first commit
This commit is contained in:
BlenderNeko
@@ -0,0 +1,674 @@
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
Version 3, 29 June 2007
|
||||
|
||||
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The GNU General Public License is a free, copyleft license for
|
||||
software and other kinds of works.
|
||||
|
||||
The licenses for most software and other practical works are designed
|
||||
to take away your freedom to share and change the works. By contrast,
|
||||
the GNU General Public License is intended to guarantee your freedom to
|
||||
share and change all versions of a program--to make sure it remains free
|
||||
software for all its users. We, the Free Software Foundation, use the
|
||||
GNU General Public License for most of our software; it applies also to
|
||||
any other work released this way by its authors. You can apply it to
|
||||
your programs, too.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
them if you wish), that you receive source code or can get it if you
|
||||
want it, that you can change the software or use pieces of it in new
|
||||
free programs, and that you know you can do these things.
|
||||
|
||||
To protect your rights, we need to prevent others from denying you
|
||||
these rights or asking you to surrender the rights. Therefore, you have
|
||||
certain responsibilities if you distribute copies of the software, or if
|
||||
you modify it: responsibilities to respect the freedom of others.
|
||||
|
||||
For example, if you distribute copies of such a program, whether
|
||||
gratis or for a fee, you must pass on to the recipients the same
|
||||
freedoms that you received. You must make sure that they, too, receive
|
||||
or can get the source code. And you must show them these terms so they
|
||||
know their rights.
|
||||
|
||||
Developers that use the GNU GPL protect your rights with two steps:
|
||||
(1) assert copyright on the software, and (2) offer you this License
|
||||
giving you legal permission to copy, distribute and/or modify it.
|
||||
|
||||
For the developers' and authors' protection, the GPL clearly explains
|
||||
that there is no warranty for this free software. For both users' and
|
||||
authors' sake, the GPL requires that modified versions be marked as
|
||||
changed, so that their problems will not be attributed erroneously to
|
||||
authors of previous versions.
|
||||
|
||||
Some devices are designed to deny users access to install or run
|
||||
modified versions of the software inside them, although the manufacturer
|
||||
can do so. This is fundamentally incompatible with the aim of
|
||||
protecting users' freedom to change the software. The systematic
|
||||
pattern of such abuse occurs in the area of products for individuals to
|
||||
use, which is precisely where it is most unacceptable. Therefore, we
|
||||
have designed this version of the GPL to prohibit the practice for those
|
||||
products. If such problems arise substantially in other domains, we
|
||||
stand ready to extend this provision to those domains in future versions
|
||||
of the GPL, as needed to protect the freedom of users.
|
||||
|
||||
Finally, every program is threatened constantly by software patents.
|
||||
States should not allow patents to restrict development and use of
|
||||
software on general-purpose computers, but in those that do, we wish to
|
||||
avoid the special danger that patents applied to a free program could
|
||||
make it effectively proprietary. To prevent this, the GPL assures that
|
||||
patents cannot be used to render the program non-free.
|
||||
|
||||
The precise terms and conditions for copying, distribution and
|
||||
modification follow.
|
||||
|
||||
TERMS AND CONDITIONS
|
||||
|
||||
0. Definitions.
|
||||
|
||||
"This License" refers to version 3 of the GNU General Public License.
|
||||
|
||||
"Copyright" also means copyright-like laws that apply to other kinds of
|
||||
works, such as semiconductor masks.
|
||||
|
||||
"The Program" refers to any copyrightable work licensed under this
|
||||
License. Each licensee is addressed as "you". "Licensees" and
|
||||
"recipients" may be individuals or organizations.
|
||||
|
||||
To "modify" a work means to copy from or adapt all or part of the work
|
||||
in a fashion requiring copyright permission, other than the making of an
|
||||
exact copy. The resulting work is called a "modified version" of the
|
||||
earlier work or a work "based on" the earlier work.
|
||||
|
||||
A "covered work" means either the unmodified Program or a work based
|
||||
on the Program.
|
||||
|
||||
To "propagate" a work means to do anything with it that, without
|
||||
permission, would make you directly or secondarily liable for
|
||||
infringement under applicable copyright law, except executing it on a
|
||||
computer or modifying a private copy. Propagation includes copying,
|
||||
distribution (with or without modification), making available to the
|
||||
public, and in some countries other activities as well.
|
||||
|
||||
To "convey" a work means any kind of propagation that enables other
|
||||
parties to make or receive copies. Mere interaction with a user through
|
||||
a computer network, with no transfer of a copy, is not conveying.
|
||||
|
||||
An interactive user interface displays "Appropriate Legal Notices"
|
||||
to the extent that it includes a convenient and prominently visible
|
||||
feature that (1) displays an appropriate copyright notice, and (2)
|
||||
tells the user that there is no warranty for the work (except to the
|
||||
extent that warranties are provided), that licensees may convey the
|
||||
work under this License, and how to view a copy of this License. If
|
||||
the interface presents a list of user commands or options, such as a
|
||||
menu, a prominent item in the list meets this criterion.
|
||||
|
||||
1. Source Code.
|
||||
|
||||
The "source code" for a work means the preferred form of the work
|
||||
for making modifications to it. "Object code" means any non-source
|
||||
form of a work.
|
||||
|
||||
A "Standard Interface" means an interface that either is an official
|
||||
standard defined by a recognized standards body, or, in the case of
|
||||
interfaces specified for a particular programming language, one that
|
||||
is widely used among developers working in that language.
|
||||
|
||||
The "System Libraries" of an executable work include anything, other
|
||||
than the work as a whole, that (a) is included in the normal form of
|
||||
packaging a Major Component, but which is not part of that Major
|
||||
Component, and (b) serves only to enable use of the work with that
|
||||
Major Component, or to implement a Standard Interface for which an
|
||||
implementation is available to the public in source code form. A
|
||||
"Major Component", in this context, means a major essential component
|
||||
(kernel, window system, and so on) of the specific operating system
|
||||
(if any) on which the executable work runs, or a compiler used to
|
||||
produce the work, or an object code interpreter used to run it.
|
||||
|
||||
The "Corresponding Source" for a work in object code form means all
|
||||
the source code needed to generate, install, and (for an executable
|
||||
work) run the object code and to modify the work, including scripts to
|
||||
control those activities. However, it does not include the work's
|
||||
System Libraries, or general-purpose tools or generally available free
|
||||
programs which are used unmodified in performing those activities but
|
||||
which are not part of the work. For example, Corresponding Source
|
||||
includes interface definition files associated with source files for
|
||||
the work, and the source code for shared libraries and dynamically
|
||||
linked subprograms that the work is specifically designed to require,
|
||||
such as by intimate data communication or control flow between those
|
||||
subprograms and other parts of the work.
|
||||
|
||||
The Corresponding Source need not include anything that users
|
||||
can regenerate automatically from other parts of the Corresponding
|
||||
Source.
|
||||
|
||||
The Corresponding Source for a work in source code form is that
|
||||
same work.
|
||||
|
||||
2. Basic Permissions.
|
||||
|
||||
All rights granted under this License are granted for the term of
|
||||
copyright on the Program, and are irrevocable provided the stated
|
||||
conditions are met. This License explicitly affirms your unlimited
|
||||
permission to run the unmodified Program. The output from running a
|
||||
covered work is covered by this License only if the output, given its
|
||||
content, constitutes a covered work. This License acknowledges your
|
||||
rights of fair use or other equivalent, as provided by copyright law.
|
||||
|
||||
You may make, run and propagate covered works that you do not
|
||||
convey, without conditions so long as your license otherwise remains
|
||||
in force. You may convey covered works to others for the sole purpose
|
||||
of having them make modifications exclusively for you, or provide you
|
||||
with facilities for running those works, provided that you comply with
|
||||
the terms of this License in conveying all material for which you do
|
||||
not control copyright. Those thus making or running the covered works
|
||||
for you must do so exclusively on your behalf, under your direction
|
||||
and control, on terms that prohibit them from making any copies of
|
||||
your copyrighted material outside their relationship with you.
|
||||
|
||||
Conveying under any other circumstances is permitted solely under
|
||||
the conditions stated below. Sublicensing is not allowed; section 10
|
||||
makes it unnecessary.
|
||||
|
||||
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
||||
|
||||
No covered work shall be deemed part of an effective technological
|
||||
measure under any applicable law fulfilling obligations under article
|
||||
11 of the WIPO copyright treaty adopted on 20 December 1996, or
|
||||
similar laws prohibiting or restricting circumvention of such
|
||||
measures.
|
||||
|
||||
When you convey a covered work, you waive any legal power to forbid
|
||||
circumvention of technological measures to the extent such circumvention
|
||||
is effected by exercising rights under this License with respect to
|
||||
the covered work, and you disclaim any intention to limit operation or
|
||||
modification of the work as a means of enforcing, against the work's
|
||||
users, your or third parties' legal rights to forbid circumvention of
|
||||
technological measures.
|
||||
|
||||
4. Conveying Verbatim Copies.
|
||||
|
||||
You may convey verbatim copies of the Program's source code as you
|
||||
receive it, in any medium, provided that you conspicuously and
|
||||
appropriately publish on each copy an appropriate copyright notice;
|
||||
keep intact all notices stating that this License and any
|
||||
non-permissive terms added in accord with section 7 apply to the code;
|
||||
keep intact all notices of the absence of any warranty; and give all
|
||||
recipients a copy of this License along with the Program.
|
||||
|
||||
You may charge any price or no price for each copy that you convey,
|
||||
and you may offer support or warranty protection for a fee.
|
||||
|
||||
5. Conveying Modified Source Versions.
|
||||
|
||||
You may convey a work based on the Program, or the modifications to
|
||||
produce it from the Program, in the form of source code under the
|
||||
terms of section 4, provided that you also meet all of these conditions:
|
||||
|
||||
a) The work must carry prominent notices stating that you modified
|
||||
it, and giving a relevant date.
|
||||
|
||||
b) The work must carry prominent notices stating that it is
|
||||
released under this License and any conditions added under section
|
||||
7. This requirement modifies the requirement in section 4 to
|
||||
"keep intact all notices".
|
||||
|
||||
c) You must license the entire work, as a whole, under this
|
||||
License to anyone who comes into possession of a copy. This
|
||||
License will therefore apply, along with any applicable section 7
|
||||
additional terms, to the whole of the work, and all its parts,
|
||||
regardless of how they are packaged. This License gives no
|
||||
permission to license the work in any other way, but it does not
|
||||
invalidate such permission if you have separately received it.
|
||||
|
||||
d) If the work has interactive user interfaces, each must display
|
||||
Appropriate Legal Notices; however, if the Program has interactive
|
||||
interfaces that do not display Appropriate Legal Notices, your
|
||||
work need not make them do so.
|
||||
|
||||
A compilation of a covered work with other separate and independent
|
||||
works, which are not by their nature extensions of the covered work,
|
||||
and which are not combined with it such as to form a larger program,
|
||||
in or on a volume of a storage or distribution medium, is called an
|
||||
"aggregate" if the compilation and its resulting copyright are not
|
||||
used to limit the access or legal rights of the compilation's users
|
||||
beyond what the individual works permit. Inclusion of a covered work
|
||||
in an aggregate does not cause this License to apply to the other
|
||||
parts of the aggregate.
|
||||
|
||||
6. Conveying Non-Source Forms.
|
||||
|
||||
You may convey a covered work in object code form under the terms
|
||||
of sections 4 and 5, provided that you also convey the
|
||||
machine-readable Corresponding Source under the terms of this License,
|
||||
in one of these ways:
|
||||
|
||||
a) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by the
|
||||
Corresponding Source fixed on a durable physical medium
|
||||
customarily used for software interchange.
|
||||
|
||||
b) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by a
|
||||
written offer, valid for at least three years and valid for as
|
||||
long as you offer spare parts or customer support for that product
|
||||
model, to give anyone who possesses the object code either (1) a
|
||||
copy of the Corresponding Source for all the software in the
|
||||
product that is covered by this License, on a durable physical
|
||||
medium customarily used for software interchange, for a price no
|
||||
more than your reasonable cost of physically performing this
|
||||
conveying of source, or (2) access to copy the
|
||||
Corresponding Source from a network server at no charge.
|
||||
|
||||
c) Convey individual copies of the object code with a copy of the
|
||||
written offer to provide the Corresponding Source. This
|
||||
alternative is allowed only occasionally and noncommercially, and
|
||||
only if you received the object code with such an offer, in accord
|
||||
with subsection 6b.
