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- .gitattributes +2 -0
- Dockerfile +2 -2
- LICENSE +661 -0
- README.md +6 -8
- __pycache__/jaa.cpython-311.pyc +0 -0
- __pycache__/settings.cpython-311.pyc +0 -0
- chain_img_processor/__init__.py +4 -0
- chain_img_processor/__pycache__/__init__.cpython-311.pyc +0 -0
- chain_img_processor/__pycache__/batchimage.cpython-311.pyc +0 -0
- chain_img_processor/__pycache__/ffmpeg_writer.cpython-311.pyc +0 -0
- chain_img_processor/__pycache__/image.cpython-311.pyc +0 -0
- chain_img_processor/__pycache__/video.cpython-311.pyc +0 -0
- chain_img_processor/batchimage.py +86 -0
- chain_img_processor/ffmpeg_writer.py +253 -0
- chain_img_processor/image.py +176 -0
- chain_img_processor/video.py +132 -0
- clip/__init__.py +1 -0
- clip/__pycache__/__init__.cpython-311.pyc +0 -0
- clip/__pycache__/clip.cpython-311.pyc +0 -0
- clip/__pycache__/model.cpython-311.pyc +0 -0
- clip/__pycache__/simple_tokenizer.cpython-311.pyc +0 -0
- clip/bpe_simple_vocab_16e6.txt.gz +3 -0
- clip/clip.py +245 -0
- clip/clipseg.py +538 -0
- clip/model.py +436 -0
- clip/simple_tokenizer.py +132 -0
- clip/vitseg.py +286 -0
- docs/faceselection.png +0 -0
- docs/finaloutput.png +3 -0
- docs/kickboxing.jpg +0 -0
- docs/musk.jpg +0 -0
- docs/screenshot.png +0 -0
- gfpgan/weights/detection_Resnet50_Final.pth +3 -0
- gfpgan/weights/parsing_parsenet.pth +3 -0
- installer/installer.py +83 -0
- installer/windows_run.bat +80 -0
- jaa.py +355 -0
- models/CLIP/rd64-uni-refined.pth +3 -0
- models/CodeFormer/codeformer.pth +3 -0
- models/CodeFormer/facelib/detection_Resnet50_Final.pth +3 -0
- models/CodeFormer/facelib/parsing_parsenet.pth +3 -0
- models/CodeFormer/realesrgan/RealESRGAN_x2plus.pth +3 -0
- models/DMDNet.pth +3 -0
- models/GFPGANv1.4.pth +3 -0
- models/inswapper_128.onnx +3 -0
- mynewshinyroop/.gitignore +2 -0
- mynewshinyroop/Lib/site-packages/_distutils_hack/__init__.py +227 -0
- mynewshinyroop/Lib/site-packages/_distutils_hack/override.py +1 -0
- mynewshinyroop/Lib/site-packages/_virtualenv.pth +3 -0
- mynewshinyroop/Lib/site-packages/_virtualenv.py +102 -0
.gitattributes
CHANGED
@@ -36,3 +36,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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app/.github/examples/snapshot.mp4 filter=lfs diff=lfs merge=lfs -text
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app/docs/finaloutput.png filter=lfs diff=lfs merge=lfs -text
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app/temp/6efe43a0630bd48ea2e9c9a638f368e81348db7b/image.png filter=lfs diff=lfs merge=lfs -text
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app/.github/examples/snapshot.mp4 filter=lfs diff=lfs merge=lfs -text
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app/docs/finaloutput.png filter=lfs diff=lfs merge=lfs -text
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app/temp/6efe43a0630bd48ea2e9c9a638f368e81348db7b/image.png filter=lfs diff=lfs merge=lfs -text
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+
docs/finaloutput.png filter=lfs diff=lfs merge=lfs -text
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+
temp/6efe43a0630bd48ea2e9c9a638f368e81348db7b/image.png filter=lfs diff=lfs merge=lfs -text
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Dockerfile
CHANGED
@@ -1,7 +1,7 @@
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FROM python:3.11
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-
WORKDIR /
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RUN apt-get update && apt-get install -y libgl1-mesa-glx
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COPY requirements.txt ./
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RUN pip install --no-cache-dir -r requirements.txt
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COPY . .
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-
CMD ["
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FROM python:3.11
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+
WORKDIR /usr/src/app
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RUN apt-get update && apt-get install -y libgl1-mesa-glx
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COPY requirements.txt ./
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RUN pip install --no-cache-dir -r requirements.txt
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COPY . .
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+
CMD ["python", "run.py"]
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LICENSE
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1 |
+
GNU AFFERO GENERAL PUBLIC LICENSE
|
2 |
+
Version 3, 19 November 2007
|
3 |
+
|
4 |
+
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
|
5 |
+
Everyone is permitted to copy and distribute verbatim copies
|
6 |
+
of this license document, but changing it is not allowed.
|
7 |
+
|
8 |
+
Preamble
|
9 |
+
|
10 |
+
The GNU Affero General Public License is a free, copyleft license for
|
11 |
+
software and other kinds of works, specifically designed to ensure
|
12 |
+
cooperation with the community in the case of network server software.
|
13 |
+
|
14 |
+
The licenses for most software and other practical works are designed
|
15 |
+
to take away your freedom to share and change the works. By contrast,
|
16 |
+
our General Public Licenses are intended to guarantee your freedom to
|
17 |
+
share and change all versions of a program--to make sure it remains free
|
18 |
+
software for all its users.
|
19 |
+
|
20 |
+
When we speak of free software, we are referring to freedom, not
|
21 |
+
price. Our General Public Licenses are designed to make sure that you
|
22 |
+
have the freedom to distribute copies of free software (and charge for
|
23 |
+
them if you wish), that you receive source code or can get it if you
|
24 |
+
want it, that you can change the software or use pieces of it in new
|
25 |
+
free programs, and that you know you can do these things.
|
26 |
+
|
27 |
+
Developers that use our General Public Licenses protect your rights
|
28 |
+
with two steps: (1) assert copyright on the software, and (2) offer
|
29 |
+
you this License which gives you legal permission to copy, distribute
|
30 |
+
and/or modify the software.
|
31 |
+
|
32 |
+
A secondary benefit of defending all users' freedom is that
|
33 |
+
improvements made in alternate versions of the program, if they
|
34 |
+
receive widespread use, become available for other developers to
|
35 |
+
incorporate. Many developers of free software are heartened and
|
36 |
+
encouraged by the resulting cooperation. However, in the case of
|
37 |
+
software used on network servers, this result may fail to come about.
|
38 |
+
The GNU General Public License permits making a modified version and
|
39 |
+
letting the public access it on a server without ever releasing its
|
40 |
+
source code to the public.
|
41 |
+
|
42 |
+
The GNU Affero General Public License is designed specifically to
|
43 |
+
ensure that, in such cases, the modified source code becomes available
|
44 |
+
to the community. It requires the operator of a network server to
|
45 |
+
provide the source code of the modified version running there to the
|
46 |
+
users of that server. Therefore, public use of a modified version, on
|
47 |
+
a publicly accessible server, gives the public access to the source
|
48 |
+
code of the modified version.
|
49 |
+
|
50 |
+
An older license, called the Affero General Public License and
|
51 |
+
published by Affero, was designed to accomplish similar goals. This is
|
52 |
+
a different license, not a version of the Affero GPL, but Affero has
|
53 |
+
released a new version of the Affero GPL which permits relicensing under
|
54 |
+
this license.
|
55 |
+
|
56 |
+
The precise terms and conditions for copying, distribution and
|
57 |
+
modification follow.
|
58 |
+
|
59 |
+
TERMS AND CONDITIONS
|
60 |
+
|
61 |
+
0. Definitions.
|
62 |
+
|
63 |
+
"This License" refers to version 3 of the GNU Affero General Public License.
|
64 |
+
|
65 |
+
"Copyright" also means copyright-like laws that apply to other kinds of
|
66 |
+
works, such as semiconductor masks.
|
67 |
+
|
68 |
+
"The Program" refers to any copyrightable work licensed under this
|
69 |
+
License. Each licensee is addressed as "you". "Licensees" and
|
70 |
+
"recipients" may be individuals or organizations.
|
71 |
+
|
72 |
+
To "modify" a work means to copy from or adapt all or part of the work
|
73 |
+
in a fashion requiring copyright permission, other than the making of an
|
74 |
+
exact copy. The resulting work is called a "modified version" of the
|
75 |
+
earlier work or a work "based on" the earlier work.
|
76 |
+
|
77 |
+
A "covered work" means either the unmodified Program or a work based
|
78 |
+
on the Program.
|
79 |
+
|
80 |
+
To "propagate" a work means to do anything with it that, without
|
81 |
+
permission, would make you directly or secondarily liable for
|
82 |
+
infringement under applicable copyright law, except executing it on a
|
83 |
+
computer or modifying a private copy. Propagation includes copying,
|
84 |
+
distribution (with or without modification), making available to the
|
85 |
+
public, and in some countries other activities as well.
|
86 |
+
|
87 |
+
To "convey" a work means any kind of propagation that enables other
|
88 |
+
parties to make or receive copies. Mere interaction with a user through
|
89 |
+
a computer network, with no transfer of a copy, is not conveying.
|
90 |
+
|
91 |
+
An interactive user interface displays "Appropriate Legal Notices"
|
92 |
+
to the extent that it includes a convenient and prominently visible
|
93 |
+
feature that (1) displays an appropriate copyright notice, and (2)
|
94 |
+
tells the user that there is no warranty for the work (except to the
|
95 |
+
extent that warranties are provided), that licensees may convey the
|
96 |
+
work under this License, and how to view a copy of this License. If
|
97 |
+
the interface presents a list of user commands or options, such as a
|
98 |
+
menu, a prominent item in the list meets this criterion.
|
99 |
+
|
100 |
+
1. Source Code.
|
101 |
+
|
102 |
+
The "source code" for a work means the preferred form of the work
|
103 |
+
for making modifications to it. "Object code" means any non-source
|
104 |
+
form of a work.
|
105 |
+
|
106 |
+
A "Standard Interface" means an interface that either is an official
|
107 |
+
standard defined by a recognized standards body, or, in the case of
|
108 |
+
interfaces specified for a particular programming language, one that
|
109 |
+
is widely used among developers working in that language.
|
110 |
+
|
111 |
+
The "System Libraries" of an executable work include anything, other
|
112 |
+
than the work as a whole, that (a) is included in the normal form of
|
113 |
+
packaging a Major Component, but which is not part of that Major
|
114 |
+
Component, and (b) serves only to enable use of the work with that
|
115 |
+
Major Component, or to implement a Standard Interface for which an
|
116 |
+
implementation is available to the public in source code form. A
|
117 |
+
"Major Component", in this context, means a major essential component
|
118 |
+
(kernel, window system, and so on) of the specific operating system
|
119 |
+
(if any) on which the executable work runs, or a compiler used to
|
120 |
+
produce the work, or an object code interpreter used to run it.
|
121 |
+
|
122 |
+
The "Corresponding Source" for a work in object code form means all
|
123 |
+
the source code needed to generate, install, and (for an executable
|
124 |
+
work) run the object code and to modify the work, including scripts to
|
125 |
+
control those activities. However, it does not include the work's
|
126 |
+
System Libraries, or general-purpose tools or generally available free
|
127 |
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programs which are used unmodified in performing those activities but
|
128 |
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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
|
133 |
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subprograms and other parts of the work.
|
134 |
+
|
135 |
+
The Corresponding Source need not include anything that users
|
136 |
+
can regenerate automatically from other parts of the Corresponding
|
137 |
+
Source.
|
138 |
+
|
139 |
+
The Corresponding Source for a work in source code form is that
|
140 |
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same work.
|
141 |
+
|
142 |
+
2. Basic Permissions.
|
143 |
+
|
144 |
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All rights granted under this License are granted for the term of
|
145 |
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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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|
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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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|
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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
|
165 |
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makes it unnecessary.
|
166 |
+
|
167 |
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3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
168 |
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|
169 |
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No covered work shall be deemed part of an effective technological
|
170 |
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measure under any applicable law fulfilling obligations under article
|
171 |
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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.
|
174 |
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|
175 |
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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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|
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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.
|
182 |
+
|
183 |
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4. Conveying Verbatim Copies.
|
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|
185 |
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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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|
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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.
|
195 |
+
|
196 |
+
5. Conveying Modified Source Versions.
|
197 |
+
|
198 |
+
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
|
208 |
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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.
|
232 |
+
|
233 |
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6. Conveying Non-Source Forms.
|
234 |
+
|
235 |
+
You may convey a covered work in object code form under the terms
|
236 |
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of sections 4 and 5, provided that you also convey the
|
237 |
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machine-readable Corresponding Source under the terms of this License,
|
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in one of these ways:
|
239 |
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|
240 |
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a) Convey the object code in, or embodied in, a physical product
|
241 |
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(including a physical distribution medium), accompanied by the
|
242 |
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Corresponding Source fixed on a durable physical medium
|
243 |
+
customarily used for software interchange.
|
244 |
+
|
245 |
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b) Convey the object code in, or embodied in, a physical product
|
246 |
+
(including a physical distribution medium), accompanied by a
|
247 |
+
written offer, valid for at least three years and valid for as
|
248 |
+
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
|
250 |
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copy of the Corresponding Source for all the software in the
|
251 |
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product that is covered by this License, on a durable physical
|
252 |
+
medium customarily used for software interchange, for a price no
|
253 |
+
more than your reasonable cost of physically performing this
|
254 |
+
conveying of source, or (2) access to copy the
|
255 |
+
Corresponding Source from a network server at no charge.
|
256 |
+
|
257 |
+
c) Convey individual copies of the object code with a copy of the
|
258 |
+
written offer to provide the Corresponding Source. This
|
259 |
+
alternative is allowed only occasionally and noncommercially, and
|
260 |
+
only if you received the object code with such an offer, in accord
|
261 |
+
with subsection 6b.
|
262 |
+
|
263 |
+
d) Convey the object code by offering access from a designated
|
264 |
+
place (gratis or for a charge), and offer equivalent access to the
|
265 |
+
Corresponding Source in the same way through the same place at no
|
266 |
+
further charge. You need not require recipients to copy the
|
267 |
+
Corresponding Source along with the object code. If the place to
|
268 |
+
copy the object code is a network server, the Corresponding Source
|
269 |
+
may be on a different server (operated by you or a third party)
|
270 |
+
that supports equivalent copying facilities, provided you maintain
|
271 |
+
clear directions next to the object code saying where to find the
|
272 |
+
Corresponding Source. Regardless of what server hosts the
|
273 |
+
Corresponding Source, you remain obligated to ensure that it is
|
274 |
+
available for as long as needed to satisfy these requirements.
|
275 |
+
|
276 |
+
e) Convey the object code using peer-to-peer transmission, provided
|
277 |
+
you inform other peers where the object code and Corresponding
|
278 |
+
Source of the work are being offered to the general public at no
|
279 |
+
charge under subsection 6d.
|
280 |
+
|
281 |
+
A separable portion of the object code, whose source code is excluded
|
282 |
+
from the Corresponding Source as a System Library, need not be
|
283 |
+
included in conveying the object code work.
|
284 |
+
|
285 |
+
A "User Product" is either (1) a "consumer product", which means any
|
286 |
+
tangible personal property which is normally used for personal, family,
|
287 |
+
or household purposes, or (2) anything designed or sold for incorporation
|
288 |
+
into a dwelling. In determining whether a product is a consumer product,
|
289 |
+
doubtful cases shall be resolved in favor of coverage. For a particular
|
290 |
+
product received by a particular user, "normally used" refers to a
|
291 |
+
typical or common use of that class of product, regardless of the status
|
292 |
+
of the particular user or of the way in which the particular user
|
293 |
+
actually uses, or expects or is expected to use, the product. A product
|
294 |
+
is a consumer product regardless of whether the product has substantial
|
295 |
+
commercial, industrial or non-consumer uses, unless such uses represent
|
296 |
+
the only significant mode of use of the product.
|
297 |
+
|
298 |
+
"Installation Information" for a User Product means any methods,
|
299 |
+
procedures, authorization keys, or other information required to install
|
300 |
+
and execute modified versions of a covered work in that User Product from
|
301 |
+
a modified version of its Corresponding Source. The information must
|
302 |
+
suffice to ensure that the continued functioning of the modified object
|
303 |
+
code is in no case prevented or interfered with solely because
|
304 |
+
modification has been made.
|
305 |
+
|
306 |
+
If you convey an object code work under this section in, or with, or
|
307 |
+
specifically for use in, a User Product, and the conveying occurs as
|
308 |
+
part of a transaction in which the right of possession and use of the
|
309 |
+
User Product is transferred to the recipient in perpetuity or for a
|
310 |
+
fixed term (regardless of how the transaction is characterized), the
|
311 |
+
Corresponding Source conveyed under this section must be accompanied
|
312 |
+
by the Installation Information. But this requirement does not apply
|
313 |
+
if neither you nor any third party retains the ability to install
|
314 |
+
modified object code on the User Product (for example, the work has
|
315 |
+
been installed in ROM).
|
316 |
+
|
317 |
+
The requirement to provide Installation Information does not include a
|
318 |
+
requirement to continue to provide support service, warranty, or updates
|
319 |
+
for a work that has been modified or installed by the recipient, or for
|
320 |
+
the User Product in which it has been modified or installed. Access to a
|
321 |
+
network may be denied when the modification itself materially and
|
322 |
+
adversely affects the operation of the network or violates the rules and
|
323 |
+
protocols for communication across the network.
|
324 |
+
|
325 |
+
Corresponding Source conveyed, and Installation Information provided,
|
326 |
+
in accord with this section must be in a format that is publicly
|
327 |
+
documented (and with an implementation available to the public in
|
328 |
+
source code form), and must require no special password or key for
|
329 |
+
unpacking, reading or copying.
|
330 |
+
|
331 |
+
7. Additional Terms.
|
332 |
+
|
333 |
+
"Additional permissions" are terms that supplement the terms of this
|
334 |
+
License by making exceptions from one or more of its conditions.
|
335 |
+
Additional permissions that are applicable to the entire Program shall
|
336 |
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be treated as though they were included in this License, to the extent
|
337 |
+
that they are valid under applicable law. If additional permissions
|
338 |
+
apply only to part of the Program, that part may be used separately
|
339 |
+
under those permissions, but the entire Program remains governed by
|
340 |
+
this License without regard to the additional permissions.
|
341 |
+
|
342 |
+
When you convey a copy of a covered work, you may at your option
|
343 |
+
remove any additional permissions from that copy, or from any part of
|
344 |
+
it. (Additional permissions may be written to require their own
|
345 |
+
removal in certain cases when you modify the work.) You may place
|
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+
additional permissions on material, added by you to a covered work,
|
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for which you have or can give appropriate copyright permission.
|
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|
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Notwithstanding any other provision of this License, for material you
|
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add to a covered work, you may (if authorized by the copyright holders of
|
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that material) supplement the terms of this License with terms:
|
352 |
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|
353 |
+
a) Disclaiming warranty or limiting liability differently from the
|
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terms of sections 15 and 16 of this License; or
|
355 |
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|
356 |
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b) Requiring preservation of specified reasonable legal notices or
|
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author attributions in that material or in the Appropriate Legal
|
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Notices displayed by works containing it; or
|
359 |
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|
360 |
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c) Prohibiting misrepresentation of the origin of that material, or
|
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requiring that modified versions of such material be marked in
|
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reasonable ways as different from the original version; or
|
363 |
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|
364 |
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d) Limiting the use for publicity purposes of names of licensors or
|
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authors of the material; or
|
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|
367 |
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e) Declining to grant rights under trademark law for use of some
|
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trade names, trademarks, or service marks; or
|
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|
370 |
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f) Requiring indemnification of licensors and authors of that
|
371 |
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material by anyone who conveys the material (or modified versions of
|
372 |
+
it) with contractual assumptions of liability to the recipient, for
|
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any liability that these contractual assumptions directly impose on
|
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those licensors and authors.
|
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|
376 |
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All other non-permissive additional terms are considered "further
|
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restrictions" within the meaning of section 10. If the Program as you
|
378 |
+
received it, or any part of it, contains a notice stating that it is
|
379 |
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governed by this License along with a term that is a further
|
380 |
+
restriction, you may remove that term. If a license document contains
|
381 |
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a further restriction but permits relicensing or conveying under this
|
382 |
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License, you may add to a covered work material governed by the terms
|
383 |
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of that license document, provided that the further restriction does
|
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not survive such relicensing or conveying.
|
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|
386 |
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If you add terms to a covered work in accord with this section, you
|
387 |
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must place, in the relevant source files, a statement of the
|
388 |
+
additional terms that apply to those files, or a notice indicating
|
389 |
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where to find the applicable terms.
|
390 |
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|
391 |
+
Additional terms, permissive or non-permissive, may be stated in the
|
392 |
+
form of a separately written license, or stated as exceptions;
|
393 |
+
the above requirements apply either way.
|
394 |
+
|
395 |
+
8. Termination.
|
396 |
+
|
397 |
+
You may not propagate or modify a covered work except as expressly
|
398 |
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provided under this License. Any attempt otherwise to propagate or
|
399 |
+
modify it is void, and will automatically terminate your rights under
|
400 |
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this License (including any patent licenses granted under the third
|
401 |
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paragraph of section 11).
|
402 |
+
|
403 |
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However, if you cease all violation of this License, then your
|
404 |
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license from a particular copyright holder is reinstated (a)
|
405 |
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provisionally, unless and until the copyright holder explicitly and
|
406 |
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finally terminates your license, and (b) permanently, if the copyright
|
407 |
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holder fails to notify you of the violation by some reasonable means
|
408 |
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prior to 60 days after the cessation.
|
409 |
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|
410 |
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Moreover, your license from a particular copyright holder is
|
411 |
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reinstated permanently if the copyright holder notifies you of the
|
412 |
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violation by some reasonable means, this is the first time you have
|
413 |
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received notice of violation of this License (for any work) from that
|
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copyright holder, and you cure the violation prior to 30 days after
|
415 |
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your receipt of the notice.
|
416 |
+
|
417 |
+
Termination of your rights under this section does not terminate the
|
418 |
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licenses of parties who have received copies or rights from you under
|
419 |
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this License. If your rights have been terminated and not permanently
|
420 |
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reinstated, you do not qualify to receive new licenses for the same
|
421 |
+
material under section 10.
|
422 |
+
|
423 |
+
9. Acceptance Not Required for Having Copies.
|
424 |
+
|
425 |
+
You are not required to accept this License in order to receive or
|
426 |
+
run a copy of the Program. Ancillary propagation of a covered work
|
427 |
+
occurring solely as a consequence of using peer-to-peer transmission
|
428 |
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to receive a copy likewise does not require acceptance. However,
|
429 |
+
nothing other than this License grants you permission to propagate or
|
430 |
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modify any covered work. These actions infringe copyright if you do
|
431 |
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not accept this License. Therefore, by modifying or propagating a
|
432 |
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covered work, you indicate your acceptance of this License to do so.
