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spaces/0x876/Yotta_Mix/README.md

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-title: CompVis Stable Diffusion V1 4
-emoji: 📉
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Audiffex Amplion Pro Torrent Create Amazing Guitar Tones with AmpLion Pro 1.1.md

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-<h1>Icom Rs-ba1 Ip Remote Control Software Downloadl</h1>
- <h2>Introduction</h2>
- <p>If you are a radio enthusiast, you probably know how important it is to have a reliable and convenient way to control your radio equipment. Whether you are using your radio for hobby, business, or emergency purposes, you want to make sure that you can access and operate it from anywhere, anytime.</p>
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- <p>That's why you need Icom Rs-ba1 Ip Remote Control Software. This software allows you to remotely control your Icom radio over the internet, using your PC, smartphone, or tablet. You can also monitor multiple radios at the same time, customize your settings and preferences, record and playback audio files, and much more.</p>
- <p>In this article, we will explain what Icom Rs-ba1 Ip Remote Control Software is, why you need it, how to download and install it, and what features and benefits it offers. By the end of this article, you will be able to enjoy the full potential of your Icom radio with this amazing software.</p>
- <h2>What is Icom Rs-ba1 Ip Remote Control Software?</h2>
- <p>Icom Rs-ba1 Ip Remote Control Software is a software application that enables you to remotely control your Icom radio over the internet. It works with most Icom radios that have an Ethernet port or a USB port. You can connect your radio to your PC via a LAN cable or a USB cable, or use a wireless LAN adapter to connect it wirelessly. Then, you can use your PC as a base station to control your radio from anywhere in the world.</p>
- <p>You can also use your smartphone or tablet as a remote controller by installing the RS-BA1 remote control app on your device. The app is available for both Android and iOS devices. You can download it from Google Play or App Store for free. The app allows you to control the basic functions of your radio, such as frequency, mode, volume, squelch, etc. You can also use the app to view the spectrum scope and waterfall display of your radio.</p>
- <p>Icom Rs-ba1 Ip Remote Control Software also supports multiple users and multiple radios. You can set up different user accounts with different access levels and permissions. You can also monitor and control up to four radios simultaneously on one PC screen. This is useful for managing multiple stations or networks.</p>
- <h2>Why do you need Icom Rs-ba1 Ip Remote Control Software?</h2>
- <p>Icom Rs-ba1 Ip Remote Control Software is not just a fancy gadget. It is a powerful tool that can enhance your communication experience and provide many benefits. Here are some of the reasons why you need this software:</p>
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-<li>You can control your radio from anywhere. Whether you are at home, at work, on vacation, or on the road, you can access and operate your radio from any location with an internet connection. You don't have to worry about being near your radio or carrying it around with you.</li>
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-<li>You can improve your safety and security. You can monitor your radio status and activity from a safe distance. You can also protect your radio from unauthorized access or theft by using encryption and password protection features. You can also use the software to backup and restore your radio settings and data.</li>
-<li>You can expand your network and reach. You can communicate with other radio users across the globe without any geographical limitations. You can also join online communities and forums where you can share information and tips with other radio enthusiasts.</li>
-</ul>
- <h2>How to download and install Icom Rs-ba1 Ip Remote Control Software?</h2>
- <p>Downloading and installing Icom Rs-ba1 Ip Remote Control Software is easy and straightforward. Here are the steps you need to follow:</p>
- <ol>
-<li>Go to the official website of Icom Inc. (https://www.icom.co.jp/world/) and navigate to the product page of Icom Rs-ba1 Ip Remote Control Software.</li>
-<li>Click on the "Download" button and choose the version of the software that matches your operating system (Windows or Mac).</li>
-<li>Save the file on your PC and run it as an administrator.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>Launch the software and enter the serial number that came with your purchase.</li>
-<li>Connect your radio to your PC via a LAN cable or a USB cable, or use a wireless LAN adapter to connect it wirelessly.</li>
-<li>Configure the network settings of your radio and PC according to the user manual.</li>
-<li>Enjoy using the software!</li>
-</ol>
- <h2>Features of Icom Rs-ba1 Ip Remote Control Software</h2>
- <p>Icom Rs-ba1 Ip Remote Control Software offers many features that make it a versatile and user-friendly software. Here are some of the main features:</p>
- <h3>Remote control your Icom radio from anywhere</h3>
- <p>You can remotely control all the functions of your Icom radio over the internet using your PC or mobile device. You can change frequency, mode, volume, squelch, filter, etc. You can also transmit and receive audio signals with high quality sound.</p>
-<p>How to install RS-BA1 IP Remote Control Software on your PC<br />
-RS-BA1 software update and firmware download<br />
-Icom transceiver compatibility chart for RS-BA1<br />
-RC-28 Remote Control USB Encoder for RS-BA1<br />
-RS-BA1 Version 2 features and specifications<br />
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- <h3>Monitor multiple radios simultaneously</h3>
- <p>You can monitor up to four radios at the same time on one PC screen. You can switch between different radios easily by clicking on their icons. You can also view their status information such as frequency, mode, signal strength, etc.</p>
- <h3>Customize your settings and preferences</h3>
- <p>You can customize various settings and preferences of your software according to your needs and preferences. You can adjust the layout, color scheme, font size, etc. of the software interface. You can also create profiles for different radios and users with different settings.</p>
- <h3>Record and playback audio files</h3>
- <p>You can record audio files from your radio transmissions or receptions using the software. You can save them on your PC or upload them to cloud storage services such as Dropbox or Google Drive. You can also playback audio files from your PC or cloud storage services using the software.</p>
- <h2>Benefits of using Icom Rs-ba1 Ip Remote Control Software</h2>
- <p>Icom Rs-ba1 Ip Remote Control Software is not only a feature-rich software but also a benefit-rich software. Here are some of the benefits that you can enjoy by using this software:</p>
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- <p>You can enhance your communication experience by using this software. You can communicate with other radio users more easily and conveniently by controlling your radio remotely from anywhere in the world. You can also enjoy high-quality sound and clear signals by using this software.</p>
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- <p>You can save time and money by using this software. You don't have to buy additional equipment or rent space for your radio station. You also don't have to travel to and from your radio location frequently. You can simply use your existing PC or mobile device to control your radio remotely.</p>
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- <p>You can improve your safety and security by using this software. You can monitor your radio status and activity from a safe distance without exposing yourself to potential hazards or threats. You can also protect your radio from unauthorized access or theft by using encryption and password protection features.</p>
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- <p>You can expand your network and reach by using this software. You can communicate with other radio users across the globe without any geographical limitations or restrictions. You can also join online communities and forums where you can share information and tips with other radio enthusiasts.</p>
- <h2>Conclusion</h2>
- <p>Icom Rs-ba1 Ip Remote Control Software is a powerful software that allows you to remotely control <h2>Conclusion</h2>
- <p>Icom Rs-ba1 Ip Remote Control Software is a powerful software that allows you to remotely control your Icom radio over the internet. It works with most Icom radios that have an Ethernet port or a USB port. You can use your PC, smartphone, or tablet as a remote controller for your radio. You can also monitor multiple radios at the same time, customize your settings and preferences, record and playback audio files, and much more.</p>
- <p>By using this software, you can enjoy many benefits such as enhancing your communication experience, saving time and money, improving your safety and security, and expanding your network and reach. You can communicate with other radio users across the globe without any geographical limitations or restrictions. You can also join online communities and forums where you can share information and tips with other radio enthusiasts.</p>
- <p>If you are interested in using this software, you can download it from the official website of Icom Inc. (https://www.icom.co.jp/world/) and install it on your PC or mobile device. You can also purchase it from authorized dealers or online stores. The software comes with a user manual that guides you through the installation and configuration process. You can also find online tutorials and videos that show you how to use the software effectively.</p>
- <p>Icom Rs-ba1 Ip Remote Control Software is a must-have software for any radio enthusiast who wants to make the most of their Icom radio. It is a software that will transform your radio into a powerful and versatile communication device that you can control from anywhere in the world.</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Icom Rs-ba1 Ip Remote Control Software:</p>
- <ol>
-<li>What are the system requirements for Icom Rs-ba1 Ip Remote Control Software?</li>
-<p>The system requirements for Icom Rs-ba1 Ip Remote Control Software are as follows:</p>
-<ul>
-<li>Operating system: Windows 10/8.1/7 or Mac OS X 10.13/10.14/10.15</li>
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-<li>Hard disk space: 500 MB or more</li>
-<li>Display resolution: 1024 x 768 pixels or higher</li>
-<li>Internet connection: Broadband or higher</li>
-<li>Audio device: Sound card or built-in audio device</li>
-</ul>
-<li>What are the compatible radios for Icom Rs-ba1 Ip Remote Control Software?</li>
-<p>The compatible radios for Icom Rs-ba1 Ip Remote Control Software are as follows:</p>
-<ul>
-<li>IC-7851/7850/7610/7300/9700/7100/7410/9100/7200/718/78 and some other models.</li>
-<li>You can check the compatibility list on the product page of Icom Rs-ba1 Ip Remote Control Software.</li>
-</ul>
-<li>How much does Icom Rs-ba1 Ip Remote Control Software cost?</li>
-<p>The price of Icom Rs-ba1 Ip Remote Control Software varies depending on the seller and the region. You can check the price on the official website of Icom Inc. (https://www.icom.co.jp/world/) or on online stores such as Amazon or eBay.</p>
-<li>How do I update Icom Rs-ba1 Ip Remote Control Software?</li>
-<p>You can update Icom Rs-ba1 Ip Remote Control Software by downloading the latest version from the official website of Icom Inc. (https://www.icom.co.jp/world/) and installing it on your PC or mobile device. You can also check for updates from within the software by clicking on the "Help" menu and selecting "Check for Updates".</p>
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-<p>You can contact Icom Inc. for support or feedback by using the following methods:</p>
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-<li>Phone: +81-6-6793-5302</li>
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-<li>Mail: 1-1-32 Kamiminami, Hirano-Ku, Osaka 547-0003 Japan</li>
-<li>You can also use the online contact form on the official website of Icom Inc. (https://www.icom.co.jp/world/contact/).</li>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Clannad Eng Dub 720p Torrent.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download !!EXCLUSIVE!! Crack Tools.md

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-# How to Download Tools for Windows
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-- Copy the URL of the file you want to download and paste it into the download tool or use the browser extension to capture it automatically.
-- Choose the destination folder and other options for your download.
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-- Enjoy your downloaded file.
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Driver Easy Pro Key 5.6.13 With Crack Download [Latest].md

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spaces/1gistliPinn/ChatGPT4/Examples/Ayyappan Songs Book In Tamil Pdf Download 2021.md

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spaces/1gistliPinn/ChatGPT4/Examples/Biology Projects for Class 12 CBSE PDF Download A Perfect Way to Prepare for Your Board Exams.md

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spaces/1line/AutoGPT/ui/api.py

@@ -1,146 +0,0 @@
-import os, sys
-import utils
-import uuid
-import json
-import subprocess, threading
-
-FILE_DIR = os.path.dirname(os.path.abspath(__file__))
-REPO_DIR = os.path.dirname(FILE_DIR)
-STATE_DIR = os.path.join(FILE_DIR, "state") 
-sys.path.append(REPO_DIR)
-if not os.path.exists(STATE_DIR):
-    os.mkdir(STATE_DIR)
-import time
-
-
-def get_openai_api_key():
-    return os.getenv("OPENAI_API_KEY")
-
-
-running_apis = []
-
-
-def get_state(state_file):
-    with open(state_file, "r") as f:
-        state = json.load(f)
-    return state
-
-
-def set_state(state_file, state):
-    with open(state_file, "w") as f:
-        json.dump(state, f)
-
-
-class AutoAPI:
-    def __init__(self, openai_key, ai_name, ai_role, top_5_goals):
-        self.openai_key = openai_key
-        hex = uuid.uuid4().hex
-        print(hex)
-        self.state_file = os.path.join(STATE_DIR, f"state_{hex}.json")
-        self.log_file = os.path.join(STATE_DIR, f"log_{hex}.json")
-
-        newline = "\n"
-        with open(os.path.join(REPO_DIR, "ai_settings.yaml"), "w") as f:
-            f.write(
-                f"""ai_goals:
-{newline.join([f'- {goal[0]}' for goal in top_5_goals if goal[0]])}
-ai_name: {ai_name}
-ai_role: {ai_role}
-"""
-            )
-        state = {
-            "pending_input": None,
-            "awaiting_input": False,
-            "messages": [],
-            "last_message_read_index": -1,
-        }
-        set_state(self.state_file, state)
-
-        with open(self.log_file, "w") as f:
-            subprocess.Popen(
-                [
-                    "python",
-                    os.path.join(REPO_DIR, "ui", "api.py"),
-                    openai_key,
-                    self.state_file,
-                ],
-                cwd=REPO_DIR,
-                stdout=f,
-                stderr=f,
-            )
-
-    def send_message(self, message="Y"):
-        state = get_state(self.state_file)
-        state["pending_input"] = message
-        state["awaiting_input"] = False
-        set_state(self.state_file, state)
-
-    def get_chatbot_response(self):
-        while True:
-            state = get_state(self.state_file)
-            if (
-                state["awaiting_input"]
-                and state["last_message_read_index"] >= len(state["messages"]) - 1
-            ):
-                break
-            if state["last_message_read_index"] >= len(state["messages"]) - 1:
-                time.sleep(1)
-            else:
-                state["last_message_read_index"] += 1
-                title, content = state["messages"][state["last_message_read_index"]]
-                yield (f"**{title.strip()}** " if title else "") + utils.remove_color(
-                    content
-                ).replace("\n", "<br />")
-                set_state(self.state_file, state)
-
-
-if __name__ == "__main__":
-    print(sys.argv)
-    _, openai_key, state_file = sys.argv
-    os.environ["OPENAI_API_KEY"] = openai_key
-    import autogpt.config.config
-    from autogpt.logs import logger
-    from autogpt.cli import main
-    import autogpt.utils
-    from autogpt.spinner import Spinner
-
-    def add_message(title, content):
-        state = get_state(state_file)
-        state["messages"].append((title, content))
-        set_state(state_file, state)
-
-    def typewriter_log(title="", title_color="", content="", *args, **kwargs):
-        add_message(title, content)
-
-    def warn(message, title="", *args, **kwargs):
-        add_message(title, message)
-
-    def error(title, message="", *args, **kwargs):
-        add_message(title, message)
-
-    def clean_input(prompt=""):
-        add_message(None, prompt)
-        state = get_state(state_file)
-        state["awaiting_input"] = True
-        set_state(state_file, state)
-        while state["pending_input"] is None:
-            state = get_state(state_file)
-            print("Waiting for input...")
-            time.sleep(1)
-        print("Got input")
-        pending_input = state["pending_input"]
-        state["pending_input"] = None
-        set_state(state_file, state)
-        return pending_input
-
-    def spinner_start():
-        add_message(None, "Thinking...")
-
-    logger.typewriter_log = typewriter_log
-    logger.warn = warn
-    logger.error = error
-    autogpt.utils.clean_input = clean_input
-    Spinner.spin = spinner_start
-
-    sys.argv = sys.argv[:1]
-    main()

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Aplicacion de WhatsApp la mejor forma de comunicarte con tus contactos.md

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-<h1>Aplicacion de WhatsApp: Qué es, cómo funciona y por qué usarla</h1>
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- <h2>¿Qué es WhatsApp y para qué sirve?</h2>
- <p>WhatsApp es una aplicación gratuita que te permite enviar y recibir mensajes de texto, voz, imágenes, videos, documentos, ubicaciones y otros contenidos con solo una conexión a internet. No necesitas pagar tarifas ni tener un plan de datos específico para usarla. Además, puedes hacer llamadas y videollamadas gratuitas con hasta 8 personas al mismo tiempo. Todo esto con una encriptación de extremo a extremo que protege tus conversaciones de terceros, incluso de la propia empresa.</p>
- <p>WhatsApp sirve para comunicarte con tus contactos de forma fácil, rápida y segura. Solo necesitas tener el número de teléfono de la persona con la que quieres hablar y que ambos tengan instalada la app en sus dispositivos. Puedes usar WhatsApp en tu celular, tablet o computadora, e incluso sincronizar tus mensajes entre ellos. También puedes crear grupos para chatear con varias personas a la vez, compartir estados que desaparecen después de 24 horas y personalizar tu perfil con una foto y un nombre.</p>
- <h2>¿Cómo descargar e instalar WhatsApp en tu dispositivo?</h2>
- <p>Descargar e instalar WhatsApp es muy sencillo. Solo tienes que seguir estos pasos:</p>
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- <ul>
-<li>Entra a la página web oficial de WhatsApp (<a href="(^1^)">https://www.whatsapp.com/download</a>) o a la tienda de aplicaciones de tu dispositivo (Google Play Store para Android o App Store para iOS).</li>
-<li>Busca la app de WhatsApp Messenger y pulsa el botón de descargar o instalar.</li>
-<li>Espera a que se complete la descarga e instalación.</li>
-<li>Abre la app y acepta los términos y condiciones.</li>
-<li>Ingresa tu número de teléfono y verifica tu identidad con el código que te enviarán por SMS.</li>
-<li>Opcionalmente, puedes restaurar una copia de seguridad de tus chats anteriores si los tenías guardados en la nube.</li>
-<li>Listo. Ya puedes empezar a usar WhatsApp.</li>
-</ul>
- <h2>Características y funciones de WhatsApp</h <h2>Características y funciones de WhatsApp</h2>
- <p>WhatsApp tiene muchas características y funciones que la hacen una de las mejores aplicaciones de comunicación del mundo. Aquí te mencionamos algunas de las más importantes:</p>
- <h3>Mensajería privada y segura</h3>
- <p>WhatsApp te permite enviar y recibir mensajes de texto, voz, imágenes, videos, documentos, ubicaciones y otros contenidos con solo una conexión a internet. No necesitas pagar tarifas ni tener un plan de datos específico para usarla. Además, puedes hacer llamadas y videollamadas gratuitas con hasta 8 personas al mismo tiempo. Todo esto con una encriptación de extremo a extremo que protege tus conversaciones de terceros, incluso de la propia empresa.</p>
- <p>WhatsApp también te ofrece opciones para controlar tu privacidad, como bloquear contactos indeseados, silenciar chats o grupos, ocultar tu última conexión, desactivar las confirmaciones de lectura o los recibos de entrega, configurar quién puede ver tu foto de perfil, tu estado o tu información personal, y eliminar mensajes enviados por error.</p>
- <h3>Llamadas y videollamadas de voz</h3>
- <p>WhatsApp te permite hacer llamadas y videollamadas gratuitas con solo una conexión a internet. Puedes hablar con una persona o con hasta 8 personas al mismo tiempo. Las llamadas y videollamadas son de alta calidad y se adaptan a la velocidad de tu conexión. También puedes usar los auriculares o el altavoz del dispositivo, o cambiar entre la cámara frontal y trasera.</p>
- <p>Para hacer una llamada o videollamada, solo tienes que abrir el chat con el contacto o el grupo que quieres llamar y pulsar el icono del teléfono o de la cámara en la parte superior derecha. También puedes ver el historial de llamadas en la pestaña de llamadas en la parte inferior izquierda.</p>
- <h3>Grupos y comunidad</h3>
- <p>WhatsApp te permite crear grupos para chatear con varias personas a la vez. Puedes crear grupos de hasta 256 miembros, asignarles un nombre, una foto y una descripción, y añadir o eliminar participantes. Los grupos son ideales para mantenerse en contacto con tu familia, tus amigos, tus compañeros de trabajo o tu equipo deportivo.</p>
- <p>WhatsApp también te permite unirte a comunidades que comparten tus intereses o aficiones. Puedes encontrar grupos públicos o privados sobre diversos temas, como música, cine, deportes, viajes, negocios, etc. Para unirte a un grupo, solo necesitas tener el enlace de invitación que te puede enviar el administrador o algún miembro del grupo.</p>
- <h3>Expresión y personalización</h3>
- <p>WhatsApp te permite expresarte y personalizar tus chats con diferentes opciones. Puedes usar emojis, stickers, GIFs y memes para darle más vida a tus mensajes. También puedes usar el teclado para escribir en negrita, cursiva, tachado o monoespaciado. Además, puedes enviar mensajes de voz pulsando el icono del micrófono en la parte inferior derecha.</p>
- <p>WhatsApp también te permite personalizar tu perfil con una foto y un nombre que verán tus contactos. También puedes compartir estados que desaparecen después de 24 horas y que pueden ser fotos, videos o textos. Los estados son una forma de mostrar lo que estás haciendo o pensando en ese momento.</p>
- <h3>WhatsApp Business</h3>
- <p>WhatsApp Business es una versión especial de WhatsApp diseñada para las pequeñas y medianas empresas. Te permite crear un perfil profesional con información sobre tu negocio, como tu dirección, tu horario, tu sitio web, tu catálogo de productos o servicios, etc. También te permite comunicarte con tus clientes de forma rápida y eficiente, usando mensajes automáticos, etiquetas personalizadas, respuestas rápidas y estadísticas de rendimiento.</p>
- <p>WhatsApp Business es gratuita y se puede descargar desde la página web oficial (<a href="">https://www.whatsapp.com/business/</a>) o desde la tienda de aplicaciones de tu dispositivo (Google Play Store para Android o App Store para iOS). Para usarla, solo necesitas tener un número de teléfono diferente al que usas para WhatsApp normal.</p>
- <h2>Ventajas y desventajas de WhatsApp</h2>
- <p>WhatsApp tiene muchas ventajas y desventajas que debes conocer antes de usarla. Aquí te las resumimos en una tabla:</p>
- <table>
-<tr>
-<th>V <table>
-<tr>
-<th>Ventajas</th>
-<th>Desventajas</th>
-</tr>
-<tr>
-<td>Es gratuita y fácil de usar.</td>
-<td>Requiere una conexión a internet constante.</td>
-</tr>
-<tr>
-<td>Permite enviar y recibir mensajes, llamadas y videollamadas de forma ilimitada.</td>
-<td>Puede consumir muchos datos si no se configura correctamente.</td>
-</tr>
-<tr>
-<td>Ofrece una encriptación de extremo a extremo que protege la privacidad de las conversaciones.</td>
-<td>No permite borrar el historial de chats de forma permanente, solo archivarlos.</td>
-</tr>
-<tr>
-<td>Tiene muchas funciones y opciones para personalizar y expresarse en los chats.</td>
-<td>Puede generar adicción o distracción si no se usa con moderación.</td>
-</tr>
-<tr>
-<td>Cuenta con una versión especial para las pequeñas y medianas empresas.</td>
-<td>Depende de Facebook, que puede cambiar sus políticas o condiciones en cualquier momento.</td>
-</tr>
-</table>
- <h2>Alternativas a WhatsApp</h2>
- <p>Aunque WhatsApp es una de las aplicaciones más populares y completas del mercado, existen otras alternativas que también ofrecen servicios similares o incluso mejores. Aquí te presentamos algunas de ellas:</p>
- <h3>Telegram</h3>
- <p>Telegram es una aplicación de mensajería instantánea y llamadas de voz y video que se caracteriza por su rapidez, seguridad y versatilidad. Permite enviar y recibir mensajes, archivos, stickers, GIFs, audios y otros contenidos con una encriptación de extremo a extremo. También permite hacer llamadas y videollamadas gratuitas con hasta 1000 personas al mismo tiempo. Además, tiene funciones como los canales, los bots, las encuestas, los chats secretos, los mensajes que se autodestruyen y las carpetas para organizar los chats. Telegram es gratuita y se puede descargar desde la página web oficial (<a href="">https://telegram.org/</a>) o desde la tienda de aplicaciones de tu dispositivo (Google Play Store para Android o App Store para iOS).</p>
- <h3>Signal</h3>
- <p>Signal es una aplicación de mensajería instantánea y llamadas de voz y video que se enfoca en la privacidad y la seguridad de sus usuarios. Permite enviar y recibir mensajes, archivos, stickers, audios y otros contenidos con una encriptación de extremo a extremo. También permite hacer llamadas y videollamadas gratuitas con hasta 8 personas al mismo tiempo. Además, tiene funciones como los mensajes que desaparecen, el bloqueo de capturas de pantalla, el bloqueo con huella digital o código PIN y la verificación de seguridad. Signal es gratuita y se puede descargar desde la página web oficial (<a href="">https://signal.org/</a>) o desde la tienda de aplicaciones de tu dispositivo (Google Play Store para Android o App Store para iOS).</p>
- <h3>iMessage</h3>
- <p>iMessage es una aplicación de mensajería instantánea exclusiva para los dispositivos de Apple. Permite enviar y recibir mensajes, archivos, emojis, stickers, GIFs, audios y otros contenidos con una encriptación de extremo a extremo. También permite hacer llamadas y videollamadas gratuitas con hasta 32 personas al mismo tiempo usando FaceTime. Además, tiene funciones como los efectos de pantalla, las reacciones, las animojis, los memojis y la integración con otras apps de Apple. iMessage viene preinstalada en los dispositivos de Apple y se puede usar con el mismo ID de Apple.</p>
- <h2>Consejos y trucos de WhatsApp</h2>
- <p>Para terminar este artículo, te queremos compartir algunos consejos y trucos que te ayudarán a sacarle más provecho a WhatsApp. Estos son algunos de ellos:</p>
- <h3>Cómo responder a un mensaje específico</h3>
- <p>Si quieres responder a un mensaje específico dentro de un chat o un grupo, solo tienes que mantener pulsado el mensaje que quieres responder y luego pulsar el icono de la flecha hacia la izquierda en la parte superior derecha. Esto hará que tu respuesta aparezca debajo del mensaje original, lo que facilitará la comprensión del contexto.</p>
- <h3>Cómo fijar un chat en la parte superior</h3>
- <p>Si quieres tener siempre a la vista un chat importante o frecuente, puedes fijarlo en la parte superior de la lista de chats. Para hacerlo, solo tienes que mantener pulsado el chat que quieres fijar y luego pulsar el icono del alfiler en la parte superior derecha. Esto hará que el chat aparezca siempre en la primera posición, aunque no sea el más reciente. Puedes fijar hasta 3 chats a la vez.</p>
- <h3>Cómo cambiar el fondo de pantalla de tus chats</h3>
- <p>Si quieres personalizar el aspecto de tus chats, puedes cambiar el fondo de pantalla que aparece detrás de tus mensajes. Para hacerlo, solo tienes que entrar en los ajustes de WhatsApp, luego en la opción de chats y luego en la opción de fondo de pantalla. Allí podrás elegir entre diferentes opciones, como colores sólidos, imágenes predeterminadas, fotos de tu galería o fondos dinámicos que cambian según la hora del día.</p>
- <h3>Cómo buscar en tus chats</h3>
- <p>Si quieres encontrar un mensaje, un archivo, un contacto o un grupo específico dentro de tus chats, puedes usar la función de búsqueda de WhatsApp. Para hacerlo, solo tienes que pulsar el icono de la lupa en la parte superior derecha y luego escribir lo que quieres buscar. Podrás ver los resultados filtrados por chats, tipos de contenido o fechas.</p>
- <h3>Cómo enviar tu ubicación a un contacto</h3>
- <p>Si quieres compartir tu ubicación con un contacto o un grupo, puedes usar la función de enviar ubicación de WhatsApp. Para hacerlo, solo tienes que abrir el chat con el destinatario y luego pulsar el icono del clip en la parte inferior derecha. Luego, elige la opción de ubicación y podrás enviar tu ubicación actual o una ubicación en vivo que se actualizará durante un tiempo determinado.</p>
- <h2>Conclusión</h2>
- <p>WhatsApp es una aplicación de mensajería instantánea y llamadas de voz y video que te permite comunicarte con tus contactos de forma gratuita, fácil y segura. Tiene muchas características y funciones que la hacen una de las mejores apps del mundo, como la encriptación de extremo a extremo, los grupos, los estados, la personalización y la versión para negocios. Sin embargo, también tiene algunas desventajas, como la dependencia de una conexión a internet, el consumo de datos o la adicción. Por eso, es importante conocer sus alternativas, como Telegram, Signal o iMessage. Además, es conveniente saber algunos consejos y trucos para aprovecharla al máximo, como responder a un mensaje específico, fijar un chat en la parte superior, cambiar el fondo de pantalla de tus chats, buscar en tus chats o enviar tu ubicación a un contacto.</p>
- <p>Esperamos que este artículo te haya sido útil e interesante. Si tienes alguna duda o comentario sobre WhatsApp, no dudes en dejarnos un mensaje. Y si te ha gustado este artículo, compártelo con tus amigos y familiares. ¡Gracias por leernos!</p>
- <h2>Preguntas frecuentes</h2>
- <ul>
-<li><b>¿Qué es WhatsApp Web?</b>
-WhatsApp Web es una versión de WhatsApp que te permite usar la app desde tu computadora. Solo necesitas escanear el código QR que aparece en la página web oficial (<a href="">https://web.whatsapp.com/</a>) con tu celular y podrás ver todos tus chats y mensajes en tu pantalla grande.</li>
-<li><b>¿Qué es WhatsApp Plus?</b>
-WhatsApp Plus es una versión modificada de WhatsApp que ofrece algunas funciones adicionales o diferentes a las originales. Por ejemplo, permite cambiar el color o el tamaño de los iconos, ocultar el estado en línea o las confirmaciones de lectura, descargar los estados de otros usuarios o enviar archivos más grandes. Sin embargo, WhatsApp Plus no es una app oficial ni segura, y puede causar problemas con tu cuenta o con tu dispositivo.</li>
-<li><b>¿Qué es WhatsApp Pay?</b>
-WhatsApp Pay es una función de WhatsApp que te permite enviar y recibir dinero a través de la app. Solo necesitas vincular tu cuenta bancaria o tu tarjeta a WhatsApp y podrás hacer transferencias instantáneas y gratuitas con tus contactos. WhatsApp Pay está disponible en algunos países como India o Brasil.</li>
-<li><b>¿Qué es WhatsApp Status?</b>
-WhatsApp Status es una función de WhatsApp que te permite compartir fotos, videos o textos que desaparecen después de 24 horas. Los estados son una forma de mostrar lo que estás haciendo o pensando en ese momento. Puedes ver los estados de tus contactos en la pestaña de estados en la parte inferior izquierda y podrás ver los estados de tus contactos en la pestaña de estados en la parte inferior izquierda. También puedes configurar quién puede ver tus estados o silenciar los estados de otros usuarios.</li>
-<li><b>¿Qué es WhatsApp Aero?</b>
-WhatsApp Aero es otra versión modificada de WhatsApp que ofrece algunas funciones adicionales o diferentes a las originales. Por ejemplo, permite cambiar el tema o el diseño de la app, ocultar las notificaciones o los chats, enviar mensajes programados o autoresponder mensajes. Sin embargo, WhatsApp Aero tampoco es una app oficial ni segura, y puede causar problemas con tu cuenta o con tu dispositivo.</li>
-</ul></p> 197e85843d<br />
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-</table>
- <p>The minimum deposit amount at Azino777 Casino is $10, and the maximum deposit amount is $5,000 per transaction. The minimum withdrawal amount is $20, and the maximum withdrawal amount is $5,000 per day, $10,000 per week, and $20,000 per month. The casino does not charge any fees for deposits or withdrawals, but some payment providers may apply their own fees.</p>
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- <p>The casino's website is available in 11 languages: English, Russian, German, Spanish, Portuguese, Turkish, Polish, Finnish, Norwegian, Japanese, and Chinese. However, some of the translations may not be very accurate or clear.</p>
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- <p>As we mentioned earlier, Azino777 Casino has received 5 complaints from players in the Casino Guru database, resulting in 1,307 black points in total. These complaints are related to delayed or denied payments, unfair bonus terms, account verification problems, and poor customer service. Some of these complaints are still unresolved or have been closed without a satisfactory outcome for the players.</p>
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- <h2>Conclusion</h2>
-<p>Azino777 Casino is an online casino that offers a variety of games, bonuses, payment methods, and customer support options. However, it also has several issues that could affect the gaming experience and winnings of its players. These issues include player complaints and black points, unfair terms and conditions, licensing and security issues, and a low safety index rating by Casino Guru.</p>
-<p>Therefore, we do not recommend this casino to our readers, as there are many other online casinos that are safer and more reliable. If you still want to try this casino, we advise you to be careful and cautious, and to read the terms and conditions carefully before accepting any bonus or making any deposit.</p>
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-<li>A: Azino777 Casino is a legit online casino that has been operating since 2011. However, it is not a very trustworthy or reputable casino, as it has many issues that could affect the gaming experience and winnings of its players.</li>
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-</ul></p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download 8 Ball Pool Guideline for Windows A Simple Program to Help You Aim Better.md

