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- spaces/1acneusushi/gradio-2dmoleculeeditor/Nafasul-Mahmoom-Urdu-Pdf-Download-NEW.md +0 -91
- spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cracked Blender Addons The Ultimate Blender No-No.md +0 -22
- spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Damaad Ke Intezaar Mein 3 Movie Hd 1) Dont Miss the Final Chapter of the Damaad Trilogy.md +0 -104
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- spaces/AIGC-Audio/AudioGPT/sound_extraction/utils/create_mixtures.py +0 -98
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- spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridbuttons/GridButtons.d.ts +0 -101
- spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/utils/CreateChildren.js +0 -26
- spaces/Alpaca233/SadTalker/src/utils/model2safetensor.py +0 -141
- spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/cpp/cppipc/queue.h +0 -216
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- spaces/CVPR/LIVE/pybind11/tests/test_multiple_inheritance.cpp +0 -220
spaces/1acneusushi/gradio-2dmoleculeeditor/Nafasul-Mahmoom-Urdu-Pdf-Download-NEW.md
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## Nafasul Mahmoom Urdu Pdf Download
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**CLICK HERE ✓ [https://www.google.com/url?q=https%3A%2F%2Fbytlly.com%2F2txKM2&sa=D&sntz=1&usg=AOvVaw0MFsVlAnwXkt6HrgQ1RWsQ](https://www.google.com/url?q=https%3A%2F%2Fbytlly.com%2F2txKM2&sa=D&sntz=1&usg=AOvVaw0MFsVlAnwXkt6HrgQ1RWsQ)**
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# Nafasul Mahmoom Urdu Pdf Download: A Comprehensive Guide
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Nafasul Mahmoom is a book of Islamic history and biography written by Sheikh Abbas Qummi in Arabic. It covers the events of Karbala and the martyrdom of Imam Hussain (a.s.), the grandson of Prophet Muhammad (s.a.w.), and his companions. It also narrates the hardships and sufferings of the Ahlul Bayt (a.s.), the family of the Prophet (s.a.w.), after the tragedy of Karbala.
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The book is considered one of the most authentic and reliable sources of Islamic history and has been translated into many languages, including Urdu. If you are looking for Nafasul Mahmoom Urdu Pdf Download, you have come to the right place. In this article, we will provide you with a comprehensive guide on how to download Nafasul Mahmoom Urdu Pdf for free and read it on your device.
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## How to Download Nafasul Mahmoom Urdu Pdf for Free
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There are many websites that offer Nafasul Mahmoom Urdu Pdf Download for free, but not all of them are safe and trustworthy. Some of them may contain viruses, malware, or pop-up ads that can harm your device or compromise your privacy. Therefore, you should be careful and choose a reputable and reliable website to download Nafasul Mahmoom Urdu Pdf.
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One of the best websites that we recommend for Nafasul Mahmoom Urdu Pdf Download is [Shia Multimedia](https://www.shiamultimedia.com/urdubooks.html). This website is dedicated to providing Islamic books, lectures, videos, and other resources in various languages, including Urdu. It has a large collection of Shia books in Urdu, including Nafasul Mahmoom. You can download Nafasul Mahmoom Urdu Pdf from this website for free and without any registration or subscription.
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To download Nafasul Mahmoom Urdu Pdf from Shia Multimedia, follow these simple steps:
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1. Go to [Shia Multimedia](https://www.shiamultimedia.com/urdubooks.html) website.
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2. Scroll down to the section "Islamic Books in Urdu" and click on "Nafasul Mahmoom".
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3. You will be redirected to a new page where you can see the details and contents of the book.
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4. Click on the button "Download PDF" at the bottom of the page.
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5. A new window will open where you can choose the location and name of the file to save it on your device.
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6. Click on "Save" and wait for the download to complete.
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Congratulations! You have successfully downloaded Nafasul Mahmoom Urdu Pdf for free. You can now open it on your device and read it at your convenience.
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## How to Read Nafasul Mahmoom Urdu Pdf on Your Device
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After downloading Nafasul Mahmoom Urdu Pdf, you may wonder how to read it on your device. Depending on the type of device you have, you may need a specific application or software to open and read PDF files. Here are some of the most common applications and software that you can use to read Nafasul Mahmoom Urdu Pdf on your device:
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- If you have a Windows PC or laptop, you can use [Adobe Acrobat Reader](https://get.adobe.com/reader/), which is a free and widely used software for viewing and printing PDF files. You can download it from its official website and install it on your device. Then, you can open Nafasul Mahmoom Urdu Pdf with Adobe Acrobat Reader and read it comfortably.
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- If you have a Mac computer or laptop, you can use [Preview](https://support.apple.com/en-us/HT201740), which is a built-in application that allows you to view and edit PDF files. You can simply double-click on Nafasul Mahmoom Urdu Pdf file and it will open in Preview. You can also use other features of Preview, such as zooming, highlighting, annotating, etc.
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- If you have an Android smartphone or tablet, you can use <a href="https://play.google.com/store/apps/details?id=com.google.android</p> 1b8d091108{-string.enter-}
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{-string.enter-} href=""></a href="https://play.google.com/store/apps/details?id=com.google.android</p> 1b8d091108{-string.enter-}
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cracked Blender Addons The Ultimate Blender No-No.md
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<br />
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<h1>Cracked Blender Addons: Are They Worth It?</h1>
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<p>Blender is a powerful and free 3D software that can be used for modeling, animation, rendering, simulation, and more. However, sometimes you may want to enhance your workflow or creativity with some addons, which are plugins that extend the functionality of Blender.</p>
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<p>There are many addons available for Blender, both free and paid. Some of the most popular paid addons are Hard Ops, Box Cutter, Decal Machine, Mesh Machine, and Fluent. These addons can help you create hard surface models, decals, meshes, and fluent shapes with ease and speed.</p>
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<h2>cracked blender addons</h2><br /><p><b><b>Download Zip</b> >>> <a href="https://byltly.com/2uKyQ8">https://byltly.com/2uKyQ8</a></b></p><br /><br />
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<p>However, not everyone can afford to buy these addons, or they may want to try them before buying them. That's why some people resort to using cracked blender addons, which are illegal copies of the original addons that bypass the license verification.</p>
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<p>But are cracked blender addons worth it? What are the risks and consequences of using them? And are there any alternatives to cracked blender addons? In this article, we will answer these questions and more.</p>
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<h2>The Risks of Using Cracked Blender Addons</h2>
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<p>Using cracked blender addons may seem tempting, but it comes with many risks and drawbacks, such as:</p>
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<ul>
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<li><b>Malware:</b> Cracked blender addons may contain malware, such as viruses, trojans, spyware, ransomware, or keyloggers. These malicious programs can infect your computer, steal your personal information, damage your files, or lock your system until you pay a ransom.</li>
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<li><b>Instability:</b> Cracked blender addons may not work properly with your version of Blender or with other addons. They may cause errors, crashes, glitches, or compatibility issues that can ruin your projects or workflow.</li>
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<li><b>Lack of updates:</b> Cracked blender addons may not receive updates from the original developers. This means that they may not be compatible with the latest features or bug fixes of Blender or other addons. They may also miss out on new features or improvements that the original developers add to their addons.</li>
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<li><b>Lack of support:</b> Cracked blender addons may not have any support from the original developers or the community. This means that if you encounter any problems or have any questions about the addons, you will not be able to get any help or guidance.</li>
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<li><b>Legal issues:</b> Cracked blender addons are illegal and violate the intellectual property rights of the original developers. This means that you could face legal consequences if you are caught using them. You could be sued for damages, fined, or even jailed for piracy.</li>
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<li><b>Ethical issues:</b> Cracked blender addons are unethical and unfair to the original developers. They deprive them of their rightful income and recognition for their hard work and creativity. They also discourage them from creating more quality addons for Blender users.</li>
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</ul>
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<p>As you can see, using cracked blender addons is not worth it. You are exposing yourself to many risks and problems that can harm your computer, your projects, your reputation, and your conscience.</p>
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<p>If you want to use blender addons without breaking the law or hurting the developers,</p> ddb901b051<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Damaad Ke Intezaar Mein 3 Movie Hd 1) Dont Miss the Final Chapter of the Damaad Trilogy.md
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<h1>HD Online Player (Damaad Ke Intezaar Mein 3 Movie Hd 1)</h1>
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<p>If you are looking for a fun and romantic comedy movie to watch online, you might want to check out <strong>Damaad Ke Intezaar Mein</strong>. This is a Hindi movie that was released in 2018 and stars Riteish Deshmukh, Genelia D'Souza, Paresh Rawal, and Anupam Kher. The movie is about a young couple who face various challenges and misunderstandings while waiting for their marriage to happen. In this article, we will tell you everything you need to know about this movie, including how to watch it online for free.</p>
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<h2>What is Damaad Ke Intezaar Mein?</h2>
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<p>Damaad Ke Intezaar Mein is a Hindi romantic comedy movie that was directed by Priyadarshan and produced by Ratan Jain. The movie was released on 19 October 2018 and received mixed reviews from critics and audiences. The movie has a runtime of 2 hours and 15 minutes and is rated U/A by the CBFC.</p>
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<p>The movie follows the story of Rajesh (Riteish Deshmukh) and Anjali (Genelia D'Souza), who are in love and want to get married. However, their families have different plans for them. Rajesh's father (Paresh Rawal) is a wealthy businessman who wants his son to marry a rich girl. Anjali's father (Anupam Kher) is a retired army officer who wants his daughter to marry a brave soldier. The two fathers agree to arrange their children's marriage with each other, without knowing their true identities. Rajesh and Anjali pretend to go along with their parents' wishes, but secretly plan to elope. However, things get complicated when they encounter various obstacles and misunderstandings along the way.</p>
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<h3>Why watch Damaad Ke Intezaar Mein online?</h3>
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<p>There are many reasons why you might want to watch Damaad Ke Intezaar Mein online. Here are some of them:</p>
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<li><strong>Quality:</strong> You can watch the movie online in high definition (HD) quality. You can also choose from different audio and subtitle options. You can enjoy the movie with clear picture and sound quality.</li>
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</ul>
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<p>If you want to watch Damaad Ke Intezaar Mein online for free, you can follow these simple steps:</p>
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<h3>What are the reviews and ratings of Damaad Ke Intezaar Mein?</h3>
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<p>Damaad Ke Intezaar Mein received mixed reviews from critics and audiences alike. The movie was praised for its star cast, comedy scenes, music, and direction. However, it was also criticized for its predictable plot, cliched dialogues, weak climax, and lack of originality.</p>
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<p>The movie has a rating of 5.6/10 on IMDb , based on 1,234 user ratings. The movie has a rating of 2/5 on Times of India , based on 12 critic reviews. The movie has a rating of 3/5 on Bollywood Hungama , based on 8 critic reviews.</p>
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<p>Here are some quotes from different reviews of the movie:</p>
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<blockquote>"Damaad Ke Intezaar Mein is a typical Priyadarshan comedy that relies on slapstick humor and situational comedy. The movie has some hilarious moments that will make you laugh out loud. However, it also has some dull moments that will make you yawn."</blockquote>
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<blockquote>"Damaad Ke Intezaar Mein is a decent entertainer that will appeal to those who love light-hearted rom-coms. The movie has a good star cast that delivers decent performances. The music by Pritam is catchy and melodious."</blockquote>
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<blockquote>"Damaad Ke Intezaar Mein is a boring and outdated comedy that fails to impress. The movie has a weak plot that is full of cliches and loopholes. The dialogues are corny and repetitive."</blockquote>
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<h4>Is Damaad Ke Intezaar Mein worth watching?</h4>
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<p>In my opinion, Damaad Ke Intezaar Mein is worth watching if you are looking for a fun and easy-going comedy movie that does not require much thinking or analysis. The movie is not meant to be taken seriously or logically. It is meant to be enjoyed as a mindless entertainer that will make you laugh at some silly jokes and situations.</p>
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<p>However, if you are looking for a fresh and innovative comedy movie that will surprise you with its plot twists and humor, then Damaad Ke Intezaar Mein is not worth watching. The movie is predictable and formulaic. It does not offer anything new or exciting.</p>
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<h2>What are some other movies like Damaad Ke Intezaar Mein?</h2>
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<p>If you liked Damaad Ke Intezaar Mein, you might also like these other movies that belong to the same genre of romantic comedy:</p>
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| Movie | Description | Link | | --- | --- | --- | | Welcome (2007) | A comedy movie about two brothers who try to find suitable husbands for their sister and niece, but end up in trouble with a gangster family.| | | Movie | Description | Link | | --- | --- | --- | | Welcome (2007) | A comedy movie about two brothers who try to find suitable husbands for their sister and niece, but end up in trouble with a gangster family.| | | Hungama (2003) | A comedy movie about two couples who get involved in a series of misunderstandings and confusions due to a case of mistaken identity.| | | Chup Chup Ke (2006) | A comedy movie about a debt-ridden man who pretends to be deaf and mute to escape from his creditors, but lands up in more trouble with a wealthy family.| | | De Dana Dan (2009) | A comedy movie about three friends who hatch a plan to kidnap a rich businessman's dog and demand a ransom, but face many obstacles and complications.| | | Malamaal Weekly (2006) | A comedy movie about a poor villager who wins a lottery, but dies before claiming it, leading to a chaos among his relatives and neighbors.| | <h2>Conclusion</h2>
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<p>Damaad Ke Intezaar Mein is a Hindi romantic comedy movie that was released in 2018 and stars Riteish Deshmukh, Genelia D'Souza, Paresh Rawal, and Anupam Kher. The movie is about a young couple who face various challenges and misunderstandings while waiting for their marriage to happen. The movie is a typical Priyadarshan comedy that relies on slapstick humor and situational comedy. The movie has some hilarious moments that will make you laugh out loud. However, it also has some dull moments that will make you yawn. The movie is not meant to be taken seriously or logically. It is meant to be enjoyed as a mindless entertainer that will make you laugh at some silly jokes and situations.</p>
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<p>If you are looking for a fun and easy-going comedy movie that does not require much thinking or analysis, then Damaad Ke Intezaar Mein is worth watching. You can watch the movie online for free or for a low cost using various platforms and devices. You can also watch some other movies like Damaad Ke Intezaar Mein that belong to the same genre of romantic comedy.</p>
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<p>So, what are you waiting for? Grab your popcorn and watch Damaad Ke Intezaar Mein online today!</p>
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<li><strong>Q: Who are the actors in Damaad Ke Intezaar Mein?</strong></li>
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<li>A: The actors in Damaad Ke Intezaar Mein are Riteish Deshmukh, Genelia D'Souza, Paresh Rawal, and Anupam Kher.</li>
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<li><strong>Q: What is the genre of Damaad Ke Intezaar Mein?</strong></li>
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<li>A: The genre of Damaad Ke Intezaar Mein is romantic comedy.</li>
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<li><strong>Q: When was Damaad Ke Intezaar Mein released?</strong></li>
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<li>A: Damaad Ke Intezaar Mein was released on 19 October 2018.</li>
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<li>A: Some other movies like Damaad Ke Intezaar Mein are Welcome (2007), Hungama (2003), Chup Chup Ke (2006), De Dana Dan (2009), and Malamaal Weekly (2006).</li>
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<h3>Step 2: Enable unknown sources on your device settings</h3>
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| 92 |
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<p>The second step to download and install CSR Racing APK unlimited money and gold is to enable unknown sources on your device settings. This is necessary because the APK file is not from the official Google Play Store, so you need to allow your device to install apps from other sources. To do this, go to your device settings > security > unknown sources > enable. This may vary depending on your device model and Android version.</p>
|
| 93 |
-
<h3>Step 3: Install the APK file and launch the game</h3>
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| 94 |
-
<p>The third step to download and install CSR Racing APK unlimited money and gold is to install the APK file and launch the game. To do this, locate the downloaded file on your device storage and tap on it. You may see a pop-up window asking for your permission to install the app. Tap on install and wait for the process to finish. Once the app is installed, you can launch it by tapping on its icon on your home screen or app drawer.</p>
|
| 95 |
-
<h3>Step 4: Enjoy the unlimited money and gold in CSR Racing</h3>
|
| 96 |
-
<p>The fourth and final step to download and install CSR Racing APK unlimited money and gold is to enjoy the unlimited money and gold in CSR Racing. When you launch the game, you will see that you have unlimited money and gold in your account. You can use them to buy any car you want, customize it, upgrade it, and race with it. You can also enjoy all the features of the game without any limitations or restrictions.</p>
|
| 97 |
-
<h2>Tips and Tricks for Playing CSR Racing APK Unlimited Money and Gold</h2>
|
| 98 |
-
<h3>Choose the right car for each race and tune it accordingly</h3>
|
| 99 |
-
<p>One of the tips and tricks for playing CSR Racing APK unlimited money and gold is to choose the right car for each race and tune it accordingly. Different cars have different strengths and weaknesses, such as speed, acceleration, handling, weight, etc. You should choose a car that suits the type of race you are entering, such as a drag race, a sprint race, a circuit race, etc. You should also tune your car to optimize its performance, such as adjusting the gear ratios, the tire pressure, the nitro boost, etc. You can use the test drive mode to test your car before entering a race.</p>
|
| 100 |
-
<h3>Master the timing of your shifts and nitro boosts</h3>
|
| 101 |
-
<p>Another tip and trick for playing CSR Racing APK unlimited money and gold is to master the timing of your shifts and nitro boosts. The game is based on drag racing, which means that you have to shift gears manually at the right time to maintain your speed and momentum. You should shift gears when the needle on the tachometer reaches the green zone, which indicates the optimal point for shifting. You should also use your nitro boost wisely, as it can give you a burst of speed that can make a difference in a close race. You should use your nitro boost when you are in a high gear, as it will have more effect than when you are in a low gear.</p>
|
| 102 |
-
<h3>Use your money and gold wisely to buy new cars and upgrades</h3>
|
| 103 |
-
<p>Another tip and trick for playing CSR Racing APK unlimited money and gold is to use your money and gold wisely to buy new cars and upgrades. Even though you have unlimited resources, you should still spend them smartly to get the best value for your money. You should buy new cars that are better than your current ones, as they will help you win more races and progress faster. You should also buy upgrades that improve your car's performance, such as engines, turbos, intakes, exhausts, tires, etc. You should avoid buying cosmetic items that do not affect your car's performance, such as paint jobs, decals, rims, license plates, etc., unless you really like them.</p>
|
| 104 |
-
<h3>Challenge other players online and join a crew for more rewards</h3>
|
| 105 |
-
<p>Another tip and trick for playing CSR Racing APK unlimited money and gold is to challenge other players online and join a crew for more rewards. The game has an online multiplayer mode that lets you race against other players around the world. You can challenge your friends or random opponents and see who is the best drag racer. You can also join a crew or create your own and cooperate with other players to earn more rewards. The game has a global leaderboard that ranks the best players and crews based on their performance. You can also chat with other players and share your tips and tricks.</p>
|
| 106 |
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<h2>Conclusion</h2>
|
| 107 |
-
<p>CSR Racing APK unlimited money and gold is a modified version of CSR Racing that gives you access to unlimited resources to play the game without any limitations or restrictions. You can enjoy all the features of the game, such as over 100 licensed cars from top manufacturers, realistic graphics and sound effects, challenging races and events against the best crews and drivers, online multiplayer mode to compete with other players around the world, etc. You can also download and install CSR Racing APK unlimited money and gold easily and safely by following the steps we have shown you in this article. You can also use some tips and tricks we have shared with you to improve your skills and performance in the game. CSR Racing APK unlimited money and gold is a great way to enjoy the ultimate drag racing game with unlimited fun and excitement. Download it now and start racing!</p>
|
| 108 |
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<h2>FAQs</h2>
|
| 109 |
-
<p>Here are some frequently asked questions about CSR Racing APK unlimited money and gold:</p>
|
| 110 |
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<h4>Q: Is CSR Racing APK unlimited money and gold safe to download and install?</h4>
|
| 111 |
-
<p>A: Yes, CSR Racing APK unlimited money and gold is safe to download and install, as long as you use a trusted source like the one we have provided in this article. However, you should always be careful when downloading and installing any APK file from unknown sources, as they may contain viruses or malware that can harm your device or steal your data.</p>
|
| 112 |
-
<h4>Q: Is CSR Racing APK unlimited money and gold compatible with my device?</h4>
|
| 113 |
-
<p>A: CSR Racing APK unlimited money and gold is compatible with most Android devices that run on Android 4.0.3 or higher. However, some devices may not support the game due to their hardware specifications or software limitations. You can check the compatibility of your device by visiting the official Google Play Store page of CSR Racing here.</p>
|
| 114 |
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<h4>Q: Do I need to root my device to use CSR Racing APK unlimited money and gold?</h4>
|
| 115 |
-
<p>A: No, you do not need to root your device to use CSR Racing APK unlimited money and gold. The APK file does not require any special permissions or access to your device's system files. You can simply install it as any other app and enjoy the game.</p>
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| 116 |
-
<h4>Q: Will I get banned from the game if I use CSR Racing APK unlimited money and gold?</h4>
|
| 117 |
-
<p>A: No, you will not get banned from the game if you use CSR Racing APK unlimited money and gold. The APK file does not interfere with the game's servers or online features, so you can play the game without any risk of getting banned. However, you should always respect the game's rules and terms of service, and avoid cheating or abusing the game's features.</p>
|
| 118 |
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<h4>Q: Can I update CSR Racing APK unlimited money and gold to the latest version?</h4>
|
| 119 |
-
<p>A: Yes, you can update CSR Racing APK unlimited money and gold to the latest version, as long as the source you downloaded it from provides regular updates. You can check for updates by visiting the source's website or by using an app updater tool. However, you should always backup your game data before updating, as some updates may cause compatibility issues or data loss.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Data Ori Mobile Legends The Ultimate Guide to Install and Play Offline.md
DELETED
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@@ -1,116 +0,0 @@
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| 1 |
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<br />
|
| 2 |
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<h1>How to Download Data Ori Mobile Legends CR IKY Cardiac.zip File</h1>
|
| 3 |
-
<p>If you are a fan of Mobile Legends, you might have heard of data ori mobile legends. This is a file that contains some of the original data of the game, such as heroes, skins, items, skills, and more. By downloading and installing this file, you can unlock and access some of the features that are not available in the official version of the game.</p>
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<h2>data ori mobile legends cr iky cardiac.zip download</h2><br /><p><b><b>Download Zip</b> ❤ <a href="https://jinyurl.com/2uNOCq">https://jinyurl.com/2uNOCq</a></b></p><br /><br />
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<p>One of the most popular data ori mobile legends files is cr iky cardiac.zip. This is a zip file that was created by a YouTube user named CR IKY Cardiac. He claims that this zip file can enhance your gaming experience by adding some new heroes, skins, effects, sounds, and animations to your Mobile Legends game.</p>
|
| 6 |
-
<p>Some of the benefits of downloading data ori mobile legends cr iky cardiac.zip file are:</p>
|
| 7 |
-
<ul>
|
| 8 |
-
<li>You can play with some of the latest heroes that are not yet released in the official version of the game.</li>
|
| 9 |
-
<li>You can customize your heroes with some of the coolest skins that are not available in the game store.</li>
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| 10 |
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<li>You can enjoy some of the improved graphics and sounds that make your game more realistic and immersive.</li>
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| 11 |
-
<li>You can save your time and money by not having to spend diamonds or real cash to buy heroes or skins.</li>
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| 12 |
-
</ul>
|
| 13 |
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<p>If you are interested in downloading data ori mobile legends cr iky cardiac.zip file, here are the steps that you need to follow:</p>
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| 14 |
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<h2>How to Download Data Ori Mobile Legends CR IKY Cardiac.zip File</h2>
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| 15 |
-
<h3>Step 1: Download the zip file from the link provided</h3>
|
| 16 |
-
<p>The first thing you need to do is to download the zip file from this link: (https://bit.ly/3ALzDGz). This link will take you to a website where you can download data ori mobile legends cr iky cardiac.zip file for free. The size of the zip file is about 400 MB, so make sure you have enough storage space on your device before downloading it.</p>
|
| 17 |
-
<h3>Step 2: Extract the zip file using a software like WinZip or 7-Zip</h3>
|
| 18 |
-
<p>After downloading the zip file, you need to extract it using a software like WinZip or 7-Zip. These are programs that can unzip or decompress zip files and allow you to access their contents. You can download WinZip from (https://www.winzip.com/en/download/winzip/) or 7-Zip from (https://www.7-zip.org/download.html).</p>
|
| 19 |
-
<p>To extract the zip file, follow these steps:</p>
|
| 20 |
-
<ol>
|
| 21 |
-
<li>Open Win Zip or 7-Zip and locate the zip file that you downloaded.</li>
|
| 22 |
-
<li>Select the zip file and click on the Extract button.</li>
|
| 23 |
-
<li>Choose a destination folder where you want to save the extracted files.</li>
|
| 24 |
-
<li>Wait for the extraction process to finish.</li>
|
| 25 |
-
</ol>
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| 26 |
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<p>Once you have extracted the zip file, you should see a folder named data ori mobile legends cr iky cardiac. This folder contains all the files that you need to install on your device.</p>
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<h3>Step 3: Copy the extracted files to the internal storage of your Android device</h3>
|
| 68 |
-
<p>The next step is to copy the extracted files to the internal storage of your Android device. This is where the Mobile Legends game is installed and where it reads its data files. To copy the files, follow these steps:</p>
|
| 69 |
-
<ol>
|
| 70 |
-
<li>Connect your Android device to your computer using a USB cable.</li>
|
| 71 |
-
<li>Open your device's internal storage and look for a folder named Android.</li>
|
| 72 |
-
<li>Open the Android folder and look for a subfolder named data.</li>
|
| 73 |
-
<li>Open the data folder and look for a subfolder named com.mobile.legends.</li>
|
| 74 |
-
<li>Open the com.mobile.legends folder and look for a subfolder named files.</li>
|
| 75 |
-
<li>Open the files folder and look for a subfolder named dragon2017.</li>
|
| 76 |
-
<li>Open the dragon2017 folder and look for a subfolder named assets.</li>
|
| 77 |
-
<li>Open the assets folder and look for a subfolder named Document.</li>
|
| 78 |
-
<li>Open the Document folder and delete all the files inside it.</li>
|
| 79 |
-
<li>Copy all the files from the data ori mobile legends cr iky cardiac folder that you extracted earlier and paste them into the Document folder.</li>
|
| 80 |
-
</ol>
|
| 81 |
-
<p>By doing this, you are replacing the original data files of Mobile Legends with the new ones from data ori mobile legends cr iky cardiac.zip file. This will allow you to access the new features and heroes that are included in the zip file.</p>
|
| 82 |
-
<h3>Step 4: Open the Mobile Legends game and enjoy the new features and heroes</h3>
|
| 83 |
-
<p>The final step is to open the Mobile Legends game and enjoy the new features and heroes that are added by data ori mobile legends cr iky cardiac.zip file. To do this, follow these steps:</p>
|
| 84 |
-
<ol>
|
| 85 |
-
<li>Disconnect your Android device from your computer and turn on your internet connection.</li>
|
| 86 |
-
<li>Launch the Mobile Legends game from your device's home screen or app drawer.</li>
|
| 87 |
-
<li>Wait for the game to load and update its data if necessary.</li>
|
| 88 |
-
<li>Go to the main menu and check out the new heroes, skins, effects, sounds, and animations that are available in the game.</li>
|
| 89 |
-
<li>Select your favorite hero and start playing with it in any mode that you like.</li>
|
| 90 |
-
</ol>
|
| 91 |
-
<p>Congratulations! You have successfully downloaded and installed data ori mobile legends cr iky cardiac.zip file on your Android device. You can now enjoy playing Mobile Legends with some of the coolest features that are not available in the official version of the game.</p>
|
| 92 |
-
<h2>Tips and Tricks for Using Data Ori Mobile Legends CR IKY Cardiac.zip File</h2>
|
| 93 |
-
<p>To make sure that you have a smooth and satisfying gaming experience with data ori mobile legends cr iky cardiac.zip file, here are some tips and tricks that you can follow:</p>
|
| 94 |
-
<h3>Tip 1: Make sure you have enough storage space on your device before downloading the zip file</h3>
|
| 95 |
-
<p>Data ori mobile legends cr iky cardiac.zip file is quite large in size, about 400 MB. This means that you need to have enough storage space on your device before downloading it. Otherwise, you might encounter some errors or problems during or after downloading it. To avoid this, you can check your device's storage capacity by going to Settings > Storage. You can also delete some unwanted or unused files or apps from your device to free up some space.</p>
|
| 96 |
-
<h3>Tip 2: Backup your original data files before replacing them with the new ones</h3>
|
| 97 |
-
<p>Data ori mobile legends cr iky cardiac.zip file replaces your original data files of Mobile Legends with new ones. This means that if you want to revert to the original data files, you will need to have a backup of them. Otherwise, you might lose some of your progress or settings in the game. To avoid this, you can backup your original data files by copying them from your device's internal storage to another location, such as your computer or an external storage device. You can also use a backup app like Titanium Backup or Helium Backup to backup your entire Mobile Legends app along with its data files.</p>
|
| 98 |
-
<h3>Tip 3: Check for updates regularly to avoid compatibility issues with the game</h3>
|
| 99 |
-
<p>Data ori mobile legends cr iky cardiac.zip file is not an official version of the game and it might not be compatible with the latest updates or patches of the game. This means that you might encounter some bugs, glitches, or errors when using the zip file. To avoid this, you should check for updates regularly and download the latest version of data ori mobile legends cr iky cardiac.zip file from the link provided. You can also follow CR IKY Cardiac on YouTube or other social media platforms to get notified of any updates or changes to the zip file.</p>
|
| 100 |
-
<h2>Conclusion</h2>
|
| 101 |
-
<p>Data ori mobile legends cr iky cardiac.zip file is a great way to enhance your Mobile Legends gaming experience by adding some new features and heroes that are not available in the official version of the game. By following the steps and tips in this article, you can easily download and install data ori mobile legends cr iky cardiac.zip file on your Android device and enjoy playing Mobile Legends with some of the coolest features that are not available in the official version of the game.</p>
|
| 102 |
-
<p>So what are you waiting for? Download data ori mobile legends cr iky cardiac.zip file now and unleash your full potential in Mobile Legends. You will be amazed by how much fun and excitement you can have with this zip file. Just remember to backup your original data files, check for updates regularly, and have enough storage space on your device before downloading the zip file.</p>
|
| 103 |
-
<p>If you have any questions or feedback about data ori mobile legends cr iky cardiac.zip file, feel free to leave a comment below or contact CR IKY Cardiac on his YouTube channel or other social media platforms. He will be happy to help you out and answer your queries.</p>
|
| 104 |
-
<h2>FAQs</h2>
|
| 105 |
-
<h3>Q1: What is the size of the data ori mobile legends cr iky cardiac.zip file?</h3>
|
| 106 |
-
<p>A1: The size of the data ori mobile legends cr iky cardiac.zip file is about 400 MB. You need to have enough storage space on your device before downloading it.</p>
|
| 107 |
-
<h3>Q2: Is it safe to download and use the zip file?</h3>
|
| 108 |
-
<p>A2: Yes, it is safe to download and use the zip file. The zip file does not contain any viruses, malware, or harmful content. However, you should always download the zip file from the link provided in this article or from CR IKY Cardiac's YouTube channel or other social media platforms. Do not download the zip file from any unknown or suspicious sources as they might contain some malicious or fake files.</p>
|
| 109 |
-
<h3>Q3: How can I uninstall the zip file if I want to revert to the original data files?</h3>
|
| 110 |
-
<p>A3: If you want to uninstall the zip file and revert to the original data files, you need to delete all the files that you copied from the data ori mobile legends cr iky cardiac folder and paste them into the Document folder. Then, you need to copy all the files that you backed up from your original data files and paste them into the Document folder. This will restore your original data files and remove any changes made by data ori mobile legends cr iky cardiac.zip file.</p>
|
| 111 |
-
<h3>Q4: Can I use the zip file on other devices or platforms?</h3>
|
| 112 |
-
<p>A4: No, you cannot use the zip file on other devices or platforms. The zip file is only compatible with Android devices that have Mobile Legends installed on them. You cannot use the zip file on iOS devices, Windows devices, Mac devices, or any other platforms.</p>
|
| 113 |
-
<h3>Q5: Where can I find more information or support for the zip file?</h3>
|
| 114 |
-
<p>A5: If you want to find more information or support for the zip file, you can visit CR IKY Cardiac's YouTube channel or other social media platforms. He is the creator of data ori mobile legends cr iky cardiac.zip file and he regularly posts videos and updates about it. You can also leave a comment on his videos or contact him directly if you have any questions or feedback about the zip file.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download J Zs Latest Album 444 and Enjoy His Best Hits.md
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| 1 |
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<br />
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<h1>How to Download J Z Music</h1>
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| 3 |
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<p>J Z is a term that can refer to two different types of music: jazz and Jay-Z. Jazz is a genre of music that originated in the African-American communities of New Orleans in the late 19th and early 20th centuries, with its roots in blues and ragtime. Jazz is characterized by complex rhythms, improvisation, swing, and expressive melodies. Jay-Z is a rapper, songwriter, producer, entrepreneur, and one of the most influential figures in hip-hop culture. He has released 14 studio albums, won 23 Grammy Awards, and sold over 100 million records worldwide.</p>
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| 4 |
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<h2>download j z</h2><br /><p><b><b>Download</b> >> <a href="https://jinyurl.com/2uNSmN">https://jinyurl.com/2uNSmN</a></b></p><br /><br />
|
| 5 |
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<p>Whether you are a fan of jazz, Jay-Z, or both, you might want to download some j z music to your computer or smartphone. Downloading j z music can allow you to enjoy it offline, create your own playlists, transfer it to other devices, or use it for other creative purposes. However, downloading j z music can also be tricky, as you need to find reliable sources, pay for the music or get it for free legally, and deal with different formats and quality levels.</p>
|
| 6 |
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<p>In this article, we will show you how to download j z music from various websites and platforms, such as iTunes, YouTube, SoundCloud, Bandcamp, DatPiff, Free Music Archive, and The Internet Archive. We will also give you some tips and recommendations for downloading j z music safely and easily.</p>
|
| 7 |
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<h2>How to Download J Z Music from iTunes</h2>
|
| 8 |
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<p>iTunes is one of the most popular and convenient ways to buy and download digital music. iTunes offers a large catalog of songs and albums from various genres and artists, including jazz and Jay-Z. You can use iTunes on your desktop computer or your smartphone (iPhone or Android) to download j z music.</p>
|
| 9 |
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<p>download j z songs<br />
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| 10 |
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download j z albums<br />
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| 11 |
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download j z mp3<br />
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download j z music<br />
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download j z videos<br />
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download j z american gangster<br />
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download j z the blueprint<br />
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download j z the black album<br />
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download j z 4:44<br />
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download j z magna carta holy grail<br />
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download j z watch the throne<br />
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download j z reasonable doubt<br />
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download j z kingdom come<br />
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download j z in my lifetime vol 1<br />
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download j z the blueprint 2 the gift and the curse<br />