|
433 |
+
|
434 |
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10. Automatic Licensing of Downstream Recipients.
|
435 |
+
|
436 |
+
Each time you convey a covered work, the recipient automatically
|
437 |
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receives a license from the original licensors, to run, modify and
|
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propagate that work, subject to this License. You are not responsible
|
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for enforcing compliance by third parties with this License.
|
440 |
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|
441 |
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An "entity transaction" is a transaction transferring control of an
|
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organization, or substantially all assets of one, or subdividing an
|
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organization, or merging organizations. If propagation of a covered
|
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work results from an entity transaction, each party to that
|
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transaction who receives a copy of the work also receives whatever
|
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licenses to the work the party's predecessor in interest had or could
|
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give under the previous paragraph, plus a right to possession of the
|
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+
Corresponding Source of the work from the predecessor in interest, if
|
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+
the predecessor has it or can get it with reasonable efforts.
|
450 |
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|
451 |
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You may not impose any further restrictions on the exercise of the
|
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rights granted or affirmed under this License. For example, you may
|
453 |
+
not impose a license fee, royalty, or other charge for exercise of
|
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rights granted under this License, and you may not initiate litigation
|
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+
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
456 |
+
any patent claim is infringed by making, using, selling, offering for
|
457 |
+
sale, or importing the Program or any portion of it.
|
458 |
+
|
459 |
+
11. Patents.
|
460 |
+
|
461 |
+
A "contributor" is a copyright holder who authorizes use under this
|
462 |
+
License of the Program or a work on which the Program is based. The
|
463 |
+
work thus licensed is called the contributor's "contributor version".
|
464 |
+
|
465 |
+
A contributor's "essential patent claims" are all patent claims
|
466 |
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owned or controlled by the contributor, whether already acquired or
|
467 |
+
hereafter acquired, that would be infringed by some manner, permitted
|
468 |
+
by this License, of making, using, or selling its contributor version,
|
469 |
+
but do not include claims that would be infringed only as a
|
470 |
+
consequence of further modification of the contributor version. For
|
471 |
+
purposes of this definition, "control" includes the right to grant
|
472 |
+
patent sublicenses in a manner consistent with the requirements of
|
473 |
+
this License.
|
474 |
+
|
475 |
+
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
476 |
+
patent license under the contributor's essential patent claims, to
|
477 |
+
make, use, sell, offer for sale, import and otherwise run, modify and
|
478 |
+
propagate the contents of its contributor version.
|
479 |
+
|
480 |
+
In the following three paragraphs, a "patent license" is any express
|
481 |
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agreement or commitment, however denominated, not to enforce a patent
|
482 |
+
(such as an express permission to practice a patent or covenant not to
|
483 |
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sue for patent infringement). To "grant" such a patent license to a
|
484 |
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party means to make such an agreement or commitment not to enforce a
|
485 |
+
patent against the party.
|
486 |
+
|
487 |
+
If you convey a covered work, knowingly relying on a patent license,
|
488 |
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and the Corresponding Source of the work is not available for anyone
|
489 |
+
to copy, free of charge and under the terms of this License, through a
|
490 |
+
publicly available network server or other readily accessible means,
|
491 |
+
then you must either (1) cause the Corresponding Source to be so
|
492 |
+
available, or (2) arrange to deprive yourself of the benefit of the
|
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+
patent license for this particular work, or (3) arrange, in a manner
|
494 |
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consistent with the requirements of this License, to extend the patent
|
495 |
+
license to downstream recipients. "Knowingly relying" means you have
|
496 |
+
actual knowledge that, but for the patent license, your conveying the
|
497 |
+
covered work in a country, or your recipient's use of the covered work
|
498 |
+
in a country, would infringe one or more identifiable patents in that
|
499 |
+
country that you have reason to believe are valid.
|
500 |
+
|
501 |
+
If, pursuant to or in connection with a single transaction or
|
502 |
+
arrangement, you convey, or propagate by procuring conveyance of, a
|
503 |
+
covered work, and grant a patent license to some of the parties
|
504 |
+
receiving the covered work authorizing them to use, propagate, modify
|
505 |
+
or convey a specific copy of the covered work, then the patent license
|
506 |
+
you grant is automatically extended to all recipients of the covered
|
507 |
+
work and works based on it.
|
508 |
+
|
509 |
+
A patent license is "discriminatory" if it does not include within
|
510 |
+
the scope of its coverage, prohibits the exercise of, or is
|
511 |
+
conditioned on the non-exercise of one or more of the rights that are
|
512 |
+
specifically granted under this License. You may not convey a covered
|
513 |
+
work if you are a party to an arrangement with a third party that is
|
514 |
+
in the business of distributing software, under which you make payment
|
515 |
+
to the third party based on the extent of your activity of conveying
|
516 |
+
the work, and under which the third party grants, to any of the
|
517 |
+
parties who would receive the covered work from you, a discriminatory
|
518 |
+
patent license (a) in connection with copies of the covered work
|
519 |
+
conveyed by you (or copies made from those copies), or (b) primarily
|
520 |
+
for and in connection with specific products or compilations that
|
521 |
+
contain the covered work, unless you entered into that arrangement,
|
522 |
+
or that patent license was granted, prior to 28 March 2007.
|
523 |
+
|
524 |
+
Nothing in this License shall be construed as excluding or limiting
|
525 |
+
any implied license or other defenses to infringement that may
|
526 |
+
otherwise be available to you under applicable patent law.
|
527 |
+
|
528 |
+
12. No Surrender of Others' Freedom.
|
529 |
+
|
530 |
+
If conditions are imposed on you (whether by court order, agreement or
|
531 |
+
otherwise) that contradict the conditions of this License, they do not
|
532 |
+
excuse you from the conditions of this License. If you cannot convey a
|
533 |
+
covered work so as to satisfy simultaneously your obligations under this
|
534 |
+
License and any other pertinent obligations, then as a consequence you may
|
535 |
+
not convey it at all. For example, if you agree to terms that obligate you
|
536 |
+
to collect a royalty for further conveying from those to whom you convey
|
537 |
+
the Program, the only way you could satisfy both those terms and this
|
538 |
+
License would be to refrain entirely from conveying the Program.
|
539 |
+
|
540 |
+
13. Remote Network Interaction; Use with the GNU General Public License.
|
541 |
+
|
542 |
+
Notwithstanding any other provision of this License, if you modify the
|
543 |
+
Program, your modified version must prominently offer all users
|
544 |
+
interacting with it remotely through a computer network (if your version
|
545 |
+
supports such interaction) an opportunity to receive the Corresponding
|
546 |
+
Source of your version by providing access to the Corresponding Source
|
547 |
+
from a network server at no charge, through some standard or customary
|
548 |
+
means of facilitating copying of software. This Corresponding Source
|
549 |
+
shall include the Corresponding Source for any work covered by version 3
|
550 |
+
of the GNU General Public License that is incorporated pursuant to the
|
551 |
+
following paragraph.
|
552 |
+
|
553 |
+
Notwithstanding any other provision of this License, you have
|
554 |
+
permission to link or combine any covered work with a work licensed
|
555 |
+
under version 3 of the GNU General Public License into a single
|
556 |
+
combined work, and to convey the resulting work. The terms of this
|
557 |
+
License will continue to apply to the part which is the covered work,
|
558 |
+
but the work with which it is combined will remain governed by version
|
559 |
+
3 of the GNU General Public License.
|
560 |
+
|
561 |
+
14. Revised Versions of this License.
|
562 |
+
|
563 |
+
The Free Software Foundation may publish revised and/or new versions of
|
564 |
+
the GNU Affero General Public License from time to time. Such new versions
|
565 |
+
will be similar in spirit to the present version, but may differ in detail to
|
566 |
+
address new problems or concerns.
|
567 |
+
|
568 |
+
Each version is given a distinguishing version number. If the
|
569 |
+
Program specifies that a certain numbered version of the GNU Affero General
|
570 |
+
Public License "or any later version" applies to it, you have the
|
571 |
+
option of following the terms and conditions either of that numbered
|
572 |
+
version or of any later version published by the Free Software
|
573 |
+
Foundation. If the Program does not specify a version number of the
|
574 |
+
GNU Affero General Public License, you may choose any version ever published
|
575 |
+
by the Free Software Foundation.
|
576 |
+
|
577 |
+
If the Program specifies that a proxy can decide which future
|
578 |
+
versions of the GNU Affero General Public License can be used, that proxy's
|
579 |
+
public statement of acceptance of a version permanently authorizes you
|
580 |
+
to choose that version for the Program.
|
581 |
+
|
582 |
+
Later license versions may give you additional or different
|
583 |
+
permissions. However, no additional obligations are imposed on any
|
584 |
+
author or copyright holder as a result of your choosing to follow a
|
585 |
+
later version.
|
586 |
+
|
587 |
+
15. Disclaimer of Warranty.
|
588 |
+
|
589 |
+
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
590 |
+
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
591 |
+
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
592 |
+
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
593 |
+
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
594 |
+
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
595 |
+
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
596 |
+
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
597 |
+
|
598 |
+
16. Limitation of Liability.
|
599 |
+
|
600 |
+
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
601 |
+
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
602 |
+
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
603 |
+
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
604 |
+
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
605 |
+
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
606 |
+
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
607 |
+
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
608 |
+
SUCH DAMAGES.
|
609 |
+
|
610 |
+
17. Interpretation of Sections 15 and 16.
|
611 |
+
|
612 |
+
If the disclaimer of warranty and limitation of liability provided
|
613 |
+
above cannot be given local legal effect according to their terms,
|
614 |
+
reviewing courts shall apply local law that most closely approximates
|
615 |
+
an absolute waiver of all civil liability in connection with the
|
616 |
+
Program, unless a warranty or assumption of liability accompanies a
|
617 |
+
copy of the Program in return for a fee.
|
618 |
+
|
619 |
+
END OF TERMS AND CONDITIONS
|
620 |
+
|
621 |
+
How to Apply These Terms to Your New Programs
|
622 |
+
|
623 |
+
If you develop a new program, and you want it to be of the greatest
|
624 |
+
possible use to the public, the best way to achieve this is to make it
|
625 |
+
free software which everyone can redistribute and change under these terms.
|
626 |
+
|
627 |
+
To do so, attach the following notices to the program. It is safest
|
628 |
+
to attach them to the start of each source file to most effectively
|
629 |
+
state the exclusion of warranty; and each file should have at least
|
630 |
+
the "copyright" line and a pointer to where the full notice is found.
|
631 |
+
|
632 |
+
<one line to give the program's name and a brief idea of what it does.>
|
633 |
+
Copyright (C) <year> <name of author>
|
634 |
+
|
635 |
+
This program is free software: you can redistribute it and/or modify
|
636 |
+
it under the terms of the GNU Affero General Public License as published
|
637 |
+
by the Free Software Foundation, either version 3 of the License, or
|
638 |
+
(at your option) any later version.
|
639 |
+
|
640 |
+
This program is distributed in the hope that it will be useful,
|
641 |
+
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
642 |
+
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
643 |
+
GNU Affero General Public License for more details.
|
644 |
+
|
645 |
+
You should have received a copy of the GNU Affero General Public License
|
646 |
+
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
647 |
+
|
648 |
+
Also add information on how to contact you by electronic and paper mail.
|
649 |
+
|
650 |
+
If your software can interact with users remotely through a computer
|
651 |
+
network, you should also make sure that it provides a way for users to
|
652 |
+
get its source. For example, if your program is a web application, its
|
653 |
+
interface could display a "Source" link that leads users to an archive
|
654 |
+
of the code. There are many ways you could offer source, and different
|
655 |
+
solutions will be better for different programs; see section 13 for the
|
656 |
+
specific requirements.
|
657 |
+
|
658 |
+
You should also get your employer (if you work as a programmer) or school,
|
659 |
+
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
660 |
+
For more information on this, and how to apply and follow the GNU AGPL, see
|
661 |
+
<https://www.gnu.org/licenses/>.
|
README.md
CHANGED
@@ -1,11 +1,9 @@
|
|
1 |
---
|
2 |
-
title:
|
3 |
-
|
4 |
-
|
5 |
-
|
6 |
-
sdk: docker
|
7 |
-
pinned: false
|
8 |
-
license: gpl-3.0
|
9 |
---
|
|
|
10 |
|
11 |
-
|
|
|
1 |
---
|
2 |
+
title: face-swap
|
3 |
+
app_file: run.py
|
4 |
+
sdk: gradio
|
5 |
+
sdk_version: 3.40.1
|
|
|
|
|
|
|
6 |
---
|
7 |
+
# roop-unleashed
|
8 |
|
9 |
+
WIP Version of roop-unleashed using Gradio UI
|
__pycache__/jaa.cpython-311.pyc
ADDED
Binary file (17.8 kB). View file
|
|
__pycache__/settings.cpython-311.pyc
ADDED
Binary file (3.43 kB). View file
|
|
chain_img_processor/__init__.py
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from .image import ChainImgProcessor, ChainImgPlugin, get_single_image_processor, version
|
2 |
+
from .video import ChainVideoProcessor, get_single_video_processor
|
3 |
+
from .batchimage import ChainBatchImageProcessor
|
4 |
+
from .ffmpeg_writer import FFMPEG_VideoWriter
|
chain_img_processor/__pycache__/__init__.cpython-311.pyc
ADDED
Binary file (584 Bytes). View file
|
|
chain_img_processor/__pycache__/batchimage.cpython-311.pyc
ADDED
Binary file (6.46 kB). View file
|
|
chain_img_processor/__pycache__/ffmpeg_writer.cpython-311.pyc
ADDED
Binary file (9.41 kB). View file
|
|
chain_img_processor/__pycache__/image.cpython-311.pyc
ADDED
Binary file (9.19 kB). View file
|
|
chain_img_processor/__pycache__/video.cpython-311.pyc
ADDED
Binary file (6.57 kB). View file
|
|
chain_img_processor/batchimage.py
ADDED
@@ -0,0 +1,86 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from typing import Any, List, Callable
|
2 |
+
import psutil
|
3 |
+
import os
|
4 |
+
from concurrent.futures import ThreadPoolExecutor, as_completed
|
5 |
+
from queue import Queue
|
6 |
+
from .image import ChainImgProcessor
|
7 |
+
from tqdm import tqdm
|
8 |
+
import cv2
|
9 |
+
|
10 |
+
def create_queue(temp_frame_paths: List[str]) -> Queue[str]:
|
11 |
+
queue: Queue[str] = Queue()
|
12 |
+
for frame_path in temp_frame_paths:
|
13 |
+
queue.put(frame_path)
|
14 |
+
return queue
|
15 |
+
|
16 |
+
|
17 |
+
def pick_queue(queue: Queue[str], queue_per_future: int) -> List[str]:
|
18 |
+
queues = []
|
19 |
+
for _ in range(queue_per_future):
|
20 |
+
if not queue.empty():
|
21 |
+
queues.append(queue.get())
|
22 |
+
return queues
|
23 |
+
|
24 |
+
|
25 |
+
|
26 |
+
class ChainBatchImageProcessor(ChainImgProcessor):
|
27 |
+
chain = None
|
28 |
+
func_params_gen = None
|
29 |
+
num_threads = 1
|
30 |
+
|
31 |
+
def __init__(self):
|
32 |
+
ChainImgProcessor.__init__(self)
|
33 |
+
|
34 |
+
|
35 |
+
def init_with_plugins(self):
|
36 |
+
self.init_plugins(["core"])
|
37 |
+
self.display_init_info()
|
38 |
+
|
39 |
+
init_on_start_arr = self.init_on_start.split(",")
|
40 |
+
for proc_id in init_on_start_arr:
|
41 |
+
self.init_processor(proc_id)
|
42 |
+
|
43 |
+
def update_progress(self, progress: Any = None) -> None:
|
44 |
+
process = psutil.Process(os.getpid())
|
45 |
+
memory_usage = process.memory_info().rss / 1024 / 1024 / 1024
|
46 |
+
progress.set_postfix({
|
47 |
+
'memory_usage': '{:.2f}'.format(memory_usage).zfill(5) + 'GB',
|
48 |
+
'execution_threads': self.num_threads
|
49 |
+
})
|
50 |
+
progress.refresh()
|
51 |
+
progress.update(1)
|
52 |
+
|
53 |
+
|
54 |
+
def process_frames(self, source_files: List[str], target_files: List[str], current_files, update: Callable[[], None]) -> None:
|
55 |
+
for f in current_files:
|
56 |
+
temp_frame = cv2.imread(f)
|
57 |
+
if temp_frame is not None:
|
58 |
+
if self.func_params_gen:
|
59 |
+
params = self.func_params_gen(None, temp_frame)
|
60 |
+
else:
|
61 |
+
params = {}
|
62 |
+
resimg, _ = self.run_chain(temp_frame, params, self.chain)
|
63 |
+
if resimg is not None:
|
64 |
+
i = source_files.index(f)
|
65 |
+
cv2.imwrite(target_files[i], resimg)
|
66 |
+
if update:
|
67 |
+
update()
|
68 |
+
|
69 |
+
|
70 |
+
def run_batch_chain(self, source_files, target_files, threads:int = 1, chain = None, params_frame_gen_func = None):
|
71 |
+
self.chain = chain
|
72 |
+
self.func_params_gen = params_frame_gen_func
|
73 |
+
progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
|
74 |
+
total = len(source_files)
|
75 |
+
self.num_threads = threads
|
76 |
+
with tqdm(total=total, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
|
77 |
+
with ThreadPoolExecutor(max_workers=threads) as executor:
|
78 |
+
futures = []
|
79 |
+
queue = create_queue(source_files)
|
80 |
+
queue_per_future = max(len(source_files) // threads, 1)
|
81 |
+
while not queue.empty():
|
82 |
+
future = executor.submit(self.process_frames, source_files, target_files, pick_queue(queue, queue_per_future), lambda: self.update_progress(progress))
|
83 |
+
futures.append(future)
|
84 |
+
for future in as_completed(futures):
|
85 |
+
future.result()
|
86 |
+
|
chain_img_processor/ffmpeg_writer.py
ADDED
@@ -0,0 +1,253 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""
|
2 |
+
FFMPEG_Writer - write set of frames to video file
|
3 |
+
|
4 |
+
original from
|
5 |
+
https://github.com/Zulko/moviepy/blob/master/moviepy/video/io/ffmpeg_writer.py
|
6 |
+
|
7 |
+
removed unnecessary dependencies
|
8 |
+
|
9 |
+
The MIT License (MIT)
|
10 |
+
|
11 |
+
Copyright (c) 2015 Zulko
|
12 |
+
Copyright (c) 2023 Janvarev Vladislav
|
13 |
+
"""
|
14 |
+
|
15 |
+
import os
|
16 |
+
import subprocess as sp
|
17 |
+
|
18 |
+
PIPE = -1
|
19 |
+
STDOUT = -2
|
20 |
+
DEVNULL = -3
|
21 |
+
|
22 |
+
FFMPEG_BINARY = "ffmpeg"
|
23 |
+
|
24 |
+
class FFMPEG_VideoWriter:
|
25 |
+
""" A class for FFMPEG-based video writing.
|
26 |
+
|
27 |
+
A class to write videos using ffmpeg. ffmpeg will write in a large
|
28 |
+
choice of formats.
|
29 |
+
|
30 |
+
Parameters
|
31 |
+
-----------
|
32 |
+
|
33 |
+
filename
|
34 |
+
Any filename like 'video.mp4' etc. but if you want to avoid
|
35 |
+
complications it is recommended to use the generic extension
|
36 |
+
'.avi' for all your videos.
|
37 |
+
|
38 |
+
size
|
39 |
+
Size (width,height) of the output video in pixels.
|
40 |
+
|
41 |
+
fps
|
42 |
+
Frames per second in the output video file.
|
43 |
+
|
44 |
+
codec
|
45 |
+
FFMPEG codec. It seems that in terms of quality the hierarchy is
|
46 |
+
'rawvideo' = 'png' > 'mpeg4' > 'libx264'
|
47 |
+
'png' manages the same lossless quality as 'rawvideo' but yields
|
48 |
+
smaller files. Type ``ffmpeg -codecs`` in a terminal to get a list
|
49 |
+
of accepted codecs.
|
50 |
+
|
51 |
+
Note for default 'libx264': by default the pixel format yuv420p
|
52 |
+
is used. If the video dimensions are not both even (e.g. 720x405)
|
53 |
+
another pixel format is used, and this can cause problem in some
|
54 |
+
video readers.
|
55 |
+
|
56 |
+
audiofile
|
57 |
+
Optional: The name of an audio file that will be incorporated
|
58 |
+
to the video.
|
59 |
+
|
60 |
+
preset
|
61 |
+
Sets the time that FFMPEG will take to compress the video. The slower,
|
62 |
+
the better the compression rate. Possibilities are: ultrafast,superfast,
|
63 |
+
veryfast, faster, fast, medium (default), slow, slower, veryslow,
|
64 |
+
placebo.
|
65 |
+
|
66 |
+
bitrate
|
67 |
+
Only relevant for codecs which accept a bitrate. "5000k" offers
|
68 |
+
nice results in general.
