Anwar Shah Kashmiri: A Renowned Scholar and Jurist of Kashmir
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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.
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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.
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.
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Anwar Shah Kashmiri authored more than 100 books and treatises on various Islamic topics. Some of his most famous works are:
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Al-Arf al-Shadhi: A commentary on Sunan al-Tirmidhi, one of the six major collections of Hadith.
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Fayd al-Bari: A commentary on Sahih al-Bukhari, the most authentic collection of Hadith.
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Tafsir al-Quran al-Azim: A commentary on the Quran that combines rational and traditional approaches.
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Al-Urf al-Shadhi: A commentary on Al-Hidayah, a classical manual of Hanafi Fiqh.
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Anwar al-Kalam: A refutation of the arguments of the Mu'tazila, a rationalist school of Islamic theology.
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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.
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).
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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).
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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.
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Baby Cats: Everything You Need to Know About These Cute Furry Friends
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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.
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Facts about Baby Cats
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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.
Baby cats go through different development stages from birth to adulthood. According to Wikipedia, these stages are:
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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.
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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.
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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.
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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.
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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.
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Unusual Stories of Baby Cats
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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.
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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.
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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.
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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
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.
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Differences between Baby Cats and Adult Cats
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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:
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Baby Cats
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Adult Cats
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Have a higher metabolism and need more calories per pound of body weight
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Have a lower metabolism and need fewer calories per pound of body weight
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Have a weaker immune system and are more susceptible to infections and diseases
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Have a stronger immune system and are more resistant to infections and diseases
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Have softer, finer fur that may change color or texture as they grow older
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Have coarser, thicker fur that usually stays the same color and texture throughout their lives
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Have blue eyes that may change color as they mature
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Have various eye colors that are usually fixed by the time they are six months old
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Have more teeth (26) that are smaller and sharper than adult teeth
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Have fewer teeth (30) that are larger and duller than baby teeth
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Are more curious, playful, and energetic, and need more stimulation and socialization
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Are more calm, relaxed, and independent, and need less stimulation and socialization
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Care Tips for Baby Cats
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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.
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Feeding and Grooming Baby Cats
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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.
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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.
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Keeping Baby Cats Warm and Safe
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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.
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Baby cat synonyms
-Kitten pictures and facts
-How to care for a newborn kitten
-Best kitten food and toys
-Baby cat breeds and characteristics
-Kitten adoption and rescue
-How to train a kitten to use the litter box
-Baby cat names and meanings
-Kitten health and vaccination
-Baby cat videos and memes
-How to introduce a kitten to other pets
-Kitten behavior and development
-Baby cat costumes and accessories
-Kitten grooming and nail trimming
-Baby cat sounds and communication
-Kitten socialization and play
-Baby cat allergies and remedies
-Kitten growth and weight chart
-Baby cat games and apps
-Kitten nutrition and feeding schedule
-How to choose a kitten from a litter
-Baby cat wallpapers and backgrounds
-Kitten anatomy and physiology
-Baby cat crafts and DIY projects
-Kitten dental care and teething
-How to make a kitten feel comfortable at home
-Baby cat quotes and sayings
-Kitten eye color and vision
-Baby cat coloring pages and activities
-Kitten ear care and cleaning
-How to travel with a kitten safely
-Baby cat calendar and planner
-Kitten genetics and coat patterns
-Baby cat jokes and puns
-Kitten enrichment and stimulation
-How to bond with a kitten emotionally
-Baby cat gifts and merchandise
-Kitten flea treatment and prevention
-Baby cat art and photography
-Kitten fur types and textures
-How to deal with a kitten's separation anxiety
-Baby cat poetry and songs
-Kitten personality types and traits
-Baby cat history and folklore
-Kitten skin care and grooming products
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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.
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Teaching Baby Cats to Use the Litter Box
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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:
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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.
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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.
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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.
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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.
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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.
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Breeds of Baby Cats
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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.
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Small Cat Breeds
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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:
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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.
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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,
Fluffy Cat Breeds
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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.
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Somali Cat
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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.
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Birman Cat
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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.
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Siberian Cat
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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.
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Norwegian Forest Cat
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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,
Kid-Friendly Cat Breeds
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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.
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Birman Cat
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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.
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Ragdoll Cat
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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.
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Himalayan Cat
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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.
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Maine Coon Cat
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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.
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Abyssinian Cat
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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.
Conclusion
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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!
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FAQs
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Here are some of the most frequently asked questions and answers about baby cats that you may find helpful.
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How long do baby cats stay with their mother?
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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.
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How often do baby cats sleep?
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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.
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How can I tell the gender of a baby cat?
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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.
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How can I name my baby cat?
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Naming your baby cat is a fun and creative process that can reflect your personality and preferences. You can choose a name based on your kitten's appearance, behavior, breed, or origin. You can also choose a name based on your favorite characters, celebrities, places, or things. You can also use online tools or books to generate or browse through thousands of possible names for your kitten. The most important thing is to choose a name that you like and that suits your kitten's personality.
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How can I train my baby cat?
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Training your baby cat is important to teach it good manners and habits, as well as to prevent or correct any unwanted behaviors. You can start training your kitten as early as possible, using positive reinforcement and gentle guidance. You can use treats, toys, praise, or affection as rewards for good behavior, and avoid using punishment or force for bad behavior. You can also use clicker training or target training to teach your kitten various commands or tricks. Some of the basic things that you can train your kitten are: how to use the litter box, how to scratch appropriately, how to come when called, how to sit or stay on command, how to walk on a leash, how to get along with other pets or people.
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Bloons TD 6 APK Ultima Version: A Guide for Android Users
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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.
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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.
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.
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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.
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Features and content of Bloons TD 6
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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:
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23 powerful Monkey Towers, each with 3 upgrade paths and unique activated abilities.
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Paragons! Explore the incredible power of the newest Paragon upgrades.
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14 diverse Heroes, with 20 signature upgrades and 2 special abilities. Plus, unlockable skins and voiceovers!
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Regular updates! Ninja Kiwi releases several updates every year with new characters, features, and gameplay.
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Boss Events! Fearsome Boss Bloons will challenge even the strongest defenses.
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Odysseys! Battle through a series of maps connected by their theme, rules, and rewards.
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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.
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Quests! Delve into what makes the Monkeys tick with Quests, crafted to tell tales and share knowledge.
-
Trophy Store! Earn Trophies to unlock dozens of cosmetic items that let
you customize your Monkeys, Bloons, and the world around you.
-
Content Browser! Create your own challenges and odysseys using the in-game editor and share them with other players online.
-
100+ original maps, each with their own unique shape, size, and theme.
-
10 special types of Bloons, each with their own abilities and resistances.
-
Colorblind mode, cloud save, offline play, and more accessibility options.
-
-
How to download and install Bloons TD 6 APK ultima version?
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Requirements and compatibility
-
To download and install Bloons TD 6 APK ultima version, you need to have an Android device that meets the following requirements:
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Android version 5.0 or higher
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At least 2 GB of RAM
-
At least 1 GB of free storage space
-
A stable internet connection
-
-
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.
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Steps to download and install
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To download and install Bloons TD 6 APK ultima version, you need to follow these steps:
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Go to a trusted website that offers Bloons TD 6 APK ultima version for download. For example, you can use this link: [text].
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Click on the download button and wait for the APK file to be downloaded to your device.
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Once the download is complete, locate the APK file in your device's file manager and tap on it to start the installation process.
-
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.
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Follow the on-screen instructions to complete the installation process.
-
Launch the game and enjoy playing Bloons TD 6 APK ultima version for free!
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Why choose Bloons TD 6 APK ultima version?
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Benefits of using APK files
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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:
-
-
You can access apps that are not available in your region or country.
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You can get apps that are not compatible with your device or operating system.
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You can get apps that are no longer supported or updated by the developers.
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You can get apps that have extra features or modifications that are not present in the official version.
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You can get apps that are free of charge or have no in-app purchases or ads.
-
-
Advantages of playing Bloons TD 6 on Android
-
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:
-
-
You can play Bloons TD 6 anytime and anywhere with your Android device, as long as you have a battery and an internet connection.
-
You can play Bloons TD 6 with touch controls that are intuitive and responsive, giving you more control over your Monkeys and Heroes.
-
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.
-
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.
-
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.
-
-
Tips and tricks for playing Bloons TD 6
-
How to use Monkey Towers and Heroes effectively
-
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:
-
-
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.
-
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.
-
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.
-
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.
-
-
How to earn Trophies and unlock cosmetic items
-
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:
-
-
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.
-
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.
-
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.
-
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.
-
-
Conclusion and FAQs
-
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.
-
If you have any questions about Bloons TD 6 APK ultima version or the game itself, you can check out these FAQs:
-
Q: Is Bloons TD 6 APK ultima version safe to use?
-
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.
-
Q: Can I play Bloons TD 6 APK ultima version online with other players?
-
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.
-
Q: Can I update Bloons TD 6 APK ultima version to get the latest features and content?
-
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.
-
Q: Can I transfer my progress or data from Bloons TD 6 APK ultima version to the official version or another device?
-
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.
-
Q: How can I contact Ninja Kiwi support if I have any issues or feedback about Bloons TD 6?
-
A: You can contact Ninja Kiwi support by using the in-game support button in the settings menu. You can also visit their website at [text] or their social media pages at [text] and [text]. Ninja Kiwi is always happy to hear from their players and will try to help you as soon as possible.
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diff --git a/spaces/1phancelerku/anime-remove-background/Build Your Dream City with Idle Island - City Idle Tycoon Mod APK - No Ads No Root.md b/spaces/1phancelerku/anime-remove-background/Build Your Dream City with Idle Island - City Idle Tycoon Mod APK - No Ads No Root.md
deleted file mode 100644
index 92ab75ee291143c00735f86bf6be91f79fcabf1f..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Build Your Dream City with Idle Island - City Idle Tycoon Mod APK - No Ads No Root.md
+++ /dev/null
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-
Idle Island City Idle Tycoon Mod APK: Build Your Dream City
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Do you love city building games? Do you want to create your own island paradise and become a tycoon? If yes, then you should try Idle Island City Idle Tycoon, a popular mobile simulator game that allows you to build your own city and become the ultimate tycoon.
Idle Island City Idle Tycoon is a game developed by RSGapps - Idle Tycoon Games. It is available for Android devices and can be downloaded from Google Play Store. In this game, you start with a small island and a few buildings. Your goal is to expand your city by building more houses, factories, shops, hotels, airports, and other facilities. You also have to manage your economy and resources, such as money, energy, population, and happiness. You can unlock new islands and buildings as you progress in the game. You can also hire managers and advisors to help you run your city more efficiently. The game has stunning graphics and animations that make your city look realistic and lively.
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Features of Idle Island City Idle Tycoon
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- Build and upgrade your city
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You can build various types of buildings in your city, such as residential, commercial, industrial, recreational, and cultural. You can also upgrade them to increase their productivity and profitability. You can customize your city by choosing different styles and themes for your buildings. You can also decorate your city with parks, trees, roads, bridges, monuments, and other items.
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- Manage your economy and resources
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You have to balance your income and expenses in your city. You have to collect money from your buildings and use it to build more facilities or upgrade them. You also have to pay taxes, salaries, maintenance costs, and other expenses. You have to monitor your energy consumption and production, as well as your population growth and happiness level. You have to make sure that your city is sustainable and profitable.
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- Unlock new islands and buildings
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You can unlock new islands as you progress in the game. Each island has its own theme and challenges. You can also unlock new buildings that offer different benefits and features. You can discover more than 100 buildings in the game.
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- Hire managers and advisors
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You can hire managers to automate your buildings and increase their efficiency. You can also hire advisors to give you tips and advice on how to improve your city. They will also reward you with bonuses and gifts.
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- Enjoy stunning graphics and animations
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The game has amazing graphics and animations that make your city look realistic and lively. You can see the day-night cycle, weather effects, traffic movements, people activities, and other details in your city. You can also zoom in and out to see your city from different angles.
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Why use Idle Island City Idle Tycoon Mod APK?
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If you want to enjoy the game without any limitations or interruptions, you should use Idle Island City Idle Tycoon Mod APK. This is a modified version of the game that gives you access to unlimited money, no ads, and easy installation.
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With Idle Island City Idle Tycoon Mod APK, you will have unlimited money in the game. This means that you can build or upgrade anything you want without worrying about the cost. You can also buy any items or boosts that you want from the shop. You can also skip the waiting time for building or upgrading your facilities. You can enjoy the game without any financial constraints.
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With Idle Island City Idle Tycoon Mod APK, you will not see any ads in the game. This means that you can play the game without any interruptions or distractions. You can also save your data and battery life by avoiding the ads. You can enjoy the game without any annoyance.
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With Idle Island City Idle Tycoon Mod APK, you will not have any trouble installing or using the game. You just need to download the APK file from a reliable source and install it on your device. You do not need to root your device or use any other tools. You can also update the game easily whenever there is a new version available. You can enjoy the game without any hassle.
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If you want to download and install Idle Island City Idle Tycoon Mod APK, you can follow these simple steps:
Allow your device to install apps from unknown sources by going to Settings > Security > Unknown Sources and enabling it.
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Locate the downloaded APK file in your file manager and tap on it to install it.
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Launch the game and enjoy building your dream city.
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Conclusion
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Idle Island City Idle Tycoon is a fun and addictive city building game that lets you create your own island paradise and become a tycoon. You can build various types of buildings, manage your economy and resources, unlock new islands and buildings, hire managers and advisors, and enjoy stunning graphics and animations. If you want to play the game without any limitations or interruptions, you should use Idle Island City Idle Tycoon Mod APK. This will give you access to unlimited money, no ads, and easy installation. You can download and install the game easily by following the steps above. So, what are you waiting for? Download Idle Island City Idle Tycoon Mod APK now and start building your dream city.
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Here are some frequently asked questions about Idle Island City Idle Tycoon Mod APK:
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Q: Is Idle Island City Idle Tycoon Mod APK safe to use?
A: Yes, Idle Island City Idle Tycoon Mod APK is safe to use as long as you download it from a trusted source. It does not contain any viruses or malware that can harm your device or data.
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Q: Do I need an internet connection to play Idle Island City Idle Tycoon Mod APK?
A: No, you do not need an internet connection to play Idle Island City Idle Tycoon Mod APK. You can play the game offline without any problem.
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Q: How can I update Idle Island City Idle Tycoon Mod APK?
A: You can update Idle Island City Idle Tycoon Mod APK by downloading the latest version of the APK file from the same source and installing it over the existing one. You do not need to uninstall the previous version.
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Q: Can I play Idle Island City Idle Tycoon Mod APK on PC?
A: Yes, you can play Idle Island City Idle Tycoon Mod APK on PC by using an Android emulator such as Bluestacks or Nox Player. You just need to install the emulator on your PC and then install the APK file on it.