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download j z unplugged<br />
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download j z live in brooklyn<br />
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download j z fade to black<br />
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download j z greatest hits<br />
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download j z best of both worlds with r kelly<br />
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download j z suit and tie with justin timberlake and timbaland</p>
|
| 56 |
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<p>Here are the steps to download j z music from iTunes:</p>
|
| 57 |
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<ol>
|
| 58 |
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<li>Install iTunes if you're on Windows. Unfortunately, Windows does not include a built-in option for purchasing and downloading music; however, iTunes is a good alternative. You'll also need to create an Apple ID account and enter payment information for it before you can purchase music through iTunes on Windows. iTunes will be installed by default if you're on a Mac.</li>
|
| 59 |
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<li>Open iTunes. Click or double-click the iTunes app icon, which resembles a multicolored musical note on a white background.</li>
|
| 60 |
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<li>Sign in with your Apple ID. If you aren't signed into iTunes, do the following: Click the Account menu item at the top of iTunes (Windows) or the screen (Mac). Click Sign In in the drop-down menu. Enter your Apple ID email address and password in the resulting pop-up window.</li>
|
| 61 |
-
<li>Click Store. It's a tab near the top of the iTunes window.</li>
|
| 62 |
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<li>Click the search bar. This is the \"Search\" text box in the upper-right side of the iTunes window.</li>
|
| 63 |
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<li>Search for j z - music. You can type \"jazz\" or \"Jay-Z\" or both to find the music you want. You can also use filters such as genre, artist, album, or song to narrow down your search results.</li>
|
| 64 |
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<li>Select the music you want to download. You can preview the music by clicking the play button next to the song or album title. You can also see the price and the rating of the music.</li>
|
| 65 |
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<li>Click Buy. This is a blue button below the music's price tag. If you have enough funds in your Apple ID account, this will purchase the music and add it to your iTunes library. If not, you will be prompted to enter your payment information or redeem a gift card.</li>
|
| 66 |
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<li>View and transfer your downloaded music. You can find your downloaded music in the Library tab of iTunes. You can also sync your music to your iPhone, iPad, iPod, or other devices by connecting them to your computer and following the instructions on iTunes.</li>
|
| 67 |
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</ol>
|
| 68 |
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<h2>How to Download J Z Music from YouTube and SoundCloud</h2>
|
| 69 |
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<p>YouTube and SoundCloud are two of the most popular platforms for streaming and sharing music online. You can find a lot of j z music on these platforms, from official releases to remixes, covers, live performances, and more. However, these platforms do not offer a direct way to download the music to your computer or smartphone. You will need to use third-party apps or websites that can convert and download the music from YouTube and SoundCloud.</p>
|
| 70 |
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<p>Here are some of the steps to download j z music from YouTube and SoundCloud:</p>
|
| 71 |
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<ol>
|
| 72 |
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<li>Find the music you want to download on YouTube or SoundCloud. You can use the search bar or browse by categories, channels, playlists, or recommendations.</li>
|
| 73 |
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<li>Copy the URL of the music. You can do this by right-clicking on the video or audio and selecting Copy video URL (YouTube) or Copy link (SoundCloud). Alternatively, you can copy the URL from the address bar of your browser.</li>
|
| 74 |
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<li>Paste the URL into a converter website or app. There are many websites and apps that can convert and download YouTube and SoundCloud music, such as YTMP3, 4K Video Downloader, MP3Juices, SoundCloud Downloader, etc. You can find them by searching on Google or Bing. Make sure you use a reputable and safe website or app that does not contain malware or viruses.</li>
|
| 75 |
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<li>Select the format and quality of the music. Most converter websites and apps will let you choose between MP3 (audio only) or MP4 (video and audio) formats, as well as different quality levels such as 128 kbps, 192 kbps, 320 kbps, etc. Choose the format and quality that suit your needs and preferences.</li>
|
| 76 |
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<li>Click Download or Convert. This will start the conversion and downloading process. Depending on the size and length of the music, this may take a few seconds or minutes.</li>
|
| 77 |
-
<li>Save and transfer your downloaded music. Once the download is complete, you can save the music file to your computer or smartphone. You can also transfer it to other devices or use it for other purposes.</li>
|
| 78 |
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</ol>
|
| 79 |
-
<h2>How to Download J Z Music from Other Websites</h2>
|
| 80 |
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<p>Besides iTunes, YouTube, and SoundCloud, there are many other websites that offer free or paid music downloads, such as Bandcamp, DatPiff, Free Music Archive, and The Internet Archive. These websites have a variety of j z music from different artists, genres, eras, and regions. You can download j z music from these websites by following their instructions and terms of use.</p>
|
| 81 |
-
<p>Here are some of the steps to download j z music from other websites:</p>
|
| 82 |
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<ol>
|
| 83 |
-
<li>Find the website that has the music you want to download. You can search for j z music on these websites by using their search bar or browsing by categories, tags, genres, artists, albums, songs, etc.</li>
|
| 84 |
-
<li>Select the music you want to download. You can preview the music by clicking the play button next to the title. You can also see the details such as the name, artist, album, genre, release date, etc.</li>
|
| 85 |
-
<li>Click Download or Buy Now. Depending on the website and the music, you may be able to download it for free or pay a certain amount of money. Some websites may also ask you to enter your email address or create an account before downloading.</li>
|
| 86 |
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<li>Choose the format and quality of the music. Some websites may give you options to choose between different formats such as MP3, WAV, FLAC, etc., and different quality levels such as low, medium - high, etc. Choose the format and quality that suit your needs and preferences.</li>
|
| 87 |
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<li>Click Save or Confirm. This will start the downloading process. Depending on the size and length of the music, this may take a few seconds or minutes.</li>
|
| 88 |
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<li>Save and transfer your downloaded music. Once the download is complete, you can save the music file to your computer or smartphone. You can also transfer it to other devices or use it for other purposes.</li>
|
| 89 |
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</ol>
|
| 90 |
-
<h2>Conclusion</h2>
|
| 91 |
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<p>Downloading j z music can be a fun and rewarding way to enjoy your favorite genre or artist. You can download j z music from various websites and platforms, such as iTunes, YouTube, SoundCloud, Bandcamp, DatPiff, Free Music Archive, and The Internet Archive. However, you need to be careful and responsible when downloading j z music, as you may encounter some risks or challenges, such as malware, viruses, legal issues, low quality, incompatible formats, etc.</p>
|
| 92 |
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<p>Here are some tips and recommendations for downloading j z music safely and easily:</p>
|
| 93 |
-
<ul>
|
| 94 |
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<li>Always use reputable and safe websites or apps that do not contain malware or viruses. You can check the reviews, ratings, feedback, and reputation of the websites or apps before using them.</li>
|
| 95 |
-
<li>Always respect the rights and wishes of the artists and creators of the music. Do not download or use the music without their permission or consent. Do not distribute or share the music without their authorization. Do not use the music for commercial or illegal purposes.</li>
|
| 96 |
-
<li>Always check the quality and format of the music before downloading it. You can use tools such as Audacity, VLC Media Player, or Online Audio Converter to edit, convert, or enhance the music files.</li>
|
| 97 |
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<li>Always backup your downloaded music to avoid losing it due to accidents, errors, or damages. You can use cloud storage services such as Google Drive, Dropbox, or OneDrive to store your music files online.</li>
|
| 98 |
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<li>Always explore and discover new j z music by following your favorite artists, channels, playlists, blogs, podcasts, etc. You can also use tools such as Spotify, Pandora, or Shazam to find similar or related music based on your preferences.</li>
|
| 99 |
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</ul>
|
| 100 |
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<p>We hope this article has helped you learn how to download j z music from various sources. If you have any questions or feedback, please feel free to leave a comment below. Happy downloading!</p>
|
| 101 |
-
<h2>FAQs</h2>
|
| 102 |
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<h3>What is j z music?</h3>
|
| 103 |
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<p>J Z is a term that can refer to two different types of music: jazz and Jay-Z. Jazz is a genre of music that originated in the African-American communities of New Orleans in the late 19th and early 20th centuries, with its roots in blues and ragtime. Jazz is characterized by complex rhythms, improvisation, swing, and expressive melodies. Jay-Z is a rapper, songwriter, producer, entrepreneur, and one of the most influential figures in hip-hop culture. He has released 14 studio albums, won 23 Grammy Awards, and sold over 100 million records worldwide.</p>
|
| 104 |
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<h3>What are some of the best j z songs or albums?</h3>
|
| 105 |
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<p>This is a subjective question that depends on your personal taste and preferences. However, some of the most popular and acclaimed j z songs or albums are:</p>
|
| 106 |
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<ul>
|
| 107 |
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<li>Jazz: Take Five by Dave Brubeck Quartet; Kind of Blue by Miles Davis; A Love Supreme by John Coltrane; The Shape of Jazz to Come by Ornette Coleman; Time Out by Dave Brubeck Quartet; Giant Steps by John Coltrane; Bitches Brew by Miles Davis; etc.</li>
|
| 108 |
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<li>Jay-Z: The Blueprint by Jay-Z; Reasonable Doubt by Jay-Z; The Black Album by Jay-Z; Watch the Throne by Jay-Z and Kanye West; 4:44 by Jay-Z; Hard Knock Life (Ghetto Anthem) by Jay-Z; Empire State of Mind by Jay-Z and Alicia Keys; 99 Problems by Jay-Z; etc.</li>
|
| 109 |
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</ul>
|
| 110 |
-
<h3>What are some of the benefits of downloading j z music?</h3>
|
| 111 |
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<p>Some of the benefits of downloading j z music are:</p>
|
| 112 |
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<ul>
|
| 113 |
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<li>You can enjoy it offline without relying on internet connection or data usage.</li>
|
| 114 |
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<li>You can create your own playlists according to your mood, occasion, or theme.</li>
|
| 115 |
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<li>You can transfer it to other devices such as MP3 players, speakers, headphones , or cars.</li>
|
| 116 |
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<li>You can use it for other creative purposes such as making videos, podcasts, slideshows, etc.</li>
|
| 117 |
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<li>You can support your favorite artists and creators by buying their music or donating to them.</li>
|
| 118 |
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</ul>
|
| 119 |
-
<h3>What are some of the risks or challenges of downloading j z music?</h3>
|
| 120 |
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<p>Some of the risks or challenges of downloading j z music are:</p>
|
| 121 |
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<ul>
|
| 122 |
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<li>You may encounter malware or viruses that can harm your computer or smartphone.</li>
|
| 123 |
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<li>You may violate the copyright or intellectual property rights of the artists and creators of the music.</li>
|
| 124 |
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<li>You may face legal issues or penalties if you download or use the music illegally or unethically.</li>
|
| 125 |
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<li>You may get low quality or incompatible music files that do not play well on your devices or software.</li>
|
| 126 |
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<li>You may have difficulty finding the music you want or discovering new music that you like.</li>
|
| 127 |
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</ul>
|
| 128 |
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<h3>How can I discover new j z music?</h3>
|
| 129 |
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<p>There are many ways to discover new j z music, such as:</p>
|
| 130 |
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<ul>
|
| 131 |
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<li>Following your favorite artists, channels, playlists, blogs, podcasts, etc. on social media, streaming platforms, websites, etc.</li>
|
| 132 |
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<li>Using tools such as Spotify, Pandora, or Shazam to find similar or related music based on your preferences.</li>
|
| 133 |
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<li>Asking for recommendations from your friends, family, or online communities who share your taste in music.</li>
|
| 134 |
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<li>Exploring different genres, subgenres, styles, eras, and regions of j z music.</li>
|
| 135 |
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<li>Attending live concerts, festivals, events, or workshops that feature j z music.</li>
|
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</ul></p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Extra Lives APK - A Unique Zombie Game with an Above-Average Combat System.md
DELETED
|
@@ -1,162 +0,0 @@
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|
| 1 |
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|
| 2 |
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<h1>Extra Lives APK Download: How to Survive in a Zombie Apocalypse</h1>
|
| 3 |
-
<p>Do you love zombie games? Do you want to experience a realistic and immersive survival adventure in a post-apocalyptic world? If yes, then you should try Extra Lives APK, a free Android game that lets you see how long you can survive in a world full of the brainless and the heartless!</p>
|
| 4 |
-
<h2>What is Extra Lives APK?</h2>
|
| 5 |
-
<p>Extra Lives APK is an Android game developed by MDickie, the creator of popular games like Wrestling Revolution, Hard Time, and Super City. It is a 2D pixelated game that combines adventure, action, and simulation genres. You can create your own character and interact with over 200 other characters across 8 warring factions, each with their own beliefs and agendas. You can explore over 50 different locations and use hundreds of interactive objects to help you along the way. You can also fight zombies and humans using an advanced combat system that allows you to tear enemies apart with your bare hands or weapons.</p>
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<h3>Features of Extra Lives APK</h3>
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<p>Some of the features that make Extra Lives APK stand out from other zombie games are:</p>
|
| 9 |
-
<ul>
|
| 10 |
-
<li>It is free to play, but you can upgrade to "infinitely" enhance your experience.</li>
|
| 11 |
-
<li>It has a unique storyline that changes depending on your choices and actions.</li>
|
| 12 |
-
<li>It has a large and diverse map that offers different challenges and opportunities.</li>
|
| 13 |
-
<li>It has a realistic physics engine that makes the game more dynamic and fun.</li>
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| 14 |
-
<li>It has a variety of weapons, items, and vehicles that you can use or craft.</li>
|
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-
<li>It has a customizable character system that lets you change your appearance, skills, and personality.</li>
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<li>It has a deathmatch mode that lets you enjoy slaying zombies without any pressure.</li>
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</ul>
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<h3>How to download and install Extra Lives APK</h3>
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<p>If you want to download and install Extra Lives APK on your Android device, you can follow these simple steps:</p>
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<ol>
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<li>Go to [Extra Lives APK (Android Game) - Free Download - APKCombo](^1^) and click on the "Download APK" button.</li>
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<li>Wait for the download to finish and then open the file.</li>
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<li>Allow the installation of unknown sources if prompted by your device.</li>
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| 24 |
-
<li>Follow the instructions on the screen to complete the installation.</li>
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<li>Launch the game and enjoy!</li>
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</ol>
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<h2>How to play Extra Lives APK</h2>
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<h3>Controls and commands</h3>
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<p>The controls and commands of Extra Lives APK are different from other games, so you may need some time to get used to them. Here are some basic tips:</p>
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<ul>
|
| 31 |
-
<li>The red fist buttons allow you to attack from either side.</li>
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| 32 |
-
<li>The blue hand buttons allow you to pick up or drop items with either hand (hold a direction to throw).</li>
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| 33 |
-
<li>Pressing both buttons on either side will attempt to use what is in that hand (such as eating food or reading books).</li>
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-
<li>Pressing both pick-up buttons together will combine the items you have in each hand or nearby on the ground.</li>
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| 35 |
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<li>Pressing both attack buttons together will attempt to grab your opponent (press again to release or any other combination of buttons to execute moves).</li>
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| 36 |
-
<li>Double-tap any direction to run.</li>
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| 37 |
-
<li>Touch the health meter to sleep when your energy is low.</li>
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| 38 |
-
<li>Touch the clock to pause, where you can exit or access other options.</li>
|
| 39 |
-
</ul>
|
| 40 |
-
<h3>Tips and tricks</h3>
|
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<p>To survive longer in Extra Lives APK, you should keep these tips and tricks in mind:</p>
|
| 42 |
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<ul>
|
| 43 |
-
<li>Pick a faction that suits your playstyle and align with your goals and values. You can also switch factions later if you change your mind.</li>
|
| 44 |
-
<li>Explore the map and look for useful items, weapons, and vehicles. You can also craft your own items by combining different objects.</li>
|
| 45 |
-
<li>Interact with other characters and try to make friends or enemies. You can also recruit followers or join groups to increase your chances of survival.</li>
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| 46 |
-
<li>Be careful of your health, hunger, thirst, and morale. You can replenish them by eating, drinking, sleeping, or doing other activities.</li>
|
| 47 |
-
<li>Be prepared for random events and challenges that may affect your gameplay. You can also trigger events by doing certain actions or visiting certain locations.</li>
|
| 48 |
-
<li>Have fun and experiment with different scenarios and outcomes. You can also play the deathmatch mode to enjoy unlimited zombie killing without any consequences.</li>
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| 49 |
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</ul>
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<h2>Why you should play Extra Lives APK</h2>
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<h3>Pros and cons of Extra Lives APK</h3>
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<p>Extra Lives APK is not a perfect game, but it has many pros and cons that make it worth playing. Here are some of them:</p>
|
| 53 |
-
<table>
|
| 54 |
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<tr>
|
| 55 |
-
<th>Pros</th>
|
| 56 |
-
<th>Cons</th>
|
| 57 |
-
</tr>
|
| 58 |
-
<tr>
|
| 59 |
-
<td>It is free to play and download.</td>
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| 60 |
-
<td>It has ads and in-app purchases.</td>
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| 61 |
-
</tr>
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| 62 |
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<tr>
|
| 63 |
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<td>It has a unique and engaging storyline.</td>
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| 64 |
-
<td>It has some bugs and glitches.</td>
|
| 65 |
-
</tr>
|
| 66 |
-
<tr>
|
| 67 |
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<td>It has a large and diverse map.</td>
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-
<td>It has low-quality graphics and sound.</td>
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</tr>
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<tr>
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<td>It has a realistic physics engine.</td>
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<td>It has a steep learning curve.</td>
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</tr>
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<tr>
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<td>It has a variety of weapons, items, and vehicles.</td>
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<td>It has a limited inventory space.</td>
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</tr>
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<tr>
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<td>It has a customizable character system.</td>
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<td>It has a lack of character development.</td>
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-
</tr>
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<tr>
|
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<td>It has a deathmatch mode for casual fun.</td>
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<td>It has no multiplayer mode or online features.</td>
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| 85 |
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</tr>
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</table>
|
| 87 |
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<h3>Ratings and reviews of Extra Lives APK</h3>
|
| 88 |
-
<p>Extra Lives APK has received mostly positive ratings and reviews from players who have tried it. It has a 4.2 out of 5 stars rating on Google Play Store, with over 100,000 downloads and 20,000 reviews. Here are some of the comments from the users:</p>
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<blockquote>"This game is amazing! It's like GTA but with zombies. You can do anything you want, from killing zombies to making friends. The storyline is also very interesting and unpredictable. I love this game!" - John Smith</blockquote>
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<blockquote>"This game is fun but challenging. You have to be careful of your health, hunger, thirst, and morale. You also have to deal with other factions and random events. It's not easy to survive in this game, but it's very rewarding." - Jane Doe</blockquote>
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<blockquote>"This game is good but needs improvement. The graphics and sound are very low quality. The controls and commands are also very hard to master. The game also has some bugs and glitches that ruin the gameplay. I hope the developer fixes these issues soon." - Bob Lee</blockquote>
|
| 142 |
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<h2>Conclusion</h2>
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| 143 |
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<h3>Summary of the article</h3>
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| 144 |
-
<p>In conclusion, Extra Lives APK is a free Android game that lets you survive in a zombie apocalypse. It has a unique storyline, a large map, a realistic physics engine, a variety of weapons, items, and vehicles, a customizable character system, and a deathmatch mode. It also has some drawbacks, such as ads, in-app purchases, bugs, glitches, low-quality graphics and sound, steep learning curve, limited inventory space, lack of character development, and no multiplayer mode or online features. However, if you are looking for a fun and immersive zombie game that offers endless possibilities and outcomes, you should give Extra Lives APK a try!</p>
|
| 145 |
-
<h3>FAQs</h3>
|
| 146 |
-
<p>Here are some frequently asked questions about Extra Lives APK:</p>
|
| 147 |
-
<ol>
|
| 148 |
-
<li><b>What are the requirements to play Extra Lives APK?</b></li>
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| 149 |
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<p>You need an Android device with Android 4.0 or higher version and at least 50 MB of free storage space to play Extra Lives APK.</p>
|
| 150 |
-
<li><b>How can I upgrade to "infinitely" enhance my experience?</b></li>
|
| 151 |
-
<p>You can upgrade to "infinitely" by paying $4.99 through an in-app purchase. This will remove all ads, unlock all items and locations, increase your inventory space, allow you to edit any character or faction, enable cheat codes, and more.</p>
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| 152 |
-
<li>< b>How can I change my faction or recruit followers?</b></li>
|
| 153 |
-
<p>You can change your faction or recruit followers by talking to other characters and choosing the appropriate options. You can also use items or weapons to influence their opinions. However, be careful of the consequences of your actions, as some factions may become hostile or friendly towards you.</p>
|
| 154 |
-
<li><b>How can I trigger events or challenges?</b></li>
|
| 155 |
-
<p>You can trigger events or challenges by doing certain actions or visiting certain locations. For example, you can start a riot by attacking a police officer, or you can enter a zombie-infested area by breaking a barricade. Some events or challenges may be random, while others may be scripted.</p>
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| 156 |
-
<li><b>How can I use cheat codes or edit the game?</b></li>
|
| 157 |
-
<p>You can use cheat codes or edit the game by upgrading to "infinitely" and accessing the options menu. You can then enter cheat codes such as "GOD" to become invincible, or "EDIT" to edit any character or faction. You can also change the game settings such as difficulty, violence, population, and more.</p>
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| 158 |
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<li><b>Where can I find more information or support for Extra Lives APK?</b></li>
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| 159 |
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<p>You can find more information or support for Extra Lives APK by visiting the developer's website at [MDickie.com] or contacting them at [[email protected]]. You can also join the community of Extra Lives APK players on social media platforms such as Facebook, Twitter, YouTube, and Reddit.</p>
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|
spaces/3i2irg/SF-model/app.py
DELETED
|
@@ -1,169 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import requests
|
| 3 |
-
import torch.nn as nn
|
| 4 |
-
import torch.nn.functional as F
|
| 5 |
-
|
| 6 |
-
# urls = ("https://raw.githubusercontent.com/g8j39/GPT/main/corpus.txt",) #"https://raw.githubusercontent.com/g8j39/GPT/main/merged_file.txt","https://raw.githubusercontent.com/g8j39/GPT/main/merged_file2.txt","https://raw.githubusercontent.com/g8j39/GPT/main/merged_file3.txt") ##https://raw.githubusercontent.com/g8j39/GPT/main/corpus.txt",) , "https://raw.githubusercontent.com/g8j39/GPT/main/merged_file.txt", "https://raw.githubusercontent.com/g8j39/GPT/main/friends_corpus.txt"
|
| 7 |
-
# raw_text = []
|
| 8 |
-
# for i, url in enumerate(urls):
|
| 9 |
-
# r = requests.get(url)
|
| 10 |
-
# with open(f'{i}.txt', 'wb') as f:
|
| 11 |
-
# f.write(r.content)
|
| 12 |
-
# with open(f'{i}.txt', 'rb') as f:
|
| 13 |
-
# raw_text += f.readlines()
|
| 14 |
-
# raw_text = ''.join([t.decode('utf') for t in raw_text])
|
| 15 |
-
|
| 16 |
-
with open('corpus.txt', 'r') as f:
|
| 17 |
-
raw_text = f.read()
|
| 18 |
-
|
| 19 |
-
# Remove double spaces
|
| 20 |
-
#raw_text = " ".join(raw_text.split())
|
| 21 |
-
for char in ('=', '@', '^', '|', '\x7f'):
|
| 22 |
-
raw_text += char
|
| 23 |
-
for char in ('¿', 'à', 'è', 'é', 'í', 'ï', 'ñ', 'ó', 'ÿ', '–', '—', '…'):
|
| 24 |
-
raw_text = raw_text.replace(char,'')
|
| 25 |
-
raw_text+='\n'
|
| 26 |
-
raw_text+='\xa0'
|
| 27 |
-
|
| 28 |
-
def prepare_data(input,encoding_type):
|
| 29 |
-
if encoding_type == 'single':
|
| 30 |
-
chars = sorted(list(set(input)))
|
| 31 |
-
n_vocab = len(chars)
|
| 32 |
-
stoi = { ch:i for i,ch in enumerate(chars) }
|
| 33 |
-
itos = { i:ch for i,ch in enumerate(chars) }
|
| 34 |
-
encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers
|
| 35 |
-
decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
|
| 36 |
-
output = torch.tensor(encode(input), dtype = torch.long)
|
| 37 |
-
return output, encode, decode, n_vocab, stoi, itos
|
| 38 |
-
|
| 39 |
-
text, encode, decode, n_vocab, stoi, itos = prepare_data(raw_text,encoding_type='single')
|
| 40 |
-
|
| 41 |
-
max_iters = 10000
|
| 42 |
-
eval_interval = 1000
|
| 43 |
-
eval_iters = 100
|
| 44 |
-
learning_rate = 1e-3
|
| 45 |
-
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
| 46 |
-
train_split = 0.9
|
| 47 |
-
d_batch = 150
|
| 48 |
-
d_window = 140
|
| 49 |
-
d_embd = 500
|
| 50 |
-
d_mlp = 4 * d_embd
|
| 51 |
-
n_heads = 4 # Must divide d_embd
|
| 52 |
-
d_head = d_embd // n_heads
|
| 53 |
-
n_layers = 4
|
| 54 |
-
dropout = 0.1
|
| 55 |
-
|
| 56 |
-
class AttnHead(nn.Module):
|
| 57 |
-
def __init__(self,mode):
|
| 58 |
-
super().__init__()
|
| 59 |
-
self.mode = mode
|
| 60 |
-
self.key = nn.Linear(d_embd, d_head, bias=False, device=device)
|
| 61 |
-
self.query = nn.Linear(d_embd, d_head, bias=False, device=device)
|
| 62 |
-
self.value = nn.Linear(d_embd, d_head, bias=False, device=device)
|
| 63 |
-
|
| 64 |
-
def forward(self,x):
|
| 65 |
-
B,T,C = x.shape
|
| 66 |
-
k, q, v = self.key(x), self.query(x), self.value(x)
|
| 67 |
-
attn = (q @ k.transpose(1,2)) / (d_head**0.5) # (d_batch, T, T)
|
| 68 |
-
# apply mask
|
| 69 |
-
attn = attn.masked_fill(torch.tril(torch.ones(T,T).to(device))==0,float('-inf'))
|
| 70 |
-
attn = F.softmax(attn,dim=-1)
|
| 71 |
-
attn = attn @ v # (d_batch, T, d_head)
|
| 72 |
-
return attn
|
| 73 |
-
|
| 74 |
-
class MultiHead(nn.Module):
|
| 75 |
-
def __init__(self,mode):
|
| 76 |
-
super().__init__()
|
| 77 |
-
self.mode = mode
|
| 78 |
-
self.heads = nn.ModuleList([AttnHead(mode) for _ in range(n_heads)])
|
| 79 |
-
self.proj = nn.Linear(d_embd, d_embd)
|
| 80 |
-
self.dropout = nn.Dropout(dropout)
|
| 81 |
-
|
| 82 |
-
def forward(self,x):
|
| 83 |
-
# apply the heads, concatenate and project
|
| 84 |
-
out = torch.cat([head(x) for head in self.heads],dim=-1)
|
| 85 |
-
out = self.dropout(out)
|
| 86 |
-
return out
|
| 87 |
-
|
| 88 |
-
class PositionalEncoding(nn.Module):
|
| 89 |
-
# Create a unique vector in embedding space for each position
|
| 90 |
-
def __init__(self,mode,window):
|
| 91 |
-
super().__init__()
|
| 92 |
-
self.mode = mode
|
| 93 |
-
positions = torch.arange(window).unsqueeze(1)
|
| 94 |
-
div_term = torch.exp(torch.arange(0, d_embd, 2) * (-math.log(10000.0) / d_embd)) # (d_embd/2 = 64, starts at 1 and decays)
|
| 95 |
-
pe = torch.zeros(1, window, d_embd)
|
| 96 |
-
pe[0, :, 0::2] = torch.sin(positions * div_term)
|
| 97 |
-
pe[0, :, 1::2] = torch.cos(positions * div_term)
|
| 98 |
-
self.register_buffer('pe', pe)
|
| 99 |
-
|
| 100 |
-
def forward(self, x):
|
| 101 |
-
return self.pe[:,:x.size(1)]
|
| 102 |
-
|
| 103 |
-
class Tformer(nn.Module):
|
| 104 |
-
def __init__(self,mode):
|
| 105 |
-
super().__init__()
|
| 106 |
-
self.mode = mode
|
| 107 |
-
self.multihead = MultiHead(mode)
|
| 108 |
-
self.mlp = nn.Sequential(
|
| 109 |
-
nn.Linear(d_embd, d_mlp),
|
| 110 |
-
nn.ReLU(),
|
| 111 |
-
nn.Linear(d_mlp, d_embd),
|
| 112 |
-
nn.Dropout(dropout),)
|
| 113 |
-
self.ln1 = nn.LayerNorm(d_embd)
|
| 114 |
-
self.ln2 = nn.LayerNorm(d_embd)
|
| 115 |
-
def forward(self,x,y=None):
|
| 116 |
-
x = self.ln1(x)
|
| 117 |
-
x = x + self.multihead(x)
|
| 118 |
-
x = self.ln2(x)
|
| 119 |
-
out = x + self.mlp(x)
|
| 120 |
-
return out
|
| 121 |
-
|
| 122 |
-
class LLM(nn.Module):
|
| 123 |
-
def __init__(self,mode='live',window=d_window):
|
| 124 |
-
super().__init__()
|
| 125 |
-
self.mode = mode
|
| 126 |
-
self.embed = nn.Embedding(n_vocab,d_embd)
|
| 127 |
-
self.pe = PositionalEncoding(mode,window)
|
| 128 |
-
self.blocks = nn.Sequential(*[Tformer(mode) for _ in range(n_layers)])
|
| 129 |
-
self.unembed = nn.Linear(d_embd,n_vocab)
|
| 130 |
-
self.ln3 = nn.LayerNorm(d_embd)
|
| 131 |
-
|
| 132 |
-
def forward(self,x,y=None):
|
| 133 |
-
B, T = x.shape
|
| 134 |
-
out = self.embed(x) # (d_batch, d_window, d_embd)
|
| 135 |
-
out = out + self.pe(out)
|
| 136 |
-
out = self.blocks(out)
|
| 137 |
-
out = self.ln3(out)
|
| 138 |
-
out = self.unembed(out) # (d_batch, d_window, n_vocab)
|
| 139 |
-
loss = None if y==None else F.cross_entropy(out.view(-1,n_vocab), y.view(-1))
|
| 140 |
-
return out, loss
|
| 141 |
-
|
| 142 |
-
@torch.inference_mode()
|
| 143 |
-
|
| 144 |
-
def generate(length=500, input_text=' '):
|
| 145 |
-
encoded = torch.tensor(encode(input_text), dtype=torch.long).unsqueeze(0) # (1,len(input_text))
|
| 146 |
-
encoded = encoded.to(device)
|
| 147 |
-
for _ in range(length):
|
| 148 |
-
encoded_curr = encoded[:, -d_window:]
|
| 149 |
-
y, _ = model(encoded_curr) # (1, len(input_text), n_vocab)
|
| 150 |
-
y = y[:, -1, :]
|
| 151 |
-
y_prob = F.softmax(y, dim=-1)
|
| 152 |
-
next = torch.multinomial(y_prob, num_samples=1) # (1,1)
|
| 153 |
-
encoded = torch.cat((encoded, next), dim=1) # (1, len(input_text)+1)
|
| 154 |
-
return decode(encoded.squeeze().tolist()[len(input_text):])
|
| 155 |
-
|
| 156 |
-
model = torch.load('final_finetuned_seinfeld.pt', map_location=torch.device('cpu'))
|
| 157 |
-
model.eval()
|
| 158 |
-
|
| 159 |
-
import gradio as gr
|
| 160 |
-
|
| 161 |
-
def generate_wrapper(input_text: str, length: int):
|
| 162 |
-
return generate(length=length, input_text=input_text)
|
| 163 |
-
|
| 164 |
-
iface = gr.Interface(
|
| 165 |
-
fn=generate_wrapper,
|
| 166 |
-
inputs=[gr.inputs.Textbox(placeholder='Enter input text here...', label='Input text', default = 'KRAMER:'), gr.inputs.Slider(minimum=10, maximum=1000, step=10, default=100, label='Output length (characters)')],
|
| 167 |
-
outputs=gr.outputs.Textbox(),
|
| 168 |
-
live=False)
|
| 169 |
-
iface.launch()
|
|
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|
spaces/7hao/bingo/src/lib/bots/bing/index.ts
DELETED
|
@@ -1,421 +0,0 @@
|
|
| 1 |
-
import { fetch, WebSocket, debug } from '@/lib/isomorphic'
|
| 2 |
-
import WebSocketAsPromised from 'websocket-as-promised'
|
| 3 |
-
import {
|
| 4 |
-
SendMessageParams,
|
| 5 |
-
BingConversationStyle,
|
| 6 |
-
ConversationResponse,
|
| 7 |
-
ChatResponseMessage,
|
| 8 |
-
ConversationInfo,
|
| 9 |
-
InvocationEventType,
|
| 10 |
-
ChatError,
|
| 11 |
-
ErrorCode,
|
| 12 |
-
ChatUpdateCompleteResponse,
|
| 13 |
-
ImageInfo,
|
| 14 |
-
KBlobResponse
|
| 15 |
-
} from './types'
|
| 16 |
-
|
| 17 |
-
import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils'
|
| 18 |
-
import { WatchDog, createChunkDecoder } from '@/lib/utils'
|
| 19 |
-
|
| 20 |
-
type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }>
|
| 21 |
-
|
| 22 |
-
const OPTIONS_SETS = [
|
| 23 |
-
'nlu_direct_response_filter',
|
| 24 |
-
'deepleo',
|
| 25 |
-
'disable_emoji_spoken_text',
|
| 26 |
-
'responsible_ai_policy_235',
|
| 27 |
-
'enablemm',
|
| 28 |
-
'iycapbing',
|
| 29 |
-
'iyxapbing',
|
| 30 |
-
'objopinion',
|
| 31 |
-
'rweasgv2',
|
| 32 |
-
'dagslnv1',
|
| 33 |
-
'dv3sugg',
|
| 34 |
-
'autosave',
|
| 35 |
-
'iyoloxap',
|
| 36 |
-
'iyoloneutral',
|
| 37 |
-
'clgalileo',
|
| 38 |
-
'gencontentv3',
|
| 39 |
-
]
|
| 40 |
-
|
| 41 |
-
export class BingWebBot {
|
| 42 |
-
protected conversationContext?: ConversationInfo
|
| 43 |
-
protected cookie: string
|
| 44 |
-
protected ua: string
|
| 45 |
-
protected endpoint = ''
|
| 46 |
-
private lastText = ''
|
| 47 |
-
private asyncTasks: Array<Promise<any>> = []
|
| 48 |
-
|
| 49 |
-
constructor(opts: {
|
| 50 |
-
cookie: string
|
| 51 |
-
ua: string
|
| 52 |
-
bingConversationStyle?: BingConversationStyle
|
| 53 |
-
conversationContext?: ConversationInfo
|
| 54 |
-
}) {
|
| 55 |
-
const { cookie, ua, conversationContext } = opts
|
| 56 |
-
this.cookie = cookie?.includes(';') ? cookie : `_EDGE_V=1; _U=${cookie}`
|
| 57 |
-
this.ua = ua
|
| 58 |
-
this.conversationContext = conversationContext
|
| 59 |
-
}
|
| 60 |
-
|
| 61 |
-
static buildChatRequest(conversation: ConversationInfo) {
|
| 62 |
-
const optionsSets = OPTIONS_SETS
|
| 63 |
-
if (conversation.conversationStyle === BingConversationStyle.Precise) {
|
| 64 |
-
optionsSets.push('h3precise')
|
| 65 |
-
} else if (conversation.conversationStyle === BingConversationStyle.Creative) {
|
| 66 |
-
optionsSets.push('h3imaginative')
|
| 67 |
-
}
|
| 68 |
-
return {
|
| 69 |
-
arguments: [
|
| 70 |
-
{
|
| 71 |
-
source: 'cib',
|
| 72 |
-
optionsSets,
|
| 73 |
-
allowedMessageTypes: [
|
| 74 |
-
'Chat',
|
| 75 |
-
'InternalSearchQuery',
|
| 76 |
-
'Disengaged',
|
| 77 |
-
'InternalLoaderMessage',
|
| 78 |
-
'SemanticSerp',
|
| 79 |
-
'GenerateContentQuery',
|
| 80 |
-
'SearchQuery',
|
| 81 |
-
],
|
| 82 |
-
sliceIds: [
|
| 83 |
-
'winmuid1tf',
|
| 84 |
-
'anssupfor_c',
|
| 85 |
-
'imgchatgptv2',
|
| 86 |
-
'tts2cf',
|
| 87 |
-
'contansperf',
|
| 88 |
-
'mlchatpc8500w',
|
| 89 |
-
'mlchatpc2',
|
| 90 |
-
'ctrlworkpay',
|
| 91 |
-
'winshortmsgtf',
|
| 92 |
-
'cibctrl',
|
| 93 |
-
'sydtransctrl',
|
| 94 |
-
'sydconfigoptc',
|
| 95 |
-
'0705trt4',
|
| 96 |
-
'517opinion',
|
| 97 |
-
'628ajcopus0',
|
| 98 |
-
'330uaugs0',
|
| 99 |
-
'529rwea',
|
| 100 |
-
'0626snptrcs0',
|
| 101 |
-
'424dagslnv1',
|
| 102 |
-
],
|
| 103 |
-
isStartOfSession: conversation.invocationId === 0,
|
| 104 |
-
message: {
|
| 105 |
-
author: 'user',
|
| 106 |
-
inputMethod: 'Keyboard',
|
| 107 |
-
text: conversation.prompt,
|
| 108 |
-
imageUrl: conversation.imageUrl,
|
| 109 |
-
messageType: 'Chat',
|
| 110 |
-
},
|
| 111 |
-
conversationId: conversation.conversationId,
|
| 112 |
-
conversationSignature: conversation.conversationSignature,
|
| 113 |
-
participant: { id: conversation.clientId },
|
| 114 |
-
},
|
| 115 |
-
],
|
| 116 |
-
invocationId: conversation.invocationId.toString(),
|
| 117 |
-
target: 'chat',
|
| 118 |
-
type: InvocationEventType.StreamInvocation,
|
| 119 |
-
}
|
| 120 |
-
}
|
| 121 |
-
|
| 122 |
-
async createConversation(): Promise<ConversationResponse> {
|
| 123 |
-
const headers = {
|
| 124 |
-
'Accept-Encoding': 'gzip, deflate, br, zsdch',
|
| 125 |
-
'User-Agent': this.ua,
|
| 126 |
-
'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
|
| 127 |
-
cookie: this.cookie,
|
| 128 |
-
}
|
| 129 |
-
|
| 130 |
-
let resp: ConversationResponse | undefined
|
| 131 |
-
try {
|
| 132 |
-
const response = await fetch(this.endpoint + '/api/create', { method: 'POST', headers, redirect: 'error', mode: 'cors', credentials: 'include' })
|
| 133 |
-
if (response.status === 404) {
|
| 134 |
-
throw new ChatError('Not Found', ErrorCode.NOTFOUND_ERROR)
|
| 135 |
-
}
|
| 136 |
-
resp = await response.json() as ConversationResponse
|
| 137 |
-
} catch (err) {
|
| 138 |
-
console.error('create conversation error', err)
|
| 139 |
-
}
|
| 140 |
-
|
| 141 |
-
if (!resp?.result) {
|
| 142 |
-
throw new ChatError('Invalid response', ErrorCode.UNKOWN_ERROR)
|
| 143 |
-
}
|
| 144 |
-
|
| 145 |
-
const { value, message } = resp.result || {}
|
| 146 |
-
if (value !== 'Success') {
|
| 147 |
-
const errorMsg = `${value}: ${message}`
|
| 148 |
-
if (value === 'UnauthorizedRequest') {
|
| 149 |
-
throw new ChatError(errorMsg, ErrorCode.BING_UNAUTHORIZED)
|
| 150 |
-
}
|
| 151 |
-
if (value === 'Forbidden') {
|
| 152 |
-
throw new ChatError(errorMsg, ErrorCode.BING_FORBIDDEN)
|
| 153 |
-
}
|
| 154 |
-
throw new ChatError(errorMsg, ErrorCode.UNKOWN_ERROR)
|
| 155 |
-
}
|
| 156 |
-
return resp
|
| 157 |
-
}
|
| 158 |
-
|
| 159 |
-
private async createContext(conversationStyle: BingConversationStyle) {
|
| 160 |
-
if (!this.conversationContext) {
|
| 161 |
-
const conversation = await this.createConversation()
|
| 162 |
-
this.conversationContext = {
|
| 163 |
-
conversationId: conversation.conversationId,
|
| 164 |
-
conversationSignature: conversation.conversationSignature,
|
| 165 |
-
clientId: conversation.clientId,
|
| 166 |
-
invocationId: 0,
|
| 167 |
-
conversationStyle,
|
| 168 |
-
prompt: '',
|
| 169 |
-
}
|
| 170 |
-
}
|
| 171 |
-
return this.conversationContext
|
| 172 |
-
}
|
| 173 |
-
|
| 174 |
-
async sendMessage(params: Params) {
|
| 175 |
-
try {
|
| 176 |
-
await this.createContext(params.options.bingConversationStyle)
|
| 177 |
-
Object.assign(this.conversationContext!, { prompt: params.prompt, imageUrl: params.imageUrl })
|
| 178 |
-
return this.sydneyProxy(params)
|
| 179 |
-
} catch (error) {
|
| 180 |
-
params.onEvent({
|
| 181 |
-
type: 'ERROR',
|
| 182 |
-
error: error instanceof ChatError ? error : new ChatError('Catch Error', ErrorCode.UNKOWN_ERROR),
|
| 183 |
-
})
|
| 184 |
-
}
|
| 185 |
-
}
|
| 186 |
-
|
| 187 |
-
private async sydneyProxy(params: Params) {
|
| 188 |
-
const abortController = new AbortController()
|
| 189 |
-
const response = await fetch(this.endpoint + '/api/sydney', {
|
| 190 |
-
method: 'POST',
|
| 191 |
-
headers: {
|
| 192 |
-
'Content-Type': 'application/json',
|
| 193 |
-
},
|
| 194 |
-
signal: abortController.signal,
|
| 195 |
-
body: JSON.stringify(this.conversationContext!)