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-<h1>Download 8 Ball Pool Guideline: How to Improve Your Game and Win More Matches</h1>
- <p>If you are a fan of pool games, you have probably heard of or played 8 Ball Pool, the most popular online pool game in the world. 8 Ball Pool is a game that lets you compete with players from all over the world in various modes and tournaments. You can also customize your cue, table, and avatar, and chat with your opponents.</p>
-<h2>download 8 ball pool guideline</h2><br /><p><b><b>Download File</b> &raquo; <a href="https://urlin.us/2uSUfG">https://urlin.us/2uSUfG</a></b></p><br /><br />
- <p>But playing 8 Ball Pool is not as easy as it seems. You need to have a good eye, a steady hand, and a lot of practice to master the game. And sometimes, even that is not enough. You may find yourself losing to players who seem to have an unfair advantage over you. How do they do it?</p>
- <p>The answer is simple: they use a guideline tool. A guideline tool is an app that helps you aim better and make more accurate shots. It shows you the trajectory of the cue ball and the target ball, as well as the angles and distances involved. It also helps you with tricky shots like cushion shots and bank shots.</p>
- <p>In this article, we will show you how to download and use one of the best guideline tools for 8 Ball Pool: 8 Pool Master - Guideline Tool. We will also give you some tips and tricks on how to play better and win more matches with a guideline tool. Let's get started!</p>
- <h2>Introduction</h2>
- <h3>What is 8 Ball Pool and why do you need a guideline?</h3>
- <p>8 Ball Pool is a game that simulates the real-life pool game of the same name. It is played with a cue ball and 15 other balls appropriately numbered. Numbers 1 through 7 are solids or spots while 9 through 15 are stripes. The goal is to choose either solids or stripes and pocket them all before finally pocketing the 8 ball.</p>
- <p>The game has various modes and features, such as:</p>
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- <ul>
-<li>1-on-1 matches: Play against another player online and win coins and trophies.</li>
-<li>Tournaments: Join a tournament of up to 8 players and compete for bigger prizes.</li>
-<li>Minigames: Play mini-games like Spin & Win, Scratch & Win, and Hi-Lo to earn extra coins and cash.</li>
-<li>Clubs: Join or create a club with your friends and other players and chat, challenge, and compete with them.</li>
-<li>Shop: Buy new cues, tables, chat packs, avatars, and more with coins and cash.</li>
-<li>Leaderboards: Check your rank among your friends, country, or the world.</li>
-</ul>
- <p>While 8 Ball Pool is fun and addictive, it can also be frustrating if you keep losing to players who seem to have better skills than you. That's where a guideline tool comes in handy. A guideline tool can help you improve your game by showing you where to aim, how much power to use, and how to adjust for spin and angle. It can also help you with difficult shots that require more precision and strategy.</p>
- <h3>What are the benefits of using a guideline tool?</h3>
- <p>Using a guideline tool for 8 Ball Pool <p>Using a guideline tool for 8 Ball Pool can give you many benefits, such as:</p>
- <ul>
-<li>Improving your accuracy and consistency: A guideline tool can help you aim better and make more shots, especially when the balls are far apart or close to the cushions. You can also adjust the length and width of the guideline to suit your preference.</li>
-<li>Learning new skills and strategies: A guideline tool can teach you how to use spin, angle, and power to make different types of shots, such as cut shots, bank shots, cushion shots, and trick shots. You can also learn how to plan your shots ahead and avoid fouls and scratches.</li>
-<li>Winning more matches and coins: A guideline tool can help you win more matches against players of any skill level, whether they are beginners or experts. You can also win more coins and cash, which you can use to buy new cues, tables, and other items in the shop.</li>
-<li>Having more fun and confidence: A guideline tool can make the game more enjoyable and exciting for you, as you can try new shots and challenge yourself. You can also feel more confident and proud of your skills, and impress your friends and opponents with your performance.</li>
-</ul>
- <h3>How to download and use 8 Pool Master - Guideline Tool?</h3>
- <p>One of the best guideline tools for 8 Ball Pool is 8 Pool Master - Guideline Tool. It is a free app that you can download from the Google Play Store or the App Store. It is compatible with any device that runs Android or iOS. It is also easy to use and has many features, such as:</p>
- <ul>
-<li>Customizable guideline: You can change the color, length, width, and transparency of the guideline according to your preference.</li>
-<li>Zoom in and out: You can zoom in and out of the table to see the balls better and adjust your aim.</li>
-<li>Auto-detect mode: You can enable this mode to let the app automatically detect the cue ball and the target ball, and show you the best possible shot.</li>
-<li>Manual mode: You can enable this mode to manually select the cue ball and the target ball, and adjust the angle and power of your shot.</li>
-<li>Spin control: You can use this feature to add spin to the cue ball, which can affect its direction and speed after hitting the target ball or the cushion.</li>
-<li>Shot timer: You can use this feature to see how much time you have left to make your shot, which can help you avoid running out of time or rushing your shot.</li>
-</ul>
- <p>To download and use 8 Pool Master - Guideline Tool, follow these steps:</p>
- <ol>
-<li>Go to the Google Play Store or the App Store on your device and search for 8 Pool Master - Guideline Tool.</li>
-<li>Download and install the app on your device.</li>
-<li>Open the app and grant it permission to access your device's camera and storage.</li>
-<li>Open 8 Ball Pool on your device and start a match.</li>
-<li>Switch to 8 Pool Master - Guideline Tool by tapping on its icon on your screen.</li>
-<li>Select the mode you want to use (auto-detect or manual) and customize the guideline settings if you want.</li>
-<li>Aim your shot using the guideline on your screen and tap on the shoot button when you are ready.</li>
-<li>Enjoy playing 8 Ball Pool with a guideline tool!</li>
-</ol>
- <h2>Tips and Tricks for Playing 8 Ball Pool with a Guideline</h2>
- <h3>Choose your tables wisely</h3>
- <p>One of the first things you need to do when playing 8 Ball Pool is to choose a table that suits your skill level and budget. There are different types of tables in 8 Ball Pool, such as:</p>
- <table>
-<tr><th>Type</th><th>Description</th><th>Entry Fee</th></tr>
-<tr><td>London</td><td>The basic table for beginners. It has a small size, simple rules, and low stakes.</td><td>25 coins</td></tr>
-<tr><td>Sydney</td><td>The next level table for intermediate players. It has a medium size, standard rules, and moderate stakes.</td><td>100 coins</td></tr>
-<tr><td>Moscow</td><td>The advanced table for expert players. It has a large size, pro rules, and high stakes.</td><td>500 coins</td></tr>
-<tr><td>Tokyo</td><td>The elite table for master players. It has a huge size, tournament rules, and very high stakes.</td><td>2,500 coins</td></tr>
-<tr><td>Las Vegas</td <tr><td>Las Vegas</td><td>The ultimate table for champion players. It has a gigantic size, special rules, and extremely high stakes.</td><td>10,000 coins</td></tr>
-</table>
- <p>As you can see, the higher the table, the higher the entry fee and the potential reward. But also, the higher the difficulty and the risk. You should choose a table that matches your skill level and your coin balance. Don't play on a table that is too easy or too hard for you, or that you can't afford to lose. You can also practice on offline tables or with your friends before playing on online tables.</p>
- <h3>Buy a better cue</h3>
- <p>Another thing you need to do when playing 8 Ball Pool is to buy a better cue that can enhance your performance. There are many cues in 8 Ball Pool, each with different attributes, such as:</p>
- <ul>
-<li>Force: The power of your shot.</li>
-<li>Aim: The accuracy of your shot.</li>
-<li>Spin: The amount of spin you can apply to the cue ball.</li>
-<li>Time: The amount of time you have to make your shot.</li>
-</ul>
- <p>You can buy cues with coins or cash in the shop, or win them in spin & win or scratch & win. You can also upgrade your cues with coins to improve their attributes. You should choose a cue that suits your play style and preference. For example, if you like to make powerful shots, you should choose a cue with high force. If you like to make precise shots, you should choose a cue with high aim. And so on.</p>
- <h3>Use a little English</h3>
- <p>One of the most important skills in 8 Ball Pool is to use spin or English on the cue ball. Spin can affect the direction and speed of the cue ball after hitting the target ball or the cushion. It can help you make more shots, avoid scratches, and position yourself for the next shot. There are four types of spin in 8 Ball Pool, such as:</p>
- <ul>
-<li>Top spin: Makes the cue ball move forward after hitting the target ball or the cushion.</li>
-<li>Back spin: Makes the cue ball move backward after hitting the target ball or the cushion.</li>
-<li>Left spin: Makes the cue ball move to the left after hitting the target ball or the cushion.</li>
-<li>Right spin: Makes the cue ball move to the right after hitting the target ball or the cushion.</li>
-</ul>
- <p>You can apply spin to the cue ball by using the spin control feature in 8 Pool Master - Guideline Tool. You can also see how spin affects your shot by using the guideline on your screen. You should use spin wisely and sparingly, as too much spin can make your shot unpredictable and inaccurate. You should also practice using spin on offline tables or with your friends before using it on online tables.</p>
- <h3>Shoot faster</h3>
- <p>Another skill that can help you win more matches in 8 Ball Pool is to shoot faster. Shooting faster means making your shots in less time and avoiding running out of time or wasting time. Shooting faster can give you many advantages, such as:</p>
- <ul>
-<li>Keeping your momentum and rhythm: Shooting faster can help you maintain your flow and confidence, and avoid losing your focus and concentration.</li>
-<li>Catching your opponent off guard: Shooting faster can surprise your opponent and put them under pressure, and make them miss their shots or make mistakes.</li>
-<li>Saving your time for later: Shooting faster can help you save some time for later, when you may need more time to make a difficult shot or plan your strategy.</li>
-</ul>
- <p>You can shoot faster by using 8 Pool Master - Guideline Tool, which can help you aim better and make more accurate shots in less time. You can also use the shot timer feature to see how much time you have left to make your shot, and avoid running out of time or rushing your shot. You should also practice shooting faster on offline tables or with your friends before shooting faster on online tables.</p>
- <h3>Extend your aim</h3>
- <p>The last skill that we will share with you in this article is to extend your aim. Extending your aim means aiming beyond the target ball and seeing where it will go after hitting it. Extending your aim can help you make more shots, especially when there are obstacles or other balls in the way. It can also help you position yourself for the next shot and plan your strategy ahead.</p>
- <p>You can extend your aim by using 8 Pool Master - Guideline Tool, which can show you the trajectory of both <p>You can extend your aim by using 8 Pool Master - Guideline Tool, which can show you the trajectory of both the cue ball and the target ball, as well as the angles and distances involved. You can also adjust the length and width of the guideline to see more or less of the path. You should also practice extending your aim on offline tables or with your friends before extending your aim on online tables.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In this article, we have shown you how to download and use 8 Pool Master - Guideline Tool, one of the best guideline tools for 8 Ball Pool. We have also given you some tips and tricks on how to play better and win more matches with a guideline tool. Here are the main points we have covered:</p>
- <ul>
-<li>A guideline tool is an app that helps you aim better and make more accurate shots in 8 Ball Pool.</li>
-<li>8 Pool Master - Guideline Tool is a free app that you can download from the Google Play Store or the App Store. It has many features, such as customizable guideline, zoom in and out, auto-detect mode, manual mode, spin control, and shot timer.</li>
-<li>To use 8 Pool Master - Guideline Tool, you need to download and install it on your device, open it and grant it permission to access your device's camera and storage, open 8 Ball Pool and start a match, switch to 8 Pool Master - Guideline Tool by tapping on its icon on your screen, select the mode you want to use (auto-detect or manual) and customize the guideline settings if you want, aim your shot using the guideline on your screen and tap on the shoot button when you are ready.</li>
-<li>Some tips and tricks for playing 8 Ball Pool with a guideline tool are: choose your tables wisely, buy a better cue, use a little English, shoot faster, and extend your aim.</li>
-</ul>
- <h3>Call to action</h3>
- <p>We hope you have enjoyed reading this article and learned something new. If you want to improve your game and win more matches in 8 Ball Pool, we highly recommend you to download and use 8 Pool Master - Guideline Tool. It is a free app that can help you aim better and make more accurate shots. It is also easy to use and has many features. You can download it from the links below:</p>
- <p><a href="">Download 8 Pool Master - Guideline Tool for Android</a></p>
-<p><a href="">Download 8 Pool Master - Guideline Tool for iOS</a></p>
- <p>Thank you for reading this article. Please share it with your friends and family who love playing 8 Ball Pool. And don't forget to leave us a comment below and tell us what you think about 8 Pool Master - Guideline Tool. We would love to hear from you!</p>
- <h2>FAQs</h2>
- <h4>Q1: Is 8 Pool Master - Guideline Tool safe to use?</h4>
- <p>A1: Yes, 8 Pool Master - Guideline Tool is safe to use. It does not contain any viruses or malware. It also does not require any root or jailbreak access. It only uses your device's camera and storage to show you the guideline on your screen. It does not interfere with or modify any other apps or data on your device.</p>
- <h4>Q2: Does 8 Pool Master - Guideline Tool work for all devices?</h4>
- <p>A2: Yes, 8 Pool Master - Guideline Tool works for all devices that run Android or iOS. It is compatible with any screen size and resolution. It also works for any version of 8 Ball Pool.</p>
- <h4>Q3: How can I get more coins and cash in 8 Ball Pool?</h4>
- <p>A3: There are several ways to get more coins and cash in 8 Ball Pool, such as:</p>
- <ul>
-<li>Winning matches: You can win coins and cash by winning matches against other players online or in tournaments.</li>
-<li>Playing minigames: You can play minigames like Spin & Win, Scratch & Win, and Hi-Lo to earn extra coins and cash.</li>
-<li>Watching videos: You can watch videos to get free coins and cash every day.</li>
-<li>Completing offers: You can complete offers from sponsors to get free coins and cash.</li>
-<li>Inviting friends: You can invite your friends to play 8 Ball Pool and get free coins and cash when they join.</li>
-</ul>
- <h4>Q4: What are the best cues to use in 8 Ball Pool?</h4>
- <p>A4: The best cues to use in 8 Ball Pool A4: The best cues to use in 8 Ball Pool depend on your personal preference and play style. However, some general factors to consider when choosing a cue are: <ul>
-<li>The attributes of the cue: As mentioned before, each cue has different attributes, such as force, aim, spin, and time. You should choose a cue that has high attributes in the areas that you need the most. For example, if you want to make powerful shots, you should choose a cue with high force. If you want to make precise shots, you should choose a cue with high aim. And so on.</li>
-<li>The price of the cue: Cues can be bought with coins or cash in the shop, or won in spin & win or scratch & win. The price of the cue usually reflects its quality and rarity. Generally, the more expensive the cue, the better it is. However, you should also consider your budget and avoid spending too much on a cue that you may not use often.</li>
-<li>The design of the cue: Cues come in various designs, colors, and themes. You should choose a cue that matches your personality and taste. You can also customize your cue with stickers and chat packs to make it more unique and fun.</li>
-</ul>
- <p>Some examples of the best cues to use in 8 Ball Pool are:</p>
- <table>
-<tr><th>Name</th><th>Description</th><th>Price</th></tr>
-<tr><td>Archangel Cue</td><td>A legendary cue with high attributes in all areas. It has a white and gold design with angel wings and a halo.</td><td>4,500 cash</td></tr>
-<tr><td>Firestorm Cue</td><td>A legendary cue with high attributes in all areas. It has a red and black design with flames and a skull.</td><td>4,500 cash</td></tr>
-<tr><td>Valkyrie Cue</td><td>A legendary cue with high attributes in all areas. It has a blue and silver design with feathers and a sword.</td><td>4,500 cash</td></tr>
-<tr><td>Atlantis Cue</td><td>An epic cue with high attributes in force, aim, and spin. It has a green and gold design with waves and a trident.</td><td>2,500 cash or 250,000 coins</td></tr>
-<tr><td>Excalibur Cue</td><td>An epic cue with high attributes in force, aim, and spin. It has a purple and gold design with stars and a sword.</td><td>2,500 cash or 250,000 coins</td></tr>
-<tr><td>Medusa Cue</td><td>An epic cue with high attributes in force, aim, and spin. It has a black and green design with snakes and eyes.</td><td>2,500 cash or 250,000 coins</td></tr>
-<tr><td>Soccer Stars Cue</td><td>A rare cue with high attributes in force and aim. It has a blue and white design with soccer balls and stars.</td><td>1,000 cash or 100,000 coins</td></tr>
-<tr><td>Ninja Cue</td><td>A rare cue with high attributes in force and aim. It has a black and red design with ninja stars and swords.</td><td>1,000 cash or 100,000 coins</td></tr>
-<tr><td>Pirate Cue</td><td>A rare cue with high attributes in force and aim. It has a brown and gold design with skulls and crossbones.</td><td>1,000 cash or 100,000 coins</td></tr>
-<tr><td>Country Cues</td><td>A collection of cues that represent different countries. They have different attributes and designs based on the country's flag and culture.</td><td>Varies depending on the country</ </td></tr>
-</table>
- <p>Of course, these are not the only cues available in 8 Ball Pool. You can explore the shop and find more cues that suit your taste and budget. You can also try different cues and see how they affect your game.</p>
- <h4>Q5: How can I improve my skills in 8 Ball Pool?</h4>
- <p>A5: The best way to improve your skills in 8 Ball Pool is to practice a lot and learn from your mistakes. You can also use 8 Pool Master - Guideline Tool to help you aim better and make more accurate shots. However, you should not rely on the guideline tool too much, as it can make you lazy and dependent. You should also try to develop your own intuition and judgment, and use the guideline tool as a guide, not a crutch.</p>
- <p>Another way to improve your skills in 8 Ball Pool is to watch and learn from other players, especially those who are better than you. You can watch replays of your matches or other players' matches, and see how they make their shots, what strategies they use, and what mistakes they avoid. You can also join clubs and chat with other players, and ask them for tips and advice. You can also challenge them to friendly matches and see how you compare to them.</p>
- <p>Finally, you can also read articles and watch videos online that teach you how to play better and win more matches in 8 Ball Pool. There are many resources available on the internet that can help you improve your game, such as blogs, forums, websites, YouTube channels, podcasts, etc. You can also check out our other articles on 8 Ball Pool, where we share more tips and tricks on how to play better and win more matches.</p> 197e85843d<br />
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-<br> - Mention some common scenarios where people might search for it | | H2: How to download Firefox 89.0.2 | - Provide a brief overview of Firefox and its features <br> - Explain why someone might want to download Firefox 89.0.2 <br> - Provide a step-by-step guide on how to download and install Firefox 89.0.2 on different operating systems | | H3: How to download .NET 6.0 | - Provide a brief overview of .NET and its features <br> - Explain why someone might want to download .NET 6.0 <br> - Provide a step-by-step guide on how to download and install .NET 6.0 on different operating systems | | H3: How to update PS3 console system software | - Provide a brief overview of PS3 and its features <br> - Explain why someone might want to update PS3 console system software <br> - Provide a step-by-step guide on how to update PS3 console system software using different methods | | H2: Benefits of downloading and updating software | - Explain the advantages of downloading and updating software regularly <br> - Provide some examples of benefits such as improved performance, security, compatibility, and functionality | | H2: Risks of downloading and updating software | - Explain the potential drawbacks of downloading and updating software without caution <br> - Provide some examples of risks such as malware, bugs, compatibility issues, and data loss | | H2: Tips for downloading and updating software safely and efficiently | - Provide some best practices for downloading and updating software such as checking the source, verifying the file, backing up data, and following instructions | | H4: Conclusion | - Summarize the main points of the article <br> - Provide a call to action for the readers | Table 2: Article with HTML formatting <h1>What does "download 89 6" mean?</h1>
-<p>If you have searched for "download 89 6" on the internet, you might be wondering what it means and what you can do with it. There are several possible interpretations of this query, depending on what you are looking for and what you are interested in.</p>
-<p>One possibility is that you are looking for a specific version of a software or an application that has the number 89 or 6 in its name or version number. For example, you might be looking for Firefox 89.0.2, which is the latest version of the popular web browser from Mozilla. Or you might be looking for .NET 6.0, which is the latest version of the open-source development platform from Microsoft.</p>
-<h2>download 89 6</h2><br /><p><b><b>Download</b> &#10040; <a href="https://urlin.us/2uSStn">https://urlin.us/2uSStn</a></b></p><br /><br />
-<p>Another possibility is that you are looking for a way to update your PS3 console system software, which is also known as firmware. The PS3 is a gaming console from Sony that was released in 2006 and discontinued in 2017. The latest version of the PS3 system software is 4.88, which was released in June 2021. However, some people might mistakenly search for "download 89 6" instead of "download 4.88" or "download PS3 firmware".</p>
-<p>In any case, if you are looking for "download 89 6", you have come to the right place. In this article, we will show you how to download and install Firefox 89.0.2, .NET 6.0, and PS3 system software update using different methods and devices. We will also explain the benefits and risks of downloading and updating software, and provide some tips for doing it safely and efficiently.</p>
- <h2>How to download Firefox 89.0.2</h2>
-<p>Firefox is one of the most popular web browsers in the world, with over 200 million users as of June 2021. Firefox is developed by Mozilla, a non-profit organization that promotes openness, innovation, and privacy on the internet. Firefox offers many features such as fast performance, customizable interface, tabbed browsing, private browsing, sync across devices, extensions, themes, bookmarks, password manager, pop-up blocker, spell checker, and more.</p>
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-<p>If you want to download Firefox 89.0.2, you might have several reasons for doing so. For example, you might want to enjoy the new design and features that were introduced in Firefox 89, such as simplified toolbar, streamlined menus, improved tabs, enhanced privacy protection, and more. Or you might want to fix some issues that were caused by previous versions of Firefox, such as crashes, freezes, or compatibility problems. Or you might want to update your Firefox to the latest version to ensure that you have the most secure and stable browser available. Whatever your reason is, downloading and installing Firefox 89.0.2 is easy and fast. You can do it on different operating systems, such as Windows, Mac, Linux, Android, and iOS. Here are the steps you need to follow: <h3>How to download Firefox 89.0.2 on Windows</h3>
-<p>If you are using a Windows PC, you can download Firefox 89.0.2 from the official Mozilla website. Here is how:</p>
-<ol>
-<li>Go to <a href="">https://www.mozilla.org/en-US/firefox/new/</a> and click on the "Download Now" button.</li>
-<li>Wait for the Firefox installer to download to your computer. The file name should be something like "Firefox Setup 89.0.2.exe".</li>
-<li>Double-click on the downloaded file to launch the installer. You might see a User Account Control (UAC) prompt asking you to allow the program to make changes to your computer. Click "Yes" to continue.</li>
-<li>Follow the on-screen instructions to complete the installation process. You can choose between a standard or a custom installation, depending on your preferences.</li>
-<li>When the installation is finished, Firefox will open automatically and ask you to import your bookmarks, passwords, and other settings from your previous browser. You can also skip this step and do it later.</li>
-<li>Congratulations! You have successfully downloaded and installed Firefox 89.0.2 on your Windows PC.</li>
-</ol>
- <h3>How to download Firefox 89.0.2 on Mac</h3>
-<p>If you are using a Mac computer, you can download Firefox 89.0.2 from the official Mozilla website. Here is how:</p>
-<ol>
-<li>Go to <a href="">https://www.mozilla.org/en-US/firefox/new/</a> and click on the "Download Now" button.</li>
-<li>Wait for the Firefox disk image file to download to your computer. The file name should be something like "Firefox 89.0.2.dmg".</li>
-<li>Double-click on the downloaded file to open it. You will see a window with the Firefox icon and a Finder icon.</li>
-<li>Drag and drop the Firefox icon onto the Finder icon to copy it to your Applications folder.</li>
-<li>Eject the disk image file by dragging it to the Trash or clicking on the Eject button next to it in the Finder sidebar.</li>
-<li>Open your Applications folder and double-click on the Firefox icon to launch it.</li>
-<li>When Firefox opens for the first time, it will ask you to confirm that you want to open it. Click "Open" to continue.</li>
-<li>Firefox will also ask you to import your bookmarks, passwords, and other settings from your previous browser. You can also skip this step and do it later.</li>
-<li>Congratulations! You have successfully downloaded and installed Firefox 89.0.2 on your Mac computer.</li>
-</ol>
- <h3>How to download Firefox 89.0.2 on Linux</h3>
-<p>If you are using a Linux system, you can download Firefox 89.0.2 from the official Mozilla website. Here is how:</p>
-<ol>
-<li>Go to <a href="">https://www.mozilla.org/en-US/firefox/new/</a> and click on the "Download Now" button.</li>
-<li>Wait for the Firefox tarball file to download to your computer. The file name should be something like "firefox-89.0.2.tar.bz2".</li>
-<li>Open a terminal window and navigate to the directory where you downloaded the file.</li>
-<li>Extract the file by typing: <code>tar xjf firefox-89.0.2.tar.bz2</code></li>
-<li>This will create a folder called "firefox" in the same directory.</li>
-<li>To run Firefox, enter the folder and type: <code>./firefox</code></li>
-<li>You can also create a shortcut or a launcher for Firefox by following these instructions.</li>
-<li>When Firefox opens for the first time, it will ask you to import your bookmarks, passwords, and other settings from your previous browser. You can also skip this step and do it later.</li>
-<li>Congratulations! You have successfully downloaded and installed Firefox 89.0.2 on your Linux system.</li>
-</ol>
- <h3>How to download Firefox 89.0.2 on Android</h3>
-<p>If you are using an Android device, such as a smartphone or a tablet, you can download Firefox 89.0.2 from the Google Play Store. Here is how:</p>
-<ol>
-<li>Go to the Google Play Store app on your device and search for "Firefox".</li>
-<li>Tap on the Firefox app icon and then tap on the "Install" button.</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>When the installation is complete, tap on the "Open" button to launch Firefox.</li>
-<li>Firefox will ask you to sign in or create a Firefox account to sync your data across devices. You can also skip this step and do it later.</li>
-<li>Firefox will also ask you to customize your browser settings, such as your default search engine, your homepage, your privacy options, and more. You can also skip this step and do it later.</li>
-<li>Congratulations! You have successfully downloaded and installed Firefox 89.0.2 on your Android device.</li>
-</ol>
- <h3>How to download Firefox 89.0.2 on iOS</h3>
-<p>If you are using an iOS device, such as an iPhone or an iPad, you can download Firefox 89.0.2 from the App Store. Here is how:</p>
-<ol>
-<li>Go to the App Store app on your device and search for "Firefox".</li>
-<li>Tap on the Firefox app icon and then tap on the "Get" button.</li>
-<li>You might need to enter your Apple ID password or use Touch ID or Face ID to confirm the download.</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>When the installation is complete, tap on the "Open" button to launch Firefox.</li>
-<li>Firefox will ask you to sign in or create a Firefox account to sync your data across devices. You can also skip this step and do it later.</li>
-<li>Firefox will also ask you to customize your browser settings, such as your default search engine, your homepage, your privacy options, and more. You can also skip this step and do it later.</li>
-<li>Congratulations! You have successfully downloaded and installed Firefox 89.0.2 on your iOS device.</li>
-</ol>
- <h2>How to download .NET 6.0</h2>
-<p>.NET is an open-source development platform that allows you to create various types of applications, such as web, mobile, desktop, cloud, gaming, IoT, and more. .NET is developed by Microsoft and supported by a large community of developers and contributors. .NET offers many features such as cross-platform compatibility, high performance, modern languages, rich libraries, integrated tools, and more.</p>
-<p>If you want to download .NET 6.0, you might have several reasons for doing so. For example, you might want to use the latest features and improvements that were introduced in .NET 6, such as minimal APIs, hot reload, Blazor desktop, MAUI, C# 10, F# 6, and more. Or you might want to fix some issues that were caused by previous versions of .NET, such as bugs, errors, or security vulnerabilities. Or you might want to update your .NET to the latest version to ensure that you have the most reliable and efficient development platform available.</p>
-<p>Whatever your reason is, downloading and installing .NET 6.0 is easy and fast. You can do it on different operating systems, such as Windows, Mac, Linux, Android, and iOS. Here are the steps you need to follow:</p>
- <h3>How to download .NET 6.0 on Windows</h3>
-<p>If you are using a Windows PC, you can download .NET 6.0 from the official Microsoft website. Here is how:</p>
-<ol>
-<li>Go to <a href="">https://dotnet.microsoft.com/download/dotnet/6.0</a> and click on the "Download .NET SDK" button under the Windows section.</li>
-<li>Wait for the .NET installer to download to your computer. The file name should be something like "dotnet-sdk-6.0.xxx-win-x64.exe".</li>
-<li>Double-click on the downloaded file to launch the installer. You might see a User Account Control (UAC) prompt asking you to allow the program to make changes to your computer. Click "Yes" to continue.</li>
-<li>Follow the on-screen instructions to complete the installation process. You can choose between a typical or a custom installation, depending on your preferences.</li>
-<li>When the installation is finished, .NET will be added to your system path and environment variables automatically.</li>
-<li>To verify that .NET is installed correctly, open a command prompt and type: <code>dotnet --version</code></li>
-<li>You should see the output: <code>6.0.xxx</code>, where xxx is the patch number.</li>
-<li>Congratulations! You have successfully downloaded and installed .NET 6.0 on your Windows PC.</li>
-</ol>
- <h3>How to download .NET 6.0 on Mac</h3>
-<p>If you are using a Mac computer, you can download .NET 6.0 from the official Microsoft website. Here is how:</p>
-<ol>
-<li>Go to <a href="">https://dotnet.microsoft.com/download/dotnet/6.0</a> and click on the "Download .NET SDK" button under the macOS section.</li>
-<li>Wait for the .NET installer to download to your computer. The file name should be something like "dotnet-sdk-6.0.xxx-osx-x64.pkg".</li>
-<li>Double-click on the downloaded file to launch the installer. You might see a security warning asking you to confirm that you want to open it. Click "Open" to continue.</li>
-<li>Follow the on-screen instructions to complete the installation process. You might need to enter your administrator password or use Touch ID to authorize the installation.</li>
-<li>When the installation is finished, .NET will be added to your system path and environment variables automatically.</li>
-<li>To verify that .NET is installed correctly, open a terminal window and type: <code>dotnet --version</code></li>
-<li>You should see the output: <code>6.0.xxx</code>, where xxx is the patch number.</li>
-<li>Congratulations! You have successfully downloaded and installed .NET 6.0 on your Mac computer.</li>
-</ol>
- <h3>How to download .NET 6.0 on Linux</h3>
-<p>If you are using a Linux system, you can download .NET 6.0 from the official Microsoft website. Here is how:</p>
-<ol>
-<li>Go to <a href="">https://dotnet.microsoft.com/download/dotnet/6.0</a> and click on the "Download .NET SDK" button under the Linux section.</li>
-<li>Select your Linux distribution and version from the drop-down menu and follow the instructions for your specific system.</li>
-<li>You might need to use commands such as <code>wget</code>, <code>curl</code>, <code>apt-get</code>, <code>yum</code>, or <code>zypper</code> to download and install .NET 6.0 on your Linux system.</li>
-<li>When the installation is finished, .NET will be added to your system path and environment variables automatically.</li>
-<li>To verify that .NET is installed correctly, open a terminal window and type: <code>dotnet --version</code></li>
-<li>You should see the output: <code>6.0.xxx</code>, where xxx is the patch number.</li>
-<li>Congratulations! You have successfully downloaded and installed .NET 6.0 on your Linux system.</li>
-</ol>
- <h2>How to update PS3 console system software</h2>
-<p>The PS3 is a gaming console from Sony that was released in 2006 and discontinued in 2017. The PS3 offers many features such as playing games, watching movies, streaming music, browsing the web, accessing online services, and more. The PS3 also has a system software, also known as firmware, that controls the basic operations of the console and provides various functions and settings.</p>
-<p>If you want to update your PS3 console system software, you might have several reasons for doing so. For example, you might want to use the latest features and improvements that were introduced in the latest version of the PS3 system software, such as stability enhancements, security patches, bug fixes, and more. Or you might want to fix some issues that were caused by previous versions of the PS3 system software, such as errors, freezes, or compatibility problems. Or you might want to update your PS3 system software to ensure that you have the most secure and stable console available.</p>
-<p>Whatever your reason is, updating your PS3 console system software is easy and fast. You can do it using different methods, such as online update, storage media update, or data transfer utility update. Here are the steps you need to follow:</p>
- <h3>How to update PS3 console system software using online update</h3>
-<p>If you have an internet connection and a PlayStation Network account, you can update your PS3 console system software using online update. Here is how:</p>
-<ol>
-<li>Turn on your PS3 console and sign in to your PlayStation Network account.</li>
-<li>Select "Settings" from the XMB (Xross Media Bar) menu and then select "System Update".</li>
-<li>Select "Update via Internet" and then press the X button.</li>
-<li>The PS3 console will automatically search for the latest version of the system software and download it to your console.</li>
-<li>When the download is complete, you will see a message asking you to accept the terms and conditions of the update. Press the X button to accept and continue.</li>
-<li>The PS3 console will restart and install the update. Do not turn off the power or remove any cables during this process.</li>
-<li>When the installation is complete, you will see a message confirming that the update was successful. Press the X button to restart your console.</li>
-<li>Congratulations! You have successfully updated your PS3 console system software using online update.</li>
-</ol>
- <h3>How to update PS3 console system software using storage media update</h3>
-<p>If you do not have an internet connection or a PlayStation Network account, you can update your PS3 console system software using storage media update. You will need a USB flash drive or a memory card with at least 200 MB of free space and a computer with an internet connection. Here is how:</p>
-<ol>
-<li>Go to <a href="">https://www.playstation.com/en-us/support/hardware/ps3/system-software/</a> on your computer and click on the "Download Update" button.</li>
-<li>Wait for the PS3 system software update file to download to your computer. The file name should be something like "PS3UPDAT.PUP".</li>
-<li>Insert your USB flash drive or memory card into your computer and create a folder named "PS3" on it.</li>
-<li>Inside the "PS3" folder, create another folder named "UPDATE".</li>
-<li>Copy the downloaded PS3 system software update file to the "UPDATE" folder.</li>
-<li>Eject your USB flash drive or memory card from your computer and insert it into your PS3 console.</li>
-<li>Turn on your PS3 console and select "Settings" from the XMB menu and then select "System Update".</li>
-<li>Select "Update via Storage Media" and then press the X button.</li>
-<li>The PS3 console will automatically detect the update file on your USB flash drive or memory card and display its version number. Press the X button to start the update.</li>
-<li>You will see a message asking you to accept the terms and conditions of the update. Press the X button to accept and continue.</li>
-<li>The PS3 console will restart and install the update. Do not turn off the power or remove any cables during this process.</li>
-<li>When the installation is complete, you will see a message confirming that the update was successful. Press the X button to restart your console.</li>
-<li>Congratulations! You have successfully updated your PS3 console system software using storage media update.</li>
-</ol>
- <h3>How to update PS3 console system software using data transfer utility update</h3>
-<p>If you have another PS3 console that has a newer version of the system software, you can update your PS3 console system software using data transfer utility update. You will need an Ethernet cable and both consoles connected to the same power source. Here is how:</p>
-<ol>
-<li>Turn on both PS3 consoles and connect them with an Ethernet cable.</li>
-<li>Select "Settings" from the XMB menu on both consoles and then select "System Settings".</li>
-<li>Select "Data Transfer Utility" on both consoles and then press the X button.</li>
-<li>On the source PS3 console (the one with the newer system software), select "1. Transfer data from this system to the other system" and then press the X button.</li>
-<li>On the destination PS3 console (the one with the older system software), select "2. Transfer data from the other system to this system" and then press the X button.</li>
-<li>You will see a message asking you to confirm that you want to transfer the data and update the system software. Press the X button to accept and continue.</li>
-<li>The data transfer and system software update will begin. Do not turn off the power or remove any cables during this process.</li>
-<li>When the data transfer and system software update are complete, you will see a message confirming that they were successful. Press the X button to restart both consoles.</li>
-<li>Congratulations! You have successfully updated your PS3 console system software using data transfer utility update.</li>
-</ol>
- <h2>Benefits of downloading and updating software</h2>
-<p>Downloading and updating software regularly can bring you many benefits, such as improved performance, security, compatibility, and functionality. Here are some examples of how downloading and updating software can enhance your experience:</p>
-<ul>
-<li>Improved performance: Downloading and updating software can make your devices run faster, smoother, and more efficiently. For example, downloading and updating Firefox can improve your web browsing speed, memory usage, and battery life. Downloading and updating .NET can improve your application development speed, quality, and productivity. Downloading and updating PS3 system software can improve your gaming performance, loading time, and stability.</li>
-<li>Improved security: Downloading and updating software can protect your devices from malware, viruses, hackers, and other threats. For example, downloading and updating Firefox can block malicious websites, trackers, and ads. Downloading and updating .NET can fix security vulnerabilities and bugs. Downloading and updating PS3 system software can prevent unauthorized access and piracy.</li>
-<li>Improved compatibility: Downloading and updating software can ensure that your devices work well with other devices, applications, and services. For example, downloading and updating Firefox can support the latest web standards, formats, and protocols. Downloading and updating .NET can support the latest platforms, languages, and frameworks. Downloading and updating PS3 system software can support the latest games, accessories, and features.</li>
-<li>Improved functionality: Downloading and updating software can enable you to use new features, functions, and options. For example, downloading and updating Firefox can give you access to new design elements, privacy settings, extensions, themes, and more. Downloading and updating .NET can give you access to new libraries, tools, APIs, and more. Downloading and updating PS3 system software can give you access to new games, modes, features, and more.</li>
-</ul>
-<p>As you can see, downloading and updating software can make your devices more powerful, secure, compatible, and functional. However, downloading and updating software also comes with some risks that you should be aware of.</p>
- <h2>Risks of downloading and updating software</h2>
-<p>Downloading and updating software without caution can expose your devices to some potential drawbacks, such as malware, bugs, compatibility issues, and data loss. Here are some examples of how downloading and updating software can harm your experience:</p>
-<ul>
-<li>Malware: Downloading and updating software from untrusted sources can infect your devices with malware, such as viruses, worms, trojans, spyware, ransomware, and more. Malware can damage your devices, steal your data, compromise your privacy, and cause other problems. For example, downloading and updating Firefox from a fake website can install a malicious extension that can monitor your online activity, display unwanted ads, or redirect you to phishing sites. Downloading and updating .NET from a corrupted file can install a backdoor that can allow hackers to access your system remotely. Downloading and updating PS3 system software from an unofficial source can install a firmware that can brick your console or disable some features.</li>
-<li>Bugs: Downloading and updating software that is not fully tested or stable can introduce bugs, errors, or glitches to your devices. Bugs can affect the performance, functionality, or usability of your devices. For example, downloading and updating Firefox to a beta version can cause some websites to load incorrectly, crash unexpectedly, or display errors. Downloading and updating .NET to a preview version can cause some applications to run slowly, behave unexpectedly, or fail to compile. Downloading and updating PS3 system software to a faulty version can cause some games to freeze, lag, or display graphical issues.</li>
-<li>Compatibility issues: Downloading and updating software that is not compatible with your devices or other software can cause conflicts, crashes, or errors. Compatibility issues can prevent your devices from working properly or at all. For example, downloading and updating Firefox to a version that is not supported by your operating system can cause Firefox to not launch, crash, or display a warning message. Downloading and updating .NET to a version that is not compatible with your application framework can cause your application to not run, crash, or display an error message. Downloading and updating PS3 system software to a version that is not compatible with your game disc or online service can cause your game to not load, crash, or display an error message.</li>
-<li>Data loss: Downloading and updating software without backing up your data can result in data loss, corruption, or deletion. Data loss can affect your personal or professional files, such as photos, videos, documents, music, contacts, messages, and more. For example, downloading and updating Firefox without exporting your bookmarks can erase your saved websites. Downloading and updating .NET without backing up your code can overwrite your project files. Downloading and updating PS3 system software without backing up your game data can delete your saved progress.</li>
-</ul>
-<p>As you can see, downloading and updating software can expose your devices to some risks that you should be careful of. However, you can avoid or minimize these risks by following some tips for downloading and updating software safely and efficiently.</p>
- <h2>Tips for downloading and updating software safely and efficiently</h2>
-<p>Downloading and updating software can be a rewarding and enjoyable experience if you do it right. Here are some best practices for downloading and updating software that can help you avoid or reduce the risks mentioned above:</p>
-<ul>
-<li>Check the source: Before downloading and updating software, make sure that you are getting it from a trusted and official source, such as the developer's website or the app store. Avoid downloading and updating software from unknown or suspicious sources, such as third-party websites or links in emails or messages. These sources might contain malware, viruses, or fake software that can harm your devices.</li>
-<li>Verify the file: After downloading and before installing software, make sure that you are getting the correct and complete file. Check the file name, size, extension, and checksum to ensure that it matches the information provided by the source. Avoid installing software that has a different or unknown file name, size, extension, or checksum. These files might be corrupted, incomplete, or tampered with.</li>
-<li>Back up data: Before updating software, make sure that you have backed up your data to a safe location, such as an external hard drive or a cloud service. This way, you can restore your data in case something goes wrong during the update process. Avoid updating software without backing up your data. You might lose your data if the update fails, crashes, or deletes it.</li>
-<li>Follow instructions: During the installation or update process, make sure that you follow the instructions provided by the source or the software. Read and understand the terms and conditions, the installation options, and the update notes. Follow the steps carefully and do not skip or modify any of them. Avoid installing or updating software without following the instructions. You might encounter errors, conflicts, or failures if you do not follow the instructions.</li>
-<li>Restart device: After installing or updating software, make sure that you restart your device to complete the process and apply the changes. Restarting your device can also clear any temporary files or cache that might interfere with the software. Avoid using your device without restarting it after installing or updating software. You might experience performance issues, crashes, or errors if you do not restart your device.</li>
-</ul>
-<p>By following these tips, you can download and update software safely and efficiently. You can enjoy the benefits of downloading and updating software without worrying about the risks.</p>
- <h4>Conclusion</h4>
-<p>In this article, we have explained what "download 89 6" means and what you can do with it. We have shown you how to download and install Firefox 89.0.2, .NET 6.0, and PS3 system software update using different methods and devices. We have also explained the benefits and risks of downloading and updating software, and provided some tips for doing it safely and efficiently.</p>
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spaces/1phancelerku/anime-remove-background/A Step-by-Step Guide to Downloading and Configuring the YouTube API Key.xml.md

