||||||
|
pp.pprint(params)
|
||||||
|
|
||||||
|
world_size, rank = init_distributed(rank_and_world_size=(rank, world_size))
|
||||||
|
logger.info(f'Running... (rank: {rank}/{world_size})')
|
||||||
|
app_main(args=params)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
num_gpus = len(args.devices)
|
||||||
|
mp.set_start_method('spawn')
|
||||||
|
|
||||||
|
for rank in range(num_gpus):
|
||||||
|
mp.Process(
|
||||||
|
target=process_main,
|
||||||
|
args=(rank, args.fname, num_gpus, args.devices)
|
||||||
|
).start()
|
||||||
@@ -0,0 +1,104 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import pprint
|
||||||
|
import sys
|
||||||
|
import yaml
|
||||||
|
|
||||||
|
import submitit
|
||||||
|
|
||||||
|
from src.train import main as app_main
|
||||||
|
|
||||||
|
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
|
||||||
|
logger = logging.getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument(
|
||||||
|
'--folder', type=str,
|
||||||
|
help='location to save submitit logs')
|
||||||
|
parser.add_argument(
|
||||||
|
'--batch-launch', action='store_true',
|
||||||
|
help='whether fname points to a file to batch-lauch several config files')
|
||||||
|
parser.add_argument(
|
||||||
|
'--fname', type=str,
|
||||||
|
help='yaml file containing config file names to launch',
|
||||||
|
default='configs.yaml')
|
||||||
|
parser.add_argument(
|
||||||
|
'--partition', type=str,
|
||||||
|
help='cluster partition to submit jobs on')
|
||||||
|
parser.add_argument(
|
||||||
|
'--nodes', type=int, default=1,
|
||||||
|
help='num. nodes to request for job')
|
||||||
|
parser.add_argument(
|
||||||
|
'--tasks-per-node', type=int, default=1,
|
||||||
|
help='num. procs to per node')
|
||||||
|
parser.add_argument(
|
||||||
|
'--time', type=int, default=4300,
|
||||||
|
help='time in minutes to run job')
|
||||||
|
|
||||||
|
|
||||||
|
class Trainer:
|
||||||
|
|
||||||
|
def __init__(self, fname='configs.yaml', load_model=None):
|
||||||
|
self.fname = fname
|
||||||
|
self.load_model = load_model
|
||||||
|
|
||||||
|
def __call__(self):
|
||||||
|
fname = self.fname
|
||||||
|
load_model = self.load_model
|
||||||
|
logger.info(f'called-params {fname}')
|
||||||
|
|
||||||
|
# -- load script params
|
||||||
|
params = None
|
||||||
|
with open(fname, 'r') as y_file:
|
||||||
|
params = yaml.load(y_file, Loader=yaml.FullLoader)
|
||||||
|
logger.info('loaded params...')
|
||||||
|
pp = pprint.PrettyPrinter(indent=4)
|
||||||
|
pp.pprint(params)
|
||||||
|
|
||||||
|
resume_preempt = False if load_model is None else load_model
|
||||||
|
app_main(args=params, resume_preempt=resume_preempt)
|
||||||
|
|
||||||
|
def checkpoint(self):
|
||||||
|
fb_trainer = Trainer(self.fname, True)
|
||||||
|
return submitit.helpers.DelayedSubmission(fb_trainer,)
|
||||||
|
|
||||||
|
|
||||||
|
def launch():
|
||||||
|
executor = submitit.AutoExecutor(
|
||||||
|
folder=os.path.join(args.folder, 'job_%j'),
|
||||||
|
slurm_max_num_timeout=20)
|
||||||
|
executor.update_parameters(
|
||||||
|
slurm_partition=args.partition,
|
||||||
|
slurm_mem_per_gpu='55G',
|
||||||
|
timeout_min=args.time,
|
||||||
|
nodes=args.nodes,
|
||||||
|
tasks_per_node=args.tasks_per_node,
|
||||||
|
cpus_per_task=10,
|
||||||
|
gpus_per_node=args.tasks_per_node)
|
||||||
|
|
||||||
|
config_fnames = [args.fname]
|
||||||
|
|
||||||
|
jobs, trainers = [], []
|
||||||
|
with executor.batch():
|
||||||
|
for cf in config_fnames:
|
||||||
|
fb_trainer = Trainer(cf)
|
||||||
|
job = executor.submit(fb_trainer,)
|
||||||
|
trainers.append(fb_trainer)
|
||||||
|
jobs.append(job)
|
||||||
|
|
||||||
|
for job in jobs:
|
||||||
|
print(job.job_id)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
args = parser.parse_args()
|
||||||
|
launch()
|
||||||
@@ -0,0 +1,223 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
import os
|
||||||
|
import subprocess
|
||||||
|
import time
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
from logging import getLogger
|
||||||
|
|
||||||
|
import torch
|
||||||
|
import torchvision
|
||||||
|
|
||||||
|
_GLOBAL_SEED = 0
|
||||||
|
logger = getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
def make_imagenet1k(
|
||||||
|
transform,
|
||||||
|
batch_size,
|
||||||
|
collator=None,
|
||||||
|
pin_mem=True,
|
||||||
|
num_workers=8,
|
||||||
|
world_size=1,
|
||||||
|
rank=0,
|
||||||
|
root_path=None,
|
||||||
|
image_folder=None,
|
||||||
|
training=True,
|
||||||
|
copy_data=False,
|
||||||
|
drop_last=True,
|
||||||
|
subset_file=None
|
||||||
|
):
|
||||||
|
dataset = ImageNet(
|
||||||
|
root=root_path,
|
||||||
|
image_folder=image_folder,
|
||||||
|
transform=transform,
|
||||||
|
train=training,
|
||||||
|
copy_data=copy_data,
|
||||||
|
index_targets=False)
|
||||||
|
if subset_file is not None:
|
||||||
|
dataset = ImageNetSubset(dataset, subset_file)
|
||||||
|
logger.info('ImageNet dataset created')
|
||||||
|
dist_sampler = torch.utils.data.distributed.DistributedSampler(
|
||||||
|
dataset=dataset,
|
||||||
|
num_replicas=world_size,
|
||||||
|
rank=rank)
|
||||||
|
data_loader = torch.utils.data.DataLoader(
|
||||||
|
dataset,
|
||||||
|
collate_fn=collator,
|
||||||
|
sampler=dist_sampler,
|
||||||
|
batch_size=batch_size,
|
||||||
|
drop_last=drop_last,
|
||||||
|
pin_memory=pin_mem,
|
||||||
|
num_workers=num_workers,
|
||||||
|
persistent_workers=False)
|
||||||
|
logger.info('ImageNet unsupervised data loader created')
|
||||||
|
|
||||||
|
return dataset, data_loader, dist_sampler
|
||||||
|
|
||||||
|
|
||||||
|
class ImageNet(torchvision.datasets.ImageFolder):
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
root,
|
||||||
|
image_folder='imagenet_full_size/061417/',
|
||||||
|
tar_file='imagenet_full_size-061417.tar.gz',
|
||||||
|
transform=None,
|
||||||
|
train=True,
|
||||||
|
job_id=None,
|
||||||
|
local_rank=None,
|
||||||
|
copy_data=True,
|
||||||
|
index_targets=False
|
||||||
|
):
|
||||||
|
"""
|
||||||
|
ImageNet
|
||||||
|
|
||||||
|
Dataset wrapper (can copy data locally to machine)
|
||||||
|
|
||||||
|
:param root: root network directory for ImageNet data
|
||||||
|
:param image_folder: path to images inside root network directory
|
||||||
|
:param tar_file: zipped image_folder inside root network directory
|
||||||
|
:param train: whether to load train data (or validation)
|
||||||
|
:param job_id: scheduler job-id used to create dir on local machine
|
||||||
|
:param copy_data: whether to copy data from network file locally
|
||||||
|
:param index_targets: whether to index the id of each labeled image
|
||||||
|
"""
|
||||||
|
|
||||||
|
suffix = 'train/' if train else 'val/'
|
||||||
|
data_path = None
|
||||||
|
if copy_data:
|
||||||
|
logger.info('copying data locally')
|
||||||
|
data_path = copy_imgnt_locally(
|
||||||
|
root=root,
|
||||||
|
suffix=suffix,
|
||||||
|
image_folder=image_folder,
|
||||||
|
tar_file=tar_file,
|
||||||
|
job_id=job_id,
|
||||||
|
local_rank=local_rank)
|
||||||
|
if (not copy_data) or (data_path is None):
|
||||||
|
data_path = os.path.join(root, image_folder, suffix)
|
||||||
|
logger.info(f'data-path {data_path}')
|
||||||
|
|
||||||
|
super(ImageNet, self).__init__(root=data_path, transform=transform)
|
||||||
|
logger.info('Initialized ImageNet')
|
||||||
|
|
||||||
|
if index_targets:
|
||||||
|
self.targets = []
|
||||||
|
for sample in self.samples:
|
||||||
|
self.targets.append(sample[1])
|
||||||
|
self.targets = np.array(self.targets)
|
||||||
|
self.samples = np.array(self.samples)
|
||||||
|
|
||||||
|
mint = None
|
||||||
|
self.target_indices = []
|
||||||
|
for t in range(len(self.classes)):
|
||||||
|
indices = np.squeeze(np.argwhere(
|
||||||
|
self.targets == t)).tolist()
|
||||||
|
self.target_indices.append(indices)
|
||||||
|
mint = len(indices) if mint is None else min(mint, len(indices))
|
||||||
|
logger.debug(f'num-labeled target {t} {len(indices)}')
|
||||||
|
logger.info(f'min. labeled indices {mint}')
|
||||||
|
|
||||||
|
|
||||||
|
class ImageNetSubset(object):
|
||||||
|
|
||||||
|
def __init__(self, dataset, subset_file):
|
||||||
|
"""
|
||||||
|
ImageNetSubset
|
||||||
|
|
||||||
|
:param dataset: ImageNet dataset object
|
||||||
|
:param subset_file: '.txt' file containing IDs of IN1K images to keep
|
||||||
|
"""
|
||||||
|
self.dataset = dataset
|
||||||
|
self.subset_file = subset_file
|
||||||
|
self.filter_dataset_(subset_file)
|
||||||
|
|
||||||
|
def filter_dataset_(self, subset_file):
|
||||||
|
""" Filter self.dataset to a subset """
|
||||||
|
root = self.dataset.root
|
||||||
|
class_to_idx = self.dataset.class_to_idx
|
||||||
|
# -- update samples to subset of IN1k targets/samples
|
||||||
|
new_samples = []
|
||||||
|
logger.info(f'Using {subset_file}')
|
||||||
|
with open(subset_file, 'r') as rfile:
|
||||||
|
for line in rfile:
|
||||||
|
class_name = line.split('_')[0]
|
||||||
|
target = class_to_idx[class_name]
|
||||||
|
img = line.split('\n')[0]
|
||||||
|
new_samples.append(
|
||||||
|
(os.path.join(root, class_name, img), target)
|
||||||
|
)
|
||||||
|
self.samples = new_samples
|
||||||
|
|
||||||
|
@property
|
||||||
|
def classes(self):
|
||||||
|
return self.dataset.classes
|
||||||
|
|
||||||
|
def __len__(self):
|
||||||
|
return len(self.samples)
|
||||||
|
|
||||||
|
def __getitem__(self, index):
|
||||||
|
path, target = self.samples[index]
|
||||||
|
img = self.dataset.loader(path)
|
||||||
|
if self.dataset.transform is not None:
|
||||||
|
img = self.dataset.transform(img)
|
||||||
|
if self.dataset.target_transform is not None:
|
||||||
|
target = self.dataset.target_transform(target)
|
||||||
|
return img, target
|
||||||
|
|
||||||
|
|
||||||
|
def copy_imgnt_locally(
|
||||||
|
root,
|
||||||
|
suffix,
|
||||||
|
image_folder='imagenet_full_size/061417/',
|
||||||
|
tar_file='imagenet_full_size-061417.tar.gz',
|
||||||
|
job_id=None,
|
||||||
|
local_rank=None
|
||||||
|
):
|
||||||
|
if job_id is None:
|
||||||
|
try:
|
||||||
|
job_id = os.environ['SLURM_JOBID']
|
||||||
|
except Exception:
|
||||||
|
logger.info('No job-id, will load directly from network file')
|
||||||
|
return None
|
||||||
|
|
||||||
|
if local_rank is None:
|
||||||
|
try:
|
||||||
|
local_rank = int(os.environ['SLURM_LOCALID'])
|
||||||
|
except Exception:
|
||||||
|
logger.info('No job-id, will load directly from network file')
|
||||||
|
return None
|
||||||
|
|
||||||
|
source_file = os.path.join(root, tar_file)
|
||||||
|
target = f'/scratch/slurm_tmpdir/{job_id}/'
|
||||||
|
target_file = os.path.join(target, tar_file)
|
||||||
|
data_path = os.path.join(target, image_folder, suffix)
|
||||||
|
logger.info(f'{source_file}\n{target}\n{target_file}\n{data_path}')
|
||||||
|
|
||||||
|
tmp_sgnl_file = os.path.join(target, 'copy_signal.txt')
|
||||||
|
|
||||||
|
if not os.path.exists(data_path):
|
||||||
|
if local_rank == 0:
|
||||||
|
commands = [
|
||||||
|
['tar', '-xf', source_file, '-C', target]]
|
||||||
|
for cmnd in commands:
|
||||||
|
start_time = time.time()
|
||||||
|
logger.info(f'Executing {cmnd}')
|
||||||
|
subprocess.run(cmnd)
|
||||||
|
logger.info(f'Cmnd took {(time.time()-start_time)/60.} min.')