|
||||
|
||||
d) Convey the object code by offering access from a designated
|
||||
place (gratis or for a charge), and offer equivalent access to the
|
||||
Corresponding Source in the same way through the same place at no
|
||||
further charge. You need not require recipients to copy the
|
||||
Corresponding Source along with the object code. If the place to
|
||||
copy the object code is a network server, the Corresponding Source
|
||||
may be on a different server (operated by you or a third party)
|
||||
that supports equivalent copying facilities, provided you maintain
|
||||
clear directions next to the object code saying where to find the
|
||||
Corresponding Source. Regardless of what server hosts the
|
||||
Corresponding Source, you remain obligated to ensure that it is
|
||||
available for as long as needed to satisfy these requirements.
|
||||
|
||||
e) Convey the object code using peer-to-peer transmission, provided
|
||||
you inform other peers where the object code and Corresponding
|
||||
Source of the work are being offered to the general public at no
|
||||
charge under subsection 6d.
|
||||
|
||||
A separable portion of the object code, whose source code is excluded
|
||||
from the Corresponding Source as a System Library, need not be
|
||||
included in conveying the object code work.
|
||||
|
||||
A "User Product" is either (1) a "consumer product", which means any
|
||||
tangible personal property which is normally used for personal, family,
|
||||
or household purposes, or (2) anything designed or sold for incorporation
|
||||
into a dwelling. In determining whether a product is a consumer product,
|
||||
doubtful cases shall be resolved in favor of coverage. For a particular
|
||||
product received by a particular user, "normally used" refers to a
|
||||
typical or common use of that class of product, regardless of the status
|
||||
of the particular user or of the way in which the particular user
|
||||
actually uses, or expects or is expected to use, the product. A product
|
||||
is a consumer product regardless of whether the product has substantial
|
||||
commercial, industrial or non-consumer uses, unless such uses represent
|
||||
the only significant mode of use of the product.
|
||||
|
||||
"Installation Information" for a User Product means any methods,
|
||||
procedures, authorization keys, or other information required to install
|
||||
and execute modified versions of a covered work in that User Product from
|
||||
a modified version of its Corresponding Source. The information must
|
||||
suffice to ensure that the continued functioning of the modified object
|
||||
code is in no case prevented or interfered with solely because
|
||||
modification has been made.
|
||||
|
||||
If you convey an object code work under this section in, or with, or
|
||||
specifically for use in, a User Product, and the conveying occurs as
|
||||
part of a transaction in which the right of possession and use of the
|
||||
User Product is transferred to the recipient in perpetuity or for a
|
||||
fixed term (regardless of how the transaction is characterized), the
|
||||
Corresponding Source conveyed under this section must be accompanied
|
||||
by the Installation Information. But this requirement does not apply
|
||||
if neither you nor any third party retains the ability to install
|
||||
modified object code on the User Product (for example, the work has
|
||||
been installed in ROM).
|
||||
|
||||
The requirement to provide Installation Information does not include a
|
||||
requirement to continue to provide support service, warranty, or updates
|
||||
for a work that has been modified or installed by the recipient, or for
|
||||
the User Product in which it has been modified or installed. Access to a
|
||||
network may be denied when the modification itself materially and
|
||||
adversely affects the operation of the network or violates the rules and
|
||||
protocols for communication across the network.
|
||||
|
||||
Corresponding Source conveyed, and Installation Information provided,
|
||||
in accord with this section must be in a format that is publicly
|
||||
documented (and with an implementation available to the public in
|
||||
source code form), and must require no special password or key for
|
||||
unpacking, reading or copying.
|
||||
|
||||
7. Additional Terms.
|
||||
|
||||
"Additional permissions" are terms that supplement the terms of this
|
||||
License by making exceptions from one or more of its conditions.
|
||||
Additional permissions that are applicable to the entire Program shall
|
||||
be treated as though they were included in this License, to the extent
|
||||
that they are valid under applicable law. If additional permissions
|
||||
apply only to part of the Program, that part may be used separately
|
||||
under those permissions, but the entire Program remains governed by
|
||||
this License without regard to the additional permissions.
|
||||
|
||||
When you convey a copy of a covered work, you may at your option
|
||||
remove any additional permissions from that copy, or from any part of
|
||||
it. (Additional permissions may be written to require their own
|
||||
removal in certain cases when you modify the work.) You may place
|
||||
additional permissions on material, added by you to a covered work,
|
||||
for which you have or can give appropriate copyright permission.
|
||||
|
||||
Notwithstanding any other provision of this License, for material you
|
||||
add to a covered work, you may (if authorized by the copyright holders of
|
||||
that material) supplement the terms of this License with terms:
|
||||
|
||||
a) Disclaiming warranty or limiting liability differently from the
|
||||
terms of sections 15 and 16 of this License; or
|
||||
|
||||
b) Requiring preservation of specified reasonable legal notices or
|
||||
author attributions in that material or in the Appropriate Legal
|
||||
Notices displayed by works containing it; or
|
||||
|
||||
c) Prohibiting misrepresentation of the origin of that material, or
|
||||
requiring that modified versions of such material be marked in
|
||||
reasonable ways as different from the original version; or
|
||||
|
||||
d) Limiting the use for publicity purposes of names of licensors or
|
||||
authors of the material; or
|
||||
|
||||
e) Declining to grant rights under trademark law for use of some
|
||||
trade names, trademarks, or service marks; or
|
||||
|
||||
f) Requiring indemnification of licensors and authors of that
|
||||
material by anyone who conveys the material (or modified versions of
|
||||
it) with contractual assumptions of liability to the recipient, for
|
||||
any liability that these contractual assumptions directly impose on
|
||||
those licensors and authors.
|
||||
|
||||
All other non-permissive additional terms are considered "further
|
||||
restrictions" within the meaning of section 10. If the Program as you
|
||||
received it, or any part of it, contains a notice stating that it is
|
||||
governed by this License along with a term that is a further
|
||||
restriction, you may remove that term. If a license document contains
|
||||
a further restriction but permits relicensing or conveying under this
|
||||
License, you may add to a covered work material governed by the terms
|
||||
of that license document, provided that the further restriction does
|
||||
not survive such relicensing or conveying.
|
||||
|
||||
If you add terms to a covered work in accord with this section, you
|
||||
must place, in the relevant source files, a statement of the
|
||||
additional terms that apply to those files, or a notice indicating
|
||||
where to find the applicable terms.
|
||||
|
||||
Additional terms, permissive or non-permissive, may be stated in the
|
||||
form of a separately written license, or stated as exceptions;
|
||||
the above requirements apply either way.
|
||||
|
||||
8. Termination.
|
||||
|
||||
You may not propagate or modify a covered work except as expressly
|
||||
provided under this License. Any attempt otherwise to propagate or
|
||||
modify it is void, and will automatically terminate your rights under
|
||||
this License (including any patent licenses granted under the third
|
||||
paragraph of section 11).
|
||||
|
||||
However, if you cease all violation of this License, then your
|
||||
license from a particular copyright holder is reinstated (a)
|
||||
provisionally, unless and until the copyright holder explicitly and
|
||||
finally terminates your license, and (b) permanently, if the copyright
|
||||
holder fails to notify you of the violation by some reasonable means
|
||||
prior to 60 days after the cessation.
|
||||
|
||||
Moreover, your license from a particular copyright holder is
|
||||
reinstated permanently if the copyright holder notifies you of the
|
||||
violation by some reasonable means, this is the first time you have
|
||||
received notice of violation of this License (for any work) from that
|
||||
copyright holder, and you cure the violation prior to 30 days after
|
||||
your receipt of the notice.
|
||||
|
||||
Termination of your rights under this section does not terminate the
|
||||
licenses of parties who have received copies or rights from you under
|
||||
this License. If your rights have been terminated and not permanently
|
||||
reinstated, you do not qualify to receive new licenses for the same
|
||||
material under section 10.
|
||||
|
||||
9. Acceptance Not Required for Having Copies.
|
||||
|
||||
You are not required to accept this License in order to receive or
|
||||
run a copy of the Program. Ancillary propagation of a covered work
|
||||
occurring solely as a consequence of using peer-to-peer transmission
|
||||
to receive a copy likewise does not require acceptance. However,
|
||||
nothing other than this License grants you permission to propagate or
|
||||
modify any covered work. These actions infringe copyright if you do
|
||||
not accept this License. Therefore, by modifying or propagating a
|
||||
covered work, you indicate your acceptance of this License to do so.
|
||||
|
||||
10. Automatic Licensing of Downstream Recipients.
|
||||
|
||||
Each time you convey a covered work, the recipient automatically
|
||||
receives a license from the original licensors, to run, modify and
|
||||
propagate that work, subject to this License. You are not responsible
|
||||
for enforcing compliance by third parties with this License.
|
||||
|
||||
An "entity transaction" is a transaction transferring control of an
|
||||
organization, or substantially all assets of one, or subdividing an
|
||||
organization, or merging organizations. If propagation of a covered
|
||||
work results from an entity transaction, each party to that
|
||||
transaction who receives a copy of the work also receives whatever
|
||||
licenses to the work the party's predecessor in interest had or could
|
||||
give under the previous paragraph, plus a right to possession of the
|
||||
Corresponding Source of the work from the predecessor in interest, if
|
||||
the predecessor has it or can get it with reasonable efforts.
|
||||
|
||||
You may not impose any further restrictions on the exercise of the
|
||||
rights granted or affirmed under this License. For example, you may
|
||||
not impose a license fee, royalty, or other charge for exercise of
|
||||
rights granted under this License, and you may not initiate litigation
|
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||
any patent claim is infringed by making, using, selling, offering for
|
||||
sale, or importing the Program or any portion of it.
|
||||
|
||||
11. Patents.
|
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this
|
||||
License of the Program or a work on which the Program is based. The
|
||||
work thus licensed is called the contributor's "contributor version".
|
||||
|
||||
A contributor's "essential patent claims" are all patent claims
|
||||
owned or controlled by the contributor, whether already acquired or
|
||||
hereafter acquired, that would be infringed by some manner, permitted
|
||||
by this License, of making, using, or selling its contributor version,
|
||||
but do not include claims that would be infringed only as a
|
||||
consequence of further modification of the contributor version. For
|
||||
purposes of this definition, "control" includes the right to grant
|
||||
patent sublicenses in a manner consistent with the requirements of
|
||||
this License.
|
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||
patent license under the contributor's essential patent claims, to
|
||||
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||
propagate the contents of its contributor version.
|
||||
|
||||
In the following three paragraphs, a "patent license" is any express
|
||||
agreement or commitment, however denominated, not to enforce a patent
|
||||
(such as an express permission to practice a patent or covenant not to
|
||||
sue for patent infringement). To "grant" such a patent license to a
|
||||
party means to make such an agreement or commitment not to enforce a
|
||||
patent against the party.
|
||||
|
||||
If you convey a covered work, knowingly relying on a patent license,
|
||||
and the Corresponding Source of the work is not available for anyone
|
||||
to copy, free of charge and under the terms of this License, through a
|
||||
publicly available network server or other readily accessible means,
|
||||
then you must either (1) cause the Corresponding Source to be so
|
||||
available, or (2) arrange to deprive yourself of the benefit of the
|
||||
patent license for this particular work, or (3) arrange, in a manner
|
||||
consistent with the requirements of this License, to extend the patent
|
||||
license to downstream recipients. "Knowingly relying" means you have
|
||||
actual knowledge that, but for the patent license, your conveying the
|
||||
covered work in a country, or your recipient's use of the covered work
|
||||
in a country, would infringe one or more identifiable patents in that
|
||||
country that you have reason to believe are valid.
|
||||
|
||||
If, pursuant to or in connection with a single transaction or
|
||||
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||
covered work, and grant a patent license to some of the parties
|
||||
receiving the covered work authorizing them to use, propagate, modify
|
||||
or convey a specific copy of the covered work, then the patent license
|
||||
you grant is automatically extended to all recipients of the covered
|
||||
work and works based on it.
|
||||
|
||||
A patent license is "discriminatory" if it does not include within
|
||||
the scope of its coverage, prohibits the exercise of, or is
|
||||
conditioned on the non-exercise of one or more of the rights that are
|
||||
specifically granted under this License. You may not convey a covered
|
||||
work if you are a party to an arrangement with a third party that is
|
||||
in the business of distributing software, under which you make payment
|
||||
to the third party based on the extent of your activity of conveying
|
||||
the work, and under which the third party grants, to any of the
|
||||
parties who would receive the covered work from you, a discriminatory
|
||||
patent license (a) in connection with copies of the covered work
|
||||
conveyed by you (or copies made from those copies), or (b) primarily
|
||||
for and in connection with specific products or compilations that
|
||||
contain the covered work, unless you entered into that arrangement,
|
||||
or that patent license was granted, prior to 28 March 2007.
|
||||
|
||||
Nothing in this License shall be construed as excluding or limiting
|
||||
any implied license or other defenses to infringement that may
|
||||
otherwise be available to you under applicable patent law.
