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GNU GENERAL PUBLIC LICENSE
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Version 3, 29 June 2007
|
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|
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Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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Everyone is permitted to copy and distribute verbatim copies
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of this license document, but changing it is not allowed.
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Preamble
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The GNU General Public License is a free, copyleft license for
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software and other kinds of works.
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|
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The licenses for most software and other practical works are designed
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to take away your freedom to share and change the works. By contrast,
|
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the GNU General Public License is intended to guarantee your freedom to
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share and change all versions of a program--to make sure it remains free
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software for all its users. We, the Free Software Foundation, use the
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GNU General Public License for most of our software; it applies also to
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any other work released this way by its authors. You can apply it to
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your programs, too.
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When we speak of free software, we are referring to freedom, not
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price. Our General Public Licenses are designed to make sure that you
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have the freedom to distribute copies of free software (and charge for
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them if you wish), that you receive source code or can get it if you
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want it, that you can change the software or use pieces of it in new
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free programs, and that you know you can do these things.
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|
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To protect your rights, we need to prevent others from denying you
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certain responsibilities if you distribute copies of the software, or if
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For example, if you distribute copies of such a program, whether
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freedoms that you received. You must make sure that they, too, receive
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Developers that use the GNU GPL protect your rights with two steps:
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giving you legal permission to copy, distribute and/or modify it.
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For the developers' and authors' protection, the GPL clearly explains
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Some devices are designed to deny users access to install or run
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Finally, every program is threatened constantly by software patents.
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States should not allow patents to restrict development and use of
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The precise terms and conditions for copying, distribution and
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TERMS AND CONDITIONS
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0. Definitions.
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|
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"This License" refers to version 3 of the GNU General Public License.
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"Copyright" also means copyright-like laws that apply to other kinds of
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works, such as semiconductor masks.
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"The Program" refers to any copyrightable work licensed under this
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A "covered work" means either the unmodified Program or a work based
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To "propagate" a work means to do anything with it that, without
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To "convey" a work means any kind of propagation that enables other
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An interactive user interface displays "Appropriate Legal Notices"
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The "source code" for a work means the preferred form of the work
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The "System Libraries" of an executable work include anything, other
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The Corresponding Source need not include anything that users
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The Corresponding Source for a work in source code form is that
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same work.
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||||
|
||||
2. Basic Permissions.
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||||
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||||
All rights granted under this License are granted for the term of
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
You may make, run and propagate covered works that you do not
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||||
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||||
not control copyright. Those thus making or running the covered works
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Conveying under any other circumstances is permitted solely under
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||||
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
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||||
No covered work shall be deemed part of an effective technological
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||||
measure under any applicable law fulfilling obligations under article
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||||
11 of the WIPO copyright treaty adopted on 20 December 1996, or
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||||
similar laws prohibiting or restricting circumvention of such
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||||
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||||
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||||
When you convey a covered work, you waive any legal power to forbid
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
4. Conveying Verbatim Copies.
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||||
You may convey verbatim copies of the Program's source code as you
|
||||
receive it, in any medium, provided that you conspicuously and
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||||
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||||
keep intact all notices stating that this License and any
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||||
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|
||||
keep intact all notices of the absence of any warranty; and give all
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||||
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||||
You may charge any price or no price for each copy that you convey,
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||||
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5. Conveying Modified Source Versions.
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||||
You may convey a work based on the Program, or the modifications to
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||||
produce it from the Program, in the form of source code under the
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||||
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||||
a) The work must carry prominent notices stating that you modified
|
||||
it, and giving a relevant date.
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||||
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||||
b) The work must carry prominent notices stating that it is
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||||
released under this License and any conditions added under section
|
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7. This requirement modifies the requirement in section 4 to
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||||
"keep intact all notices".
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||||
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||||
c) You must license the entire work, as a whole, under this
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||||
License to anyone who comes into possession of a copy. This
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||||
License will therefore apply, along with any applicable section 7
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||||
additional terms, to the whole of the work, and all its parts,
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||||
regardless of how they are packaged. This License gives no
|
||||
permission to license the work in any other way, but it does not
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||||
invalidate such permission if you have separately received it.
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||||
|
||||
d) If the work has interactive user interfaces, each must display
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||||
Appropriate Legal Notices; however, if the Program has interactive
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||||
interfaces that do not display Appropriate Legal Notices, your
|
||||
work need not make them do so.
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||||
A compilation of a covered work with other separate and independent
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works, which are not by their nature extensions of the covered work,
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and which are not combined with it such as to form a larger program,
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||||
in or on a volume of a storage or distribution medium, is called an
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||||
"aggregate" if the compilation and its resulting copyright are not
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||||
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.
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||||
|
||||
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:
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||||
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||||
a) Convey the object code in, or embodied in, a physical product
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||||
(including a physical distribution medium), accompanied by the
|
||||
Corresponding Source fixed on a durable physical medium
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||||
customarily used for software interchange.
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||||
b) Convey the object code in, or embodied in, a physical product
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written offer, valid for at least three years and valid for as
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||||
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||||
model, to give anyone who possesses the object code either (1) a
|
||||
copy of the Corresponding Source for all the software in the
|
||||
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||||
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conveying of source, or (2) access to copy the
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Corresponding Source from a network server at no charge.
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||||
c) Convey individual copies of the object code with a copy of the
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written offer to provide the Corresponding Source. This
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alternative is allowed only occasionally and noncommercially, and
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only if you received the object code with such an offer, in accord
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||||
with subsection 6b.
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|
||||
d) Convey the object code by offering access from a designated
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||||
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Corresponding Source in the same way through the same place at no
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||||
Corresponding Source along with the object code. If the place to
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||||
copy the object code is a network server, the Corresponding Source
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||||
may be on a different server (operated by you or a third party)
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that supports equivalent copying facilities, provided you maintain
|
||||
clear directions next to the object code saying where to find the
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Corresponding Source, you remain obligated to ensure that it is
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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
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Source of the work are being offered to the general public at no
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charge under subsection 6d.
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|
||||
A separable portion of the object code, whose source code is excluded
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||||
from the Corresponding Source as a System Library, need not be
|
||||
included in conveying the object code work.
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|
||||
A "User Product" is either (1) a "consumer product", which means any
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tangible personal property which is normally used for personal, family,
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||||
or household purposes, or (2) anything designed or sold for incorporation
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||||
into a dwelling. In determining whether a product is a consumer product,
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||||
doubtful cases shall be resolved in favor of coverage. For a particular
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||||
product received by a particular user, "normally used" refers to a
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||||
typical or common use of that class of product, regardless of the status
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||||
of the particular user or of the way in which the particular user
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actually uses, or expects or is expected to use, the product. A product
|
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is a consumer product regardless of whether the product has substantial
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||||
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,
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||||
procedures, authorization keys, or other information required to install
|
||||
and execute modified versions of a covered work in that User Product from
|
||||
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|
||||
suffice to ensure that the continued functioning of the modified object
|
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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
|
||||
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|
||||
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|
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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
|
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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.
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|
||||
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
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||||
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|
||||
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|
||||
|
||||
Notwithstanding any other provision of this License, for material you
|
||||
add to a covered work, you may (if authorized by the copyright holders of
|
||||
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|
||||
|
||||
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
|
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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
|
||||
|
||||
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|
||||
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|
||||
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||||
|
||||
f) Requiring indemnification of licensors and authors of that
|
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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
|
||||
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||||
restriction, you may remove that term. If a license document contains
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||||
a further restriction but permits relicensing or conveying under this
|
||||
License, you may add to a covered work material governed by the terms
|
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of that license document, provided that the further restriction does
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||||
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||||
If you add terms to a covered work in accord with this section, you
|
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must place, in the relevant source files, a statement of the
|
||||
additional terms that apply to those files, or a notice indicating
|
||||
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|
||||
|
||||
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
|
||||
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|
||||
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|
||||
|
||||
However, if you cease all violation of this License, then your
|
||||
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|
||||
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|
||||
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|
||||
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|
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prior to 60 days after the cessation.
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||||
|
||||
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
|
||||
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||||
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|
||||
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|
||||
|
||||
Termination of your rights under this section does not terminate the
|
||||
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|
||||
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|
||||
reinstated, you do not qualify to receive new licenses for the same
|
||||
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|
||||
|
||||
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 <http://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
|
||||
<http://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
|
||||
<http://www.gnu.org/philosophy/why-not-lgpl.html>.
|
||||
Binary file not shown.
@ -0,0 +1,67 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
|
||||
# Example usage: python train.py --data Argoverse.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── Argoverse ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/Argoverse # dataset root dir
|
||||
train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
|
||||
val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
|
||||
test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
|
||||
|
||||
# Classes
|
||||
nc: 8 # number of classes
|
||||
names: ['person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import json
|
||||
|
||||
from tqdm import tqdm
|
||||
from utils.general import download, Path
|
||||
|
||||
|
||||
def argoverse2yolo(set):
|
||||
labels = {}
|
||||
a = json.load(open(set, "rb"))
|
||||
for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
|
||||
img_id = annot['image_id']
|
||||
img_name = a['images'][img_id]['name']
|
||||
img_label_name = img_name[:-3] + "txt"
|
||||
|
||||
cls = annot['category_id'] # instance class id
|
||||
x_center, y_center, width, height = annot['bbox']
|
||||
x_center = (x_center + width / 2) / 1920.0 # offset and scale
|
||||
y_center = (y_center + height / 2) / 1200.0 # offset and scale
|
||||
width /= 1920.0 # scale
|
||||
height /= 1200.0 # scale
|
||||
|
||||
img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
|
||||
if not img_dir.exists():
|
||||
img_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
k = str(img_dir / img_label_name)
|
||||
if k not in labels:
|
||||
labels[k] = []
|
||||
labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
|
||||
|
||||
for k in labels:
|
||||
with open(k, "w") as f:
|
||||
f.writelines(labels[k])
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path('../datasets/Argoverse') # dataset root dir
|
||||
urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
|
||||
download(urls, dir=dir, delete=False)
|
||||
|
||||
# Convert
|
||||
annotations_dir = 'Argoverse-HD/annotations/'
|
||||
(dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
|
||||
for d in "train.json", "val.json":
|
||||
argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels
|
||||
@ -0,0 +1,53 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Global Wheat 2020 dataset http://www.global-wheat.com/
|
||||
# Example usage: python train.py --data GlobalWheat2020.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── GlobalWheat2020 ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/GlobalWheat2020 # dataset root dir
|
||||
train: # train images (relative to 'path') 3422 images
|
||||
- images/arvalis_1
|
||||
- images/arvalis_2
|
||||
- images/arvalis_3
|
||||
- images/ethz_1
|
||||
- images/rres_1
|
||||
- images/inrae_1
|
||||
- images/usask_1
|
||||
val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
|
||||
- images/ethz_1
|
||||
test: # test images (optional) 1276 images
|
||||
- images/utokyo_1
|
||||
- images/utokyo_2
|
||||
- images/nau_1
|
||||
- images/uq_1
|
||||
|
||||
# Classes
|
||||
nc: 1 # number of classes
|
||||
names: ['wheat_head'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from utils.general import download, Path
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
|
||||
'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
|
||||
download(urls, dir=dir)
|
||||
|
||||
# Make Directories
|
||||
for p in 'annotations', 'images', 'labels':
|
||||
(dir / p).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Move
|
||||
for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
|
||||
'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
|
||||
(dir / p).rename(dir / 'images' / p) # move to /images
|
||||
f = (dir / p).with_suffix('.json') # json file
|
||||
if f.exists():
|
||||
f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
|
||||
@ -0,0 +1,112 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Objects365 dataset https://www.objects365.org/
|
||||
# Example usage: python train.py --data Objects365.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── Objects365 ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/Objects365 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1742289 images
|
||||
val: images/val # val images (relative to 'path') 80000 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
nc: 365 # number of classes
|
||||
names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup',
|
||||
'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book',
|
||||
'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag',
|
||||
'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV',
|
||||
'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle',
|
||||
'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird',
|
||||
'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
|
||||
'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning',
|
||||
'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife',
|
||||
'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock',
|
||||
'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish',
|
||||
'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan',
|
||||
'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard',
|
||||
'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign',
|
||||
'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
|
||||
'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard',
|
||||
'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry',
|
||||
'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
|
||||
'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors',
|
||||
'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape',
|
||||
'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck',
|
||||
'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette',
|
||||
'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket',
|
||||
'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine',
|
||||
'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine',
|
||||
'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon',
|
||||
'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse',
|
||||
'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball',
|
||||
'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin',
|
||||
'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts',
|
||||
'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
|
||||
'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD',
|
||||
'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder',
|
||||
'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips',
|
||||
'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab',
|
||||
'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
|
||||
'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart',
|
||||
'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French',
|
||||
'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell',
|
||||
'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil',
|
||||
'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis']
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from pycocotools.coco import COCO
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils.general import Path, download, np, xyxy2xywhn
|
||||
|
||||
# Make Directories
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
for p in 'images', 'labels':
|
||||
(dir / p).mkdir(parents=True, exist_ok=True)
|
||||
for q in 'train', 'val':
|
||||
(dir / p / q).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Train, Val Splits
|
||||
for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
|
||||
print(f"Processing {split} in {patches} patches ...")
|
||||
images, labels = dir / 'images' / split, dir / 'labels' / split
|
||||
|
||||
# Download
|
||||
url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
|
||||
if split == 'train':
|
||||
download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json
|
||||
download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
|
||||
elif split == 'val':
|
||||
download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json
|
||||
download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
|
||||
download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
|
||||
|
||||
# Move
|
||||
for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
|
||||
f.rename(images / f.name) # move to /images/{split}
|
||||
|
||||
# Labels
|
||||
coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
|
||||
names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
|
||||
for cid, cat in enumerate(names):
|
||||
catIds = coco.getCatIds(catNms=[cat])
|
||||
imgIds = coco.getImgIds(catIds=catIds)
|
||||
for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
|
||||
width, height = im["width"], im["height"]
|
||||
path = Path(im["file_name"]) # image filename
|
||||
try:
|
||||
with open(labels / path.with_suffix('.txt').name, 'a') as file:
|
||||
annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
|
||||
for a in coco.loadAnns(annIds):
|
||||
x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
|
||||
xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
|
||||
x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
|
||||
file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
@ -0,0 +1,52 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
|
||||
# Example usage: python train.py --data SKU-110K.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── SKU-110K ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/SKU-110K # dataset root dir
|
||||
train: train.txt # train images (relative to 'path') 8219 images
|
||||
val: val.txt # val images (relative to 'path') 588 images
|
||||
test: test.txt # test images (optional) 2936 images
|
||||
|
||||
# Classes
|
||||
nc: 1 # number of classes
|
||||
names: ['object'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import shutil
|
||||
from tqdm import tqdm
|
||||
from utils.general import np, pd, Path, download, xyxy2xywh
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
parent = Path(dir.parent) # download dir
|
||||
urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
|
||||
download(urls, dir=parent, delete=False)
|
||||
|
||||
# Rename directories
|
||||
if dir.exists():
|
||||
shutil.rmtree(dir)
|
||||
(parent / 'SKU110K_fixed').rename(dir) # rename dir
|
||||
(dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir
|
||||
|
||||
# Convert labels
|
||||
names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names
|
||||
for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
|
||||
x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations
|
||||
images, unique_images = x[:, 0], np.unique(x[:, 0])
|
||||
with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
|
||||
f.writelines(f'./images/{s}\n' for s in unique_images)
|
||||
for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
|
||||
cls = 0 # single-class dataset
|
||||
