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-3. Refractor it and add it to [./g4f](https://github.com/xtekky/gpt4free/tree/main/g4f)
-
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diff --git a/spaces/17TheWord/vits-models/text/cleaners.py b/spaces/17TheWord/vits-models/text/cleaners.py
deleted file mode 100644
index d26581deb399609163518054718ad80ecca5d934..0000000000000000000000000000000000000000
--- a/spaces/17TheWord/vits-models/text/cleaners.py
+++ /dev/null
@@ -1,475 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-import pyopenjtalk
-from jamo import h2j, j2hcj
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba, cn2an
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄳ', 'ㄱㅅ'),
- ('ㄵ', 'ㄴㅈ'),
- ('ㄶ', 'ㄴㅎ'),
- ('ㄺ', 'ㄹㄱ'),
- ('ㄻ', 'ㄹㅁ'),
- ('ㄼ', 'ㄹㅂ'),
- ('ㄽ', 'ㄹㅅ'),
- ('ㄾ', 'ㄹㅌ'),
- ('ㄿ', 'ㄹㅍ'),
- ('ㅀ', 'ㄹㅎ'),
- ('ㅄ', 'ㅂㅅ'),
- ('ㅘ', 'ㅗㅏ'),
- ('ㅙ', 'ㅗㅐ'),
- ('ㅚ', 'ㅗㅣ'),
- ('ㅝ', 'ㅜㅓ'),
- ('ㅞ', 'ㅜㅔ'),
- ('ㅟ', 'ㅜㅣ'),
- ('ㅢ', 'ㅡㅣ'),
- ('ㅑ', 'ㅣㅏ'),
- ('ㅒ', 'ㅣㅐ'),
- ('ㅕ', 'ㅣㅓ'),
- ('ㅖ', 'ㅣㅔ'),
- ('ㅛ', 'ㅣㅗ'),
- ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', '에이'),
- ('b', '비'),
- ('c', '시'),
- ('d', '디'),
- ('e', '이'),
- ('f', '에프'),
- ('g', '지'),
- ('h', '에이치'),
- ('i', '아이'),
- ('j', '제이'),
- ('k', '케이'),
- ('l', '엘'),
- ('m', '엠'),
- ('n', '엔'),
- ('o', '오'),
- ('p', '피'),
- ('q', '큐'),
- ('r', '아르'),
- ('s', '에스'),
- ('t', '티'),
- ('u', '유'),
- ('v', '브이'),
- ('w', '더블유'),
- ('x', '엑스'),
- ('y', '와이'),
- ('z', '제트')
-]]
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'ㄟˉ'),
- ('b', 'ㄅㄧˋ'),
- ('c', 'ㄙㄧˉ'),
- ('d', 'ㄉㄧˋ'),
- ('e', 'ㄧˋ'),
- ('f', 'ㄝˊㄈㄨˋ'),
- ('g', 'ㄐㄧˋ'),
- ('h', 'ㄝˇㄑㄩˋ'),
- ('i', 'ㄞˋ'),
- ('j', 'ㄐㄟˋ'),
- ('k', 'ㄎㄟˋ'),
- ('l', 'ㄝˊㄛˋ'),
- ('m', 'ㄝˊㄇㄨˋ'),
- ('n', 'ㄣˉ'),
- ('o', 'ㄡˉ'),
- ('p', 'ㄆㄧˉ'),
- ('q', 'ㄎㄧㄡˉ'),
- ('r', 'ㄚˋ'),
- ('s', 'ㄝˊㄙˋ'),
- ('t', 'ㄊㄧˋ'),
- ('u', 'ㄧㄡˉ'),
- ('v', 'ㄨㄧˉ'),
- ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
- ('x', 'ㄝˉㄎㄨˋㄙˋ'),
- ('y', 'ㄨㄞˋ'),
- ('z', 'ㄗㄟˋ')
-]]
-
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('ㄅㄛ', 'p⁼wo'),
- ('ㄆㄛ', 'pʰwo'),
- ('ㄇㄛ', 'mwo'),
- ('ㄈㄛ', 'fwo'),
- ('ㄅ', 'p⁼'),
- ('ㄆ', 'pʰ'),
- ('ㄇ', 'm'),
- ('ㄈ', 'f'),
- ('ㄉ', 't⁼'),
- ('ㄊ', 'tʰ'),
- ('ㄋ', 'n'),
- ('ㄌ', 'l'),
- ('ㄍ', 'k⁼'),
- ('ㄎ', 'kʰ'),
- ('ㄏ', 'h'),
- ('ㄐ', 'ʧ⁼'),
- ('ㄑ', 'ʧʰ'),
- ('ㄒ', 'ʃ'),
- ('ㄓ', 'ʦ`⁼'),
- ('ㄔ', 'ʦ`ʰ'),
- ('ㄕ', 's`'),
- ('ㄖ', 'ɹ`'),
- ('ㄗ', 'ʦ⁼'),
- ('ㄘ', 'ʦʰ'),
- ('ㄙ', 's'),
- ('ㄚ', 'a'),
- ('ㄛ', 'o'),
- ('ㄜ', 'ə'),
- ('ㄝ', 'e'),
- ('ㄞ', 'ai'),
- ('ㄟ', 'ei'),
- ('ㄠ', 'au'),
- ('ㄡ', 'ou'),
- ('ㄧㄢ', 'yeNN'),
- ('ㄢ', 'aNN'),
- ('ㄧㄣ', 'iNN'),
- ('ㄣ', 'əNN'),
- ('ㄤ', 'aNg'),
- ('ㄧㄥ', 'iNg'),
- ('ㄨㄥ', 'uNg'),
- ('ㄩㄥ', 'yuNg'),
- ('ㄥ', 'əNg'),
- ('ㄦ', 'əɻ'),
- ('ㄧ', 'i'),
- ('ㄨ', 'u'),
- ('ㄩ', 'ɥ'),
- ('ˉ', '→'),
- ('ˊ', '↑'),
- ('ˇ', '↓↑'),
- ('ˋ', '↓'),
- ('˙', ''),
- ('，', ','),
- ('。', '.'),
- ('！', '!'),
- ('？', '?'),
- ('—', '-')
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def lowercase(text):
- return text.lower()
-
-
-def collapse_whitespace(text):
- return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
- return unidecode(text)
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text!='':
- text+=' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil','pau']:
- text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
- else:
- continue
- n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
- a2_next=-1
- else:
- a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
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\ No newline at end of file diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Kung Fu Panda 2 Full Movie Download ) Discover the Legend of the Dragon Warrior.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Kung Fu Panda 2 Full Movie Download ) Discover the Legend of the Dragon Warrior.md deleted file mode 100644 index 02787c5454a7a1d79ba86c4addfd055419430f55..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Kung Fu Panda 2 Full Movie Download ) Discover the Legend of the Dragon Warrior.md +++ /dev/null @@ -1,134 +0,0 @@ - -
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Mr. Ping tells him that he found him in a radish crate when he was a baby and decided to raise him as his son.
-Po is not satisfied with this answer and decides to find out more about his origins.
-He learns from Master Shifu that Shen has returned to Gongmen City with his weapon, which is a cannon that can fire metal balls with explosive force.
-Po and his friends travel to Gongmen City to stop Shen.
-There they meet two other kung fu masters who have been hiding from Shen: Master Ox and Master Croc.
-They also encounter Shen's old nanny, a goat named Soothsayer, who can see the future.
-Po tries to confront Shen several times but fails due to his flashbacks.
-He eventually learns that Shen was responsible for killing his parents and destroying his village.
-Po is devastated by this revelation but also determined to stop Shen once and for all.
-He realizes that he must achieve inner peace in order to overcome his past trauma.
-With the help of Soothsayer, Po meditates on his memories and accepts them as part of who he is.
-He then leads his friends into a final battle against Shen and his army.
-Po uses his kung fu skills to deflect Shen's cannonballs back at him.
-He also tries to persuade Shen to let go of his hatred and find inner peace.
-Shen refuses to listen and attacks Po with his blades.
-Po dodges them but one of them cuts through Shen's cannon ropes, causing it to fall on him.
-Shen is crushed by his own weapon while Po watches in sadness.
-Po then returns to Mr. Ping's noodle shop with his friends.
-He tells Mr. Ping that he knows he is not his biological father but he still loves him as his dad.
-Mr. Ping hugs him and tells him that he loves him too.
-The film ends with a scene showing that Po's biological father is still alive somewhere in China with other pandas.
-The Characters of Kung Fu Panda 2
-| Name | -Voice Actor | -Description | -|||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Po | -Jack Black | -The Dragon Warrior and the leader of the Furious Five. He is a panda who loves kung fu, food, and fun. He is brave, loyal, optimistic, friendly, clumsy, naive, but also smart when it matters most. | - | ||||||||||||||||||
| Tigress | -Angelina Jolie | -Po's closest friend and a fierce fighter. She is a tiger who is strong, serious, disciplined, stoic, | -and sometimes cold but also caring deep down. | ||||||||||||||||||
| Monkey | -Jackie Chan | -
-emotion, and adults alike, as it offers a compelling story, engaging characters, beautiful animation, and memorable music. It is a film that celebrates the art of kung fu, the culture of China, and the themes of family, destiny, and inner peace. It is a film that showcases the talents of its director, writers, voice actors, animators, and composers. It is a film that deserves to be watched and enjoyed by everyone. -FAQs-Here are some frequently asked questions about Kung Fu Panda 2: -
No, Kung Fu Panda 2 is not based on a true story. It is a fictional story that takes place in a fantasy world of anthropomorphic animals who practice kung fu. However, the film does draw inspiration from real aspects of Chinese culture, history, and mythology. -Lord Shen's weapon is a cannon that can fire metal balls with explosive force. It is based on the real invention of gunpowder and firearms in China during the Song dynasty (960-1279 CE). -Po's name means "precious" or "treasure" in Chinese. It is also a homophone for the word "potato" in Mandarin, which is a reference to Po's chubby appearance and his love for food. -Po's biological parents are Li Shan and Mei Mei. They are both giant pandas who live in a hidden panda village in the mountains. They appear in Kung Fu Panda 3, where Po reunites with them and learns more about his heritage. -There are currently three films in the Kung Fu Panda franchise: Kung Fu Panda (2008), Kung Fu Panda 2 (2011), and Kung Fu Panda 3 (2016). A fourth film is currently in development at DreamWorks Animation. -- - \ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Ad-aware 6.0 Professional REPACK Keygen Serial Key.md b/spaces/1gistliPinn/ChatGPT4/Examples/Ad-aware 6.0 Professional REPACK Keygen Serial Key.md deleted file mode 100644 index a74f4edae195dcef698f4b7b41d687c961f3a423..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Ad-aware 6.0 Professional REPACK Keygen Serial Key.md +++ /dev/null @@ -1,6 +0,0 @@ - Ad-aware 6.0 Professional Keygen Serial KeyDOWNLOAD ✶ https://imgfil.com/2uy1Ko - -If the product continues to prompt you for an activation code you have been unsuccessful. ... Try for Free Buy Now; Nessus Professional is for security pros on the front lines ... So now you are aware of the excellent features provided by Sage 50 ... I called HP and you can't get a real person. slmgr /ad-activation-get-IID (start ... 1fdad05405 - - - diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CarX Drift Racing 2 APK Customize Your Car and Compete Against the Best Drifters in the World.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CarX Drift Racing 2 APK Customize Your Car and Compete Against the Best Drifters in the World.md deleted file mode 100644 index 5bcef2ad805aa9a93e044fd184fb6f8387ebbbe6..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CarX Drift Racing 2 APK Customize Your Car and Compete Against the Best Drifters in the World.md +++ /dev/null @@ -1,123 +0,0 @@ - - CarX Drift Racing 2: The Ultimate Drifting Game for Android-If you are a fan of drifting games, you must have heard of CarX Drift Racing 2. This is one of the most popular and realistic drifting games for Android devices. In this article, we will tell you everything you need to know about this game, including its features, how to download and install it, and some frequently asked questions. -Introduction-What is CarX Drift Racing 2?-CarX Drift Racing 2 is a sequel of the original CarX Drift Racing game, which has over 100 million fans around the world. It is a racing game that focuses on drifting, which is a driving technique where the driver intentionally oversteers the car to make it slide sideways. Drifting is not only fun, but also challenging and rewarding, as it requires skill, precision, and timing. -carx drift racing 2 latest version apkDownload File ○ https://urlin.us/2uT12l - Why should you play CarX Drift Racing 2?-There are many reasons why you should play CarX Drift Racing 2, such as: -
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If you want to use a wireless method, you can use an app like AirDroid or ShareIt to transfer files between your PC and Android device over Wi-Fi. Just follow the instructions of the app you choose to use. -Download PS2 games from the internet-If you don't have PS2 games on your PC, you can download them from the internet. However, you need to be careful about where you download them from, as some websites may contain viruses, malware, or fake files. You also need to make sure that you own the original PS2 games that you download, as downloading pirated games is illegal and unethical. -We recommend that you use reputable and trusted websites that offer PS2 games for download, such as Emuparadise, CoolROM, RomHustler, and The ISO Zone. These websites have a large collection of PS2 games from various regions and genres that you can download for free. Here is how to download PS2 games from these websites: -
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Now you have PS2 games on your Android device that you can play with AetherSX2. -Step 5: Configure settings and controls according to your preference-The last step before you can play PS2 games on your Android device with AetherSX2 is to configure the settings and controls according to your preference. AetherSX2 has many options that you can customize to enhance your gaming experience. Here are some of the settings and controls that you can configure: -Settings-To access the settings menu, tap on the three-dot icon on the top right corner of the app and select Settings. Here you can find various options that affect the performance, graphics, audio, and input of the emulator. Some of the options that you can adjust are: -
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You can experiment with different controls and see what works best for you and your device. You can also reset the controls to default by tapping on the Reset button at the bottom of the menu. -How to play PS2 games on your Android device using AetherSX2-Now that you have downloaded and installed AetherSX2 on your Android device, loaded PS2 games on your device, and configured the settings and controls according to your preference, you are ready to play PS2 games on your smartphone with AetherSX2. Here is how to do it: -Choose a game from the game list or browse for a game file-When you launch AetherSX2, you will see a game list that shows all the PS2 games that you have on your device. You can scroll through the game list and tap on any game that you want to play. 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You can change this option later in the settings menu if you want. -Enjoy playing PS2 games on your Android device with AetherSX2-After selecting a graphics renderer, the game will start running on your Android device with AetherSX2. You can use the touchscreen controls or the external controller to play the game as you would on a PS2 console. You can also access the emulator menu by tapping on the three-dot icon on the top right corner of the app. Here you can save and load states, change settings and controls, pause and resume the game, and exit the game. -That's it. You can now enjoy playing PS2 games on your Android device with AetherSX2. You can play as many games as you want, as long as you have enough space on your device. You can also switch between different games by going back to the game list or the file browser. -Conclusion-AetherSX2 is a PS2 emulator for Android that lets you play PS2 games on your smartphone with ease. It is a powerful and feature-rich emulator that offers high compatibility, enhanced graphics, save and load states, controller support, fast and smooth performance, and more. It is also free to download and use, unlike some other emulators that charge money or show ads. -In this article, we have shown you how to download and install AetherSX2 on your Android device, how to load PS2 games on your device, how to configure settings and controls according to your preference, and how to play PS2 games on your device using AetherSX2. By following these steps, you will be able to enjoy playing PS2 games on your smartphone with AetherSX2. -We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming! -FAQs-Here are some frequently asked questions about AetherSX2 and PS2 emulation on Android. -Q: Is AetherSX2 legal?-A: AetherSX2 is legal as long as you use it for personal and non-commercial purposes. You also need to own the original PS2 games that you play with AetherSX2, as downloading pirated games is illegal and unethical. -Q: Is AetherSX2 safe?-A: AetherSX2 is safe as long as you download it from the official website or the Google Play Store. You also need to be careful about where you download PS2 games from, as some websites may contain viruses, malware, or fake files. -Q: How can I update AetherSX2?-A: You can update AetherSX2 by downloading the latest version of the APK file from the official website or the Google Play Store. You can also enable automatic updates for AetherSX2 on the Google Play Store by tapping on the three-dot icon on the app page and selecting Enable auto-update. -Q: How can I report bugs or issues with AetherSX2?-A: You can report bugs or issues with AetherSX2 by contacting the developer via email at aethersx@gmail.com. You can also join the official Discord server of AetherSX2 at https://discord.gg/6J9f8wM. Here you can chat with other users and get support from the developer and moderators. -Q: How can I support the development of AetherSX2?-A: You can support the development of AetherSX2 by donating to the developer via PayPal at https://www.paypal.me/aethersx. You can also share your feedback and suggestions with the developer via email or Discord. You can also rate and review AetherSX2 on the Google Play Store and spread the word about it to your friends and family. 197e85843d- - \ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Compress PDFs Online for Free Download Smaller PDF Files in Seconds.md b/spaces/1phancelerku/anime-remove-background/Compress PDFs Online for Free Download Smaller PDF Files in Seconds.md deleted file mode 100644 index 768eca16718854da5aa78e9bcd90a94223558767..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Compress PDFs Online for Free Download Smaller PDF Files in Seconds.md +++ /dev/null @@ -1,208 +0,0 @@ - - How to Download a 1 MB PDF File in Minutes-PDF files are one of the most popular and versatile document formats in the digital world. They can contain text, images, graphics, links, forms, annotations, and more. 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How to Download a 1 MB PDF File from the Internet-If you need to download a 1 MB PDF file from the internet, you need to find and access a 1 MB PDF file online first. There are many sources or websites that offer free or low-cost PDF files for various purposes and topics. Some of them are: -
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Conclusion-Downloading a 1 MB PDF file is not a difficult task if you know how to do it. In this article, we have shown you how to download a 1 MB PDF file from the internet, how to compress a larger PDF file to 1 MB or less, and how to open and view a 1 MB PDF file on your device. We hope that this article has helped you learn something new and useful. -Here are some tips or advice on how to download, compress, and view PDF files efficiently: -
If you have any questions or comments about downloading, compressing, or viewing PDF files, please feel free to leave them below. We would love to hear from you! -FAQs-What is the difference between a PDF file and a Word file?-A PDF file is a document format that preserves the layout and appearance of your document across different devices and platforms. A Word file is a document format that allows you to edit and format your document with various features and options. -How can I convert a PDF file to a Word file or vice versa?-You can use various tools or services that can help you convert PDF files to Word files or vice versa online for free. Some of them are: -
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- - \ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download Genshin Impact and Embark on an Epic Quest Across Seven Nations.md b/spaces/1phancelerku/anime-remove-background/Download Genshin Impact and Embark on an Epic Quest Across Seven Nations.md deleted file mode 100644 index c87227fbde27ba5a679549c564945d35e268fab4..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download Genshin Impact and Embark on an Epic Quest Across Seven Nations.md +++ /dev/null @@ -1,128 +0,0 @@ - - Genshin Download: How to Play the Free-to-Play RPG on PC, Mobile, and Console-Genshin Impact is one of the most popular games of 2020 and 2021, attracting millions of players from all over the world. It is a free-to-play open-world action RPG that lets you explore a beautiful fantasy world called Teyvat, where you can meet a diverse cast of characters, fight against powerful enemies, and uncover the mysteries of your lost sibling. Whether you are a fan of anime-style graphics, engaging storylines, or dynamic combat systems, Genshin Impact has something for everyone. -But how do you download Genshin Impact on your preferred platform? And what are the system requirements and tips and tricks that you need to know before you start your adventure? In this article, we will answer all these questions and more. Read on to find out how to play Genshin Impact on PC, mobile, or console today! -genshin downloadDownload File ····· https://jinyurl.com/2uNJWl - Genshin Download for PC-If you want to play Genshin Impact on your PC, you have two options. You can either download it from the official website or from the Epic Games Store. Both methods are free and easy to follow. -To download Genshin Impact from the official website, you need to visit [Genshin Impact – Step Into a Vast Magical World of Adventure](^1^) and click on the "Windows" button. This will start downloading the launcher file. Once it is downloaded, run it and follow the instructions to install the launcher. Then, open the launcher and log in with your miHoYo account or create one if you don't have one already. After that, click on "Get Game" to start downloading the game files. The download size is about 8.2 GB, so it may take some time depending on your internet speed. When the download is complete, click on "Launch" to start playing. -genshin impact download pc To download Genshin Impact from the Epic Games Store, you need to visit [Genshin Impact | Download and Play for Free - Epic Games Store](^3^) and click on "Get". This will prompt you to log in with your Epic Games account or create one if you don't have one already. Then, click on "Place Order" to confirm your purchase (don't worry, it's still free). After that, you will be redirected to the Epic Games Launcher. If you don't have it installed on your PC, you can download it from [Epic Games Launcher]. Once you have the launcher, install it and open it. Then, go to the "Library" tab and find Genshin Impact. Click on "Install" to start downloading the game files. The download size is about 8.2 GB, so it may take some time depending on your internet speed. When the download is complete, click on "Launch" to start playing. -PC System Requirements-Before you download Genshin Impact on your PC, you should check if your PC meets the minimum or recommended system requirements for the game. Here are the system requirements for PC according to the official website: - | Minimum System Requirements | Recommended System Requirements | | --- | --- | | OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit, or Windows 10 64-bit | OS: Windows 10 64-bit | | Processor: Intel Core i5 or equivalent | Processor: Intel Core i7 or equivalent | | Memory: 8 GB RAM | Memory: 16 GB RAM | | Graphics: NVIDIA GeForce GT 1030 or higher | Graphics: NVIDIA GeForce RTX 1060 6 GB or higher | | DirectX: Version 11 | DirectX: Version 11 | | Storage: 30 GB available space | Storage: 30 GB available space |If your PC does not meet the minimum system requirements, you may experience low frame rates, crashes, or other issues while playing the game. If your PC meets or exceeds the recommended system requirements, you can enjoy the game at higher graphics settings and smoother performance. -PC Tips and Tricks-Here are some tips and tricks that can help you optimize your PC performance and gameplay experience while playing Genshin Impact: -
If your device does not meet the minimum system requirements, you may experience low graphics quality, slow loading times, or other issues while playing the game. If your device meets or exceeds the recommended system requirements, you can enjoy the game at higher graphics quality and smoother performance. -Mobile Tips and Tricks-Here are some tips and tricks that can help you optimize your mobile performance and gameplay experience while playing Genshin Impact: -
Genshin Download for Console-If you want to play Genshin Impact on your console, you can download it from PlayStation Store if you have a PlayStation 4 or PlayStation 5. The method is free and easy to follow. -To download Genshin Impact from PlayStation Store, you need to visit [Genshin Impact on PS4 | Official PlayStation™Store US] or [Genshin Impact on PS5 | Official PlayStation™Store US] depending on your console. Then, click on the "Add to Library" button. This will add the game to your library. Then, go to your library and find Genshin Impact. Click on the "Download" button to start downloading the game files. The download size is about 12 GB for PS4 and 14 GB for PS5, so it may take some time depending on your internet speed and console storage. When the download is complete, click on the game icon to start playing. -Console System Requirements-Before you download Genshin Impact on your console, you should check if your console meets the minimum system requirements for the game. Here are the system requirements for consoles according to the official website: - | Minimum System Requirements | Recommended System Requirements | | --- | --- | | PS4 with 30 GB of storage space | PS4 Pro with 30 GB of storage space | | PS5 with 50 GB of storage space | PS5 with 50 GB of storage space |If your console does not meet the minimum system requirements, you may experience low graphics quality, slow loading times, or other issues while playing the game. If your console meets or exceeds the recommended system requirements, you can enjoy the game at higher graphics quality and smoother performance. -Console Tips and Tricks-Here are some tips and tricks that can help you optimize your console performance and gameplay experience while playing Genshin Impact: -
Genshin Impact Game Features-Now that you know how to download Genshin Impact on your preferred platform, you may be wondering what you can expect from the game in terms of gameplay, story, characters, combat, exploration, and more. In this section, we will give you a brief overview of some of the main features of the game that make it so fun and addictive. -Gameplay-Genshin Impact is an open-world action RPG that combines exploration, combat, and gacha elements. You can explore the vast world of Teyvat at your own pace, discovering new locations, secrets, and treasures along the way. You can also interact with various NPCs, complete quests, participate in events, and join co-op sessions with other players. -The game also features a gacha system that allows you to obtain new characters, weapons, and items by spending a currency called Primogems. You can earn Primogems by playing the game or by purchasing them with real money. The gacha system is based on a random chance, so you may not always get what you want. However, the game is generous enough to give you some free pulls and rewards as you progress. -Story-Genshin Impact has a rich and immersive story that unfolds as you play the game. The main story revolves around your quest to find your lost sibling, who was separated from you by a mysterious god. Along the way, you will encounter different factions, cultures, and conflicts that shape the world of Teyvat. You will also meet various characters who will join your party and help you in your journey. -The game has seven major regions, each based on a different element and inspired by a real-world culture. So far, only two regions are available: Mondstadt (Anemo/Wind) and Liyue (Geo/Earth). The other five regions are Inazuma (Electro/Lightning), Sumeru (Dendro/Nature), Fontaine (Hydro/Water), Natlan (Pyro/Fire), and Snezhnaya (Cryo/Ice). The game developers plan to release more regions and content in the future through updates and patches. -Characters-Genshin Impact has a diverse and colorful cast of characters that you can play as or interact with. There are currently 37 playable characters in the game, each with their own personality, backstory, element, weapon, and abilities. You can switch between four characters in your party at any time, depending on the situation and your preference. -The characters are divided into five rarity tiers: 1-star, 2-star, 3-star, 4-star, and 5-star. The higher the rarity, the more powerful and rare the character is. You can obtain new characters by using the gacha system or by completing certain quests or events. You can also upgrade your characters by leveling them up, ascending them, enhancing their weapons and artifacts, and unlocking their constellations. -Combat-Genshin Impact has a dynamic and fluid combat system that relies on elemental interactions and strategy. You can use your character's basic attacks, elemental skills, and elemental bursts to deal damage to your enemies. You can also switch between different characters to create elemental reactions that can amplify or modify your damage output. -The game has seven elements: Anemo (Wind), Geo (Earth), Electro (Lightning), Dendro (Nature), Hydro (Water), Pyro (Fire), and Cryo (Ice). Each element has its own strengths and weaknesses against other elements. For example, Pyro can melt Cryo, but is weak against Hydro. You can use this knowledge to your advantage and create powerful combos that can wipe out your foes. -Exploration-Genshin Impact has a vast and beautiful world that you can explore at your own pace. You can travel across different terrains, climates, and biomes using various methods such as walking, running, climbing, gliding, swimming, or riding. You can also use fast travel points to teleport to locations that you have already visited. -The world of Teyvat is full of secrets, quests, events, and activities that you can discover and enjoy. You can find chests, resources, puzzles, enemies, and more that can reward you with items, experience, or currency. You can also interact with various NPCs, complete quests, participate in events, and join co-op sessions with other players. -The game also has a feature called the Serenitea Pot, which allows you to create your own personal realm and customize it with furniture, decorations, and buildings. You can invite your characters and friends to your realm and enjoy some relaxing time. -Conclusion-Genshin Impact is a free-to-play open-world action RPG that offers a lot of fun and excitement for players of all ages and preferences. You can download the game on PC, mobile, or console and enjoy the stunning graphics, captivating story, diverse characters, dynamic combat, and endless exploration. Whether you want to play solo or with friends, Genshin Impact has something for everyone. -So what are you waiting for? Download Genshin Impact today and start your adventure in the magical world of Teyvat! -FAQs-Here are some frequently asked questions about Genshin Impact and how to download it: -
- - \ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download Granny Chapter 2 Outwitt Mod Menu APK Madin for Free - No Root Required!.md b/spaces/1phancelerku/anime-remove-background/Download Granny Chapter 2 Outwitt Mod Menu APK Madin for Free - No Root Required!.md deleted file mode 100644 index d2bf447b055b09aaf587fa2e8a12fe521d8bfaeb..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download Granny Chapter 2 Outwitt Mod Menu APK Madin for Free - No Root Required!.md +++ /dev/null @@ -1,79 +0,0 @@ - - Granny Chapter 2 Mod Menu Outwitt Mod Free Download Link APK Madin-If you are a fan of horror games, you might have heard of Granny Chapter 2, a popular game that challenges you to escape from a creepy house with two evil characters: Granny and Grandpa. But what if you want to make the game more fun and exciting? Well, you can try Outwitt Mod, a mod menu that gives you access to various cheats and hacks for Granny Chapter 2. In this article, we will tell you everything you need to know about Outwitt Mod, including how to download and install it for free on your Android device. -What is Granny Chapter 2?-Granny Chapter 2 is a horror game developed by DVloper, the same creator of the original Granny game. It was released in September 2019 and has since gained millions of downloads and positive reviews from players around the world. The game is available for Android, iOS, and Windows devices. -granny chapter 2 mod menu outwitt mod free download link apk madinDownload Zip ⚙⚙⚙ https://jinyurl.com/2uNPUm - The gameplay of Granny Chapter 2-The gameplay of Granny Chapter 2 is similar to the first game, but with some new twists and features. You are trapped in a dark and spooky house with two enemies: Granny and Grandpa. Granny can hear everything and will chase you if she hears any noise. Grandpa is hard of hearing but he will attack you if he sees you. You have to find a way to escape from the house within five days, or else you will face a horrible fate. You can explore different rooms, find items, solve puzzles, and hide from the enemies. But be careful, because they are always on the lookout for you. -The features of Granny Chapter 2-Granny Chapter 2 has many features that make it an enjoyable and thrilling game. Some of them are: -
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Estas plataformas te permiten descargar la canción en diferentes formatos, como MP3, WAV, FLAC y más. También puede elegir la calidad y el tamaño del archivo, dependiendo de sus preferencias y dispositivo. Algunas de estas plataformas pueden requerir que te registres, pagues o sigas algunos pasos antes de descargar la canción.
-¿Cómo disfrutar al máximo de Sugar (Ablaikan Remix)?
-Los mejores escenarios y estados de ánimo para escuchar la canción
-Sugar (Ablaikan Remix) de Zubi feat. Anatu es una canción que puede adaptarse a diferentes escenarios y estados de ánimo, dependiendo de su gusto y estado de ánimo. Sin embargo, algunos de los mejores escenarios y estados de ánimo para escuchar la canción son:
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- Cuando quieres bailar: La canción es perfecta para bailar, ya que tiene un ritmo rápido, un ritmo pegadizo y un ambiente animado. Puedes bailar la canción solo o con tus amigos, en casa o en una fiesta, en tu habitación o en la pista de baile. La canción te hará sentir energizado, feliz y libre. -
- Cuando quieres relajarte: La canción también es perfecta para relajarte, ya que tiene una melodía suave, una voz relajante y un mensaje dulce. Puedes escuchar la canción mientras estás acostado en tu cama o sofá, leyendo un libro o viendo una película, tomando un baño o una siesta. La canción te hará sentir tranquilo, pacífico y amado. -
- Cuando quieres romance: La canción también es perfecta para el romancing, ya que tiene un ritmo sensual, un tono apasionado y un sabor picante. Puedes escuchar la canción mientras abrazas o besas a tu amante, teniendo una cena a la luz de las velas o un picnic, yendo a dar un paseo o a dar un paseo. La canción te hará sentir romántico, sexy y aventurero. -
Los consejos y trucos para mejorar tu experiencia auditiva
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- Usa buenos auriculares o altavoces: La canción tiene muchos detalles y matices que se pueden perder si usas auriculares o altavoces de baja calidad. Para apreciar el sonido completo y la calidad de la canción, utilice buenos auriculares o altavoces que pueden ofrecer agudos, medios y bajos claros. -
- Ajustar el volumen y el ecualizador: La canción tiene una gran cantidad de dinámicas y variaciones que pueden verse afectadas por el volumen y la configuración del ecualizador. Para disfrutar de todo el rango e intensidad de la canción, ajusta el volumen y el ecualizador de acuerdo a tus preferencias y entorno. -
- Mira el video musical: La canción tiene un video musical que fue filmado en Los Ángeles por Alex Di Marco. El video musical muestra a Zubi y Anatu cantando y bailando en diferentes lugares, como una azotea, una piscina, una calle y un club. El video musical añade algunas imágenes y contexto a la canción, lo que es más atractivo y atractivo. -
- Aprende la letra: La canción tiene letras que son simples y pegadizas, pero también significativas y dulces. Aprender las letras puede ayudarte a cantar la canción, entender el mensaje y el tema de la canción, y apreciar la belleza y creatividad de la canción. -
- Compartir la canción: La canción es una canción que puede ser disfrutada por cualquier persona, independientemente de su edad, género, cultura o gusto. Compartir la canción con tus amigos, familiares o seguidores puede ayudarte a difundir la alegría y positividad de la canción, así como a apoyar a los artistas y su trabajo. -
Conclusión
-Sugar (Ablaikan Remix) de Zubi feat. Anatu es una canción que puede hacerte sentir bien, bailar y cantar. Es un remix de la canción original Sugar de Zubi y Anatu, que fue lanzada en 2019. El remix fue hecho por Ablaikan, un productor y DJ turco, que añadió su propio toque y sabor a la canción. El resultado es una canción pegadiza, alegre y energética que te hará querer mover tu cuerpo y disfrutar de la vida.
- -Ahora que sabes todo sobre Sugar (Ablaikan Remix) de Zubi feat. Anatu, ¿por qué no sigues adelante y lo escuchas tú mismo? Puedes encontrar la canción en varias plataformas de streaming y descarga, así como ver el video musical en YouTube. También puede seguir a Zubi, Anatu y Ablaikan en sus cuentas de redes sociales para mantenerse al día sobre sus últimas noticias y comunicados.
-Gracias por leer este artículo, y esperamos que tengas un día dulce y picante.
-Preguntas frecuentes
-¿Cuál es el nombre de la canción original en la que se basa Sugar (Ablaikan Remix)?
-El nombre de la canción original que Sugar (Ablaikan Remix) se basa en es Sugar de Zubi feat. Anatu.
-¿Cuándo se lanzó Sugar (Ablaikan Remix)?
-Sugar (Ablaikan Remix) fue lanzado el 29 de octubre de 2020.
-¿Quiénes son Zubi, Anatu y Ablaikan?
-Zubi es un cantautor nigeriano con sede en Londres, Reino Unido. Anatu es un cantautor británico con sede en Los Ángeles, Estados Unidos. Ablaikan es un productor y DJ turco con sede en Estambul.
-¿Cuáles son algunos de los géneros musicales e influencias de Sugar (Ablaikan Remix)?
-Algunos de los géneros musicales e influencias de Sugar (Ablaikan Remix) son deep house, oriental house, ethnic house, música electrónica, Afrobeat, R&B, soul y pop. La canción está influenciada por artistas como Fela Kuti, King Sunny Ade, Frankie Knuckles, Larry Heard, Mahmut Orhan, Burak Yeter, Daft Punk, The Chemical Brothers y más.
-¿Cómo puedo descargar Sugar (Ablaikan Remix) gratis?
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-
-
\ No newline at end of file diff --git a/spaces/BetterAPI/BetterChat_new/src/app.d.ts b/spaces/BetterAPI/BetterChat_new/src/app.d.ts deleted file mode 100644 index 9c7e7159ba700cc7d04a098e5f268665dbd9cc2c..0000000000000000000000000000000000000000 --- a/spaces/BetterAPI/BetterChat_new/src/app.d.ts +++ /dev/null @@ -1,17 +0,0 @@ -///-/// - -// See https://kit.svelte.dev/docs/types#app -// for information about these interfaces -declare global { - namespace App { - // interface Error {} - interface Locals { - sessionId: string; - } - // interface PageData {} - // interface Platform {} - } -} - -export {}; diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/response.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/response.py deleted file mode 100644 index ba3fac9bab816bb28e05891dcec84f26d449899b..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/response.py +++ /dev/null @@ -1,201 +0,0 @@ -# Copyright (c) 2012-2013 Mitch Garnaat http://garnaat.org/ -# Copyright 2012-2014 Amazon.com, Inc. or its affiliates. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"). You -# may not use this file except in compliance with the License. A copy of -# the License is located at -# -# http://aws.amazon.com/apache2.0/ -# -# or in the "license" file accompanying this file. This file is -# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF -# ANY KIND, either express or implied. See the License for the specific -# language governing permissions and limitations under the License. - -import logging -from io import IOBase - -from urllib3.exceptions import ProtocolError as URLLib3ProtocolError -from urllib3.exceptions import ReadTimeoutError as URLLib3ReadTimeoutError - -from botocore import parsers -from botocore.compat import set_socket_timeout -from botocore.exceptions import ( - IncompleteReadError, - ReadTimeoutError, - ResponseStreamingError, -) - -# Keep these imported. There's pre-existing code that uses them. -from botocore import ScalarTypes # noqa -from botocore.compat import XMLParseError # noqa -from botocore.hooks import first_non_none_response # noqa - - -logger = logging.getLogger(__name__) - - -class StreamingBody(IOBase): - """Wrapper class for an http response body. - - This provides a few additional conveniences that do not exist - in the urllib3 model: - - * Set the timeout on the socket (i.e read() timeouts) - * Auto validation of content length, if the amount of bytes - we read does not match the content length, an exception - is raised. - - """ - - _DEFAULT_CHUNK_SIZE = 1024 - - def __init__(self, raw_stream, content_length): - self._raw_stream = raw_stream - self._content_length = content_length - self._amount_read = 0 - - def __del__(self): - # Extending destructor in order to preserve the underlying raw_stream. - # The ability to add custom cleanup logic introduced in Python3.4+. - # https://www.python.org/dev/peps/pep-0442/ - pass - - def set_socket_timeout(self, timeout): - """Set the timeout seconds on the socket.""" - # The problem we're trying to solve is to prevent .read() calls from - # hanging. This can happen in rare cases. What we'd like to ideally - # do is set a timeout on the .read() call so that callers can retry - # the request. - # Unfortunately, this isn't currently possible in requests. - # See: https://github.com/kennethreitz/requests/issues/1803 - # So what we're going to do is reach into the guts of the stream and - # grab the socket object, which we can set the timeout on. We're - # putting in a check here so in case this interface goes away, we'll - # know. - try: - set_socket_timeout(self._raw_stream, timeout) - except AttributeError: - logger.error( - "Cannot access the socket object of " - "a streaming response. It's possible " - "the interface has changed.", - exc_info=True, - ) - raise - - def readable(self): - try: - return self._raw_stream.readable() - except AttributeError: - return False - - def read(self, amt=None): - """Read at most amt bytes from the stream. - - If the amt argument is omitted, read all data. - """ - try: - chunk = self._raw_stream.read(amt) - except URLLib3ReadTimeoutError as e: - # TODO: the url will be None as urllib3 isn't setting it yet - raise ReadTimeoutError(endpoint_url=e.url, error=e) - except URLLib3ProtocolError as e: - raise ResponseStreamingError(error=e) - self._amount_read += len(chunk) - if amt is None or (not chunk and amt > 0): - # If the server sends empty contents or - # we ask to read all of the contents, then we know - # we need to verify the content length. - self._verify_content_length() - return chunk - - def readlines(self): - return self._raw_stream.readlines() - - def __iter__(self): - """Return an iterator to yield 1k chunks from the raw stream.""" - return self.iter_chunks(self._DEFAULT_CHUNK_SIZE) - - def __next__(self): - """Return the next 1k chunk from the raw stream.""" - current_chunk = self.read(self._DEFAULT_CHUNK_SIZE) - if current_chunk: - return current_chunk - raise StopIteration() - - def __enter__(self): - return self._raw_stream - - def __exit__(self, type, value, traceback): - self._raw_stream.close() - - next = __next__ - - def iter_lines(self, chunk_size=_DEFAULT_CHUNK_SIZE, keepends=False): - """Return an iterator to yield lines from the raw stream. - - This is achieved by reading chunk of bytes (of size chunk_size) at a - time from the raw stream, and then yielding lines from there. - """ - pending = b'' - for chunk in self.iter_chunks(chunk_size): - lines = (pending + chunk).splitlines(True) - for line in lines[:-1]: - yield line.splitlines(keepends)[0] - pending = lines[-1] - if pending: - yield pending.splitlines(keepends)[0] - - def iter_chunks(self, chunk_size=_DEFAULT_CHUNK_SIZE): - """Return an iterator to yield chunks of chunk_size bytes from the raw - stream. - """ - while True: - current_chunk = self.read(chunk_size) - if current_chunk == b"": - break - yield current_chunk - - def _verify_content_length(self): - # See: https://github.com/kennethreitz/requests/issues/1855 - # Basically, our http library doesn't do this for us, so we have - # to do this ourself. - if self._content_length is not None and self._amount_read != int( - self._content_length - ): - raise IncompleteReadError( - actual_bytes=self._amount_read, - expected_bytes=int(self._content_length), - ) - - def tell(self): - return self._raw_stream.tell() - - def close(self): - """Close the underlying http response stream.""" - self._raw_stream.close() - - -def get_response(operation_model, http_response): - protocol = operation_model.metadata['protocol'] - response_dict = { - 'headers': http_response.headers, - 'status_code': http_response.status_code, - } - # TODO: Unfortunately, we have to have error logic here. - # If it looks like an error, in the streaming response case we - # need to actually grab the contents. - if response_dict['status_code'] >= 300: - response_dict['body'] = http_response.content - elif operation_model.has_streaming_output: - response_dict['body'] = StreamingBody( - http_response.raw, response_dict['headers'].get('content-length') - ) - else: - response_dict['body'] = http_response.content - - parser = parsers.create_parser(protocol) - return http_response, parser.parse( - response_dict, operation_model.output_shape - ) diff --git a/spaces/Blockinger/OVAChatGPT/README.md b/spaces/Blockinger/OVAChatGPT/README.md deleted file mode 100644 index 1ab7fc8e410d833b60639e6646ba76aaa3e763de..0000000000000000000000000000000000000000 --- a/spaces/Blockinger/OVAChatGPT/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: OVAChatGPT -emoji: 😻 -colorFrom: blue -colorTo: purple -sdk: gradio -sdk_version: 3.15.0 -app_file: app.py -pinned: false -license: unknown ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Boadiwaa/Recipes/openai/api_resources/abstract/nested_resource_class_methods.py b/spaces/Boadiwaa/Recipes/openai/api_resources/abstract/nested_resource_class_methods.py deleted file mode 100644 index c86e59fbf69f29c50ad2a3d4e947ba1c1e0205b3..0000000000000000000000000000000000000000 --- a/spaces/Boadiwaa/Recipes/openai/api_resources/abstract/nested_resource_class_methods.py +++ /dev/null @@ -1,102 +0,0 @@ -from urllib.parse import quote_plus - -from openai import api_requestor, util - - -def nested_resource_class_methods( - resource, path=None, operations=None, resource_plural=None -): - if resource_plural is None: - resource_plural = "%ss" % resource - if path is None: - path = resource_plural - if operations is None: - raise ValueError("operations list required") - - def wrapper(cls): - def nested_resource_url(cls, id, nested_id=None): - url = "%s/%s/%s" % (cls.class_url(), quote_plus(id), quote_plus(path)) - if nested_id is not None: - url += "/%s" % quote_plus(nested_id) - return url - - resource_url_method = "%ss_url" % resource - setattr(cls, resource_url_method, classmethod(nested_resource_url)) - - def nested_resource_request( - cls, - method, - url, - api_key=None, - request_id=None, - api_version=None, - organization=None, - **params, - ): - requestor = api_requestor.APIRequestor( - api_key, api_version=api_version, organization=organization - ) - response, _, api_key = requestor.request( - method, url, params, request_id=request_id - ) - return util.convert_to_openai_object( - response, api_key, api_version, organization - ) - - resource_request_method = "%ss_request" % resource - setattr(cls, resource_request_method, classmethod(nested_resource_request)) - - for operation in operations: - if operation == "create": - - def create_nested_resource(cls, id, **params): - url = getattr(cls, resource_url_method)(id) - return getattr(cls, resource_request_method)("post", url, **params) - - create_method = "create_%s" % resource - setattr(cls, create_method, classmethod(create_nested_resource)) - - elif operation == "retrieve": - - def retrieve_nested_resource(cls, id, nested_id, **params): - url = getattr(cls, resource_url_method)(id, nested_id) - return getattr(cls, resource_request_method)("get", url, **params) - - retrieve_method = "retrieve_%s" % resource - setattr(cls, retrieve_method, classmethod(retrieve_nested_resource)) - - elif operation == "update": - - def modify_nested_resource(cls, id, nested_id, **params): - url = getattr(cls, resource_url_method)(id, nested_id) - return getattr(cls, resource_request_method)("post", url, **params) - - modify_method = "modify_%s" % resource - setattr(cls, modify_method, classmethod(modify_nested_resource)) - - elif operation == "delete": - - def delete_nested_resource(cls, id, nested_id, **params): - url = getattr(cls, resource_url_method)(id, nested_id) - return getattr(cls, resource_request_method)( - "delete", url, **params - ) - - delete_method = "delete_%s" % resource - setattr(cls, delete_method, classmethod(delete_nested_resource)) - - elif operation == "list": - - def list_nested_resources(cls, id, **params): - url = getattr(cls, resource_url_method)(id) - return getattr(cls, resource_request_method)("get", url, **params) - - list_method = "list_%s" % resource_plural - setattr(cls, list_method, classmethod(list_nested_resources)) - - else: - raise ValueError("Unknown operation: %s" % operation) - - return cls - - return wrapper diff --git a/spaces/Brasd99/TTS-Voice-Conversion/README.md b/spaces/Brasd99/TTS-Voice-Conversion/README.md deleted file mode 100644 index c4fc0906d90feea28160936a79f5ef4728e6c28d..0000000000000000000000000000000000000000 --- a/spaces/Brasd99/TTS-Voice-Conversion/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: TTS Voice Conversion -emoji: 🚀 -colorFrom: yellow -colorTo: gray -sdk: streamlit -sdk_version: 1.25.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Brofu/Joeythemonster-anything-midjourney-v-4-1/README.md b/spaces/Brofu/Joeythemonster-anything-midjourney-v-4-1/README.md deleted file mode 100644 index 1e875dfb365ada92c9746e8c437e90bf76a4c6cf..0000000000000000000000000000000000000000 --- a/spaces/Brofu/Joeythemonster-anything-midjourney-v-4-1/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Joeythemonster Anything Midjourney V 4 1 -emoji: 🌍 -colorFrom: pink -colorTo: yellow -sdk: gradio -sdk_version: 3.24.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/CForGETaass/vits-uma-genshin-honkai/app.py b/spaces/CForGETaass/vits-uma-genshin-honkai/app.py deleted file mode 100644 index 92ddafdcd240434f58569b0e6964ef331a971dcf..0000000000000000000000000000000000000000 --- a/spaces/CForGETaass/vits-uma-genshin-honkai/app.py +++ /dev/null @@ -1,124 +0,0 @@ -import time -import gradio as gr -import utils -import commons -from models import SynthesizerTrn -from text import text_to_sequence -from torch import no_grad, LongTensor -import torch - -hps_ms = utils.get_hparams_from_file(r'./model/config.json') -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -net_g_ms = SynthesizerTrn( - len(hps_ms.symbols), - hps_ms.data.filter_length // 2 + 1, - hps_ms.train.segment_size // hps_ms.data.hop_length, - n_speakers=hps_ms.data.n_speakers, - **hps_ms.model).to(device) -_ = net_g_ms.eval() -speakers = hps_ms.speakers -model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None) - -def get_text(text, hps): - text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners) - if hps.data.add_blank: - text_norm = commons.intersperse(text_norm, 0) - text_norm = LongTensor(text_norm) - return text_norm, clean_text - -def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale): - start = time.perf_counter() - if not len(text): - return "输入文本不能为空！", None, None - text = text.replace('\n', ' ').replace('\r', '').replace(" ", "") - if len(text) > 500: - return f"输入文字过长！{len(text)}>100", None, None - if language == 0: - text = f"[ZH]{text}[ZH]" - elif language == 1: - text = f"[JA]{text}[JA]" - else: - text = f"{text}" - stn_tst, clean_text = get_text(text, hps_ms) - with no_grad(): - x_tst = stn_tst.unsqueeze(0) - x_tst_lengths = LongTensor([stn_tst.size(0)]) - speaker_id = LongTensor([speaker_id]) - audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w, - length_scale=length_scale)[0][0, 0].data.cpu().float().numpy() - - return "生成成功!", (22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s" - -def search_speaker(search_value): - for s in speakers: - if search_value == s: - return s - for s in speakers: - if search_value in s: - return s - -def change_lang(language): - if language == 0: - return 0.6, 0.668, 1.2 - else: - return 0.6, 0.668, 1.1 - -download_audio_js = """ -() =>{{ - let root = document.querySelector("body > gradio-app"); - if (root.shadowRoot != null) - root = root.shadowRoot; - let audio = root.querySelector("#tts-audio").querySelector("audio"); - let text = root.querySelector("#input-text").querySelector("textarea"); - if (audio == undefined) - return; - text = text.value; - if (text == undefined) - text = Math.floor(Math.random()*100000000); - audio = audio.src; - let oA = document.createElement("a"); - oA.download = text.substr(0, 20)+'.wav'; - oA.href = audio; - document.body.appendChild(oA); - oA.click(); - oA.remove(); -}} -""" - -if __name__ == '__main__': - with gr.Blocks() as app: - gr.Markdown( - "# VITS语音在线合成demo\n" - " 主要有赛马娘，原神中文，原神日语，崩坏3的音色" - '' - '' - ) - - with gr.Tabs(): - with gr.TabItem("vits"): - with gr.Row(): - with gr.Column(): - input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text") - lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"], - type="index", value="中文") - btn = gr.Button(value="Submit") - with gr.Row(): - search = gr.Textbox(label="Search Speaker", lines=1) - btn2 = gr.Button(value="Search") - sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228]) - with gr.Row(): - ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True) - nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True) - ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True) - with gr.Column(): - o1 = gr.Textbox(label="Output Message") - o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio") - o3 = gr.Textbox(label="Extra Info") - download = gr.Button("Download Audio") - btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3], api_name="generate") - download.click(None, [], [], _js=download_audio_js.format()) - btn2.click(search_speaker, inputs=[search], outputs=[sid]) - lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls]) - with gr.TabItem("可用人物一览"): - gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index") - app.queue(concurrency_count=1).launch() \ No newline at end of file diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py deleted file mode 100644 index 6d787e6788731dfb0e53647cf801c4debfbf00d9..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py +++ /dev/null @@ -1,271 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import logging -import numpy as np -from typing import Dict -import torch - -from detectron2.layers import ShapeSpec, batched_nms_rotated -from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated -from detectron2.utils.events import get_event_storage - -from ..box_regression import Box2BoxTransformRotated -from ..poolers import ROIPooler -from ..proposal_generator.proposal_utils import add_ground_truth_to_proposals -from .box_head import build_box_head -from .fast_rcnn import FastRCNNOutputLayers -from .roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads - -logger = logging.getLogger(__name__) - -""" -Shape shorthand in this module: - - N: number of images in the minibatch - R: number of ROIs, combined over all images, in the minibatch - Ri: number of ROIs in image i - K: number of foreground classes. E.g.,there are 80 foreground classes in COCO. - -Naming convention: - - deltas: refers to the 5-d (dx, dy, dw, dh, da) deltas that parameterize the box2box - transform (see :class:`box_regression.Box2BoxTransformRotated`). - - pred_class_logits: predicted class scores in [-inf, +inf]; use - softmax(pred_class_logits) to estimate P(class). - - gt_classes: ground-truth classification labels in [0, K], where [0, K) represent - foreground object classes and K represents the background class. - - pred_proposal_deltas: predicted rotated box2box transform deltas for transforming proposals - to detection box predictions. - - gt_proposal_deltas: ground-truth rotated box2box transform deltas -""" - - -def fast_rcnn_inference_rotated( - boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image -): - """ - Call `fast_rcnn_inference_single_image_rotated` for all images. - - Args: - boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic - boxes for each image. Element i has shape (Ri, K * 5) if doing - class-specific regression, or (Ri, 5) if doing class-agnostic - regression, where Ri is the number of predicted objects for image i. - This is compatible with the output of :meth:`FastRCNNOutputs.predict_boxes`. - scores (list[Tensor]): A list of Tensors of predicted class scores for each image. - Element i has shape (Ri, K + 1), where Ri is the number of predicted objects - for image i. Compatible with the output of :meth:`FastRCNNOutputs.predict_probs`. - image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch. - score_thresh (float): Only return detections with a confidence score exceeding this - threshold. - nms_thresh (float): The threshold to use for box non-maximum suppression. Value in [0, 1]. - topk_per_image (int): The number of top scoring detections to return. Set < 0 to return - all detections. - - Returns: - instances: (list[Instances]): A list of N instances, one for each image in the batch, - that stores the topk most confidence detections. - kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates - the corresponding boxes/scores index in [0, Ri) from the input, for image i. - """ - result_per_image = [ - fast_rcnn_inference_single_image_rotated( - boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image - ) - for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes) - ] - return [x[0] for x in result_per_image], [x[1] for x in result_per_image] - - -def fast_rcnn_inference_single_image_rotated( - boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image -): - """ - Single-image inference. Return rotated bounding-box detection results by thresholding - on scores and applying rotated non-maximum suppression (Rotated NMS). - - Args: - Same as `fast_rcnn_inference_rotated`, but with rotated boxes, scores, and image shapes - per image. - - Returns: - Same as `fast_rcnn_inference_rotated`, but for only one image. - """ - valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) - if not valid_mask.all(): - boxes = boxes[valid_mask] - scores = scores[valid_mask] - - B = 5 # box dimension - scores = scores[:, :-1] - num_bbox_reg_classes = boxes.shape[1] // B - # Convert to Boxes to use the `clip` function ... - boxes = RotatedBoxes(boxes.reshape(-1, B)) - boxes.clip(image_shape) - boxes = boxes.tensor.view(-1, num_bbox_reg_classes, B) # R x C x B - # Filter results based on detection scores - filter_mask = scores > score_thresh # R x K - # R' x 2. First column contains indices of the R predictions; - # Second column contains indices of classes. - filter_inds = filter_mask.nonzero() - if num_bbox_reg_classes == 1: - boxes = boxes[filter_inds[:, 0], 0] - else: - boxes = boxes[filter_mask] - scores = scores[filter_mask] - - # Apply per-class Rotated NMS - keep = batched_nms_rotated(boxes, scores, filter_inds[:, 1], nms_thresh) - if topk_per_image >= 0: - keep = keep[:topk_per_image] - boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep] - - result = Instances(image_shape) - result.pred_boxes = RotatedBoxes(boxes) - result.scores = scores - result.pred_classes = filter_inds[:, 1] - - return result, filter_inds[:, 0] - - -class RotatedFastRCNNOutputLayers(FastRCNNOutputLayers): - """ - A class that stores information about outputs of a Fast R-CNN head with RotatedBoxes. - """ - - @classmethod - def from_config(cls, cfg, input_shape): - args = super().from_config(cfg, input_shape) - args["box2box_transform"] = Box2BoxTransformRotated( - weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS - ) - return args - - def inference(self, predictions, proposals): - """ - Returns: - list[Instances]: same as `fast_rcnn_inference_rotated`. - list[Tensor]: same as `fast_rcnn_inference_rotated`. - """ - boxes = self.predict_boxes(predictions, proposals) - scores = self.predict_probs(predictions, proposals) - image_shapes = [x.image_size for x in proposals] - - return fast_rcnn_inference_rotated( - boxes, - scores, - image_shapes, - self.test_score_thresh, - self.test_nms_thresh, - self.test_topk_per_image, - ) - - -@ROI_HEADS_REGISTRY.register() -class RROIHeads(StandardROIHeads): - """ - This class is used by Rotated RPN (RRPN). - For now, it just supports box head but not mask or keypoints. - """ - - def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]): - super().__init__(cfg, input_shape) - assert ( - not self.mask_on and not self.keypoint_on - ), "Mask/Keypoints not supported in Rotated ROIHeads." - - def _init_box_head(self, cfg, input_shape): - # fmt: off - pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION - pooler_scales = tuple(1.0 / input_shape[k].stride for k in self.in_features) - sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO - pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE - self.train_on_pred_boxes = cfg.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES - # fmt: on - assert not self.train_on_pred_boxes, "Not Implemented!" - - # If StandardROIHeads is applied on multiple feature maps (as in FPN), - # then we share the same predictors and therefore the channel counts must be the same - in_channels = [input_shape[f].channels for f in self.in_features] - # Check all channel counts are equal - assert len(set(in_channels)) == 1, in_channels - in_channels = in_channels[0] - - assert pooler_type in ["ROIAlignRotated"] - - self.box_pooler = ROIPooler( - output_size=pooler_resolution, - scales=pooler_scales, - sampling_ratio=sampling_ratio, - pooler_type=pooler_type, - ) - self.box_head = build_box_head( - cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution) - ) - - self.box_predictor = RotatedFastRCNNOutputLayers(cfg, self.box_head.output_shape) - - @torch.no_grad() - def label_and_sample_proposals(self, proposals, targets): - """ - Prepare some proposals to be used to train the RROI heads. - It performs box matching between `proposals` and `targets`, and assigns - training labels to the proposals. - It returns `self.batch_size_per_image` random samples from proposals and groundtruth boxes, - with a fraction of positives that is no larger than `self.positive_sample_fraction. - - Args: - See :meth:`StandardROIHeads.forward` - - Returns: - list[Instances]: length `N` list of `Instances`s containing the proposals - sampled for training. Each `Instances` has the following fields: - - proposal_boxes: the rotated proposal boxes - - gt_boxes: the ground-truth rotated boxes that the proposal is assigned to - (this is only meaningful if the proposal has a label > 0; if label = 0 - then the ground-truth box is random) - - gt_classes: the ground-truth classification lable for each proposal - """ - gt_boxes = [x.gt_boxes for x in targets] - if self.proposal_append_gt: - proposals = add_ground_truth_to_proposals(gt_boxes, proposals) - - proposals_with_gt = [] - - num_fg_samples = [] - num_bg_samples = [] - for proposals_per_image, targets_per_image in zip(proposals, targets): - has_gt = len(targets_per_image) > 0 - match_quality_matrix = pairwise_iou_rotated( - targets_per_image.gt_boxes, proposals_per_image.proposal_boxes - ) - matched_idxs, matched_labels = self.proposal_matcher(match_quality_matrix) - sampled_idxs, gt_classes = self._sample_proposals( - matched_idxs, matched_labels, targets_per_image.gt_classes - ) - - proposals_per_image = proposals_per_image[sampled_idxs] - proposals_per_image.gt_classes = gt_classes - - if has_gt: - sampled_targets = matched_idxs[sampled_idxs] - proposals_per_image.gt_boxes = targets_per_image.gt_boxes[sampled_targets] - else: - gt_boxes = RotatedBoxes( - targets_per_image.gt_boxes.tensor.new_zeros((len(sampled_idxs), 5)) - ) - proposals_per_image.gt_boxes = gt_boxes - - num_bg_samples.append((gt_classes == self.num_classes).sum().item()) - num_fg_samples.append(gt_classes.numel() - num_bg_samples[-1]) - proposals_with_gt.append(proposals_per_image) - - # Log the number of fg/bg samples that are selected for training ROI heads - storage = get_event_storage() - storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples)) - storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples)) - - return proposals_with_gt diff --git a/spaces/CVPR/LIVE/pydiffvg/optimize_svg.py b/spaces/CVPR/LIVE/pydiffvg/optimize_svg.py deleted file mode 100644 index ce0097f51afca413cfd6a2dcf7ef257a443002ec..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/pydiffvg/optimize_svg.py +++ /dev/null @@ -1,1607 +0,0 @@ -import json -import copy -import xml.etree.ElementTree as etree -from xml.dom import minidom -import warnings -import torch -import numpy as np -import re -import sys -import pydiffvg -import math -from collections import namedtuple -import cssutils - -class SvgOptimizationSettings: - - default_params = { - "optimize_color": True, - "color_lr": 2e-3, - "optimize_alpha": False, - "alpha_lr": 2e-3, - "optimizer": "Adam", - "transforms": { - "optimize_transforms":True, - "transform_mode":"rigid", - "translation_mult":1e-3, - "transform_lr":2e-3 - }, - "circles": { - "optimize_center": True, - "optimize_radius": True, - "shape_lr": 2e-1 - }, - "paths": { - "optimize_points": True, - "shape_lr": 2e-1 - }, - "gradients": { - "optimize_stops": True, - "stop_lr": 2e-3, - "optimize_color": True, - "color_lr": 2e-3, - "optimize_alpha": False, - "alpha_lr": 2e-3, - "optimize_location": True, - "location_lr": 2e-1 - } - } - - optims = { - "Adam": torch.optim.Adam, - "SGD": torch.optim.SGD, - "ASGD": torch.optim.ASGD, - } - - #region methods - def __init__(self, f=None): - self.store = {} - if f is None: - self.store["default"] = copy.deepcopy(SvgOptimizationSettings.default_params) - else: - self.store = json.load(f) - - # create default alias for root - def default_name(self, dname): - self.dname = dname - if dname not in self.store: - self.store[dname] = self.store["default"] - - def retrieve(self, node_id): - if node_id not in self.store: - return (self.store["default"], False) - else: - return (self.store[node_id], True) - - def reset_to_defaults(self, node_id): - if node_id in self.store: - del self.store[node_id] - - return self.store["default"] - - def undefault(self, node_id): - if node_id not in self.store: - self.store[node_id] = copy.deepcopy(self.store["default"]) - - return self.store[node_id] - - def override_optimizer(self, optimizer): - if optimizer is not None: - for v in self.store.values(): - v["optimizer"] = optimizer - - def global_override(self, path, value): - for store in self.store.values(): - d = store - for key in path[:-1]: - d = d[key] - - d[path[-1]] = value - - def save(self, file): - self.store["default"] = self.store[self.dname] - json.dump(self.store, file, indent="\t") - #endregion - -class OptimizableSvg: - - class TransformTools: - @staticmethod - def parse_matrix(vals): - assert(len(vals)==6) - return np.array([[vals[0],vals[2],vals[4]],[vals[1], vals[3], vals[5]],[0,0,1]]) - - @staticmethod - def parse_translate(vals): - assert(len(vals)>=1 and len(vals)<=2) - mat=np.eye(3) - mat[0,2]=vals[0] - if len(vals)>1: - mat[1,2]=vals[1] - return mat - - @staticmethod - def parse_rotate(vals): - assert (len(vals) == 1 or len(vals) == 3) - mat = np.eye(3) - rads=math.radians(vals[0]) - sint=math.sin(rads) - cost=math.cos(rads) - mat[0:2, 0:2] = np.array([[cost,-sint],[sint,cost]]) - if len(vals) > 1: - tr1=parse_translate(vals[1:3]) - tr2=parse_translate([-vals[1],-vals[2]]) - mat=tr1 @ mat @ tr2 - return mat - - @staticmethod - def parse_scale(vals): - assert (len(vals) >= 1 and len(vals) <= 2) - d=np.array([vals[0], vals[1] if len(vals)>1 else vals[0],1]) - return np.diag(d) - - @staticmethod - def parse_skewx(vals): - assert(len(vals)==1) - m=np.eye(3) - m[0,1]=vals[0] - return m - - @staticmethod - def parse_skewy(vals): - assert (len(vals) == 1) - m = np.eye(3) - m[1, 0] = vals[0] - return m - - @staticmethod - def transformPoints(pointsTensor, transform): - assert(transform is not None) - one=torch.ones((pointsTensor.shape[0],1),device=pointsTensor.device) - homo_points = torch.cat([pointsTensor, one], dim=1) - mult = transform.mm(homo_points.permute(1,0)).permute(1,0) - tfpoints=mult[:, 0:2].contiguous() - #print(torch.norm(mult[:,2]-one)) - assert(pointsTensor.shape == tfpoints.shape) - return tfpoints - - @staticmethod - def promote_numpy(M): - ret = np.eye(3) - ret[0:2, 0:2] = M - return ret - - @staticmethod - def recompose_numpy(Theta,ScaleXY,ShearX,TXY): - cost=math.cos(Theta) - sint=math.sin(Theta) - Rot=np.array([[cost, -sint],[sint, cost]]) - Scale=np.diag(ScaleXY) - Shear=np.eye(2) - Shear[0,1]=ShearX - - Translate=np.eye(3) - Translate[0:2,2]=TXY - - M=OptimizableSvg.TransformTools.promote_numpy(Rot @ Scale @ Shear) @ Translate - return M - - @staticmethod - def promote(m): - M=torch.eye(3).to(m.device) - M[0:2,0:2]=m - return M - - @staticmethod - def make_rot(Theta): - sint=Theta.sin().squeeze() - cost=Theta.cos().squeeze() - #m=torch.tensor([[cost, -sint],[sint, cost]]) - Rot=torch.stack((torch.stack((cost,-sint)),torch.stack((sint,cost)))) - return Rot - - @staticmethod - def make_scale(ScaleXY): - if ScaleXY.squeeze().dim()==0: - ScaleXY=ScaleXY.squeeze() - #uniform scale - return torch.diag(torch.stack([ScaleXY,ScaleXY])).to(ScaleXY.device) - else: - return torch.diag(ScaleXY).to(ScaleXY.device) - - @staticmethod - def make_shear(ShearX): - m=torch.eye(2).to(ShearX.device) - m[0,1]=ShearX - return m - - @staticmethod - def make_translate(TXY): - m=torch.eye(3).to(TXY.device) - m[0:2,2]=TXY - return m - - @staticmethod - def recompose(Theta,ScaleXY,ShearX,TXY): - Rot=OptimizableSvg.TransformTools.make_rot(Theta) - Scale=OptimizableSvg.TransformTools.make_scale(ScaleXY) - Shear=OptimizableSvg.TransformTools.make_shear(ShearX) - Translate=OptimizableSvg.TransformTools.make_translate(TXY) - - return OptimizableSvg.TransformTools.promote(Rot.mm(Scale).mm(Shear)).mm(Translate) - - TransformDecomposition=namedtuple("TransformDecomposition","theta scale shear translate") - TransformProperties=namedtuple("TransformProperties", "has_rotation has_scale has_mirror scale_uniform has_shear has_translation") - - @staticmethod - def make_named(decomp): - if not isinstance(decomp,OptimizableSvg.TransformTools.TransformDecomposition): - decomp=OptimizableSvg.TransformTools.TransformDecomposition(theta=decomp[0],scale=decomp[1],shear=decomp[2],translate=decomp[3]) - return decomp - - @staticmethod - def analyze_transform(decomp): - decomp=OptimizableSvg.TransformTools.make_named(decomp) - epsilon=1e-3 - has_rotation=abs(decomp.theta)>epsilon - has_scale=abs((abs(decomp.scale)-1)).max()>epsilon - scale_len=decomp.scale.squeeze().ndim>0 if isinstance(decomp.scale,np.ndarray) else decomp.scale.squeeze().dim() > 0 - has_mirror=scale_len and decomp.scale[0]*decomp.scale[1] < 0 - scale_uniform=not scale_len or abs(abs(decomp.scale[0])-abs(decomp.scale[1]))epsilon - has_translate=max(abs(decomp.translate[0]),abs(decomp.translate[1]))>epsilon - - return OptimizableSvg.TransformTools.TransformProperties(has_rotation=has_rotation,has_scale=has_scale,has_mirror=has_mirror,scale_uniform=scale_uniform,has_shear=has_shear,has_translation=has_translate) - - @staticmethod - def check_and_decomp(M): - decomp=OptimizableSvg.TransformTools.decompose(M) if M is not None else OptimizableSvg.TransformTools.TransformDecomposition(theta=0,scale=(1,1),shear=0,translate=(0,0)) - props=OptimizableSvg.TransformTools.analyze_transform(decomp) - return (decomp, props) - - @staticmethod - def tf_to_string(M): - tfstring = "matrix({} {} {} {} {} {})".format(M[0, 0], M[1, 0], M[0, 1], M[1, 1], M[0, 2], M[1, 2]) - return tfstring - - @staticmethod - def decomp_to_string(decomp): - decomp = OptimizableSvg.TransformTools.make_named(decomp) - ret="" - props=OptimizableSvg.TransformTools.analyze_transform(decomp) - if props.has_rotation: - ret+="rotate({}) ".format(math.degrees(decomp.theta.item())) - if props.has_scale: - if decomp.scale.dim()==0: - ret += "scale({}) ".format(decomp.scale.item()) - else: - ret+="scale({} {}) ".format(decomp.scale[0], decomp.scale[1]) - if props.has_shear: - ret+="skewX({}) ".format(decomp.shear.item()) - if props.has_translation: - ret+="translate({} {}) ".format(decomp.translate[0],decomp.translate[1]) - - return ret - - @staticmethod - def decompose(M): - m = M[0:2, 0:2] - t0=M[0:2, 2] - #get translation so that we can post-multiply with it - TXY=np.linalg.solve(m,t0) - - T=np.eye(3) - T[0:2,2]=TXY - - q, r = np.linalg.qr(m) - - ref = np.array([[1, 0], [0, np.sign(np.linalg.det(q))]]) - - Rot = np.dot(q, ref) - - ref2 = np.array([[1, 0], [0, np.sign(np.linalg.det(r))]]) - - r2 = np.dot(ref2, r) - - Ref = np.dot(ref, ref2) - - sc = np.diag(r2) - Scale = np.diagflat(sc) - - Shear = np.eye(2) - Shear[0, 1] = r2[0, 1] / sc[0] - #the actual shear coefficient - ShearX=r2[0, 1] / sc[0] - - if np.sum(sc) < 0: - # both scales are negative, flip this and add a 180 rotation - Rot = np.dot(Rot, -np.eye(2)) - Scale = -Scale - - Theta = math.atan2(Rot[1, 0], Rot[0, 0]) - ScaleXY = np.array([Scale[0,0],Scale[1,1]*Ref[1,1]]) - - return OptimizableSvg.TransformTools.TransformDecomposition(theta=Theta, scale=ScaleXY, shear=ShearX, translate=TXY) - - #region suboptimizers - - #optimizes color, but really any tensor that needs to stay between 0 and 1 per-entry - class ColorOptimizer: - def __init__(self,tensor,optim_type,lr): - self.tensor=tensor - self.optim=optim_type([tensor],lr=lr) - - def zero_grad(self): - self.optim.zero_grad() - - def step(self): - self.optim.step() - self.tensor.data.clamp_(min=1e-4,max=1.) - - #optimizes gradient stop positions - class StopOptimizer: - def __init__(self,stops,optim_type,lr): - self.stops=stops - self.optim=optim_type([stops],lr=lr) - - def zero_grad(self): - self.optim.zero_grad() - - def step(self): - self.optim.step() - self.stops.data.clamp_(min=0., max=1.) - self.stops.data, _ = self.stops.sort() - self.stops.data[0] = 0. - self.stops.data[-1]=1. - - #optimizes gradient: stop, positions, colors+opacities, locations - class GradientOptimizer: - def __init__(self, begin, end, offsets, stops, optim_params): - self.begin=begin.clone().detach() if begin is not None else None - self.end=end.clone().detach() if end is not None else None - self.offsets=offsets.clone().detach() if offsets is not None else None - self.stop_colors=stops[:,0:3].clone().detach() if stops is not None else None - self.stop_alphas=stops[:,3].clone().detach() if stops is not None else None - self.optimizers=[] - - if optim_params["gradients"]["optimize_stops"] and self.offsets is not None: - self.offsets.requires_grad_(True) - self.optimizers.append(OptimizableSvg.StopOptimizer(self.offsets,SvgOptimizationSettings.optims[optim_params["optimizer"]],optim_params["gradients"]["stop_lr"])) - if optim_params["gradients"]["optimize_color"] and self.stop_colors is not None: - self.stop_colors.requires_grad_(True) - self.optimizers.append(OptimizableSvg.ColorOptimizer(self.stop_colors,SvgOptimizationSettings.optims[optim_params["optimizer"]],optim_params["gradients"]["color_lr"])) - if optim_params["gradients"]["optimize_alpha"] and self.stop_alphas is not None: - self.stop_alphas.requires_grad_(True) - self.optimizers.append(OptimizableSvg.ColorOptimizer(self.stop_alphas,SvgOptimizationSettings.optims[optim_params["optimizer"]],optim_params["gradients"]["alpha_lr"])) - if optim_params["gradients"]["optimize_location"] and self.begin is not None and self.end is not None: - self.begin.requires_grad_(True) - self.end.requires_grad_(True) - self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.begin,self.end],lr=optim_params["gradients"]["location_lr"])) - - - def get_vals(self): - return self.begin, self.end, self.offsets, torch.cat((self.stop_colors,self.stop_alphas.unsqueeze(1)),1) if self.stop_colors is not None and self.stop_alphas is not None else None - - def zero_grad(self): - for optim in self.optimizers: - optim.zero_grad() - - def step(self): - for optim in self.optimizers: - optim.step() - - class TransformOptimizer: - def __init__(self,transform,optim_params): - self.transform=transform - self.optimizes=optim_params["transforms"]["optimize_transforms"] and transform is not None - self.params=copy.deepcopy(optim_params) - self.transform_mode=optim_params["transforms"]["transform_mode"] - - if self.optimizes: - optimvars=[] - self.residual=None - lr=optim_params["transforms"]["transform_lr"] - tmult=optim_params["transforms"]["translation_mult"] - decomp,props=OptimizableSvg.TransformTools.check_and_decomp(transform.cpu().numpy()) - if self.transform_mode=="move": - #only translation and rotation should be set - if props.has_scale or props.has_shear or props.has_mirror: - print("Warning: set to optimize move only, but input transform has residual scale or shear") - self.residual=self.transform.clone().detach().requires_grad_(False) - self.Theta=torch.tensor(0,dtype=torch.float32,requires_grad=True,device=transform.device) - self.translation=torch.tensor([0, 0],dtype=torch.float32,requires_grad=True,device=transform.device) - else: - self.residual=None - self.Theta=torch.tensor(decomp.theta,dtype=torch.float32,requires_grad=True,device=transform.device) - self.translation=torch.tensor(decomp.translate,dtype=torch.float32,requires_grad=True,device=transform.device) - optimvars+=[{'params':x,'lr':lr} for x in [self.Theta]]+[{'params':self.translation,'lr':lr*tmult}] - elif self.transform_mode=="rigid": - #only translation, rotation, and uniform scale should be set - if props.has_shear or props.has_mirror or not props.scale_uniform: - print("Warning: set to optimize rigid transform only, but input transform has residual shear, mirror or non-uniform scale") - self.residual = self.transform.clone().detach().requires_grad_(False) - self.Theta = torch.tensor(0, dtype=torch.float32, requires_grad=True,device=transform.device) - self.translation = torch.tensor([0, 0], dtype=torch.float32, requires_grad=True,device=transform.device) - self.scale=torch.tensor(1, dtype=torch.float32, requires_grad=True,device=transform.device) - else: - self.residual = None - self.Theta = torch.tensor(decomp.theta, dtype=torch.float32, requires_grad=True,device=transform.device) - self.translation = torch.tensor(decomp.translate, dtype=torch.float32, requires_grad=True,device=transform.device) - self.scale = torch.tensor(decomp.scale[0], dtype=torch.float32, requires_grad=True,device=transform.device) - optimvars += [{'params':x,'lr':lr} for x in [self.Theta, self.scale]]+[{'params':self.translation,'lr':lr*tmult}] - elif self.transform_mode=="similarity": - if props.has_shear or not props.scale_uniform: - print("Warning: set to optimize rigid transform only, but input transform has residual shear or non-uniform scale") - self.residual = self.transform.clone().detach().requires_grad_(False) - self.Theta = torch.tensor(0, dtype=torch.float32, requires_grad=True,device=transform.device) - self.translation = torch.tensor([0, 0], dtype=torch.float32, requires_grad=True,device=transform.device) - self.scale=torch.tensor(1, dtype=torch.float32, requires_grad=True,device=transform.device) - self.scale_sign=torch.tensor(1,dtype=torch.float32,requires_grad=False,device=transform.device) - else: - self.residual = None - self.Theta = torch.tensor(decomp.theta, dtype=torch.float32, requires_grad=True,device=transform.device) - self.translation = torch.tensor(decomp.translate, dtype=torch.float32, requires_grad=True,device=transform.device) - self.scale = torch.tensor(decomp.scale[0], dtype=torch.float32, requires_grad=True,device=transform.device) - self.scale_sign = torch.tensor(np.sign(decomp.scale[0]*decomp.scale[1]), dtype=torch.float32, requires_grad=False,device=transform.device) - optimvars += [{'params':x,'lr':lr} for x in [self.Theta, self.scale]]+[{'params':self.translation,'lr':lr*tmult}] - elif self.transform_mode=="affine": - self.Theta = torch.tensor(decomp.theta, dtype=torch.float32, requires_grad=True,device=transform.device) - self.translation = torch.tensor(decomp.translate, dtype=torch.float32, requires_grad=True,device=transform.device) - self.scale = torch.tensor(decomp.scale, dtype=torch.float32, requires_grad=True,device=transform.device) - self.shear = torch.tensor(decomp.shear, dtype=torch.float32, requires_grad=True,device=transform.device) - optimvars += [{'params':x,'lr':lr} for x in [self.Theta, self.scale, self.shear]]+[{'params':self.translation,'lr':lr*tmult}] - else: - raise ValueError("Unrecognized transform mode '{}'".format(self.transform_mode)) - self.optimizer=SvgOptimizationSettings.optims[optim_params["optimizer"]](optimvars) - - def get_transform(self): - if not self.optimizes: - return self.transform - else: - if self.transform_mode == "move": - composed=OptimizableSvg.TransformTools.recompose(self.Theta,torch.tensor([1.],device=self.Theta.device),torch.tensor(0.,device=self.Theta.device),self.translation) - return self.residual.mm(composed) if self.residual is not None else composed - elif self.transform_mode == "rigid": - composed = OptimizableSvg.TransformTools.recompose(self.Theta, self.scale, torch.tensor(0.,device=self.Theta.device), - self.translation) - return self.residual.mm(composed) if self.residual is not None else composed - elif self.transform_mode == "similarity": - composed=OptimizableSvg.TransformTools.recompose(self.Theta, torch.cat((self.scale,self.scale*self.scale_sign)),torch.tensor(0.,device=self.Theta.device),self.translation) - return self.residual.mm(composed) if self.residual is not None else composed - elif self.transform_mode == "affine": - composed = OptimizableSvg.TransformTools.recompose(self.Theta, self.scale, self.shear, self.translation) - return composed - else: - raise ValueError("Unrecognized transform mode '{}'".format(self.transform_mode)) - - def tfToString(self): - if self.transform is None: - return None - elif not self.optimizes: - return OptimizableSvg.TransformTools.tf_to_string(self.transform) - else: - if self.transform_mode == "move": - str=OptimizableSvg.TransformTools.decomp_to_string((self.Theta,torch.tensor([1.]),torch.tensor(0.),self.translation)) - return (OptimizableSvg.TransformTools.tf_to_string(self.residual) if self.residual is not None else "")+" "+str - elif self.transform_mode == "rigid": - str = OptimizableSvg.TransformTools.decomp_to_string((self.Theta, self.scale, torch.tensor(0.), - self.translation)) - return (OptimizableSvg.TransformTools.tf_to_string(self.residual) if self.residual is not None else "")+" "+str - elif self.transform_mode == "similarity": - str=OptimizableSvg.TransformTools.decomp_to_string((self.Theta, torch.cat((self.scale,self.scale*self.scale_sign)),torch.tensor(0.),self.translation)) - return (OptimizableSvg.TransformTools.tf_to_string(self.residual) if self.residual is not None else "")+" "+str - elif self.transform_mode == "affine": - str = OptimizableSvg.TransformTools.decomp_to_string((self.Theta, self.scale, self.shear, self.translation)) - return composed - - def zero_grad(self): - if self.optimizes: - self.optimizer.zero_grad() - - def step(self): - if self.optimizes: - self.optimizer.step() - - #endregion - - #region Nodes - class SvgNode: - def __init__(self,id,transform,appearance,settings): - self.id=id - self.children=[] - self.optimizers=[] - self.device = settings.device - self.transform=torch.tensor(transform,dtype=torch.float32,device=self.device) if transform is not None else None - self.transform_optim=OptimizableSvg.TransformOptimizer(self.transform,settings.retrieve(self.id)[0]) - self.optimizers.append(self.transform_optim) - self.proc_appearance(appearance,settings.retrieve(self.id)[0]) - - def tftostring(self): - return self.transform_optim.tfToString() - - def appearanceToString(self): - appstring="" - for key,value in self.appearance.items(): - if key in ["fill", "stroke"]: - #a paint-type value - if value[0] == "none": - appstring+="{}:none;".format(key) - elif value[0] == "solid": - appstring += "{}:{};".format(key,OptimizableSvg.rgb_to_string(value[1])) - elif value[0] == "url": - appstring += "{}:url(#{});".format(key,value[1].id) - #appstring += "{}:{};".format(key,"#ff00ff") - elif key in ["opacity", "fill-opacity", "stroke-opacity", "stroke-width", "fill-rule"]: - appstring+="{}:{};".format(key,value) - else: - raise ValueError("Don't know how to write appearance parameter '{}'".format(key)) - return appstring - - - def write_xml_common_attrib(self,node,tfname="transform"): - if self.transform is not None: - node.set(tfname,self.tftostring()) - if len(self.appearance)>0: - node.set('style',self.appearanceToString()) - if self.id is not None: - node.set('id',self.id) - - - def proc_appearance(self,appearance,optim_params): - self.appearance=appearance - for key, value in appearance.items(): - if key == "fill" or key == "stroke": - if optim_params["optimize_color"] and value[0]=="solid": - value[1].requires_grad_(True) - self.optimizers.append(OptimizableSvg.ColorOptimizer(value[1],SvgOptimizationSettings.optims[optim_params["optimizer"]],optim_params["color_lr"])) - elif key == "fill-opacity" or key == "stroke-opacity" or key == "opacity": - if optim_params["optimize_alpha"]: - value[1].requires_grad_(True) - self.optimizers.append(OptimizableSvg.ColorOptimizer(value[1], optim_params["optimizer"], - optim_params["alpha_lr"])) - elif key == "fill-rule" or key == "stroke-width": - pass - else: - raise RuntimeError("Unrecognized appearance key '{}'".format(key)) - - def prop_transform(self,intform): - return intform.matmul(self.transform_optim.get_transform()) if self.transform is not None else intform - - def prop_appearance(self,inappearance): - outappearance=copy.copy(inappearance) - for key,value in self.appearance.items(): - if key == "fill": - #gets replaced - outappearance[key]=value - elif key == "fill-opacity": - #gets multiplied - outappearance[key] = outappearance[key]*value - elif key == "fill-rule": - #gets replaced - outappearance[key] = value - elif key =="opacity": - # gets multiplied - outappearance[key] = outappearance[key]*value - elif key == "stroke": - # gets replaced - outappearance[key] = value - elif key == "stroke-opacity": - # gets multiplied - outappearance[key] = outappearance[key]*value - elif key =="stroke-width": - # gets replaced - outappearance[key] = value - else: - raise RuntimeError("Unrecognized appearance key '{}'".format(key)) - return outappearance - - def zero_grad(self): - for optim in self.optimizers: - optim.zero_grad() - for child in self.children: - child.zero_grad() - - def step(self): - for optim in self.optimizers: - optim.step() - for child in self.children: - child.step() - - def get_type(self): - return "Generic node" - - def is_shape(self): - return False - - def build_scene(self,shapes,shape_groups,transform,appearance): - raise NotImplementedError("Abstract SvgNode cannot recurse") - - class GroupNode(SvgNode): - def __init__(self, id, transform, appearance,settings): - super().__init__(id, transform, appearance,settings) - - def get_type(self): - return "Group node" - - def build_scene(self,shapes,shape_groups,transform,appearance): - outtf=self.prop_transform(transform) - outapp=self.prop_appearance(appearance) - for child in self.children: - child.build_scene(shapes,shape_groups,outtf,outapp) - - def write_xml(self, parent): - elm=etree.SubElement(parent,"g") - self.write_xml_common_attrib(elm) - - for child in self.children: - child.write_xml(elm) - - class RootNode(SvgNode): - def __init__(self, id, transform, appearance,settings): - super().__init__(id, transform, appearance,settings) - - def write_xml(self,document): - elm=etree.Element('svg') - self.write_xml_common_attrib(elm) - elm.set("version","2.0") - elm.set("width",str(document.canvas[0])) - elm.set("height", str(document.canvas[1])) - elm.set("xmlns","http://www.w3.org/2000/svg") - elm.set("xmlns:xlink","http://www.w3.org/1999/xlink") - #write definitions before we write any children - document.write_defs(elm) - - #write the children - for child in self.children: - child.write_xml(elm) - - return elm - - def get_type(self): - return "Root node" - - def build_scene(self,shapes,shape_groups,transform,appearance): - outtf = self.prop_transform(transform).to(self.device) - for child in self.children: - child.build_scene(shapes,shape_groups,outtf,appearance) - - @staticmethod - def get_default_appearance(device): - default_appearance = {"fill": ("solid", torch.tensor([0., 0., 0.],device=device)), - "fill-opacity": torch.tensor([1.],device=device), - "fill-rule": "nonzero", - "opacity": torch.tensor([1.],device=device), - "stroke": ("none", None), - "stroke-opacity": torch.tensor([1.],device=device), - "stroke-width": torch.tensor([0.],device=device)} - return default_appearance - - @staticmethod - def get_default_transform(): - return torch.eye(3) - - - - class ShapeNode(SvgNode): - def __init__(self, id, transform, appearance,settings): - super().__init__(id, transform, appearance,settings) - - def get_type(self): - return "Generic shape node" - - def is_shape(self): - return True - - def construct_paint(self,value,combined_opacity,transform): - if value[0] == "none": - return None - elif value[0] == "solid": - return torch.cat([value[1],combined_opacity]).to(self.device) - elif value[0] == "url": - #get the gradient object from this node - return value[1].getGrad(combined_opacity,transform) - else: - raise ValueError("Unknown paint value type '{}'".format(value[0])) - - def make_shape_group(self,appearance,transform,num_shapes,num_subobjects): - fill=self.construct_paint(appearance["fill"],appearance["opacity"]*appearance["fill-opacity"],transform) - stroke=self.construct_paint(appearance["stroke"],appearance["opacity"]*appearance["stroke-opacity"],transform) - sg = pydiffvg.ShapeGroup(shape_ids=torch.tensor(range(num_shapes, num_shapes + num_subobjects)), - fill_color=fill, - use_even_odd_rule=appearance["fill-rule"]=="evenodd", - stroke_color=stroke, - shape_to_canvas=transform, - id=self.id) - return sg - - class PathNode(ShapeNode): - def __init__(self, id, transform, appearance,settings, paths): - super().__init__(id, transform, appearance,settings) - self.proc_paths(paths,settings.retrieve(self.id)[0]) - - def proc_paths(self,paths,optim_params): - self.paths=paths - if optim_params["paths"]["optimize_points"]: - ptlist=[] - for path in paths: - ptlist.append(path.points.requires_grad_(True)) - self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]](ptlist,lr=optim_params["paths"]["shape_lr"])) - - def get_type(self): - return "Path node" - - def build_scene(self,shapes,shape_groups,transform,appearance): - applytf=self.prop_transform(transform) - applyapp = self.prop_appearance(appearance) - sg=self.make_shape_group(applyapp,applytf,len(shapes),len(self.paths)) - for path in self.paths: - disp_path=pydiffvg.Path(path.num_control_points,path.points,path.is_closed,applyapp["stroke-width"],path.id) - shapes.append(disp_path) - shape_groups.append(sg) - - def path_to_string(self,path): - path_string = "M {},{} ".format(path.points[0][0].item(), path.points[0][1].item()) - idx = 1 - numpoints = path.points.shape[0] - for type in path.num_control_points: - toproc = type + 1 - if type == 0: - # add line - path_string += "L " - elif type == 1: - # add quadric - path_string += "Q " - elif type == 2: - # add cubic - path_string += "C " - while toproc > 0: - path_string += "{},{} ".format(path.points[idx % numpoints][0].item(), - path.points[idx % numpoints][1].item()) - idx += 1 - toproc -= 1 - if path.is_closed: - path_string += "Z " - - return path_string - - def paths_string(self): - pstr="" - for path in self.paths: - pstr+=self.path_to_string(path) - return pstr - - def write_xml(self, parent): - elm = etree.SubElement(parent, "path") - self.write_xml_common_attrib(elm) - elm.set("d",self.paths_string()) - - for child in self.children: - child.write_xml(elm) - - class RectNode(ShapeNode): - def __init__(self, id, transform, appearance,settings, rect): - super().__init__(id, transform, appearance,settings) - self.rect=torch.tensor(rect,dtype=torch.float,device=settings.device) - optim_params=settings.retrieve(self.id)[0] - #borrowing path settings for this - if optim_params["paths"]["optimize_points"]: - self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.rect],lr=optim_params["paths"]["shape_lr"])) - - def get_type(self): - return "Rect node" - - def build_scene(self,shapes,shape_groups,transform,appearance): - applytf=self.prop_transform(transform) - applyapp = self.prop_appearance(appearance) - sg=self.make_shape_group(applyapp,applytf,len(shapes),1) - shapes.append(pydiffvg.Rect(self.rect[0:2],self.rect[0:2]+self.rect[2:4],applyapp["stroke-width"],self.id)) - shape_groups.append(sg) - - def write_xml(self, parent): - elm = etree.SubElement(parent, "rect") - self.write_xml_common_attrib(elm) - elm.set("x",str(self.rect[0])) - elm.set("y", str(self.rect[1])) - elm.set("width", str(self.rect[2])) - elm.set("height", str(self.rect[3])) - - for child in self.children: - child.write_xml(elm) - - class CircleNode(ShapeNode): - def __init__(self, id, transform, appearance,settings, rect): - super().__init__(id, transform, appearance,settings) - self.circle=torch.tensor(rect,dtype=torch.float,device=settings.device) - optim_params=settings.retrieve(self.id)[0] - #borrowing path settings for this - if optim_params["paths"]["optimize_points"]: - self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.circle],lr=optim_params["paths"]["shape_lr"])) - - def get_type(self): - return "Circle node" - - def build_scene(self,shapes,shape_groups,transform,appearance): - applytf=self.prop_transform(transform) - applyapp = self.prop_appearance(appearance) - sg=self.make_shape_group(applyapp,applytf,len(shapes),1) - shapes.append(pydiffvg.Circle(self.circle[2],self.circle[0:2],applyapp["stroke-width"],self.id)) - shape_groups.append(sg) - - def write_xml(self, parent): - elm = etree.SubElement(parent, "circle") - self.write_xml_common_attrib(elm) - elm.set("cx",str(self.circle[0])) - elm.set("cy", str(self.circle[1])) - elm.set("r", str(self.circle[2])) - - for child in self.children: - child.write_xml(elm) - - - class EllipseNode(ShapeNode): - def __init__(self, id, transform, appearance,settings, ellipse): - super().__init__(id, transform, appearance,settings) - self.ellipse=torch.tensor(ellipse,dtype=torch.float,device=settings.device) - optim_params=settings.retrieve(self.id)[0] - #borrowing path settings for this - if optim_params["paths"]["optimize_points"]: - self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.ellipse],lr=optim_params["paths"]["shape_lr"])) - - def get_type(self): - return "Ellipse node" - - def build_scene(self,shapes,shape_groups,transform,appearance): - applytf=self.prop_transform(transform) - applyapp = self.prop_appearance(appearance) - sg=self.make_shape_group(applyapp,applytf,len(shapes),1) - shapes.append(pydiffvg.Ellipse(self.ellipse[2:4],self.ellipse[0:2],applyapp["stroke-width"],self.id)) - shape_groups.append(sg) - - def write_xml(self, parent): - elm = etree.SubElement(parent, "ellipse") - self.write_xml_common_attrib(elm) - elm.set("cx", str(self.ellipse[0])) - elm.set("cy", str(self.ellipse[1])) - elm.set("rx", str(self.ellipse[2])) - elm.set("ry", str(self.ellipse[3])) - - for child in self.children: - child.write_xml(elm) - - class PolygonNode(ShapeNode): - def __init__(self, id, transform, appearance,settings, points): - super().__init__(id, transform, appearance,settings) - self.points=points - optim_params=settings.retrieve(self.id)[0] - #borrowing path settings for this - if optim_params["paths"]["optimize_points"]: - self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.points],lr=optim_params["paths"]["shape_lr"])) - - def get_type(self): - return "Polygon node" - - def build_scene(self,shapes,shape_groups,transform,appearance): - applytf=self.prop_transform(transform) - applyapp = self.prop_appearance(appearance) - sg=self.make_shape_group(applyapp,applytf,len(shapes),1) - shapes.append(pydiffvg.Polygon(self.points,True,applyapp["stroke-width"],self.id)) - shape_groups.append(sg) - - def point_string(self): - ret="" - for i in range(self.points.shape[0]): - pt=self.points[i,:] - #assert pt.shape == (1,2) - ret+= str(pt[0])+","+str(pt[1])+" " - return ret - - def write_xml(self, parent): - elm = etree.SubElement(parent, "polygon") - self.write_xml_common_attrib(elm) - elm.set("points",self.point_string()) - - for child in self.children: - child.write_xml(elm) - - class GradientNode(SvgNode): - def __init__(self, id, transform,settings,begin,end,offsets,stops,href): - super().__init__(id, transform, {},settings) - self.optim=OptimizableSvg.GradientOptimizer(begin, end, offsets, stops, settings.retrieve(id)[0]) - self.optimizers.append(self.optim) - self.href=href - - def is_ref(self): - return self.href is not None - - def get_type(self): - return "Gradient node" - - def get_stops(self): - _, _, offsets, stops=self.optim.get_vals() - return offsets, stops - - def get_points(self): - begin, end, _, _ =self.optim.get_vals() - return begin, end - - def write_xml(self, parent): - elm = etree.SubElement(parent, "linearGradient") - self.write_xml_common_attrib(elm,tfname="gradientTransform") - - begin, end, offsets, stops = self.optim.get_vals() - - if self.href is None: - #we have stops - for idx, offset in enumerate(offsets): - stop=etree.SubElement(elm,"stop") - stop.set("offset",str(offset.item())) - stop.set("stop-color",OptimizableSvg.rgb_to_string(stops[idx,0:3])) - stop.set("stop-opacity",str(stops[idx,3].item())) - else: - elm.set('xlink:href', "#{}".format(self.href.id)) - - if begin is not None and end is not None: - #no stops - elm.set('x1', str(begin[0].item())) - elm.set('y1', str(begin[1].item())) - elm.set('x2', str(end[0].item())) - elm.set('y2', str(end[1].item())) - - # magic value to make this work - elm.set("gradientUnits", "userSpaceOnUse") - - for child in self.children: - child.write_xml(elm) - - def getGrad(self,combined_opacity,transform): - if self.is_ref(): - offsets, stops=self.href.get_stops() - else: - offsets, stops=self.get_stops() - - stops=stops.clone() - stops[:,3]*=combined_opacity - - begin,end = self.get_points() - - applytf=self.prop_transform(transform) - begin=OptimizableSvg.TransformTools.transformPoints(begin.unsqueeze(0),applytf).squeeze() - end = OptimizableSvg.TransformTools.transformPoints(end.unsqueeze(0), applytf).squeeze() - - return pydiffvg.LinearGradient(begin, end, offsets, stops) - #endregion - - def __init__(self, filename, settings=SvgOptimizationSettings(),optimize_background=False, verbose=False, device=torch.device("cpu")): - self.settings=settings - self.verbose=verbose - self.device=device - self.settings.device=device - - tree = etree.parse(filename) - root = tree.getroot() - - #in case we need global optimization - self.optimizers=[] - self.background=torch.tensor([1.,1.,1.],dtype=torch.float32,requires_grad=optimize_background,device=self.device) - - if optimize_background: - p=settings.retrieve("default")[0] - self.optimizers.append(OptimizableSvg.ColorOptimizer(self.background,SvgOptimizationSettings.optims[p["optimizer"]],p["color_lr"])) - - self.defs={} - - self.depth=0 - - self.dirty=True - self.scene=None - - self.parseRoot(root) - - recognised_shapes=["path","circle","rect","ellipse","polygon"] - - #region core functionality - def build_scene(self): - if self.dirty: - shape_groups=[] - shapes=[] - self.root.build_scene(shapes,shape_groups,OptimizableSvg.RootNode.get_default_transform().to(self.device),OptimizableSvg.RootNode.get_default_appearance(self.device)) - self.scene=(self.canvas[0],self.canvas[1],shapes,shape_groups) - self.dirty=False - return self.scene - - def zero_grad(self): - self.root.zero_grad() - for optim in self.optimizers: - optim.zero_grad() - for item in self.defs.values(): - if issubclass(item.__class__,OptimizableSvg.SvgNode): - item.zero_grad() - - def render(self,scale=None,seed=0): - #render at native resolution - scene = self.build_scene() - scene_args = pydiffvg.RenderFunction.serialize_scene(*scene) - render = pydiffvg.RenderFunction.apply - out_size=(scene[0],scene[1]) if scale is None else (int(scene[0]*scale),int(scene[1]*scale)) - img = render(out_size[0], # width - out_size[1], # height - 2, # num_samples_x - 2, # num_samples_y - seed, # seed - None, # background_image - *scene_args) - return img - - def step(self): - self.dirty=True - self.root.step() - for optim in self.optimizers: - optim.step() - for item in self.defs.values(): - if issubclass(item.__class__, OptimizableSvg.SvgNode): - item.step() - #endregion - - #region reporting - - def offset_str(self,s): - return ("\t"*self.depth)+s - - def reportSkippedAttribs(self, node, non_skipped=[]): - skipped=set([k for k in node.attrib.keys() if not OptimizableSvg.is_namespace(k)])-set(non_skipped) - if len(skipped)>0: - tag=OptimizableSvg.remove_namespace(node.tag) if "id" not in node.attrib else "{}#{}".format(OptimizableSvg.remove_namespace(node.tag),node.attrib["id"]) - print(self.offset_str("Warning: Skipping the following attributes of node '{}': {}".format(tag,", ".join(["'{}'".format(atr) for atr in skipped])))) - - def reportSkippedChildren(self,node,skipped): - skipped_names=["{}#{}".format(elm.tag,elm.attrib["id"]) if "id" in elm.attrib else elm.tag for elm in skipped] - if len(skipped)>0: - tag = OptimizableSvg.remove_namespace(node.tag) if "id" not in node.attrib else "{}#{}".format(OptimizableSvg.remove_namespace(node.tag), - node.attrib["id"]) - print(self.offset_str("Warning: Skipping the following children of node '{}': {}".format(tag,", ".join(["'{}'".format(name) for name in skipped_names])))) - - #endregion - - #region parsing - @staticmethod - def remove_namespace(s): - """ - {...} ... -> ... - """ - return re.sub('{.*}', '', s) - - @staticmethod - def is_namespace(s): - return re.match('{.*}', s) is not None - - @staticmethod - def parseTransform(node): - if "transform" not in node.attrib and "gradientTransform" not in node.attrib: - return None - - tf_string=node.attrib["transform"] if "transform" in node.attrib else node.attrib["gradientTransform"] - tforms=tf_string.split(")")[:-1] - mat=np.eye(3) - for tform in tforms: - type = tform.split("(")[0] - args = [float(val) for val in re.split("[, ]+",tform.split("(")[1])] - if type == "matrix": - mat=mat @ OptimizableSvg.TransformTools.parse_matrix(args) - elif type == "translate": - mat = mat @ OptimizableSvg.TransformTools.parse_translate(args) - elif type == "rotate": - mat = mat @ OptimizableSvg.TransformTools.parse_rotate(args) - elif type == "scale": - mat = mat @ OptimizableSvg.TransformTools.parse_scale(args) - elif type == "skewX": - mat = mat @ OptimizableSvg.TransformTools.parse_skewx(args) - elif type == "skewY": - mat = mat @ OptimizableSvg.TransformTools.parse_skewy(args) - else: - raise ValueError("Unknown transform type '{}'".format(type)) - return mat - - #dictionary that defines what constant do we need to multiply different units to get the value in pixels - #gleaned from the CSS definition - unit_dict = {"px":1, - "mm":4, - "cm":40, - "in":25.4*4, - "pt":25.4*4/72, - "pc":25.4*4/6 - } - - @staticmethod - def parseLength(s): - #length is a number followed possibly by a unit definition - #we assume that default unit is the pixel (px) equal to 0.25mm - #last two characters might be unit - val=None - for i in range(len(s)): - try: - val=float(s[:len(s)-i]) - unit=s[len(s)-i:] - break - except ValueError: - continue - if len(unit)>0 and unit not in OptimizableSvg.unit_dict: - raise ValueError("Unknown or unsupported unit '{}' encountered while parsing".format(unit)) - if unit != "": - val*=OptimizableSvg.unit_dict[unit] - return val - - @staticmethod - def parseOpacity(s): - is_percent=s.endswith("%") - s=s.rstrip("%") - val=float(s) - if is_percent: - val=val/100 - return np.clip(val,0.,1.) - - @staticmethod - def parse_color(s): - """ - Hex to tuple - """ - if s[0] != '#': - raise ValueError("Color argument `{}` not supported".format(s)) - s = s.lstrip('#') - if len(s)==6: - rgb = tuple(int(s[i:i + 2], 16) for i in (0, 2, 4)) - return torch.tensor([rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0]) - elif len(s)==3: - rgb = tuple((int(s[i:i + 1], 16)) for i in (0, 1, 2)) - return torch.tensor([rgb[0] / 15.0, rgb[1] / 15.0, rgb[2] / 15.0]) - else: - raise ValueError("Color argument `{}` not supported".format(s)) - # sRGB to RGB - # return torch.pow(torch.tensor([rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0]), 2.2) - - - @staticmethod - def rgb_to_string(val): - byte_rgb=(val.clone().detach()*255).type(torch.int) - byte_rgb.clamp_(min=0,max=255) - s="#{:02x}{:02x}{:02x}".format(*byte_rgb) - return s - - #parses a "paint" string for use in fill and stroke definitions - @staticmethod - def parsePaint(paintStr,defs,device): - paintStr=paintStr.strip() - if paintStr=="none": - return ("none", None) - elif paintStr[0]=="#": - return ("solid",OptimizableSvg.parse_color(paintStr).to(device)) - elif paintStr.startswith("url"): - url=paintStr.lstrip("url(").rstrip(")").strip("\'\"").lstrip("#") - if url not in defs: - raise ValueError("Paint-type attribute referencing an unknown object with ID '#{}'".format(url)) - return ("url",defs[url]) - else: - raise ValueError("Unrecognized paint string: '{}'".format(paintStr)) - - appearance_keys=["fill","fill-opacity","fill-rule","opacity","stroke","stroke-opacity","stroke-width"] - - @staticmethod - def parseAppearance(node, defs, device): - ret={} - parse_keys = OptimizableSvg.appearance_keys - local_dict={key:value for key,value in node.attrib.items() if key in parse_keys} - css_dict={} - style_dict={} - appearance_dict={} - if "class" in node.attrib: - cls=node.attrib["class"] - if "."+cls in defs: - css_string=defs["."