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Q: Can I transfer my progress from the original game to Idle Island City Idle Tycoon Mod APK?
A: Yes, you can transfer your progress from the original game to Idle Island City Idle Tycoon Mod APK by using a cloud save feature. You just need to connect your game account to Google Play Games or Facebook and then sync your data across devices.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Driven The Movie That Changed the Face of Motorsports.md b/spaces/1phancelerku/anime-remove-background/Download Driven The Movie That Changed the Face of Motorsports.md
deleted file mode 100644
index 70796d9850e0924064c14e1691269a2ecdfd22f4..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Driven The Movie That Changed the Face of Motorsports.md
+++ /dev/null
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Download Driven Marketing: How to Use Data to Boost Your Marketing ROI
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Data is the new oil in the digital economy. It fuels innovation, growth, and competitive advantage. But how can you use data to power up your marketing efforts?
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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.
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.
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What is download driven marketing and why is it important?
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Download driven marketing is the use of data from downloads to optimize marketing campaigns and strategies.
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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:
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Create relevant and valuable content and offers that attract and engage your audience.
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Segment your audience based on their download behavior and interests.
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Personalize your content and offers based on their download history and profile.
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Measure the effectiveness of your marketing efforts and improve your conversion rates.
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Download driven marketing can help you:
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- Understand your audience's needs, preferences, and behaviors.
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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.
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- Segment your audience based on their download behavior and interests.
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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:
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The type of content they download (e.g., ebooks, podcasts, videos).
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The topic of the content they download (e.g., social media, SEO, email marketing).
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The frequency of their downloads (e.g., once a month, once a week, once a day).
- Personalize your content and offers based on their download history and profile.
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By using the data from downloads, you can personalize your content and offers based on their download history and profile. For example, you can:
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Send them follow-up emails with more content and offers related to their previous downloads.
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Show them personalized recommendations and suggestions based on their download preferences.
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Create dynamic landing pages and web pages that display content and offers tailored to their download interests.
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- Measure the effectiveness of your marketing efforts and improve your conversion rates.
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By using the data from downloads, you can measure the effectiveness of your marketing efforts and improve your conversion rates. For example, you can:
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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.
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Identify the best practices and the areas of improvement for your download campaigns and strategies, such as content quality, design, format, distribution, and promotion.
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Test and optimize your download campaigns and strategies based on data-driven insights, such as A/B testing, multivariate testing, and user feedback.
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How to implement download driven marketing in your business?
-
To implement download driven marketing, you need to:
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- Identify your download goals and metrics.
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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:
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To generate more leads for your business.
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To increase brand awareness and authority in your industry.
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To educate and inform your audience about your products or services.
To nurture and convert your leads into customers.
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Your download metrics could be:
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The number of downloads per content type, topic, or channel.
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The percentage of downloads that result in leads, subscribers, or customers.
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The cost per download, lead, subscriber, or customer.
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The revenue per download, lead, subscriber, or customer.
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-
- Choose the right tools and platforms to collect, store, and analyze your download data.
-
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:
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-
Capture the data from downloads, such as the user's name, email, location, device, browser, etc.
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Store the data from downloads in a secure and accessible database or cloud service.
-
Analyze the data from downloads using tools such as Google Analytics, Microsoft Power BI, Tableau, etc.
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-
- Create relevant and valuable content and offers that attract and engage your audience.
-
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:
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Solve a problem or answer a question that your audience has.
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Provide useful information or insights that your audience can benefit from.
Match the tone and style of your brand and your audience.
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Include a clear and compelling call to action that encourages your audience to download your content or offer.
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- Test and optimize your download campaigns and strategies based on data-driven insights.
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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:
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Identify the best practices and the areas of improvement for your download campaigns and strategies.
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Experiment with different variables and factors that affect your download results, such as content type, topic, format, design, distribution, promotion, etc.
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Implement the changes and improvements that lead to better download outcomes and higher marketing ROI.
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Examples of successful download driven marketing campaigns
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Here are some examples of how brands have used download driven marketing to achieve their marketing goals:
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- Netflix used download data to create personalized recommendations and increase customer retention.
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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.
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- HubSpot used download data to generate leads and nurture them through email marketing.
-
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.
-
- Spotify used download data to create customized playlists and enhance user experience.
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.
-
Conclusion
-
Download driven marketing is a powerful way to use data to improve your marketing ROI. By using download data, you can:
-
- Know your audience better and tailor your content and offers to their needs and interests.
-
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.
-
- Segment your audience based on their download behavior and deliver personalized messages and experiences.
-
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.
-
- Track and measure the impact of your download campaigns and strategies and optimize them accordingly.
-
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.
-
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".
-
FAQs
-
What is download driven marketing?
-
Download driven marketing is the use of data from downloads to optimize marketing campaigns and strategies.
-
What are the benefits of download driven marketing?
-
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.
-
What are some examples of download driven marketing campaigns?
Some examples of download driven marketing campaigns are:
-
-
Netflix used download data to create personalized recommendations and increase customer retention.
-
HubSpot used download data to generate leads and nurture them through email marketing.
-
Spotify used download data to create customized playlists and enhance user experience.
-
-
What are the best tools and platforms for download driven marketing?
-
There are many tools and platforms that can help you with download driven marketing, such as:
-
-
Google Analytics: A web analytics tool that can help you track and analyze your download data and performance.
-
Microsoft Power BI: A business intelligence tool that can help you visualize and report your download data and insights.
-
Tableau: A data visualization tool that can help you create interactive dashboards and charts based on your download data.
-
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.
-
WordPress: A content management system that can help you create and manage your content and offers for download, such as ebooks, reports, webinars, etc.
-
-
How to create content and offers that attract and engage your audience?
To create content and offers that attract and engage your audience, you need to:
-
-
Research your audience and understand their pain points, challenges, goals, and interests.
-
Create content and offers that solve their problems, answer their questions, and provide them with value.
-
Use catchy headlines, compelling introductions, and clear conclusions to capture their attention and interest.
-
Use simple, conversational, and engaging language to communicate your message and connect with your audience.
-
Use visuals, such as images, videos, infographics, etc., to enhance your content and offer and make them more appealing and memorable.
-
Include a clear and compelling call to action that encourages your audience to download your content or offer.
-
-
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!
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/1toTree/lora_test/ppdiffusers/schedulers/scheduling_karras_ve.py b/spaces/1toTree/lora_test/ppdiffusers/schedulers/scheduling_karras_ve.py
deleted file mode 100644
index 20c45556c3bc60884068fbafbaef986bfc4808b0..0000000000000000000000000000000000000000
--- a/spaces/1toTree/lora_test/ppdiffusers/schedulers/scheduling_karras_ve.py
+++ /dev/null
@@ -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()
diff --git a/spaces/2ndelement/voicevox/Dockerfile b/spaces/2ndelement/voicevox/Dockerfile
deleted file mode 100644
index c32138339e4a73d00fbc64e90f2ac02ce606bd54..0000000000000000000000000000000000000000
--- a/spaces/2ndelement/voicevox/Dockerfile
+++ /dev/null
@@ -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 <= 0.11.0 (ONNX)
-ARG TARGETPLATFORM
-ARG USE_GPU=false
-ARG VOICEVOX_CORE_VERSION=0.14.3
-
-RUN < /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 < /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 < /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 < /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 < /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" ]
diff --git a/spaces/7hao/bingo/src/components/button-scroll-to-bottom.tsx b/spaces/7hao/bingo/src/components/button-scroll-to-bottom.tsx
deleted file mode 100644
index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000
--- a/spaces/7hao/bingo/src/components/button-scroll-to-bottom.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-import { Button, type ButtonProps } from '@/components/ui/button'
-import { IconArrowDown } from '@/components/ui/icons'
-
-export function ButtonScrollToBottom({ className, ...props }: ButtonProps) {
- const isAtBottom = useAtBottom()
-
- return (
-
- )
-}
diff --git a/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Getting Started 6bc871dcdd4a4554b5b22c0c40740841/Example sub-page 48f64d6186ec4428b2e4180475245a9c.md b/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Getting Started 6bc871dcdd4a4554b5b22c0c40740841/Example sub-page 48f64d6186ec4428b2e4180475245a9c.md
deleted file mode 100644
index 93828724f7000ec2b17a2396de7bab7ba5150b59..0000000000000000000000000000000000000000
--- a/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Getting Started 6bc871dcdd4a4554b5b22c0c40740841/Example sub-page 48f64d6186ec4428b2e4180475245a9c.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Example sub-page
-
-Last edited time: March 31, 2023 1:49 PM
-Owner: Anonymous
-Tags: Testing
\ No newline at end of file
diff --git a/spaces/AI-Naga/Parking_Space_Counter/app.py b/spaces/AI-Naga/Parking_Space_Counter/app.py
deleted file mode 100644
index d63634031b031955c3caade97e3cfe2891c6050b..0000000000000000000000000000000000000000
--- a/spaces/AI-Naga/Parking_Space_Counter/app.py
+++ /dev/null
@@ -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()
\ No newline at end of file
diff --git a/spaces/AIFILMS/generate_human_motion/pyrender/pyrender/node.py b/spaces/AIFILMS/generate_human_motion/pyrender/pyrender/node.py
deleted file mode 100644
index 1f37f7856cc732a37dc58253022a7c331489493e..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/generate_human_motion/pyrender/pyrender/node.py
+++ /dev/null
@@ -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))
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/midas/midas/blocks.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/midas/midas/blocks.py
deleted file mode 100644
index 2145d18fa98060a618536d9a64fe6589e9be4f78..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/midas/midas/blocks.py
+++ /dev/null
@@ -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
-
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/models/melgan.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/models/melgan.py
deleted file mode 100644
index f0bc957ff29ba5a54f5913685ac35d6da70e88c6..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/models/melgan.py
+++ /dev/null
@@ -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)
diff --git a/spaces/AIWaves/SOP_Generation-single/Environment/base_environment.py b/spaces/AIWaves/SOP_Generation-single/Environment/base_environment.py
deleted file mode 100644
index 1aa9f9c15d1e759a9f6cc4076aa8a61d3efd2e4e..0000000000000000000000000000000000000000
--- a/spaces/AIWaves/SOP_Generation-single/Environment/base_environment.py
+++ /dev/null
@@ -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}
-
-
diff --git a/spaces/Abhilashvj/planogram-compliance/utils/aws/userdata.sh b/spaces/Abhilashvj/planogram-compliance/utils/aws/userdata.sh
deleted file mode 100644
index 5fc1332ac1b0d1794cf8f8c5f6918059ae5dc381..0000000000000000000000000000000000000000
--- a/spaces/Abhilashvj/planogram-compliance/utils/aws/userdata.sh
+++ /dev/null
@@ -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
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/methods/ColorPicker.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/methods/ColorPicker.js
deleted file mode 100644
index 22d36b3a727424b51fcaf2c1d4e169cb94021f9c..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/methods/ColorPicker.js
+++ /dev/null
@@ -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;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dropdownlist/methods/listpanel/CloseListPanel.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dropdownlist/methods/listpanel/CloseListPanel.js
deleted file mode 100644
index 343fd49ab604aea13871eeeef846fdd7b4a64d1b..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dropdownlist/methods/listpanel/CloseListPanel.js
+++ /dev/null
@@ -1,11 +0,0 @@
-var CloseListPanel = function () {
- if (!this.dropDownBehavior) {
- return this;
- }
-
- this.dropDownBehavior.requestClose();
-
- return this;
-}
-
-export default CloseListPanel;
\ No newline at end of file
diff --git a/spaces/AkitoP/umamusume_bert_vits2/app0.py b/spaces/AkitoP/umamusume_bert_vits2/app0.py
deleted file mode 100644
index 033e0da1791bb7ed95b44d939d5d403651f3fea0..0000000000000000000000000000000000000000
--- a/spaces/AkitoP/umamusume_bert_vits2/app0.py
+++ /dev/null
@@ -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")
diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/training/coaches/base_coach.py b/spaces/Amrrs/DragGan-Inversion/PTI/training/coaches/base_coach.py
deleted file mode 100644
index ccea133353df1f6b6737f9672ae7e2cb9438071d..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/PTI/training/coaches/base_coach.py
+++ /dev/null
@@ -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
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/utils/models_utils.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/utils/models_utils.py
deleted file mode 100644
index 53b2c3fa9d7035364dd34384fcdab78c1ae5c6af..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/utils/models_utils.py
+++ /dev/null
@@ -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
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/model_editing.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/model_editing.md
deleted file mode 100644
index 4aa8a1d83fe4ebd2b697b93243298275260a3cb8..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/model_editing.md
+++ /dev/null
@@ -1,35 +0,0 @@
-
-
-# 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).
-
-
-
-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.