with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
|
||||
for r in x[images == im]:
|
||||
w, h = r[6], r[7] # image width, height
|
||||
xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance
|
||||
f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label
|
||||
@ -0,0 +1,80 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC
|
||||
# Example usage: python train.py --data VOC.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── VOC ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/VOC
|
||||
train: # train images (relative to 'path') 16551 images
|
||||
- images/train2012
|
||||
- images/train2007
|
||||
- images/val2012
|
||||
- images/val2007
|
||||
val: # val images (relative to 'path') 4952 images
|
||||
- images/test2007
|
||||
test: # test images (optional)
|
||||
- images/test2007
|
||||
|
||||
# Classes
|
||||
nc: 20 # number of classes
|
||||
names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
|
||||
'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
from tqdm import tqdm
|
||||
from utils.general import download, Path
|
||||
|
||||
|
||||
def convert_label(path, lb_path, year, image_id):
|
||||
def convert_box(size, box):
|
||||
dw, dh = 1. / size[0], 1. / size[1]
|
||||
x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
|
||||
return x * dw, y * dh, w * dw, h * dh
|
||||
|
||||
in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
|
||||
out_file = open(lb_path, 'w')
|
||||
tree = ET.parse(in_file)
|
||||
root = tree.getroot()
|
||||
size = root.find('size')
|
||||
w = int(size.find('width').text)
|
||||
h = int(size.find('height').text)
|
||||
|
||||
for obj in root.iter('object'):
|
||||
cls = obj.find('name').text
|
||||
if cls in yaml['names'] and not int(obj.find('difficult').text) == 1:
|
||||
xmlbox = obj.find('bndbox')
|
||||
bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
|
||||
cls_id = yaml['names'].index(cls) # class id
|
||||
out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
|
||||
urls = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
|
||||
url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
|
||||
url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
|
||||
download(urls, dir=dir / 'images', delete=False)
|
||||
|
||||
# Convert
|
||||
path = dir / f'images/VOCdevkit'
|
||||
for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
|
||||
imgs_path = dir / 'images' / f'{image_set}{year}'
|
||||
lbs_path = dir / 'labels' / f'{image_set}{year}'
|
||||
imgs_path.mkdir(exist_ok=True, parents=True)
|
||||
lbs_path.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').read().strip().split()
|
||||
for id in tqdm(image_ids, desc=f'{image_set}{year}'):
|
||||
f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
|
||||
lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
|
||||
f.rename(imgs_path / f.name) # move image
|
||||
convert_label(path, lb_path, year, id) # convert labels to YOLO format
|
||||
@ -0,0 +1,61 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
|
||||
# Example usage: python train.py --data VisDrone.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── VisDrone ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/VisDrone # dataset root dir
|
||||
train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
|
||||
val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
|
||||
test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
|
||||
|
||||
# Classes
|
||||
nc: 10 # number of classes
|
||||
names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from utils.general import download, os, Path
|
||||
|
||||
def visdrone2yolo(dir):
|
||||
from PIL import Image
|
||||
from tqdm import tqdm
|
||||
|
||||
def convert_box(size, box):
|
||||
# Convert VisDrone box to YOLO xywh box
|
||||
dw = 1. / size[0]
|
||||
dh = 1. / size[1]
|
||||
return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
|
||||
|
||||
(dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
|
||||
pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
|
||||
for f in pbar:
|
||||
img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
|
||||
lines = []
|
||||
with open(f, 'r') as file: # read annotation.txt
|
||||
for row in [x.split(',') for x in file.read().strip().splitlines()]:
|
||||
if row[4] == '0': # VisDrone 'ignored regions' class 0
|
||||
continue
|
||||
cls = int(row[5]) - 1
|
||||
box = convert_box(img_size, tuple(map(int, row[:4])))
|
||||
lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
|
||||
with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
|
||||
fl.writelines(lines) # write label.txt
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
|
||||
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
|
||||
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
|
||||
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
|
||||
download(urls, dir=dir)
|
||||
|
||||
# Convert
|
||||
for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
|
||||
visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
|
||||
@ -0,0 +1,44 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# COCO 2017 dataset http://cocodataset.org
|
||||
# Example usage: python train.py --data coco.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── coco ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco # dataset root dir
|
||||
train: train2017.txt # train images (relative to 'path') 118287 images
|
||||
val: val2017.txt # train images (relative to 'path') 5000 images
|
||||
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||
|
||||
# Classes
|
||||
nc: 80 # number of classes
|
||||
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
|
||||
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
|
||||
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
|
||||
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
|
||||
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
|
||||
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
|
||||
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
|
||||
'hair drier', 'toothbrush'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: |
|
||||
from utils.general import download, Path
|
||||
|
||||
# Download labels
|
||||
segments = False # segment or box labels
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
|
||||
urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
|
||||
download(urls, dir=dir.parent)
|
||||
|
||||
# Download data
|
||||
urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
|
||||
'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
|
||||
'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
|
||||
download(urls, dir=dir / 'images', threads=3)
|
||||
@ -0,0 +1,30 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
|
||||
# Example usage: python train.py --data coco128.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── coco128 ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco128 # dataset root dir
|
||||
train: images/train2017 # train images (relative to 'path') 128 images
|
||||
val: images/train2017 # val images (relative to 'path') 128 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
nc: 80 # number of classes
|
||||
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
|
||||
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
|
||||
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
|
||||
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
|
||||
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
|
||||
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
|
||||
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
|
||||
'hair drier', 'toothbrush'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/coco128.zip
|
||||
@ -0,0 +1,12 @@
|
||||
# Custom data for safety helmet
|
||||
|
||||
|
||||
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: /home/data/yolo_format/images/train
|
||||
val: /home/data/yolo_format/images/val
|
||||
|
||||
# number of classes
|
||||
nc: 2
|
||||
|
||||
# class names
|
||||
names: ['phone', 'person']
|
||||
@ -0,0 +1,39 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Hyperparameters for VOC finetuning
|
||||
# python train.py --batch 64 --weights yolov5m.pt --data VOC.yaml --img 512 --epochs 50
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
# Hyperparameter Evolution Results
|
||||
# Generations: 306
|
||||
# P R mAP.5 mAP.5:.95 box obj cls
|
||||
# Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146
|
||||
|
||||
lr0: 0.0032
|
||||
lrf: 0.12
|
||||
momentum: 0.843
|
||||
weight_decay: 0.00036
|
||||
warmup_epochs: 2.0
|
||||
warmup_momentum: 0.5
|
||||
warmup_bias_lr: 0.05
|
||||
box: 0.0296
|
||||
cls: 0.243
|
||||
cls_pw: 0.631
|
||||
obj: 0.301
|
||||
obj_pw: 0.911
|
||||
iou_t: 0.2
|
||||
anchor_t: 2.91
|
||||
# anchors: 3.63
|
||||
fl_gamma: 0.0
|
||||
hsv_h: 0.0138
|
||||
hsv_s: 0.664
|
||||
hsv_v: 0.464
|
||||
degrees: 0.373
|
||||
translate: 0.245
|
||||
scale: 0.898
|
||||
shear: 0.602
|
||||
perspective: 0.0
|
||||
flipud: 0.00856
|
||||
fliplr: 0.5
|
||||
mosaic: 1.0
|
||||
mixup: 0.243
|
||||
copy_paste: 0.0
|
||||
@ -0,0 +1,31 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
lr0: 0.00258
|
||||
lrf: 0.17
|
||||
momentum: 0.779
|
||||
weight_decay: 0.00058
|
||||
warmup_epochs: 1.33
|
||||
warmup_momentum: 0.86
|
||||
warmup_bias_lr: 0.0711
|
||||
box: 0.0539
|
||||
cls: 0.299
|
||||
cls_pw: 0.825
|
||||
obj: 0.632
|
||||
obj_pw: 1.0
|
||||
iou_t: 0.2
|
||||
anchor_t: 3.44
|
||||
anchors: 3.2
|
||||
fl_gamma: 0.0
|
||||
hsv_h: 0.0188
|
||||
hsv_s: 0.704
|
||||
hsv_v: 0.36
|
||||
degrees: 0.0
|
||||
translate: 0.0902
|
||||
scale: 0.491
|
||||
shear: 0.0
|
||||
perspective: 0.0
|
||||
flipud: 0.0
|
||||
fliplr: 0.5
|
||||
mosaic: 1.0
|
||||
mixup: 0.0
|
||||
copy_paste: 0.0
|
||||
@ -0,0 +1,34 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Hyperparameters for high-augmentation COCO training from scratch
|
||||
# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # warmup initial bias lr
|
||||
box: 0.05 # box loss gain
|
||||
cls: 0.3 # cls loss gain
|
||||
cls_pw: 1.0 # cls BCELoss positive_weight
|
||||
obj: 0.7 # obj loss gain (scale with pixels)
|
||||
obj_pw: 1.0 # obj BCELoss positive_weight
|
||||
iou_t: 0.20 # IoU training threshold
|
||||
anchor_t: 4.0 # anchor-multiple threshold
|
||||
# anchors: 3 # anchors per output layer (0 to ignore)
|
||||
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
|
||||
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # image rotation (+/- deg)
|
||||
translate: 0.1 # image translation (+/- fraction)
|
||||
scale: 0.9 # image scale (+/- gain)
|
||||
shear: 0.0 # image shear (+/- deg)
|
||||
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # image flip up-down (probability)
|
||||
fliplr: 0.5 # image flip left-right (probability)
|
||||
mosaic: 1.0 # image mosaic (probability)
|
||||
mixup: 0.1 # image mixup (probability)
|
||||
copy_paste: 0.1 # segment copy-paste (probability)
|
||||
@ -0,0 +1,34 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Hyperparameters for low-augmentation COCO training from scratch
|
||||
# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # warmup initial bias lr
|
||||
box: 0.05 # box loss gain
|
||||
cls: 0.5 # cls loss gain
|
||||
cls_pw: 1.0 # cls BCELoss positive_weight
|
||||
obj: 1.0 # obj loss gain (scale with pixels)
|
||||
obj_pw: 1.0 # obj BCELoss positive_weight
|
||||
iou_t: 0.20 # IoU training threshold
|
||||
anchor_t: 4.0 # anchor-multiple threshold
|
||||
# anchors: 3 # anchors per output layer (0 to ignore)
|
||||
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
|
||||
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # image rotation (+/- deg)
|
||||
translate: 0.1 # image translation (+/- fraction)
|
||||
scale: 0.5 # image scale (+/- gain)
|
||||
shear: 0.0 # image shear (+/- deg)
|
||||
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # image flip up-down (probability)
|
||||
fliplr: 0.5 # image flip left-right (probability)
|
||||
mosaic: 1.0 # image mosaic (probability)
|
||||
mixup: 0.0 # image mixup (probability)
|
||||
copy_paste: 0.0 # segment copy-paste (probability)
|
||||
@ -0,0 +1,34 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Hyperparameters for medium-augmentation COCO training from scratch
|
||||
# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # warmup initial bias lr
|
||||
box: 0.05 # box loss gain
|
||||
cls: 0.3 # cls loss gain
|
||||
cls_pw: 1.0 # cls BCELoss positive_weight
|
||||
obj: 0.7 # obj loss gain (scale with pixels)
|
||||
obj_pw: 1.0 # obj BCELoss positive_weight
|
||||
iou_t: 0.20 # IoU training threshold
|
||||
anchor_t: 4.0 # anchor-multiple threshold
|
||||
# anchors: 3 # anchors per output layer (0 to ignore)
|
||||
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
|
||||
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # image rotation (+/- deg)
|
||||
translate: 0.1 # image translation (+/- fraction)
|
||||
scale: 0.9 # image scale (+/- gain)
|
||||
shear: 0.0 # image shear (+/- deg)
|
||||
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # image flip up-down (probability)
|
||||
fliplr: 0.5 # image flip left-right (probability)
|
||||
mosaic: 1.0 # image mosaic (probability)
|
||||
mixup: 0.1 # image mixup (probability)
|
||||
copy_paste: 0.0 # segment copy-paste (probability)
|
||||
@ -0,0 +1,34 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Hyperparameters for COCO training from scratch
|
||||
# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # warmup initial bias lr
|
||||
box: 0.05 # box loss gain
|
||||
cls: 0.5 # cls loss gain
|
||||
cls_pw: 1.0 # cls BCELoss positive_weight
|
||||
obj: 1.0 # obj loss gain (scale with pixels)
|
||||
obj_pw: 1.0 # obj BCELoss positive_weight
|
||||
iou_t: 0.20 # IoU training threshold
|
||||
anchor_t: 4.0 # anchor-multiple threshold
|
||||
# anchors: 3 # anchors per output layer (0 to ignore)
|
||||
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
|
||||
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # image rotation (+/- deg)
|
||||
translate: 0.1 # image translation (+/- fraction)
|
||||
scale: 0.5 # image scale (+/- gain)
|
||||
shear: 0.0 # image shear (+/- deg)
|
||||
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # image flip up-down (probability)
|
||||
fliplr: 0.5 # image flip left-right (probability)
|
||||
mosaic: 1.0 # image mosaic (probability)
|
||||
mixup: 0.0 # image mixup (probability)
|
||||
copy_paste: 0.0 # segment copy-paste (probability)
|
||||
|
After Width: | Height: | Size: 476 KiB |
|
After Width: | Height: | Size: 74 KiB |
|
After Width: | Height: | Size: 392 KiB |
|
After Width: | Height: | Size: 223 KiB |
|
After Width: | Height: | Size: 181 KiB |
|
After Width: | Height: | Size: 197 KiB |
|
After Width: | Height: | Size: 646 KiB |
|
After Width: | Height: | Size: 344 KiB |
|
After Width: | Height: | Size: 315 KiB |
|
After Width: | Height: | Size: 444 KiB |
|
After Width: | Height: | Size: 165 KiB |
@ -0,0 +1,12 @@
|
||||
# Custom data for safety helmet
|
||||
|
||||
|
||||
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: F:/up/1212/YOLO_Mask/score/images/train
|
||||
val: F:/up/1212/YOLO_Mask/score/images/val
|
||||
|
||||
# number of classes
|
||||
nc: 2
|
||||
|
||||
# class names
|
||||
names: ['mask', 'face']
|
||||
@ -0,0 +1,20 @@
|
||||
#!/bin/bash
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Download latest models from https://github.com/ultralytics/yolov5/releases
|
||||
# Example usage: bash path/to/download_weights.sh
|
||||
# parent
|
||||
# └── yolov5
|
||||
# ├── yolov5s.pt ← downloads here
|
||||
# ├── yolov5m.pt
|
||||
# └── ...
|
||||
|
||||
python - <<EOF
|
||||
from utils.downloads import attempt_download
|
||||
|
||||
models = ['n', 's', 'm', 'l', 'x']
|
||||
models.extend([x + '6' for x in models]) # add P6 models
|
||||
|
||||
for x in models:
|
||||
attempt_download(f'yolov5{x}.pt')
|
||||
|
||||
EOF
|
||||
@ -0,0 +1,27 @@
|
||||
#!/bin/bash
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Download COCO 2017 dataset http://cocodataset.org
|
||||
# Example usage: bash data/scripts/get_coco.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── coco ← downloads here
|
||||
|
||||
# Download/unzip labels
|
||||
d='../datasets' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
|
||||
echo 'Downloading' $url$f ' ...'
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
|
||||
|
||||
# Download/unzip images
|
||||
d='../datasets/coco/images' # unzip directory
|
||||
url=http://images.cocodataset.org/zips/
|
||||
f1='train2017.zip' # 19G, 118k images
|
||||
f2='val2017.zip' # 1G, 5k images
|
||||
f3='test2017.zip' # 7G, 41k images (optional)
|
||||
for f in $f1 $f2; do
|
||||
echo 'Downloading' $url$f '...'
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
|
||||
done
|
||||
wait # finish background tasks
|
||||
@ -0,0 +1,17 @@
|
||||
#!/bin/bash
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
|
||||
# Example usage: bash data/scripts/get_coco128.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── coco128 ← downloads here
|
||||
|
||||
# Download/unzip images and labels
|
||||
d='../datasets' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f='coco128.zip' # or 'coco128-segments.zip', 68 MB
|
||||
echo 'Downloading' $url$f ' ...'
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
|
||||
|
||||
wait # finish background tasks
|
||||
@ -0,0 +1,102 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# xView 2018 dataset https://challenge.xviewdataset.org
|
||||
# -------- DOWNLOAD DATA MANUALLY from URL above and unzip to 'datasets/xView' before running train command! --------
|
||||
# Example usage: python train.py --data xView.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── xView ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/xView # dataset root dir
|
||||
train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
|
||||
val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
|
||||
|
||||
# Classes
|
||||
nc: 60 # number of classes
|
||||
names: ['Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', 'Passenger Vehicle', 'Small Car', 'Bus',
|
||||
'Pickup Truck', 'Utility Truck', 'Truck', 'Cargo Truck', 'Truck w/Box', 'Truck Tractor', 'Trailer',
|
||||
'Truck w/Flatbed', 'Truck w/Liquid', 'Crane Truck', 'Railway Vehicle', 'Passenger Car', 'Cargo Car',
|
||||
'Flat Car', 'Tank car', 'Locomotive', 'Maritime Vessel', 'Motorboat', 'Sailboat', 'Tugboat', 'Barge',
|
||||
'Fishing Vessel', 'Ferry', 'Yacht', 'Container Ship', 'Oil Tanker', 'Engineering Vehicle', 'Tower crane',
|
||||
'Container Crane', 'Reach Stacker', 'Straddle Carrier', 'Mobile Crane', 'Dump Truck', 'Haul Truck',
|
||||
'Scraper/Tractor', 'Front loader/Bulldozer', 'Excavator', 'Cement Mixer', 'Ground Grader', 'Hut/Tent', 'Shed',
|
||||
'Building', 'Aircraft Hangar', 'Damaged Building', 'Facility', 'Construction Site', 'Vehicle Lot', 'Helipad',
|
||||
'Storage Tank', 'Shipping container lot', 'Shipping Container', 'Pylon', 'Tower'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils.datasets import autosplit
|
||||
from utils.general import download, xyxy2xywhn
|
||||
|
||||
|
||||
def convert_labels(fname=Path('xView/xView_train.geojson')):
|
||||
# Convert xView geoJSON labels to YOLO format
|
||||
path = fname.parent
|
||||
with open(fname) as f:
|
||||
print(f'Loading {fname}...')
|
||||
data = json.load(f)
|
||||
|
||||
# Make dirs
|
||||
labels = Path(path / 'labels' / 'train')
|
||||
os.system(f'rm -rf {labels}')
|
||||
labels.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# xView classes 11-94 to 0-59
|
||||
xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
|
||||
12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
|
||||
29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
|
||||
47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
|
||||
|
||||
shapes = {}
|
||||
for feature in tqdm(data['features'], desc=f'Converting {fname}'):
|
||||
p = feature['properties']
|
||||
if p['bounds_imcoords']:
|
||||
id = p['image_id']
|
||||
file = path / 'train_images' / id
|
||||
if file.exists(): # 1395.tif missing
|
||||
try:
|
||||
box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
|
||||
assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
|
||||
cls = p['type_id']
|
||||
cls = xview_class2index[int(cls)] # xView class to 0-60
|
||||
assert 59 >= cls >= 0, f'incorrect class index {cls}'
|
||||
|
||||
# Write YOLO label
|
||||
if id not in shapes:
|
||||
shapes[id] = Image.open(file).size
|
||||
box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
|
||||
with open((labels / id).with_suffix('.txt'), 'a') as f:
|
||||
f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt
|
||||
except Exception as e:
|
||||
print(f'WARNING: skipping one label for {file}: {e}')
|
||||
|
||||
|
||||
# Download manually from https://challenge.xviewdataset.org
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
# urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels
|
||||
# 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images
|
||||
# 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels)
|
||||
# download(urls, dir=dir, delete=False)
|
||||
|
||||
# Convert labels
|
||||
convert_labels(dir / 'xView_train.geojson')
|
||||
|
||||
# Move images
|
||||
images = Path(dir / 'images')
|
||||
images.mkdir(parents=True, exist_ok=True)
|
||||
Path(dir / 'train_images').rename(dir / 'images' / 'train')
|
||||
Path(dir / 'val_images').rename(dir / 'images' / 'val')
|
||||
|
||||
# Split
|
||||
autosplit(dir / 'images' / 'train')
|
||||
@ -0,0 +1,246 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Run inference on images, videos, directories, streams, etc.
|
||||
|
||||
Usage:
|
||||
$ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam
|
||||
img.jpg # image
|
||||
vid.mp4 # video
|
||||
path/ # directory
|
||||
path/*.jpg # glob
|
||||
'https://youtu.be/Zgi9g1ksQHc' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import torch
|
||||
import torch.backends.cudnn as cudnn
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[0] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from models.common import DetectMultiBackend
|
||||
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
|
||||
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
|
||||
increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
|
||||
from utils.plots import Annotator, colors, save_one_box
|
||||
from utils.torch_utils import select_device, time_sync
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
|
||||
source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
|
||||
imgsz=640, # inference size (pixels)
|
||||
conf_thres=0.25, # confidence threshold
|
||||
iou_thres=0.45, # NMS IOU threshold
|
||||
max_det=1000, # maximum detections per image
|
||||
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
view_img=False, # show results
|
||||
save_txt=False, # save results to *.txt
|
||||
save_conf=False, # save confidences in --save-txt labels
|
||||
save_crop=False, # save cropped prediction boxes
|
||||
nosave=False, # do not save images/videos
|
||||
classes=None, # filter by class: --class 0, or --class 0 2 3
|
||||
agnostic_nms=False, # class-agnostic NMS
|
||||
augment=False, # augmented inference
|
||||
visualize=False, # visualize features
|
||||
update=False, # update all models
|
||||
project=ROOT / 'runs/detect', # save results to project/name
|
||||
name='exp', # save results to project/name
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
line_thickness=3, # bounding box thickness (pixels)
|
||||
hide_labels=False, # hide labels
|
||||
hide_conf=False, # hide confidences
|
||||
half=False, # use FP16 half-precision inference
|
||||
dnn=False, # use OpenCV DNN for ONNX inference
|
||||
):
|
||||
source = str(source)
|
||||
save_img = not nosave and not source.endswith('.txt') # save inference images
|
||||
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
||||
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
|
||||
webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
|
||||
if is_url and is_file:
|
||||
source = check_file(source) # download
|
||||
|
||||
# Directories
|
||||
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
|
||||
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
device = select_device(device)
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn)
|
||||
stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
|
||||
# Half
|
||||
half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
|
||||
if pt:
|
||||
model.model.half() if half else model.model.float()
|
||||
|
||||
# Dataloader
|
||||
if webcam:
|
||||
view_img = check_imshow()
|
||||
cudnn.benchmark = True # set True to speed up constant image size inference
|
||||
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
|
||||
bs = len(dataset) # batch_size
|
||||
else:
|
||||
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
|
||||
bs = 1 # batch_size
|
||||
vid_path, vid_writer = [None] * bs, [None] * bs
|
||||
|
||||
# Run inference
|
||||
if pt and device.type != 'cpu':
|
||||
model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters()))) # warmup
|
||||
dt, seen = [0.0, 0.0, 0.0], 0
|
||||
for path, im, im0s, vid_cap, s in dataset:
|
||||
t1 = time_sync()
|
||||
im = torch.from_numpy(im).to(device)
|
||||
im = im.half() if half else im.float() # uint8 to fp16/32
|
||||
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
if len(im.shape) == 3:
|
||||
im = im[None] # expand for batch dim
|
||||
t2 = time_sync()
|
||||
dt[0] += t2 - t1
|
||||
|
||||
# Inference
|
||||
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
|
||||
pred = model(im, augment=augment, visualize=visualize)
|
||||
t3 = time_sync()
|
||||
dt[1] += t3 - t2
|
||||
|
||||
# NMS
|
||||
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
|
||||
dt[2] += time_sync() - t3
|
||||
|
||||
# Second-stage classifier (optional)
|
||||
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
|
||||
|
||||
# Process predictions
|
||||
for i, det in enumerate(pred): # per image
|
||||
seen += 1
|
||||
if webcam: # batch_size >= 1
|
||||
p, im0, frame = path[i], im0s[i].copy(), dataset.count
|
||||
s += f'{i}: '
|
||||
else:
|
||||
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
|
||||
|
||||
p = Path(p) # to Path
|
||||
save_path = str(save_dir / p.name) # im.jpg
|
||||
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
|
||||
s += '%gx%g ' % im.shape[2:] # print string
|
||||
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
|
||||
imc = im0.copy() if save_crop else im0 # for save_crop
|
||||
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
|
||||
if len(det):
|
||||
# Rescale boxes from img_size to im0 size
|
||||
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
|
||||
|
||||
# Print results
|
||||
for c in det[:, -1].unique():
|
||||
n = (det[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||||
|
||||
# Write results
|
||||
for *xyxy, conf, cls in reversed(det):
|
||||
if save_txt: # Write to file
|
||||
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
|
||||
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
|
||||
with open(txt_path + '.txt', 'a') as f:
|
||||
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||
|
||||
if save_img or save_crop or view_img: # Add bbox to image
|
||||
c = int(cls) # integer class
|
||||
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
|
||||
annotator.box_label(xyxy, label, color=colors(c, True))
|
||||
if save_crop:
|
||||
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
|
||||
|
||||
# Print time (inference-only)
|
||||
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
|
||||
|
||||
# Stream results
|
||||
im0 = annotator.result()
|
||||
if view_img:
|
||||
cv2.imshow(str(p), im0)
|
||||
cv2.waitKey(1) # 1 millisecond
|
||||
|
||||
# Save results (image with detections)
|
||||
if save_img:
|
||||
if dataset.mode == 'image':
|
||||
cv2.imwrite(save_path, im0)
|
||||
else: # 'video' or 'stream'
|
||||
if vid_path[i] != save_path: # new video
|
||||
vid_path[i] = save_path
|
||||
if isinstance(vid_writer[i], cv2.VideoWriter):
|
||||
vid_writer[i].release() # release previous video writer
|
||||
if vid_cap: # video
|
||||
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
||||
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
else: # stream
|
||||
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||
save_path += '.mp4'
|
||||
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
||||
vid_writer[i].write(im0)
|
||||
|
||||
# Print results
|
||||
t = tuple(x / seen * 1E3 for x in dt) # speeds per image
|
||||
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
|
||||
if save_txt or save_img:
|
||||
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
|
||||
if update:
|
||||
strip_optimizer(weights) # update model (to fix SourceChangeWarning)
|
||||
|
||||
|
||||
def parse_opt():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
|
||||
parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.5, help='confidence threshold')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
|
||||
parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--view-img', action='store_true', help='show results')
|
||||
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
|
||||
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
|
||||
parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
|
||||
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
|
||||
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
|
||||
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
|
||||
parser.add_argument('--augment', action='store_true', help='augmented inference')
|
||||
parser.add_argument('--visualize', action='store_true', help='visualize features')
|
||||
parser.add_argument('--update', action='store_true', help='update all models')
|
||||
parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
|
||||
parser.add_argument('--name', default='exp', help='save results to project/name')
|
||||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||
parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
|
||||
parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
|
||||
parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
|
||||
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
|
||||
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
|
||||
opt = parser.parse_args()
|
||||
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
||||
print_args(FILE.stem, opt)
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
check_requirements(exclude=('tensorboard', 'thop'))
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
# 命令使用
|
||||
# python detect.py --weights runs/train/exp_yolov5s/weights/best.pt --source data/images/fishman.jpg # webcam
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
@ -0,0 +1,61 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
|
||||
FROM nvcr.io/nvidia/pytorch:21.10-py3
|
||||
|
||||
# Install linux packages
|
||||
RUN apt update && apt install -y zip htop screen libgl1-mesa-glx
|
||||
|
||||
# Install python dependencies
|
||||
COPY ../requirements.txt .