|
||||
|
||||
12. No Surrender of Others' Freedom.
|
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot convey a
|
||||
covered work so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you may
|
||||
not convey it at all. For example, if you agree to terms that obligate you
|
||||
to collect a royalty for further conveying from those to whom you convey
|
||||
the Program, the only way you could satisfy both those terms and this
|
||||
License would be to refrain entirely from conveying the Program.
|
||||
|
||||
13. Use with the GNU Affero General Public License.
|
||||
|
||||
Notwithstanding any other provision of this License, you have
|
||||
permission to link or combine any covered work with a work licensed
|
||||
under version 3 of the GNU Affero General Public License into a single
|
||||
combined work, and to convey the resulting work. The terms of this
|
||||
License will continue to apply to the part which is the covered work,
|
||||
but the special requirements of the GNU Affero General Public License,
|
||||
section 13, concerning interaction through a network will apply to the
|
||||
combination as such.
|
||||
|
||||
14. Revised Versions of this License.
|
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of
|
||||
the GNU General Public License from time to time. Such new versions will
|
||||
be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the
|
||||
Program specifies that a certain numbered version of the GNU General
|
||||
Public License "or any later version" applies to it, you have the
|
||||
option of following the terms and conditions either of that numbered
|
||||
version or of any later version published by the Free Software
|
||||
Foundation. If the Program does not specify a version number of the
|
||||
GNU General Public License, you may choose any version ever published
|
||||
by the Free Software Foundation.
|
||||
|
||||
If the Program specifies that a proxy can decide which future
|
||||
versions of the GNU General Public License can be used, that proxy's
|
||||
public statement of acceptance of a version permanently authorizes you
|
||||
to choose that version for the Program.
|
||||
|
||||
Later license versions may give you additional or different
|
||||
permissions. However, no additional obligations are imposed on any
|
||||
author or copyright holder as a result of your choosing to follow a
|
||||
later version.
|
||||
|
||||
15. Disclaimer of Warranty.
|
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||
|
||||
16. Limitation of Liability.
|
||||
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
||||
SUCH DAMAGES.
|
||||
|
||||
17. Interpretation of Sections 15 and 16.
|
||||
|
||||
If the disclaimer of warranty and limitation of liability provided
|
||||
above cannot be given local legal effect according to their terms,
|
||||
reviewing courts shall apply local law that most closely approximates
|
||||
an absolute waiver of all civil liability in connection with the
|
||||
Program, unless a warranty or assumption of liability accompanies a
|
||||
copy of the Program in return for a fee.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
state the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
<one line to give the program's name and a brief idea of what it does.>
|
||||
Copyright (C) <year> <name of author>
|
||||
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If the program does terminal interaction, make it output a short
|
||||
notice like this when it starts in an interactive mode:
|
||||
|
||||
<program> Copyright (C) <year> <name of author>
|
||||
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
||||
This is free software, and you are welcome to redistribute it
|
||||
under certain conditions; type `show c' for details.
|
||||
|
||||
The hypothetical commands `show w' and `show c' should show the appropriate
|
||||
parts of the General Public License. Of course, your program's commands
|
||||
might be different; for a GUI interface, you would use an "about box".
|
||||
|
||||
You should also get your employer (if you work as a programmer) or school,
|
||||
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||
For more information on this, and how to apply and follow the GNU GPL, see
|
||||
<https://www.gnu.org/licenses/>.
|
||||
|
||||
The GNU General Public License does not permit incorporating your program
|
||||
into proprietary programs. If your program is a subroutine library, you
|
||||
may consider it more useful to permit linking proprietary applications with
|
||||
the library. If this is what you want to do, use the GNU Lesser General
|
||||
Public License instead of this License. But first, please read
|
||||
<https://www.gnu.org/licenses/why-not-lgpl.html>.
|
||||
@@ -0,0 +1,27 @@
|
||||
# ComfyUI SeeCoder nodes
|
||||
|
||||
This repo contains 2 experimental WIP nodes for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) that let's you use [SeeCoders](https://github.com/SHI-Labs/Prompt-Free-Diffusion).
|
||||
|
||||
## getting SeeCoders
|
||||
You can find the seecoders [here](https://huggingface.co/shi-labs/prompt-free-diffusion). They have to be placed at `models/seecoders`
|
||||
|
||||
## nodes:
|
||||
|
||||
### SEECoderImageEncode
|
||||
|
||||
this node can be used to create an embedding from an image
|
||||
|
||||
- **image**: the image to encode
|
||||
- **seecoder_name**: the name of the seecoder
|
||||
|
||||
### ConcatConditioning
|
||||
|
||||
this node can be used to concat different embeddings together, so you can e.g. create both a text and a visual embedding and concat them together.
|
||||
|
||||
- **conditioning_to**: a set of embeddings to concat something to
|
||||
- **conditioning_from**: the embedding to concat behind those in **conditioning_to**
|
||||
|
||||
## TODO:
|
||||
|
||||
- [ ] support for non safetensor formats
|
||||
- [ ] bring attention layers in line with ones used in comfy
|
||||
@@ -0,0 +1,3 @@
|
||||
from .nodes import NODE_CLASS_MAPPINGS
|
||||
|
||||
__all__ = ['NODE_CLASS_MAPPINGS']
|
||||
@@ -0,0 +1,117 @@
|
||||
|
||||
import folder_paths
|
||||
import torch
|
||||
from seecoder.seecoder import SemanticExtractionEncoder, QueryTransformer, Decoder
|
||||
from seecoder.swin import SwinTransformer
|
||||
import safetensors.torch
|
||||
import os
|
||||
import sys
|
||||
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
|
||||
|
||||
import comfy.model_management
|
||||
|
||||
folder_paths.folder_names_and_paths["seecoder"] = ([os.path.join(folder_paths.models_dir, "seecoders")], folder_paths.supported_ckpt_extensions)
|
||||
|
||||
_swine_config = {
|
||||
"embed_dim" : 192,
|
||||
"depths" : [ 2, 2, 18, 2 ],
|
||||
"num_heads" : [ 6, 12, 24, 48 ],
|
||||
"window_size" : 12,
|
||||
"ape" : False,
|
||||
"drop_path_rate" : 0.3,
|
||||
"patch_norm" : True,
|
||||
}
|
||||
|
||||
_decoder_config = {
|
||||
"inchannels" : {'res3' : 384, 'res4' : 768, 'res5' : 1536},
|
||||
"trans_input_tags" : ['res3', 'res4', 'res5'],
|
||||
"trans_dim" : 768,
|
||||
"trans_dropout" : 0.1,
|
||||
"trans_nheads" : 8,
|
||||
"trans_feedforward_dim" : 1024,
|
||||
"trans_num_layers" : 6,
|
||||
}
|
||||
|
||||
_qt_config = {
|
||||
"in_channels":768,
|
||||
"hidden_dim":768,
|
||||
"num_queries":[4, 144],
|
||||
"nheads":8,
|
||||
"num_layers":9,
|
||||
"feedforward_dim":2048,
|
||||
"pre_norm":False,
|
||||
"num_feature_levels":3,
|
||||
"enforce_input_project":False,
|
||||
"with_fea2d_pos":False
|
||||
}
|
||||
|
||||
class SEECoderImageEncode:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"seecoder_name": (folder_paths.get_filename_list("seecoder"), ),
|
||||
"image": ("IMAGE",),
|
||||
}}
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "SEECoderEncode"
|
||||
|
||||
CATEGORY = "conditioning"
|
||||
|
||||
def SEECoderEncode(self, seecoder_name, image):
|
||||
device = comfy.model_management.get_torch_device()
|
||||
path = folder_paths.get_full_path("seecoder", seecoder_name)
|
||||
sd = safetensors.torch.load_file(path, device="cpu")
|
||||
sd = {k[10:] if k.startswith('ctx.image.') else k: v for k,v in sd.items()}
|
||||
is_pa = any([x.startswith("qtransformer.pe_layer") for x in sd.keys()])
|
||||
|
||||
swine_config = _swine_config.copy()
|
||||
decoder_config = _decoder_config.copy()
|
||||
qt_config = _qt_config.copy()
|
||||
if is_pa:
|
||||
qt_config['with_fea2d_pos'] = True
|
||||
|
||||
swine = SwinTransformer(**swine_config)
|
||||
decoder = Decoder(**decoder_config)
|
||||
queryTransformer = QueryTransformer(**qt_config)
|
||||
|
||||
SEE_encoder = SemanticExtractionEncoder(swine, decoder, queryTransformer)
|
||||
SEE_encoder.load_state_dict(sd)
|
||||
SEE_encoder = SEE_encoder.to(device)
|
||||
SEE_encoder.eval()
|
||||
encoding = SEE_encoder(image.movedim(-1,1).to(device)).cpu()
|
||||
|
||||
return ([[encoding, {}]], )
|
||||
|
||||
class ConcatConditioning:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"conditioning_to": ("CONDITIONING",),
|
||||
"conditioning_from": ("CONDITIONING",),
|
||||
}}
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "SEECoderEncode"
|
||||
|
||||
CATEGORY = "_for_testing"
|
||||
|
||||
def SEECoderEncode(self, conditioning_to, conditioning_from):
|
||||
out = []
|
||||
|
||||
if len(conditioning_from) > 1:
|
||||
print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
|
||||
|
||||
cond_from = conditioning_from[0][0]
|
||||
|
||||
for i in range(len(conditioning_to)):
|
||||
t1 = conditioning_to[i][0]
|
||||
tw = torch.cat((t1, cond_from),1)
|
||||
n = [tw, conditioning_to[i][1].copy()]
|
||||
out.append(n)
|
||||
|
||||
return (out, )
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"SEECoderImageEncode": SEECoderImageEncode,
|
||||
"ConcatConditioning": ConcatConditioning,
|
||||
}
|
||||
+570
@@ -0,0 +1,570 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import copy
|
||||
|
||||
from .seecoder_utils import with_pos_embed
|
||||
|
||||
###########
|
||||
# helpers #
|
||||
###########
|
||||
|
||||
def _get_clones(module, N):
|
||||
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
|
||||
|
||||
def _get_activation_fn(activation):
|
||||
"""Return an activation function given a string"""
|
||||
if activation == "relu":
|
||||
return F.relu
|
||||
if activation == "gelu":
|
||||
return F.gelu
|
||||
if activation == "glu":
|
||||
return F.glu
|
||||
raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
|
||||
|
||||
def c2_xavier_fill(module):
|
||||
# Caffe2 implementation of XavierFill in fact
|
||||
nn.init.kaiming_uniform_(module.weight, a=1)
|
||||
if module.bias is not None:
|
||||
nn.init.constant_(module.bias, 0)
|
||||