-
-
-
-## StableDiffusionModelEditingPipeline
-[[autodoc]] StableDiffusionModelEditingPipeline
- - __call__
- - all
-
-## StableDiffusionPipelineOutput
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
\ No newline at end of file
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/detr/detr_r50_8x2_150e_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/detr/detr_r50_8x2_150e_coco.py
deleted file mode 100644
index ba276f447c2a858f6ae454fdd1cb0c95c831092c..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/detr/detr_r50_8x2_150e_coco.py
+++ /dev/null
@@ -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)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py
deleted file mode 100644
index fe1d659f1a58ddb6e662d74a41c77005d2ee0638..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py
+++ /dev/null
@@ -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))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fast_scnn.py b/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fast_scnn.py
deleted file mode 100644
index 32fdeb659355a5ce5ef2cc7c2f30742703811cdf..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fast_scnn.py
+++ /dev/null
@@ -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'))
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/midas/utils.py b/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/midas/utils.py
deleted file mode 100644
index 9a9d3b5b66370fa98da9e067ba53ead848ea9a59..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/midas/utils.py
+++ /dev/null
@@ -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
diff --git a/spaces/AntNikYab/NaturalLanguageProcessing/function/lstm_preprocessing.py b/spaces/AntNikYab/NaturalLanguageProcessing/function/lstm_preprocessing.py
deleted file mode 100644
index 302f1291a34515be9ca062d381bac92649fed704..0000000000000000000000000000000000000000
--- a/spaces/AntNikYab/NaturalLanguageProcessing/function/lstm_preprocessing.py
+++ /dev/null
@@ -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'
\ No newline at end of file
diff --git a/spaces/Ariharasudhan/YoloV5/utils/loggers/comet/__init__.py b/spaces/Ariharasudhan/YoloV5/utils/loggers/comet/__init__.py
deleted file mode 100644
index b0318f88d6a63a6ba37fd2bf7ec4869084a45966..0000000000000000000000000000000000000000
--- a/spaces/Ariharasudhan/YoloV5/utils/loggers/comet/__init__.py
+++ /dev/null
@@ -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(
- {
- 'mAP@.5': ap50[i],
- 'mAP@.5:.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()
diff --git a/spaces/Arsenii2023/Demo1/demo1.py b/spaces/Arsenii2023/Demo1/demo1.py
deleted file mode 100644
index 84835fc0394fc3c7e7598d5419284631b4127c95..0000000000000000000000000000000000000000
--- a/spaces/Arsenii2023/Demo1/demo1.py
+++ /dev/null
@@ -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)
\ No newline at end of file
diff --git a/spaces/Awesimo/jojogan/e4e/criteria/w_norm.py b/spaces/Awesimo/jojogan/e4e/criteria/w_norm.py
deleted file mode 100644
index a45ab6f67d8a3f7051be4b7236fa2f38446fd2c1..0000000000000000000000000000000000000000
--- a/spaces/Awesimo/jojogan/e4e/criteria/w_norm.py
+++ /dev/null
@@ -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]
diff --git a/spaces/Bart92/RVC_HF/go-applio.bat b/spaces/Bart92/RVC_HF/go-applio.bat
deleted file mode 100644
index 60c0c41d34a8aee5e14e744accb33d028d807245..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/go-applio.bat
+++ /dev/null
@@ -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
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/constants.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/constants.py
deleted file mode 100644
index 570aa2ea78748e469c966b720646364dba61594b..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/constants.py
+++ /dev/null
@@ -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
diff --git a/spaces/Blaise-g/summarize-biomedical-papers-long-summary-or-tldr/README.md b/spaces/Blaise-g/summarize-biomedical-papers-long-summary-or-tldr/README.md
deleted file mode 100644
index 50c050585bb0cebabaf8c707ed367f5e2a97b061..0000000000000000000000000000000000000000
--- a/spaces/Blaise-g/summarize-biomedical-papers-long-summary-or-tldr/README.md
+++ /dev/null
@@ -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
----
\ No newline at end of file
diff --git a/spaces/BwayKC/darkstorm2150-Protogen_v2.2_Official_Release/app.py b/spaces/BwayKC/darkstorm2150-Protogen_v2.2_Official_Release/app.py
deleted file mode 100644
index aca6cf204d6e8a1aecfd27e41ea3c114089a936c..0000000000000000000000000000000000000000
--- a/spaces/BwayKC/darkstorm2150-Protogen_v2.2_Official_Release/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/darkstorm2150/Protogen_v2.2_Official_Release").launch()
\ No newline at end of file
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/samplers/distributed_sampler.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/samplers/distributed_sampler.py
deleted file mode 100644
index 05c162205e8ed7c30269d03aed441d738f9b5b0a..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/samplers/distributed_sampler.py
+++ /dev/null
@@ -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)
diff --git a/spaces/CVPR/LIVE/pybind11/tests/test_callbacks.cpp b/spaces/CVPR/LIVE/pybind11/tests/test_callbacks.cpp
deleted file mode 100644
index 71b88c44c7650a7e7b3f37cee19359e15bbb0270..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/tests/test_callbacks.cpp
+++ /dev/null
@@ -1,168 +0,0 @@
-/*
- tests/test_callbacks.cpp -- callbacks
-
- Copyright (c) 2016 Wenzel Jakob
-
- 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
-#include
-
-
-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 &func) {
- return "func(43) = " + std::to_string(func(43)); });
- m.def("test_callback4", []() -> std::function { 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);
- /* Test cleanup of lambda closure */
- m.def("test_cleanup", []() -> std::function {
- 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 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 &f) -> std::string {
- using fn_type = int (*)(int);
- auto result = f.target();
- 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) { });
-
- 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_(m, "MovableObject");
-
- // test_movable_object
- m.def("callback_with_movable", [](std::function 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_(m, "CppBoundMethodTest")
- .def(py::init<>())
- .def("triple", [](CppBoundMethodTest &, int val) { return 3 * val; });
-
- // test async Python callbacks
- using callback_f = std::function;
- 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(i));
- });
-}
diff --git a/spaces/CVPR/LIVE/thrust/testing/unittest/special_types.h b/spaces/CVPR/LIVE/thrust/testing/unittest/special_types.h
deleted file mode 100644
index b046a96eec4d80ff907f84d994b8dd04a9be0506..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/testing/unittest/special_types.h
+++ /dev/null
@@ -1,184 +0,0 @@
-#pragma once
-
-#include
-#include
-
-template
-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
- 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
-{
- 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 {};
-
-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;
-
-
-}
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/device_reference.h b/spaces/CVPR/LIVE/thrust/thrust/device_reference.h
deleted file mode 100644
index 6d8538b2fbfe4149f0dc56650eb9eb3c49ff0b91..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/device_reference.h
+++ /dev/null
@@ -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
-#include
-#include
-#include
-
-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
- *
- * int main(void)
- * {
- * thrust::device_vector vec(1, 13);
- *
- * thrust::device_reference 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
- * #include
- *
- * int main(void)
- * {
- * thrust::device_vector vec(1, 13);
- *
- * thrust::device_reference 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
- * #include
- *
- * int main(void)
- * {
- * thrust::device_vector 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
- *
- * struct foo
- * {
- * int x;
- * };
- *
- * int main(void)
- * {
- * thrust::device_vector foo_vec(1);
- *
- * thrust::device_reference 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
- *
- * struct foo
- * {
- * int x;
- * };
- *
- * int main(void)
- * {
- * thrust::device_vector foo_vec(1);
- *
- * // create a local host-side foo object
- * foo host_foo;
- * host_foo.x = 13;
- *
- * thrust::device_reference 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
- * #include
- *
- * int main(void)
- * {
- * thrust::device_vector 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
- class device_reference
- : public thrust::reference<
- T,
- thrust::device_ptr,
- thrust::device_reference
- >
-{
- private:
- typedef thrust::reference<
- T,
- thrust::device_ptr,
- thrust::device_reference
- > 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 &ref, where ref 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
- * #include
- * ...
- * thrust::device_vector v(1,0);
- * thrust::device_reference 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
- * device_reference from device_reference.
- */
- template
- __host__ __device__
- device_reference(const device_reference &other,
- typename thrust::detail::enable_if_convertible<
- typename device_reference::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
- * #include
- * ...
- * thrust::device_vector v(1,0);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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 *this
- */
- template
- __host__ __device__
- device_reference &operator=(const device_reference &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 *this
- */
- __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 *this
- *
- * The following code snippet demonstrates the semantics of
- * \p device_reference's prefix increment operator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector v(1,0);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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
- * #include
- * ...
- * thrust::device_vector v(1,0);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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 *this.
- *
- * The following code snippet demonstrates the semantics of
- * \p device_reference's addition assignment operator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector v(1,0);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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 *this
- *
- * The following code snippet demonstrates the semantics of
- * \p device_reference's prefix decrement operator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector v(1,0);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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
- * #include
- * ...
- * thrust::device_vector v(1,0);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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 *this.
- *
- * The following code snippet demonstrates the semantics of
- * \p device_reference's addition assignment operator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector v(1,0);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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 *this.
- *
- * The following code snippet demonstrates the semantics of
- * \p device_reference's multiply assignment operator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector v(1,1);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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 *this.
- *
- * The following code snippet demonstrates the semantics of
- * \p device_reference's divide assignment operator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector v(1,5);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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 *this.
- *
- * The following code snippet demonstrates the semantics of
- * \p device_reference's divide assignment operator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector v(1,5);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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 *this.
- *
- * The following code snippet demonstrates the semantics of
- * \p device_reference's left shift assignment operator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector v(1,1);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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 *this.
- *
- * The following code snippet demonstrates the semantics of
- * \p device_reference's right shift assignment operator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector v(1,2);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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 *this.
- *
- * The following code snippet demonstrates the semantics of
- * \p device_reference's AND assignment operator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector v(1,1);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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 *this.
- *
- * The following code snippet demonstrates the semantics of
- * \p device_reference's OR assignment operator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector v(1,0);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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 *this.
- *
- * The following code snippet demonstrates the semantics of
- * \p device_reference's XOR assignment operator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector v(1,1);
- * thrust::device_ptr ptr = &v[0];
- * thrust::device_reference 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
-__host__ __device__
-void swap(device_reference x, device_reference 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
-std::basic_ostream &
-operator<<(std::basic_ostream &os, const device_reference &y);
-#endif
-
-/*! \}
- */
-
-} // end thrust
-
-#include
-
diff --git a/spaces/CVPR/Object-Detection-With-DETR-and-YOLOS/app.py b/spaces/CVPR/Object-Detection-With-DETR-and-YOLOS/app.py
deleted file mode 100644
index 7cf99c6bbbf0d5bb5d6b76580999e4d545c99056..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Object-Detection-With-DETR-and-YOLOS/app.py
+++ /dev/null
@@ -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 = """
Object Detection App with DETR and YOLOS
"""
-
-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://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("")
-
-
-demo.launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/CVPR/WALT/mmdet/core/bbox/samplers/sampling_result.py b/spaces/CVPR/WALT/mmdet/core/bbox/samplers/sampling_result.py
deleted file mode 100644
index 419a8e39a3c307a7cd9cfd0565a20037ded0d646..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/core/bbox/samplers/sampling_result.py
+++ /dev/null
@@ -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 =
- """
-
- 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
diff --git a/spaces/CVPR/flava-multimodal-zero-shot/app.py b/spaces/CVPR/flava-multimodal-zero-shot/app.py
deleted file mode 100644
index ee4c007dc117558b8014950320f471b37668e576..0000000000000000000000000000000000000000
--- a/spaces/CVPR/flava-multimodal-zero-shot/app.py
+++ /dev/null
@@ -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()
\ No newline at end of file
diff --git a/spaces/CVPR/lama-example/bin/calc_dataset_stats.py b/spaces/CVPR/lama-example/bin/calc_dataset_stats.py
deleted file mode 100644
index 5086fea1bab691892f2e52e3c59e5ef048bcfac0..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/bin/calc_dataset_stats.py
+++ /dev/null
@@ -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())
diff --git a/spaces/Cpp4App/Cpp4App/CDM/run_single.py b/spaces/Cpp4App/Cpp4App/CDM/run_single.py
deleted file mode 100644
index dabbe7dbfd0692d67124c5b4975542edc7e2913c..0000000000000000000000000000000000000000
--- a/spaces/Cpp4App/Cpp4App/CDM/run_single.py
+++ /dev/null
@@ -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
-
-
diff --git a/spaces/Cvandi/remake/realesrgan/__init__.py b/spaces/Cvandi/remake/realesrgan/__init__.py
deleted file mode 100644
index bfea78f284116dee22510d4aa91f9e44afb7d472..0000000000000000000000000000000000000000
--- a/spaces/Cvandi/remake/realesrgan/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# flake8: noqa
-from .archs import *
-from .data import *
-from .models import *
-from .utils import *
-#from .version import *
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/cu2qu/errors.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/cu2qu/errors.py
deleted file mode 100644
index fa3dc42937131c5db54890dde8f519b15f5d0ff1..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/cu2qu/errors.py
+++ /dev/null
@@ -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()))
- )
diff --git a/spaces/Detomo/Image-Classification/app.py b/spaces/Detomo/Image-Classification/app.py
deleted file mode 100644
index fdd9102f03eafbef37e2807d4850847df09dbcc3..0000000000000000000000000000000000000000
--- a/spaces/Detomo/Image-Classification/app.py
+++ /dev/null
@@ -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: Vu Minh Chien.",
-)
-iface.launch()
diff --git a/spaces/EdBianchi/ThemeParksAccidents_RDF-SPARQL/app.py b/spaces/EdBianchi/ThemeParksAccidents_RDF-SPARQL/app.py
deleted file mode 100644
index 3e71f820decfad6075df8e717601849a0a9ff8be..0000000000000000000000000000000000000000
--- a/spaces/EdBianchi/ThemeParksAccidents_RDF-SPARQL/app.py
+++ /dev/null
@@ -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:
- PREFIX acc:
- PREFIX 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:
-
- SELECT DISTINCT ?name
- WHERE {
- ?ride ride:name ?name .
- }
-"""
-# num of accidents per injured people
-query_1 = """
- PREFIX r:
- PREFIX a:
-
- 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:
- PREFIX 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:
- PREFIX acc:
- PREFIX 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:
- PREFIX ride_type:
- PREFIX acc:
- PREFIX 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:
- PREFIX acc:
-
- 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:
-
- 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:
- PREFIX acc:
-
- 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:
- 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
- """)
diff --git a/spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/nets_new.py b/spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/nets_new.py
deleted file mode 100644
index 1c0f4fa96d921e979fe31bd4151701b7783fbcea..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/nets_new.py
+++ /dev/null
@@ -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
diff --git a/spaces/Felix123456/bingo/tests/kblob.ts b/spaces/Felix123456/bingo/tests/kblob.ts
deleted file mode 100644
index 9e15b41c1c94a690beb61b23cdb42fc78767ccd2..0000000000000000000000000000000000000000
--- a/spaces/Felix123456/bingo/tests/kblob.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-import FormData from 'form-data'
-
-import { fetch } from '@/lib/isomorphic'
-
-const formData = new FormData()
-
-const knowledgeRequest = {"imageInfo":{"url":"https://www.baidu.com/img/PCfb_5bf082d29588c07f842ccde3f97243ea.png"},"knowledgeRequest":{"invokedSkills":["ImageById"],"subscriptionId":"Bing.Chat.Multimodal","invokedSkillsRequestData":{"enableFaceBlur":true},"convoData":{"convoid":"51D|BingProdUnAuthenticatedUsers|E3DCA904FF236C67C3450163BCEC64CFF3F618CC8A4AFD75FD518F5ED0ADA080","convotone":"Creative"}}}
-
-formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
-
-
-fetch('https://bing.vcanbb.top/images/kblob',
- {
- method: 'POST',
- body: formData.getBuffer(),
- headers: {
- "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
- "sec-ch-ua-mobile": "?0",
- "sec-ch-ua-platform": "\"Windows\"",
- "Referer": "https://bing.vcanbb.top/web/index.html",
- "Referrer-Policy": "origin-when-cross-origin",
- ...formData.getHeaders()
- }
-
- }
-).then(res => res.text())
-.then(res => console.log('res', res))
diff --git a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/utils/download_util.py b/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/utils/download_util.py
deleted file mode 100644
index 2a267915743ee3f3232bc8fe992466b52468979a..0000000000000000000000000000000000000000
--- a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/utils/download_util.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import math
-import os
-import requests
-from torch.hub import download_url_to_file, get_dir
-from tqdm import tqdm
-from urllib.parse import urlparse
-
-from .misc import sizeof_fmt
-
-
-def download_file_from_google_drive(file_id, save_path):
- """Download files from google drive.