|
||||||
|
with open(tmp_sgnl_file, '+w') as f:
|
||||||
|
print('Done copying locally.', file=f)
|
||||||
|
else:
|
||||||
|
while not os.path.exists(tmp_sgnl_file):
|
||||||
|
time.sleep(60)
|
||||||
|
logger.info(f'{local_rank}: Checking {tmp_sgnl_file}')
|
||||||
|
|
||||||
|
return data_path
|
||||||
+156
@@ -0,0 +1,156 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
import logging
|
||||||
|
import sys
|
||||||
|
|
||||||
|
import torch
|
||||||
|
|
||||||
|
import src.models.vision_transformer as vit
|
||||||
|
from src.utils.schedulers import (
|
||||||
|
WarmupCosineSchedule,
|
||||||
|
CosineWDSchedule)
|
||||||
|
from src.utils.tensors import trunc_normal_
|
||||||
|
|
||||||
|
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
|
||||||
|
logger = logging.getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
def load_checkpoint(
|
||||||
|
device,
|
||||||
|
r_path,
|
||||||
|
encoder,
|
||||||
|
predictor,
|
||||||
|
target_encoder,
|
||||||
|
opt,
|
||||||
|
scaler,
|
||||||
|
):
|
||||||
|
try:
|
||||||
|
checkpoint = torch.load(r_path, map_location=torch.device('cpu'))
|
||||||
|
epoch = checkpoint['epoch']
|
||||||
|
|
||||||
|
# -- loading encoder
|
||||||
|
pretrained_dict = checkpoint['encoder']
|
||||||
|
msg = encoder.load_state_dict(pretrained_dict)
|
||||||
|
logger.info(f'loaded pretrained encoder from epoch {epoch} with msg: {msg}')
|
||||||
|
|
||||||
|
# -- loading predictor
|
||||||
|
pretrained_dict = checkpoint['predictor']
|
||||||
|
msg = predictor.load_state_dict(pretrained_dict)
|
||||||
|
logger.info(f'loaded pretrained encoder from epoch {epoch} with msg: {msg}')
|
||||||
|
|
||||||
|
# -- loading target_encoder
|
||||||
|
if target_encoder is not None:
|
||||||
|
print(list(checkpoint.keys()))
|
||||||
|
pretrained_dict = checkpoint['target_encoder']
|
||||||
|
msg = target_encoder.load_state_dict(pretrained_dict)
|
||||||
|
logger.info(f'loaded pretrained encoder from epoch {epoch} with msg: {msg}')
|
||||||
|
|
||||||
|
# -- loading optimizer
|
||||||
|
opt.load_state_dict(checkpoint['opt'])
|
||||||
|
if scaler is not None:
|
||||||
|
scaler.load_state_dict(checkpoint['scaler'])
|
||||||
|
logger.info(f'loaded optimizers from epoch {epoch}')
|
||||||
|
logger.info(f'read-path: {r_path}')
|
||||||
|
del checkpoint
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.info(f'Encountered exception when loading checkpoint {e}')
|
||||||
|
epoch = 0
|
||||||
|
|
||||||
|
return encoder, predictor, target_encoder, opt, scaler, epoch
|
||||||
|
|
||||||
|
|
||||||
|
def init_model(
|
||||||
|
device,
|
||||||
|
patch_size=16,
|
||||||
|
model_name='vit_base',
|
||||||
|
crop_size=224,
|
||||||
|
pred_depth=6,
|
||||||
|
pred_emb_dim=384
|
||||||
|
):
|
||||||
|
encoder = vit.__dict__[model_name](
|
||||||
|
img_size=[crop_size],
|
||||||
|
patch_size=patch_size)
|
||||||
|
predictor = vit.__dict__['vit_predictor'](
|
||||||
|
num_patches=encoder.patch_embed.num_patches,
|
||||||
|
embed_dim=encoder.embed_dim,
|
||||||
|
predictor_embed_dim=pred_emb_dim,
|
||||||
|
depth=pred_depth,
|
||||||
|
num_heads=encoder.num_heads)
|
||||||
|
|
||||||
|
def init_weights(m):
|
||||||
|
if isinstance(m, torch.nn.Linear):
|
||||||
|
trunc_normal_(m.weight, std=0.02)
|
||||||
|
if m.bias is not None:
|
||||||
|
torch.nn.init.constant_(m.bias, 0)
|
||||||
|
elif isinstance(m, torch.nn.LayerNorm):
|
||||||
|
torch.nn.init.constant_(m.bias, 0)
|
||||||
|
torch.nn.init.constant_(m.weight, 1.0)
|
||||||
|
|
||||||
|
for m in encoder.modules():
|
||||||
|
init_weights(m)
|
||||||
|
|
||||||
|
for m in predictor.modules():
|
||||||
|
init_weights(m)
|
||||||
|
|
||||||
|
encoder.to(device)
|
||||||
|
predictor.to(device)
|
||||||
|
logger.info(encoder)
|
||||||
|
return encoder, predictor
|
||||||
|
|
||||||
|
|
||||||
|
def init_opt(
|
||||||
|
encoder,
|
||||||
|
predictor,
|
||||||
|
iterations_per_epoch,
|
||||||
|
start_lr,
|
||||||
|
ref_lr,
|
||||||
|
warmup,
|
||||||
|
num_epochs,
|
||||||
|
wd=1e-6,
|
||||||
|
final_wd=1e-6,
|
||||||
|
final_lr=0.0,
|
||||||
|
use_bfloat16=False,
|
||||||
|
ipe_scale=1.25
|
||||||
|
):
|
||||||
|
param_groups = [
|
||||||
|
{
|
||||||
|
'params': (p for n, p in encoder.named_parameters()
|
||||||
|
if ('bias' not in n) and (len(p.shape) != 1))
|
||||||
|
}, {
|
||||||
|
'params': (p for n, p in predictor.named_parameters()
|
||||||
|
if ('bias' not in n) and (len(p.shape) != 1))
|
||||||
|
}, {
|
||||||
|
'params': (p for n, p in encoder.named_parameters()
|
||||||
|
if ('bias' in n) or (len(p.shape) == 1)),
|
||||||
|
'WD_exclude': True,
|
||||||
|
'weight_decay': 0
|
||||||
|
}, {
|
||||||
|
'params': (p for n, p in predictor.named_parameters()
|
||||||
|
if ('bias' in n) or (len(p.shape) == 1)),
|
||||||
|
'WD_exclude': True,
|
||||||
|
'weight_decay': 0
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
logger.info('Using AdamW')
|
||||||
|
optimizer = torch.optim.AdamW(param_groups)
|
||||||
|
scheduler = WarmupCosineSchedule(
|
||||||
|
optimizer,
|
||||||
|
warmup_steps=int(warmup*iterations_per_epoch),
|
||||||
|
start_lr=start_lr,
|
||||||
|
ref_lr=ref_lr,
|
||||||
|
final_lr=final_lr,
|
||||||
|
T_max=int(ipe_scale*num_epochs*iterations_per_epoch))
|
||||||
|
wd_scheduler = CosineWDSchedule(
|
||||||
|
optimizer,
|
||||||
|
ref_wd=wd,
|
||||||
|
final_wd=final_wd,
|
||||||
|
T_max=int(ipe_scale*num_epochs*iterations_per_epoch))
|
||||||
|
scaler = torch.cuda.amp.GradScaler() if use_bfloat16 else None
|
||||||
|
return optimizer, scaler, scheduler, wd_scheduler
|
||||||
@@ -0,0 +1,21 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
from logging import getLogger
|
||||||
|
|
||||||
|
import torch
|
||||||
|
|
||||||
|
_GLOBAL_SEED = 0
|
||||||
|
logger = getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
class DefaultCollator(object):
|
||||||
|
|
||||||
|
def __call__(self, batch):
|
||||||
|
|
||||||
|
collated_batch = torch.utils.data.default_collate(batch)
|
||||||
|
return collated_batch, None, None
|
||||||
@@ -0,0 +1,170 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
import math
|
||||||
|
|
||||||
|
from multiprocessing import Value
|
||||||
|
|
||||||
|
from logging import getLogger
|
||||||
|
|
||||||
|
import torch
|
||||||
|
|
||||||
|
_GLOBAL_SEED = 0
|
||||||
|
logger = getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
class MaskCollator(object):
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
input_size=(224, 224),
|
||||||
|
patch_size=16,
|
||||||
|
enc_mask_scale=(0.2, 0.8),
|
||||||
|
pred_mask_scale=(0.2, 0.8),
|
||||||
|
aspect_ratio=(0.3, 3.0),
|
||||||
|
nenc=1,
|
||||||
|
npred=2,
|
||||||
|
min_keep=4,
|
||||||
|
allow_overlap=False
|
||||||
|
):
|
||||||
|
super(MaskCollator, self).__init__()
|
||||||
|
if not isinstance(input_size, tuple):
|
||||||
|
input_size = (input_size, ) * 2
|
||||||
|
self.patch_size = patch_size
|
||||||
|
self.height, self.width = input_size[0] // patch_size, input_size[1] // patch_size
|
||||||
|
self.enc_mask_scale = enc_mask_scale
|
||||||
|
self.pred_mask_scale = pred_mask_scale
|
||||||
|
self.aspect_ratio = aspect_ratio
|
||||||
|
self.nenc = nenc
|
||||||
|
self.npred = npred
|
||||||
|
self.min_keep = min_keep # minimum number of patches to keep
|
||||||
|
self.allow_overlap = allow_overlap # whether to allow overlap b/w enc and pred masks
|
||||||
|
self._itr_counter = Value('i', -1) # collator is shared across worker processes
|
||||||
|
|
||||||
|
def step(self):
|
||||||
|
i = self._itr_counter
|
||||||
|
with i.get_lock():
|
||||||
|
i.value += 1
|
||||||
|
v = i.value
|
||||||
|
return v
|
||||||
|
|
||||||
|
def _sample_block_size(self, generator, scale, aspect_ratio_scale):
|
||||||
|
_rand = torch.rand(1, generator=generator).item()
|
||||||
|
# -- Sample block scale
|
||||||
|
min_s, max_s = scale
|
||||||
|
mask_scale = min_s + _rand * (max_s - min_s)
|
||||||
|
max_keep = int(self.height * self.width * mask_scale)
|
||||||
|
# -- Sample block aspect-ratio
|
||||||
|
min_ar, max_ar = aspect_ratio_scale
|
||||||
|
aspect_ratio = min_ar + _rand * (max_ar - min_ar)
|
||||||
|
# -- Compute block height and width (given scale and aspect-ratio)
|
||||||
|
h = int(round(math.sqrt(max_keep * aspect_ratio)))
|
||||||
|
w = int(round(math.sqrt(max_keep / aspect_ratio)))
|
||||||
|
while h >= self.height:
|
||||||
|
h -= 1
|
||||||
|
while w >= self.width:
|
||||||
|
w -= 1
|
||||||
|
|
||||||
|
return (h, w)
|
||||||
|
|
||||||
|
def _sample_block_mask(self, b_size, acceptable_regions=None):
|
||||||
|
h, w = b_size
|
||||||
|
|
||||||
|
def constrain_mask(mask, tries=0):
|
||||||
|
""" Helper to restrict given mask to a set of acceptable regions """
|
||||||
|
N = max(int(len(acceptable_regions)-tries), 0)
|
||||||
|
for k in range(N):
|
||||||
|
mask *= acceptable_regions[k]
|
||||||
|
# --
|
||||||
|
# -- Loop to sample masks until we find a valid one
|
||||||
|
tries = 0
|
||||||
|
timeout = og_timeout = 20
|
||||||
|
valid_mask = False
|
||||||
|
while not valid_mask:
|
||||||
|
# -- Sample block top-left corner
|
||||||
|