|
| 196 |
-
})
|
| 197 |
-
if (response.status !== 200) {
|
| 198 |
-
params.onEvent({
|
| 199 |
-
type: 'ERROR',
|
| 200 |
-
error: new ChatError(
|
| 201 |
-
'Unknown error',
|
| 202 |
-
ErrorCode.UNKOWN_ERROR,
|
| 203 |
-
),
|
| 204 |
-
})
|
| 205 |
-
}
|
| 206 |
-
params.signal?.addEventListener('abort', () => {
|
| 207 |
-
abortController.abort()
|
| 208 |
-
})
|
| 209 |
-
|
| 210 |
-
const textDecoder = createChunkDecoder()
|
| 211 |
-
for await (const chunk of streamAsyncIterable(response.body!)) {
|
| 212 |
-
this.parseEvents(params, websocketUtils.unpackMessage(textDecoder(chunk)))
|
| 213 |
-
}
|
| 214 |
-
}
|
| 215 |
-
|
| 216 |
-
async sendWs() {
|
| 217 |
-
const wsConfig: ConstructorParameters<typeof WebSocketAsPromised>[1] = {
|
| 218 |
-
packMessage: websocketUtils.packMessage,
|
| 219 |
-
unpackMessage: websocketUtils.unpackMessage,
|
| 220 |
-
createWebSocket: (url) => new WebSocket(url, {
|
| 221 |
-
headers: {
|
| 222 |
-
'accept-language': 'zh-CN,zh;q=0.9',
|
| 223 |
-
'cache-control': 'no-cache',
|
| 224 |
-
'User-Agent': this.ua,
|
| 225 |
-
pragma: 'no-cache',
|
| 226 |
-
cookie: this.cookie,
|
| 227 |
-
}
|
| 228 |
-
})
|
| 229 |
-
}
|
| 230 |
-
const wsp = new WebSocketAsPromised('wss://sydney.bing.com/sydney/ChatHub', wsConfig)
|
| 231 |
-
|
| 232 |
-
wsp.open().then(() => {
|
| 233 |
-
wsp.sendPacked({ protocol: 'json', version: 1 })
|
| 234 |
-
wsp.sendPacked({ type: 6 })
|
| 235 |
-
wsp.sendPacked(BingWebBot.buildChatRequest(this.conversationContext!))
|
| 236 |
-
})
|
| 237 |
-
|
| 238 |
-
return wsp
|
| 239 |
-
}
|
| 240 |
-
|
| 241 |
-
private async useWs(params: Params) {
|
| 242 |
-
const wsp = await this.sendWs()
|
| 243 |
-
const watchDog = new WatchDog()
|
| 244 |
-
wsp.onUnpackedMessage.addListener((events) => {
|
| 245 |
-
watchDog.watch(() => {
|
| 246 |
-
wsp.sendPacked({ type: 6 })
|
| 247 |
-
})
|
| 248 |
-
this.parseEvents(params, events)
|
| 249 |
-
})
|
| 250 |
-
|
| 251 |
-
wsp.onClose.addListener(() => {
|
| 252 |
-
watchDog.reset()
|
| 253 |
-
params.onEvent({ type: 'DONE' })
|
| 254 |
-
wsp.removeAllListeners()
|
| 255 |
-
})
|
| 256 |
-
|
| 257 |
-
params.signal?.addEventListener('abort', () => {
|
| 258 |
-
wsp.removeAllListeners()
|
| 259 |
-
wsp.close()
|
| 260 |
-
})
|
| 261 |
-
}
|
| 262 |
-
|
| 263 |
-
private async createImage(prompt: string, id: string) {
|
| 264 |
-
try {
|
| 265 |
-
const headers = {
|
| 266 |
-
'Accept-Encoding': 'gzip, deflate, br, zsdch',
|
| 267 |
-
'User-Agent': this.ua,
|
| 268 |
-
'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
|
| 269 |
-
cookie: this.cookie,
|
| 270 |
-
}
|
| 271 |
-
const query = new URLSearchParams({
|
| 272 |
-
prompt,
|
| 273 |
-
id
|
| 274 |
-
})
|
| 275 |
-
const response = await fetch(this.endpoint + '/api/image?' + query.toString(),
|
| 276 |
-
{
|
| 277 |
-
method: 'POST',
|
| 278 |
-
headers,
|
| 279 |
-
mode: 'cors',
|
| 280 |
-
credentials: 'include'
|
| 281 |
-
})
|
| 282 |
-
.then(res => res.text())
|
| 283 |
-
if (response) {
|
| 284 |
-
this.lastText += '\n' + response
|
| 285 |
-
}
|
| 286 |
-
} catch (err) {
|
| 287 |
-
console.error('Create Image Error', err)
|
| 288 |
-
}
|
| 289 |
-
}
|
| 290 |
-
|
| 291 |
-
private buildKnowledgeApiPayload(imageUrl: string, conversationStyle: BingConversationStyle) {
|
| 292 |
-
const imageInfo: ImageInfo = {}
|
| 293 |
-
let imageBase64: string | undefined = undefined
|
| 294 |
-
const knowledgeRequest = {
|
| 295 |
-
imageInfo,
|
| 296 |
-
knowledgeRequest: {
|
| 297 |
-
invokedSkills: [
|
| 298 |
-
'ImageById'
|
| 299 |
-
],
|
| 300 |
-
subscriptionId: 'Bing.Chat.Multimodal',
|
| 301 |
-
invokedSkillsRequestData: {
|
| 302 |
-
enableFaceBlur: true
|
| 303 |
-
},
|
| 304 |
-
convoData: {
|
| 305 |
-
convoid: this.conversationContext?.conversationId,
|
| 306 |
-
convotone: conversationStyle,
|
| 307 |
-
}
|
| 308 |
-
},
|
| 309 |
-
}
|
| 310 |
-
|
| 311 |
-
if (imageUrl.startsWith('data:image/')) {
|
| 312 |
-
imageBase64 = imageUrl.replace('data:image/', '');
|
| 313 |
-
const partIndex = imageBase64.indexOf(',')
|
| 314 |
-
if (partIndex) {
|
| 315 |
-
imageBase64 = imageBase64.substring(partIndex + 1)
|
| 316 |
-
}
|
| 317 |
-
} else {
|
| 318 |
-
imageInfo.url = imageUrl
|
| 319 |
-
}
|
| 320 |
-
return { knowledgeRequest, imageBase64 }
|
| 321 |
-
}
|
| 322 |
-
|
| 323 |
-
async uploadImage(imageUrl: string, conversationStyle: BingConversationStyle = BingConversationStyle.Creative): Promise<KBlobResponse | undefined> {
|
| 324 |
-
if (!imageUrl) {
|
| 325 |
-
return
|
| 326 |
-
}
|
| 327 |
-
await this.createContext(conversationStyle)
|
| 328 |
-
const payload = this.buildKnowledgeApiPayload(imageUrl, conversationStyle)
|
| 329 |
-
|
| 330 |
-
const response = await fetch(this.endpoint + '/api/kblob',
|
| 331 |
-
{
|
| 332 |
-
headers: {
|
| 333 |
-
'Content-Type': 'application/json',
|
| 334 |
-
},
|
| 335 |
-
method: 'POST',
|
| 336 |
-
mode: 'cors',
|
| 337 |
-
credentials: 'include',
|
| 338 |
-
body: JSON.stringify(payload),
|
| 339 |
-
})
|
| 340 |
-
.then(res => res.json())
|
| 341 |
-
.catch(e => {
|
| 342 |
-
console.log('Error', e)
|
| 343 |
-
})
|
| 344 |
-
return response
|
| 345 |
-
}
|
| 346 |
-
|
| 347 |
-
private async generateContent(message: ChatResponseMessage) {
|
| 348 |
-
if (message.contentType === 'IMAGE') {
|
| 349 |
-
this.asyncTasks.push(this.createImage(message.text, message.messageId))
|
| 350 |
-
}
|
| 351 |
-
}
|
| 352 |
-
|
| 353 |
-
private async parseEvents(params: Params, events: any) {
|
| 354 |
-
const conversation = this.conversationContext!
|
| 355 |
-
|
| 356 |
-
events?.forEach(async (event: ChatUpdateCompleteResponse) => {
|
| 357 |
-
debug('bing event', event)
|
| 358 |
-
if (event.type === 3) {
|
| 359 |
-
await Promise.all(this.asyncTasks)
|
| 360 |
-
this.asyncTasks = []
|
| 361 |
-
params.onEvent({ type: 'UPDATE_ANSWER', data: { text: this.lastText } })
|
| 362 |
-
params.onEvent({ type: 'DONE' })
|
| 363 |
-
conversation.invocationId = parseInt(event.invocationId, 10) + 1
|
| 364 |
-
} else if (event.type === 1) {
|
| 365 |
-
const messages = event.arguments[0].messages
|
| 366 |
-
if (messages) {
|
| 367 |
-
const text = convertMessageToMarkdown(messages[0])
|
| 368 |
-
this.lastText = text
|
| 369 |
-
params.onEvent({ type: 'UPDATE_ANSWER', data: { text, spokenText: messages[0].text, throttling: event.arguments[0].throttling } })
|
| 370 |
-
}
|
| 371 |
-
} else if (event.type === 2) {
|
| 372 |
-
const messages = event.item.messages as ChatResponseMessage[] | undefined
|
| 373 |
-
if (!messages) {
|
| 374 |
-
params.onEvent({
|
| 375 |
-
type: 'ERROR',
|
| 376 |
-
error: new ChatError(
|
| 377 |
-
event.item.result.error || 'Unknown error',
|
| 378 |
-
event.item.result.value === 'Throttled' ? ErrorCode.THROTTLE_LIMIT
|
| 379 |
-
: event.item.result.value === 'CaptchaChallenge' ? (this.conversationContext?.conversationId?.includes('BingProdUnAuthenticatedUsers') ? ErrorCode.BING_UNAUTHORIZED : ErrorCode.BING_CAPTCHA)
|
| 380 |
-
: ErrorCode.UNKOWN_ERROR
|
| 381 |
-
),
|
| 382 |
-
})
|
| 383 |
-
return
|
| 384 |
-
}
|
| 385 |
-
const limited = messages.some((message) =>
|
| 386 |
-
message.contentOrigin === 'TurnLimiter'
|
| 387 |
-
|| message.messageType === 'Disengaged'
|
| 388 |
-
)
|
| 389 |
-
if (limited) {
|
| 390 |
-
params.onEvent({
|
| 391 |
-
type: 'ERROR',
|
| 392 |
-
error: new ChatError(
|
| 393 |
-
'Sorry, you have reached chat limit in this conversation.',
|
| 394 |
-
ErrorCode.CONVERSATION_LIMIT,
|
| 395 |
-
),
|
| 396 |
-
})
|
| 397 |
-
return
|
| 398 |
-
}
|
| 399 |
-
|
| 400 |
-
const lastMessage = event.item.messages.at(-1) as ChatResponseMessage
|
| 401 |
-
const specialMessage = event.item.messages.find(message => message.author === 'bot' && message.contentType === 'IMAGE')
|
| 402 |
-
if (specialMessage) {
|
| 403 |
-
this.generateContent(specialMessage)
|
| 404 |
-
}
|
| 405 |
-
|
| 406 |
-
if (lastMessage) {
|
| 407 |
-
const text = convertMessageToMarkdown(lastMessage)
|
| 408 |
-
this.lastText = text
|
| 409 |
-
params.onEvent({
|
| 410 |
-
type: 'UPDATE_ANSWER',
|
| 411 |
-
data: { text, throttling: event.item.throttling, suggestedResponses: lastMessage.suggestedResponses, sourceAttributions: lastMessage.sourceAttributions },
|
| 412 |
-
})
|
| 413 |
-
}
|
| 414 |
-
}
|
| 415 |
-
})
|
| 416 |
-
}
|
| 417 |
-
|
| 418 |
-
resetConversation() {
|
| 419 |
-
this.conversationContext = undefined
|
| 420 |
-
}
|
| 421 |
-
}
|
|
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spaces/ADOPLE/AdopleAI-ResumeAnalyzer/README.md
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---
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title: AdopleAIResumeAnalyser
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emoji: 🏃
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colorFrom: gray
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.35.2
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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spaces/AIGC-Audio/AudioGPT/sound_extraction/utils/create_mixtures.py
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import torch
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import numpy as np
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def add_noise_and_scale(front, noise, snr_l=0, snr_h=0, scale_lower=1.0, scale_upper=1.0):
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"""
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| 6 |
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:param front: front-head audio, like vocal [samples,channel], will be normlized so any scale will be fine
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:param noise: noise, [samples,channel], any scale
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:param snr_l: Optional
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| 9 |
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:param snr_h: Optional
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:param scale_lower: Optional
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| 11 |
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:param scale_upper: Optional
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:return: scaled front and noise (noisy = front + noise), all_mel_e2e outputs are noramlized within [-1 , 1]
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| 13 |
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"""
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| 14 |
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snr = None
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| 15 |
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noise, front = normalize_energy_torch(noise), normalize_energy_torch(front) # set noise and vocal to equal range [-1,1]
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| 16 |
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# print("normalize:",torch.max(noise),torch.max(front))
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| 17 |
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if snr_l is not None and snr_h is not None:
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| 18 |
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front, noise, snr = _random_noise(front, noise, snr_l=snr_l, snr_h=snr_h) # remix them with a specific snr
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| 19 |
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| 20 |
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noisy, noise, front = unify_energy_torch(noise + front, noise, front) # normalize noisy, noise and vocal energy into [-1,1]
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| 21 |
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| 22 |
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# print("unify:", torch.max(noise), torch.max(front), torch.max(noisy))
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| 23 |
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scale = _random_scale(scale_lower, scale_upper) # random scale these three signal
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| 24 |
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| 25 |
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# print("Scale",scale)
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| 26 |
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noisy, noise, front = noisy * scale, noise * scale, front * scale # apply scale
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# print("after scale", torch.max(noisy), torch.max(noise), torch.max(front), snr, scale)
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| 28 |
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| 29 |
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front, noise = _to_numpy(front), _to_numpy(noise) # [num_samples]
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| 30 |
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mixed_wav = front + noise
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| 32 |
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return front, noise, mixed_wav, snr, scale
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| 33 |
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| 34 |
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def _random_scale(lower=0.3, upper=0.9):
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| 35 |
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return float(uniform_torch(lower, upper))
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| 37 |
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def _random_noise(clean, noise, snr_l=None, snr_h=None):
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| 38 |
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snr = uniform_torch(snr_l,snr_h)
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| 39 |
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clean_weight = 10 ** (float(snr) / 20)
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| 40 |
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return clean, noise/clean_weight, snr
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| 41 |
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| 42 |
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def _to_numpy(wav):
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| 43 |
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return np.transpose(wav, (1, 0))[0].numpy() # [num_samples]
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| 44 |
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| 45 |
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def normalize_energy(audio, alpha = 1):
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| 46 |
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'''
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| 47 |
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:param audio: 1d waveform, [batchsize, *],
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:param alpha: the value of output range from: [-alpha,alpha]
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| 49 |
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:return: 1d waveform which value range from: [-alpha,alpha]
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| 50 |
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'''
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| 51 |
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val_max = activelev(audio)
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return (audio / val_max) * alpha
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| 54 |
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def normalize_energy_torch(audio, alpha = 1):
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| 55 |
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'''
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| 56 |
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If the signal is almost empty(determined by threshold), if will only be divided by 2**15
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:param audio: 1d waveform, 2**15
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| 58 |
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:param alpha: the value of output range from: [-alpha,alpha]
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:return: 1d waveform which value range from: [-alpha,alpha]
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| 60 |
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'''
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| 61 |
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val_max = activelev_torch([audio])
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| 62 |
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return (audio / val_max) * alpha
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| 63 |
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| 64 |
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def unify_energy(*args):
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| 65 |
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max_amp = activelev(args)
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| 66 |
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mix_scale = 1.0/max_amp
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| 67 |
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return [x * mix_scale for x in args]
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| 68 |
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| 69 |
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def unify_energy_torch(*args):
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| 70 |
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max_amp = activelev_torch(args)
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| 71 |
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mix_scale = 1.0/max_amp
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| 72 |
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return [x * mix_scale for x in args]
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| 73 |
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| 74 |
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def activelev(*args):
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| 75 |
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'''
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| 76 |
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need to update like matlab
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| 77 |
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'''
|
| 78 |
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return np.max(np.abs([*args]))
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| 79 |
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| 80 |
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def activelev_torch(*args):
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| 81 |
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'''
|
| 82 |
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need to update like matlab
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| 83 |
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'''
|
| 84 |
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res = []
|
| 85 |
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args = args[0]
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| 86 |
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for each in args:
|
| 87 |
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res.append(torch.max(torch.abs(each)))
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| 88 |
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return max(res)
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| 89 |
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| 90 |
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def uniform_torch(lower, upper):
|
| 91 |
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if(abs(lower-upper)<1e-5):
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| 92 |
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return upper
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| 93 |
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return (upper-lower)*torch.rand(1)+lower
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| 94 |
-
|
| 95 |
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if __name__ == "__main__":
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| 96 |
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wav1 = torch.randn(1, 32000)
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| 97 |
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wav2 = torch.randn(1, 32000)
|
| 98 |
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target, noise, snr, scale = add_noise_and_scale(wav1, wav2)
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spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/metrics/ssim.py
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@@ -1,84 +0,0 @@
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| 1 |
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"""
|
| 2 |
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Adapted from https://github.com/Po-Hsun-Su/pytorch-ssim
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| 3 |
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"""
|
| 4 |
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|
| 5 |
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import torch
|
| 6 |
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import torch.nn.functional as F
|
| 7 |
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from torch.autograd import Variable
|
| 8 |
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import numpy as np
|
| 9 |
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from math import exp
|
| 10 |
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|
| 11 |
-
|
| 12 |
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def gaussian(window_size, sigma):
|
| 13 |
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gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
|
| 14 |
-
return gauss / gauss.sum()
|
| 15 |
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|
| 16 |
-
|
| 17 |
-
def create_window(window_size, channel):
|
| 18 |
-
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
|
| 19 |
-
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
|
| 20 |
-
window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
|
| 21 |
-
return window
|
| 22 |
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|
| 23 |
-
|
| 24 |
-
def _ssim(img1, img2, window, window_size, channel, size_average=True):
|
| 25 |
-
mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
|
| 26 |
-
mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
|
| 27 |
-
|
| 28 |
-
mu1_sq = mu1.pow(2)
|
| 29 |
-
mu2_sq = mu2.pow(2)
|
| 30 |
-
mu1_mu2 = mu1 * mu2
|
| 31 |
-
|
| 32 |
-
sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
|
| 33 |
-
sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
|
| 34 |
-
sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
|
| 35 |
-
|
| 36 |
-
C1 = 0.01 ** 2
|
| 37 |
-
C2 = 0.03 ** 2
|
| 38 |
-
|
| 39 |
-
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
|
| 40 |
-
|
| 41 |
-
if size_average:
|
| 42 |
-
return ssim_map.mean()
|
| 43 |
-
else:
|
| 44 |
-
return ssim_map.mean(1)
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
class SSIM(torch.nn.Module):
|
| 48 |
-
def __init__(self, window_size=11, size_average=True):
|
| 49 |
-
super(SSIM, self).__init__()
|
| 50 |
-
self.window_size = window_size
|
| 51 |
-
self.size_average = size_average
|
| 52 |
-
self.channel = 1
|
| 53 |
-
self.window = create_window(window_size, self.channel)
|
| 54 |
-
|
| 55 |
-
def forward(self, img1, img2):
|
| 56 |
-
(_, channel, _, _) = img1.size()
|
| 57 |
-
|
| 58 |
-
if channel == self.channel and self.window.data.type() == img1.data.type():
|
| 59 |
-
window = self.window
|
| 60 |
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else:
|
| 61 |
-
window = create_window(self.window_size, channel)
|
| 62 |
-
|
| 63 |
-
if img1.is_cuda:
|
| 64 |
-
window = window.cuda(img1.get_device())
|
| 65 |
-
window = window.type_as(img1)
|
| 66 |
-
|
| 67 |
-
self.window = window
|
| 68 |
-
self.channel = channel
|
| 69 |
-
|
| 70 |
-
return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
|
| 71 |
-
|
| 72 |
-
|
| 73 |
-
window = None
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
def ssim(img1, img2, window_size=11, size_average=True):
|
| 77 |
-
(_, channel, _, _) = img1.size()
|
| 78 |
-
global window
|
| 79 |
-
if window is None:
|
| 80 |
-
window = create_window(window_size, channel)
|
| 81 |
-
if img1.is_cuda:
|
| 82 |
-
window = window.cuda(img1.get_device())
|
| 83 |
-
window = window.type_as(img1)
|
| 84 |
-
return _ssim(img1, img2, window, window_size, channel, size_average)
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spaces/AILab-CVC/SEED-LLaMA/scripts/seed_llama_inference_8B.py
DELETED
|
@@ -1,120 +0,0 @@
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|
| 1 |
-
import hydra
|
| 2 |
-
|
| 3 |
-
import pyrootutils
|
| 4 |
-
import os
|
| 5 |
-
import torch
|
| 6 |
-
|
| 7 |
-
from omegaconf import OmegaConf
|
| 8 |
-
import json
|
| 9 |
-
from typing import Optional
|
| 10 |
-
import transformers
|
| 11 |
-
from PIL import Image
|
| 12 |
-
from torchvision.transforms.functional import InterpolationMode
|
| 13 |
-
|
| 14 |
-
pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
|
| 15 |
-
|
| 16 |
-
BOI_TOKEN = '<img>'
|
| 17 |
-
EOI_TOKEN = '</img>'
|
| 18 |
-
IMG_TOKEN = '<img_{:05d}>'
|
| 19 |
-
|
| 20 |
-
IMG_FLAG = '<image>'
|
| 21 |
-
NUM_IMG_TOKNES = 32
|
| 22 |
-
NUM_IMG_CODES = 8192
|
| 23 |
-
image_id_shift = 32000
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
def generate(tokenizer, input_tokens, generation_config, model):
|
| 29 |
-
|
| 30 |
-
input_ids = tokenizer(input_tokens, add_special_tokens=False, return_tensors='pt').input_ids
|
| 31 |
-
input_ids = input_ids.to("cuda")
|
| 32 |
-
|
| 33 |
-
generate_ids = model.generate(
|
| 34 |
-
input_ids=input_ids,
|
| 35 |
-
**generation_config
|
| 36 |
-
)
|
| 37 |
-
generate_ids = generate_ids[0][input_ids.shape[1]:]
|
| 38 |
-
|
| 39 |
-
return generate_ids
|
| 40 |
-
|
| 41 |
-
def decode_image_text(generate_ids, tokenizer, save_path=None):
|
| 42 |
-
|
| 43 |
-
boi_list = torch.where(generate_ids == tokenizer(BOI_TOKEN, add_special_tokens=False).input_ids[0])[0]
|
| 44 |
-
eoi_list = torch.where(generate_ids == tokenizer(EOI_TOKEN, add_special_tokens=False).input_ids[0])[0]
|
| 45 |
-
|
| 46 |
-
if len(boi_list) == 0 and len(eoi_list) == 0:
|
| 47 |
-
text_ids = generate_ids
|
| 48 |
-
texts = tokenizer.decode(text_ids, skip_special_tokens=True)
|
| 49 |
-
print(texts)
|
| 50 |
-
|
| 51 |
-
else:
|
| 52 |
-
boi_index = boi_list[0]
|
| 53 |
-
eoi_index = eoi_list[0]
|
| 54 |
-
|
| 55 |
-
text_ids = generate_ids[:boi_index]
|
| 56 |
-
if len(text_ids) != 0:
|
| 57 |
-
texts = tokenizer.decode(text_ids, skip_special_tokens=True)
|
| 58 |
-
print(texts)
|
| 59 |
-
|
| 60 |
-
image_ids = (generate_ids[boi_index+1:eoi_index] - image_id_shift).reshape(1,-1)
|
| 61 |
-
|
| 62 |
-
images = tokenizer.decode_image(image_ids)
|
| 63 |
-
|
| 64 |
-
images[0].save(save_path)
|
| 65 |
-
|
| 66 |
-
|
| 67 |
-
device = "cuda"
|
| 68 |
-
|
| 69 |
-
tokenizer_cfg_path = 'configs/tokenizer/seed_llama_tokenizer.yaml'
|
| 70 |
-
tokenizer_cfg = OmegaConf.load(tokenizer_cfg_path)
|
| 71 |
-
tokenizer = hydra.utils.instantiate(tokenizer_cfg, device=device, load_diffusion=True)
|
| 72 |
-
|
| 73 |
-
transform_cfg_path = 'configs/transform/clip_transform.yaml'
|
| 74 |
-
transform_cfg = OmegaConf.load(transform_cfg_path)
|
| 75 |
-
transform = hydra.utils.instantiate(transform_cfg)
|
| 76 |
-
|
| 77 |
-
model_cfg = OmegaConf.load('configs/llm/seed_llama_8b.yaml')
|
| 78 |
-
model = hydra.utils.instantiate(model_cfg, torch_dtype=torch.float16)
|
| 79 |
-
model = model.eval().to(device)
|
| 80 |
-
|
| 81 |
-
generation_config = {
|
| 82 |
-
'temperature': 1.0,
|
| 83 |
-
'num_beams': 1,
|
| 84 |
-
'max_new_tokens': 512,
|
| 85 |
-
'top_p': 0.5,
|
| 86 |
-
'do_sample': True
|
| 87 |
-
}
|
| 88 |
-
|
| 89 |
-
s_token = "USER:"
|
| 90 |
-
e_token = "ASSISTANT:"
|
| 91 |
-
sep = "\n"
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
### visual question answering
|
| 95 |
-
image_path = "images/cat.jpg"
|
| 96 |
-
image = Image.open(image_path).convert('RGB')
|
| 97 |
-
image_tensor = transform(image).to(device)
|
| 98 |
-
img_ids = tokenizer.encode_image(image_torch=image_tensor)
|
| 99 |
-
img_ids = img_ids.view(-1).cpu().numpy()
|
| 100 |
-
img_tokens = BOI_TOKEN + ''.join([IMG_TOKEN.format(item) for item in img_ids]) + EOI_TOKEN
|
| 101 |
-
|
| 102 |
-
question = "What is this animal?"
|
| 103 |
-
|
| 104 |
-
input_tokens = tokenizer.bos_token + s_token + " " + img_tokens + question + sep + e_token
|
| 105 |
-
generate_ids = generate(tokenizer, input_tokens, generation_config, model)
|
| 106 |
-
decode_image_text(generate_ids, tokenizer)
|
| 107 |
-
|
| 108 |
-
### text-to-image generation
|
| 109 |
-
prompt = "Can you generate an image of a dog on the green grass?"
|
| 110 |
-
input_tokens = tokenizer.bos_token + s_token + " " + prompt + sep + e_token
|
| 111 |
-
generate_ids = generate(tokenizer, input_tokens, generation_config, model)
|
| 112 |
-
save_path = 'dog.jpg'
|
| 113 |
-
decode_image_text(generate_ids, tokenizer, save_path)
|
| 114 |
-
|
| 115 |
-
### multimodal prompt image generation
|
| 116 |
-
instruction = "Can you make the cat wear sunglasses?"
|
| 117 |
-
input_tokens = tokenizer.bos_token + s_token + " " + img_tokens + instruction + sep + e_token
|
| 118 |
-
generate_ids = generate(tokenizer, input_tokens, generation_config, model)
|
| 119 |
-
save_path = 'cat_sunglasses.jpg'
|
| 120 |
-
decode_image_text(generate_ids, tokenizer, save_path)
|
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|
spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py
DELETED
|
@@ -1,292 +0,0 @@
|
|
| 1 |
-
_base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']
|
| 2 |
-
|
| 3 |
-
# ========================Frequently modified parameters======================
|
| 4 |
-
# -----data related-----
|
| 5 |
-
data_root = 'data/coco/' # Root path of data
|
| 6 |
-
# Path of train annotation file
|
| 7 |
-
train_ann_file = 'annotations/instances_train2017.json'
|
| 8 |
-
train_data_prefix = 'train2017/' # Prefix of train image path
|
| 9 |
-
# Path of val annotation file
|
| 10 |
-
val_ann_file = 'annotations/instances_val2017.json'
|
| 11 |
-
val_data_prefix = 'val2017/' # Prefix of val image path
|
| 12 |
-
|
| 13 |
-
num_classes = 80 # Number of classes for classification
|
| 14 |
-
# Batch size of a single GPU during training
|
| 15 |
-
train_batch_size_per_gpu = 16
|
| 16 |
-
# Worker to pre-fetch data for each single GPU during training
|
| 17 |
-
train_num_workers = 8
|
| 18 |
-
# persistent_workers must be False if num_workers is 0
|
| 19 |
-
persistent_workers = True
|
| 20 |
-
|
| 21 |
-
# -----model related-----
|
| 22 |
-
# Basic size of multi-scale prior box
|
| 23 |
-
anchors = [
|
| 24 |
-
[(10, 13), (16, 30), (33, 23)], # P3/8
|
| 25 |
-
[(30, 61), (62, 45), (59, 119)], # P4/16
|
| 26 |
-
[(116, 90), (156, 198), (373, 326)] # P5/32
|
| 27 |
-
]
|
| 28 |
-
|
| 29 |
-
# -----train val related-----
|
| 30 |
-
# Base learning rate for optim_wrapper. Corresponding to 8xb16=128 bs
|
| 31 |
-
base_lr = 0.01
|
| 32 |
-
max_epochs = 300 # Maximum training epochs
|
| 33 |
-
|
| 34 |
-
model_test_cfg = dict(
|
| 35 |
-
# The config of multi-label for multi-class prediction.
|
| 36 |
-
multi_label=True,
|
| 37 |
-
# The number of boxes before NMS
|
| 38 |
-
nms_pre=30000,
|
| 39 |
-
score_thr=0.001, # Threshold to filter out boxes.
|
| 40 |
-
nms=dict(type='nms', iou_threshold=0.65), # NMS type and threshold
|
| 41 |
-
max_per_img=300) # Max number of detections of each image
|
| 42 |
-
|
| 43 |
-
# ========================Possible modified parameters========================
|
| 44 |
-
# -----data related-----
|
| 45 |
-
img_scale = (640, 640) # width, height
|
| 46 |
-
# Dataset type, this will be used to define the dataset
|
| 47 |
-
dataset_type = 'YOLOv5CocoDataset'
|
| 48 |
-
# Batch size of a single GPU during validation
|
| 49 |
-
val_batch_size_per_gpu = 1
|
| 50 |
-
# Worker to pre-fetch data for each single GPU during validation
|
| 51 |
-
val_num_workers = 2
|
| 52 |
-
|
| 53 |
-
# Config of batch shapes. Only on val.
|
| 54 |
-
# It means not used if batch_shapes_cfg is None.
|
| 55 |
-
batch_shapes_cfg = dict(
|
| 56 |
-
type='BatchShapePolicy',
|
| 57 |
-
batch_size=val_batch_size_per_gpu,
|
| 58 |
-
img_size=img_scale[0],
|
| 59 |
-
# The image scale of padding should be divided by pad_size_divisor
|
| 60 |
-
size_divisor=32,
|
| 61 |
-
# Additional paddings for pixel scale
|
| 62 |
-
extra_pad_ratio=0.5)
|
| 63 |
-
|
| 64 |
-
# -----model related-----
|
| 65 |
-
# The scaling factor that controls the depth of the network structure
|
| 66 |
-
deepen_factor = 0.33
|
| 67 |
-
# The scaling factor that controls the width of the network structure
|
| 68 |
-
widen_factor = 0.5
|
| 69 |
-
# Strides of multi-scale prior box
|
| 70 |
-
strides = [8, 16, 32]
|
| 71 |
-
num_det_layers = 3 # The number of model output scales
|
| 72 |
-
norm_cfg = dict(type='BN', momentum=0.03, eps=0.001) # Normalization config
|
| 73 |
-
|
| 74 |
-
# -----train val related-----
|
| 75 |
-
affine_scale = 0.5 # YOLOv5RandomAffine scaling ratio
|
| 76 |
-
loss_cls_weight = 0.5
|
| 77 |
-
loss_bbox_weight = 0.05
|
| 78 |
-
loss_obj_weight = 1.0
|
| 79 |
-
prior_match_thr = 4. # Priori box matching threshold
|
| 80 |
-
# The obj loss weights of the three output layers
|
| 81 |
-
obj_level_weights = [4., 1., 0.4]
|
| 82 |
-
lr_factor = 0.01 # Learning rate scaling factor
|
| 83 |
-
weight_decay = 0.0005
|
| 84 |
-
# Save model checkpoint and validation intervals
|
| 85 |
-
save_checkpoint_intervals = 10
|
| 86 |
-
# The maximum checkpoints to keep.
|
| 87 |
-
max_keep_ckpts = 3
|
| 88 |
-
# Single-scale training is recommended to
|
| 89 |
-
# be turned on, which can speed up training.
|
| 90 |
-
env_cfg = dict(cudnn_benchmark=True)
|
| 91 |
-
|
| 92 |
-
# ===============================Unmodified in most cases====================
|
| 93 |
-
model = dict(
|
| 94 |
-
type='YOLODetector',
|
| 95 |
-
data_preprocessor=dict(
|
| 96 |
-
type='mmdet.DetDataPreprocessor',
|
| 97 |
-
mean=[0., 0., 0.],
|
| 98 |
-
std=[255., 255., 255.],
|
| 99 |
-
bgr_to_rgb=True),
|
| 100 |
-
backbone=dict(
|
| 101 |
-
type='YOLOv5CSPDarknet',
|
| 102 |
-
deepen_factor=deepen_factor,
|
| 103 |
-
widen_factor=widen_factor,
|
| 104 |
-
norm_cfg=norm_cfg,
|
| 105 |
-
act_cfg=dict(type='SiLU', inplace=True)),
|
| 106 |
-
neck=dict(
|
| 107 |
-
type='YOLOv5PAFPN',
|
| 108 |
-
deepen_factor=deepen_factor,
|
| 109 |
-
widen_factor=widen_factor,
|
| 110 |
-
in_channels=[256, 512, 1024],
|
| 111 |
-
out_channels=[256, 512, 1024],
|
| 112 |
-
num_csp_blocks=3,
|
| 113 |
-
norm_cfg=norm_cfg,
|
| 114 |
-
act_cfg=dict(type='SiLU', inplace=True)),
|
| 115 |
-
bbox_head=dict(
|
| 116 |
-
type='YOLOv5Head',
|
| 117 |
-
head_module=dict(
|
| 118 |
-
type='YOLOv5HeadModule',
|
| 119 |
-
num_classes=num_classes,
|
| 120 |
-
in_channels=[256, 512, 1024],
|
| 121 |
-
widen_factor=widen_factor,
|
| 122 |
-
featmap_strides=strides,
|
| 123 |
-
num_base_priors=3),
|
| 124 |
-
prior_generator=dict(
|
| 125 |
-
type='mmdet.YOLOAnchorGenerator',
|
| 126 |
-
base_sizes=anchors,
|
| 127 |
-
strides=strides),
|
| 128 |
-
# scaled based on number of detection layers
|
| 129 |
-
loss_cls=dict(
|
| 130 |
-
type='mmdet.CrossEntropyLoss',
|
| 131 |
-
use_sigmoid=True,
|
| 132 |
-
reduction='mean',
|
| 133 |
-
loss_weight=loss_cls_weight *
|
| 134 |
-
(num_classes / 80 * 3 / num_det_layers)),
|
| 135 |
-
loss_bbox=dict(
|
| 136 |
-
type='IoULoss',
|
| 137 |
-
iou_mode='ciou',
|
| 138 |
-
bbox_format='xywh',
|
| 139 |
-
eps=1e-7,
|
| 140 |
-
reduction='mean',
|
| 141 |
-
loss_weight=loss_bbox_weight * (3 / num_det_layers),
|
| 142 |
-
return_iou=True),
|
| 143 |
-
loss_obj=dict(
|
| 144 |
-
type='mmdet.CrossEntropyLoss',
|
| 145 |
-
use_sigmoid=True,
|
| 146 |
-
reduction='mean',
|
| 147 |
-
loss_weight=loss_obj_weight *
|
| 148 |
-
((img_scale[0] / 640)**2 * 3 / num_det_layers)),
|
| 149 |
-
prior_match_thr=prior_match_thr,
|
| 150 |
-
obj_level_weights=obj_level_weights),
|
| 151 |
-
test_cfg=model_test_cfg)
|
| 152 |
-
|
| 153 |
-
albu_train_transforms = [
|
| 154 |
-
dict(type='Blur', p=0.01),
|
| 155 |
-
dict(type='MedianBlur', p=0.01),
|
| 156 |
-
dict(type='ToGray', p=0.01),
|
| 157 |
-
dict(type='CLAHE', p=0.01)
|
| 158 |
-
]
|
| 159 |
-
|
| 160 |
-
pre_transform = [
|
| 161 |
-
dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
|
| 162 |
-
dict(type='LoadAnnotations', with_bbox=True)
|
| 163 |
-
]
|
| 164 |
-
|
| 165 |
-
train_pipeline = [
|
| 166 |
-
*pre_transform,
|
| 167 |
-
dict(
|
| 168 |
-
type='Mosaic',
|
| 169 |
-
img_scale=img_scale,
|
| 170 |
-
pad_val=114.0,
|
| 171 |
-
pre_transform=pre_transform),
|
| 172 |
-
dict(
|
| 173 |
-
type='YOLOv5RandomAffine',
|
| 174 |
-
max_rotate_degree=0.0,
|
| 175 |
-
max_shear_degree=0.0,
|
| 176 |
-
scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
|
| 177 |
-
# img_scale is (width, height)
|
| 178 |
-
border=(-img_scale[0] // 2, -img_scale[1] // 2),
|
| 179 |
-
border_val=(114, 114, 114)),
|
| 180 |
-
dict(
|
| 181 |
-
type='mmdet.Albu',
|
| 182 |
-
transforms=albu_train_transforms,
|
| 183 |
-
bbox_params=dict(
|
| 184 |
-
type='BboxParams',
|
| 185 |
-
format='pascal_voc',
|
| 186 |
-
label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
|
| 187 |
-
keymap={
|
| 188 |
-
'img': 'image',
|
| 189 |
-
'gt_bboxes': 'bboxes'
|
| 190 |
-
}),
|
| 191 |
-
dict(type='YOLOv5HSVRandomAug'),
|
| 192 |
-
dict(type='mmdet.RandomFlip', prob=0.5),
|
| 193 |
-
dict(
|
| 194 |
-
type='mmdet.PackDetInputs',
|
| 195 |
-
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
|
| 196 |
-
'flip_direction'))
|
| 197 |
-
]
|
| 198 |
-
|
| 199 |
-
train_dataloader = dict(
|
| 200 |
-
batch_size=train_batch_size_per_gpu,
|
| 201 |
-
num_workers=train_num_workers,
|
| 202 |
-
persistent_workers=persistent_workers,
|
| 203 |
-
pin_memory=True,
|
| 204 |
-
sampler=dict(type='DefaultSampler', shuffle=True),
|
| 205 |
-
dataset=dict(
|
| 206 |
-
type=dataset_type,
|
| 207 |
-
data_root=data_root,
|
| 208 |
-
ann_file=train_ann_file,
|
| 209 |
-
data_prefix=dict(img=train_data_prefix),
|
| 210 |
-
filter_cfg=dict(filter_empty_gt=False, min_size=32),
|
| 211 |
-
pipeline=train_pipeline))
|
| 212 |
-
|
| 213 |
-
test_pipeline = [
|
| 214 |
-
dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
|
| 215 |
-
dict(type='YOLOv5KeepRatioResize', scale=img_scale),
|
| 216 |
-
dict(
|
| 217 |
-
type='LetterResize',
|
| 218 |
-
scale=img_scale,
|
| 219 |
-
allow_scale_up=False,
|
| 220 |
-
pad_val=dict(img=114)),
|
| 221 |
-
dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
|
| 222 |
-
dict(
|
| 223 |
-
type='mmdet.PackDetInputs',
|
| 224 |
-
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
|
| 225 |
-
'scale_factor', 'pad_param'))
|
| 226 |
-
]
|
| 227 |
-
|
| 228 |
-
val_dataloader = dict(
|
| 229 |
-
batch_size=val_batch_size_per_gpu,
|
| 230 |
-
num_workers=val_num_workers,
|
| 231 |
-
persistent_workers=persistent_workers,
|
| 232 |
-
pin_memory=True,
|
| 233 |
-
drop_last=False,
|
| 234 |
-
sampler=dict(type='DefaultSampler', shuffle=False),
|
| 235 |
-
dataset=dict(
|
| 236 |
-
type=dataset_type,
|
| 237 |
-
data_root=data_root,
|
| 238 |
-
test_mode=True,
|
| 239 |
-
data_prefix=dict(img=val_data_prefix),
|
| 240 |
-
ann_file=val_ann_file,
|
| 241 |
-
pipeline=test_pipeline,
|
| 242 |
-
batch_shapes_cfg=batch_shapes_cfg))
|
| 243 |
-
|
| 244 |
-
test_dataloader = val_dataloader
|
| 245 |
-
|
| 246 |
-
param_scheduler = None
|
| 247 |
-
optim_wrapper = dict(
|
| 248 |
-
type='OptimWrapper',
|
| 249 |
-
optimizer=dict(
|
| 250 |
-
type='SGD',
|
| 251 |
-
lr=base_lr,
|
| 252 |
-
momentum=0.937,
|
| 253 |
-
weight_decay=weight_decay,
|
| 254 |
-
nesterov=True,
|
| 255 |
-
batch_size_per_gpu=train_batch_size_per_gpu),
|
| 256 |
-
constructor='YOLOv5OptimizerConstructor')
|
| 257 |
-
|
| 258 |
-
default_hooks = dict(
|
| 259 |
-
param_scheduler=dict(
|
| 260 |
-
type='YOLOv5ParamSchedulerHook',
|
| 261 |
-
scheduler_type='linear',
|
| 262 |
-
lr_factor=lr_factor,
|
| 263 |
-
max_epochs=max_epochs),
|
| 264 |
-
checkpoint=dict(
|
| 265 |
-
type='CheckpointHook',
|
| 266 |
-
interval=save_checkpoint_intervals,
|
| 267 |
-
save_best='auto',
|
| 268 |
-
max_keep_ckpts=max_keep_ckpts))
|
| 269 |
-
|
| 270 |
-
custom_hooks = [
|
| 271 |
-
dict(
|
| 272 |
-
type='EMAHook',
|
| 273 |
-
ema_type='ExpMomentumEMA',
|
| 274 |
-
momentum=0.0001,
|
| 275 |
-
update_buffers=True,
|
| 276 |
-
strict_load=False,
|
| 277 |
-
priority=49)
|
| 278 |
-
]
|
| 279 |
-
|
| 280 |
-
val_evaluator = dict(
|
| 281 |
-
type='mmdet.CocoMetric',
|
| 282 |
-
proposal_nums=(100, 1, 10),
|
| 283 |
-
ann_file=data_root + val_ann_file,
|
| 284 |
-
metric='bbox')
|
| 285 |
-
test_evaluator = val_evaluator
|
| 286 |
-
|
| 287 |
-
train_cfg = dict(
|
| 288 |
-
type='EpochBasedTrainLoop',
|
| 289 |
-
max_epochs=max_epochs,
|
| 290 |
-
val_interval=save_checkpoint_intervals)
|
| 291 |
-
val_cfg = dict(type='ValLoop')
|
| 292 |
-
test_cfg = dict(type='TestLoop')
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|
spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnetv1c50_8xb32_in1k.py
DELETED
|
@@ -1,5 +0,0 @@
|
|
| 1 |
-
_base_ = [
|
| 2 |
-
'../_base_/models/resnetv1c50.py',
|
| 3 |
-
'../_base_/datasets/imagenet_bs32_pil_resize.py',
|
| 4 |
-
'../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
|
| 5 |
-
]
|
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spaces/AchyuthGamer/NeonAI-Chat-UI/neon.ai.py
DELETED
|
@@ -1,37 +0,0 @@
|
|
| 1 |
-
import random
|
| 2 |
-
import gradio as gr
|
| 3 |
-
import openai
|
| 4 |
-
|
| 5 |
-
openai.api_type = "azure"
|
| 6 |
-
openai.api_base = "https://hrangaopenaillm.openai.azure.com"
|
| 7 |
-
openai.api_version = "2023-03-15-preview"
|
| 8 |
-
openai.api_key = "e951b48da7c548e18af601a15cb6aefa"
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
def gptresponse(message, history):
|
| 12 |
-
system_prompt = "You are OpenGPT chatbot developed by Achyuth to help people. Your developer is 13 years old and a young programmer."
