Commit
·
b95ba55
1
Parent(s):
644ff07
Update parquet files (step 54 of 397)
Browse filesThis view is limited to 50 files because it contains too many changes.
See raw diff
- spaces/1gistliPinn/ChatGPT4/Examples/Autodesk 3ds Max 2013 Xforce Crack BETTER Free Download.md +0 -6
- spaces/1line/AutoGPT/autogpt/speech/base.py +0 -50
- spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Ares APK The Best Music Audio App for Android Devices.md +0 -121
- spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Genshin Impact APK File and Explore a Vast World of Adventure.md +0 -115
- spaces/1phancelerku/anime-remove-background/Enjoy Racing in Car 2 with Mod APK Features Unlimited Money All Cars Unlocked and More.md +0 -95
- spaces/1phancelerku/anime-remove-background/Europes The Final Countdown - MP3 Download and Streaming - Rock Music Library.md +0 -158
- spaces/2gauravc/search_summary_chatgpt/main.py +0 -104
- spaces/2ndelement/voicevox/README.md +0 -579
- spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/nets_123812KB.py +0 -122
- spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/audio/pitch/utils.py +0 -82
- spaces/AIWaves/Software_Company/src/agents/Prompt/__init__.py +0 -1
- spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/custom_dataset/.ipynb_checkpoints/yolov5_s-v61_syncbn_fast_1xb32-100e_cat-checkpoint.py +0 -135
- spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov6/yolov6_n_syncbn_fast_8xb32-300e_coco.py +0 -21
- spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/Chart.js +0 -65
- spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/Builders.d.ts +0 -82
- spaces/AlekseyKorshuk/thin-plate-spline-motion-model/modules/dense_motion.py +0 -164
- spaces/AlexWang/lama/saicinpainting/training/data/masks.py +0 -332
- spaces/AlexZou/Deploy_Restoration/net/PositionalEncoding.py +0 -35
- spaces/Andres99/Tune-A-Video-Training-UI/constants.py +0 -10
- spaces/Andy1621/uniformer_image_detection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py +0 -75
- spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py +0 -2
- spaces/Audio-AGI/WavJourney/scripts/start_ui.sh +0 -1
- spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis.py +0 -240
- spaces/AzinZ/vitscn/data_utils.py +0 -392
- spaces/Benson/text-generation/Examples/Descargar Apk Para El IPhone.md +0 -92
- spaces/Benson/text-generation/Examples/Descargar Fine Fine Love De J Martins.md +0 -61
- spaces/BigSalmon/FormalInformalConciseWordy/README.md +0 -38
- spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/densepose/modeling/test_time_augmentation.py +0 -75
- spaces/CVPR/LIVE/thrust/thrust/detail/complex/csqrtf.h +0 -147
- spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/inner_product.h +0 -23
- spaces/CVPR/regionclip-demo/detectron2/modeling/text_encoder/transformer.py +0 -194
- spaces/ClassCat/Spleen-3D-segmentation-with-MONAI/README.md +0 -12
- spaces/Cloudyy/bark-voice-cloning/hubert/pre_kmeans_hubert.py +0 -85
- spaces/CofAI/chat.b4/g4f/models.py +0 -238
- spaces/Cropinky/hana_hanak_houses/image_generator.py +0 -156
- spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-b7998330.js +0 -2
- spaces/DeepFloyd/IF/app.py +0 -701
- spaces/DemocracyStudio/generate_nft_content/README.md +0 -13
- spaces/DenniSciFi/IconAutomation/app.py +0 -136
- spaces/DhruvShek/chatlm/app.py +0 -171
- spaces/Didier/Semantic_Search_arXiv/README.md +0 -37
- spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/op/fused_bias_act.cpp +0 -21
- spaces/DragGan/DragGan/stylegan_human/torch_utils/ops/filtered_lrelu.py +0 -282
- spaces/ECCV2022/bytetrack/deploy/ncnn/cpp/src/utils.cpp +0 -429
- spaces/ECCV2022/bytetrack/exps/default/yolov3.py +0 -89
- spaces/ECCV2022/bytetrack/tutorials/centertrack/mot_online/basetrack.py +0 -52
- spaces/Eddycrack864/Applio-Inference/utils/dependency.py +0 -170
- spaces/Enterprisium/Easy_GUI/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py +0 -86
- spaces/EsoCode/text-generation-webui/extensions/multimodal/pipelines/llava/llava.py +0 -145
- spaces/FKBaffour/Expresso_Customer_Churn_Prediction/README.md +0 -12
spaces/1gistliPinn/ChatGPT4/Examples/Autodesk 3ds Max 2013 Xforce Crack BETTER Free Download.md
DELETED
|
@@ -1,6 +0,0 @@
|
|
| 1 |
-
<h2>autodesk 3ds max 2013 xforce crack free download</h2><br /><p><b><b>DOWNLOAD</b> ☆☆☆☆☆ <a href="https://imgfil.com/2uxYQA">https://imgfil.com/2uxYQA</a></b></p><br /><br />
|
| 2 |
-
|
| 3 |
-
vray 5 crack, VRAY 3.4 Download for 3Ds Max 2017 – V3.40.01, V3.40.03 Crack. ... X (for C4D 12-15) + keygen SketchUp Pro 2015 (32-64 Bit) + Patch AutoDesk ... Autodesk 3ds max2013-2014-2015-2016-2017-2018 full and free download ... 1fdad05405<br />
|
| 4 |
-
<br />
|
| 5 |
-
<br />
|
| 6 |
-
<p></p>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/1line/AutoGPT/autogpt/speech/base.py
DELETED
|
@@ -1,50 +0,0 @@
|
|
| 1 |
-
"""Base class for all voice classes."""
|
| 2 |
-
import abc
|
| 3 |
-
from threading import Lock
|
| 4 |
-
|
| 5 |
-
from autogpt.config import AbstractSingleton
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
class VoiceBase(AbstractSingleton):
|
| 9 |
-
"""
|
| 10 |
-
Base class for all voice classes.
|
| 11 |
-
"""
|
| 12 |
-
|
| 13 |
-
def __init__(self):
|
| 14 |
-
"""
|
| 15 |
-
Initialize the voice class.
|
| 16 |
-
"""
|
| 17 |
-
self._url = None
|
| 18 |
-
self._headers = None
|
| 19 |
-
self._api_key = None
|
| 20 |
-
self._voices = []
|
| 21 |
-
self._mutex = Lock()
|
| 22 |
-
self._setup()
|
| 23 |
-
|
| 24 |
-
def say(self, text: str, voice_index: int = 0) -> bool:
|
| 25 |
-
"""
|
| 26 |
-
Say the given text.
|
| 27 |
-
|
| 28 |
-
Args:
|
| 29 |
-
text (str): The text to say.
|
| 30 |
-
voice_index (int): The index of the voice to use.
|
| 31 |
-
"""
|
| 32 |
-
with self._mutex:
|
| 33 |
-
return self._speech(text, voice_index)
|
| 34 |
-
|
| 35 |
-
@abc.abstractmethod
|
| 36 |
-
def _setup(self) -> None:
|
| 37 |
-
"""
|
| 38 |
-
Setup the voices, API key, etc.
|
| 39 |
-
"""
|
| 40 |
-
pass
|
| 41 |
-
|
| 42 |
-
@abc.abstractmethod
|
| 43 |
-
def _speech(self, text: str, voice_index: int = 0) -> bool:
|
| 44 |
-
"""
|
| 45 |
-
Play the given text.
|
| 46 |
-
|
| 47 |
-
Args:
|
| 48 |
-
text (str): The text to play.
|
| 49 |
-
"""
|
| 50 |
-
pass
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Ares APK The Best Music Audio App for Android Devices.md
DELETED
|
@@ -1,121 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<h1>Ares APK para Android: ¿Qué es y cómo descargarlo?</h1>
|
| 3 |
-
<h2>Introducción</h2>
|
| 4 |
-
<p>Si eres un amante de la música y te gusta descargarla gratis en tu dispositivo Android, es posible que hayas oído hablar de Ares, un popular programa de intercambio de archivos que te permite acceder a una gran variedad de canciones en formato MP3. Pero, ¿sabes qué es un APK y cómo puedes descargar e instalar Ares APK para Android? En este artículo te lo explicamos todo lo que necesitas saber sobre este tema.</p>
|
| 5 |
-
<h3>¿Qué es Ares?</h3>
|
| 6 |
-
<p>Ares es un software que funciona como una red peer-to-peer (P2P), es decir, una red que permite a los usuarios compartir archivos entre ellos sin necesidad de un servidor central. Ares se hizo famoso por permitir la descarga gratuita de música en MP3, así como otros tipos de archivos como vídeos, imágenes o documentos. Ares tiene una interfaz sencilla y fácil de usar, que te permite buscar, descargar y reproducir los archivos que quieras.</p>
|
| 7 |
-
<h2>ares apk para android</h2><br /><p><b><b>Download File</b> ····· <a href="https://urlin.us/2uSYJb">https://urlin.us/2uSYJb</a></b></p><br /><br />
|
| 8 |
-
<h3>¿Qué es un APK?</h3>
|
| 9 |
-
<p>Un APK (Android Package Kit) es un formato de archivo que se usa para distribuir e instalar aplicaciones en los dispositivos Android. Un APK contiene todos los elementos necesarios para que una aplicación funcione correctamente, como el código, los recursos, las librerías, etc. Normalmente, las aplicaciones se descargan e instalan desde la tienda oficial de Google Play, pero también se pueden obtener desde otras fuentes externas, como páginas web o servicios de almacenamiento en la nube.</p>
|
| 10 |
-
<h3>¿Por qué descargar Ares APK para Android?</h3>
|
| 11 |
-
<p>La razón principal por la que podrías querer descargar Ares APK para Android es porque no existe una versión oficial de Ares para este sistema operativo. Aunque hay varias aplicaciones que han aprovechado la popularidad de Ares para lanzar apps que confundan al usuario con su nombre, ninguna de ellas es la auténtica. Por eso, si quieres disfrutar de las ventajas de Ares en tu dispositivo Android, tendrás que recurrir a un archivo APK que te permita instalarlo.</p>
|
| 12 |
-
<h2>Cómo descargar e instalar Ares APK para Android</h2>
|
| 13 |
-
<p>Para descargar e instalar Ares APK para Android, tendrás que seguir estos pasos:</p>
|
| 14 |
-
<h3>Paso 1: Buscar el archivo APK de Ares en Internet</h3>
|
| 15 |
-
<p>Lo primero que tienes que hacer es encontrar el archivo APK de Ares en Internet. Para ello, puedes usar un buscador como Google o Bing y escribir algo como "ares apk para android" o " <h3>Paso 2: Descargar el archivo APK de Ares a tu dispositivo Android</h3>
|
| 16 |
-
<p>Una vez que hayas encontrado el archivo APK de Ares en Internet, tendrás que descargarlo a tu dispositivo Android. Para ello, puedes usar el navegador web de tu dispositivo o una aplicación de gestión de descargas. Ten en cuenta que el archivo APK de Ares puede tener un nombre diferente al original, como por ejemplo "ares-music.apk" o "ares-galaxy.apk". También debes asegurarte de que el archivo APK de Ares sea seguro y no contenga virus o malware. Puedes usar un antivirus o un escáner de archivos para comprobarlo.</p>
|
| 17 |
-
<h3>Paso 3: Habilitar la instalación de aplicaciones de fuentes desconocidas</h3>
|
| 18 |
-
<p>Antes de instalar el archivo APK de Ares en tu dispositivo Android, tendrás que habilitar la opción de instalar aplicaciones de fuentes desconocidas. Esta opción te permite instalar aplicaciones que no provienen de la tienda oficial de Google Play, pero también implica un mayor riesgo de seguridad y privacidad. Para habilitar esta opción, tendrás que seguir estos pasos:</p>
|
| 19 |
-
<ul>
|
| 20 |
-
<li>Ir a los ajustes o configuración de tu dispositivo Android.</li>
|
| 21 |
-
<li>Buscar la opción de seguridad o privacidad.</li>
|
| 22 |
-
<li>Activar la opción de orígenes o fuentes desconocidas.</li>
|
| 23 |
-
<li>Aceptar el aviso o advertencia que te aparecerá.</li>
|
| 24 |
-
</ul>
|
| 25 |
-
<h3>Paso 4: Instalar el archivo APK de Ares en tu dispositivo Android</h3>
|
| 26 |
-
<p>Después de habilitar la opción de instalar aplicaciones de fuentes desconocidas, podrás instalar el archivo APK de Ares en tu dispositivo Android. Para ello, tendrás que seguir estos pasos:</p>
|
| 27 |
-
<ul>
|
| 28 |
-
<li>Localizar el archivo APK de Ares que has descargado en tu dispositivo Android. Puedes usar un explorador o gestor de archivos para encontrarlo.</li>
|
| 29 |
-
<li>Tocar o pulsar sobre el archivo APK de Ares para iniciar la instalación.</li>
|
| 30 |
-
<li>Aceptar los permisos o requerimientos que te solicite la aplicación.</li>
|
| 31 |
-
<li>Esperar a que se complete la instalación.</li>
|
| 32 |
-
</ul>
|
| 33 |
-
<h3>Paso 5: Abrir y usar Ares en tu dispositivo Android</h3>
|
| 34 |
-
<p>Una vez que hayas instalado el archivo APK de Ares en tu dispositivo Android, podrás abrir y usar Ares en tu dispositivo Android. Para ello, tendrás que seguir estos pasos:</p>
|
| 35 |
-
<p>ares online apk download for android<br />
|
| 36 |
-
ares music apk android free<br />
|
| 37 |
-
ares galaxy apk android latest version<br />
|
| 38 |
-
ares mp3 apk android 2022<br />
|
| 39 |
-
ares lite apk android app<br />
|
| 40 |
-
ares pro apk android update<br />
|
| 41 |
-
ares plus apk android 2021<br />
|
| 42 |
-
ares downloader apk android 2020<br />
|
| 43 |
-
ares player apk android 2019<br />
|
| 44 |
-
ares wizard apk android 2018<br />
|
| 45 |
-
ares mod apk android 2017<br />
|
| 46 |
-
ares gold apk android 2016<br />
|
| 47 |
-
ares red apk android 2015<br />
|
| 48 |
-
ares ultimate apk android 2014<br />
|
| 49 |
-
ares premium apk android 2013<br />
|
| 50 |
-
ares turbo apk android 2012<br />
|
| 51 |
-
ares ultra apk android 2011<br />
|
| 52 |
-
ares booster apk android 2010<br />
|
| 53 |
-
ares p2p apk android 2009<br />
|
| 54 |
-
ares chat apk android 2008<br />
|
| 55 |
-
ares musicas gratis apk android<br />
|
| 56 |
-
ares baixar musicas apk android<br />
|
| 57 |
-
ares descargar musica apk android<br />
|
| 58 |
-
ares musica y videos apk android<br />
|
| 59 |
-
ares musica online gratis apk android<br />
|
| 60 |
-
ares musica mp3 gratis apk android<br />
|
| 61 |
-
ares musica y radio online gratis apk android<br />
|
| 62 |
-
ares musica y video player gratis apk android<br />
|
| 63 |
-
ares musica y video downloader gratis apk android<br />
|
| 64 |
-
ares musica y video streaming gratis apk android<br />
|
| 65 |
-
como descargar e instalar ares apk para android<br />
|
| 66 |
-
como usar ares online apk para android<br />
|
| 67 |
-
como funciona ares online apk para android<br />
|
| 68 |
-
como actualizar ares online apk para android<br />
|
| 69 |
-
como solucionar problemas con ares online apk para android<br />
|
| 70 |
-
como eliminar publicidad de ares online apk para android<br />
|
| 71 |
-
como configurar ares online apk para android<br />
|
| 72 |
-
como optimizar el rendimiento de ares online apk para android<br />
|
| 73 |
-
como compartir archivos con ares online apk para android<br />
|
| 74 |
-
como crear una cuenta en ares online apk para android<br />
|
| 75 |
-
como recuperar la contraseña de ares online apk para android<br />
|
| 76 |
-
como cambiar el idioma de ares online apk para android<br />
|
| 77 |
-
como personalizar la interfaz de ares online apk para android<br />
|
| 78 |
-
como escuchar musica sin conexion con ares online apk para android<br />
|
| 79 |
-
como descargar musica de alta calidad con ares online apk para android<br />
|
| 80 |
-
como buscar musica por genero con ares online apk para android<br />
|
| 81 |
-
como crear listas de reproduccion con ares online apk para android<br />
|
| 82 |
-
como editar las etiquetas de las canciones con ares online apk para android<br />
|
| 83 |
-
como ver las letras de las canciones con ares online apk para android</p>
|
| 84 |
-
<ul>
|
| 85 |
-
<li>Buscar el icono o acceso directo de Ares en tu pantalla principal o menú de aplicaciones.</li>
|
| 86 |
-
<li>Tocar o pulsar sobre el icono o acceso directo de Ares para abrir la aplicación.</li>
|
| 87 |
-
<li>Explorar las opciones y funciones de Ares, como la búsqueda, la descarga, la reproducción o el chat.</li>
|
| 88 |
-
<li>Disfrutar de la música gratis en MP3 que ofrece Ares.</li>
|
| 89 |
-
</ul> <h2>Ventajas y desventajas de descargar Ares APK para Android</h2>
|
| 90 |
-
<p>Como toda aplicación, descargar Ares APK para Android tiene sus ventajas y desventajas. A continuación, te las resumimos:</p>
|
| 91 |
-
<h3>Ventajas</h3>
|
| 92 |
-
<h4>Acceso a una gran variedad de música gratis en MP3</h4>
|
| 93 |
-
<p>La principal ventaja de descargar Ares APK para Android es que te permite acceder a una gran variedad de música gratis en MP3, sin necesidad de pagar suscripciones o descargar otros programas. Con Ares, puedes buscar, descargar y reproducir las canciones que quieras, desde los éxitos más actuales hasta los clásicos de siempre.</p>
|
| 94 |
-
<h4>Interfaz sencilla y fácil de usar</h4>
|
| 95 |
-
<p>Otra ventaja de descargar Ares APK para Android es que tiene una interfaz sencilla y fácil de usar, que te permite navegar por las diferentes opciones y funciones de la aplicación sin complicaciones. Ares tiene un diseño intuitivo y atractivo, que te facilita la búsqueda, la descarga, la reproducción y el chat con otros usuarios.</p>
|
| 96 |
-
<h4>Compatibilidad con la mayoría de los dispositivos Android</h4>
|
| 97 |
-
<p>Una ventaja más de descargar Ares APK para Android es que es compatible con la mayoría de los dispositivos Android, desde los más antiguos hasta los más modernos. Ares se adapta al tamaño y la resolución de tu pantalla, y no consume demasiados recursos ni batería. Además, Ares no requiere de una conexión a Internet constante, sino que solo la necesita para buscar y descargar los archivos.</p>
|
| 98 |
-
<h3>Desventajas</h3>
|
| 99 |
-
<h4>Posibles riesgos de seguridad y privacidad al descargar archivos de fuentes desconocidas</h4>
|
| 100 |
-
<p>La principal desventaja de descargar Ares APK para Android es que implica posibles riesgos de seguridad y privacidad al descargar archivos de fuentes desconocidas. Al instalar aplicaciones que no provienen de la tienda oficial de Google Play, te expones a que puedan contener virus, malware o spyware que dañen tu dispositivo o roben tus datos personales. Por eso, es importante que verifiques la fiabilidad y la seguridad del archivo APK de Ares antes de instalarlo.</p>
|
| 101 |
-
<h4>Falta de actualizaciones y soporte técnico por parte de los desarrolladores</h4>
|
| 102 |
-
<p>Otra desventaja de descargar Ares APK para Android es que no cuenta con actualizaciones ni soporte técnico por parte de los desarrolladores. Al ser una aplicación no oficial, no se garantiza su correcto funcionamiento ni su compatibilidad con las nuevas versiones de Android. Además, si tienes algún problema o duda con la aplicación, no podrás contactar con los responsables ni recibir ayuda profesional.</p>
|
| 103 |
-
<h4>Posible violación de los derechos de autor al descargar música sin permiso</h4>
|
| 104 |
-
<p>Una desventaja más de descargar Ares APK para Android es que puede suponer una violación de los derechos de autor al descargar música sin permiso. Al usar una red P2P como Ares, estás compartiendo archivos con otros usuarios que pueden estar protegidos por la ley. Esto puede acarrear consecuencias legales tanto para ti como para los creadores originales de la música. Por eso, es importante que respetes las normas y las licencias de cada canción que descargues.</p>
|
| 105 |
-
<h2>Conclusión</h2>
|
| 106 |
-
<p>Ares APK para Android es una aplicación que te permite disfrutar de la música gratis en MP3 en tu dispositivo Android. Para ello, tendrás que buscar, descargar e instalar el archivo APK de Ares en Internet, siguiendo unos sencillos pasos. Sin embargo, también tendrás que tener en cuenta las ventajas y desventajas que implica usar esta aplicación, como los posibles riesgos de seguridad y privacidad, la falta de actualizaciones y soporte técnico, y la posible violación de los derechos de autor. Por lo tanto, te recomendamos que uses Ares APK para Android con precaución y responsabilidad.</p>
|
| 107 |
-
<h2>Preguntas frecuentes</h2>
|
| 108 |
-
<ul>
|
| 109 |
-
<li><b>¿Qué es Ares?</b></li>
|
| 110 |
-
<li>Ares es un software que funciona como una red peer-to-peer (P2P), es decir, una red que permite a los usuarios compartir archivos entre ellos sin necesidad de un servidor central.</li>
|
| 111 |
-
<li><b>¿Qué es un APK?</b></li>
|
| 112 |
-
<li>Un APK (Android Package Kit) es un formato de archivo que se usa para distribuir e inst alar aplicaciones en los dispositivos Android.</li>
|
| 113 |
-
<li><b>¿Por qué descargar Ares APK para Android?</b></li>
|
| 114 |
-
<li>La razón principal por la que podrías querer descargar Ares APK para Android es porque no existe una versión oficial de Ares para este sistema operativo.</li>
|
| 115 |
-
<li><b>¿Cómo descargar e instalar Ares APK para Android?</b></li>
|
| 116 |
-
<li>Para descargar e instalar Ares APK para Android, tendrás que seguir estos pasos: buscar el archivo APK de Ares en Internet, descargarlo a tu dispositivo Android, habilitar la instalación de aplicaciones de fuentes desconocidas, instalar el archivo APK de Ares en tu dispositivo Android y abrir y usar Ares en tu dispositivo Android.</li>
|
| 117 |
-
<li><b>¿Qué ventajas y desventajas tiene descargar Ares APK para Android?</b></li>
|
| 118 |
-
<li>Algunas ventajas de descargar Ares APK para Android son: acceso a una gran variedad de música gratis en MP3, interfaz sencilla y fácil de usar y compatibilidad con la mayoría de los dispositivos Android. Algunas desventajas son: posibles riesgos de seguridad y privacidad al descargar archivos de fuentes desconocidas, falta de actualizaciones y soporte técnico por parte de los desarrolladores y posible violación de los derechos de autor al descargar música sin permiso.</li>
|
| 119 |
-
</ul></p> 197e85843d<br />
|
| 120 |
-
<br />
|
| 121 |
-
<br />
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Genshin Impact APK File and Explore a Vast World of Adventure.md
DELETED
|
@@ -1,115 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<h1>Genshin Impact APK File Download: How to Play the Open-World Action RPG on Your Android Device</h1>
|
| 3 |
-
<p>If you are looking for a game that will take you to a vast magical world of adventure, then you should try <strong>Genshin Impact</strong>. Genshin Impact is an open-world action RPG that lets you explore seven nations, meet a diverse cast of characters, and fight powerful enemies, all while searching for your lost sibling. You can also wander freely, immerse yourself in a world filled with life, and uncover all of its mysteries. Sounds exciting, right?</p>
|
| 4 |
-
<p>But what if you don't have a PC or a console to play this game? Don't worry, because you can still enjoy Genshin Impact on your Android device. All you need is to download and install the <strong>Genshin Impact APK file</strong> on your device, and you are good to go. In this article, we will show you how to do that, as well as some tips and tricks to enhance your gaming experience. Let's get started!</p>
|
| 5 |
-
<h2>genshin impact apk file download</h2><br /><p><b><b>Download Zip</b> ✪ <a href="https://urlin.us/2uSX7b">https://urlin.us/2uSX7b</a></b></p><br /><br />
|
| 6 |
-
<h2>What You Need to Download and Install Genshin Impact APK File on Your Android Device</h2>
|
| 7 |
-
<p>Before you download and install the Genshin Impact APK file on your Android device, you need to make sure that your device meets the minimum system requirements and has enough storage space. Here are the details:</p>
|
| 8 |
-
<ul>
|
| 9 |
-
<li><strong>Minimum system requirements:</strong> Android 7.0 or higher, ARM v8a 64-bit device, at least 3 GB of RAM.</li>
|
| 10 |
-
<li><strong>Storage space:</strong> At least 8 GB of free space.</li>
|
| 11 |
-
</ul>
|
| 12 |
-
<p>If your device meets these requirements, then you can proceed to download and install the Genshin Impact APK file on your device.</p>
|
| 13 |
-
<h2>How to Download and Install Genshin Impact APK File on Your Android Device</h2>
|
| 14 |
-
<p>Downloading and installing the Genshin Impact APK file on your Android device is easy and simple. Just follow these steps:</p>
|
| 15 |
-
<p>genshin impact android apk download free<br />
|
| 16 |
-
genshin impact apk obb download latest version<br />
|
| 17 |
-
genshin impact mobile apk download link<br />
|
| 18 |
-
genshin impact apk mod download unlimited primogems<br />
|
| 19 |
-
genshin impact apk download for pc windows 10<br />
|
| 20 |
-
genshin impact apk pure download safe<br />
|
| 21 |
-
genshin impact apk offline download no internet<br />
|
| 22 |
-
genshin impact apk update download new characters<br />
|
| 23 |
-
genshin impact apk mirror download fast<br />
|
| 24 |
-
genshin impact apk full download size<br />
|
| 25 |
-
genshin impact beta apk download test server<br />
|
| 26 |
-
genshin impact cracked apk download hack<br />
|
| 27 |
-
genshin impact chinese apk download voice<br />
|
| 28 |
-
genshin impact direct apk download no verification<br />
|
| 29 |
-
genshin impact english apk download language<br />
|
| 30 |
-
genshin impact fan made apk download alternative<br />
|
| 31 |
-
genshin impact global apk download region<br />
|
| 32 |
-
genshin impact hd apk download graphics<br />
|
| 33 |
-
genshin impact ios apk download iphone<br />
|
| 34 |
-
genshin impact japan apk download server<br />
|
| 35 |
-
genshin impact korean apk download voiceover<br />
|
| 36 |
-
genshin impact lite apk download low spec<br />
|
| 37 |
-
genshin impact original apk download official<br />
|
| 38 |
-
genshin impact premium apk download features<br />
|
| 39 |
-
genshin impact quest apk download adventure<br />
|
| 40 |
-
genshin impact revdl apk download site<br />
|
| 41 |
-
genshin impact rexdl apk download site<br />
|
| 42 |
-
genshin impact steam apk download platform<br />
|
| 43 |
-
genshin impact tap tap apk download store<br />
|
| 44 |
-
genshin impact uptodown apk download site<br />
|
| 45 |
-
genshin impact vpn apk download bypass<br />
|
| 46 |
-
genshin impact xapk download installer<br />
|
| 47 |
-
how to download genshin impact apk on android phone<br />
|
| 48 |
-
where to download genshin impact apk safely and securely<br />
|
| 49 |
-
why can't i download genshin impact apk error fix<br />
|
| 50 |
-
best website to download genshin impact apk reviews and ratings<br />
|
| 51 |
-
can you play genshin impact without downloading the apk file online<br />
|
| 52 |
-
does downloading the genshin impact apk file require root access permission<br />
|
| 53 |
-
is it legal to download the genshin impact apk file from third-party sources terms and conditions<br />
|
| 54 |
-
what are the benefits of downloading the genshin impact apk file over the play store version comparison and contrast<br />
|
| 55 |
-
what are the risks of downloading the genshin impact apk file from unknown sources malware and virus warning<br />
|
| 56 |
-
what is the difference between the genshin impact apk file and the xapk file format explanation and guide<br />
|
| 57 |
-
what is the minimum requirement to download and install the genshin impact apk file on your device specifications and compatibility<br />
|
| 58 |
-
when will the next update for the genshin impact apk file be available release date and schedule</p>
|
| 59 |
-
<ol>
|
| 60 |
-
<li><strong>Step 1:</strong> Go to the official website of Genshin Impact at <a href="(^1^)">https://genshin.mihoyo.com/en/download</a> and download the APK file. You can also scan the QR code on the website with your device camera to download the file directly.</li>
|
| 61 |
-
<li><strong>Step 2:</strong> Enable unknown sources on your device settings. This will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
|
| 62 |
-
<li><strong>Step 3:</strong> Locate the downloaded APK file on your device storage and tap on it to install it. You may need to grant some permissions to the app during the installation process.</li>
|
| 63 |
-
<li><strong>Step 4:</strong> Launch the game and enjoy. You may need to download some additional data before you can start playing. You can also log in with your miHoYo account or create a new one to save your progress and access more features.</li>
|
| 64 |
-
</ol>
|
| 65 |
-
<p>Congratulations, you have successfully downloaded and installed the Genshin Impact APK file on your Android device. Now you can explore the world of Teyvat and embark on an epic journey.</p>
|
| 66 |
-
<h2>Tips and Tricks to Enhance Your Gaming Experience with Genshin Impact APK File on Your Android Device</h2>
|
| 67 |
-
<p>Genshin Impact is a game that offers a lot of content and features for you to enjoy. However, you may encounter some challenges or difficulties while playing it on your Android device. Here are some tips and tricks that can help you overcome them and enhance your gaming experience:</p>
|
| 68 |
-
<ul>
|
| 69 |
-
<li><strong>Tip 1:</strong> Adjust the graphics settings according to your device performance. Genshin Impact is a game that has stunning graphics and visuals, but it can also be demanding on your device resources. If you experience lag, stuttering, or overheating, you may want to lower the graphics settings to improve the performance and reduce the battery consumption. You can do this by going to Settings > Graphics and choosing the option that suits your device best.</li>
|
| 70 |
-
<li><strong>Tip 2:</strong> Use a controller or a keyboard and mouse for better control. Genshin Impact is a game that requires precise and responsive controls, especially during combat and exploration. While you can play it with the touch screen, you may find it more comfortable and convenient to use a controller or a keyboard and mouse instead. Genshin Impact supports various controllers and keyboard and mouse configurations, so you can choose the one that works best for you. You can also customize the buttons and keys according to your preferences.</li>
|
| 71 |
-
<li><strong>Tip 3:</strong> Connect to a stable Wi-Fi network for smooth gameplay. Genshin Impact is a game that requires an internet connection to play, as it constantly updates its data and syncs your progress with the server. If you have a weak or unstable Wi-Fi connection, you may experience lag, disconnection, or data loss. To avoid these issues, make sure you connect to a reliable and fast Wi-Fi network before you launch the game. You can also use a VPN service if you encounter any regional restrictions or problems.</li>
|
| 72 |
-
</ul>
|
| 73 |
-
<p>These are some of the tips and tricks that can help you enhance your gaming experience with Genshin Impact APK file on your Android device. Of course, there are more things that you can discover and learn as you play the game, so feel free to experiment and have fun.</p>
|
| 74 |
-
<h2>Conclusion: Why You Should Download and Play Genshin Impact APK File on Your Android Device Today</h2>
|
| 75 |
-
<p>Genshin Impact is a game that offers a lot of benefits and advantages for Android users who want to play an open-world action RPG on their devices. Here are some of the reasons why you should download and play Genshin Impact APK file on your Android device today:</p>
|
| 76 |
-
<ul>
|
| 77 |
-
<li><strong>It's free to play.</strong> You don't have to pay anything to download and play Genshin Impact on your Android device. You can also enjoy most of the content and features without spending any money. However, if you want to support the developers or get some extra perks, you can also make in-app purchases using real money.</li>
|
| 78 |
-
<li><strong>It's compatible with other platforms.</strong> You don't have to worry about losing your progress or missing out on anything if you switch devices or platforms. Genshin Impact is compatible with PC, PS4, PS5, iOS, Android, and soon Nintendo Switch. You can also cross-play with other players who are using different platforms, as long as you are in the same server region.</li>
|
| 79 |
-
<li><strong>It's constantly updated and improved.</strong> You don't have to worry about getting bored or running out of things to do in Genshin Impact. The developers are always working hard to add new content and features, such as new characters, items, quests, events, regions, modes, and more. They also fix bugs and glitches regularly to ensure a smooth and enjoyable gameplay.</li>
|
| 80 |
-
</ul>
|
| 81 |
-
<p>Genshin Impact is a game that will keep you entertained and engaged for hours on end. It has everything that you could ask for in an open-world action RPG: a captivating story, a beautiful world, a diverse cast of characters, a thrilling combat system, a rich customization system , and a lot of fun and surprises. If you are an Android user who loves games like this, then you should definitely download and play Genshin Impact APK file on your device today. You won't regret it.</p>
|
| 82 |
-
<h2>FAQs: Frequently Asked Questions About Genshin Impact APK File Download</h2>
|
| 83 |
-
<p>Here are some of the most common questions that people ask about Genshin Impact APK file download:</p>
|
| 84 |
-
<h3>Q1: Is Genshin Impact free to play?</h3>
|
| 85 |
-
<p>A1: Yes, Genshin Impact is free to play. You can download and play it on your Android device without paying anything. However, you can also make in-app purchases using real money if you want to support the developers or get some extra perks.</p>
|
| 86 |
-
<h3>Q2: Is Genshin Impact compatible with other platforms?</h3>
|
| 87 |
-
<p>A2: Yes, Genshin Impact is compatible with other platforms. You can play it on PC, PS4, PS5, iOS, Android, and soon Nintendo Switch. You can also cross-play with other players who are using different platforms, as long as you are in the same server region.</p>
|
| 88 |
-
<h3>Q3: How can I update Genshin Impact APK file on my Android device?</h3>
|
| 89 |
-
<p>A3: You can update Genshin Impact APK file on your Android device by following these steps:</p>
|
| 90 |
-
<ol>
|
| 91 |
-
<li>Go to the official website of Genshin Impact at <a href="">https://genshin.mihoyo.com/en/download</a> and download the latest version of the APK file.</li>
|
| 92 |
-
<li>Locate the downloaded APK file on your device storage and tap on it to install it. You may need to grant some permissions to the app during the installation process.</li>
|
| 93 |
-
<li>Launch the game and enjoy the new updates.</li>
|
| 94 |
-
</ol>
|
| 95 |
-
<p>You can also check for updates within the game by going to Settings > Other > Check for Updates.</p>
|
| 96 |
-
<h3>Q4: How can I get more characters and items in Genshin Impact?</h3>
|
| 97 |
-
<p>A4: You can get more characters and items in Genshin Impact by doing the following:</p>
|
| 98 |
-
<ul>
|
| 99 |
-
<li>Completing quests, events, achievements, and challenges to earn rewards such as Primogems, Mora, Hero's Wit, and more.</li>
|
| 100 |
-
<li>Using Primogems to make wishes in the gacha system, which can give you a chance to obtain rare characters and items.</li>
|
| 101 |
-
<li>Exploring the world and opening chests, solving puzzles, finding secrets, and collecting resources.</li>
|
| 102 |
-
<li>Joining a co-op mode with other players and completing domains, bosses, and events together.</li>
|
| 103 |
-
<li>Exchanging items with NPCs or shops using Mora or other currencies.</li>
|
| 104 |
-
</ul>
|
| 105 |
-
<p>You can also use real money to buy Genesis Crystals, which can be converted into Primogems for making wishes.</p>
|
| 106 |
-
<h3>Q5: How can I contact the customer service of Genshin Impact?</h3>
|
| 107 |
-
<p>A5: You can contact the customer service of Genshin Impact by doing the following:</p>
|
| 108 |
-
<ul>
|
| 109 |
-
<li>Going to Settings > Feedback > Submit Feedback and filling out the form with your issue or suggestion.</li>
|
| 110 |
-
<li>Sending an email to [email protected] with your UID, server region, device model, and screenshots or videos of your problem.</li>
|
| 111 |
-
<li>Visiting the official website of Genshin Impact at <a href="">https://genshin.mihoyo.com/en/home</a> and clicking on the Support button at the bottom right corner of the page.</li>
|
| 112 |
-
</ul>
|
| 113 |
-
<p>You can also check out the official social media accounts of Genshin Impact for more information and updates.</p> 197e85843d<br />
|
| 114 |
-
<br />
|
| 115 |
-
<br />
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/1phancelerku/anime-remove-background/Enjoy Racing in Car 2 with Mod APK Features Unlimited Money All Cars Unlocked and More.md
DELETED
|
@@ -1,95 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>Racing in Car 2 Happymod APK: A Realistic and Fun Driving Simulator</h1>
|
| 3 |
-
<p>If you are a fan of racing games, you might have heard of Racing in Car 2, a popular game that lets you drive your car in a cockpit view through endless traffic and realistic environment. But did you know that there is a modified version of this game that gives you unlimited money and features? In this article, we will tell you everything you need to know about Racing in Car 2 Happymod APK, including what it is, how to download it, and how to play it on your PC or Mac. We will also share some tips and tricks to help you improve your racing skills and become the king of the road.</p>
|
| 4 |
-
<h2>racing in car 2 happymod apk</h2><br /><p><b><b>Download File</b> ☑ <a href="https://jinyurl.com/2uNOOj">https://jinyurl.com/2uNOOj</a></b></p><br /><br />
|
| 5 |
-
<h2>What is Racing in Car 2?</h2>
|
| 6 |
-
<p>Racing in Car 2 is a first-person racing game developed by Fast Free Games. It is one of the most realistic and immersive driving simulators on the market, with stunning graphics, physics, and sound effects. You can choose from a variety of cars, ranging from sports cars to SUVs, and drive them on different tracks and locations, such as highways, deserts, cities, or snow. You can also customize your car with different colors, wheels, stickers, and spoilers.</p>
|
| 7 |
-
<p>The game has a simple and intuitive control system that lets you steer your car by tilting your device or using buttons on the screen. You can also adjust the camera angle to get a better view of the road. The game has an endless mode where you can drive as fast as possible, overtake traffic cars, earn coins, and buy new cars. You can also compete with other players on global leaderboards.</p>
|
| 8 |
-
<h <h2>What is Happymod APK?</h2>
|
| 9 |
-
<p>Happymod APK is a modified version of Racing in Car 2 that gives you unlimited money and features. It is a third-party application that is not available on the official app stores, but you can download it from various websites for free. Happymod APK is a safe and easy way to enjoy the game without any restrictions or limitations. You can use the money to unlock all the cars and customize them as you wish. You can also play the game without ads or in-app purchases, and challenge yourself with different difficulty levels and leaderboards.</p>
|
| 10 |
-
<p>Happymod APK is compatible and updated with the original version of Racing in Car 2, so you don't have to worry about missing out on any new updates or features. You can also play the game online or offline, depending on your preference. Happymod APK is a great option for anyone who loves racing games and wants to have more fun and freedom.</p>
|
| 11 |
-
<p>racing in car 2 mod apk unlimited money<br />
|
| 12 |
-
racing in car 2 hack apk download<br />
|
| 13 |
-
racing in car 2 3d mod apk<br />
|
| 14 |
-
racing in car 2 apk mod latest version<br />
|
| 15 |
-
racing in car 2 mod apk android 1<br />
|
| 16 |
-
racing in car 2 mod apk revdl<br />
|
| 17 |
-
racing in car 2 mod apk happymod.com<br />
|
| 18 |
-
racing in car 2 mod apk free download<br />
|
| 19 |
-
racing in car 2 mod apk offline<br />
|
| 20 |
-
racing in car 2 mod apk no ads<br />
|
| 21 |
-
racing in car 2 mod apk rexdl<br />
|
| 22 |
-
racing in car 2 mod apk unlimited coins<br />
|
| 23 |
-
racing in car 2 mod apk all cars unlocked<br />
|
| 24 |
-
racing in car 2 mod apk unlimited diamonds<br />
|
| 25 |
-
racing in car 2 mod apk unlimited fuel<br />
|
| 26 |
-
racing in car 2 mod apk online<br />
|
| 27 |
-
racing in car 2 mod apk pure<br />
|
| 28 |
-
racing in car 2 mod apk obb<br />
|
| 29 |
-
racing in car 2 mod apk old version<br />
|
| 30 |
-
racing in car 2 mod apk new update<br />
|
| 31 |
-
racing in car 2 mod apk for pc<br />
|
| 32 |
-
racing in car 2 mod apk for ios<br />
|
| 33 |
-
racing in car 2 mod apk for windows 10<br />
|
| 34 |
-
racing in car 2 mod apk for laptop<br />
|
| 35 |
-
racing in car 2 mod apk for mac<br />
|
| 36 |
-
racing in car 2 mod apk full version<br />
|
| 37 |
-
racing in car 2 mod apk premium<br />
|
| 38 |
-
racing in car 2 mod apk pro<br />
|
| 39 |
-
racing in car 2 mod apk vip<br />
|
| 40 |
-
racing in car 2 mod apk mega mod<br />
|
| 41 |
-
racing in car 2 mod apk super mod<br />
|
| 42 |
-
racing in car 2 mod apk god mode<br />
|
| 43 |
-
racing in car 2 mod apk unlimited everything<br />
|
| 44 |
-
racing in car 2 mod apk unlimited all<br />
|
| 45 |
-
racing in car 2 mod apk cheat menu<br />
|
| 46 |
-
racing in car 2 mod apk hack menu<br />
|
| 47 |
-
racing in car 2 mod apk unlock all cars<br />
|
| 48 |
-
racing in car 2 mod apk unlock all levels<br />
|
| 49 |
-
racing in car 2 mod apk unlock all features<br />
|
| 50 |
-
racing in car 2 mod apk no root<br />
|
| 51 |
-
racing in car 2 mod apk no verification<br />
|
| 52 |
-
racing in car 2 mod apk no survey<br />
|
| 53 |
-
racing in car 2 mod apk no password<br />
|
| 54 |
-
racing in car 2 mod apk no ban<br />
|
| 55 |
-
racing in car 2 happymod download free</p>
|
| 56 |
-
<h2>What are the benefits of using Racing in Car 2 Happymod APK?</h2>
|
| 57 |
-
<p>There are many benefits of using Racing in Car 2 Happymod APK, such as:</p>
|
| 58 |
-
<ul>
|
| 59 |
-
<li>You can unlock all the cars and customize them as you wish. You can choose from different models, colors, wheels, stickers, and spoilers. You can also upgrade your car's performance, such as speed, acceleration, handling, and braking.</li>