|
||||
def with_pos_embed(x, pos):
|
||||
return x if pos is None else x + pos
|
||||
|
||||
###########
|
||||
# Modules #
|
||||
###########
|
||||
|
||||
class Conv2d_Convenience(nn.Conv2d):
|
||||
def __init__(self, *args, **kwargs):
|
||||
norm = kwargs.pop("norm", None)
|
||||
activation = kwargs.pop("activation", None)
|
||||
super().__init__(*args, **kwargs)
|
||||
self.norm = norm
|
||||
self.activation = activation
|
||||
|
||||
def forward(self, x):
|
||||
x = F.conv2d(
|
||||
x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
|
||||
if self.norm is not None:
|
||||
x = self.norm(x)
|
||||
if self.activation is not None:
|
||||
x = self.activation(x)
|
||||
return x
|
||||
|
||||
class DecoderLayer(nn.Module):
|
||||
def __init__(self,
|
||||
dim=256,
|
||||
feedforward_dim=1024,
|
||||
dropout=0.1,
|
||||
activation="relu",
|
||||
n_heads=8,):
|
||||
|
||||
super().__init__()
|
||||
|
||||
self.self_attn = nn.MultiheadAttention(dim, n_heads, dropout=dropout)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.norm1 = nn.LayerNorm(dim)
|
||||
|
||||
self.linear1 = nn.Linear(dim, feedforward_dim)
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(feedforward_dim, dim)
|
||||
self.dropout3 = nn.Dropout(dropout)
|
||||
self.norm2 = nn.LayerNorm(dim)
|
||||
|
||||
def forward(self, x):
|
||||
h = x
|
||||
h1 = self.self_attn(x, x, x, attn_mask=None)[0]
|
||||
h = h + self.dropout1(h1)
|
||||
h = self.norm1(h)
|
||||
|
||||
h2 = self.linear2(self.dropout2(self.activation(self.linear1(h))))
|
||||
h = h + self.dropout3(h2)
|
||||
h = self.norm2(h)
|
||||
return h
|
||||
|
||||
class DecoderLayerStacked(nn.Module):
|
||||
def __init__(self, layer, num_layers, norm=None):
|
||||
super().__init__()
|
||||
self.layers = _get_clones(layer, num_layers)
|
||||
self.num_layers = num_layers
|
||||
self.norm = norm
|
||||
|
||||
def forward(self, x):
|
||||
h = x
|
||||
for _, layer in enumerate(self.layers):
|
||||
h = layer(h)
|
||||
if self.norm is not None:
|
||||
h = self.norm(h)
|
||||
return h
|
||||
|
||||
class SelfAttentionLayer(nn.Module):
|
||||
def __init__(self, channels, nhead, dropout=0.0,
|
||||
activation="relu", normalize_before=False):
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(channels, nhead, dropout=dropout)
|
||||
|
||||
self.norm = nn.LayerNorm(channels)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
def _reset_parameters(self):
|
||||
for p in self.parameters():
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
|
||||
def forward_post(self,
|
||||
qkv,
|
||||
qk_pos = None,
|
||||
mask = None,):
|
||||
h = qkv
|
||||
qk = with_pos_embed(qkv, qk_pos).transpose(0, 1)
|
||||
v = qkv.transpose(0, 1)
|
||||
h1 = self.self_attn(qk, qk, v, attn_mask=mask)[0]
|
||||
h1 = h1.transpose(0, 1)
|
||||
h = h + self.dropout(h1)
|
||||
h = self.norm(h)
|
||||
return h
|
||||
|
||||
def forward_pre(self, tgt,
|
||||
tgt_mask = None,
|
||||
tgt_key_padding_mask = None,
|
||||
query_pos = None):
|
||||
# deprecated
|
||||
assert False
|
||||
tgt2 = self.norm(tgt)
|
||||
q = k = self.with_pos_embed(tgt2, query_pos)
|
||||
tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
|
||||
key_padding_mask=tgt_key_padding_mask)[0]
|
||||
tgt = tgt + self.dropout(tgt2)
|
||||
return tgt
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
if self.normalize_before:
|
||||
return self.forward_pre(*args, **kwargs)
|
||||
return self.forward_post(*args, **kwargs)
|
||||
|
||||
class CrossAttentionLayer(nn.Module):
|
||||
def __init__(self, channels, nhead, dropout=0.0,
|
||||
activation="relu", normalize_before=False):
|
||||
super().__init__()
|
||||
self.multihead_attn = nn.MultiheadAttention(channels, nhead, dropout=dropout)
|
||||
|
||||
self.norm = nn.LayerNorm(channels)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
def _reset_parameters(self):
|
||||
for p in self.parameters():
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
|
||||
def forward_post(self,
|
||||
q,
|
||||
kv,
|
||||
q_pos = None,
|
||||
k_pos = None,
|
||||
mask = None,):
|
||||
h = q
|
||||
q = with_pos_embed(q, q_pos).transpose(0, 1)
|
||||
k = with_pos_embed(kv, k_pos).transpose(0, 1)
|
||||
v = kv.transpose(0, 1)
|
||||
h1 = self.multihead_attn(q, k, v, attn_mask=mask)[0]
|
||||
h1 = h1.transpose(0, 1)
|
||||
h = h + self.dropout(h1)
|
||||
h = self.norm(h)
|
||||
return h
|
||||
|
||||
def forward_pre(self, tgt, memory,
|
||||
memory_mask = None,
|
||||
memory_key_padding_mask = None,
|
||||
pos = None,
|
||||
query_pos = None):
|
||||
# Deprecated
|
||||
assert False
|
||||
tgt2 = self.norm(tgt)
|
||||
tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
|
||||
key=self.with_pos_embed(memory, pos),
|
||||
value=memory, attn_mask=memory_mask,
|
||||
key_padding_mask=memory_key_padding_mask)[0]
|
||||
tgt = tgt + self.dropout(tgt2)
|
||||
return tgt
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
if self.normalize_before:
|
||||
return self.forward_pre(*args, **kwargs)
|
||||
return self.forward_post(*args, **kwargs)
|
||||
|
||||
class FeedForwardLayer(nn.Module):
|
||||
def __init__(self, channels, hidden_channels=2048, dropout=0.0,
|
||||
activation="relu", normalize_before=False):
|
||||
super().__init__()
|
||||
self.linear1 = nn.Linear(channels, hidden_channels)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(hidden_channels, channels)
|
||||
self.norm = nn.LayerNorm(channels)
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
self._reset_parameters()
|
||||
|
||||
def _reset_parameters(self):
|
||||
for p in self.parameters():
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
|
||||
def forward_post(self, x):
|
||||
h = x
|
||||
h1 = self.linear2(self.dropout(self.activation(self.linear1(h))))
|
||||
h = h + self.dropout(h1)
|
||||
h = self.norm(h)
|
||||
return h
|
||||
|
||||
def forward_pre(self, x):
|
||||
xn = self.norm(x)
|
||||
h = x
|
||||
h1 = self.linear2(self.dropout(self.activation(self.linear1(xn))))
|
||||
h = h + self.dropout(h1)
|
||||
return h
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
if self.normalize_before:
|
||||
return self.forward_pre(*args, **kwargs)
|
||||
return self.forward_post(*args, **kwargs)
|
||||
|
||||
class MLP(nn.Module):
|
||||
def __init__(self, in_channels, channels, out_channels, num_layers):
|
||||
super().__init__()
|
||||
self.num_layers = num_layers
|
||||
h = [channels] * (num_layers - 1)
|
||||
self.layers = nn.ModuleList(
|
||||
nn.Linear(n, k)
|
||||
for n, k in zip([in_channels]+h, h+[out_channels]))
|
||||
|
||||
def forward(self, x):
|
||||
for i, layer in enumerate(self.layers):
|
||||
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
|
||||
return x
|
||||
|
||||
class PPE_MLP(nn.Module):
|
||||
def __init__(self, freq_num=20, freq_max=None, out_channel=768, mlp_layer=3):
|
||||
import math
|
||||
super().__init__()
|
||||
self.freq_num = freq_num
|
||||
self.freq_max = freq_max
|
||||
self.out_channel = out_channel
|
||||
self.mlp_layer = mlp_layer
|
||||
self.twopi = 2 * math.pi
|
||||
|
||||
mlp = []
|
||||
in_channel = freq_num*4
|
||||
for idx in range(mlp_layer):
|
||||
linear = nn.Linear(in_channel, out_channel, bias=True)
|
||||
nn.init.xavier_normal_(linear.weight)
|
||||
nn.init.constant_(linear.bias, 0)
|
||||
mlp.append(linear)
|
||||
if idx != mlp_layer-1:
|
||||
mlp.append(nn.SiLU())
|
||||
in_channel = out_channel
|
||||
self.mlp = nn.Sequential(*mlp)
|
||||
nn.init.constant_(self.mlp[-1].weight, 0)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
assert mask is None, "Mask not implemented"
|
||||
h, w = x.shape[-2:]
|
||||
minlen = min(h, w)
|
||||
|
||||
h_embed, w_embed = torch.meshgrid(torch.arange(h), torch.arange(w), indexing='ij')
|
||||
if self.training:
|
||||
import numpy.random as npr
|
||||
pertube_h, pertube_w = npr.uniform(-0.5, 0.5), npr.uniform(-0.5, 0.5)
|
||||
else:
|
||||
pertube_h, pertube_w = 0, 0
|
||||
|
||||
h_embed = (h_embed+0.5 - h/2 + pertube_h) / (minlen) * self.twopi
|
||||
w_embed = (w_embed+0.5 - w/2 + pertube_w) / (minlen) * self.twopi
|
||||
h_embed, w_embed = h_embed.to(x.device).to(x.dtype), w_embed.to(x.device).to(x.dtype)
|
||||
|
||||
dim_t = torch.linspace(0, 1, self.freq_num, dtype=torch.float32, device=x.device)
|
||||
freq_max = self.freq_max if self.freq_max is not None else minlen/2
|
||||
dim_t = freq_max ** dim_t.to(x.dtype)
|
||||
|
||||
pos_h = h_embed[:, :, None] * dim_t
|
||||
pos_w = w_embed[:, :, None] * dim_t
|
||||
pos = torch.cat((pos_h.sin(), pos_h.cos(), pos_w.sin(), pos_w.cos()), dim=-1)
|
||||
pos = self.mlp(pos)
|
||||
pos = pos.permute(2, 0, 1)[None]
|
||||
return pos
|
||||
|
||||
def __repr__(self, _repr_indent=4):
|
||||
head = "Positional encoding " + self.__class__.__name__
|
||||
body = [
|
||||
"num_pos_feats: {}".format(self.num_pos_feats),
|
||||
"temperature: {}".format(self.temperature),
|
||||
"normalize: {}".format(self.normalize),
|
||||
"scale: {}".format(self.scale),
|
||||
]
|
||||
# _repr_indent = 4
|
||||
lines = [head] + [" " * _repr_indent + line for line in body]
|
||||
return "\n".join(lines)
|
||||
|
||||
###########
|
||||
# Decoder #
|
||||
###########
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
inchannels,
|
||||
trans_input_tags,
|
||||
trans_num_layers,
|
||||
trans_dim,
|
||||
trans_nheads,
|
||||
trans_dropout,
|
||||
trans_feedforward_dim,):
|
||||
|
||||
super().__init__()
|
||||
trans_inchannels = {
|
||||
k: v for k, v in inchannels.items() if k in trans_input_tags}
|
||||
fpn_inchannels = {
|
||||
k: v for k, v in inchannels.items() if k not in trans_input_tags}
|
||||
|
||||
self.trans_tags = sorted(list(trans_inchannels.keys()))
|
||||
self.fpn_tags = sorted(list(fpn_inchannels.keys()))
|
||||
self.all_tags = sorted(list(inchannels.keys()))
|
||||
|
||||
if len(self.trans_tags)==0:
|
||||
assert False # Not allowed
|
||||
|
||||
self.num_trans_lvls = len(self.trans_tags)
|
||||
|
||||
self.inproj_layers = nn.ModuleDict()
|
||||
for tagi in self.trans_tags:
|
||||
layeri = nn.Sequential(
|
||||
nn.Conv2d(trans_inchannels[tagi], trans_dim, kernel_size=1),
|
||||
nn.GroupNorm(32, trans_dim),)
|
||||
nn.init.xavier_uniform_(layeri[0].weight, gain=1)
|
||||
nn.init.constant_(layeri[0].bias, 0)
|
||||
self.inproj_layers[tagi] = layeri
|
||||
|
||||
tlayer = DecoderLayer(
|
||||
dim = trans_dim,
|
||||
n_heads = trans_nheads,
|
||||
dropout = trans_dropout,
|
||||
feedforward_dim = trans_feedforward_dim,
|
||||
activation = 'relu',)
|
||||
|
||||
self.transformer = DecoderLayerStacked(tlayer, trans_num_layers)
|
||||
for p in self.transformer.parameters():
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