|
| 13 |
-
|
| 14 |
-
messages = [{"role":"system","content":system_prompt}]
|
| 15 |
-
for human, assistant in history:
|
| 16 |
-
messages.append({"role":"user", "content":human})
|
| 17 |
-
messages.append({"role":"assistant", "content":assistant})
|
| 18 |
-
|
| 19 |
-
if message != '':
|
| 20 |
-
messages.append({"role":"user", "content":message})
|
| 21 |
-
|
| 22 |
-
response = openai.ChatCompletion.create(engine = "NGA_AI_ASSISTANT",
|
| 23 |
-
messages = messages,
|
| 24 |
-
temperature =0.7,
|
| 25 |
-
max_tokens = 4000,
|
| 26 |
-
top_p = 0.95,
|
| 27 |
-
frequency_penalty = 0,
|
| 28 |
-
presence_penalty = 0,
|
| 29 |
-
stop = None)
|
| 30 |
-
|
| 31 |
-
return response["choices"][0]["message"]["content"]
|
| 32 |
-
|
| 33 |
-
title = "NeonAI Chat✨"
|
| 34 |
-
|
| 35 |
-
gr.HTML(title)
|
| 36 |
-
|
| 37 |
-
gr.ChatInterface(gptresponse, title=title).launch()
|
|
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|
spaces/AdVisual/MaskCut/predict.py
DELETED
|
@@ -1,27 +0,0 @@
|
|
| 1 |
-
import base64
|
| 2 |
-
from io import BytesIO
|
| 3 |
-
from PIL import Image
|
| 4 |
-
import numpy as np
|
| 5 |
-
from model import Model
|
| 6 |
-
|
| 7 |
-
def predict(package, image_base64: str, threshold: float, num_objects: int):
|
| 8 |
-
# Decode the image from base64 to PIL.Image
|
| 9 |
-
# We use BytesIO to convert the base64 to bytes
|
| 10 |
-
base64_split = image_base64.split(',')[1]
|
| 11 |
-
buf = BytesIO(base64.b64decode(base64_split))
|
| 12 |
-
|
| 13 |
-
image = Image.open(buf)
|
| 14 |
-
|
| 15 |
-
# Get the image path from tmp_image
|
| 16 |
-
canvas = Image.new('RGB', image.size, (0, 0, 0))
|
| 17 |
-
|
| 18 |
-
# We copy the image that and fill it with black, to get the dimensions
|
| 19 |
-
rgb = np.array(canvas)
|
| 20 |
-
model : Model = package.get('model')
|
| 21 |
-
masks = model(image, threshold, num_objects)
|
| 22 |
-
|
| 23 |
-
for mask in masks:
|
| 24 |
-
fg = mask > 0.5
|
| 25 |
-
rgb[fg] = 255
|
| 26 |
-
|
| 27 |
-
return Image.fromarray(rgb)
|
|
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|
spaces/Adeeb-F/AI-Genrated-Image-Detector/app.py
DELETED
|
@@ -1,30 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
import numpy as np
|
| 3 |
-
from tensorflow.keras.models import load_model
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
model = load_model("large_model_3lakh_v1.h5")
|
| 7 |
-
|
| 8 |
-
title = '🧠 AI FORGED IMAGE DETECTOR'
|
| 9 |
-
|
| 10 |
-
description = 'THROUGH THIS APPLICATION YOU CAN INPUT AN IMAGE AND THE WEBSITE WILL TELL WHETHER THE IMAGE IS AI GENERATED OR NOT.'
|
| 11 |
-
list_num = [0, 1]
|
| 12 |
-
#0 is fake 1 is true
|
| 13 |
-
|
| 14 |
-
def closest(lst, K):
|
| 15 |
-
return lst[min(range(len(lst)), key=lambda i: abs(lst[i] - K))]
|
| 16 |
-
def hell(image):
|
| 17 |
-
pred = model.predict(np.expand_dims(image / 255, 0))
|
| 18 |
-
result = closest(list_num, pred[0])
|
| 19 |
-
if result == 0:
|
| 20 |
-
return "The image is generated by AI"
|
| 21 |
-
if result == 1:
|
| 22 |
-
return "The Image is not generated by AI"
|
| 23 |
-
|
| 24 |
-
demo = gr.Interface(fn=hell, inputs=[gr.Image(shape=(256,256))], outputs=["text"],
|
| 25 |
-
# Pass through title and description
|
| 26 |
-
title=title, description=description,
|
| 27 |
-
# Set theme and launch parameters
|
| 28 |
-
theme='finlaymacklon/boxy_violet')
|
| 29 |
-
|
| 30 |
-
demo.launch()
|
|
|
|
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|
spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/decision_maker/__init__.py
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
from agentverse.registry import Registry
|
| 2 |
-
|
| 3 |
-
decision_maker_registry = Registry(name="DecisionMakerRegistry")
|
| 4 |
-
|
| 5 |
-
from .base import BaseDecisionMaker, DummyDecisionMaker
|
| 6 |
-
from .horizontal import HorizontalDecisionMaker
|
| 7 |
-
from .vertical import VerticalDecisionMaker
|
| 8 |
-
from .dynamic import DynamicDecisionMaker
|
| 9 |
-
from .vertical_solver_first import VerticalSolverFirstDecisionMaker
|
| 10 |
-
from .concurrent import ConcurrentDecisionMaker
|
| 11 |
-
from .horizontal_tool import HorizontalToolDecisionMaker
|
| 12 |
-
from .central import CentralDecisionMaker
|
| 13 |
-
from .brainstorming import BrainstormingDecisionMaker
|
|
|
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/board/match/GetAllMatch.js
DELETED
|
@@ -1,35 +0,0 @@
|
|
| 1 |
-
import RefreshSymbolCache from './RefreshSymbolCache.js';
|
| 2 |
-
import GetMatchN from './GetMatchN.js';
|
| 3 |
-
|
| 4 |
-
const SetStruct = Phaser.Structs.Set;
|
| 5 |
-
var GetAllMatch = function () {
|
| 6 |
-
RefreshSymbolCache.call(this) // only refresh symbol cache once
|
| 7 |
-
// Get match5, match4, match3
|
| 8 |
-
var self = this;
|
| 9 |
-
var matchLines = [];
|
| 10 |
-
for (var n = 5; n >= 3; n--) {
|
| 11 |
-
GetMatchN.call(this, n, function (result, board) {
|
| 12 |
-
var newSet = new SetStruct(board.tileXYArrayToChessArray(result.tileXY, self.chessTileZ));
|
| 13 |
-
for (var i = 0, cnt = matchLines.length; i < cnt; i++) {
|
| 14 |
-
if (subSetTest(matchLines[i], newSet)) {
|
| 15 |
-
return; // not a new set
|
| 16 |
-
}
|
| 17 |
-
}
|
| 18 |
-
matchLines.push(newSet);
|
| 19 |
-
});
|
| 20 |
-
}
|
| 21 |
-
return matchLines;
|
| 22 |
-
}
|
| 23 |
-
|
| 24 |
-
var subSetTest = function (setA, setB) {
|
| 25 |
-
// Return true if setB is a subset of setA
|
| 26 |
-
var itemsA = setA.entries;
|
| 27 |
-
for (var i = 0, cnt = itemsA.length; i < cnt; i++) {
|
| 28 |
-
if (!setB.contains(itemsA[i])) {
|
| 29 |
-
return false;
|
| 30 |
-
}
|
| 31 |
-
}
|
| 32 |
-
return true;
|
| 33 |
-
};
|
| 34 |
-
|
| 35 |
-
export default GetAllMatch;
|
|
|
|
|
|
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridbuttons/GridButtons.d.ts
DELETED
|
@@ -1,101 +0,0 @@
|
|
| 1 |
-
// import * as Phaser from 'phaser';
|
| 2 |
-
import GridSizer from '../gridsizer/GridSizer';
|
| 3 |
-
import { IConfig as IConfigButtons } from '../utils/buttongroup/Buttons';
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
export default GridButtons;
|
| 7 |
-
|
| 8 |
-
declare namespace GridButtons {
|
| 9 |
-
type CreateCellContainerCallbackType = (
|
| 10 |
-
scene: Phaser.Scene,
|
| 11 |
-
x: number, y: number,
|
| 12 |
-
config: {
|
| 13 |
-
column?: number, row?: number,
|
| 14 |
-
|
| 15 |
-
align?: GridSizer.AlignTypes,
|
| 16 |
-
padding?: GridSizer.PaddingTypes,
|
| 17 |
-
expand?: boolean,
|
| 18 |
-
key?: string
|
| 19 |
-
}
|
| 20 |
-
) => Phaser.GameObjects.GameObject;
|
| 21 |
-
|
| 22 |
-
interface IConfig extends GridSizer.IConfig, IConfigButtons {
|
| 23 |
-
background?: Phaser.GameObjects.GameObject,
|
| 24 |
-
|
| 25 |
-
buttons?: Phaser.GameObjects.GameObject[][],
|
| 26 |
-
createCellContainerCallback?: CreateCellContainerCallbackType
|
| 27 |
-
}
|
| 28 |
-
}
|
| 29 |
-
|
| 30 |
-
declare class GridButtons extends GridSizer {
|
| 31 |
-
constructor(
|
| 32 |
-
scene: Phaser.Scene,
|
| 33 |
-
config?: GridButtons.IConfig
|
| 34 |
-
);
|
| 35 |
-
|
| 36 |
-
emitButtonClick(
|
| 37 |
-
index: number | Phaser.GameObjects.GameObject
|
| 38 |
-
): this;
|
| 39 |
-
|
| 40 |
-
setButtonEnable(
|
| 41 |
-
index?: number | Phaser.GameObjects.GameObject | boolean,
|
| 42 |
-
enable?: boolean
|
| 43 |
-
): this;
|
| 44 |
-
|
| 45 |
-
toggleButtonEnable(
|
| 46 |
-
index?: number | Phaser.GameObjects.GameObject
|
| 47 |
-
): this;
|
| 48 |
-
|
| 49 |
-
getButtonEnable(
|
| 50 |
-
index: number | Phaser.GameObjects.GameObject
|
| 51 |
-
): boolean;
|
| 52 |
-
|
| 53 |
-
getButton(
|
| 54 |
-
index: number
|
| 55 |
-
): Phaser.GameObjects.GameObject | null;
|
| 56 |
-
|
| 57 |
-
addButton(
|
| 58 |
-
gameObject: Phaser.GameObjects.GameObject
|
| 59 |
-
): this;
|
| 60 |
-
|
| 61 |
-
removeButton(
|
| 62 |
-
gameObject: Phaser.GameObjects.GameObject,
|
| 63 |
-
destroyChild?: boolean
|
| 64 |
-
): this;
|
| 65 |
-
|
| 66 |
-
clearButtons(
|
| 67 |
-
destroyChild?: boolean
|
| 68 |
-
): this;
|
| 69 |
-
|
| 70 |
-
showButton(
|
| 71 |
-
index: number | Phaser.GameObjects.GameObject
|
| 72 |
-
): this;
|
| 73 |
-
|
| 74 |
-
hideButton(
|
| 75 |
-
index: number | Phaser.GameObjects.GameObject
|
| 76 |
-
): this;
|
| 77 |
-
|
| 78 |
-
forEachButtton(
|
| 79 |
-
callback: (button: Phaser.GameObjects.GameObject, index: number, buttons: Phaser.GameObjects.GameObject[]) => void,
|
| 80 |
-
scop?: unknown
|
| 81 |
-
): this;
|
| 82 |
-
|
| 83 |
-
readonly buttons: Phaser.GameObjects.GameObject[];
|
| 84 |
-
|
| 85 |
-
value: unknown;
|
| 86 |
-
|
| 87 |
-
setSelectedButtonName(
|
| 88 |
-
name: string
|
| 89 |
-
): this;
|
| 90 |
-
|
| 91 |
-
getSelectedButtonName(): string;
|
| 92 |
-
|
| 93 |
-
setButtonState(
|
| 94 |
-
name: string,
|
| 95 |
-
state?: boolean
|
| 96 |
-
): this;
|
| 97 |
-
|
| 98 |
-
getButtonState(
|
| 99 |
-
name: string
|
| 100 |
-
): boolean;
|
| 101 |
-
}
|
|
|
|
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/utils/CreateChildren.js
DELETED
|
@@ -1,26 +0,0 @@
|
|
| 1 |
-
import CreateChild from './CreateChild.js';
|
| 2 |
-
|
| 3 |
-
var CreateChildren = function (scene, data, subKey, view, styles, customBuilders) {
|
| 4 |
-
var childData = data[subKey];
|
| 5 |
-
if (!childData) {
|
| 6 |
-
return undefined;
|
| 7 |
-
}
|
| 8 |
-
|
| 9 |
-
if (Array.isArray(childData)) {
|
| 10 |
-
for (var i = 0, cnt = childData.length; i < cnt; i++) {
|
| 11 |
-
if (Array.isArray(childData[i])) { // Nested array
|
| 12 |
-
CreateChildren(scene, childData, i, view, styles, customBuilders);
|
| 13 |
-
} else {
|
| 14 |
-
CreateChild(scene, childData, i, view, styles, customBuilders);
|
| 15 |
-
}
|
| 16 |
-
}
|
| 17 |
-
} else {
|
| 18 |
-
for (var key in childData) {
|
| 19 |
-
CreateChild(scene, childData, key, view, styles, customBuilders);
|
| 20 |
-
}
|
| 21 |
-
}
|
| 22 |
-
|
| 23 |
-
return childData;
|
| 24 |
-
}
|
| 25 |
-
|
| 26 |
-
export default CreateChildren;
|
|
|
|
|
|
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|
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|
spaces/Alpaca233/SadTalker/src/utils/model2safetensor.py
DELETED
|
@@ -1,141 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import yaml
|
| 3 |
-
import os
|
| 4 |
-
|
| 5 |
-
import safetensors
|
| 6 |
-
from safetensors.torch import save_file
|
| 7 |
-
from yacs.config import CfgNode as CN
|
| 8 |
-
import sys
|
| 9 |
-
|
| 10 |
-
sys.path.append('/apdcephfs/private_shadowcun/SadTalker')
|
| 11 |
-
|
| 12 |
-
from src.face3d.models import networks
|
| 13 |
-
|
| 14 |
-
from src.facerender.modules.keypoint_detector import HEEstimator, KPDetector
|
| 15 |
-
from src.facerender.modules.mapping import MappingNet
|
| 16 |
-
from src.facerender.modules.generator import OcclusionAwareGenerator, OcclusionAwareSPADEGenerator
|
| 17 |
-
|
| 18 |
-
from src.audio2pose_models.audio2pose import Audio2Pose
|
| 19 |
-
from src.audio2exp_models.networks import SimpleWrapperV2
|
| 20 |
-
from src.test_audio2coeff import load_cpk
|
| 21 |
-
|
| 22 |
-
size = 256
|
| 23 |
-
############ face vid2vid
|
| 24 |
-
config_path = os.path.join('src', 'config', 'facerender.yaml')
|
| 25 |
-
current_root_path = '.'
|
| 26 |
-
|
| 27 |
-
path_of_net_recon_model = os.path.join(current_root_path, 'checkpoints', 'epoch_20.pth')
|
| 28 |
-
net_recon = networks.define_net_recon(net_recon='resnet50', use_last_fc=False, init_path='')
|
| 29 |
-
checkpoint = torch.load(path_of_net_recon_model, map_location='cpu')
|
| 30 |
-
net_recon.load_state_dict(checkpoint['net_recon'])
|
| 31 |
-
|
| 32 |
-
with open(config_path) as f:
|
| 33 |
-
config = yaml.safe_load(f)
|
| 34 |
-
|
| 35 |
-
generator = OcclusionAwareSPADEGenerator(**config['model_params']['generator_params'],
|
| 36 |
-
**config['model_params']['common_params'])
|
| 37 |
-
kp_extractor = KPDetector(**config['model_params']['kp_detector_params'],
|
| 38 |
-
**config['model_params']['common_params'])
|
| 39 |
-
he_estimator = HEEstimator(**config['model_params']['he_estimator_params'],
|
| 40 |
-
**config['model_params']['common_params'])
|
| 41 |
-
mapping = MappingNet(**config['model_params']['mapping_params'])
|
| 42 |
-
|
| 43 |
-
def load_cpk_facevid2vid(checkpoint_path, generator=None, discriminator=None,
|
| 44 |
-
kp_detector=None, he_estimator=None, optimizer_generator=None,
|
| 45 |
-
optimizer_discriminator=None, optimizer_kp_detector=None,
|
| 46 |
-
optimizer_he_estimator=None, device="cpu"):
|
| 47 |
-
|
| 48 |
-
checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
|
| 49 |
-
if generator is not None:
|
| 50 |
-
generator.load_state_dict(checkpoint['generator'])
|
| 51 |
-
if kp_detector is not None:
|
| 52 |
-
kp_detector.load_state_dict(checkpoint['kp_detector'])
|
| 53 |
-
if he_estimator is not None:
|
| 54 |
-
he_estimator.load_state_dict(checkpoint['he_estimator'])
|
| 55 |
-
if discriminator is not None:
|
| 56 |
-
try:
|
| 57 |
-
discriminator.load_state_dict(checkpoint['discriminator'])
|
| 58 |
-
except:
|
| 59 |
-
print ('No discriminator in the state-dict. Dicriminator will be randomly initialized')
|
| 60 |
-
if optimizer_generator is not None:
|
| 61 |
-
optimizer_generator.load_state_dict(checkpoint['optimizer_generator'])
|
| 62 |
-
if optimizer_discriminator is not None:
|
| 63 |
-
try:
|
| 64 |
-
optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
|
| 65 |
-
except RuntimeError as e:
|
| 66 |
-
print ('No discriminator optimizer in the state-dict. Optimizer will be not initialized')
|
| 67 |
-
if optimizer_kp_detector is not None:
|
| 68 |
-
optimizer_kp_detector.load_state_dict(checkpoint['optimizer_kp_detector'])
|
| 69 |
-
if optimizer_he_estimator is not None:
|
| 70 |
-
optimizer_he_estimator.load_state_dict(checkpoint['optimizer_he_estimator'])
|
| 71 |
-
|
| 72 |
-
return checkpoint['epoch']
|
| 73 |
-
|
| 74 |
-
|
| 75 |
-
def load_cpk_facevid2vid_safetensor(checkpoint_path, generator=None,
|
| 76 |
-
kp_detector=None, he_estimator=None,
|
| 77 |
-
device="cpu"):
|
| 78 |
-
|
| 79 |
-
checkpoint = safetensors.torch.load_file(checkpoint_path)
|
| 80 |
-
|
| 81 |
-
if generator is not None:
|
| 82 |
-
x_generator = {}
|
| 83 |
-
for k,v in checkpoint.items():
|
| 84 |
-
if 'generator' in k:
|
| 85 |
-
x_generator[k.replace('generator.', '')] = v
|
| 86 |
-
generator.load_state_dict(x_generator)
|
| 87 |
-
if kp_detector is not None:
|
| 88 |
-
x_generator = {}
|
| 89 |
-
for k,v in checkpoint.items():
|
| 90 |
-
if 'kp_extractor' in k:
|
| 91 |
-
x_generator[k.replace('kp_extractor.', '')] = v
|
| 92 |
-
kp_detector.load_state_dict(x_generator)
|
| 93 |
-
if he_estimator is not None:
|
| 94 |
-
x_generator = {}
|
| 95 |
-
for k,v in checkpoint.items():
|
| 96 |
-
if 'he_estimator' in k:
|
| 97 |
-
x_generator[k.replace('he_estimator.', '')] = v
|
| 98 |
-
he_estimator.load_state_dict(x_generator)
|
| 99 |
-
|
| 100 |
-
return None
|
| 101 |
-
|
| 102 |
-
free_view_checkpoint = '/apdcephfs/private_shadowcun/SadTalker/checkpoints/facevid2vid_'+str(size)+'-model.pth.tar'
|
| 103 |
-
load_cpk_facevid2vid(free_view_checkpoint, kp_detector=kp_extractor, generator=generator, he_estimator=he_estimator)
|
| 104 |
-
|
| 105 |
-
wav2lip_checkpoint = os.path.join(current_root_path, 'checkpoints', 'wav2lip.pth')
|
| 106 |
-
|
| 107 |
-
audio2pose_checkpoint = os.path.join(current_root_path, 'checkpoints', 'auido2pose_00140-model.pth')
|
| 108 |
-
audio2pose_yaml_path = os.path.join(current_root_path, 'src', 'config', 'auido2pose.yaml')
|
| 109 |
-
|
| 110 |
-
audio2exp_checkpoint = os.path.join(current_root_path, 'checkpoints', 'auido2exp_00300-model.pth')
|
| 111 |
-
audio2exp_yaml_path = os.path.join(current_root_path, 'src', 'config', 'auido2exp.yaml')
|
| 112 |
-
|
| 113 |
-
fcfg_pose = open(audio2pose_yaml_path)
|
| 114 |
-
cfg_pose = CN.load_cfg(fcfg_pose)
|
| 115 |
-
cfg_pose.freeze()
|
| 116 |
-
audio2pose_model = Audio2Pose(cfg_pose, wav2lip_checkpoint)
|
| 117 |
-
audio2pose_model.eval()
|
| 118 |
-
load_cpk(audio2pose_checkpoint, model=audio2pose_model, device='cpu')
|
| 119 |
-
|
| 120 |
-
# load audio2exp_model
|
| 121 |
-
netG = SimpleWrapperV2()
|
| 122 |
-
netG.eval()
|
| 123 |
-
load_cpk(audio2exp_checkpoint, model=netG, device='cpu')
|
| 124 |
-
|
| 125 |
-
class SadTalker(torch.nn.Module):
|
| 126 |
-
def __init__(self, kp_extractor, generator, netG, audio2pose, face_3drecon):
|
| 127 |
-
super(SadTalker, self).__init__()
|
| 128 |
-
self.kp_extractor = kp_extractor
|
| 129 |
-
self.generator = generator
|
| 130 |
-
self.audio2exp = netG
|
| 131 |
-
self.audio2pose = audio2pose
|
| 132 |
-
self.face_3drecon = face_3drecon
|
| 133 |
-
|
| 134 |
-
|
| 135 |
-
model = SadTalker(kp_extractor, generator, netG, audio2pose_model, net_recon)
|
| 136 |
-
|
| 137 |
-
# here, we want to convert it to safetensor
|
| 138 |
-
save_file(model.state_dict(), "checkpoints/SadTalker_V0.0.2_"+str(size)+".safetensors")
|
| 139 |
-
|
| 140 |
-
### test
|
| 141 |
-
load_cpk_facevid2vid_safetensor('checkpoints/SadTalker_V0.0.2_'+str(size)+'.safetensors', kp_detector=kp_extractor, generator=generator, he_estimator=None)
|
|
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|
spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/cpp/cppipc/queue.h
DELETED
|
@@ -1,216 +0,0 @@
|
|
| 1 |
-
#pragma once
|
| 2 |
-
|
| 3 |
-
#include <type_traits>
|
| 4 |
-
#include <new>
|
| 5 |
-
#include <utility> // [[since C++14]]: std::exchange
|
| 6 |
-
#include <algorithm>
|
| 7 |
-
#include <atomic>
|
| 8 |
-
#include <tuple>
|
| 9 |
-
#include <thread>
|
| 10 |
-
#include <chrono>
|
| 11 |
-
#include <string>
|
| 12 |
-
#include <cassert> // assert
|
| 13 |
-
|
| 14 |
-
#include "libipc/def.h"
|
| 15 |
-
#include "libipc/shm.h"
|
| 16 |
-
#include "libipc/rw_lock.h"
|
| 17 |
-
|
| 18 |
-
#include "libipc/utility/log.h"
|
| 19 |
-
#include "libipc/platform/detail.h"
|
| 20 |
-
#include "libipc/circ/elem_def.h"
|
| 21 |
-
|
| 22 |
-
namespace ipc {
|
| 23 |
-
namespace detail {
|
| 24 |
-
|
| 25 |
-
class queue_conn {
|
| 26 |
-
protected:
|
| 27 |
-
circ::cc_t connected_ = 0;
|
| 28 |
-
shm::handle elems_h_;
|
| 29 |
-
|
| 30 |
-
template <typename Elems>
|
| 31 |
-
Elems* open(char const * name) {
|
| 32 |
-
if (name == nullptr || name[0] == '\0') {
|
| 33 |
-
ipc::error("fail open waiter: name is empty!\n");
|
| 34 |
-
return nullptr;
|
| 35 |
-
}
|
| 36 |
-
if (!elems_h_.acquire(name, sizeof(Elems))) {
|
| 37 |
-
return nullptr;
|
| 38 |
-
}
|
| 39 |
-
auto elems = static_cast<Elems*>(elems_h_.get());
|
| 40 |
-
if (elems == nullptr) {
|
| 41 |
-
ipc::error("fail acquire elems: %s\n", name);
|
| 42 |
-
return nullptr;
|
| 43 |
-
}
|
| 44 |
-
elems->init();
|
| 45 |
-
return elems;
|
| 46 |
-
}
|
| 47 |
-
|
| 48 |
-
void close() {
|
| 49 |
-
elems_h_.release();
|
| 50 |
-
}
|
| 51 |
-
|
| 52 |
-
public:
|
| 53 |
-
queue_conn() = default;
|
| 54 |
-
queue_conn(const queue_conn&) = delete;
|
| 55 |
-
queue_conn& operator=(const queue_conn&) = delete;
|
| 56 |
-
|
| 57 |
-
bool connected() const noexcept {
|
| 58 |
-
return connected_ != 0;
|
| 59 |
-
}
|
| 60 |
-
|
| 61 |
-
circ::cc_t connected_id() const noexcept {
|
| 62 |
-
return connected_;
|
| 63 |
-
}
|
| 64 |
-
|
| 65 |
-
template <typename Elems>
|
| 66 |
-
auto connect(Elems* elems) noexcept
|
| 67 |
-
/*needs 'optional' here*/
|
| 68 |
-
-> std::tuple<bool, bool, decltype(std::declval<Elems>().cursor())> {
|
| 69 |
-
if (elems == nullptr) return {};
|
| 70 |
-
// if it's already connected, just return
|
| 71 |
-
if (connected()) return {connected(), false, 0};
|
| 72 |
-
connected_ = elems->connect_receiver();
|
| 73 |
-
return {connected(), true, elems->cursor()};
|
| 74 |
-
}
|
| 75 |
-
|
| 76 |
-
template <typename Elems>
|
| 77 |
-
bool disconnect(Elems* elems) noexcept {
|
| 78 |
-
if (elems == nullptr) return false;
|
| 79 |
-
// if it's already disconnected, just return false
|
| 80 |
-
if (!connected()) return false;
|
| 81 |
-
elems->disconnect_receiver(std::exchange(connected_, 0));
|
| 82 |
-
return true;
|
| 83 |
-
}
|
| 84 |
-
};
|
| 85 |
-
|
| 86 |
-
template <typename Elems>
|
| 87 |
-
class queue_base : public queue_conn {
|
| 88 |
-
using base_t = queue_conn;
|
| 89 |
-
|
| 90 |
-
public:
|
| 91 |
-
using elems_t = Elems;
|
| 92 |
-
using policy_t = typename elems_t::policy_t;
|
| 93 |
-
|
| 94 |
-
protected:
|
| 95 |
-
elems_t * elems_ = nullptr;
|
| 96 |
-
decltype(std::declval<elems_t>().cursor()) cursor_ = 0;
|
| 97 |
-
bool sender_flag_ = false;
|
| 98 |
-
|
| 99 |
-
public:
|
| 100 |
-
using base_t::base_t;
|
| 101 |
-
|
| 102 |
-
queue_base() = default;
|
| 103 |
-
|
| 104 |
-
explicit queue_base(char const * name)
|
| 105 |
-
: queue_base{} {
|
| 106 |
-
elems_ = open<elems_t>(name);
|
| 107 |
-
}
|
| 108 |
-
|
| 109 |
-
explicit queue_base(elems_t * elems) noexcept
|
| 110 |
-
: queue_base{} {
|
| 111 |
-
assert(elems != nullptr);
|
| 112 |
-
elems_ = elems;
|
| 113 |
-
}
|
| 114 |
-
|
| 115 |
-
/* not virtual */ ~queue_base() {
|
| 116 |
-
base_t::close();
|
| 117 |
-
}
|
| 118 |
-
|
| 119 |
-
elems_t * elems() noexcept { return elems_; }
|
| 120 |
-
elems_t const * elems() const noexcept { return elems_; }
|
| 121 |
-
|
| 122 |
-
bool ready_sending() noexcept {
|
| 123 |
-
if (elems_ == nullptr) return false;
|
| 124 |
-
return sender_flag_ || (sender_flag_ = elems_->connect_sender());
|
| 125 |
-
}
|
| 126 |
-
|
| 127 |
-
void shut_sending() noexcept {
|
| 128 |
-
if (elems_ == nullptr) return;
|
| 129 |
-
if (!sender_flag_) return;
|
| 130 |
-
elems_->disconnect_sender();
|
| 131 |
-
}
|
| 132 |
-
|
| 133 |
-
bool connect() noexcept {
|
| 134 |
-
auto tp = base_t::connect(elems_);
|
| 135 |
-
if (std::get<0>(tp) && std::get<1>(tp)) {
|
| 136 |
-
cursor_ = std::get<2>(tp);
|
| 137 |
-
return true;
|
| 138 |
-
}
|
| 139 |
-
return std::get<0>(tp);
|
| 140 |
-
}
|
| 141 |
-
|
| 142 |
-
bool disconnect() noexcept {
|
| 143 |
-
return base_t::disconnect(elems_);
|
| 144 |
-
}
|
| 145 |
-
|
| 146 |
-
std::size_t conn_count() const noexcept {
|
| 147 |
-
return (elems_ == nullptr) ? static_cast<std::size_t>(invalid_value) : elems_->conn_count();
|
| 148 |
-
}
|
| 149 |
-
|
| 150 |
-
bool valid() const noexcept {
|
| 151 |
-
return elems_ != nullptr;
|
| 152 |
-
}
|
| 153 |
-
|
| 154 |
-
bool empty() const noexcept {
|
| 155 |
-
return !valid() || (cursor_ == elems_->cursor());
|
| 156 |
-
}
|
| 157 |
-
|
| 158 |
-
template <typename T, typename F, typename... P>
|
| 159 |
-
bool push(F&& prep, P&&... params) {
|
| 160 |
-
if (elems_ == nullptr) return false;
|
| 161 |
-
return elems_->push(this, [&](void* p) {
|
| 162 |
-
if (prep(p)) ::new (p) T(std::forward<P>(params)...);
|
| 163 |
-
});
|
| 164 |
-
}
|
| 165 |
-
|
| 166 |
-
template <typename T, typename F, typename... P>
|
| 167 |
-
bool force_push(F&& prep, P&&... params) {
|
| 168 |
-
if (elems_ == nullptr) return false;
|
| 169 |
-
return elems_->force_push(this, [&](void* p) {
|
| 170 |
-
if (prep(p)) ::new (p) T(std::forward<P>(params)...);
|
| 171 |
-
});
|
| 172 |
-
}
|
| 173 |
-
|
| 174 |
-
template <typename T, typename F>
|
| 175 |
-
bool pop(T& item, F&& out) {
|
| 176 |
-
if (elems_ == nullptr) {
|
| 177 |
-
return false;
|
| 178 |
-
}
|
| 179 |
-
return elems_->pop(this, &(this->cursor_), [&item](void* p) {
|
| 180 |
-
::new (&item) T(std::move(*static_cast<T*>(p)));
|
| 181 |
-
}, std::forward<F>(out));
|
| 182 |
-
}
|
| 183 |
-
};
|
| 184 |
-
|
| 185 |
-
} // namespace detail
|
| 186 |
-
|
| 187 |
-
template <typename T, typename Policy>
|
| 188 |
-
class queue final : public detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>> {
|
| 189 |
-
using base_t = detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>>;
|
| 190 |
-
|
| 191 |
-
public:
|
| 192 |
-
using value_t = T;
|
| 193 |
-
|
| 194 |
-
using base_t::base_t;
|
| 195 |
-
|
| 196 |
-
template <typename... P>
|
| 197 |
-
bool push(P&&... params) {
|
| 198 |
-
return base_t::template push<T>(std::forward<P>(params)...);
|
| 199 |
-
}
|
| 200 |
-
|
| 201 |
-
template <typename... P>
|
| 202 |
-
bool force_push(P&&... params) {
|
| 203 |
-
return base_t::template force_push<T>(std::forward<P>(params)...);
|
| 204 |
-
}
|
| 205 |
-
|
| 206 |
-
bool pop(T& item) {
|
| 207 |
-
return base_t::pop(item, [](bool) {});
|
| 208 |
-
}
|
| 209 |
-
|
| 210 |
-
template <typename F>
|
| 211 |
-
bool pop(T& item, F&& out) {
|
| 212 |
-
return base_t::pop(item, std::forward<F>(out));
|
| 213 |
-
}
|
| 214 |
-
};
|
| 215 |
-
|
| 216 |
-
} // namespace ipc
|
|
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|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/pndm.md
DELETED
|
@@ -1,20 +0,0 @@
|
|
| 1 |
-
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
|
| 2 |
-
|
| 3 |
-
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
| 4 |
-
the License. You may obtain a copy of the License at
|
| 5 |
-
|
| 6 |
-
http://www.apache.org/licenses/LICENSE-2.0
|
| 7 |
-
|
| 8 |
-
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
| 9 |
-
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
| 10 |
-
specific language governing permissions and limitations under the License.
|
| 11 |
-
-->
|
| 12 |
-
|
| 13 |
-
# Pseudo numerical methods for diffusion models (PNDM)
|
| 14 |
-
|
| 15 |
-
## Overview
|
| 16 |
-
|
| 17 |
-
Original implementation can be found [here](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181).