- Ref:
- https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive # noqa E501
- Args:
- file_id (str): File id.
- save_path (str): Save path.
- """
-
- session = requests.Session()
- URL = 'https://docs.google.com/uc?export=download'
- params = {'id': file_id}
-
- response = session.get(URL, params=params, stream=True)
- token = get_confirm_token(response)
- if token:
- params['confirm'] = token
- response = session.get(URL, params=params, stream=True)
-
- # get file size
- response_file_size = session.get(URL, params=params, stream=True, headers={'Range': 'bytes=0-2'})
- print(response_file_size)
- if 'Content-Range' in response_file_size.headers:
- file_size = int(response_file_size.headers['Content-Range'].split('/')[1])
- else:
- file_size = None
-
- save_response_content(response, save_path, file_size)
-
-
-def get_confirm_token(response):
- for key, value in response.cookies.items():
- if key.startswith('download_warning'):
- return value
- return None
-
-
-def save_response_content(response, destination, file_size=None, chunk_size=32768):
- if file_size is not None:
- pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk')
-
- readable_file_size = sizeof_fmt(file_size)
- else:
- pbar = None
-
- with open(destination, 'wb') as f:
- downloaded_size = 0
- for chunk in response.iter_content(chunk_size):
- downloaded_size += chunk_size
- if pbar is not None:
- pbar.update(1)
- pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} / {readable_file_size}')
- if chunk: # filter out keep-alive new chunks
- f.write(chunk)
- if pbar is not None:
- pbar.close()
-
-
-def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
- """Load file form http url, will download models if necessary.
- Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
- Args:
- url (str): URL to be downloaded.
- model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir.
- Default: None.
- progress (bool): Whether to show the download progress. Default: True.
- file_name (str): The downloaded file name. If None, use the file name in the url. Default: None.
- Returns:
- str: The path to the downloaded file.
- """
- if model_dir is None: # use the pytorch hub_dir
- hub_dir = get_dir()
- model_dir = os.path.join(hub_dir, 'checkpoints')
-
- os.makedirs(model_dir, exist_ok=True)
-
- parts = urlparse(url)
- filename = os.path.basename(parts.path)
- if file_name is not None:
- filename = file_name
- cached_file = os.path.abspath(os.path.join(model_dir, filename))
- if not os.path.exists(cached_file):
- print(f'Downloading: "{url}" to {cached_file}\n')
- download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
- return cached_file
\ No newline at end of file
diff --git a/spaces/Fredithefish/PixelRevive/README.md b/spaces/Fredithefish/PixelRevive/README.md
deleted file mode 100644
index f4b729ea029f50a0f20673d5b4386abe5fd80081..0000000000000000000000000000000000000000
--- a/spaces/Fredithefish/PixelRevive/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: PixelRevive
-emoji: 🐨
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/models/resnet_lat_origin.py b/spaces/Gen-Sim/Gen-Sim/cliport/models/resnet_lat_origin.py
deleted file mode 100644
index 9b2776979b2b6e11d416ab0aef30de5384a7d8d3..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/models/resnet_lat_origin.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import cliport.utils.utils as utils
-
-from cliport.models.resnet import ConvBlock, IdentityBlock
-
-class ResNet45_10s_origin(nn.Module):
- def __init__(self, input_shape, output_dim, cfg, device, preprocess):
- super(ResNet45_10s_origin, self).__init__()
- self.input_shape = input_shape
- self.input_dim = input_shape[-1]
- self.output_dim = output_dim
- self.cfg = cfg
- self.device = device
- self.batchnorm = self.cfg['train']['batchnorm']
- self.preprocess = preprocess
-
- self._make_layers()
-
- def _make_layers(self):
- # conv1
- self.conv1 = nn.Sequential(
- nn.Conv2d(self.input_dim, 64, stride=1, kernel_size=3, padding=1),
- nn.BatchNorm2d(64) if self.batchnorm else nn.Identity(),
- nn.ReLU(True),
- )
-
- # fcn
- self.layer1 = nn.Sequential(
- ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- )
-
- self.layer2 = nn.Sequential(
- ConvBlock(64, [128, 128, 128], kernel_size=3, stride=2, batchnorm=self.batchnorm),
- IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- )
-
- self.layer3 = nn.Sequential(
- ConvBlock(128, [256, 256, 256], kernel_size=3, stride=2, batchnorm=self.batchnorm),
- IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- )
-
- self.layer4 = nn.Sequential(
- ConvBlock(256, [512, 512, 512], kernel_size=3, stride=2, batchnorm=self.batchnorm),
- IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- )
-
- self.layer5 = nn.Sequential(
- ConvBlock(512, [1024, 1024, 1024], kernel_size=3, stride=2, batchnorm=self.batchnorm),
- IdentityBlock(1024, [1024, 1024, 1024], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- )
-
- # head
- self.layer6 = nn.Sequential(
- ConvBlock(1024, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- nn.UpsamplingBilinear2d(scale_factor=2),
- )
-
- self.layer7 = nn.Sequential(
- ConvBlock(512, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- nn.UpsamplingBilinear2d(scale_factor=2),
- )
-
- self.layer8 = nn.Sequential(
- ConvBlock(256, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- nn.UpsamplingBilinear2d(scale_factor=2),
- )
-
- self.layer9 = nn.Sequential(
- ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- nn.UpsamplingBilinear2d(scale_factor=2),
- )
-
- self.layer10 = nn.Sequential(
- ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
- nn.UpsamplingBilinear2d(scale_factor=2),
- )
-
- # conv2
- self.conv2 = nn.Sequential(
- ConvBlock(32, [16, 16, self.output_dim], kernel_size=3, stride=1,
- final_relu=False, batchnorm=self.batchnorm),
- IdentityBlock(self.output_dim, [16, 16, self.output_dim], kernel_size=3, stride=1,
- final_relu=False, batchnorm=self.batchnorm)
- )
-
- def forward(self, x):
- x = self.preprocess(x, dist='transporter')
- in_shape = x.shape
-
- # encoder
- for layer in [self.conv1, self.layer1, self.layer2, self.layer3, self.layer4, self.layer5]:
- x = layer(x)
-
- # decoder
- im = []
- for layer in [self.layer6, self.layer7, self.layer8, self.layer9, self.layer10, self.conv2]:
- im.append(x)
- x = layer(x)
-
- x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear')
- return x, im
\ No newline at end of file
diff --git a/spaces/GeorgeOrville/bingo/README.md b/spaces/GeorgeOrville/bingo/README.md
deleted file mode 100644
index 6010177f05bf837aa164d6a0fd98c06c50c5523e..0000000000000000000000000000000000000000
--- a/spaces/GeorgeOrville/bingo/README.md
+++ /dev/null
@@ -1,196 +0,0 @@
----
-title: bingo
-emoji: 📉
-colorFrom: red
-colorTo: red
-sdk: docker
-license: mit
-duplicated_from: hf4all/bingo
----
-
-
-
-# Bingo
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-
-
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://github.com/weaigc/bingo/blob/main/license)
-
-
- */
- var abr_switch_map = [
- new ABRPresets(8, 9, 9, 0, 0, 6.60, 145, 0, 0.95, 0, -30.0, 11, 0.0012, 1), /* 8, impossible to use in stereo */
- new ABRPresets(16, 9, 9, 0, 0, 6.60, 145, 0, 0.95, 0, -25.0, 11, 0.0010, 1), /* 16 */
- new ABRPresets(24, 9, 9, 0, 0, 6.60, 145, 0, 0.95, 0, -20.0, 11, 0.0010, 1), /* 24 */
- new ABRPresets(32, 9, 9, 0, 0, 6.60, 145, 0, 0.95, 0, -15.0, 11, 0.0010, 1), /* 32 */
- new ABRPresets(40, 9, 9, 0, 0, 6.60, 145, 0, 0.95, 0, -10.0, 11, 0.0009, 1), /* 40 */
- new ABRPresets(48, 9, 9, 0, 0, 6.60, 145, 0, 0.95, 0, -10.0, 11, 0.0009, 1), /* 48 */
- new ABRPresets(56, 9, 9, 0, 0, 6.60, 145, 0, 0.95, 0, -6.0, 11, 0.0008, 1), /* 56 */
- new ABRPresets(64, 9, 9, 0, 0, 6.60, 145, 0, 0.95, 0, -2.0, 11, 0.0008, 1), /* 64 */
- new ABRPresets(80, 9, 9, 0, 0, 6.60, 145, 0, 0.95, 0, .0, 8, 0.0007, 1), /* 80 */
- new ABRPresets(96, 9, 9, 0, 2.50, 6.60, 145, 0, 0.95, 0, 1.0, 5.5, 0.0006, 1), /* 96 */
- new ABRPresets(112, 9, 9, 0, 2.25, 6.60, 145, 0, 0.95, 0, 2.0, 4.5, 0.0005, 1), /* 112 */
- new ABRPresets(128, 9, 9, 0, 1.95, 6.40, 140, 0, 0.95, 0, 3.0, 4, 0.0002, 1), /* 128 */
- new ABRPresets(160, 9, 9, 1, 1.79, 6.00, 135, 0, 0.95, -2, 5.0, 3.5, 0, 1), /* 160 */
- new ABRPresets(192, 9, 9, 1, 1.49, 5.60, 125, 0, 0.97, -4, 7.0, 3, 0, 0), /* 192 */
- new ABRPresets(224, 9, 9, 1, 1.25, 5.20, 125, 0, 0.98, -6, 9.0, 2, 0, 0), /* 224 */
- new ABRPresets(256, 9, 9, 1, 0.97, 5.20, 125, 0, 1.00, -8, 10.0, 1, 0, 0), /* 256 */
- new ABRPresets(320, 9, 9, 1, 0.90, 5.20, 125, 0, 1.00, -10, 12.0, 0, 0, 0) /* 320 */
- ];
-
- function apply_abr_preset(gfp, preset, enforce) {
- /* Variables for the ABR stuff */
- var actual_bitrate = preset;
-
- var r = lame.nearestBitrateFullIndex(preset);
-
- gfp.VBR = VbrMode.vbr_abr;
- gfp.VBR_mean_bitrate_kbps = actual_bitrate;
- gfp.VBR_mean_bitrate_kbps = Math.min(gfp.VBR_mean_bitrate_kbps, 320);
- gfp.VBR_mean_bitrate_kbps = Math.max(gfp.VBR_mean_bitrate_kbps, 8);
- gfp.brate = gfp.VBR_mean_bitrate_kbps;
- if (gfp.VBR_mean_bitrate_kbps > 320) {
- gfp.disable_reservoir = true;
- }
-
- /* parameters for which there is no proper set/get interface */
- if (abr_switch_map[r].safejoint > 0)
- gfp.exp_nspsytune = gfp.exp_nspsytune | 2;
- /* safejoint */
-
- if (abr_switch_map[r].sfscale > 0) {
- gfp.internal_flags.noise_shaping = 2;
- }
- /* ns-bass tweaks */
- if (Math.abs(abr_switch_map[r].nsbass) > 0) {
- var k = (int)(abr_switch_map[r].nsbass * 4);
- if (k < 0)
- k += 64;
- gfp.exp_nspsytune = gfp.exp_nspsytune | (k << 2);
- }
-
- if (enforce != 0)
- gfp.quant_comp = abr_switch_map[r].quant_comp;
- else if (!(Math.abs(gfp.quant_comp - -1) > 0))
- gfp.quant_comp = abr_switch_map[r].quant_comp;
- // SET_OPTION(quant_comp, abr_switch_map[r].quant_comp, -1);
- if (enforce != 0)
- gfp.quant_comp_short = abr_switch_map[r].quant_comp_s;
- else if (!(Math.abs(gfp.quant_comp_short - -1) > 0))
- gfp.quant_comp_short = abr_switch_map[r].quant_comp_s;
- // SET_OPTION(quant_comp_short, abr_switch_map[r].quant_comp_s, -1);
-
- if (enforce != 0)
- gfp.msfix = abr_switch_map[r].nsmsfix;
- else if (!(Math.abs(gfp.msfix - -1) > 0))
- gfp.msfix = abr_switch_map[r].nsmsfix;
- // SET_OPTION(msfix, abr_switch_map[r].nsmsfix, -1);
-
- if (enforce != 0)
- gfp.internal_flags.nsPsy.attackthre = abr_switch_map[r].st_lrm;
- else if (!(Math.abs(gfp.internal_flags.nsPsy.attackthre - -1) > 0))
- gfp.internal_flags.nsPsy.attackthre = abr_switch_map[r].st_lrm;
- // SET_OPTION(short_threshold_lrm, abr_switch_map[r].st_lrm, -1);
- if (enforce != 0)
- gfp.internal_flags.nsPsy.attackthre_s = abr_switch_map[r].st_s;
- else if (!(Math.abs(gfp.internal_flags.nsPsy.attackthre_s - -1) > 0))
- gfp.internal_flags.nsPsy.attackthre_s = abr_switch_map[r].st_s;
- // SET_OPTION(short_threshold_s, abr_switch_map[r].st_s, -1);
-
- /*
- * ABR seems to have big problems with clipping, especially at low
- * bitrates
- */
- /*
- * so we compensate for that here by using a scale value depending on
- * bitrate
- */
- if (enforce != 0)
- gfp.scale = abr_switch_map[r].scale;
- else if (!(Math.abs(gfp.scale - -1) > 0))
- gfp.scale = abr_switch_map[r].scale;
- // SET_OPTION(scale, abr_switch_map[r].scale, -1);
-
- if (enforce != 0)
- gfp.maskingadjust = abr_switch_map[r].masking_adj;
- else if (!(Math.abs(gfp.maskingadjust - 0) > 0))
- gfp.maskingadjust = abr_switch_map[r].masking_adj;
- // SET_OPTION(maskingadjust, abr_switch_map[r].masking_adj, 0);
- if (abr_switch_map[r].masking_adj > 0) {
- if (enforce != 0)
- gfp.maskingadjust_short = (abr_switch_map[r].masking_adj * .9);
- else if (!(Math.abs(gfp.maskingadjust_short - 0) > 0))
- gfp.maskingadjust_short = (abr_switch_map[r].masking_adj * .9);
- // SET_OPTION(maskingadjust_short, abr_switch_map[r].masking_adj *
- // .9, 0);
- } else {
- if (enforce != 0)
- gfp.maskingadjust_short = (abr_switch_map[r].masking_adj * 1.1);
- else if (!(Math.abs(gfp.maskingadjust_short - 0) > 0))
- gfp.maskingadjust_short = (abr_switch_map[r].masking_adj * 1.1);
- // SET_OPTION(maskingadjust_short, abr_switch_map[r].masking_adj *
- // 1.1, 0);
- }
-
- if (enforce != 0)
- gfp.ATHlower = -abr_switch_map[r].ath_lower / 10.;
- else if (!(Math.abs((-gfp.ATHlower * 10.) - 0) > 0))
- gfp.ATHlower = -abr_switch_map[r].ath_lower / 10.;
- // SET_OPTION(ATHlower, abr_switch_map[r].ath_lower, 0);
- if (enforce != 0)
- gfp.ATHcurve = abr_switch_map[r].ath_curve;
- else if (!(Math.abs(gfp.ATHcurve - -1) > 0))
- gfp.ATHcurve = abr_switch_map[r].ath_curve;
- // SET_OPTION(ATHcurve, abr_switch_map[r].ath_curve, -1);
-
- if (enforce != 0)
- gfp.interChRatio = abr_switch_map[r].interch;
- else if (!(Math.abs(gfp.interChRatio - -1) > 0))
- gfp.interChRatio = abr_switch_map[r].interch;
- // SET_OPTION(interChRatio, abr_switch_map[r].interch, -1);
-
- return preset;
- }
-
- this.apply_preset = function(gfp, preset, enforce) {
- /* translate legacy presets */
- switch (preset) {
- case Lame.R3MIX:
- {
- preset = Lame.V3;
- gfp.VBR = VbrMode.vbr_mtrh;
- break;
- }
- case Lame.MEDIUM:
- {
- preset = Lame.V4;
- gfp.VBR = VbrMode.vbr_rh;
- break;
- }
- case Lame.MEDIUM_FAST:
- {
- preset = Lame.V4;
- gfp.VBR = VbrMode.vbr_mtrh;
- break;
- }
- case Lame.STANDARD:
- {
- preset = Lame.V2;
- gfp.VBR = VbrMode.vbr_rh;
- break;
- }
- case Lame.STANDARD_FAST:
- {
- preset = Lame.V2;
- gfp.VBR = VbrMode.vbr_mtrh;
- break;
- }
- case Lame.EXTREME:
- {
- preset = Lame.V0;
- gfp.VBR = VbrMode.vbr_rh;
- break;
- }
- case Lame.EXTREME_FAST:
- {
- preset = Lame.V0;
- gfp.VBR = VbrMode.vbr_mtrh;
- break;
- }
- case Lame.INSANE:
- {
- preset = 320;
- gfp.preset = preset;
- apply_abr_preset(gfp, preset, enforce);
- gfp.VBR = VbrMode.vbr_off;
- return preset;
- }
- }
-
- gfp.preset = preset;
- {
- switch (preset) {
- case Lame.V9:
- apply_vbr_preset(gfp, 9, enforce);
- return preset;
- case Lame.V8:
- apply_vbr_preset(gfp, 8, enforce);
- return preset;
- case Lame.V7:
- apply_vbr_preset(gfp, 7, enforce);
- return preset;
- case Lame.V6:
- apply_vbr_preset(gfp, 6, enforce);
- return preset;
- case Lame.V5:
- apply_vbr_preset(gfp, 5, enforce);
- return preset;
- case Lame.V4:
- apply_vbr_preset(gfp, 4, enforce);
- return preset;
- case Lame.V3:
- apply_vbr_preset(gfp, 3, enforce);
- return preset;
- case Lame.V2:
- apply_vbr_preset(gfp, 2, enforce);
- return preset;
- case Lame.V1:
- apply_vbr_preset(gfp, 1, enforce);
- return preset;
- case Lame.V0:
- apply_vbr_preset(gfp, 0, enforce);
- return preset;
- default:
- break;
- }
- }
- if (8 <= preset && preset <= 320) {
- return apply_abr_preset(gfp, preset, enforce);
- }
-
- /* no corresponding preset found */
- gfp.preset = 0;
- return preset;
- }
-
- // Rest from getset.c:
-
- /**
- * VBR quality level.