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-<h1>How to Download YouTube API Key.xml File</h1>
-<p>If you want to interact with YouTube using your application, you need to have a YouTube API key.xml file. This file contains your authorization credentials that allow you to access the YouTube Data API v3. In this article, you will learn what is YouTube API key.xml file, why do you need it, how to create it, and how to use it.</p>
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-<p>A YouTube API key.xml file is a file that contains your API key, which is a unique identifier that your application uses to communicate with the YouTube Data API v3. The API key is a string of characters that you can find in the Google API Console. The XML format of the file makes it easy to store and transfer the key.</p>
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-<p>You need YouTube API key.xml file because it enables you to use the YouTube Data API v3, which is a service that allows you to add a variety of YouTube features to your application. For example, you can use the API to upload videos, manage playlists and subscriptions, update channel settings, and more. Without the API key, you cannot access the API and perform these operations.</p>
- <h3>How to create YouTube API Key.xml File?</h3>
-<p>To create YouTube API key.xml file, you need to follow these steps:</p>
- <h4>Step 1: Create a project in Google API Console</h4>
-<p>Go to <a href="(^1^)">https://developers.google.com/youtube/registering_an_application</a> and sign in with your Google account. Click on "Create Project" and enter a name for your project. Click on "Create" and wait for the project to be created.</p>
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- <h4>Step 2: Enable YouTube Data API v3</h4>
-<p>In the Google API Console, select your project and go to "APIs & Services" > "Library". Search for "YouTube Data API v3" and click on it. Click on "Enable" and wait for the API to be enabled.</p>
- <h4>Step 3: Create an API key</h4>
-<p>In the Google API Console, go to "APIs & Services" > "Credentials". Click on "Create Credentials" > "API key". A pop-up window will show your new API key. Copy the key and click on "Close". You can also restrict the key by clicking on "Restrict key" and selecting one of the options.</p>
- <h4>Step 4: Download the API key.xml file</h4>
-<p>In the Google API Console, go to "APIs & Services" > "Credentials". Click on the name of your API key. Under "Key restrictions", click on "Download XML". A file named api_key.xml will be downloaded to your computer. This is your YouTube API key.xml file.</p>
- <h2>How to use YouTube API Key.xml File?</h2>
-<p>To use YouTube API key.xml file, you need to include it in your application code and send it along with your requests to the YouTube Data API v3. Depending on the type of request, you may also need to send an OAuth 2.0 token, which is another type of authorization credential that grants access to private user data. You can learn more about OAuth 2.0 <a href="(^2^)">here</a>.</p>
- <h3>How to upload videos using YouTube API Key.xml File?</h3>
-<p>To upload videos using YouTube API Key.xml file, you need to use the videos.insert method of the YouTube Data API v3. You also need to send an OAuth 2.0 token that has the https://www.googleapis.com/auth/youtube.upload scope. You can use one of the following methods to upload videos: - Simple upload: This method is suitable for small files (less than 15 MB) and does not support resumable uploads. You can use the HTTP POST method and send the video metadata and content in a single request. You can learn more about simple upload <a href="">here</a>. - Resumable upload: This method is suitable for large files (more than 15 MB) and supports resumable uploads. You can use the HTTP POST method and send the video metadata in the first request, and then send the video content in one or more subsequent requests. You can learn more about resumable upload <a href="">here</a>. <h3>How to manage playlists and subscriptions using YouTube API Key.xml File?</h3>
-<p>To manage playlists and subscriptions using YouTube API Key.xml file, you need to use the playlists and subscriptions resources of the YouTube Data API v3. You also need to send an OAuth 2.0 token that has the https://www.googleapis.com/auth/youtube scope. You can use the following methods to manage playlists and subscriptions: - playlists.insert: This method allows you to create a new playlist. You need to send a playlist resource that contains the playlist title, description, and privacy status. You can learn more about playlists.insert <a href="">here</a>. - playlists.update: This method allows you to update an existing playlist. You need to send a playlist resource that contains the playlist ID and the updated fields. You can learn more about playlists.update <a href="">here</a>. - playlists.delete: This method allows you to delete an existing playlist. You need to send the playlist ID as a parameter. You can learn more about playlists.delete <a href="">here</a>. - subscriptions.insert: This method allows you to subscribe to a channel. You need to send a subscription resource that contains the channel ID of the channel you want to subscribe to. You can learn more about subscriptions.insert <a href="">here</a>. - subscriptions.delete: This method allows you to unsubscribe from a channel. You need to send the subscription ID as a parameter. You can learn more about subscriptions.delete <a href="">here</a>.</p>
- <h3>How to update channel settings using YouTube API Key.xml File?</h3>
-<p>To update channel settings using YouTube API Key.xml file, you need to use the channels resource of the YouTube Data API v3. You also need to send an OAuth 2.0 token that has the https://www.googleapis.com/auth/youtube scope. You can use the following method to update channel settings: - channels.update: This method allows you to update your channel's metadata, branding, and features. You need to send a channel resource that contains the channel ID and the updated fields. You can learn more about channels.update <a href="">here</a>.</p>
- <h2>Conclusion</h2>
-<p>In this article, you learned how to download YouTube API key.xml file, which is a file that contains your authorization credentials for accessing the YouTube Data API v3. You also learned how to use YouTube API key.xml file to upload videos, manage playlists and subscriptions, and update channel settings using the YouTube Data API v3.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about YouTube API key.xml file:</p>
- <h4>Q: How do I get an OAuth 2.0 token?</h4>
-<p>A: To get an OAuth 2.0 token, you need to follow the OAuth 2.0 authorization flow, which involves requesting user consent, exchanging authorization code for access token, and refreshing access token when it expires. You can learn more about OAuth 2.0 authorization flow <a href="">here</a>.</p>
- <h4>Q: How do I store and secure my YouTube API key.xml file?</h4>
-<p>A: To store and secure your YouTube API key.xml file, you need to follow some best practices, such as encrypting the file, storing it in a safe location, limiting its access, and rotating it regularly. You can learn more about storing and securing your YouTube API key.xml file <a href="">here</a>.</p>
- <h4>Q: How do I troubleshoot errors when using YouTube API key.xml file?</h4>
-<p>A: To troubleshoot errors when using YouTube API key.xml file, you need to check the error response code and message, which indicate the cause of the error and possible solutions. You can learn more about troubleshooting errors when using YouTube API key.xml file <a href=" ">here</a>.</p>
- <h4>Q: How do I monitor and optimize the performance of my YouTube API key.xml file?</h4>
-<p>A: To monitor and optimize the performance of your YouTube API key.xml file, you need to use the Google API Console, which provides various tools and reports to help you track and improve your API usage, quota, and billing. You can learn more about monitoring and optimizing the performance of your YouTube API key.xml file <a href="">here</a>.</p>
- <h4>Q: How do I find more resources and support for using YouTube API key.xml file?</h4>
-<p>A: To find more resources and support for using YouTube API key.xml file, you can visit the following websites: - YouTube Data API v3 Documentation: This website provides detailed information and examples on how to use the YouTube Data API v3. You can visit the website <a href="">here</a>. - YouTube Data API v3 Reference: This website provides a complete list and description of all the methods, parameters, and resources of the YouTube Data API v3. You can visit the website <a href="">here</a>. - YouTube Data API v3 Forum: This website provides a platform for developers to ask questions, share ideas, and get help from other developers who use the YouTube Data API v3. You can visit the website <a href="">here</a>.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download Beach Buggy Racing for Free and Enjoy Off-Road Mayhem.md

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-<h1>Beach Buggy Racing Download: A Guide to the Best Kart Racing Game for Your Device</h1>
-<p>If you're looking for a fun and exciting kart racing game that will make you feel like you're on a tropical island, then you should definitely check out Beach Buggy Racing. This game is a sequel to the popular Beach Buggy Blitz, and it offers more features, more tracks, more cars, more power-ups, and more fun than ever before. In this article, we'll tell you what Beach Buggy Racing is, why you should download it, and how to get it on your device.</p>
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-<h2>What is Beach Buggy Racing?</h2>
-<h3>A fun and colorful kart racing game with power-ups and customization</h3>
-<p>Beach Buggy Racing is a 3D kart racing game that lets you drive into an action-packed world of off-road mayhem. You can race against a field of rival drivers, each with their own personality and special ability. You can also collect and upgrade a variety of cars, from dune buggies to muscle cars to lunar rovers. And you can use over 25 different power-ups, like Dodgeball Frenzy, Fireball, and Oil Slick, to fight your way to the finish line.</p>
-<h3>The sequel to the popular Beach Buggy Blitz</h3>
-<p>Beach Buggy Racing is the official sequel to Beach Buggy Blitz, the free driving game with over 30 million players worldwide. Beach Buggy Blitz was a simple but addictive game that challenged you to drive as far as possible on a randomly generated island. Beach Buggy Racing takes this concept to the next level by adding more depth, variety, and challenge to the gameplay.</p>
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-<p>Beach Buggy Racing is available for Android, iOS, Windows, and Xbox One devices. You can download it for free from the respective app stores or online platforms. The game is supported by ads and in-app purchases, but you can also upgrade to the premium version for a one-time fee. The premium version gives you infinite tickets (which you need to enter races), split-screen multiplayer mode (where you can race with up to four friends on one device), and removes all ads.</p>
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-<li><h4>Race against a field of rival drivers with unique personalities and abilities</h4>
-<p>In the main mode of the game, you can choose from 12 different drivers, each with their own backstory, voice, and special power. For example, you can play as Rez, the alien who can create a wormhole to teleport ahead of the pack, or Roxie, the rock star who can unleash a sonic boom to blast away obstacles. You can also unlock more drivers as you progress through the game.</p></li>
-<li><h4>Collect and upgrade a garage full of cool cars, from dune buggies to monster trucks</h4>
-<p>Another cool feature of the game is that you can collect and customize a variety of cars, each with their own stats and style. You can start with a basic beach buggy, but you can also unlock and upgrade more exotic vehicles, like a lunar rover, a pirate ship, or a dragon. You can also change the color, paint job, and decals of your cars to make them look more awesome.</p></li>
-<li><h4>Use over 25 different power-ups to blast your way to the finish line</h4>
-<p>Beach Buggy Racing is not just about driving fast, it's also about using power-ups to gain an edge over your opponents. You can collect power-ups by driving over glowing orbs on the track, and you can use them by tapping on the screen. There are over 25 different power-ups in the game, ranging from offensive ones like rockets, fireballs, and oil slicks, to defensive ones like shields, boosts, and magnets. You can also upgrade your power-ups to make them more effective.</p></li>
-<li><h4>Explore 15 spectacular race tracks with hidden shortcuts and surprises</h4>
-<p>The game also features 15 different race tracks, each with its own theme and scenery. You can race on a sunny beach, a spooky forest, a snowy mountain, a volcanic island, and more. Each track also has hidden shortcuts and secrets that you can discover by exploring the environment. For example, you can drive through a waterfall, jump over a bridge, or crash through a temple.</p></li>
-<li><h4>Recruit a team of racers with special powers to help you win</h4>
-<p>As you play the game, you can also recruit other racers to join your team. Each racer has a special power that can help you in different ways. For example, you can recruit McSkelly, the skeleton pirate who can summon a ghost ship to block your enemies, or Tiki Mon, the tiki mask who can create a trail of fire behind him. You can also switch between racers before each race to choose the best one for the situation.</p></li>
-<li><h4>Compete with your friends on leaderboards, achievements, and split-screen multiplayer (premium version only)</h4>
-<p>If you want to challenge your friends or show off your skills, you can also connect your game to Google Play Games or Game Center (depending on your device) and access leaderboards and achievements. You can see how you rank against other players around the world or compare your scores with your friends. You can also unlock achievements by completing various tasks in the game. And if you upgrade to the premium version of the game, you can also enjoy split-screen multiplayer mode, where you can race with up to four friends on one device.</p></li>
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-<h3>High-quality graphics and sound that make you feel like you're on vacation</h3>
-<p>Besides the gameplay features, Beach Buggy Racing also impresses with its graphics and sound quality. The game has stunning 3D graphics that bring the tropical island to life. You can see the palm trees swaying in the wind, the waves crashing on the shore, and the birds flying in the sky. The game also has bouncy surf music that fits the cheery theme of the game. The sound effects are also realistic and fun, from the engine noises to the power-up sounds.</p>
-<h2>How to download Beach Buggy Racing?</h2>
-<h3>Follow these simple steps to get the game on your device</h3>
-<p>If you're convinced that Beach Buggy Racing is the game for you, then here's how you can download it on your device:</p>
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-<li><h4>For Android devices, go to the Google Play Store and search for Beach Buggy Racing or click here</h4>
-<p>Once you find the game on the Google Play Store, tap on Install and p>Once you find the game on the Google Play Store, tap on Install and wait for the game to download and install on your device. You may need to grant some permissions to the game, such as access to your storage and network. After the installation is complete, you can tap on Open to launch the game and start playing.</p></li>
-<li><h4>For iOS devices, go to the App Store and search for Beach Buggy Racing or click here</h4>
-<p>Similarly, you can find the game on the App Store by searching for Beach Buggy Racing or clicking on this link. Then, tap on Get and confirm your purchase with your Apple ID or Touch ID. The game will download and install on your device automatically. You can then tap on the game icon to open it and enjoy the game.</p></li>
-<li><h4>For Windows devices, go to the Microsoft Store and search for Beach Buggy Racing or click here</h4>
-<p>If you have a Windows device, such as a PC, laptop, tablet, or phone, you can also download Beach Buggy Racing from the Microsoft Store. Just search for Beach Buggy Racing or click on this link and then click on Get. The game will download and install on your device in a few minutes. You can then click on Play to start the game.</p></li>
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-<p>Finally, if you have an Xbox One console, you can also get Beach Buggy Racing from the Xbox Store. Just search for Beach Buggy Racing or click on this link and then click on Buy. You may need to sign in with your Microsoft account or Xbox Live account to complete the purchase. The game will download and install on your console automatically. You can then launch the game from your home screen or library.</p></li>
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-<h2>Conclusion</h2>
-<p>Beach Buggy Racing is a fun and exciting kart racing game that will make you feel like you're on a tropical island. It has a lot of features and modes that will keep you entertained for hours. You can race against a field of rival drivers, collect and upgrade a garage full of cool cars, use over 25 different power-ups, explore 15 spectacular race tracks, recruit a team of racers with special powers, compete with your friends on leaderboards, achievements, and split-screen multiplayer (premium version only), and enjoy high-quality graphics and sound that make you feel like you're on vacation. If you want to download Beach Buggy Racing, just follow the simple steps we provided above for your device. You won't regret it!</p>
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-<li><p><b>Q: How much space does Beach Buggy Racing take up on my device?</b></p>
-<p>A: The size of the game may vary depending on your device and platform, but it's generally around 100 MB. You may need to clear some space on your device before downloading the game.</p></li>
-<li><p><b>Q: How do I control my car in Beach Buggy Racing?</b></p>
-<p>A: You can control your car by tilting your device left or right to steer, tapping on the screen to use power-ups, and swiping up or down to jump or duck. You can also change the control settings in the options menu if you prefer.</p></li>
-<li><p><b>Q: How do I unlock more cars, drivers, power-ups, and tracks in Beach Buggy Racing?</b></p>
-<p>A: You can unlock more content by playing the game and earning coins and gems. Coins are used to buy and upgrade cars and power-ups, while gems are used to unlock drivers and tracks. You can also earn coins and gems by completing achievements, watching ads, or buying them with real money.</p></li>
-<li><p><b>Q: How do I play split-screen multiplayer mode in Beach Buggy Racing?</b></p>
-<p>A: Split-screen multiplayer mode is only available in the premium version of the game, which you can buy for a one-time fee. To play split-screen multiplayer mode, you need to have an Android TV device or an Xbox One console with up to four controllers connected. Then, you can select multiplayer mode from the main menu and choose your settings.</p></li>
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-<p>I hope you enjoyed this article and learned something new about Beach Buggy Racing. If you have any questions or feedback, feel free to leave a comment below. And if you're ready to download the game and have some fun, just follow the links we provided above for your device. Happy racing!</p> 401be4b1e0<br />
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spaces/2ndelement/voicevox/build_util/merge_update_infos.py

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-import argparse
-import json
-from collections import OrderedDict
-from pathlib import Path
-from typing import Dict, List, Union
-
-
-def merge_json_string(src: str, dst: str) -> str:
-    """
-    バージョンが同じ場合は要素を結合する
-    >>> src = '[{"version": "0.0.1", "a": ["a1"], "b": ["b1", "b2"]}]'
-    >>> dst = '[{"version": "0.0.1", "a": ["a2"], "b": ["b1", "b3"]}]'
-    >>> merge_json_string(src, dst)
-    '[{"version": "0.0.1", "a": ["a1", "a2"], "b": ["b1", "b2", "b3"]}]'
-
-    バージョンが無かった場合は無視される
-    >>> src = '[{"version": "1"}]'
-    >>> dst = '[{"version": "1"}, {"version": "2"}]'
-    >>> merge_json_string(src, dst)
-    '[{"version": "1"}]'
-    """
-    src_json: List[Dict[str, Union[str, List[str]]]] = json.loads(src)
-    dst_json: List[Dict[str, Union[str, List[str]]]] = json.loads(dst)
-
-    for src_item in src_json:
-        for dst_item in dst_json:
-            if src_item["version"] == dst_item["version"]:
-                for key in src_item:
-                    if key == "version":
-                        continue
-
-                    # 異なるものがあった場合だけ後ろに付け足す
-                    src_item[key] = list(
-                        OrderedDict.fromkeys(src_item[key] + dst_item[key])
-                    )
-
-    return json.dumps(src_json)
-
-
-def merge_update_infos(src_path: Path, dst_path: Path, output_path: Path) -> None:
-    src = src_path.read_text(encoding="utf-8")
-    dst = dst_path.read_text(encoding="utf-8")
-    merged = merge_json_string(src, dst)
-    output_path.write_text(merged)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("src_path", type=Path)
-    parser.add_argument("dst_path", type=Path)
-    parser.add_argument("output_path", type=Path)
-    args = parser.parse_args()
-    merge_update_infos(args.src_path, args.dst_path, args.output_path)

spaces/2ndelement/voicevox/voicevox_engine/preset/__init__.py

@@ -1,9 +0,0 @@
-from .Preset import Preset
-from .PresetError import PresetError
-from .PresetManager import PresetManager
-
-__all__ = [
-    "Preset",
-    "PresetManager",
-    "PresetError",
-]

spaces/AI-Dashboards/ScrabbleSolverWordThesaurus/app.py

@@ -1,67 +0,0 @@
-import streamlit as st
-import itertools
-import nltk
-from nltk.corpus import wordnet, words
-
-# Download the necessary resources
-nltk.download("wordnet")
-nltk.download("words")
-
-def get_related_words(word):
-    synonyms = set()
-    antonyms = set()
-    hypernyms = set()
-    hyponyms = set()
-
-    for syn in wordnet.synsets(word):
-        for lemma in syn.lemmas():
-            synonyms.add(lemma.name())
-            if lemma.antonyms():
-                antonyms.add(lemma.antonyms()[0].name())
-
-        for hyper in syn.hypernyms():
-            hypernyms.update(lemma.name() for lemma in hyper.lemmas())
-
-        for hypo in syn.hyponyms():
-            hyponyms.update(lemma.name() for lemma in hypo.lemmas())
-
-    return {
-        "synonyms": list(synonyms),
-        "antonyms": list(antonyms),
-        "hypernyms": list(hypernyms),
-        "hyponyms": list(hyponyms),
-    }
-
-def generate_words(letters, length=None):
-    english_words = set(words.words())
-    permutations = set()
-    for i in range(1, len(letters) + 1):
-        for p in itertools.permutations(letters, i):
-            word = "".join(p).lower()  # Convert the word to lowercase
-            if (length is None or len(word) == length) and word in english_words:
-                permutations.add(word)
-    return permutations
-
-
-st.title("Scrabble Helper")
-
-letters = st.text_input("Enter the letters you have:")
-word_length = st.number_input("Enter the word length (optional):", min_value=0, value=0, step=1)
-
-if letters:
-    st.header("Generated Words")
-    words = generate_words(letters, length=word_length if word_length > 0 else None)
-    st.write(words)
-
-    st.header("Thesaurus and Related Words Lookup")
-    selected_word = st.selectbox("Select a word to look up related words:", [""] + sorted(words))
-    if selected_word:
-        related_words = get_related_words(selected_word)
-        st.subheader("Synonyms")
-        st.write(related_words["synonyms"])
-        st.subheader("Antonyms")
-        st.write(related_words["antonyms"])
-        st.subheader("Hypernyms (more general terms)")
-        st.write(related_words["hypernyms"])
-        st.subheader("Hyponyms (more specific terms)")
-        st.write(related_words["hyponyms"])

spaces/AIZero2HeroBootcamp/ClassDescriptionAndExamplesStreamlit/README.md

@@ -1,13 +0,0 @@
----
-title: ClassDescriptionAndExamplesStreamlit
-emoji: 🐨
-colorFrom: green
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AbelKidane/headdetector/app.py

@@ -1,76 +0,0 @@
-import streamlit as st
-from PIL import Image
-from prediction import prediction
-from prediction import confidence
-from prediction import iou_thresold
-from prediction import Display_Confidence
-from prediction import Display_Class
-import streamlit as st
-import time
-import os
-
-# Global variables
-uploaded_file = None
-path_to_image = None
-
-def make_prediction():
-
-    global confidence
-    global path_to_image
-    global uploaded_file
-    global iou_thresold
-
-    if uploaded_file is not None:
-        with st.spinner(f"Detecting heads in the image. Please wait..."):
-            annotatedImage = prediction(path_to_image, confidence, 
-            disp_Class=Display_Class, disp_Confidence=Display_Confidence)
-        st.image(annotatedImage, caption=f'Model Prediction')
-    
-def upload_file():
-
-    global path_to_image
-    global uploaded_file
-    global confidence
-
-    uploaded_file = st.file_uploader("Upload an image",type=['jpg','png','jpeg'])
-    if uploaded_file is not None:
-        path_to_image = "image/"+uploaded_file.name
-        image = Image.open(uploaded_file)
-        # Save image to the directory 'image' if it doesn't exist
-        if not os.path.exists(path_to_image):
-            image.save(path_to_image)
-        make_prediction() 
-
-def side_bar():
-
-    global confidence
-    global uploaded_file
-    global iou_thresold
-    global Display_Confidence
-    global Display_Class
-
-    with st.sidebar:
-        st.subheader("Modify parameters")
-        confidence = st.slider('Confidence %', 0, 100, 80)
-        iou_thresold = st.slider('IOU Threshold %', 0, 100, 30)
-        
-        # Checkboxes to display class and confidence for each detection
-        Display_Class = st.checkbox('Display Class', value=True)   
-        Display_Confidence = st.checkbox('Display Confidence', value=True)
-
-        url = "https://github.com/AbelKidane-abita/Reports"
-        # st.write("check out this [link](%s)" % url)
-        st.markdown("[GitHub](%s)" % url)
-            
-
-def main_func():
-    
-    st.title('YoloV8 Head Detector Model') #display title
-    st.text('This is a YoloV8 object detection model that detects human heads.') #display description
-    
-    side_bar() #display side bar
-    upload_file() #display the button to upload the file from file explorer
-
-
-if __name__=='__main__':
-    main_func()

spaces/Aditya9790/yolo7-object-tracking/deploy/triton-inference-server/processing.py

@@ -1,51 +0,0 @@
-from boundingbox import BoundingBox
-
-import cv2
-import numpy as np
-
-def preprocess(img, input_shape, letter_box=True):
-    if letter_box:
-        img_h, img_w, _ = img.shape
-        new_h, new_w = input_shape[0], input_shape[1]
-        offset_h, offset_w = 0, 0
-        if (new_w / img_w) <= (new_h / img_h):
-            new_h = int(img_h * new_w / img_w)
-            offset_h = (input_shape[0] - new_h) // 2
-        else:
-            new_w = int(img_w * new_h / img_h)
-            offset_w = (input_shape[1] - new_w) // 2
-        resized = cv2.resize(img, (new_w, new_h))
-        img = np.full((input_shape[0], input_shape[1], 3), 127, dtype=np.uint8)
-        img[offset_h:(offset_h + new_h), offset_w:(offset_w + new_w), :] = resized
-    else:
-        img = cv2.resize(img, (input_shape[1], input_shape[0]))
-
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    img = img.transpose((2, 0, 1)).astype(np.float32)
-    img /= 255.0
-    return img
-
-def postprocess(num_dets, det_boxes, det_scores, det_classes, img_w, img_h, input_shape, letter_box=True):
-    boxes = det_boxes[0, :num_dets[0][0]] / np.array([input_shape[0], input_shape[1], input_shape[0], input_shape[1]], dtype=np.float32)
-    scores = det_scores[0, :num_dets[0][0]]
-    classes = det_classes[0, :num_dets[0][0]].astype(np.int)
-
-    old_h, old_w = img_h, img_w
-    offset_h, offset_w = 0, 0
-    if letter_box:
-        if (img_w / input_shape[1]) >= (img_h / input_shape[0]):
-            old_h = int(input_shape[0] * img_w / input_shape[1])
-            offset_h = (old_h - img_h) // 2
-        else:
-            old_w = int(input_shape[1] * img_h / input_shape[0])
-            offset_w = (old_w - img_w) // 2
-
-    boxes = boxes * np.array([old_w, old_h, old_w, old_h], dtype=np.float32)
-    if letter_box:
-        boxes -= np.array([offset_w, offset_h, offset_w, offset_h], dtype=np.float32)
-    boxes = boxes.astype(np.int)
-
-    detected_objects = []
-    for box, score, label in zip(boxes, scores, classes):
-        detected_objects.append(BoundingBox(label, score, box[0], box[2], box[1], box[3], img_w, img_h))
-    return detected_objects

spaces/AdityaMahimkar/ParaPhraser/README.md

@@ -1,13 +0,0 @@
----
-title: ParaPhraser
-emoji: 👁
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 2.9.1
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/AhmedKhairullah/dmo/app.py

@@ -1,49 +0,0 @@
-
-# import gradio as gr
-
-# def greet(name, is_morning, temperature):
-#     salutation = "Good morning" if is_morning else "Good evening"
-#     greeting = f"{salutation} {name}. It is {temperature} degrees today"
-#     celsius = (temperature - 32) * 5 / 9
-#     return greeting, round(celsius, 2)
-
-# demo = gr.Interface(
-#     fn=greet,
-#     inputs=["text", "checkbox", gr.Slider(0, 100)],
-#     outputs=["text", "number"],
-# )
-# demo.launch()
-
-
-
-from fastai.vision.all import *
-import gradio as gr
-def is_cat (x):
-    return x[0].isupper()
-
-learn=load_learner("model.pkl")
-
-categories=('dog','cat')
-
-def classify_image (img):
-    pred,idx,probs =learn.predict(img)
-    return dict(zip(categories,map(float,probs)))
-
-image=gr.inputs.Image(shape=(192,192))
-label=gr.outputs.Label()
-examples=['cat.jpg','dog.jpg']
-
-intf=gr.Interface(fn=classify_image,inputs=image,outputs=label,examples=examples)
-intf.launch(inline=False)
-
-
-
-
-
-
-
-
-
-
-
-

spaces/AkshayKollimarala/MYAIVOICESPEECH/README.md

@@ -1,12 +0,0 @@
----
-title: MYAIVOICESPEECH
-emoji: 🏆
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlexMaoMao/ostris-ikea-instructions-lora-sdxl/README.md

@@ -1,12 +0,0 @@
----
-title: Ostris Ikea Instructions Lora Sdxl
-emoji: 🚀
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.45.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AmazonScience/QA-NLU/app.py