self.level_embed = nn.Parameter(torch.Tensor(len(self.trans_tags), trans_dim))
|
||||
nn.init.normal_(self.level_embed)
|
||||
|
||||
self.lateral_layers = nn.ModuleDict()
|
||||
self.output_layers = nn.ModuleDict()
|
||||
for tagi in self.all_tags:
|
||||
lateral_conv = Conv2d_Convenience(
|
||||
inchannels[tagi], trans_dim, kernel_size=1,
|
||||
bias=False, norm=nn.GroupNorm(32, trans_dim))
|
||||
c2_xavier_fill(lateral_conv)
|
||||
self.lateral_layers[tagi] = lateral_conv
|
||||
|
||||
for tagi in self.fpn_tags:
|
||||
output_conv = Conv2d_Convenience(
|
||||
trans_dim, trans_dim, kernel_size=3, stride=1, padding=1,
|
||||
bias=False, norm=nn.GroupNorm(32, trans_dim), activation=F.relu,)
|
||||
c2_xavier_fill(output_conv)
|
||||
self.output_layers[tagi] = output_conv
|
||||
|
||||
def forward(self, features):
|
||||
x = []
|
||||
spatial_shapes = {}
|
||||
for idx, tagi in enumerate(self.trans_tags[::-1]):
|
||||
xi = features[tagi]
|
||||
xi = self.inproj_layers[tagi](xi)
|
||||
bs, _, h, w = xi.shape
|
||||
spatial_shapes[tagi] = (h, w)
|
||||
xi = xi.flatten(2).transpose(1, 2) + self.level_embed[idx].view(1, 1, -1)
|
||||
x.append(xi)
|
||||
|
||||
x_length = [xi.shape[1] for xi in x]
|
||||
x_concat = torch.cat(x, 1)
|
||||
y_concat = self.transformer(x_concat)
|
||||
y = torch.split(y_concat, x_length, dim=1)
|
||||
|
||||
out = {}
|
||||
for idx, tagi in enumerate(self.trans_tags[::-1]):
|
||||
h, w = spatial_shapes[tagi]
|
||||
yi = y[idx].transpose(1, 2).view(bs, -1, h, w)
|
||||
out[tagi] = yi
|
||||
|
||||
for idx, tagi in enumerate(self.all_tags[::-1]):
|
||||
lconv = self.lateral_layers[tagi]
|
||||
if tagi in self.trans_tags:
|
||||
out[tagi] = out[tagi] + lconv(features[tagi])
|
||||
tag_save = tagi
|
||||
else:
|
||||
oconv = self.output_layers[tagi]
|
||||
h = lconv(features[tagi])
|
||||
oprev = out[tag_save]
|
||||
h = h + F.interpolate(oconv(oprev), size=h.shape[-2:], mode="bilinear", align_corners=False)
|
||||
out[tagi] = h
|
||||
|
||||
return out
|
||||
|
||||
#####################
|
||||
# Query Transformer #
|
||||
#####################
|
||||
|
||||
class QueryTransformer(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
hidden_dim,
|
||||
num_queries = [8, 144],
|
||||
nheads = 8,
|
||||
num_layers = 9,
|
||||
feedforward_dim = 2048,
|
||||
mask_dim = 256,
|
||||
pre_norm = False,
|
||||
num_feature_levels = 3,
|
||||
enforce_input_project = False,
|
||||
with_fea2d_pos = True):
|
||||
|
||||
super().__init__()
|
||||
|
||||
if with_fea2d_pos:
|
||||
self.pe_layer = PPE_MLP(freq_num=20, freq_max=None, out_channel=hidden_dim, mlp_layer=3)
|
||||
else:
|
||||
self.pe_layer = None
|
||||
|
||||
if in_channels!=hidden_dim or enforce_input_project:
|
||||
self.input_proj = nn.ModuleList()
|
||||
for _ in range(num_feature_levels):
|
||||
self.input_proj.append(nn.Conv2d(in_channels, hidden_dim, kernel_size=1))
|
||||
c2_xavier_fill(self.input_proj[-1])
|
||||
else:
|
||||
self.input_proj = None
|
||||
|
||||
self.num_heads = nheads
|
||||
self.num_layers = num_layers
|
||||
self.transformer_selfatt_layers = nn.ModuleList()
|
||||
self.transformer_crossatt_layers = nn.ModuleList()
|
||||
self.transformer_feedforward_layers = nn.ModuleList()
|
||||
|
||||
for _ in range(self.num_layers):
|
||||
self.transformer_selfatt_layers.append(
|
||||
SelfAttentionLayer(
|
||||
channels=hidden_dim,
|
||||
nhead=nheads,
|
||||
dropout=0.0,
|
||||
normalize_before=pre_norm, ))
|
||||
|
||||
self.transformer_crossatt_layers.append(
|
||||
CrossAttentionLayer(
|
||||
channels=hidden_dim,
|
||||
nhead=nheads,
|
||||
dropout=0.0,
|
||||
normalize_before=pre_norm, ))
|
||||
|
||||
self.transformer_feedforward_layers.append(
|
||||
FeedForwardLayer(
|
||||
channels=hidden_dim,
|
||||
hidden_channels=feedforward_dim,
|
||||
dropout=0.0,
|
||||
normalize_before=pre_norm, ))
|
||||
|
||||
self.num_queries = num_queries
|
||||
num_gq, num_lq = self.num_queries
|
||||
self.init_query = nn.Embedding(num_gq+num_lq, hidden_dim)
|
||||
self.query_pos_embedding = nn.Embedding(num_gq+num_lq, hidden_dim)
|
||||
|
||||
self.num_feature_levels = num_feature_levels
|
||||
self.level_embed = nn.Embedding(num_feature_levels, hidden_dim)
|
||||
|
||||
def forward(self, x):
|
||||
# x is a list of multi-scale feature
|
||||
assert len(x) == self.num_feature_levels
|
||||
fea2d = []
|
||||
fea2d_pos = []
|
||||
size_list = []
|
||||
|
||||
for i in range(self.num_feature_levels):
|
||||
size_list.append(x[i].shape[-2:])
|
||||
if self.pe_layer is not None:
|
||||
pi = self.pe_layer(x[i], None).flatten(2)
|
||||
pi = pi.transpose(1, 2)
|
||||
else:
|
||||
pi = None
|
||||
xi = self.input_proj[i](x[i]) if self.input_proj is not None else x[i]
|
||||
xi = xi.flatten(2) + self.level_embed.weight[i][None, :, None]
|
||||
xi = xi.transpose(1, 2)
|
||||
fea2d.append(xi)
|
||||
fea2d_pos.append(pi)
|
||||
|
||||
bs, _, _ = fea2d[0].shape
|
||||
num_gq, num_lq = self.num_queries
|
||||
gquery = self.init_query.weight[:num_gq].unsqueeze(0).repeat(bs, 1, 1)
|
||||
lquery = self.init_query.weight[num_gq:].unsqueeze(0).repeat(bs, 1, 1)
|
||||
|
||||
gquery_pos = self.query_pos_embedding.weight[:num_gq].unsqueeze(0).repeat(bs, 1, 1)
|
||||
lquery_pos = self.query_pos_embedding.weight[num_gq:].unsqueeze(0).repeat(bs, 1, 1)
|
||||
|
||||
for i in range(self.num_layers):
|
||||
level_index = i % self.num_feature_levels
|
||||
|
||||
qout = self.transformer_crossatt_layers[i](
|
||||
q = lquery,
|
||||
kv = fea2d[level_index],
|
||||
q_pos = lquery_pos,
|
||||
k_pos = fea2d_pos[level_index],
|
||||
mask = None,)
|
||||
lquery = qout
|
||||
|
||||
qout = self.transformer_selfatt_layers[i](
|
||||
qkv = torch.cat([gquery, lquery], dim=1),
|
||||
qk_pos = torch.cat([gquery_pos, lquery_pos], dim=1),)
|
||||
|
||||
qout = self.transformer_feedforward_layers[i](qout)
|
||||
|
||||
gquery = qout[:, :num_gq]
|
||||
lquery = qout[:, num_gq:]
|
||||
|
||||
output = torch.cat([gquery, lquery], dim=1)
|
||||
|
||||
return output
|
||||
|
||||
##################
|
||||
# Main structure #
|
||||
##################
|
||||
|
||||
class SemanticExtractionEncoder(nn.Module):
|
||||
def __init__(self,
|
||||
imencoder_cfg,
|
||||
imdecoder_cfg,
|
||||
qtransformer_cfg):
|
||||
super().__init__()
|
||||
self.imencoder = imencoder_cfg
|
||||
self.imdecoder = imdecoder_cfg
|
||||
self.qtransformer = qtransformer_cfg
|
||||
|
||||
def forward(self, x):
|
||||
fea = self.imencoder(x)
|
||||
hs = {'res3' : fea['res3'],
|
||||
'res4' : fea['res4'],
|
||||
'res5' : fea['res5'], }
|
||||
hs = self.imdecoder(hs)
|
||||
hs = [hs['res3'], hs['res4'], hs['res5']]
|
||||
q = self.qtransformer(hs)
|
||||
return q
|
||||
|
||||
def encode(self, x):
|
||||
return self(x)
|
||||
@@ -0,0 +1,108 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
|
||||
import math
|
||||
import copy
|
||||
|
||||
def _get_clones(module, N):
|
||||
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
|
||||
|
||||
def _get_activation_fn(activation):
|
||||
"""Return an activation function given a string"""
|
||||
if activation == "relu":
|
||||
return F.relu
|
||||
if activation == "gelu":
|
||||
return F.gelu
|
||||
if activation == "glu":
|
||||
return F.glu
|
||||
raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
|
||||
|
||||
def _is_power_of_2(n):
|
||||
if (not isinstance(n, int)) or (n < 0):
|
||||
raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
|
||||
return (n & (n-1) == 0) and n != 0
|
||||
|
||||
def c2_xavier_fill(module):
|
||||
# Caffe2 implementation of XavierFill in fact
|
||||
nn.init.kaiming_uniform_(module.weight, a=1)
|
||||
if module.bias is not None:
|
||||
nn.init.constant_(module.bias, 0)
|
||||
|
||||
def with_pos_embed(x, pos):
|
||||
return x if pos is None else x + pos
|
||||
|
||||
class PositionEmbeddingSine(nn.Module):
|
||||
def __init__(self, num_pos_feats=64, temperature=256, normalize=False, scale=None):
|
||||
super().__init__()
|
||||
self.num_pos_feats = num_pos_feats
|
||||
self.temperature = temperature
|
||||
self.normalize = normalize
|
||||
if scale is not None and normalize is False:
|
||||
raise ValueError("normalize should be True if scale is passed")
|
||||
if scale is None:
|
||||
scale = 2 * math.pi
|
||||
self.scale = scale
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
if mask is None:
|
||||
mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
|
||||
not_mask = ~mask
|
||||
h, w = not_mask.shape[-2:]
|
||||
minlen = min(h, w)
|
||||
h_embed = not_mask.cumsum(1, dtype=torch.float32)
|
||||
w_embed = not_mask.cumsum(2, dtype=torch.float32)
|
||||
if self.normalize:
|
||||
eps = 1e-6
|
||||
h_embed = (h_embed - h/2) / (minlen + eps) * self.scale
|
||||
w_embed = (w_embed - w/2) / (minlen + eps) * self.scale
|
||||
|
||||
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
|
||||
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
|
||||
|
||||
pos_w = w_embed[:, :, :, None] / dim_t
|
||||
pos_h = h_embed[:, :, :, None] / dim_t
|
||||
pos_w = torch.stack(
|
||||
(pos_w[:, :, :, 0::2].sin(), pos_w[:, :, :, 1::2].cos()), dim=4
|
||||
).flatten(3)
|
||||
pos_h = torch.stack(
|
||||
(pos_h[:, :, :, 0::2].sin(), pos_h[:, :, :, 1::2].cos()), dim=4
|
||||
).flatten(3)
|
||||
pos = torch.cat((pos_h, pos_w), dim=3).permute(0, 3, 1, 2)
|
||||
return pos
|
||||
|
||||
def __repr__(self, _repr_indent=4):
|
||||
head = "Positional encoding " + self.__class__.__name__
|
||||
body = [
|
||||
"num_pos_feats: {}".format(self.num_pos_feats),
|
||||
"temperature: {}".format(self.temperature),
|
||||
"normalize: {}".format(self.normalize),
|
||||
"scale: {}".format(self.scale),
|
||||
]
|
||||
# _repr_indent = 4
|
||||
lines = [head] + [" " * _repr_indent + line for line in body]
|
||||
return "\n".join(lines)
|
||||
|
||||
class Conv2d_Convenience(nn.Conv2d):
|
||||
def __init__(self, *args, **kwargs):
|
||||
norm = kwargs.pop("norm", None)
|
||||
activation = kwargs.pop("activation", None)
|
||||
super().__init__(*args, **kwargs)
|
||||
self.norm = norm
|
||||
self.activation = activation
|
||||
|
||||
def forward(self, x):
|
||||
if not torch.jit.is_scripting():
|
||||
if x.numel() == 0 and self.training:
|
||||
assert not isinstance(
|
||||
self.norm, torch.nn.SyncBatchNorm
|
||||
), "SyncBatchNorm does not support empty inputs!"
|
||||
x = F.conv2d(
|
||||
x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups
|
||||
)
|
||||
if self.norm is not None:
|
||||
x = self.norm(x)
|
||||
if self.activation is not None:
|
||||
x = self.activation(x)
|
||||
return x
|
||||
|
||||
@@ -0,0 +1,697 @@
|
||||
#import fvcore.nn.weight_init as weight_init #dependency only needed for training?
