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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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||||||
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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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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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have the freedom to distribute copies of free software (and charge for
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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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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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you modify it: responsibilities to respect the freedom of others.
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For example, if you distribute copies of such a program, whether
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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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products. If such problems arise substantially in other domains, we
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stand ready to extend this provision to those domains in future versions
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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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"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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"The Program" refers to any copyrightable work licensed under this
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To "modify" a work means to copy from or adapt all or part of the work
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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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permission, would make you directly or secondarily liable for
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infringement under applicable copyright law, except executing it on a
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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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A "Standard Interface" means an interface that either is an official
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The "System Libraries" of an executable work include anything, other
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"Major Component", in this context, means a major essential component
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The "Corresponding Source" for a work in object code form means all
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the source code needed to generate, install, and (for an executable
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work) run the object code and to modify the work, including scripts to
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control those activities. However, it does not include the work's
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System Libraries, or general-purpose tools or generally available free
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||||||
programs which are used unmodified in performing those activities but
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||||||
which are not part of the work. For example, Corresponding Source
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includes interface definition files associated with source files for
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the work, and the source code for shared libraries and dynamically
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linked subprograms that the work is specifically designed to require,
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such as by intimate data communication or control flow between those
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subprograms and other parts of the work.
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||||||
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|
||||||
The Corresponding Source need not include anything that users
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||||||
can regenerate automatically from other parts of the Corresponding
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||||||
Source.
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||||||
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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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||||||
copyright on the Program, and are irrevocable provided the stated
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||||||
conditions are met. This License explicitly affirms your unlimited
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||||||
permission to run the unmodified Program. The output from running a
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||||||
covered work is covered by this License only if the output, given its
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content, constitutes a covered work. This License acknowledges your
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||||||
rights of fair use or other equivalent, as provided by copyright law.
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You may make, run and propagate covered works that you do not
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convey, without conditions so long as your license otherwise remains
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in force. You may convey covered works to others for the sole purpose
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of having them make modifications exclusively for you, or provide you
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with facilities for running those works, provided that you comply with
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the terms of this License in conveying all material for which you do
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not control copyright. Those thus making or running the covered works
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for you must do so exclusively on your behalf, under your direction
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and control, on terms that prohibit them from making any copies of
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your copyrighted material outside their relationship with you.
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|
||||||
Conveying under any other circumstances is permitted solely under
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|
||||||
the conditions stated below. Sublicensing is not allowed; section 10
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|
||||||
makes it unnecessary.
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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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measures.
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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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||||||
circumvention of technological measures to the extent such circumvention
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||||||
is effected by exercising rights under this License with respect to
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the covered work, and you disclaim any intention to limit operation or
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modification of the work as a means of enforcing, against the work's
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users, your or third parties' legal rights to forbid circumvention of
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technological measures.
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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
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|
||||||
receive it, in any medium, provided that you conspicuously and
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||||||
appropriately publish on each copy an appropriate copyright notice;
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||||||
keep intact all notices stating that this License and any
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||||||
non-permissive terms added in accord with section 7 apply to the code;
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||||||
keep intact all notices of the absence of any warranty; and give all
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||||||
recipients a copy of this License along with the Program.
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You may charge any price or no price for each copy that you convey,
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and you may offer support or warranty protection for a fee.
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|
||||||
5. Conveying Modified Source Versions.
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||||||
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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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|
||||||
terms of section 4, provided that you also meet all of these conditions:
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|
||||||
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|
||||||
a) The work must carry prominent notices stating that you modified
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|
||||||
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
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|
||||||
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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|
||||||
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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
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|
||||||
work need not make them do so.
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||||||
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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
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|
||||||
beyond what the individual works permit. Inclusion of a covered work
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|
||||||
in an aggregate does not cause this License to apply to the other
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|
||||||
parts of the aggregate.
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|
||||||
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|
||||||
6. Conveying Non-Source Forms.
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|
||||||
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|
||||||
You may convey a covered work in object code form under the terms
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|
||||||
of sections 4 and 5, provided that you also convey the
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|
||||||
machine-readable Corresponding Source under the terms of this License,
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|
||||||
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
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|
||||||
Corresponding Source fixed on a durable physical medium
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|
||||||
customarily used for software interchange.
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|
||||||
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|
||||||
b) Convey the object code in, or embodied in, a physical product
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|
||||||
(including a physical distribution medium), accompanied by a
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|
||||||
written offer, valid for at least three years and valid for as
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|
||||||
long as you offer spare parts or customer support for that product
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|
||||||
model, to give anyone who possesses the object code either (1) a
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|
||||||
copy of the Corresponding Source for all the software in the
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|
||||||
product that is covered by this License, on a durable physical
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|
||||||
medium customarily used for software interchange, for a price no
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|
||||||
more than your reasonable cost of physically performing this
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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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|
||||||
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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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|
||||||
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|
||||||
d) Convey the object code by offering access from a designated
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|
||||||
place (gratis or for a charge), and offer equivalent access to the
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|
||||||
Corresponding Source in the same way through the same place at no
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|
||||||
further charge. You need not require recipients to copy the
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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
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|
||||||
clear directions next to the object code saying where to find the
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|
||||||
Corresponding Source. Regardless of what server hosts the
|
|
||||||
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
|
|
||||||
Source of the work are being offered to the general public at no
|
|
||||||
charge under subsection 6d.
|
|
||||||
|
|
||||||
A separable portion of the object code, whose source code is excluded
|
|
||||||
from the Corresponding Source as a System Library, need not be
|
|
||||||
included in conveying the object code work.
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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
|
|
||||||
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
|
|
||||||
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,
|
|
||||||
procedures, authorization keys, or other information required to install
|
|
||||||
and execute modified versions of a covered work in that User Product from
|
|
||||||
a modified version of its Corresponding Source. The information must
|
|
||||||
suffice to ensure that the continued functioning of the modified object
|
|
||||||
code is in no case prevented or interfered with solely because
|
|
||||||
modification has been made.
|
|
||||||
|
|
||||||
If you convey an object code work under this section in, or with, or
|
|
||||||
specifically for use in, a User Product, and the conveying occurs as
|
|
||||||
part of a transaction in which the right of possession and use of the
|
|
||||||
User Product is transferred to the recipient in perpetuity or for a
|
|
||||||
fixed term (regardless of how the transaction is characterized), the
|
|
||||||
Corresponding Source conveyed under this section must be accompanied
|
|
||||||
by the Installation Information. But this requirement does not apply
|
|
||||||
if neither you nor any third party retains the ability to install
|
|
||||||
modified object code on the User Product (for example, the work has
|
|
||||||
been installed in ROM).
|
|
||||||
|
|
||||||
The requirement to provide Installation Information does not include a
|
|
||||||
requirement to continue to provide support service, warranty, or updates
|
|
||||||
for a work that has been modified or installed by the recipient, or for
|
|
||||||
the User Product in which it has been modified or installed. Access to a
|
|
||||||
network may be denied when the modification itself materially and
|
|
||||||
adversely affects the operation of the network or violates the rules and
|
|
||||||
protocols for communication across the network.
|
|
||||||
|
|
||||||
Corresponding Source conveyed, and Installation Information provided,
|
|
||||||
in accord with this section must be in a format that is publicly
|
|
||||||
documented (and with an implementation available to the public in
|
|
||||||
source code form), and must require no special password or key for
|
|
||||||
unpacking, reading or copying.