@@ -1,202 +0,0 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
-
-st.title('Question-Answering NLU')
-
-st.sidebar.title('Navigation')
-menu = st.sidebar.radio("", options=["Demo", "Parsing NLU data into SQuAD 2.0", "Training",
-                                     "Evaluation"], index=0)
-
-
-if menu == "Demo":
-
-    st.markdown('''
-
-        Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering, 
-        leveraging pre-trained question-answering models to perform well on few-shot settings. Instead of 
-        training an intent classifier or a slot tagger, for example, we can ask the model intent- and 
-        slot-related questions in natural language: 
-        
-        ```
-        Context : I'm looking for a cheap flight to Boston.
-        
-        Question: Is the user looking to book a flight?
-        Answer  : Yes
-        
-        Question: Is the user asking about departure time?
-        Answer  : No
-        
-        Question: What price is the user looking for?
-        Answer  : cheap
-        
-        Question: Where is the user flying from?
-        Answer  : (empty)
-        ```
-        
-        Thus, by asking questions for each intent and slot in natural language, we can effectively construct an NLU hypothesis. For more details,
-        please read the paper: 
-        [Language model is all you need: Natural language understanding as question answering](https://assets.amazon.science/33/ea/800419b24a09876601d8ab99bfb9/language-model-is-all-you-need-natural-language-understanding-as-question-answering.pdf).
-        
-        In this Space, we will see how to transform an example
-        NLU dataset (e.g. utterances and intent / slot annotations) into [SQuAD 2.0 format](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/)
-        question-answering data that can be used by QANLU.
-        
-        ### Demo
-
-        Feel free to query the pre-trained QA-NLU model using the buttons below. 
-        
-        *Please note that this model has been trained on ATIS and may be need to be further fine-tuned to support intents and slots that are not covered in ATIS*.
-    ''')
-    
-    tokenizer = AutoTokenizer.from_pretrained("AmazonScience/qanlu")
-
-    model = AutoModelForQuestionAnswering.from_pretrained("AmazonScience/qanlu")
-
-    qa_pipeline = pipeline('question-answering', model=model, tokenizer=tokenizer)
-    
-    context = st.text_input(
-        'Please enter the context (remember to include "Yes. No. " in the beginning):',
-        value="Yes. No. I want a cheap flight to Boston."
-    )
-    question = st.text_input(
-        'Please enter the intent question:',
-        value="Are they looking for a flight?"
-    )
-
-
-    qa_input = {
-      'context': context,
-      'question': question
-    }
-
-    if st.button('Ask QANLU'):
-        answer = qa_pipeline(qa_input)
-        st.write(answer)
-
-elif menu == "Parsing NLU data into SQuAD 2.0":
-    st.header('QA-NLU Data Parsing')
-    
-    st.markdown('''
-        Here, we show a small example of how NLU data can be transformed into QANLU data.
-        The same method can be used to transform [MATIS++](https://github.com/amazon-research/multiatis) 
-        NLU data (e.g. utterances and intent / slot annotations) into [SQuAD 2.0 format](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/)
-        question-answering data that can be used by QANLU.
-        
-        Here is an example dataset with three intents and two examples per intent: 
-        
-        ````
-        restaurant, I am looking for some Vietnamese food
-        restaurant, What is there to eat around here?
-        music, Play my workout playlist
-        music, Can you find Bob Dylan songs?
-        flight, Show me flights from Oakland to Dallas
-        flight, I want two economy tickets from Miami to Chicago
-        ````
-        
-        Now, we need to define some questions, per intent. We can use free-form questions or use templates.
-        
-        ````
-        {
-            'restaurant': [
-                'Did they ask for a restaurant?',
-                'Did they mention a restaurant?'
-            ],
-            'music': [
-                'Did they ask for music?',
-                'Do they want to play music?'
-            ],
-            'flight': [
-                'Did they ask for a flight?',
-                'Do they want to book a flight?'
-            ]
-        }
-        ````
-        
-        The next step is to run the `atis.py` script from the [QA-NLU Amazon Research repository](https://github.com/amazon-research/question-answering-nlu).
-        That script will produce a json file that looks like this:
-        
-        ````
-        {
-        "version": 1.0,
-        "data": [
-            {
-                "title": "MultiATIS++",
-                "paragraphs": [
-                    {
-                        "context": "yes. no. i am looking for some vietnamese food",
-                        "qas": [
-                            {
-                                "question": "did they ask for a restaurant?",
-                                "id": "49f1180cb9ce4178a8a90f76c21f69b4",
-                                "is_impossible": false,
-                                "answers": [
-                                    {
-                                        "text": "yes",
-                                        "answer_start": 0
-                                    }
-                                ],
-                                "slot": "",
-                                "intent": "restaurant"
-                            },
-                            {
-                                "question": "did they ask for music?",
-                                "id": "a7ffe039fb3e4843ae16d5a68194f45e",
-                                "is_impossible": false,
-                                "answers": [
-                                    {
-                                        "text": "no",
-                                        "answer_start": 5
-                                    }
-                                ],
-                                "slot": "",
-                                "intent": "restaurant"
-                            },
-                            ... <More questions>
-                            
-                ... <More paragraphs>
-        ````
-        
-        There are many tunable parameters when generating the above file, such as how many negative examples to include per question. Follow the same process for training a slot-tagging model.
-        
-    ''')
-    
-elif menu == "Training":
-    st.header('QA-NLU Training')
-    
-    st.markdown('''
-        To train a QA-NLU model on the data we created, we use the `run_squad.py` script from [huggingface](https://github.com/huggingface/transformers/blob/master/examples/legacy/question-answering/run_squad.py) and a SQuAD-trained QA model as our base. As an example, we can use `deepset/roberta-base-squad2` model from [here](https://huggingface.co/deepset/roberta-base-squad2) (assuming 8 GPUs are present):
-    ''')
-    
-    st.code('''
-        mkdir models
-        
-        python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \\
-            --model_type roberta \\
-            --model_name_or_path deepset/roberta-base-squad2 \\
-            --do_train \\
-            --do_eval \\
-            --do_lower_case \\
-            --train_file data/matis_en_train_squad.json \\
-            --predict_file data/matis_en_test_squad.json \\
-            --learning_rate 3e-5 \\
-            --num_train_epochs 2 \\
-            --max_seq_length 384 \\
-            --doc_stride 64 \\
-            --output_dir models/qanlu/ \\
-            --per_gpu_train_batch_size 8 \\
-            --overwrite_output_dir \\
-            --version_2_with_negative \\
-            --save_steps 100000 \\
-            --gradient_accumulation_steps 8 \\
-            --seed $RANDOM
-    ''')
-
-elif menu == "Evaluation":
-    st.header('QA-NLU Evaluation')
-    
-    st.markdown('''
-        To assess the performance of the trained model, we can use the `calculate_pr.py` script from the [QA-NLU Amazon Research repository](https://github.com/amazon-research/question-answering-nlu).
-        
-        Feel free to query the pre-trained QA-NLU model in the Demo section.
-    ''')
-   

spaces/Amrrs/DragGan-Inversion/PTI/utils/align_data.py

@@ -1,37 +0,0 @@
-import sys
-sys.path.append('.')
-from configs import paths_config
-import dlib
-import glob
-import os
-from tqdm import tqdm
-from utils.alignment import align_face
-
-
-def pre_process_images(raw_images_path):
-    current_directory = os.getcwd()
-
-    IMAGE_SIZE = 1024
-    predictor = dlib.shape_predictor(paths_config.dlib)
-    os.chdir(raw_images_path)
-    images_names = glob.glob(f'*')
-
-    aligned_images = []
-    for image_name in tqdm(images_names):
-        try:
-            aligned_image = align_face(filepath=f'{raw_images_path}/{image_name}',
-                                       predictor=predictor, output_size=IMAGE_SIZE)
-            aligned_images.append(aligned_image)
-        except Exception as e:
-            print(e)
-
-    os.makedirs(paths_config.input_data_path, exist_ok=True)
-    for image, name in zip(aligned_images, images_names):
-        real_name = name.split('.')[0]
-        image.save(f'{paths_config.input_data_path}/{real_name}.jpeg')
-
-    os.chdir(current_directory)
-
-
-if __name__ == "__main__":
-    pre_process_images('/home/zhizizhang/Documents2/projects/PTI/docs')

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/training/lora.md

@@ -1,128 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Low-Rank Adaptation of Large Language Models (LoRA)
-
-[[open-in-colab]]
-
-<Tip warning={true}>
-
-현재 LoRA는 [`UNet2DConditionalModel`]의 어텐션 레이어에서만 지원됩니다.
-
-</Tip>
-
-[LoRA(Low-Rank Adaptation of Large Language Models)](https://arxiv.org/abs/2106.09685)는 메모리를 적게 사용하면서 대규모 모델의 학습을 가속화하는 학습 방법입니다. 이는 rank-decomposition weight 행렬 쌍(**업데이트 행렬**이라고 함)을 추가하고 새로 추가된 가중치**만** 학습합니다. 여기에는 몇 가지 장점이 있습니다.
-
-- 이전에 미리 학습된 가중치는 고정된 상태로 유지되므로 모델이 [치명적인 망각](https://www.pnas.org/doi/10.1073/pnas.1611835114) 경향이 없습니다.
-- Rank-decomposition 행렬은 원래 모델보다 파라메터 수가 훨씬 적으므로 학습된 LoRA 가중치를 쉽게 끼워넣을 수 있습니다.
-- LoRA 매트릭스는 일반적으로 원본 모델의 어텐션 레이어에 추가됩니다. 🧨 Diffusers는 [`~diffusers.loaders.UNet2DConditionLoadersMixin.load_attn_procs`] 메서드를 제공하여 LoRA 가중치를 모델의 어텐션 레이어로 불러옵니다. `scale` 매개변수를 통해 모델이 새로운 학습 이미지에 맞게 조정되는 범위를 제어할 수 있습니다.
-- 메모리 효율성이 향상되어 Tesla T4, RTX 3080 또는 RTX 2080 Ti와 같은 소비자용 GPU에서 파인튜닝을 실행할 수 있습니다! T4와 같은 GPU는 무료이며 Kaggle 또는 Google Colab 노트북에서 쉽게 액세스할 수 있습니다.
-
-
-<Tip>
-
-💡 LoRA는 어텐션 레이어에만 한정되지는 않습니다. 저자는 언어 모델의 어텐션 레이어를 수정하는 것이 매우 효율적으로 죻은 성능을 얻기에 충분하다는 것을 발견했습니다. 이것이 LoRA 가중치를 모델의 어텐션 레이어에 추가하는 것이 일반적인 이유입니다. LoRA 작동 방식에 대한 자세한 내용은 [Using LoRA for effective Stable Diffusion fine-tuning](https://huggingface.co/blog/lora) 블로그를 확인하세요!
-
-</Tip>
-
-[cloneofsimo](https://github.com/cloneofsimo)는 인기 있는 [lora](https://github.com/cloneofsimo/lora) GitHub 리포지토리에서 Stable Diffusion을 위한 LoRA 학습을 최초로 시도했습니다. 🧨 Diffusers는 [text-to-image 생성](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image#training-with-lora) 및 [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth#training-with-low-rank-adaptation-of-large-language-models-lora)을 지원합니다. 이 가이드는 두 가지를 모두 수행하는 방법을 보여줍니다.
-
-모델을 저장하거나 커뮤니티와 공유하려면 Hugging Face 계정에 로그인하세요(아직 계정이 없는 경우 [생성](hf.co/join)하세요):
-
-```bash
-huggingface-cli login
-```
-
-## Text-to-image
-
-수십억 개의 파라메터들이 있는 Stable Diffusion과 같은 모델을 파인튜닝하는 것은 느리고 어려울 수 있습니다. LoRA를 사용하면 diffusion 모델을 파인튜닝하는 것이 훨씬 쉽고 빠릅니다. 8비트 옵티마이저와 같은 트릭에 의존하지 않고도 11GB의 GPU RAM으로 하드웨어에서 실행할 수 있습니다.
-
-
-### 학습 [[text-to-image 학습]]
-
-[Pokémon BLIP 캡션](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) 데이터셋으로 [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)를 파인튜닝해 나만의 포켓몬을 생성해 보겠습니다.
-
-시작하려면 `MODEL_NAME` 및 `DATASET_NAME` 환경 변수가 설정되어 있는지 확인하십시오. `OUTPUT_DIR` 및 `HUB_MODEL_ID` 변수는 선택 사항이며 허브에서 모델을 저장할 위치를 지정합니다.
-
-```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="/sddata/finetune/lora/pokemon"
-export HUB_MODEL_ID="pokemon-lora"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
-```
-
-학습을 시작하기 전에 알아야 할 몇 가지 플래그가 있습니다.
-
-* `--push_to_hub`를 명시하면 학습된 LoRA 임베딩을 허브에 저장합니다.
-* `--report_to=wandb`는 학습 결과를 가중치 및 편향 대시보드에 보고하고 기록합니다(예를 들어, 이 [보고서](https://wandb.ai/pcuenq/text2image-fine-tune/run/b4k1w0tn?workspace=user-pcuenq)를 ���조하세요).
-* `--learning_rate=1e-04`, 일반적으로 LoRA에서 사용하는 것보다 더 높은 학습률을 사용할 수 있습니다.
-
-이제 학습을 시작할 준비가 되었습니다 (전체 학습 스크립트는 [여기](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py)에서 찾을 수 있습니다).
-
-```bash
-accelerate launch train_dreambooth_lora.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --instance_prompt="a photo of sks dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=1 \
-  --checkpointing_steps=100 \
-  --learning_rate=1e-4 \
-  --report_to="wandb" \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --max_train_steps=500 \
-  --validation_prompt="A photo of sks dog in a bucket" \
-  --validation_epochs=50 \
-  --seed="0" \
-  --push_to_hub
-```
-
-### 추론 [[dreambooth 추론]]
-
-이제 [`StableDiffusionPipeline`]에서 기본 모델을 불러와 추론을 위해 모델을 사용할 수 있습니다:
-
-```py
->>> import torch
->>> from diffusers import StableDiffusionPipeline
-
->>> model_base = "runwayml/stable-diffusion-v1-5"
-
->>> pipe = StableDiffusionPipeline.from_pretrained(model_base, torch_dtype=torch.float16)
-```
-
-*기본 모델의 가중치 위에* 파인튜닝된 DreamBooth 모델에서 LoRA 가중치를 불러온 다음, 더 빠른 추론을 위해 파이프라인을 GPU로 이동합니다. LoRA 가중치를 프리징된 사전 훈련된 모델 가중치와 병합할 때, 선택적으로 'scale' 매개변수로 어느 정도의 가중치를 병합할 지 조절할 수 있습니다:
-
-<Tip>
-
-💡 `0`의 `scale` 값은 LoRA 가중치를 사용하지 않아 원래 모델의 가중치만 사용한 것과 같고, `1`의 `scale` 값은 파인튜닝된 LoRA 가중치만 사용함을 의미합니다. 0과 1 사이의 값들은 두 결과들 사이로 보간됩니다.
-
-</Tip>
-
-```py
->>> pipe.unet.load_attn_procs(model_path)
->>> pipe.to("cuda")
-# LoRA 파인튜닝된 모델의 가중치 절반과 기본 모델의 가중치 절반 사용
-
->>> image = pipe(
-...     "A picture of a sks dog in a bucket.",
-...     num_inference_steps=25,
-...     guidance_scale=7.5,
-...     cross_attention_kwargs={"scale": 0.5},
-... ).images[0]
-# 완전히 파인튜닝된 LoRA 모델의 가중치 사용
-
->>> image = pipe("A picture of a sks dog in a bucket.", num_inference_steps=25, guidance_scale=7.5).images[0]
->>> image.save("bucket-dog.png")
-```

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/instruct_pix2pix/README.md

@@ -1,193 +0,0 @@
-# InstructPix2Pix training example
-
-[InstructPix2Pix](https://arxiv.org/abs/2211.09800) is a method to fine-tune text-conditioned diffusion models such that they can follow an edit instruction for an input image. Models fine-tuned using this method take the following as inputs:
-
-<p align="center">
-    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-instruction.png" alt="instructpix2pix-inputs" width=600/>
-</p>
-
-The output is an "edited" image that reflects the edit instruction applied on the input image:
-
-<p align="center">
-    <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/output-gs%407-igs%401-steps%4050.png" alt="instructpix2pix-output" width=600/>
-</p>
-
-The `train_instruct_pix2pix.py` script shows how to implement the training procedure and adapt it for Stable Diffusion.
-
-***Disclaimer: Even though `train_instruct_pix2pix.py` implements the InstructPix2Pix
-training procedure while being faithful to the [original implementation](https://github.com/timothybrooks/instruct-pix2pix) we have only tested it on a [small-scale dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples). This can impact the end results. For better results, we recommend longer training runs with a larger dataset. [Here](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered) you can find a large dataset for InstructPix2Pix training.***
-
-## Running locally with PyTorch
-
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install -e .
-```
-
-Then cd in the example folder and run
-```bash
-pip install -r requirements.txt
-```
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-Or for a default accelerate configuration without answering questions about your environment
-
-```bash
-accelerate config default
-```
-
-Or if your environment doesn't support an interactive shell e.g. a notebook
-
-```python
-from accelerate.utils import write_basic_config
-write_basic_config()
-```
-
-### Toy example
-
-As mentioned before, we'll use a [small toy dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples) for training. The dataset 
-is a smaller version of the [original dataset](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered) used in the InstructPix2Pix paper.
-
-Configure environment variables such as the dataset identifier and the Stable Diffusion
-checkpoint:
-
-```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-export DATASET_ID="fusing/instructpix2pix-1000-samples"
-```
-
-Now, we can launch training:
-
-```bash
-accelerate launch --mixed_precision="fp16" train_instruct_pix2pix.py \
-    --pretrained_model_name_or_path=$MODEL_NAME \
-    --dataset_name=$DATASET_ID \
-    --enable_xformers_memory_efficient_attention \
-    --resolution=256 --random_flip \
-    --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \
-    --max_train_steps=15000 \
-    --checkpointing_steps=5000 --checkpoints_total_limit=1 \
-    --learning_rate=5e-05 --max_grad_norm=1 --lr_warmup_steps=0 \
-    --conditioning_dropout_prob=0.05 \
-    --mixed_precision=fp16 \
-    --seed=42 
-```
-
-Additionally, we support performing validation inference to monitor training progress
-with Weights and Biases. You can enable this feature with `report_to="wandb"`:
-
-```bash
-accelerate launch --mixed_precision="fp16" train_instruct_pix2pix.py \
-    --pretrained_model_name_or_path=$MODEL_NAME \
-    --dataset_name=$DATASET_ID \
-    --enable_xformers_memory_efficient_attention \
-    --resolution=256 --random_flip \
-    --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \
-    --max_train_steps=15000 \
-    --checkpointing_steps=5000 --checkpoints_total_limit=1 \
-    --learning_rate=5e-05 --max_grad_norm=1 --lr_warmup_steps=0 \
-    --conditioning_dropout_prob=0.05 \
-    --mixed_precision=fp16 \
-    --val_image_url="https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png" \
-    --validation_prompt="make the mountains snowy" \
-    --seed=42 \
-    --report_to=wandb 
- ```
-
- We recommend this type of validation as it can be useful for model debugging. Note that you need `wandb` installed to use this. You can install `wandb` by running `pip install wandb`. 
-
- [Here](https://wandb.ai/sayakpaul/instruct-pix2pix/runs/ctr3kovq), you can find an example training run that includes some validation samples and the training hyperparameters.
-
- ***Note: In the original paper, the authors observed that even when the model is trained with an image resolution of 256x256, it generalizes well to bigger resolutions such as 512x512. This is likely because of the larger dataset they used during training.***
-
- ## Training with multiple GPUs
-
-`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)
-for running distributed training with `accelerate`. Here is an example command:
-
-```bash 
-accelerate launch --mixed_precision="fp16" --multi_gpu train_instruct_pix2pix.py \
- --pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5 \
- --dataset_name=sayakpaul/instructpix2pix-1000-samples \
- --use_ema \
- --enable_xformers_memory_efficient_attention \
- --resolution=512 --random_flip \
- --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \
- --max_train_steps=15000 \
- --checkpointing_steps=5000 --checkpoints_total_limit=1 \
- --learning_rate=5e-05 --lr_warmup_steps=0 \
- --conditioning_dropout_prob=0.05 \
- --mixed_precision=fp16 \
- --seed=42 
-```
-
- ## Inference
-
- Once training is complete, we can perform inference:
-
- ```python
-import PIL
-import requests
-import torch
-from diffusers import StableDiffusionInstructPix2PixPipeline
-
-model_id = "your_model_id" # <- replace this 
-pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-generator = torch.Generator("cuda").manual_seed(0)
-
-url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/test_pix2pix_4.png"
-
-
-def download_image(url):
-    image = PIL.Image.open(requests.get(url, stream=True).raw)
-    image = PIL.ImageOps.exif_transpose(image)
-    image = image.convert("RGB")
-    return image
-
-image = download_image(url)
-prompt = "wipe out the lake"
-num_inference_steps = 20
-image_guidance_scale = 1.5
-guidance_scale = 10
-
-edited_image = pipe(prompt, 
-    image=image, 
-    num_inference_steps=num_inference_steps, 
-    image_guidance_scale=image_guidance_scale, 
-    guidance_scale=guidance_scale,
-    generator=generator,
-).images[0]
-edited_image.save("edited_image.png")
-```
-
-An example model repo obtained using this training script can be found
-here - [sayakpaul/instruct-pix2pix](https://huggingface.co/sayakpaul/instruct-pix2pix).
-
-We encourage you to play with the following three parameters to control
-speed and quality during performance:
-
-* `num_inference_steps`
-* `image_guidance_scale`
-* `guidance_scale`
-
-Particularly, `image_guidance_scale` and `guidance_scale` can have a profound impact
-on the generated ("edited") image (see [here](https://twitter.com/RisingSayak/status/1628392199196151808?s=20) for an example).
-
-If you're looking for some interesting ways to use the InstructPix2Pix training methodology, we welcome you to check out this blog post: [Instruction-tuning Stable Diffusion with InstructPix2Pix](https://huggingface.co/blog/instruction-tuning-sd). 
-
-## Stable Diffusion XL
-
-We support fine-tuning of the UNet shipped in [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) with DreamBooth and LoRA via the `train_dreambooth_lora_sdxl.py` script. Please refer to the docs [here](./README_sdxl.md).

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/audio_diffusion/__init__.py

@@ -1,2 +0,0 @@
-from .mel import Mel
-from .pipeline_audio_diffusion import AudioDiffusionPipeline

spaces/Andy1621/uniformer_image_detection/configs/yolact/README.md

@@ -1,71 +0,0 @@
-# **Y**ou **O**nly **L**ook **A**t **C**oefficien**T**s
-
-[ALGORITHM]
-
-```
-    ██╗   ██╗ ██████╗ ██╗      █████╗  ██████╗████████╗
-    ╚██╗ ██╔╝██╔═══██╗██║     ██╔══██╗██╔════╝╚══██╔══╝
-     ╚████╔╝ ██║   ██║██║     ███████║██║        ██║
-      ╚██╔╝  ██║   ██║██║     ██╔══██║██║        ██║
-       ██║   ╚██████╔╝███████╗██║  ██║╚██████╗   ██║
-       ╚═╝    ╚═════╝ ╚══════╝╚═╝  ╚═╝ ╚═════╝   ╚═╝
-```
-
-A simple, fully convolutional model for real-time instance segmentation. This is the code for our paper:
-
-- [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689)
- <!-- - [YOLACT++: Better Real-time Instance Segmentation](https://arxiv.org/abs/1912.06218) -->
-
-For a real-time demo, check out our ICCV video:
-[![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/0pMfmo8qfpQ/0.jpg)](https://www.youtube.com/watch?v=0pMfmo8qfpQ)
-
-## Evaluation
-
-Here are our YOLACT models along with their FPS on a Titan Xp and mAP on COCO's `val`:
-
-| Image Size | GPU x BS | Backbone      | *FPS  | mAP  | Weights | Configs | Download |
-|:----------:|:--------:|:-------------:|:-----:|:----:|:-------:|:------:|:--------:|
-| 550        | 1x8      | Resnet50-FPN  | 42.5 | 29.0 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact_r50_1x8_coco.py) |[model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco_20200908-f38d58df.pth) |
-| 550        | 8x8      | Resnet50-FPN  | 42.5 | 28.4 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact_r50_8x8_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_8x8_coco_20200908-ca34f5db.pth) |
-| 550        | 1x8      | Resnet101-FPN | 33.5 | 30.4 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact_r101_1x8_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r101_1x8_coco_20200908-4cbe9101.pth) |
-
-*Note: The FPS is evaluated by the [original implementation](https://github.com/dbolya/yolact). When calculating FPS, only the model inference time is taken into account. Data loading and post-processing operations such as converting masks to RLE code, generating COCO JSON results, image rendering are not included.
-
-## Training
-
-All the aforementioned models are trained with a single GPU. It typically takes ~12GB VRAM when using resnet-101 as the backbone. If you want to try multiple GPUs training, you may have to modify the configuration files accordingly, such as adjusting the training schedule and freezing batch norm.
-
-```Shell
-# Trains using the resnet-101 backbone with a batch size of 8 on a single GPU.
-./tools/dist_train.sh configs/yolact/yolact_r101.py 1
-```
-
-## Testing
-
-Please refer to [mmdetection/docs/getting_started.md](https://github.com/open-mmlab/mmdetection/blob/master/docs/getting_started.md#inference-with-pretrained-models).
-
-## Citation
-
-If you use YOLACT or this code base in your work, please cite
-
-```latex
-@inproceedings{yolact-iccv2019,
-  author    = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
-  title     = {YOLACT: {Real-time} Instance Segmentation},
-  booktitle = {ICCV},
-  year      = {2019},
-}
-```
-
-<!-- For YOLACT++, please cite
-
-```latex
-@misc{yolact-plus-arxiv2019,
-  title         = {YOLACT++: Better Real-time Instance Segmentation},
-  author        = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
-  year          = {2019},
-  eprint        = {1912.06218},
-  archivePrefix = {arXiv},
-  primaryClass  = {cs.CV}
-}
-``` -->

spaces/Andy1621/uniformer_image_detection/mmdet/models/necks/pafpn.py

@@ -1,142 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule
-from mmcv.runner import auto_fp16
-
-from ..builder import NECKS
-from .fpn import FPN
-
-
-@NECKS.register_module()
-class PAFPN(FPN):
-    """Path Aggregation Network for Instance Segmentation.
-
-    This is an implementation of the `PAFPN in Path Aggregation Network
-    <https://arxiv.org/abs/1803.01534>`_.
-
-    Args:
-        in_channels (List[int]): Number of input channels per scale.
-        out_channels (int): Number of output channels (used at each scale)
-        num_outs (int): Number of output scales.
-        start_level (int): Index of the start input backbone level used to
-            build the feature pyramid. Default: 0.
-        end_level (int): Index of the end input backbone level (exclusive) to
-            build the feature pyramid. Default: -1, which means the last level.
-        add_extra_convs (bool): Whether to add conv layers on top of the
-            original feature maps. Default: False.
-        extra_convs_on_inputs (bool): Whether to apply extra conv on
-            the original feature from the backbone. Default: False.
-        relu_before_extra_convs (bool): Whether to apply relu before the extra
-            conv. Default: False.
-        no_norm_on_lateral (bool): Whether to apply norm on lateral.
-            Default: False.
-        conv_cfg (dict): Config dict for convolution layer. Default: None.
-        norm_cfg (dict): Config dict for normalization layer. Default: None.
-        act_cfg (str): Config dict for activation layer in ConvModule.
-            Default: None.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 num_outs,
-                 start_level=0,
-                 end_level=-1,
-                 add_extra_convs=False,
-                 extra_convs_on_inputs=True,
-                 relu_before_extra_convs=False,
-                 no_norm_on_lateral=False,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 act_cfg=None):
-        super(PAFPN,
-              self).__init__(in_channels, out_channels, num_outs, start_level,
-                             end_level, add_extra_convs, extra_convs_on_inputs,
-                             relu_before_extra_convs, no_norm_on_lateral,
-                             conv_cfg, norm_cfg, act_cfg)
-        # add extra bottom up pathway
-        self.downsample_convs = nn.ModuleList()
-        self.pafpn_convs = nn.ModuleList()
-        for i in range(self.start_level + 1, self.backbone_end_level):
-            d_conv = ConvModule(
-                out_channels,
-                out_channels,
-                3,
-                stride=2,
-                padding=1,
-                conv_cfg=conv_cfg,
-                norm_cfg=norm_cfg,
-                act_cfg=act_cfg,
-                inplace=False)
-            pafpn_conv = ConvModule(
-                out_channels,
-                out_channels,
-                3,
-                padding=1,
-                conv_cfg=conv_cfg,
-                norm_cfg=norm_cfg,
-                act_cfg=act_cfg,
-                inplace=False)
-            self.downsample_convs.append(d_conv)
-            self.pafpn_convs.append(pafpn_conv)
-
-    @auto_fp16()
-    def forward(self, inputs):
-        """Forward function."""
-        assert len(inputs) == len(self.in_channels)
-
-        # build laterals
-        laterals = [
-            lateral_conv(inputs[i + self.start_level])
-            for i, lateral_conv in enumerate(self.lateral_convs)
-        ]
-
-        # build top-down path
-        used_backbone_levels = len(laterals)
-        for i in range(used_backbone_levels - 1, 0, -1):
-            prev_shape = laterals[i - 1].shape[2:]
-            laterals[i - 1] += F.interpolate(
-                laterals[i], size=prev_shape, mode='nearest')
-
-        # build outputs
-        # part 1: from original levels
-        inter_outs = [
-            self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)
-        ]
-
-        # part 2: add bottom-up path
-        for i in range(0, used_backbone_levels - 1):
-            inter_outs[i + 1] += self.downsample_convs[i](inter_outs[i])
-
-        outs = []
-        outs.append(inter_outs[0])
-        outs.extend([
-            self.pafpn_convs[i - 1](inter_outs[i])
-            for i in range(1, used_backbone_levels)
-        ])
-
-        # part 3: add extra levels
-        if self.num_outs > len(outs):
-            # use max pool to get more levels on top of outputs
-            # (e.g., Faster R-CNN, Mask R-CNN)
-            if not self.add_extra_convs:
-                for i in range(self.num_outs - used_backbone_levels):
-                    outs.append(F.max_pool2d(outs[-1], 1, stride=2))
-            # add conv layers on top of original feature maps (RetinaNet)
-            else:
-                if self.add_extra_convs == 'on_input':
-                    orig = inputs[self.backbone_end_level - 1]
-                    outs.append(self.fpn_convs[used_backbone_levels](orig))
-                elif self.add_extra_convs == 'on_lateral':
-                    outs.append(self.fpn_convs[used_backbone_levels](
-                        laterals[-1]))
-                elif self.add_extra_convs == 'on_output':
-                    outs.append(self.fpn_convs[used_backbone_levels](outs[-1]))
-                else:
-                    raise NotImplementedError
-                for i in range(used_backbone_levels + 1, self.num_outs):
-                    if self.relu_before_extra_convs:
-                        outs.append(self.fpn_convs[i](F.relu(outs[-1])))
-                    else:
-                        outs.append(self.fpn_convs[i](outs[-1]))
-        return tuple(outs)

spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/scripts/super_res_train.py

@@ -1,98 +0,0 @@
-"""
-Train a super-resolution model.
-"""
-
-import argparse
-
-import torch.nn.functional as F
-
-from guided_diffusion import dist_util, logger
-from guided_diffusion.image_datasets import load_data
-from guided_diffusion.resample import create_named_schedule_sampler
-from guided_diffusion.script_util import (
-    sr_model_and_diffusion_defaults,
-    sr_create_model_and_diffusion,
-    args_to_dict,
-    add_dict_to_argparser,
-)
-from guided_diffusion.train_util import TrainLoop
-
-
-def main():
-    args = create_argparser().parse_args()
-
-    dist_util.setup_dist()
-    logger.configure()
-
-    logger.log("creating model...")
-    model, diffusion = sr_create_model_and_diffusion(
-        **args_to_dict(args, sr_model_and_diffusion_defaults().keys())
-    )
-    model.to(dist_util.dev())
-    schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
-
-    logger.log("creating data loader...")
-    data = load_superres_data(
-        args.data_dir,
-        args.batch_size,
-        large_size=args.large_size,
-        small_size=args.small_size,
-        class_cond=args.class_cond,
-    )
-
-    logger.log("training...")
-    TrainLoop(
-        model=model,
-        diffusion=diffusion,
-        data=data,
-        batch_size=args.batch_size,
-        microbatch=args.microbatch,
-        lr=args.lr,
-        ema_rate=args.ema_rate,
-        log_interval=args.log_interval,
-        save_interval=args.save_interval,
-        resume_checkpoint=args.resume_checkpoint,
-        use_fp16=args.use_fp16,
-        fp16_scale_growth=args.fp16_scale_growth,
-        schedule_sampler=schedule_sampler,
-        weight_decay=args.weight_decay,
-        lr_anneal_steps=args.lr_anneal_steps,
-    ).run_loop()
-
-
-def load_superres_data(data_dir, batch_size, large_size, small_size, class_cond=False):
-    data = load_data(
-        data_dir=data_dir,
-        batch_size=batch_size,
-        image_size=large_size,
-        class_cond=class_cond,
-    )
-    for large_batch, model_kwargs in data:
-        model_kwargs["low_res"] = F.interpolate(large_batch, small_size, mode="area")
-        yield large_batch, model_kwargs
-
-
-def create_argparser():
-    defaults = dict(
-        data_dir="",
-        schedule_sampler="uniform",
-        lr=1e-4,
-        weight_decay=0.0,
-        lr_anneal_steps=0,
-        batch_size=1,
-        microbatch=-1,
-        ema_rate="0.9999",
-        log_interval=10,
-        save_interval=10000,
-        resume_checkpoint="",
-        use_fp16=False,
-        fp16_scale_growth=1e-3,
-    )
-    defaults.update(sr_model_and_diffusion_defaults())
-    parser = argparse.ArgumentParser()
-    add_dict_to_argparser(parser, defaults)
-    return parser
-
-
-if __name__ == "__main__":
-    main()