- * 0 = highest
- * 9 = lowest
- */
- function lame_set_VBR_q(gfp, VBR_q) {
- var ret = 0;
-
- if (0 > VBR_q) {
- /* Unknown VBR quality level! */
- ret = -1;
- VBR_q = 0;
- }
- if (9 < VBR_q) {
- ret = -1;
- VBR_q = 9;
- }
-
- gfp.VBR_q = VBR_q;
- gfp.VBR_q_frac = 0;
- return ret;
- }
-
-}
-
-/*
- * bit reservoir source file
- *
- * Copyright (c) 1999-2000 Mark Taylor
- *
- * This library is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2 of the License, or (at your option) any later version.
- *
- * This library is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Library General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with this library; if not, write to the
- * Free Software Foundation, Inc., 59 Temple Place - Suite 330,
- * Boston, MA 02111-1307, USA.
- */
-
-/* $Id: Reservoir.java,v 1.9 2011/05/24 20:48:06 kenchis Exp $ */
-
-//package mp3;
-
-/**
- * ResvFrameBegin:
- * Called (repeatedly) at the beginning of a frame. Updates the maximum size of
- * the reservoir, and checks to make sure main_data_begin was set properly by
- * the formatter
- * Background information:
- *
- * This is the original text from the ISO standard. Because of sooo many bugs
- * and irritations correcting comments are added in brackets []. A '^W' means
- * you should remove the last word.
- *
- *
- * 1. The following rule can be used to calculate the maximum
- * number of bits used for one granule [^W frame]:
- * At the highest possible bitrate of Layer III (320 kbps
- * per stereo signal [^W^W^W], 48 kHz) the frames must be of
- * [^W^W^W are designed to have] constant length, i.e.
- * one buffer [^W^W the frame] length is:
- *
- * 320 kbps * 1152/48 kHz = 7680 bit = 960 byte
- *
- * This value is used as the maximum buffer per channel [^W^W] at
- * lower bitrates [than 320 kbps]. At 64 kbps mono or 128 kbps
- * stereo the main granule length is 64 kbps * 576/48 kHz = 768 bit
- * [per granule and channel] at 48 kHz sampling frequency.
- * This means that there is a maximum deviation (short time buffer
- * [= reservoir]) of 7680 - 2*2*768 = 4608 bits is allowed at 64 kbps.
- * The actual deviation is equal to the number of bytes [with the
- * meaning of octets] denoted by the main_data_end offset pointer.
- * The actual maximum deviation is (2^9-1)*8 bit = 4088 bits
- * [for MPEG-1 and (2^8-1)*8 bit for MPEG-2, both are hard limits].
- * ... The xchange of buffer bits between the left and right channel
- * is allowed without restrictions [exception: dual channel].
- * Because of the [constructed] constraint on the buffer size
- * main_data_end is always set to 0 in the case of bit_rate_index==14,
- * i.e. data rate 320 kbps per stereo signal [^W^W^W]. In this case
- * all data are allocated between adjacent header [^W sync] words
- * [, i.e. there is no buffering at all].
- *
- * Meaning of the variables:
- * resvLimit: (0, 8, ..., 8*255 (MPEG-2), 8*511 (MPEG-1))
- * Number of bits can be stored in previous frame(s) due to
- * counter size constaints
- * maxmp3buf: ( ??? ... 8*1951 (MPEG-1 and 2), 8*2047 (MPEG-2.5))
- * Number of bits allowed to encode one frame (you can take 8*511 bit
- * from the bit reservoir and at most 8*1440 bit from the current
- * frame (320 kbps, 32 kHz), so 8*1951 bit is the largest possible
- * value for MPEG-1 and -2)
- *
- * maximum allowed granule/channel size times 4 = 8*2047 bits.,
- * so this is the absolute maximum supported by the format.
- *
- *
- * fullFrameBits: maximum number of bits available for encoding
- * the current frame.
- *
- * mean_bits: target number of bits per granule.
- *
- * frameLength:
- *
- * gfc.ResvMax: maximum allowed reservoir
- *
- * gfc.ResvSize: current reservoir size
- *
- * l3_side.resvDrain_pre:
- * ancillary data to be added to previous frame:
- * (only usefull in VBR modes if it is possible to have
- * maxmp3buf < fullFrameBits)). Currently disabled,
- * see #define NEW_DRAIN
- * 2010-02-13: RH now enabled, it seems to be needed for CBR too,
- * as there exists one example, where the FhG decoder
- * can't decode a -b320 CBR file anymore.
- *
- * l3_side.resvDrain_post:
- * ancillary data to be added to this frame:
- *
- *
- */
-
- /* main_data_begin has 9 bits in MPEG-1, 8 bits MPEG-2 */
- var resvLimit = (8 * 256) * gfc.mode_gr - 8;
-
- /*
- * maximum allowed frame size. dont use more than this number of bits,
- * even if the frame has the space for them:
- */
- if (gfp.brate > 320) {
- /* in freeformat the buffer is constant */
- maxmp3buf = 8 * ((int) ((gfp.brate * 1000)
- / (gfp.out_samplerate / 1152) / 8 + .5));
- } else {
- /*
- * all mp3 decoders should have enough buffer to handle this value:
- * size of a 320kbps 32kHz frame
- */
- maxmp3buf = 8 * 1440;
-
- /*
- * Bouvigne suggests this more lax interpretation of the ISO doc
- * instead of using 8*960.
- */
-
- if (gfp.strict_ISO) {
- maxmp3buf = 8 * ((int) (320000 / (gfp.out_samplerate / 1152) / 8 + .5));
- }
- }
-
- gfc.ResvMax = maxmp3buf - frameLength;
- if (gfc.ResvMax > resvLimit)
- gfc.ResvMax = resvLimit;
- if (gfc.ResvMax < 0 || gfp.disable_reservoir)
- gfc.ResvMax = 0;
-
- var fullFrameBits = mean_bits.bits * gfc.mode_gr
- + Math.min(gfc.ResvSize, gfc.ResvMax);
-
- if (fullFrameBits > maxmp3buf)
- fullFrameBits = maxmp3buf;
-
-
- l3_side.resvDrain_pre = 0;
-
- // frame analyzer code
- if (gfc.pinfo != null) {
- /*
- * expected bits per channel per granule [is this also right for
- * mono/stereo, MPEG-1/2 ?]
- */
- gfc.pinfo.mean_bits = mean_bits.bits / 2;
- gfc.pinfo.resvsize = gfc.ResvSize;
- }
-
- return fullFrameBits;
- }
-
- /**
- * returns targ_bits: target number of bits to use for 1 granule
- * extra_bits: amount extra available from reservoir
- * Mark Taylor 4/99
- */
- this.ResvMaxBits = function(gfp, mean_bits, targ_bits, cbr) {
- var gfc = gfp.internal_flags;
- var add_bits;
- var ResvSize = gfc.ResvSize, ResvMax = gfc.ResvMax;
-
- /* compensate the saved bits used in the 1st granule */
- if (cbr != 0)
- ResvSize += mean_bits;
-
- if ((gfc.substep_shaping & 1) != 0)
- ResvMax *= 0.9;
-
- targ_bits.bits = mean_bits;
-
- /* extra bits if the reservoir is almost full */
- if (ResvSize * 10 > ResvMax * 9) {
- add_bits = ResvSize - (ResvMax * 9) / 10;
- targ_bits.bits += add_bits;
- gfc.substep_shaping |= 0x80;
- } else {
- add_bits = 0;
- gfc.substep_shaping &= 0x7f;
- /*
- * build up reservoir. this builds the reservoir a little slower
- * than FhG. It could simple be mean_bits/15, but this was rigged to
- * always produce 100 (the old value) at 128kbs
- */
- if (!gfp.disable_reservoir && 0 == (gfc.substep_shaping & 1))
- targ_bits.bits -= .1 * mean_bits;
- }
-
- /* amount from the reservoir we are allowed to use. ISO says 6/10 */
- var extra_bits = (ResvSize < (gfc.ResvMax * 6) / 10 ? ResvSize
- : (gfc.ResvMax * 6) / 10);
- extra_bits -= add_bits;
-
- if (extra_bits < 0)
- extra_bits = 0;
- return extra_bits;
- }
-
- /**
- * Called after a granule's bit allocation. Readjusts the size of the
- * reservoir to reflect the granule's usage.
- */
- this.ResvAdjust = function(gfc, gi) {
- gfc.ResvSize -= gi.part2_3_length + gi.part2_length;
- }
-
- /**
- * Called after all granules in a frame have been allocated. Makes sure that
- * the reservoir size is within limits, possibly by adding stuffing bits.
- */
- this.ResvFrameEnd = function(gfc, mean_bits) {
- var over_bits;
- var l3_side = gfc.l3_side;
-
- gfc.ResvSize += mean_bits * gfc.mode_gr;
- var stuffingBits = 0;
- l3_side.resvDrain_post = 0;
- l3_side.resvDrain_pre = 0;
-
- /* we must be byte aligned */
- if ((over_bits = gfc.ResvSize % 8) != 0)
- stuffingBits += over_bits;
-
- over_bits = (gfc.ResvSize - stuffingBits) - gfc.ResvMax;
- if (over_bits > 0) {
- stuffingBits += over_bits;
- }
-
- /*
- * NOTE: enabling the NEW_DRAIN code fixes some problems with FhG
- * decoder shipped with MS Windows operating systems. Using this, it is
- * even possible to use Gabriel's lax buffer consideration again, which
- * assumes, any decoder should have a buffer large enough for a 320 kbps
- * frame at 32 kHz sample rate.
- *
- * old drain code: lame -b320 BlackBird.wav --. does not play with
- * GraphEdit.exe using FhG decoder V1.5 Build 50
- *
- * new drain code: lame -b320 BlackBird.wav --. plays fine with
- * GraphEdit.exe using FhG decoder V1.5 Build 50
- *
- * Robert Hegemann, 2010-02-13.
- */
- /*
- * drain as many bits as possible into previous frame ancillary data In
- * particular, in VBR mode ResvMax may have changed, and we have to make
- * sure main_data_begin does not create a reservoir bigger than ResvMax
- * mt 4/00
- */
- {
- var mdb_bytes = Math.min(l3_side.main_data_begin * 8, stuffingBits) / 8;
- l3_side.resvDrain_pre += 8 * mdb_bytes;
- stuffingBits -= 8 * mdb_bytes;
- gfc.ResvSize -= 8 * mdb_bytes;
- l3_side.main_data_begin -= mdb_bytes;
- }
- /* drain the rest into this frames ancillary data */
- l3_side.resvDrain_post += stuffingBits;
- gfc.ResvSize -= stuffingBits;
- }
-}
-
-
-/**
- * A Vbr header may be present in the ancillary data field of the first frame of
- * an mp3 bitstream
- * The Vbr header (optionally) contains
- *
- *
frames total number of audio frames in the bitstream
- *
bytes total number of bytes in the bitstream
- *
toc table of contents
- *
- *
- * toc (table of contents) gives seek points for random access.
- * The ith entry determines the seek point for i-percent duration.