|
|
||||||
|
|
||||||
7. Additional Terms.
|
|
||||||
|
|
||||||
"Additional permissions" are terms that supplement the terms of this
|
|
||||||
License by making exceptions from one or more of its conditions.
|
|
||||||
Additional permissions that are applicable to the entire Program shall
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|
||||||
be treated as though they were included in this License, to the extent
|
|
||||||
that they are valid under applicable law. If additional permissions
|
|
||||||
apply only to part of the Program, that part may be used separately
|
|
||||||
under those permissions, but the entire Program remains governed by
|
|
||||||
this License without regard to the additional permissions.
|
|
||||||
|
|
||||||
When you convey a copy of a covered work, you may at your option
|
|
||||||
remove any additional permissions from that copy, or from any part of
|
|
||||||
it. (Additional permissions may be written to require their own
|
|
||||||
removal in certain cases when you modify the work.) You may place
|
|
||||||
additional permissions on material, added by you to a covered work,
|
|
||||||
for which you have or can give appropriate copyright permission.
|
|
||||||
|
|
||||||
Notwithstanding any other provision of this License, for material you
|
|
||||||
add to a covered work, you may (if authorized by the copyright holders of
|
|
||||||
that material) supplement the terms of this License with terms:
|
|
||||||
|
|
||||||
a) Disclaiming warranty or limiting liability differently from the
|
|
||||||
terms of sections 15 and 16 of this License; or
|
|
||||||
|
|
||||||
b) Requiring preservation of specified reasonable legal notices or
|
|
||||||
author attributions in that material or in the Appropriate Legal
|
|
||||||
Notices displayed by works containing it; or
|
|
||||||
|
|
||||||
c) Prohibiting misrepresentation of the origin of that material, or
|
|
||||||
requiring that modified versions of such material be marked in
|
|
||||||
reasonable ways as different from the original version; or
|
|
||||||
|
|
||||||
d) Limiting the use for publicity purposes of names of licensors or
|
|
||||||
authors of the material; or
|
|
||||||
|
|
||||||
e) Declining to grant rights under trademark law for use of some
|
|
||||||
trade names, trademarks, or service marks; or
|
|
||||||
|
|
||||||
f) Requiring indemnification of licensors and authors of that
|
|
||||||
material by anyone who conveys the material (or modified versions of
|
|
||||||
it) with contractual assumptions of liability to the recipient, for
|
|
||||||
any liability that these contractual assumptions directly impose on
|
|
||||||
those licensors and authors.
|
|
||||||
|
|
||||||
All other non-permissive additional terms are considered "further
|
|
||||||
restrictions" within the meaning of section 10. If the Program as you
|
|
||||||
received it, or any part of it, contains a notice stating that it is
|
|
||||||
governed by this License along with a term that is a further
|
|
||||||
restriction, you may remove that term. If a license document contains
|
|
||||||
a further restriction but permits relicensing or conveying under this
|
|
||||||
License, you may add to a covered work material governed by the terms
|
|
||||||
of that license document, provided that the further restriction does
|
|
||||||
not survive such relicensing or conveying.
|
|
||||||
|
|
||||||
If you add terms to a covered work in accord with this section, you
|
|
||||||
must place, in the relevant source files, a statement of the
|
|
||||||
additional terms that apply to those files, or a notice indicating
|
|
||||||
where to find the applicable terms.
|
|
||||||
|
|
||||||
Additional terms, permissive or non-permissive, may be stated in the
|
|
||||||
form of a separately written license, or stated as exceptions;
|
|
||||||
the above requirements apply either way.
|
|
||||||
|
|
||||||
8. Termination.
|
|
||||||
|
|
||||||
You may not propagate or modify a covered work except as expressly
|
|
||||||
provided under this License. Any attempt otherwise to propagate or
|
|
||||||
modify it is void, and will automatically terminate your rights under
|
|
||||||
this License (including any patent licenses granted under the third
|
|
||||||
paragraph of section 11).
|
|
||||||
|
|
||||||
However, if you cease all violation of this License, then your
|
|
||||||
license from a particular copyright holder is reinstated (a)
|
|
||||||
provisionally, unless and until the copyright holder explicitly and
|
|
||||||
finally terminates your license, and (b) permanently, if the copyright
|
|
||||||
holder fails to notify you of the violation by some reasonable means
|
|
||||||
prior to 60 days after the cessation.
|
|
||||||
|
|
||||||
Moreover, your license from a particular copyright holder is
|
|
||||||
reinstated permanently if the copyright holder notifies you of the
|
|
||||||
violation by some reasonable means, this is the first time you have
|
|
||||||
received notice of violation of this License (for any work) from that
|
|
||||||
copyright holder, and you cure the violation prior to 30 days after
|
|
||||||
your receipt of the notice.
|
|
||||||
|
|
||||||
Termination of your rights under this section does not terminate the
|
|
||||||
licenses of parties who have received copies or rights from you under
|
|
||||||
this License. If your rights have been terminated and not permanently
|
|
||||||
reinstated, you do not qualify to receive new licenses for the same
|
|
||||||
material under section 10.
|
|
||||||
|
|
||||||
9. Acceptance Not Required for Having Copies.
|
|
||||||
|
|
||||||
You are not required to accept this License in order to receive or
|
|
||||||
run a copy of the Program. Ancillary propagation of a covered work
|
|
||||||
occurring solely as a consequence of using peer-to-peer transmission
|
|
||||||
to receive a copy likewise does not require acceptance. However,
|
|
||||||
nothing other than this License grants you permission to propagate or
|
|
||||||
modify any covered work. These actions infringe copyright if you do
|
|
||||||
not accept this License. Therefore, by modifying or propagating a
|
|
||||||
covered work, you indicate your acceptance of this License to do so.
|
|
||||||
|
|
||||||
10. Automatic Licensing of Downstream Recipients.
|
|
||||||
|
|
||||||
Each time you convey a covered work, the recipient automatically
|
|
||||||
receives a license from the original licensors, to run, modify and
|
|
||||||
propagate that work, subject to this License. You are not responsible
|
|
||||||
for enforcing compliance by third parties with this License.
|
|
||||||
|
|
||||||
An "entity transaction" is a transaction transferring control of an
|
|
||||||
organization, or substantially all assets of one, or subdividing an
|
|
||||||
organization, or merging organizations. If propagation of a covered
|
|
||||||
work results from an entity transaction, each party to that
|
|
||||||
transaction who receives a copy of the work also receives whatever
|
|
||||||
licenses to the work the party's predecessor in interest had or could
|
|
||||||
give under the previous paragraph, plus a right to possession of the
|
|
||||||
Corresponding Source of the work from the predecessor in interest, if
|
|
||||||
the predecessor has it or can get it with reasonable efforts.
|
|
||||||
|
|
||||||
You may not impose any further restrictions on the exercise of the
|
|
||||||
rights granted or affirmed under this License. For example, you may
|
|
||||||
not impose a license fee, royalty, or other charge for exercise of
|
|
||||||
rights granted under this License, and you may not initiate litigation
|
|
||||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
|
||||||
any patent claim is infringed by making, using, selling, offering for
|
|
||||||
sale, or importing the Program or any portion of it.
|
|
||||||
|
|
||||||
11. Patents.
|
|
||||||
|
|
||||||
A "contributor" is a copyright holder who authorizes use under this
|
|
||||||
License of the Program or a work on which the Program is based. The
|
|
||||||
work thus licensed is called the contributor's "contributor version".