|
| 18 |
-
|
| 19 |
-
## PNDMScheduler
|
| 20 |
-
[[autodoc]] PNDMScheduler
|
|
|
|
|
|
|
|
|
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|
|
|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/text_to_video_synthesis/__init__.py
DELETED
|
@@ -1,32 +0,0 @@
|
|
| 1 |
-
from dataclasses import dataclass
|
| 2 |
-
from typing import List, Optional, Union
|
| 3 |
-
|
| 4 |
-
import numpy as np
|
| 5 |
-
import torch
|
| 6 |
-
|
| 7 |
-
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
@dataclass
|
| 11 |
-
class TextToVideoSDPipelineOutput(BaseOutput):
|
| 12 |
-
"""
|
| 13 |
-
Output class for text-to-video pipelines.
|
| 14 |
-
|
| 15 |
-
Args:
|
| 16 |
-
frames (`List[np.ndarray]` or `torch.FloatTensor`)
|
| 17 |
-
List of denoised frames (essentially images) as NumPy arrays of shape `(height, width, num_channels)` or as
|
| 18 |
-
a `torch` tensor. The length of the list denotes the video length (the number of frames).
|
| 19 |
-
"""
|
| 20 |
-
|
| 21 |
-
frames: Union[List[np.ndarray], torch.FloatTensor]
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
try:
|
| 25 |
-
if not (is_transformers_available() and is_torch_available()):
|
| 26 |
-
raise OptionalDependencyNotAvailable()
|
| 27 |
-
except OptionalDependencyNotAvailable:
|
| 28 |
-
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
|
| 29 |
-
else:
|
| 30 |
-
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
|
| 31 |
-
from .pipeline_text_to_video_synth_img2img import VideoToVideoSDPipeline # noqa: F401
|
| 32 |
-
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
|
|
|
|
|
|
|
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|
|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/fixtures/custom_pipeline/pipeline.py
DELETED
|
@@ -1,101 +0,0 @@
|
|
| 1 |
-
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
|
| 14 |
-
# limitations under the License.
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
from typing import Optional, Tuple, Union
|
| 18 |
-
|
| 19 |
-
import torch
|
| 20 |
-
|
| 21 |
-
from diffusers import DiffusionPipeline, ImagePipelineOutput
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
class CustomLocalPipeline(DiffusionPipeline):
|
| 25 |
-
r"""
|
| 26 |
-
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
| 27 |
-
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
|
| 28 |
-
|
| 29 |
-
Parameters:
|
| 30 |
-
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
|
| 31 |
-
scheduler ([`SchedulerMixin`]):
|
| 32 |
-
A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
|
| 33 |
-
[`DDPMScheduler`], or [`DDIMScheduler`].
|
| 34 |
-
"""
|
| 35 |
-
|
| 36 |
-
def __init__(self, unet, scheduler):
|
| 37 |
-
super().__init__()
|
| 38 |
-
self.register_modules(unet=unet, scheduler=scheduler)
|
| 39 |
-
|
| 40 |
-
@torch.no_grad()
|
| 41 |
-
def __call__(
|
| 42 |
-
self,
|
| 43 |
-
batch_size: int = 1,
|
| 44 |
-
generator: Optional[torch.Generator] = None,
|
| 45 |
-
num_inference_steps: int = 50,
|
| 46 |
-
output_type: Optional[str] = "pil",
|
| 47 |
-
return_dict: bool = True,
|
| 48 |
-
**kwargs,
|
| 49 |
-
) -> Union[ImagePipelineOutput, Tuple]:
|
| 50 |
-
r"""
|
| 51 |
-
Args:
|
| 52 |
-
batch_size (`int`, *optional*, defaults to 1):
|
| 53 |
-
The number of images to generate.
|
| 54 |
-
generator (`torch.Generator`, *optional*):
|
| 55 |
-
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
|
| 56 |
-
deterministic.
|
| 57 |
-
eta (`float`, *optional*, defaults to 0.0):
|
| 58 |
-
The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
|
| 59 |
-
num_inference_steps (`int`, *optional*, defaults to 50):
|
| 60 |
-
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
| 61 |
-
expense of slower inference.
|
| 62 |
-
output_type (`str`, *optional*, defaults to `"pil"`):
|
| 63 |
-
The output format of the generate image. Choose between
|
| 64 |
-
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
| 65 |
-
return_dict (`bool`, *optional*, defaults to `True`):
|
| 66 |
-
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
|
| 67 |
-
|
| 68 |
-
Returns:
|
| 69 |
-
[`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
|
| 70 |
-
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
|
| 71 |
-
generated images.
|
| 72 |
-
"""
|
| 73 |
-
|
| 74 |
-
# Sample gaussian noise to begin loop
|
| 75 |
-
image = torch.randn(
|
| 76 |
-
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
|
| 77 |
-
generator=generator,
|
| 78 |
-
)
|
| 79 |
-
image = image.to(self.device)
|
| 80 |
-
|
| 81 |
-
# set step values
|
| 82 |
-
self.scheduler.set_timesteps(num_inference_steps)
|
| 83 |
-
|
| 84 |
-
for t in self.progress_bar(self.scheduler.timesteps):
|
| 85 |
-
# 1. predict noise model_output
|
| 86 |
-
model_output = self.unet(image, t).sample
|
| 87 |
-
|
| 88 |
-
# 2. predict previous mean of image x_t-1 and add variance depending on eta
|
| 89 |
-
# eta corresponds to η in paper and should be between [0, 1]
|
| 90 |
-
# do x_t -> x_t-1
|
| 91 |
-
image = self.scheduler.step(model_output, t, image).prev_sample
|
| 92 |
-
|
| 93 |
-
image = (image / 2 + 0.5).clamp(0, 1)
|
| 94 |
-
image = image.cpu().permute(0, 2, 3, 1).numpy()
|
| 95 |
-
if output_type == "pil":
|
| 96 |
-
image = self.numpy_to_pil(image)
|
| 97 |
-
|
| 98 |
-
if not return_dict:
|
| 99 |
-
return (image,), "This is a local test"
|
| 100 |
-
|
| 101 |
-
return ImagePipelineOutput(images=image), "This is a local test"
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|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_ddim_inverse.py
DELETED
|
@@ -1,135 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
|
| 3 |
-
from diffusers import DDIMInverseScheduler
|
| 4 |
-
|
| 5 |
-
from .test_schedulers import SchedulerCommonTest
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
class DDIMInverseSchedulerTest(SchedulerCommonTest):
|
| 9 |
-
scheduler_classes = (DDIMInverseScheduler,)
|
| 10 |
-
forward_default_kwargs = (("eta", 0.0), ("num_inference_steps", 50))
|
| 11 |
-
|
| 12 |
-
def get_scheduler_config(self, **kwargs):
|
| 13 |
-
config = {
|
| 14 |
-
"num_train_timesteps": 1000,
|
| 15 |
-
"beta_start": 0.0001,
|
| 16 |
-
"beta_end": 0.02,
|
| 17 |
-
"beta_schedule": "linear",
|
| 18 |
-
"clip_sample": True,
|
| 19 |
-
}
|
| 20 |
-
|
| 21 |
-
config.update(**kwargs)
|
| 22 |
-
return config
|
| 23 |
-
|
| 24 |
-
def full_loop(self, **config):
|
| 25 |
-
scheduler_class = self.scheduler_classes[0]
|
| 26 |
-
scheduler_config = self.get_scheduler_config(**config)
|
| 27 |
-
scheduler = scheduler_class(**scheduler_config)
|
| 28 |
-
|
| 29 |
-
num_inference_steps, eta = 10, 0.0
|
| 30 |
-
|
| 31 |
-
model = self.dummy_model()
|
| 32 |
-
sample = self.dummy_sample_deter
|
| 33 |
-
|
| 34 |
-
scheduler.set_timesteps(num_inference_steps)
|
| 35 |
-
|
| 36 |
-
for t in scheduler.timesteps:
|
| 37 |
-
residual = model(sample, t)
|
| 38 |
-
sample = scheduler.step(residual, t, sample, eta).prev_sample
|
| 39 |
-
|
| 40 |
-
return sample
|
| 41 |
-
|
| 42 |
-
def test_timesteps(self):
|
| 43 |
-
for timesteps in [100, 500, 1000]:
|
| 44 |
-
self.check_over_configs(num_train_timesteps=timesteps)
|
| 45 |
-
|
| 46 |
-
def test_steps_offset(self):
|
| 47 |
-
for steps_offset in [0, 1]:
|
| 48 |
-
self.check_over_configs(steps_offset=steps_offset)
|
| 49 |
-
|
| 50 |
-
scheduler_class = self.scheduler_classes[0]
|
| 51 |
-
scheduler_config = self.get_scheduler_config(steps_offset=1)
|
| 52 |
-
scheduler = scheduler_class(**scheduler_config)
|
| 53 |
-
scheduler.set_timesteps(5)
|
| 54 |
-
assert torch.equal(scheduler.timesteps, torch.LongTensor([-199, 1, 201, 401, 601]))
|
| 55 |
-
|
| 56 |
-
def test_betas(self):
|
| 57 |
-
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):
|
| 58 |
-
self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
|
| 59 |
-
|
| 60 |
-
def test_schedules(self):
|
| 61 |
-
for schedule in ["linear", "squaredcos_cap_v2"]:
|
| 62 |
-
self.check_over_configs(beta_schedule=schedule)
|
| 63 |
-
|
| 64 |
-
def test_prediction_type(self):
|
| 65 |
-
for prediction_type in ["epsilon", "v_prediction"]:
|
| 66 |
-
self.check_over_configs(prediction_type=prediction_type)
|
| 67 |
-
|
| 68 |
-
def test_clip_sample(self):
|
| 69 |
-
for clip_sample in [True, False]:
|
| 70 |
-
self.check_over_configs(clip_sample=clip_sample)
|
| 71 |
-
|
| 72 |
-
def test_timestep_spacing(self):
|
| 73 |
-
for timestep_spacing in ["trailing", "leading"]:
|
| 74 |
-
self.check_over_configs(timestep_spacing=timestep_spacing)
|
| 75 |
-
|
| 76 |
-
def test_rescale_betas_zero_snr(self):
|
| 77 |
-
for rescale_betas_zero_snr in [True, False]:
|
| 78 |
-
self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)
|
| 79 |
-
|
| 80 |
-
def test_thresholding(self):
|
| 81 |
-
self.check_over_configs(thresholding=False)
|
| 82 |
-
for threshold in [0.5, 1.0, 2.0]:
|
| 83 |
-
for prediction_type in ["epsilon", "v_prediction"]:
|
| 84 |
-
self.check_over_configs(
|
| 85 |
-
thresholding=True,
|
| 86 |
-
prediction_type=prediction_type,
|
| 87 |
-
sample_max_value=threshold,
|
| 88 |
-
)
|
| 89 |
-
|
| 90 |
-
def test_time_indices(self):
|
| 91 |
-
for t in [1, 10, 49]:
|
| 92 |
-
self.check_over_forward(time_step=t)
|
| 93 |
-
|
| 94 |
-
def test_inference_steps(self):
|
| 95 |
-
for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]):
|
| 96 |
-
self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)
|
| 97 |
-
|
| 98 |
-
def test_add_noise_device(self):
|
| 99 |
-
pass
|
| 100 |
-
|
| 101 |
-
def test_full_loop_no_noise(self):
|
| 102 |
-
sample = self.full_loop()
|
| 103 |
-
|
| 104 |
-
result_sum = torch.sum(torch.abs(sample))
|
| 105 |
-
result_mean = torch.mean(torch.abs(sample))
|
| 106 |
-
|
| 107 |
-
assert abs(result_sum.item() - 509.1079) < 1e-2
|
| 108 |
-
assert abs(result_mean.item() - 0.6629) < 1e-3
|
| 109 |
-
|
| 110 |
-
def test_full_loop_with_v_prediction(self):
|
| 111 |
-
sample = self.full_loop(prediction_type="v_prediction")
|
| 112 |
-
|
| 113 |
-
result_sum = torch.sum(torch.abs(sample))
|
| 114 |
-
result_mean = torch.mean(torch.abs(sample))
|
| 115 |
-
|
| 116 |
-
assert abs(result_sum.item() - 1029.129) < 1e-2
|
| 117 |
-
assert abs(result_mean.item() - 1.3400) < 1e-3
|
| 118 |
-
|
| 119 |
-
def test_full_loop_with_set_alpha_to_one(self):
|
| 120 |
-
# We specify different beta, so that the first alpha is 0.99
|
| 121 |
-
sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)
|
| 122 |
-
result_sum = torch.sum(torch.abs(sample))
|
| 123 |
-
result_mean = torch.mean(torch.abs(sample))
|
| 124 |
-
|
| 125 |
-
assert abs(result_sum.item() - 259.8116) < 1e-2
|
| 126 |
-
assert abs(result_mean.item() - 0.3383) < 1e-3
|
| 127 |
-
|
| 128 |
-
def test_full_loop_with_no_set_alpha_to_one(self):
|
| 129 |
-
# We specify different beta, so that the first alpha is 0.99
|
| 130 |
-
sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)
|
| 131 |
-
result_sum = torch.sum(torch.abs(sample))
|
| 132 |
-
result_mean = torch.mean(torch.abs(sample))
|
| 133 |
-
|
| 134 |
-
assert abs(result_sum.item() - 239.055) < 1e-2
|
| 135 |
-
assert abs(result_mean.item() - 0.3113) < 1e-3
|
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|
spaces/Andy0409/text_generator/app.py
DELETED
|
@@ -1,11 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
from gradio.mix import Parallel
|
| 3 |
-
|
| 4 |
-
ttl="Doing magic"
|
| 5 |
-
desc="Generate now"
|
| 6 |
-
|
| 7 |
-
model1 = gr.Interface.load("huggingface/gpt2")
|
| 8 |
-
model2 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B")
|
| 9 |
-
model3 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-1.3B")
|
| 10 |
-
|
| 11 |
-
gr.Parallel(model1, model2, model3, title=ttl, description=desc).launch()
|
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|
spaces/Andy1621/uniformer_image_detection/configs/_base_/models/faster_rcnn_r50_fpn.py
DELETED
|
@@ -1,107 +0,0 @@
|
|
| 1 |
-
model = dict(
|
| 2 |
-
type='FasterRCNN',
|
| 3 |
-
pretrained='torchvision://resnet50',
|
| 4 |
-
backbone=dict(
|
| 5 |
-
type='ResNet',
|
| 6 |
-
depth=50,
|
| 7 |
-
num_stages=4,
|
| 8 |
-
out_indices=(0, 1, 2, 3),
|
| 9 |
-
frozen_stages=1,
|
| 10 |
-
norm_cfg=dict(type='BN', requires_grad=True),
|
| 11 |
-
norm_eval=True,
|
| 12 |
-
style='pytorch'),
|
| 13 |
-
neck=dict(
|
| 14 |
-
type='FPN',
|
| 15 |
-
in_channels=[256, 512, 1024, 2048],
|
| 16 |
-
out_channels=256,
|
| 17 |
-
num_outs=5),
|
| 18 |
-
rpn_head=dict(
|
| 19 |
-
type='RPNHead',
|
| 20 |
-
in_channels=256,
|
| 21 |
-
feat_channels=256,
|
| 22 |
-
anchor_generator=dict(
|
| 23 |
-
type='AnchorGenerator',
|
| 24 |
-
scales=[8],
|
| 25 |
-
ratios=[0.5, 1.0, 2.0],
|
| 26 |
-
strides=[4, 8, 16, 32, 64]),
|
| 27 |
-
bbox_coder=dict(
|
| 28 |
-
type='DeltaXYWHBBoxCoder',
|
| 29 |
-
target_means=[.0, .0, .0, .0],
|
| 30 |
-
target_stds=[1.0, 1.0, 1.0, 1.0]),
|
| 31 |
-
loss_cls=dict(
|
| 32 |
-
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
|
| 33 |
-
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
|
| 34 |
-
roi_head=dict(
|
| 35 |
-
type='StandardRoIHead',
|
| 36 |
-
bbox_roi_extractor=dict(
|
| 37 |
-
type='SingleRoIExtractor',
|
| 38 |
-
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
|
| 39 |
-
out_channels=256,
|
| 40 |
-
featmap_strides=[4, 8, 16, 32]),
|
| 41 |
-
bbox_head=dict(
|
| 42 |
-
type='Shared2FCBBoxHead',
|
| 43 |
-
in_channels=256,
|
| 44 |
-
fc_out_channels=1024,
|
| 45 |
-
roi_feat_size=7,
|
| 46 |
-
num_classes=80,
|
| 47 |
-
bbox_coder=dict(
|
| 48 |
-
type='DeltaXYWHBBoxCoder',
|
| 49 |
-
target_means=[0., 0., 0., 0.],
|
| 50 |
-
target_stds=[0.1, 0.1, 0.2, 0.2]),
|
| 51 |
-
reg_class_agnostic=False,
|
| 52 |
-
loss_cls=dict(
|
| 53 |
-
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
|
| 54 |
-
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
|
| 55 |
-
# model training and testing settings
|
| 56 |
-
train_cfg=dict(
|
| 57 |
-
rpn=dict(
|
| 58 |
-
assigner=dict(
|
| 59 |
-
type='MaxIoUAssigner',
|
| 60 |
-
pos_iou_thr=0.7,
|
| 61 |
-
neg_iou_thr=0.3,
|
| 62 |
-
min_pos_iou=0.3,
|
| 63 |
-
match_low_quality=True,
|
| 64 |
-
ignore_iof_thr=-1),
|
| 65 |
-
sampler=dict(
|
| 66 |
-
type='RandomSampler',
|
| 67 |
-
num=256,
|
| 68 |
-
pos_fraction=0.5,
|
| 69 |
-
neg_pos_ub=-1,
|
| 70 |
-
add_gt_as_proposals=False),
|
| 71 |
-
allowed_border=-1,
|
| 72 |
-
pos_weight=-1,
|
| 73 |
-
debug=False),
|
| 74 |
-
rpn_proposal=dict(
|
| 75 |
-
nms_pre=2000,
|
| 76 |
-
max_per_img=1000,
|
| 77 |
-
nms=dict(type='nms', iou_threshold=0.7),
|
| 78 |
-
min_bbox_size=0),
|
| 79 |
-
rcnn=dict(
|
| 80 |
-
assigner=dict(
|
| 81 |
-
type='MaxIoUAssigner',
|
| 82 |
-
pos_iou_thr=0.5,
|
| 83 |
-
neg_iou_thr=0.5,
|
| 84 |
-
min_pos_iou=0.5,
|
| 85 |
-
match_low_quality=False,
|
| 86 |
-
ignore_iof_thr=-1),
|
| 87 |
-
sampler=dict(
|
| 88 |
-
type='RandomSampler',
|
| 89 |
-
num=512,
|
| 90 |
-
pos_fraction=0.25,
|
| 91 |
-
neg_pos_ub=-1,
|
| 92 |
-
add_gt_as_proposals=True),
|
| 93 |
-
pos_weight=-1,
|
| 94 |
-
debug=False)),
|
| 95 |
-
test_cfg=dict(
|
| 96 |
-
rpn=dict(
|
| 97 |
-
nms_pre=1000,
|
| 98 |
-
max_per_img=1000,
|
| 99 |
-
nms=dict(type='nms', iou_threshold=0.7),
|
| 100 |
-
min_bbox_size=0),
|
| 101 |
-
rcnn=dict(
|
| 102 |
-
score_thr=0.05,
|
| 103 |
-
nms=dict(type='nms', iou_threshold=0.5),
|
| 104 |
-
max_per_img=100)
|
| 105 |
-
# soft-nms is also supported for rcnn testing
|
| 106 |
-
# e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
|
| 107 |
-
))
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spaces/Andy1621/uniformer_image_detection/mmdet/datasets/pipelines/test_time_aug.py
DELETED
|
@@ -1,119 +0,0 @@
|
|
| 1 |
-
import warnings
|
| 2 |
-
|
| 3 |
-
import mmcv
|
| 4 |
-
|
| 5 |
-
from ..builder import PIPELINES
|
| 6 |
-
from .compose import Compose
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
@PIPELINES.register_module()
|
| 10 |
-
class MultiScaleFlipAug(object):
|
| 11 |
-
"""Test-time augmentation with multiple scales and flipping.
|
| 12 |
-
|
| 13 |
-
An example configuration is as followed:
|
| 14 |
-
|
| 15 |
-
.. code-block::
|
| 16 |
-
|
| 17 |
-
img_scale=[(1333, 400), (1333, 800)],
|
| 18 |
-
flip=True,
|
| 19 |
-
transforms=[
|
| 20 |
-
dict(type='Resize', keep_ratio=True),
|
| 21 |
-
dict(type='RandomFlip'),
|
| 22 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 23 |
-
dict(type='Pad', size_divisor=32),
|
| 24 |
-
dict(type='ImageToTensor', keys=['img']),
|
| 25 |
-
dict(type='Collect', keys=['img']),
|
| 26 |
-
]
|
| 27 |
-
|
| 28 |
-
After MultiScaleFLipAug with above configuration, the results are wrapped
|
| 29 |
-
into lists of the same length as followed:
|
| 30 |
-
|
| 31 |
-
.. code-block::
|
| 32 |
-
|
| 33 |
-
dict(
|
| 34 |
-
img=[...],
|
| 35 |
-
img_shape=[...],
|
| 36 |
-
scale=[(1333, 400), (1333, 400), (1333, 800), (1333, 800)]
|
| 37 |
-
flip=[False, True, False, True]
|
| 38 |
-
...
|
| 39 |
-
)
|
| 40 |
-
|
| 41 |
-
Args:
|
| 42 |
-
transforms (list[dict]): Transforms to apply in each augmentation.
|
| 43 |
-
img_scale (tuple | list[tuple] | None): Images scales for resizing.
|
| 44 |
-
scale_factor (float | list[float] | None): Scale factors for resizing.
|
| 45 |
-
flip (bool): Whether apply flip augmentation. Default: False.
|
| 46 |
-
flip_direction (str | list[str]): Flip augmentation directions,
|
| 47 |
-
options are "horizontal" and "vertical". If flip_direction is list,
|
| 48 |
-
multiple flip augmentations will be applied.
|
| 49 |
-
It has no effect when flip == False. Default: "horizontal".
|
| 50 |
-
"""
|
| 51 |
-
|
| 52 |
-
def __init__(self,
|
| 53 |
-
transforms,
|
| 54 |
-
img_scale=None,
|
| 55 |
-
scale_factor=None,
|
| 56 |
-
flip=False,
|
| 57 |
-
flip_direction='horizontal'):
|
| 58 |
-
self.transforms = Compose(transforms)
|
| 59 |
-
assert (img_scale is None) ^ (scale_factor is None), (
|
| 60 |
-
'Must have but only one variable can be setted')
|
| 61 |
-
if img_scale is not None:
|
| 62 |
-
self.img_scale = img_scale if isinstance(img_scale,
|
| 63 |
-
list) else [img_scale]
|
| 64 |
-
self.scale_key = 'scale'
|
| 65 |
-
assert mmcv.is_list_of(self.img_scale, tuple)
|
| 66 |
-
else:
|
| 67 |
-
self.img_scale = scale_factor if isinstance(
|
| 68 |
-
scale_factor, list) else [scale_factor]
|
| 69 |
-
self.scale_key = 'scale_factor'
|
| 70 |
-
|
| 71 |
-
self.flip = flip
|
| 72 |
-
self.flip_direction = flip_direction if isinstance(
|
| 73 |
-
flip_direction, list) else [flip_direction]
|
| 74 |
-
assert mmcv.is_list_of(self.flip_direction, str)
|
| 75 |
-
if not self.flip and self.flip_direction != ['horizontal']:
|
| 76 |
-
warnings.warn(
|
| 77 |
-
'flip_direction has no effect when flip is set to False')
|
| 78 |
-
if (self.flip
|
| 79 |
-
and not any([t['type'] == 'RandomFlip' for t in transforms])):
|
| 80 |
-
warnings.warn(
|
| 81 |
-
'flip has no effect when RandomFlip is not in transforms')
|
| 82 |
-
|
| 83 |
-
def __call__(self, results):
|
| 84 |
-
"""Call function to apply test time augment transforms on results.
|
| 85 |
-
|
| 86 |
-
Args:
|
| 87 |
-
results (dict): Result dict contains the data to transform.
|
| 88 |
-
|
| 89 |
-
Returns:
|
| 90 |
-
dict[str: list]: The augmented data, where each value is wrapped
|
| 91 |
-
into a list.
|
| 92 |
-
"""
|
| 93 |
-
|
| 94 |
-
aug_data = []
|
| 95 |
-
flip_args = [(False, None)]
|
| 96 |
-
if self.flip:
|
| 97 |
-
flip_args += [(True, direction)
|
| 98 |
-
for direction in self.flip_direction]
|
| 99 |
-
for scale in self.img_scale:
|
| 100 |
-
for flip, direction in flip_args:
|
| 101 |
-
_results = results.copy()
|
| 102 |
-
_results[self.scale_key] = scale
|
| 103 |
-
_results['flip'] = flip
|
| 104 |
-
_results['flip_direction'] = direction
|
| 105 |
-
data = self.transforms(_results)
|
| 106 |
-
aug_data.append(data)
|
| 107 |
-
# list of dict to dict of list
|
| 108 |
-
aug_data_dict = {key: [] for key in aug_data[0]}
|
| 109 |
-
for data in aug_data:
|
| 110 |
-
for key, val in data.items():
|
| 111 |
-
aug_data_dict[key].append(val)
|
| 112 |
-
return aug_data_dict
|
| 113 |
-
|
| 114 |
-
def __repr__(self):
|
| 115 |
-
repr_str = self.__class__.__name__
|
| 116 |
-
repr_str += f'(transforms={self.transforms}, '
|
| 117 |
-
repr_str += f'img_scale={self.img_scale}, flip={self.flip}, '
|
| 118 |
-
repr_str += f'flip_direction={self.flip_direction})'
|
| 119 |
-
return repr_str
|
|
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spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/atss.py
DELETED
|
@@ -1,17 +0,0 @@
|
|
| 1 |
-
from ..builder import DETECTORS
|
| 2 |
-
from .single_stage import SingleStageDetector
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
@DETECTORS.register_module()
|
| 6 |
-
class ATSS(SingleStageDetector):
|
| 7 |
-
"""Implementation of `ATSS <https://arxiv.org/abs/1912.02424>`_."""
|
| 8 |
-
|
| 9 |
-
def __init__(self,
|
| 10 |
-
backbone,
|
| 11 |
-
neck,
|
| 12 |
-
bbox_head,
|
| 13 |
-
train_cfg=None,
|
| 14 |
-
test_cfg=None,
|
| 15 |
-
pretrained=None):
|
| 16 |
-
super(ATSS, self).__init__(backbone, neck, bbox_head, train_cfg,
|
| 17 |
-
test_cfg, pretrained)
|
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|
spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py
DELETED
|
@@ -1,6 +0,0 @@
|
|
| 1 |
-
_base_ = [
|
| 2 |
-
'../_base_/models/fcn_r50-d8.py', '../_base_/datasets/pascal_voc12_aug.py',
|
| 3 |
-
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
| 4 |
-
]
|
| 5 |
-
model = dict(
|
| 6 |
-
decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
|
|
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|
spaces/Arnaudding001/OpenAI_whisperLive/app-local.py
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
# Run the app with no audio file restrictions
|
| 2 |
-
from app import create_ui
|
| 3 |
-
create_ui(-1)
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Artrajz/vits-simple-api/vits/__init__.py
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
from .vits import VITS
|
|
|
|
|
|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/modeline.py
DELETED
|
@@ -1,43 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
pygments.modeline
|
| 3 |
-
~~~~~~~~~~~~~~~~~
|
| 4 |
-
|
| 5 |
-
A simple modeline parser (based on pymodeline).
|
| 6 |
-
|
| 7 |
-
:copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
|
| 8 |
-
:license: BSD, see LICENSE for details.
|
| 9 |
-
"""
|
| 10 |
-
|
| 11 |
-
import re
|
| 12 |
-
|
| 13 |
-
__all__ = ['get_filetype_from_buffer']
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
modeline_re = re.compile(r'''
|
| 17 |
-
(?: vi | vim | ex ) (?: [<=>]? \d* )? :
|
| 18 |
-
.* (?: ft | filetype | syn | syntax ) = ( [^:\s]+ )
|
| 19 |
-
''', re.VERBOSE)
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
def get_filetype_from_line(l):
|
| 23 |
-
m = modeline_re.search(l)
|
| 24 |
-
if m:
|
| 25 |
-
return m.group(1)
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
def get_filetype_from_buffer(buf, max_lines=5):
|
| 29 |
-
"""
|
| 30 |
-
Scan the buffer for modelines and return filetype if one is found.
|
| 31 |
-
"""
|
| 32 |
-
lines = buf.splitlines()
|
| 33 |
-
for l in lines[-1:-max_lines-1:-1]:
|
| 34 |
-
ret = get_filetype_from_line(l)
|
| 35 |
-
if ret:
|
| 36 |
-
return ret
|
| 37 |
-
for i in range(max_lines, -1, -1):
|
| 38 |
-
if i < len(lines):
|
| 39 |
-
ret = get_filetype_from_line(lines[i])
|
| 40 |
-
if ret:
|
| 41 |
-
return ret
|
| 42 |
-
|
| 43 |
-
return None
|
|
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spaces/Atom007/SDXL-base-9-CPU/app.py
DELETED
|
@@ -1,28 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
import torch
|
| 3 |
-
import numpy as np
|
| 4 |
-
import modin.pandas as pd
|
| 5 |
-
from PIL import Image
|
| 6 |
-
from diffusers import DiffusionPipeline
|
| 7 |
-
from huggingface_hub import login
|
| 8 |
-
import os
|
| 9 |
-
|
| 10 |
-
login(token=os.environ.get('HF_KEY'))
|
| 11 |
-
|
| 12 |
-
device = "cuda" if torch.cuda.is_available() else "cpu"
|
| 13 |
-
|
| 14 |
-
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9", add_to_git_credential=True)
|
| 15 |
-
pipe = pipe.to(device)
|
| 16 |
-
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
|
| 17 |
-
|
| 18 |
-
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9")
|
| 19 |
-
refiner = refiner.to(device)
|
| 20 |
-
refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
|
| 21 |
-
|
| 22 |
-
def genie (prompt, negative_prompt, scale, steps, seed):
|
| 23 |
-
generator = torch.Generator(device=device).manual_seed(seed)
|
| 24 |
-
int_image = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=scale, num_images_per_prompt=1, generator=generator, width=768, height=768, output_type="latent").images
|
| 25 |
-
image = refiner(prompt=prompt, image=int_image).images[0]
|
| 26 |
-
return image
|
| 27 |
-
|
| 28 |
-
gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), gr.Textbox(label='What you Do Not want the AI-model to generate.'), gr.Slider(1, 15, 10), gr.Slider(25, maximum=50, value=25, step=1), gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True)], outputs='image', title="Stable Diffusion XL base 9 CPU", description="SDXL-base-9 CPU. <b>WARNING:</b> Extremely Slow. 65seconds/Iteration. Expected time to wait 25-50 minutes for an image of 25-50 iterations respectively.", article = "Code: <a href=\"https://huggingface.co/Atom007\">Atom007</a>").launch(debug=True, max_threads=80)
|
|
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|
spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/retinanet.py
DELETED
|
@@ -1,439 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
-
import logging
|
| 3 |
-
import math
|
| 4 |
-
from typing import List, Tuple
|
| 5 |
-
import torch
|
| 6 |
-
from fvcore.nn import sigmoid_focal_loss_jit
|
| 7 |
-
from torch import Tensor, nn
|
| 8 |
-
from torch.nn import functional as F
|
| 9 |
-
|
| 10 |
-
from detectron2.config import configurable
|
| 11 |
-
from detectron2.layers import CycleBatchNormList, ShapeSpec, batched_nms, cat, get_norm
|
| 12 |
-
from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
|
| 13 |
-
from detectron2.utils.events import get_event_storage
|
| 14 |
-
|
| 15 |
-
from ..anchor_generator import build_anchor_generator
|
| 16 |
-
from ..backbone import Backbone, build_backbone
|
| 17 |
-
from ..box_regression import Box2BoxTransform, _dense_box_regression_loss
|
| 18 |
-
from ..matcher import Matcher
|
| 19 |
-
from .build import META_ARCH_REGISTRY
|
| 20 |
-
from .dense_detector import DenseDetector, permute_to_N_HWA_K # noqa
|
| 21 |
-
|
| 22 |
-
__all__ = ["RetinaNet"]
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
logger = logging.getLogger(__name__)
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
@META_ARCH_REGISTRY.register()
|
| 29 |
-
class RetinaNet(DenseDetector):
|
| 30 |
-
"""
|
| 31 |
-
Implement RetinaNet in :paper:`RetinaNet`.
|
| 32 |
-
"""
|
| 33 |
-
|
| 34 |
-
@configurable
|
| 35 |
-
def __init__(
|
| 36 |
-
self,
|
| 37 |
-
*,
|
| 38 |
-
backbone: Backbone,
|
| 39 |
-
head: nn.Module,
|
| 40 |
-
head_in_features,
|
| 41 |
-
anchor_generator,
|
| 42 |
-
box2box_transform,
|
| 43 |
-
anchor_matcher,
|
| 44 |
-
num_classes,
|
| 45 |
-
focal_loss_alpha=0.25,
|
| 46 |
-
focal_loss_gamma=2.0,
|
| 47 |
-
smooth_l1_beta=0.0,
|
| 48 |
-
box_reg_loss_type="smooth_l1",
|
| 49 |
-
test_score_thresh=0.05,
|
| 50 |
-
test_topk_candidates=1000,
|
| 51 |
-
test_nms_thresh=0.5,
|
| 52 |
-
max_detections_per_image=100,
|
| 53 |
-
pixel_mean,
|
| 54 |
-
pixel_std,
|
| 55 |
-
vis_period=0,
|
| 56 |
-
input_format="BGR",
|
| 57 |
-
):
|
| 58 |
-
"""
|
| 59 |
-
NOTE: this interface is experimental.
|
| 60 |
-
|
| 61 |
-
Args:
|
| 62 |
-
backbone: a backbone module, must follow detectron2's backbone interface
|
| 63 |
-
head (nn.Module): a module that predicts logits and regression deltas
|
| 64 |
-
for each level from a list of per-level features
|
| 65 |
-
head_in_features (Tuple[str]): Names of the input feature maps to be used in head
|
| 66 |
-
anchor_generator (nn.Module): a module that creates anchors from a
|
| 67 |
-
list of features. Usually an instance of :class:`AnchorGenerator`
|
| 68 |
-
box2box_transform (Box2BoxTransform): defines the transform from anchors boxes to
|
| 69 |
-
instance boxes
|
| 70 |
-
anchor_matcher (Matcher): label the anchors by matching them with ground truth.
|
| 71 |
-
num_classes (int): number of classes. Used to label background proposals.
|
| 72 |
-
|
| 73 |
-
# Loss parameters:
|
| 74 |
-
focal_loss_alpha (float): focal_loss_alpha
|
| 75 |
-
focal_loss_gamma (float): focal_loss_gamma
|
| 76 |
-
smooth_l1_beta (float): smooth_l1_beta
|
| 77 |
-
box_reg_loss_type (str): Options are "smooth_l1", "giou", "diou", "ciou"
|
| 78 |
-
|
| 79 |
-
# Inference parameters:
|
| 80 |
-
test_score_thresh (float): Inference cls score threshold, only anchors with
|
| 81 |
-
score > INFERENCE_TH are considered for inference (to improve speed)
|
| 82 |
-
test_topk_candidates (int): Select topk candidates before NMS
|
| 83 |
-
test_nms_thresh (float): Overlap threshold used for non-maximum suppression
|
| 84 |
-
(suppress boxes with IoU >= this threshold)
|
| 85 |
-
max_detections_per_image (int):
|
| 86 |
-
Maximum number of detections to return per image during inference
|
| 87 |
-
(100 is based on the limit established for the COCO dataset).
|
| 88 |
-
|
| 89 |
-
pixel_mean, pixel_std: see :class:`DenseDetector`.
|
| 90 |
-
"""
|
| 91 |
-
super().__init__(
|
| 92 |
-
backbone, head, head_in_features, pixel_mean=pixel_mean, pixel_std=pixel_std
|
| 93 |
-
)
|
| 94 |
-
self.num_classes = num_classes
|
| 95 |
-
|
| 96 |
-
# Anchors
|
| 97 |
-
self.anchor_generator = anchor_generator
|
| 98 |
-
self.box2box_transform = box2box_transform
|
| 99 |
-
self.anchor_matcher = anchor_matcher
|
| 100 |
-
|
| 101 |
-
# Loss parameters:
|
| 102 |
-
self.focal_loss_alpha = focal_loss_alpha
|
| 103 |
-
self.focal_loss_gamma = focal_loss_gamma
|
| 104 |
-
self.smooth_l1_beta = smooth_l1_beta
|
| 105 |
-
self.box_reg_loss_type = box_reg_loss_type
|
| 106 |
-
# Inference parameters:
|
| 107 |
-
self.test_score_thresh = test_score_thresh
|
| 108 |
-
self.test_topk_candidates = test_topk_candidates
|
| 109 |
-
self.test_nms_thresh = test_nms_thresh
|
| 110 |
-
self.max_detections_per_image = max_detections_per_image
|
| 111 |
-
# Vis parameters
|
| 112 |
-
self.vis_period = vis_period
|
| 113 |
-
self.input_format = input_format
|
| 114 |
-
|
| 115 |
-
@classmethod
|
| 116 |
-
def from_config(cls, cfg):
|
| 117 |
-
backbone = build_backbone(cfg)
|
| 118 |
-
backbone_shape = backbone.output_shape()
|
| 119 |
-
feature_shapes = [backbone_shape[f] for f in cfg.MODEL.RETINANET.IN_FEATURES]
|
| 120 |
-
head = RetinaNetHead(cfg, feature_shapes)
|
| 121 |
-
anchor_generator = build_anchor_generator(cfg, feature_shapes)
|
| 122 |
-
return {
|
| 123 |
-
"backbone": backbone,
|
| 124 |
-
"head": head,
|
| 125 |
-
"anchor_generator": anchor_generator,
|
| 126 |
-
"box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RETINANET.BBOX_REG_WEIGHTS),
|
| 127 |
-
"anchor_matcher": Matcher(
|
| 128 |
-
cfg.MODEL.RETINANET.IOU_THRESHOLDS,
|
| 129 |
-
cfg.MODEL.RETINANET.IOU_LABELS,
|
| 130 |
-
allow_low_quality_matches=True,
|
| 131 |
-
),
|
| 132 |
-
"pixel_mean": cfg.MODEL.PIXEL_MEAN,
|
| 133 |
-
"pixel_std": cfg.MODEL.PIXEL_STD,
|
| 134 |
-
"num_classes": cfg.MODEL.RETINANET.NUM_CLASSES,
|
| 135 |
-
"head_in_features": cfg.MODEL.RETINANET.IN_FEATURES,
|
| 136 |
-
# Loss parameters:
|
| 137 |
-
"focal_loss_alpha": cfg.MODEL.RETINANET.FOCAL_LOSS_ALPHA,
|
| 138 |
-
"focal_loss_gamma": cfg.MODEL.RETINANET.FOCAL_LOSS_GAMMA,
|
| 139 |
-
"smooth_l1_beta": cfg.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA,
|
| 140 |
-
"box_reg_loss_type": cfg.MODEL.RETINANET.BBOX_REG_LOSS_TYPE,
|
| 141 |
-
# Inference parameters:
|
| 142 |
-
"test_score_thresh": cfg.MODEL.RETINANET.SCORE_THRESH_TEST,
|
| 143 |
-
"test_topk_candidates": cfg.MODEL.RETINANET.TOPK_CANDIDATES_TEST,
|
| 144 |
-
"test_nms_thresh": cfg.MODEL.RETINANET.NMS_THRESH_TEST,
|
| 145 |
-
"max_detections_per_image": cfg.TEST.DETECTIONS_PER_IMAGE,
|
| 146 |
-
# Vis parameters
|
| 147 |
-
"vis_period": cfg.VIS_PERIOD,
|
| 148 |
-
"input_format": cfg.INPUT.FORMAT,
|
| 149 |
-
}
|
| 150 |
-
|
| 151 |
-
def forward_training(self, images, features, predictions, gt_instances):
|
| 152 |
-
# Transpose the Hi*Wi*A dimension to the middle:
|
| 153 |
-
pred_logits, pred_anchor_deltas = self._transpose_dense_predictions(
|
| 154 |
-
predictions, [self.num_classes, 4]
|
| 155 |
-
)
|
| 156 |
-
anchors = self.anchor_generator(features)
|
| 157 |
-
gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances)
|
| 158 |
-
return self.losses(anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes)
|
| 159 |
-
|
| 160 |
-
def losses(self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes):
|
| 161 |
-
"""
|
| 162 |
-
Args:
|
| 163 |
-
anchors (list[Boxes]): a list of #feature level Boxes
|
| 164 |
-
gt_labels, gt_boxes: see output of :meth:`RetinaNet.label_anchors`.