- * seek point in bytes = (toc[i]/256.0) * total_bitstream_bytes
- * e.g. half duration seek point = (toc[50]/256.0) * total_bitstream_bytes
- */
-VBRTag.NUMTOCENTRIES = 100;
-VBRTag.MAXFRAMESIZE = 2880;
-
-function VBRTag() {
-
- var lame;
- var bs;
- var v;
-
- this.setModules = function (_lame, _bs, _v) {
- lame = _lame;
- bs = _bs;
- v = _v;
- };
-
- //fix 精简
-
- /**
- * Lookup table for fast CRC-16 computation. Uses the polynomial
- * x^16+x^15+x^2+1
- */
- var crc16Lookup = [0x0000, 0xC0C1, 0xC181, 0x0140,
- 0xC301, 0x03C0, 0x0280, 0xC241, 0xC601, 0x06C0, 0x0780, 0xC741,
- 0x0500, 0xC5C1, 0xC481, 0x0440, 0xCC01, 0x0CC0, 0x0D80, 0xCD41,
- 0x0F00, 0xCFC1, 0xCE81, 0x0E40, 0x0A00, 0xCAC1, 0xCB81, 0x0B40,
- 0xC901, 0x09C0, 0x0880, 0xC841, 0xD801, 0x18C0, 0x1980, 0xD941,
- 0x1B00, 0xDBC1, 0xDA81, 0x1A40, 0x1E00, 0xDEC1, 0xDF81, 0x1F40,
- 0xDD01, 0x1DC0, 0x1C80, 0xDC41, 0x1400, 0xD4C1, 0xD581, 0x1540,
- 0xD701, 0x17C0, 0x1680, 0xD641, 0xD201, 0x12C0, 0x1380, 0xD341,
- 0x1100, 0xD1C1, 0xD081, 0x1040, 0xF001, 0x30C0, 0x3180, 0xF141,
- 0x3300, 0xF3C1, 0xF281, 0x3240, 0x3600, 0xF6C1, 0xF781, 0x3740,
- 0xF501, 0x35C0, 0x3480, 0xF441, 0x3C00, 0xFCC1, 0xFD81, 0x3D40,
- 0xFF01, 0x3FC0, 0x3E80, 0xFE41, 0xFA01, 0x3AC0, 0x3B80, 0xFB41,
- 0x3900, 0xF9C1, 0xF881, 0x3840, 0x2800, 0xE8C1, 0xE981, 0x2940,
- 0xEB01, 0x2BC0, 0x2A80, 0xEA41, 0xEE01, 0x2EC0, 0x2F80, 0xEF41,
- 0x2D00, 0xEDC1, 0xEC81, 0x2C40, 0xE401, 0x24C0, 0x2580, 0xE541,
- 0x2700, 0xE7C1, 0xE681, 0x2640, 0x2200, 0xE2C1, 0xE381, 0x2340,
- 0xE101, 0x21C0, 0x2080, 0xE041, 0xA001, 0x60C0, 0x6180, 0xA141,
- 0x6300, 0xA3C1, 0xA281, 0x6240, 0x6600, 0xA6C1, 0xA781, 0x6740,
- 0xA501, 0x65C0, 0x6480, 0xA441, 0x6C00, 0xACC1, 0xAD81, 0x6D40,
- 0xAF01, 0x6FC0, 0x6E80, 0xAE41, 0xAA01, 0x6AC0, 0x6B80, 0xAB41,
- 0x6900, 0xA9C1, 0xA881, 0x6840, 0x7800, 0xB8C1, 0xB981, 0x7940,
- 0xBB01, 0x7BC0, 0x7A80, 0xBA41, 0xBE01, 0x7EC0, 0x7F80, 0xBF41,
- 0x7D00, 0xBDC1, 0xBC81, 0x7C40, 0xB401, 0x74C0, 0x7580, 0xB541,
- 0x7700, 0xB7C1, 0xB681, 0x7640, 0x7200, 0xB2C1, 0xB381, 0x7340,
- 0xB101, 0x71C0, 0x7080, 0xB041, 0x5000, 0x90C1, 0x9181, 0x5140,
- 0x9301, 0x53C0, 0x5280, 0x9241, 0x9601, 0x56C0, 0x5780, 0x9741,
- 0x5500, 0x95C1, 0x9481, 0x5440, 0x9C01, 0x5CC0, 0x5D80, 0x9D41,
- 0x5F00, 0x9FC1, 0x9E81, 0x5E40, 0x5A00, 0x9AC1, 0x9B81, 0x5B40,
- 0x9901, 0x59C0, 0x5880, 0x9841, 0x8801, 0x48C0, 0x4980, 0x8941,
- 0x4B00, 0x8BC1, 0x8A81, 0x4A40, 0x4E00, 0x8EC1, 0x8F81, 0x4F40,
- 0x8D01, 0x4DC0, 0x4C80, 0x8C41, 0x4400, 0x84C1, 0x8581, 0x4540,
- 0x8701, 0x47C0, 0x4680, 0x8641, 0x8201, 0x42C0, 0x4380, 0x8341,
- 0x4100, 0x81C1, 0x8081, 0x4040];
-
- //fix 精简
-
- /**
- * Fast CRC-16 computation (uses table crc16Lookup).
- *
- * @param value
- * @param crc
- * @return
- */
- function crcUpdateLookup(value, crc) {
- var tmp = crc ^ value;
- crc = (crc >> 8) ^ crc16Lookup[tmp & 0xff];
- return crc;
- }
-
- this.updateMusicCRC = function (crc, buffer, bufferPos, size) {
- for (var i = 0; i < size; ++i)
- crc[0] = crcUpdateLookup(buffer[bufferPos + i], crc[0]);
- }
-
- //fix 精简
-}
-
-
-
-BitStream.EQ = function (a, b) {
- return (Math.abs(a) > Math.abs(b)) ? (Math.abs((a) - (b)) <= (Math
- .abs(a) * 1e-6))
- : (Math.abs((a) - (b)) <= (Math.abs(b) * 1e-6));
-};
-
-BitStream.NEQ = function (a, b) {
- return !BitStream.EQ(a, b);
-};
-
-function BitStream() {
- var self = this;
- var CRC16_POLYNOMIAL = 0x8005;
-
- /*
- * we work with ints, so when doing bit manipulation, we limit ourselves to
- * MAX_LENGTH-2 just to be on the safe side
- */
- var MAX_LENGTH = 32;
-
- //GainAnalysis ga;
- //MPGLib mpg;
- //Version ver;
- //VBRTag vbr;
- var ga = null;
- var mpg = null;
- var ver = null;
- var vbr = null;
-
- //public final void setModules(GainAnalysis ga, MPGLib mpg, Version ver,
- // VBRTag vbr) {
-
- this.setModules = function (_ga, _mpg, _ver, _vbr) {
- ga = _ga;
- mpg = _mpg;
- ver = _ver;
- vbr = _vbr;
- };
-
- /**
- * Bit stream buffer.
- */
- //private byte[] buf;
- var buf = null;
- /**
- * Bit counter of bit stream.
- */
- var totbit = 0;
- /**
- * Pointer to top byte in buffer.
- */
- var bufByteIdx = 0;
- /**
- * Pointer to top bit of top byte in buffer.
- */
- var bufBitIdx = 0;
-
- /**
- * compute bitsperframe and mean_bits for a layer III frame
- */
- this.getframebits = function (gfp) {
- var gfc = gfp.internal_flags;
- var bit_rate;
-
- /* get bitrate in kbps [?] */
- if (gfc.bitrate_index != 0)
- bit_rate = Tables.bitrate_table[gfp.version][gfc.bitrate_index];
- else
- bit_rate = gfp.brate;
-
- /* main encoding routine toggles padding on and off */
- /* one Layer3 Slot consists of 8 bits */
- var bytes = 0 | (gfp.version + 1) * 72000 * bit_rate / gfp.out_samplerate + gfc.padding;
- return 8 * bytes;
- };
-
- function putheader_bits(gfc) {
- System.arraycopy(gfc.header[gfc.w_ptr].buf, 0, buf, bufByteIdx, gfc.sideinfo_len);
- bufByteIdx += gfc.sideinfo_len;
- totbit += gfc.sideinfo_len * 8;
- gfc.w_ptr = (gfc.w_ptr + 1) & (LameInternalFlags.MAX_HEADER_BUF - 1);
- }
-
- /**
- * write j bits into the bit stream
- */
- function putbits2(gfc, val, j) {
-
-
- while (j > 0) {
- var k;
- if (bufBitIdx == 0) {
- bufBitIdx = 8;
- bufByteIdx++;
- if (gfc.header[gfc.w_ptr].write_timing == totbit) {
- putheader_bits(gfc);
- }
- buf[bufByteIdx] = 0;
- }
-
- k = Math.min(j, bufBitIdx);
- j -= k;
-
- bufBitIdx -= k;
-
- /* 32 too large on 32 bit machines */
-
- buf[bufByteIdx] |= ((val >> j) << bufBitIdx);
- totbit += k;
- }
- }
-
- /**
- * write j bits into the bit stream, ignoring frame headers
- */
- function putbits_noheaders(gfc, val, j) {
-
- while (j > 0) {
- var k;
- if (bufBitIdx == 0) {
- bufBitIdx = 8;
- bufByteIdx++;
- buf[bufByteIdx] = 0;
- }
-
- k = Math.min(j, bufBitIdx);
- j -= k;
-
- bufBitIdx -= k;
-
- /* 32 too large on 32 bit machines */
-
- buf[bufByteIdx] |= ((val >> j) << bufBitIdx);
- totbit += k;
- }
- }
-
- /**
- * Some combinations of bitrate, Fs, and stereo make it impossible to stuff
- * out a frame using just main_data, due to the limited number of bits to
- * indicate main_data_length. In these situations, we put stuffing bits into
- * the ancillary data...
- */
- function drain_into_ancillary(gfp, remainingBits) {
- var gfc = gfp.internal_flags;
- var i;
-
- if (remainingBits >= 8) {
- putbits2(gfc, 0x4c, 8);
- remainingBits -= 8;
- }
- if (remainingBits >= 8) {
- putbits2(gfc, 0x41, 8);
- remainingBits -= 8;
- }
- if (remainingBits >= 8) {
- putbits2(gfc, 0x4d, 8);
- remainingBits -= 8;
- }
- if (remainingBits >= 8) {
- putbits2(gfc, 0x45, 8);
- remainingBits -= 8;
- }
-
- if (remainingBits >= 32) {
- var version = ver.getLameShortVersion();
- if (remainingBits >= 32)
- for (i = 0; i < version.length && remainingBits >= 8; ++i) {
- remainingBits -= 8;
- putbits2(gfc, version.charCodeAt(i), 8); //fix 错误的使用charAt
- }
- }
-
- for (; remainingBits >= 1; remainingBits -= 1) {
- putbits2(gfc, gfc.ancillary_flag, 1);
- gfc.ancillary_flag ^= (!gfp.disable_reservoir ? 1 : 0);
- }
-
-
- }
-
- /**
- * write N bits into the header
- */
- function writeheader(gfc, val, j) {
- var ptr = gfc.header[gfc.h_ptr].ptr;
-
- while (j > 0) {
- var k = Math.min(j, 8 - (ptr & 7));
- j -= k;
- /* >> 32 too large for 32 bit machines */
-
- gfc.header[gfc.h_ptr].buf[ptr >> 3] |= ((val >> j)) << (8 - (ptr & 7) - k);
- ptr += k;
- }
- gfc.header[gfc.h_ptr].ptr = ptr;
- }
-
- function CRC_update(value, crc) {
- value <<= 8;
- for (var i = 0; i < 8; i++) {
- value <<= 1;
- crc <<= 1;
-
- if ((((crc ^ value) & 0x10000) != 0))
- crc ^= CRC16_POLYNOMIAL;
- }
- return crc;
- }
-
- this.CRC_writeheader = function (gfc, header) {
- var crc = 0xffff;
- /* (jo) init crc16 for error_protection */
-
- crc = CRC_update(header[2] & 0xff, crc);
- crc = CRC_update(header[3] & 0xff, crc);
- for (var i = 6; i < gfc.sideinfo_len; i++) {
- crc = CRC_update(header[i] & 0xff, crc);
- }
-
- header[4] = (byte)(crc >> 8);
- header[5] = (byte)(crc & 255);
- };
-
- function encodeSideInfo2(gfp, bitsPerFrame) {
- var gfc = gfp.internal_flags;
- var l3_side;
- var gr, ch;
-
- l3_side = gfc.l3_side;
- gfc.header[gfc.h_ptr].ptr = 0;
- Arrays.fill(gfc.header[gfc.h_ptr].buf, 0, gfc.sideinfo_len, 0);
- if (gfp.out_samplerate < 16000)
- writeheader(gfc, 0xffe, 12);
- else
- writeheader(gfc, 0xfff, 12);
- writeheader(gfc, (gfp.version), 1);
- writeheader(gfc, 4 - 3, 2);
- writeheader(gfc, (!gfp.error_protection ? 1 : 0), 1);
- writeheader(gfc, (gfc.bitrate_index), 4);
- writeheader(gfc, (gfc.samplerate_index), 2);
- writeheader(gfc, (gfc.padding), 1);
- writeheader(gfc, (gfp.extension), 1);
- writeheader(gfc, (gfp.mode.ordinal()), 2);
- writeheader(gfc, (gfc.mode_ext), 2);
- writeheader(gfc, (gfp.copyright), 1);
- writeheader(gfc, (gfp.original), 1);
- writeheader(gfc, (gfp.emphasis), 2);
- if (gfp.error_protection) {
- writeheader(gfc, 0, 16);
- /* dummy */
- }
-
- if (gfp.version == 1) {
- /* MPEG1 */
- writeheader(gfc, (l3_side.main_data_begin), 9);
-
- if (gfc.channels_out == 2)
- writeheader(gfc, l3_side.private_bits, 3);
- else
- writeheader(gfc, l3_side.private_bits, 5);
-
- for (ch = 0; ch < gfc.channels_out; ch++) {
- var band;
- for (band = 0; band < 4; band++) {
- writeheader(gfc, l3_side.scfsi[ch][band], 1);
- }
- }
-
- for (gr = 0; gr < 2; gr++) {
- for (ch = 0; ch < gfc.channels_out; ch++) {
- var gi = l3_side.tt[gr][ch];
- writeheader(gfc, gi.part2_3_length + gi.part2_length, 12);
- writeheader(gfc, gi.big_values / 2, 9);
- writeheader(gfc, gi.global_gain, 8);
- writeheader(gfc, gi.scalefac_compress, 4);
-
- if (gi.block_type != Encoder.NORM_TYPE) {
- writeheader(gfc, 1, 1);
- /* window_switching_flag */
- writeheader(gfc, gi.block_type, 2);
- writeheader(gfc, gi.mixed_block_flag, 1);
-
- if (gi.table_select[0] == 14)
- gi.table_select[0] = 16;
- writeheader(gfc, gi.table_select[0], 5);
- if (gi.table_select[1] == 14)
- gi.table_select[1] = 16;
- writeheader(gfc, gi.table_select[1], 5);
-
- writeheader(gfc, gi.subblock_gain[0], 3);
- writeheader(gfc, gi.subblock_gain[1], 3);
- writeheader(gfc, gi.subblock_gain[2], 3);
- } else {
- writeheader(gfc, 0, 1);
- /* window_switching_flag */
- if (gi.table_select[0] == 14)
- gi.table_select[0] = 16;
- writeheader(gfc, gi.table_select[0], 5);
- if (gi.table_select[1] == 14)
- gi.table_select[1] = 16;
- writeheader(gfc, gi.table_select[1], 5);
- if (gi.table_select[2] == 14)
- gi.table_select[2] = 16;
- writeheader(gfc, gi.table_select[2], 5);
-
- writeheader(gfc, gi.region0_count, 4);
- writeheader(gfc, gi.region1_count, 3);
- }
- writeheader(gfc, gi.preflag, 1);
- writeheader(gfc, gi.scalefac_scale, 1);
- writeheader(gfc, gi.count1table_select, 1);
- }
- }
- } else {
- /* MPEG2 */
- writeheader(gfc, (l3_side.main_data_begin), 8);
- writeheader(gfc, l3_side.private_bits, gfc.channels_out);
-
- gr = 0;
- for (ch = 0; ch < gfc.channels_out; ch++) {
- var gi = l3_side.tt[gr][ch];
- writeheader(gfc, gi.part2_3_length + gi.part2_length, 12);
