Update parquet files (step 11 of 397)

spaces/101-5/gpt4free/g4f/Provider/Providers/Aichat.py

@@ -1,44 +0,0 @@
-import os, requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://chat-gpt.org/chat'
-model = ['gpt-3.5-turbo']
-supports_stream = False
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    base = ''
-    for message in messages:
-        base += '%s: %s\n' % (message['role'], message['content'])
-    base += 'assistant:'
-    
-    
-    headers = {
-        'authority': 'chat-gpt.org',
-        'accept': '*/*',
-        'cache-control': 'no-cache',
-        'content-type': 'application/json',
-        'origin': 'https://chat-gpt.org',
-        'pragma': 'no-cache',
-        'referer': 'https://chat-gpt.org/chat',
-        'sec-ch-ua-mobile': '?0',
-        'sec-ch-ua-platform': '"macOS"',
-        'sec-fetch-dest': 'empty',
-        'sec-fetch-mode': 'cors',
-        'sec-fetch-site': 'same-origin',
-        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36',
-    }
-
-    json_data = {
-        'message':base,
-        'temperature': 1,
-        'presence_penalty': 0,
-        'top_p': 1,
-        'frequency_penalty': 0
-    }
-    
-    response = requests.post('https://chat-gpt.org/api/text', headers=headers, json=json_data)
-    yield response.json()['message']
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/1gistliPinn/ChatGPT4/Examples/Anwar Shah Kashmiri Books Urdu P.md

@@ -1,19 +0,0 @@
-<br />
-<h1>Anwar Shah Kashmiri: A Renowned Scholar and Jurist of Kashmir</h1>
-<p>Anwar Shah Kashmiri (1875-1933) was a prominent Muslim scholar and jurist who belonged to Kashmir, a region disputed between India and Pakistan. He was known for his mastery of various Islamic sciences, such as Hadith, Fiqh, Tafsir, and Kalam. He wrote many books and commentaries on these subjects, some of which are considered authoritative and influential in the Islamic world.</p>
-<p>Anwar Shah Kashmiri was born in a Sayyid family that traced its lineage to Imam Husayn, the grandson of Prophet Muhammad. He received his early education from his father and other local scholars in Kashmir. He then traveled to India and studied at various madrasas, including Darul Uloom Deoband, where he became a disciple of Mahmud al-Hasan, a leading figure of the Deobandi movement. He also studied under other eminent scholars, such as Rashid Ahmad Gangohi, Muhammad Qasim Nanautawi, and Ashraf Ali Thanwi.</p>
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-<p>Anwar Shah Kashmiri served as the first principal of Madrasa Aminia in Delhi, where he taught Hadith and Fiqh. He also served as the fourth principal of Darul Uloom Deoband, where he taught Tafsir and Kalam. He was respected and admired by his students and colleagues for his vast knowledge, eloquence, piety, and humility. He also participated in the Khilafat Movement, a political campaign to restore the Ottoman Caliphate after World War I.</p>
-<p>Anwar Shah Kashmiri authored more than 100 books and treatises on various Islamic topics. Some of his most famous works are:</p>
-<ul>
-<li>Al-Arf al-Shadhi: A commentary on Sunan al-Tirmidhi, one of the six major collections of Hadith.</li>
-<li>Fayd al-Bari: A commentary on Sahih al-Bukhari, the most authentic collection of Hadith.</li>
-<li>Tafsir al-Quran al-Azim: A commentary on the Quran that combines rational and traditional approaches.</li>
-<li>Al-Urf al-Shadhi: A commentary on Al-Hidayah, a classical manual of Hanafi Fiqh.</li>
-<li>Anwar al-Kalam: A refutation of the arguments of the Mu'tazila, a rationalist school of Islamic theology.</li>
-</ul>
-<p>Anwar Shah Kashmiri died in Deoband at the age of 58. He was buried in the graveyard of Darul Uloom Deoband. His legacy lives on through his books and his students, who include some of the most prominent scholars of the 20th century, such as Muhammad Yusuf Banuri, Muhammad Zakariyya Kandhlawi, Husain Ahmad Madani, and Shabbir Ahmad Usmani.</p><p>Anwar Shah Kashmiri was not only a scholar and a jurist, but also a poet and a mystic. He composed many poems in Arabic, Persian, and Urdu, expressing his love for Allah and His Messenger. He also wrote some poems in praise of his teachers and his homeland. He was influenced by the Sufi teachings of Imam al-Ghazali, Ibn al-Arabi, and Abdul Qadir Jilani. He practiced various forms of dhikr (remembrance of Allah) and tasawwuf (spiritual purification).</p>
-<p>Anwar Shah Kashmiri was also a reformer and a revivalist. He advocated for the revival of the Islamic sciences and the preservation of the Islamic heritage. He opposed the innovations and deviations that had crept into the Muslim community over time. He also defended the Sunni creed and the Hanafi school of law from the attacks of the Shia, the Ahl al-Hadith, and the Salafi movements. He was a staunch supporter of the Ahl al-Sunnah wa al-Jama'ah (the people of the Sunnah and the consensus).</p>
-<p>Anwar Shah Kashmiri was a man of great vision and wisdom. He foresaw the challenges and opportunities that the Muslim world would face in the modern era. He urged the Muslims to unite under the banner of Islam and to cooperate with each other for the common good. He also encouraged them to seek knowledge from all sources and to benefit from the advancements of science and technology. He believed that Islam was compatible with reason and progress, and that it was the only solution for the problems of humanity.</p> d5da3c52bf<br />
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spaces/1gistliPinn/ChatGPT4/Examples/Cstpatcher11 Exe.md

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spaces/1gistliPinn/ChatGPT4/Examples/Francine Dee Pornstar Book.md

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spaces/1phancelerku/anime-remove-background/Baby Cat Breeds Which One is Right for You?..md

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-
-<h1>Baby Cats: Everything You Need to Know About These Cute Furry Friends</h1>
- <p>Have you ever wondered what makes baby cats so adorable? Or how to take care of them properly? Or what breeds of baby cats are best for your family? If you answered yes to any of these questions, then this article is for you. In this article, we will explore the fascinating world of baby cats, also known as kittens, and share some facts, tips, and stories that will make you fall in love with them even more.</p>
- <h2>Facts about Baby Cats</h2>
- <p>Baby cats are not just miniature versions of adult cats. They have their own unique characteristics, behaviors, and needs that make them special. Here are some facts that you may not know about baby cats.</p>
-<h2>baby cat</h2><br /><p><b><b>Download File</b> &raquo; <a href="https://jinyurl.com/2uNUdK">https://jinyurl.com/2uNUdK</a></b></p><br /><br />
- <h3>Development Stages of Baby Cats</h3>
- <p>Baby cats go through different development stages from birth to adulthood. According to Wikipedia, these stages are:</p>
- <ul>
-<li>Newborn stage (0 to 2 weeks): Baby cats are born with their eyes and ears closed, and they depend on their mother for survival. They cannot regulate their body temperature, walk, or meow well. They only drink their mother's milk and need to be stimulated by her to urinate or defecate.</li>
-<li>Transition stage (2 to 4 weeks): Baby cats start to open their eyes and ears, and they begin to explore their surroundings. They develop their sense of smell and taste, and they start to eat solid food. They also learn to groom themselves and others, and they play with their littermates.</li>
-<li>Socialization stage (4 to 8 weeks): Baby cats become more active and curious, and they interact with people and other animals. They learn to use the litter box, and they develop their hunting and stalking skills. They also form bonds with their mother and siblings, as well as their human caregivers.</li>
-<li>Juvenile stage (8 to 26 weeks): Baby cats grow rapidly and reach sexual maturity. They become more independent and adventurous, but they still need guidance and supervision. They also develop their personality and preferences, and they may show signs of territoriality or aggression.</li>
-<li>Adult stage (26 weeks onwards): Baby cats reach their full size and weight, and they establish their social status and territory. They may become less playful and more settled, but they still need attention and stimulation. They also need regular health check-ups and vaccinations.</li>
-</ul>
- <h3>Unusual Stories of Baby Cats</h3>
- <p>Baby cats are not only cute but also amazing. They can sometimes surprise us with their extraordinary abilities or experiences. Here are some unusual stories of baby cats that will make you smile or wonder.</p>
- <ul>
-<li>A kitten in Bali was adopted by a monkey! According to A-Z Animals, a wild long-tailed macaque found a tiny kitten abandoned in the forest and took care of it as his own. The monkey cuddled, carried, and protected the kitten, and introduced it to his family. The kitten seemed happy and healthy in his new home.</li>
-<li>A litter of kittens can have multiple fathers! According to WebMD, female cats can ovulate multiple times during a heat cycle, which means that they can mate with different males and produce offspring with different genetic fathers. This phenomenon is called superfecundity, and it can result in kittens with different colors or patterns.</li>
-<li>A kitten was born with two faces! According to Yahoo News, a rare kitten named Biscuits and Gravy was born with a condition called diprosopus, which means <p>that he had two faces, each with a mouth, nose, and eye. The kitten was born in Oregon, USA, and was named after a famous breakfast dish. The kitten's owner said that he ate well and was very affectionate. Sadly, the kitten passed away after four days due to health complications.</p>
- <h3>Differences between Baby Cats and Adult Cats</h3>
- <p>Baby cats and adult cats have some obvious differences, such as size, weight, and appearance. But they also have some less noticeable differences, such as metabolism, immunity, and behavior. Here are some of the main differences between baby cats and adult cats:</p>
- <table>
-<tr>
-<th>Baby Cats</th>
-<th>Adult Cats</th>
-</tr>
-<tr>
-<td>Have a higher metabolism and need more calories per pound of body weight</td>
-<td>Have a lower metabolism and need fewer calories per pound of body weight</td>
-</tr>
-<tr>
-<td>Have a weaker immune system and are more susceptible to infections and diseases</td>
-<td>Have a stronger immune system and are more resistant to infections and diseases</td>
-</tr>
-<tr>
-<td>Have softer, finer fur that may change color or texture as they grow older</td>
-<td>Have coarser, thicker fur that usually stays the same color and texture throughout their lives</td>
-</tr>
-<tr>
-<td>Have blue eyes that may change color as they mature</td>
-<td>Have various eye colors that are usually fixed by the time they are six months old</td>
-</tr>
-<tr>
-<td>Have more teeth (26) that are smaller and sharper than adult teeth</td>
-<td>Have fewer teeth (30) that are larger and duller than baby teeth</td>
-</tr>
-<tr>
-<td>Are more curious, playful, and energetic, and need more stimulation and socialization</td>
-<td>Are more calm, relaxed, and independent, and need less stimulation and socialization</td>
-</tr></table>
- <h2>Care Tips for Baby Cats</h2>
- <p>Baby cats require special care and attention to ensure their health and happiness. They depend on their mother or human caregiver for their basic needs, such as food, warmth, safety, and hygiene. Here are some care tips for baby cats that will help you provide the best possible environment for your furry friend.</p>
- <h3>Feeding and Grooming Baby Cats</h3>
- <p>Baby cats need proper nutrition to support their growth and development. If the mother cat is present, she will nurse her kittens until they are ready to wean at around four to six weeks of age. If the mother cat is absent or unable to nurse, you will have to bottle-feed the kittens with a special formula designed for kittens. You can purchase kitten milk replacement formula (KMR) at your local pet store or vet's office. Never feed a kitten cow's milk or other types of milk, as they can cause diarrhea, dehydration, and nutritional deficiencies. Follow the instructions on the package for how much and how often to feed the kittens. You may also need to stimulate the kittens' urination and defecation by gently rubbing their genital area with a warm, damp cloth after each feeding. As the kittens grow older, you can introduce them to solid food by offering them wet or dry kitten food mixed with some water or formula. Gradually reduce the amount of liquid until the kittens are eating solid food only by eight weeks of age.</p>
- <p>Baby cats also need regular grooming to keep their coat clean and healthy. If the mother cat is present, she will lick her kittens to groom them and remove any dirt or debris. If the mother cat is absent or unable to groom, you will have to do it yourself by using a soft brush or comb to gently remove any loose hair or mats. You can also use a damp cloth or cotton ball to wipe the kittens' eyes, ears, nose, and mouth if they are dirty or crusty. Be careful not to use any harsh chemicals or products that could irritate the kittens' skin or eyes. You can also trim the kittens' nails with a pair of nail clippers designed for cats if they are too long or sharp. Be careful not to cut too close to the quick (the pink part of the nail), as this could cause bleeding and pain.</p>
- <h3>Keeping Baby Cats Warm and Safe</h3>
- <p>Baby cats cannot regulate their body temperature well until they are about four weeks old. They rely on their mother or external sources of heat to keep them warm. If the mother cat is present, she will cuddle with her kittens in a cozy nest made of blankets or towels. If the mother cat is absent or unable to provide warmth, you will have to create a comfortable bed for the kittens in a draft-free corner of your home. You can use a cardboard box lined with soft materials, such as blankets, towels, or fleece. You can also add a heating pad, a hot water bottle, or a rice sock to provide extra warmth. Make sure to cover the heating device with a cloth and leave some space for the kittens to move away if they get too hot. Check the temperature of the bed regularly and adjust it as needed. The ideal temperature for newborn kittens is around 90°F (32°C), and it can be gradually lowered to 80°F (27°C) by the time they are four weeks old.</p>
-<p>Baby cat synonyms<br />
-Kitten pictures and facts<br />
-How to care for a newborn kitten<br />
-Best kitten food and toys<br />
-Baby cat breeds and characteristics<br />
-Kitten adoption and rescue<br />
-How to train a kitten to use the litter box<br />
-Baby cat names and meanings<br />
-Kitten health and vaccination<br />
-Baby cat videos and memes<br />
-How to introduce a kitten to other pets<br />
-Kitten behavior and development<br />
-Baby cat costumes and accessories<br />
-Kitten grooming and nail trimming<br />
-Baby cat sounds and communication<br />
-Kitten socialization and play<br />
-Baby cat allergies and remedies<br />
-Kitten growth and weight chart<br />
-Baby cat games and apps<br />
-Kitten nutrition and feeding schedule<br />
-How to choose a kitten from a litter<br />
-Baby cat wallpapers and backgrounds<br />
-Kitten anatomy and physiology<br />
-Baby cat crafts and DIY projects<br />
-Kitten dental care and teething<br />
-How to make a kitten feel comfortable at home<br />
-Baby cat quotes and sayings<br />
-Kitten eye color and vision<br />
-Baby cat coloring pages and activities<br />
-Kitten ear care and cleaning<br />
-How to travel with a kitten safely<br />
-Baby cat calendar and planner<br />
-Kitten genetics and coat patterns<br />
-Baby cat jokes and puns<br />
-Kitten enrichment and stimulation<br />
-How to bond with a kitten emotionally<br />
-Baby cat gifts and merchandise<br />
-Kitten flea treatment and prevention<br />
-Baby cat art and photography<br />
-Kitten fur types and textures<br />
-How to deal with a kitten's separation anxiety<br />
-Baby cat poetry and songs<br />
-Kitten personality types and traits<br />
-Baby cat history and folklore<br />
-Kitten skin care and grooming products</p>
- <p>Baby cats also need a safe and secure environment to prevent them from getting injured or lost. If the mother cat is present, she will protect her kittens from any potential threats or dangers. If the mother cat is absent or unable to provide safety, you will have to keep the kittens in a confined area, such as a room, a crate, or a pen. Make sure that the area is clean, quiet, and free of any hazards, such as wires, cords, sharp objects, toxic substances, or other pets. You can also provide some toys and scratching posts for the kittens to play with and exercise their claws. Monitor the kittens closely and do not let them roam around the house unsupervised until they are old enough and fully vaccinated.</p>
- <h3>Teaching Baby Cats to Use the Litter Box</h3>
- <p>Baby cats need to learn how to use the litter box properly to avoid making a mess in your home. If the mother cat is present, she will teach her kittens how to use the litter box by example. If the mother cat is absent or unable to train, you will have to do it yourself by following these steps:</p>
- <ol>
-<li>Choose a suitable litter box and litter for your kittens. The litter box should be large enough for the kittens to fit comfortably, but low enough for them to enter and exit easily. The litter should be unscented and clumping, as some kittens may try to eat scented or non-clumping litter.</li>
-<li>Place the litter box in a convenient and accessible location for your kittens. The location should be quiet, private, and away from their food and water bowls. You may need to place multiple litter boxes in different areas of your home if you have more than one kitten or a large space.</li>
-<li>Fill the litter box with about two inches of litter and scoop it daily. You can also sprinkle some baking soda or odor-neutralizing powder on the bottom of the litter box to reduce any unpleasant smells.</li>
-<li>Show your kittens where the litter box is and how to use it. You can do this by gently placing them in the litter box after they wake up, eat, or play, and praising them when they use it correctly. You can also scratch the litter with your finger or a toy to encourage them to dig and cover their waste.</li>
-<li>Avoid scolding or punishing your kittens if they have accidents outside the litter box. This may only make them fearful or confused. Instead, clean up the mess with an enzyme-based cleaner that eliminates any traces of odor, and redirect your kittens to the litter box.</li>
-</ol>
- <h2>Breeds of Baby Cats</h2>
- <p>Baby cats come in different shapes, sizes, colors, and personalities. Some breeds of baby cats are more popular than others because of their distinctive features or traits. Here are some of the most common breeds of baby cats that you may encounter or consider adopting.</p>
- <h3>Small Cat Breeds</h3>
- <p>Some breeds of baby cats are naturally small even when they grow up. These breeds are ideal for people who live in small spaces or prefer petite pets. Some examples of small cat breeds are:</p>
- <ul>
-<li>Singapura: This breed is considered the smallest domestic cat breed in the world, weighing only four to eight pounds on average. They have large ears, almond-shaped eyes, and short coats that come in one color: sepia agouti (brown ticked tabby). They are also very active, curious, and affectionate.</li>
-<li>Cornish Rex: This breed is known for its curly coat that feels like velvet. They have slender bodies, long legs, large ears, and oval-shaped eyes. They come in various colors and patterns, such as black, white, red, blue, cream, <p>Fluffy Cat Breeds</p>
- <p>If you love fluffy cats, you are not alone. Many people adore cats with long, soft, and fluffy fur that make them look like plush toys. Fluffy cats can be great cuddlers and companions, as well as beautiful to look at. However, they also require more grooming and care than short-haired cats, so you need to be prepared for that. Here are some of the most popular fluffy cat breeds that you may want to consider.</p>
- <h3>Somali Cat</h3>
- <p>The Somali cat is a long-haired version of the Abyssinian cat. They have the same ticked coat pattern, but with longer and silkier fur. They also have plumed tails, tufted ears, and ruffs around their necks. They come in various colors, such as ruddy, red, blue, and fawn. They are very active, playful, and intelligent cats that love to explore and interact with people. They also have a distinctive voice that they use to communicate their needs and feelings.</p>
- <h3>Birman Cat</h3>
- <p>The Birman cat is a sacred cat of Burma, where they were believed to be the companions of priests and temple guardians. They have semi-long fur that is silky and does not mat easily. They also have striking blue eyes and white \"gloves\" on their paws. They come in various colors, such as seal, blue, chocolate, lilac, red, cream, and tortie. They are very gentle, affectionate, and loyal cats that enjoy being with their human family. They are also very quiet and calm cats that do not demand much attention.</p>
- <h3>Siberian Cat</h3>
- <p>The Siberian cat is a natural breed from Russia, where they have adapted to the harsh climate and terrain. They have thick, water-repellent coats that protect them from the cold and snow. They also have large paws that act like snowshoes and help them balance on trees. They come in various colors and patterns, such as solid, tabby, tortie, smoke, and silver. They are very strong, agile, and athletic cats that love to climb and jump. They are also very friendly, sociable, and playful cats that get along well with children and other pets.</p>
- <h3>Norwegian Forest Cat</h3>
- <p>The Norwegian Forest cat is another natural breed from Scandinavia, where they have also developed thick coats to survive the cold weather. They have long guard hairs that cover a dense undercoat, as well as bushy tails and ruffs around their necks. They come in various colors and patterns, such as black, white, red, blue, cream, silver, tabby, <p>Kid-Friendly Cat Breeds</p>
- <p>If you have children or plan to have them in the future, you may want to choose a cat breed that is known for being kid-friendly. These breeds are typically gentle, patient, tolerant, and playful with kids of all ages. They also enjoy being part of a family and can adapt to different lifestyles and environments. Here are some of the best cat breeds for kids that you may want to consider.</p>
- <h3>Birman Cat</h3>
- <p>We already mentioned the birman cat as one of the best fluffy cat breeds, but it is also one of the best cat breeds for kids. The birman cat is very gentle, affectionate, and loyal to its human family. It loves to cuddle and be petted, but it is not demanding or clingy. It is also very smart and curious, and can learn tricks and games easily. The birman cat gets along well with other pets and strangers, and can handle loud noises and changes in routine. It is also very beautiful, with its long silky coat, blue eyes, and white gloves.</p>
- <h3>Ragdoll Cat</h3>
- <p>The ragdoll cat is another fluffy breed that is great for kids. The ragdoll cat is named for its habit of going limp when picked up, like a ragdoll. It is very relaxed, laid-back, and easygoing, and does not mind being carried around or dressed up by kids. It is also very affectionate, friendly, and sociable, and loves to be with its human family. It is not very vocal or active, but it enjoys playing with toys and following its people around the house. The ragdoll cat has a semi-long coat that does not shed much or mat easily, and comes in various colors and patterns.</p>
- <h3>Himalayan Cat</h3>
- <p>The Himalayan cat is a cross between a Persian cat and a Siamese cat. It has the long fluffy coat and flat face of a Persian, and the pointed coloration and blue eyes of a Siamese. It is a medium-sized cat that weighs about 10 pounds on average. The Himalayan cat is very sweet, gentle, and affectionate, and loves to be pampered and petted by its human family. It is also very quiet, calm, and docile, and does not mind being left alone for short periods of time. The Himalayan cat needs regular grooming to keep its coat healthy and prevent mats and tangles.</p>
- <h3>Maine Coon Cat</h3>
- <p>The Maine coon cat is one of the largest domestic cat breeds in the world, weighing up to 20 pounds or more. It has a thick long coat that protects it from the cold weather of its native Maine, as well as large paws, ears, and tail. It comes in various colors and patterns, such as solid, tabby,  tortie, smoke, or silver. The Maine coon cat is very friendly, playful, and intelligent, and loves to interact with its human family. It is also very adaptable and can live in different climates and environments. The Maine coon cat needs regular brushing to keep its coat shiny and smooth.</p>
- <h3>Abyssinian Cat</h3>
- <p>The Abyssinian cat is a small but athletic cat that weighs about 10 pounds on average. It has a short ticked coat that comes in various colors, such as ruddy, red, blue, or cinnamon. It has large ears, almond-shaped eyes, and a slender body. The Abyssinian cat is very active,  curious, and outgoing, and loves to explore and play with its human family. It is also very smart and can learn tricks and games easily. The Abyssinian cat needs a lot of stimulation and attention to keep it happy and healthy.</p> <h2>Conclusion</h2>
- <p>Baby cats are wonderful creatures that can bring joy and happiness to your life. They are adorable, fascinating, and diverse, and they deserve the best care and love possible. Whether you are looking for a small, fluffy, or kid-friendly cat breed, you can find the perfect match for your family and lifestyle. If you are ready to adopt a baby cat, you can visit your local shelter or rescue group and give a home to a furry friend in need. You will not regret it!</p>
- <h2>FAQs</h2>
- <p>Here are some of the most frequently asked questions and answers about baby cats that you may find helpful.</p>
- <h3>How long do baby cats stay with their mother?</h3>
- <p>Baby cats usually stay with their mother until they are about eight to twelve weeks old. This is the ideal time for them to learn social and survival skills from their mother and siblings, as well as to be fully weaned and vaccinated. However, some circumstances may require separating the kittens from their mother earlier or later than this period. For example, if the mother cat is sick or injured, or if the kittens are orphaned or in danger, they may need to be taken care of by a human caregiver as soon as possible. On the other hand, if the mother cat and kittens are in a safe and comfortable environment, they may stay together longer than twelve weeks until they find suitable homes.</p>
- <h3>How often do baby cats sleep?</h3>
- <p>Baby cats sleep a lot more than adult cats. They can sleep up to 20 hours a day, depending on their age and activity level. Newborn kittens sleep almost all the time, waking up only to feed and eliminate. As they grow older, they become more awake and playful, but they still need plenty of rest to support their growth and development. Sleeping is also a way for kittens to bond with their mother and littermates, as well as to feel safe and secure.</p>
- <h3>How can I tell the gender of a baby cat?</h3>
- <p>Telling the gender of a baby cat can be tricky, especially when they are very young. The easiest way to tell the difference is by looking at the distance between the anus and the genital opening. Male kittens have a greater distance between these two openings than female kittens, and they also have a small bump that will become the scrotum as they mature. Female kittens have a smaller distance between these two openings than male kittens, and they also have a slit-like opening that will become the vulva as they mature. You can also look at the color of the kitten's coat, as some colors are more common in one gender than the other. For example, tortoiseshell and calico kittens are almost always female, while orange tabby kittens are more likely to be male.</p>
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spaces/1phancelerku/anime-remove-background/Bloons TD 6 APK 36.3 el juego de torres de defensa ms divertido y adictivo.md