|
||||
from typing import Optional
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from .seecoder_utils import PositionEmbeddingSine, _get_clones, _get_activation_fn
|
||||
|
||||
##########
|
||||
# helper #
|
||||
##########
|
||||
|
||||
def with_pos_embed(x, pos):
|
||||
return x if pos is None else x + pos
|
||||
|
||||
##############
|
||||
# One Former #
|
||||
##############
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self,
|
||||
d_model=512,
|
||||
nhead=8,
|
||||
num_encoder_layers=6,
|
||||
num_decoder_layers=6,
|
||||
dim_feedforward=2048,
|
||||
dropout=0.1,
|
||||
activation="relu",
|
||||
normalize_before=False,
|
||||
return_intermediate_dec=False,):
|
||||
|
||||
super().__init__()
|
||||
encoder_layer = TransformerEncoderLayer(
|
||||
d_model, nhead, dim_feedforward, dropout, activation, normalize_before)
|
||||
encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
|
||||
self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
|
||||
|
||||
decoder_layer = TransformerDecoderLayer(
|
||||
d_model, nhead, dim_feedforward, dropout, activation, normalize_before)
|
||||
decoder_norm = nn.LayerNorm(d_model)
|
||||
self.decoder = TransformerDecoder(
|
||||
decoder_layer,
|
||||
num_decoder_layers,
|
||||
decoder_norm,
|
||||
return_intermediate=return_intermediate_dec,)
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
self.d_model = d_model
|
||||
self.nhead = nhead
|
||||
|
||||
def _reset_parameters(self):
|
||||
for p in self.parameters():
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
|
||||
def forward(self, src, mask, query_embed, pos_embed, task_token=None):
|
||||
# flatten NxCxHxW to HWxNxC
|
||||
bs, c, h, w = src.shape
|
||||
src = src.flatten(2).permute(2, 0, 1)
|
||||
pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
|
||||
query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
|
||||
if mask is not None:
|
||||
mask = mask.flatten(1)
|
||||
|
||||
if task_token is None:
|
||||
tgt = torch.zeros_like(query_embed)
|
||||
else:
|
||||
tgt = task_token.repeat(query_embed.shape[0], 1, 1)
|
||||
|
||||
memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) # src = memory
|
||||
hs = self.decoder(
|
||||
tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed
|
||||
)
|
||||
return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)
|
||||
|
||||
class TransformerEncoder(nn.Module):
|
||||
def __init__(self, encoder_layer, num_layers, norm=None):
|
||||
super().__init__()
|
||||
self.layers = _get_clones(encoder_layer, num_layers)
|
||||
self.num_layers = num_layers
|
||||
self.norm = norm
|
||||
|
||||
def forward(self, src, mask=None, src_key_padding_mask=None, pos=None,):
|
||||
output = src
|
||||
for layer in self.layers:
|
||||
output = layer(
|
||||
output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos
|
||||
)
|
||||
if self.norm is not None:
|
||||
output = self.norm(output)
|
||||
return output
|
||||
|
||||
class TransformerDecoder(nn.Module):
|
||||
def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
|
||||
super().__init__()
|
||||
self.layers = _get_clones(decoder_layer, num_layers)
|
||||
self.num_layers = num_layers
|
||||
self.norm = norm
|
||||
self.return_intermediate = return_intermediate
|
||||
|
||||
def forward(
|
||||
self,
|
||||
tgt,
|
||||
memory,
|
||||
tgt_mask=None,
|
||||
memory_mask=None,
|
||||
tgt_key_padding_mask=None,
|
||||
memory_key_padding_mask=None,
|
||||
pos=None,
|
||||
query_pos=None,):
|
||||
|
||||
output = tgt
|
||||
intermediate = []
|
||||
for layer in self.layers:
|
||||
output = layer(
|
||||
output,
|
||||
memory,
|
||||
tgt_mask=tgt_mask,
|
||||
memory_mask=memory_mask,
|
||||
tgt_key_padding_mask=tgt_key_padding_mask,
|
||||
memory_key_padding_mask=memory_key_padding_mask,
|
||||
pos=pos,
|
||||
query_pos=query_pos,
|
||||
)
|
||||
if self.return_intermediate:
|
||||
intermediate.append(self.norm(output))
|
||||
|
||||
if self.norm is not None:
|
||||
output = self.norm(output)
|
||||
if self.return_intermediate:
|
||||
intermediate.pop()
|
||||
intermediate.append(output)
|
||||
|
||||
if self.return_intermediate:
|
||||
return torch.stack(intermediate)
|
||||
|
||||
return output.unsqueeze(0)
|
||||
|
||||
class TransformerEncoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_model,
|
||||
nhead,
|
||||
dim_feedforward=2048,
|
||||
dropout=0.1,
|
||||
activation="relu",
|
||||
normalize_before=False, ):
|
||||
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
|
||||
# Implementation of Feedforward model
|
||||
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
||||
|
||||
self.norm1 = nn.LayerNorm(d_model)
|
||||
self.norm2 = nn.LayerNorm(d_model)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
def with_pos_embed(self, x, pos):
|
||||
return x if pos is None else x + pos
|
||||
|
||||
def forward_post(
|
||||
self,
|
||||
src,
|
||||
src_mask = None,
|
||||
src_key_padding_mask = None,
|
||||
pos = None,):
|
||||
|
||||
q = k = self.with_pos_embed(src, pos)
|
||||
src2 = self.self_attn(
|
||||
q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
|
||||
)[0]
|
||||
src = src + self.dropout1(src2)
|
||||
src = self.norm1(src)
|
||||
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
|
||||
src = src + self.dropout2(src2)
|
||||
src = self.norm2(src)
|
||||
return src
|
||||
|
||||
def forward_pre(
|
||||
self,
|
||||
src,
|
||||
src_mask = None,
|
||||
src_key_padding_mask = None,
|
||||
pos = None,):
|
||||
|
||||
src2 = self.norm1(src)
|
||||
q = k = self.with_pos_embed(src2, pos)
|
||||
src2 = self.self_attn(
|
||||
q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
|
||||
)[0]
|
||||
src = src + self.dropout1(src2)
|
||||
src2 = self.norm2(src)
|
||||
src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
|
||||
src = src + self.dropout2(src2)
|
||||
return src
|
||||
|
||||
def forward(
|
||||
self,
|
||||
src,
|
||||
src_mask = None,
|
||||
src_key_padding_mask = None,
|
||||
pos = None,):
|
||||
if self.normalize_before:
|
||||
return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
|
||||
return self.forward_post(src, src_mask, src_key_padding_mask, pos)
|
||||
|
||||
class TransformerDecoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_model,
|
||||
nhead,
|
||||
dim_feedforward=2048,
|
||||
dropout=0.1,
|
||||
activation="relu",
|
||||
normalize_before=False,):
|
||||
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
|
||||
self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
|
||||
# Implementation of Feedforward model
|
||||
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
||||
|
||||
self.norm1 = nn.LayerNorm(d_model)
|
||||
self.norm2 = nn.LayerNorm(d_model)
|
||||
self.norm3 = nn.LayerNorm(d_model)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
self.dropout3 = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
def with_pos_embed(self, x, pos):
|
||||
return x if pos is None else x + pos
|
||||
|
||||
def forward_post(
|
||||
self,
|
||||
tgt,
|
||||
memory,
|
||||
tgt_mask = None,
|
||||
memory_mask = None,
|
||||
tgt_key_padding_mask = None,
|
||||
memory_key_padding_mask = None,
|
||||
pos = None,
|
||||
query_pos = None,):
|
||||
|
||||
q = k = self.with_pos_embed(tgt, query_pos)
|
||||
tgt2 = self.self_attn(
|
||||
q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0]
|
||||
tgt = tgt + self.dropout1(tgt2)
|
||||
tgt = self.norm1(tgt)
|
||||
tgt2 = self.multihead_attn(
|
||||
query=self.with_pos_embed(tgt, query_pos),
|
||||
key=self.with_pos_embed(memory, pos),
|
||||
value=memory,
|
||||
attn_mask=memory_mask,
|
||||
key_padding_mask=memory_key_padding_mask,)[0]
|
||||
tgt = tgt + self.dropout2(tgt2)
|
||||
tgt = self.norm2(tgt)
|
||||
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
|
||||
tgt = tgt + self.dropout3(tgt2)
|
||||
tgt = self.norm3(tgt)
|
||||
return tgt
|
||||
|
||||
def forward_pre(
|
||||
self,
|
||||
tgt,
|
||||
memory,
|
||||
tgt_mask = None,
|
||||
memory_mask = None,
|
||||
tgt_key_padding_mask = None,
|
||||
memory_key_padding_mask = None,
|
||||
pos = None,
|
||||
query_pos = None,):
|
||||
|
||||
tgt2 = self.norm1(tgt)
|
||||
q = k = self.with_pos_embed(tgt2, query_pos)
|
||||
tgt2 = self.self_attn(
|
||||
q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
|
||||
)[0]
|
||||
tgt = tgt + self.dropout1(tgt2)
|
||||
tgt2 = self.norm2(tgt)
|
||||
tgt2 = self.multihead_attn(
|
||||
query=self.with_pos_embed(tgt2, query_pos),
|
||||
key=self.with_pos_embed(memory, pos),
|
||||
value=memory,
|
||||
attn_mask=memory_mask,
|
||||
key_padding_mask=memory_key_padding_mask,
|
||||
)[0]
|
||||
tgt = tgt + self.dropout2(tgt2)
|
||||
tgt2 = self.norm3(tgt)
|
||||
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
|
||||
tgt = tgt + self.dropout3(tgt2)
|
||||
return tgt
|
||||
|
||||
def forward(
|
||||
self,
|
||||
tgt,
|
||||
memory,
|
||||
tgt_mask = None,
|
||||
memory_mask = None,
|
||||
tgt_key_padding_mask = None,
|
||||
memory_key_padding_mask = None,
|
||||
pos = None,
|
||||
query_pos = None, ):
|
||||
|
||||
if self.normalize_before:
|
||||
return self.forward_pre(
|
||||
tgt,
|
||||
memory,
|
||||
tgt_mask,
|
||||
memory_mask,
|
||||
tgt_key_padding_mask,
|
||||
memory_key_padding_mask,
|
||||
pos,
|
||||
query_pos,)
|
||||
return self.forward_post(
|
||||
tgt,
|
||||
memory,
|
||||
tgt_mask,
|
||||
memory_mask,
|
||||
tgt_key_padding_mask,
|
||||
memory_key_padding_mask,
|
||||
pos,
|
||||
query_pos,)
|
||||
|
||||
class SelfAttentionLayer(nn.Module):
|
||||
|
||||
def __init__(self, d_model, nhead, dropout=0.0,
|
||||
activation="relu", normalize_before=False):
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
|
||||
|
||||
self.norm = nn.LayerNorm(d_model)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
def _reset_parameters(self):
|
||||
for p in self.parameters():
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
|
||||
def with_pos_embed(self, tensor, pos):
|
||||
return tensor if pos is None else tensor + pos
|
||||
|
||||
def forward_post(self, tgt,
|
||||
tgt_mask = None,
|
||||
tgt_key_padding_mask = None,
|
||||
query_pos = None):
|
||||
q = k = self.with_pos_embed(tgt, query_pos).transpose(0 ,1)
|
||||
tgt2 = self.self_attn(q, k, value=tgt.transpose(0 ,1), attn_mask=tgt_mask,
|
||||
key_padding_mask=tgt_key_padding_mask)[0]
|
||||
tgt = tgt + self.dropout(tgt2.transpose(0 ,1))
|
||||
tgt = self.norm(tgt)
|
||||
|
||||
return tgt
|
||||
|
||||
def forward_pre(self, tgt,
|
||||
tgt_mask = None,
|
||||
tgt_key_padding_mask = None,
|
||||
query_pos = None):
|
||||
tgt2 = self.norm(tgt)
|
||||
q = k = self.with_pos_embed(tgt2, query_pos)
|
||||
tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
|
||||
key_padding_mask=tgt_key_padding_mask)[0]
|
||||
tgt = tgt + self.dropout(tgt2)
|
||||
|
||||
return tgt
|
||||
|
||||
def forward(self, tgt,
|
||||
tgt_mask = None,
|
||||
tgt_key_padding_mask = None,
|
||||
query_pos = None):
|
||||
if self.normalize_before:
|
||||
return self.forward_pre(tgt, tgt_mask,
|
||||
tgt_key_padding_mask, query_pos)
|
||||
return self.forward_post(tgt, tgt_mask,
|
||||
tgt_key_padding_mask, query_pos)
|
||||
|
||||
class CrossAttentionLayer(nn.Module):
|
||||
|
||||
def __init__(self, d_model, nhead, dropout=0.0,
|
||||
activation="relu", normalize_before=False):
|
||||
super().__init__()
|
||||
self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
|
||||
|
||||
self.norm = nn.LayerNorm(d_model)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
def _reset_parameters(self):
|
||||
for p in self.parameters():
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
|
||||
def with_pos_embed(self, tensor, pos):
|
||||
return tensor if pos is None else tensor + pos
|
||||
|
||||
def forward_post(self, tgt, memory,
|
||||
memory_mask = None,
|
||||
memory_key_padding_mask = None,
|
||||
pos = None,
|
||||
query_pos = None):
|
||||
tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos).transpose(0, 1),
|
||||
key=self.with_pos_embed(memory, pos).transpose(0, 1),
|
||||
value=memory.transpose(0, 1), attn_mask=memory_mask,
|
||||
key_padding_mask=memory_key_padding_mask)[0]
|
||||
tgt = tgt + self.dropout(tgt2.transpose(0, 1))
|
||||
tgt = self.norm(tgt)
|
||||
|
||||
return tgt
|
||||
|
||||