|
||||
RUN python -m pip install --upgrade pip
|
||||
RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof
|
||||
RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook wandb>=0.12.2
|
||||
RUN pip install --no-cache -U torch torchvision numpy Pillow
|
||||
# RUN pip install --no-cache torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
|
||||
|
||||
# Create working directory
|
||||
RUN mkdir -p /usr/src/app
|
||||
WORKDIR /usr/src/app
|
||||
|
||||
# Copy contents
|
||||
COPY .. /usr/src/app
|
||||
|
||||
# Downloads to user config dir
|
||||
ADD https://ultralytics.com/assets/Arial.ttf /root/.config/Ultralytics/
|
||||
|
||||
# Set environment variables
|
||||
# ENV HOME=/usr/src/app
|
||||
|
||||
|
||||
# Usage Examples -------------------------------------------------------------------------------------------------------
|
||||
|
||||
# Build and Push
|
||||
# t=ultralytics/yolov5:latest && sudo docker build -t $t . && sudo docker push $t
|
||||
|
||||
# Pull and Run
|
||||
# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
|
||||
|
||||
# Pull and Run with local directory access
|
||||
# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
|
||||
|
||||
# Kill all
|
||||
# sudo docker kill $(sudo docker ps -q)
|
||||
|
||||
# Kill all image-based
|
||||
# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
|
||||
|
||||
# Bash into running container
|
||||
# sudo docker exec -it 5a9b5863d93d bash
|
||||
|
||||
# Bash into stopped container
|
||||
# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash
|
||||
|
||||
# Clean up
|
||||
# docker system prune -a --volumes
|
||||
|
||||
# Update Ubuntu drivers
|
||||
# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
|
||||
|
||||
# DDP test
|
||||
# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
|
||||
@ -0,0 +1,292 @@
|
||||
<div align="center">
|
||||
<p>
|
||||
<a align="left" href="https://ultralytics.com/yolov5" target="_blank">
|
||||
<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
|
||||
</p>
|
||||
<br>
|
||||
<div>
|
||||
<a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
|
||||
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
|
||||
<br>
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
<a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
|
||||
<a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
|
||||
</div>
|
||||
<br>
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/>
|
||||
</a>
|
||||
<img width="2%" />
|
||||
<a href="https://www.linkedin.com/company/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/>
|
||||
</a>
|
||||
<img width="2%" />
|
||||
<a href="https://twitter.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
|
||||
</a>
|
||||
<img width="2%" />
|
||||
<a href="https://youtube.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
|
||||
</a>
|
||||
<img width="2%" />
|
||||
<a href="https://www.facebook.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
|
||||
</a>
|
||||
<img width="2%" />
|
||||
<a href="https://www.instagram.com/ultralytics/">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
<br>
|
||||
<p>
|
||||
YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
|
||||
open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
|
||||
</p>
|
||||
|
||||
<!--
|
||||
<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
|
||||
<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
|
||||
-->
|
||||
|
||||
</div>
|
||||
|
||||
## <div align="center">Documentation</div>
|
||||
|
||||
See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
|
||||
|
||||
## <div align="center">Quick Start Examples</div>
|
||||
|
||||
<details open>
|
||||
<summary>Install</summary>
|
||||
|
||||
[**Python>=3.6.0**](https://www.python.org/) is required with all
|
||||
[requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including
|
||||
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
|
||||
<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
|
||||
|
||||
```bash
|
||||
$ git clone https://github.com/ultralytics/yolov5
|
||||
$ cd yolov5
|
||||
$ pip install -r requirements.txt
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details open>
|
||||
<summary>Inference</summary>
|
||||
|
||||
Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download
|
||||
from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases).
|
||||
|
||||
```python
|
||||
import torch
|
||||
|
||||
# Model
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom
|
||||
|
||||
# Images
|
||||
img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
|
||||
|
||||
# Inference
|
||||
results = model(img)
|
||||
|
||||
# Results
|
||||
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
<summary>Inference with detect.py</summary>
|
||||
|
||||
`detect.py` runs inference on a variety of sources, downloading models automatically from
|
||||
the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
|
||||
|
||||
```bash
|
||||
$ python detect.py --source 0 # webcam
|
||||
img.jpg # image
|
||||
vid.mp4 # video
|
||||
path/ # directory
|
||||
path/*.jpg # glob
|
||||
'https://youtu.be/Zgi9g1ksQHc' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Training</summary>
|
||||
|
||||
Run commands below to reproduce results
|
||||
on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on
|
||||
first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the
|
||||
largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices).
|
||||
|
||||
```bash
|
||||
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
|
||||
yolov5m 40
|
||||
yolov5l 24
|
||||
yolov5x 16
|
||||
```
|
||||
|
||||
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
|
||||
|
||||
</details>
|
||||
|
||||
<details open>
|
||||
<summary>Tutorials</summary>
|
||||
|
||||
* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 RECOMMENDED
|
||||
* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️
|
||||
RECOMMENDED
|
||||
* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW
|
||||
* [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) 🌟 NEW
|
||||
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
|
||||
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ NEW
|
||||
* [TorchScript, ONNX, CoreML Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
|
||||
* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
|
||||
* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
|
||||
* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
|
||||
* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
|
||||
* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) ⭐ NEW
|
||||
* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">Environments</div>
|
||||
|
||||
Get started in seconds with our verified environments. Click each icon below for details.
|
||||
|
||||
<div align="center">
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
|
||||
</a>
|
||||
<a href="https://www.kaggle.com/ultralytics/yolov5">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
|
||||
</a>
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov5">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/>
|
||||
</a>
|
||||
<a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/>
|
||||
</a>
|
||||
<a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
## <div align="center">Integrations</div>
|
||||
|
||||
<div align="center">
|
||||
<a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-long.png" width="49%"/>
|
||||
</a>
|
||||
<a href="https://roboflow.com/?ref=ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow-long.png" width="49%"/>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
|Weights and Biases|Roboflow ⭐ NEW|
|
||||
|:-:|:-:|
|
||||
|Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |
|
||||
|
||||
|
||||
<!-- ## <div align="center">Compete and Win</div>
|
||||
|
||||
We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!
|
||||
|
||||
<p align="center">
|
||||
<a href="https://github.com/ultralytics/yolov5/discussions/3213">
|
||||
<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"></a>
|
||||
</p> -->
|
||||
|
||||
## <div align="center">Why YOLOv5</div>
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136901921-abcfcd9d-f978-4942-9b97-0e3f202907df.png"></p>
|
||||
<details>
|
||||
<summary>YOLOv5-P5 640 Figure (click to expand)</summary>
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136763877-b174052b-c12f-48d2-8bc4-545e3853398e.png"></p>
|
||||
</details>
|
||||
<details>
|
||||
<summary>Figure Notes (click to expand)</summary>
|
||||
|
||||
* **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
|
||||
* **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
|
||||
* **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
|
||||
* **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
|
||||
</details>
|
||||
|
||||
### Pretrained Checkpoints
|
||||
|
||||
[assets]: https://github.com/ultralytics/yolov5/releases
|
||||
[TTA]: https://github.com/ultralytics/yolov5/issues/303
|
||||
|
||||
|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>CPU b1<br>(ms) |Speed<br><sup>V100 b1<br>(ms) |Speed<br><sup>V100 b32<br>(ms) |params<br><sup>(M) |FLOPs<br><sup>@640 (B)
|
||||
|--- |--- |--- |--- |--- |--- |--- |--- |---
|
||||
|[YOLOv5n][assets] |640 |28.4 |46.0 |**45** |**6.3**|**0.6**|**1.9**|**4.5**
|
||||
|[YOLOv5s][assets] |640 |37.2 |56.0 |98 |6.4 |0.9 |7.2 |16.5
|
||||
|[YOLOv5m][assets] |640 |45.2 |63.9 |224 |8.2 |1.7 |21.2 |49.0
|
||||
|[YOLOv5l][assets] |640 |48.8 |67.2 |430 |10.1 |2.7 |46.5 |109.1
|
||||
|[YOLOv5x][assets] |640 |50.7 |68.9 |766 |12.1 |4.8 |86.7 |205.7
|
||||
| | | | | | | | |
|
||||
|[YOLOv5n6][assets] |1280 |34.0 |50.7 |153 |8.1 |2.1 |3.2 |4.6
|
||||
|[YOLOv5s6][assets] |1280 |44.5 |63.0 |385 |8.2 |3.6 |16.8 |12.6
|
||||
|[YOLOv5m6][assets] |1280 |51.0 |69.0 |887 |11.1 |6.8 |35.7 |50.0
|
||||
|[YOLOv5l6][assets] |1280 |53.6 |71.6 |1784 |15.8 |10.5 |76.8 |111.4
|
||||
|[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |54.7<br>**55.4** |**72.4**<br>72.3 |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>-
|
||||
|
||||
<details>
|
||||
<summary>Table Notes (click to expand)</summary>
|
||||
|
||||
* All checkpoints are trained to 300 epochs with default settings and hyperparameters.
|
||||
* **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
|
||||
* **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
|
||||
* **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">Contribute</div>
|
||||
|
||||
We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!
|
||||
|
||||
<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://opencollective.com/ultralytics/contributors.svg?width=990" /></a>
|
||||
|
||||
|
||||
## <div align="center">Contact</div>
|
||||
|
||||
For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries or
|
||||
professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
|
||||
|
||||
<br>
|
||||
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="3%"/>
|
||||
</a>
|
||||
<img width="3%" />
|
||||
<a href="https://www.linkedin.com/company/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/>
|
||||
</a>
|
||||
<img width="3%" />
|
||||
<a href="https://twitter.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="3%"/>
|
||||
</a>
|
||||
<img width="3%" />
|
||||
<a href="https://youtube.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="3%"/>
|
||||
</a>
|
||||
<img width="3%" />
|
||||
<a href="https://www.facebook.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="3%"/>
|
||||
</a>
|
||||
<img width="3%" />
|
||||
<a href="https://www.instagram.com/ultralytics/">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/>
|
||||
</a>
|
||||
</div>
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,369 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Export a YOLOv5 PyTorch model to TorchScript, ONNX, CoreML, TensorFlow (saved_model, pb, TFLite, TF.js,) formats
|
||||
TensorFlow exports authored by https://github.com/zldrobit
|
||||
|
||||
Usage:
|
||||
$ python path/to/export.py --weights yolov5s.pt --include torchscript onnx coreml saved_model pb tflite tfjs
|
||||
|
||||
Inference:
|
||||
$ python path/to/detect.py --weights yolov5s.pt
|
||||
yolov5s.onnx (must export with --dynamic)
|
||||
yolov5s_saved_model
|
||||
yolov5s.pb
|
||||
yolov5s.tflite
|
||||
|
||||
TensorFlow.js:
|
||||
$ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
|
||||
$ npm install
|
||||
$ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
|
||||
$ npm start
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.utils.mobile_optimizer import optimize_for_mobile
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[0] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from models.common import Conv
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import Detect
|
||||
from utils.activations import SiLU
|
||||
from utils.datasets import LoadImages
|
||||
from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, colorstr, file_size, print_args,
|
||||
url2file)
|
||||
from utils.torch_utils import select_device
|
||||
|
||||
|
||||
def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
|
||||
# YOLOv5 TorchScript model export
|
||||
try:
|
||||
LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
|
||||
f = file.with_suffix('.torchscript.pt')
|
||||
|
||||
ts = torch.jit.trace(model, im, strict=False)
|
||||
d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
|
||||
extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
|
||||
(optimize_for_mobile(ts) if optimize else ts).save(f, _extra_files=extra_files)
|
||||
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix} export failure: {e}')
|
||||
|
||||
|
||||
def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')):
|
||||
# YOLOv5 ONNX export
|
||||
try:
|
||||
check_requirements(('onnx',))
|
||||
import onnx
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
|
||||
f = file.with_suffix('.onnx')
|
||||
|
||||
torch.onnx.export(model, im, f, verbose=False, opset_version=opset,
|
||||
training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
|
||||
do_constant_folding=not train,
|
||||
input_names=['images'],
|
||||
output_names=['output'],
|
||||
dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640)
|
||||
'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
|
||||
} if dynamic else None)
|
||||
|
||||
# Checks
|
||||
model_onnx = onnx.load(f) # load onnx model
|
||||
onnx.checker.check_model(model_onnx) # check onnx model
|
||||
# LOGGER.info(onnx.helper.printable_graph(model_onnx.graph)) # print
|
||||
|
||||
# Simplify
|
||||
if simplify:
|
||||
try:
|
||||
check_requirements(('onnx-simplifier',))
|
||||
import onnxsim
|
||||
|
||||
LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
|
||||
model_onnx, check = onnxsim.simplify(
|
||||
model_onnx,
|
||||
dynamic_input_shape=dynamic,
|
||||
input_shapes={'images': list(im.shape)} if dynamic else None)
|
||||
assert check, 'assert check failed'
|
||||
onnx.save(model_onnx, f)
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix} simplifier failure: {e}')
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
LOGGER.info(f"{prefix} run --dynamic ONNX model inference with: 'python detect.py --weights {f}'")
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix} export failure: {e}')
|
||||
|
||||
|
||||
def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
|
||||
# YOLOv5 CoreML export
|
||||
ct_model = None
|
||||
try:
|
||||
check_requirements(('coremltools',))
|
||||
import coremltools as ct
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
|
||||
f = file.with_suffix('.mlmodel')
|
||||
|
||||
model.train() # CoreML exports should be placed in model.train() mode
|
||||
ts = torch.jit.trace(model, im, strict=False) # TorchScript model
|
||||
ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
|
||||
ct_model.save(f)
|
||||
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
|
||||
return ct_model
|
||||
|
||||
|
||||
def export_saved_model(model, im, file, dynamic,
|
||||
tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
|
||||
conf_thres=0.25, prefix=colorstr('TensorFlow saved_model:')):
|
||||
# YOLOv5 TensorFlow saved_model export
|
||||
keras_model = None
|
||||
try:
|
||||
import tensorflow as tf
|
||||
from tensorflow import keras
|
||||
|
||||
from models.tf import TFDetect, TFModel
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
f = str(file).replace('.pt', '_saved_model')
|
||||
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
||||
|
||||
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
|
||||
im = tf.zeros((batch_size, *imgsz, 3)) # BHWC order for TensorFlow
|
||||
y = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
||||
inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
|
||||
outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
||||
keras_model = keras.Model(inputs=inputs, outputs=outputs)
|
||||
keras_model.trainable = False
|
||||
keras_model.summary()
|
||||
keras_model.save(f, save_format='tf')
|
||||
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
|
||||
return keras_model
|
||||
|
||||
|
||||
def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
|
||||
# YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
|
||||
try:
|
||||
import tensorflow as tf
|
||||
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
f = file.with_suffix('.pb')
|
||||
|
||||
m = tf.function(lambda x: keras_model(x)) # full model
|
||||
m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
|
||||
frozen_func = convert_variables_to_constants_v2(m)
|
||||
frozen_func.graph.as_graph_def()
|
||||
tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
|
||||
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
|
||||
|
||||
def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('TensorFlow Lite:')):
|
||||
# YOLOv5 TensorFlow Lite export
|
||||
try:
|
||||
import tensorflow as tf
|
||||
|
||||
from models.tf import representative_dataset_gen
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
||||
f = str(file).replace('.pt', '-fp16.tflite')
|
||||
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
|
||||
converter.target_spec.supported_types = [tf.float16]
|
||||
converter.optimizations = [tf.lite.Optimize.DEFAULT]
|
||||
if int8:
|
||||
dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False) # representative data
|
||||
converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib)
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
|
||||
converter.target_spec.supported_types = []
|
||||
converter.inference_input_type = tf.uint8 # or tf.int8
|
||||
converter.inference_output_type = tf.uint8 # or tf.int8
|
||||
converter.experimental_new_quantizer = False
|
||||
f = str(file).replace('.pt', '-int8.tflite')
|
||||
|
||||
tflite_model = converter.convert()
|
||||
open(f, "wb").write(tflite_model)
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
|
||||
|
||||
def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
|
||||
# YOLOv5 TensorFlow.js export
|
||||
try:
|
||||
check_requirements(('tensorflowjs',))
|
||||
import re
|
||||
|
||||
import tensorflowjs as tfjs
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
|
||||
f = str(file).replace('.pt', '_web_model') # js dir
|
||||
f_pb = file.with_suffix('.pb') # *.pb path
|
||||
f_json = f + '/model.json' # *.json path
|
||||
|
||||
cmd = f"tensorflowjs_converter --input_format=tf_frozen_model " \
|
||||
f"--output_node_names='Identity,Identity_1,Identity_2,Identity_3' {f_pb} {f}"
|
||||
subprocess.run(cmd, shell=True)
|
||||
|
||||
json = open(f_json).read()
|
||||
with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order
|
||||
subst = re.sub(
|
||||
r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}}}',
|
||||
r'{"outputs": {"Identity": {"name": "Identity"}, '
|
||||
r'"Identity_1": {"name": "Identity_1"}, '
|
||||
r'"Identity_2": {"name": "Identity_2"}, '
|
||||
r'"Identity_3": {"name": "Identity_3"}}}',
|
||||
json)
|
||||
j.write(subst)
|
||||
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
|
||||
weights=ROOT / 'yolov5s.pt', # weights path
|
||||
imgsz=(640, 640), # image (height, width)
|
||||
batch_size=1, # batch size
|
||||
device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
include=('torchscript', 'onnx', 'coreml'), # include formats
|
||||
half=False, # FP16 half-precision export
|
||||
inplace=False, # set YOLOv5 Detect() inplace=True
|
||||
train=False, # model.train() mode
|
||||
optimize=False, # TorchScript: optimize for mobile
|
||||
int8=False, # CoreML/TF INT8 quantization
|
||||
dynamic=False, # ONNX/TF: dynamic axes
|
||||
simplify=False, # ONNX: simplify model
|
||||
opset=12, # ONNX: opset version