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/test.py

@@ -1,54 +0,0 @@
-import os,time
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-from options.test_options import TestOptions
-from dataloader.data_loader import dataloader
-from model import create_model
-from itertools import islice
-from util.visualizer import save_images
-from util import html
-
-if __name__=='__main__':
-    opt = TestOptions().parse()       # get test options
-    opt.name = 'imagenet'
-    opt.img_file='../../tmp/img/'
-    opt.mask_file='../../tmp/mask/'
-    opt.results_dir='../../results'
-    opt.model='tc'
-    opt.coarse_or_refine='refine'
-    opt.gpu_id=0
-    opt.no_shuffle=True
-    opt.batch_size=1
-    opt.preprocess='scale_shortside'
-    opt.mask_type=3
-    opt.load_size=512
-    opt.attn_G=True
-    opt.add_noise=True
-    dataset = dataloader(opt)       # create a dataset
-    dataset_size = len(dataset) * opt.batch_size
-    print('testing images = %d' % dataset_size)
-    model = create_model(opt)       # create a model
-    # create a website
-    opt.epoch = '%d' % opt.which_iter if opt.which_iter > 0 else opt.epoch
-    web_dir = os.path.join(opt.results_dir, opt.name, '{}_{}'.format(opt.phase, opt.epoch))  # define the website directory
-    print('creating web directory', web_dir)
-    opt.save_dir = web_dir
-    webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
-    opt.how_many = dataset_size if opt.how_many == float("inf") else opt.how_many
-
-    iter_data_time = time.time()
-    for i, data in enumerate(islice(dataset, opt.how_many)):
-        if i == 0:
-            model.setup(opt)
-            model.parallelize()
-            model.eval()
-        model.set_input(data)
-        model.test()
-        visuals = model.get_current_visuals()
-        img_path = model.get_image_paths()
-        save_images(webpage, visuals, img_path, width=opt.display_winsize)
-        if i % 5 == 0:
-            print('processing (%04d)-th image... %s' % (i, img_path))
-    total_time = time.time() - iter_data_time
-    print('the total evaluation time %f' % (total_time))
-    webpage.save()

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/optimizer.py

@@ -1,508 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-from collections import defaultdict
-from itertools import chain
-
-from torch.nn.utils import clip_grad
-
-from annotator.uniformer.mmcv.utils import TORCH_VERSION, _BatchNorm, digit_version
-from ..dist_utils import allreduce_grads
-from ..fp16_utils import LossScaler, wrap_fp16_model
-from .hook import HOOKS, Hook
-
-try:
-    # If PyTorch version >= 1.6.0, torch.cuda.amp.GradScaler would be imported
-    # and used; otherwise, auto fp16 will adopt mmcv's implementation.
-    from torch.cuda.amp import GradScaler
-except ImportError:
-    pass
-
-
-@HOOKS.register_module()
-class OptimizerHook(Hook):
-
-    def __init__(self, grad_clip=None):
-        self.grad_clip = grad_clip
-
-    def clip_grads(self, params):
-        params = list(
-            filter(lambda p: p.requires_grad and p.grad is not None, params))
-        if len(params) > 0:
-            return clip_grad.clip_grad_norm_(params, **self.grad_clip)
-
-    def after_train_iter(self, runner):
-        runner.optimizer.zero_grad()
-        runner.outputs['loss'].backward()
-        if self.grad_clip is not None:
-            grad_norm = self.clip_grads(runner.model.parameters())
-            if grad_norm is not None:
-                # Add grad norm to the logger
-                runner.log_buffer.update({'grad_norm': float(grad_norm)},
-                                         runner.outputs['num_samples'])
-        runner.optimizer.step()
-
-
-@HOOKS.register_module()
-class GradientCumulativeOptimizerHook(OptimizerHook):
-    """Optimizer Hook implements multi-iters gradient cumulating.
-
-    Args:
-        cumulative_iters (int, optional): Num of gradient cumulative iters.
-            The optimizer will step every `cumulative_iters` iters.
-            Defaults to 1.
-
-    Examples:
-        >>> # Use cumulative_iters to simulate a large batch size
-        >>> # It is helpful when the hardware cannot handle a large batch size.
-        >>> loader = DataLoader(data, batch_size=64)
-        >>> optim_hook = GradientCumulativeOptimizerHook(cumulative_iters=4)
-        >>> # almost equals to
-        >>> loader = DataLoader(data, batch_size=256)
-        >>> optim_hook = OptimizerHook()
-    """
-
-    def __init__(self, cumulative_iters=1, **kwargs):
-        super(GradientCumulativeOptimizerHook, self).__init__(**kwargs)
-
-        assert isinstance(cumulative_iters, int) and cumulative_iters > 0, \
-            f'cumulative_iters only accepts positive int, but got ' \
-            f'{type(cumulative_iters)} instead.'
-
-        self.cumulative_iters = cumulative_iters
-        self.divisible_iters = 0
-        self.remainder_iters = 0
-        self.initialized = False
-
-    def has_batch_norm(self, module):
-        if isinstance(module, _BatchNorm):
-            return True
-        for m in module.children():
-            if self.has_batch_norm(m):
-                return True
-        return False
-
-    def _init(self, runner):
-        if runner.iter % self.cumulative_iters != 0:
-            runner.logger.warning(
-                'Resume iter number is not divisible by cumulative_iters in '
-                'GradientCumulativeOptimizerHook, which means the gradient of '
-                'some iters is lost and the result may be influenced slightly.'
-            )
-
-        if self.has_batch_norm(runner.model) and self.cumulative_iters > 1:
-            runner.logger.warning(
-                'GradientCumulativeOptimizerHook may slightly decrease '
-                'performance if the model has BatchNorm layers.')
-
-        residual_iters = runner.max_iters - runner.iter
-
-        self.divisible_iters = (
-            residual_iters // self.cumulative_iters * self.cumulative_iters)
-        self.remainder_iters = residual_iters - self.divisible_iters
-
-        self.initialized = True
-
-    def after_train_iter(self, runner):
-        if not self.initialized:
-            self._init(runner)
-
-        if runner.iter < self.divisible_iters:
-            loss_factor = self.cumulative_iters
-        else:
-            loss_factor = self.remainder_iters
-        loss = runner.outputs['loss']
-        loss = loss / loss_factor
-        loss.backward()
-
-        if (self.every_n_iters(runner, self.cumulative_iters)
-                or self.is_last_iter(runner)):
-
-            if self.grad_clip is not None:
-                grad_norm = self.clip_grads(runner.model.parameters())
-                if grad_norm is not None:
-                    # Add grad norm to the logger
-                    runner.log_buffer.update({'grad_norm': float(grad_norm)},
-                                             runner.outputs['num_samples'])
-            runner.optimizer.step()
-            runner.optimizer.zero_grad()
-
-
-if (TORCH_VERSION != 'parrots'
-        and digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
-
-    @HOOKS.register_module()
-    class Fp16OptimizerHook(OptimizerHook):
-        """FP16 optimizer hook (using PyTorch's implementation).
-
-        If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend,
-        to take care of the optimization procedure.
-
-        Args:
-            loss_scale (float | str | dict): Scale factor configuration.
-                If loss_scale is a float, static loss scaling will be used with
-                the specified scale. If loss_scale is a string, it must be
-                'dynamic', then dynamic loss scaling will be used.
-                It can also be a dict containing arguments of GradScalar.
-                Defaults to 512. For Pytorch >= 1.6, mmcv uses official
-                implementation of GradScaler. If you use a dict version of
-                loss_scale to create GradScaler, please refer to:
-                https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler
-                for the parameters.
-
-        Examples:
-            >>> loss_scale = dict(
-            ...     init_scale=65536.0,
-            ...     growth_factor=2.0,
-            ...     backoff_factor=0.5,
-            ...     growth_interval=2000
-            ... )
-            >>> optimizer_hook = Fp16OptimizerHook(loss_scale=loss_scale)
-        """
-
-        def __init__(self,
-                     grad_clip=None,
-                     coalesce=True,
-                     bucket_size_mb=-1,
-                     loss_scale=512.,
-                     distributed=True):
-            self.grad_clip = grad_clip
-            self.coalesce = coalesce
-            self.bucket_size_mb = bucket_size_mb
-            self.distributed = distributed
-            self._scale_update_param = None
-            if loss_scale == 'dynamic':
-                self.loss_scaler = GradScaler()
-            elif isinstance(loss_scale, float):
-                self._scale_update_param = loss_scale
-                self.loss_scaler = GradScaler(init_scale=loss_scale)
-            elif isinstance(loss_scale, dict):
-                self.loss_scaler = GradScaler(**loss_scale)
-            else:
-                raise ValueError('loss_scale must be of type float, dict, or '
-                                 f'"dynamic", got {loss_scale}')
-
-        def before_run(self, runner):
-            """Preparing steps before Mixed Precision Training."""
-            # wrap model mode to fp16
-            wrap_fp16_model(runner.model)
-            # resume from state dict
-            if 'fp16' in runner.meta and 'loss_scaler' in runner.meta['fp16']:
-                scaler_state_dict = runner.meta['fp16']['loss_scaler']
-                self.loss_scaler.load_state_dict(scaler_state_dict)
-
-        def copy_grads_to_fp32(self, fp16_net, fp32_weights):
-            """Copy gradients from fp16 model to fp32 weight copy."""
-            for fp32_param, fp16_param in zip(fp32_weights,
-                                              fp16_net.parameters()):
-                if fp16_param.grad is not None:
-                    if fp32_param.grad is None:
-                        fp32_param.grad = fp32_param.data.new(
-                            fp32_param.size())
-                    fp32_param.grad.copy_(fp16_param.grad)
-
-        def copy_params_to_fp16(self, fp16_net, fp32_weights):
-            """Copy updated params from fp32 weight copy to fp16 model."""
-            for fp16_param, fp32_param in zip(fp16_net.parameters(),
-                                              fp32_weights):
-                fp16_param.data.copy_(fp32_param.data)
-
-        def after_train_iter(self, runner):
-            """Backward optimization steps for Mixed Precision Training. For
-            dynamic loss scaling, please refer to
-            https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler.
-
-            1. Scale the loss by a scale factor.
-            2. Backward the loss to obtain the gradients.
-            3. Unscale the optimizer’s gradient tensors.
-            4. Call optimizer.step() and update scale factor.
-            5. Save loss_scaler state_dict for resume purpose.
-            """
-            # clear grads of last iteration
-            runner.model.zero_grad()
-            runner.optimizer.zero_grad()
-
-            self.loss_scaler.scale(runner.outputs['loss']).backward()
-            self.loss_scaler.unscale_(runner.optimizer)
-            # grad clip
-            if self.grad_clip is not None:
-                grad_norm = self.clip_grads(runner.model.parameters())
-                if grad_norm is not None:
-                    # Add grad norm to the logger
-                    runner.log_buffer.update({'grad_norm': float(grad_norm)},
-                                             runner.outputs['num_samples'])
-            # backward and update scaler
-            self.loss_scaler.step(runner.optimizer)
-            self.loss_scaler.update(self._scale_update_param)
-
-            # save state_dict of loss_scaler
-            runner.meta.setdefault(
-                'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
-
-    @HOOKS.register_module()
-    class GradientCumulativeFp16OptimizerHook(GradientCumulativeOptimizerHook,
-                                              Fp16OptimizerHook):
-        """Fp16 optimizer Hook (using PyTorch's implementation) implements
-        multi-iters gradient cumulating.
-
-        If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend,
-        to take care of the optimization procedure.
-        """
-
-        def __init__(self, *args, **kwargs):
-            super(GradientCumulativeFp16OptimizerHook,
-                  self).__init__(*args, **kwargs)
-
-        def after_train_iter(self, runner):
-            if not self.initialized:
-                self._init(runner)
-
-            if runner.iter < self.divisible_iters:
-                loss_factor = self.cumulative_iters
-            else:
-                loss_factor = self.remainder_iters
-            loss = runner.outputs['loss']
-            loss = loss / loss_factor
-
-            self.loss_scaler.scale(loss).backward()
-
-            if (self.every_n_iters(runner, self.cumulative_iters)
-                    or self.is_last_iter(runner)):
-
-                # copy fp16 grads in the model to fp32 params in the optimizer
-                self.loss_scaler.unscale_(runner.optimizer)
-
-                if self.grad_clip is not None:
-                    grad_norm = self.clip_grads(runner.model.parameters())
-                    if grad_norm is not None:
-                        # Add grad norm to the logger
-                        runner.log_buffer.update(
-                            {'grad_norm': float(grad_norm)},
-                            runner.outputs['num_samples'])
-
-                # backward and update scaler
-                self.loss_scaler.step(runner.optimizer)
-                self.loss_scaler.update(self._scale_update_param)
-
-                # save state_dict of loss_scaler
-                runner.meta.setdefault(
-                    'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
-
-                # clear grads
-                runner.model.zero_grad()
-                runner.optimizer.zero_grad()
-
-else:
-
-    @HOOKS.register_module()
-    class Fp16OptimizerHook(OptimizerHook):
-        """FP16 optimizer hook (mmcv's implementation).
-
-        The steps of fp16 optimizer is as follows.
-        1. Scale the loss value.
-        2. BP in the fp16 model.
-        2. Copy gradients from fp16 model to fp32 weights.
-        3. Update fp32 weights.
-        4. Copy updated parameters from fp32 weights to fp16 model.
-
-        Refer to https://arxiv.org/abs/1710.03740 for more details.
-
-        Args:
-            loss_scale (float | str | dict): Scale factor configuration.
-                If loss_scale is a float, static loss scaling will be used with
-                the specified scale. If loss_scale is a string, it must be
-                'dynamic', then dynamic loss scaling will be used.
-                It can also be a dict containing arguments of LossScaler.
-                Defaults to 512.
-        """
-
-        def __init__(self,
-                     grad_clip=None,
-                     coalesce=True,
-                     bucket_size_mb=-1,
-                     loss_scale=512.,
-                     distributed=True):
-            self.grad_clip = grad_clip
-            self.coalesce = coalesce
-            self.bucket_size_mb = bucket_size_mb
-            self.distributed = distributed
-            if loss_scale == 'dynamic':
-                self.loss_scaler = LossScaler(mode='dynamic')
-            elif isinstance(loss_scale, float):
-                self.loss_scaler = LossScaler(
-                    init_scale=loss_scale, mode='static')
-            elif isinstance(loss_scale, dict):
-                self.loss_scaler = LossScaler(**loss_scale)
-            else:
-                raise ValueError('loss_scale must be of type float, dict, or '
-                                 f'"dynamic", got {loss_scale}')
-
-        def before_run(self, runner):
-            """Preparing steps before Mixed Precision Training.
-
-            1. Make a master copy of fp32 weights for optimization.
-            2. Convert the main model from fp32 to fp16.
-            """
-            # keep a copy of fp32 weights
-            old_groups = runner.optimizer.param_groups
-            runner.optimizer.param_groups = copy.deepcopy(
-                runner.optimizer.param_groups)
-            state = defaultdict(dict)
-            p_map = {
-                old_p: p
-                for old_p, p in zip(
-                    chain(*(g['params'] for g in old_groups)),
-                    chain(*(g['params']
-                            for g in runner.optimizer.param_groups)))
-            }
-            for k, v in runner.optimizer.state.items():
-                state[p_map[k]] = v
-            runner.optimizer.state = state
-            # convert model to fp16
-            wrap_fp16_model(runner.model)
-            # resume from state dict
-            if 'fp16' in runner.meta and 'loss_scaler' in runner.meta['fp16']:
-                scaler_state_dict = runner.meta['fp16']['loss_scaler']
-                self.loss_scaler.load_state_dict(scaler_state_dict)
-
-        def copy_grads_to_fp32(self, fp16_net, fp32_weights):
-            """Copy gradients from fp16 model to fp32 weight copy."""
-            for fp32_param, fp16_param in zip(fp32_weights,
-                                              fp16_net.parameters()):
-                if fp16_param.grad is not None:
-                    if fp32_param.grad is None:
-                        fp32_param.grad = fp32_param.data.new(
-                            fp32_param.size())
-                    fp32_param.grad.copy_(fp16_param.grad)
-
-        def copy_params_to_fp16(self, fp16_net, fp32_weights):
-            """Copy updated params from fp32 weight copy to fp16 model."""
-            for fp16_param, fp32_param in zip(fp16_net.parameters(),
-                                              fp32_weights):
-                fp16_param.data.copy_(fp32_param.data)
-
-        def after_train_iter(self, runner):
-            """Backward optimization steps for Mixed Precision Training. For
-            dynamic loss scaling, please refer `loss_scalar.py`
-
-            1. Scale the loss by a scale factor.
-            2. Backward the loss to obtain the gradients (fp16).
-            3. Copy gradients from the model to the fp32 weight copy.
-            4. Scale the gradients back and update the fp32 weight copy.
-            5. Copy back the params from fp32 weight copy to the fp16 model.
-            6. Save loss_scaler state_dict for resume purpose.
-            """
-            # clear grads of last iteration
-            runner.model.zero_grad()
-            runner.optimizer.zero_grad()
-            # scale the loss value
-            scaled_loss = runner.outputs['loss'] * self.loss_scaler.loss_scale
-            scaled_loss.backward()
-            # copy fp16 grads in the model to fp32 params in the optimizer
-
-            fp32_weights = []
-            for param_group in runner.optimizer.param_groups:
-                fp32_weights += param_group['params']
-            self.copy_grads_to_fp32(runner.model, fp32_weights)
-            # allreduce grads
-            if self.distributed:
-                allreduce_grads(fp32_weights, self.coalesce,
-                                self.bucket_size_mb)
-
-            has_overflow = self.loss_scaler.has_overflow(fp32_weights)
-            # if has overflow, skip this iteration
-            if not has_overflow:
-                # scale the gradients back
-                for param in fp32_weights:
-                    if param.grad is not None:
-                        param.grad.div_(self.loss_scaler.loss_scale)
-                if self.grad_clip is not None:
-                    grad_norm = self.clip_grads(fp32_weights)
-                    if grad_norm is not None:
-                        # Add grad norm to the logger
-                        runner.log_buffer.update(
-                            {'grad_norm': float(grad_norm)},
-                            runner.outputs['num_samples'])
-                # update fp32 params
-                runner.optimizer.step()
-                # copy fp32 params to the fp16 model
-                self.copy_params_to_fp16(runner.model, fp32_weights)
-            self.loss_scaler.update_scale(has_overflow)
-            if has_overflow:
-                runner.logger.warning('Check overflow, downscale loss scale '
-                                      f'to {self.loss_scaler.cur_scale}')
-
-            # save state_dict of loss_scaler
-            runner.meta.setdefault(
-                'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
-
-    @HOOKS.register_module()
-    class GradientCumulativeFp16OptimizerHook(GradientCumulativeOptimizerHook,
-                                              Fp16OptimizerHook):
-        """Fp16 optimizer Hook (using mmcv implementation) implements multi-
-        iters gradient cumulating."""
-
-        def __init__(self, *args, **kwargs):
-            super(GradientCumulativeFp16OptimizerHook,
-                  self).__init__(*args, **kwargs)
-
-        def after_train_iter(self, runner):
-            if not self.initialized:
-                self._init(runner)
-
-            if runner.iter < self.divisible_iters:
-                loss_factor = self.cumulative_iters
-            else:
-                loss_factor = self.remainder_iters
-
-            loss = runner.outputs['loss']
-            loss = loss / loss_factor
-
-            # scale the loss value
-            scaled_loss = loss * self.loss_scaler.loss_scale
-            scaled_loss.backward()
-
-            if (self.every_n_iters(runner, self.cumulative_iters)
-                    or self.is_last_iter(runner)):
-
-                # copy fp16 grads in the model to fp32 params in the optimizer
-                fp32_weights = []
-                for param_group in runner.optimizer.param_groups:
-                    fp32_weights += param_group['params']
-                self.copy_grads_to_fp32(runner.model, fp32_weights)
-                # allreduce grads
-                if self.distributed:
-                    allreduce_grads(fp32_weights, self.coalesce,
-                                    self.bucket_size_mb)
-
-                has_overflow = self.loss_scaler.has_overflow(fp32_weights)
-                # if has overflow, skip this iteration
-                if not has_overflow:
-                    # scale the gradients back
-                    for param in fp32_weights:
-                        if param.grad is not None:
-                            param.grad.div_(self.loss_scaler.loss_scale)
-                    if self.grad_clip is not None:
-                        grad_norm = self.clip_grads(fp32_weights)
-                        if grad_norm is not None:
-                            # Add grad norm to the logger
-                            runner.log_buffer.update(
-                                {'grad_norm': float(grad_norm)},
-                                runner.outputs['num_samples'])
-                    # update fp32 params
-                    runner.optimizer.step()
-                    # copy fp32 params to the fp16 model
-                    self.copy_params_to_fp16(runner.model, fp32_weights)
-                else:
-                    runner.logger.warning(
-                        'Check overflow, downscale loss scale '
-                        f'to {self.loss_scaler.cur_scale}')
-
-                self.loss_scaler.update_scale(has_overflow)
-
-                # save state_dict of loss_scaler
-                runner.meta.setdefault(
-                    'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
-
-                # clear grads
-                runner.model.zero_grad()
-                runner.optimizer.zero_grad()

spaces/AsakuraMizu/moe-tts/mel_processing.py

@@ -1,101 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/columns.py

@@ -1,187 +0,0 @@
-from collections import defaultdict
-from itertools import chain
-from operator import itemgetter
-from typing import Dict, Iterable, List, Optional, Tuple
-
-from .align import Align, AlignMethod
-from .console import Console, ConsoleOptions, RenderableType, RenderResult
-from .constrain import Constrain
-from .measure import Measurement
-from .padding import Padding, PaddingDimensions
-from .table import Table
-from .text import TextType
-from .jupyter import JupyterMixin
-
-
-class Columns(JupyterMixin):
-    """Display renderables in neat columns.
-
-    Args:
-        renderables (Iterable[RenderableType]): Any number of Rich renderables (including str).
-        width (int, optional): The desired width of the columns, or None to auto detect. Defaults to None.
-        padding (PaddingDimensions, optional): Optional padding around cells. Defaults to (0, 1).
-        expand (bool, optional): Expand columns to full width. Defaults to False.
-        equal (bool, optional): Arrange in to equal sized columns. Defaults to False.
-        column_first (bool, optional): Align items from top to bottom (rather than left to right). Defaults to False.
-        right_to_left (bool, optional): Start column from right hand side. Defaults to False.
-        align (str, optional): Align value ("left", "right", or "center") or None for default. Defaults to None.
-        title (TextType, optional): Optional title for Columns.
-    """
-
-    def __init__(
-        self,
-        renderables: Optional[Iterable[RenderableType]] = None,
-        padding: PaddingDimensions = (0, 1),
-        *,
-        width: Optional[int] = None,
-        expand: bool = False,
-        equal: bool = False,
-        column_first: bool = False,
-        right_to_left: bool = False,
-        align: Optional[AlignMethod] = None,
-        title: Optional[TextType] = None,
-    ) -> None:
-        self.renderables = list(renderables or [])
-        self.width = width
-        self.padding = padding
-        self.expand = expand
-        self.equal = equal
-        self.column_first = column_first
-        self.right_to_left = right_to_left
-        self.align: Optional[AlignMethod] = align
-        self.title = title
-
-    def add_renderable(self, renderable: RenderableType) -> None:
-        """Add a renderable to the columns.
-
-        Args:
-            renderable (RenderableType): Any renderable object.
-        """
-        self.renderables.append(renderable)
-
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-        render_str = console.render_str
-        renderables = [
-            render_str(renderable) if isinstance(renderable, str) else renderable
-            for renderable in self.renderables
-        ]
-        if not renderables:
-            return
-        _top, right, _bottom, left = Padding.unpack(self.padding)
-        width_padding = max(left, right)
-        max_width = options.max_width
-        widths: Dict[int, int] = defaultdict(int)
-        column_count = len(renderables)
-
-        get_measurement = Measurement.get
-        renderable_widths = [
-            get_measurement(console, options, renderable).maximum
-            for renderable in renderables
-        ]
-        if self.equal:
-            renderable_widths = [max(renderable_widths)] * len(renderable_widths)
-
-        def iter_renderables(
-            column_count: int,
-        ) -> Iterable[Tuple[int, Optional[RenderableType]]]:
-            item_count = len(renderables)
-            if self.column_first:
-                width_renderables = list(zip(renderable_widths, renderables))
-
-                column_lengths: List[int] = [item_count // column_count] * column_count
-                for col_no in range(item_count % column_count):
-                    column_lengths[col_no] += 1
-
-                row_count = (item_count + column_count - 1) // column_count
-                cells = [[-1] * column_count for _ in range(row_count)]
-                row = col = 0
-                for index in range(item_count):
-                    cells[row][col] = index
-                    column_lengths[col] -= 1
-                    if column_lengths[col]:
-                        row += 1
-                    else:
-                        col += 1
-                        row = 0
-                for index in chain.from_iterable(cells):
-                    if index == -1:
-                        break
-                    yield width_renderables[index]
-            else:
-                yield from zip(renderable_widths, renderables)
-            # Pad odd elements with spaces
-            if item_count % column_count:
-                for _ in range(column_count - (item_count % column_count)):
-                    yield 0, None
-
-        table = Table.grid(padding=self.padding, collapse_padding=True, pad_edge=False)
-        table.expand = self.expand
-        table.title = self.title
-
-        if self.width is not None:
-            column_count = (max_width) // (self.width + width_padding)
-            for _ in range(column_count):
-                table.add_column(width=self.width)
-        else:
-            while column_count > 1:
-                widths.clear()
-                column_no = 0
-                for renderable_width, _ in iter_renderables(column_count):
-                    widths[column_no] = max(widths[column_no], renderable_width)
-                    total_width = sum(widths.values()) + width_padding * (
-                        len(widths) - 1
-                    )
-                    if total_width > max_width:
-                        column_count = len(widths) - 1
-                        break
-                    else:
-                        column_no = (column_no + 1) % column_count
-                else:
-                    break
-
-        get_renderable = itemgetter(1)
-        _renderables = [
-            get_renderable(_renderable)
-            for _renderable in iter_renderables(column_count)
-        ]
-        if self.equal:
-            _renderables = [
-                None
-                if renderable is None
-                else Constrain(renderable, renderable_widths[0])
-                for renderable in _renderables
-            ]
-        if self.align:
-            align = self.align
-            _Align = Align
-            _renderables = [
-                None if renderable is None else _Align(renderable, align)
-                for renderable in _renderables
-            ]
-
-        right_to_left = self.right_to_left
-        add_row = table.add_row
-        for start in range(0, len(_renderables), column_count):
-            row = _renderables[start : start + column_count]
-            if right_to_left:
-                row = row[::-1]
-            add_row(*row)
-        yield table
-
-
-if __name__ == "__main__":  # pragma: no cover
-    import os
-
-    console = Console()
-
-    files = [f"{i} {s}" for i, s in enumerate(sorted(os.listdir()))]
-    columns = Columns(files, padding=(0, 1), expand=False, equal=False)
-    console.print(columns)
-    console.rule()
-    columns.column_first = True
-    console.print(columns)
-    columns.right_to_left = True
-    console.rule()
-    console.print(columns)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/MODEL_ZOO.md