top = torch.randint(0, self.height - h, (1,))
|
||||||
|
left = torch.randint(0, self.width - w, (1,))
|
||||||
|
mask = torch.zeros((self.height, self.width), dtype=torch.int32)
|
||||||
|
mask[top:top+h, left:left+w] = 1
|
||||||
|
# -- Constrain mask to a set of acceptable regions
|
||||||
|
if acceptable_regions is not None:
|
||||||
|
constrain_mask(mask, tries)
|
||||||
|
mask = torch.nonzero(mask.flatten())
|
||||||
|
# -- If mask too small try again
|
||||||
|
valid_mask = len(mask) > self.min_keep
|
||||||
|
if not valid_mask:
|
||||||
|
timeout -= 1
|
||||||
|
if timeout == 0:
|
||||||
|
tries += 1
|
||||||
|
timeout = og_timeout
|
||||||
|
logger.warning(f'Mask generator says: "Valid mask not found, decreasing acceptable-regions [{tries}]"')
|
||||||
|
mask = mask.squeeze()
|
||||||
|
# --
|
||||||
|
mask_complement = torch.ones((self.height, self.width), dtype=torch.int32)
|
||||||
|
mask_complement[top:top+h, left:left+w] = 0
|
||||||
|
# --
|
||||||
|
return mask, mask_complement
|
||||||
|
|
||||||
|
def __call__(self, batch):
|
||||||
|
'''
|
||||||
|
Create encoder and predictor masks when collating imgs into a batch
|
||||||
|
# 1. sample enc block (size + location) using seed
|
||||||
|
# 2. sample pred block (size) using seed
|
||||||
|
# 3. sample several enc block locations for each image (w/o seed)
|
||||||
|
# 4. sample several pred block locations for each image (w/o seed)
|
||||||
|
# 5. return enc mask and pred mask
|
||||||
|
'''
|
||||||
|
B = len(batch)
|
||||||
|
|
||||||
|
collated_batch = torch.utils.data.default_collate(batch)
|
||||||
|
|
||||||
|
seed = self.step()
|
||||||
|
g = torch.Generator()
|
||||||
|
g.manual_seed(seed)
|
||||||
|
p_size = self._sample_block_size(
|
||||||
|
generator=g,
|
||||||
|
scale=self.pred_mask_scale,
|
||||||
|
aspect_ratio_scale=self.aspect_ratio)
|
||||||
|
e_size = self._sample_block_size(
|
||||||
|
generator=g,
|
||||||
|
scale=self.enc_mask_scale,
|
||||||
|
aspect_ratio_scale=(1., 1.))
|
||||||
|
|
||||||
|
collated_masks_pred, collated_masks_enc = [], []
|
||||||
|
min_keep_pred = self.height * self.width
|
||||||
|
min_keep_enc = self.height * self.width
|
||||||
|
for _ in range(B):
|
||||||
|
|
||||||
|
masks_p, masks_C = [], []
|
||||||
|
for _ in range(self.npred):
|
||||||
|
mask, mask_C = self._sample_block_mask(p_size)
|
||||||
|
masks_p.append(mask)
|
||||||
|
masks_C.append(mask_C)
|
||||||
|
min_keep_pred = min(min_keep_pred, len(mask))
|
||||||
|
collated_masks_pred.append(masks_p)
|
||||||
|
|
||||||
|
acceptable_regions = masks_C
|
||||||
|
try:
|
||||||
|
if self.allow_overlap:
|
||||||
|
acceptable_regions= None
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f'Encountered exception in mask-generator {e}')
|
||||||
|
|
||||||
|
masks_e = []
|
||||||
|
for _ in range(self.nenc):
|
||||||
|
mask, _ = self._sample_block_mask(e_size, acceptable_regions=acceptable_regions)
|
||||||
|
masks_e.append(mask)
|
||||||
|
min_keep_enc = min(min_keep_enc, len(mask))
|
||||||
|
collated_masks_enc.append(masks_e)
|
||||||
|
|
||||||
|
collated_masks_pred = [[cm[:min_keep_pred] for cm in cm_list] for cm_list in collated_masks_pred]
|
||||||
|
collated_masks_pred = torch.utils.data.default_collate(collated_masks_pred)
|
||||||
|
# --
|
||||||
|
collated_masks_enc = [[cm[:min_keep_enc] for cm in cm_list] for cm_list in collated_masks_enc]
|
||||||
|
collated_masks_enc = torch.utils.data.default_collate(collated_masks_enc)
|
||||||
|
|
||||||
|
return collated_batch, collated_masks_enc, collated_masks_pred
|
||||||
@@ -0,0 +1,72 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
from multiprocessing import Value
|
||||||
|
|
||||||
|
from logging import getLogger
|
||||||
|
|
||||||
|
import torch
|
||||||
|
|
||||||
|
_GLOBAL_SEED = 0
|
||||||
|
logger = getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
class MaskCollator(object):
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
ratio=(0.4, 0.6),
|
||||||
|
input_size=(224, 224),
|
||||||
|
patch_size=16,
|
||||||
|
):
|
||||||
|
super(MaskCollator, self).__init__()
|
||||||
|
if not isinstance(input_size, tuple):
|
||||||
|
input_size = (input_size, ) * 2
|
||||||
|
self.patch_size = patch_size
|
||||||
|
self.height, self.width = input_size[0] // patch_size, input_size[1] // patch_size
|
||||||
|
self.ratio = ratio
|
||||||
|
self._itr_counter = Value('i', -1) # collator is shared across worker processes
|
||||||
|
|
||||||
|
def step(self):
|
||||||
|
i = self._itr_counter
|
||||||
|
with i.get_lock():
|
||||||
|
i.value += 1
|
||||||
|
v = i.value
|
||||||
|
return v
|
||||||
|
|
||||||
|
def __call__(self, batch):
|
||||||
|
'''
|
||||||
|
Create encoder and predictor masks when collating imgs into a batch
|
||||||
|
# 1. sample enc block (size + location) using seed
|
||||||
|
# 2. sample pred block (size) using seed
|
||||||
|
# 3. sample several enc block locations for each image (w/o seed)
|
||||||
|
# 4. sample several pred block locations for each image (w/o seed)
|
||||||
|
# 5. return enc mask and pred mask
|
||||||
|
'''
|
||||||
|
B = len(batch)
|
||||||
|
|
||||||
|
collated_batch = torch.utils.data.default_collate(batch)
|
||||||
|
|
||||||
|
seed = self.step()
|
||||||
|
g = torch.Generator()
|
||||||
|
g.manual_seed(seed)
|
||||||
|
ratio = self.ratio
|
||||||
|
ratio = ratio[0] + torch.rand(1, generator=g).item() * (ratio[1] - ratio[0])
|
||||||
|
num_patches = self.height * self.width
|
||||||
|
num_keep = int(num_patches * (1. - ratio))
|
||||||
|
|
||||||
|
collated_masks_pred, collated_masks_enc = [], []
|
||||||
|
for _ in range(B):
|
||||||
|
|
||||||
|
m = torch.randperm(num_patches)
|
||||||
|
collated_masks_enc.append([m[:num_keep]])
|
||||||
|
collated_masks_pred.append([m[num_keep:]])
|
||||||
|
|
||||||
|
collated_masks_pred = torch.utils.data.default_collate(collated_masks_pred)
|
||||||
|
collated_masks_enc = torch.utils.data.default_collate(collated_masks_enc)
|
||||||
|
|
||||||
|
return collated_batch, collated_masks_enc, collated_masks_pred
|
||||||
@@ -0,0 +1,20 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
import torch
|
||||||
|
|
||||||
|
|
||||||
|
def apply_masks(x, masks):
|
||||||
|
"""
|
||||||
|
:param x: tensor of shape [B (batch-size), N (num-patches), D (feature-dim)]
|
||||||
|
:param masks: list of tensors containing indices of patches in [N] to keep
|
||||||
|
"""
|
||||||
|
all_x = []
|
||||||
|
for m in masks:
|
||||||
|
mask_keep = m.unsqueeze(-1).repeat(1, 1, x.size(-1))
|
||||||
|
all_x += [torch.gather(x, dim=1, index=mask_keep)]
|
||||||
|
return torch.cat(all_x, dim=0)
|
||||||
@@ -0,0 +1,500 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
import math
|
||||||
|
from functools import partial
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
import torch
|
||||||
|
import torch.nn as nn
|
||||||
|
|
||||||
|
from src.utils.tensors import (
|
||||||
|
trunc_normal_,
|
||||||
|
repeat_interleave_batch
|
||||||
|
)
|
||||||
|
from src.masks.utils import apply_masks
|
||||||
|
|
||||||
|
|
||||||
|
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
|
||||||
|
"""
|
||||||
|
grid_size: int of the grid height and width
|
||||||
|
return:
|
||||||
|
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
|
||||||
|
"""
|
||||||
|
grid_h = np.arange(grid_size, dtype=float)
|
||||||
|
grid_w = np.arange(grid_size, dtype=float)
|
||||||
|
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
||||||
|
grid = np.stack(grid, axis=0)
|
||||||
|
|
||||||
|
grid = grid.reshape([2, 1, grid_size, grid_size])
|
||||||
|
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
||||||
|
if cls_token:
|
||||||
|
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
|
||||||
|
return pos_embed
|
||||||
|
|
||||||
|
|
||||||
|
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
||||||
|
assert embed_dim % 2 == 0
|
||||||
|
|
||||||
|
# use half of dimensions to encode grid_h
|
||||||
|
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
|
||||||
|
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
|
||||||
|
|
||||||
|
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
|
||||||
|
return emb
|
||||||
|
|
||||||
|
|
||||||
|
def get_1d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
|
||||||
|
"""
|
||||||
|
grid_size: int of the grid length
|
||||||
|
return:
|
||||||
|
pos_embed: [grid_size, embed_dim] or [1+grid_size, embed_dim] (w/ or w/o cls_token)
|
||||||
|
"""
|
||||||
|
grid = np.arange(grid_size, dtype=float)
|
||||||
|
pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, grid)
|
||||||
|
if cls_token:
|
||||||
|
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
|
||||||
|
return pos_embed
|
||||||
|
|
||||||
|
|
||||||
|
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
|
||||||
|
"""
|
||||||
|
embed_dim: output dimension for each position
|
||||||
|
pos: a list of positions to be encoded: size (M,)
|
||||||
|
out: (M, D)
|
||||||
|
"""
|
||||||
|
assert embed_dim % 2 == 0
|
||||||
|
omega = np.arange(embed_dim // 2, dtype=float)
|
||||||
|
omega /= embed_dim / 2.