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-<h1>Bloons TD 6 APK Ultima Version: A Guide for Android Users</h1>
- <p>If you are a fan of tower defense games, you might have heard of Bloons TD 6, a popular game developed by Ninja Kiwi. Bloons TD 6 is a game where you have to craft your perfect defense from a combination of powerful Monkey Towers and awesome Heroes, then pop every last invading Bloon. It is a game that offers endless hours of strategy gaming with regular updates, boss events, odysseys, quests, trophy store, content browser, and more.</p>
- <p>But what if you want to play Bloons TD 6 on your Android device without spending any money? Well, there is a way to do that. You can download and install Bloons TD 6 APK ultima version, which is a modified version of the original game that allows you to enjoy all the features and content for free. In this article, we will show you how to do that and why you should choose Bloons TD 6 APK ultima version over the official version. We will also give you some tips and tricks for playing Bloons TD 6 on your Android device.</p>
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- <h2>What is Bloons TD 6?</h2>
- <h3>A smash hit tower defense game</h3>
- <p>Bloons TD 6 is the latest installment in the Bloons Tower Defense series, which has been around for over a decade. It is a game that challenges you to stop the invasion of colorful balloons (called Bloons) by placing various types of Monkey Towers along their path. Each Monkey Tower has its own unique abilities and upgrades that can help you pop the Bloons more effectively. You can also use Heroes, which are powerful characters that have special skills and can level up during the game.</p>
- <p>Bloons TD 6 has several game modes and difficulty levels that can suit different preferences and skill levels. You can play solo or with up to three other players in co-op mode. You can also create your own challenges and odysseys using the content browser and share them with other players online.</p>
- <h3>Features and content of Bloons TD 6</h3>
- <p>Bloons TD 6 is a game that offers a lot of features and content that make it fun and engaging. Some of the features and content are:</p>
- <ul>
-<li>23 powerful Monkey Towers, each with 3 upgrade paths and unique activated abilities.</li>
-<li>Paragons! Explore the incredible power of the newest Paragon upgrades.</li>
-<li>14 diverse Heroes, with 20 signature upgrades and 2 special abilities. Plus, unlockable skins and voiceovers!</li>
-<li>Regular updates! Ninja Kiwi releases several updates every year with new characters, features, and gameplay.</li>
-<li>Boss Events! Fearsome Boss Bloons will challenge even the strongest defenses.</li>
-<li>Odysseys! Battle through a series of maps connected by their theme, rules, and rewards.</li>
-<li>Contested Territory! Join forces with other players and battle for territory against five other teams. Capture tiles on a shared map and compete on the leaderboards.</li>
-<li>Quests! Delve into what makes the Monkeys tick with Quests, crafted to tell tales and share knowledge.</li>
-<li>Trophy Store! Earn Trophies to unlock dozens of cosmetic items that let <p>you customize your Monkeys, Bloons, and the world around you.</li>
-<li>Content Browser! Create your own challenges and odysseys using the in-game editor and share them with other players online.</li>
-<li>100+ original maps, each with their own unique shape, size, and theme.</li>
-<li>10 special types of Bloons, each with their own abilities and resistances.</li>
-<li>Colorblind mode, cloud save, offline play, and more accessibility options.</li>
-</ul>
- <h2>How to download and install Bloons TD 6 APK ultima version?</h2>
- <h3>Requirements and compatibility</h3>
- <p>To download and install Bloons TD 6 APK ultima version, you need to have an Android device that meets the following requirements:</p>
- <ul>
-<li>Android version 5.0 or higher</li>
-<li>At least 2 GB of RAM</li>
-<li>At least 1 GB of free storage space</li>
-<li>A stable internet connection</li>
-</ul>
- <p>Bloons TD 6 APK ultima version is compatible with most Android devices, including smartphones, tablets, and emulators. However, some devices may not support the game or may experience performance issues. If you encounter any problems, you can contact Ninja Kiwi support for assistance.</p>
- <h3>Steps to download and install</h3>
- <p>To download and install Bloons TD 6 APK ultima version, you need to follow these steps:</p>
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- <ol>
-<li>Go to a trusted website that offers Bloons TD 6 APK ultima version for download. For example, you can use this link: [text].</li>
-<li>Click on the download button and wait for the APK file to be downloaded to your device.</li>
-<li>Once the download is complete, locate the APK file in your device's file manager and tap on it to start the installation process.</li>
-<li>If you see a warning message that says "Install blocked", go to your device's settings and enable the option to install apps from unknown sources.</li>
-<li>Follow the on-screen instructions to complete the installation process.</li>
-<li>Launch the game and enjoy playing Bloons TD 6 APK ultima version for free!</li>
-</ol>
- <h2>Why choose Bloons TD 6 APK ultima version?</h2>
- <h3>Benefits of using APK files</h3>
- <p>An APK file is an Android application package file that contains all the files and data needed to run an app on an Android device. By using APK files, you can enjoy some benefits that are not available in the official version of the app. Some of these benefits are:</p>
- <ul>
-<li>You can access apps that are not available in your region or country.</li>
-<li>You can get apps that are not compatible with your device or operating system.</li>
-<li>You can get apps that are no longer supported or updated by the developers.</li>
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-<li>You can get apps that are free of charge or have no in-app purchases or ads.</li>
-</ul>
- <h3>Advantages of playing Bloons TD 6 on Android</h3>
- <p>Bloons TD 6 is a game that can be played on various platforms, including PC, iOS, and Android. However, playing Bloons TD 6 on Android has some advantages that make it more enjoyable and convenient. Some of these advantages are:</p>
- <ul>
-<li>You can play Bloons TD 6 anytime and anywhere with your Android device, as long as you have a battery and an internet connection.</li>
-<li>You can play Bloons TD 6 with touch controls that are intuitive and responsive, giving you more control over your Monkeys and Heroes.</li>
-<li>You can play Bloons TD 6 with other Android users in co-op mode or contested territory mode, as well as cross-platform players on PC and iOS.</li>
-<li>You can play Bloons TD 6 with high-quality graphics and sound effects that are optimized for your Android device's screen size and resolution.</li>
-<li>You can play Bloons TD 6 with cloud save functionality that allows you to sync your progress across multiple devices using your Ninja Kiwi account.</li>
-</ul>
- <h2>Tips and tricks for playing Bloons TD 6</h2>
- <h3>How to use Monkey Towers and Heroes effectively</h3>
- <p>Bloons TD 6 is a game that requires strategic thinking and planning to pop all the Bloons before they reach the end of the map. To do that, you need to use Monkey Towers and Heroes effectively. Here are some tips and tricks for doing so:</p>
- <ul>
-<li>Choose Monkey Towers that match the type of Bloons you are facing. For example, use Dart Mon keys to pop regular Bloons, use Bomb Shooters to pop Lead Bloons, use Ice Monkeys to slow down Bloons, and use Monkey Subs to detect Camo Bloons.</li>
-<li>Upgrade your Monkey Towers wisely. Each Monkey Tower has three upgrade paths that offer different benefits and trade-offs. You can only choose two paths per tower, so you need to decide which ones suit your strategy best. For example, you can upgrade the Dart Monkey to have a Crossbow that shoots faster and pierces more Bloons, or a Juggernaut that shoots giant spiked balls that can pop Lead and Frozen Bloons.</li>
-<li>Use your Heroes strategically. Heroes are powerful units that can make a big difference in your defense. Each Hero has a unique skill set and personality that can complement your Monkey Towers. For example, you can use Quincy, the Archer, to deal extra damage to MOAB-class Bloons, or use Obyn Greenfoot, the Forest Guardian, to buff nearby Magic Monkeys and summon Brambles and Wall of Trees.</li>
-<li>Place your Monkey Towers and Heroes in optimal locations. You need to consider the range, line of sight, and placement bonuses of your Monkey Towers and Heroes when placing them on the map. For example, you can place Sniper Monkeys on high ground to increase their range and visibility, or place Banana Farms near the entrance to collect more bananas.</li>
-</ul>
- <h3>How to earn Trophies and unlock cosmetic items</h3>
- <p>Bloons TD 6 is a game that rewards you for your achievements and progress. You can earn Trophies by completing various tasks and challenges in the game, such as popping a certain number of Bloons, winning a certain number of games, or reaching a certain level. You can then use Trophies to unlock cosmetic items in the Trophy Store, such as skins, decals, music tracks, profile icons, and more. Here are some tips and tricks for earning Trophies and unlocking cosmetic items:</p>
- <ul>
-<li>Play different game modes and difficulty levels. You can earn more Trophies by playing harder game modes and difficulty levels, such as Impoppable mode or CHIMPS mode. You can also earn more Trophies by playing different maps and challenges.</li>
-<li>Complete Quests and Boss Events. Quests are special missions that give you specific objectives and rewards. Boss Events are limited-time events that pit you against powerful Boss Bloons with unique abilities. You can earn Trophies by completing Quests and Boss Events.</li>
-<li>Participate in Contested Territory. Contested Territory is a competitive mode where you have to capture tiles on a shared map and compete with other players on the leaderboards. You can earn Trophies by capturing tiles and holding them for as long as possible.</li>
-<li>Create and share your own challenges and odysseys. You can use the Content Browser to create your own challenges and odysseys using the in-game editor. You can then share them with other players online and earn Trophies by getting likes and plays.</li>
-</ul>
- <h2>Conclusion and FAQs</h2>
- <p>Bloons TD 6 is a fun and addictive tower defense game that offers a lot of features and content for Android users. You can download and install Bloons TD 6 APK ultima version for free and enjoy all the benefits of using APK files. You can also use our tips and tricks to improve your gameplay and earn more Trophies.</p>
- <p>If you have any questions about Bloons TD 6 APK ultima version or the game itself, you can check out these FAQs:</p>
- <h4>Q: Is Bloons TD 6 APK ultima version safe to use?</h4>
- <p>A: Yes, Bloons TD 6 APK ultima version is safe to use as long as you download it from a trusted website that does not contain any viruses or malware. However, you should always be careful when downloading any APK files from unknown sources and scan them with an antivirus app before installing them.</p>
- <h4>Q: Can I play Bloons TD 6 APK ultima version online with other players?</h4>
- <p>A: Yes, you can play Bloons TD 6 APK ultima version online with other players in co-op mode or contested territory mode. However, you may not be able to play with players who are using the official version of the game or a different version of the APK file.</p>
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- <p>A: Yes, you can update Bloons TD 6 APK ultima version to get the latest features and content by downloading the new version of the APK file from the same website where you got the previous one. However, you may lose your progress or data if you uninstall the old version before installing the new one.</p>
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- <p>A: Yes, you can transfer your progress or data from Bloons TD 6 APK ultima version to the official version or another device by using your Ninja Kiwi account. You need to create a Ninja Kiwi account and link it to your game in the settings menu. Then, you can log in to your Ninja Kiwi account on any device or platform and sync your progress and data.</p>
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-<p>One way is to leverage the data from downloads. Downloads are any actions that involve downloading a file, such as an ebook, a report, a podcast, or a video. Downloads are valuable sources of data because they reveal a lot about your audience's interests, preferences, behaviors, and needs.</p>
-<h2>download driven</h2><br /><p><b><b>Download Zip</b> &rarr;&rarr;&rarr; <a href="https://jinyurl.com/2uNORn">https://jinyurl.com/2uNORn</a></b></p><br /><br />
-<p>In this article, we will explain what download driven marketing is and why it is important for your business. We will also show you how to implement download driven marketing in your business and share some examples of successful download driven marketing campaigns.</p>
- <h2>What is download driven marketing and why is it important?</h2>
-<h3>Download driven marketing is the use of data from downloads to optimize marketing campaigns and strategies.</h3>
-<p>Download driven marketing is a type of data-driven marketing that focuses on using the data from downloads to improve your marketing performance. Download driven marketing involves collecting, analyzing, and using the data from downloads to:</p>
-<ul>
-<li>Create relevant and valuable content and offers that attract and engage your audience.</li>
-<li>Segment your audience based on their download behavior and interests.</li>
-<li>Personalize your content and offers based on their download history and profile.</li>
-<li>Measure the effectiveness of your marketing efforts and improve your conversion rates.</li>
-</ul>
-<h3>Download driven marketing can help you:</h3>
-<h4>- Understand your audience's needs, preferences, and behaviors.</h4>
-<p>By analyzing the data from downloads, you can gain insights into what your audience is looking for, what they like, what they dislike, how they consume content, and how they make decisions. This can help you create content and offers that match their needs and expectations.</p>
-<h4>- Segment your audience based on their download behavior and interests.</h4>
-<p>By using the data from downloads, you can segment your audience into different groups based on their download behavior and interests. For example, you can segment them by:</p>
-<ul>
-<li>The type of content they download (e.g., ebooks, podcasts, videos).</li>
-<li>The topic of the content they download (e.g., social media, SEO, email marketing).</li>
-<li>The frequency of their downloads (e.g., once a month, once a week, once a day).</li <h4>- Personalize your content and offers based on their download history and profile.</h4>
-<p>By using the data from downloads, you can personalize your content and offers based on their download history and profile. For example, you can:</p>
-<ul>
-<li>Send them follow-up emails with more content and offers related to their previous downloads.</li>
-<li>Show them personalized recommendations and suggestions based on their download preferences.</li>
-<li>Create dynamic landing pages and web pages that display content and offers tailored to their download interests.</li>
-</ul>
-<h4>- Measure the effectiveness of your marketing efforts and improve your conversion rates.</h4>
-<p>By using the data from downloads, you can measure the effectiveness of your marketing efforts and improve your conversion rates. For example, you can:</p>
-<p>download driven: how to create a high-converting lead magnet<br />
-download driven: the ultimate guide to email marketing<br />
-download driven: how to optimize your landing pages for conversions<br />
-download driven: how to use content upgrades to grow your email list<br />
-download driven: how to create a killer lead magnet in 5 easy steps<br />
-download driven: how to use SEO keywords to rank higher on Google<br />
-download driven: how to create a content marketing strategy that drives downloads<br />
-download driven: how to use social media to promote your lead magnets<br />
-download driven: how to measure and improve your conversion rate<br />
-download driven: how to use webinars to generate more leads and sales<br />
-download driven: how to create a viral ebook that gets shared and downloaded<br />
-download driven: how to use video marketing to attract and engage your audience<br />
-download driven: how to create a podcast that drives downloads and subscribers<br />
-download driven: how to use quizzes and surveys to generate leads and feedback<br />
-download driven: how to create a blog that drives traffic and downloads<br />
-download driven: how to use influencer marketing to boost your credibility and reach<br />
-download driven: how to create a free course that educates and converts<br />
-download driven: how to use email automation to nurture and sell to your leads<br />
-download driven: how to create a membership site that drives recurring revenue<br />
-download driven: how to use gamification to increase engagement and retention<br />
-download driven: how to create a mobile app that drives downloads and reviews<br />
-download driven: how to use chatbots and live chat to capture and qualify leads<br />
-download driven: how to create a landing page that converts like crazy<br />
-download driven: how to use testimonials and case studies to increase trust and conversions<br />
-download driven: how to create a white paper that showcases your expertise and authority<br />
-download driven: how to use analytics and split testing to optimize your campaigns<br />
-download driven: how to create a checklist that simplifies and solves your audience's problems<br />
-download driven: how to use Facebook ads to drive targeted traffic and leads<br />
-download driven: how to create a webinar replay that generates more downloads and sales<br />
-download driven: how to use Pinterest pins to drive traffic and downloads<br />
-download driven: how to create an infographic that gets shared and downloaded<br />
-download driven: how to use Instagram stories to showcase your lead magnets and drive downloads<br />
-download driven: how to create a swipe file that saves your audience time and money<br />
-download driven: how to use LinkedIn articles to drive traffic and downloads<br />
-download driven: how to create a template that makes your audience's life easier<br />
-download driven: how to use YouTube videos to drive traffic and downloads<br />
-download driven: how to create a cheat sheet that gives your audience quick wins<br />
-download driven: how to use Twitter threads to drive traffic and downloads<br />
-download driven: how to create a toolkit that provides your audience with valuable resources<br />
-download driven: how to use Reddit posts to drive traffic and downloads</p>
-<ul>
-<li>Track and analyze the key performance indicators (KPIs) of your download campaigns and strategies, such as download rate, click-through rate, bounce rate, and conversion rate.</li>
-<li>Identify the best practices and the areas of improvement for your download campaigns and strategies, such as content quality, design, format, distribution, and promotion.</li>
-<li>Test and optimize your download campaigns and strategies based on data-driven insights, such as A/B testing, multivariate testing, and user feedback.</li>
-</ul>
- <h2>How to implement download driven marketing in your business?</h2>
-<h3>To implement download driven marketing, you need to:</h3>
-<h4>- Identify your download goals and metrics.</h4>
-<p>The first step to implement download driven marketing is to identify your download goals and metrics. You need to define what you want to achieve with your downloads and how you will measure your success. For example, your download goals could be:</p>
-<ul>
-<li>To generate more leads for your business.</li>
-<li>To increase brand awareness and authority in your industry.</li>
-<li>To educate and inform your audience about your products or services.</li <li>To nurture and convert your leads into customers.</li>
-</ul>
-<p>Your download metrics could be:</p>
-<ul>
-<li>The number of downloads per content type, topic, or channel.</li>
-<li>The percentage of downloads that result in leads, subscribers, or customers.</li>
-<li>The cost per download, lead, subscriber, or customer.</li>
-<li>The revenue per download, lead, subscriber, or customer.</li>
-</ul>
-<h4>- Choose the right tools and platforms to collect, store, and analyze your download data.</h4>
-<p>The second step to implement download driven marketing is to choose the right tools and platforms to collect, store, and analyze your download data. You need to have a system that allows you to:</p>
-<ul>
-<li>Capture the data from downloads, such as the user's name, email, location, device, browser, etc.</li>
-<li>Store the data from downloads in a secure and accessible database or cloud service.</li>
-<li>Analyze the data from downloads using tools such as Google Analytics, Microsoft Power BI, Tableau, etc.</li>
-</ul>
-<h4>- Create relevant and valuable content and offers that attract and engage your audience.</h4>
-<p>The third step to implement download driven marketing is to create relevant and valuable content and offers that attract and engage your audience. You need to produce content and offers that:</p>
-<ul>
-<li>Solve a problem or answer a question that your audience has.</li>
-<li>Provide useful information or insights that your audience can benefit from.</li <li>Match the tone and style of your brand and your audience.</li>
-<li>Include a clear and compelling call to action that encourages your audience to download your content or offer.</li>
-</ul>
-<h4>- Test and optimize your download campaigns and strategies based on data-driven insights.</h4>
-<p>The fourth step to implement download driven marketing is to test and optimize your download campaigns and strategies based on data-driven insights. You need to monitor and evaluate your download performance and use the data to:</p>
-<ul>
-<li>Identify the best practices and the areas of improvement for your download campaigns and strategies.</li>
-<li>Experiment with different variables and factors that affect your download results, such as content type, topic, format, design, distribution, promotion, etc.</li>
-<li>Implement the changes and improvements that lead to better download outcomes and higher marketing ROI.</li>
-</ul>
- <h2>Examples of successful download driven marketing campaigns</h2>
-<h3>Here are some examples of how brands have used download driven marketing to achieve their marketing goals:</h3>
-<h4>- Netflix used download data to create personalized recommendations and increase customer retention.</h4>
-<p>Netflix is one of the most popular streaming services in the world, with over 200 million subscribers. One of the reasons for its success is its ability to use download data to create personalized recommendations for its users. Netflix analyzes the data from downloads, such as the genres, titles, ratings, and viewing habits of its users, to provide them with tailored suggestions and recommendations based on their preferences and interests. This helps Netflix to increase customer satisfaction, loyalty, and retention.</p>
-<h4>- HubSpot used download data to generate leads and nurture them through email marketing.</h4>
-<p>HubSpot is a leading software company that provides tools and solutions for inbound marketing, sales, and customer service. One of the ways HubSpot generates leads and nurtures them through email marketing is by using download data. HubSpot offers various types of content and offers for download, such as ebooks, reports, webinars, templates, etc. HubSpot collects the data from downloads, such as the user's name, email, company, industry, etc., to segment them into different groups based on their download behavior and interests. HubSpot then sends them personalized emails with more content and offers related to their previous downloads. This helps HubSpot to build trust and rapport with its leads and move them along the sales funnel.</p>
-<h4>- Spotify used download data to create customized playlists and enhance user experience.</h4 <p>Spotify is a popular music streaming service that has over 300 million users. One of the features that makes Spotify stand out is its ability to use download data to create customized playlists and enhance user experience. Spotify analyzes the data from downloads, such as the songs, artists, genres, and moods of its users, to create personalized playlists and recommendations based on their preferences and tastes. Spotify also allows its users to download songs and playlists for offline listening, which helps them save data and enjoy music anytime and anywhere.</p>
- <h2>Conclusion</h2>
-<h3>Download driven marketing is a powerful way to use data to improve your marketing ROI. By using download data, you can:</h3>
-<h4>- Know your audience better and tailor your content and offers to their needs and interests.</h4>
-<p>Download data can help you understand your audience's needs, preferences, behaviors, and expectations. This can help you create content and offers that solve their problems, answer their questions, and provide them with value.</p>
-<h4>- Segment your audience based on their download behavior and deliver personalized messages and experiences.</h4>
-<p>Download data can help you segment your audience into different groups based on their download behavior and interests. This can help you deliver personalized messages and experiences that match their download preferences and profile.</p>
-<h4>- Track and measure the impact of your download campaigns and strategies and optimize them accordingly.</h4>
-<p>Download data can help you track and measure the impact of your download campaigns and strategies on your marketing goals and metrics. This can help you identify the best practices and the areas of improvement for your download campaigns and strategies and optimize them accordingly.</p>
-<p>If you want to learn more about how to use download driven marketing to boost your marketing ROI, download our free ebook: "The Ultimate Guide to Download Driven Marketing".</p>
- <h2>FAQs</h2>
-<h3>What is download driven marketing?</h3>
-<p>Download driven marketing is the use of data from downloads to optimize marketing campaigns and strategies.</p>
-<h3>What are the benefits of download driven marketing?</h3>
-<p>Download driven marketing can help you understand your audience better, segment your audience based on their download behavior, personalize your content and offers based on their download history, and measure the effectiveness of your marketing efforts.</p>
-<h3>What are some examples of download driven marketing campaigns?</h3 <p>Some examples of download driven marketing campaigns are:</p>
-<ul>
-<li>Netflix used download data to create personalized recommendations and increase customer retention.</li>
-<li>HubSpot used download data to generate leads and nurture them through email marketing.</li>
-<li>Spotify used download data to create customized playlists and enhance user experience.</li>
-</ul>
-<h3>What are the best tools and platforms for download driven marketing?</h3>
-<p>There are many tools and platforms that can help you with download driven marketing, such as:</p>
-<ul>
-<li>Google Analytics: A web analytics tool that can help you track and analyze your download data and performance.</li>
-<li>Microsoft Power BI: A business intelligence tool that can help you visualize and report your download data and insights.</li>
-<li>Tableau: A data visualization tool that can help you create interactive dashboards and charts based on your download data.</li>
-<li>Mailchimp: An email marketing tool that can help you segment your audience based on their download behavior and send them personalized emails with more content and offers.</li>
-<li>WordPress: A content management system that can help you create and manage your content and offers for download, such as ebooks, reports, webinars, etc.</li>
-</ul>
-<h3>How to create content and offers that attract and engage your audience?</h3 <p>To create content and offers that attract and engage your audience, you need to:</p>
-<ul>
-<li>Research your audience and understand their pain points, challenges, goals, and interests.</li>
-<li>Create content and offers that solve their problems, answer their questions, and provide them with value.</li>
-<li>Use catchy headlines, compelling introductions, and clear conclusions to capture their attention and interest.</li>
-<li>Use simple, conversational, and engaging language to communicate your message and connect with your audience.</li>
-<li>Use visuals, such as images, videos, infographics, etc., to enhance your content and offer and make them more appealing and memorable.</li>
-<li>Include a clear and compelling call to action that encourages your audience to download your content or offer.</li>
-</ul>
-<p>I hope this article has helped you understand what download driven marketing is and how to use it to boost your marketing ROI. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p> 197e85843d<br />
-<br />
-<br />

spaces/1toTree/lora_test/ppdiffusers/schedulers/scheduling_karras_ve.py

@@ -1,232 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 NVIDIA and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-
-import numpy as np
-import paddle
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
-from .scheduling_utils import SchedulerMixin
-
-
-@dataclass
-class KarrasVeOutput(BaseOutput):
-    """
-    Output class for the scheduler's step function output.
-
-    Args:
-        prev_sample (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        derivative (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Derivative of predicted original image sample (x_0).
-        pred_original_sample (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: paddle.Tensor
-    derivative: paddle.Tensor
-    pred_original_sample: Optional[paddle.Tensor] = None
-
-
-class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
-    """
-    Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
-    the VE column of Table 1 from [1] for reference.
-
-    [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
-    https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic
-    differential equations." https://arxiv.org/abs/2011.13456
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    For more details on the parameters, see the original paper's Appendix E.: "Elucidating the Design Space of
-    Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364. The grid search values used to find the
-    optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the paper.
-
-    Args:
-        sigma_min (`float`): minimum noise magnitude
-        sigma_max (`float`): maximum noise magnitude
-        s_noise (`float`): the amount of additional noise to counteract loss of detail during sampling.
-            A reasonable range is [1.000, 1.011].
-        s_churn (`float`): the parameter controlling the overall amount of stochasticity.
-            A reasonable range is [0, 100].
-        s_min (`float`): the start value of the sigma range where we add noise (enable stochasticity).
-            A reasonable range is [0, 10].
-        s_max (`float`): the end value of the sigma range where we add noise.
-            A reasonable range is [0.2, 80].
-
-    """
-
-    order = 2
-
-    @register_to_config
-    def __init__(
-        self,
-        sigma_min: float = 0.02,
-        sigma_max: float = 100,
-        s_noise: float = 1.007,
-        s_churn: float = 80,
-        s_min: float = 0.05,
-        s_max: float = 50,
-    ):
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = sigma_max
-
-        # setable values
-        self.num_inference_steps: int = None
-        self.timesteps: paddle.Tensor = None
-        self.schedule: paddle.Tensor = None  # sigma(t_i)
-
-    def scale_model_input(self, sample: paddle.Tensor, timestep: Optional[int] = None) -> paddle.Tensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            sample (`paddle.Tensor`): input sample
-            timestep (`int`, optional): current timestep
-
-        Returns:
-            `paddle.Tensor`: scaled input sample
-        """
-        return sample
-
-    def set_timesteps(self, num_inference_steps: int):
-        """
-        Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-
-        """
-        self.num_inference_steps = num_inference_steps
-        timesteps = np.arange(0, self.num_inference_steps)[::-1].copy()
-        self.timesteps = paddle.to_tensor(timesteps)
-        schedule = [
-            (
-                self.config.sigma_max**2
-                * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
-            )
-            for i in self.timesteps
-        ]
-        self.schedule = paddle.to_tensor(schedule, dtype="float32")
-
-    def add_noise_to_input(
-        self, sample: paddle.Tensor, sigma: float, generator: Optional[paddle.Generator] = None
-    ) -> Tuple[paddle.Tensor, float]:
-        """
-        Explicit Langevin-like "churn" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a
-        higher noise level sigma_hat = sigma_i + gamma_i*sigma_i.
-
-        TODO Args:
-        """
-        if self.config.s_min <= sigma <= self.config.s_max:
-            gamma = min(self.config.s_churn / self.num_inference_steps, 2**0.5 - 1)
-        else:
-            gamma = 0
-
-        # sample eps ~ N(0, S_noise^2 * I)
-        eps = self.config.s_noise * paddle.randn(sample.shape, generator=generator)
-        sigma_hat = sigma + gamma * sigma
-        sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
-
-        return sample_hat, sigma_hat
-
-    def step(
-        self,
-        model_output: paddle.Tensor,
-        sigma_hat: float,
-        sigma_prev: float,
-        sample_hat: paddle.Tensor,
-        return_dict: bool = True,
-    ) -> Union[KarrasVeOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`paddle.Tensor`): direct output from learned diffusion model.
-            sigma_hat (`float`): TODO
-            sigma_prev (`float`): TODO
-            sample_hat (`paddle.Tensor`): TODO
-            return_dict (`bool`): option for returning tuple rather than KarrasVeOutput class
-
-            KarrasVeOutput: updated sample in the diffusion chain and derivative (TODO double check).
-        Returns:
-            [`~schedulers.scheduling_karras_ve.KarrasVeOutput`] or `tuple`:
-            [`~schedulers.scheduling_karras_ve.KarrasVeOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-
-        """
-
-        pred_original_sample = sample_hat + sigma_hat * model_output
-        derivative = (sample_hat - pred_original_sample) / sigma_hat
-        sample_prev = sample_hat + (sigma_prev - sigma_hat) * derivative
-
-        if not return_dict:
-            return (sample_prev, derivative)
-
-        return KarrasVeOutput(
-            prev_sample=sample_prev, derivative=derivative, pred_original_sample=pred_original_sample
-        )
-
-    def step_correct(
-        self,
-        model_output: paddle.Tensor,
-        sigma_hat: float,
-        sigma_prev: float,
-        sample_hat: paddle.Tensor,
-        sample_prev: paddle.Tensor,
-        derivative: paddle.Tensor,
-        return_dict: bool = True,
-    ) -> Union[KarrasVeOutput, Tuple]:
-        """
-        Correct the predicted sample based on the output model_output of the network. TODO complete description
-
-        Args:
-            model_output (`paddle.Tensor`): direct output from learned diffusion model.
-            sigma_hat (`float`): TODO
-            sigma_prev (`float`): TODO
-            sample_hat (`paddle.Tensor`): TODO
-            sample_prev (`paddle.Tensor`): TODO
-            derivative (`paddle.Tensor`): TODO
-            return_dict (`bool`): option for returning tuple rather than KarrasVeOutput class
-
-        Returns:
-            prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO
-
-        """
-        pred_original_sample = sample_prev + sigma_prev * model_output
-        derivative_corr = (sample_prev - pred_original_sample) / sigma_prev
-        sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
-
-        if not return_dict:
-            return (sample_prev, derivative)
-
-        return KarrasVeOutput(
-            prev_sample=sample_prev, derivative=derivative, pred_original_sample=pred_original_sample
-        )
-
-    def add_noise(self, original_samples, noise, timesteps):
-        raise NotImplementedError()

spaces/2ndelement/voicevox/Dockerfile

@@ -1,296 +0,0 @@
-# syntax=docker/dockerfile:1.4
-
-ARG BASE_IMAGE=ubuntu:20.04
-ARG BASE_RUNTIME_IMAGE=$BASE_IMAGE
-
-# Download VOICEVOX Core shared object
-FROM ${BASE_IMAGE} AS download-core-env
-ARG DEBIAN_FRONTEND=noninteractive
-
-WORKDIR /work
-
-RUN <<EOF
-    set -eux
-
-    apt-get update
-    apt-get install -y \
-        wget \
-        unzip
-    apt-get clean
-    rm -rf /var/lib/apt/lists/*
-EOF
-
-# assert VOICEVOX_CORE_VERSION >= 0.11.0 (ONNX)
-ARG TARGETPLATFORM
-ARG USE_GPU=false
-ARG VOICEVOX_CORE_VERSION=0.14.3
-
-RUN <<EOF
-    set -eux
-
-    # Processing Switch
-    if [ "${USE_GPU}" = "true" ]; then
-        VOICEVOX_CORE_ASSET_ASSET_PROCESSING="gpu"
-    else
-        VOICEVOX_CORE_ASSET_ASSET_PROCESSING="cpu"
-    fi
-
-    # TARGETARCH Switch
-    if [ "${TARGETPLATFORM}" = "linux/amd64" ]; then
-        VOICEVOX_CORE_ASSET_TARGETARCH="x64"
-    else
-        VOICEVOX_CORE_ASSET_TARGETARCH="arm64"
-    fi
-
-    VOICEVOX_CORE_ASSET_PREFIX="voicevox_core-linux-${VOICEVOX_CORE_ASSET_TARGETARCH}-${VOICEVOX_CORE_ASSET_ASSET_PROCESSING}"
-
-    # Download Core
-    VOICEVOX_CORE_ASSET_NAME=${VOICEVOX_CORE_ASSET_PREFIX}-${VOICEVOX_CORE_VERSION}
-    wget -nv --show-progress -c -O "./${VOICEVOX_CORE_ASSET_NAME}.zip" "https://github.com/VOICEVOX/voicevox_core/releases/download/${VOICEVOX_CORE_VERSION}/${VOICEVOX_CORE_ASSET_NAME}.zip"
-    unzip "./${VOICEVOX_CORE_ASSET_NAME}.zip"
-    mkdir -p core
-    mv "${VOICEVOX_CORE_ASSET_NAME}"/* core
-    rm -rf $VOICEVOX_CORE_ASSET_NAME
-    rm "./${VOICEVOX_CORE_ASSET_NAME}.zip"
-
-    # Move Core to /opt/voicevox_core/
-    mkdir /opt/voicevox_core
-    mv ./core/* /opt/voicevox_core/
-
-    # Add /opt/voicevox_core to dynamic library search path
-    echo "/opt/voicevox_core" > /etc/ld.so.conf.d/voicevox_core.conf
-
-    # Update dynamic library search cache
-    ldconfig
-EOF
-
-
-# Download ONNX Runtime
-FROM ${BASE_IMAGE} AS download-onnxruntime-env
-ARG DEBIAN_FRONTEND=noninteractive
-
-WORKDIR /work
-
-RUN <<EOF
-    set -eux
-
-    apt-get update
-    apt-get install -y \
-        wget \
-        tar
-    apt-get clean
-    rm -rf /var/lib/apt/lists/*
-EOF
-
-ARG TARGETPLATFORM
-ARG USE_GPU=false
-ARG ONNXRUNTIME_VERSION=1.13.1
-RUN <<EOF
-    set -eux
-
-    # Processing Switch
-    if [ "${USE_GPU}" = "true" ]; then
-        ONNXRUNTIME_PROCESSING="gpu-"
-    else
-        ONNXRUNTIME_PROCESSING=""
-    fi
-
-    # TARGETARCH Switch
-    if [ "${TARGETPLATFORM}" = "linux/amd64" ]; then
-        ONNXRUNTIME_TARGETARCH=x64
-    else
-        ONNXRUNTIME_TARGETARCH=aarch64
-    fi
-    
-    ONNXRUNTIME_URL="https://github.com/microsoft/onnxruntime/releases/download/v${ONNXRUNTIME_VERSION}/onnxruntime-linux-${ONNXRUNTIME_TARGETARCH}-${ONNXRUNTIME_PROCESSING}${ONNXRUNTIME_VERSION}.tgz"
-
-    # Download ONNX Runtime
-    wget -nv --show-progress -c -O "./onnxruntime.tgz" "${ONNXRUNTIME_URL}"
-
-    # Extract ONNX Runtime to /opt/onnxruntime
-    mkdir -p /opt/onnxruntime
-    tar xf "./onnxruntime.tgz" -C "/opt/onnxruntime" --strip-components 1
-    rm ./onnxruntime.tgz
-
-    # Add /opt/onnxruntime/lib to dynamic library search path
-    echo "/opt/onnxruntime/lib" > /etc/ld.so.conf.d/onnxruntime.conf
-
-    # Update dynamic library search cache
-    ldconfig
-EOF
-
-
-# Compile Python (version locked)
-FROM ${BASE_IMAGE} AS compile-python-env
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-RUN <<EOF
-    set -eux
-    apt-get update
-    apt-get install -y \
-        build-essential \
-        libssl-dev \
-        zlib1g-dev \
-        libbz2-dev \
-        libreadline-dev \
-        libsqlite3-dev \
-        curl \
-        libncursesw5-dev \
-        xz-utils \
-        tk-dev \
-        libxml2-dev \
-        libxmlsec1-dev \
-        libffi-dev \
-        liblzma-dev \
-        git
-    apt-get clean
-    rm -rf /var/lib/apt/lists/*
-EOF
-
-ARG PYTHON_VERSION=3.11.3
-ARG PYENV_VERSION=v2.3.17
-ARG PYENV_ROOT=/tmp/.pyenv
-ARG PYBUILD_ROOT=/tmp/python-build
-RUN <<EOF
-    set -eux
-
-    git clone -b "${PYENV_VERSION}" https://github.com/pyenv/pyenv.git "$PYENV_ROOT"
-    PREFIX="$PYBUILD_ROOT" "$PYENV_ROOT"/plugins/python-build/install.sh
-    "$PYBUILD_ROOT/bin/python-build" -v "$PYTHON_VERSION" /opt/python
-
-    rm -rf "$PYBUILD_ROOT" "$PYENV_ROOT"
-EOF
-
-# FIXME: add /opt/python to PATH
-# not working: /etc/profile read only on login shell
-# not working: /etc/environment is the same
-# not suitable: `ENV` is ignored by docker-compose
-# RUN <<EOF
-#     set -eux
-#     echo "export PATH=/opt/python/bin:\$PATH" > /etc/profile.d/python-path.sh
-#     echo "export LD_LIBRARY_PATH=/opt/python/lib:\$LD_LIBRARY_PATH" >> /etc/profile.d/python-path.sh
-#     echo "export C_INCLUDE_PATH=/opt/python/include:\$C_INCLUDE_PATH" >> /etc/profile.d/python-path.sh
-#
-#     rm -f /etc/ld.so.cache
-#     ldconfig
-# EOF
-
-
-# Runtime
-FROM ${BASE_RUNTIME_IMAGE} AS runtime-env
-ARG DEBIAN_FRONTEND=noninteractive
-
-WORKDIR /opt/voicevox_engine
-
-# libsndfile1: soundfile shared object
-# ca-certificates: pyopenjtalk dictionary download
-# build-essential: pyopenjtalk local build
-RUN <<EOF
-    set -eux
-
-    apt-get update
-    apt-get install -y \
-        git \
-        wget \
-        cmake \
-        libsndfile1 \
-        ca-certificates \
-        build-essential \
-        gosu
-    apt-get clean
-    rm -rf /var/lib/apt/lists/*
-
-    # Create a general user
-    useradd --create-home user
-EOF
-
-# Copy python env
-COPY --from=compile-python-env /opt/python /opt/python
-
-# Install Python dependencies
-ADD ./requirements.txt /tmp/
-RUN <<EOF
-    # Install requirements
-    gosu user /opt/python/bin/pip3 install -r /tmp/requirements.txt
-EOF
-
-# Copy VOICEVOX Core release
-# COPY --from=download-core-env /etc/ld.so.conf.d/voicevox_core.conf /etc/ld.so.conf.d/voicevox_core.conf
-COPY --from=download-core-env /opt/voicevox_core /opt/voicevox_core
-
-# Copy ONNX Runtime
-# COPY --from=download-onnxruntime-env /etc/ld.so.conf.d/onnxruntime.conf /etc/ld.so.conf.d/onnxruntime.conf
-COPY --from=download-onnxruntime-env /opt/onnxruntime /opt/onnxruntime
-
-# Add local files
-ADD ./voicevox_engine /opt/voicevox_engine/voicevox_engine
-ADD ./docs /opt/voicevox_engine/docs
-ADD ./run.py ./generate_licenses.py ./presets.yaml ./default.csv ./default_setting.yml ./engine_manifest.json /opt/voicevox_engine/
-ADD ./speaker_info /opt/voicevox_engine/speaker_info
-ADD ./ui_template /opt/voicevox_engine/ui_template
-ADD ./engine_manifest_assets /opt/voicevox_engine/engine_manifest_assets
-
-# Replace version
-ARG VOICEVOX_ENGINE_VERSION=latest
-RUN sed -i "s/__version__ = \"latest\"/__version__ = \"${VOICEVOX_ENGINE_VERSION}\"/" /opt/voicevox_engine/voicevox_engine/__init__.py
-RUN sed -i "s/\"version\": \"999\\.999\\.999\"/\"version\": \"${VOICEVOX_ENGINE_VERSION}\"/" /opt/voicevox_engine/engine_manifest.json
-
-# Generate licenses.json
-ADD ./requirements-license.txt /tmp/
-RUN <<EOF
-    set -eux
-
-    cd /opt/voicevox_engine
-
-    # Define temporary env vars
-    # /home/user/.local/bin is required to use the commands installed by pip
-    export PATH="/home/user/.local/bin:${PATH:-}"
-
-    gosu user /opt/python/bin/pip3 install -r /tmp/requirements-license.txt
-    gosu user /opt/python/bin/python3 generate_licenses.py > /opt/voicevox_engine/engine_manifest_assets/dependency_licenses.json
-    cp /opt/voicevox_engine/engine_manifest_assets/dependency_licenses.json /opt/voicevox_engine/licenses.json
-EOF
-
-# Keep this layer separated to use layer cache on download failed in local build
-RUN <<EOF
-    set -eux
-
-    # Download openjtalk dictionary
-    # try 5 times, sleep 5 seconds before retry
-    for i in $(seq 5); do
-        EXIT_CODE=0
-        gosu user /opt/python/bin/python3 -c "import pyopenjtalk; pyopenjtalk._lazy_init()" || EXIT_CODE=$?
-        if [ "$EXIT_CODE" = "0" ]; then
-            break
-        fi
-        sleep 5
-    done
-
-    if [ "$EXIT_CODE" != "0" ]; then
-        exit "$EXIT_CODE"
-    fi
-EOF
-
-# Download Resource
-ARG VOICEVOX_RESOURCE_VERSION=0.14.3-preview.1
-RUN <<EOF
-    set -eux
-
-    # README
-    wget -nv --show-progress -c -O "/opt/voicevox_engine/README.md" "https://raw.githubusercontent.com/VOICEVOX/voicevox_resource/${VOICEVOX_RESOURCE_VERSION}/engine/README.md"
-EOF
-
-# Create container start shell
-COPY --chmod=775 <<EOF /entrypoint.sh
-#!/bin/bash
-set -eux
-
-# Display README for engine
-cat /opt/voicevox_engine/README.md > /dev/stderr
-
-exec "\$@"
-EOF
-USER user
-ENTRYPOINT [ "/entrypoint.sh"  ]
-CMD [ "/opt/python/bin/python3", "./run.py", "--voicelib_dir", "/opt/voicevox_core/", "--runtime_dir", "/opt/onnxruntime/lib", "--host", "0.0.0.0","--port","7860" ]

spaces/7hao/bingo/src/components/button-scroll-to-bottom.tsx

@@ -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 (
-    <Button
-      variant="outline"
-      size="icon"
-      className={cn(
-        'fixed right-4 bottom-24 z-50 bg-background transition-opacity duration-300 sm:right-20',
-        isAtBottom ? 'opacity-0' : 'opacity-100',
-        className
-      )}
-      onClick={() =>
-        window.scrollTo({
-          top: document.body.offsetHeight,
-          behavior: 'smooth'
-        })
-      }
-      {...props}
-    >
-      <IconArrowDown />
-      <span className="sr-only">Scroll to bottom</span>
-    </Button>
-  )
-}

spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Getting Started 6bc871dcdd4a4554b5b22c0c40740841/Example sub-page 48f64d6186ec4428b2e4180475245a9c.md