@@ -1,1052 +0,0 @@
-# Detectron2 Model Zoo and Baselines
-
-## Introduction
-
-This file documents a large collection of baselines trained
-with detectron2 in Sep-Oct, 2019.
-All numbers were obtained on [Big Basin](https://engineering.fb.com/data-center-engineering/introducing-big-basin-our-next-generation-ai-hardware/)
-servers with 8 NVIDIA V100 GPUs & NVLink. The speed numbers are periodically updated with latest PyTorch/CUDA/cuDNN versions.
-You can access these models from code using [detectron2.model_zoo](https://detectron2.readthedocs.io/modules/model_zoo.html) APIs.
-
-In addition to these official baseline models, you can find more models in [projects/](projects/).
-
-#### How to Read the Tables
-* The "Name" column contains a link to the config file. Models can be reproduced using `tools/train_net.py` with the corresponding yaml config file,
-  or `tools/lazyconfig_train_net.py` for python config files.
-* Training speed is averaged across the entire training.
-  We keep updating the speed with latest version of detectron2/pytorch/etc.,
-  so they might be different from the `metrics` file.
-  Training speed for multi-machine jobs is not provided.
-* Inference speed is measured by `tools/train_net.py --eval-only`, or [inference_on_dataset()](https://detectron2.readthedocs.io/modules/evaluation.html#detectron2.evaluation.inference_on_dataset),
-  with batch size 1 in detectron2 directly.
-  Measuring it with custom code may introduce other overhead.
-  Actual deployment in production should in general be faster than the given inference
-  speed due to more optimizations.
-* The *model id* column is provided for ease of reference.
-  To check downloaded file integrity, any model on this page contains its md5 prefix in its file name.
-* Training curves and other statistics can be found in `metrics` for each model.
-
-#### Common Settings for COCO Models
-* All COCO models were trained on `train2017` and evaluated on `val2017`.
-* The default settings are __not directly comparable__ with Detectron's standard settings.
-  For example, our default training data augmentation uses scale jittering in addition to horizontal flipping.
-
-  To make fair comparisons with Detectron's settings, see
-  [Detectron1-Comparisons](configs/Detectron1-Comparisons/) for accuracy comparison,
-  and [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html)
-  for speed comparison.
-* For Faster/Mask R-CNN, we provide baselines based on __3 different backbone combinations__:
-  * __FPN__: Use a ResNet+FPN backbone with standard conv and FC heads for mask and box prediction,
-    respectively. It obtains the best
-    speed/accuracy tradeoff, but the other two are still useful for research.
-  * __C4__: Use a ResNet conv4 backbone with conv5 head. The original baseline in the Faster R-CNN paper.
-  * __DC5__ (Dilated-C5): Use a ResNet conv5 backbone with dilations in conv5, and standard conv and FC heads
-    for mask and box prediction, respectively.
-    This is used by the Deformable ConvNet paper.
-* Most models are trained with the 3x schedule (~37 COCO epochs).
-  Although 1x models are heavily under-trained, we provide some ResNet-50 models with the 1x (~12 COCO epochs)
-  training schedule for comparison when doing quick research iteration.
-
-#### ImageNet Pretrained Models
-
-It's common to initialize from backbone models pre-trained on ImageNet classification tasks. The following backbone models are available:
-
-* [R-50.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl): converted copy of [MSRA's original ResNet-50](https://github.com/KaimingHe/deep-residual-networks) model.
-* [R-101.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-101.pkl): converted copy of [MSRA's original ResNet-101](https://github.com/KaimingHe/deep-residual-networks) model.
-* [X-101-32x8d.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/FAIR/X-101-32x8d.pkl): ResNeXt-101-32x8d model trained with Caffe2 at FB.
-* [R-50.pkl (torchvision)](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/torchvision/R-50.pkl): converted copy of [torchvision's ResNet-50](https://pytorch.org/docs/stable/torchvision/models.html#torchvision.models.resnet50) model.
-  More details can be found in [the conversion script](tools/convert-torchvision-to-d2.py).
-
-Note that the above models have __different__ format from those provided in Detectron: we do not fuse BatchNorm into an affine layer.
-Pretrained models in Detectron's format can still be used. For example:
-* [X-152-32x8d-IN5k.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl):
-  ResNeXt-152-32x8d model trained on ImageNet-5k with Caffe2 at FB (see ResNeXt paper for details on ImageNet-5k).
-* [R-50-GN.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/47261647/R-50-GN.pkl):
-  ResNet-50 with Group Normalization.
-* [R-101-GN.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/47592356/R-101-GN.pkl):
-  ResNet-101 with Group Normalization.
-
-These models require slightly different settings regarding normalization and architecture. See the model zoo configs for reference.
-
-#### License
-
-All models available for download through this document are licensed under the
-[Creative Commons Attribution-ShareAlike 3.0 license](https://creativecommons.org/licenses/by-sa/3.0/).
-
-### COCO Object Detection Baselines
-
-#### Faster R-CNN:
-<!--
-(fb only) To update the table in vim:
-1. Remove the old table: d}
-2. Copy the below command to the place of the table
-3. :.!bash
-
-./gen_html_table.py --config 'COCO-Detection/faster*50*'{1x,3x}'*' 'COCO-Detection/faster*101*' --name R50-C4 R50-DC5 R50-FPN R50-C4 R50-DC5 R50-FPN R101-C4 R101-DC5 R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: faster_rcnn_R_50_C4_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml">R50-C4</a></td>
-<td align="center">1x</td>
-<td align="center">0.551</td>
-<td align="center">0.102</td>
-<td align="center">4.8</td>
-<td align="center">35.7</td>
-<td align="center">137257644</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_1x/137257644/model_final_721ade.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_1x/137257644/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_50_DC5_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml">R50-DC5</a></td>
-<td align="center">1x</td>
-<td align="center">0.380</td>
-<td align="center">0.068</td>
-<td align="center">5.0</td>
-<td align="center">37.3</td>
-<td align="center">137847829</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_DC5_1x/137847829/model_final_51d356.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_DC5_1x/137847829/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.210</td>
-<td align="center">0.038</td>
-<td align="center">3.0</td>
-<td align="center">37.9</td>
-<td align="center">137257794</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_50_C4_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml">R50-C4</a></td>
-<td align="center">3x</td>
-<td align="center">0.543</td>
-<td align="center">0.104</td>
-<td align="center">4.8</td>
-<td align="center">38.4</td>
-<td align="center">137849393</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_3x/137849393/model_final_f97cb7.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_3x/137849393/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_50_DC5_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml">R50-DC5</a></td>
-<td align="center">3x</td>
-<td align="center">0.378</td>
-<td align="center">0.070</td>
-<td align="center">5.0</td>
-<td align="center">39.0</td>
-<td align="center">137849425</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_DC5_3x/137849425/model_final_68d202.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_DC5_3x/137849425/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml">R50-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.209</td>
-<td align="center">0.038</td>
-<td align="center">3.0</td>
-<td align="center">40.2</td>
-<td align="center">137849458</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_101_C4_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml">R101-C4</a></td>
-<td align="center">3x</td>
-<td align="center">0.619</td>
-<td align="center">0.139</td>
-<td align="center">5.9</td>
-<td align="center">41.1</td>
-<td align="center">138204752</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_C4_3x/138204752/model_final_298dad.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_C4_3x/138204752/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_101_DC5_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml">R101-DC5</a></td>
-<td align="center">3x</td>
-<td align="center">0.452</td>
-<td align="center">0.086</td>
-<td align="center">6.1</td>
-<td align="center">40.6</td>
-<td align="center">138204841</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_DC5_3x/138204841/model_final_3e0943.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_DC5_3x/138204841/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_101_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml">R101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.286</td>
-<td align="center">0.051</td>
-<td align="center">4.1</td>
-<td align="center">42.0</td>
-<td align="center">137851257</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_FPN_3x/137851257/model_final_f6e8b1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_FPN_3x/137851257/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_X_101_32x8d_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml">X101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.638</td>
-<td align="center">0.098</td>
-<td align="center">6.7</td>
-<td align="center">43.0</td>
-<td align="center">139173657</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x/139173657/model_final_68b088.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x/139173657/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-#### RetinaNet:
-<!--
-./gen_html_table.py --config 'COCO-Detection/retina*50*' 'COCO-Detection/retina*101*' --name R50 R50 R101 --fields lr_sched train_speed inference_speed mem box_AP
--->
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: retinanet_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml">R50</a></td>
-<td align="center">1x</td>
-<td align="center">0.205</td>
-<td align="center">0.041</td>
-<td align="center">4.1</td>
-<td align="center">37.4</td>
-<td align="center">190397773</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_1x/190397773/model_final_bfca0b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_1x/190397773/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: retinanet_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml">R50</a></td>
-<td align="center">3x</td>
-<td align="center">0.205</td>
-<td align="center">0.041</td>
-<td align="center">4.1</td>
-<td align="center">38.7</td>
-<td align="center">190397829</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/190397829/model_final_5bd44e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/190397829/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: retinanet_R_101_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml">R101</a></td>
-<td align="center">3x</td>
-<td align="center">0.291</td>
-<td align="center">0.054</td>
-<td align="center">5.2</td>
-<td align="center">40.4</td>
-<td align="center">190397697</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/190397697/model_final_971ab9.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/190397697/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-#### RPN & Fast R-CNN:
-<!--
-./gen_html_table.py --config 'COCO-Detection/rpn*' 'COCO-Detection/fast_rcnn*' --name "RPN R50-C4" "RPN R50-FPN" "Fast R-CNN R50-FPN" --fields lr_sched train_speed inference_speed mem box_AP prop_AR
--->
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">prop.<br/>AR</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: rpn_R_50_C4_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/rpn_R_50_C4_1x.yaml">RPN R50-C4</a></td>
-<td align="center">1x</td>
-<td align="center">0.130</td>
-<td align="center">0.034</td>
-<td align="center">1.5</td>
-<td align="center"></td>
-<td align="center">51.6</td>
-<td align="center">137258005</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_C4_1x/137258005/model_final_450694.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_C4_1x/137258005/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: rpn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/rpn_R_50_FPN_1x.yaml">RPN R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.186</td>
-<td align="center">0.032</td>
-<td align="center">2.7</td>
-<td align="center"></td>
-<td align="center">58.0</td>
-<td align="center">137258492</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_FPN_1x/137258492/model_final_02ce48.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_FPN_1x/137258492/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: fast_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml">Fast R-CNN R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.140</td>
-<td align="center">0.029</td>
-<td align="center">2.6</td>
-<td align="center">37.8</td>
-<td align="center"></td>
-<td align="center">137635226</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/model_final_e5f7ce.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-### COCO Instance Segmentation Baselines with Mask R-CNN
-<!--
-./gen_html_table.py --config 'COCO-InstanceSegmentation/mask*50*'{1x,3x}'*' 'COCO-InstanceSegmentation/mask*101*' --name R50-C4 R50-DC5 R50-FPN R50-C4 R50-DC5 R50-FPN R101-C4 R101-DC5 R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP mask_AP
--->
-
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_R_50_C4_1x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml">R50-C4</a></td>
-<td align="center">1x</td>
-<td align="center">0.584</td>
-<td align="center">0.110</td>
-<td align="center">5.2</td>
-<td align="center">36.8</td>
-<td align="center">32.2</td>
-<td align="center">137259246</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x/137259246/model_final_9243eb.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x/137259246/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_DC5_1x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml">R50-DC5</a></td>
-<td align="center">1x</td>
-<td align="center">0.471</td>
-<td align="center">0.076</td>
-<td align="center">6.5</td>
-<td align="center">38.3</td>
-<td align="center">34.2</td>
-<td align="center">137260150</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x/137260150/model_final_4f86c3.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x/137260150/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.261</td>
-<td align="center">0.043</td>
-<td align="center">3.4</td>
-<td align="center">38.6</td>
-<td align="center">35.2</td>
-<td align="center">137260431</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/model_final_a54504.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_C4_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml">R50-C4</a></td>
-<td align="center">3x</td>
-<td align="center">0.575</td>
-<td align="center">0.111</td>
-<td align="center">5.2</td>
-<td align="center">39.8</td>
-<td align="center">34.4</td>
-<td align="center">137849525</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/model_final_4ce675.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_DC5_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml">R50-DC5</a></td>
-<td align="center">3x</td>
-<td align="center">0.470</td>
-<td align="center">0.076</td>
-<td align="center">6.5</td>
-<td align="center">40.0</td>
-<td align="center">35.9</td>
-<td align="center">137849551</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/model_final_84107b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml">R50-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.261</td>
-<td align="center">0.043</td>
-<td align="center">3.4</td>
-<td align="center">41.0</td>
-<td align="center">37.2</td>
-<td align="center">137849600</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_101_C4_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml">R101-C4</a></td>
-<td align="center">3x</td>
-<td align="center">0.652</td>
-<td align="center">0.145</td>
-<td align="center">6.3</td>
-<td align="center">42.6</td>
-<td align="center">36.7</td>
-<td align="center">138363239</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x/138363239/model_final_a2914c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x/138363239/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_101_DC5_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml">R101-DC5</a></td>
-<td align="center">3x</td>
-<td align="center">0.545</td>
-<td align="center">0.092</td>
-<td align="center">7.6</td>
-<td align="center">41.9</td>
-<td align="center">37.3</td>
-<td align="center">138363294</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x/138363294/model_final_0464b7.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x/138363294/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_101_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml">R101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.340</td>
-<td align="center">0.056</td>
-<td align="center">4.6</td>
-<td align="center">42.9</td>
-<td align="center">38.6</td>
-<td align="center">138205316</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x/138205316/model_final_a3ec72.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x/138205316/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_X_101_32x8d_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml">X101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.690</td>
-<td align="center">0.103</td>
-<td align="center">7.2</td>
-<td align="center">44.3</td>
-<td align="center">39.5</td>
-<td align="center">139653917</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x/139653917/model_final_2d9806.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x/139653917/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-
-#### New baselines using Large-Scale Jitter and Longer Training Schedule
-
-The following baselines of COCO Instance Segmentation with Mask R-CNN are generated
-using a longer training schedule and large-scale jitter as described in Google's
-[Simple Copy-Paste Data Augmentation](https://arxiv.org/pdf/2012.07177.pdf) paper. These
-models are trained from scratch using random initialization. These baselines exceed the
-previous Mask R-CNN baselines.
-
-In the following table, one epoch consists of training on 118000 COCO images.
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">epochs</th>
-<th valign="bottom">train<br/>time<br/>(s/im)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_R_50_FPN_100ep_LSJ -->
- <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py">R50-FPN</a></td>
-<td align="center">100</td>
-<td align="center">0.376</td>
-<td align="center">0.069</td>
-<td align="center">44.6</td>
-<td align="center">40.3</td>
-<td align="center">42047764</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ/42047764/model_final_bb69de.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ/42047764/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_200ep_LSJ -->
- <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ.py">R50-FPN</a></td>
-<td align="center">200</td>
-<td align="center">0.376</td>
-<td align="center">0.069</td>
-<td align="center">46.3</td>
-<td align="center">41.7</td>
-<td align="center">42047638</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ/42047638/model_final_89a8d3.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ/42047638/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_400ep_LSJ -->
- <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ.py">R50-FPN</a></td>
-<td align="center">400</td>
-<td align="center">0.376</td>
-<td align="center">0.069</td>
-<td align="center">47.4</td>
-<td align="center">42.5</td>
-<td align="center">42019571</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ/42019571/model_final_14d201.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ/42019571/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_101_FPN_100ep_LSJ -->
- <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ.py">R101-FPN</a></td>
-<td align="center">100</td>
-<td align="center">0.518</td>
-<td align="center">0.073</td>
-<td align="center">46.4</td>
-<td align="center">41.6</td>
-<td align="center">42025812</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ/42025812/model_final_4f7b58.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ/42025812/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_101_FPN_200ep_LSJ -->
- <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ.py">R101-FPN</a></td>
-<td align="center">200</td>
-<td align="center">0.518</td>
-<td align="center">0.073</td>
-<td align="center">48.0</td>
-<td align="center">43.1</td>
-<td align="center">42131867</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ/42131867/model_final_0bb7ae.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ/42131867/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_101_FPN_400ep_LSJ -->
- <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py">R101-FPN</a></td>
-<td align="center">400</td>
-<td align="center">0.518</td>
-<td align="center">0.073</td>
-<td align="center">48.9</td>
-<td align="center">43.7</td>
-<td align="center">42073830</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ/42073830/model_final_f96b26.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ/42073830/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ -->
- <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py">regnetx_4gf_dds_FPN</a></td>
-<td align="center">100</td>
-<td align="center">0.474</td>
-<td align="center">0.071</td>
-<td align="center">46.0</td>
-<td align="center">41.3</td>
-<td align="center">42047771</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ/42047771/model_final_b7fbab.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ/42047771/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ -->
- <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py">regnetx_4gf_dds_FPN</a></td>
-<td align="center">200</td>
-<td align="center">0.474</td>
-<td align="center">0.071</td>
-<td align="center">48.1</td>
-<td align="center">43.1</td>
-<td align="center">42132721</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ/42132721/model_final_5d87c1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ/42132721/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ -->
- <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py">regnetx_4gf_dds_FPN</a></td>
-<td align="center">400</td>
-<td align="center">0.474</td>
-<td align="center">0.071</td>
-<td align="center">48.6</td>
-<td align="center">43.5</td>
-<td align="center">42025447</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ/42025447/model_final_f1362d.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ/42025447/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ -->
- <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ.py">regnety_4gf_dds_FPN</a></td>
-<td align="center">100</td>
-<td align="center">0.487</td>
-<td align="center">0.073</td>
-<td align="center">46.1</td>
-<td align="center">41.6</td>
-<td align="center">42047784</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ/42047784/model_final_6ba57e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ/42047784/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ -->
- <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py">regnety_4gf_dds_FPN</a></td>
-<td align="center">200</td>
-<td align="center">0.487</td>
-<td align="center">0.072</td>
-<td align="center">47.8</td>
-<td align="center">43.0</td>
-<td align="center">42047642</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ/42047642/model_final_27b9c1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ/42047642/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ -->
- <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py">regnety_4gf_dds_FPN</a></td>
-<td align="center">400</td>
-<td align="center">0.487</td>
-<td align="center">0.072</td>
-<td align="center">48.2</td>
-<td align="center">43.3</td>
-<td align="center">42045954</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ/42045954/model_final_ef3a80.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ/42045954/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-### COCO Person Keypoint Detection Baselines with Keypoint R-CNN
-<!--
-./gen_html_table.py --config 'COCO-Keypoints/*50*' 'COCO-Keypoints/*101*'  --name R50-FPN R50-FPN R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP keypoint_AP
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">kp.<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: keypoint_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.315</td>
-<td align="center">0.072</td>
-<td align="center">5.0</td>
-<td align="center">53.6</td>
-<td align="center">64.0</td>
-<td align="center">137261548</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x/137261548/model_final_04e291.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x/137261548/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: keypoint_rcnn_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml">R50-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.316</td>
-<td align="center">0.066</td>
-<td align="center">5.0</td>
-<td align="center">55.4</td>
-<td align="center">65.5</td>
-<td align="center">137849621</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/model_final_a6e10b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: keypoint_rcnn_R_101_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml">R101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.390</td>
-<td align="center">0.076</td>
-<td align="center">6.1</td>
-<td align="center">56.4</td>
-<td align="center">66.1</td>
-<td align="center">138363331</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x/138363331/model_final_997cc7.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x/138363331/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: keypoint_rcnn_X_101_32x8d_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml">X101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.738</td>
-<td align="center">0.121</td>
-<td align="center">8.7</td>
-<td align="center">57.3</td>
-<td align="center">66.0</td>
-<td align="center">139686956</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x/139686956/model_final_5ad38f.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x/139686956/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-### COCO Panoptic Segmentation Baselines with Panoptic FPN
-<!--
-./gen_html_table.py --config 'COCO-PanopticSegmentation/*50*' 'COCO-PanopticSegmentation/*101*'  --name R50-FPN R50-FPN R101-FPN --fields lr_sched train_speed inference_speed mem box_AP mask_AP PQ
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">PQ</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: panoptic_fpn_R_50_1x -->
- <tr><td align="left"><a href="configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml">R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.304</td>
-<td align="center">0.053</td>
-<td align="center">4.8</td>
-<td align="center">37.6</td>
-<td align="center">34.7</td>
-<td align="center">39.4</td>
-<td align="center">139514544</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x/139514544/model_final_dbfeb4.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x/139514544/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: panoptic_fpn_R_50_3x -->
- <tr><td align="left"><a href="configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml">R50-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.302</td>
-<td align="center">0.053</td>
-<td align="center">4.8</td>
-<td align="center">40.0</td>
-<td align="center">36.5</td>
-<td align="center">41.5</td>
-<td align="center">139514569</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/model_final_c10459.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: panoptic_fpn_R_101_3x -->
- <tr><td align="left"><a href="configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml">R101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.392</td>
-<td align="center">0.066</td>
-<td align="center">6.0</td>
-<td align="center">42.4</td>
-<td align="center">38.5</td>
-<td align="center">43.0</td>
-<td align="center">139514519</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x/139514519/model_final_cafdb1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x/139514519/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-### LVIS Instance Segmentation Baselines with Mask R-CNN
-
-Mask R-CNN baselines on the [LVIS dataset](https://lvisdataset.org), v0.5.
-These baselines are described in Table 3(c) of the [LVIS paper](https://arxiv.org/abs/1908.03195).
-
-NOTE: the 1x schedule here has the same amount of __iterations__ as the COCO 1x baselines.
-They are roughly 24 epochs of LVISv0.5 data.
-The final results of these configs have large variance across different runs.
-
-<!--
-./gen_html_table.py --config 'LVISv0.5-InstanceSegmentation/mask*50*' 'LVISv0.5-InstanceSegmentation/mask*101*' --name R50-FPN R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP mask_AP
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.292</td>
-<td align="center">0.107</td>
-<td align="center">7.1</td>
-<td align="center">23.6</td>
-<td align="center">24.4</td>
-<td align="center">144219072</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/144219072/model_final_571f7c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/144219072/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_101_FPN_1x -->
- <tr><td align="left"><a href="configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml">R101-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.371</td>
-<td align="center">0.114</td>
-<td align="center">7.8</td>
-<td align="center">25.6</td>
-<td align="center">25.9</td>
-<td align="center">144219035</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x/144219035/model_final_824ab5.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x/144219035/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_X_101_32x8d_FPN_1x -->
- <tr><td align="left"><a href="configs/LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml">X101-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.712</td>
-<td align="center">0.151</td>
-<td align="center">10.2</td>
-<td align="center">26.7</td>
-<td align="center">27.1</td>
-<td align="center">144219108</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x/144219108/model_final_5e3439.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x/144219108/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-
-### Cityscapes & Pascal VOC Baselines
-
-Simple baselines for
-* Mask R-CNN on Cityscapes instance segmentation (initialized from COCO pre-training, then trained on Cityscapes fine annotations only)
-* Faster R-CNN on PASCAL VOC object detection (trained on VOC 2007 train+val + VOC 2012 train+val, tested on VOC 2007 using 11-point interpolated AP)
-
-<!--
-./gen_html_table.py --config 'Cityscapes/*' 'PascalVOC-Detection/*' --name "R50-FPN, Cityscapes" "R50-C4, VOC" --fields train_speed inference_speed mem box_AP box_AP50 mask_AP
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">box<br/>AP50</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_R_50_FPN -->
- <tr><td align="left"><a href="configs/Cityscapes/mask_rcnn_R_50_FPN.yaml">R50-FPN, Cityscapes</a></td>
-<td align="center">0.240</td>
-<td align="center">0.078</td>
-<td align="center">4.4</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center">36.5</td>
-<td align="center">142423278</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Cityscapes/mask_rcnn_R_50_FPN/142423278/model_final_af9cf5.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Cityscapes/mask_rcnn_R_50_FPN/142423278/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_50_C4 -->
- <tr><td align="left"><a href="configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml">R50-C4, VOC</a></td>
-<td align="center">0.537</td>
-<td align="center">0.081</td>
-<td align="center">4.8</td>
-<td align="center">51.9</td>
-<td align="center">80.3</td>
-<td align="center"></td>
-<td align="center">142202221</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PascalVOC-Detection/faster_rcnn_R_50_C4/142202221/model_final_b1acc2.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PascalVOC-Detection/faster_rcnn_R_50_C4/142202221/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-
-### Other Settings
-
-Ablations for Deformable Conv and Cascade R-CNN:
-
-<!--
-./gen_html_table.py --config 'COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml' 'Misc/*R_50_FPN_1x_dconv*' 'Misc/cascade*1x.yaml' 'COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml' 'Misc/*R_50_FPN_3x_dconv*' 'Misc/cascade*3x.yaml' --name "Baseline R50-FPN" "Deformable Conv" "Cascade R-CNN" "Baseline R50-FPN" "Deformable Conv" "Cascade R-CNN"  --fields lr_sched train_speed inference_speed mem box_AP mask_AP
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml">Baseline R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.261</td>
-<td align="center">0.043</td>
-<td align="center">3.4</td>
-<td align="center">38.6</td>
-<td align="center">35.2</td>
-<td align="center">137260431</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/model_final_a54504.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_1x_dconv_c3-c5 -->
- <tr><td align="left"><a href="configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml">Deformable Conv</a></td>
-<td align="center">1x</td>
-<td align="center">0.342</td>
-<td align="center">0.048</td>
-<td align="center">3.5</td>
-<td align="center">41.5</td>
-<td align="center">37.5</td>
-<td align="center">138602867</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5/138602867/model_final_65c703.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5/138602867/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml">Cascade R-CNN</a></td>
-<td align="center">1x</td>
-<td align="center">0.317</td>
-<td align="center">0.052</td>
-<td align="center">4.0</td>
-<td align="center">42.1</td>
-<td align="center">36.4</td>
-<td align="center">138602847</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_R_50_FPN_1x/138602847/model_final_e9d89b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_R_50_FPN_1x/138602847/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml">Baseline R50-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.261</td>
-<td align="center">0.043</td>
-<td align="center">3.4</td>
-<td align="center">41.0</td>
-<td align="center">37.2</td>
-<td align="center">137849600</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_3x_dconv_c3-c5 -->
- <tr><td align="left"><a href="configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml">Deformable Conv</a></td>
-<td align="center">3x</td>
-<td align="center">0.349</td>
-<td align="center">0.047</td>
-<td align="center">3.5</td>
-<td align="center">42.7</td>
-<td align="center">38.5</td>
-<td align="center">144998336</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5/144998336/model_final_821d0b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5/144998336/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml">Cascade R-CNN</a></td>
-<td align="center">3x</td>
-<td align="center">0.328</td>
-<td align="center">0.053</td>
-<td align="center">4.0</td>
-<td align="center">44.3</td>
-<td align="center">38.5</td>
-<td align="center">144998488</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_R_50_FPN_3x/144998488/model_final_480dd8.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_R_50_FPN_3x/144998488/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-Ablations for normalization methods, and a few models trained from scratch following [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883).
-(Note: The baseline uses `2fc` head while the others use [`4conv1fc` head](https://arxiv.org/abs/1803.08494))
-<!--
-./gen_html_table.py --config 'COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml' 'Misc/mask*50_FPN_3x_gn.yaml' 'Misc/mask*50_FPN_3x_syncbn.yaml' 'Misc/scratch*' --name "Baseline R50-FPN" "GN" "SyncBN" "GN (from scratch)" "GN (from scratch)" "SyncBN (from scratch)" --fields lr_sched train_speed inference_speed mem box_AP mask_AP
-   -->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml">Baseline R50-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.261</td>
-<td align="center">0.043</td>
-<td align="center">3.4</td>
-<td align="center">41.0</td>
-<td align="center">37.2</td>
-<td align="center">137849600</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_3x_gn -->
- <tr><td align="left"><a href="configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml">GN</a></td>
-<td align="center">3x</td>
-<td align="center">0.309</td>
-<td align="center">0.060</td>
-<td align="center">5.6</td>
-<td align="center">42.6</td>
-<td align="center">38.6</td>
-<td align="center">138602888</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_gn/138602888/model_final_dc5d9e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_gn/138602888/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_3x_syncbn -->
- <tr><td align="left"><a href="configs/Misc/mask_rcnn_R_50_FPN_3x_syncbn.yaml">SyncBN</a></td>
-<td align="center">3x</td>
-<td align="center">0.345</td>
-<td align="center">0.053</td>
-<td align="center">5.5</td>
-<td align="center">41.9</td>
-<td align="center">37.8</td>
-<td align="center">169527823</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_syncbn/169527823/model_final_3b3c51.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_syncbn/169527823/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: scratch_mask_rcnn_R_50_FPN_3x_gn -->
- <tr><td align="left"><a href="configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml">GN (from scratch)</a></td>
-<td align="center">3x</td>
-<td align="center">0.338</td>
-<td align="center">0.061</td>
-<td align="center">7.2</td>
-<td align="center">39.9</td>
-<td align="center">36.6</td>
-<td align="center">138602908</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn/138602908/model_final_01ca85.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn/138602908/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: scratch_mask_rcnn_R_50_FPN_9x_gn -->
- <tr><td align="left"><a href="configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml">GN (from scratch)</a></td>
-<td align="center">9x</td>
-<td align="center">N/A</td>
-<td align="center">0.061</td>
-<td align="center">7.2</td>
-<td align="center">43.7</td>
-<td align="center">39.6</td>
-<td align="center">183808979</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn/183808979/model_final_da7b4c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn/183808979/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: scratch_mask_rcnn_R_50_FPN_9x_syncbn -->
- <tr><td align="left"><a href="configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml">SyncBN (from scratch)</a></td>
-<td align="center">9x</td>
-<td align="center">N/A</td>
-<td align="center">0.055</td>
-<td align="center">7.2</td>
-<td align="center">43.6</td>
-<td align="center">39.3</td>
-<td align="center">184226666</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn/184226666/model_final_5ce33e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn/184226666/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-A few very large models trained for a long time, for demo purposes. They are trained using multiple machines:
-
-<!--
-./gen_html_table.py --config 'Misc/panoptic_*dconv*' 'Misc/cascade_*152*' --name "Panoptic FPN R101" "Mask R-CNN X152" --fields inference_speed mem box_AP mask_AP PQ
-# manually add TTA results
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">PQ</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: panoptic_fpn_R_101_dconv_cascade_gn_3x -->
- <tr><td align="left"><a href="configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml">Panoptic FPN R101</a></td>
-<td align="center">0.098</td>
-<td align="center">11.4</td>
-<td align="center">47.4</td>
-<td align="center">41.3</td>
-<td align="center">46.1</td>
-<td align="center">139797668</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x/139797668/model_final_be35db.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x/139797668/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv -->
- <tr><td align="left"><a href="configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml">Mask R-CNN X152</a></td>
-<td align="center">0.234</td>
-<td align="center">15.1</td>
-<td align="center">50.2</td>
-<td align="center">44.0</td>
-<td align="center"></td>
-<td align="center">18131413</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv/18131413/model_0039999_e76410.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv/18131413/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: TTA cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv -->
- <tr><td align="left">above + test-time aug.</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center">51.9</td>
-<td align="center">45.9</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
-</tr>
-</tbody></table>