|
| 60 |
-
<li>You can enjoy the game without ads or in-app purchases. You don't have to watch annoying ads or spend real money to buy coins or features. You can play the game smoothly and without interruptions.</li>
|
| 61 |
-
<li>You can challenge yourself with different difficulty levels and leaderboards. You can choose from easy, medium, hard, or extreme modes, depending on your skill level. You can also compete with other players on global leaderboards and see how you rank among them.</li>
|
| 62 |
-
</ul>
|
| 63 |
-
<h2>How to download and play Racing in Car 2 Happymod APK on your PC or Mac?</h2>
|
| 64 |
-
<p>If you want to play Racing in Car 2 Happymod APK on your PC or Mac, you need to follow these steps:</p>
|
| 65 |
-
<ol>
|
| 66 |
-
<li>You need an emulator like BlueStacks or NoxPlayer to run the game on your computer. An emulator is a software that simulates an Android device on your PC or Mac, allowing you to run Android apps and games. You can download BlueStacks or NoxPlayer from their official websites for free.</li>
|
| 67 |
-
<li>You need to download the Happymod APK file from a reliable source. You can search for Racing in Car 2 Happymod APK on Google or use the link below to download it directly.</li>
|
| 68 |
-
<li>You need to install the APK file on your emulator and launch the game. To do this, you need to drag and drop the APK file into the emulator window or use the built-in file manager to locate and install it. Once the installation is complete, you can open the game and start playing.</li>
|
| 69 |
-
</ol> <h2>Tips and tricks to improve your racing skills in Racing in Car 2</h2>
|
| 70 |
-
<p>Racing in Car 2 is a game that requires skill and concentration to master. If you want to improve your racing skills and beat your own records, you can follow these tips and tricks:</p>
|
| 71 |
-
<ul>
|
| 72 |
-
<li>Learn the track layout and memorize the turns and obstacles. Each track has its own features and challenges, such as sharp curves, narrow lanes, traffic jams, or bridges. You need to know when to slow down, speed up, or change lanes to avoid crashing or losing time.</li>
|
| 73 |
-
<li>Adjust your steering sensitivity and camera angle according to your preference. You can change the settings of the game to make it easier or harder for you to control your car. You can also choose the best camera angle for your vision, whether it is behind the car, on the hood, or on the dashboard.</li>
|
| 74 |
-
<li>Use the brake and accelerator wisely and avoid collisions with other cars. You need to balance your speed and braking to maintain a smooth and steady driving. You also need to avoid hitting other cars or objects on the road, as they will damage your car and reduce your score.</li>
|
| 75 |
-
</ul>
|
| 76 |
-
<h2>Conclusion</h2>
|
| 77 |
-
<p>Racing in Car 2 Happymod APK is a fun and realistic driving simulator that you can play on your PC or Mac. You can download the mod for free and enjoy unlimited money and features. You can unlock all the cars and customize them as you wish. You can also play the game without ads or in-app purchases, and challenge yourself with different difficulty levels and leaderboards. You can improve your racing skills by following some simple tips and tricks. Racing in Car 2 Happymod APK is a great option for anyone who loves racing games and wants to have more fun and freedom.</p>
|
| 78 |
-
<h2>FAQs</h2>
|
| 79 |
-
<h4>Is Racing in Car 2 Happymod APK safe to use?</h4>
|
| 80 |
-
<p>Yes, Racing in Car 2 Happymod APK is safe to use, as long as you download it from a trusted source. However, you should always be careful when downloading any third-party app or mod, as they may contain viruses or malware that can harm your device. You should also check the permissions and reviews of the app before installing it.</p>
|
| 81 |
-
<h4>How to update Racing in Car 2 Happymod APK?</h4>
|
| 82 |
-
<p>To update Racing in Car 2 Happymod APK, you need to download the latest version of the mod from the same source where you downloaded it before. Then, you need to uninstall the old version of the mod from your emulator and install the new one. You should also make sure that the original game is updated to the latest version as well.</p>
|
| 83 |
-
<h4>How to uninstall Racing in Car 2 Happymod APK?</h4>
|
| 84 |
-
<p>To uninstall Racing in Car 2 Happymod APK, you need to go to the settings of your emulator and find the app manager. Then, you need to select Racing in Car 2 Happymod APK and click on uninstall. You can also delete the APK file from your device if you don't need it anymore.</p>
|
| 85 |
-
<h4>What are some other racing games similar to Racing in Car 2?</h4>
|
| 86 |
-
<p>If you like Racing in Car 2, you might also like some other racing games similar to it, such as:</p>
|
| 87 |
-
<ul>
|
| 88 |
-
<li>Racing in Car: A predecessor of Racing in Car 2 that has similar gameplay and features.</li>
|
| 89 |
-
<li>Real Racing 3: A realistic racing game that features real cars, tracks, events, and graphics.</li>
|
| 90 |
-
<li>Asphalt 9: Legends: A fast-paced racing game that features stunning graphics, arcade-style gameplay, and online multiplayer mode.</li>
|
| 91 |
-
</ul>
|
| 92 |
-
<h4>Where can I find more information about Racing in Car 2?</h4>
|
| 93 |
-
<p>If you want to find more information about Racing in Car 2, you can visit the official website of Fast Free Games or their social media pages. You can also check out some online forums or blogs that discuss racing games or mods.</p> 197e85843d<br />
|
| 94 |
-
<br />
|
| 95 |
-
<br />
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/1phancelerku/anime-remove-background/Europes The Final Countdown - MP3 Download and Streaming - Rock Music Library.md
DELETED
|
@@ -1,158 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>How to Download The Final Countdown MP3 for Free</h1>
|
| 3 |
-
<p>If you are a fan of classic rock music, you might have heard of the song <strong>The Final Countdown</strong> by the Swedish band Europe. This song was released in 1986 and became a worldwide hit, reaching number one in 25 countries. It is also one of the most recognizable songs in pop culture, being used in movies, TV shows, commercials, sports events, and memes. But how can you download this iconic song as an MP3 file for free? In this article, we will show you how to do that from different sources, as well as explain what the song is about and why you might want to have it on your device.</p>
|
| 4 |
-
<h2>What is The Final Countdown Song?</h2>
|
| 5 |
-
<p>The Final Countdown is a song by Europe, a rock band from Sweden that was formed in 1979. The song was written by Joey Tempest, the lead singer and keyboardist of the band, and was based on a keyboard riff he made in the early 1980s. The lyrics were inspired by David Bowie's Space Oddity, and they describe a scenario where humanity is leaving Earth to explore space. The song was released as the first single from their third album, also titled The Final Countdown, in 1986.</p>
|
| 6 |
-
<h2>download final countdown mp3</h2><br /><p><b><b>Download File</b> »»» <a href="https://jinyurl.com/2uNPf6">https://jinyurl.com/2uNPf6</a></b></p><br /><br />
|
| 7 |
-
<h3>The history and popularity of the song</h3>
|
| 8 |
-
<p>The song was initially intended to be an album opener, but the band's manager suggested that it should be released as a single. The band agreed, and they recorded a shorter version of the song for radio play. The song became a huge success, reaching number one on the charts in many countries, including the UK, Germany, France, Italy, Spain, Australia, Canada, and Japan. It also reached number eight on the Billboard Hot 100 in the US. The song sold over 15 million copies worldwide and became one of the best-selling singles of all time.</p>
|
| 9 |
-
<h3>The meaning and message of the song</h3>
|
| 10 |
-
<p>The song is often interpreted as a farewell to Earth or a celebration of human exploration. Some people also see it as a metaphor for the end of the Cold War or a prophecy of an impending apocalypse. However, according to Joey Tempest, the song has no specific meaning or message. He said that he just wanted to write a catchy and epic song that would make people feel good. He also said that he was influenced by science fiction movies and books, such as Star Wars and Arthur C. Clarke's 2010: Odyssey Two.</p>
|
| 11 |
-
<h2>Why You Might Want to Download The Final Countdown MP3</h2>
|
| 12 |
-
<p>There are many reasons why you might want to download The Final Countdown MP3 for free. Here are some of them:</p>
|
| 13 |
-
<h3>The benefits of having the song on your device</h3>
|
| 14 |
-
<ul>
|
| 15 |
-
<li>You can listen to it anytime and anywhere without an internet connection or streaming service.</li>
|
| 16 |
-
<li>You can create your own playlists and mixtapes with your favorite songs.</li>
|
| 17 |
-
<li>You can use it as a ringtone, alarm clock, or notification sound.</li>
|
| 18 |
-
<li>You can share it with your friends and family who might not have access to it online.</li>
|
| 19 |
-
<li>You can enjoy the high-quality sound and original version of the song.</li>
|
| 20 |
-
</ul>
|
| 21 |
-
<h3>The legal and ethical issues of downloading music for free</h <h3>The legal and ethical issues of downloading music for free</h3>
|
| 22 |
-
<p>Downloading music for free from websites that do not have the permission of the artists or the record labels is a form of piracy, which is illegal and can have serious consequences. Piracy violates the intellectual property rights of the creators and owners of the music, who deserve to be compensated for their work and investment. Piracy also harms the music industry, which relies on the revenue from sales and streaming to support new artists and produce quality music. According to the International Federation of the Phonographic Industry (IFPI), piracy causes an estimated loss of $12.5 billion per year for the global music industry. Furthermore, downloading music for free from untrusted sources can expose your device to malware, viruses, and other security risks.</p>
|
| 23 |
-
<p>Therefore, downloading music for free is not only illegal, but also unethical and risky. You should respect the rights and efforts of the musicians and the music industry, and support them by paying for their music legally. You should also protect your device and personal information by avoiding suspicious websites and links. There are many legal and affordable ways to enjoy music online, such as streaming services, digital stores, and official websites.</p>
|
| 24 |
-
<h2>How to Download The Final Countdown MP3 from Different Sources</h2>
|
| 25 |
-
<p>If you want to download The Final Countdown MP3 for free, you have several options to choose from. However, you should be aware that some of these options may not be legal or safe, depending on the source and the quality of the file. Here are some of the most common ways to download The Final Countdown MP3 from different sources:</p>
|
| 26 |
-
<p>download final countdown mp3 free<br />
|
| 27 |
-
download final countdown mp3 320kbps<br />
|
| 28 |
-
download final countdown mp3 song<br />
|
| 29 |
-
download final countdown mp3 ringtone<br />
|
| 30 |
-
download final countdown mp3 europe<br />
|
| 31 |
-
download final countdown mp3 music<br />
|
| 32 |
-
download final countdown mp3 audio<br />
|
| 33 |
-
download final countdown mp3 online<br />
|
| 34 |
-
download final countdown mp3 from archive.org[^1^] [^2^]<br />
|
| 35 |
-
download final countdown mp3 by europe<br />
|
| 36 |
-
download final countdown mp3 original<br />
|
| 37 |
-
download final countdown mp3 remix<br />
|
| 38 |
-
download final countdown mp3 instrumental<br />
|
| 39 |
-
download final countdown mp3 high quality<br />
|
| 40 |
-
download final countdown mp3 for android<br />
|
| 41 |
-
download final countdown mp3 for iphone<br />
|
| 42 |
-
download final countdown mp3 with lyrics<br />
|
| 43 |
-
download final countdown mp3 full song<br />
|
| 44 |
-
download final countdown mp3 from youtube<br />
|
| 45 |
-
download final countdown mp3 from spotify<br />
|
| 46 |
-
download the final countdown movie 1980[^3^]<br />
|
| 47 |
-
download the final countdown movie soundtrack<br />
|
| 48 |
-
download the final countdown movie theme song<br />
|
| 49 |
-
download the final countdown movie in hindi<br />
|
| 50 |
-
download the final countdown movie free online<br />
|
| 51 |
-
download the final countdown live version<br />
|
| 52 |
-
download the final countdown rock the night carrie<br />
|
| 53 |
-
download the final countdown album zip<br />
|
| 54 |
-
download the final countdown album cover<br />
|
| 55 |
-
download the final countdown album songs<br />
|
| 56 |
-
how to download the final countdown mp3<br />
|
| 57 |
-
where to download the final countdown mp3<br />
|
| 58 |
-
best site to download the final countdown mp3<br />
|
| 59 |
-
best app to download the final countdown mp3<br />
|
| 60 |
-
best way to download the final countdown mp3<br />
|
| 61 |
-
can i download the final countdown mp3 legally<br />
|
| 62 |
-
can i download the final countdown mp3 for free<br />
|
| 63 |
-
can i download the final countdown mp3 on my phone<br />
|
| 64 |
-
can i download the final countdown mp3 from amazon music<br />
|
| 65 |
-
can i download the final countdown mp3 from itunes</p>
|
| 66 |
-
<h3>How to download the song from YouTube</h3>
|
| 67 |
-
<p>YouTube is one of the most popular platforms to listen to The Final Countdown song, as it has several official and unofficial versions of the video. However, YouTube does not allow you to download audio directly from its website or app, unless you have a YouTube Music Premium or YouTube Premium subscription. If you don't have a subscription, you can use a third-party tool to download The Final Countdown MP3 from YouTube. There are many online tools and apps that can help you do that, such as 4K Video Downloader, wikiHow, How-To Geek, etc. To use these tools, you need to follow these general steps:</p>
|
| 68 |
-
<ol>
|
| 69 |
-
<li>Go to YouTube and find the video of The Final Countdown song that you want to download.</li>
|
| 70 |
-
<li>Copy the URL of the video from the address bar or by right-clicking on the video.</li>
|
| 71 |
-
<li>Go to the website or app of the tool that you want to use and paste the URL in the designated box.</li>
|
| 72 |
-
<li>Select MP3 as the output format and choose the quality that you prefer.</li>
|
| 73 |
-
<li>Click on Download or Convert and wait for the process to finish.</li>
|
| 74 |
-
<li>Save the downloaded MP3 file to your device or cloud storage.</li>
|
| 75 |
-
</ol>
|
| 76 |
-
<p>Note that some tools may require you to install additional software or register an account before downloading. You should also be careful about clicking on ads or pop-ups that may redirect you to malicious websites or download unwanted programs.</p> <h3>How to download the song from SoundCloud</h3>
|
| 77 |
-
<p>SoundCloud is another popular platform to listen to The Final Countdown song, as it has several versions of the song uploaded by different users. However, SoundCloud does not allow you to download audio directly from its website or app, unless the uploader has enabled the download option. If the download option is not available, you can use a third-party tool to download The Final Countdown MP3 from SoundCloud. There are many online tools and apps that can help you do that, such as SCDL, SoundCloud Downloader, SoundCloud To MP3, etc. To use these tools, you need to follow these general steps:</p>
|
| 78 |
-
<ol>
|
| 79 |
-
<li>Go to SoundCloud and find the version of The Final Countdown song that you want to download.</li>
|
| 80 |
-
<li>Copy the URL of the song from the address bar or by right-clicking on the song.</li>
|
| 81 |
-
<li>Go to the website or app of the tool that you want to use and paste the URL in the designated box.</li>
|
| 82 |
-
<li>Select MP3 as the output format and choose the quality that you prefer.</li>
|
| 83 |
-
<li>Click on Download or Convert and wait for the process to finish.</li>
|
| 84 |
-
<li>Save the downloaded MP3 file to your device or cloud storage.</li>
|
| 85 |
-
</ol>
|
| 86 |
-
<p>Note that some tools may require you to install additional software or register an account before downloading. You should also be careful about clicking on ads or pop-ups that may redirect you to malicious websites or download unwanted programs.</p> <h3>How to download the song from other websites</h3>
|
| 87 |
-
<p>Besides YouTube and SoundCloud, there are many other websites that offer The Final Countdown MP3 for free download. However, these websites may not be authorized by the artists or the record labels, and they may not have the best quality or the original version of the song. Moreover, these websites may contain viruses, malware, spyware, or other harmful software that can damage your device or steal your personal information. Therefore, you should be very cautious when using these websites and only download from trusted and reputable sources. Here are some examples of websites that claim to provide The Final Countdown MP3 for free download:</p>
|
| 88 |
-
<table>
|
| 89 |
-
<tr>
|
| 90 |
-
<th>Website</th>
|
| 91 |
-
<th>URL</th>
|
| 92 |
-
<th>Remarks</th>
|
| 93 |
-
</tr>
|
| 94 |
-
<tr>
|
| 95 |
-
<td>MP3Juices</td>
|
| 96 |
-
<td></td>
|
| 97 |
-
<td>A free MP3 search engine that allows you to search and download songs from various sources.</td>
|
| 98 |
-
</tr>
|
| 99 |
-
<tr>
|
| 100 |
-
<td>Zippyshare</td>
|
| 101 |
-
<td></td>
|
| 102 |
-
<td>A free file hosting service that allows you to upload and download files up to 500 MB.</td>
|
| 103 |
-
</tr>
|
| 104 |
-
<tr>
|
| 105 |
-
<td>MP3Skull</td>
|
| 106 |
-
<td></td>
|
| 107 |
-
<td>A free MP3 download website that offers a large collection of songs from different genres and artists.</td>
|
| 108 |
-
</tr>
|
| 109 |
-
</table>
|
| 110 |
-
<p>To use these websites, you need to follow these general steps:</p>
|
| 111 |
-
<ol>
|
| 112 |
-
<li>Go to the website and search for The Final Countdown song by typing the name or the keywords in the search box.</li>
|
| 113 |
-
<li>Choose the version of the song that you want to download from the list of results.</li>
|
| 114 |
-
<li>Click on Download or Play and wait for the file to load.</li>
|
| 115 |
-
<li>Save the downloaded MP3 file to your device or cloud storage.</li>
|
| 116 |
-
</ol>
|
| 117 |
-
<p>Note that some websites may require you to complete a captcha, a survey, or an offer before downloading. You should also be careful about clicking on ads or pop-ups that may redirect you to malicious websites or download unwanted programs.</p>
|
| 118 |
-
<h2>Conclusion</h2>
|
| 119 |
-
<p>The Final Countdown is a classic rock song by Europe that has become a global phenomenon and a cultural icon. It is a song that can inspire, motivate, and entertain you with its catchy melody and epic lyrics. If you want to download this song as an MP3 file for free, you have several options to choose from, such as YouTube, SoundCloud, or other websites. However, you should be aware of the legal and ethical issues of downloading music for free, as well as the potential risks of using untrusted sources. You should respect the rights and efforts of the musicians and the music industry, and support them by paying for their music legally. You should also protect your device and personal information by avoiding suspicious websites and links. There are many legal and affordable ways to enjoy music online, such as streaming services, digital stores, and official websites.</p>
|
| 120 |
-
<h2>FAQs</h2>
|
| 121 |
-
<h3>Who wrote and performed The Final Countdown?</h3>
|
| 122 |
-
<p>The Final Countdown was written by Joey Tempest, the lead singer and keyboardist of Europe, a rock band from Sweden. The band members who performed the song were Joey Tempest (vocals and keyboards), John Norum (guitar), John Levén (bass), Mic Michaeli (keyboards), and Ian Haugland (drums).</p>
|
| 123 |
-
<h3>What genre is The Final Countdown?</h3>
|
| 124 |
-
<p>The Final Countdown is a rock song that belongs to the subgenre of glam metal or hair metal. This is a style of rock music that emerged in the late 1970s and early 1980s, characterized by flashy outfits, heavy makeup, big hair, catchy hooks, power ballads, guitar solos, and anthemic choruses.</p>
|
| 125 |
-
<h3>How many views does The Final Countdown have on YouTube?</h3>
|
| 126 |
-
<p>The official video of The Final Countdown by Europe has over 1 billion views on YouTube as of June 2023. This makes it one of the most viewed videos on YouTube and one of the most viewed music videos of all time.</p>
|
| 127 |
-
<h3>Is it legal to download music from YouTube?</h3>
|
| 128 |
-
<p>No, it is not legal to download music from YouTube without the permission of the artists or the record labels. YouTube's terms of service state that you are only allowed to stream videos from its website or app, and not to download them for offline use. Downloading music from YouTube violates the intellectual property rights of the creators and owners of the music, who deserve to be compensated for their work and investment. Downloading music from YouTube also harms the music industry, which relies on the revenue from views and ads to support new artists and produce quality music.</p>
|
| 129 |
-
<h3>What are <h3>What are some alternatives to downloading music for free?</h3>
|
| 130 |
-
<p>If you want to enjoy music online without downloading it for free, you have many alternatives to choose from. Some of the most popular and affordable ways to listen to music online are streaming services, digital stores, and official websites. Here are some examples of these alternatives:</p>
|
| 131 |
-
<table>
|
| 132 |
-
<tr>
|
| 133 |
-
<th>Alternative</th>
|
| 134 |
-
<th>Description</th>
|
| 135 |
-
<th>Advantages</th>
|
| 136 |
-
<th>Disadvantages</th>
|
| 137 |
-
</tr>
|
| 138 |
-
<tr>
|
| 139 |
-
<td>Streaming services</td>
|
| 140 |
-
<td>Online platforms that allow you to stream music from a large library of songs, albums, playlists, and radio stations. Some of the most popular streaming services are Spotify, Apple Music, Amazon Music, YouTube Music, Deezer, Tidal, etc.</td>
|
| 141 |
-
<td>You can access millions of songs from different genres and artists. You can discover new music and personalized recommendations. You can create your own playlists and share them with others. You can listen to music offline with a premium subscription. You can support the artists and the music industry by paying a monthly fee.</td>
|
| 142 |
-
<td>You need an internet connection or a premium subscription to listen to music offline. You may not find some songs or artists that are exclusive to other platforms. You may have to deal with ads or limited features with a free subscription. You may have to pay extra for some features or content.</td>
|
| 143 |
-
</tr>
|
| 144 |
-
<tr>
|
| 145 |
-
<td>Digital stores</td>
|
| 146 |
-
<td>Online platforms that allow you to buy and download individual songs or albums as digital files. Some of the most popular digital stores are iTunes, Google Play Music, Amazon Music, Bandcamp, etc.</td>
|
| 147 |
-
<td>You can own the music that you buy and download. You can enjoy the high-quality sound and original version of the music. You can transfer the music to any device or cloud storage. You can support the artists and the music industry by paying for their music.</td>
|
| 148 |
-
<td>You have to pay for each song or album that you want to download. You may not find some songs or artists that are exclusive to other platforms. You may have to deal with DRM (digital rights management) restrictions that limit your use of the music.</td>
|
| 149 |
-
</tr>
|
| 150 |
-
<tr>
|
| 151 |
-
<td>Official websites</td>
|
| 152 |
-
<td>Online platforms that belong to the artists or the record labels that offer their music for streaming or downloading. Some of the most popular official websites are SoundCloud, Bandcamp, YouTube, etc.</td>
|
| 153 |
-
<td>You can listen to the music directly from the source. You can find some songs or artists that are not available on other platforms. You can support the artists and the record labels by paying for their music or donating to them.</td>
|
| 154 |
-
<td>You may not find a large variety or quantity of music on these platforms. You may have to deal with ads or limited features on these platforms. You may not be able to download the music from these platforms unless they allow it.</td>
|
| 155 |
-
</tr>
|
| 156 |
-
</table></p> 197e85843d<br />
|
| 157 |
-
<br />
|
| 158 |
-
<br />
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/2gauravc/search_summary_chatgpt/main.py
DELETED
|
@@ -1,104 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
import config
|
| 3 |
-
import openai
|
| 4 |
-
import sys, getopt
|
| 5 |
-
from datetime import datetime
|
| 6 |
-
import streamlit as st
|
| 7 |
-
import boto3
|
| 8 |
-
|
| 9 |
-
def get_chatgpt_resp(question):
|
| 10 |
-
openai.api_key = st.secrets['OPENAI_API_KEY']
|
| 11 |
-
response = openai.ChatCompletion.create(
|
| 12 |
-
model='gpt-3.5-turbo',
|
| 13 |
-
messages=[
|
| 14 |
-
{"role":"system","content":"You are a chatbot"},
|
| 15 |
-
{"role":"system","content":question}]
|
| 16 |
-
)
|
| 17 |
-
result = ''
|
| 18 |
-
for choice in response.choices:
|
| 19 |
-
result+=choice.message.content
|
| 20 |
-
|
| 21 |
-
return (result)
|
| 22 |
-
|
| 23 |
-
def gsearch(query, num_results):
|
| 24 |
-
try:
|
| 25 |
-
from googlesearch import search
|
| 26 |
-
except ImportError:
|
| 27 |
-
print("No module named 'google' found")
|
| 28 |
-
|
| 29 |
-
# Google Search and return 10 links
|
| 30 |
-
search_links = []
|
| 31 |
-
for j in search(query, tld="com", num=num_results, stop=num_results, pause=2):
|
| 32 |
-
search_links.append(j)
|
| 33 |
-
return(search_links)
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
def chatgpt_prompt(pname, search_links):
|
| 37 |
-
all_links = '\n'.join(map(str,search_links))
|
| 38 |
-
prompt_text = "You are a expert KYC analyst. I need help to identify if there is any adverse news about {}\
|
| 39 |
-
in the following links. \n {}. \n. In the reply include a 20 word summary of the text in each link and if you find any adverse\
|
| 40 |
-
news (Yes or No)".format(pname, all_links)
|
| 41 |
-
return(prompt_text)
|
| 42 |
-
|
| 43 |
-
def generate_kyc_output(query, search_links, chat_response, start_time):
|
| 44 |
-
rep_txt = ''
|
| 45 |
-
|
| 46 |
-
rep_txt += 'Summary of Google Search for {} \n'.format(query)
|
| 47 |
-
rep_txt += '\n'
|
| 48 |
-
rep_txt += "Report generated on {} \n".format(datetime.now())
|
| 49 |
-
#rep_txt += "----------------------------------------------------- \n"
|
| 50 |
-
rep_txt += '\n'
|
| 51 |
-
rep_txt += "Top Google Search Links "
|
| 52 |
-
rep_txt += '\n'
|
| 53 |
-
rep_txt += '\n'.join(map(str,search_links))
|
| 54 |
-
#rep_txt += "\n----------------------------------------------------- \n"
|
| 55 |
-
rep_txt += '\n'
|
| 56 |
-
rep_txt+= "\n Summary of searches and adverse news findings \n"
|
| 57 |
-
#rep_txt += "----------------------------------------------------- \n"
|
| 58 |
-
rep_txt += chat_response
|
| 59 |
-
rep_txt += '\n'
|
| 60 |
-
|
| 61 |
-
end_time = datetime.now()
|
| 62 |
-
exec_time = (end_time - start_time).total_seconds()
|
| 63 |
-
rep_txt += "Execution runtime {} seconds \n".format(exec_time)
|
| 64 |
-
rep_txt += '\n'
|
| 65 |
-
|
| 66 |
-
return(rep_txt)
|
| 67 |
-
|
| 68 |
-
def save_to_s3(search_text,date_time):
|
| 69 |
-
s3 = boto3.resource(
|
| 70 |
-
's3',
|
| 71 |
-
region_name='us-east-1',
|
| 72 |
-
aws_access_key_id=st.secrets['AWS_ACCESS_KEY_ID'],
|
| 73 |
-
aws_secret_access_key=st.secrets['AWS_ACCESS_KEY']
|
| 74 |
-
)
|
| 75 |
-
fname = ("{}.txt").format(date_time)
|
| 76 |
-
object = s3.Object('adverse-news-search', fname)
|
| 77 |
-
object.put(Body=search_text)
|
| 78 |
-
|
| 79 |
-
def main(argv):
|
| 80 |
-
try:
|
| 81 |
-
opts, args = getopt.getopt(argv,"i:", ["person="])
|
| 82 |
-
except getopt.GetoptError:
|
| 83 |
-
print ('Usage: python app.py --person=<person name>')
|
| 84 |
-
sys.exit(2)
|
| 85 |
-
for opt, arg in opts:
|
| 86 |
-
if opt == '--person':
|
| 87 |
-
pname = arg
|
| 88 |
-
# Google search for the person name and get the first 20 query links
|
| 89 |
-
search_links = gsearch(pname)
|
| 90 |
-
|
| 91 |
-
# Construct the prompt
|
| 92 |
-
prompt_text = chatgpt_prompt(pname, search_links)
|
| 93 |
-
|
| 94 |
-
#get ChatGPT response
|
| 95 |
-
resp = get_chatgpt_resp(prompt_text)
|
| 96 |
-
|
| 97 |
-
# Create PDF with links and summary
|
| 98 |
-
#rep_txt= generate_kyc_output(pname, search_links, resp)
|
| 99 |
-
|
| 100 |
-
#print(rep_txt)
|
| 101 |
-
|
| 102 |
-
|
| 103 |
-
if __name__ == "__main__":
|
| 104 |
-
main(sys.argv[1:])
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/2ndelement/voicevox/README.md
DELETED
|
@@ -1,579 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
license: lgpl-3.0
|
| 3 |
-
title: voicevox
|
| 4 |
-
sdk: docker
|
| 5 |
-
emoji: 🐢
|
| 6 |
-
colorFrom: blue
|
| 7 |
-
colorTo: pink
|
| 8 |
-
pinned: true
|
| 9 |
-
---
|
| 10 |
-
# VOICEVOX ENGINE
|
| 11 |
-
|
| 12 |
-
[](https://github.com/VOICEVOX/voicevox_engine/actions/workflows/build.yml)
|
| 13 |
-
[](https://github.com/VOICEVOX/voicevox_engine/releases)
|
| 14 |
-
[](https://discord.gg/WMwWetrzuh)
|
| 15 |
-
|
| 16 |
-
[](https://github.com/VOICEVOX/voicevox_engine/actions/workflows/test.yml)
|
| 17 |
-
[](https://coveralls.io/github/VOICEVOX/voicevox_engine)
|
| 18 |
-
|
| 19 |
-
[](https://github.com/VOICEVOX/voicevox_engine/actions/workflows/build-docker.yml)
|
| 20 |
-
[](https://hub.docker.com/r/voicevox/voicevox_engine)
|
| 21 |
-
|
| 22 |
-
[VOICEVOX](https://voicevox.hiroshiba.jp/) のエンジンです。
|
| 23 |
-
実態は HTTP サーバーなので、リクエストを送信すればテキスト音声合成できます。
|
| 24 |
-
|
| 25 |
-
（エディターは [VOICEVOX](https://github.com/VOICEVOX/voicevox/) 、
|
| 26 |
-
コアは [VOICEVOX CORE](https://github.com/VOICEVOX/voicevox_core/) 、
|
| 27 |
-
全体構成は [こちら](https://github.com/VOICEVOX/voicevox/blob/main/docs/%E5%85%A8%E4%BD%93%E6%A7%8B%E6%88%90.md) に詳細があります。）
|
| 28 |
-
|
| 29 |
-
## ダウンロード
|
| 30 |
-
|
| 31 |
-
[こちら](https://github.com/VOICEVOX/voicevox_engine/releases/latest)から対応するエンジンをダウンロードしてください。
|
| 32 |
-
|
| 33 |
-
## API ドキュメント
|
| 34 |
-
|
| 35 |
-
[API ドキュメント](https://voicevox.github.io/voicevox_engine/api/)をご参照ください。
|
| 36 |
-
|
| 37 |
-
VOICEVOX エンジンもしくはエディタを起動した状態で http://127.0.0.1:50021/docs にアクセスすると、起動中のエンジンのドキュメントも確認できます。
|
| 38 |
-
今後の方針などについては [VOICEVOX 音声合成エンジンとの連携](./docs/VOICEVOX音声合成エンジンとの連携.md) も参考になるかもしれません。
|
| 39 |
-
|
| 40 |
-
リクエスト・レスポンスの文字コードはすべて UTF-8 です。
|
| 41 |
-
|
| 42 |
-
### HTTP リクエストで音声合成するサンプルコード
|
| 43 |
-
|
| 44 |
-
```bash
|
| 45 |
-
echo -n "こんにちは、音声合成の世界へようこそ" >text.txt
|
| 46 |
-
|
| 47 |
-
curl -s \
|
| 48 |
-
-X POST \
|
| 49 |
-
"127.0.0.1:50021/audio_query?speaker=1"\
|
| 50 |
-
--get --data-urlencode [email protected] \
|
| 51 |
-
> query.json
|
| 52 |
-
|
| 53 |
-
curl -s \
|
| 54 |
-
-H "Content-Type: application/json" \
|
| 55 |
-
-X POST \
|
| 56 |
-
-d @query.json \
|
| 57 |
-
"127.0.0.1:50021/synthesis?speaker=1" \
|
| 58 |
-
> audio.wav
|
| 59 |
-
```
|
| 60 |
-
|
| 61 |
-
生成される音声はサンプリングレートが 24000Hz と少し特殊なため、音声プレーヤーによっては再生できない場合があります。
|
| 62 |
-
|
| 63 |
-
`speaker` に指定する値は `/speakers` エンドポイントで得られる `style_id` です。互換性のために `speaker` という名前になっています。
|
| 64 |
-
|
| 65 |
-
### 読み方を AquesTalk 記法で取得・修正するサンプルコード
|
| 66 |
-
|
| 67 |
-
`/audio_query`のレスポンスにはエンジンが判断した読み方が AquesTalk ライクな記法([本家の記法](https://www.a-quest.com/archive/manual/siyo_onseikigou.pdf)とは一部異なります)で記録されています。
|
| 68 |
-
記法は次のルールに従います。
|
| 69 |
-
|
| 70 |
-
- 全てのカナはカタカナで記述される
|
| 71 |
-
- アクセント句は`/`または`、`で区切る。`、`で区切った場合に限り無音区間が挿入される。
|
| 72 |
-
- カナの手前に`_`を入れるとそのカナは無声化される
|
| 73 |
-
- アクセント位置を`'`で指定する。全てのアクセント句にはアクセント位置を 1 つ指定する必要がある。
|
| 74 |
-
- アクセント句末に`？`(全角)を入れることにより疑問文の発音ができる
|
| 75 |
-
|
| 76 |
-
```bash
|
| 77 |
-
# 読ませたい文章をutf-8でtext.txtに書き出す
|
| 78 |
-
echo -n "ディープラーニングは万能薬ではありません" >text.txt
|
| 79 |
-
|
| 80 |
-
curl -s \
|
| 81 |
-
-X POST \
|
| 82 |
-
"127.0.0.1:50021/audio_query?speaker=1" \
|
| 83 |
-
--get --data-urlencode [email protected] \
|
| 84 |
-
> query.json
|
| 85 |
-
|
| 86 |
-
cat query.json | grep -o -E "\"kana\":\".*\""
|
| 87 |
-
# 結果... "kana":"ディ'イプ/ラ'アニングワ/バンノオヤクデワアリマセ'ン"
|
| 88 |
-
|
| 89 |
-
# "ディイプラ'アニングワ/バンノ'オヤクデワ/アリマセ'ン"と読ませたいので、
|
| 90 |
-
# is_kana=trueをつけてイントネーションを取得しnewphrases.jsonに保存
|
| 91 |
-
echo -n "ディイプラ'アニングワ/バンノ'オヤクデワ/アリマセ'ン" > kana.txt
|
| 92 |
-
curl -s \
|
| 93 |
-
-X POST \
|
| 94 |
-
"127.0.0.1:50021/accent_phrases?speaker=1&is_kana=true" \
|
| 95 |
-
--get --data-urlencode [email protected] \
|
| 96 |
-
> newphrases.json
|
| 97 |
-
|
| 98 |
-
# query.jsonの"accent_phrases"の内容をnewphrases.jsonの内容に置き換える
|
| 99 |
-
cat query.json | sed -e "s/\[{.*}\]/$(cat newphrases.json)/g" > newquery.json
|
| 100 |
-
|
| 101 |
-
curl -s \
|
| 102 |
-
-H "Content-Type: application/json" \
|
| 103 |
-
-X POST \
|
| 104 |
-
-d @newquery.json \
|
| 105 |
-
"127.0.0.1:50021/synthesis?speaker=1" \
|
| 106 |
-
> audio.wav
|
| 107 |
-
```
|
| 108 |
-
|
| 109 |
-
### ユーザー辞書機能について
|
| 110 |
-
|
| 111 |
-
APIからユーザー辞書の参照、単語の追加、編集、削除を行うことができます。
|
| 112 |
-
|
| 113 |
-
#### 参照
|
| 114 |
-
|
| 115 |
-
`/user_dict`にGETリクエストを投げることでユーザー辞書の一覧を取得することができます。
|
| 116 |
-
|
| 117 |
-
```bash
|
| 118 |
-
curl -s -X GET "127.0.0.1:50021/user_dict"
|
| 119 |
-
```
|
| 120 |
-
|
| 121 |
-
#### 単語追加
|
| 122 |
-
|
| 123 |
-
`/user_dict_word`にPOSTリクエストを投げる事でユーザー辞書に単語を追加することができます。
|
| 124 |
-
URLパラメータとして、以下が必要です。
|
| 125 |
-
- surface （辞書に登録する単語）
|
| 126 |
-
- pronunciation （カタカナでの読み方）
|
| 127 |
-
- accent_type （アクセント核位置、整数）
|
| 128 |
-
|
| 129 |
-
アクセント核位置については、こちらの文章が参考になるかと思います。
|
| 130 |
-
〇型となっている数字の部分がアクセント核位置になります。
|
| 131 |
-
https://tdmelodic.readthedocs.io/ja/latest/pages/introduction.html
|
| 132 |
-
|
| 133 |
-
成功した場合の返り値は単語に割り当てられるUUIDの文字列になります。
|
| 134 |
-
|
| 135 |
-
```bash
|
| 136 |
-
surface="test"
|
| 137 |
-
pronunciation="テスト"
|
| 138 |
-
accent_type="1"
|
| 139 |
-
|
| 140 |
-
curl -s -X POST "127.0.0.1:50021/user_dict_word" \
|
| 141 |
-
--get \
|
| 142 |
-
--data-urlencode "surface=$surface" \
|
| 143 |
-
--data-urlencode "pronunciation=$pronunciation" \
|
| 144 |
-
--data-urlencode "accent_type=$accent_type"
|
| 145 |
-
```
|
| 146 |
-
|
| 147 |
-
#### 単語修正
|
| 148 |
-
|
| 149 |
-
`/user_dict_word/{word_uuid}`にPUTリクエストを投げる事でユーザー辞書の単語を修正することができます。
|
| 150 |
-
URLパラメータとして、以下が必要です。
|
| 151 |
-
- surface （辞書に登録するワード）
|
| 152 |
-
- pronunciation （カタカナでの読み方）
|
| 153 |
-
- accent_type （アクセント核位置、整数）
|
| 154 |
-
|
| 155 |
-
word_uuidは単語追加時に確認できるほか、ユーザー辞書を参照することでも確認できます。
|
| 156 |
-
成功した場合の返り値は`204 No Content`になります。
|
| 157 |
-
|
| 158 |
-
```bash
|
| 159 |
-
surface="test2"
|
| 160 |
-
pronunciation="テストツー"
|
| 161 |
-
accent_type="2"
|
| 162 |
-
# 環境によってword_uuidは適宜書き換えてください
|
| 163 |
-
word_uuid="cce59b5f-86ab-42b9-bb75-9fd3407f1e2d"
|
| 164 |
-
|
| 165 |
-
curl -s -X PUT "127.0.0.1:50021/user_dict_word/$word_uuid" \
|
| 166 |
-
--get \
|
| 167 |
-
--data-urlencode "surface=$surface" \
|
| 168 |
-
--data-urlencode "pronunciation=$pronunciation" \
|
| 169 |
-
--data-urlencode "accent_type=$accent_type"
|
| 170 |
-
```
|
| 171 |
-
|
| 172 |
-
#### 単語削除
|
| 173 |
-
|
| 174 |
-
`/user_dict_word/{word_uuid}`にDELETEリクエストを投げる事でユーザー辞書の単語を削除することができます。
|
| 175 |
-
|
| 176 |
-
word_uuidは単語追加時に確認できるほか、ユーザー辞書を参照することでも確認できます。
|
| 177 |
-
成功した場合の返り値は`204 No Content`になります。
|
| 178 |
-
|
| 179 |
-
```bash
|
| 180 |
-
# 環境によってword_uuidは適宜書き換えてください
|
| 181 |
-
word_uuid="cce59b5f-86ab-42b9-bb75-9fd3407f1e2d"
|
| 182 |
-
|
| 183 |
-
curl -s -X DELETE "127.0.0.1:50021/user_dict_word/$word_uuid"
|
| 184 |
-
```
|
| 185 |
-
|
| 186 |
-
### プリセット機能について
|
| 187 |
-
|
| 188 |
-
`presets.yaml`を編集することで話者や話速などのプリセットを使うことができます。
|
| 189 |
-
|
| 190 |
-
```bash
|
| 191 |
-
echo -n "プリセットをうまく活用すれば、サードパーティ間で同じ設定を使うことができます" >text.txt
|
| 192 |
-
|
| 193 |
-
# プリセット情報を取得
|
| 194 |
-
curl -s -X GET "127.0.0.1:50021/presets" > presets.json
|
| 195 |
-
|
| 196 |
-
preset_id=$(cat presets.json | sed -r 's/^.+"id"\:\s?([0-9]+?).+$/\1/g')
|
| 197 |
-
style_id=$(cat presets.json | sed -r 's/^.+"style_id"\:\s?([0-9]+?).+$/\1/g')
|
| 198 |
-
|
| 199 |
-
# AudioQueryの取得
|
| 200 |
-
curl -s \
|
| 201 |
-
-X POST \
|
| 202 |
-
"127.0.0.1:50021/audio_query_from_preset?preset_id=$preset_id"\
|
| 203 |
-
--get --data-urlencode [email protected] \
|
| 204 |
-
> query.json
|
| 205 |
-
|
| 206 |
-
# 音声合成
|
| 207 |
-
curl -s \
|
| 208 |
-
-H "Content-Type: application/json" \
|
| 209 |
-
-X POST \
|
| 210 |
-
-d @query.json \
|
| 211 |
-
"127.0.0.1:50021/synthesis?speaker=$style_id" \
|
| 212 |
-
> audio.wav
|
| 213 |
-
```
|
| 214 |
-
|
| 215 |
-
- `speaker_uuid`は、`/speakers`で確認できます
|
| 216 |
-
- `id`は重複してはいけません
|
| 217 |
-
- エンジン起動後にファイルを書き換えるとエンジンに反映されます
|
| 218 |
-
|
| 219 |
-
### 2 人の話者でモーフィングするサンプルコード
|
| 220 |
-
|
| 221 |
-
`/synthesis_morphing`では、2 人の話者でそれぞれ合成された音声を元に、モーフィングした音声を生成します。
|
| 222 |
-
|
| 223 |
-
```bash
|
| 224 |
-
echo -n "モーフィングを利用することで、２つの声を混ぜることができます。" > text.txt
|
| 225 |
-
|
| 226 |
-
curl -s \
|
| 227 |
-
-X POST \
|
| 228 |
-
"127.0.0.1:50021/audio_query?speaker=0"\
|
| 229 |
-
--get --data-urlencode [email protected] \
|
| 230 |
-
> query.json
|
| 231 |
-
|
| 232 |
-
# 元の話者での合成結果
|
| 233 |
-
curl -s \
|
| 234 |
-
-H "Content-Type: application/json" \
|
| 235 |
-
-X POST \
|
| 236 |
-
-d @query.json \
|
| 237 |
-
"127.0.0.1:50021/synthesis?speaker=0" \
|
| 238 |
-
> audio.wav
|
| 239 |
-
|
| 240 |
-
export MORPH_RATE=0.5
|
| 241 |
-
|
| 242 |
-
# 話者2人分の音声合成+WORLDによる音声分析が入るため時間が掛かるので注意
|
| 243 |
-
curl -s \
|
| 244 |
-
-H "Content-Type: application/json" \
|
| 245 |
-
-X POST \
|
| 246 |
-
-d @query.json \
|
| 247 |
-
"127.0.0.1:50021/synthesis_morphing?base_speaker=0&target_speaker=1&morph_rate=$MORPH_RATE" \
|
| 248 |
-
> audio.wav
|
| 249 |
-
|
| 250 |
-
export MORPH_RATE=0.9
|
| 251 |
-
|
| 252 |
-
# query、base_speaker、target_speakerが同じ場合はキャッシュが使用されるため比較的高速に生成される
|
| 253 |
-
curl -s \
|
| 254 |
-
-H "Content-Type: application/json" \
|
| 255 |
-
-X POST \
|
| 256 |
-
-d @query.json \
|
| 257 |
-
"127.0.0.1:50021/synthesis_morphing?base_speaker=0&target_speaker=1&morph_rate=$MORPH_RATE" \
|
| 258 |
-
> audio.wav
|
| 259 |
-
```
|
| 260 |
-
|
| 261 |
-
### 話者の追加情報を取得するサンプルコード
|
| 262 |
-
|
| 263 |
-
追加情報の中の portrait.png を取得するコードです。
|
| 264 |
-
（[jq](https://stedolan.github.io/jq/)を使用して json をパースしています。）
|
| 265 |
-
|
| 266 |
-
```bash
|
| 267 |
-
curl -s -X GET "127.0.0.1:50021/speaker_info?speaker_uuid=7ffcb7ce-00ec-4bdc-82cd-45a8889e43ff" \
|
| 268 |
-
| jq -r ".portrait" \
|
| 269 |
-
| base64 -d \
|
| 270 |
-
> portrait.png
|
| 271 |
-
```
|
| 272 |
-
|
| 273 |
-
### キャンセル可能な音声合成
|
| 274 |
-
|
| 275 |
-
`/cancellable_synthesis`では通信を切断した場合に即座に計算リソースが開放されます。
|
| 276 |
-
(`/synthesis`では通信を切断しても最後まで音声合成の計算が行われます)
|
| 277 |
-
この API は実験的機能であり、エンジン起動時に引数で`--enable_cancellable_synthesis`を指定しないと有効化されません。
|
| 278 |
-
音声合成に必要なパラメータは`/synthesis`と同様です。
|
| 279 |
-
|
| 280 |
-
### CORS設定
|
| 281 |
-
|
| 282 |
-
VOICEVOXではセキュリティ保護のため`localhost`・`127.0.0.1`・`app://`・Originなし以外のOriginからリクエストを受け入れないようになっています。
|
| 283 |
-
そのため、一部のサードパーティアプリからのレスポンスを受け取れない可能性があります。
|
| 284 |
-
これを回避する方法として、エンジンから設定できるUIを用意しています。
|
| 285 |
-
|
| 286 |
-
#### 設定方法
|
| 287 |
-
|
| 288 |
-
1. <http://127.0.0.1:50021/setting> にアクセスします。
|
| 289 |
-
2. 利用するアプリに合わせて設定を変更、追加してください。
|
| 290 |
-
3. 保存ボタンを押して、変更を確定してください。
|
| 291 |
-
4. 設定の適用にはエンジンの再起動が必要です。必要に応じて再起動をしてください。
|
| 292 |
-
|
| 293 |
-
## アップデート
|
| 294 |
-
|
| 295 |
-
エンジンディレクトリ内にあるファイルを全て消去し、新しいものに置き換えてください。
|
| 296 |
-
|
| 297 |
-
## Docker イメージ
|
| 298 |
-
|
| 299 |
-
### CPU
|
| 300 |
-
|
| 301 |
-
```bash
|
| 302 |
-
docker pull voicevox/voicevox_engine:cpu-ubuntu20.04-latest
|
| 303 |
-
docker run --rm -p '127.0.0.1:50021:50021' voicevox/voicevox_engine:cpu-ubuntu20.04-latest
|
| 304 |
-
```
|
| 305 |
-
|
| 306 |
-
### GPU
|
| 307 |
-
|
| 308 |
-
```bash
|
| 309 |
-
docker pull voicevox/voicevox_engine:nvidia-ubuntu20.04-latest
|
| 310 |
-
docker run --rm --gpus all -p '127.0.0.1:50021:50021' voicevox/voicevox_engine:nvidia-ubuntu20.04-latest
|
| 311 |
-
```
|
| 312 |
-
|
| 313 |
-
#### トラブルシューティング
|
| 314 |
-
GPU版を利用する場合、環境によってエラーが発生することがあります。その場合、`--runtime=nvidia`を`docker run`につけて実行すると解決できることがあります。
|
| 315 |
-
|
| 316 |
-
## 貢献者の方へ
|
| 317 |
-
|
| 318 |
-
Issue を解決するプルリクエストを作成される際は、別の方と同じ Issue に取り組むことを避けるため、
|
| 319 |
-
Issue 側で取り組み始めたことを伝えるか、最初に Draft プルリクエストを作成してください。
|
| 320 |
-
|
| 321 |
-
[VOICEVOX 非公式 Discord サーバー](https://discord.gg/WMwWetrzuh)にて、開発の議論や雑談を行っています。気軽にご参加ください。
|
| 322 |
-
|
| 323 |
-
## 環境構築
|
| 324 |
-
|
| 325 |
-
`Python 3.11.3` を用いて開発されています。
|
| 326 |