|
69 |
+
|
70 |
+
"""
|
71 |
+
|
72 |
+
def __init__(self, filename, size, fps, codec="libx265", crf=14, audiofile=None,
|
73 |
+
preset="medium", bitrate=None,
|
74 |
+
logfile=None, threads=None, ffmpeg_params=None):
|
75 |
+
|
76 |
+
if logfile is None:
|
77 |
+
logfile = sp.PIPE
|
78 |
+
|
79 |
+
self.filename = filename
|
80 |
+
self.codec = codec
|
81 |
+
self.ext = self.filename.split(".")[-1]
|
82 |
+
w = size[0] - 1 if size[0] % 2 != 0 else size[0]
|
83 |
+
h = size[1] - 1 if size[1] % 2 != 0 else size[1]
|
84 |
+
|
85 |
+
|
86 |
+
# order is important
|
87 |
+
cmd = [
|
88 |
+
FFMPEG_BINARY,
|
89 |
+
'-hide_banner',
|
90 |
+
'-hwaccel', 'auto',
|
91 |
+
'-y',
|
92 |
+
'-loglevel', 'error' if logfile == sp.PIPE else 'info',
|
93 |
+
'-f', 'rawvideo',
|
94 |
+
'-vcodec', 'rawvideo',
|
95 |
+
'-s', '%dx%d' % (size[0], size[1]),
|
96 |
+
#'-pix_fmt', 'rgba' if withmask else 'rgb24',
|
97 |
+
'-pix_fmt', 'bgr24',
|
98 |
+
'-r', str(fps),
|
99 |
+
'-an', '-i', '-'
|
100 |
+
]
|
101 |
+
|
102 |
+
if audiofile is not None:
|
103 |
+
cmd.extend([
|
104 |
+
'-i', audiofile,
|
105 |
+
'-acodec', 'copy'
|
106 |
+
])
|
107 |
+
|
108 |
+
cmd.extend([
|
109 |
+
'-vcodec', codec,
|
110 |
+
'-crf', str(crf)
|
111 |
+
#'-preset', preset,
|
112 |
+
])
|
113 |
+
if ffmpeg_params is not None:
|
114 |
+
cmd.extend(ffmpeg_params)
|
115 |
+
if bitrate is not None:
|
116 |
+
cmd.extend([
|
117 |
+
'-b', bitrate
|
118 |
+
])
|
119 |
+
|
120 |
+
# scale to a resolution divisible by 2 if not even
|
121 |
+
cmd.extend(['-vf', f'scale={w}:{h}' if w != size[0] or h != size[1] else 'colorspace=bt709:iall=bt601-6-625:fast=1'])
|
122 |
+
|
123 |
+
if threads is not None:
|
124 |
+
cmd.extend(["-threads", str(threads)])
|
125 |
+
|
126 |
+
cmd.extend([
|
127 |
+
'-pix_fmt', 'yuv420p',
|
128 |
+
|
129 |
+
])
|
130 |
+
cmd.extend([
|
131 |
+
filename
|
132 |
+
])
|
133 |
+
|
134 |
+
test = str(cmd)
|
135 |
+
print(test)
|
136 |
+
|
137 |
+
popen_params = {"stdout": DEVNULL,
|
138 |
+
"stderr": logfile,
|
139 |
+
"stdin": sp.PIPE}
|
140 |
+
|
141 |
+
# This was added so that no extra unwanted window opens on windows
|
142 |
+
# when the child process is created
|
143 |
+
if os.name == "nt":
|
144 |
+
popen_params["creationflags"] = 0x08000000 # CREATE_NO_WINDOW
|
145 |
+
|
146 |
+
self.proc = sp.Popen(cmd, **popen_params)
|
147 |
+
|
148 |
+
|
149 |
+
def write_frame(self, img_array):
|
150 |
+
""" Writes one frame in the file."""
|
151 |
+
try:
|
152 |
+
#if PY3:
|
153 |
+
self.proc.stdin.write(img_array.tobytes())
|
154 |
+
# else:
|
155 |
+
# self.proc.stdin.write(img_array.tostring())
|
156 |
+
except IOError as err:
|
157 |
+
_, ffmpeg_error = self.proc.communicate()
|
158 |
+
error = (str(err) + ("\n\nMoviePy error: FFMPEG encountered "
|
159 |
+
"the following error while writing file %s:"
|
160 |
+
"\n\n %s" % (self.filename, str(ffmpeg_error))))
|
161 |
+
|
162 |
+
if b"Unknown encoder" in ffmpeg_error:
|
163 |
+
|
164 |
+
error = error+("\n\nThe video export "
|
165 |
+
"failed because FFMPEG didn't find the specified "
|
166 |
+
"codec for video encoding (%s). Please install "
|
167 |
+
"this codec or change the codec when calling "
|
168 |
+
"write_videofile. For instance:\n"
|
169 |
+
" >>> clip.write_videofile('myvid.webm', codec='libvpx')")%(self.codec)
|
170 |
+
|
171 |
+
elif b"incorrect codec parameters ?" in ffmpeg_error:
|
172 |
+
|
173 |
+
error = error+("\n\nThe video export "
|
174 |
+
"failed, possibly because the codec specified for "
|
175 |
+
"the video (%s) is not compatible with the given "
|
176 |
+
"extension (%s). Please specify a valid 'codec' "
|
177 |
+
"argument in write_videofile. This would be 'libx264' "
|
178 |
+
"or 'mpeg4' for mp4, 'libtheora' for ogv, 'libvpx for webm. "
|
179 |
+
"Another possible reason is that the audio codec was not "
|
180 |
+
"compatible with the video codec. For instance the video "
|
181 |
+
"extensions 'ogv' and 'webm' only allow 'libvorbis' (default) as a"
|
182 |
+
"video codec."
|
183 |
+
)%(self.codec, self.ext)
|
184 |
+
|
185 |
+
elif b"encoder setup failed" in ffmpeg_error:
|
186 |
+
|
187 |
+
error = error+("\n\nThe video export "
|
188 |
+
"failed, possibly because the bitrate you specified "
|
189 |
+
"was too high or too low for the video codec.")
|
190 |
+
|
191 |
+
elif b"Invalid encoder type" in ffmpeg_error:
|
192 |
+
|
193 |
+
error = error + ("\n\nThe video export failed because the codec "
|
194 |
+
"or file extension you provided is not a video")
|
195 |
+
|
196 |
+
|
197 |
+
raise IOError(error)
|
198 |
+
|
199 |
+
def close(self):
|
200 |
+
if self.proc:
|
201 |
+
self.proc.stdin.close()
|
202 |
+
if self.proc.stderr is not None:
|
203 |
+
self.proc.stderr.close()
|
204 |
+
self.proc.wait()
|
205 |
+
|
206 |
+
self.proc = None
|
207 |
+
|
208 |
+
# Support the Context Manager protocol, to ensure that resources are cleaned up.
|
209 |
+
|
210 |
+
def __enter__(self):
|
211 |
+
return self
|
212 |
+
|
213 |
+
def __exit__(self, exc_type, exc_value, traceback):
|
214 |
+
self.close()
|
215 |
+
|
216 |
+
|
217 |
+
|
218 |
+
def ffmpeg_write_image(filename, image, logfile=False):
|
219 |
+
""" Writes an image (HxWx3 or HxWx4 numpy array) to a file, using
|
220 |
+
ffmpeg. """
|
221 |
+
|
222 |
+
if image.dtype != 'uint8':
|
223 |
+
image = image.astype("uint8")
|
224 |
+
|
225 |
+
cmd = [ FFMPEG_BINARY, '-y',
|
226 |
+
'-s', "%dx%d"%(image.shape[:2][::-1]),
|
227 |
+
"-f", 'rawvideo',
|
228 |
+
'-pix_fmt', "rgba" if (image.shape[2] == 4) else "rgb24",
|
229 |
+
'-i','-', filename]
|
230 |
+
|
231 |
+
if logfile:
|
232 |
+
log_file = open(filename + ".log", 'w+')
|
233 |
+
else:
|
234 |
+
log_file = sp.PIPE
|
235 |
+
|
236 |
+
popen_params = {"stdout": DEVNULL,
|
237 |
+
"stderr": log_file,
|
238 |
+
"stdin": sp.PIPE}
|
239 |
+
|
240 |
+
if os.name == "nt":
|
241 |
+
popen_params["creationflags"] = 0x08000000
|
242 |
+
|
243 |
+
proc = sp.Popen(cmd, **popen_params)
|
244 |
+
out, err = proc.communicate(image.tostring())
|
245 |
+
|
246 |
+
if proc.returncode:
|
247 |
+
err = "\n".join(["[MoviePy] Running : %s\n" % cmd,
|
248 |
+
"WARNING: this command returned an error:",
|
249 |
+
err.decode('utf8')])
|
250 |
+
raise IOError(err)
|
251 |
+
|
252 |
+
del proc
|
253 |
+
|
chain_img_processor/image.py
ADDED
@@ -0,0 +1,176 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from jaa import JaaCore
|
2 |
+
from roop.utilities import get_device
|
3 |
+
|
4 |
+
|
5 |
+
from typing import Any
|
6 |
+
|
7 |
+
version = "4.0.0"
|
8 |
+
|
9 |
+
class ChainImgProcessor(JaaCore):
|
10 |
+
|
11 |
+
def __init__(self):
|
12 |
+
JaaCore.__init__(self)
|
13 |
+
|
14 |
+
self.processors:dict = {
|
15 |
+
}
|
16 |
+
|
17 |
+
self.processors_objects:dict[str,list[ChainImgPlugin]] = {}
|
18 |
+
|
19 |
+
self.default_chain = ""
|
20 |
+
self.init_on_start = ""
|
21 |
+
|
22 |
+
self.inited_processors = []
|
23 |
+
|
24 |
+
self.is_demo_row_render = False
|
25 |
+
|
26 |
+
def process_plugin_manifest(self, modname, manifest):
|
27 |
+
# adding processors from plugin manifest
|
28 |
+
if "img_processor" in manifest: # process commands
|
29 |
+
for cmd in manifest["img_processor"].keys():
|
30 |
+
self.processors[cmd] = manifest["img_processor"][cmd]
|
31 |
+
|
32 |
+
return manifest
|
33 |
+
|
34 |
+
def init_with_plugins(self):
|
35 |
+
self.init_plugins(["core"])
|
36 |
+
self.display_init_info()
|
37 |
+
|
38 |
+
#self.init_translator_engine(self.default_translator)
|
39 |
+
init_on_start_arr = self.init_on_start.split(",")
|
40 |
+
for proc_id in init_on_start_arr:
|
41 |
+
self.init_processor(proc_id)
|
42 |
+
|
43 |
+
def run_chain(self, img, params:dict[str,Any] = None, chain:str = None, thread_index:int = 0):
|
44 |
+
if chain is None:
|
45 |
+
chain = self.default_chain
|
46 |
+
if params is None:
|
47 |
+
params = {}
|
48 |
+
params["_thread_index"] = thread_index
|
49 |
+
chain_ar = chain.split(",")
|
50 |
+
# init all not inited processors first
|
51 |
+
for proc_id in chain_ar:
|
52 |
+
if proc_id != "":
|
53 |
+
if not proc_id in self.inited_processors:
|
54 |
+
self.init_processor(proc_id)
|
55 |
+
|
56 |
+
|
57 |
+
|
58 |
+
# run processing
|
59 |
+
if self.is_demo_row_render:
|
60 |
+
import cv2
|
61 |
+
import numpy as np
|
62 |
+
height, width, channels = img.shape
|
63 |
+
img_blank = np.zeros((height+30, width*(1+len(chain_ar)), 3), dtype=np.uint8)
|
64 |
+
img_blank.fill(255)
|
65 |
+
|
66 |
+
y = 30
|
67 |
+
x = 0
|
68 |
+
img_blank[y:y + height, x:x + width] = img
|
69 |
+
|
70 |
+
# Set the font scale and thickness
|
71 |
+
font_scale = 1
|
72 |
+
thickness = 2
|
73 |
+
|
74 |
+
# Set the font face to a monospace font
|
75 |
+
font_face = cv2.FONT_HERSHEY_SIMPLEX
|
76 |
+
|
77 |
+
cv2.putText(img_blank, "original", (x+4, y-7), font_face, font_scale, (0, 0, 0), thickness)
|
78 |
+
|
79 |
+
|
80 |
+
i = 0
|
81 |
+
for proc_id in chain_ar:
|
82 |
+
i += 1
|
83 |
+
if proc_id != "":
|
84 |
+
#img = self.processors[proc_id][1](self, img, params) # params can be modified inside
|
85 |
+
y = 30
|
86 |
+
img = self.processors_objects[proc_id][thread_index].process(img,params)
|
87 |
+
if self.is_demo_row_render:
|
88 |
+
x = width*i
|
89 |
+
img_blank[y:y + height, x:x + width] = img
|
90 |
+
cv2.putText(img_blank, proc_id, (x + 4, y - 7), font_face, font_scale, (0, 0, 0), thickness)
|
91 |
+
|
92 |
+
if self.is_demo_row_render:
|
93 |
+
return img_blank, params
|
94 |
+
|
95 |
+
return img, params
|
96 |
+
|
97 |
+
# ---------------- init translation stuff ----------------
|
98 |
+
def fill_processors_for_thread_chains(self, threads:int = 1, chain:str = None):
|
99 |
+
if chain is None:
|
100 |
+
chain = self.default_chain
|
101 |
+
|
102 |
+
chain_ar = chain.split(",")
|
103 |
+
# init all not initialized processors first
|
104 |
+
for processor_id in chain_ar:
|
105 |
+
if processor_id != "":
|
106 |
+
if self.processors_objects.get(processor_id) is None:
|
107 |
+
self.processors_objects[processor_id] = []
|
108 |
+
while len(self.processors_objects[processor_id]) < threads:
|
109 |
+
self.add_processor_to_list(processor_id)
|
110 |
+
|
111 |
+
def add_processor_to_list(self, processor_id: str):
|
112 |
+
obj = self.processors[processor_id](self)
|
113 |
+
obj.init_plugin()
|
114 |
+
if self.processors_objects.get(processor_id) is None:
|
115 |
+
self.processors_objects[processor_id] = []
|
116 |
+
self.processors_objects[processor_id].append(obj)
|
117 |
+
def init_processor(self, processor_id: str):
|
118 |
+
if processor_id == "": # blank line case
|
119 |
+
return
|
120 |
+
|
121 |
+
if processor_id in self.inited_processors:
|
122 |
+
return
|
123 |
+
|
124 |
+
try:
|
125 |
+
if self.verbose:
|
126 |
+
self.print_blue("TRY: init processor plugin '{0}'...".format(processor_id))
|
127 |
+
self.add_processor_to_list(processor_id)
|
128 |
+
self.inited_processors.append(processor_id)
|
129 |
+
if self.verbose:
|
130 |
+
self.print_blue("SUCCESS: '{0}' initialized!".format(processor_id))
|
131 |
+
|
132 |
+
except Exception as e:
|
133 |
+
self.print_error("Error init processor plugin {0}...".format(processor_id), e)
|
134 |
+
|
135 |
+
# ------------ formatting stuff -------------------
|
136 |
+
def display_init_info(self):
|
137 |
+
if self.verbose:
|
138 |
+
print("ChainImgProcessor v{0}:".format(version))
|
139 |
+
self.format_print_key_list("processors:", self.processors.keys())
|
140 |
+
|
141 |
+
def format_print_key_list(self, key:str, value:list):
|
142 |
+
print(key+": ".join(value))
|
143 |
+
|
144 |
+
def print_error(self,err_txt,e:Exception = None):
|
145 |
+
print(err_txt,"red")
|
146 |
+
# if e != None:
|
147 |
+
# cprint(e,"red")
|
148 |
+
import traceback
|
149 |
+
traceback.print_exc()
|
150 |
+
|
151 |
+
def print_red(self,txt):
|
152 |
+
print(txt)
|
153 |
+
|
154 |
+
def print_blue(self, txt):
|
155 |
+
print(txt)
|
156 |
+
|
157 |
+
class ChainImgPlugin:
|
158 |
+
|
159 |
+
device = 'cpu'
|
160 |
+
|
161 |
+
def __init__(self, core: ChainImgProcessor):
|
162 |
+
self.core = core
|
163 |
+
self.device = get_device()
|
164 |
+
|
165 |
+
def init_plugin(self): # here you can init something. Called once
|
166 |
+
pass
|
167 |
+
def process(self, img, params:dict): # process img. Called multiple
|
168 |
+
return img
|
169 |
+
|
170 |
+
_img_processor:ChainImgProcessor = None
|
171 |
+
def get_single_image_processor() -> ChainImgProcessor:
|
172 |
+
global _img_processor
|
173 |
+
if _img_processor is None:
|
174 |
+
_img_processor = ChainImgProcessor()
|
175 |
+
_img_processor.init_with_plugins()
|
176 |
+
return _img_processor
|
chain_img_processor/video.py
ADDED
@@ -0,0 +1,132 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import roop.globals
|
2 |
+
|
3 |
+
from threading import Thread
|
4 |
+
from chain_img_processor import ChainImgProcessor
|
5 |
+
|
6 |
+
class ThreadWithReturnValue(Thread):
|
7 |
+
|
8 |
+
def __init__(self, group=None, target=None, name=None,
|
9 |
+
args=(), kwargs={}, Verbose=None):
|
10 |
+
Thread.__init__(self, group, target, name, args, kwargs)
|
11 |
+
self._return = None
|
12 |
+
|
13 |
+
def run(self):
|
14 |
+
if self._target is not None:
|
15 |
+
self._return = self._target(*self._args,
|
16 |
+
**self._kwargs)
|
17 |
+
|
18 |
+
def join(self, *args):
|
19 |
+
Thread.join(self, *args)
|
20 |
+
return self._return
|
21 |
+
|
22 |
+
|
23 |
+
# in beta
|
24 |
+
class ChainVideoProcessor(ChainImgProcessor):
|
25 |
+
def __init__(self):
|
26 |
+
ChainImgProcessor.__init__(self)
|
27 |
+
|
28 |
+
self.video_save_codec = "libx264"
|
29 |
+
self.video_save_crf = 14
|
30 |
+
|
31 |
+
def init_with_plugins(self):
|
32 |
+
self.init_plugins(["core","core_video"])
|
33 |
+
self.display_init_info()
|
34 |
+
|
35 |
+
init_on_start_arr = self.init_on_start.split(",")
|
36 |
+
for proc_id in init_on_start_arr:
|
37 |
+
self.init_processor(proc_id)
|
38 |
+
|
39 |
+
def run_video_chain(self, source_video, target_video, fps, threads:int = 1, chain = None, params_frame_gen_func = None, video_audio = None):
|
40 |
+
import cv2
|
41 |
+
from tqdm import tqdm
|
42 |
+
from chain_img_processor.ffmpeg_writer import FFMPEG_VideoWriter # ffmpeg install needed
|
43 |
+
|
44 |
+
cap = cv2.VideoCapture(source_video)
|
45 |
+
# width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
46 |
+
# height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
47 |
+
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
48 |
+
|
49 |
+
# first frame do manually - because upscale may happen, we need to estimate width/height
|
50 |
+
ret, frame = cap.read()
|
51 |
+
if params_frame_gen_func is not None:
|
52 |
+
params = params_frame_gen_func(self, frame)
|
53 |
+
else:
|
54 |
+
params = {}
|
55 |
+
params["original_frame"] = frame
|
56 |
+
frame_processed, params = self.run_chain(frame,params,chain)
|
57 |
+
height, width, channels = frame_processed.shape
|
58 |
+
|
59 |
+
self.fill_processors_for_thread_chains(threads,chain)
|
60 |
+
#print(self.processors_objects)
|
61 |
+
#import threading
|
62 |
+
#locks:list[threading.Lock] = []
|
63 |
+
locks: list[bool] = []
|
64 |
+
for i in range(threads):
|
65 |
+
#locks.append(threading.Lock())
|
66 |
+
locks.append(False)
|
67 |
+
|
68 |
+
temp = []
|
69 |
+
with FFMPEG_VideoWriter(target_video, (width, height), fps, codec=roop.globals.video_encoder, crf=roop.globals.video_quality, audiofile=video_audio) as output_video_ff:
|
70 |
+
with tqdm(total=frame_count, desc='Processing', unit="frame", dynamic_ncols=True,
|
71 |
+
bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]') as progress:
|
72 |
+
|
73 |
+
# do first frame
|
74 |
+
output_video_ff.write_frame(frame_processed)
|
75 |
+
progress.update(1) #
|
76 |
+
cnt_frames = 0
|
77 |
+
|
78 |
+
# do rest frames
|
79 |
+
while True:
|
80 |
+
# getting frame
|
81 |
+
ret, frame = cap.read()
|
82 |
+
|
83 |
+
if not ret:
|
84 |
+
break
|
85 |
+
cnt_frames+=1
|
86 |
+
thread_ind = cnt_frames % threads
|
87 |
+
# we are having an array of length %gpu_threads%, running in parallel
|
88 |
+
# so if array is equal or longer than gpu threads, waiting
|
89 |
+
#while len(temp) >= threads:
|
90 |
+
while locks[thread_ind]:
|
91 |
+
#print('WAIT', thread_ind)
|
92 |
+
# we are order dependent, so we are forced to wait for first element to finish. When finished removing thread from the list
|
93 |
+
frame_processed, params = temp.pop(0).join()
|
94 |
+
locks[params["_thread_index"]] = False
|
95 |
+
#print('OFF',cnt_frames,locks[params["_thread_index"]],locks)
|
96 |
+
# writing into output
|
97 |
+
output_video_ff.write_frame(frame_processed)
|
98 |
+
# updating the status
|
99 |
+
progress.update(1)
|
100 |
+
|
101 |
+
# calc params for frame
|
102 |
+
if params_frame_gen_func is not None:
|
103 |
+
params = params_frame_gen_func(self,frame)
|
104 |
+
else:
|
105 |
+
params = {}
|
106 |
+
|
107 |
+
# adding new frame to the list and starting it
|
108 |
+
locks[thread_ind] = True
|
109 |
+
#print('ON', cnt_frames, thread_ind, locks)
|
110 |
+
params["original_frame"] = frame
|
111 |
+
temp.append(
|
112 |
+
ThreadWithReturnValue(target=self.run_chain, args=(frame, params, chain, thread_ind)))
|
113 |
+
temp[-1].start()
|
114 |
+
|
115 |
+
while len(temp) > 0:
|
116 |
+
# we are order dependent, so we are forced to wait for first element to finish. When finished removing thread from the list
|
117 |
+
frame_processed, params = temp.pop(0).join()
|
118 |
+
locks[params["_thread_index"]] = False
|
119 |
+
# writing into output
|
120 |
+
output_video_ff.write_frame(frame_processed)
|
121 |
+
|
122 |
+
progress.update(1)
|
123 |
+
|
124 |
+
#print("FINAL", locks)
|
125 |
+
|
126 |
+
_video_processor:ChainVideoProcessor = None
|
127 |
+
def get_single_video_processor() -> ChainVideoProcessor:
|
128 |
+
global _video_processor
|
129 |
+
if _video_processor is None:
|
130 |
+
_video_processor = ChainVideoProcessor()
|
131 |
+
_video_processor.init_with_plugins()
|
132 |
+
return _video_processor
|
clip/__init__.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
from .clip import *
|
clip/__pycache__/__init__.cpython-311.pyc
ADDED
Binary file (185 Bytes). View file
|
|
clip/__pycache__/clip.cpython-311.pyc
ADDED
Binary file (16.9 kB). View file
|
|
clip/__pycache__/model.cpython-311.pyc
ADDED
Binary file (31.8 kB). View file
|
|
clip/__pycache__/simple_tokenizer.cpython-311.pyc
ADDED
Binary file (11.1 kB). View file
|
|
clip/bpe_simple_vocab_16e6.txt.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
|
3 |
+
size 1356917
|
clip/clip.py
ADDED
@@ -0,0 +1,245 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import hashlib
|
2 |
+
import os
|
3 |
+
import urllib
|
4 |
+
import warnings
|
5 |
+
from typing import Any, Union, List
|
6 |
+
from pkg_resources import packaging
|
7 |
+
|
8 |
+
import torch
|
9 |
+
from PIL import Image
|
10 |
+
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
|
11 |
+
from tqdm import tqdm
|
12 |
+
|
13 |
+
from .model import build_model
|
14 |
+
from .simple_tokenizer import SimpleTokenizer as _Tokenizer
|
15 |
+
|
16 |
+
try:
|
17 |
+
from torchvision.transforms import InterpolationMode
|
18 |
+
BICUBIC = InterpolationMode.BICUBIC
|
19 |
+
except ImportError:
|
20 |
+
BICUBIC = Image.BICUBIC
|
21 |
+
|
22 |
+
|
23 |
+
if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
|
24 |
+
warnings.warn("PyTorch version 1.7.1 or higher is recommended")
|
25 |
+
|
26 |
+
|
27 |
+
__all__ = ["available_models", "load", "tokenize"]
|
28 |
+
_tokenizer = _Tokenizer()
|
29 |
+
|
30 |
+
_MODELS = {
|
31 |
+
"RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
|
32 |
+
"RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
|
33 |
+
"RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
|
34 |
+
"RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
|
35 |
+
"RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt",
|
36 |
+
"ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
|
37 |
+
"ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
|
38 |
+
"ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
|
39 |
+
"ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
|
40 |
+
}
|
41 |
+
|
42 |
+
|
43 |
+
def _download(url: str, root: str):
|
44 |
+
os.makedirs(root, exist_ok=True)
|
45 |
+
filename = os.path.basename(url)
|
46 |
+
|
47 |
+
expected_sha256 = url.split("/")[-2]
|
48 |
+
download_target = os.path.join(root, filename)
|
49 |
+
|
50 |
+
if os.path.exists(download_target) and not os.path.isfile(download_target):
|
51 |
+
raise RuntimeError(f"{download_target} exists and is not a regular file")
|
52 |
+
|
53 |
+
if os.path.isfile(download_target):
|
54 |
+
if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
|
55 |
+
return download_target
|
56 |
+
else:
|
57 |
+
warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
|
58 |
+
|
59 |
+
with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
|
60 |
+
with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
|
61 |
+
while True:
|
62 |
+
buffer = source.read(8192)
|
63 |
+
if not buffer:
|
64 |
+
break
|
65 |
+
|
66 |
+
output.write(buffer)
|
67 |
+
loop.update(len(buffer))
|
68 |
+
|
69 |
+
if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
|
70 |
+
raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")
|
71 |
+
|
72 |
+
return download_target
|
73 |
+
|
74 |
+
|
75 |
+
def _convert_image_to_rgb(image):
|
76 |
+
return image.convert("RGB")
|
77 |
+
|
78 |
+
|
79 |
+
def _transform(n_px):
|
80 |
+
return Compose([
|
81 |
+
Resize(n_px, interpolation=BICUBIC),
|
82 |
+
CenterCrop(n_px),
|
83 |
+
_convert_image_to_rgb,
|
84 |
+
ToTensor(),
|
85 |
+
Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
|
86 |
+
])
|
87 |
+
|
88 |
+
|
89 |
+
def available_models() -> List[str]:
|
90 |
+
"""Returns the names of available CLIP models"""
|
91 |
+
return list(_MODELS.keys())
|
92 |
+
|
93 |
+
|
94 |
+
def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None):
|
95 |
+
"""Load a CLIP model
|
96 |
+
|
97 |
+
Parameters
|
98 |
+
----------
|
99 |
+
name : str
|
100 |
+
A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
|
101 |
+
|
102 |
+
device : Union[str, torch.device]
|
103 |
+
The device to put the loaded model
|
104 |
+
|
105 |
+
jit : bool
|
106 |
+
Whether to load the optimized JIT model or more hackable non-JIT model (default).