|
| 165 |
-
Their shapes are (N, R) and (N, R, 4), respectively, where R is
|
| 166 |
-
the total number of anchors across levels, i.e. sum(Hi x Wi x Ai)
|
| 167 |
-
pred_logits, pred_anchor_deltas: both are list[Tensor]. Each element in the
|
| 168 |
-
list corresponds to one level and has shape (N, Hi * Wi * Ai, K or 4).
|
| 169 |
-
Where K is the number of classes used in `pred_logits`.
|
| 170 |
-
|
| 171 |
-
Returns:
|
| 172 |
-
dict[str, Tensor]:
|
| 173 |
-
mapping from a named loss to a scalar tensor storing the loss.
|
| 174 |
-
Used during training only. The dict keys are: "loss_cls" and "loss_box_reg"
|
| 175 |
-
"""
|
| 176 |
-
num_images = len(gt_labels)
|
| 177 |
-
gt_labels = torch.stack(gt_labels) # (N, R)
|
| 178 |
-
|
| 179 |
-
valid_mask = gt_labels >= 0
|
| 180 |
-
pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes)
|
| 181 |
-
num_pos_anchors = pos_mask.sum().item()
|
| 182 |
-
get_event_storage().put_scalar("num_pos_anchors", num_pos_anchors / num_images)
|
| 183 |
-
normalizer = self._ema_update("loss_normalizer", max(num_pos_anchors, 1), 100)
|
| 184 |
-
|
| 185 |
-
# classification and regression loss
|
| 186 |
-
gt_labels_target = F.one_hot(gt_labels[valid_mask], num_classes=self.num_classes + 1)[
|
| 187 |
-
:, :-1
|
| 188 |
-
] # no loss for the last (background) class
|
| 189 |
-
loss_cls = sigmoid_focal_loss_jit(
|
| 190 |
-
cat(pred_logits, dim=1)[valid_mask],
|
| 191 |
-
gt_labels_target.to(pred_logits[0].dtype),
|
| 192 |
-
alpha=self.focal_loss_alpha,
|
| 193 |
-
gamma=self.focal_loss_gamma,
|
| 194 |
-
reduction="sum",
|
| 195 |
-
)
|
| 196 |
-
|
| 197 |
-
loss_box_reg = _dense_box_regression_loss(
|
| 198 |
-
anchors,
|
| 199 |
-
self.box2box_transform,
|
| 200 |
-
pred_anchor_deltas,
|
| 201 |
-
gt_boxes,
|
| 202 |
-
pos_mask,
|
| 203 |
-
box_reg_loss_type=self.box_reg_loss_type,
|
| 204 |
-
smooth_l1_beta=self.smooth_l1_beta,
|
| 205 |
-
)
|
| 206 |
-
|
| 207 |
-
return {
|
| 208 |
-
"loss_cls": loss_cls / normalizer,
|
| 209 |
-
"loss_box_reg": loss_box_reg / normalizer,
|
| 210 |
-
}
|
| 211 |
-
|
| 212 |
-
@torch.no_grad()
|
| 213 |
-
def label_anchors(self, anchors, gt_instances):
|
| 214 |
-
"""
|
| 215 |
-
Args:
|
| 216 |
-
anchors (list[Boxes]): A list of #feature level Boxes.
|
| 217 |
-
The Boxes contains anchors of this image on the specific feature level.
|
| 218 |
-
gt_instances (list[Instances]): a list of N `Instances`s. The i-th
|
| 219 |
-
`Instances` contains the ground-truth per-instance annotations
|
| 220 |
-
for the i-th input image.
|
| 221 |
-
|
| 222 |
-
Returns:
|
| 223 |
-
list[Tensor]: List of #img tensors. i-th element is a vector of labels whose length is
|
| 224 |
-
the total number of anchors across all feature maps (sum(Hi * Wi * A)).
|
| 225 |
-
Label values are in {-1, 0, ..., K}, with -1 means ignore, and K means background.
|
| 226 |
-
|
| 227 |
-
list[Tensor]: i-th element is a Rx4 tensor, where R is the total number of anchors
|
| 228 |
-
across feature maps. The values are the matched gt boxes for each anchor.
|
| 229 |
-
Values are undefined for those anchors not labeled as foreground.
|
| 230 |
-
"""
|
| 231 |
-
anchors = Boxes.cat(anchors) # Rx4
|
| 232 |
-
|
| 233 |
-
gt_labels = []
|
| 234 |
-
matched_gt_boxes = []
|
| 235 |
-
for gt_per_image in gt_instances:
|
| 236 |
-
match_quality_matrix = pairwise_iou(gt_per_image.gt_boxes, anchors)
|
| 237 |
-
matched_idxs, anchor_labels = self.anchor_matcher(match_quality_matrix)
|
| 238 |
-
del match_quality_matrix
|
| 239 |
-
|
| 240 |
-
if len(gt_per_image) > 0:
|
| 241 |
-
matched_gt_boxes_i = gt_per_image.gt_boxes.tensor[matched_idxs]
|
| 242 |
-
|
| 243 |
-
gt_labels_i = gt_per_image.gt_classes[matched_idxs]
|
| 244 |
-
# Anchors with label 0 are treated as background.
|
| 245 |
-
gt_labels_i[anchor_labels == 0] = self.num_classes
|
| 246 |
-
# Anchors with label -1 are ignored.
|
| 247 |
-
gt_labels_i[anchor_labels == -1] = -1
|
| 248 |
-
else:
|
| 249 |
-
matched_gt_boxes_i = torch.zeros_like(anchors.tensor)
|
| 250 |
-
gt_labels_i = torch.zeros_like(matched_idxs) + self.num_classes
|
| 251 |
-
|
| 252 |
-
gt_labels.append(gt_labels_i)
|
| 253 |
-
matched_gt_boxes.append(matched_gt_boxes_i)
|
| 254 |
-
|
| 255 |
-
return gt_labels, matched_gt_boxes
|
| 256 |
-
|
| 257 |
-
def forward_inference(
|
| 258 |
-
self, images: ImageList, features: List[Tensor], predictions: List[List[Tensor]]
|
| 259 |
-
):
|
| 260 |
-
pred_logits, pred_anchor_deltas = self._transpose_dense_predictions(
|
| 261 |
-
predictions, [self.num_classes, 4]
|
| 262 |
-
)
|
| 263 |
-
anchors = self.anchor_generator(features)
|
| 264 |
-
|
| 265 |
-
results: List[Instances] = []
|
| 266 |
-
for img_idx, image_size in enumerate(images.image_sizes):
|
| 267 |
-
scores_per_image = [x[img_idx].sigmoid_() for x in pred_logits]
|
| 268 |
-
deltas_per_image = [x[img_idx] for x in pred_anchor_deltas]
|
| 269 |
-
results_per_image = self.inference_single_image(
|
| 270 |
-
anchors, scores_per_image, deltas_per_image, image_size
|
| 271 |
-
)
|
| 272 |
-
results.append(results_per_image)
|
| 273 |
-
return results
|
| 274 |
-
|
| 275 |
-
def inference_single_image(
|
| 276 |
-
self,
|
| 277 |
-
anchors: List[Boxes],
|
| 278 |
-
box_cls: List[Tensor],
|
| 279 |
-
box_delta: List[Tensor],
|
| 280 |
-
image_size: Tuple[int, int],
|
| 281 |
-
):
|
| 282 |
-
"""
|
| 283 |
-
Single-image inference. Return bounding-box detection results by thresholding
|
| 284 |
-
on scores and applying non-maximum suppression (NMS).
|
| 285 |
-
|
| 286 |
-
Arguments:
|
| 287 |
-
anchors (list[Boxes]): list of #feature levels. Each entry contains
|
| 288 |
-
a Boxes object, which contains all the anchors in that feature level.
|
| 289 |
-
box_cls (list[Tensor]): list of #feature levels. Each entry contains
|
| 290 |
-
tensor of size (H x W x A, K)
|
| 291 |
-
box_delta (list[Tensor]): Same shape as 'box_cls' except that K becomes 4.
|
| 292 |
-
image_size (tuple(H, W)): a tuple of the image height and width.
|
| 293 |
-
|
| 294 |
-
Returns:
|
| 295 |
-
Same as `inference`, but for only one image.
|
| 296 |
-
"""
|
| 297 |
-
pred = self._decode_multi_level_predictions(
|
| 298 |
-
anchors,
|
| 299 |
-
box_cls,
|
| 300 |
-
box_delta,
|
| 301 |
-
self.test_score_thresh,
|
| 302 |
-
self.test_topk_candidates,
|
| 303 |
-
image_size,
|
| 304 |
-
)
|
| 305 |
-
keep = batched_nms( # per-class NMS
|
| 306 |
-
pred.pred_boxes.tensor, pred.scores, pred.pred_classes, self.test_nms_thresh
|
| 307 |
-
)
|
| 308 |
-
return pred[keep[: self.max_detections_per_image]]
|
| 309 |
-
|
| 310 |
-
|
| 311 |
-
class RetinaNetHead(nn.Module):
|
| 312 |
-
"""
|
| 313 |
-
The head used in RetinaNet for object classification and box regression.
|
| 314 |
-
It has two subnets for the two tasks, with a common structure but separate parameters.
|
| 315 |
-
"""
|
| 316 |
-
|
| 317 |
-
@configurable
|
| 318 |
-
def __init__(
|
| 319 |
-
self,
|
| 320 |
-
*,
|
| 321 |
-
input_shape: List[ShapeSpec],
|
| 322 |
-
num_classes,
|
| 323 |
-
num_anchors,
|
| 324 |
-
conv_dims: List[int],
|
| 325 |
-
norm="",
|
| 326 |
-
prior_prob=0.01,
|
| 327 |
-
):
|
| 328 |
-
"""
|
| 329 |
-
NOTE: this interface is experimental.
|
| 330 |
-
|
| 331 |
-
Args:
|
| 332 |
-
input_shape (List[ShapeSpec]): input shape
|
| 333 |
-
num_classes (int): number of classes. Used to label background proposals.
|
| 334 |
-
num_anchors (int): number of generated anchors
|
| 335 |
-
conv_dims (List[int]): dimensions for each convolution layer
|
| 336 |
-
norm (str or callable):
|
| 337 |
-
Normalization for conv layers except for the two output layers.
|
| 338 |
-
See :func:`detectron2.layers.get_norm` for supported types.
|
| 339 |
-
prior_prob (float): Prior weight for computing bias
|
| 340 |
-
"""
|
| 341 |
-
super().__init__()
|
| 342 |
-
|
| 343 |
-
self._num_features = len(input_shape)
|
| 344 |
-
if norm == "BN" or norm == "SyncBN":
|
| 345 |
-
logger.info(
|
| 346 |
-
f"Using domain-specific {norm} in RetinaNetHead with len={self._num_features}."
|
| 347 |
-
)
|
| 348 |
-
bn_class = nn.BatchNorm2d if norm == "BN" else nn.SyncBatchNorm
|
| 349 |
-
|
| 350 |
-
def norm(c):
|
| 351 |
-
return CycleBatchNormList(
|
| 352 |
-
length=self._num_features, bn_class=bn_class, num_features=c
|
| 353 |
-
)
|
| 354 |
-
|
| 355 |
-
else:
|
| 356 |
-
norm_name = str(type(get_norm(norm, 1)))
|
| 357 |
-
if "BN" in norm_name:
|
| 358 |
-
logger.warning(
|
| 359 |
-
f"Shared BatchNorm (type={norm_name}) may not work well in RetinaNetHead."
|
| 360 |
-
)
|
| 361 |
-
|
| 362 |
-
cls_subnet = []
|
| 363 |
-
bbox_subnet = []
|
| 364 |
-
for in_channels, out_channels in zip(
|
| 365 |
-
[input_shape[0].channels] + list(conv_dims), conv_dims
|
| 366 |
-
):
|
| 367 |
-
cls_subnet.append(
|
| 368 |
-
nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
| 369 |
-
)
|
| 370 |
-
if norm:
|
| 371 |
-
cls_subnet.append(get_norm(norm, out_channels))
|
| 372 |
-
cls_subnet.append(nn.ReLU())
|
| 373 |
-
bbox_subnet.append(
|
| 374 |
-
nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
| 375 |
-
)
|
| 376 |
-
if norm:
|
| 377 |
-
bbox_subnet.append(get_norm(norm, out_channels))
|
| 378 |
-
bbox_subnet.append(nn.ReLU())
|
| 379 |
-
|
| 380 |
-
self.cls_subnet = nn.Sequential(*cls_subnet)
|
| 381 |
-
self.bbox_subnet = nn.Sequential(*bbox_subnet)
|
| 382 |
-
self.cls_score = nn.Conv2d(
|
| 383 |
-
conv_dims[-1], num_anchors * num_classes, kernel_size=3, stride=1, padding=1
|
| 384 |
-
)
|
| 385 |
-
self.bbox_pred = nn.Conv2d(
|
| 386 |
-
conv_dims[-1], num_anchors * 4, kernel_size=3, stride=1, padding=1
|
| 387 |
-
)
|
| 388 |
-
|
| 389 |
-
# Initialization
|
| 390 |
-
for modules in [self.cls_subnet, self.bbox_subnet, self.cls_score, self.bbox_pred]:
|
| 391 |
-
for layer in modules.modules():
|
| 392 |
-
if isinstance(layer, nn.Conv2d):
|
| 393 |
-
torch.nn.init.normal_(layer.weight, mean=0, std=0.01)
|
| 394 |
-
torch.nn.init.constant_(layer.bias, 0)
|
| 395 |
-
|
| 396 |
-
# Use prior in model initialization to improve stability
|
| 397 |
-
bias_value = -(math.log((1 - prior_prob) / prior_prob))
|
| 398 |
-
torch.nn.init.constant_(self.cls_score.bias, bias_value)
|
| 399 |
-
|
| 400 |
-
@classmethod
|
| 401 |
-
def from_config(cls, cfg, input_shape: List[ShapeSpec]):
|
| 402 |
-
num_anchors = build_anchor_generator(cfg, input_shape).num_cell_anchors
|
| 403 |
-
assert (
|
| 404 |
-
len(set(num_anchors)) == 1
|
| 405 |
-
), "Using different number of anchors between levels is not currently supported!"
|
| 406 |
-
num_anchors = num_anchors[0]
|
| 407 |
-
|
| 408 |
-
return {
|
| 409 |
-
"input_shape": input_shape,
|
| 410 |
-
"num_classes": cfg.MODEL.RETINANET.NUM_CLASSES,
|
| 411 |
-
"conv_dims": [input_shape[0].channels] * cfg.MODEL.RETINANET.NUM_CONVS,
|
| 412 |
-
"prior_prob": cfg.MODEL.RETINANET.PRIOR_PROB,
|
| 413 |
-
"norm": cfg.MODEL.RETINANET.NORM,
|
| 414 |
-
"num_anchors": num_anchors,
|
| 415 |
-
}
|
| 416 |
-
|
| 417 |
-
def forward(self, features: List[Tensor]):
|
| 418 |
-
"""
|
| 419 |
-
Arguments:
|
| 420 |
-
features (list[Tensor]): FPN feature map tensors in high to low resolution.
|
| 421 |
-
Each tensor in the list correspond to different feature levels.
|
| 422 |
-
|
| 423 |
-
Returns:
|
| 424 |
-
logits (list[Tensor]): #lvl tensors, each has shape (N, AxK, Hi, Wi).
|
| 425 |
-
The tensor predicts the classification probability
|
| 426 |
-
at each spatial position for each of the A anchors and K object
|
| 427 |
-
classes.
|
| 428 |
-
bbox_reg (list[Tensor]): #lvl tensors, each has shape (N, Ax4, Hi, Wi).
|
| 429 |
-
The tensor predicts 4-vector (dx,dy,dw,dh) box
|
| 430 |
-
regression values for every anchor. These values are the
|
| 431 |
-
relative offset between the anchor and the ground truth box.
|
| 432 |
-
"""
|
| 433 |
-
assert len(features) == self._num_features
|
| 434 |
-
logits = []
|
| 435 |
-
bbox_reg = []
|
| 436 |
-
for feature in features:
|
| 437 |
-
logits.append(self.cls_score(self.cls_subnet(feature)))
|
| 438 |
-
bbox_reg.append(self.bbox_pred(self.bbox_subnet(feature)))
|
| 439 |
-
return logits, bbox_reg
|
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|
spaces/BWQ/Chatgpt/README.md
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Chatgpt
|
| 3 |
-
emoji: 🌍
|
| 4 |
-
colorFrom: pink
|
| 5 |
-
colorTo: purple
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.24.1
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: mit
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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spaces/BasalGanglia/stabilityai-stable-diffusion-2/app.py
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import gradio as gr
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gr.Interface.load("models/stabilityai/stable-diffusion-2").launch()
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spaces/Benson/text-generation/Examples/Artesana Y Construccin.md
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| 1 |
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| 2 |
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<h1>Arte y Construcción: Una Guía para Principiantes</h1>
|
| 3 |
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<p>¿Te gustan los juegos de construcción? ¿Quieres crear tu propia casa o castillo o la mía? ¿Quieres explorar un vasto mundo lleno de sorpresas y aventuras? Si respondiste sí a cualquiera de estas preguntas, ¡deberías probar con juegos de manualidades y construcción! </p>
|
| 4 |
-
<p>Los juegos de manualidades y construcción son un género de videojuegos que te permiten crear cualquier cosa que puedas imaginar usando varios bloques y elementos. También puede explorar diferentes mundos e interactuar con otros jugadores en línea o fuera de línea. Los juegos de manualidades y construcción son divertidos y populares porque te permiten expresar tu creatividad e imaginación en un entorno virtual. </p>
|
| 5 |
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<h2>artesanía y construcción</h2><br /><p><b><b>Download File</b> ---> <a href="https://bltlly.com/2v6JZS">https://bltlly.com/2v6JZS</a></b></p><br /><br />
|
| 6 |
-
<p>Pero ¿cuáles son los beneficios de jugar a juegos de manualidades y construcción? Bueno, aquí están algunos de ellos:</p>
|
| 7 |
-
<ul>
|
| 8 |
-
<li>Mejoran tus habilidades espaciales y de resolución de problemas. </li>
|
| 9 |
-
<li>Mejoran tu capacidad de atención y concentración. </li>
|
| 10 |
-
<li>Estimulan el cerebro y la memoria. </li>
|
| 11 |
-
<li>Fomentan tus habilidades de trabajo en equipo y comunicación. </li>
|
| 12 |
-
<li>Relajan tu mente y estado de ánimo. </li>
|
| 13 |
-
</ul>
|
| 14 |
-
<p>Así que si usted está interesado en el arte y la construcción de juegos, pero no saben por dónde empezar o cómo jugar, no se preocupe! ¡Este artículo le guiará a través de los fundamentos de los juegos de artesanía y construcción y le ayudará a construir su casa de ensueño en ningún momento! </p>
|
| 15 |
-
<h2>Cómo empezar a crear y construir</h2>
|
| 16 |
-
<h3>Elegir un juego</h3>
|
| 17 |
-
<p>Lo primero que tienes que hacer es elegir un juego que se adapte a tus preferencias e intereses. Hay muchos juegos de manualidades y construcción disponibles en diferentes plataformas, como PC, móvil, consola, etc. Algunos de los más populares son:</p>
|
| 18 |
-
<ul>
|
| 19 |
-
<li><strong>Elaboración y construcción</strong> : Este es un juego gratuito para dispositivos Android que te permite construir tu propio mundo con recursos ilimitados. También puedes explorar diferentes mapas, como ciudad, bosque, desierto, etc. También puedes personalizar tu personaje y jugar con tus amigos online. </li>
|
| 20 |
-
|
| 21 |
-
<li><strong>Minecraft</strong>: Este es el juego de artesanía y construcción más famoso y popular del mundo. Está disponible en PC, móvil, consola y otras plataformas. Puedes crear tu propio mundo con diferentes modos, como supervivencia, creatividad, aventura, etc. También puedes explorar un mundo enorme con diferentes biomas, animales, pueblos, mazmorras, etc. También puedes jugar con millones de jugadores online o offline. </li>
|
| 22 |
-
</ul>
|
| 23 |
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<p>Hay muchos otros juegos de artesanía y construcción que puedes probar, como Roblox, Terraria, Stardew Valley, etc. Cada juego tiene sus propias características y ventajas. Puedes elegir el que más te guste y se ajuste a tu presupuesto y dispositivo. </p>
|
| 24 |
-
<p>Para principiantes, recomendamos <strong>Elaboración y Construcción</strong> o <strong>Artesano: Construcción de Artesanía</strong> porque son libres y fáciles de jugar en dispositivos móviles. También tienen muchos recursos y mapas para elegir. Sin embargo, si quieres una experiencia más avanzada e inmersiva, puedes probar <strong>Minecraft</strong>, que tiene más opciones y desafíos. </p>
|
| 25 |
-
<h3>Aprender lo básico</h3>
|
| 26 |
-
<p>Una vez que hayas elegido un juego, necesitas aprender los fundamentos de cómo jugarlo. Los conceptos básicos incluyen cómo controlar a tu personaje, cómo acceder a los menús, cómo usar las herramientas y los bloques, etc. Cada juego tiene su propio tutorial o guía que puedes seguir para aprender lo básico. También puede ver algunos videos o leer algunos artículos en línea para obtener algunos consejos y trucos. </p>
|
| 27 |
-
<p>Aquí hay algunos consejos generales sobre cómo aprender los fundamentos de los juegos de artesanía y construcción:</p>
|
| 28 |
-
<p></p>
|
| 29 |
-
<ul>
|
| 30 |
-
<li>Usa la pantalla táctil o el teclado y el ratón para mover a tu personaje. También puedes usar los botones o las teclas para saltar, agacharse, volar, etc.</li>
|
| 31 |
-
<li>Utilice el menú o el inventario para acceder a sus herramientas y bloques. También puede usar los botones o teclas para cambiar entre ellos. </li>
|
| 32 |
-
<li>Usa las herramientas y los bloques para crear o destruir cualquier cosa en el mundo. También puedes usar los botones o teclas para colocarlos o romperlos. </li>
|
| 33 |
-
|
| 34 |
-
<li>Usa la ayuda o el icono de interrogación para obtener más información sobre las características y funciones del juego. </li>
|
| 35 |
-
</ul>
|
| 36 |
-
<p>Aquí hay algunas capturas de pantalla o videos que muestran cómo jugar <strong>Crafting y Building</strong, o <strong>Craftsman: Building Craft</strong> :</p> <p><img src="( 1 )" alt="Crafting y Building screenshot" width="300" height=">200">img src=( 5 )" =alt"Craftsman: Building Craft screenshot" width="300" height="200"></p>
|
| 37 |
-
<p>Como puedes ver, ambos juegos tienen gráficos y jugabilidad similares, pero tienen algunas diferencias en los mapas, elementos y modos. Puedes probar ambos juegos y ver cuál te gusta más. </p>
|
| 38 |
-
<h3>Encontrar una ubicación</h3>
|
| 39 |
-
<p>Después de haber aprendido los fundamentos del juego, es necesario encontrar una ubicación para su edificio. La ubicación es importante porque afecta el estilo, el tamaño y la forma de su edificio. También debe considerar el medio ambiente, los recursos y los peligros a su alrededor. </p>
|
| 40 |
-
<p>Cada juego tiene diferentes mundos o mapas que puedes explorar. Algunos de ellos son generados aleatoriamente, mientras que otros son pre-hechos. Algunos de ellos se basan en lugares de la vida real, mientras que otros son ficticios o de fantasía. Algunos de ellos son planos y simples, mientras que otros son complejos y diversos. </p>
|
| 41 |
-
<p>Aquí hay algunos ejemplos de diferentes biomas o terrenos que puedes encontrar en juegos de artesanía y construcción:</p>
|
| 42 |
-
<ul>
|
| 43 |
-
<li><strong>Bosque</strong>: Este es un bioma que tiene muchos árboles, hierba, flores y animales. Es un buen lugar para encontrar madera y comida. También es un lugar hermoso y tranquilo para construir su casa. </li>
|
| 44 |
-
<li><strong>Desierto</strong>: Este es un bioma que tiene mucha arena, cactus y arbustos muertos. Es un buen lugar para encontrar arenisca y vidrio. También es un lugar desafiante y caliente para construir su casa. </li>
|
| 45 |
-
<li><strong>Nieve</strong>: Este es un bioma que tiene mucha nieve, hielo y muñecos de nieve. Es un buen lugar para encontrar bolas de nieve y bloques de hielo. También es un lugar frío y festivo para construir su casa. </li>
|
| 46 |
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|
| 47 |
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<li><strong>Ocean</strong>: Este es un bioma que tiene mucha agua, coral y peces. Es un buen lugar para encontrar prismarinas y linternas de mar. También es un lugar húmedo y aventurero para construir su casa. </li>
|
| 48 |
-
</ul>
|
| 49 |
-
<p>Puedes elegir cualquier bioma o terreno que te guste para tu edificio. También puede mezclar y combinar diferentes biomas o terrenos para crear su propio mundo único. ¡El único límite es su imaginación! </p> <h2>Cómo crear y construir la casa de tus sueños</h2>
|
| 50 |
-
<h3>Planificación de su diseño</h3>
|
| 51 |
-
<p>Ahora que ha encontrado una ubicación para su edificio, debe planificar su diseño antes de comenzar a construir. Planificar su diseño le ayudará a ahorrar tiempo, recursos y esfuerzo. También le ayudará a crear una casa más hermosa y funcional. </p>
|
| 52 |
-
<p>Aquí hay algunos pasos sobre cómo planificar su diseño:</p>
|
| 53 |
-
<ol>
|
| 54 |
-
<li><strong>Dibuja tu idea</strong>: Puedes usar un papel y un lápiz o una aplicación digital para esbozar tu idea de cómo quieres que se vea tu casa. Puede dibujar la forma, el tamaño, el estilo y el color de su casa. También puede agregar algunos detalles, como ventanas, puertas, techo, etc.</li>
|
| 55 |
-
<li><strong>Mide tu espacio</strong>: Puedes usar una regla o una cinta métrica para medir tu espacio en el juego. También puede utilizar los bloques como unidad de medida. Por ejemplo, un bloque es igual a un metro. A continuación, puede calcular cuántos bloques necesita para cada parte de su casa. </li>
|
| 56 |
-
<li><strong>Elige un tema</strong>: Puedes elegir un tema para tu casa que coincida con tu personalidad y preferencia. Un tema es una idea o concepto general que guía su diseño. Por ejemplo, puede elegir un tema moderno, un tema medieval, un tema de fantasía, etc. También puede elegir un esquema de color que complemente su tema. </li>
|
| 57 |
-
</ol>
|
| 58 |
-
<p>Aquí hay algunos ejemplos de diferentes temas y esquemas de color para juegos de artesanía y construcción:</p>
|
| 59 |
-
<tabla>
|
| 60 |
-
<tr>
|
| 61 |
-
<th>Tema</th>
|
| 62 |
-
<th>Esquema de colores</th>
|
| 63 |
-
</tr>
|
| 64 |
-
<tr>
|
| 65 |
-
<td>Moderno</td>
|
| 66 |
-
<td>Blanco, negro, gris, azul, etc.</td>
|
| 67 |
-
</tr>
|
| 68 |
-
<tr>
|
| 69 |
-
|
| 70 |
-
<td>Marrón, beige, verde, rojo, etc.</td>
|
| 71 |
-
</tr>
|
| 72 |
-
<tr>
|
| 73 |
-
<td>Fantasía</td>
|
| 74 |
-
<td>Púrpura, rosa, amarillo, naranja, etc.</td>
|
| 75 |
-
</tr>
|
| 76 |
-
</tabla>
|
| 77 |
-
<h3>Recopilación de recursos</h3>
|
| 78 |
-
<p>Después de haber planeado su diseño, necesita reunir recursos para construir. Los recursos son los materiales que utilizas para crear o crear objetos y bloques en el juego. Puedes encontrar recursos en diferentes lugares del mundo del juego, como árboles, rocas, minerales, plantas, etc.</p>
|
| 79 |
-
<p>Aquí hay algunos consejos sobre cómo reunir recursos:</p>
|
| 80 |
-
<ul>
|
| 81 |
-
<li><strong>Mine them</strong>: Puede utilizar herramientas como picos, ejes, palas, etc. para extraer recursos de la tierra o el medio ambiente. También puede usar explosivos como TNT o dinamita para detonarlos. Tenga cuidado de no dañar su entorno o usted mismo. </li>
|
| 82 |
-
<li><strong>Craft them</strong>: Puedes usar recursos que hayas extraído o recopilado para crear otros recursos que necesites. Puedes usar mesas de manualidades u otros dispositivos para crear objetos y bloques en el juego. También puedes usar recetas o planos para guiarte. </li>
|
| 83 |
-
<li><strong>Guárdelos</strong>: Puede usar contenedores como cofres, barriles, cajas, etc. para almacenar sus recursos en el juego. También puede usar etiquetas o letreros para organizarlos. Puede colocar sus contenedores cerca de su sitio de construcción o en un lugar seguro. </li>
|
| 84 |
-
</ul>
|
| 85 |
-
<h3>Construyendo su estructura</h3> <p>Después de haber reunido suficientes recursos, puede comenzar a construir su estructura. Su estructura es la parte principal de su casa que define su forma y tamaño. Puede construir su estructura paso a paso, a partir de la base, luego las paredes, luego el techo, etc.</p>
|
| 86 |
-
<p>Aquí hay algunos consejos sobre cómo construir su estructura:</p>
|
| 87 |
-
<ul>
|
| 88 |
-
<li><strong>Echa los cimientos</strong>: Puedes usar bloques como piedra, ladrillo, hormigón, etc. para poner los cimientos de tu casa. También puedes usar bloques como hierba, tierra, arena, etc. para nivelar el suelo. Puedes usar una cuadrícula o un plano para guiarte. </li>
|
| 89 |
-
|
| 90 |
-
<li><strong>Construye el techo</strong>: Puedes usar bloques como madera, piedra, ladrillo, vidrio, etc. para construir el techo de tu casa. También puede utilizar bloques como escaleras, losas, vallas, etc. para crear diferentes pendientes y ángulos. También puede añadir tragaluces y chimeneas a su techo. </li>
|
| 91 |
-
</ul>
|
| 92 |
-
<p>Aquí hay algunas capturas de pantalla o videos que le muestran cómo construir su estructura en <strong>Elaboración y construcción</strong> o <strong>Craftsman: Building Craft</strong> :</p>
|
| 93 |
-
<p><img src="" alt="Crafting and Building video" width="300" height="200"><img src="" alt="Craftsman: Building Craft video" width="300" height="></p>
|
| 94 |
-
<p>Como puedes ver, ambos juegos tienen herramientas y bloques similares para construir tu estructura, pero tienen algunas diferencias en la calidad y variedad de ellos. Puedes probar ambos juegos y ver cuál prefieres. </p>
|
| 95 |
-
<h3>Decorar su casa</h3>
|
| 96 |
-
<p>Después de haber construido su estructura, puede comenzar a decorar su casa con muebles, pinturas, plantas, iluminación, etc. Decorar su casa la hará más acogedora y atractiva. También puede añadir algunos toques personales y detalles a su casa. </p>
|
| 97 |
-
<p>Aquí hay algunos consejos sobre cómo decorar su casa:</p>
|
| 98 |
-
<ul>
|
| 99 |
-
<li><strong>Añadir muebles</strong>: Puede utilizar artículos como camas, cofres, mesas, sillas, sofás, etc. para agregar muebles a su casa. También puede elaborar o encontrar algunos artículos especiales como estanterías, soportes de armadura, rocolas, etc. Puede colocar sus muebles en diferentes habitaciones de acuerdo a sus funciones. </li>
|
| 100 |
-
<li><strong>Añadir pinturas</strong>: Puede utilizar artículos como pinturas, pancartas, carteles, etc. para agregar pinturas a su casa. También puede crear o encontrar algunas pinturas personalizadas que se adapten a su tema y estilo. Puede colgar sus pinturas en las paredes o colocarlas en caballetes. </li>
|
| 101 |
-
|
| 102 |
-
<li><strong>Añadir iluminación</strong>: Puede utilizar elementos como antorchas, linternas, velas, etc. para añadir iluminación a su casa. También puede utilizar elementos como glowstone, linternas de mar, lámparas de redstone, etc. para crear diferentes colores y efectos de iluminación. Puede colocar su iluminación en las paredes o en el techo. </li>
|
| 103 |
-
</ul>
|
| 104 |
-
<p>Aquí hay algunas capturas de pantalla o videos que muestran cómo decorar su casa en <strong>Elaboración y construcción</strong> o <strong>Craftsman: Building Craft</strong> :</p>
|
| 105 |
-
<p><img src="" alt="Crafting and Building screenshot" width="300" height="200"><img src="" alt="Craftsman: Building Craft screenshot" width="300" height="></p>
|
| 106 |
-
<p>Como puedes ver, ambos juegos tienen elementos y bloques similares para decorar tu casa, pero tienen algunas diferencias en la calidad y variedad de ellos. Puedes probar ambos juegos y ver cuál te gusta más. </p>
|
| 107 |
-
<h2>Cómo explorar e interactuar con otros jugadores</h2>
|
| 108 |
-
<h3>Explorando el mundo</h3>
|
| 109 |
-
<p>Después de haber decorado su casa, puede comenzar a explorar el mundo alrededor de su casa. El mundo está lleno de sorpresas y aventuras que puedes descubrir y disfrutar. También puede encontrar nuevos recursos, elementos y bloques que puede usar para su construcción o elaboración. </p>
|
| 110 |
-
<p>Aquí hay algunos consejos sobre cómo explorar el mundo:</p>
|
| 111 |
-
<ul>
|
| 112 |
-
<li><strong>Encuentra nuevos biomas</strong>: Puedes encontrar nuevos biomas o terrenos que tienen diferentes características. También puede encontrar nuevos animales, plantas y estructuras que son únicas para cada bioma. Por ejemplo, puedes encontrar osos polares en el bioma de la nieve, pandas en el bioma de la selva, o templos en el bioma del desierto. </li>
|
| 113 |
-
<li><strong>Encuentra nuevos pueblos</strong>: Puedes encontrar nuevos pueblos que tienen diferentes tipos de aldeanos y edificios. También puede comerciar con los aldeanos, ayudarlos con sus tareas o asaltar sus pechos. Por ejemplo, puedes encontrar agricultores, herreros, bibliotecarios, etc. en los pueblos. </li>
|
| 114 |
-
|
| 115 |
-
</ul>
|
| 116 |
-
<p>Aquí hay algunas capturas de pantalla o videos que muestran cómo explorar el mundo en <strong>Elaboración y construcción</strong> o <strong>Craftsman: Building Craft</strong> :</p>
|
| 117 |
-
<p><img src="" alt="Crafting and Building video" width="300" height="200"><img src="" alt="Craftsman: Building Craft video" width="300" height="></p>
|
| 118 |
-
<p>Como puedes ver, ambos juegos tienen mundos o mapas similares para explorar, pero tienen algunas diferencias en el tamaño y la diversidad de ellos. Puedes probar ambos juegos y ver cuál te gusta más. </p>
|
| 119 |
-
<h3>Jugando con amigos</h3>
|
| 120 |
-
<p>Después de haber explorado el mundo, puede comenzar a jugar con amigos en línea o fuera de línea en el juego. Jugar con amigos hará que tu juego sea más divertido y social. También puedes cooperar o competir con tus amigos en diferentes modos y actividades del juego. </p>
|
| 121 |
-
<p>Aquí hay algunos consejos sobre cómo jugar con amigos:</p>
|
| 122 |
-
<ul>
|
| 123 |
-
<li><strong>Únete o crea un servidor o un mundo</strong>: Puedes unirte o crear un servidor o un mundo que te permita jugar con otros jugadores online o offline. También puede elegir el nombre, la contraseña, el modo y la configuración de su servidor o mundo. </li>
|
| 124 |
-
<li><strong>Elige un modo multijugador</strong>: Puedes elegir un modo multijugador que se adapte a tus preferencias y objetivos. Por ejemplo, puedes elegir un modo de supervivencia, donde tienes que reunir recursos y sobrevivir contra enemigos; un modo creativo, donde tienes recursos ilimitados y no enemigos; un modo de aventura, donde tienes que completar misiones y desafíos; etc.</li>
|
| 125 |
-
<li><strong>Elige una actividad multijugador</strong>: Puedes elegir una actividad multijugador que se adapte a tu interés y habilidad. Por ejemplo, puedes elegir una actividad de construcción, donde tienes que construir algo juntos o por separado; una actividad de lucha, donde tienes que luchar unos contra otros o contra enemigos; una actividad de carreras, donde tienes que competir contra otros o contra el tiempo; etc.</li>
|
| 126 |
-
</ul>
|
| 127 |
-
|
| 128 |
-
<p><img src="" alt="Crafting and Building screenshot" width="300" height="200"><img src="" alt="Craftsman: Building Craft screenshot" width="300" height="></p>
|
| 129 |
-
<p>Como puedes ver, ambos juegos tienen opciones y características similares para jugar con amigos, pero tienen algunas diferencias en la calidad y variedad de ellos. Puedes probar ambos juegos y ver cuál te gusta más. </p>
|
| 130 |
-
<h3>Compartir tus creaciones</h3>
|
| 131 |
-
<p>Después de haber jugado con tus amigos, puedes empezar a compartir tus creaciones con otros jugadores o con el mundo. Compartir tus creaciones hará que tu juego sea más gratificante e inspirador. También puedes recibir comentarios, sugerencias y cumplidos de otros jugadores. </p>
|
| 132 |
-
<p>Aquí hay algunos consejos sobre cómo compartir tus creaciones:</p>
|
| 133 |
-
<ul>
|
| 134 |
-
<li><strong>Toma capturas de pantalla o videos</strong>: Puedes usar la cámara o la grabadora en el juego para tomar capturas de pantalla o videos de tu casa o mundo. También puede usar aplicaciones o dispositivos externos para capturar su pantalla o grabar su voz. </li>
|
| 135 |
-
<li><strong>Edita y guarda tus archivos</strong>: Puedes usar el editor o la galería del juego para editar y guardar tus archivos. También puede usar aplicaciones o dispositivos externos para editar y guardar sus archivos. Puede agregar algunos efectos, filtros, subtítulos, etc. a sus archivos. </li>
|
| 136 |
-
<li><strong>Sube y comparte tus archivos</strong>: Puedes usar el cargador o las redes sociales en el juego para subir y compartir tus archivos. También puede utilizar plataformas externas o sitios web para subir y compartir sus archivos. Puede elegir el nombre, descripción, etiquetas, etc. de sus archivos. </li>
|
| 137 |
-
</ul>
|
| 138 |
-
<p>Aquí hay algunos ejemplos de plataformas o sitios web donde puedes compartir tus creaciones:</p>
|
| 139 |
-
<ul>
|
| 140 |
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<li><strong>YouTube</strong>: Esta es una plataforma para compartir videos donde puedes subir y ver videos de juegos de manualidades y construcción. También puede comentar, como, suscribirse, etc. en los vídeos. </li>
|
| 141 |
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| 142 |
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<li><strong>Reddit</strong>: Esta es una plataforma de discusión donde puedes publicar y leer publicaciones sobre juegos de manualidades y construcción. También puedes comentar, upvote, downvote, etc. en los posts. </li>
|
| 143 |
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</ul>
|
| 144 |