- writeheader(gfc, gi.big_values / 2, 9);
- writeheader(gfc, gi.global_gain, 8);
- writeheader(gfc, gi.scalefac_compress, 9);
-
- if (gi.block_type != Encoder.NORM_TYPE) {
- writeheader(gfc, 1, 1);
- /* window_switching_flag */
- writeheader(gfc, gi.block_type, 2);
- writeheader(gfc, gi.mixed_block_flag, 1);
-
- if (gi.table_select[0] == 14)
- gi.table_select[0] = 16;
- writeheader(gfc, gi.table_select[0], 5);
- if (gi.table_select[1] == 14)
- gi.table_select[1] = 16;
- writeheader(gfc, gi.table_select[1], 5);
-
- writeheader(gfc, gi.subblock_gain[0], 3);
- writeheader(gfc, gi.subblock_gain[1], 3);
- writeheader(gfc, gi.subblock_gain[2], 3);
- } else {
- writeheader(gfc, 0, 1);
- /* window_switching_flag */
- if (gi.table_select[0] == 14)
- gi.table_select[0] = 16;
- writeheader(gfc, gi.table_select[0], 5);
- if (gi.table_select[1] == 14)
- gi.table_select[1] = 16;
- writeheader(gfc, gi.table_select[1], 5);
- if (gi.table_select[2] == 14)
- gi.table_select[2] = 16;
- writeheader(gfc, gi.table_select[2], 5);
-
- writeheader(gfc, gi.region0_count, 4);
- writeheader(gfc, gi.region1_count, 3);
- }
-
- writeheader(gfc, gi.scalefac_scale, 1);
- writeheader(gfc, gi.count1table_select, 1);
- }
- }
-
- if (gfp.error_protection) {
- /* (jo) error_protection: add crc16 information to header */
- CRC_writeheader(gfc, gfc.header[gfc.h_ptr].buf);
- }
-
- {
- var old = gfc.h_ptr;
-
- gfc.h_ptr = (old + 1) & (LameInternalFlags.MAX_HEADER_BUF - 1);
- gfc.header[gfc.h_ptr].write_timing = gfc.header[old].write_timing
- + bitsPerFrame;
-
- if (gfc.h_ptr == gfc.w_ptr) {
- /* yikes! we are out of header buffer space */
- System.err
- .println("Error: MAX_HEADER_BUF too small in bitstream.c \n");
- }
-
- }
- }
-
- function huffman_coder_count1(gfc, gi) {
- /* Write count1 area */
- var h = Tables.ht[gi.count1table_select + 32];
- var i, bits = 0;
-
- var ix = gi.big_values;
- var xr = gi.big_values;
-
- for (i = (gi.count1 - gi.big_values) / 4; i > 0; --i) {
- var huffbits = 0;
- var p = 0, v;
-
- v = gi.l3_enc[ix + 0];
- if (v != 0) {
- p += 8;
- if (gi.xr[xr + 0] < 0)
- huffbits++;
- }
-
- v = gi.l3_enc[ix + 1];
- if (v != 0) {
- p += 4;
- huffbits *= 2;
- if (gi.xr[xr + 1] < 0)
- huffbits++;
- }
-
- v = gi.l3_enc[ix + 2];
- if (v != 0) {
- p += 2;
- huffbits *= 2;
- if (gi.xr[xr + 2] < 0)
- huffbits++;
- }
-
- v = gi.l3_enc[ix + 3];
- if (v != 0) {
- p++;
- huffbits *= 2;
- if (gi.xr[xr + 3] < 0)
- huffbits++;
- }
-
- ix += 4;
- xr += 4;
- putbits2(gfc, huffbits + h.table[p], h.hlen[p]);
- bits += h.hlen[p];
- }
- return bits;
- }
-
- /**
- * Implements the pseudocode of page 98 of the IS
- */
- function Huffmancode(gfc, tableindex, start, end, gi) {
- var h = Tables.ht[tableindex];
- var bits = 0;
-
- if (0 == tableindex)
- return bits;
-
- for (var i = start; i < end; i += 2) {
- var cbits = 0;
- var xbits = 0;
- var linbits = h.xlen;
- var xlen = h.xlen;
- var ext = 0;
- var x1 = gi.l3_enc[i];
- var x2 = gi.l3_enc[i + 1];
-
- if (x1 != 0) {
- if (gi.xr[i] < 0)
- ext++;
- cbits--;
- }
-
- if (tableindex > 15) {
- /* use ESC-words */
- if (x1 > 14) {
- var linbits_x1 = x1 - 15;
- ext |= linbits_x1 << 1;
- xbits = linbits;
- x1 = 15;
- }
-
- if (x2 > 14) {
- var linbits_x2 = x2 - 15;
- ext <<= linbits;
- ext |= linbits_x2;
- xbits += linbits;
- x2 = 15;
- }
- xlen = 16;
- }
-
- if (x2 != 0) {
- ext <<= 1;
- if (gi.xr[i + 1] < 0)
- ext++;
- cbits--;
- }
-
-
- x1 = x1 * xlen + x2;
- xbits -= cbits;
- cbits += h.hlen[x1];
-
-
- putbits2(gfc, h.table[x1], cbits);
- putbits2(gfc, ext, xbits);
- bits += cbits + xbits;
- }
- return bits;
- }
-
- /**
- * Note the discussion of huffmancodebits() on pages 28 and 29 of the IS, as
- * well as the definitions of the side information on pages 26 and 27.
- */
- function ShortHuffmancodebits(gfc, gi) {
- var region1Start = 3 * gfc.scalefac_band.s[3];
- if (region1Start > gi.big_values)
- region1Start = gi.big_values;
-
- /* short blocks do not have a region2 */
- var bits = Huffmancode(gfc, gi.table_select[0], 0, region1Start, gi);
- bits += Huffmancode(gfc, gi.table_select[1], region1Start,
- gi.big_values, gi);
- return bits;
- }
-
- function LongHuffmancodebits(gfc, gi) {
- var bigvalues, bits;
- var region1Start, region2Start;
-
- bigvalues = gi.big_values;
-
- var i = gi.region0_count + 1;
- region1Start = gfc.scalefac_band.l[i];
- i += gi.region1_count + 1;
- region2Start = gfc.scalefac_band.l[i];
-
- if (region1Start > bigvalues)
- region1Start = bigvalues;
-
- if (region2Start > bigvalues)
- region2Start = bigvalues;
-
- bits = Huffmancode(gfc, gi.table_select[0], 0, region1Start, gi);
- bits += Huffmancode(gfc, gi.table_select[1], region1Start,
- region2Start, gi);
- bits += Huffmancode(gfc, gi.table_select[2], region2Start, bigvalues,
- gi);
- return bits;
- }
-
- function writeMainData(gfp) {
- var gr, ch, sfb, data_bits, tot_bits = 0;
- var gfc = gfp.internal_flags;
- var l3_side = gfc.l3_side;
-
- if (gfp.version == 1) {
- /* MPEG 1 */
- for (gr = 0; gr < 2; gr++) {
- for (ch = 0; ch < gfc.channels_out; ch++) {
- var gi = l3_side.tt[gr][ch];
- var slen1 = Takehiro.slen1_tab[gi.scalefac_compress];
- var slen2 = Takehiro.slen2_tab[gi.scalefac_compress];
- data_bits = 0;
- for (sfb = 0; sfb < gi.sfbdivide; sfb++) {
- if (gi.scalefac[sfb] == -1)
- continue;
- /* scfsi is used */
- putbits2(gfc, gi.scalefac[sfb], slen1);
- data_bits += slen1;
- }
- for (; sfb < gi.sfbmax; sfb++) {
- if (gi.scalefac[sfb] == -1)
- continue;
- /* scfsi is used */
- putbits2(gfc, gi.scalefac[sfb], slen2);
- data_bits += slen2;
- }
-
- if (gi.block_type == Encoder.SHORT_TYPE) {
- data_bits += ShortHuffmancodebits(gfc, gi);
- } else {
- data_bits += LongHuffmancodebits(gfc, gi);
- }
- data_bits += huffman_coder_count1(gfc, gi);
- /* does bitcount in quantize.c agree with actual bit count? */
- tot_bits += data_bits;
- }
- /* for ch */
- }
- /* for gr */
- } else {
- /* MPEG 2 */
- gr = 0;
- for (ch = 0; ch < gfc.channels_out; ch++) {
- var gi = l3_side.tt[gr][ch];
- var i, sfb_partition, scale_bits = 0;
- data_bits = 0;
- sfb = 0;
- sfb_partition = 0;
-
- if (gi.block_type == Encoder.SHORT_TYPE) {
- for (; sfb_partition < 4; sfb_partition++) {
- var sfbs = gi.sfb_partition_table[sfb_partition] / 3;
- var slen = gi.slen[sfb_partition];
- for (i = 0; i < sfbs; i++, sfb++) {
- putbits2(gfc,
- Math.max(gi.scalefac[sfb * 3 + 0], 0), slen);
- putbits2(gfc,
- Math.max(gi.scalefac[sfb * 3 + 1], 0), slen);
- putbits2(gfc,
- Math.max(gi.scalefac[sfb * 3 + 2], 0), slen);
- scale_bits += 3 * slen;
- }
- }
- data_bits += ShortHuffmancodebits(gfc, gi);
- } else {
- for (; sfb_partition < 4; sfb_partition++) {
- var sfbs = gi.sfb_partition_table[sfb_partition];
- var slen = gi.slen[sfb_partition];
- for (i = 0; i < sfbs; i++, sfb++) {
- putbits2(gfc, Math.max(gi.scalefac[sfb], 0), slen);
- scale_bits += slen;
- }
- }
- data_bits += LongHuffmancodebits(gfc, gi);
- }
- data_bits += huffman_coder_count1(gfc, gi);
- /* does bitcount in quantize.c agree with actual bit count? */
- tot_bits += scale_bits + data_bits;
- }
- /* for ch */
- }
- /* for gf */
- return tot_bits;
- }
-
- /* main_data */
-
- function TotalBytes() {
- this.total = 0;
- }
-
- /*
- * compute the number of bits required to flush all mp3 frames currently in
- * the buffer. This should be the same as the reservoir size. Only call this
- * routine between frames - i.e. only after all headers and data have been
- * added to the buffer by format_bitstream().
- *
- * Also compute total_bits_output = size of mp3 buffer (including frame
- * headers which may not have yet been send to the mp3 buffer) + number of
- * bits needed to flush all mp3 frames.
- *
- * total_bytes_output is the size of the mp3 output buffer if
- * lame_encode_flush_nogap() was called right now.
- */
- function compute_flushbits(gfp, total_bytes_output) {
- var gfc = gfp.internal_flags;
- var flushbits, remaining_headers;
- var bitsPerFrame;
- var last_ptr, first_ptr;
- first_ptr = gfc.w_ptr;
- /* first header to add to bitstream */
- last_ptr = gfc.h_ptr - 1;
- /* last header to add to bitstream */
- if (last_ptr == -1)
- last_ptr = LameInternalFlags.MAX_HEADER_BUF - 1;
-
- /* add this many bits to bitstream so we can flush all headers */
- flushbits = gfc.header[last_ptr].write_timing - totbit;
- total_bytes_output.total = flushbits;
-
- if (flushbits >= 0) {
- /* if flushbits >= 0, some headers have not yet been written */
- /* reduce flushbits by the size of the headers */
- remaining_headers = 1 + last_ptr - first_ptr;
- if (last_ptr < first_ptr)
- remaining_headers = 1 + last_ptr - first_ptr
- + LameInternalFlags.MAX_HEADER_BUF;
- flushbits -= remaining_headers * 8 * gfc.sideinfo_len;
- }
-
- /*
- * finally, add some bits so that the last frame is complete these bits
- * are not necessary to decode the last frame, but some decoders will
- * ignore last frame if these bits are missing
- */
- bitsPerFrame = self.getframebits(gfp);
- flushbits += bitsPerFrame;
- total_bytes_output.total += bitsPerFrame;
- /* round up: */
- if ((total_bytes_output.total % 8) != 0)
- total_bytes_output.total = 1 + (total_bytes_output.total / 8);
- else
- total_bytes_output.total = (total_bytes_output.total / 8);
- total_bytes_output.total += bufByteIdx + 1;
-
- if (flushbits < 0) {
- System.err.println("strange error flushing buffer ... \n");
- }
- return flushbits;
- }
-
- this.flush_bitstream = function (gfp) {
- var gfc = gfp.internal_flags;
- var l3_side;
- var flushbits;
- var last_ptr = gfc.h_ptr - 1;
- /* last header to add to bitstream */
- if (last_ptr == -1)
- last_ptr = LameInternalFlags.MAX_HEADER_BUF - 1;
- l3_side = gfc.l3_side;
-
- if ((flushbits = compute_flushbits(gfp, new TotalBytes())) < 0)
- return;
- drain_into_ancillary(gfp, flushbits);
-
- /* check that the 100% of the last frame has been written to bitstream */
-
- /*
- * we have padded out all frames with ancillary data, which is the same
- * as filling the bitreservoir with ancillary data, so :
- */
- gfc.ResvSize = 0;
- l3_side.main_data_begin = 0;
-
- /* save the ReplayGain value */
- if (gfc.findReplayGain) {
- var RadioGain = ga.GetTitleGain(gfc.rgdata);
- gfc.RadioGain = Math.floor(RadioGain * 10.0 + 0.5) | 0;
- /* round to nearest */
- }
-
- /* find the gain and scale change required for no clipping */
- if (gfc.findPeakSample) {
- gfc.noclipGainChange = Math.ceil(
- Math_log10(gfc.PeakSample / 32767.0) * 20.0 * 10.0) | 0;
- /* round up */
-
- if (gfc.noclipGainChange > 0) {
- /* clipping occurs */
- if (EQ(gfp.scale, 1.0) || EQ(gfp.scale, 0.0))
- gfc.noclipScale = (Math
- .floor((32767.0 / gfc.PeakSample) * 100.0) / 100.0);
- /* round down */
- else {
- /*
- * the user specified his own scaling factor. We could
- * suggest the scaling factor of
- * (32767.0/gfp.PeakSample)*(gfp.scale) but it's usually
- * very inaccurate. So we'd rather not advice him on the
- * scaling factor.