|
||||||
|
omega = 1. / 10000**omega # (D/2,)
|
||||||
|
|
||||||
|
pos = pos.reshape(-1) # (M,)
|
||||||
|
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
|
||||||
|
|
||||||
|
emb_sin = np.sin(out) # (M, D/2)
|
||||||
|
emb_cos = np.cos(out) # (M, D/2)
|
||||||
|
|
||||||
|
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
||||||
|
return emb
|
||||||
|
|
||||||
|
|
||||||
|
def drop_path(x, drop_prob: float = 0., training: bool = False):
|
||||||
|
if drop_prob == 0. or not training:
|
||||||
|
return x
|
||||||
|
keep_prob = 1 - drop_prob
|
||||||
|
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
|
||||||
|
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
|
||||||
|
random_tensor.floor_() # binarize
|
||||||
|
output = x.div(keep_prob) * random_tensor
|
||||||
|
return output
|
||||||
|
|
||||||
|
|
||||||
|
class DropPath(nn.Module):
|
||||||
|
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
||||||
|
"""
|
||||||
|
def __init__(self, drop_prob=None):
|
||||||
|
super(DropPath, self).__init__()
|
||||||
|
self.drop_prob = drop_prob
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
return drop_path(x, self.drop_prob, self.training)
|
||||||
|
|
||||||
|
|
||||||
|
class MLP(nn.Module):
|
||||||
|
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||||
|
super().__init__()
|
||||||
|
out_features = out_features or in_features
|
||||||
|
hidden_features = hidden_features or in_features
|
||||||
|
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||||
|
self.act = act_layer()
|
||||||
|
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||||
|
self.drop = nn.Dropout(drop)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
x = self.fc1(x)
|
||||||
|
x = self.act(x)
|
||||||
|
x = self.drop(x)
|
||||||
|
x = self.fc2(x)
|
||||||
|
x = self.drop(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
class Attention(nn.Module):
|
||||||
|
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||||
|
super().__init__()
|
||||||
|
self.num_heads = num_heads
|
||||||
|
head_dim = dim // num_heads
|
||||||
|
self.scale = qk_scale or head_dim ** -0.5
|
||||||
|
|
||||||
|
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||||
|
self.attn_drop = nn.Dropout(attn_drop)
|
||||||
|
self.proj = nn.Linear(dim, dim)
|
||||||
|
self.proj_drop = nn.Dropout(proj_drop)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
B, N, C = x.shape
|
||||||
|
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||||
|
q, k, v = qkv[0], qkv[1], qkv[2]
|
||||||
|
|
||||||
|
attn = (q @ k.transpose(-2, -1)) * self.scale
|
||||||
|
attn = attn.softmax(dim=-1)
|
||||||
|
attn = self.attn_drop(attn)
|
||||||
|
|
||||||
|
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
||||||
|
x = self.proj(x)
|
||||||
|
x = self.proj_drop(x)
|
||||||
|
return x, attn
|
||||||
|
|
||||||
|
|
||||||
|
class Block(nn.Module):
|
||||||
|
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
||||||
|
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||||
|
super().__init__()
|
||||||
|
self.norm1 = norm_layer(dim)
|
||||||
|
self.attn = Attention(
|
||||||
|
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
||||||
|
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||||
|
self.norm2 = norm_layer(dim)
|
||||||
|
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||||
|
self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||||
|
|
||||||
|
def forward(self, x, return_attention=False):
|
||||||
|
y, attn = self.attn(self.norm1(x))
|
||||||
|
if return_attention:
|
||||||
|
return attn
|
||||||
|
x = x + self.drop_path(y)
|
||||||
|
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
class PatchEmbed(nn.Module):
|
||||||
|
""" Image to Patch Embedding
|
||||||
|
"""
|
||||||
|
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
|
||||||
|
super().__init__()
|
||||||
|
num_patches = (img_size // patch_size) * (img_size // patch_size)
|
||||||
|
self.img_size = img_size
|
||||||
|
self.patch_size = patch_size
|
||||||
|
self.num_patches = num_patches
|
||||||
|
|
||||||
|
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
B, C, H, W = x.shape
|
||||||
|
x = self.proj(x).flatten(2).transpose(1, 2)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
class ConvEmbed(nn.Module):
|
||||||
|
"""
|
||||||
|
3x3 Convolution stems for ViT following ViTC models
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, channels, strides, img_size=224, in_chans=3, batch_norm=True):
|
||||||
|
super().__init__()
|
||||||
|
# Build the stems
|
||||||
|
stem = []
|
||||||
|
channels = [in_chans] + channels
|
||||||
|
for i in range(len(channels) - 2):
|
||||||
|
stem += [nn.Conv2d(channels[i], channels[i+1], kernel_size=3,
|
||||||
|
stride=strides[i], padding=1, bias=(not batch_norm))]
|
||||||
|
if batch_norm:
|
||||||
|
stem += [nn.BatchNorm2d(channels[i+1])]
|
||||||
|
stem += [nn.ReLU(inplace=True)]
|
||||||
|
stem += [nn.Conv2d(channels[-2], channels[-1], kernel_size=1, stride=strides[-1])]
|
||||||
|
self.stem = nn.Sequential(*stem)
|
||||||
|
|
||||||
|
# Comptute the number of patches
|
||||||
|
stride_prod = int(np.prod(strides))
|
||||||
|
self.num_patches = (img_size[0] // stride_prod)**2
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
p = self.stem(x)
|
||||||
|
return p.flatten(2).transpose(1, 2)
|
||||||
|
|
||||||
|
|
||||||
|
class VisionTransformerPredictor(nn.Module):
|
||||||
|
""" Vision Transformer """
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
num_patches,
|
||||||
|
embed_dim=768,
|
||||||
|
predictor_embed_dim=384,
|
||||||
|
depth=6,
|
||||||
|
num_heads=12,
|
||||||
|
mlp_ratio=4.0,
|
||||||
|
qkv_bias=True,
|
||||||
|
qk_scale=None,
|
||||||
|
drop_rate=0.0,
|
||||||
|
attn_drop_rate=0.0,
|
||||||
|
drop_path_rate=0.0,
|
||||||
|
norm_layer=nn.LayerNorm,
|
||||||
|
init_std=0.02,
|
||||||
|
**kwargs
|
||||||
|
):
|
||||||
|
super().__init__()
|
||||||
|
self.predictor_embed = nn.Linear(embed_dim, predictor_embed_dim, bias=True)
|
||||||
|
self.mask_token = nn.Parameter(torch.zeros(1, 1, predictor_embed_dim))
|
||||||
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
||||||
|
# --
|
||||||
|
self.predictor_pos_embed = nn.Parameter(torch.zeros(1, num_patches, predictor_embed_dim),
|
||||||
|
requires_grad=False)
|
||||||
|
predictor_pos_embed = get_2d_sincos_pos_embed(self.predictor_pos_embed.shape[-1],
|
||||||
|
int(num_patches**.5),
|
||||||
|
cls_token=False)
|
||||||
|
self.predictor_pos_embed.data.copy_(torch.from_numpy(predictor_pos_embed).float().unsqueeze(0))
|
||||||
|
# --
|
||||||
|
self.predictor_blocks = nn.ModuleList([
|
||||||
|
Block(
|
||||||
|
dim=predictor_embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||||
|
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
|
||||||
|
for i in range(depth)])
|
||||||
|
self.predictor_norm = norm_layer(predictor_embed_dim)
|
||||||
|
self.predictor_proj = nn.Linear(predictor_embed_dim, embed_dim, bias=True)
|
||||||
|
# ------
|
||||||
|
self.init_std = init_std
|
||||||
|
trunc_normal_(self.mask_token, std=self.init_std)
|
||||||
|
self.apply(self._init_weights)
|
||||||
|
self.fix_init_weight()
|
||||||
|
|
||||||
|
def fix_init_weight(self):
|
||||||
|
def rescale(param, layer_id):
|
||||||
|
param.div_(math.sqrt(2.0 * layer_id))
|
||||||
|
|
||||||
|
for layer_id, layer in enumerate(self.predictor_blocks):
|
||||||
|
rescale(layer.attn.proj.weight.data, layer_id + 1)
|
||||||
|
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
|
||||||
|
|
||||||
|
def _init_weights(self, m):
|
||||||
|
if isinstance(m, nn.Linear):
|
||||||
|
trunc_normal_(m.weight, std=self.init_std)
|
||||||
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||||
|
nn.init.constant_(m.bias, 0)
|
||||||
|
elif isinstance(m, nn.LayerNorm):
|
||||||
|
nn.init.constant_(m.bias, 0)
|
||||||
|
nn.init.constant_(m.weight, 1.0)
|
||||||
|
elif isinstance(m, nn.Conv2d):
|
||||||
|
trunc_normal_(m.weight, std=self.init_std)
|
||||||
|
if m.bias is not None:
|
||||||
|
nn.init.constant_(m.bias, 0)
|
||||||
|
|
||||||
|
def forward(self, x, masks_x, masks):
|
||||||
|
assert (masks is not None) and (masks_x is not None), 'Cannot run predictor without mask indices'
|
||||||
|
|
||||||
|
if not isinstance(masks_x, list):
|
||||||
|
masks_x = [masks_x]
|
||||||
|
|
||||||
|
if not isinstance(masks, list):
|
||||||
|
masks = [masks]
|
||||||
|
|
||||||
|
# -- Batch Size
|
||||||
|
B = len(x) // len(masks_x)
|
||||||
|
|
||||||
|
# -- map from encoder-dim to pedictor-dim
|
||||||
|
x = self.predictor_embed(x)
|
||||||
|
|
||||||
|
# -- add positional embedding to x tokens
|
||||||
|
x_pos_embed = self.predictor_pos_embed.repeat(B, 1, 1)
|
||||||
|
x += apply_masks(x_pos_embed, masks_x)
|
||||||
|
|
||||||
|
_, N_ctxt, D = x.shape
|
||||||
|
|
||||||
|
# -- concat mask tokens to x
|
||||||
|
pos_embs = self.predictor_pos_embed.repeat(B, 1, 1)
|
||||||
|
pos_embs = apply_masks(pos_embs, masks)
|
||||||
|