|
|
||||||
|
|
||||||
A contributor's "essential patent claims" are all patent claims
|
|
||||||
owned or controlled by the contributor, whether already acquired or
|
|
||||||
hereafter acquired, that would be infringed by some manner, permitted
|
|
||||||
by this License, of making, using, or selling its contributor version,
|
|
||||||
but do not include claims that would be infringed only as a
|
|
||||||
consequence of further modification of the contributor version. For
|
|
||||||
purposes of this definition, "control" includes the right to grant
|
|
||||||
patent sublicenses in a manner consistent with the requirements of
|
|
||||||
this License.
|
|
||||||
|
|
||||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
|
||||||
patent license under the contributor's essential patent claims, to
|
|
||||||
make, use, sell, offer for sale, import and otherwise run, modify and
|
|
||||||
propagate the contents of its contributor version.
|
|
||||||
|
|
||||||
In the following three paragraphs, a "patent license" is any express
|
|
||||||
agreement or commitment, however denominated, not to enforce a patent
|
|
||||||
(such as an express permission to practice a patent or covenant not to
|
|
||||||
sue for patent infringement). To "grant" such a patent license to a
|
|
||||||
party means to make such an agreement or commitment not to enforce a
|
|
||||||
patent against the party.
|
|
||||||
|
|
||||||
If you convey a covered work, knowingly relying on a patent license,
|
|
||||||
and the Corresponding Source of the work is not available for anyone
|
|
||||||
to copy, free of charge and under the terms of this License, through a
|
|
||||||
publicly available network server or other readily accessible means,
|
|
||||||
then you must either (1) cause the Corresponding Source to be so
|
|
||||||
available, or (2) arrange to deprive yourself of the benefit of the
|
|
||||||
patent license for this particular work, or (3) arrange, in a manner
|
|
||||||
consistent with the requirements of this License, to extend the patent
|
|
||||||
license to downstream recipients. "Knowingly relying" means you have
|
|
||||||
actual knowledge that, but for the patent license, your conveying the
|
|
||||||
covered work in a country, or your recipient's use of the covered work
|
|
||||||
in a country, would infringe one or more identifiable patents in that
|
|
||||||
country that you have reason to believe are valid.
|
|
||||||
|
|
||||||
If, pursuant to or in connection with a single transaction or
|
|
||||||
arrangement, you convey, or propagate by procuring conveyance of, a
|
|
||||||
covered work, and grant a patent license to some of the parties
|
|
||||||
receiving the covered work authorizing them to use, propagate, modify
|
|
||||||
or convey a specific copy of the covered work, then the patent license
|
|
||||||
you grant is automatically extended to all recipients of the covered
|
|
||||||
work and works based on it.
|
|
||||||
|
|
||||||
A patent license is "discriminatory" if it does not include within
|
|
||||||
the scope of its coverage, prohibits the exercise of, or is
|
|
||||||
conditioned on the non-exercise of one or more of the rights that are
|
|
||||||
specifically granted under this License. You may not convey a covered
|
|
||||||
work if you are a party to an arrangement with a third party that is
|
|
||||||
in the business of distributing software, under which you make payment
|
|
||||||
to the third party based on the extent of your activity of conveying
|
|
||||||
the work, and under which the third party grants, to any of the
|
|
||||||
parties who would receive the covered work from you, a discriminatory
|
|
||||||
patent license (a) in connection with copies of the covered work
|
|
||||||
conveyed by you (or copies made from those copies), or (b) primarily
|
|
||||||
for and in connection with specific products or compilations that
|
|
||||||
contain the covered work, unless you entered into that arrangement,
|
|
||||||
or that patent license was granted, prior to 28 March 2007.
|
|
||||||
|
|
||||||
Nothing in this License shall be construed as excluding or limiting
|
|
||||||
any implied license or other defenses to infringement that may
|
|
||||||
otherwise be available to you under applicable patent law.
|
|
||||||
|
|
||||||
12. No Surrender of Others' Freedom.
|
|
||||||
|
|
||||||
If conditions are imposed on you (whether by court order, agreement or
|
|
||||||
otherwise) that contradict the conditions of this License, they do not
|
|
||||||
excuse you from the conditions of this License. If you cannot convey a
|
|
||||||
covered work so as to satisfy simultaneously your obligations under this
|
|
||||||
License and any other pertinent obligations, then as a consequence you may
|
|
||||||
not convey it at all. For example, if you agree to terms that obligate you
|
|
||||||
to collect a royalty for further conveying from those to whom you convey
|
|
||||||
the Program, the only way you could satisfy both those terms and this
|
|
||||||
License would be to refrain entirely from conveying the Program.
|
|
||||||
|
|
||||||
13. Use with the GNU Affero General Public License.
|
|
||||||
|
|
||||||
Notwithstanding any other provision of this License, you have
|
|
||||||
permission to link or combine any covered work with a work licensed
|
|
||||||
under version 3 of the GNU Affero General Public License into a single
|
|
||||||
combined work, and to convey the resulting work. The terms of this
|
|
||||||
License will continue to apply to the part which is the covered work,
|
|
||||||
but the special requirements of the GNU Affero General Public License,
|
|
||||||
section 13, concerning interaction through a network will apply to the
|
|
||||||
combination as such.
|
|
||||||
|
|
||||||
14. Revised Versions of this License.
|
|
||||||
|
|
||||||
The Free Software Foundation may publish revised and/or new versions of
|
|
||||||
the GNU General Public License from time to time. Such new versions will
|
|
||||||
be similar in spirit to the present version, but may differ in detail to
|
|
||||||
address new problems or concerns.
|
|
||||||
|
|
||||||
Each version is given a distinguishing version number. If the
|
|
||||||
Program specifies that a certain numbered version of the GNU General
|
|
||||||
Public License "or any later version" applies to it, you have the
|
|
||||||
option of following the terms and conditions either of that numbered
|
|
||||||
version or of any later version published by the Free Software
|
|
||||||
Foundation. If the Program does not specify a version number of the
|
|
||||||
GNU General Public License, you may choose any version ever published
|
|
||||||
by the Free Software Foundation.
|
|
||||||
|
|
||||||
If the Program specifies that a proxy can decide which future
|
|
||||||
versions of the GNU General Public License can be used, that proxy's
|
|
||||||
public statement of acceptance of a version permanently authorizes you
|
|
||||||
to choose that version for the Program.
|
|
||||||
|
|
||||||
Later license versions may give you additional or different
|
|
||||||
permissions. However, no additional obligations are imposed on any
|
|
||||||
author or copyright holder as a result of your choosing to follow a
|
|
||||||
later version.
|
|
||||||
|
|
||||||
15. Disclaimer of Warranty.
|
|
||||||
|
|
||||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
|
||||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
|
||||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
|
||||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
|
||||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
|
||||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
|
||||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
|
||||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
|
||||||
|
|
||||||
16. Limitation of Liability.
|
|
||||||
|
|
||||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
|
||||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
|
||||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
|
||||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
|
||||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
|
||||||
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
|
||||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
|
||||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
|
||||||
SUCH DAMAGES.