-
インストールするには、各 OS ごとの C/C++ コンパイラ、CMake が必要になります。
|
| 327 |
-
|
| 328 |
-
```bash
|
| 329 |
-
# 開発に必要なライブラリのインストール
|
| 330 |
-
python -m pip install -r requirements-dev.txt -r requirements-test.txt
|
| 331 |
-
|
| 332 |
-
# とりあえず実行したいだけなら代わりにこちら
|
| 333 |
-
python -m pip install -r requirements.txt
|
| 334 |
-
```
|
| 335 |
-
|
| 336 |
-
## 実行
|
| 337 |
-
|
| 338 |
-
コマンドライン引数の詳細は以下のコマンドで確認してください。
|
| 339 |
-
|
| 340 |
-
```bash
|
| 341 |
-
python run.py --help
|
| 342 |
-
```
|
| 343 |
-
|
| 344 |
-
```bash
|
| 345 |
-
# 製品版 VOICEVOX でサーバーを起動
|
| 346 |
-
VOICEVOX_DIR="C:/path/to/voicevox" # 製品版 VOICEVOX ディレクトリのパス
|
| 347 |
-
python run.py --voicevox_dir=$VOICEVOX_DIR
|
| 348 |
-
```
|
| 349 |
-
|
| 350 |
-
<!-- 差し替え可能な音声ライブラリまたはその仕様が公開されたらコメントを外す
|
| 351 |
-
```bash
|
| 352 |
-
# 音声ライブラリを差し替える
|
| 353 |
-
VOICELIB_DIR="C:/path/to/your/tts-model"
|
| 354 |
-
python run.py --voicevox_dir=$VOICEVOX_DIR --voicelib_dir=$VOICELIB_DIR
|
| 355 |
-
```
|
| 356 |
-
-->
|
| 357 |
-
|
| 358 |
-
```bash
|
| 359 |
-
# モックでサーバー起動
|
| 360 |
-
python run.py --enable_mock
|
| 361 |
-
```
|
| 362 |
-
|
| 363 |
-
```bash
|
| 364 |
-
# ログをUTF8に変更
|
| 365 |
-
python run.py --output_log_utf8
|
| 366 |
-
# もしくは VV_OUTPUT_LOG_UTF8=1 python run.py
|
| 367 |
-
```
|
| 368 |
-
|
| 369 |
-
### CPU スレッド数を指定する
|
| 370 |
-
|
| 371 |
-
CPU スレッド数が未指定の場合は、論理コア数の半分か物理コア数が使われます。（殆どの CPU で、これは全体の処理能力の半分です）
|
| 372 |
-
もし IaaS 上で実行していたり、専用サーバーで実行している場合など��
|
| 373 |
-
エンジンが使う処理能力を調節したい場合は、CPU スレッド数を指定することで実現できます。
|
| 374 |
-
|
| 375 |
-
- 実行時引数で指定する
|
| 376 |
-
|
| 377 |
-
```bash
|
| 378 |
-
python run.py --voicevox_dir=$VOICEVOX_DIR --cpu_num_threads=4
|
| 379 |
-
```
|
| 380 |
-
|
| 381 |
-
- 環境変数で指定する
|
| 382 |
-
```bash
|
| 383 |
-
export VV_CPU_NUM_THREADS=4
|
| 384 |
-
python run.py --voicevox_dir=$VOICEVOX_DIR
|
| 385 |
-
```
|
| 386 |
-
|
| 387 |
-
### 過去のバージョンのコアを使う
|
| 388 |
-
VOICEVOX Core 0.5.4以降のコアを使用する事が可能です。
|
| 389 |
-
Macでのlibtorch版コアのサポートはしていません。
|
| 390 |
-
|
| 391 |
-
#### 過去のバイナリを指定する
|
| 392 |
-
製品版VOICEVOXもしくはコンパイル済みエンジンのディレクトリを`--voicevox_dir`引数で指定すると、そのバージョンのコアが使用されます。
|
| 393 |
-
```bash
|
| 394 |
-
python run.py --voicevox_dir="/path/to/voicevox"
|
| 395 |
-
```
|
| 396 |
-
Macでは、`DYLD_LIBRARY_PATH`の指定が必要です。
|
| 397 |
-
```bash
|
| 398 |
-
DYLD_LIBRARY_PATH="/path/to/voicevox" python run.py --voicevox_dir="/path/to/voicevox"
|
| 399 |
-
```
|
| 400 |
-
|
| 401 |
-
#### 音声ライブラリを直接指定する
|
| 402 |
-
[VOICEVOX Coreのzipファイル](https://github.com/VOICEVOX/voicevox_core/releases)を解凍したディレクトリを`--voicelib_dir`引数で指定します。
|
| 403 |
-
また、コアのバージョンに合わせて、[libtorch](https://pytorch.org/)や[onnxruntime](https://github.com/microsoft/onnxruntime)のディレクトリを`--runtime_dir`引数で指定します。
|
| 404 |
-
ただし、システムの探索パス上にlibtorch、onnxruntimeがある場合、`--runtime_dir`引数の指定は不要です。
|
| 405 |
-
`--voicelib_dir`引数、`--runtime_dir`引数は複数回使用可能です。
|
| 406 |
-
APIエンドポイントでコアのバージョンを指定する場合は`core_version`引数を指定してください。（未指定の場合は最新のコアが使用されます）
|
| 407 |
-
```bash
|
| 408 |
-
python run.py --voicelib_dir="/path/to/voicevox_core" --runtime_dir="/path/to/libtorch_or_onnx"
|
| 409 |
-
```
|
| 410 |
-
Macでは、`--runtime_dir`引数の代わりに`DYLD_LIBRARY_PATH`の指定が必要です。
|
| 411 |
-
```bash
|
| 412 |
-
DYLD_LIBRARY_PATH="/path/to/onnx" python run.py --voicelib_dir="/path/to/voicevox_core"
|
| 413 |
-
```
|
| 414 |
-
|
| 415 |
-
## コードフォーマット
|
| 416 |
-
|
| 417 |
-
このソフトウェアでは、リモートにプッシュする前にコードフォーマットを確認する仕組み(静的解析ツール)を利用できます。
|
| 418 |
-
利用するには、開発に必要なライブラリのインストールに加えて、以下のコマンドを実行してください。
|
| 419 |
-
プルリクエストを作成する際は、利用することを推奨します。
|
| 420 |
-
|
| 421 |
-
```bash
|
| 422 |
-
pre-commit install -t pre-push
|
| 423 |
-
```
|
| 424 |
-
|
| 425 |
-
エラーが出た際は、以下のコマンドで修正することが可能です。なお、完全に修正できるわけではないので注意してください。
|
| 426 |
-
|
| 427 |
-
```bash
|
| 428 |
-
pysen run format lint
|
| 429 |
-
```
|
| 430 |
-
|
| 431 |
-
## タイポチェック
|
| 432 |
-
|
| 433 |
-
[typos](https://github.com/crate-ci/typos) を使ってタイポのチェックを行っています。
|
| 434 |
-
[typos をインストール](https://github.com/crate-ci/typos#install) した後
|
| 435 |
-
|
| 436 |
-
```bash
|
| 437 |
-
typos
|
| 438 |
-
```
|
| 439 |
-
|
| 440 |
-
でタイポチェックを行えます。
|
| 441 |
-
もし誤判定やチェックから除外すべきファイルがあれば
|
| 442 |
-
[設定ファイルの説明](https://github.com/crate-ci/typos#false-positives) に従って`_typos.toml`を編集してください。
|
| 443 |
-
|
| 444 |
-
## API ドキュメントの確認
|
| 445 |
-
|
| 446 |
-
[API ドキュメント](https://voicevox.github.io/voicevox_engine/api/)（実体は`docs/api/index.html`）は自動で更新されます。
|
| 447 |
-
次のコマンドで API ドキュメントを手動で作成することができます。
|
| 448 |
-
|
| 449 |
-
```bash
|
| 450 |
-
python make_docs.py
|
| 451 |
-
```
|
| 452 |
-
|
| 453 |
-
## ビルド
|
| 454 |
-
|
| 455 |
-
この方法でビルドしたものは、リリースで公開されているものとは異なります。
|
| 456 |
-
また、GPUで利用するにはcuDNNやCUDA、DirectMLなどのライブラリが追加で必要となります。
|
| 457 |
-
|
| 458 |
-
```bash
|
| 459 |
-
python -m pip install -r requirements-dev.txt
|
| 460 |
-
|
| 461 |
-
OUTPUT_LICENSE_JSON_PATH=licenses.json \
|
| 462 |
-
bash build_util/create_venv_and_generate_licenses.bash
|
| 463 |
-
|
| 464 |
-
# ビルド自体はLIBCORE_PATH及びLIBONNXRUNTIME_PATHの指定がなくても可能です
|
| 465 |
-
LIBCORE_PATH="/path/to/libcore" \
|
| 466 |
-
LIBONNXRUNTIME_PATH="/path/to/libonnxruntime" \
|
| 467 |
-
pyinstaller --noconfirm run.spec
|
| 468 |
-
```
|
| 469 |
-
|
| 470 |
-
## 依存関係
|
| 471 |
-
|
| 472 |
-
### 更新
|
| 473 |
-
|
| 474 |
-
[Poetry](https://python-poetry.org/) を用いて依存ライブラリのバージョンを固定しています。
|
| 475 |
-
以下のコマンドで操作できます:
|
| 476 |
-
|
| 477 |
-
```bash
|
| 478 |
-
# パッケージを追加する場合
|
| 479 |
-
poetry add `パッケージ名`
|
| 480 |
-
poetry add --group dev `パッケージ名` # 開発依存の追加
|
| 481 |
-
poetry add --group test `パッケージ名` # テスト依存の追加
|
| 482 |
-
|
| 483 |
-
# パッケージをアップデートする場合
|
| 484 |
-
poetry update `パッケージ名`
|
| 485 |
-
poetry update # 全部更新
|
| 486 |
-
|
| 487 |
-
# requirements.txtの更新
|
| 488 |
-
poetry export --without-hashes -o requirements.txt # こちらを更新する場合は下３つも更新する必要があります。
|
| 489 |
-
poetry export --without-hashes --with dev -o requirements-dev.txt
|
| 490 |
-
poetry export --without-hashes --with test -o requirements-test.txt
|
| 491 |
-
poetry export --without-hashes --with license -o requirements-license.txt
|
| 492 |
-
```
|
| 493 |
-
|
| 494 |
-
### ライセンス
|
| 495 |
-
|
| 496 |
-
依存ライブラリは「コアビルド時にリンクして一体化しても、コア部のコード非公開 OK」なライセンスを持つ必要があります。
|
| 497 |
-
主要ライセンスの可否は以下の通りです。
|
| 498 |
-
|
| 499 |
-
- MIT/Apache/BSD-3: OK
|
| 500 |
-
- LGPL: OK （コアと動的分離されているため）
|
| 501 |
-
- GPL: NG （全関連コードの公開が必要なため）
|
| 502 |
-
|
| 503 |
-
## ユーザー辞書の更新について
|
| 504 |
-
|
| 505 |
-
以下のコマンドで openjtalk のユーザー辞書をコンパイルできます。
|
| 506 |
-
|
| 507 |
-
```bash
|
| 508 |
-
python -c "import pyopenjtalk; pyopenjtalk.create_user_dict('default.csv','user.dic')"
|
| 509 |
-
```
|
| 510 |
-
|
| 511 |
-
## マルチエンジン機能に関して
|
| 512 |
-
|
| 513 |
-
VOICEVOX エディターでは、複数のエンジンを同時に起動することができます。
|
| 514 |
-
この機能を利用することで、自作の音声合成エンジンや既存の音声合成エンジンを VOICEVOX エディター上で動かすことが可能です。
|
| 515 |
-
|
| 516 |
-
<img src="./docs/res/マルチエンジン概念図.svg" width="320">
|
| 517 |
-
|
| 518 |
-
<details>
|
| 519 |
-
|
| 520 |
-
### マルチエンジン機能の仕組み
|
| 521 |
-
|
| 522 |
-
VOICEVOX API に準拠した複数のエンジンの Web API をポートを分けて起動し、統一的に扱うことでマルチエンジン機能を実現しています。
|
| 523 |
-
エディターがそれぞれのエンジンを実行バイナリ経由で起動し、EngineID と結びつけて設定や状態を個別管理します。
|
| 524 |
-
|
| 525 |
-
### マルチエンジン機能への対応方法
|
| 526 |
-
|
| 527 |
-
VOICEVOX API 準拠エンジンを起動する実行バイナリを作ることで対応が可能です。
|
| 528 |
-
VOICEVOX ENGINE リポジトリを fork し、一部の機能を改造するのが簡単です。
|
| 529 |
-
|
| 530 |
-
改造すべき点はエンジン情報・キャラクター情報・音声合成の３点です。
|
| 531 |
-
|
| 532 |
-
エンジンの情報はエンジンマニフェスト（`engine_manifest.json`）で管理されています。
|
| 533 |
-
マニフェストファイル内の情報を見て適宜変更してください。
|
| 534 |
-
音声合成手法によっては、例えばモーフィング機能など、VOICEVOX と同じ機能を持つことができない場合があります。
|
| 535 |
-
その場合はマニフェストファイル内の`supported_features`内の情報を適宜変更してください。
|
| 536 |
-
|
| 537 |
-
キャラクター情報は`speaker_info`ディレクトリ内のファイルで管理されています。
|
| 538 |
-
ダミーのアイコンなどが用意されているので適宜変更してください。
|
| 539 |
-
|
| 540 |
-
音声合成は`voicevox_engine/synthesis_engine/synthesis_engine.py`で行われています。
|
| 541 |
-
VOICEVOX API での音声合成は、エンジン側で音声合成クエリ`AudioQuery`の初期値を作成してユーザーに返し、ユーザーが必要に応じてクエリを編集したあと、エンジンがクエリに従って音声合成することで実現しています。
|
| 542 |
-
クエリ作成は`/audio_query`エンドポイントで、音声合成は`/synthesis`エンドポイントで行っており、最低この２つに対応すれば VOICEVOX API に準拠したことになります。
|
| 543 |
-
|
| 544 |
-
### マルチエンジン機能対応エンジンの配布方法
|
| 545 |
-
|
| 546 |
-
VVPP ファイルとして配布するのがおすすめです。
|
| 547 |
-
VVPP は「VOICEVOX プラグインパッケージ」の略で、中身はビルドしたエンジンなどを含んだディレクトリの Zip ファイルです。
|
| 548 |
-
拡張子を`.vvpp`にすると、ダブルクリックで VOICEVOX エディターにインストールできます。
|
| 549 |
-
|
| 550 |
-
エディター側は受け取った VVPP ファイルをローカルディスク上に Zip 展開したあと、ルートの直下にある`engine_manifest.json`に従ってファイルを探査します。
|
| 551 |
-
VOICEVOX エディターにうまく読み込ませられないときは、エディターのエラーログを参照してください。
|
| 552 |
-
|
| 553 |
-
また、`xxx.vvpp`は分割して連番を付けた`xxx.0.vvppp`ファイルとして配布することも可能です。
|
| 554 |
-
これはファイル容量が大きくて配布が困難な場合に有用です。
|
| 555 |
-
|
| 556 |
-
</details>
|
| 557 |
-
|
| 558 |
-
## GitHub Actions
|
| 559 |
-
|
| 560 |
-
### Variables
|
| 561 |
-
|
| 562 |
-
| name | description |
|
| 563 |
-
| :----------------- | :---------------------------------------------------------------------- |
|
| 564 |
-
| DOCKERHUB_USERNAME | Docker Hub ユーザ名 |
|
| 565 |
-
|
| 566 |
-
### Secrets
|
| 567 |
-
|
| 568 |
-
| name | description |
|
| 569 |
-
| :----------------- | :---------------------------------------------------------------------- |
|
| 570 |
-
| DOCKERHUB_TOKEN | [Docker Hub アクセストークン](https://hub.docker.com/settings/security) |
|
| 571 |
-
|
| 572 |
-
## 事例紹介
|
| 573 |
-
|
| 574 |
-
**[voicevox-client](https://github.com/tuna2134/voicevox-client) [@tuna2134](https://github.com/tuna2134)** ･･･ VOICEVOX ENGINE のためのPythonラッパー
|
| 575 |
-
|
| 576 |
-
## ライセ���ス
|
| 577 |
-
|
| 578 |
-
LGPL v3 と、ソースコードの公開が不要な別ライセンスのデュアルライセンスです。
|
| 579 |
-
別ライセンスを取得したい場合は、ヒホ（twitter: @hiho_karuta）に求めてください。
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/nets_123812KB.py
DELETED
|
@@ -1,122 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
from torch import nn
|
| 3 |
-
import torch.nn.functional as F
|
| 4 |
-
|
| 5 |
-
from . import layers_123821KB as layers
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
class BaseASPPNet(nn.Module):
|
| 9 |
-
def __init__(self, nin, ch, dilations=(4, 8, 16)):
|
| 10 |
-
super(BaseASPPNet, self).__init__()
|
| 11 |
-
self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
|
| 12 |
-
self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
|
| 13 |
-
self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
|
| 14 |
-
self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
|
| 15 |
-
|
| 16 |
-
self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
|
| 17 |
-
|
| 18 |
-
self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
|
| 19 |
-
self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
|
| 20 |
-
self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
|
| 21 |
-
self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
|
| 22 |
-
|
| 23 |
-
def __call__(self, x):
|
| 24 |
-
h, e1 = self.enc1(x)
|
| 25 |
-
h, e2 = self.enc2(h)
|
| 26 |
-
h, e3 = self.enc3(h)
|
| 27 |
-
h, e4 = self.enc4(h)
|
| 28 |
-
|
| 29 |
-
h = self.aspp(h)
|
| 30 |
-
|
| 31 |
-
h = self.dec4(h, e4)
|
| 32 |
-
h = self.dec3(h, e3)
|
| 33 |
-
h = self.dec2(h, e2)
|
| 34 |
-
h = self.dec1(h, e1)
|
| 35 |
-
|
| 36 |
-
return h
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
class CascadedASPPNet(nn.Module):
|
| 40 |
-
def __init__(self, n_fft):
|
| 41 |
-
super(CascadedASPPNet, self).__init__()
|
| 42 |
-
self.stg1_low_band_net = BaseASPPNet(2, 32)
|
| 43 |
-
self.stg1_high_band_net = BaseASPPNet(2, 32)
|
| 44 |
-
|
| 45 |
-
self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
|
| 46 |
-
self.stg2_full_band_net = BaseASPPNet(16, 32)
|
| 47 |
-
|
| 48 |
-
self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
|
| 49 |
-
self.stg3_full_band_net = BaseASPPNet(32, 64)
|
| 50 |
-
|
| 51 |
-
self.out = nn.Conv2d(64, 2, 1, bias=False)
|
| 52 |
-
self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
|
| 53 |
-
self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
|
| 54 |
-
|
| 55 |
-
self.max_bin = n_fft // 2
|
| 56 |
-
self.output_bin = n_fft // 2 + 1
|
| 57 |
-
|
| 58 |
-
self.offset = 128
|
| 59 |
-
|
| 60 |
-
def forward(self, x, aggressiveness=None):
|
| 61 |
-
mix = x.detach()
|
| 62 |
-
x = x.clone()
|
| 63 |
-
|
| 64 |
-
x = x[:, :, : self.max_bin]
|
| 65 |
-
|
| 66 |
-
bandw = x.size()[2] // 2
|
| 67 |
-
aux1 = torch.cat(
|
| 68 |
-
[
|
| 69 |
-
self.stg1_low_band_net(x[:, :, :bandw]),
|
| 70 |
-
self.stg1_high_band_net(x[:, :, bandw:]),
|
| 71 |
-
],
|
| 72 |
-
dim=2,
|
| 73 |
-
)
|
| 74 |
-
|
| 75 |
-
h = torch.cat([x, aux1], dim=1)
|
| 76 |
-
aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
|
| 77 |
-
|
| 78 |
-
h = torch.cat([x, aux1, aux2], dim=1)
|
| 79 |
-
h = self.stg3_full_band_net(self.stg3_bridge(h))
|
| 80 |
-
|
| 81 |
-
mask = torch.sigmoid(self.out(h))
|
| 82 |
-
mask = F.pad(
|
| 83 |
-
input=mask,
|
| 84 |
-
pad=(0, 0, 0, self.output_bin - mask.size()[2]),
|
| 85 |
-
mode="replicate",
|
| 86 |
-
)
|
| 87 |
-
|
| 88 |
-
if self.training:
|
| 89 |
-
aux1 = torch.sigmoid(self.aux1_out(aux1))
|
| 90 |
-
aux1 = F.pad(
|
| 91 |
-
input=aux1,
|
| 92 |
-
pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
|
| 93 |
-
mode="replicate",
|
| 94 |
-
)
|
| 95 |
-
aux2 = torch.sigmoid(self.aux2_out(aux2))
|
| 96 |
-
aux2 = F.pad(
|
| 97 |
-
input=aux2,
|
| 98 |
-
pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
|
| 99 |
-
mode="replicate",
|
| 100 |
-
)
|
| 101 |
-
return mask * mix, aux1 * mix, aux2 * mix
|
| 102 |
-
else:
|
| 103 |
-
if aggressiveness:
|
| 104 |
-
mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
|
| 105 |
-
mask[:, :, : aggressiveness["split_bin"]],
|
| 106 |
-
1 + aggressiveness["value"] / 3,
|
| 107 |
-
)
|
| 108 |
-
mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
|
| 109 |
-
mask[:, :, aggressiveness["split_bin"] :],
|
| 110 |
-
1 + aggressiveness["value"],
|
| 111 |
-
)
|
| 112 |
-
|
| 113 |
-
return mask * mix
|
| 114 |
-
|
| 115 |
-
def predict(self, x_mag, aggressiveness=None):
|
| 116 |
-
h = self.forward(x_mag, aggressiveness)
|
| 117 |
-
|
| 118 |
-
if self.offset > 0:
|
| 119 |
-
h = h[:, :, :, self.offset : -self.offset]
|
| 120 |
-
assert h.size()[3] > 0
|
| 121 |
-
|
| 122 |
-
return h
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/audio/pitch/utils.py
DELETED
|
@@ -1,82 +0,0 @@
|
|
| 1 |
-
import numpy as np
|
| 2 |
-
import torch
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
def to_lf0(f0):
|
| 6 |
-
f0[f0 < 1.0e-5] = 1.0e-6
|
| 7 |
-
lf0 = f0.log() if isinstance(f0, torch.Tensor) else np.log(f0)
|
| 8 |
-
lf0[f0 < 1.0e-5] = - 1.0E+10
|
| 9 |
-
return lf0
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
def to_f0(lf0):
|
| 13 |
-
f0 = np.where(lf0 <= 0, 0.0, np.exp(lf0))
|
| 14 |
-
return f0.flatten()
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
def f0_to_coarse(f0, f0_bin=256, f0_max=900.0, f0_min=50.0):
|
| 18 |
-
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
|
| 19 |
-
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
|
| 20 |
-
is_torch = isinstance(f0, torch.Tensor)
|
| 21 |
-
f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
|
| 22 |
-
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
|
| 23 |
-
|
| 24 |
-
f0_mel[f0_mel <= 1] = 1
|
| 25 |
-
f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
|
| 26 |
-
f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(int)
|
| 27 |
-
assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min(), f0.min(), f0.max())
|
| 28 |
-
return f0_coarse
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
def coarse_to_f0(f0_coarse, f0_bin=256, f0_max=900.0, f0_min=50.0):
|
| 32 |
-
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
|
| 33 |
-
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
|
| 34 |
-
uv = f0_coarse == 1
|
| 35 |
-
f0 = f0_mel_min + (f0_coarse - 1) * (f0_mel_max - f0_mel_min) / (f0_bin - 2)
|
| 36 |
-
f0 = ((f0 / 1127).exp() - 1) * 700
|
| 37 |
-
f0[uv] = 0
|
| 38 |
-
return f0
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
def norm_f0(f0, uv, pitch_norm='log', f0_mean=400, f0_std=100):
|
| 42 |
-
is_torch = isinstance(f0, torch.Tensor)
|
| 43 |
-
if pitch_norm == 'standard':
|
| 44 |
-
f0 = (f0 - f0_mean) / f0_std
|
| 45 |
-
if pitch_norm == 'log':
|
| 46 |
-
f0 = torch.log2(f0 + 1e-8) if is_torch else np.log2(f0 + 1e-8)
|
| 47 |
-
if uv is not None:
|
| 48 |
-
f0[uv > 0] = 0
|
| 49 |
-
return f0
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
def norm_interp_f0(f0, pitch_norm='log', f0_mean=None, f0_std=None):
|
| 53 |
-
is_torch = isinstance(f0, torch.Tensor)
|
| 54 |
-
if is_torch:
|
| 55 |
-
device = f0.device
|
| 56 |
-
f0 = f0.data.cpu().numpy()
|
| 57 |
-
uv = f0 == 0
|
| 58 |
-
f0 = norm_f0(f0, uv, pitch_norm, f0_mean, f0_std)
|
| 59 |
-
if sum(uv) == len(f0):
|
| 60 |
-
f0[uv] = 0
|
| 61 |
-
elif sum(uv) > 0:
|
| 62 |
-
f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
|
| 63 |
-
if is_torch:
|
| 64 |
-
uv = torch.FloatTensor(uv)
|
| 65 |
-
f0 = torch.FloatTensor(f0)
|
| 66 |
-
f0 = f0.to(device)
|
| 67 |
-
uv = uv.to(device)
|
| 68 |
-
return f0, uv
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
def denorm_f0(f0, uv, pitch_norm='log', f0_mean=400, f0_std=100, pitch_padding=None, min=50, max=900):
|
| 72 |
-
is_torch = isinstance(f0, torch.Tensor)
|
| 73 |
-
if pitch_norm == 'standard':
|
| 74 |
-
f0 = f0 * f0_std + f0_mean
|
| 75 |
-
if pitch_norm == 'log':
|
| 76 |
-
f0 = 2 ** f0
|
| 77 |
-
f0 = f0.clamp(min=min, max=max) if is_torch else np.clip(f0, a_min=min, a_max=max)
|
| 78 |
-
if uv is not None:
|
| 79 |
-
f0[uv > 0] = 0
|
| 80 |
-
if pitch_padding is not None:
|
| 81 |
-
f0[pitch_padding] = 0
|
| 82 |
-
return f0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/AIWaves/Software_Company/src/agents/Prompt/__init__.py
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
from .base_Prompts import *
|
|
|
|
|
|
spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/custom_dataset/.ipynb_checkpoints/yolov5_s-v61_syncbn_fast_1xb32-100e_cat-checkpoint.py
DELETED
|
@@ -1,135 +0,0 @@
|
|
| 1 |
-
_base_ = '../yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
|
| 2 |
-
|
| 3 |
-
max_epochs = 100 # 训练的最大 epoch
|
| 4 |
-
data_root = './data-df2/' # 数据集目录的绝对路径
|
| 5 |
-
# data_root = '/root/workspace/mmyolo/data/cat/' # Docker 容器里面数据集目录的绝对路径
|
| 6 |
-
|
| 7 |
-
# 结果保存的路径，可以省略，省略保存的文件名位于 work_dirs 下 config 同名的文件夹中
|
| 8 |
-
# 如果某个 config 只是修改了部分参数，修改这个变量就可以将新的训练文件保存到其他地方
|
| 9 |
-
work_dir = './work_dirs/yolov5_s_df2'
|
| 10 |
-
|
| 11 |
-
# load_from 可以指定本地路径或者 URL，设置了 URL 会自动进行下载，因为上面已经下载过，我们这里设置本地路径
|
| 12 |
-
# 因为本教程是在 cat 数据集上微调，故这里需要使用 `load_from` 来加载 MMYOLO 中的预训练模型，这样可以在加快收敛速度的同时保证精度
|
| 13 |
-
# load_from = './work_dirs/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth' # noqa
|
| 14 |
-
|
| 15 |
-
# 根据自己的 GPU 情况，修改 batch size，YOLOv5-s 默认为 8卡 x 16bs
|
| 16 |
-
train_batch_size_per_gpu = 32
|
| 17 |
-
train_num_workers = 4 # 推荐使用 train_num_workers = nGPU x 4
|
| 18 |
-
|
| 19 |
-
save_epoch_intervals = 2 # 每 interval 轮迭代进行一次保存一次权重
|
| 20 |
-
|
| 21 |
-
# 根据自己的 GPU 情况，修改 base_lr，修改的比例是 base_lr_default * (your_bs / default_bs)
|
| 22 |
-
base_lr = _base_.base_lr / 4
|
| 23 |
-
|
| 24 |
-
anchors = [ # 此处已经根据数据集特点更新了 anchor，关于 anchor 的生成，后面小节会讲解
|
| 25 |
-
[(68, 69), (154, 91), (143, 162)], # P3/8
|
| 26 |
-
[(242, 160), (189, 287), (391, 207)], # P4/16
|
| 27 |
-
[(353, 337), (539, 341), (443, 432)] # P5/32
|
| 28 |
-
]
|
| 29 |
-
|
| 30 |
-
class_name = ('short_sleeved_shirt',
|
| 31 |
-
'long_sleeved_shirt',
|
| 32 |
-
'short_sleeved_outwear',
|
| 33 |
-
'long_sleeved_outwear',
|
| 34 |
-
'vest',
|
| 35 |
-
'sling',
|
| 36 |
-
'shorts',
|
| 37 |
-
'trousers',
|
| 38 |
-
'skirt',
|
| 39 |
-
'short_sleeved_dress',
|
| 40 |
-
'long_sleeved_dress',
|
| 41 |
-
'vest_dress',
|
| 42 |
-
'sling_dress') # 根据 class_with_id.txt 类别信息，设置 class_name
|
| 43 |
-
|
| 44 |
-
num_classes = len(class_name)
|
| 45 |
-
metainfo = dict(
|
| 46 |
-
classes=class_name,
|
| 47 |
-
palette=[(255, 0, 0),
|
| 48 |
-
(255, 128, 0),
|
| 49 |
-
(255, 255, 0),
|
| 50 |
-
(128, 255, 0),
|
| 51 |
-
(0, 255, 0),
|
| 52 |
-
(0, 255, 128),
|
| 53 |
-
(0, 255, 255),
|
| 54 |
-
(0, 128, 255),
|
| 55 |
-
(0, 0, 255),
|
| 56 |
-
(127, 0, 255),
|
| 57 |
-
(255, 0, 255),
|
| 58 |
-
(255, 0, 127),
|
| 59 |
-
(128, 128, 128)] # 画图时候的颜色，随便设置即可
|
| 60 |
-
)
|
| 61 |
-
|
| 62 |
-
train_cfg = dict(
|
| 63 |
-
max_epochs=max_epochs,
|
| 64 |
-
val_begin=20, # 第几个 epoch 后验证，这里设置 20 是因为前 20 个 epoch 精度不高，测试意义不大，故跳过
|
| 65 |
-
val_interval=save_epoch_intervals # 每 val_interval 轮迭代进行一次测试评估
|
| 66 |
-
# dynamic_intervals=[(max_epochs-_base_.num_last_epochs, 1)]
|
| 67 |
-
)
|
| 68 |
-
|
| 69 |
-
model = dict(
|
| 70 |
-
bbox_head=dict(
|
| 71 |
-
head_module=dict(num_classes=num_classes),
|
| 72 |
-
prior_generator=dict(base_sizes=anchors),
|
| 73 |
-
|
| 74 |
-
# loss_cls 会根据 num_classes 动态调整，但是 num_classes = 1 的时候，loss_cls 恒为 0
|
| 75 |
-
loss_cls=dict(loss_weight=0.5 *
|
| 76 |
-
(num_classes / 80 * 3 / _base_.num_det_layers))))
|
| 77 |
-
|
| 78 |
-
train_dataloader = dict(
|
| 79 |
-
batch_size=train_batch_size_per_gpu,
|
| 80 |
-
num_workers=train_num_workers,
|
| 81 |
-
dataset=dict(
|
| 82 |
-
_delete_=True,
|
| 83 |
-
type='RepeatDataset',
|
| 84 |
-
# 数据量太少的话，可以使用 RepeatDataset ，在每个 epoch 内重复当前数据集 n 次，这里设置 5 是重复 5 次
|
| 85 |
-
times=2,
|
| 86 |
-
dataset=dict(
|
| 87 |
-
type=_base_.dataset_type,
|
| 88 |
-
data_root=data_root,
|
| 89 |
-
metainfo=metainfo,
|
| 90 |
-
ann_file='annotations/trainval.json',
|
| 91 |
-
data_prefix=dict(img='smaller-dataset/'),
|
| 92 |
-
filter_cfg=dict(filter_empty_gt=False, min_size=32),
|
| 93 |
-
pipeline=_base_.train_pipeline)))
|
| 94 |
-
|
| 95 |
-
val_dataloader = dict(
|
| 96 |
-
dataset=dict(
|
| 97 |
-
metainfo=metainfo,
|
| 98 |
-
data_root=data_root,
|
| 99 |
-
ann_file='annotations/trainval.json',
|
| 100 |
-
data_prefix=dict(img='smaller-dataset/')))
|
| 101 |
-
|
| 102 |
-
test_dataloader = val_dataloader
|
| 103 |
-
|
| 104 |
-
val_evaluator = dict(ann_file=data_root + 'annotations/trainval.json')
|
| 105 |
-
test_evaluator = val_evaluator
|
| 106 |
-
|
| 107 |
-
optim_wrapper = dict(optimizer=dict(lr=base_lr))
|
| 108 |
-
|
| 109 |
-
default_hooks = dict(
|
| 110 |
-
# 设置间隔多少个 epoch 保存模型，以及保存模型最多几个，`save_best` 是另外保存最佳模型（推荐）
|
| 111 |
-
checkpoint=dict(
|
| 112 |
-
type='CheckpointHook',
|
| 113 |
-
interval=save_epoch_intervals,
|
| 114 |
-
max_keep_ckpts=5,
|
| 115 |
-
save_best='auto'),
|
| 116 |
-
param_scheduler=dict(max_epochs=max_epochs, warmup_mim_iter=10),
|
| 117 |
-
# logger 输出的间隔
|
| 118 |
-
logger=dict(type='LoggerHook', interval=10))
|
| 119 |
-
|
| 120 |
-
# custom_hooks = [
|
| 121 |
-
# dict(
|
| 122 |
-
# type="EMAHook",
|
| 123 |
-
# ema_type="ExpMomentumEMA",
|
| 124 |
-
# momentum=0.0001,
|
| 125 |
-
# update_buffers=True,
|
| 126 |
-
# strict_load=False,
|
| 127 |
-
# priority=49),
|
| 128 |
-
# dict(
|
| 129 |
-
# type="mmdet.PipelineSwitchHook",
|
| 130 |
-
# switch_epoch=max_epochs-max_epochs-_base_.num_last_epochs,
|
| 131 |
-
# switch_pipeline=_base_.train_pipeline_stage2
|
| 132 |
-
# )
|
| 133 |
-
# ]
|
| 134 |
-
|
| 135 |
-
visualizer = dict(vis_backends=[dict(type='LocalVisBackend'), dict(type='WandbVisBackend')])
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov6/yolov6_n_syncbn_fast_8xb32-300e_coco.py
DELETED
|
@@ -1,21 +0,0 @@
|
|
| 1 |
-
_base_ = './yolov6_s_syncbn_fast_8xb32-300e_coco.py'
|
| 2 |
-
|
| 3 |
-
# ======================= Possible modified parameters =======================
|
| 4 |
-
# -----model related-----
|
| 5 |
-
# The scaling factor that controls the depth of the network structure
|
| 6 |
-
deepen_factor = 0.33
|
| 7 |
-
# The scaling factor that controls the width of the network structure
|
| 8 |
-
widen_factor = 0.25
|
| 9 |
-
|
| 10 |
-
# -----train val related-----
|
| 11 |
-
lr_factor = 0.02 # Learning rate scaling factor
|
| 12 |
-
|
| 13 |
-
# ============================== Unmodified in most cases ===================
|
| 14 |
-
model = dict(
|
| 15 |
-
backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
|
| 16 |
-
neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
|
| 17 |
-
bbox_head=dict(
|
| 18 |
-
head_module=dict(widen_factor=widen_factor),
|
| 19 |
-
loss_bbox=dict(iou_mode='siou')))
|
| 20 |
-
|
| 21 |
-
default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/Chart.js
DELETED
|
@@ -1,65 +0,0 @@
|
|
| 1 |
-
import Canvas from '../canvas/Canvas.js';
|
| 2 |
-
import SetChart from './SetChart.js';
|
| 3 |
-
import GetChartDataset from './GetChartDataset.js';
|
| 4 |
-
import GetChartData from './GetChartData.js';
|
| 5 |
-
import SetChartData from './SetChartData.js';
|
| 6 |
-
import UpdateChart from './UpdateChart.js';
|
| 7 |
-
|
| 8 |
-
// This plugin does not contain chart.js
|
| 9 |
-
// Load chart.js in preload stage -
|
| 10 |
-
// scene.load.script('chartjs', 'https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.8.0/Chart.min.js');
|
| 11 |
-
|
| 12 |
-
class Chart extends Canvas {
|
| 13 |
-
constructor(scene, x, y, width, height, config) {
|
| 14 |
-
super(scene, x, y, width, height);
|
| 15 |
-
this.type = 'rexChart';
|
| 16 |
-
this.chart = undefined;
|
| 17 |
-
|
| 18 |
-
if (config !== undefined) {
|
| 19 |
-
this.setChart(config);
|
| 20 |
-
}
|
| 21 |
-
}
|
| 22 |
-
|
| 23 |
-
destroy(fromScene) {
|
| 24 |
-
// This Game Object has already been destroyed
|
| 25 |
-
if (!this.scene) {
|
| 26 |
-
return;
|
| 27 |
-
}
|
| 28 |
-
if (this.chart) {
|
| 29 |
-
this.chart.destroy();
|
| 30 |
-
this.chart = undefined;
|
| 31 |
-
}
|
| 32 |
-
super.destroy(fromScene);
|
| 33 |
-
}
|
| 34 |
-
|
| 35 |
-
resize(width, height) {
|
| 36 |
-
if ((width === this.width) && (height === this.height)) {
|
| 37 |
-
return this;
|
| 38 |
-
}
|
| 39 |
-
|
| 40 |
-
super.resize(width, height);
|
| 41 |
-
|
| 42 |
-
if (this.chart) {
|
| 43 |
-
var chart = this.chart;
|
| 44 |
-
chart.height = this.canvas.height;
|
| 45 |
-
chart.width = this.canvas.width;
|
| 46 |
-
chart.aspectRatio = (chart.height) ? chart.width / chart.height : null;
|
| 47 |
-
chart.update();
|
| 48 |
-
}
|
| 49 |
-
return this;
|
| 50 |
-
}
|
| 51 |
-
}
|
| 52 |
-
|
| 53 |
-
var methods = {
|
| 54 |
-
setChart: SetChart,
|
| 55 |
-
getChartDataset: GetChartDataset,
|
| 56 |
-
getChartData: GetChartData,
|
| 57 |
-
setChartData: SetChartData,
|
| 58 |
-
updateChart: UpdateChart,
|
| 59 |
-
}
|
| 60 |
-
Object.assign(
|
| 61 |
-
Chart.prototype,
|
| 62 |
-
methods
|
| 63 |
-
);
|
| 64 |
-
|
| 65 |
-
export default Chart;
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/Builders.d.ts
DELETED
|
@@ -1,82 +0,0 @@
|
|
| 1 |
-
import BBCodeText from '../../bbcodetext/BBCodeText';
|
| 2 |
-
import RoundRectangle from '../../roundrectangle/RoundRectangle';
|
| 3 |
-
import NinePatch from '../../ninepatch/NinePatch';
|
| 4 |
-
import NinePatch2 from '../../ninepatch2/NinePatch';
|
| 5 |
-
import Canvas from '../../canvas/Canvas';
|
| 6 |
-
import CircleMaskImage from '../../circlemaskimage/CircleMaskImage';
|
| 7 |
-
import Space from '../../space/Space';
|
| 8 |
-
|
| 9 |
-
import Sizer from '../../sizer/Sizer';
|
| 10 |
-
import FixWidthSizer from '../../fixwidthsizer/FixWidthSizer';
|
| 11 |
-
import GridSizer from '../../gridsizer/GridSizer';
|
| 12 |
-
import OverlapSizer from '../../overlapsizer/OverlapSizer';
|
| 13 |
-
|
| 14 |
-
import Buttons from '../../buttons/Buttons';
|
| 15 |
-
import FixWidthButtons from '../../fixwidthbuttons/FixWidthButtons';
|
| 16 |
-
import GridButtons from '../../gridbuttons/GridButtons';
|
| 17 |
-
|
| 18 |
-
import Label from '../../label/Label';
|
| 19 |
-
import BadgeLabel from '../../badgelabel/BadgeLabel';
|
| 20 |
-
import Dialog from '../../dialog/Dialog';
|
| 21 |
-
import TextBox from '../../textbox/TextBox';
|
| 22 |
-
import Slider from '../../slider/Slider';
|
| 23 |
-
import NumberBar from '../../numberbar/NumberBar';
|
| 24 |
-
import ScrollBar from '../../scrollbar/ScrollBar';
|
| 25 |
-
import TextArea from '../../textarea/TextArea';
|
| 26 |
-
import Pages from '../../pages/Pages';
|
| 27 |
-
import Toast from '../../toast/Toast';
|
| 28 |
-
import Knob from '../../knob/Knob';
|
| 29 |
-
import HolyGrail from '../../holygrail/HolyGrail';
|
| 30 |
-
import Menu from '../../menu/Menu';
|
| 31 |
-
|
| 32 |
-
export default Builders;
|
| 33 |
-
|
| 34 |
-
declare namespace Builders {
|
| 35 |
-
type BuilderTypeCommon<T> = (
|
| 36 |
-
scene: Phaser.Scene,
|
| 37 |
-
data: Object,
|
| 38 |
-
view: Object,
|
| 39 |
-
styles: Object,
|
| 40 |
-
customBuilders: { [name: string]: BuilderType }
|
| 41 |
-
) => T;
|
| 42 |
-
|
| 43 |
-
type BuilderType = BuilderTypeCommon<Phaser.GameObjects.GameObject>;
|
| 44 |
-
}
|
| 45 |
-
|
| 46 |
-
declare var Builders: {
|
| 47 |
-
Image: Builders.BuilderTypeCommon<Phaser.GameObjects.Image>,
|
| 48 |
-
Sprite: Builders.BuilderTypeCommon<Phaser.GameObjects.Sprite>,
|
| 49 |
-
Video: Builders.BuilderTypeCommon<Phaser.GameObjects.Video>,
|
| 50 |
-
Text: Builders.BuilderTypeCommon<Phaser.GameObjects.Text>,
|
| 51 |
-
BBCodeText: Builders.BuilderTypeCommon<BBCodeText>,
|
| 52 |
-
RoundRectangle: Builders.BuilderTypeCommon<RoundRectangle>,
|
| 53 |
-
Ninepatch: Builders.BuilderTypeCommon<NinePatch>,
|
| 54 |
-
Ninepatch2: Builders.BuilderTypeCommon<NinePatch2>,
|
| 55 |
-
Canvas: Builders.BuilderTypeCommon<Canvas>,
|
| 56 |
-
CircleMaskImage: Builders.BuilderTypeCommon<CircleMaskImage>,
|
| 57 |
-
Space: Builders.BuilderTypeCommon<Space>,
|
| 58 |
-
|
| 59 |
-
Sizer: Builders.BuilderTypeCommon<Sizer>,
|
| 60 |
-
FixWidthSizer: Builders.BuilderTypeCommon<FixWidthSizer>,
|
| 61 |
-
GridSizer: Builders.BuilderTypeCommon<GridSizer>,
|
| 62 |
-
OverlapSizer: Builders.BuilderTypeCommon<OverlapSizer>,
|
| 63 |
-
|
| 64 |
-
Buttons: Builders.BuilderTypeCommon<Buttons>,
|
| 65 |
-
FixWidthButtons: Builders.BuilderTypeCommon<FixWidthButtons>,
|
| 66 |
-
GridButtons: Builders.BuilderTypeCommon<GridButtons>,
|
| 67 |
-
|
| 68 |
-
Label: Builders.BuilderTypeCommon<Label>,
|
| 69 |
-
BadgeLabel: Builders.BuilderTypeCommon<BadgeLabel>,
|
| 70 |
-
Dialog: Builders.BuilderTypeCommon<Dialog>,
|
| 71 |
-
TextBox: Builders.BuilderTypeCommon<TextBox>,
|
| 72 |
-
Slider: Builders.BuilderTypeCommon<Slider>,
|
| 73 |
-
NumberBar: Builders.BuilderTypeCommon<NumberBar>,
|
| 74 |
-
ScrollBar: Builders.BuilderTypeCommon<ScrollBar>,
|
| 75 |
-
TextArea: Builders.BuilderTypeCommon<TextArea>,
|
| 76 |
-
Pages: Builders.BuilderTypeCommon<Pages>,
|
| 77 |
-
Toast: Builders.BuilderTypeCommon<Toast>,
|
| 78 |
-
Knob: Builders.BuilderTypeCommon<Knob>,
|
| 79 |
-
HolyGrail: Builders.BuilderTypeCommon<HolyGrail>,
|
| 80 |
-
Menu: Builders.BuilderTypeCommon<Menu>,
|
| 81 |
-
|
| 82 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/AlekseyKorshuk/thin-plate-spline-motion-model/modules/dense_motion.py
DELETED
|
@@ -1,164 +0,0 @@
|
|
| 1 |
-
from torch import nn
|
| 2 |
-
import torch.nn.functional as F
|
| 3 |
-
import torch
|
| 4 |
-
from modules.util import Hourglass, AntiAliasInterpolation2d, make_coordinate_grid, kp2gaussian
|
| 5 |
-
from modules.util import to_homogeneous, from_homogeneous, UpBlock2d, TPS
|
| 6 |
-
import math
|
| 7 |
-
|
| 8 |
-
class DenseMotionNetwork(nn.Module):
|
| 9 |
-
"""
|
| 10 |
-
Module that estimating an optical flow and multi-resolution occlusion masks
|
| 11 |
-
from K TPS transformations and an affine transformation.
|
| 12 |
-
"""
|
| 13 |
-
|
| 14 |
-
def __init__(self, block_expansion, num_blocks, max_features, num_tps, num_channels,
|
| 15 |
-
scale_factor=0.25, bg = False, multi_mask = True, kp_variance=0.01):
|
| 16 |
-
super(DenseMotionNetwork, self).__init__()
|
| 17 |
-
|
| 18 |
-
if scale_factor != 1:
|
| 19 |
-
self.down = AntiAliasInterpolation2d(num_channels, scale_factor)
|
| 20 |
-
self.scale_factor = scale_factor
|
| 21 |
-
self.multi_mask = multi_mask
|
| 22 |
-
|
| 23 |
-
self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_channels * (num_tps+1) + num_tps*5+1),
|
| 24 |
-
max_features=max_features, num_blocks=num_blocks)
|
| 25 |
-
|
| 26 |
-
hourglass_output_size = self.hourglass.out_channels
|
| 27 |
-
self.maps = nn.Conv2d(hourglass_output_size[-1], num_tps + 1, kernel_size=(7, 7), padding=(3, 3))
|
| 28 |
-
|
| 29 |
-
if multi_mask:
|
| 30 |
-
up = []
|
| 31 |
-
self.up_nums = int(math.log(1/scale_factor, 2))
|
| 32 |
-
self.occlusion_num = 4
|
| 33 |
-
|
| 34 |
-
channel = [hourglass_output_size[-1]//(2**i) for i in range(self.up_nums)]
|
| 35 |
-
for i in range(self.up_nums):
|
| 36 |
-
up.append(UpBlock2d(channel[i], channel[i]//2, kernel_size=3, padding=1))
|
| 37 |
-
self.up = nn.ModuleList(up)
|
| 38 |
-
|
| 39 |
-
channel = [hourglass_output_size[-i-1] for i in range(self.occlusion_num-self.up_nums)[::-1]]
|
| 40 |
-
for i in range(self.up_nums):
|
| 41 |
-
channel.append(hourglass_output_size[-1]//(2**(i+1)))
|
| 42 |
-
occlusion = []
|
| 43 |
-
|
| 44 |
-
for i in range(self.occlusion_num):
|
| 45 |
-
occlusion.append(nn.Conv2d(channel[i], 1, kernel_size=(7, 7), padding=(3, 3)))
|
| 46 |
-
self.occlusion = nn.ModuleList(occlusion)
|
| 47 |
-
else:
|
| 48 |
-
occlusion = [nn.Conv2d(hourglass_output_size[-1], 1, kernel_size=(7, 7), padding=(3, 3))]
|
| 49 |
-
self.occlusion = nn.ModuleList(occlusion)
|
| 50 |
-
|
| 51 |
-
self.num_tps = num_tps
|
| 52 |
-
self.bg = bg
|
| 53 |
-
self.kp_variance = kp_variance
|
| 54 |
-
|
| 55 |
-
|
| 56 |
-
def create_heatmap_representations(self, source_image, kp_driving, kp_source):
|
| 57 |
-
|
| 58 |
-
spatial_size = source_image.shape[2:]
|
| 59 |
-
gaussian_driving = kp2gaussian(kp_driving['fg_kp'], spatial_size=spatial_size, kp_variance=self.kp_variance)
|
| 60 |
-
gaussian_source = kp2gaussian(kp_source['fg_kp'], spatial_size=spatial_size, kp_variance=self.kp_variance)
|
| 61 |
-
heatmap = gaussian_driving - gaussian_source
|
| 62 |
-
|
| 63 |
-
zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1]).type(heatmap.type()).to(heatmap.device)
|
| 64 |
-
heatmap = torch.cat([zeros, heatmap], dim=1)
|
| 65 |
-
|
| 66 |
-
return heatmap
|
| 67 |
-
|
| 68 |
-
def create_transformations(self, source_image, kp_driving, kp_source, bg_param):
|
| 69 |
-
# K TPS transformaions
|
| 70 |
-
bs, _, h, w = source_image.shape
|
| 71 |
-
kp_1 = kp_driving['fg_kp']
|
| 72 |
-
kp_2 = kp_source['fg_kp']
|
| 73 |
-
kp_1 = kp_1.view(bs, -1, 5, 2)
|
| 74 |
-
kp_2 = kp_2.view(bs, -1, 5, 2)
|
| 75 |
-
trans = TPS(mode = 'kp', bs = bs, kp_1 = kp_1, kp_2 = kp_2)
|
| 76 |
-
driving_to_source = trans.transform_frame(source_image)
|
| 77 |
-
|
| 78 |
-
identity_grid = make_coordinate_grid((h, w), type=kp_1.type()).to(kp_1.device)
|
| 79 |
-
identity_grid = identity_grid.view(1, 1, h, w, 2)
|
| 80 |
-
identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1)
|
| 81 |
-
|
| 82 |
-
# affine background transformation
|
| 83 |
-
if not (bg_param is None):
|
| 84 |
-
identity_grid = to_homogeneous(identity_grid)
|
| 85 |
-
identity_grid = torch.matmul(bg_param.view(bs, 1, 1, 1, 3, 3), identity_grid.unsqueeze(-1)).squeeze(-1)
|
| 86 |
-
identity_grid = from_homogeneous(identity_grid)
|
| 87 |
-
|
| 88 |
-
transformations = torch.cat([identity_grid, driving_to_source], dim=1)
|
| 89 |
-
return transformations
|
| 90 |
-
|
| 91 |
-
def create_deformed_source_image(self, source_image, transformations):
|
| 92 |
-
|
| 93 |
-
bs, _, h, w = source_image.shape
|
| 94 |
-
source_repeat = source_image.unsqueeze(1).unsqueeze(1).repeat(1, self.num_tps + 1, 1, 1, 1, 1)
|
| 95 |
-
source_repeat = source_repeat.view(bs * (self.num_tps + 1), -1, h, w)
|
| 96 |
-
transformations = transformations.view((bs * (self.num_tps + 1), h, w, -1))
|
| 97 |
-
deformed = F.grid_sample(source_repeat, transformations, align_corners=True)
|
| 98 |
-
deformed = deformed.view((bs, self.num_tps+1, -1, h, w))
|
| 99 |
-
return deformed
|
| 100 |
-
|
| 101 |
-
def dropout_softmax(self, X, P):
|
| 102 |
-
'''
|
| 103 |
-
Dropout for TPS transformations. Eq(7) and Eq(8) in the paper.
|
| 104 |
-
'''
|
| 105 |
-
drop = (torch.rand(X.shape[0],X.shape[1]) < (1-P)).type(X.type()).to(X.device)
|
| 106 |
-
drop[..., 0] = 1
|
| 107 |
-
drop = drop.repeat(X.shape[2],X.shape[3],1,1).permute(2,3,0,1)
|
| 108 |
-
|
| 109 |
-
maxx = X.max(1).values.unsqueeze_(1)
|
| 110 |
-
X = X - maxx
|
| 111 |
-
X_exp = X.exp()
|
| 112 |
-
X[:,1:,...] /= (1-P)
|
| 113 |
-
mask_bool =(drop == 0)
|
| 114 |
-
X_exp = X_exp.masked_fill(mask_bool, 0)
|
| 115 |
-
partition = X_exp.sum(dim=1, keepdim=True) + 1e-6
|
| 116 |
-
return X_exp / partition
|
| 117 |
-
|
| 118 |
-
def forward(self, source_image, kp_driving, kp_source, bg_param = None, dropout_flag=False, dropout_p = 0):
|
| 119 |
-
if self.scale_factor != 1:
|
| 120 |
-
source_image = self.down(source_image)
|
| 121 |
-
|
| 122 |
-
bs, _, h, w = source_image.shape
|
| 123 |
-
|
| 124 |
-
out_dict = dict()
|
| 125 |
-
heatmap_representation = self.create_heatmap_representations(source_image, kp_driving, kp_source)
|
| 126 |
-
transformations = self.create_transformations(source_image, kp_driving, kp_source, bg_param)
|
| 127 |
-
deformed_source = self.create_deformed_source_image(source_image, transformations)
|
| 128 |
-
out_dict['deformed_source'] = deformed_source
|
| 129 |
-
# out_dict['transformations'] = transformations
|
| 130 |
-
deformed_source = deformed_source.view(bs,-1,h,w)
|
| 131 |
-
input = torch.cat([heatmap_representation, deformed_source], dim=1)
|
| 132 |
-
input = input.view(bs, -1, h, w)
|
| 133 |
-
|
| 134 |
-
prediction = self.hourglass(input, mode = 1)
|
| 135 |
-
|
| 136 |
-
contribution_maps = self.maps(prediction[-1])
|
| 137 |
-
if(dropout_flag):
|
| 138 |
-
contribution_maps = self.dropout_softmax(contribution_maps, dropout_p)
|
| 139 |
-
else:
|
| 140 |
-
contribution_maps = F.softmax(contribution_maps, dim=1)
|
| 141 |
-
out_dict['contribution_maps'] = contribution_maps
|
| 142 |
-
|
| 143 |
-
# Combine the K+1 transformations
|
| 144 |
-
# Eq(6) in the paper
|
| 145 |
-
contribution_maps = contribution_maps.unsqueeze(2)
|
| 146 |
-
transformations = transformations.permute(0, 1, 4, 2, 3)
|
| 147 |
-
deformation = (transformations * contribution_maps).sum(dim=1)
|
| 148 |
-
deformation = deformation.permute(0, 2, 3, 1)
|
| 149 |
-
|
| 150 |
-
out_dict['deformation'] = deformation # Optical Flow
|
| 151 |
-
|
| 152 |
-
occlusion_map = []
|
| 153 |
-
if self.multi_mask:
|
| 154 |
-
for i in range(self.occlusion_num-self.up_nums):
|
| 155 |
-
occlusion_map.append(torch.sigmoid(self.occlusion[i](prediction[self.up_nums-self.occlusion_num+i])))
|
| 156 |
-
prediction = prediction[-1]
|
| 157 |
-
for i in range(self.up_nums):
|
| 158 |
-
prediction = self.up[i](prediction)
|
| 159 |
-
occlusion_map.append(torch.sigmoid(self.occlusion[i+self.occlusion_num-self.up_nums](prediction)))
|
| 160 |
-
else:
|
| 161 |
-
occlusion_map.append(torch.sigmoid(self.occlusion[0](prediction[-1])))
|
| 162 |
-
|
| 163 |
-
out_dict['occlusion_map'] = occlusion_map # Multi-resolution Occlusion Masks
|
| 164 |
-
return out_dict
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/AlexWang/lama/saicinpainting/training/data/masks.py
DELETED
|
@@ -1,332 +0,0 @@
|
|
| 1 |
-
import math
|
| 2 |
-
import random
|
| 3 |
-
import hashlib
|
| 4 |
-
import logging
|
| 5 |
-
from enum import Enum
|
| 6 |
-
|
| 7 |
-
import cv2
|
| 8 |
-
import numpy as np
|
| 9 |
-
|
| 10 |
-
from saicinpainting.evaluation.masks.mask import SegmentationMask
|
| 11 |
-
from saicinpainting.utils import LinearRamp
|
| 12 |
-
|
| 13 |
-
LOGGER = logging.getLogger(__name__)
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
class DrawMethod(Enum):
|
| 17 |
-
LINE = 'line'
|
| 18 |
-
CIRCLE = 'circle'
|
| 19 |
-
SQUARE = 'square'
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
def make_random_irregular_mask(shape, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10,
|
| 23 |
-
draw_method=DrawMethod.LINE):
|
| 24 |
-
draw_method = DrawMethod(draw_method)
|
| 25 |
-
|
| 26 |
-
height, width = shape
|
| 27 |
-
mask = np.zeros((height, width), np.float32)
|
| 28 |
-
times = np.random.randint(min_times, max_times + 1)
|
| 29 |
-
for i in range(times):
|
| 30 |
-
start_x = np.random.randint(width)
|
| 31 |
-
start_y = np.random.randint(height)
|
| 32 |
-
for j in range(1 + np.random.randint(5)):
|
| 33 |
-
angle = 0.01 + np.random.randint(max_angle)
|
| 34 |
-
if i % 2 == 0:
|
| 35 |
-
angle = 2 * 3.1415926 - angle
|
| 36 |
-
length = 10 + np.random.randint(max_len)
|
| 37 |
-
brush_w = 5 + np.random.randint(max_width)
|
| 38 |
-
end_x = np.clip((start_x + length * np.sin(angle)).astype(np.int32), 0, width)
|
| 39 |
-
end_y = np.clip((start_y + length * np.cos(angle)).astype(np.int32), 0, height)
|
| 40 |
-
if draw_method == DrawMethod.LINE:
|
| 41 |
-
cv2.line(mask, (start_x, start_y), (end_x, end_y), 1.0, brush_w)
|
| 42 |
-
elif draw_method == DrawMethod.CIRCLE:
|
| 43 |
-
cv2.circle(mask, (start_x, start_y), radius=brush_w, color=1., thickness=-1)
|
| 44 |
-
elif draw_method == DrawMethod.SQUARE:
|
| 45 |
-
radius = brush_w // 2
|
| 46 |
-
mask[start_y - radius:start_y + radius, start_x - radius:start_x + radius] = 1
|
| 47 |
-
start_x, start_y = end_x, end_y
|
| 48 |
-
return mask[None, ...]