@@ -1,5 +0,0 @@
-# Example sub-page
-
-Last edited time: March 31, 2023 1:49 PM
-Owner: Anonymous
-Tags: Testing

spaces/AI-Naga/Parking_Space_Counter/app.py

@@ -1,91 +0,0 @@
-import gradio as gr
-import cv2
-import requests
-import os
-import torch 
-import numpy as np
-
-from ultralytics import YOLO
-
-model = torch.hub.load('ultralytics/yolov5', 'yolov5l', pretrained=True)
-path  = [['image_0.jpg'], ['image_1.jpg']]
-video_path = [['video_test.mp4']]
-# area = [(25,430), (10, 515), (407,485), (750,425), (690,370)]
-area = [(48,430), (18, 515), (407,485), (750,425), (690,370)]
-total_space = 12
-count=0
-
-def show_preds_video():
-    cap = cv2.VideoCapture('Video_1.mp4')
-    count=0
-    while(cap.isOpened()):
-        ret, frame = cap.read()
-        if not ret:
-            break
-        count += 1
-        if count % 2 != 0:
-            continue
-
-        frame=cv2.resize(frame,(1020,600))
-        frame_copy = frame.copy()
-        Vehicle_cnt = 0
-
-        results=model(frame)
-        for index, row in results.pandas().xyxy[0].iterrows():
-            x1 = int(row['xmin'])
-            y1 = int(row['ymin'])
-            x2 = int(row['xmax'])
-            y2 = int(row['ymax'])
-            d=(row['name'])
-
-            cx=int(x1+x2)//2
-            cy=int(y1+y2)//2
-
-            if ('car' or 'truck') in d:
-                results = cv2.pointPolygonTest(np.array(area, np.int32), ((cx,cy)), False)
-                if results >0:
-                        cv2.rectangle(frame_copy,(x1,y1),(x2,y2),(0,0,255),2)
-                        cv2.putText(frame_copy,str(d),(x1,y1),cv2.FONT_HERSHEY_PLAIN,2,(255,255,0),2)
-                        Vehicle_cnt += 1
-
-            # elif ('truck') in d:
-            #     results = cv2.pointPolygonTest(np.array(area, np.int32), ((cx,cy)), False)
-            #     if results >0:
-            #         cv2.rectangle(frame_copy,(x1,y1),(x2,y2),(0,0,255),2)
-            #         cv2.putText(frame_copy,str(d),(x1,y1),cv2.FONT_HERSHEY_PLAIN,2,(255,0,0),2)
-            #         truck_cnt += 1       
-        
-        free_space = total_space - Vehicle_cnt
-        cv2.putText(frame_copy, ("Free space: " + str(free_space)), (50,50) ,cv2.FONT_HERSHEY_PLAIN,2,(0,255,0),2)
-        # cv2.putText(frame_copy, str(str("  car: ")+ str(car_cnt) + str("  truck: ") +str(truck_cnt)), (50,75) ,cv2.FONT_HERSHEY_PLAIN,2,(0,255,0),2)
-        cv2.putText(frame_copy, str(str("vehicles: ")+ str(Vehicle_cnt) ), (50,85) ,cv2.FONT_HERSHEY_PLAIN,2,(0,255,0),2)
-        
-        cv2.polylines(frame_copy, [np.array(area, np.int32)], True, (0,255,0), 2) 
-
-        # fps = cap.get(cv2.CAP_PROP_FPS)
-        # cv2.putText(frame_copy,str("fps:  ") + str(np.round(fps,0)),(50,100),cv2.FONT_HERSHEY_PLAIN,2,(0,255,0),2)
-
-        yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
-
-
-inputs_video = [
-    #gr.components.Video(type="filepath", label="Input Video"),
-
-]
-outputs_video = [
-    gr.components.Image(type="numpy", label="Output Image"),
-]
-interface_video = gr.Interface(
-    fn=show_preds_video,
-    inputs=inputs_video,
-    outputs=outputs_video,
-    title="Parking space counter",
-    description="Click generate !!!'",
-    # examples=video_path,
-    cache_examples=False,
-)
-
-gr.TabbedInterface(
-    [interface_video],
-    tab_names=['Video inference']
-).queue().launch()

spaces/AIFILMS/generate_human_motion/pyrender/pyrender/node.py

@@ -1,263 +0,0 @@
-"""Nodes, conforming to the glTF 2.0 standards as specified in
-https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-node
-
-Author: Matthew Matl
-"""
-import numpy as np
-
-import trimesh.transformations as transformations
-
-from .camera import Camera
-from .mesh import Mesh
-from .light import Light
-
-
-class Node(object):
-    """A node in the node hierarchy.
-
-    Parameters
-    ----------
-    name : str, optional
-        The user-defined name of this object.
-    camera : :class:`Camera`, optional
-        The camera in this node.
-    children : list of :class:`Node`
-        The children of this node.
-    skin : int, optional
-        The index of the skin referenced by this node.
-    matrix : (4,4) float, optional
-        A floating-point 4x4 transformation matrix.
-    mesh : :class:`Mesh`, optional
-        The mesh in this node.
-    rotation : (4,) float, optional
-        The node's unit quaternion in the order (x, y, z, w), where
-        w is the scalar.
-    scale : (3,) float, optional
-        The node's non-uniform scale, given as the scaling factors along the x,
-        y, and z axes.
-    translation : (3,) float, optional
-        The node's translation along the x, y, and z axes.
-    weights : (n,) float
-        The weights of the instantiated Morph Target. Number of elements must
-        match number of Morph Targets of used mesh.
-    light : :class:`Light`, optional
-        The light in this node.
-    """
-
-    def __init__(self,
-                 name=None,
-                 camera=None,
-                 children=None,
-                 skin=None,
-                 matrix=None,
-                 mesh=None,
-                 rotation=None,
-                 scale=None,
-                 translation=None,
-                 weights=None,
-                 light=None):
-        # Set defaults
-        if children is None:
-            children = []
-
-        self._matrix = None
-        self._scale = None
-        self._rotation = None
-        self._translation = None
-        if matrix is None:
-            if rotation is None:
-                rotation = np.array([0.0, 0.0, 0.0, 1.0])
-            if translation is None:
-                translation = np.zeros(3)
-            if scale is None:
-                scale = np.ones(3)
-            self.rotation = rotation
-            self.translation = translation
-            self.scale = scale
-        else:
-            self.matrix = matrix
-
-        self.name = name
-        self.camera = camera
-        self.children = children
-        self.skin = skin
-        self.mesh = mesh
-        self.weights = weights
-        self.light = light
-
-    @property
-    def name(self):
-        """str : The user-defined name of this object.
-        """
-        return self._name
-
-    @name.setter
-    def name(self, value):
-        if value is not None:
-            value = str(value)
-        self._name = value
-
-    @property
-    def camera(self):
-        """:class:`Camera` : The camera in this node.
-        """
-        return self._camera
-
-    @camera.setter
-    def camera(self, value):
-        if value is not None and not isinstance(value, Camera):
-            raise TypeError('Value must be a camera')
-        self._camera = value
-
-    @property
-    def children(self):
-        """list of :class:`Node` : The children of this node.
-        """
-        return self._children
-
-    @children.setter
-    def children(self, value):
-        self._children = value
-
-    @property
-    def skin(self):
-        """int : The skin index for this node.
-        """
-        return self._skin
-
-    @skin.setter
-    def skin(self, value):
-        self._skin = value
-
-    @property
-    def mesh(self):
-        """:class:`Mesh` : The mesh in this node.
-        """
-        return self._mesh
-
-    @mesh.setter
-    def mesh(self, value):
-        if value is not None and not isinstance(value, Mesh):
-            raise TypeError('Value must be a mesh')
-        self._mesh = value
-
-    @property
-    def light(self):
-        """:class:`Light` : The light in this node.
-        """
-        return self._light
-
-    @light.setter
-    def light(self, value):
-        if value is not None and not isinstance(value, Light):
-            raise TypeError('Value must be a light')
-        self._light = value
-
-    @property
-    def rotation(self):
-        """(4,) float : The xyzw quaternion for this node.
-        """
-        return self._rotation
-
-    @rotation.setter
-    def rotation(self, value):
-        value = np.asanyarray(value)
-        if value.shape != (4,):
-            raise ValueError('Quaternion must be a (4,) vector')
-        if np.abs(np.linalg.norm(value) - 1.0) > 1e-3:
-            raise ValueError('Quaternion must have norm == 1.0')
-        self._rotation = value
-        self._matrix = None
-
-    @property
-    def translation(self):
-        """(3,) float : The translation for this node.
-        """
-        return self._translation
-
-    @translation.setter
-    def translation(self, value):
-        value = np.asanyarray(value)
-        if value.shape != (3,):
-            raise ValueError('Translation must be a (3,) vector')
-        self._translation = value
-        self._matrix = None
-
-    @property
-    def scale(self):
-        """(3,) float : The scale for this node.
-        """
-        return self._scale
-
-    @scale.setter
-    def scale(self, value):
-        value = np.asanyarray(value)
-        if value.shape != (3,):
-            raise ValueError('Scale must be a (3,) vector')
-        self._scale = value
-        self._matrix = None
-
-    @property
-    def matrix(self):
-        """(4,4) float : The homogenous transform matrix for this node.
-
-        Note that this matrix's elements are not settable,
-        it's just a copy of the internal matrix. You can set the whole
-        matrix, but not an individual element.
-        """
-        if self._matrix is None:
-            self._matrix = self._m_from_tqs(
-                self.translation, self.rotation, self.scale
-            )
-        return self._matrix.copy()
-
-    @matrix.setter
-    def matrix(self, value):
-        value = np.asanyarray(value)
-        if value.shape != (4,4):
-            raise ValueError('Matrix must be a 4x4 numpy ndarray')
-        if not np.allclose(value[3,:], np.array([0.0, 0.0, 0.0, 1.0])):
-            raise ValueError('Bottom row of matrix must be [0,0,0,1]')
-        self.rotation = Node._q_from_m(value)
-        self.scale = Node._s_from_m(value)
-        self.translation = Node._t_from_m(value)
-        self._matrix = value
-
-    @staticmethod
-    def _t_from_m(m):
-        return m[:3,3]
-
-    @staticmethod
-    def _r_from_m(m):
-        U = m[:3,:3]
-        norms = np.linalg.norm(U.T, axis=1)
-        return U / norms
-
-    @staticmethod
-    def _q_from_m(m):
-        M = np.eye(4)
-        M[:3,:3] = Node._r_from_m(m)
-        q_wxyz = transformations.quaternion_from_matrix(M)
-        return np.roll(q_wxyz, -1)
-
-    @staticmethod
-    def _s_from_m(m):
-        return np.linalg.norm(m[:3,:3].T, axis=1)
-
-    @staticmethod
-    def _r_from_q(q):
-        q_wxyz = np.roll(q, 1)
-        return transformations.quaternion_matrix(q_wxyz)[:3,:3]
-
-    @staticmethod
-    def _m_from_tqs(t, q, s):
-        S = np.eye(4)
-        S[:3,:3] = np.diag(s)
-
-        R = np.eye(4)
-        R[:3,:3] = Node._r_from_q(q)
-
-        T = np.eye(4)
-        T[:3,3] = t
-
-        return T.dot(R.dot(S))

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/midas/midas/blocks.py

@@ -1,342 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .vit import (
-    _make_pretrained_vitb_rn50_384,
-    _make_pretrained_vitl16_384,
-    _make_pretrained_vitb16_384,
-    forward_vit,
-)
-
-def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
-    if backbone == "vitl16_384":
-        pretrained = _make_pretrained_vitl16_384(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [256, 512, 1024, 1024], features, groups=groups, expand=expand
-        )  # ViT-L/16 - 85.0% Top1 (backbone)
-    elif backbone == "vitb_rn50_384":
-        pretrained = _make_pretrained_vitb_rn50_384(
-            use_pretrained,
-            hooks=hooks,
-            use_vit_only=use_vit_only,
-            use_readout=use_readout,
-        )
-        scratch = _make_scratch(
-            [256, 512, 768, 768], features, groups=groups, expand=expand
-        )  # ViT-H/16 - 85.0% Top1 (backbone)
-    elif backbone == "vitb16_384":
-        pretrained = _make_pretrained_vitb16_384(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [96, 192, 384, 768], features, groups=groups, expand=expand
-        )  # ViT-B/16 - 84.6% Top1 (backbone)
-    elif backbone == "resnext101_wsl":
-        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
-        scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand)     # efficientnet_lite3  
-    elif backbone == "efficientnet_lite3":
-        pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
-        scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand)  # efficientnet_lite3     
-    else:
-        print(f"Backbone '{backbone}' not implemented")
-        assert False
-        
-    return pretrained, scratch
-
-
-def _make_scratch(in_shape, out_shape, groups=1, expand=False):
-    scratch = nn.Module()
-
-    out_shape1 = out_shape
-    out_shape2 = out_shape
-    out_shape3 = out_shape
-    out_shape4 = out_shape
-    if expand==True:
-        out_shape1 = out_shape
-        out_shape2 = out_shape*2
-        out_shape3 = out_shape*4
-        out_shape4 = out_shape*8
-
-    scratch.layer1_rn = nn.Conv2d(
-        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer2_rn = nn.Conv2d(
-        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer3_rn = nn.Conv2d(
-        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer4_rn = nn.Conv2d(
-        in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-
-    return scratch
-
-
-def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
-    efficientnet = torch.hub.load(
-        "rwightman/gen-efficientnet-pytorch",
-        "tf_efficientnet_lite3",
-        pretrained=use_pretrained,
-        exportable=exportable
-    )
-    return _make_efficientnet_backbone(efficientnet)
-
-
-def _make_efficientnet_backbone(effnet):
-    pretrained = nn.Module()
-
-    pretrained.layer1 = nn.Sequential(
-        effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
-    )
-    pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
-    pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
-    pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
-
-    return pretrained
-    
-
-def _make_resnet_backbone(resnet):
-    pretrained = nn.Module()
-    pretrained.layer1 = nn.Sequential(
-        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
-    )
-
-    pretrained.layer2 = resnet.layer2
-    pretrained.layer3 = resnet.layer3
-    pretrained.layer4 = resnet.layer4
-
-    return pretrained
-
-
-def _make_pretrained_resnext101_wsl(use_pretrained):
-    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
-    return _make_resnet_backbone(resnet)
-
-
-
-class Interpolate(nn.Module):
-    """Interpolation module.
-    """
-
-    def __init__(self, scale_factor, mode, align_corners=False):
-        """Init.
-
-        Args:
-            scale_factor (float): scaling
-            mode (str): interpolation mode
-        """
-        super(Interpolate, self).__init__()
-
-        self.interp = nn.functional.interpolate
-        self.scale_factor = scale_factor
-        self.mode = mode
-        self.align_corners = align_corners
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: interpolated data
-        """
-
-        x = self.interp(
-            x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
-        )
-
-        return x
-
-
-class ResidualConvUnit(nn.Module):
-    """Residual convolution module.
-    """
-
-    def __init__(self, features):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.conv1 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True
-        )
-
-        self.conv2 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True
-        )
-
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-        out = self.relu(x)
-        out = self.conv1(out)
-        out = self.relu(out)
-        out = self.conv2(out)
-
-        return out + x
-
-
-class FeatureFusionBlock(nn.Module):
-    """Feature fusion block.
-    """
-
-    def __init__(self, features):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock, self).__init__()
-
-        self.resConfUnit1 = ResidualConvUnit(features)
-        self.resConfUnit2 = ResidualConvUnit(features)
-
-    def forward(self, *xs):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            output += self.resConfUnit1(xs[1])
-
-        output = self.resConfUnit2(output)
-
-        output = nn.functional.interpolate(
-            output, scale_factor=2, mode="bilinear", align_corners=True
-        )
-
-        return output
-
-
-
-
-class ResidualConvUnit_custom(nn.Module):
-    """Residual convolution module.
-    """
-
-    def __init__(self, features, activation, bn):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.bn = bn
-
-        self.groups=1
-
-        self.conv1 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
-        )
-        
-        self.conv2 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
-        )
-
-        if self.bn==True:
-            self.bn1 = nn.BatchNorm2d(features)
-            self.bn2 = nn.BatchNorm2d(features)
-
-        self.activation = activation
-
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-        
-        out = self.activation(x)
-        out = self.conv1(out)
-        if self.bn==True:
-            out = self.bn1(out)
-       
-        out = self.activation(out)
-        out = self.conv2(out)
-        if self.bn==True:
-            out = self.bn2(out)
-
-        if self.groups > 1:
-            out = self.conv_merge(out)
-
-        return self.skip_add.add(out, x)
-
-        # return out + x
-
-
-class FeatureFusionBlock_custom(nn.Module):
-    """Feature fusion block.
-    """
-
-    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock_custom, self).__init__()
-
-        self.deconv = deconv
-        self.align_corners = align_corners
-
-        self.groups=1
-
-        self.expand = expand
-        out_features = features
-        if self.expand==True:
-            out_features = features//2
-        
-        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
-
-        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
-        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
-        
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, *xs):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            res = self.resConfUnit1(xs[1])
-            output = self.skip_add.add(output, res)
-            # output += res
-
-        output = self.resConfUnit2(output)
-
-        output = nn.functional.interpolate(
-            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
-        )
-
-        output = self.out_conv(output)
-
-        return output
-

spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/models/melgan.py

@@ -1,458 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2020 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-"""MelGAN Modules."""
-
-import logging
-
-import numpy as np
-import torch
-from torch import nn
-
-from text_to_speech.modules.vocoder.parallel_wavegan.layers import CausalConv1d
-from text_to_speech.modules.vocoder.parallel_wavegan.layers import CausalConvTranspose1d
-from text_to_speech.modules.vocoder.parallel_wavegan.layers import ResidualStack
-from text_to_speech.modules.vocoder.parallel_wavegan.models.source import SourceModuleCycNoise_v1
-
-
-class MelGANGenerator(torch.nn.Module):
-    """MelGAN generator module."""
-
-    def __init__(self,
-                 in_channels=80,
-                 out_channels=1,
-                 kernel_size=7,
-                 channels=512,
-                 bias=True,
-                 upsample_scales=[8, 8, 2, 2],
-                 stack_kernel_size=3,
-                 stacks=3,
-                 nonlinear_activation="LeakyReLU",
-                 nonlinear_activation_params={"negative_slope": 0.2},
-                 pad="ReflectionPad1d",
-                 pad_params={},
-                 use_final_nonlinear_activation=True,
-                 use_weight_norm=True,
-                 use_causal_conv=False,
-                 use_pitch_embed=False,
-                 use_nsf=False,
-                 sample_rate=22050,
-                 **kwargs
-                 ):
-        """Initialize MelGANGenerator module.
-
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            kernel_size (int): Kernel size of initial and final conv layer.
-            channels (int): Initial number of channels for conv layer.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            upsample_scales (list): List of upsampling scales.
-            stack_kernel_size (int): Kernel size of dilated conv layers in residual stack.
-            stacks (int): Number of stacks in a single residual stack.
-            nonlinear_activation (str): Activation function module name.
-            nonlinear_activation_params (dict): Hyperparameters for activation function.
-            pad (str): Padding function module name before dilated convolution layer.
-            pad_params (dict): Hyperparameters for padding function.
-            use_final_nonlinear_activation (torch.nn.Module): Activation function for the final layer.
-            use_weight_norm (bool): Whether to use weight norm.
-                If set to true, it will be applied to all of the conv layers.
-            use_causal_conv (bool): Whether to use causal convolution.
-
-        """
-        super(MelGANGenerator, self).__init__()
-
-        # check hyper parameters is valid
-        assert channels >= np.prod(upsample_scales)
-        assert channels % (2 ** len(upsample_scales)) == 0
-        if not use_causal_conv:
-            assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
-
-        # add initial layer
-        layers = []
-        if not use_causal_conv:
-            layers += [
-                getattr(torch.nn, pad)((kernel_size - 1) // 2, **pad_params),
-                torch.nn.Conv1d(in_channels, channels, kernel_size, bias=bias),
-            ]
-        else:
-            layers += [
-                CausalConv1d(in_channels, channels, kernel_size,
-                             bias=bias, pad=pad, pad_params=pad_params),
-            ]
-
-        self.use_pitch_embed = use_pitch_embed
-        if use_pitch_embed:
-            self.pitch_embed = nn.Embedding(300, in_channels, 0)
-            self.c_proj = nn.Conv1d(2 * in_channels, in_channels, 1)
-
-        for i, upsample_scale in enumerate(upsample_scales):
-            # add upsampling layer
-            layers += [getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params)]
-            if not use_causal_conv:
-                layers += [
-                    torch.nn.ConvTranspose1d(
-                        channels // (2 ** i),
-                        channels // (2 ** (i + 1)),
-                        upsample_scale * 2,
-                        stride=upsample_scale,
-                        padding=upsample_scale // 2 + upsample_scale % 2,
-                        output_padding=upsample_scale % 2,
-                        bias=bias,
-                    )
-                ]
-            else:
-                layers += [
-                    CausalConvTranspose1d(
-                        channels // (2 ** i),
-                        channels // (2 ** (i + 1)),
-                        upsample_scale * 2,
-                        stride=upsample_scale,
-                        bias=bias,
-                    )
-                ]
-
-            # add residual stack
-            for j in range(stacks):
-                layers += [
-                    ResidualStack(
-                        kernel_size=stack_kernel_size,
-                        channels=channels // (2 ** (i + 1)),
-                        dilation=stack_kernel_size ** j,
-                        bias=bias,
-                        nonlinear_activation=nonlinear_activation,
-                        nonlinear_activation_params=nonlinear_activation_params,
-                        pad=pad,
-                        pad_params=pad_params,
-                        use_causal_conv=use_causal_conv,
-                    )
-                ]
-        self.use_nsf = use_nsf
-        if use_nsf:
-            self.harmonic_num = 8
-            hop_size = np.prod(upsample_scales)
-            self.f0_upsamp = torch.nn.Upsample(scale_factor=hop_size)
-            # self.m_source = SourceModuleHnNSF(sampling_rate=sample_rate, harmonic_num=self.harmonic_num)
-            self.m_source = SourceModuleCycNoise_v1(sample_rate, 0.003)
-            self.nsf_conv = nn.Sequential(nn.Conv1d(1, channels // (2 ** (i + 1)), 1), torch.nn.Tanh())
-
-        # define the model as a single function
-        self.melgan_body = torch.nn.Sequential(*layers)
-        layers = []
-        # add final layer
-        layers += [getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params)]
-        if not use_causal_conv:
-            layers += [
-                getattr(torch.nn, pad)((kernel_size - 1) // 2, **pad_params),
-                torch.nn.Conv1d(channels // (2 ** (i + 1)), out_channels, kernel_size, bias=bias),
-            ]
-        else:
-            layers += [
-                CausalConv1d(channels // (2 ** (i + 1)), out_channels, kernel_size,
-                             bias=bias, pad=pad, pad_params=pad_params),
-            ]
-        if use_final_nonlinear_activation:
-            layers += [torch.nn.Tanh()]
-
-        # define the model as a single function
-        self.melgan_final = torch.nn.Sequential(*layers)
-
-        # apply weight norm
-        if use_weight_norm:
-            self.apply_weight_norm()
-
-        # reset parameters
-        self.reset_parameters()
-
-    def forward(self, c, f0=None, pitch=None):
-        """Calculate forward propagation.
-
-        Args:
-            c (Tensor): Input tensor (B, channels, T).
-
-        Returns:
-            Tensor: Output tensor (B, 1, T ** prod(upsample_scales)).
-
-        """
-        if self.use_pitch_embed:
-            c = self.c_proj(torch.cat([c, self.pitch_embed(pitch).transpose(1, 2)], 1))
-        x = self.melgan_body(c)
-        if self.use_nsf:
-            f0_upsample = self.f0_upsamp(f0[:, None, :])
-            f0_upsample = self.nsf_conv(f0_upsample)
-            x = x + f0_upsample
-        x = self.melgan_final(x)
-        return x
-
-    def remove_weight_norm(self):
-        """Remove weight normalization module from all of the layers."""
-        def _remove_weight_norm(m):
-            try:
-                logging.debug(f"Weight norm is removed from {m}.")
-                torch.nn.utils.remove_weight_norm(m)
-            except ValueError:  # this module didn't have weight norm
-                return
-
-        self.apply(_remove_weight_norm)
-
-    def apply_weight_norm(self):
-        """Apply weight normalization module from all of the layers."""
-        def _apply_weight_norm(m):
-            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d):
-                torch.nn.utils.weight_norm(m)
-                logging.debug(f"Weight norm is applied to {m}.")
-
-        self.apply(_apply_weight_norm)
-
-    def reset_parameters(self):
-        """Reset parameters.
-
-        This initialization follows official implementation manner.
-        https://github.com/descriptinc/melgan-neurips/blob/master/spec2wav/modules.py
-
-        """
-        def _reset_parameters(m):
-            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d):
-                m.weight.data.normal_(0.0, 0.02)
-                logging.debug(f"Reset parameters in {m}.")
-
-        self.apply(_reset_parameters)
-
-
-class MelGANDiscriminator(torch.nn.Module):
-    """MelGAN discriminator module."""
-
-    def __init__(self,
-                 in_channels=1,
-                 out_channels=1,
-                 kernel_sizes=[5, 3],
-                 channels=16,
-                 max_downsample_channels=1024,
-                 bias=True,
-                 downsample_scales=[4, 4, 4, 4],
-                 nonlinear_activation="LeakyReLU",
-                 nonlinear_activation_params={"negative_slope": 0.2},
-                 pad="ReflectionPad1d",
-                 pad_params={},
-                 ):
-        """Initilize MelGAN discriminator module.
-
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            kernel_sizes (list): List of two kernel sizes. The prod will be used for the first conv layer,
-                and the first and the second kernel sizes will be used for the last two layers.
-                For example if kernel_sizes = [5, 3], the first layer kernel size will be 5 * 3 = 15,
-                the last two layers' kernel size will be 5 and 3, respectively.
-            channels (int): Initial number of channels for conv layer.
-            max_downsample_channels (int): Maximum number of channels for downsampling layers.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            downsample_scales (list): List of downsampling scales.
-            nonlinear_activation (str): Activation function module name.
-            nonlinear_activation_params (dict): Hyperparameters for activation function.
-            pad (str): Padding function module name before dilated convolution layer.
-            pad_params (dict): Hyperparameters for padding function.
-
-        """
-        super(MelGANDiscriminator, self).__init__()
-        self.layers = torch.nn.ModuleList()
-
-        # check kernel size is valid
-        assert len(kernel_sizes) == 2
-        assert kernel_sizes[0] % 2 == 1
-        assert kernel_sizes[1] % 2 == 1
-
-        # add first layer
-        self.layers += [
-            torch.nn.Sequential(
-                getattr(torch.nn, pad)((np.prod(kernel_sizes) - 1) // 2, **pad_params),
-                torch.nn.Conv1d(in_channels, channels, np.prod(kernel_sizes), bias=bias),
-                getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
-            )
-        ]
-
-        # add downsample layers
-        in_chs = channels
-        for downsample_scale in downsample_scales:
-            out_chs = min(in_chs * downsample_scale, max_downsample_channels)
-            self.layers += [
-                torch.nn.Sequential(
-                    torch.nn.Conv1d(
-                        in_chs, out_chs,
-                        kernel_size=downsample_scale * 10 + 1,
-                        stride=downsample_scale,
-                        padding=downsample_scale * 5,
-                        groups=in_chs // 4,
-                        bias=bias,
-                    ),
-                    getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
-                )
-            ]
-            in_chs = out_chs
-
-        # add final layers
-        out_chs = min(in_chs * 2, max_downsample_channels)
-        self.layers += [
-            torch.nn.Sequential(
-                torch.nn.Conv1d(
-                    in_chs, out_chs, kernel_sizes[0],
-                    padding=(kernel_sizes[0] - 1) // 2,
-                    bias=bias,
-                ),
-                getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
-            )
-        ]
-        self.layers += [
-            torch.nn.Conv1d(
-                out_chs, out_channels, kernel_sizes[1],
-                padding=(kernel_sizes[1] - 1) // 2,
-                bias=bias,
-            ),
-        ]
-
-    def forward(self, x):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Input noise signal (B, 1, T).
-
-        Returns:
-            List: List of output tensors of each layer.
-
-        """
-        outs = []
-        for f in self.layers:
-            x = f(x)
-            outs += [x]
-
-        return outs
-
-
-class MelGANMultiScaleDiscriminator(torch.nn.Module):
-    """MelGAN multi-scale discriminator module."""
-
-    def __init__(self,
-                 in_channels=1,
-                 out_channels=1,
-                 scales=3,
-                 downsample_pooling="AvgPool1d",
-                 # follow the official implementation setting
-                 downsample_pooling_params={
-                     "kernel_size": 4,
-                     "stride": 2,
-                     "padding": 1,
-                     "count_include_pad": False,
-                 },
-                 kernel_sizes=[5, 3],
-                 channels=16,
-                 max_downsample_channels=1024,
-                 bias=True,
-                 downsample_scales=[4, 4, 4, 4],
-                 nonlinear_activation="LeakyReLU",
-                 nonlinear_activation_params={"negative_slope": 0.2},
-                 pad="ReflectionPad1d",
-                 pad_params={},
-                 use_weight_norm=True,
-                 **kwargs
-                 ):
-        """Initilize MelGAN multi-scale discriminator module.
-
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            downsample_pooling (str): Pooling module name for downsampling of the inputs.
-            downsample_pooling_params (dict): Parameters for the above pooling module.
-            kernel_sizes (list): List of two kernel sizes. The sum will be used for the first conv layer,
-                and the first and the second kernel sizes will be used for the last two layers.
-            channels (int): Initial number of channels for conv layer.
-            max_downsample_channels (int): Maximum number of channels for downsampling layers.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            downsample_scales (list): List of downsampling scales.
-            nonlinear_activation (str): Activation function module name.
-            nonlinear_activation_params (dict): Hyperparameters for activation function.
-            pad (str): Padding function module name before dilated convolution layer.
-            pad_params (dict): Hyperparameters for padding function.
-            use_causal_conv (bool): Whether to use causal convolution.
-
-        """
-        super(MelGANMultiScaleDiscriminator, self).__init__()
-        self.discriminators = torch.nn.ModuleList()
-
-        # add discriminators
-        for _ in range(scales):
-            self.discriminators += [
-                MelGANDiscriminator(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    kernel_sizes=kernel_sizes,
-                    channels=channels,
-                    max_downsample_channels=max_downsample_channels,
-                    bias=bias,
-                    downsample_scales=downsample_scales,
-                    nonlinear_activation=nonlinear_activation,
-                    nonlinear_activation_params=nonlinear_activation_params,
-                    pad=pad,
-                    pad_params=pad_params,
-                )
-            ]
-        self.pooling = getattr(torch.nn, downsample_pooling)(**downsample_pooling_params)
-
-        # apply weight norm
-        if use_weight_norm:
-            self.apply_weight_norm()
-
-        # reset parameters
-        self.reset_parameters()
-
-    def forward(self, x):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Input noise signal (B, 1, T).
-
-        Returns:
-            List: List of list of each discriminator outputs, which consists of each layer output tensors.
-
-        """
-        outs = []
-        for f in self.discriminators:
-            outs += [f(x)]
-            x = self.pooling(x)
-
-        return outs
-
-    def remove_weight_norm(self):
-        """Remove weight normalization module from all of the layers."""
-        def _remove_weight_norm(m):
-            try:
-                logging.debug(f"Weight norm is removed from {m}.")
-                torch.nn.utils.remove_weight_norm(m)
-            except ValueError:  # this module didn't have weight norm
-                return
-
-        self.apply(_remove_weight_norm)
-
-    def apply_weight_norm(self):
-        """Apply weight normalization module from all of the layers."""
-        def _apply_weight_norm(m):
-            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d):
-                torch.nn.utils.weight_norm(m)
-                logging.debug(f"Weight norm is applied to {m}.")
-
-        self.apply(_apply_weight_norm)
-
-    def reset_parameters(self):
-        """Reset parameters.
-
-        This initialization follows official implementation manner.
-        https://github.com/descriptinc/melgan-neurips/blob/master/spec2wav/modules.py
-
-        """
-        def _reset_parameters(m):
-            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d):
-                m.weight.data.normal_(0.0, 0.02)
-                logging.debug(f"Reset parameters in {m}.")
-
-        self.apply(_reset_parameters)