|
107 |
+
|
108 |
+
download_root: str
|
109 |
+
path to download the model files; by default, it uses "~/.cache/clip"
|
110 |
+
|
111 |
+
Returns
|
112 |
+
-------
|
113 |
+
model : torch.nn.Module
|
114 |
+
The CLIP model
|
115 |
+
|
116 |
+
preprocess : Callable[[PIL.Image], torch.Tensor]
|
117 |
+
A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
|
118 |
+
"""
|
119 |
+
if name in _MODELS:
|
120 |
+
model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
|
121 |
+
elif os.path.isfile(name):
|
122 |
+
model_path = name
|
123 |
+
else:
|
124 |
+
raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
|
125 |
+
|
126 |
+
with open(model_path, 'rb') as opened_file:
|
127 |
+
try:
|
128 |
+
# loading JIT archive
|
129 |
+
model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval()
|
130 |
+
state_dict = None
|
131 |
+
except RuntimeError:
|
132 |
+
# loading saved state dict
|
133 |
+
if jit:
|
134 |
+
warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
|
135 |
+
jit = False
|
136 |
+
state_dict = torch.load(opened_file, map_location="cpu")
|
137 |
+
|
138 |
+
if not jit:
|
139 |
+
model = build_model(state_dict or model.state_dict()).to(device)
|
140 |
+
if str(device) == "cpu":
|
141 |
+
model.float()
|
142 |
+
return model, _transform(model.visual.input_resolution)
|
143 |
+
|
144 |
+
# patch the device names
|
145 |
+
device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
|
146 |
+
device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
|
147 |
+
|
148 |
+
def _node_get(node: torch._C.Node, key: str):
|
149 |
+
"""Gets attributes of a node which is polymorphic over return type.
|
150 |
+
|
151 |
+
From https://github.com/pytorch/pytorch/pull/82628
|
152 |
+
"""
|
153 |
+
sel = node.kindOf(key)
|
154 |
+
return getattr(node, sel)(key)
|
155 |
+
|
156 |
+
def patch_device(module):
|
157 |
+
try:
|
158 |
+
graphs = [module.graph] if hasattr(module, "graph") else []
|
159 |
+
except RuntimeError:
|
160 |
+
graphs = []
|
161 |
+
|
162 |
+
if hasattr(module, "forward1"):
|
163 |
+
graphs.append(module.forward1.graph)
|
164 |
+
|
165 |
+
for graph in graphs:
|
166 |
+
for node in graph.findAllNodes("prim::Constant"):
|
167 |
+
if "value" in node.attributeNames() and str(_node_get(node, "value")).startswith("cuda"):
|
168 |
+
node.copyAttributes(device_node)
|
169 |
+
|
170 |
+
model.apply(patch_device)
|
171 |
+
patch_device(model.encode_image)
|
172 |
+
patch_device(model.encode_text)
|
173 |
+
|
174 |
+
# patch dtype to float32 on CPU
|
175 |
+
if str(device) == "cpu":
|
176 |
+
float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
|
177 |
+
float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
|
178 |
+
float_node = float_input.node()
|
179 |
+
|
180 |
+
def patch_float(module):
|
181 |
+
try:
|
182 |
+
graphs = [module.graph] if hasattr(module, "graph") else []
|
183 |
+
except RuntimeError:
|
184 |
+
graphs = []
|
185 |
+
|
186 |
+
if hasattr(module, "forward1"):
|
187 |
+
graphs.append(module.forward1.graph)
|
188 |
+
|
189 |
+
for graph in graphs:
|
190 |
+
for node in graph.findAllNodes("aten::to"):
|
191 |
+
inputs = list(node.inputs())
|
192 |
+
for i in [1, 2]: # dtype can be the second or third argument to aten::to()
|
193 |
+
if _node_get(inputs[i].node(), "value") == 5:
|
194 |
+
inputs[i].node().copyAttributes(float_node)
|
195 |
+
|
196 |
+
model.apply(patch_float)
|
197 |
+
patch_float(model.encode_image)
|
198 |
+
patch_float(model.encode_text)
|
199 |
+
|
200 |
+
model.float()
|
201 |
+
|
202 |
+
return model, _transform(model.input_resolution.item())
|
203 |
+
|
204 |
+
|
205 |
+
def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]:
|
206 |
+
"""
|
207 |
+
Returns the tokenized representation of given input string(s)
|
208 |
+
|
209 |
+
Parameters
|
210 |
+
----------
|
211 |
+
texts : Union[str, List[str]]
|
212 |
+
An input string or a list of input strings to tokenize
|
213 |
+
|
214 |
+
context_length : int
|
215 |
+
The context length to use; all CLIP models use 77 as the context length
|
216 |
+
|
217 |
+
truncate: bool
|
218 |
+
Whether to truncate the text in case its encoding is longer than the context length
|
219 |
+
|
220 |
+
Returns
|
221 |
+
-------
|
222 |
+
A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
|
223 |
+
We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
|
224 |
+
"""
|
225 |
+
if isinstance(texts, str):
|
226 |
+
texts = [texts]
|
227 |
+
|
228 |
+
sot_token = _tokenizer.encoder["<|startoftext|>"]
|
229 |
+
eot_token = _tokenizer.encoder["<|endoftext|>"]
|
230 |
+
all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
|
231 |
+
if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
|
232 |
+
result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
|
233 |
+
else:
|
234 |
+
result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)
|
235 |
+
|
236 |
+
for i, tokens in enumerate(all_tokens):
|
237 |
+
if len(tokens) > context_length:
|
238 |
+
if truncate:
|
239 |
+
tokens = tokens[:context_length]
|
240 |
+
tokens[-1] = eot_token
|
241 |
+
else:
|
242 |
+
raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
|
243 |
+
result[i, :len(tokens)] = torch.tensor(tokens)
|
244 |
+
|
245 |
+
return result
|
clip/clipseg.py
ADDED
@@ -0,0 +1,538 @@
|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
|
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|
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|
|
|
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|
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|
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|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
from os.path import basename, dirname, join, isfile
|
3 |
+
import torch
|
4 |
+
from torch import nn
|
5 |
+
from torch.nn import functional as nnf
|
6 |
+
from torch.nn.modules.activation import ReLU
|
7 |
+
|
8 |
+
|
9 |
+
def get_prompt_list(prompt):
|
10 |
+
if prompt == 'plain':
|
11 |
+
return ['{}']
|
12 |
+
elif prompt == 'fixed':
|
13 |
+
return ['a photo of a {}.']
|
14 |
+
elif prompt == 'shuffle':
|
15 |
+
return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
|
16 |
+
elif prompt == 'shuffle+':
|
17 |
+
return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
|
18 |
+
'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
|
19 |
+
'a bad photo of a {}.', 'a photo of the {}.']
|
20 |
+
else:
|
21 |
+
raise ValueError('Invalid value for prompt')
|
22 |
+
|
23 |
+
|
24 |
+
def forward_multihead_attention(x, b, with_aff=False, attn_mask=None):
|
25 |
+
"""
|
26 |
+
Simplified version of multihead attention (taken from torch source code but without tons of if clauses).
|
27 |
+
The mlp and layer norm come from CLIP.
|
28 |
+
x: input.
|
29 |
+
b: multihead attention module.
|
30 |
+
"""
|
31 |
+
|
32 |
+
x_ = b.ln_1(x)
|
33 |
+
q, k, v = nnf.linear(x_, b.attn.in_proj_weight, b.attn.in_proj_bias).chunk(3, dim=-1)
|
34 |
+
tgt_len, bsz, embed_dim = q.size()
|
35 |
+
|
36 |
+
head_dim = embed_dim // b.attn.num_heads
|
37 |
+
scaling = float(head_dim) ** -0.5
|
38 |
+
|
39 |
+
q = q.contiguous().view(tgt_len, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
|
40 |
+
k = k.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
|
41 |
+
v = v.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
|
42 |
+
|
43 |
+
q = q * scaling
|
44 |
+
|
45 |
+
attn_output_weights = torch.bmm(q, k.transpose(1, 2)) # n_heads * batch_size, tokens^2, tokens^2
|
46 |
+
if attn_mask is not None:
|
47 |
+
|
48 |
+
|
49 |
+
attn_mask_type, attn_mask = attn_mask
|
50 |
+
n_heads = attn_output_weights.size(0) // attn_mask.size(0)
|
51 |
+
attn_mask = attn_mask.repeat(n_heads, 1)
|
52 |
+
|
53 |
+
if attn_mask_type == 'cls_token':
|
54 |
+
# the mask only affects similarities compared to the readout-token.
|
55 |
+
attn_output_weights[:, 0, 1:] = attn_output_weights[:, 0, 1:] * attn_mask[None,...]
|
56 |
+
# attn_output_weights[:, 0, 0] = 0*attn_output_weights[:, 0, 0]
|
57 |
+
|
58 |
+
if attn_mask_type == 'all':
|
59 |
+
# print(attn_output_weights.shape, attn_mask[:, None].shape)
|
60 |
+
attn_output_weights[:, 1:, 1:] = attn_output_weights[:, 1:, 1:] * attn_mask[:, None]
|
61 |
+
|
62 |
+
|
63 |
+
attn_output_weights = torch.softmax(attn_output_weights, dim=-1)
|
64 |
+
|
65 |
+
attn_output = torch.bmm(attn_output_weights, v)
|
66 |
+
attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
|
67 |
+
attn_output = b.attn.out_proj(attn_output)
|
68 |
+
|
69 |
+
x = x + attn_output
|
70 |
+
x = x + b.mlp(b.ln_2(x))
|
71 |
+
|
72 |
+
if with_aff:
|
73 |
+
return x, attn_output_weights
|
74 |
+
else:
|
75 |
+
return x
|
76 |
+
|
77 |
+
|
78 |
+
class CLIPDenseBase(nn.Module):
|
79 |
+
|
80 |
+
def __init__(self, version, reduce_cond, reduce_dim, prompt, n_tokens):
|
81 |
+
super().__init__()
|
82 |
+
|
83 |
+
import clip
|
84 |
+
|
85 |
+
# prec = torch.FloatTensor
|
86 |
+
self.clip_model, _ = clip.load(version, device='cpu', jit=False)
|
87 |
+
self.model = self.clip_model.visual
|
88 |
+
|
89 |
+
# if not None, scale conv weights such that we obtain n_tokens.
|
90 |
+
self.n_tokens = n_tokens
|
91 |
+
|
92 |
+
for p in self.clip_model.parameters():
|
93 |
+
p.requires_grad_(False)
|
94 |
+
|
95 |
+
# conditional
|
96 |
+
if reduce_cond is not None:
|
97 |
+
self.reduce_cond = nn.Linear(512, reduce_cond)
|
98 |
+
for p in self.reduce_cond.parameters():
|
99 |
+
p.requires_grad_(False)
|
100 |
+
else:
|
101 |
+
self.reduce_cond = None
|
102 |
+
|
103 |
+
self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
|
104 |
+
self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
|
105 |
+
|
106 |
+
self.reduce = nn.Linear(768, reduce_dim)
|
107 |
+
|
108 |
+
self.prompt_list = get_prompt_list(prompt)
|
109 |
+
|
110 |
+
# precomputed prompts
|
111 |
+
import pickle
|
112 |
+
if isfile('precomputed_prompt_vectors.pickle'):
|
113 |
+
precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
|
114 |
+
self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
|
115 |
+
else:
|
116 |
+
self.precomputed_prompts = dict()
|
117 |
+
|
118 |
+
def rescaled_pos_emb(self, new_size):
|
119 |
+
assert len(new_size) == 2
|
120 |
+
|
121 |
+
a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
|
122 |
+
b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
|
123 |
+
return torch.cat([self.model.positional_embedding[:1], b])
|
124 |
+
|
125 |
+
def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
|
126 |
+
|
127 |
+
|
128 |
+
with torch.no_grad():
|
129 |
+
|
130 |
+
inp_size = x_inp.shape[2:]
|
131 |
+
|
132 |
+
if self.n_tokens is not None:
|
133 |
+
stride2 = x_inp.shape[2] // self.n_tokens
|
134 |
+
conv_weight2 = nnf.interpolate(self.model.conv1.weight, (stride2, stride2), mode='bilinear', align_corners=True)
|
135 |
+
x = nnf.conv2d(x_inp, conv_weight2, bias=self.model.conv1.bias, stride=stride2, dilation=self.model.conv1.dilation)
|
136 |
+
else:
|
137 |
+
x = self.model.conv1(x_inp) # shape = [*, width, grid, grid]
|
138 |
+
|
139 |
+
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
|
140 |
+
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
|
141 |
+
|
142 |
+
x = torch.cat([self.model.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width]
|
143 |
+
|
144 |
+
standard_n_tokens = 50 if self.model.conv1.kernel_size[0] == 32 else 197
|
145 |
+
|
146 |
+
if x.shape[1] != standard_n_tokens:
|
147 |
+
new_shape = int(math.sqrt(x.shape[1]-1))
|
148 |
+
x = x + self.rescaled_pos_emb((new_shape, new_shape)).to(x.dtype)[None,:,:]
|
149 |
+
else:
|
150 |
+
x = x + self.model.positional_embedding.to(x.dtype)
|
151 |
+
|
152 |
+
x = self.model.ln_pre(x)
|
153 |
+
|
154 |
+
x = x.permute(1, 0, 2) # NLD -> LND
|
155 |
+
|
156 |
+
activations, affinities = [], []
|
157 |
+
for i, res_block in enumerate(self.model.transformer.resblocks):
|
158 |
+
|
159 |
+
if mask is not None:
|
160 |
+
mask_layer, mask_type, mask_tensor = mask
|
161 |
+
if mask_layer == i or mask_layer == 'all':
|
162 |
+
# import ipdb; ipdb.set_trace()
|
163 |
+
size = int(math.sqrt(x.shape[0] - 1))
|
164 |
+
|
165 |
+
attn_mask = (mask_type, nnf.interpolate(mask_tensor.unsqueeze(1).float(), (size, size)).view(mask_tensor.shape[0], size * size))
|
166 |
+
|
167 |
+
else:
|
168 |
+
attn_mask = None
|
169 |
+
else:
|
170 |
+
attn_mask = None
|
171 |
+
|
172 |
+
x, aff_per_head = forward_multihead_attention(x, res_block, with_aff=True, attn_mask=attn_mask)
|
173 |
+
|
174 |
+
if i in extract_layers:
|
175 |
+
affinities += [aff_per_head]
|
176 |
+
|
177 |
+
#if self.n_tokens is not None:
|
178 |
+
# activations += [nnf.interpolate(x, inp_size, mode='bilinear', align_corners=True)]
|
179 |
+
#else:
|
180 |
+
activations += [x]
|
181 |
+
|
182 |
+
if len(extract_layers) > 0 and i == max(extract_layers) and skip:
|
183 |
+
print('early skip')
|
184 |
+
break
|
185 |
+
|
186 |
+
x = x.permute(1, 0, 2) # LND -> NLD
|
187 |
+
x = self.model.ln_post(x[:, 0, :])
|
188 |
+
|
189 |
+
if self.model.proj is not None:
|
190 |
+
x = x @ self.model.proj
|
191 |
+
|
192 |
+
return x, activations, affinities
|
193 |
+
|
194 |
+
def sample_prompts(self, words, prompt_list=None):
|
195 |
+
|
196 |
+
prompt_list = prompt_list if prompt_list is not None else self.prompt_list
|
197 |
+
|
198 |
+
prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
|
199 |
+
prompts = [prompt_list[i] for i in prompt_indices]
|
200 |
+
return [promt.format(w) for promt, w in zip(prompts, words)]
|
201 |
+
|
202 |
+
def get_cond_vec(self, conditional, batch_size):
|
203 |
+
# compute conditional from a single string
|
204 |
+
if conditional is not None and type(conditional) == str:
|
205 |
+
cond = self.compute_conditional(conditional)
|
206 |
+
cond = cond.repeat(batch_size, 1)
|
207 |
+
|
208 |
+
# compute conditional from string list/tuple
|
209 |
+
elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
|
210 |
+
assert len(conditional) == batch_size
|
211 |
+
cond = self.compute_conditional(conditional)
|
212 |
+
|
213 |
+
# use conditional directly
|
214 |
+
elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
|
215 |
+
cond = conditional
|
216 |
+
|
217 |
+
# compute conditional from image
|
218 |
+
elif conditional is not None and type(conditional) == torch.Tensor:
|
219 |
+
with torch.no_grad():
|
220 |
+
cond, _, _ = self.visual_forward(conditional)
|
221 |
+
else:
|
222 |
+
raise ValueError('invalid conditional')
|
223 |
+
return cond
|
224 |
+
|
225 |
+
def compute_conditional(self, conditional):
|
226 |
+
import clip
|
227 |
+
|
228 |
+
dev = next(self.parameters()).device
|
229 |
+
|
230 |
+
if type(conditional) in {list, tuple}:
|
231 |
+
text_tokens = clip.tokenize(conditional).to(dev)
|
232 |
+
cond = self.clip_model.encode_text(text_tokens)
|
233 |
+
else:
|
234 |
+
if conditional in self.precomputed_prompts:
|
235 |
+
cond = self.precomputed_prompts[conditional].float().to(dev)
|
236 |
+
else:
|
237 |
+
text_tokens = clip.tokenize([conditional]).to(dev)
|
238 |
+
cond = self.clip_model.encode_text(text_tokens)[0]
|
239 |
+
|
240 |
+
if self.shift_vector is not None:
|
241 |
+
return cond + self.shift_vector
|
242 |
+
else:
|
243 |
+
return cond
|
244 |
+
|
245 |
+
|
246 |
+
def clip_load_untrained(version):
|
247 |
+
assert version == 'ViT-B/16'
|
248 |
+
from clip.model import CLIP
|
249 |
+
from clip.clip import _MODELS, _download
|
250 |
+
model = torch.jit.load(_download(_MODELS['ViT-B/16'])).eval()
|
251 |
+
state_dict = model.state_dict()
|
252 |
+
|
253 |
+
vision_width = state_dict["visual.conv1.weight"].shape[0]
|
254 |
+
vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
|
255 |
+
vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
|
256 |
+
grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
|
257 |
+
image_resolution = vision_patch_size * grid_size
|
258 |
+
embed_dim = state_dict["text_projection"].shape[1]
|
259 |
+
context_length = state_dict["positional_embedding"].shape[0]
|
260 |
+
vocab_size = state_dict["token_embedding.weight"].shape[0]
|
261 |
+
transformer_width = state_dict["ln_final.weight"].shape[0]
|
262 |
+
transformer_heads = transformer_width // 64
|
263 |
+
transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
|
264 |
+
|
265 |
+
return CLIP(embed_dim, image_resolution, vision_layers, vision_width, vision_patch_size,