+cls] - css_dict={item.split(":")[0]:item.split(":")[1] for item in css_string.split(";") if len(item)>0 and item.split(":")[0] in parse_keys} - if "style" in node.attrib: - style_string=node.attrib["style"] - style_dict={item.split(":")[0]:item.split(":")[1] for item in style_string.split(";") if len(item)>0 and item.split(":")[0] in parse_keys} - appearance_dict.update(css_dict) - appearance_dict.update(style_dict) - appearance_dict.update(local_dict) - for key,value in appearance_dict.items(): - if key=="fill": - ret[key]=OptimizableSvg.parsePaint(value,defs,device) - elif key == "fill-opacity": - ret[key]=torch.tensor(OptimizableSvg.parseOpacity(value),device=device) - elif key == "fill-rule": - ret[key]=value - elif key == "opacity": - ret[key]=torch.tensor(OptimizableSvg.parseOpacity(value),device=device) - elif key == "stroke": - ret[key]=OptimizableSvg.parsePaint(value,defs,device) - elif key == "stroke-opacity": - ret[key]=torch.tensor(OptimizableSvg.parseOpacity(value),device=device) - elif key == "stroke-width": - ret[key]=torch.tensor(OptimizableSvg.parseLength(value),device=device) - else: - raise ValueError("Error while parsing appearance attributes: key '{}' should not be here".format(key)) - - return ret - - def parseRoot(self,root): - if self.verbose: - print(self.offset_str("Parsing root")) - self.depth += 1 - - # get document canvas dimensions - self.parseViewport(root) - canvmax=np.max(self.canvas) - self.settings.global_override(["transforms","translation_mult"],canvmax) - id=root.attrib["id"] if "id" in root.attrib else None - - transform=OptimizableSvg.parseTransform(root) - appearance=OptimizableSvg.parseAppearance(root,self.defs,self.device) - - version=root.attrib["version"] if "version" in root.attrib else " " - if version != "2.0": - print(self.offset_str("Warning: Version {} is not 2.0, strange things may happen".format(version))) - - self.root=OptimizableSvg.RootNode(id,transform,appearance,self.settings) - - if self.verbose: - self.reportSkippedAttribs(root, ["width", "height", "id", "transform","version", "style"]+OptimizableSvg.appearance_keys) - - #go through the root children and parse them appropriately - skipped=[] - for child in root: - if OptimizableSvg.remove_namespace(child.tag) in OptimizableSvg.recognised_shapes: - self.parseShape(child,self.root) - elif OptimizableSvg.remove_namespace(child.tag) == "defs": - self.parseDefs(child) - elif OptimizableSvg.remove_namespace(child.tag) == "style": - self.parseStyle(child) - elif OptimizableSvg.remove_namespace(child.tag) == "g": - self.parseGroup(child,self.root) - else: - skipped.append(child) - - if self.verbose: - self.reportSkippedChildren(root,skipped) - - self.depth-=1 - - def parseShape(self,shape,parent): - tag=OptimizableSvg.remove_namespace(shape.tag) - if self.verbose: - print(self.offset_str("Parsing {}#{}".format(tag,shape.attrib["id"] if "id" in shape.attrib else " "))) - - self.depth+=1 - if tag == "path": - self.parsePath(shape,parent) - elif tag == "circle": - self.parseCircle(shape,parent) - elif tag == "rect": - self.parseRect(shape,parent) - elif tag == "ellipse": - self.parseEllipse(shape,parent) - elif tag == "polygon": - self.parsePolygon(shape,parent) - else: - raise ValueError("Encountered unknown shape type '{}'".format(tag)) - self.depth -= 1 - - def parsePath(self,shape,parent): - path_string=shape.attrib['d'] - name = '' - if 'id' in shape.attrib: - name = shape.attrib['id'] - paths = pydiffvg.from_svg_path(path_string) - for idx, path in enumerate(paths): - path.stroke_width = torch.tensor([0.],device=self.device) - path.num_control_points=path.num_control_points.to(self.device) - path.points=path.points.to(self.device) - path.source_id = name - path.id = "{}-{}".format(name,idx) if len(paths)>1 else name - transform = OptimizableSvg.parseTransform(shape) - appearance = OptimizableSvg.parseAppearance(shape,self.defs,self.device) - node=OptimizableSvg.PathNode(name,transform,appearance,self.settings,paths) - parent.children.append(node) - - if self.verbose: - self.reportSkippedAttribs(shape, ["id","d","transform","style"]+OptimizableSvg.appearance_keys) - self.reportSkippedChildren(shape,list(shape)) - - def parseEllipse(self, shape, parent): - cx = float(shape.attrib["cx"]) if "cx" in shape.attrib else 0. - cy = float(shape.attrib["cy"]) if "cy" in shape.attrib else 0. - rx = float(shape.attrib["rx"]) - ry = float(shape.attrib["ry"]) - name = '' - if 'id' in shape.attrib: - name = shape.attrib['id'] - transform = OptimizableSvg.parseTransform(shape) - appearance = OptimizableSvg.parseAppearance(shape, self.defs, self.device) - node = OptimizableSvg.EllipseNode(name, transform, appearance, self.settings, (cx, cy, rx, ry)) - parent.children.append(node) - - if self.verbose: - self.reportSkippedAttribs(shape, ["id", "x", "y", "r", "transform", - "style"] + OptimizableSvg.appearance_keys) - self.reportSkippedChildren(shape, list(shape)) - - def parsePolygon(self, shape, parent): - points_string = shape.attrib['points'] - name = '' - points=[] - for point_string in points_string.split(" "): - if len(point_string) == 0: - continue - coord_strings=point_string.split(",") - assert len(coord_strings)==2 - points.append([float(coord_strings[0]),float(coord_strings[1])]) - points=torch.tensor(points,dtype=torch.float,device=self.device) - if 'id' in shape.attrib: - name = shape.attrib['id'] - transform = OptimizableSvg.parseTransform(shape) - appearance = OptimizableSvg.parseAppearance(shape, self.defs, self.device) - node = OptimizableSvg.PolygonNode(name, transform, appearance, self.settings, points) - parent.children.append(node) - - if self.verbose: - self.reportSkippedAttribs(shape, ["id", "points", "transform", "style"] + OptimizableSvg.appearance_keys) - self.reportSkippedChildren(shape, list(shape)) - - def parseCircle(self,shape,parent): - cx = float(shape.attrib["cx"]) if "cx" in shape.attrib else 0. - cy = float(shape.attrib["cy"]) if "cy" in shape.attrib else 0. - r = float(shape.attrib["r"]) - name = '' - if 'id' in shape.attrib: - name = shape.attrib['id'] - transform = OptimizableSvg.parseTransform(shape) - appearance = OptimizableSvg.parseAppearance(shape, self.defs, self.device) - node = OptimizableSvg.CircleNode(name, transform, appearance, self.settings, (cx, cy, r)) - parent.children.append(node) - - if self.verbose: - self.reportSkippedAttribs(shape, ["id", "x", "y", "r", "transform", - "style"] + OptimizableSvg.appearance_keys) - self.reportSkippedChildren(shape, list(shape)) - - def parseRect(self,shape,parent): - x = float(shape.attrib["x"]) if "x" in shape.attrib else 0. - y = float(shape.attrib["y"]) if "y" in shape.attrib else 0. - width = float(shape.attrib["width"]) - height = float(shape.attrib["height"]) - name = '' - if 'id' in shape.attrib: - name = shape.attrib['id'] - transform = OptimizableSvg.parseTransform(shape) - appearance = OptimizableSvg.parseAppearance(shape, self.defs, self.device) - node = OptimizableSvg.RectNode(name, transform, appearance, self.settings, (x,y,width,height)) - parent.children.append(node) - - if self.verbose: - self.reportSkippedAttribs(shape, ["id", "x", "y", "width", "height", "transform", "style"] + OptimizableSvg.appearance_keys) - self.reportSkippedChildren(shape, list(shape)) - - def parseGroup(self,group,parent): - tag = OptimizableSvg.remove_namespace(group.tag) - id = group.attrib["id"] if "id" in group.attrib else " " - if self.verbose: - print(self.offset_str("Parsing {}#{}".format(tag, id))) - - self.depth+=1 - - transform=self.parseTransform(group) - - #todo process more attributes - appearance=OptimizableSvg.parseAppearance(group,self.defs,self.device) - node=OptimizableSvg.GroupNode(id,transform,appearance,self.settings) - parent.children.append(node) - - if self.verbose: - self.reportSkippedAttribs(group,["id","transform","style"]+OptimizableSvg.appearance_keys) - - skipped_children=[] - for child in group: - if OptimizableSvg.remove_namespace(child.tag) in OptimizableSvg.recognised_shapes: - self.parseShape(child,node) - elif OptimizableSvg.remove_namespace(child.tag) == "defs": - self.parseDefs(child) - elif OptimizableSvg.remove_namespace(child.tag) == "style": - self.parseStyle(child) - elif OptimizableSvg.remove_namespace(child.tag) == "g": - self.parseGroup(child,node) - else: - skipped_children.append(child) - - if self.verbose: - self.reportSkippedChildren(group,skipped_children) - - self.depth-=1 - - def parseStyle(self,style_node): - tag = OptimizableSvg.remove_namespace(style_node.tag) - id = style_node.attrib["id"] if "id" in style_node.attrib else " " - if self.verbose: - print(self.offset_str("Parsing {}#{}".format(tag, id))) - - if style_node.attrib["type"] != "text/css": - raise ValueError("Only text/css style recognized, got {}".format(style_node.attrib["type"])) - - self.depth += 1 - - # creating only a dummy node - node = OptimizableSvg.SvgNode(id, None, {}, self.settings) - - if self.verbose: - self.reportSkippedAttribs(def_node, ["id"]) - - if len(style_node)>0: - raise ValueError("Style node should not have children (has {})".format(len(style_node))) - - # collect CSS classes - sheet = cssutils.parseString(style_node.text) - for rule in sheet: - if hasattr(rule, 'selectorText') and hasattr(rule, 'style'): - name = rule.selectorText - if len(name) >= 2 and name[0] == '.': - self.defs[name] = rule.style.getCssText().replace("\n","") - else: - raise ValueError("Unrecognized CSS selector {}".format(name)) - else: - raise ValueError("No style or selector text in CSS rule") - - if self.verbose: - self.reportSkippedChildren(def_node, skipped_children) - - self.depth -= 1 - - def parseDefs(self,def_node): - #only linear gradients are currently supported - tag = OptimizableSvg.remove_namespace(def_node.tag) - id = def_node.attrib["id"] if "id" in def_node.attrib else " " - if self.verbose: - print(self.offset_str("Parsing {}#{}".format(tag, id))) - - self.depth += 1 - - - # creating only a dummy node - node = OptimizableSvg.SvgNode(id, None, {},self.settings) - - if self.verbose: - self.reportSkippedAttribs(def_node, ["id"]) - - skipped_children = [] - for child in def_node: - if OptimizableSvg.remove_namespace(child.tag) == "linearGradient": - self.parseGradient(child,node) - elif OptimizableSvg.remove_namespace(child.tag) in OptimizableSvg.recognised_shapes: - raise NotImplementedError("Definition/instantiation of shapes not supported") - elif OptimizableSvg.remove_namespace(child.tag) == "defs": - raise NotImplementedError("Definition within definition not supported") - elif OptimizableSvg.remove_namespace(child.tag) == "g": - raise NotImplementedError("Groups within definition not supported") - else: - skipped_children.append(child) - - if len(node.children)>0: - #take this node out and enter it into defs - self.defs[node.children[0].id]=node.children[0] - node.children.pop() - - - if self.verbose: - self.reportSkippedChildren(def_node, skipped_children) - - self.depth -= 1 - - def parseGradientStop(self,stop): - param_dict={key:value for key,value in stop.attrib.items() if key in ["id","offset","stop-color","stop-opacity"]} - style_dict={} - if "style" in stop.attrib: - style_dict={item.split(":")[0]:item.split(":")[1] for item in stop.attrib["style"].split(";") if len(item)>0} - param_dict.update(style_dict) - - offset=OptimizableSvg.parseOpacity(param_dict["offset"]) - color=OptimizableSvg.parse_color(param_dict["stop-color"]) - opacity=OptimizableSvg.parseOpacity(param_dict["stop-opacity"]) if "stop-opacity" in param_dict else 1. - - return offset, color, opacity - - def parseGradient(self, gradient_node, parent): - tag = OptimizableSvg.remove_namespace(gradient_node.tag) - id = gradient_node.attrib["id"] if "id" in gradient_node.attrib else " " - if self.verbose: - print(self.offset_str("Parsing {}#{}".format(tag, id))) - - self.depth += 1 - if "stop" not in [OptimizableSvg.remove_namespace(child.tag) for child in gradient_node]\ - and "href" not in [OptimizableSvg.remove_namespace(key) for key in gradient_node.attrib.keys()]: - raise ValueError("Gradient {} has neither stops nor a href link to them".format(id)) - - transform=self.parseTransform(gradient_node) - begin=None - end = None - offsets=[] - stops=[] - href=None - - if "x1" in gradient_node.attrib or "y1" in gradient_node.attrib: - begin=np.array([0.,0.]) - if "x1" in gradient_node.attrib: - begin[0] = float(gradient_node.attrib["x1"]) - if "y1" in gradient_node.attrib: - begin[1] = float(gradient_node.attrib["y1"]) - begin = torch.tensor(begin.transpose(),dtype=torch.float32) - - if "x2" in gradient_node.attrib or "y2" in gradient_node.attrib: - end=np.array([0.,0.]) - if "x2" in gradient_node.attrib: - end[0] = float(gradient_node.attrib["x2"]) - if "y2" in gradient_node.attrib: - end[1] = float(gradient_node.attrib["y2"]) - end=torch.tensor(end.transpose(),dtype=torch.float32) - - stop_nodes=[node for node in list(gradient_node) if OptimizableSvg.remove_namespace(node.tag)=="stop"] - if len(stop_nodes)>0: - stop_nodes=sorted(stop_nodes,key=lambda n: float(n.attrib["offset"])) - - for stop in stop_nodes: - offset, color, opacity = self.parseGradientStop(stop) - offsets.append(offset) - stops.append(np.concatenate((color,np.array([opacity])))) - - hkey=next((value for key,value in gradient_node.attrib.items() if OptimizableSvg.remove_namespace(key)=="href"),None) - if hkey is not None: - href=self.defs[hkey.lstrip("#")] - - parent.children.append(OptimizableSvg.GradientNode(id,transform,self.settings,begin.to(self.device) if begin is not None else begin,end.to(self.device) if end is not None else end,torch.tensor(offsets,dtype=torch.float32,device=self.device) if len(offsets)>0 else None,torch.tensor(np.array(stops),dtype=torch.float32,device=self.device) if len(stops)>0 else None,href)) - - self.depth -= 1 - - def parseViewport(self, root): - if "width" in root.attrib and "height" in root.attrib: - self.canvas = np.array([int(math.ceil(float(root.attrib["width"]))), int(math.ceil(float(root.attrib["height"])))]) - elif "viewBox" in root.attrib: - s=root.attrib["viewBox"].split(" ") - w=s[2] - h=s[3] - self.canvas = np.array( - [int(math.ceil(float(w))), int(math.ceil(float(h)))]) - else: - raise ValueError("Size information is missing from document definition") - #endregion - - #region writing - def write_xml(self): - tree=self.root.write_xml(self) - - return minidom.parseString(etree.tostring(tree, 'utf-8')).toprettyxml(indent=" ") - - def write_defs(self,root): - if len(self.defs)==0: - return - - defnode = etree.SubElement(root, 'defs') - stylenode = etree.SubElement(root,'style') - stylenode.set('type','text/css') - stylenode.text="" - - defcpy=copy.copy(self.defs) - while len(defcpy)>0: - torem=[] - for key,value in defcpy.items(): - if issubclass(value.__class__,OptimizableSvg.SvgNode): - if value.href is None or value.href not in defcpy: - value.write_xml(defnode) - torem.append(key) - else: - continue - else: - #this is a string, and hence a CSS attribute - stylenode.text+=key+" {"+value+"}\n" - torem.append(key) - - for key in torem: - del defcpy[key] - #endregion - - diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/partition.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/partition.h deleted file mode 100644 index fdd158c4cc13ebb81f6fc407276aeada8d1201c5..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/partition.h +++ /dev/null @@ -1,170 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - - -/*! \file partition.h - * \brief Generic implementations of partition functions. - */ - -#pragma once - -#include -#include - -namespace thrust -{ -namespace system -{ -namespace detail -{ -namespace generic -{ - - -template -__host__ __device__ - ForwardIterator stable_partition(thrust::execution_policy &exec, - ForwardIterator first, - ForwardIterator last, - Predicate pred); - -template -__host__ __device__ - ForwardIterator stable_partition(thrust::execution_policy &exec, - ForwardIterator first, - ForwardIterator last, - InputIterator stencil, - Predicate pred); - - -template -__host__ __device__ - thrust::pair &exec, - InputIterator first, - InputIterator last, - OutputIterator1 out_true, - OutputIterator2 out_false, - Predicate pred); - - -template -__host__ __device__ - thrust::pair &exec, - InputIterator1 first, - InputIterator1 last, - InputIterator2 stencil, - OutputIterator1 out_true, - OutputIterator2 out_false, - Predicate pred); - - -template -__host__ __device__ - ForwardIterator partition(thrust::execution_policy &exec, - ForwardIterator first, - ForwardIterator last, - Predicate pred); - - -template -__host__ __device__ - ForwardIterator partition(thrust::execution_policy &exec, - ForwardIterator first, - ForwardIterator last, - InputIterator stencil, - Predicate pred); - - -template -__host__ __device__ - thrust::pair &exec, - InputIterator first, - InputIterator last, - OutputIterator1 out_true, - OutputIterator2 out_false, - Predicate pred); - - -template -__host__ __device__ - thrust::pair &exec, - InputIterator1 first, - InputIterator1 last, - InputIterator2 stencil, - OutputIterator1 out_true, - OutputIterator2 out_false, - Predicate pred); - - -template -__host__ __device__ - ForwardIterator partition_point(thrust::execution_policy &exec, - ForwardIterator first, - ForwardIterator last, - Predicate pred); - - -template -__host__ __device__ - bool is_partitioned(thrust::execution_policy &exec, - InputIterator first, - InputIterator last, - Predicate pred); - - -} // end namespace generic -} // end namespace detail -} // end namespace system -} // end namespace thrust - -#include - diff --git a/spaces/Caoyunkang/Segment-Any-Anomaly/SAM/segment_anything/build_sam.py b/spaces/Caoyunkang/Segment-Any-Anomaly/SAM/segment_anything/build_sam.py deleted file mode 100644 index e41e064e8dfd11131e833c7a1468a38bc6ea6764..0000000000000000000000000000000000000000 --- a/spaces/Caoyunkang/Segment-Any-Anomaly/SAM/segment_anything/build_sam.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -from functools import partial - -from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer - - -def build_sam_vit_h(checkpoint=None): - print('build_sam_vit_h') - return _build_sam( - encoder_embed_dim=1280, - encoder_depth=32, - encoder_num_heads=16, - encoder_global_attn_indexes=[7, 15, 23, 31], - checkpoint=checkpoint, - ) - - -def build_sam_vit_l(checkpoint=None): - print('build_sam_vit_l') - return _build_sam( - encoder_embed_dim=1024, - encoder_depth=24, - encoder_num_heads=16, - encoder_global_attn_indexes=[5, 11, 17, 23], - checkpoint=checkpoint, - ) - - -def build_sam_vit_b(checkpoint=None): - print('build_sam_vit_b') - return _build_sam( - encoder_embed_dim=768, - encoder_depth=12, - encoder_num_heads=12, - encoder_global_attn_indexes=[2, 5, 8, 11], - checkpoint=checkpoint, - ) - -build_sam = build_sam_vit_h - - - -sam_model_registry = { - "default": build_sam, - "vit_h": build_sam_vit_h, - "vit_l": build_sam_vit_l, - "vit_b": build_sam_vit_b, -} - - -def _build_sam( - encoder_embed_dim, - encoder_depth, - encoder_num_heads, - encoder_global_attn_indexes, - checkpoint=None, -): - prompt_embed_dim = 256 - image_size = 1024 - vit_patch_size = 16 - image_embedding_size = image_size // vit_patch_size - sam = Sam( - image_encoder=ImageEncoderViT( - depth=encoder_depth, - embed_dim=encoder_embed_dim, - img_size=image_size, - mlp_ratio=4, - norm_layer=partial(torch.nn.LayerNorm, eps=1e-6), - num_heads=encoder_num_heads, - patch_size=vit_patch_size, - qkv_bias=True, - use_rel_pos=True, - global_attn_indexes=encoder_global_attn_indexes, - window_size=14, - out_chans=prompt_embed_dim, - ), - prompt_encoder=PromptEncoder( - embed_dim=prompt_embed_dim, - image_embedding_size=(image_embedding_size, image_embedding_size), - input_image_size=(image_size, image_size), - mask_in_chans=16, - ), - mask_decoder=MaskDecoder( - num_multimask_outputs=3, - transformer=TwoWayTransformer( - depth=2, - embedding_dim=prompt_embed_dim, - mlp_dim=2048, - num_heads=8, - ), - transformer_dim=prompt_embed_dim, - iou_head_depth=3, - iou_head_hidden_dim=256, - ), - pixel_mean=[123.675, 116.28, 103.53], - pixel_std=[58.395, 57.12, 57.375], - ) - sam.eval() - if checkpoint is not None: - with open(checkpoint, "rb") as f: - state_dict = torch.load(f) - sam.load_state_dict(state_dict) - return sam diff --git a/spaces/CarlDennis/HYTTS/app.py b/spaces/CarlDennis/HYTTS/app.py deleted file mode 100644 index de5919c67a7e3dfc8d1b7afac4d8986c29f43210..0000000000000000000000000000000000000000 --- a/spaces/CarlDennis/HYTTS/app.py +++ /dev/null @@ -1,131 +0,0 @@ -import re -import time -import gradio as gr -import torch -import commons -import utils -from models import SynthesizerTrn -from text import text_to_sequence - -config_json = "configs//multi.json" -pth_path = "model//G=728.pth" - -lan = ["中文", "日文", "英文", "德语", "克罗地亚语"] - - -def get_text(text, hps, cleaned=False): - if cleaned: - text_norm = text_to_sequence(text, hps.symbols, []) - else: - text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners) - if hps.data.add_blank: - text_norm = commons.intersperse(text_norm, 0) - text_norm = torch.LongTensor(text_norm) - return text_norm - - -def get_label(text, label): - if f'[{label}]' in text: - return True, text.replace(f'[{label}]', '') - else: - return False, text - - -def sle(language, tts_input0): - if language == "中文": - tts_input1 = "[ZH]" + tts_input0.replace('\n', '。') + "[ZH]" - return tts_input1 - if language == "英文": - tts_input1 = "[EN]" + tts_input0.replace('\n', '.') + "[EN]" - return tts_input1 - elif language == "日文": - tts_input1 = "[JA]" + tts_input0.replace('\n', '。') + "[JA]" - return tts_input1 - elif language == "德语": - tts_input1 = "[DE]" + tts_input0.replace('\n', '.') + "[DE]" - return tts_input1 - elif language == "克罗地亚语": - tts_input1 = "[CR]" + tts_input0.replace('\n', '.') + "[CR]" - return tts_input1 - - -def load_model(config_json, pth_path): - dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - hps_ms = utils.get_hparams_from_file(f"{config_json}") - n_speakers = hps_ms.data.n_speakers if 'n_speakers' in hps_ms.data.keys() else 0 - n_symbols = len(hps_ms.symbols) if 'symbols' in hps_ms.keys() else 0 - net_g_ms = SynthesizerTrn( - n_symbols, - hps_ms.data.filter_length // 2 + 1, - hps_ms.train.segment_size // hps_ms.data.hop_length, - n_speakers=n_speakers, - **hps_ms.model).to(dev) - _ = net_g_ms.eval() - _ = utils.load_checkpoint(pth_path, net_g_ms) - return net_g_ms - - -net_g_ms = load_model(config_json, pth_path) - - -def infer(language, text, speaker_id, n_scale=0.667, n_scale_w=0.8, l_scale=1): - hps_ms = utils.get_hparams_from_file(f"{config_json}") - stn_tst = get_text(sle(language, text), hps_ms) - speaker_id = int(i_dict[speaker_id]) - dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - with torch.no_grad(): - x_tst = stn_tst.unsqueeze(0).to(dev) - t1 = time.time() - x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev) - sid = torch.LongTensor([speaker_id]).to(dev) - audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=n_scale, noise_scale_w=n_scale_w, - length_scale=l_scale)[0][ - 0, 0].data.cpu().float().numpy() - t2 = time.time() - spending_time = "推理时间：" + str(t2 - t1) + "s" - print(spending_time) - return (hps_ms.data.sampling_rate, audio) - - -i_dict = { - "ことり(JAP)": 1, - "うみ(JAP)": 0, - "えり(JAP)": 6, - "小文(CHN)": 9, - "小菊(CHN)": 10, - "小标(CHN)": 11, - "Helena(HRV)": 14, - "Erika(DEU)": 19, - "Diana(ENG)": 26, - "Michelle(ENG)": 30, -} - -idols = [ - "ことり(JAP)", - "うみ(JAP)", - "えり(JAP)", - "小文(CHN)", - "小菊(CHN)", - "小标(CHN)", - "Helena(HRV)", - "Erika(DEU)", - "Diana(ENG)", - "Michelle(ENG)" -] - -app = gr.Blocks() -with app: - with gr.Tabs(): - with gr.TabItem("幻音文字转语音"): - tts_input1 = gr.TextArea(label="支持英语、日语、德语、中文、克罗地亚语", value="大家好") - language = gr.Dropdown(label="选择语言", choices=lan, value="中文", interactive=True) - para_input1 = gr.Slider(minimum=0.01, maximum=1.0, label="更改噪声比例", value=0.667) - para_input2 = gr.Slider(minimum=0.01, maximum=1.0, label="更改噪声偏差", value=0.8) - para_input3 = gr.Slider(minimum=0.1, maximum=10, label="更改时间比例", value=1) - tts_submit = gr.Button("Generate", variant="primary") - speaker1 = gr.Dropdown(label="选择说话人", choices=idols, value="小文(CHN)", interactive=True) - tts_output2 = gr.Audio(label="Output") - - tts_submit.click(infer, [language, tts_input1, speaker1, para_input1, para_input2, para_input3], - [tts_output2]) - app.launch() diff --git a/spaces/CarlDennis/Lovelive-VITS-JPZH/text/__init__.py b/spaces/CarlDennis/Lovelive-VITS-JPZH/text/__init__.py deleted file mode 100644 index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000 --- a/spaces/CarlDennis/Lovelive-VITS-JPZH/text/__init__.py +++ /dev/null @@ -1,32 +0,0 @@ -""" from https://github.com/keithito/tacotron """ -from text import cleaners - - -def text_to_sequence(text, symbols, cleaner_names): - '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text. - Args: - text: string to convert to a sequence - cleaner_names: names of the cleaner functions to run the text through - Returns: - List of integers corresponding to the symbols in the text - ''' - _symbol_to_id = {s: i for i, s in enumerate(symbols)} - - sequence = [] - - clean_text = _clean_text(text, cleaner_names) - for symbol in clean_text: - if symbol not in _symbol_to_id.keys(): - continue - symbol_id = _symbol_to_id[symbol] - sequence += [symbol_id] - return sequence - - -def _clean_text(text, cleaner_names): - for name in cleaner_names: - cleaner = getattr(cleaners, name) - if not cleaner: - raise Exception('Unknown cleaner: %s' % name) - text = cleaner(text) - return text diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/word_eval.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/word_eval.py deleted file mode 100644 index 462875e4d54adf792039b2eac4b83da0c0d07423..0000000000000000000000000000000000000000 --- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/word_eval.py +++ /dev/null @@ -1,782 +0,0 @@ -import logging -import tempfile -import os -import torch -from collections import OrderedDict -import itertools -from tqdm import tqdm -from .util import io_ -from maskrcnn_benchmark.modeling.roi_heads.boundary_head.inference import Masker -from maskrcnn_benchmark.structures.bounding_box import BoxList -from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou - -from maskrcnn_benchmark.config import cfg -from shapely.geometry import * -import cv2 -import numpy as np -import csv -from .alfashape import getAlfaShapes -import torch.nn as nn - - -def do_coco_evaluation( - dataset, - predictions, - box_only, # False - output_folder, - iou_types, # 'segm' - expected_results, # [] - expected_results_sigma_tol, # 4 -): - logger = logging.getLogger("maskrcnn_benchmark.inference") - - if box_only: - logger.info("Evaluating bbox proposals") - areas = {"all": "", "small": "s", "medium": "m", "large": "l"} - res = COCOResults("box_proposal") - for limit in [100, 1000]: - for area, suffix in areas.items(): - stats = evaluate_box_proposals( - predictions, dataset, area=area, limit=limit - ) - key = "AR{}@{:d}".format(suffix, limit) - res.results["box_proposal"][key] = stats["ar"].item() - logger.info(res) - check_expected_results(res, expected_results, expected_results_sigma_tol) - if output_folder: - torch.save(res, os.path.join(output_folder, "box_proposals.pth")) - return - logger.info("Preparing results for COCO format") - coco_results = {} - if "bbox" in iou_types: - logger.info("Preparing bbox results") - coco_results["bbox"] = prepare_for_coco_detection(predictions, dataset) - if "bo" in iou_types: - logger.info("Preparing bo results") - coco_results["bo"] = prepare_for_boundary_segmentation(predictions, dataset) - logger.info("Do not apply evaluating predictions") - for iou_type in iou_types: - with tempfile.NamedTemporaryFile() as f: - file_path = f.name - if output_folder: - if not os.path.isdir(output_folder): - print('creating dir: ' + output_folder) - os.mkdir(output_folder) - file_path = os.path.join(output_folder, iou_type + ".json") - res = evaluate_predictions_on_coco( - dataset.coco, coco_results[iou_type], file_path, iou_type - ) - - return None - - -def prepare_for_coco_detection(predictions, dataset): - # assert isinstance(dataset, COCODataset) - coco_results = [] - for image_id, prediction in enumerate(predictions): - original_id = dataset.id_to_img_map[image_id] - if len(prediction) == 0: - continue - - # TODO replace with get_img_info? - image_width = dataset.coco.imgs[original_id]["width"] - image_height = dataset.coco.imgs[original_id]["height"] - prediction = prediction.resize((image_width, image_height)) - prediction = prediction.convert("xywh") - - boxes = prediction.bbox.tolist() - scores = prediction.get_field("scores").tolist() - labels = prediction.get_field("labels").tolist() - - mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels] - - coco_results.extend( - [ - { - "image_id": original_id, - "category_id": mapped_labels[k], - "bbox": box, - "score": scores[k], - } - for k, box in enumerate(boxes) - ] - ) - return coco_results - - -def contour_to_xys(cnt, image_shape): - """Convert rect to xys, i.e., eight points - The `image_shape` is used to to make sure all points return are valid, i.e., within image area - """ - rect = cv2.minAreaRect(cnt) - h, w = image_shape[0:2] - - def get_valid_x(x): - if x < 0: - return 0 - if x >= w: - return w - 1 - return x - - def get_valid_y(y): - if y < 0: - return 0 - if y >= h: - return h - 1 - return y - - points = cv2.boxPoints(rect) - points = np.int0(points) - for i_xy, (x, y) in enumerate(points): - x = get_valid_x(x) - y = get_valid_y(y) - points[i_xy, :] = [x, y] - points = np.reshape(points, -1) - return points - - -def contour_to_valid(cnt, image_shape): - """Convert rect to xys, i.e., eight points - The `image_shape` is used to to make sure all points return are valid, i.e., within image area - """ - # rect = cv2.minAreaRect(cnt) - if len(cnt.shape) != 3: - assert 1 < 0 - rect = cnt.reshape([cnt.shape[0], cnt.shape[2]]) - h, w = image_shape[0:2] - - def get_valid_x(x): - if x < 0: - return 0 - if x >= w: - return w - 1 - return x - - def get_valid_y(y): - if y < 0: - return 0 - if y >= h: - return h - 1 - return y - for i_xy, (x, y) in enumerate(rect): - x = get_valid_x(x) - y = get_valid_y(y) - rect[i_xy, :] = [x, y] - - points = np.reshape(rect, -1) - return points - - -def _nms_y(heat, kernel=3): - pad = (kernel - 1) // 2 - hmax = nn.functional.max_pool2d( - heat, (1, kernel), stride=1, padding=(0, pad)) - keep = (hmax == heat).float() - return heat * keep - - -def _nms_x(heat, kernel=3): - pad = (kernel - 1) // 2 - hmax = nn.functional.max_pool2d( - heat, (kernel, 1), stride=1, padding=(pad, 0)) - keep = (hmax == heat).float() - return heat * keep - -def CTW_order_lr(map_in): - - line_out_l2r = [] - line_out_r2l = [] - - map_in = torch.tensor(map_in) - value, top = torch.topk(map_in, 2, dim=0) - value = value.numpy() - top = top.numpy() - top_th = np.where(value[1] > 0.1)[0] # L - # print(top_th) - if len(top_th) == 0: - return [] - top1 = np.sort(top, axis=0) - for i in range(len(top_th)): - line_out_l2r.append([top_th[i], top1[0][top_th[i]]]) - line_out_r2l.append([top_th[i], top1[1][top_th[i]]]) - line_out = line_out_l2r+line_out_r2l[::-1] - # print(line_out) - return line_out - -def CTW_order_bt(map_in): - - line_out_t2b = [] - line_out_b2t = [] - - map_in = torch.tensor(map_in) - value, top = torch.topk(map_in, 2, dim=1) - value = value.numpy() - top = top.numpy() - top_th = np.where(value[:, 1] > 0.1)[0] # H - if len(top_th) == 0: - return [] - top1 = np.sort(top, axis=1) - for i in range(len(top_th)): - line_out_b2t.append([top1[top_th[i]][0], top_th[i]]) - line_out_t2b.append([top1[top_th[i]][1], top_th[i]]) - line_out = line_out_b2t[::-1] + line_out_t2b - # print(line_out) - return line_out - -def boundary_to_mask_ic(bo_x, bo_y, name, num): - - # NMS Hmap and Vmap - Vmap = _nms_x(bo_x, kernel=5) - Hmap = _nms_y(bo_y, kernel=3) - Vmap = Vmap[0] - Hmap = Hmap[0] - ploys_Alfa_x = Vmap.clone().numpy() - ploys_Alfa_y = Hmap.clone().numpy() - - # Threshold Hmap and Vmap - thresh = 0.5 - ploys_Alfa_x[ploys_Alfa_x < thresh] = 0 - ploys_Alfa_x[ploys_Alfa_x >= thresh] = 1 - ploys_Alfa_y[ploys_Alfa_y < thresh] = 0 - ploys_Alfa_y[ploys_Alfa_y >= thresh] = 1 - # Output points with strong texture inforamtion in both maps - ploys_Alfa = ploys_Alfa_x + ploys_Alfa_y - ploys_Alfa[ploys_Alfa < 2] = 0 - ploys_Alfa[ploys_Alfa == 2] = 1 - img_draw = np.zeros([ploys_Alfa_y.shape[-1], ploys_Alfa_y.shape[-1]], dtype=np.uint8) - - # calculate polygon by Alpha-Shape Algorithm - if ploys_Alfa.sum() == 0: - return img_draw - ploys_Alfa_inds = np.argwhere(ploys_Alfa == 1) - zero_detect_x = ploys_Alfa_inds[:, 0] - ploys_Alfa_inds[0, 0] - zero_detect_y = ploys_Alfa_inds[:, 1] - ploys_Alfa_inds[0, 1] - if np.where(zero_detect_x != 0)[0].shape[0] == 0 or np.where(zero_detect_y != 0)[0].shape[0] == 0 or \ - ploys_Alfa_inds.shape[0] < 4: - draw_line = ploys_Alfa_inds[np.newaxis, np.newaxis, :, :] - cv2.fillPoly(img_draw, draw_line, 1) - return img_draw - ploys_Alfa_inds = ploys_Alfa_inds.tolist() - ploys_Alfa_inds = [tuple(ploys_Alfa_ind) for ploys_Alfa_ind in ploys_Alfa_inds] - lines = getAlfaShapes(ploys_Alfa_inds, alfas=[1]) - draw_line = np.array(lines) - if len(draw_line.shape) == 4: - if draw_line.shape[1] == 1: - draw_line[0, 0, :, :] = draw_line[0, 0, :, ::-1] - cv2.fillPoly(img_draw, draw_line, 1) - else: - i_draw = 0 - for draw_l in draw_line[0]: - img_draw_new = np.zeros([28, 28], dtype=np.uint8) - draw_l = draw_l[np.newaxis, np.newaxis, :, :] - cv2.fillPoly(img_draw, np.int32(draw_l), 1) - cv2.fillPoly(img_draw_new, np.int32(draw_l), 1) - i_draw += 1 - - else: - for i, line in enumerate(lines[0]): - draw_line = np.array(line) - draw_line = draw_line[np.newaxis, np.newaxis, :, :] - draw_line[0, 0, :, :] = draw_line[0, 0, :, ::-1] - cv2.fillPoly(img_draw, draw_line, 1) - return img_draw - -def boundary_to_mask_ctw(bo_x,bo_y, name, num, image_name_name,p_temp_box): - w_half = (p_temp_box[2] - p_temp_box[0]) * .5 - h_half = (p_temp_box[3] - p_temp_box[1]) * .5 - thresh_total = 0.5 - - if w_half >= h_half: - # point re-scoring - bo_x = _nms_x(bo_x, kernel=9) - bo_x = bo_x[0] - bo_y = bo_y[0] - ploys_Alfa_x = bo_x.clone().numpy() - ploys_Alfa_y = bo_y.clone().numpy() - thresh_x = thresh_total - thresh_y = thresh_total - ploys_Alfa_x_1 = bo_x.clone().numpy() - ploys_Alfa_y_1 = bo_y.clone().numpy() - ploys_Alfa__1 = ploys_Alfa_x_1 + ploys_Alfa_y_1 - ploys_Alfa_x[ploys_Alfa_x < thresh_x] = 0 - ploys_Alfa_x[ploys_Alfa_x >= thresh_x] = 1 - ploys_Alfa_y[ploys_Alfa_y < thresh_y] = 0 - ploys_Alfa_y[ploys_Alfa_y >= thresh_y] = 1 - ploys_Alfa = ploys_Alfa_x + ploys_Alfa_y - ploys_Alfa[ploys_Alfa < 2] = 0 - ploys_Alfa[ploys_Alfa == 2] = 1 - ploys_Alfa *= ploys_Alfa__1 - # rebuild text region from contour points - img_draw = np.zeros([ploys_Alfa_y.shape[-1], ploys_Alfa_y.shape[-1]], dtype=np.uint8) - if ploys_Alfa.sum() == 0: - return img_draw - lines = CTW_order_lr(ploys_Alfa) - else: - bo_y = _nms_y(bo_y,kernel=9) - bo_x = bo_x[0] - bo_y = bo_y[0] - ploys_Alfa_x = bo_x.clone().numpy() - ploys_Alfa_y = bo_y.clone().numpy() - thresh_x = thresh_total - thresh_y = thresh_total - ploys_Alfa_x_1 = bo_x.clone().numpy() - ploys_Alfa_y_1 = bo_y.clone().numpy() - ploys_Alfa__1 = ploys_Alfa_x_1 + ploys_Alfa_y_1 - ploys_Alfa_x[ploys_Alfa_x < thresh_x] = 0 - ploys_Alfa_x[ploys_Alfa_x >= thresh_x] = 1 - ploys_Alfa_y[ploys_Alfa_y < thresh_y] = 0 - ploys_Alfa_y[ploys_Alfa_y >= thresh_y] = 1 - ploys_Alfa = ploys_Alfa_x + ploys_Alfa_y - ploys_Alfa[ploys_Alfa < 2] = 0 - ploys_Alfa[ploys_Alfa == 2] = 1 - ploys_Alfa *= ploys_Alfa__1 - img_draw = np.zeros([ploys_Alfa_y.shape[-1], ploys_Alfa_y.shape[-1]], dtype=np.uint8) - if ploys_Alfa.sum() == 0: - return img_draw - lines = CTW_order_bt(ploys_Alfa) - if len(lines) <=10: - return img_draw - draw_line = np.array(lines) - draw_line = draw_line[np.newaxis, np.newaxis, :, :] - cv2.fillPoly(img_draw, draw_line, 1) - img_draw = img_draw.astype(np.uint8) - kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) - img_draw = cv2.morphologyEx(img_draw, cv2.MORPH_CLOSE, kernel) - return img_draw - -def mask_to_roRect(mask, img_shape): - ## convert mask into rotated rect - e = mask[0, :, :] - _, countours, hier = cv2.findContours(e.clone().numpy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) # Aarlog - if len(countours) == 0: - return np.zeros((1, 8)) - t_c = countours[0].copy() - quad = contour_to_xys(t_c, img_shape) - return quad - - -def mask_to_contours(mask, img_shape): - e = mask[0, :, :] - - _, countours, hier = cv2.findContours(e.clone().numpy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) # Aarlog - - if len(countours) == 0: - return np.zeros((1, 8)) - t_c = countours[0].copy() - quad = contour_to_valid(t_c, img_shape) - return quad - - -def write_result_as_txt(image_name, bboxes, path): - if not os.path.exists(path): - os.makedirs(path) - - filename = io_.join_path(path, '%s.txt' % (image_name)) - lines = [] - for b_idx, bbox in enumerate(bboxes): - if len(bbox) < 6: - continue - values = [int(v) for v in bbox] - # line = "%d, %d, %d, %d, %d, %d, %d, %d\n"%tuple(values) - line = "%d" % values[0] - for v_id in range(1, len(values)): - line += ", %d" % values[v_id] - line += '\n' - lines.append(line) - io_.write_lines(filename, lines) - - -def prepare_for_boundary_segmentation(predictions, dataset): - import pycocotools.mask as mask_util - import numpy as np - - masker = Masker(threshold=0.5, padding=1) - coco_results = [] - - for image_id, prediction in tqdm(enumerate(predictions)): - original_id = dataset.id_to_img_map[image_id] - image_name = dataset.coco.imgs[original_id]["file_name"].split('.')[0] - im_w_name = dataset.coco.imgs[original_id]["file_name"] - if len(prediction) == 0: - continue - - # TODO replace with get_img_info? - image_width = dataset.coco.imgs[original_id]["width"] - image_height = dataset.coco.imgs[original_id]["height"] - prediction = prediction.resize((image_width, image_height)) - masks_x = prediction.get_field("mask_x") - masks_y = prediction.get_field("mask_y") - - if 'ic15' in cfg.DATASETS.TEST[0]: - masks = [boundary_to_mask_ic(mask_x, mask_y, dataset.coco.imgs[original_id]["file_name"], number) for - mask_x, mask_y, number in zip(masks_x, masks_y,list(range(masks_x.shape[0])))] - elif 'CTW' in cfg.DATASETS.TEST[0]: - masks = [boundary_to_mask_ctw(mask_x, mask_y, dataset.coco.imgs[original_id]["file_name"], number, image_name, - p_temp) for - mask_x, mask_y, number, p_temp in zip(masks_x, masks_y, - list(range(masks_x.shape[0])), prediction.bbox)] - else: - print('Please add your own construction code!') - assert 1<0 - - masks = torch.from_numpy(np.array(masks)[:, np.newaxis, :, :]) - # Masker is necessary only if masks haven't been already resized. - if list(masks.shape[-2:]) != [image_height, image_width]: - masks = masker(masks.expand(1, -1, -1, -1, -1), prediction) - masks = masks[0] - - scores = prediction.get_field("scores").tolist() - labels = prediction.get_field("labels").tolist() - if 'ic15' in cfg.DATASETS.TEST[0]: - rects = [mask_to_roRect(mask, [image_height, image_width]) for mask in masks] - if 'CTW' in cfg.DATASETS.TEST[0]: - contours = [mask_to_contours(mask, [image_height, image_width]) for mask in masks] - # output for evaluation - write_result_as_txt(image_name, contours, './output/ctw/results.txt') - # visualization - if cfg.DATASETS.Test_Visual: - im_write = cv2.imread( - '../ct/dataset/ctw/ctw_test_images/' + im_w_name)[:, :,::-1] - for box in contours: - box = np.array(box) - box = np.around(box).astype(np.int32) - cv2.polylines(im_write[:, :, ::-1], [box.astype(np.int32).reshape((-1, 1, 2))], True, color=(0, 255, 0), thickness=2) # 0,255,255 y 0,255,0 g - cv2.imwrite('./det_visual/' + im_w_name,im_write[:, :, ::-1]) - - if 'ic15' in cfg.DATASETS.TEST[0]: - mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels] - esd = [] - for k, rect in enumerate(rects): - if rect.all() == 0: - continue - else: - esd.append( - { - "image_id": original_id, - "category_id": mapped_labels[k], - "seg_rorect": rect.tolist(), - "score": scores[k], - } - ) - if cfg.PROCESS.PNMS: - pnms_thresh = cfg.PROCESS.NMS_THRESH - keep = esd_pnms(esd, pnms_thresh) - new_esd = [] - for i in keep: - new_esd.append(esd[i]) - coco_results.extend(new_esd) - # visualization - if cfg.DATASETS.Test_Visual: - im_write = cv2.imread( - '../ct/dataset/ic15/ic15_test_images/' + im_w_name)[ - :, :, ::-1] - for i in keep: - box = esd[i] - # print(box) - # assert 1<0 - box = np.array(box['seg_rorect']) - box = np.around(box).astype(np.int32) - cv2.polylines(im_write[:, :, ::-1], [box.astype(np.int32).reshape((-1, 1, 2))], True, - color=(0, 255, 0), thickness=2) # 0,255,255 y 0,255,0 g - cv2.imwrite('./det_visual/' + im_w_name, im_write[:, :, ::-1]) - else: - coco_results.extend(esd) - - - return coco_results - -def ke_to_quad(ke, mty, img_shape): - mt = mty[:].argmax() - quad = paraToQuad_v3(ke, mt) - return quad - - -# polynms -def py_cpu_pnms(dets, scores, thresh): - pts = [] - for det in dets: - pts.append([[det[i][0], det[i][1]] for i in range(len(det))]) - order = scores.argsort()[::-1] - areas = np.zeros(scores.shape) - order = scores.argsort()[::-1] - inter_areas = np.zeros((scores.shape[0], scores.shape[0])) - for il in range(len(pts)): - poly = Polygon(pts[il]) - areas[il] = poly.area - for jl in range(il, len(pts)): - polyj = Polygon(pts[jl]) - try: - inS = poly.intersection(polyj) - except: - print(poly, polyj) - inter_areas[il][jl] = inS.area - inter_areas[jl][il] = inS.area - - keep = [] - while order.size > 0: - i = order[0] - keep.append(i) - ovr = inter_areas[i][order[1:]] / (areas[i] + areas[order[1:]] - inter_areas[i][order[1:]]) - inds = np.where(ovr <= thresh)[0] - order = order[inds + 1] - return keep - - -def esd_pnms(esd, pnms_thresh): - scores = [] - dets = [] - for ele in esd: - score = ele['score'] - quad = ele['seg_rorect'] - # det = np.array([[quad[0][0], quad[0][1]], [quad[1][0], quad[1][1]],[quad[2][0], quad[2][1]],[quad[3][0], quad[3][1]]]) - det = np.array([[quad[0], quad[1]], [quad[2], quad[3]], [quad[4], quad[5]], [quad[6], quad[7]]]) - scores.append(score) - dets.append(det) - scores = np.array(scores) - dets = np.array(dets) - keep = py_cpu_pnms(dets, scores, pnms_thresh) - return keep - - -# inspired from Detectron -def evaluate_box_proposals( - predictions, dataset, thresholds=None, area="all", limit=None -): - """Evaluate detection proposal recall metrics. This function is a much - faster alternative to the official COCO API recall evaluation code. However, - it produces slightly different results. - """ - # Record max overlap value for each gt box - # Return vector of overlap values - areas = { - "all": 0, - "small": 1, - "medium": 2, - "large": 3, - "96-128": 4, - "128-256": 5, - "256-512": 6, - "512-inf": 7, - } - area_ranges = [ - [0 ** 2, 1e5 ** 2], # all - [0 ** 2, 32 ** 2], # small - [32 ** 2, 96 ** 2], # medium - [96 ** 2, 1e5 ** 2], # large - [96 ** 2, 128 ** 2], # 96-128 - [128 ** 2, 256 ** 2], # 128-256 - [256 ** 2, 512 ** 2], # 256-512 - [512 ** 2, 1e5 ** 2], - ] # 512-inf - assert area in areas, "Unknown area range: {}".format(area) - area_range = area_ranges[areas[area]] - gt_overlaps = [] - num_pos = 0 - - for image_id, prediction in enumerate(predictions): - original_id = dataset.id_to_img_map[image_id] - - # TODO replace with get_img_info? - image_width = dataset.coco.imgs[original_id]["width"] - image_height = dataset.coco.imgs[original_id]["height"] - prediction = prediction.resize((image_width, image_height)) - - # sort predictions in descending order - # TODO maybe remove this and make it explicit in the documentation - inds = prediction.get_field("objectness").sort(descending=True)[1] - prediction = prediction[inds] - - ann_ids = dataset.coco.getAnnIds(imgIds=original_id) - anno = dataset.coco.loadAnns(ann_ids) - gt_boxes = [obj["bbox"] for obj in anno if obj["iscrowd"] == 0] - gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes - gt_boxes = BoxList(gt_boxes, (image_width, image_height), mode="xywh").convert( - "xyxy" - ) - gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0]) - - if len(gt_boxes) == 0: - continue - - valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) - gt_boxes = gt_boxes[valid_gt_inds] - - num_pos += len(gt_boxes) - - if len(gt_boxes) == 0: - continue - - if len(prediction) == 0: - continue - - if limit is not None and len(prediction) > limit: - prediction = prediction[:limit] - - overlaps = boxlist_iou(prediction, gt_boxes) - - _gt_overlaps = torch.zeros(len(gt_boxes)) - for j in range(min(len(prediction), len(gt_boxes))): - # find which proposal box maximally covers each gt box - # and get the iou amount of coverage for each gt box - max_overlaps, argmax_overlaps = overlaps.max(dim=0) - - # find which gt box is 'best' covered (i.e. 'best' = most iou) - gt_ovr, gt_ind = max_overlaps.max(dim=0) - assert gt_ovr >= 0 - # find the proposal box that covers the best covered gt box - box_ind = argmax_overlaps[gt_ind] - # record the iou coverage of this gt box - _gt_overlaps[j] = overlaps[box_ind, gt_ind] - assert _gt_overlaps[j] == gt_ovr - # mark the proposal box and the gt box as used - overlaps[box_ind, :] = -1 - overlaps[:, gt_ind] = -1 - - # append recorded iou coverage level - gt_overlaps.append(_gt_overlaps) - gt_overlaps = torch.cat(gt_overlaps, dim=0) - gt_overlaps, _ = torch.sort(gt_overlaps) - - if thresholds is None: - step = 0.05 - thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) - recalls = torch.zeros_like(thresholds) - # compute recall for each iou threshold - for i, t in enumerate(thresholds): - recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) - # ar = 2 * np.trapz(recalls, thresholds) - ar = recalls.mean() - return { - "ar": ar, - "recalls": recalls, - "thresholds": thresholds, - "gt_overlaps": gt_overlaps, - "num_pos": num_pos, - } - - -def evaluate_predictions_on_coco( - coco_gt, coco_results, json_result_file, iou_type="bbox" -): - import json - - print('writing results to ' + json_result_file) - with open(json_result_file, "w") as f: - json.dump(coco_results, f) - - # from pycocotools.cocoeval import COCOeval - - # coco_dt = coco_gt.loadRes(str(json_result_file)) - # # coco_dt = coco_gt.loadRes(coco_results) - # coco_eval = COCOeval(coco_gt, coco_dt, iou_type) - # coco_eval.evaluate() - # coco_eval.accumulate() - # coco_eval.summarize() - # return coco_eval - return None - - -class COCOResults(object): - METRICS = { - "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"], - "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"], - "box_proposal": [ - "AR@100", - "ARs@100", - "ARm@100", - "ARl@100", - "AR@1000", - "ARs@1000", - "ARm@1000", - "ARl@1000", - ], - "keypoint": ["AP", "AP50", "AP75", "APm", "APl"], - } - - def __init__(self, *iou_types): - allowed_types = ("box_proposal", "bbox", "segm") - assert all(iou_type in allowed_types for iou_type in iou_types) - results = OrderedDict() - for iou_type in iou_types: - results[iou_type] = OrderedDict( - [(metric, -1) for metric in COCOResults.METRICS[iou_type]] - ) - self.results = results - - def update(self, coco_eval): - if coco_eval is None: - return - from pycocotools.cocoeval import COCOeval - - assert isinstance(coco_eval, COCOeval) - s = coco_eval.stats - iou_type = coco_eval.params.iouType - res = self.results[iou_type] - metrics = COCOResults.METRICS[iou_type] - for idx, metric in enumerate(metrics): - res[metric] = s[idx] - - def __repr__(self): - # TODO make it pretty - return repr(self.results) - - -def check_expected_results(results, expected_results, sigma_tol): - if not expected_results: - return - - logger = logging.getLogger("maskrcnn_benchmark.inference") - for task, metric, (mean, std) in expected_results: - actual_val = results.results[task][metric] - lo = mean - sigma_tol * std - hi = mean + sigma_tol * std - ok = (lo < actual_val) and (actual_val < hi) - msg = ( - "{} > {} sanity check (actual vs. expected): " - "{:.3f} vs. mean={:.4f}, std={:.4}, range=({:.4f}, {:.4f})" - ).format(task, metric, actual_val, mean, std, lo, hi) - if not ok: - msg = "FAIL: " + msg - logger.error(msg) - else: - msg = "PASS: " + msg - logger.info(msg) - - -def paraToQuad_v3(kes, mt): - ms = (kes[0, 0], kes[6, 0]) - xs = [kes[i, 0] for i in range(1, 5)] # 1 2 3 4 - ys = [kes[i, 0] for i in range(7, 11)] # 7 8 9 10 - crs = (kes[5, 0], kes[11, 0]) - ms = Point(ms) - crs = Point(crs) - vp = [] - all_types = [[1, 2, 3, 4], [1, 2, 4, 3], [1, 3, 2, 4], [1, 3, 4, 2], [1, 4, 2, 3], [1, 4, 3, 2], \ - [2, 1, 3, 4], [2, 1, 4, 3], [2, 3, 1, 4], [2, 3, 4, 1], [2, 4, 1, 3], [2, 4, 3, 1], \ - [3, 1, 2, 4], [3, 1, 4, 2], [3, 2, 1, 4], [3, 2, 4, 1], [3, 4, 1, 2], [3, 4, 2, 1], \ - [4, 1, 2, 3], [4, 1, 3, 2], [4, 2, 1, 3], [4, 2, 3, 1], [4, 3, 1, 2], [4, 3, 2, 1]] - all_types = [[all_types[iat][0] - 1, all_types[iat][1] - 1, all_types[iat][2] - 1, all_types[iat][3] - 1] for iat in - range(24)] - - tpe = all_types[mt] - p1 = Point((xs[0], ys[tpe[0]])) - p2 = Point((xs[1], ys[tpe[1]])) - p3 = Point((xs[2], ys[tpe[2]])) - p4 = Point((xs[3], ys[tpe[3]])) - pts = [p1, p2, p3, p4] - scs = [0, 1, 2, 3] - for it in itertools.permutations(scs, 4): - poly = Polygon([(pts[it[0]].x, pts[it[0]].y), (pts[it[1]].x, pts[it[1]].y),(pts[it[2]].x, pts[it[2]].y), (pts[it[3]].x, pts[it[3]].y)]) - if poly.is_valid and ms.within(poly) and crs.within(poly): - quad = [(pts[it[0]].x, pts[it[0]].y), (pts[it[1]].x, pts[it[1]].y),(pts[it[2]].x, pts[it[2]].y), (pts[it[3]].x, pts[it[3]].y)] - lr = LinearRing(quad) - if lr.is_ccw: - return [(int(iq[0]), int(iq[1])) for iq in quad] - else: - quad = [quad[0], quad[3], quad[2], quad[1]] - return [(int(iq[0]), int(iq[1])) for iq in quad] - - return [(int(iq[0]), int(iq[1])) for iq in quad] - - return None - diff --git a/spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/datasets/data_utils.py b/spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/datasets/data_utils.py deleted file mode 100644 index 8fe6a567bae667f00ef0ee1d4d9075649107b471..0000000000000000000000000000000000000000 --- a/spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/datasets/data_utils.py +++ /dev/null @@ -1,196 +0,0 @@ -""" - Copyright (c) 2022, salesforce.com, inc. - All rights reserved. - SPDX-License-Identifier: BSD-3-Clause - For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause -""" - -import gzip -import logging -import os -import random as rnd -import tarfile -import zipfile -import random -from typing import List -from tqdm import tqdm - -import decord -from decord import VideoReader -import webdataset as wds -import numpy as np -import torch -from torch.utils.data.dataset import IterableDataset - -from video_llama.common.registry import registry -from video_llama.datasets.datasets.base_dataset import ConcatDataset - - -decord.bridge.set_bridge("torch") -MAX_INT = registry.get("MAX_INT") - - -class ChainDataset(wds.DataPipeline): - r"""Dataset for chaining multiple :class:`DataPipeline` s. - - This class is useful to assemble different existing dataset streams. The - chaining operation is done on-the-fly, so concatenating large-scale - datasets with this class will be efficient. - - Args: - datasets (iterable of IterableDataset): datasets to be chained together - """ - def __init__(self, datasets: List[wds.DataPipeline]) -> None: - super().__init__() - self.datasets = datasets - self.prob = [] - self.names = [] - for dataset in self.datasets: - if hasattr(dataset, 'name'): - self.names.append(dataset.name) - else: - self.names.append('Unknown') - if hasattr(dataset, 'sample_ratio'): - self.prob.append(dataset.sample_ratio) - else: - self.prob.append(1) - logging.info("One of the datapipeline doesn't define ratio and set to 1 automatically.") - - def __iter__(self): - datastreams = [iter(dataset) for dataset in self.datasets] - while True: - select_datastream = random.choices(datastreams, weights=self.prob, k=1)[0] - yield next(select_datastream) - - -def apply_to_sample(f, sample): - if len(sample) == 0: - return {} - - def _apply(x): - if torch.is_tensor(x): - return f(x) - elif isinstance(x, dict): - return {key: _apply(value) for key, value in x.items()} - elif isinstance(x, list): - return [_apply(x) for x in x] - else: - return x - - return _apply(sample) - - -def move_to_cuda(sample): - def _move_to_cuda(tensor): - return tensor.cuda() - - return apply_to_sample(_move_to_cuda, sample) - - -def prepare_sample(samples, cuda_enabled=True): - if cuda_enabled: - samples = move_to_cuda(samples) - - # TODO fp16 support - - return samples - - -def reorg_datasets_by_split(datasets): - """ - Organizes datasets by split. - - Args: - datasets: dict of torch.utils.data.Dataset objects by name. - - Returns: - Dict of datasets by split {split_name: List[Datasets]}. - """ - # if len(datasets) == 1: - # return datasets[list(datasets.keys())[0]] - # else: - reorg_datasets = dict() - - # reorganize by split - for _, dataset in datasets.items(): - for split_name, dataset_split in dataset.items(): - if split_name not in reorg_datasets: - reorg_datasets[split_name] = [dataset_split] - else: - reorg_datasets[split_name].append(dataset_split) - - return reorg_datasets - - -def concat_datasets(datasets): - """ - Concatenates multiple datasets into a single dataset. - - It supports may-style datasets and DataPipeline from WebDataset. Currently, does not support - generic IterableDataset because it requires creating separate samplers. - - Now only supports conctenating training datasets and assuming validation and testing - have only a single dataset. This is because metrics should not be computed on the concatenated - datasets. - - Args: - datasets: dict of torch.utils.data.Dataset objects by split. - - Returns: - Dict of concatenated datasets by split, "train" is the concatenation of multiple datasets, - "val" and "test" remain the same. - - If the input training datasets contain both map-style and DataPipeline datasets, returns - a tuple, where the first element is a concatenated map-style dataset and the second - element is a chained DataPipeline dataset. - - """ - # concatenate datasets in the same split - for split_name in datasets: - if split_name != "train": - assert ( - len(datasets[split_name]) == 1 - ), "Do not support multiple {} datasets.".format(split_name) - datasets[split_name] = datasets[split_name][0] - else: - iterable_datasets, map_datasets = [], [] - for dataset in datasets[split_name]: - if isinstance(dataset, wds.DataPipeline): - logging.info( - "Dataset {} is IterableDataset, can't be concatenated.".format( - dataset - ) - ) - iterable_datasets.append(dataset) - elif isinstance(dataset, IterableDataset): - raise NotImplementedError( - "Do not support concatenation of generic IterableDataset." - ) - else: - map_datasets.append(dataset) - - # if len(iterable_datasets) > 0: - # concatenate map-style datasets and iterable-style datasets separately - if len(iterable_datasets) > 1: - chained_datasets = ( - ChainDataset(iterable_datasets) - ) - elif len(iterable_datasets) == 1: - chained_datasets = iterable_datasets[0] - else: - chained_datasets = None - - concat_datasets = ( - ConcatDataset(map_datasets) if len(map_datasets) > 0 else None - ) - - train_datasets = concat_datasets, chained_datasets - train_datasets = tuple([x for x in train_datasets if x is not None]) - train_datasets = ( - train_datasets[0] if len(train_datasets) == 1 else train_datasets - ) - - datasets[split_name] = train_datasets - - return datasets - diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/varLib/errors.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/varLib/errors.py deleted file mode 100644 index 4f30f901babed2b985ae5c333420b6a9e7a3baa8..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/varLib/errors.py +++ /dev/null @@ -1,219 +0,0 @@ -import textwrap - - -class VarLibError(Exception): - """Base exception for the varLib module.""" - - -class VarLibValidationError(VarLibError): - """Raised when input data is invalid from varLib's point of view.""" - - -class VarLibMergeError(VarLibError): - """Raised when input data cannot be merged into a variable font.""" - - def __init__(self, merger=None, **kwargs): - self.merger = merger - if not kwargs: - kwargs = {} - if "stack" in kwargs: - self.stack = kwargs["stack"] - del kwargs["stack"] - else: - self.stack = [] - self.cause = kwargs - - @property - def reason(self): - return self.__doc__ - - def _master_name(self, ix): - if self.merger is not None: - ttf = self.merger.ttfs[ix] - if "name" in ttf and ttf["name"].getBestFullName(): - return ttf["name"].getBestFullName() - elif hasattr(ttf.reader, "file") and hasattr(ttf.reader.file, "name"): - return ttf.reader.file.name - return f"master number {ix}" - - @property - def offender(self): - if "expected" in self.cause and "got" in self.cause: - index = [x == self.cause["expected"] for x in self.cause["got"]].index( - False - ) - master_name = self._master_name(index) - if "location" in self.cause: - master_name = f"{master_name} ({self.cause['location']})" - return index, master_name - return None, None - - @property - def details(self): - if "expected" in self.cause and "got" in self.cause: - offender_index, offender = self.offender - got = self.cause["got"][offender_index] - return f"Expected to see {self.stack[0]}=={self.cause['expected']!r}, instead saw {got!r}\n" - return "" - - def __str__(self): - offender_index, offender = self.offender - location = "" - if offender: - location = f"\n\nThe problem is likely to be in {offender}:\n" - context = "".join(reversed(self.stack)) - basic = textwrap.fill( - f"Couldn't merge the fonts, because {self.reason}. " - f"This happened while performing the following operation: {context}", - width=78, - ) - return "\n\n" + basic + location + self.details - - -class ShouldBeConstant(VarLibMergeError): - """some values were different, but should have been the same""" - - @property - def details(self): - basic_message = super().details - - if self.stack[0] != ".FeatureCount" or self.merger is None: - return basic_message - - assert self.stack[0] == ".FeatureCount" - offender_index, _ = self.offender - bad_ttf = self.merger.ttfs[offender_index] - good_ttf = next( - ttf - for ttf in self.merger.ttfs - if self.stack[-1] in ttf - and ttf[self.stack[-1]].table.FeatureList.FeatureCount - == self.cause["expected"] - ) - - good_features = [ - x.FeatureTag - for x in good_ttf[self.stack[-1]].table.FeatureList.FeatureRecord - ] - bad_features = [ - x.FeatureTag - for x in bad_ttf[self.stack[-1]].table.FeatureList.FeatureRecord - ] - return basic_message + ( - "\nIncompatible features between masters.\n" - f"Expected: {', '.join(good_features)}.\n" - f"Got: {', '.join(bad_features)}.\n" - ) - - -class FoundANone(VarLibMergeError): - """one of the values in a list was empty when it shouldn't have been""" - - @property - def offender(self): - index = [x is None for x in self.cause["got"]].index(True) - return index, self._master_name(index) - - @property - def details(self): - cause, stack = self.cause, self.stack - return f"{stack[0]}=={cause['got']}\n" - - -class NotANone(VarLibMergeError): - """one of the values in a list was not empty when it should have been""" - - @property - def offender(self): - index = [x is not None for x in self.cause["got"]].index(True) - return index, self._master_name(index) - - @property - def details(self): - cause, stack = self.cause, self.stack - return f"{stack[0]}=={cause['got']}\n" - - -class MismatchedTypes(VarLibMergeError): - """data had inconsistent types""" - - -class LengthsDiffer(VarLibMergeError): - """a list of objects had inconsistent lengths""" - - -class KeysDiffer(VarLibMergeError): - """a list of objects had different keys""" - - -class InconsistentGlyphOrder(VarLibMergeError): - """the glyph order was inconsistent between masters""" - - -class InconsistentExtensions(VarLibMergeError): - """the masters use extension lookups in inconsistent ways""" - - -class UnsupportedFormat(VarLibMergeError): - """an OpenType subtable (%s) had a format I didn't expect""" - - def __init__(self, merger=None, **kwargs): - super().__init__(merger, **kwargs) - if not self.stack: - self.stack = [".Format"] - - @property - def reason(self): - s = self.__doc__ % self.cause["subtable"] - if "value" in self.cause: - s += f" ({self.cause['value']!r})" - return s - - -class InconsistentFormats(UnsupportedFormat): - """an OpenType subtable (%s) had inconsistent formats between masters""" - - -class VarLibCFFMergeError(VarLibError): - pass - - -class VarLibCFFDictMergeError(VarLibCFFMergeError): - """Raised when a CFF PrivateDict cannot be merged.""" - - def __init__(self, key, value, values): - error_msg = ( - f"For the Private Dict key '{key}', the default font value list:" - f"\n\t{value}\nhad a different number of values than a region font:" - ) - for region_value in values: - error_msg += f"\n\t{region_value}" - self.args = (error_msg,) - - -class VarLibCFFPointTypeMergeError(VarLibCFFMergeError): - """Raised when a CFF glyph cannot be merged because of point type differences.""" - - def __init__(self, point_type, pt_index, m_index, default_type, glyph_name): - error_msg = ( - f"Glyph '{glyph_name}': '{point_type}' at point index {pt_index} in " - f"master index {m_index} differs from the default font point type " - f"'{default_type}'" - ) - self.args = (error_msg,) - - -class VarLibCFFHintTypeMergeError(VarLibCFFMergeError): - """Raised when a CFF glyph cannot be merged because of hint type differences.""" - - def __init__(self, hint_type, cmd_index, m_index, default_type, glyph_name): - error_msg = ( - f"Glyph '{glyph_name}': '{hint_type}' at index {cmd_index} in " - f"master index {m_index} differs from the default font hint type " - f"'{default_type}'" - ) - self.args = (error_msg,) - - -class VariationModelError(VarLibError): - """Raised when a variation model is faulty.""" diff --git a/spaces/DaFujaTyping/hf-Chat-ui/src/lib/utils/randomUuid.ts b/spaces/DaFujaTyping/hf-Chat-ui/src/lib/utils/randomUuid.ts deleted file mode 100644 index 9d536365c57659305ad28d6fc06b89d77ab337ab..0000000000000000000000000000000000000000 --- a/spaces/DaFujaTyping/hf-Chat-ui/src/lib/utils/randomUuid.ts +++ /dev/null @@ -1,14 +0,0 @@ -type UUID = ReturnType ; - -export function randomUUID(): UUID { - // Only on old safari / ios - if (!("randomUUID" in crypto)) { - return "10000000-1000-4000-8000-100000000000".replace(/[018]/g, (c) => - ( - Number(c) ^ - (crypto.getRandomValues(new Uint8Array(1))[0] & (15 >> (Number(c) / 4))) - ).toString(16) - ) as UUID; - } - return crypto.randomUUID(); -} diff --git a/spaces/Datasculptor/MusicGen/tests/modules/test_lstm.py b/spaces/Datasculptor/MusicGen/tests/modules/test_lstm.py deleted file mode 100644 index 1248964c8191e19f27661f0974bef9cc967eb015..0000000000000000000000000000000000000000 --- a/spaces/Datasculptor/MusicGen/tests/modules/test_lstm.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import random -import torch - -from audiocraft.modules.lstm import StreamableLSTM - - -class TestStreamableLSTM: - - def test_lstm(self): - B, C, T = 4, 2, random.randint(1, 100) - - lstm = StreamableLSTM(C, 3, skip=False) - x = torch.randn(B, C, T) - y = lstm(x) - - print(y.shape) - assert y.shape == torch.Size([B, C, T]) - - def test_lstm_skip(self): - B, C, T = 4, 2, random.randint(1, 100) - - lstm = StreamableLSTM(C, 3, skip=True) - x = torch.randn(B, C, T) - y = lstm(x) - - assert y.shape == torch.Size([B, C, T]) diff --git a/spaces/DebateLabKIT/deepa2-demo/README.md b/spaces/DebateLabKIT/deepa2-demo/README.md deleted file mode 100644 index 1da43e12200334c88c709bba5573542c24f35602..0000000000000000000000000000000000000000 --- a/spaces/DebateLabKIT/deepa2-demo/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: DeepA2 Demo -emoji: 🕵️♀️ -colorFrom: green -colorTo: yellow -sdk: streamlit -app_file: app.py -pinned: true ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/metrics/__init__.py b/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/metrics/__init__.py deleted file mode 100644 index db8124b132f91216c0ded226f20ea3a046734728..0000000000000000000000000000000000000000 --- a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/metrics/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# -# This work is licensed under the Creative Commons Attribution-NonCommercial -# 4.0 International License. To view a copy of this license, visit -# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to -# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. - -# empty diff --git a/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/include/dataType.h b/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/include/dataType.h deleted file mode 100644 index a7821a395c1c03db137587b879b255846fb0ca16..0000000000000000000000000000000000000000 --- a/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/include/dataType.h +++ /dev/null @@ -1,36 +0,0 @@ -#pragma once - -#include -#include - -#include -#include -typedef Eigen::Matrix FEATURESS; - -//Kalmanfilter -//typedef Eigen::Matrix matches; - std::vector unmatched_tracks; - std::vector unmatched_detections; -}TRACHER_MATCHD; - -//linear_assignment: -typedef Eigen::Matrix = memo(({ language, value }) => { - const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 }) - - const downloadAsFile = () => { - if (typeof window === 'undefined') { - return - } - const fileExtension = programmingLanguages[language] || '.file' - const suggestedFileName = `file-${generateRandomString( - 3, - true - )}${fileExtension}` - const fileName = window.prompt('Enter file name' || '', suggestedFileName) - - if (!fileName) { - // User pressed cancel on prompt. - return - } - - const blob = new Blob([value], { type: 'text/plain' }) - const url = URL.createObjectURL(blob) - const link = document.createElement('a') - link.download = fileName - link.href = url - link.style.display = 'none' - document.body.appendChild(link) - link.click() - document.body.removeChild(link) - URL.revokeObjectURL(url) - } - - const onCopy = () => { - if (isCopied) return - copyToClipboard(value) - } - - return ( - -- ) -}) -CodeBlock.displayName = 'CodeBlock' - -export { CodeBlock } diff --git a/spaces/Gladiator/Sartorius-Cell-Segmentation/app.py b/spaces/Gladiator/Sartorius-Cell-Segmentation/app.py deleted file mode 100644 index 1cbab0be85e1242df43bbb394cb0674daa301e3d..0000000000000000000000000000000000000000 --- a/spaces/Gladiator/Sartorius-Cell-Segmentation/app.py +++ /dev/null @@ -1,62 +0,0 @@ -import streamlit as st -import numpy as np -import matplotlib.pyplot as plt -from PIL import Image -from cellpose import models - - -@st.cache() -def load_model(model_path): - inf_model = models.CellposeModel(gpu=False, pretrained_model=model_path) - return inf_model - -#def inference(img, model_path, **model_params): -# inf_model = models.CellposeModel(gpu=False, pretrained_model=model_path) -# preds, flows, _ = inf_model.eval([img], **model_params) -# return preds, flows - - -if __name__ == "__main__": - - st.title("Sartorius Neuronal Cell Segmentation") - model_path = "./cellpose_residual_on_style_on_concatenation_off_fold1_ep_649_cv_0.2834" - inf_model = models.CellposeModel(gpu=False, pretrained_model=model_path) - uploaded_img = st.file_uploader(label="Upload neuronal cell image") - - with st.expander("View input image"): - if uploaded_img is not None: - st.image(uploaded_img) - else: - st.warning("Please upload an image") - - segment = st.button("Perform segmentation") - - if uploaded_img is not None and segment: - img = Image.open(uploaded_img) - img = np.array(img) - - model_params = { - "diameter": 19.0, - "channels": [0, 0], - "augment": True, - "resample": True, - } - with st.spinner("Performing segmentation. This might take a while..."): - #preds, flows = inference( - # img=img, - # model_path="./cellpose_residual_on_style_on_concatenation_off_fold1_ep_649_cv_0.2834", - # **model_params - #) - preds, flows, _ = inf_model.eval([img], **model_params) - - fig, (ax1, ax2, ax3) = plt.subplots(1, 3) - ax1.axis("off") - ax2.axis("off") - ax3.axis("off") - ax1.set_title("Original Image") - ax1.imshow(img, cmap="gray") - ax2.set_title("Segmented image") - ax2.imshow(preds[0]) - ax3.set_title("Image flows") - ax3.imshow(flows[0][0]) - st.pyplot(fig) diff --git a/spaces/Godrose0728/Aisound02/text/english.py b/spaces/Godrose0728/Aisound02/text/english.py deleted file mode 100644 index 6817392ba8a9eb830351de89fb7afc5ad72f5e42..0000000000000000000000000000000000000000 --- a/spaces/Godrose0728/Aisound02/text/english.py +++ /dev/null @@ -1,188 +0,0 @@ -""" from https://github.com/keithito/tacotron """ - -''' -Cleaners are transformations that run over the input text at both training and eval time. - -Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners" -hyperparameter. Some cleaners are English-specific. You'll typically want to use: - 1. "english_cleaners" for English text - 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using - the Unidecode library (https://pypi.python.org/pypi/Unidecode) - 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update - the symbols in symbols.py to match your data). -''' - - -# Regular expression matching whitespace: - - -import re -import inflect -from unidecode import unidecode -import eng_to_ipa as ipa -_inflect = inflect.engine() -_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])') -_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)') -_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)') -_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)') -_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)') -_number_re = re.compile(r'[0-9]+') - -# List of (regular expression, replacement) pairs for abbreviations: -_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [ - ('mrs', 'misess'), - ('mr', 'mister'), - ('dr', 'doctor'), - ('st', 'saint'), - ('co', 'company'), - ('jr', 'junior'), - ('maj', 'major'), - ('gen', 'general'), - ('drs', 'doctors'), - ('rev', 'reverend'), - ('lt', 'lieutenant'), - ('hon', 'honorable'), - ('sgt', 'sergeant'), - ('capt', 'captain'), - ('esq', 'esquire'), - ('ltd', 'limited'), - ('col', 'colonel'), - ('ft', 'fort'), -]] - - -# List of (ipa, lazy ipa) pairs: -_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('r', 'ɹ'), - ('æ', 'e'), - ('ɑ', 'a'), - ('ɔ', 'o'), - ('ð', 'z'), - ('θ', 's'), - ('ɛ', 'e'), - ('ɪ', 'i'), - ('ʊ', 'u'), - ('ʒ', 'ʥ'), - ('ʤ', 'ʥ'), - ('ˈ', '↓'), -]] - -# List of (ipa, lazy ipa2) pairs: -_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('r', 'ɹ'), - ('ð', 'z'), - ('θ', 's'), - ('ʒ', 'ʑ'), - ('ʤ', 'dʑ'), - ('ˈ', '↓'), -]] - -# List of (ipa, ipa2) pairs -_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('r', 'ɹ'), - ('ʤ', 'dʒ'), - ('ʧ', 'tʃ') -]] - - -def expand_abbreviations(text): - for regex, replacement in _abbreviations: - text = re.sub(regex, replacement, text) - return text - - -def collapse_whitespace(text): - return re.sub(r'\s+', ' ', text) - - -def _remove_commas(m): - return m.group(1).replace(',', '') - - -def _expand_decimal_point(m): - return m.group(1).replace('.', ' point ') - - -def _expand_dollars(m): - match = m.group(1) - parts = match.split('.') - if len(parts) > 2: - return match + ' dollars' # Unexpected format - dollars = int(parts[0]) if parts[0] else 0 - cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 - if dollars and cents: - dollar_unit = 'dollar' if dollars == 1 else 'dollars' - cent_unit = 'cent' if cents == 1 else 'cents' - return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit) - elif dollars: - dollar_unit = 'dollar' if dollars == 1 else 'dollars' - return '%s %s' % (dollars, dollar_unit) - elif cents: - cent_unit = 'cent' if cents == 1 else 'cents' - return '%s %s' % (cents, cent_unit) - else: - return 'zero dollars' - - -def _expand_ordinal(m): - return _inflect.number_to_words(m.group(0)) - - -def _expand_number(m): - num = int(m.group(0)) - if num > 1000 and num < 3000: - if num == 2000: - return 'two thousand' - elif num > 2000 and num < 2010: - return 'two thousand ' + _inflect.number_to_words(num % 100) - elif num % 100 == 0: - return _inflect.number_to_words(num // 100) + ' hundred' - else: - return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ') - else: - return _inflect.number_to_words(num, andword='') - - -def normalize_numbers(text): - text = re.sub(_comma_number_re, _remove_commas, text) - text = re.sub(_pounds_re, r'\1 pounds', text) - text = re.sub(_dollars_re, _expand_dollars, text) - text = re.sub(_decimal_number_re, _expand_decimal_point, text) - text = re.sub(_ordinal_re, _expand_ordinal, text) - text = re.sub(_number_re, _expand_number, text) - return text - - -def mark_dark_l(text): - return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text) - - -def english_to_ipa(text): - text = unidecode(text).lower() - text = expand_abbreviations(text) - text = normalize_numbers(text) - phonemes = ipa.convert(text) - phonemes = collapse_whitespace(phonemes) - return phonemes - - -def english_to_lazy_ipa(text): - text = english_to_ipa(text) - for regex, replacement in _lazy_ipa: - text = re.sub(regex, replacement, text) - return text - - -def english_to_ipa2(text): - text = english_to_ipa(text) - text = mark_dark_l(text) - for regex, replacement in _ipa_to_ipa2: - text = re.sub(regex, replacement, text) - return text.replace('...', '…') - - -def english_to_lazy_ipa2(text): - text = english_to_ipa(text) - for regex, replacement in _lazy_ipa2: - text = re.sub(regex, replacement, text) - return text diff --git a/spaces/Gradio-Blocks/Gradio_YOLOv5_Det/util/fonts_opt.py b/spaces/Gradio-Blocks/Gradio_YOLOv5_Det/util/fonts_opt.py deleted file mode 100644 index ccea77ad00c644cedf38ec0336dcf124a8b6448d..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/Gradio_YOLOv5_Det/util/fonts_opt.py +++ /dev/null @@ -1,69 +0,0 @@ -# font management -# author: Zeng Yifu（曾逸夫） -# creation time: 2022-05-01 -# email: zyfiy1314@163.com -# project homepage: https://gitee.com/CV_Lab/gradio_yolov5_det - -import os -import sys -from pathlib import Path - -import wget -from rich.console import Console - -ROOT_PATH = sys.path[0] # Project root directory - -# Chinese, English, Russian, Spanish, Arabic, Korean -fonts_list = ["SimSun.ttf", "TimesNewRoman.ttf", "malgun.ttf"] # font list -fonts_suffix = ["ttc", "ttf", "otf"] # font suffix - -data_url_dict = { - "SimSun.ttf": "https://gitee.com/CV_Lab/gradio_yolov5_det/attach_files/1053539/download/SimSun.ttf", - "TimesNewRoman.ttf": "https://gitee.com/CV_Lab/gradio_yolov5_det/attach_files/1053537/download/TimesNewRoman.ttf", - "malgun.ttf": "https://gitee.com/CV_Lab/gradio_yolov5_det/attach_files/1053538/download/malgun.ttf",} - -console = Console() - - -# create font library -def add_fronts(font_diff): - - global font_name - - for k, v in data_url_dict.items(): - if k in font_diff: - font_name = v.split("/")[-1] # font name - Path(f"{ROOT_PATH}/fonts").mkdir(parents=True, exist_ok=True) # Create a directory - - file_path = f"{ROOT_PATH}/fonts/{font_name}" # font path - - try: - # Download font file - wget.download(v, file_path) - except Exception as e: - print("Path error! Program ended!") - print(e) - sys.exit() - else: - print() - console.print(f"{font_name} [bold green]font file download complete![/bold green] has been saved to: {file_path}") - - -# Determine the font file -def is_fonts(fonts_dir): - if os.path.isdir(fonts_dir): - # if the font library exists - f_list = os.listdir(fonts_dir) # local font library - - font_diff = list(set(fonts_list).difference(set(f_list))) - - if font_diff != []: - # font does not exist - console.print("[bold red] font does not exist, loading...[/bold red]") - add_fronts(font_diff) # Create a font library - else: - console.print(f"{fonts_list}[bold green]font already exists![/bold green]") - else: - # The font library does not exist, create a font library - console.print("[bold red]font library does not exist, creating...[/bold red]") - add_fronts(fonts_list) # Create a font library \ No newline at end of file diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py deleted file mode 100644 index de4a8a5e9f030f1e8a8802596885186163f23eed..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,11 +0,0 @@ -_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py' -model = dict( - pretrained='open-mmlab://resnet101_v1c', - backbone=dict( - depth=101, - dilations=(1, 1, 1, 2), - strides=(1, 2, 2, 1), - multi_grid=(1, 2, 4)), - decode_head=dict( - dilations=(1, 6, 12, 18), - sampler=dict(type='OHEMPixelSampler', min_kept=100000))) diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/data/audio_dataset.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/data/audio_dataset.py deleted file mode 100644 index 9d7442526186b3712f5d4754f928a40ecd964174..0000000000000000000000000000000000000000 --- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/data/audio_dataset.py +++ /dev/null @@ -1,587 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -"""AudioDataset support. In order to handle a larger number of files -without having to scan again the folders, we precompute some metadata -(filename, sample rate, duration), and use that to efficiently sample audio segments. -""" -import argparse -import copy -from concurrent.futures import ThreadPoolExecutor, Future -from dataclasses import dataclass, fields -from contextlib import ExitStack -from functools import lru_cache -import gzip -import json -import logging -import os -from pathlib import Path -import random -import sys -import typing as tp - -import torch -import torch.nn.functional as F - -from .audio import audio_read, audio_info -from .audio_utils import convert_audio -from .zip import PathInZip - -try: - import dora -except ImportError: - dora = None # type: ignore - - -@dataclass(order=True) -class BaseInfo: - - @classmethod - def _dict2fields(cls, dictionary: dict): - return { - field.name: dictionary[field.name] - for field in fields(cls) if field.name in dictionary - } - - @classmethod - def from_dict(cls, dictionary: dict): - _dictionary = cls._dict2fields(dictionary) - return cls(**_dictionary) - - def to_dict(self): - return { - field.name: self.__getattribute__(field.name) - for field in fields(self) - } - - -@dataclass(order=True) -class AudioMeta(BaseInfo): - path: str - duration: float - sample_rate: int - amplitude: tp.Optional[float] = None - weight: tp.Optional[float] = None - # info_path is used to load additional information about the audio file that is stored in zip files. - info_path: tp.Optional[PathInZip] = None - - @classmethod - def from_dict(cls, dictionary: dict): - base = cls._dict2fields(dictionary) - if 'info_path' in base and base['info_path'] is not None: - base['info_path'] = PathInZip(base['info_path']) - return cls(**base) - - def to_dict(self): - d = super().to_dict() - if d['info_path'] is not None: - d['info_path'] = str(d['info_path']) - return d - - -@dataclass(order=True) -class SegmentInfo(BaseInfo): - meta: AudioMeta - seek_time: float - # The following values are given once the audio is processed, e.g. - # at the target sample rate and target number of channels. - n_frames: int # actual number of frames without padding - total_frames: int # total number of frames, padding included - sample_rate: int # actual sample rate - channels: int # number of audio channels. - - -DEFAULT_EXTS = ['.wav', '.mp3', '.flac', '.ogg', '.m4a'] - -logger = logging.getLogger(__name__) - - -def _get_audio_meta(file_path: str, minimal: bool = True) -> AudioMeta: - """AudioMeta from a path to an audio file. - - Args: - file_path (str): Resolved path of valid audio file. - minimal (bool): Whether to only load the minimal set of metadata (takes longer if not). - Returns: - AudioMeta: Audio file path and its metadata. - """ - info = audio_info(file_path) - amplitude: tp.Optional[float] = None - if not minimal: - wav, sr = audio_read(file_path) - amplitude = wav.abs().max().item() - return AudioMeta(file_path, info.duration, info.sample_rate, amplitude) - - -def _resolve_audio_meta(m: AudioMeta, fast: bool = True) -> AudioMeta: - """If Dora is available as a dependency, try to resolve potential relative paths - in list of AudioMeta. This method is expected to be used when loading meta from file. - - Args: - m (AudioMeta): Audio meta to resolve. - fast (bool): If True, uses a really fast check for determining if a file - is already absolute or not. Only valid on Linux/Mac. - Returns: - AudioMeta: Audio meta with resolved path. - """ - def is_abs(m): - if fast: - return str(m)[0] == '/' - else: - os.path.isabs(str(m)) - - if not dora: - return m - - if not is_abs(m.path): - m.path = dora.git_save.to_absolute_path(m.path) - if m.info_path is not None and not is_abs(m.info_path.zip_path): - m.info_path.zip_path = dora.git_save.to_absolute_path(m.path) - return m - - -def find_audio_files(path: tp.Union[Path, str], - exts: tp.List[str] = DEFAULT_EXTS, - resolve: bool = True, - minimal: bool = True, - progress: bool = False, - workers: int = 0) -> tp.List[AudioMeta]: - """Build a list of AudioMeta from a given path, - collecting relevant audio files and fetching meta info. - - Args: - path (str or Path): Path to folder containing audio files. - exts (list of str): List of file extensions to consider for audio files. - minimal (bool): Whether to only load the minimal set of metadata (takes longer if not). - progress (bool): Whether to log progress on audio files collection. - workers (int): number of parallel workers, if 0, use only the current thread. - Returns: - list of AudioMeta: List of audio file path and its metadata. - """ - audio_files = [] - futures: tp.List[Future] = [] - pool: tp.Optional[ThreadPoolExecutor] = None - with ExitStack() as stack: - if workers > 0: - pool = ThreadPoolExecutor(workers) - stack.enter_context(pool) - - if progress: - print("Finding audio files...") - for root, folders, files in os.walk(path, followlinks=True): - for file in files: - full_path = Path(root) / file - if full_path.suffix.lower() in exts: - audio_files.append(full_path) - if pool is not None: - futures.append(pool.submit(_get_audio_meta, str(audio_files[-1]), minimal)) - if progress: - print(format(len(audio_files), " 8d"), end='\r', file=sys.stderr) - - if progress: - print("Getting audio metadata...") - meta: tp.List[AudioMeta] = [] - for idx, file_path in enumerate(audio_files): - try: - if pool is None: - m = _get_audio_meta(str(file_path), minimal) - else: - m = futures[idx].result() - if resolve: - m = _resolve_audio_meta(m) - except Exception as err: - print("Error with", str(file_path), err, file=sys.stderr) - continue - meta.append(m) - if progress: - print(format((1 + idx) / len(audio_files), " 3.1%"), end='\r', file=sys.stderr) - meta.sort() - return meta - - -def load_audio_meta(path: tp.Union[str, Path], - resolve: bool = True, fast: bool = True) -> tp.List[AudioMeta]: - """Load list of AudioMeta from an optionally compressed json file. - - Args: - path (str or Path): Path to JSON file. - resolve (bool): Whether to resolve the path from AudioMeta (default=True). - fast (bool): activates some tricks to make things faster. - Returns: - list of AudioMeta: List of audio file path and its total duration. - """ - open_fn = gzip.open if str(path).lower().endswith('.gz') else open - with open_fn(path, 'rb') as fp: # type: ignore - lines = fp.readlines() - meta = [] - for line in lines: - d = json.loads(line) - m = AudioMeta.from_dict(d) - if resolve: - m = _resolve_audio_meta(m, fast=fast) - meta.append(m) - return meta - - -def save_audio_meta(path: tp.Union[str, Path], meta: tp.List[AudioMeta]): - """Save the audio metadata to the file pointer as json. - - Args: - path (str or Path): Path to JSON file. - metadata (list of BaseAudioMeta): List of audio meta to save. - """ - Path(path).parent.mkdir(exist_ok=True, parents=True) - open_fn = gzip.open if str(path).lower().endswith('.gz') else open - with open_fn(path, 'wb') as fp: # type: ignore - for m in meta: - json_str = json.dumps(m.to_dict()) + '\n' - json_bytes = json_str.encode('utf-8') - fp.write(json_bytes) - - -class AudioDataset: - """Base audio dataset. - - The dataset takes a list of AudioMeta and create a dataset composed of segments of audio - and potentially additional information, by creating random segments from the list of audio - files referenced in the metadata and applying minimal data pre-processing such as resampling, - mixing of channels, padding, etc. - - If no segment_duration value is provided, the AudioDataset will return the full wav for each - audio file. Otherwise, it will randomly sample audio files and create a segment of the specified - duration, applying padding if required. - - By default, only the torch Tensor corresponding to the waveform is returned. Setting return_info=True - allows to return a tuple containing the torch Tensor and additional metadata on the segment and the - original audio meta. - - Note that you can call `start_epoch(epoch)` in order to get - a deterministic "randomization" for `shuffle=True`. - For a given epoch and dataset index, this will always return the same extract. - You can get back some diversity by setting the `shuffle_seed` param. - - Args: - meta (list of AudioMeta): List of audio files metadata. - segment_duration (float, optional): Optional segment duration of audio to load. - If not specified, the dataset will load the full audio segment from the file. - shuffle (bool): Set to `True` to have the data reshuffled at every epoch. - sample_rate (int): Target sample rate of the loaded audio samples. - channels (int): Target number of channels of the loaded audio samples. - sample_on_duration (bool): Set to `True` to sample segments with probability - dependent on audio file duration. This is only used if `segment_duration` is provided. - sample_on_weight (bool): Set to `True` to sample segments using the `weight` entry of - `AudioMeta`. If `sample_on_duration` is also True, the actual weight will be the product - of the file duration and file weight. This is only used if `segment_duration` is provided. - min_segment_ratio (float): Minimum segment ratio to use when the audio file - is shorter than the desired segment. - max_read_retry (int): Maximum number of retries to sample an audio segment from the dataset. - return_info (bool): Whether to return the wav only or return wav along with segment info and metadata. - min_audio_duration (float, optional): Minimum audio file duration, in seconds, if provided - audio shorter than this will be filtered out. - max_audio_duration (float, optional): Maximal audio file duration in seconds, if provided - audio longer than this will be filtered out. - shuffle_seed (int): can be used to further randomize - load_wav (bool): if False, skip loading the wav but returns a tensor of 0 - with the expected segment_duration (which must be provided if load_wav is False). - permutation_on_files (bool): only if `sample_on_weight` and `sample_on_duration` - are False. Will ensure a permutation on files when going through the dataset. - In that case the epoch number must be provided in order for the model - to continue the permutation across epochs. In that case, it is assumed - that `num_samples = total_batch_size * num_updates_per_epoch`, with - `total_batch_size` the overall batch size accounting for all gpus. - """ - def __init__(self, - meta: tp.List[AudioMeta], - segment_duration: tp.Optional[float] = None, - shuffle: bool = True, - num_samples: int = 10_000, - sample_rate: int = 48_000, - channels: int = 2, - pad: bool = True, - sample_on_duration: bool = True, - sample_on_weight: bool = True, - min_segment_ratio: float = 0.5, - max_read_retry: int = 10, - return_info: bool = False, - min_audio_duration: tp.Optional[float] = None, - max_audio_duration: tp.Optional[float] = None, - shuffle_seed: int = 0, - load_wav: bool = True, - permutation_on_files: bool = False, - ): - assert len(meta) > 0, "No audio meta provided to AudioDataset. Please check loading of audio meta." - assert segment_duration is None or segment_duration > 0 - assert segment_duration is None or min_segment_ratio >= 0 - self.segment_duration = segment_duration - self.min_segment_ratio = min_segment_ratio - self.max_audio_duration = max_audio_duration - self.min_audio_duration = min_audio_duration - if self.min_audio_duration is not None and self.max_audio_duration is not None: - assert self.min_audio_duration <= self.max_audio_duration - self.meta: tp.List[AudioMeta] = self._filter_duration(meta) - assert len(self.meta) # Fail fast if all data has been filtered. - self.total_duration = sum(d.duration for d in self.meta) - - if segment_duration is None: - num_samples = len(self.meta) - self.num_samples = num_samples - self.shuffle = shuffle - self.sample_rate = sample_rate - self.channels = channels - self.pad = pad - self.sample_on_weight = sample_on_weight - self.sample_on_duration = sample_on_duration - self.sampling_probabilities = self._get_sampling_probabilities() - self.max_read_retry = max_read_retry - self.return_info = return_info - self.shuffle_seed = shuffle_seed - self.current_epoch: tp.Optional[int] = None - self.load_wav = load_wav - if not load_wav: - assert segment_duration is not None - self.permutation_on_files = permutation_on_files - if permutation_on_files: - assert not self.sample_on_duration - assert not self.sample_on_weight - assert self.shuffle - - def start_epoch(self, epoch: int): - self.current_epoch = epoch - - def __len__(self): - return self.num_samples - - def _get_sampling_probabilities(self, normalized: bool = True): - """Return the sampling probabilities for each file inside `self.meta`.""" - scores: tp.List[float] = [] - for file_meta in self.meta: - score = 1. - if self.sample_on_weight and file_meta.weight is not None: - score *= file_meta.weight - if self.sample_on_duration: - score *= file_meta.duration - scores.append(score) - probabilities = torch.tensor(scores) - if normalized: - probabilities /= probabilities.sum() - return probabilities - - @staticmethod - @lru_cache(16) - def _get_file_permutation(num_files: int, permutation_index: int, base_seed: int): - # Used to keep the most recent files permutation in memory implicitely. - # will work unless someone is using a lot of Datasets in parallel. - rng = torch.Generator() - rng.manual_seed(base_seed + permutation_index) - return torch.randperm(num_files, generator=rng) - - def sample_file(self, index: int, rng: torch.Generator) -> AudioMeta: - """Sample a given file from `self.meta`. Can be overridden in subclasses. - This is only called if `segment_duration` is not None. - - You must use the provided random number generator `rng` for reproducibility. - You can further make use of the index accessed. - """ - if self.permutation_on_files: - assert self.current_epoch is not None - total_index = self.current_epoch * len(self) + index - permutation_index = total_index // len(self.meta) - relative_index = total_index % len(self.meta) - permutation = AudioDataset._get_file_permutation( - len(self.meta), permutation_index, self.shuffle_seed) - file_index = permutation[relative_index] - return self.meta[file_index] - - if not self.sample_on_weight and not self.sample_on_duration: - file_index = int(torch.randint(len(self.sampling_probabilities), (1,), generator=rng).item()) - else: - file_index = int(torch.multinomial(self.sampling_probabilities, 1, generator=rng).item()) - - return self.meta[file_index] - - def _audio_read(self, path: str, seek_time: float = 0, duration: float = -1): - # Override this method in subclass if needed. - if self.load_wav: - return audio_read(path, seek_time, duration, pad=False) - else: - assert self.segment_duration is not None - n_frames = int(self.sample_rate * self.segment_duration) - return torch.zeros(self.channels, n_frames), self.sample_rate - - def __getitem__(self, index: int) -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, SegmentInfo]]: - if self.segment_duration is None: - file_meta = self.meta[index] - out, sr = audio_read(file_meta.path) - out = convert_audio(out, sr, self.sample_rate, self.channels) - n_frames = out.shape[-1] - segment_info = SegmentInfo(file_meta, seek_time=0., n_frames=n_frames, total_frames=n_frames, - sample_rate=self.sample_rate, channels=out.shape[0]) - else: - rng = torch.Generator() - if self.shuffle: - # We use index, plus extra randomness, either totally random if we don't know the epoch. - # otherwise we make use of the epoch number and optional shuffle_seed. - if self.current_epoch is None: - rng.manual_seed(index + self.num_samples * random.randint(0, 2**24)) - else: - rng.manual_seed(index + self.num_samples * (self.current_epoch + self.shuffle_seed)) - else: - # We only use index - rng.manual_seed(index) - - for retry in range(self.max_read_retry): - file_meta = self.sample_file(index, rng) - # We add some variance in the file position even if audio file is smaller than segment - # without ending up with empty segments - max_seek = max(0, file_meta.duration - self.segment_duration * self.min_segment_ratio) - seek_time = torch.rand(1, generator=rng).item() * max_seek - try: - out, sr = audio_read(file_meta.path, seek_time, self.segment_duration, pad=False) - out = convert_audio(out, sr, self.sample_rate, self.channels) - n_frames = out.shape[-1] - target_frames = int(self.segment_duration * self.sample_rate) - if self.pad: - out = F.pad(out, (0, target_frames - n_frames)) - segment_info = SegmentInfo(file_meta, seek_time, n_frames=n_frames, total_frames=target_frames, - sample_rate=self.sample_rate, channels=out.shape[0]) - except Exception as exc: - logger.warning("Error opening file %s: %r", file_meta.path, exc) - if retry == self.max_read_retry - 1: - raise - else: - break - - if self.return_info: - # Returns the wav and additional information on the wave segment - return out, segment_info - else: - return out - - def collater(self, samples): - """The collater function has to be provided to the dataloader - if AudioDataset has return_info=True in order to properly collate - the samples of a batch. - """ - if self.segment_duration is None and len(samples) > 1: - assert self.pad, "Must allow padding when batching examples of different durations." - - # In this case the audio reaching the collater is of variable length as segment_duration=None. - to_pad = self.segment_duration is None and self.pad - if to_pad: - max_len = max([wav.shape[-1] for wav, _ in samples]) - - def _pad_wav(wav): - return F.pad(wav, (0, max_len - wav.shape[-1])) - - if self.return_info: - if len(samples) > 0: - assert len(samples[0]) == 2 - assert isinstance(samples[0][0], torch.Tensor) - assert isinstance(samples[0][1], SegmentInfo) - - wavs = [wav for wav, _ in samples] - segment_infos = [copy.deepcopy(info) for _, info in samples] - - if to_pad: - # Each wav could be of a different duration as they are not segmented. - for i in range(len(samples)): - # Determines the total length of the signal with padding, so we update here as we pad. - segment_infos[i].total_frames = max_len - wavs[i] = _pad_wav(wavs[i]) - - wav = torch.stack(wavs) - return wav, segment_infos - else: - assert isinstance(samples[0], torch.Tensor) - if to_pad: - samples = [_pad_wav(s) for s in samples] - return torch.stack(samples) - - def _filter_duration(self, meta: tp.List[AudioMeta]) -> tp.List[AudioMeta]: - """Filters out audio files with audio durations that will not allow to sample examples from them.""" - orig_len = len(meta) - - # Filter data that is too short. - if self.min_audio_duration is not None: - meta = [m for m in meta if m.duration >= self.min_audio_duration] - - # Filter data that is too long. - if self.max_audio_duration is not None: - meta = [m for m in meta if m.duration <= self.max_audio_duration] - - filtered_len = len(meta) - removed_percentage = 100*(1-float(filtered_len)/orig_len) - msg = 'Removed %.2f percent of the data because it was too short or too long.' % removed_percentage - if removed_percentage < 10: - logging.debug(msg) - else: - logging.warning(msg) - return meta - - @classmethod - def from_meta(cls, root: tp.Union[str, Path], **kwargs): - """Instantiate AudioDataset from a path to a directory containing a manifest as a jsonl file. - - Args: - root (str or Path): Path to root folder containing audio files. - kwargs: Additional keyword arguments for the AudioDataset. - """ - root = Path(root) - if root.is_dir(): - if (root / 'data.jsonl').exists(): - root = root / 'data.jsonl' - elif (root / 'data.jsonl.gz').exists(): - root = root / 'data.jsonl.gz' - else: - raise ValueError("Don't know where to read metadata from in the dir. " - "Expecting either a data.jsonl or data.jsonl.gz file but none found.") - meta = load_audio_meta(root) - return cls(meta, **kwargs) - - @classmethod - def from_path(cls, root: tp.Union[str, Path], minimal_meta: bool = True, - exts: tp.List[str] = DEFAULT_EXTS, **kwargs): - """Instantiate AudioDataset from a path containing (possibly nested) audio files. - - Args: - root (str or Path): Path to root folder containing audio files. - minimal_meta (bool): Whether to only load minimal metadata or not. - exts (list of str): Extensions for audio files. - kwargs: Additional keyword arguments for the AudioDataset. - """ - root = Path(root) - if root.is_file(): - meta = load_audio_meta(root, resolve=True) - else: - meta = find_audio_files(root, exts, minimal=minimal_meta, resolve=True) - return cls(meta, **kwargs) - - -def main(): - logging.basicConfig(stream=sys.stderr, level=logging.INFO) - parser = argparse.ArgumentParser( - prog='audio_dataset', - description='Generate .jsonl files by scanning a folder.') - parser.add_argument('root', help='Root folder with all the audio files') - parser.add_argument('output_meta_file', - help='Output file to store the metadata, ') - parser.add_argument('--complete', - action='store_false', dest='minimal', default=True, - help='Retrieve all metadata, even the one that are expansive ' - 'to compute (e.g. normalization).') - parser.add_argument('--resolve', - action='store_true', default=False, - help='Resolve the paths to be absolute and with no symlinks.') - parser.add_argument('--workers', - default=10, type=int, - help='Number of workers.') - args = parser.parse_args() - meta = find_audio_files(args.root, DEFAULT_EXTS, progress=True, - resolve=args.resolve, minimal=args.minimal, workers=args.workers) - save_audio_meta(args.output_meta_file, meta) - - -if __name__ == '__main__': - main() diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_text_joint_to_text/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_text_joint_to_text/__init__.py deleted file mode 100644 index 239d2e69f9a235095dee1ea7b3a94164a77273f5..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_text_joint_to_text/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from . import tasks, criterions, models # noqa diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/data/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/data/__init__.py deleted file mode 100644 index d0545627efc9a6f9bb180e351ead519a2cb6dea7..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/data/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .extracted_features_dataset import ExtractedFeaturesDataset -from .random_input_dataset import RandomInputDataset - - -__all__ = [ - "ExtractedFeaturesDataset", - "RandomInputDataset", -] diff --git a/spaces/Harshveer/Finetuned_Diffusion_Max/README.md b/spaces/Harshveer/Finetuned_Diffusion_Max/README.md deleted file mode 100644 index 6abd059c49a7dcb579ebf390c2f689c991edd06c..0000000000000000000000000000000000000000 --- a/spaces/Harshveer/Finetuned_Diffusion_Max/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Finetuned Diffusion -emoji: 🪄🖼️ -colorFrom: red -colorTo: pink -sdk: gradio -sdk_version: 3.16.2 -app_file: app.py -pinned: true -license: mit -duplicated_from: ivanmeyer/Finetuned_Diffusion_Max ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/stft.py b/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/stft.py deleted file mode 100644 index 5852bd20904c9c206030523737ce3fbd64300a0c..0000000000000000000000000000000000000000 --- a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/stft.py +++ /dev/null @@ -1,185 +0,0 @@ -""" -BSD 3-Clause License - -Copyright (c) 2017, Prem Seetharaman -All rights reserved. - -* Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, this - list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -* Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from this - software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -""" - -import torch -import numpy as np -import torch.nn.functional as F -from torch.autograd import Variable -from scipy.signal import get_window -from librosa.util import pad_center, tiny -from librosa import stft, istft -from audio_processing import window_sumsquare - - -class STFT(torch.nn.Module): - """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft""" - - def __init__( - self, filter_length=800, hop_length=200, win_length=800, window="hann" - ): - super(STFT, self).__init__() - self.filter_length = filter_length - self.hop_length = hop_length - self.win_length = win_length - self.window = window - self.forward_transform = None - scale = self.filter_length / self.hop_length - fourier_basis = np.fft.fft(np.eye(self.filter_length)) - - cutoff = int((self.filter_length / 2 + 1)) - fourier_basis = np.vstack( - [np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])] - ) - - forward_basis = torch.FloatTensor(fourier_basis[:, None, :]) - inverse_basis = torch.FloatTensor( - np.linalg.pinv(scale * fourier_basis).T[:, None, :] - ) - - if window is not None: - assert filter_length >= win_length - # get window and zero center pad it to filter_length - fft_window = get_window(window, win_length, fftbins=True) - fft_window = pad_center(fft_window, filter_length) - fft_window = torch.from_numpy(fft_window).float() - - # window the bases - forward_basis *= fft_window - inverse_basis *= fft_window - - self.register_buffer("forward_basis", forward_basis.float()) - self.register_buffer("inverse_basis", inverse_basis.float()) - - def transform(self, input_data): - num_batches = input_data.size(0) - num_samples = input_data.size(1) - - self.num_samples = num_samples - - if input_data.device.type == "cuda": - # similar to librosa, reflect-pad the input - input_data = input_data.view(num_batches, 1, num_samples) - input_data = F.pad( - input_data.unsqueeze(1), - (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0), - mode="reflect", - ) - input_data = input_data.squeeze(1) - - forward_transform = F.conv1d( - input_data, self.forward_basis, stride=self.hop_length, padding=0 - ) - - cutoff = int((self.filter_length / 2) + 1) - real_part = forward_transform[:, :cutoff, :] - imag_part = forward_transform[:, cutoff:, :] - else: - x = input_data.detach().numpy() - real_part = [] - imag_part = [] - for y in x: - y_ = stft( - y, self.filter_length, self.hop_length, self.win_length, self.window - ) - real_part.append(y_.real[None, :, :]) - imag_part.append(y_.imag[None, :, :]) - real_part = np.concatenate(real_part, 0) - imag_part = np.concatenate(imag_part, 0) - - real_part = torch.from_numpy(real_part).to(input_data.dtype) - imag_part = torch.from_numpy(imag_part).to(input_data.dtype) - - magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2) - phase = torch.atan2(imag_part.data, real_part.data) - - return magnitude, phase - - def inverse(self, magnitude, phase): - recombine_magnitude_phase = torch.cat( - [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1 - ) - - if magnitude.device.type == "cuda": - inverse_transform = F.conv_transpose1d( - recombine_magnitude_phase, - self.inverse_basis, - stride=self.hop_length, - padding=0, - ) - - if self.window is not None: - window_sum = window_sumsquare( - self.window, - magnitude.size(-1), - hop_length=self.hop_length, - win_length=self.win_length, - n_fft=self.filter_length, - dtype=np.float32, - ) - # remove modulation effects - approx_nonzero_indices = torch.from_numpy( - np.where(window_sum > tiny(window_sum))[0] - ) - window_sum = torch.from_numpy(window_sum).to(inverse_transform.device) - inverse_transform[:, :, approx_nonzero_indices] /= window_sum[ - approx_nonzero_indices - ] - - # scale by hop ratio - inverse_transform *= float(self.filter_length) / self.hop_length - - inverse_transform = inverse_transform[:, :, int(self.filter_length / 2) :] - inverse_transform = inverse_transform[ - :, :, : -int(self.filter_length / 2) : - ] - inverse_transform = inverse_transform.squeeze(1) - else: - x_org = recombine_magnitude_phase.detach().numpy() - n_b, n_f, n_t = x_org.shape - x = np.empty([n_b, n_f // 2, n_t], dtype=np.complex64) - x.real = x_org[:, : n_f // 2] - x.imag = x_org[:, n_f // 2 :] - inverse_transform = [] - for y in x: - y_ = istft(y, self.hop_length, self.win_length, self.window) - inverse_transform.append(y_[None, :]) - inverse_transform = np.concatenate(inverse_transform, 0) - inverse_transform = torch.from_numpy(inverse_transform).to( - recombine_magnitude_phase.dtype - ) - - return inverse_transform - - def forward(self, input_data): - self.magnitude, self.phase = self.transform(input_data) - reconstruction = self.inverse(self.magnitude, self.phase) - return reconstruction diff --git a/spaces/Hina4867/bingo/src/components/button-scroll-to-bottom.tsx b/spaces/Hina4867/bingo/src/components/button-scroll-to-bottom.tsx deleted file mode 100644 index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000 --- a/spaces/Hina4867/bingo/src/components/button-scroll-to-bottom.tsx +++ /dev/null @@ -1,34 +0,0 @@ -'use client' - -import * as React from 'react' - -import { cn } from '@/lib/utils' -import { useAtBottom } from '@/lib/hooks/use-at-bottom' -import { Button, type ButtonProps } from '@/components/ui/button' -import { IconArrowDown } from '@/components/ui/icons' - -export function ButtonScrollToBottom({ className, ...props }: ButtonProps) { - const isAtBottom = useAtBottom() - - return ( - - ) -} diff --git a/spaces/HumanDesignHub/Ra-Diffusion_v.1/README.md b/spaces/HumanDesignHub/Ra-Diffusion_v.1/README.md deleted file mode 100644 index 20c6281318c1403430de84aac307e3b747c52722..0000000000000000000000000000000000000000 --- a/spaces/HumanDesignHub/Ra-Diffusion_v.1/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Ra Diffusion -emoji: 📚 -colorFrom: yellow -colorTo: yellow -sdk: gradio -sdk_version: 3.16.2 -app_file: app.py -pinned: false -license: openrail ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/ICML2022/OFA/fairseq/examples/speech_synthesis/preprocessing/get_vctk_audio_manifest.py b/spaces/ICML2022/OFA/fairseq/examples/speech_synthesis/preprocessing/get_vctk_audio_manifest.py deleted file mode 100644 index 7afa40fcd195465a225c9f251734e84fe6b3c7ef..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/speech_synthesis/preprocessing/get_vctk_audio_manifest.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import logging -import numpy as np -import re -from pathlib import Path -from collections import defaultdict - -import pandas as pd -from torchaudio.datasets import VCTK -from tqdm import tqdm - -from examples.speech_to_text.data_utils import save_df_to_tsv - - -log = logging.getLogger(__name__) - -SPLITS = ["train", "dev", "test"] - - -def normalize_text(text): - return re.sub(r"[^a-zA-Z.?!,'\- ]", '', text) - - -def process(args): - out_root = Path(args.output_data_root).absolute() - out_root.mkdir(parents=True, exist_ok=True) - - # Generate TSV manifest - print("Generating manifest...") - dataset = VCTK(out_root.as_posix(), download=False) - ids = list(dataset._walker) - np.random.seed(args.seed) - np.random.shuffle(ids) - n_train = len(ids) - args.n_dev - args.n_test - _split = ["train"] * n_train + ["dev"] * args.n_dev + ["test"] * args.n_test - id_to_split = dict(zip(ids, _split)) - manifest_by_split = {split: defaultdict(list) for split in SPLITS} - progress = tqdm(enumerate(dataset), total=len(dataset)) - for i, (waveform, _, text, speaker_id, _) in progress: - sample_id = dataset._walker[i] - _split = id_to_split[sample_id] - audio_dir = Path(dataset._path) / dataset._folder_audio / speaker_id - audio_path = audio_dir / f"{sample_id}.wav" - text = normalize_text(text) - manifest_by_split[_split]["id"].append(sample_id) - manifest_by_split[_split]["audio"].append(audio_path.as_posix()) - manifest_by_split[_split]["n_frames"].append(len(waveform[0])) - manifest_by_split[_split]["tgt_text"].append(text) - manifest_by_split[_split]["speaker"].append(speaker_id) - manifest_by_split[_split]["src_text"].append(text) - - manifest_root = Path(args.output_manifest_root).absolute() - manifest_root.mkdir(parents=True, exist_ok=True) - for _split in SPLITS: - save_df_to_tsv( - pd.DataFrame.from_dict(manifest_by_split[_split]), - manifest_root / f"{_split}.audio.tsv" - ) - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--output-data-root", "-d", required=True, type=str) - parser.add_argument("--output-manifest-root", "-m", required=True, type=str) - parser.add_argument("--n-dev", default=50, type=int) - parser.add_argument("--n-test", default=100, type=int) - parser.add_argument("--seed", "-s", default=1234, type=int) - args = parser.parse_args() - - process(args) - - -if __name__ == "__main__": - main() diff --git a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/cmd.sh b/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/cmd.sh deleted file mode 100644 index e74953194d41f0d93855d41b2acef08556d92477..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/cmd.sh +++ /dev/null @@ -1,15 +0,0 @@ -# you can change cmd.sh depending on what type of queue you are using. -# If you have no queueing system and want to run on a local machine, you -# can change all instances 'queue.pl' to run.pl (but be careful and run -# commands one by one: most recipes will exhaust the memory on your -# machine). queue.pl works with GridEngine (qsub). slurm.pl works -# with slurm. Different queues are configured differently, with different -# queue names and different ways of specifying things like memory; -# to account for these differences you can create and edit the file -# conf/queue.conf to match your queue's configuration. Search for -# conf/queue.conf in http://kaldi-asr.org/doc/queue.html for more information, -# or search for the string 'default_config' in utils/queue.pl or utils/slurm.pl. - -export train_cmd="run.pl --mem 2G" -export decode_cmd="run.pl --mem 4G" -export mkgraph_cmd="run.pl --mem 8G" diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda.cpp b/spaces/ICML2022/OFA/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda.cpp deleted file mode 100644 index ece47a8d908b93cec102743070c9057986d39d3f..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda.cpp +++ /dev/null @@ -1,51 +0,0 @@ -/** - * Copyright (c) Facebook, Inc. and its affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */ - -#include- {language} --- - --- {value} - --#include - -std::vector Ask the AI coach about techniques and strategies in the game of padel. The coach can answer depending on the level of you as a player, whether you are a beginner, average, or pro.