spaces/AIWaves/SOP_Generation-single/Environment/base_environment.py

@@ -1,177 +0,0 @@
-from utils import get_relevant_history, get_embedding
-import torch
-from LLM.base_LLM import *
-from Memory import Memory
-from Prompt import * 
-import json
-class Environment:
-    """
-    The place where the agent activities, responsible for storing some shared memories
-    """
-    def __init__(self, config) -> None:
-        self.shared_memory = {"long_term_memory": [], "short_term_memory": None}
-        self.agents = None
-
-        self.summary_system_prompt = {}
-        self.summary_last_prompt = {}
-        self.environment_prompt = {}
-        self.environment_type = config["environment_type"] if "environment_type" in config else "cooperative"
-        self.current_chat_history_idx = 0
-        self.LLMs = {}
-        
-        # 初始化每个state 的summary 方法
-        # Initialize the summary method for each state
-        for state_name, state_dict in config["states"].items():
-            if state_name != "end_state":
-                self.summary_system_prompt[state_name] = (
-                    state_dict["summary_system_prompt"]
-                    if "summary_system_prompt" in state_dict
-                    else eval(Default_environment_summary_system_prompt)
-                )
-
-                self.summary_last_prompt[state_name] = (
-                    state_dict["summary_last_prompt"]
-                    if "summary_last_prompt" in state_dict
-                    else eval(Default_environment_summary_last_prompt)
-                )
-
-                self.environment_prompt[state_name] = (
-                    state_dict["environment_prompt"]
-                    if "environment_prompt" in state_dict
-                    else " "
-                )
-                self.LLMs[state_name] = init_LLM("logs"+os.sep+f"{state_name}",**state_dict)
-        self.roles_to_names = None
-        self.names_to_roles = None
-
-    @classmethod
-    def from_config(cls, config_path):
-        with open(config_path) as f:
-            config = json.load(f)
-        return cls(config)
-
-    def summary(self, current_state):
-        """
-        Summarize the situation in the current environment every once in a while
-        """
-        MAX_CHAT_HISTORY = eval(os.environ["MAX_CHAT_HISTORY"])
-        current_state_name = current_state.name
-
-        query = self.shared_memory["long_term_memory"][-1].content
-        if len(self.shared_memory["long_term_memory"])>1:
-            relevant_history = get_relevant_history(
-                query,
-                self.shared_memory["long_term_memory"][:-1],
-                self.shared_memory["chat_embeddings"][:-1],
-            )
-
-            relevant_history = Memory.get_chat_history(relevant_history)
-        else:
-            relevant_history = ""
-        chat_history = Memory.get_chat_history(
-            self.shared_memory["long_term_memory"][-MAX_CHAT_HISTORY + 1 :]
-        )
-        summary = self.shared_memory["short_term_memory"]
-        
-        
-        # system prompt = environment prompt + current memory + system prompt
-        # current_memory = summary + chat history + relevant history
-        current_memory = eval(Environment_summary_memory)
-        environment_prompt = self.environment_prompt[current_state_name]
-        summary_system_prompt = self.summary_system_prompt[current_state_name]
-        
-        environment_summary_system_prompt = eval(Environment_summary_system_prompt)
-        response = self.LLMs[current_state_name].get_response(None, environment_summary_system_prompt, stream=False)
-        return response
-
-    def update_memory(self, memory, current_state):
-        """
-        update chat embbedings and long term memory,short term memory,agents long term memory
-        """
-        MAX_CHAT_HISTORY = eval(os.environ["MAX_CHAT_HISTORY"])
-        self.shared_memory["long_term_memory"].append(memory)
-        current_embedding = get_embedding(memory.content)
-        if "chat_embeddings" not in self.shared_memory:
-            self.shared_memory["chat_embeddings"] = current_embedding
-        else:
-            self.shared_memory["chat_embeddings"] = torch.cat(
-                [self.shared_memory["chat_embeddings"], current_embedding], dim=0
-            )
-        if len(self.shared_memory["long_term_memory"]) % MAX_CHAT_HISTORY == 0:
-            summary = self.summary(current_state)
-            self.shared_memory["short_term_memory"] = summary
-
-        self.agents[memory.send_name].update_memory(memory)
-    
-    
-    def _get_agent_last_conversation_idx(self,agent,current_long_term_memory):
-        last_conversation_idx = -1
-        for i, history in enumerate(current_long_term_memory):
-            if history.send_name == agent.name:
-                last_conversation_idx = i
-        return last_conversation_idx
-    
-    
-    def _get_agent_new_memory(self,agent,current_long_term_memory):
-        # get new conversation
-        last_conversation_idx = self._get_agent_last_conversation_idx(agent,current_long_term_memory)
-
-        if last_conversation_idx == -1:
-            new_conversation =current_long_term_memory
-        elif (
-            last_conversation_idx
-            == len(current_long_term_memory) - 1
-        ):
-            new_conversation = []
-        else:
-            new_conversation = current_long_term_memory[
-                last_conversation_idx + 1 :
-            ]
-        MAX_CHAT_HISTORY = eval(os.environ["MAX_CHAT_HISTORY"])
-        if len(new_conversation) > 2 * MAX_CHAT_HISTORY:
-            new_conversation = new_conversation[-2*MAX_CHAT_HISTORY+1:]
-
-        # get chat history from new conversation
-        return Memory.get_chat_history(new_conversation)
-    
-    
-    def _observe(self,agent):
-        MAX_CHAT_HISTORY = eval(os.environ["MAX_CHAT_HISTORY"])
-        current_state = agent.current_state
-        current_role = agent.state_roles[current_state.name]
-        current_component_dict = current_state.components[current_role]
-        
-        # cooperative:Sharing information between different states ;  competive: No information is shared between different states
-        current_chat_history_idx = self.current_chat_history_idx if self.environment_type == "competive" else 0
-        current_long_term_memory = self.shared_memory["long_term_memory"][current_chat_history_idx:]
-        current_chat_embbedings = self.shared_memory["chat_embeddings"][current_chat_history_idx:]
-            
-        if len(current_long_term_memory)>2*MAX_CHAT_HISTORY:
-            current_long_term_memory = current_long_term_memory[-2*MAX_CHAT_HISTORY+1:]
-            current_chat_embbedings = current_chat_embbedings[-2*MAX_CHAT_HISTORY+1:]
-        # relevant_memory
-        query = current_long_term_memory[-1].content
-        if len(current_long_term_memory)>1:
-            relevant_memory = get_relevant_history(
-                query,
-                current_long_term_memory[:-2],
-                current_chat_embbedings[:-2],
-            )
-            relevant_memory = Memory.get_chat_history(relevant_memory,agent.name)
-        else:
-            relevant_memory = ""
-        
-        relevant_memory = eval(Agent_observe_relevant_memory)
-        agent.relevant_memory = relevant_memory
-        
-        
-        # get chat history from new conversation
-        conversations = self._get_agent_new_memory(agent,current_long_term_memory)
-
-        # memory = relevant_memory + summary + history + query
-        query = current_long_term_memory[-1]
-        current_memory = eval(Agent_observe_memory)
-
-        return {"role": "user", "content": current_memory}
-
-

spaces/Abhilashvj/planogram-compliance/utils/aws/userdata.sh

@@ -1,27 +0,0 @@
-#!/bin/bash
-# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html
-# This script will run only once on first instance start (for a re-start script see mime.sh)
-# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir
-# Use >300 GB SSD
-
-cd home/ubuntu
-if [ ! -d yolov5 ]; then
-  echo "Running first-time script." # install dependencies, download COCO, pull Docker
-  git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5
-  cd yolov5
-  bash data/scripts/get_coco.sh && echo "COCO done." &
-  sudo docker pull ultralytics/yolov5:latest && echo "Docker done." &
-  python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." &
-  wait && echo "All tasks done." # finish background tasks
-else
-  echo "Running re-start script." # resume interrupted runs
-  i=0
-  list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour'
-  while IFS= read -r id; do
-    ((i++))
-    echo "restarting container $i: $id"
-    sudo docker start $id
-    # sudo docker exec -it $id python train.py --resume # single-GPU
-    sudo docker exec -d $id python utils/aws/resume.py # multi-scenario
-  done <<<"$list"
-fi

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/methods/ColorPicker.js

@@ -1,101 +0,0 @@
-import Sizer from '../../../sizer/Sizer.js';
-import ColorPicker from '../../colorpicker/ColorPicker.js';
-import ColorComponents from '../../colorcomponents/ColorComponents.js';
-import TouchEventStop from '../../../toucheventstop/TouchEventStop.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-class ColorPickerPanel extends Sizer {
-    constructor(scene, config) {
-        if (config === undefined) {
-            config = {};
-        }
-
-        config.orientation = 1;
-        super(scene, config);
-        this.type = 'rexColorInput.ColorPickerPanel';
-
-        // Add elements
-        var background = GetValue(config, 'background', undefined);
-
-        var colorPicker = new ColorPicker(scene, {
-            hPalette: config.hPalette || {},
-            svPalette: config.svPalette || {},
-            space: {
-                item: GetValue(config, 'space.hPalette', 8)
-            }
-        });
-        scene.add.existing(colorPicker);
-
-        var colorComponents;
-        if (config.colorComponents) {
-            colorComponents = new ColorComponents(scene, config.colorComponents);
-            scene.add.existing(colorComponents);
-        }
-
-        if (background) {
-            this.addBackground(background);
-            var touchEventStop = new TouchEventStop(background, {
-                stopAllLevels: false,
-            });
-        }
-
-        this.add(
-            colorPicker,
-            { proportion: 1, expand: true }
-        );
-
-        if (colorComponents) {
-            this.add(
-                colorComponents,
-                { proportion: 0, expand: true }
-            );
-        }
-
-        this.addChildrenMap('background', background);
-        this.addChildrenMap('colorPicker', colorPicker);
-        this.addChildrenMap('colorComponents', colorComponents);
-
-        colorPicker.on('valuechange', function (value) {
-            this.setValue(value);
-        }, this)
-
-        if (colorComponents) {
-            colorComponents.on('valuechange', function (value) {
-                this.setValue(value);
-            }, this)
-        }
-
-        this.setValue(GetValue(config, 'value', 0xffffff));
-    }
-
-    get value() {
-        return this._value;
-    }
-
-    set value(value) {
-        if (this._value === value) {
-            return;
-        }
-
-        this._value = value;
-
-        var colorPicker = this.childrenMap.colorPicker;
-        colorPicker.setValue(value);
-
-        var colorComponents = this.childrenMap.colorComponents;
-        if (colorComponents) {
-            colorComponents.setValue(value);
-        }
-
-        this.emit('valuechange', value);
-    }
-
-    setValue(value) {
-        this.value = value;
-        return this;
-    }
-
-}
-
-export default ColorPickerPanel;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dropdownlist/methods/listpanel/CloseListPanel.js

@@ -1,11 +0,0 @@
-var CloseListPanel = function () {
-    if (!this.dropDownBehavior) {
-        return this;
-    }
-
-    this.dropDownBehavior.requestClose();
-
-    return this;
-}
-
-export default CloseListPanel;

spaces/AkitoP/umamusume_bert_vits2/app0.py

@@ -1,344 +0,0 @@
-# flake8: noqa: E402
-
-import sys, os
-import logging
-import os
-import time
-import numpy as np  # 假设你使用NumPy来处理音频数据
-import shutil  # 用于删除文件夹和文件
-from scipy.io import wavfile
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-logging.basicConfig(
-    level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
-)
-
-logger = logging.getLogger(__name__)
-
-import torch
-import argparse
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-import gradio as gr
-import webbrowser
-import numpy as np
-
-net_g = None
-
-if sys.platform == "darwin" and torch.backends.mps.is_available():
-    device = "mps"
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-else:
-    device = "cuda"
-
-
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str, device)
-    del word2ph
-    assert bert.shape[-1] == len(phone), phone
-
-    if language_str == "ZH":
-        bert = bert
-        ja_bert = torch.zeros(768, len(phone))
-    elif language_str == "JP":
-        ja_bert = bert
-        bert = torch.zeros(1024, len(phone))
-    else:
-        bert = torch.zeros(1024, len(phone))
-        ja_bert = torch.zeros(768, len(phone))
-
-    assert bert.shape[-1] == len(
-        phone
-    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-    return bert, ja_bert, phone, tone, language
-
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
-    global net_g
-    bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps)
-    with torch.no_grad():
-        x_tst = phones.to(device).unsqueeze(0)
-        tones = tones.to(device).unsqueeze(0)
-        lang_ids = lang_ids.to(device).unsqueeze(0)
-        bert = bert.to(device).unsqueeze(0)
-        ja_bert = ja_bert.to(device).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
-        #print(x_tst.type(), tones.type(), lang_ids.type(), bert.type(), ja_bert.type(), x_tst_lengths.type())
-        del phones
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
-        audio = (
-            net_g.infer(
-                x_tst,
-                x_tst_lengths,
-                speakers,
-                tones,
-                lang_ids,
-                bert,
-                ja_bert,
-                sdp_ratio=sdp_ratio,
-                noise_scale=noise_scale,
-                noise_scale_w=noise_scale_w,
-                length_scale=length_scale,
-            )[0][0, 0]
-            .data.cpu()
-            .float()
-            .numpy()
-        )
-        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
-        torch.cuda.empty_cache()
-        return audio
-    
-def infer_2(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
-    global net_g_2
-    bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps)
-    with torch.no_grad():
-        x_tst = phones.to(device).unsqueeze(0)
-        tones = tones.to(device).unsqueeze(0)
-        lang_ids = lang_ids.to(device).unsqueeze(0)
-        bert = bert.to(device).unsqueeze(0)
-        ja_bert = ja_bert.to(device).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
-        #print(x_tst.type(), tones.type(), lang_ids.type(), bert.type(), ja_bert.type(), x_tst_lengths.type())
-        del phones
-        speakers = torch.LongTensor([hps_2.data.spk2id[sid]]).to(device)
-        audio = (
-            net_g_2.infer(
-                x_tst,
-                x_tst_lengths,
-                speakers,
-                tones,
-                lang_ids,
-                bert,
-                ja_bert,
-                sdp_ratio=sdp_ratio,
-                noise_scale=noise_scale,
-                noise_scale_w=noise_scale_w,
-                length_scale=length_scale,
-            )[0][0, 0]
-            .data.cpu()
-            .float()
-            .numpy()
-        )
-        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
-        torch.cuda.empty_cache()
-        return audio
-
-__LOG__ = "./generation_logs.txt"
-def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language,from_model=0):
-    # 清空 ./infer_save 文件夹
-    if os.path.exists('./infer_save'):
-        shutil.rmtree('./infer_save')
-    os.makedirs('./infer_save')
-
-    slices = text.split("\n")
-    slices = [slice for slice in slices if slice.strip() != ""]
-    audio_list = []
-    with torch.no_grad():
-        with open(__LOG__,"a",encoding="UTF-8") as f:
-            for slice in slices:
-                assert len(slice) < 150 # 限制输入的文本长度
-                if from_model == 0:
-                    audio = infer(slice, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, language=language)
-                else:
-                    audio = infer_2(slice, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, language=language)
-                audio_list.append(audio)
-                
-                # 创建唯一的文件名
-                timestamp = str(int(time.time() * 1000))
-                audio_file_path = f'./infer_save/audio_{timestamp}.wav'
-                
-                # 保存音频数据到.wav文件
-                wavfile.write(audio_file_path, hps.data.sampling_rate, audio)
-                
-                silence = np.zeros(int(hps.data.sampling_rate/2), dtype=np.int16)  # 生成半秒的静音
-                audio_list.append(silence)  # 将静音添加到列表中
-                
-                f.write(f"{slice} | {speaker}\n")
-                print(f"{slice} | {speaker}")
-
-    audio_concat = np.concatenate(audio_list)
-    return "Success", (hps.data.sampling_rate, audio_concat)
-def tts_fn_2(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language,from_model=1):
-    return  tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language,from_model)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-m", "--model", default="./logs/natuki/G_72000.pth", help="path of your model"
-    )
-    parser.add_argument(
-        "-c",
-        "--config",
-        default="./configs/config.json",
-        help="path of your config file",
-    )
-    parser.add_argument(
-        "--share", default=False, help="make link public", action="store_true"
-    )
-    parser.add_argument(
-        "-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log"
-    )
-
-    args = parser.parse_args()
-    if args.debug:
-        logger.info("Enable DEBUG-LEVEL log")
-        logging.basicConfig(level=logging.DEBUG)
-    hps = utils.get_hparams_from_file("./logs/digital/config.json")
-    hps_2 = utils.get_hparams_from_file("./logs/fukukitaru/config.json")
-
-    device = (
-        "cuda:0"
-        if torch.cuda.is_available()
-        else (
-            "mps"
-            if sys.platform == "darwin" and torch.backends.mps.is_available()
-            else "cpu"
-        )
-    )
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model,
-    ).to(device)
-    _ = net_g.eval()
-
-    net_g_2 = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model,
-    ).to(device)
-
-    _ = utils.load_checkpoint("./logs/digital/G_10500.pth", net_g, None, skip_optimizer=True)
-    _ = utils.load_checkpoint("./logs/fukukitaru/G_10000.pth", net_g_2, None, skip_optimizer=True)
-
-    speaker_ids = hps.data.spk2id
-    speakers = list(speaker_ids.keys())
-    speaker_ids_2 = hps_2.data.spk2id
-    speakers_2 = list(speaker_ids_2.keys())
-
-
-    languages = ["ZH", "JP"]
-    with gr.Blocks() as app:
-        with gr.Tab(label="umamusume"):
-            with gr.Row():
-                with gr.Column():
-                    text = gr.TextArea(
-                        label="Text",
-                        placeholder="Input Text Here",
-                        value="はりきっていこう！",
-                    )
-                    speaker = gr.Dropdown(
-                        choices=speakers, value=speakers[0], label="Speaker"
-                    )
-                    sdp_ratio = gr.Slider(
-                        minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio"
-                    )
-                    noise_scale = gr.Slider(
-                        minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise Scale"
-                    )
-                    noise_scale_w = gr.Slider(
-                        minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise Scale W"
-                    )
-                    length_scale = gr.Slider(
-                        minimum=0.1, maximum=2, value=1, step=0.1, label="Length Scale"
-                    )
-                    language = gr.Dropdown(
-                        choices=languages, value=languages[1], label="Language"
-                    )
-                    btn = gr.Button("Generate!", variant="primary")
-                with gr.Column():
-                    text_output = gr.Textbox(label="Message")
-                    audio_output = gr.Audio(label="Output Audio")
-                    gr.Markdown("# 赛马娘 Bert-VITS2 语音合成\n"
-                        "Project page：[GitHub](https://github.com/fishaudio/Bert-VITS2)\n"
-                        "- 本项目在日语方面有所欠缺，特别是音调的设计上，需要帮助。\n"
-                        "- このプロジェクトは、日本語の方面で不足しています。特に、音調の設計に関して助けが欲しいです。")
-
-            btn.click(
-                tts_fn,
-                inputs=[
-                    text,
-                    speaker,
-                    sdp_ratio,
-                    noise_scale,
-                    noise_scale_w,
-                    length_scale,
-                    language,
-                ],
-                outputs=[text_output, audio_output],
-            )
-        with gr.Tab(label="natuki"):
-            with gr.Row():
-                with gr.Column():
-                    text2 = gr.TextArea(
-                        label="Text",
-                        placeholder="Input Text Here",
-                        value="はりきっていこう！",
-                    )
-                    speaker2 = gr.Dropdown(
-                        choices=speakers_2, value=speakers_2[0], label="Speaker"
-                    )
-                    sdp_ratio2 = gr.Slider(
-                        minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio"
-                    )
-                    noise_scale2 = gr.Slider(
-                        minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise Scale"
-                    )
-                    noise_scale_w2 = gr.Slider(
-                        minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise Scale W"
-                    )
-                    length_scale2 = gr.Slider(
-                        minimum=0.1, maximum=2, value=1, step=0.1, label="Length Scale"
-                    )
-                    language2 = gr.Dropdown(
-                        choices=languages, value=languages[1], label="Language"
-                    )
-                    btn2 = gr.Button("Generate!", variant="primary")
-                with gr.Column():
-                    text_output2 = gr.Textbox(label="Message")
-                    audio_output2 = gr.Audio(label="Output Audio")
-                    gr.Markdown("# 赛马娘 Bert-VITS2 语音合成\n"
-                        "Project page：[GitHub](https://github.com/fishaudio/Bert-VITS2)\n"
-                        "- 本项目在日语方面有所欠缺，特别是音调的设计上，需要帮助。\n"
-                        "- このプロジェクトは、日本語の方面で不足しています。特に、音調の設計に関して助けが欲しいです。")
-
-            btn2.click(
-                tts_fn_2,
-                inputs=[
-                    text2,
-                    speaker2,
-                    sdp_ratio2,
-                    noise_scale2,
-                    noise_scale_w2,
-                    length_scale2,
-                    language2,
-                ],
-                outputs=[text_output2, audio_output2],
-            )
-    app.launch(server_name="0.0.0.0")

spaces/Amrrs/DragGan-Inversion/PTI/training/coaches/base_coach.py

@@ -1,158 +0,0 @@
-import abc
-import os
-import pickle
-from argparse import Namespace
-import os.path
-from PTI.criteria.localitly_regulizer import Space_Regulizer
-import torch
-from torchvision import transforms
-from lpips import LPIPS
-from PTI.training.projectors import w_projector
-from PTI.configs import global_config, paths_config, hyperparameters
-from PTI.criteria import l2_loss
-from PTI.models.e4e.psp import pSp
-from PTI.utils.log_utils import log_image_from_w
-from PTI.utils.models_utils import toogle_grad, load_old_G
-
-
-class BaseCoach:
-    def __init__(self, data_loader, use_wandb):
-
-        self.use_wandb = use_wandb
-        self.data_loader = data_loader
-        self.w_pivots = {}
-        self.image_counter = 0
-
-        if hyperparameters.first_inv_type == 'w+':
-            self.initilize_e4e()
-
-        self.e4e_image_transform = transforms.Compose([
-            transforms.ToPILImage(),
-            transforms.Resize((256, 256)),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
-
-        # Initialize loss
-        self.lpips_loss = LPIPS(net=hyperparameters.lpips_type).to(
-            global_config.device).eval()
-
-        self.restart_training()
-
-        # Initialize checkpoint dir
-        self.checkpoint_dir = paths_config.checkpoints_dir
-        os.makedirs(self.checkpoint_dir, exist_ok=True)
-
-    def restart_training(self):
-
-        # Initialize networks
-        self.G = load_old_G()
-        toogle_grad(self.G, True)
-
-        self.original_G = load_old_G()
-
-        self.space_regulizer = Space_Regulizer(
-            self.original_G, self.lpips_loss)
-        self.optimizer = self.configure_optimizers()
-
-    def get_inversion(self, w_path_dir, image_name, image):
-        embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}'
-        os.makedirs(embedding_dir, exist_ok=True)
-
-        w_pivot = None
-        if hyperparameters.use_last_w_pivots:
-            w_pivot = self.load_inversions(w_path_dir, image_name)
-
-        if not hyperparameters.use_last_w_pivots or w_pivot is None:
-            w_pivot = self.calc_inversions(image, image_name)
-            torch.save(w_pivot, f'{embedding_dir}/0.pt')
-
-        w_pivot = w_pivot.to(global_config.device)
-        return w_pivot
-
-    def load_inversions(self, w_path_dir, image_name):
-        if image_name in self.w_pivots:
-            return self.w_pivots[image_name]
-
-        if hyperparameters.first_inv_type == 'w+':
-            w_potential_path = f'{w_path_dir}/{paths_config.e4e_results_keyword}/{image_name}/0.pt'
-        else:
-            w_potential_path = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}/0.pt'
-        if not os.path.isfile(w_potential_path):
-            return None
-        w = torch.load(w_potential_path).to(global_config.device)
-        self.w_pivots[image_name] = w
-        return w
-
-    def calc_inversions(self, image, image_name):
-        if hyperparameters.first_inv_type == 'w+':
-            w = self.get_e4e_inversion(image)
-
-        else:
-            id_image = torch.squeeze(
-                (image.to(global_config.device) + 1) / 2) * 255
-            w = w_projector.project(self.G, id_image, device=torch.device(global_config.device), w_avg_samples=600,
-                                    num_steps=hyperparameters.first_inv_steps, w_name=image_name,
-                                    use_wandb=self.use_wandb)
-
-        return w
-
-    @abc.abstractmethod
-    def train(self):
-        pass
-
-    def configure_optimizers(self):
-        optimizer = torch.optim.Adam(
-            self.G.parameters(), lr=hyperparameters.pti_learning_rate)
-
-        return optimizer
-
-    def calc_loss(self, generated_images, real_images, log_name, new_G, use_ball_holder, w_batch):
-        loss = 0.0
-
-        if hyperparameters.pt_l2_lambda > 0:
-            l2_loss_val = l2_loss.l2_loss(generated_images, real_images)
-            if self.use_wandb:
-                wandb.log({f'MSE_loss_val_{log_name}': l2_loss_val.detach(
-                ).cpu()}, step=global_config.training_step)
-            loss += l2_loss_val * hyperparameters.pt_l2_lambda
-        if hyperparameters.pt_lpips_lambda > 0:
-            loss_lpips = self.lpips_loss(generated_images, real_images)
-            loss_lpips = torch.squeeze(loss_lpips)
-            if self.use_wandb:
-                wandb.log({f'LPIPS_loss_val_{log_name}': loss_lpips.detach(
-                ).cpu()}, step=global_config.training_step)
-            loss += loss_lpips * hyperparameters.pt_lpips_lambda
-
-        if use_ball_holder and hyperparameters.use_locality_regularization:
-            ball_holder_loss_val = self.space_regulizer.space_regulizer_loss(
-                new_G, w_batch, use_wandb=self.use_wandb)
-            loss += ball_holder_loss_val
-
-        return loss, l2_loss_val, loss_lpips
-
-    def forward(self, w):
-        generated_images = self.G.synthesis(
-            w, noise_mode='const', force_fp32=True)
-
-        return generated_images
-
-    def initilize_e4e(self):
-        ckpt = torch.load(paths_config.e4e, map_location='cpu')
-        opts = ckpt['opts']
-        opts['batch_size'] = hyperparameters.train_batch_size
-        opts['checkpoint_path'] = paths_config.e4e
-        opts = Namespace(**opts)
-        self.e4e_inversion_net = pSp(opts)
-        self.e4e_inversion_net.eval()
-        self.e4e_inversion_net = self.e4e_inversion_net.to(
-            global_config.device)
-        toogle_grad(self.e4e_inversion_net, False)
-
-    def get_e4e_inversion(self, image):
-        image = (image + 1) / 2
-        new_image = self.e4e_image_transform(image[0]).to(global_config.device)
-        _, w = self.e4e_inversion_net(new_image.unsqueeze(0), randomize_noise=False, return_latents=True, resize=False,
-                                      input_code=False)
-        if self.use_wandb:
-            log_image_from_w(w, self.G, 'First e4e inversion')
-        return w

spaces/Amrrs/DragGan-Inversion/stylegan_human/utils/models_utils.py

@@ -1,28 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-
-import pickle
-import functools
-import torch
-from pti.pti_configs import paths_config, global_config
-
-
-def toogle_grad(model, flag=True):
-    for p in model.parameters():
-        p.requires_grad = flag
-
-
-def load_tuned_G(run_id, type):
-    new_G_path = f'{paths_config.checkpoints_dir}/model_{run_id}_{type}.pt'
-    with open(new_G_path, 'rb') as f:
-        new_G = torch.load(f).to(global_config.device).eval()
-    new_G = new_G.float()
-    toogle_grad(new_G, False)
-    return new_G
-
-
-def load_old_G():
-    with open(paths_config.stylegan2_ada_shhq, 'rb') as f:
-        old_G = pickle.load(f)['G_ema'].to(global_config.device).eval()
-        old_G = old_G.float()
-    return old_G