|
|
||||||
|
|
||||||
17. Interpretation of Sections 15 and 16.
|
|
||||||
|
|
||||||
If the disclaimer of warranty and limitation of liability provided
|
|
||||||
above cannot be given local legal effect according to their terms,
|
|
||||||
reviewing courts shall apply local law that most closely approximates
|
|
||||||
an absolute waiver of all civil liability in connection with the
|
|
||||||
Program, unless a warranty or assumption of liability accompanies a
|
|
||||||
copy of the Program in return for a fee.
|
|
||||||
|
|
||||||
END OF TERMS AND CONDITIONS
|
|
||||||
|
|
||||||
How to Apply These Terms to Your New Programs
|
|
||||||
|
|
||||||
If you develop a new program, and you want it to be of the greatest
|
|
||||||
possible use to the public, the best way to achieve this is to make it
|
|
||||||
free software which everyone can redistribute and change under these terms.
|
|
||||||
|
|
||||||
To do so, attach the following notices to the program. It is safest
|
|
||||||
to attach them to the start of each source file to most effectively
|
|
||||||
state the exclusion of warranty; and each file should have at least
|
|
||||||
the "copyright" line and a pointer to where the full notice is found.
|
|
||||||
|
|
||||||
<one line to give the program's name and a brief idea of what it does.>
|
|
||||||
Copyright (C) <year> <name of author>
|
|
||||||
|
|
||||||
This program is free software: you can redistribute it and/or modify
|
|
||||||
it under the terms of the GNU General Public License as published by
|
|
||||||
the Free Software Foundation, either version 3 of the License, or
|
|
||||||
(at your option) any later version.
|
|
||||||
|
|
||||||
This program is distributed in the hope that it will be useful,
|
|
||||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
||||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
||||||
GNU General Public License for more details.
|
|
||||||
|
|
||||||
You should have received a copy of the GNU General Public License
|
|
||||||
along with this program. If not, see <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.
@ -1,67 +0,0 @@
|
|||||||
# 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
|
|
||||||
@ -1,53 +0,0 @@
|
|||||||
# 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
|
|
||||||
@ -1,112 +0,0 @@
|
|||||||
# 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)
|
|
||||||
@ -1,52 +0,0 @@
|
|||||||
# 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
|
|
||||||
@ -1,80 +0,0 @@
|
|||||||
# 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
|
|
||||||
@ -1,61 +0,0 @@
|
|||||||
# 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
|
|
||||||
@ -1,44 +0,0 @@
|
|||||||
# 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)
|
|
||||||
@ -1,30 +0,0 @@
|
|||||||
# 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
|
|
||||||
@ -1,13 +0,0 @@
|
|||||||
# 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']
|
|
||||||
|
|
||||||
@ -1,39 +0,0 @@
|
|||||||
# 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
|
|
||||||
@ -1,31 +0,0 @@
|
|||||||
# 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
|
|
||||||
@ -1,34 +0,0 @@
|
|||||||
# 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)
|
|
||||||
@ -1,34 +0,0 @@
|
|||||||
# 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)
|
|
||||||
@ -1,34 +0,0 @@
|
|||||||
# 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)
|
|
||||||
@ -1,34 +0,0 @@
|
|||||||
# 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)
|
|
||||||
|
Before Width: | Height: | Size: 476 KiB |
|
Before Width: | Height: | Size: 74 KiB |
|
Before Width: | Height: | Size: 392 KiB |
|
Before Width: | Height: | Size: 223 KiB |
|
Before Width: | Height: | Size: 181 KiB |
|
Before Width: | Height: | Size: 197 KiB |
|
Before Width: | Height: | Size: 646 KiB |
|
Before Width: | Height: | Size: 344 KiB |
|
Before Width: | Height: | Size: 315 KiB |
|
Before Width: | Height: | Size: 444 KiB |
|
Before Width: | Height: | Size: 165 KiB |
@ -1,12 +0,0 @@
|
|||||||
# 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']
|
|
||||||
@ -1,20 +0,0 @@
|
|||||||
#!/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
|
|
||||||
@ -1,27 +0,0 @@
|
|||||||
#!/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
|
|
||||||
@ -1,17 +0,0 @@
|
|||||||
#!/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
|
|
||||||
@ -1,102 +0,0 @@
|
|||||||
# 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')
|
|
||||||
@ -1,246 +0,0 @@
|
|||||||
# 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)
|
|
||||||
@ -1,61 +0,0 @@