", -) - -Padelcoach.launch() diff --git a/spaces/JonysArcanjo/App_predict_House_price/README.md b/spaces/JonysArcanjo/App_predict_House_price/README.md deleted file mode 100644 index e468bf2187552bff549935c2796ee4070f316fd6..0000000000000000000000000000000000000000 --- a/spaces/JonysArcanjo/App_predict_House_price/README.md +++ /dev/null @@ -1,74 +0,0 @@ - ---- -title: App Predict House Price -sdk: gradio -sdk_version: 3.23.0 -app_file: app.py ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference - - -# Predict House Price - - - - - - -### Welcome to this Data Science project! - -The objective of this project is to predict house prices using machine learning techniques. To achieve this, I performed a brief exploratory data analysis and directed the study to evaluate the Boruta feature selection technique, comparing its performance with the neural network both with and without the use of this technique. - -## About Dataset - -The dataset used contains house sale prices for King County, which includes Seattle, and it includes homes sold between May 2014 and May 2015. - -Dataset source: [Kaggle](https://www.kaggle.com/harlfoxem/housesalesprediction) - -## Libraries Used - -- Tensorflow -- Pandas -- Numpy -- Matplotlib -- Boruta (for feature selection) -- Gradio - user interface (ux) - -## Metrics Used - -The following metrics were used to assess the performance of the models: - -- RMSE: Root Mean Squared Error -- MSE: Mean Squared Error -- MAE: Mean Absolute Error -- R2: The determination coefficient -- Adjusted R2 - -## Application in PRD -Below are some characteristics of the house as input for the application to return the forecast of the house's value. - - - -Below is a list of the features used: -- sqft_living: Living area square footage. -- -sqft_lot: Lot size square footage. -- waterfront: Indicates if the property has a waterfront view (1) or not (0). -- view: Number of times the property has been viewed. -- grade: Overall grade given to the housing unit, based on the King County grading system. -- sqft_above: Square footage of the home's interior living space above ground level. -- yr_built: Year the house was built. -- zipcode: Zip code of the area where the property is located. -- lat: Latitude of the property. -- long: Longitude of the property. -- sqft_living15: Average living area square footage of the nearest 15 houses. -- sqft_lot15: Average lot size square footage of the nearest 15 houses. - -## Conclusion - -The results of this project indicate that the model that uses all resources performed better than the model that used the Boruta feature selection technique. However, when choosing the best model for a specific application, it is important to consider not only the performance but also the computational cost and the efficiency in the selection of resources. For future versions of this model, it would be interesting to explore other feature selection and feature engineering techniques. - -## License - -This project is licensed under the MIT License. - diff --git a/spaces/JunghunleePhD/catsClassification/app.py b/spaces/JunghunleePhD/catsClassification/app.py deleted file mode 100644 index 57add93a4005dd499d9030f7d2342ca59d589782..0000000000000000000000000000000000000000 --- a/spaces/JunghunleePhD/catsClassification/app.py +++ /dev/null @@ -1,33 +0,0 @@ -from fastbook import * -from random import sample -import gradio as gr - -def get_images(): - listOfCats = sorted(['Lioness', 'Lion', 'Tiger', 'White tiger', 'White lion', \ - 'Leopard', 'Snow leopard', 'Lynx', 'Jaguar', 'Cheetah', \ - 'Jaguarundi', 'Leopard cat', 'Canada lynx', 'Caracal', \ - 'Ocelot', 'Leopard cat', 'Marble cat', 'Puma', 'Black panther', \ - 'Margay', 'Serval', 'Fishing cat']) - cats = sample(listOfCats, len(listOfCats))[:2] - imagess = [sample(search_images_ddg(f'{cat} photo', max_images=5), 5) for cat in cats] - return [images[:1] for images in imagess] - -def quiz_builder(input): - model = load_learner("cats.pkl") - is_it, _, probs = model.predict(input) - return f"The cat in this image is a {is_it}!" - # return {is_it: f"{max(probs[0], probs[1], probs[2]):.4f}"} - -demo = gr.Interface( - quiz_builder, - [ - gr.Image(value=None) - ], - "text", - examples=[ - *get_images(), - ] -) - -if __name__ == "__main__": - demo.launch() diff --git a/spaces/KazeDevID/RVC-Model/README.md b/spaces/KazeDevID/RVC-Model/README.md deleted file mode 100644 index 5777f585dc8e5d87ec519c59111d77331c73005e..0000000000000000000000000000000000000000 --- a/spaces/KazeDevID/RVC-Model/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Hololive Rvc Models -emoji: 🎤🌸▶️ -colorFrom: red -colorTo: blue -sdk: gradio -sdk_version: 3.27.0 -app_file: app.py -pinned: false -license: mit -duplicated_from: YanzBotz/Waifu-YanzBotz ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/KeeganFdes/stack_onnx/README.md b/spaces/KeeganFdes/stack_onnx/README.md deleted file mode 100644 index a3c30f6e33df5321c7909cc003ca2645c0f79720..0000000000000000000000000000000000000000 --- a/spaces/KeeganFdes/stack_onnx/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Stack Onnx -emoji: 📚 -colorFrom: pink -colorTo: purple -sdk: gradio -sdk_version: 3.41.2 -app_file: app.py -pinned: false -license: openrail ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Kevin676/Real-Time-Voice-Cloning/encoder/config.py b/spaces/Kevin676/Real-Time-Voice-Cloning/encoder/config.py deleted file mode 100644 index 1c21312f3de971bfa008254c6035cebc09f05e4c..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/Real-Time-Voice-Cloning/encoder/config.py +++ /dev/null @@ -1,45 +0,0 @@ -librispeech_datasets = { - "train": { - "clean": ["LibriSpeech/train-clean-100", "LibriSpeech/train-clean-360"], - "other": ["LibriSpeech/train-other-500"] - }, - "test": { - "clean": ["LibriSpeech/test-clean"], - "other": ["LibriSpeech/test-other"] - }, - "dev": { - "clean": ["LibriSpeech/dev-clean"], - "other": ["LibriSpeech/dev-other"] - }, -} -libritts_datasets = { - "train": { - "clean": ["LibriTTS/train-clean-100", "LibriTTS/train-clean-360"], - "other": ["LibriTTS/train-other-500"] - }, - "test": { - "clean": ["LibriTTS/test-clean"], - "other": ["LibriTTS/test-other"] - }, - "dev": { - "clean": ["LibriTTS/dev-clean"], - "other": ["LibriTTS/dev-other"] - }, -} -voxceleb_datasets = { - "voxceleb1" : { - "train": ["VoxCeleb1/wav"], - "test": ["VoxCeleb1/test_wav"] - }, - "voxceleb2" : { - "train": ["VoxCeleb2/dev/aac"], - "test": ["VoxCeleb2/test_wav"] - } -} - -other_datasets = [ - "LJSpeech-1.1", - "VCTK-Corpus/wav48", -] - -anglophone_nationalites = ["australia", "canada", "ireland", "uk", "usa"] diff --git a/spaces/Kimata/Sanskrit-TTS/utils/updated_cleaner_utils.py b/spaces/Kimata/Sanskrit-TTS/utils/updated_cleaner_utils.py deleted file mode 100644 index 0608fedddb1fc7b68ba925e6129f2b2084e35a77..0000000000000000000000000000000000000000 --- a/spaces/Kimata/Sanskrit-TTS/utils/updated_cleaner_utils.py +++ /dev/null @@ -1,139 +0,0 @@ -import re -def run(): - - # The path to the local git repo for Indic NLP library - INDIC_NLP_LIB_HOME=r"./indic_nlp_library" - - # The path to the local git repo for Indic NLP Resources - INDIC_NLP_RESOURCES=r"./indic_nlp_resources" - import sys - sys.path.append(r'{}'.format(INDIC_NLP_LIB_HOME)) - - from indicnlp import common - common.set_resources_path(INDIC_NLP_RESOURCES) - - from indicnlp import loader - loader.load() - -run() - -from indicnlp.normalize.indic_normalize import IndicNormalizerFactory -from indicnlp.tokenize import sentence_tokenize -from indicnlp.syllable import syllabifier - -lang='sa' -factory=IndicNormalizerFactory() -normalizer=factory.get_normalizer("hi") -DEPENDENT_VOWELS = ["ा", "ि", "ी", "ु", "ू", "े", "ै", "ो", "ौ", "ं", "ः", "ृ", "ॄ"] - -dict_num = {"०": "शून्य", "१": "एक", "२": "द्वि", "३": "त्रि", - "४": "चतुर्", "५": "पञ्च", "६": "षट्", "७": "सप्त", "८": "अष्ट", "९": "नव"} - -DEFAULT_TEXT = "अयं द्वितीयशब्दः २ अस्ति। प्रथमः शब्दः १ अस्ति। अन्ये शब्दाः सर्वे द्वितीयं शब्दं प्रयोजयन्ति। इत्थं सप्ततिः शब्दाः लिखिताः सन्ति। अस्मिन लेखने सर्वे अक्षराः संस्कृते लिखिताः सन्ति। अन्ये लिखन्ति ३, ४, ५ इत्यादि। तथापि, अहं एकं अक्षरं एव उपयोगामि।" - - -""" -Text cleaning pipeline. -1. Tokenize sentence. -2. Segment sentence into individual words. -3. Normalize words. -4. Clean normalized words. -5. Split wwords with the # delimiter. -3. Syllabify delimited sentences. - - - -1. Denote tokenized text with a special character such as #. -2. Segment the whole text into individual words. -3. Identify numbers in the text and normalize them. -4. Clean the whole text. -5. Syllabify the cleaned text. -""" - -def tokenize_sentence(text): - '''Tokenize a paragraph into sentences''' - sentences = sentence_tokenize.sentence_split(text, lang='sa') - sentences = "#".join(sentences) - return sentences - - -def segment_sentence(text): - '''Segment a sentence into individual words''' - - -def clean_text(text): - - processed_text = re.sub(r'\+ +', '', text) - processed_text = re.sub(': +', '\n \n', processed_text) - processed_text = re.sub(r'\+ ।', '\n \n', processed_text) - processed_text = re.sub(r'\+$', '', processed_text) - return processed_text - -def syllabify_text(text): - text_list = [] - #Syllabify text - for char in text: - if char in DEPENDENT_VOWELS: - char = "(" + char + ")" - text_list.append(char) - else: - text_list.append(char) - - full_text = " + ".join(text_list).replace("'", "") - return full_text - - -def normalize_text(text): - output_string = "" - #Map sanskrit numbers to their normalized form. - for char in text: - if char in dict_num: - output_string += dict_num[char] - else: - output_string += char - return output_string - - -def preprocess_text(text): - '''Cleans, tokenizes and normalizes text''' - #Normalize text - normalized_text = normalize_text(text) - - #Tokenize text. - tokenized_text = tokenize_sentence(normalized_text) - tokenized_text = "\n".join(tokenized_text) - - #Syllabify_text - syllabified_text = syllabify_text(tokenized_text) - - #Clean text - cleaned_text = clean_text(syllabified_text) - - #Remove unnecessary characters from a string. - text_cleaned = [] - for index, text in enumerate(cleaned_text.split('\n')): - if text.startswith('+'): - text = text[2:] - - elif text.startswith(' +'): - text = text[3:] - - elif text.endswith('+') or text.endswith(' +'): - text = text[:-2] - - text_cleaned.append(text) - - text_cleaned_str = "\n".join(text_cleaned) - - return text_cleaned_str - - -# DEFAULT_TEXT = """तो क्या विश्व कप 2019 में मैच का बॉस टॉस है? यानी मैच में हार-जीत में \ -# टॉस की भूमिका अहम है? आप ऐसा सोच सकते हैं। विश्वकप के अपने-अपने पहले मैच में बुरी तरह हारने वाली एशिया की दो टीमों \ -# पाकिस्तान और श्रीलंका के कप्तान ने हालांकि अपने हार के पीछे टॉस की दलील तो नहीं दी, लेकिन यह जरूर कहा था कि वह एक अहम टॉस हार गए थे।""" -# DEFAULT_TEXT='संस्कृतम् जगतः एकतमा अतिप्राचीना समृद्धा शास्त्रीया च भाषासु वर्तते । संस्कृतं भारतस्य जगत: वा भाषासु एकतमा प्राचीनतमा ।' - -print(f"Default text is: {DEFAULT_TEXT}") -print('\n \n') -NORMALIZED_TEXT = preprocess_text(DEFAULT_TEXT) -print(f"Syllabified text is: {NORMALIZED_TEXT}") diff --git a/spaces/KindUnes/ImageNet/app.py b/spaces/KindUnes/ImageNet/app.py deleted file mode 100644 index eb53ba976b5b5da554fccea49a497526293b8e6c..0000000000000000000000000000000000000000 --- a/spaces/KindUnes/ImageNet/app.py +++ /dev/null @@ -1,36 +0,0 @@ -import gradio as gr -import tensorflow as tf -import requests -from PIL import Image -import numpy as np - -# Load pre-trained MobileNetV2 model -model = tf.keras.applications.MobileNetV2(weights='imagenet') -labels = requests.get("https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json").json() - -def classify_image(input_image): - # Preprocess the image - img = input_image.resize((224, 224)) - img = tf.keras.preprocessing.image.img_to_array(img) - img = tf.keras.applications.mobilenet_v2.preprocess_input(img) - img = tf.expand_dims(img, axis=0) - - # Make predictions - predictions = model.predict(img) - decoded_predictions = tf.keras.applications.mobilenet_v2.decode_predictions(predictions)[0] - - # Format and return results as HTML - results_html = "- "
- for rank, (class_id, label, score) in enumerate(decoded_predictions, start=1):
- results_html += f"
- {rank}. {label} ({score:.2f}) " - results_html += "
", - "optimizer_params": { - "lr": 2e-05 - }, - "scheduler": "WarmupLinear", - "steps_per_epoch": null, - "warmup_steps": 713, - "weight_decay": 0.01 -} -``` - - -## Full Model Architecture -``` -SentenceTransformer( - (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: DistilBertModel - (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) -) -``` - -## Citing & Authors - - \ No newline at end of file diff --git a/spaces/MoyerLiu/ChatGPT-Next-Web/README.md b/spaces/MoyerLiu/ChatGPT-Next-Web/README.md deleted file mode 100644 index da91c1035a3830fb3dbb11a374ff77c547ae82b5..0000000000000000000000000000000000000000 --- a/spaces/MoyerLiu/ChatGPT-Next-Web/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: ChatGPT Next Web -emoji: 📊 -colorFrom: blue -colorTo: gray -sdk: docker -pinned: false -license: apache-2.0 -app_port: 3000 -duplicated_from: bwbmfya/ChatGPT-Next-Web ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/networks/transformer_encoder.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/networks/transformer_encoder.py deleted file mode 100644 index 7c6054ddcc242d5184c6e0e4dcd5102e6955b915..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/networks/transformer_encoder.py +++ /dev/null @@ -1,238 +0,0 @@ -# Copyright 2019 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Transformer-based text encoder network.""" -# pylint: disable=g-classes-have-attributes -from __future__ import absolute_import -from __future__ import division -# from __future__ import google_type_annotations -from __future__ import print_function - -import tensorflow as tf - -from official.modeling import activations -from official.nlp.modeling import layers - - -@tf.keras.utils.register_keras_serializable(package='Text') -class TransformerEncoder(tf.keras.Model): - """Bi-directional Transformer-based encoder network. - - This network implements a bi-directional Transformer-based encoder as - described in "BERT: Pre-training of Deep Bidirectional Transformers for - Language Understanding" (https://arxiv.org/abs/1810.04805). It includes the - embedding lookups and transformer layers, but not the masked language model - or classification task networks. - - The default values for this object are taken from the BERT-Base implementation - in "BERT: Pre-training of Deep Bidirectional Transformers for Language - Understanding". - - Arguments: - vocab_size: The size of the token vocabulary. - hidden_size: The size of the transformer hidden layers. - num_layers: The number of transformer layers. - num_attention_heads: The number of attention heads for each transformer. The - hidden size must be divisible by the number of attention heads. - sequence_length: The sequence length that this encoder expects. If None, the - sequence length is dynamic; if an integer, the encoder will require - sequences padded to this length. - max_sequence_length: The maximum sequence length that this encoder can - consume. If None, max_sequence_length uses the value from sequence length. - This determines the variable shape for positional embeddings. - type_vocab_size: The number of types that the 'type_ids' input can take. - intermediate_size: The intermediate size for the transformer layers. - activation: The activation to use for the transformer layers. - dropout_rate: The dropout rate to use for the transformer layers. - attention_dropout_rate: The dropout rate to use for the attention layers - within the transformer layers. - initializer: The initialzer to use for all weights in this encoder. - return_all_encoder_outputs: Whether to output sequence embedding outputs of - all encoder transformer layers. - output_range: The sequence output range, [0, output_range), by slicing the - target sequence of the last transformer layer. `None` means the entire - target sequence will attend to the source sequence, which yeilds the full - output. - embedding_width: The width of the word embeddings. If the embedding width - is not equal to hidden size, embedding parameters will be factorized into - two matrices in the shape of ['vocab_size', 'embedding_width'] and - ['embedding_width', 'hidden_size'] ('embedding_width' is usually much - smaller than 'hidden_size'). - embedding_layer: The word embedding layer. `None` means we will create a new - embedding layer. Otherwise, we will reuse the given embedding layer. This - parameter is originally added for ELECTRA model which needs to tie the - generator embeddings with the discriminator embeddings. - """ - - def __init__(self, - vocab_size, - hidden_size=768, - num_layers=12, - num_attention_heads=12, - sequence_length=512, - max_sequence_length=None, - type_vocab_size=16, - intermediate_size=3072, - activation=activations.gelu, - dropout_rate=0.1, - attention_dropout_rate=0.1, - initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02), - return_all_encoder_outputs=False, - output_range=None, - embedding_width=None, - embedding_layer=None, - **kwargs): - activation = tf.keras.activations.get(activation) - initializer = tf.keras.initializers.get(initializer) - - if not max_sequence_length: - max_sequence_length = sequence_length - self._self_setattr_tracking = False - self._config_dict = { - 'vocab_size': vocab_size, - 'hidden_size': hidden_size, - 'num_layers': num_layers, - 'num_attention_heads': num_attention_heads, - 'sequence_length': sequence_length, - 'max_sequence_length': max_sequence_length, - 'type_vocab_size': type_vocab_size, - 'intermediate_size': intermediate_size, - 'activation': tf.keras.activations.serialize(activation), - 'dropout_rate': dropout_rate, - 'attention_dropout_rate': attention_dropout_rate, - 'initializer': tf.keras.initializers.serialize(initializer), - 'return_all_encoder_outputs': return_all_encoder_outputs, - 'output_range': output_range, - 'embedding_width': embedding_width, - } - - word_ids = tf.keras.layers.Input( - shape=(sequence_length,), dtype=tf.int32, name='input_word_ids') - mask = tf.keras.layers.Input( - shape=(sequence_length,), dtype=tf.int32, name='input_mask') - type_ids = tf.keras.layers.Input( - shape=(sequence_length,), dtype=tf.int32, name='input_type_ids') - - if embedding_width is None: - embedding_width = hidden_size - if embedding_layer is None: - self._embedding_layer = layers.OnDeviceEmbedding( - vocab_size=vocab_size, - embedding_width=embedding_width, - initializer=initializer, - name='word_embeddings') - else: - self._embedding_layer = embedding_layer - word_embeddings = self._embedding_layer(word_ids) - - # Always uses dynamic slicing for simplicity. - self._position_embedding_layer = layers.PositionEmbedding( - initializer=initializer, - use_dynamic_slicing=True, - max_sequence_length=max_sequence_length, - name='position_embedding') - position_embeddings = self._position_embedding_layer(word_embeddings) - self._type_embedding_layer = layers.OnDeviceEmbedding( - vocab_size=type_vocab_size, - embedding_width=embedding_width, - initializer=initializer, - use_one_hot=True, - name='type_embeddings') - type_embeddings = self._type_embedding_layer(type_ids) - - embeddings = tf.keras.layers.Add()( - [word_embeddings, position_embeddings, type_embeddings]) - - embeddings = ( - tf.keras.layers.LayerNormalization( - name='embeddings/layer_norm', - axis=-1, - epsilon=1e-12, - dtype=tf.float32)(embeddings)) - embeddings = ( - tf.keras.layers.Dropout(rate=dropout_rate)(embeddings)) - - # We project the 'embedding' output to 'hidden_size' if it is not already - # 'hidden_size'. - if embedding_width != hidden_size: - self._embedding_projection = tf.keras.layers.experimental.EinsumDense( - '...x,xy->...y', - output_shape=hidden_size, - bias_axes='y', - kernel_initializer=initializer, - name='embedding_projection') - embeddings = self._embedding_projection(embeddings) - - self._transformer_layers = [] - data = embeddings - attention_mask = layers.SelfAttentionMask()([data, mask]) - encoder_outputs = [] - for i in range(num_layers): - if i == num_layers - 1 and output_range is not None: - transformer_output_range = output_range - else: - transformer_output_range = None - layer = layers.Transformer( - num_attention_heads=num_attention_heads, - intermediate_size=intermediate_size, - intermediate_activation=activation, - dropout_rate=dropout_rate, - attention_dropout_rate=attention_dropout_rate, - output_range=transformer_output_range, - kernel_initializer=initializer, - name='transformer/layer_%d' % i) - self._transformer_layers.append(layer) - data = layer([data, attention_mask]) - encoder_outputs.append(data) - - first_token_tensor = ( - tf.keras.layers.Lambda(lambda x: tf.squeeze(x[:, 0:1, :], axis=1))( - encoder_outputs[-1])) - self._pooler_layer = tf.keras.layers.Dense( - units=hidden_size, - activation='tanh', - kernel_initializer=initializer, - name='pooler_transform') - cls_output = self._pooler_layer(first_token_tensor) - - if return_all_encoder_outputs: - outputs = [encoder_outputs, cls_output] - else: - outputs = [encoder_outputs[-1], cls_output] - - super(TransformerEncoder, self).__init__( - inputs=[word_ids, mask, type_ids], outputs=outputs, **kwargs) - - def get_embedding_table(self): - return self._embedding_layer.embeddings - - def get_embedding_layer(self): - return self._embedding_layer - - def get_config(self): - return self._config_dict - - @property - def transformer_layers(self): - """List of Transformer layers in the encoder.""" - return self._transformer_layers - - @property - def pooler_layer(self): - """The pooler dense layer after the transformer layers.""" - return self._pooler_layer - - @classmethod - def from_config(cls, config, custom_objects=None): - return cls(**config) diff --git a/spaces/OAOA/DifFace/basicsr/models/video_recurrent_model.py b/spaces/OAOA/DifFace/basicsr/models/video_recurrent_model.py deleted file mode 100644 index 796ee57d5aeb84e81fe8dc769facc8339798cc3e..0000000000000000000000000000000000000000 --- a/spaces/OAOA/DifFace/basicsr/models/video_recurrent_model.py +++ /dev/null @@ -1,197 +0,0 @@ -import torch -from collections import Counter -from os import path as osp -from torch import distributed as dist -from tqdm import tqdm - -from basicsr.metrics import calculate_metric -from basicsr.utils import get_root_logger, imwrite, tensor2img -from basicsr.utils.dist_util import get_dist_info -from basicsr.utils.registry import MODEL_REGISTRY -from .video_base_model import VideoBaseModel - - -@MODEL_REGISTRY.register() -class VideoRecurrentModel(VideoBaseModel): - - def __init__(self, opt): - super(VideoRecurrentModel, self).__init__(opt) - if self.is_train: - self.fix_flow_iter = opt['train'].get('fix_flow') - - def setup_optimizers(self): - train_opt = self.opt['train'] - flow_lr_mul = train_opt.get('flow_lr_mul', 1) - logger = get_root_logger() - logger.info(f'Multiple the learning rate for flow network with {flow_lr_mul}.') - if flow_lr_mul == 1: - optim_params = self.net_g.parameters() - else: # separate flow params and normal params for different lr - normal_params = [] - flow_params = [] - for name, param in self.net_g.named_parameters(): - if 'spynet' in name: - flow_params.append(param) - else: - normal_params.append(param) - optim_params = [ - { # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }, - { - 'params': flow_params, - 'lr': train_opt['optim_g']['lr'] * flow_lr_mul - }, - ] - - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - - def optimize_parameters(self, current_iter): - if self.fix_flow_iter: - logger = get_root_logger() - if current_iter == 1: - logger.info(f'Fix flow network and feature extractor for {self.fix_flow_iter} iters.') - for name, param in self.net_g.named_parameters(): - if 'spynet' in name or 'edvr' in name: - param.requires_grad_(False) - elif current_iter == self.fix_flow_iter: - logger.warning('Train all the parameters.') - self.net_g.requires_grad_(True) - - super(VideoRecurrentModel, self).optimize_parameters(current_iter) - - def dist_validation(self, dataloader, current_iter, tb_logger, save_img): - dataset = dataloader.dataset - dataset_name = dataset.opt['name'] - with_metrics = self.opt['val']['metrics'] is not None - # initialize self.metric_results - # It is a dict: { - # 'folder1': tensor (num_frame x len(metrics)), - # 'folder2': tensor (num_frame x len(metrics)) - # } - if with_metrics: - if not hasattr(self, 'metric_results'): # only execute in the first run - self.metric_results = {} - num_frame_each_folder = Counter(dataset.data_info['folder']) - for folder, num_frame in num_frame_each_folder.items(): - self.metric_results[folder] = torch.zeros( - num_frame, len(self.opt['val']['metrics']), dtype=torch.float32, device='cuda') - # initialize the best metric results - self._initialize_best_metric_results(dataset_name) - # zero self.metric_results - rank, world_size = get_dist_info() - if with_metrics: - for _, tensor in self.metric_results.items(): - tensor.zero_() - - metric_data = dict() - num_folders = len(dataset) - num_pad = (world_size - (num_folders % world_size)) % world_size - if rank == 0: - pbar = tqdm(total=len(dataset), unit='folder') - # Will evaluate (num_folders + num_pad) times, but only the first num_folders results will be recorded. - # (To avoid wait-dead) - for i in range(rank, num_folders + num_pad, world_size): - idx = min(i, num_folders - 1) - val_data = dataset[idx] - folder = val_data['folder'] - - # compute outputs - val_data['lq'].unsqueeze_(0) - val_data['gt'].unsqueeze_(0) - self.feed_data(val_data) - val_data['lq'].squeeze_(0) - val_data['gt'].squeeze_(0) - - self.test() - visuals = self.get_current_visuals() - - # tentative for out of GPU memory - del self.lq - del self.output - if 'gt' in visuals: - del self.gt - torch.cuda.empty_cache() - - if self.center_frame_only: - visuals['result'] = visuals['result'].unsqueeze(1) - if 'gt' in visuals: - visuals['gt'] = visuals['gt'].unsqueeze(1) - - # evaluate - if i < num_folders: - for idx in range(visuals['result'].size(1)): - result = visuals['result'][0, idx, :, :, :] - result_img = tensor2img([result]) # uint8, bgr - metric_data['img'] = result_img - if 'gt' in visuals: - gt = visuals['gt'][0, idx, :, :, :] - gt_img = tensor2img([gt]) # uint8, bgr - metric_data['img2'] = gt_img - - if save_img: - if self.opt['is_train']: - raise NotImplementedError('saving image is not supported during training.') - else: - if self.center_frame_only: # vimeo-90k - clip_ = val_data['lq_path'].split('/')[-3] - seq_ = val_data['lq_path'].split('/')[-2] - name_ = f'{clip_}_{seq_}' - img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, - f"{name_}_{self.opt['name']}.png") - else: # others - img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, - f"{idx:08d}_{self.opt['name']}.png") - # image name only for REDS dataset - imwrite(result_img, img_path) - - # calculate metrics - if with_metrics: - for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()): - result = calculate_metric(metric_data, opt_) - self.metric_results[folder][idx, metric_idx] += result - - # progress bar - if rank == 0: - for _ in range(world_size): - pbar.update(1) - pbar.set_description(f'Folder: {folder}') - - if rank == 0: - pbar.close() - - if with_metrics: - if self.opt['dist']: - # collect data among GPUs - for _, tensor in self.metric_results.items(): - dist.reduce(tensor, 0) - dist.barrier() - - if rank == 0: - self._log_validation_metric_values(current_iter, dataset_name, tb_logger) - - def test(self): - n = self.lq.size(1) - self.net_g.eval() - - flip_seq = self.opt['val'].get('flip_seq', False) - self.center_frame_only = self.opt['val'].get('center_frame_only', False) - - if flip_seq: - self.lq = torch.cat([self.lq, self.lq.flip(1)], dim=1) - - with torch.no_grad(): - self.output = self.net_g(self.lq) - - if flip_seq: - output_1 = self.output[:, :n, :, :, :] - output_2 = self.output[:, n:, :, :, :].flip(1) - self.output = 0.5 * (output_1 + output_2) - - if self.center_frame_only: - self.output = self.output[:, n // 2, :, :, :] - - self.net_g.train() diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/byte_level_bpe/README.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/byte_level_bpe/README.md deleted file mode 100644 index 657092660eae42d20f67647417623b8b8cb7b66c..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/byte_level_bpe/README.md +++ /dev/null @@ -1,88 +0,0 @@ -# Neural Machine Translation with Byte-Level Subwords - -https://arxiv.org/abs/1909.03341 - -We provide an implementation of byte-level byte-pair encoding (BBPE), taking IWSLT 2017 Fr-En translation as -example. - -## Data -Get data and generate fairseq binary dataset: -```bash -bash ./get_data.sh -``` - -## Model Training -Train Transformer model with Bi-GRU embedding contextualization (implemented in `gru_transformer.py`): -```bash -# VOCAB=bytes -# VOCAB=chars -VOCAB=bbpe2048 -# VOCAB=bpe2048 -# VOCAB=bbpe4096 -# VOCAB=bpe4096 -# VOCAB=bpe16384 -``` -```bash -fairseq-train "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \ - --arch gru_transformer --encoder-layers 2 --decoder-layers 2 --dropout 0.3 --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --log-format 'simple' --log-interval 100 --save-dir "checkpoints/${VOCAB}" \ - --batch-size 100 --max-update 100000 --update-freq 2 -``` - -## Generation -`fairseq-generate` requires bytes (BBPE) decoder to convert byte-level representation back to characters: -```bash -# BPE=--bpe bytes -# BPE=--bpe characters -BPE=--bpe byte_bpe --sentencepiece-model-path data/spm_bbpe2048.model -# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe2048.model -# BPE=--bpe byte_bpe --sentencepiece-model-path data/spm_bbpe4096.model -# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe4096.model -# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe16384.model -``` - -```bash -fairseq-generate "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \ - --source-lang fr --gen-subset test --sacrebleu --path "checkpoints/${VOCAB}/checkpoint_last.pt" \ - --tokenizer moses --moses-target-lang en ${BPE} -``` -When using `fairseq-interactive`, bytes (BBPE) encoder/decoder is required to tokenize input data and detokenize model predictions: -```bash -fairseq-interactive "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \ - --path "checkpoints/${VOCAB}/checkpoint_last.pt" --input data/test.fr --tokenizer moses --moses-source-lang fr \ - --moses-target-lang en ${BPE} --buffer-size 1000 --max-tokens 10000 -``` - -## Results -| Vocabulary | Model | BLEU | -|:-------------:|:-------------:|:-------------:| -| Joint BPE 16k ([Kudo, 2018](https://arxiv.org/abs/1804.10959)) | 512d LSTM 2+2 | 33.81 | -| Joint BPE 16k | Transformer base 2+2 (w/ GRU) | 36.64 (36.72) | -| Joint BPE 4k | Transformer base 2+2 (w/ GRU) | 35.49 (36.10) | -| Joint BBPE 4k | Transformer base 2+2 (w/ GRU) | 35.61 (35.82) | -| Joint BPE 2k | Transformer base 2+2 (w/ GRU) | 34.87 (36.13) | -| Joint BBPE 2k | Transformer base 2+2 (w/ GRU) | 34.98 (35.43) | -| Characters | Transformer base 2+2 (w/ GRU) | 31.78 (33.30) | -| Bytes | Transformer base 2+2 (w/ GRU) | 31.57 (33.62) | - - -## Citation -``` -@misc{wang2019neural, - title={Neural Machine Translation with Byte-Level Subwords}, - author={Changhan Wang and Kyunghyun Cho and Jiatao Gu}, - year={2019}, - eprint={1909.03341}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} -``` - - -## Contact -Changhan Wang ([changhan@fb.com](mailto:changhan@fb.com)), -Kyunghyun Cho ([kyunghyuncho@fb.com](mailto:kyunghyuncho@fb.com)), -Jiatao Gu ([jgu@fb.com](mailto:jgu@fb.com)) diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/logging/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/logging/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/scripts/average_checkpoints.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/scripts/average_checkpoints.py deleted file mode 100644 index c512f802bce6b3395cc42a0e4eb39181e9f8c873..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/scripts/average_checkpoints.py +++ /dev/null @@ -1,158 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import collections -import os -import re - -import torch -from fairseq.file_io import PathManager - - -def average_checkpoints(inputs): - """Loads checkpoints from inputs and returns a model with averaged weights. - - Args: - inputs: An iterable of string paths of checkpoints to load from. - - Returns: - A dict of string keys mapping to various values. The 'model' key - from the returned dict should correspond to an OrderedDict mapping - string parameter names to torch Tensors. - """ - params_dict = collections.OrderedDict() - params_keys = None - new_state = None - num_models = len(inputs) - - for fpath in inputs: - with PathManager.open(fpath, "rb") as f: - state = torch.load( - f, - map_location=( - lambda s, _: torch.serialization.default_restore_location(s, "cpu") - ), - ) - # Copies over the settings from the first checkpoint - if new_state is None: - new_state = state - - model_params = state["model"] - - model_params_keys = list(model_params.keys()) - if params_keys is None: - params_keys = model_params_keys - elif params_keys != model_params_keys: - raise KeyError( - "For checkpoint {}, expected list of params: {}, " - "but found: {}".format(f, params_keys, model_params_keys) - ) - - for k in params_keys: - p = model_params[k] - if isinstance(p, torch.HalfTensor): - p = p.float() - if k not in params_dict: - params_dict[k] = p.clone() - # NOTE: clone() is needed in case of p is a shared parameter - else: - params_dict[k] += p - - averaged_params = collections.OrderedDict() - for k, v in params_dict.items(): - averaged_params[k] = v - if averaged_params[k].is_floating_point(): - averaged_params[k].div_(num_models) - else: - averaged_params[k] //= num_models - new_state["model"] = averaged_params - return new_state - - -def last_n_checkpoints(paths, n, update_based, upper_bound=None): - assert len(paths) == 1 - path = paths[0] - if update_based: - pt_regexp = re.compile(r"checkpoint_\d+_(\d+)\.pt") - else: - pt_regexp = re.compile(r"checkpoint(\d+)\.pt") - files = PathManager.ls(path) - - entries = [] - for f in files: - m = pt_regexp.fullmatch(f) - if m is not None: - sort_key = int(m.group(1)) - if upper_bound is None or sort_key <= upper_bound: - entries.append((sort_key, m.group(0))) - if len(entries) < n: - raise Exception( - "Found {} checkpoint files but need at least {}", len(entries), n - ) - return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)[:n]] - - -def main(): - parser = argparse.ArgumentParser( - description="Tool to average the params of input checkpoints to " - "produce a new checkpoint", - ) - # fmt: off - parser.add_argument('--inputs', required=True, nargs='+', - help='Input checkpoint file paths.') - parser.add_argument('--output', required=True, metavar='FILE', - help='Write the new checkpoint containing the averaged weights to this path.') - num_group = parser.add_mutually_exclusive_group() - num_group.add_argument('--num-epoch-checkpoints', type=int, - help='if set, will try to find checkpoints with names checkpoint_xx.pt in the path specified by input, ' - 'and average last this many of them.') - num_group.add_argument('--num-update-checkpoints', type=int, - help='if set, will try to find checkpoints with names checkpoint_ee_xx.pt in the path specified by input, ' - 'and average last this many of them.') - parser.add_argument('--checkpoint-upper-bound', type=int, - help='when using --num-epoch-checkpoints, this will set an upper bound on which epoch to use, ' - 'when using --num-update-checkpoints, this will set an upper bound on which update to use' - 'e.g., with --num-epoch-checkpoints=10 --checkpoint-upper-bound=50, checkpoints 41-50 would be averaged.' - 'e.g., with --num-update-checkpoints=10 --checkpoint-upper-bound=50000, checkpoints 40500-50000 would be averaged assuming --save-interval-updates 500' - ) - # fmt: on - args = parser.parse_args() - print(args) - - num = None - is_update_based = False - if args.num_update_checkpoints is not None: - num = args.num_update_checkpoints - is_update_based = True - elif args.num_epoch_checkpoints is not None: - num = args.num_epoch_checkpoints - - assert args.checkpoint_upper_bound is None or ( - args.num_epoch_checkpoints is not None - or args.num_update_checkpoints is not None - ), "--checkpoint-upper-bound requires --num-epoch-checkpoints or --num-update-checkpoints" - assert ( - args.num_epoch_checkpoints is None or args.num_update_checkpoints is None - ), "Cannot combine --num-epoch-checkpoints and --num-update-checkpoints" - - if num is not None: - args.inputs = last_n_checkpoints( - args.inputs, - num, - is_update_based, - upper_bound=args.checkpoint_upper_bound, - ) - print("averaging checkpoints: ", args.inputs) - - new_state = average_checkpoints(args.inputs) - with PathManager.open(args.output, "wb") as f: - torch.save(new_state, f) - print("Finished writing averaged checkpoint to {}".format(args.output)) - - -if __name__ == "__main__": - main() diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/multihead_attention.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/multihead_attention.py deleted file mode 100644 index a2516356117847b0d46d965ee942354a2ed23189..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/multihead_attention.py +++ /dev/null @@ -1,500 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import Dict, Optional, Tuple - -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.incremental_decoding_utils import with_incremental_state -from fairseq.modules.fairseq_dropout import FairseqDropout -from fairseq.modules.quant_noise import quant_noise -from torch import Tensor, nn -from torch.nn import Parameter - - -@with_incremental_state -class MultiheadAttention(nn.Module): - """Multi-headed attention. - - See "Attention Is All You Need" for more details. - """ - - def __init__( - self, - embed_dim, - num_heads, - kdim=None, - vdim=None, - dropout=0.0, - bias=True, - add_bias_kv=False, - add_zero_attn=False, - self_attention=False, - encoder_decoder_attention=False, - q_noise=0.0, - qn_block_size=8, - ): - super().__init__() - self.embed_dim = embed_dim - self.kdim = kdim if kdim is not None else embed_dim - self.vdim = vdim if vdim is not None else embed_dim - self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim - - self.num_heads = num_heads - self.dropout_module = FairseqDropout( - dropout, module_name=self.__class__.__name__ - ) - - self.head_dim = embed_dim // num_heads - assert ( - self.head_dim * num_heads == self.embed_dim - ), "embed_dim must be divisible by num_heads" - self.scaling = self.head_dim ** -0.5 - - self.self_attention = self_attention - self.encoder_decoder_attention = encoder_decoder_attention - - assert not self.self_attention or self.qkv_same_dim, ( - "Self-attention requires query, key and " "value to be of the same size" - ) - - self.k_proj = quant_noise( - nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size - ) - self.v_proj = quant_noise( - nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size - ) - self.q_proj = quant_noise( - nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size - ) - - self.out_proj = quant_noise( - nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size - ) - - if add_bias_kv: - self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) - self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) - else: - self.bias_k = self.bias_v = None - - self.add_zero_attn = add_zero_attn - - self.reset_parameters() - - self.onnx_trace = False - - def prepare_for_onnx_export_(self): - self.onnx_trace = True - - def reset_parameters(self): - if self.qkv_same_dim: - # Empirically observed the convergence to be much better with - # the scaled initialization - nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2)) - nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2)) - nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2)) - else: - nn.init.xavier_uniform_(self.k_proj.weight) - nn.init.xavier_uniform_(self.v_proj.weight) - nn.init.xavier_uniform_(self.q_proj.weight) - - nn.init.xavier_uniform_(self.out_proj.weight) - if self.out_proj.bias is not None: - nn.init.constant_(self.out_proj.bias, 0.0) - if self.bias_k is not None: - nn.init.xavier_normal_(self.bias_k) - if self.bias_v is not None: - nn.init.xavier_normal_(self.bias_v) - - def forward( - self, - query, - key: Optional[Tensor], - value: Optional[Tensor], - key_padding_mask: Optional[Tensor] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - need_weights: bool = True, - static_kv: bool = False, - attn_mask: Optional[Tensor] = None, - before_softmax: bool = False, - need_head_weights: bool = False, - ) -> Tuple[Tensor, Optional[Tensor]]: - """Input shape: Time x Batch x Channel - - Args: - key_padding_mask (ByteTensor, optional): mask to exclude - keys that are pads, of shape `(batch, src_len)`, where - padding elements are indicated by 1s. - need_weights (bool, optional): return the attention weights, - averaged over heads (default: False). - attn_mask (ByteTensor, optional): typically used to - implement causal attention, where the mask prevents the - attention from looking forward in time (default: None). - before_softmax (bool, optional): return the raw attention - weights and values before the attention softmax. - need_head_weights (bool, optional): return the attention - weights for each head. Implies *need_weights*. Default: - return the average attention weights over all heads. - """ - if need_head_weights: - need_weights = True - - is_tpu = query.device.type == "xla" - - tgt_len, bsz, embed_dim = query.size() - src_len = tgt_len - assert embed_dim == self.embed_dim, f"query dim {embed_dim} != {self.embed_dim}" - assert list(query.size()) == [tgt_len, bsz, embed_dim] - if key is not None: - src_len, key_bsz, _ = key.size() - if not torch.jit.is_scripting(): - assert key_bsz == bsz - assert value is not None - assert src_len, bsz == value.shape[:2] - - if ( - not self.onnx_trace - and not is_tpu # don't use PyTorch version on TPUs - and incremental_state is None - and not static_kv - # A workaround for quantization to work. Otherwise JIT compilation - # treats bias in linear module as method. - and not torch.jit.is_scripting() - ): - assert key is not None and value is not None - return F.multi_head_attention_forward( - query, - key, - value, - self.embed_dim, - self.num_heads, - torch.empty([0]), - torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)), - self.bias_k, - self.bias_v, - self.add_zero_attn, - self.dropout_module.p, - self.out_proj.weight, - self.out_proj.bias, - self.training or self.dropout_module.apply_during_inference, - key_padding_mask, - need_weights, - attn_mask, - use_separate_proj_weight=True, - q_proj_weight=self.q_proj.weight, - k_proj_weight=self.k_proj.weight, - v_proj_weight=self.v_proj.weight, - ) - - if incremental_state is not None: - saved_state = self._get_input_buffer(incremental_state) - if saved_state is not None and "prev_key" in saved_state: - # previous time steps are cached - no need to recompute - # key and value if they are static - if static_kv: - assert self.encoder_decoder_attention and not self.self_attention - key = value = None - else: - saved_state = None - - if self.self_attention: - q = self.q_proj(query) - k = self.k_proj(query) - v = self.v_proj(query) - elif self.encoder_decoder_attention: - # encoder-decoder attention - q = self.q_proj(query) - if key is None: - assert value is None - k = v = None - else: - k = self.k_proj(key) - v = self.v_proj(key) - - else: - assert key is not None and value is not None - q = self.q_proj(query) - k = self.k_proj(key) - v = self.v_proj(value) - q *= self.scaling - - if self.bias_k is not None: - assert self.bias_v is not None - k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) - v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) - if attn_mask is not None: - attn_mask = torch.cat( - [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 - ) - if key_padding_mask is not None: - key_padding_mask = torch.cat( - [ - key_padding_mask, - key_padding_mask.new_zeros(key_padding_mask.size(0), 1), - ], - dim=1, - ) - - q = ( - q.contiguous() - .view(tgt_len, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - if k is not None: - k = ( - k.contiguous() - .view(-1, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - if v is not None: - v = ( - v.contiguous() - .view(-1, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - - if saved_state is not None: - # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) - if "prev_key" in saved_state: - _prev_key = saved_state["prev_key"] - assert _prev_key is not None - prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim) - if static_kv: - k = prev_key - else: - assert k is not None - k = torch.cat([prev_key, k], dim=1) - src_len = k.size(1) - if "prev_value" in saved_state: - _prev_value = saved_state["prev_value"] - assert _prev_value is not None - prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim) - if static_kv: - v = prev_value - else: - assert v is not None - v = torch.cat([prev_value, v], dim=1) - prev_key_padding_mask: Optional[Tensor] = None - if "prev_key_padding_mask" in saved_state: - prev_key_padding_mask = saved_state["prev_key_padding_mask"] - assert k is not None and v is not None - key_padding_mask = MultiheadAttention._append_prev_key_padding_mask( - key_padding_mask=key_padding_mask, - prev_key_padding_mask=prev_key_padding_mask, - batch_size=bsz, - src_len=k.size(1), - static_kv=static_kv, - ) - - saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim) - saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim) - saved_state["prev_key_padding_mask"] = key_padding_mask - # In this branch incremental_state is never None - assert incremental_state is not None - incremental_state = self._set_input_buffer(incremental_state, saved_state) - assert k is not None - assert k.size(1) == src_len - - # This is part of a workaround to get around fork/join parallelism - # not supporting Optional types. - if key_padding_mask is not None and key_padding_mask.dim() == 0: - key_padding_mask = None - - if key_padding_mask is not None: - assert key_padding_mask.size(0) == bsz - assert key_padding_mask.size(1) == src_len - - if self.add_zero_attn: - assert v is not None - src_len += 1 - k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) - v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) - if attn_mask is not None: - attn_mask = torch.cat( - [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 - ) - if key_padding_mask is not None: - key_padding_mask = torch.cat( - [ - key_padding_mask, - torch.zeros(key_padding_mask.size(0), 1).type_as( - key_padding_mask - ), - ], - dim=1, - ) - - attn_weights = torch.bmm(q, k.transpose(1, 2)) - attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) - - assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] - - if attn_mask is not None: - attn_mask = attn_mask.unsqueeze(0) - if self.onnx_trace: - attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1) - attn_weights += attn_mask - - if key_padding_mask is not None: - # don't attend to padding symbols - attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) - if not is_tpu: - attn_weights = attn_weights.masked_fill( - key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), - float("-inf"), - ) - else: - attn_weights = attn_weights.transpose(0, 2) - attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf")) - attn_weights = attn_weights.transpose(0, 2) - attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) - - if before_softmax: - return attn_weights, v - - attn_weights_float = utils.softmax( - attn_weights, dim=-1, onnx_trace=self.onnx_trace - ) - attn_weights = attn_weights_float.type_as(attn_weights) - attn_probs = self.dropout_module(attn_weights) - - assert v is not None - attn = torch.bmm(attn_probs, v) - assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] - if self.onnx_trace and attn.size(1) == 1: - # when ONNX tracing a single decoder step (sequence length == 1) - # the transpose is a no-op copy before view, thus unnecessary - attn = attn.contiguous().view(tgt_len, bsz, embed_dim) - else: - attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) - attn = self.out_proj(attn) - attn_weights: Optional[Tensor] = None - if need_weights: - attn_weights = attn_weights_float.view( - bsz, self.num_heads, tgt_len, src_len - ).transpose(1, 0) - if not need_head_weights: - # average attention weights over heads - attn_weights = attn_weights.mean(dim=0) - - return attn, attn_weights - - @staticmethod - def _append_prev_key_padding_mask( - key_padding_mask: Optional[Tensor], - prev_key_padding_mask: Optional[Tensor], - batch_size: int, - src_len: int, - static_kv: bool, - ) -> Optional[Tensor]: - # saved key padding masks have shape (bsz, seq_len) - if prev_key_padding_mask is not None and static_kv: - new_key_padding_mask = prev_key_padding_mask - elif prev_key_padding_mask is not None and key_padding_mask is not None: - new_key_padding_mask = torch.cat( - [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1 - ) - # During incremental decoding, as the padding token enters and - # leaves the frame, there will be a time when prev or current - # is None - elif prev_key_padding_mask is not None: - if src_len > prev_key_padding_mask.size(1): - filler = torch.zeros( - (batch_size, src_len - prev_key_padding_mask.size(1)), - device=prev_key_padding_mask.device, - ) - new_key_padding_mask = torch.cat( - [prev_key_padding_mask.float(), filler.float()], dim=1 - ) - else: - new_key_padding_mask = prev_key_padding_mask.float() - elif key_padding_mask is not None: - if src_len > key_padding_mask.size(1): - filler = torch.zeros( - (batch_size, src_len - key_padding_mask.size(1)), - device=key_padding_mask.device, - ) - new_key_padding_mask = torch.cat( - [filler.float(), key_padding_mask.float()], dim=1 - ) - else: - new_key_padding_mask = key_padding_mask.float() - else: - new_key_padding_mask = prev_key_padding_mask - return new_key_padding_mask - - @torch.jit.export - def reorder_incremental_state( - self, - incremental_state: Dict[str, Dict[str, Optional[Tensor]]], - new_order: Tensor, - ): - """Reorder buffered internal state (for incremental generation).""" - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is not None: - for k in input_buffer.keys(): - input_buffer_k = input_buffer[k] - if input_buffer_k is not None: - if self.encoder_decoder_attention and input_buffer_k.size( - 0 - ) == new_order.size(0): - break - input_buffer[k] = input_buffer_k.index_select(0, new_order) - incremental_state = self._set_input_buffer(incremental_state, input_buffer) - return incremental_state - - def _get_input_buffer( - self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] - ) -> Dict[str, Optional[Tensor]]: - result = self.get_incremental_state(incremental_state, "attn_state") - if result is not None: - return result - else: - empty_result: Dict[str, Optional[Tensor]] = {} - return empty_result - - def _set_input_buffer( - self, - incremental_state: Dict[str, Dict[str, Optional[Tensor]]], - buffer: Dict[str, Optional[Tensor]], - ): - return self.set_incremental_state(incremental_state, "attn_state", buffer) - - def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int): - return attn_weights - - def upgrade_state_dict_named(self, state_dict, name): - prefix = name + "." if name != "" else "" - items_to_add = {} - keys_to_remove = [] - for k in state_dict.keys(): - if k.endswith(prefix + "in_proj_weight"): - # in_proj_weight used to be q + k + v with same dimensions - dim = int(state_dict[k].shape[0] / 3) - items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim] - items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim] - items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :] - - keys_to_remove.append(k) - - k_bias = prefix + "in_proj_bias" - if k_bias in state_dict.keys(): - dim = int(state_dict[k].shape[0] / 3) - items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim] - items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][ - dim : 2 * dim - ] - items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :] - - keys_to_remove.append(prefix + "in_proj_bias") - - for k in keys_to_remove: - del state_dict[k] - - for key, value in items_to_add.items(): - state_dict[key] = value diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/bart/README.md b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/bart/README.md deleted file mode 100644 index 4050a724ee6a2f20c9998a95df48c58b64764ab1..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/bart/README.md +++ /dev/null @@ -1,228 +0,0 @@ -# BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension - -[https://arxiv.org/abs/1910.13461](https://arxiv.org/abs/1910.13461) - -## Introduction - -BART is sequence-to-sequence model trained with denoising as pretraining objective. We show that this pretraining objective is more generic and show that we can match [RoBERTa](../roberta) results on SQuAD and GLUE and gain state-of-the-art results on summarization (XSum, CNN dataset), long form generative question answering (ELI5) and dialog response genration (ConvAI2). See the associated paper for more details. - -## Pre-trained models - -Model | Description | # params | Download ----|---|---|--- -`bart.base` | BART model with 6 encoder and decoder layers | 140M | [bart.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz) -`bart.large` | BART model with 12 encoder and decoder layers | 400M | [bart.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz) -`bart.large.mnli` | `bart.large` finetuned on `MNLI` | 400M | [bart.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz) -`bart.large.cnn` | `bart.large` finetuned on `CNN-DM` | 400M | [bart.large.cnn.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz) -`bart.large.xsum` | `bart.large` finetuned on `Xsum` | 400M | [bart.large.xsum.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz) - -## Results - -**[GLUE (Wang et al., 2019)](https://gluebenchmark.com/)** -_(dev set, single model, single-task finetuning)_ - -Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B ----|---|---|---|---|---|---|---|--- -`roberta.large` | 90.2 | 94.7 | 92.2 | 86.6 | 96.4 | 90.9 | 68.0 | 92.4 -`bart.large` | 89.9 | 94.9 | 92.5 | 87.0 | 96.6 | 90.4 | 62.8 | 91.2 - -**[SQuAD (Rajpurkar et al., 2018)](https://rajpurkar.github.io/SQuAD-explorer/)** -_(dev set, no additional data used)_ - -Model | SQuAD 1.1 EM/F1 | SQuAD 2.0 EM/F1 ----|---|--- -`roberta.large` | 88.9/94.6 | 86.5/89.4 -`bart.large` | 88.8/94.6 | 86.1/89.2 - -**[CNN/Daily Mail](http://nlpprogress.com/english/summarization.html)** -_(test set, no additional data used)_ - -Model | R1 | R2 | RL ----|---|---|--- -`BERTSUMEXTABS` | 42.13 | 19.60 | 39.18 -`bart.large` | 44.16 | 21.28 | 40.90 - -## Example usage - -##### Load BART from torch.hub (PyTorch >= 1.1): -```python -import torch -bart = torch.hub.load('pytorch/fairseq', 'bart.large') -bart.eval() # disable dropout (or leave in train mode to finetune) -``` - -##### Load BART (for PyTorch 1.0 or custom models): -```python -# Download bart.large model -wget https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz -tar -xzvf bart.large.tar.gz - -# Load the model in fairseq -from fairseq.models.bart import BARTModel -bart = BARTModel.from_pretrained('/path/to/bart.large', checkpoint_file='model.pt') -bart.eval() # disable dropout (or leave in train mode to finetune) -``` - -##### Apply Byte-Pair Encoding (BPE) to input text: -```python -tokens = bart.encode('Hello world!') -assert tokens.tolist() == [0, 31414, 232, 328, 2] -bart.decode(tokens) # 'Hello world!' -``` - -##### Extract features from BART: -```python -# Extract the last layer's features -last_layer_features = bart.extract_features(tokens) -assert last_layer_features.size() == torch.Size([1, 5, 1024]) - -# Extract all layer's features from decoder (layer 0 is the embedding layer) -all_layers = bart.extract_features(tokens, return_all_hiddens=True) -assert len(all_layers) == 13 -assert torch.all(all_layers[-1] == last_layer_features) -``` - -##### Use BART for sentence-pair classification tasks: -```python -# Download BART already finetuned for MNLI -bart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli') -bart.eval() # disable dropout for evaluation - -# Encode a pair of sentences and make a prediction -tokens = bart.encode('BART is a seq2seq model.', 'BART is not sequence to sequence.') -bart.predict('mnli', tokens).argmax() # 0: contradiction - -# Encode another pair of sentences -tokens = bart.encode('BART is denoising autoencoder.', 'BART is version of autoencoder.') -bart.predict('mnli', tokens).argmax() # 2: entailment -``` - -##### Register a new (randomly initialized) classification head: -```python -bart.register_classification_head('new_task', num_classes=3) -logprobs = bart.predict('new_task', tokens) -``` - -##### Batched prediction: -```python -import torch -from fairseq.data.data_utils import collate_tokens - -bart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli') -bart.eval() - -batch_of_pairs = [ - ['BART is a seq2seq model.', 'BART is not sequence to sequence.'], - ['BART is denoising autoencoder.', 'BART is version of autoencoder.'], -] - -batch = collate_tokens( - [bart.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1 -) - -logprobs = bart.predict('mnli', batch) -print(logprobs.argmax(dim=1)) -# tensor([0, 2]) -``` - -##### Using the GPU: -```python -bart.cuda() -bart.predict('new_task', tokens) -``` - -#### Filling masks: - -BART can be used to fill multiple ` ` tokens in the input. -```python -bart = torch.hub.load('pytorch/fairseq', 'bart.base') -bart.eval() -bart.fill_mask(['The cat on the .'], topk=3, beam=10) -# [[('The cat was on the ground.', tensor(-0.6183)), ('The cat was on the floor.', tensor(-0.6798)), ('The cat sleeps on the couch.', tensor(-0.6830))]] -``` - -Note that by default we enforce the output length to match the input length. -This can be disabled by setting ``match_source_len=False``: -``` -bart.fill_mask(['The cat on the .'], topk=3, beam=10, match_source_len=False) -# [[('The cat was on the ground.', tensor(-0.6185)), ('The cat was asleep on the couch.', tensor(-0.6276)), ('The cat was on the floor.', tensor(-0.6800))]] -``` - -Example code to fill masks for a batch of sentences using GPU -``` -bart.cuda() -bart.fill_mask(['The cat on the .', 'The dog on the .'], topk=3, beam=10) -# [[('The cat was on the ground.', tensor(-0.6183)), ('The cat was on the floor.', tensor(-0.6798)), ('The cat sleeps on the couch.', tensor(-0.6830))], [('The dog was on the ground.', tensor(-0.6190)), ('The dog lay on the ground.', tensor(-0.6711)), -('The dog was asleep on the couch', tensor(-0.6796))]] -``` - -#### Evaluating the `bart.large.mnli` model: - -Example python code snippet to evaluate accuracy on the MNLI `dev_matched` set. -```python -label_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'} -ncorrect, nsamples = 0, 0 -bart.cuda() -bart.eval() -with open('glue_data/MNLI/dev_matched.tsv') as fin: - fin.readline() - for index, line in enumerate(fin): - tokens = line.strip().split('\t') - sent1, sent2, target = tokens[8], tokens[9], tokens[-1] - tokens = bart.encode(sent1, sent2) - prediction = bart.predict('mnli', tokens).argmax().item() - prediction_label = label_map[prediction] - ncorrect += int(prediction_label == target) - nsamples += 1 - print('| Accuracy: ', float(ncorrect)/float(nsamples)) -# Expected output: 0.9010 -``` - -#### Evaluating the `bart.large.cnn` model: -- Follow instructions [here](https://github.com/abisee/cnn-dailymail) to download and process into data-files such that `test.source` and `test.target` has one line for each non-tokenized sample. -- For simpler preprocessing, you can also `wget https://cdn-datasets.huggingface.co/summarization/cnn_dm_v2.tgz`, although there is no guarantee of identical scores -- `huggingface/transformers` has a simpler interface that supports [single-gpu](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/run_eval.py) and [multi-gpu](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/run_distributed_eval.py) beam search. - In `huggingface/transformers`, the BART models' paths are `facebook/bart-large-cnn` and `facebook/bart-large-xsum`. - -In `fairseq`, summaries can be generated using: - -```bash -cp data-bin/cnn_dm/dict.source.txt checkpoints/ -python examples/bart/summarize.py \ - --model-dir pytorch/fairseq \ - --model-file bart.large.cnn \ - --src cnn_dm/test.source \ - --out cnn_dm/test.hypo -``` - -For calculating rouge, install `files2rouge` from [here](https://github.com/pltrdy/files2rouge). - -```bash -export CLASSPATH=/path/to/stanford-corenlp-full-2016-10-31/stanford-corenlp-3.7.0.jar - -# Tokenize hypothesis and target files. -cat test.hypo | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > test.hypo.tokenized -cat test.target | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > test.hypo.target -files2rouge test.hypo.tokenized test.hypo.target -# Expected output: (ROUGE-2 Average_F: 0.21238) -``` - - -## Finetuning - -- [Finetuning on GLUE](README.glue.md) -- [Finetuning on CNN-DM](README.summarization.md) - -## Citation - -```bibtex -@article{lewis2019bart, - title = {BART: Denoising Sequence-to-Sequence Pre-training for Natural -Language Generation, Translation, and Comprehension}, - author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and - Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov - and Luke Zettlemoyer }, - journal={arXiv preprint arXiv:1910.13461}, - year = {2019}, -} -``` diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/config/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/config/__init__.py deleted file mode 100644 index 6264236915a7269a4d920ee8213004374dd86a9a..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/config/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/model_parallel/models/transformer_lm.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/model_parallel/models/transformer_lm.py deleted file mode 100644 index dc52f6e8dd3899b6bf9bebae7415cee20baf9884..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/model_parallel/models/transformer_lm.py +++ /dev/null @@ -1,174 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.nn as nn -from fairseq.model_parallel.models.transformer import ModelParallelTransformerDecoder -from fairseq.models import register_model, register_model_architecture -from fairseq.models.transformer_lm import TransformerLanguageModel - - -try: - from fairseq.model_parallel.megatron.mpu import VocabParallelEmbedding - - has_megatron_submodule = True -except (ImportError, ModuleNotFoundError): - has_megatron_submodule = False - - -DEFAULT_MAX_TARGET_POSITIONS = 1024 - - -@register_model("model_parallel_transformer_lm") -class ModelParallelTransformerLanguageModel(TransformerLanguageModel): - - @staticmethod - def add_args(parser): - TransformerLanguageModel.add_args(parser) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - if not has_megatron_submodule: - raise ImportError( - "\n\nPlease install the megatron submodule:" - "\n\n git submodule update --init " - "fairseq/model_parallel/megatron" - ) - - # make sure all arguments are present in older models - base_lm_architecture(args) - - task.source_dictionary.pad_to_multiple_(args.model_parallel_size * 8) - task.target_dictionary.pad_to_multiple_(args.model_parallel_size * 8) - - if args.decoder_layers_to_keep: - args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) - - if getattr(args, "max_target_positions", None) is None: - args.max_target_positions = getattr( - args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS - ) - - if args.character_embeddings: - raise NotImplementedError( - "Character embeddings is not supported for model parallel" - ) - elif args.adaptive_input: - raise NotImplementedError( - "Adaptive input is not supported for model parallel" - ) - else: - embed_tokens = cls.build_embedding( - args, task.source_dictionary, args.decoder_input_dim - ) - - decoder = ModelParallelTransformerDecoder( - args, - task.target_dictionary, - embed_tokens, - no_encoder_attn=True, - ) - return cls(decoder) - - @staticmethod - def add_args(parser): - TransformerLanguageModel.add_args(parser) - - @classmethod - def build_embedding(cls, args, dictionary, embed_dim, path=None): - def _vocab_init(tensor, **kwargs): - nn.init.normal_(tensor, mean=0, std=embed_dim ** -0.5) - nn.init.constant_(tensor[1], 0) - - embed_tokens = VocabParallelEmbedding( - len(dictionary), embed_dim, dictionary.pad(), init_method=_vocab_init - ) - return embed_tokens - - -def base_lm_architecture(args): - # backward compatibility for older model checkpoints - if hasattr(args, "no_tie_adaptive_proj"): - # previous models defined --no-tie-adaptive-proj, so use the existence of - # that option to determine if this is an "old" model checkpoint - args.no_decoder_final_norm = True # old models always set this to True - if args.no_tie_adaptive_proj is False: - args.tie_adaptive_proj = True - if hasattr(args, "decoder_final_norm"): - args.no_decoder_final_norm = not args.decoder_final_norm - - args.activation_fn = getattr(args, "activation_fn", "relu") - args.dropout = getattr(args, "dropout", 0.1) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.activation_dropout = getattr(args, "activation_dropout", 0.0) - args.relu_dropout = getattr(args, "relu_dropout", 0.0) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - # Model training is not stable without this - args.decoder_normalize_before = True - args.no_decoder_final_norm = getattr(args, "no_decoder_final_norm", False) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.adaptive_softmax_factor = getattr(args, "adaptive_softmax_factor", 4) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.character_embeddings = getattr(args, "character_embeddings", False) - args.character_filters = getattr( - args, - "character_filters", - "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]", - ) - args.character_embedding_dim = getattr(args, "character_embedding_dim", 4) - args.char_embedder_highway_layers = getattr(args, "char_embedder_highway_layers", 2) - args.adaptive_input = getattr(args, "adaptive_input", False) - args.adaptive_input_factor = getattr(args, "adaptive_input_factor", 4) - args.adaptive_input_cutoff = getattr(args, "adaptive_input_cutoff", None) - args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) - args.tie_adaptive_proj = getattr(args, "tie_adaptive_proj", False) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) - args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None) - args.layernorm_embedding = getattr(args, "layernorm_embedding", False) - args.no_scale_embedding = getattr(args, "no_scale_embedding", False) - args.quant_noise_pq = getattr(args, "quant_noise_pq", 0.0) - args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8) - args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0.0) - args.add_bos_token = getattr(args, "add_bos_token", False) - - -@register_model_architecture("model_parallel_transformer_lm", "transformer_lm_megatron") -def transformer_lm_megatron(args): - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 3072) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 3072 * 4) - args.decoder_layers = getattr(args, "decoder_layers", 72) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) - args.dropout = getattr(args, "dropout", 0.1) - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - args.activation_fn = getattr(args, "activation_fn", "gelu") - base_lm_architecture(args) - - -@register_model_architecture( - "model_parallel_transformer_lm", "transformer_lm_megatron_11b" -) -def transformer_lm_megatron_11b(args): - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 3072) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 3072 * 6) - args.decoder_layers = getattr(args, "decoder_layers", 72) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) - args.dropout = getattr(args, "dropout", 0.1) - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - args.activation_fn = getattr(args, "activation_fn", "gelu") - base_lm_architecture(args) diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/tasks/audio_pretraining.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/tasks/audio_pretraining.py deleted file mode 100644 index cc310088db8852e80cd2e65d51f06f8f7cb592e3..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/tasks/audio_pretraining.py +++ /dev/null @@ -1,206 +0,0 @@ -# Copyright (c) 2017-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the LICENSE file in -# the root directory of this source tree. An additional grant of patent rights -# can be found in the PATENTS file in the same directory. - -import logging -import os -import sys - -from argparse import Namespace -from dataclasses import dataclass, field -from typing import Optional -from omegaconf import MISSING, II, OmegaConf - -from fairseq.data import BinarizedAudioDataset, FileAudioDataset -from fairseq.dataclass import FairseqDataclass, ChoiceEnum -from fairseq.data.text_compressor import TextCompressionLevel - -from . import FairseqTask, register_task - - -logger = logging.getLogger(__name__) - - -@dataclass -class InferredW2vConfig: - # The following are needed to precompute mask and mask channel indices - # before model's forward. - mask_length: Optional[int] = II("model.mask_length") - mask_prob: Optional[float] = II("model.mask_prob") - mask_selection: Optional[str] = II("model.mask_selection") - mask_other: Optional[float] = II("model.mask_other") - no_mask_overlap: Optional[bool] = II("model.no_mask_overlap") - mask_min_space: Optional[int] = II("model.mask_min_space") - mask_channel_length: Optional[int] = II("model.mask_channel_length") - mask_channel_prob: Optional[float] = II("model.mask_channel_prob") - mask_channel_selection: Optional[str] = II("model.mask_channel_selection") - mask_channel_other: Optional[float] = II("model.mask_channel_other") - no_mask_channel_overlap: Optional[bool] = II("model.no_mask_channel_overlap") - mask_channel_min_space: Optional[int] = II("model.mask_channel_min_space") - - conv_feature_layers: Optional[str] = II("model.conv_feature_layers") - encoder_embed_dim: Optional[int] = II("model.encoder_embed_dim") - - -@dataclass -class AudioPretrainingConfig(FairseqDataclass): - data: str = field(default=MISSING, metadata={"help": "path to data directory"}) - labels: Optional[str] = field( - default=None, - metadata={ - "help": "extension of the label file to load, used for fine-tuning"}, - ) - binarized_dataset: bool = field( - default=False, - metadata={ - "help": "if true, loads binarized dataset (useful for very large datasets). " - "See examples/wav2vec/scripts/binarize_manifest.sh" - }, - ) - sample_rate: int = field( - default=16_000, - metadata={ - "help": "target sample rate. audio files will be up/down sampled to this rate" - }, - ) - normalize: bool = field( - default=False, - metadata={"help": "if set, normalizes input to have 0 mean and unit variance"}, - ) - enable_padding: bool = field( - default=False, metadata={"help": "pad shorter samples instead of cropping"} - ) - max_sample_size: Optional[int] = field( - default=None, metadata={"help": "max sample size to crop to for batching"} - ) - min_sample_size: Optional[int] = field( - default=None, metadata={"help": "min sample size to skip small examples"} - ) - num_batch_buckets: int = field( - default=0, - metadata={"help": "number of buckets"}, - ) - precompute_mask_indices: bool = field( - default=False, - metadata={ - "help": "flag to compute mask indices in data preparation.", - }, - ) - - inferred_w2v_config: Optional[InferredW2vConfig] = field( - default=None, - metadata={ - "help": "wav2vec 2.0 masking arguments used to pre-compute masks (required for TPU)", - }, - ) - - tpu: bool = II("common.tpu") - text_compression_level: ChoiceEnum([x.name for x in TextCompressionLevel]) = field( - default="none", - metadata={ - "help": "compression level for texts (e.g. audio filenames, " - "target texts): none/low/high (default: none). " - } - ) - - -@register_task("audio_pretraining", dataclass=AudioPretrainingConfig) -class AudioPretrainingTask(FairseqTask): - """ """ - - cfg: AudioPretrainingConfig - - @classmethod - def setup_task(cls, cfg: AudioPretrainingConfig, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - cfg (AudioPretrainingConfig): configuration of this task - """ - - return cls(cfg) - - def _get_mask_precompute_kwargs(self, cfg): - if self.cfg.precompute_mask_indices or self.cfg.tpu: - assert ( - cfg.inferred_w2v_config is not None - ), "inferred_w2v_config must be set" - return OmegaConf.to_container( - cfg.inferred_w2v_config, resolve=True, enum_to_str=True - ) - else: - return {} - - def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): - data_path = self.cfg.data - task_cfg = task_cfg or self.cfg - - # upgrade old task - if isinstance(task_cfg, Namespace): - if not hasattr(task_cfg, "autoregressive"): - task_cfg.autoregressive = not task_cfg.criterion == "ctc" - - text_compression_level = getattr( - TextCompressionLevel, str(self.cfg.text_compression_level) - ) - if getattr(task_cfg, "binarized_dataset", False): - self.datasets[split] = BinarizedAudioDataset( - data_path, - split=split, - sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate), - max_sample_size=self.cfg.max_sample_size, - min_sample_size=self.cfg.min_sample_size, - pad=task_cfg.labels is not None or task_cfg.enable_padding, - normalize=task_cfg.normalize, - num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu), - compute_mask_indices=(self.cfg.precompute_mask_indices or self.cfg.tpu), - **self._get_mask_precompute_kwargs(task_cfg), - ) - else: - manifest_path = os.path.join(data_path, "{}.tsv".format(split)) - - self.datasets[split] = FileAudioDataset( - manifest_path=manifest_path, - sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate), - max_sample_size=self.cfg.max_sample_size, - min_sample_size=self.cfg.min_sample_size, - pad=task_cfg.labels is not None or task_cfg.enable_padding, - normalize=task_cfg.normalize, - num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu), - compute_mask_indices=(self.cfg.precompute_mask_indices or self.cfg.tpu), - text_compression_level=text_compression_level, - **self._get_mask_precompute_kwargs(task_cfg), - ) - - if self.cfg.tpu and task_cfg.inferred_w2v_config.mask_channel_prob == 0.0: - logger.info( - "Pretraining on TPUs may suffer convergence " - "issues when training with `mask_channel_prob` value of " - "0. You may want to set this to a low value close to 0." - ) - - @property - def source_dictionary(self): - return None - - @property - def target_dictionary(self): - return None - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return sys.maxsize, sys.maxsize - - def build_model(self, model_cfg: FairseqDataclass): - model = super().build_model(model_cfg) - - actualized_cfg = getattr(model, "cfg", None) - if actualized_cfg is not None: - # if "w2v_args" in actualized_cfg: - if hasattr(actualized_cfg, "w2v_args"): - model_cfg.w2v_args = actualized_cfg.w2v_args - - return model diff --git a/spaces/Olivier-Truong/XTTS_V1_CPU_working/app.py b/spaces/Olivier-Truong/XTTS_V1_CPU_working/app.py deleted file mode 100644 index eefe1bb0828c30efd6d373d4844500e6c9698550..0000000000000000000000000000000000000000 --- a/spaces/Olivier-Truong/XTTS_V1_CPU_working/app.py +++ /dev/null @@ -1,258 +0,0 @@ -import sys -import os -# By using XTTS you agree to CPML license https://coqui.ai/cpml -os.environ["COQUI_TOS_AGREED"] = "1" - -import gradio as gr -from TTS.api import TTS - -model_names = TTS().list_models() -m = model_names[0] -print(model_names) -tts = TTS(m, gpu=False) -tts.to("cpu") # no GPU or Amd -#tts.to("cuda") # cuda only - -def predict(prompt, language, audio_file_pth, mic_file_path, use_mic, agree): - if agree == True: - if use_mic == True: - if mic_file_path is not None: - speaker_wav=mic_file_path - else: - gr.Warning("Please record your voice with Microphone, or uncheck Use Microphone to use reference audios") - return ( - None, - None, - ) - - else: - speaker_wav=audio_file_pth - - if len(prompt)<2: - gr.Warning("Please give a longer prompt text") - return ( - None, - None, - ) - if len(prompt)>10000: - gr.Warning("Text length limited to 10000 characters for this demo, please try shorter text") - return ( - None, - None, - ) - try: - if language == "fr": - if m.find("your") != -1: - language = "fr-fr" - if m.find("/fr/") != -1: - language = None - tts.tts_to_file( - text=prompt, - file_path="output.wav", - speaker_wav=speaker_wav, - language=language - ) - except RuntimeError as e : - if "device-assert" in str(e): - # cannot do anything on cuda device side error, need tor estart - gr.Warning("Unhandled Exception encounter, please retry in a minute") - print("Cuda device-assert Runtime encountered need restart") - sys.exit("Exit due to cuda device-assert") - else: - raise e - - return ( - gr.make_waveform( - audio="output.wav", - ), - "output.wav", - ) - else: - gr.Warning("Please accept the Terms & Condition!") - return ( - None, - None, - ) - - -title = "XTTS Glz's remake (Fonctional Text-2-Speech)" - -description = """ -XTTS is a Voice generation model that lets you clone voices into different languages by using just a quick 3-second audio clip. -
-XTTS is built on previous research, like Tortoise, with additional architectural innovations and training to make cross-language voice cloning and multilingual speech generation possible. -
-This is the same model that powers our creator application Coqui Studio as well as the Coqui API. In production we apply modifications to make low-latency streaming possible. -
-Leave a star on the Github TTS, where our open-source inference and training code lives. -
-For faster inference without waiting in the queue, you should duplicate this space and upgrade to GPU via the settings. -
-""" - -article = """ -
- --
--""" -examples = [ - [ - "Hello, World !, here is an example of light voice cloning. Try to upload your best audio samples quality", - "en", - "examples/female.wav", - None, - False, - True, - ], - [ - "Je suis un lycéen français de 17 ans, passioner par la Cyber-Sécuritée et les models d'IA.", - "fr", - "examples/male.wav", - None, - False, - True, - ], - [ - "Als ich sechs war, sah ich einmal ein wunderbares Bild", - "de", - "examples/female.wav", - None, - False, - True, - ], - [ - "Cuando tenía seis años, vi una vez una imagen magnífica", - "es", - "examples/male.wav", - None, - False, - True, - ], - [ - "Quando eu tinha seis anos eu vi, uma vez, uma imagem magnífica", - "pt", - "examples/female.wav", - None, - False, - True, - ], - [ - "Kiedy miałem sześć lat, zobaczyłem pewnego razu wspaniały obrazek", - "pl", - "examples/male.wav", - None, - False, - True, - ], - [ - "Un tempo lontano, quando avevo sei anni, vidi un magnifico disegno", - "it", - "examples/female.wav", - None, - False, - True, - ], - [ - "Bir zamanlar, altı yaşındayken, muhteşem bir resim gördüm", - "tr", - "examples/female.wav", - None, - False, - True, - ], - [ - "Когда мне было шесть лет, я увидел однажды удивительную картинку", - "ru", - "examples/female.wav", - None, - False, - True, - ], - [ - "Toen ik een jaar of zes was, zag ik op een keer een prachtige plaat", - "nl", - "examples/male.wav", - None, - False, - True, - ], - [ - "Když mi bylo šest let, viděl jsem jednou nádherný obrázek", - "cs", - "examples/female.wav", - None, - False, - True, - ], - [ - "当我还只有六岁的时候， 看到了一副精彩的插画", - "zh-cn", - "examples/female.wav", - None, - False, - True, - ], -] - - - -gr.Interface( - fn=predict, - inputs=[ - gr.Textbox( - label="Text Prompt", - info="One or two sentences at a time is better", - value="Hello, World !, here is an example of light voice cloning. Try to upload your best audio samples quality", - ), - gr.Dropdown( - label="Language", - info="Select an output language for the synthesised speech", - choices=[ - "en", - "es", - "fr", - "de", - "it", - "pt", - "pl", - "tr", - "ru", - "nl", - "cs", - "ar", - "zh-cn", - ], - max_choices=1, - value="en", - ), - gr.Audio( - label="Reference Audio", - info="Click on the ✎ button to upload your own target speaker audio", - type="filepath", - value="examples/female.wav", - ), - gr.Audio(source="microphone", - type="filepath", - info="Use your microphone to record audio", - label="Use Microphone for Reference"), - gr.Checkbox(label="Check to use Microphone as Reference", - value=False, - info="Notice: Microphone input may not work properly under traffic",), - gr.Checkbox( - label="Agree", - value=True, - info="I agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml", - ), - ], - outputs=[ - gr.Video(label="Waveform Visual"), - gr.Audio(label="Synthesised Audio"), - ], - title=title, - description=description, - article=article, - examples=examples, -).queue().launch(debug=True) \ No newline at end of file diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/datasets/prepare_panoptic_fpn.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/datasets/prepare_panoptic_fpn.py deleted file mode 100644 index 597d791afab1bcc0013203a66c7fba225065eebe..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/datasets/prepare_panoptic_fpn.py +++ /dev/null @@ -1,116 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. - -import functools -import json -import multiprocessing as mp -import numpy as np -import os -import time -from fvcore.common.download import download -from panopticapi.utils import rgb2id -from PIL import Image - -from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES - - -def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map): - panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32) - panoptic = rgb2id(panoptic) - output = np.zeros_like(panoptic, dtype=np.uint8) + 255 - for seg in segments: - cat_id = seg["category_id"] - new_cat_id = id_map[cat_id] - output[panoptic == seg["id"]] = new_cat_id - Image.fromarray(output).save(output_semantic) - - -def separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories): - """ - Create semantic segmentation annotations from panoptic segmentation - annotations, to be used by PanopticFPN. - - It maps all thing categories to class 0, and maps all unlabeled pixels to class 255. - It maps all stuff categories to contiguous ids starting from 1. - - Args: - panoptic_json (str): path to the panoptic json file, in COCO's format. - panoptic_root (str): a directory with panoptic annotation files, in COCO's format. - sem_seg_root (str): a directory to output semantic annotation files - categories (list[dict]): category metadata. Each dict needs to have: - "id": corresponds to the "category_id" in the json annotations - "isthing": 0 or 1 - """ - os.makedirs(sem_seg_root, exist_ok=True) - - stuff_ids = [k["id"] for k in categories if k["isthing"] == 0] - thing_ids = [k["id"] for k in categories if k["isthing"] == 1] - id_map = {} # map from category id to id in the output semantic annotation - assert len(stuff_ids) <= 254 - for i, stuff_id in enumerate(stuff_ids): - id_map[stuff_id] = i + 1 - for thing_id in thing_ids: - id_map[thing_id] = 0 - id_map[0] = 255 - - with open(panoptic_json) as f: - obj = json.load(f) - - pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4)) - - def iter_annotations(): - for anno in obj["annotations"]: - file_name = anno["file_name"] - segments = anno["segments_info"] - input = os.path.join(panoptic_root, file_name) - output = os.path.join(sem_seg_root, file_name) - yield input, output, segments - - print("Start writing to {} ...".format(sem_seg_root)) - start = time.time() - pool.starmap( - functools.partial(_process_panoptic_to_semantic, id_map=id_map), - iter_annotations(), - chunksize=100, - ) - print("Finished. time: {:.2f}s".format(time.time() - start)) - - -if __name__ == "__main__": - dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco") - for s in ["val2017", "train2017"]: - separate_coco_semantic_from_panoptic( - os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)), - os.path.join(dataset_dir, "panoptic_{}".format(s)), - os.path.join(dataset_dir, "panoptic_stuff_{}".format(s)), - COCO_CATEGORIES, - ) - - # Prepare val2017_100 for quick testing: - - dest_dir = os.path.join(dataset_dir, "annotations/") - URL_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/" - download(URL_PREFIX + "annotations/coco/panoptic_val2017_100.json", dest_dir) - with open(os.path.join(dest_dir, "panoptic_val2017_100.json")) as f: - obj = json.load(f) - - def link_val100(dir_full, dir_100): - print("Creating " + dir_100 + " ...") - os.makedirs(dir_100, exist_ok=True) - for img in obj["images"]: - basename = os.path.splitext(img["file_name"])[0] - src = os.path.join(dir_full, basename + ".png") - dst = os.path.join(dir_100, basename + ".png") - src = os.path.relpath(src, start=dir_100) - os.symlink(src, dst) - - link_val100( - os.path.join(dataset_dir, "panoptic_val2017"), - os.path.join(dataset_dir, "panoptic_val2017_100"), - ) - - link_val100( - os.path.join(dataset_dir, "panoptic_stuff_val2017"), - os.path.join(dataset_dir, "panoptic_stuff_val2017_100"), - ) diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/models/ade20k/segm_lib/utils/data/dataloader.py b/spaces/OpenGVLab/InternGPT/third-party/lama/bin/models/ade20k/segm_lib/utils/data/dataloader.py deleted file mode 100644 index 039b9ec3645b2a4626ff47c221e372f32a6ad339..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/models/ade20k/segm_lib/utils/data/dataloader.py +++ /dev/null @@ -1,425 +0,0 @@ -import torch -import torch.multiprocessing as multiprocessing -from torch._C import _set_worker_signal_handlers, \ - _remove_worker_pids, _error_if_any_worker_fails -try: - from torch._C import _set_worker_pids -except: - from torch._C import _update_worker_pids as _set_worker_pids -from .sampler import SequentialSampler, RandomSampler, BatchSampler -import signal -import collections -import re -import sys -import threading -import traceback -from torch._six import string_classes, int_classes -import numpy as np - -if sys.version_info[0] == 2: - import Queue as queue -else: - import queue - - -class ExceptionWrapper(object): - r"Wraps an exception plus traceback to communicate across threads" - - def __init__(self, exc_info): - self.exc_type = exc_info[0] - self.exc_msg = "".join(traceback.format_exception(*exc_info)) - - -_use_shared_memory = False -"""Whether to use shared memory in default_collate""" - - -def _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn, worker_id): - global _use_shared_memory - _use_shared_memory = True - - # Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal - # module's handlers are executed after Python returns from C low-level - # handlers, likely when the same fatal signal happened again already. - # https://docs.python.org/3/library/signal.html Sec. 18.8.1.1 - _set_worker_signal_handlers() - - torch.set_num_threads(1) - torch.manual_seed(seed) - np.random.seed(seed) - - if init_fn is not None: - init_fn(worker_id) - - while True: - r = index_queue.get() - if r is None: - break - idx, batch_indices = r - try: - samples = collate_fn([dataset[i] for i in batch_indices]) - except Exception: - data_queue.put((idx, ExceptionWrapper(sys.exc_info()))) - else: - data_queue.put((idx, samples)) - - -def _worker_manager_loop(in_queue, out_queue, done_event, pin_memory, device_id): - if pin_memory: - torch.cuda.set_device(device_id) - - while True: - try: - r = in_queue.get() - except Exception: - if done_event.is_set(): - return - raise - if r is None: - break - if isinstance(r[1], ExceptionWrapper): - out_queue.put(r) - continue - idx, batch = r - try: - if pin_memory: - batch = pin_memory_batch(batch) - except Exception: - out_queue.put((idx, ExceptionWrapper(sys.exc_info()))) - else: - out_queue.put((idx, batch)) - -numpy_type_map = { - 'float64': torch.DoubleTensor, - 'float32': torch.FloatTensor, - 'float16': torch.HalfTensor, - 'int64': torch.LongTensor, - 'int32': torch.IntTensor, - 'int16': torch.ShortTensor, - 'int8': torch.CharTensor, - 'uint8': torch.ByteTensor, -} - - -def default_collate(batch): - "Puts each data field into a tensor with outer dimension batch size" - - error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" - elem_type = type(batch[0]) - if torch.is_tensor(batch[0]): - out = None - if _use_shared_memory: - # If we're in a background process, concatenate directly into a - # shared memory tensor to avoid an extra copy - numel = sum([x.numel() for x in batch]) - storage = batch[0].storage()._new_shared(numel) - out = batch[0].new(storage) - return torch.stack(batch, 0, out=out) - elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ - and elem_type.__name__ != 'string_': - elem = batch[0] - if elem_type.__name__ == 'ndarray': - # array of string classes and object - if re.search('[SaUO]', elem.dtype.str) is not None: - raise TypeError(error_msg.format(elem.dtype)) - - return torch.stack([torch.from_numpy(b) for b in batch], 0) - if elem.shape == (): # scalars - py_type = float if elem.dtype.name.startswith('float') else int - return numpy_type_map[elem.dtype.name](list(map(py_type, batch))) - elif isinstance(batch[0], int_classes): - return torch.LongTensor(batch) - elif isinstance(batch[0], float): - return torch.DoubleTensor(batch) - elif isinstance(batch[0], string_classes): - return batch - elif isinstance(batch[0], collections.Mapping): - return {key: default_collate([d[key] for d in batch]) for key in batch[0]} - elif isinstance(batch[0], collections.Sequence): - transposed = zip(*batch) - return [default_collate(samples) for samples in transposed] - - raise TypeError((error_msg.format(type(batch[0])))) - - -def pin_memory_batch(batch): - if torch.is_tensor(batch): - return batch.pin_memory() - elif isinstance(batch, string_classes): - return batch - elif isinstance(batch, collections.Mapping): - return {k: pin_memory_batch(sample) for k, sample in batch.items()} - elif isinstance(batch, collections.Sequence): - return [pin_memory_batch(sample) for sample in batch] - else: - return batch - - -_SIGCHLD_handler_set = False -"""Whether SIGCHLD handler is set for DataLoader worker failures. Only one -handler needs to be set for all DataLoaders in a process.""" - - -def _set_SIGCHLD_handler(): - # Windows doesn't support SIGCHLD handler - if sys.platform == 'win32': - return - # can't set signal in child threads - if not isinstance(threading.current_thread(), threading._MainThread): - return - global _SIGCHLD_handler_set - if _SIGCHLD_handler_set: - return - previous_handler = signal.getsignal(signal.SIGCHLD) - if not callable(previous_handler): - previous_handler = None - - def handler(signum, frame): - # This following call uses `waitid` with WNOHANG from C side. Therefore, - # Python can still get and update the process status successfully. - _error_if_any_worker_fails() - if previous_handler is not None: - previous_handler(signum, frame) - - signal.signal(signal.SIGCHLD, handler) - _SIGCHLD_handler_set = True - - -class DataLoaderIter(object): - "Iterates once over the DataLoader's dataset, as specified by the sampler" - - def __init__(self, loader): - self.dataset = loader.dataset - self.collate_fn = loader.collate_fn - self.batch_sampler = loader.batch_sampler - self.num_workers = loader.num_workers - self.pin_memory = loader.pin_memory and torch.cuda.is_available() - self.timeout = loader.timeout - self.done_event = threading.Event() - - self.sample_iter = iter(self.batch_sampler) - - if self.num_workers > 0: - self.worker_init_fn = loader.worker_init_fn - self.index_queue = multiprocessing.SimpleQueue() - self.worker_result_queue = multiprocessing.SimpleQueue() - self.batches_outstanding = 0 - self.worker_pids_set = False - self.shutdown = False - self.send_idx = 0 - self.rcvd_idx = 0 - self.reorder_dict = {} - - base_seed = torch.LongTensor(1).random_(0, 2**31-1)[0] - self.workers = [ - multiprocessing.Process( - target=_worker_loop, - args=(self.dataset, self.index_queue, self.worker_result_queue, self.collate_fn, - base_seed + i, self.worker_init_fn, i)) - for i in range(self.num_workers)] - - if self.pin_memory or self.timeout > 0: - self.data_queue = queue.Queue() - if self.pin_memory: - maybe_device_id = torch.cuda.current_device() - else: - # do not initialize cuda context if not necessary - maybe_device_id = None - self.worker_manager_thread = threading.Thread( - target=_worker_manager_loop, - args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory, - maybe_device_id)) - self.worker_manager_thread.daemon = True - self.worker_manager_thread.start() - else: - self.data_queue = self.worker_result_queue - - for w in self.workers: - w.daemon = True # ensure that the worker exits on process exit - w.start() - - _set_worker_pids(id(self), tuple(w.pid for w in self.workers)) - _set_SIGCHLD_handler() - self.worker_pids_set = True - - # prime the prefetch loop - for _ in range(2 * self.num_workers): - self._put_indices() - - def __len__(self): - return len(self.batch_sampler) - - def _get_batch(self): - if self.timeout > 0: - try: - return self.data_queue.get(timeout=self.timeout) - except queue.Empty: - raise RuntimeError('DataLoader timed out after {} seconds'.format(self.timeout)) - else: - return self.data_queue.get() - - def __next__(self): - if self.num_workers == 0: # same-process loading - indices = next(self.sample_iter) # may raise StopIteration - batch = self.collate_fn([self.dataset[i] for i in indices]) - if self.pin_memory: - batch = pin_memory_batch(batch) - return batch - - # check if the next sample has already been generated - if self.rcvd_idx in self.reorder_dict: - batch = self.reorder_dict.pop(self.rcvd_idx) - return self._process_next_batch(batch) - - if self.batches_outstanding == 0: - self._shutdown_workers() - raise StopIteration - - while True: - assert (not self.shutdown and self.batches_outstanding > 0) - idx, batch = self._get_batch() - self.batches_outstanding -= 1 - if idx != self.rcvd_idx: - # store out-of-order samples - self.reorder_dict[idx] = batch - continue - return self._process_next_batch(batch) - - next = __next__ # Python 2 compatibility - - def __iter__(self): - return self - - def _put_indices(self): - assert self.batches_outstanding < 2 * self.num_workers - indices = next(self.sample_iter, None) - if indices is None: - return - self.index_queue.put((self.send_idx, indices)) - self.batches_outstanding += 1 - self.send_idx += 1 - - def _process_next_batch(self, batch): - self.rcvd_idx += 1 - self._put_indices() - if isinstance(batch, ExceptionWrapper): - raise batch.exc_type(batch.exc_msg) - return batch - - def __getstate__(self): - # TODO: add limited pickling support for sharing an iterator - # across multiple threads for HOGWILD. - # Probably the best way to do this is by moving the sample pushing - # to a separate thread and then just sharing the data queue - # but signalling the end is tricky without a non-blocking API - raise NotImplementedError("DataLoaderIterator cannot be pickled") - - def _shutdown_workers(self): - try: - if not self.shutdown: - self.shutdown = True - self.done_event.set() - # if worker_manager_thread is waiting to put - while not self.data_queue.empty(): - self.data_queue.get() - for _ in self.workers: - self.index_queue.put(None) - # done_event should be sufficient to exit worker_manager_thread, - # but be safe here and put another None - self.worker_result_queue.put(None) - finally: - # removes pids no matter what - if self.worker_pids_set: - _remove_worker_pids(id(self)) - self.worker_pids_set = False - - def __del__(self): - if self.num_workers > 0: - self._shutdown_workers() - - -class DataLoader(object): - """ - Data loader. Combines a dataset and a sampler, and provides - single- or multi-process iterators over the dataset. - - Arguments: - dataset (Dataset): dataset from which to load the data. - batch_size (int, optional): how many samples per batch to load - (default: 1). - shuffle (bool, optional): set to ``True`` to have the data reshuffled - at every epoch (default: False). - sampler (Sampler, optional): defines the strategy to draw samples from - the dataset. If specified, ``shuffle`` must be False. - batch_sampler (Sampler, optional): like sampler, but returns a batch of - indices at a time. Mutually exclusive with batch_size, shuffle, - sampler, and drop_last. - num_workers (int, optional): how many subprocesses to use for data - loading. 