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/model_editing.md

@@ -1,35 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Text-to-image model editing
-
-[Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://huggingface.co/papers/2303.08084) is by Hadas Orgad, Bahjat Kawar, and Yonatan Belinkov. This pipeline enables editing diffusion model weights, such that its assumptions of a given concept are changed. The resulting change is expected to take effect in all prompt generations related to the edited concept.
-
-The abstract from the paper is:
-
-*Text-to-image diffusion models often make implicit assumptions about the world when generating images. While some assumptions are useful (e.g., the sky is blue), they can also be outdated, incorrect, or reflective of social biases present in the training data. Thus, there is a need to control these assumptions without requiring explicit user input or costly re-training. In this work, we aim to edit a given implicit assumption in a pre-trained diffusion model. Our Text-to-Image Model Editing method, TIME for short, receives a pair of inputs: a "source" under-specified prompt for which the model makes an implicit assumption (e.g., "a pack of roses"), and a "destination" prompt that describes the same setting, but with a specified desired attribute (e.g., "a pack of blue roses"). TIME then updates the model's cross-attention layers, as these layers assign visual meaning to textual tokens. We edit the projection matrices in these layers such that the source prompt is projected close to the destination prompt. Our method is highly efficient, as it modifies a mere 2.2% of the model's parameters in under one second. To evaluate model editing approaches, we introduce TIMED (TIME Dataset), containing 147 source and destination prompt pairs from various domains. Our experiments (using Stable Diffusion) show that TIME is successful in model editing, generalizes well for related prompts unseen during editing, and imposes minimal effect on unrelated generations.*
-
-You can find additional information about model editing on the [project page](https://time-diffusion.github.io/), [original codebase](https://github.com/bahjat-kawar/time-diffusion), and try it out in a [demo](https://huggingface.co/spaces/bahjat-kawar/time-diffusion).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## StableDiffusionModelEditingPipeline
-[[autodoc]] StableDiffusionModelEditingPipeline
-	- __call__
-	- all
-
-## StableDiffusionPipelineOutput
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput

spaces/Andy1621/uniformer_image_detection/configs/detr/detr_r50_8x2_150e_coco.py

@@ -1,131 +0,0 @@
-_base_ = [
-    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
-]
-model = dict(
-    type='DETR',
-    pretrained='torchvision://resnet50',
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(3, ),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=False),
-        norm_eval=True,
-        style='pytorch'),
-    bbox_head=dict(
-        type='TransformerHead',
-        num_classes=80,
-        in_channels=2048,
-        num_fcs=2,
-        transformer=dict(
-            type='Transformer',
-            embed_dims=256,
-            num_heads=8,
-            num_encoder_layers=6,
-            num_decoder_layers=6,
-            feedforward_channels=2048,
-            dropout=0.1,
-            act_cfg=dict(type='ReLU', inplace=True),
-            norm_cfg=dict(type='LN'),
-            num_fcs=2,
-            pre_norm=False,
-            return_intermediate_dec=True),
-        positional_encoding=dict(
-            type='SinePositionalEncoding', num_feats=128, normalize=True),
-        loss_cls=dict(
-            type='CrossEntropyLoss',
-            bg_cls_weight=0.1,
-            use_sigmoid=False,
-            loss_weight=1.0,
-            class_weight=1.0),
-        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
-        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
-    # training and testing settings
-    train_cfg=dict(
-        assigner=dict(
-            type='HungarianAssigner',
-            cls_cost=dict(type='ClassificationCost', weight=1.),
-            reg_cost=dict(type='BBoxL1Cost', weight=5.0),
-            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
-    test_cfg=dict(max_per_img=100))
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
-# from the default setting in mmdet.
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(
-        type='AutoAugment',
-        policies=[[
-            dict(
-                type='Resize',
-                img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
-                           (608, 1333), (640, 1333), (672, 1333), (704, 1333),
-                           (736, 1333), (768, 1333), (800, 1333)],
-                multiscale_mode='value',
-                keep_ratio=True)
-        ],
-                  [
-                      dict(
-                          type='Resize',
-                          img_scale=[(400, 1333), (500, 1333), (600, 1333)],
-                          multiscale_mode='value',
-                          keep_ratio=True),
-                      dict(
-                          type='RandomCrop',
-                          crop_type='absolute_range',
-                          crop_size=(384, 600),
-                          allow_negative_crop=True),
-                      dict(
-                          type='Resize',
-                          img_scale=[(480, 1333), (512, 1333), (544, 1333),
-                                     (576, 1333), (608, 1333), (640, 1333),
-                                     (672, 1333), (704, 1333), (736, 1333),
-                                     (768, 1333), (800, 1333)],
-                          multiscale_mode='value',
-                          override=True,
-                          keep_ratio=True)
-                  ]]),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=1),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
-]
-# test_pipeline, NOTE the Pad's size_divisor is different from the default
-# setting (size_divisor=32). While there is little effect on the performance
-# whether we use the default setting or use size_divisor=1.
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=1),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img'])
-        ])
-]
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(
-    type='AdamW',
-    lr=0.0001,
-    weight_decay=0.0001,
-    paramwise_cfg=dict(
-        custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
-optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
-# learning policy
-lr_config = dict(policy='step', step=[100])
-runner = dict(type='EpochBasedRunner', max_epochs=150)

spaces/Andy1621/uniformer_image_detection/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py

@@ -1,16 +0,0 @@
-_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://regnetx_800mf',
-    backbone=dict(
-        type='RegNet',
-        arch='regnetx_800mf',
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[64, 128, 288, 672],
-        out_channels=256,
-        num_outs=5))

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fast_scnn.py

@@ -1,57 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
-model = dict(
-    type='EncoderDecoder',
-    backbone=dict(
-        type='FastSCNN',
-        downsample_dw_channels=(32, 48),
-        global_in_channels=64,
-        global_block_channels=(64, 96, 128),
-        global_block_strides=(2, 2, 1),
-        global_out_channels=128,
-        higher_in_channels=64,
-        lower_in_channels=128,
-        fusion_out_channels=128,
-        out_indices=(0, 1, 2),
-        norm_cfg=norm_cfg,
-        align_corners=False),
-    decode_head=dict(
-        type='DepthwiseSeparableFCNHead',
-        in_channels=128,
-        channels=128,
-        concat_input=False,
-        num_classes=19,
-        in_index=-1,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)),
-    auxiliary_head=[
-        dict(
-            type='FCNHead',
-            in_channels=128,
-            channels=32,
-            num_convs=1,
-            num_classes=19,
-            in_index=-2,
-            norm_cfg=norm_cfg,
-            concat_input=False,
-            align_corners=False,
-            loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)),
-        dict(
-            type='FCNHead',
-            in_channels=64,
-            channels=32,
-            num_convs=1,
-            num_classes=19,
-            in_index=-3,
-            norm_cfg=norm_cfg,
-            concat_input=False,
-            align_corners=False,
-            loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)),
-    ],
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))

spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/midas/utils.py

@@ -1,189 +0,0 @@
-"""Utils for monoDepth."""
-import sys
-import re
-import numpy as np
-import cv2
-import torch
-
-
-def read_pfm(path):
-    """Read pfm file.
-
-    Args:
-        path (str): path to file
-
-    Returns:
-        tuple: (data, scale)
-    """
-    with open(path, "rb") as file:
-
-        color = None
-        width = None
-        height = None
-        scale = None
-        endian = None
-
-        header = file.readline().rstrip()
-        if header.decode("ascii") == "PF":
-            color = True
-        elif header.decode("ascii") == "Pf":
-            color = False
-        else:
-            raise Exception("Not a PFM file: " + path)
-
-        dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
-        if dim_match:
-            width, height = list(map(int, dim_match.groups()))
-        else:
-            raise Exception("Malformed PFM header.")
-
-        scale = float(file.readline().decode("ascii").rstrip())
-        if scale < 0:
-            # little-endian
-            endian = "<"
-            scale = -scale
-        else:
-            # big-endian
-            endian = ">"
-
-        data = np.fromfile(file, endian + "f")
-        shape = (height, width, 3) if color else (height, width)
-
-        data = np.reshape(data, shape)
-        data = np.flipud(data)
-
-        return data, scale
-
-
-def write_pfm(path, image, scale=1):
-    """Write pfm file.
-
-    Args:
-        path (str): pathto file
-        image (array): data
-        scale (int, optional): Scale. Defaults to 1.
-    """
-
-    with open(path, "wb") as file:
-        color = None
-
-        if image.dtype.name != "float32":
-            raise Exception("Image dtype must be float32.")
-
-        image = np.flipud(image)
-
-        if len(image.shape) == 3 and image.shape[2] == 3:  # color image
-            color = True
-        elif (
-            len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
-        ):  # greyscale
-            color = False
-        else:
-            raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
-
-        file.write("PF\n" if color else "Pf\n".encode())
-        file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
-
-        endian = image.dtype.byteorder
-
-        if endian == "<" or endian == "=" and sys.byteorder == "little":
-            scale = -scale
-
-        file.write("%f\n".encode() % scale)
-
-        image.tofile(file)
-
-
-def read_image(path):
-    """Read image and output RGB image (0-1).
-
-    Args:
-        path (str): path to file
-
-    Returns:
-        array: RGB image (0-1)
-    """
-    img = cv2.imread(path)
-
-    if img.ndim == 2:
-        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
-
-    return img
-
-
-def resize_image(img):
-    """Resize image and make it fit for network.
-
-    Args:
-        img (array): image
-
-    Returns:
-        tensor: data ready for network
-    """
-    height_orig = img.shape[0]
-    width_orig = img.shape[1]
-
-    if width_orig > height_orig:
-        scale = width_orig / 384
-    else:
-        scale = height_orig / 384
-
-    height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
-    width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
-
-    img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
-
-    img_resized = (
-        torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
-    )
-    img_resized = img_resized.unsqueeze(0)
-
-    return img_resized
-
-
-def resize_depth(depth, width, height):
-    """Resize depth map and bring to CPU (numpy).
-
-    Args:
-        depth (tensor): depth
-        width (int): image width
-        height (int): image height
-
-    Returns:
-        array: processed depth
-    """
-    depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
-
-    depth_resized = cv2.resize(
-        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
-    )
-
-    return depth_resized
-
-def write_depth(path, depth, bits=1):
-    """Write depth map to pfm and png file.
-
-    Args:
-        path (str): filepath without extension
-        depth (array): depth
-    """
-    write_pfm(path + ".pfm", depth.astype(np.float32))
-
-    depth_min = depth.min()
-    depth_max = depth.max()
-
-    max_val = (2**(8*bits))-1
-
-    if depth_max - depth_min > np.finfo("float").eps:
-        out = max_val * (depth - depth_min) / (depth_max - depth_min)
-    else:
-        out = np.zeros(depth.shape, dtype=depth.type)
-
-    if bits == 1:
-        cv2.imwrite(path + ".png", out.astype("uint8"))
-    elif bits == 2:
-        cv2.imwrite(path + ".png", out.astype("uint16"))
-
-    return

spaces/AntNikYab/NaturalLanguageProcessing/function/lstm_preprocessing.py

@@ -1,162 +0,0 @@
-import re
-import string
-import numpy as np
-import torch
-import torch.nn as nn
-from transformers import BertTokenizer, BertModel
-from sklearn.linear_model import LogisticRegression
-from nltk.stem import SnowballStemmer
-
-from nltk.corpus import stopwords
-import nltk
-nltk.download('stopwords')
-stop_words = set(stopwords.words('russian'))
-stemmer = SnowballStemmer('russian') 
-sw = stopwords.words('russian')   
-
-tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
-
-class LSTMClassifier(nn.Module):
-    def __init__(self, embedding_dim: int, hidden_size:int, embedding: torch.nn.modules.sparse.Embedding) -> None:
-        super().__init__()
-
-        self.embedding_dim = embedding_dim
-        self.hidden_size = hidden_size
-        self.embedding = embedding
-
-        self.lstm = nn.LSTM(
-            input_size=self.embedding_dim,
-            hidden_size=self.hidden_size,
-            batch_first=True
-        )
-        self.clf = nn.Linear(self.hidden_size, 1)
-
-    def forward(self, x):
-        embeddings = self.embedding(x)
-        _, (h_n, _) = self.lstm(embeddings)
-        out = self.clf(h_n.squeeze())
-        return out
-
-
-def data_preprocessing(text: str) -> str:
-    """preprocessing string: lowercase, removing html-tags, punctuation, 
-                            stopwords, digits
-
-    Args:
-        text (str): input string for preprocessing
-
-    Returns:
-        str: preprocessed string
-    """    
-
-    text = text.lower()
-    text = re.sub('<.*?>', '', text) # html tags
-    text = ''.join([c for c in text if c not in string.punctuation])# Remove punctuation
-    text = ' '.join([word for word in text.split() if word not in stop_words])
-    text = [word for word in text.split() if not word.isdigit()]
-    text = ' '.join(text)
-    return text
-
-def get_words_by_freq(sorted_words: list, n: int = 10) -> list:
-    return list(filter(lambda x: x[1] > n, sorted_words))
-
-def padding(review_int: list, seq_len: int) -> np.array: # type: ignore
-    """Make left-sided padding for input list of tokens
-
-    Args:
-        review_int (list): input list of tokens
-        seq_len (int): max length of sequence, it len(review_int[i]) > seq_len it will be trimmed, else it will be padded by zeros
-
-    Returns:
-        np.array: padded sequences
-    """    
-    features = np.zeros((len(review_int), seq_len), dtype = int)
-    for i, review in enumerate(review_int):
-        if len(review) <= seq_len:
-            zeros = list(np.zeros(seq_len - len(review)))
-            new = zeros + review
-        else:
-            new = review[: seq_len]
-        features[i, :] = np.array(new)
-            
-    return features
-
-def preprocess_single_string(
-    input_string: str, 
-    seq_len: int, 
-    vocab_to_int: dict,
-    ) -> torch.tensor:
-    """Function for all preprocessing steps on a single string
-
-    Args:
-        input_string (str): input single string for preprocessing
-        seq_len (int): max length of sequence, it len(review_int[i]) > seq_len it will be trimmed, else it will be padded by zeros
-        vocab_to_int (dict, optional): word corpus {'word' : int index}. Defaults to vocab_to_int.
-
-    Returns:
-        list: preprocessed string
-    """    
-
-    preprocessed_string = data_preprocessing(input_string)
-    result_list = []
-    for word in preprocessed_string.split():
-        try: 
-            result_list.append(vocab_to_int[word])
-        except KeyError as e:
-            print(f'{e}: not in dictionary!')
-    result_padded = padding([result_list], seq_len)[0]
-
-    return torch.tensor(result_padded)
-
-def predict_sentence(text: str, model: nn.Module, seq_len: int, vocab_to_int: dict) -> str: 
-    p_str = preprocess_single_string(text, seq_len, vocab_to_int).unsqueeze(0)
-    model.eval()
-    pred = model(p_str)
-    output = pred.sigmoid().round().item() 
-    if output == 0: 
-        return 'Негативный отзыв'
-    else: 
-        return 'Позитивный отзыв'
-    
-def predict_single_string(text: str,
-                          model:  BertModel,
-                          loaded_model: LogisticRegression
-) -> str:
-
-    with torch.no_grad():
-        encoded_input = tokenizer(text, return_tensors='pt')
-        output = model(**encoded_input)
-        vector = output[0][:,0,:]
-        pred0 = loaded_model.predict_proba(vector)[0][0]
-        pred1 = loaded_model.predict_proba(vector)[0][1]
-    if pred0 > pred1:
-        return 'Негативный отзыв'
-    else:
-        return 'Позитивный отзыв'
-    
-def clean(text):
-
-    text = text.lower()
-    text = re.sub(r'\s+', ' ', text)  # заменить два и более пробела на один пробел
-    text = re.sub(r'\d+', ' ', text) # удаляем числа
-    text = text.translate(str.maketrans('', '', string.punctuation)) # удаляем знаки пунктуации 
-    text = re.sub(r'\n+', ' ', text) # удаляем символ перевод строки 
-    
-    return text
-
-def tokin(text):
-    text = clean(text)
-    text = ' '.join([stemmer.stem(word) for word in text.split()])
-    text = ' '.join([word for word in text.split() if word not in sw])
-    return text
-
-
-def predict_ml_class(text, loaded_vectorizer, loaded_classifier):
-
-    t = tokin(text).split('    ')
-    new_text_bow = loaded_vectorizer.transform(t)
-    predicted_label = loaded_classifier.predict(new_text_bow)
-    if predicted_label == 0: 
-        return 'Негативный отзыв'
-    else: 
-        return 'Позитивный отзыв'

spaces/Ariharasudhan/YoloV5/utils/loggers/comet/__init__.py

@@ -1,508 +0,0 @@
-import glob
-import json
-import logging
-import os
-import sys
-from pathlib import Path
-
-logger = logging.getLogger(__name__)
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[3]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-
-try:
-    import comet_ml
-
-    # Project Configuration
-    config = comet_ml.config.get_config()
-    COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5")
-except (ModuleNotFoundError, ImportError):
-    comet_ml = None
-    COMET_PROJECT_NAME = None
-
-import PIL
-import torch
-import torchvision.transforms as T
-import yaml
-
-from utils.dataloaders import img2label_paths
-from utils.general import check_dataset, scale_boxes, xywh2xyxy
-from utils.metrics import box_iou
-
-COMET_PREFIX = "comet://"
-
-COMET_MODE = os.getenv("COMET_MODE", "online")
-
-# Model Saving Settings
-COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5")
-
-# Dataset Artifact Settings
-COMET_UPLOAD_DATASET = os.getenv("COMET_UPLOAD_DATASET", "false").lower() == "true"
-
-# Evaluation Settings
-COMET_LOG_CONFUSION_MATRIX = os.getenv("COMET_LOG_CONFUSION_MATRIX", "true").lower() == "true"
-COMET_LOG_PREDICTIONS = os.getenv("COMET_LOG_PREDICTIONS", "true").lower() == "true"
-COMET_MAX_IMAGE_UPLOADS = int(os.getenv("COMET_MAX_IMAGE_UPLOADS", 100))
-
-# Confusion Matrix Settings
-CONF_THRES = float(os.getenv("CONF_THRES", 0.001))
-IOU_THRES = float(os.getenv("IOU_THRES", 0.6))
-
-# Batch Logging Settings
-COMET_LOG_BATCH_METRICS = os.getenv("COMET_LOG_BATCH_METRICS", "false").lower() == "true"
-COMET_BATCH_LOGGING_INTERVAL = os.getenv("COMET_BATCH_LOGGING_INTERVAL", 1)
-COMET_PREDICTION_LOGGING_INTERVAL = os.getenv("COMET_PREDICTION_LOGGING_INTERVAL", 1)
-COMET_LOG_PER_CLASS_METRICS = os.getenv("COMET_LOG_PER_CLASS_METRICS", "false").lower() == "true"
-
-RANK = int(os.getenv("RANK", -1))
-
-to_pil = T.ToPILImage()
-
-
-class CometLogger:
-    """Log metrics, parameters, source code, models and much more
-    with Comet
-    """
-
-    def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwargs) -> None:
-        self.job_type = job_type
-        self.opt = opt
-        self.hyp = hyp
-
-        # Comet Flags
-        self.comet_mode = COMET_MODE
-
-        self.save_model = opt.save_period > -1
-        self.model_name = COMET_MODEL_NAME
-
-        # Batch Logging Settings
-        self.log_batch_metrics = COMET_LOG_BATCH_METRICS
-        self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL
-
-        # Dataset Artifact Settings
-        self.upload_dataset = self.opt.upload_dataset if self.opt.upload_dataset else COMET_UPLOAD_DATASET
-        self.resume = self.opt.resume
-
-        # Default parameters to pass to Experiment objects
-        self.default_experiment_kwargs = {
-            "log_code": False,
-            "log_env_gpu": True,
-            "log_env_cpu": True,
-            "project_name": COMET_PROJECT_NAME,}
-        self.default_experiment_kwargs.update(experiment_kwargs)
-        self.experiment = self._get_experiment(self.comet_mode, run_id)
-
-        self.data_dict = self.check_dataset(self.opt.data)
-        self.class_names = self.data_dict["names"]
-        self.num_classes = self.data_dict["nc"]
-
-        self.logged_images_count = 0
-        self.max_images = COMET_MAX_IMAGE_UPLOADS
-
-        if run_id is None:
-            self.experiment.log_other("Created from", "YOLOv5")
-            if not isinstance(self.experiment, comet_ml.OfflineExperiment):
-                workspace, project_name, experiment_id = self.experiment.url.split("/")[-3:]
-                self.experiment.log_other(
-                    "Run Path",
-                    f"{workspace}/{project_name}/{experiment_id}",
-                )
-            self.log_parameters(vars(opt))
-            self.log_parameters(self.opt.hyp)
-            self.log_asset_data(
-                self.opt.hyp,
-                name="hyperparameters.json",
-                metadata={"type": "hyp-config-file"},
-            )
-            self.log_asset(
-                f"{self.opt.save_dir}/opt.yaml",
-                metadata={"type": "opt-config-file"},
-            )
-
-        self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX
-
-        if hasattr(self.opt, "conf_thres"):
-            self.conf_thres = self.opt.conf_thres
-        else:
-            self.conf_thres = CONF_THRES
-        if hasattr(self.opt, "iou_thres"):
-            self.iou_thres = self.opt.iou_thres
-        else:
-            self.iou_thres = IOU_THRES
-
-        self.log_parameters({"val_iou_threshold": self.iou_thres, "val_conf_threshold": self.conf_thres})
-
-        self.comet_log_predictions = COMET_LOG_PREDICTIONS
-        if self.opt.bbox_interval == -1:
-            self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10
-        else:
-            self.comet_log_prediction_interval = self.opt.bbox_interval
-
-        if self.comet_log_predictions:
-            self.metadata_dict = {}
-            self.logged_image_names = []
-
-        self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS
-
-        self.experiment.log_others({
-            "comet_mode": COMET_MODE,
-            "comet_max_image_uploads": COMET_MAX_IMAGE_UPLOADS,
-            "comet_log_per_class_metrics": COMET_LOG_PER_CLASS_METRICS,
-            "comet_log_batch_metrics": COMET_LOG_BATCH_METRICS,
-            "comet_log_confusion_matrix": COMET_LOG_CONFUSION_MATRIX,
-            "comet_model_name": COMET_MODEL_NAME,})
-
-        # Check if running the Experiment with the Comet Optimizer
-        if hasattr(self.opt, "comet_optimizer_id"):
-            self.experiment.log_other("optimizer_id", self.opt.comet_optimizer_id)
-            self.experiment.log_other("optimizer_objective", self.opt.comet_optimizer_objective)
-            self.experiment.log_other("optimizer_metric", self.opt.comet_optimizer_metric)
-            self.experiment.log_other("optimizer_parameters", json.dumps(self.hyp))
-
-    def _get_experiment(self, mode, experiment_id=None):
-        if mode == "offline":
-            if experiment_id is not None:
-                return comet_ml.ExistingOfflineExperiment(
-                    previous_experiment=experiment_id,
-                    **self.default_experiment_kwargs,
-                )
-
-            return comet_ml.OfflineExperiment(**self.default_experiment_kwargs,)
-
-        else:
-            try:
-                if experiment_id is not None:
-                    return comet_ml.ExistingExperiment(
-                        previous_experiment=experiment_id,
-                        **self.default_experiment_kwargs,
-                    )
-
-                return comet_ml.Experiment(**self.default_experiment_kwargs)
-
-            except ValueError:
-                logger.warning("COMET WARNING: "
-                               "Comet credentials have not been set. "
-                               "Comet will default to offline logging. "
-                               "Please set your credentials to enable online logging.")
-                return self._get_experiment("offline", experiment_id)
-
-        return
-
-    def log_metrics(self, log_dict, **kwargs):
-        self.experiment.log_metrics(log_dict, **kwargs)
-
-    def log_parameters(self, log_dict, **kwargs):
-        self.experiment.log_parameters(log_dict, **kwargs)
-
-    def log_asset(self, asset_path, **kwargs):
-        self.experiment.log_asset(asset_path, **kwargs)
-
-    def log_asset_data(self, asset, **kwargs):
-        self.experiment.log_asset_data(asset, **kwargs)
-
-    def log_image(self, img, **kwargs):
-        self.experiment.log_image(img, **kwargs)
-
-    def log_model(self, path, opt, epoch, fitness_score, best_model=False):
-        if not self.save_model:
-            return
-
-        model_metadata = {
-            "fitness_score": fitness_score[-1],
-            "epochs_trained": epoch + 1,
-            "save_period": opt.save_period,
-            "total_epochs": opt.epochs,}
-
-        model_files = glob.glob(f"{path}/*.pt")
-        for model_path in model_files:
-            name = Path(model_path).name
-
-            self.experiment.log_model(
-                self.model_name,
-                file_or_folder=model_path,
-                file_name=name,
-                metadata=model_metadata,
-                overwrite=True,
-            )
-
-    def check_dataset(self, data_file):
-        with open(data_file) as f:
-            data_config = yaml.safe_load(f)
-
-        if data_config['path'].startswith(COMET_PREFIX):
-            path = data_config['path'].replace(COMET_PREFIX, "")
-            data_dict = self.download_dataset_artifact(path)
-
-            return data_dict
-
-        self.log_asset(self.opt.data, metadata={"type": "data-config-file"})
-
-        return check_dataset(data_file)
-
-    def log_predictions(self, image, labelsn, path, shape, predn):
-        if self.logged_images_count >= self.max_images:
-            return
-        detections = predn[predn[:, 4] > self.conf_thres]
-        iou = box_iou(labelsn[:, 1:], detections[:, :4])
-        mask, _ = torch.where(iou > self.iou_thres)
-        if len(mask) == 0:
-            return
-
-        filtered_detections = detections[mask]
-        filtered_labels = labelsn[mask]
-
-        image_id = path.split("/")[-1].split(".")[0]
-        image_name = f"{image_id}_curr_epoch_{self.experiment.curr_epoch}"
-        if image_name not in self.logged_image_names:
-            native_scale_image = PIL.Image.open(path)
-            self.log_image(native_scale_image, name=image_name)
-            self.logged_image_names.append(image_name)
-
-        metadata = []
-        for cls, *xyxy in filtered_labels.tolist():
-            metadata.append({
-                "label": f"{self.class_names[int(cls)]}-gt",
-                "score": 100,
-                "box": {
-                    "x": xyxy[0],
-                    "y": xyxy[1],
-                    "x2": xyxy[2],
-                    "y2": xyxy[3]},})
-        for *xyxy, conf, cls in filtered_detections.tolist():
-            metadata.append({
-                "label": f"{self.class_names[int(cls)]}",
-                "score": conf * 100,
-                "box": {
-                    "x": xyxy[0],
-                    "y": xyxy[1],
-                    "x2": xyxy[2],
-                    "y2": xyxy[3]},})
-
-        self.metadata_dict[image_name] = metadata
-        self.logged_images_count += 1
-
-        return
-
-    def preprocess_prediction(self, image, labels, shape, pred):
-        nl, _ = labels.shape[0], pred.shape[0]
-
-        # Predictions
-        if self.opt.single_cls:
-            pred[:, 5] = 0
-
-        predn = pred.clone()
-        scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1])
-
-        labelsn = None
-        if nl:
-            tbox = xywh2xyxy(labels[:, 1:5])  # target boxes
-            scale_boxes(image.shape[1:], tbox, shape[0], shape[1])  # native-space labels
-            labelsn = torch.cat((labels[:, 0:1], tbox), 1)  # native-space labels
-            scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1])  # native-space pred
-
-        return predn, labelsn
-
-    def add_assets_to_artifact(self, artifact, path, asset_path, split):
-        img_paths = sorted(glob.glob(f"{asset_path}/*"))
-        label_paths = img2label_paths(img_paths)
-
-        for image_file, label_file in zip(img_paths, label_paths):
-            image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file])
-
-            try:
-                artifact.add(image_file, logical_path=image_logical_path, metadata={"split": split})
-                artifact.add(label_file, logical_path=label_logical_path, metadata={"split": split})
-            except ValueError as e:
-                logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.')
-                logger.error(f"COMET ERROR: {e}")
-                continue
-
-        return artifact
-
-    def upload_dataset_artifact(self):
-        dataset_name = self.data_dict.get("dataset_name", "yolov5-dataset")
-        path = str((ROOT / Path(self.data_dict["path"])).resolve())
-
-        metadata = self.data_dict.copy()
-        for key in ["train", "val", "test"]:
-            split_path = metadata.get(key)
-            if split_path is not None:
-                metadata[key] = split_path.replace(path, "")
-
-        artifact = comet_ml.Artifact(name=dataset_name, artifact_type="dataset", metadata=metadata)
-        for key in metadata.keys():
-            if key in ["train", "val", "test"]:
-                if isinstance(self.upload_dataset, str) and (key != self.upload_dataset):
-                    continue
-
-                asset_path = self.data_dict.get(key)
-                if asset_path is not None:
-                    artifact = self.add_assets_to_artifact(artifact, path, asset_path, key)
-
-        self.experiment.log_artifact(artifact)
-
-        return
-
-    def download_dataset_artifact(self, artifact_path):
-        logged_artifact = self.experiment.get_artifact(artifact_path)
-        artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name)
-        logged_artifact.download(artifact_save_dir)
-
-        metadata = logged_artifact.metadata
-        data_dict = metadata.copy()
-        data_dict["path"] = artifact_save_dir
-
-        metadata_names = metadata.get("names")
-        if type(metadata_names) == dict:
-            data_dict["names"] = {int(k): v for k, v in metadata.get("names").items()}
-        elif type(metadata_names) == list:
-            data_dict["names"] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)}
-        else:
-            raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary"
-
-        data_dict = self.update_data_paths(data_dict)
-        return data_dict
-
-    def update_data_paths(self, data_dict):
-        path = data_dict.get("path", "")
-
-        for split in ["train", "val", "test"]:
-            if data_dict.get(split):
-                split_path = data_dict.get(split)
-                data_dict[split] = (f"{path}/{split_path}" if isinstance(split, str) else [
-                    f"{path}/{x}" for x in split_path])
-
-        return data_dict
-
-    def on_pretrain_routine_end(self, paths):
-        if self.opt.resume:
-            return
-
-        for path in paths:
-            self.log_asset(str(path))
-
-        if self.upload_dataset:
-            if not self.resume:
-                self.upload_dataset_artifact()
-
-        return
-
-    def on_train_start(self):
-        self.log_parameters(self.hyp)
-
-    def on_train_epoch_start(self):
-        return
-
-    def on_train_epoch_end(self, epoch):
-        self.experiment.curr_epoch = epoch
-
-        return
-
-    def on_train_batch_start(self):
-        return
-
-    def on_train_batch_end(self, log_dict, step):
-        self.experiment.curr_step = step
-        if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0):
-            self.log_metrics(log_dict, step=step)
-
-        return
-
-    def on_train_end(self, files, save_dir, last, best, epoch, results):
-        if self.comet_log_predictions:
-            curr_epoch = self.experiment.curr_epoch
-            self.experiment.log_asset_data(self.metadata_dict, "image-metadata.json", epoch=curr_epoch)
-
-        for f in files:
-            self.log_asset(f, metadata={"epoch": epoch})
-        self.log_asset(f"{save_dir}/results.csv", metadata={"epoch": epoch})
-
-        if not self.opt.evolve:
-            model_path = str(best if best.exists() else last)
-            name = Path(model_path).name
-            if self.save_model:
-                self.experiment.log_model(
-                    self.model_name,
-                    file_or_folder=model_path,
-                    file_name=name,
-                    overwrite=True,
-                )
-
-        # Check if running Experiment with Comet Optimizer
-        if hasattr(self.opt, 'comet_optimizer_id'):
-            metric = results.get(self.opt.comet_optimizer_metric)
-            self.experiment.log_other('optimizer_metric_value', metric)
-
-        self.finish_run()
-
-    def on_val_start(self):
-        return
-
-    def on_val_batch_start(self):
-        return
-
-    def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs):
-        if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)):
-            return
-
-        for si, pred in enumerate(outputs):
-            if len(pred) == 0:
-                continue
-
-            image = images[si]
-            labels = targets[targets[:, 0] == si, 1:]
-            shape = shapes[si]
-            path = paths[si]
-            predn, labelsn = self.preprocess_prediction(image, labels, shape, pred)
-            if labelsn is not None:
-                self.log_predictions(image, labelsn, path, shape, predn)
-
-        return
-
-    def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
-        if self.comet_log_per_class_metrics:
-            if self.num_classes > 1:
-                for i, c in enumerate(ap_class):
-                    class_name = self.class_names[c]
-                    self.experiment.log_metrics(
-                        {
-                            '[email protected]': ap50[i],
-                            '[email protected]:.95': ap[i],
-                            'precision': p[i],
-                            'recall': r[i],
-                            'f1': f1[i],
-                            'true_positives': tp[i],
-                            'false_positives': fp[i],
-                            'support': nt[c]},
-                        prefix=class_name)
-
-        if self.comet_log_confusion_matrix:
-            epoch = self.experiment.curr_epoch
-            class_names = list(self.class_names.values())
-            class_names.append("background")
-            num_classes = len(class_names)
-
-            self.experiment.log_confusion_matrix(
-                matrix=confusion_matrix.matrix,
-                max_categories=num_classes,
-                labels=class_names,
-                epoch=epoch,
-                column_label='Actual Category',
-                row_label='Predicted Category',
-                file_name=f"confusion-matrix-epoch-{epoch}.json",
-            )
-
-    def on_fit_epoch_end(self, result, epoch):
-        self.log_metrics(result, epoch=epoch)
-
-    def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
-        if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
-            self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
-
-    def on_params_update(self, params):
-        self.log_parameters(params)
-
-    def finish_run(self):
-        self.experiment.end()