def forward_pre(self, tgt, memory,
|
||||
memory_mask = None,
|
||||
memory_key_padding_mask = None,
|
||||
pos = None,
|
||||
query_pos = None):
|
||||
tgt2 = self.norm(tgt)
|
||||
tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
|
||||
key=self.with_pos_embed(memory, pos),
|
||||
value=memory, attn_mask=memory_mask,
|
||||
key_padding_mask=memory_key_padding_mask)[0]
|
||||
tgt = tgt + self.dropout(tgt2)
|
||||
|
||||
return tgt
|
||||
|
||||
def forward(self, tgt, memory,
|
||||
memory_mask = None,
|
||||
memory_key_padding_mask = None,
|
||||
pos = None,
|
||||
query_pos = None):
|
||||
if self.normalize_before:
|
||||
return self.forward_pre(tgt, memory, memory_mask,
|
||||
memory_key_padding_mask, pos, query_pos)
|
||||
return self.forward_post(tgt, memory, memory_mask,
|
||||
memory_key_padding_mask, pos, query_pos)
|
||||
|
||||
class FFNLayer(nn.Module):
|
||||
|
||||
def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,
|
||||
activation="relu", normalize_before=False):
|
||||
super().__init__()
|
||||
# Implementation of Feedforward model
|
||||
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
||||
|
||||
self.norm = nn.LayerNorm(d_model)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
def _reset_parameters(self):
|
||||
for p in self.parameters():
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
|
||||
def with_pos_embed(self, tensor, pos):
|
||||
return tensor if pos is None else tensor + pos
|
||||
|
||||
def forward_post(self, tgt):
|
||||
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
|
||||
tgt = tgt + self.dropout(tgt2)
|
||||
tgt = self.norm(tgt)
|
||||
return tgt
|
||||
|
||||
def forward_pre(self, tgt):
|
||||
tgt2 = self.norm(tgt)
|
||||
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
|
||||
tgt = tgt + self.dropout(tgt2)
|
||||
return tgt
|
||||
|
||||
def forward(self, tgt):
|
||||
if self.normalize_before:
|
||||
return self.forward_pre(tgt)
|
||||
return self.forward_post(tgt)
|
||||
|
||||
class MLP(nn.Module):
|
||||
""" Very simple multi-layer perceptron (also called FFN)"""
|
||||
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
|
||||
super().__init__()
|
||||
self.num_layers = num_layers
|
||||
h = [hidden_dim] * (num_layers - 1)
|
||||
self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
|
||||
|
||||
def forward(self, x):
|
||||
for i, layer in enumerate(self.layers):
|
||||
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
|
||||
return x
|
||||
|
||||
class Seet_OneFormer_TDecoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
mask_classification,
|
||||
num_classes,
|
||||
hidden_dim,
|
||||
num_queries,
|
||||
nheads,
|
||||
dropout,
|
||||
dim_feedforward,
|
||||
enc_layers,
|
||||
is_train,
|
||||
dec_layers,
|
||||
class_dec_layers,
|
||||
pre_norm,
|
||||
mask_dim,
|
||||
enforce_input_project,
|
||||
use_task_norm,):
|
||||
|
||||
super().__init__()
|
||||
|
||||
assert mask_classification, "Only support mask classification model"
|
||||
self.mask_classification = mask_classification
|
||||
self.is_train = is_train
|
||||
self.use_task_norm = use_task_norm
|
||||
|
||||
# positional encoding
|
||||
N_steps = hidden_dim // 2
|
||||
self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
|
||||
|
||||
self.class_transformer = Transformer(
|
||||
d_model=hidden_dim,
|
||||
dropout=dropout,
|
||||
nhead=nheads,
|
||||
dim_feedforward=dim_feedforward,
|
||||
num_encoder_layers=enc_layers,
|
||||
num_decoder_layers=class_dec_layers,
|
||||
normalize_before=pre_norm,
|
||||
return_intermediate_dec=False,
|
||||
)
|
||||
|
||||
# define Transformer decoder here
|
||||
self.num_heads = nheads
|
||||
self.num_layers = dec_layers
|
||||
self.transformer_self_attention_layers = nn.ModuleList()
|
||||
self.transformer_cross_attention_layers = nn.ModuleList()
|
||||
self.transformer_ffn_layers = nn.ModuleList()
|
||||
|
||||
for _ in range(self.num_layers):
|
||||
self.transformer_self_attention_layers.append(
|
||||
SelfAttentionLayer(
|
||||
d_model=hidden_dim,
|
||||
nhead=nheads,
|
||||
dropout=0.0,
|
||||
normalize_before=pre_norm,
|
||||
)
|
||||
)
|
||||
|
||||
self.transformer_cross_attention_layers.append(
|
||||
CrossAttentionLayer(
|
||||
d_model=hidden_dim,
|
||||
nhead=nheads,
|
||||
dropout=0.0,
|
||||
normalize_before=pre_norm,
|
||||
)
|
||||
)
|
||||
|
||||
self.transformer_ffn_layers.append(
|
||||
FFNLayer(
|
||||
d_model=hidden_dim,
|
||||
dim_feedforward=dim_feedforward,
|
||||
dropout=0.0,
|
||||
normalize_before=pre_norm,
|
||||
)
|
||||
)
|
||||
|
||||
self.decoder_norm = nn.LayerNorm(hidden_dim)
|
||||
|
||||
self.num_queries = num_queries
|
||||
# learnable query p.e.
|
||||
self.query_embed = nn.Embedding(num_queries, hidden_dim)
|
||||
|
||||
# level embedding (we always use 3 scales)
|
||||
self.num_feature_levels = 3
|
||||
self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)
|
||||
self.input_proj = nn.ModuleList()
|
||||
for _ in range(self.num_feature_levels):
|
||||
if in_channels != hidden_dim or enforce_input_project:
|
||||
self.input_proj.append(nn.Conv2d(in_channels, hidden_dim, kernel_size=1))
|
||||
#weight_init.c2_xavier_fill(self.input_proj[-1])
|
||||
else:
|
||||
self.input_proj.append(nn.Sequential())
|
||||
|
||||
self.class_input_proj = nn.Conv2d(in_channels, hidden_dim, kernel_size=1)
|
||||
#weight_init.c2_xavier_fill(self.class_input_proj)
|
||||
|
||||
# output FFNs
|
||||
if self.mask_classification:
|
||||
self.class_embed = nn.Linear(hidden_dim, num_classes + 1)
|
||||
self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)
|
||||
|
||||
def forward(self, x, mask_features, tasks):
|
||||
# x is a list of multi-scale feature
|
||||
assert len(x) == self.num_feature_levels
|
||||
src = []
|
||||
pos = []
|
||||
size_list = []
|
||||
|
||||
for i in range(self.num_feature_levels):
|
||||
size_list.append(x[i].shape[-2:])
|
||||
pos.append(self.pe_layer(x[i], None).flatten(2))
|
||||
src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None])
|
||||
pos[-1] = pos[-1].transpose(1, 2)
|
||||
src[-1] = src[-1].transpose(1, 2)
|
||||
|
||||
bs, _, _ = src[0].shape
|
||||
|
||||
query_embed = self.query_embed.weight.unsqueeze(0).repeat(bs, 1, 1)
|
||||
|
||||
tasks = tasks.unsqueeze(0)
|
||||
if self.use_task_norm:
|
||||
tasks = self.decoder_norm(tasks)
|
||||
|
||||
feats = self.pe_layer(mask_features, None)
|
||||
|
||||
out_t, _ = self.class_transformer(
|
||||
feats, None,
|
||||
self.query_embed.weight[:-1],
|
||||
self.class_input_proj(mask_features),
|
||||
tasks if self.use_task_norm else None)
|
||||
out_t = out_t[0]
|
||||
|
||||
out = torch.cat([out_t, tasks], dim=1)
|
||||
|
||||
output = out.clone()
|
||||
|
||||
predictions_class = []
|
||||
predictions_mask = []
|
||||
|
||||
# prediction heads on learnable query features
|
||||
outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(
|
||||
output, mask_features, attn_mask_target_size=size_list[0])
|
||||
predictions_class.append(outputs_class)
|
||||
predictions_mask.append(outputs_mask)
|
||||
|
||||
for i in range(self.num_layers):
|
||||
level_index = i % self.num_feature_levels
|
||||
attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False
|
||||
|
||||
output = self.transformer_cross_attention_layers[i](
|
||||
output, src[level_index],
|
||||
memory_mask=attn_mask,
|
||||
memory_key_padding_mask=None,
|
||||
pos=pos[level_index], query_pos=query_embed, )
|
||||
|
||||
output = self.transformer_self_attention_layers[i](
|
||||
output, tgt_mask=None,
|
||||
tgt_key_padding_mask=None,
|
||||
query_pos=query_embed, )
|
||||
|
||||
# FFN
|
||||
output = self.transformer_ffn_layers[i](output)
|
||||
|
||||
outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(
|
||||
output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels])
|
||||
predictions_class.append(outputs_class)
|
||||
predictions_mask.append(outputs_mask)
|
||||
|
||||
assert len(predictions_class) == self.num_layers + 1
|
||||
|
||||
out = {
|
||||
'pred_logits': predictions_class[-1],
|
||||
'pred_masks': predictions_mask[-1],}
|
||||
|
||||
return out
|
||||
|
||||
def forward_prediction_heads(self, output, mask_features, attn_mask_target_size):
|
||||
decoder_output = self.decoder_norm(output)
|
||||
outputs_class = self.class_embed(decoder_output)
|
||||
mask_embed = self.mask_embed(decoder_output)
|
||||
outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features)
|
||||
|
||||
attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode="bilinear", align_corners=False)
|
||||
attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool()
|
||||
attn_mask = attn_mask.detach()
|
||||
|
||||
return outputs_class, outputs_mask, attn_mask
|
||||
@@ -0,0 +1,657 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint as checkpoint
|
||||
import numpy as np
|
||||
|
||||
|
||||
##############################
|
||||
# timm.models.layers helpers #
|
||||
##############################
|
||||
|
||||
def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
|
||||
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 = x.new_empty(shape).bernoulli_(keep_prob)
|
||||
if keep_prob > 0.0 and scale_by_keep:
|
||||
random_tensor.div_(keep_prob)
|
||||
return x * random_tensor
|
||||
|
||||
class DropPath(nn.Module):
|
||||
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
||||
"""
|
||||
def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
|
||||
super(DropPath, self).__init__()
|
||||
self.drop_prob = drop_prob
|
||||
self.scale_by_keep = scale_by_keep
|
||||
|
||||
def forward(self, x):
|
||||
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
|
||||
|
||||
def extra_repr(self):
|
||||
return f'drop_prob={round(self.drop_prob,3):0.3f}'
|
||||
|
||||
def _ntuple(n):
|
||||
def parse(x):
|
||||
from itertools import repeat
|
||||
import collections.abc
|
||||
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
|
||||
return tuple(x)
|
||||
return tuple(repeat(x, n))
|
||||
return parse
|
||||
|
||||
to_1tuple = _ntuple(1)
|
||||
to_2tuple = _ntuple(2)
|
||||
to_3tuple = _ntuple(3)
|
||||
to_4tuple = _ntuple(4)
|
||||
to_ntuple = _ntuple
|
||||
|
||||
def _trunc_normal_(tensor, mean, std, a, b):
|
||||
import warnings
|
||||
import math
|
||||
|
||||
def norm_cdf(x):
|
||||
return (1. + math.erf(x / math.sqrt(2.))) / 2.
|
||||
|
||||
if (mean < a - 2 * std) or (mean > b + 2 * std):
|
||||
warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
|
||||
"The distribution of values may be incorrect.",
|
||||
stacklevel=2)
|
||||
|
||||
l = norm_cdf((a - mean) / std)
|
||||
u = norm_cdf((b - mean) / std)
|
||||
tensor.uniform_(2 * l - 1, 2 * u - 1)
|
||||
tensor.erfinv_()
|
||||
tensor.mul_(std * math.sqrt(2.))
|
||||
tensor.add_(mean)
|
||||
tensor.clamp_(min=a, max=b)
|
||||
return tensor
|
||||
|
||||
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
|
||||
with torch.no_grad():
|
||||
return _trunc_normal_(tensor, mean, std, a, b)
|
||||
|
||||
#############
|
||||
# main swin #
|
||||
#############
|
||||
|
||||
class Mlp(nn.Module):
|
||||
""" Multilayer perceptron."""
|
||||
|
||||
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
|
||||
|
||||
|
||||
def window_partition(x, window_size):
|
||||
"""
|
||||
Args:
|
||||
x: (B, H, W, C)
|
||||
window_size (int): window size
|
||||
Returns:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
"""
|
||||
B, H, W, C = x.shape
|
||||
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
|
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
||||
return windows
|
||||
|
||||
|
||||
def window_reverse(windows, window_size, H, W):
|
||||
"""
|
||||
Args:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
window_size (int): Window size
|
||||
H (int): Height of image
|
||||
W (int): Width of image
|
||||
Returns:
|
||||
x: (B, H, W, C)
|
||||
"""
|
||||
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
||||
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
|
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
||||
return x
|
||||
|
||||
|
||||
class WindowAttention(nn.Module):
|
||||
""" Window based multi-head self attention (W-MSA) module with relative position bias.
|
||||
It supports both of shifted and non-shifted window.