|
|||||||
# 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
|
|
||||||
@ -1,292 +0,0 @@
|
|||||||
<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
@ -1,369 +0,0 @@
|
|||||||
# 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)
|
|
||||||
@ -1,142 +0,0 @@
|
|||||||
# 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()
|
|
||||||
|
Before Width: | Height: | Size: 33 KiB |
|
Before Width: | Height: | Size: 216 KiB |
|
Before Width: | Height: | Size: 151 KiB |
|
Before Width: | Height: | Size: 26 KiB |
|
Before Width: | Height: | Size: 28 KiB |
|
Before Width: | Height: | Size: 27 KiB |
|
Before Width: | Height: | Size: 90 KiB |
|
Before Width: | Height: | Size: 54 KiB |
|
Before Width: | Height: | Size: 21 KiB |
|
Before Width: | Height: | Size: 59 KiB |
|
Before Width: | Height: | Size: 1.6 MiB |
|
Before Width: | Height: | Size: 88 KiB |
|
Before Width: | Height: | Size: 21 KiB |
|
Before Width: | Height: | Size: 33 KiB |
|
Before Width: | Height: | Size: 2.4 KiB |
@ -1,242 +0,0 @@
|
|||||||
2024年 07月 01日 星期一 21:13:13 CST: wenzhi.py 被停止, PID: 29387
|
|
||||||
2024年 07月 01日 星期一 21:13:13 CST: tts.py 运行失败，退出状态码 1, PID: 29405
|
|
||||||
2024年 07月 01日 星期一 21:13:18 CST: wenzhi.py 被停止, PID: 29408
|
|
||||||
2024年 07月 01日 星期一 21:13:18 CST: tts.py 运行失败，退出状态码 1, PID: 29435
|
|
||||||
2024年 07月 01日 星期一 21:13:23 CST: wenzhi.py 被停止, PID: 29456
|
|
||||||
2024年 07月 01日 星期一 21:13:23 CST: tts.py 运行失败，退出状态码 1, PID: 29474
|
|
||||||
2024年 07月 01日 星期一 21:14:26 CST: wenzhi.py 被停止, PID: 29817
|
|
||||||
2024年 07月 01日 星期一 21:14:26 CST: tts.py 运行失败，退出状态码 1, PID: 29835
|
|
||||||
2024年 07月 01日 星期一 21:14:31 CST: wenzhi.py 被停止, PID: 29838
|
|
||||||
2024年 07月 01日 星期一 21:14:31 CST: tts.py 运行失败，退出状态码 1, PID: 29865
|
|
||||||
2024年 07月 01日 星期一 21:16:08 CST: wenzhi.py 被停止, PID: 30373
|
|
||||||
2024年 07月 01日 星期一 21:16:08 CST: tts.py 运行失败，退出状态码 1, PID: 30391
|
|
||||||
2024年 07月 01日 星期一 21:16:13 CST: wenzhi.py 被停止, PID: 30412
|
|
||||||
2024年 07月 01日 星期一 21:16:13 CST: tts.py 运行失败，退出状态码 1, PID: 30466
|
|
||||||
2024年 07月 01日 星期一 21:16:18 CST: wenzhi.py 被停止, PID: 30469
|
|
||||||
2024年 07月 01日 星期一 21:16:18 CST: tts.py 运行失败，退出状态码 1, PID: 30550
|
|
||||||
2024年 07月 01日 星期一 21:16:42 CST: wenzhi.py 被停止, PID: 30664
|
|
||||||
2024年 07月 01日 星期一 21:16:42 CST: tts.py 运行失败，退出状态码 1, PID: 30691
|
|
||||||
2024年 07月 01日 星期一 21:16:47 CST: wenzhi.py 被停止, PID: 30694
|
|
||||||
2024年 07月 01日 星期一 21:16:47 CST: tts.py 运行失败，退出状态码 1, PID: 30721
|
|
||||||
2024年 07月 01日 星期一 21:16:52 CST: wenzhi.py 被停止, PID: 30733
|
|
||||||
2024年 07月 01日 星期一 21:16:52 CST: tts.py 运行失败，退出状态码 1, PID: 30769
|
|
||||||
2024年 07月 01日 星期一 21:16:57 CST: wenzhi.py 被停止, PID: 30781
|
|
||||||
2024年 07月 01日 星期一 21:16:57 CST: tts.py 运行失败，退出状态码 1, PID: 30835
|
|
||||||
2024年 07月 01日 星期一 21:17:02 CST: wenzhi.py 被停止, PID: 30838
|
|
||||||
2024年 07月 01日 星期一 21:17:02 CST: tts.py 运行失败，退出状态码 1, PID: 30895
|
|
||||||
2024年 07月 01日 星期一 21:19:05 CST: wenzhi.py 被停止, PID: 31525
|
|
||||||
2024年 07月 01日 星期一 21:19:05 CST: tts.py 运行失败，退出状态码 1, PID: 31543
|
|
||||||
2024年 07月 01日 星期一 21:19:10 CST: wenzhi.py 被停止, PID: 31546
|
|
||||||
2024年 07月 01日 星期一 21:19:10 CST: tts.py 运行失败，退出状态码 1, PID: 31573
|
|
||||||
2024年 07月 01日 星期一 21:20:16 CST: wenzhi.py 被停止, PID: 31924
|
|
||||||
2024年 07月 01日 星期一 21:20:17 CST: tts.py 运行失败，退出状态码 1, PID: 31960
|
|
||||||
2024年 07月 01日 星期一 21:20:22 CST: wenzhi.py 被停止, PID: 31981
|
|
||||||
2024年 07月 01日 星期一 21:20:22 CST: tts.py 运行失败，退出状态码 1, PID: 32008
|
|
||||||
2024年 07月 01日 星期一 21:20:27 CST: wenzhi.py 被停止, PID: 32029
|
|
||||||
2024年 07月 01日 星期一 21:20:27 CST: tts.py 运行失败，退出状态码 1, PID: 32056
|
|
||||||
2024年 07月 01日 星期一 21:20:32 CST: wenzhi.py 被停止, PID: 32059
|
|
||||||
2024年 07月 01日 星期一 21:20:32 CST: tts.py 运行失败，退出状态码 1, PID: 32086
|
|
||||||
2024年 07月 01日 星期一 21:20:37 CST: wenzhi.py 被停止, PID: 32125
|
|
||||||
2024年 07月 01日 星期一 21:20:37 CST: tts.py 运行失败，退出状态码 1, PID: 32161
|
|
||||||
2024年 07月 01日 星期一 21:20:42 CST: wenzhi.py 被停止, PID: 32191
|
|
||||||
2024年 07月 01日 星期一 21:20:42 CST: tts.py 运行失败，退出状态码 1, PID: 32264
|
|
||||||
2024年 07月 01日 星期一 21:20:47 CST: wenzhi.py 被停止, PID: 32267
|
|
||||||
2024年 07月 01日 星期一 21:20:47 CST: tts.py 运行失败，退出状态码 1, PID: 32303
|
|
||||||
2024年 07月 01日 星期一 21:20:52 CST: wenzhi.py 被停止, PID: 32306
|
|
||||||
2024年 07月 01日 星期一 21:20:52 CST: tts.py 运行失败，退出状态码 1, PID: 32351
|
|
||||||
2024年 07月 01日 星期一 21:20:57 CST: wenzhi.py 被停止, PID: 32354
|
|
||||||
2024年 07月 01日 星期一 21:20:57 CST: tts.py 运行失败，退出状态码 1, PID: 32426
|
|
||||||
2024年 07月 01日 星期一 23:05:26 CST: wenzhi.py 被停止, PID: 8301
|
|
||||||
2024年 07月 01日 星期一 23:05:26 CST: tts.py 运行失败，退出状态码 1, PID: 8318