0 means that the data will be loaded in the main process. - (default: 0) - collate_fn (callable, optional): merges a list of samples to form a mini-batch. - pin_memory (bool, optional): If ``True``, the data loader will copy tensors - into CUDA pinned memory before returning them. - drop_last (bool, optional): set to ``True`` to drop the last incomplete batch, - if the dataset size is not divisible by the batch size. If ``False`` and - the size of dataset is not divisible by the batch size, then the last batch - will be smaller. (default: False) - timeout (numeric, optional): if positive, the timeout value for collecting a batch - from workers. Should always be non-negative. (default: 0) - worker_init_fn (callable, optional): If not None, this will be called on each - worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as - input, after seeding and before data loading. (default: None) - - .. note:: By default, each worker will have its PyTorch seed set to - ``base_seed + worker_id``, where ``base_seed`` is a long generated - by main process using its RNG. You may use ``torch.initial_seed()`` to access - this value in :attr:`worker_init_fn`, which can be used to set other seeds - (e.g. NumPy) before data loading. - - .. warning:: If ``spawn'' start method is used, :attr:`worker_init_fn` cannot be an - unpicklable object, e.g., a lambda function. - """ - - def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, - num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False, - timeout=0, worker_init_fn=None): - self.dataset = dataset - self.batch_size = batch_size - self.num_workers = num_workers - self.collate_fn = collate_fn - self.pin_memory = pin_memory - self.drop_last = drop_last - self.timeout = timeout - self.worker_init_fn = worker_init_fn - - if timeout < 0: - raise ValueError('timeout option should be non-negative') - - if batch_sampler is not None: - if batch_size > 1 or shuffle or sampler is not None or drop_last: - raise ValueError('batch_sampler is mutually exclusive with ' - 'batch_size, shuffle, sampler, and drop_last') - - if sampler is not None and shuffle: - raise ValueError('sampler is mutually exclusive with shuffle') - - if self.num_workers < 0: - raise ValueError('num_workers cannot be negative; ' - 'use num_workers=0 to disable multiprocessing.') - - if batch_sampler is None: - if sampler is None: - if shuffle: - sampler = RandomSampler(dataset) - else: - sampler = SequentialSampler(dataset) - batch_sampler = BatchSampler(sampler, batch_size, drop_last) - - self.sampler = sampler - self.batch_sampler = batch_sampler - - def __iter__(self): - return DataLoaderIter(self) - - def __len__(self): - return len(self.batch_sampler) diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/modules/base.py b/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/modules/base.py deleted file mode 100644 index a50c3fc7753a0bba64a5ab8c1ed64ff97e62313f..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/modules/base.py +++ /dev/null @@ -1,80 +0,0 @@ -import abc -from typing import Tuple, List - -import torch -import torch.nn as nn - -from saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv -from saicinpainting.training.modules.multidilated_conv import MultidilatedConv - - -class BaseDiscriminator(nn.Module): - @abc.abstractmethod - def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]: - """ - Predict scores and get intermediate activations. Useful for feature matching loss - :return tuple (scores, list of intermediate activations) - """ - raise NotImplemented() - - -def get_conv_block_ctor(kind='default'): - if not isinstance(kind, str): - return kind - if kind == 'default': - return nn.Conv2d - if kind == 'depthwise': - return DepthWiseSeperableConv - if kind == 'multidilated': - return MultidilatedConv - raise ValueError(f'Unknown convolutional block kind {kind}') - - -def get_norm_layer(kind='bn'): - if not isinstance(kind, str): - return kind - if kind == 'bn': - return nn.BatchNorm2d - if kind == 'in': - return nn.InstanceNorm2d - raise ValueError(f'Unknown norm block kind {kind}') - - -def get_activation(kind='tanh'): - if kind == 'tanh': - return nn.Tanh() - if kind == 'sigmoid': - return nn.Sigmoid() - if kind is False: - return nn.Identity() - raise ValueError(f'Unknown activation kind {kind}') - - -class SimpleMultiStepGenerator(nn.Module): - def __init__(self, steps: List[nn.Module]): - super().__init__() - self.steps = nn.ModuleList(steps) - - def forward(self, x): - cur_in = x - outs = [] - for step in self.steps: - cur_out = step(cur_in) - outs.append(cur_out) - cur_in = torch.cat((cur_in, cur_out), dim=1) - return torch.cat(outs[::-1], dim=1) - -def deconv_factory(kind, ngf, mult, norm_layer, activation, max_features): - if kind == 'convtranspose': - return [nn.ConvTranspose2d(min(max_features, ngf * mult), - min(max_features, int(ngf * mult / 2)), - kernel_size=3, stride=2, padding=1, output_padding=1), - norm_layer(min(max_features, int(ngf * mult / 2))), activation] - elif kind == 'bilinear': - return [nn.Upsample(scale_factor=2, mode='bilinear'), - DepthWiseSeperableConv(min(max_features, ngf * mult), - min(max_features, int(ngf * mult / 2)), - kernel_size=3, stride=1, padding=1), - norm_layer(min(max_features, int(ngf * mult / 2))), activation] - else: - raise Exception(f"Invalid deconv kind: {kind}") \ No newline at end of file diff --git a/spaces/OpenMotionLab/MotionGPT/mGPT/metrics/base.py b/spaces/OpenMotionLab/MotionGPT/mGPT/metrics/base.py deleted file mode 100644 index 540771902f3f91bf926f5fbf1e170242f8b26181..0000000000000000000000000000000000000000 --- a/spaces/OpenMotionLab/MotionGPT/mGPT/metrics/base.py +++ /dev/null @@ -1,46 +0,0 @@ -from torch import Tensor, nn -from os.path import join as pjoin -from .mr import MRMetrics -from .t2m import TM2TMetrics -from .mm import MMMetrics -from .m2t import M2TMetrics -from .m2m import PredMetrics - - -class BaseMetrics(nn.Module): - def __init__(self, cfg, datamodule, debug, **kwargs) -> None: - super().__init__() - - njoints = datamodule.njoints - - data_name = datamodule.name - if data_name in ["humanml3d", "kit"]: - self.TM2TMetrics = TM2TMetrics( - cfg=cfg, - dataname=data_name, - diversity_times=30 if debug else cfg.METRIC.DIVERSITY_TIMES, - dist_sync_on_step=cfg.METRIC.DIST_SYNC_ON_STEP, - ) - self.M2TMetrics = M2TMetrics( - cfg=cfg, - w_vectorizer=datamodule.hparams.w_vectorizer, - diversity_times=30 if debug else cfg.METRIC.DIVERSITY_TIMES, - dist_sync_on_step=cfg.METRIC.DIST_SYNC_ON_STEP) - self.MMMetrics = MMMetrics( - cfg=cfg, - mm_num_times=cfg.METRIC.MM_NUM_TIMES, - dist_sync_on_step=cfg.METRIC.DIST_SYNC_ON_STEP, - ) - - self.MRMetrics = MRMetrics( - njoints=njoints, - jointstype=cfg.DATASET.JOINT_TYPE, - dist_sync_on_step=cfg.METRIC.DIST_SYNC_ON_STEP, - ) - self.PredMetrics = PredMetrics( - cfg=cfg, - njoints=njoints, - jointstype=cfg.DATASET.JOINT_TYPE, - dist_sync_on_step=cfg.METRIC.DIST_SYNC_ON_STEP, - task=cfg.model.params.task, - ) diff --git a/spaces/Oumar199/Fake-Real-Face-Detection/fake_face_detection/trainers/custom_trainer.py b/spaces/Oumar199/Fake-Real-Face-Detection/fake_face_detection/trainers/custom_trainer.py deleted file mode 100644 index 6575eef80157f93143fe3e15d00c137f9fa2c7c3..0000000000000000000000000000000000000000 --- a/spaces/Oumar199/Fake-Real-Face-Detection/fake_face_detection/trainers/custom_trainer.py +++ /dev/null @@ -1,27 +0,0 @@ - -from transformers import Trainer -import torch - -def get_custom_trainer(weights: torch.Tensor): - - class CustomTrainer(Trainer): # got from https://huggingface.co/docs/transformers/main_classes/trainer - - def compute_loss(self, model, inputs, return_outputs=False): - - # recuperate labels - labels = inputs.get("labels") - - # forward pass - outputs = model(**inputs) - - # recuperate logits - logits = outputs.get("logits") - - # compute custom loss (passing the weights) - loss_fct = nn.CrossEntropyLoss(weight=weights) - - loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1)) - - return (loss, outputs) if return_outputs else loss - - return CustomTrainer diff --git a/spaces/PAIR/PAIR-Diffusion/ldm/models/diffusion/ddpm.py b/spaces/PAIR/PAIR-Diffusion/ldm/models/diffusion/ddpm.py deleted file mode 100644 index f71a44af48c8cba8e97849b7e6813b3e6f9fe83c..0000000000000000000000000000000000000000 --- a/spaces/PAIR/PAIR-Diffusion/ldm/models/diffusion/ddpm.py +++ /dev/null @@ -1,1797 +0,0 @@ -""" -wild mixture of -https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py -https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py -https://github.com/CompVis/taming-transformers --- merci -""" - -import torch -import torch.nn as nn -import numpy as np -import pytorch_lightning as pl -from torch.optim.lr_scheduler import LambdaLR -from einops import rearrange, repeat -from contextlib import contextmanager, nullcontext -from functools import partial -import itertools -from tqdm import tqdm -from torchvision.utils import make_grid -from pytorch_lightning.utilities.distributed import rank_zero_only -from omegaconf import ListConfig - -from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config -from ldm.modules.ema import LitEma -from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution -from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL -from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like -from ldm.models.diffusion.ddim import DDIMSampler - - -__conditioning_keys__ = {'concat': 'c_concat', - 'crossattn': 'c_crossattn', - 'adm': 'y'} - - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -def uniform_on_device(r1, r2, shape, device): - return (r1 - r2) * torch.rand(*shape, device=device) + r2 - - -class DDPM(pl.LightningModule): - # classic DDPM with Gaussian diffusion, in image space - def __init__(self, - unet_config, - timesteps=1000, - beta_schedule="linear", - loss_type="l2", - ckpt_path=None, - ignore_keys=[], - load_only_unet=False, - monitor="val/loss", - use_ema=True, - first_stage_key="image", - image_size=256, - channels=3, - log_every_t=100, - clip_denoised=True, - linear_start=1e-4, - linear_end=2e-2, - cosine_s=8e-3, - given_betas=None, - original_elbo_weight=0., - v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta - l_simple_weight=1., - conditioning_key=None, - parameterization="eps", # all assuming fixed variance schedules - scheduler_config=None, - use_positional_encodings=False, - learn_logvar=False, - logvar_init=0., - make_it_fit=False, - ucg_training=None, - reset_ema=False, - reset_num_ema_updates=False, - ): - super().__init__() - assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"' - self.parameterization = parameterization - print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") - self.cond_stage_model = None - self.clip_denoised = clip_denoised - self.log_every_t = log_every_t - self.first_stage_key = first_stage_key - self.image_size = image_size # try conv? - self.channels = channels - self.use_positional_encodings = use_positional_encodings - self.model = DiffusionWrapper(unet_config, conditioning_key) - count_params(self.model, verbose=True) - self.use_ema = use_ema - if self.use_ema: - self.model_ema = LitEma(self.model) - print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") - - self.use_scheduler = scheduler_config is not None - if self.use_scheduler: - self.scheduler_config = scheduler_config - - self.v_posterior = v_posterior - self.original_elbo_weight = original_elbo_weight - self.l_simple_weight = l_simple_weight - - if monitor is not None: - self.monitor = monitor - self.make_it_fit = make_it_fit - if reset_ema: assert exists(ckpt_path) - if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) - if reset_ema: - assert self.use_ema - print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.") - self.model_ema = LitEma(self.model) - if reset_num_ema_updates: - print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ") - assert self.use_ema - self.model_ema.reset_num_updates() - - self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, - linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) - - self.loss_type = loss_type - - self.learn_logvar = learn_logvar - logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) - if self.learn_logvar: - self.logvar = nn.Parameter(self.logvar, requires_grad=True) - else: - self.register_buffer('logvar', logvar) - - self.ucg_training = ucg_training or dict() - if self.ucg_training: - self.ucg_prng = np.random.RandomState() - - def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, - linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - if exists(given_betas): - betas = given_betas - else: - betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, - cosine_s=cosine_s) - alphas = 1. - betas - alphas_cumprod = np.cumprod(alphas, axis=0) - alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) - - timesteps, = betas.shape - self.num_timesteps = int(timesteps) - self.linear_start = linear_start - self.linear_end = linear_end - assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' - - to_torch = partial(torch.tensor, dtype=torch.float32) - - self.register_buffer('betas', to_torch(betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) - - # calculations for diffusion q(x_t | x_{t-1}) and others - self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) - - # calculations for posterior q(x_{t-1} | x_t, x_0) - posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( - 1. - alphas_cumprod) + self.v_posterior * betas - # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) - self.register_buffer('posterior_variance', to_torch(posterior_variance)) - # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain - self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) - self.register_buffer('posterior_mean_coef1', to_torch( - betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) - self.register_buffer('posterior_mean_coef2', to_torch( - (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) - - if self.parameterization == "eps": - lvlb_weights = self.betas ** 2 / ( - 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) - elif self.parameterization == "x0": - lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) - elif self.parameterization == "v": - lvlb_weights = torch.ones_like(self.betas ** 2 / ( - 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))) - else: - raise NotImplementedError("mu not supported") - lvlb_weights[0] = lvlb_weights[1] - self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) - assert not torch.isnan(self.lvlb_weights).all() - - @contextmanager - def ema_scope(self, context=None): - if self.use_ema: - self.model_ema.store(self.model.parameters()) - self.model_ema.copy_to(self.model) - if context is not None: - print(f"{context}: Switched to EMA weights") - try: - yield None - finally: - if self.use_ema: - self.model_ema.restore(self.model.parameters()) - if context is not None: - print(f"{context}: Restored training weights") - - @torch.no_grad() - def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): - sd = torch.load(path, map_location="cpu") - if "state_dict" in list(sd.keys()): - sd = sd["state_dict"] - keys = list(sd.keys()) - for k in keys: - for ik in ignore_keys: - if k.startswith(ik): - print("Deleting key {} from state_dict.".format(k)) - del sd[k] - if self.make_it_fit: - n_params = len([name for name, _ in - itertools.chain(self.named_parameters(), - self.named_buffers())]) - for name, param in tqdm( - itertools.chain(self.named_parameters(), - self.named_buffers()), - desc="Fitting old weights to new weights", - total=n_params - ): - if not name in sd: - continue - old_shape = sd[name].shape - new_shape = param.shape - assert len(old_shape) == len(new_shape) - if len(new_shape) > 2: - # we only modify first two axes - assert new_shape[2:] == old_shape[2:] - # assumes first axis corresponds to output dim - if not new_shape == old_shape: - new_param = param.clone() - old_param = sd[name] - if len(new_shape) == 1: - for i in range(new_param.shape[0]): - new_param[i] = old_param[i % old_shape[0]] - elif len(new_shape) >= 2: - for i in range(new_param.shape[0]): - for j in range(new_param.shape[1]): - new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]] - - n_used_old = torch.ones(old_shape[1]) - for j in range(new_param.shape[1]): - n_used_old[j % old_shape[1]] += 1 - n_used_new = torch.zeros(new_shape[1]) - for j in range(new_param.shape[1]): - n_used_new[j] = n_used_old[j % old_shape[1]] - - n_used_new = n_used_new[None, :] - while len(n_used_new.shape) < len(new_shape): - n_used_new = n_used_new.unsqueeze(-1) - new_param /= n_used_new - - sd[name] = new_param - - missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( - sd, strict=False) - print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") - if len(missing) > 0: - print(f"Missing Keys:\n {missing}") - if len(unexpected) > 0: - print(f"\nUnexpected Keys:\n {unexpected}") - - def q_mean_variance(self, x_start, t): - """ - Get the distribution q(x_t | x_0). - :param x_start: the [N x C x ...] tensor of noiseless inputs. - :param t: the number of diffusion steps (minus 1). Here, 0 means one step. - :return: A tuple (mean, variance, log_variance), all of x_start's shape. - """ - mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) - variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) - log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) - return mean, variance, log_variance - - def predict_start_from_noise(self, x_t, t, noise): - return ( - extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise - ) - - def predict_start_from_z_and_v(self, x_t, t, v): - # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) - # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) - return ( - extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t - - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v - ) - - def predict_eps_from_z_and_v(self, x_t, t, v): - return ( - extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v + - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t - ) - - def q_posterior(self, x_start, x_t, t): - posterior_mean = ( - extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + - extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t - ) - posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) - posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) - return posterior_mean, posterior_variance, posterior_log_variance_clipped - - def p_mean_variance(self, x, t, clip_denoised: bool): - model_out = self.model(x, t) - if self.parameterization == "eps": - x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) - elif self.parameterization == "x0": - x_recon = model_out - if clip_denoised: - x_recon.clamp_(-1., 1.) - - model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) - return model_mean, posterior_variance, posterior_log_variance - - @torch.no_grad() - def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): - b, *_, device = *x.shape, x.device - model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) - noise = noise_like(x.shape, device, repeat_noise) - # no noise when t == 0 - nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise - - @torch.no_grad() - def p_sample_loop(self, shape, return_intermediates=False): - device = self.betas.device - b = shape[0] - img = torch.randn(shape, device=device) - intermediates = [img] - for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): - img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), - clip_denoised=self.clip_denoised) - if i % self.log_every_t == 0 or i == self.num_timesteps - 1: - intermediates.append(img) - if return_intermediates: - return img, intermediates - return img - - @torch.no_grad() - def sample(self, batch_size=16, return_intermediates=False): - image_size = self.image_size - channels = self.channels - return self.p_sample_loop((batch_size, channels, image_size, image_size), - return_intermediates=return_intermediates) - - def q_sample(self, x_start, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) - - def get_v(self, x, noise, t): - return ( - extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise - - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x - ) - - def get_loss(self, pred, target, mean=True): - if self.loss_type == 'l1': - loss = (target - pred).abs() - if mean: - loss = loss.mean() - elif self.loss_type == 'l2': - if mean: - loss = torch.nn.functional.mse_loss(target, pred) - else: - loss = torch.nn.functional.mse_loss(target, pred, reduction='none') - else: - raise NotImplementedError("unknown loss type '{loss_type}'") - - return loss - - def p_losses(self, x_start, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - model_out = self.model(x_noisy, t) - - loss_dict = {} - if self.parameterization == "eps": - target = noise - elif self.parameterization == "x0": - target = x_start - elif self.parameterization == "v": - target = self.get_v(x_start, noise, t) - else: - raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported") - - loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) - - log_prefix = 'train' if self.training else 'val' - - loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) - loss_simple = loss.mean() * self.l_simple_weight - - loss_vlb = (self.lvlb_weights[t] * loss).mean() - loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) - - loss = loss_simple + self.original_elbo_weight * loss_vlb - - loss_dict.update({f'{log_prefix}/loss': loss}) - - return loss, loss_dict - - def forward(self, x, *args, **kwargs): - # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size - # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' - t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() - return self.p_losses(x, t, *args, **kwargs) - - def get_input(self, batch, k): - x = batch[k] - if len(x.shape) == 3: - x = x[..., None] - x = rearrange(x, 'b h w c -> b c h w') - x = x.to(memory_format=torch.contiguous_format).float() - return x - - def shared_step(self, batch): - x = self.get_input(batch, self.first_stage_key) - loss, loss_dict = self(x) - return loss, loss_dict - - def training_step(self, batch, batch_idx): - for k in self.ucg_training: - p = self.ucg_training[k]["p"] - val = self.ucg_training[k]["val"] - if val is None: - val = "" - for i in range(len(batch[k])): - if self.ucg_prng.choice(2, p=[1 - p, p]): - batch[k][i] = val - - loss, loss_dict = self.shared_step(batch) - - self.log_dict(loss_dict, prog_bar=True, - logger=True, on_step=True, on_epoch=True) - - self.log("global_step", self.global_step, - prog_bar=True, logger=True, on_step=True, on_epoch=False) - - if self.use_scheduler: - lr = self.optimizers().param_groups[0]['lr'] - self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) - - return loss - - @torch.no_grad() - def validation_step(self, batch, batch_idx): - _, loss_dict_no_ema = self.shared_step(batch) - with self.ema_scope(): - _, loss_dict_ema = self.shared_step(batch) - loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} - self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) - self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) - - def on_train_batch_end(self, *args, **kwargs): - if self.use_ema: - self.model_ema(self.model) - - def _get_rows_from_list(self, samples): - n_imgs_per_row = len(samples) - denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') - denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') - denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) - return denoise_grid - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): - log = dict() - x = self.get_input(batch, self.first_stage_key) - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - x = x.to(self.device)[:N] - log["inputs"] = x - - # get diffusion row - diffusion_row = list() - x_start = x[:n_row] - - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(x_start) - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - diffusion_row.append(x_noisy) - - log["diffusion_row"] = self._get_rows_from_list(diffusion_row) - - if sample: - # get denoise row - with self.ema_scope("Plotting"): - samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) - - log["samples"] = samples - log["denoise_row"] = self._get_rows_from_list(denoise_row) - - if return_keys: - if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: - return log - else: - return {key: log[key] for key in return_keys} - return log - - def configure_optimizers(self): - lr = self.learning_rate - params = list(self.model.parameters()) - if self.learn_logvar: - params = params + [self.logvar] - opt = torch.optim.AdamW(params, lr=lr) - return opt - - -class LatentDiffusion(DDPM): - """main class""" - - def __init__(self, - first_stage_config, - cond_stage_config, - num_timesteps_cond=None, - cond_stage_key="image", - cond_stage_trainable=False, - concat_mode=True, - cond_stage_forward=None, - conditioning_key=None, - scale_factor=1.0, - scale_by_std=False, - force_null_conditioning=False, - *args, **kwargs): - self.force_null_conditioning = force_null_conditioning - self.num_timesteps_cond = default(num_timesteps_cond, 1) - self.scale_by_std = scale_by_std - assert self.num_timesteps_cond <= kwargs['timesteps'] - # for backwards compatibility after implementation of DiffusionWrapper - if conditioning_key is None: - conditioning_key = 'concat' if concat_mode else 'crossattn' - if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning: - conditioning_key = None - ckpt_path = kwargs.pop("ckpt_path", None) - reset_ema = kwargs.pop("reset_ema", False) - reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False) - ignore_keys = kwargs.pop("ignore_keys", []) - super().__init__(conditioning_key=conditioning_key, *args, **kwargs) - self.concat_mode = concat_mode - self.cond_stage_trainable = cond_stage_trainable - self.cond_stage_key = cond_stage_key - try: - self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 - except: - self.num_downs = 0 - if not scale_by_std: - self.scale_factor = scale_factor - else: - self.register_buffer('scale_factor', torch.tensor(scale_factor)) - self.instantiate_first_stage(first_stage_config) - self.instantiate_cond_stage(cond_stage_config) - self.cond_stage_forward = cond_stage_forward - self.clip_denoised = False - self.bbox_tokenizer = None - - self.restarted_from_ckpt = False - if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys) - self.restarted_from_ckpt = True - if reset_ema: - assert self.use_ema - print( - f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.") - self.model_ema = LitEma(self.model) - if reset_num_ema_updates: - print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ") - assert self.use_ema - self.model_ema.reset_num_updates() - - def make_cond_schedule(self, ): - self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) - ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() - self.cond_ids[:self.num_timesteps_cond] = ids - - @rank_zero_only - @torch.no_grad() - def on_train_batch_start(self, batch, batch_idx, dataloader_idx): - # only for very first batch - if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: - assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' - # set rescale weight to 1./std of encodings - print("### USING STD-RESCALING ###") - x = super().get_input(batch, self.first_stage_key) - x = x.to(self.device) - encoder_posterior = self.encode_first_stage(x) - z = self.get_first_stage_encoding(encoder_posterior).detach() - del self.scale_factor - self.register_buffer('scale_factor', 1. / z.flatten().std()) - print(f"setting self.scale_factor to {self.scale_factor}") - print("### USING STD-RESCALING ###") - - def register_schedule(self, - given_betas=None, beta_schedule="linear", timesteps=1000, - linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) - - self.shorten_cond_schedule = self.num_timesteps_cond > 1 - if self.shorten_cond_schedule: - self.make_cond_schedule() - - def instantiate_first_stage(self, config): - model = instantiate_from_config(config) - self.first_stage_model = model.eval() - self.first_stage_model.train = disabled_train - for param in self.first_stage_model.parameters(): - param.requires_grad = False - - def instantiate_cond_stage(self, config): - if not self.cond_stage_trainable: - if config == "__is_first_stage__": - print("Using first stage also as cond stage.") - self.cond_stage_model = self.first_stage_model - elif config == "__is_unconditional__": - print(f"Training {self.__class__.__name__} as an unconditional model.") - self.cond_stage_model = None - # self.be_unconditional = True - else: - model = instantiate_from_config(config) - self.cond_stage_model = model.eval() - self.cond_stage_model.train = disabled_train - for param in self.cond_stage_model.parameters(): - param.requires_grad = False - else: - assert config != '__is_first_stage__' - assert config != '__is_unconditional__' - model = instantiate_from_config(config) - self.cond_stage_model = model - - def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): - denoise_row = [] - for zd in tqdm(samples, desc=desc): - denoise_row.append(self.decode_first_stage(zd.to(self.device), - force_not_quantize=force_no_decoder_quantization)) - n_imgs_per_row = len(denoise_row) - denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W - denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') - denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') - denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) - return denoise_grid - - def get_first_stage_encoding(self, encoder_posterior): - if isinstance(encoder_posterior, DiagonalGaussianDistribution): - z = encoder_posterior.sample() - elif isinstance(encoder_posterior, torch.Tensor): - z = encoder_posterior - else: - raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") - return self.scale_factor * z - - def get_learned_conditioning(self, c): - if self.cond_stage_forward is None: - if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): - c = self.cond_stage_model.encode(c) - if isinstance(c, DiagonalGaussianDistribution): - c = c.mode() - else: - c = self.cond_stage_model(c) - else: - assert hasattr(self.cond_stage_model, self.cond_stage_forward) - c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) - return c - - def meshgrid(self, h, w): - y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) - x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) - - arr = torch.cat([y, x], dim=-1) - return arr - - def delta_border(self, h, w): - """ - :param h: height - :param w: width - :return: normalized distance to image border, - wtith min distance = 0 at border and max dist = 0.5 at image center - """ - lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) - arr = self.meshgrid(h, w) / lower_right_corner - dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] - dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] - edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] - return edge_dist - - def get_weighting(self, h, w, Ly, Lx, device): - weighting = self.delta_border(h, w) - weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], - self.split_input_params["clip_max_weight"], ) - weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) - - if self.split_input_params["tie_braker"]: - L_weighting = self.delta_border(Ly, Lx) - L_weighting = torch.clip(L_weighting, - self.split_input_params["clip_min_tie_weight"], - self.split_input_params["clip_max_tie_weight"]) - - L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) - weighting = weighting * L_weighting - return weighting - - def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code - """ - :param x: img of size (bs, c, h, w) - :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) - """ - bs, nc, h, w = x.shape - - # number of crops in image - Ly = (h - kernel_size[0]) // stride[0] + 1 - Lx = (w - kernel_size[1]) // stride[1] + 1 - - if uf == 1 and df == 1: - fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) - unfold = torch.nn.Unfold(**fold_params) - - fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) - - weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) - normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap - weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) - - elif uf > 1 and df == 1: - fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) - unfold = torch.nn.Unfold(**fold_params) - - fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), - dilation=1, padding=0, - stride=(stride[0] * uf, stride[1] * uf)) - fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) - - weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) - normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap - weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) - - elif df > 1 and uf == 1: - fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) - unfold = torch.nn.Unfold(**fold_params) - - fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), - dilation=1, padding=0, - stride=(stride[0] // df, stride[1] // df)) - fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) - - weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) - normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap - weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) - - else: - raise NotImplementedError - - return fold, unfold, normalization, weighting - - @torch.no_grad() - def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, - cond_key=None, return_original_cond=False, bs=None, return_x=False): - x = super().get_input(batch, k) - if bs is not None: - x = x[:bs] - x = x.to(self.device) - encoder_posterior = self.encode_first_stage(x) - z = self.get_first_stage_encoding(encoder_posterior).detach() - - if self.model.conditioning_key is not None and not self.force_null_conditioning: - if cond_key is None: - cond_key = self.cond_stage_key - if cond_key != self.first_stage_key: - if cond_key in ['caption', 'coordinates_bbox', "txt"]: - xc = batch[cond_key] - elif cond_key in ['class_label', 'cls']: - xc = batch - else: - xc = super().get_input(batch, cond_key).to(self.device) - else: - xc = x - if not self.cond_stage_trainable or force_c_encode: - if isinstance(xc, dict) or isinstance(xc, list): - c = self.get_learned_conditioning(xc) - else: - c = self.get_learned_conditioning(xc.to(self.device)) - else: - c = xc - if bs is not None: - c = c[:bs] - - if self.use_positional_encodings: - pos_x, pos_y = self.compute_latent_shifts(batch) - ckey = __conditioning_keys__[self.model.conditioning_key] - c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} - - else: - c = None - xc = None - if self.use_positional_encodings: - pos_x, pos_y = self.compute_latent_shifts(batch) - c = {'pos_x': pos_x, 'pos_y': pos_y} - out = [z, c] - if return_first_stage_outputs: - xrec = self.decode_first_stage(z) - out.extend([x, xrec]) - if return_x: - out.extend([x]) - if return_original_cond: - out.append(xc) - return out - - @torch.no_grad() - def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): - if predict_cids: - if z.dim() == 4: - z = torch.argmax(z.exp(), dim=1).long() - z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) - z = rearrange(z, 'b h w c -> b c h w').contiguous() - - z = 1. / self.scale_factor * z - return self.first_stage_model.decode(z) - - @torch.no_grad() - def encode_first_stage(self, x): - return self.first_stage_model.encode(x) - - def shared_step(self, batch, **kwargs): - x, c = self.get_input(batch, self.first_stage_key) - loss = self(x, c) - return loss - - def forward(self, x, c, *args, **kwargs): - t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() - if self.model.conditioning_key is not None: - assert c is not None - if self.cond_stage_trainable: - c = self.get_learned_conditioning(c) - if self.shorten_cond_schedule: # TODO: drop this option - tc = self.cond_ids[t].to(self.device) - c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) - return self.p_losses(x, c, t, *args, **kwargs) - - def apply_model(self, x_noisy, t, cond, return_ids=False): - if isinstance(cond, dict): - # hybrid case, cond is expected to be a dict - pass - else: - if not isinstance(cond, list): - cond = [cond] - key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' - cond = {key: cond} - - x_recon = self.model(x_noisy, t, **cond) - - if isinstance(x_recon, tuple) and not return_ids: - return x_recon[0] - else: - return x_recon - - def _predict_eps_from_xstart(self, x_t, t, pred_xstart): - return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) - - def _prior_bpd(self, x_start): - """ - Get the prior KL term for the variational lower-bound, measured in - bits-per-dim. - This term can't be optimized, as it only depends on the encoder. - :param x_start: the [N x C x ...] tensor of inputs. - :return: a batch of [N] KL values (in bits), one per batch element. - """ - batch_size = x_start.shape[0] - t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) - qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) - kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) - return mean_flat(kl_prior) / np.log(2.0) - - def p_losses(self, x_start, cond, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - model_output = self.apply_model(x_noisy, t, cond) - - loss_dict = {} - prefix = 'train' if self.training else 'val' - - if self.parameterization == "x0": - target = x_start - elif self.parameterization == "eps": - target = noise - elif self.parameterization == "v": - target = self.get_v(x_start, noise, t) - else: - raise NotImplementedError() - - loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) - loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) - - logvar_t = self.logvar[t].to(self.device) - loss = loss_simple / torch.exp(logvar_t) + logvar_t - # loss = loss_simple / torch.exp(self.logvar) + self.logvar - if self.learn_logvar: - loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) - loss_dict.update({'logvar': self.logvar.data.mean()}) - - loss = self.l_simple_weight * loss.mean() - - loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) - loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() - loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) - loss += (self.original_elbo_weight * loss_vlb) - loss_dict.update({f'{prefix}/loss': loss}) - - return loss, loss_dict - - def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, - return_x0=False, score_corrector=None, corrector_kwargs=None): - t_in = t - model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) - - if score_corrector is not None: - assert self.parameterization == "eps" - model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) - - if return_codebook_ids: - model_out, logits = model_out - - if self.parameterization == "eps": - x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) - elif self.parameterization == "x0": - x_recon = model_out - else: - raise NotImplementedError() - - if clip_denoised: - x_recon.clamp_(-1., 1.) - if quantize_denoised: - x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) - model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) - if return_codebook_ids: - return model_mean, posterior_variance, posterior_log_variance, logits - elif return_x0: - return model_mean, posterior_variance, posterior_log_variance, x_recon - else: - return model_mean, posterior_variance, posterior_log_variance - - @torch.no_grad() - def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, - return_codebook_ids=False, quantize_denoised=False, return_x0=False, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): - b, *_, device = *x.shape, x.device - outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, - return_codebook_ids=return_codebook_ids, - quantize_denoised=quantize_denoised, - return_x0=return_x0, - score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) - if return_codebook_ids: - raise DeprecationWarning("Support dropped.") - model_mean, _, model_log_variance, logits = outputs - elif return_x0: - model_mean, _, model_log_variance, x0 = outputs - else: - model_mean, _, model_log_variance = outputs - - noise = noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: - noise = torch.nn.functional.dropout(noise, p=noise_dropout) - # no noise when t == 0 - nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) - - if return_codebook_ids: - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) - if return_x0: - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 - else: - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise - - @torch.no_grad() - def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, - img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., - score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, - log_every_t=None): - if not log_every_t: - log_every_t = self.log_every_t - timesteps = self.num_timesteps - if batch_size is not None: - b = batch_size if batch_size is not None else shape[0] - shape = [batch_size] + list(shape) - else: - b = batch_size = shape[0] - if x_T is None: - img = torch.randn(shape, device=self.device) - else: - img = x_T - intermediates = [] - if cond is not None: - if isinstance(cond, dict): - cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} - else: - cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] - - if start_T is not None: - timesteps = min(timesteps, start_T) - iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', - total=timesteps) if verbose else reversed( - range(0, timesteps)) - if type(temperature) == float: - temperature = [temperature] * timesteps - - for i in iterator: - ts = torch.full((b,), i, device=self.device, dtype=torch.long) - if self.shorten_cond_schedule: - assert self.model.conditioning_key != 'hybrid' - tc = self.cond_ids[ts].to(cond.device) - cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) - - img, x0_partial = self.p_sample(img, cond, ts, - clip_denoised=self.clip_denoised, - quantize_denoised=quantize_denoised, return_x0=True, - temperature=temperature[i], noise_dropout=noise_dropout, - score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) - if mask is not None: - assert x0 is not None - img_orig = self.q_sample(x0, ts) - img = img_orig * mask + (1. - mask) * img - - if i % log_every_t == 0 or i == timesteps - 1: - intermediates.append(x0_partial) - if callback: callback(i) - if img_callback: img_callback(img, i) - return img, intermediates - - @torch.no_grad() - def p_sample_loop(self, cond, shape, return_intermediates=False, - x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, - mask=None, x0=None, img_callback=None, start_T=None, - log_every_t=None): - - if not log_every_t: - log_every_t = self.log_every_t - device = self.betas.device - b = shape[0] - if x_T is None: - img = torch.randn(shape, device=device) - else: - img = x_T - - intermediates = [img] - if timesteps is None: - timesteps = self.num_timesteps - - if start_T is not None: - timesteps = min(timesteps, start_T) - iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( - range(0, timesteps)) - - if mask is not None: - assert x0 is not None - assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match - - for i in iterator: - ts = torch.full((b,), i, device=device, dtype=torch.long) - if self.shorten_cond_schedule: - assert self.model.conditioning_key != 'hybrid' - tc = self.cond_ids[ts].to(cond.device) - cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) - - img = self.p_sample(img, cond, ts, - clip_denoised=self.clip_denoised, - quantize_denoised=quantize_denoised) - if mask is not None: - img_orig = self.q_sample(x0, ts) - img = img_orig * mask + (1. - mask) * img - - if i % log_every_t == 0 or i == timesteps - 1: - intermediates.append(img) - if callback: callback(i) - if img_callback: img_callback(img, i) - - if return_intermediates: - return img, intermediates - return img - - @torch.no_grad() - def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, - verbose=True, timesteps=None, quantize_denoised=False, - mask=None, x0=None, shape=None, **kwargs): - if shape is None: - shape = (batch_size, self.channels, self.image_size, self.image_size) - if cond is not None: - if isinstance(cond, dict): - cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} - else: - cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] - return self.p_sample_loop(cond, - shape, - return_intermediates=return_intermediates, x_T=x_T, - verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, - mask=mask, x0=x0) - - @torch.no_grad() - def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): - if ddim: - ddim_sampler = DDIMSampler(self) - shape = (self.channels, self.image_size, self.image_size) - samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, - shape, cond, verbose=False, **kwargs) - - else: - samples, intermediates = self.sample(cond=cond, batch_size=batch_size, - return_intermediates=True, **kwargs) - - return samples, intermediates - - @torch.no_grad() - def get_unconditional_conditioning(self, batch_size, null_label=None): - if null_label is not None: - xc = null_label - if isinstance(xc, ListConfig): - xc = list(xc) - if isinstance(xc, dict) or isinstance(xc, list): - c = self.get_learned_conditioning(xc) - else: - if hasattr(xc, "to"): - xc = xc.to(self.device) - c = self.get_learned_conditioning(xc) - else: - if self.cond_stage_key in ["class_label", "cls"]: - xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device) - return self.get_learned_conditioning(xc) - else: - raise NotImplementedError("todo") - if isinstance(c, list): # in case the encoder gives us a list - for i in range(len(c)): - c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device) - else: - c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device) - return c - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None, - quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, - plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, - use_ema_scope=True, - **kwargs): - ema_scope = self.ema_scope if use_ema_scope else nullcontext - use_ddim = ddim_steps is not None - - log = dict() - z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, - return_first_stage_outputs=True, - force_c_encode=True, - return_original_cond=True, - bs=N) - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - log["inputs"] = x - log["reconstruction"] = xrec - if self.model.conditioning_key is not None: - if hasattr(self.cond_stage_model, "decode"): - xc = self.cond_stage_model.decode(c) - log["conditioning"] = xc - elif self.cond_stage_key in ["caption", "txt"]: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25) - log["conditioning"] = xc - elif self.cond_stage_key in ['class_label', "cls"]: - try: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25) - log['conditioning'] = xc - except KeyError: - # probably no "human_label" in batch - pass - elif isimage(xc): - log["conditioning"] = xc - if ismap(xc): - log["original_conditioning"] = self.to_rgb(xc) - - if plot_diffusion_rows: - # get diffusion row - diffusion_row = list() - z_start = z[:n_row] - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(z_start) - z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) - diffusion_row.append(self.decode_first_stage(z_noisy)) - - diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W - diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') - diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') - diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) - log["diffusion_row"] = diffusion_grid - - if sample: - # get denoise row - with ema_scope("Sampling"): - samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - if plot_denoise_rows: - denoise_grid = self._get_denoise_row_from_list(z_denoise_row) - log["denoise_row"] = denoise_grid - - if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( - self.first_stage_model, IdentityFirstStage): - # also display when quantizing x0 while sampling - with ema_scope("Plotting Quantized Denoised"): - samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - quantize_denoised=True) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, - # quantize_denoised=True) - x_samples = self.decode_first_stage(samples.to(self.device)) - log["samples_x0_quantized"] = x_samples - - if unconditional_guidance_scale > 1.0: - uc = self.get_unconditional_conditioning(N, unconditional_guidance_label) - if self.model.conditioning_key == "crossattn-adm": - uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]} - with ema_scope("Sampling with classifier-free guidance"): - samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc, - ) - x_samples_cfg = self.decode_first_stage(samples_cfg) - log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg - - if inpaint: - # make a simple center square - b, h, w = z.shape[0], z.shape[2], z.shape[3] - mask = torch.ones(N, h, w).to(self.device) - # zeros will be filled in - mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. - mask = mask[:, None, ...] - with ema_scope("Plotting Inpaint"): - samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta, - ddim_steps=ddim_steps, x0=z[:N], mask=mask) - x_samples = self.decode_first_stage(samples.to(self.device)) - log["samples_inpainting"] = x_samples - log["mask"] = mask - - # outpaint - mask = 1. - mask - with ema_scope("Plotting Outpaint"): - samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta, - ddim_steps=ddim_steps, x0=z[:N], mask=mask) - x_samples = self.decode_first_stage(samples.to(self.device)) - log["samples_outpainting"] = x_samples - - if plot_progressive_rows: - with ema_scope("Plotting Progressives"): - img, progressives = self.progressive_denoising(c, - shape=(self.channels, self.image_size, self.image_size), - batch_size=N) - prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") - log["progressive_row"] = prog_row - - if return_keys: - if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: - return log - else: - return {key: log[key] for key in return_keys} - return log - - def configure_optimizers(self): - lr = self.learning_rate - params = list(self.model.parameters()) - if self.cond_stage_trainable: - print(f"{self.__class__.__name__}: Also optimizing conditioner params!") - params = params + list(self.cond_stage_model.parameters()) - if self.learn_logvar: - print('Diffusion model optimizing logvar') - params.append(self.logvar) - opt = torch.optim.AdamW(params, lr=lr) - if self.use_scheduler: - assert 'target' in self.scheduler_config - scheduler = instantiate_from_config(self.scheduler_config) - - print("Setting up LambdaLR scheduler...") - scheduler = [ - { - 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), - 'interval': 'step', - 'frequency': 1 - }] - return [opt], scheduler - return opt - - @torch.no_grad() - def to_rgb(self, x): - x = x.float() - if not hasattr(self, "colorize"): - self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) - x = nn.functional.conv2d(x, weight=self.colorize) - x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. - return x - - -class DiffusionWrapper(pl.LightningModule): - def __init__(self, diff_model_config, conditioning_key): - super().__init__() - self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False) - self.diffusion_model = instantiate_from_config(diff_model_config) - self.conditioning_key = conditioning_key - assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm'] - - def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None): - if self.conditioning_key is None: - out = self.diffusion_model(x, t) - elif self.conditioning_key == 'concat': - xc = torch.cat([x] + c_concat, dim=1) - out = self.diffusion_model(xc, t) - elif self.conditioning_key == 'crossattn': - if not self.sequential_cross_attn: - cc = torch.cat(c_crossattn, 1) - else: - cc = c_crossattn - out = self.diffusion_model(x, t, context=cc) - elif self.conditioning_key == 'hybrid': - xc = torch.cat([x] + c_concat, dim=1) - cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(xc, t, context=cc) - elif self.conditioning_key == 'hybrid-adm': - assert c_adm is not None - xc = torch.cat([x] + c_concat, dim=1) - cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(xc, t, context=cc, y=c_adm) - elif self.conditioning_key == 'crossattn-adm': - assert c_adm is not None - cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(x, t, context=cc, y=c_adm) - elif self.conditioning_key == 'adm': - cc = c_crossattn[0] - out = self.diffusion_model(x, t, y=cc) - else: - raise NotImplementedError() - - return out - - -class LatentUpscaleDiffusion(LatentDiffusion): - def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key=None, **kwargs): - super().__init__(*args, **kwargs) - # assumes that neither the cond_stage nor the low_scale_model contain trainable params - assert not self.cond_stage_trainable - self.instantiate_low_stage(low_scale_config) - self.low_scale_key = low_scale_key - self.noise_level_key = noise_level_key - - def instantiate_low_stage(self, config): - model = instantiate_from_config(config) - self.low_scale_model = model.eval() - self.low_scale_model.train = disabled_train - for param in self.low_scale_model.parameters(): - param.requires_grad = False - - @torch.no_grad() - def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): - if not log_mode: - z, c = super().get_input(batch, k, force_c_encode=True, bs=bs) - else: - z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, - force_c_encode=True, return_original_cond=True, bs=bs) - x_low = batch[self.low_scale_key][:bs] - x_low = rearrange(x_low, 'b h w c -> b c h w') - x_low = x_low.to(memory_format=torch.contiguous_format).float() - zx, noise_level = self.low_scale_model(x_low) - if self.noise_level_key is not None: - # get noise level from batch instead, e.g. when extracting a custom noise level for bsr - raise NotImplementedError('TODO') - - all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level} - if log_mode: - # TODO: maybe disable if too expensive - x_low_rec = self.low_scale_model.decode(zx) - return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level - return z, all_conds - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, - plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, - unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True, - **kwargs): - ema_scope = self.ema_scope if use_ema_scope else nullcontext - use_ddim = ddim_steps is not None - - log = dict() - z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N, - log_mode=True) - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - log["inputs"] = x - log["reconstruction"] = xrec - log["x_lr"] = x_low - log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec - if self.model.conditioning_key is not None: - if hasattr(self.cond_stage_model, "decode"): - xc = self.cond_stage_model.decode(c) - log["conditioning"] = xc - elif self.cond_stage_key in ["caption", "txt"]: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25) - log["conditioning"] = xc - elif self.cond_stage_key in ['class_label', 'cls']: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25) - log['conditioning'] = xc - elif isimage(xc): - log["conditioning"] = xc - if ismap(xc): - log["original_conditioning"] = self.to_rgb(xc) - - if plot_diffusion_rows: - # get diffusion row - diffusion_row = list() - z_start = z[:n_row] - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(z_start) - z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) - diffusion_row.append(self.decode_first_stage(z_noisy)) - - diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W - diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') - diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') - diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) - log["diffusion_row"] = diffusion_grid - - if sample: - # get denoise row - with ema_scope("Sampling"): - samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - if plot_denoise_rows: - denoise_grid = self._get_denoise_row_from_list(z_denoise_row) - log["denoise_row"] = denoise_grid - - if unconditional_guidance_scale > 1.0: - uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label) - # TODO explore better "unconditional" choices for the other keys - # maybe guide away from empty text label and highest noise level and maximally degraded zx? - uc = dict() - for k in c: - if k == "c_crossattn": - assert isinstance(c[k], list) and len(c[k]) == 1 - uc[k] = [uc_tmp] - elif k == "c_adm": # todo: only run with text-based guidance? - assert isinstance(c[k], torch.Tensor) - #uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level - uc[k] = c[k] - elif isinstance(c[k], list): - uc[k] = [c[k][i] for i in range(len(c[k]))] - else: - uc[k] = c[k] - - with ema_scope("Sampling with classifier-free guidance"): - samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc, - ) - x_samples_cfg = self.decode_first_stage(samples_cfg) - log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg - - if plot_progressive_rows: - with ema_scope("Plotting Progressives"): - img, progressives = self.progressive_denoising(c, - shape=(self.channels, self.image_size, self.image_size), - batch_size=N) - prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") - log["progressive_row"] = prog_row - - return log - - -class LatentFinetuneDiffusion(LatentDiffusion): - """ - Basis for different finetunas, such as inpainting or depth2image - To disable finetuning mode, set finetune_keys to None - """ - - def __init__(self, - concat_keys: tuple, - finetune_keys=("model.diffusion_model.input_blocks.0.0.weight", - "model_ema.diffusion_modelinput_blocks00weight" - ), - keep_finetune_dims=4, - # if model was trained without concat mode before and we would like to keep these channels - c_concat_log_start=None, # to log reconstruction of c_concat codes - c_concat_log_end=None, - *args, **kwargs - ): - ckpt_path = kwargs.pop("ckpt_path", None) - ignore_keys = kwargs.pop("ignore_keys", list()) - super().__init__(*args, **kwargs) - self.finetune_keys = finetune_keys - self.concat_keys = concat_keys - self.keep_dims = keep_finetune_dims - self.c_concat_log_start = c_concat_log_start - self.c_concat_log_end = c_concat_log_end - if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint' - if exists(ckpt_path): - self.init_from_ckpt(ckpt_path, ignore_keys) - - def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): - sd = torch.load(path, map_location="cpu") - if "state_dict" in list(sd.keys()): - sd = sd["state_dict"] - keys = list(sd.keys()) - for k in keys: - for ik in ignore_keys: - if k.startswith(ik): - print("Deleting key {} from state_dict.".format(k)) - del sd[k] - - # make it explicit, finetune by including extra input channels - if exists(self.finetune_keys) and k in self.finetune_keys: - new_entry = None - for name, param in self.named_parameters(): - if name in self.finetune_keys: - print( - f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only") - new_entry = torch.zeros_like(param) # zero init - assert exists(new_entry), 'did not find matching parameter to modify' - new_entry[:, :self.keep_dims, ...] = sd[k] - sd[k] = new_entry - - missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( - sd, strict=False) - print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") - if len(missing) > 0: - print(f"Missing Keys: {missing}") - if len(unexpected) > 0: - print(f"Unexpected Keys: {unexpected}") - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, - quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, - plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, - use_ema_scope=True, - **kwargs): - ema_scope = self.ema_scope if use_ema_scope else nullcontext - use_ddim = ddim_steps is not None - - log = dict() - z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True) - c_cat, c = c["c_concat"][0], c["c_crossattn"][0] - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - log["inputs"] = x - log["reconstruction"] = xrec - if self.model.conditioning_key is not None: - if hasattr(self.cond_stage_model, "decode"): - xc = self.cond_stage_model.decode(c) - log["conditioning"] = xc - elif self.cond_stage_key in ["caption", "txt"]: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25) - log["conditioning"] = xc - elif self.cond_stage_key in ['class_label', 'cls']: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25) - log['conditioning'] = xc - elif isimage(xc): - log["conditioning"] = xc - if ismap(xc): - log["original_conditioning"] = self.to_rgb(xc) - - if not (self.c_concat_log_start is None and self.c_concat_log_end is None): - log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end]) - - if plot_diffusion_rows: - # get diffusion row - diffusion_row = list() - z_start = z[:n_row] - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(z_start) - z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) - diffusion_row.append(self.decode_first_stage(z_noisy)) - - diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W - diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') - diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') - diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) - log["diffusion_row"] = diffusion_grid - - if sample: - # get denoise row - with ema_scope("Sampling"): - samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, - batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - if plot_denoise_rows: - denoise_grid = self._get_denoise_row_from_list(z_denoise_row) - log["denoise_row"] = denoise_grid - - if unconditional_guidance_scale > 1.0: - uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label) - uc_cat = c_cat - uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]} - with ema_scope("Sampling with classifier-free guidance"): - samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, - batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc_full, - ) - x_samples_cfg = self.decode_first_stage(samples_cfg) - log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg - - return log - - -class LatentInpaintDiffusion(LatentFinetuneDiffusion): - """ - can either run as pure inpainting model (only concat mode) or with mixed conditionings, - e.g. mask as concat and text via cross-attn. - To disable finetuning mode, set finetune_keys to None - """ - - def __init__(self, - concat_keys=("mask", "masked_image"), - masked_image_key="masked_image", - *args, **kwargs - ): - super().__init__(concat_keys, *args, **kwargs) - self.masked_image_key = masked_image_key - assert self.masked_image_key in concat_keys - - @torch.no_grad() - def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): - # note: restricted to non-trainable encoders currently - assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting' - z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, - force_c_encode=True, return_original_cond=True, bs=bs) - - assert exists(self.concat_keys) - c_cat = list() - for ck in self.concat_keys: - cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() - if bs is not None: - cc = cc[:bs] - cc = cc.to(self.device) - bchw = z.shape - if ck != self.masked_image_key: - cc = torch.nn.functional.interpolate(cc, size=bchw[-2:]) - else: - cc = self.get_first_stage_encoding(self.encode_first_stage(cc)) - c_cat.append(cc) - c_cat = torch.cat(c_cat, dim=1) - all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} - if return_first_stage_outputs: - return z, all_conds, x, xrec, xc - return z, all_conds - - @torch.no_grad() - def log_images(self, *args, **kwargs): - log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs) - log["masked_image"] = rearrange(args[0]["masked_image"], - 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() - return log - - -class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion): - """ - condition on monocular depth estimation - """ - - def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs): - super().__init__(concat_keys=concat_keys, *args, **kwargs) - self.depth_model = instantiate_from_config(depth_stage_config) - self.depth_stage_key = concat_keys[0] - - @torch.no_grad() - def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): - # note: restricted to non-trainable encoders currently - assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for depth2img' - z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, - force_c_encode=True, return_original_cond=True, bs=bs) - - assert exists(self.concat_keys) - assert len(self.concat_keys) == 1 - c_cat = list() - for ck in self.concat_keys: - cc = batch[ck] - if bs is not None: - cc = cc[:bs] - cc = cc.to(self.device) - cc = self.depth_model(cc) - cc = torch.nn.functional.interpolate( - cc, - size=z.shape[2:], - mode="bicubic", - align_corners=False, - ) - - depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3], - keepdim=True) - cc = 2. * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1. - c_cat.append(cc) - c_cat = torch.cat(c_cat, dim=1) - all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} - if return_first_stage_outputs: - return z, all_conds, x, xrec, xc - return z, all_conds - - @torch.no_grad() - def log_images(self, *args, **kwargs): - log = super().log_images(*args, **kwargs) - depth = self.depth_model(args[0][self.depth_stage_key]) - depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), \ - torch.amax(depth, dim=[1, 2, 3], keepdim=True) - log["depth"] = 2. * (depth - depth_min) / (depth_max - depth_min) - 1. - return log - - -class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion): - """ - condition on low-res image (and optionally on some spatial noise augmentation) - """ - def __init__(self, concat_keys=("lr",), reshuffle_patch_size=None, - low_scale_config=None, low_scale_key=None, *args, **kwargs): - super().__init__(concat_keys=concat_keys, *args, **kwargs) - self.reshuffle_patch_size = reshuffle_patch_size - self.low_scale_model = None - if low_scale_config is not None: - print("Initializing a low-scale model") - assert exists(low_scale_key) - self.instantiate_low_stage(low_scale_config) - self.low_scale_key = low_scale_key - - def instantiate_low_stage(self, config): - model = instantiate_from_config(config) - self.low_scale_model = model.eval() - self.low_scale_model.train = disabled_train - for param in self.low_scale_model.parameters(): - param.requires_grad = False - - @torch.no_grad() - def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): - # note: restricted to non-trainable encoders currently - assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for upscaling-ft' - z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, - force_c_encode=True, return_original_cond=True, bs=bs) - - assert exists(self.concat_keys) - assert len(self.concat_keys) == 1 - # optionally make spatial noise_level here - c_cat = list() - noise_level = None - for ck in self.concat_keys: - cc = batch[ck] - cc = rearrange(cc, 'b h w c -> b c h w') - if exists(self.reshuffle_patch_size): - assert isinstance(self.reshuffle_patch_size, int) - cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w', - p1=self.reshuffle_patch_size, p2=self.reshuffle_patch_size) - if bs is not None: - cc = cc[:bs] - cc = cc.to(self.device) - if exists(self.low_scale_model) and ck == self.low_scale_key: - cc, noise_level = self.low_scale_model(cc) - c_cat.append(cc) - c_cat = torch.cat(c_cat, dim=1) - if exists(noise_level): - all_conds = {"c_concat": [c_cat], "c_crossattn": [c], "c_adm": noise_level} - else: - all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} - if return_first_stage_outputs: - return z, all_conds, x, xrec, xc - return z, all_conds - - @torch.no_grad() - def log_images(self, *args, **kwargs): - log = super().log_images(*args, **kwargs) - log["lr"] = rearrange(args[0]["lr"], 'b h w c -> b c h w') - return log diff --git a/spaces/PKUWilliamYang/VToonify/vtoonify/util.py b/spaces/PKUWilliamYang/VToonify/vtoonify/util.py deleted file mode 100644 index 01ad2930c55d07866dee02e019d359bb78f65fc7..0000000000000000000000000000000000000000 --- a/spaces/PKUWilliamYang/VToonify/vtoonify/util.py +++ /dev/null @@ -1,229 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from PIL import Image -import cv2 -import random -import math -import argparse -import torch -from torch.utils import data -from torch.nn import functional as F -from torch import autograd -from torch.nn import init -import torchvision.transforms as transforms -from model.stylegan.op import conv2d_gradfix -from model.encoder.encoders.psp_encoders import GradualStyleEncoder -from model.encoder.align_all_parallel import get_landmark - -def visualize(img_arr, dpi): - plt.figure(figsize=(10,10),dpi=dpi) - plt.imshow(((img_arr.detach().cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8)) - plt.axis('off') - plt.show() - -def save_image(img, filename): - tmp = ((img.detach().cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8) - cv2.imwrite(filename, cv2.cvtColor(tmp, cv2.COLOR_RGB2BGR)) - -def load_image(filename): - transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]), - ]) - - img = Image.open(filename) - img = transform(img) - return img.unsqueeze(dim=0) - -def data_sampler(dataset, shuffle, distributed): - if distributed: - return data.distributed.DistributedSampler(dataset, shuffle=shuffle) - - if shuffle: - return data.RandomSampler(dataset) - - else: - return data.SequentialSampler(dataset) - - -def requires_grad(model, flag=True): - for p in model.parameters(): - p.requires_grad = flag - - -def accumulate(model1, model2, decay=0.999): - par1 = dict(model1.named_parameters()) - par2 = dict(model2.named_parameters()) - - for k in par1.keys(): - par1[k].data.mul_(decay).add_(par2[k].data, alpha=1 - decay) - - -def sample_data(loader): - while True: - for batch in loader: - yield batch - - -def d_logistic_loss(real_pred, fake_pred): - real_loss = F.softplus(-real_pred) - fake_loss = F.softplus(fake_pred) - - return real_loss.mean() + fake_loss.mean() - - -def d_r1_loss(real_pred, real_img): - with conv2d_gradfix.no_weight_gradients(): - grad_real, = autograd.grad( - outputs=real_pred.sum(), inputs=real_img, create_graph=True - ) - grad_penalty = grad_real.pow(2).reshape(grad_real.shape[0], -1).sum(1).mean() - - return grad_penalty - - -def g_nonsaturating_loss(fake_pred): - loss = F.softplus(-fake_pred).mean() - - return loss - - -def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01): - noise = torch.randn_like(fake_img) / math.sqrt( - fake_img.shape[2] * fake_img.shape[3] - ) - grad, = autograd.grad( - outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True - ) - path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1)) - - path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length) - - path_penalty = (path_lengths - path_mean).pow(2).mean() - - return path_penalty, path_mean.detach(), path_lengths - - -def make_noise(batch, latent_dim, n_noise, device): - if n_noise == 1: - return torch.randn(batch, latent_dim, device=device) - - noises = torch.randn(n_noise, batch, latent_dim, device=device).unbind(0) - - return noises - - -def mixing_noise(batch, latent_dim, prob, device): - if prob > 0 and random.random() < prob: - return make_noise(batch, latent_dim, 2, device) - - else: - return [make_noise(batch, latent_dim, 1, device)] - - -def set_grad_none(model, targets): - for n, p in model.named_parameters(): - if n in targets: - p.grad = None - - -def weights_init(m): - classname = m.__class__.__name__ - if classname.find('BatchNorm2d') != -1: - if hasattr(m, 'weight') and m.weight is not None: - init.normal_(m.weight.data, 1.0, 0.02) - if hasattr(m, 'bias') and m.bias is not None: - init.constant_(m.bias.data, 0.0) - elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): - init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') - if hasattr(m, 'bias') and m.bias is not None: - init.constant_(m.bias.data, 0.0) - - -def load_psp_standalone(checkpoint_path, device='cuda'): - ckpt = torch.load(checkpoint_path, map_location='cpu') - opts = ckpt['opts'] - if 'output_size' not in opts: - opts['output_size'] = 1024 - opts['n_styles'] = int(math.log(opts['output_size'], 2)) * 2 - 2 - opts = argparse.Namespace(**opts) - psp = GradualStyleEncoder(50, 'ir_se', opts) - psp_dict = {k.replace('encoder.', ''): v for k, v in ckpt['state_dict'].items() if k.startswith('encoder.')} - psp.load_state_dict(psp_dict) - psp.eval() - psp = psp.to(device) - latent_avg = ckpt['latent_avg'].to(device) - - def add_latent_avg(model, inputs, outputs): - return outputs + latent_avg.repeat(outputs.shape[0], 1, 1) - - psp.register_forward_hook(add_latent_avg) - return psp - -def get_video_crop_parameter(filepath, predictor, padding=[200,200,200,200]): - if type(filepath) == str: - img = dlib.load_rgb_image(filepath) - else: - img = filepath - lm = get_landmark(img, predictor) - if lm is None: - return None - lm_chin = lm[0 : 17] # left-right - lm_eyebrow_left = lm[17 : 22] # left-right - lm_eyebrow_right = lm[22 : 27] # left-right - lm_nose = lm[27 : 31] # top-down - lm_nostrils = lm[31 : 36] # top-down - lm_eye_left = lm[36 : 42] # left-clockwise - lm_eye_right = lm[42 : 48] # left-clockwise - lm_mouth_outer = lm[48 : 60] # left-clockwise - lm_mouth_inner = lm[60 : 68] # left-clockwise - - scale = 64. / (np.mean(lm_eye_right[:,0])-np.mean(lm_eye_left[:,0])) - center = ((np.mean(lm_eye_right, axis=0)+np.mean(lm_eye_left, axis=0)) / 2) * scale - h, w = round(img.shape[0] * scale), round(img.shape[1] * scale) - left = max(round(center[0] - padding[0]), 0) // 8 * 8 - right = min(round(center[0] + padding[1]), w) // 8 * 8 - top = max(round(center[1] - padding[2]), 0) // 8 * 8 - bottom = min(round(center[1] + padding[3]), h) // 8 * 8 - return h,w,top,bottom,left,right,scale - -def tensor2cv2(img): - tmp = ((img.cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8) - return cv2.cvtColor(tmp, cv2.COLOR_RGB2BGR) - -# get parameters from the stylegan and mark them with their layers -def gather_params(G): - params = dict( - [(res, {}) for res in range(18)] + [("others", {})] - ) - for n, p in sorted(list(G.named_buffers()) + list(G.named_parameters())): - if n.startswith("convs"): - layer = int(n.split(".")[1]) + 1 - params[layer][n] = p - elif n.startswith("to_rgbs"): - layer = int(n.split(".")[1]) * 2 + 3 - params[layer][n] = p - elif n.startswith("conv1"): - params[0][n] = p - elif n.startswith("to_rgb1"): - params[1][n] = p - else: - params["others"][n] = p - return params - -# blend the ffhq stylegan model and the finetuned model for toonify -# see ``Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains'' -def blend_models(G_low, G_high, weight=[1]*7+[0]*11): - params_low = gather_params(G_low) - params_high = gather_params(G_high) - - for res in range(18): - for n, p in params_high[res].items(): - params_high[res][n] = params_high[res][n] * (1-weight[res]) + params_low[res][n] * weight[res] - - state_dict = {} - for _, p in params_high.items(): - state_dict.update(p) - - return state_dict - diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/fdes-finalizers.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/fdes-finalizers.go deleted file mode 100644 index 9f558a42274f565e41f7156238c731386c23b4c9..0000000000000000000000000000000000000000 Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/fdes-finalizers.go and /dev/null differ diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-28.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-28.go deleted file mode 100644 index f5a7db5bfaacc10c20584f22dc3524c2ea3c53b4..0000000000000000000000000000000000000000 Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-28.go and /dev/null differ diff --git a/spaces/PeepDaSlan9/OpenAssistant-reward-model-deberta-v3-large-v2/README.md b/spaces/PeepDaSlan9/OpenAssistant-reward-model-deberta-v3-large-v2/README.md deleted file mode 100644 index d9f0dec6044afde3bb32ba6e33b9df5583063a14..0000000000000000000000000000000000000000 --- a/spaces/PeepDaSlan9/OpenAssistant-reward-model-deberta-v3-large-v2/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: OpenAssistant Reward Model Deberta V3 Large V2 -emoji: 👁 -colorFrom: yellow -colorTo: purple -sdk: gradio -sdk_version: 3.33.1 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/flickr.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/flickr.py deleted file mode 100644 index 5dfff031ea80e012d9b6a5b547b5e12cb50a0896..0000000000000000000000000000000000000000 --- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/flickr.py +++ /dev/null @@ -1,8 +0,0 @@ -import torch -import torchvision -import torch.utils.data as data -from maskrcnn_benchmark.data.datasets.modulated_coco import ModulatedDataset - - -class FlickrDataset(ModulatedDataset): - pass diff --git a/spaces/PixArt-alpha/PixArt-alpha/README.md b/spaces/PixArt-alpha/PixArt-alpha/README.md deleted file mode 100644 index 5a00d07f34c605d555af8dcc23af5a027a075531..0000000000000000000000000000000000000000 --- a/spaces/PixArt-alpha/PixArt-alpha/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Pixart-α -emoji: 👀 -colorFrom: pink -colorTo: purple -sdk: gradio -sdk_version: 4.1.1 -app_file: app.py -pinned: false ---- - -Inference Code: https://github.com/PixArt-alpha/PixArt-alpha -Paper: https://arxiv.org/abs/2310.00426 diff --git a/spaces/Rajagopal/ImageBind_zeroshot_demo2/models/transformer.py b/spaces/Rajagopal/ImageBind_zeroshot_demo2/models/transformer.py deleted file mode 100644 index 98902ac8f08868c486a7c74781e952bee444c2e6..0000000000000000000000000000000000000000 --- a/spaces/Rajagopal/ImageBind_zeroshot_demo2/models/transformer.py +++ /dev/null @@ -1,284 +0,0 @@ -#!/usr/bin/env python3 -# Portions Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -# Code modified from -# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py ; -# https://github.com/facebookresearch/deit/blob/main/models.py -# and https://github.com/facebookresearch/vissl/blob/main/vissl/models/trunks/vision_transformer.py - - -import copy -import fnmatch -import logging -from functools import partial -from typing import Callable, List - -import torch -import torch.nn as nn -import torch.utils.checkpoint as checkpoint - -from timm.models.layers import DropPath, trunc_normal_ - - -class Attention(nn.Module): - def __init__( - self, - dim, - num_heads=8, - qkv_bias=False, - qk_scale=None, - attn_drop=0.0, - proj_drop=0.0, - ): - super().__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - # NOTE scale factor was wrong in my original version, - # can set manually to be compat with prev weights - self.scale = qk_scale or head_dim**-0.5 - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x): - B, N, C = x.shape - qkv = ( - self.qkv(x) - .reshape(B, N, 3, self.num_heads, C // self.num_heads) - .permute(2, 0, 3, 1, 4) - ) - q, k, v = ( - qkv[0], - qkv[1], - qkv[2], - ) # make torchscript happy (cannot use tensor as tuple) - - attn = (q @ k.transpose(-2, -1)) * self.scale - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class Mlp(nn.Module): - def __init__( - self, - in_features, - hidden_features=None, - out_features=None, - act_layer=nn.GELU, - drop=0.0, - ): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -class MultiheadAttention(nn.MultiheadAttention): - def forward(self, x: torch.Tensor, attn_mask: torch.Tensor): - return super().forward(x, x, x, need_weights=False, attn_mask=attn_mask)[0] - - -class ViTAttention(Attention): - def forward(self, x: torch.Tensor, attn_mask: torch.Tensor): - assert attn_mask is None - return super().forward(x) - - -class BlockWithMasking(nn.Module): - def __init__( - self, - dim: int, - attn_target: Callable, - mlp_ratio: int = 4, - act_layer: Callable = nn.GELU, - norm_layer: Callable = nn.LayerNorm, - ffn_dropout_rate: float = 0.0, - drop_path: float = 0.0, - layer_scale_type: str = None, - layer_scale_init_value: float = 1e-4, - ): - super().__init__() - - assert not isinstance( - attn_target, nn.Module - ), "attn_target should be a Callable. Otherwise attn_target is shared across blocks!" - self.attn = attn_target() - if drop_path > 0.0: - self.drop_path = DropPath(drop_path) - else: - self.drop_path = nn.Identity() - self.norm_1 = norm_layer(dim) - mlp_hidden_dim = int(mlp_ratio * dim) - self.mlp = Mlp( - in_features=dim, - hidden_features=mlp_hidden_dim, - act_layer=act_layer, - drop=ffn_dropout_rate, - ) - self.norm_2 = norm_layer(dim) - self.layer_scale_type = layer_scale_type - if self.layer_scale_type is not None: - assert self.layer_scale_type in [ - "per_channel", - "scalar", - ], f"Found Layer scale type {self.layer_scale_type}" - if self.layer_scale_type == "per_channel": - # one gamma value per channel - gamma_shape = [1, 1, dim] - elif self.layer_scale_type == "scalar": - # single gamma value for all channels - gamma_shape = [1, 1, 1] - # two gammas: for each part of the fwd in the encoder - self.layer_scale_gamma1 = nn.Parameter( - torch.ones(size=gamma_shape) * layer_scale_init_value, - requires_grad=True, - ) - self.layer_scale_gamma2 = nn.Parameter( - torch.ones(size=gamma_shape) * layer_scale_init_value, - requires_grad=True, - ) - - def forward(self, x: torch.Tensor, attn_mask: torch.Tensor): - if self.layer_scale_type is None: - x = x + self.drop_path(self.attn(self.norm_1(x), attn_mask)) - x = x + self.drop_path(self.mlp(self.norm_2(x))) - else: - x = ( - x - + self.drop_path(self.attn(self.norm_1(x), attn_mask)) - * self.layer_scale_gamma1 - ) - x = x + self.drop_path(self.mlp(self.norm_2(x))) * self.layer_scale_gamma2 - return x - - -_LAYER_NORM = partial(nn.LayerNorm, eps=1e-6) - - -class SimpleTransformer(nn.Module): - def __init__( - self, - attn_target: Callable, - embed_dim: int, - num_blocks: int, - block: Callable = BlockWithMasking, - pre_transformer_layer: Callable = None, - post_transformer_layer: Callable = None, - drop_path_rate: float = 0.0, - drop_path_type: str = "progressive", - norm_layer: Callable = _LAYER_NORM, - mlp_ratio: int = 4, - ffn_dropout_rate: float = 0.0, - layer_scale_type: str = None, # from cait; possible values are None, "per_channel", "scalar" - layer_scale_init_value: float = 1e-4, # from cait; float - weight_init_style: str = "jax", # possible values jax or pytorch - ): - """ - Simple Transformer with the following features - 1. Supports masked attention - 2. Supports DropPath - 3. Supports LayerScale - 4. Supports Dropout in Attention and FFN - 5. Makes few assumptions about the input except that it is a Tensor - """ - super().__init__() - self.pre_transformer_layer = pre_transformer_layer - if drop_path_type == "progressive": - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, num_blocks)] - elif drop_path_type == "uniform": - dpr = [drop_path_rate for i in range(num_blocks)] - else: - raise ValueError(f"Unknown drop_path_type: {drop_path_type}") - - self.blocks = nn.Sequential( - *[ - block( - dim=embed_dim, - attn_target=attn_target, - mlp_ratio=mlp_ratio, - ffn_dropout_rate=ffn_dropout_rate, - drop_path=dpr[i], - norm_layer=norm_layer, - layer_scale_type=layer_scale_type, - layer_scale_init_value=layer_scale_init_value, - ) - for i in range(num_blocks) - ] - ) - self.post_transformer_layer = post_transformer_layer - self.weight_init_style = weight_init_style - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - if self.weight_init_style == "jax": - # Based on MAE and official Jax ViT implementation - torch.nn.init.xavier_uniform_(m.weight) - elif self.weight_init_style == "pytorch": - # PyTorch ViT uses trunc_normal_ - trunc_normal_(m.weight, std=0.02) - - if m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, (nn.LayerNorm)): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def forward( - self, - tokens: torch.Tensor, - attn_mask: torch.Tensor = None, - use_checkpoint: bool = False, - checkpoint_every_n: int = 1, - checkpoint_blk_ids: List[int] = None, - ): - """ - Inputs - - tokens: data of shape N x L x D (or L x N x D depending on the attention implementation) - - attn: mask of shape L x L - - Output - - x: data of shape N x L x D (or L x N x D depending on the attention implementation) - """ - if self.pre_transformer_layer: - tokens = self.pre_transformer_layer(tokens) - if use_checkpoint and checkpoint_blk_ids is None: - checkpoint_blk_ids = [ - blk_id - for blk_id in range(len(self.blocks)) - if blk_id % checkpoint_every_n == 0 - ] - if checkpoint_blk_ids: - checkpoint_blk_ids = set(checkpoint_blk_ids) - for blk_id, blk in enumerate(self.blocks): - if use_checkpoint and blk_id in checkpoint_blk_ids: - tokens = checkpoint.checkpoint( - blk, tokens, attn_mask, use_reentrant=False - ) - else: - tokens = blk(tokens, attn_mask=attn_mask) - if self.post_transformer_layer: - tokens = self.post_transformer_layer(tokens) - return tokens diff --git a/spaces/RajkNakka/speech-to-speech-translation/app.py b/spaces/RajkNakka/speech-to-speech-translation/app.py deleted file mode 100644 index 19a8cd51b6df63491b92588b17f202f1faafd3de..0000000000000000000000000000000000000000 --- a/spaces/RajkNakka/speech-to-speech-translation/app.py +++ /dev/null @@ -1,73 +0,0 @@ -import gradio as gr -import numpy as np -import torch -from datasets import load_dataset - -from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline - - -device = "cuda:0" if torch.cuda.is_available() else "cpu" - -# load speech translation checkpoint -asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device) - -# load text-to-speech checkpoint and speaker embeddings -# processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") -processor = SpeechT5Processor.from_pretrained("RajkNakka/speecht5_finetuned_voxpopuli_nl") - -model = SpeechT5ForTextToSpeech.from_pretrained("RajkNakka/speecht5_finetuned_voxpopuli_nl").to(device) -vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device) - -embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") -speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) - - -def translate(audio): - outputs = asr_pipe(audio, max_new_tokens=128, generate_kwargs={"task": "transcribe", "language": "nl"}) - return outputs["text"] - - -def synthesise(text): - inputs = processor(text=text, return_tensors="pt") - speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder) - return speech.cpu() - - -def speech_to_speech_translation(audio): - translated_text = translate(audio) - synthesised_speech = synthesise(translated_text) - synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16) - return 16000, synthesised_speech - - -title = "Cascaded STST" -description = """ -Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in English. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Microsoft's -[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech: - - -""" - -demo = gr.Blocks() - -mic_translate = gr.Interface( - fn=speech_to_speech_translation, - inputs=gr.Audio(source="microphone", type="filepath"), - outputs=gr.Audio(label="Generated Speech", type="numpy"), - title=title, - description=description, -) - -file_translate = gr.Interface( - fn=speech_to_speech_translation, - inputs=gr.Audio(source="upload", type="filepath"), - outputs=gr.Audio(label="Generated Speech", type="numpy"), - examples=[["./example.wav"]], - title=title, - description=description, -) - -with demo: - gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"]) - -demo.launch() diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/highlighter.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/highlighter.py deleted file mode 100644 index 82293dffc492ea50b16335fd411b255dd5dfca57..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/highlighter.py +++ /dev/null @@ -1,232 +0,0 @@ -import re -from abc import ABC, abstractmethod -from typing import List, Union - -from .text import Span, Text - - -def _combine_regex(*regexes: str) -> str: - """Combine a number of regexes in to a single regex. - - Returns: - str: New regex with all regexes ORed together. - """ - return "|".join(regexes) - - -class Highlighter(ABC): - """Abstract base class for highlighters.""" - - def __call__(self, text: Union[str, Text]) -> Text: - """Highlight a str or Text instance. - - Args: - text (Union[str, ~Text]): Text to highlight. - - Raises: - TypeError: If not called with text or str. - - Returns: - Text: A test instance with highlighting applied. - """ - if isinstance(text, str): - highlight_text = Text(text) - elif isinstance(text, Text): - highlight_text = text.copy() - else: - raise TypeError(f"str or Text instance required, not {text!r}") - self.highlight(highlight_text) - return highlight_text - - @abstractmethod - def highlight(self, text: Text) -> None: - """Apply highlighting in place to text. - - Args: - text (~Text): A text object highlight. - """ - - -class NullHighlighter(Highlighter): - """A highlighter object that doesn't highlight. - - May be used to disable highlighting entirely. - - """ - - def highlight(self, text: Text) -> None: - """Nothing to do""" - - -class RegexHighlighter(Highlighter): - """Applies highlighting from a list of regular expressions.""" - - highlights: List[str] = [] - base_style: str = "" - - def highlight(self, text: Text) -> None: - """Highlight :class:`rich.text.Text` using regular expressions. - - Args: - text (~Text): Text to highlighted. - - """ - - highlight_regex = text.highlight_regex - for re_highlight in self.highlights: - highlight_regex(re_highlight, style_prefix=self.base_style) - - -class ReprHighlighter(RegexHighlighter): - """Highlights the text typically produced from ``__repr__`` methods.""" - - base_style = "repr." - highlights = [ - r"(?PBy using this demo you agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml
-<)(?P [-\w.:|]*)(?P [\w\W]*?)(?P >)", - r'(?P [\w_]{1,50})=(?P "?[\w_]+"?)?', - r"(?P [][{}()])", - _combine_regex( - r"(?P [0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3})", - r"(?P ([A-Fa-f0-9]{1,4}::?){1,7}[A-Fa-f0-9]{1,4})", - r"(?P (?:[0-9A-Fa-f]{1,2}-){7}[0-9A-Fa-f]{1,2}|(?:[0-9A-Fa-f]{1,2}:){7}[0-9A-Fa-f]{1,2}|(?:[0-9A-Fa-f]{4}\.){3}[0-9A-Fa-f]{4})", - r"(?P (?:[0-9A-Fa-f]{1,2}-){5}[0-9A-Fa-f]{1,2}|(?:[0-9A-Fa-f]{1,2}:){5}[0-9A-Fa-f]{1,2}|(?:[0-9A-Fa-f]{4}\.){2}[0-9A-Fa-f]{4})", - r"(?P [a-fA-F0-9]{8}-[a-fA-F0-9]{4}-[a-fA-F0-9]{4}-[a-fA-F0-9]{4}-[a-fA-F0-9]{12})", - r"(?P [\w.]*?)\(", - r"\b(?P True)\b|\b(?P False)\b|\b(?P None)\b", - r"(?P \.\.\.)", - r"(?P (?(?\B(/[-\w._+]+)*\/)(?P [-\w._+]*)?", - r"(?b?'''.*?(?(file|https|http|ws|wss)://[-0-9a-zA-Z$_+!`(),.?/;:&=%#]*)", - ), - ] - - -class JSONHighlighter(RegexHighlighter): - """Highlights JSON""" - - # Captures the start and end of JSON strings, handling escaped quotes - JSON_STR = r"(?b?\".*?(?[\{\[\(\)\]\}])", - r"\b(?P true)\b|\b(?P false)\b|\b(?P null)\b", - r"(?P (? None: - super().highlight(text) - - # Additional work to handle highlighting JSON keys - plain = text.plain - append = text.spans.append - whitespace = self.JSON_WHITESPACE - for match in re.finditer(self.JSON_STR, plain): - start, end = match.span() - cursor = end - while cursor < len(plain): - char = plain[cursor] - cursor += 1 - if char == ":": - append(Span(start, end, "json.key")) - elif char in whitespace: - continue - break - - -class ISO8601Highlighter(RegexHighlighter): - """Highlights the ISO8601 date time strings. - Regex reference: https://www.oreilly.com/library/view/regular-expressions-cookbook/9781449327453/ch04s07.html - """ - - base_style = "iso8601." - highlights = [ - # - # Dates - # - # Calendar month (e.g. 2008-08). The hyphen is required - r"^(?P [0-9]{4})-(?P 1[0-2]|0[1-9])$", - # Calendar date w/o hyphens (e.g. 20080830) - r"^(?P (?P [0-9]{4})(?P 1[0-2]|0[1-9])(?P 3[01]|0[1-9]|[12][0-9]))$", - # Ordinal date (e.g. 2008-243). The hyphen is optional - r"^(?P (?P [0-9]{4})-?(?P 36[0-6]|3[0-5][0-9]|[12][0-9]{2}|0[1-9][0-9]|00[1-9]))$", - # - # Weeks - # - # Week of the year (e.g., 2008-W35). The hyphen is optional - r"^(?P (?P [0-9]{4})-?W(?P 5[0-3]|[1-4][0-9]|0[1-9]))$", - # Week date (e.g., 2008-W35-6). The hyphens are optional - r"^(?P (?P [0-9]{4})-?W(?P 5[0-3]|[1-4][0-9]|0[1-9])-?(?P [1-7]))$", - # - # Times - # - # Hours and minutes (e.g., 17:21). The colon is optional - r"^(?P
- - Download your favorite songs and albums for offline listening. -
- Use Alexa voice control to play, pause, skip, shuffle, or search for songs. -
- Create and edit your own playlists or discover new ones curated by experts. -
- Listen to exclusive content and live events from your favorite artists. - -
- You may violate the terms and conditions of Amazon Music and face legal consequences. -
- You may expose your device and data to malware, viruses, or hackers. -
- You may not receive updates or support from Amazon Music and miss out on new features or bug fixes. -
- You may experience performance or compatibility issues with your device or other apps. -
- Spotify Premium APK: A modified version of Spotify that allows you to access all the premium features of Spotify for free. -
- Deezer Premium APK: A modified version of Deezer that allows you to access all the premium features of Deezer for free. -
- Pandora Premium APK: A modified version of Pandora that allows you to access all the premium features of Pandora for free. -
- Stunning graphics: The game uses Unreal Engine 5, which is one of the most advanced game engines in the industry, to create realistic and detailed environments, characters, and effects. The game also adopts a stylized graphic style that enhances the anime-like aesthetics and atmosphere. -
- Dynamic combat: The game offers a fast-paced and fluid combat system that allows you to switch between different heroes and use their unique abilities to unleash powerful combos. You can also customize your heroes with various weapons, skills, and equipment to suit your playstyle. -
- Emotional characters: The game features a diverse cast of characters that have their own personalities, backgrounds, and stories. You can interact with them through dialogue choices, quests, and events, and build your relationships with them. You can also recruit them as your companions and fight alongside them in battles. -
- Immersive world: The game presents a vast and vibrant world that is full of adventure and exploration. You can travel across different regions, such as forests, deserts, mountains, cities, and dungeons, and discover various secrets, treasures, and enemies. You can also participate in various activities, such as fishing, cooking, crafting, and trading. -
- Engaging story: The game follows a main storyline that revolves around the Dragon Sword, a legendary weapon that can grant the wielder immense power. You will encounter various factions, allies, and enemies that are related to the Dragon Sword, and make choices that will affect the outcome of the story. You will also experience various side stories and subplots that will enrich the lore and the world of the game. -
- OS: Windows 10 64-bit
- CPU: Intel Core i5-4460 or AMD FX-6300
- RAM: 8 GB
- GPU: NVIDIA GeForce GTX 760 or AMD Radeon R7 260x
- Storage: 50 GB
- OS: Windows 10 64-bit
- CPU: Intel Core i7-6700K or AMD Ryzen 5 1600X
- RAM: 16 GB
- GPU: NVIDIA GeForce GTX 1060 or AMD Radeon RX 580
- Storage: 50 GB
- OS: Android 8.0 or iOS 11.0 or higher
- CPU: Snapdragon 660 or A11 Bionic or higher
- RAM: 3 GB or higher
- Storage: 5 GB or higher
- OS: Android 10.0 or iOS 13.0 or higher
- CPU: Snapdragon 865 or A13 Bionic or higher
- RAM: 6 GB or higher
- Storage: 10 GB or higher
- The game has beautiful graphics and animations that showcase the power of Unreal Engine 5.