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<h2>Conclusión</h2>
|
| 145 |
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<p>Los juegos de manualidades y construcción son una excelente manera de expresar tu creatividad e imaginación en un entorno virtual. También son divertidos y populares porque te permiten explorar diferentes mundos e interactuar con otros jugadores online o offline. </p>
|
| 146 |
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<p>En este artículo, te hemos guiado a través de los fundamentos de los juegos de artesanía y construcción y te hemos ayudado a construir la casa de tus sueños en poco tiempo. También te hemos dado algunos consejos sobre cómo explorar el mundo, jugar con amigos y compartir tus creaciones con otros jugadores o con el mundo. </p>
|
| 147 |
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<p>Esperamos que hayas disfrutado de este artículo y hayas aprendido algo nuevo de él. También esperamos que hayas encontrado un juego que se adapte a tus preferencias e intereses. ¡Ahora es el momento de que pruebes a crear y construir juegos por ti mismo y ver lo divertidos que son! </p>
|
| 148 |
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<h2>Preguntas frecuentes</h2>
|
| 149 |
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<p>Aquí hay algunas preguntas frecuentes sobre juegos de artesanía y construcción:</p>
|
| 150 |
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<ol>
|
| 151 |
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<li><strong>¿Qué otros juegos de manualidades y construcción puedo probar? </strong></li>
|
| 152 |
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<p>Algunos otros juegos de manualidades y construcción que puedes probar son Roblox, Terraria, Stardew Valley, Lego Worlds, etc. Cada juego tiene sus propias características y ventajas que puedes explorar y disfrutar. </p>
|
| 153 |
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<li><strong>¿Cómo puedo mejorar mis habilidades en juegos de manualidades y construcción? </strong></li>
|
| 154 |
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<p>Puedes mejorar tus habilidades en manualidades y juegos de construcción practicando regularmente, viendo tutoriales o guías en línea, obteniendo retroalimentación de otros jugadores, uniéndote a concursos o desafíos, etc.</p>
|
| 155 |
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<li><strong>¿Cómo puedo ganar dinero con juegos de artesanía y construcción? </strong></li>
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| 157 |
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<li><strong>¿Son seguros los juegos de artesanía y construcción para los niños? </strong></li>
|
| 158 |
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<p>Los juegos de manualidades y construcción son generalmente seguros para los niños siempre y cuando sean supervisados por adultos, jueguen con los ajustes y modos apropiados, eviten contenido o interacciones inapropiadas, etc.</p>
|
| 159 |
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<li><strong>¿Los juegos de manualidades y construcción son buenos para la educación? </strong></li>
|
| 160 |
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<p>Los juegos de artesanía y construcción son buenos para la educación, ya que pueden enseñar a los niños diversas habilidades como habilidades espaciales, habilidades para resolver problemas, habilidades de creatividad, habilidades de trabajo en equipo, etc. También pueden inspirar a los niños a aprender más sobre diferentes temas, como la ciencia, historia, arte, etc.</p>
|
| 161 |
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</ol></p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Bus Simulator Ultimate Son Srm Apk Happymod.md
DELETED
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|
| 1 |
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<br />
|
| 2 |
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<h1>Bus Simulator Ultimate: El mejor juego de conducción de autobuses para Android</h1>
|
| 3 |
-
<p>¿Te encanta conducir autobuses y explorar diferentes ciudades? ¿Quieres experimentar los retos y recompensas realistas de dirigir tu propia compañía de autobuses? Si es así, entonces deberías probar Bus Simulator Ultimate, el juego de conducción de autobuses más popular y realista para dispositivos Android. </p>
|
| 4 |
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<h2>bus simulator ultimate son sürüm apk happymod</h2><br /><p><b><b>Download File</b> • <a href="https://bltlly.com/2v6JsS">https://bltlly.com/2v6JsS</a></b></p><br /><br />
|
| 5 |
-
<p>Bus Simulator Ultimate es un juego que te permite crear tu propia compañía de autobuses y operar rutas a través de varios países, como Alemania, Turquía, Italia, Francia, España y más. Puede personalizar sus autobuses, contratar conductores, administrar sus ingresos y gastos, e interactuar con los pasajeros y otros conductores. También puede conducir su autobús en diferentes condiciones climáticas, situaciones de tráfico y eventos en la carretera. </p>
|
| 6 |
-
<p>En este artículo, le diremos todo lo que necesita saber sobre Bus Simulator Ultimate, incluidas sus características, cómo descargarlo e instalarlo en su dispositivo Android usando happymod, por qué debe elegir la versión happymod y algunos consejos y trucos para jugar el juego. ¡Vamos a empezar! </p>
|
| 7 |
-
<h2>¿Qué es Bus Simulator Ultimate? </h2>
|
| 8 |
-
<p>Bus Simulator Ultimate es un juego de simulación desarrollado por Zuuks Games, un estudio de juegos turco que también creó otros juegos populares como Truck Simulator 2018: Europe y Euro Truck Driver 2018. El juego fue lanzado en agosto de 2019 y desde entonces ha sido descargado más de 50 millones de veces en Google Play Store. También ha recibido críticas positivas de jugadores y críticos, que elogiaron sus gráficos, jugabilidad y realismo. </p>
|
| 9 |
-
<p>Como su nombre indica, Bus Simulator Ultimate es un juego que simula la experiencia de conducir un autobús y gestionar una empresa de autobuses. Puede elegir entre más de 30 autobuses diferentes, cada uno con sus propias especificaciones y características. También puede diseñar sus propios autobuses cambiando sus colores, pieles, interiores y accesorios. </p>
|
| 10 |
-
|
| 11 |
-
<p>Puede conducir su autobús en entornos 3D realistas que se basan en ubicaciones reales. Verá monumentos, edificios, puentes y paisajes famosos mientras conduce. También encontrará diferentes condiciones climáticas, como lluvia, nieve, niebla y noche. Usted tendrá que seguir las reglas de tráfico y señales, evitar accidentes y violaciones, y respetar los límites de velocidad. También tendrá que interactuar con sus pasajeros y otros conductores a través de un sistema de radio. </p>
|
| 12 |
-
<p></p>
|
| 13 |
-
<h3>Características de Bus Simulator Ultimate</h3>
|
| 14 |
-
<p>Bus Simulator Ultimate es un juego que ofrece muchas características que lo hacen divertido y atractivo. Algunas de estas características son:</p>
|
| 15 |
-
<ul>
|
| 16 |
-
<li>Modo multijugador: Puedes jugar online con tus amigos u otros jugadores de todo el mundo. Puede unirse o crear un convoy y conducir juntos en la misma ruta. También puede chatear con otros jugadores utilizando la función de chat de voz o texto. </li>
|
| 17 |
-
<li>Sistema de radio: Puede escuchar varias estaciones de radio que reproducen música, noticias, deportes y más. También puede crear su propia estación de radio agregando sus canciones favoritas desde su dispositivo. </li>
|
| 18 |
-
<li>Sonidos realistas: Puede escuchar los sonidos realistas de su motor de autobús, bocina, frenos, limpiaparabrisas, puertas y más. También puede escuchar los sonidos del tráfico, el clima, los pasajeros y otros conductores. </li>
|
| 19 |
-
<li>Sistema de retroalimentación: Puede obtener retroalimentación de sus pasajeros y conductores en función de su rendimiento. Te evaluarán en diferentes aspectos, como habilidades de conducción, seguridad, comodidad, puntualidad y servicio. También puedes ver sus comentarios y sugerencias sobre cómo mejorar tu negocio. </li>
|
| 20 |
-
<li>Logros y tablas de clasificación: Puedes desbloquear varios logros completando ciertas tareas o objetivos en el juego. También puedes ver tu rango y progreso en las tablas de clasificación globales y regionales. </li>
|
| 21 |
-
</ul>
|
| 22 |
-
<h3>Cómo descargar <h3>Cómo descargar e instalar Bus Simulator Ultimate son sürüm apk happymod</h3>
|
| 23 |
-
|
| 24 |
-
<p>Descargar e instalar Bus Simulator Ultimate son sürüm apk happymod es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
|
| 25 |
-
<ol>
|
| 26 |
-
<li>Ir al sitio web oficial de happymod o la tienda de aplicaciones Uptodown y buscar Bus Simulator Ultimate.</li>
|
| 27 |
-
<li>Seleccione la última versión del juego y haga clic en el botón de descarga. Verá una ventana emergente pidiéndole que confirme la descarga. Haga clic en Aceptar.</li>
|
| 28 |
-
<li>Espere a que la descarga termine y luego abra el archivo apk. Es posible que necesite habilitar la instalación desde fuentes desconocidas en la configuración del dispositivo. </li>
|
| 29 |
-
<li>Siga las instrucciones en la pantalla e instale el juego en su dispositivo. </li>
|
| 30 |
-
<li>Iniciar el juego y disfrutar de Bus Simulator Ultimate son sürüm apk happymod! </li>
|
| 31 |
-
</ol> <h2>¿Por qué elegir Bus Simulator Ultimate son sürüm apk happymod? </h2>
|
| 32 |
-
<p>Como mencionamos anteriormente, Bus Simulator Ultimate son sürüm apk happymod es una versión modificada del juego que le da más ventajas y características que la versión original. Estas son algunas de las razones por las que debería elegir esta versión:</p>
|
| 33 |
-
<ul>
|
| 34 |
-
<li>Dinero ilimitado: Puedes obtener dinero ilimitado en el juego, que puedes usar para comprar nuevos autobuses, actualizar los existentes, contratar más conductores, expandir tus rutas y más. También puede gastar su dinero en otras cosas, como combustible, mantenimiento, impuestos y salarios. </li>
|
| 35 |
-
<li>Autobuses desbloqueados: Puede desbloquear todos los autobuses en el juego, que normalmente están disponibles solo después de completar ciertos niveles o pagar dinero real. Puede elegir entre más de 30 autobuses diferentes, cada uno con sus propias especificaciones y características. También puede personalizar sus autobuses como desee. </li>
|
| 36 |
-
<li>Compras gratis: Puedes comprar cualquier cosa en el juego sin gastar dinero. Puede comprar nuevas pieles, interiores, accesorios y más para sus autobuses. También puede comprar regalos para sus pasajeros y conductores para aumentar su satisfacción y lealtad. </li>
|
| 37 |
-
|
| 38 |
-
</ul>
|
| 39 |
-
<p>Estos son solo algunos de los beneficios de usar Bus Simulator Ultimate son sürüm apk happymod. Hay muchas más características y opciones que puedes descubrir y disfrutar jugando el juego tú mismo. </p>
|
| 40 |
-
<h3>Consejos y trucos para jugar Bus Simulator Ultimate</h3>
|
| 41 |
-
<p>Bus Simulator Ultimate es un juego que requiere habilidad, estrategia y paciencia. No se trata solo de conducir un autobús, sino también de gestionar una empresa de autobuses y satisfacer a sus clientes. Aquí hay algunos consejos y trucos que pueden ayudarte a jugar mejor:</p>
|
| 42 |
-
<ul>
|
| 43 |
-
<li>Planifique sus rutas cuidadosamente: Antes de comenzar una ruta, debe revisar el mapa y ver la distancia, el tráfico, el clima y las condiciones de la carretera. También debe considerar la demanda y las preferencias de sus pasajeros. Debe elegir una ruta que sea rentable, segura y cómoda para usted y sus clientes. </li>
|
| 44 |
-
<li>Conduzca con seguridad y sin problemas: Cuando conduzca su autobús, debe seguir las reglas de tráfico y las señales, evitar accidentes y violaciones, y respetar los límites de velocidad. También debe conducir sin problemas y evitar el frenado repentino o la aceleración. Esto mejorará sus habilidades de conducción, seguridad, comodidad, puntualidad y calificaciones de servicio. </li>
|
| 45 |
-
<li>Interactuar con sus pasajeros y conductores: Debe comunicarse con sus pasajeros y conductores a través del sistema de radio. Debe saludarlos, informarles sobre la ruta y el destino, agradecerles por elegir su empresa y disculparse por cualquier inconveniente o retraso. También debe escuchar sus comentarios y sugerencias sobre cómo mejorar su servicio. </li>
|
| 46 |
-
<li>Actualizar sus autobuses y la empresa: Usted debe invertir su dinero en la mejora de sus autobuses y la empresa. Deberías comprar nuevos autobuses, mejorar los existentes, contratar más conductores, expandir tus rutas y más. Esto aumentará sus ingresos, cuota de mercado, satisfacción del cliente y reputación. </li>
|
| 47 |
-
</ul>
|
| 48 |
-
<h2>Conclusión</h2>
|
| 49 |
-
|
| 50 |
-
<p>Si desea disfrutar de Bus Simulator Ultimate con más características y beneficios, debe descargar e instalar la versión apk happymod son sürüm. Esta es una versión modificada del juego que le da acceso a dinero ilimitado, autobuses desbloqueados, compras gratuitas y más. También puede deshacerse de los anuncios y disfrutar de descargas más rápidas con happymod. </p>
|
| 51 |
-
<p>Esperamos que este artículo le ha ayudado a aprender más acerca de Bus Simulator Ultimate son sürüm apk happymod. Si tiene alguna pregunta o comentario, por favor siéntase libre de dejarlos abajo. ¡Gracias por leer! </p>
|
| 52 |
-
<h3>Preguntas frecuentes</h3>
|
| 53 |
-
<p>Aquí están algunas de las preguntas más frecuentes sobre Bus Simulator Ultimate son sürüm apk happymod:</p>
|
| 54 |
-
<ol>
|
| 55 |
-
<li><b> ¿Es seguro usar Bus Simulator Ultimate son sürüm apk happymod? </b><br>
|
| 56 |
-
Sí, Bus Simulator Ultimate son sürüm apk happymod es seguro de usar siempre y cuando se descarga de una fuente de confianza como happymod o Uptodown. Estas fuentes escanean los archivos apk en busca de virus y malware antes de subirlos a sus sitios web. Sin embargo, siempre debe tener cuidado al descargar cualquier archivo de Internet y verificar sus permisos antes de instalarlo en su dispositivo. </ <li><b>¿Cuáles son los requisitos para jugar Bus Simulator Ultimate son sürüm apk happymod? </b><br>
|
| 57 |
-
Para jugar Bus Simulator Ultimate son sürüm apk happymod, es necesario tener un dispositivo Android que se ejecuta en Android 5.0 o superior. También necesita tener al menos 1 GB de RAM y 500 MB de espacio de almacenamiento gratuito. También es posible que necesite una conexión a Internet para jugar en línea o acceder a algunas características del juego. </li>
|
| 58 |
-
<li><b>¿Cómo puedo actualizar Bus Simulator Ultimate son sürüm apk happymod? </b><br>
|
| 59 |
-
Para actualizar Bus Simulator Ultimate son sürüm apk happymod, es necesario visitar el sitio web de happymod o Uptodown y descargar la última versión del juego. A continuación, puede instalarlo sobre la versión existente en su dispositivo. Es posible que necesite habilitar la instalación desde fuentes desconocidas en la configuración del dispositivo. </li>
|
| 60 |
-
|
| 61 |
-
Sí, se puede jugar Bus Simulator Ultimate son sürüm apk happymod en el PC con un emulador de Android. Un emulador de Android es un software que le permite ejecutar aplicaciones y juegos de Android en su PC. Algunos de los emuladores populares de Android son BlueStacks, NoxPlayer y LDPlayer. Puede descargar e instalar cualquiera de estos emuladores en su PC y luego descargar e instalar Bus Simulator Ultimate son sürüm apk happymod de happymod o Uptodown.</li>
|
| 62 |
-
<li><b>¿Puedo transferir mi progreso desde la versión original de Bus Simulator Ultimate a la versión happymod? </b><br>
|
| 63 |
-
Desafortunadamente, no. No puede transferir su progreso desde la versión original de Bus Simulator Ultimate a la versión happymod. La versión happymod es una versión modificada del juego que tiene diferentes archivos y datos que la versión original. Por lo tanto, tendrá que empezar desde cero si cambia a la versión happymod. </li>
|
| 64 |
-
</ol></p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Ciudad Smash Descarga Gratuita Apk.md
DELETED
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<br />
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| 2 |
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<br> - Dos métodos para descargar archivos APK de Google Play Store: utilizando una herramienta web o una aplicación extractora de APK. <br> - Cómo instalar archivos APK en dispositivos Android. | | H2: Cómo descargar muy pequeñas pesadillas 2 APK de Google Play Store | - Cómo utilizar APK Mirror o Evozi’s APK Downloader para generar y guardar el archivo APK para Very Little Nightmares 2. <br> - Cómo utilizar App APK Extractor & Analyzer para extraer el archivo APK de la aplicación instalada. | | H2: Cómo descargar muy pequeñas pesadillas 2 APK de tiendas de aplicaciones alternativas | - Cómo utilizar Aptoide o APKPure para encontrar y descargar el archivo APK para Very Little Nightmares 2. <br> - Cómo comprobar la seguridad y compatibilidad del archivo APK antes de instalarlo. | | H2: Conclusión y preguntas frecuentes | - Resumen de los principales puntos y consejos para descargar Muy Pequeñas Pesadillas 2 APK. <br> - Cinco preguntas frecuentes únicas con respuestas. | Tabla 2: Artículo con formato HTML <h1>Cómo descargar pesadillas muy pequeñas 2 APK</h1>
|
| 3 |
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<p>Si eres un fan de los juegos de aventura de terror, es posible que hayas oído hablar de <strong>Very Little Nightmares 2</strong>, una secuela del popular juego <em>Little Nightmares</em>. En este juego, juegas como Mono, un joven que tiene que escapar de un mundo retorcido que está controlado por una torre de señal misteriosa. En el camino, te encontrarás con muchas criaturas aterradoras y rompecabezas que pondrán a prueba tu coraje e ingenio. </p>
|
| 4 |
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<p>Very Little Nightmares 2 está disponible en Steam, PlayStation, Xbox, Nintendo Switch y Google Play Store. Sin embargo, si quieres descargar el juego como un archivo <strong>APK</strong>, necesitarás usar algunos métodos alternativos. En este artículo, le mostraremos cómo descargar Very Little Nightmares 2 APK de diferentes fuentes y cómo instalarlo en su dispositivo Android. </p>
|
| 5 |
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<h2>ciudad smash descarga gratuita apk</h2><br /><p><b><b>DOWNLOAD</b> ⇔ <a href="https://bltlly.com/2v6Lfe">https://bltlly.com/2v6Lfe</a></b></p><br /><br />
|
| 6 |
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<h2> ¿Qué es un archivo APK y cómo descargarlo</h2>
|
| 7 |
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|
| 8 |
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<p>Para descargar archivos APK, hay varios métodos. Una forma es ir a <a href="( 1 )">APK Mirror</a> en su dispositivo Android a través de Chrome u otro navegador, buscar la aplicación que desee, y toque en 'Descargar APK'. Otra forma es utilizar un <a href="( 4 )">APK downloader web</a>, como un <href=( 10 )"Evozi’s APK Downer/a, para guardar el APK en su teléfono, tableta u ordenador. También puede descargar archivos APK directamente en su dispositivo mediante una conexión a Internet y un navegador para dirigirlo a un sitio de descarga APK.</p>
|
| 9 |
-
<p>Para instalar un archivo APK en tu Android, descarga el archivo usando el navegador predeterminado, Chrome, y acepta cualquier ventana emergente. Es importante descargar solo archivos APK de fuentes confiables y hacer una búsqueda rápida en Google para verificar la reputación de la aplicación o empresa. </p>
|
| 10 |
-
<h2>Cómo descargar muy pequeñas pesadillas 2 APK de Google Play Store</h2>
|
| 11 |
-
<p>Si quieres obtener la versión oficial de Very Little Nightmares 2 de Google Play Store, puedes usar uno de estos dos métodos:</p>
|
| 12 |
-
<h3>Método 1: Usar una herramienta web</h3>
|
| 13 |
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<ol>
|
| 14 |
-
<li>Abra el <a href="( 1 )">Google Play Store</a> en su dispositivo Android o computadora y busque Very Little Nightmares 2.</li>
|
| 15 |
-
<li>Copiar la URL de la aplicación desde la barra de direcciones. </li>
|
| 16 |
-
<li>Ir a <a href="( 10 )">Evozi’s APK Downloader</a> en un navegador web y pegar la URL en el cuadro en la parte superior. </li>
|
| 17 |
-
<li>Seleccione un tipo de dispositivo en el menú desplegable "Dispositivo" y haga clic en el botón "Generar enlace de descarga". </li>
|
| 18 |
-
<li>Haga clic en el verde "Haga clic aquí para descargar Very Little Nightmares 2 APK" botón y guardar el archivo en su dispositivo u ordenador. </li>
|
| 19 |
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</ol>
|
| 20 |
-
<h3>Método 2: Usar una aplicación APK Extractor</h3>
|
| 21 |
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<ol>
|
| 22 |
-
<li>Descargar e instalar <a href="">App APK Extractor & Analyzer</a> desde la Google Play Store en su dispositivo Android. </li>
|
| 23 |
-
<li>Abra la aplicación y otorgue los permisos necesarios para acceder a sus archivos y aplicaciones. </li>
|
| 24 |
-
<li>Encuentra muy pequeñas pesadillas 2 en la lista de aplicaciones instaladas y toque en él. </li>
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| 25 |
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| 26 |
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<li>Transfiera el archivo a otro dispositivo u ordenador si lo desea. </li>
|
| 27 |
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</ol>
|
| 28 |
-
<h2>Cómo descargar muy pequeñas pesadillas 2 APK de tiendas de aplicaciones alternativas</h2>
|
| 29 |
-
<p>Si no puedes encontrar Very Little Nightmares 2 en Google Play Store, o quieres probar una versión diferente del juego, puedes usar una de estas tiendas de aplicaciones alternativas:</p>
|
| 30 |
-
<h3>Aptoide</h3>
|
| 31 |
-
<p><a href="">Aptoide</a> es una popular tienda de aplicaciones que ofrece millones de aplicaciones y juegos gratis. Puede descargar Aptoide desde su sitio web oficial o desde otras fuentes. Para descargar Very Little Nightmares 2 APK de Aptoide, sigue estos pasos:</p>
|
| 32 |
-
<ol>
|
| 33 |
-
<li>Abra Aptoide en su dispositivo Android o computadora y busque Very Little Nightmares 2.</li>
|
| 34 |
-
<li> Toque en el icono de la aplicación y leer la descripción, calificaciones y comentarios. </li>
|
| 35 |
-
<li>Toque en el botón "Instalar" y espere a que termine la descarga. </li>
|
| 36 |
-
<li>Abra el administrador de archivos en su dispositivo y busque el archivo APK descargado. </li>
|
| 37 |
-
<li>Toque en él y siga las instrucciones para instalarlo. </li>
|
| 38 |
-
</ol>
|
| 39 |
-
<h3>APKPure</h3>
|
| 40 |
-
<p><a href="">APKPure</a> es otra tienda de aplicaciones que ofrece aplicaciones y juegos gratuitos y seguros. Puede descargar APKPure desde su sitio web oficial o desde otras fuentes. Para descargar Very Little Nightmares 2 APK de APKPure, sigue estos pasos:</p>
|
| 41 |
-
<ol>
|
| 42 |
-
<li>Abra APKPure en su dispositivo Android o computadora y busque Very Little Nightmares 2.</li>
|
| 43 |
-
<li> Toque en el icono de la aplicación y leer la descripción, calificaciones y comentarios. </li>
|
| 44 |
-
<li>Toque en el botón "Descargar APK" y guarde el archivo en su dispositivo u ordenador. </li>
|
| 45 |
-
<li>Abra el administrador de archivos en su dispositivo y busque el archivo APK descargado. </li>
|
| 46 |
-
<li>Toque en él y siga las instrucciones para instalarlo. </li>
|
| 47 |
-
</ol>
|
| 48 |
-
<h2>Conclusión y preguntas frecuentes</h2>
|
| 49 |
-
<p>En este artículo, le hemos mostrado cómo descargar Very Little Nightmares 2 APK de diferentes fuentes y cómo instalarlo en su dispositivo Android. Esperamos que haya disfrutado de esta guía y la haya encontrado útil. Aquí hay algunas preguntas frecuentes que podrían ayudarlo más:</p>
|
| 50 |
-
<p></p>
|
| 51 |
-
|
| 52 |
-
<p>A: Generalmente, sí, siempre y cuando lo descargue de fuentes confiables y verifique su seguridad antes de instalarlo. Sin embargo, siempre hay un riesgo de malware o virus al descargar cualquier archivo de fuentes desconocidas, así que tenga cuidado y utilice una aplicación antivirus confiable. </p>
|
| 53 |
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<h4>Q: Es muy pequeñas pesadillas 2 APK compatible con mi dispositivo? </h4>
|
| 54 |
-
<p>A: La compatibilidad de Very Little Nightmares 2 APK depende de su modelo de dispositivo, versión de Android, y el espacio de almacenamiento disponible. Puede comprobar la compatibilidad del archivo APK leyendo su descripción, requisitos y comentarios en la tienda de aplicaciones o sitio web donde lo descargó. También puede utilizar una aplicación como <a href="">APK Analyzer</a> para comprobar los detalles del archivo APK antes de instalarlo. </p>
|
| 55 |
-
<h4>Q: ¿Cómo puedo actualizar muy pequeñas pesadillas 2 APK? </h4>
|
| 56 |
-
<p>A: Para actualizar Very Little Nightmares 2 APK, tendrá que descargar la última versión del archivo APK de la misma fuente donde lo obtuvo antes. Luego, puede desinstalar la versión anterior e instalar la nueva, o instalar la nueva sobre la anterior. Sin embargo, es posible que algunas actualizaciones no funcionen correctamente si las instalas en una versión anterior, por lo que es mejor desinstalarlas primero. </p>
|
| 57 |
-
<h4>Q: ¿Cómo puedo desinstalar muy pequeñas pesadillas 2 APK? </h4>
|
| 58 |
-
<p>A: Para desinstalar Very Little Nightmares 2 APK, puede ir a la configuración de su dispositivo > aplicaciones > Very Little Nightmares 2 > desinstalar, o utilizar una aplicación como <a href=">Easy Uninstaller</a> para eliminarlo rápida y fácilmente. </p>
|
| 59 |
-
<h4>Q: ¿Dónde puedo encontrar más información acerca de Very Little Nightmares 2 APK? </h4>
|
| 60 |
-
<p>A: Usted puede encontrar más información sobre Very Little Nightmares 2 APK visitando el sitio web oficial del juego, el blog del desarrollador, o los foros de fans. También puedes ver vídeos, trailers y reseñas de juegos en YouTube u otras plataformas. </p> 64aa2da5cf<br />
|
| 61 |
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spaces/BernardoOlisan/vqganclip/taming-transformers/taming/modules/transformer/mingpt.py
DELETED
|
@@ -1,415 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
taken from: https://github.com/karpathy/minGPT/
|
| 3 |
-
GPT model:
|
| 4 |
-
- the initial stem consists of a combination of token encoding and a positional encoding
|
| 5 |
-
- the meat of it is a uniform sequence of Transformer blocks
|
| 6 |
-
- each Transformer is a sequential combination of a 1-hidden-layer MLP block and a self-attention block
|
| 7 |
-
- all blocks feed into a central residual pathway similar to resnets
|
| 8 |
-
- the final decoder is a linear projection into a vanilla Softmax classifier
|
| 9 |
-
"""
|
| 10 |
-
|
| 11 |
-
import math
|
| 12 |
-
import logging
|
| 13 |
-
|
| 14 |
-
import torch
|
| 15 |
-
import torch.nn as nn
|
| 16 |
-
from torch.nn import functional as F
|
| 17 |
-
from transformers import top_k_top_p_filtering
|
| 18 |
-
|
| 19 |
-
logger = logging.getLogger(__name__)
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
class GPTConfig:
|
| 23 |
-
""" base GPT config, params common to all GPT versions """
|
| 24 |
-
embd_pdrop = 0.1
|
| 25 |
-
resid_pdrop = 0.1
|
| 26 |
-
attn_pdrop = 0.1
|
| 27 |
-
|
| 28 |
-
def __init__(self, vocab_size, block_size, **kwargs):
|
| 29 |
-
self.vocab_size = vocab_size
|
| 30 |
-
self.block_size = block_size
|
| 31 |
-
for k,v in kwargs.items():
|
| 32 |
-
setattr(self, k, v)
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
class GPT1Config(GPTConfig):
|
| 36 |
-
""" GPT-1 like network roughly 125M params """
|
| 37 |
-
n_layer = 12
|
| 38 |
-
n_head = 12
|
| 39 |
-
n_embd = 768
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
class CausalSelfAttention(nn.Module):
|
| 43 |
-
"""
|
| 44 |
-
A vanilla multi-head masked self-attention layer with a projection at the end.
|
| 45 |
-
It is possible to use torch.nn.MultiheadAttention here but I am including an
|
| 46 |
-
explicit implementation here to show that there is nothing too scary here.
|
| 47 |
-
"""
|
| 48 |
-
|
| 49 |
-
def __init__(self, config):
|
| 50 |
-
super().__init__()
|
| 51 |
-
assert config.n_embd % config.n_head == 0
|
| 52 |
-
# key, query, value projections for all heads
|
| 53 |
-
self.key = nn.Linear(config.n_embd, config.n_embd)
|
| 54 |
-
self.query = nn.Linear(config.n_embd, config.n_embd)
|
| 55 |
-
self.value = nn.Linear(config.n_embd, config.n_embd)
|
| 56 |
-
# regularization
|
| 57 |
-
self.attn_drop = nn.Dropout(config.attn_pdrop)
|
| 58 |
-
self.resid_drop = nn.Dropout(config.resid_pdrop)
|
| 59 |
-
# output projection
|
| 60 |
-
self.proj = nn.Linear(config.n_embd, config.n_embd)
|
| 61 |
-
# causal mask to ensure that attention is only applied to the left in the input sequence
|
| 62 |
-
mask = torch.tril(torch.ones(config.block_size,
|
| 63 |
-
config.block_size))
|
| 64 |
-
if hasattr(config, "n_unmasked"):
|
| 65 |
-
mask[:config.n_unmasked, :config.n_unmasked] = 1
|
| 66 |
-
self.register_buffer("mask", mask.view(1, 1, config.block_size, config.block_size))
|
| 67 |
-
self.n_head = config.n_head
|
| 68 |
-
|
| 69 |
-
def forward(self, x, layer_past=None):
|
| 70 |
-
B, T, C = x.size()
|
| 71 |
-
|
| 72 |
-
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
|
| 73 |
-
k = self.key(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
|
| 74 |
-
q = self.query(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
|
| 75 |
-
v = self.value(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
|
| 76 |
-
|
| 77 |
-
present = torch.stack((k, v))
|
| 78 |
-
if layer_past is not None:
|
| 79 |
-
past_key, past_value = layer_past
|
| 80 |
-
k = torch.cat((past_key, k), dim=-2)
|
| 81 |
-
v = torch.cat((past_value, v), dim=-2)
|
| 82 |
-
|
| 83 |
-
# causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
|
| 84 |
-
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
|
| 85 |
-
if layer_past is None:
|
| 86 |
-
att = att.masked_fill(self.mask[:,:,:T,:T] == 0, float('-inf'))
|
| 87 |
-
|
| 88 |
-
att = F.softmax(att, dim=-1)
|
| 89 |
-
att = self.attn_drop(att)
|
| 90 |
-
y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
|
| 91 |
-
y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
|
| 92 |
-
|
| 93 |
-
# output projection
|
| 94 |
-
y = self.resid_drop(self.proj(y))
|
| 95 |
-
return y, present # TODO: check that this does not break anything
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
class Block(nn.Module):
|
| 99 |
-
""" an unassuming Transformer block """
|
| 100 |
-
def __init__(self, config):
|
| 101 |
-
super().__init__()
|
| 102 |
-
self.ln1 = nn.LayerNorm(config.n_embd)
|
| 103 |
-
self.ln2 = nn.LayerNorm(config.n_embd)
|
| 104 |
-
self.attn = CausalSelfAttention(config)
|
| 105 |
-
self.mlp = nn.Sequential(
|
| 106 |
-
nn.Linear(config.n_embd, 4 * config.n_embd),
|
| 107 |
-
nn.GELU(), # nice
|
| 108 |
-
nn.Linear(4 * config.n_embd, config.n_embd),
|
| 109 |
-
nn.Dropout(config.resid_pdrop),
|
| 110 |
-
)
|
| 111 |
-
|
| 112 |
-
def forward(self, x, layer_past=None, return_present=False):
|
| 113 |
-
# TODO: check that training still works
|
| 114 |
-
if return_present: assert not self.training
|
| 115 |
-
# layer past: tuple of length two with B, nh, T, hs
|
| 116 |
-
attn, present = self.attn(self.ln1(x), layer_past=layer_past)
|
| 117 |
-
|
| 118 |
-
x = x + attn
|
| 119 |
-
x = x + self.mlp(self.ln2(x))
|
| 120 |
-
if layer_past is not None or return_present:
|
| 121 |
-
return x, present
|
| 122 |
-
return x
|
| 123 |
-
|
| 124 |
-
|
| 125 |
-
class GPT(nn.Module):
|
| 126 |
-
""" the full GPT language model, with a context size of block_size """
|
| 127 |
-
def __init__(self, vocab_size, block_size, n_layer=12, n_head=8, n_embd=256,
|
| 128 |
-
embd_pdrop=0., resid_pdrop=0., attn_pdrop=0., n_unmasked=0):
|
| 129 |
-
super().__init__()
|
| 130 |
-
config = GPTConfig(vocab_size=vocab_size, block_size=block_size,
|
| 131 |
-
embd_pdrop=embd_pdrop, resid_pdrop=resid_pdrop, attn_pdrop=attn_pdrop,
|
| 132 |
-
n_layer=n_layer, n_head=n_head, n_embd=n_embd,
|
| 133 |
-
n_unmasked=n_unmasked)
|
| 134 |
-
# input embedding stem
|
| 135 |
-
self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
|
| 136 |
-
self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
|
| 137 |
-
self.drop = nn.Dropout(config.embd_pdrop)
|
| 138 |
-
# transformer
|
| 139 |
-
self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
|
| 140 |
-
# decoder head
|
| 141 |
-
self.ln_f = nn.LayerNorm(config.n_embd)
|
| 142 |
-
self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
|
| 143 |
-
self.block_size = config.block_size
|
| 144 |
-
self.apply(self._init_weights)
|
| 145 |
-
self.config = config
|
| 146 |
-
logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
|
| 147 |
-
|
| 148 |
-
def get_block_size(self):
|
| 149 |
-
return self.block_size
|
| 150 |
-
|
| 151 |
-
def _init_weights(self, module):
|
| 152 |
-
if isinstance(module, (nn.Linear, nn.Embedding)):
|
| 153 |
-
module.weight.data.normal_(mean=0.0, std=0.02)
|
| 154 |
-
if isinstance(module, nn.Linear) and module.bias is not None:
|
| 155 |
-
module.bias.data.zero_()
|
| 156 |
-
elif isinstance(module, nn.LayerNorm):
|
| 157 |
-
module.bias.data.zero_()
|
| 158 |
-
module.weight.data.fill_(1.0)
|
| 159 |
-
|
| 160 |
-
def forward(self, idx, embeddings=None, targets=None):
|
| 161 |
-
# forward the GPT model
|
| 162 |
-
token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector
|
| 163 |
-
|
| 164 |
-
if embeddings is not None: # prepend explicit embeddings
|
| 165 |
-
token_embeddings = torch.cat((embeddings, token_embeddings), dim=1)
|
| 166 |
-
|
| 167 |
-
t = token_embeddings.shape[1]
|
| 168 |
-
assert t <= self.block_size, "Cannot forward, model block size is exhausted."