|
||||
topk_per_class=100, # TF.js NMS: topk per class to keep
|
||||
topk_all=100, # TF.js NMS: topk for all classes to keep
|
||||
iou_thres=0.45, # TF.js NMS: IoU threshold
|
||||
conf_thres=0.25 # TF.js NMS: confidence threshold
|
||||
):
|
||||
t = time.time()
|
||||
include = [x.lower() for x in include]
|
||||
tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'tfjs')) # TensorFlow exports
|
||||
imgsz *= 2 if len(imgsz) == 1 else 1 # expand
|
||||
file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)
|
||||
|
||||
# Load PyTorch model
|
||||
device = select_device(device)
|
||||
assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
|
||||
model = attempt_load(weights, map_location=device, inplace=True, fuse=True) # load FP32 model
|
||||
nc, names = model.nc, model.names # number of classes, class names
|
||||
|
||||
# Input
|
||||
gs = int(max(model.stride)) # grid size (max stride)
|
||||
imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples
|
||||
im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
|
||||
|
||||
# Update model
|
||||
if half:
|
||||
im, model = im.half(), model.half() # to FP16
|
||||
model.train() if train else model.eval() # training mode = no Detect() layer grid construction
|
||||
for k, m in model.named_modules():
|
||||
if isinstance(m, Conv): # assign export-friendly activations
|
||||
if isinstance(m.act, nn.SiLU):
|
||||
m.act = SiLU()
|
||||
elif isinstance(m, Detect):
|
||||
m.inplace = inplace
|
||||
m.onnx_dynamic = dynamic
|
||||
# m.forward = m.forward_export # assign forward (optional)
|
||||
|
||||
for _ in range(2):
|
||||
y = model(im) # dry runs
|
||||
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)")
|
||||
|
||||
# Exports
|
||||
if 'torchscript' in include:
|
||||
export_torchscript(model, im, file, optimize)
|
||||
if 'onnx' in include:
|
||||
export_onnx(model, im, file, opset, train, dynamic, simplify)
|
||||
if 'coreml' in include:
|
||||
export_coreml(model, im, file)
|
||||
|
||||
# TensorFlow Exports
|
||||
if any(tf_exports):
|
||||
pb, tflite, tfjs = tf_exports[1:]
|
||||
assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
|
||||
model = export_saved_model(model, im, file, dynamic, tf_nms=tfjs, agnostic_nms=tfjs,
|
||||
topk_per_class=topk_per_class, topk_all=topk_all, conf_thres=conf_thres,
|
||||
iou_thres=iou_thres) # keras model
|
||||
if pb or tfjs: # pb prerequisite to tfjs
|
||||
export_pb(model, im, file)
|
||||
if tflite:
|
||||
export_tflite(model, im, file, int8=int8, data=data, ncalib=100)
|
||||
if tfjs:
|
||||
export_tfjs(model, im, file)
|
||||
|
||||
# Finish
|
||||
LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
|
||||
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
|
||||
f'\nVisualize with https://netron.app')
|
||||
|
||||
|
||||
def parse_opt():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
|
||||
parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)')
|
||||
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
|
||||
parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
|
||||
parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
|
||||
parser.add_argument('--train', action='store_true', help='model.train() mode')
|
||||
parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
|
||||
parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
|
||||
parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes')
|
||||
parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
|
||||
parser.add_argument('--opset', type=int, default=13, help='ONNX: opset version')
|
||||
parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep')
|
||||
parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
|
||||
parser.add_argument('--include', nargs='+',
|
||||
default=['torchscript', 'onnx'],
|
||||
help='available formats are (torchscript, onnx, coreml, saved_model, pb, tflite, tfjs)')
|
||||
opt = parser.parse_args()
|
||||
print_args(FILE.stem, opt)
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
@ -0,0 +1,142 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
|
||||
|
||||
Usage:
|
||||
import torch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
|
||||
"""
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
"""Creates a specified YOLOv5 model
|
||||
|
||||
Arguments:
|
||||
name (str): name of model, i.e. 'yolov5s'
|
||||
pretrained (bool): load pretrained weights into the model
|
||||
channels (int): number of input channels
|
||||
classes (int): number of model classes
|
||||
autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
|
||||
verbose (bool): print all information to screen
|
||||
device (str, torch.device, None): device to use for model parameters
|
||||
|
||||
Returns:
|
||||
YOLOv5 pytorch model
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import Model
|
||||
from utils.downloads import attempt_download
|
||||
from utils.general import check_requirements, intersect_dicts, set_logging
|
||||
from utils.torch_utils import select_device
|
||||
|
||||
file = Path(__file__).resolve()
|
||||
check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
|
||||
set_logging(verbose=verbose)
|
||||
|
||||
save_dir = Path('') if str(name).endswith('.pt') else file.parent
|
||||
path = (save_dir / name).with_suffix('.pt') # checkpoint path
|
||||
try:
|
||||
device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)
|
||||
|
||||
if pretrained and channels == 3 and classes == 80:
|
||||
model = attempt_load(path, map_location=device) # download/load FP32 model
|
||||
else:
|
||||
cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path
|
||||
model = Model(cfg, channels, classes) # create model
|
||||
if pretrained:
|
||||
ckpt = torch.load(attempt_download(path), map_location=device) # load
|
||||
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
|
||||
csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors']) # intersect
|
||||
model.load_state_dict(csd, strict=False) # load
|
||||
if len(ckpt['model'].names) == classes:
|
||||
model.names = ckpt['model'].names # set class names attribute
|
||||
if autoshape:
|
||||
model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS
|
||||
return model.to(device)
|
||||
|
||||
except Exception as e:
|
||||
help_url = 'https://github.com/ultralytics/yolov5/issues/36'
|
||||
s = 'Cache may be out of date, try `force_reload=True`. See %s for help.' % help_url
|
||||
raise Exception(s) from e
|
||||
|
||||
|
||||
def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None):
|
||||
# YOLOv5 custom or local model
|
||||
return _create(path, autoshape=autoshape, verbose=verbose, device=device)
|
||||
|
||||
|
||||
def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv5-nano model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5n', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv5-small model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv5-medium model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5m', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv5-large model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5l', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv5-xlarge model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5x', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5n6', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5m6', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5l6', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5x6', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # pretrained
|
||||
# model = custom(path='path/to/model.pt') # custom
|
||||
|
||||
# Verify inference
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
imgs = ['data/images/zidane.jpg', # filename
|
||||
Path('data/images/zidane.jpg'), # Path
|
||||
'https://ultralytics.com/images/zidane.jpg', # URI
|
||||
cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV
|
||||
Image.open('data/images/bus.jpg'), # PIL
|
||||
np.zeros((320, 640, 3))] # numpy
|
||||
|
||||
results = model(imgs) # batched inference
|
||||
results.print()
|
||||
results.save()
|
||||
|
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|
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|
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|
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|
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|
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|
After Width: | Height: | Size: 90 KiB |
|
After Width: | Height: | Size: 54 KiB |
|
After Width: | Height: | Size: 21 KiB |
|
After Width: | Height: | Size: 59 KiB |
|
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|
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|
After Width: | Height: | Size: 21 KiB |
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@ -0,0 +1,591 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Common modules
|
||||
"""
|
||||
|
||||
import json
|
||||
import math
|
||||
import platform
|
||||
import warnings
|
||||
from copy import copy
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import requests
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from PIL import Image
|
||||
from torch.cuda import amp
|
||||
|
||||
from utils.datasets import exif_transpose, letterbox
|
||||
from utils.general import (LOGGER, check_requirements, check_suffix, colorstr, increment_path, make_divisible,
|
||||
non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh)
|
||||
from utils.plots import Annotator, colors, save_one_box
|
||||
from utils.torch_utils import time_sync
|
||||
|
||||
|
||||
def autopad(k, p=None): # kernel, padding
|
||||
# Pad to 'same'
|
||||
if p is None:
|
||||
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
|
||||
return p
|
||||
|
||||
|
||||
class Conv(nn.Module):
|
||||
# Standard convolution
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super().__init__()
|
||||
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
|
||||
self.bn = nn.BatchNorm2d(c2)
|
||||
self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
|
||||
|
||||
def forward(self, x):
|
||||
return self.act(self.bn(self.conv(x)))
|
||||
|
||||
def forward_fuse(self, x):
|
||||
return self.act(self.conv(x))
|
||||
|
||||
|
||||
class DWConv(Conv):
|
||||
# Depth-wise convolution class
|
||||
def __init__(self, c1, c2, k=1, s=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
|
||||
|
||||
|
||||
class TransformerLayer(nn.Module):
|
||||
# Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
|
||||
def __init__(self, c, num_heads):
|
||||
super().__init__()
|
||||
self.q = nn.Linear(c, c, bias=False)
|
||||
self.k = nn.Linear(c, c, bias=False)
|
||||
self.v = nn.Linear(c, c, bias=False)
|
||||
self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
|
||||
self.fc1 = nn.Linear(c, c, bias=False)
|
||||
self.fc2 = nn.Linear(c, c, bias=False)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
|
||||
x = self.fc2(self.fc1(x)) + x
|
||||
return x
|
||||
|
||||
|
||||
class TransformerBlock(nn.Module):
|
||||
# Vision Transformer https://arxiv.org/abs/2010.11929
|
||||
def __init__(self, c1, c2, num_heads, num_layers):
|
||||
super().__init__()
|
||||
self.conv = None
|
||||
if c1 != c2:
|
||||
self.conv = Conv(c1, c2)
|
||||
self.linear = nn.Linear(c2, c2) # learnable position embedding
|
||||
self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
|
||||
self.c2 = c2
|
||||
|
||||
def forward(self, x):
|
||||
if self.conv is not None:
|
||||
x = self.conv(x)
|
||||
b, _, w, h = x.shape
|
||||
p = x.flatten(2).permute(2, 0, 1)
|
||||
return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)
|
||||
|
||||
|
||||
class Bottleneck(nn.Module):
|
||||
# Standard bottleneck
|
||||
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = Conv(c1, c_, 1, 1)
|
||||
self.cv2 = Conv(c_, c2, 3, 1, g=g)
|
||||
self.add = shortcut and c1 == c2
|
||||
|
||||
def forward(self, x):
|
||||
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
|
||||
|
||||
|
||||
class BottleneckCSP(nn.Module):
|
||||
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = Conv(c1, c_, 1, 1)
|
||||
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
|
||||
self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
|
||||
self.cv4 = Conv(2 * c_, c2, 1, 1)
|
||||
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
|
||||
self.act = nn.SiLU()
|
||||
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
|
||||
|
||||
def forward(self, x):
|
||||
y1 = self.cv3(self.m(self.cv1(x)))
|
||||
y2 = self.cv2(x)
|
||||
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
|
||||
|
||||
|
||||
class C3(nn.Module):
|
||||
# CSP Bottleneck with 3 convolutions
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = Conv(c1, c_, 1, 1)
|
||||
self.cv2 = Conv(c1, c_, 1, 1)
|
||||
self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
|
||||
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
|
||||
# self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
|
||||
|
||||
def forward(self, x):
|
||||
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
|
||||
|
||||
|
||||
class C3TR(C3):
|
||||
# C3 module with TransformerBlock()
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
|
||||
super().__init__(c1, c2, n, shortcut, g, e)
|
||||
c_ = int(c2 * e)
|
||||
self.m = TransformerBlock(c_, c_, 4, n)
|
||||
|
||||
|
||||
class C3SPP(C3):
|
||||
# C3 module with SPP()
|
||||
def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
|
||||
super().__init__(c1, c2, n, shortcut, g, e)
|
||||
c_ = int(c2 * e)
|
||||
self.m = SPP(c_, c_, k)
|
||||
|
||||
|
||||
class C3Ghost(C3):
|
||||
# C3 module with GhostBottleneck()
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
|
||||
super().__init__(c1, c2, n, shortcut, g, e)
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))
|
||||
|
||||
|
||||
class SPP(nn.Module):
|
||||
# Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
|
||||
def __init__(self, c1, c2, k=(5, 9, 13)):
|
||||
super().__init__()
|
||||
c_ = c1 // 2 # hidden channels
|
||||
self.cv1 = Conv(c1, c_, 1, 1)
|
||||
self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
|
||||
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
|
||||
|
||||
def forward(self, x):
|
||||
x = self.cv1(x)
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
|
||||
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
|
||||
|
||||
|
||||
class SPPF(nn.Module):
|
||||
# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
|
||||
def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
|
||||
super().__init__()
|
||||
c_ = c1 // 2 # hidden channels
|
||||
self.cv1 = Conv(c1, c_, 1, 1)
|
||||
self.cv2 = Conv(c_ * 4, c2, 1, 1)
|
||||
self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.cv1(x)
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
|
||||
y1 = self.m(x)
|
||||
y2 = self.m(y1)
|
||||
return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
|
||||
|
||||
|
||||
class Focus(nn.Module):
|
||||
# Focus wh information into c-space
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super().__init__()
|
||||
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
|
||||
# self.contract = Contract(gain=2)
|
||||
|
||||
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
|
||||
return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
|
||||
# return self.conv(self.contract(x))
|
||||
|
||||
|
||||
class GhostConv(nn.Module):
|
||||
# Ghost Convolution https://github.com/huawei-noah/ghostnet
|
||||
def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
|
||||
super().__init__()
|
||||
c_ = c2 // 2 # hidden channels
|
||||
self.cv1 = Conv(c1, c_, k, s, None, g, act)
|
||||
self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
|
||||
|
||||
def forward(self, x):
|
||||
y = self.cv1(x)
|
||||
return torch.cat([y, self.cv2(y)], 1)
|
||||
|
||||
|
||||
class GhostBottleneck(nn.Module):
|
||||
# Ghost Bottleneck https://github.com/huawei-noah/ghostnet
|
||||
def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
|
||||
super().__init__()
|
||||
c_ = c2 // 2
|
||||
self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
|
||||
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
|
||||
GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
|
||||
self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
|
||||
Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv(x) + self.shortcut(x)
|
||||
|
||||
|
||||
class Contract(nn.Module):
|
||||
# Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
|
||||
def __init__(self, gain=2):
|
||||
super().__init__()
|
||||
self.gain = gain
|
||||
|
||||
def forward(self, x):
|
||||
b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
|
||||
s = self.gain
|
||||
x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2)
|
||||
x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
|
||||
return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40)
|
||||
|
||||
|
||||
class Expand(nn.Module):
|
||||
# Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
|
||||
def __init__(self, gain=2):
|
||||
super().__init__()
|
||||
self.gain = gain
|
||||
|
||||
def forward(self, x):
|
||||
b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
|
||||
s = self.gain
|
||||
x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80)
|
||||
x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
|
||||
return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160)
|
||||
|
||||
|
||||
class Concat(nn.Module):
|
||||
# Concatenate a list of tensors along dimension
|
||||
def __init__(self, dimension=1):
|
||||
super().__init__()
|
||||
self.d = dimension
|
||||
|
||||
def forward(self, x):
|
||||
return torch.cat(x, self.d)
|
||||
|
||||
|
||||
class DetectMultiBackend(nn.Module):
|
||||
# YOLOv5 MultiBackend class for python inference on various backends
|
||||
def __init__(self, weights='yolov5s.pt', device=None, dnn=True):
|
||||
# Usage:
|
||||
# PyTorch: weights = *.pt
|
||||
# TorchScript: *.torchscript.pt
|
||||
# CoreML: *.mlmodel
|
||||
# TensorFlow: *_saved_model
|
||||
# TensorFlow: *.pb
|
||||
# TensorFlow Lite: *.tflite
|
||||
# ONNX Runtime: *.onnx
|
||||
# OpenCV DNN: *.onnx with dnn=True
|
||||
super().__init__()
|
||||
w = str(weights[0] if isinstance(weights, list) else weights)
|
||||
suffix, suffixes = Path(w).suffix.lower(), ['.pt', '.onnx', '.tflite', '.pb', '', '.mlmodel']
|
||||
check_suffix(w, suffixes) # check weights have acceptable suffix
|
||||
pt, onnx, tflite, pb, saved_model, coreml = (suffix == x for x in suffixes) # backend booleans
|
||||
jit = pt and 'torchscript' in w.lower()
|
||||
stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults
|
||||
|
||||
if jit: # TorchScript
|
||||
LOGGER.info(f'Loading {w} for TorchScript inference...')
|
||||
extra_files = {'config.txt': ''} # model metadata
|
||||
model = torch.jit.load(w, _extra_files=extra_files)
|
||||
if extra_files['config.txt']:
|
||||
d = json.loads(extra_files['config.txt']) # extra_files dict
|
||||
stride, names = int(d['stride']), d['names']
|
||||
elif pt: # PyTorch
|
||||
from models.experimental import attempt_load # scoped to avoid circular import
|
||||
model = torch.jit.load(w) if 'torchscript' in w else attempt_load(weights, map_location=device)
|
||||
stride = int(model.stride.max()) # model stride
|
||||
names = model.module.names if hasattr(model, 'module') else model.names # get class names
|
||||
elif coreml: # CoreML *.mlmodel
|
||||
import coremltools as ct
|
||||
model = ct.models.MLModel(w)
|
||||
elif dnn: # ONNX OpenCV DNN
|
||||
LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
|
||||
check_requirements(('opencv-python>=4.5.4',))
|
||||
net = cv2.dnn.readNetFromONNX(w)
|
||||
elif onnx: # ONNX Runtime
|
||||
LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
|
||||
check_requirements(('onnx', 'onnxruntime-gpu' if torch.has_cuda else 'onnxruntime'))
|
||||
import onnxruntime
|
||||
session = onnxruntime.InferenceSession(w, None)
|
||||
else: # TensorFlow model (TFLite, pb, saved_model)
|
||||
import tensorflow as tf
|
||||
if pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
|
||||
def wrap_frozen_graph(gd, inputs, outputs):
|
||||
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped
|
||||
return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs),
|
||||
tf.nest.map_structure(x.graph.as_graph_element, outputs))
|
||||
|
||||
LOGGER.info(f'Loading {w} for TensorFlow *.pb inference...')
|
||||
graph_def = tf.Graph().as_graph_def()
|
||||
graph_def.ParseFromString(open(w, 'rb').read())
|
||||
frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0")
|
||||
elif saved_model:
|
||||
LOGGER.info(f'Loading {w} for TensorFlow saved_model inference...')