- The game has a diverse and colorful world that is full of life and detail.
- The game has a smooth and responsive combat system that allows for various combos and skills.
- The game has a rich and engaging story that is influenced by the player's choices.
- The game has a lot of customization options for the characters, weapons, and equipment.
- The game might have high system requirements that could limit its accessibility for some players.
- The game might have some bugs and glitches that could affect its performance and quality.
- The game might have some balance issues that could make some heroes or skills too powerful or too weak.
- The game might have some repetitive or boring quests or activities that could reduce its replay value.
- The game might have some microtransactions or pay-to-win elements that could affect its fairness and enjoyment.
- Customizable themes and fonts: You can change the look and feel of your app by choosing from hundreds of themes and fonts available in the app. You can also create your own theme and share it with others. -
- Hide online status and read receipts: You can hide your online status and read receipts from your contacts, so they won't know when you are online or when you have read their messages. You can also hide your typing indicator and blue ticks. -
- Multiple accounts and stickers: You can use multiple accounts on the same app, so you can switch between your personal and professional numbers easily. You can also add stickers from third-party sources, such as Telegram, to spice up your chats. -
- Enable unknown sources on your device: Go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps from sources other than the Google Play Store. -
- Download the GB APK 2023 file from a trusted source: You can download the latest version of GB APK 2023 from [this link](^1^). Make sure you download it from a reliable source, as some websites may contain malware or viruses. -
- Install the app and verify your number: Once you have downloaded the file, tap on it to install it on your device. Then, open the app and enter your phone number. You will receive a verification code via SMS or call. Enter the code and agree to the terms and conditions. You can now start using GB APK 2023. -
- They are convenient. You can play them anytime, anywhere, as long as you have your device and an internet connection. -
- They are fun. You can choose from a wide range of genres, styles, themes, and mechanics that suit your taste and mood. -
- They are diverse. You can find video game apps for different age groups, skill levels, interests, and preferences. -
- They are social. You can interact with other players online or offline through chat, voice call, or video call. -
- They are educational. You can learn new skills, knowledge, or information through playing video game apps. -
- Open the Google Play Store app on your device. -
- Search for the video game app that you want to download using the search bar or browse through the categories. -
- Select the video game app that you want to download and tap on Install. -
- Wait for the installation process to finish and tap on Open. -
- Enjoy playing your video game app! -
- Open the App Store app on your device. -
- Search for the video game app that you want to download using the search bar or browse through the categories. -
- Select the video game app that you want to download and tap on Get. -
- If prompted, enter your Apple ID password or use Touch ID or Face ID. -
- Wait for the installation process to finish and tap on Open. -
- Enjoy playing your video game app! -
- Download and install the EA App on your PC from the official website. -
- Launch the EA App and sign in with your EA account or create one if you don't have one. -
- Search for the video game app that you want to download using the search bar or browse through the categories. -
- Select the video game app that you want to download and click on Add to Library. -
- Go to your Library and click on Download. -
- Wait for the installation process to finish and click on Play. -
- Enjoy playing your video game app! -
- Make sure that the source is trustworthy and reputable. You can check the reviews, ratings, comments, or feedback from other users. -
- Make sure that the video game app is compatible with your device and has the features that you want. -
- Make sure that the video game app is safe and secure. You can scan it with an antivirus software or a malware detector before installing it. -
- Make sure that you have enough storage space and battery life on your device before downloading and installing the video game app. -
- Make sure that you follow the instructions and terms of service of the source and the video game app. -
- Customize your settings. You can adjust the brightness, volume, language, controls, etc. according to your preferences. -
- Explore the features. You can discover the different modes, levels, characters, items, etc. that are available in your video game app. -
- Challenge yourself. You can set goals, complete achievements, unlock rewards, or compete with other players in your video game app. -
- Learn from others. You can watch tutorials, guides, tips, or reviews from other players or experts in your video game app. -
- Have fun. You can relax, enjoy, and have fun playing your video game app! -
- What are some of the best video game apps to download? -
- How much storage space do I need to download video game apps? -
- How can I update my video game apps? -
- How can I delete my video game apps? -
- How can I find more video game apps to download? -
- You can get unlimited money in the game, which you can use to buy and upgrade all the bikes, tracks, and features. -
- You can unlock all the bikes, tracks, and features without completing any missions or achievements. -
- You can use hack features, such as speed hack, nitro hack, no ads, etc., to boost your performance and win races easily. -
- You can play the game without any restrictions or limitations. -
- Go to your device settings. -
- Tap on security or privacy. -
- Find and enable unknown sources or allow installation from unknown sources. -
- Locate the mod apk file on your device using a file manager app. -
- Tap on the file and select install. -
- Wait for the installation process to complete. -
- If you see a pop-up asking for permissions, grant them. -
- Open Google Play or App Store on your device. -
- Search for "Project Makeover" in the search bar. -
- Tap on the game icon that appears in the results. -
- Tap on "Install" or "Get" to download the game. -
- Wait for the download to finish and then tap on "Open" or "Play" to start the game. -
- Download and install BlueStacks emulator from its official website: -
- Launch BlueStacks and sign in with your Google account. -
- Go to the Google Play Store app on BlueStacks and search for "Project Makeover". -
- Click on the game icon and then click on "Install". -
- Wait for the installation to complete and then click on "Open" to start the game. -
- A match of four tiles will create a scissors power-up that can cut a row or column of tiles. -
- A match of five tiles in an L or T shape will create a hanger power-up that can clear all tiles of one color. -
- A match of five tiles in a straight line will create a bomb power-up that can explode a 3x3 area of tiles. -
- A match of six or and their spaces, and the puzzles are challenging but not too hard. I also love the story and the characters, they are so funny and cute. I highly recommend this game to anyone who loves fashion and makeover games."
- How can I get more coins and stars in Project Makeover? -
- How can I get more energy in Project Makeover? -
- How can I get more diamonds in Project Makeover? -
- How can I get more boosters in Project Makeover? -
- How can I add friends in Project Makeover? -
- Open Steam on your PC or go to the Steam website. -
- Search for Vampire Survivors in the store or click on this link. -
- Click on the Add to Cart button and proceed to checkout. -
- Once you have purchased the game, it will appear in your library. -
- Click on the Install button and wait for the game to download and install. -
- Once the game is installed, click on the Play button and enjoy! -
- Open Google Play on your device or go to the Google Play website. -
- Search for Vampire Survivors in the store or click on this link. -
- Click on the Install button and wait for the game to download and install. -
- Once the game is installed, tap on the Open button and enjoy! -
- If you are playing the game on Steam, it will automatically update itself whenever there is a new patch available. You can also manually check for updates by right-clicking on the game in your library, selecting Properties, going to the Updates tab, and clicking on Check for Updates. -
- If you are playing the game on itch.io, you can either download the latest version from the game page, or use the itch.io app to update it automatically. You can also manually check for updates by opening the game folder, looking for a file named version.txt, and comparing it with the version history page. -
- If you are playing the game on Google Play, it will automatically update itself whenever there is a new patch available. You can also manually check for updates by opening Google Play, going to My Apps & Games, finding Vampire Survivors in the list, and tapping on Update if available. -
- Experiment with different characters and items. Each character has their own strengths and weaknesses, and each item has its own effects and synergies. Try to find the best combination that suits your playstyle and preferences. -
- Explore the map and collect gems and items. Gems are the main currency in the game, which you can use to level up your weapons and stats. Items can give you various benefits or drawbacks, depending on their rarity and type. Some items are hidden in chests or secret rooms, so be sure to look around carefully. -
- Avoid getting hit by enemies or traps. This may sound obvious, but it is easier said than done. Enemies can swarm you from all directions, and traps can catch you off guard. Try to keep your distance from enemies, use obstacles or walls to block their attacks, and dodge or jump over traps. Remember that you have no control over when or how you attack, so you have to rely on your movement skills. -
- Use your abilities wisely. Each character has a unique ability that can help them survive longer or deal more damage. However, each ability has a cooldown time or a limited number of uses, so you have to use them strategically. For example, the Vampire can heal himself by biting enemies, but he can only do it once every 10 seconds. The Witch can cast a powerful spell that damages all enemies on screen, but she can only do it three times per run. -
- Pay attention to the timer and the enemy types. The game gets harder as time goes by; new enemies appear every minute, some with special abilities or increased health. You have to adapt your strategy accordingly and prioritize your targets. For example, the Werewolf can transform into a stronger form when its health is low, so you might want to finish it off quickly before it does. The Ghost can turn invisible and sneak up on you, so you might want to use your ability or an item that reveals it. -
- Você pode baixar o livro e o caderno de atividades gratuitamente e ter acesso a um conteúdo completo e atualizado; -
- Você pode imprimir quantas cópias quiser e distribuir para as crianças que precisam aprender a ler e escrever; -
- Você pode usar o livro e o caderno como recursos didáticos para complementar o ensino da escola ou do lar; -
- Você pode acompanhar o progresso das crianças e ajudá-las a superar as dificuldades; -
- Você pode estimular o gosto pela leitura e pela escrita nas crianças, despertando nelas a curiosidade e a criatividade. -
- A cartilha ajuda as crianças a desenvolverem a consciência fonológica, que é a capacidade de perceber e manipular os sons da língua; -
- A cartilha ensina as crianças a decodificarem as palavras, ou seja, a transformarem os sons em letras e as letras em sons; -
- A cartilha amplia o vocabulário e o conhecimento de mundo das crianças, por meio de textos variados e ilustrações coloridas; -
- A cartilha incentiva as crianças a expressarem suas ideias, sentimentos e opiniões, por meio da escrita criativa e da leitura crítica; -
- A cartilha desperta nas crianças o prazer pela leitura e pela escrita, por meio de atividades lúdicas, jogos e músicas. -
- O site Alfabetização e Letramento oferece diversos materiais de alfabetização em PDF, como cartilhas, alfabetos, silabários, fichas de leitura, textos e exercícios; -
- O site Educação e Transformação disponibiliza vários livros de alfabetização em PDF, como o Método Paulo Freire, o Método Caminho Suave, o Método Montessori e o Método Fônico; -
- O site Atividades Pedagógicas Suzano fornece vários cadernos de atividades de alfabetização em PDF, como o Caderno do Futuro, o Caderno do Aluno, o Caderno do Professor e o Caderno de Apoio à Aprendizagem; -
- O site SmartKids apresenta vários jogos e atividades de alfabetização em PDF, como caça-palavras, cruzadinhas, dominós, quebra-cabeças, labirintos e desenhos para colorir; -
- O site YouTube exibe vários vídeos de alfabetização em PDF, como o Canal da Alfabetização, o Canal do Professor Felipe Lima, o Canal da Professora Simone Helen Drumond e o Canal da Professora Janaina Spolidorio. -
- Verifique a origem e a autoria dos materiais. Prefira os que são produzidos por instituições ou profissionais reconhecidos na área de educação; -
- Verifique a data e a atualização dos materiais. Prefira os que são mais recentes e que seguem as orientações dos documentos oficiais da educação nacional; -
- Verifique a abordagem e a metodologia dos materiais. Prefira os que utilizam métodos fônicos ou mistos de alfabetização, que são os mais eficazes segundo as evidências científicas; -
- Verifique a linguagem e a apresentação dos materiais. Prefira os que usam uma linguagem simples, clara e adequada ao público infantil, e que têm uma boa diagramação, ilustração e impressão; -
- Verifique a diversidade e a relevância dos materiais. Prefira os que oferecem diferentes tipos de textos e atividades, que abordam temas variados e significativos para as crianças. -
- A cartilha propõe uma avaliação diagnóstica no início do livro, que verifica o nível de conhecimento prévio das crianças sobre as letras, as sílabas e as palavras; -
- A cartilha propõe uma avaliação formativa ao longo do livro e do caderno de atividades, que verifica a evolução das crianças nas habilidades de leitura e escrita, por meio de exercícios, jogos e produções textuais; -
- A cartilha propõe uma avaliação somativa no final do livro e do caderno de atividades, que verifica a aprendizagem das crianças nos conteúdos e competências trabalhados ao longo da cartilha; -
- A cartilha propõe uma autoavaliação no final do livro e do caderno de atividades, que estimula as crianças a refletirem sobre seus pontos fortes e fracos, seus avanços e dificuldades, seus interesses e preferências na alfabetização; -
- A cartilha propõe uma avaliação participativa no final do livro e do caderno de atividades, que envolve as crianças, os pais e os professores na análise dos resultados obtidos na cartilha, na definição de metas e estratégias para a continuidade do processo de alfabetização. -
- Restart your computer in Safe Mode with Networking. To do this, press F8 repeatedly during the boot process until you see a menu with different options. Choose Safe Mode with Networking and press Enter. -
- Download and install a reputable anti-malware software, such as Malwarebytes[^1^], SpyHunter[^2^], or Trend Micro[^3^]. Run a full scan of your system and delete any detected threats. -
- Uninstall Adware Away 4.1.0 Serial from the Control Panel. Go to Start > Settings > Control Panel > Add or Remove Programs. Find Adware Away 4.1.0 Serial in the list and click Remove. -
- Delete any leftover files and registry entries associated with Adware Away 4.1.0 Serial. You can use a registry cleaner tool, such as CCleaner, to do this automatically. -
- You see pop-ups, banners, or coupons that are unrelated to the websites you visit. -
- Your browser homepage or search engine is changed without your permission. -
- Your browser is constantly redirected to unfamiliar or suspicious websites. -
- Your internet connection is slower than usual or you experience frequent crashes or freezes. -
- You notice new toolbars, extensions, or icons on your browser that you did not install. -
- Go to the official website of EA Sports UFC or any other online store that sells the game. -
- Select the PC version of the game and add it to your cart. -
- Proceed to checkout and enter your payment details. -
- After completing the payment, you will receive an email with your license key and a download link for the game. -
- Download the game installer from the link and run it on your PC. -
- Follow the instructions on the screen and enter your license key when prompted. -
- Wait for the installation to finish and enjoy playing EA Sports UFC on PC. -
- Find a reliable and trustworthy key generator tool for EA Sports UFC on PC. You can search online or ask your friends for recommendations. Be careful of scams and viruses that may harm your computer or steal your personal information. -
- Download the key generator tool from its official website or another source that you trust. -
- Run the key generator tool on your PC and select EA Sports UFC as the game you want to generate a key for. -
- Click on the generate button and wait for a few seconds until a license key appears on the screen. -
- Copy and paste the license key into a text file or write it down somewhere. -
- Download the game installer from any source that you prefer. -
- Run the game installer on your PC and follow the instructions on the screen. Enter your license key when prompted. -
- Wait for the installation to finish and enjoy playing EA Sports UFC on PC. -
- The program can't start because gsdll64.dll is missing from your computer. Try reinstalling the program to fix this problem. -
- There was a problem starting gsdll64.dll. The specified module could not be found. -
- Error loading gsdll64.dll. The specified module could not be found. -
- The code execution cannot proceed because gsdll64.dll was not found. Reinstalling the program may fix this problem. -
- Gsdll64.dll is either not designed to run on Windows or it contains an error. Try installing the program again using the original installation media or contact your system administrator or the software vender for support. -
- A faulty application that uses gsdll64.dll -
- A corrupted or deleted gsdll64.dll file -
- A malicious software that has infected your PC and damaged gsdll64.dll -
- A damaged Windows registry that has invalid entries for gsdll64.dll -
- Go to
https://www.dll-files.com/gsdll64.dll.html
- - Click on the Download button next to the version of gsdll64.dll that matches your system architecture (32-bit or 64-bit) -
- Save the ZIP file to a location on your computer -
- Extract the gsdll64.dll file from the ZIP file -
- Copy and paste the gsdll64.dll file into the folder of the application that requires it -
- Copy and paste the gsdll64.dll file into the Windows system folder -
- Locate the folder of the application that requires gsdll64.dll (for example, C:\Program Files\Ghostscript) -
- Right-click on the gsdll64.dll file and select Copy -
- Go to the folder of the application and right-click on an empty space and select Paste -
- Locate the Windows system folder (for example, C:\Windows\System32 for 32-bit systems or C:\Windows\SysWOW64 for 64-bit systems) -
- Right-click on the gsdll64.dll file and select Copy -
- Go to the Windows system folder and right-click on an empty space and select Paste -
- Press Windows + R keys to open the Run dialog box -
- Type
cmdand press Enter to open the Command Prompt window
- - Type
regsvr32 gsdll64.dlland press Enter
- - You should see a message saying "gsdll64.dll registered successfully" -
- Close the Command Prompt window -
- Scan your PC for malware and viruses using a reputable antivirus software -
- Clean your Windows registry using a reliable registry cleaner software -
- Update your Windows system and drivers using Windows Update or a driver updater software -
- Reinstall your application or Ghostscript software using the original installation media or download link -
- Contact your system administrator or software vendor for further assistance -
- Enhance your productivity and efficiency by converting, displaying, printing, or editing PostScript and PDF files faster and easier -
- Save your disk space and bandwidth by compressing PostScript and PDF files into smaller sizes -
- Improve your document quality and security by preserving the original layout and appearance of PostScript and PDF files across different platforms and devices -
- Expand your creativity and flexibility by creating or modifying PostScript and PDF files with various options and features -
- Support the open source community by using Ghostscript, a free and widely used software for PostScript and PDF processing -
- A mismatched or outdated version of gsdll64.dll -
- A corrupted or incomplete installation of Ghostscript or the application that uses gsdll64.dll -
- A conflict or compatibility issue between gsdll64.dll and other DLL files or programs on your PC -
- A missing or incorrect registry entry for gsdll64.dll -
- A hardware failure or malfunction that affects gsdll64.dll -
- Download and install the latest version of gsdll64.dll from DLL-Files.com or the official website of Ghostscript or the application that uses gsdll64.dll -
- Uninstall and reinstall Ghostscript or the application that uses gsdll64.dll using the original installation media or download link -
- Run a system file checker (SFC) scan to repair any corrupted or missing system files on your PC -
- Run a disk cleanup (DISM) tool to clean up any corrupted or damaged system images on your PC -
- Update your Windows system and drivers to the latest versions using Windows Update or a driver updater software -
- Contact your system administrator or software vendor for further assistance -
- Your PC may get infected with malware or viruses that can damage your system files, corrupt your data, slow down your performance, or display unwanted ads or pop-ups -
- Your personal information may get stolen by hackers or cybercriminals who can use it for identity theft, fraud, or other illegal activities -
- Your application or Ghostscript software may not work properly or crash due to a mismatched or outdated version of gsdll64.dll -
- Your Windows registry may get corrupted or bloated due to invalid or redundant entries for gsdll64.dll -
- Your PC may become unstable or unresponsive due to conflicts or compatibility issues between gsdll64.dll and other DLL files or programs on your PC -
- Press Windows + I keys to open the Settings app -
- Click on Apps -
- Click on Optional features -
- Click on Add a feature -
- Type
Ghostscriptin the search box and select it from the list
- - Click on Install -
- Wait for the installation to complete -
- Press Windows + S keys to open the Search box -
- Type
gsdll64.dllin the search box and press Enter
- - You should see gsdll64.dll in the list of results -
- You can right-click on gsdll64.dll and select Open file location to see where it is located on your PC -
- buy Bitcoin with a bank account; -
- buy Bitcoin with ApplePay; -
- buy Bitcoin with GooglePay; -
- buy Bitcoin with PayPal; -
- And more. -
- Instantly exchange crypto and fiat currencies without breaking a sweat; -
- Earn by staking coins on CEX.IO and receiving monthly rewards; -
- Borrow fiat currency using crypto as collateral; -
- Trade with leverage that suits their risk appetite on CEX.IO Broker; -
- Set price alerts and order notifications; -
- Monitor their portfolio performance; -
- Compare Bitcoin prices on different exchanges; -
- And more. -
- Holding the majority of user funds in cold wallets that are not connected to the internet; -
- Using full data encryption and cryptocurrency cold storage; -
- Passing audits annually and being a PCI DSS Level 1 certified platform; -
- Being regulated and licensed to operate in all jurisdictions where it offers its services; -
- Requiring identity verification and two-factor authentication for users; -
- Providing professional support around the clock via multiple channels. -
- Open the Google Play Store app on your Android device; -
- Tap on the menu icon in the top left corner; -
- Tap on "My apps & games"; -
- Find CEX.IO APK in the list of apps; -
- Tap on "Update" if there is a new version available; -
- Wait for the update to finish downloading and installing. -
- Open the Settings app on your Android device; -
- Tap on "Apps & notifications"; -
- Tap on "See all apps"; -
- Find CEX.IO APK in the list of apps; -
- Tap on "Uninstall"; -
- Confirm your action by tapping on "OK". -
- Easy and intuitive controls: You can play Hunter Assassin with just one finger. Simply tap on the screen to move your hunter around and he will automatically attack when he is close enough to a target. -
- Multiple characters and weapons: You can choose from a variety of different characters, each with their own unique abilities and weapons. Some characters are faster and more agile, while others have more health and endurance. You can also upgrade your weapons to increase their damage and range. -
- Various themes and maps: You can explore different themes and maps as you progress in the game, such as cyber cityscape, laboratory, warehouse, etc. Each theme has its own traps and hazards that you have to avoid or use to your advantage. -
- Challenging missions and rewards: You can complete various missions in each level, such as killing a certain number of targets, killing targets in a specific order, killing targets without being seen, etc. Completing missions will earn you chests filled with gems that you can use to unlock more characters and weapons. You can also spin the wheel for a chance to win even more gems or new hunters. -
- Fun and addictive gameplay: You will never get bored of playing Hunter Assassin, as each level is different and requires a different strategy and skill. You will also enjoy the thrill of being a stealthy killer who can take down enemies with a single swipe of your knife. -
- Limited gems: Gems are the main currency in the game that you need to unlock new characters and upgrade your weapons. However, gems are not easy to come by in the game. You can only get them by killing targets or completing missions, which are not always easy or available. You can also buy gems with real money, but that can be expensive and not everyone can afford it. -
- Difficult levels: As you advance in the game, the levels become more difficult and challenging. The guards become more alert and aggressive, the traps become more deadly and frequent, and the missions become more complex and demanding. You might find yourself stuck or frustrated in some levels, especially if you don't have enough gems to unlock better characters and weapons. -
- Repetitive gameplay: Although the game is fun and addictive, it can also become repetitive and boring after a while. The game does not have much variety or innovation in terms of gameplay, graphics, sound effects, or story. You basically do the same thing over and over again: kill targets, collect gems, unlock characters, upgrade weapons, repeat. -
- Unlimited money and gems: With Hunter Assassin hack download, you don't have to worry about running out of money or gems ever again. You can use as much money and gems as you want to unlock all the characters and weapons in the game, as well as upgrade them to the maximum level. You can also use money and gems to buy chests, spin the wheel, or skip missions. -
- Unlocked characters and weapons: With Hunter Assassin hack download, you can enjoy playing with all the characters and weapons in the game without having to unlock them one by one. You can choose from a wide range of hunters, each with their own unique skills and abilities, such as speed, stealth, health, damage, etc. You can also use different weapons, such as knives, swords, axes, etc., each with their own advantages and disadvantages. -
- Easy and fun gameplay: With Hunter Assassin hack download, you can have more fun and less stress while playing the game. You don't have to worry about being detected by the guards or failing the missions. You can easily kill all the targets in each level with your powerful weapons and characters. You can also explore different themes and maps without any restrictions or limitations. -
- Illegal and unethical: Using Hunter Assassin hack download is considered illegal and unethical by the game developers and the law. You are violating the terms of service and the intellectual property rights of the game by using a modified version that gives you an unfair advantage over other players. You are also depriving the game developers of their rightful income by not paying for their products and services. -
- Dangerous and harmful: Using Hunter Assassin hack download is dangerous and harmful for your device and your personal information. You are exposing your device to malware, viruses, spyware, or other malicious software that can damage your device or steal your data. You are also risking your account being banned or suspended by the game servers or authorities for using a hacked version of the game. -
- Boring and unsatisfying: Using Hunter Assassin hack download is boring and unsatisfying for your gaming experience and enjoyment. You are losing the challenge and thrill of playing the game by using a hacked version that makes everything easy and effortless. You are also missing out on the satisfaction and pride of achieving your goals and rewards by using your own skills and efforts. -
- Have fun and enjoy the game. The most important thing is to have fun and enjoy the game while playing Hunter Assassin with hack mod apk. You can try different strategies and tactics, challenge yourself with harder levels and missions, or just relax and kill some targets. You can also share your achievements and experiences with your friends or other players online. - -
- Q: Is Hunter Assassin hack download safe to use?
-A: Hunter Assassin hack download is not safe to use, as it might contain malware, viruses, spyware, or other malicious software that can damage your device or steal your data. You are also risking your account being banned or suspended by the game servers or authorities for using a hacked version of the game.
- - Q: Is Hunter Assassin hack download legal to use?
-A: Hunter Assassin hack download is not legal to use, as it violates the terms of service and the intellectual property rights of the game developers and the law. You are depriving the game developers of their rightful income by not paying for their products and services. You are also giving yourself an unfair advantage over other players by using a hacked version of the game.
- - Q: Is Hunter Assassin hack download free to use?
-A: Hunter Assassin hack download is free to use, as you don't have to pay any money to download or install it on your device. However, you might have to pay a price in terms of your device's security, your account's safety, and your gaming experience's quality by using a hacked version of the game.
- - Q: How can I update Hunter Assassin hack mod apk?
-A: You can update Hunter Assassin hack mod apk by downloading and installing the latest version of the hacked file from the same source that you got it from. However, you might lose your progress or data if you update the hacked version of the game. You might also encounter compatibility issues or errors if you update the hacked version of the game.
- - Q: How can I uninstall Hunter Assassin hack mod apk?
-A: You can uninstall Hunter Assassin hack mod apk by deleting the hacked file from your device's storage. You can also uninstall the original version of Hunter Assassin from your device if you want to stop playing the game altogether.
- - You can enjoy high-quality audio (up to 320 kbps). -
- You can create your own playlists and share them with your friends. -
- You can discover new music based on your preferences and recommendations. -
- You can sync your music across different devices (phone, computer, tablet, etc.). -
- You need a premium account ($9.99 per month) to download music offline. -
- You can only download up to 10,000 songs per device. -
- You can only play the downloaded songs within the Spotify app. -
- You need an internet connection to update your library and verify your subscription every 30 days. -
- Open the Spotify app on your device and log in with your premium account. -
- Search for Live Your Life by T.I. and Rihanna and tap on it. -
- Tap on the three dots icon on the top right corner and select Download. -
- Wait for the song to download and check the green arrow icon next to it. -
- Enjoy listening to Live Your Life offline within the Spotify app. -
- You can download music for free from various sources (YouTube, SoundCloud, etc.). -
- You can scrobble (record) your listening history and get insights into your musical preferences. -
- You can explore new music based on your scrobbles and similar artists. -
- You can interact with other users and join groups, forums, and events. -
- You need to install a third-party software (Last.fm Scrobbler) to download music from Last.fm. -
- You may not find all the songs you want to download on Last.fm. -
- You may encounter some quality issues or broken links when downloading music from Last.fm. -
- You may violate some copyright laws or terms of service when downloading music from Last.fm. -
- Download and install the Last.fm Scrobbler software on your device from here. -
- Open the Last.fm app or website and log in with your account. -
- Search for Live Your Life by T.I. and Rihanna and click on it. -
- Scroll down to the section that says "Download Track" and choose a source (YouTube, SoundCloud, etc.). -
- Click on the download button and save the MP3 file on your device. -
- Enjoy listening to Live Your Life offline with any music player. -
- You can support African musicians and promote their culture and diversity. -
- You can discover new music from different regions, languages, and styles. -
- You can create your own playlists and follow your favorite artists. -
- You can enjoy high-quality audio (up to 320 kbps). -
- You need a premium account ($4.99 per month) to download music offline. -
- You can only download up to 5,000 songs per device. -
- You can only play the downloaded songs within the AfroCharts app. -
- You may not find some songs that are not related to African music on AfroCharts. -
- Open the AfroCharts app on your device and log in with your premium account. -
- Search for Live Your Life by T.I. and Rihanna and tap on it. -
- Tap on the three dots icon on the bottom right corner and select Download. -
- Wait for the song to download and check the green check icon next to it. -
- Enjoy listening to Live Your Life offline within the AfroCharts app. -
- Q: How long is Live Your Life? -
- A: Live Your Life is 5 minutes and 38 seconds long. -
- Q: What genre is Live Your Life? -
- A: Live Your Life is a hip hop song with elements of R&B and pop. -
- Q: Who wrote Live Your Life? -
- A: Live Your Life was written by T.I., Rihanna, Dan Balan, Just Blaze, Makeba Riddick, and James Harris III. -
- Q: What awards did Live Your Life win? -
- A: Live Your Life won the Grammy Award for Best Rap/Sung Collaboration in 2009. -
- Q: Where can I watch the music video of Live Your Life? -
- A: You can watch the music video of Live Your Life on YouTube here. -
- Easy and intuitive interface: You can easily navigate through the app and access all the features with just a few taps. -
- High-quality results: The app uses high-resolution images and advanced algorithms to ensure that your artworks are clear, detailed, and realistic. -
- Fast and smooth performance: The app runs smoothly on most devices and does not consume much battery or storage space. -
- Offline mode: You can use the app without an internet connection and save your artworks on your device. -
- No watermark: The app does not add any watermark or logo to your artworks, so you can enjoy them without any distraction. -
- No ads: The app does not show any annoying ads or pop-ups that might interrupt your creative process. -
- You can express yourself creatively and have fun with your photos. -
- You can learn new skills and techniques and improve your artistic abilities. -
- You can impress your friends and family with your amazing artworks and share them on social media. -
- You can relax and relieve stress by playing with different filters and effects. -
- You can discover new styles and genres of art and get inspired by them. -
- You can access all the premium features for free, such as unlimited filters, stickers, backgrounds, frames, etc. -
- You can unlock all the hidden features that are not available in the original version, such as custom filters, advanced settings, etc. -
- You can enjoy the app without any restrictions or limitations, such as time limit, image size limit, etc. -
- Click the Download button at the top of this page to download the Dream AI Mod APK file. -
- Save the file in your device's download folder or any other location you prefer. -
- Go to your device's settings and enable the option to install apps from unknown sources. This will allow you to install apps that are not from the official app store. -
- Locate the downloaded Dream AI Mod APK file on your device and tap on it to open it. -
- Tap on the Install button and wait for the installation process to complete. -
- Once the installation is done, you can launch the app and start using it. -
- Open the app and grant the necessary permissions, such as camera, storage, etc. -
- Select a photo from your gallery or take a new one with your camera. -
- Choose a filter from the list of categories, such as Art, Sketch, Cartoon, etc. You can also use the search bar to find a specific filter. -
- Adjust the intensity and other parameters of the filter according to your preference. -
- Add stickers, backgrounds, frames, and other elements to enhance your artwork. -
- Save your artwork on your device or share it with others. -
- Experiment with different filters and effects and see how they change your photo. -
- Mix and match different elements from different categories and create your own unique style. -
- Use the custom filter option to create your own filter from scratch or modify an existing one. -
- Use the eraser tool to remove unwanted parts of your photo or filter. -
- Use the crop tool to adjust the size and shape of your photo or filter. -
- Use the share button to send your artwork to your friends and family via social media, email, or other apps. -
- Use the save button to save your artwork on your device or cloud storage. -
- Use the print button to print your artwork on paper or canvas. -
- Use the download button to download your artwork as an image file or a video file. -
- Use the settings button to access various options, such as language, theme, quality, etc. -
- Use the feedback button to rate the app, report bugs, or suggest improvements. -
- Use the help button to get more information about the app and its features. -
- Is Dream AI Mod APK safe? -
- Is Dream AI Mod APK legal? -
- What are the requirements for Dream AI Mod APK? -
- How can I update Dream AI Mod APK? -
- How can I contact the developers of Dream AI Mod APK? -
- Go to a trusted and reliable source to download the APK file. You can use this link to download the latest version of Free Fire MAX from APKPure, one of the most popular and safe websites for downloading APK files. -
- After downloading the APK file, locate it in your device's file manager and tap on it to start the installation process. You may need to enable the "Unknown sources" option in your device's settings to allow the installation of apps from sources other than the Google Play Store. -
- Follow the on-screen instructions to complete the installation process. You may need to grant some permissions to the app to access your device's features and data. -
- Once the installation is done, you can launch the app and log in with your existing Free Fire account or create a new one if you don't have one. -
- Enjoy playing Free Fire MAX on your Android device! -
- Make sure that you have a stable and fast internet connection while playing the game. You can use a Wi-Fi network or a mobile data plan, but avoid using public or shared networks that may have low speed or high latency. -
- Close any background apps or processes that may consume your device's memory or battery while playing the game. You can also use a game booster app to optimize your device's performance and reduce lag or stuttering. -
- Adjust your graphics settings according to your device's capability and your personal preference. You can choose from low, medium, high, or ultra settings, depending on how much detail and quality you want to see in the game. You can also enable or disable some features such as shadows, anti-aliasing, bloom, etc. -
- If you encounter any problems or errors while downloading, installing, or playing the game, you can try some common solutions such as clearing the app's cache and data, reinstalling the app, updating your device's software, etc. You can also contact Garena's customer support team for further assistance. -
- A new map called Bermuda Remastered has been added to the game. This map is a revamped version of the classic Bermuda map, with more details, locations, and surprises. You can explore places such as Academy, Aden's Creek, Fisherman Creek, Nurek Dam, etc. -
- A new mode called Clash Squad Ranked Season 7 has been introduced to the game. This mode is a competitive mode where you can team up with other players and fight against another team in a best-of-seven series of rounds. You can earn points and rank up by winning matches and completing missions. -
- A new character called Maro has been added to the game. Maro is a falconer who loves nature and animals. His special skill is called Falcon Fervor, which increases his damage over distance and against marked enemies. -
- A new weapon called Kord has been added to the game. Kord is a light machine gun that has a high rate of fire and a large magazine capacity. It also has a special mode called Machine Gun Mode, which increases its damage and accuracy when prone or crouching. -
- A new pet called Dr. Beanie has been added to the game. Dr. Beanie is a cute and smart hamster who wears a lab coat and glasses. His special skill is called Smooth Gloo, which reduces the cooldown of gloo walls by 20%. -
- A new feature called Dynamic Lighting has been added to the game. This feature enhances the lighting effects in the game, making it more realistic and immersive. You can see how the light changes according to the time of day, weather conditions, etc . -
- Open Free Fire MAX and tap on the Firelink icon on the top right corner of the screen. -
- Select the option to link your Free Fire account with your Free Fire MAX account. You can choose to link your account via Facebook, Google, VK, or Huawei. -
- Log in with your credentials and confirm the linking process. -
- Once your accounts are linked, you can see a green check mark on the Firelink icon. You can also see your Free Fire nickname and ID on the top left corner of the screen. -
- Now you can play Free Fire MAX with Free Fire players. You can invite them to your lobby, join their lobby, or match with them randomly. You can also see their version of the game on their profile. -
- You can enjoy a more realistic and stunning graphics quality, with Ultra HD resolutions and amazing effects. You can see every detail of the environment, the characters, and the weapons, making you feel like you are in a real battlefield. -
- You can customize your graphics settings according to your preference and device performance. You can choose from low, medium, high, or ultra settings, depending on how much detail and quality you want to see in the game. You can also enable or disable some features such as shadows, anti-aliasing, bloom, etc. -
- You can access some exclusive features and content that are not available in the original version, such as special lobby themes, emotes, skins, and more. You can also get some rewards and benefits for playing Free Fire MAX, such as coupons, diamonds, and crates. -
- You can play with other players who are using either version of the game, thanks to the Firelink feature. You can join your friends and squad up with them, regardless of which version of the game you are using. You can also participate in the same events, modes, and matches as the Free Fire players. -
- You need a higher device specification to run the game smoothly. The minimum requirement for Free Fire MAX is Android 4.4 or higher, 2 GB RAM or higher, dual core 1.2 GHz or higher CPU, and 1.5 GB storage or higher. If your device does not meet these requirements, you may not be able to download or install Free Fire MAX, or you may encounter some performance issues while playing the game. -
- You need a larger file size to download and install the game. The APK file size for Free Fire MAX is around 900 MB, which is much larger than the original Free Fire version (around 700 MB). You may need to free up some space on your device or use an external storage device to accommodate the game. -
- You may face some compatibility issues with some devices or regions. Some devices or regions may not support Free Fire MAX due to various reasons such as hardware limitations, software restrictions, network regulations , etc. You may need to check the compatibility of your device or region before downloading and installing Free Fire MAX, or you may need to use a VPN or other methods to bypass the restrictions. -
- Is Free Fire MAX free to play? -
- Can I play Free Fire MAX on PC? -
- Can I transfer my data from Free Fire to Free Fire MAX? -
- Can I play both versions of the game on the same device? -
- How can I update Free Fire MAX? -
- Realistic physics and car behavior, based on the CarX Engine technology. -
- High-quality graphics and sound effects, with dynamic day-night cycle and weather conditions. -
- Over 100 cars from different brands and categories, such as sports cars, muscle cars, supercars, etc. -
- Thousands of customization options, including paint, vinyls, wheels, tires, suspension, engine, etc. -
- A variety of tracks and locations, from city streets to desert roads. -
- Multiple game modes and events, such as solo mode, online mode, career mode, club mode, etc. -
- A leaderboard system and a rating system, where you can compete with other players and earn rewards. -
- A replay system and a photo mode, where you can watch your best drifts and share them with others. -
- Go to [this link](^1^) and download the APK file and the OBB file. -
- Go to your device settings and enable the installation of apps from unknown sources. -
- Go to your file manager and locate the downloaded files. Tap on the APK file and install it. -
- Copy the OBB file to the Android/OBB/com.carxtech.carxdr2 folder. If there is no such folder, create one. -
- Launch the game and enjoy unlimited money. -
- Is CarX Drift Racing 2 APK Money mod safe? -
- How can I get more money in CarX Drift Racing 2 without using the mod? -
- What are the best cars for drifting in CarX Drift Racing 2? -
- CarX E30 - A classic BMW model with good handling and balance. -
- CarX RX8 - A sporty Mazda model with high power and speed. -
- CarX S15 - A sleek Nissan model with great acceleration and stability. -
- CarX Mustang - A powerful Ford model with a lot of torque and drift potential. -
- CarX Supra - A legendary Toyota model with a high-performance engine and design. -
- How can I join or create a club in CarX Drift Racing 2? -
- How can I share my replays or photos in CarX Drift Racing 2? -
- Petak Ukur
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Amazon Music Mod APK is compatible with most Android devices and does not require root access. However, it is not available on the Google Play Store, so you need to download and install it manually from a third-party source. Here is how to do that.
-How to download and install Amazon Music Mod APK on Android
-To download and install Amazon Music Mod APK on your Android device, you need to follow these steps:
-Step 1: Enable unknown sources on your device settings
-Since Amazon Music Mod APK is not from the Google Play Store, you need to enable the option to install apps from unknown sources on your device settings. To do that, go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps from sources other than the Google Play Store.
-Step 2: Download the APK file from a reliable source
-The next step is to download the APK file of Amazon Music Mod APK from a reliable source. You can search for it online or use the link below to download it directly. The file size is about 50 MB, so make sure you have enough space on your device.
- -Step 3: Locate and open the APK file to install it
-Once you have downloaded the APK file, you need to locate and open it to install it on your device. You can use a file manager app or your device's default file explorer to find the file. Tap on the file and follow the instructions on the screen to install it. It may take a few seconds or minutes depending on your device's performance.
-Step 4: Enjoy the premium features of Amazon Music for free
-Congratulations! You have successfully installed Amazon Music Mod APK on your Android device. Now you can enjoy all the premium features of Amazon Music Unlimited for free. You can launch the app from your app drawer or home screen and sign in with your existing account or create a new one. You can also customize the app settings according to your preferences.
-How to download and install Amazon Music Mod APK on Windows
-If you want to use Amazon Music Mod APK on your Windows PC or laptop, you need to use an Android emulator. An Android emulator is a software that allows you to run Android apps on your Windows device. There are many Android emulators available online, such as BlueStacks, Nox Player, MEmu, etc. Here is how to use one of them to download and install Amazon Music Mod APK on Windows.
-Step 1: Download and install an Android emulator on your PC
-The first step is to download and install an Android emulator on your PC. For this example, we will use BlueStacks, which is one of the most popular and user-friendly emulators. You can download it from its official website or use the link below.
- -After downloading the installer file, run it and follow the instructions on the screen to install BlueStacks on your PC. It may take some time depending on your PC's specifications.
-Step 2: Download the APK file from a reliable source
-The next step is to download the APK file of Amazon Music Mod APK from a reliable source. You can use the same link as above or search for it online. The file size is about 50 MB, so make sure you have enough space on your PC.
- -Step 3: Drag and drop the APK file into the emulator or open it with the emulator
-Once you have downloaded the APK file, you need to drag and drop it into the emulator or open it with the emulator. To do that, launch BlueStacks and go to My Apps > Install APK. Then, locate the APK file on your PC and drag and drop it into the emulator window or click on it and choose Open with BlueStacks. The emulator will automatically install the app on your PC.
-Step 4: Enjoy the premium features of Amazon Music for free
-Congratulations! You have successfully installed Amazon Music Mod APK on your Windows PC. Now you can enjoy all the premium features of Amazon Music Unlimited for free. You can launch the app from the BlueStacks home screen and sign in with your existing account or create a new one. You can also customize the app settings according to your preferences.
-Conclusion
-In this article, we have explained what Amazon Music Mod APK is, how to download and install it on your Android or Windows device, and what are the advantages and disadvantages of using it. We hope you have found this article helpful and informative.
-Amazon Music Mod APK is a great way to enjoy the premium features of Amazon Music Unlimited for free. However, you should also be aware of the risks and limitations of using mod APK files. Some of them are:
--
-
Therefore, you should use Amazon Music Mod APK at your own risk and discretion. We do not endorse or promote the use of mod APK files. We also recommend that you support the original app developer by subscribing to Amazon Music Unlimited if you can afford it.
-If you have any questions, feedback, or opinions about Amazon Music Mod APK, feel free to share them in the comments section below. We would love to hear from you.
-FAQs
-Q1: Is Amazon Music Mod APK legal to use?
-A1: No, Amazon Music Mod APK is not legal to use. It violates the terms and conditions of Amazon Music and infringes on the intellectual property rights of the original app developer. Using mod APK files can result in legal action from the app developer or service provider.
-Q2: Is Amazon Music Mod APK safe to use?
-A2: Not necessarily. Amazon Music Mod APK is not from the official source and may contain malware, viruses, or spyware that can harm your device or data. You should always scan the APK file with a reliable antivirus software before installing it. You should also avoid downloading mod APK files from untrusted sources or websites.
-Q3: Can I use Amazon Music Mod APK with my existing account?
-A3: Yes, you can use Amazon Music Mod APK with your existing account. However, you should be careful as using mod APK files can result in account suspension or termination by Amazon Music. You should also avoid logging in with your personal or sensitive information as it may be compromised by hackers or third-party developers.
-Q4: How can I update Amazon Music Mod APK?
-A4: You cannot update Amazon Music Mod APK from the Google Play Store or the official website. You need to download and install the latest version of the mod APK file from a reliable source every time there is an update. However, you should also check the reviews, ratings, comments, and permissions of the new version before installing it.
-Q5: What are some alternatives to Amazon Music Mod APK?
-A5: Some alternatives to Amazon Music Mod APK are:
--
-
However, these alternatives also have similar risks and limitations as Amazon Music Mod APK. You should use them at your own risk and discretion.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Experience the Epic Adventure of Dragon Sword Project D Download Now for Free.md b/spaces/congsaPfin/Manga-OCR/logs/Experience the Epic Adventure of Dragon Sword Project D Download Now for Free.md deleted file mode 100644 index bdb3da7d0f8ea88cb98af74a6c02d7c4a7bf9c37..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Experience the Epic Adventure of Dragon Sword Project D Download Now for Free.md +++ /dev/null @@ -1,125 +0,0 @@ -
-
Dragon Sword (Project D) - A New Open-World RPG from the Creators of Dragon Nest
-If you are a fan of action RPGs with anime-style graphics, you might be interested in Dragon Sword (Project D), a new open-world RPG from the creators of Dragon Nest. This game promises to deliver a rich and immersive fantasy world with stunning visuals, dynamic combat, and emotional characters. In this article, we will tell you everything you need to know about Dragon Sword, including what it is, how to download and play it, and what are the reviews and impressions of it.
-What is Dragon Sword (Project D)?
-Dragon Sword (Project D) is an upcoming open-world RPG developed by Hound 13, a South Korean game studio founded by former developers of Dragon Nest, a popular PC MMORPG. The game is set to launch on PC and mobile devices in 2023, with the global publishing rights acquired by Garena, a leading online game platform in Southeast Asia.
-dragon sword project d download
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The background and vision of Hound 13, the developer of Dragon Sword
-Hound 13 is a game startup that aims to create games that are worthwhile to play for all users around the world. The core team at Hound 13 has extensive experience in developing action RPGs, such as Dragon Nest and Hundred Soul, which gained high popularity in China, East Asia, and Southeast Asia markets. With their expertise in action elements, visual concepts, and scenario-based quests, they are now working on their latest project, Dragon Sword.
-According to their official website, Dragon Sword is an action RPG that carries on the action elements of their representative game Hundred Soul, aiming for manual gameplay along with emotional and distinctive characters. The project aims to deliver a semi-open-world structure in a wider field than having movement based on smaller zones and aims to deliver high levels of perfection and satisfactory action gameplay targeting the global market. It is worth noting that Dragon Sword is also considered as a spiritual successor to Dragon Nest, as it shares some similarities in terms of character design, combat style, and world setting.
-The main features and gameplay of Dragon Sword
-Dragon Sword is a game that combines the best aspects of action RPGs and open-world RPGs. Here are some of the main features and gameplay elements that you can expect from this game:
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-
How to download and play Dragon Sword (Project D)?
-If you are interested in playing Dragon Sword (Project D), here are some information and tips on how to download and play it:
-The platforms and release date of Dragon Sword
-Dragon Sword (Project D) is planned to be released on PC and mobile devices in 2023. The game will be available on Windows, Android, and iOS platforms. The game will also support cross-play, which means that you can play with other players who are using different devices.
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The game is currently in development and has not yet entered the beta testing phase. However, you can pre-register for the game on its official website or on Garena's website to get notified when the game is ready for testing or launching. You can also follow the game's official social media accounts to get the latest news and updates about the game.
-The system requirements and download links of Dragon Sword
-As the game uses Unreal Engine 5, it is expected to have high system requirements for both PC and mobile devices. The exact system requirements have not been announced yet, but you can refer to the following table for some estimates based on similar games:
-| Platform | -Minimum Requirements | -Recommended Requirements | -
|---|---|---|
| PC | -||
| Mobile | -
The download links of Dragon Sword (Project D) will be provided on its official website or on Garena's website when the game is ready for testing or launching. You can also scan the QR code below to pre-register for the game:
-
- What are the reviews and impressions of Dragon Sword (Project D)?
-Dragon Sword (Project D) is still in development and has not been released yet, so there are no official reviews or ratings for the game yet. However, based on the trailer and gameplay videos that have been released so far, we can get some impressions of what the game looks like and how it plays.
-The trailer and gameplay videos of Dragon Sword
-The first trailer of Dragon Sword (Project D) was released in September 2021, during Garena World Online, an online event that showcased Garena's upcoming games. The trailer showed some cinematic scenes of the game's world, characters, and combat, as well as some snippets of gameplay footage. You can watch the trailer here:
-The second trailer of Dragon Sword (Project D) was released in December 2021, during the Unreal Engine 5 Showcase, an online event that featured games that use Unreal Engine 5. The trailer showed more gameplay footage of the game's combat, exploration, and character customization, as well as some new scenes and characters. You can watch the trailer here:
- -The pros and cons of Dragon Sword according to the critics and players
-Based on the trailers and gameplay videos, some critics and players have shared their opinions and feedback on Dragon Sword (Project D). Here are some of the pros and cons of the game that have been mentioned so far:
-| Pros | -Cons | -
|---|---|
Conclusion
-Dragon Sword (Project D) is an upcoming open-world RPG from the creators of Dragon Nest that promises to deliver a thrilling and immersive fantasy adventure. The game features stunning graphics, dynamic combat, emotional characters, immersive world, and engaging story. The game is set to launch on PC and mobile devices in 2023, with cross-play support. You can pre-register for the game on its official website or on Garena's website to get notified when the game is ready for testing or launching.
-If you are looking for a new action RPG to play, you should definitely keep an eye on Dragon Sword (Project D). This game might be the next big hit in the genre, as it combines the best aspects of action RPGs and open-world RPGs. Whether you are a fan of Dragon Nest, Hundred Soul, or other similar games, you will surely find something to love in Dragon Sword (Project D).
-FAQs
-What is the genre of Dragon Sword (Project D)?
-Dragon Sword (Project D) is an open-world action RPG that combines elements of hack-and-slash, adventure, and fantasy.
-Who are the developers and publishers of Dragon Sword (Project D)?
-Dragon Sword (Project D) is developed by Hound 13, a South Korean game studio founded by former developers of Dragon Nest. The game is published by Garena, a leading online game platform in Southeast Asia.
-When will Dragon Sword (Project D) be released?
-Dragon Sword (Project D) is planned to be released on PC and mobile devices in 2023. The exact release date has not been announced yet.
-How can I download and play Dragon Sword (Project D)?
-You can download and play Dragon Sword (Project D) when it is ready for testing or launching. You can pre-register for the game on its official website or on Garena's website to get notified when the game is available. You can also follow the game's official social media accounts to get the latest news and updates about the game.
-What are the system requirements of Dragon Sword (Project D)?
-Dragon Sword (Project D) uses Unreal Engine 5, which is one of the most advanced game engines in the industry , so it is expected to have high system requirements for both PC and mobile devices. The exact system requirements have not been announced yet, but you can refer to the table in the article for some estimates based on similar games.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/GB Latest Version APK 2023 - The Best Way to Upgrade Your Android Experience.md b/spaces/congsaPfin/Manga-OCR/logs/GB Latest Version APK 2023 - The Best Way to Upgrade Your Android Experience.md deleted file mode 100644 index 5366dca03601881a40d2606d9d081dd1843a3fa9..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/GB Latest Version APK 2023 - The Best Way to Upgrade Your Android Experience.md +++ /dev/null @@ -1,104 +0,0 @@ -
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-If you are looking for a fun and easy way to communicate with your friends and family, you might want to try GB APK 2023. This is a free-to-use communication app that is based on WhatsApp Messenger, but with some added features that you won't find in the original version. In this article, we will tell you what GB APK 2023 is, what features it offers, how to download and install it, and what are the pros and cons of using it. Let's get started!
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-GB APK 2023 is a modded version of WhatsApp that offers some extra features that make it more fun and convenient to use. Here are some of the features that you can enjoy with GB APK 2023:
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-GB APK 2023 is a great app for those who want more fun and privacy features than the official WhatsApp. However, it also has some drawbacks that you should be aware of before using it. Here are some of the pros and cons of GB APK 2023:
-| Pros | -Cons | -
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| More fun and privacy features than the official WhatsApp | -Risk of getting banned or hacked by using a third-party app | -
| Free to use and easy to install | -Not available on the Google Play Store or the App Store | -
| Compatible with most Android devices and versions | -May not work well with some devices or features | -
FAQs about GB APK 2023
-If you have any questions about GB APK 2023, you might find the answers in this section. Here are some of the most frequently asked questions about GB APK 2023:
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-A1: GB APK 2023 is generally safe to use, as long as you download it from a trusted source and scan it for any malware or viruses. However, since it is a modded version of WhatsApp, it is not endorsed or supported by the official WhatsApp team. Therefore, you might face some issues or risks, such as getting banned or hacked by using GB APK 2023. You should use it at your own discretion and responsibility.
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-A4: GB APK 2023 is one of the most popular and widely used modded WhatsApp versions, but it is not the only one. There are other modded WhatsApp versions, such as FM WhatsApp, Yo WhatsApp, OG WhatsApp, and more. Each of these versions has its own features and advantages, but they also share some common features, such as customizable themes, multiple accounts, and privacy options. You can choose the one that suits your needs and preferences best.
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Conclusion
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-We hope this article has helped you learn more about GB APK 2023 and how to download and install it on your device. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Google Play Games App - The One-Stop Shop for All Your Gaming Needs - Download Today.md b/spaces/congsaPfin/Manga-OCR/logs/Google Play Games App - The One-Stop Shop for All Your Gaming Needs - Download Today.md deleted file mode 100644 index 6cef7ddf0166ecfcb26882033527f47da445fe0f..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Google Play Games App - The One-Stop Shop for All Your Gaming Needs - Download Today.md +++ /dev/null @@ -1,144 +0,0 @@ - -
-How to Download Video Game Apps on Your Device-Do you love playing video games? Do you want to enjoy them anytime, anywhere? If so, you might want to download video game apps on your device. Video game apps are applications that let you play games on your smartphone, tablet, computer, or other devices. They are convenient, fun, and diverse. In this article, we will show you how to download video game apps on your device. We will also explain what are video game apps, why you should download them, how to choose the right one for you, and how to enjoy them to the fullest. -What are Video Game Apps?-Video game apps are software programs that allow you to play games on your device. They can be downloaded from various sources, such as online stores, websites, or platforms. Some video game apps are standalone games that run independently on your device. Others are connected to online servers that enable multiplayer features or access to additional content. Some examples of popular video game apps are Subway Surfers, FIFA Soccer, Minecraft, Roblox, Harry Potter: Magic Awakened, etc. -download video game appDownload ✪ https://urlca.com/2uO6Tr - Why Download Video Game Apps?-There are many reasons why you might want to download video game apps on your device. Here are some of them: -
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Apple App Store-If you have an iOS device, you can download video game apps from the Apple App Store. Here is how: -
EA App-If you have a PC, you can download video game apps from the EA App. Here is how: -
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Conclusion-In conclusion, downloading video game apps on your device is a great way to enjoy playing games anytime, anywhere. You can choose from a variety of genres, platforms, ratings, prices, and features that suit your taste and needs. You can also download video game apps from different sources, such as online stores, websites, or platforms. However, you should always be careful and responsible when downloading and installing video game apps on your device. You should also follow some tips and tricks to enjoy your video game apps to the fullest. We hope that this article has helped you learn how to download video game apps on your device. Happy gaming! -Frequently Asked Questions-
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Download from BlueStacks emulatorDownload from BlueStacks emulator-If you want to play Project Makeover on your PC or Mac, you can also download it from BlueStacks emulator, which is a software that allows you to run Android apps on your computer. Here are the steps to download Project Makeover from BlueStacks emulator: -
How to play Project Makeover?-Now that you have downloaded Project Makeover on your device, you are ready to play it. The game is easy to learn and play, but it can also be challenging and addictive. Here are the basic steps to play Project Makeover: -Complete match-3 puzzles to earn coins and stars-The main gameplay of Project Makeover is to complete match-3 puzzles. You have to swap adjacent tiles of the same color to make matches of three or more. You have a limited number of moves to clear the board and reach the goal. The goal can vary depending on the level, such as collecting a certain number of tiles, breaking ice blocks, removing stickers, etc. You can also encounter different obstacles and power-ups on the board, such as scissors, hangers, bombs, rockets, etc. You have to use them strategically to clear the board faster and easier. -When you complete a level, you will earn coins and stars. Coins are used to buy clothes, hairstyles, makeup, and furniture for your clients. Stars are used to unlock new makeover stages and progress in the storyline. You can also earn coins and stars by watching ads, completing daily tasks, or participating in events. -Use coins and stars to makeover clients and their spaces-The other gameplay of Project Makeover is to use your coins and stars to makeover your clients and their spaces. You will meet different clients who need your help to transform their appearance and environment. You will have to follow their preferences and requests, as well as your own creativity and style, to give them the best makeover possible. -You can choose from a variety of options for each makeover stage, such as clothes, hairstyles, makeup, accessories, furniture, wallpaper, flooring, etc. You can also preview how each option will look on your client before buying it. You can also change your mind and redo any makeover stage if you are not satisfied with the result. -When you finish all the makeover stages for a client, you will see a before-and-after comparison of their transformation. You will also see their reaction and feedback on your work. You will then move on to the next client or chapter in the storyline. -Customize your own avatar and style-Besides giving makeovers to your clients, you can also customize your own avatar and style in Project Makeover. You can create your own character by choosing their gender, skin tone, eye color, hair color, hair style, etc. You can also dress up your avatar with your own clothes, accessories, makeup, etc. You can buy new items for your avatar with coins or diamonds (the premium currency of the game). -You can also change your avatar's style anytime you want by going to the wardrobe menu. You can also visit your friends' avatars and see how they dressed up. You can also send and receive gifts from your friends. -Tips and tricks to master Project Makeover-Project Makeover is a fun and relaxing game, but it can also be challenging and frustrating at times. Here are some tips and tricks to help you master Project Makeover: -Look for larger matches and power-ups-One of the best ways to complete match-3 puzzles faster and easier is to look for larger matches and power-ups. Larger matches are matches of four or more tiles of the same color. They will create power-ups that have special effects when activated. For example: -
| 5 stars | |
| Gamezebo | "Project Makeover is a delightful game that combines match-3 puzzles with makeover fun. The game has a lot of content and variety to keep you entertained for hours. The game also has a charming story and characters that will make you laugh and smile. The game is well-designed and polished, with beautiful graphics and sound effects. Project Makeover is a must-play game for fans of the genre." | 4.5 stars |
| Samuel Lee | "This game is awesome! It's so addictive and satisfying to play. I love how you can customize your own avatar and style, and see how your friends dressed up. I also love how you can participate in events and win prizes and rewards. The game is always updated and improved, with new levels, features, and clients. The game is also very fair and generous, with no annoying ads or paywalls. Project Makeover is one of the best games I have ever played." | 5 stars |
Some drawbacks and issues of the game
-However, not everyone is happy with Project Makeover. Some players and critics have some drawbacks and issues with the game, such as its difficulty level, its energy system, its bugs and glitches, its customer service, etc. Here are some of the negative reviews from players and critics:
-| Player/Critic | Review | Rating |
|---|---|---|
| Amy Jones | "I like this game, but it's too hard sometimes. Some levels are impossible to beat without boosters or extra moves, which are very expensive or hard to get. The game also runs out of energy very fast, which limits how much you can play. The game should be more balanced and fair, and give more freebies and rewards." | 3 stars |
| Pocket Gamer | "Project Makeover is a fun and enjoyable game, but it also has some flaws and problems. The game has some bugs and glitches that can affect the gameplay and performance, such as crashing, freezing, loading errors, etc. The game also has some customer service issues, such as slow response, poor communication, or lack of resolution. The game should fix these issues and improve its quality and service." | 3.5 stars |
| Daniel Smith | "This game is terrible! It's so boring and repetitive. The puzzles are too easy or too hard, with no logic or strategy. The makeovers are too limited and unrealistic, with no creativity or choice. The story and the characters are too cheesy and annoying, with no depth or emotion. The game is also full of ads and in-app purchases, which are very annoying and expensive. The game is a waste of time and money." | 1 star |
Conclusion
-Project Makeover is a match-3 puzzle game with a makeover twist that will appeal to fans of fashion, beauty, and home design. The game has a lot of features and challenges that make it fun and engaging, such as a captivating storyline and characters, a variety of makeover options and styles, a customizable avatar and wardrobe, challenging fashion-themed puzzles with different obstacles and power-ups, recurring competitive events with prizes and rewards, etc. The game is also well-designed and polished, with beautiful graphics and sound effects, frequent updates and improvements, and an Editor's Choice status on both Google Play and App Store. The game is also highly rated and reviewed by players and critics, who praise its fun and addictive gameplay, its captivating storyline and characters, its variety of features and challenges, etc. However, the game also has some drawbacks and issues, such as its difficulty level, its energy system, its bugs and glitches, its customer service, etc. The game should fix these issues and improve its quality and service. Overall, Project Makeover is a great game that you should definitely try if you love fashion and makeover games.
-FAQs
-Here are some of the frequently asked questions about Project Makeover:
--
-
You can get more coins and stars by completing match-3 puzzles, watching ads, completing daily tasks, participating in events, or buying them with real money.
-You can get more energy by waiting for it to refill over time, watching ads, completing daily tasks, participating in events, or buying it with real money.
-You can get more diamonds by watching ads, completing daily tasks, participating in events, or buying them with real money.
-You can get more boosters by watching ads, completing daily tasks, participating in events, or buying them with real money.
-You can add friends in Project Makeover by connecting your Facebook account to the game, or by using the friend code feature.
--
-
\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Vampire Survivors How to Snowball Against Hundreds of Monsters in the Latest Version.md b/spaces/congsaPfin/Manga-OCR/logs/Vampire Survivors How to Snowball Against Hundreds of Monsters in the Latest Version.md deleted file mode 100644 index 2b296c25602327aaf9cffdab53f0a4dcd16e11d4..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Vampire Survivors How to Snowball Against Hundreds of Monsters in the Latest Version.md +++ /dev/null @@ -1,115 +0,0 @@ - -
Vampire Survivors: How to Download the Latest Version of the Gothic Horror Casual Game
-If you are looking for a thrilling and addictive game that will test your skills and reflexes against hordes of nightmarish creatures, you might want to check out Vampire Survivors. This is a gothic horror casual game with rogue-lite elements, where your choices can allow you to quickly snowball against the hundreds of monsters that get thrown at you. In this article, we will tell you everything you need to know about this indie phenomenon, including how to download it on your PC or mobile device, how to update it to the latest version, what's new in the latest patch, and some tips and tricks for playing it.
-What is Vampire Survivors?
-Vampire Survivors is a time survival game with minimalistic gameplay and roguelite elements. It was developed and published by indie developer poncle. The game was originally coded using HTML5 and was released on itch.io as a browser game for free on March 31, 2021. It later came out on Steam on December 17, 2021, and on Google Play on June 8, 2023. The game supports mouse, keyboard, controller, and touch screen inputs.
-vampire survivors last version download
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The premise of the game is simple: you play as one of several characters who have to survive as long as possible against waves of vampires, werewolves, zombies, skeletons, ghosts, demons, and other supernatural enemies. You have no control over when or how you attack; your weapons fire automatically based on your position and your enemies' proximity. Your only task is to move around the map, collect gems and items that drop from enemies or chests, level up your weapons and stats, and avoid getting hit by enemies or traps. The game gets progressively harder as time goes by; new enemies appear every minute, some with special abilities or increased health. The game ends when you die or when you reach 30 minutes of survival time.
-Why You Should Play Vampire Survivors?
-Vampire Survivors is one of those games that are easy to pick up but hard to put down. It has a simple but addictive gameplay loop that keeps you hooked for hours. It also has a lot of variety and replay value thanks to its roguelite elements. You can choose from eight different characters, each with their own unique weapons, abilities, and perks. You can also unlock and equip various items that can boost your stats, grant you special effects, or alter the gameplay in some way. You can also customize your game mode by choosing from four difficulty levels, three map sizes, and three game speeds. You can also play with up to four friends in local co-op mode, or challenge yourself in endless mode or daily challenge mode.
-Vampire Survivors has received overwhelmingly positive reviews from both critics and players. It has a 9/10 rating on Steam, a 4.8/5 rating on Google Play, and a 4.6/5 rating on itch.io. Some of the praises that the game has received include its charming pixel art graphics, its catchy retro soundtrack, its smooth and responsive controls, its balanced and rewarding gameplay, its high replay value, and its fun and humorous tone. Some of the criticisms that the game has received include its lack of online multiplayer, its occasional bugs and glitches, and its high difficulty curve.
-How to Download Vampire Survivors on PC?
-If you want to play Vampire Survivors on your PC, you have two options: you can either play it on your browser for free on itch.io, or you can buy it on Steam for $4.99 USD. Here are the steps to download the game on Steam:
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-
How to Download Vampire Survivors on Mobile?
-If you want to play Vampire Survivors on your mobile device, you can download it for free on Google Play. Here are the steps to download the game on your Android phone or tablet:
--
-
How to Update Vampire Survivors to the Latest Version?
-Vampire Survivors is constantly being updated by the developer with new features, fixes, and improvements. To make sure that you are playing the latest version of the game, you need to check for updates and install them on your PC or mobile device. Here is how to do that:
-On PC:
--
-
On Mobile:
--
-
What's New in the Latest Version of Vampire Survivors?
-The latest version of Vampire Survivors as of June 20, 2023 is version 1.6.0. This version was released on June 15, 2023 and introduced several new features, fixes, and improvements. Here are some of the main changes and additions in this patch:
-| Feature | Description |
|---|---|
| New character: The Hunter | A A skilled marksman who uses a crossbow and a knife to hunt down his prey. He has a high critical chance and can deal extra damage to enemies with low health. He also has a passive ability that allows him to see enemies through walls. |
| New item: The Holy Grail | A rare and powerful item that grants the user immortality. It heals the user for 1 HP every second and prevents them from dying. However, it also curses the user with eternal thirst, which reduces their movement speed by 50% and makes them unable to use other items. |
| New enemy: The Reaper | A terrifying and deadly enemy that appears after 20 minutes of survival time. It is immune to all damage and can kill the user instantly with its scythe. It can also teleport around the map and summon other enemies. The only way to escape from it is to reach 30 minutes of survival time or use the Holy Grail. |
| New feature: Achievements | A new feature that rewards the user for completing various challenges and tasks in the game. There are 50 achievements in total, ranging from easy to hard. Some of them unlock new items or characters, while others grant the user bragging rights. |
| New feature: Leaderboards | A new feature that ranks the user based on their best survival time, score, kills, and gems collected in the game. There are separate leaderboards for each game mode, difficulty level, map size, and game speed. The user can also compare their stats with their friends or other players around the world. |
| Various bug fixes and improvements | A number of bug fixes and improvements that enhance the performance, stability, and quality of the game. Some of them include fixing typos, glitches, crashes, exploits, balancing issues, and adding more options and settings. |
For more details on the latest version of Vampire Survivors, you can visit the version history page on itch.io or the update news page on Steam.
-Tips and Tricks for Playing Vampire Survivors
-Vampire Survivors is a challenging and fun game that requires skill, strategy, and luck to survive. Here are some tips and tricks that can help you improve your gameplay and have more fun:
--
-
Conclusion
-Vampire Survivors is a gothic horror casual game with rogue-lite elements that will keep you entertained for hours. It has a simple but addictive gameplay loop that challenges your skills and reflexes against waves of supernatural enemies. It also has a lot of variety and replay value thanks to its roguelite elements, such as different characters, items, modes, difficulties, maps, speeds, achievements, and leader boards. You can download the game for free on your browser or mobile device, or buy it for a low price on Steam. You can also update the game to the latest version to enjoy the new features, fixes, and improvements. If you are looking for a thrilling and addictive game that will test your skills and reflexes against hordes of nightmarish creatures, you might want to check out Vampire Survivors. You won't regret it!
-FAQs
-Is Vampire Survivors free to play?
-Yes, Vampire Survivors is free to play on your browser or mobile device. You can play the game without any ads or in-app purchases. However, if you want to support the developer and enjoy some extra benefits, you can buy the game on Steam for $4.99 USD. This will give you access to achievements, leaderboards, cloud saves, and future updates.
-Is Vampire Survivors multiplayer?
-Yes, Vampire Survivors has a local co-op mode that allows you to play with up to four friends on the same device. You can either share the same screen or use multiple controllers or touch screens. However, the game does not have an online multiplayer mode at the moment.
-Is Vampire Survivors available on other platforms?
-No, Vampire Survivors is only available on PC and Android devices at the moment. The developer has not announced any plans to release the game on other platforms such as iOS, Mac, Linux, or consoles.
-How long is a run in Vampire Survivors?
-A run in Vampire Survivors can last anywhere from a few seconds to 30 minutes, depending on your skill level and luck. The game ends when you die or when you reach 30 minutes of survival time. The longer you survive, the higher your score and rank will be.
-How many characters are there in Vampire Survivors?
-There are eight characters in Vampire Survivors, each with their own unique weapons, abilities, and perks. They are: The Vampire, The Witch, The Priest, The Knight, The Hunter, The Scientist, The Ninja, and The Pirate. You can unlock them by playing the game and collecting gems.
197e85843d-
-
\ No newline at end of file diff --git a/spaces/contluForse/HuggingGPT/assets/Cartilha Alegria De Saber Em Pdf Downloadl UPDATED.md b/spaces/contluForse/HuggingGPT/assets/Cartilha Alegria De Saber Em Pdf Downloadl UPDATED.md deleted file mode 100644 index ae0979c956c05970cf3d55788b7b179f6ff3d33b..0000000000000000000000000000000000000000 --- a/spaces/contluForse/HuggingGPT/assets/Cartilha Alegria De Saber Em Pdf Downloadl UPDATED.md +++ /dev/null @@ -1,104 +0,0 @@ -
-
Cartilha Alegria de Saber em PDF: um guia para alfabetizar crianças
- -A Cartilha Alegria de Saber é um livro de alfabetização que utiliza o método fônico para ensinar as crianças a ler e escrever. O método fônico consiste em associar os sons das letras e das sílabas com as imagens dos objetos que representam. Assim, a criança aprende a reconhecer e a produzir as palavras de forma lúdica e divertida.
-Cartilha Alegria De Saber Em Pdf Downloadl
Download ✶ https://ssurll.com/2uzxnf
- -
A Cartilha Alegria de Saber foi elaborada por uma equipe de pedagogos e professores especializados em alfabetização, com base em pesquisas científicas e experiências práticas. O livro é composto por 69 páginas, divididas em quatro partes: vogais, consoantes, sílabas simples e sílabas complexas. Cada parte contém textos, ilustrações, atividades, jogos e músicas que estimulam o desenvolvimento cognitivo, afetivo e social da criança.
- -Além do livro, a Cartilha Alegria de Saber também oferece um caderno de atividades complementares, que pode ser baixado gratuitamente em formato PDF. O caderno contém exercícios de escrita, leitura, interpretação, ortografia, gramática e matemática, que reforçam o aprendizado da cartilha. O caderno também pode ser usado como material de apoio para os pais e professores que acompanham o processo de alfabetização das crianças.
- -Como baixar a Cartilha Alegria de Saber em PDF?
- -Se você quer baixar a Cartilha Alegria de Saber em PDF, basta acessar o site oficial do livro e clicar no botão "Baixar". Você será redirecionado para uma página onde poderá escolher entre as opções de download: Power Point ou imagem por imagem. Depois, é só salvar o arquivo no seu computador ou dispositivo móvel e imprimir se desejar.
- -Ao baixar a Cartilha Alegria de Saber em PDF, você terá acesso a um material de qualidade, que já foi utilizado por milhares de crianças em todo o Brasil. A cartilha é recomendada por especialistas em educação e aprovada pelo Ministério da Educação (MEC). Além disso, ao adquirir o livro, você estará contribuindo para um projeto social que doa exemplares da cartilha para escolas públicas e comunidades carentes.
- -Por que usar a Cartilha Alegria de Saber em PDF?
- -A Cartilha Alegria de Saber em PDF é uma ótima opção para quem quer alfabetizar crianças de forma eficiente e divertida. Veja alguns benefícios de usar esse material:
- --
-
Portanto, se você quer alfabetizar crianças com sucesso, não perca tempo e baixe agora mesmo a Cartilha Alegria de Saber em PDF. Você vai se surpreender com os resultados!
- -Como usar a Cartilha Alegria de Saber em PDF na sala de aula?
- -A Cartilha Alegria de Saber em PDF pode ser usada na sala de aula como um recurso didático para facilitar o ensino e a aprendizagem da alfabetização. Os professores podem seguir as orientações do livro e do caderno de atividades, que apresentam uma sequência lógica e progressiva de conteúdos e habilidades. Os professores também podem adaptar as atividades de acordo com as necessidades e o ritmo de cada turma e de cada aluno.
- -A Cartilha Alegria de Saber em PDF também permite que os professores trabalhem de forma interdisciplinar, integrando as áreas de linguagem, matemática, ciências, artes e educação física. Além disso, o livro e o caderno estimulam a participação dos pais e da comunidade no processo de alfabetização das crianças, promovendo a parceria entre a escola e a família.
- -Quais são os benefícios da Cartilha Alegria de Saber em PDF para as crianças?
- -A Cartilha Alegria de Saber em PDF traz diversos benefícios para as crianças que estão aprendendo a ler e escrever. Veja alguns deles:
- --
-
Portanto, a Cartilha Alegria de Saber em PDF é um material que contribui para o desenvolvimento integral das crianças, preparando-as para os desafios da vida escolar e social.
-A Cartilha Alegria de Saber em PDF não é o único material de alfabetização que você pode baixar gratuitamente na internet. Existem outros sites que disponibilizam livros, cadernos, jogos, atividades e vídeos que podem auxiliar no ensino e na aprendizagem da leitura e da escrita. Veja alguns exemplos:
- --
-
Portanto, se você quer ter acesso a mais materiais de alfabetização em PDF, basta pesquisar na internet e escolher os que mais se adequam às suas necessidades e preferências.
- -Nem todos os materiais de alfabetização em PDF que você encontra na internet são confiáveis e adequados. Alguns podem conter erros conceituais, metodológicos ou gramaticais, que podem prejudicar o processo de alfabetização das crianças. Por isso, é importante que você avalie a qualidade dos materiais antes de baixá-los e usá-los. Veja algumas dicas para fazer isso:
- --
-
Portanto, se você quer garantir a qualidade dos materiais de alfabetização em PDF que você baixa na internet, siga essas dicas e faça uma avaliação criteriosa antes de usá-los.
-A Cartilha Alegria de Saber em PDF também pode ser usada em casa, como um recurso para apoiar a alfabetização das crianças que estudam na modalidade de ensino remoto ou híbrido, ou que precisam de reforço escolar. Os pais ou responsáveis podem seguir as orientações do livro e do caderno de atividades, que explicam passo a passo como ensinar as crianças a ler e escrever.
- -A Cartilha Alegria de Saber em PDF também permite que os pais ou responsáveis criem um ambiente favorável à alfabetização em casa, proporcionando momentos de leitura compartilhada, de escrita criativa e de brincadeiras educativas com as crianças. Além disso, o livro e o caderno incentivam o diálogo e a troca de experiências entre as crianças, os pais e os professores, promovendo a parceria entre a família e a escola.
- -A Cartilha Alegria de Saber em PDF é um material que permite avaliar o desempenho das crianças na alfabetização, por meio de diferentes instrumentos e critérios. Veja alguns exemplos:
- --
-
Portanto, a Cartilha Alegria de Saber em PDF é um material que possibilita uma avaliação contínua, diversificada e democrática da alfabetização das crianças, contribuindo para o aprimoramento da qualidade da educação.
-A Cartilha Alegria de Saber em PDF é um material de alfabetização que pode trazer muitos benefícios para as crianças, os pais e os professores que desejam ensinar e aprender a ler e escrever de forma eficaz, divertida e significativa. A cartilha oferece uma proposta pedagógica baseada em evidências científicas e em boas práticas educacionais, que respeita a diversidade cultural e linguística do país e que promove o desenvolvimento integral das crianças.
- -A Cartilha Alegria de Saber em PDF também pode ser usada em diferentes contextos e situações, como na sala de aula, em casa, no ensino remoto ou híbrido, ou no reforço escolar. A cartilha disponibiliza o livro e o caderno de atividades em formato PDF, facilitando o acesso e a distribuição do material para as escolas, os professores e as famílias. A cartilha também possibilita uma avaliação contínua, diversificada e democrática da alfabetização das crianças, contribuindo para o aprimoramento da qualidade da educação.
- -Portanto, se você quer ter acesso a um material de alfabetização que pode fazer a diferença na vida das crianças brasileiras, baixe agora mesmo a Cartilha Alegria de Saber em PDF e aproveite todos os seus recursos e vantagens.
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-
\ No newline at end of file diff --git a/spaces/contluForse/HuggingGPT/assets/Collapse Devastated World English Patch Review and Rating by Gamers.md b/spaces/contluForse/HuggingGPT/assets/Collapse Devastated World English Patch Review and Rating by Gamers.md deleted file mode 100644 index 1c60a0e0bb758a5f358113ef524dfb26859193e5..0000000000000000000000000000000000000000 --- a/spaces/contluForse/HuggingGPT/assets/Collapse Devastated World English Patch Review and Rating by Gamers.md +++ /dev/null @@ -1,6 +0,0 @@ - -
The world has changed dramatically in the three months since our last update of the World Economic Outlook in January. A rare disaster, a coronavirus pandemic, has resulted in a tragically large number of human lives being lost. As countries implement necessary quarantines and social distancing practices to contain the pandemic, the world has been put in a Great Lockdown. The magnitude and speed of collapse in activity that has followed is unlike anything experienced in our lifetimes.
-The reasons Venezuela is in crisis are years of hyperinflation, violence, and food and medicine shortages. The country was once considered the richest in Latin America, thanks to having the largest oil reserves in the world. But more than a decade of declining oil revenue and poor governance led to the collapse of the national economy, and the government has not been able to provide adequate social services.
-Collapse Devastated World English Patch
Download File ——— https://ssurll.com/2uzxGH
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-
\ No newline at end of file diff --git a/spaces/contluForse/HuggingGPT/assets/Download Bollebof 101 Barz 2013 for Free The Airline Bof EP.md b/spaces/contluForse/HuggingGPT/assets/Download Bollebof 101 Barz 2013 for Free The Airline Bof EP.md deleted file mode 100644 index 52cc9c8527475481a14c7ea6b5875ad253a44e4f..0000000000000000000000000000000000000000 --- a/spaces/contluForse/HuggingGPT/assets/Download Bollebof 101 Barz 2013 for Free The Airline Bof EP.md +++ /dev/null @@ -1,6 +0,0 @@ -
bollebof 101 barz 2013 download
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- - aaccfb2cb3
-
-
- diff --git a/spaces/cozyanduofen/bingo/src/lib/hooks/use-copy-to-clipboard.tsx b/spaces/cozyanduofen/bingo/src/lib/hooks/use-copy-to-clipboard.tsx deleted file mode 100644 index 62f7156dca246c46b213151af003a3a177977ccf..0000000000000000000000000000000000000000 --- a/spaces/cozyanduofen/bingo/src/lib/hooks/use-copy-to-clipboard.tsx +++ /dev/null @@ -1,33 +0,0 @@ -'use client' - -import * as React from 'react' - -export interface useCopyToClipboardProps { - timeout?: number -} - -export function useCopyToClipboard({ - timeout = 2000 -}: useCopyToClipboardProps) { - const [isCopied, setIsCopied] = React.useState
元素，则不添加按钮
- }
- var firstChild = code.firstChild;
- if (!firstChild) {
- return; // 如果 元素没有子节点，则不添加按钮
- }
- var button = document.createElement('button');
- button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本
- button.style.position = 'relative';
- button.style.float = 'right';
- button.style.fontSize = '1em'; // 可选：调整按钮大小
- button.style.background = 'none'; // 可选：去掉背景颜色
- button.style.border = 'none'; // 可选：去掉边框
- button.style.cursor = 'pointer'; // 可选：显示指针样式
- button.addEventListener('click', function () {
- var range = document.createRange();
- range.selectNodeContents(code);
- range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前
- var selection = window.getSelection();
- selection.removeAllRanges();
- selection.addRange(range);
-
- try {
- var success = document.execCommand('copy');
- if (success) {
- button.textContent = '\u2714';
- setTimeout(function () {
- button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制”
- }, 2000);
- } else {
- button.textContent = '\u2716';
- }
- } catch (e) {
- console.error(e);
- button.textContent = '\u2716';
- }
-
- selection.removeAllRanges();
- });
- code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前
- }
-
- function handleNewElements(mutationsList, observer) {
- for (var mutation of mutationsList) {
- if (mutation.type === 'childList') {
- for (var node of mutation.addedNodes) {
- if (node.nodeName === 'PRE') {
- addCopyButton(node);
- }
- }
- }
- }
- }
-
- var observer = new MutationObserver(handleNewElements);
- observer.observe(document.documentElement, { childList: true, subtree: true });
-
- document.querySelectorAll('pre').forEach(addCopyButton);
-})();
diff --git a/spaces/daydayup1225/Chat-web/assets/custom.js b/spaces/daydayup1225/Chat-web/assets/custom.js
deleted file mode 100644
index 7b1761043149ff97ca498501c87a0d15db5258ee..0000000000000000000000000000000000000000
--- a/spaces/daydayup1225/Chat-web/assets/custom.js
+++ /dev/null
@@ -1 +0,0 @@
-// custom javascript here
\ No newline at end of file
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/PSDraw.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/PSDraw.py
deleted file mode 100644
index 13b3048f67e18ac58170c3a1bd25cb18d66b30fe..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/PSDraw.py
+++ /dev/null
@@ -1,229 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# Simple PostScript graphics interface
-#
-# History:
-# 1996-04-20 fl Created
-# 1999-01-10 fl Added gsave/grestore to image method
-# 2005-05-04 fl Fixed floating point issue in image (from Eric Etheridge)
-#
-# Copyright (c) 1997-2005 by Secret Labs AB. All rights reserved.
-# Copyright (c) 1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import sys
-
-from . import EpsImagePlugin
-
-##
-# Simple PostScript graphics interface.
-
-
-class PSDraw:
- """
- Sets up printing to the given file. If ``fp`` is omitted,
- ``sys.stdout.buffer`` or ``sys.stdout`` is assumed.
- """
-
- def __init__(self, fp=None):
- if not fp:
- try:
- fp = sys.stdout.buffer
- except AttributeError:
- fp = sys.stdout
- self.fp = fp
-
- def begin_document(self, id=None):
- """Set up printing of a document. (Write PostScript DSC header.)"""