|
|
||||||
2024年 07月 01日 星期一 23:05:28 CST: wenzhi.py 被停止, PID: 8322
|
|
||||||
2024年 07月 01日 星期一 23:05:28 CST: tts.py 运行失败，退出状态码 1, PID: 8339
|
|
||||||
2024年 07月 01日 星期一 23:05:30 CST: wenzhi.py 被停止, PID: 8352
|
|
||||||
2024年 07月 01日 星期一 23:05:30 CST: tts.py 运行失败，退出状态码 1, PID: 8369
|
|
||||||
2024年 07月 01日 星期一 23:05:32 CST: wenzhi.py 被停止, PID: 8373
|
|
||||||
2024年 07月 01日 星期一 23:05:32 CST: tts.py 运行失败，退出状态码 1, PID: 8400
|
|
||||||
2024年 07月 01日 星期一 23:05:34 CST: wenzhi.py 被停止, PID: 8422
|
|
||||||
2024年 07月 01日 星期一 23:05:34 CST: tts.py 运行失败，退出状态码 1, PID: 8448
|
|
||||||
2024年 07月 01日 星期一 23:09:24 CST: wenzhi.py 被停止, PID: 10042
|
|
||||||
2024年 07月 01日 星期一 23:09:24 CST: tts.py 运行失败，退出状态码 1, PID: 10069
|
|
||||||
2024年 07月 01日 星期一 23:09:26 CST: wenzhi.py 被停止, PID: 10073
|
|
||||||
2024年 07月 01日 星期一 23:09:27 CST: tts.py 运行失败，退出状态码 1, PID: 10090
|
|
||||||
2024年 07月 01日 星期一 23:09:29 CST: wenzhi.py 被停止, PID: 10121
|
|
||||||
2024年 07月 01日 星期一 23:09:29 CST: tts.py 运行失败，退出状态码 1, PID: 10129
|
|
||||||
2024年 07月 01日 星期一 23:09:31 CST: wenzhi.py 被停止, PID: 10178
|
|
||||||
2024年 07月 01日 星期一 23:09:31 CST: tts.py 运行失败，退出状态码 1, PID: 10195
|
|
||||||
2024年 07月 01日 星期一 23:09:33 CST: wenzhi.py 被停止, PID: 10208
|
|
||||||
2024年 07月 01日 星期一 23:09:33 CST: tts.py 运行失败，退出状态码 1, PID: 10225
|
|
||||||
2024年 07月 01日 星期一 23:09:35 CST: wenzhi.py 被停止, PID: 10229
|
|
||||||
2024年 07月 01日 星期一 23:09:35 CST: tts.py 运行失败，退出状态码 1, PID: 10237
|
|
||||||
2024年 07月 01日 星期一 23:09:37 CST: wenzhi.py 被停止, PID: 10241
|
|
||||||
2024年 07月 01日 星期一 23:09:37 CST: tts.py 运行失败，退出状态码 1, PID: 10249
|
|
||||||
2024年 07月 01日 星期一 23:09:39 CST: wenzhi.py 被停止, PID: 10289
|
|
||||||
2024年 07月 01日 星期一 23:09:39 CST: tts.py 运行失败，退出状态码 1, PID: 10306
|
|
||||||
2024年 07月 01日 星期一 23:09:41 CST: wenzhi.py 被停止, PID: 10310
|
|
||||||
2024年 07月 01日 星期一 23:09:41 CST: tts.py 运行失败，退出状态码 1, PID: 10327
|
|
||||||
2024年 07月 01日 星期一 23:09:43 CST: wenzhi.py 被停止, PID: 10340
|
|
||||||
2024年 07月 01日 星期一 23:09:43 CST: tts.py 运行失败，退出状态码 1, PID: 10357
|
|
||||||
2024年 07月 01日 星期一 23:09:45 CST: wenzhi.py 被停止, PID: 10379
|
|
||||||
2024年 07月 01日 星期一 23:09:45 CST: tts.py 运行失败，退出状态码 1, PID: 10396
|
|
||||||
2024年 07月 01日 星期一 23:13:21 CST: wenzhi.py 被停止, PID: 11686
|
|
||||||
2024年 07月 01日 星期一 23:13:21 CST: tts.py 运行成功, PID: 11694
|
|
||||||
2024年 07月 01日 星期一 23:13:23 CST: wenzhi.py 被停止, PID: 11698
|
|
||||||
2024年 07月 01日 星期一 23:13:23 CST: tts.py 运行成功, PID: 11733
|
|
||||||
2024年 07月 01日 星期一 23:14:23 CST: wenzhi.py 被停止, PID: 12133
|
|
||||||
2024年 07月 01日 星期一 23:14:23 CST: tts.py 运行成功, PID: 12141
|
|
||||||
2024年 07月 01日 星期一 23:14:25 CST: wenzhi.py 被停止, PID: 12154
|
|
||||||
2024年 07月 01日 星期一 23:14:25 CST: tts.py 运行成功, PID: 12180
|
|
||||||
2024年 07月 01日 星期一 23:16:13 CST: wenzhi.py 被停止, PID: 12910
|
|
||||||
2024年 07月 01日 星期一 23:16:13 CST: tts.py 运行成功, PID: 12918
|
|
||||||
2024年 07月 01日 星期一 23:16:15 CST: wenzhi.py 被停止, PID: 12940
|
|
||||||
2024年 07月 01日 星期一 23:16:15 CST: tts.py 运行成功, PID: 12957
|
|
||||||
2024年 07月 01日 星期一 23:16:17 CST: wenzhi.py 被停止, PID: 12961
|
|
||||||
2024年 07月 01日 星期一 23:16:17 CST: tts.py 运行成功, PID: 12978
|
|
||||||
2024年 07月 01日 星期一 23:16:19 CST: wenzhi.py 被停止, PID: 12982
|
|
||||||
2024年 07月 01日 星期一 23:16:19 CST: tts.py 运行成功, PID: 12990
|
|
||||||
2024年 07月 01日 星期一 23:16:21 CST: wenzhi.py 被停止, PID: 12994
|
|
||||||
2024年 07月 01日 星期一 23:16:21 CST: tts.py 运行成功, PID: 13002
|
|
||||||
2024年 07月 01日 星期一 23:16:23 CST: wenzhi.py 被停止, PID: 13024
|
|
||||||
2024年 07月 01日 星期一 23:16:23 CST: tts.py 运行成功, PID: 13041
|
|
||||||
2024年 07月 01日 星期一 23:16:25 CST: wenzhi.py 被停止, PID: 13054
|
|
||||||
2024年 07月 01日 星期一 23:16:25 CST: tts.py 运行成功, PID: 13062
|
|
||||||
2024年 07月 01日 星期一 23:16:27 CST: wenzhi.py 被停止, PID: 13075
|
|
||||||
2024年 07月 01日 星期一 23:16:27 CST: tts.py 运行成功, PID: 13083
|
|
||||||
2024年 07月 01日 星期一 23:17:10 CST: wenzhi.py 被停止, PID: 13388
|
|
||||||
2024年 07月 01日 星期一 23:17:10 CST: tts.py 运行成功, PID: 13396
|
|
||||||
2024年 07月 01日 星期一 23:17:12 CST: wenzhi.py 被停止, PID: 13400
|
|
||||||
2024年 07月 01日 星期一 23:17:12 CST: tts.py 运行成功, PID: 13408
|
|
||||||
2024年 07月 01日 星期一 23:17:23 CST: wenzhi.py 被停止, PID: 13502
|
|
||||||
2024年 07月 01日 星期一 23:17:23 CST: tts.py 运行成功, PID: 13510
|
|
||||||
2024年 07月 01日 星期一 23:17:25 CST: wenzhi.py 被停止, PID: 13514
|
|
||||||
2024年 07月 01日 星期一 23:17:25 CST: tts.py 运行成功, PID: 13522
|
|
||||||
2024年 07月 01日 星期一 23:17:27 CST: wenzhi.py 被停止, PID: 13526
|
|
||||||
2024年 07月 01日 星期一 23:17:27 CST: tts.py 运行成功, PID: 13534
|
|
||||||
2024年 07月 01日 星期一 23:17:59 CST: wenzhi.py 被停止, PID: 13762
|
|
||||||
2024年 07月 01日 星期一 23:17:59 CST: tts.py 运行失败，退出状态码 1, PID: 13779