|
||||
model = tf.keras.models.load_model(w)
|
||||
elif tflite: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
|
||||
if 'edgetpu' in w.lower():
|
||||
LOGGER.info(f'Loading {w} for TensorFlow Edge TPU inference...')
|
||||
import tflite_runtime.interpreter as tfli
|
||||
delegate = {'Linux': 'libedgetpu.so.1', # install https://coral.ai/software/#edgetpu-runtime
|
||||
'Darwin': 'libedgetpu.1.dylib',
|
||||
'Windows': 'edgetpu.dll'}[platform.system()]
|
||||
interpreter = tfli.Interpreter(model_path=w, experimental_delegates=[tfli.load_delegate(delegate)])
|
||||
else:
|
||||
LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
|
||||
interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model
|
||||
interpreter.allocate_tensors() # allocate
|
||||
input_details = interpreter.get_input_details() # inputs
|
||||
output_details = interpreter.get_output_details() # outputs
|
||||
self.__dict__.update(locals()) # assign all variables to self
|
||||
|
||||
def forward(self, im, augment=False, visualize=False, val=False):
|
||||
# YOLOv5 MultiBackend inference
|
||||
b, ch, h, w = im.shape # batch, channel, height, width
|
||||
if self.pt: # PyTorch
|
||||
y = self.model(im) if self.jit else self.model(im, augment=augment, visualize=visualize)
|
||||
return y if val else y[0]
|
||||
elif self.coreml: # CoreML *.mlmodel
|
||||
im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3)
|
||||
im = Image.fromarray((im[0] * 255).astype('uint8'))
|
||||
# im = im.resize((192, 320), Image.ANTIALIAS)
|
||||
y = self.model.predict({'image': im}) # coordinates are xywh normalized
|
||||
box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
|
||||
conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
|
||||
y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
|
||||
elif self.onnx: # ONNX
|
||||
im = im.cpu().numpy() # torch to numpy
|
||||
if self.dnn: # ONNX OpenCV DNN
|
||||
self.net.setInput(im)
|
||||
y = self.net.forward()
|
||||
else: # ONNX Runtime
|
||||
y = self.session.run([self.session.get_outputs()[0].name], {self.session.get_inputs()[0].name: im})[0]
|
||||
else: # TensorFlow model (TFLite, pb, saved_model)
|
||||
im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3)
|
||||
if self.pb:
|
||||
y = self.frozen_func(x=self.tf.constant(im)).numpy()
|
||||
elif self.saved_model:
|
||||
y = self.model(im, training=False).numpy()
|
||||
elif self.tflite:
|
||||
input, output = self.input_details[0], self.output_details[0]
|
||||
int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model
|
||||
if int8:
|
||||
scale, zero_point = input['quantization']
|
||||
im = (im / scale + zero_point).astype(np.uint8) # de-scale
|
||||
self.interpreter.set_tensor(input['index'], im)
|
||||
self.interpreter.invoke()
|
||||
y = self.interpreter.get_tensor(output['index'])
|
||||
if int8:
|
||||
scale, zero_point = output['quantization']
|
||||
y = (y.astype(np.float32) - zero_point) * scale # re-scale
|
||||
y[..., 0] *= w # x
|
||||
y[..., 1] *= h # y
|
||||
y[..., 2] *= w # w
|
||||
y[..., 3] *= h # h
|
||||
y = torch.tensor(y)
|
||||
return (y, []) if val else y
|
||||
|
||||
|
||||
class AutoShape(nn.Module):
|
||||
# YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
|
||||
conf = 0.25 # NMS confidence threshold
|
||||
iou = 0.45 # NMS IoU threshold
|
||||
classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
|
||||
multi_label = False # NMS multiple labels per box
|
||||
max_det = 1000 # maximum number of detections per image
|
||||
|
||||
def __init__(self, model):
|
||||
super().__init__()
|
||||
self.model = model.eval()
|
||||
|
||||
def autoshape(self):
|
||||
LOGGER.info('AutoShape already enabled, skipping... ') # model already converted to model.autoshape()
|
||||
return self
|
||||
|
||||
def _apply(self, fn):
|
||||
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
|
||||
self = super()._apply(fn)
|
||||
m = self.model.model[-1] # Detect()
|
||||
m.stride = fn(m.stride)
|
||||
m.grid = list(map(fn, m.grid))
|
||||
if isinstance(m.anchor_grid, list):
|
||||
m.anchor_grid = list(map(fn, m.anchor_grid))
|
||||
return self
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(self, imgs, size=640, augment=False, profile=False):
|
||||
# Inference from various sources. For height=640, width=1280, RGB images example inputs are:
|
||||
# file: imgs = 'data/images/zidane.jpg' # str or PosixPath
|
||||
# URI: = 'https://ultralytics.com/images/zidane.jpg'
|
||||
# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
|
||||
# PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3)
|
||||
# numpy: = np.zeros((640,1280,3)) # HWC
|
||||
# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
|
||||
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
|
||||
|
||||
t = [time_sync()]
|
||||
p = next(self.model.parameters()) # for device and type
|
||||
if isinstance(imgs, torch.Tensor): # torch
|
||||
with amp.autocast(enabled=p.device.type != 'cpu'):
|
||||
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
|
||||
|
||||
# Pre-process
|
||||
n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
|
||||
shape0, shape1, files = [], [], [] # image and inference shapes, filenames
|
||||
for i, im in enumerate(imgs):
|
||||
f = f'image{i}' # filename
|
||||
if isinstance(im, (str, Path)): # filename or uri
|
||||
im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
|
||||
im = np.asarray(exif_transpose(im))
|
||||
elif isinstance(im, Image.Image): # PIL Image
|
||||
im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
|
||||
files.append(Path(f).with_suffix('.jpg').name)
|
||||
if im.shape[0] < 5: # image in CHW
|
||||
im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
|
||||
im = im[..., :3] if im.ndim == 3 else np.tile(im[..., None], 3) # enforce 3ch input
|
||||
s = im.shape[:2] # HWC
|
||||
shape0.append(s) # image shape
|
||||
g = (size / max(s)) # gain
|
||||
shape1.append([y * g for y in s])
|
||||
imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
|
||||
shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
|
||||
x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
|
||||
x = np.stack(x, 0) if n > 1 else x[0][None] # stack
|
||||
x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
|
||||
x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32
|
||||
t.append(time_sync())
|
||||
|
||||
with amp.autocast(enabled=p.device.type != 'cpu'):
|
||||
# Inference
|
||||
y = self.model(x, augment, profile)[0] # forward
|
||||
t.append(time_sync())
|
||||
|
||||
# Post-process
|
||||
y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes,
|
||||
multi_label=self.multi_label, max_det=self.max_det) # NMS
|
||||
for i in range(n):
|
||||
scale_coords(shape1, y[i][:, :4], shape0[i])
|
||||
|
||||
t.append(time_sync())
|
||||
return Detections(imgs, y, files, t, self.names, x.shape)
|
||||
|
||||
|
||||
class Detections:
|
||||
# YOLOv5 detections class for inference results
|
||||
def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
|
||||
super().__init__()
|
||||
d = pred[0].device # device
|
||||
gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs] # normalizations
|
||||
self.imgs = imgs # list of images as numpy arrays
|
||||
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
|
||||
self.names = names # class names
|
||||
self.files = files # image filenames
|
||||
self.xyxy = pred # xyxy pixels
|
||||
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
|
||||
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
|
||||
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
|
||||
self.n = len(self.pred) # number of images (batch size)
|
||||
self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms)
|
||||
self.s = shape # inference BCHW shape
|
||||
|
||||
def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
|
||||
crops = []
|
||||
for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
|
||||
s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string
|
||||
if pred.shape[0]:
|
||||
for c in pred[:, -1].unique():
|
||||
n = (pred[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||||
if show or save or render or crop:
|
||||
annotator = Annotator(im, example=str(self.names))
|
||||
for *box, conf, cls in reversed(pred): # xyxy, confidence, class
|
||||
label = f'{self.names[int(cls)]} {conf:.2f}'
|
||||
if crop:
|
||||
file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
|
||||
crops.append({'box': box, 'conf': conf, 'cls': cls, 'label': label,
|
||||
'im': save_one_box(box, im, file=file, save=save)})
|
||||
else: # all others
|
||||
annotator.box_label(box, label, color=colors(cls))
|
||||
im = annotator.im
|
||||
else:
|
||||
s += '(no detections)'
|
||||
|
||||
im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
|
||||
if pprint:
|
||||
LOGGER.info(s.rstrip(', '))
|
||||
if show:
|
||||
im.show(self.files[i]) # show
|
||||
if save:
|
||||
f = self.files[i]
|
||||
im.save(save_dir / f) # save
|
||||
if i == self.n - 1:
|
||||
LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
|
||||
if render:
|
||||
self.imgs[i] = np.asarray(im)
|
||||
if crop:
|
||||
if save:
|
||||
LOGGER.info(f'Saved results to {save_dir}\n')
|
||||
return crops
|
||||
|
||||
def print(self):
|
||||
self.display(pprint=True) # print results
|
||||
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
|
||||
self.t)
|
||||
|
||||
def show(self):
|
||||
self.display(show=True) # show results
|
||||
|
||||
def save(self, save_dir='runs/detect/exp'):
|
||||
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
|
||||
self.display(save=True, save_dir=save_dir) # save results
|
||||
|
||||
def crop(self, save=True, save_dir='runs/detect/exp'):
|
||||
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) if save else None
|
||||
return self.display(crop=True, save=save, save_dir=save_dir) # crop results
|
||||
|
||||
def render(self):
|
||||
self.display(render=True) # render results
|
||||
return self.imgs
|
||||
|
||||
def pandas(self):
|
||||
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
|
||||
new = copy(self) # return copy
|
||||
ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
|
||||
cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
|
||||
for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
|
||||
a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
|
||||
setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
|
||||
return new
|
||||
|
||||
def tolist(self):
|
||||
# return a list of Detections objects, i.e. 'for result in results.tolist():'
|
||||
x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)]
|
||||
for d in x:
|
||||
for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
|
||||
setattr(d, k, getattr(d, k)[0]) # pop out of list
|
||||
return x
|
||||
|
||||
def __len__(self):
|
||||
return self.n
|
||||
|
||||
|
||||
class Classify(nn.Module):
|
||||
# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super().__init__()
|
||||
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
|
||||
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
|
||||
self.flat = nn.Flatten()
|
||||
|
||||
def forward(self, x):
|
||||
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
|
||||
return self.flat(self.conv(z)) # flatten to x(b,c2)
|
||||
@ -0,0 +1,120 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Experimental modules
|
||||
"""
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from models.common import Conv
|
||||
from utils.downloads import attempt_download
|
||||
|
||||
|
||||
class CrossConv(nn.Module):
|
||||
# Cross Convolution Downsample
|
||||
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
|
||||
# ch_in, ch_out, kernel, stride, groups, expansion, shortcut
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = Conv(c1, c_, (1, k), (1, s))
|
||||
self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
|
||||
self.add = shortcut and c1 == c2
|
||||
|
||||
def forward(self, x):
|
||||
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
|
||||
|
||||
|
||||
class Sum(nn.Module):
|
||||
# Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
|
||||
def __init__(self, n, weight=False): # n: number of inputs
|
||||
super().__init__()
|
||||
self.weight = weight # apply weights boolean
|
||||
self.iter = range(n - 1) # iter object
|
||||
if weight:
|
||||
self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights
|
||||
|
||||
def forward(self, x):
|
||||
y = x[0] # no weight
|
||||
if self.weight:
|
||||
w = torch.sigmoid(self.w) * 2
|
||||
for i in self.iter:
|
||||
y = y + x[i + 1] * w[i]
|
||||
else:
|
||||
for i in self.iter:
|
||||
y = y + x[i + 1]
|
||||
return y
|
||||
|
||||
|
||||
class MixConv2d(nn.Module):
|
||||
# Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
|
||||
def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy
|
||||
super().__init__()
|
||||
n = len(k) # number of convolutions
|
||||
if equal_ch: # equal c_ per group
|
||||
i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices
|
||||
c_ = [(i == g).sum() for g in range(n)] # intermediate channels
|
||||
else: # equal weight.numel() per group
|
||||
b = [c2] + [0] * n
|
||||
a = np.eye(n + 1, n, k=-1)
|
||||
a -= np.roll(a, 1, axis=1)
|
||||
a *= np.array(k) ** 2
|
||||
a[0] = 1
|
||||
c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
|
||||
|
||||
self.m = nn.ModuleList(
|
||||
[nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
|
||||
self.bn = nn.BatchNorm2d(c2)
|
||||
self.act = nn.SiLU()
|
||||
|
||||
def forward(self, x):
|
||||
return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
|
||||
|
||||
|
||||
class Ensemble(nn.ModuleList):
|
||||
# Ensemble of models
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def forward(self, x, augment=False, profile=False, visualize=False):
|
||||
y = []
|
||||
for module in self:
|
||||
y.append(module(x, augment, profile, visualize)[0])
|
||||
# y = torch.stack(y).max(0)[0] # max ensemble
|
||||
# y = torch.stack(y).mean(0) # mean ensemble
|
||||
y = torch.cat(y, 1) # nms ensemble
|
||||
return y, None # inference, train output
|
||||
|
||||
|
||||
def attempt_load(weights, map_location=None, inplace=True, fuse=True):
|
||||
from models.yolo import Detect, Model
|
||||
|
||||
# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
|
||||
model = Ensemble()
|
||||
for w in weights if isinstance(weights, list) else [weights]:
|
||||
ckpt = torch.load(attempt_download(w), map_location=map_location) # load
|
||||
if fuse:
|
||||
model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model
|
||||
else:
|
||||
model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval()) # without layer fuse
|
||||
|
||||
# Compatibility updates
|
||||
for m in model.modules():
|
||||
if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
|
||||
m.inplace = inplace # pytorch 1.7.0 compatibility
|
||||
if type(m) is Detect:
|
||||
if not isinstance(m.anchor_grid, list): # new Detect Layer compatibility
|
||||
delattr(m, 'anchor_grid')
|
||||
setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
|
||||
elif type(m) is Conv:
|
||||
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
|
||||
|
||||
if len(model) == 1:
|
||||
return model[-1] # return model
|
||||
else:
|
||||
print(f'Ensemble created with {weights}\n')
|
||||
for k in ['names']:
|
||||
setattr(model, k, getattr(model[-1], k))
|
||||
model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride
|
||||
return model # return ensemble
|
||||
@ -0,0 +1,59 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Default anchors for COCO data
|
||||
|
||||
|
||||
# P5 -------------------------------------------------------------------------------------------------------------------
|
||||
# P5-640:
|
||||
anchors_p5_640:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
|
||||
# P6 -------------------------------------------------------------------------------------------------------------------
|
||||
# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387
|
||||
anchors_p6_640:
|
||||
- [9,11, 21,19, 17,41] # P3/8
|
||||
- [43,32, 39,70, 86,64] # P4/16
|
||||
- [65,131, 134,130, 120,265] # P5/32
|
||||
- [282,180, 247,354, 512,387] # P6/64
|
||||
|
||||
# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792
|
||||
anchors_p6_1280:
|
||||
- [19,27, 44,40, 38,94] # P3/8
|
||||
- [96,68, 86,152, 180,137] # P4/16
|
||||
- [140,301, 303,264, 238,542] # P5/32
|
||||
- [436,615, 739,380, 925,792] # P6/64
|
||||
|
||||
# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187
|
||||
anchors_p6_1920:
|
||||
- [28,41, 67,59, 57,141] # P3/8
|
||||
- [144,103, 129,227, 270,205] # P4/16
|
||||
- [209,452, 455,396, 358,812] # P5/32
|
||||
- [653,922, 1109,570, 1387,1187] # P6/64
|
||||
|
||||
|
||||
# P7 -------------------------------------------------------------------------------------------------------------------
|
||||
# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372
|
||||
anchors_p7_640:
|
||||
- [11,11, 13,30, 29,20] # P3/8
|
||||
- [30,46, 61,38, 39,92] # P4/16
|
||||
- [78,80, 146,66, 79,163] # P5/32
|
||||
- [149,150, 321,143, 157,303] # P6/64
|
||||
- [257,402, 359,290, 524,372] # P7/128
|
||||
|
||||
# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818
|
||||
anchors_p7_1280:
|
||||
- [19,22, 54,36, 32,77] # P3/8
|
||||
- [70,83, 138,71, 75,173] # P4/16
|
||||
- [165,159, 148,334, 375,151] # P5/32
|
||||
- [334,317, 251,626, 499,474] # P6/64
|
||||
- [750,326, 534,814, 1079,818] # P7/128
|
||||
|
||||
# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227
|
||||
anchors_p7_1920:
|
||||
- [29,34, 81,55, 47,115] # P3/8
|
||||
- [105,124, 207,107, 113,259] # P4/16
|
||||
- [247,238, 222,500, 563,227] # P5/32
|
||||
- [501,476, 376,939, 749,711] # P6/64
|
||||
- [1126,489, 801,1222, 1618,1227] # P7/128
|
||||
@ -0,0 +1,51 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# darknet53 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [32, 3, 1]], # 0
|
||||
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
|
||||
[-1, 1, Bottleneck, [64]],
|
||||
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
|
||||
[-1, 2, Bottleneck, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
|
||||
[-1, 8, Bottleneck, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
|
||||
[-1, 8, Bottleneck, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
|
||||
[-1, 4, Bottleneck, [1024]], # 10
|
||||
]
|
||||
|
||||
# YOLOv3-SPP head
|
||||
head:
|
||||
[[-1, 1, Bottleneck, [1024, False]],
|
||||
[-1, 1, SPP, [512, [5, 9, 13]]],
|
||||
[-1, 1, Conv, [1024, 3, 1]],
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
|
||||
|
||||
[-2, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
|
||||
|
||||
[-2, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 1, Bottleneck, [256, False]],
|
||||
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
|
||||
|
||||
[[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,41 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10,14, 23,27, 37,58] # P4/16
|
||||
- [81,82, 135,169, 344,319] # P5/32
|
||||
|
||||
# YOLOv3-tiny backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [16, 3, 1]], # 0
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2
|
||||
[-1, 1, Conv, [32, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4
|
||||
[-1, 1, Conv, [64, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8
|
||||
[-1, 1, Conv, [128, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16
|
||||
[-1, 1, Conv, [256, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32
|
||||
[-1, 1, Conv, [512, 3, 1]],
|
||||
[-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11
|
||||
[-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12
|
||||
]
|
||||
|
||||
# YOLOv3-tiny head
|
||||
head:
|
||||
[[-1, 1, Conv, [1024, 3, 1]],
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large)
|
||||
|
||||
[-2, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)
|
||||
|
||||
[[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5)
|
||||
]
|
||||
@ -0,0 +1,51 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# darknet53 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [32, 3, 1]], # 0
|
||||
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
|
||||
[-1, 1, Bottleneck, [64]],
|
||||
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
|
||||
[-1, 2, Bottleneck, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
|
||||
[-1, 8, Bottleneck, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
|
||||
[-1, 8, Bottleneck, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
|
||||
[-1, 4, Bottleneck, [1024]], # 10
|
||||
]
|
||||
|
||||
# YOLOv3 head
|
||||
head:
|
||||
[[-1, 1, Bottleneck, [1024, False]],
|
||||
[-1, 1, Conv, [512, [1, 1]]],
|
||||
[-1, 1, Conv, [1024, 3, 1]],
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
|
||||
|
||||
[-2, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
|
||||
|
||||
[-2, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 1, Bottleneck, [256, False]],
|
||||
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
|
||||
|
||||
[[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,48 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 BiFPN head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,42 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 FPN head
|
||||
head:
|
||||
[[-1, 3, C3, [1024, False]], # 10 (P5/32-large)
|
||||
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 3, C3, [512, False]], # 14 (P4/16-medium)
|
||||
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 3, C3, [256, False]], # 18 (P3/8-small)
|
||||
|
||||
[[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,54 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors: 3 # auto-anchor evolves 3 anchors per P output layer
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 2], 1, Concat, [1]], # cat backbone P2
|
||||
[-1, 1, C3, [128, False]], # 21 (P2/4-xsmall)
|
||||
|
||||
[-1, 1, Conv, [128, 3, 2]],
|
||||
[[-1, 18], 1, Concat, [1]], # cat head P3
|
||||
[-1, 3, C3, [256, False]], # 24 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 27 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 30 (P5/32-large)
|
||||
|
||||
[[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,56 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors: 3 # auto-anchor 3 anchors per P output layer
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
@ -0,0 +1,67 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors: 3 # auto-anchor 3 anchors per P output layer
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, Conv, [1280, 3, 2]], # 11-P7/128
|
||||
[-1, 3, C3, [1280]],
|
||||
[-1, 1, SPPF, [1280, 5]], # 13
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[[-1, 1, Conv, [1024, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 10], 1, Concat, [1]], # cat backbone P6
|
||||
[-1, 3, C3, [1024, False]], # 17
|
||||
|
||||
[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 21
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 25
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 29 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 26], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 32 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 22], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 35 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 18], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge)
|
||||
|
||||
[-1, 1, Conv, [1024, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P7
|
||||
[-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge)
|
||||
|
||||
[[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7)
|
||||
]
|
||||
@ -0,0 +1,48 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 PANet head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,60 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [19,27, 44,40, 38,94] # P3/8
|
||||
- [96,68, 86,152, 180,137] # P4/16
|
||||
- [140,301, 303,264, 238,542] # P5/32
|
||||
- [436,615, 739,380, 925,792] # P6/64
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
@ -0,0 +1,60 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.67 # model depth multiple
|
||||
width_multiple: 0.75 # layer channel multiple
|
||||
anchors:
|
||||
- [19,27, 44,40, 38,94] # P3/8
|
||||
- [96,68, 86,152, 180,137] # P4/16
|
||||
- [140,301, 303,264, 238,542] # P5/32
|
||||
- [436,615, 739,380, 925,792] # P6/64
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
@ -0,0 +1,60 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.25 # layer channel multiple
|
||||
anchors:
|
||||
- [19,27, 44,40, 38,94] # P3/8
|
||||
- [96,68, 86,152, 180,137] # P4/16
|
||||
- [140,301, 303,264, 238,542] # P5/32
|
||||
- [436,615, 739,380, 925,792] # P6/64
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
@ -0,0 +1,48 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3Ghost, [128]],
|
||||
[-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3Ghost, [256]],
|
||||
[-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3Ghost, [512]],
|
||||
[-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3Ghost, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, GhostConv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3Ghost, [512, False]], # 13
|
||||
|
||||
[-1, 1, GhostConv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, GhostConv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, GhostConv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,48 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,60 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
anchors:
|
||||
- [19,27, 44,40, 38,94] # P3/8
|
||||
- [96,68, 86,152, 180,137] # P4/16
|
||||
- [140,301, 303,264, 238,542] # P5/32
|
||||
- [436,615, 739,380, 925,792] # P6/64
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
@ -0,0 +1,60 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.33 # model depth multiple
|
||||
width_multiple: 1.25 # layer channel multiple
|
||||
anchors:
|
||||
- [19,27, 44,40, 38,94] # P3/8
|
||||
- [96,68, 86,152, 180,137] # P4/16
|
||||
- [140,301, 303,264, 238,542] # P5/32
|
||||
- [436,615, 739,380, 925,792] # P6/64
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
@ -0,0 +1,48 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 2 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,48 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 2 # number of classes
|
||||
depth_multiple: 0.67 # model depth multiple
|
||||
width_multiple: 0.75 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,48 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 2 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,465 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
TensorFlow, Keras and TFLite versions of YOLOv5
|
||||
Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127
|
||||
|
||||
Usage:
|
||||
$ python models/tf.py --weights yolov5s.pt
|
||||
|
||||
Export:
|
||||
$ python path/to/export.py --weights yolov5s.pt --include saved_model pb tflite tfjs
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import sys
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
# ROOT = ROOT.relative_to(Path.cwd()) # relative
|
||||
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from tensorflow import keras
|
||||
|
||||
from models.common import C3, SPP, SPPF, Bottleneck, BottleneckCSP, Concat, Conv, DWConv, Focus, autopad
|
||||
from models.experimental import CrossConv, MixConv2d, attempt_load
|
||||
from models.yolo import Detect
|
||||
from utils.activations import SiLU
|
||||
from utils.general import LOGGER, make_divisible, print_args
|
||||
|
||||
|
||||
class TFBN(keras.layers.Layer):
|
||||
# TensorFlow BatchNormalization wrapper
|
||||
def __init__(self, w=None):
|
||||
super().__init__()
|
||||
self.bn = keras.layers.BatchNormalization(
|
||||
beta_initializer=keras.initializers.Constant(w.bias.numpy()),
|
||||
gamma_initializer=keras.initializers.Constant(w.weight.numpy()),
|
||||
moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()),
|
||||
moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()),
|
||||
epsilon=w.eps)
|
||||
|
||||
def call(self, inputs):
|
||||
return self.bn(inputs)
|
||||
|
||||
|
||||
class TFPad(keras.layers.Layer):
|
||||
def __init__(self, pad):
|
||||
super().__init__()
|
||||
self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])
|
||||
|
||||
def call(self, inputs):
|
||||
return tf.pad(inputs, self.pad, mode='constant', constant_values=0)
|
||||
|
||||
|
||||
class TFConv(keras.layers.Layer):
|
||||
# Standard convolution
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
|
||||
# ch_in, ch_out, weights, kernel, stride, padding, groups
|
||||
super().__init__()
|
||||
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
|
||||
assert isinstance(k, int), "Convolution with multiple kernels are not allowed."