- # FIXME: incomplete
- self.fp.write(
- b"%!PS-Adobe-3.0\n"
- b"save\n"
- b"/showpage { } def\n"
- b"%%EndComments\n"
- b"%%BeginDocument\n"
- )
- # self.fp.write(ERROR_PS) # debugging!
- self.fp.write(EDROFF_PS)
- self.fp.write(VDI_PS)
- self.fp.write(b"%%EndProlog\n")
- self.isofont = {}
-
- def end_document(self):
- """Ends printing. (Write PostScript DSC footer.)"""
- self.fp.write(b"%%EndDocument\nrestore showpage\n%%End\n")
- if hasattr(self.fp, "flush"):
- self.fp.flush()
-
- def setfont(self, font, size):
- """
- Selects which font to use.
-
- :param font: A PostScript font name
- :param size: Size in points.
- """
- font = bytes(font, "UTF-8")
- if font not in self.isofont:
- # reencode font
- self.fp.write(b"/PSDraw-%s ISOLatin1Encoding /%s E\n" % (font, font))
- self.isofont[font] = 1
- # rough
- self.fp.write(b"/F0 %d /PSDraw-%s F\n" % (size, font))
-
- def line(self, xy0, xy1):
- """
- Draws a line between the two points. Coordinates are given in
- PostScript point coordinates (72 points per inch, (0, 0) is the lower
- left corner of the page).
- """
- self.fp.write(b"%d %d %d %d Vl\n" % (*xy0, *xy1))
-
- def rectangle(self, box):
- """
- Draws a rectangle.
-
- :param box: A tuple of four integers, specifying left, bottom, width and
- height.
- """
- self.fp.write(b"%d %d M 0 %d %d Vr\n" % box)
-
- def text(self, xy, text):
- """
- Draws text at the given position. You must use
- :py:meth:`~PIL.PSDraw.PSDraw.setfont` before calling this method.
- """
- text = bytes(text, "UTF-8")
- text = b"\\(".join(text.split(b"("))
- text = b"\\)".join(text.split(b")"))
- xy += (text,)
- self.fp.write(b"%d %d M (%s) S\n" % xy)
-
- def image(self, box, im, dpi=None):
- """Draw a PIL image, centered in the given box."""
- # default resolution depends on mode
- if not dpi:
- if im.mode == "1":
- dpi = 200 # fax
- else:
- dpi = 100 # greyscale
- # image size (on paper)
- x = im.size[0] * 72 / dpi
- y = im.size[1] * 72 / dpi
- # max allowed size
- xmax = float(box[2] - box[0])
- ymax = float(box[3] - box[1])
- if x > xmax:
- y = y * xmax / x
- x = xmax
- if y > ymax:
- x = x * ymax / y
- y = ymax
- dx = (xmax - x) / 2 + box[0]
- dy = (ymax - y) / 2 + box[1]
- self.fp.write(b"gsave\n%f %f translate\n" % (dx, dy))
- if (x, y) != im.size:
- # EpsImagePlugin._save prints the image at (0,0,xsize,ysize)
- sx = x / im.size[0]
- sy = y / im.size[1]
- self.fp.write(b"%f %f scale\n" % (sx, sy))
- EpsImagePlugin._save(im, self.fp, None, 0)
- self.fp.write(b"\ngrestore\n")
-
-
-# --------------------------------------------------------------------
-# PostScript driver
-
-#
-# EDROFF.PS -- PostScript driver for Edroff 2
-#
-# History:
-# 94-01-25 fl: created (edroff 2.04)
-#
-# Copyright (c) Fredrik Lundh 1994.
-#
-
-
-EDROFF_PS = b"""\
-/S { show } bind def
-/P { moveto show } bind def
-/M { moveto } bind def
-/X { 0 rmoveto } bind def
-/Y { 0 exch rmoveto } bind def
-/E { findfont
- dup maxlength dict begin
- {
- 1 index /FID ne { def } { pop pop } ifelse
- } forall
- /Encoding exch def
- dup /FontName exch def
- currentdict end definefont pop
-} bind def
-/F { findfont exch scalefont dup setfont
- [ exch /setfont cvx ] cvx bind def
-} bind def
-"""
-
-#
-# VDI.PS -- PostScript driver for VDI meta commands
-#
-# History:
-# 94-01-25 fl: created (edroff 2.04)
-#
-# Copyright (c) Fredrik Lundh 1994.
-#
-
-VDI_PS = b"""\
-/Vm { moveto } bind def
-/Va { newpath arcn stroke } bind def
-/Vl { moveto lineto stroke } bind def
-/Vc { newpath 0 360 arc closepath } bind def
-/Vr { exch dup 0 rlineto
- exch dup 0 exch rlineto
- exch neg 0 rlineto
- 0 exch neg rlineto
- setgray fill } bind def
-/Tm matrix def
-/Ve { Tm currentmatrix pop
- translate scale newpath 0 0 .5 0 360 arc closepath
- Tm setmatrix
-} bind def
-/Vf { currentgray exch setgray fill setgray } bind def
-"""
-
-#
-# ERROR.PS -- Error handler
-#
-# History:
-# 89-11-21 fl: created (pslist 1.10)
-#
-
-ERROR_PS = b"""\
-/landscape false def
-/errorBUF 200 string def
-/errorNL { currentpoint 10 sub exch pop 72 exch moveto } def
-errordict begin /handleerror {
- initmatrix /Courier findfont 10 scalefont setfont
- newpath 72 720 moveto $error begin /newerror false def
- (PostScript Error) show errorNL errorNL
- (Error: ) show
- /errorname load errorBUF cvs show errorNL errorNL
- (Command: ) show
- /command load dup type /stringtype ne { errorBUF cvs } if show
- errorNL errorNL
- (VMstatus: ) show
- vmstatus errorBUF cvs show ( bytes available, ) show
- errorBUF cvs show ( bytes used at level ) show
- errorBUF cvs show errorNL errorNL
- (Operand stargck: ) show errorNL /ostargck load {
- dup type /stringtype ne { errorBUF cvs } if 72 0 rmoveto show errorNL
- } forall errorNL
- (Execution stargck: ) show errorNL /estargck load {
- dup type /stringtype ne { errorBUF cvs } if 72 0 rmoveto show errorNL
- } forall
- end showpage
-} def end
-"""
diff --git a/spaces/deelerb/3dselfie/PIFu/lib/renderer/gl/cam_render.py b/spaces/deelerb/3dselfie/PIFu/lib/renderer/gl/cam_render.py
deleted file mode 100644
index 7b766af057b9c052388aceb152b0191fa2e4ea25..0000000000000000000000000000000000000000
--- a/spaces/deelerb/3dselfie/PIFu/lib/renderer/gl/cam_render.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from .render import Render
-
-GLUT = None
-
-class CamRender(Render):
- def __init__(self, width=1600, height=1200, name='Cam Renderer',
- program_files=['simple.fs', 'simple.vs'], color_size=1, ms_rate=1, egl=False):
- Render.__init__(self, width, height, name, program_files, color_size, ms_rate=ms_rate, egl=egl)
- self.camera = None
-
- if not egl:
- global GLUT
- import OpenGL.GLUT as GLUT
- GLUT.glutDisplayFunc(self.display)
- GLUT.glutKeyboardFunc(self.keyboard)
-
- def set_camera(self, camera):
- self.camera = camera
- self.projection_matrix, self.model_view_matrix = camera.get_gl_matrix()
-
- def keyboard(self, key, x, y):
- # up
- eps = 1
- # print(key)
- if key == b'w':
- self.camera.center += eps * self.camera.direction
- elif key == b's':
- self.camera.center -= eps * self.camera.direction
- if key == b'a':
- self.camera.center -= eps * self.camera.right
- elif key == b'd':
- self.camera.center += eps * self.camera.right
- if key == b' ':
- self.camera.center += eps * self.camera.up
- elif key == b'x':
- self.camera.center -= eps * self.camera.up
- elif key == b'i':
- self.camera.near += 0.1 * eps
- self.camera.far += 0.1 * eps
- elif key == b'o':
- self.camera.near -= 0.1 * eps
- self.camera.far -= 0.1 * eps
-
- self.projection_matrix, self.model_view_matrix = self.camera.get_gl_matrix()
-
- def show(self):
- if GLUT is not None:
- GLUT.glutMainLoop()
diff --git a/spaces/diacanFperku/AutoGPT/Adware Away 4.1.0 Serial.md b/spaces/diacanFperku/AutoGPT/Adware Away 4.1.0 Serial.md
deleted file mode 100644
index 645cad072a9825a12092d6afe41fbad38a487a70..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Adware Away 4.1.0 Serial.md
+++ /dev/null
@@ -1,26 +0,0 @@
-
-How to Remove Adware Away 4.1.0 Serial from Your PC
-Adware Away 4.1.0 Serial is a malicious program that claims to be a legitimate anti-virus software. However, it is actually a rogue application that infects your computer with adware, spyware, and other malware. It also displays fake security alerts and pop-ups to scare you into buying its full version.
-Adware Away 4.1.0 Serial
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-If you have downloaded Adware Away 4.1.0 Serial from a suspicious website or received it as an email attachment, you should remove it as soon as possible. Here are some steps to help you get rid of this nasty program:
-
-
-Congratulations! You have successfully removed Adware Away 4.1.0 Serial from your PC. To prevent future infections, you should always be careful about what you download and open online. You should also keep your anti-malware software updated and run regular scans of your system.
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-Some signs that you may have adware on your PC are:
-
-
-If you notice any of these symptoms, you should scan your PC with a reliable anti-malware software and remove any detected adware. You should also avoid clicking on suspicious links or ads, and only download software from trusted sources.
In conclusion, Adware Away 4.1.0 Serial is a rogue program that poses as a legitimate anti-virus software. It infects your PC with adware and other malware, and tries to trick you into buying its full version. You should remove it as soon as possible using the steps outlined above.
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-Adware is a common and annoying type of malware that displays unwanted ads on your computer. It can also compromise your privacy and security, and affect your system performance. You should always be careful about what you download and open online, and keep your anti-malware software updated and active.
-By following these tips, you can protect your PC from adware and other malware threats. Stay safe and enjoy a clean and fast computer!
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diff --git a/spaces/diacanFperku/AutoGPT/Bangalore Days Movie Download With Subtitles In Utorrent.md b/spaces/diacanFperku/AutoGPT/Bangalore Days Movie Download With Subtitles In Utorrent.md
deleted file mode 100644
index 310a3af55401f89bd936b426d5002d0c0baf725a..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Bangalore Days Movie Download With Subtitles In Utorrent.md
+++ /dev/null
@@ -1,40 +0,0 @@
-Bangalore Days Movie Download With Subtitles In Utorrent
Download ⇒ https://gohhs.com/2uFUHv
-
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-
-Bangalore days movie with subtitles in uk
-
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-
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-This is the prologue of my current project. "After the 4fefd39f24
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diff --git a/spaces/diacanFperku/AutoGPT/Dxcpl.exe [VERIFIED] Download Windows 7 32-bit.md b/spaces/diacanFperku/AutoGPT/Dxcpl.exe [VERIFIED] Download Windows 7 32-bit.md
deleted file mode 100644
index fd45395dbbec7f6686b0fcf1db5e78410f972231..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Dxcpl.exe [VERIFIED] Download Windows 7 32-bit.md
+++ /dev/null
@@ -1,6 +0,0 @@
-dxcpl.exe download windows 7 32-bit
Download File ✵ https://gohhs.com/2uFTof
-
-. [file size:1631KB] Download: [DirectX Diagnostic Tool download] 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 4fefd39f24
-
-
-
diff --git a/spaces/diacanFperku/AutoGPT/Ea Sports Ufc Pc Version License Key.txt.md b/spaces/diacanFperku/AutoGPT/Ea Sports Ufc Pc Version License Key.txt.md
deleted file mode 100644
index da5494fe3bb07cb3d78fba7f25dd158a0fb81ffc..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Ea Sports Ufc Pc Version License Key.txt.md
+++ /dev/null
@@ -1,35 +0,0 @@
-
-How to Download and Install EA Sports UFC on PC
-EA Sports UFC is a mixed martial arts fighting video game developed by Electronic Arts for the PlayStation 4 and Xbox One. It is based on the Ultimate Fighting Championship brand and was released on June 15, 2014. It features realistic graphics, physics, and gameplay, as well as over 200 fighters with their own styles and skills.
-If you want to play EA Sports UFC on your PC, you will need a license key to activate the game. A license key is a unique code that verifies that you have purchased the game legally. Without a license key, you will not be able to install or run the game on your computer.
-ea sports ufc pc version license key.txt
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-
-
-Disclaimer
-This article is for informational purposes only. We do not endorse or promote any illegal or unethical activities, such as piracy or hacking. We are not responsible for any consequences that may arise from using a key generator tool or downloading an unauthorized copy of EA Sports UFC on PC. Please use this method at your own risk and discretion.
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diff --git a/spaces/diacanFperku/AutoGPT/Free Download !!INSTALL!! Cheat G-cash Generator.md b/spaces/diacanFperku/AutoGPT/Free Download !!INSTALL!! Cheat G-cash Generator.md
deleted file mode 100644
index dea01eb59ee973b1aa49f88e683c5bc945a4b907..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Free Download !!INSTALL!! Cheat G-cash Generator.md
+++ /dev/null
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diff --git a/spaces/diacanFperku/AutoGPT/More To Read 1 Pdf Indir VERIFIED.md b/spaces/diacanFperku/AutoGPT/More To Read 1 Pdf Indir VERIFIED.md
deleted file mode 100644
index 1c689c0ba17a5a9834b2b542fb978c3cf92df9dd..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/More To Read 1 Pdf Indir VERIFIED.md
+++ /dev/null
@@ -1,6 +0,0 @@
-more to read 1 pdf indir
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diff --git a/spaces/diacanFperku/AutoGPT/Motorola Radius Sm50 Manual.zip.md b/spaces/diacanFperku/AutoGPT/Motorola Radius Sm50 Manual.zip.md
deleted file mode 100644
index 19ed5fc1cba4026df4aa1aa73e209dac01be4878..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Motorola Radius Sm50 Manual.zip.md
+++ /dev/null
@@ -1,10 +0,0 @@
-
-Motorola supplies an express mail warranty card to you upon delivery of your Product. All express warranty claims should be made by completing the warranty claims card and returning it to Motorola or its authorised third party by post or with an express mail service.
-This warranty is in addition to any warranty(s) contained in any sales or other document/s provided by Motorola or any third party. Motorola has the right to carry out any repair to your Product at its premises or at a location chosen by you as part of the trade-in or repair program provided that Motorola is available to carry out such repair. You must provide proof of purchase to Motorola and have the original sales receipt or proof of purchase of the Product in your possession.
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-Motorola Corporation reserves the right to change, add or delete any warranty provision for the Products without prior written notice to the Owner. Other than this, you have no other implied warranty, including that of merchantability or fitness for a particular purpose.
-The Motorola RA2021 Mobile Communicator is the latest model in the Motorola range, with a choice of five color variations, a built in optical digital camera and the latest voice and data communications features, as well as 4.3 inch touch screen display.
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-For example, the unit is configured to perform dedicated transmit only at full power, allowing the unit to operate for several days on a single battery charge. The motorola has a data cable that connects the unit to your handheld or other mobile phone. You must have this cable with you when you activate your unit and send your profile. If the unit is not connected to a phone, you need to connect it to a cellular or mobile phone in order to activate the unit.
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diff --git a/spaces/elsamueldev/gpt4all/tries/1.md b/spaces/elsamueldev/gpt4all/tries/1.md
deleted file mode 100644
index 8d0f737373812f1f43f27d911614cb0960333836..0000000000000000000000000000000000000000
--- a/spaces/elsamueldev/gpt4all/tries/1.md
+++ /dev/null
@@ -1,23 +0,0 @@
-```python
-import gradio as gr
-from gpt4all import GPT4All
-
-GPT4All.download_model("wizardlm-13b-v1.1-superhot-8k.ggmlv3.q4_0.bin", model_path="/home/user/app/")
-
-model = GPT4All(
- model_name="wizardlm-13b-v1.1-superhot-8k.ggmlv3.q4_0.bin",
- model_path="/home/user/app/"
-)
-
-def responder(pregunta):
- respuesta = model.generate(pregunta)
- return respuesta
-
-gr.Interface(
- fn=responder,
- inputs="text",
- outputs="text"
-).launch()
-```
-
-No responde las preguntas, las toma como si fuera un texto que completar
\ No newline at end of file
diff --git a/spaces/emc348/faces-through-time/models/StyleCLIP/models/stylegan2/op/__init__.py b/spaces/emc348/faces-through-time/models/StyleCLIP/models/stylegan2/op/__init__.py
deleted file mode 100644
index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000
--- a/spaces/emc348/faces-through-time/models/StyleCLIP/models/stylegan2/op/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
diff --git a/spaces/erwann/Face-editor/edit.py b/spaces/erwann/Face-editor/edit.py
deleted file mode 100644
index 04f392ae557a266a89590937f7029ac6a027bf3a..0000000000000000000000000000000000000000
--- a/spaces/erwann/Face-editor/edit.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import os
-import sys
-
-from img_processing import custom_to_pil, preprocess, preprocess_vqgan
-
-sys.path.append("taming-transformers")
-import glob
-
-import gradio as gr
-import matplotlib.pyplot as plt
-import PIL
-import taming
-import torch
-
-from loaders import load_config, load_default
-from utils import get_device
-
-
-def get_embedding(model, path=None, img=None, device="cpu"):
- assert path or img, "Input either path or tensor"
- if img is not None:
- raise NotImplementedError
- x = preprocess(PIL.Image.open(path), target_image_size=256).to(device)
- x_processed = preprocess_vqgan(x)
- z, _, [_, _, indices] = model.encode(x_processed)
- return z
-
-
-def blend_paths(
- model, path1, path2, quantize=False, weight=0.5, show=True, device="cuda"
-):
- x = preprocess(PIL.Image.open(path1), target_image_size=256).to(device)
- y = preprocess(PIL.Image.open(path2), target_image_size=256).to(device)
- x_latent = get_embedding(model, path=path1, device=device)
- y_latent = get_embedding(model, path=path2, device=device)
- z = torch.lerp(x_latent, y_latent, weight)
- if quantize:
- z = model.quantize(z)[0]
- decoded = model.decode(z)[0]
- if show:
- plt.figure(figsize=(10, 20))
- plt.subplot(1, 3, 1)
- plt.imshow(x.cpu().permute(0, 2, 3, 1)[0])
- plt.subplot(1, 3, 2)
- plt.imshow(custom_to_pil(decoded))
- plt.subplot(1, 3, 3)
- plt.imshow(y.cpu().permute(0, 2, 3, 1)[0])
- plt.show()
- return custom_to_pil(decoded), z
-
-
-if __name__ == "__main__":
- device = get_device()
- model = load_default(device)
- model.to(device)
- blend_paths(
- model,
- "./test_pics/face.jpeg",
- "./test_pics/face2.jpeg",
- quantize=False,
- weight=0.5,
- )
- plt.show()
diff --git a/spaces/falterWliame/Face_Mask_Detection/Download !!INSTALL!! Iggle Pop Full Version.md b/spaces/falterWliame/Face_Mask_Detection/Download !!INSTALL!! Iggle Pop Full Version.md
deleted file mode 100644
index df248e0d10fed61077d22641ce16dab241f43f83..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Download !!INSTALL!! Iggle Pop Full Version.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
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Iggle Pop Deluxe is a time limited demo version that will expire after 60 minutes of use. To keep on playing the game must be registered.
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Download Zip 🔗 https://urlca.com/2uDdCw
-Iggle Pop Deluxe is an arcade game developed by Sprout Games and published by PopCap. This game has really low system requirements, a 350Mhz CPU powered by Windows 98 (or later) and 128 Mb RAM will be enough to enjoy Iggle Pop Deluxe.
Iggle Pop Deluxe is an arcade game that strongly resembles
When we first play Iggle Pop we must create a new user. Multi-user capability is a great feature if we are using a shared computer. We can see two different game modes: Adventure game and Retro Game (Retro game is not available in demo version). After selecting game mode we must choose our hero, Fizzy or Dizzy. The aim of the game is to move through the mazes and free the trapped iggles.
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Download ((TOP)) Gds64dll.md b/spaces/falterWliame/Face_Mask_Detection/Download ((TOP)) Gds64dll.md
deleted file mode 100644
index e2173dc83d895f6413ee10e7c753a3b139de6268..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Download ((TOP)) Gds64dll.md
+++ /dev/null
@@ -1,182 +0,0 @@
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\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/CEX.IO App A Multi-Asset Crypto Wallet with Staking and DeFi Features.md b/spaces/fatiXbelha/sd/CEX.IO App A Multi-Asset Crypto Wallet with Staking and DeFi Features.md
deleted file mode 100644
index f83c13b244912b784adad4f7f8088153f783a5d0..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/CEX.IO App A Multi-Asset Crypto Wallet with Staking and DeFi Features.md
+++ /dev/null
@@ -1,127 +0,0 @@
-
-CEX.IO APK: A Convenient and Secure Crypto Trading App
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diff --git a/spaces/fatiXbelha/sd/Download Hunter Assassin Hack for Android Unlock All Characters and Levels.md b/spaces/fatiXbelha/sd/Download Hunter Assassin Hack for Android Unlock All Characters and Levels.md
deleted file mode 100644
index 921d768d51059e94bbeaf7736571281eb29d39bd..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Hunter Assassin Hack for Android Unlock All Characters and Levels.md
+++ /dev/null
@@ -1,114 +0,0 @@
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diff --git a/spaces/fatiXbelha/sd/Download Live Your Life MP3 - The Ultimate Motivational Song by T.I. and Rihanna.md b/spaces/fatiXbelha/sd/Download Live Your Life MP3 - The Ultimate Motivational Song by T.I. and Rihanna.md
deleted file mode 100644
index 32cd3381bd775cf35279952226e7c29e44cae359..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Live Your Life MP3 - The Ultimate Motivational Song by T.I. and Rihanna.md
+++ /dev/null
@@ -1,156 +0,0 @@
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diff --git a/spaces/fatiXbelha/sd/Dream AI How to Use the MOD APK to Create Amazing Art - Download Now.md b/spaces/fatiXbelha/sd/Dream AI How to Use the MOD APK to Create Amazing Art - Download Now.md
deleted file mode 100644
index 41fb752e200e3c0b64b147516eb3cde2429b8a33..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Dream AI How to Use the MOD APK to Create Amazing Art - Download Now.md
+++ /dev/null
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diff --git a/spaces/fatiXbelha/sd/Experience Premium Gameplay with Free Fire MAX 1.94.1 APK - Latest Version.md b/spaces/fatiXbelha/sd/Experience Premium Gameplay with Free Fire MAX 1.94.1 APK - Latest Version.md
deleted file mode 100644
index 47486fb1ac65050feb243d2200527ce36b816c05..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Experience Premium Gameplay with Free Fire MAX 1.94.1 APK - Latest Version.md
+++ /dev/null
@@ -1,150 +0,0 @@
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-Yes, Free Fire MAX is free to play, just like the original Free Fire version. You don't have to pay anything to download or install the game, or to access its features and content. However, you can choose to purchase some in-game items or services with real money if you want to enhance your gaming experience.
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diff --git a/spaces/fcakyon/zero-shot-video-classification/README.md b/spaces/fcakyon/zero-shot-video-classification/README.md
deleted file mode 100644
index b7c5e4880a879007c0a56405ce3fb58fc4372608..0000000000000000000000000000000000000000
--- a/spaces/fcakyon/zero-shot-video-classification/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Zero Shot Video Classification
-emoji: 🔥
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: true
-license: apache-2.0
-tags:
-- making-demos
----
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/CarX Drift Racing 2 Money Mod APK Upgrade Your Cars and Customize Your Drifts.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/CarX Drift Racing 2 Money Mod APK Upgrade Your Cars and Customize Your Drifts.md
deleted file mode 100644
index 85ea6cfab2582931dd9c507f02ae2a7efce3f4ce..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/CarX Drift Racing 2 Money Mod APK Upgrade Your Cars and Customize Your Drifts.md
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@@ -1,76 +0,0 @@
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401be4b1e0
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diff --git a/spaces/fffiloni/AnimateDiff-Image-Init/download_bashscripts/0-MotionModule.sh b/spaces/fffiloni/AnimateDiff-Image-Init/download_bashscripts/0-MotionModule.sh
deleted file mode 100644
index 8e2007ee6210f45e6f904ccecaad66eeff5e59ec..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/AnimateDiff-Image-Init/download_bashscripts/0-MotionModule.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-gdown 1RqkQuGPaCO5sGZ6V6KZ-jUWmsRu48Kdq -O models/Motion_Module/
-gdown 1ql0g_Ys4UCz2RnokYlBjyOYPbttbIpbu -O models/Motion_Module/
\ No newline at end of file
diff --git a/spaces/fffiloni/ControlVideo/inference.sh b/spaces/fffiloni/ControlVideo/inference.sh
deleted file mode 100644
index 2ff056ed060222067cdbc762dc087f71214b27fa..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/ControlVideo/inference.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-python inference.py \
- --prompt "A striking mallard floats effortlessly on the sparkling pond." \
- --condition "depth" \
- --video_path "data/mallard-water.mp4" \
- --output_path "outputs/" \
- --video_length 15 \
- --smoother_steps 19 20 \
- --width 512 \
- --height 512 \
- # --is_long_video
\ No newline at end of file
diff --git a/spaces/fffiloni/stable-diffusion-color-sketch/app.py b/spaces/fffiloni/stable-diffusion-color-sketch/app.py
deleted file mode 100644
index fe301a5863167c1398966294634f7ff48de0d888..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/stable-diffusion-color-sketch/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import gradio as gr
-#import torch
-#from torch import autocast // only for GPU
-
-from PIL import Image
-import numpy as np
-from io import BytesIO
-import os
-MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
-
-#from diffusers import StableDiffusionPipeline
-from diffusers import StableDiffusionImg2ImgPipeline
-
-print("hello sylvain")
-
-YOUR_TOKEN=MY_SECRET_TOKEN
-
-device="cpu"
-
-#prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
-#prompt_pipe.to(device)
-
-img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
-img_pipe.to(device)
-
-source_img = gr.Image(source="canvas", type="filepath", tool='color-sketch', label="new gradio color sketch")
-
-gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
-
-def resize(value,img):
- #baseheight = value
- img = Image.open(img)
- #hpercent = (baseheight/float(img.size[1]))
- #wsize = int((float(img.size[0])*float(hpercent)))
- #img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS)
- img = img.resize((value,value), Image.Resampling.LANCZOS)
- return img
-
-
-def infer(source_img, prompt):
-
- source_image = resize(512, source_img)
- source_image.save('source.png')
- images_list = img_pipe([prompt] * 2, init_image=source_image, strength=0.75)
- images = []
- safe_image = Image.open(r"unsafe.png")
- for i, image in enumerate(images_list["sample"]):
- if(images_list["nsfw_content_detected"][i]):
- images.append(safe_image)
- else:
- images.append(image)
- return images
-
-print("Great sylvain ! Everything is working fine !")
-
-title="Paint Stable Diffusion CPU"
-description="Img-2-Img Stable Diffusion example using CPU and the beta color-sketch gradio tool.
Warning: Slow process... ~5/10 min inference time. NSFW filter enabled."
-custom_css = "style.css"
-
-gr.Interface(fn=infer, inputs=[source_img, "text"], outputs=gallery,title=title,description=description,css=custom_css).queue(max_size=100).launch(enable_queue=True)
-
diff --git a/spaces/fracapuano/AISandbox/mailing/mailing.py b/spaces/fracapuano/AISandbox/mailing/mailing.py
deleted file mode 100644
index a40bb2a4f135e386270f48aaa8b5f8eb65b718c1..0000000000000000000000000000000000000000
--- a/spaces/fracapuano/AISandbox/mailing/mailing.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import smtplib
-from email.mime.text import MIMEText
-from email.mime.multipart import MIMEMultipart
-from typing import Text, Union, Iterable
-
-
-def mailing_main(subject:Text, body:Text, to_address:Union[Text, Iterable[Text]]):
- """Sends the email with the given subject and body to the given address (accepts also list of addresses)."""
- # Mailing server configuration
- smtp_server = 'smtp.gmail.com.'
- smtp_port = 587
- sender_email = 'bainhackathon@gmail.com'
- sender_password = 'onyghfffdbmurjdf'
-
- # This creates the actual email message
- msg = MIMEMultipart()
- msg['From'] = sender_email
- msg['To'] = to_address
- msg['Subject'] = subject
- msg.attach(MIMEText(body, 'plain'))
-
- # Connects to SMTP server and then sends the actual email
- try:
- server = smtplib.SMTP(smtp_server, smtp_port)
- server.starttls()
- server.login(sender_email, sender_password)
- server.sendmail(sender_email, to_address, msg.as_string())
- server.quit()
- print("Email sent successfully!")
- except Exception as e:
- print("Error sending email:", e)
-
diff --git a/spaces/gagan3012/T5-Summarization/Makefile b/spaces/gagan3012/T5-Summarization/Makefile
deleted file mode 100644
index e8352210d475daa7a1dfff65e41bc84f05cd3a28..0000000000000000000000000000000000000000
--- a/spaces/gagan3012/T5-Summarization/Makefile
+++ /dev/null
@@ -1,95 +0,0 @@
-.PHONY: clean dirs virtualenv lint requirements push pull run
-
-#################################################################################
-# GLOBALS #
-#################################################################################
-
-PROJECT_DIR := $(shell dirname $(realpath $(lastword $(MAKEFILE_LIST))))
-PYTHON_INTERPRETER = python
-
-#################################################################################
-# COMMANDS #
-#################################################################################
-
-## Create virtualenv.
-## Activate with the command:
-## source env/bin/activate
-virtualenv:
- virtualenv -p $(PYTHON_INTERPRETER) env
-
-## Install Python Dependencies.
-## Make sure you activate the virtualenv first!
-requirements:
- $(PYTHON_INTERPRETER) -m pip install -U pip setuptools wheel
- $(PYTHON_INTERPRETER) -m pip install -r requirements.txt
-
-## Create directories that are ignored by git but required for the project
-dirs:
- mkdir -p data/raw data/processed models
-
-## Delete all compiled Python files
-clean:
- find . -type f -name "*.py[co]" -delete
- find . -type d -name "__pycache__" -delete
-
-## Lint using flake8
-lint:
- flake8 src
-
-## Upload Data to default DVC remote
-push:
- dvc push -r origin
-
-
-## Download Data from default DVC remote
-pull:
- dvc pull
-
-## run the DVC pipeline - recompute any modified outputs such as processed data or trained models
-run:
- dvc repro dvc.yaml
-
-#################################################################################
-# PROJECT RULES #
-#################################################################################
-
-
-
-#################################################################################
-# Self Documenting Commands #
-#################################################################################
-
-.DEFAULT_GOAL := help
-
-# Inspired by
-# sed script explained:
-# /^##/:
-# * save line in hold space
-# * purge line
-# * Loop:
-# * append newline + line to hold space
-# * go to next line
-# * if line starts with doc comment, strip comment character off and loop
-# * remove target prerequisites
-# * append hold space (+ newline) to line
-# * replace newline plus comments by `---`
-# * print line
-# Separate expressions are necessary because labels cannot be delimited by
-# semicolon; see
-.PHONY: help
-help:
- @echo "$$(tput bold)Available rules:$$(tput sgr0)"
- @echo
- @sed -n -e "/^## / Missing" $Missing \
- | LC_ALL='C' sort --ignore-case \
- | awk -F '---' \
- -v ncol=$$(tput cols) \
- -v indent=19 \
- -v col_on="$$(tput setaf 6)" \
- -v col_off="$$(tput sgr0)" \
- 'Missing \
- printf "%s ", words[i]; \
- } \
- printf "\n"; \
- }' \
- | more $(shell test $(shell uname) = Darwin && echo '--no-init --raw-control-chars')
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/contour_expand.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/contour_expand.py
deleted file mode 100644
index ea1111e1768b5f27e118bf7dbc0d9c70a7afd6d7..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/contour_expand.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import numpy as np
-import torch
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', ['contour_expand'])
-
-
-def contour_expand(kernel_mask, internal_kernel_label, min_kernel_area,
- kernel_num):
- """Expand kernel contours so that foreground pixels are assigned into
- instances.
-
- Arguments:
- kernel_mask (np.array or Tensor): The instance kernel mask with
- size hxw.
- internal_kernel_label (np.array or Tensor): The instance internal
- kernel label with size hxw.
- min_kernel_area (int): The minimum kernel area.
- kernel_num (int): The instance kernel number.
-
- Returns:
- label (list): The instance index map with size hxw.
- """
- assert isinstance(kernel_mask, (torch.Tensor, np.ndarray))
- assert isinstance(internal_kernel_label, (torch.Tensor, np.ndarray))
- assert isinstance(min_kernel_area, int)
- assert isinstance(kernel_num, int)
-
- if isinstance(kernel_mask, np.ndarray):
- kernel_mask = torch.from_numpy(kernel_mask)
- if isinstance(internal_kernel_label, np.ndarray):
- internal_kernel_label = torch.from_numpy(internal_kernel_label)
-
- if torch.__version__ == 'parrots':
- if kernel_mask.shape[0] == 0 or internal_kernel_label.shape[0] == 0:
- label = []
- else:
- label = ext_module.contour_expand(
- kernel_mask,
- internal_kernel_label,
- min_kernel_area=min_kernel_area,
- kernel_num=kernel_num)
- label = label.tolist()
- else:
- label = ext_module.contour_expand(kernel_mask, internal_kernel_label,
- min_kernel_area, kernel_num)
- return label
diff --git a/spaces/giswqs/Streamlit/apps/xy.py b/spaces/giswqs/Streamlit/apps/xy.py
deleted file mode 100644
index 1ca2cef25553a8b1d52c19db0aac6c3ca37a6858..0000000000000000000000000000000000000000
--- a/spaces/giswqs/Streamlit/apps/xy.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import leafmap.foliumap as leafmap
-import pandas as pd
-import streamlit as st
-
-
-def app():
-
- st.title("Add Points from XY")
-
- sample_url = "https://raw.githubusercontent.com/giswqs/leafmap/master/examples/data/world_cities.csv"
- url = st.text_input("Enter URL:", sample_url)
- m = leafmap.Map(locate_control=True, plugin_LatLngPopup=False)
-
- if url:
-
- try:
- df = pd.read_csv(url)
-
- columns = df.columns.values.tolist()
- row1_col1, row1_col2, row1_col3, row1_col4, row1_col5 = st.columns(
- [1, 1, 3, 1, 1]
- )
-
- lon_index = 0
- lat_index = 0
-
- for col in columns:
- if col.lower() in ["lon", "longitude", "long", "lng"]:
- lon_index = columns.index(col)
- elif col.lower() in ["lat", "latitude"]:
- lat_index = columns.index(col)
-
- with row1_col1:
- x = st.selectbox("Select longitude column", columns, lon_index)
-
- with row1_col2:
- y = st.selectbox("Select latitude column", columns, lat_index)
-
- with row1_col3:
- popups = st.multiselect("Select popup columns", columns, columns)
-
- with row1_col4:
- heatmap = st.checkbox("Add heatmap")
-
- if heatmap:
- with row1_col5:
- if "pop_max" in columns:
- index = columns.index("pop_max")
- else:
- index = 0
- heatmap_col = st.selectbox("Select heatmap column", columns, index)
- try:
- m.add_heatmap(df, y, x, heatmap_col)
- except:
- st.error("Please select a numeric column")
-
- try:
- m.add_points_from_xy(df, x, y, popups)
- except:
- st.error("Please select a numeric column")
-
- except Exception as e:
- st.error(e)
-
- m.to_streamlit()
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/BAHAN AJAR - Universitas Udayana[2].md b/spaces/gotiQspiryo/whisper-ui/examples/BAHAN AJAR - Universitas Udayana[2].md
deleted file mode 100644
index 67e2b8fdeafaa7e2dbba855732bbf718893c07ca..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/BAHAN AJAR - Universitas Udayana[2].md
+++ /dev/null
@@ -1,8 +0,0 @@
-
-Bismillah sya mau bertnya ya
*Mohon bantuannya*
1. Pada kondisi yang bagaimana anda menggunakan rancangan acak lengkap dalam suatu penelitian *
2. Pada kondisi yang bagaimana anda menggunakan rancangan acak kelompok dalam suatu penelian
3. Pada kondisi yang bagaimana anda menggunakan rancangan faktorial dalam suatu penelitian?
4. Berikan gambaran secara lengkap dan jelas (dijelaskan juga perlakuan, pengacakan dan kondisi/lokasi penelitiannya) suatu penelitian yang menggunakan rancangan acak lengkap? *
5. Berikan gambaran secara lengkap dan jelas (dijelaskan juga perlakuan, pengacakan dan kondisi/lokasi penelitiannya) suatu penelitian yang menggunakan rancangan acak kelompok? *
-Misalnya, sebagai contoh sederhana dari penelitian eksperimental, peneliti ingin mengetahui efektivitas penggunaan sistem alarm rokok di ruang publik untuk mengurangi konsumsi rokok di tempat umum. Beberapa ruang publik dibangun menjadi rokok, yang lain dengan fitur yang sama tidak dilengkapi dengan alarm rokok.
-contoh soal rancangan acak lengkap pdf to jpg
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-1Inventarisasi Hutan
2Analisis Keanekaragaman Flora dan Fauna
3Penyuluhan Kehutanan
4Pemanenan Hasil hutan
5Perlindungan Hutan
6Pengantar Konservasi Sumber Daya Hutan
7Hutan Kota
8Rancangan Percobaan
9Perencanaan Hutan
10Biometrika Hutan
11Pengelolaan Daerah Aliran Sungai
12Klimatologi Pertanian
13Agroforestry
14Bioteknologi Kehutanan
15Dasar-dasar Pengelolaan Kayu
16Pengelolaan Hutan Rakyat
18Teknik Sampling
19Sistem informasi Geografi
20Fisika DasarBab 1 Penyusunan dan Penguraian VektorÂ
-Penelitian ini bertujuan untuk mengetahui pengaruh pembedaan kualitas konsentrat terhadap pertumbuhan ukuran-ukuran tubuh, bobot badan dan konsumsi pakan pedet FH betina lepas sapih. Penelitian ini menggunakan 12 ekor pedet FH betina lepas sapih, bobot badan rata-rata 84,25 ± 15,16 kg (CV= 18,62%), rata-rata tinggi pundak awal 90,56 ± 4,86 cm (CV= 5,73%), rata-rata panjang badan awal 75,15 ± 5,43 cm (CV= 2,23%), rata-rata lingkar dada awal 104,98 ± 5,87 cm (CV= 5,59%), rata-rata panjang tulang carpus awal 3,30 ± 0,61 cm (CV= 18,48%), rata-rata tulang metacarpus awal 15,79 ± 1,91 cm (CV= 7,45%). Pakan
penelitian yang digunakan adalah rumput gajah dan formulasi konsentrat I dan formulasi konsentrat II dengan perbedaan protein kasar (PK) dan total digestibel nutrien (TDN). Parameter yang diukur antara lain pertambahan bobot badan dan ukuran-ukuran tubuh meliputi lingkar dada (LD), panjang badan (PB), tinggi pundak (TP), panjang tulang carpus dan metacarpus, serta konsumsi pakan. Penganbilan sampel dilakukan secara purposive sampling berdasarkan umur pedet yaitu rata-rata 3,5 bulan. Rancangan percobaan menggunakan rancangan acak lengkap (RAL) dengan dua perlakuan dan masing-masing diulang enam kali. Semua data dianalisa secara statistik menggunakan uji t dengan peluang kesalahan 5%. Simpulan penelitian ini adalah bahwa formulasi konsentrat I dan formulasi konsentrat II dapat menjadi pakan bagi pedet FH betina lepas sapih dan layak untuk dijadikan calon induk pengganti.
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Download Jungle Ki Sherni full movie in hindi in 3gp The story of a tigress of the forests and her tribal allies.md b/spaces/gotiQspiryo/whisper-ui/examples/Download Jungle Ki Sherni full movie in hindi in 3gp The story of a tigress of the forests and her tribal allies.md
deleted file mode 100644
index f39baf66ba2a93d608507f4fb38c847f5c53906d..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Download Jungle Ki Sherni full movie in hindi in 3gp The story of a tigress of the forests and her tribal allies.md
+++ /dev/null
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diff --git a/spaces/gulabpatel/GFP_GAN/PaperModel.md b/spaces/gulabpatel/GFP_GAN/PaperModel.md
deleted file mode 100644
index aec81d31de56df74c19ae840d44ad2b2a1f06d28..0000000000000000000000000000000000000000
--- a/spaces/gulabpatel/GFP_GAN/PaperModel.md
+++ /dev/null
@@ -1,76 +0,0 @@
-# Installation
-
-We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. See [here](README.md#installation) for this easier installation.
-If you want want to use the original model in our paper, please follow the instructions below.
-
-1. Clone repo
-
- ```bash
- git clone https://github.com/xinntao/GFPGAN.git
- cd GFPGAN
- ```
-
-1. Install dependent packages
-
- As StyleGAN2 uses customized PyTorch C++ extensions, you need to **compile them during installation** or **load them just-in-time(JIT)**.
- You can refer to [BasicSR-INSTALL.md](https://github.com/xinntao/BasicSR/blob/master/INSTALL.md) for more details.
-
- **Option 1: Load extensions just-in-time(JIT)** (For those just want to do simple inferences, may have less issues)
-
- ```bash
- # Install basicsr - https://github.com/xinntao/BasicSR
- # We use BasicSR for both training and inference
- pip install basicsr
-
- # Install facexlib - https://github.com/xinntao/facexlib
- # We use face detection and face restoration helper in the facexlib package
- pip install facexlib
-
- pip install -r requirements.txt
- python setup.py develop
-
- # remember to set BASICSR_JIT=True before your running commands
- ```
-
- **Option 2: Compile extensions during installation** (For those need to train/inference for many times)
-
- ```bash
- # Install basicsr - https://github.com/xinntao/BasicSR
- # We use BasicSR for both training and inference
- # Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient
- # Add -vvv for detailed log prints
- BASICSR_EXT=True pip install basicsr -vvv
-
- # Install facexlib - https://github.com/xinntao/facexlib
- # We use face detection and face restoration helper in the facexlib package
- pip install facexlib
-
- pip install -r requirements.txt
- python setup.py develop
- ```
-
-## :zap: Quick Inference
-
-Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
-
-```bash
-wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
-```
-
-- Option 1: Load extensions just-in-time(JIT)
-
- ```bash
- BASICSR_JIT=True python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
-
- # for aligned images
- BASICSR_JIT=True python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned
- ```
-
-- Option 2: Have successfully compiled extensions during installation
-
- ```bash
- python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
-
- # for aligned images
- python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned
- ```
diff --git a/spaces/gundruke/ua-thesis-absa/models/layers/__init__.py b/spaces/gundruke/ua-thesis-absa/models/layers/__init__.py
deleted file mode 100644
index 2ee872937f8b5718aaf0faacb6f22e024ec55a87..0000000000000000000000000000000000000000
--- a/spaces/gundruke/ua-thesis-absa/models/layers/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .CRF import CRF
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/structures/image_list.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/structures/image_list.py
deleted file mode 100644
index 2d89224b64402badf7f0b113188b5f653df912ac..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/structures/image_list.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-from __future__ import division
-from typing import Any, List, Sequence, Tuple, Union
-import torch
-from torch.nn import functional as F
-
-
-class ImageList(object):
- """
- Structure that holds a list of images (of possibly
- varying sizes) as a single tensor.
- This works by padding the images to the same size,
- and storing in a field the original sizes of each image
-
- Attributes:
- image_sizes (list[tuple[int, int]]): each tuple is (h, w)
- """
-
- def __init__(self, tensor: torch.Tensor, image_sizes: List[Tuple[int, int]]):
- """
- Arguments:
- tensor (Tensor): of shape (N, H, W) or (N, C_1, ..., C_K, H, W) where K >= 1
- image_sizes (list[tuple[int, int]]): Each tuple is (h, w). It can
- be smaller than (H, W) due to padding.
- """
- self.tensor = tensor
- self.image_sizes = image_sizes
-
- def __len__(self) -> int:
- return len(self.image_sizes)
-
- def __getitem__(self, idx: Union[int, slice]) -> torch.Tensor:
- """
- Access the individual image in its original size.
-
- Returns:
- Tensor: an image of shape (H, W) or (C_1, ..., C_K, H, W) where K >= 1
- """
- size = self.image_sizes[idx]
- return self.tensor[idx, ..., : size[0], : size[1]] # type: ignore
-
- def to(self, *args: Any, **kwargs: Any) -> "ImageList":
- cast_tensor = self.tensor.to(*args, **kwargs)
- return ImageList(cast_tensor, self.image_sizes)
-
- @property
- def device(self) -> torch.device:
- return self.tensor.device
-
- @staticmethod
- def from_tensors(
- tensors: Sequence[torch.Tensor], size_divisibility: int = 0, pad_value: float = 0.0
- ) -> "ImageList":
- """
- Args:
- tensors: a tuple or list of `torch.Tensors`, each of shape (Hi, Wi) or
- (C_1, ..., C_K, Hi, Wi) where K >= 1. The Tensors will be padded
- to the same shape with `pad_value`.
- size_divisibility (int): If `size_divisibility > 0`, add padding to ensure
- the common height and width is divisible by `size_divisibility`.
- This depends on the model and many models need a divisibility of 32.
- pad_value (float): value to pad
-
- Returns:
- an `ImageList`.
- """
- assert len(tensors) > 0
- assert isinstance(tensors, (tuple, list))
- for t in tensors:
- assert isinstance(t, torch.Tensor), type(t)
- assert t.shape[1:-2] == tensors[0].shape[1:-2], t.shape
- # per dimension maximum (H, W) or (C_1, ..., C_K, H, W) where K >= 1 among all tensors
- max_size = (
- # In tracing mode, x.shape[i] is Tensor, and should not be converted
- # to int: this will cause the traced graph to have hard-coded shapes.
- # Instead we should make max_size a Tensor that depends on these tensors.
- # Using torch.stack twice seems to be the best way to convert
- # list[list[ScalarTensor]] to a Tensor
- torch.stack(
- [
- torch.stack([torch.as_tensor(dim) for dim in size])
- for size in [tuple(img.shape) for img in tensors]
- ]
- )
- .max(0)
- .values
- )
-
- if size_divisibility > 0:
- stride = size_divisibility
- # the last two dims are H,W, both subject to divisibility requirement
- max_size = torch.cat([max_size[:-2], (max_size[-2:] + (stride - 1)) // stride * stride])
-
- image_sizes = [tuple(im.shape[-2:]) for im in tensors]
-
- if len(tensors) == 1:
- # This seems slightly (2%) faster.
- # TODO: check whether it's faster for multiple images as well
- image_size = image_sizes[0]
- padding_size = [0, max_size[-1] - image_size[1], 0, max_size[-2] - image_size[0]]
- if all(x == 0 for x in padding_size): # https://github.com/pytorch/pytorch/issues/31734
- batched_imgs = tensors[0].unsqueeze(0)
- else:
- padded = F.pad(tensors[0], padding_size, value=pad_value)
- batched_imgs = padded.unsqueeze_(0)
- else:
- # max_size can be a tensor in tracing mode, therefore use tuple()
- batch_shape = (len(tensors),) + tuple(max_size)
- batched_imgs = tensors[0].new_full(batch_shape, pad_value)
- for img, pad_img in zip(tensors, batched_imgs):
- pad_img[..., : img.shape[-2], : img.shape[-1]].copy_(img)
-
- return ImageList(batched_imgs.contiguous(), image_sizes)
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tools/deploy/README.md b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tools/deploy/README.md
deleted file mode 100644
index b9d5b15512c0bd160accbb1823236b8954a37b86..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tools/deploy/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-
-This directory contains:
-
-1. A script that converts a detectron2 model to caffe2 format.
-
-2. An example that loads a Mask R-CNN model in caffe2 format and runs inference.
-
-See [tutorial](https://detectron2.readthedocs.io/tutorials/deployment.html)
-for their usage.
diff --git a/spaces/hf-audio/open_asr_leaderboard/init.py b/spaces/hf-audio/open_asr_leaderboard/init.py
deleted file mode 100644
index 2e24a0345a30dd3eb1a79616f81a65efa36a1956..0000000000000000000000000000000000000000
--- a/spaces/hf-audio/open_asr_leaderboard/init.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import os
-from constants import EVAL_REQUESTS_PATH
-from pathlib import Path
-from huggingface_hub import HfApi, Repository
-
-TOKEN_HUB = os.environ.get("TOKEN_HUB", None)
-QUEUE_REPO = os.environ.get("QUEUE_REPO", None)
-QUEUE_PATH = os.environ.get("QUEUE_PATH", None)
-
-hf_api = HfApi(
- endpoint="https://huggingface.co",
- token=TOKEN_HUB,
-)
-
-def load_all_info_from_dataset_hub():
- eval_queue_repo = None
- results_csv_path = None
- requested_models = None
-
- passed = True
- if TOKEN_HUB is None:
- passed = False
- else:
- print("Pulling evaluation requests and results.")
-
- eval_queue_repo = Repository(
- local_dir=QUEUE_PATH,
- clone_from=QUEUE_REPO,
- use_auth_token=TOKEN_HUB,
- repo_type="dataset",
- )
- eval_queue_repo.git_pull()
-
- # Local directory where dataset repo is cloned + folder with eval requests
- directory = QUEUE_PATH / EVAL_REQUESTS_PATH
- requested_models = get_all_requested_models(directory)
- requested_models = [p.stem for p in requested_models]
- # Local directory where dataset repo is cloned
- csv_results = get_csv_with_results(QUEUE_PATH)
- if csv_results is None:
- passed = False
- if not passed:
- print("No HuggingFace token provided. Skipping evaluation requests and results.")
-
- return eval_queue_repo, requested_models, csv_results
-
-
-def upload_file(requested_model_name, path_or_fileobj):
- dest_repo_file = Path(EVAL_REQUESTS_PATH) / path_or_fileobj.name
- dest_repo_file = str(dest_repo_file)
- hf_api.upload_file(
- path_or_fileobj=path_or_fileobj,
- path_in_repo=str(dest_repo_file),
- repo_id=QUEUE_REPO,
- token=TOKEN_HUB,
- repo_type="dataset",
- commit_message=f"Add {requested_model_name} to eval queue")
-
-def get_all_requested_models(directory):
- directory = Path(directory)
- all_requested_models = list(directory.glob("*.txt"))
- return all_requested_models
-
-def get_csv_with_results(directory):
- directory = Path(directory)
- all_csv_files = list(directory.glob("*.csv"))
- latest = [f for f in all_csv_files if f.stem.endswith("latest")]
- if len(latest) != 1:
- return None
- return latest[0]
-
-
-
-def is_model_on_hub(model_name, revision="main") -> bool:
- try:
- model_name = model_name.replace(" ","")
- author = model_name.split("/")[0]
- model_id = model_name.split("/")[1]
- if len(author) == 0 or len(model_id) == 0:
- return False, "is not a valid model name. Please use the format `author/model_name`."
- except Exception as e:
- return False, "is not a valid model name. Please use the format `author/model_name`."
-
- try:
- models = list(hf_api.list_models(author=author, search=model_id))
- matched = [model_name for m in models if m.modelId == model_name]
- if len(matched) != 1:
- return False, "was not found on the hub!"
- else:
- return True, None
- except Exception as e:
- print(f"Could not get the model from the hub.: {e}")
- return False, "was not found on hub!"
\ No newline at end of file
diff --git a/spaces/hhalim/hadi_first_day_in_HF/README.md b/spaces/hhalim/hadi_first_day_in_HF/README.md
deleted file mode 100644
index f71c508603356006ad9676a0602d35969180425e..0000000000000000000000000000000000000000
--- a/spaces/hhalim/hadi_first_day_in_HF/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Hadi First Day In HF
-emoji: 🐠
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hra/Curriculum-BabyAGI/app.py b/spaces/hra/Curriculum-BabyAGI/app.py
deleted file mode 100644
index 74214146d6f8eb8d4e945c180f1e5d5280afef6b..0000000000000000000000000000000000000000
--- a/spaces/hra/Curriculum-BabyAGI/app.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import json
-import openai
-import os
-
-import pandas as pd
-
-import gradio as gr
-from collections import deque
-from typing import Dict, List, Optional, Any
-
-from langchain import LLMChain, OpenAI, PromptTemplate
-import datetime
-from datetime import datetime, date, time, timedelta
-from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor, Document, ServiceContext
-from langchain.llms import OpenAIChat
-
-import feedparser
-import pandas as pd
-import numpy as np
-
-from duckduckgo_search import ddg_videos
-from duckduckgo_search import ddg
-
-def get_learning_curriculum(openapikey,topic):
- dateforfilesave=datetime.today().strftime("%d-%m-%Y %I:%M%p")
- print(topic)
- print(dateforfilesave)
- if openapikey=='':
- return pd.DataFrame(["Please provide OpenAPI Key"],columns=['ERROR'])
-
- os.environ['OPENAI_API_KEY'] = str(openapikey)
-
-
- ###Task Creation Agent
-
- prompt='You are a training center AI. Give me a detailed curriculum to learn about "{topicforquery}" using search. The curriculum will be a series of learning tasks to be achieved. Give output as a python list of jsons with "task name", "search keyword" to search to complete the task. Donot repeat the taks. For each task name also add a list of "questions" to ask the search results data to select specific articles and complete the curriculum. Remember the search list will be a dataframe of titles & body of the searched article and you may not be able to go through the full article hence these questions should be of types "Which article best suits a learning curriculum?", "Which article is learning oriented?. To reiterate output should be in json with keys task name ex: get beginner training articles for painting, search keyword ex: beginner painting & questions ex: What are top articles for painting?'.format(topicforquery=topic)
- openai.api_key = os.getenv("OPENAI_API_KEY")
- resp=openai.ChatCompletion.create(
- model="gpt-3.5-turbo",
- messages=[
- {"role": "user", "content": prompt}
- ]
- )
- tasklist=json.loads(resp['choices'][0]['message']['content'])
-
- ###Function to search the internet using DuckDuckGo exposed as a tool
- def research_search(search_keyword,question_to_ask,topic):
- llm_predictor = LLMPredictor(llm=OpenAIChat(temperature=0, model_name="gpt-3.5-turbo"))
- service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
- keyword=search_keyword
- keyword="+".join(keyword.lower().split())
- keyword=keyword.replace(' and ',' AND ')
- posts = ddg(keyword+' '+topic, safesearch='Off', page=1)
- latestnews_df=pd.DataFrame(posts)
- print(latestnews_df.columns)
- #latestnews_df=latestnews_df.drop_duplicates(subset=['title','link','published'])
- latestnews_df['text']='Title: '+latestnews_df['title']+' Description: '+latestnews_df['body']
- print(latestnews_df['text'].tolist())
- documents=[Document(t) for t in latestnews_df['text'].tolist()]
- index = GPTSimpleVectorIndex.from_documents(documents)
- prompt_query=question_to_ask
- respstr=str(index.query(prompt_query,
- service_context=service_context,
- response_mode="tree_summarize",
- similarity_top_k=10))
- print("Search response: ",respstr)
- return respstr
-
- ###Task Execution Agent loop
- list1=[]
- list2=[]
- list3=[]
- for i in range(len(tasklist)):
- taskstuff=tasklist[i]
- search_keyword=taskstuff['search keyword']
- for question in taskstuff['questions']:
- response_string=research_search(search_keyword,question,topic)
- list1.append(taskstuff['task name'])
- list2.append(question)
- list3.append(response_string)
-
- ###Create dataframe to display
- outputdf=pd.DataFrame()
- outputdf['Task']=list1
- outputdf['Question']=list2
- outputdf['Learning']=list3
-
- return outputdf
-
-with gr.Blocks() as demo:
- gr.Markdown("BabyAGI creates Learning Curriculum
")
- gr.Markdown(
- """ This is the first step of an experiment using BabyAGI as a "framework" to construct focused use cases (ex: learning curriculums). The flow uses two AI agents 1) Task creation agent: to create a task list & questions 2) Task execution agent: to execute the tasks & find answers to the questions. Unlike the original BabyAGI concept, this is not open-ended. \n\nNote: This is a series of experiments to understand AI agents and hence do check the quality of output. OpenAI agents (gpt-3.5-turbo), llama-index & DuckDuckGo search are used. The analysis takes roughly 120 secs & may not always be consistent. An error occurs when the OpenAI Api key is not provided/ ChatGPT API is overloaded/ ChatGPT is unable to correctly decipher & format the output\n\n Future directions: 1) Make the task creation more open-ended or longer. 2) Discover multiple learning paths and make ChatGPT introspect on them before finalizing the optimal one 3)Learn from the answers and change the curriculum"""
- )
-
- with gr.Row() as row:
- with gr.Column():
- textboxtopic = gr.Textbox(placeholder="Enter Topic for Curriculum...", lines=1,label='Topic')
- with gr.Column():
- textboxopenapi = gr.Textbox(placeholder="Enter OpenAPI Key...", lines=1,label='OpenAPI Key')
- with gr.Row() as row:
- examples = gr.Examples(examples=['Acrylic painting','Generative AI','latest NLP topic models','FIFA mobile game','Telemedicine'],
- inputs=[textboxtopic])
- with gr.Row() as row:
- btn = gr.Button("Generate \nCurriculum")
-
- with gr.Row() as row:
- table1=gr.Dataframe(
- #headers=["Item", "Cost"],
- #datatype=["str", "str","str"],
- label="Learning Curriculum",
- )
-
- btn.click(get_learning_curriculum, inputs=[textboxopenapi,textboxtopic],outputs=[table1])
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/hysts/zeroscope-v2/README.md b/spaces/hysts/zeroscope-v2/README.md
deleted file mode 100644
index 803a683fa3488ffd9086df37d9f8237e98777b9d..0000000000000000000000000000000000000000
--- a/spaces/hysts/zeroscope-v2/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Zeroscope V2
-emoji: 🌖
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.45.2
-app_file: app.py
-pinned: false
-license: mit
-suggested_hardware: t4-small
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/idlsono/Idksono4/README.md b/spaces/idlsono/Idksono4/README.md
deleted file mode 100644
index 484f63344c33755efc6687ddcb752305dc60f3f7..0000000000000000000000000000000000000000
--- a/spaces/idlsono/Idksono4/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Lolcats39
-emoji: 🌍
-colorFrom: yellow
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/imseldrith/BotX/Uploader/config.py b/spaces/imseldrith/BotX/Uploader/config.py
deleted file mode 100644
index e97d8fce7dc3879ab97081ed271023b9420ad602..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/BotX/Uploader/config.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Hash Minner
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE
-
-import os
-
-import logging
-
-logging.basicConfig(
- format='%(name)s - %(levelname)s - %(message)s',
- handlers=[logging.FileHandler('log.txt'),
- logging.StreamHandler()],
- level=logging.INFO
-)
-
-
-class Config(object):
- WEBHOOK = os.environ.get("BOT_TOKEN", False)
- # Get a token from @BotFather
- BOT_TOKEN = os.environ.get("BOT_TOKEN", "")
- # The Telegram API things
- API_ID = int(os.environ.get("API_ID", 12345))
- API_HASH = os.environ.get("API_HASH")
- # Get these values from my.telegram.org
- # Array to store users who are authorized to use the bot
-
- # File /video download location
- DOWNLOAD_LOCATION = "./DOWNLOADS"
-
- MEGA_EMAIL = os.environ.get("MEGA_EMAIL", "None")
- # If deploying on vps edit the above value as example := Mega_email = "Your-Mega_email-inside-inverted-commas."
-
- # This is not necessary! Enter your mega password only if you have a mega.nz account with pro/business features.
- MEGA_PASSWORD = os.environ.get("MEGA_PASSWORD", "None")
- # If deploying on vps edit the above value as example := Mega_password = "Your-Mega_password-inside-inverted-commas."
- # Telegram maximum file upload size
- TG_MAX_FILE_SIZE = 4194304000
-
- # Chunk size that should be used with requests
- CHUNK_SIZE = int(os.environ.get("CHUNK_SIZE", 128))
- # Proxy for accessing youtube-dl in GeoRestricted Areas
- # Get your own proxy from https://github.com/rg3/youtube-dl/issues/1091#issuecomment-230163061
- HTTP_PROXY = os.environ.get("HTTP_PROXY", "")
-
- # Set timeout for subprcess
- PROCESS_MAX_TIMEOUT = 3700
-
- LOG_CHANNEL = int(os.environ.get("LOG_CHANNEL", -100))
- OWNER_ID = int(os.environ.get("OWNER_ID", "12356"))
- BOT_USERNAME = os.environ.get("BOT_USERNAME", "")
- ADL_BOT_RQ = {}
- AUTH_USERS = list({int(x)
- for x in os.environ.get("AUTH_USERS", "0").split()})
- AUTH_USERS.append(OWNER_ID)
diff --git a/spaces/inamXcontru/PoeticTTS/Californication S06e01 Hdtv X264 Evolve English Subtitles Free Dvd Download.md b/spaces/inamXcontru/PoeticTTS/Californication S06e01 Hdtv X264 Evolve English Subtitles Free Dvd Download.md
deleted file mode 100644
index dbae6f903e7eb3c62cb40762a0eebd4f90ddc89e..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Californication S06e01 Hdtv X264 Evolve English Subtitles Free Dvd Download.md
+++ /dev/null
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-Californication S06e01 Hdtv X264 Evolve English Subtitles : Free Dvd
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deleted file mode 100644
index 5491205c9b24c2ec4ea7fa3bda21afac80478bc9..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/7 Data Recovery Crack Serial Key.md
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deleted file mode 100644
index e1fbcb942a7e55392aeabae31da038aaa4d0f625..0000000000000000000000000000000000000000
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deleted file mode 100644
index 2cab3dc3e81a17344f822c68505f24ab4eded080..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Mere Brother Ki Dulhan 3 1080p Full Movie Download PATCHED.md
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index a33a4bd7cab7d4594fb92617025f700cee8ca08b..0000000000000000000000000000000000000000
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deleted file mode 100644
index 1124a0d58fd14f1f47096bcde3b219a280f1eb5e..0000000000000000000000000000000000000000
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diff --git a/spaces/inreVtussa/clothingai/Examples/Bhugol Aur Aap Pdf Download REPACK.md b/spaces/inreVtussa/clothingai/Examples/Bhugol Aur Aap Pdf Download REPACK.md
deleted file mode 100644
index 52173559576554396a0fc209375eea7fb8095f9f..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Bhugol Aur Aap Pdf Download REPACK.md
+++ /dev/null
@@ -1,10 +0,0 @@
-bhugol aur aap pdf download
Download File →→→ https://tiurll.com/2uCiIv
-
-July 4, 2020 - "Geography" and "You", commonly known as G'nY (in English) and its counterpart Bhugol Aur Aap ( in Hindi), published twice a month. magazines that ... read more
-July 3, 2020 - Aaj Kal (in English) and Bhugol Aur Aap (in Hindi) are released every month, which means you can enjoy two magazines at the same time.
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diff --git a/spaces/inreVtussa/clothingai/Examples/Brooklyn Nine-Nine Season 1-2 Complete 1080p Plus Subtitles [rarbg][rartv] UPD.md b/spaces/inreVtussa/clothingai/Examples/Brooklyn Nine-Nine Season 1-2 Complete 1080p Plus Subtitles [rarbg][rartv] UPD.md
deleted file mode 100644
index cb7e9e7cb50c7be0b2b0a0863d1106cd76a208f0..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Brooklyn Nine-Nine Season 1-2 Complete 1080p Plus Subtitles [rarbg][rartv] UPD.md
+++ /dev/null
@@ -1,17 +0,0 @@
-
-How to Watch Brooklyn Nine-Nine Season 1-2 Complete 1080p Plus Subtitles [rarbg][rartv]
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diff --git a/spaces/izumi-lab/llama-13b-japanese-lora-v0-1ep/README.md b/spaces/izumi-lab/llama-13b-japanese-lora-v0-1ep/README.md
deleted file mode 100644
index 3666c04df9e84d8f96e743091a0714d19b13b346..0000000000000000000000000000000000000000
--- a/spaces/izumi-lab/llama-13b-japanese-lora-v0-1ep/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: LLaMA 13B Japanese LoRA v0 1 epoch
-emoji: 🐨
-colorFrom: gray
-colorTo: gray
-sdk: docker
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/jackyccl/segment-anything/groundingdino/util/__init__.py b/spaces/jackyccl/segment-anything/groundingdino/util/__init__.py
deleted file mode 100644
index 168f9979a4623806934b0ff1102ac166704e7dec..0000000000000000000000000000000000000000
--- a/spaces/jackyccl/segment-anything/groundingdino/util/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
diff --git a/spaces/jeffrymahbuubi/foodvision-mini/app.py b/spaces/jeffrymahbuubi/foodvision-mini/app.py
deleted file mode 100644
index 4cd715f723f6f42cc3c86fd54d25df129d61bc3f..0000000000000000000000000000000000000000
--- a/spaces/jeffrymahbuubi/foodvision-mini/app.py
+++ /dev/null
@@ -1,75 +0,0 @@
-
-### 1. Imports and class names setup ###
-import gradio as gr
-import os
-import torch
-
-from model import create_effnetb2_model
-from timeit import default_timer as timer
-from typing import Tuple, Dict
-
-# Setup class names
-class_names = ['pizza', 'steak', 'sushi']
-
-### 2. Model and transforms prepration ###
-effnetb2, effnetb2_tranforms = create_effnetb2_model(
- num_classes=3
-)
-
-# Load saved weights
-effnetb2.load_state_dict(
- torch.load(
- f"09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
- map_location=torch.device('cpu')
- )
-)
-
-### 3. Predict function ###
-def predict(img) -> Tuple[Dict, float]:
- """
- Transforms and performs a prediction on img and returns prediction and time taken.
- """
-
- # Start the timer
- start_time = timer()
-
- # Transform the target image and add a batch dimension
- img = effnetb2_tranforms(img).unsqueeze(0)
-
- # Put model into evaluation mode and turn on inference mode
- effnetb2.eval()
- with torch.inference_mode():
- # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
- pred_probs = torch.softmax(effnetb2(img), dim=1)
-
- # Create a prediction label and prediction probability for each prediction class
- pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
-
- # Calculate the prediction time
- pred_time = round(timer() - start_time, 5)
-
- # Return the prediction dictionary and prediction time
- return pred_labels_and_probs, pred_time
-
-### 4. Gradio app ###
-# Create title, description and article strings
-title = "FoodVision Mini 🍕🥩🍣"
-description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
-article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
-
-# Create examples list from "examples/" directory
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-# Create the Gradio demo
-demo = gr.Interface(fn=predict, # mapping function from input to output
- inputs=gr.Image(type="pil"), # what are the inputs?
- outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
- gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
- # Create examples list from "examples/" directory
- examples=example_list,
- title=title,
- description=description,
- article=article)
-
-# Launch the demo!
-demo.launch()
diff --git a/spaces/jgurzoni/image_background_swapper/saicinpainting/evaluation/losses/lpips.py b/spaces/jgurzoni/image_background_swapper/saicinpainting/evaluation/losses/lpips.py
deleted file mode 100644
index b5f19b747f2457902695213f7efcde4fdc306c1f..0000000000000000000000000000000000000000
--- a/spaces/jgurzoni/image_background_swapper/saicinpainting/evaluation/losses/lpips.py
+++ /dev/null
@@ -1,891 +0,0 @@
-############################################################
-# The contents below have been combined using files in the #
-# following repository: #
-# https://github.com/richzhang/PerceptualSimilarity #
-############################################################
-
-############################################################
-# __init__.py #
-############################################################
-
-import numpy as np
-from skimage.metrics import structural_similarity
-import torch
-
-from saicinpainting.utils import get_shape
-
-
-class PerceptualLoss(torch.nn.Module):
- def __init__(self, model='net-lin', net='alex', colorspace='rgb', model_path=None, spatial=False, use_gpu=True):
- # VGG using our perceptually-learned weights (LPIPS metric)
- # def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss
- super(PerceptualLoss, self).__init__()
- self.use_gpu = use_gpu
- self.spatial = spatial
- self.model = DistModel()
- self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace,
- model_path=model_path, spatial=self.spatial)
-
- def forward(self, pred, target, normalize=True):
- """
- Pred and target are Variables.
- If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1]
- If normalize is False, assumes the images are already between [-1,+1]
- Inputs pred and target are Nx3xHxW
- Output pytorch Variable N long
- """
-
- if normalize:
- target = 2 * target - 1
- pred = 2 * pred - 1
-
- return self.model(target, pred)
-
-
-def normalize_tensor(in_feat, eps=1e-10):
- norm_factor = torch.sqrt(torch.sum(in_feat ** 2, dim=1, keepdim=True))
- return in_feat / (norm_factor + eps)
-
-
-def l2(p0, p1, range=255.):
- return .5 * np.mean((p0 / range - p1 / range) ** 2)
-
-
-def psnr(p0, p1, peak=255.):
- return 10 * np.log10(peak ** 2 / np.mean((1. * p0 - 1. * p1) ** 2))
-
-
-def dssim(p0, p1, range=255.):
- return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2.
-
-
-def rgb2lab(in_img, mean_cent=False):
- from skimage import color
- img_lab = color.rgb2lab(in_img)
- if (mean_cent):
- img_lab[:, :, 0] = img_lab[:, :, 0] - 50
- return img_lab
-
-
-def tensor2np(tensor_obj):
- # change dimension of a tensor object into a numpy array
- return tensor_obj[0].cpu().float().numpy().transpose((1, 2, 0))
-
-
-def np2tensor(np_obj):
- # change dimenion of np array into tensor array
- return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
-
-
-def tensor2tensorlab(image_tensor, to_norm=True, mc_only=False):
- # image tensor to lab tensor
- from skimage import color
-
- img = tensor2im(image_tensor)
- img_lab = color.rgb2lab(img)
- if (mc_only):
- img_lab[:, :, 0] = img_lab[:, :, 0] - 50
- if (to_norm and not mc_only):
- img_lab[:, :, 0] = img_lab[:, :, 0] - 50
- img_lab = img_lab / 100.
-
- return np2tensor(img_lab)
-
-
-def tensorlab2tensor(lab_tensor, return_inbnd=False):
- from skimage import color
- import warnings
- warnings.filterwarnings("ignore")
-
- lab = tensor2np(lab_tensor) * 100.
- lab[:, :, 0] = lab[:, :, 0] + 50
-
- rgb_back = 255. * np.clip(color.lab2rgb(lab.astype('float')), 0, 1)
- if (return_inbnd):
- # convert back to lab, see if we match
- lab_back = color.rgb2lab(rgb_back.astype('uint8'))
- mask = 1. * np.isclose(lab_back, lab, atol=2.)
- mask = np2tensor(np.prod(mask, axis=2)[:, :, np.newaxis])
- return (im2tensor(rgb_back), mask)
- else:
- return im2tensor(rgb_back)
-
-
-def rgb2lab(input):
- from skimage import color
- return color.rgb2lab(input / 255.)
-
-
-def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255. / 2.):
- image_numpy = image_tensor[0].cpu().float().numpy()
- image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
- return image_numpy.astype(imtype)
-
-
-def im2tensor(image, imtype=np.uint8, cent=1., factor=255. / 2.):
- return torch.Tensor((image / factor - cent)
- [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
-
-
-def tensor2vec(vector_tensor):
- return vector_tensor.data.cpu().numpy()[:, :, 0, 0]
-
-
-def voc_ap(rec, prec, use_07_metric=False):
- """ ap = voc_ap(rec, prec, [use_07_metric])
- Compute VOC AP given precision and recall.
- If use_07_metric is true, uses the
- VOC 07 11 point method (default:False).
- """
- if use_07_metric:
- # 11 point metric
- ap = 0.
- for t in np.arange(0., 1.1, 0.1):
- if np.sum(rec >= t) == 0:
- p = 0
- else:
- p = np.max(prec[rec >= t])
- ap = ap + p / 11.
- else:
- # correct AP calculation
- # first append sentinel values at the end
- mrec = np.concatenate(([0.], rec, [1.]))
- mpre = np.concatenate(([0.], prec, [0.]))
-
- # compute the precision envelope
- for i in range(mpre.size - 1, 0, -1):
- mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
-
- # to calculate area under PR curve, look for points
- # where X axis (recall) changes value
- i = np.where(mrec[1:] != mrec[:-1])[0]
-
- # and sum (\Delta recall) * prec
- ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
- return ap
-
-
-def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255. / 2.):
- # def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.):
- image_numpy = image_tensor[0].cpu().float().numpy()
- image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
- return image_numpy.astype(imtype)
-
-
-def im2tensor(image, imtype=np.uint8, cent=1., factor=255. / 2.):
- # def im2tensor(image, imtype=np.uint8, cent=1., factor=1.):
- return torch.Tensor((image / factor - cent)
- [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
-
-
-############################################################
-# base_model.py #
-############################################################
-
-
-class BaseModel(torch.nn.Module):
- def __init__(self):
- super().__init__()
-
- def name(self):
- return 'BaseModel'
-
- def initialize(self, use_gpu=True):
- self.use_gpu = use_gpu
-
- def forward(self):
- pass
-
- def get_image_paths(self):
- pass
-
- def optimize_parameters(self):
- pass
-
- def get_current_visuals(self):
- return self.input
-
- def get_current_errors(self):
- return {}
-
- def save(self, label):
- pass
-
- # helper saving function that can be used by subclasses
- def save_network(self, network, path, network_label, epoch_label):
- save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
- save_path = os.path.join(path, save_filename)
- torch.save(network.state_dict(), save_path)
-
- # helper loading function that can be used by subclasses
- def load_network(self, network, network_label, epoch_label):
- save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
- save_path = os.path.join(self.save_dir, save_filename)
- print('Loading network from %s' % save_path)
- network.load_state_dict(torch.load(save_path, map_location='cpu'))
-
- def update_learning_rate():
- pass
-
- def get_image_paths(self):
- return self.image_paths
-
- def save_done(self, flag=False):
- np.save(os.path.join(self.save_dir, 'done_flag'), flag)
- np.savetxt(os.path.join(self.save_dir, 'done_flag'), [flag, ], fmt='%i')
-
-
-############################################################
-# dist_model.py #
-############################################################
-
-import os
-from collections import OrderedDict
-from scipy.ndimage import zoom
-from tqdm import tqdm
-
-
-class DistModel(BaseModel):
- def name(self):
- return self.model_name
-
- def initialize(self, model='net-lin', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False,
- model_path=None,
- use_gpu=True, printNet=False, spatial=False,
- is_train=False, lr=.0001, beta1=0.5, version='0.1'):
- '''
- INPUTS
- model - ['net-lin'] for linearly calibrated network
- ['net'] for off-the-shelf network
- ['L2'] for L2 distance in Lab colorspace
- ['SSIM'] for ssim in RGB colorspace
- net - ['squeeze','alex','vgg']
- model_path - if None, will look in weights/[NET_NAME].pth
- colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM
- use_gpu - bool - whether or not to use a GPU
- printNet - bool - whether or not to print network architecture out
- spatial - bool - whether to output an array containing varying distances across spatial dimensions
- spatial_shape - if given, output spatial shape. if None then spatial shape is determined automatically via spatial_factor (see below).
- spatial_factor - if given, specifies upsampling factor relative to the largest spatial extent of a convolutional layer. if None then resized to size of input images.
- spatial_order - spline order of filter for upsampling in spatial mode, by default 1 (bilinear).
- is_train - bool - [True] for training mode
- lr - float - initial learning rate
- beta1 - float - initial momentum term for adam
- version - 0.1 for latest, 0.0 was original (with a bug)
- '''
- BaseModel.initialize(self, use_gpu=use_gpu)
-
- self.model = model
- self.net = net
- self.is_train = is_train
- self.spatial = spatial
- self.model_name = '%s [%s]' % (model, net)
-
- if (self.model == 'net-lin'): # pretrained net + linear layer
- self.net = PNetLin(pnet_rand=pnet_rand, pnet_tune=pnet_tune, pnet_type=net,
- use_dropout=True, spatial=spatial, version=version, lpips=True)
- kw = dict(map_location='cpu')
- if (model_path is None):
- import inspect
- model_path = os.path.abspath(
- os.path.join(os.path.dirname(__file__), '..', '..', '..', 'models', 'lpips_models', f'{net}.pth'))
-
- if (not is_train):
- self.net.load_state_dict(torch.load(model_path, **kw), strict=False)
-
- elif (self.model == 'net'): # pretrained network
- self.net = PNetLin(pnet_rand=pnet_rand, pnet_type=net, lpips=False)
- elif (self.model in ['L2', 'l2']):
- self.net = L2(use_gpu=use_gpu, colorspace=colorspace) # not really a network, only for testing
- self.model_name = 'L2'
- elif (self.model in ['DSSIM', 'dssim', 'SSIM', 'ssim']):
- self.net = DSSIM(use_gpu=use_gpu, colorspace=colorspace)
- self.model_name = 'SSIM'
- else:
- raise ValueError("Model [%s] not recognized." % self.model)
-
- self.trainable_parameters = list(self.net.parameters())
-
- if self.is_train: # training mode
- # extra network on top to go from distances (d0,d1) => predicted human judgment (h*)
- self.rankLoss = BCERankingLoss()
- self.trainable_parameters += list(self.rankLoss.net.parameters())
- self.lr = lr
- self.old_lr = lr
- self.optimizer_net = torch.optim.Adam(self.trainable_parameters, lr=lr, betas=(beta1, 0.999))
- else: # test mode
- self.net.eval()
-
- # if (use_gpu):
- # self.net.to(gpu_ids[0])
- # self.net = torch.nn.DataParallel(self.net, device_ids=gpu_ids)
- # if (self.is_train):
- # self.rankLoss = self.rankLoss.to(device=gpu_ids[0]) # just put this on GPU0
-
- if (printNet):
- print('---------- Networks initialized -------------')
- print_network(self.net)
- print('-----------------------------------------------')
-
- def forward(self, in0, in1, retPerLayer=False):
- ''' Function computes the distance between image patches in0 and in1
- INPUTS
- in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1]
- OUTPUT
- computed distances between in0 and in1
- '''
-
- return self.net(in0, in1, retPerLayer=retPerLayer)
-
- # ***** TRAINING FUNCTIONS *****
- def optimize_parameters(self):
- self.forward_train()
- self.optimizer_net.zero_grad()
- self.backward_train()
- self.optimizer_net.step()
- self.clamp_weights()
-
- def clamp_weights(self):
- for module in self.net.modules():
- if (hasattr(module, 'weight') and module.kernel_size == (1, 1)):
- module.weight.data = torch.clamp(module.weight.data, min=0)
-
- def set_input(self, data):
- self.input_ref = data['ref']
- self.input_p0 = data['p0']
- self.input_p1 = data['p1']
- self.input_judge = data['judge']
-
- # if (self.use_gpu):
- # self.input_ref = self.input_ref.to(device=self.gpu_ids[0])
- # self.input_p0 = self.input_p0.to(device=self.gpu_ids[0])
- # self.input_p1 = self.input_p1.to(device=self.gpu_ids[0])
- # self.input_judge = self.input_judge.to(device=self.gpu_ids[0])
-
- # self.var_ref = Variable(self.input_ref, requires_grad=True)
- # self.var_p0 = Variable(self.input_p0, requires_grad=True)
- # self.var_p1 = Variable(self.input_p1, requires_grad=True)
-
- def forward_train(self): # run forward pass
- # print(self.net.module.scaling_layer.shift)
- # print(torch.norm(self.net.module.net.slice1[0].weight).item(), torch.norm(self.net.module.lin0.model[1].weight).item())
-
- assert False, "We shoud've not get here when using LPIPS as a metric"
-
- self.d0 = self(self.var_ref, self.var_p0)
- self.d1 = self(self.var_ref, self.var_p1)
- self.acc_r = self.compute_accuracy(self.d0, self.d1, self.input_judge)
-
- self.var_judge = Variable(1. * self.input_judge).view(self.d0.size())
-
- self.loss_total = self.rankLoss(self.d0, self.d1, self.var_judge * 2. - 1.)
-
- return self.loss_total
-
- def backward_train(self):
- torch.mean(self.loss_total).backward()
-
- def compute_accuracy(self, d0, d1, judge):
- ''' d0, d1 are Variables, judge is a Tensor '''
- d1_lt_d0 = (d1 < d0).cpu().data.numpy().flatten()
- judge_per = judge.cpu().numpy().flatten()
- return d1_lt_d0 * judge_per + (1 - d1_lt_d0) * (1 - judge_per)
-
- def get_current_errors(self):
- retDict = OrderedDict([('loss_total', self.loss_total.data.cpu().numpy()),
- ('acc_r', self.acc_r)])
-
- for key in retDict.keys():
- retDict[key] = np.mean(retDict[key])
-
- return retDict
-
- def get_current_visuals(self):
- zoom_factor = 256 / self.var_ref.data.size()[2]
-
- ref_img = tensor2im(self.var_ref.data)
- p0_img = tensor2im(self.var_p0.data)
- p1_img = tensor2im(self.var_p1.data)
-
- ref_img_vis = zoom(ref_img, [zoom_factor, zoom_factor, 1], order=0)
- p0_img_vis = zoom(p0_img, [zoom_factor, zoom_factor, 1], order=0)
- p1_img_vis = zoom(p1_img, [zoom_factor, zoom_factor, 1], order=0)
-
- return OrderedDict([('ref', ref_img_vis),
- ('p0', p0_img_vis),
- ('p1', p1_img_vis)])
-
- def save(self, path, label):
- if (self.use_gpu):
- self.save_network(self.net.module, path, '', label)
- else:
- self.save_network(self.net, path, '', label)
- self.save_network(self.rankLoss.net, path, 'rank', label)
-
- def update_learning_rate(self, nepoch_decay):
- lrd = self.lr / nepoch_decay
- lr = self.old_lr - lrd
-
- for param_group in self.optimizer_net.param_groups:
- param_group['lr'] = lr
-
- print('update lr [%s] decay: %f -> %f' % (type, self.old_lr, lr))
- self.old_lr = lr
-
-
-def score_2afc_dataset(data_loader, func, name=''):
- ''' Function computes Two Alternative Forced Choice (2AFC) score using
- distance function 'func' in dataset 'data_loader'
- INPUTS
- data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside
- func - callable distance function - calling d=func(in0,in1) should take 2
- pytorch tensors with shape Nx3xXxY, and return numpy array of length N
- OUTPUTS
- [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators
- [1] - dictionary with following elements
- d0s,d1s - N arrays containing distances between reference patch to perturbed patches
- gts - N array in [0,1], preferred patch selected by human evaluators
- (closer to "0" for left patch p0, "1" for right patch p1,
- "0.6" means 60pct people preferred right patch, 40pct preferred left)
- scores - N array in [0,1], corresponding to what percentage function agreed with humans
- CONSTS
- N - number of test triplets in data_loader
- '''
-
- d0s = []
- d1s = []
- gts = []
-
- for data in tqdm(data_loader.load_data(), desc=name):
- d0s += func(data['ref'], data['p0']).data.cpu().numpy().flatten().tolist()
- d1s += func(data['ref'], data['p1']).data.cpu().numpy().flatten().tolist()
- gts += data['judge'].cpu().numpy().flatten().tolist()
-
- d0s = np.array(d0s)
- d1s = np.array(d1s)
- gts = np.array(gts)
- scores = (d0s < d1s) * (1. - gts) + (d1s < d0s) * gts + (d1s == d0s) * .5
-
- return (np.mean(scores), dict(d0s=d0s, d1s=d1s, gts=gts, scores=scores))
-
-
-def score_jnd_dataset(data_loader, func, name=''):
- ''' Function computes JND score using distance function 'func' in dataset 'data_loader'
- INPUTS
- data_loader - CustomDatasetDataLoader object - contains a JNDDataset inside
- func - callable distance function - calling d=func(in0,in1) should take 2
- pytorch tensors with shape Nx3xXxY, and return pytorch array of length N
- OUTPUTS
- [0] - JND score in [0,1], mAP score (area under precision-recall curve)
- [1] - dictionary with following elements
- ds - N array containing distances between two patches shown to human evaluator
- sames - N array containing fraction of people who thought the two patches were identical
- CONSTS
- N - number of test triplets in data_loader
- '''
-
- ds = []
- gts = []
-
- for data in tqdm(data_loader.load_data(), desc=name):
- ds += func(data['p0'], data['p1']).data.cpu().numpy().tolist()
- gts += data['same'].cpu().numpy().flatten().tolist()
-
- sames = np.array(gts)
- ds = np.array(ds)
-
- sorted_inds = np.argsort(ds)
- ds_sorted = ds[sorted_inds]
- sames_sorted = sames[sorted_inds]
-
- TPs = np.cumsum(sames_sorted)
- FPs = np.cumsum(1 - sames_sorted)
- FNs = np.sum(sames_sorted) - TPs
-
- precs = TPs / (TPs + FPs)
- recs = TPs / (TPs + FNs)
- score = voc_ap(recs, precs)
-
- return (score, dict(ds=ds, sames=sames))
-
-
-############################################################
-# networks_basic.py #
-############################################################
-
-import torch.nn as nn
-from torch.autograd import Variable
-import numpy as np
-
-
-def spatial_average(in_tens, keepdim=True):
- return in_tens.mean([2, 3], keepdim=keepdim)
-
-
-def upsample(in_tens, out_H=64): # assumes scale factor is same for H and W
- in_H = in_tens.shape[2]
- scale_factor = 1. * out_H / in_H
-
- return nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=False)(in_tens)
-
-
-# Learned perceptual metric
-class PNetLin(nn.Module):
- def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False,
- version='0.1', lpips=True):
- super(PNetLin, self).__init__()
-
- self.pnet_type = pnet_type
- self.pnet_tune = pnet_tune
- self.pnet_rand = pnet_rand
- self.spatial = spatial
- self.lpips = lpips
- self.version = version
- self.scaling_layer = ScalingLayer()
-
- if (self.pnet_type in ['vgg', 'vgg16']):
- net_type = vgg16
- self.chns = [64, 128, 256, 512, 512]
- elif (self.pnet_type == 'alex'):
- net_type = alexnet
- self.chns = [64, 192, 384, 256, 256]
- elif (self.pnet_type == 'squeeze'):
- net_type = squeezenet
- self.chns = [64, 128, 256, 384, 384, 512, 512]
- self.L = len(self.chns)
-
- self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune)
-
- if (lpips):
- self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
- self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
- self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
- self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
- self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
- self.lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
- if (self.pnet_type == 'squeeze'): # 7 layers for squeezenet
- self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout)
- self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout)
- self.lins += [self.lin5, self.lin6]
-
- def forward(self, in0, in1, retPerLayer=False):
- # v0.0 - original release had a bug, where input was not scaled
- in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version == '0.1' else (
- in0, in1)
- outs0, outs1 = self.net(in0_input), self.net(in1_input)
- feats0, feats1, diffs = {}, {}, {}
-
- for kk in range(self.L):
- feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])
- diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
-
- if (self.lpips):
- if (self.spatial):
- res = [upsample(self.lins[kk].model(diffs[kk]), out_H=in0.shape[2]) for kk in range(self.L)]
- else:
- res = [spatial_average(self.lins[kk].model(diffs[kk]), keepdim=True) for kk in range(self.L)]
- else:
- if (self.spatial):
- res = [upsample(diffs[kk].sum(dim=1, keepdim=True), out_H=in0.shape[2]) for kk in range(self.L)]
- else:
- res = [spatial_average(diffs[kk].sum(dim=1, keepdim=True), keepdim=True) for kk in range(self.L)]
-
- val = res[0]
- for l in range(1, self.L):
- val += res[l]
-
- if (retPerLayer):
- return (val, res)
- else:
- return val
-
-
-class ScalingLayer(nn.Module):
- def __init__(self):
- super(ScalingLayer, self).__init__()
- self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])
- self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])
-
- def forward(self, inp):
- return (inp - self.shift) / self.scale
-
-
-class NetLinLayer(nn.Module):
- ''' A single linear layer which does a 1x1 conv '''
-
- def __init__(self, chn_in, chn_out=1, use_dropout=False):
- super(NetLinLayer, self).__init__()
-
- layers = [nn.Dropout(), ] if (use_dropout) else []
- layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]
- self.model = nn.Sequential(*layers)
-
-
-class Dist2LogitLayer(nn.Module):
- ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) '''
-
- def __init__(self, chn_mid=32, use_sigmoid=True):
- super(Dist2LogitLayer, self).__init__()
-
- layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True), ]
- layers += [nn.LeakyReLU(0.2, True), ]
- layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True), ]
- layers += [nn.LeakyReLU(0.2, True), ]
- layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True), ]
- if (use_sigmoid):
- layers += [nn.Sigmoid(), ]
- self.model = nn.Sequential(*layers)
-
- def forward(self, d0, d1, eps=0.1):
- return self.model(torch.cat((d0, d1, d0 - d1, d0 / (d1 + eps), d1 / (d0 + eps)), dim=1))
-
-
-class BCERankingLoss(nn.Module):
- def __init__(self, chn_mid=32):
- super(BCERankingLoss, self).__init__()
- self.net = Dist2LogitLayer(chn_mid=chn_mid)
- # self.parameters = list(self.net.parameters())
- self.loss = torch.nn.BCELoss()
-
- def forward(self, d0, d1, judge):
- per = (judge + 1.) / 2.
- self.logit = self.net(d0, d1)
- return self.loss(self.logit, per)
-
-
-# L2, DSSIM metrics
-class FakeNet(nn.Module):
- def __init__(self, use_gpu=True, colorspace='Lab'):
- super(FakeNet, self).__init__()
- self.use_gpu = use_gpu
- self.colorspace = colorspace
-
-
-class L2(FakeNet):
-
- def forward(self, in0, in1, retPerLayer=None):
- assert (in0.size()[0] == 1) # currently only supports batchSize 1
-
- if (self.colorspace == 'RGB'):
- (N, C, X, Y) = in0.size()
- value = torch.mean(torch.mean(torch.mean((in0 - in1) ** 2, dim=1).view(N, 1, X, Y), dim=2).view(N, 1, 1, Y),
- dim=3).view(N)
- return value
- elif (self.colorspace == 'Lab'):
- value = l2(tensor2np(tensor2tensorlab(in0.data, to_norm=False)),
- tensor2np(tensor2tensorlab(in1.data, to_norm=False)), range=100.).astype('float')
- ret_var = Variable(torch.Tensor((value,)))
- # if (self.use_gpu):
- # ret_var = ret_var.cuda()
- return ret_var
-
-
-class DSSIM(FakeNet):
-
- def forward(self, in0, in1, retPerLayer=None):
- assert (in0.size()[0] == 1) # currently only supports batchSize 1
-
- if (self.colorspace == 'RGB'):
- value = dssim(1. * tensor2im(in0.data), 1. * tensor2im(in1.data), range=255.).astype('float')
- elif (self.colorspace == 'Lab'):
- value = dssim(tensor2np(tensor2tensorlab(in0.data, to_norm=False)),
- tensor2np(tensor2tensorlab(in1.data, to_norm=False)), range=100.).astype('float')
- ret_var = Variable(torch.Tensor((value,)))
- # if (self.use_gpu):
- # ret_var = ret_var.cuda()
- return ret_var
-
-
-def print_network(net):
- num_params = 0
- for param in net.parameters():
- num_params += param.numel()
- print('Network', net)
- print('Total number of parameters: %d' % num_params)
-
-
-############################################################
-# pretrained_networks.py #
-############################################################
-
-from collections import namedtuple
-import torch
-from torchvision import models as tv
-
-
-class squeezenet(torch.nn.Module):
- def __init__(self, requires_grad=False, pretrained=True):
- super(squeezenet, self).__init__()
- pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features
- self.slice1 = torch.nn.Sequential()
- self.slice2 = torch.nn.Sequential()
- self.slice3 = torch.nn.Sequential()
- self.slice4 = torch.nn.Sequential()
- self.slice5 = torch.nn.Sequential()
- self.slice6 = torch.nn.Sequential()
- self.slice7 = torch.nn.Sequential()
- self.N_slices = 7
- for x in range(2):
- self.slice1.add_module(str(x), pretrained_features[x])
- for x in range(2, 5):
- self.slice2.add_module(str(x), pretrained_features[x])
- for x in range(5, 8):
- self.slice3.add_module(str(x), pretrained_features[x])
- for x in range(8, 10):
- self.slice4.add_module(str(x), pretrained_features[x])
- for x in range(10, 11):
- self.slice5.add_module(str(x), pretrained_features[x])
- for x in range(11, 12):
- self.slice6.add_module(str(x), pretrained_features[x])
- for x in range(12, 13):
- self.slice7.add_module(str(x), pretrained_features[x])
- if not requires_grad:
- for param in self.parameters():
- param.requires_grad = False
-
- def forward(self, X):
- h = self.slice1(X)
- h_relu1 = h
- h = self.slice2(h)
- h_relu2 = h
- h = self.slice3(h)
- h_relu3 = h
- h = self.slice4(h)
- h_relu4 = h
- h = self.slice5(h)
- h_relu5 = h
- h = self.slice6(h)
- h_relu6 = h
- h = self.slice7(h)
- h_relu7 = h
- vgg_outputs = namedtuple("SqueezeOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5', 'relu6', 'relu7'])
- out = vgg_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5, h_relu6, h_relu7)
-
- return out
-
-
-class alexnet(torch.nn.Module):
- def __init__(self, requires_grad=False, pretrained=True):
- super(alexnet, self).__init__()
- alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features
- self.slice1 = torch.nn.Sequential()
- self.slice2 = torch.nn.Sequential()
- self.slice3 = torch.nn.Sequential()
- self.slice4 = torch.nn.Sequential()
- self.slice5 = torch.nn.Sequential()
- self.N_slices = 5
- for x in range(2):
- self.slice1.add_module(str(x), alexnet_pretrained_features[x])
- for x in range(2, 5):
- self.slice2.add_module(str(x), alexnet_pretrained_features[x])
- for x in range(5, 8):
- self.slice3.add_module(str(x), alexnet_pretrained_features[x])
- for x in range(8, 10):
- self.slice4.add_module(str(x), alexnet_pretrained_features[x])
- for x in range(10, 12):
- self.slice5.add_module(str(x), alexnet_pretrained_features[x])
- if not requires_grad:
- for param in self.parameters():
- param.requires_grad = False
-
- def forward(self, X):
- h = self.slice1(X)
- h_relu1 = h
- h = self.slice2(h)
- h_relu2 = h
- h = self.slice3(h)
- h_relu3 = h
- h = self.slice4(h)
- h_relu4 = h
- h = self.slice5(h)
- h_relu5 = h
- alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])
- out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
-
- return out
-
-
-class vgg16(torch.nn.Module):
- def __init__(self, requires_grad=False, pretrained=True):
- super(vgg16, self).__init__()
- vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features
- self.slice1 = torch.nn.Sequential()
- self.slice2 = torch.nn.Sequential()
- self.slice3 = torch.nn.Sequential()
- self.slice4 = torch.nn.Sequential()
- self.slice5 = torch.nn.Sequential()
- self.N_slices = 5
- for x in range(4):
- self.slice1.add_module(str(x), vgg_pretrained_features[x])
- for x in range(4, 9):
- self.slice2.add_module(str(x), vgg_pretrained_features[x])
- for x in range(9, 16):
- self.slice3.add_module(str(x), vgg_pretrained_features[x])
- for x in range(16, 23):
- self.slice4.add_module(str(x), vgg_pretrained_features[x])
- for x in range(23, 30):
- self.slice5.add_module(str(x), vgg_pretrained_features[x])
- if not requires_grad:
- for param in self.parameters():
- param.requires_grad = False
-
- def forward(self, X):
- h = self.slice1(X)
- h_relu1_2 = h
- h = self.slice2(h)
- h_relu2_2 = h
- h = self.slice3(h)
- h_relu3_3 = h
- h = self.slice4(h)
- h_relu4_3 = h
- h = self.slice5(h)
- h_relu5_3 = h
- vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
- out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
-
- return out
-
-
-class resnet(torch.nn.Module):
- def __init__(self, requires_grad=False, pretrained=True, num=18):
- super(resnet, self).__init__()
- if (num == 18):
- self.net = tv.resnet18(pretrained=pretrained)
- elif (num == 34):
- self.net = tv.resnet34(pretrained=pretrained)
- elif (num == 50):
- self.net = tv.resnet50(pretrained=pretrained)
- elif (num == 101):
- self.net = tv.resnet101(pretrained=pretrained)
- elif (num == 152):
- self.net = tv.resnet152(pretrained=pretrained)
- self.N_slices = 5
-
- self.conv1 = self.net.conv1
- self.bn1 = self.net.bn1
- self.relu = self.net.relu
- self.maxpool = self.net.maxpool
- self.layer1 = self.net.layer1
- self.layer2 = self.net.layer2
- self.layer3 = self.net.layer3
- self.layer4 = self.net.layer4
-
- def forward(self, X):
- h = self.conv1(X)
- h = self.bn1(h)
- h = self.relu(h)
- h_relu1 = h
- h = self.maxpool(h)
- h = self.layer1(h)
- h_conv2 = h
- h = self.layer2(h)
- h_conv3 = h
- h = self.layer3(h)
- h_conv4 = h
- h = self.layer4(h)
- h_conv5 = h
-
- outputs = namedtuple("Outputs", ['relu1', 'conv2', 'conv3', 'conv4', 'conv5'])
- out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)
-
- return out
diff --git a/spaces/jhwen/bingo/src/pages/api/sydney.ts b/spaces/jhwen/bingo/src/pages/api/sydney.ts
deleted file mode 100644
index 8bd7074bc72bd2803e4acf89d3814908893ff044..0000000000000000000000000000000000000000
--- a/spaces/jhwen/bingo/src/pages/api/sydney.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-import { NextApiRequest, NextApiResponse } from 'next'
-import { WebSocket, debug } from '@/lib/isomorphic'
-import { BingWebBot } from '@/lib/bots/bing'
-import { websocketUtils } from '@/lib/bots/bing/utils'
-import { WatchDog, createHeaders } from '@/lib/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- const conversationContext = req.body
- const headers = createHeaders(req.cookies)
- const id = headers['x-forwarded-for']
-
- debug(id, headers)
- res.setHeader('Content-Type', 'text/stream; charset=UTF-8')
-
- const ws = new WebSocket('wss://sydney.bing.com/sydney/ChatHub', {
- headers: {
- ...headers,
- 'accept-language': 'zh-CN,zh;q=0.9',
- 'cache-control': 'no-cache',
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- pragma: 'no-cache',
- }
- })
-
- const closeDog = new WatchDog()
- const timeoutDog = new WatchDog()
- ws.onmessage = (event) => {
- timeoutDog.watch(() => {
- debug(id, 'timeout')
- ws.send(websocketUtils.packMessage({ type: 6 }))
- }, 3000)
- closeDog.watch(() => {
- debug(id, 'timeout close')
- ws.close()
- }, 20000)
- res.write(event.data)
- if (/\{"type":([367])\}/.test(String(event.data))) {
- const type = parseInt(RegExp.$1, 10)
- debug(id, 'connection type', type)
- if (type === 3) {
- ws.close()
- } else {
- ws.send(websocketUtils.packMessage({ type }))
- }
- }
- }
-
- ws.onclose = () => {
- timeoutDog.reset()
- closeDog.reset()
- debug(id, 'ws close')
- res.end()
- }
-
- await new Promise((resolve) => ws.onopen = resolve)
- ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 }))
- ws.send(websocketUtils.packMessage({ type: 6 }))
- ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!)))