|
|
||||||
2024年 07月 01日 星期一 23:18:01 CST: wenzhi.py 被停止, PID: 13783
|
|
||||||
2024年 07月 01日 星期一 23:18:01 CST: tts.py 运行失败，退出状态码 1, PID: 13791
|
|
||||||
2024年 07月 01日 星期一 23:18:03 CST: wenzhi.py 被停止, PID: 13795
|
|
||||||
2024年 07月 01日 星期一 23:18:03 CST: tts.py 运行失败，退出状态码 1, PID: 13821
|
|
||||||
2024年 07月 01日 星期一 23:18:05 CST: wenzhi.py 被停止, PID: 13825
|
|
||||||
2024年 07月 01日 星期一 23:18:05 CST: tts.py 运行失败，退出状态码 1, PID: 13833
|
|
||||||
2024年 07月 01日 星期一 23:18:07 CST: wenzhi.py 被停止, PID: 13837
|
|
||||||
2024年 07月 01日 星期一 23:18:07 CST: tts.py 运行失败，退出状态码 1, PID: 13854
|
|
||||||
2024年 07月 01日 星期一 23:18:09 CST: wenzhi.py 被停止, PID: 13867
|
|
||||||
2024年 07月 01日 星期一 23:18:09 CST: tts.py 运行失败，退出状态码 1, PID: 13875
|
|
||||||
2024年 07月 01日 星期一 23:18:11 CST: wenzhi.py 被停止, PID: 13879
|
|
||||||
2024年 07月 01日 星期一 23:18:11 CST: tts.py 运行失败，退出状态码 1, PID: 13887
|
|
||||||
2024年 07月 01日 星期一 23:18:13 CST: wenzhi.py 被停止, PID: 13891
|
|
||||||
2024年 07月 01日 星期一 23:18:13 CST: tts.py 运行失败，退出状态码 1, PID: 13899
|
|
||||||
2024年 07月 01日 星期一 23:18:15 CST: wenzhi.py 被停止, PID: 13903
|
|
||||||
2024年 07月 01日 星期一 23:18:15 CST: tts.py 运行失败，退出状态码 1, PID: 13911
|
|
||||||
2024年 07月 01日 星期一 23:18:17 CST: wenzhi.py 被停止, PID: 13915
|
|
||||||
2024年 07月 01日 星期一 23:18:18 CST: tts.py 运行失败，退出状态码 1, PID: 13923
|
|
||||||
2024年 07月 01日 星期一 23:18:20 CST: wenzhi.py 被停止, PID: 13927
|
|
||||||
2024年 07月 01日 星期一 23:18:20 CST: tts.py 运行失败，退出状态码 1, PID: 13935
|
|
||||||
2024年 07月 01日 星期一 23:18:22 CST: wenzhi.py 被停止, PID: 13939
|
|
||||||
2024年 07月 01日 星期一 23:18:22 CST: tts.py 运行失败，退出状态码 1, PID: 13947
|
|
||||||
2024年 07月 01日 星期一 23:18:24 CST: wenzhi.py 被停止, PID: 13960
|
|
||||||
2024年 07月 01日 星期一 23:18:24 CST: tts.py 运行失败，退出状态码 1, PID: 13968
|
|
||||||
2024年 07月 01日 星期一 23:18:26 CST: wenzhi.py 被停止, PID: 13972
|
|
||||||
2024年 07月 01日 星期一 23:18:26 CST: tts.py 运行失败，退出状态码 1, PID: 13980
|
|
||||||
2024年 07月 01日 星期一 23:18:28 CST: wenzhi.py 被停止, PID: 13984
|
|
||||||
2024年 07月 01日 星期一 23:18:28 CST: tts.py 运行失败，退出状态码 1, PID: 13992
|
|
||||||
2024年 07月 01日 星期一 23:18:30 CST: wenzhi.py 被停止, PID: 13996
|
|
||||||
2024年 07月 01日 星期一 23:18:30 CST: tts.py 运行失败，退出状态码 1, PID: 14004
|
|
||||||
2024年 07月 01日 星期一 23:18:32 CST: wenzhi.py 被停止, PID: 14017
|
|
||||||
2024年 07月 01日 星期一 23:18:32 CST: tts.py 运行失败，退出状态码 1, PID: 14025
|
|
||||||
2024年 07月 01日 星期一 23:18:34 CST: wenzhi.py 被停止, PID: 14029
|
|
||||||
2024年 07月 01日 星期一 23:18:34 CST: tts.py 运行失败，退出状态码 1, PID: 14046
|
|
||||||
2024年 07月 01日 星期一 23:18:36 CST: wenzhi.py 被停止, PID: 14050
|
|
||||||
2024年 07月 01日 星期一 23:18:36 CST: tts.py 运行失败，退出状态码 1, PID: 14076
|
|
||||||
2024年 07月 01日 星期一 23:18:38 CST: wenzhi.py 被停止, PID: 14080
|
|
||||||
2024年 07月 01日 星期一 23:18:38 CST: tts.py 运行失败，退出状态码 1, PID: 14088
|
|
||||||
2024年 07月 01日 星期一 23:18:40 CST: wenzhi.py 被停止, PID: 14092
|
|
||||||
2024年 07月 01日 星期一 23:18:40 CST: tts.py 运行失败，退出状态码 1, PID: 14109
|
|
||||||
2024年 07月 01日 星期一 23:18:42 CST: wenzhi.py 被停止, PID: 14113
|
|
||||||
2024年 07月 01日 星期一 23:18:42 CST: tts.py 运行失败，退出状态码 1, PID: 14130
|
|
||||||
2024年 07月 01日 星期一 23:18:44 CST: wenzhi.py 被停止, PID: 14134
|
|
||||||
2024年 07月 01日 星期一 23:18:44 CST: tts.py 运行失败，退出状态码 1, PID: 14142
|
|
||||||
2024年 07月 01日 星期一 23:18:46 CST: wenzhi.py 被停止, PID: 14146
|
|
||||||
2024年 07月 01日 星期一 23:18:46 CST: tts.py 运行失败，退出状态码 1, PID: 14154
|
|
||||||
2024年 07月 01日 星期一 23:18:48 CST: wenzhi.py 被停止, PID: 14167
|
|
||||||
2024年 07月 01日 星期一 23:18:48 CST: tts.py 运行失败，退出状态码 1, PID: 14184
|
|
||||||
2024年 07月 01日 星期一 23:18:50 CST: wenzhi.py 被停止, PID: 14197
|
|
||||||
2024年 07月 01日 星期一 23:18:50 CST: tts.py 运行失败，退出状态码 1, PID: 14223
|
|
||||||
2024年 07月 01日 星期一 23:18:52 CST: wenzhi.py 被停止, PID: 14227
|
|
||||||
2024年 07月 01日 星期一 23:18:52 CST: tts.py 运行失败，退出状态码 1, PID: 14235
|
|
||||||
2024年 07月 01日 星期一 23:18:54 CST: wenzhi.py 被停止, PID: 14248
|
|
||||||
2024年 07月 01日 星期一 23:18:54 CST: tts.py 运行失败，退出状态码 1, PID: 14282
|
|
||||||
2024年 07月 01日 星期一 23:18:56 CST: wenzhi.py 被停止, PID: 14296
|
|
||||||
2024年 07月 01日 星期一 23:18:56 CST: tts.py 运行失败，退出状态码 1, PID: 14313
|
|
||||||
2024年 07月 01日 星期一 23:18:58 CST: wenzhi.py 被停止, PID: 14334
|
|
||||||
2024年 07月 01日 星期一 23:18:58 CST: tts.py 运行失败，退出状态码 1, PID: 14361
|
|
||||||
2024年 07月 01日 星期一 23:19:00 CST: wenzhi.py 被停止, PID: 14374
|
|
||||||
2024年 07月 01日 星期一 23:19:00 CST: tts.py 运行失败，退出状态码 1, PID: 14391
|
|
||||||
2024年 07月 01日 星期一 23:19:02 CST: wenzhi.py 被停止, PID: 14395
|
|
||||||
2024年 07月 01日 星期一 23:19:02 CST: tts.py 运行失败，退出状态码 1, PID: 14403
|
|
||||||
2024年 07月 01日 星期一 23:19:04 CST: wenzhi.py 被停止, PID: 14425
|
|
||||||