|
||||
# TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
|
||||
# see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch
|
||||
|
||||
conv = keras.layers.Conv2D(
|
||||
c2, k, s, 'SAME' if s == 1 else 'VALID', use_bias=False if hasattr(w, 'bn') else True,
|
||||
kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
|
||||
bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
|
||||
self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
|
||||
self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
|
||||
|
||||
# YOLOv5 activations
|
||||
if isinstance(w.act, nn.LeakyReLU):
|
||||
self.act = (lambda x: keras.activations.relu(x, alpha=0.1)) if act else tf.identity
|
||||
elif isinstance(w.act, nn.Hardswish):
|
||||
self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667) if act else tf.identity
|
||||
elif isinstance(w.act, (nn.SiLU, SiLU)):
|
||||
self.act = (lambda x: keras.activations.swish(x)) if act else tf.identity
|
||||
else:
|
||||
raise Exception(f'no matching TensorFlow activation found for {w.act}')
|
||||
|
||||
def call(self, inputs):
|
||||
return self.act(self.bn(self.conv(inputs)))
|
||||
|
||||
|
||||
class TFFocus(keras.layers.Layer):
|
||||
# Focus wh information into c-space
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
|
||||
# ch_in, ch_out, kernel, stride, padding, groups
|
||||
super().__init__()
|
||||
self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)
|
||||
|
||||
def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c)
|
||||
# inputs = inputs / 255 # normalize 0-255 to 0-1
|
||||
return self.conv(tf.concat([inputs[:, ::2, ::2, :],
|
||||
inputs[:, 1::2, ::2, :],
|
||||
inputs[:, ::2, 1::2, :],
|
||||
inputs[:, 1::2, 1::2, :]], 3))
|
||||
|
||||
|
||||
class TFBottleneck(keras.layers.Layer):
|
||||
# Standard bottleneck
|
||||
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
|
||||
self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2)
|
||||
self.add = shortcut and c1 == c2
|
||||
|
||||
def call(self, inputs):
|
||||
return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
|
||||
|
||||
|
||||
class TFConv2d(keras.layers.Layer):
|
||||
# Substitution for PyTorch nn.Conv2D
|
||||
def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
|
||||
super().__init__()
|
||||
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
|
||||
self.conv = keras.layers.Conv2D(
|
||||
c2, k, s, 'VALID', use_bias=bias,
|
||||
kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()),
|
||||
bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None, )
|
||||
|
||||
def call(self, inputs):
|
||||
return self.conv(inputs)
|
||||
|
||||
|
||||
class TFBottleneckCSP(keras.layers.Layer):
|
||||
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
|
||||
# ch_in, ch_out, number, shortcut, groups, expansion
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
|
||||
self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2)
|
||||
self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3)
|
||||
self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4)
|
||||
self.bn = TFBN(w.bn)
|
||||
self.act = lambda x: keras.activations.relu(x, alpha=0.1)
|
||||
self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
|
||||
|
||||
def call(self, inputs):
|
||||
y1 = self.cv3(self.m(self.cv1(inputs)))
|
||||
y2 = self.cv2(inputs)
|
||||
return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3))))
|
||||
|
||||
|
||||
class TFC3(keras.layers.Layer):
|
||||
# CSP Bottleneck with 3 convolutions
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
|
||||
# ch_in, ch_out, number, shortcut, groups, expansion
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
|
||||
self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
|
||||
self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)
|
||||
self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
|
||||
|
||||
def call(self, inputs):
|
||||
return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
|
||||
|
||||
|
||||
class TFSPP(keras.layers.Layer):
|
||||
# Spatial pyramid pooling layer used in YOLOv3-SPP
|
||||
def __init__(self, c1, c2, k=(5, 9, 13), w=None):
|
||||
super().__init__()
|
||||
c_ = c1 // 2 # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
|
||||
self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2)
|
||||
self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k]
|
||||
|
||||
def call(self, inputs):
|
||||
x = self.cv1(inputs)
|
||||
return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3))
|
||||
|
||||
|
||||
class TFSPPF(keras.layers.Layer):
|
||||
# Spatial pyramid pooling-Fast layer
|
||||
def __init__(self, c1, c2, k=5, w=None):
|
||||
super().__init__()
|
||||
c_ = c1 // 2 # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
|
||||
self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)
|
||||
self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding='SAME')
|
||||
|
||||
def call(self, inputs):
|
||||
x = self.cv1(inputs)
|
||||
y1 = self.m(x)
|
||||
y2 = self.m(y1)
|
||||
return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3))
|
||||
|
||||
|
||||
class TFDetect(keras.layers.Layer):
|
||||
def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer
|
||||
super().__init__()
|
||||
self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32)
|
||||
self.nc = nc # number of classes
|
||||
self.no = nc + 5 # number of outputs per anchor
|
||||
self.nl = len(anchors) # number of detection layers
|
||||
self.na = len(anchors[0]) // 2 # number of anchors
|
||||
self.grid = [tf.zeros(1)] * self.nl # init grid
|
||||
self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32)
|
||||
self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]),
|
||||
[self.nl, 1, -1, 1, 2])
|
||||
self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)]
|
||||
self.training = False # set to False after building model
|
||||
self.imgsz = imgsz
|
||||
for i in range(self.nl):
|
||||
ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
|
||||
self.grid[i] = self._make_grid(nx, ny)
|
||||
|
||||
def call(self, inputs):
|
||||
z = [] # inference output
|
||||
x = []
|
||||
for i in range(self.nl):
|
||||
x.append(self.m[i](inputs[i]))
|
||||
# x(bs,20,20,255) to x(bs,3,20,20,85)
|
||||
ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
|
||||
x[i] = tf.transpose(tf.reshape(x[i], [-1, ny * nx, self.na, self.no]), [0, 2, 1, 3])
|
||||
|
||||
if not self.training: # inference
|
||||
y = tf.sigmoid(x[i])
|
||||
xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
|
||||
wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
|
||||
# Normalize xywh to 0-1 to reduce calibration error
|
||||
xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
|
||||
wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
|
||||
y = tf.concat([xy, wh, y[..., 4:]], -1)
|
||||
z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no]))
|
||||
|
||||
return x if self.training else (tf.concat(z, 1), x)
|
||||
|
||||
@staticmethod
|
||||
def _make_grid(nx=20, ny=20):
|
||||
# yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
|
||||
# return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
|
||||
xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny))
|
||||
return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32)
|
||||
|
||||
|
||||
class TFUpsample(keras.layers.Layer):
|
||||
def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w'
|
||||
super().__init__()
|
||||
assert scale_factor == 2, "scale_factor must be 2"
|
||||
self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode)
|
||||
# self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
|
||||
# with default arguments: align_corners=False, half_pixel_centers=False
|
||||
# self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x,
|
||||
# size=(x.shape[1] * 2, x.shape[2] * 2))
|
||||
|
||||
def call(self, inputs):
|
||||
return self.upsample(inputs)
|
||||
|
||||
|
||||
class TFConcat(keras.layers.Layer):
|
||||
def __init__(self, dimension=1, w=None):
|
||||
super().__init__()
|
||||
assert dimension == 1, "convert only NCHW to NHWC concat"
|
||||
self.d = 3
|
||||
|
||||
def call(self, inputs):
|
||||
return tf.concat(inputs, self.d)
|
||||
|
||||
|
||||
def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
|
||||
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
|
||||
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
|
||||
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
|
||||
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
|
||||
|
||||
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
|
||||
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
|
||||
m_str = m
|
||||
m = eval(m) if isinstance(m, str) else m # eval strings
|
||||
for j, a in enumerate(args):
|
||||
try:
|
||||
args[j] = eval(a) if isinstance(a, str) else a # eval strings
|
||||
except NameError:
|
||||
pass
|
||||
|
||||
n = max(round(n * gd), 1) if n > 1 else n # depth gain
|
||||
if m in [nn.Conv2d, Conv, Bottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
|
||||
c1, c2 = ch[f], args[0]
|
||||
c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
|
||||
|
||||
args = [c1, c2, *args[1:]]
|
||||
if m in [BottleneckCSP, C3]:
|
||||
args.insert(2, n)
|
||||
n = 1
|
||||
elif m is nn.BatchNorm2d:
|
||||
args = [ch[f]]
|
||||
elif m is Concat:
|
||||
c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
|
||||
elif m is Detect:
|
||||
args.append([ch[x + 1] for x in f])
|
||||
if isinstance(args[1], int): # number of anchors
|
||||
args[1] = [list(range(args[1] * 2))] * len(f)
|
||||
args.append(imgsz)
|
||||
else:
|
||||
c2 = ch[f]
|
||||
|
||||
tf_m = eval('TF' + m_str.replace('nn.', ''))
|
||||
m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \
|
||||
else tf_m(*args, w=model.model[i]) # module
|
||||
|
||||
torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
|
||||
t = str(m)[8:-2].replace('__main__.', '') # module type
|
||||
np = sum(x.numel() for x in torch_m_.parameters()) # number params
|
||||
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
|
||||
LOGGER.info(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}') # print
|
||||
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
|
||||
layers.append(m_)
|
||||
ch.append(c2)
|
||||
return keras.Sequential(layers), sorted(save)
|
||||
|
||||
|
||||
class TFModel:
|
||||
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes
|
||||
super().__init__()
|
||||
if isinstance(cfg, dict):
|
||||
self.yaml = cfg # model dict
|
||||
else: # is *.yaml
|
||||
import yaml # for torch hub
|
||||
self.yaml_file = Path(cfg).name
|
||||
with open(cfg) as f:
|
||||
self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict
|
||||
|
||||
# Define model
|
||||
if nc and nc != self.yaml['nc']:
|
||||
LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
|
||||
self.yaml['nc'] = nc # override yaml value
|
||||
self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)
|
||||
|
||||
def predict(self, inputs, tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
|
||||
conf_thres=0.25):
|
||||
y = [] # outputs
|
||||
x = inputs
|
||||
for i, m in enumerate(self.model.layers):
|
||||
if m.f != -1: # if not from previous layer
|
||||
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
||||
|
||||
x = m(x) # run
|
||||
y.append(x if m.i in self.savelist else None) # save output
|
||||
|
||||
# Add TensorFlow NMS
|
||||
if tf_nms:
|
||||
boxes = self._xywh2xyxy(x[0][..., :4])
|
||||
probs = x[0][:, :, 4:5]
|
||||
classes = x[0][:, :, 5:]
|
||||
scores = probs * classes
|
||||
if agnostic_nms:
|
||||
nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres)
|
||||
return nms, x[1]
|
||||
else:
|
||||
boxes = tf.expand_dims(boxes, 2)
|
||||
nms = tf.image.combined_non_max_suppression(
|
||||
boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False)
|
||||
return nms, x[1]
|
||||
|
||||
return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...]
|
||||
# x = x[0][0] # [x(1,6300,85), ...] to x(6300,85)
|
||||
# xywh = x[..., :4] # x(6300,4) boxes
|
||||
# conf = x[..., 4:5] # x(6300,1) confidences
|
||||
# cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes
|
||||
# return tf.concat([conf, cls, xywh], 1)
|
||||
|
||||
@staticmethod
|
||||
def _xywh2xyxy(xywh):
|
||||
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
|
||||
x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1)
|
||||
return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1)
|
||||
|
||||
|
||||
class AgnosticNMS(keras.layers.Layer):
|
||||
# TF Agnostic NMS
|
||||
def call(self, input, topk_all, iou_thres, conf_thres):
|
||||
# wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450
|
||||
return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres), input,
|
||||
fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32),
|
||||
name='agnostic_nms')
|
||||
|
||||
@staticmethod
|
||||
def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25): # agnostic NMS
|
||||
boxes, classes, scores = x
|
||||
class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32)
|
||||
scores_inp = tf.reduce_max(scores, -1)
|
||||
selected_inds = tf.image.non_max_suppression(
|
||||
boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres)
|
||||
selected_boxes = tf.gather(boxes, selected_inds)
|
||||
padded_boxes = tf.pad(selected_boxes,
|
||||
paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]],
|
||||
mode="CONSTANT", constant_values=0.0)
|
||||
selected_scores = tf.gather(scores_inp, selected_inds)
|
||||
padded_scores = tf.pad(selected_scores,
|
||||
paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
|
||||
mode="CONSTANT", constant_values=-1.0)
|
||||
selected_classes = tf.gather(class_inds, selected_inds)
|
||||
padded_classes = tf.pad(selected_classes,
|
||||
paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
|
||||
mode="CONSTANT", constant_values=-1.0)
|
||||
valid_detections = tf.shape(selected_inds)[0]
|
||||
return padded_boxes, padded_scores, padded_classes, valid_detections
|
||||
|
||||
|
||||
def representative_dataset_gen(dataset, ncalib=100):
|
||||
# Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays
|
||||
for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
|
||||
input = np.transpose(img, [1, 2, 0])
|
||||
input = np.expand_dims(input, axis=0).astype(np.float32)
|
||||
input /= 255
|
||||
yield [input]
|
||||
if n >= ncalib:
|
||||
break
|
||||
|
||||
|
||||
def run(weights=ROOT / 'yolov5s.pt', # weights path
|
||||
imgsz=(640, 640), # inference size h,w
|
||||
batch_size=1, # batch size
|
||||
dynamic=False, # dynamic batch size
|
||||
):
|
||||
# PyTorch model
|
||||
im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image
|
||||
model = attempt_load(weights, map_location=torch.device('cpu'), inplace=True, fuse=False)
|
||||
y = model(im) # inference
|
||||
model.info()
|
||||
|
||||
# TensorFlow model
|
||||
im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image
|
||||
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
|
||||
y = tf_model.predict(im) # inference
|
||||
|
||||
# Keras model
|
||||
im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
|
||||
keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im))
|
||||
keras_model.summary()
|
||||
|
||||
LOGGER.info('PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.')