- req.socket.once('close', () => {
- debug(id, 'connection close')
- ws.close()
- if (!res.closed) {
- res.end()
- }
- })
-}
diff --git a/spaces/jiawei011/dreamgaussian/mesh_utils.py b/spaces/jiawei011/dreamgaussian/mesh_utils.py
deleted file mode 100644
index ca9fce9232f5133d6f91d5cf64d9e17b0725a5c9..0000000000000000000000000000000000000000
--- a/spaces/jiawei011/dreamgaussian/mesh_utils.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import numpy as np
-import pymeshlab as pml
-
-
-def poisson_mesh_reconstruction(points, normals=None):
- # points/normals: [N, 3] np.ndarray
-
- import open3d as o3d
-
- pcd = o3d.geometry.PointCloud()
- pcd.points = o3d.utility.Vector3dVector(points)
-
- # outlier removal
- pcd, ind = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=10)
-
- # normals
- if normals is None:
- pcd.estimate_normals()
- else:
- pcd.normals = o3d.utility.Vector3dVector(normals[ind])
-
- # visualize
- o3d.visualization.draw_geometries([pcd], point_show_normal=False)
-
- mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
- pcd, depth=9
- )
- vertices_to_remove = densities < np.quantile(densities, 0.1)
- mesh.remove_vertices_by_mask(vertices_to_remove)
-
- # visualize
- o3d.visualization.draw_geometries([mesh])
-
- vertices = np.asarray(mesh.vertices)
- triangles = np.asarray(mesh.triangles)
-
- print(
- f"[INFO] poisson mesh reconstruction: {points.shape} --> {vertices.shape} / {triangles.shape}"
- )
-
- return vertices, triangles
-
-
-def decimate_mesh(
- verts, faces, target, backend="pymeshlab", remesh=False, optimalplacement=True
-):
- # optimalplacement: default is True, but for flat mesh must turn False to prevent spike artifect.
-
- _ori_vert_shape = verts.shape
- _ori_face_shape = faces.shape
-
- if backend == "pyfqmr":
- import pyfqmr
-
- solver = pyfqmr.Simplify()
- solver.setMesh(verts, faces)
- solver.simplify_mesh(target_count=target, preserve_border=False, verbose=False)
- verts, faces, normals = solver.getMesh()
- else:
- m = pml.Mesh(verts, faces)
- ms = pml.MeshSet()
- ms.add_mesh(m, "mesh") # will copy!
-
- # filters
- # ms.meshing_decimation_clustering(threshold=pml.Percentage(1))
- ms.meshing_decimation_quadric_edge_collapse(
- targetfacenum=int(target), optimalplacement=optimalplacement
- )
-
- if remesh:
- # ms.apply_coord_taubin_smoothing()
- ms.meshing_isotropic_explicit_remeshing(
- iterations=3, targetlen=pml.Percentage(1)
- )
-
- # extract mesh
- m = ms.current_mesh()
- verts = m.vertex_matrix()
- faces = m.face_matrix()
-
- print(
- f"[INFO] mesh decimation: {_ori_vert_shape} --> {verts.shape}, {_ori_face_shape} --> {faces.shape}"
- )
-
- return verts, faces
-
-
-def clean_mesh(
- verts,
- faces,
- v_pct=1,
- min_f=64,
- min_d=20,
- repair=True,
- remesh=True,
- remesh_size=0.01,
-):
- # verts: [N, 3]
- # faces: [N, 3]
-
- _ori_vert_shape = verts.shape
- _ori_face_shape = faces.shape
-
- m = pml.Mesh(verts, faces)
- ms = pml.MeshSet()
- ms.add_mesh(m, "mesh") # will copy!
-
- # filters
- ms.meshing_remove_unreferenced_vertices() # verts not refed by any faces
-
- if v_pct > 0:
- ms.meshing_merge_close_vertices(
- threshold=pml.Percentage(v_pct)
- ) # 1/10000 of bounding box diagonal
-
- ms.meshing_remove_duplicate_faces() # faces defined by the same verts
- ms.meshing_remove_null_faces() # faces with area == 0
-
- if min_d > 0:
- ms.meshing_remove_connected_component_by_diameter(
- mincomponentdiag=pml.Percentage(min_d)
- )
-
- if min_f > 0:
- ms.meshing_remove_connected_component_by_face_number(mincomponentsize=min_f)
-
- if repair:
- # ms.meshing_remove_t_vertices(method=0, threshold=40, repeat=True)
- ms.meshing_repair_non_manifold_edges(method=0)
- ms.meshing_repair_non_manifold_vertices(vertdispratio=0)
-
- if remesh:
- # ms.apply_coord_taubin_smoothing()
- ms.meshing_isotropic_explicit_remeshing(
- iterations=3, targetlen=pml.AbsoluteValue(remesh_size)
- )
-
- # extract mesh
- m = ms.current_mesh()
- verts = m.vertex_matrix()
- faces = m.face_matrix()
-
- print(
- f"[INFO] mesh cleaning: {_ori_vert_shape} --> {verts.shape}, {_ori_face_shape} --> {faces.shape}"
- )
-
- return verts, faces
diff --git a/spaces/jmyungjoon/cartoon/network/__init__.py b/spaces/jmyungjoon/cartoon/network/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/file/video_audio.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/file/video_audio.py
deleted file mode 100644
index d0d3742e829121099f7808ba6a68f3a9f0803e5f..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/file/video_audio.py
+++ /dev/null
@@ -1,63 +0,0 @@
-"""Video audio parser.
-
-Contains parsers for mp3, mp4 files.
-
-"""
-from pathlib import Path
-from typing import Any, Dict, cast
-
-from gpt_index.readers.file.base_parser import BaseParser
-
-
-class VideoAudioParser(BaseParser):
- """Video audio parser.
-
- Extract text from transcript of video/audio files.
-
- """
-
- def __init__(self, *args: Any, model_version: str = "base", **kwargs: Any) -> None:
- """Init params."""
- super().__init__(*args, **kwargs)
- self._model_version = model_version
-
- def _init_parser(self) -> Dict:
- """Init parser."""
- try:
- import whisper
- except ImportError:
- raise ImportError(
- "Please install OpenAI whisper model "
- "'pip install git+https://github.com/openai/whisper.git' "
- "to use the model"
- )
-
- model = whisper.load_model(self._model_version)
-
- return {"model": model}
-
- def parse_file(self, file: Path, errors: str = "ignore") -> str:
- """Parse file."""
- import whisper
-
- if file.name.endswith("mp4"):
- try:
- from pydub import AudioSegment # noqa: F401
- except ImportError:
- raise ImportError("Please install pydub 'pip install pydub' ")
- # open file
- video = AudioSegment.from_file(file, format="mp4")
-
- # Extract audio from video
- audio = video.split_to_mono()[0]
-
- file_str = str(file)[:-4] + ".mp3"
- # export file
- audio.export(file_str, format="mp3")
-
- model = cast(whisper.Whisper, self.parser_config["model"])
- result = model.transcribe(str(file))
-
- transcript = result["text"]
-
- return transcript
diff --git a/spaces/johnslegers/stable-diffusion-gui-test/modules/runtime.py b/spaces/johnslegers/stable-diffusion-gui-test/modules/runtime.py
deleted file mode 100644
index 8d4bcb75ebe1b4419443669b91da7e3bfe5704c2..0000000000000000000000000000000000000000
--- a/spaces/johnslegers/stable-diffusion-gui-test/modules/runtime.py
+++ /dev/null
@@ -1,682 +0,0 @@
-import json
-import os, re
-import traceback
-import torch
-import numpy as np
-from omegaconf import OmegaConf
-from PIL import Image, ImageOps
-from tqdm import tqdm, trange
-from itertools import islice
-from einops import rearrange
-import time
-from pytorch_lightning import seed_everything
-from torch import autocast
-from contextlib import nullcontext
-from einops import rearrange, repeat
-from ldmlib.util import instantiate_from_config
-from optimizedSD.optimUtils import split_weighted_subprompts
-from transformers import logging
-
-from gfpgan import GFPGANer
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from realesrgan import RealESRGANer
-
-import uuid
-
-logging.set_verbosity_error()
-
-# consts
-config_yaml = "optimizedSD/v1-inference.yaml"
-filename_regex = re.compile('[^a-zA-Z0-9]')
-
-# api stuff
-from sd_internal import Request, Response, Image as ResponseImage
-import base64
-from io import BytesIO
-#from colorama import Fore
-
-# local
-stop_processing = False
-temp_images = {}
-
-ckpt_file = None
-gfpgan_file = None
-real_esrgan_file = None
-
-model = None
-modelCS = None
-modelFS = None
-model_gfpgan = None
-model_real_esrgan = None
-
-model_is_half = False
-model_fs_is_half = False
-device = None
-unet_bs = 1
-precision = 'autocast'
-sampler_plms = None
-sampler_ddim = None
-
-has_valid_gpu = False
-force_full_precision = False
-try:
- gpu = torch.cuda.current_device()
- gpu_name = torch.cuda.get_device_name(gpu)
- print('GPU detected: ', gpu_name)
-
- force_full_precision = ('nvidia' in gpu_name.lower() or 'geforce' in gpu_name.lower()) and (' 1660' in gpu_name or ' 1650' in gpu_name) # otherwise these NVIDIA cards create green images
- if force_full_precision:
- print('forcing full precision on NVIDIA 16xx cards, to avoid green images. GPU detected: ', gpu_name)
-
- mem_free, mem_total = torch.cuda.mem_get_info(gpu)
- mem_total /= float(10**9)
- if mem_total < 3.0:
- print("GPUs with less than 3 GB of VRAM are not compatible with Stable Diffusion")
- raise Exception()
-
- has_valid_gpu = True
-except:
- print('WARNING: No compatible GPU found. Using the CPU, but this will be very slow!')
- pass
-
-def load_model_ckpt(ckpt_to_use, device_to_use='cuda', turbo=False, unet_bs_to_use=1, precision_to_use='autocast'):
- global ckpt_file, model, modelCS, modelFS, model_is_half, device, unet_bs, precision, model_fs_is_half
-
- device = device_to_use if has_valid_gpu else 'cpu'
- precision = precision_to_use if not force_full_precision else 'full'
- unet_bs = unet_bs_to_use
-
- unload_model()
-
- if device == 'cpu':
- precision = 'full'
-
- sd = load_model_from_config(f"{ckpt_to_use}.ckpt")
- li, lo = [], []
- for key, value in sd.items():
- sp = key.split(".")
- if (sp[0]) == "model":
- if "input_blocks" in sp:
- li.append(key)
- elif "middle_block" in sp:
- li.append(key)
- elif "time_embed" in sp:
- li.append(key)
- else:
- lo.append(key)
- for key in li:
- sd["model1." + key[6:]] = sd.pop(key)
- for key in lo:
- sd["model2." + key[6:]] = sd.pop(key)
-
- config = OmegaConf.load(f"{config_yaml}")
-
- model = instantiate_from_config(config.modelUNet)
- _, _ = model.load_state_dict(sd, strict=False)
- model.eval()
- model.cdevice = device
- model.unet_bs = unet_bs
- model.turbo = turbo
-
- modelCS = instantiate_from_config(config.modelCondStage)
- _, _ = modelCS.load_state_dict(sd, strict=False)
- modelCS.eval()
- modelCS.cond_stage_model.device = device
-
- modelFS = instantiate_from_config(config.modelFirstStage)
- _, _ = modelFS.load_state_dict(sd, strict=False)
- modelFS.eval()
- del sd
-
- if device != "cpu" and precision == "autocast":
- model.half()
- modelCS.half()
- modelFS.half()
- model_is_half = True
- model_fs_is_half = True
- else:
- model_is_half = False
- model_fs_is_half = False
-
- ckpt_file = ckpt_to_use
-
- print('loaded ', ckpt_file, 'to', device, 'precision', precision)
-
-def unload_model():
- global model, modelCS, modelFS
-
- if model is not None:
- del model
- del modelCS
- del modelFS
-
- model = None
- modelCS = None
- modelFS = None
-
-def load_model_gfpgan(gfpgan_to_use):
- global gfpgan_file, model_gfpgan
-
- if gfpgan_to_use is None:
- return
-
- gfpgan_file = gfpgan_to_use
- model_path = gfpgan_to_use + ".pth"
-
- if device == 'cpu':
- model_gfpgan = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device('cpu'))
- else:
- model_gfpgan = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device('cuda'))
-
- print('loaded ', gfpgan_to_use, 'to', device, 'precision', precision)
-
-def load_model_real_esrgan(real_esrgan_to_use):
- global real_esrgan_file, model_real_esrgan
-
- if real_esrgan_to_use is None:
- return
-
- real_esrgan_file = real_esrgan_to_use
- model_path = real_esrgan_to_use + ".pth"
-
- RealESRGAN_models = {
- 'RealESRGAN_x4plus': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4),
- 'RealESRGAN_x4plus_anime_6B': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
- }
-
- model_to_use = RealESRGAN_models[real_esrgan_to_use]
-
- if device == 'cpu':
- model_real_esrgan = RealESRGANer(scale=2, model_path=model_path, model=model_to_use, pre_pad=0, half=False) # cpu does not support half
- model_real_esrgan.device = torch.device('cpu')
- model_real_esrgan.model.to('cpu')
- else:
- model_real_esrgan = RealESRGANer(scale=2, model_path=model_path, model=model_to_use, pre_pad=0, half=model_is_half)
-
- model_real_esrgan.model.name = real_esrgan_to_use
-
- print('loaded ', real_esrgan_to_use, 'to', device, 'precision', precision)
-
-def mk_img(req: Request):
- try:
- yield from do_mk_img(req)
- except Exception as e:
- print(traceback.format_exc())
-
- gc()
-
- if device != "cpu":
- modelFS.to("cpu")
- modelCS.to("cpu")
-
- model.model1.to("cpu")
- model.model2.to("cpu")
-
- gc()
-
- yield json.dumps({
- "status": 'failed',
- "detail": str(e)
- })
-
-def do_mk_img(req: Request):
- global ckpt_file
- global model, modelCS, modelFS, device
- global model_gfpgan, model_real_esrgan
- global stop_processing
-
- stop_processing = False
-
- res = Response()
- res.request = req
- res.images = []
-
- temp_images.clear()
-
- # custom model support:
- # the req.use_stable_diffusion_model needs to be a valid path
- # to the ckpt file (without the extension).
-
- needs_model_reload = False
- ckpt_to_use = ckpt_file
- if ckpt_to_use != req.use_stable_diffusion_model:
- ckpt_to_use = req.use_stable_diffusion_model
- needs_model_reload = True
-
- model.turbo = req.turbo
- if req.use_cpu:
- if device != 'cpu':
- device = 'cpu'
-
- if model_is_half:
- load_model_ckpt(ckpt_to_use, device)
- needs_model_reload = False
-
- load_model_gfpgan(gfpgan_file)
- load_model_real_esrgan(real_esrgan_file)
- else:
- if has_valid_gpu:
- prev_device = device
- device = 'cuda'
-
- if (precision == 'autocast' and (req.use_full_precision or not model_is_half)) or \
- (precision == 'full' and not req.use_full_precision and not force_full_precision):
-
- load_model_ckpt(ckpt_to_use, device, req.turbo, unet_bs, ('full' if req.use_full_precision else 'autocast'))
- needs_model_reload = False
-
- if prev_device != device:
- load_model_gfpgan(gfpgan_file)
- load_model_real_esrgan(real_esrgan_file)
-
- if needs_model_reload:
- load_model_ckpt(ckpt_to_use, device, req.turbo, unet_bs, precision)
-
- if req.use_face_correction != gfpgan_file:
- load_model_gfpgan(req.use_face_correction)
-
- if req.use_upscale != real_esrgan_file:
- load_model_real_esrgan(req.use_upscale)
-
- model.cdevice = device
- modelCS.cond_stage_model.device = device
-
- opt_prompt = req.prompt
- opt_seed = req.seed
- opt_n_samples = req.num_outputs
- opt_n_iter = 1
- opt_scale = req.guidance_scale
- opt_C = 4
- opt_H = req.height
- opt_W = req.width
- opt_f = 8
- opt_ddim_steps = req.num_inference_steps
- opt_ddim_eta = 0.0
- opt_strength = req.prompt_strength
- opt_save_to_disk_path = req.save_to_disk_path
- opt_init_img = req.init_image
- opt_use_face_correction = req.use_face_correction
- opt_use_upscale = req.use_upscale
- opt_show_only_filtered = req.show_only_filtered_image
- opt_format = req.output_format
- opt_sampler_name = req.sampler
-
- print(req.to_string(), '\n device', device)
-
- print('\n\n Using precision:', precision)
-
- seed_everything(opt_seed)
-
- batch_size = opt_n_samples
- prompt = opt_prompt
- assert prompt is not None
- data = [batch_size * [prompt]]
-
- if precision == "autocast" and device != "cpu":
- precision_scope = autocast
- else:
- precision_scope = nullcontext
-
- mask = None
-
- if req.init_image is None:
- handler = _txt2img
-
- init_latent = None
- t_enc = None
- else:
- handler = _img2img
-
- init_image = load_img(req.init_image, opt_W, opt_H)
- init_image = init_image.to(device)
-
- if device != "cpu" and precision == "autocast":
- init_image = init_image.half()
-
- modelFS.to(device)
-
- init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
- init_latent = modelFS.get_first_stage_encoding(modelFS.encode_first_stage(init_image)) # move to latent space
-
- if req.mask is not None:
- mask = load_mask(req.mask, opt_W, opt_H, init_latent.shape[2], init_latent.shape[3], True).to(device)
- mask = mask[0][0].unsqueeze(0).repeat(4, 1, 1).unsqueeze(0)
- mask = repeat(mask, '1 ... -> b ...', b=batch_size)
-
- if device != "cpu" and precision == "autocast":
- mask = mask.half()
-
- move_fs_to_cpu()
-
- assert 0. <= opt_strength <= 1., 'can only work with strength in [0.0, 1.0]'
- t_enc = int(opt_strength * opt_ddim_steps)
- print(f"target t_enc is {t_enc} steps")
-
- if opt_save_to_disk_path is not None:
- session_out_path = os.path.join(opt_save_to_disk_path, req.session_id)
- os.makedirs(session_out_path, exist_ok=True)
- else:
- session_out_path = None
-
- seeds = ""
- with torch.no_grad():
- for n in trange(opt_n_iter, desc="Sampling"):
- for prompts in tqdm(data, desc="data"):
-
- with precision_scope("cuda"):
- modelCS.to(device)
- uc = None
- if opt_scale != 1.0:
- uc = modelCS.get_learned_conditioning(batch_size * [req.negative_prompt])
- if isinstance(prompts, tuple):
- prompts = list(prompts)
-
- subprompts, weights = split_weighted_subprompts(prompts[0])
- if len(subprompts) > 1:
- c = torch.zeros_like(uc)
- totalWeight = sum(weights)
- # normalize each "sub prompt" and add it
- for i in range(len(subprompts)):
- weight = weights[i]
- # if not skip_normalize:
- weight = weight / totalWeight
- c = torch.add(c, modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
- else:
- c = modelCS.get_learned_conditioning(prompts)
-
- modelFS.to(device)
-
- partial_x_samples = None
- def img_callback(x_samples, i):
- nonlocal partial_x_samples
-
- partial_x_samples = x_samples
-
- if req.stream_progress_updates:
- n_steps = opt_ddim_steps if req.init_image is None else t_enc
- progress = {"step": i, "total_steps": n_steps}
-
- if req.stream_image_progress and i % 5 == 0:
- partial_images = []
-
- for i in range(batch_size):
- x_samples_ddim = modelFS.decode_first_stage(x_samples[i].unsqueeze(0))
- x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
- x_sample = 255.0 * rearrange(x_sample[0].cpu().numpy(), "c h w -> h w c")
- x_sample = x_sample.astype(np.uint8)
- img = Image.fromarray(x_sample)
- buf = BytesIO()
- img.save(buf, format='JPEG')
- buf.seek(0)
-
- del img, x_sample, x_samples_ddim
- # don't delete x_samples, it is used in the code that called this callback
-
- temp_images[str(req.session_id) + '/' + str(i)] = buf
- partial_images.append({'path': f'/image/tmp/{req.session_id}/{i}'})
-
- progress['output'] = partial_images
-
- yield json.dumps(progress)
-
- if stop_processing:
- raise UserInitiatedStop("User requested that we stop processing")
-
- # run the handler
- try:
- if handler == _txt2img:
- x_samples = _txt2img(opt_W, opt_H, opt_n_samples, opt_ddim_steps, opt_scale, None, opt_C, opt_f, opt_ddim_eta, c, uc, opt_seed, img_callback, mask, opt_sampler_name)
- else:
- x_samples = _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, opt_ddim_eta, opt_seed, img_callback, mask)
-
- yield from x_samples
-
- x_samples = partial_x_samples
- except UserInitiatedStop:
- if partial_x_samples is None:
- continue
-
- x_samples = partial_x_samples
-
- print("saving images")
- for i in range(batch_size):
-
- x_samples_ddim = modelFS.decode_first_stage(x_samples[i].unsqueeze(0))
- x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
- x_sample = 255.0 * rearrange(x_sample[0].cpu().numpy(), "c h w -> h w c")
- x_sample = x_sample.astype(np.uint8)
- img = Image.fromarray(x_sample)
-
- has_filters = (opt_use_face_correction is not None and opt_use_face_correction.startswith('GFPGAN')) or \
- (opt_use_upscale is not None and opt_use_upscale.startswith('RealESRGAN'))
-
- return_orig_img = not has_filters or not opt_show_only_filtered
-
- if stop_processing:
- return_orig_img = True
-
- if opt_save_to_disk_path is not None:
- prompt_flattened = filename_regex.sub('_', prompts[0])
- prompt_flattened = prompt_flattened[:50]
-
- img_id = str(uuid.uuid4())[-8:]
-
- file_path = f"{prompt_flattened}_{img_id}"
- img_out_path = os.path.join(session_out_path, f"{file_path}.{opt_format}")
- meta_out_path = os.path.join(session_out_path, f"{file_path}.txt")
-
- if return_orig_img:
- save_image(img, img_out_path)
-
- save_metadata(meta_out_path, prompts, opt_seed, opt_W, opt_H, opt_ddim_steps, opt_scale, opt_strength, opt_use_face_correction, opt_use_upscale, opt_sampler_name, req.negative_prompt, ckpt_file)
-
- if return_orig_img:
- img_data = img_to_base64_str(img, opt_format)
- res_image_orig = ResponseImage(data=img_data, seed=opt_seed)
- res.images.append(res_image_orig)
-
- if opt_save_to_disk_path is not None:
- res_image_orig.path_abs = img_out_path
-
- del img
-
- if has_filters and not stop_processing:
- print('Applying filters..')
-
- gc()
- filters_applied = []
-
- if opt_use_face_correction:
- _, _, output = model_gfpgan.enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
- x_sample = output[:,:,::-1]
- filters_applied.append(opt_use_face_correction)
-
- if opt_use_upscale:
- output, _ = model_real_esrgan.enhance(x_sample[:,:,::-1])
- x_sample = output[:,:,::-1]
- filters_applied.append(opt_use_upscale)
-
- filtered_image = Image.fromarray(x_sample)
-
- filtered_img_data = img_to_base64_str(filtered_image, opt_format)
- res_image_filtered = ResponseImage(data=filtered_img_data, seed=opt_seed)
- res.images.append(res_image_filtered)
-
- filters_applied = "_".join(filters_applied)
-
- if opt_save_to_disk_path is not None:
- filtered_img_out_path = os.path.join(session_out_path, f"{file_path}_{filters_applied}.{opt_format}")
- save_image(filtered_image, filtered_img_out_path)
- res_image_filtered.path_abs = filtered_img_out_path
-
- del filtered_image
-
- seeds += str(opt_seed) + ","
- opt_seed += 1
-
- move_fs_to_cpu()
- gc()
- del x_samples, x_samples_ddim, x_sample
- print("memory_final = ", torch.cuda.memory_allocated() / 1e6)
-
- print('Task completed')
-
- yield json.dumps(res.json())
-
-def save_image(img, img_out_path):
- try:
- img.save(img_out_path)
- except:
- print('could not save the file', traceback.format_exc())
-
-def save_metadata(meta_out_path, prompts, opt_seed, opt_W, opt_H, opt_ddim_steps, opt_scale, opt_prompt_strength, opt_correct_face, opt_upscale, sampler_name, negative_prompt, ckpt_file):
- metadata = f"{prompts[0]}\nWidth: {opt_W}\nHeight: {opt_H}\nSeed: {opt_seed}\nSteps: {opt_ddim_steps}\nGuidance Scale: {opt_scale}\nPrompt Strength: {opt_prompt_strength}\nUse Face Correction: {opt_correct_face}\nUse Upscaling: {opt_upscale}\nSampler: {sampler_name}\nNegative Prompt: {negative_prompt}\nStable Diffusion Model: {ckpt_file + '.ckpt'}"
-
- try:
- with open(meta_out_path, 'w') as f:
- f.write(metadata)
- except:
- print('could not save the file', traceback.format_exc())
-
-def _txt2img(opt_W, opt_H, opt_n_samples, opt_ddim_steps, opt_scale, start_code, opt_C, opt_f, opt_ddim_eta, c, uc, opt_seed, img_callback, mask, sampler_name):
- shape = [opt_n_samples, opt_C, opt_H // opt_f, opt_W // opt_f]
-
- if device != "cpu":
- mem = torch.cuda.memory_allocated() / 1e6
- modelCS.to("cpu")
- while torch.cuda.memory_allocated() / 1e6 >= mem:
- time.sleep(1)
-
- if sampler_name == 'ddim':
- model.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
-
- samples_ddim = model.sample(
- S=opt_ddim_steps,
- conditioning=c,
- seed=opt_seed,
- shape=shape,
- verbose=False,
- unconditional_guidance_scale=opt_scale,
- unconditional_conditioning=uc,
- eta=opt_ddim_eta,
- x_T=start_code,
- img_callback=img_callback,
- mask=mask,
- sampler = sampler_name,
- )
-
- yield from samples_ddim
-
-def _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, opt_ddim_eta, opt_seed, img_callback, mask):
- # encode (scaled latent)
- z_enc = model.stochastic_encode(
- init_latent,
- torch.tensor([t_enc] * batch_size).to(device),
- opt_seed,
- opt_ddim_eta,
- opt_ddim_steps,
- )
- x_T = None if mask is None else init_latent
-
- # decode it
- samples_ddim = model.sample(
- t_enc,
- c,
- z_enc,
- unconditional_guidance_scale=opt_scale,
- unconditional_conditioning=uc,
- img_callback=img_callback,
- mask=mask,
- x_T=x_T,
- sampler = 'ddim'
- )
-
- yield from samples_ddim
-
-def move_fs_to_cpu():
- if device != "cpu":
- mem = torch.cuda.memory_allocated() / 1e6
- modelFS.to("cpu")
- while torch.cuda.memory_allocated() / 1e6 >= mem:
- time.sleep(1)
-
-def gc():
- if device == 'cpu':
- return
-
- torch.cuda.empty_cache()
- torch.cuda.ipc_collect()
-
-# internal
-
-def chunk(it, size):
- it = iter(it)
- return iter(lambda: tuple(islice(it, size)), ())
-
-
-def load_model_from_config(ckpt, verbose=False):
- print(f"Loading model from {ckpt}")
- pl_sd = torch.load(ckpt, map_location="cpu")
- if "global_step" in pl_sd:
- print(f"Global Step: {pl_sd['global_step']}")
- sd = pl_sd["state_dict"]
- return sd
-
-# utils
-class UserInitiatedStop(Exception):
- pass
-
-def load_img(img_str, w0, h0):
- image = base64_str_to_img(img_str).convert("RGB")
- w, h = image.size
- print(f"loaded input image of size ({w}, {h}) from base64")
- if h0 is not None and w0 is not None:
- h, w = h0, w0
-
- w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64
- image = image.resize((w, h), resample=Image.Resampling.LANCZOS)
- image = np.array(image).astype(np.float32) / 255.0
- image = image[None].transpose(0, 3, 1, 2)
- image = torch.from_numpy(image)
- return 2.*image - 1.
-
-def load_mask(mask_str, h0, w0, newH, newW, invert=False):
- image = base64_str_to_img(mask_str).convert("RGB")
- w, h = image.size
- print(f"loaded input mask of size ({w}, {h})")
-
- if invert:
- print("inverted")
- image = ImageOps.invert(image)
- # where_0, where_1 = np.where(image == 0), np.where(image == 255)
- # image[where_0], image[where_1] = 255, 0
-
- if h0 is not None and w0 is not None:
- h, w = h0, w0
-
- w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64
-
- print(f"New mask size ({w}, {h})")
- image = image.resize((newW, newH), resample=Image.Resampling.LANCZOS)
- image = np.array(image)
-
- image = image.astype(np.float32) / 255.0
- image = image[None].transpose(0, 3, 1, 2)
- image = torch.from_numpy(image)
- return image
-
-# https://stackoverflow.com/a/61114178
-def img_to_base64_str(img, output_format="PNG"):
- buffered = BytesIO()
- img.save(buffered, format=output_format)
- buffered.seek(0)
- img_byte = buffered.getvalue()
- img_str = "data:image/png;base64," + base64.b64encode(img_byte).decode()
- return img_str
-
-def base64_str_to_img(img_str):
- img_str = img_str[len("data:image/png;base64,"):]
- data = base64.b64decode(img_str)
- buffered = BytesIO(data)
- img = Image.open(buffered)
- return img
diff --git a/spaces/jonas/sdg-policy-tracing/analyse_site.py b/spaces/jonas/sdg-policy-tracing/analyse_site.py
deleted file mode 100644
index c6d702c678e16023d9b0d6613f5dbef2ce6b7f93..0000000000000000000000000000000000000000
--- a/spaces/jonas/sdg-policy-tracing/analyse_site.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import streamlit as st
-
-import glob, os, sys; sys.path.append('/src')
-#import helper
-from src import preprocessing as pre
-from src import cleaning as clean
-
-def app():
- # Sidebar
- st.sidebar.title('Analyse Policy Document')
-
- # Container
- with st.container():
- st.markdown("SDSN X GIZ Policy Tracing
",
- unsafe_allow_html=True)
-
- file = st.file_uploader('Upload PDF File', type=['pdf', 'docx', 'txt'])
-
- if file is not None:
- st.write("Filename: ", file.name)
- # text = []
- # with pdfplumber.open(file) as pdf:
- # for page in pdf.pages:
- # text.append(page.extract_text())
- # text_str = ' '.join([page for page in text])
-
- # st.write('Number of pages:',len(pdf.pages))
-
- # load document
- docs = pre.load_document(file)
-
- # preprocess document
- docs_processed, df, all_text = clean.preprocessing(docs)
-
-
-
- st.write('... ')
-
- else:
- st.write(' ')
- st.write(' ')
- st.markdown("no PDF uploaded ...
",
- unsafe_allow_html=True)
diff --git a/spaces/jone/GFPGAN/setup.py b/spaces/jone/GFPGAN/setup.py
deleted file mode 100644
index 474e9188aa2dc5c19614921760ce4ad99bd19c13..0000000000000000000000000000000000000000
--- a/spaces/jone/GFPGAN/setup.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python
-
-from setuptools import find_packages, setup
-
-import os
-import subprocess
-import time
-
-version_file = 'gfpgan/version.py'
-
-
-def readme():
- with open('README.md', encoding='utf-8') as f:
- content = f.read()
- return content
-
-
-def get_git_hash():
-
- def _minimal_ext_cmd(cmd):
- # construct minimal environment
- env = {}
- for k in ['SYSTEMROOT', 'PATH', 'HOME']:
- v = os.environ.get(k)
- if v is not None:
- env[k] = v
- # LANGUAGE is used on win32
- env['LANGUAGE'] = 'C'
- env['LANG'] = 'C'
- env['LC_ALL'] = 'C'
- out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
- return out
-
- try:
- out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
- sha = out.strip().decode('ascii')
- except OSError:
- sha = 'unknown'
-
- return sha
-
-
-def get_hash():
- if os.path.exists('.git'):
- sha = get_git_hash()[:7]
- else:
- sha = 'unknown'
-
- return sha
-
-
-def write_version_py():
- content = """# GENERATED VERSION FILE
-# TIME: {}
-__version__ = '{}'
-__gitsha__ = '{}'
-version_info = ({})
-"""
- sha = get_hash()
- with open('VERSION', 'r') as f:
- SHORT_VERSION = f.read().strip()
- VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
-
- version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
- with open(version_file, 'w') as f:
- f.write(version_file_str)
-
-
-def get_version():
- with open(version_file, 'r') as f:
- exec(compile(f.read(), version_file, 'exec'))
- return locals()['__version__']
-
-
-def get_requirements(filename='requirements.txt'):
- here = os.path.dirname(os.path.realpath(__file__))
- with open(os.path.join(here, filename), 'r') as f:
- requires = [line.replace('\n', '') for line in f.readlines()]
- return requires
-
-
-if __name__ == '__main__':
- write_version_py()
- setup(
- name='gfpgan',
- version=get_version(),
- description='GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration',
- long_description=readme(),
- long_description_content_type='text/markdown',
- author='Xintao Wang',
- author_email='xintao.wang@outlook.com',
- keywords='computer vision, pytorch, image restoration, super-resolution, face restoration, gan, gfpgan',
- url='https://github.com/TencentARC/GFPGAN',
- include_package_data=True,
- packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
- classifiers=[
- 'Development Status :: 4 - Beta',
- 'License :: OSI Approved :: Apache Software License',
- 'Operating System :: OS Independent',
- 'Programming Language :: Python :: 3',
- 'Programming Language :: Python :: 3.7',
- 'Programming Language :: Python :: 3.8',
- ],
- license='Apache License Version 2.0',
- setup_requires=['cython', 'numpy'],
- install_requires=get_requirements(),
- zip_safe=False)
diff --git a/spaces/joshen/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp b/spaces/joshen/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
deleted file mode 100644
index 36d06c8e9068570c3e7624895d474f33dbfe3d29..0000000000000000000000000000000000000000
--- a/spaces/joshen/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
+++ /dev/null
@@ -1,3276 +0,0 @@
-// jpgd.cpp - C++ class for JPEG decompression.
-// Public domain, Rich Geldreich
-// Last updated Apr. 16, 2011
-// Alex Evans: Linear memory allocator (taken from jpge.h).
-//
-// Supports progressive and baseline sequential JPEG image files, and the most common chroma subsampling factors: Y, H1V1, H2V1, H1V2, and H2V2.
-//
-// Chroma upsampling quality: H2V2 is upsampled in the frequency domain, H2V1 and H1V2 are upsampled using point sampling.
-// Chroma upsampling reference: "Fast Scheme for Image Size Change in the Compressed Domain"
-// http://vision.ai.uiuc.edu/~dugad/research/dct/index.html
-
-#include "jpgd.h"
-#include
-
-#include
-// BEGIN EPIC MOD
-#define JPGD_ASSERT(x) { assert(x); CA_ASSUME(x); } (void)0
-// END EPIC MOD
-
-#ifdef _MSC_VER
-#pragma warning (disable : 4611) // warning C4611: interaction between '_setjmp' and C++ object destruction is non-portable
-#endif
-
-// Set to 1 to enable freq. domain chroma upsampling on images using H2V2 subsampling (0=faster nearest neighbor sampling).
-// This is slower, but results in higher quality on images with highly saturated colors.
-#define JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING 1
-
-#define JPGD_TRUE (1)
-#define JPGD_FALSE (0)
-
-#define JPGD_MAX(a,b) (((a)>(b)) ? (a) : (b))
-#define JPGD_MIN(a,b) (((a)<(b)) ? (a) : (b))
-
-namespace jpgd {
-
- static inline void *jpgd_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
- static inline void jpgd_free(void *p) { FMemory::Free(p); }
-
-// BEGIN EPIC MOD
-//@UE3 - use UE3 BGRA encoding instead of assuming RGBA
- // stolen from IImageWrapper.h
- enum ERGBFormatJPG
- {
- Invalid = -1,
- RGBA = 0,
- BGRA = 1,
- Gray = 2,
- };
- static ERGBFormatJPG jpg_format;
-// END EPIC MOD
-
- // DCT coefficients are stored in this sequence.
- static int g_ZAG[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-
- enum JPEG_MARKER
- {
- M_SOF0 = 0xC0, M_SOF1 = 0xC1, M_SOF2 = 0xC2, M_SOF3 = 0xC3, M_SOF5 = 0xC5, M_SOF6 = 0xC6, M_SOF7 = 0xC7, M_JPG = 0xC8,
- M_SOF9 = 0xC9, M_SOF10 = 0xCA, M_SOF11 = 0xCB, M_SOF13 = 0xCD, M_SOF14 = 0xCE, M_SOF15 = 0xCF, M_DHT = 0xC4, M_DAC = 0xCC,
- M_RST0 = 0xD0, M_RST1 = 0xD1, M_RST2 = 0xD2, M_RST3 = 0xD3, M_RST4 = 0xD4, M_RST5 = 0xD5, M_RST6 = 0xD6, M_RST7 = 0xD7,
- M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_DNL = 0xDC, M_DRI = 0xDD, M_DHP = 0xDE, M_EXP = 0xDF,
- M_APP0 = 0xE0, M_APP15 = 0xEF, M_JPG0 = 0xF0, M_JPG13 = 0xFD, M_COM = 0xFE, M_TEM = 0x01, M_ERROR = 0x100, RST0 = 0xD0
- };
-
- enum JPEG_SUBSAMPLING { JPGD_GRAYSCALE = 0, JPGD_YH1V1, JPGD_YH2V1, JPGD_YH1V2, JPGD_YH2V2 };
-
-#define CONST_BITS 13
-#define PASS1_BITS 2
-#define SCALEDONE ((int32)1)
-
-#define FIX_0_298631336 ((int32)2446) /* FIX(0.298631336) */
-#define FIX_0_390180644 ((int32)3196) /* FIX(0.390180644) */
-#define FIX_0_541196100 ((int32)4433) /* FIX(0.541196100) */
-#define FIX_0_765366865 ((int32)6270) /* FIX(0.765366865) */
-#define FIX_0_899976223 ((int32)7373) /* FIX(0.899976223) */
-#define FIX_1_175875602 ((int32)9633) /* FIX(1.175875602) */
-#define FIX_1_501321110 ((int32)12299) /* FIX(1.501321110) */
-#define FIX_1_847759065 ((int32)15137) /* FIX(1.847759065) */
-#define FIX_1_961570560 ((int32)16069) /* FIX(1.961570560) */
-#define FIX_2_053119869 ((int32)16819) /* FIX(2.053119869) */
-#define FIX_2_562915447 ((int32)20995) /* FIX(2.562915447) */
-#define FIX_3_072711026 ((int32)25172) /* FIX(3.072711026) */
-
-#define DESCALE(x,n) (((x) + (SCALEDONE << ((n)-1))) >> (n))
-#define DESCALE_ZEROSHIFT(x,n) (((x) + (128 << (n)) + (SCALEDONE << ((n)-1))) >> (n))
-
-#define MULTIPLY(var, cnst) ((var) * (cnst))
-
-#define CLAMP(i) ((static_cast(i) > 255) ? (((~i) >> 31) & 0xFF) : (i))
-
- // Compiler creates a fast path 1D IDCT for X non-zero columns
- template
- struct Row
- {
- static void idct(int* pTemp, const jpgd_block_t* pSrc)
- {
- // ACCESS_COL() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_COL(x) (((x) < NONZERO_COLS) ? (int)pSrc[x] : 0)
-
- const int z2 = ACCESS_COL(2), z3 = ACCESS_COL(6);
-
- const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
- const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
- const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
- const int tmp0 = (ACCESS_COL(0) + ACCESS_COL(4)) << CONST_BITS;
- const int tmp1 = (ACCESS_COL(0) - ACCESS_COL(4)) << CONST_BITS;
-
- const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
- const int atmp0 = ACCESS_COL(7), atmp1 = ACCESS_COL(5), atmp2 = ACCESS_COL(3), atmp3 = ACCESS_COL(1);
-
- const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
- const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
- const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
- const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
- const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
- const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
- const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
- const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
- const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
- const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
- pTemp[0] = DESCALE(tmp10 + btmp3, CONST_BITS-PASS1_BITS);
- pTemp[7] = DESCALE(tmp10 - btmp3, CONST_BITS-PASS1_BITS);
- pTemp[1] = DESCALE(tmp11 + btmp2, CONST_BITS-PASS1_BITS);
- pTemp[6] = DESCALE(tmp11 - btmp2, CONST_BITS-PASS1_BITS);
- pTemp[2] = DESCALE(tmp12 + btmp1, CONST_BITS-PASS1_BITS);
- pTemp[5] = DESCALE(tmp12 - btmp1, CONST_BITS-PASS1_BITS);
- pTemp[3] = DESCALE(tmp13 + btmp0, CONST_BITS-PASS1_BITS);
- pTemp[4] = DESCALE(tmp13 - btmp0, CONST_BITS-PASS1_BITS);
- }
- };
-
- template <>
- struct Row<0>
- {
- static void idct(int* pTemp, const jpgd_block_t* pSrc)
- {
-#ifdef _MSC_VER
- pTemp; pSrc;
-#endif
- }
- };
-
- template <>
- struct Row<1>
- {
- static void idct(int* pTemp, const jpgd_block_t* pSrc)
- {
- const int dcval = (pSrc[0] << PASS1_BITS);
-
- pTemp[0] = dcval;
- pTemp[1] = dcval;
- pTemp[2] = dcval;
- pTemp[3] = dcval;
- pTemp[4] = dcval;
- pTemp[5] = dcval;
- pTemp[6] = dcval;
- pTemp[7] = dcval;
- }
- };
-
- // Compiler creates a fast path 1D IDCT for X non-zero rows
- template
- struct Col
- {
- static void idct(uint8* pDst_ptr, const int* pTemp)
- {
- // ACCESS_ROW() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_ROW(x) (((x) < NONZERO_ROWS) ? pTemp[x * 8] : 0)
-
- const int z2 = ACCESS_ROW(2);
- const int z3 = ACCESS_ROW(6);
-
- const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
- const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
- const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
- const int tmp0 = (ACCESS_ROW(0) + ACCESS_ROW(4)) << CONST_BITS;
- const int tmp1 = (ACCESS_ROW(0) - ACCESS_ROW(4)) << CONST_BITS;
-
- const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
- const int atmp0 = ACCESS_ROW(7), atmp1 = ACCESS_ROW(5), atmp2 = ACCESS_ROW(3), atmp3 = ACCESS_ROW(1);
-
- const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
- const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
- const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
- const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
- const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
- const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
- const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
- const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
- const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
- const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
- int i = DESCALE_ZEROSHIFT(tmp10 + btmp3, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*0] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp10 - btmp3, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*7] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp11 + btmp2, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*1] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp11 - btmp2, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*6] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp12 + btmp1, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*2] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp12 - btmp1, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*5] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp13 + btmp0, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*3] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp13 - btmp0, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*4] = (uint8)CLAMP(i);
- }
- };
-
- template <>
- struct Col<1>
- {
- static void idct(uint8* pDst_ptr, const int* pTemp)
- {
- int dcval = DESCALE_ZEROSHIFT(pTemp[0], PASS1_BITS+3);
- const uint8 dcval_clamped = (uint8)CLAMP(dcval);
- pDst_ptr[0*8] = dcval_clamped;
- pDst_ptr[1*8] = dcval_clamped;
- pDst_ptr[2*8] = dcval_clamped;
- pDst_ptr[3*8] = dcval_clamped;
- pDst_ptr[4*8] = dcval_clamped;
- pDst_ptr[5*8] = dcval_clamped;
- pDst_ptr[6*8] = dcval_clamped;
- pDst_ptr[7*8] = dcval_clamped;
- }
- };
-
- static const uint8 s_idct_row_table[] =
- {
- 1,0,0,0,0,0,0,0, 2,0,0,0,0,0,0,0, 2,1,0,0,0,0,0,0, 2,1,1,0,0,0,0,0, 2,2,1,0,0,0,0,0, 3,2,1,0,0,0,0,0, 4,2,1,0,0,0,0,0, 4,3,1,0,0,0,0,0,
- 4,3,2,0,0,0,0,0, 4,3,2,1,0,0,0,0, 4,3,2,1,1,0,0,0, 4,3,2,2,1,0,0,0, 4,3,3,2,1,0,0,0, 4,4,3,2,1,0,0,0, 5,4,3,2,1,0,0,0, 6,4,3,2,1,0,0,0,
- 6,5,3,2,1,0,0,0, 6,5,4,2,1,0,0,0, 6,5,4,3,1,0,0,0, 6,5,4,3,2,0,0,0, 6,5,4,3,2,1,0,0, 6,5,4,3,2,1,1,0, 6,5,4,3,2,2,1,0, 6,5,4,3,3,2,1,0,
- 6,5,4,4,3,2,1,0, 6,5,5,4,3,2,1,0, 6,6,5,4,3,2,1,0, 7,6,5,4,3,2,1,0, 8,6,5,4,3,2,1,0, 8,7,5,4,3,2,1,0, 8,7,6,4,3,2,1,0, 8,7,6,5,3,2,1,0,
- 8,7,6,5,4,2,1,0, 8,7,6,5,4,3,1,0, 8,7,6,5,4,3,2,0, 8,7,6,5,4,3,2,1, 8,7,6,5,4,3,2,2, 8,7,6,5,4,3,3,2, 8,7,6,5,4,4,3,2, 8,7,6,5,5,4,3,2,
- 8,7,6,6,5,4,3,2, 8,7,7,6,5,4,3,2, 8,8,7,6,5,4,3,2, 8,8,8,6,5,4,3,2, 8,8,8,7,5,4,3,2, 8,8,8,7,6,4,3,2, 8,8,8,7,6,5,3,2, 8,8,8,7,6,5,4,2,
- 8,8,8,7,6,5,4,3, 8,8,8,7,6,5,4,4, 8,8,8,7,6,5,5,4, 8,8,8,7,6,6,5,4, 8,8,8,7,7,6,5,4, 8,8,8,8,7,6,5,4, 8,8,8,8,8,6,5,4, 8,8,8,8,8,7,5,4,
- 8,8,8,8,8,7,6,4, 8,8,8,8,8,7,6,5, 8,8,8,8,8,7,6,6, 8,8,8,8,8,7,7,6, 8,8,8,8,8,8,7,6, 8,8,8,8,8,8,8,6, 8,8,8,8,8,8,8,7, 8,8,8,8,8,8,8,8,
- };
-
- static const uint8 s_idct_col_table[] = { 1, 1, 2, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 };
-
- void idct(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr, int block_max_zag)
- {
- JPGD_ASSERT(block_max_zag >= 1);
- JPGD_ASSERT(block_max_zag <= 64);
-
- if (block_max_zag == 1)
- {
- int k = ((pSrc_ptr[0] + 4) >> 3) + 128;
- k = CLAMP(k);
- k = k | (k<<8);
- k = k | (k<<16);
-
- for (int i = 8; i > 0; i--)
- {
- *(int*)&pDst_ptr[0] = k;
- *(int*)&pDst_ptr[4] = k;
- pDst_ptr += 8;
- }
- return;
- }
-
- int temp[64];
-
- const jpgd_block_t* pSrc = pSrc_ptr;
- int* pTemp = temp;
-
- const uint8* pRow_tab = &s_idct_row_table[(block_max_zag - 1) * 8];
- int i;
- for (i = 8; i > 0; i--, pRow_tab++)
- {
- switch (*pRow_tab)
- {
- case 0: Row<0>::idct(pTemp, pSrc); break;
- case 1: Row<1>::idct(pTemp, pSrc); break;
- case 2: Row<2>::idct(pTemp, pSrc); break;
- case 3: Row<3>::idct(pTemp, pSrc); break;
- case 4: Row<4>::idct(pTemp, pSrc); break;
- case 5: Row<5>::idct(pTemp, pSrc); break;
- case 6: Row<6>::idct(pTemp, pSrc); break;
- case 7: Row<7>::idct(pTemp, pSrc); break;
- case 8: Row<8>::idct(pTemp, pSrc); break;
- }
-
- pSrc += 8;
- pTemp += 8;
- }
-
- pTemp = temp;
-
- const int nonzero_rows = s_idct_col_table[block_max_zag - 1];
- for (i = 8; i > 0; i--)
- {
- switch (nonzero_rows)
- {
- case 1: Col<1>::idct(pDst_ptr, pTemp); break;
- case 2: Col<2>::idct(pDst_ptr, pTemp); break;
- case 3: Col<3>::idct(pDst_ptr, pTemp); break;
- case 4: Col<4>::idct(pDst_ptr, pTemp); break;
- case 5: Col<5>::idct(pDst_ptr, pTemp); break;
- case 6: Col<6>::idct(pDst_ptr, pTemp); break;
- case 7: Col<7>::idct(pDst_ptr, pTemp); break;
- case 8: Col<8>::idct(pDst_ptr, pTemp); break;
- }
-
- pTemp++;
- pDst_ptr++;
- }
- }
-
- void idct_4x4(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr)
- {
- int temp[64];
- int* pTemp = temp;
- const jpgd_block_t* pSrc = pSrc_ptr;
-
- for (int i = 4; i > 0; i--)
- {
- Row<4>::idct(pTemp, pSrc);
- pSrc += 8;
- pTemp += 8;
- }
-
- pTemp = temp;
- for (int i = 8; i > 0; i--)
- {
- Col<4>::idct(pDst_ptr, pTemp);
- pTemp++;
- pDst_ptr++;
- }
- }
-
- // Retrieve one character from the input stream.
- inline uint jpeg_decoder::get_char()
- {
- // Any bytes remaining in buffer?
- if (!m_in_buf_left)
- {
- // Try to get more bytes.
- prep_in_buffer();
- // Still nothing to get?
- if (!m_in_buf_left)
- {
- // Pad the end of the stream with 0xFF 0xD9 (EOI marker)
- int t = m_tem_flag;
- m_tem_flag ^= 1;
- if (t)
- return 0xD9;
- else
- return 0xFF;
- }
- }
-
- uint c = *m_pIn_buf_ofs++;
- m_in_buf_left--;
-
- return c;
- }
-
- // Same as previous method, except can indicate if the character is a pad character or not.
- inline uint jpeg_decoder::get_char(bool *pPadding_flag)
- {
- if (!m_in_buf_left)
- {
- prep_in_buffer();
- if (!m_in_buf_left)
- {
- *pPadding_flag = true;
- int t = m_tem_flag;
- m_tem_flag ^= 1;
- if (t)
- return 0xD9;
- else
- return 0xFF;
- }
- }
-
- *pPadding_flag = false;
-
- uint c = *m_pIn_buf_ofs++;
- m_in_buf_left--;
-
- return c;
- }
-
- // Inserts a previously retrieved character back into the input buffer.
- inline void jpeg_decoder::stuff_char(uint8 q)
- {
- *(--m_pIn_buf_ofs) = q;
- m_in_buf_left++;
- }
-
- // Retrieves one character from the input stream, but does not read past markers. Will continue to return 0xFF when a marker is encountered.
- inline uint8 jpeg_decoder::get_octet()
- {
- bool padding_flag;
- int c = get_char(&padding_flag);
-
- if (c == 0xFF)
- {
- if (padding_flag)
- return 0xFF;
-
- c = get_char(&padding_flag);
- if (padding_flag)
- {
- stuff_char(0xFF);
- return 0xFF;
- }
-
- if (c == 0x00)
- return 0xFF;
- else
- {
- stuff_char(static_cast(c));
- stuff_char(0xFF);
- return 0xFF;
- }
- }
-
- return static_cast(c);
- }
-
- // Retrieves a variable number of bits from the input stream. Does not recognize markers.
- inline uint jpeg_decoder::get_bits(int num_bits)
- {
- if (!num_bits)
- return 0;
-
- uint i = m_bit_buf >> (32 - num_bits);
-
- if ((m_bits_left -= num_bits) <= 0)
- {
- m_bit_buf <<= (num_bits += m_bits_left);
-
- uint c1 = get_char();
- uint c2 = get_char();
- m_bit_buf = (m_bit_buf & 0xFFFF0000) | (c1 << 8) | c2;
-
- m_bit_buf <<= -m_bits_left;
-
- m_bits_left += 16;
-
- JPGD_ASSERT(m_bits_left >= 0);
- }
- else
- m_bit_buf <<= num_bits;
-
- return i;
- }
-
- // Retrieves a variable number of bits from the input stream. Markers will not be read into the input bit buffer. Instead, an infinite number of all 1's will be returned when a marker is encountered.
- inline uint jpeg_decoder::get_bits_no_markers(int num_bits)
- {
- if (!num_bits)
- return 0;
-
- uint i = m_bit_buf >> (32 - num_bits);
-
- if ((m_bits_left -= num_bits) <= 0)
- {
- m_bit_buf <<= (num_bits += m_bits_left);
-
- if ((m_in_buf_left < 2) || (m_pIn_buf_ofs[0] == 0xFF) || (m_pIn_buf_ofs[1] == 0xFF))
- {
- uint c1 = get_octet();
- uint c2 = get_octet();
- m_bit_buf |= (c1 << 8) | c2;
- }
- else
- {
- m_bit_buf |= ((uint)m_pIn_buf_ofs[0] << 8) | m_pIn_buf_ofs[1];
- m_in_buf_left -= 2;
- m_pIn_buf_ofs += 2;
- }
-
- m_bit_buf <<= -m_bits_left;
-
- m_bits_left += 16;
-
- JPGD_ASSERT(m_bits_left >= 0);
- }
- else
- m_bit_buf <<= num_bits;
-
- return i;
- }
-
- // Decodes a Huffman encoded symbol.
- inline int jpeg_decoder::huff_decode(huff_tables *pH)
- {
- int symbol;
-
- // Check first 8-bits: do we have a complete symbol?
- if ((symbol = pH->look_up[m_bit_buf >> 24]) < 0)
- {
- // Decode more bits, use a tree traversal to find symbol.
- int ofs = 23;
- do
- {
- symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
- ofs--;
- } while (symbol < 0);
-
- get_bits_no_markers(8 + (23 - ofs));
- }
- else
- get_bits_no_markers(pH->code_size[symbol]);
-
- return symbol;
- }
-
- // Decodes a Huffman encoded symbol.
- inline int jpeg_decoder::huff_decode(huff_tables *pH, int& extra_bits)
- {
- int symbol;
-
- // Check first 8-bits: do we have a complete symbol?
- if ((symbol = pH->look_up2[m_bit_buf >> 24]) < 0)
- {
- // Use a tree traversal to find symbol.
- int ofs = 23;
- do
- {
- symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
- ofs--;
- } while (symbol < 0);
-
- get_bits_no_markers(8 + (23 - ofs));
-
- extra_bits = get_bits_no_markers(symbol & 0xF);
- }
- else
- {
- JPGD_ASSERT(((symbol >> 8) & 31) == pH->code_size[symbol & 255] + ((symbol & 0x8000) ? (symbol & 15) : 0));
-
- if (symbol & 0x8000)
- {
- get_bits_no_markers((symbol >> 8) & 31);
- extra_bits = symbol >> 16;
- }
- else
- {
- int code_size = (symbol >> 8) & 31;
- int num_extra_bits = symbol & 0xF;
- int bits = code_size + num_extra_bits;
- if (bits <= (m_bits_left + 16))
- extra_bits = get_bits_no_markers(bits) & ((1 << num_extra_bits) - 1);
- else
- {
- get_bits_no_markers(code_size);
- extra_bits = get_bits_no_markers(num_extra_bits);
- }
- }
-
- symbol &= 0xFF;
- }
-
- return symbol;
- }
-
- // Tables and macro used to fully decode the DPCM differences.
- static const int s_extend_test[16] = { 0, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100, 0x0200, 0x0400, 0x0800, 0x1000, 0x2000, 0x4000 };
- static const int s_extend_offset[16] = { 0, -1, -3, -7, -15, -31, -63, -127, -255, -511, -1023, -2047, -4095, -8191, -16383, -32767 };
- static const int s_extend_mask[] = { 0, (1<<0), (1<<1), (1<<2), (1<<3), (1<<4), (1<<5), (1<<6), (1<<7), (1<<8), (1<<9), (1<<10), (1<<11), (1<<12), (1<<13), (1<<14), (1<<15), (1<<16) };
-#define HUFF_EXTEND(x,s) ((x) < s_extend_test[s] ? (x) + s_extend_offset[s] : (x))
-
- // Clamps a value between 0-255.
- inline uint8 jpeg_decoder::clamp(int i)
- {
- if (static_cast(i) > 255)
- i = (((~i) >> 31) & 0xFF);
-
- return static_cast(i);
- }
-
- namespace DCT_Upsample
- {
- struct Matrix44
- {
- typedef int Element_Type;
- enum { NUM_ROWS = 4, NUM_COLS = 4 };
-
- Element_Type v[NUM_ROWS][NUM_COLS];
-
- inline int rows() const { return NUM_ROWS; }
- inline int cols() const { return NUM_COLS; }
-
- inline const Element_Type & at(int r, int c) const { return v[r][c]; }
- inline Element_Type & at(int r, int c) { return v[r][c]; }
-
- inline Matrix44() { }
-
- inline Matrix44& operator += (const Matrix44& a)
- {
- for (int r = 0; r < NUM_ROWS; r++)
- {
- at(r, 0) += a.at(r, 0);
- at(r, 1) += a.at(r, 1);
- at(r, 2) += a.at(r, 2);
- at(r, 3) += a.at(r, 3);
- }
- return *this;
- }
-
- inline Matrix44& operator -= (const Matrix44& a)
- {
- for (int r = 0; r < NUM_ROWS; r++)
- {
- at(r, 0) -= a.at(r, 0);
- at(r, 1) -= a.at(r, 1);
- at(r, 2) -= a.at(r, 2);
- at(r, 3) -= a.at(r, 3);
- }
- return *this;
- }
-
- friend inline Matrix44 operator + (const Matrix44& a, const Matrix44& b)
- {
- Matrix44 ret;
- for (int r = 0; r < NUM_ROWS; r++)
- {
- ret.at(r, 0) = a.at(r, 0) + b.at(r, 0);
- ret.at(r, 1) = a.at(r, 1) + b.at(r, 1);
- ret.at(r, 2) = a.at(r, 2) + b.at(r, 2);
- ret.at(r, 3) = a.at(r, 3) + b.at(r, 3);
- }
- return ret;
- }
-
- friend inline Matrix44 operator - (const Matrix44& a, const Matrix44& b)
- {
- Matrix44 ret;
- for (int r = 0; r < NUM_ROWS; r++)
- {
- ret.at(r, 0) = a.at(r, 0) - b.at(r, 0);
- ret.at(r, 1) = a.at(r, 1) - b.at(r, 1);
- ret.at(r, 2) = a.at(r, 2) - b.at(r, 2);
- ret.at(r, 3) = a.at(r, 3) - b.at(r, 3);
- }
- return ret;
- }
-
- static inline void add_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
- {
- for (int r = 0; r < 4; r++)
- {
- pDst[0*8 + r] = static_cast(a.at(r, 0) + b.at(r, 0));
- pDst[1*8 + r] = static_cast(a.at(r, 1) + b.at(r, 1));
- pDst[2*8 + r] = static_cast(a.at(r, 2) + b.at(r, 2));
- pDst[3*8 + r] = static_cast(a.at(r, 3) + b.at(r, 3));
- }
- }
-
- static inline void sub_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
- {
- for (int r = 0; r < 4; r++)
- {
- pDst[0*8 + r] = static_cast(a.at(r, 0) - b.at(r, 0));
- pDst[1*8 + r] = static_cast(a.at(r, 1) - b.at(r, 1));
- pDst[2*8 + r] = static_cast(a.at(r, 2) - b.at(r, 2));
- pDst[3*8 + r] = static_cast(a.at(r, 3) - b.at(r, 3));
- }
- }
- };
-
- const int FRACT_BITS = 10;
- const int SCALE = 1 << FRACT_BITS;
-
- typedef int Temp_Type;
-#define D(i) (((i) + (SCALE >> 1)) >> FRACT_BITS)
-#define F(i) ((int)((i) * SCALE + .5f))
-
- // Any decent C++ compiler will optimize this at compile time to a 0, or an array access.
-#define AT(c, r) ((((c)>=NUM_COLS)||((r)>=NUM_ROWS)) ? 0 : pSrc[(c)+(r)*8])
-
- // NUM_ROWS/NUM_COLS = # of non-zero rows/cols in input matrix
- template
- struct P_Q
- {
- static void calc(Matrix44& P, Matrix44& Q, const jpgd_block_t* pSrc)
- {
- // 4x8 = 4x8 times 8x8, matrix 0 is constant
- const Temp_Type X000 = AT(0, 0);
- const Temp_Type X001 = AT(0, 1);
- const Temp_Type X002 = AT(0, 2);
- const Temp_Type X003 = AT(0, 3);
- const Temp_Type X004 = AT(0, 4);
- const Temp_Type X005 = AT(0, 5);
- const Temp_Type X006 = AT(0, 6);
- const Temp_Type X007 = AT(0, 7);
- const Temp_Type X010 = D(F(0.415735f) * AT(1, 0) + F(0.791065f) * AT(3, 0) + F(-0.352443f) * AT(5, 0) + F(0.277785f) * AT(7, 0));
- const Temp_Type X011 = D(F(0.415735f) * AT(1, 1) + F(0.791065f) * AT(3, 1) + F(-0.352443f) * AT(5, 1) + F(0.277785f) * AT(7, 1));
- const Temp_Type X012 = D(F(0.415735f) * AT(1, 2) + F(0.791065f) * AT(3, 2) + F(-0.352443f) * AT(5, 2) + F(0.277785f) * AT(7, 2));
- const Temp_Type X013 = D(F(0.415735f) * AT(1, 3) + F(0.791065f) * AT(3, 3) + F(-0.352443f) * AT(5, 3) + F(0.277785f) * AT(7, 3));
- const Temp_Type X014 = D(F(0.415735f) * AT(1, 4) + F(0.791065f) * AT(3, 4) + F(-0.352443f) * AT(5, 4) + F(0.277785f) * AT(7, 4));
- const Temp_Type X015 = D(F(0.415735f) * AT(1, 5) + F(0.791065f) * AT(3, 5) + F(-0.352443f) * AT(5, 5) + F(0.277785f) * AT(7, 5));
- const Temp_Type X016 = D(F(0.415735f) * AT(1, 6) + F(0.791065f) * AT(3, 6) + F(-0.352443f) * AT(5, 6) + F(0.277785f) * AT(7, 6));
- const Temp_Type X017 = D(F(0.415735f) * AT(1, 7) + F(0.791065f) * AT(3, 7) + F(-0.352443f) * AT(5, 7) + F(0.277785f) * AT(7, 7));
- const Temp_Type X020 = AT(4, 0);
- const Temp_Type X021 = AT(4, 1);
- const Temp_Type X022 = AT(4, 2);
- const Temp_Type X023 = AT(4, 3);
- const Temp_Type X024 = AT(4, 4);
- const Temp_Type X025 = AT(4, 5);
- const Temp_Type X026 = AT(4, 6);
- const Temp_Type X027 = AT(4, 7);
- const Temp_Type X030 = D(F(0.022887f) * AT(1, 0) + F(-0.097545f) * AT(3, 0) + F(0.490393f) * AT(5, 0) + F(0.865723f) * AT(7, 0));
- const Temp_Type X031 = D(F(0.022887f) * AT(1, 1) + F(-0.097545f) * AT(3, 1) + F(0.490393f) * AT(5, 1) + F(0.865723f) * AT(7, 1));
- const Temp_Type X032 = D(F(0.022887f) * AT(1, 2) + F(-0.097545f) * AT(3, 2) + F(0.490393f) * AT(5, 2) + F(0.865723f) * AT(7, 2));
- const Temp_Type X033 = D(F(0.022887f) * AT(1, 3) + F(-0.097545f) * AT(3, 3) + F(0.490393f) * AT(5, 3) + F(0.865723f) * AT(7, 3));
- const Temp_Type X034 = D(F(0.022887f) * AT(1, 4) + F(-0.097545f) * AT(3, 4) + F(0.490393f) * AT(5, 4) + F(0.865723f) * AT(7, 4));
- const Temp_Type X035 = D(F(0.022887f) * AT(1, 5) + F(-0.097545f) * AT(3, 5) + F(0.490393f) * AT(5, 5) + F(0.865723f) * AT(7, 5));
- const Temp_Type X036 = D(F(0.022887f) * AT(1, 6) + F(-0.097545f) * AT(3, 6) + F(0.490393f) * AT(5, 6) + F(0.865723f) * AT(7, 6));
- const Temp_Type X037 = D(F(0.022887f) * AT(1, 7) + F(-0.097545f) * AT(3, 7) + F(0.490393f) * AT(5, 7) + F(0.865723f) * AT(7, 7));
-
- // 4x4 = 4x8 times 8x4, matrix 1 is constant
- P.at(0, 0) = X000;
- P.at(0, 1) = D(X001 * F(0.415735f) + X003 * F(0.791065f) + X005 * F(-0.352443f) + X007 * F(0.277785f));
- P.at(0, 2) = X004;
- P.at(0, 3) = D(X001 * F(0.022887f) + X003 * F(-0.097545f) + X005 * F(0.490393f) + X007 * F(0.865723f));
- P.at(1, 0) = X010;
- P.at(1, 1) = D(X011 * F(0.415735f) + X013 * F(0.791065f) + X015 * F(-0.352443f) + X017 * F(0.277785f));
- P.at(1, 2) = X014;
- P.at(1, 3) = D(X011 * F(0.022887f) + X013 * F(-0.097545f) + X015 * F(0.490393f) + X017 * F(0.865723f));
- P.at(2, 0) = X020;
- P.at(2, 1) = D(X021 * F(0.415735f) + X023 * F(0.791065f) + X025 * F(-0.352443f) + X027 * F(0.277785f));
- P.at(2, 2) = X024;
- P.at(2, 3) = D(X021 * F(0.022887f) + X023 * F(-0.097545f) + X025 * F(0.490393f) + X027 * F(0.865723f));
- P.at(3, 0) = X030;
- P.at(3, 1) = D(X031 * F(0.415735f) + X033 * F(0.791065f) + X035 * F(-0.352443f) + X037 * F(0.277785f));
- P.at(3, 2) = X034;
- P.at(3, 3) = D(X031 * F(0.022887f) + X033 * F(-0.097545f) + X035 * F(0.490393f) + X037 * F(0.865723f));
- // 40 muls 24 adds
-
- // 4x4 = 4x8 times 8x4, matrix 1 is constant
- Q.at(0, 0) = D(X001 * F(0.906127f) + X003 * F(-0.318190f) + X005 * F(0.212608f) + X007 * F(-0.180240f));
- Q.at(0, 1) = X002;
- Q.at(0, 2) = D(X001 * F(-0.074658f) + X003 * F(0.513280f) + X005 * F(0.768178f) + X007 * F(-0.375330f));
- Q.at(0, 3) = X006;
- Q.at(1, 0) = D(X011 * F(0.906127f) + X013 * F(-0.318190f) + X015 * F(0.212608f) + X017 * F(-0.180240f));
- Q.at(1, 1) = X012;
- Q.at(1, 2) = D(X011 * F(-0.074658f) + X013 * F(0.513280f) + X015 * F(0.768178f) + X017 * F(-0.375330f));
- Q.at(1, 3) = X016;
- Q.at(2, 0) = D(X021 * F(0.906127f) + X023 * F(-0.318190f) + X025 * F(0.212608f) + X027 * F(-0.180240f));
- Q.at(2, 1) = X022;
- Q.at(2, 2) = D(X021 * F(-0.074658f) + X023 * F(0.513280f) + X025 * F(0.768178f) + X027 * F(-0.375330f));
- Q.at(2, 3) = X026;
- Q.at(3, 0) = D(X031 * F(0.906127f) + X033 * F(-0.318190f) + X035 * F(0.212608f) + X037 * F(-0.180240f));
- Q.at(3, 1) = X032;
- Q.at(3, 2) = D(X031 * F(-0.074658f) + X033 * F(0.513280f) + X035 * F(0.768178f) + X037 * F(-0.375330f));
- Q.at(3, 3) = X036;
- // 40 muls 24 adds
- }
- };
-
- template
- struct R_S
- {
- static void calc(Matrix44& R, Matrix44& S, const jpgd_block_t* pSrc)
- {
- // 4x8 = 4x8 times 8x8, matrix 0 is constant
- const Temp_Type X100 = D(F(0.906127f) * AT(1, 0) + F(-0.318190f) * AT(3, 0) + F(0.212608f) * AT(5, 0) + F(-0.180240f) * AT(7, 0));
- const Temp_Type X101 = D(F(0.906127f) * AT(1, 1) + F(-0.318190f) * AT(3, 1) + F(0.212608f) * AT(5, 1) + F(-0.180240f) * AT(7, 1));
- const Temp_Type X102 = D(F(0.906127f) * AT(1, 2) + F(-0.318190f) * AT(3, 2) + F(0.212608f) * AT(5, 2) + F(-0.180240f) * AT(7, 2));
- const Temp_Type X103 = D(F(0.906127f) * AT(1, 3) + F(-0.318190f) * AT(3, 3) + F(0.212608f) * AT(5, 3) + F(-0.180240f) * AT(7, 3));
- const Temp_Type X104 = D(F(0.906127f) * AT(1, 4) + F(-0.318190f) * AT(3, 4) + F(0.212608f) * AT(5, 4) + F(-0.180240f) * AT(7, 4));
- const Temp_Type X105 = D(F(0.906127f) * AT(1, 5) + F(-0.318190f) * AT(3, 5) + F(0.212608f) * AT(5, 5) + F(-0.180240f) * AT(7, 5));
- const Temp_Type X106 = D(F(0.906127f) * AT(1, 6) + F(-0.318190f) * AT(3, 6) + F(0.212608f) * AT(5, 6) + F(-0.180240f) * AT(7, 6));
- const Temp_Type X107 = D(F(0.906127f) * AT(1, 7) + F(-0.318190f) * AT(3, 7) + F(0.212608f) * AT(5, 7) + F(-0.180240f) * AT(7, 7));
- const Temp_Type X110 = AT(2, 0);
- const Temp_Type X111 = AT(2, 1);
- const Temp_Type X112 = AT(2, 2);
- const Temp_Type X113 = AT(2, 3);
- const Temp_Type X114 = AT(2, 4);
- const Temp_Type X115 = AT(2, 5);
- const Temp_Type X116 = AT(2, 6);
- const Temp_Type X117 = AT(2, 7);
- const Temp_Type X120 = D(F(-0.074658f) * AT(1, 0) + F(0.513280f) * AT(3, 0) + F(0.768178f) * AT(5, 0) + F(-0.375330f) * AT(7, 0));
- const Temp_Type X121 = D(F(-0.074658f) * AT(1, 1) + F(0.513280f) * AT(3, 1) + F(0.768178f) * AT(5, 1) + F(-0.375330f) * AT(7, 1));
- const Temp_Type X122 = D(F(-0.074658f) * AT(1, 2) + F(0.513280f) * AT(3, 2) + F(0.768178f) * AT(5, 2) + F(-0.375330f) * AT(7, 2));
- const Temp_Type X123 = D(F(-0.074658f) * AT(1, 3) + F(0.513280f) * AT(3, 3) + F(0.768178f) * AT(5, 3) + F(-0.375330f) * AT(7, 3));
- const Temp_Type X124 = D(F(-0.074658f) * AT(1, 4) + F(0.513280f) * AT(3, 4) + F(0.768178f) * AT(5, 4) + F(-0.375330f) * AT(7, 4));
- const Temp_Type X125 = D(F(-0.074658f) * AT(1, 5) + F(0.513280f) * AT(3, 5) + F(0.768178f) * AT(5, 5) + F(-0.375330f) * AT(7, 5));
- const Temp_Type X126 = D(F(-0.074658f) * AT(1, 6) + F(0.513280f) * AT(3, 6) + F(0.768178f) * AT(5, 6) + F(-0.375330f) * AT(7, 6));
- const Temp_Type X127 = D(F(-0.074658f) * AT(1, 7) + F(0.513280f) * AT(3, 7) + F(0.768178f) * AT(5, 7) + F(-0.375330f) * AT(7, 7));
- const Temp_Type X130 = AT(6, 0);
- const Temp_Type X131 = AT(6, 1);
- const Temp_Type X132 = AT(6, 2);
- const Temp_Type X133 = AT(6, 3);
- const Temp_Type X134 = AT(6, 4);
- const Temp_Type X135 = AT(6, 5);
- const Temp_Type X136 = AT(6, 6);
- const Temp_Type X137 = AT(6, 7);
- // 80 muls 48 adds
-
- // 4x4 = 4x8 times 8x4, matrix 1 is constant
- R.at(0, 0) = X100;
- R.at(0, 1) = D(X101 * F(0.415735f) + X103 * F(0.791065f) + X105 * F(-0.352443f) + X107 * F(0.277785f));
- R.at(0, 2) = X104;
- R.at(0, 3) = D(X101 * F(0.022887f) + X103 * F(-0.097545f) + X105 * F(0.490393f) + X107 * F(0.865723f));
- R.at(1, 0) = X110;
- R.at(1, 1) = D(X111 * F(0.415735f) + X113 * F(0.791065f) + X115 * F(-0.352443f) + X117 * F(0.277785f));
- R.at(1, 2) = X114;
- R.at(1, 3) = D(X111 * F(0.022887f) + X113 * F(-0.097545f) + X115 * F(0.490393f) + X117 * F(0.865723f));
- R.at(2, 0) = X120;
- R.at(2, 1) = D(X121 * F(0.415735f) + X123 * F(0.791065f) + X125 * F(-0.352443f) + X127 * F(0.277785f));
- R.at(2, 2) = X124;
- R.at(2, 3) = D(X121 * F(0.022887f) + X123 * F(-0.097545f) + X125 * F(0.490393f) + X127 * F(0.865723f));
- R.at(3, 0) = X130;
- R.at(3, 1) = D(X131 * F(0.415735f) + X133 * F(0.791065f) + X135 * F(-0.352443f) + X137 * F(0.277785f));
- R.at(3, 2) = X134;
- R.at(3, 3) = D(X131 * F(0.022887f) + X133 * F(-0.097545f) + X135 * F(0.490393f) + X137 * F(0.865723f));
- // 40 muls 24 adds
- // 4x4 = 4x8 times 8x4, matrix 1 is constant
- S.at(0, 0) = D(X101 * F(0.906127f) + X103 * F(-0.318190f) + X105 * F(0.212608f) + X107 * F(-0.180240f));
- S.at(0, 1) = X102;
- S.at(0, 2) = D(X101 * F(-0.074658f) + X103 * F(0.513280f) + X105 * F(0.768178f) + X107 * F(-0.375330f));
- S.at(0, 3) = X106;
- S.at(1, 0) = D(X111 * F(0.906127f) + X113 * F(-0.318190f) + X115 * F(0.212608f) + X117 * F(-0.180240f));
- S.at(1, 1) = X112;
- S.at(1, 2) = D(X111 * F(-0.074658f) + X113 * F(0.513280f) + X115 * F(0.768178f) + X117 * F(-0.375330f));
- S.at(1, 3) = X116;
- S.at(2, 0) = D(X121 * F(0.906127f) + X123 * F(-0.318190f) + X125 * F(0.212608f) + X127 * F(-0.180240f));
- S.at(2, 1) = X122;
- S.at(2, 2) = D(X121 * F(-0.074658f) + X123 * F(0.513280f) + X125 * F(0.768178f) + X127 * F(-0.375330f));
- S.at(2, 3) = X126;
- S.at(3, 0) = D(X131 * F(0.906127f) + X133 * F(-0.318190f) + X135 * F(0.212608f) + X137 * F(-0.180240f));
- S.at(3, 1) = X132;
- S.at(3, 2) = D(X131 * F(-0.074658f) + X133 * F(0.513280f) + X135 * F(0.768178f) + X137 * F(-0.375330f));
- S.at(3, 3) = X136;
- // 40 muls 24 adds
- }
- };
- } // end namespace DCT_Upsample
-
- // Unconditionally frees all allocated m_blocks.
- void jpeg_decoder::free_all_blocks()
- {
- m_pStream = NULL;
- for (mem_block *b = m_pMem_blocks; b; )
- {
- mem_block *n = b->m_pNext;
- jpgd_free(b);
- b = n;
- }
- m_pMem_blocks = NULL;
- }
-
- // This method handles all errors.
- // It could easily be changed to use C++ exceptions.
- void jpeg_decoder::stop_decoding(jpgd_status status)
- {
- m_error_code = status;
- free_all_blocks();
- longjmp(m_jmp_state, status);
-
- // we shouldn't get here as longjmp shouldn't return, but we put it here to make it explicit
- // that this function doesn't return, otherwise we get this error:
- //
- // error : function declared 'noreturn' should not return
- exit(1);
- }
-
- void *jpeg_decoder::alloc(size_t nSize, bool zero)
- {
- nSize = (JPGD_MAX(nSize, 1) + 3) & ~3;
- char *rv = NULL;
- for (mem_block *b = m_pMem_blocks; b; b = b->m_pNext)
- {
- if ((b->m_used_count + nSize) <= b->m_size)
- {
- rv = b->m_data + b->m_used_count;
- b->m_used_count += nSize;
- break;
- }
- }
- if (!rv)
- {
- int capacity = JPGD_MAX(32768 - 256, (nSize + 2047) & ~2047);
- mem_block *b = (mem_block*)jpgd_malloc(sizeof(mem_block) + capacity);
- if (!b) stop_decoding(JPGD_NOTENOUGHMEM);
- b->m_pNext = m_pMem_blocks; m_pMem_blocks = b;
- b->m_used_count = nSize;
- b->m_size = capacity;
- rv = b->m_data;
- }
- if (zero) memset(rv, 0, nSize);
- return rv;
- }
-
- void jpeg_decoder::word_clear(void *p, uint16 c, uint n)
- {
- uint8 *pD = (uint8*)p;
- const uint8 l = c & 0xFF, h = (c >> 8) & 0xFF;
- while (n)
- {
- pD[0] = l; pD[1] = h; pD += 2;
- n--;
- }
- }
-
- // Refill the input buffer.
- // This method will sit in a loop until (A) the buffer is full or (B)
- // the stream's read() method reports and end of file condition.
- void jpeg_decoder::prep_in_buffer()
- {
- m_in_buf_left = 0;
- m_pIn_buf_ofs = m_in_buf;
-
- if (m_eof_flag)
- return;
-
- do
- {
- int bytes_read = m_pStream->read(m_in_buf + m_in_buf_left, JPGD_IN_BUF_SIZE - m_in_buf_left, &m_eof_flag);
- if (bytes_read == -1)
- stop_decoding(JPGD_STREAM_READ);
-
- m_in_buf_left += bytes_read;
- } while ((m_in_buf_left < JPGD_IN_BUF_SIZE) && (!m_eof_flag));
-
- m_total_bytes_read += m_in_buf_left;
-
- // Pad the end of the block with M_EOI (prevents the decompressor from going off the rails if the stream is invalid).
- // (This dates way back to when this decompressor was written in C/asm, and the all-asm Huffman decoder did some fancy things to increase perf.)
- word_clear(m_pIn_buf_ofs + m_in_buf_left, 0xD9FF, 64);
- }
-
- // Read a Huffman code table.
- void jpeg_decoder::read_dht_marker()
- {
- int i, index, count;
- uint8 huff_num[17];
- uint8 huff_val[256];
-
- uint num_left = get_bits(16);
-
- if (num_left < 2)
- stop_decoding(JPGD_BAD_DHT_MARKER);
-
- num_left -= 2;
-
- while (num_left)
- {
- index = get_bits(8);
-
- huff_num[0] = 0;
-
- count = 0;
-
- for (i = 1; i <= 16; i++)
- {
- huff_num[i] = static_cast(get_bits(8));
- count += huff_num[i];
- }
-
- if (count > 255)
- stop_decoding(JPGD_BAD_DHT_COUNTS);
-
- for (i = 0; i < count; i++)
- huff_val[i] = static_cast(get_bits(8));
-
- i = 1 + 16 + count;
-
- if (num_left < (uint)i)
- stop_decoding(JPGD_BAD_DHT_MARKER);
-
- num_left -= i;
-
- if ((index & 0x10) > 0x10)
- stop_decoding(JPGD_BAD_DHT_INDEX);
-
- index = (index & 0x0F) + ((index & 0x10) >> 4) * (JPGD_MAX_HUFF_TABLES >> 1);
-
- if (index >= JPGD_MAX_HUFF_TABLES)
- stop_decoding(JPGD_BAD_DHT_INDEX);
-
- if (!m_huff_num[index])
- m_huff_num[index] = (uint8 *)alloc(17);
-
- if (!m_huff_val[index])
- m_huff_val[index] = (uint8 *)alloc(256);
-
- m_huff_ac[index] = (index & 0x10) != 0;
- memcpy(m_huff_num[index], huff_num, 17);
- memcpy(m_huff_val[index], huff_val, 256);
- }
- }
-
- // Read a quantization table.
- void jpeg_decoder::read_dqt_marker()
- {
- int n, i, prec;
- uint num_left;
- uint temp;
-
- num_left = get_bits(16);
-
- if (num_left < 2)
- stop_decoding(JPGD_BAD_DQT_MARKER);
-
- num_left -= 2;
-
- while (num_left)
- {
- n = get_bits(8);
- prec = n >> 4;
- n &= 0x0F;
-
- if (n >= JPGD_MAX_QUANT_TABLES)
- stop_decoding(JPGD_BAD_DQT_TABLE);
-
- if (!m_quant[n])
- m_quant[n] = (jpgd_quant_t *)alloc(64 * sizeof(jpgd_quant_t));
-
- // read quantization entries, in zag order
- for (i = 0; i < 64; i++)
- {
- temp = get_bits(8);
-
- if (prec)
- temp = (temp << 8) + get_bits(8);
-
- m_quant[n][i] = static_cast(temp);
- }
-
- i = 64 + 1;
-
- if (prec)
- i += 64;
-
- if (num_left < (uint)i)
- stop_decoding(JPGD_BAD_DQT_LENGTH);
-
- num_left -= i;
- }
- }
-
- // Read the start of frame (SOF) marker.
- void jpeg_decoder::read_sof_marker()
- {
- int i;
- uint num_left;
-
- num_left = get_bits(16);
-
- if (get_bits(8) != 8) /* precision: sorry, only 8-bit precision is supported right now */
- stop_decoding(JPGD_BAD_PRECISION);
-
- m_image_y_size = get_bits(16);
-
- if ((m_image_y_size < 1) || (m_image_y_size > JPGD_MAX_HEIGHT))
- stop_decoding(JPGD_BAD_HEIGHT);
-
- m_image_x_size = get_bits(16);
-
- if ((m_image_x_size < 1) || (m_image_x_size > JPGD_MAX_WIDTH))
- stop_decoding(JPGD_BAD_WIDTH);
-
- m_comps_in_frame = get_bits(8);
-
- if (m_comps_in_frame > JPGD_MAX_COMPONENTS)
- stop_decoding(JPGD_TOO_MANY_COMPONENTS);
-
- if (num_left != (uint)(m_comps_in_frame * 3 + 8))
- stop_decoding(JPGD_BAD_SOF_LENGTH);
-
- for (i = 0; i < m_comps_in_frame; i++)
- {
- m_comp_ident[i] = get_bits(8);
- m_comp_h_samp[i] = get_bits(4);
- m_comp_v_samp[i] = get_bits(4);
- m_comp_quant[i] = get_bits(8);
- }
- }
-
- // Used to skip unrecognized markers.
- void jpeg_decoder::skip_variable_marker()
- {
- uint num_left;
-
- num_left = get_bits(16);
-
- if (num_left < 2)
- stop_decoding(JPGD_BAD_VARIABLE_MARKER);
-
- num_left -= 2;
-
- while (num_left)
- {
- get_bits(8);
- num_left--;
- }
- }
-
- // Read a define restart interval (DRI) marker.
- void jpeg_decoder::read_dri_marker()
- {
- if (get_bits(16) != 4)
- stop_decoding(JPGD_BAD_DRI_LENGTH);
-
- m_restart_interval = get_bits(16);
- }
-
- // Read a start of scan (SOS) marker.
- void jpeg_decoder::read_sos_marker()
- {
- uint num_left;
- int i, ci, n, c, cc;
-
- num_left = get_bits(16);
-
- n = get_bits(8);
-
- m_comps_in_scan = n;
-
- num_left -= 3;
-
- if ( (num_left != (uint)(n * 2 + 3)) || (n < 1) || (n > JPGD_MAX_COMPS_IN_SCAN) )
- stop_decoding(JPGD_BAD_SOS_LENGTH);
-
- for (i = 0; i < n; i++)
- {
- cc = get_bits(8);
- c = get_bits(8);
- num_left -= 2;
-
- for (ci = 0; ci < m_comps_in_frame; ci++)
- if (cc == m_comp_ident[ci])
- break;
-
- if (ci >= m_comps_in_frame)
- stop_decoding(JPGD_BAD_SOS_COMP_ID);
-
- m_comp_list[i] = ci;
- m_comp_dc_tab[ci] = (c >> 4) & 15;
- m_comp_ac_tab[ci] = (c & 15) + (JPGD_MAX_HUFF_TABLES >> 1);
- }
-
- m_spectral_start = get_bits(8);
- m_spectral_end = get_bits(8);
- m_successive_high = get_bits(4);
- m_successive_low = get_bits(4);
-
- if (!m_progressive_flag)
- {
- m_spectral_start = 0;
- m_spectral_end = 63;
- }
-
- num_left -= 3;
-
- while (num_left) /* read past whatever is num_left */
- {
- get_bits(8);
- num_left--;
- }
- }
-
- // Finds the next marker.
- int jpeg_decoder::next_marker()
- {
- uint c, bytes;
-
- bytes = 0;
-
- do
- {
- do
- {
- bytes++;
- c = get_bits(8);
- } while (c != 0xFF);
-
- do
- {
- c = get_bits(8);
- } while (c == 0xFF);
-
- } while (c == 0);
-
- // If bytes > 0 here, there where extra bytes before the marker (not good).
-
- return c;
- }
-
- // Process markers. Returns when an SOFx, SOI, EOI, or SOS marker is
- // encountered.
- int jpeg_decoder::process_markers()
- {
- int c;
-
- for ( ; ; )
- {
- c = next_marker();
-
- switch (c)
- {
- case M_SOF0:
- case M_SOF1:
- case M_SOF2:
- case M_SOF3:
- case M_SOF5:
- case M_SOF6:
- case M_SOF7:
- // case M_JPG:
- case M_SOF9:
- case M_SOF10:
- case M_SOF11:
- case M_SOF13:
- case M_SOF14:
- case M_SOF15:
- case M_SOI:
- case M_EOI:
- case M_SOS:
- {
- return c;
- }
- case M_DHT:
- {
- read_dht_marker();
- break;
- }
- // No arithmitic support - dumb patents!
- case M_DAC:
- {
- stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
- break;
- }
- case M_DQT:
- {
- read_dqt_marker();
- break;
- }
- case M_DRI:
- {
- read_dri_marker();
- break;
- }
- //case M_APP0: /* no need to read the JFIF marker */
-
- case M_JPG:
- case M_RST0: /* no parameters */
- case M_RST1:
- case M_RST2:
- case M_RST3:
- case M_RST4:
- case M_RST5:
- case M_RST6:
- case M_RST7:
- case M_TEM:
- {
- stop_decoding(JPGD_UNEXPECTED_MARKER);
- break;
- }
- default: /* must be DNL, DHP, EXP, APPn, JPGn, COM, or RESn or APP0 */
- {
- skip_variable_marker();
- break;
- }
- }
- }
- }
-
- // Finds the start of image (SOI) marker.
- // This code is rather defensive: it only checks the first 512 bytes to avoid
- // false positives.
- void jpeg_decoder::locate_soi_marker()
- {
- uint lastchar, thischar;
- uint bytesleft;
-
- lastchar = get_bits(8);
-
- thischar = get_bits(8);
-
- /* ok if it's a normal JPEG file without a special header */
-
- if ((lastchar == 0xFF) && (thischar == M_SOI))
- return;
-
- bytesleft = 4096; //512;
-
- for ( ; ; )
- {
- if (--bytesleft == 0)
- stop_decoding(JPGD_NOT_JPEG);
-
- lastchar = thischar;
-
- thischar = get_bits(8);
-
- if (lastchar == 0xFF)
- {
- if (thischar == M_SOI)
- break;
- else if (thischar == M_EOI) // get_bits will keep returning M_EOI if we read past the end
- stop_decoding(JPGD_NOT_JPEG);
- }
- }
-
- // Check the next character after marker: if it's not 0xFF, it can't be the start of the next marker, so the file is bad.
- thischar = (m_bit_buf >> 24) & 0xFF;
-
- if (thischar != 0xFF)
- stop_decoding(JPGD_NOT_JPEG);
- }
-
- // Find a start of frame (SOF) marker.
- void jpeg_decoder::locate_sof_marker()
- {
- locate_soi_marker();
-
- int c = process_markers();
-
- switch (c)
- {
- case M_SOF2:
- m_progressive_flag = JPGD_TRUE;
- case M_SOF0: /* baseline DCT */
- case M_SOF1: /* extended sequential DCT */
- {
- read_sof_marker();
- break;
- }
- case M_SOF9: /* Arithmitic coding */
- {
- stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
- break;
- }
- default:
- {
- stop_decoding(JPGD_UNSUPPORTED_MARKER);
- break;
- }
- }
- }
-
- // Find a start of scan (SOS) marker.
- int jpeg_decoder::locate_sos_marker()
- {
- int c;
-
- c = process_markers();
-
- if (c == M_EOI)
- return JPGD_FALSE;
- else if (c != M_SOS)
- stop_decoding(JPGD_UNEXPECTED_MARKER);
-
- read_sos_marker();
-
- return JPGD_TRUE;
- }
-
- // Reset everything to default/uninitialized state.