2024年 07月 01日 星期一 23:19:04 CST: tts.py 运行失败，退出状态码 1, PID: 14433
|
|
||||||
2024年 07月 01日 星期一 23:19:06 CST: wenzhi.py 被停止, PID: 14446
|
|
||||||
2024年 07月 01日 星期一 23:19:07 CST: tts.py 运行失败，退出状态码 1, PID: 14463
|
|
||||||
2024年 07月 01日 星期一 23:19:09 CST: wenzhi.py 被停止, PID: 14485
|
|
||||||
2024年 07月 01日 星期一 23:19:09 CST: tts.py 运行失败，退出状态码 1, PID: 14502
|
|
||||||
2024年 07月 01日 星期一 23:19:11 CST: wenzhi.py 被停止, PID: 14515
|
|
||||||
2024年 07月 01日 星期一 23:19:11 CST: tts.py 运行失败，退出状态码 1, PID: 14532
|
|
||||||
2024年 07月 01日 星期一 23:19:13 CST: wenzhi.py 被停止, PID: 14536
|
|
||||||
2024年 07月 01日 星期一 23:19:13 CST: tts.py 运行失败，退出状态码 1, PID: 14544
|
|
||||||
2024年 07月 01日 星期一 23:19:15 CST: wenzhi.py 被停止, PID: 14548
|
|
||||||
2024年 07月 01日 星期一 23:19:15 CST: tts.py 运行失败，退出状态码 1, PID: 14574
|
|
||||||
2024年 07月 01日 星期一 23:19:17 CST: wenzhi.py 被停止, PID: 14578
|
|
||||||
2024年 07月 01日 星期一 23:19:17 CST: tts.py 运行失败，退出状态码 1, PID: 14586
|
|
||||||
2024年 07月 01日 星期一 23:19:19 CST: wenzhi.py 被停止, PID: 14599
|
|
||||||
2024年 07月 01日 星期一 23:19:19 CST: tts.py 运行失败，退出状态码 1, PID: 14616
|
|
||||||
2024年 07月 01日 星期一 23:19:21 CST: wenzhi.py 被停止, PID: 14620
|
|
||||||
2024年 07月 01日 星期一 23:19:21 CST: tts.py 运行失败，退出状态码 1, PID: 14646
|
|
||||||
2024年 07月 01日 星期一 23:19:23 CST: wenzhi.py 被停止, PID: 14650
|
|
||||||
2024年 07月 01日 星期一 23:19:23 CST: tts.py 运行失败，退出状态码 1, PID: 14658
|
|
||||||
2024年 07月 01日 星期一 23:19:25 CST: wenzhi.py 被停止, PID: 14671
|
|
||||||
2024年 07月 01日 星期一 23:19:25 CST: tts.py 运行失败，退出状态码 1, PID: 14679
|
|
||||||
2024年 07月 01日 星期一 23:19:27 CST: wenzhi.py 被停止, PID: 14692
|
|
||||||
2024年 07月 01日 星期一 23:19:27 CST: tts.py 运行失败，退出状态码 1, PID: 14709
|
|
||||||
2024年 07月 01日 星期一 23:19:29 CST: wenzhi.py 被停止, PID: 14713
|
|
||||||
2024年 07月 01日 星期一 23:19:29 CST: tts.py 运行失败，退出状态码 1, PID: 14721
|
|
||||||
2024年 07月 01日 星期一 23:19:31 CST: wenzhi.py 被停止, PID: 14735
|
|
||||||
2024年 07月 01日 星期一 23:19:31 CST: tts.py 运行失败，退出状态码 1, PID: 14743
|
|
||||||
2024年 07月 01日 星期一 23:19:33 CST: wenzhi.py 被停止, PID: 14756
|
|
||||||
2024年 07月 01日 星期一 23:19:33 CST: tts.py 运行失败，退出状态码 1, PID: 14773
|
|
||||||
2024年 07月 01日 星期一 23:19:35 CST: wenzhi.py 被停止, PID: 14777
|
|
||||||
2024年 07月 01日 星期一 23:19:35 CST: tts.py 运行失败，退出状态码 1, PID: 14785
|
|
||||||
2024年 07月 01日 星期一 23:19:37 CST: wenzhi.py 被停止, PID: 14798
|
|
||||||
2024年 07月 01日 星期一 23:19:37 CST: tts.py 运行失败，退出状态码 1, PID: 14815
|
|
||||||
2024年 07月 01日 星期一 23:19:39 CST: wenzhi.py 被停止, PID: 14819
|
|
||||||
2024年 07月 01日 星期一 23:19:39 CST: tts.py 运行失败，退出状态码 1, PID: 14836
|
|
||||||
2024年 07月 01日 星期一 23:19:41 CST: wenzhi.py 被停止, PID: 14840
|
|
||||||
2024年 07月 01日 星期一 23:19:41 CST: tts.py 运行失败，退出状态码 1, PID: 14848
|
|
||||||
2024年 07月 01日 星期一 23:19:43 CST: wenzhi.py 被停止, PID: 14852
|
|
||||||
2024年 07月 01日 星期一 23:19:43 CST: tts.py 运行失败，退出状态码 1, PID: 14860
|
|
||||||
2024年 07月 01日 星期一 23:19:45 CST: wenzhi.py 被停止, PID: 14864
|
|
||||||
2024年 07月 01日 星期一 23:19:45 CST: tts.py 运行失败，退出状态码 1, PID: 14872
|
|
||||||
2024年 07月 01日 星期一 23:19:47 CST: wenzhi.py 被停止, PID: 14885
|
|
||||||
2024年 07月 01日 星期一 23:19:47 CST: tts.py 运行失败，退出状态码 1, PID: 14893
|
|
||||||
2024年 07月 01日 星期一 23:19:49 CST: wenzhi.py 被停止, PID: 14897
|
|
||||||
2024年 07月 01日 星期一 23:19:49 CST: tts.py 运行失败，退出状态码 1, PID: 14905
|
|
||||||
2024年 07月 01日 星期一 23:19:51 CST: wenzhi.py 被停止, PID: 14909
|
|
||||||
2024年 07月 01日 星期一 23:19:51 CST: tts.py 运行失败，退出状态码 1, PID: 14926
|
|
||||||
2024年 07月 01日 星期一 23:19:53 CST: wenzhi.py 被停止, PID: 14948
|
|
||||||
2024年 07月 01日 星期一 23:19:53 CST: tts.py 运行失败，退出状态码 1, PID: 14956
|
|
||||||
2024年 07月 01日 星期一 23:19:55 CST: wenzhi.py 被停止, PID: 14969
|
|
||||||
2024年 07月 01日 星期一 23:19:55 CST: tts.py 运行失败，退出状态码 1, PID: 14977
|
|
||||||
2024年 07月 01日 星期一 23:19:58 CST: wenzhi.py 被停止, PID: 14981
|
|
||||||
2024年 07月 01日 星期一 23:19:58 CST: tts.py 运行失败，退出状态码 1, PID: 15007
|
|
||||||
2024年 07月 01日 星期一 23:20:00 CST: wenzhi.py 被停止, PID: 15020
|
|
||||||
2024年 07月 01日 星期一 23:20:00 CST: tts.py 运行失败，退出状态码 1, PID: 15037
|
|
||||||
2024年 07月 01日 星期一 23:20:02 CST: wenzhi.py 被停止, PID: 15041
|
|
||||||
2024年 07月 01日 星期一 23:20:02 CST: tts.py 运行失败，退出状态码 1, PID: 15049
|
|
||||||
2024年 07月 01日 星期一 23:20:04 CST: wenzhi.py 被停止, PID: 15053
|
|
||||||
2024年 07月 01日 星期一 23:20:04 CST: tts.py 运行失败，退出状态码 1, PID: 15061
|
|
||||||
2024年 07月 01日 星期一 23:20:06 CST: wenzhi.py 被停止, PID: 15067
|
|
||||||
2024年 07月 01日 星期一 23:20:06 CST: tts.py 运行失败，退出状态码 1, PID: 15075
|
|
||||||
2024年 07月 01日 星期一 23:20:08 CST: wenzhi.py 被停止, PID: 15079
|
|
||||||
2024年 07月 01日 星期一 23:20:08 CST: tts.py 运行失败，退出状态码 1, PID: 15105
|
|
||||||
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Reference in new issue