pos_embs = repeat_interleave_batch(pos_embs, B, repeat=len(masks_x))
|
||||||
|
# --
|
||||||
|
pred_tokens = self.mask_token.repeat(pos_embs.size(0), pos_embs.size(1), 1)
|
||||||
|
# --
|
||||||
|
pred_tokens += pos_embs
|
||||||
|
x = x.repeat(len(masks), 1, 1)
|
||||||
|
x = torch.cat([x, pred_tokens], dim=1)
|
||||||
|
|
||||||
|
# -- fwd prop
|
||||||
|
for blk in self.predictor_blocks:
|
||||||
|
x = blk(x)
|
||||||
|
x = self.predictor_norm(x)
|
||||||
|
|
||||||
|
# -- return preds for mask tokens
|
||||||
|
x = x[:, N_ctxt:]
|
||||||
|
x = self.predictor_proj(x)
|
||||||
|
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
class VisionTransformer(nn.Module):
|
||||||
|
""" Vision Transformer """
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
img_size=[224],
|
||||||
|
patch_size=16,
|
||||||
|
in_chans=3,
|
||||||
|
embed_dim=768,
|
||||||
|
predictor_embed_dim=384,
|
||||||
|
depth=12,
|
||||||
|
predictor_depth=12,
|
||||||
|
num_heads=12,
|
||||||
|
mlp_ratio=4.0,
|
||||||
|
qkv_bias=True,
|
||||||
|
qk_scale=None,
|
||||||
|
drop_rate=0.0,
|
||||||
|
attn_drop_rate=0.0,
|
||||||
|
drop_path_rate=0.0,
|
||||||
|
norm_layer=nn.LayerNorm,
|
||||||
|
init_std=0.02,
|
||||||
|
**kwargs
|
||||||
|
):
|
||||||
|
super().__init__()
|
||||||
|
self.num_features = self.embed_dim = embed_dim
|
||||||
|
self.num_heads = num_heads
|
||||||
|
# --
|
||||||
|
self.patch_embed = PatchEmbed(
|
||||||
|
img_size=img_size[0],
|
||||||
|
patch_size=patch_size,
|
||||||
|
in_chans=in_chans,
|
||||||
|
embed_dim=embed_dim)
|
||||||
|
num_patches = self.patch_embed.num_patches
|
||||||
|
# --
|
||||||
|
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim), requires_grad=False)
|
||||||
|
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1],
|
||||||
|
int(self.patch_embed.num_patches**.5),
|
||||||
|
cls_token=False)
|
||||||
|
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
|
||||||
|
# --
|
||||||
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
||||||
|
self.blocks = nn.ModuleList([
|
||||||
|
Block(
|
||||||
|
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||||
|
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
|
||||||
|
for i in range(depth)])
|
||||||
|
self.norm = norm_layer(embed_dim)
|
||||||
|
# ------
|
||||||
|
self.init_std = init_std
|
||||||
|
self.apply(self._init_weights)
|
||||||
|
self.fix_init_weight()
|
||||||
|
|
||||||
|
def fix_init_weight(self):
|
||||||
|
def rescale(param, layer_id):
|
||||||
|
param.div_(math.sqrt(2.0 * layer_id))
|
||||||
|
|
||||||
|
for layer_id, layer in enumerate(self.blocks):
|
||||||
|
rescale(layer.attn.proj.weight.data, layer_id + 1)
|
||||||
|
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
|
||||||
|
|
||||||
|
def _init_weights(self, m):
|
||||||
|
if isinstance(m, nn.Linear):
|
||||||
|
trunc_normal_(m.weight, std=self.init_std)
|
||||||
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||||
|
nn.init.constant_(m.bias, 0)
|
||||||
|
elif isinstance(m, nn.LayerNorm):
|
||||||
|
nn.init.constant_(m.bias, 0)
|
||||||
|
nn.init.constant_(m.weight, 1.0)
|
||||||
|
elif isinstance(m, nn.Conv2d):
|
||||||
|
trunc_normal_(m.weight, std=self.init_std)
|
||||||
|
if m.bias is not None:
|
||||||
|
nn.init.constant_(m.bias, 0)
|
||||||
|
|
||||||
|
def forward(self, x, masks=None):
|
||||||
|
if masks is not None:
|
||||||
|
if not isinstance(masks, list):
|
||||||
|
masks = [masks]
|
||||||
|
|
||||||
|
# -- patchify x
|
||||||
|
x = self.patch_embed(x)
|
||||||
|
B, N, D = x.shape
|
||||||
|
|
||||||
|
# -- add positional embedding to x
|
||||||
|
pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
|
||||||
|
x = x + pos_embed
|
||||||
|
|
||||||
|
# -- mask x
|
||||||
|
if masks is not None:
|
||||||
|
x = apply_masks(x, masks)
|
||||||
|
|
||||||
|
# -- fwd prop
|
||||||
|
for i, blk in enumerate(self.blocks):
|
||||||
|
x = blk(x)
|
||||||
|
|
||||||
|
if self.norm is not None:
|
||||||
|
x = self.norm(x)
|
||||||
|
|
||||||
|
return x
|
||||||
|
|
||||||
|
def interpolate_pos_encoding(self, x, pos_embed):
|
||||||
|
npatch = x.shape[1] - 1
|
||||||
|
N = pos_embed.shape[1] - 1
|
||||||
|
if npatch == N:
|
||||||
|
return pos_embed
|
||||||
|
class_emb = pos_embed[:, 0]
|
||||||
|
pos_embed = pos_embed[:, 1:]
|
||||||
|
dim = x.shape[-1]
|
||||||
|
pos_embed = nn.functional.interpolate(
|
||||||
|
pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
|
||||||
|
scale_factor=math.sqrt(npatch / N),
|
||||||
|
mode='bicubic',
|
||||||
|
)
|
||||||
|
pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
|
||||||
|
return torch.cat((class_emb.unsqueeze(0), pos_embed), dim=1)
|
||||||
|
|
||||||
|
|
||||||
|
def vit_predictor(**kwargs):
|
||||||
|
model = VisionTransformerPredictor(
|
||||||
|
mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
||||||
|
**kwargs)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
def vit_tiny(patch_size=16, **kwargs):
|
||||||
|
model = VisionTransformer(
|
||||||
|
patch_size=patch_size, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4,
|
||||||
|
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
def vit_small(patch_size=16, **kwargs):
|
||||||
|
model = VisionTransformer(
|
||||||
|
patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4,
|
||||||
|
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
def vit_base(patch_size=16, **kwargs):
|
||||||
|
model = VisionTransformer(
|
||||||
|
patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
|
||||||
|
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
def vit_large(patch_size=16, **kwargs):
|
||||||
|
model = VisionTransformer(
|
||||||
|
patch_size=patch_size, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,
|
||||||
|
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
def vit_huge(patch_size=16, **kwargs):
|
||||||
|
model = VisionTransformer(
|
||||||
|
patch_size=patch_size, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4,
|
||||||
|
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
def vit_giant(patch_size=16, **kwargs):
|
||||||
|
model = VisionTransformer(
|
||||||
|
patch_size=patch_size, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=48/11,
|
||||||
|
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
VIT_EMBED_DIMS = {
|
||||||
|
'vit_tiny': 192,
|
||||||
|
'vit_small': 384,
|
||||||
|
'vit_base': 768,
|
||||||
|
'vit_large': 1024,
|
||||||
|
'vit_huge': 1280,
|
||||||
|
'vit_giant': 1408,
|
||||||
|
}
|
||||||
+379
@@ -0,0 +1,379 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
import os
|
||||||
|
|
||||||
|
# -- FOR DISTRIBUTED TRAINING ENSURE ONLY 1 DEVICE VISIBLE PER PROCESS
|
||||||
|
try:
|
||||||
|
# -- WARNING: IF DOING DISTRIBUTED TRAINING ON A NON-SLURM CLUSTER, MAKE
|
||||||
|
# -- SURE TO UPDATE THIS TO GET LOCAL-RANK ON NODE, OR ENSURE
|
||||||
|
# -- THAT YOUR JOBS ARE LAUNCHED WITH ONLY 1 DEVICE VISIBLE
|
||||||
|
# -- TO EACH PROCESS
|
||||||
|
os.environ['CUDA_VISIBLE_DEVICES'] = os.environ['SLURM_LOCALID']
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
|
||||||
|
import copy
|
||||||
|
import logging
|
||||||
|
import sys
|
||||||
|
import yaml
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
import torch
|
||||||
|
import torch.multiprocessing as mp
|
||||||
|
import torch.nn.functional as F
|
||||||
|
from torch.nn.parallel import DistributedDataParallel
|
||||||
|
|
||||||
|
from src.masks.multiblock import MaskCollator as MBMaskCollator
|
||||||
|
from src.masks.utils import apply_masks
|
||||||
|
from src.utils.distributed import (
|
||||||
|
init_distributed,
|
||||||
|
AllReduce
|
||||||
|
)
|
||||||
|
from src.utils.logging import (
|
||||||
|
CSVLogger,
|
||||||
|
gpu_timer,
|
||||||
|
grad_logger,
|
||||||
|
AverageMeter)
|
||||||
|
from src.utils.tensors import repeat_interleave_batch
|
||||||
|
from src.datasets.imagenet1k import make_imagenet1k
|
||||||
|
|
||||||
|
from src.helper import (
|
||||||
|
load_checkpoint,
|
||||||
|
init_model,
|
||||||
|
init_opt)
|
||||||
|
from src.transforms import make_transforms
|
||||||
|
|
||||||
|
# --
|
||||||
|
log_timings = True
|
||||||
|
log_freq = 10
|
||||||
|
checkpoint_freq = 50
|
||||||
|
# --
|
||||||
|
|
||||||
|
_GLOBAL_SEED = 0
|
||||||
|
np.random.seed(_GLOBAL_SEED)
|
||||||
|
torch.manual_seed(_GLOBAL_SEED)
|
||||||
|
torch.backends.cudnn.benchmark = True
|
||||||
|
|
||||||
|
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
|
||||||
|
logger = logging.getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
def main(args, resume_preempt=False):
|
||||||
|
|
||||||
|
# ----------------------------------------------------------------------- #
|
||||||
|
# PASSED IN PARAMS FROM CONFIG FILE
|
||||||
|
# ----------------------------------------------------------------------- #
|
||||||
|
|
||||||
|
# -- META
|
||||||
|
use_bfloat16 = args['meta']['use_bfloat16']
|
||||||
|
model_name = args['meta']['model_name']
|
||||||
|