|
||||
|
||||
|
||||
def parse_opt():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
|
||||
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
|
||||
parser.add_argument('--dynamic', action='store_true', help='dynamic batch size')
|
||||
opt = parser.parse_args()
|
||||
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
||||
print_args(FILE.stem, opt)
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
@ -0,0 +1,336 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
YOLO-specific modules
|
||||
|
||||
Usage:
|
||||
$ python path/to/models/yolo.py --cfg yolov5s.yaml
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
# ROOT = ROOT.relative_to(Path.cwd()) # relative
|
||||
|
||||
from models.common import *
|
||||
from models.experimental import *
|
||||
from utils.autoanchor import check_anchor_order
|
||||
from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
|
||||
from utils.plots import feature_visualization
|
||||
from utils.torch_utils import (copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, select_device,
|
||||
time_sync)
|
||||
|
||||
try:
|
||||
import thop # for FLOPs computation
|
||||
except ImportError:
|
||||
thop = None
|
||||
|
||||
|
||||
class Detect(nn.Module):
|
||||
stride = None # strides computed during build
|
||||
onnx_dynamic = False # ONNX export parameter
|
||||
|
||||
def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer
|
||||
super().__init__()
|
||||
self.nc = nc # number of classes
|
||||
self.no = nc + 5 # number of outputs per anchor
|
||||
self.nl = len(anchors) # number of detection layers
|
||||
self.na = len(anchors[0]) // 2 # number of anchors
|
||||
self.grid = [torch.zeros(1)] * self.nl # init grid
|
||||
self.anchor_grid = [torch.zeros(1)] * self.nl # init anchor grid
|
||||
self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
|
||||
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
|
||||
self.inplace = inplace # use in-place ops (e.g. slice assignment)
|
||||
|
||||
def forward(self, x):
|
||||
z = [] # inference output
|
||||
for i in range(self.nl):
|
||||
x[i] = self.m[i](x[i]) # conv
|
||||
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
|
||||
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
|
||||
|
||||
if not self.training: # inference
|
||||
if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
|
||||
self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
|
||||
|
||||
y = x[i].sigmoid()
|
||||
if self.inplace:
|
||||
y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
|
||||
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
|
||||
else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
|
||||
xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
|
||||
wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
|
||||
y = torch.cat((xy, wh, y[..., 4:]), -1)
|
||||
z.append(y.view(bs, -1, self.no))
|
||||
|
||||
return x if self.training else (torch.cat(z, 1), x)
|
||||
|
||||
def _make_grid(self, nx=20, ny=20, i=0):
|
||||
d = self.anchors[i].device
|
||||
if check_version(torch.__version__, '1.10.0'): # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
|
||||
yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)], indexing='ij')
|
||||
else:
|
||||
yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)])
|
||||
grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float()
|
||||
anchor_grid = (self.anchors[i].clone() * self.stride[i]) \
|
||||
.view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float()
|
||||
return grid, anchor_grid
|
||||
|
||||
|
||||
class Model(nn.Module):
|
||||
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
|
||||
super().__init__()
|
||||
if isinstance(cfg, dict):
|
||||
self.yaml = cfg # model dict
|
||||
else: # is *.yaml
|
||||
import yaml # for torch hub
|
||||
self.yaml_file = Path(cfg).name
|
||||
with open(cfg, encoding='ascii', errors='ignore') as f:
|
||||
self.yaml = yaml.safe_load(f) # model dict
|
||||
|
||||
# Define model
|
||||
ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
|
||||
if nc and nc != self.yaml['nc']:
|
||||
LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
|
||||
self.yaml['nc'] = nc # override yaml value
|
||||
if anchors:
|
||||
LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
|
||||
self.yaml['anchors'] = round(anchors) # override yaml value
|
||||
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
|
||||
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
|
||||
self.inplace = self.yaml.get('inplace', True)
|
||||
|
||||
# Build strides, anchors
|
||||
m = self.model[-1] # Detect()
|
||||
if isinstance(m, Detect):
|
||||
s = 256 # 2x min stride
|
||||
m.inplace = self.inplace
|
||||
m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
|
||||
m.anchors /= m.stride.view(-1, 1, 1)
|
||||
check_anchor_order(m)
|
||||
self.stride = m.stride
|
||||
self._initialize_biases() # only run once
|
||||
|
||||
# Init weights, biases
|
||||
initialize_weights(self)
|
||||
self.info()
|
||||
LOGGER.info('')
|
||||
|
||||
def forward(self, x, augment=False, profile=False, visualize=False):
|
||||
if augment:
|
||||
return self._forward_augment(x) # augmented inference, None
|
||||
return self._forward_once(x, profile, visualize) # single-scale inference, train
|
||||
|
||||
def _forward_augment(self, x):
|
||||
img_size = x.shape[-2:] # height, width
|
||||
s = [1, 0.83, 0.67] # scales
|
||||
f = [None, 3, None] # flips (2-ud, 3-lr)
|
||||
y = [] # outputs
|
||||
for si, fi in zip(s, f):
|
||||
xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
|
||||
yi = self._forward_once(xi)[0] # forward
|
||||
# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
|
||||
yi = self._descale_pred(yi, fi, si, img_size)
|
||||
y.append(yi)
|
||||
y = self._clip_augmented(y) # clip augmented tails
|
||||
return torch.cat(y, 1), None # augmented inference, train
|
||||
|
||||
def _forward_once(self, x, profile=False, visualize=False):
|
||||
y, dt = [], [] # outputs
|
||||
for m in self.model:
|
||||
if m.f != -1: # if not from previous layer
|
||||
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
||||
if profile:
|
||||
self._profile_one_layer(m, x, dt)
|
||||
x = m(x) # run
|
||||
y.append(x if m.i in self.save else None) # save output
|
||||
if visualize:
|
||||
feature_visualization(x, m.type, m.i, save_dir=visualize)
|
||||
return x
|
||||
|
||||
def _descale_pred(self, p, flips, scale, img_size):
|
||||
# de-scale predictions following augmented inference (inverse operation)
|
||||
if self.inplace:
|
||||
p[..., :4] /= scale # de-scale
|
||||
if flips == 2:
|
||||
p[..., 1] = img_size[0] - p[..., 1] # de-flip ud
|
||||
elif flips == 3:
|
||||
p[..., 0] = img_size[1] - p[..., 0] # de-flip lr
|
||||
else:
|
||||
x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale
|
||||
if flips == 2:
|
||||
y = img_size[0] - y # de-flip ud
|
||||
elif flips == 3:
|
||||
x = img_size[1] - x # de-flip lr
|
||||
p = torch.cat((x, y, wh, p[..., 4:]), -1)
|
||||
return p
|
||||
|
||||
def _clip_augmented(self, y):
|
||||
# Clip YOLOv5 augmented inference tails
|
||||
nl = self.model[-1].nl # number of detection layers (P3-P5)
|
||||
g = sum(4 ** x for x in range(nl)) # grid points
|
||||
e = 1 # exclude layer count
|
||||
i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e)) # indices
|
||||
y[0] = y[0][:, :-i] # large
|
||||
i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices
|
||||
y[-1] = y[-1][:, i:] # small
|
||||
return y
|
||||
|
||||
def _profile_one_layer(self, m, x, dt):
|
||||
c = isinstance(m, Detect) # is final layer, copy input as inplace fix
|
||||
o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
|
||||
t = time_sync()
|
||||
for _ in range(10):
|
||||
m(x.copy() if c else x)
|
||||
dt.append((time_sync() - t) * 100)
|
||||
if m == self.model[0]:
|
||||
LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} {'module'}")
|
||||
LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
|
||||
if c:
|
||||
LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
|
||||
|
||||
def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
|
||||
# https://arxiv.org/abs/1708.02002 section 3.3
|
||||
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
|
||||
m = self.model[-1] # Detect() module
|
||||
for mi, s in zip(m.m, m.stride): # from
|
||||
b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
|
||||
b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
|
||||
b.data[:, 5:] += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # cls
|
||||
mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
|
||||
|
||||
def _print_biases(self):
|
||||
m = self.model[-1] # Detect() module
|
||||
for mi in m.m: # from
|
||||
b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
|
||||
LOGGER.info(
|
||||
('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
|
||||
|
||||
# def _print_weights(self):
|
||||
# for m in self.model.modules():
|
||||
# if type(m) is Bottleneck:
|
||||
# LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
|
||||
|
||||
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
|
||||
LOGGER.info('Fusing layers... ')
|
||||
for m in self.model.modules():
|
||||
if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
|
||||
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
|
||||
delattr(m, 'bn') # remove batchnorm
|
||||
m.forward = m.forward_fuse # update forward
|
||||
self.info()
|
||||
return self
|
||||
|
||||
def autoshape(self): # add AutoShape module
|
||||
LOGGER.info('Adding AutoShape... ')
|
||||
m = AutoShape(self) # wrap model
|
||||
copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes
|
||||
return m
|
||||
|
||||
def info(self, verbose=False, img_size=640): # print model information
|
||||
model_info(self, verbose, img_size)
|
||||
|
||||
def _apply(self, fn):
|
||||
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
|
||||
self = super()._apply(fn)
|
||||
m = self.model[-1] # Detect()
|
||||
if isinstance(m, Detect):
|
||||
m.stride = fn(m.stride)
|
||||
m.grid = list(map(fn, m.grid))
|
||||
if isinstance(m.anchor_grid, list):
|
||||
m.anchor_grid = list(map(fn, m.anchor_grid))
|
||||
return self
|
||||
|
||||
|
||||
def parse_model(d, ch): # model_dict, input_channels(3)
|
||||
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
|
||||
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
|
||||
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
|
||||
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
|
||||
|
||||
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
|
||||
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
|
||||
m = eval(m) if isinstance(m, str) else m # eval strings
|
||||
for j, a in enumerate(args):
|
||||
try:
|
||||
args[j] = eval(a) if isinstance(a, str) else a # eval strings
|
||||
except NameError:
|
||||
pass
|
||||
|
||||
n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
|
||||
if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
|
||||
BottleneckCSP, C3, C3TR, C3SPP, C3Ghost]:
|
||||
c1, c2 = ch[f], args[0]
|
||||
if c2 != no: # if not output
|
||||
c2 = make_divisible(c2 * gw, 8)
|
||||
|
||||
args = [c1, c2, *args[1:]]
|
||||
if m in [BottleneckCSP, C3, C3TR, C3Ghost]:
|
||||
args.insert(2, n) # number of repeats
|
||||
n = 1
|
||||
elif m is nn.BatchNorm2d:
|
||||
args = [ch[f]]
|
||||
elif m is Concat:
|
||||
c2 = sum(ch[x] for x in f)
|
||||
elif m is Detect:
|
||||
args.append([ch[x] for x in f])
|
||||
if isinstance(args[1], int): # number of anchors
|
||||
args[1] = [list(range(args[1] * 2))] * len(f)
|
||||
elif m is Contract:
|
||||
c2 = ch[f] * args[0] ** 2
|
||||
elif m is Expand:
|
||||
c2 = ch[f] // args[0] ** 2
|
||||
else:
|
||||
c2 = ch[f]
|
||||
|
||||
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
|
||||
t = str(m)[8:-2].replace('__main__.', '') # module type
|
||||
np = sum(x.numel() for x in m_.parameters()) # number params
|
||||
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
|
||||
LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # print
|
||||
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
|
||||
layers.append(m_)
|
||||
if i == 0:
|
||||
ch = []
|
||||
ch.append(c2)
|
||||
return nn.Sequential(*layers), sorted(save)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--profile', action='store_true', help='profile model speed')
|
||||
parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')
|
||||
opt = parser.parse_args()
|
||||
opt.cfg = check_yaml(opt.cfg) # check YAML
|
||||
print_args(FILE.stem, opt)
|
||||
device = select_device(opt.device)
|
||||
|
||||
# Create model
|
||||
model = Model(opt.cfg).to(device)
|
||||
model.train()
|
||||
|
||||
# Profile
|
||||
if opt.profile:
|
||||
img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
|
||||
y = model(img, profile=True)
|
||||
|
||||
# Test all models
|
||||
if opt.test:
|
||||
for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
|
||||
try:
|
||||
_ = Model(cfg)
|
||||
except Exception as e:
|
||||
print(f'Error in {cfg}: {e}')
|
||||
|
||||
# Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)
|
||||
# from torch.utils.tensorboard import SummaryWriter
|
||||
# tb_writer = SummaryWriter('.')
|
||||
# LOGGER.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
|
||||
# tb_writer.add_graph(torch.jit.trace(model, img, strict=False), []) # add model graph
|
||||
@ -0,0 +1,48 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 2 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,48 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 2 # number of classes
|
||||
depth_multiple: 0.67 # model depth multiple
|
||||
width_multiple: 0.75 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,48 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 2 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.25 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,48 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 2 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -0,0 +1,48 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 2 # number of classes
|
||||
depth_multiple: 1.33 # model depth multiple
|
||||
width_multiple: 1.25 # layer channel multiple
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -0,0 +1,36 @@
|
||||
# pip install -r requirements.txt
|
||||
|
||||
# Base ----------------------------------------
|
||||
matplotlib>=3.2.2
|
||||
numpy>=1.18.5
|
||||
opencv-python>=4.1.2
|
||||
Pillow>=7.1.2
|
||||
PyYAML>=5.3.1
|
||||
requests>=2.23.0
|
||||
scipy>=1.4.1
|
||||
# torch>=1.7.0
|
||||
# torchvision>=0.8.1
|
||||
tqdm>=4.41.0
|
||||
|
||||
# Logging -------------------------------------
|
||||
tensorboard>=2.4.1
|
||||
# wandb
|
||||
|
||||
# Plotting ------------------------------------
|
||||
pandas>=1.1.4
|
||||
seaborn>=0.11.0
|
||||
|
||||
# Export --------------------------------------
|
||||
# coremltools>=4.1 # CoreML export
|
||||
# onnx>=1.9.0 # ONNX export
|
||||
# onnx-simplifier>=0.3.6 # ONNX simplifier
|
||||
# scikit-learn==0.19.2 # CoreML quantization
|
||||
# tensorflow>=2.4.1 # TFLite export
|
||||
# tensorflowjs>=3.9.0 # TF.js export
|
||||
|
||||
# Extras --------------------------------------
|
||||
# albumentations>=1.0.3
|
||||
# Cython # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
|
||||
# pycocotools>=2.0 # COCO mAP
|
||||
# roboflow
|
||||
thop # FLOPs computation
|
||||
|
After Width: | Height: | Size: 177 KiB |
@ -0,0 +1,51 @@
|
||||
# Project-wide configuration file, can be used for package metadata and other toll configurations
|
||||
# Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments
|
||||
|
||||
[metadata]
|
||||
license_file = LICENSE
|
||||
description-file = README.md
|
||||
|
||||
|
||||
[tool:pytest]
|
||||
norecursedirs =
|
||||
.git
|
||||
dist
|
||||
build
|
||||
addopts =
|
||||
--doctest-modules
|
||||
--durations=25
|
||||
--color=yes
|
||||
|
||||
|
||||
[flake8]
|
||||
max-line-length = 120
|
||||
exclude = .tox,*.egg,build,temp
|
||||
select = E,W,F
|
||||
doctests = True
|
||||
verbose = 2
|
||||
# https://pep8.readthedocs.io/en/latest/intro.html#error-codes
|
||||
format = pylint
|
||||
# see: https://www.flake8rules.com/
|
||||
ignore =
|
||||
E731 # Do not assign a lambda expression, use a def
|
||||
F405
|
||||
E402
|
||||
F841
|
||||
E741
|
||||
F821
|
||||
E722
|
||||
F401
|
||||
W504
|
||||
E127
|
||||
W504
|
||||
E231
|
||||
E501
|
||||
F403
|
||||
E302
|
||||
F541
|
||||
|
||||
|
||||
[isort]
|
||||
# https://pycqa.github.io/isort/docs/configuration/options.html
|
||||
line_length = 120
|
||||
multi_line_output = 0
|
||||
@ -0,0 +1,630 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Train a YOLOv5 model on a custom dataset
|
||||
|
||||
Usage:
|
||||
$ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640
|
||||
"""
|
||||
import argparse
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import sys
|
||||
import time
|
||||
from copy import deepcopy
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
import yaml
|
||||
from torch.cuda import amp
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.optim import SGD, Adam, lr_scheduler
|
||||
from tqdm import tqdm
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[0] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
import val # for end-of-epoch mAP
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import Model
|
||||
from utils.autoanchor import check_anchors
|
||||
from utils.autobatch import check_train_batch_size
|
||||
from utils.callbacks import Callbacks
|
||||
from utils.datasets import create_dataloader
|
||||
from utils.downloads import attempt_download
|
||||
from utils.general import (LOGGER, NCOLS, check_dataset, check_file, check_git_status, check_img_size,
|
||||
check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path,
|
||||
init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods,
|
||||
one_cycle, print_args, print_mutation, strip_optimizer)
|
||||
from utils.loggers import Loggers
|
||||
from utils.loggers.wandb.wandb_utils import check_wandb_resume
|
||||
from utils.loss import ComputeLoss
|
||||
from utils.metrics import fitness
|
||||
from utils.plots import plot_evolve, plot_labels
|
||||
from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, select_device, torch_distributed_zero_first
|
||||
|
||||
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
|
||||
RANK = int(os.getenv('RANK', -1))
|
||||
WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
|
||||
|
||||
|
||||
def train(hyp, # path/to/hyp.yaml or hyp dictionary
|
||||
opt,
|
||||
device,
|
||||
callbacks
|
||||
):
|
||||
save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, = \
|
||||
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
|
||||
opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
|
||||
|
||||
# Directories
|
||||
w = save_dir / 'weights' # weights dir
|
||||
(w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir
|
||||
last, best = w / 'last.pt', w / 'best.pt'
|
||||
|
||||
# Hyperparameters
|
||||
if isinstance(hyp, str):
|
||||
with open(hyp, errors='ignore') as f:
|
||||
hyp = yaml.safe_load(f) # load hyps dict
|
||||
LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
|
||||
|
||||
# Save run settings
|
||||
with open(save_dir / 'hyp.yaml', 'w') as f:
|
||||
yaml.safe_dump(hyp, f, sort_keys=False)
|
||||
with open(save_dir / 'opt.yaml', 'w') as f:
|
||||
yaml.safe_dump(vars(opt), f, sort_keys=False)
|
||||
data_dict = None
|
||||
|
||||
# Loggers
|
||||
if RANK in [-1, 0]:
|
||||
loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance
|
||||
if loggers.wandb:
|
||||
data_dict = loggers.wandb.data_dict
|
||||
if resume:
|
||||
weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp
|
||||
|
||||
# Register actions
|
||||
for k in methods(loggers):
|
||||
callbacks.register_action(k, callback=getattr(loggers, k))
|
||||
|
||||
# Config
|
||||
plots = not evolve # create plots
|
||||
cuda = device.type != 'cpu'
|
||||
init_seeds(1 + RANK)
|
||||
with torch_distributed_zero_first(LOCAL_RANK):
|
||||
data_dict = data_dict or check_dataset(data) # check if None
|
||||
train_path, val_path = data_dict['train'], data_dict['val']
|
||||
nc = 1 if single_cls else int(data_dict['nc']) # number of classes
|
||||
names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
|
||||
assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}' # check
|
||||
is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset
|
||||
|
||||
# Model
|
||||
check_suffix(weights, '.pt') # check weights
|
||||
pretrained = weights.endswith('.pt')
|
||||
if pretrained:
|
||||
with torch_distributed_zero_first(LOCAL_RANK):
|
||||
weights = attempt_download(weights) # download if not found locally
|
||||
ckpt = torch.load(weights, map_location=device) # load checkpoint
|
||||
model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
|
||||
exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys
|
||||
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
|
||||
csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect
|
||||
model.load_state_dict(csd, strict=False) # load
|
||||
LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report
|
||||
else:
|
||||
model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
|
||||
|
||||
# Freeze
|
||||
freeze = [f'model.{x}.' for x in range(freeze)] # layers to freeze
|
||||
for k, v in model.named_parameters():
|
||||
v.requires_grad = True # train all layers
|
||||
if any(x in k for x in freeze):
|
||||
LOGGER.info(f'freezing {k}')
|
||||
v.requires_grad = False
|
||||
|
||||
# Image size
|
||||
gs = max(int(model.stride.max()), 32) # grid size (max stride)
|
||||
imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
|
||||
|
||||
# Batch size
|
||||
if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
|
||||
batch_size = check_train_batch_size(model, imgsz)
|
||||
|
||||
# Optimizer
|
||||
nbs = 64 # nominal batch size
|
||||
accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
|
||||
hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay
|
||||
LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}")
|
||||
|
||||
g0, g1, g2 = [], [], [] # optimizer parameter groups
|
||||
for v in model.modules():
|
||||
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): # bias
|
||||
g2.append(v.bias)
|
||||
if isinstance(v, nn.BatchNorm2d): # weight (no decay)
|
||||
g0.append(v.weight)
|
||||
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): # weight (with decay)
|
||||
g1.append(v.weight)
|
||||
|
||||
if opt.adam:
|
||||
optimizer = Adam(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
|
||||
else:
|
||||
optimizer = SGD(g0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
|
||||
|
||||
optimizer.add_param_group({'params': g1, 'weight_decay': hyp['weight_decay']}) # add g1 with weight_decay
|
||||
optimizer.add_param_group({'params': g2}) # add g2 (biases)
|
||||
LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups "
|
||||
f"{len(g0)} weight, {len(g1)} weight (no decay), {len(g2)} bias")
|
||||
del g0, g1, g2
|
||||
|
||||
# Scheduler
|
||||
if opt.linear_lr:
|
||||
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
|
||||
else:
|
||||
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
|
||||
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)
|
||||
|
||||
# EMA
|
||||
ema = ModelEMA(model) if RANK in [-1, 0] else None
|
||||
|
||||
# Resume
|
||||
start_epoch, best_fitness = 0, 0.0
|
||||
if pretrained:
|
||||
# Optimizer
|
||||
if ckpt['optimizer'] is not None:
|
||||
optimizer.load_state_dict(ckpt['optimizer'])
|
||||
best_fitness = ckpt['best_fitness']
|
||||
|
||||
# EMA
|
||||
if ema and ckpt.get('ema'):
|
||||
ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
|
||||
ema.updates = ckpt['updates']
|
||||
|
||||
# Epochs
|
||||
start_epoch = ckpt['epoch'] + 1
|
||||
if resume:
|
||||
assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.'