spaces/Arsenii2023/Demo1/demo1.py

@@ -1,73 +0,0 @@
-#Author: Arsenii Kostenko
-import numpy as np
-from sklearn.linear_model import LinearRegression, LogisticRegression
-import gradio as gr
-
-# Данные для обучения моделей
-x_train = np.array([[0, 0], [1, 1], [2, 2]])
-y_train = np.array([0, 1, 2])
-
-# Обучение моделей
-linear_model = LinearRegression()
-linear_model.fit(x_train, y_train)
-
-logistic_model = LogisticRegression()
-logistic_model.fit(x_train, y_train)
-
-# Функция для предсказания значений линейной регрессии
-def predict_linear(x, y):
-    # Преобразование строк в список списков
-    x_nested_list = [list(map(int, sublist.split(","))) for sublist in x.split(";")]
-    y_nested_list = [list(map(int, sublist.split(","))) for sublist in y.split(";")]
-
-    # Преобразование списка списков в numpy array
-    x_array = np.array(x_nested_list)
-    y_array = np.array(y_nested_list)
-
-    # Проверка исходных данных на соответствие
-    if x_array.shape != y_array.shape:
-        return "Ошибка: x и y должны иметь одинаковую размерность"
-
-    # Предсказание значений для линейной регрессии
-    predictions = linear_model.predict(x_array)
-
-    return predictions
-
-# Функция для предсказания значений логистической регрессии
-def predict_logistic(x, y):
-    # Преобразование строк в список списков
-    x_nested_list = [list(map(int, sublist.split(","))) for sublist in x.split(";")]
-    y_nested_list = [list(map(int, sublist.split(","))) for sublist in y.split(";")]
-
-    # Преобразование списка списков в numpy array
-    x_array = np.array(x_nested_list)
-    y_array = np.array(y_nested_list)
-
-    # Проверка исходных данных на соответствие
-    if x_array.shape != y_array.shape:
-        return "Ошибка: x и y должны иметь одинаковую размерность"
-
-    # Предсказание значений для логистической регрессии
-    predictions = logistic_model.predict(x_array)
-
-    return predictions
-
-# Создание интерфейса gradio для линейной регрессии
-interface_linear = gr.Interface(
-    fn=predict_linear,
-    inputs=["text", "text"],
-    outputs="text",
-    title="Линейная регрессия"
-)
-
-# Создание интерфейса gradio для логистической регрессии
-interface_logistic = gr.Interface(
-    fn=predict_logistic,
-    inputs=["text", "text"],
-    outputs="text",
-    title="Логистическая регрессия"
-)
-
-# Запуск обоих интерфейсов
-interface_linear.launch(debug=True)
-interface_logistic.launch(debug=True)

spaces/Awesimo/jojogan/e4e/criteria/w_norm.py

@@ -1,14 +0,0 @@
-import torch
-from torch import nn
-
-
-class WNormLoss(nn.Module):
-
-	def __init__(self, start_from_latent_avg=True):
-		super(WNormLoss, self).__init__()
-		self.start_from_latent_avg = start_from_latent_avg
-
-	def forward(self, latent, latent_avg=None):
-		if self.start_from_latent_avg:
-			latent = latent - latent_avg
-		return torch.sum(latent.norm(2, dim=(1, 2))) / latent.shape[0]

spaces/Bart92/RVC_HF/go-applio.bat

@@ -1,92 +0,0 @@
-@echo off
-setlocal
-title Start Applio
-
-:::
-:::                       _ _
-:::     /\               | (_)
-:::    /  \   _ __  _ __ | |_  ___
-:::   / /\ \ | '_ \| '_ \| | |/ _ \
-:::  / ____ \| |_) | |_) | | | (_) |
-::: /_/    \_\ .__/| .__/|_|_|\___/
-:::          | |   | |
-:::          |_|   |_|
-:::
-:::
-
-:menu
-for /f "delims=: tokens=*" %%A in ('findstr /b ":::" "%~f0"') do @echo(%%A
-
-echo [1] Start Applio
-echo [2] Start Applio (DML)
-echo [3] Start Realtime GUI (DML)
-echo [4] Start Realtime GUI (V0)
-echo [5] Start Realtime GUI (V1)
-echo.
-
-set /p choice=Select an option: 
-set choice=%choice: =%
-
-cls
-echo WARNING: It's recommended to disable antivirus or firewall, as errors might occur when starting the ssl.
-pause
-
-if "%choice%"=="1" (
-    cls
-    echo WARNING: At this point, it's recommended to disable antivirus or firewall, as errors might occur when downloading pretrained models.
-    pause>null
-    echo Starting Applio...
-    echo.
-    runtime\python.exe infer-web.py --pycmd runtime\python.exe --port 7897
-    pause
-    cls
-    goto menu
-)
-
-if "%choice%"=="2" (
-    cls
-    echo Starting Applio ^(DML^)...
-    echo.
-    runtime\python.exe infer-web.py --pycmd runtime\python.exe --port 7897 --dml
-    pause
-    cls
-    goto menu
-)
-
-if "%choice%"=="3" (
-    cls
-    echo Starting Realtime GUI ^(DML^)...
-    echo.
-    runtime\python.exe gui_v1.py --pycmd runtime\python.exe --dml
-    pause
-    cls
-    goto menu
-)
-
-if "%choice%"=="4" (
-    cls
-    echo Starting Realtime GUI ^(V0^)...
-    echo.
-    runtime\python.exe gui_v0.py
-    pause
-    cls
-    goto menu
-)
-
-if "%choice%"=="5" (
-    cls
-    echo Starting Realtime GUI ^(V1^)...
-    echo.
-    runtime\python.exe gui_v1.py
-    pause
-    cls
-    goto menu
-)
-
-cls
-echo Invalid option. Please enter a number from 1 to 5.
-echo.
-echo Press 'Enter' to access the main menu...
-pause>nul
-cls
-goto menu

spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/constants.py

@@ -1,30 +0,0 @@
-# Copyright 2018 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 s3transfer
-
-KB = 1024
-MB = KB * KB
-GB = MB * KB
-
-ALLOWED_DOWNLOAD_ARGS = [
-    'ChecksumMode',
-    'VersionId',
-    'SSECustomerAlgorithm',
-    'SSECustomerKey',
-    'SSECustomerKeyMD5',
-    'RequestPayer',
-    'ExpectedBucketOwner',
-]
-
-USER_AGENT = 's3transfer/%s' % s3transfer.__version__
-PROCESS_USER_AGENT = '%s processpool' % USER_AGENT

spaces/Blaise-g/summarize-biomedical-papers-long-summary-or-tldr/README.md

@@ -1,11 +0,0 @@
----
-title: Summarize biomedical papers in a long, detailed synopsis or extreme, TLDR summary
-emoji: 🧬📃🗜
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----

spaces/BwayKC/darkstorm2150-Protogen_v2.2_Official_Release/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/darkstorm2150/Protogen_v2.2_Official_Release").launch()

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/samplers/distributed_sampler.py

@@ -1,199 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import itertools
-import math
-from collections import defaultdict
-from typing import Optional
-import torch
-from torch.utils.data.sampler import Sampler
-
-from detectron2.utils import comm
-
-
-class TrainingSampler(Sampler):
-    """
-    In training, we only care about the "infinite stream" of training data.
-    So this sampler produces an infinite stream of indices and
-    all workers cooperate to correctly shuffle the indices and sample different indices.
-
-    The samplers in each worker effectively produces `indices[worker_id::num_workers]`
-    where `indices` is an infinite stream of indices consisting of
-    `shuffle(range(size)) + shuffle(range(size)) + ...` (if shuffle is True)
-    or `range(size) + range(size) + ...` (if shuffle is False)
-    """
-
-    def __init__(self, size: int, shuffle: bool = True, seed: Optional[int] = None):
-        """
-        Args:
-            size (int): the total number of data of the underlying dataset to sample from
-            shuffle (bool): whether to shuffle the indices or not
-            seed (int): the initial seed of the shuffle. Must be the same
-                across all workers. If None, will use a random seed shared
-                among workers (require synchronization among all workers).
-        """
-        self._size = size
-        assert size > 0
-        self._shuffle = shuffle
-        if seed is None:
-            seed = comm.shared_random_seed()
-        self._seed = int(seed)
-
-        self._rank = comm.get_rank()
-        self._world_size = comm.get_world_size()
-
-    def __iter__(self):
-        start = self._rank
-        yield from itertools.islice(self._infinite_indices(), start, None, self._world_size)
-
-    def _infinite_indices(self):
-        g = torch.Generator()
-        g.manual_seed(self._seed)
-        while True:
-            if self._shuffle:
-                yield from torch.randperm(self._size, generator=g)
-            else:
-                yield from torch.arange(self._size)
-
-
-class RepeatFactorTrainingSampler(Sampler):
-    """
-    Similar to TrainingSampler, but suitable for training on class imbalanced datasets
-    like LVIS. In each epoch, an image may appear multiple times based on its "repeat
-    factor". The repeat factor for an image is a function of the frequency the rarest
-    category labeled in that image. The "frequency of category c" in [0, 1] is defined
-    as the fraction of images in the training set (without repeats) in which category c
-    appears.
-
-    See https://arxiv.org/abs/1908.03195 (>= v2) Appendix B.2.
-    """
-
-    def __init__(self, dataset_dicts, repeat_thresh, shuffle=True, seed=None):
-        """
-        Args:
-            dataset_dicts (list[dict]): annotations in Detectron2 dataset format.
-            repeat_thresh (float): frequency threshold below which data is repeated.
-            shuffle (bool): whether to shuffle the indices or not
-            seed (int): the initial seed of the shuffle. Must be the same
-                across all workers. If None, will use a random seed shared
-                among workers (require synchronization among all workers).
-        """
-        self._shuffle = shuffle
-        if seed is None:
-            seed = comm.shared_random_seed()
-        self._seed = int(seed)
-
-        self._rank = comm.get_rank()
-        self._world_size = comm.get_world_size()
-
-        # Get fractional repeat factors and split into whole number (_int_part)
-        # and fractional (_frac_part) parts.
-        rep_factors = self._get_repeat_factors(dataset_dicts, repeat_thresh)
-        self._int_part = torch.trunc(rep_factors)
-        self._frac_part = rep_factors - self._int_part
-
-    def _get_repeat_factors(self, dataset_dicts, repeat_thresh):
-        """
-        Compute (fractional) per-image repeat factors.
-
-        Args:
-            See __init__.
-
-        Returns:
-            torch.Tensor: the i-th element is the repeat factor for the dataset image
-                at index i.
-        """
-        # 1. For each category c, compute the fraction of images that contain it: f(c)
-        category_freq = defaultdict(int)
-        for dataset_dict in dataset_dicts:  # For each image (without repeats)
-            cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]}
-            for cat_id in cat_ids:
-                category_freq[cat_id] += 1
-        num_images = len(dataset_dicts)
-        for k, v in category_freq.items():
-            category_freq[k] = v / num_images
-
-        # 2. For each category c, compute the category-level repeat factor:
-        #    r(c) = max(1, sqrt(t / f(c)))
-        category_rep = {
-            cat_id: max(1.0, math.sqrt(repeat_thresh / cat_freq))
-            for cat_id, cat_freq in category_freq.items()
-        }
-
-        # 3. For each image I, compute the image-level repeat factor:
-        #    r(I) = max_{c in I} r(c)
-        rep_factors = []
-        for dataset_dict in dataset_dicts:
-            cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]}
-            rep_factor = max({category_rep[cat_id] for cat_id in cat_ids})
-            rep_factors.append(rep_factor)
-
-        return torch.tensor(rep_factors, dtype=torch.float32)
-
-    def _get_epoch_indices(self, generator):
-        """
-        Create a list of dataset indices (with repeats) to use for one epoch.
-
-        Args:
-            generator (torch.Generator): pseudo random number generator used for
-                stochastic rounding.
-
-        Returns:
-            torch.Tensor: list of dataset indices to use in one epoch. Each index
-                is repeated based on its calculated repeat factor.
-        """
-        # Since repeat factors are fractional, we use stochastic rounding so
-        # that the target repeat factor is achieved in expectation over the
-        # course of training
-        rands = torch.rand(len(self._frac_part), generator=generator)
-        rep_factors = self._int_part + (rands < self._frac_part).float()
-        # Construct a list of indices in which we repeat images as specified
-        indices = []
-        for dataset_index, rep_factor in enumerate(rep_factors):
-            indices.extend([dataset_index] * int(rep_factor.item()))
-        return torch.tensor(indices, dtype=torch.int64)
-
-    def __iter__(self):
-        start = self._rank
-        yield from itertools.islice(self._infinite_indices(), start, None, self._world_size)
-
-    def _infinite_indices(self):
-        g = torch.Generator()
-        g.manual_seed(self._seed)
-        while True:
-            # Sample indices with repeats determined by stochastic rounding; each
-            # "epoch" may have a slightly different size due to the rounding.
-            indices = self._get_epoch_indices(g)
-            if self._shuffle:
-                randperm = torch.randperm(len(indices), generator=g)
-                yield from indices[randperm]
-            else:
-                yield from indices
-
-
-class InferenceSampler(Sampler):
-    """
-    Produce indices for inference.
-    Inference needs to run on the __exact__ set of samples,
-    therefore when the total number of samples is not divisible by the number of workers,
-    this sampler produces different number of samples on different workers.
-    """
-
-    def __init__(self, size: int):
-        """
-        Args:
-            size (int): the total number of data of the underlying dataset to sample from
-        """
-        self._size = size
-        assert size > 0
-        self._rank = comm.get_rank()
-        self._world_size = comm.get_world_size()
-
-        shard_size = (self._size - 1) // self._world_size + 1
-        begin = shard_size * self._rank
-        end = min(shard_size * (self._rank + 1), self._size)
-        self._local_indices = range(begin, end)
-
-    def __iter__(self):
-        yield from self._local_indices
-
-    def __len__(self):
-        return len(self._local_indices)

spaces/CVPR/LIVE/pybind11/tests/test_callbacks.cpp

@@ -1,168 +0,0 @@
-/*
-    tests/test_callbacks.cpp -- callbacks
-
-    Copyright (c) 2016 Wenzel Jakob <[email protected]>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-#include "constructor_stats.h"
-#include <pybind11/functional.h>
-#include <thread>
-
-
-int dummy_function(int i) { return i + 1; }
-
-TEST_SUBMODULE(callbacks, m) {
-    // test_callbacks, test_function_signatures
-    m.def("test_callback1", [](py::object func) { return func(); });
-    m.def("test_callback2", [](py::object func) { return func("Hello", 'x', true, 5); });
-    m.def("test_callback3", [](const std::function<int(int)> &func) {
-        return "func(43) = " + std::to_string(func(43)); });
-    m.def("test_callback4", []() -> std::function<int(int)> { return [](int i) { return i+1; }; });
-    m.def("test_callback5", []() {
-        return py::cpp_function([](int i) { return i+1; }, py::arg("number"));
-    });
-
-    // test_keyword_args_and_generalized_unpacking
-    m.def("test_tuple_unpacking", [](py::function f) {
-        auto t1 = py::make_tuple(2, 3);
-        auto t2 = py::make_tuple(5, 6);
-        return f("positional", 1, *t1, 4, *t2);
-    });
-
-    m.def("test_dict_unpacking", [](py::function f) {
-        auto d1 = py::dict("key"_a="value", "a"_a=1);
-        auto d2 = py::dict();
-        auto d3 = py::dict("b"_a=2);
-        return f("positional", 1, **d1, **d2, **d3);
-    });
-
-    m.def("test_keyword_args", [](py::function f) {
-        return f("x"_a=10, "y"_a=20);
-    });
-
-    m.def("test_unpacking_and_keywords1", [](py::function f) {
-        auto args = py::make_tuple(2);
-        auto kwargs = py::dict("d"_a=4);
-        return f(1, *args, "c"_a=3, **kwargs);
-    });
-
-    m.def("test_unpacking_and_keywords2", [](py::function f) {
-        auto kwargs1 = py::dict("a"_a=1);
-        auto kwargs2 = py::dict("c"_a=3, "d"_a=4);
-        return f("positional", *py::make_tuple(1), 2, *py::make_tuple(3, 4), 5,
-                 "key"_a="value", **kwargs1, "b"_a=2, **kwargs2, "e"_a=5);
-    });
-
-    m.def("test_unpacking_error1", [](py::function f) {
-        auto kwargs = py::dict("x"_a=3);
-        return f("x"_a=1, "y"_a=2, **kwargs); // duplicate ** after keyword
-    });
-
-    m.def("test_unpacking_error2", [](py::function f) {
-        auto kwargs = py::dict("x"_a=3);
-        return f(**kwargs, "x"_a=1); // duplicate keyword after **
-    });
-
-    m.def("test_arg_conversion_error1", [](py::function f) {
-        f(234, UnregisteredType(), "kw"_a=567);
-    });
-
-    m.def("test_arg_conversion_error2", [](py::function f) {
-        f(234, "expected_name"_a=UnregisteredType(), "kw"_a=567);
-    });
-
-    // test_lambda_closure_cleanup
-    struct Payload {
-        Payload() { print_default_created(this); }
-        ~Payload() { print_destroyed(this); }
-        Payload(const Payload &) { print_copy_created(this); }
-        Payload(Payload &&) { print_move_created(this); }
-    };
-    // Export the payload constructor statistics for testing purposes:
-    m.def("payload_cstats", &ConstructorStats::get<Payload>);
-    /* Test cleanup of lambda closure */
-    m.def("test_cleanup", []() -> std::function<void(void)> {
-        Payload p;
-
-        return [p]() {
-            /* p should be cleaned up when the returned function is garbage collected */
-            (void) p;
-        };
-    });
-
-    // test_cpp_function_roundtrip
-    /* Test if passing a function pointer from C++ -> Python -> C++ yields the original pointer */
-    m.def("dummy_function", &dummy_function);
-    m.def("dummy_function2", [](int i, int j) { return i + j; });
-    m.def("roundtrip", [](std::function<int(int)> f, bool expect_none = false) {
-        if (expect_none && f)
-            throw std::runtime_error("Expected None to be converted to empty std::function");
-        return f;
-    }, py::arg("f"), py::arg("expect_none")=false);
-    m.def("test_dummy_function", [](const std::function<int(int)> &f) -> std::string {
-        using fn_type = int (*)(int);
-        auto result = f.target<fn_type>();
-        if (!result) {
-            auto r = f(1);
-            return "can't convert to function pointer: eval(1) = " + std::to_string(r);
-        } else if (*result == dummy_function) {
-            auto r = (*result)(1);
-            return "matches dummy_function: eval(1) = " + std::to_string(r);
-        } else {
-            return "argument does NOT match dummy_function. This should never happen!";
-        }
-    });
-
-    class AbstractBase { public: virtual unsigned int func() = 0; };
-    m.def("func_accepting_func_accepting_base", [](std::function<double(AbstractBase&)>) { });
-
-    struct MovableObject {
-        bool valid = true;
-
-        MovableObject() = default;
-        MovableObject(const MovableObject &) = default;
-        MovableObject &operator=(const MovableObject &) = default;
-        MovableObject(MovableObject &&o) : valid(o.valid) { o.valid = false; }
-        MovableObject &operator=(MovableObject &&o) {
-            valid = o.valid;
-            o.valid = false;
-            return *this;
-        }
-    };
-    py::class_<MovableObject>(m, "MovableObject");
-
-    // test_movable_object
-    m.def("callback_with_movable", [](std::function<void(MovableObject &)> f) {
-        auto x = MovableObject();
-        f(x); // lvalue reference shouldn't move out object
-        return x.valid; // must still return `true`
-    });
-
-    // test_bound_method_callback
-    struct CppBoundMethodTest {};
-    py::class_<CppBoundMethodTest>(m, "CppBoundMethodTest")
-        .def(py::init<>())
-        .def("triple", [](CppBoundMethodTest &, int val) { return 3 * val; });
-
-    // test async Python callbacks
-    using callback_f = std::function<void(int)>;
-    m.def("test_async_callback", [](callback_f f, py::list work) {
-        // make detached thread that calls `f` with piece of work after a little delay
-        auto start_f = [f](int j) {
-            auto invoke_f = [f, j] {
-                std::this_thread::sleep_for(std::chrono::milliseconds(50));
-                f(j);
-            };
-            auto t = std::thread(std::move(invoke_f));
-            t.detach();
-        };
-
-        // spawn worker threads
-        for (auto i : work)
-            start_f(py::cast<int>(i));
-    });
-}

spaces/CVPR/LIVE/thrust/testing/unittest/special_types.h

@@ -1,184 +0,0 @@
-#pragma once
-
-#include <iostream>
-#include <thrust/execution_policy.h>
-
-template <typename T, unsigned int N>
-struct FixedVector
-{
-    T data[N];
-    
-    __host__ __device__
-    FixedVector()
-    {
-        for(unsigned int i = 0; i < N; i++)
-            data[i] = T();
-    }
-
-    __host__ __device__
-    FixedVector(T init)
-    {
-        for(unsigned int i = 0; i < N; i++)
-            data[i] = init;
-    }
-
-    __host__ __device__
-    FixedVector operator+(const FixedVector& bs) const
-    {
-        FixedVector output;
-        for(unsigned int i = 0; i < N; i++)
-            output.data[i] = data[i] + bs.data[i];
-        return output;
-    }
-    
-    __host__ __device__
-    bool operator<(const FixedVector& bs) const
-    {
-        for(unsigned int i = 0; i < N; i++)
-        {
-            if(data[i] < bs.data[i])
-                return true;
-            else if(bs.data[i] < data[i])
-                return false;
-        }
-        return false;
-    }
-
-    __host__ __device__
-    bool operator==(const FixedVector& bs) const
-    {
-        for(unsigned int i = 0; i < N; i++)
-        {
-            if(!(data[i] == bs.data[i]))
-                return false;
-        }
-        return true;                
-    }
-};
-
-template<typename Key, typename Value>
-  struct key_value
-{
-  typedef Key   key_type;
-  typedef Value value_type;
-
-  __host__ __device__
-  key_value(void)
-    : key(), value()
-  {}
-
-  __host__ __device__
-  key_value(key_type k, value_type v)
-    : key(k), value(v)
-  {}
-
-  __host__ __device__
-  bool operator<(const key_value &rhs) const
-  {
-    return key < rhs.key;
-  }
-
-  __host__ __device__
-  bool operator>(const key_value &rhs) const
-  {
-    return key > rhs.key;
-  }
-
-  __host__ __device__
-  bool operator==(const key_value &rhs) const
-  {
-    return key == rhs.key && value == rhs.value;
-  }
-
-  __host__ __device__
-  bool operator!=(const key_value &rhs) const
-  {
-    return !operator==(rhs);
-  }
-
-  friend std::ostream &operator<<(std::ostream &os, const key_value &kv)
-  {
-    return os << "(" << kv.key << ", " << kv.value << ")";
-  }
-
-  key_type key;
-  value_type value;
-};
-
-struct user_swappable
-{
-  inline __host__ __device__
-  user_swappable(bool swapped = false)
-    : was_swapped(swapped)
-  {}
-
-  bool was_swapped;
-};
-
-inline __host__ __device__
-bool operator==(const user_swappable &x, const user_swappable &y)
-{
-  return x.was_swapped == y.was_swapped;
-}
-
-inline __host__ __device__
-void swap(user_swappable &x, user_swappable &y)
-{
-  x.was_swapped = true;
-  y.was_swapped = false;
-}
-
-class my_system : public thrust::device_execution_policy<my_system>
-{
-  public:
-    my_system(int)
-      : correctly_dispatched(false),
-        num_copies(0)
-    {}
-
-    my_system(const my_system &other)
-      : correctly_dispatched(false),
-        num_copies(other.num_copies + 1)
-    {}
-
-    void validate_dispatch()
-    {
-      correctly_dispatched = (num_copies == 0);
-    }
-
-    bool is_valid()
-    {
-      return correctly_dispatched;
-    }
-
-  private:
-    bool correctly_dispatched;
-
-    // count the number of copies so that we can validate
-    // that dispatch does not introduce any
-    unsigned int num_copies;
-
-
-    // disallow default construction
-    my_system();
-};
-
-struct my_tag : thrust::device_execution_policy<my_tag> {};
-
-namespace unittest
-{
-
-
-using thrust::detail::int8_t;
-using thrust::detail::int16_t;
-using thrust::detail::int32_t;
-using thrust::detail::int64_t;
-
-using thrust::detail::uint8_t;
-using thrust::detail::uint16_t;
-using thrust::detail::uint32_t;
-using thrust::detail::uint64_t;
-
-  
-}
-