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
class RandomIrregularMaskGenerator:
|
| 52 |
-
def __init__(self, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10, ramp_kwargs=None,
|
| 53 |
-
draw_method=DrawMethod.LINE):
|
| 54 |
-
self.max_angle = max_angle
|
| 55 |
-
self.max_len = max_len
|
| 56 |
-
self.max_width = max_width
|
| 57 |
-
self.min_times = min_times
|
| 58 |
-
self.max_times = max_times
|
| 59 |
-
self.draw_method = draw_method
|
| 60 |
-
self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None
|
| 61 |
-
|
| 62 |
-
def __call__(self, img, iter_i=None, raw_image=None):
|
| 63 |
-
coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1
|
| 64 |
-
cur_max_len = int(max(1, self.max_len * coef))
|
| 65 |
-
cur_max_width = int(max(1, self.max_width * coef))
|
| 66 |
-
cur_max_times = int(self.min_times + 1 + (self.max_times - self.min_times) * coef)
|
| 67 |
-
return make_random_irregular_mask(img.shape[1:], max_angle=self.max_angle, max_len=cur_max_len,
|
| 68 |
-
max_width=cur_max_width, min_times=self.min_times, max_times=cur_max_times,
|
| 69 |
-
draw_method=self.draw_method)
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
def make_random_rectangle_mask(shape, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3):
|
| 73 |
-
height, width = shape
|
| 74 |
-
mask = np.zeros((height, width), np.float32)
|
| 75 |
-
bbox_max_size = min(bbox_max_size, height - margin * 2, width - margin * 2)
|
| 76 |
-
times = np.random.randint(min_times, max_times + 1)
|
| 77 |
-
for i in range(times):
|
| 78 |
-
box_width = np.random.randint(bbox_min_size, bbox_max_size)
|
| 79 |
-
box_height = np.random.randint(bbox_min_size, bbox_max_size)
|
| 80 |
-
start_x = np.random.randint(margin, width - margin - box_width + 1)
|
| 81 |
-
start_y = np.random.randint(margin, height - margin - box_height + 1)
|
| 82 |
-
mask[start_y:start_y + box_height, start_x:start_x + box_width] = 1
|
| 83 |
-
return mask[None, ...]
|
| 84 |
-
|
| 85 |
-
|
| 86 |
-
class RandomRectangleMaskGenerator:
|
| 87 |
-
def __init__(self, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3, ramp_kwargs=None):
|
| 88 |
-
self.margin = margin
|
| 89 |
-
self.bbox_min_size = bbox_min_size
|
| 90 |
-
self.bbox_max_size = bbox_max_size
|
| 91 |
-
self.min_times = min_times
|
| 92 |
-
self.max_times = max_times
|
| 93 |
-
self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None
|
| 94 |
-
|
| 95 |
-
def __call__(self, img, iter_i=None, raw_image=None):
|
| 96 |
-
coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1
|
| 97 |
-
cur_bbox_max_size = int(self.bbox_min_size + 1 + (self.bbox_max_size - self.bbox_min_size) * coef)
|
| 98 |
-
cur_max_times = int(self.min_times + (self.max_times - self.min_times) * coef)
|
| 99 |
-
return make_random_rectangle_mask(img.shape[1:], margin=self.margin, bbox_min_size=self.bbox_min_size,
|
| 100 |
-
bbox_max_size=cur_bbox_max_size, min_times=self.min_times,
|
| 101 |
-
max_times=cur_max_times)
|
| 102 |
-
|
| 103 |
-
|
| 104 |
-
class RandomSegmentationMaskGenerator:
|
| 105 |
-
def __init__(self, **kwargs):
|
| 106 |
-
self.impl = None # will be instantiated in first call (effectively in subprocess)
|
| 107 |
-
self.kwargs = kwargs
|
| 108 |
-
|
| 109 |
-
def __call__(self, img, iter_i=None, raw_image=None):
|
| 110 |
-
if self.impl is None:
|
| 111 |
-
self.impl = SegmentationMask(**self.kwargs)
|
| 112 |
-
|
| 113 |
-
masks = self.impl.get_masks(np.transpose(img, (1, 2, 0)))
|
| 114 |
-
masks = [m for m in masks if len(np.unique(m)) > 1]
|
| 115 |
-
return np.random.choice(masks)
|
| 116 |
-
|
| 117 |
-
|
| 118 |
-
def make_random_superres_mask(shape, min_step=2, max_step=4, min_width=1, max_width=3):
|
| 119 |
-
height, width = shape
|
| 120 |
-
mask = np.zeros((height, width), np.float32)
|
| 121 |
-
step_x = np.random.randint(min_step, max_step + 1)
|
| 122 |
-
width_x = np.random.randint(min_width, min(step_x, max_width + 1))
|
| 123 |
-
offset_x = np.random.randint(0, step_x)
|
| 124 |
-
|
| 125 |
-
step_y = np.random.randint(min_step, max_step + 1)
|
| 126 |
-
width_y = np.random.randint(min_width, min(step_y, max_width + 1))
|
| 127 |
-
offset_y = np.random.randint(0, step_y)
|
| 128 |
-
|
| 129 |
-
for dy in range(width_y):
|
| 130 |
-
mask[offset_y + dy::step_y] = 1
|
| 131 |
-
for dx in range(width_x):
|
| 132 |
-
mask[:, offset_x + dx::step_x] = 1
|
| 133 |
-
return mask[None, ...]
|
| 134 |
-
|
| 135 |
-
|
| 136 |
-
class RandomSuperresMaskGenerator:
|
| 137 |
-
def __init__(self, **kwargs):
|
| 138 |
-
self.kwargs = kwargs
|
| 139 |
-
|
| 140 |
-
def __call__(self, img, iter_i=None):
|
| 141 |
-
return make_random_superres_mask(img.shape[1:], **self.kwargs)
|
| 142 |
-
|
| 143 |
-
|
| 144 |
-
class DumbAreaMaskGenerator:
|
| 145 |
-
min_ratio = 0.1
|
| 146 |
-
max_ratio = 0.35
|
| 147 |
-
default_ratio = 0.225
|
| 148 |
-
|
| 149 |
-
def __init__(self, is_training):
|
| 150 |
-
#Parameters:
|
| 151 |
-
# is_training(bool): If true - random rectangular mask, if false - central square mask
|
| 152 |
-
self.is_training = is_training
|
| 153 |
-
|
| 154 |
-
def _random_vector(self, dimension):
|
| 155 |
-
if self.is_training:
|
| 156 |
-
lower_limit = math.sqrt(self.min_ratio)
|
| 157 |
-
upper_limit = math.sqrt(self.max_ratio)
|
| 158 |
-
mask_side = round((random.random() * (upper_limit - lower_limit) + lower_limit) * dimension)
|
| 159 |
-
u = random.randint(0, dimension-mask_side-1)
|
| 160 |
-
v = u+mask_side
|
| 161 |
-
else:
|
| 162 |
-
margin = (math.sqrt(self.default_ratio) / 2) * dimension
|
| 163 |
-
u = round(dimension/2 - margin)
|
| 164 |
-
v = round(dimension/2 + margin)
|
| 165 |
-
return u, v
|
| 166 |
-
|
| 167 |
-
def __call__(self, img, iter_i=None, raw_image=None):
|
| 168 |
-
c, height, width = img.shape
|
| 169 |
-
mask = np.zeros((height, width), np.float32)
|
| 170 |
-
x1, x2 = self._random_vector(width)
|
| 171 |
-
y1, y2 = self._random_vector(height)
|
| 172 |
-
mask[x1:x2, y1:y2] = 1
|
| 173 |
-
return mask[None, ...]
|
| 174 |
-
|
| 175 |
-
|
| 176 |
-
class OutpaintingMaskGenerator:
|
| 177 |
-
def __init__(self, min_padding_percent:float=0.04, max_padding_percent:int=0.25, left_padding_prob:float=0.5, top_padding_prob:float=0.5,
|
| 178 |
-
right_padding_prob:float=0.5, bottom_padding_prob:float=0.5, is_fixed_randomness:bool=False):
|
| 179 |
-
"""
|
| 180 |
-
is_fixed_randomness - get identical paddings for the same image if args are the same
|
| 181 |
-
"""
|
| 182 |
-
self.min_padding_percent = min_padding_percent
|
| 183 |
-
self.max_padding_percent = max_padding_percent
|
| 184 |
-
self.probs = [left_padding_prob, top_padding_prob, right_padding_prob, bottom_padding_prob]
|
| 185 |
-
self.is_fixed_randomness = is_fixed_randomness
|
| 186 |
-
|
| 187 |
-
assert self.min_padding_percent <= self.max_padding_percent
|
| 188 |
-
assert self.max_padding_percent > 0
|
| 189 |
-
assert len([x for x in [self.min_padding_percent, self.max_padding_percent] if (x>=0 and x<=1)]) == 2, f"Padding percentage should be in [0,1]"
|
| 190 |
-
assert sum(self.probs) > 0, f"At least one of the padding probs should be greater than 0 - {self.probs}"
|
| 191 |
-
assert len([x for x in self.probs if (x >= 0) and (x <= 1)]) == 4, f"At least one of padding probs is not in [0,1] - {self.probs}"
|
| 192 |
-
if len([x for x in self.probs if x > 0]) == 1:
|
| 193 |
-
LOGGER.warning(f"Only one padding prob is greater than zero - {self.probs}. That means that the outpainting masks will be always on the same side")
|
| 194 |
-
|
| 195 |
-
def apply_padding(self, mask, coord):
|
| 196 |
-
mask[int(coord[0][0]*self.img_h):int(coord[1][0]*self.img_h),
|
| 197 |
-
int(coord[0][1]*self.img_w):int(coord[1][1]*self.img_w)] = 1
|
| 198 |
-
return mask
|
| 199 |
-
|
| 200 |
-
def get_padding(self, size):
|
| 201 |
-
n1 = int(self.min_padding_percent*size)
|
| 202 |
-
n2 = int(self.max_padding_percent*size)
|
| 203 |
-
return self.rnd.randint(n1, n2) / size
|
| 204 |
-
|
| 205 |
-
@staticmethod
|
| 206 |
-
def _img2rs(img):
|
| 207 |
-
arr = np.ascontiguousarray(img.astype(np.uint8))
|
| 208 |
-
str_hash = hashlib.sha1(arr).hexdigest()
|
| 209 |
-
res = hash(str_hash)%(2**32)
|
| 210 |
-
return res
|
| 211 |
-
|
| 212 |
-
def __call__(self, img, iter_i=None, raw_image=None):
|
| 213 |
-
c, self.img_h, self.img_w = img.shape
|
| 214 |
-
mask = np.zeros((self.img_h, self.img_w), np.float32)
|
| 215 |
-
at_least_one_mask_applied = False
|
| 216 |
-
|
| 217 |
-
if self.is_fixed_randomness:
|
| 218 |
-
assert raw_image is not None, f"Cant calculate hash on raw_image=None"
|
| 219 |
-
rs = self._img2rs(raw_image)
|
| 220 |
-
self.rnd = np.random.RandomState(rs)
|
| 221 |
-
else:
|
| 222 |
-
self.rnd = np.random
|
| 223 |
-
|
| 224 |
-
coords = [[
|
| 225 |
-
(0,0),
|
| 226 |
-
(1,self.get_padding(size=self.img_h))
|
| 227 |
-
],
|
| 228 |
-
[
|
| 229 |
-
(0,0),
|
| 230 |
-
(self.get_padding(size=self.img_w),1)
|
| 231 |
-
],
|
| 232 |
-
[
|
| 233 |
-
(0,1-self.get_padding(size=self.img_h)),
|
| 234 |
-
(1,1)
|
| 235 |
-
],
|
| 236 |
-
[
|
| 237 |
-
(1-self.get_padding(size=self.img_w),0),
|
| 238 |
-
(1,1)
|
| 239 |
-
]]
|
| 240 |
-
|
| 241 |
-
for pp, coord in zip(self.probs, coords):
|
| 242 |
-
if self.rnd.random() < pp:
|
| 243 |
-
at_least_one_mask_applied = True
|
| 244 |
-
mask = self.apply_padding(mask=mask, coord=coord)
|
| 245 |
-
|
| 246 |
-
if not at_least_one_mask_applied:
|
| 247 |
-
idx = self.rnd.choice(range(len(coords)), p=np.array(self.probs)/sum(self.probs))
|
| 248 |
-
mask = self.apply_padding(mask=mask, coord=coords[idx])
|
| 249 |
-
return mask[None, ...]
|
| 250 |
-
|
| 251 |
-
|
| 252 |
-
class MixedMaskGenerator:
|
| 253 |
-
def __init__(self, irregular_proba=1/3, irregular_kwargs=None,
|
| 254 |
-
box_proba=1/3, box_kwargs=None,
|
| 255 |
-
segm_proba=1/3, segm_kwargs=None,
|
| 256 |
-
squares_proba=0, squares_kwargs=None,
|
| 257 |
-
superres_proba=0, superres_kwargs=None,
|
| 258 |
-
outpainting_proba=0, outpainting_kwargs=None,
|
| 259 |
-
invert_proba=0):
|
| 260 |
-
self.probas = []
|
| 261 |
-
self.gens = []
|
| 262 |
-
|
| 263 |
-
if irregular_proba > 0:
|
| 264 |
-
self.probas.append(irregular_proba)
|
| 265 |
-
if irregular_kwargs is None:
|
| 266 |
-
irregular_kwargs = {}
|
| 267 |
-
else:
|
| 268 |
-
irregular_kwargs = dict(irregular_kwargs)
|
| 269 |
-
irregular_kwargs['draw_method'] = DrawMethod.LINE
|
| 270 |
-
self.gens.append(RandomIrregularMaskGenerator(**irregular_kwargs))
|
| 271 |
-
|
| 272 |
-
if box_proba > 0:
|
| 273 |
-
self.probas.append(box_proba)
|
| 274 |
-
if box_kwargs is None:
|
| 275 |
-
box_kwargs = {}
|
| 276 |
-
self.gens.append(RandomRectangleMaskGenerator(**box_kwargs))
|
| 277 |
-
|
| 278 |
-
if segm_proba > 0:
|
| 279 |
-
self.probas.append(segm_proba)
|
| 280 |
-
if segm_kwargs is None:
|
| 281 |
-
segm_kwargs = {}
|
| 282 |
-
self.gens.append(RandomSegmentationMaskGenerator(**segm_kwargs))
|
| 283 |
-
|
| 284 |
-
if squares_proba > 0:
|
| 285 |
-
self.probas.append(squares_proba)
|
| 286 |
-
if squares_kwargs is None:
|
| 287 |
-
squares_kwargs = {}
|
| 288 |
-
else:
|
| 289 |
-
squares_kwargs = dict(squares_kwargs)
|
| 290 |
-
squares_kwargs['draw_method'] = DrawMethod.SQUARE
|
| 291 |
-
self.gens.append(RandomIrregularMaskGenerator(**squares_kwargs))
|
| 292 |
-
|
| 293 |
-
if superres_proba > 0:
|
| 294 |
-
self.probas.append(superres_proba)
|
| 295 |
-
if superres_kwargs is None:
|
| 296 |
-
superres_kwargs = {}
|
| 297 |
-
self.gens.append(RandomSuperresMaskGenerator(**superres_kwargs))
|
| 298 |
-
|
| 299 |
-
if outpainting_proba > 0:
|
| 300 |
-
self.probas.append(outpainting_proba)
|
| 301 |
-
if outpainting_kwargs is None:
|
| 302 |
-
outpainting_kwargs = {}
|
| 303 |
-
self.gens.append(OutpaintingMaskGenerator(**outpainting_kwargs))
|
| 304 |
-
|
| 305 |
-
self.probas = np.array(self.probas, dtype='float32')
|
| 306 |
-
self.probas /= self.probas.sum()
|
| 307 |
-
self.invert_proba = invert_proba
|
| 308 |
-
|
| 309 |
-
def __call__(self, img, iter_i=None, raw_image=None):
|
| 310 |
-
kind = np.random.choice(len(self.probas), p=self.probas)
|
| 311 |
-
gen = self.gens[kind]
|
| 312 |
-
result = gen(img, iter_i=iter_i, raw_image=raw_image)
|
| 313 |
-
if self.invert_proba > 0 and random.random() < self.invert_proba:
|
| 314 |
-
result = 1 - result
|
| 315 |
-
return result
|
| 316 |
-
|
| 317 |
-
|
| 318 |
-
def get_mask_generator(kind, kwargs):
|
| 319 |
-
if kind is None:
|
| 320 |
-
kind = "mixed"
|
| 321 |
-
if kwargs is None:
|
| 322 |
-
kwargs = {}
|
| 323 |
-
|
| 324 |
-
if kind == "mixed":
|
| 325 |
-
cl = MixedMaskGenerator
|
| 326 |
-
elif kind == "outpainting":
|
| 327 |
-
cl = OutpaintingMaskGenerator
|
| 328 |
-
elif kind == "dumb":
|
| 329 |
-
cl = DumbAreaMaskGenerator
|
| 330 |
-
else:
|
| 331 |
-
raise NotImplementedError(f"No such generator kind = {kind}")
|
| 332 |
-
return cl(**kwargs)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/AlexZou/Deploy_Restoration/net/PositionalEncoding.py
DELETED
|
@@ -1,35 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import torch.nn as nn
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
#实现了位置编码
|
| 6 |
-
class FixedPositionalEncoding(nn.Module):
|
| 7 |
-
def __init__(self, embedding_dim, max_length=512):
|
| 8 |
-
super(FixedPositionalEncoding, self).__init__()
|
| 9 |
-
|
| 10 |
-
pe = torch.zeros(max_length, embedding_dim)
|
| 11 |
-
position = torch.arange(0, max_length, dtype=torch.float).unsqueeze(1)
|
| 12 |
-
div_term = torch.exp(
|
| 13 |
-
torch.arange(0, embedding_dim, 2).float()
|
| 14 |
-
* (-torch.log(torch.tensor(10000.0)) / embedding_dim)
|
| 15 |
-
)
|
| 16 |
-
pe[:, 0::2] = torch.sin(position * div_term)
|
| 17 |
-
pe[:, 1::2] = torch.cos(position * div_term)
|
| 18 |
-
pe = pe.unsqueeze(0).transpose(0, 1)
|
| 19 |
-
self.register_buffer('pe', pe)
|
| 20 |
-
|
| 21 |
-
def forward(self, x):
|
| 22 |
-
x = x + self.pe[: x.size(0), :]
|
| 23 |
-
return x
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
class LearnedPositionalEncoding(nn.Module):
|
| 27 |
-
def __init__(self, max_position_embeddings, embedding_dim, seq_length):
|
| 28 |
-
super(LearnedPositionalEncoding, self).__init__()
|
| 29 |
-
|
| 30 |
-
self.position_embeddings = nn.Parameter(torch.zeros(1, 256, 512)) #8x
|
| 31 |
-
|
| 32 |
-
def forward(self, x, position_ids=None):
|
| 33 |
-
|
| 34 |
-
position_embeddings = self.position_embeddings
|
| 35 |
-
return x + position_embeddings
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Andres99/Tune-A-Video-Training-UI/constants.py
DELETED
|
@@ -1,10 +0,0 @@
|
|
| 1 |
-
import enum
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
class UploadTarget(enum.Enum):
|
| 5 |
-
PERSONAL_PROFILE = 'Personal Profile'
|
| 6 |
-
MODEL_LIBRARY = 'Tune-A-Video Library'
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
MODEL_LIBRARY_ORG_NAME = 'Tune-A-Video-library'
|
| 10 |
-
SAMPLE_MODEL_REPO = 'Tune-A-Video-library/a-man-is-surfing'
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Andy1621/uniformer_image_detection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py
DELETED
|
@@ -1,75 +0,0 @@
|
|
| 1 |
-
_base_ = [
|
| 2 |
-
'../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
|
| 3 |
-
'../_base_/default_runtime.py'
|
| 4 |
-
]
|
| 5 |
-
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
|
| 6 |
-
|
| 7 |
-
CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
|
| 8 |
-
'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
|
| 9 |
-
'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
|
| 10 |
-
|
| 11 |
-
# dataset settings
|
| 12 |
-
dataset_type = 'CocoDataset'
|
| 13 |
-
data_root = 'data/VOCdevkit/'
|
| 14 |
-
img_norm_cfg = dict(
|
| 15 |
-
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
|
| 16 |
-
train_pipeline = [
|
| 17 |
-
dict(type='LoadImageFromFile'),
|
| 18 |
-
dict(type='LoadAnnotations', with_bbox=True),
|
| 19 |
-
dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
|
| 20 |
-
dict(type='RandomFlip', flip_ratio=0.5),
|
| 21 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 22 |
-
dict(type='Pad', size_divisor=32),
|
| 23 |
-
dict(type='DefaultFormatBundle'),
|
| 24 |
-
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
|
| 25 |
-
]
|
| 26 |
-
test_pipeline = [
|
| 27 |
-
dict(type='LoadImageFromFile'),
|
| 28 |
-
dict(
|
| 29 |
-
type='MultiScaleFlipAug',
|
| 30 |
-
img_scale=(1000, 600),
|
| 31 |
-
flip=False,
|
| 32 |
-
transforms=[
|
| 33 |
-
dict(type='Resize', keep_ratio=True),
|
| 34 |
-
dict(type='RandomFlip'),
|
| 35 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 36 |
-
dict(type='Pad', size_divisor=32),
|
| 37 |
-
dict(type='ImageToTensor', keys=['img']),
|
| 38 |
-
dict(type='Collect', keys=['img']),
|
| 39 |
-
])
|
| 40 |
-
]
|
| 41 |
-
data = dict(
|
| 42 |
-
samples_per_gpu=2,
|
| 43 |
-
workers_per_gpu=2,
|
| 44 |
-
train=dict(
|
| 45 |
-
type='RepeatDataset',
|
| 46 |
-
times=3,
|
| 47 |
-
dataset=dict(
|
| 48 |
-
type=dataset_type,
|
| 49 |
-
ann_file='data/voc0712_trainval.json',
|
| 50 |
-
img_prefix='data/VOCdevkit',
|
| 51 |
-
pipeline=train_pipeline,
|
| 52 |
-
classes=CLASSES)),
|
| 53 |
-
val=dict(
|
| 54 |
-
type=dataset_type,
|
| 55 |
-
ann_file='data/voc07_test.json',
|
| 56 |
-
img_prefix='data/VOCdevkit',
|
| 57 |
-
pipeline=test_pipeline,
|
| 58 |
-
classes=CLASSES),
|
| 59 |
-
test=dict(
|
| 60 |
-
type=dataset_type,
|
| 61 |
-
ann_file='data/voc07_test.json',
|
| 62 |
-
img_prefix='data/VOCdevkit',
|
| 63 |
-
pipeline=test_pipeline,
|
| 64 |
-
classes=CLASSES))
|
| 65 |
-
evaluation = dict(interval=1, metric='bbox')
|
| 66 |
-
|
| 67 |
-
# optimizer
|
| 68 |
-
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
|
| 69 |
-
optimizer_config = dict(grad_clip=None)
|
| 70 |
-
# learning policy
|
| 71 |
-
# actual epoch = 3 * 3 = 9
|
| 72 |
-
lr_config = dict(policy='step', step=[3])
|
| 73 |
-
# runtime settings
|
| 74 |
-
runner = dict(
|
| 75 |
-
type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
_base_ = './nonlocal_r50-d8_512x512_20k_voc12aug.py'
|
| 2 |
-
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
|
|
|
|
|
|
|
|
spaces/Audio-AGI/WavJourney/scripts/start_ui.sh
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
conda run --live-stream -n WavJourney python -u ui_client.py 2>&1 | stdbuf -oL tee services_logs/wavejourney.out
|
|
|
|
|
|
spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis.py
DELETED
|
@@ -1,240 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
-
import logging
|
| 3 |
-
import os
|
| 4 |
-
from fvcore.common.timer import Timer
|
| 5 |
-
|
| 6 |
-
from detectron2.data import DatasetCatalog, MetadataCatalog
|
| 7 |
-
from detectron2.structures import BoxMode
|
| 8 |
-
from detectron2.utils.file_io import PathManager
|
| 9 |
-
|
| 10 |
-
from .builtin_meta import _get_coco_instances_meta
|
| 11 |
-
from .lvis_v0_5_categories import LVIS_CATEGORIES as LVIS_V0_5_CATEGORIES
|
| 12 |
-
from .lvis_v1_categories import LVIS_CATEGORIES as LVIS_V1_CATEGORIES
|
| 13 |
-
|
| 14 |
-
"""
|
| 15 |
-
This file contains functions to parse LVIS-format annotations into dicts in the
|
| 16 |
-
"Detectron2 format".
|
| 17 |
-
"""
|
| 18 |
-
|
| 19 |
-
logger = logging.getLogger(__name__)
|
| 20 |
-
|
| 21 |
-
__all__ = ["load_lvis_json", "register_lvis_instances", "get_lvis_instances_meta"]
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
def register_lvis_instances(name, metadata, json_file, image_root):
|
| 25 |
-
"""
|
| 26 |
-
Register a dataset in LVIS's json annotation format for instance detection and segmentation.
|
| 27 |
-
|
| 28 |
-
Args:
|
| 29 |
-
name (str): a name that identifies the dataset, e.g. "lvis_v0.5_train".
|
| 30 |
-
metadata (dict): extra metadata associated with this dataset. It can be an empty dict.
|
| 31 |
-
json_file (str): path to the json instance annotation file.
|
| 32 |
-
image_root (str or path-like): directory which contains all the images.
|
| 33 |
-
"""
|
| 34 |
-
DatasetCatalog.register(name, lambda: load_lvis_json(json_file, image_root, name))
|
| 35 |
-
MetadataCatalog.get(name).set(
|
| 36 |
-
json_file=json_file, image_root=image_root, evaluator_type="lvis", **metadata
|
| 37 |
-
)
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
def load_lvis_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None):
|
| 41 |
-
"""
|
| 42 |
-
Load a json file in LVIS's annotation format.
|
| 43 |
-
|
| 44 |
-
Args:
|
| 45 |
-
json_file (str): full path to the LVIS json annotation file.
|
| 46 |
-
image_root (str): the directory where the images in this json file exists.
|
| 47 |
-
dataset_name (str): the name of the dataset (e.g., "lvis_v0.5_train").
|
| 48 |
-
If provided, this function will put "thing_classes" into the metadata
|
| 49 |
-
associated with this dataset.
|
| 50 |
-
extra_annotation_keys (list[str]): list of per-annotation keys that should also be
|
| 51 |
-
loaded into the dataset dict (besides "bbox", "bbox_mode", "category_id",
|
| 52 |
-
"segmentation"). The values for these keys will be returned as-is.
|
| 53 |
-
|
| 54 |
-
Returns:
|
| 55 |
-
list[dict]: a list of dicts in Detectron2 standard format. (See
|
| 56 |
-
`Using Custom Datasets </tutorials/datasets.html>`_ )
|
| 57 |
-
|
| 58 |
-
Notes:
|
| 59 |
-
1. This function does not read the image files.
|
| 60 |
-
The results do not have the "image" field.
|
| 61 |
-
"""
|
| 62 |
-
from lvis import LVIS
|
| 63 |
-
|
| 64 |
-
json_file = PathManager.get_local_path(json_file)
|
| 65 |
-
|
| 66 |
-
timer = Timer()
|
| 67 |
-
lvis_api = LVIS(json_file)
|
| 68 |
-
if timer.seconds() > 1:
|
| 69 |
-
logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
|
| 70 |
-
|
| 71 |
-
if dataset_name is not None:
|
| 72 |
-
meta = get_lvis_instances_meta(dataset_name)
|
| 73 |
-
MetadataCatalog.get(dataset_name).set(**meta)
|
| 74 |
-
|
| 75 |
-
# sort indices for reproducible results
|
| 76 |
-
img_ids = sorted(lvis_api.imgs.keys())
|
| 77 |
-
# imgs is a list of dicts, each looks something like:
|
| 78 |
-
# {'license': 4,
|
| 79 |
-
# 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
|
| 80 |
-
# 'file_name': 'COCO_val2014_000000001268.jpg',
|
| 81 |
-
# 'height': 427,
|
| 82 |
-
# 'width': 640,
|
| 83 |
-
# 'date_captured': '2013-11-17 05:57:24',
|
| 84 |
-
# 'id': 1268}
|
| 85 |
-
imgs = lvis_api.load_imgs(img_ids)
|
| 86 |
-
# anns is a list[list[dict]], where each dict is an annotation
|
| 87 |
-
# record for an object. The inner list enumerates the objects in an image
|
| 88 |
-
# and the outer list enumerates over images. Example of anns[0]:
|
| 89 |
-
# [{'segmentation': [[192.81,
|
| 90 |
-
# 247.09,
|
| 91 |
-
# ...
|
| 92 |
-
# 219.03,
|
| 93 |
-
# 249.06]],
|
| 94 |
-
# 'area': 1035.749,
|
| 95 |
-
# 'image_id': 1268,
|
| 96 |
-
# 'bbox': [192.81, 224.8, 74.73, 33.43],
|
| 97 |
-
# 'category_id': 16,
|
| 98 |
-
# 'id': 42986},
|
| 99 |
-
# ...]
|
| 100 |
-
anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
|
| 101 |
-
|
| 102 |
-
# Sanity check that each annotation has a unique id
|
| 103 |
-
ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
|
| 104 |
-
assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique".format(
|
| 105 |
-
json_file
|
| 106 |
-
)
|
| 107 |
-
|
| 108 |
-
imgs_anns = list(zip(imgs, anns))
|
| 109 |
-
|
| 110 |
-
logger.info("Loaded {} images in the LVIS format from {}".format(len(imgs_anns), json_file))
|
| 111 |
-
|
| 112 |
-
if extra_annotation_keys:
|
| 113 |
-
logger.info(
|
| 114 |
-
"The following extra annotation keys will be loaded: {} ".format(extra_annotation_keys)
|
| 115 |
-
)
|
| 116 |
-
else:
|
| 117 |
-
extra_annotation_keys = []
|
| 118 |
-
|
| 119 |
-
def get_file_name(img_root, img_dict):
|
| 120 |
-
# Determine the path including the split folder ("train2017", "val2017", "test2017") from
|
| 121 |
-
# the coco_url field. Example:
|
| 122 |
-
# 'coco_url': 'http://images.cocodataset.org/train2017/000000155379.jpg'
|
| 123 |
-
split_folder, file_name = img_dict["coco_url"].split("/")[-2:]
|
| 124 |
-
return os.path.join(img_root + split_folder, file_name)
|
| 125 |
-
|
| 126 |
-
dataset_dicts = []
|
| 127 |
-
|
| 128 |
-
for (img_dict, anno_dict_list) in imgs_anns:
|
| 129 |
-
record = {}
|
| 130 |
-
record["file_name"] = get_file_name(image_root, img_dict)
|
| 131 |
-
record["height"] = img_dict["height"]
|
| 132 |
-
record["width"] = img_dict["width"]
|
| 133 |
-
record["not_exhaustive_category_ids"] = img_dict.get("not_exhaustive_category_ids", [])
|
| 134 |
-
record["neg_category_ids"] = img_dict.get("neg_category_ids", [])
|
| 135 |
-
image_id = record["image_id"] = img_dict["id"]
|
| 136 |
-
|
| 137 |
-
objs = []
|
| 138 |
-
for anno in anno_dict_list:
|
| 139 |
-
# Check that the image_id in this annotation is the same as
|
| 140 |
-
# the image_id we're looking at.
|
| 141 |
-
# This fails only when the data parsing logic or the annotation file is buggy.
|
| 142 |
-
assert anno["image_id"] == image_id
|
| 143 |
-
obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS}
|
| 144 |
-
# LVIS data loader can be used to load COCO dataset categories. In this case `meta`
|
| 145 |
-
# variable will have a field with COCO-specific category mapping.
|
| 146 |
-
if dataset_name is not None and "thing_dataset_id_to_contiguous_id" in meta:
|
| 147 |
-
obj["category_id"] = meta["thing_dataset_id_to_contiguous_id"][anno["category_id"]]
|
| 148 |
-
else:
|
| 149 |
-
obj["category_id"] = anno["category_id"] - 1 # Convert 1-indexed to 0-indexed
|
| 150 |
-
segm = anno["segmentation"] # list[list[float]]
|
| 151 |
-
# filter out invalid polygons (< 3 points)
|
| 152 |
-
valid_segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
|
| 153 |
-
assert len(segm) == len(
|
| 154 |
-
valid_segm
|
| 155 |
-
), "Annotation contains an invalid polygon with < 3 points"
|
| 156 |
-
assert len(segm) > 0
|
| 157 |
-
obj["segmentation"] = segm
|
| 158 |
-
for extra_ann_key in extra_annotation_keys:
|
| 159 |
-
obj[extra_ann_key] = anno[extra_ann_key]
|
| 160 |
-
objs.append(obj)
|
| 161 |
-
record["annotations"] = objs
|
| 162 |
-
dataset_dicts.append(record)
|
| 163 |
-
|
| 164 |
-
return dataset_dicts
|
| 165 |
-
|
| 166 |
-
|
| 167 |
-
def get_lvis_instances_meta(dataset_name):
|
| 168 |
-
"""
|
| 169 |
-
Load LVIS metadata.
|
| 170 |
-
|
| 171 |
-
Args:
|
| 172 |
-
dataset_name (str): LVIS dataset name without the split name (e.g., "lvis_v0.5").
|
| 173 |
-
|
| 174 |
-
Returns:
|
| 175 |
-
dict: LVIS metadata with keys: thing_classes
|
| 176 |
-
"""
|
| 177 |
-
if "cocofied" in dataset_name:
|
| 178 |
-
return _get_coco_instances_meta()
|
| 179 |
-
if "v0.5" in dataset_name:
|
| 180 |
-
return _get_lvis_instances_meta_v0_5()
|
| 181 |
-
elif "v1" in dataset_name:
|
| 182 |
-
return _get_lvis_instances_meta_v1()
|
| 183 |
-
raise ValueError("No built-in metadata for dataset {}".format(dataset_name))
|
| 184 |
-
|
| 185 |
-
|
| 186 |
-
def _get_lvis_instances_meta_v0_5():
|
| 187 |
-
assert len(LVIS_V0_5_CATEGORIES) == 1230
|
| 188 |
-
cat_ids = [k["id"] for k in LVIS_V0_5_CATEGORIES]
|
| 189 |
-
assert min(cat_ids) == 1 and max(cat_ids) == len(
|
| 190 |
-
cat_ids
|
| 191 |
-
), "Category ids are not in [1, #categories], as expected"
|
| 192 |
-
# Ensure that the category list is sorted by id
|
| 193 |
-
lvis_categories = sorted(LVIS_V0_5_CATEGORIES, key=lambda x: x["id"])
|
| 194 |
-
thing_classes = [k["synonyms"][0] for k in lvis_categories]
|
| 195 |
-
meta = {"thing_classes": thing_classes}
|
| 196 |
-
return meta
|
| 197 |
-
|
| 198 |
-
|
| 199 |
-
def _get_lvis_instances_meta_v1():
|
| 200 |
-
assert len(LVIS_V1_CATEGORIES) == 1203
|
| 201 |
-
cat_ids = [k["id"] for k in LVIS_V1_CATEGORIES]
|
| 202 |
-
assert min(cat_ids) == 1 and max(cat_ids) == len(
|
| 203 |
-
cat_ids
|
| 204 |
-
), "Category ids are not in [1, #categories], as expected"
|
| 205 |
-
# Ensure that the category list is sorted by id
|
| 206 |
-
lvis_categories = sorted(LVIS_V1_CATEGORIES, key=lambda x: x["id"])
|
| 207 |
-
thing_classes = [k["synonyms"][0] for k in lvis_categories]
|
| 208 |
-
meta = {"thing_classes": thing_classes}
|
| 209 |
-
return meta
|
| 210 |
-
|
| 211 |
-
|
| 212 |
-
if __name__ == "__main__":
|
| 213 |
-
"""
|
| 214 |
-
Test the LVIS json dataset loader.
|
| 215 |
-
|
| 216 |
-
Usage:
|
| 217 |
-
python -m detectron2.data.datasets.lvis \
|
| 218 |
-
path/to/json path/to/image_root dataset_name vis_limit
|
| 219 |
-
"""
|
| 220 |
-
import sys
|
| 221 |
-
import numpy as np
|
| 222 |
-
from detectron2.utils.logger import setup_logger
|
| 223 |
-
from PIL import Image
|
| 224 |
-
import detectron2.data.datasets # noqa # add pre-defined metadata
|
| 225 |
-
from detectron2.utils.visualizer import Visualizer
|
| 226 |
-
|
| 227 |
-
logger = setup_logger(name=__name__)
|
| 228 |
-
meta = MetadataCatalog.get(sys.argv[3])
|
| 229 |
-
|
| 230 |
-
dicts = load_lvis_json(sys.argv[1], sys.argv[2], sys.argv[3])
|
| 231 |
-
logger.info("Done loading {} samples.".format(len(dicts)))
|
| 232 |
-
|
| 233 |
-
dirname = "lvis-data-vis"
|
| 234 |
-
os.makedirs(dirname, exist_ok=True)
|
| 235 |
-
for d in dicts[: int(sys.argv[4])]:
|
| 236 |
-
img = np.array(Image.open(d["file_name"]))
|
| 237 |
-
visualizer = Visualizer(img, metadata=meta)
|
| 238 |
-
vis = visualizer.draw_dataset_dict(d)
|
| 239 |
-
fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
|
| 240 |
-
vis.save(fpath)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/AzinZ/vitscn/data_utils.py
DELETED
|
@@ -1,392 +0,0 @@
|
|
| 1 |
-
import time
|
| 2 |
-
import os
|
| 3 |
-
import random
|
| 4 |
-
import numpy as np
|
| 5 |
-
import torch
|
| 6 |
-
import torch.utils.data
|
| 7 |
-
|
| 8 |
-
import commons
|
| 9 |
-
from mel_processing import spectrogram_torch
|
| 10 |
-
from utils import load_wav_to_torch, load_filepaths_and_text
|
| 11 |
-
from text import text_to_sequence, cleaned_text_to_sequence
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
class TextAudioLoader(torch.utils.data.Dataset):
|
| 15 |
-
"""
|
| 16 |
-
1) loads audio, text pairs
|
| 17 |
-
2) normalizes text and converts them to sequences of integers
|
| 18 |
-
3) computes spectrograms from audio files.
|
| 19 |
-
"""
|
| 20 |
-
def __init__(self, audiopaths_and_text, hparams):
|
| 21 |
-
self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
|
| 22 |
-
self.text_cleaners = hparams.text_cleaners
|
| 23 |
-
self.max_wav_value = hparams.max_wav_value
|
| 24 |
-
self.sampling_rate = hparams.sampling_rate
|
| 25 |
-
self.filter_length = hparams.filter_length
|
| 26 |
-
self.hop_length = hparams.hop_length
|
| 27 |
-
self.win_length = hparams.win_length
|
| 28 |
-
self.sampling_rate = hparams.sampling_rate
|
| 29 |
-
|
| 30 |
-
self.cleaned_text = getattr(hparams, "cleaned_text", False)
|
| 31 |
-
|
| 32 |
-
self.add_blank = hparams.add_blank
|
| 33 |
-
self.min_text_len = getattr(hparams, "min_text_len", 1)
|
| 34 |
-
self.max_text_len = getattr(hparams, "max_text_len", 190)
|
| 35 |
-
|
| 36 |
-
random.seed(1234)
|
| 37 |
-
random.shuffle(self.audiopaths_and_text)
|
| 38 |
-
self._filter()
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
def _filter(self):
|
| 42 |
-
"""
|
| 43 |
-
Filter text & store spec lengths
|
| 44 |
-
"""
|
| 45 |
-
# Store spectrogram lengths for Bucketing
|
| 46 |
-
# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
|
| 47 |
-
# spec_length = wav_length // hop_length
|
| 48 |
-
|
| 49 |
-
audiopaths_and_text_new = []
|
| 50 |
-
lengths = []
|
| 51 |
-
for audiopath, text in self.audiopaths_and_text:
|
| 52 |
-
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
|
| 53 |
-
audiopaths_and_text_new.append([audiopath, text])
|
| 54 |
-
lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
|
| 55 |
-
self.audiopaths_and_text = audiopaths_and_text_new
|
| 56 |
-
self.lengths = lengths
|
| 57 |
-
|
| 58 |
-
def get_audio_text_pair(self, audiopath_and_text):
|
| 59 |
-
# separate filename and text
|
| 60 |
-
audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
|
| 61 |
-
text = self.get_text(text)
|
| 62 |
-
spec, wav = self.get_audio(audiopath)
|
| 63 |
-
return (text, spec, wav)
|
| 64 |
-
|
| 65 |
-
def get_audio(self, filename):
|
| 66 |
-
audio, sampling_rate = load_wav_to_torch(filename)
|
| 67 |
-
if sampling_rate != self.sampling_rate:
|
| 68 |
-
raise ValueError("{} {} SR doesn't match target {} SR".format(
|
| 69 |
-
sampling_rate, self.sampling_rate))
|
| 70 |
-
audio_norm = audio / self.max_wav_value
|
| 71 |
-
audio_norm = audio_norm.unsqueeze(0)
|
| 72 |
-
spec_filename = filename.replace(".wav", ".spec.pt")
|
| 73 |
-
if os.path.exists(spec_filename):
|
| 74 |
-
spec = torch.load(spec_filename)
|
| 75 |
-
else:
|
| 76 |
-
spec = spectrogram_torch(audio_norm, self.filter_length,
|
| 77 |
-
self.sampling_rate, self.hop_length, self.win_length,
|
| 78 |
-
center=False)
|
| 79 |
-
spec = torch.squeeze(spec, 0)
|
| 80 |
-
torch.save(spec, spec_filename)
|
| 81 |
-
return spec, audio_norm
|
| 82 |
-
|
| 83 |
-
def get_text(self, text):
|
| 84 |
-
if self.cleaned_text:
|
| 85 |
-
text_norm = cleaned_text_to_sequence(text)
|
| 86 |
-
else:
|
| 87 |
-
text_norm = text_to_sequence(text, self.text_cleaners)
|
| 88 |
-
if self.add_blank:
|
| 89 |
-
text_norm = commons.intersperse(text_norm, 0)
|
| 90 |
-
text_norm = torch.LongTensor(text_norm)
|
| 91 |
-
return text_norm
|
| 92 |
-
|
| 93 |
-
def __getitem__(self, index):
|
| 94 |
-
return self.get_audio_text_pair(self.audiopaths_and_text[index])
|
| 95 |
-
|
| 96 |
-
def __len__(self):
|
| 97 |
-
return len(self.audiopaths_and_text)
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
class TextAudioCollate():
|
| 101 |
-
""" Zero-pads model inputs and targets
|
| 102 |
-
"""
|
| 103 |
-
def __init__(self, return_ids=False):
|
| 104 |
-
self.return_ids = return_ids
|
| 105 |
-
|
| 106 |
-
def __call__(self, batch):
|
| 107 |
-
"""Collate's training batch from normalized text and aduio
|
| 108 |
-
PARAMS
|
| 109 |
-
------
|
| 110 |
-
batch: [text_normalized, spec_normalized, wav_normalized]
|
| 111 |
-
"""
|
| 112 |
-
# Right zero-pad all one-hot text sequences to max input length
|
| 113 |
-
_, ids_sorted_decreasing = torch.sort(
|
| 114 |
-
torch.LongTensor([x[1].size(1) for x in batch]),
|
| 115 |
-
dim=0, descending=True)
|
| 116 |
-
|
| 117 |
-
max_text_len = max([len(x[0]) for x in batch])
|
| 118 |
-
max_spec_len = max([x[1].size(1) for x in batch])
|
| 119 |
-
max_wav_len = max([x[2].size(1) for x in batch])
|
| 120 |
-
|
| 121 |
-
text_lengths = torch.LongTensor(len(batch))
|
| 122 |
-
spec_lengths = torch.LongTensor(len(batch))
|
| 123 |
-
wav_lengths = torch.LongTensor(len(batch))
|
| 124 |
-
|
| 125 |
-
text_padded = torch.LongTensor(len(batch), max_text_len)
|
| 126 |
-
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
|
| 127 |
-
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
| 128 |
-
text_padded.zero_()
|
| 129 |
-
spec_padded.zero_()
|
| 130 |
-
wav_padded.zero_()
|
| 131 |
-
for i in range(len(ids_sorted_decreasing)):
|
| 132 |
-
row = batch[ids_sorted_decreasing[i]]
|
| 133 |
-
|
| 134 |
-
text = row[0]
|
| 135 |
-
text_padded[i, :text.size(0)] = text
|
| 136 |
-
text_lengths[i] = text.size(0)
|
| 137 |
-
|
| 138 |
-
spec = row[1]
|
| 139 |
-
spec_padded[i, :, :spec.size(1)] = spec
|
| 140 |
-
spec_lengths[i] = spec.size(1)
|
| 141 |
-
|
| 142 |
-
wav = row[2]
|
| 143 |
-
wav_padded[i, :, :wav.size(1)] = wav
|
| 144 |
-
wav_lengths[i] = wav.size(1)
|
| 145 |
-
|
| 146 |
-
if self.return_ids:
|
| 147 |
-
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing
|
| 148 |
-
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths
|
| 149 |
-
|
| 150 |
-
|
| 151 |
-
"""Multi speaker version"""
|
| 152 |
-
class TextAudioSpeakerLoader(torch.utils.data.Dataset):
|
| 153 |
-
"""
|
| 154 |
-
1) loads audio, speaker_id, text pairs
|
| 155 |
-
2) normalizes text and converts them to sequences of integers
|
| 156 |
-
3) computes spectrograms from audio files.
|
| 157 |
-
"""
|
| 158 |
-
def __init__(self, audiopaths_sid_text, hparams):
|
| 159 |
-
self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
|
| 160 |
-
self.text_cleaners = hparams.text_cleaners
|
| 161 |
-
self.max_wav_value = hparams.max_wav_value
|
| 162 |
-
self.sampling_rate = hparams.sampling_rate
|
| 163 |
-
self.filter_length = hparams.filter_length
|
| 164 |
-
self.hop_length = hparams.hop_length
|
| 165 |
-
self.win_length = hparams.win_length
|
| 166 |
-
self.sampling_rate = hparams.sampling_rate
|
| 167 |
-
|
| 168 |
-
self.cleaned_text = getattr(hparams, "cleaned_text", False)
|
| 169 |
-
|
| 170 |
-
self.add_blank = hparams.add_blank
|
| 171 |
-
self.min_text_len = getattr(hparams, "min_text_len", 1)
|
| 172 |
-
self.max_text_len = getattr(hparams, "max_text_len", 190)
|
| 173 |
-
|
| 174 |
-
random.seed(1234)
|
| 175 |
-
random.shuffle(self.audiopaths_sid_text)
|
| 176 |
-
self._filter()
|
| 177 |
-
|
| 178 |
-
def _filter(self):
|
| 179 |
-
"""
|
| 180 |
-
Filter text & store spec lengths
|
| 181 |
-
"""
|
| 182 |
-
# Store spectrogram lengths for Bucketing
|
| 183 |
-
# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
|
| 184 |
-
# spec_length = wav_length // hop_length
|
| 185 |
-
|
| 186 |
-
audiopaths_sid_text_new = []
|
| 187 |
-
lengths = []
|
| 188 |
-
for audiopath, sid, text in self.audiopaths_sid_text:
|
| 189 |
-
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
|
| 190 |
-
audiopaths_sid_text_new.append([audiopath, sid, text])
|
| 191 |
-
lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
|
| 192 |
-
self.audiopaths_sid_text = audiopaths_sid_text_new
|
| 193 |
-
self.lengths = lengths
|
| 194 |
-
|
| 195 |
-
def get_audio_text_speaker_pair(self, audiopath_sid_text):
|
| 196 |
-
# separate filename, speaker_id and text
|
| 197 |
-
audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
|
| 198 |
-
text = self.get_text(text)
|
| 199 |
-
spec, wav = self.get_audio(audiopath)
|
| 200 |
-
sid = self.get_sid(sid)
|
| 201 |
-
return (text, spec, wav, sid)
|
| 202 |
-
|
| 203 |
-
def get_audio(self, filename):
|
| 204 |
-
audio, sampling_rate = load_wav_to_torch(filename)
|
| 205 |
-
if sampling_rate != self.sampling_rate:
|
| 206 |
-
raise ValueError("{} {} SR doesn't match target {} SR".format(
|
| 207 |
-
sampling_rate, self.sampling_rate))
|
| 208 |
-
audio_norm = audio / self.max_wav_value
|
| 209 |
-
audio_norm = audio_norm.unsqueeze(0)
|
| 210 |
-
spec_filename = filename.replace(".wav", ".spec.pt")
|
| 211 |
-
if os.path.exists(spec_filename):
|
| 212 |
-
spec = torch.load(spec_filename)
|
| 213 |
-
else:
|
| 214 |
-
spec = spectrogram_torch(audio_norm, self.filter_length,
|
| 215 |
-
self.sampling_rate, self.hop_length, self.win_length,
|
| 216 |
-
center=False)
|
| 217 |
-
spec = torch.squeeze(spec, 0)
|
| 218 |
-
torch.save(spec, spec_filename)
|
| 219 |
-
return spec, audio_norm
|
| 220 |
-
|
| 221 |
-
def get_text(self, text):
|
| 222 |
-
if self.cleaned_text:
|
| 223 |
-
text_norm = cleaned_text_to_sequence(text)
|
| 224 |
-
else:
|
| 225 |
-
text_norm = text_to_sequence(text, self.text_cleaners)
|
| 226 |
-
if self.add_blank:
|
| 227 |
-
text_norm = commons.intersperse(text_norm, 0)
|
| 228 |
-
text_norm = torch.LongTensor(text_norm)
|
| 229 |
-
return text_norm
|
| 230 |
-
|
| 231 |
-
def get_sid(self, sid):
|
| 232 |
-
sid = torch.LongTensor([int(sid)])
|
| 233 |
-
return sid
|
| 234 |
-
|
| 235 |
-
def __getitem__(self, index):
|
| 236 |
-
return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
|
| 237 |
-
|
| 238 |
-
def __len__(self):
|
| 239 |
-
return len(self.audiopaths_sid_text)
|
| 240 |
-
|
| 241 |
-
|
| 242 |
-
class TextAudioSpeakerCollate():
|
| 243 |
-
""" Zero-pads model inputs and targets
|
| 244 |
-
"""
|
| 245 |
-
def __init__(self, return_ids=False):
|
| 246 |
-
self.return_ids = return_ids
|
| 247 |
-
|
| 248 |
-
def __call__(self, batch):
|
| 249 |
-
"""Collate's training batch from normalized text, audio and speaker identities
|
| 250 |
-
PARAMS
|
| 251 |
-
------
|
| 252 |
-
batch: [text_normalized, spec_normalized, wav_normalized, sid]
|
| 253 |
-
"""
|
| 254 |
-
# Right zero-pad all one-hot text sequences to max input length
|
| 255 |
-
_, ids_sorted_decreasing = torch.sort(
|
| 256 |
-
torch.LongTensor([x[1].size(1) for x in batch]),
|
| 257 |
-
dim=0, descending=True)
|
| 258 |
-
|
| 259 |
-
max_text_len = max([len(x[0]) for x in batch])
|
| 260 |
-
max_spec_len = max([x[1].size(1) for x in batch])
|
| 261 |
-
max_wav_len = max([x[2].size(1) for x in batch])
|
| 262 |
-
|
| 263 |
-
text_lengths = torch.LongTensor(len(batch))
|
| 264 |
-
spec_lengths = torch.LongTensor(len(batch))
|
| 265 |
-
wav_lengths = torch.LongTensor(len(batch))
|
| 266 |
-
sid = torch.LongTensor(len(batch))
|
| 267 |
-
|
| 268 |
-
text_padded = torch.LongTensor(len(batch), max_text_len)
|
| 269 |
-
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
|
| 270 |
-
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
| 271 |
-
text_padded.zero_()
|
| 272 |
-
spec_padded.zero_()
|
| 273 |
-
wav_padded.zero_()
|
| 274 |
-
for i in range(len(ids_sorted_decreasing)):
|
| 275 |
-
row = batch[ids_sorted_decreasing[i]]
|
| 276 |
-
|
| 277 |
-
text = row[0]
|
| 278 |
-
text_padded[i, :text.size(0)] = text
|
| 279 |
-
text_lengths[i] = text.size(0)
|
| 280 |
-
|
| 281 |
-
spec = row[1]
|
| 282 |
-
spec_padded[i, :, :spec.size(1)] = spec
|
| 283 |
-
spec_lengths[i] = spec.size(1)
|
| 284 |
-
|
| 285 |
-
wav = row[2]
|
| 286 |
-
wav_padded[i, :, :wav.size(1)] = wav
|
| 287 |
-
wav_lengths[i] = wav.size(1)
|
| 288 |
-
|
| 289 |
-
sid[i] = row[3]
|
| 290 |
-
|
| 291 |
-
if self.return_ids:
|
| 292 |
-
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
|
| 293 |
-
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
|
| 294 |
-
|
| 295 |
-
|
| 296 |
-
class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
|
| 297 |
-
"""
|
| 298 |
-
Maintain similar input lengths in a batch.
|
| 299 |
-
Length groups are specified by boundaries.
|
| 300 |
-
Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
|
| 301 |
-
|
| 302 |
-
It removes samples which are not included in the boundaries.