|
||||
if epochs < start_epoch:
|
||||
LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.")
|
||||
epochs += ckpt['epoch'] # finetune additional epochs
|
||||
|
||||
del ckpt, csd
|
||||
|
||||
# DP mode
|
||||
if cuda and RANK == -1 and torch.cuda.device_count() > 1:
|
||||
LOGGER.warning('WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
|
||||
'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
|
||||
model = torch.nn.DataParallel(model)
|
||||
|
||||
# SyncBatchNorm
|
||||
if opt.sync_bn and cuda and RANK != -1:
|
||||
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
|
||||
LOGGER.info('Using SyncBatchNorm()')
|
||||
|
||||
# Trainloader
|
||||
train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
|
||||
hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=LOCAL_RANK,
|
||||
workers=workers, image_weights=opt.image_weights, quad=opt.quad,
|
||||
prefix=colorstr('train: '), shuffle=True)
|
||||
mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max()) # max label class
|
||||
nb = len(train_loader) # number of batches
|
||||
assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
|
||||
|
||||
# Process 0
|
||||
if RANK in [-1, 0]:
|
||||
val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls,
|
||||
hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1,
|
||||
workers=workers, pad=0.5,
|
||||
prefix=colorstr('val: '))[0]
|
||||
|
||||
if not resume:
|
||||
labels = np.concatenate(dataset.labels, 0)
|
||||
# c = torch.tensor(labels[:, 0]) # classes
|
||||
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
|
||||
# model._initialize_biases(cf.to(device))
|
||||
if plots:
|
||||
plot_labels(labels, names, save_dir)
|
||||
|
||||
# Anchors
|
||||
if not opt.noautoanchor:
|
||||
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
|
||||
model.half().float() # pre-reduce anchor precision
|
||||
|
||||
callbacks.run('on_pretrain_routine_end')
|
||||
|
||||
# DDP mode
|
||||
if cuda and RANK != -1:
|
||||
model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
|
||||
|
||||
# Model attributes
|
||||
nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)
|
||||
hyp['box'] *= 3 / nl # scale to layers
|
||||
hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers
|
||||
hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
|
||||
hyp['label_smoothing'] = opt.label_smoothing
|
||||
model.nc = nc # attach number of classes to model
|
||||
model.hyp = hyp # attach hyperparameters to model
|
||||
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
|
||||
model.names = names
|
||||
|
||||
# Start training
|
||||
t0 = time.time()
|
||||
nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations)
|
||||
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
|
||||
last_opt_step = -1
|
||||
maps = np.zeros(nc) # mAP per class
|
||||
results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
|
||||
scheduler.last_epoch = start_epoch - 1 # do not move
|
||||
scaler = amp.GradScaler(enabled=cuda)
|
||||
stopper = EarlyStopping(patience=opt.patience)
|
||||
compute_loss = ComputeLoss(model) # init loss class
|
||||
LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
|
||||
f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
|
||||
f"Logging results to {colorstr('bold', save_dir)}\n"
|
||||
f'Starting training for {epochs} epochs...')
|
||||
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
|
||||
model.train()
|
||||
|
||||
# Update image weights (optional, single-GPU only)
|
||||
if opt.image_weights:
|
||||
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
|
||||
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
|
||||
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
|
||||
|
||||
# Update mosaic border (optional)
|
||||
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
|
||||
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
|
||||
|
||||
mloss = torch.zeros(3, device=device) # mean losses
|
||||
if RANK != -1:
|
||||
train_loader.sampler.set_epoch(epoch)
|
||||
pbar = enumerate(train_loader)
|
||||
LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
|
||||
if RANK in [-1, 0]:
|
||||
pbar = tqdm(pbar, total=nb, ncols=NCOLS, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
|
||||
optimizer.zero_grad()
|
||||
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
|
||||
ni = i + nb * epoch # number integrated batches (since train start)
|
||||
imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0
|
||||
|
||||
# Warmup
|
||||
if ni <= nw:
|
||||
xi = [0, nw] # x interp
|
||||
# compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
|
||||
accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
|
||||
for j, x in enumerate(optimizer.param_groups):
|
||||
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
|
||||
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
|
||||
if 'momentum' in x:
|
||||
x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
|
||||
|
||||
# Multi-scale
|
||||
if opt.multi_scale:
|
||||
sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
|
||||
sf = sz / max(imgs.shape[2:]) # scale factor
|
||||
if sf != 1:
|
||||
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
|
||||
imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
|
||||
|
||||
# Forward
|
||||
with amp.autocast(enabled=cuda):
|
||||
pred = model(imgs) # forward
|
||||
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
|
||||
if RANK != -1:
|
||||
loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
|
||||
if opt.quad:
|
||||
loss *= 4.
|
||||
|
||||
# Backward
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
# Optimize
|
||||
if ni - last_opt_step >= accumulate:
|
||||
scaler.step(optimizer) # optimizer.step
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
if ema:
|
||||
ema.update(model)
|
||||
last_opt_step = ni
|
||||
|
||||
# Log
|
||||
if RANK in [-1, 0]:
|
||||
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
|
||||
mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB)
|
||||
pbar.set_description(('%10s' * 2 + '%10.4g' * 5) % (
|
||||
f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
|
||||
callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots, opt.sync_bn)
|
||||
# end batch ------------------------------------------------------------------------------------------------
|
||||
|
||||
# Scheduler
|
||||
lr = [x['lr'] for x in optimizer.param_groups] # for loggers
|
||||
scheduler.step()
|
||||
|
||||
if RANK in [-1, 0]:
|
||||
# mAP
|
||||
callbacks.run('on_train_epoch_end', epoch=epoch)
|
||||
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
|
||||
final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
|
||||
if not noval or final_epoch: # Calculate mAP
|
||||
results, maps, _ = val.run(data_dict,
|
||||
batch_size=batch_size // WORLD_SIZE * 2,
|
||||
imgsz=imgsz,
|
||||
model=ema.ema,
|
||||
single_cls=single_cls,
|
||||
dataloader=val_loader,
|
||||
save_dir=save_dir,
|
||||
plots=False,
|
||||
callbacks=callbacks,
|
||||
compute_loss=compute_loss)
|
||||
|
||||
# Update best mAP
|
||||
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
|
||||
if fi > best_fitness:
|
||||
best_fitness = fi
|
||||
log_vals = list(mloss) + list(results) + lr
|
||||
callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)
|
||||
|
||||
# Save model
|
||||
if (not nosave) or (final_epoch and not evolve): # if save
|
||||
ckpt = {'epoch': epoch,
|
||||
'best_fitness': best_fitness,
|
||||
'model': deepcopy(de_parallel(model)).half(),
|
||||
'ema': deepcopy(ema.ema).half(),
|
||||
'updates': ema.updates,
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
|
||||
'date': datetime.now().isoformat()}
|
||||
|
||||
# Save last, best and delete
|
||||
torch.save(ckpt, last)
|
||||
if best_fitness == fi:
|
||||
torch.save(ckpt, best)
|
||||
if (epoch > 0) and (opt.save_period > 0) and (epoch % opt.save_period == 0):
|
||||
torch.save(ckpt, w / f'epoch{epoch}.pt')
|
||||
del ckpt
|
||||
callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)
|
||||
|
||||
# Stop Single-GPU
|
||||
if RANK == -1 and stopper(epoch=epoch, fitness=fi):
|
||||
break
|
||||
|
||||
# Stop DDP TODO: known issues shttps://github.com/ultralytics/yolov5/pull/4576
|
||||
# stop = stopper(epoch=epoch, fitness=fi)
|
||||
# if RANK == 0:
|
||||
# dist.broadcast_object_list([stop], 0) # broadcast 'stop' to all ranks
|
||||
|
||||
# Stop DPP
|
||||
# with torch_distributed_zero_first(RANK):
|
||||
# if stop:
|
||||
# break # must break all DDP ranks
|
||||
|
||||
# end epoch ----------------------------------------------------------------------------------------------------
|
||||
# end training -----------------------------------------------------------------------------------------------------
|
||||
if RANK in [-1, 0]:
|
||||
LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
|
||||
for f in last, best:
|
||||
if f.exists():
|
||||
strip_optimizer(f) # strip optimizers
|
||||
if f is best:
|
||||
LOGGER.info(f'\nValidating {f}...')
|
||||
results, _, _ = val.run(data_dict,
|
||||
batch_size=batch_size // WORLD_SIZE * 2,
|
||||
imgsz=imgsz,
|
||||
model=attempt_load(f, device).half(),
|
||||
iou_thres=0.65 if is_coco else 0.60, # best pycocotools results at 0.65
|
||||
single_cls=single_cls,
|
||||
dataloader=val_loader,
|
||||
save_dir=save_dir,
|
||||
save_json=is_coco,
|
||||
verbose=True,
|
||||
plots=True,
|
||||
callbacks=callbacks,
|
||||
compute_loss=compute_loss) # val best model with plots
|
||||
if is_coco:
|
||||
callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
|
||||
|
||||
callbacks.run('on_train_end', last, best, plots, epoch, results)
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
return results
|
||||
|
||||
|
||||
# 明天把这些模型都试试效果先，一波波给他训练完毕，找个公开的数据集测试一下。
|
||||
def parse_opt(known=False):
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--weights', type=str, default=ROOT / 'pretrained/yolov5s.pt', help='initial weights path')
|
||||
parser.add_argument('--cfg', type=str, default=ROOT / 'models/yolov5s.yaml', help='model.yaml path')
|
||||
parser.add_argument('--data', type=str, default=ROOT / 'data/data.yaml', help='dataset.yaml path')
|
||||
parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path')
|
||||
parser.add_argument('--epochs', type=int, default=300)
|
||||
parser.add_argument('--batch-size', type=int, default=4, help='total batch size for all GPUs, -1 for autobatch')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
|
||||
parser.add_argument('--rect', action='store_true', help='rectangular training')
|
||||
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
|
||||
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
|
||||
parser.add_argument('--noval', action='store_true', help='only validate final epoch')
|
||||
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
|
||||
parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
|
||||
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
|
||||
parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
|
||||
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
# parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
|
||||
parser.add_argument('--multi-scale', default=True, help='vary img-size +/- 50%%')
|
||||
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
|
||||
parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
|
||||
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
|
||||
parser.add_argument('--workers', type=int, default=0, help='max dataloader workers (per RANK in DDP mode)')
|
||||
parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
|
||||
parser.add_argument('--name', default='exp', help='save to project/name')
|
||||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||
parser.add_argument('--quad', action='store_true', help='quad dataloader')
|
||||
parser.add_argument('--linear-lr', action='store_true', help='linear LR')
|
||||
parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
|
||||
parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
|
||||
parser.add_argument('--freeze', type=int, default=0, help='Number of layers to freeze. backbone=10, all=24')
|
||||
parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
|
||||
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
|
||||
# Weights & Biases arguments
|
||||
parser.add_argument('--entity', default=None, help='W&B: Entity')
|
||||
parser.add_argument('--upload_dataset', action='store_true', help='W&B: Upload dataset as artifact table')
|
||||
parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')
|
||||
parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
|
||||
|
||||
opt = parser.parse_known_args()[0] if known else parser.parse_args()
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt, callbacks=Callbacks()):
|
||||
|
||||
# Checks
|
||||
if RANK in [-1, 0]:
|
||||
print_args(FILE.stem, opt)
|
||||
check_git_status()
|
||||
check_requirements(exclude=['thop'])
|
||||
|
||||
# Resume
|
||||
if opt.resume and not check_wandb_resume(opt) and not opt.evolve: # resume an interrupted run
|
||||
ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path
|
||||
assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
|
||||
with open(Path(ckpt).parent.parent / 'opt.yaml', errors='ignore') as f:
|
||||
opt = argparse.Namespace(**yaml.safe_load(f)) # replace
|
||||
opt.cfg, opt.weights, opt.resume = '', ckpt, True # reinstate
|
||||
LOGGER.info(f'Resuming training from {ckpt}')
|
||||
else:
|
||||
opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \
|
||||
check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks
|
||||
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
|
||||
if opt.evolve:
|
||||
opt.project = str(ROOT / 'runs/evolve')
|
||||
opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume
|
||||
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
|
||||
|
||||
# DDP mode
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
if LOCAL_RANK != -1:
|
||||
assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
|
||||
assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count'
|
||||
assert not opt.image_weights, '--image-weights argument is not compatible with DDP training'
|
||||
assert not opt.evolve, '--evolve argument is not compatible with DDP training'
|
||||
torch.cuda.set_device(LOCAL_RANK)
|
||||
device = torch.device('cuda', LOCAL_RANK)
|
||||
dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
|
||||
|
||||
# Train
|
||||
if not opt.evolve:
|
||||
train(opt.hyp, opt, device, callbacks)
|
||||
if WORLD_SIZE > 1 and RANK == 0:
|
||||
LOGGER.info('Destroying process group... ')
|
||||
dist.destroy_process_group()
|
||||
|
||||
# Evolve hyperparameters (optional)
|
||||
else:
|
||||
# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
|
||||
meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
|
||||
'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
|
||||
'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
|
||||
'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
|
||||
'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
|
||||
'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
|
||||
'box': (1, 0.02, 0.2), # box loss gain
|
||||
'cls': (1, 0.2, 4.0), # cls loss gain
|
||||
'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
|
||||
'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
|
||||
'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
|
||||
'iou_t': (0, 0.1, 0.7), # IoU training threshold
|
||||
'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
|
||||
'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
|
||||
'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
|
||||
'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
|
||||
'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
|
||||
'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
|
||||
'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
|
||||
'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
|
||||
'scale': (1, 0.0, 0.9), # image scale (+/- gain)
|
||||
'shear': (1, 0.0, 10.0), # image shear (+/- deg)
|
||||
'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
|
||||
'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
|
||||
'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
|
||||
'mosaic': (1, 0.0, 1.0), # image mixup (probability)
|
||||
'mixup': (1, 0.0, 1.0), # image mixup (probability)
|
||||
'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability)
|
||||
|
||||
with open(opt.hyp, errors='ignore') as f:
|
||||
hyp = yaml.safe_load(f) # load hyps dict
|
||||
if 'anchors' not in hyp: # anchors commented in hyp.yaml
|
||||
hyp['anchors'] = 3
|
||||
opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
|
||||
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
|
||||
evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'
|
||||
if opt.bucket:
|
||||
os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {save_dir}') # download evolve.csv if exists
|
||||
|
||||
for _ in range(opt.evolve): # generations to evolve
|
||||
if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
|
||||
# Select parent(s)
|
||||
parent = 'single' # parent selection method: 'single' or 'weighted'
|
||||
x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
|
||||
n = min(5, len(x)) # number of previous results to consider
|
||||
x = x[np.argsort(-fitness(x))][:n] # top n mutations
|
||||
w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0)
|
||||
if parent == 'single' or len(x) == 1:
|
||||
# x = x[random.randint(0, n - 1)] # random selection
|
||||
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
|
||||
elif parent == 'weighted':
|
||||
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
|
||||
|
||||
# Mutate
|
||||
mp, s = 0.8, 0.2 # mutation probability, sigma
|
||||
npr = np.random
|
||||
npr.seed(int(time.time()))
|
||||
g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1
|
||||
ng = len(meta)
|
||||
v = np.ones(ng)
|
||||
while all(v == 1): # mutate until a change occurs (prevent duplicates)
|
||||
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
|
||||
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
|
||||
hyp[k] = float(x[i + 7] * v[i]) # mutate
|
||||
|
||||
# Constrain to limits
|
||||
for k, v in meta.items():
|
||||
hyp[k] = max(hyp[k], v[1]) # lower limit
|
||||
hyp[k] = min(hyp[k], v[2]) # upper limit
|
||||
hyp[k] = round(hyp[k], 5) # significant digits
|
||||
|
||||
# Train mutation
|
||||
results = train(hyp.copy(), opt, device, callbacks)
|
||||
|
||||
# Write mutation results
|
||||
print_mutation(results, hyp.copy(), save_dir, opt.bucket)
|
||||
|
||||
# Plot results
|
||||
plot_evolve(evolve_csv)
|
||||
LOGGER.info(f'Hyperparameter evolution finished\n'
|
||||
f"Results saved to {colorstr('bold', save_dir)}\n"
|
||||
f'Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}')
|
||||
|
||||
|
||||
def run(**kwargs):
|
||||
# Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
|
||||
opt = parse_opt(True)
|
||||
for k, v in kwargs.items():
|
||||
setattr(opt, k, v)
|
||||
main(opt)
|
||||
|
||||
|
||||
# python train.py --data mask_data.yaml --cfg mask_yolov5s.yaml --weights pretrained/yolov5s.pt --epoch 100 --batch-size 4 --device cpu
|
||||
# python train.py --data mask_data.yaml --cfg mask_yolov5l.yaml --weights pretrained/yolov5l.pt --epoch 100 --batch-size 4
|
||||
# python train.py --data mask_data.yaml --cfg mask_yolov5m.yaml --weights pretrained/yolov5m.pt --epoch 100 --batch-size 4
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
@ -0,0 +1,18 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
utils/initialization
|
||||
"""
|
||||
|
||||
|
||||
def notebook_init():
|
||||
# For YOLOv5 notebooks
|
||||
print('Checking setup...')
|
||||
from IPython import display # to display images and clear console output
|
||||
|
||||
from utils.general import emojis
|
||||
from utils.torch_utils import select_device # YOLOv5 imports
|
||||
|
||||
display.clear_output()
|
||||
select_device(newline=False)
|
||||
print(emojis('Setup complete ✅'))
|
||||
return display
|
||||
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