|
266 |
+
context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)
|
267 |
+
|
268 |
+
|
269 |
+
class CLIPDensePredT(CLIPDenseBase):
|
270 |
+
|
271 |
+
def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed',
|
272 |
+
extra_blocks=0, reduce_cond=None, fix_shift=False,
|
273 |
+
learn_trans_conv_only=False, limit_to_clip_only=False, upsample=False,
|
274 |
+
add_calibration=False, rev_activations=False, trans_conv=None, n_tokens=None, complex_trans_conv=False):
|
275 |
+
|
276 |
+
super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
|
277 |
+
# device = 'cpu'
|
278 |
+
|
279 |
+
self.extract_layers = extract_layers
|
280 |
+
self.cond_layer = cond_layer
|
281 |
+
self.limit_to_clip_only = limit_to_clip_only
|
282 |
+
self.process_cond = None
|
283 |
+
self.rev_activations = rev_activations
|
284 |
+
|
285 |
+
depth = len(extract_layers)
|
286 |
+
|
287 |
+
if add_calibration:
|
288 |
+
self.calibration_conds = 1
|
289 |
+
|
290 |
+
self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
|
291 |
+
|
292 |
+
self.add_activation1 = True
|
293 |
+
|
294 |
+
self.version = version
|
295 |
+
|
296 |
+
self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
|
297 |
+
|
298 |
+
if fix_shift:
|
299 |
+
# self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'clip_text_shift_vector.pth')), requires_grad=False)
|
300 |
+
self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'shift_text_to_vis.pth')), requires_grad=False)
|
301 |
+
# self.shift_vector = nn.Parameter(-1*torch.load(join(dirname(basename(__file__)), 'shift2.pth')), requires_grad=False)
|
302 |
+
else:
|
303 |
+
self.shift_vector = None
|
304 |
+
|
305 |
+
if trans_conv is None:
|
306 |
+
trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
|
307 |
+
else:
|
308 |
+
# explicitly define transposed conv kernel size
|
309 |
+
trans_conv_ks = (trans_conv, trans_conv)
|
310 |
+
|
311 |
+
if not complex_trans_conv:
|
312 |
+
self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
|
313 |
+
else:
|
314 |
+
assert trans_conv_ks[0] == trans_conv_ks[1]
|
315 |
+
|
316 |
+
tp_kernels = (trans_conv_ks[0] // 4, trans_conv_ks[0] // 4)
|
317 |
+
|
318 |
+
self.trans_conv = nn.Sequential(
|
319 |
+
nn.Conv2d(reduce_dim, reduce_dim, kernel_size=3, padding=1),
|
320 |
+
nn.ReLU(),
|
321 |
+
nn.ConvTranspose2d(reduce_dim, reduce_dim // 2, kernel_size=tp_kernels[0], stride=tp_kernels[0]),
|
322 |
+
nn.ReLU(),
|
323 |
+
nn.ConvTranspose2d(reduce_dim // 2, 1, kernel_size=tp_kernels[1], stride=tp_kernels[1]),
|
324 |
+
)
|
325 |
+
|
326 |
+
# self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
|
327 |
+
|
328 |
+
assert len(self.extract_layers) == depth
|
329 |
+
|
330 |
+
self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
|
331 |
+
self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
|
332 |
+
self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
|
333 |
+
|
334 |
+
# refinement and trans conv
|
335 |
+
|
336 |
+
if learn_trans_conv_only:
|
337 |
+
for p in self.parameters():
|
338 |
+
p.requires_grad_(False)
|
339 |
+
|
340 |
+
for p in self.trans_conv.parameters():
|
341 |
+
p.requires_grad_(True)
|
342 |
+
|
343 |
+
self.prompt_list = get_prompt_list(prompt)
|
344 |
+
|
345 |
+
|
346 |
+
def forward(self, inp_image, conditional=None, return_features=False, mask=None):
|
347 |
+
|
348 |
+
assert type(return_features) == bool
|
349 |
+
|
350 |
+
inp_image = inp_image.to(self.model.positional_embedding.device)
|
351 |
+
|
352 |
+
if mask is not None:
|
353 |
+
raise ValueError('mask not supported')
|
354 |
+
|
355 |
+
# x_inp = normalize(inp_image)
|
356 |
+
x_inp = inp_image
|
357 |
+
|
358 |
+
bs, dev = inp_image.shape[0], x_inp.device
|
359 |
+
|
360 |
+
cond = self.get_cond_vec(conditional, bs)
|
361 |
+
|
362 |
+
visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
|
363 |
+
|
364 |
+
activation1 = activations[0]
|
365 |
+
activations = activations[1:]
|
366 |
+
|
367 |
+
_activations = activations[::-1] if not self.rev_activations else activations
|
368 |
+
|
369 |
+
a = None
|
370 |
+
for i, (activation, block, reduce) in enumerate(zip(_activations, self.blocks, self.reduces)):
|
371 |
+
|
372 |
+
if a is not None:
|
373 |
+
a = reduce(activation) + a
|
374 |
+
else:
|
375 |
+
a = reduce(activation)
|
376 |
+
|
377 |
+
if i == self.cond_layer:
|
378 |
+
if self.reduce_cond is not None:
|
379 |
+
cond = self.reduce_cond(cond)
|
380 |
+
|
381 |
+
a = self.film_mul(cond) * a + self.film_add(cond)
|
382 |
+
|
383 |
+
a = block(a)
|
384 |
+
|
385 |
+
for block in self.extra_blocks:
|
386 |
+
a = a + block(a)
|
387 |
+
|
388 |
+
a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
|
389 |
+
|
390 |
+
size = int(math.sqrt(a.shape[2]))
|
391 |
+
|
392 |
+
a = a.view(bs, a.shape[1], size, size)
|
393 |
+
|
394 |
+
a = self.trans_conv(a)
|
395 |
+
|
396 |
+
if self.n_tokens is not None:
|
397 |
+
a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear', align_corners=True)
|
398 |
+
|
399 |
+
if self.upsample_proj is not None:
|
400 |
+
a = self.upsample_proj(a)
|
401 |
+
a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
|
402 |
+
|
403 |
+
if return_features:
|
404 |
+
return a, visual_q, cond, [activation1] + activations
|
405 |
+
else:
|
406 |
+
return a,
|
407 |
+
|
408 |
+
|
409 |
+
|
410 |
+
class CLIPDensePredTMasked(CLIPDensePredT):
|
411 |
+
|
412 |
+
def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4,
|
413 |
+
prompt='fixed', extra_blocks=0, reduce_cond=None, fix_shift=False, learn_trans_conv_only=False,
|
414 |
+
refine=None, limit_to_clip_only=False, upsample=False, add_calibration=False, n_tokens=None):
|
415 |
+
|
416 |
+
super().__init__(version=version, extract_layers=extract_layers, cond_layer=cond_layer, reduce_dim=reduce_dim,
|
417 |
+
n_heads=n_heads, prompt=prompt, extra_blocks=extra_blocks, reduce_cond=reduce_cond,
|
418 |
+
fix_shift=fix_shift, learn_trans_conv_only=learn_trans_conv_only,
|
419 |
+
limit_to_clip_only=limit_to_clip_only, upsample=upsample, add_calibration=add_calibration,
|
420 |
+
n_tokens=n_tokens)
|
421 |
+
|
422 |
+
def visual_forward_masked(self, img_s, seg_s):
|
423 |
+
return super().visual_forward(img_s, mask=('all', 'cls_token', seg_s))
|
424 |
+
|
425 |
+
def forward(self, img_q, cond_or_img_s, seg_s=None, return_features=False):
|
426 |
+
|
427 |
+
if seg_s is None:
|
428 |
+
cond = cond_or_img_s
|
429 |
+
else:
|
430 |
+
img_s = cond_or_img_s
|
431 |
+
|
432 |
+
with torch.no_grad():
|
433 |
+
cond, _, _ = self.visual_forward_masked(img_s, seg_s)
|
434 |
+
|
435 |
+
return super().forward(img_q, cond, return_features=return_features)
|
436 |
+
|
437 |
+
|
438 |
+
|
439 |
+
class CLIPDenseBaseline(CLIPDenseBase):
|
440 |
+
|
441 |
+
def __init__(self, version='ViT-B/32', cond_layer=0,
|
442 |
+
extract_layer=9, reduce_dim=128, reduce2_dim=None, prompt='fixed',
|
443 |
+
reduce_cond=None, limit_to_clip_only=False, n_tokens=None):
|
444 |
+
|
445 |
+
super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
|
446 |
+
device = 'cpu'
|
447 |
+
|
448 |
+
# self.cond_layer = cond_layer
|
449 |
+
self.extract_layer = extract_layer
|
450 |
+
self.limit_to_clip_only = limit_to_clip_only
|
451 |
+
self.shift_vector = None
|
452 |
+
|
453 |
+
self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
|
454 |
+
|
455 |
+
assert reduce2_dim is not None
|
456 |
+
|
457 |
+
self.reduce2 = nn.Sequential(
|
458 |
+
nn.Linear(reduce_dim, reduce2_dim),
|
459 |
+
nn.ReLU(),
|
460 |
+
nn.Linear(reduce2_dim, reduce_dim)
|
461 |
+
)
|
462 |
+
|
463 |
+
trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
|
464 |
+
self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
|
465 |
+
|
466 |
+
|
467 |
+
def forward(self, inp_image, conditional=None, return_features=False):
|
468 |
+
|
469 |
+
inp_image = inp_image.to(self.model.positional_embedding.device)
|
470 |
+
|
471 |
+
# x_inp = normalize(inp_image)
|
472 |
+
x_inp = inp_image
|
473 |
+
|
474 |
+
bs, dev = inp_image.shape[0], x_inp.device
|
475 |
+
|
476 |
+
cond = self.get_cond_vec(conditional, bs)
|
477 |
+
|
478 |
+
visual_q, activations, affinities = self.visual_forward(x_inp, extract_layers=[self.extract_layer])
|
479 |
+
|
480 |
+
a = activations[0]
|
481 |
+
a = self.reduce(a)
|
482 |
+
a = self.film_mul(cond) * a + self.film_add(cond)
|
483 |
+
|
484 |
+
if self.reduce2 is not None:
|
485 |
+
a = self.reduce2(a)
|
486 |
+
|
487 |
+
# the original model would execute a transformer block here
|
488 |
+
|
489 |
+
a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
|
490 |
+
|
491 |
+
size = int(math.sqrt(a.shape[2]))
|
492 |
+
|
493 |
+
a = a.view(bs, a.shape[1], size, size)
|
494 |
+
a = self.trans_conv(a)
|
495 |
+
|
496 |
+
if return_features:
|
497 |
+
return a, visual_q, cond, activations
|
498 |
+
else:
|
499 |
+
return a,
|
500 |
+
|
501 |
+
|
502 |
+
class CLIPSegMultiLabel(nn.Module):
|
503 |
+
|
504 |
+
def __init__(self, model) -> None:
|
505 |
+
super().__init__()
|
506 |
+
|
507 |
+
from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC
|
508 |
+
|
509 |
+
self.pascal_classes = VOC
|
510 |
+
|
511 |
+
from clip.clipseg import CLIPDensePredT
|
512 |
+
from general_utils import load_model
|
513 |
+
# self.clipseg = load_model('rd64-vit16-neg0.2-phrasecut', strict=False)
|
514 |
+
self.clipseg = load_model(model, strict=False)
|
515 |
+
|
516 |
+
self.clipseg.eval()
|
517 |
+
|
518 |
+
def forward(self, x):
|
519 |
+
|
520 |
+
bs = x.shape[0]
|
521 |
+
out = torch.ones(21, bs, 352, 352).to(x.device) * -10
|
522 |
+
|
523 |
+
for class_id, class_name in enumerate(self.pascal_classes):
|
524 |
+
|
525 |
+
fac = 3 if class_name == 'background' else 1
|
526 |
+
|
527 |
+
with torch.no_grad():
|
528 |
+
pred = torch.sigmoid(self.clipseg(x, class_name)[0][:,0]) * fac
|
529 |
+
|
530 |
+
out[class_id] += pred
|
531 |
+
|
532 |
+
|
533 |
+
out = out.permute(1, 0, 2, 3)
|
534 |
+
|
535 |
+
return out
|
536 |
+
|
537 |
+
# construct output tensor
|
538 |
+
|
clip/model.py
ADDED
@@ -0,0 +1,436 @@
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
|
|
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|
|
|
|
|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from collections import OrderedDict
|
2 |
+
from typing import Tuple, Union
|
3 |
+
|
4 |
+
import numpy as np
|
5 |
+
import torch
|
6 |
+
import torch.nn.functional as F
|
7 |
+
from torch import nn
|
8 |
+
|
9 |
+
|
10 |
+
class Bottleneck(nn.Module):
|
11 |
+
expansion = 4
|
12 |
+
|
13 |
+
def __init__(self, inplanes, planes, stride=1):
|
14 |
+
super().__init__()
|
15 |
+
|
16 |
+
# all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
|
17 |
+
self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
|
18 |
+
self.bn1 = nn.BatchNorm2d(planes)
|
19 |
+
self.relu1 = nn.ReLU(inplace=True)
|
20 |
+
|
21 |
+
self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
|
22 |
+
self.bn2 = nn.BatchNorm2d(planes)
|
23 |
+
self.relu2 = nn.ReLU(inplace=True)
|
24 |
+
|
25 |
+
self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
|
26 |
+
|
27 |
+
self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
|
28 |
+
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
|
29 |
+
self.relu3 = nn.ReLU(inplace=True)
|
30 |
+
|
31 |
+
self.downsample = None
|
32 |
+
self.stride = stride
|
33 |
+
|
34 |
+
if stride > 1 or inplanes != planes * Bottleneck.expansion:
|
35 |
+
# downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
|
36 |
+
self.downsample = nn.Sequential(OrderedDict([
|
37 |
+
("-1", nn.AvgPool2d(stride)),
|
38 |
+
("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
|
39 |
+
("1", nn.BatchNorm2d(planes * self.expansion))
|
40 |
+
]))
|
41 |
+
|
42 |
+
def forward(self, x: torch.Tensor):
|
43 |
+
identity = x
|
44 |
+
|
45 |
+
out = self.relu1(self.bn1(self.conv1(x)))
|
46 |
+
out = self.relu2(self.bn2(self.conv2(out)))
|
47 |
+
out = self.avgpool(out)
|
48 |
+
out = self.bn3(self.conv3(out))
|
49 |
+
|
50 |
+
if self.downsample is not None:
|
51 |
+
identity = self.downsample(x)
|
52 |
+
|
53 |
+
out += identity
|
54 |
+
out = self.relu3(out)
|
55 |
+
return out
|
56 |
+
|
57 |
+
|
58 |
+
class AttentionPool2d(nn.Module):
|
59 |
+
def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
|
60 |
+
super().__init__()
|
61 |
+
self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
|
62 |
+
self.k_proj = nn.Linear(embed_dim, embed_dim)
|
63 |
+
self.q_proj = nn.Linear(embed_dim, embed_dim)
|
64 |
+
self.v_proj = nn.Linear(embed_dim, embed_dim)
|
65 |
+
self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
|
66 |
+
self.num_heads = num_heads
|
67 |
+
|
68 |
+
def forward(self, x):
|
69 |
+
x = x.flatten(start_dim=2).permute(2, 0, 1) # NCHW -> (HW)NC
|
70 |
+
x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC
|
71 |
+
x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC
|
72 |
+
x, _ = F.multi_head_attention_forward(
|
73 |
+
query=x[:1], key=x, value=x,
|
74 |
+
embed_dim_to_check=x.shape[-1],
|
75 |
+
num_heads=self.num_heads,
|
76 |
+
q_proj_weight=self.q_proj.weight,
|
77 |
+
k_proj_weight=self.k_proj.weight,
|
78 |
+
v_proj_weight=self.v_proj.weight,
|
79 |
+
in_proj_weight=None,
|
80 |
+
in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
|
81 |
+
bias_k=None,
|
82 |
+
bias_v=None,
|
83 |
+
add_zero_attn=False,
|
84 |
+
dropout_p=0,
|
85 |
+
out_proj_weight=self.c_proj.weight,
|
86 |
+
out_proj_bias=self.c_proj.bias,
|
87 |
+
use_separate_proj_weight=True,
|
88 |
+
training=self.training,
|
89 |
+
need_weights=False
|
90 |
+
)
|
91 |
+
return x.squeeze(0)
|
92 |
+
|
93 |
+
|
94 |
+
class ModifiedResNet(nn.Module):
|
95 |
+
"""
|
96 |
+
A ResNet class that is similar to torchvision's but contains the following changes:
|
97 |
+
- There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
|
98 |
+
- Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
|
99 |
+
- The final pooling layer is a QKV attention instead of an average pool
|
100 |
+
"""
|
101 |
+
|
102 |
+
def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
|
103 |
+
super().__init__()
|
104 |
+
self.output_dim = output_dim
|
105 |
+
self.input_resolution = input_resolution
|
106 |
+
|
107 |
+
# the 3-layer stem
|
108 |
+
self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
|
109 |
+
self.bn1 = nn.BatchNorm2d(width // 2)
|
110 |
+
self.relu1 = nn.ReLU(inplace=True)
|
111 |
+
self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
|
112 |
+
self.bn2 = nn.BatchNorm2d(width // 2)
|
113 |
+
self.relu2 = nn.ReLU(inplace=True)
|
114 |
+
self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
|
115 |
+
self.bn3 = nn.BatchNorm2d(width)
|
116 |
+
self.relu3 = nn.ReLU(inplace=True)
|
117 |
+
self.avgpool = nn.AvgPool2d(2)
|
118 |
+
|
119 |
+
# residual layers
|
120 |
+
self._inplanes = width # this is a *mutable* variable used during construction
|
121 |
+
self.layer1 = self._make_layer(width, layers[0])
|
122 |
+
self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
|
123 |
+
self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
|
124 |
+
self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
|
125 |
+
|
126 |
+
embed_dim = width * 32 # the ResNet feature dimension
|
127 |
+
self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
|
128 |
+
|
129 |
+
def _make_layer(self, planes, blocks, stride=1):
|
130 |
+
layers = [Bottleneck(self._inplanes, planes, stride)]
|
131 |
+
|
132 |
+
self._inplanes = planes * Bottleneck.expansion
|
133 |
+
for _ in range(1, blocks):
|
134 |
+
layers.append(Bottleneck(self._inplanes, planes))
|
135 |
+
|
136 |
+
return nn.Sequential(*layers)
|
137 |
+
|
138 |
+
def forward(self, x):
|
139 |
+
def stem(x):
|
140 |
+
x = self.relu1(self.bn1(self.conv1(x)))
|
141 |
+
x = self.relu2(self.bn2(self.conv2(x)))
|
142 |
+
x = self.relu3(self.bn3(self.conv3(x)))
|
143 |
+
x = self.avgpool(x)
|
144 |
+
return x
|
145 |
+
|
146 |
+
x = x.type(self.conv1.weight.dtype)
|
147 |
+
x = stem(x)
|
148 |
+
x = self.layer1(x)
|
149 |
+
x = self.layer2(x)
|
150 |
+
x = self.layer3(x)
|
151 |
+
x = self.layer4(x)
|
152 |
+
x = self.attnpool(x)
|
153 |
+
|
154 |
+
return x
|
155 |
+
|
156 |
+
|
157 |
+
class LayerNorm(nn.LayerNorm):
|
158 |
+
"""Subclass torch's LayerNorm to handle fp16."""