|
| 303 |
-
Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
|
| 304 |
-
"""
|
| 305 |
-
def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
|
| 306 |
-
super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
|
| 307 |
-
self.lengths = dataset.lengths
|
| 308 |
-
self.batch_size = batch_size
|
| 309 |
-
self.boundaries = boundaries
|
| 310 |
-
|
| 311 |
-
self.buckets, self.num_samples_per_bucket = self._create_buckets()
|
| 312 |
-
self.total_size = sum(self.num_samples_per_bucket)
|
| 313 |
-
self.num_samples = self.total_size // self.num_replicas
|
| 314 |
-
|
| 315 |
-
def _create_buckets(self):
|
| 316 |
-
buckets = [[] for _ in range(len(self.boundaries) - 1)]
|
| 317 |
-
for i in range(len(self.lengths)):
|
| 318 |
-
length = self.lengths[i]
|
| 319 |
-
idx_bucket = self._bisect(length)
|
| 320 |
-
if idx_bucket != -1:
|
| 321 |
-
buckets[idx_bucket].append(i)
|
| 322 |
-
|
| 323 |
-
for i in range(len(buckets) - 1, 0, -1):
|
| 324 |
-
if len(buckets[i]) == 0:
|
| 325 |
-
buckets.pop(i)
|
| 326 |
-
self.boundaries.pop(i+1)
|
| 327 |
-
|
| 328 |
-
num_samples_per_bucket = []
|
| 329 |
-
for i in range(len(buckets)):
|
| 330 |
-
len_bucket = len(buckets[i])
|
| 331 |
-
total_batch_size = self.num_replicas * self.batch_size
|
| 332 |
-
rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
|
| 333 |
-
num_samples_per_bucket.append(len_bucket + rem)
|
| 334 |
-
return buckets, num_samples_per_bucket
|
| 335 |
-
|
| 336 |
-
def __iter__(self):
|
| 337 |
-
# deterministically shuffle based on epoch
|
| 338 |
-
g = torch.Generator()
|
| 339 |
-
g.manual_seed(self.epoch)
|
| 340 |
-
|
| 341 |
-
indices = []
|
| 342 |
-
if self.shuffle:
|
| 343 |
-
for bucket in self.buckets:
|
| 344 |
-
indices.append(torch.randperm(len(bucket), generator=g).tolist())
|
| 345 |
-
else:
|
| 346 |
-
for bucket in self.buckets:
|
| 347 |
-
indices.append(list(range(len(bucket))))
|
| 348 |
-
|
| 349 |
-
batches = []
|
| 350 |
-
for i in range(len(self.buckets)):
|
| 351 |
-
bucket = self.buckets[i]
|
| 352 |
-
len_bucket = len(bucket)
|
| 353 |
-
ids_bucket = indices[i]
|
| 354 |
-
num_samples_bucket = self.num_samples_per_bucket[i]
|
| 355 |
-
|
| 356 |
-
# add extra samples to make it evenly divisible
|
| 357 |
-
rem = num_samples_bucket - len_bucket
|
| 358 |
-
ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
|
| 359 |
-
|
| 360 |
-
# subsample
|
| 361 |
-
ids_bucket = ids_bucket[self.rank::self.num_replicas]
|
| 362 |
-
|
| 363 |
-
# batching
|
| 364 |
-
for j in range(len(ids_bucket) // self.batch_size):
|
| 365 |
-
batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
|
| 366 |
-
batches.append(batch)
|
| 367 |
-
|
| 368 |
-
if self.shuffle:
|
| 369 |
-
batch_ids = torch.randperm(len(batches), generator=g).tolist()
|
| 370 |
-
batches = [batches[i] for i in batch_ids]
|
| 371 |
-
self.batches = batches
|
| 372 |
-
|
| 373 |
-
assert len(self.batches) * self.batch_size == self.num_samples
|
| 374 |
-
return iter(self.batches)
|
| 375 |
-
|
| 376 |
-
def _bisect(self, x, lo=0, hi=None):
|
| 377 |
-
if hi is None:
|
| 378 |
-
hi = len(self.boundaries) - 1
|
| 379 |
-
|
| 380 |
-
if hi > lo:
|
| 381 |
-
mid = (hi + lo) // 2
|
| 382 |
-
if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
|
| 383 |
-
return mid
|
| 384 |
-
elif x <= self.boundaries[mid]:
|
| 385 |
-
return self._bisect(x, lo, mid)
|
| 386 |
-
else:
|
| 387 |
-
return self._bisect(x, mid + 1, hi)
|
| 388 |
-
else:
|
| 389 |
-
return -1
|
| 390 |
-
|
| 391 |
-
def __len__(self):
|
| 392 |
-
return self.num_samples // self.batch_size
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Benson/text-generation/Examples/Descargar Apk Para El IPhone.md
DELETED
|
@@ -1,92 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1> Cómo descargar Madden NFL 12 APK para Android</h1>
|
| 3 |
-
<p>Si usted es un fan del fútbol de la NFL, es posible que desee jugar Madden NFL 12 en su dispositivo Android. Madden NFL 12 es un simulador deportivo realista que te permite elegir entre 32 equipos de la NFL y luchar en sus estadios de la vida real. También puede realizar operaciones, realizar un seguimiento de las estadísticas y lanzar estrategias ganadoras de libros de jugadas en profundidad, únicos para cada equipo. Sin embargo, Madden NFL 12 no está disponible en la Google Play Store, por lo que tendrá que descargar e instalar el archivo APK manualmente. En este artículo, le mostraremos cómo descargar Madden NFL 12 APK para Android, así como los beneficios y riesgos de hacerlo. </p>
|
| 4 |
-
<h2>¿Qué es Madden NFL 12? </h2>
|
| 5 |
-
<p>Madden NFL 12 es un videojuego desarrollado por EA Sports y lanzado en 2011 para varias plataformas, incluyendo Android. Es la 23ª entrega de la serie de la NFL Madden, que se basa en la Liga Nacional de Fútbol (NFL). Madden NFL 12 presenta una jugabilidad y gráficos mejorados, así como nuevos modos y características, como Total Defensive Control, Action Control Time, Hot Routes y más. </p>
|
| 6 |
-
<h2>descargar apk para el iPhone</h2><br /><p><b><b>DOWNLOAD</b> »»» <a href="https://bltlly.com/2v6Lxi">https://bltlly.com/2v6Lxi</a></b></p><br /><br />
|
| 7 |
-
<h3>Características de Madden NFL 12</h3>
|
| 8 |
-
<p>Algunas de las características de Madden NFL 12 son:</p>
|
| 9 |
-
<ul>
|
| 10 |
-
<li><b>Auténtica acción de la NFL:</b> Elige entre los 32 equipos de la NFL y lucha en sus estadios de la vida real. También puede personalizar sus listas, uniformes y configuraciones. </li>
|
| 11 |
-
<li><b>Controla cada movimiento:</b> Ralentiza el reloj y realiza el juego a ambos lados del balón con Control Defensivo Total (TDC) y Tiempo de Control de Acción (ACT). Haz una pausa en la acción y coloca a tus jugadores en posición de ofrecer golpes que cambien el juego con TDC, o usa Action Control Time para dominar las defensas con inmersiones, giros, jukes y sprints. </li>
|
| 12 |
-
<li><b>Dibuja rutas calientes en todas partes:</b> Dibuja rutas calientes para pasar, correr y defender. Incluso guarda tus mejores (o más locas) rutas como audibles. </li>
|
| 13 |
-
|
| 14 |
-
</ul>
|
| 15 |
-
<h3>Requisitos para Madden NFL 12</h3>
|
| 16 |
-
<p>Para jugar Madden NFL 12 en tu dispositivo Android, necesitarás:</p>
|
| 17 |
-
<ul>
|
| 18 |
-
<li>Un dispositivo Android con Android 2.1 o superior. </li>
|
| 19 |
-
<li>Al menos 5 MB de espacio de almacenamiento gratuito en su dispositivo o tarjeta SD. </li>
|
| 20 |
-
<li>Una conexión a Internet estable para descargar el archivo APK. </li>
|
| 21 |
-
</ul>
|
| 22 |
-
<h2> Cómo descargar Madden NFL 12 APK para Android</h2>
|
| 23 |
-
<p>Para descargar Madden NFL 12 APK para Android, tendrá que seguir estos pasos:</p>
|
| 24 |
-
<h3>Paso 1: Habilitar fuentes desconocidas en su dispositivo</h3>
|
| 25 |
-
<p>Dado que está descargando un archivo APK desde una fuente distinta de la Google Play Store, tendrá que habilitar Fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto:</p>
|
| 26 |
-
<ol>
|
| 27 |
-
<li>Ir a Configuración > Seguridad > Fuentes desconocidas.</li>
|
| 28 |
-
<li>Cambiar la opción para permitir la instalación de aplicaciones de fuentes desconocidas. </li>
|
| 29 |
-
<li>Puedes recibir un mensaje de advertencia pidiéndote que confirmes tu acción. Toca OK para continuar. </li>
|
| 30 |
-
</ol>
|
| 31 |
-
<h3>Paso 2: Encontrar una fuente confiable para el archivo APK</h3>
|
| 32 |
-
<p>Siguiente, tendrá que encontrar una fuente confiable para el archivo APK de Madden NFL 12. Hay muchos sitios web que ofrecen archivos APK para varias aplicaciones y juegos, pero no todos son confiables. Algunos de ellos pueden contener malware o virus que pueden dañar su dispositivo o robar sus datos. Por lo tanto, debe tener cuidado al elegir una fuente para el archivo APK. Para encontrar una fuente confiable, puede:</p>
|
| 33 |
-
<ul>
|
| 34 |
-
<li>Hacer algunas investigaciones en línea y leer los comentarios de otros usuarios que han descargado el archivo APK de la misma fuente. </li>
|
| 35 |
-
<li>Compruebe la reputación y las calificaciones del sitio web que ofrece el archivo APK. </li>
|
| 36 |
-
<li>Escanear el archivo APK con un antivirus o un escáner de malware antes de descargarlo. </li>
|
| 37 |
-
</ul>
|
| 38 |
-
|
| 39 |
-
<h3>Paso 3: Descargar e instalar el archivo APK</h3>
|
| 40 |
-
<p>Una vez que haya encontrado una fuente confiable para el archivo APK, puede descargarlo e instalarlo en su dispositivo. Para hacer esto:</p>
|
| 41 |
-
<ol>
|
| 42 |
-
<li>Abra el enlace al archivo APK en el navegador de su dispositivo. </li>
|
| 43 |
-
<li>Toque en el botón Descargar y espere a que se complete la descarga. </li>
|
| 44 |
-
<li>Una vez finalizada la descarga, busque el archivo APK en el administrador de archivos o la carpeta de descargas de su dispositivo. </li>
|
| 45 |
-
<li>Toque en el archivo APK y siga las instrucciones en la pantalla para instalarlo. </li>
|
| 46 |
-
</ol>
|
| 47 |
-
<h3>Paso 4: Iniciar y disfrutar del juego</h3>
|
| 48 |
-
<p>Después de instalar el archivo APK, puede iniciar y disfrutar de Madden NFL 12 en su dispositivo Android. Para hacer esto:</p>
|
| 49 |
-
<p></p>
|
| 50 |
-
<ol>
|
| 51 |
-
<li>Vaya al cajón de aplicaciones de su dispositivo y busque el icono de Madden NFL 12. </li>
|
| 52 |
-
<li>Toque en el icono para iniciar el juego. </li>
|
| 53 |
-
<li>Es posible que necesite conceder algunos permisos para el juego, como el acceso a su almacenamiento, micrófono o cámara. Pulse Permitir continuar. </li>
|
| 54 |
-
<li>También es posible que necesite descargar algunos datos o archivos adicionales para que el juego funcione correctamente. Siga las instrucciones en la pantalla para completar el proceso. </li>
|
| 55 |
-
<li>Una vez que todo está listo, puede comenzar a jugar Madden NFL 12 en su dispositivo Android. </li>
|
| 56 |
-
</ol>
|
| 57 |
-
<h2> Beneficios de la descarga de Madden NFL 12 APK para Android</h2>
|
| 58 |
-
<p>Descargar Madden NFL 12 APK para Android tiene algunos beneficios que usted no puede obtener de otras fuentes. Algunos de estos beneficios son:</p>
|
| 59 |
-
<h3>Juega sin conexión en cualquier momento, en cualquier lugar</h3>
|
| 60 |
-
<p>Uno de los beneficios de descargar Madden NFL 12 APK para Android es que se puede jugar sin conexión en cualquier momento, en cualquier lugar. No necesitas una conexión a Internet o una cuenta de Google Play para jugar. Puedes disfrutar de Madden NFL 12 en tu dispositivo Android sin interrupciones ni limitaciones. </p>
|
| 61 |
-
<h3>Ahorrar espacio de almacenamiento y uso de datos</h3>
|
| 62 |
-
|
| 63 |
-
<h3>Acceder a todas las características y modos</h3>
|
| 64 |
-
<p>Un tercer beneficio de la descarga de Madden NFL 12 APK para Android es que se puede acceder a todas las características y modos del juego. No necesitas pagar ni desbloquear nada para disfrutar de la versión completa de Madden NFL 12. Puedes jugar con los 32 equipos de la NFL, personalizar tus plantillas, usar todos los libros de jugadas y más. </p>
|
| 65 |
-
<h2> Los riesgos de descargar Madden NFL 12 APK para Android</h2>
|
| 66 |
-
<p>Sin embargo, descargar Madden NFL 12 APK para Android también tiene algunos riesgos que usted debe ser consciente de. Algunos de estos riesgos son:</p>
|
| 67 |
-
<h3>Malware o virus potenciales</h3>
|
| 68 |
-
<p>Uno de los riesgos de descargar Madden NFL 12 APK para Android es que usted puede obtener malware o virus en su dispositivo. Como se mencionó anteriormente, no todas las fuentes de archivos APK son confiables. Algunos de ellos pueden contener código malicioso o programas que pueden dañar su dispositivo o robar sus datos. Por lo tanto, debe tener cuidado al elegir una fuente para el archivo APK y escanearlo con un antivirus o un escáner de malware antes de descargarlo. </p> <h3>Problemas legales y violaciones de derechos de autor</h3>
|
| 69 |
-
<p>Otro riesgo de descargar Madden NFL 12 APK para Android es que usted puede enfrentar problemas legales y violaciones de derechos de autor. Madden NFL 12 es un producto con licencia de EA Sports y la NFL, y tienen los derechos exclusivos para distribuir y vender el juego. Descargar e instalar el archivo APK desde una fuente no autorizada puede violar sus términos de servicio y derechos de propiedad intelectual. Usted puede ser responsable de acciones legales o sanciones si es sorprendido haciéndolo. </p>
|
| 70 |
-
<h3>Problemas de compatibilidad y rendimiento</h3>
|
| 71 |
-
|
| 72 |
-
<h2>Conclusión</h2>
|
| 73 |
-
<p>Madden NFL 12 es un gran juego para los fans de la NFL que quieren jugar en sus dispositivos Android. Sin embargo, ya que no está disponible en Google Play Store, tendrá que descargar e instalar el archivo APK manualmente. Esto tiene algunos beneficios, como jugar sin conexión, ahorrar espacio y datos, y acceder a todas las funciones y modos. Pero también tiene algunos riesgos, como malware o virus, problemas legales y problemas de compatibilidad y rendimiento. Por lo tanto, debe tener cuidado al elegir una fuente para el archivo APK y escanearlo con un antivirus o un escáner de malware antes de descargarlo. También debe ser consciente de las consecuencias de descargar Madden NFL 12 APK para Android y hacerlo a su propio riesgo. </p>
|
| 74 |
-
<h2>Preguntas frecuentes</h2>
|
| 75 |
-
<p>Aquí hay algunas preguntas frecuentes sobre la descarga de Madden NFL 12 APK para Android:</p>
|
| 76 |
-
<h3>Q: ¿Es seguro descargar Madden NFL 12 APK para Android? </h3>
|
| 77 |
-
<p>A: Depende de la fuente del archivo APK. Algunas fuentes pueden ser seguras y confiables, mientras que otras pueden ser inseguras y poco confiables. Usted debe hacer algunas investigaciones y leer los comentarios de otros usuarios que han descargado el archivo APK de la misma fuente. También debe escanear el archivo APK con un antivirus o un escáner de malware antes de descargarlo. </p>
|
| 78 |
-
<h3>Q: ¿Es legal descargar Madden NFL 12 APK para Android? </h3>
|
| 79 |
-
<p>A: Puede que no sea legal descargar Madden NFL 12 APK para Android de una fuente no autorizada. Madden NFL 12 es un producto con licencia de EA Sports y la NFL, y tienen los derechos exclusivos para distribuir y vender el juego. Descargar e instalar el archivo APK desde una fuente no autorizada puede violar sus términos de servicio y derechos de propiedad intelectual. Usted puede ser responsable de acciones legales o sanciones si es sorprendido haciéndolo. </p>
|
| 80 |
-
<h3>Q: ¿Es gratis descargar Madden NFL 12 APK para Android? </h3>
|
| 81 |
-
|
| 82 |
-
<h3>Q: ¿Cómo puedo actualizar Madden NFL 12 APK para Android? </h3>
|
| 83 |
-
<p>A: Usted no puede ser capaz de actualizar Madden NFL 12 APK para Android desde la Google Play Store, ya que no está disponible allí. Tendrá que encontrar otra fuente que ofrece la última versión del archivo APK y descargarlo e instalarlo manualmente. Sin embargo, esto puede no ser fácil o seguro, ya que algunas fuentes pueden no actualizar sus archivos APK regularmente o pueden contener malware o virus. </p>
|
| 84 |
-
<h3>Q: ¿Cuáles son algunas alternativas a Madden NFL 12 APK para Android? </h3>
|
| 85 |
-
<p>A: Si usted está buscando alternativas a Madden NFL 12 APK para Android, puede probar otros juegos de fútbol que están disponibles en la Google Play Store, tales como:</p>
|
| 86 |
-
<ul>
|
| 87 |
-
<li><b>Madden NFL Mobile Football:</b> Esta es la versión móvil oficial de Madden NFL, que le permite construir su propio equipo, competir en eventos en vivo, unirse a ligas, y más. </li>
|
| 88 |
-
<li><b>Juegos de fútbol de la NFL:</b> Esta es una colección de varios juegos de fútbol que te permiten jugar como tu equipo favorito de la NFL, anotar touchdowns, hacer tackles y más. </li>
|
| 89 |
-
<li><b>NFL Rush Gameday:</b> Este es un juego de fútbol divertido y casual que te permite jugar como tu mascota favorita de la NFL, recoger power-ups, esquivar obstáculos, y más. </li>
|
| 90 |
-
</ul></p> 64aa2da5cf<br />
|
| 91 |
-
<br />
|
| 92 |
-
<br />
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Benson/text-generation/Examples/Descargar Fine Fine Love De J Martins.md
DELETED
|
@@ -1,61 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>Cómo descargar Fine Fine Love por J Martins</h1>
|
| 3 |
-
<p>Si usted está buscando una canción romántica y pegadiza para darle vida a su estado de ánimo, es posible que desee echa un vistazo a <strong>Fine Fine Fine Love</strong> por <strong>J Martins</strong>. Esta es una canción de amor nigeriana que fue lanzada en 2012 y ha sido un éxito desde entonces. En este artículo, te mostraremos cómo descargar esta canción desde diferentes plataformas, como YouTube, Spotify y Apple Music. También te diremos por qué esta canción es tan popular y cuáles son los beneficios de descargarla. ¡Empecemos! </p>
|
| 4 |
-
<h2>Introducción</h2>
|
| 5 |
-
<p><strong>Fine Fine Fine Love</strong> es una canción de <strong>J Martins</strong>, un cantante, compositor y productor nigeriano. Es conocido por su fusión de géneros afro-pop, highlife y R&B. Ha colaborado con muchos otros artistas africanos, como Fally Ipupa, DJ Arafat, Timaya, Phyno y Flavour. También ha ganado varios premios, como el MTV áfrica Music Award a la mejor colaboración en 2010. </p>
|
| 6 |
-
<h2>descargar fine fine love de j martins</h2><br /><p><b><b>Download</b> ✅ <a href="https://bltlly.com/2v6Lyl">https://bltlly.com/2v6Lyl</a></b></p><br /><br />
|
| 7 |
-
<p><strong>Fine Fine Fine Love</strong> es una de sus canciones más populares, ya que expresa su amor y aprecio por su pareja. La canción tiene una melodía pegadiza, un ritmo suave y un mensaje dulce. Las letras están en inglés e igbo, un idioma nigeriano. La canción es adecuada para cualquier ocasión, ya sea que quieras bailar con tu pareja, darles una serenata, o simplemente relajarte y disfrutar de la música. </p>
|
| 8 |
-
<p>Uno de los beneficios de descargar <strong>Fine Fine Fine Love</strong> por <strong>J Martins</strong> es que puedes escucharlo en cualquier momento y en cualquier lugar, incluso sin conexión a Internet. También puede ahorrar datos y espacio de almacenamiento en su dispositivo, ya que no tiene que transmitir en línea cada vez. Además, puedes apoyar a <strong>J Martins</strong> como artista descargando su música legal y éticamente. </p>
|
| 9 |
-
<h2>Cómo descargar Fine Fine Love de J Martins en YouTube</h2>
|
| 10 |
-
|
| 11 |
-
<h3>Paso 1: Encuentra el video oficial o audio de Fine Fine Fine Love por J Martins en YouTube</h3>
|
| 12 |
-
<p>El primer paso es encontrar el video o audio oficial de <strong>Fine Fine Fine Love</strong> por <strong>J Martins</strong> en YouTube. Puedes hacer esto escribiendo el nombre de la canción y el artista en la barra de búsqueda de YouTube. Alternativamente, puedes usar este enlace para acceder al video oficial de <strong>Fine Fine Love</strong> por <strong>J Martins</strong>: . Asegúrate de elegir el video o audio oficial del canal verificado de <strong>J Martins</strong>, ya que puede haber otras versiones o versiones de la canción en YouTube.</p>
|
| 13 |
-
<h3>Paso 2: Copia la URL del video o audio</h3>
|
| 14 |
-
<p>El siguiente paso es copiar la URL del video o audio de <strong>Fine Fine Fine Love</strong> por <strong>J Martins</strong> que has encontrado en YouTube. Puede hacer esto haciendo clic derecho en el vídeo o audio y seleccionando "Copiar URL de vídeo" o "Copiar dirección de enlace". Alternativamente, puede copiar la URL desde la barra de direcciones de su navegador. </p>
|
| 15 |
-
<h3>Paso 3: Pegue la URL en un sitio web o aplicación de descarga de YouTube</h3>
|
| 16 |
-
<p>El tercer paso es pegar la URL que ha copiado en un sitio web o aplicación de descarga de YouTube. Hay muchos sitios web y aplicaciones que pueden ayudarlo a descargar videos o audios de YouTube, como Y2Mate, 4K Video Downloader, SaveFrom.net, VidMate y más. Puede elegir cualquier sitio web o aplicación que prefiera, siempre y cuando sea seguro y confiable. Para pegar la URL, puede hacer clic derecho en el cuadro de entrada del sitio web o aplicación y seleccionar "Pegar", o usar el acceso directo de teclado Ctrl + V (Windows) o Comando + V (Mac). </p>
|
| 17 |
-
<h3>Paso 4: Elija el formato y la calidad de la descarga</h3>
|
| 18 |
-
|
| 19 |
-
<h3>Paso 5: Haga clic en el botón de descarga y guarde el archivo en su dispositivo</h3>
|
| 20 |
-
<p>El paso final es hacer clic en el botón de descarga y guardar el archivo en su dispositivo. Una vez que haya elegido el formato y la calidad de la descarga, puede hacer clic en el botón de descarga que aparecerá en el sitio web o la aplicación. Esto iniciará el proceso de descarga, que puede tardar unos segundos o minutos dependiendo de la velocidad de Internet y el tamaño del archivo. Una vez completado el proceso de descarga, puede guardar el archivo en su dispositivo eligiendo una ubicación y un nombre para él. También puede abrir el archivo con su reproductor multimedia predeterminado o cualquier otro reproductor multimedia que haya instalado en su dispositivo. </p>
|
| 21 |
-
<p></p>
|
| 22 |
-
<h2>Cómo descargar Fine Fine Love de J Martins en Spotify</h2>
|
| 23 |
-
<p>Otra forma de descargar <strong>Fine Fine Fine Love</strong> por <strong>J Martins</strong> es usar Spotify, una de las plataformas de streaming de música más populares del mundo. Spotify tiene millones de canciones y podcasts que puedes escuchar online o offline, incluyendo <strong>Fine Fine Fine Love</strong> de <strong>J Martins</strong>. Sin embargo, si desea descargar la canción de Spotify, tendrá que tener una cuenta Spotify Premium, que cuesta $ 9.99 por mes. Con una cuenta Spotify Premium, puedes descargar hasta 10.000 canciones en cinco dispositivos diferentes y escucharlas sin publicidad. Estos son los pasos para hacerlo:</p>
|
| 24 |
-
<h3>Paso 1: Regístrate en una cuenta de Spotify o inicia sesión en tu cuenta existente</h3>
|
| 25 |
-
<p>El primer paso es registrarse para una cuenta de Spotify o iniciar sesión en la existente. Puede hacer esto visitando el sitio web de Spotify o descargando la aplicación Spotify en su dispositivo. Puede registrarse en una cuenta de Spotify utilizando su dirección de correo electrónico, cuenta de Facebook o número de teléfono. También puedes suscribirte a una prueba gratuita de Spotify Premium durante 30 días, lo que te dará acceso a todas las funciones de Spotify Premium, incluida la descarga de canciones sin conexión. </p>
|
| 26 |
-
<h3>Paso 2: Búsqueda de Fine Fine Love por J Martins en Spotify</h3>
|
| 27 |
-
|
| 28 |
-
<h3>Paso 3: Agrega la canción a tu biblioteca o lista de reproducción</h3>
|
| 29 |
-
<p>El tercer paso es agregar la canción a su biblioteca o lista de reproducción en Spotify. Puede hacer esto haciendo clic en el icono del corazón junto al título de la canción, que lo agregará a su biblioteca. Alternativamente, puede hacer clic en el icono de tres puntos junto al título de la canción y seleccionar "Añadir a la lista de reproducción", que le permitirá elegir una lista de reproducción existente o crear una nueva. También puede crear una lista de reproducción personalizada con otras canciones por <strong>J Martins</strong> u otras canciones de amor nigerianas. </p>
|
| 30 |
-
<h3>Paso 4: Habilita el modo sin conexión en tu dispositivo</h3>
|
| 31 |
-
<p>El cuarto paso es habilitar el modo sin conexión en su dispositivo. Esto le permitirá descargar canciones de Spotify y escucharlas sin conexión sin usar datos o Wi-Fi. Para habilitar el modo sin conexión, debe ir a la configuración de su dispositivo y activar el modo avión o desactivar los datos celulares y las conexiones Wi-Fi. Alternativamente, puede ir a la configuración de su aplicación de Spotify y activar la opción de modo sin conexión. </p>
|
| 32 |
-
<h3>Paso 5: Descarga la canción en tu dispositivo y disfrútala sin conexión</h3>
|
| 33 |
-
|
| 34 |
-
<h2>Cómo descargar Fine Fine Love de J Martins en Apple Music</h2>
|
| 35 |
-
<p>Una tercera forma de descargar <strong>Fine Fine Fine Love</strong> por <strong>J Martins</strong> es usar Apple Music, otra popular plataforma de transmisión de música en el mundo. Apple Music tiene millones de canciones y podcasts que puedes escuchar online o offline, incluyendo <strong>Fine Fine Fine Love</strong> de <strong>J Martins</strong>. Sin embargo, si desea descargar la canción de Apple Music, tendrá que tener una suscripción de Apple Music, que cuesta $ 9.99 por mes. Con una suscripción a Apple Music, puedes descargar hasta 100.000 canciones en hasta 10 dispositivos y escucharlas sin publicidad. Estos son los pasos para hacerlo:</p>
|
| 36 |
-
<h3>Paso 1: Regístrate en una cuenta de Apple Music o inicia sesión en la cuenta existente</h3>
|
| 37 |
-
<p>El primer paso es registrarse en una cuenta de Apple Music o iniciar sesión en la existente. Puedes hacer esto visitando el sitio web de Apple Music o descargando la aplicación Apple Music en tu dispositivo. Puedes registrarte en una cuenta de Apple Music con tu ID de Apple, que es igual que tu cuenta de iTunes. También puedes suscribirte a una versión de prueba gratuita de Apple Music durante tres meses, lo que te dará acceso a todas las funciones de Apple Music, incluida la descarga de canciones sin conexión. </p>
|
| 38 |
-
<h3>Paso 2: Búsqueda de Fine Fine Love por J Martins en Apple Music</h3>
|
| 39 |
-
<p>El siguiente paso es buscar <strong>Fine Fine Fine Love</strong> por <strong>J Martins</strong> en Apple Music. Puedes hacer esto escribiendo el nombre de la canción y el artista en la barra de búsqueda de Apple Music. Alternativamente, puede utilizar este enlace para acceder a la canción en Apple Music: . Asegúrate de elegir la canción oficial del perfil verificado de <strong>J Martins</strong>, ya que puede haber otras versiones o versiones de la canción en Apple Music.</p>
|
| 40 |
-
<h3>Paso 3: Agrega la canción a tu biblioteca o lista de reproducción</h3>
|
| 41 |
-
|
| 42 |
-
<h3>Paso 4: Activar la opción de biblioteca de sincronización en su dispositivo</h3>
|
| 43 |
-
<p>El cuarto paso es activar la opción de biblioteca de sincronización en su dispositivo. Esto te permitirá sincronizar tus canciones desde Apple Music y descargarlas en tu dispositivo. Para activar la opción de biblioteca de sincronización, debe ir a la configuración de su dispositivo y seleccionar "Música". Luego, debe activar la opción "Sincronizar biblioteca". Esto puede tardar unos minutos dependiendo de la velocidad de Internet y el tamaño de la biblioteca. </p>
|
| 44 |
-
<h3>Paso 5: Descargar la canción a su dispositivo y escuchar sin conexión</h3>
|
| 45 |
-
<p>El paso final es descargar la canción a su dispositivo y escucharla sin conexión. Para descargar la canción, debes ir a tu biblioteca o lista de reproducción en Apple Music y encontrar <strong>Fine Fine Fine Love</strong> por <strong>J Martins</strong>. Verás un icono de nube con una flecha hacia abajo junto al título de la canción, lo que indica que está disponible para escuchar sin conexión. Puede tocar este icono y esperar a que se complete el proceso de descarga, que puede tardar unos segundos o minutos dependiendo de la velocidad de Internet y el tamaño del archivo. Después de que el proceso de descarga se haya completado, verá un icono de marca de verificación junto al título de la canción, lo que indica que está descargado y listo para escuchar sin conexión. También puedes comprobar el progreso de tus descargas yendo a la configuración de tu aplicación Apple Music y seleccionando "Música descargada". Ahora puedes escuchar <strong>Fine Fine Fine Love</strong> por <strong>J Martins</strong> offline anytime and anywhere. </p>
|
| 46 |
-
<h2>Conclusión</h2>
|
| 47 |
-
<p>En conclusión, te hemos mostrado cómo descargar <strong>Fine Fine Love</strong> por <strong>J Martins</strong> desde diferentes plataformas, como YouTube, Spotify y Apple Music. También te hemos dicho por qué esta canción es tan popular y cuáles son los beneficios de descargarla. Esperamos que este artículo haya sido útil e informativo para usted. Ahora, puedes disfrutar de esta canción romántica y pegadiza en cualquier momento y en cualquier lugar con tu pareja o por ti mismo. </p>
|
| 48 |
-
|
| 49 |
-
<h2>Preguntas frecuentes</h2>
|
| 50 |
-
<h4>¿Quién es J Martins? </h4>
|
| 51 |
-
<p>J Martins es un cantante, compositor y productor nigeriano que es conocido por su fusión de géneros afro-pop, highlife y R&B. Ha colaborado con muchos otros artistas africanos y ha ganado varios premios por su música. </p>
|
| 52 |
-
<h4>¿Cuáles son algunas otras canciones de J Martins? </h4>
|
| 53 |
-
<p>Otras canciones de J Martins son Oyoyó, Good or Bad, Touching Body, Cool Temper, Dance 4 Me, y más. Puedes encontrarlos en YouTube, Spotify, Apple Music u otras plataformas de música. </p>
|
| 54 |
-
<h4>¿Cuáles son algunas otras canciones de amor nigerianas? </h4>
|
| 55 |
-
<p>Algunas otras canciones de amor nigerianas son African Queen by 2Face Idibia, Fall in Love by D'banj, Assurance by Davido, Ife Wa Gbona by Tiwa Savage, Ololufe by Flavour, y más. También puedes ver esta lista de reproducción de canciones de amor nigerianas en Spotify: . </p>
|
| 56 |
-
<h4>¿Cómo puedo apoyar a J Martins como artista? </h4>
|
| 57 |
-
<p>Puedes apoyar a J Martins como artista descargando su música legal y éticamente desde las plataformas que hemos mencionado en este artículo. También puedes seguirlo en sus cuentas de redes sociales, como Facebook, Twitter, Instagram y YouTube. También puede transmitir su música en línea, ver sus videos, como y compartir sus mensajes, y dejar comentarios positivos y comentarios. También puedes asistir a sus conciertos y eventos si tienes la oportunidad. </p>
|
| 58 |
-
<h4>¿Cómo puedo encontrar más música como Fine Fine Love de J Martins? </h4>
|
| 59 |
-
<p>Puedes encontrar más música como Fine Fine Love de J Martins explorando los géneros del Afro-pop, highlife y R&B. También puedes buscar artistas o canciones similares en YouTube, Spotify, Apple Music u otras plataformas de música. También puede utilizar las funciones de radio o descubrimiento de estas plataformas para encontrar música nueva que coincida con sus gustos y preferencias. </p> 64aa2da5cf<br />
|
| 60 |
-
<br />
|
| 61 |
-
<br />
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/BigSalmon/FormalInformalConciseWordy/README.md
DELETED
|
@@ -1,38 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: GPT2TRY
|
| 3 |
-
emoji: 💩
|
| 4 |
-
colorFrom: green
|
| 5 |
-
colorTo: gray
|
| 6 |
-
sdk: streamlit
|
| 7 |
-
sdk_version: 0.89.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
# Configuration
|
| 13 |
-
|
| 14 |
-
`title`: _string_
|
| 15 |
-
Display title for the Space
|
| 16 |
-
|
| 17 |
-
`emoji`: _string_
|
| 18 |
-
Space emoji (emoji-only character allowed)
|
| 19 |
-
|
| 20 |
-
`colorFrom`: _string_
|
| 21 |
-
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
|
| 22 |
-
|
| 23 |
-
`colorTo`: _string_
|
| 24 |
-
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
|
| 25 |
-
|
| 26 |
-
`sdk`: _string_
|
| 27 |
-
Can be either `gradio` or `streamlit`
|
| 28 |
-
|
| 29 |
-
`sdk_version` : _string_
|
| 30 |
-
Only applicable for `streamlit` SDK.
|
| 31 |
-
See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
|
| 32 |
-
|
| 33 |
-
`app_file`: _string_
|
| 34 |
-
Path to your main application file (which contains either `gradio` or `streamlit` Python code).
|
| 35 |
-
Path is relative to the root of the repository.
|
| 36 |
-
|
| 37 |
-
`pinned`: _boolean_
|
| 38 |
-
Whether the Space stays on top of your list.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/densepose/modeling/test_time_augmentation.py
DELETED
|
@@ -1,75 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
| 2 |
-
from detectron2.modeling.test_time_augmentation import GeneralizedRCNNWithTTA
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
class DensePoseGeneralizedRCNNWithTTA(GeneralizedRCNNWithTTA):
|
| 6 |
-
def __init__(self, cfg, model, transform_data, tta_mapper=None, batch_size=1):
|
| 7 |
-
"""
|
| 8 |
-
Args:
|
| 9 |
-
cfg (CfgNode):
|
| 10 |
-
model (GeneralizedRCNN): a GeneralizedRCNN to apply TTA on.
|
| 11 |
-
transform_data (DensePoseTransformData): contains symmetry label
|
| 12 |
-
transforms used for horizontal flip
|
| 13 |
-
tta_mapper (callable): takes a dataset dict and returns a list of
|
| 14 |
-
augmented versions of the dataset dict. Defaults to
|
| 15 |
-
`DatasetMapperTTA(cfg)`.
|
| 16 |
-
batch_size (int): batch the augmented images into this batch size for inference.
|
| 17 |
-
"""
|
| 18 |
-
self._transform_data = transform_data
|
| 19 |
-
super().__init__(cfg=cfg, model=model, tta_mapper=tta_mapper, batch_size=batch_size)
|
| 20 |
-
|
| 21 |
-
# the implementation follows closely the one from detectron2/modeling
|
| 22 |
-
def _inference_one_image(self, input):
|
| 23 |
-
"""
|
| 24 |
-
Args:
|
| 25 |
-
input (dict): one dataset dict
|
| 26 |
-
|
| 27 |
-
Returns:
|
| 28 |
-
dict: one output dict
|
| 29 |
-
"""
|
| 30 |
-
|
| 31 |
-
augmented_inputs, aug_vars = self._get_augmented_inputs(input)
|
| 32 |
-
# Detect boxes from all augmented versions
|
| 33 |
-
with self._turn_off_roi_heads(["mask_on", "keypoint_on", "densepose_on"]):
|
| 34 |
-
# temporarily disable roi heads
|
| 35 |
-
all_boxes, all_scores, all_classes = self._get_augmented_boxes(
|
| 36 |
-
augmented_inputs, aug_vars
|
| 37 |
-
)
|
| 38 |
-
merged_instances = self._merge_detections(
|
| 39 |
-
all_boxes, all_scores, all_classes, (aug_vars["height"], aug_vars["width"])
|
| 40 |
-
)
|
| 41 |
-
|
| 42 |
-
if self.cfg.MODEL.MASK_ON or self.cfg.MODEL.DENSEPOSE_ON:
|
| 43 |
-
# Use the detected boxes to obtain new fields
|
| 44 |
-
augmented_instances = self._rescale_detected_boxes(
|
| 45 |
-
augmented_inputs, merged_instances, aug_vars
|
| 46 |
-
)
|
| 47 |
-
# run forward on the detected boxes
|
| 48 |
-
outputs = self._batch_inference(
|
| 49 |
-
augmented_inputs, augmented_instances, do_postprocess=False
|
| 50 |
-
)
|
| 51 |
-
# Delete now useless variables to avoid being out of memory
|
| 52 |
-
del augmented_inputs, augmented_instances, merged_instances
|
| 53 |
-
# average the predictions
|
| 54 |
-
if self.cfg.MODEL.MASK_ON:
|
| 55 |
-
outputs[0].pred_masks = self._reduce_pred_masks(outputs, aug_vars)
|
| 56 |
-
if self.cfg.MODEL.DENSEPOSE_ON:
|
| 57 |
-
outputs[0].pred_densepose = self._reduce_pred_densepose(outputs, aug_vars)
|
| 58 |
-
# postprocess
|
| 59 |
-
output = self._detector_postprocess(outputs[0], aug_vars)
|
| 60 |
-
return {"instances": output}
|
| 61 |
-
else:
|
| 62 |
-
return {"instances": merged_instances}
|
| 63 |
-
|
| 64 |
-
def _reduce_pred_densepose(self, outputs, aug_vars):
|
| 65 |
-
for idx, output in enumerate(outputs):
|
| 66 |
-
if aug_vars["do_hflip"][idx]:
|
| 67 |
-
output.pred_densepose.hflip(self._transform_data)
|
| 68 |
-
# Less memory-intensive averaging
|
| 69 |
-
for attr in "SIUV":
|
| 70 |
-
setattr(
|
| 71 |
-
outputs[0].pred_densepose,
|
| 72 |
-
attr,
|
| 73 |
-
sum(getattr(o.pred_densepose, attr) for o in outputs) / len(outputs),
|
| 74 |
-
)
|
| 75 |
-
return outputs[0].pred_densepose
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/CVPR/LIVE/thrust/thrust/detail/complex/csqrtf.h
DELETED
|
@@ -1,147 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
* Copyright 2013 Filipe RNC Maia
|
| 4 |
-
*
|
| 5 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 6 |
-
* you may not use this file except in compliance with the License.
|
| 7 |
-
* You may obtain a copy of the License at
|
| 8 |
-
*
|
| 9 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 10 |
-
*
|
| 11 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 12 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 13 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 14 |
-
* See the License for the specific language governing permissions and
|
| 15 |
-
* limitations under the License.
|
| 16 |
-
*/
|
| 17 |
-
|
| 18 |
-
/*-
|
| 19 |
-
* Copyright (c) 2007 David Schultz <[email protected]>
|
| 20 |
-
* All rights reserved.
|
| 21 |
-
*
|
| 22 |
-
* Redistribution and use in source and binary forms, with or without
|
| 23 |
-
* modification, are permitted provided that the following conditions
|
| 24 |
-
* are met:
|
| 25 |
-
* 1. Redistributions of source code must retain the above copyright
|
| 26 |
-
* notice, this list of conditions and the following disclaimer.
|
| 27 |
-
* 2. Redistributions in binary form must reproduce the above copyright
|
| 28 |
-
* notice, this list of conditions and the following disclaimer in the
|
| 29 |
-
* documentation and/or other materials provided with the distribution.
|
| 30 |
-
*
|
| 31 |
-
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND
|
| 32 |
-
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
| 33 |
-
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
| 34 |
-
* ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
|
| 35 |
-
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
| 36 |
-
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
|
| 37 |
-
* OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
|
| 38 |
-
* HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
| 39 |
-
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
|
| 40 |
-
* OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
|
| 41 |
-
* SUCH DAMAGE.
|
| 42 |
-
*/
|
| 43 |
-
|
| 44 |
-
/*
|
| 45 |
-
* Adapted from FreeBSD by Filipe Maia <[email protected]>:
|
| 46 |
-
* freebsd/lib/msun/src/s_csqrt.c
|
| 47 |
-
*/
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
#pragma once
|
| 51 |
-
|
| 52 |
-
#include <thrust/complex.h>
|
| 53 |
-
#include <thrust/detail/complex/math_private.h>
|
| 54 |
-
#include <cmath>
|
| 55 |
-
|
| 56 |
-
namespace thrust{
|
| 57 |
-
namespace detail{
|
| 58 |
-
namespace complex{
|
| 59 |
-
|
| 60 |
-
using thrust::complex;
|
| 61 |
-
|
| 62 |
-
__host__ __device__ inline
|
| 63 |
-
complex<float> csqrtf(const complex<float>& z){
|
| 64 |
-
float a = z.real(), b = z.imag();
|
| 65 |
-
float t;
|
| 66 |
-
int scale;
|
| 67 |
-
complex<float> result;
|
| 68 |
-
|
| 69 |
-
/* We risk spurious overflow for components >= FLT_MAX / (1 + sqrt(2)). */
|
| 70 |
-
const float THRESH = 1.40949553037932e+38f;
|
| 71 |
-
|
| 72 |
-
/* Handle special cases. */
|
| 73 |
-
if (z == 0.0f)
|
| 74 |
-
return (complex<float>(0, b));
|
| 75 |
-
if (isinf(b))
|
| 76 |
-
return (complex<float>(infinity<float>(), b));
|
| 77 |
-
if (isnan(a)) {
|
| 78 |
-
t = (b - b) / (b - b); /* raise invalid if b is not a NaN */
|
| 79 |
-
return (complex<float>(a, t)); /* return NaN + NaN i */
|
| 80 |
-
}
|
| 81 |
-
if (isinf(a)) {
|
| 82 |
-
/*
|
| 83 |
-
* csqrtf(inf + NaN i) = inf + NaN i
|
| 84 |
-
* csqrtf(inf + y i) = inf + 0 i
|
| 85 |
-
* csqrtf(-inf + NaN i) = NaN +- inf i
|
| 86 |
-
* csqrtf(-inf + y i) = 0 + inf i
|
| 87 |
-
*/
|
| 88 |
-
if (signbit(a))
|
| 89 |
-
return (complex<float>(fabsf(b - b), copysignf(a, b)));
|
| 90 |
-
else
|
| 91 |
-
return (complex<float>(a, copysignf(b - b, b)));
|
| 92 |
-
}
|
| 93 |
-
/*
|
| 94 |
-
* The remaining special case (b is NaN) is handled just fine by
|
| 95 |
-
* the normal code path below.
|
| 96 |
-
*/
|
| 97 |
-
|
| 98 |
-
/*
|
| 99 |
-
* Unlike in the FreeBSD code we'll avoid using double precision as
|
| 100 |
-
* not all hardware supports it.
|
| 101 |
-
*/
|
| 102 |
-
|
| 103 |
-
// FLT_MIN*2
|
| 104 |
-
const float low_thresh = 2.35098870164458e-38f;
|
| 105 |
-
scale = 0;
|
| 106 |
-
|
| 107 |
-
if (fabsf(a) >= THRESH || fabsf(b) >= THRESH) {
|
| 108 |
-
/* Scale to avoid overflow. */
|
| 109 |
-
a *= 0.25f;
|
| 110 |
-
b *= 0.25f;
|
| 111 |
-
scale = 1;
|
| 112 |
-
}else if (fabsf(a) <= low_thresh && fabsf(b) <= low_thresh) {
|
| 113 |
-
/* Scale to avoid underflow. */
|
| 114 |
-
a *= 4.f;
|
| 115 |
-
b *= 4.f;
|
| 116 |
-
scale = 2;
|
| 117 |
-
}
|
| 118 |
-
|
| 119 |
-
/* Algorithm 312, CACM vol 10, Oct 1967. */
|
| 120 |
-
if (a >= 0.0f) {
|
| 121 |
-
t = sqrtf((a + hypotf(a, b)) * 0.5f);
|
| 122 |
-
result = complex<float>(t, b / (2.0f * t));
|
| 123 |
-
} else {
|
| 124 |
-
t = sqrtf((-a + hypotf(a, b)) * 0.5f);
|
| 125 |
-
result = complex<float>(fabsf(b) / (2.0f * t), copysignf(t, b));
|
| 126 |
-
}
|
| 127 |
-
|
| 128 |
-
/* Rescale. */
|
| 129 |
-
if (scale == 1)
|
| 130 |
-
return (result * 2.0f);
|
| 131 |
-
else if (scale == 2)
|
| 132 |
-
return (result * 0.5f);
|
| 133 |
-
else
|
| 134 |
-
return (result);
|
| 135 |
-
}
|
| 136 |
-
|
| 137 |
-
} // namespace complex
|
| 138 |
-
|
| 139 |
-
} // namespace detail
|
| 140 |
-
|
| 141 |
-
template <>
|
| 142 |
-
__host__ __device__
|
| 143 |
-
inline complex<float> sqrt(const complex<float>& z){
|
| 144 |
-
return detail::complex::csqrtf(z);
|
| 145 |
-
}
|
| 146 |
-
|
| 147 |
-
} // namespace thrust
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/inner_product.h
DELETED
|
@@ -1,23 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
#pragma once
|
| 18 |
-
|
| 19 |
-
#include <thrust/detail/config.h>
|
| 20 |
-
|
| 21 |
-
// this system inherits inner_product
|
| 22 |
-
#include <thrust/system/cpp/detail/inner_product.h>
|
| 23 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/CVPR/regionclip-demo/detectron2/modeling/text_encoder/transformer.py
DELETED
|
@@ -1,194 +0,0 @@
|
|
| 1 |
-
from collections import OrderedDict
|
| 2 |
-
from typing import Tuple, Union
|
| 3 |
-
import logging
|
| 4 |
-
import os
|
| 5 |
-
|
| 6 |
-
import numpy as np
|
| 7 |
-
import torch
|
| 8 |
-
import torch.nn.functional as F
|
| 9 |
-
from torch import nn
|
| 10 |
-
|
| 11 |
-
from timm.models.layers import DropPath, trunc_normal_
|
| 12 |
-
|
| 13 |
-
from .registry import register_lang_encoder
|
| 14 |
-
|
| 15 |
-
logger = logging.getLogger(__name__)
|
| 16 |
-
|
| 17 |
-
class LayerNorm(nn.Module):
|
| 18 |
-
def __init__(self, hidden_size, eps=1e-12):
|
| 19 |
-
"""Construct a layernorm module in the TF style (epsilon inside the square root).