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/resnet.py

@@ -1,694 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-import fvcore.nn.weight_init as weight_init
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from detectron2.layers import (
-    CNNBlockBase,
-    Conv2d,
-    DeformConv,
-    ModulatedDeformConv,
-    ShapeSpec,
-    get_norm,
-)
-
-from .backbone import Backbone
-from .build import BACKBONE_REGISTRY
-
-__all__ = [
-    "ResNetBlockBase",
-    "BasicBlock",
-    "BottleneckBlock",
-    "DeformBottleneckBlock",
-    "BasicStem",
-    "ResNet",
-    "make_stage",
-    "build_resnet_backbone",
-]
-
-
-class BasicBlock(CNNBlockBase):
-    """
-    The basic residual block for ResNet-18 and ResNet-34 defined in :paper:`ResNet`,
-    with two 3x3 conv layers and a projection shortcut if needed.
-    """
-
-    def __init__(self, in_channels, out_channels, *, stride=1, norm="BN"):
-        """
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            stride (int): Stride for the first conv.
-            norm (str or callable): normalization for all conv layers.
-                See :func:`layers.get_norm` for supported format.
-        """
-        super().__init__(in_channels, out_channels, stride)
-
-        if in_channels != out_channels:
-            self.shortcut = Conv2d(
-                in_channels,
-                out_channels,
-                kernel_size=1,
-                stride=stride,
-                bias=False,
-                norm=get_norm(norm, out_channels),
-            )
-        else:
-            self.shortcut = None
-
-        self.conv1 = Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size=3,
-            stride=stride,
-            padding=1,
-            bias=False,
-            norm=get_norm(norm, out_channels),
-        )
-
-        self.conv2 = Conv2d(
-            out_channels,
-            out_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            bias=False,
-            norm=get_norm(norm, out_channels),
-        )
-
-        for layer in [self.conv1, self.conv2, self.shortcut]:
-            if layer is not None:  # shortcut can be None
-                weight_init.c2_msra_fill(layer)
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = F.relu_(out)
-        out = self.conv2(out)
-
-        if self.shortcut is not None:
-            shortcut = self.shortcut(x)
-        else:
-            shortcut = x
-
-        out += shortcut
-        out = F.relu_(out)
-        return out
-
-
-class BottleneckBlock(CNNBlockBase):
-    """
-    The standard bottleneck residual block used by ResNet-50, 101 and 152
-    defined in :paper:`ResNet`.  It contains 3 conv layers with kernels
-    1x1, 3x3, 1x1, and a projection shortcut if needed.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        *,
-        bottleneck_channels,
-        stride=1,
-        num_groups=1,
-        norm="BN",
-        stride_in_1x1=False,
-        dilation=1,
-    ):
-        """
-        Args:
-            bottleneck_channels (int): number of output channels for the 3x3
-                "bottleneck" conv layers.
-            num_groups (int): number of groups for the 3x3 conv layer.
-            norm (str or callable): normalization for all conv layers.
-                See :func:`layers.get_norm` for supported format.
-            stride_in_1x1 (bool): when stride>1, whether to put stride in the
-                first 1x1 convolution or the bottleneck 3x3 convolution.
-            dilation (int): the dilation rate of the 3x3 conv layer.
-        """
-        super().__init__(in_channels, out_channels, stride)
-
-        if in_channels != out_channels:
-            self.shortcut = Conv2d(
-                in_channels,
-                out_channels,
-                kernel_size=1,
-                stride=stride,
-                bias=False,
-                norm=get_norm(norm, out_channels),
-            )
-        else:
-            self.shortcut = None
-
-        # The original MSRA ResNet models have stride in the first 1x1 conv
-        # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have
-        # stride in the 3x3 conv
-        stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
-
-        self.conv1 = Conv2d(
-            in_channels,
-            bottleneck_channels,
-            kernel_size=1,
-            stride=stride_1x1,
-            bias=False,
-            norm=get_norm(norm, bottleneck_channels),
-        )
-
-        self.conv2 = Conv2d(
-            bottleneck_channels,
-            bottleneck_channels,
-            kernel_size=3,
-            stride=stride_3x3,
-            padding=1 * dilation,
-            bias=False,
-            groups=num_groups,
-            dilation=dilation,
-            norm=get_norm(norm, bottleneck_channels),
-        )
-
-        self.conv3 = Conv2d(
-            bottleneck_channels,
-            out_channels,
-            kernel_size=1,
-            bias=False,
-            norm=get_norm(norm, out_channels),
-        )
-
-        for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]:
-            if layer is not None:  # shortcut can be None
-                weight_init.c2_msra_fill(layer)
-
-        # Zero-initialize the last normalization in each residual branch,
-        # so that at the beginning, the residual branch starts with zeros,
-        # and each residual block behaves like an identity.
-        # See Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour":
-        # "For BN layers, the learnable scaling coefficient γ is initialized
-        # to be 1, except for each residual block's last BN
-        # where γ is initialized to be 0."
-
-        # nn.init.constant_(self.conv3.norm.weight, 0)
-        # TODO this somehow hurts performance when training GN models from scratch.
-        # Add it as an option when we need to use this code to train a backbone.
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = F.relu_(out)
-
-        out = self.conv2(out)
-        out = F.relu_(out)
-
-        out = self.conv3(out)
-
-        if self.shortcut is not None:
-            shortcut = self.shortcut(x)
-        else:
-            shortcut = x
-
-        out += shortcut
-        out = F.relu_(out)
-        return out
-
-
-class DeformBottleneckBlock(CNNBlockBase):
-    """
-    Similar to :class:`BottleneckBlock`, but with :paper:`deformable conv <deformconv>`
-    in the 3x3 convolution.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        *,
-        bottleneck_channels,
-        stride=1,
-        num_groups=1,
-        norm="BN",
-        stride_in_1x1=False,
-        dilation=1,
-        deform_modulated=False,
-        deform_num_groups=1,
-    ):
-        super().__init__(in_channels, out_channels, stride)
-        self.deform_modulated = deform_modulated
-
-        if in_channels != out_channels:
-            self.shortcut = Conv2d(
-                in_channels,
-                out_channels,
-                kernel_size=1,
-                stride=stride,
-                bias=False,
-                norm=get_norm(norm, out_channels),
-            )
-        else:
-            self.shortcut = None
-
-        stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
-
-        self.conv1 = Conv2d(
-            in_channels,
-            bottleneck_channels,
-            kernel_size=1,
-            stride=stride_1x1,
-            bias=False,
-            norm=get_norm(norm, bottleneck_channels),
-        )
-
-        if deform_modulated:
-            deform_conv_op = ModulatedDeformConv
-            # offset channels are 2 or 3 (if with modulated) * kernel_size * kernel_size
-            offset_channels = 27
-        else:
-            deform_conv_op = DeformConv
-            offset_channels = 18
-
-        self.conv2_offset = Conv2d(
-            bottleneck_channels,
-            offset_channels * deform_num_groups,
-            kernel_size=3,
-            stride=stride_3x3,
-            padding=1 * dilation,
-            dilation=dilation,
-        )
-        self.conv2 = deform_conv_op(
-            bottleneck_channels,
-            bottleneck_channels,
-            kernel_size=3,
-            stride=stride_3x3,
-            padding=1 * dilation,
-            bias=False,
-            groups=num_groups,
-            dilation=dilation,
-            deformable_groups=deform_num_groups,
-            norm=get_norm(norm, bottleneck_channels),
-        )
-
-        self.conv3 = Conv2d(
-            bottleneck_channels,
-            out_channels,
-            kernel_size=1,
-            bias=False,
-            norm=get_norm(norm, out_channels),
-        )
-
-        for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]:
-            if layer is not None:  # shortcut can be None
-                weight_init.c2_msra_fill(layer)
-
-        nn.init.constant_(self.conv2_offset.weight, 0)
-        nn.init.constant_(self.conv2_offset.bias, 0)
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = F.relu_(out)
-
-        if self.deform_modulated:
-            offset_mask = self.conv2_offset(out)
-            offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1)
-            offset = torch.cat((offset_x, offset_y), dim=1)
-            mask = mask.sigmoid()
-            out = self.conv2(out, offset, mask)
-        else:
-            offset = self.conv2_offset(out)
-            out = self.conv2(out, offset)
-        out = F.relu_(out)
-
-        out = self.conv3(out)
-
-        if self.shortcut is not None:
-            shortcut = self.shortcut(x)
-        else:
-            shortcut = x
-
-        out += shortcut
-        out = F.relu_(out)
-        return out
-
-
-class BasicStem(CNNBlockBase):
-    """
-    The standard ResNet stem (layers before the first residual block),
-    with a conv, relu and max_pool.
-    """
-
-    def __init__(self, in_channels=3, out_channels=64, norm="BN"):
-        """
-        Args:
-            norm (str or callable): norm after the first conv layer.
-                See :func:`layers.get_norm` for supported format.
-        """
-        super().__init__(in_channels, out_channels, 4)
-        self.in_channels = in_channels
-        self.conv1 = Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size=7,
-            stride=2,
-            padding=3,
-            bias=False,
-            norm=get_norm(norm, out_channels),
-        )
-        weight_init.c2_msra_fill(self.conv1)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = F.relu_(x)
-        x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
-        return x
-
-
-class ResNet(Backbone):
-    """
-    Implement :paper:`ResNet`.
-    """
-
-    def __init__(self, stem, stages, num_classes=None, out_features=None, freeze_at=0):
-        """
-        Args:
-            stem (nn.Module): a stem module
-            stages (list[list[CNNBlockBase]]): several (typically 4) stages,
-                each contains multiple :class:`CNNBlockBase`.
-            num_classes (None or int): if None, will not perform classification.
-                Otherwise, will create a linear layer.
-            out_features (list[str]): name of the layers whose outputs should
-                be returned in forward. Can be anything in "stem", "linear", or "res2" ...
-                If None, will return the output of the last layer.
-            freeze_at (int): The number of stages at the beginning to freeze.
-                see :meth:`freeze` for detailed explanation.
-        """
-        super().__init__()
-        self.stem = stem
-        self.num_classes = num_classes
-
-        current_stride = self.stem.stride
-        self._out_feature_strides = {"stem": current_stride}
-        self._out_feature_channels = {"stem": self.stem.out_channels}
-
-        self.stage_names, self.stages = [], []
-
-        if out_features is not None:
-            # Avoid keeping unused layers in this module. They consume extra memory
-            # and may cause allreduce to fail
-            num_stages = max(
-                [{"res2": 1, "res3": 2, "res4": 3, "res5": 4}.get(f, 0) for f in out_features]
-            )
-            stages = stages[:num_stages]
-        for i, blocks in enumerate(stages):
-            assert len(blocks) > 0, len(blocks)
-            for block in blocks:
-                assert isinstance(block, CNNBlockBase), block
-
-            name = "res" + str(i + 2)
-            stage = nn.Sequential(*blocks)
-
-            self.add_module(name, stage)
-            self.stage_names.append(name)
-            self.stages.append(stage)
-
-            self._out_feature_strides[name] = current_stride = int(
-                current_stride * np.prod([k.stride for k in blocks])
-            )
-            self._out_feature_channels[name] = curr_channels = blocks[-1].out_channels
-        self.stage_names = tuple(self.stage_names)  # Make it static for scripting
-
-        if num_classes is not None:
-            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-            self.linear = nn.Linear(curr_channels, num_classes)
-
-            # Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour":
-            # "The 1000-way fully-connected layer is initialized by
-            # drawing weights from a zero-mean Gaussian with standard deviation of 0.01."
-            nn.init.normal_(self.linear.weight, std=0.01)
-            name = "linear"
-
-        if out_features is None:
-            out_features = [name]
-        self._out_features = out_features
-        assert len(self._out_features)
-        children = [x[0] for x in self.named_children()]
-        for out_feature in self._out_features:
-            assert out_feature in children, "Available children: {}".format(", ".join(children))
-        self.freeze(freeze_at)
-
-    def forward(self, x):
-        """
-        Args:
-            x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
-
-        Returns:
-            dict[str->Tensor]: names and the corresponding features
-        """
-        assert x.dim() == 4, f"ResNet takes an input of shape (N, C, H, W). Got {x.shape} instead!"
-        outputs = {}
-        x = self.stem(x)
-        if "stem" in self._out_features:
-            outputs["stem"] = x
-        for name, stage in zip(self.stage_names, self.stages):
-            x = stage(x)
-            if name in self._out_features:
-                outputs[name] = x
-        if self.num_classes is not None:
-            x = self.avgpool(x)
-            x = torch.flatten(x, 1)
-            x = self.linear(x)
-            if "linear" in self._out_features:
-                outputs["linear"] = x
-        return outputs
-
-    def output_shape(self):
-        return {
-            name: ShapeSpec(
-                channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
-            )
-            for name in self._out_features
-        }
-
-    def freeze(self, freeze_at=0):
-        """
-        Freeze the first several stages of the ResNet. Commonly used in
-        fine-tuning.
-
-        Layers that produce the same feature map spatial size are defined as one
-        "stage" by :paper:`FPN`.
-
-        Args:
-            freeze_at (int): number of stages to freeze.
-                `1` means freezing the stem. `2` means freezing the stem and
-                one residual stage, etc.
-
-        Returns:
-            nn.Module: this ResNet itself
-        """
-        if freeze_at >= 1:
-            self.stem.freeze()
-        for idx, stage in enumerate(self.stages, start=2):
-            if freeze_at >= idx:
-                for block in stage.children():
-                    block.freeze()
-        return self
-
-    @staticmethod
-    def make_stage(block_class, num_blocks, *, in_channels, out_channels, **kwargs):
-        """
-        Create a list of blocks of the same type that forms one ResNet stage.
-
-        Args:
-            block_class (type): a subclass of CNNBlockBase that's used to create all blocks in this
-                stage. A module of this type must not change spatial resolution of inputs unless its
-                stride != 1.
-            num_blocks (int): number of blocks in this stage
-            in_channels (int): input channels of the entire stage.
-            out_channels (int): output channels of **every block** in the stage.
-            kwargs: other arguments passed to the constructor of
-                `block_class`. If the argument name is "xx_per_block", the
-                argument is a list of values to be passed to each block in the
-                stage. Otherwise, the same argument is passed to every block
-                in the stage.
-
-        Returns:
-            list[CNNBlockBase]: a list of block module.
-
-        Examples:
-        ::
-            stage = ResNet.make_stage(
-                BottleneckBlock, 3, in_channels=16, out_channels=64,
-                bottleneck_channels=16, num_groups=1,
-                stride_per_block=[2, 1, 1],
-                dilations_per_block=[1, 1, 2]
-            )
-
-        Usually, layers that produce the same feature map spatial size are defined as one
-        "stage" (in :paper:`FPN`). Under such definition, ``stride_per_block[1:]`` should
-        all be 1.
-        """
-        blocks = []
-        for i in range(num_blocks):
-            curr_kwargs = {}
-            for k, v in kwargs.items():
-                if k.endswith("_per_block"):
-                    assert len(v) == num_blocks, (
-                        f"Argument '{k}' of make_stage should have the "
-                        f"same length as num_blocks={num_blocks}."
-                    )
-                    newk = k[: -len("_per_block")]
-                    assert newk not in kwargs, f"Cannot call make_stage with both {k} and {newk}!"
-                    curr_kwargs[newk] = v[i]
-                else:
-                    curr_kwargs[k] = v
-
-            blocks.append(
-                block_class(in_channels=in_channels, out_channels=out_channels, **curr_kwargs)
-            )
-            in_channels = out_channels
-        return blocks
-
-    @staticmethod
-    def make_default_stages(depth, block_class=None, **kwargs):
-        """
-        Created list of ResNet stages from pre-defined depth (one of 18, 34, 50, 101, 152).
-        If it doesn't create the ResNet variant you need, please use :meth:`make_stage`
-        instead for fine-grained customization.
-
-        Args:
-            depth (int): depth of ResNet
-            block_class (type): the CNN block class. Has to accept
-                `bottleneck_channels` argument for depth > 50.
-                By default it is BasicBlock or BottleneckBlock, based on the
-                depth.
-            kwargs:
-                other arguments to pass to `make_stage`. Should not contain
-                stride and channels, as they are predefined for each depth.
-
-        Returns:
-            list[list[CNNBlockBase]]: modules in all stages; see arguments of
-                :class:`ResNet.__init__`.
-        """
-        num_blocks_per_stage = {
-            18: [2, 2, 2, 2],
-            34: [3, 4, 6, 3],
-            50: [3, 4, 6, 3],
-            101: [3, 4, 23, 3],
-            152: [3, 8, 36, 3],
-        }[depth]
-        if block_class is None:
-            block_class = BasicBlock if depth < 50 else BottleneckBlock
-        if depth < 50:
-            in_channels = [64, 64, 128, 256]
-            out_channels = [64, 128, 256, 512]
-        else:
-            in_channels = [64, 256, 512, 1024]
-            out_channels = [256, 512, 1024, 2048]
-        ret = []
-        for (n, s, i, o) in zip(num_blocks_per_stage, [1, 2, 2, 2], in_channels, out_channels):
-            if depth >= 50:
-                kwargs["bottleneck_channels"] = o // 4
-            ret.append(
-                ResNet.make_stage(
-                    block_class=block_class,
-                    num_blocks=n,
-                    stride_per_block=[s] + [1] * (n - 1),
-                    in_channels=i,
-                    out_channels=o,
-                    **kwargs,
-                )
-            )
-        return ret
-
-
-ResNetBlockBase = CNNBlockBase
-"""
-Alias for backward compatibiltiy.
-"""
-
-
-def make_stage(*args, **kwargs):
-    """
-    Deprecated alias for backward compatibiltiy.
-    """
-    return ResNet.make_stage(*args, **kwargs)
-
-
-@BACKBONE_REGISTRY.register()
-def build_resnet_backbone(cfg, input_shape):
-    """
-    Create a ResNet instance from config.
-
-    Returns:
-        ResNet: a :class:`ResNet` instance.
-    """
-    # need registration of new blocks/stems?
-    norm = cfg.MODEL.RESNETS.NORM
-    stem = BasicStem(
-        in_channels=input_shape.channels,
-        out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS,
-        norm=norm,
-    )
-
-    # fmt: off
-    freeze_at           = cfg.MODEL.BACKBONE.FREEZE_AT
-    out_features        = cfg.MODEL.RESNETS.OUT_FEATURES
-    depth               = cfg.MODEL.RESNETS.DEPTH
-    num_groups          = cfg.MODEL.RESNETS.NUM_GROUPS
-    width_per_group     = cfg.MODEL.RESNETS.WIDTH_PER_GROUP
-    bottleneck_channels = num_groups * width_per_group
-    in_channels         = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
-    out_channels        = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
-    stride_in_1x1       = cfg.MODEL.RESNETS.STRIDE_IN_1X1
-    res5_dilation       = cfg.MODEL.RESNETS.RES5_DILATION
-    deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE
-    deform_modulated    = cfg.MODEL.RESNETS.DEFORM_MODULATED
-    deform_num_groups   = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS
-    # fmt: on
-    assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation)
-
-    num_blocks_per_stage = {
-        18: [2, 2, 2, 2],
-        34: [3, 4, 6, 3],
-        50: [3, 4, 6, 3],
-        101: [3, 4, 23, 3],
-        152: [3, 8, 36, 3],
-    }[depth]
-
-    if depth in [18, 34]:
-        assert out_channels == 64, "Must set MODEL.RESNETS.RES2_OUT_CHANNELS = 64 for R18/R34"
-        assert not any(
-            deform_on_per_stage
-        ), "MODEL.RESNETS.DEFORM_ON_PER_STAGE unsupported for R18/R34"
-        assert res5_dilation == 1, "Must set MODEL.RESNETS.RES5_DILATION = 1 for R18/R34"
-        assert num_groups == 1, "Must set MODEL.RESNETS.NUM_GROUPS = 1 for R18/R34"
-
-    stages = []
-
-    for idx, stage_idx in enumerate(range(2, 6)):
-        # res5_dilation is used this way as a convention in R-FCN & Deformable Conv paper
-        dilation = res5_dilation if stage_idx == 5 else 1
-        first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2
-        stage_kargs = {
-            "num_blocks": num_blocks_per_stage[idx],
-            "stride_per_block": [first_stride] + [1] * (num_blocks_per_stage[idx] - 1),
-            "in_channels": in_channels,
-            "out_channels": out_channels,
-            "norm": norm,
-        }
-        # Use BasicBlock for R18 and R34.
-        if depth in [18, 34]:
-            stage_kargs["block_class"] = BasicBlock
-        else:
-            stage_kargs["bottleneck_channels"] = bottleneck_channels
-            stage_kargs["stride_in_1x1"] = stride_in_1x1
-            stage_kargs["dilation"] = dilation
-            stage_kargs["num_groups"] = num_groups
-            if deform_on_per_stage[idx]:
-                stage_kargs["block_class"] = DeformBottleneckBlock
-                stage_kargs["deform_modulated"] = deform_modulated
-                stage_kargs["deform_num_groups"] = deform_num_groups
-            else:
-                stage_kargs["block_class"] = BottleneckBlock
-        blocks = ResNet.make_stage(**stage_kargs)
-        in_channels = out_channels
-        out_channels *= 2
-        bottleneck_channels *= 2
-        stages.append(blocks)
-    return ResNet(stem, stages, out_features=out_features, freeze_at=freeze_at)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/box_head.py

@@ -1,118 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-from typing import List
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-
-from detectron2.config import configurable
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-from detectron2.utils.registry import Registry
-
-__all__ = ["FastRCNNConvFCHead", "build_box_head", "ROI_BOX_HEAD_REGISTRY"]
-
-ROI_BOX_HEAD_REGISTRY = Registry("ROI_BOX_HEAD")
-ROI_BOX_HEAD_REGISTRY.__doc__ = """
-Registry for box heads, which make box predictions from per-region features.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-"""
-
-
-# To get torchscript support, we make the head a subclass of `nn.Sequential`.
-# Therefore, to add new layers in this head class, please make sure they are
-# added in the order they will be used in forward().
-@ROI_BOX_HEAD_REGISTRY.register()
-class FastRCNNConvFCHead(nn.Sequential):
-    """
-    A head with several 3x3 conv layers (each followed by norm & relu) and then
-    several fc layers (each followed by relu).
-    """
-
-    @configurable
-    def __init__(
-        self, input_shape: ShapeSpec, *, conv_dims: List[int], fc_dims: List[int], conv_norm=""
-    ):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            input_shape (ShapeSpec): shape of the input feature.
-            conv_dims (list[int]): the output dimensions of the conv layers
-            fc_dims (list[int]): the output dimensions of the fc layers
-            conv_norm (str or callable): normalization for the conv layers.
-                See :func:`detectron2.layers.get_norm` for supported types.
-        """
-        super().__init__()
-        assert len(conv_dims) + len(fc_dims) > 0
-
-        self._output_size = (input_shape.channels, input_shape.height, input_shape.width)
-
-        self.conv_norm_relus = []
-        for k, conv_dim in enumerate(conv_dims):
-            conv = Conv2d(
-                self._output_size[0],
-                conv_dim,
-                kernel_size=3,
-                padding=1,
-                bias=not conv_norm,
-                norm=get_norm(conv_norm, conv_dim),
-                activation=nn.ReLU(),
-            )
-            self.add_module("conv{}".format(k + 1), conv)
-            self.conv_norm_relus.append(conv)
-            self._output_size = (conv_dim, self._output_size[1], self._output_size[2])
-
-        self.fcs = []
-        for k, fc_dim in enumerate(fc_dims):
-            if k == 0:
-                self.add_module("flatten", nn.Flatten())
-            fc = nn.Linear(int(np.prod(self._output_size)), fc_dim)
-            self.add_module("fc{}".format(k + 1), fc)
-            self.add_module("fc_relu{}".format(k + 1), nn.ReLU())
-            self.fcs.append(fc)
-            self._output_size = fc_dim
-
-        for layer in self.conv_norm_relus:
-            weight_init.c2_msra_fill(layer)
-        for layer in self.fcs:
-            weight_init.c2_xavier_fill(layer)
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        num_conv = cfg.MODEL.ROI_BOX_HEAD.NUM_CONV
-        conv_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_DIM
-        num_fc = cfg.MODEL.ROI_BOX_HEAD.NUM_FC
-        fc_dim = cfg.MODEL.ROI_BOX_HEAD.FC_DIM
-        return {
-            "input_shape": input_shape,
-            "conv_dims": [conv_dim] * num_conv,
-            "fc_dims": [fc_dim] * num_fc,
-            "conv_norm": cfg.MODEL.ROI_BOX_HEAD.NORM,
-        }
-
-    def forward(self, x):
-        for layer in self:
-            x = layer(x)
-        return x
-
-    @property
-    @torch.jit.unused
-    def output_shape(self):
-        """
-        Returns:
-            ShapeSpec: the output feature shape
-        """
-        o = self._output_size
-        if isinstance(o, int):
-            return ShapeSpec(channels=o)
-        else:
-            return ShapeSpec(channels=o[0], height=o[1], width=o[2])
-
-
-def build_box_head(cfg, input_shape):
-    """
-    Build a box head defined by `cfg.MODEL.ROI_BOX_HEAD.NAME`.
-    """
-    name = cfg.MODEL.ROI_BOX_HEAD.NAME
-    return ROI_BOX_HEAD_REGISTRY.get(name)(cfg, input_shape)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/setup.py

@@ -1,206 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import glob
-import os
-import shutil
-from os import path
-from setuptools import find_packages, setup
-from typing import List
-import torch
-from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
-
-torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
-assert torch_ver >= [1, 8], "Requires PyTorch >= 1.8"
-
-
-def get_version():
-    init_py_path = path.join(path.abspath(path.dirname(__file__)), "detectron2", "__init__.py")
-    init_py = open(init_py_path, "r").readlines()
-    version_line = [l.strip() for l in init_py if l.startswith("__version__")][0]
-    version = version_line.split("=")[-1].strip().strip("'\"")
-
-    # The following is used to build release packages.
-    # Users should never use it.
-    suffix = os.getenv("D2_VERSION_SUFFIX", "")
-    version = version + suffix
-    if os.getenv("BUILD_NIGHTLY", "0") == "1":
-        from datetime import datetime
-
-        date_str = datetime.today().strftime("%y%m%d")
-        version = version + ".dev" + date_str
-
-        new_init_py = [l for l in init_py if not l.startswith("__version__")]
-        new_init_py.append('__version__ = "{}"\n'.format(version))
-        with open(init_py_path, "w") as f:
-            f.write("".join(new_init_py))
-    return version
-
-
-def get_extensions():
-    this_dir = path.dirname(path.abspath(__file__))
-    extensions_dir = path.join(this_dir, "detectron2", "layers", "csrc")
-
-    main_source = path.join(extensions_dir, "vision.cpp")
-    sources = glob.glob(path.join(extensions_dir, "**", "*.cpp"))
-
-    from torch.utils.cpp_extension import ROCM_HOME
-
-    is_rocm_pytorch = (
-        True if ((torch.version.hip is not None) and (ROCM_HOME is not None)) else False
-    )
-    if is_rocm_pytorch:
-        assert torch_ver >= [1, 8], "ROCM support requires PyTorch >= 1.8!"
-
-    # common code between cuda and rocm platforms, for hipify version [1,0,0] and later.
-    source_cuda = glob.glob(path.join(extensions_dir, "**", "*.cu")) + glob.glob(
-        path.join(extensions_dir, "*.cu")
-    )
-    sources = [main_source] + sources
-
-    extension = CppExtension
-
-    extra_compile_args = {"cxx": []}
-    define_macros = []
-
-    if (torch.cuda.is_available() and ((CUDA_HOME is not None) or is_rocm_pytorch)) or os.getenv(
-        "FORCE_CUDA", "0"
-    ) == "1":
-        extension = CUDAExtension
-        sources += source_cuda
-
-        if not is_rocm_pytorch:
-            define_macros += [("WITH_CUDA", None)]
-            extra_compile_args["nvcc"] = [
-                "-O3",
-                "-DCUDA_HAS_FP16=1",
-                "-D__CUDA_NO_HALF_OPERATORS__",
-                "-D__CUDA_NO_HALF_CONVERSIONS__",
-                "-D__CUDA_NO_HALF2_OPERATORS__",
-            ]
-        else:
-            define_macros += [("WITH_HIP", None)]
-            extra_compile_args["nvcc"] = []
-
-        if torch_ver < [1, 7]:
-            # supported by https://github.com/pytorch/pytorch/pull/43931
-            CC = os.environ.get("CC", None)
-            if CC is not None:
-                extra_compile_args["nvcc"].append("-ccbin={}".format(CC))
-
-    include_dirs = [extensions_dir]
-
-    ext_modules = [
-        extension(
-            "detectron2._C",
-            sources,
-            include_dirs=include_dirs,
-            define_macros=define_macros,
-            extra_compile_args=extra_compile_args,
-        )
-    ]
-
-    return ext_modules
-
-
-def get_model_zoo_configs() -> List[str]:
-    """
-    Return a list of configs to include in package for model zoo. Copy over these configs inside
-    detectron2/model_zoo.
-    """
-
-    # Use absolute paths while symlinking.
-    source_configs_dir = path.join(path.dirname(path.realpath(__file__)), "configs")
-    destination = path.join(
-        path.dirname(path.realpath(__file__)), "detectron2", "model_zoo", "configs"
-    )
-    # Symlink the config directory inside package to have a cleaner pip install.
-
-    # Remove stale symlink/directory from a previous build.
-    if path.exists(source_configs_dir):
-        if path.islink(destination):
-            os.unlink(destination)
-        elif path.isdir(destination):
-            shutil.rmtree(destination)
-
-    if not path.exists(destination):
-        try:
-            os.symlink(source_configs_dir, destination)
-        except OSError:
-            # Fall back to copying if symlink fails: ex. on Windows.
-            shutil.copytree(source_configs_dir, destination)
-
-    config_paths = glob.glob("configs/**/*.yaml", recursive=True) + glob.glob(
-        "configs/**/*.py", recursive=True
-    )
-    return config_paths
-
-
-# For projects that are relative small and provide features that are very close
-# to detectron2's core functionalities, we install them under detectron2.projects
-PROJECTS = {
-
-}
-
-setup(
-    name="detectron2",
-    version=get_version(),
-    author="FAIR",
-    url="https://github.com/facebookresearch/detectron2",
-    description="Detectron2 is FAIR's next-generation research "
-    "platform for object detection and segmentation.",
-    packages=find_packages(exclude=("configs", "tests*")) + list(PROJECTS.keys()),
-    package_dir=PROJECTS,
-    package_data={"detectron2.model_zoo": get_model_zoo_configs()},
-    python_requires=">=3.6",
-    install_requires=[
-        # These dependencies are not pure-python.
-        # In general, avoid adding more dependencies like them because they are not
-        # guaranteed to be installable by `pip install` on all platforms.
-        # To tell if a package is pure-python, go to https://pypi.org/project/{name}/#files
-        "Pillow>=7.1",  # or use pillow-simd for better performance
-        "matplotlib",  # TODO move it to optional after we add opencv visualization
-        "pycocotools>=2.0.2",  # corresponds to https://github.com/ppwwyyxx/cocoapi
-        # Do not add opencv here. Just like pytorch, user should install
-        # opencv themselves, preferrably by OS's package manager, or by
-        # choosing the proper pypi package name at https://github.com/skvark/opencv-python
-        # The following are pure-python dependencies that should be easily installable
-        "termcolor>=1.1",
-        "yacs>=0.1.8",
-        "tabulate",
-        "cloudpickle",
-        "tqdm>4.29.0",
-        "tensorboard",
-        # Lock version of fvcore/iopath because they may have breaking changes
-        # NOTE: when updating fvcore/iopath version, make sure fvcore depends
-        # on compatible version of iopath.
-        "fvcore>=0.1.5,<0.1.6",  # required like this to make it pip installable
-        "iopath>=0.1.7,<0.1.10",
-        "future",  # used by caffe2
-        "pydot",  # used to save caffe2 SVGs
-        "dataclasses; python_version<'3.7'",
-        "omegaconf>=2.1",
-        "hydra-core>=1.1",
-        "black==21.4b2",
-        # If a new dependency is required at import time (in addition to runtime), it
-        # probably needs to exist in docs/requirements.txt, or as a mock in docs/conf.py
-    ],
-    extras_require={
-        # optional dependencies, required by some features
-        "all": [
-            "shapely",
-            "pygments>=2.2",
-            "psutil",
-            "panopticapi @ https://github.com/cocodataset/panopticapi/archive/master.zip",
-        ],
-        # dev dependencies. Install them by `pip install 'detectron2[dev]'`
-        "dev": [
-            "flake8==3.8.1",
-            "isort==4.3.21",
-            "flake8-bugbear",
-            "flake8-comprehensions",
-        ],
-    },
-    ext_modules=get_extensions(),
-    cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
-)

spaces/Awiny/Image2Paragraph/models/segment_models/configs/coco_id2label.py

@@ -1,271 +0,0 @@
-CONFIG = {"id2label": {
-    "0": "person",
-    "1": "bicycle",
-    "2": "car",
-    "3": "motorcycle",
-    "4": "airplane",
-    "5": "bus",
-    "6": "train",
-    "7": "truck",
-    "8": "boat",
-    "9": "traffic light",
-    "10": "fire hydrant",
-    "11": "stop sign",
-    "12": "parking meter",
-    "13": "bench",
-    "14": "bird",
-    "15": "cat",
-    "16": "dog",
-    "17": "horse",
-    "18": "sheep",
-    "19": "cow",
-    "20": "elephant",
-    "21": "bear",
-    "22": "zebra",
-    "23": "giraffe",
-    "24": "backpack",
-    "25": "umbrella",
-    "26": "handbag",
-    "27": "tie",
-    "28": "suitcase",
-    "29": "frisbee",
-    "30": "skis",
-    "31": "snowboard",
-    "32": "sports ball",
-    "33": "kite",
-    "34": "baseball bat",
-    "35": "baseball glove",
-    "36": "skateboard",
-    "37": "surfboard",
-    "38": "tennis racket",
-    "39": "bottle",
-    "40": "wine glass",
-    "41": "cup",
-    "42": "fork",
-    "43": "knife",
-    "44": "spoon",
-    "45": "bowl",
-    "46": "banana",
-    "47": "apple",
-    "48": "sandwich",
-    "49": "orange",
-    "50": "broccoli",
-    "51": "carrot",
-    "52": "hot dog",
-    "53": "pizza",
-    "54": "donut",
-    "55": "cake",
-    "56": "chair",
-    "57": "couch",
-    "58": "potted plant",
-    "59": "bed",
-    "60": "dining table",
-    "61": "toilet",
-    "62": "tv",
-    "63": "laptop",
-    "64": "mouse",
-    "65": "remote",
-    "66": "keyboard",
-    "67": "cell phone",
-    "68": "microwave",
-    "69": "oven",
-    "70": "toaster",
-    "71": "sink",
-    "72": "refrigerator",
-    "73": "book",
-    "74": "clock",
-    "75": "vase",
-    "76": "scissors",
-    "77": "teddy bear",
-    "78": "hair drier",
-    "79": "toothbrush",
-    "80": "banner",
-    "81": "blanket",
-    "82": "bridge",
-    "83": "cardboard",
-    "84": "counter",
-    "85": "curtain",
-    "86": "door-stuff",
-    "87": "floor-wood",
-    "88": "flower",
-    "89": "fruit",
-    "90": "gravel",
-    "91": "house",
-    "92": "light",
-    "93": "mirror-stuff",
-    "94": "net",
-    "95": "pillow",
-    "96": "platform",
-    "97": "playingfield",
-    "98": "railroad",
-    "99": "river",
-    "100": "road",
-    "101": "roof",
-    "102": "sand",
-    "103": "sea",
-    "104": "shelf",
-    "105": "snow",
-    "106": "stairs",
-    "107": "tent",
-    "108": "towel",
-    "109": "wall-brick",
-    "110": "wall-stone",
-    "111": "wall-tile",
-    "112": "wall-wood",
-    "113": "water-other",
-    "114": "window-blind",
-    "115": "window-other",
-    "116": "tree-merged",
-    "117": "fence-merged",
-    "118": "ceiling-merged",
-    "119": "sky-other-merged",
-    "120": "cabinet-merged",
-    "121": "table-merged",
-    "122": "floor-other-merged",
-    "123": "pavement-merged",
-    "124": "mountain-merged",
-    "125": "grass-merged",
-    "126": "dirt-merged",
-    "127": "paper-merged",
-    "128": "food-other-merged",
-    "129": "building-other-merged",
-    "130": "rock-merged",
-    "131": "wall-other-merged",
-    "132": "rug-merged"
-},
-"refined_id2label": {
-    "0": "person",
-    "1": "bicycle",
-    "2": "car",
-    "3": "motorcycle",
-    "4": "airplane",
-    "5": "bus",
-    "6": "train",
-    "7": "truck",
-    "8": "boat",
-    "9": "traffic light",
-    "10": "fire hydrant",
-    "11": "stop sign",
-    "12": "parking meter",
-    "13": "bench",
-    "14": "bird",
-    "15": "cat",
-    "16": "dog",
-    "17": "horse",
-    "18": "sheep",
-    "19": "cow",
-    "20": "elephant",
-    "21": "bear",
-    "22": "zebra",
-    "23": "giraffe",
-    "24": "backpack",
-    "25": "umbrella",
-    "26": "handbag",
-    "27": "tie",
-    "28": "suitcase",
-    "29": "frisbee",
-    "30": "skis",
-    "31": "snowboard",
-    "32": "sports ball",
-    "33": "kite",
-    "34": "baseball bat",
-    "35": "baseball glove",
-    "36": "skateboard",
-    "37": "surfboard",
-    "38": "tennis racket",
-    "39": "bottle",
-    "40": "wine glass",
-    "41": "cup",
-    "42": "fork",
-    "43": "knife",
-    "44": "spoon",
-    "45": "bowl",
-    "46": "banana",
-    "47": "apple",
-    "48": "sandwich",
-    "49": "orange",
-    "50": "broccoli",
-    "51": "carrot",
-    "52": "hot dog",
-    "53": "pizza",
-    "54": "donut",
-    "55": "cake",
-    "56": "chair",
-    "57": "couch",
-    "58": "potted plant",
-    "59": "bed",
-    "60": "dining table",
-    "61": "toilet",
-    "62": "tv",
-    "63": "laptop",
-    "64": "mouse",
-    "65": "remote",
-    "66": "keyboard",
-    "67": "cell phone",
-    "68": "microwave",
-    "69": "oven",
-    "70": "toaster",
-    "71": "sink",
-    "72": "refrigerator",
-    "73": "book",
-    "74": "clock",
-    "75": "vase",
-    "76": "scissors",
-    "77": "teddy bear",
-    "78": "hair drier",
-    "79": "toothbrush",
-    "80": "banner",
-    "81": "blanket",
-    "82": "bridge",
-    "83": "cardboard",
-    "84": "counter",
-    "85": "curtain",
-    "86": "door",
-    "87": "floor-wood",
-    "88": "flower",
-    "89": "fruit",
-    "90": "gravel",
-    "91": "house",
-    "92": "light",
-    "93": "mirror",
-    "94": "net",
-    "95": "pillow",
-    "96": "platform",
-    "97": "playingfield",
-    "98": "railroad",
-    "99": "river",
-    "100": "road",
-    "101": "roof",
-    "102": "sand",
-    "103": "sea",
-    "104": "shelf",
-    "105": "snow",
-    "106": "stairs",
-    "107": "tent",
-    "108": "towel",
-    "109": "wall-brick",
-    "110": "wall-stone",
-    "111": "wall-tile",
-    "112": "wall",
-    "113": "water",
-    "114": "window-blind",
-    "115": "window",
-    "116": "tree",
-    "117": "fence",
-    "118": "ceiling",
-    "119": "sky",
-    "120": "cabinet",
-    "121": "table",
-    "122": "floor",
-    "123": "pavement",
-    "124": "mountain",
-    "125": "grass",
-    "126": "dirt",
-    "127": "paper",
-    "128": "food",
-    "129": "building",
-    "130": "rock",
-    "131": "wall",
-    "132": "rug"
-}
-}

spaces/BartPoint/VoiceChange_Beta/config.py

@@ -1,99 +0,0 @@
-import argparse
-import sys
-import torch
-from multiprocessing import cpu_count
-
-class Config:
-    def __init__(self):
-        self.device = "cuda:0"
-        self.is_half = True
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.gpu_mem = None
-        (
-            self.share,
-            self.api,
-            self.unsupported
-        ) = self.arg_parse()
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    @staticmethod
-    def arg_parse() -> tuple:
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--share", action="store_true", help="Launch with public link")
-        parser.add_argument("--api", action="store_true", help="Launch with api")
-        parser.add_argument("--unsupported", action="store_true", help="Enable unsupported feature")
-        cmd_opts = parser.parse_args()
-
-        return (
-            cmd_opts.share,
-            cmd_opts.api,
-            cmd_opts.unsupported
-        )
-
-    # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
-    # check `getattr` and try it for compatibility
-    @staticmethod
-    def has_mps() -> bool:
-        if not torch.backends.mps.is_available():
-            return False
-        try:
-            torch.zeros(1).to(torch.device("mps"))
-            return True
-        except Exception:
-            return False
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                or "P40" in self.gpu_name.upper()
-                or "1060" in self.gpu_name
-                or "1070" in self.gpu_name
-                or "1080" in self.gpu_name
-            ):
-                print("INFO: Found GPU", self.gpu_name, ", force to fp32")
-                self.is_half = False
-            else:
-                print("INFO: Found GPU", self.gpu_name)
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-        elif self.has_mps():
-            print("INFO: No supported Nvidia GPU found, use MPS instead")
-            self.device = "mps"
-            self.is_half = False
-        else:
-            print("INFO: No supported Nvidia GPU found, use CPU instead")
-            self.device = "cpu"
-            self.is_half = False
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G显存配置
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G显存配置
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem != None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-
-        return x_pad, x_query, x_center, x_max