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
window_size (tuple[int]): The height and width of the window.
|
||||
num_heads (int): Number of attention heads.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
|
||||
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
|
||||
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
|
||||
"""
|
||||
|
||||
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.window_size = window_size # Wh, Ww
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
# define a parameter table of relative position bias
|
||||
self.relative_position_bias_table = nn.Parameter(
|
||||
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
|
||||
|
||||
# get pair-wise relative position index for each token inside the window
|
||||
coords_h = torch.arange(self.window_size[0])
|
||||
coords_w = torch.arange(self.window_size[1])
|
||||
coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij')) # 2, Wh, Ww
|
||||
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
||||
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||||
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
|
||||
relative_coords[:, :, 1] += self.window_size[1] - 1
|
||||
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
||||
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||||
self.register_buffer("relative_position_index", relative_position_index)
|
||||
|
||||
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)
|
||||
|
||||
trunc_normal_(self.relative_position_bias_table, std=.02)
|
||||
self.softmax = nn.Softmax(dim=-1)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
""" Forward function.
|
||||
Args:
|
||||
x: input features with shape of (num_windows*B, N, C)
|
||||
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
|
||||
"""
|
||||
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] # make torchscript happy (cannot use tensor as tuple)
|
||||
|
||||
q = q * self.scale
|
||||
attn = (q @ k.transpose(-2, -1))
|
||||
|
||||
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
||||
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
|
||||
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
||||
attn = attn + relative_position_bias.unsqueeze(0)
|
||||
|
||||
if mask is not None:
|
||||
nW = mask.shape[0]
|
||||
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
|
||||
attn = attn.view(-1, self.num_heads, N, N)
|
||||
attn = self.softmax(attn)
|
||||
else:
|
||||
attn = self.softmax(attn)
|
||||
|
||||
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
|
||||
|
||||
|
||||
class SwinTransformerBlock(nn.Module):
|
||||
""" Swin Transformer Block.
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Window size.
|
||||
shift_size (int): Shift size for SW-MSA.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float, optional): Stochastic depth rate. Default: 0.0
|
||||
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(self, dim, num_heads, window_size=7, shift_size=0,
|
||||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
|
||||
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.window_size = window_size
|
||||
self.shift_size = shift_size
|
||||
self.mlp_ratio = mlp_ratio
|
||||
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
|
||||
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = WindowAttention(
|
||||
dim, window_size=to_2tuple(self.window_size), 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)
|
||||
|
||||
self.H = None
|
||||
self.W = None
|
||||
|
||||
def forward(self, x, mask_matrix):
|
||||
""" Forward function.
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
mask_matrix: Attention mask for cyclic shift.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
H, W = self.H, self.W
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
shortcut = x
|
||||
x = self.norm1(x)
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# pad feature maps to multiples of window size
|
||||
pad_l = pad_t = 0
|
||||
pad_r = (self.window_size - W % self.window_size) % self.window_size
|
||||
pad_b = (self.window_size - H % self.window_size) % self.window_size
|
||||
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
|
||||
_, Hp, Wp, _ = x.shape
|
||||
|
||||
# cyclic shift
|
||||
if self.shift_size > 0:
|
||||
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
||||
attn_mask = mask_matrix
|
||||
else:
|
||||
shifted_x = x
|
||||
attn_mask = None
|
||||
|
||||
# partition windows
|
||||
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
|
||||
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
|
||||
|
||||
# W-MSA/SW-MSA
|
||||
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
|
||||
|
||||
# merge windows
|
||||
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
||||
shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
|
||||
|
||||
# reverse cyclic shift
|
||||
if self.shift_size > 0:
|
||||
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
x = shifted_x
|
||||
|
||||
if pad_r > 0 or pad_b > 0:
|
||||
x = x[:, :H, :W, :].contiguous()
|
||||
|
||||
x = x.view(B, H * W, C)
|
||||
|
||||
# FFN
|
||||
x = shortcut + self.drop_path(x)
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class PatchMerging(nn.Module):
|
||||
""" Patch Merging Layer
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
def __init__(self, dim, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
||||
self.norm = norm_layer(4 * dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
""" Forward function.
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# padding
|
||||
pad_input = (H % 2 == 1) or (W % 2 == 1)
|
||||
if pad_input:
|
||||
x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
|
||||
|
||||
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
||||
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
||||
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
||||
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
||||
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
||||
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
||||
|
||||
x = self.norm(x)
|
||||
x = self.reduction(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class BasicLayer(nn.Module):
|
||||
""" A basic Swin Transformer layer for one stage.
|
||||
Args:
|
||||
dim (int): Number of feature channels
|
||||
depth (int): Depths of this stage.
|
||||
num_heads (int): Number of attention head.
|
||||
window_size (int): Local window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
dim,
|
||||
depth,
|
||||
num_heads,
|
||||
window_size=7,
|
||||
mlp_ratio=4.,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop=0.,
|
||||
attn_drop=0.,
|
||||
drop_path=0.,
|
||||
norm_layer=nn.LayerNorm,
|
||||
downsample=None,
|
||||
use_checkpoint=False):
|
||||
super().__init__()
|
||||
self.window_size = window_size
|
||||
self.shift_size = window_size // 2
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.ModuleList([
|
||||
SwinTransformerBlock(
|
||||
dim=dim,
|
||||
num_heads=num_heads,
|
||||
window_size=window_size,
|
||||
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop,
|
||||
attn_drop=attn_drop,
|
||||
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||||
norm_layer=norm_layer)
|
||||
for i in range(depth)])
|
||||
|
||||
# patch merging layer
|
||||
if downsample is not None:
|
||||
self.downsample = downsample(dim=dim, norm_layer=norm_layer)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x, H, W):
|
||||
""" Forward function.
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
|
||||
# calculate attention mask for SW-MSA
|
||||
Hp = int(np.ceil(H / self.window_size)) * self.window_size
|
||||
Wp = int(np.ceil(W / self.window_size)) * self.window_size
|
||||
img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device, dtype=x.dtype) # 1 Hp Wp 1
|
||||
h_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
w_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
cnt = 0
|
||||
for h in h_slices:
|
||||
for w in w_slices:
|
||||
img_mask[:, h, w, :] = cnt
|
||||
cnt += 1
|
||||
|
||||
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
|
||||
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||||
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
||||
|
||||
for blk in self.blocks:
|
||||
blk.H, blk.W = H, W
|
||||
if self.use_checkpoint:
|
||||
x = checkpoint.checkpoint(blk, x, attn_mask)
|
||||
else:
|
||||
x = blk(x, attn_mask)
|
||||
if self.downsample is not None:
|
||||
x_down = self.downsample(x, H, W)
|
||||
Wh, Ww = (H + 1) // 2, (W + 1) // 2
|
||||
return x, H, W, x_down, Wh, Ww
|
||||
else:
|
||||
return x, H, W, x, H, W
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
Args:
|
||||
patch_size (int): Patch token size. Default: 4.
|
||||
in_chans (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
||||
"""
|
||||
|
||||
def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
|
||||
super().__init__()
|
||||
patch_size = to_2tuple(patch_size)
|
||||
self.patch_size = patch_size
|
||||
|
||||
self.in_chans = in_chans
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
||||
if norm_layer is not None:
|
||||
self.norm = norm_layer(embed_dim)
|
||||
else:
|
||||
self.norm = None
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
# padding
|
||||
_, _, H, W = x.size()
|
||||
if W % self.patch_size[1] != 0:
|
||||
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
|
||||
if H % self.patch_size[0] != 0:
|
||||
x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
|
||||
|
||||
x = self.proj(x) # B C Wh Ww
|
||||
if self.norm is not None:
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformer(nn.Module):
|
||||
""" Swin Transformer backbone.
|
||||
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
||||
https://arxiv.org/pdf/2103.14030
|
||||
Args:
|
||||
pretrain_img_size (int): Input image size for training the pretrained model,
|
||||
used in absolute postion embedding. Default 224.
|
||||
patch_size (int | tuple(int)): Patch size. Default: 4.
|
||||
in_chans (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
depths (tuple[int]): Depths of each Swin Transformer stage.
|
||||
num_heads (tuple[int]): Number of attention head of each stage.
|
||||
window_size (int): Window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop_rate (float): Dropout rate.
|
||||
attn_drop_rate (float): Attention dropout rate. Default: 0.
|
||||
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
|
||||
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
||||
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
|
||||
patch_norm (bool): If True, add normalization after patch embedding. Default: True.
|
||||
out_indices (Sequence[int]): Output from which stages.
|
||||
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
|
||||
-1 means not freezing any parameters.
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
pretrain_img_size=224,
|
||||
patch_size=4,
|
||||
in_chans=3,
|
||||
embed_dim=96,
|
||||
depths=[2, 2, 6, 2],
|
||||
num_heads=[3, 6, 12, 24],
|
||||
window_size=7,
|
||||
mlp_ratio=4.,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop_rate=0.,
|
||||
attn_drop_rate=0.,
|
||||
drop_path_rate=0.2,
|
||||
norm_layer=nn.LayerNorm,
|
||||
ape=False,
|
||||
patch_norm=True,
|
||||
out_indices=(0, 1, 2, 3),
|
||||
frozen_stages=-1,
|
||||
use_checkpoint=False):
|
||||
super().__init__()
|
||||
|
||||
self.pretrain_img_size = pretrain_img_size
|
||||
self.num_layers = len(depths)
|
||||
self.embed_dim = embed_dim
|
||||
self.ape = ape
|
||||
self.patch_norm = patch_norm
|
||||
self.out_indices = out_indices
|
||||
self.frozen_stages = frozen_stages
|
||||
|
||||
# split image into non-overlapping patches
|
||||
self.patch_embed = PatchEmbed(
|
||||
patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
|
||||
norm_layer=norm_layer if self.patch_norm else None)
|
||||
|
||||
# absolute position embedding
|
||||
if self.ape:
|
||||
pretrain_img_size = to_2tuple(pretrain_img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
|
||||
|
||||
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
|
||||
trunc_normal_(self.absolute_pos_embed, std=.02)
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
# stochastic depth
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||||
|
||||
# build layers
|
||||
self.layers = nn.ModuleList()
|
||||
for i_layer in range(self.num_layers):
|
||||
layer = BasicLayer(
|
||||
dim=int(embed_dim * 2 ** i_layer),
|
||||
depth=depths[i_layer],
|
||||
num_heads=num_heads[i_layer],
|
||||
window_size=window_size,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop_rate,
|
||||
attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
|
||||
norm_layer=norm_layer,
|
||||
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
|
||||
use_checkpoint=use_checkpoint)
|
||||
self.layers.append(layer)
|
||||
|
||||
num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
|
||||
self.num_features = num_features
|
||||
|
||||
# add a norm layer for each output
|
||||
for i_layer in out_indices:
|
||||
layer = norm_layer(num_features[i_layer])
|
||||
layer_name = f'norm{i_layer}'
|
||||
self.add_module(layer_name, layer)
|
||||
|
||||
self._freeze_stages()
|
||||
|
||||
def _freeze_stages(self):
|
||||
if self.frozen_stages >= 0:
|
||||
self.patch_embed.eval()
|
||||
for param in self.patch_embed.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 1 and self.ape:
|
||||
self.absolute_pos_embed.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 2:
|
||||
self.pos_drop.eval()
|
||||
for i in range(0, self.frozen_stages - 1):
|
||||
m = self.layers[i]
|
||||
m.eval()
|
||||
for param in m.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
x = self.patch_embed(x)
|
||||
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
if self.ape:
|
||||
# interpolate the position embedding to the corresponding size
|
||||
absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
|
||||
x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
|
||||
else:
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.pos_drop(x)
|
||||
|
||||
outs = []
|
||||
for i in range(self.num_layers):
|
||||
layer = self.layers[i]
|
||||
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
|
||||
|
||||
if i in self.out_indices:
|
||||
norm_layer = getattr(self, f'norm{i}')
|
||||
x_out = norm_layer(x_out)
|
||||
|
||||
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(out)
|
||||
|
||||
outputs = {
|
||||
'res2' : outs[0],
|
||||
'res3' : outs[1],
|
||||
'res4' : outs[2],
|
||||
'res5' : outs[3],}
|
||||
return outputs
|
||||
|
||||
def train(self, mode=True):
|
||||
"""Convert the model into training mode while keep layers freezed."""
|
||||
super(SwinTransformer, self).train(mode)
|
||||
self._freeze_stages()
|
||||
return self
|
||||
Reference in New Issue
Block a user