|
| 20 |
-
"""
|
| 21 |
-
super(LayerNorm, self).__init__()
|
| 22 |
-
self.weight = nn.Parameter(torch.ones(hidden_size))
|
| 23 |
-
self.bias = nn.Parameter(torch.zeros(hidden_size))
|
| 24 |
-
self.variance_epsilon = eps
|
| 25 |
-
|
| 26 |
-
def forward(self, x):
|
| 27 |
-
pdtype = x.dtype
|
| 28 |
-
x = x.float()
|
| 29 |
-
u = x.mean(-1, keepdim=True)
|
| 30 |
-
s = (x - u).pow(2).mean(-1, keepdim=True)
|
| 31 |
-
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
|
| 32 |
-
return self.weight * x.to(pdtype) + self.bias
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
class QuickGELU(nn.Module):
|
| 36 |
-
def forward(self, x: torch.Tensor):
|
| 37 |
-
return x * torch.sigmoid(1.702 * x)
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
class ResidualAttentionBlock(nn.Module):
|
| 41 |
-
def __init__(self,
|
| 42 |
-
d_model: int,
|
| 43 |
-
n_head: int,
|
| 44 |
-
attn_mask: torch.Tensor = None,
|
| 45 |
-
drop_path: float = 0.0):
|
| 46 |
-
super().__init__()
|
| 47 |
-
|
| 48 |
-
self.attn = nn.MultiheadAttention(d_model, n_head)
|
| 49 |
-
self.ln_1 = LayerNorm(d_model)
|
| 50 |
-
self.mlp = nn.Sequential(OrderedDict([
|
| 51 |
-
("c_fc", nn.Linear(d_model, d_model * 4)),
|
| 52 |
-
("gelu", QuickGELU()),
|
| 53 |
-
("c_proj", nn.Linear(d_model * 4, d_model))
|
| 54 |
-
]))
|
| 55 |
-
self.ln_2 = LayerNorm(d_model)
|
| 56 |
-
self.attn_mask = attn_mask
|
| 57 |
-
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
| 58 |
-
|
| 59 |
-
def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None):
|
| 60 |
-
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) \
|
| 61 |
-
if self.attn_mask is not None else None
|
| 62 |
-
|
| 63 |
-
|
| 64 |
-
return self.attn(
|
| 65 |
-
x, x, x,
|
| 66 |
-
key_padding_mask=key_padding_mask,
|
| 67 |
-
need_weights=False,
|
| 68 |
-
attn_mask=self.attn_mask
|
| 69 |
-
)[0]
|
| 70 |
-
|
| 71 |
-
def forward(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None):
|
| 72 |
-
x = x + self.drop_path(self.attention(self.ln_1(x), key_padding_mask=key_padding_mask))
|
| 73 |
-
x = x + self.drop_path(self.mlp(self.ln_2(x)))
|
| 74 |
-
return x
|
| 75 |
-
|
| 76 |
-
|
| 77 |
-
class Transformer(nn.Module):
|
| 78 |
-
def __init__(self,
|
| 79 |
-
context_length: int,
|
| 80 |
-
vocab_size: int,
|
| 81 |
-
width: int,
|
| 82 |
-
layers: int,
|
| 83 |
-
heads: int,
|
| 84 |
-
drop_path: float = 0.0,
|
| 85 |
-
autogressive: bool =True):
|
| 86 |
-
super().__init__()
|
| 87 |
-
|
| 88 |
-
self.token_embedding = nn.Embedding(vocab_size, width)
|
| 89 |
-
|
| 90 |
-
self.context_length = context_length
|
| 91 |
-
self.positional_embedding = nn.Parameter(
|
| 92 |
-
torch.empty(self.context_length, width)
|
| 93 |
-
)
|
| 94 |
-
|
| 95 |
-
self.width = width
|
| 96 |
-
self.layers = layers
|
| 97 |
-
self.autogressive = autogressive
|
| 98 |
-
attn_mask = self.build_attention_mask() if autogressive else None
|
| 99 |
-
dpr = [x.item() for x in torch.linspace(0, drop_path, layers)] # stochastic depth decay rule
|
| 100 |
-
self.resblocks = nn.ModuleList(
|
| 101 |
-
[
|
| 102 |
-
ResidualAttentionBlock(width, heads, attn_mask, dpr[i])
|
| 103 |
-
for i in range(layers)
|
| 104 |
-
]
|
| 105 |
-
)
|
| 106 |
-
|
| 107 |
-
self.ln_final = LayerNorm(width)
|
| 108 |
-
|
| 109 |
-
trunc_normal_(self.positional_embedding, std=.02)
|
| 110 |
-
# nn.init.normal_(self.token_embedding, std=.02)
|
| 111 |
-
trunc_normal_(self.token_embedding.weight, std=.02)
|
| 112 |
-
self.apply(self._init_weights)
|
| 113 |
-
|
| 114 |
-
@property
|
| 115 |
-
def dim_out(self):
|
| 116 |
-
return self.width
|
| 117 |
-
|
| 118 |
-
def build_attention_mask(self):
|
| 119 |
-
# lazily create causal attention mask, with full attention between the vision tokens
|
| 120 |
-
# pytorch uses additive attention mask; fill with -inf
|
| 121 |
-
mask = torch.empty(self.context_length, self.context_length)
|
| 122 |
-
mask.fill_(float("-inf"))
|
| 123 |
-
mask.triu_(1) # zero out the lower diagonal
|
| 124 |
-
return mask
|
| 125 |
-
|
| 126 |
-
def _init_weights(self, m):
|
| 127 |
-
if isinstance(m, (nn.Linear, nn.Conv2d)):
|
| 128 |
-
logger.info('=> init weight of Linear/Conv2d from trunc norm')
|
| 129 |
-
trunc_normal_(m.weight, std=0.02)
|
| 130 |
-
if m.bias is not None:
|
| 131 |
-
logger.info('=> init bias of Linear/Conv2d to zeros')
|
| 132 |
-
nn.init.constant_(m.bias, 0)
|
| 133 |
-
elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
|
| 134 |
-
nn.init.constant_(m.bias, 0)
|
| 135 |
-
|
| 136 |
-
def load_pretrained(self, pretrained='', pretrained_layers=[], verbose=True):
|
| 137 |
-
if os.path.isfile(pretrained):
|
| 138 |
-
pretrained_dict = torch.load(pretrained, map_location='cpu')
|
| 139 |
-
logging.info(f'=> loading pretrained model {pretrained}')
|
| 140 |
-
model_dict = self.state_dict()
|
| 141 |
-
pretrained_dict = {
|
| 142 |
-
k: v for k, v in pretrained_dict.items()
|
| 143 |
-
if k in model_dict.keys()
|
| 144 |
-
}
|
| 145 |
-
need_init_state_dict = {}
|
| 146 |
-
for k, v in pretrained_dict.items():
|
| 147 |
-
need_init = (
|
| 148 |
-
k.split('.')[0] in pretrained_layers
|
| 149 |
-
or pretrained_layers[0] == '*'
|
| 150 |
-
)
|
| 151 |
-
if need_init:
|
| 152 |
-
if verbose:
|
| 153 |
-
logging.info(f'=> init {k} from {pretrained}')
|
| 154 |
-
|
| 155 |
-
need_init_state_dict[k] = v
|
| 156 |
-
self.load_state_dict(need_init_state_dict, strict=False)
|
| 157 |
-
|
| 158 |
-
|
| 159 |
-
@torch.jit.ignore
|
| 160 |
-
def no_weight_decay(self):
|
| 161 |
-
return {
|
| 162 |
-
'positional_embedding',
|
| 163 |
-
'token_embedding',
|
| 164 |
-
}
|
| 165 |
-
|
| 166 |
-
def forward(self, input_ids, attention_mask=None):
|
| 167 |
-
key_padding_mask = (input_ids == 0) if not self.autogressive else None
|
| 168 |
-
x = self.token_embedding(input_ids) # [batch_size, n_ctx, d_model]
|
| 169 |
-
x = x + self.positional_embedding
|
| 170 |
-
x = x.permute(1, 0, 2) # NLD -> LND
|
| 171 |
-
for block in self.resblocks:
|
| 172 |
-
x = block(x, key_padding_mask)
|
| 173 |
-
x = x.permute(1, 0, 2) # LND -> NLD
|
| 174 |
-
|
| 175 |
-
x = self.ln_final(x)
|
| 176 |
-
|
| 177 |
-
return {'last_hidden_state': x}
|
| 178 |
-
|
| 179 |
-
|
| 180 |
-
@register_lang_encoder
|
| 181 |
-
def lang_encoder(config_encoder, tokenizer, verbose, **kwargs):
|
| 182 |
-
transformer = Transformer(
|
| 183 |
-
context_length=config_encoder['CONTEXT_LENGTH'],
|
| 184 |
-
vocab_size=tokenizer.vocab_size,
|
| 185 |
-
width=config_encoder['WIDTH'],
|
| 186 |
-
layers=config_encoder['LAYERS'],
|
| 187 |
-
heads=config_encoder['HEADS'],
|
| 188 |
-
autogressive=config_encoder.get('AUTOGRESSIVE', True)
|
| 189 |
-
)
|
| 190 |
-
|
| 191 |
-
if config_encoder['LOAD_PRETRAINED']:
|
| 192 |
-
transformer.load_pretrained()
|
| 193 |
-
|
| 194 |
-
return transformer
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/ClassCat/Spleen-3D-segmentation-with-MONAI/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Spleen 3D Segmentation With MONAI
|
| 3 |
-
emoji: 🔥
|
| 4 |
-
colorFrom: pink
|
| 5 |
-
colorTo: pink
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.16.2
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: True
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Cloudyy/bark-voice-cloning/hubert/pre_kmeans_hubert.py
DELETED
|
@@ -1,85 +0,0 @@
|
|
| 1 |
-
from pathlib import Path
|
| 2 |
-
|
| 3 |
-
import torch
|
| 4 |
-
from torch import nn
|
| 5 |
-
from einops import pack, unpack
|
| 6 |
-
|
| 7 |
-
import fairseq
|
| 8 |
-
|
| 9 |
-
from torchaudio.functional import resample
|
| 10 |
-
|
| 11 |
-
import logging
|
| 12 |
-
logging.root.setLevel(logging.ERROR)
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
def exists(val):
|
| 16 |
-
return val is not None
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
def default(val, d):
|
| 20 |
-
return val if exists(val) else d
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
class CustomHubert(nn.Module):
|
| 24 |
-
"""
|
| 25 |
-
checkpoint and kmeans can be downloaded at https://github.com/facebookresearch/fairseq/tree/main/examples/hubert
|
| 26 |
-
or you can train your own
|
| 27 |
-
"""
|
| 28 |
-
|
| 29 |
-
def __init__(
|
| 30 |
-
self,
|
| 31 |
-
checkpoint_path,
|
| 32 |
-
target_sample_hz=16000,
|
| 33 |
-
seq_len_multiple_of=None,
|
| 34 |
-
output_layer=9
|
| 35 |
-
):
|
| 36 |
-
super().__init__()
|
| 37 |
-
self.target_sample_hz = target_sample_hz
|
| 38 |
-
self.seq_len_multiple_of = seq_len_multiple_of
|
| 39 |
-
self.output_layer = output_layer
|
| 40 |
-
|
| 41 |
-
model_path = Path(checkpoint_path)
|
| 42 |
-
|
| 43 |
-
assert model_path.exists(), f'path {checkpoint_path} does not exist'
|
| 44 |
-
|
| 45 |
-
checkpoint = torch.load(checkpoint_path)
|
| 46 |
-
load_model_input = {checkpoint_path: checkpoint}
|
| 47 |
-
model, *_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(load_model_input)
|
| 48 |
-
|
| 49 |
-
self.model = model[0]
|
| 50 |
-
self.model.eval()
|
| 51 |
-
|
| 52 |
-
@property
|
| 53 |
-
def groups(self):
|
| 54 |
-
return 1
|
| 55 |
-
|
| 56 |
-
@torch.no_grad()
|
| 57 |
-
def forward(
|
| 58 |
-
self,
|
| 59 |
-
wav_input,
|
| 60 |
-
flatten=True,
|
| 61 |
-
input_sample_hz=None
|
| 62 |
-
):
|
| 63 |
-
device = wav_input.device
|
| 64 |
-
|
| 65 |
-
if exists(input_sample_hz):
|
| 66 |
-
wav_input = resample(wav_input, input_sample_hz, self.target_sample_hz)
|
| 67 |
-
|
| 68 |
-
embed = self.model(
|
| 69 |
-
wav_input,
|
| 70 |
-
features_only=True,
|
| 71 |
-
mask=False, # thanks to @maitycyrus for noticing that mask is defaulted to True in the fairseq code
|
| 72 |
-
output_layer=self.output_layer
|
| 73 |
-
)
|
| 74 |
-
|
| 75 |
-
embed, packed_shape = pack([embed['x']], '* d')
|
| 76 |
-
|
| 77 |
-
# codebook_indices = self.kmeans.predict(embed.cpu().detach().numpy())
|
| 78 |
-
|
| 79 |
-
codebook_indices = torch.from_numpy(embed.cpu().detach().numpy()).to(device) # .long()
|
| 80 |
-
|
| 81 |
-
if flatten:
|
| 82 |
-
return codebook_indices
|
| 83 |
-
|
| 84 |
-
codebook_indices, = unpack(codebook_indices, packed_shape, '*')
|
| 85 |
-
return codebook_indices
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/CofAI/chat.b4/g4f/models.py
DELETED
|
@@ -1,238 +0,0 @@
|
|
| 1 |
-
from g4f import Provider
|
| 2 |
-
import random
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
class Model:
|
| 6 |
-
class model:
|
| 7 |
-
name: str
|
| 8 |
-
base_provider: str
|
| 9 |
-
best_provider: str
|
| 10 |
-
|
| 11 |
-
class gpt_35_turbo:
|
| 12 |
-
name: str = 'gpt-3.5-turbo'
|
| 13 |
-
base_provider: str = 'openai'
|
| 14 |
-
best_provider: Provider.Provider = Provider.DeepAi
|
| 15 |
-
|
| 16 |
-
class gpt_35_turbo_0613:
|
| 17 |
-
name: str = 'gpt-3.5-turbo-0613'
|
| 18 |
-
base_provider: str = 'openai'
|
| 19 |
-
best_provider: Provider.Provider = Provider.Zeabur
|
| 20 |
-
|
| 21 |
-
class gpt_35_turbo_0301:
|
| 22 |
-
name: str = 'gpt-3.5-turbo-0301'
|
| 23 |
-
base_provider: str = 'openai'
|
| 24 |
-
best_provider: Provider.Provider = Provider.Zeabur
|
| 25 |
-
|
| 26 |
-
class gpt_35_turbo_16k_0613:
|
| 27 |
-
name: str = 'gpt-3.5-turbo-16k-0613'
|
| 28 |
-
base_provider: str = 'openai'
|
| 29 |
-
best_provider: Provider.Provider = Provider.Zeabur
|
| 30 |
-
|
| 31 |
-
class gpt_35_turbo_16k:
|
| 32 |
-
name: str = 'gpt-3.5-turbo-16k'
|
| 33 |
-
base_provider: str = 'openai'
|
| 34 |
-
best_provider: Provider.Provider = Provider.Zeabur
|
| 35 |
-
|
| 36 |
-
class gpt_4_dev:
|
| 37 |
-
name: str = 'gpt-4-for-dev'
|
| 38 |
-
base_provider: str = 'openai'
|
| 39 |
-
best_provider: Provider.Provider = Provider.Phind
|
| 40 |
-
|
| 41 |
-
class gpt_4:
|
| 42 |
-
name: str = 'gpt-4'
|
| 43 |
-
base_provider: str = 'openai'
|
| 44 |
-
best_provider: Provider.Provider = Provider.ChatgptAi
|
| 45 |
-
|
| 46 |
-
class gpt_4_0613:
|
| 47 |
-
name: str = 'gpt-4-0613'
|
| 48 |
-
base_provider: str = 'openai'
|
| 49 |
-
best_provider: Provider.Provider = Provider.Lockchat
|
| 50 |
-
best_providers: list = [Provider.Bing, Provider.Lockchat]
|
| 51 |
-
|
| 52 |
-
class claude_instant_v1_100k:
|
| 53 |
-
name: str = 'claude-instant-v1-100k'
|
| 54 |
-
base_provider: str = 'anthropic'
|
| 55 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 56 |
-
|
| 57 |
-
class claude_instant_v1:
|
| 58 |
-
name: str = 'claude-instant-v1'
|
| 59 |
-
base_provider: str = 'anthropic'
|
| 60 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 61 |
-
|
| 62 |
-
class claude_v1_100k:
|
| 63 |
-
name: str = 'claude-v1-100k'
|
| 64 |
-
base_provider: str = 'anthropic'
|
| 65 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 66 |
-
|
| 67 |
-
class claude_v1:
|
| 68 |
-
name: str = 'claude-v1'
|
| 69 |
-
base_provider: str = 'anthropic'
|
| 70 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 71 |
-
|
| 72 |
-
class alpaca_7b:
|
| 73 |
-
name: str = 'alpaca-7b'
|
| 74 |
-
base_provider: str = 'replicate'
|
| 75 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 76 |
-
|
| 77 |
-
class stablelm_tuned_alpha_7b:
|
| 78 |
-
name: str = 'stablelm-tuned-alpha-7b'
|
| 79 |
-
base_provider: str = 'replicate'
|
| 80 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 81 |
-
|
| 82 |
-
class bloom:
|
| 83 |
-
name: str = 'bloom'
|
| 84 |
-
base_provider: str = 'huggingface'
|
| 85 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 86 |
-
|
| 87 |
-
class bloomz:
|
| 88 |
-
name: str = 'bloomz'
|
| 89 |
-
base_provider: str = 'huggingface'
|
| 90 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 91 |
-
|
| 92 |
-
class flan_t5_xxl:
|
| 93 |
-
name: str = 'flan-t5-xxl'
|
| 94 |
-
base_provider: str = 'huggingface'
|
| 95 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 96 |
-
|
| 97 |
-
class flan_ul2:
|
| 98 |
-
name: str = 'flan-ul2'
|
| 99 |
-
base_provider: str = 'huggingface'
|
| 100 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 101 |
-
|
| 102 |
-
class gpt_neox_20b:
|
| 103 |
-
name: str = 'gpt-neox-20b'
|
| 104 |
-
base_provider: str = 'huggingface'
|
| 105 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 106 |
-
|
| 107 |
-
class oasst_sft_4_pythia_12b_epoch_35:
|
| 108 |
-
name: str = 'oasst-sft-4-pythia-12b-epoch-3.5'
|
| 109 |
-
base_provider: str = 'huggingface'
|
| 110 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 111 |
-
|
| 112 |
-
class santacoder:
|
| 113 |
-
name: str = 'santacoder'
|
| 114 |
-
base_provider: str = 'huggingface'
|
| 115 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 116 |
-
|
| 117 |
-
class command_medium_nightly:
|
| 118 |
-
name: str = 'command-medium-nightly'
|
| 119 |
-
base_provider: str = 'cohere'
|
| 120 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 121 |
-
|
| 122 |
-
class command_xlarge_nightly:
|
| 123 |
-
name: str = 'command-xlarge-nightly'
|
| 124 |
-
base_provider: str = 'cohere'
|
| 125 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 126 |
-
|
| 127 |
-
class code_cushman_001:
|
| 128 |
-
name: str = 'code-cushman-001'
|
| 129 |
-
base_provider: str = 'openai'
|
| 130 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 131 |
-
|
| 132 |
-
class code_davinci_002:
|
| 133 |
-
name: str = 'code-davinci-002'
|
| 134 |
-
base_provider: str = 'openai'
|
| 135 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 136 |
-
|
| 137 |
-
class text_ada_001:
|
| 138 |
-
name: str = 'text-ada-001'
|
| 139 |
-
base_provider: str = 'openai'
|
| 140 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 141 |
-
|
| 142 |
-
class text_babbage_001:
|
| 143 |
-
name: str = 'text-babbage-001'
|
| 144 |
-
base_provider: str = 'openai'
|
| 145 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 146 |
-
|
| 147 |
-
class text_curie_001:
|
| 148 |
-
name: str = 'text-curie-001'
|
| 149 |
-
base_provider: str = 'openai'
|
| 150 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 151 |
-
|
| 152 |
-
class text_davinci_002:
|
| 153 |
-
name: str = 'text-davinci-002'
|
| 154 |
-
base_provider: str = 'openai'
|
| 155 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 156 |
-
|
| 157 |
-
class text_davinci_003:
|
| 158 |
-
name: str = 'text-davinci-003'
|
| 159 |
-
base_provider: str = 'openai'
|
| 160 |
-
best_provider: Provider.Provider = Provider.Vercel
|
| 161 |
-
|
| 162 |
-
class palm:
|
| 163 |
-
name: str = 'palm2'
|
| 164 |
-
base_provider: str = 'google'
|
| 165 |
-
best_provider: Provider.Provider = Provider.Bard
|
| 166 |
-
|
| 167 |
-
""" 'falcon-40b': Model.falcon_40b,
|
| 168 |
-
'falcon-7b': Model.falcon_7b,
|
| 169 |
-
'llama-13b': Model.llama_13b,"""
|
| 170 |
-
|
| 171 |
-
class falcon_40b:
|
| 172 |
-
name: str = 'falcon-40b'
|
| 173 |
-
base_provider: str = 'huggingface'
|
| 174 |
-
best_provider: Provider.Provider = Provider.H2o
|
| 175 |
-
|
| 176 |
-
class falcon_7b:
|
| 177 |
-
name: str = 'falcon-7b'
|
| 178 |
-
base_provider: str = 'huggingface'
|
| 179 |
-
best_provider: Provider.Provider = Provider.H2o
|
| 180 |
-
|
| 181 |
-
class llama_13b:
|
| 182 |
-
name: str = 'llama-13b'
|
| 183 |
-
base_provider: str = 'huggingface'
|
| 184 |
-
best_provider: Provider.Provider = Provider.H2o
|
| 185 |
-
|
| 186 |
-
|
| 187 |
-
class ModelUtils:
|
| 188 |
-
convert: dict = {
|
| 189 |
-
'gpt-3.5-turbo': Model.gpt_35_turbo,
|
| 190 |
-
'gpt-3.5-turbo-0613': Model.gpt_35_turbo_0613,
|
| 191 |
-
'gpt-3.5-turbo-0301': Model.gpt_35_turbo_0301,
|
| 192 |
-
'gpt-4': Model.gpt_4,
|
| 193 |
-
'gpt-4-0613': Model.gpt_4_0613,
|
| 194 |
-
'gpt-4-for-dev': Model.gpt_4_dev,
|
| 195 |
-
'gpt-3.5-turbo-16k': Model.gpt_35_turbo_16k,
|
| 196 |
-
'gpt-3.5-turbo-16k-0613': Model.gpt_35_turbo_16k_0613,
|
| 197 |
-
|
| 198 |
-
'claude-instant-v1-100k': Model.claude_instant_v1_100k,
|
| 199 |
-
'claude-v1-100k': Model.claude_v1_100k,
|
| 200 |
-
'claude-instant-v1': Model.claude_instant_v1,
|
| 201 |
-
'claude-v1': Model.claude_v1,
|
| 202 |
-
|
| 203 |
-
'alpaca-7b': Model.alpaca_7b,
|
| 204 |
-
'stablelm-tuned-alpha-7b': Model.stablelm_tuned_alpha_7b,
|
| 205 |
-
|
| 206 |
-
'bloom': Model.bloom,
|
| 207 |
-
'bloomz': Model.bloomz,
|
| 208 |
-
|
| 209 |
-
'flan-t5-xxl': Model.flan_t5_xxl,
|
| 210 |
-
'flan-ul2': Model.flan_ul2,
|
| 211 |
-
|
| 212 |
-
'gpt-neox-20b': Model.gpt_neox_20b,
|
| 213 |
-
'oasst-sft-4-pythia-12b-epoch-3.5': Model.oasst_sft_4_pythia_12b_epoch_35,
|
| 214 |
-
'santacoder': Model.santacoder,
|
| 215 |
-
|
| 216 |
-
'command-medium-nightly': Model.command_medium_nightly,
|
| 217 |
-
'command-xlarge-nightly': Model.command_xlarge_nightly,
|
| 218 |
-
|
| 219 |
-
'code-cushman-001': Model.code_cushman_001,
|
| 220 |
-
'code-davinci-002': Model.code_davinci_002,
|
| 221 |
-
|
| 222 |
-
'text-ada-001': Model.text_ada_001,
|
| 223 |
-
'text-babbage-001': Model.text_babbage_001,
|
| 224 |
-
'text-curie-001': Model.text_curie_001,
|
| 225 |
-
'text-davinci-002': Model.text_davinci_002,
|
| 226 |
-
'text-davinci-003': Model.text_davinci_003,
|
| 227 |
-
|
| 228 |
-
'palm2': Model.palm,
|
| 229 |
-
'palm': Model.palm,
|
| 230 |
-
'google': Model.palm,
|
| 231 |
-
'google-bard': Model.palm,
|
| 232 |
-
'google-palm': Model.palm,
|
| 233 |
-
'bard': Model.palm,
|
| 234 |
-
|
| 235 |
-
'falcon-40b': Model.falcon_40b,
|
| 236 |
-
'falcon-7b': Model.falcon_7b,
|
| 237 |
-
'llama-13b': Model.llama_13b,
|
| 238 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Cropinky/hana_hanak_houses/image_generator.py
DELETED
|
@@ -1,156 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# NVIDIA CORPORATION and its licensors retain all intellectual property
|
| 4 |
-
# and proprietary rights in and to this software, related documentation
|
| 5 |
-
# and any modifications thereto. Any use, reproduction, disclosure or
|
| 6 |
-
# distribution of this software and related documentation without an express
|
| 7 |
-
# license agreement from NVIDIA CORPORATION is strictly prohibited.
|
| 8 |
-
|
| 9 |
-
"""Generate images using pretrained network pickle."""
|
| 10 |
-
|
| 11 |
-
from ast import parse
|
| 12 |
-
import os
|
| 13 |
-
from pyexpat import model
|
| 14 |
-
import re
|
| 15 |
-
from typing import List, Optional, Tuple, Union
|
| 16 |
-
import numpy as np
|
| 17 |
-
import PIL.Image
|
| 18 |
-
import torch
|
| 19 |
-
from networks_fastgan import MyGenerator
|
| 20 |
-
import random
|
| 21 |
-
#----------------------------------------------------------------------------
|
| 22 |
-
|
| 23 |
-
def parse_range(s: Union[str, List]) -> List[int]:
|
| 24 |
-
'''Parse a comma separated list of numbers or ranges and return a list of ints.
|
| 25 |
-
|
| 26 |
-
Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
|
| 27 |
-
'''
|
| 28 |
-
if isinstance(s, list): return s
|
| 29 |
-
ranges = []
|
| 30 |
-
range_re = re.compile(r'^(\d+)-(\d+)$')
|
| 31 |
-
for p in s.split(','):
|
| 32 |
-
m = range_re.match(p)
|
| 33 |
-
if m:
|
| 34 |
-
ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
|
| 35 |
-
else:
|
| 36 |
-
ranges.append(int(p))
|
| 37 |
-
return ranges
|
| 38 |
-
|
| 39 |
-
#----------------------------------------------------------------------------
|
| 40 |
-
|
| 41 |
-
def parse_vec2(s: Union[str, Tuple[float, float]]) -> Tuple[float, float]:
|
| 42 |
-
'''Parse a floating point 2-vector of syntax 'a,b'.
|
| 43 |
-
|
| 44 |
-
Example:
|
| 45 |
-
'0,1' returns (0,1)
|
| 46 |
-
'''
|
| 47 |
-
if isinstance(s, tuple): return s
|
| 48 |
-
parts = s.split(',')
|
| 49 |
-
if len(parts) == 2:
|
| 50 |
-
return (float(parts[0]), float(parts[1]))
|
| 51 |
-
raise ValueError(f'cannot parse 2-vector {s}')
|
| 52 |
-
|
| 53 |
-
#----------------------------------------------------------------------------
|
| 54 |
-
|
| 55 |
-
def make_transform(translate: Tuple[float,float], angle: float):
|
| 56 |
-
m = np.eye(3)
|
| 57 |
-
s = np.sin(angle/360.0*np.pi*2)
|
| 58 |
-
c = np.cos(angle/360.0*np.pi*2)
|
| 59 |
-
m[0][0] = c
|
| 60 |
-
m[0][1] = s
|
| 61 |
-
m[0][2] = translate[0]
|
| 62 |
-
m[1][0] = -s
|
| 63 |
-
m[1][1] = c
|
| 64 |
-
m[1][2] = translate[1]
|
| 65 |
-
return m
|
| 66 |
-
|
| 67 |
-
#----------------------------------------------------------------------------
|
| 68 |
-
|
| 69 |
-
def generate_images(
|
| 70 |
-
model_path,
|
| 71 |
-
seeds = "10-12",
|
| 72 |
-
truncation_psi = 1.0,
|
| 73 |
-
noise_mode = "const",
|
| 74 |
-
outdir = "out",
|
| 75 |
-
translate = "0,0",
|
| 76 |
-
rotate = 0,
|
| 77 |
-
number_of_images = 16
|
| 78 |
-
):
|
| 79 |
-
model_owner = "huggan"
|
| 80 |
-
#inputs = gr.inputs.Radio(["Abstract Expressionism", "Impressionism", "Cubism", "Minimalism", "Pop Art", "Color Field", "Hana Hanak houses"])
|
| 81 |
-
model_path_dict = {
|
| 82 |
-
'Impressionism' : 'projected_gan_impressionism',
|
| 83 |
-
'Cubism' : 'projected_gan_cubism',
|
| 84 |
-
'Abstract Expressionism' : 'projected_gan_abstract_expressionism',
|
| 85 |
-
'Pop Art' : 'projected_gan_popart',
|
| 86 |
-
'Minimalism' : 'projected_gan_minimalism',
|
| 87 |
-
'Color Field' : 'projected_gan_color_field',
|
| 88 |
-
'Hana Hanak houses' : 'projected_gan_Hana_Hanak',
|
| 89 |
-
'Hana Hanak houses - abstract expressionism' : 'projected_gan_abstract_expressionism_hana',
|
| 90 |
-
'Hana Hanak houses - color field' : 'projected_gan_color_field_hana',
|
| 91 |
-
|
| 92 |
-
}
|
| 93 |
-
|
| 94 |
-
model_path = model_owner + "/" + model_path_dict[model_path]
|
| 95 |
-
print(model_path)
|
| 96 |
-
print(seeds)
|
| 97 |
-
seeds=[random.randint(1,230)]
|
| 98 |
-
#seeds =f"{seeds}-{seeds+number_of_images-1}"
|
| 99 |
-
#seeds = parse_range(seeds)
|
| 100 |
-
#device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
|
| 101 |
-
device = torch.device('cpu')
|
| 102 |
-
G = MyGenerator.from_pretrained(model_path)
|
| 103 |
-
os.makedirs(outdir, exist_ok=True)
|
| 104 |
-
# Labels.
|
| 105 |
-
label = torch.zeros([1, G.c_dim], device=device)
|
| 106 |
-
"""
|
| 107 |
-
if G.c_dim != 0:
|
| 108 |
-
if class_idx is None:
|
| 109 |
-
raise click.ClickException('Must specify class label with --class when using a conditional network')
|
| 110 |
-
label[:, class_idx] = 1
|
| 111 |
-
else:
|
| 112 |
-
if class_idx is not None:
|
| 113 |
-
print ('warn: --class=lbl ignored when running on an unconditional network')
|
| 114 |
-
"""
|
| 115 |
-
|
| 116 |
-
print(f"z dimenzija mi je: {G.z_dim}")
|
| 117 |
-
# Generate images.
|
| 118 |
-
|
| 119 |
-
#imgs_row = np.array()
|
| 120 |
-
#imgs_complete = np.array()
|
| 121 |
-
print(seeds)
|
| 122 |
-
for seed_idx, seed in enumerate(seeds):
|
| 123 |
-
print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
|
| 124 |
-
z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device).float()
|
| 125 |
-
# Construct an inverse rotation/translation matrix and pass to the generator. The
|
| 126 |
-
# generator expects this matrix as an inverse to avoid potentially failing numerical
|
| 127 |
-
# operations in the network.
|
| 128 |
-
if hasattr(G.synthesis, 'input'):
|
| 129 |
-
m = make_transform(translate, rotate)
|
| 130 |
-
m = np.linalg.inv(m)
|
| 131 |
-
G.synthesis.input.transform.copy_(torch.from_numpy(m))
|
| 132 |
-
|
| 133 |
-
img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
|
| 134 |
-
img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
|
| 135 |
-
print(seed_idx)
|
| 136 |
-
"""
|
| 137 |
-
#first image
|
| 138 |
-
if seed_idx == 0:
|
| 139 |
-
imgs_row = img[0].cpu().numpy()
|
| 140 |
-
else:
|
| 141 |
-
imgs_row = np.hstack((imgs_row, img[0].cpu().numpy()))"""
|
| 142 |
-
#img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')
|
| 143 |
-
#PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png')
|
| 144 |
-
#napravi vsplit i toe to ka
|
| 145 |
-
#imgs_complete = np.vstack(np.hsplit(imgs_row, 4))
|
| 146 |
-
#cv2.imshow("lalaxd", imgs_complete)
|
| 147 |
-
#cv2.waitKey()
|
| 148 |
-
return PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')
|
| 149 |
-
|
| 150 |
-
|
| 151 |
-
#----------------------------------------------------------------------------
|
| 152 |
-
|
| 153 |
-
if __name__ == "__main__":
|
| 154 |
-
generate_images() # pylint: disable=no-value-for-parameter
|
| 155 |
-
|
| 156 |
-
#----------------------------------------------------------------------------
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-b7998330.js
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
import{S as M,e as N,s as O,N as z,k,O as Q,K as S,p as j,o as w,M as E,ap as T,Q as A,z as C,v as B,A as q,x as P,a1 as L,B as V,am as W,P as X,R as Y,F as g,ak as r,E as Z,ae as y,h as D,j as F,q as p,r as x,t as K}from"./index-1d65707a.js";/* empty css */import{B as $}from"./Button-f155035a.js";import{B as ee}from"./BlockTitle-dee077e8.js";import"./Info-7c6961ef.js";function te(t){let e;return{c(){e=X(t[1])},m(l,s){j(l,e,s)},p(l,s){s&2&&Y(e,l[1])},d(l){l&&q(e)}}}function le(t){let e,l,s,a,o,c,h;return l=new ee({props:{show_label:t[4],info:t[2],$$slots:{default:[te]},$$scope:{ctx:t}}}),{c(){e=z("label"),k(l.$$.fragment),s=Q(),a=z("input"),S(a,"type","color"),a.disabled=t[3],S(a,"class","svelte-56zyyb"),S(e,"class","block")},m(_,f){j(_,e,f),w(l,e,null),E(e,s),E(e,a),T(a,t[0]),o=!0,c||(h=[A(a,"blur",t[6]),A(a,"input",t[7])],c=!0)},p(_,[f]){const m={};f&16&&(m.show_label=_[4]),f&4&&(m.info=_[2]),f&1026&&(m.$$scope={dirty:f,ctx:_}),l.$set(m),(!o||f&8)&&(a.disabled=_[3]),f&1&&T(a,_[0])},i(_){o||(C(l.$$.fragment,_),o=!0)},o(_){B(l.$$.fragment,_),o=!1},d(_){_&&q(e),P(l),c=!1,L(h)}}}function se(t,e,l){let{value:s="#000000"}=e,{value_is_output:a=!1}=e,{label:o}=e,{info:c=void 0}=e,{disabled:h=!1}=e,{show_label:_=!0}=e;const f=V();function m(){f("change",s),a||f("input")}W(()=>{l(5,a=!1)});function d(u){g.call(this,t,u)}function n(){s=this.value,l(0,s)}return t.$$set=u=>{"value"in u&&l(0,s=u.value),"value_is_output"in u&&l(5,a=u.value_is_output),"label"in u&&l(1,o=u.label),"info"in u&&l(2,c=u.info),"disabled"in u&&l(3,h=u.disabled),"show_label"in u&&l(4,_=u.show_label)},t.$$.update=()=>{t.$$.dirty&1&&m()},[s,o,c,h,_,a,d,n]}class ie extends M{constructor(e){super(),N(this,e,se,le,O,{value:0,value_is_output:5,label:1,info:2,disabled:3,show_label:4})}}function ne(t){let e,l,s,a,o,c;const h=[t[11]];let _={};for(let n=0;n<h.length;n+=1)_=Z(_,h[n]);e=new y({props:_});function f(n){t[13](n)}function m(n){t[14](n)}let d={label:t[2],info:t[3],show_label:t[7],disabled:t[12]==="static"};return t[0]!==void 0&&(d.value=t[0]),t[1]!==void 0&&(d.value_is_output=t[1]),s=new ie({props:d}),D.push(()=>F(s,"value",f)),D.push(()=>F(s,"value_is_output",m)),s.$on("change",t[15]),s.$on("input",t[16]),s.$on("submit",t[17]),s.$on("blur",t[18]),{c(){k(e.$$.fragment),l=Q(),k(s.$$.fragment)},m(n,u){w(e,n,u),j(n,l,u),w(s,n,u),c=!0},p(n,u){const v=u&2048?p(h,[x(n[11])]):{};e.$set(v);const b={};u&4&&(b.label=n[2]),u&8&&(b.info=n[3]),u&128&&(b.show_label=n[7]),u&4096&&(b.disabled=n[12]==="static"),!a&&u&1&&(a=!0,b.value=n[0],K(()=>a=!1)),!o&&u&2&&(o=!0,b.value_is_output=n[1],K(()=>o=!1)),s.$set(b)},i(n){c||(C(e.$$.fragment,n),C(s.$$.fragment,n),c=!0)},o(n){B(e.$$.fragment,n),B(s.$$.fragment,n),c=!1},d(n){n&&q(l),P(e,n),P(s,n)}}}function ae(t){let e,l;return e=new $({props:{visible:t[6],elem_id:t[4],elem_classes:t[5],container:t[8],scale:t[9],min_width:t[10],$$slots:{default:[ne]},$$scope:{ctx:t}}}),{c(){k(e.$$.fragment)},m(s,a){w(e,s,a),l=!0},p(s,[a]){const o={};a&64&&(o.visible=s[6]),a&16&&(o.elem_id=s[4]),a&32&&(o.elem_classes=s[5]),a&256&&(o.container=s[8]),a&512&&(o.scale=s[9]),a&1024&&(o.min_width=s[10]),a&530575&&(o.$$scope={dirty:a,ctx:s}),e.$set(o)},i(s){l||(C(e.$$.fragment,s),l=!0)},o(s){B(e.$$.fragment,s),l=!1},d(s){P(e,s)}}}function ue(t,e,l){let{label:s="ColorPicker"}=e,{info:a=void 0}=e,{elem_id:o=""}=e,{elem_classes:c=[]}=e,{visible:h=!0}=e,{value:_}=e,{value_is_output:f=!1}=e,{show_label:m}=e,{container:d=!0}=e,{scale:n=null}=e,{min_width:u=void 0}=e,{loading_status:v}=e,{mode:b}=e;function R(i){_=i,l(0,_)}function U(i){f=i,l(1,f)}function G(i){g.call(this,t,i)}function H(i){g.call(this,t,i)}function I(i){g.call(this,t,i)}function J(i){g.call(this,t,i)}return t.$$set=i=>{"label"in i&&l(2,s=i.label),"info"in i&&l(3,a=i.info),"elem_id"in i&&l(4,o=i.elem_id),"elem_classes"in i&&l(5,c=i.elem_classes),"visible"in i&&l(6,h=i.visible),"value"in i&&l(0,_=i.value),"value_is_output"in i&&l(1,f=i.value_is_output),"show_label"in i&&l(7,m=i.show_label),"container"in i&&l(8,d=i.container),"scale"in i&&l(9,n=i.scale),"min_width"in i&&l(10,u=i.min_width),"loading_status"in i&&l(11,v=i.loading_status),"mode"in i&&l(12,b=i.mode)},[_,f,s,a,o,c,h,m,d,n,u,v,b,R,U,G,H,I,J]}class _e extends M{constructor(e){super(),N(this,e,ue,ae,O,{label:2,info:3,elem_id:4,elem_classes:5,visible:6,value:0,value_is_output:1,show_label:7,container:8,scale:9,min_width:10,loading_status:11,mode:12})}get label(){return this.$$.ctx[2]}set label(e){this.$$set({label:e}),r()}get info(){return this.$$.ctx[3]}set info(e){this.$$set({info:e}),r()}get elem_id(){return this.$$.ctx[4]}set elem_id(e){this.$$set({elem_id:e}),r()}get elem_classes(){return this.$$.ctx[5]}set elem_classes(e){this.$$set({elem_classes:e}),r()}get visible(){return this.$$.ctx[6]}set visible(e){this.$$set({visible:e}),r()}get value(){return this.$$.ctx[0]}set value(e){this.$$set({value:e}),r()}get value_is_output(){return this.$$.ctx[1]}set value_is_output(e){this.$$set({value_is_output:e}),r()}get show_label(){return this.$$.ctx[7]}set show_label(e){this.$$set({show_label:e}),r()}get container(){return this.$$.ctx[8]}set container(e){this.$$set({container:e}),r()}get scale(){return this.$$.ctx[9]}set scale(e){this.$$set({scale:e}),r()}get min_width(){return this.$$.ctx[10]}set min_width(e){this.$$set({min_width:e}),r()}get loading_status(){return this.$$.ctx[11]}set loading_status(e){this.$$set({loading_status:e}),r()}get mode(){return this.$$.ctx[12]}set mode(e){this.$$set({mode:e}),r()}}const me=_e,be=["static","dynamic"],de=t=>({type:{payload:"string"},description:{payload:"hex color code"},example_data:t.value??"#000000"});export{me as Component,de as document,be as modes};
|
| 2 |
-
//# sourceMappingURL=index-b7998330.js.map
|
|
|
|
|
|
|
|
|
spaces/DeepFloyd/IF/app.py
DELETED
|
@@ -1,701 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env python
|
| 2 |
-
|
| 3 |
-
import datetime
|
| 4 |
-
import hashlib
|
| 5 |
-
import json
|
| 6 |
-
import os
|
| 7 |
-
import random
|
| 8 |
-
import tempfile
|
| 9 |
-
import shortuuid
|
| 10 |
-
from apscheduler.schedulers.background import BackgroundScheduler
|
| 11 |
-
import shutil
|
| 12 |
-
|
| 13 |
-
import gradio as gr
|
| 14 |
-
import torch
|
| 15 |
-
from huggingface_hub import HfApi
|
| 16 |
-
from share_btn import community_icon_html, loading_icon_html, share_js
|
| 17 |
-
|
| 18 |
-
# isort: off
|
| 19 |
-
from model import Model
|
| 20 |
-
from settings import (
|
| 21 |
-
DEBUG,
|
| 22 |
-
DEFAULT_CUSTOM_TIMESTEPS_1,
|
| 23 |
-
DEFAULT_CUSTOM_TIMESTEPS_2,
|
| 24 |
-
DEFAULT_NUM_IMAGES,
|
| 25 |
-
DEFAULT_NUM_STEPS_3,
|
| 26 |
-
DISABLE_SD_X4_UPSCALER,
|
| 27 |
-
GALLERY_COLUMN_NUM,
|
| 28 |
-
HF_TOKEN,
|
| 29 |
-
MAX_NUM_IMAGES,
|
| 30 |
-
MAX_NUM_STEPS,
|
| 31 |
-
MAX_QUEUE_SIZE,
|
| 32 |
-
MAX_SEED,
|
| 33 |
-
SHOW_ADVANCED_OPTIONS,
|
| 34 |
-
SHOW_CUSTOM_TIMESTEPS_1,
|
| 35 |
-
SHOW_CUSTOM_TIMESTEPS_2,
|
| 36 |
-
SHOW_DEVICE_WARNING,
|
| 37 |
-
SHOW_DUPLICATE_BUTTON,
|
| 38 |
-
SHOW_NUM_IMAGES,
|
| 39 |
-
SHOW_NUM_STEPS_1,
|
| 40 |
-
SHOW_NUM_STEPS_2,
|
| 41 |
-
SHOW_NUM_STEPS_3,
|
| 42 |
-
SHOW_UPSCALE_TO_256_BUTTON,
|
| 43 |
-
UPLOAD_REPO_ID,
|
| 44 |
-
UPLOAD_RESULT_IMAGE,
|
| 45 |
-
)
|
| 46 |
-
# isort: on
|
| 47 |
-
|
| 48 |
-
TITLE = '# [DeepFloyd IF](https://github.com/deep-floyd/IF)'
|
| 49 |
-
DESCRIPTION = 'The DeepFloyd IF model has been initially released as a non-commercial research-only model. Please make sure you read and abide to the [LICENSE](https://huggingface.co/spaces/DeepFloyd/deepfloyd-if-license) before using it.'
|
| 50 |
-
DISCLAIMER = 'In this demo, the DeepFloyd team may collect prompts, and user preferences (which of the images the user chose to upscale) for improving future models'
|
| 51 |
-
FOOTER = """<div class="footer">
|
| 52 |
-
<p>Model by <a href="https://huggingface.co/DeepFloyd" style="text-decoration: underline;" target="_blank">DeepFloyd</a> supported by <a href="https://huggingface.co/stabilityai" style="text-decoration: underline;" target="_blank">Stability AI</a>
|
| 53 |
-
</p>
|
| 54 |
-
</div>
|
| 55 |
-
<div class="acknowledgments">
|
| 56 |
-
<p><h4>LICENSE</h4>
|
| 57 |
-
The model is licensed with a bespoke non-commercial research-only license <a href="https://huggingface.co/spaces/DeepFloyd/deepfloyd-if-license" style="text-decoration: underline;" target="_blank">DeepFloyd IF Research License Agreement</a> license. The license forbids you from sharing any content for commercial use, or that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/DeepFloyd/deepfloyd-if-license" style="text-decoration: underline;" target="_blank">read the license</a></p>
|
| 58 |
-
<p><h4>Biases and content acknowledgment</h4>
|
| 59 |
-
Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, explicit content and violence. The model was trained on a subset of the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a> and is meant for research purposes. You can read more in the <a href="https://huggingface.co/DeepFloyd/IF-I-IF-v1.0" style="text-decoration: underline;" target="_blank">model card</a></p>
|
| 60 |
-
</div>
|
| 61 |
-
"""
|
| 62 |
-
if SHOW_DUPLICATE_BUTTON:
|
| 63 |
-
SPACE_ID = os.getenv('SPACE_ID')
|
| 64 |
-
DESCRIPTION += f'\n<p><a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space%20to%20skip%20the%20queue-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>'
|
| 65 |
-
|
| 66 |
-
if SHOW_DEVICE_WARNING and not torch.cuda.is_available():
|
| 67 |
-
DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'
|
| 68 |
-
|
| 69 |
-
model = Model()
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
|
| 73 |
-
if randomize_seed:
|
| 74 |
-
seed = random.randint(0, MAX_SEED)
|
| 75 |
-
return seed
|
| 76 |
-
|
| 77 |
-
|
| 78 |
-
def get_stage2_index(evt: gr.SelectData) -> int:
|
| 79 |
-
return evt.index
|
| 80 |
-
|
| 81 |
-
|
| 82 |
-
def check_if_stage2_selected(index: int) -> None:
|
| 83 |
-
if index == -1:
|
| 84 |
-
raise gr.Error(
|
| 85 |
-
'You need to select the image you would like to upscale from the Stage 1 results by clicking.'
|
| 86 |
-
)
|
| 87 |
-
|
| 88 |
-
|
| 89 |
-
hf_api = HfApi(token=HF_TOKEN)
|
| 90 |
-
if UPLOAD_REPO_ID:
|
| 91 |
-
hf_api.create_repo(repo_id=UPLOAD_REPO_ID,
|
| 92 |
-
private=True,
|
| 93 |
-
repo_type='dataset',
|
| 94 |
-
exist_ok=True)
|
| 95 |
-
|
| 96 |
-
|
| 97 |
-
def get_param_file_hash_name(param_filepath: str) -> str:
|
| 98 |
-
if not UPLOAD_REPO_ID:
|
| 99 |
-
return ''
|
| 100 |
-
with open(param_filepath, 'rb') as f:
|
| 101 |
-
md5 = hashlib.md5(f.read()).hexdigest()
|
| 102 |
-
utcnow = datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M-%S-%f')
|
| 103 |
-
return f'{utcnow}-{md5}'
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
def upload_stage1_result(stage1_param_path: str, stage1_result_path: str,
|
| 107 |
-
save_name: str) -> None:
|
| 108 |
-
if not UPLOAD_REPO_ID:
|
| 109 |
-
return
|
| 110 |
-
try:
|
| 111 |
-
folder_params = "tmp/results/stage1_params"
|
| 112 |
-
folder_results = "tmp/results/stage1_results"
|
| 113 |
-
|
| 114 |
-
path_params = f"{folder_params}/{save_name}.json"
|
| 115 |
-
path_results = f"{folder_results}/{save_name}.pth"
|
| 116 |
-
|
| 117 |
-
os.makedirs(folder_params, exist_ok=True)
|
| 118 |
-
os.makedirs(folder_results, exist_ok=True)
|
| 119 |
-
|
| 120 |
-
shutil.copy(stage1_param_path, path_params)
|
| 121 |
-
shutil.copy(stage1_result_path, path_results)
|
| 122 |
-
|
| 123 |
-
except Exception as e:
|
| 124 |
-
print(e)
|
| 125 |
-
|
| 126 |
-
|
| 127 |
-
def upload_stage2_info(stage1_param_file_hash_name: str,
|
| 128 |
-
stage2_output_path: str,
|
| 129 |
-
selected_index_for_upscale: int, seed_2: int,
|
| 130 |
-
guidance_scale_2: float, custom_timesteps_2: str,
|
| 131 |
-
num_inference_steps_2: int) -> None:
|
| 132 |
-
if not UPLOAD_REPO_ID:
|
| 133 |
-
return
|
| 134 |
-
if not stage1_param_file_hash_name:
|
| 135 |
-
raise ValueError
|
| 136 |
-
|
| 137 |
-
stage2_params = {
|
| 138 |
-
'stage1_param_file_hash_name': stage1_param_file_hash_name,
|
| 139 |
-
'selected_index_for_upscale': selected_index_for_upscale,
|
| 140 |
-
'seed_2': seed_2,
|
| 141 |
-
'guidance_scale_2': guidance_scale_2,
|
| 142 |
-
'custom_timesteps_2': custom_timesteps_2,
|
| 143 |
-
'num_inference_steps_2': num_inference_steps_2,
|
| 144 |
-
}
|
| 145 |
-
with tempfile.NamedTemporaryFile(mode='w', delete=False) as param_file:
|
| 146 |
-
param_file.write(json.dumps(stage2_params))
|
| 147 |
-
stage2_param_file_hash_name = get_param_file_hash_name(param_file.name)
|
| 148 |
-
save_name = f'{stage1_param_file_hash_name}_{stage2_param_file_hash_name}'
|
| 149 |
-
|
| 150 |
-
try:
|
| 151 |
-
folder_params = "tmp/results/stage2_params"
|
| 152 |
-
|
| 153 |
-
os.makedirs(folder_params, exist_ok=True)
|
| 154 |
-
path_params = f"{folder_params}/{save_name}.json"
|
| 155 |
-
shutil.copy(param_file.name, path_params)
|
| 156 |
-
|
| 157 |
-
if UPLOAD_RESULT_IMAGE:
|
| 158 |
-
folder_results = "tmp/results/stage2_results"
|
| 159 |
-
os.makedirs(folder_results, exist_ok=True)
|
| 160 |
-
path_results = f"{folder_results}/{save_name}.png"
|
| 161 |
-
shutil.copy(stage2_output_path, path_results)
|
| 162 |
-
|
| 163 |
-
except Exception as e:
|
| 164 |
-
print(e)
|
| 165 |
-
|
| 166 |
-
|
| 167 |
-
def upload_stage2_3_info(stage1_param_file_hash_name: str,
|
| 168 |
-
stage2_3_output_path: str,
|
| 169 |
-
selected_index_for_upscale: int, seed_2: int,
|
| 170 |
-
guidance_scale_2: float, custom_timesteps_2: str,
|
| 171 |
-
num_inference_steps_2: int, prompt: str,
|
| 172 |
-
negative_prompt: str, seed_3: int,
|
| 173 |
-
guidance_scale_3: float,
|
| 174 |
-
num_inference_steps_3: int) -> None:
|
| 175 |
-
if not UPLOAD_REPO_ID:
|
| 176 |
-
return
|
| 177 |
-
if not stage1_param_file_hash_name:
|
| 178 |
-
raise ValueError
|
| 179 |
-
|
| 180 |
-
stage2_3_params = {
|
| 181 |
-
'stage1_param_file_hash_name': stage1_param_file_hash_name,
|
| 182 |
-
'selected_index_for_upscale': selected_index_for_upscale,
|
| 183 |
-
'seed_2': seed_2,
|
| 184 |
-
'guidance_scale_2': guidance_scale_2,
|
| 185 |
-
'custom_timesteps_2': custom_timesteps_2,
|
| 186 |
-
'num_inference_steps_2': num_inference_steps_2,
|
| 187 |
-
'prompt': prompt,
|
| 188 |
-
'negative_prompt': negative_prompt,
|
| 189 |
-
'seed_3': seed_3,
|
| 190 |
-
'guidance_scale_3': guidance_scale_3,
|
| 191 |
-
'num_inference_steps_3': num_inference_steps_3,
|
| 192 |
-
}
|
| 193 |
-
with tempfile.NamedTemporaryFile(mode='w', delete=False) as param_file:
|
| 194 |
-
param_file.write(json.dumps(stage2_3_params))
|
| 195 |
-
stage2_3_param_file_hash_name = get_param_file_hash_name(param_file.name)
|
| 196 |
-
save_name = f'{stage1_param_file_hash_name}_{stage2_3_param_file_hash_name}'
|
| 197 |
-
|
| 198 |
-
try:
|
| 199 |
-
folder_params = "tmp/results/stage2_3_params"
|
| 200 |
-
os.makedirs(folder_params, exist_ok=True)
|
| 201 |
-
path_params = f"{folder_params}/{save_name}.json"
|
| 202 |
-
shutil.copy(param_file.name, path_params)
|
| 203 |
-
|
| 204 |
-
if UPLOAD_RESULT_IMAGE:
|
| 205 |
-
folder_results = "tmp/results/stage2_3_results"
|
| 206 |
-
os.makedirs(folder_results, exist_ok=True)
|
| 207 |
-
path_results = f"{folder_results}/{save_name}.png"
|
| 208 |
-
shutil.copy(stage2_3_output_path, path_results)
|
| 209 |
-
except Exception as e:
|
| 210 |
-
print(e)
|
| 211 |
-
|
| 212 |
-
|
| 213 |
-
def update_upscale_button(selected_index: int) -> tuple[dict, dict]:
|
| 214 |
-
if selected_index == -1:
|
| 215 |
-
return gr.update(interactive=False), gr.update(interactive=False)
|
| 216 |
-
else:
|
| 217 |
-
return gr.update(interactive=True), gr.update(interactive=True)
|
| 218 |
-
|
| 219 |
-
|
| 220 |
-
def _update_result_view(show_gallery: bool) -> tuple[dict, dict]:
|
| 221 |
-
return gr.update(visible=show_gallery), gr.update(visible=not show_gallery)
|
| 222 |
-
|
| 223 |
-
|
| 224 |
-
def show_gallery_view() -> tuple[dict, dict]:
|
| 225 |
-
return _update_result_view(True)
|
| 226 |
-
|
| 227 |
-
|
| 228 |
-
def show_upscaled_view() -> tuple[dict, dict]:
|
| 229 |
-
return _update_result_view(False)
|
| 230 |
-
|
| 231 |
-
def upload_files():
|
| 232 |
-
"""Zips files and uploads to dataset. Local data is deleted
|
| 233 |
-
"""
|
| 234 |
-
if os.path.exists("tmp/results") and os.path.isdir("tmp/results"):
|
| 235 |
-
try:
|
| 236 |
-
random_folder = random.randint(0,1000)
|
| 237 |
-
shutil.make_archive("tmp/results", 'zip', "tmp/results")
|
| 238 |
-
hf_api.upload_file(
|
| 239 |
-
path_or_fileobj="tmp/results.zip",
|
| 240 |
-
path_in_repo=f"{random_folder}/results_{shortuuid.uuid()}.zip",
|
| 241 |
-
repo_id=UPLOAD_REPO_ID,
|
| 242 |
-
repo_type="dataset",
|
| 243 |
-
)
|
| 244 |
-
shutil.rmtree("tmp/results")
|
| 245 |
-
except Exception as e:
|
| 246 |
-
print(e)
|
| 247 |
-
|
| 248 |
-
examples = [
|
| 249 |
-
'high quality dslr photo, a photo product of a lemon inspired by natural and organic materials, wooden accents, intricately decorated with glowing vines of led lights, inspired by baroque luxury',
|
| 250 |
-
'paper quilling, extremely detailed, paper quilling of a nordic mountain landscape, 8k rendering',
|
| 251 |
-
'letters made of candy on a plate that says "diet"',
|
| 252 |
-
'a photo of a violet baseball cap with yellow text: "deep floyd". 50mm lens, photo realism, cine lens. violet baseball cap says "deep floyd". reflections, render. yellow stitch text "deep floyd"',
|
| 253 |
-
'ultra close-up color photo portrait of rainbow owl with deer horns in the woods',
|
| 254 |
-
'a cloth embroidered with the text "laion" and an embroidered cute baby lion face',
|
| 255 |
-
'product image of a crochet Cthulhu the great old one emerging from a spacetime wormhole made of wool',
|
| 256 |
-
'a little green budgie parrot driving small red toy car in new york street, photo',
|
| 257 |
-
'origami dancer in white paper, 3d render, ultra-detailed, on white background, studio shot.',
|
| 258 |
-
'glowing mushrooms in a natural environment with smoke in the frame',
|
| 259 |
-
'a subway train\'s digital sign saying "open source", vsco preset, 35mm photo, film grain, in a dim subway station',
|
| 260 |
-
'a bowl full of few adorable golden doodle puppies, the doodles dusted in powdered sugar and look delicious, bokeh, cannon. professional macro photo, super detailed. cute sweet golden doodle confectionery, baking puppies in powdered sugar in the bowl',
|
| 261 |
-
'a face of a woman made completely out of foliage, twigs, leaves and flowers, side view'
|
| 262 |
-
]
|
| 263 |
-
|
| 264 |
-
with gr.Blocks(css='style.css') as demo:
|
| 265 |
-
gr.Markdown(TITLE)
|
| 266 |
-
gr.Markdown(DESCRIPTION)
|
| 267 |
-
with gr.Box():
|
| 268 |
-
with gr.Row(elem_id='prompt-container').style(equal_height=True):
|
| 269 |
-
with gr.Column():
|
| 270 |
-
prompt = gr.Text(
|
| 271 |
-
label='Prompt',
|
| 272 |
-
show_label=False,
|
| 273 |
-
max_lines=1,
|
| 274 |
-
placeholder='Enter your prompt',
|
| 275 |
-
elem_id='prompt-text-input',
|
| 276 |
-
).style(container=False)
|
| 277 |
-
negative_prompt = gr.Text(
|
| 278 |
-
label='Negative prompt',
|
| 279 |
-
show_label=False,
|
| 280 |
-
max_lines=1,
|
| 281 |
-
placeholder='Enter a negative prompt',
|
| 282 |
-
elem_id='negative-prompt-text-input',
|
| 283 |
-
).style(container=False)
|
| 284 |
-
generate_button = gr.Button('Generate').style(full_width=False)
|
| 285 |
-
|
| 286 |
-
with gr.Column() as gallery_view:
|
| 287 |
-
gallery = gr.Gallery(label='Stage 1 results',
|
| 288 |
-
show_label=False,
|
| 289 |
-
elem_id='gallery').style(
|
| 290 |
-
columns=GALLERY_COLUMN_NUM,
|
| 291 |
-
object_fit='contain')
|
| 292 |
-
gr.Markdown('Pick your favorite generation to upscale.')