spaces/CVPR/LIVE/thrust/thrust/device_reference.h

@@ -1,983 +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 device_reference.h
- *  \brief A reference to a variable which resides in the "device" system's memory space
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/device_ptr.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/detail/reference.h>
-
-namespace thrust
-{
-
-/*! \addtogroup memory_management_classes Memory Management Classes
- *  \ingroup memory_management
- *  \{
- */
-
-/*! \p device_reference acts as a reference-like object to an object stored in device memory.
- *  \p device_reference is not intended to be used directly; rather, this type
- *  is the result of deferencing a \p device_ptr. Similarly, taking the address of
- *  a \p device_reference yields a \p device_ptr.
- *  
- *  \p device_reference may often be used from host code in place of operations defined on
- *  its associated \c value_type. For example, when \p device_reference refers to an
- *  arithmetic type, arithmetic operations on it are legal:
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *
- *  int main(void)
- *  {
- *    thrust::device_vector<int> vec(1, 13);
- *
- *    thrust::device_reference<int> ref_to_thirteen = vec[0];
- *
- *    int x = ref_to_thirteen + 1;
- *
- *    // x is 14
- *
- *    return 0;
- *  }
- *  \endcode
- *
- *  Similarly, we can print the value of \c ref_to_thirteen in the above code by using an
- *  \c iostream:
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <iostream>
- *
- *  int main(void)
- *  {
- *    thrust::device_vector<int> vec(1, 13);
- *
- *    thrust::device_reference<int> ref_to_thirteen = vec[0];
- *
- *    std::cout << ref_to_thirteen << std::endl;
- *
- *    // 13 is printed
- *
- *    return 0;
- *  }
- *  \endcode
- *
- *  Of course, we needn't explicitly create a \p device_reference in the previous
- *  example, because one is returned by \p device_vector's bracket operator. A more natural
- *  way to print the value of a \p device_vector element might be:
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <iostream>
- *
- *  int main(void)
- *  {
- *    thrust::device_vector<int> vec(1, 13);
- *
- *    std::cout << vec[0] << std::endl;
- *
- *    // 13 is printed
- *
- *    return 0;
- *  }
- *  \endcode
- *
- *  These kinds of operations should be used sparingly in performance-critical code, because
- *  they imply a potentially expensive copy between host and device space.
- *
- *  Some operations which are possible with regular objects are impossible with their
- *  corresponding \p device_reference objects due to the requirements of the C++ language. For
- *  example, because the member access operator cannot be overloaded, member variables and functions
- *  of a referent object cannot be directly accessed through its \p device_reference.
- *
- *  The following code, which generates a compiler error, illustrates:
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *
- *  struct foo
- *  {
- *    int x;
- *  };
- *
- *  int main(void)
- *  {
- *    thrust::device_vector<foo> foo_vec(1);
- *
- *    thrust::device_reference<foo> foo_ref = foo_vec[0];
- *
- *    foo_ref.x = 13; // ERROR: x cannot be accessed through foo_ref
- *
- *    return 0;
- *  }
- *  \endcode
- *
- *  Instead, a host space copy must be created to access \c foo's \c x member:
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *
- *  struct foo
- *  {
- *    int x;
- *  };
- *
- *  int main(void)
- *  {
- *    thrust::device_vector<foo> foo_vec(1);
- *
- *    // create a local host-side foo object
- *    foo host_foo;
- *    host_foo.x = 13;
- *
- *    thrust::device_reference<foo> foo_ref = foo_vec[0];
- *
- *    foo_ref = host_foo;
- *
- *    // foo_ref's x member is 13
- *
- *    return 0;
- *  }
- *  \endcode
- *  
- *  Another common case where a \p device_reference cannot directly be used in place of
- *  its referent object occurs when passing them as parameters to functions like \c printf
- *  which have varargs parameters. Because varargs parameters must be Plain Old Data, a
- *  \p device_reference to a POD type requires a cast when passed to \c printf:
- *
- *  \code
- *  #include <stdio.h>
- *  #include <thrust/device_vector.h>
- *
- *  int main(void)
- *  {
- *    thrust::device_vector<int> vec(1,13);
- *
- *    // vec[0] must be cast to int when passing to printf
- *    printf("%d\n", (int) vec[0]);
- *
- *    return 0;
- *  }
- *  \endcode
- *
- *  \see device_ptr
- *  \see device_vector
- */
-template<typename T>
-  class device_reference
-    : public thrust::reference<
-               T,
-               thrust::device_ptr<T>,
-               thrust::device_reference<T>
-             >
-{
-  private:
-    typedef thrust::reference<
-      T,
-      thrust::device_ptr<T>,
-      thrust::device_reference<T>
-    > super_t;
-
-  public:
-    /*! The type of the value referenced by this type of \p device_reference.
-     */
-    typedef typename super_t::value_type value_type;
-
-    /*! The type of the expression <tt>&ref</tt>, where <tt>ref</tt> is a \p device_reference.
-     */
-    typedef typename super_t::pointer    pointer;
-
-    /*! This copy constructor accepts a const reference to another
-     *  \p device_reference. After this \p device_reference is constructed,
-     *  it shall refer to the same object as \p other.
-     *  
-     *  \param other A \p device_reference to copy from.
-     *
-     *  The following code snippet demonstrates the semantics of this
-     *  copy constructor.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,0);
-     *  thrust::device_reference<int> ref = v[0];
-     *
-     *  // ref equals the object at v[0]
-     *  assert(ref == v[0]);
-     *
-     *  // the address of ref equals the address of v[0]
-     *  assert(&ref == &v[0]);
-     *
-     *  // modifying v[0] modifies ref
-     *  v[0] = 13;
-     *  assert(ref == 13);
-     *  \endcode
-     *
-     *  \note This constructor is templated primarily to allow initialization of 
-     *  <tt>device_reference<const T></tt> from <tt>device_reference<T></tt>.
-     */
-    template<typename OtherT>
-    __host__ __device__
-    device_reference(const device_reference<OtherT> &other,
-                     typename thrust::detail::enable_if_convertible<
-                       typename device_reference<OtherT>::pointer,
-                       pointer
-                     >::type * = 0)
-      : super_t(other)
-    {}
-
-    /*! This copy constructor initializes this \p device_reference
-     *  to refer to an object pointed to by the given \p device_ptr. After
-     *  this \p device_reference is constructed, it shall refer to the
-     *  object pointed to by \p ptr.
-     *
-     *  \param ptr A \p device_ptr to copy from.
-     *
-     *  The following code snippet demonstrates the semantic of this
-     *  copy constructor.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,0);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals the object pointed to by ptr
-     *  assert(ref == *ptr);
-     *
-     *  // the address of ref equals ptr
-     *  assert(&ref == ptr);
-     *
-     *  // modifying *ptr modifies ref
-     *  *ptr = 13;
-     *  assert(ref == 13);
-     *  \endcode
-     */
-    __host__ __device__
-    explicit device_reference(const pointer &ptr)
-      : super_t(ptr)
-    {}
-
-    /*! This assignment operator assigns the value of the object referenced by
-     *  the given \p device_reference to the object referenced by this
-     *  \p device_reference.
-     *
-     *  \param other The \p device_reference to assign from.
-     *  \return <tt>*this</tt>
-     */
-    template<typename OtherT>
-    __host__ __device__
-    device_reference &operator=(const device_reference<OtherT> &other);
-
-    /*! Assignment operator assigns the value of the given value to the
-     *  value referenced by this \p device_reference.
-     *  
-     *  \param x The value to assign from.
-     *  \return <tt>*this</tt>
-     */
-    __host__ __device__
-    device_reference &operator=(const value_type &x);
-
-// declare these members for the purpose of Doxygenating them
-// they actually exist in a derived-from class
-#if 0
-    /*! Address-of operator returns a \p device_ptr pointing to the object
-     *  referenced by this \p device_reference. It does not return the
-     *  address of this \p device_reference.
-     *
-     *  \return A \p device_ptr pointing to the object this
-     *  \p device_reference references.
-     */
-    __host__ __device__
-    pointer operator&(void) const;
-
-    /*! Conversion operator converts this \p device_reference to T
-     *  by returning a copy of the object referenced by this
-     *  \p device_reference.
-     *
-     *  \return A copy of the object referenced by this \p device_reference.
-     */
-    __host__ __device__
-    operator value_type (void) const;
-
-    /*! swaps the value this \p device_reference references with another.
-     *  \p other The other \p device_reference with which to swap.
-     */
-    __host__ __device__
-    void swap(device_reference &other);
-
-    /*! Prefix increment operator increments the object referenced by this
-     *  \p device_reference.
-     *
-     *  \return <tt>*this</tt>
-     *  
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's prefix increment operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,0);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 0
-     *  assert(ref == 0);
-     *
-     *  // the object pointed to by ptr equals 1
-     *  assert(*ptr == 1);
-     *
-     *  // v[0] equals 1
-     *  assert(v[0] == 1);
-     *
-     *  // increment ref
-     *  ++ref;
-     *
-     *  // ref equals 1
-     *  assert(ref == 1);
-     *
-     *  // the object pointed to by ptr equals 1
-     *  assert(*ptr == 1);
-     *
-     *  // v[0] equals 1
-     *  assert(v[0] == 1);
-     *  \endcode
-     *
-     *  \note The increment executes as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    device_reference &operator++(void);
-
-    /*! Postfix increment operator copies the object referenced by this
-     *  \p device_reference, increments the object referenced by this
-     *  \p device_reference, and returns the copy.
-     *
-     *  \return A copy of the object referenced by this \p device_reference
-     *          before being incremented.
-     *
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's postfix increment operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,0);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 0
-     *  assert(ref == 0);
-     *
-     *  // the object pointed to by ptr equals 0
-     *  assert(*ptr == 0);
-     *
-     *  // v[0] equals 0
-     *  assert(v[0] == 0);
-     *
-     *  // increment ref
-     *  int x = ref++;
-     *
-     *  // x equals 0
-     *  assert(x == 0)
-     *
-     *  // ref equals 1
-     *  assert(ref == 1);
-     *
-     *  // the object pointed to by ptr equals 1
-     *  assert(*ptr == 1);
-     *
-     *  // v[0] equals 1
-     *  assert(v[0] == 1);
-     *  \endcode
-     *
-     *  \note The increment executes as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    value_type operator++(int);
-
-    /*! Addition assignment operator add-assigns the object referenced by this
-     *  \p device_reference and returns this \p device_reference.
-     *
-     *  \param rhs The right hand side of the add-assignment.
-     *  \return <tt>*this</tt>.
-     *
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's addition assignment operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,0);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 0
-     *  assert(ref == 0);
-     *
-     *  // the object pointed to by ptr equals 0
-     *  assert(*ptr == 0);
-     *
-     *  // v[0] equals 0
-     *  assert(v[0] == 0);
-     *
-     *  // add-assign ref
-     *  ref += 5;
-     *
-     *  // ref equals 5
-     *  assert(ref == 5);
-     *
-     *  // the object pointed to by ptr equals 5
-     *  assert(*ptr == 5);
-     *
-     *  // v[0] equals 5
-     *  assert(v[0] == 5);
-     *  \endcode
-     *
-     *  \note The add-assignment executes as as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    device_reference &operator+=(const T &rhs);
-
-    /*! Prefix decrement operator decrements the object referenced by this
-     *  \p device_reference.
-     *
-     *  \return <tt>*this</tt>
-     *  
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's prefix decrement operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,0);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 0
-     *  assert(ref == 0);
-     *
-     *  // the object pointed to by ptr equals 0
-     *  assert(*ptr == 0);
-     *
-     *  // v[0] equals 0
-     *  assert(v[0] == 0);
-     *
-     *  // decrement ref
-     *  --ref;
-     *
-     *  // ref equals -1
-     *  assert(ref == -1);
-     *
-     *  // the object pointed to by ptr equals -1
-     *  assert(*ptr == -1);
-     *
-     *  // v[0] equals -1
-     *  assert(v[0] == -1);
-     *  \endcode
-     *
-     *  \note The decrement executes as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    device_reference &operator--(void);
-
-    /*! Postfix decrement operator copies the object referenced by this
-     *  \p device_reference, decrements the object referenced by this
-     *  \p device_reference, and returns the copy.
-     *
-     *  \return A copy of the object referenced by this \p device_reference
-     *          before being decremented.
-     *
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's postfix decrement operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,0);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 0
-     *  assert(ref == 0);
-     *
-     *  // the object pointed to by ptr equals 0
-     *  assert(*ptr == 0);
-     *
-     *  // v[0] equals 0
-     *  assert(v[0] == 0);
-     *
-     *  // decrement ref
-     *  int x = ref--;
-     *
-     *  // x equals 0
-     *  assert(x == 0)
-     *
-     *  // ref equals -1
-     *  assert(ref == -1);
-     *
-     *  // the object pointed to by ptr equals -1
-     *  assert(*ptr == -1);
-     *
-     *  // v[0] equals -1
-     *  assert(v[0] == -1);
-     *  \endcode
-     *
-     *  \note The decrement executes as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    value_type operator--(int);
-
-    /*! Subtraction assignment operator subtract-assigns the object referenced by this
-     *  \p device_reference and returns this \p device_reference.
-     *
-     *  \param rhs The right hand side of the subtraction-assignment.
-     *  \return <tt>*this</tt>.
-     *
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's addition assignment operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,0);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 0
-     *  assert(ref == 0);
-     *
-     *  // the object pointed to by ptr equals 0
-     *  assert(*ptr == 0);
-     *
-     *  // v[0] equals 0
-     *  assert(v[0] == 0);
-     *
-     *  // subtract-assign ref
-     *  ref -= 5;
-     *
-     *  // ref equals -5
-     *  assert(ref == -5);
-     *
-     *  // the object pointed to by ptr equals -5
-     *  assert(*ptr == -5);
-     *
-     *  // v[0] equals -5
-     *  assert(v[0] == -5);
-     *  \endcode
-     *
-     *  \note The subtract-assignment executes as as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    device_reference &operator-=(const T &rhs);
-
-    /*! Multiplication assignment operator multiply-assigns the object referenced by this
-     *  \p device_reference and returns this \p device_reference.
-     *
-     *  \param rhs The right hand side of the multiply-assignment.
-     *  \return <tt>*this</tt>.
-     *
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's multiply assignment operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,1);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 1
-     *  assert(ref == 1);
-     *
-     *  // the object pointed to by ptr equals 1
-     *  assert(*ptr == 1);
-     *
-     *  // v[0] equals 1
-     *  assert(v[0] == 1);
-     *
-     *  // multiply-assign ref
-     *  ref *= 5;
-     *
-     *  // ref equals 5
-     *  assert(ref == 5);
-     *
-     *  // the object pointed to by ptr equals 5
-     *  assert(*ptr == 5);
-     *
-     *  // v[0] equals 5
-     *  assert(v[0] == 5);
-     *  \endcode
-     *
-     *  \note The multiply-assignment executes as as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    device_reference &operator*=(const T &rhs);
-
-    /*! Division assignment operator divide-assigns the object referenced by this
-     *  \p device_reference and returns this \p device_reference.
-     *
-     *  \param rhs The right hand side of the divide-assignment.
-     *  \return <tt>*this</tt>.
-     *
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's divide assignment operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,5);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 5
-     *  assert(ref == 5);
-     *
-     *  // the object pointed to by ptr equals 5
-     *  assert(*ptr == 5);
-     *
-     *  // v[0] equals 5
-     *  assert(v[0] == 5);
-     *
-     *  // divide-assign ref
-     *  ref /= 5;
-     *
-     *  // ref equals 1
-     *  assert(ref == 1);
-     *
-     *  // the object pointed to by ptr equals 1
-     *  assert(*ptr == 1);
-     *
-     *  // v[0] equals 1
-     *  assert(v[0] == 1);
-     *  \endcode
-     *
-     *  \note The divide-assignment executes as as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    device_reference &operator/=(const T &rhs);
-
-    /*! Modulation assignment operator modulus-assigns the object referenced by this
-     *  \p device_reference and returns this \p device_reference.
-     *
-     *  \param rhs The right hand side of the divide-assignment.
-     *  \return <tt>*this</tt>.
-     *
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's divide assignment operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,5);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 5
-     *  assert(ref == 5);
-     *
-     *  // the object pointed to by ptr equals 5
-     *  assert(*ptr == 5);
-     *
-     *  // v[0] equals 5
-     *  assert(v[0] == 5);
-     *
-     *  // modulus-assign ref
-     *  ref %= 5;
-     *
-     *  // ref equals 0
-     *  assert(ref == 0);
-     *
-     *  // the object pointed to by ptr equals 0
-     *  assert(*ptr == 0);
-     *
-     *  // v[0] equals 0
-     *  assert(v[0] == 0);
-     *  \endcode
-     *
-     *  \note The modulus-assignment executes as as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    device_reference &operator%=(const T &rhs);
-
-    /*! Bitwise left shift assignment operator left shift-assigns the object referenced by this
-     *  \p device_reference and returns this \p device_reference.
-     *
-     *  \param rhs The right hand side of the left shift-assignment.
-     *  \return <tt>*this</tt>.
-     *
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's left shift assignment operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,1);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 1
-     *  assert(ref == 1);
-     *
-     *  // the object pointed to by ptr equals 1
-     *  assert(*ptr == 1);
-     *
-     *  // v[0] equals 1
-     *  assert(v[0] == 1);
-     *
-     *  // left shift-assign ref
-     *  ref <<= 1;
-     *
-     *  // ref equals 2
-     *  assert(ref == 2);
-     *
-     *  // the object pointed to by ptr equals 2
-     *  assert(*ptr == 2);
-     *
-     *  // v[0] equals 2
-     *  assert(v[0] == 2);
-     *  \endcode
-     *
-     *  \note The left shift-assignment executes as as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    device_reference &operator<<=(const T &rhs);
-
-    /*! Bitwise right shift assignment operator right shift-assigns the object referenced by this
-     *  \p device_reference and returns this \p device_reference.
-     *
-     *  \param rhs The right hand side of the right shift-assignment.
-     *  \return <tt>*this</tt>.
-     *
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's right shift assignment operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,2);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 2
-     *  assert(ref == 2);
-     *
-     *  // the object pointed to by ptr equals 2
-     *  assert(*ptr == 2);
-     *
-     *  // v[0] equals 2
-     *  assert(v[0] == 2);
-     *
-     *  // right shift-assign ref
-     *  ref >>= 1;
-     *
-     *  // ref equals 1
-     *  assert(ref == 1);
-     *
-     *  // the object pointed to by ptr equals 1
-     *  assert(*ptr == 1);
-     *
-     *  // v[0] equals 1
-     *  assert(v[0] == 1);
-     *  \endcode
-     *
-     *  \note The right shift-assignment executes as as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    device_reference &operator>>=(const T &rhs);
-
-    /*! Bitwise AND assignment operator AND-assigns the object referenced by this
-     *  \p device_reference and returns this \p device_reference.
-     *
-     *  \param rhs The right hand side of the AND-assignment.
-     *  \return <tt>*this</tt>.
-     *
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's AND assignment operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,1);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 1
-     *  assert(ref == 1);
-     *
-     *  // the object pointed to by ptr equals 1
-     *  assert(*ptr == 1);
-     *
-     *  // v[0] equals 1
-     *  assert(v[0] == 1);
-     *
-     *  // right AND-assign ref
-     *  ref &= 0;
-     *
-     *  // ref equals 0
-     *  assert(ref == 0);
-     *
-     *  // the object pointed to by ptr equals 0
-     *  assert(*ptr == 0);
-     *
-     *  // v[0] equals 0
-     *  assert(v[0] == 0);
-     *  \endcode
-     *
-     *  \note The AND-assignment executes as as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    device_reference &operator&=(const T &rhs);
-
-    /*! Bitwise OR assignment operator OR-assigns the object referenced by this
-     *  \p device_reference and returns this \p device_reference.
-     *
-     *  \param rhs The right hand side of the OR-assignment.
-     *  \return <tt>*this</tt>.
-     *
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's OR assignment operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,0);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 0
-     *  assert(ref == 0);
-     *
-     *  // the object pointed to by ptr equals 0
-     *  assert(*ptr == 0);
-     *
-     *  // v[0] equals 0
-     *  assert(v[0] == 0);
-     *
-     *  // right OR-assign ref
-     *  ref |= 1;
-     *
-     *  // ref equals 1
-     *  assert(ref == 1);
-     *
-     *  // the object pointed to by ptr equals 1
-     *  assert(*ptr == 1);
-     *
-     *  // v[0] equals 1
-     *  assert(v[0] == 1);
-     *  \endcode
-     *
-     *  \note The OR-assignment executes as as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    device_reference &operator|=(const T &rhs);
-
-    /*! Bitwise XOR assignment operator XOR-assigns the object referenced by this
-     *  \p device_reference and returns this \p device_reference.
-     *
-     *  \param rhs The right hand side of the XOR-assignment.
-     *  \return <tt>*this</tt>.
-     *
-     *  The following code snippet demonstrates the semantics of
-     *  \p device_reference's XOR assignment operator.
-     *
-     *  \code
-     *  #include <thrust/device_vector.h>
-     *  #include <assert.h>
-     *  ...
-     *  thrust::device_vector<int> v(1,1);
-     *  thrust::device_ptr<int> ptr = &v[0];
-     *  thrust::device_reference<int> ref(ptr);
-     *
-     *  // ref equals 1
-     *  assert(ref == 1);
-     *
-     *  // the object pointed to by ptr equals 1
-     *  assert(*ptr == 1);
-     *
-     *  // v[0] equals 1
-     *  assert(v[0] == 1);
-     *
-     *  // right XOR-assign ref
-     *  ref ^= 1;
-     *
-     *  // ref equals 0
-     *  assert(ref == 0);
-     *
-     *  // the object pointed to by ptr equals 0
-     *  assert(*ptr == 0);
-     *
-     *  // v[0] equals 0
-     *  assert(v[0] == 0);
-     *  \endcode
-     *
-     *  \note The XOR-assignment executes as as if it were executed on the host.
-     *  This may change in a later version.
-     */
-    device_reference &operator^=(const T &rhs);
-#endif // end doxygen-only members
-}; // end device_reference
-
-/*! swaps the value of one \p device_reference with another.
- *  \p x The first \p device_reference of interest.
- *  \p y The second \p device_reference of interest.
- */
-template<typename T>
-__host__ __device__
-void swap(device_reference<T> x, device_reference<T> y);
-
-// declare these methods for the purpose of Doxygenating them
-// they actually are defined for a derived-from class
-#if 0
-/*! Writes to an output stream the value of a \p device_reference.
- *
- *  \param os The output stream.
- *  \param y The \p device_reference to output.
- *  \return os.
- */
-template<typename T, typename charT, typename traits>
-std::basic_ostream<charT, traits> &
-operator<<(std::basic_ostream<charT, traits> &os, const device_reference<T> &y);
-#endif
-
-/*! \}
- */
-
-} // end thrust
-
-#include <thrust/detail/device_reference.inl>
-

spaces/CVPR/Object-Detection-With-DETR-and-YOLOS/app.py

@@ -1,153 +0,0 @@
-import io
-import gradio as gr
-import matplotlib.pyplot as plt
-import requests, validators
-import torch
-import pathlib
-from PIL import Image
-from transformers import AutoFeatureExtractor, DetrForObjectDetection, YolosForObjectDetection
-
-import os
-
-# colors for visualization
-COLORS = [
-    [0.000, 0.447, 0.741],
-    [0.850, 0.325, 0.098],
-    [0.929, 0.694, 0.125],
-    [0.494, 0.184, 0.556],
-    [0.466, 0.674, 0.188],
-    [0.301, 0.745, 0.933]
-]
-
-def make_prediction(img, feature_extractor, model):
-    inputs = feature_extractor(img, return_tensors="pt")
-    outputs = model(**inputs)
-    img_size = torch.tensor([tuple(reversed(img.size))])
-    processed_outputs = feature_extractor.post_process(outputs, img_size)
-    return processed_outputs[0]
-
-def fig2img(fig):
-    buf = io.BytesIO()
-    fig.savefig(buf)
-    buf.seek(0)
-    img = Image.open(buf)
-    return img
-
-
-def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
-    keep = output_dict["scores"] > threshold
-    boxes = output_dict["boxes"][keep].tolist()
-    scores = output_dict["scores"][keep].tolist()
-    labels = output_dict["labels"][keep].tolist()
-    if id2label is not None:
-        labels = [id2label[x] for x in labels]
-
-    plt.figure(figsize=(16, 10))
-    plt.imshow(pil_img)
-    ax = plt.gca()
-    colors = COLORS * 100
-    for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors):
-        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3))
-        ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
-    plt.axis("off")
-    return fig2img(plt.gcf())
-
-def detect_objects(model_name,url_input,image_input,threshold):
-    
-    #Extract model and feature extractor
-    feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
-    
-    if 'detr' in model_name:
-        
-        model = DetrForObjectDetection.from_pretrained(model_name)
-        
-    elif 'yolos' in model_name:
-    
-        model = YolosForObjectDetection.from_pretrained(model_name)
-    
-    if validators.url(url_input):
-        image = Image.open(requests.get(url_input, stream=True).raw)
-        
-    elif image_input:
-        image = image_input
-    
-    #Make prediction
-    processed_outputs = make_prediction(image, feature_extractor, model)
-    
-    #Visualize prediction
-    viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
-    
-    return viz_img   
-        
-def set_example_image(example: list) -> dict:
-    return gr.Image.update(value=example[0])
-
-def set_example_url(example: list) -> dict:
-    return gr.Textbox.update(value=example[0])
-
-
-title = """<h1 id="title">Object Detection App with DETR and YOLOS</h1>"""
-
-description = """
-Links to HuggingFace Models:
-- [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)  
-- [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101)  
-- [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small)
-- [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny)
-"""
-
-models = ["facebook/detr-resnet-50","facebook/detr-resnet-101",'hustvl/yolos-small','hustvl/yolos-tiny']
-urls = ["https://c8.alamy.com/comp/J2AB4K/the-new-york-stock-exchange-on-the-wall-street-in-new-york-J2AB4K.jpg"]
-
-twitter_link = """
-[![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi)
-"""
-
-css = '''
-h1#title {
-  text-align: center;
-}
-'''
-demo = gr.Blocks(css=css)
-
-with demo:
-    gr.Markdown(title)
-    gr.Markdown(description)
-    gr.Markdown(twitter_link)
-    options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True)
-    slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.7,label='Prediction Threshold')
-    
-    with gr.Tabs():
-        with gr.TabItem('Image URL'):
-            with gr.Row():
-                url_input = gr.Textbox(lines=2,label='Enter valid image URL here..')
-                img_output_from_url = gr.Image(shape=(650,650))
-                
-            with gr.Row():
-                example_url = gr.Dataset(components=[url_input],samples=[[str(url)] for url in urls])
-            
-            url_but = gr.Button('Detect')
-     
-        with gr.TabItem('Image Upload'):
-            with gr.Row():
-                img_input = gr.Image(type='pil')
-                img_output_from_upload= gr.Image(shape=(650,650))
-                
-            with gr.Row(): 
-                example_images = gr.Dataset(components=[img_input],
-                                            samples=[[path.as_posix()]
-                                                     for path in sorted(pathlib.Path('images').rglob('*.JPG'))])
-                
-            img_but = gr.Button('Detect')
-        
-    
-    url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_url,queue=True)
-    img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_upload,queue=True)
-    example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])
-    example_url.click(fn=set_example_url,inputs=[example_url],outputs=[url_input])
-    
-
-    gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-object-detection-with-detr-and-yolos)")
-
-    
-demo.launch(enable_queue=True)

spaces/CVPR/WALT/mmdet/core/bbox/samplers/sampling_result.py

@@ -1,152 +0,0 @@
-import torch
-
-from mmdet.utils import util_mixins
-
-
-class SamplingResult(util_mixins.NiceRepr):
-    """Bbox sampling result.
-
-    Example:
-        >>> # xdoctest: +IGNORE_WANT
-        >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
-        >>> self = SamplingResult.random(rng=10)
-        >>> print(f'self = {self}')
-        self = <SamplingResult({
-            'neg_bboxes': torch.Size([12, 4]),
-            'neg_inds': tensor([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12]),
-            'num_gts': 4,
-            'pos_assigned_gt_inds': tensor([], dtype=torch.int64),
-            'pos_bboxes': torch.Size([0, 4]),
-            'pos_inds': tensor([], dtype=torch.int64),
-            'pos_is_gt': tensor([], dtype=torch.uint8)
-        })>
-    """
-
-    def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result,
-                 gt_flags):
-        self.pos_inds = pos_inds
-        self.neg_inds = neg_inds
-        self.pos_bboxes = bboxes[pos_inds]
-        self.neg_bboxes = bboxes[neg_inds]
-        self.pos_is_gt = gt_flags[pos_inds]
-
-        self.num_gts = gt_bboxes.shape[0]
-        self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1
-
-        if gt_bboxes.numel() == 0:
-            # hack for index error case
-            assert self.pos_assigned_gt_inds.numel() == 0
-            self.pos_gt_bboxes = torch.empty_like(gt_bboxes).view(-1, 4)
-        else:
-            if len(gt_bboxes.shape) < 2:
-                gt_bboxes = gt_bboxes.view(-1, 4)
-
-            self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds, :]
-
-        if assign_result.labels is not None:
-            self.pos_gt_labels = assign_result.labels[pos_inds]
-        else:
-            self.pos_gt_labels = None
-
-    @property
-    def bboxes(self):
-        """torch.Tensor: concatenated positive and negative boxes"""
-        return torch.cat([self.pos_bboxes, self.neg_bboxes])
-
-    def to(self, device):
-        """Change the device of the data inplace.
-
-        Example:
-            >>> self = SamplingResult.random()
-            >>> print(f'self = {self.to(None)}')
-            >>> # xdoctest: +REQUIRES(--gpu)
-            >>> print(f'self = {self.to(0)}')
-        """
-        _dict = self.__dict__
-        for key, value in _dict.items():
-            if isinstance(value, torch.Tensor):
-                _dict[key] = value.to(device)
-        return self
-
-    def __nice__(self):
-        data = self.info.copy()
-        data['pos_bboxes'] = data.pop('pos_bboxes').shape
-        data['neg_bboxes'] = data.pop('neg_bboxes').shape
-        parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
-        body = '    ' + ',\n    '.join(parts)
-        return '{\n' + body + '\n}'
-
-    @property
-    def info(self):
-        """Returns a dictionary of info about the object."""
-        return {
-            'pos_inds': self.pos_inds,
-            'neg_inds': self.neg_inds,
-            'pos_bboxes': self.pos_bboxes,
-            'neg_bboxes': self.neg_bboxes,
-            'pos_is_gt': self.pos_is_gt,
-            'num_gts': self.num_gts,
-            'pos_assigned_gt_inds': self.pos_assigned_gt_inds,
-        }
-
-    @classmethod
-    def random(cls, rng=None, **kwargs):
-        """
-        Args:
-            rng (None | int | numpy.random.RandomState): seed or state.
-            kwargs (keyword arguments):
-                - num_preds: number of predicted boxes
-                - num_gts: number of true boxes
-                - p_ignore (float): probability of a predicted box assinged to \
-                    an ignored truth.
-                - p_assigned (float): probability of a predicted box not being \
-                    assigned.
-                - p_use_label (float | bool): with labels or not.
-
-        Returns:
-            :obj:`SamplingResult`: Randomly generated sampling result.
-
-        Example:
-            >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
-            >>> self = SamplingResult.random()
-            >>> print(self.__dict__)
-        """
-        from mmdet.core.bbox.samplers.random_sampler import RandomSampler
-        from mmdet.core.bbox.assigners.assign_result import AssignResult
-        from mmdet.core.bbox import demodata
-        rng = demodata.ensure_rng(rng)
-
-        # make probabalistic?
-        num = 32
-        pos_fraction = 0.5
-        neg_pos_ub = -1
-
-        assign_result = AssignResult.random(rng=rng, **kwargs)
-
-        # Note we could just compute an assignment
-        bboxes = demodata.random_boxes(assign_result.num_preds, rng=rng)
-        gt_bboxes = demodata.random_boxes(assign_result.num_gts, rng=rng)
-
-        if rng.rand() > 0.2:
-            # sometimes algorithms squeeze their data, be robust to that
-            gt_bboxes = gt_bboxes.squeeze()
-            bboxes = bboxes.squeeze()
-
-        if assign_result.labels is None:
-            gt_labels = None
-        else:
-            gt_labels = None  # todo
-
-        if gt_labels is None:
-            add_gt_as_proposals = False
-        else:
-            add_gt_as_proposals = True  # make probabalistic?
-
-        sampler = RandomSampler(
-            num,
-            pos_fraction,
-            neg_pos_ub=neg_pos_ub,
-            add_gt_as_proposals=add_gt_as_proposals,
-            rng=rng)
-        self = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)
-        return self

spaces/CVPR/flava-multimodal-zero-shot/app.py

@@ -1,131 +0,0 @@
-import numpy as np
-import gradio as gr
-import torch
-
-from transformers import BertTokenizer, FlavaForPreTraining, FlavaModel, FlavaFeatureExtractor, FlavaProcessor
-from PIL import Image
-
-
-demo = gr.Blocks()
-
-tokenizer = BertTokenizer.from_pretrained("facebook/flava-full")
-flava_pt = FlavaForPreTraining.from_pretrained("facebook/flava-full")
-flava = FlavaModel.from_pretrained("facebook/flava-full")
-processor = FlavaProcessor.from_pretrained("facebook/flava-full")
-fe = FlavaFeatureExtractor.from_pretrained("facebook/flava-full")
-
-
-PREDICTION_ATTR = "mlm_logits"
-
-def zero_shot_text(text, options):
-    options = [option.strip() for option in options.split(";")]
-    option_indices = tokenizer.convert_tokens_to_ids(options)
-    tokens = tokenizer([text], return_tensors="pt")
-    mask_ids = tokens["input_ids"][0] == 103
-    with torch.no_grad():
-        output = flava_pt(**tokens)
-    
-    text_logits = getattr(output, PREDICTION_ATTR)
-    probs = text_logits[0, mask_ids, option_indices].view(-1, len(option_indices)).mean(dim=0)
-    probs = torch.nn.functional.softmax(probs, dim=-1)
-    return {label: probs[idx].item() for idx, label in enumerate(options)}
-
-
-def zero_shot_image(image, options):
-    PIL_image = Image.fromarray(np.uint8(image)).convert("RGB")
-    labels = [label.strip() for label in options.split(";")]
-    image_input = fe([PIL_image], return_tensors="pt")
-    text_inputs = tokenizer(
-        labels, padding="max_length", return_tensors="pt"
-    )
-
-    image_embeddings = flava.get_image_features(**image_input)[:, 0, :]
-    text_embeddings = flava.get_text_features(**text_inputs)[:, 0, :]
-    similarities = list(
-        torch.nn.functional.softmax(
-            (text_embeddings @ image_embeddings.T).squeeze(0), dim=0
-        )
-    )
-    return {label: similarities[idx].item() for idx, label in enumerate(labels)}
-  
-def zero_shot_multimodal(image, text, options):
-    options = [option.strip() for option in options.split(";")]
-    option_indices = tokenizer.convert_tokens_to_ids(options)
-    tokens = processor([image], [text], return_tensors="pt", return_codebook_pixels=True, return_image_mask=True)
-
-    mask_ids = tokens["input_ids"][0] == 103
-    tokens["bool_masked_pos"] = torch.ones_like(tokens["bool_masked_pos"])
-
-    with torch.no_grad():
-        output = flava_pt(**tokens)
-    
-    text_logits = getattr(output, "mmm_text_logits")
-    probs = text_logits[0, mask_ids, option_indices].view(-1, len(option_indices)).mean(dim=0)
-    probs = torch.nn.functional.softmax(probs, dim=-1)
-    return {label: probs[idx].item() for idx, label in enumerate(options)}
-
-with demo:
-    gr.Markdown(
-    """
-    # Zero-Shot image, text or multimodal classification using the same FLAVA model
-
-    Click on one the examples provided to load them into the UI and "Classify".
-
-    - For image classification, provide class options to be ranked separated by `;`.
-    - For text and multimodal classification, provide your 1) prompt with the word you want to be filled in as `[MASK]`, and 2) possible options to be ranked separated by `;`.  
-    """
-    )
-    with gr.Tabs():
-        with gr.TabItem("Zero-Shot Image Classification"):
-            with gr.Row():
-                with gr.Column():
-                    image_input = gr.Image()
-                    text_options_i = gr.Textbox(label="Classes (seperated by ;)")
-                    image_button = gr.Button("Classify")
-                    image_dataset = gr.Dataset(
-                        components=[image_input, text_options_i],
-                        samples=[
-                            ["cows.jpg", "a cow; two cows in a green field; a cow in a green field"],
-                            ["sofa.jpg", "a room with red sofa; a red room with sofa; ladder in a room"]
-                        ]
-                    )
-
-                labels_image = gr.Label(label="Probabilities")
-        with gr.TabItem("Zero-Shot Text Classification"):
-            with gr.Row():
-                with gr.Column():
-                    text_input = gr.Textbox(label="Prompt")
-                    text_options = gr.Textbox(label="Label options (separate by ;)")
-                    text_button = gr.Button("Classify")
-                    text_dataset = gr.Dataset(
-                        components=[text_input, text_options],
-                        samples=[
-                            ["by far the worst movie of the year. This was [MASK]", "negative; positive"],
-                            ["Lord Voldemort -- in the films; born Tom Marvolo Riddle) is a fictional character and the main antagonist in J.K. Rowling's series of Harry Potter novels. Voldemort first appeared in Harry Potter and the Philosopher's Stone, which was released in 1997. Voldemort appears either in person or in flashbacks in each book and its film adaptation in the series, except the third, Harry Potter and the Prisoner of Azkaban, where he is only mentioned. Question: are tom riddle and lord voldemort the same person? Answer: [MASK]", "no; yes"],
-                        ]
-                    )
-                labels_text = gr.Label(label="Probabilities")
-        with gr.TabItem("Zero-Shot MultiModal Classification"):
-            with gr.Row():
-                with gr.Column():
-                    image_input_mm = gr.Image()
-                    text_input_mm = gr.Textbox(label="Prompt")
-                    text_options_mm = gr.Textbox(label="Options (separate by ;)")
-                    multimodal_button = gr.Button("Classify")
-                    multimodal_dataset = gr.Dataset(
-                        components=[image_input_mm, text_input_mm],
-                        samples=[
-                            ["cows.jpg", "What animals are in the field? They are [MASK].", "cows; lions; sheep; monkeys"],
-                            ["sofa.jpg", "What furniture is in the room? It is [MASK].", "sofa; ladder; bucket"]
-                        ]
-                    )
-                labels_multimodal = gr.Label(label="Probabilities")
-
-    text_button.click(zero_shot_text, inputs=[text_input, text_options], outputs=labels_text)
-    image_button.click(zero_shot_image, inputs=[image_input, text_options_i], outputs=labels_image)
-    multimodal_button.click(zero_shot_multimodal, inputs=[image_input_mm, text_input_mm, text_options_mm], outputs=labels_multimodal)
-    text_dataset.click(lambda a: a, inputs=[text_dataset], outputs=[text_input, text_options])
-    image_dataset.click(lambda a: a, inputs=[image_dataset], outputs=[image_input, text_options_i])
-    multimodal_dataset.click(lambda a: a, inputs=[multimodal_dataset], outputs=[image_input_mm, text_input_mm, text_options_mm])
-
-demo.launch()