|
159 |
+
|
160 |
+
def forward(self, x: torch.Tensor):
|
161 |
+
orig_type = x.dtype
|
162 |
+
ret = super().forward(x.type(torch.float32))
|
163 |
+
return ret.type(orig_type)
|
164 |
+
|
165 |
+
|
166 |
+
class QuickGELU(nn.Module):
|
167 |
+
def forward(self, x: torch.Tensor):
|
168 |
+
return x * torch.sigmoid(1.702 * x)
|
169 |
+
|
170 |
+
|
171 |
+
class ResidualAttentionBlock(nn.Module):
|
172 |
+
def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
|
173 |
+
super().__init__()
|
174 |
+
|
175 |
+
self.attn = nn.MultiheadAttention(d_model, n_head)
|
176 |
+
self.ln_1 = LayerNorm(d_model)
|
177 |
+
self.mlp = nn.Sequential(OrderedDict([
|
178 |
+
("c_fc", nn.Linear(d_model, d_model * 4)),
|
179 |
+
("gelu", QuickGELU()),
|
180 |
+
("c_proj", nn.Linear(d_model * 4, d_model))
|
181 |
+
]))
|
182 |
+
self.ln_2 = LayerNorm(d_model)
|
183 |
+
self.attn_mask = attn_mask
|
184 |
+
|
185 |
+
def attention(self, x: torch.Tensor):
|
186 |
+
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
|
187 |
+
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
|
188 |
+
|
189 |
+
def forward(self, x: torch.Tensor):
|
190 |
+
x = x + self.attention(self.ln_1(x))
|
191 |
+
x = x + self.mlp(self.ln_2(x))
|
192 |
+
return x
|
193 |
+
|
194 |
+
|
195 |
+
class Transformer(nn.Module):
|
196 |
+
def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
|
197 |
+
super().__init__()
|
198 |
+
self.width = width
|
199 |
+
self.layers = layers
|
200 |
+
self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
|
201 |
+
|
202 |
+
def forward(self, x: torch.Tensor):
|
203 |
+
return self.resblocks(x)
|
204 |
+
|
205 |
+
|
206 |
+
class VisionTransformer(nn.Module):
|
207 |
+
def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
|
208 |
+
super().__init__()
|
209 |
+
self.input_resolution = input_resolution
|
210 |
+
self.output_dim = output_dim
|
211 |
+
self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
|
212 |
+
|
213 |
+
scale = width ** -0.5
|
214 |
+
self.class_embedding = nn.Parameter(scale * torch.randn(width))
|
215 |
+
self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
|
216 |
+
self.ln_pre = LayerNorm(width)
|
217 |
+
|
218 |
+
self.transformer = Transformer(width, layers, heads)
|
219 |
+
|
220 |
+
self.ln_post = LayerNorm(width)
|
221 |
+
self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
|
222 |
+
|
223 |
+
def forward(self, x: torch.Tensor):
|
224 |
+
x = self.conv1(x) # shape = [*, width, grid, grid]
|
225 |
+
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
|
226 |
+
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
|
227 |
+
x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width]
|
228 |
+
x = x + self.positional_embedding.to(x.dtype)
|
229 |
+
x = self.ln_pre(x)
|
230 |
+
|
231 |
+
x = x.permute(1, 0, 2) # NLD -> LND
|
232 |
+
x = self.transformer(x)
|
233 |
+
x = x.permute(1, 0, 2) # LND -> NLD
|
234 |
+
|
235 |
+
x = self.ln_post(x[:, 0, :])
|
236 |
+
|
237 |
+
if self.proj is not None:
|
238 |
+
x = x @ self.proj
|
239 |
+
|
240 |
+
return x
|
241 |
+
|
242 |
+
|
243 |
+
class CLIP(nn.Module):
|
244 |
+
def __init__(self,
|
245 |
+
embed_dim: int,
|
246 |
+
# vision
|
247 |
+
image_resolution: int,
|
248 |
+
vision_layers: Union[Tuple[int, int, int, int], int],
|
249 |
+
vision_width: int,
|
250 |
+
vision_patch_size: int,
|
251 |
+
# text
|
252 |
+
context_length: int,
|
253 |
+
vocab_size: int,
|
254 |
+
transformer_width: int,
|
255 |
+
transformer_heads: int,
|
256 |
+
transformer_layers: int
|
257 |
+
):
|
258 |
+
super().__init__()
|
259 |
+
|
260 |
+
self.context_length = context_length
|
261 |
+
|
262 |
+
if isinstance(vision_layers, (tuple, list)):
|
263 |
+
vision_heads = vision_width * 32 // 64
|
264 |
+
self.visual = ModifiedResNet(
|
265 |
+
layers=vision_layers,
|
266 |
+
output_dim=embed_dim,
|
267 |
+
heads=vision_heads,
|
268 |
+
input_resolution=image_resolution,
|
269 |
+
width=vision_width
|
270 |
+
)
|
271 |
+
else:
|
272 |
+
vision_heads = vision_width // 64
|
273 |
+
self.visual = VisionTransformer(
|
274 |
+
input_resolution=image_resolution,
|
275 |
+
patch_size=vision_patch_size,
|
276 |
+
width=vision_width,
|
277 |
+
layers=vision_layers,
|
278 |
+
heads=vision_heads,
|
279 |
+
output_dim=embed_dim
|
280 |
+
)
|
281 |
+
|
282 |
+
self.transformer = Transformer(
|
283 |
+
width=transformer_width,
|
284 |
+
layers=transformer_layers,
|
285 |
+
heads=transformer_heads,
|
286 |
+
attn_mask=self.build_attention_mask()
|
287 |
+
)
|
288 |
+
|
289 |
+
self.vocab_size = vocab_size
|
290 |
+
self.token_embedding = nn.Embedding(vocab_size, transformer_width)
|
291 |
+
self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
|
292 |
+
self.ln_final = LayerNorm(transformer_width)
|
293 |
+
|
294 |
+
self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
|
295 |
+
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
|
296 |
+
|
297 |
+
self.initialize_parameters()
|
298 |
+
|
299 |
+
def initialize_parameters(self):
|
300 |
+
nn.init.normal_(self.token_embedding.weight, std=0.02)
|
301 |
+
nn.init.normal_(self.positional_embedding, std=0.01)
|
302 |
+
|
303 |
+
if isinstance(self.visual, ModifiedResNet):
|
304 |
+
if self.visual.attnpool is not None:
|
305 |
+
std = self.visual.attnpool.c_proj.in_features ** -0.5
|
306 |
+
nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
|
307 |
+
nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
|
308 |
+
nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
|
309 |
+
nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
|
310 |
+
|
311 |
+
for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
|
312 |
+
for name, param in resnet_block.named_parameters():
|
313 |
+
if name.endswith("bn3.weight"):
|
314 |
+
nn.init.zeros_(param)
|
315 |
+
|
316 |
+
proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
|
317 |
+
attn_std = self.transformer.width ** -0.5
|
318 |
+
fc_std = (2 * self.transformer.width) ** -0.5
|
319 |
+
for block in self.transformer.resblocks:
|
320 |
+
nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
|
321 |
+
nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
|
322 |
+
nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
|
323 |
+
nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
|
324 |
+
|
325 |
+
if self.text_projection is not None:
|
326 |
+
nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
|
327 |
+
|
328 |
+
def build_attention_mask(self):
|
329 |
+
# lazily create causal attention mask, with full attention between the vision tokens
|
330 |
+
# pytorch uses additive attention mask; fill with -inf
|
331 |
+
mask = torch.empty(self.context_length, self.context_length)
|
332 |
+
mask.fill_(float("-inf"))
|
333 |
+
mask.triu_(1) # zero out the lower diagonal
|
334 |
+
return mask
|
335 |
+
|
336 |
+
@property
|
337 |
+
def dtype(self):
|
338 |
+
return self.visual.conv1.weight.dtype
|
339 |
+
|
340 |
+
def encode_image(self, image):
|
341 |
+
return self.visual(image.type(self.dtype))
|
342 |
+
|
343 |
+
def encode_text(self, text):
|
344 |
+
x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model]
|
345 |
+
|
346 |
+
x = x + self.positional_embedding.type(self.dtype)
|
347 |
+
x = x.permute(1, 0, 2) # NLD -> LND
|
348 |
+
x = self.transformer(x)
|
349 |
+
x = x.permute(1, 0, 2) # LND -> NLD
|
350 |
+
x = self.ln_final(x).type(self.dtype)
|
351 |
+
|
352 |
+
# x.shape = [batch_size, n_ctx, transformer.width]
|
353 |
+
# take features from the eot embedding (eot_token is the highest number in each sequence)
|
354 |
+
x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
|
355 |
+
|
356 |
+
return x
|
357 |
+
|
358 |
+
def forward(self, image, text):
|
359 |
+
image_features = self.encode_image(image)
|
360 |
+
text_features = self.encode_text(text)
|
361 |
+
|
362 |
+
# normalized features
|
363 |
+
image_features = image_features / image_features.norm(dim=1, keepdim=True)
|
364 |
+
text_features = text_features / text_features.norm(dim=1, keepdim=True)
|
365 |
+
|
366 |
+
# cosine similarity as logits
|
367 |
+
logit_scale = self.logit_scale.exp()
|
368 |
+
logits_per_image = logit_scale * image_features @ text_features.t()
|
369 |
+
logits_per_text = logits_per_image.t()
|
370 |
+
|
371 |
+
# shape = [global_batch_size, global_batch_size]
|
372 |
+
return logits_per_image, logits_per_text
|
373 |
+
|
374 |
+
|
375 |
+
def convert_weights(model: nn.Module):
|
376 |
+
"""Convert applicable model parameters to fp16"""
|
377 |
+
|
378 |
+
def _convert_weights_to_fp16(l):
|
379 |
+
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
|
380 |
+
l.weight.data = l.weight.data.half()
|
381 |
+
if l.bias is not None:
|
382 |
+
l.bias.data = l.bias.data.half()
|
383 |
+
|
384 |
+
if isinstance(l, nn.MultiheadAttention):
|
385 |
+
for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
|
386 |
+
tensor = getattr(l, attr)
|
387 |
+
if tensor is not None:
|
388 |
+
tensor.data = tensor.data.half()
|
389 |
+
|
390 |
+
for name in ["text_projection", "proj"]:
|
391 |
+
if hasattr(l, name):
|
392 |
+
attr = getattr(l, name)
|
393 |
+
if attr is not None:
|
394 |
+
attr.data = attr.data.half()
|
395 |
+
|
396 |
+
model.apply(_convert_weights_to_fp16)
|
397 |
+
|
398 |
+
|
399 |
+
def build_model(state_dict: dict):
|
400 |
+
vit = "visual.proj" in state_dict
|
401 |
+
|
402 |
+
if vit:
|
403 |
+
vision_width = state_dict["visual.conv1.weight"].shape[0]
|
404 |
+
vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
|
405 |
+
vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
|
406 |
+
grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
|
407 |
+
image_resolution = vision_patch_size * grid_size
|
408 |
+
else:
|
409 |
+
counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
|
410 |
+
vision_layers = tuple(counts)
|
411 |
+
vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
|
412 |
+
output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
|
413 |
+
vision_patch_size = None
|
414 |
+
assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
|
415 |
+
image_resolution = output_width * 32
|
416 |
+
|
417 |
+
embed_dim = state_dict["text_projection"].shape[1]
|
418 |
+
context_length = state_dict["positional_embedding"].shape[0]
|
419 |
+
vocab_size = state_dict["token_embedding.weight"].shape[0]
|
420 |
+
transformer_width = state_dict["ln_final.weight"].shape[0]
|
421 |
+
transformer_heads = transformer_width // 64
|
422 |
+
transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))
|
423 |
+
|
424 |
+
model = CLIP(
|
425 |
+
embed_dim,
|
426 |
+
image_resolution, vision_layers, vision_width, vision_patch_size,
|
427 |
+
context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
|
428 |
+
)
|
429 |
+
|
430 |
+
for key in ["input_resolution", "context_length", "vocab_size"]:
|
431 |
+
if key in state_dict:
|
432 |
+
del state_dict[key]
|
433 |
+
|
434 |
+
convert_weights(model)
|
435 |
+
model.load_state_dict(state_dict)
|
436 |
+
return model.eval()
|
clip/simple_tokenizer.py
ADDED
@@ -0,0 +1,132 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import gzip
|
2 |
+
import html
|
3 |
+
import os
|
4 |
+
from functools import lru_cache
|
5 |
+
|
6 |
+
import ftfy
|
7 |
+
import regex as re
|
8 |
+
|
9 |
+
|
10 |
+
@lru_cache()
|
11 |
+
def default_bpe():
|
12 |
+
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
|
13 |
+
|
14 |
+
|
15 |
+
@lru_cache()
|
16 |
+
def bytes_to_unicode():
|
17 |
+
"""
|
18 |
+
Returns list of utf-8 byte and a corresponding list of unicode strings.
|
19 |
+
The reversible bpe codes work on unicode strings.
|
20 |
+
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
|
21 |
+
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
|
22 |
+
This is a signficant percentage of your normal, say, 32K bpe vocab.
|
23 |
+
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
|
24 |
+
And avoids mapping to whitespace/control characters the bpe code barfs on.
|
25 |
+
"""
|
26 |
+
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
|
27 |
+
cs = bs[:]
|
28 |
+
n = 0
|
29 |
+
for b in range(2**8):
|
30 |
+
if b not in bs:
|
31 |
+
bs.append(b)
|
32 |
+
cs.append(2**8+n)
|
33 |
+
n += 1
|
34 |
+
cs = [chr(n) for n in cs]
|
35 |
+
return dict(zip(bs, cs))
|
36 |
+
|
37 |
+
|
38 |
+
def get_pairs(word):
|
39 |
+
"""Return set of symbol pairs in a word.
|
40 |
+
Word is represented as tuple of symbols (symbols being variable-length strings).
|
41 |
+
"""
|
42 |
+
pairs = set()
|
43 |
+
prev_char = word[0]
|
44 |
+
for char in word[1:]:
|
45 |
+
pairs.add((prev_char, char))
|
46 |
+
prev_char = char
|
47 |
+
return pairs
|
48 |
+
|
49 |
+
|
50 |
+
def basic_clean(text):
|
51 |
+
text = ftfy.fix_text(text)
|
52 |
+
text = html.unescape(html.unescape(text))
|
53 |
+
return text.strip()
|
54 |
+
|
55 |
+
|
56 |
+
def whitespace_clean(text):
|
57 |
+
text = re.sub(r'\s+', ' ', text)
|
58 |
+
text = text.strip()
|
59 |
+
return text
|
60 |
+
|
61 |
+
|
62 |
+
class SimpleTokenizer(object):
|
63 |
+
def __init__(self, bpe_path: str = default_bpe()):
|
64 |
+
self.byte_encoder = bytes_to_unicode()
|
65 |
+
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
|
66 |
+
merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
|
67 |
+
merges = merges[1:49152-256-2+1]
|
68 |
+
merges = [tuple(merge.split()) for merge in merges]
|
69 |
+
vocab = list(bytes_to_unicode().values())
|
70 |
+
vocab = vocab + [v+'</w>' for v in vocab]
|
71 |
+
for merge in merges:
|
72 |
+
vocab.append(''.join(merge))
|
73 |
+
vocab.extend(['<|startoftext|>', '<|endoftext|>'])
|
74 |
+
self.encoder = dict(zip(vocab, range(len(vocab))))
|
75 |
+
self.decoder = {v: k for k, v in self.encoder.items()}
|
76 |
+
self.bpe_ranks = dict(zip(merges, range(len(merges))))
|
77 |
+
self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
|
78 |
+
self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
|
79 |
+
|
80 |
+
def bpe(self, token):
|
81 |
+
if token in self.cache:
|
82 |
+
return self.cache[token]
|
83 |
+
word = tuple(token[:-1]) + ( token[-1] + '</w>',)
|
84 |
+
pairs = get_pairs(word)
|
85 |
+
|
86 |
+
if not pairs:
|
87 |
+
return token+'</w>'
|
88 |
+
|
89 |
+
while True:
|
90 |
+
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
|
91 |
+
if bigram not in self.bpe_ranks:
|
92 |
+
break
|
93 |
+
first, second = bigram
|
94 |
+
new_word = []
|
95 |
+
i = 0
|
96 |
+
while i < len(word):
|
97 |
+
try:
|
98 |
+
j = word.index(first, i)
|
99 |
+
new_word.extend(word[i:j])
|
100 |
+
i = j
|
101 |
+
except:
|
102 |
+
new_word.extend(word[i:])
|
103 |
+
break
|
104 |
+
|
105 |
+
if word[i] == first and i < len(word)-1 and word[i+1] == second:
|
106 |
+
new_word.append(first+second)
|
107 |
+
i += 2
|
108 |
+
else:
|
109 |
+
new_word.append(word[i])
|
110 |
+
i += 1
|
111 |
+
new_word = tuple(new_word)
|
112 |
+
word = new_word
|
113 |
+
if len(word) == 1:
|
114 |
+
break
|
115 |
+
else:
|
116 |
+
pairs = get_pairs(word)
|
117 |
+
word = ' '.join(word)
|
118 |
+
self.cache[token] = word
|
119 |
+
return word
|
120 |
+
|
121 |
+
def encode(self, text):
|
122 |
+
bpe_tokens = []
|
123 |
+
text = whitespace_clean(basic_clean(text)).lower()
|
124 |
+
for token in re.findall(self.pat, text):
|
125 |
+
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
|
126 |
+
bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
|
127 |
+
return bpe_tokens
|
128 |
+
|
129 |
+
def decode(self, tokens):
|
130 |
+
text = ''.join([self.decoder[token] for token in tokens])
|
131 |
+
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
|
132 |
+
return text
|
clip/vitseg.py
ADDED
@@ -0,0 +1,286 @@
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
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|
|
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|
|
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|
|
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|
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|
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|
|
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|
|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
from posixpath import basename, dirname, join
|
3 |
+
# import clip
|
4 |
+
from clip.model import convert_weights
|
5 |
+
import torch
|
6 |
+
import json
|
7 |
+
from torch import nn
|
8 |
+
from torch.nn import functional as nnf
|
9 |
+
from torch.nn.modules import activation
|
10 |
+
from torch.nn.modules.activation import ReLU
|
11 |
+
from torchvision import transforms
|
12 |
+
|
13 |
+
normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
|
14 |
+
|
15 |
+
from torchvision.models import ResNet
|
16 |
+
|
17 |
+
|
18 |
+
def process_prompts(conditional, prompt_list, conditional_map):
|
19 |
+
# DEPRECATED
|
20 |
+
|
21 |
+
# randomly sample a synonym
|
22 |
+
words = [conditional_map[int(i)] for i in conditional]
|
23 |
+
words = [syns[torch.multinomial(torch.ones(len(syns)), 1, replacement=True).item()] for syns in words]
|
24 |
+
words = [w.replace('_', ' ') for w in words]
|
25 |
+
|
26 |
+
if prompt_list is not None:
|
27 |
+
prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
|
28 |
+
prompts = [prompt_list[i] for i in prompt_indices]
|
29 |
+
else:
|
30 |
+
prompts = ['a photo of {}'] * (len(words))
|
31 |
+
|
32 |
+
return [promt.format(w) for promt, w in zip(prompts, words)]
|
33 |
+
|
34 |
+
|
35 |
+
class VITDenseBase(nn.Module):
|
36 |
+
|
37 |
+
def rescaled_pos_emb(self, new_size):
|
38 |
+
assert len(new_size) == 2
|
39 |
+
|
40 |
+
a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
|
41 |
+
b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
|
42 |
+
return torch.cat([self.model.positional_embedding[:1], b])
|
43 |
+
|
44 |
+
def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
|
45 |
+
|
46 |
+
with torch.no_grad():
|
47 |
+
|
48 |
+
x_inp = nnf.interpolate(x_inp, (384, 384))
|
49 |
+
|
50 |
+
x = self.model.patch_embed(x_inp)
|
51 |
+
cls_token = self.model.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
52 |
+
if self.model.dist_token is None:
|
53 |
+
x = torch.cat((cls_token, x), dim=1)
|
54 |
+
else:
|
55 |
+
x = torch.cat((cls_token, self.model.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
|
56 |
+
x = self.model.pos_drop(x + self.model.pos_embed)
|
57 |
+
|
58 |
+
activations = []
|
59 |
+
for i, block in enumerate(self.model.blocks):
|
60 |
+
x = block(x)
|
61 |
+
|
62 |
+
if i in extract_layers:
|
63 |
+
# permute to be compatible with CLIP
|
64 |
+
activations += [x.permute(1,0,2)]
|
65 |
+
|
66 |
+
x = self.model.norm(x)
|
67 |
+
x = self.model.head(self.model.pre_logits(x[:, 0]))
|
68 |
+
|
69 |
+
# again for CLIP compatibility
|
70 |
+
# x = x.permute(1, 0, 2)
|
71 |
+
|
72 |
+
return x, activations, None
|
73 |
+
|
74 |
+
def sample_prompts(self, words, prompt_list=None):
|
75 |
+
|
76 |
+
prompt_list = prompt_list if prompt_list is not None else self.prompt_list
|
77 |
+
|
78 |
+
prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
|
79 |
+
prompts = [prompt_list[i] for i in prompt_indices]
|
80 |
+
return [promt.format(w) for promt, w in zip(prompts, words)]
|
81 |
+
|
82 |
+
def get_cond_vec(self, conditional, batch_size):
|
83 |
+
# compute conditional from a single string
|
84 |
+
if conditional is not None and type(conditional) == str:
|
85 |
+
cond = self.compute_conditional(conditional)
|
86 |
+
cond = cond.repeat(batch_size, 1)
|
87 |
+
|
88 |
+
# compute conditional from string list/tuple
|
89 |
+
elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
|
90 |
+
assert len(conditional) == batch_size
|
91 |
+
cond = self.compute_conditional(conditional)
|
92 |
+
|
93 |
+
# use conditional directly
|
94 |
+
elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
|
95 |
+
cond = conditional
|
96 |
+
|
97 |
+
# compute conditional from image
|
98 |
+
elif conditional is not None and type(conditional) == torch.Tensor:
|
99 |
+
with torch.no_grad():
|
100 |
+
cond, _, _ = self.visual_forward(conditional)
|
101 |
+
else:
|
102 |
+
raise ValueError('invalid conditional')
|
103 |
+
return cond
|
104 |
+
|
105 |
+
def compute_conditional(self, conditional):
|
106 |
+
import clip
|
107 |
+
|
108 |
+
dev = next(self.parameters()).device
|
109 |
+
|
110 |
+
if type(conditional) in {list, tuple}:
|
111 |
+
text_tokens = clip.tokenize(conditional).to(dev)
|
112 |
+
cond = self.clip_model.encode_text(text_tokens)
|
113 |
+
else:
|
114 |
+
if conditional in self.precomputed_prompts:
|
115 |
+
cond = self.precomputed_prompts[conditional].float().to(dev)
|
116 |
+
else:
|
117 |
+
text_tokens = clip.tokenize([conditional]).to(dev)
|
118 |
+
cond = self.clip_model.encode_text(text_tokens)[0]
|
119 |
+
|
120 |
+
return cond
|
121 |
+
|
122 |
+
|
123 |
+
class VITDensePredT(VITDenseBase):
|
124 |
+
|
125 |
+
def __init__(self, extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed',
|
126 |
+
depth=3, extra_blocks=0, reduce_cond=None, fix_shift=False,
|
127 |
+
learn_trans_conv_only=False, refine=None, limit_to_clip_only=False, upsample=False,
|
128 |
+
add_calibration=False, process_cond=None, not_pretrained=False):
|
129 |
+
super().__init__()
|
130 |
+
# device = 'cpu'
|
131 |
+
|
132 |
+
self.extract_layers = extract_layers
|
133 |
+
self.cond_layer = cond_layer
|
134 |
+
self.limit_to_clip_only = limit_to_clip_only
|
135 |
+
self.process_cond = None
|
136 |
+
|
137 |
+
if add_calibration:
|
138 |
+
self.calibration_conds = 1
|
139 |
+
|
140 |
+
self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
|
141 |
+
|
142 |
+
self.add_activation1 = True
|
143 |
+
|
144 |
+
import timm
|
145 |
+
self.model = timm.create_model('vit_base_patch16_384', pretrained=True)
|
146 |
+
self.model.head = nn.Linear(768, 512 if reduce_cond is None else reduce_cond)
|
147 |
+
|
148 |
+
for p in self.model.parameters():
|
149 |
+
p.requires_grad_(False)
|
150 |
+
|
151 |
+
import clip
|
152 |
+
self.clip_model, _ = clip.load('ViT-B/16', device='cpu', jit=False)
|
153 |
+
# del self.clip_model.visual
|
154 |
+
|
155 |
+
|
156 |
+
self.token_shape = (14, 14)
|
157 |
+
|
158 |
+
# conditional
|
159 |
+
if reduce_cond is not None:
|
160 |
+
self.reduce_cond = nn.Linear(512, reduce_cond)
|
161 |
+
for p in self.reduce_cond.parameters():
|
162 |
+
p.requires_grad_(False)
|
163 |
+
else:
|
164 |
+
self.reduce_cond = None
|
165 |
+
|
166 |
+
# self.film = AVAILABLE_BLOCKS['film'](512, 128)
|
167 |
+
self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
|
168 |
+
self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
|
169 |
+
|
170 |
+
# DEPRECATED
|
171 |
+
# self.conditional_map = {c['id']: c['synonyms'] for c in json.load(open(cond_map))}
|
172 |
+
|
173 |
+
assert len(self.extract_layers) == depth
|
174 |
+
|
175 |
+
self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
|
176 |
+
self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
|
177 |
+
self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
|
178 |
+
|
179 |
+
trans_conv_ks = (16, 16)
|
180 |
+
self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
|
181 |
+
|
182 |
+
# refinement and trans conv
|
183 |
+
|
184 |
+
if learn_trans_conv_only:
|
185 |
+
for p in self.parameters():
|
186 |
+
p.requires_grad_(False)
|
187 |
+
|
188 |
+
for p in self.trans_conv.parameters():
|
189 |
+
p.requires_grad_(True)
|
190 |
+
|
191 |
+
if prompt == 'fixed':
|
192 |
+
self.prompt_list = ['a photo of a {}.']
|
193 |
+
elif prompt == 'shuffle':
|
194 |
+
self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
|
195 |
+
elif prompt == 'shuffle+':
|
196 |
+
self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
|
197 |
+
'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
|
198 |
+
'a bad photo of a {}.', 'a photo of the {}.']