- void jpeg_decoder::init(jpeg_decoder_stream *pStream)
- {
- m_pMem_blocks = NULL;
- m_error_code = JPGD_SUCCESS;
- m_ready_flag = false;
- m_image_x_size = m_image_y_size = 0;
- m_pStream = pStream;
- m_progressive_flag = JPGD_FALSE;
-
- memset(m_huff_ac, 0, sizeof(m_huff_ac));
- memset(m_huff_num, 0, sizeof(m_huff_num));
- memset(m_huff_val, 0, sizeof(m_huff_val));
- memset(m_quant, 0, sizeof(m_quant));
-
- m_scan_type = 0;
- m_comps_in_frame = 0;
-
- memset(m_comp_h_samp, 0, sizeof(m_comp_h_samp));
- memset(m_comp_v_samp, 0, sizeof(m_comp_v_samp));
- memset(m_comp_quant, 0, sizeof(m_comp_quant));
- memset(m_comp_ident, 0, sizeof(m_comp_ident));
- memset(m_comp_h_blocks, 0, sizeof(m_comp_h_blocks));
- memset(m_comp_v_blocks, 0, sizeof(m_comp_v_blocks));
-
- m_comps_in_scan = 0;
- memset(m_comp_list, 0, sizeof(m_comp_list));
- memset(m_comp_dc_tab, 0, sizeof(m_comp_dc_tab));
- memset(m_comp_ac_tab, 0, sizeof(m_comp_ac_tab));
-
- m_spectral_start = 0;
- m_spectral_end = 0;
- m_successive_low = 0;
- m_successive_high = 0;
- m_max_mcu_x_size = 0;
- m_max_mcu_y_size = 0;
- m_blocks_per_mcu = 0;
- m_max_blocks_per_row = 0;
- m_mcus_per_row = 0;
- m_mcus_per_col = 0;
- m_expanded_blocks_per_component = 0;
- m_expanded_blocks_per_mcu = 0;
- m_expanded_blocks_per_row = 0;
- m_freq_domain_chroma_upsample = false;
-
- memset(m_mcu_org, 0, sizeof(m_mcu_org));
-
- m_total_lines_left = 0;
- m_mcu_lines_left = 0;
- m_real_dest_bytes_per_scan_line = 0;
- m_dest_bytes_per_scan_line = 0;
- m_dest_bytes_per_pixel = 0;
-
- memset(m_pHuff_tabs, 0, sizeof(m_pHuff_tabs));
-
- memset(m_dc_coeffs, 0, sizeof(m_dc_coeffs));
- memset(m_ac_coeffs, 0, sizeof(m_ac_coeffs));
- memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
- m_eob_run = 0;
-
- memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
- m_pIn_buf_ofs = m_in_buf;
- m_in_buf_left = 0;
- m_eof_flag = false;
- m_tem_flag = 0;
-
- memset(m_in_buf_pad_start, 0, sizeof(m_in_buf_pad_start));
- memset(m_in_buf, 0, sizeof(m_in_buf));
- memset(m_in_buf_pad_end, 0, sizeof(m_in_buf_pad_end));
-
- m_restart_interval = 0;
- m_restarts_left = 0;
- m_next_restart_num = 0;
-
- m_max_mcus_per_row = 0;
- m_max_blocks_per_mcu = 0;
- m_max_mcus_per_col = 0;
-
- memset(m_last_dc_val, 0, sizeof(m_last_dc_val));
- m_pMCU_coefficients = NULL;
- m_pSample_buf = NULL;
-
- m_total_bytes_read = 0;
-
- m_pScan_line_0 = NULL;
- m_pScan_line_1 = NULL;
-
- // Ready the input buffer.
- prep_in_buffer();
-
- // Prime the bit buffer.
- m_bits_left = 16;
- m_bit_buf = 0;
-
- get_bits(16);
- get_bits(16);
-
- for (int i = 0; i < JPGD_MAX_BLOCKS_PER_MCU; i++)
- m_mcu_block_max_zag[i] = 64;
- }
-
-#define SCALEBITS 16
-#define ONE_HALF ((int) 1 << (SCALEBITS-1))
-#define FIX(x) ((int) ((x) * (1L<> SCALEBITS;
- m_cbb[i] = ( FIX(1.77200f) * k + ONE_HALF) >> SCALEBITS;
- m_crg[i] = (-FIX(0.71414f)) * k;
- m_cbg[i] = (-FIX(0.34414f)) * k + ONE_HALF;
- }
- }
-
- // This method throws back into the stream any bytes that where read
- // into the bit buffer during initial marker scanning.
- void jpeg_decoder::fix_in_buffer()
- {
- // In case any 0xFF's where pulled into the buffer during marker scanning.
- JPGD_ASSERT((m_bits_left & 7) == 0);
-
- if (m_bits_left == 16)
- stuff_char( (uint8)(m_bit_buf & 0xFF));
-
- if (m_bits_left >= 8)
- stuff_char( (uint8)((m_bit_buf >> 8) & 0xFF));
-
- stuff_char((uint8)((m_bit_buf >> 16) & 0xFF));
- stuff_char((uint8)((m_bit_buf >> 24) & 0xFF));
-
- m_bits_left = 16;
- get_bits_no_markers(16);
- get_bits_no_markers(16);
- }
-
- void jpeg_decoder::transform_mcu(int mcu_row)
- {
- jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
- uint8* pDst_ptr = m_pSample_buf + mcu_row * m_blocks_per_mcu * 64;
-
- for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
- {
- idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
- pSrc_ptr += 64;
- pDst_ptr += 64;
- }
- }
-
- static const uint8 s_max_rc[64] =
- {
- 17, 18, 34, 50, 50, 51, 52, 52, 52, 68, 84, 84, 84, 84, 85, 86, 86, 86, 86, 86,
- 102, 118, 118, 118, 118, 118, 118, 119, 120, 120, 120, 120, 120, 120, 120, 136,
- 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
- 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136
- };
-
- void jpeg_decoder::transform_mcu_expand(int mcu_row)
- {
- jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
- uint8* pDst_ptr = m_pSample_buf + mcu_row * m_expanded_blocks_per_mcu * 64;
-
- // Y IDCT
- int mcu_block;
- for (mcu_block = 0; mcu_block < m_expanded_blocks_per_component; mcu_block++)
- {
- idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
- pSrc_ptr += 64;
- pDst_ptr += 64;
- }
-
- // Chroma IDCT, with upsampling
- jpgd_block_t temp_block[64];
-
- for (int i = 0; i < 2; i++)
- {
- DCT_Upsample::Matrix44 P, Q, R, S;
-
- JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] >= 1);
- JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] <= 64);
-
- switch (s_max_rc[m_mcu_block_max_zag[mcu_block++] - 1])
- {
- case 1*16+1:
- DCT_Upsample::P_Q<1, 1>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<1, 1>::calc(R, S, pSrc_ptr);
- break;
- case 1*16+2:
- DCT_Upsample::P_Q<1, 2>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<1, 2>::calc(R, S, pSrc_ptr);
- break;
- case 2*16+2:
- DCT_Upsample::P_Q<2, 2>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<2, 2>::calc(R, S, pSrc_ptr);
- break;
- case 3*16+2:
- DCT_Upsample::P_Q<3, 2>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<3, 2>::calc(R, S, pSrc_ptr);
- break;
- case 3*16+3:
- DCT_Upsample::P_Q<3, 3>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<3, 3>::calc(R, S, pSrc_ptr);
- break;
- case 3*16+4:
- DCT_Upsample::P_Q<3, 4>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<3, 4>::calc(R, S, pSrc_ptr);
- break;
- case 4*16+4:
- DCT_Upsample::P_Q<4, 4>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<4, 4>::calc(R, S, pSrc_ptr);
- break;
- case 5*16+4:
- DCT_Upsample::P_Q<5, 4>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<5, 4>::calc(R, S, pSrc_ptr);
- break;
- case 5*16+5:
- DCT_Upsample::P_Q<5, 5>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<5, 5>::calc(R, S, pSrc_ptr);
- break;
- case 5*16+6:
- DCT_Upsample::P_Q<5, 6>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<5, 6>::calc(R, S, pSrc_ptr);
- break;
- case 6*16+6:
- DCT_Upsample::P_Q<6, 6>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<6, 6>::calc(R, S, pSrc_ptr);
- break;
- case 7*16+6:
- DCT_Upsample::P_Q<7, 6>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<7, 6>::calc(R, S, pSrc_ptr);
- break;
- case 7*16+7:
- DCT_Upsample::P_Q<7, 7>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<7, 7>::calc(R, S, pSrc_ptr);
- break;
- case 7*16+8:
- DCT_Upsample::P_Q<7, 8>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<7, 8>::calc(R, S, pSrc_ptr);
- break;
- case 8*16+8:
- DCT_Upsample::P_Q<8, 8>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<8, 8>::calc(R, S, pSrc_ptr);
- break;
- default:
- JPGD_ASSERT(false);
- }
-
- DCT_Upsample::Matrix44 a(P + Q); P -= Q;
- DCT_Upsample::Matrix44& b = P;
- DCT_Upsample::Matrix44 c(R + S); R -= S;
- DCT_Upsample::Matrix44& d = R;
-
- DCT_Upsample::Matrix44::add_and_store(temp_block, a, c);
- idct_4x4(temp_block, pDst_ptr);
- pDst_ptr += 64;
-
- DCT_Upsample::Matrix44::sub_and_store(temp_block, a, c);
- idct_4x4(temp_block, pDst_ptr);
- pDst_ptr += 64;
-
- DCT_Upsample::Matrix44::add_and_store(temp_block, b, d);
- idct_4x4(temp_block, pDst_ptr);
- pDst_ptr += 64;
-
- DCT_Upsample::Matrix44::sub_and_store(temp_block, b, d);
- idct_4x4(temp_block, pDst_ptr);
- pDst_ptr += 64;
-
- pSrc_ptr += 64;
- }
- }
-
- // Loads and dequantizes the next row of (already decoded) coefficients.
- // Progressive images only.
- void jpeg_decoder::load_next_row()
- {
- int i;
- jpgd_block_t *p;
- jpgd_quant_t *q;
- int mcu_row, mcu_block, row_block = 0;
- int component_num, component_id;
- int block_x_mcu[JPGD_MAX_COMPONENTS];
-
- memset(block_x_mcu, 0, JPGD_MAX_COMPONENTS * sizeof(int));
-
- for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
- {
- int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
- for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
- {
- component_id = m_mcu_org[mcu_block];
- q = m_quant[m_comp_quant[component_id]];
-
- p = m_pMCU_coefficients + 64 * mcu_block;
-
- jpgd_block_t* pAC = coeff_buf_getp(m_ac_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
- jpgd_block_t* pDC = coeff_buf_getp(m_dc_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
- p[0] = pDC[0];
- memcpy(&p[1], &pAC[1], 63 * sizeof(jpgd_block_t));
-
- for (i = 63; i > 0; i--)
- if (p[g_ZAG[i]])
- break;
-
- m_mcu_block_max_zag[mcu_block] = i + 1;
-
- for ( ; i >= 0; i--)
- if (p[g_ZAG[i]])
- p[g_ZAG[i]] = static_cast(p[g_ZAG[i]] * q[i]);
-
- row_block++;
-
- if (m_comps_in_scan == 1)
- block_x_mcu[component_id]++;
- else
- {
- if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
- {
- block_x_mcu_ofs = 0;
-
- if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
- {
- block_y_mcu_ofs = 0;
-
- block_x_mcu[component_id] += m_comp_h_samp[component_id];
- }
- }
- }
- }
-
- if (m_freq_domain_chroma_upsample)
- transform_mcu_expand(mcu_row);
- else
- transform_mcu(mcu_row);
- }
-
- if (m_comps_in_scan == 1)
- m_block_y_mcu[m_comp_list[0]]++;
- else
- {
- for (component_num = 0; component_num < m_comps_in_scan; component_num++)
- {
- component_id = m_comp_list[component_num];
-
- m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
- }
- }
- }
-
- // Restart interval processing.
- void jpeg_decoder::process_restart()
- {
- int i;
- int c = 0;
-
- // Align to a byte boundry
- // FIXME: Is this really necessary? get_bits_no_markers() never reads in markers!
- //get_bits_no_markers(m_bits_left & 7);
-
- // Let's scan a little bit to find the marker, but not _too_ far.
- // 1536 is a "fudge factor" that determines how much to scan.
- for (i = 1536; i > 0; i--)
- if (get_char() == 0xFF)
- break;
-
- if (i == 0)
- stop_decoding(JPGD_BAD_RESTART_MARKER);
-
- for ( ; i > 0; i--)
- if ((c = get_char()) != 0xFF)
- break;
-
- if (i == 0)
- stop_decoding(JPGD_BAD_RESTART_MARKER);
-
- // Is it the expected marker? If not, something bad happened.
- if (c != (m_next_restart_num + M_RST0))
- stop_decoding(JPGD_BAD_RESTART_MARKER);
-
- // Reset each component's DC prediction values.
- memset(&m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
- m_eob_run = 0;
-
- m_restarts_left = m_restart_interval;
-
- m_next_restart_num = (m_next_restart_num + 1) & 7;
-
- // Get the bit buffer going again...
-
- m_bits_left = 16;
- get_bits_no_markers(16);
- get_bits_no_markers(16);
- }
-
- static inline int dequantize_ac(int c, int q) { c *= q; return c; }
-
- // Decodes and dequantizes the next row of coefficients.
- void jpeg_decoder::decode_next_row()
- {
- int row_block = 0;
-
- for (int mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
- {
- if ((m_restart_interval) && (m_restarts_left == 0))
- process_restart();
-
- jpgd_block_t* p = m_pMCU_coefficients;
- for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++, p += 64)
- {
- int component_id = m_mcu_org[mcu_block];
- jpgd_quant_t* q = m_quant[m_comp_quant[component_id]];
-
- int r, s;
- s = huff_decode(m_pHuff_tabs[m_comp_dc_tab[component_id]], r);
- s = HUFF_EXTEND(r, s);
-
- m_last_dc_val[component_id] = (s += m_last_dc_val[component_id]);
-
- p[0] = static_cast(s * q[0]);
-
- int prev_num_set = m_mcu_block_max_zag[mcu_block];
-
- huff_tables *pH = m_pHuff_tabs[m_comp_ac_tab[component_id]];
-
- int k;
- for (k = 1; k < 64; k++)
- {
- int extra_bits;
- s = huff_decode(pH, extra_bits);
-
- r = s >> 4;
- s &= 15;
-
- if (s)
- {
- if (r)
- {
- if ((k + r) > 63)
- stop_decoding(JPGD_DECODE_ERROR);
-
- if (k < prev_num_set)
- {
- int n = JPGD_MIN(r, prev_num_set - k);
- int kt = k;
- while (n--)
- p[g_ZAG[kt++]] = 0;
- }
-
- k += r;
- }
-
- s = HUFF_EXTEND(extra_bits, s);
-
- JPGD_ASSERT(k < 64);
-
- p[g_ZAG[k]] = static_cast(dequantize_ac(s, q[k])); //s * q[k];
- }
- else
- {
- if (r == 15)
- {
- if ((k + 16) > 64)
- stop_decoding(JPGD_DECODE_ERROR);
-
- if (k < prev_num_set)
- {
- int n = JPGD_MIN(16, prev_num_set - k);
- int kt = k;
- while (n--)
- {
- JPGD_ASSERT(kt <= 63);
- p[g_ZAG[kt++]] = 0;
- }
- }
-
- k += 16 - 1; // - 1 because the loop counter is k
- // BEGIN EPIC MOD
- JPGD_ASSERT(k < 64 && p[g_ZAG[k]] == 0);
- // END EPIC MOD
- }
- else
- break;
- }
- }
-
- if (k < prev_num_set)
- {
- int kt = k;
- while (kt < prev_num_set)
- p[g_ZAG[kt++]] = 0;
- }
-
- m_mcu_block_max_zag[mcu_block] = k;
-
- row_block++;
- }
-
- if (m_freq_domain_chroma_upsample)
- transform_mcu_expand(mcu_row);
- else
- transform_mcu(mcu_row);
-
- m_restarts_left--;
- }
- }
-
- // YCbCr H1V1 (1x1:1:1, 3 m_blocks per MCU) to RGB
- void jpeg_decoder::H1V1Convert()
- {
- int row = m_max_mcu_y_size - m_mcu_lines_left;
- uint8 *d = m_pScan_line_0;
- uint8 *s = m_pSample_buf + row * 8;
-
- for (int i = m_max_mcus_per_row; i > 0; i--)
- {
- for (int j = 0; j < 8; j++)
- {
- int y = s[j];
- int cb = s[64+j];
- int cr = s[128+j];
-
- if (jpg_format == ERGBFormatJPG::BGRA)
- {
- d[0] = clamp(y + m_cbb[cb]);
- d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
- d[2] = clamp(y + m_crr[cr]);
- d[3] = 255;
- }
- else
- {
- d[0] = clamp(y + m_crr[cr]);
- d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
- d[2] = clamp(y + m_cbb[cb]);
- d[3] = 255;
- }
- d += 4;
- }
-
- s += 64*3;
- }
- }
-
- // YCbCr H2V1 (2x1:1:1, 4 m_blocks per MCU) to RGB
- void jpeg_decoder::H2V1Convert()
- {
- int row = m_max_mcu_y_size - m_mcu_lines_left;
- uint8 *d0 = m_pScan_line_0;
- uint8 *y = m_pSample_buf + row * 8;
- uint8 *c = m_pSample_buf + 2*64 + row * 8;
-
- for (int i = m_max_mcus_per_row; i > 0; i--)
- {
- for (int l = 0; l < 2; l++)
- {
- for (int j = 0; j < 4; j++)
- {
- int cb = c[0];
- int cr = c[64];
-
- int rc = m_crr[cr];
- int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
- int bc = m_cbb[cb];
-
- int yy = y[j<<1];
- if (jpg_format == ERGBFormatJPG::BGRA)
- {
- d0[0] = clamp(yy+bc);
- d0[1] = clamp(yy+gc);
- d0[2] = clamp(yy+rc);
- d0[3] = 255;
- yy = y[(j<<1)+1];
- d0[4] = clamp(yy+bc);
- d0[5] = clamp(yy+gc);
- d0[6] = clamp(yy+rc);
- d0[7] = 255;
- }
- else
- {
- d0[0] = clamp(yy+rc);
- d0[1] = clamp(yy+gc);
- d0[2] = clamp(yy+bc);
- d0[3] = 255;
- yy = y[(j<<1)+1];
- d0[4] = clamp(yy+rc);
- d0[5] = clamp(yy+gc);
- d0[6] = clamp(yy+bc);
- d0[7] = 255;
- }
-
- d0 += 8;
-
- c++;
- }
- y += 64;
- }
-
- y += 64*4 - 64*2;
- c += 64*4 - 8;
- }
- }
-
- // YCbCr H2V1 (1x2:1:1, 4 m_blocks per MCU) to RGB
- void jpeg_decoder::H1V2Convert()
- {
- int row = m_max_mcu_y_size - m_mcu_lines_left;
- uint8 *d0 = m_pScan_line_0;
- uint8 *d1 = m_pScan_line_1;
- uint8 *y;
- uint8 *c;
-
- if (row < 8)
- y = m_pSample_buf + row * 8;
- else
- y = m_pSample_buf + 64*1 + (row & 7) * 8;
-
- c = m_pSample_buf + 64*2 + (row >> 1) * 8;
-
- for (int i = m_max_mcus_per_row; i > 0; i--)
- {
- for (int j = 0; j < 8; j++)
- {
- int cb = c[0+j];
- int cr = c[64+j];
-
- int rc = m_crr[cr];
- int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
- int bc = m_cbb[cb];
-
- int yy = y[j];
- if (jpg_format == ERGBFormatJPG::BGRA)
- {
- d0[0] = clamp(yy+bc);
- d0[1] = clamp(yy+gc);
- d0[2] = clamp(yy+rc);
- d0[3] = 255;
- yy = y[8+j];
- d1[0] = clamp(yy+bc);
- d1[1] = clamp(yy+gc);
- d1[2] = clamp(yy+rc);
- d1[3] = 255;
- }
- else
- {
- d0[0] = clamp(yy+rc);
- d0[1] = clamp(yy+gc);
- d0[2] = clamp(yy+bc);
- d0[3] = 255;
- yy = y[8+j];
- d1[0] = clamp(yy+rc);
- d1[1] = clamp(yy+gc);
- d1[2] = clamp(yy+bc);
- d1[3] = 255;
- }
-
- d0 += 4;
- d1 += 4;
- }
-
- y += 64*4;
- c += 64*4;
- }
- }
-
- // YCbCr H2V2 (2x2:1:1, 6 m_blocks per MCU) to RGB
- void jpeg_decoder::H2V2Convert()
- {
- int row = m_max_mcu_y_size - m_mcu_lines_left;
- uint8 *d0 = m_pScan_line_0;
- uint8 *d1 = m_pScan_line_1;
- uint8 *y;
- uint8 *c;
-
- if (row < 8)
- y = m_pSample_buf + row * 8;
- else
- y = m_pSample_buf + 64*2 + (row & 7) * 8;
-
- c = m_pSample_buf + 64*4 + (row >> 1) * 8;
-
- for (int i = m_max_mcus_per_row; i > 0; i--)
- {
- for (int l = 0; l < 2; l++)
- {
- for (int j = 0; j < 8; j += 2)
- {
- int cb = c[0];
- int cr = c[64];
-
- int rc = m_crr[cr];
- int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
- int bc = m_cbb[cb];
-
- int yy = y[j];
- if (jpg_format == ERGBFormatJPG::BGRA)
- {
- d0[0] = clamp(yy+bc);
- d0[1] = clamp(yy+gc);
- d0[2] = clamp(yy+rc);
- d0[3] = 255;
- yy = y[j+1];
- d0[4] = clamp(yy+bc);
- d0[5] = clamp(yy+gc);
- d0[6] = clamp(yy+rc);
- d0[7] = 255;
- yy = y[j+8];
- d1[0] = clamp(yy+bc);
- d1[1] = clamp(yy+gc);
- d1[2] = clamp(yy+rc);
- d1[3] = 255;
- yy = y[j+8+1];
- d1[4] = clamp(yy+bc);
- d1[5] = clamp(yy+gc);
- d1[6] = clamp(yy+rc);
- d1[7] = 255;
- }
- else
- {
- d0[0] = clamp(yy+rc);
- d0[1] = clamp(yy+gc);
- d0[2] = clamp(yy+bc);
- d0[3] = 255;
- yy = y[j+1];
- d0[4] = clamp(yy+rc);
- d0[5] = clamp(yy+gc);
- d0[6] = clamp(yy+bc);
- d0[7] = 255;
- yy = y[j+8];
- d1[0] = clamp(yy+rc);
- d1[1] = clamp(yy+gc);
- d1[2] = clamp(yy+bc);
- d1[3] = 255;
- yy = y[j+8+1];
- d1[4] = clamp(yy+rc);
- d1[5] = clamp(yy+gc);
- d1[6] = clamp(yy+bc);
- d1[7] = 255;
- }
-
- d0 += 8;
- d1 += 8;
-
- c++;
- }
- y += 64;
- }
-
- y += 64*6 - 64*2;
- c += 64*6 - 8;
- }
- }
-
- // Y (1 block per MCU) to 8-bit grayscale
- void jpeg_decoder::gray_convert()
- {
- int row = m_max_mcu_y_size - m_mcu_lines_left;
- uint8 *d = m_pScan_line_0;
- uint8 *s = m_pSample_buf + row * 8;
-
- for (int i = m_max_mcus_per_row; i > 0; i--)
- {
- *(uint *)d = *(uint *)s;
- *(uint *)(&d[4]) = *(uint *)(&s[4]);
-
- s += 64;
- d += 8;
- }
- }
-
- void jpeg_decoder::expanded_convert()
- {
- int row = m_max_mcu_y_size - m_mcu_lines_left;
-
- uint8* Py = m_pSample_buf + (row / 8) * 64 * m_comp_h_samp[0] + (row & 7) * 8;
-
- uint8* d = m_pScan_line_0;
-
- for (int i = m_max_mcus_per_row; i > 0; i--)
- {
- for (int k = 0; k < m_max_mcu_x_size; k += 8)
- {
- const int Y_ofs = k * 8;
- const int Cb_ofs = Y_ofs + 64 * m_expanded_blocks_per_component;
- const int Cr_ofs = Y_ofs + 64 * m_expanded_blocks_per_component * 2;
- for (int j = 0; j < 8; j++)
- {
- int y = Py[Y_ofs + j];
- int cb = Py[Cb_ofs + j];
- int cr = Py[Cr_ofs + j];
-
- if (jpg_format == ERGBFormatJPG::BGRA)
- {
- d[0] = clamp(y + m_cbb[cb]);
- d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
- d[2] = clamp(y + m_crr[cr]);
- d[3] = 255;
- }
- else
- {
- d[0] = clamp(y + m_crr[cr]);
- d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
- d[2] = clamp(y + m_cbb[cb]);
- d[3] = 255;
- }
-
- d += 4;
- }
- }
-
- Py += 64 * m_expanded_blocks_per_mcu;
- }
- }
-
- // Find end of image (EOI) marker, so we can return to the user the exact size of the input stream.
- void jpeg_decoder::find_eoi()
- {
- if (!m_progressive_flag)
- {
- // Attempt to read the EOI marker.
- //get_bits_no_markers(m_bits_left & 7);
-
- // Prime the bit buffer
- m_bits_left = 16;
- get_bits(16);
- get_bits(16);
-
- // The next marker _should_ be EOI
- process_markers();
- }
-
- m_total_bytes_read -= m_in_buf_left;
- }
-
- int jpeg_decoder::decode(const void** pScan_line, uint* pScan_line_len)
- {
- if ((m_error_code) || (!m_ready_flag))
- return JPGD_FAILED;
-
- if (m_total_lines_left == 0)
- return JPGD_DONE;
-
- if (m_mcu_lines_left == 0)
- {
- if (setjmp(m_jmp_state))
- return JPGD_FAILED;
-
- if (m_progressive_flag)
- load_next_row();
- else
- decode_next_row();
-
- // Find the EOI marker if that was the last row.
- if (m_total_lines_left <= m_max_mcu_y_size)
- find_eoi();
-
- m_mcu_lines_left = m_max_mcu_y_size;
- }
-
- if (m_freq_domain_chroma_upsample)
- {
- expanded_convert();
- *pScan_line = m_pScan_line_0;
- }
- else
- {
- switch (m_scan_type)
- {
- case JPGD_YH2V2:
- {
- if ((m_mcu_lines_left & 1) == 0)
- {
- H2V2Convert();
- *pScan_line = m_pScan_line_0;
- }
- else
- *pScan_line = m_pScan_line_1;
-
- break;
- }
- case JPGD_YH2V1:
- {
- H2V1Convert();
- *pScan_line = m_pScan_line_0;
- break;
- }
- case JPGD_YH1V2:
- {
- if ((m_mcu_lines_left & 1) == 0)
- {
- H1V2Convert();
- *pScan_line = m_pScan_line_0;
- }
- else
- *pScan_line = m_pScan_line_1;
-
- break;
- }
- case JPGD_YH1V1:
- {
- H1V1Convert();
- *pScan_line = m_pScan_line_0;
- break;
- }
- case JPGD_GRAYSCALE:
- {
- gray_convert();
- *pScan_line = m_pScan_line_0;
-
- break;
- }
- }
- }
-
- *pScan_line_len = m_real_dest_bytes_per_scan_line;
-
- m_mcu_lines_left--;
- m_total_lines_left--;
-
- return JPGD_SUCCESS;
- }
-
- // Creates the tables needed for efficient Huffman decoding.
- void jpeg_decoder::make_huff_table(int index, huff_tables *pH)
- {
- int p, i, l, si;
- uint8 huffsize[257];
- uint huffcode[257];
- uint code;
- uint subtree;
- int code_size;
- int lastp;
- int nextfreeentry;
- int currententry;
-
- pH->ac_table = m_huff_ac[index] != 0;
-
- p = 0;
-
- for (l = 1; l <= 16; l++)
- {
- for (i = 1; i <= m_huff_num[index][l]; i++)
- huffsize[p++] = static_cast(l);
- }
-
- huffsize[p] = 0;
-
- lastp = p;
-
- code = 0;
- si = huffsize[0];
- p = 0;
-
- while (huffsize[p])
- {
- while (huffsize[p] == si)
- {
- huffcode[p++] = code;
- code++;
- }
-
- code <<= 1;
- si++;
- }
-
- memset(pH->look_up, 0, sizeof(pH->look_up));
- memset(pH->look_up2, 0, sizeof(pH->look_up2));
- memset(pH->tree, 0, sizeof(pH->tree));
- memset(pH->code_size, 0, sizeof(pH->code_size));
-
- nextfreeentry = -1;
-
- p = 0;
-
- while (p < lastp)
- {
- i = m_huff_val[index][p];
- code = huffcode[p];
- code_size = huffsize[p];
-
- pH->code_size[i] = static_cast(code_size);
-
- if (code_size <= 8)
- {
- code <<= (8 - code_size);
-
- for (l = 1 << (8 - code_size); l > 0; l--)
- {
- JPGD_ASSERT(i < 256);
-
- pH->look_up[code] = i;
-
- bool has_extrabits = false;
- int extra_bits = 0;
- int num_extra_bits = i & 15;
-
- int bits_to_fetch = code_size;
- if (num_extra_bits)
- {
- int total_codesize = code_size + num_extra_bits;
- if (total_codesize <= 8)
- {
- has_extrabits = true;
- extra_bits = ((1 << num_extra_bits) - 1) & (code >> (8 - total_codesize));
- JPGD_ASSERT(extra_bits <= 0x7FFF);
- bits_to_fetch += num_extra_bits;
- }
- }
-
- if (!has_extrabits)
- pH->look_up2[code] = i | (bits_to_fetch << 8);
- else
- pH->look_up2[code] = i | 0x8000 | (extra_bits << 16) | (bits_to_fetch << 8);
-
- code++;
- }
- }
- else
- {
- subtree = (code >> (code_size - 8)) & 0xFF;
-
- currententry = pH->look_up[subtree];
-
- if (currententry == 0)
- {
- pH->look_up[subtree] = currententry = nextfreeentry;
- pH->look_up2[subtree] = currententry = nextfreeentry;
-
- nextfreeentry -= 2;
- }
-
- code <<= (16 - (code_size - 8));
-
- for (l = code_size; l > 9; l--)
- {
- if ((code & 0x8000) == 0)
- currententry--;
-
- if (pH->tree[-currententry - 1] == 0)
- {
- pH->tree[-currententry - 1] = nextfreeentry;
-
- currententry = nextfreeentry;
-
- nextfreeentry -= 2;
- }
- else
- currententry = pH->tree[-currententry - 1];
-
- code <<= 1;
- }
-
- if ((code & 0x8000) == 0)
- currententry--;
-
- pH->tree[-currententry - 1] = i;
- }
-
- p++;
- }
- }
-
- // Verifies the quantization tables needed for this scan are available.
- void jpeg_decoder::check_quant_tables()
- {
- for (int i = 0; i < m_comps_in_scan; i++)
- if (m_quant[m_comp_quant[m_comp_list[i]]] == NULL)
- stop_decoding(JPGD_UNDEFINED_QUANT_TABLE);
- }
-
- // Verifies that all the Huffman tables needed for this scan are available.
- void jpeg_decoder::check_huff_tables()
- {
- for (int i = 0; i < m_comps_in_scan; i++)
- {
- if ((m_spectral_start == 0) && (m_huff_num[m_comp_dc_tab[m_comp_list[i]]] == NULL))
- stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
-
- if ((m_spectral_end > 0) && (m_huff_num[m_comp_ac_tab[m_comp_list[i]]] == NULL))
- stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
- }
-
- for (int i = 0; i < JPGD_MAX_HUFF_TABLES; i++)
- if (m_huff_num[i])
- {
- if (!m_pHuff_tabs[i])
- m_pHuff_tabs[i] = (huff_tables *)alloc(sizeof(huff_tables));
-
- make_huff_table(i, m_pHuff_tabs[i]);
- }
- }
-
- // Determines the component order inside each MCU.
- // Also calcs how many MCU's are on each row, etc.
- void jpeg_decoder::calc_mcu_block_order()
- {
- int component_num, component_id;
- int max_h_samp = 0, max_v_samp = 0;
-
- for (component_id = 0; component_id < m_comps_in_frame; component_id++)
- {
- if (m_comp_h_samp[component_id] > max_h_samp)
- max_h_samp = m_comp_h_samp[component_id];
-
- if (m_comp_v_samp[component_id] > max_v_samp)
- max_v_samp = m_comp_v_samp[component_id];
- }
-
- for (component_id = 0; component_id < m_comps_in_frame; component_id++)
- {
- m_comp_h_blocks[component_id] = ((((m_image_x_size * m_comp_h_samp[component_id]) + (max_h_samp - 1)) / max_h_samp) + 7) / 8;
- m_comp_v_blocks[component_id] = ((((m_image_y_size * m_comp_v_samp[component_id]) + (max_v_samp - 1)) / max_v_samp) + 7) / 8;
- }
-
- if (m_comps_in_scan == 1)
- {
- m_mcus_per_row = m_comp_h_blocks[m_comp_list[0]];
- m_mcus_per_col = m_comp_v_blocks[m_comp_list[0]];
- }
- else
- {
- m_mcus_per_row = (((m_image_x_size + 7) / 8) + (max_h_samp - 1)) / max_h_samp;
- m_mcus_per_col = (((m_image_y_size + 7) / 8) + (max_v_samp - 1)) / max_v_samp;
- }
-
- if (m_comps_in_scan == 1)
- {
- m_mcu_org[0] = m_comp_list[0];
-
- m_blocks_per_mcu = 1;
- }
- else
- {
- m_blocks_per_mcu = 0;
-
- for (component_num = 0; component_num < m_comps_in_scan; component_num++)
- {
- int num_blocks;
-
- component_id = m_comp_list[component_num];
-
- num_blocks = m_comp_h_samp[component_id] * m_comp_v_samp[component_id];
-
- while (num_blocks--)
- m_mcu_org[m_blocks_per_mcu++] = component_id;
- }
- }
- }
-
- // Starts a new scan.
- int jpeg_decoder::init_scan()
- {
- if (!locate_sos_marker())
- return JPGD_FALSE;
-
- calc_mcu_block_order();
-
- check_huff_tables();
-
- check_quant_tables();
-
- memset(m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
- m_eob_run = 0;
-
- if (m_restart_interval)
- {
- m_restarts_left = m_restart_interval;
- m_next_restart_num = 0;
- }
-
- fix_in_buffer();
-
- return JPGD_TRUE;
- }
-
- // Starts a frame. Determines if the number of components or sampling factors
- // are supported.
- void jpeg_decoder::init_frame()
- {
- int i;
-
- if (m_comps_in_frame == 1)
- {
- if ((m_comp_h_samp[0] != 1) || (m_comp_v_samp[0] != 1))
- stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
- m_scan_type = JPGD_GRAYSCALE;
- m_max_blocks_per_mcu = 1;
- m_max_mcu_x_size = 8;
- m_max_mcu_y_size = 8;
- }
- else if (m_comps_in_frame == 3)
- {
- if ( ((m_comp_h_samp[1] != 1) || (m_comp_v_samp[1] != 1)) ||
- ((m_comp_h_samp[2] != 1) || (m_comp_v_samp[2] != 1)) )
- stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
- if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
- {
- m_scan_type = JPGD_YH1V1;
-
- m_max_blocks_per_mcu = 3;
- m_max_mcu_x_size = 8;
- m_max_mcu_y_size = 8;
- }
- else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
- {
- m_scan_type = JPGD_YH2V1;
- m_max_blocks_per_mcu = 4;
- m_max_mcu_x_size = 16;
- m_max_mcu_y_size = 8;
- }
- else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 2))
- {
- m_scan_type = JPGD_YH1V2;
- m_max_blocks_per_mcu = 4;
- m_max_mcu_x_size = 8;
- m_max_mcu_y_size = 16;
- }
- else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
- {
- m_scan_type = JPGD_YH2V2;
- m_max_blocks_per_mcu = 6;
- m_max_mcu_x_size = 16;
- m_max_mcu_y_size = 16;
- }
- else
- stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
- }
- else
- stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
- m_max_mcus_per_row = (m_image_x_size + (m_max_mcu_x_size - 1)) / m_max_mcu_x_size;
- m_max_mcus_per_col = (m_image_y_size + (m_max_mcu_y_size - 1)) / m_max_mcu_y_size;
-
- // These values are for the *destination* pixels: after conversion.
- if (m_scan_type == JPGD_GRAYSCALE)
- m_dest_bytes_per_pixel = 1;
- else
- m_dest_bytes_per_pixel = 4;
-
- m_dest_bytes_per_scan_line = ((m_image_x_size + 15) & 0xFFF0) * m_dest_bytes_per_pixel;
-
- m_real_dest_bytes_per_scan_line = (m_image_x_size * m_dest_bytes_per_pixel);
-
- // Initialize two scan line buffers.
- m_pScan_line_0 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
- if ((m_scan_type == JPGD_YH1V2) || (m_scan_type == JPGD_YH2V2))
- m_pScan_line_1 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
-
- m_max_blocks_per_row = m_max_mcus_per_row * m_max_blocks_per_mcu;
-
- // Should never happen
- if (m_max_blocks_per_row > JPGD_MAX_BLOCKS_PER_ROW)
- stop_decoding(JPGD_ASSERTION_ERROR);
-
- // Allocate the coefficient buffer, enough for one MCU
- m_pMCU_coefficients = (jpgd_block_t*)alloc(m_max_blocks_per_mcu * 64 * sizeof(jpgd_block_t));
-
- for (i = 0; i < m_max_blocks_per_mcu; i++)
- m_mcu_block_max_zag[i] = 64;
-
- m_expanded_blocks_per_component = m_comp_h_samp[0] * m_comp_v_samp[0];
- m_expanded_blocks_per_mcu = m_expanded_blocks_per_component * m_comps_in_frame;
- m_expanded_blocks_per_row = m_max_mcus_per_row * m_expanded_blocks_per_mcu;
- // Freq. domain chroma upsampling is only supported for H2V2 subsampling factor.
-// BEGIN EPIC MOD
-#if JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING
- m_freq_domain_chroma_upsample = (m_expanded_blocks_per_mcu == 4*3);
-#else
- m_freq_domain_chroma_upsample = 0;
-#endif
-// END EPIC MOD
-
- if (m_freq_domain_chroma_upsample)
- m_pSample_buf = (uint8 *)alloc(m_expanded_blocks_per_row * 64);
- else
- m_pSample_buf = (uint8 *)alloc(m_max_blocks_per_row * 64);
-
- m_total_lines_left = m_image_y_size;
-
- m_mcu_lines_left = 0;
-
- create_look_ups();
- }
-
- // The coeff_buf series of methods originally stored the coefficients
- // into a "virtual" file which was located in EMS, XMS, or a disk file. A cache
- // was used to make this process more efficient. Now, we can store the entire
- // thing in RAM.
- jpeg_decoder::coeff_buf* jpeg_decoder::coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y)
- {
- coeff_buf* cb = (coeff_buf*)alloc(sizeof(coeff_buf));
-
- cb->block_num_x = block_num_x;
- cb->block_num_y = block_num_y;
- cb->block_len_x = block_len_x;
- cb->block_len_y = block_len_y;
- cb->block_size = (block_len_x * block_len_y) * sizeof(jpgd_block_t);
- cb->pData = (uint8 *)alloc(cb->block_size * block_num_x * block_num_y, true);
- return cb;
- }
-
- inline jpgd_block_t *jpeg_decoder::coeff_buf_getp(coeff_buf *cb, int block_x, int block_y)
- {
- JPGD_ASSERT((block_x < cb->block_num_x) && (block_y < cb->block_num_y));
- return (jpgd_block_t *)(cb->pData + block_x * cb->block_size + block_y * (cb->block_size * cb->block_num_x));
- }
-
- // The following methods decode the various types of m_blocks encountered
- // in progressively encoded images.
- void jpeg_decoder::decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
- {
- int s, r;
- jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
- if ((s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_dc_tab[component_id]])) != 0)
- {
- r = pD->get_bits_no_markers(s);
- s = HUFF_EXTEND(r, s);
- }
-
- pD->m_last_dc_val[component_id] = (s += pD->m_last_dc_val[component_id]);
-
- p[0] = static_cast(s << pD->m_successive_low);
- }
-
- void jpeg_decoder::decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
- {
- if (pD->get_bits_no_markers(1))
- {
- jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
- p[0] |= (1 << pD->m_successive_low);
- }
- }
-
- void jpeg_decoder::decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
- {
- int k, s, r;
-
- if (pD->m_eob_run)
- {
- pD->m_eob_run--;
- return;
- }
-
- jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
- for (k = pD->m_spectral_start; k <= pD->m_spectral_end; k++)
- {
- s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
- r = s >> 4;
- s &= 15;
-
- if (s)
- {
- if ((k += r) > 63)
- pD->stop_decoding(JPGD_DECODE_ERROR);
-
- r = pD->get_bits_no_markers(s);
- s = HUFF_EXTEND(r, s);
-
- p[g_ZAG[k]] = static_cast(s << pD->m_successive_low);
- }
- else
- {
- if (r == 15)
- {
- if ((k += 15) > 63)
- pD->stop_decoding(JPGD_DECODE_ERROR);
- }
- else
- {
- pD->m_eob_run = 1 << r;
-
- if (r)
- pD->m_eob_run += pD->get_bits_no_markers(r);
-
- pD->m_eob_run--;
-
- break;
- }
- }
- }
- }
-
- void jpeg_decoder::decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
- {
- int s, k, r;
- int p1 = 1 << pD->m_successive_low;
- int m1 = (-1) << pD->m_successive_low;
- jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
- k = pD->m_spectral_start;
-
- if (pD->m_eob_run == 0)
- {
- for ( ; k <= pD->m_spectral_end; k++)
- {
- s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
- r = s >> 4;
- s &= 15;
-
- if (s)
- {
- if (s != 1)
- pD->stop_decoding(JPGD_DECODE_ERROR);
-
- if (pD->get_bits_no_markers(1))
- s = p1;
- else
- s = m1;
- }
- else
- {
- if (r != 15)
- {
- pD->m_eob_run = 1 << r;
-
- if (r)
- pD->m_eob_run += pD->get_bits_no_markers(r);
-
- break;
- }
- }
-
- do
- {
- // BEGIN EPIC MOD
- JPGD_ASSERT(k < 64);
- // END EPIC MOD
-
- jpgd_block_t *this_coef = p + g_ZAG[k];
-
- if (*this_coef != 0)
- {
- if (pD->get_bits_no_markers(1))
- {
- if ((*this_coef & p1) == 0)
- {
- if (*this_coef >= 0)
- *this_coef = static_cast(*this_coef + p1);
- else
- *this_coef = static_cast(*this_coef + m1);
- }
- }
- }
- else
- {
- if (--r < 0)
- break;
- }
-
- k++;
-
- } while (k <= pD->m_spectral_end);
-
- if ((s) && (k < 64))
- {
- p[g_ZAG[k]] = static_cast(s);
- }
- }
- }
-
- if (pD->m_eob_run > 0)
- {
- for ( ; k <= pD->m_spectral_end; k++)
- {
- // BEGIN EPIC MOD
- JPGD_ASSERT(k < 64);
- // END EPIC MOD
-
- jpgd_block_t *this_coef = p + g_ZAG[k];
-
- if (*this_coef != 0)
- {
- if (pD->get_bits_no_markers(1))
- {
- if ((*this_coef & p1) == 0)
- {
- if (*this_coef >= 0)
- *this_coef = static_cast(*this_coef + p1);
- else
- *this_coef = static_cast(*this_coef + m1);
- }
- }
- }
- }
-
- pD->m_eob_run--;
- }
- }
-
- // Decode a scan in a progressively encoded image.
- void jpeg_decoder::decode_scan(pDecode_block_func decode_block_func)
- {
- int mcu_row, mcu_col, mcu_block;
- int block_x_mcu[JPGD_MAX_COMPONENTS], m_block_y_mcu[JPGD_MAX_COMPONENTS];
-
- memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
- for (mcu_col = 0; mcu_col < m_mcus_per_col; mcu_col++)
- {
- int component_num, component_id;
-
- memset(block_x_mcu, 0, sizeof(block_x_mcu));
-
- for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
- {
- int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
- if ((m_restart_interval) && (m_restarts_left == 0))
- process_restart();
-
- for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
- {
- component_id = m_mcu_org[mcu_block];
-
- decode_block_func(this, component_id, block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-
- if (m_comps_in_scan == 1)
- block_x_mcu[component_id]++;
- else
- {
- if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
- {
- block_x_mcu_ofs = 0;
-
- if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
- {
- block_y_mcu_ofs = 0;
- block_x_mcu[component_id] += m_comp_h_samp[component_id];
- }
- }
- }
- }
-
- m_restarts_left--;
- }
-
- if (m_comps_in_scan == 1)
- m_block_y_mcu[m_comp_list[0]]++;
- else
- {
- for (component_num = 0; component_num < m_comps_in_scan; component_num++)
- {
- component_id = m_comp_list[component_num];
- m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
- }
- }
- }
- }
-
- // Decode a progressively encoded image.
- void jpeg_decoder::init_progressive()
- {
- int i;
-
- if (m_comps_in_frame == 4)
- stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
- // Allocate the coefficient buffers.
- for (i = 0; i < m_comps_in_frame; i++)
- {
- m_dc_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 1, 1);
- m_ac_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 8, 8);
- }
-
- for ( ; ; )
- {
- int dc_only_scan, refinement_scan;
- pDecode_block_func decode_block_func;
-
- if (!init_scan())
- break;
-
- dc_only_scan = (m_spectral_start == 0);
- refinement_scan = (m_successive_high != 0);
-
- if ((m_spectral_start > m_spectral_end) || (m_spectral_end > 63))
- stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
- if (dc_only_scan)
- {
- if (m_spectral_end)
- stop_decoding(JPGD_BAD_SOS_SPECTRAL);
- }
- else if (m_comps_in_scan != 1) /* AC scans can only contain one component */
- stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
- if ((refinement_scan) && (m_successive_low != m_successive_high - 1))
- stop_decoding(JPGD_BAD_SOS_SUCCESSIVE);
-
- if (dc_only_scan)
- {
- if (refinement_scan)
- decode_block_func = decode_block_dc_refine;
- else
- decode_block_func = decode_block_dc_first;
- }
- else
- {
- if (refinement_scan)
- decode_block_func = decode_block_ac_refine;
- else
- decode_block_func = decode_block_ac_first;
- }
-
- decode_scan(decode_block_func);
-
- m_bits_left = 16;
- get_bits(16);
- get_bits(16);
- }
-
- m_comps_in_scan = m_comps_in_frame;
-
- for (i = 0; i < m_comps_in_frame; i++)
- m_comp_list[i] = i;
-
- calc_mcu_block_order();
- }
-
- void jpeg_decoder::init_sequential()
- {
- if (!init_scan())
- stop_decoding(JPGD_UNEXPECTED_MARKER);
- }
-
- void jpeg_decoder::decode_start()
- {
- init_frame();
-
- if (m_progressive_flag)
- init_progressive();
- else
- init_sequential();
- }
-
- void jpeg_decoder::decode_init(jpeg_decoder_stream *pStream)
- {
- init(pStream);
- locate_sof_marker();
- }
-
- jpeg_decoder::jpeg_decoder(jpeg_decoder_stream *pStream)
- {
- if (setjmp(m_jmp_state))
- return;
- decode_init(pStream);
- }
-
- int jpeg_decoder::begin_decoding()
- {
- if (m_ready_flag)
- return JPGD_SUCCESS;
-
- if (m_error_code)
- return JPGD_FAILED;
-
- if (setjmp(m_jmp_state))
- return JPGD_FAILED;
-
- decode_start();
-
- m_ready_flag = true;
-
- return JPGD_SUCCESS;
- }
-
- jpeg_decoder::~jpeg_decoder()
- {
- free_all_blocks();
- }
-
- jpeg_decoder_file_stream::jpeg_decoder_file_stream()
- {
- m_pFile = NULL;
- m_eof_flag = false;
- m_error_flag = false;
- }
-
- void jpeg_decoder_file_stream::close()
- {
- if (m_pFile)
- {
- fclose(m_pFile);
- m_pFile = NULL;
- }
-
- m_eof_flag = false;
- m_error_flag = false;
- }
-
- jpeg_decoder_file_stream::~jpeg_decoder_file_stream()
- {
- close();
- }
-
- bool jpeg_decoder_file_stream::open(const char *Pfilename)
- {
- close();
-
- m_eof_flag = false;
- m_error_flag = false;
-
-#if defined(_MSC_VER)
- m_pFile = NULL;
- fopen_s(&m_pFile, Pfilename, "rb");
-#else
- m_pFile = fopen(Pfilename, "rb");
-#endif
- return m_pFile != NULL;
- }
-
- int jpeg_decoder_file_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
- {
- if (!m_pFile)
- return -1;
-
- if (m_eof_flag)
- {
- *pEOF_flag = true;
- return 0;
- }
-
- if (m_error_flag)
- return -1;
-
- int bytes_read = static_cast(fread(pBuf, 1, max_bytes_to_read, m_pFile));
- if (bytes_read < max_bytes_to_read)
- {
- if (ferror(m_pFile))
- {
- m_error_flag = true;
- return -1;
- }
-
- m_eof_flag = true;
- *pEOF_flag = true;
- }
-
- return bytes_read;
- }
-
- bool jpeg_decoder_mem_stream::open(const uint8 *pSrc_data, uint size)
- {
- close();
- m_pSrc_data = pSrc_data;
- m_ofs = 0;
- m_size = size;
- return true;
- }
-
- int jpeg_decoder_mem_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
- {
- *pEOF_flag = false;
-
- if (!m_pSrc_data)
- return -1;
-
- uint bytes_remaining = m_size - m_ofs;
- if ((uint)max_bytes_to_read > bytes_remaining)
- {
- max_bytes_to_read = bytes_remaining;
- *pEOF_flag = true;
- }
-
- memcpy(pBuf, m_pSrc_data + m_ofs, max_bytes_to_read);
- m_ofs += max_bytes_to_read;
-
- return max_bytes_to_read;
- }
-
- unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps)
- {
- if (!actual_comps)
- return NULL;
- *actual_comps = 0;
-
- if ((!pStream) || (!width) || (!height) || (!req_comps))
- return NULL;
-
- if ((req_comps != 1) && (req_comps != 3) && (req_comps != 4))
- return NULL;
-
- jpeg_decoder decoder(pStream);
- if (decoder.get_error_code() != JPGD_SUCCESS)
- return NULL;
-
- const int image_width = decoder.get_width(), image_height = decoder.get_height();
- *width = image_width;
- *height = image_height;
- *actual_comps = decoder.get_num_components();
-
- if (decoder.begin_decoding() != JPGD_SUCCESS)
- return NULL;
-
- const int dst_bpl = image_width * req_comps;
-
- uint8 *pImage_data = (uint8*)jpgd_malloc(dst_bpl * image_height);
- if (!pImage_data)
- return NULL;
-
- for (int y = 0; y < image_height; y++)
- {
- const uint8* pScan_line = 0;
- uint scan_line_len;
- if (decoder.decode((const void**)&pScan_line, &scan_line_len) != JPGD_SUCCESS)
- {
- jpgd_free(pImage_data);
- return NULL;
- }
-
- uint8 *pDst = pImage_data + y * dst_bpl;
-
- if (((req_comps == 4) && (decoder.get_num_components() == 3)) ||
- ((req_comps == 1) && (decoder.get_num_components() == 1)))
- {
- memcpy(pDst, pScan_line, dst_bpl);
- }
- else if (decoder.get_num_components() == 1)
- {
- if (req_comps == 3)
- {
- for (int x = 0; x < image_width; x++)
- {
- uint8 luma = pScan_line[x];
- pDst[0] = luma;
- pDst[1] = luma;
- pDst[2] = luma;
- pDst += 3;
- }
- }
- else
- {
- for (int x = 0; x < image_width; x++)
- {
- uint8 luma = pScan_line[x];
- pDst[0] = luma;
- pDst[1] = luma;
- pDst[2] = luma;
- pDst[3] = 255;
- pDst += 4;
- }
- }
- }
- else if (decoder.get_num_components() == 3)
- {
- if (req_comps == 1)
- {
- const int YR = 19595, YG = 38470, YB = 7471;
- for (int x = 0; x < image_width; x++)
- {
- int r = pScan_line[x*4+0];
- int g = pScan_line[x*4+1];
- int b = pScan_line[x*4+2];
- *pDst++ = static_cast((r * YR + g * YG + b * YB + 32768) >> 16);
- }
- }
- else
- {
- for (int x = 0; x < image_width; x++)
- {
- pDst[0] = pScan_line[x*4+0];
- pDst[1] = pScan_line[x*4+1];
- pDst[2] = pScan_line[x*4+2];
- pDst += 3;
- }
- }
- }
- }
-
- return pImage_data;
- }
-
-// BEGIN EPIC MOD
- unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format)
- {
- jpg_format = (ERGBFormatJPG)format;
-// EMD EPIC MOD
- jpgd::jpeg_decoder_mem_stream mem_stream(pSrc_data, src_data_size);
- return decompress_jpeg_image_from_stream(&mem_stream, width, height, actual_comps, req_comps);
- }
-
- unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps)
- {
- jpgd::jpeg_decoder_file_stream file_stream;
- if (!file_stream.open(pSrc_filename))
- return NULL;
- return decompress_jpeg_image_from_stream(&file_stream, width, height, actual_comps, req_comps);
- }
-
-} // namespace jpgd
diff --git a/spaces/joushe/moe-tts/mel_processing.py b/spaces/joushe/moe-tts/mel_processing.py
deleted file mode 100644
index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000
--- a/spaces/joushe/moe-tts/mel_processing.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- output = dynamic_range_compression_torch(magnitudes)
- return output
-
-
-def spectral_de_normalize_torch(magnitudes):
- output = dynamic_range_decompression_torch(magnitudes)
- return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- global mel_basis
- dtype_device = str(spec.dtype) + '_' + str(spec.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
- return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global mel_basis, hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
-
- return spec
diff --git a/spaces/jpdiazpardo/jpdiazpardo-whisper-tiny-metal/functions/charts.py b/spaces/jpdiazpardo/jpdiazpardo-whisper-tiny-metal/functions/charts.py
deleted file mode 100644
index e28f7844dddd82f027881fdf35dbc4f9b0042f43..0000000000000000000000000000000000000000
--- a/spaces/jpdiazpardo/jpdiazpardo-whisper-tiny-metal/functions/charts.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import plotly.graph_objects as go
-
-def add_emoji(emotions_list):
- for s in range(len(emotions_list)):
- if emotions_list[s]=="surprise": emotions_list[s]="surprise 😲"
- elif emotions_list[s]=="joy": emotions_list[s]="joy 😀"
- elif emotions_list[s]=="anger": emotions_list[s]="anger 🤬"
- elif emotions_list[s]=="neutral": emotions_list[s]="neutral 😐"
- elif emotions_list[s]=="disgust": emotions_list[s]="disgust 🤢"
- elif emotions_list[s]=="fear": emotions_list[s]="fear 😨"
- elif emotions_list[s]=="sadness": emotions_list[s]="sadness 😭"
- else: print(s)
-
- return emotions_list
-
-
-def spider_chart(dictionary):
-
- fig = go.Figure(data=go.Scatterpolar(
- r=[round(v*100,2) for v in dictionary.values()],
- theta= add_emoji([k for k in dictionary.keys()]),
- fill='toself'))
-
- fig.update_layout(
- polar=dict(
- radialaxis=dict(
- visible=True
- ),
- ),
- showlegend=False,
- width = 400, height = 400,
- title = "Audio Sentiment Analysis", title_x=0.5)
-
- return fig
\ No newline at end of file
diff --git a/spaces/katanaml-org/sparrow-ui/views/data_review.py b/spaces/katanaml-org/sparrow-ui/views/data_review.py
deleted file mode 100644
index d5d3c719ce7870e3cb2e5f227a57c34fd2e671cf..0000000000000000000000000000000000000000
--- a/spaces/katanaml-org/sparrow-ui/views/data_review.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import streamlit as st
-from natsort import natsorted
-import os
-from PIL import Image
-import math
-from streamlit_sparrow_labeling import st_sparrow_labeling
-import json
-
-
-class DataReview:
- class Model:
- # pageTitle = "Data Review"
- subheader_2 = "Select"
- subheader_3 = "Result"
- selection_text = "File to review"
- initial_msg = "Please select a file to review"
-
- img_file = None
-
- def set_image_file(self, img_file):
- st.session_state['img_file_review'] = img_file
-
- def get_image_file(self):
- if 'img_file_review' not in st.session_state:
- return None
- return st.session_state['img_file_review']
-
- json_file = None
-
- def set_json_file(self, json_file):
- st.session_state['json_file_review'] = json_file
-
- def get_json_file(self):
- if 'json_file_review' not in st.session_state:
- return None
- return st.session_state['json_file_review']
-
- def view(self, model, ui_width, device_type, device_width):
- # st.title(model.pageTitle)
-
- with st.sidebar:
- st.markdown("---")
- st.subheader(model.subheader_2)
-
- # get list of files in inference directory
- processed_file_names = self.get_processed_file_names('docs/inference/')
-
- if 'selection_index' not in st.session_state:
- st.session_state['selection_index'] = 0
- selection_index = 0
- else:
- selection_index = st.session_state['selection_index']
-
- selection = st.selectbox(model.selection_text, processed_file_names, index=selection_index)
-
- selection_index = self.get_selection_index(selection, processed_file_names)
- st.session_state['selection_index'] = selection_index
-
- img_file = "docs/inference/" + selection + ".jpg"
- json_file = "docs/inference/" + selection + ".json"
-
- model.set_image_file(img_file)
- model.set_json_file(json_file)
-
- if model.get_image_file() is not None:
- doc_img = Image.open(model.get_image_file())
- doc_height = doc_img.height
- doc_width = doc_img.width
-
- canvas_width, number_of_columns = self.canvas_available_width(ui_width, doc_width, device_type,
- device_width)
-
- if number_of_columns > 1:
- col1, col2 = st.columns([number_of_columns, 10 - number_of_columns])
- with col1:
- pass
- self.render_doc(model, doc_img, canvas_width, doc_height, doc_width)
- with col2:
- pass
- self.render_results(model)
- else:
- pass
- self.render_doc(model, doc_img, canvas_width, doc_height, doc_width)
- self.render_results(model)
- else:
- st.title(model.initial_msg)
-
-
- def get_processed_file_names(self, dir_name):
- # get ordered list of files without file extension, excluding hidden files, with JSON extension only
- file_names = [os.path.splitext(f)[0] for f in os.listdir(dir_name) if
- os.path.isfile(os.path.join(dir_name, f)) and not f.startswith('.') and f.endswith('.json')]
- file_names = natsorted(file_names)
- return file_names
-
- def get_selection_index(self, file, files_list):
- return files_list.index(file)
-
- def canvas_available_width(self, ui_width, doc_width, device_type, device_width):
- doc_width_pct = (doc_width * 100) / ui_width
- if doc_width_pct < 45:
- canvas_width_pct = 37
- elif doc_width_pct < 55:
- canvas_width_pct = 49
- else:
- canvas_width_pct = 60
-
- if ui_width > 700 and canvas_width_pct == 37 and device_type == "desktop":
- return math.floor(canvas_width_pct * ui_width / 100), 4
- elif ui_width > 700 and canvas_width_pct == 49 and device_type == "desktop":
- return math.floor(canvas_width_pct * ui_width / 100), 5
- elif ui_width > 700 and canvas_width_pct == 60 and device_type == "desktop":
- return math.floor(canvas_width_pct * ui_width / 100), 6
- else:
- if device_type == "desktop":
- ui_width = device_width - math.floor((device_width * 22) / 100)
- elif device_type == "mobile":
- ui_width = device_width - math.floor((device_width * 13) / 100)
- return ui_width, 1
-
-
- def render_doc(self, model, doc_img, canvas_width, doc_height, doc_width):
- height = 1296
- width = 864
-
- annotations_json = {
- "meta": {
- "version": "v0.1",
- "split": "train",
- "image_id": 0,
- "image_size": {
- "width": doc_width,
- "height": doc_height
- }
- },
- "words": []
- }
-
- st_sparrow_labeling(
- fill_color="rgba(0, 151, 255, 0.3)",
- stroke_width=2,
- stroke_color="rgba(0, 50, 255, 0.7)",
- background_image=doc_img,
- initial_rects=annotations_json,
- height=height,
- width=width,
- drawing_mode="transform",
- display_toolbar=False,
- update_streamlit=False,
- canvas_width=canvas_width,
- doc_height=doc_height,
- doc_width=doc_width,
- image_rescale=True,
- key="doc_annotation" + model.get_image_file()
- )
-
- def render_results(self, model):
- json_file = model.get_json_file()
- if json_file is not None:
- with open(json_file) as f:
- data_json = json.load(f)
- st.subheader(model.subheader_3)
- st.markdown("---")
- st.json(data_json)
- st.markdown("---")
\ No newline at end of file
diff --git a/spaces/kepl/gpt/run.py b/spaces/kepl/gpt/run.py
deleted file mode 100644
index 3b9ca0f439c4dd6a791f7eed62d942d096562b61..0000000000000000000000000000000000000000
--- a/spaces/kepl/gpt/run.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import secrets
-
-from server.bp import bp
-from server.website import Website
-from server.backend import Backend_Api
-from server.babel import create_babel
-from json import load
-from flask import Flask
-
-if __name__ == '__main__':
-
- # Load configuration from config.json
- config = load(open('config.json', 'r'))
- site_config = config['site_config']
- url_prefix = config.pop('url_prefix')
-
- # Create the app
- app = Flask(__name__)
- app.secret_key = secrets.token_hex(16)
-
- # Set up Babel
- create_babel(app)
-
- # Set up the website routes
- site = Website(bp, url_prefix)
- for route in site.routes:
- bp.add_url_rule(
- route,
- view_func=site.routes[route]['function'],
- methods=site.routes[route]['methods'],
- )
-
- # Set up the backend API routes
- backend_api = Backend_Api(bp, config)
- for route in backend_api.routes:
- bp.add_url_rule(
- route,
- view_func=backend_api.routes[route]['function'],
- methods=backend_api.routes[route]['methods'],
- )
-
- # Register the blueprint
- app.register_blueprint(bp, url_prefix=url_prefix)
-
- # Run the Flask server
- print(f"Running on {site_config['port']}{url_prefix}")
- app.run(**site_config)
- print(f"Closing port {site_config['port']}")
diff --git a/spaces/keremberke/valorant-object-detection/README.md b/spaces/keremberke/valorant-object-detection/README.md
deleted file mode 100644
index bbb470b3979a43eeb4acfcbb2dd0cd4b6ceb8f98..0000000000000000000000000000000000000000
--- a/spaces/keremberke/valorant-object-detection/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Valorant Object Detection
-emoji: 🎮
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/docs/speed_benchmark.md b/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/docs/speed_benchmark.md
deleted file mode 100644
index 055aee0defe2c43a523ced48260242f0f99b7cea..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/docs/speed_benchmark.md
+++ /dev/null
@@ -1,93 +0,0 @@
-## Test Training Speed
-
-- Test Commands
-
-You need to use the following two commands to test the Partial FC training performance.
-The number of identites is **3 millions** (synthetic data), turn mixed precision training on, backbone is resnet50,
-batch size is 1024.
-```shell
-# Model Parallel
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/3millions
-# Partial FC 0.1
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/3millions_pfc
-```
-
-- GPU Memory
-
-```
-# (Model Parallel) gpustat -i
-[0] Tesla V100-SXM2-32GB | 64'C, 94 % | 30338 / 32510 MB
-[1] Tesla V100-SXM2-32GB | 60'C, 99 % | 28876 / 32510 MB
-[2] Tesla V100-SXM2-32GB | 60'C, 99 % | 28872 / 32510 MB
-[3] Tesla V100-SXM2-32GB | 69'C, 99 % | 28872 / 32510 MB
-[4] Tesla V100-SXM2-32GB | 66'C, 99 % | 28888 / 32510 MB
-[5] Tesla V100-SXM2-32GB | 60'C, 99 % | 28932 / 32510 MB
-[6] Tesla V100-SXM2-32GB | 68'C, 100 % | 28916 / 32510 MB
-[7] Tesla V100-SXM2-32GB | 65'C, 99 % | 28860 / 32510 MB
-
-# (Partial FC 0.1) gpustat -i
-[0] Tesla V100-SXM2-32GB | 60'C, 95 % | 10488 / 32510 MB │·······················
-[1] Tesla V100-SXM2-32GB | 60'C, 97 % | 10344 / 32510 MB │·······················
-[2] Tesla V100-SXM2-32GB | 61'C, 95 % | 10340 / 32510 MB │·······················
-[3] Tesla V100-SXM2-32GB | 66'C, 95 % | 10340 / 32510 MB │·······················
-[4] Tesla V100-SXM2-32GB | 65'C, 94 % | 10356 / 32510 MB │·······················
-[5] Tesla V100-SXM2-32GB | 61'C, 95 % | 10400 / 32510 MB │·······················
-[6] Tesla V100-SXM2-32GB | 68'C, 96 % | 10384 / 32510 MB │·······················
-[7] Tesla V100-SXM2-32GB | 64'C, 95 % | 10328 / 32510 MB │·······················
-```
-
-- Training Speed
-
-```python
-# (Model Parallel) trainging.log
-Training: Speed 2271.33 samples/sec Loss 1.1624 LearningRate 0.2000 Epoch: 0 Global Step: 100
-Training: Speed 2269.94 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 150
-Training: Speed 2272.67 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 200
-Training: Speed 2266.55 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 250
-Training: Speed 2272.54 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 300
-
-# (Partial FC 0.1) trainging.log
-Training: Speed 5299.56 samples/sec Loss 1.0965 LearningRate 0.2000 Epoch: 0 Global Step: 100
-Training: Speed 5296.37 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 150
-Training: Speed 5304.37 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 200
-Training: Speed 5274.43 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 250
-Training: Speed 5300.10 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 300
-```
-
-In this test case, Partial FC 0.1 only use1 1/3 of the GPU memory of the model parallel,
-and the training speed is 2.5 times faster than the model parallel.
-
-
-## Speed Benchmark
-
-1. Training speed of different parallel methods (samples/second), Tesla V100 32GB * 8. (Larger is better)
-
-| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
-| :--- | :--- | :--- | :--- |
-|125000 | 4681 | 4824 | 5004 |
-|250000 | 4047 | 4521 | 4976 |
-|500000 | 3087 | 4013 | 4900 |
-|1000000 | 2090 | 3449 | 4803 |
-|1400000 | 1672 | 3043 | 4738 |
-|2000000 | - | 2593 | 4626 |
-|4000000 | - | 1748 | 4208 |
-|5500000 | - | 1389 | 3975 |
-|8000000 | - | - | 3565 |
-|16000000 | - | - | 2679 |
-|29000000 | - | - | 1855 |
-
-2. GPU memory cost of different parallel methods (GB per GPU), Tesla V100 32GB * 8. (Smaller is better)
-
-| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
-| :--- | :--- | :--- | :--- |
-|125000 | 7358 | 5306 | 4868 |
-|250000 | 9940 | 5826 | 5004 |
-|500000 | 14220 | 7114 | 5202 |
-|1000000 | 23708 | 9966 | 5620 |
-|1400000 | 32252 | 11178 | 6056 |
-|2000000 | - | 13978 | 6472 |
-|4000000 | - | 23238 | 8284 |
-|5500000 | - | 32188 | 9854 |
-|8000000 | - | - | 12310 |
-|16000000 | - | - | 19950 |
-|29000000 | - | - | 32324 |
diff --git a/spaces/kevinwang676/rvc-mlbb-v2/lib/infer_pack/onnx_inference.py b/spaces/kevinwang676/rvc-mlbb-v2/lib/infer_pack/onnx_inference.py
deleted file mode 100644
index c78324cbc08414fffcc689f325312de0e51bd6b4..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/rvc-mlbb-v2/lib/infer_pack/onnx_inference.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import onnxruntime
-import librosa
-import numpy as np
-import soundfile
-
-
-class ContentVec:
- def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
- print("load model(s) from {}".format(vec_path))
- if device == "cpu" or device is None:
- providers = ["CPUExecutionProvider"]
- elif device == "cuda":
- providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
- elif device == "dml":
- providers = ["DmlExecutionProvider"]
- else:
- raise RuntimeError("Unsportted Device")
- self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
-
- def __call__(self, wav):
- return self.forward(wav)
-
- def forward(self, wav):
- feats = wav
- if feats.ndim == 2: # double channels
- feats = feats.mean(-1)
- assert feats.ndim == 1, feats.ndim
- feats = np.expand_dims(np.expand_dims(feats, 0), 0)
- onnx_input = {self.model.get_inputs()[0].name: feats}
- logits = self.model.run(None, onnx_input)[0]
- return logits.transpose(0, 2, 1)
-
-
-def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
- if f0_predictor == "pm":
- from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
-
- f0_predictor_object = PMF0Predictor(
- hop_length=hop_length, sampling_rate=sampling_rate
- )
- elif f0_predictor == "harvest":
- from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
-
- f0_predictor_object = HarvestF0Predictor(
- hop_length=hop_length, sampling_rate=sampling_rate
- )
- elif f0_predictor == "dio":
- from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
-
- f0_predictor_object = DioF0Predictor(
- hop_length=hop_length, sampling_rate=sampling_rate
- )
- else:
- raise Exception("Unknown f0 predictor")
- return f0_predictor_object
-
-
-class OnnxRVC:
- def __init__(
- self,
- model_path,
- sr=40000,
- hop_size=512,
- vec_path="vec-768-layer-12",
- device="cpu",
- ):
- vec_path = f"pretrained/{vec_path}.onnx"
- self.vec_model = ContentVec(vec_path, device)
- if device == "cpu" or device is None:
- providers = ["CPUExecutionProvider"]
- elif device == "cuda":
- providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
- elif device == "dml":
- providers = ["DmlExecutionProvider"]
- else:
- raise RuntimeError("Unsportted Device")
- self.model = onnxruntime.InferenceSession(model_path, providers=providers)
- self.sampling_rate = sr
- self.hop_size = hop_size
-
- def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
- onnx_input = {
- self.model.get_inputs()[0].name: hubert,
- self.model.get_inputs()[1].name: hubert_length,
- self.model.get_inputs()[2].name: pitch,
- self.model.get_inputs()[3].name: pitchf,
- self.model.get_inputs()[4].name: ds,
- self.model.get_inputs()[5].name: rnd,
- }
- return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16)
-
- def inference(
- self,
- raw_path,
- sid,
- f0_method="dio",
- f0_up_key=0,
- pad_time=0.5,
- cr_threshold=0.02,
- ):
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
- f0_predictor = get_f0_predictor(
- f0_method,
- hop_length=self.hop_size,
- sampling_rate=self.sampling_rate,
- threshold=cr_threshold,
- )
- wav, sr = librosa.load(raw_path, sr=self.sampling_rate)
- org_length = len(wav)
- if org_length / sr > 50.0:
- raise RuntimeError("Reached Max Length")
-
- wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
- wav16k = wav16k
-
- hubert = self.vec_model(wav16k)
- hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32)
- hubert_length = hubert.shape[1]
-
- pitchf = f0_predictor.compute_f0(wav, hubert_length)
- pitchf = pitchf * 2 ** (f0_up_key / 12)
- pitch = pitchf.copy()
- f0_mel = 1127 * np.log(1 + pitch / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- pitch = np.rint(f0_mel).astype(np.int64)
-
- pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
- pitch = pitch.reshape(1, len(pitch))
- ds = np.array([sid]).astype(np.int64)
-
- rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
- hubert_length = np.array([hubert_length]).astype(np.int64)
-
- out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
- out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
- return out_wav[0:org_length]
diff --git a/spaces/kevinwang676/rvc-models-new/infer_pack/models_onnx_moess.py b/spaces/kevinwang676/rvc-models-new/infer_pack/models_onnx_moess.py
deleted file mode 100644
index 12efb0629a2e3d0d746a34f467254536c2bdbe5f..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/rvc-models-new/infer_pack/models_onnx_moess.py
+++ /dev/null
@@ -1,849 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(256, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(256, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- x = self.proj(x) * x_mask
- return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(
- self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0,
- ):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(
- modules.ResidualCouplingLayer(
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- mean_only=True,
- )
- )
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
- def remove_weight_norm(self):
- for i in range(self.n_flows):
- self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=0,
- ):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(resblock_kernel_sizes, resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
- """Definition of sine generator
- SineGen(samp_rate, harmonic_num = 0,
- sine_amp = 0.1, noise_std = 0.003,
- voiced_threshold = 0,
- flag_for_pulse=False)
- samp_rate: sampling rate in Hz
- harmonic_num: number of harmonic overtones (default 0)
- sine_amp: amplitude of sine-wavefrom (default 0.1)
- noise_std: std of Gaussian noise (default 0.003)
- voiced_thoreshold: F0 threshold for U/V classification (default 0)
- flag_for_pulse: this SinGen is used inside PulseGen (default False)
- Note: when flag_for_pulse is True, the first time step of a voiced
- segment is always sin(np.pi) or cos(0)
- """
-
- def __init__(
- self,
- samp_rate,
- harmonic_num=0,
- sine_amp=0.1,
- noise_std=0.003,
- voiced_threshold=0,
- flag_for_pulse=False,
- ):
- super(SineGen, self).__init__()
- self.sine_amp = sine_amp
- self.noise_std = noise_std
- self.harmonic_num = harmonic_num
- self.dim = self.harmonic_num + 1
- self.sampling_rate = samp_rate
- self.voiced_threshold = voiced_threshold
-
- def _f02uv(self, f0):
- # generate uv signal
- uv = torch.ones_like(f0)
- uv = uv * (f0 > self.voiced_threshold)
- return uv
-
- def forward(self, f0, upp):
- """sine_tensor, uv = forward(f0)
- input F0: tensor(batchsize=1, length, dim=1)
- f0 for unvoiced steps should be 0
- output sine_tensor: tensor(batchsize=1, length, dim)
- output uv: tensor(batchsize=1, length, 1)
- """
- with torch.no_grad():
- f0 = f0[:, None].transpose(1, 2)
- f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
- # fundamental component
- f0_buf[:, :, 0] = f0[:, :, 0]
- for idx in np.arange(self.harmonic_num):
- f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
- idx + 2
- ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
- rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
- rand_ini = torch.rand(
- f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
- )
- rand_ini[:, 0] = 0
- rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
- tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
- tmp_over_one *= upp
- tmp_over_one = F.interpolate(
- tmp_over_one.transpose(2, 1),
- scale_factor=upp,
- mode="linear",
- align_corners=True,
- ).transpose(2, 1)
- rad_values = F.interpolate(
- rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(
- 2, 1
- ) #######
- tmp_over_one %= 1
- tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
- cumsum_shift = torch.zeros_like(rad_values)
- cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
- sine_waves = torch.sin(
- torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
- )
- sine_waves = sine_waves * self.sine_amp
- uv = self._f02uv(f0)
- uv = F.interpolate(
- uv.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(2, 1)
- noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
- noise = noise_amp * torch.randn_like(sine_waves)
- sine_waves = sine_waves * uv + noise
- return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
- """SourceModule for hn-nsf
- SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
- add_noise_std=0.003, voiced_threshod=0)
- sampling_rate: sampling_rate in Hz
- harmonic_num: number of harmonic above F0 (default: 0)
- sine_amp: amplitude of sine source signal (default: 0.1)
- add_noise_std: std of additive Gaussian noise (default: 0.003)
- note that amplitude of noise in unvoiced is decided
- by sine_amp
- voiced_threshold: threhold to set U/V given F0 (default: 0)
- Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
- F0_sampled (batchsize, length, 1)
- Sine_source (batchsize, length, 1)
- noise_source (batchsize, length 1)
- uv (batchsize, length, 1)
- """
-
- def __init__(
- self,
- sampling_rate,
- harmonic_num=0,
- sine_amp=0.1,
- add_noise_std=0.003,
- voiced_threshod=0,
- is_half=True,
- ):
- super(SourceModuleHnNSF, self).__init__()
-
- self.sine_amp = sine_amp
- self.noise_std = add_noise_std
- self.is_half = is_half
- # to produce sine waveforms
- self.l_sin_gen = SineGen(
- sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
- )
-
- # to merge source harmonics into a single excitation
- self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
- self.l_tanh = torch.nn.Tanh()
-
- def forward(self, x, upp=None):
- sine_wavs, uv, _ = self.l_sin_gen(x, upp)
- if self.is_half:
- sine_wavs = sine_wavs.half()
- sine_merge = self.l_tanh(self.l_linear(sine_wavs))
- return sine_merge, None, None # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- sr,
- is_half=False,
- ):
- super(GeneratorNSF, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
-
- self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
- self.m_source = SourceModuleHnNSF(
- sampling_rate=sr, harmonic_num=0, is_half=is_half
- )
- self.noise_convs = nn.ModuleList()
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- c_cur = upsample_initial_channel // (2 ** (i + 1))
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
- if i + 1 < len(upsample_rates):
- stride_f0 = np.prod(upsample_rates[i + 1 :])
- self.noise_convs.append(
- Conv1d(
- 1,
- c_cur,
- kernel_size=stride_f0 * 2,
- stride=stride_f0,
- padding=stride_f0 // 2,
- )
- )
- else:
- self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(resblock_kernel_sizes, resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- self.upp = np.prod(upsample_rates)
-
- def forward(self, x, f0, g=None):
- har_source, noi_source, uv = self.m_source(f0, self.upp)
- har_source = har_source.transpose(1, 2)
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- x_source = self.noise_convs[i](har_source)
- x = x + x_source
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-sr2sr = {
- "32k": 32000,
- "40k": 40000,
- "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsidM(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr,
- **kwargs
- ):
- super().__init__()
- if type(sr) == type("strr"):
- sr = sr2sr[sr]
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- sr=sr,
- is_half=kwargs["is_half"],
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
- return o
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- # hop_length,
- gin_channels=0,
- use_sdp=True,
- **kwargs
- ):
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256Sim(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- is_half=kwargs["is_half"],
- )
-
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(
- self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
- ): # y是spec不需要了现在
- g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- x, x_mask = self.enc_p(phone, pitch, phone_lengths)
- x = self.flow(x, x_mask, g=g, reverse=True)
- o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
- return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2, 3, 5, 7, 11, 17]
- # periods = [3, 5, 7, 11, 17, 23, 37]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [
- DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
- ]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = [] #
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- # for j in range(len(fmap_r)):
- # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ]
- )
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(
- Conv2d(
- 1,
- 32,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 32,
- 128,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 128,
- 512,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 512,
- 1024,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 1024,
- 1024,
- (kernel_size, 1),
- 1,
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- ]
- )
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/web/static/js/wav.js b/spaces/kira4424/Tacotron-zero-short-voice-clone/web/static/js/wav.js
deleted file mode 100644
index 461908c4fd68655a6f4cbc573b469fd97ed6ed8d..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/web/static/js/wav.js
+++ /dev/null
@@ -1,86 +0,0 @@
-/*
-wav编码器+编码引擎
-https://github.com/xiangyuecn/Recorder
-
-当然最佳推荐使用mp3、wav格式，代码也是优先照顾这两种格式
-浏览器支持情况
-https://developer.mozilla.org/en-US/docs/Web/HTML/Supported_media_formats
-
-编码原理：给pcm数据加上一个44直接的wav头即成wav文件；pcm数据就是Recorder中的buffers原始数据（重新采样），16位时为LE小端模式（Little Endian），实质上是未经过任何编码处理
-*/
-(function(){
-"use strict";
-
-Recorder.prototype.enc_wav={
- stable:true
- ,testmsg:"支持位数8位、16位（填在比特率里面），采样率取值无限制"
-};
-Recorder.prototype.wav=function(res,True,False){
- var This=this,set=This.set
- ,size=res.length
- ,sampleRate=set.sampleRate
- ,bitRate=set.bitRate==8?8:16;
-
- //编码数据 https://github.com/mattdiamond/Recorderjs https://www.cnblogs.com/blqw/p/3782420.html https://www.cnblogs.com/xiaoqi/p/6993912.html
- var dataLength=size*(bitRate/8);
- var buffer=new ArrayBuffer(44+dataLength);
- var data=new DataView(buffer);
-
- var offset=0;
- var writeString=function(str){
- for (var i=0;i>8)+128;
- data.setInt8(offset,val,true);
- };
- }else{
- for (var i=0;i list[tuple[str, str]]:
- """Extract hyperlinks from a BeautifulSoup object
-
- Args:
- soup (BeautifulSoup): The BeautifulSoup object
- base_url (str): The base URL
-
- Returns:
- List[Tuple[str, str]]: The extracted hyperlinks
- """
- return [
- (link.text, urljoin(base_url, link["href"]))
- for link in soup.find_all("a", href=True)
- ]
-
-
-def format_hyperlinks(hyperlinks: list[tuple[str, str]]) -> list[str]:
- """Format hyperlinks to be displayed to the user
-
- Args:
- hyperlinks (List[Tuple[str, str]]): The hyperlinks to format
-
- Returns:
- List[str]: The formatted hyperlinks
- """
- return [f"{link_text} ({link_url})" for link_text, link_url in hyperlinks]
diff --git a/spaces/kukuhtw/VToonify/vtoonify/model/raft/README.md b/spaces/kukuhtw/VToonify/vtoonify/model/raft/README.md
deleted file mode 100644
index 650275ed7c4cda12822587c6a4358f057fffe494..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/VToonify/vtoonify/model/raft/README.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# RAFT
-This repository contains the source code for our paper:
-
-[RAFT: Recurrent All Pairs Field Transforms for Optical Flow](https://arxiv.org/pdf/2003.12039.pdf)
-ECCV 2020
-Zachary Teed and Jia Deng
-
-
-
-## Requirements
-The code has been tested with PyTorch 1.6 and Cuda 10.1.
-```Shell
-conda create --name raft
-conda activate raft
-conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 matplotlib tensorboard scipy opencv -c pytorch
-```
-
-## Demos
-Pretrained models can be downloaded by running
-```Shell
-./download_models.sh
-```
-or downloaded from [google drive](https://drive.google.com/drive/folders/1sWDsfuZ3Up38EUQt7-JDTT1HcGHuJgvT?usp=sharing)
-
-You can demo a trained model on a sequence of frames
-```Shell
-python demo.py --model=models/raft-things.pth --path=demo-frames
-```
-
-## Required Data
-To evaluate/train RAFT, you will need to download the required datasets.
-* [FlyingChairs](https://lmb.informatik.uni-freiburg.de/resources/datasets/FlyingChairs.en.html#flyingchairs)
-* [FlyingThings3D](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)
-* [Sintel](http://sintel.is.tue.mpg.de/)
-* [KITTI](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=flow)
-* [HD1K](http://hci-benchmark.iwr.uni-heidelberg.de/) (optional)
-
-
-By default `datasets.py` will search for the datasets in these locations. You can create symbolic links to wherever the datasets were downloaded in the `datasets` folder
-
-```Shell
-├── datasets
- ├── Sintel
- ├── test
- ├── training
- ├── KITTI
- ├── testing
- ├── training
- ├── devkit
- ├── FlyingChairs_release
- ├── data
- ├── FlyingThings3D
- ├── frames_cleanpass
- ├── frames_finalpass
- ├── optical_flow
-```
-
-## Evaluation
-You can evaluate a trained model using `evaluate.py`
-```Shell
-python evaluate.py --model=models/raft-things.pth --dataset=sintel --mixed_precision
-```
-
-## Training
-We used the following training schedule in our paper (2 GPUs). Training logs will be written to the `runs` which can be visualized using tensorboard
-```Shell
-./train_standard.sh
-```
-
-If you have a RTX GPU, training can be accelerated using mixed precision. You can expect similiar results in this setting (1 GPU)
-```Shell
-./train_mixed.sh
-```
-
-## (Optional) Efficent Implementation
-You can optionally use our alternate (efficent) implementation by compiling the provided cuda extension
-```Shell
-cd alt_cuda_corr && python setup.py install && cd ..
-```
-and running `demo.py` and `evaluate.py` with the `--alternate_corr` flag Note, this implementation is somewhat slower than all-pairs, but uses significantly less GPU memory during the forward pass.
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/__init__.py
deleted file mode 100644
index caaad2cc7b96947857fe3ba3de903be65644bde6..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/__init__.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import pkgutil
-
-import gradio.components as components
-import gradio.inputs as inputs
-import gradio.outputs as outputs
-import gradio.processing_utils
-import gradio.templates
-import gradio.themes as themes
-from gradio.blocks import Blocks
-from gradio.components import (
- HTML,
- JSON,
- AnnotatedImage,
- Annotatedimage,
- Audio,
- BarPlot,
- Button,
- Carousel,
- Chatbot,
- Checkbox,
- CheckboxGroup,
- Checkboxgroup,
- Code,
- ColorPicker,
- DataFrame,
- Dataframe,
- Dataset,
- Dropdown,
- File,
- Gallery,
- Highlight,
- HighlightedText,
- Highlightedtext,
- Image,
- Interpretation,
- Json,
- Label,
- LinePlot,
- Markdown,
- Model3D,
- Number,
- Plot,
- Radio,
- ScatterPlot,
- Slider,
- State,
- StatusTracker,
- Text,
- Textbox,
- TimeSeries,
- Timeseries,
- UploadButton,
- Variable,
- Video,
- component,
-)
-from gradio.deploy_space import deploy
-from gradio.events import SelectData
-from gradio.exceptions import Error
-from gradio.external import load
-from gradio.flagging import (
- CSVLogger,
- FlaggingCallback,
- HuggingFaceDatasetJSONSaver,
- HuggingFaceDatasetSaver,
- SimpleCSVLogger,
-)
-from gradio.helpers import EventData, Progress, make_waveform, skip, update
-from gradio.helpers import create_examples as Examples # noqa: N812
-from gradio.interface import Interface, TabbedInterface, close_all
-from gradio.ipython_ext import load_ipython_extension
-from gradio.layouts import Accordion, Box, Column, Group, Row, Tab, TabItem, Tabs
-from gradio.mix import Parallel, Series
-from gradio.routes import Request, mount_gradio_app
-from gradio.templates import (
- Files,
- ImageMask,
- ImagePaint,
- List,
- Matrix,
- Mic,
- Microphone,
- Numpy,
- Paint,
- Pil,
- PlayableVideo,
- Sketchpad,
- TextArea,
- Webcam,
-)
-from gradio.themes import Base as Theme
-
-current_pkg_version = (
- (pkgutil.get_data(__name__, "version.txt") or b"").decode("ascii").strip()
-)
-__version__ = current_pkg_version
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Blocks-005a10ea.css b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Blocks-005a10ea.css
deleted file mode 100644
index 1feac101230266e476fc5f389f286813260505b5..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Blocks-005a10ea.css
+++ /dev/null
@@ -1 +0,0 @@
-.wrap.svelte-1i3r921.svelte-1i3r921{padding:var(--size-6)}.attention.svelte-1i3r921.svelte-1i3r921{font-weight:var(--weight-bold);font-size:var(--text-lg)}.attention.svelte-1i3r921 code.svelte-1i3r921{border:none;background:none;color:var(--color-accent);font-weight:var(--weight-bold)}button.svelte-1i3r921.svelte-1i3r921{position:absolute;top:var(--size-5);right:var(--size-6);width:var(--size-4);color:var(--body-text-color)}button.svelte-1i3r921.svelte-1i3r921:hover{color:var(--color-accent)}@media (min-width: 768px){button.svelte-1i3r921.svelte-1i3r921{top:var(--size-6)}}h2.svelte-9i27qi.svelte-9i27qi{display:flex;color:var(--body-text-color);font-weight:var(--weight-semibold)}h2.svelte-9i27qi img.svelte-9i27qi{margin-right:var(--size-2);width:var(--size-4)}span.svelte-9i27qi.svelte-9i27qi{color:var(--color-accent)}button.svelte-9i27qi.svelte-9i27qi{position:absolute;top:var(--size-5);right:var(--size-6);width:var(--size-4);color:var(--body-text-color)}button.svelte-9i27qi.svelte-9i27qi:hover{color:var(--color-accent)}@media (min-width: 768px){button.svelte-9i27qi.svelte-9i27qi{top:var(--size-6)}h2.svelte-9i27qi 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diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/DropdownArrow-5fa4dd09.css b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/DropdownArrow-5fa4dd09.css
deleted file mode 100644
index c47d6f6f010f0626b0036068fe41d683b37b2954..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/DropdownArrow-5fa4dd09.css
+++ /dev/null
@@ -1 +0,0 @@
-.dropdown-arrow.svelte-p5edak{fill:var(--body-text-color);margin-right:var(--size-2);width:var(--size-5)}
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/tests/test_afm.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/tests/test_afm.py
deleted file mode 100644
index e5c6a83937cd68e3ae20a14d8babb9a99bd5a4f5..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/tests/test_afm.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from io import BytesIO
-import pytest
-import logging
-
-from matplotlib import _afm
-from matplotlib import font_manager as fm
-
-
-# See note in afm.py re: use of comma as decimal separator in the
-# UnderlineThickness field and re: use of non-ASCII characters in the Notice
-# field.
-AFM_TEST_DATA = b"""StartFontMetrics 2.0
-Comment Comments are ignored.
-Comment Creation Date:Mon Nov 13 12:34:11 GMT 2017
-FontName MyFont-Bold
-EncodingScheme FontSpecific
-FullName My Font Bold
-FamilyName Test Fonts
-Weight Bold
-ItalicAngle 0.0
-IsFixedPitch false
-UnderlinePosition -100
-UnderlineThickness 56,789
-Version 001.000
-Notice Copyright \xa9 2017 No one.
-FontBBox 0 -321 1234 369
-StartCharMetrics 3
-C 0 ; WX 250 ; N space ; B 0 0 0 0 ;
-C 42 ; WX 1141 ; N foo ; B 40 60 800 360 ;
-C 99 ; WX 583 ; N bar ; B 40 -10 543 210 ;
-EndCharMetrics
-EndFontMetrics
-"""
-
-
-def test_nonascii_str():
- # This tests that we also decode bytes as utf-8 properly.
- # Else, font files with non ascii characters fail to load.
- inp_str = "привет"
- byte_str = inp_str.encode("utf8")
-
- ret = _afm._to_str(byte_str)
- assert ret == inp_str
-
-
-def test_parse_header():
- fh = BytesIO(AFM_TEST_DATA)
- header = _afm._parse_header(fh)
- assert header == {
- b'StartFontMetrics': 2.0,
- b'FontName': 'MyFont-Bold',
- b'EncodingScheme': 'FontSpecific',
- b'FullName': 'My Font Bold',
- b'FamilyName': 'Test Fonts',
- b'Weight': 'Bold',
- b'ItalicAngle': 0.0,
- b'IsFixedPitch': False,
- b'UnderlinePosition': -100,
- b'UnderlineThickness': 56.789,
- b'Version': '001.000',
- b'Notice': b'Copyright \xa9 2017 No one.',
- b'FontBBox': [0, -321, 1234, 369],
- b'StartCharMetrics': 3,
- }
-
-
-def test_parse_char_metrics():
- fh = BytesIO(AFM_TEST_DATA)
- _afm._parse_header(fh) # position
- metrics = _afm._parse_char_metrics(fh)
- assert metrics == (
- {0: (250.0, 'space', [0, 0, 0, 0]),
- 42: (1141.0, 'foo', [40, 60, 800, 360]),
- 99: (583.0, 'bar', [40, -10, 543, 210]),
- },
- {'space': (250.0, 'space', [0, 0, 0, 0]),
- 'foo': (1141.0, 'foo', [40, 60, 800, 360]),
- 'bar': (583.0, 'bar', [40, -10, 543, 210]),
- })
-
-
-def test_get_familyname_guessed():
- fh = BytesIO(AFM_TEST_DATA)
- font = _afm.AFM(fh)
- del font._header[b'FamilyName'] # remove FamilyName, so we have to guess
- assert font.get_familyname() == 'My Font'
-
-
-def test_font_manager_weight_normalization():
- font = _afm.AFM(BytesIO(
- AFM_TEST_DATA.replace(b"Weight Bold\n", b"Weight Custom\n")))
- assert fm.afmFontProperty("", font).weight == "normal"
-
-
-@pytest.mark.parametrize(
- "afm_data",
- [
- b"""nope
-really nope""",
- b"""StartFontMetrics 2.0
-Comment Comments are ignored.
-Comment Creation Date:Mon Nov 13 12:34:11 GMT 2017
-FontName MyFont-Bold
-EncodingScheme FontSpecific""",
- ],
-)
-def test_bad_afm(afm_data):
- fh = BytesIO(afm_data)
- with pytest.raises(RuntimeError):
- _afm._parse_header(fh)
-
-
-@pytest.mark.parametrize(
- "afm_data",
- [
- b"""StartFontMetrics 2.0
-Comment Comments are ignored.
-Comment Creation Date:Mon Nov 13 12:34:11 GMT 2017
-Aardvark bob
-FontName MyFont-Bold
-EncodingScheme FontSpecific
-StartCharMetrics 3""",
- b"""StartFontMetrics 2.0
-Comment Comments are ignored.
-Comment Creation Date:Mon Nov 13 12:34:11 GMT 2017
-ItalicAngle zero degrees
-FontName MyFont-Bold
-EncodingScheme FontSpecific
-StartCharMetrics 3""",
- ],
-)
-def test_malformed_header(afm_data, caplog):
- fh = BytesIO(afm_data)
- with caplog.at_level(logging.ERROR):
- _afm._parse_header(fh)
-
- assert len(caplog.records) == 1
diff --git a/spaces/laocao1798/laocaoAI/Dockerfile b/spaces/laocao1798/laocaoAI/Dockerfile
deleted file mode 100644
index 3698c7cb7938e025afc53b18a571ae2961fbdffe..0000000000000000000000000000000000000000
--- a/spaces/laocao1798/laocaoAI/Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="kJs8hD92ncMzLaoQWYtX5rG6bE3fZ4iO"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/lc202301/ChuanhuChatGPT/overwrites.py b/spaces/lc202301/ChuanhuChatGPT/overwrites.py
deleted file mode 100644
index a87499a81bb3c23bf34c1faadcc02085567cd447..0000000000000000000000000000000000000000
--- a/spaces/lc202301/ChuanhuChatGPT/overwrites.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from __future__ import annotations
-import logging
-
-from llama_index import Prompt
-from typing import List, Tuple
-import mdtex2html
-
-from presets import *
-from llama_func import *
-
-
-def compact_text_chunks(self, prompt: Prompt, text_chunks: List[str]) -> List[str]:
- logging.debug("Compacting text chunks...🚀🚀🚀")
- combined_str = [c.strip() for c in text_chunks if c.strip()]
- combined_str = [f"[{index+1}] {c}" for index, c in enumerate(combined_str)]
- combined_str = "\n\n".join(combined_str)
- # resplit based on self.max_chunk_overlap
- text_splitter = self.get_text_splitter_given_prompt(prompt, 1, padding=1)
- return text_splitter.split_text(combined_str)
-
-
-def postprocess(
- self, y: List[Tuple[str | None, str | None]]
-) -> List[Tuple[str | None, str | None]]:
- """
- Parameters:
- y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.
- Returns:
- List of tuples representing the message and response. Each message and response will be a string of HTML.
- """
- if y is None or y == []:
- return []
- tag_regex = re.compile(r"^<\w+>[^<]+")
- if tag_regex.search(y[-1][1]):
- y[-1] = (convert_user(y[-1][0]), y[-1][1])
- else:
- y[-1] = (convert_user(y[-1][0]), convert_mdtext(y[-1][1]))
- return y
-
-with open("./assets/custom.js", "r", encoding="utf-8") as f, open("./assets/Kelpy-Codos.js", "r", encoding="utf-8") as f2:
- customJS = f.read()
- kelpyCodos = f2.read()
-
-def reload_javascript():
- print("Reloading javascript...")
- js = f''
- def template_response(*args, **kwargs):
- res = GradioTemplateResponseOriginal(*args, **kwargs)
- res.body = res.body.replace(b'