load_model = args['meta']['load_checkpoint'] or resume_preempt
|
||||||
|
r_file = args['meta']['read_checkpoint']
|
||||||
|
copy_data = args['meta']['copy_data']
|
||||||
|
pred_depth = args['meta']['pred_depth']
|
||||||
|
pred_emb_dim = args['meta']['pred_emb_dim']
|
||||||
|
if not torch.cuda.is_available():
|
||||||
|
device = torch.device('cpu')
|
||||||
|
else:
|
||||||
|
device = torch.device('cuda:0')
|
||||||
|
torch.cuda.set_device(device)
|
||||||
|
|
||||||
|
# -- DATA
|
||||||
|
use_gaussian_blur = args['data']['use_gaussian_blur']
|
||||||
|
use_horizontal_flip = args['data']['use_horizontal_flip']
|
||||||
|
use_color_distortion = args['data']['use_color_distortion']
|
||||||
|
color_jitter = args['data']['color_jitter_strength']
|
||||||
|
# --
|
||||||
|
batch_size = args['data']['batch_size']
|
||||||
|
pin_mem = args['data']['pin_mem']
|
||||||
|
num_workers = args['data']['num_workers']
|
||||||
|
root_path = args['data']['root_path']
|
||||||
|
image_folder = args['data']['image_folder']
|
||||||
|
crop_size = args['data']['crop_size']
|
||||||
|
crop_scale = args['data']['crop_scale']
|
||||||
|
# --
|
||||||
|
|
||||||
|
# -- MASK
|
||||||
|
allow_overlap = args['mask']['allow_overlap'] # whether to allow overlap b/w context and target blocks
|
||||||
|
patch_size = args['mask']['patch_size'] # patch-size for model training
|
||||||
|
num_enc_masks = args['mask']['num_enc_masks'] # number of context blocks
|
||||||
|
min_keep = args['mask']['min_keep'] # min number of patches in context block
|
||||||
|
enc_mask_scale = args['mask']['enc_mask_scale'] # scale of context blocks
|
||||||
|
num_pred_masks = args['mask']['num_pred_masks'] # number of target blocks
|
||||||
|
pred_mask_scale = args['mask']['pred_mask_scale'] # scale of target blocks
|
||||||
|
aspect_ratio = args['mask']['aspect_ratio'] # aspect ratio of target blocks
|
||||||
|
# --
|
||||||
|
|
||||||
|
# -- OPTIMIZATION
|
||||||
|
ema = args['optimization']['ema']
|
||||||
|
ipe_scale = args['optimization']['ipe_scale'] # scheduler scale factor (def: 1.0)
|
||||||
|
wd = float(args['optimization']['weight_decay'])
|
||||||
|
final_wd = float(args['optimization']['final_weight_decay'])
|
||||||
|
num_epochs = args['optimization']['epochs']
|
||||||
|
warmup = args['optimization']['warmup']
|
||||||
|
start_lr = args['optimization']['start_lr']
|
||||||
|
lr = args['optimization']['lr']
|
||||||
|
final_lr = args['optimization']['final_lr']
|
||||||
|
|
||||||
|
# -- LOGGING
|
||||||
|
folder = args['logging']['folder']
|
||||||
|
tag = args['logging']['write_tag']
|
||||||
|
|
||||||
|
dump = os.path.join(folder, 'params-ijepa.yaml')
|
||||||
|
with open(dump, 'w') as f:
|
||||||
|
yaml.dump(args, f)
|
||||||
|
# ----------------------------------------------------------------------- #
|
||||||
|
|
||||||
|
try:
|
||||||
|
mp.set_start_method('spawn')
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
|
||||||
|
# -- init torch distributed backend
|
||||||
|
world_size, rank = init_distributed()
|
||||||
|
logger.info(f'Initialized (rank/world-size) {rank}/{world_size}')
|
||||||
|
if rank > 0:
|
||||||
|
logger.setLevel(logging.ERROR)
|
||||||
|
|
||||||
|
# -- log/checkpointing paths
|
||||||
|
log_file = os.path.join(folder, f'{tag}_r{rank}.csv')
|
||||||
|
save_path = os.path.join(folder, f'{tag}' + '-ep{epoch}.pth.tar')
|
||||||
|
latest_path = os.path.join(folder, f'{tag}-latest.pth.tar')
|
||||||
|
load_path = None
|
||||||
|
if load_model:
|
||||||
|
load_path = os.path.join(folder, r_file) if r_file is not None else latest_path
|
||||||
|
|
||||||
|
# -- make csv_logger
|
||||||
|
csv_logger = CSVLogger(log_file,
|
||||||
|
('%d', 'epoch'),
|
||||||
|
('%d', 'itr'),
|
||||||
|
('%.5f', 'loss'),
|
||||||
|
('%.5f', 'mask-A'),
|
||||||
|
('%.5f', 'mask-B'),
|
||||||
|
('%d', 'time (ms)'))
|
||||||
|
|
||||||
|
# -- init model
|
||||||
|
encoder, predictor = init_model(
|
||||||
|
device=device,
|
||||||
|
patch_size=patch_size,
|
||||||
|
crop_size=crop_size,
|
||||||
|
pred_depth=pred_depth,
|
||||||
|
pred_emb_dim=pred_emb_dim,
|
||||||
|
model_name=model_name)
|
||||||
|
target_encoder = copy.deepcopy(encoder)
|
||||||
|
|
||||||
|
# -- make data transforms
|
||||||
|
mask_collator = MBMaskCollator(
|
||||||
|
input_size=crop_size,
|
||||||
|
patch_size=patch_size,
|
||||||
|
pred_mask_scale=pred_mask_scale,
|
||||||
|
enc_mask_scale=enc_mask_scale,
|
||||||
|
aspect_ratio=aspect_ratio,
|
||||||
|
nenc=num_enc_masks,
|
||||||
|
npred=num_pred_masks,
|
||||||
|
allow_overlap=allow_overlap,
|
||||||
|
min_keep=min_keep)
|
||||||
|
|
||||||
|
transform = make_transforms(
|
||||||
|
crop_size=crop_size,
|
||||||
|
crop_scale=crop_scale,
|
||||||
|
gaussian_blur=use_gaussian_blur,
|
||||||
|
horizontal_flip=use_horizontal_flip,
|
||||||
|
color_distortion=use_color_distortion,
|
||||||
|
color_jitter=color_jitter)
|
||||||
|
|
||||||
|
# -- init data-loaders/samplers
|
||||||
|
_, unsupervised_loader, unsupervised_sampler = make_imagenet1k(
|
||||||
|
transform=transform,
|
||||||
|
batch_size=batch_size,
|
||||||
|
collator=mask_collator,
|
||||||
|
pin_mem=pin_mem,
|
||||||
|
training=True,
|
||||||
|
num_workers=num_workers,
|
||||||
|
world_size=world_size,
|
||||||
|
rank=rank,
|
||||||
|
root_path=root_path,
|
||||||
|
image_folder=image_folder,
|
||||||
|
copy_data=copy_data,
|
||||||
|
drop_last=True)
|
||||||
|
ipe = len(unsupervised_loader)
|
||||||
|
|
||||||
|
# -- init optimizer and scheduler
|
||||||
|
optimizer, scaler, scheduler, wd_scheduler = init_opt(
|
||||||
|
encoder=encoder,
|
||||||
|
predictor=predictor,
|
||||||
|
wd=wd,
|
||||||
|
final_wd=final_wd,
|
||||||
|
start_lr=start_lr,
|
||||||
|
ref_lr=lr,
|
||||||
|
final_lr=final_lr,
|
||||||
|
iterations_per_epoch=ipe,
|
||||||
|
warmup=warmup,
|
||||||
|
num_epochs=num_epochs,
|
||||||
|
ipe_scale=ipe_scale,
|
||||||
|
use_bfloat16=use_bfloat16)
|
||||||
|
encoder = DistributedDataParallel(encoder, static_graph=True)
|
||||||
|
predictor = DistributedDataParallel(predictor, static_graph=True)
|
||||||
|
target_encoder = DistributedDataParallel(target_encoder)
|
||||||
|
for p in target_encoder.parameters():
|
||||||
|
p.requires_grad = False
|
||||||
|
|
||||||
|
# -- momentum schedule
|
||||||
|
momentum_scheduler = (ema[0] + i*(ema[1]-ema[0])/(ipe*num_epochs*ipe_scale)
|
||||||
|
for i in range(int(ipe*num_epochs*ipe_scale)+1))
|
||||||
|
|
||||||
|
start_epoch = 0
|
||||||
|
# -- load training checkpoint
|
||||||
|
if load_model:
|
||||||
|
encoder, predictor, target_encoder, optimizer, scaler, start_epoch = load_checkpoint(
|
||||||
|
device=device,
|
||||||
|
r_path=load_path,
|
||||||
|
encoder=encoder,
|
||||||
|
predictor=predictor,
|
||||||
|
target_encoder=target_encoder,
|
||||||
|
opt=optimizer,
|
||||||
|
scaler=scaler)
|
||||||
|
for _ in range(start_epoch*ipe):
|
||||||
|
scheduler.step()
|
||||||
|
wd_scheduler.step()
|
||||||
|
next(momentum_scheduler)
|
||||||
|
mask_collator.step()
|
||||||
|
|
||||||
|
def save_checkpoint(epoch):
|
||||||
|
save_dict = {
|
||||||
|
'encoder': encoder.state_dict(),
|
||||||
|
'predictor': predictor.state_dict(),
|
||||||
|
'target_encoder': target_encoder.state_dict(),
|
||||||
|
'opt': optimizer.state_dict(),
|
||||||
|
'scaler': None if scaler is None else scaler.state_dict(),
|
||||||
|
'epoch': epoch,
|
||||||
|
'loss': loss_meter.avg,
|
||||||
|
'batch_size': batch_size,
|
||||||
|
'world_size': world_size,
|
||||||
|
'lr': lr
|
||||||
|
}
|
||||||
|
if rank == 0:
|
||||||
|
torch.save(save_dict, latest_path)
|
||||||
|
if (epoch + 1) % checkpoint_freq == 0:
|
||||||
|
torch.save(save_dict, save_path.format(epoch=f'{epoch + 1}'))
|
||||||
|
|
||||||
|
# -- TRAINING LOOP
|
||||||
|
for epoch in range(start_epoch, num_epochs):
|
||||||
|
logger.info('Epoch %d' % (epoch + 1))
|
||||||
|
|
||||||
|
# -- update distributed-data-loader epoch
|
||||||
|
unsupervised_sampler.set_epoch(epoch)
|
||||||
|
|
||||||
|
loss_meter = AverageMeter()
|
||||||
|
maskA_meter = AverageMeter()
|
||||||
|
maskB_meter = AverageMeter()
|
||||||
|
time_meter = AverageMeter()
|
||||||
|
|
||||||
|
for itr, (udata, masks_enc, masks_pred) in enumerate(unsupervised_loader):
|
||||||
|
|
||||||
|
def load_imgs():
|
||||||
|
# -- unsupervised imgs
|
||||||
|
imgs = udata[0].to(device, non_blocking=True)
|
||||||
|
masks_1 = [u.to(device, non_blocking=True) for u in masks_enc]
|
||||||
|
masks_2 = [u.to(device, non_blocking=True) for u in masks_pred]
|
||||||
|
return (imgs, masks_1, masks_2)
|
||||||
|
imgs, masks_enc, masks_pred = load_imgs()
|
||||||
|
maskA_meter.update(len(masks_enc[0][0]))
|
||||||
|
maskB_meter.update(len(masks_pred[0][0]))
|
||||||
|
|
||||||
|
def train_step():
|
||||||
|
_new_lr = scheduler.step()
|
||||||
|
_new_wd = wd_scheduler.step()
|
||||||
|
# --
|
||||||
|
|
||||||
|
def forward_target():
|
||||||
|
with torch.no_grad():
|
||||||
|
h = target_encoder(imgs)