|
199 |
+
elif prompt == 'shuffle_clip':
|
200 |
+
from models.clip_prompts import imagenet_templates
|
201 |
+
self.prompt_list = imagenet_templates
|
202 |
+
|
203 |
+
if process_cond is not None:
|
204 |
+
if process_cond == 'clamp' or process_cond[0] == 'clamp':
|
205 |
+
|
206 |
+
val = process_cond[1] if type(process_cond) in {list, tuple} else 0.2
|
207 |
+
|
208 |
+
def clamp_vec(x):
|
209 |
+
return torch.clamp(x, -val, val)
|
210 |
+
|
211 |
+
self.process_cond = clamp_vec
|
212 |
+
|
213 |
+
elif process_cond.endswith('.pth'):
|
214 |
+
|
215 |
+
shift = torch.load(process_cond)
|
216 |
+
def add_shift(x):
|
217 |
+
return x + shift.to(x.device)
|
218 |
+
|
219 |
+
self.process_cond = add_shift
|
220 |
+
|
221 |
+
import pickle
|
222 |
+
precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
|
223 |
+
self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
|
224 |
+
|
225 |
+
|
226 |
+
def forward(self, inp_image, conditional=None, return_features=False, mask=None):
|
227 |
+
|
228 |
+
assert type(return_features) == bool
|
229 |
+
|
230 |
+
# inp_image = inp_image.to(self.model.positional_embedding.device)
|
231 |
+
|
232 |
+
if mask is not None:
|
233 |
+
raise ValueError('mask not supported')
|
234 |
+
|
235 |
+
# x_inp = normalize(inp_image)
|
236 |
+
x_inp = inp_image
|
237 |
+
|
238 |
+
bs, dev = inp_image.shape[0], x_inp.device
|
239 |
+
|
240 |
+
inp_image_size = inp_image.shape[2:]
|
241 |
+
|
242 |
+
cond = self.get_cond_vec(conditional, bs)
|
243 |
+
|
244 |
+
visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
|
245 |
+
|
246 |
+
activation1 = activations[0]
|
247 |
+
activations = activations[1:]
|
248 |
+
|
249 |
+
a = None
|
250 |
+
for i, (activation, block, reduce) in enumerate(zip(activations[::-1], self.blocks, self.reduces)):
|
251 |
+
|
252 |
+
if a is not None:
|
253 |
+
a = reduce(activation) + a
|
254 |
+
else:
|
255 |
+
a = reduce(activation)
|
256 |
+
|
257 |
+
if i == self.cond_layer:
|
258 |
+
if self.reduce_cond is not None:
|
259 |
+
cond = self.reduce_cond(cond)
|
260 |
+
|
261 |
+
a = self.film_mul(cond) * a + self.film_add(cond)
|
262 |
+
|
263 |
+
a = block(a)
|
264 |
+
|
265 |
+
for block in self.extra_blocks:
|
266 |
+
a = a + block(a)
|
267 |
+
|
268 |
+
a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
|
269 |
+
|
270 |
+
size = int(math.sqrt(a.shape[2]))
|
271 |
+
|
272 |
+
a = a.view(bs, a.shape[1], size, size)
|
273 |
+
|
274 |
+
if self.trans_conv is not None:
|
275 |
+
a = self.trans_conv(a)
|
276 |
+
|
277 |
+
if self.upsample_proj is not None:
|
278 |
+
a = self.upsample_proj(a)
|
279 |
+
a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
|
280 |
+
|
281 |
+
a = nnf.interpolate(a, inp_image_size)
|
282 |
+
|
283 |
+
if return_features:
|
284 |
+
return a, visual_q, cond, [activation1] + activations
|
285 |
+
else:
|
286 |
+
return a,
|
docs/faceselection.png
ADDED
docs/finaloutput.png
ADDED
Git LFS Details
|
docs/kickboxing.jpg
ADDED
docs/musk.jpg
ADDED
docs/screenshot.png
ADDED
gfpgan/weights/detection_Resnet50_Final.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6d1de9c2944f2ccddca5f5e010ea5ae64a39845a86311af6fdf30841b0a5a16d
|
3 |
+
size 109497761
|
gfpgan/weights/parsing_parsenet.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3d558d8d0e42c20224f13cf5a29c79eba2d59913419f945545d8cf7b72920de2
|
3 |
+
size 85331193
|
installer/installer.py
ADDED
@@ -0,0 +1,83 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import argparse
|
2 |
+
import glob
|
3 |
+
import os
|
4 |
+
import shutil
|
5 |
+
import site
|
6 |
+
import subprocess
|
7 |
+
import sys
|
8 |
+
|
9 |
+
|
10 |
+
script_dir = os.getcwd()
|
11 |
+
|
12 |
+
|
13 |
+
def run_cmd(cmd, capture_output=False, env=None):
|
14 |
+
# Run shell commands
|
15 |
+
return subprocess.run(cmd, shell=True, capture_output=capture_output, env=env)
|
16 |
+
|
17 |
+
|
18 |
+
def check_env():
|
19 |
+
# If we have access to conda, we are probably in an environment
|
20 |
+
conda_not_exist = run_cmd("conda", capture_output=True).returncode
|
21 |
+
if conda_not_exist:
|
22 |
+
print("Conda is not installed. Exiting...")
|
23 |
+
sys.exit()
|
24 |
+
|
25 |
+
# Ensure this is a new environment and not the base environment
|
26 |
+
if os.environ["CONDA_DEFAULT_ENV"] == "base":
|
27 |
+
print("Create an environment for this project and activate it. Exiting...")
|
28 |
+
sys.exit()
|
29 |
+
|
30 |
+
|
31 |
+
def install_dependencies():
|
32 |
+
# Install Git and clone repo
|
33 |
+
run_cmd("conda install -y -k git")
|
34 |
+
run_cmd("git clone https://github.com/C0untFloyd/roop-unleashed.git")
|
35 |
+
|
36 |
+
# Install the webui dependencies
|
37 |
+
update_dependencies()
|
38 |
+
|
39 |
+
|
40 |
+
def update_dependencies():
|
41 |
+
global MY_PATH
|
42 |
+
|
43 |
+
os.chdir(MY_PATH)
|
44 |
+
# do a hard reset for to update even if there are local changes
|
45 |
+
run_cmd("git fetch --all")
|
46 |
+
run_cmd("git reset --hard origin/main")
|
47 |
+
run_cmd("git pull")
|
48 |
+
# Installs/Updates dependencies from all requirements.txt
|
49 |
+
run_cmd("python -m pip install -r requirements.txt")
|
50 |
+
|
51 |
+
|
52 |
+
def start_app():
|
53 |
+
global MY_PATH
|
54 |
+
|
55 |
+
os.chdir(MY_PATH)
|
56 |
+
# forward commandline arguments
|
57 |
+
sys.argv.pop(0)
|
58 |
+
args = ' '.join(sys.argv)
|
59 |
+
print("Launching App")
|
60 |
+
run_cmd(f'python run.py {args}')
|
61 |
+
|
62 |
+
|
63 |
+
if __name__ == "__main__":
|
64 |
+
global MY_PATH
|
65 |
+
|
66 |
+
MY_PATH = "roop-unleashed"
|
67 |
+
|
68 |
+
|
69 |
+
# Verifies we are in a conda environment
|
70 |
+
check_env()
|
71 |
+
|
72 |
+
# If webui has already been installed, skip and run
|
73 |
+
if not os.path.exists(MY_PATH):
|
74 |
+
install_dependencies()
|
75 |
+
else:
|
76 |
+
# moved update from batch to here, because of batch limitations
|
77 |
+
updatechoice = input("Check for Updates? [y/n]").lower()
|
78 |
+
if updatechoice == "y":
|
79 |
+
update_dependencies()
|
80 |
+
|
81 |
+
# Run the model with webui
|
82 |
+
os.chdir(script_dir)
|
83 |
+
start_app()
|
installer/windows_run.bat
ADDED
@@ -0,0 +1,80 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
@echo off
|
2 |
+
REM Please set the following commandline arguments to your prefered settings
|
3 |
+
set COMMANDLINE_ARGS=--execution-provider cuda --frame-processor face_swapper face_enhancer --video-encoder libvpx-vp9
|
4 |
+
|
5 |
+
cd /D "%~dp0"
|
6 |
+
|
7 |
+
echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end
|
8 |
+
|
9 |
+
set PATH=%PATH%;%SystemRoot%\system32
|
10 |
+
|
11 |
+
@rem config
|
12 |
+
set INSTALL_DIR=%cd%\installer_files
|
13 |
+
set CONDA_ROOT_PREFIX=%cd%\installer_files\conda
|
14 |
+
set INSTALL_ENV_DIR=%cd%\installer_files\env
|
15 |
+
set MINICONDA_DOWNLOAD_URL=https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe
|
16 |
+
set FFMPEG_DOWNLOAD_URL=https://github.com/GyanD/codexffmpeg/releases/download/2023-06-21-git-1bcb8a7338/ffmpeg-2023-06-21-git-1bcb8a7338-essentials_build.zip
|
17 |
+
set INSTALL_FFMPEG_DIR=%cd%\installer_files\ffmpeg
|
18 |
+
set conda_exists=F
|
19 |
+
|
20 |
+
@rem figure out whether git and conda needs to be installed
|
21 |
+
call "%CONDA_ROOT_PREFIX%\_conda.exe" --version >nul 2>&1
|
22 |
+
if "%ERRORLEVEL%" EQU "0" set conda_exists=T
|
23 |
+
|
24 |
+
@rem (if necessary) install git and conda into a contained environment
|
25 |
+
@rem download conda
|
26 |
+
if "%conda_exists%" == "F" (
|
27 |
+
echo Downloading Miniconda from %MINICONDA_DOWNLOAD_URL% to %INSTALL_DIR%\miniconda_installer.exe
|
28 |
+
|
29 |
+
mkdir "%INSTALL_DIR%"
|
30 |
+
call curl -Lk "%MINICONDA_DOWNLOAD_URL%" > "%INSTALL_DIR%\miniconda_installer.exe" || ( echo. && echo Miniconda failed to download. && goto end )
|
31 |
+
|
32 |
+
echo Installing Miniconda to %CONDA_ROOT_PREFIX%
|
33 |
+
start /wait "" "%INSTALL_DIR%\miniconda_installer.exe" /InstallationType=JustMe /NoShortcuts=1 /AddToPath=0 /RegisterPython=0 /NoRegistry=1 /S /D=%CONDA_ROOT_PREFIX%
|
34 |
+
|
35 |
+
@rem test the conda binary
|
36 |
+
echo Miniconda version:
|
37 |
+
call "%CONDA_ROOT_PREFIX%\_conda.exe" --version || ( echo. && echo Miniconda not found. && goto end )
|
38 |
+
)
|
39 |
+
|
40 |
+
@rem create the installer env
|
41 |
+
if not exist "%INSTALL_ENV_DIR%" (
|
42 |
+
echo Packages to install: %PACKAGES_TO_INSTALL%
|
43 |
+
call "%CONDA_ROOT_PREFIX%\_conda.exe" create --no-shortcuts -y -k --prefix "%INSTALL_ENV_DIR%" python=3.10 || ( echo. && echo Conda environment creation failed. && goto end )
|
44 |
+
)
|
45 |
+
|
46 |
+
if not exist "%INSTALL_FFMPEG_DIR%" (
|
47 |
+
echo Downloading ffmpeg from %FFMPEG_DOWNLOAD_URL% to %INSTALL_DIR%
|
48 |
+
call curl -Lk "%FFMPEG_DOWNLOAD_URL%" > "%INSTALL_DIR%\ffmpeg.zip" || ( echo. && echo ffmpeg failed to download. && goto end )
|
49 |
+
call powershell -command "Expand-Archive -Force '%INSTALL_DIR%\ffmpeg.zip' '%INSTALL_DIR%\'"
|
50 |
+
|
51 |
+
cd "installer_files"
|
52 |
+
setlocal EnableExtensions EnableDelayedExpansion
|
53 |
+
|
54 |
+
for /f "tokens=*" %%f in ('dir /s /b /ad "ffmpeg*"') do (
|
55 |
+
ren "%%f" "ffmpeg"
|
56 |
+
)
|
57 |
+
endlocal
|
58 |
+
setx PATH "%INSTALL_FFMPEG_DIR%\bin\;%PATH%"
|
59 |
+
echo To use videos, you need to restart roop after this installation.
|
60 |
+
cd ..
|
61 |
+
)
|
62 |
+
|
63 |
+
@rem check if conda environment was actually created
|
64 |
+
if not exist "%INSTALL_ENV_DIR%\python.exe" ( echo. && echo ERROR: Conda environment is empty. && goto end )
|
65 |
+
|
66 |
+
@rem activate installer env
|
67 |
+
call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo Miniconda hook not found. && goto end )
|
68 |
+
|
69 |
+
@rem setup installer env
|
70 |
+
echo Launching roop unleashed - please edit windows_run.bat to customize commandline arguments
|
71 |
+
call python installer.py %COMMANDLINE_ARGS%
|
72 |
+
|
73 |
+
echo.
|
74 |
+
echo Done!
|
75 |
+
|
76 |
+
:end
|
77 |
+
pause
|
78 |
+
|
79 |
+
|
80 |
+
|
jaa.py
ADDED
@@ -0,0 +1,355 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
1 |
+
"""
|
2 |
+
Jaa.py Plugin Framework
|
3 |
+
Author: Janvarev Vladislav
|
4 |
+
|
5 |
+
Jaa.py - minimalistic one-file plugin framework with no dependencies.
|
6 |
+
Main functions:
|
7 |
+
- run all plugins files from "plugins" folder, base on filename
|
8 |
+
- save each plugin options in "options" folder in JSON text files for further editing
|
9 |
+
|
10 |
+
- Plugins
|
11 |
+
must located in plugins/ folder
|
12 |
+
must have "start(core)" function, that returns manifest dict
|
13 |
+
manifest must contain keys "name" and "version"
|
14 |
+
can contain "default_options"
|
15 |
+
- if contain - options will be saved in "options" folder and reload instead next time
|
16 |
+
- if contain - "start_with_options(core,manifest)" function will run with manifest with "options" key
|
17 |
+
manifest will be processed in "process_plugin_manifest" function if you override it
|
18 |
+
|
19 |
+
- Options (for plugins)
|
20 |
+
are saved under "options" folder in JSON format
|
21 |
+
created at first run plugin with "default_options"
|
22 |
+
updated when plugin change "version"
|
23 |
+
|
24 |
+
- Example usage:
|
25 |
+
class VoiceAssCore(JaaCore): # class must override JaaCore
|
26 |
+
def __init__(self):
|
27 |
+
JaaCore.__init__(self,__file__)
|
28 |
+
...
|
29 |
+
|
30 |
+
main = VoiceAssCore()
|
31 |
+
main.init_plugins(["core"]) # 1 param - first plugins to be initialized
|
32 |
+
# Good if you need some "core" options/plugin to be loaded before others
|
33 |
+
# not necessary starts with "plugin_" prefix
|
34 |
+
|
35 |
+
also can be run like
|
36 |
+
|
37 |
+
main.init_plugins()
|
38 |
+
|
39 |
+
- Requirements
|
40 |
+
Python 3.5+ (due to dict mix in final_options calc), can be relaxed
|
41 |
+
"""
|
42 |
+
|
43 |
+
import os
|
44 |
+
import traceback
|
45 |
+
import json
|
46 |
+
|
47 |
+
# here we trying to use termcolor to highlight plugin info and errors during load
|
48 |
+
try:
|
49 |
+
from termcolor import cprint
|
50 |
+
except Exception as e:
|
51 |
+
# not found? making a stub!
|
52 |
+
def cprint(p,color=None):
|
53 |
+
if color == None:
|
54 |
+
print(p)
|
55 |
+
else:
|
56 |
+
print(str(color).upper(),p)
|
57 |
+
|
58 |
+
version = "2.2.0"
|
59 |
+
|
60 |
+
class JaaCore:
|
61 |
+
verbose = False
|
62 |
+
|
63 |
+
def __init__(self,root_file = __file__):
|
64 |
+
self.jaaPluginPrefix = "plugin_"
|
65 |
+
self.jaaVersion = version
|
66 |
+
self.jaaRootFolder = os.path.dirname(root_file)
|
67 |
+
self.jaaOptionsPath = self.jaaRootFolder+os.path.sep+"plugin_options"
|
68 |
+
self.jaaShowTracebackOnPluginErrors = False
|
69 |
+
if self.verbose:
|
70 |
+
cprint("JAA.PY v{0} class created!".format(version),"blue")
|
71 |
+
|
72 |
+
# ------------- plugins -----------------
|
73 |
+
def init_plugins(self, list_first_plugins = []):
|
74 |
+
self.plugin_manifests = {}
|
75 |
+
|
76 |
+
# 1. run first plugins first!
|
77 |
+
for modname in list_first_plugins:
|
78 |
+
self.init_plugin(modname)
|
79 |
+
|
80 |
+
# 2. run all plugins from plugins folder
|
81 |
+
from os import listdir
|
82 |
+
from os.path import isfile, join
|
83 |
+
pluginpath = self.jaaRootFolder+"/plugins"
|
84 |
+
files = [f for f in listdir(pluginpath) if isfile(join(pluginpath, f))]
|
85 |
+
|
86 |
+
for fil in files:
|
87 |
+
# print fil[:-3]
|
88 |
+
if fil.startswith(self.jaaPluginPrefix) and fil.endswith(".py"):
|
89 |
+
modfile = fil[:-3]
|
90 |
+
self.init_plugin(modfile)
|
91 |
+
|
92 |
+
|
93 |
+
|
94 |
+
def init_plugin(self,modname):
|
95 |
+
# import
|
96 |
+
try:
|
97 |
+
mod = self.import_plugin("plugins."+modname)
|
98 |
+
except Exception as e:
|
99 |
+
self.print_error("JAA PLUGIN ERROR: {0} error on load: {1}".format(modname, str(e)))
|
100 |
+
return False
|
101 |
+
|
102 |
+
# run start function
|
103 |
+
try:
|
104 |
+
res = mod.start(self)
|
105 |
+
except Exception as e:
|
106 |
+
self.print_error("JAA PLUGIN ERROR: {0} error on start: {1}".format(modname, str(e)))
|
107 |
+
return False
|
108 |
+
|
109 |
+
# if plugin has an options
|
110 |
+
if "default_options" in res:
|
111 |
+
try:
|
112 |
+
# saved options try to read
|
113 |
+
saved_options = {}
|
114 |
+
try:
|
115 |
+
with open(self.jaaOptionsPath+'/'+modname+'.json', 'r', encoding="utf-8") as f:
|
116 |
+
s = f.read()
|
117 |
+
saved_options = json.loads(s)
|
118 |
+
#print("Saved options", saved_options)
|
119 |
+
except Exception as e:
|
120 |
+
pass
|
121 |
+
|
122 |
+
res["default_options"]["v"] = res["version"]
|
123 |
+
|
124 |
+
|
125 |
+
# only string needs Python 3.5
|
126 |
+
final_options = {**res["default_options"], **saved_options}
|
127 |
+
|
128 |
+
# if no option found or version is differ from mod version
|
129 |
+
if len(saved_options) == 0 or saved_options["v"] != res["version"]:
|
130 |
+
final_options["v"] = res["version"]
|
131 |
+
self.save_plugin_options(modname,final_options)
|
132 |
+
|
133 |
+
res["options"] = final_options
|
134 |
+
|
135 |
+
try:
|
136 |
+
res2 = mod.start_with_options(self,res)
|
137 |
+
if res2 != None:
|
138 |
+
res = res2
|
139 |
+
except Exception as e:
|
140 |
+
self.print_error("JAA PLUGIN ERROR: {0} error on start_with_options processing: {1}".format(modname, str(e)))
|
141 |
+
return False
|
142 |
+
|
143 |
+
except Exception as e:
|
144 |
+
self.print_error("JAA PLUGIN ERROR: {0} error on options processing: {1}".format(modname, str(e)))
|
145 |
+
return False
|
146 |
+
|
147 |
+
|
148 |
+
# processing plugin manifest
|
149 |
+
try:
|
150 |
+
# set up name and version
|
151 |
+
plugin_name = res["name"]
|
152 |
+
plugin_version = res["version"]
|
153 |
+
|
154 |
+
|
155 |
+
self.process_plugin_manifest(modname,res)
|
156 |
+
|
157 |
+
except Exception as e:
|
158 |
+
print("JAA PLUGIN ERROR: {0} error on process startup options: {1}".format(modname, str(e)))
|
159 |
+
return False
|
160 |
+
|
161 |
+
self.plugin_manifests[modname] = res
|
162 |
+
|
163 |
+
self.on_succ_plugin_start(modname,plugin_name,plugin_version)
|
164 |
+
return True
|
165 |
+
|
166 |
+
def on_succ_plugin_start(self, modname, plugin_name, plugin_version):
|
167 |
+
if self.verbose:
|
168 |
+
cprint("JAA PLUGIN: {1} {2} ({0}) started!".format(modname, plugin_name, plugin_version))
|
169 |
+
|
170 |
+
def print_error(self,p):
|
171 |
+
cprint(p,"red")
|
172 |
+
if self.jaaShowTracebackOnPluginErrors:
|
173 |
+
traceback.print_exc()
|
174 |
+
|
175 |
+
def import_plugin(self, module_name):
|
176 |
+
import sys
|
177 |
+
|
178 |
+
__import__(module_name)
|
179 |
+
|
180 |
+
if module_name in sys.modules:
|
181 |
+
return sys.modules[module_name]
|
182 |
+
return None
|
183 |
+
|
184 |
+
def save_plugin_options(self,modname,options):
|
185 |
+
# check folder exists
|
186 |
+
if not os.path.exists(self.jaaOptionsPath):
|
187 |
+
os.makedirs(self.jaaOptionsPath)
|
188 |
+
|
189 |
+
str_options = json.dumps(options, sort_keys=True, indent=4, ensure_ascii=False)
|
190 |
+
with open(self.jaaOptionsPath+'/'+modname+'.json', 'w', encoding="utf-8") as f:
|
191 |
+
f.write(str_options)
|
192 |
+
f.close()
|
193 |
+
|
194 |
+
# process manifest must be overrided in inherit class
|
195 |
+
def process_plugin_manifest(self,modname,manifest):
|
196 |
+
print("JAA PLUGIN: {0} manifest dummy procession (override 'process_plugin_manifest' function)".format(modname))
|
197 |
+
return
|
198 |
+
|
199 |
+
def plugin_manifest(self,pluginname):
|
200 |
+
if pluginname in self.plugin_manifests:
|
201 |
+
return self.plugin_manifests[pluginname]
|
202 |
+
return {}
|
203 |
+
|
204 |
+
def plugin_options(self,pluginname):
|
205 |
+
manifest = self.plugin_manifest(pluginname)
|
206 |
+
if "options" in manifest:
|
207 |
+
return manifest["options"]
|
208 |
+
return None
|
209 |
+
|
210 |
+
# ------------ gradio stuff --------------
|
211 |
+
def gradio_save(self,pluginname):
|
212 |
+
print("Saving options for {0}!".format(pluginname))
|
213 |
+
self.save_plugin_options(pluginname,self.plugin_options(pluginname))
|
214 |
+
|
215 |
+
def gradio_upd(self, pluginname, option, val):
|
216 |
+
options = self.plugin_options(pluginname)
|
217 |
+
|
218 |
+
# special case
|
219 |
+
if isinstance(options[option], (list, dict)) and isinstance(val, str):
|
220 |
+
import json
|
221 |
+
try:
|
222 |
+
options[option] = json.loads(val)
|
223 |
+
except Exception as e:
|
224 |
+
print(e)
|
225 |
+
pass
|
226 |
+
else:
|
227 |
+
options[option] = val
|
228 |
+
print(option,val,options)
|
229 |
+
|
230 |
+
def gradio_render_settings_interface(self, title:str="Settings manager", required_fields_to_show_plugin:list=["default_options"]):
|
231 |
+
import gradio as gr
|
232 |
+
|
233 |
+
with gr.Blocks() as gr_interface:
|
234 |
+
gr.Markdown("# {0}".format(title))
|
235 |
+
for pluginname in self.plugin_manifests:
|
236 |
+
manifest = self.plugin_manifests[pluginname]
|
237 |
+
|
238 |
+
# calculate if we show plugin
|
239 |
+
is_show_plugin = False
|
240 |
+
if len(required_fields_to_show_plugin) == 0:
|
241 |
+
is_show_plugin = True
|
242 |
+
else:
|
243 |
+
for k in required_fields_to_show_plugin:
|
244 |
+
if manifest.get(k) is not None:
|
245 |
+
is_show_plugin = True
|
246 |
+
|
247 |
+
if is_show_plugin:
|
248 |
+
with gr.Tab(pluginname):
|
249 |
+
gr.Markdown("## {0} v{1}".format(manifest["name"],manifest["version"]))
|
250 |
+
if manifest.get("description") is not None:
|
251 |
+
gr.Markdown(manifest.get("description"))
|
252 |
+
|
253 |
+
if manifest.get("url") is not None:
|
254 |
+
gr.Markdown("**URL:** [{0}]({0})".format(manifest.get("url")))
|
255 |
+
|
256 |
+
|
257 |
+
if "options" in manifest:
|
258 |
+
options = manifest["options"]
|
259 |
+
if len(options) > 1: # not only v
|
260 |
+
text_button = gr.Button("Save options".format(pluginname))
|
261 |
+
#options_int_list = []
|
262 |
+
for option in options:
|
263 |
+
|
264 |
+
#gr.Label(label=option)
|
265 |
+
if option != "v":
|
266 |
+
val = options[option]
|
267 |
+
label = option
|
268 |
+
|
269 |
+
if manifest.get("options_label") is not None:
|
270 |
+
if manifest.get("options_label").get(option) is not None:
|
271 |
+
label = option+": "+manifest.get("options_label").get(option)
|
272 |
+
|
273 |
+
|
274 |
+
if isinstance(val, (bool, )):
|
275 |
+
gr_elem = gr.Checkbox(value=val,label=label)
|
276 |
+
elif isinstance(val, (dict,list)):
|
277 |
+
import json
|
278 |
+
gr_elem = gr.Textbox(value=json.dumps(val,ensure_ascii=False), label=label)
|
279 |
+
else:
|
280 |
+
gr_elem = gr.Textbox(value=val, label=label)
|
281 |
+
|
282 |
+
def handler(x,pluginname=pluginname,option=option):
|
283 |
+
self.gradio_upd(pluginname, option, x)
|
284 |
+
|
285 |
+
gr_elem.change(handler, gr_elem, None)
|
286 |
+
|
287 |
+
def handler_save(pluginname=pluginname):
|
288 |
+
self.gradio_save(pluginname)
|
289 |
+
|
290 |
+
text_button.click(handler_save,inputs=None,outputs=None)
|
291 |
+
else:
|
292 |
+
gr.Markdown("_No options for this plugin_")
|
293 |
+
|
294 |
+
return gr_interface
|
295 |
+
|
296 |
+
|
297 |
+
def load_options(options_file=None,py_file=None,default_options={}):
|
298 |
+
# 1. calculating options filename
|
299 |
+
if options_file == None:
|
300 |
+
if py_file == None:
|
301 |
+
raise Exception('JAA: Options or PY file is not defined, cant calc options filename')
|
302 |
+
else:
|
303 |
+
options_file = py_file[:-3]+'.json'
|
304 |
+
|
305 |
+
# 2. try to read saved options
|
306 |
+
saved_options = {}
|
307 |
+
try:
|
308 |
+
with open(options_file, 'r', encoding="utf-8") as f:
|
309 |
+
s = f.read()
|
310 |
+
saved_options = json.loads(s)
|
311 |
+
#print("Saved options", saved_options)
|
312 |
+
except Exception as e:
|
313 |
+
pass
|
314 |
+
|
315 |
+
# 3. calculating final options
|
316 |
+
|
317 |
+
# only string needs Python 3.5
|
318 |
+
final_options = {**default_options, **saved_options}
|
319 |
+
|
320 |
+
# 4. calculating hash from def options to check - is file rewrite needed?
|
321 |
+
import hashlib
|
322 |
+
hash = hashlib.md5((json.dumps(default_options, sort_keys=True)).encode('utf-8')).hexdigest()
|
323 |
+
|
324 |
+
# 5. if no option file found or hash was from other default options
|
325 |
+
if len(saved_options) == 0 or not ("hash" in saved_options.keys()) or saved_options["hash"] != hash:
|
326 |
+
final_options["hash"] = hash
|
327 |
+
#self.save_plugin_options(modname,final_options)
|
328 |
+
|
329 |
+
# saving in file
|
330 |
+
str_options = json.dumps(final_options, sort_keys=True, indent=4, ensure_ascii=False)
|
331 |
+
with open(options_file, 'w', encoding="utf-8") as f:
|
332 |
+
f.write(str_options)
|
333 |
+
f.close()
|
334 |
+
|
335 |
+
return final_options
|
336 |
+
|
337 |
+
"""
|
338 |
+
The MIT License (MIT)
|
339 |
+
Copyright (c) 2021 Janvarev Vladislav
|
340 |
+
|
341 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
342 |
+
of this software and associated documentation files (the “Software”), to deal
|
343 |
+
in the Software without restriction, including without limitation the rights to use,
|
344 |
+
copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
|
345 |
+
and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
|
346 |
+
|
347 |
+
The above copyright notice and this permission notice shall be included in all copies or
|
348 |
+
substantial portions of the Software.
|
349 |
+
|
350 |
+
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
|
351 |
+
INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
|
352 |
+
PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE
|
353 |
+
FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
|
354 |
+
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
|
355 |
+
"""
|
models/CLIP/rd64-uni-refined.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a4956f9a7978a75630b08c9d6ec075b7c51cf43b4751b686e3a011d4012ddc9d
|
3 |
+
size 4720707
|
models/CodeFormer/codeformer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:1009e537e0c2a07d4cabce6355f53cb66767cd4b4297ec7a4a64ca4b8a5684b7
|
3 |
+
size 376637898
|
models/CodeFormer/facelib/detection_Resnet50_Final.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6d1de9c2944f2ccddca5f5e010ea5ae64a39845a86311af6fdf30841b0a5a16d
|
3 |
+
size 109497761
|
models/CodeFormer/facelib/parsing_parsenet.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3d558d8d0e42c20224f13cf5a29c79eba2d59913419f945545d8cf7b72920de2
|
3 |
+
size 85331193
|
models/CodeFormer/realesrgan/RealESRGAN_x2plus.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:49fafd45f8fd7aa8d31ab2a22d14d91b536c34494a5cfe31eb5d89c2fa266abb
|
3 |
+
size 67061725
|
models/DMDNet.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:70daeb4b1fd10f241043b587d892a941f2651d7322db02f06ff64b166537f65c
|
3 |
+
size 603684323
|
models/GFPGANv1.4.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e2cd4703ab14f4d01fd1383a8a8b266f9a5833dacee8e6a79d3bf21a1b6be5ad
|
3 |
+
size 348632874
|
models/inswapper_128.onnx
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e4a3f08c753cb72d04e10aa0f7dbe3deebbf39567d4ead6dce08e98aa49e16af
|
3 |
+
size 554253681
|
mynewshinyroop/.gitignore
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
# created by virtualenv automatically
|
2 |
+
*
|
mynewshinyroop/Lib/site-packages/_distutils_hack/__init__.py
ADDED
@@ -0,0 +1,227 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# don't import any costly modules
|
2 |
+
import sys
|
3 |
+
import os
|
4 |
+
|
5 |
+
|
6 |
+
is_pypy = '__pypy__' in sys.builtin_module_names
|
7 |
+
|
8 |
+
|
9 |
+
def warn_distutils_present():
|
10 |
+
if 'distutils' not in sys.modules:
|
11 |
+
return
|
12 |
+
if is_pypy and sys.version_info < (3, 7):
|
13 |
+
# PyPy for 3.6 unconditionally imports distutils, so bypass the warning
|
14 |
+
# https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
|
15 |
+
return
|
16 |
+
import warnings
|
17 |
+
|
18 |
+
warnings.warn(
|
19 |
+
"Distutils was imported before Setuptools, but importing Setuptools "
|
20 |
+
"also replaces the `distutils` module in `sys.modules`. This may lead "
|
21 |
+
"to undesirable behaviors or errors. To avoid these issues, avoid "
|
22 |
+
"using distutils directly, ensure that setuptools is installed in the "
|
23 |
+
"traditional way (e.g. not an editable install), and/or make sure "
|
24 |
+
"that setuptools is always imported before distutils."
|
25 |
+
)
|
26 |
+
|
27 |
+
|
28 |
+
def clear_distutils():
|
29 |
+
if 'distutils' not in sys.modules:
|
30 |
+
return
|
31 |
+
import warnings
|
32 |
+
|
33 |
+
warnings.warn("Setuptools is replacing distutils.")
|
34 |
+
mods = [
|
35 |
+
name
|
36 |
+
for name in sys.modules
|
37 |
+
if name == "distutils" or name.startswith("distutils.")
|
38 |
+
]
|
39 |
+
for name in mods:
|
40 |
+
del sys.modules[name]
|
41 |
+
|
42 |
+
|
43 |
+
def enabled():
|
44 |
+
"""
|
45 |
+
Allow selection of distutils by environment variable.
|
46 |
+
"""
|
47 |
+
which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'local')
|
48 |
+
return which == 'local'
|
49 |
+
|
50 |
+
|
51 |
+
def ensure_local_distutils():
|
52 |
+
import importlib
|
53 |
+
|
54 |
+
clear_distutils()
|
55 |
+
|
56 |
+
# With the DistutilsMetaFinder in place,
|
57 |
+
# perform an import to cause distutils to be
|
58 |
+
# loaded from setuptools._distutils. Ref #2906.
|
59 |
+
with shim():
|
60 |
+
importlib.import_module('distutils')
|
61 |
+
|
62 |
+
# check that submodules load as expected
|
63 |
+
core = importlib.import_module('distutils.core')
|
64 |
+
assert '_distutils' in core.__file__, core.__file__
|
65 |
+
assert 'setuptools._distutils.log' not in sys.modules
|
66 |
+
|
67 |
+
|
68 |
+
def do_override():
|
69 |
+
"""
|
70 |
+
Ensure that the local copy of distutils is preferred over stdlib.
|
71 |
+
|
72 |
+
See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
|
73 |
+
for more motivation.
|
74 |
+
"""
|
75 |
+
if enabled():
|
76 |
+
warn_distutils_present()
|
77 |
+
ensure_local_distutils()
|
78 |
+
|
79 |
+
|
80 |
+
class _TrivialRe:
|
81 |
+
def __init__(self, *patterns):
|
82 |
+
self._patterns = patterns
|
83 |
+
|
84 |
+
def match(self, string):
|
85 |
+
return all(pat in string for pat in self._patterns)
|
86 |
+
|
87 |
+
|
88 |
+
class DistutilsMetaFinder:
|
89 |
+
def find_spec(self, fullname, path, target=None):
|
90 |
+
# optimization: only consider top level modules and those
|
91 |
+
# found in the CPython test suite.
|
92 |
+
if path is not None and not fullname.startswith('test.'):
|
93 |
+
return
|
94 |
+
|
95 |
+
method_name = 'spec_for_{fullname}'.format(**locals())
|
96 |
+
method = getattr(self, method_name, lambda: None)
|
97 |
+
return method()
|
98 |
+
|
99 |
+
def spec_for_distutils(self):
|
100 |
+
if self.is_cpython():
|
101 |
+
return
|
102 |
+
|
103 |
+
import importlib
|
104 |
+
import importlib.abc
|
105 |
+
import importlib.util
|
106 |
+
|
107 |
+
try:
|
108 |
+
mod = importlib.import_module('setuptools._distutils')
|
109 |
+
except Exception:
|
110 |
+
# There are a couple of cases where setuptools._distutils
|
111 |
+
# may not be present:
|
112 |
+
# - An older Setuptools without a local distutils is
|
113 |
+
# taking precedence. Ref #2957.
|
114 |
+
# - Path manipulation during sitecustomize removes
|
115 |
+
# setuptools from the path but only after the hook
|
116 |
+
# has been loaded. Ref #2980.
|
117 |
+
# In either case, fall back to stdlib behavior.
|
118 |
+
return
|
119 |
+
|
120 |
+
class DistutilsLoader(importlib.abc.Loader):
|
121 |
+
def create_module(self, spec):
|
122 |
+
mod.__name__ = 'distutils'
|
123 |
+
return mod
|
124 |
+
|
125 |
+
def exec_module(self, module):
|
126 |
+
pass
|
127 |
+
|
128 |
+
return importlib.util.spec_from_loader(
|
129 |
+
'distutils', DistutilsLoader(), origin=mod.__file__
|
130 |
+
)
|
131 |
+
|
132 |
+
@staticmethod
|
133 |
+
def is_cpython():
|
134 |
+
"""
|
135 |
+
Suppress supplying distutils for CPython (build and tests).
|
136 |
+
Ref #2965 and #3007.
|
137 |
+
"""
|
138 |
+
return os.path.isfile('pybuilddir.txt')
|
139 |
+
|
140 |
+
def spec_for_pip(self):
|
141 |
+
"""
|
142 |
+
Ensure stdlib distutils when running under pip.
|
143 |
+
See pypa/pip#8761 for rationale.
|
144 |
+
"""
|
145 |
+
if sys.version_info >= (3, 12) or self.pip_imported_during_build():
|
146 |
+
return
|
147 |
+
clear_distutils()
|
148 |
+
self.spec_for_distutils = lambda: None
|
149 |
+
|
150 |
+
@classmethod
|
151 |
+
def pip_imported_during_build(cls):
|
152 |
+
"""
|
153 |
+
Detect if pip is being imported in a build script. Ref #2355.
|
154 |
+
"""
|
155 |
+
import traceback
|
156 |
+
|
157 |
+
return any(
|
158 |
+
cls.frame_file_is_setup(frame) for frame, line in traceback.walk_stack(None)
|
159 |
+
)
|
160 |
+
|
161 |
+
@staticmethod
|
162 |
+
def frame_file_is_setup(frame):
|
163 |
+
"""
|
164 |
+
Return True if the indicated frame suggests a setup.py file.
|
165 |
+
"""
|
166 |
+
# some frames may not have __file__ (#2940)
|
167 |
+
return frame.f_globals.get('__file__', '').endswith('setup.py')
|
168 |
+
|
169 |
+
def spec_for_sensitive_tests(self):
|
170 |
+
"""
|
171 |
+
Ensure stdlib distutils when running select tests under CPython.
|
172 |
+
|
173 |
+
python/cpython#91169
|
174 |
+
"""
|
175 |
+
clear_distutils()
|
176 |
+
self.spec_for_distutils = lambda: None
|
177 |
+
|
178 |
+
sensitive_tests = (
|
179 |
+
[
|
180 |
+
'test.test_distutils',
|
181 |
+
'test.test_peg_generator',
|
182 |
+
'test.test_importlib',
|
183 |
+
]
|
184 |
+
if sys.version_info < (3, 10)
|
185 |
+
else [
|
186 |
+
'test.test_distutils',
|
187 |
+
]
|
188 |
+
)
|
189 |
+
|
190 |
+
|
191 |
+
for name in DistutilsMetaFinder.sensitive_tests:
|
192 |
+
setattr(
|
193 |
+
DistutilsMetaFinder,
|
194 |
+
f'spec_for_{name}',
|
195 |
+
DistutilsMetaFinder.spec_for_sensitive_tests,
|
196 |
+
)
|
197 |
+
|
198 |
+
|
199 |
+
DISTUTILS_FINDER = DistutilsMetaFinder()
|
200 |
+
|
201 |
+
|
202 |
+
def add_shim():
|
203 |
+
DISTUTILS_FINDER in sys.meta_path or insert_shim()
|
204 |
+
|
205 |
+
|
206 |
+
class shim:
|
207 |
+
def __enter__(self):
|
208 |
+
insert_shim()
|
209 |
+
|
210 |
+
def __exit__(self, exc, value, tb):
|
211 |
+
_remove_shim()
|
212 |
+
|
213 |
+
|
214 |
+
def insert_shim():
|
215 |
+
sys.meta_path.insert(0, DISTUTILS_FINDER)
|
216 |
+
|
217 |
+
|
218 |
+
def _remove_shim():
|
219 |
+
try:
|
220 |
+
sys.meta_path.remove(DISTUTILS_FINDER)
|
221 |
+
except ValueError:
|
222 |
+
pass
|
223 |
+
|
224 |
+
|
225 |
+
if sys.version_info < (3, 12):
|
226 |
+
# DistutilsMetaFinder can only be disabled in Python < 3.12 (PEP 632)
|
227 |
+
remove_shim = _remove_shim
|
mynewshinyroop/Lib/site-packages/_distutils_hack/override.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
__import__('_distutils_hack').do_override()
|
mynewshinyroop/Lib/site-packages/_virtualenv.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:69ac3d8f27e679c81b94ab30b3b56e9cd138219b1ba94a1fa3606d5a76a1433d
|
3 |
+
size 18
|
mynewshinyroop/Lib/site-packages/_virtualenv.py
ADDED
@@ -0,0 +1,102 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""Patches that are applied at runtime to the virtual environment."""
|
2 |
+
|
3 |
+
from __future__ import annotations
|
4 |
+
|
5 |
+
import os
|
6 |
+
import sys
|
7 |
+
from contextlib import suppress
|
8 |
+
|
9 |
+
VIRTUALENV_PATCH_FILE = os.path.join(__file__)
|
10 |
+
|
11 |
+
|
12 |
+
def patch_dist(dist):
|
13 |
+
"""
|
14 |
+
Distutils allows user to configure some arguments via a configuration file:
|
15 |
+
https://docs.python.org/3/install/index.html#distutils-configuration-files.
|
16 |
+
|
17 |
+
Some of this arguments though don't make sense in context of the virtual environment files, let's fix them up.
|
18 |
+
""" # noqa: D205
|
19 |
+
# we cannot allow some install config as that would get packages installed outside of the virtual environment
|
20 |
+
old_parse_config_files = dist.Distribution.parse_config_files
|
21 |
+
|
22 |
+
def parse_config_files(self, *args, **kwargs):
|
23 |
+
result = old_parse_config_files(self, *args, **kwargs)
|
24 |
+
install = self.get_option_dict("install")
|
25 |
+
|
26 |
+
if "prefix" in install: # the prefix governs where to install the libraries
|
27 |
+
install["prefix"] = VIRTUALENV_PATCH_FILE, os.path.abspath(sys.prefix)
|
28 |
+
for base in ("purelib", "platlib", "headers", "scripts", "data"):
|
29 |
+
key = f"install_{base}"
|
30 |
+
if key in install: # do not allow global configs to hijack venv paths
|
31 |
+
install.pop(key, None)
|
32 |
+
return result
|
33 |
+
|
34 |
+
dist.Distribution.parse_config_files = parse_config_files
|
35 |
+
|
36 |
+
|
37 |
+
# Import hook that patches some modules to ignore configuration values that break package installation in case
|
38 |
+
# of virtual environments.
|
39 |
+
_DISTUTILS_PATCH = "distutils.dist", "setuptools.dist"
|
40 |
+
# https://docs.python.org/3/library/importlib.html#setting-up-an-importer
|
41 |
+
|
42 |
+
|
43 |
+
class _Finder:
|
44 |
+
"""A meta path finder that allows patching the imported distutils modules."""
|
45 |
+
|
46 |
+
fullname = None
|
47 |
+
|
48 |
+
# lock[0] is threading.Lock(), but initialized lazily to avoid importing threading very early at startup,
|
49 |
+
# because there are gevent-based applications that need to be first to import threading by themselves.
|
50 |
+
# See https://github.com/pypa/virtualenv/issues/1895 for details.
|
51 |
+
lock = [] # noqa: RUF012
|
52 |
+
|
53 |
+
def find_spec(self, fullname, path, target=None): # noqa: ARG002
|
54 |
+
if fullname in _DISTUTILS_PATCH and self.fullname is None:
|
55 |
+
# initialize lock[0] lazily
|
56 |
+
if len(self.lock) == 0:
|
57 |
+
import threading
|
58 |
+
|
59 |
+
lock = threading.Lock()
|
60 |
+
# there is possibility that two threads T1 and T2 are simultaneously running into find_spec,
|
61 |
+
# observing .lock as empty, and further going into hereby initialization. However due to the GIL,
|
62 |
+
# list.append() operation is atomic and this way only one of the threads will "win" to put the lock
|
63 |
+
# - that every thread will use - into .lock[0].
|
64 |
+
# https://docs.python.org/3/faq/library.html#what-kinds-of-global-value-mutation-are-thread-safe
|
65 |
+
self.lock.append(lock)
|
66 |
+
|
67 |
+
from functools import partial
|
68 |
+
from importlib.util import find_spec
|
69 |
+
|
70 |
+
with self.lock[0]:
|
71 |
+
self.fullname = fullname
|
72 |
+
try:
|
73 |
+
spec = find_spec(fullname, path)
|
74 |
+
if spec is not None:
|
75 |
+
# https://www.python.org/dev/peps/pep-0451/#how-loading-will-work
|
76 |
+
is_new_api = hasattr(spec.loader, "exec_module")
|
77 |
+
func_name = "exec_module" if is_new_api else "load_module"
|
78 |
+
old = getattr(spec.loader, func_name)
|
79 |
+
func = self.exec_module if is_new_api else self.load_module
|
80 |
+
if old is not func:
|
81 |
+
with suppress(AttributeError): # C-Extension loaders are r/o such as zipimporter with <3.7
|
82 |
+
setattr(spec.loader, func_name, partial(func, old))
|
83 |
+
return spec
|
84 |
+
finally:
|
85 |
+
self.fullname = None
|
86 |
+
return None
|
87 |
+
|
88 |
+
@staticmethod
|
89 |
+
def exec_module(old, module):
|
90 |
+
old(module)
|
91 |
+
if module.__name__ in _DISTUTILS_PATCH:
|
92 |
+
patch_dist(module)
|
93 |
+
|
94 |
+
@staticmethod
|
95 |
+
def load_module(old, name):
|
96 |
+
module = old(name)
|
97 |
+
if module.__name__ in _DISTUTILS_PATCH:
|
98 |
+
patch_dist(module)
|
99 |
+
return module
|
100 |
+
|
101 |
+
|
102 |
+
sys.meta_path.insert(0, _Finder())
|