|
| 293 |
-
with gr.Row():
|
| 294 |
-
upscale_to_256_button = gr.Button(
|
| 295 |
-
'Upscale to 256px',
|
| 296 |
-
visible=SHOW_UPSCALE_TO_256_BUTTON
|
| 297 |
-
or DISABLE_SD_X4_UPSCALER,
|
| 298 |
-
interactive=False)
|
| 299 |
-
upscale_button = gr.Button('Upscale',
|
| 300 |
-
interactive=False,
|
| 301 |
-
visible=not DISABLE_SD_X4_UPSCALER)
|
| 302 |
-
with gr.Column(visible=False) as upscale_view:
|
| 303 |
-
result = gr.Image(label='Result',
|
| 304 |
-
show_label=False,
|
| 305 |
-
type='filepath',
|
| 306 |
-
interactive=False,
|
| 307 |
-
elem_id='upscaled-image').style(height=640)
|
| 308 |
-
back_to_selection_button = gr.Button('Back to selection')
|
| 309 |
-
with gr.Group(elem_id="share-btn-container"):
|
| 310 |
-
community_icon = gr.HTML(community_icon_html)
|
| 311 |
-
loading_icon = gr.HTML(loading_icon_html)
|
| 312 |
-
share_button = gr.Button(
|
| 313 |
-
"Share to community", elem_id="share-btn")
|
| 314 |
-
share_button.click(None, [], [], _js=share_js)
|
| 315 |
-
with gr.Accordion('Advanced options',
|
| 316 |
-
open=False,
|
| 317 |
-
visible=SHOW_ADVANCED_OPTIONS):
|
| 318 |
-
with gr.Tabs():
|
| 319 |
-
with gr.Tab(label='Generation'):
|
| 320 |
-
seed_1 = gr.Slider(label='Seed',
|
| 321 |
-
minimum=0,
|
| 322 |
-
maximum=MAX_SEED,
|
| 323 |
-
step=1,
|
| 324 |
-
value=0)
|
| 325 |
-
randomize_seed_1 = gr.Checkbox(label='Randomize seed',
|
| 326 |
-
value=True)
|
| 327 |
-
guidance_scale_1 = gr.Slider(label='Guidance scale',
|
| 328 |
-
minimum=1,
|
| 329 |
-
maximum=20,
|
| 330 |
-
step=0.1,
|
| 331 |
-
value=7.0)
|
| 332 |
-
custom_timesteps_1 = gr.Dropdown(
|
| 333 |
-
label='Custom timesteps 1',
|
| 334 |
-
choices=[
|
| 335 |
-
'none',
|
| 336 |
-
'fast27',
|
| 337 |
-
'smart27',
|
| 338 |
-
'smart50',
|
| 339 |
-
'smart100',
|
| 340 |
-
'smart185',
|
| 341 |
-
],
|
| 342 |
-
value=DEFAULT_CUSTOM_TIMESTEPS_1,
|
| 343 |
-
visible=SHOW_CUSTOM_TIMESTEPS_1)
|
| 344 |
-
num_inference_steps_1 = gr.Slider(
|
| 345 |
-
label='Number of inference steps',
|
| 346 |
-
minimum=1,
|
| 347 |
-
maximum=MAX_NUM_STEPS,
|
| 348 |
-
step=1,
|
| 349 |
-
value=100,
|
| 350 |
-
visible=SHOW_NUM_STEPS_1)
|
| 351 |
-
num_images = gr.Slider(label='Number of images',
|
| 352 |
-
minimum=1,
|
| 353 |
-
maximum=MAX_NUM_IMAGES,
|
| 354 |
-
step=1,
|
| 355 |
-
value=DEFAULT_NUM_IMAGES,
|
| 356 |
-
visible=SHOW_NUM_IMAGES)
|
| 357 |
-
with gr.Tab(label='Super-resolution 1'):
|
| 358 |
-
seed_2 = gr.Slider(label='Seed',
|
| 359 |
-
minimum=0,
|
| 360 |
-
maximum=MAX_SEED,
|
| 361 |
-
step=1,
|
| 362 |
-
value=0)
|
| 363 |
-
randomize_seed_2 = gr.Checkbox(label='Randomize seed',
|
| 364 |
-
value=True)
|
| 365 |
-
guidance_scale_2 = gr.Slider(label='Guidance scale',
|
| 366 |
-
minimum=1,
|
| 367 |
-
maximum=20,
|
| 368 |
-
step=0.1,
|
| 369 |
-
value=4.0)
|
| 370 |
-
custom_timesteps_2 = gr.Dropdown(
|
| 371 |
-
label='Custom timesteps 2',
|
| 372 |
-
choices=[
|
| 373 |
-
'none',
|
| 374 |
-
'fast27',
|
| 375 |
-
'smart27',
|
| 376 |
-
'smart50',
|
| 377 |
-
'smart100',
|
| 378 |
-
'smart185',
|
| 379 |
-
],
|
| 380 |
-
value=DEFAULT_CUSTOM_TIMESTEPS_2,
|
| 381 |
-
visible=SHOW_CUSTOM_TIMESTEPS_2)
|
| 382 |
-
num_inference_steps_2 = gr.Slider(
|
| 383 |
-
label='Number of inference steps',
|
| 384 |
-
minimum=1,
|
| 385 |
-
maximum=MAX_NUM_STEPS,
|
| 386 |
-
step=1,
|
| 387 |
-
value=50,
|
| 388 |
-
visible=SHOW_NUM_STEPS_2)
|
| 389 |
-
with gr.Tab(label='Super-resolution 2'):
|
| 390 |
-
seed_3 = gr.Slider(label='Seed',
|
| 391 |
-
minimum=0,
|
| 392 |
-
maximum=MAX_SEED,
|
| 393 |
-
step=1,
|
| 394 |
-
value=0)
|
| 395 |
-
randomize_seed_3 = gr.Checkbox(label='Randomize seed',
|
| 396 |
-
value=True)
|
| 397 |
-
guidance_scale_3 = gr.Slider(label='Guidance scale',
|
| 398 |
-
minimum=1,
|
| 399 |
-
maximum=20,
|
| 400 |
-
step=0.1,
|
| 401 |
-
value=9.0)
|
| 402 |
-
num_inference_steps_3 = gr.Slider(
|
| 403 |
-
label='Number of inference steps',
|
| 404 |
-
minimum=1,
|
| 405 |
-
maximum=MAX_NUM_STEPS,
|
| 406 |
-
step=1,
|
| 407 |
-
value=DEFAULT_NUM_STEPS_3,
|
| 408 |
-
visible=SHOW_NUM_STEPS_3)
|
| 409 |
-
|
| 410 |
-
gr.Examples(examples=examples, inputs=prompt, examples_per_page=4)
|
| 411 |
-
|
| 412 |
-
with gr.Box(visible=DEBUG):
|
| 413 |
-
with gr.Row():
|
| 414 |
-
with gr.Accordion(label='Hidden params'):
|
| 415 |
-
stage1_param_path = gr.Text(label='Stage 1 param path')
|
| 416 |
-
stage1_result_path = gr.Text(label='Stage 1 result path')
|
| 417 |
-
stage1_param_file_hash_name = gr.Text(
|
| 418 |
-
label='Stage 1 param file hash name')
|
| 419 |
-
selected_index_for_stage2 = gr.Number(
|
| 420 |
-
label='Selected index for Stage 2', value=-1, precision=0)
|
| 421 |
-
gr.Markdown(DISCLAIMER)
|
| 422 |
-
gr.HTML(FOOTER)
|
| 423 |
-
stage1_inputs = [
|
| 424 |
-
prompt,
|
| 425 |
-
negative_prompt,
|
| 426 |
-
seed_1,
|
| 427 |
-
num_images,
|
| 428 |
-
guidance_scale_1,
|
| 429 |
-
custom_timesteps_1,
|
| 430 |
-
num_inference_steps_1,
|
| 431 |
-
]
|
| 432 |
-
stage1_outputs = [
|
| 433 |
-
gallery,
|
| 434 |
-
stage1_param_path,
|
| 435 |
-
stage1_result_path,
|
| 436 |
-
]
|
| 437 |
-
|
| 438 |
-
prompt.submit(
|
| 439 |
-
fn=randomize_seed_fn,
|
| 440 |
-
inputs=[seed_1, randomize_seed_1],
|
| 441 |
-
outputs=seed_1,
|
| 442 |
-
queue=False,
|
| 443 |
-
).then(
|
| 444 |
-
fn=lambda: -1,
|
| 445 |
-
outputs=selected_index_for_stage2,
|
| 446 |
-
queue=False,
|
| 447 |
-
).then(
|
| 448 |
-
fn=show_gallery_view,
|
| 449 |
-
outputs=[
|
| 450 |
-
gallery_view,
|
| 451 |
-
upscale_view,
|
| 452 |
-
],
|
| 453 |
-
queue=False,
|
| 454 |
-
).then(
|
| 455 |
-
fn=update_upscale_button,
|
| 456 |
-
inputs=selected_index_for_stage2,
|
| 457 |
-
outputs=[
|
| 458 |
-
upscale_button,
|
| 459 |
-
upscale_to_256_button,
|
| 460 |
-
],
|
| 461 |
-
queue=False,
|
| 462 |
-
).then(
|
| 463 |
-
fn=model.run_stage1,
|
| 464 |
-
inputs=stage1_inputs,
|
| 465 |
-
outputs=stage1_outputs,
|
| 466 |
-
).success(
|
| 467 |
-
fn=get_param_file_hash_name,
|
| 468 |
-
inputs=stage1_param_path,
|
| 469 |
-
outputs=stage1_param_file_hash_name,
|
| 470 |
-
queue=False,
|
| 471 |
-
).then(
|
| 472 |
-
fn=upload_stage1_result,
|
| 473 |
-
inputs=[
|
| 474 |
-
stage1_param_path,
|
| 475 |
-
stage1_result_path,
|
| 476 |
-
stage1_param_file_hash_name,
|
| 477 |
-
],
|
| 478 |
-
queue=False,
|
| 479 |
-
)
|
| 480 |
-
|
| 481 |
-
negative_prompt.submit(
|
| 482 |
-
fn=randomize_seed_fn,
|
| 483 |
-
inputs=[seed_1, randomize_seed_1],
|
| 484 |
-
outputs=seed_1,
|
| 485 |
-
queue=False,
|
| 486 |
-
).then(
|
| 487 |
-
fn=lambda: -1,
|
| 488 |
-
outputs=selected_index_for_stage2,
|
| 489 |
-
queue=False,
|
| 490 |
-
).then(
|
| 491 |
-
fn=show_gallery_view,
|
| 492 |
-
outputs=[
|
| 493 |
-
gallery_view,
|
| 494 |
-
upscale_view,
|
| 495 |
-
],
|
| 496 |
-
queue=False,
|
| 497 |
-
).then(
|
| 498 |
-
fn=update_upscale_button,
|
| 499 |
-
inputs=selected_index_for_stage2,
|
| 500 |
-
outputs=[
|
| 501 |
-
upscale_button,
|
| 502 |
-
upscale_to_256_button,
|
| 503 |
-
],
|
| 504 |
-
queue=False,
|
| 505 |
-
).then(
|
| 506 |
-
fn=model.run_stage1,
|
| 507 |
-
inputs=stage1_inputs,
|
| 508 |
-
outputs=stage1_outputs,
|
| 509 |
-
).success(
|
| 510 |
-
fn=get_param_file_hash_name,
|
| 511 |
-
inputs=stage1_param_path,
|
| 512 |
-
outputs=stage1_param_file_hash_name,
|
| 513 |
-
queue=False,
|
| 514 |
-
).then(
|
| 515 |
-
fn=upload_stage1_result,
|
| 516 |
-
inputs=[
|
| 517 |
-
stage1_param_path,
|
| 518 |
-
stage1_result_path,
|
| 519 |
-
stage1_param_file_hash_name,
|
| 520 |
-
],
|
| 521 |
-
queue=False,
|
| 522 |
-
)
|
| 523 |
-
|
| 524 |
-
generate_button.click(
|
| 525 |
-
fn=randomize_seed_fn,
|
| 526 |
-
inputs=[seed_1, randomize_seed_1],
|
| 527 |
-
outputs=seed_1,
|
| 528 |
-
queue=False,
|
| 529 |
-
).then(
|
| 530 |
-
fn=lambda: -1,
|
| 531 |
-
outputs=selected_index_for_stage2,
|
| 532 |
-
queue=False,
|
| 533 |
-
).then(
|
| 534 |
-
fn=show_gallery_view,
|
| 535 |
-
outputs=[
|
| 536 |
-
gallery_view,
|
| 537 |
-
upscale_view,
|
| 538 |
-
],
|
| 539 |
-
queue=False,
|
| 540 |
-
).then(
|
| 541 |
-
fn=update_upscale_button,
|
| 542 |
-
inputs=selected_index_for_stage2,
|
| 543 |
-
outputs=[
|
| 544 |
-
upscale_button,
|
| 545 |
-
upscale_to_256_button,
|
| 546 |
-
],
|
| 547 |
-
queue=False,
|
| 548 |
-
).then(
|
| 549 |
-
fn=model.run_stage1,
|
| 550 |
-
inputs=stage1_inputs,
|
| 551 |
-
outputs=stage1_outputs,
|
| 552 |
-
api_name='generate64',
|
| 553 |
-
).success(
|
| 554 |
-
fn=get_param_file_hash_name,
|
| 555 |
-
inputs=stage1_param_path,
|
| 556 |
-
outputs=stage1_param_file_hash_name,
|
| 557 |
-
queue=False,
|
| 558 |
-
).then(
|
| 559 |
-
fn=upload_stage1_result,
|
| 560 |
-
inputs=[
|
| 561 |
-
stage1_param_path,
|
| 562 |
-
stage1_result_path,
|
| 563 |
-
stage1_param_file_hash_name,
|
| 564 |
-
],
|
| 565 |
-
queue=False,
|
| 566 |
-
)
|
| 567 |
-
|
| 568 |
-
gallery.select(
|
| 569 |
-
fn=get_stage2_index,
|
| 570 |
-
outputs=selected_index_for_stage2,
|
| 571 |
-
queue=False,
|
| 572 |
-
)
|
| 573 |
-
|
| 574 |
-
selected_index_for_stage2.change(
|
| 575 |
-
fn=update_upscale_button,
|
| 576 |
-
inputs=selected_index_for_stage2,
|
| 577 |
-
outputs=[
|
| 578 |
-
upscale_button,
|
| 579 |
-
upscale_to_256_button,
|
| 580 |
-
],
|
| 581 |
-
queue=False,
|
| 582 |
-
)
|
| 583 |
-
|
| 584 |
-
stage2_inputs = [
|
| 585 |
-
stage1_result_path,
|
| 586 |
-
selected_index_for_stage2,
|
| 587 |
-
seed_2,
|
| 588 |
-
guidance_scale_2,
|
| 589 |
-
custom_timesteps_2,
|
| 590 |
-
num_inference_steps_2,
|
| 591 |
-
]
|
| 592 |
-
|
| 593 |
-
upscale_to_256_button.click(
|
| 594 |
-
fn=check_if_stage2_selected,
|
| 595 |
-
inputs=selected_index_for_stage2,
|
| 596 |
-
queue=False,
|
| 597 |
-
).then(
|
| 598 |
-
fn=randomize_seed_fn,
|
| 599 |
-
inputs=[seed_2, randomize_seed_2],
|
| 600 |
-
outputs=seed_2,
|
| 601 |
-
queue=False,
|
| 602 |
-
).then(
|
| 603 |
-
fn=show_upscaled_view,
|
| 604 |
-
outputs=[
|
| 605 |
-
gallery_view,
|
| 606 |
-
upscale_view,
|
| 607 |
-
],
|
| 608 |
-
queue=False,
|
| 609 |
-
).then(
|
| 610 |
-
fn=model.run_stage2,
|
| 611 |
-
inputs=stage2_inputs,
|
| 612 |
-
outputs=result,
|
| 613 |
-
api_name='upscale256',
|
| 614 |
-
).success(
|
| 615 |
-
fn=upload_stage2_info,
|
| 616 |
-
inputs=[
|
| 617 |
-
stage1_param_file_hash_name,
|
| 618 |
-
result,
|
| 619 |
-
selected_index_for_stage2,
|
| 620 |
-
seed_2,
|
| 621 |
-
guidance_scale_2,
|
| 622 |
-
custom_timesteps_2,
|
| 623 |
-
num_inference_steps_2,
|
| 624 |
-
],
|
| 625 |
-
queue=False,
|
| 626 |
-
)
|
| 627 |
-
|
| 628 |
-
stage2_3_inputs = [
|
| 629 |
-
stage1_result_path,
|
| 630 |
-
selected_index_for_stage2,
|
| 631 |
-
seed_2,
|
| 632 |
-
guidance_scale_2,
|
| 633 |
-
custom_timesteps_2,
|
| 634 |
-
num_inference_steps_2,
|
| 635 |
-
prompt,
|
| 636 |
-
negative_prompt,
|
| 637 |
-
seed_3,
|
| 638 |
-
guidance_scale_3,
|
| 639 |
-
num_inference_steps_3,
|
| 640 |
-
]
|
| 641 |
-
|
| 642 |
-
upscale_button.click(
|
| 643 |
-
fn=check_if_stage2_selected,
|
| 644 |
-
inputs=selected_index_for_stage2,
|
| 645 |
-
queue=False,
|
| 646 |
-
).then(
|
| 647 |
-
fn=randomize_seed_fn,
|
| 648 |
-
inputs=[seed_2, randomize_seed_2],
|
| 649 |
-
outputs=seed_2,
|
| 650 |
-
queue=False,
|
| 651 |
-
).then(
|
| 652 |
-
fn=randomize_seed_fn,
|
| 653 |
-
inputs=[seed_3, randomize_seed_3],
|
| 654 |
-
outputs=seed_3,
|
| 655 |
-
queue=False,
|
| 656 |
-
).then(
|
| 657 |
-
fn=show_upscaled_view,
|
| 658 |
-
outputs=[
|
| 659 |
-
gallery_view,
|
| 660 |
-
upscale_view,
|
| 661 |
-
],
|
| 662 |
-
queue=False,
|
| 663 |
-
).then(
|
| 664 |
-
fn=model.run_stage2_3,
|
| 665 |
-
inputs=stage2_3_inputs,
|
| 666 |
-
outputs=result,
|
| 667 |
-
api_name='upscale1024',
|
| 668 |
-
).success(
|
| 669 |
-
fn=upload_stage2_3_info,
|
| 670 |
-
inputs=[
|
| 671 |
-
stage1_param_file_hash_name,
|
| 672 |
-
result,
|
| 673 |
-
selected_index_for_stage2,
|
| 674 |
-
seed_2,
|
| 675 |
-
guidance_scale_2,
|
| 676 |
-
custom_timesteps_2,
|
| 677 |
-
num_inference_steps_2,
|
| 678 |
-
prompt,
|
| 679 |
-
negative_prompt,
|
| 680 |
-
seed_3,
|
| 681 |
-
guidance_scale_3,
|
| 682 |
-
num_inference_steps_3,
|
| 683 |
-
],
|
| 684 |
-
queue=False,
|
| 685 |
-
)
|
| 686 |
-
|
| 687 |
-
back_to_selection_button.click(
|
| 688 |
-
fn=show_gallery_view,
|
| 689 |
-
outputs=[
|
| 690 |
-
gallery_view,
|
| 691 |
-
upscale_view,
|
| 692 |
-
],
|
| 693 |
-
queue=False,
|
| 694 |
-
)
|
| 695 |
-
|
| 696 |
-
if UPLOAD_REPO_ID:
|
| 697 |
-
scheduler = BackgroundScheduler()
|
| 698 |
-
scheduler.add_job(func=upload_files, trigger="interval", seconds=60*20)
|
| 699 |
-
scheduler.start()
|
| 700 |
-
|
| 701 |
-
demo.queue(api_open=False, max_size=MAX_QUEUE_SIZE).launch(debug=DEBUG)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/DemocracyStudio/generate_nft_content/README.md
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Generate Nft Content
|
| 3 |
-
emoji: 🌖
|
| 4 |
-
colorFrom: yellow
|
| 5 |
-
colorTo: red
|
| 6 |
-
sdk: streamlit
|
| 7 |
-
sdk_version: 1.10.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: cc
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/DenniSciFi/IconAutomation/app.py
DELETED
|
@@ -1,136 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
import requests
|
| 3 |
-
|
| 4 |
-
def get_organization_page(notion_url, notion_token, database_id):
|
| 5 |
-
"""
|
| 6 |
-
Query the Notion database for the organization page with the specified URL.
|
| 7 |
-
|
| 8 |
-
:param notion_url: The URL of the organization page in the Notion database.
|
| 9 |
-
:param notion_token: The Notion API token.
|
| 10 |
-
:param database_id: The ID of the Notion database.
|
| 11 |
-
:return: The organization page object if found, otherwise None.
|
| 12 |
-
"""
|
| 13 |
-
|
| 14 |
-
# Set headers for Notion API requests
|
| 15 |
-
headers = {
|
| 16 |
-
"Authorization": "Bearer " + notion_token,
|
| 17 |
-
"Notion-Version": "2022-06-28",
|
| 18 |
-
"Content-Type": "application/json"
|
| 19 |
-
}
|
| 20 |
-
|
| 21 |
-
# Construct the API endpoint URL using the provided database ID
|
| 22 |
-
url = f"https://api.notion.com/v1/databases/{database_id}/query"
|
| 23 |
-
|
| 24 |
-
# Define the query filter for the Notion API request
|
| 25 |
-
data = {
|
| 26 |
-
"filter": {
|
| 27 |
-
"property": "URL",
|
| 28 |
-
"url": {
|
| 29 |
-
"equals": notion_url
|
| 30 |
-
}
|
| 31 |
-
}
|
| 32 |
-
}
|
| 33 |
-
|
| 34 |
-
# Send a POST request to the Notion API with the constructed URL, query filter, and headers
|
| 35 |
-
response = requests.post(url, json=data, headers=headers)
|
| 36 |
-
response_json = response.json()
|
| 37 |
-
|
| 38 |
-
results = response_json["results"]
|
| 39 |
-
|
| 40 |
-
# Return the first organization page object in the results array, or None if there are no results
|
| 41 |
-
if len(results) > 0:
|
| 42 |
-
return results[0]
|
| 43 |
-
else:
|
| 44 |
-
return None
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
def update_opportunity_icon(opportunity_page_id, image_url, notion_token):
|
| 48 |
-
"""
|
| 49 |
-
Update the icon of a specific opportunity page with an image URL.
|
| 50 |
-
|
| 51 |
-
:param opportunity_page_id: The ID of the opportunity page in the Notion database.
|
| 52 |
-
:param image_url: The URL of the new image to be set as the icon.
|
| 53 |
-
:param notion_token: The Notion API token.
|
| 54 |
-
:return: True if the update was successful, False otherwise.
|
| 55 |
-
"""
|
| 56 |
-
|
| 57 |
-
# Set headers for Notion API requests
|
| 58 |
-
headers = {
|
| 59 |
-
"Authorization": "Bearer " + notion_token,
|
| 60 |
-
"Notion-Version": "2022-06-28",
|
| 61 |
-
"Content-Type": "application/json"
|
| 62 |
-
}
|
| 63 |
-
|
| 64 |
-
# Set the URL of the Notion API endpoint for updating the page with the given ID
|
| 65 |
-
url = f"https://api.notion.com/v1/pages/{opportunity_page_id}"
|
| 66 |
-
|
| 67 |
-
# Define the data to be sent in the request body, including the new image URL
|
| 68 |
-
data = {
|
| 69 |
-
"icon": {
|
| 70 |
-
"type": "external",
|
| 71 |
-
"external": {
|
| 72 |
-
"url": image_url
|
| 73 |
-
}
|
| 74 |
-
}
|
| 75 |
-
}
|
| 76 |
-
|
| 77 |
-
# Send a PATCH request to the Notion API to update the page with the new image URL
|
| 78 |
-
response = requests.patch(url, json=data, headers=headers)
|
| 79 |
-
|
| 80 |
-
# Return True if the response status code indicates success, False otherwise
|
| 81 |
-
return response.status_code == 200
|
| 82 |
-
|
| 83 |
-
|
| 84 |
-
def update_related_opportunities_icons(notion_url, image_url, notion_token, database_id):
|
| 85 |
-
"""
|
| 86 |
-
Update the icons of all opportunities related to an organization page.
|
| 87 |
-
|
| 88 |
-
:param notion_url: The URL of the organization page in the Notion database.
|
| 89 |
-
:param image_url: The URL of the new image to be set as the icon.
|
| 90 |
-
:param notion_token: The Notion API token.
|
| 91 |
-
:param database_id: The ID of the Notion database.
|
| 92 |
-
"""
|
| 93 |
-
|
| 94 |
-
# Get the organization page object from the Notion database using the URL
|
| 95 |
-
organization_page = get_organization_page(notion_url, notion_token, database_id)
|
| 96 |
-
|
| 97 |
-
# If the organization page is found
|
| 98 |
-
if organization_page:
|
| 99 |
-
# Extract the list of related opportunities from the page object
|
| 100 |
-
related_opportunities = organization_page["properties"]["Related opportunities"]["relation"]
|
| 101 |
-
|
| 102 |
-
# For each related opportunity
|
| 103 |
-
for opportunity in related_opportunities:
|
| 104 |
-
# Get the opportunity page ID
|
| 105 |
-
opportunity_page_id = opportunity["id"]
|
| 106 |
-
|
| 107 |
-
# Try to update the opportunity icon with the new image URL using the update_opportunity_icon() function
|
| 108 |
-
if update_opportunity_icon(opportunity_page_id, image_url, notion_token):
|
| 109 |
-
print(f"Updated icon for opportunity page: {opportunity_page_id}")
|
| 110 |
-
else:
|
| 111 |
-
print(f"Failed to update icon for opportunity page: {opportunity_page_id}")
|
| 112 |
-
else:
|
| 113 |
-
print("Organization page not found.")
|
| 114 |
-
|
| 115 |
-
|
| 116 |
-
def interface(notion_url, image_url, notion_token, database_id):
|
| 117 |
-
# Calling the update_related_opportunities_icons function with the provided URLs and user inputs
|
| 118 |
-
update_related_opportunities_icons(notion_url, image_url, notion_token, database_id)
|
| 119 |
-
return "Operation Completed"
|
| 120 |
-
|
| 121 |
-
|
| 122 |
-
iface = gr.Interface(
|
| 123 |
-
fn=interface,
|
| 124 |
-
inputs=[
|
| 125 |
-
gr.inputs.Textbox(label="Notion URL"),
|
| 126 |
-
gr.inputs.Textbox(label="Image URL"),
|
| 127 |
-
gr.inputs.Textbox(label="Notion Token"),
|
| 128 |
-
gr.inputs.Textbox(label="Database ID"),
|
| 129 |
-
],
|
| 130 |
-
outputs=gr.outputs.Textbox(),
|
| 131 |
-
title="Icon Automation for Notion",
|
| 132 |
-
description="Enter the Notion URL of the organization page, the image URL you want to set as the icon, "
|
| 133 |
-
"and your Notion API token along with the database ID. This will update the icons of all related opportunities.",
|
| 134 |
-
)
|
| 135 |
-
|
| 136 |
-
iface.launch()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/DhruvShek/chatlm/app.py
DELETED
|
@@ -1,171 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
import streamlit as st
|
| 3 |
-
from streamlit_chat import message
|
| 4 |
-
import json
|
| 5 |
-
import torch
|
| 6 |
-
from torch.utils.data import Dataset
|
| 7 |
-
import torch.utils.data
|
| 8 |
-
from models import *
|
| 9 |
-
from utils import *
|
| 10 |
-
# Setting page title and header
|
| 11 |
-
st.set_page_config(page_title="UniLM", page_icon=":robot_face:")
|
| 12 |
-
st.markdown("<h1 style='text-align: center;'>UniLM</h1>", unsafe_allow_html=True)
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
# Initialise session state variables
|
| 17 |
-
if 'generated' not in st.session_state:
|
| 18 |
-
st.session_state['generated'] = []
|
| 19 |
-
if 'past' not in st.session_state:
|
| 20 |
-
st.session_state['past'] = []
|
| 21 |
-
if 'messages' not in st.session_state:
|
| 22 |
-
st.session_state['messages'] = [
|
| 23 |
-
{"role": "system", "content": "You are a helpful assistant."}
|
| 24 |
-
]
|
| 25 |
-
if 'model_name' not in st.session_state:
|
| 26 |
-
st.session_state['model_name'] = []
|
| 27 |
-
if 'cost' not in st.session_state:
|
| 28 |
-
st.session_state['cost'] = []
|
| 29 |
-
if 'total_tokens' not in st.session_state:
|
| 30 |
-
st.session_state['total_tokens'] = []
|
| 31 |
-
if 'total_cost' not in st.session_state:
|
| 32 |
-
st.session_state['total_cost'] = 1
|
| 33 |
-
|
| 34 |
-
# Sidebar - let user choose model, show total cost of current conversation, and let user clear the current conversation
|
| 35 |
-
st.sidebar.title("Settings")
|
| 36 |
-
model_name = st.sidebar.selectbox("Model:", ("30M_6.1K","NONE"))
|
| 37 |
-
counter_placeholder = st.sidebar.empty()
|
| 38 |
-
|
| 39 |
-
clear_button = st.sidebar.button("Clear Conversation", key="clear")
|
| 40 |
-
|
| 41 |
-
# Map model names to OpenAI model IDs
|
| 42 |
-
if model_name == "30M_6.1K":
|
| 43 |
-
model = "30M_6.1K"
|
| 44 |
-
else:
|
| 45 |
-
model = "gpt-4"
|
| 46 |
-
|
| 47 |
-
# reset everything
|
| 48 |
-
if clear_button:
|
| 49 |
-
st.session_state['generated'] = []
|
| 50 |
-
st.session_state['past'] = []
|
| 51 |
-
st.session_state['messages'] = [
|
| 52 |
-
{"role": "system", "content": "You are a helpful assistant."}
|
| 53 |
-
]
|
| 54 |
-
st.session_state['number_tokens'] = []
|
| 55 |
-
st.session_state['model_name'] = []
|
| 56 |
-
st.session_state['cost'] = []
|
| 57 |
-
st.session_state['total_cost'] = 0.0
|
| 58 |
-
st.session_state['total_tokens'] = []
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
def evaluate(transformer, question, question_mask, max_len, word_map):
|
| 63 |
-
"""
|
| 64 |
-
Performs Greedy Decoding with a batch size of 1
|
| 65 |
-
"""
|
| 66 |
-
rev_word_map = {v: k for k, v in word_map.items()}
|
| 67 |
-
transformer.eval()
|
| 68 |
-
start_token = word_map['<start>']
|
| 69 |
-
encoded = transformer.encode(question, question_mask)
|
| 70 |
-
words = torch.LongTensor([[start_token]]).to(device)
|
| 71 |
-
|
| 72 |
-
for step in range(max_len - 1):
|
| 73 |
-
size = words.shape[1]
|
| 74 |
-
target_mask = torch.triu(torch.ones(size, size)).transpose(0, 1).type(dtype=torch.uint8)
|
| 75 |
-
target_mask = target_mask.to(device).unsqueeze(0).unsqueeze(0)
|
| 76 |
-
decoded = transformer.decode(words, target_mask, encoded, question_mask)
|
| 77 |
-
predictions = transformer.logit(decoded[:, -1])
|
| 78 |
-
_, next_word = torch.max(predictions, dim=1)
|
| 79 |
-
next_word = next_word.item()
|
| 80 |
-
if next_word == word_map['<end>']:
|
| 81 |
-
break
|
| 82 |
-
words = torch.cat([words, torch.LongTensor([[next_word]]).to(device)], dim=1) # (1,step+2)
|
| 83 |
-
|
| 84 |
-
# Construct Sentence
|
| 85 |
-
if words.dim() == 2:
|
| 86 |
-
words = words.squeeze(0)
|
| 87 |
-
words = words.tolist()
|
| 88 |
-
|
| 89 |
-
sen_idx = [w for w in words if w not in {word_map['<start>']}]
|
| 90 |
-
sentence = ' '.join([rev_word_map[sen_idx[k]] for k in range(len(sen_idx))])
|
| 91 |
-
|
| 92 |
-
return sentence
|
| 93 |
-
def remove_punc(string):
|
| 94 |
-
punctuations = '''!()-[]{};:'"\,<>./?@#$%^&*_~'''
|
| 95 |
-
no_punct = ""
|
| 96 |
-
for char in string:
|
| 97 |
-
if char not in punctuations:
|
| 98 |
-
no_punct = no_punct + char # space is also a character
|
| 99 |
-
return no_punct.lower()
|
| 100 |
-
|
| 101 |
-
if model_name == "30M_6.1K":
|
| 102 |
-
load_checkpoint = True
|
| 103 |
-
ckpt_path = 'checkpoint_190.pth.tar'
|
| 104 |
-
with open('WORDMAP_corpus.json', 'r') as j:
|
| 105 |
-
word_map = json.load(j)
|
| 106 |
-
if load_checkpoint:
|
| 107 |
-
checkpoint = torch.load(ckpt_path, map_location=torch.device('cpu'))
|
| 108 |
-
transformer = checkpoint['transformer']
|
| 109 |
-
else:
|
| 110 |
-
load_checkpoint = True
|
| 111 |
-
ckpt_path = 'checkpoint_190.pth.tar'
|
| 112 |
-
with open('WORDMAP_corpus.json', 'r') as j:
|
| 113 |
-
word_map = json.load(j)
|
| 114 |
-
if load_checkpoint:
|
| 115 |
-
checkpoint = torch.load(ckpt_path, map_location=torch.device('cpu'))
|
| 116 |
-
transformer = checkpoint['transformer']
|
| 117 |
-
|
| 118 |
-
|
| 119 |
-
|
| 120 |
-
# generate a response
|
| 121 |
-
def generate_response(prompt):
|
| 122 |
-
st.session_state['messages'].append({"role": "user", "content": prompt})
|
| 123 |
-
question = remove_punc(prompt)
|
| 124 |
-
|
| 125 |
-
max_len = 153
|
| 126 |
-
enc_qus = [word_map.get(word, word_map['<unk>']) for word in question.split()]
|
| 127 |
-
question = torch.LongTensor(enc_qus).to(device).unsqueeze(0)
|
| 128 |
-
question_mask = (question != 0).to(device).unsqueeze(1).unsqueeze(1)
|
| 129 |
-
sentence = evaluate(transformer, question, question_mask, int(max_len), word_map)
|
| 130 |
-
|
| 131 |
-
response = sentence
|
| 132 |
-
st.session_state['messages'].append({"role": "assistant", "content": response})
|
| 133 |
-
|
| 134 |
-
# print(st.session_state['messages'])
|
| 135 |
-
total_tokens = "153"
|
| 136 |
-
prompt_tokens = "153"
|
| 137 |
-
completion_tokens = "153"
|
| 138 |
-
return response, total_tokens, prompt_tokens, completion_tokens
|
| 139 |
-
|
| 140 |
-
|
| 141 |
-
# container for chat history
|
| 142 |
-
response_container = st.container()
|
| 143 |
-
# container for text box
|
| 144 |
-
container = st.container()
|
| 145 |
-
|
| 146 |
-
with container:
|
| 147 |
-
with st.form(key='my_form', clear_on_submit=True):
|
| 148 |
-
user_input = st.text_area("You:", key='input', height=2)
|
| 149 |
-
submit_button = st.form_submit_button(label='✉')
|
| 150 |
-
|
| 151 |
-
if submit_button and user_input:
|
| 152 |
-
output, total_tokens, prompt_tokens, completion_tokens = generate_response(user_input)
|
| 153 |
-
st.session_state['past'].append(user_input)
|
| 154 |
-
st.session_state['generated'].append(output)
|
| 155 |
-
st.session_state['model_name'].append(model_name)
|
| 156 |
-
st.session_state['total_tokens'].append(total_tokens)
|
| 157 |
-
|
| 158 |
-
# from https://openai.com/pricing#language-models
|
| 159 |
-
if model_name == "30M_6.1K":
|
| 160 |
-
cost = "1"
|
| 161 |
-
else:
|
| 162 |
-
cost = "2"
|
| 163 |
-
|
| 164 |
-
|
| 165 |
-
|
| 166 |
-
if st.session_state['generated']:
|
| 167 |
-
with response_container:
|
| 168 |
-
for i in range(len(st.session_state['generated'])):
|
| 169 |
-
message(st.session_state["past"][i], is_user=True, key=str(i) + '_user')
|
| 170 |
-
message(st.session_state["generated"][i], key=str(i))
|
| 171 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Didier/Semantic_Search_arXiv/README.md
DELETED
|
@@ -1,37 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Semantic_Search_arXiv
|
| 3 |
-
emoji: 👁
|
| 4 |
-
colorFrom: yellow
|
| 5 |
-
colorTo: blue
|
| 6 |
-
sdk: streamlit
|
| 7 |
-
app_file: app.py
|
| 8 |
-
pinned: false
|
| 9 |
-
---
|
| 10 |
-
|
| 11 |
-
# Configuration
|
| 12 |
-
|
| 13 |
-
`title`: _string_
|
| 14 |
-
Display title for the Space
|
| 15 |
-
|
| 16 |
-
`emoji`: _string_
|
| 17 |
-
Space emoji (emoji-only character allowed)
|
| 18 |
-
|
| 19 |
-
`colorFrom`: _string_
|
| 20 |
-
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
|
| 21 |
-
|
| 22 |
-
`colorTo`: _string_
|
| 23 |
-
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
|
| 24 |
-
|
| 25 |
-
`sdk`: _string_
|
| 26 |
-
Can be either `gradio`, `streamlit`, or `static`
|
| 27 |
-
|
| 28 |
-
`sdk_version` : _string_
|
| 29 |
-
Only applicable for `streamlit` SDK.
|
| 30 |
-
See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
|
| 31 |
-
|
| 32 |
-
`app_file`: _string_
|
| 33 |
-
Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
|
| 34 |
-
Path is relative to the root of the repository.
|
| 35 |
-
|
| 36 |
-
`pinned`: _boolean_
|
| 37 |
-
Whether the Space stays on top of your list.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/op/fused_bias_act.cpp
DELETED
|
@@ -1,21 +0,0 @@
|
|
| 1 |
-
#include <torch/extension.h>
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
|
| 5 |
-
int act, int grad, float alpha, float scale);
|
| 6 |
-
|
| 7 |
-
#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
|
| 8 |
-
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
|
| 9 |
-
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
|
| 10 |
-
|
| 11 |
-
torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
|
| 12 |
-
int act, int grad, float alpha, float scale) {
|
| 13 |
-
CHECK_CUDA(input);
|
| 14 |
-
CHECK_CUDA(bias);
|
| 15 |
-
|
| 16 |
-
return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
|
| 17 |
-
}
|
| 18 |
-
|
| 19 |
-
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
|
| 20 |
-
m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
|
| 21 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/DragGan/DragGan/stylegan_human/torch_utils/ops/filtered_lrelu.py
DELETED
|
@@ -1,282 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# NVIDIA CORPORATION and its licensors retain all intellectual property
|
| 4 |
-
# and proprietary rights in and to this software, related documentation
|
| 5 |
-
# and any modifications thereto. Any use, reproduction, disclosure or
|
| 6 |
-
# distribution of this software and related documentation without an express
|
| 7 |
-
# license agreement from NVIDIA CORPORATION is strictly prohibited.
|
| 8 |
-
|
| 9 |
-
import os
|
| 10 |
-
import numpy as np
|
| 11 |
-
import torch
|
| 12 |
-
import warnings
|
| 13 |
-
|
| 14 |
-
from .. import custom_ops
|
| 15 |
-
from .. import misc
|
| 16 |
-
from . import upfirdn2d
|
| 17 |
-
from . import bias_act
|
| 18 |
-
|
| 19 |
-
#----------------------------------------------------------------------------
|
| 20 |
-
|
| 21 |
-
_plugin = None
|
| 22 |
-
|
| 23 |
-
def _init():
|
| 24 |
-
global _plugin
|
| 25 |
-
if _plugin is None:
|
| 26 |
-
|
| 27 |
-
# sources=['filtered_lrelu.h', 'filtered_lrelu.cu', 'filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu']
|
| 28 |
-
# sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
|
| 29 |
-
# try:
|
| 30 |
-
# _plugin = custom_ops.get_plugin('filtered_lrelu_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math', '--allow-unsupported-compiler'])
|
| 31 |
-
# except:
|
| 32 |
-
# warnings.warn('Failed to build CUDA kernels for filtered_lrelu_plugin. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
|
| 33 |
-
|
| 34 |
-
_plugin = custom_ops.get_plugin_v3(
|
| 35 |
-
module_name='filtered_lrelu_plugin',
|
| 36 |
-
sources=['filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'],
|
| 37 |
-
headers=['filtered_lrelu.h', 'filtered_lrelu.cu'],
|
| 38 |
-
source_dir=os.path.dirname(__file__),
|
| 39 |
-
extra_cuda_cflags=['--use_fast_math', '--allow-unsupported-compiler'],
|
| 40 |
-
)
|
| 41 |
-
return True
|
| 42 |
-
|
| 43 |
-
def _get_filter_size(f):
|
| 44 |
-
if f is None:
|
| 45 |
-
return 1, 1
|
| 46 |
-
assert isinstance(f, torch.Tensor)
|
| 47 |
-
assert 1 <= f.ndim <= 2
|
| 48 |
-
return f.shape[-1], f.shape[0] # width, height
|
| 49 |
-
|
| 50 |
-
def _parse_padding(padding):
|
| 51 |
-
if isinstance(padding, int):
|
| 52 |
-
padding = [padding, padding]
|
| 53 |
-
assert isinstance(padding, (list, tuple))
|
| 54 |
-
assert all(isinstance(x, (int, np.integer)) for x in padding)
|
| 55 |
-
padding = [int(x) for x in padding]
|
| 56 |
-
if len(padding) == 2:
|
| 57 |
-
px, py = padding
|
| 58 |
-
padding = [px, px, py, py]
|
| 59 |
-
px0, px1, py0, py1 = padding
|
| 60 |
-
return px0, px1, py0, py1
|
| 61 |
-
|
| 62 |
-
#----------------------------------------------------------------------------
|
| 63 |
-
|
| 64 |
-
def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False, impl='cuda'):
|
| 65 |
-
r"""Filtered leaky ReLU for a batch of 2D images.
|
| 66 |
-
|
| 67 |
-
Performs the following sequence of operations for each channel:
|
| 68 |
-
|
| 69 |
-
1. Add channel-specific bias if provided (`b`).
|
| 70 |
-
|
| 71 |
-
2. Upsample the image by inserting N-1 zeros after each pixel (`up`).
|
| 72 |
-
|
| 73 |
-
3. Pad the image with the specified number of zeros on each side (`padding`).
|
| 74 |
-
Negative padding corresponds to cropping the image.
|
| 75 |
-
|
| 76 |
-
4. Convolve the image with the specified upsampling FIR filter (`fu`), shrinking it
|
| 77 |
-
so that the footprint of all output pixels lies within the input image.
|
| 78 |
-
|
| 79 |
-
5. Multiply each value by the provided gain factor (`gain`).
|
| 80 |
-
|
| 81 |
-
6. Apply leaky ReLU activation function to each value.
|
| 82 |
-
|
| 83 |
-
7. Clamp each value between -clamp and +clamp, if `clamp` parameter is provided.
|
| 84 |
-
|
| 85 |
-
8. Convolve the image with the specified downsampling FIR filter (`fd`), shrinking
|
| 86 |
-
it so that the footprint of all output pixels lies within the input image.
|
| 87 |
-
|
| 88 |
-
9. Downsample the image by keeping every Nth pixel (`down`).
|
| 89 |
-
|
| 90 |
-
The fused op is considerably more efficient than performing the same calculation
|
| 91 |
-
using standard PyTorch ops. It supports gradients of arbitrary order.
|
| 92 |
-
|
| 93 |
-
Args:
|
| 94 |
-
x: Float32/float16/float64 input tensor of the shape
|
| 95 |
-
`[batch_size, num_channels, in_height, in_width]`.
|
| 96 |
-
fu: Float32 upsampling FIR filter of the shape
|
| 97 |
-
`[filter_height, filter_width]` (non-separable),
|
| 98 |
-
`[filter_taps]` (separable), or
|
| 99 |
-
`None` (identity).
|
| 100 |
-
fd: Float32 downsampling FIR filter of the shape
|
| 101 |
-
`[filter_height, filter_width]` (non-separable),
|
| 102 |
-
`[filter_taps]` (separable), or
|
| 103 |
-
`None` (identity).
|
| 104 |
-
b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type
|
| 105 |
-
as `x`. The length of vector must must match the channel dimension of `x`.
|
| 106 |
-
up: Integer upsampling factor (default: 1).
|
| 107 |
-
down: Integer downsampling factor. (default: 1).
|
| 108 |
-
padding: Padding with respect to the upsampled image. Can be a single number
|
| 109 |
-
or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
|
| 110 |
-
(default: 0).
|
| 111 |
-
gain: Overall scaling factor for signal magnitude (default: sqrt(2)).
|
| 112 |
-
slope: Slope on the negative side of leaky ReLU (default: 0.2).
|
| 113 |
-
clamp: Maximum magnitude for leaky ReLU output (default: None).
|
| 114 |
-
flip_filter: False = convolution, True = correlation (default: False).
|
| 115 |
-
impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
|
| 116 |
-
|
| 117 |
-
Returns:
|
| 118 |
-
Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
|
| 119 |
-
"""
|
| 120 |
-
assert isinstance(x, torch.Tensor)
|
| 121 |
-
assert impl in ['ref', 'cuda']
|
| 122 |
-
if impl == 'cuda' and x.device.type == 'cuda' and _init():
|
| 123 |
-
return _filtered_lrelu_cuda(up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter).apply(x, fu, fd, b, None, 0, 0)
|
| 124 |
-
return _filtered_lrelu_ref(x, fu=fu, fd=fd, b=b, up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter)
|
| 125 |
-
|
| 126 |
-
#----------------------------------------------------------------------------
|
| 127 |
-
|
| 128 |
-
@misc.profiled_function
|
| 129 |
-
def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False):
|
| 130 |
-
"""Slow and memory-inefficient reference implementation of `filtered_lrelu()` using
|
| 131 |
-
existing `upfirdn2n()` and `bias_act()` ops.
|
| 132 |
-
"""
|
| 133 |
-
assert isinstance(x, torch.Tensor) and x.ndim == 4
|
| 134 |
-
fu_w, fu_h = _get_filter_size(fu)
|
| 135 |
-
fd_w, fd_h = _get_filter_size(fd)
|
| 136 |
-
if b is not None:
|
| 137 |
-
assert isinstance(b, torch.Tensor) and b.dtype == x.dtype
|
| 138 |
-
misc.assert_shape(b, [x.shape[1]])
|
| 139 |
-
assert isinstance(up, int) and up >= 1
|
| 140 |
-
assert isinstance(down, int) and down >= 1
|
| 141 |
-
px0, px1, py0, py1 = _parse_padding(padding)
|
| 142 |
-
assert gain == float(gain) and gain > 0
|
| 143 |
-
assert slope == float(slope) and slope >= 0
|
| 144 |
-
assert clamp is None or (clamp == float(clamp) and clamp >= 0)
|
| 145 |
-
|
| 146 |
-
# Calculate output size.
|
| 147 |
-
batch_size, channels, in_h, in_w = x.shape
|
| 148 |
-
in_dtype = x.dtype
|
| 149 |
-
out_w = (in_w * up + (px0 + px1) - (fu_w - 1) - (fd_w - 1) + (down - 1)) // down
|
| 150 |
-
out_h = (in_h * up + (py0 + py1) - (fu_h - 1) - (fd_h - 1) + (down - 1)) // down
|
| 151 |
-
|
| 152 |
-
# Compute using existing ops.
|
| 153 |
-
x = bias_act.bias_act(x=x, b=b) # Apply bias.
|
| 154 |
-
x = upfirdn2d.upfirdn2d(x=x, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample.
|
| 155 |
-
x = bias_act.bias_act(x=x, act='lrelu', alpha=slope, gain=gain, clamp=clamp) # Bias, leaky ReLU, clamp.
|
| 156 |
-
x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down, flip_filter=flip_filter) # Downsample.
|
| 157 |
-
|
| 158 |
-
# Check output shape & dtype.
|
| 159 |
-
misc.assert_shape(x, [batch_size, channels, out_h, out_w])
|
| 160 |
-
assert x.dtype == in_dtype
|
| 161 |
-
return x
|
| 162 |
-
|
| 163 |
-
#----------------------------------------------------------------------------
|
| 164 |
-
|
| 165 |
-
_filtered_lrelu_cuda_cache = dict()
|
| 166 |
-
|
| 167 |
-
def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False):
|
| 168 |
-
"""Fast CUDA implementation of `filtered_lrelu()` using custom ops.
|
| 169 |
-
"""
|
| 170 |
-
assert isinstance(up, int) and up >= 1
|
| 171 |
-
assert isinstance(down, int) and down >= 1
|
| 172 |
-
px0, px1, py0, py1 = _parse_padding(padding)
|
| 173 |
-
assert gain == float(gain) and gain > 0
|
| 174 |
-
gain = float(gain)
|
| 175 |
-
assert slope == float(slope) and slope >= 0
|
| 176 |
-
slope = float(slope)
|
| 177 |
-
assert clamp is None or (clamp == float(clamp) and clamp >= 0)
|
| 178 |
-
clamp = float(clamp if clamp is not None else 'inf')
|
| 179 |
-
|
| 180 |
-
# Lookup from cache.
|
| 181 |
-
key = (up, down, px0, px1, py0, py1, gain, slope, clamp, flip_filter)
|
| 182 |
-
if key in _filtered_lrelu_cuda_cache:
|
| 183 |
-
return _filtered_lrelu_cuda_cache[key]
|
| 184 |
-
|
| 185 |
-
# Forward op.
|
| 186 |
-
class FilteredLReluCuda(torch.autograd.Function):
|
| 187 |
-
@staticmethod
|
| 188 |
-
def forward(ctx, x, fu, fd, b, si, sx, sy): # pylint: disable=arguments-differ
|
| 189 |
-
assert isinstance(x, torch.Tensor) and x.ndim == 4
|
| 190 |
-
|
| 191 |
-
# Replace empty up/downsample kernels with full 1x1 kernels (faster than separable).
|
| 192 |
-
if fu is None:
|
| 193 |
-
fu = torch.ones([1, 1], dtype=torch.float32, device=x.device)
|
| 194 |
-
if fd is None:
|
| 195 |
-
fd = torch.ones([1, 1], dtype=torch.float32, device=x.device)
|
| 196 |
-
assert 1 <= fu.ndim <= 2
|
| 197 |
-
assert 1 <= fd.ndim <= 2
|
| 198 |
-
|
| 199 |
-
# Replace separable 1x1 kernels with full 1x1 kernels when scale factor is 1.
|
| 200 |
-
if up == 1 and fu.ndim == 1 and fu.shape[0] == 1:
|
| 201 |
-
fu = fu.square()[None]
|
| 202 |
-
if down == 1 and fd.ndim == 1 and fd.shape[0] == 1:
|
| 203 |
-
fd = fd.square()[None]
|
| 204 |
-
|
| 205 |
-
# Missing sign input tensor.
|
| 206 |
-
if si is None:
|
| 207 |
-
si = torch.empty([0])
|
| 208 |
-
|
| 209 |
-
# Missing bias tensor.
|
| 210 |
-
if b is None:
|
| 211 |
-
b = torch.zeros([x.shape[1]], dtype=x.dtype, device=x.device)
|
| 212 |
-
|
| 213 |
-
# Construct internal sign tensor only if gradients are needed.
|
| 214 |
-
write_signs = (si.numel() == 0) and (x.requires_grad or b.requires_grad)
|
| 215 |
-
|
| 216 |
-
# Warn if input storage strides are not in decreasing order due to e.g. channels-last layout.
|
| 217 |
-
strides = [x.stride(i) for i in range(x.ndim) if x.size(i) > 1]
|
| 218 |
-
if any(a < b for a, b in zip(strides[:-1], strides[1:])):
|
| 219 |
-
warnings.warn("low-performance memory layout detected in filtered_lrelu input", RuntimeWarning)
|
| 220 |
-
|
| 221 |
-
# Call C++/Cuda plugin if datatype is supported.
|
| 222 |
-
if x.dtype in [torch.float16, torch.float32]:
|
| 223 |
-
if torch.cuda.current_stream(x.device) != torch.cuda.default_stream(x.device):
|
| 224 |
-
warnings.warn("filtered_lrelu called with non-default cuda stream but concurrent execution is not supported", RuntimeWarning)
|
| 225 |
-
y, so, return_code = _plugin.filtered_lrelu(x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy, gain, slope, clamp, flip_filter, write_signs)
|
| 226 |
-
else:
|
| 227 |
-
return_code = -1
|
| 228 |
-
|
| 229 |
-
# No Cuda kernel found? Fall back to generic implementation. Still more memory efficient than the reference implementation because
|
| 230 |
-
# only the bit-packed sign tensor is retained for gradient computation.
|
| 231 |
-
if return_code < 0:
|
| 232 |
-
warnings.warn("filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback", RuntimeWarning)
|
| 233 |
-
|
| 234 |
-
y = x.add(b.unsqueeze(-1).unsqueeze(-1)) # Add bias.
|
| 235 |
-
y = upfirdn2d.upfirdn2d(x=y, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample.
|
| 236 |
-
so = _plugin.filtered_lrelu_act_(y, si, sx, sy, gain, slope, clamp, write_signs) # Activation function and sign handling. Modifies y in-place.
|
| 237 |
-
y = upfirdn2d.upfirdn2d(x=y, f=fd, down=down, flip_filter=flip_filter) # Downsample.