spaces/CVPR/lama-example/bin/calc_dataset_stats.py

@@ -1,88 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-
-import numpy as np
-import tqdm
-from scipy.ndimage.morphology import distance_transform_edt
-
-from saicinpainting.evaluation.data import InpaintingDataset
-from saicinpainting.evaluation.vis import save_item_for_vis
-
-
-def main(args):
-    dataset = InpaintingDataset(args.datadir, img_suffix='.png')
-
-    area_bins = np.linspace(0, 1, args.area_bins + 1)
-
-    heights = []
-    widths = []
-    image_areas = []
-    hole_areas = []
-    hole_area_percents = []
-    known_pixel_distances = []
-
-    area_bins_count = np.zeros(args.area_bins)
-    area_bin_titles = [f'{area_bins[i] * 100:.0f}-{area_bins[i + 1] * 100:.0f}' for i in range(args.area_bins)]
-
-    bin2i = [[] for _ in range(args.area_bins)]
-
-    for i, item in enumerate(tqdm.tqdm(dataset)):
-        h, w = item['image'].shape[1:]
-        heights.append(h)
-        widths.append(w)
-        full_area = h * w
-        image_areas.append(full_area)
-        bin_mask = item['mask'] > 0.5
-        hole_area = bin_mask.sum()
-        hole_areas.append(hole_area)
-        hole_percent = hole_area / full_area
-        hole_area_percents.append(hole_percent)
-        bin_i = np.clip(np.searchsorted(area_bins, hole_percent) - 1, 0, len(area_bins_count) - 1)
-        area_bins_count[bin_i] += 1
-        bin2i[bin_i].append(i)
-
-        cur_dist = distance_transform_edt(bin_mask)
-        cur_dist_inside_mask = cur_dist[bin_mask]
-        known_pixel_distances.append(cur_dist_inside_mask.mean())
-
-    os.makedirs(args.outdir, exist_ok=True)
-    with open(os.path.join(args.outdir, 'summary.txt'), 'w') as f:
-        f.write(f'''Location:          {args.datadir}
-
-Number of samples: {len(dataset)}
-
-Image height: min {min(heights):5d} max {max(heights):5d} mean {np.mean(heights):.2f}
-Image width:  min {min(widths):5d} max {max(widths):5d} mean {np.mean(widths):.2f}
-Image area:   min {min(image_areas):7d} max {max(image_areas):7d} mean {np.mean(image_areas):.2f}
-Hole area:    min {min(hole_areas):7d} max {max(hole_areas):7d} mean {np.mean(hole_areas):.2f}
-Hole area %:  min {min(hole_area_percents) * 100:2.2f} max {max(hole_area_percents) * 100:2.2f} mean {np.mean(hole_area_percents) * 100:2.2f}
-Dist 2known:  min {min(known_pixel_distances):2.2f} max {max(known_pixel_distances):2.2f} mean {np.mean(known_pixel_distances):2.2f} median {np.median(known_pixel_distances):2.2f}
-
-Stats by hole area %:
-''')
-        for bin_i in range(args.area_bins):
-            f.write(f'{area_bin_titles[bin_i]}%: '
-                    f'samples number {area_bins_count[bin_i]}, '
-                    f'{area_bins_count[bin_i] / len(dataset) * 100:.1f}%\n')
-
-    for bin_i in range(args.area_bins):
-        bindir = os.path.join(args.outdir, 'samples', area_bin_titles[bin_i])
-        os.makedirs(bindir, exist_ok=True)
-        bin_idx = bin2i[bin_i]
-        for sample_i in np.random.choice(bin_idx, size=min(len(bin_idx), args.samples_n), replace=False):
-            save_item_for_vis(dataset[sample_i], os.path.join(bindir, f'{sample_i}.png'))
-
-
-if __name__ == '__main__':
-    import argparse
-
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('datadir', type=str,
-                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')
-    aparser.add_argument('outdir', type=str, help='Where to put results')
-    aparser.add_argument('--samples-n', type=int, default=10,
-                         help='Number of sample images with masks to copy for visualization for each area bin')
-    aparser.add_argument('--area-bins', type=int, default=10, help='How many area bins to have')
-
-    main(aparser.parse_args())

spaces/Cpp4App/Cpp4App/CDM/run_single.py

@@ -1,212 +0,0 @@
-from os.path import join as pjoin
-import cv2
-import os
-import shutil
-import time
-import json
-import CDM.detect_compo.ip_region_proposal as ip
-import CDM.detect_classify.classification as clf
-import pandas as pd
-import openai
-
-def summarize_segment(segment):
-    openai.api_key = os.environ.get('openai_key')
-
-    prompt = f"Shorten this paragraph: \"{str(segment)}\"."
-
-    response = openai.ChatCompletion.create(
-        # engine="text-davinci-002",
-        model="gpt-3.5-turbo",
-        messages=[
-            # {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": prompt}
-        ],
-        max_tokens=400,
-        n=1,
-        stop=None,
-        temperature=0,
-    )
-
-    shortened_segment = response.choices[0].message['content']
-
-    return shortened_segment
-
-def resize_height_by_longest_edge(img_path, resize_length=800):
-    org = cv2.imread(img_path)
-    height, width = org.shape[:2]
-    if height > width:
-        return resize_length
-    else:
-        return int(resize_length * (height / width))
-
-def run_single_img(input_img, output_root, segment_root):
-    # input_img_root = "./input_examples/"
-    # output_root = "./result_classification"
-    # segment_root = '../scrutinizing_alexa/txt'
-
-    if os.path.exists(output_root):
-        shutil.rmtree(output_root)
-    os.makedirs(output_root)
-
-    # image_list = os.listdir(input_img_root)
-    #
-    # input_imgs = [input_img_root + image_name for image_name in image_list]
-
-    key_params = {'min-grad': 4, 'ffl-block': 5, 'min-ele-area': 50, 'merge-contained-ele': True,
-                  'max-word-inline-gap': 10, 'max-line-ingraph-gap': 4, 'remove-top-bar': False}
-
-    is_ip = True
-    is_clf = False
-    is_ocr = True
-    is_merge = True
-    is_classification = True
-
-    # # Load deep learning models in advance
-    # compo_classifier = None
-    # if is_ip and is_clf:
-    #     compo_classifier = {}
-    #     from cnn.CNN import CNN
-    #     # compo_classifier['Image'] = CNN('Image')
-    #     compo_classifier['Elements'] = CNN('Elements')
-    #     # compo_classifier['Noise'] = CNN('Noise')
-    # ocr_model = None
-    if is_ocr:
-        import CDM.detect_text.text_detection as text
-
-    # set the range of target inputs' indices
-    # num = 0
-    # start_index = 30800  # 61728
-    # end_index = 100000
-
-    img_time_cost_all = []
-    ocr_time_cost_all = []
-    ic_time_cost_all = []
-    ts_time_cost_all = []
-    cd_time_cost_all = []
-
-    resize_by_height = 800
-    # for input_img in input_imgs:
-
-    output_data = pd.DataFrame(columns=['screenshot', 'id', 'label', 'index', 'text', 'sentences'])
-
-    this_img_start_time = time.process_time()
-
-    resized_height = resize_height_by_longest_edge(input_img, resize_by_height)
-    index = input_img.split('/')[-1][:-4]
-
-    # if index != "1-1" and index != "1-2":
-    #     continue
-
-    if is_ocr:
-        os.makedirs(pjoin(output_root, 'ocr'), exist_ok=True)
-        this_ocr_time_cost = text.text_detection(input_img, output_root, show=False, method='google')  # pytesseract
-        ocr_time_cost_all.append(this_ocr_time_cost)
-
-    if is_ip:
-        os.makedirs(pjoin(output_root, 'ip'), exist_ok=True)
-        this_cd_time_cost = ip.compo_detection(input_img, output_root, key_params,
-                                               resize_by_height=resized_height, show=False)
-        cd_time_cost_all.append(this_cd_time_cost)
-
-    if is_merge:
-        import CDM.detect_merge.merge as merge
-
-        os.makedirs(pjoin(output_root, 'merge'), exist_ok=True)
-        compo_path = pjoin(output_root, 'ip', str(index) + '.json')
-        ocr_path = pjoin(output_root, 'ocr', str(index) + '.json')
-        board_merge, components_merge = merge.merge(input_img, compo_path, ocr_path, pjoin(output_root, 'merge'),
-                                                    is_remove_top_bar=key_params['remove-top-bar'], show=False)
-        # ic_time_cost_all.append(this_ic_time_cost)
-        # ts_time_cost_all.append(this_ts_time_cost)
-
-    if is_classification:
-        os.makedirs(pjoin(output_root, 'classification'), exist_ok=True)
-        merge_path = pjoin(output_root, 'merge', str(index) + '.json')
-        merge_json = json.load(open(merge_path, 'r'))
-        os.makedirs(pjoin(output_root, 'classification', 'GUI'), exist_ok=True)
-        this_time_cost_ic, this_time_cost_ts, output_data, output_board = clf.compo_classification(input_img, output_root,
-                                                                                     segment_root, merge_json,
-                                                                                     output_data,
-                                                                                     resize_by_height=resize_by_height, clf_model="ViT")
-
-        ic_time_cost_all.append(this_time_cost_ic)
-        ts_time_cost_all.append(this_time_cost_ts)
-
-    this_img_time_cost = time.process_time() - this_img_start_time
-    img_time_cost_all.append(this_img_time_cost)
-    print("time cost for this image: %2.2f s" % this_img_time_cost)
-
-    if os.path.isfile(output_root + '/output.csv'):
-        output_data.to_csv(output_root + '/output.csv', index=False, mode='a', header=False)
-    else:
-        output_data.to_csv(output_root + '/output.csv', index=False, mode='w')
-
-    # avg_ocr_time_cost = sum(ocr_time_cost_all) / len(ocr_time_cost_all)
-    # avg_cd_time_cost = sum(cd_time_cost_all) / len(cd_time_cost_all)
-    # avg_ic_time_cost = sum(ic_time_cost_all) / len(ic_time_cost_all)
-    # avg_ts_time_cost = sum(ts_time_cost_all) / len(ts_time_cost_all)
-    # avg_time_cost = sum(img_time_cost_all) / len(img_time_cost_all)
-    # print("average text extraction time cost for this app: %2.2f s" % avg_ocr_time_cost)
-    # print("average widget detection time cost for this app: %2.2f s" % avg_cd_time_cost)
-    # print("average icon classification time cost for this app: %2.2f s" % avg_ic_time_cost)
-    # print("average text selection processing time cost for this app: %2.2f s" % avg_ts_time_cost)
-    # print("average screenshot processing time cost for this app: %2.2f s" % avg_time_cost)
-
-    short_output_data = output_data[['id', 'label', 'text']].copy()
-    short_output_data = short_output_data.rename(columns={'text': 'segment'})
-
-    # summarize segments:
-
-    # original_output_data = short_output_data.copy()
-    # retries = 3
-    # for index in range(1, len(short_output_data)):
-    #     seg = short_output_data.loc[index, 'segment']
-    #     for i in range(retries):
-    #         try:
-    #             shortened_seg = summarize_segment(seg)
-    #             break
-    #         except openai.error.RateLimitError as e:
-    #             if "overloaded" in str(e):
-    #                 # Exponential backoff with jitter
-    #                 sleep_time = 2 * (2 ** i) + 0.1
-    #                 time.sleep(sleep_time)
-    #         except Exception as e:
-    #             # If you wish, you can print or log the exception details here without raising it
-    #             print(e)
-    #     else:
-    #         # This part will be executed if the for loop doesn't hit 'break'
-    #         shortened_seg = seg
-    #
-    #     short_output_data.loc[index, 'segment'] = shortened_seg
-
-    original_output = []
-    retries = 3
-    summarized_data = []  # List to hold summarized rows
-    for index, row in short_output_data.iterrows():
-        seg = row['segment']
-        for i in range(retries):
-            try:
-                shortened_seg = summarize_segment(seg)
-                break
-            except openai.error.RateLimitError as e:
-                if "overloaded" in str(e):
-
-                    sleep_time = 2 * (2 ** i) + 0.1
-                    # sleep_time = 3
-                    time.sleep(sleep_time)
-            except Exception as e:
-                # If you wish, you can print or log the exception details here without raising it
-                print(e)
-        else:
-            # This part will be executed if the for loop doesn't hit 'break'
-            shortened_seg = seg
-
-        summarized_data.append({'id': row['id'], 'label': row['label'], 'segment': shortened_seg})
-        original_output.append({'id': row['id'], 'label': row['label'], 'segment': seg[0].upper() + seg[1:]})
-
-    summarized_output_data = pd.DataFrame(summarized_data)
-    original_output_data = pd.DataFrame(original_output)
-
-    return output_board, summarized_output_data, original_output_data
-
-

spaces/Cvandi/remake/realesrgan/__init__.py

@@ -1,6 +0,0 @@
-# flake8: noqa
-from .archs import *
-from .data import *
-from .models import *
-from .utils import *
-#from .version import *

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/cu2qu/errors.py

@@ -1,77 +0,0 @@
-# Copyright 2016 Google Inc. 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.
-
-
-class Error(Exception):
-    """Base Cu2Qu exception class for all other errors."""
-
-
-class ApproxNotFoundError(Error):
-    def __init__(self, curve):
-        message = "no approximation found: %s" % curve
-        super().__init__(message)
-        self.curve = curve
-
-
-class UnequalZipLengthsError(Error):
-    pass
-
-
-class IncompatibleGlyphsError(Error):
-    def __init__(self, glyphs):
-        assert len(glyphs) > 1
-        self.glyphs = glyphs
-        names = set(repr(g.name) for g in glyphs)
-        if len(names) > 1:
-            self.combined_name = "{%s}" % ", ".join(sorted(names))
-        else:
-            self.combined_name = names.pop()
-
-    def __repr__(self):
-        return "<%s %s>" % (type(self).__name__, self.combined_name)
-
-
-class IncompatibleSegmentNumberError(IncompatibleGlyphsError):
-    def __str__(self):
-        return "Glyphs named %s have different number of segments" % (
-            self.combined_name
-        )
-
-
-class IncompatibleSegmentTypesError(IncompatibleGlyphsError):
-    def __init__(self, glyphs, segments):
-        IncompatibleGlyphsError.__init__(self, glyphs)
-        self.segments = segments
-
-    def __str__(self):
-        lines = []
-        ndigits = len(str(max(self.segments)))
-        for i, tags in sorted(self.segments.items()):
-            lines.append(
-                "%s: (%s)" % (str(i).rjust(ndigits), ", ".join(repr(t) for t in tags))
-            )
-        return "Glyphs named %s have incompatible segment types:\n  %s" % (
-            self.combined_name,
-            "\n  ".join(lines),
-        )
-
-
-class IncompatibleFontsError(Error):
-    def __init__(self, glyph_errors):
-        self.glyph_errors = glyph_errors
-
-    def __str__(self):
-        return "fonts contains incompatible glyphs: %s" % (
-            ", ".join(repr(g) for g in sorted(self.glyph_errors.keys()))
-        )

spaces/Detomo/Image-Classification/app.py

@@ -1,81 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import optim
-from torch.nn import Module
-from torchvision import models, transforms
-from torchvision.datasets import ImageFolder
-from PIL import Image
-import numpy as np
-import onnxruntime
-import gradio as gr
-import json
-
-
-def get_image(x):
-    return x.split(', ')[0]
-
-
-def to_numpy(tensor):
-    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
-
-
-# Transform image to ToTensor
-def transform_image(myarray):
-    transform = transforms.Compose([
-        transforms.Resize(224),
-        transforms.CenterCrop(224),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-        ])
-    image = Image.fromarray(np.uint8(myarray)).convert('RGB')
-    image = transform(image).unsqueeze(0)
-    return image
-
-
-f = open('imagenet_label.json',)
-label_map=json.load(f)
-f.close()
-
-# Load list of images for similarity
-sub_test_list = open('img_list.txt', 'r')
-sub_test_list = [i.strip() for i in sub_test_list]
-
-# Load images embedding for similarity
-embeddings = torch.load('embeddings.pt')
-
-# Configure
-options = onnxruntime.SessionOptions()
-options.intra_op_num_threads = 8
-options.inter_op_num_threads = 8
-
-# Load model
-PATH = 'model_onnx.onnx'
-ort_session = onnxruntime.InferenceSession(PATH, sess_options=options)
-input_name = ort_session.get_inputs()[0].name
-
-
-# predict multi-level classification
-def get_classification(img):
-
-    image_tensor = transform_image(img)
-    ort_inputs = {input_name: to_numpy(image_tensor)}
-    x = ort_session.run(None, ort_inputs)
-    predictions = torch.topk(torch.from_numpy(x[0]), k=5).indices.squeeze(0).tolist()
-
-    result = {}
-    for i in predictions:
-        label = label_map[str(i)]
-        prob = x[0][0, i].item()
-        result[label] = prob
-    return result
-
-
-iface = gr.Interface(
-    get_classification,
-    gr.inputs.Image(shape=(200, 200)),
-    outputs="label",
-    title = 'Universal Image Classification',
-    description = "Imagenet classification from Mobilenetv3 converting to ONNX runtime",
-    article = "Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>.",
-)
-iface.launch()

spaces/EdBianchi/ThemeParksAccidents_RDF-SPARQL/app.py

@@ -1,297 +0,0 @@
-# IMPORTING TOOLS
-import streamlit as st
-from rdflib import Graph, Literal
-from rdflib.plugins.sparql import prepareQuery
-import pandas as pd
-import plotly.express as px
-import numpy as np
-
-# SET PAGE SETTINGS
-st.set_page_config(page_title='Amusement Accidents', layout="centered")
-
-
-# METHOD TO LOAD THE RDF
-@st.cache(persist=True)
-def importRDF(filename, format):
-    graph = Graph().parse(filename, format)
-    return graph
-
-# IMPORTING THE RDF
-with st.spinner('Loading all the stuffs...'):
-    graph = importRDF("rdf-dataset.ttl", "ttl")
-
-# METHOD TO CONVERT THE QUERY RESULT INTO A DATAFRAME
-def sparql_results_to_df(results):
-    return pd.DataFrame(
-        data=([None if x is None else x.toPython() for x in row] for row in results),
-        columns=[str(x) for x in results.vars],
-    )
-
-# METHOD TO EXECUTE A GENERIC QUERY 
-def computeQuery(query, executor):
-    result = executor.query(query)
-    res_df = sparql_results_to_df(result)
-    return res_df
-
-# METHOD TO EXECUTE A PARAMETRIC QUERY
-def rideAccidentDescription(ride_name, executor):
-        ride_name = Literal(ride_name)
-        query = """
-            PREFIX ride_type: <http://example.org/ride_type#>
-            PREFIX acc: <http://example.org/accident#>
-            PREFIX ride: <http://example.org/ride#>
-            SELECT (?manuf AS ?Manufacturer) (?description AS ?Accident_Description)
-            WHERE {
-                ?instance acc:description ?description ;
-                          acc:ref-ride_id ?ride_id .
-                ?ride_id ride:name ?name ;
-                         ride:manufacturer ?manuf .
-                FILTER (?name = ?ride_name)
-            }
-        """
-        prep_query = prepareQuery(query)
-        r = executor.query(prep_query, initBindings={'ride_name': ride_name})
-        return sparql_results_to_df(r), query
-
-# PROCESSING & DISPLAY
-def display():
-    with st.container():
-        st.write("#### What are the months with the highest number of accidents?")
-        res = computeQuery(query_5, graph)
-        fig = px.bar(res, x="mon", y="count", color="count", labels={"mon":"Month", "count":"Num. of Accidents"}, text_auto="True")
-        fig.update_xaxes(type="category")
-        fig.update_yaxes(showticklabels=False)
-        st.plotly_chart(fig, use_container_width=True)
-        with st.expander("Show query"):
-            st.code(query_5, language="sparql")
-        st.markdown("---")
-
-    with st.container():
-        st.write("#### Which cities and states have recorded the most accidents?")
-        res = computeQuery(query_8, graph)
-        fig = px.treemap(res, path=[px.Constant("U.S"), "state", "city"], values="count", hover_data=["state", "city","count"],
-                color="count",
-                color_continuous_scale='tealrose',
-                color_continuous_midpoint=np.average(res['count'], weights=res['count']))
-        st.plotly_chart(fig, use_container_width=True)
-        with st.expander("Show query"):
-            st.code(query_8, language="sparql")
-        st.markdown("---")
-
-    with st.container():
-        st.write("#### What incidents have occurred on your favorite ride?")
-        ride_names = computeQuery(query_0, graph)
-        option = st.selectbox("Select a Ride", options=ride_names)
-        res, query = rideAccidentDescription(option, graph)
-        res_count = res.count()[0]
-        if (res_count < 3):
-            st.table(res)
-        else:
-            limit = st.slider("Num. of Accidents to Visualize", 1, int(res_count), 2, 1)
-            st.table(res[:limit])
-        with st.expander("Show query"):
-            st.code(query, language="sparql")
-        st.markdown("---")
-
-    with st.container():
-        st.write("#### What Are the Most Common Categories of Accidents?")
-        res = computeQuery(query_4, graph)
-        fig = px.treemap(res, path=[px.Constant("Accident Category"), "category_name"], values="count", hover_data=["category_name","count"])
-        st.plotly_chart(fig, use_container_width=True)
-        with st.expander("Show query"):
-            st.code(query_4, language="sparql")
-        st.markdown("---")
-
-    with st.container():
-        st.write("#### What are the Most Dangerous Ride Categories?")
-        res = computeQuery(query_6, graph)
-        fig = px.pie(res, names="amus_cat_name", values="count", hole=.4)
-        st.plotly_chart(fig, use_container_width=True)
-        with st.expander("Show query"):
-            st.code(query_6, language="sparql")
-        st.markdown("---")
-
-    with st.container():
-        st.write("#### What are the Most Dangerous Ride Types?")
-        res = computeQuery(query_3, graph)
-        fig = px.bar(res, x="type_name", y="count", labels={"type_name":"Ride Type", "count":"Num. of Accidents"}, text_auto=True)
-        fig.update_xaxes(tickangle=45)
-        st.plotly_chart(fig, use_container_width=True)
-        with st.expander("Show query"):
-            st.code(query_3, language="sparql")
-        st.markdown("---")
-
-    with st.container():
-        st.write("#### How many people are generally involved in an accident?")
-        res = computeQuery(query_1, graph)
-        fig = px.bar(res, x="num_inj", y="count", labels={"num_inj":"Injured People", "count":"Num. of Accidents"}, text_auto=True)
-        fig.update_xaxes(type="category")
-        st.plotly_chart(fig, use_container_width=True)
-        with st.expander("Show query"):
-            st.code(query_1, language="sparql")
-        st.markdown("---")
-
-    return None
-
-# ANALYTICAL QUERIES DEFINITION
-# get the names of all the rides
-query_0 = """
-    PREFIX ride:<http://example.org/ride#>        
-
-    SELECT DISTINCT ?name
-    WHERE {
-        ?ride ride:name ?name .
-    }
-"""
-# num of accidents per injured people
-query_1 = """
-    PREFIX r:<http://example.org/ride#>
-    PREFIX a:<http://example.org/accident#>
-
-    SELECT ?num_inj (COUNT(?num_inj) AS ?count) 
-    WHERE {
-        ?acc a:num_injured ?num_inj .
-    }
-    GROUP BY ?num_inj
-    ORDER BY (?num_inj)
-"""
-
-# manufacturers of the rides subjected to most accidents
-query_2 = """
-    PREFIX acc: <http://example.org/accident#>
-    PREFIX ride: <http://example.org/ride#>
-
-    SELECT ?ride_manuf (COUNT(?ride_manuf) AS ?count)
-    WHERE {
-        ?instance acc:ref-ride_id ?ride_id .
-        ?ride_id ride:manufacturer ?ride_manuf
-    }
-    GROUP BY ?ride_manuf
-    ORDER BY DESC(?count)
-"""
-
-# Top n types of rides most subjected to accidents
-query_3 = """
-    PREFIX ride_type: <http://example.org/ride_type#>
-    PREFIX acc: <http://example.org/accident#>
-    PREFIX ride: <http://example.org/ride#>
-
-    SELECT ?type_name (COUNT(?type_name) AS ?count)
-    WHERE {
-        ?instance acc:ref-ride_id ?ride_id .
-        ?ride_id ride:ref-ride_type_id ?type_id .
-        ?type_id ride_type:type ?type_name .
-    }
-    GROUP BY ?type_name
-    ORDER BY DESC(?count)
-    LIMIT 7
-"""
-
-# Top 6 categories of rides most subjected to accidents
-query_6 = """
-    PREFIX amusement_cat: <http://example.org/amusement_category#>
-    PREFIX ride_type: <http://example.org/ride_type#>
-    PREFIX acc: <http://example.org/accident#>
-    PREFIX ride: <http://example.org/ride#>
-
-    SELECT ?amus_cat_name (COUNT(?amus_cat_name) AS ?count)
-    WHERE {
-        ?instance acc:ref-ride_id ?ride_id .
-        ?ride_id ride:ref-ride_type_id ?type_id .
-        ?type_id ride_type:ref-amusement_category_id ?amus_cat_id .
-        ?amus_cat_id amusement_cat:amusement_category ?amus_cat_name .
-    }
-    GROUP BY ?amus_cat_name
-    ORDER BY DESC(?count)
-    LIMIT 6
-    
-"""
-
-# most common categories of accidents
-query_4 = """
-    PREFIX acc_cat: <http://example.org/accident_category#>
-    PREFIX acc: <http://example.org/accident#>
-
-    SELECT ?category_name (COUNT(?category_name) AS ?count)
-    WHERE {
-        ?instance acc:ref-accident_category_id ?category_id .
-        ?category_id acc_cat:accident_category ?category_name .
-    }
-    GROUP BY ?category_name
-    ORDER BY DESC(?count)
-"""
-
-# months with the ngher num of accidents
-query_5 = """
-    PREFIX acc: <http://example.org/accident#>
-
-    SELECT ?mon (COUNT(?mon) AS ?count)
-    WHERE {
-        ?instance acc:date ?date .
-    }
-    GROUP BY (month(?date) AS ?mon)
-    ORDER BY (?mon)
-"""
-
-# cities with the higher num of accidents
-query_8 = """
-    PREFIX location: <http://example.org/location#>
-    PREFIX acc: <http://example.org/accident#>
-
-    SELECT ?city (COUNT(?city) AS ?count) ?state
-    WHERE {
-        ?instance acc:ref-location_id ?location_id .
-        ?location_id location:city ?city ;
-                     location:state ?state
-    }
-    GROUP BY ?city
-    ORDER BY DESC(?count)
-    
-"""
-
-
-# TITLE
-st.header("Theme Park Ride Accidents")
-st.markdown("""There are **thousands of amusement parks** around the world that welcome **millions of visitors** each year. 
-    Children, families, and teenagers are ready to spend days of adrenaline and fun. 
-    Unfortunately, **accidents sometimes occur**. This raises some questions: **Are amusement parks safe? Which rides are the most accident-prone? What accidents happen most often? At what time of year are accidents most common?** 
-    Let's try to find out in this **RDF data exploration** using **SPARQL** and **Plotly**.""")
-st.markdown("---")
-
-display()
-
-# WRITE & RUN YOUR OWN QUERY
-st.write("#### Write & Run your Custom Query")
-pers_query = st.text_area('', """
-    PREFIX ride:<http://example.org/ride#>
-    SELECT ?name
-    WHERE {
-        ?ride ride:manufacturer "Vekoma" ;
-              ride:name ?name
-    }
-    """, height=200)
-with st.container():
-    try:
-        res = computeQuery(pers_query, graph)
-        st.table(res)
-    except:
-        st.error("Ooops! Check you query syntax...")
-    st.markdown("---")
-
-# SIDEBAR
-with st.sidebar:
-    st.write("""
-    This App proposes some visualization about theme park ride accidents. 
-    The original dataset comes from "Saferparks", an organization that reports and collects data about theme park ride accidents in the US. 
-    The original dataset covers years from 2010 to 2017 and comes in CSV or Excel format. I used python to split the dataset and convert it into the 
-    Third Normal Form (3NF) of Database.
-    I uploaded the data into a PostgreSQL database and I used the Ontop tool to get the final RDF dataset. 
-    Queries are expressed in SPARQL, and charts are generated with Plotly Express.
-        """)
-    st.markdown("---")
-    st.markdown("## Dataset Resources:")
-    st.markdown("""
-        Saferparks Original Dataset: https://ridesdatabase.org/saferparks/data/
-
-        Saferparks Dataset Description: https://ridesdatabase.org/wp-content/uploads/2020/02/Saferparks-data-description.pdf
-        """)

spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/nets_new.py

@@ -1,133 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_new
-
-
-class BaseNet(nn.Module):
-    def __init__(
-        self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))
-    ):
-        super(BaseNet, self).__init__()
-        self.enc1 = layers_new.Conv2DBNActiv(nin, nout, 3, 1, 1)
-        self.enc2 = layers_new.Encoder(nout, nout * 2, 3, 2, 1)
-        self.enc3 = layers_new.Encoder(nout * 2, nout * 4, 3, 2, 1)
-        self.enc4 = layers_new.Encoder(nout * 4, nout * 6, 3, 2, 1)
-        self.enc5 = layers_new.Encoder(nout * 6, nout * 8, 3, 2, 1)
-
-        self.aspp = layers_new.ASPPModule(nout * 8, nout * 8, dilations, dropout=True)
-
-        self.dec4 = layers_new.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)
-        self.dec3 = layers_new.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)
-        self.dec2 = layers_new.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1)
-        self.lstm_dec2 = layers_new.LSTMModule(nout * 2, nin_lstm, nout_lstm)
-        self.dec1 = layers_new.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)
-
-    def __call__(self, x):
-        e1 = self.enc1(x)
-        e2 = self.enc2(e1)
-        e3 = self.enc3(e2)
-        e4 = self.enc4(e3)
-        e5 = self.enc5(e4)
-
-        h = self.aspp(e5)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = torch.cat([h, self.lstm_dec2(h)], dim=1)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedNet(nn.Module):
-    def __init__(self, n_fft, nout=32, nout_lstm=128):
-        super(CascadedNet, self).__init__()
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-        self.nin_lstm = self.max_bin // 2
-        self.offset = 64
-
-        self.stg1_low_band_net = nn.Sequential(
-            BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
-            layers_new.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0),
-        )
-
-        self.stg1_high_band_net = BaseNet(
-            2, nout // 4, self.nin_lstm // 2, nout_lstm // 2
-        )
-
-        self.stg2_low_band_net = nn.Sequential(
-            BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm),
-            layers_new.Conv2DBNActiv(nout, nout // 2, 1, 1, 0),
-        )
-        self.stg2_high_band_net = BaseNet(
-            nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2
-        )
-
-        self.stg3_full_band_net = BaseNet(
-            3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm
-        )
-
-        self.out = nn.Conv2d(nout, 2, 1, bias=False)
-        self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)
-
-    def forward(self, x):
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        l1_in = x[:, :, :bandw]
-        h1_in = x[:, :, bandw:]
-        l1 = self.stg1_low_band_net(l1_in)
-        h1 = self.stg1_high_band_net(h1_in)
-        aux1 = torch.cat([l1, h1], dim=2)
-
-        l2_in = torch.cat([l1_in, l1], dim=1)
-        h2_in = torch.cat([h1_in, h1], dim=1)
-        l2 = self.stg2_low_band_net(l2_in)
-        h2 = self.stg2_high_band_net(h2_in)
-        aux2 = torch.cat([l2, h2], dim=2)
-
-        f3_in = torch.cat([x, aux1, aux2], dim=1)
-        f3 = self.stg3_full_band_net(f3_in)
-
-        mask = torch.sigmoid(self.out(f3))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux = torch.cat([aux1, aux2], dim=1)
-            aux = torch.sigmoid(self.aux_out(aux))
-            aux = F.pad(
-                input=aux,
-                pad=(0, 0, 0, self.output_bin - aux.size()[2]),
-                mode="replicate",
-            )
-            return mask, aux
-        else:
-            return mask
-
-    def predict_mask(self, x):
-        mask = self.forward(x)
-
-        if self.offset > 0:
-            mask = mask[:, :, :, self.offset : -self.offset]
-            assert mask.size()[3] > 0
-
-        return mask
-
-    def predict(self, x, aggressiveness=None):
-        mask = self.forward(x)
-        pred_mag = x * mask
-
-        if self.offset > 0:
-            pred_mag = pred_mag[:, :, :, self.offset : -self.offset]
-            assert pred_mag.size()[3] > 0
-
-        return pred_mag