|
||||||
|
h = F.layer_norm(h, (h.size(-1),)) # normalize over feature-dim
|
||||||
|
B = len(h)
|
||||||
|
# -- create targets (masked regions of h)
|
||||||
|
h = apply_masks(h, masks_pred)
|
||||||
|
h = repeat_interleave_batch(h, B, repeat=len(masks_enc))
|
||||||
|
return h
|
||||||
|
|
||||||
|
def forward_context():
|
||||||
|
z = encoder(imgs, masks_enc)
|
||||||
|
z = predictor(z, masks_enc, masks_pred)
|
||||||
|
return z
|
||||||
|
|
||||||
|
def loss_fn(z, h):
|
||||||
|
loss = F.smooth_l1_loss(z, h)
|
||||||
|
loss = AllReduce.apply(loss)
|
||||||
|
return loss
|
||||||
|
|
||||||
|
# Step 1. Forward
|
||||||
|
with torch.cuda.amp.autocast(dtype=torch.bfloat16, enabled=use_bfloat16):
|
||||||
|
h = forward_target()
|
||||||
|
z = forward_context()
|
||||||
|
loss = loss_fn(z, h)
|
||||||
|
|
||||||
|
# Step 2. Backward & step
|
||||||
|
if use_bfloat16:
|
||||||
|
scaler.scale(loss).backward()
|
||||||
|
scaler.step(optimizer)
|
||||||
|
scaler.update()
|
||||||
|
else:
|
||||||
|
loss.backward()
|
||||||
|
optimizer.step()
|
||||||
|
grad_stats = grad_logger(encoder.named_parameters())
|
||||||
|
optimizer.zero_grad()
|
||||||
|
|
||||||
|
# Step 3. momentum update of target encoder
|
||||||
|
with torch.no_grad():
|
||||||
|
m = next(momentum_scheduler)
|
||||||
|
for param_q, param_k in zip(encoder.parameters(), target_encoder.parameters()):
|
||||||
|
param_k.data.mul_(m).add_((1.-m) * param_q.detach().data)
|
||||||
|
|
||||||
|
return (float(loss), _new_lr, _new_wd, grad_stats)
|
||||||
|
(loss, _new_lr, _new_wd, grad_stats), etime = gpu_timer(train_step)
|
||||||
|
loss_meter.update(loss)
|
||||||
|
time_meter.update(etime)
|
||||||
|
|
||||||
|
# -- Logging
|
||||||
|
def log_stats():
|
||||||
|
csv_logger.log(epoch + 1, itr, loss, maskA_meter.val, maskB_meter.val, etime)
|
||||||
|
if (itr % log_freq == 0) or np.isnan(loss) or np.isinf(loss):
|
||||||
|
logger.info('[%d, %5d] loss: %.3f '
|
||||||
|
'masks: %.1f %.1f '
|
||||||
|
'[wd: %.2e] [lr: %.2e] '
|
||||||
|
'[mem: %.2e] '
|
||||||
|
'(%.1f ms)'
|
||||||
|
% (epoch + 1, itr,
|
||||||
|
loss_meter.avg,
|
||||||
|
maskA_meter.avg,
|
||||||
|
maskB_meter.avg,
|
||||||
|
_new_wd,
|
||||||
|
_new_lr,
|
||||||
|
torch.cuda.max_memory_allocated() / 1024.**2,
|
||||||
|
time_meter.avg))
|
||||||
|
|
||||||
|
if grad_stats is not None:
|
||||||
|
logger.info('[%d, %5d] grad_stats: [%.2e %.2e] (%.2e, %.2e)'
|
||||||
|
% (epoch + 1, itr,
|
||||||
|
grad_stats.first_layer,
|
||||||
|
grad_stats.last_layer,
|
||||||
|
grad_stats.min,
|
||||||
|
grad_stats.max))
|
||||||
|
|
||||||
|
log_stats()
|
||||||
|
|
||||||
|
assert not np.isnan(loss), 'loss is nan'
|
||||||
|
|
||||||
|
# -- Save Checkpoint after every epoch
|
||||||
|
logger.info('avg. loss %.3f' % loss_meter.avg)
|
||||||
|
save_checkpoint(epoch+1)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
@@ -0,0 +1,67 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
from logging import getLogger
|
||||||
|
|
||||||
|
from PIL import ImageFilter
|
||||||
|
|
||||||
|
import torch
|
||||||
|
import torchvision.transforms as transforms
|
||||||
|
|
||||||
|
_GLOBAL_SEED = 0
|
||||||
|
logger = getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
def make_transforms(
|
||||||
|
crop_size=224,
|
||||||
|
crop_scale=(0.3, 1.0),
|
||||||
|
color_jitter=1.0,
|
||||||
|
horizontal_flip=False,
|
||||||
|
color_distortion=False,
|
||||||
|
gaussian_blur=False,
|
||||||
|
normalization=((0.485, 0.456, 0.406),
|
||||||
|
(0.229, 0.224, 0.225))
|
||||||
|
):
|
||||||
|
logger.info('making imagenet data transforms')
|
||||||
|
|
||||||
|
def get_color_distortion(s=1.0):
|
||||||
|
# s is the strength of color distortion.
|
||||||
|
color_jitter = transforms.ColorJitter(0.8*s, 0.8*s, 0.8*s, 0.2*s)
|
||||||
|
rnd_color_jitter = transforms.RandomApply([color_jitter], p=0.8)
|
||||||
|
rnd_gray = transforms.RandomGrayscale(p=0.2)
|
||||||
|
color_distort = transforms.Compose([
|
||||||
|
rnd_color_jitter,
|
||||||
|
rnd_gray])
|
||||||
|
return color_distort
|
||||||
|
|
||||||
|
transform_list = []
|
||||||
|
transform_list += [transforms.RandomResizedCrop(crop_size, scale=crop_scale)]
|
||||||
|
if horizontal_flip:
|
||||||
|
transform_list += [transforms.RandomHorizontalFlip()]
|
||||||
|
if color_distortion:
|
||||||
|
transform_list += [get_color_distortion(s=color_jitter)]
|
||||||
|
if gaussian_blur:
|
||||||
|
transform_list += [GaussianBlur(p=0.5)]
|
||||||
|
transform_list += [transforms.ToTensor()]
|
||||||
|
transform_list += [transforms.Normalize(normalization[0], normalization[1])]
|
||||||
|
|
||||||
|
transform = transforms.Compose(transform_list)
|
||||||
|
return transform
|
||||||
|
|
||||||
|
|
||||||
|
class GaussianBlur(object):
|
||||||
|
def __init__(self, p=0.5, radius_min=0.1, radius_max=2.):
|
||||||
|
self.prob = p
|
||||||
|
self.radius_min = radius_min
|
||||||
|
self.radius_max = radius_max
|
||||||
|
|
||||||
|
def __call__(self, img):
|
||||||
|
if torch.bernoulli(torch.tensor(self.prob)) == 0:
|
||||||
|
return img
|
||||||
|
|
||||||
|
radius = self.radius_min + torch.rand(1) * (self.radius_max - self.radius_min)
|
||||||
|
return img.filter(ImageFilter.GaussianBlur(radius=radius))
|
||||||
@@ -0,0 +1,112 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
import os
|
||||||
|
|
||||||
|
import torch
|
||||||
|
import torch.distributed as dist
|
||||||
|
|
||||||
|
from logging import getLogger
|
||||||
|
|
||||||
|
logger = getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
def init_distributed(port=40112, rank_and_world_size=(None, None)):
|
||||||
|
|
||||||
|
if dist.is_available() and dist.is_initialized():
|
||||||
|
return dist.get_world_size(), dist.get_rank()
|
||||||
|
|
||||||
|
rank, world_size = rank_and_world_size
|
||||||
|
os.environ['MASTER_ADDR'] = 'localhost'
|
||||||
|
|
||||||
|
if (rank is None) or (world_size is None):
|
||||||
|
try:
|
||||||
|
world_size = int(os.environ['SLURM_NTASKS'])
|
||||||
|
rank = int(os.environ['SLURM_PROCID'])
|
||||||
|
os.environ['MASTER_ADDR'] = os.environ['HOSTNAME']
|
||||||
|
except Exception:
|
||||||
|
logger.info('SLURM vars not set (distributed training not available)')
|
||||||
|
world_size, rank = 1, 0
|
||||||
|
return world_size, rank
|
||||||
|
|
||||||
|
try:
|
||||||
|
os.environ['MASTER_PORT'] = str(port)
|
||||||
|
torch.distributed.init_process_group(
|
||||||
|
backend='nccl',
|
||||||
|
world_size=world_size,
|
||||||
|
rank=rank)
|
||||||
|
except Exception as e:
|
||||||
|
world_size, rank = 1, 0
|
||||||
|
logger.info(f'distributed training not available {e}')
|
||||||
|
|
||||||
|
return world_size, rank
|
||||||
|
|
||||||
|
|
||||||
|
class AllGather(torch.autograd.Function):
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def forward(ctx, x):
|
||||||
|
if (
|
||||||
|
dist.is_available()
|
||||||
|
and dist.is_initialized()
|
||||||
|
and (dist.get_world_size() > 1)
|
||||||
|
):
|
||||||
|
x = x.contiguous()
|
||||||
|
outputs = [torch.zeros_like(x) for _ in range(dist.get_world_size())]
|
||||||
|
dist.all_gather(outputs, x)
|
||||||
|
return torch.cat(outputs, 0)
|
||||||
|
return x
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def backward(ctx, grads):
|
||||||
|
if (
|
||||||
|
dist.is_available()
|
||||||
|
and dist.is_initialized()
|
||||||
|
and (dist.get_world_size() > 1)
|
||||||
|
):
|
||||||
|
s = (grads.shape[0] // dist.get_world_size()) * dist.get_rank()
|
||||||
|
e = (grads.shape[0] // dist.get_world_size()) * (dist.get_rank() + 1)
|
||||||
|
grads = grads.contiguous()
|
||||||
|
dist.all_reduce(grads)
|
||||||
|
return grads[s:e]
|
||||||
|
return grads
|
||||||
|
|
||||||
|
|
||||||
|
class AllReduceSum(torch.autograd.Function):
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def forward(ctx, x):
|
||||||
|
if (
|
||||||
|
dist.is_available()
|
||||||
|
and dist.is_initialized()
|
||||||
|
and (dist.get_world_size() > 1)
|
||||||
|
):
|
||||||
|
x = x.contiguous()
|
||||||
|
dist.all_reduce(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def backward(ctx, grads):
|
||||||
|
return grads
|
||||||
|
|
||||||
|
|
||||||
|
class AllReduce(torch.autograd.Function):
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def forward(ctx, x):
|
||||||
|
if (
|
||||||
|
dist.is_available()
|
||||||
|
and dist.is_initialized()
|
||||||
|
and (dist.get_world_size() > 1)
|
||||||
|
):
|
||||||
|
x = x.contiguous() / dist.get_world_size()
|
||||||
|
dist.all_reduce(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def backward(ctx, grads):
|
||||||
|
return grads
|
||||||
@@ -0,0 +1,92 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
import torch
|
||||||
|
|
||||||
|
|
||||||
|
def gpu_timer(closure, log_timings=True):
|
||||||
|
""" Helper to time gpu-time to execute closure() """
|
||||||
|
log_timings = log_timings and torch.cuda.is_available()
|
||||||
|
|
||||||
|
elapsed_time = -1.