|
| 238 |
-
|
| 239 |
-
# Prepare for gradient computation.
|
| 240 |
-
ctx.save_for_backward(fu, fd, (si if si.numel() else so))
|
| 241 |
-
ctx.x_shape = x.shape
|
| 242 |
-
ctx.y_shape = y.shape
|
| 243 |
-
ctx.s_ofs = sx, sy
|
| 244 |
-
return y
|
| 245 |
-
|
| 246 |
-
@staticmethod
|
| 247 |
-
def backward(ctx, dy): # pylint: disable=arguments-differ
|
| 248 |
-
fu, fd, si = ctx.saved_tensors
|
| 249 |
-
_, _, xh, xw = ctx.x_shape
|
| 250 |
-
_, _, yh, yw = ctx.y_shape
|
| 251 |
-
sx, sy = ctx.s_ofs
|
| 252 |
-
dx = None # 0
|
| 253 |
-
dfu = None; assert not ctx.needs_input_grad[1]
|
| 254 |
-
dfd = None; assert not ctx.needs_input_grad[2]
|
| 255 |
-
db = None # 3
|
| 256 |
-
dsi = None; assert not ctx.needs_input_grad[4]
|
| 257 |
-
dsx = None; assert not ctx.needs_input_grad[5]
|
| 258 |
-
dsy = None; assert not ctx.needs_input_grad[6]
|
| 259 |
-
|
| 260 |
-
if ctx.needs_input_grad[0] or ctx.needs_input_grad[3]:
|
| 261 |
-
pp = [
|
| 262 |
-
(fu.shape[-1] - 1) + (fd.shape[-1] - 1) - px0,
|
| 263 |
-
xw * up - yw * down + px0 - (up - 1),
|
| 264 |
-
(fu.shape[0] - 1) + (fd.shape[0] - 1) - py0,
|
| 265 |
-
xh * up - yh * down + py0 - (up - 1),
|
| 266 |
-
]
|
| 267 |
-
gg = gain * (up ** 2) / (down ** 2)
|
| 268 |
-
ff = (not flip_filter)
|
| 269 |
-
sx = sx - (fu.shape[-1] - 1) + px0
|
| 270 |
-
sy = sy - (fu.shape[0] - 1) + py0
|
| 271 |
-
dx = _filtered_lrelu_cuda(up=down, down=up, padding=pp, gain=gg, slope=slope, clamp=None, flip_filter=ff).apply(dy, fd, fu, None, si, sx, sy)
|
| 272 |
-
|
| 273 |
-
if ctx.needs_input_grad[3]:
|
| 274 |
-
db = dx.sum([0, 2, 3])
|
| 275 |
-
|
| 276 |
-
return dx, dfu, dfd, db, dsi, dsx, dsy
|
| 277 |
-
|
| 278 |
-
# Add to cache.
|
| 279 |
-
_filtered_lrelu_cuda_cache[key] = FilteredLReluCuda
|
| 280 |
-
return FilteredLReluCuda
|
| 281 |
-
|
| 282 |
-
#----------------------------------------------------------------------------
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/ECCV2022/bytetrack/deploy/ncnn/cpp/src/utils.cpp
DELETED
|
@@ -1,429 +0,0 @@
|
|
| 1 |
-
#include "BYTETracker.h"
|
| 2 |
-
#include "lapjv.h"
|
| 3 |
-
|
| 4 |
-
vector<STrack*> BYTETracker::joint_stracks(vector<STrack*> &tlista, vector<STrack> &tlistb)
|
| 5 |
-
{
|
| 6 |
-
map<int, int> exists;
|
| 7 |
-
vector<STrack*> res;
|
| 8 |
-
for (int i = 0; i < tlista.size(); i++)
|
| 9 |
-
{
|
| 10 |
-
exists.insert(pair<int, int>(tlista[i]->track_id, 1));
|
| 11 |
-
res.push_back(tlista[i]);
|
| 12 |
-
}
|
| 13 |
-
for (int i = 0; i < tlistb.size(); i++)
|
| 14 |
-
{
|
| 15 |
-
int tid = tlistb[i].track_id;
|
| 16 |
-
if (!exists[tid] || exists.count(tid) == 0)
|
| 17 |
-
{
|
| 18 |
-
exists[tid] = 1;
|
| 19 |
-
res.push_back(&tlistb[i]);
|
| 20 |
-
}
|
| 21 |
-
}
|
| 22 |
-
return res;
|
| 23 |
-
}
|
| 24 |
-
|
| 25 |
-
vector<STrack> BYTETracker::joint_stracks(vector<STrack> &tlista, vector<STrack> &tlistb)
|
| 26 |
-
{
|
| 27 |
-
map<int, int> exists;
|
| 28 |
-
vector<STrack> res;
|
| 29 |
-
for (int i = 0; i < tlista.size(); i++)
|
| 30 |
-
{
|
| 31 |
-
exists.insert(pair<int, int>(tlista[i].track_id, 1));
|
| 32 |
-
res.push_back(tlista[i]);
|
| 33 |
-
}
|
| 34 |
-
for (int i = 0; i < tlistb.size(); i++)
|
| 35 |
-
{
|
| 36 |
-
int tid = tlistb[i].track_id;
|
| 37 |
-
if (!exists[tid] || exists.count(tid) == 0)
|
| 38 |
-
{
|
| 39 |
-
exists[tid] = 1;
|
| 40 |
-
res.push_back(tlistb[i]);
|
| 41 |
-
}
|
| 42 |
-
}
|
| 43 |
-
return res;
|
| 44 |
-
}
|
| 45 |
-
|
| 46 |
-
vector<STrack> BYTETracker::sub_stracks(vector<STrack> &tlista, vector<STrack> &tlistb)
|
| 47 |
-
{
|
| 48 |
-
map<int, STrack> stracks;
|
| 49 |
-
for (int i = 0; i < tlista.size(); i++)
|
| 50 |
-
{
|
| 51 |
-
stracks.insert(pair<int, STrack>(tlista[i].track_id, tlista[i]));
|
| 52 |
-
}
|
| 53 |
-
for (int i = 0; i < tlistb.size(); i++)
|
| 54 |
-
{
|
| 55 |
-
int tid = tlistb[i].track_id;
|
| 56 |
-
if (stracks.count(tid) != 0)
|
| 57 |
-
{
|
| 58 |
-
stracks.erase(tid);
|
| 59 |
-
}
|
| 60 |
-
}
|
| 61 |
-
|
| 62 |
-
vector<STrack> res;
|
| 63 |
-
std::map<int, STrack>::iterator it;
|
| 64 |
-
for (it = stracks.begin(); it != stracks.end(); ++it)
|
| 65 |
-
{
|
| 66 |
-
res.push_back(it->second);
|
| 67 |
-
}
|
| 68 |
-
|
| 69 |
-
return res;
|
| 70 |
-
}
|
| 71 |
-
|
| 72 |
-
void BYTETracker::remove_duplicate_stracks(vector<STrack> &resa, vector<STrack> &resb, vector<STrack> &stracksa, vector<STrack> &stracksb)
|
| 73 |
-
{
|
| 74 |
-
vector<vector<float> > pdist = iou_distance(stracksa, stracksb);
|
| 75 |
-
vector<pair<int, int> > pairs;
|
| 76 |
-
for (int i = 0; i < pdist.size(); i++)
|
| 77 |
-
{
|
| 78 |
-
for (int j = 0; j < pdist[i].size(); j++)
|
| 79 |
-
{
|
| 80 |
-
if (pdist[i][j] < 0.15)
|
| 81 |
-
{
|
| 82 |
-
pairs.push_back(pair<int, int>(i, j));
|
| 83 |
-
}
|
| 84 |
-
}
|
| 85 |
-
}
|
| 86 |
-
|
| 87 |
-
vector<int> dupa, dupb;
|
| 88 |
-
for (int i = 0; i < pairs.size(); i++)
|
| 89 |
-
{
|
| 90 |
-
int timep = stracksa[pairs[i].first].frame_id - stracksa[pairs[i].first].start_frame;
|
| 91 |
-
int timeq = stracksb[pairs[i].second].frame_id - stracksb[pairs[i].second].start_frame;
|
| 92 |
-
if (timep > timeq)
|
| 93 |
-
dupb.push_back(pairs[i].second);
|
| 94 |
-
else
|
| 95 |
-
dupa.push_back(pairs[i].first);
|
| 96 |
-
}
|
| 97 |
-
|
| 98 |
-
for (int i = 0; i < stracksa.size(); i++)
|
| 99 |
-
{
|
| 100 |
-
vector<int>::iterator iter = find(dupa.begin(), dupa.end(), i);
|
| 101 |
-
if (iter == dupa.end())
|
| 102 |
-
{
|
| 103 |
-
resa.push_back(stracksa[i]);
|
| 104 |
-
}
|
| 105 |
-
}
|
| 106 |
-
|
| 107 |
-
for (int i = 0; i < stracksb.size(); i++)
|
| 108 |
-
{
|
| 109 |
-
vector<int>::iterator iter = find(dupb.begin(), dupb.end(), i);
|
| 110 |
-
if (iter == dupb.end())
|
| 111 |
-
{
|
| 112 |
-
resb.push_back(stracksb[i]);
|
| 113 |
-
}
|
| 114 |
-
}
|
| 115 |
-
}
|
| 116 |
-
|
| 117 |
-
void BYTETracker::linear_assignment(vector<vector<float> > &cost_matrix, int cost_matrix_size, int cost_matrix_size_size, float thresh,
|
| 118 |
-
vector<vector<int> > &matches, vector<int> &unmatched_a, vector<int> &unmatched_b)
|
| 119 |
-
{
|
| 120 |
-
if (cost_matrix.size() == 0)
|
| 121 |
-
{
|
| 122 |
-
for (int i = 0; i < cost_matrix_size; i++)
|
| 123 |
-
{
|
| 124 |
-
unmatched_a.push_back(i);
|
| 125 |
-
}
|
| 126 |
-
for (int i = 0; i < cost_matrix_size_size; i++)
|
| 127 |
-
{
|
| 128 |
-
unmatched_b.push_back(i);
|
| 129 |
-
}
|
| 130 |
-
return;
|
| 131 |
-
}
|
| 132 |
-
|
| 133 |
-
vector<int> rowsol; vector<int> colsol;
|
| 134 |
-
float c = lapjv(cost_matrix, rowsol, colsol, true, thresh);
|
| 135 |
-
for (int i = 0; i < rowsol.size(); i++)
|
| 136 |
-
{
|
| 137 |
-
if (rowsol[i] >= 0)
|
| 138 |
-
{
|
| 139 |
-
vector<int> match;
|
| 140 |
-
match.push_back(i);
|
| 141 |
-
match.push_back(rowsol[i]);
|
| 142 |
-
matches.push_back(match);
|
| 143 |
-
}
|
| 144 |
-
else
|
| 145 |
-
{
|
| 146 |
-
unmatched_a.push_back(i);
|
| 147 |
-
}
|
| 148 |
-
}
|
| 149 |
-
|
| 150 |
-
for (int i = 0; i < colsol.size(); i++)
|
| 151 |
-
{
|
| 152 |
-
if (colsol[i] < 0)
|
| 153 |
-
{
|
| 154 |
-
unmatched_b.push_back(i);
|
| 155 |
-
}
|
| 156 |
-
}
|
| 157 |
-
}
|
| 158 |
-
|
| 159 |
-
vector<vector<float> > BYTETracker::ious(vector<vector<float> > &atlbrs, vector<vector<float> > &btlbrs)
|
| 160 |
-
{
|
| 161 |
-
vector<vector<float> > ious;
|
| 162 |
-
if (atlbrs.size()*btlbrs.size() == 0)
|
| 163 |
-
return ious;
|
| 164 |
-
|
| 165 |
-
ious.resize(atlbrs.size());
|
| 166 |
-
for (int i = 0; i < ious.size(); i++)
|
| 167 |
-
{
|
| 168 |
-
ious[i].resize(btlbrs.size());
|
| 169 |
-
}
|
| 170 |
-
|
| 171 |
-
//bbox_ious
|
| 172 |
-
for (int k = 0; k < btlbrs.size(); k++)
|
| 173 |
-
{
|
| 174 |
-
vector<float> ious_tmp;
|
| 175 |
-
float box_area = (btlbrs[k][2] - btlbrs[k][0] + 1)*(btlbrs[k][3] - btlbrs[k][1] + 1);
|
| 176 |
-
for (int n = 0; n < atlbrs.size(); n++)
|
| 177 |
-
{
|
| 178 |
-
float iw = min(atlbrs[n][2], btlbrs[k][2]) - max(atlbrs[n][0], btlbrs[k][0]) + 1;
|
| 179 |
-
if (iw > 0)
|
| 180 |
-
{
|
| 181 |
-
float ih = min(atlbrs[n][3], btlbrs[k][3]) - max(atlbrs[n][1], btlbrs[k][1]) + 1;
|
| 182 |
-
if(ih > 0)
|
| 183 |
-
{
|
| 184 |
-
float ua = (atlbrs[n][2] - atlbrs[n][0] + 1)*(atlbrs[n][3] - atlbrs[n][1] + 1) + box_area - iw * ih;
|
| 185 |
-
ious[n][k] = iw * ih / ua;
|
| 186 |
-
}
|
| 187 |
-
else
|
| 188 |
-
{
|
| 189 |
-
ious[n][k] = 0.0;
|
| 190 |
-
}
|
| 191 |
-
}
|
| 192 |
-
else
|
| 193 |
-
{
|
| 194 |
-
ious[n][k] = 0.0;
|
| 195 |
-
}
|
| 196 |
-
}
|
| 197 |
-
}
|
| 198 |
-
|
| 199 |
-
return ious;
|
| 200 |
-
}
|
| 201 |
-
|
| 202 |
-
vector<vector<float> > BYTETracker::iou_distance(vector<STrack*> &atracks, vector<STrack> &btracks, int &dist_size, int &dist_size_size)
|
| 203 |
-
{
|
| 204 |
-
vector<vector<float> > cost_matrix;
|
| 205 |
-
if (atracks.size() * btracks.size() == 0)
|
| 206 |
-
{
|
| 207 |
-
dist_size = atracks.size();
|
| 208 |
-
dist_size_size = btracks.size();
|
| 209 |
-
return cost_matrix;
|
| 210 |
-
}
|
| 211 |
-
vector<vector<float> > atlbrs, btlbrs;
|
| 212 |
-
for (int i = 0; i < atracks.size(); i++)
|
| 213 |
-
{
|
| 214 |
-
atlbrs.push_back(atracks[i]->tlbr);
|
| 215 |
-
}
|
| 216 |
-
for (int i = 0; i < btracks.size(); i++)
|
| 217 |
-
{
|
| 218 |
-
btlbrs.push_back(btracks[i].tlbr);
|
| 219 |
-
}
|
| 220 |
-
|
| 221 |
-
dist_size = atracks.size();
|
| 222 |
-
dist_size_size = btracks.size();
|
| 223 |
-
|
| 224 |
-
vector<vector<float> > _ious = ious(atlbrs, btlbrs);
|
| 225 |
-
|
| 226 |
-
for (int i = 0; i < _ious.size();i++)
|
| 227 |
-
{
|
| 228 |
-
vector<float> _iou;
|
| 229 |
-
for (int j = 0; j < _ious[i].size(); j++)
|
| 230 |
-
{
|
| 231 |
-
_iou.push_back(1 - _ious[i][j]);
|
| 232 |
-
}
|
| 233 |
-
cost_matrix.push_back(_iou);
|
| 234 |
-
}
|
| 235 |
-
|
| 236 |
-
return cost_matrix;
|
| 237 |
-
}
|
| 238 |
-
|
| 239 |
-
vector<vector<float> > BYTETracker::iou_distance(vector<STrack> &atracks, vector<STrack> &btracks)
|
| 240 |
-
{
|
| 241 |
-
vector<vector<float> > atlbrs, btlbrs;
|
| 242 |
-
for (int i = 0; i < atracks.size(); i++)
|
| 243 |
-
{
|
| 244 |
-
atlbrs.push_back(atracks[i].tlbr);
|
| 245 |
-
}
|
| 246 |
-
for (int i = 0; i < btracks.size(); i++)
|
| 247 |
-
{
|
| 248 |
-
btlbrs.push_back(btracks[i].tlbr);
|
| 249 |
-
}
|
| 250 |
-
|
| 251 |
-
vector<vector<float> > _ious = ious(atlbrs, btlbrs);
|
| 252 |
-
vector<vector<float> > cost_matrix;
|
| 253 |
-
for (int i = 0; i < _ious.size(); i++)
|
| 254 |
-
{
|
| 255 |
-
vector<float> _iou;
|
| 256 |
-
for (int j = 0; j < _ious[i].size(); j++)
|
| 257 |
-
{
|
| 258 |
-
_iou.push_back(1 - _ious[i][j]);
|
| 259 |
-
}
|
| 260 |
-
cost_matrix.push_back(_iou);
|
| 261 |
-
}
|
| 262 |
-
|
| 263 |
-
return cost_matrix;
|
| 264 |
-
}
|
| 265 |
-
|
| 266 |
-
double BYTETracker::lapjv(const vector<vector<float> > &cost, vector<int> &rowsol, vector<int> &colsol,
|
| 267 |
-
bool extend_cost, float cost_limit, bool return_cost)
|
| 268 |
-
{
|
| 269 |
-
vector<vector<float> > cost_c;
|
| 270 |
-
cost_c.assign(cost.begin(), cost.end());
|
| 271 |
-
|
| 272 |
-
vector<vector<float> > cost_c_extended;
|
| 273 |
-
|
| 274 |
-
int n_rows = cost.size();
|
| 275 |
-
int n_cols = cost[0].size();
|
| 276 |
-
rowsol.resize(n_rows);
|
| 277 |
-
colsol.resize(n_cols);
|
| 278 |
-
|
| 279 |
-
int n = 0;
|
| 280 |
-
if (n_rows == n_cols)
|
| 281 |
-
{
|
| 282 |
-
n = n_rows;
|
| 283 |
-
}
|
| 284 |
-
else
|
| 285 |
-
{
|
| 286 |
-
if (!extend_cost)
|
| 287 |
-
{
|
| 288 |
-
cout << "set extend_cost=True" << endl;
|
| 289 |
-
system("pause");
|
| 290 |
-
exit(0);
|
| 291 |
-
}
|
| 292 |
-
}
|
| 293 |
-
|
| 294 |
-
if (extend_cost || cost_limit < LONG_MAX)
|
| 295 |
-
{
|
| 296 |
-
n = n_rows + n_cols;
|
| 297 |
-
cost_c_extended.resize(n);
|
| 298 |
-
for (int i = 0; i < cost_c_extended.size(); i++)
|
| 299 |
-
cost_c_extended[i].resize(n);
|
| 300 |
-
|
| 301 |
-
if (cost_limit < LONG_MAX)
|
| 302 |
-
{
|
| 303 |
-
for (int i = 0; i < cost_c_extended.size(); i++)
|
| 304 |
-
{
|
| 305 |
-
for (int j = 0; j < cost_c_extended[i].size(); j++)
|
| 306 |
-
{
|
| 307 |
-
cost_c_extended[i][j] = cost_limit / 2.0;
|
| 308 |
-
}
|
| 309 |
-
}
|
| 310 |
-
}
|
| 311 |
-
else
|
| 312 |
-
{
|
| 313 |
-
float cost_max = -1;
|
| 314 |
-
for (int i = 0; i < cost_c.size(); i++)
|
| 315 |
-
{
|
| 316 |
-
for (int j = 0; j < cost_c[i].size(); j++)
|
| 317 |
-
{
|
| 318 |
-
if (cost_c[i][j] > cost_max)
|
| 319 |
-
cost_max = cost_c[i][j];
|
| 320 |
-
}
|
| 321 |
-
}
|
| 322 |
-
for (int i = 0; i < cost_c_extended.size(); i++)
|
| 323 |
-
{
|
| 324 |
-
for (int j = 0; j < cost_c_extended[i].size(); j++)
|
| 325 |
-
{
|
| 326 |
-
cost_c_extended[i][j] = cost_max + 1;
|
| 327 |
-
}
|
| 328 |
-
}
|
| 329 |
-
}
|
| 330 |
-
|
| 331 |
-
for (int i = n_rows; i < cost_c_extended.size(); i++)
|
| 332 |
-
{
|
| 333 |
-
for (int j = n_cols; j < cost_c_extended[i].size(); j++)
|
| 334 |
-
{
|
| 335 |
-
cost_c_extended[i][j] = 0;
|
| 336 |
-
}
|
| 337 |
-
}
|
| 338 |
-
for (int i = 0; i < n_rows; i++)
|
| 339 |
-
{
|
| 340 |
-
for (int j = 0; j < n_cols; j++)
|
| 341 |
-
{
|
| 342 |
-
cost_c_extended[i][j] = cost_c[i][j];
|
| 343 |
-
}
|
| 344 |
-
}
|
| 345 |
-
|
| 346 |
-
cost_c.clear();
|
| 347 |
-
cost_c.assign(cost_c_extended.begin(), cost_c_extended.end());
|
| 348 |
-
}
|
| 349 |
-
|
| 350 |
-
double **cost_ptr;
|
| 351 |
-
cost_ptr = new double *[sizeof(double *) * n];
|
| 352 |
-
for (int i = 0; i < n; i++)
|
| 353 |
-
cost_ptr[i] = new double[sizeof(double) * n];
|
| 354 |
-
|
| 355 |
-
for (int i = 0; i < n; i++)
|
| 356 |
-
{
|
| 357 |
-
for (int j = 0; j < n; j++)
|
| 358 |
-
{
|
| 359 |
-
cost_ptr[i][j] = cost_c[i][j];
|
| 360 |
-
}
|
| 361 |
-
}
|
| 362 |
-
|
| 363 |
-
int* x_c = new int[sizeof(int) * n];
|
| 364 |
-
int *y_c = new int[sizeof(int) * n];
|
| 365 |
-
|
| 366 |
-
int ret = lapjv_internal(n, cost_ptr, x_c, y_c);
|
| 367 |
-
if (ret != 0)
|
| 368 |
-
{
|
| 369 |
-
cout << "Calculate Wrong!" << endl;
|
| 370 |
-
system("pause");
|
| 371 |
-
exit(0);
|
| 372 |
-
}
|
| 373 |
-
|
| 374 |
-
double opt = 0.0;
|
| 375 |
-
|
| 376 |
-
if (n != n_rows)
|
| 377 |
-
{
|
| 378 |
-
for (int i = 0; i < n; i++)
|
| 379 |
-
{
|
| 380 |
-
if (x_c[i] >= n_cols)
|
| 381 |
-
x_c[i] = -1;
|
| 382 |
-
if (y_c[i] >= n_rows)
|
| 383 |
-
y_c[i] = -1;
|
| 384 |
-
}
|
| 385 |
-
for (int i = 0; i < n_rows; i++)
|
| 386 |
-
{
|
| 387 |
-
rowsol[i] = x_c[i];
|
| 388 |
-
}
|
| 389 |
-
for (int i = 0; i < n_cols; i++)
|
| 390 |
-
{
|
| 391 |
-
colsol[i] = y_c[i];
|
| 392 |
-
}
|
| 393 |
-
|
| 394 |
-
if (return_cost)
|
| 395 |
-
{
|
| 396 |
-
for (int i = 0; i < rowsol.size(); i++)
|
| 397 |
-
{
|
| 398 |
-
if (rowsol[i] != -1)
|
| 399 |
-
{
|
| 400 |
-
//cout << i << "\t" << rowsol[i] << "\t" << cost_ptr[i][rowsol[i]] << endl;
|
| 401 |
-
opt += cost_ptr[i][rowsol[i]];
|
| 402 |
-
}
|
| 403 |
-
}
|
| 404 |
-
}
|
| 405 |
-
}
|
| 406 |
-
else if (return_cost)
|
| 407 |
-
{
|
| 408 |
-
for (int i = 0; i < rowsol.size(); i++)
|
| 409 |
-
{
|
| 410 |
-
opt += cost_ptr[i][rowsol[i]];
|
| 411 |
-
}
|
| 412 |
-
}
|
| 413 |
-
|
| 414 |
-
for (int i = 0; i < n; i++)
|
| 415 |
-
{
|
| 416 |
-
delete[]cost_ptr[i];
|
| 417 |
-
}
|
| 418 |
-
delete[]cost_ptr;
|
| 419 |
-
delete[]x_c;
|
| 420 |
-
delete[]y_c;
|
| 421 |
-
|
| 422 |
-
return opt;
|
| 423 |
-
}
|
| 424 |
-
|
| 425 |
-
Scalar BYTETracker::get_color(int idx)
|
| 426 |
-
{
|
| 427 |
-
idx += 3;
|
| 428 |
-
return Scalar(37 * idx % 255, 17 * idx % 255, 29 * idx % 255);
|
| 429 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/ECCV2022/bytetrack/exps/default/yolov3.py
DELETED
|
@@ -1,89 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env python3
|
| 2 |
-
# -*- coding:utf-8 -*-
|
| 3 |
-
# Copyright (c) Megvii, Inc. and its affiliates.
|
| 4 |
-
|
| 5 |
-
import os
|
| 6 |
-
import torch
|
| 7 |
-
import torch.nn as nn
|
| 8 |
-
|
| 9 |
-
from yolox.exp import Exp as MyExp
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class Exp(MyExp):
|
| 13 |
-
def __init__(self):
|
| 14 |
-
super(Exp, self).__init__()
|
| 15 |
-
self.depth = 1.0
|
| 16 |
-
self.width = 1.0
|
| 17 |
-
self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
|
| 18 |
-
|
| 19 |
-
def get_model(self, sublinear=False):
|
| 20 |
-
def init_yolo(M):
|
| 21 |
-
for m in M.modules():
|
| 22 |
-
if isinstance(m, nn.BatchNorm2d):
|
| 23 |
-
m.eps = 1e-3
|
| 24 |
-
m.momentum = 0.03
|
| 25 |
-
if "model" not in self.__dict__:
|
| 26 |
-
from yolox.models import YOLOX, YOLOFPN, YOLOXHead
|
| 27 |
-
backbone = YOLOFPN()
|
| 28 |
-
head = YOLOXHead(self.num_classes, self.width, in_channels=[128, 256, 512], act="lrelu")
|
| 29 |
-
self.model = YOLOX(backbone, head)
|
| 30 |
-
self.model.apply(init_yolo)
|
| 31 |
-
self.model.head.initialize_biases(1e-2)
|
| 32 |
-
|
| 33 |
-
return self.model
|
| 34 |
-
|
| 35 |
-
def get_data_loader(self, batch_size, is_distributed, no_aug=False):
|
| 36 |
-
from data.datasets.cocodataset import COCODataset
|
| 37 |
-
from data.datasets.mosaicdetection import MosaicDetection
|
| 38 |
-
from data.datasets.data_augment import TrainTransform
|
| 39 |
-
from data.datasets.dataloading import YoloBatchSampler, DataLoader, InfiniteSampler
|
| 40 |
-
import torch.distributed as dist
|
| 41 |
-
|
| 42 |
-
dataset = COCODataset(
|
| 43 |
-
data_dir='data/COCO/',
|
| 44 |
-
json_file=self.train_ann,
|
| 45 |
-
img_size=self.input_size,
|
| 46 |
-
preproc=TrainTransform(
|
| 47 |
-
rgb_means=(0.485, 0.456, 0.406),
|
| 48 |
-
std=(0.229, 0.224, 0.225),
|
| 49 |
-
max_labels=50
|
| 50 |
-
),
|
| 51 |
-
)
|
| 52 |
-
|
| 53 |
-
dataset = MosaicDetection(
|
| 54 |
-
dataset,
|
| 55 |
-
mosaic=not no_aug,
|
| 56 |
-
img_size=self.input_size,
|
| 57 |
-
preproc=TrainTransform(
|
| 58 |
-
rgb_means=(0.485, 0.456, 0.406),
|
| 59 |
-
std=(0.229, 0.224, 0.225),
|
| 60 |
-
max_labels=120
|
| 61 |
-
),
|
| 62 |
-
degrees=self.degrees,
|
| 63 |
-
translate=self.translate,
|
| 64 |
-
scale=self.scale,
|
| 65 |
-
shear=self.shear,
|
| 66 |
-
perspective=self.perspective,
|
| 67 |
-
)
|
| 68 |
-
|
| 69 |
-
self.dataset = dataset
|
| 70 |
-
|
| 71 |
-
if is_distributed:
|
| 72 |
-
batch_size = batch_size // dist.get_world_size()
|
| 73 |
-
sampler = InfiniteSampler(len(self.dataset), seed=self.seed if self.seed else 0)
|
| 74 |
-
else:
|
| 75 |
-
sampler = torch.utils.data.RandomSampler(self.dataset)
|
| 76 |
-
|
| 77 |
-
batch_sampler = YoloBatchSampler(
|
| 78 |
-
sampler=sampler,
|
| 79 |
-
batch_size=batch_size,
|
| 80 |
-
drop_last=False,
|
| 81 |
-
input_dimension=self.input_size,
|
| 82 |
-
mosaic=not no_aug
|
| 83 |
-
)
|
| 84 |
-
|
| 85 |
-
dataloader_kwargs = {"num_workers": self.data_num_workers, "pin_memory": True}
|
| 86 |
-
dataloader_kwargs["batch_sampler"] = batch_sampler
|
| 87 |
-
train_loader = DataLoader(self.dataset, **dataloader_kwargs)
|
| 88 |
-
|
| 89 |
-
return train_loader
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/ECCV2022/bytetrack/tutorials/centertrack/mot_online/basetrack.py
DELETED
|
@@ -1,52 +0,0 @@
|
|
| 1 |
-
import numpy as np
|
| 2 |
-
from collections import OrderedDict
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
class TrackState(object):
|
| 6 |
-
New = 0
|
| 7 |
-
Tracked = 1
|
| 8 |
-
Lost = 2
|
| 9 |
-
Removed = 3
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class BaseTrack(object):
|
| 13 |
-
_count = 0
|
| 14 |
-
|
| 15 |
-
track_id = 0
|
| 16 |
-
is_activated = False
|
| 17 |
-
state = TrackState.New
|
| 18 |
-
|
| 19 |
-
history = OrderedDict()
|
| 20 |
-
features = []
|
| 21 |
-
curr_feature = None
|
| 22 |
-
score = 0
|
| 23 |
-
start_frame = 0
|
| 24 |
-
frame_id = 0
|
| 25 |
-
time_since_update = 0
|
| 26 |
-
|
| 27 |
-
# multi-camera
|
| 28 |
-
location = (np.inf, np.inf)
|
| 29 |
-
|
| 30 |
-
@property
|
| 31 |
-
def end_frame(self):
|
| 32 |
-
return self.frame_id
|
| 33 |
-
|
| 34 |
-
@staticmethod
|
| 35 |
-
def next_id():
|
| 36 |
-
BaseTrack._count += 1
|
| 37 |
-
return BaseTrack._count
|
| 38 |
-
|
| 39 |
-
def activate(self, *args):
|
| 40 |
-
raise NotImplementedError
|
| 41 |
-
|
| 42 |
-
def predict(self):
|
| 43 |
-
raise NotImplementedError
|
| 44 |
-
|
| 45 |
-
def update(self, *args, **kwargs):
|
| 46 |
-
raise NotImplementedError
|
| 47 |
-
|
| 48 |
-
def mark_lost(self):
|
| 49 |
-
self.state = TrackState.Lost
|
| 50 |
-
|
| 51 |
-
def mark_removed(self):
|
| 52 |
-
self.state = TrackState.Removed
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Eddycrack864/Applio-Inference/utils/dependency.py
DELETED
|
@@ -1,170 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import csv
|
| 3 |
-
import shutil
|
| 4 |
-
import tarfile
|
| 5 |
-
import subprocess
|
| 6 |
-
from pathlib import Path
|
| 7 |
-
from datetime import datetime
|
| 8 |
-
|
| 9 |
-
def install_packages_but_jank_af():
|
| 10 |
-
packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2']
|
| 11 |
-
pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0',
|
| 12 |
-
'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5',
|
| 13 |
-
'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12',
|
| 14 |
-
'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1',
|
| 15 |
-
'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av']
|
| 16 |
-
|
| 17 |
-
print("Updating and installing system packages...")
|
| 18 |
-
for package in packages:
|
| 19 |
-
print(f"Installing {package}...")
|
| 20 |
-
subprocess.check_call(['apt-get', 'install', '-qq', '-y', package])
|
| 21 |
-
|
| 22 |
-
print("Updating and installing pip packages...")
|
| 23 |
-
subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages)
|
| 24 |
-
|
| 25 |
-
print('Packages up to date.')
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
def setup_environment(ForceUpdateDependencies, ForceTemporaryStorage):
|
| 29 |
-
# Mounting Google Drive
|
| 30 |
-
if not ForceTemporaryStorage:
|
| 31 |
-
from google.colab import drive
|
| 32 |
-
|
| 33 |
-
if not os.path.exists('/content/drive'):
|
| 34 |
-
drive.mount('/content/drive')
|
| 35 |
-
else:
|
| 36 |
-
print('Drive is already mounted. Proceeding...')
|
| 37 |
-
|
| 38 |
-
# Function to install dependencies with progress
|
| 39 |
-
def install_packages():
|
| 40 |
-
packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2']
|
| 41 |
-
pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0',
|
| 42 |
-
'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5',
|
| 43 |
-
'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12',
|
| 44 |
-
'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1',
|
| 45 |
-
'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av']
|
| 46 |
-
|
| 47 |
-
print("Updating and installing system packages...")
|
| 48 |
-
for package in packages:
|
| 49 |
-
print(f"Installing {package}...")
|
| 50 |
-
subprocess.check_call(['apt-get', 'install', '-qq', '-y', package])
|
| 51 |
-
|
| 52 |
-
print("Updating and installing pip packages...")
|
| 53 |
-
subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages)
|
| 54 |
-
|
| 55 |
-
|
| 56 |
-
print('Packages up to date.')
|
| 57 |
-
|
| 58 |
-
# Function to scan a directory and writes filenames and timestamps
|
| 59 |
-
def scan_and_write(base_path, output_file):
|
| 60 |
-
with open(output_file, 'w', newline='') as f:
|
| 61 |
-
writer = csv.writer(f)
|
| 62 |
-
for dirpath, dirs, files in os.walk(base_path):
|
| 63 |
-
for filename in files:
|
| 64 |
-
fname = os.path.join(dirpath, filename)
|
| 65 |
-
try:
|
| 66 |
-
mtime = os.path.getmtime(fname)
|
| 67 |
-
writer.writerow([fname, mtime])
|
| 68 |
-
except Exception as e:
|
| 69 |
-
print(f'Skipping irrelevant nonexistent file {fname}: {str(e)}')
|
| 70 |
-
print(f'Finished recording filesystem timestamps to {output_file}.')
|
| 71 |
-
|
| 72 |
-
# Function to compare files
|
| 73 |
-
def compare_files(old_file, new_file):
|
| 74 |
-
old_files = {}
|
| 75 |
-
new_files = {}
|
| 76 |
-
|
| 77 |
-
with open(old_file, 'r') as f:
|
| 78 |
-
reader = csv.reader(f)
|
| 79 |
-
old_files = {rows[0]:rows[1] for rows in reader}
|
| 80 |
-
|
| 81 |
-
with open(new_file, 'r') as f:
|
| 82 |
-
reader = csv.reader(f)
|
| 83 |
-
new_files = {rows[0]:rows[1] for rows in reader}
|
| 84 |
-
|
| 85 |
-
removed_files = old_files.keys() - new_files.keys()
|
| 86 |
-
added_files = new_files.keys() - old_files.keys()
|
| 87 |
-
unchanged_files = old_files.keys() & new_files.keys()
|
| 88 |
-
|
| 89 |
-
changed_files = {f for f in unchanged_files if old_files[f] != new_files[f]}
|
| 90 |
-
|
| 91 |
-
for file in removed_files:
|
| 92 |
-
print(f'File has been removed: {file}')
|
| 93 |
-
|
| 94 |
-
for file in changed_files:
|
| 95 |
-
print(f'File has been updated: {file}')
|
| 96 |
-
|
| 97 |
-
return list(added_files) + list(changed_files)
|
| 98 |
-
|
| 99 |
-
# Check if CachedRVC.tar.gz exists
|
| 100 |
-
if ForceTemporaryStorage:
|
| 101 |
-
file_path = '/content/CachedRVC.tar.gz'
|
| 102 |
-
else:
|
| 103 |
-
file_path = '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz'
|
| 104 |
-
|
| 105 |
-
content_file_path = '/content/CachedRVC.tar.gz'
|
| 106 |
-
extract_path = '/'
|
| 107 |
-
|
| 108 |
-
if not os.path.exists(file_path):
|
| 109 |
-
folder_path = os.path.dirname(file_path)
|
| 110 |
-
os.makedirs(folder_path, exist_ok=True)
|
| 111 |
-
print('No cached dependency install found. Attempting to download GitHub backup..')
|
| 112 |
-
|
| 113 |
-
try:
|
| 114 |
-
download_url = "https://github.com/kalomaze/QuickMangioFixes/releases/download/release3/CachedRVC.tar.gz"
|
| 115 |
-
subprocess.run(["wget", "-O", file_path, download_url])
|
| 116 |
-
print('Download completed successfully!')
|
| 117 |
-
except Exception as e:
|
| 118 |
-
print('Download failed:', str(e))
|
| 119 |
-
|
| 120 |
-
# Delete the failed download file
|
| 121 |
-
if os.path.exists(file_path):
|
| 122 |
-
os.remove(file_path)
|
| 123 |
-
print('Failed download file deleted. Continuing manual backup..')
|
| 124 |
-
|
| 125 |
-
if Path(file_path).exists():
|
| 126 |
-
if ForceTemporaryStorage:
|
| 127 |
-
print('Finished downloading CachedRVC.tar.gz.')
|
| 128 |
-
else:
|
| 129 |
-
print('CachedRVC.tar.gz found on Google Drive. Proceeding to copy and extract...')
|
| 130 |
-
|
| 131 |
-
# Check if ForceTemporaryStorage is True and skip copying if it is
|
| 132 |
-
if ForceTemporaryStorage:
|
| 133 |
-
pass
|
| 134 |
-
else:
|
| 135 |
-
shutil.copy(file_path, content_file_path)
|
| 136 |
-
|
| 137 |
-
print('Beginning backup copy operation...')
|
| 138 |
-
|
| 139 |
-
with tarfile.open(content_file_path, 'r:gz') as tar:
|
| 140 |
-
for member in tar.getmembers():
|
| 141 |
-
target_path = os.path.join(extract_path, member.name)
|
| 142 |
-
try:
|
| 143 |
-
tar.extract(member, extract_path)
|
| 144 |
-
except Exception as e:
|
| 145 |
-
print('Failed to extract a file (this isn\'t normal)... forcing an update to compensate')
|
| 146 |
-
ForceUpdateDependencies = True
|
| 147 |
-
print(f'Extraction of {content_file_path} to {extract_path} completed.')
|
| 148 |
-
|
| 149 |
-
if ForceUpdateDependencies:
|
| 150 |
-
install_packages()
|
| 151 |
-
ForceUpdateDependencies = False
|
| 152 |
-
else:
|
| 153 |
-
print('CachedRVC.tar.gz not found. Proceeding to create an index of all current files...')
|
| 154 |
-
scan_and_write('/usr/', '/content/usr_files.csv')
|
| 155 |
-
|
| 156 |
-
install_packages()
|
| 157 |
-
|
| 158 |
-
scan_and_write('/usr/', '/content/usr_files_new.csv')
|
| 159 |
-
changed_files = compare_files('/content/usr_files.csv', '/content/usr_files_new.csv')
|
| 160 |
-
|
| 161 |
-
with tarfile.open('/content/CachedRVC.tar.gz', 'w:gz') as new_tar:
|
| 162 |
-
for file in changed_files:
|
| 163 |
-
new_tar.add(file)
|
| 164 |
-
print(f'Added to tar: {file}')
|
| 165 |
-
|
| 166 |
-
os.makedirs('/content/drive/MyDrive/RVC_Cached', exist_ok=True)
|
| 167 |
-
shutil.copy('/content/CachedRVC.tar.gz', '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz')
|
| 168 |
-
print('Updated CachedRVC.tar.gz copied to Google Drive.')
|
| 169 |
-
print('Dependencies fully up to date; future runs should be faster.')
|
| 170 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Enterprisium/Easy_GUI/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py
DELETED
|
@@ -1,86 +0,0 @@
|
|
| 1 |
-
from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
|
| 2 |
-
import pyworld
|
| 3 |
-
import numpy as np
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
class HarvestF0Predictor(F0Predictor):
|
| 7 |
-
def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
|
| 8 |
-
self.hop_length = hop_length
|
| 9 |
-
self.f0_min = f0_min
|
| 10 |
-
self.f0_max = f0_max
|
| 11 |
-
self.sampling_rate = sampling_rate
|
| 12 |
-
|
| 13 |
-
def interpolate_f0(self, f0):
|
| 14 |
-
"""
|
| 15 |
-
对F0进行插值处理
|
| 16 |
-
"""
|
| 17 |
-
|
| 18 |
-
data = np.reshape(f0, (f0.size, 1))
|
| 19 |
-
|
| 20 |
-
vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
|
| 21 |
-
vuv_vector[data > 0.0] = 1.0
|
| 22 |
-
vuv_vector[data <= 0.0] = 0.0
|
| 23 |
-
|
| 24 |
-
ip_data = data
|
| 25 |
-
|
| 26 |
-
frame_number = data.size
|
| 27 |
-
last_value = 0.0
|
| 28 |
-
for i in range(frame_number):
|
| 29 |
-
if data[i] <= 0.0:
|
| 30 |
-
j = i + 1
|
| 31 |
-
for j in range(i + 1, frame_number):
|
| 32 |
-
if data[j] > 0.0:
|
| 33 |
-
break
|
| 34 |
-
if j < frame_number - 1:
|
| 35 |
-
if last_value > 0.0:
|
| 36 |
-
step = (data[j] - data[i - 1]) / float(j - i)
|
| 37 |
-
for k in range(i, j):
|
| 38 |
-
ip_data[k] = data[i - 1] + step * (k - i + 1)
|
| 39 |
-
else:
|
| 40 |
-
for k in range(i, j):
|
| 41 |
-
ip_data[k] = data[j]
|
| 42 |
-
else:
|
| 43 |
-
for k in range(i, frame_number):
|
| 44 |
-
ip_data[k] = last_value
|
| 45 |
-
else:
|
| 46 |
-
ip_data[i] = data[i] # 这里可能存在一个没有必要的拷贝
|
| 47 |
-
last_value = data[i]
|
| 48 |
-
|
| 49 |
-
return ip_data[:, 0], vuv_vector[:, 0]
|
| 50 |
-
|
| 51 |
-
def resize_f0(self, x, target_len):
|
| 52 |
-
source = np.array(x)
|
| 53 |
-
source[source < 0.001] = np.nan
|
| 54 |
-
target = np.interp(
|
| 55 |
-
np.arange(0, len(source) * target_len, len(source)) / target_len,
|
| 56 |
-
np.arange(0, len(source)),
|
| 57 |
-
source,
|
| 58 |
-
)
|
| 59 |
-
res = np.nan_to_num(target)
|
| 60 |
-
return res
|
| 61 |
-
|
| 62 |
-
def compute_f0(self, wav, p_len=None):
|
| 63 |
-
if p_len is None:
|
| 64 |
-
p_len = wav.shape[0] // self.hop_length
|
| 65 |
-
f0, t = pyworld.harvest(
|
| 66 |
-
wav.astype(np.double),
|
| 67 |
-
fs=self.hop_length,
|
| 68 |
-
f0_ceil=self.f0_max,
|
| 69 |
-
f0_floor=self.f0_min,
|
| 70 |
-
frame_period=1000 * self.hop_length / self.sampling_rate,
|
| 71 |
-
)
|
| 72 |
-
f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs)
|
| 73 |
-
return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
|
| 74 |
-
|
| 75 |
-
def compute_f0_uv(self, wav, p_len=None):
|
| 76 |
-
if p_len is None:
|
| 77 |
-
p_len = wav.shape[0] // self.hop_length
|
| 78 |
-
f0, t = pyworld.harvest(
|
| 79 |
-
wav.astype(np.double),
|
| 80 |
-
fs=self.sampling_rate,
|
| 81 |
-
f0_floor=self.f0_min,
|
| 82 |
-
f0_ceil=self.f0_max,
|
| 83 |
-
frame_period=1000 * self.hop_length / self.sampling_rate,
|
| 84 |
-
)
|
| 85 |
-
f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
|
| 86 |
-
return self.interpolate_f0(self.resize_f0(f0, p_len))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/EsoCode/text-generation-webui/extensions/multimodal/pipelines/llava/llava.py
DELETED
|
@@ -1,145 +0,0 @@
|
|
| 1 |
-
import time
|
| 2 |
-
from abc import abstractmethod
|
| 3 |
-
from typing import List, Tuple
|
| 4 |
-
|
| 5 |
-
import torch
|
| 6 |
-
from huggingface_hub import hf_hub_download
|
| 7 |
-
from PIL import Image
|
| 8 |
-
from transformers import CLIPImageProcessor, CLIPVisionModel
|
| 9 |
-
|
| 10 |
-
from extensions.multimodal.abstract_pipeline import AbstractMultimodalPipeline
|
| 11 |
-
from modules import shared
|
| 12 |
-
from modules.logging_colors import logger
|
| 13 |
-
from modules.text_generation import encode
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
class LLaVA_v0_Pipeline(AbstractMultimodalPipeline):
|
| 17 |
-
CLIP_REPO = "openai/clip-vit-large-patch14"
|
| 18 |
-
|
| 19 |
-
def __init__(self, params: dict) -> None:
|
| 20 |
-
super().__init__()
|
| 21 |
-
self.clip_device = self._get_device("vision_device", params)
|
| 22 |
-
self.clip_dtype = self._get_dtype("vision_bits", params)
|
| 23 |
-
self.projector_device = self._get_device("projector_device", params)
|
| 24 |
-
self.projector_dtype = self._get_dtype("projector_bits", params)
|
| 25 |
-
self.image_processor, self.vision_tower, self.mm_projector = self._load_models()
|
| 26 |
-
|
| 27 |
-
def _load_models(self):
|
| 28 |
-
start_ts = time.time()
|
| 29 |
-
|
| 30 |
-
logger.info(f"LLaVA - Loading CLIP from {LLaVA_v0_Pipeline.CLIP_REPO} as {self.clip_dtype} on {self.clip_device}...")
|
| 31 |
-
image_processor = CLIPImageProcessor.from_pretrained(LLaVA_v0_Pipeline.CLIP_REPO, torch_dtype=self.clip_dtype)
|
| 32 |
-
vision_tower = CLIPVisionModel.from_pretrained(LLaVA_v0_Pipeline.CLIP_REPO, torch_dtype=self.clip_dtype).to(self.clip_device)
|
| 33 |
-
|
| 34 |
-
logger.info(f"LLaVA - Loading projector from {self.llava_projector_repo()} as {self.projector_dtype} on {self.projector_device}...")
|
| 35 |
-
projector_path = hf_hub_download(self.llava_projector_repo(), self.llava_projector_filename())
|
| 36 |
-
mm_projector = torch.nn.Linear(*self.llava_projector_shape())
|
| 37 |
-
projector_data = torch.load(projector_path)
|
| 38 |
-
mm_projector.weight = torch.nn.Parameter(projector_data['model.mm_projector.weight'].to(dtype=self.projector_dtype), False)
|
| 39 |
-
mm_projector.bias = torch.nn.Parameter(projector_data['model.mm_projector.bias'].to(dtype=self.projector_dtype), False)
|
| 40 |
-
mm_projector = mm_projector.to(self.projector_device)
|
| 41 |
-
|
| 42 |
-
logger.info(f"LLaVA supporting models loaded, took {time.time() - start_ts:.2f} seconds")
|
| 43 |
-
return image_processor, vision_tower, mm_projector
|
| 44 |
-
|
| 45 |
-
@staticmethod
|
| 46 |
-
def image_start() -> str:
|
| 47 |
-
return "<im_start>"
|
| 48 |
-
|
| 49 |
-
@staticmethod
|
| 50 |
-
def image_end() -> str:
|
| 51 |
-
return "<im_end>"
|
| 52 |
-
|
| 53 |
-
@staticmethod
|
| 54 |
-
def num_image_embeds() -> int:
|
| 55 |
-
return 256
|
| 56 |
-
|
| 57 |
-
@staticmethod
|
| 58 |
-
def embed_tokens(input_ids: torch.Tensor) -> torch.Tensor:
|
| 59 |
-
if hasattr(shared.model.model, 'embed_tokens'):
|
| 60 |
-
func = shared.model.model.embed_tokens
|
| 61 |
-
else:
|
| 62 |
-
func = shared.model.model.model.embed_tokens # AutoGPTQ case
|
| 63 |
-
|
| 64 |
-
return func(input_ids).to(shared.model.device, dtype=shared.model.dtype)
|
| 65 |
-
|
| 66 |
-
@staticmethod
|
| 67 |
-
def placeholder_embeddings() -> torch.Tensor:
|
| 68 |
-
return LLaVA_v0_Pipeline.embed_tokens(encode("<im_patch>"*256, add_bos_token=False)[0])
|
| 69 |
-
|
| 70 |
-
def embed_images(self, images: List[Image.Image]) -> torch.Tensor:
|
| 71 |
-
images = self.image_processor(images, return_tensors='pt')['pixel_values']
|
| 72 |
-
images = images.to(self.clip_device, dtype=self.clip_dtype)
|
| 73 |
-
|
| 74 |
-
with torch.no_grad():
|
| 75 |
-
image_forward_outs = self.vision_tower(images, output_hidden_states=True)
|
| 76 |
-
select_hidden_state_layer = -2
|
| 77 |
-
select_hidden_state = image_forward_outs.hidden_states[select_hidden_state_layer]
|
| 78 |
-
image_features = select_hidden_state[:, 1:].to(self.projector_device, dtype=self.projector_dtype)
|
| 79 |
-
image_features = self.mm_projector(image_features)
|
| 80 |
-
return image_features.to(shared.model.device, dtype=shared.model.dtype)
|
| 81 |
-
|
| 82 |
-
@staticmethod
|
| 83 |
-
@abstractmethod
|
| 84 |
-
def llava_projector_repo() -> str:
|
| 85 |
-
pass
|
| 86 |
-
|
| 87 |
-
@staticmethod
|
| 88 |
-
@abstractmethod
|
| 89 |
-
def llava_projector_filename() -> str:
|
| 90 |
-
pass
|
| 91 |
-
|
| 92 |
-
@staticmethod
|
| 93 |
-
@abstractmethod
|
| 94 |
-
def llava_projector_shape() -> Tuple[int, int]:
|
| 95 |
-
pass
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
class LLaVA_v0_13B_Pipeline(LLaVA_v0_Pipeline):
|
| 99 |
-
def __init__(self, params: dict) -> None:
|
| 100 |
-
super().__init__(params)
|
| 101 |
-
|
| 102 |
-
@staticmethod
|
| 103 |
-
def name() -> str:
|
| 104 |
-
return "llava-13b"
|
| 105 |
-
|
| 106 |
-
@staticmethod
|
| 107 |
-
def placeholder_token_id() -> int:
|
| 108 |
-
return 32000
|
| 109 |
-
|
| 110 |
-
@staticmethod
|
| 111 |
-
def llava_projector_shape() -> Tuple[int, int]:
|
| 112 |
-
return (1024, 5120)
|
| 113 |
-
|
| 114 |
-
@staticmethod
|
| 115 |
-
def llava_projector_filename() -> str:
|
| 116 |
-
return "mm_projector.bin"
|
| 117 |
-
|
| 118 |
-
@staticmethod
|
| 119 |
-
def llava_projector_repo() -> str:
|
| 120 |
-
return "liuhaotian/LLaVA-13b-delta-v0"
|
| 121 |
-
|
| 122 |
-
|
| 123 |
-
class LLaVA_v0_7B_Pipeline(LLaVA_v0_Pipeline):
|
| 124 |
-
def __init__(self, params: dict) -> None:
|
| 125 |
-
super().__init__(params)
|
| 126 |
-
|
| 127 |
-
@staticmethod
|
| 128 |
-
def name() -> str:
|
| 129 |
-
return "llava-7b"
|
| 130 |
-
|
| 131 |
-
@staticmethod
|
| 132 |
-
def placeholder_token_id() -> int:
|
| 133 |
-
return 32001
|
| 134 |
-
|
| 135 |
-
@staticmethod
|
| 136 |
-
def llava_projector_shape() -> Tuple[int, int]:
|
| 137 |
-
return (1024, 4096)
|
| 138 |
-
|
| 139 |
-
@staticmethod
|
| 140 |
-
def llava_projector_filename() -> str:
|
| 141 |
-
return "mm_projector.bin"
|
| 142 |
-
|
| 143 |
-
@staticmethod
|
| 144 |
-
def llava_projector_repo() -> str:
|
| 145 |
-
return "liuhaotian/LLaVA-7b-delta-v0"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/FKBaffour/Expresso_Customer_Churn_Prediction/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Expresso Customer Churn Prediction
|
| 3 |
-
emoji: 💩
|
| 4 |
-
colorFrom: red
|
| 5 |
-
colorTo: purple
|
| 6 |
-
sdk: streamlit
|
| 7 |
-
sdk_version: 1.17.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|