|
| 169 |
-
position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector
|
| 170 |
-
x = self.drop(token_embeddings + position_embeddings)
|
| 171 |
-
x = self.blocks(x)
|
| 172 |
-
x = self.ln_f(x)
|
| 173 |
-
logits = self.head(x)
|
| 174 |
-
|
| 175 |
-
# if we are given some desired targets also calculate the loss
|
| 176 |
-
loss = None
|
| 177 |
-
if targets is not None:
|
| 178 |
-
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
|
| 179 |
-
|
| 180 |
-
return logits, loss
|
| 181 |
-
|
| 182 |
-
def forward_with_past(self, idx, embeddings=None, targets=None, past=None, past_length=None):
|
| 183 |
-
# inference only
|
| 184 |
-
assert not self.training
|
| 185 |
-
token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector
|
| 186 |
-
if embeddings is not None: # prepend explicit embeddings
|
| 187 |
-
token_embeddings = torch.cat((embeddings, token_embeddings), dim=1)
|
| 188 |
-
|
| 189 |
-
if past is not None:
|
| 190 |
-
assert past_length is not None
|
| 191 |
-
past = torch.cat(past, dim=-2) # n_layer, 2, b, nh, len_past, dim_head
|
| 192 |
-
past_shape = list(past.shape)
|
| 193 |
-
expected_shape = [self.config.n_layer, 2, idx.shape[0], self.config.n_head, past_length, self.config.n_embd//self.config.n_head]
|
| 194 |
-
assert past_shape == expected_shape, f"{past_shape} =/= {expected_shape}"
|
| 195 |
-
position_embeddings = self.pos_emb[:, past_length, :] # each position maps to a (learnable) vector
|
| 196 |
-
else:
|
| 197 |
-
position_embeddings = self.pos_emb[:, :token_embeddings.shape[1], :]
|
| 198 |
-
|
| 199 |
-
x = self.drop(token_embeddings + position_embeddings)
|
| 200 |
-
presents = [] # accumulate over layers
|
| 201 |
-
for i, block in enumerate(self.blocks):
|
| 202 |
-
x, present = block(x, layer_past=past[i, ...] if past is not None else None, return_present=True)
|
| 203 |
-
presents.append(present)
|
| 204 |
-
|
| 205 |
-
x = self.ln_f(x)
|
| 206 |
-
logits = self.head(x)
|
| 207 |
-
# if we are given some desired targets also calculate the loss
|
| 208 |
-
loss = None
|
| 209 |
-
if targets is not None:
|
| 210 |
-
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
|
| 211 |
-
|
| 212 |
-
return logits, loss, torch.stack(presents) # _, _, n_layer, 2, b, nh, 1, dim_head
|
| 213 |
-
|
| 214 |
-
|
| 215 |
-
class DummyGPT(nn.Module):
|
| 216 |
-
# for debugging
|
| 217 |
-
def __init__(self, add_value=1):
|
| 218 |
-
super().__init__()
|
| 219 |
-
self.add_value = add_value
|
| 220 |
-
|
| 221 |
-
def forward(self, idx):
|
| 222 |
-
return idx + self.add_value, None
|
| 223 |
-
|
| 224 |
-
|
| 225 |
-
class CodeGPT(nn.Module):
|
| 226 |
-
"""Takes in semi-embeddings"""
|
| 227 |
-
def __init__(self, vocab_size, block_size, in_channels, n_layer=12, n_head=8, n_embd=256,
|
| 228 |
-
embd_pdrop=0., resid_pdrop=0., attn_pdrop=0., n_unmasked=0):
|
| 229 |
-
super().__init__()
|
| 230 |
-
config = GPTConfig(vocab_size=vocab_size, block_size=block_size,
|
| 231 |
-
embd_pdrop=embd_pdrop, resid_pdrop=resid_pdrop, attn_pdrop=attn_pdrop,
|
| 232 |
-
n_layer=n_layer, n_head=n_head, n_embd=n_embd,
|
| 233 |
-
n_unmasked=n_unmasked)
|
| 234 |
-
# input embedding stem
|
| 235 |
-
self.tok_emb = nn.Linear(in_channels, config.n_embd)
|
| 236 |
-
self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
|
| 237 |
-
self.drop = nn.Dropout(config.embd_pdrop)
|
| 238 |
-
# transformer
|
| 239 |
-
self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
|
| 240 |
-
# decoder head
|
| 241 |
-
self.ln_f = nn.LayerNorm(config.n_embd)
|
| 242 |
-
self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
|
| 243 |
-
self.block_size = config.block_size
|
| 244 |
-
self.apply(self._init_weights)
|
| 245 |
-
self.config = config
|
| 246 |
-
logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
|
| 247 |
-
|
| 248 |
-
def get_block_size(self):
|
| 249 |
-
return self.block_size
|
| 250 |
-
|
| 251 |
-
def _init_weights(self, module):
|
| 252 |
-
if isinstance(module, (nn.Linear, nn.Embedding)):
|
| 253 |
-
module.weight.data.normal_(mean=0.0, std=0.02)
|
| 254 |
-
if isinstance(module, nn.Linear) and module.bias is not None:
|
| 255 |
-
module.bias.data.zero_()
|
| 256 |
-
elif isinstance(module, nn.LayerNorm):
|
| 257 |
-
module.bias.data.zero_()
|
| 258 |
-
module.weight.data.fill_(1.0)
|
| 259 |
-
|
| 260 |
-
def forward(self, idx, embeddings=None, targets=None):
|
| 261 |
-
# forward the GPT model
|
| 262 |
-
token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector
|
| 263 |
-
|
| 264 |
-
if embeddings is not None: # prepend explicit embeddings
|
| 265 |
-
token_embeddings = torch.cat((embeddings, token_embeddings), dim=1)
|
| 266 |
-
|
| 267 |
-
t = token_embeddings.shape[1]
|
| 268 |
-
assert t <= self.block_size, "Cannot forward, model block size is exhausted."
|
| 269 |
-
position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector
|
| 270 |
-
x = self.drop(token_embeddings + position_embeddings)
|
| 271 |
-
x = self.blocks(x)
|
| 272 |
-
x = self.taming_cinln_f(x)
|
| 273 |
-
logits = self.head(x)
|
| 274 |
-
|
| 275 |
-
# if we are given some desired targets also calculate the loss
|
| 276 |
-
loss = None
|
| 277 |
-
if targets is not None:
|
| 278 |
-
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
|
| 279 |
-
|
| 280 |
-
return logits, loss
|
| 281 |
-
|
| 282 |
-
|
| 283 |
-
|
| 284 |
-
#### sampling utils
|
| 285 |
-
|
| 286 |
-
def top_k_logits(logits, k):
|
| 287 |
-
v, ix = torch.topk(logits, k)
|
| 288 |
-
out = logits.clone()
|
| 289 |
-
out[out < v[:, [-1]]] = -float('Inf')
|
| 290 |
-
return out
|
| 291 |
-
|
| 292 |
-
@torch.no_grad()
|
| 293 |
-
def sample(model, x, steps, temperature=1.0, sample=False, top_k=None):
|
| 294 |
-
"""
|
| 295 |
-
take a conditioning sequence of indices in x (of shape (b,t)) and predict the next token in
|
| 296 |
-
the sequence, feeding the predictions back into the model each time. Clearly the sampling
|
| 297 |
-
has quadratic complexity unlike an RNN that is only linear, and has a finite context window
|
| 298 |
-
of block_size, unlike an RNN that has an infinite context window.
|
| 299 |
-
"""
|
| 300 |
-
block_size = model.get_block_size()
|
| 301 |
-
model.eval()
|
| 302 |
-
for k in range(steps):
|
| 303 |
-
x_cond = x if x.size(1) <= block_size else x[:, -block_size:] # crop context if needed
|
| 304 |
-
logits, _ = model(x_cond)
|
| 305 |
-
# pluck the logits at the final step and scale by temperature
|
| 306 |
-
logits = logits[:, -1, :] / temperature
|
| 307 |
-
# optionally crop probabilities to only the top k options
|
| 308 |
-
if top_k is not None:
|
| 309 |
-
logits = top_k_logits(logits, top_k)
|
| 310 |
-
# apply softmax to convert to probabilities
|
| 311 |
-
probs = F.softmax(logits, dim=-1)
|
| 312 |
-
# sample from the distribution or take the most likely
|
| 313 |
-
if sample:
|
| 314 |
-
ix = torch.multinomial(probs, num_samples=1)
|
| 315 |
-
else:
|
| 316 |
-
_, ix = torch.topk(probs, k=1, dim=-1)
|
| 317 |
-
# append to the sequence and continue
|
| 318 |
-
x = torch.cat((x, ix), dim=1)
|
| 319 |
-
|
| 320 |
-
return x
|
| 321 |
-
|
| 322 |
-
|
| 323 |
-
@torch.no_grad()
|
| 324 |
-
def sample_with_past(x, model, steps, temperature=1., sample_logits=True,
|
| 325 |
-
top_k=None, top_p=None, callback=None):
|
| 326 |
-
# x is conditioning
|
| 327 |
-
sample = x
|
| 328 |
-
cond_len = x.shape[1]
|
| 329 |
-
past = None
|
| 330 |
-
for n in range(steps):
|
| 331 |
-
if callback is not None:
|
| 332 |
-
callback(n)
|
| 333 |
-
logits, _, present = model.forward_with_past(x, past=past, past_length=(n+cond_len-1))
|
| 334 |
-
if past is None:
|
| 335 |
-
past = [present]
|
| 336 |
-
else:
|
| 337 |
-
past.append(present)
|
| 338 |
-
logits = logits[:, -1, :] / temperature
|
| 339 |
-
if top_k is not None:
|
| 340 |
-
logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
|
| 341 |
-
|
| 342 |
-
probs = F.softmax(logits, dim=-1)
|
| 343 |
-
if not sample_logits:
|
| 344 |
-
_, x = torch.topk(probs, k=1, dim=-1)
|
| 345 |
-
else:
|
| 346 |
-
x = torch.multinomial(probs, num_samples=1)
|
| 347 |
-
# append to the sequence and continue
|
| 348 |
-
sample = torch.cat((sample, x), dim=1)
|
| 349 |
-
del past
|
| 350 |
-
sample = sample[:, cond_len:] # cut conditioning off
|
| 351 |
-
return sample
|
| 352 |
-
|
| 353 |
-
|
| 354 |
-
#### clustering utils
|
| 355 |
-
|
| 356 |
-
class KMeans(nn.Module):
|
| 357 |
-
def __init__(self, ncluster=512, nc=3, niter=10):
|
| 358 |
-
super().__init__()
|
| 359 |
-
self.ncluster = ncluster
|
| 360 |
-
self.nc = nc
|
| 361 |
-
self.niter = niter
|
| 362 |
-
self.shape = (3,32,32)
|
| 363 |
-
self.register_buffer("C", torch.zeros(self.ncluster,nc))
|
| 364 |
-
self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))
|
| 365 |
-
|
| 366 |
-
def is_initialized(self):
|
| 367 |
-
return self.initialized.item() == 1
|
| 368 |
-
|
| 369 |
-
@torch.no_grad()
|
| 370 |
-
def initialize(self, x):
|
| 371 |
-
N, D = x.shape
|
| 372 |
-
assert D == self.nc, D
|
| 373 |
-
c = x[torch.randperm(N)[:self.ncluster]] # init clusters at random
|
| 374 |
-
for i in range(self.niter):
|
| 375 |
-
# assign all pixels to the closest codebook element
|
| 376 |
-
a = ((x[:, None, :] - c[None, :, :])**2).sum(-1).argmin(1)
|
| 377 |
-
# move each codebook element to be the mean of the pixels that assigned to it
|
| 378 |
-
c = torch.stack([x[a==k].mean(0) for k in range(self.ncluster)])
|
| 379 |
-
# re-assign any poorly positioned codebook elements
|
| 380 |
-
nanix = torch.any(torch.isnan(c), dim=1)
|
| 381 |
-
ndead = nanix.sum().item()
|
| 382 |
-
print('done step %d/%d, re-initialized %d dead clusters' % (i+1, self.niter, ndead))
|
| 383 |
-
c[nanix] = x[torch.randperm(N)[:ndead]] # re-init dead clusters
|
| 384 |
-
|
| 385 |
-
self.C.copy_(c)
|
| 386 |
-
self.initialized.fill_(1)
|
| 387 |
-
|
| 388 |
-
|
| 389 |
-
def forward(self, x, reverse=False, shape=None):
|
| 390 |
-
if not reverse:
|
| 391 |
-
# flatten
|
| 392 |
-
bs,c,h,w = x.shape
|
| 393 |
-
assert c == self.nc
|
| 394 |
-
x = x.reshape(bs,c,h*w,1)
|
| 395 |
-
C = self.C.permute(1,0)
|
| 396 |
-
C = C.reshape(1,c,1,self.ncluster)
|
| 397 |
-
a = ((x-C)**2).sum(1).argmin(-1) # bs, h*w indices
|
| 398 |
-
return a
|
| 399 |
-
else:
|
| 400 |
-
# flatten
|
| 401 |
-
bs, HW = x.shape
|
| 402 |
-
"""
|
| 403 |
-
c = self.C.reshape( 1, self.nc, 1, self.ncluster)
|
| 404 |
-
c = c[bs*[0],:,:,:]
|
| 405 |
-
c = c[:,:,HW*[0],:]
|
| 406 |
-
x = x.reshape(bs, 1, HW, 1)
|
| 407 |
-
x = x[:,3*[0],:,:]
|
| 408 |
-
x = torch.gather(c, dim=3, index=x)
|
| 409 |
-
"""
|
| 410 |
-
x = self.C[x]
|
| 411 |
-
x = x.permute(0,2,1)
|
| 412 |
-
shape = shape if shape is not None else self.shape
|
| 413 |
-
x = x.reshape(bs, *shape)
|
| 414 |
-
|
| 415 |
-
return x
|
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spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/__init__.py
DELETED
|
File without changes
|
spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/cachecontrol/heuristics.py
DELETED
|
@@ -1,139 +0,0 @@
|
|
| 1 |
-
# SPDX-FileCopyrightText: 2015 Eric Larson
|
| 2 |
-
#
|
| 3 |
-
# SPDX-License-Identifier: Apache-2.0
|
| 4 |
-
|
| 5 |
-
import calendar
|
| 6 |
-
import time
|
| 7 |
-
|
| 8 |
-
from email.utils import formatdate, parsedate, parsedate_tz
|
| 9 |
-
|
| 10 |
-
from datetime import datetime, timedelta
|
| 11 |
-
|
| 12 |
-
TIME_FMT = "%a, %d %b %Y %H:%M:%S GMT"
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
def expire_after(delta, date=None):
|
| 16 |
-
date = date or datetime.utcnow()
|
| 17 |
-
return date + delta
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
def datetime_to_header(dt):
|
| 21 |
-
return formatdate(calendar.timegm(dt.timetuple()))
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
class BaseHeuristic(object):
|
| 25 |
-
|
| 26 |
-
def warning(self, response):
|
| 27 |
-
"""
|
| 28 |
-
Return a valid 1xx warning header value describing the cache
|
| 29 |
-
adjustments.
|
| 30 |
-
|
| 31 |
-
The response is provided too allow warnings like 113
|
| 32 |
-
http://tools.ietf.org/html/rfc7234#section-5.5.4 where we need
|
| 33 |
-
to explicitly say response is over 24 hours old.
|
| 34 |
-
"""
|
| 35 |
-
return '110 - "Response is Stale"'
|
| 36 |
-
|
| 37 |
-
def update_headers(self, response):
|
| 38 |
-
"""Update the response headers with any new headers.
|
| 39 |
-
|
| 40 |
-
NOTE: This SHOULD always include some Warning header to
|
| 41 |
-
signify that the response was cached by the client, not
|
| 42 |
-
by way of the provided headers.
|
| 43 |
-
"""
|
| 44 |
-
return {}
|
| 45 |
-
|
| 46 |
-
def apply(self, response):
|
| 47 |
-
updated_headers = self.update_headers(response)
|
| 48 |
-
|
| 49 |
-
if updated_headers:
|
| 50 |
-
response.headers.update(updated_headers)
|
| 51 |
-
warning_header_value = self.warning(response)
|
| 52 |
-
if warning_header_value is not None:
|
| 53 |
-
response.headers.update({"Warning": warning_header_value})
|
| 54 |
-
|
| 55 |
-
return response
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
class OneDayCache(BaseHeuristic):
|
| 59 |
-
"""
|
| 60 |
-
Cache the response by providing an expires 1 day in the
|
| 61 |
-
future.
|
| 62 |
-
"""
|
| 63 |
-
|
| 64 |
-
def update_headers(self, response):
|
| 65 |
-
headers = {}
|
| 66 |
-
|
| 67 |
-
if "expires" not in response.headers:
|
| 68 |
-
date = parsedate(response.headers["date"])
|
| 69 |
-
expires = expire_after(timedelta(days=1), date=datetime(*date[:6]))
|
| 70 |
-
headers["expires"] = datetime_to_header(expires)
|
| 71 |
-
headers["cache-control"] = "public"
|
| 72 |
-
return headers
|
| 73 |
-
|
| 74 |
-
|
| 75 |
-
class ExpiresAfter(BaseHeuristic):
|
| 76 |
-
"""
|
| 77 |
-
Cache **all** requests for a defined time period.
|
| 78 |
-
"""
|
| 79 |
-
|
| 80 |
-
def __init__(self, **kw):
|
| 81 |
-
self.delta = timedelta(**kw)
|
| 82 |
-
|
| 83 |
-
def update_headers(self, response):
|
| 84 |
-
expires = expire_after(self.delta)
|
| 85 |
-
return {"expires": datetime_to_header(expires), "cache-control": "public"}
|
| 86 |
-
|
| 87 |
-
def warning(self, response):
|
| 88 |
-
tmpl = "110 - Automatically cached for %s. Response might be stale"
|
| 89 |
-
return tmpl % self.delta
|
| 90 |
-
|
| 91 |
-
|
| 92 |
-
class LastModified(BaseHeuristic):
|
| 93 |
-
"""
|
| 94 |
-
If there is no Expires header already, fall back on Last-Modified
|
| 95 |
-
using the heuristic from
|
| 96 |
-
http://tools.ietf.org/html/rfc7234#section-4.2.2
|
| 97 |
-
to calculate a reasonable value.
|
| 98 |
-
|
| 99 |
-
Firefox also does something like this per
|
| 100 |
-
https://developer.mozilla.org/en-US/docs/Web/HTTP/Caching_FAQ
|
| 101 |
-
http://lxr.mozilla.org/mozilla-release/source/netwerk/protocol/http/nsHttpResponseHead.cpp#397
|
| 102 |
-
Unlike mozilla we limit this to 24-hr.
|
| 103 |
-
"""
|
| 104 |
-
cacheable_by_default_statuses = {
|
| 105 |
-
200, 203, 204, 206, 300, 301, 404, 405, 410, 414, 501
|
| 106 |
-
}
|
| 107 |
-
|
| 108 |
-
def update_headers(self, resp):
|
| 109 |
-
headers = resp.headers
|
| 110 |
-
|
| 111 |
-
if "expires" in headers:
|
| 112 |
-
return {}
|
| 113 |
-
|
| 114 |
-
if "cache-control" in headers and headers["cache-control"] != "public":
|
| 115 |
-
return {}
|
| 116 |
-
|
| 117 |
-
if resp.status not in self.cacheable_by_default_statuses:
|
| 118 |
-
return {}
|
| 119 |
-
|
| 120 |
-
if "date" not in headers or "last-modified" not in headers:
|
| 121 |
-
return {}
|
| 122 |
-
|
| 123 |
-
date = calendar.timegm(parsedate_tz(headers["date"]))
|
| 124 |
-
last_modified = parsedate(headers["last-modified"])
|
| 125 |
-
if date is None or last_modified is None:
|
| 126 |
-
return {}
|
| 127 |
-
|
| 128 |
-
now = time.time()
|
| 129 |
-
current_age = max(0, now - date)
|
| 130 |
-
delta = date - calendar.timegm(last_modified)
|
| 131 |
-
freshness_lifetime = max(0, min(delta / 10, 24 * 3600))
|
| 132 |
-
if freshness_lifetime <= current_age:
|
| 133 |
-
return {}
|
| 134 |
-
|
| 135 |
-
expires = date + freshness_lifetime
|
| 136 |
-
return {"expires": time.strftime(TIME_FMT, time.gmtime(expires))}
|
| 137 |
-
|
| 138 |
-
def warning(self, resp):
|
| 139 |
-
return None
|
|
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|
spaces/Blessin/one-liners/app.py
DELETED
|
@@ -1,33 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
import random
|
| 3 |
-
from datasets import load_dataset
|
| 4 |
-
|
| 5 |
-
# Load the dataset from Hugging Face
|
| 6 |
-
dataset = load_dataset("Blessin/dialogues-one-liners")
|
| 7 |
-
|
| 8 |
-
# Extract the dialogues from the dataset
|
| 9 |
-
DIALOGUES = dataset["train"]["dialogues"]
|
| 10 |
-
|
| 11 |
-
def generate_statement():
|
| 12 |
-
"""Return a random dialogue from the dataset."""
|
| 13 |
-
# Pick a random sublist from the dataset
|
| 14 |
-
random_dialogue_list = random.choice(DIALOGUES)
|
| 15 |
-
# Pick a random dialogue from the sublist
|
| 16 |
-
return random.choice(random_dialogue_list)
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
def main():
|
| 20 |
-
# Define the UI using gr.Interface
|
| 21 |
-
interface = gr.Interface(
|
| 22 |
-
fn=generate_statement, # Function to call on button press
|
| 23 |
-
inputs=[], # No inputs required
|
| 24 |
-
outputs="text", # Output is a text area
|
| 25 |
-
live=False, # Only generate statement after button press
|
| 26 |
-
description="Press the button to generate a random statement from the dataset."
|
| 27 |
-
)
|
| 28 |
-
|
| 29 |
-
# Launch the UI
|
| 30 |
-
interface.launch(share=True)
|
| 31 |
-
|
| 32 |
-
if __name__ == "__main__":
|
| 33 |
-
main()
|
|
|
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|
spaces/CVPR/GFPGAN-example/tests/test_arcface_arch.py
DELETED
|
@@ -1,49 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
|
| 3 |
-
from gfpgan.archs.arcface_arch import BasicBlock, Bottleneck, ResNetArcFace
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
def test_resnetarcface():
|
| 7 |
-
"""Test arch: ResNetArcFace."""
|
| 8 |
-
|
| 9 |
-
# model init and forward (gpu)
|
| 10 |
-
if torch.cuda.is_available():
|
| 11 |
-
net = ResNetArcFace(block='IRBlock', layers=(2, 2, 2, 2), use_se=True).cuda().eval()
|
| 12 |
-
img = torch.rand((1, 1, 128, 128), dtype=torch.float32).cuda()
|
| 13 |
-
output = net(img)
|
| 14 |
-
assert output.shape == (1, 512)
|
| 15 |
-
|
| 16 |
-
# -------------------- without SE block ----------------------- #
|
| 17 |
-
net = ResNetArcFace(block='IRBlock', layers=(2, 2, 2, 2), use_se=False).cuda().eval()
|
| 18 |
-
output = net(img)
|
| 19 |
-
assert output.shape == (1, 512)
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
def test_basicblock():
|
| 23 |
-
"""Test the BasicBlock in arcface_arch"""
|
| 24 |
-
block = BasicBlock(1, 3, stride=1, downsample=None).cuda()
|
| 25 |
-
img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda()
|
| 26 |
-
output = block(img)
|
| 27 |
-
assert output.shape == (1, 3, 12, 12)
|
| 28 |
-
|
| 29 |
-
# ----------------- use the downsmaple module--------------- #
|
| 30 |
-
downsample = torch.nn.UpsamplingNearest2d(scale_factor=0.5).cuda()
|
| 31 |
-
block = BasicBlock(1, 3, stride=2, downsample=downsample).cuda()
|
| 32 |
-
img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda()
|
| 33 |
-
output = block(img)
|
| 34 |
-
assert output.shape == (1, 3, 6, 6)
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
def test_bottleneck():
|
| 38 |
-
"""Test the Bottleneck in arcface_arch"""
|
| 39 |
-
block = Bottleneck(1, 1, stride=1, downsample=None).cuda()
|
| 40 |
-
img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda()
|
| 41 |
-
output = block(img)
|
| 42 |
-
assert output.shape == (1, 4, 12, 12)
|
| 43 |
-
|
| 44 |
-
# ----------------- use the downsmaple module--------------- #
|
| 45 |
-
downsample = torch.nn.UpsamplingNearest2d(scale_factor=0.5).cuda()
|
| 46 |
-
block = Bottleneck(1, 1, stride=2, downsample=downsample).cuda()
|
| 47 |
-
img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda()
|
| 48 |
-
output = block(img)
|
| 49 |
-
assert output.shape == (1, 4, 6, 6)
|
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spaces/CVPR/LIVE/pybind11/tests/test_multiple_inheritance.cpp
DELETED
|
@@ -1,220 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
tests/test_multiple_inheritance.cpp -- multiple inheritance,
|
| 3 |
-
implicit MI casts
|
| 4 |
-
|
| 5 |
-
Copyright (c) 2016 Wenzel Jakob <[email protected]>
|
| 6 |
-
|
| 7 |
-
All rights reserved. Use of this source code is governed by a
|
| 8 |
-
BSD-style license that can be found in the LICENSE file.
|
| 9 |
-
*/
|
| 10 |
-
|
| 11 |
-
#include "pybind11_tests.h"
|
| 12 |
-
#include "constructor_stats.h"
|
| 13 |
-
|
| 14 |
-
// Many bases for testing that multiple inheritance from many classes (i.e. requiring extra
|
| 15 |
-
// space for holder constructed flags) works.
|
| 16 |
-
template <int N> struct BaseN {
|
| 17 |
-
BaseN(int i) : i(i) { }
|
| 18 |
-
int i;
|
| 19 |
-
};
|
| 20 |
-
|
| 21 |
-
// test_mi_static_properties
|
| 22 |
-
struct Vanilla {
|
| 23 |
-
std::string vanilla() { return "Vanilla"; };
|
| 24 |
-
};
|
| 25 |
-
struct WithStatic1 {
|
| 26 |
-
static std::string static_func1() { return "WithStatic1"; };
|
| 27 |
-
static int static_value1;
|
| 28 |
-
};
|
| 29 |
-
struct WithStatic2 {
|
| 30 |
-
static std::string static_func2() { return "WithStatic2"; };
|
| 31 |
-
static int static_value2;
|
| 32 |
-
};
|
| 33 |
-
struct VanillaStaticMix1 : Vanilla, WithStatic1, WithStatic2 {
|
| 34 |
-
static std::string static_func() { return "VanillaStaticMix1"; }
|
| 35 |
-
static int static_value;
|
| 36 |
-
};
|
| 37 |
-
struct VanillaStaticMix2 : WithStatic1, Vanilla, WithStatic2 {
|
| 38 |
-
static std::string static_func() { return "VanillaStaticMix2"; }
|
| 39 |
-
static int static_value;
|
| 40 |
-
};
|
| 41 |
-
int WithStatic1::static_value1 = 1;
|
| 42 |
-
int WithStatic2::static_value2 = 2;
|
| 43 |
-
int VanillaStaticMix1::static_value = 12;
|
| 44 |
-
int VanillaStaticMix2::static_value = 12;
|
| 45 |
-
|
| 46 |
-
TEST_SUBMODULE(multiple_inheritance, m) {
|
| 47 |
-
|
| 48 |
-
// test_multiple_inheritance_mix1
|
| 49 |
-
// test_multiple_inheritance_mix2
|
| 50 |
-
struct Base1 {
|
| 51 |
-
Base1(int i) : i(i) { }
|
| 52 |
-
int foo() { return i; }
|
| 53 |
-
int i;
|
| 54 |
-
};
|
| 55 |
-
py::class_<Base1> b1(m, "Base1");
|
| 56 |
-
b1.def(py::init<int>())
|
| 57 |
-
.def("foo", &Base1::foo);
|
| 58 |
-
|
| 59 |
-
struct Base2 {
|
| 60 |
-
Base2(int i) : i(i) { }
|
| 61 |
-
int bar() { return i; }
|
| 62 |
-
int i;
|
| 63 |
-
};
|
| 64 |
-
py::class_<Base2> b2(m, "Base2");
|
| 65 |
-
b2.def(py::init<int>())
|
| 66 |
-
.def("bar", &Base2::bar);
|
| 67 |
-
|
| 68 |
-
|
| 69 |
-
// test_multiple_inheritance_cpp
|
| 70 |
-
struct Base12 : Base1, Base2 {
|
| 71 |
-
Base12(int i, int j) : Base1(i), Base2(j) { }
|
| 72 |
-
};
|
| 73 |
-
struct MIType : Base12 {
|
| 74 |
-
MIType(int i, int j) : Base12(i, j) { }
|
| 75 |
-
};
|
| 76 |
-
py::class_<Base12, Base1, Base2>(m, "Base12");
|
| 77 |
-
py::class_<MIType, Base12>(m, "MIType")
|
| 78 |
-
.def(py::init<int, int>());
|
| 79 |
-
|
| 80 |
-
|
| 81 |
-
// test_multiple_inheritance_python_many_bases
|
| 82 |
-
#define PYBIND11_BASEN(N) py::class_<BaseN<N>>(m, "BaseN" #N).def(py::init<int>()).def("f" #N, [](BaseN<N> &b) { return b.i + N; })
|
| 83 |
-
PYBIND11_BASEN( 1); PYBIND11_BASEN( 2); PYBIND11_BASEN( 3); PYBIND11_BASEN( 4);
|
| 84 |
-
PYBIND11_BASEN( 5); PYBIND11_BASEN( 6); PYBIND11_BASEN( 7); PYBIND11_BASEN( 8);
|
| 85 |
-
PYBIND11_BASEN( 9); PYBIND11_BASEN(10); PYBIND11_BASEN(11); PYBIND11_BASEN(12);
|
| 86 |
-
PYBIND11_BASEN(13); PYBIND11_BASEN(14); PYBIND11_BASEN(15); PYBIND11_BASEN(16);
|
| 87 |
-
PYBIND11_BASEN(17);
|
| 88 |
-
|
| 89 |
-
// Uncommenting this should result in a compile time failure (MI can only be specified via
|
| 90 |
-
// template parameters because pybind has to know the types involved; see discussion in #742 for
|
| 91 |
-
// details).
|
| 92 |
-
// struct Base12v2 : Base1, Base2 {
|
| 93 |
-
// Base12v2(int i, int j) : Base1(i), Base2(j) { }
|
| 94 |
-
// };
|
| 95 |
-
// py::class_<Base12v2>(m, "Base12v2", b1, b2)
|
| 96 |
-
// .def(py::init<int, int>());
|
| 97 |
-
|
| 98 |
-
|
| 99 |
-
// test_multiple_inheritance_virtbase
|
| 100 |
-
// Test the case where not all base classes are specified, and where pybind11 requires the
|
| 101 |
-
// py::multiple_inheritance flag to perform proper casting between types.
|
| 102 |
-
struct Base1a {
|
| 103 |
-
Base1a(int i) : i(i) { }
|
| 104 |
-
int foo() { return i; }
|
| 105 |
-
int i;
|
| 106 |
-
};
|
| 107 |
-
py::class_<Base1a, std::shared_ptr<Base1a>>(m, "Base1a")
|
| 108 |
-
.def(py::init<int>())
|
| 109 |
-
.def("foo", &Base1a::foo);
|
| 110 |
-
|
| 111 |
-
struct Base2a {
|
| 112 |
-
Base2a(int i) : i(i) { }
|
| 113 |
-
int bar() { return i; }
|
| 114 |
-
int i;
|
| 115 |
-
};
|
| 116 |
-
py::class_<Base2a, std::shared_ptr<Base2a>>(m, "Base2a")
|
| 117 |
-
.def(py::init<int>())
|
| 118 |
-
.def("bar", &Base2a::bar);
|
| 119 |
-
|
| 120 |
-
struct Base12a : Base1a, Base2a {
|
| 121 |
-
Base12a(int i, int j) : Base1a(i), Base2a(j) { }
|
| 122 |
-
};
|
| 123 |
-
py::class_<Base12a, /* Base1 missing */ Base2a,
|
| 124 |
-
std::shared_ptr<Base12a>>(m, "Base12a", py::multiple_inheritance())
|
| 125 |
-
.def(py::init<int, int>());
|
| 126 |
-
|
| 127 |
-
m.def("bar_base2a", [](Base2a *b) { return b->bar(); });
|
| 128 |
-
m.def("bar_base2a_sharedptr", [](std::shared_ptr<Base2a> b) { return b->bar(); });
|
| 129 |
-
|
| 130 |
-
// test_mi_unaligned_base
|
| 131 |
-
// test_mi_base_return
|
| 132 |
-
// Issue #801: invalid casting to derived type with MI bases
|
| 133 |
-
struct I801B1 { int a = 1; I801B1() = default; I801B1(const I801B1 &) = default; virtual ~I801B1() = default; };
|
| 134 |
-
struct I801B2 { int b = 2; I801B2() = default; I801B2(const I801B2 &) = default; virtual ~I801B2() = default; };
|
| 135 |
-
struct I801C : I801B1, I801B2 {};
|
| 136 |
-
struct I801D : I801C {}; // Indirect MI
|
| 137 |
-
// Unregistered classes:
|
| 138 |
-
struct I801B3 { int c = 3; virtual ~I801B3() = default; };
|
| 139 |
-
struct I801E : I801B3, I801D {};
|
| 140 |
-
|
| 141 |
-
py::class_<I801B1, std::shared_ptr<I801B1>>(m, "I801B1").def(py::init<>()).def_readonly("a", &I801B1::a);
|
| 142 |
-
py::class_<I801B2, std::shared_ptr<I801B2>>(m, "I801B2").def(py::init<>()).def_readonly("b", &I801B2::b);
|
| 143 |
-
py::class_<I801C, I801B1, I801B2, std::shared_ptr<I801C>>(m, "I801C").def(py::init<>());
|
| 144 |
-
py::class_<I801D, I801C, std::shared_ptr<I801D>>(m, "I801D").def(py::init<>());
|
| 145 |
-
|
| 146 |
-
// Two separate issues here: first, we want to recognize a pointer to a base type as being a
|
| 147 |
-
// known instance even when the pointer value is unequal (i.e. due to a non-first
|
| 148 |
-
// multiple-inheritance base class):
|
| 149 |
-
m.def("i801b1_c", [](I801C *c) { return static_cast<I801B1 *>(c); });
|
| 150 |
-
m.def("i801b2_c", [](I801C *c) { return static_cast<I801B2 *>(c); });
|
| 151 |
-
m.def("i801b1_d", [](I801D *d) { return static_cast<I801B1 *>(d); });
|
| 152 |
-
m.def("i801b2_d", [](I801D *d) { return static_cast<I801B2 *>(d); });
|
| 153 |
-
|
| 154 |
-
// Second, when returned a base class pointer to a derived instance, we cannot assume that the
|
| 155 |
-
// pointer is `reinterpret_cast`able to the derived pointer because, like above, the base class
|
| 156 |
-
// pointer could be offset.
|
| 157 |
-
m.def("i801c_b1", []() -> I801B1 * { return new I801C(); });
|
| 158 |
-
m.def("i801c_b2", []() -> I801B2 * { return new I801C(); });
|
| 159 |
-
m.def("i801d_b1", []() -> I801B1 * { return new I801D(); });
|
| 160 |
-
m.def("i801d_b2", []() -> I801B2 * { return new I801D(); });
|
| 161 |
-
|
| 162 |
-
// Return a base class pointer to a pybind-registered type when the actual derived type
|
| 163 |
-
// isn't pybind-registered (and uses multiple-inheritance to offset the pybind base)
|
| 164 |
-
m.def("i801e_c", []() -> I801C * { return new I801E(); });
|
| 165 |
-
m.def("i801e_b2", []() -> I801B2 * { return new I801E(); });
|
| 166 |
-
|
| 167 |
-
|
| 168 |
-
// test_mi_static_properties
|
| 169 |
-
py::class_<Vanilla>(m, "Vanilla")
|
| 170 |
-
.def(py::init<>())
|
| 171 |
-
.def("vanilla", &Vanilla::vanilla);
|
| 172 |
-
|
| 173 |
-
py::class_<WithStatic1>(m, "WithStatic1")
|
| 174 |
-
.def(py::init<>())
|
| 175 |
-
.def_static("static_func1", &WithStatic1::static_func1)
|
| 176 |
-
.def_readwrite_static("static_value1", &WithStatic1::static_value1);
|
| 177 |
-
|
| 178 |
-
py::class_<WithStatic2>(m, "WithStatic2")
|
| 179 |
-
.def(py::init<>())
|
| 180 |
-
.def_static("static_func2", &WithStatic2::static_func2)
|
| 181 |
-
.def_readwrite_static("static_value2", &WithStatic2::static_value2);
|
| 182 |
-
|
| 183 |
-
py::class_<VanillaStaticMix1, Vanilla, WithStatic1, WithStatic2>(
|
| 184 |
-
m, "VanillaStaticMix1")
|
| 185 |
-
.def(py::init<>())
|
| 186 |
-
.def_static("static_func", &VanillaStaticMix1::static_func)
|
| 187 |
-
.def_readwrite_static("static_value", &VanillaStaticMix1::static_value);
|
| 188 |
-
|
| 189 |
-
py::class_<VanillaStaticMix2, WithStatic1, Vanilla, WithStatic2>(
|
| 190 |
-
m, "VanillaStaticMix2")
|
| 191 |
-
.def(py::init<>())
|
| 192 |
-
.def_static("static_func", &VanillaStaticMix2::static_func)
|
| 193 |
-
.def_readwrite_static("static_value", &VanillaStaticMix2::static_value);
|
| 194 |
-
|
| 195 |
-
|
| 196 |
-
#if !(defined(PYPY_VERSION) && (PYPY_VERSION_NUM < 0x06000000))
|
| 197 |
-
struct WithDict { };
|
| 198 |
-
struct VanillaDictMix1 : Vanilla, WithDict { };
|
| 199 |
-
struct VanillaDictMix2 : WithDict, Vanilla { };
|
| 200 |
-
py::class_<WithDict>(m, "WithDict", py::dynamic_attr()).def(py::init<>());
|
| 201 |
-
py::class_<VanillaDictMix1, Vanilla, WithDict>(m, "VanillaDictMix1").def(py::init<>());
|
| 202 |
-
py::class_<VanillaDictMix2, WithDict, Vanilla>(m, "VanillaDictMix2").def(py::init<>());
|
| 203 |
-
#endif
|
| 204 |
-
|
| 205 |
-
// test_diamond_inheritance
|
| 206 |
-
// Issue #959: segfault when constructing diamond inheritance instance
|
| 207 |
-
// All of these have int members so that there will be various unequal pointers involved.
|
| 208 |
-
struct B { int b; B() = default; B(const B&) = default; virtual ~B() = default; };
|
| 209 |
-
struct C0 : public virtual B { int c0; };
|
| 210 |
-
struct C1 : public virtual B { int c1; };
|
| 211 |
-
struct D : public C0, public C1 { int d; };
|
| 212 |
-
py::class_<B>(m, "B")
|
| 213 |
-
.def("b", [](B *self) { return self; });
|
| 214 |
-
py::class_<C0, B>(m, "C0")
|
| 215 |
-
.def("c0", [](C0 *self) { return self; });
|
| 216 |
-
py::class_<C1, B>(m, "C1")
|
| 217 |
-
.def("c1", [](C1 *self) { return self; });
|
| 218 |
-
py::class_<D, C0, C1>(m, "D")
|
| 219 |
-
.def(py::init<>());
|
| 220 |
-
}
|
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