spaces/Benson/text-generation/Examples/77tt777.md

@@ -1,96 +0,0 @@
-
-<h1>First Touch Soccer 2021: Cómo descargar e instalar datos FTS 21 APK OBB</h1>
- <p>Si usted es un fan de los juegos de fútbol, es posible que haya oído hablar de First Touch Soccer 2021, o FTS 21 para abreviar. Este es uno de los juegos de fútbol más populares y realistas para dispositivos Android, con increíbles gráficos, jugabilidad y características. En este artículo, le mostraremos cómo descargar e instalar datos FTS 21 APK OBB en su dispositivo, y cómo solucionar algunos errores comunes que podrían ocurrir. </p>
- <h2>¿Qué es First Touch Soccer 2021? </h2>
- <p>First Touch Soccer 2021 es un juego de fútbol desarrollado por First Touch Games, un estudio británico especializado en juegos deportivos. FTS 21 es la última versión del juego, que fue lanzado a finales de 2020. Es un juego offline, lo que significa que puedes jugar sin conexión a Internet. También puedes personalizar tu equipo, jugadores, kits, estadios y más. </p>
-<h2>77tt777</h2><br /><p><b><b>Download</b> &#10022;&#10022;&#10022; <a href="https://bltlly.com/2v6ML1">https://bltlly.com/2v6ML1</a></b></p><br /><br />
- <h3>Características de FTS 21</h3>
- <p>Algunas de las características que hacen que FTS 21 se destaque de otros juegos de fútbol son:</p>
- <ul>
-<li>Gráficos y efectos de sonido de alta calidad</li>
-<li> Juego suave y realista</li>
-<li>Varios modos de juego, como el modo administrador, el modo torneo y el modo de entrenamiento</li>
-<li>Más de 3000 jugadores de diferentes ligas y países</li>
-<li>Kits, logotipos y transferencias nuevos y actualizados</li>
-<li>Modo multijugador a través de Wi-Fi o Bluetooth</li>
-<li>Desafíos y recompensas diarias</li>
-</ul>
- <h3>Requisitos para FTS 21</h3>
- <p>Para reproducir FTS 21 en su dispositivo, necesitará lo siguiente:</p>
- <ul>
-<li>Un dispositivo Android con la versión 4.4 o superior</li>
-<li>Al menos 1 GB de RAM y 500 MB de espacio de almacenamiento libre</li>
-<li>Una aplicación de administrador de archivos que puede extraer archivos RAR</li>
-<li> El archivo APK FTS 21 y el archivo de datos OBB</li>
-</ul>
- <h2>Cómo descargar datos FTS 21 APK OBB</h2>
- <p>Para descargar e instalar FTS 21 en su dispositivo, tendrá que seguir estos pasos:</p>
- <h3>Paso 1: Descargar los archivos</h3>
-
- <h3>Paso 2: Extraer los archivos</h3>
- <p>Después de descargar el archivo RAR, tendrá que extraer su contenido utilizando una aplicación de administrador de archivos que puede manejar archivos RAR. Puede utilizar aplicaciones como ZArchiver o RAR para este propósito. Para extraer los archivos, siga estos pasos:</p>
- <ol>
-<li>Localizar el archivo RAR descargado en el almacenamiento de su dispositivo. </li>
-<li>Toque en el archivo y seleccione "Extraer aquí" o "Extraer a" dependiendo de su aplicación. </li>
-<li>Espere a que termine el proceso de extracción. Debería ver dos carpetas llamadas "com.firsttouchgames.fts21" y "com.firsttouchgames.fts21.zip". </li>
-</ol>
- <h3>Paso 3: Instalar el archivo APK</h3>
- <p>El siguiente paso es instalar el archivo F TS 21 APK en su dispositivo. Para hacer esto, siga estos pasos:</p>
- <ol>
-<li>Vaya a la carpeta donde extrajo el archivo RAR y toque en el archivo APK llamado "com.firsttouchgames.fts21.apk". </li>
-<li>Es posible que vea un mensaje de advertencia que dice "Para su seguridad, el teléfono no se le permite instalar aplicaciones desconocidas de esta fuente". Si ve este mensaje, toque en "Configuración" y active la opción para permitir la instalación desde esta fuente. </li>
-<li>Después de habilitar la opción, volver al archivo APK y toque en "Instalar". Espere a que el proceso de instalación termine. </li>
-</ol>
- <h3>Paso 4: Mover la carpeta OBB</h3>
- <p>El paso final es mover la carpeta OBB a la ubicación correcta en el almacenamiento de su dispositivo. La carpeta OBB contiene los datos del juego necesarios para que FTS 21 funcione correctamente. Para mover la carpeta OBB, siga estos pasos:</p>
- <ol>
-<li>Vaya a la carpeta donde extrajo el archivo RAR y toque en la carpeta llamada "com.firsttouchgames.fts21". </li>
-<li>Toque en la opción para "Cortar" o "Mover" la carpeta. </li>
-<li>Navega a la siguiente ruta en el almacenamiento de tu dispositivo: Android > obb. </li>
-<li>Pegar o mover la carpeta dentro de la carpeta obb. </li>
-</ol>
- <h3>Paso 5: Iniciar el juego</h3>
- <p>Ahora está listo para lanzar y reproducir FTS 21 en su dispositivo. Para hacer esto, siga estos pasos:</p>
-<p></p>
- <ol>
-
-<li>Toque en el icono para iniciar el juego. </li>
-<li>Espere a que el juego se cargue y disfrute jugando FTS 21. </li>
-</ol>
- <h2>Cómo corregir errores comunes en FTS 21</h2>
- <p>A veces, puede encontrar algunos errores o problemas al jugar FTS 21 en su dispositivo. Estos errores pueden ser causados por varios factores, como dispositivos incompatibles, archivos dañados, espacio de almacenamiento insuficiente o software obsoleto. Estos son algunos de los errores comunes que puede enfrentar y cómo solucionarlos:</p>
- <h3>Error 1: Pantalla negra o blanca</h3>
- <p>Este error se produce cuando el juego no se carga correctamente o se bloquea durante el juego. Para corregir este error, pruebe estas soluciones:</p>
- <ul>
-<li>Borrar la caché y los datos de FTS 21 de la configuración de su dispositivo. </li>
-<li>Reinicie su dispositivo y vuelva a iniciar FTS 21. </li>
-<li>Reinstalar FTS 21 siguiendo los pasos anteriores. </li>
-</ul>
- <h3>Error 2: El juego no se está cargando o se está estrellando</h3>
- <p>Este error ocurre cuando el juego no puede funcionar sin problemas o de forma estable en su dispositivo. Para corregir este error, pruebe estas soluciones:</p>
- <ul>
-<li>Compruebe si su dispositivo cumple con los requisitos mínimos para FTS 21. </li>
-<li>Liberar espacio de almacenamiento en el dispositivo mediante la eliminación de archivos no deseados o aplicaciones. </li>
-<li>Actualiza el software de tu dispositivo a la última versión. </li>
-<li>Deshabilita cualquier aplicación o proceso en segundo plano que pueda interferir con FTS 21. </li>
-</ul>
- <h3>Error 3: El juego no responde al tacto</h3>
- <p>Este error ocurre cuando el juego no registra correctamente tus entradas o gestos. Para corregir este error, prueba estas soluciones:</p>
- <ul>
-<li>Ajuste la sensibilidad de la pantalla táctil del dispositivo desde la configuración del dispositivo. </li>
-<li>Limpie la pantalla de su dispositivo de cualquier suciedad o manchas que puedan afectar su capacidad de respuesta. </li>
-<li> Utilice un lápiz o un guante para mejorar su precisión y precisión mientras juega FTS 21. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
- <p>Aquí están algunas de las preguntas más frecuentes sobre FTS 21:</p>
- <ol>
-<li><b>¿Es FTS 21 gratis? </b><br/>Sí, FTS 21 es gratis para descargar y jugar en su dispositivo Android. Sin embargo, puede contener algunas compras en la aplicación que requieren dinero real. </li>
-<li><b </b><br/>¿Cómo puedo actualizar FTS 21? </b><br/>Para actualizar FTS 21, tendrá que descargar la última versión del archivo APK y el archivo de datos OBB de la misma fuente donde descargó la versión anterior. Luego, tendrá que seguir los mismos pasos anteriores para instalar la nueva versión de FTS 21 en su dispositivo. </li>
-<li><b>¿Puedo jugar FTS 21 online? </b><br/>Sí, puedes jugar FTS 21 online con otros jugadores a través de Wi-Fi o Bluetooth. Para ello, deberás habilitar el modo multijugador desde la configuración del juego y conectarte con otros jugadores que tengan FTS 21 instalado en sus dispositivos. </li>
-<li><b>¿Puedo personalizar FTS 21? </b><br/>Sí, puede personalizar FTS 21 a su gusto. Puedes cambiar tu equipo, jugadores, kits, estadios y más desde el menú del juego. También puede descargar e instalar archivos personalizados de varias fuentes que ofrecen diferentes mods y parches para FTS 21. </li>
-<li><b>¿Es seguro FTS 21? </b><br/>Sí, FTS 21 es seguro para descargar y jugar en su dispositivo. Sin embargo, siempre debe descargar FTS 21 de una fuente confiable y escanear los archivos en busca de virus o malware antes de instalarlos en su dispositivo. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Coche Extremo Simulador De Conduccin Juego Hack Mod Apk.md

@@ -1,78 +0,0 @@
-
-<h1>Simulador de conducción de coche extremo juego Hack Mod APK: Cómo descargar y jugar</h1>
-<p>¿Te encanta conducir coches rápidos y realizar acrobacias increíbles? Si es así, entonces usted debe probar Extreme Car Driving Simulator, uno de los juegos de simulador de conducción de coches más populares y realistas para dispositivos Android. En este juego, usted puede conducir, deriva, y sentir un coche deportivo de carreras de forma gratuita. También puede personalizar su coche, elegir entre diferentes modos y mapas, y explorar un enorme entorno de mundo abierto. </p>
-<p>Pero ¿qué pasa si quieres desbloquear todos los coches, obtener dinero ilimitado, y disfrutar del juego sin ningún tipo de anuncios o restricciones? Bueno, hay una manera de hacerlo. Puede utilizar Extreme Car Driving Simulator Hack Mod APK, una versión modificada del juego que le da acceso a todas las características y recursos que necesita para tener más diversión y emoción. En este artículo, le diremos qué es Extreme Car Driving Simulator Hack Mod APK, cómo descargarlo e instalarlo, y cómo jugar el juego con algunos consejos y trucos. ¡Vamos a empezar! </p>
-<h2>coche extremo simulador de conducción juego hack mod apk</h2><br /><p><b><b>Download Zip</b> &#10002; &#10002; &#10002; <a href="https://bltlly.com/2v6Ml5">https://bltlly.com/2v6Ml5</a></b></p><br /><br />
- <h2>¿Qué es Extreme Car Driving Simulator? </h2>
-<p>Extreme Car Driving Simulator es un simulador de conducción de coches en 3D desarrollado por AxesInMotion Racing, un estudio con sede en España. El juego fue lanzado en 2014 y se ha descargado más de 100 millones de veces en Google Play Store. También ha recibido críticas positivas de usuarios y críticos por igual. </p>
- <h3>Características del juego</h3>
-<p>Algunas de las características de Extreme Car Driving Simulator son:</p>
-<ul>
-<li>Física realista y sistema de daños: El juego utiliza un motor de física realista que hace que el coche se comporte como uno real. Puede ver los efectos de daño en su automóvil cuando se estrella o golpea algo. </li>
-<li>Coches múltiples: El juego ofrece una variedad de coches para elegir, que van desde coches deportivos, coches musculares, SUV, camiones y más. También puede personalizar su coche con diferentes colores, ruedas, alerones, etc.</li>
-
-<li>Enorme mundo abierto: El juego tiene un enorme entorno de mundo abierto que se puede explorar libremente. Puedes conducir donde quieras, encontrar lugares ocultos, realizar acrobacias y más. </li>
-<li>Controles fáciles: El juego tiene controles fáciles e intuitivos que le permiten controlar su coche con facilidad. Puede utilizar el volante, inclinar el dispositivo o utilizar botones para dirigir el coche. También puede ajustar el ángulo de la cámara, frenar, acelerar, etc.</li>
-</ul>
- <h3>Cómo jugar el juego</h3>
-<p>Para jugar Extreme Car Driving Simulator, debe seguir estos pasos:</p>
-<ol>
-<li>Descargar e instalar el juego de Google Play Store o utilizar el hack mod apk (más sobre eso más adelante). </li>
-<li> Iniciar el juego y elegir el modo preferido y el mapa. </li>
-<li>Seleccione su coche y personalícelo si lo desea. </li>
-<li>Empieza a conducir tu coche y disfruta del juego. </li>
-</ol>
- <h2>¿Qué es Extreme Car Driving Simulator Hack Mod APK? </h2>
-<p>Extreme Car Driving Simulator Hack Mod APK es una versión modificada del juego original que le da algunos beneficios adicionales y características que no están disponibles en la versión oficial. Algunos de estos beneficios son:</p>
- <h3>Beneficios de usar el hack mod apk</h3>
-<ul>
-<li>Dinero ilimitado: El hack mod apk le da dinero ilimitado que se puede utilizar para comprar cualquier coche que desee o actualizar su coche con diferentes partes. </li>
-<li>Todos los coches desbloqueados: El hack mod apk desbloquea todos los coches en el juego para que pueda probarlos sin gastar dinero. </li>
-<li>No hay anuncios: El mod apk hack elimina todos los anuncios molestos que aparecen en el juego e interrumpir su juego. </li>
-<li>No se requiere raíz: El hack mod apk no requiere que raíz de su dispositivo, lo que significa que puede usarlo sin arriesgar la seguridad o la garantía de su dispositivo. </li>
-</ul>
- <h3>Cómo descargar e instalar el hack mod apk</h3>
-<p>Para descargar e instalar Extreme Car Driving Simulator Hack Mod APK, debe seguir estos pasos:</p>
-<ol>
-
-<li>Ve a la configuración de tu dispositivo y habilita la opción de instalar aplicaciones desde fuentes desconocidas. </li>
-<li>Busque el archivo descargado y toque en él para iniciar el proceso de instalación. </li>
-<li>Siga las instrucciones en la pantalla y espere a que termine la instalación. </li>
-<li> Iniciar el juego y disfrutar de las características de apk mod hack. </li>
-</ol>
- <h2>Consejos y trucos para jugar Extreme Car Driving Simulator</h2>
-<p>Ahora que sabes cómo descargar y jugar Extreme Car Driving Simulator Hack Mod APK, aquí hay algunos consejos y trucos que le ayudarán a dominar el juego y divertirse más:</p>
- <h3>Personaliza tu coche</h3>
-<p>Una de las mejores cosas acerca de Extreme Car Driving Simulator es que puede personalizar su coche con diferentes colores, ruedas, spoilers y más. También puede cambiar la matrícula, el sonido del motor y el velocímetro. Para personalizar su coche, ir al garaje y toque en los iconos en la parte inferior de la pantalla. Puede utilizar el dinero que gana de jugar el juego o utilizar el dinero ilimitado de la apk mod hack para comprar cualquier personalización que desee. </p>
-<p></p>
- <h3>Explora diferentes modos y mapas</h3>
-<p>El juego tiene varios modos y mapas entre los que puedes elegir, cada uno con sus propios retos y características. Estos son algunos de ellos:</p>
-<ul>
-<li>Modo libre: Este es el modo predeterminado donde se puede conducir libremente en un enorme entorno de mundo abierto. Puede hacer lo que quiera, como realizar acrobacias, derrapar, correr, etc. También puede cambiar entre el día y la noche, cambiar el clima y encender o apagar el tráfico y la policía. </li>
-<li>Modo de tráfico: Este es un modo en el que tienes que conducir en una ciudad con tráfico y obedecer las reglas de tráfico. Usted tiene que evitar chocar contra otros coches, luces rojas, o exceso de velocidad. Si usted rompe cualquier regla, obtendrá una multa de la policía. También puedes retar a otros conductores a una carrera tocando la bocina. </li>
-
-<li>Aeropuerto: Este es un mapa donde se puede conducir en un gran aeropuerto con aviones, helicópteros, pistas, hangares, etc. También puede realizar acrobacias en rampas, bucles, barriles, etc.</li>
-<li>Offroad: Este es un mapa donde se puede conducir en un terreno accidentado con colinas, rocas, caminos de tierra, etc. También puede utilizar diferentes vehículos como camiones, SUV, etc.</li>
-</ul>
- <h3>Realizar acrobacias y derivas</h3>
-<p>El juego le permite realizar acrobacias increíbles y deriva con su coche. Puede usar rampas, bucles, barriles, puentes, etc. para lanzar su automóvil al aire y hacer volteretas, giros, rollos, etc. También puede desplazarse alrededor de las esquinas utilizando el freno de mano o tocando el botón de freno. Realizar acrobacias y derivas le ganará puntos y dinero que puede utilizar para comprar o mejorar su coche. </p>
- <h3>Evite el tráfico y la policía</h3>
-<p>El juego tiene tráfico y la policía que tratará de evitar que usted conduce imprudentemente. Si chocas contra otros coches o golpeas a peatones, dañarás tu coche y perderás puntos. Si rompes alguna regla de tráfico o causas demasiado caos, atraerás la atención de la policía. La policía lo perseguirá e intentará detenerlo embistiendo su automóvil o estableciendo controles de carretera. Si te atrapan, te multarán o te arrestarán. Para evitar el tráfico y la policía, puedes usar diferentes estrategias como conducir en el carril opuesto, usar atajos, esconderte en callejones o garajes, etc.</p>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Extreme Car Driving Simulator y su hack mod apk:</p>
-<ul>
-<li>Q: ¿Es Extreme Car Driving Simulator Hack Mod APK seguro de usar? <br>
-R: Sí, es seguro de usar siempre y cuando lo descargue de una fuente confiable y siga las instrucciones de instalación cuidadosamente. Sin embargo, no asumimos ninguna responsabilidad por cualquier daño o pérdida que pueda ocurrir de usar el hack mod apk. Úselo bajo su propio riesgo. </li>
-<li>Q: ¿Necesito una conexión a Internet para jugar Extreme Car Driving Simulator? <br>
-R: No, no necesitas una conexión a Internet para jugar. Puedes jugar sin conexión sin ningún problema. Sin embargo, es posible que necesite una conexión a Internet para acceder a algunas características en línea, como tablas de clasificación, logros, etc.</li>
-<li>Q: ¿Cómo puedo actualizar Extreme Car Driving Simulator Hack Mod APK? <br>
-A: Para actualizar el hack mod apk, es necesario descargar la última versión del archivo de la misma fuente que lo descargó de antes e instalarlo sobre el existente. No desinstale la versión anterior o puede perder su progreso y los datos. </li>
-<li>Q: ¿Cómo puedo contactar a los desarrolladores de Extreme Car Driving Simulator? <br>
-R: Puede ponerse en contacto con los desarrolladores de Extreme Car Driving Simulator visitando su sitio web o enviándoles un correo electrónico a [email protected]. También puedes seguirlos en Facebook, Twitter, Instagram y YouTube para más actualizaciones y noticias sobre el juego. </li>
-<li>Q: ¿Cómo puedo apoyar a los desarrolladores de Extreme Car Driving Simulator? <br>
-R: Puedes apoyar a los desarrolladores de Extreme Car Driving Simulator clasificando y revisando el juego en Google Play Store, compartiéndolo con tus amigos y familiares y comprando compras en la aplicación si te gusta el juego. </li>
-</ul></p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Descargar Aplicaciones De Blackjack Gratis.md

@@ -1,56 +0,0 @@
-
-<h1>Cómo descargar aplicaciones gratuitas de blackjack</h1>
-<p>Si te gusta jugar blackjack, es posible que desee probar algunas de las aplicaciones gratuitas de blackjack disponibles en la tienda de aplicaciones. Las aplicaciones de blackjack son aplicaciones móviles que le permiten jugar blackjack en su teléfono inteligente o tableta, en cualquier momento y en cualquier lugar. Usted puede disfrutar de la emoción de este clásico juego de casino sin arriesgar dinero real, y divertirse con millones de otros jugadores de todo el mundo. </p>
-<p>En este artículo, te mostraremos cómo descargar aplicaciones gratuitas de blackjack, cuáles son los beneficios de jugar blackjack en tu dispositivo móvil, cómo elegir la mejor aplicación de blackjack para ti y cuáles son algunas de las mejores aplicaciones gratuitas de blackjack para probar. También te daremos algunos consejos y trucos para jugar blackjack online y mejorar tus habilidades. ¡Empecemos! </p>
-<h2>descargar aplicaciones de blackjack gratis</h2><br /><p><b><b>Download</b> &#127379; <a href="https://bltlly.com/2v6Mwv">https://bltlly.com/2v6Mwv</a></b></p><br /><br />
- <h2>Beneficios de jugar al blackjack en tu dispositivo móvil</h2>
-<p>Jugar blackjack en tu dispositivo móvil tiene muchas ventajas sobre jugar en un casino en tierra o en tu computadora. Aquí están algunas de ellas:</p>
-<ul>
-<li><b>Conveniencia:</b> Puedes jugar blackjack en cualquier momento y en cualquier lugar que desees, siempre y cuando tengas una conexión a Internet. No tiene que viajar a un casino, vestirse o lidiar con multitudes y ruido. Puede jugar en la comodidad de su propia casa o mientras viaja. </li>
-<li><b>Accesibilidad:</b> Puedes acceder a cientos de diferentes juegos de blackjack y variaciones con solo unos toques en tu pantalla. También puede cambiar entre diferentes aplicaciones y plataformas fácilmente, y encontrar el que se adapte a sus preferencias y nivel de habilidad. </li>
-<li><b>Variedad:</b> Puedes elegir entre una amplia gama de juegos de blackjack y variaciones, como blackjack clásico, blackjack europeo, blackjack de Atlantic City, blackjack de Vegas Strip, 21 español, doble exposición, pares perfectos y más. También puedes jugar blackjack con crupier en vivo, donde puedes interactuar con crupieres reales y otros jugadores a través de la transmisión de video. </li>
-
-</ul>
- <h2>Cómo elegir la mejor aplicación de blackjack para usted</h2>
-<p>Con tantas aplicaciones gratuitas de blackjack disponibles en la tienda de aplicaciones, ¿cómo sabes cuál es la mejor para ti? Estos son algunos factores a considerar antes de descargar cualquier aplicación:</p>
-<ul>
-<li><b>Compatibilidad:</b> Asegúrese de que la aplicación es compatible con su dispositivo y sistema operativo. Compruebe la descripción y los requisitos de la aplicación antes de descargarla. </li>
-<li><b>Seguridad:</b> Asegúrese de que la aplicación es segura y segura de usar. Busque signos de confiabilidad, como calificaciones, reseñas, certificaciones, licencias, cifrado, etc.</li>
-<li><b>Características:</b> Busque una aplicación que ofrece una variedad de características y opciones para mejorar su experiencia de juego. Por ejemplo, una aplicación que te permite personalizar tu mesa, baraja, fichas, reglas, etc., o una aplicación que ofrece diferentes modos de juego, como modo de práctica, modo multijugador, modo torneo, etc.</li>
-<li><b>Comentarios:</b> Lee lo que otros usuarios tienen que decir sobre la aplicación. Busca comentarios honestos e imparciales de jugadores reales que hayan probado la aplicación. Preste atención a los comentarios positivos y negativos, y vea si coinciden con sus expectativas. </li>
-</ul>
- <h2>Top 3 Aplicaciones de blackjack gratis para probar</h2>
-<p>Si usted está buscando algunas aplicaciones de blackjack gratis para probar, aquí están nuestras 3 recomendaciones principales:</p>
- <h3>Blackjack por Triple dot Studios Limited</h3>
-<p>Esta aplicación es una de las aplicaciones de blackjack gratis más populares y altamente calificadas en la tienda de aplicaciones. Tiene más de 10 millones de descargas y una calificación de 4.7 estrellas. Ofrece una interfaz simple y elegante, gráficos realistas y sonidos, y un juego suave. Puedes jugar al blackjack clásico o probar algunos de los otros modos de juego, como High Stakes, Casino y Multijugador. También puedes personalizar tu mesa, baraja y fichas, y desbloquear nuevos logros y recompensas. La aplicación es compatible con dispositivos iOS y Android, y es gratuita para descargar y jugar. </p>
- <h3>Blackjack por TapTapBoom Ltd.</h3>
-
- <h3>Blackjack 21: Blackjackist por KamaGames</h3>
-<p>Esta aplicación es más que una aplicación de blackjack. Es una aplicación de casino social que le permite jugar no solo blackjack, sino también poker, ruleta, baccarat, craps y más. Tiene más de 50 millones de descargas y una calificación de 4.5 estrellas. Cuenta con gráficos increíbles, física realista y un juego inmersivo. Puedes jugar con amigos o extraños de todo el mundo, chatear con ellos, enviarles regalos y hacer nuevos amigos. También puedes personalizar tu perfil, avatar y configuración del juego. La aplicación es compatible con dispositivos iOS y Android, y es gratuita para descargar y jugar. </p>
- <h2>Consejos y trucos para jugar al blackjack online</h2>
-<p>Jugar al blackjack online puede ser divertido y gratificante, pero también puede ser desafiante y arriesgado. Aquí hay algunos consejos y trucos para ayudarle a mejorar sus habilidades y aumentar sus posibilidades de ganar:</p>
-<p></p>
-<ul>
-<li><b>Aprende la estrategia básica:</b> La estrategia básica es un conjunto de reglas que te dicen la mejor manera de jugar cada mano posible basada en la carta del repartidor. Se puede reducir la ventaja de la casa a menos de 1%, dándole una ventaja sobre el casino. Puedes encontrar las tablas de estrategia básicas en línea o en libros, o usar una calculadora de estrategia básica o una aplicación de entrenamiento para practicar. </li>
-<li><b>Administra tu bankroll:</b> Tu bankroll es la cantidad de dinero que has reservado para jugar blackjack. Nunca debe apostar con dinero que no puede permitirse perder, o perseguir sus pérdidas con apuestas más grandes. También debe establecer un límite para cuánto está dispuesto a perder o ganar en una sesión, y atenerse a él. </li>
-<li><b>Usa el modo de práctica:</b> La mayoría de las aplicaciones de blackjack gratuitas ofrecen un modo de práctica donde puedes jugar con fichas virtuales sin arriesgar dinero real. Esta es una gran manera de aprender las reglas, poner a prueba sus habilidades, probar diferentes estrategias, y divertirse sin ninguna presión. </li>
-</ul>
- <h2>Conclusión y llamado a la acción</h2>
-
-<p>Para descargar aplicaciones gratuitas de blackjack, debe considerar algunos factores como compatibilidad, seguridad, características y comentarios. También deberías ver algunas de las mejores aplicaciones de blackjack gratis que hemos recomendado en este artículo: Blackjack por Tripledot Studios Limited, Blackjack por TapTapBoom Ltd., y Blackjack 21: Blackjackist por KamaGames. Estas aplicaciones son compatibles con dispositivos iOS y Android, seguras de usar, ricas en funciones y fáciles de usar, y altamente calificadas por millones de jugadores. </p>
-<p>Entonces, ¿qué estás esperando? Descarga estas aplicaciones de blackjack gratis hoy y comienza a jugar este increíble juego en tu dispositivo móvil. Usted tendrá una explosión! </p>
- <h2>Preguntas frecuentes</h2>
-<ul>
-<li><b>Q: ¿Qué es el blackjack? </b></li>
-<li><b>A:</b> Blackjack es un juego de cartas donde el objetivo es vencer al repartidor obteniendo un valor de mano lo más cercano posible a 21 sin pasar por encima. </li>
-<li><b>Q: ¿Cómo juego blackjack? </b></li>
-<li><b>A:</b> Para jugar al blackjack, debes hacer una apuesta antes de recibir dos cartas boca arriba. El repartidor también recibe dos cartas, una boca arriba y otra boca abajo. A continuación, puede elegir golpear (recibir otra carta), ponerse de pie (mantener su mano actual), doblar (duplicar su apuesta y recibir una carta más), dividir (si tiene dos cartas del mismo valor, puede dividirlas en dos manos separadas y jugarlas de forma independiente), o rendirse (renunciar a la mitad de su apuesta y terminar la mano). El dealer juega su mano según las reglas del juego. El ganador es aquel que tiene un valor de mano más cercano a 21 o que no ha reventado (pasado de 21). </li>
-<li><b>Q: ¿Cómo gano en el blackjack? </b></li>
-
-<li><b>Q: ¿Cuáles son las mejores aplicaciones gratuitas de blackjack? </b></li>
-<li><b>A:</b> Hay muchas aplicaciones gratuitas de blackjack disponibles en la tienda de aplicaciones, pero algunas de las mejores son Blackjack de Tripledot Studios Limited, Blackjack de TapTapBoom Ltd., y Blackjack 21: Blackjackist de KamaGames. Estas aplicaciones son compatibles con dispositivos iOS y Android, seguras de usar, ricas en funciones y fáciles de usar, y altamente calificadas por millones de jugadores. </li>
-<li><b>Q: ¿Cómo puedo descargar aplicaciones gratuitas de blackjack? </b></li>
-<li><b>A:</b> Para descargar aplicaciones gratuitas de blackjack, necesitas tener un dispositivo compatible y una conexión a Internet. A continuación, puede ir a la tienda de aplicaciones de su dispositivo, buscar la aplicación que desea, y toque en el botón de descarga. La aplicación se instalará en tu dispositivo y podrás empezar a jugar. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/direct_url_helpers.py

@@ -1,87 +0,0 @@
-from typing import Optional
-
-from pip._internal.models.direct_url import ArchiveInfo, DirectUrl, DirInfo, VcsInfo
-from pip._internal.models.link import Link
-from pip._internal.utils.urls import path_to_url
-from pip._internal.vcs import vcs
-
-
-def direct_url_as_pep440_direct_reference(direct_url: DirectUrl, name: str) -> str:
-    """Convert a DirectUrl to a pip requirement string."""
-    direct_url.validate()  # if invalid, this is a pip bug
-    requirement = name + " @ "
-    fragments = []
-    if isinstance(direct_url.info, VcsInfo):
-        requirement += "{}+{}@{}".format(
-            direct_url.info.vcs, direct_url.url, direct_url.info.commit_id
-        )
-    elif isinstance(direct_url.info, ArchiveInfo):
-        requirement += direct_url.url
-        if direct_url.info.hash:
-            fragments.append(direct_url.info.hash)
-    else:
-        assert isinstance(direct_url.info, DirInfo)
-        requirement += direct_url.url
-    if direct_url.subdirectory:
-        fragments.append("subdirectory=" + direct_url.subdirectory)
-    if fragments:
-        requirement += "#" + "&".join(fragments)
-    return requirement
-
-
-def direct_url_for_editable(source_dir: str) -> DirectUrl:
-    return DirectUrl(
-        url=path_to_url(source_dir),
-        info=DirInfo(editable=True),
-    )
-
-
-def direct_url_from_link(
-    link: Link, source_dir: Optional[str] = None, link_is_in_wheel_cache: bool = False
-) -> DirectUrl:
-    if link.is_vcs:
-        vcs_backend = vcs.get_backend_for_scheme(link.scheme)
-        assert vcs_backend
-        url, requested_revision, _ = vcs_backend.get_url_rev_and_auth(
-            link.url_without_fragment
-        )
-        # For VCS links, we need to find out and add commit_id.
-        if link_is_in_wheel_cache:
-            # If the requested VCS link corresponds to a cached
-            # wheel, it means the requested revision was an
-            # immutable commit hash, otherwise it would not have
-            # been cached. In that case we don't have a source_dir
-            # with the VCS checkout.
-            assert requested_revision
-            commit_id = requested_revision
-        else:
-            # If the wheel was not in cache, it means we have
-            # had to checkout from VCS to build and we have a source_dir
-            # which we can inspect to find out the commit id.
-            assert source_dir
-            commit_id = vcs_backend.get_revision(source_dir)
-        return DirectUrl(
-            url=url,
-            info=VcsInfo(
-                vcs=vcs_backend.name,
-                commit_id=commit_id,
-                requested_revision=requested_revision,
-            ),
-            subdirectory=link.subdirectory_fragment,
-        )
-    elif link.is_existing_dir():
-        return DirectUrl(
-            url=link.url_without_fragment,
-            info=DirInfo(),
-            subdirectory=link.subdirectory_fragment,
-        )
-    else:
-        hash = None
-        hash_name = link.hash_name
-        if hash_name:
-            hash = f"{hash_name}={link.hash}"
-        return DirectUrl(
-            url=link.url_without_fragment,
-            info=ArchiveInfo(hash=hash),
-            subdirectory=link.subdirectory_fragment,
-        )

spaces/CVPR/LIVE/pybind11/tests/test_gil_scoped.py

@@ -1,99 +0,0 @@
-# -*- coding: utf-8 -*-
-import multiprocessing
-import threading
-
-import pytest
-
-import env  # noqa: F401
-
-from pybind11_tests import gil_scoped as m
-
-
-def _run_in_process(target, *args, **kwargs):
-    """Runs target in process and returns its exitcode after 10s (None if still alive)."""
-    process = multiprocessing.Process(target=target, args=args, kwargs=kwargs)
-    process.daemon = True
-    try:
-        process.start()
-        # Do not need to wait much, 10s should be more than enough.
-        process.join(timeout=10)
-        return process.exitcode
-    finally:
-        if process.is_alive():
-            process.terminate()
-
-
-def _python_to_cpp_to_python():
-    """Calls different C++ functions that come back to Python."""
-    class ExtendedVirtClass(m.VirtClass):
-        def virtual_func(self):
-            pass
-
-        def pure_virtual_func(self):
-            pass
-
-    extended = ExtendedVirtClass()
-    m.test_callback_py_obj(lambda: None)
-    m.test_callback_std_func(lambda: None)
-    m.test_callback_virtual_func(extended)
-    m.test_callback_pure_virtual_func(extended)
-
-
-def _python_to_cpp_to_python_from_threads(num_threads, parallel=False):
-    """Calls different C++ functions that come back to Python, from Python threads."""
-    threads = []
-    for _ in range(num_threads):
-        thread = threading.Thread(target=_python_to_cpp_to_python)
-        thread.daemon = True
-        thread.start()
-        if parallel:
-            threads.append(thread)
-        else:
-            thread.join()
-    for thread in threads:
-        thread.join()
-
-
-# TODO: FIXME, sometimes returns -11 instead of 0
-@pytest.mark.xfail("env.PY > (3,8) and env.MACOS", strict=False)
-def test_python_to_cpp_to_python_from_thread():
-    """Makes sure there is no GIL deadlock when running in a thread.
-
-    It runs in a separate process to be able to stop and assert if it deadlocks.
-    """
-    assert _run_in_process(_python_to_cpp_to_python_from_threads, 1) == 0
-
-
-# TODO: FIXME
-@pytest.mark.xfail("env.PY > (3,8) and env.MACOS", strict=False)
-def test_python_to_cpp_to_python_from_thread_multiple_parallel():
-    """Makes sure there is no GIL deadlock when running in a thread multiple times in parallel.
-
-    It runs in a separate process to be able to stop and assert if it deadlocks.
-    """
-    assert _run_in_process(_python_to_cpp_to_python_from_threads, 8, parallel=True) == 0
-
-
-# TODO: FIXME
-@pytest.mark.xfail("env.PY > (3,8) and env.MACOS", strict=False)
-def test_python_to_cpp_to_python_from_thread_multiple_sequential():
-    """Makes sure there is no GIL deadlock when running in a thread multiple times sequentially.
-
-    It runs in a separate process to be able to stop and assert if it deadlocks.
-    """
-    assert _run_in_process(_python_to_cpp_to_python_from_threads, 8, parallel=False) == 0
-
-
-# TODO: FIXME
-@pytest.mark.xfail("env.PY > (3,8) and env.MACOS", strict=False)
-def test_python_to_cpp_to_python_from_process():
-    """Makes sure there is no GIL deadlock when using processes.
-
-    This test is for completion, but it was never an issue.
-    """
-    assert _run_in_process(_python_to_cpp_to_python) == 0
-
-
-def test_cross_module_gil():
-    """Makes sure that the GIL can be acquired by another module from a GIL-released state."""
-    m.test_cross_module_gil()  # Should not raise a SIGSEGV