|
||||||
|
if log_timings:
|
||||||
|
start = torch.cuda.Event(enable_timing=True)
|
||||||
|
end = torch.cuda.Event(enable_timing=True)
|
||||||
|
start.record()
|
||||||
|
|
||||||
|
result = closure()
|
||||||
|
|
||||||
|
if log_timings:
|
||||||
|
end.record()
|
||||||
|
torch.cuda.synchronize()
|
||||||
|
elapsed_time = start.elapsed_time(end)
|
||||||
|
|
||||||
|
return result, elapsed_time
|
||||||
|
|
||||||
|
|
||||||
|
class CSVLogger(object):
|
||||||
|
|
||||||
|
def __init__(self, fname, *argv):
|
||||||
|
self.fname = fname
|
||||||
|
self.types = []
|
||||||
|
# -- print headers
|
||||||
|
with open(self.fname, '+a') as f:
|
||||||
|
for i, v in enumerate(argv, 1):
|
||||||
|
self.types.append(v[0])
|
||||||
|
if i < len(argv):
|
||||||
|
print(v[1], end=',', file=f)
|
||||||
|
else:
|
||||||
|
print(v[1], end='\n', file=f)
|
||||||
|
|
||||||
|
def log(self, *argv):
|
||||||
|
with open(self.fname, '+a') as f:
|
||||||
|
for i, tv in enumerate(zip(self.types, argv), 1):
|
||||||
|
end = ',' if i < len(argv) else '\n'
|
||||||
|
print(tv[0] % tv[1], end=end, file=f)
|
||||||
|
|
||||||
|
|
||||||
|
class AverageMeter(object):
|
||||||
|
"""computes and stores the average and current value"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.reset()
|
||||||
|
|
||||||
|
def reset(self):
|
||||||
|
self.val = 0
|
||||||
|
self.avg = 0
|
||||||
|
self.max = float('-inf')
|
||||||
|
self.min = float('inf')
|
||||||
|
self.sum = 0
|
||||||
|
self.count = 0
|
||||||
|
|
||||||
|
def update(self, val, n=1):
|
||||||
|
self.val = val
|
||||||
|
try:
|
||||||
|
self.max = max(val, self.max)
|
||||||
|
self.min = min(val, self.min)
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
self.sum += val * n
|
||||||
|
self.count += n
|
||||||
|
self.avg = self.sum / self.count
|
||||||
|
|
||||||
|
|
||||||
|
def grad_logger(named_params):
|
||||||
|
stats = AverageMeter()
|
||||||
|
stats.first_layer = None
|
||||||
|
stats.last_layer = None
|
||||||
|
for n, p in named_params:
|
||||||
|
if (p.grad is not None) and not (n.endswith('.bias') or len(p.shape) == 1):
|
||||||
|
grad_norm = float(torch.norm(p.grad.data))
|
||||||
|
stats.update(grad_norm)
|
||||||
|
if 'qkv' in n:
|
||||||
|
stats.last_layer = grad_norm
|
||||||
|
if stats.first_layer is None:
|
||||||
|
stats.first_layer = grad_norm
|
||||||
|
if stats.first_layer is None or stats.last_layer is None:
|
||||||
|
stats.first_layer = stats.last_layer = 0.
|
||||||
|
return stats
|
||||||
@@ -0,0 +1,76 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
import math
|
||||||
|
|
||||||
|
|
||||||
|
class WarmupCosineSchedule(object):
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
optimizer,
|
||||||
|
warmup_steps,
|
||||||
|
start_lr,
|
||||||
|
ref_lr,
|
||||||
|
T_max,
|
||||||
|
last_epoch=-1,
|
||||||
|
final_lr=0.
|
||||||
|
):
|
||||||
|
self.optimizer = optimizer
|
||||||
|
self.start_lr = start_lr
|
||||||
|
self.ref_lr = ref_lr
|
||||||
|
self.final_lr = final_lr
|
||||||
|
self.warmup_steps = warmup_steps
|
||||||
|
self.T_max = T_max - warmup_steps
|
||||||
|
self._step = 0.
|
||||||
|
|
||||||
|
def step(self):
|
||||||
|
self._step += 1
|
||||||
|
if self._step < self.warmup_steps:
|
||||||
|
progress = float(self._step) / float(max(1, self.warmup_steps))
|
||||||
|
new_lr = self.start_lr + progress * (self.ref_lr - self.start_lr)
|
||||||
|
else:
|
||||||
|
# -- progress after warmup
|
||||||
|
progress = float(self._step - self.warmup_steps) / float(max(1, self.T_max))
|
||||||
|
new_lr = max(self.final_lr,
|
||||||
|
self.final_lr + (self.ref_lr - self.final_lr) * 0.5 * (1. + math.cos(math.pi * progress)))
|
||||||
|
|
||||||
|
for group in self.optimizer.param_groups:
|
||||||
|
group['lr'] = new_lr
|
||||||
|
|
||||||
|
return new_lr
|
||||||
|
|
||||||
|
|
||||||
|
class CosineWDSchedule(object):
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
optimizer,
|
||||||
|
ref_wd,
|
||||||
|
T_max,
|
||||||
|
final_wd=0.
|
||||||
|
):
|
||||||
|
self.optimizer = optimizer
|
||||||
|
self.ref_wd = ref_wd
|
||||||
|
self.final_wd = final_wd
|
||||||
|
self.T_max = T_max
|
||||||
|
self._step = 0.
|
||||||
|
|
||||||
|
def step(self):
|
||||||
|
self._step += 1
|
||||||
|
progress = self._step / self.T_max
|
||||||
|
new_wd = self.final_wd + (self.ref_wd - self.final_wd) * 0.5 * (1. + math.cos(math.pi * progress))
|
||||||
|
|
||||||
|
if self.final_wd <= self.ref_wd:
|
||||||
|
new_wd = max(self.final_wd, new_wd)
|
||||||
|
else:
|
||||||
|
new_wd = min(self.final_wd, new_wd)
|
||||||
|
|
||||||
|
for group in self.optimizer.param_groups:
|
||||||
|
if ('WD_exclude' not in group) or not group['WD_exclude']:
|
||||||
|
group['weight_decay'] = new_wd
|
||||||
|
return new_wd
|
||||||
@@ -0,0 +1,71 @@
|
|||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the license found in the
|
||||||
|
# LICENSE file in the root directory of this source tree.
|
||||||
|
#
|
||||||
|
|
||||||
|
import math
|
||||||
|
|
||||||
|
import torch
|
||||||
|
|
||||||
|
from logging import getLogger
|
||||||
|
|
||||||
|
logger = getLogger()
|
||||||
|
|
||||||
|
|
||||||
|
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
|
||||||
|
# Cut & paste from PyTorch official master until it's in a few official releases - RW
|
||||||
|
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
|
||||||
|
def norm_cdf(x):
|
||||||
|
# Computes standard normal cumulative distribution function
|
||||||
|
return (1. + math.erf(x / math.sqrt(2.))) / 2.
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
# Values are generated by using a truncated uniform distribution and
|
||||||
|
# then using the inverse CDF for the normal distribution.
|
||||||
|
# Get upper and lower cdf values
|
||||||
|
l = norm_cdf((a - mean) / std)
|
||||||
|
u = norm_cdf((b - mean) / std)
|
||||||
|
|
||||||
|
# Uniformly fill tensor with values from [l, u], then translate to
|
||||||
|
# [2l-1, 2u-1].
|
||||||
|
tensor.uniform_(2 * l - 1, 2 * u - 1)
|
||||||
|
|
||||||
|
# Use inverse cdf transform for normal distribution to get truncated
|
||||||
|
# standard normal
|
||||||
|
tensor.erfinv_()
|
||||||
|
|
||||||
|
# Transform to proper mean, std
|
||||||
|
tensor.mul_(std * math.sqrt(2.))
|
||||||
|
tensor.add_(mean)
|
||||||
|
|
||||||
|
# Clamp to ensure it's in the proper range
|
||||||
|
tensor.clamp_(min=a, max=b)
|
||||||
|
return tensor
|
||||||
|
|
||||||
|
|
||||||
|
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
|
||||||
|
# type: (Tensor, float, float, float, float) -> Tensor
|
||||||
|
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
|
||||||
|
|
||||||
|
|
||||||
|
def apply_masks(x, masks):
|
||||||
|
"""
|
||||||
|
:param x: tensor of shape [B (batch-size), N (num-patches), D (feature-dim)]
|
||||||
|
:param masks: list of tensors containing indices of patches in [N] to keep
|
||||||
|
"""
|
||||||
|
all_x = []
|
||||||
|
for m in masks:
|
||||||
|
mask_keep = m.unsqueeze(-1).repeat(1, 1, x.size(-1))
|
||||||
|
all_x += [torch.gather(x, dim=1, index=mask_keep)]
|
||||||
|
return torch.cat(all_x, dim=0)
|
||||||
|
|
||||||
|
|
||||||
|
def repeat_interleave_batch(x, B, repeat):
|
||||||
|
N = len(x) // B
|
||||||
|
x = torch.cat([
|
||||||
|
torch.cat([x[i*B:(i+1)*B] for _ in range(repeat)], dim=0)
|
||||||
|
for i in range(N)
|
||||||
|
], dim=0)
|
||||||
|
return x
|
||||||
Reference in New Issue
Block a user