- */
- gfc.noclipScale = -1;
- }
- } else
- /* no clipping */
- gfc.noclipScale = -1;
- }
- };
-
- this.add_dummy_byte = function (gfp, val, n) {
- var gfc = gfp.internal_flags;
- var i;
-
- while (n-- > 0) {
- putbits_noheaders(gfc, val, 8);
-
- for (i = 0; i < LameInternalFlags.MAX_HEADER_BUF; ++i)
- gfc.header[i].write_timing += 8;
- }
- };
-
- /**
- * This is called after a frame of audio has been quantized and coded. It
- * will write the encoded audio to the bitstream. Note that from a layer3
- * encoder's perspective the bit stream is primarily a series of main_data()
- * blocks, with header and side information inserted at the proper locations
- * to maintain framing. (See Figure A.7 in the IS).
- */
- this.format_bitstream = function (gfp) {
- var gfc = gfp.internal_flags;
- var l3_side;
- l3_side = gfc.l3_side;
-
- var bitsPerFrame = this.getframebits(gfp);
- drain_into_ancillary(gfp, l3_side.resvDrain_pre);
-
- encodeSideInfo2(gfp, bitsPerFrame);
- var bits = 8 * gfc.sideinfo_len;
- bits += writeMainData(gfp);
- drain_into_ancillary(gfp, l3_side.resvDrain_post);
- bits += l3_side.resvDrain_post;
-
- l3_side.main_data_begin += (bitsPerFrame - bits) / 8;
-
- /*
- * compare number of bits needed to clear all buffered mp3 frames with
- * what we think the resvsize is:
- */
- if (compute_flushbits(gfp, new TotalBytes()) != gfc.ResvSize) {
- System.err.println("Internal buffer inconsistency. flushbits <> ResvSize");
- }
-
- /*
- * compare main_data_begin for the next frame with what we think the
- * resvsize is:
- */
- if ((l3_side.main_data_begin * 8) != gfc.ResvSize) {
- System.err.printf("bit reservoir error: \n"
- + "l3_side.main_data_begin: %d \n"
- + "Resvoir size: %d \n"
- + "resv drain (post) %d \n"
- + "resv drain (pre) %d \n"
- + "header and sideinfo: %d \n"
- + "data bits: %d \n"
- + "total bits: %d (remainder: %d) \n"
- + "bitsperframe: %d \n",
- 8 * l3_side.main_data_begin, gfc.ResvSize,
- l3_side.resvDrain_post, l3_side.resvDrain_pre,
- 8 * gfc.sideinfo_len, bits - l3_side.resvDrain_post - 8
- * gfc.sideinfo_len, bits, bits % 8, bitsPerFrame);
-
- System.err.println("This is a fatal error. It has several possible causes:");
- System.err.println("90%% LAME compiled with buggy version of gcc using advanced optimizations");
- System.err.println(" 9%% Your system is overclocked");
- System.err.println(" 1%% bug in LAME encoding library");
-
- gfc.ResvSize = l3_side.main_data_begin * 8;
- }
- //;
-
- if (totbit > 1000000000) {
- /*
- * to avoid totbit overflow, (at 8h encoding at 128kbs) lets reset
- * bit counter
- */
- var i;
- for (i = 0; i < LameInternalFlags.MAX_HEADER_BUF; ++i)
- gfc.header[i].write_timing -= totbit;
- totbit = 0;
- }
-
- return 0;
- };
-
- /**
- *
- * copy data out of the internal MP3 bit buffer into a user supplied
- * unsigned char buffer.
- *
- * mp3data=0 indicates data in buffer is an id3tags and VBR tags
- * mp3data=1 data is real mp3 frame data.
- *
- * compute the ATH for each scalefactor band cd range: 0..96db
- *
- * Input: 3.3kHz signal 32767 amplitude (3.3kHz is where ATH is smallest =
- * -5db) longblocks: sfb=12 en0/bw=-11db max_en0 = 1.3db shortblocks: sfb=5
- * -9db 0db
- *
- * Input: 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 (repeated) longblocks: amp=1
- * sfb=12 en0/bw=-103 db max_en0 = -92db amp=32767 sfb=12 -12 db -1.4db
- *
- * Input: 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 (repeated) shortblocks: amp=1
- * sfb=5 en0/bw= -99 -86 amp=32767 sfb=5 -9 db 4db
- *
- *
- * MAX energy of largest wave at 3.3kHz = 1db AVE energy of largest wave at
- * 3.3kHz = -11db Let's take AVE: -11db = maximum signal in sfb=12. Dynamic
- * range of CD: 96db. Therefor energy of smallest audible wave in sfb=12 =
- * -11 - 96 = -107db = ATH at 3.3kHz.
- *
- * ATH formula for this wave: -5db. To adjust to LAME scaling, we need ATH =
- * ATH_formula - 103 (db) ATH = ATH * 2.5e-10 (ener)
- *
- */
- function ATHmdct(gfp, f) {
- var ath = psy.ATHformula(f, gfp);
-
- ath -= NSATHSCALE;
-
- /* modify the MDCT scaling for the ATH and convert to energy */
- ath = Math.pow(10.0, ath / 10.0 + gfp.ATHlower);
- return ath;
- }
-
- function compute_ath(gfp) {
- var ATH_l = gfp.internal_flags.ATH.l;
- var ATH_psfb21 = gfp.internal_flags.ATH.psfb21;
- var ATH_s = gfp.internal_flags.ATH.s;
- var ATH_psfb12 = gfp.internal_flags.ATH.psfb12;
- var gfc = gfp.internal_flags;
- var samp_freq = gfp.out_samplerate;
-
- for (var sfb = 0; sfb < Encoder.SBMAX_l; sfb++) {
- var start = gfc.scalefac_band.l[sfb];
- var end = gfc.scalefac_band.l[sfb + 1];
- ATH_l[sfb] = Float.MAX_VALUE;
- for (var i = start; i < end; i++) {
- var freq = i * samp_freq / (2 * 576);
- var ATH_f = ATHmdct(gfp, freq);
- /* freq in kHz */
- ATH_l[sfb] = Math.min(ATH_l[sfb], ATH_f);
- }
- }
-
- for (var sfb = 0; sfb < Encoder.PSFB21; sfb++) {
- var start = gfc.scalefac_band.psfb21[sfb];
- var end = gfc.scalefac_band.psfb21[sfb + 1];
- ATH_psfb21[sfb] = Float.MAX_VALUE;
- for (var i = start; i < end; i++) {
- var freq = i * samp_freq / (2 * 576);
- var ATH_f = ATHmdct(gfp, freq);
- /* freq in kHz */
- ATH_psfb21[sfb] = Math.min(ATH_psfb21[sfb], ATH_f);
- }
- }
-
- for (var sfb = 0; sfb < Encoder.SBMAX_s; sfb++) {
- var start = gfc.scalefac_band.s[sfb];
- var end = gfc.scalefac_band.s[sfb + 1];
- ATH_s[sfb] = Float.MAX_VALUE;
- for (var i = start; i < end; i++) {
- var freq = i * samp_freq / (2 * 192);
- var ATH_f = ATHmdct(gfp, freq);
- /* freq in kHz */
- ATH_s[sfb] = Math.min(ATH_s[sfb], ATH_f);
- }
- ATH_s[sfb] *= (gfc.scalefac_band.s[sfb + 1] - gfc.scalefac_band.s[sfb]);
- }
-
- for (var sfb = 0; sfb < Encoder.PSFB12; sfb++) {
- var start = gfc.scalefac_band.psfb12[sfb];
- var end = gfc.scalefac_band.psfb12[sfb + 1];
- ATH_psfb12[sfb] = Float.MAX_VALUE;
- for (var i = start; i < end; i++) {
- var freq = i * samp_freq / (2 * 192);
- var ATH_f = ATHmdct(gfp, freq);
- /* freq in kHz */
- ATH_psfb12[sfb] = Math.min(ATH_psfb12[sfb], ATH_f);
- }
- /* not sure about the following */
- ATH_psfb12[sfb] *= (gfc.scalefac_band.s[13] - gfc.scalefac_band.s[12]);
- }
-
- /*
- * no-ATH mode: reduce ATH to -200 dB
- */
- if (gfp.noATH) {
- for (var sfb = 0; sfb < Encoder.SBMAX_l; sfb++) {
- ATH_l[sfb] = 1E-20;
- }
- for (var sfb = 0; sfb < Encoder.PSFB21; sfb++) {
- ATH_psfb21[sfb] = 1E-20;
- }
- for (var sfb = 0; sfb < Encoder.SBMAX_s; sfb++) {
- ATH_s[sfb] = 1E-20;
- }
- for (var sfb = 0; sfb < Encoder.PSFB12; sfb++) {
- ATH_psfb12[sfb] = 1E-20;
- }
- }
-
- /*
- * work in progress, don't rely on it too much
- */
- gfc.ATH.floor = 10. * Math_log10(ATHmdct(gfp, -1.));
- }
-
- /**
- * initialization for iteration_loop
- */
- this.iteration_init = function (gfp) {
- var gfc = gfp.internal_flags;
- var l3_side = gfc.l3_side;
- var i;
-
- if (gfc.iteration_init_init == 0) {
- gfc.iteration_init_init = 1;
-
- l3_side.main_data_begin = 0;
- compute_ath(gfp);
-
- pow43[0] = 0.0;
- for (i = 1; i < PRECALC_SIZE; i++)
- pow43[i] = Math.pow(i, 4.0 / 3.0);
-
- for (i = 0; i < PRECALC_SIZE - 1; i++)
- adj43[i] = ((i + 1) - Math.pow(
- 0.5 * (pow43[i] + pow43[i + 1]), 0.75));
- adj43[i] = 0.5;
-
- for (i = 0; i < Q_MAX; i++)
- ipow20[i] = Math.pow(2.0, (i - 210) * -0.1875);
- for (i = 0; i <= Q_MAX + Q_MAX2; i++)
- pow20[i] = Math.pow(2.0, (i - 210 - Q_MAX2) * 0.25);
-
- tak.huffman_init(gfc);
-
- {
- var bass, alto, treble, sfb21;
-
- i = (gfp.exp_nspsytune >> 2) & 63;
- if (i >= 32)
- i -= 64;
- bass = Math.pow(10, i / 4.0 / 10.0);
-
- i = (gfp.exp_nspsytune >> 8) & 63;
- if (i >= 32)
- i -= 64;
- alto = Math.pow(10, i / 4.0 / 10.0);
-
- i = (gfp.exp_nspsytune >> 14) & 63;
- if (i >= 32)
- i -= 64;
- treble = Math.pow(10, i / 4.0 / 10.0);
-
- /*
- * to be compatible with Naoki's original code, the next 6 bits
- * define only the amount of changing treble for sfb21
- */
- i = (gfp.exp_nspsytune >> 20) & 63;
- if (i >= 32)
- i -= 64;
- sfb21 = treble * Math.pow(10, i / 4.0 / 10.0);
- for (i = 0; i < Encoder.SBMAX_l; i++) {
- var f;
- if (i <= 6)
- f = bass;
- else if (i <= 13)
- f = alto;
- else if (i <= 20)
- f = treble;
- else
- f = sfb21;
-
- gfc.nsPsy.longfact[i] = f;
- }
- for (i = 0; i < Encoder.SBMAX_s; i++) {
- var f;
- if (i <= 5)
- f = bass;
- else if (i <= 10)
- f = alto;
- else if (i <= 11)
- f = treble;
- else
- f = sfb21;
-
- gfc.nsPsy.shortfact[i] = f;
- }
- }
- }
- }
-
- /**
- * allocate bits among 2 channels based on PE
- * mt 6/99
- * bugfixes rh 8/01: often allocated more than the allowed 4095 bits
- */
- this.on_pe = function (gfp, pe,
- targ_bits, mean_bits, gr, cbr) {
- var gfc = gfp.internal_flags;
- var tbits = 0, bits;
- var add_bits = new_int(2);
- var ch;
-
- /* allocate targ_bits for granule */
- var mb = new MeanBits(tbits);
- var extra_bits = rv.ResvMaxBits(gfp, mean_bits, mb, cbr);
- tbits = mb.bits;
- /* maximum allowed bits for this granule */
- var max_bits = tbits + extra_bits;
- if (max_bits > LameInternalFlags.MAX_BITS_PER_GRANULE) {
- // hard limit per granule
- max_bits = LameInternalFlags.MAX_BITS_PER_GRANULE;
- }
- for (bits = 0, ch = 0; ch < gfc.channels_out; ++ch) {
- /******************************************************************
- * allocate bits for each channel
- ******************************************************************/
- targ_bits[ch] = Math.min(LameInternalFlags.MAX_BITS_PER_CHANNEL,
- tbits / gfc.channels_out);
-
- add_bits[ch] = 0 | (targ_bits[ch] * pe[gr][ch] / 700.0 - targ_bits[ch]);
-
- /* at most increase bits by 1.5*average */
- if (add_bits[ch] > mean_bits * 3 / 4)
- add_bits[ch] = mean_bits * 3 / 4;
-
- if (add_bits[ch] < 0)
- add_bits[ch] = 0;
-
- if (add_bits[ch] + targ_bits[ch] > LameInternalFlags.MAX_BITS_PER_CHANNEL)
- add_bits[ch] = Math.max(0,
- LameInternalFlags.MAX_BITS_PER_CHANNEL - targ_bits[ch]);
-
- bits += add_bits[ch];
- }
- if (bits > extra_bits) {
- for (ch = 0; ch < gfc.channels_out; ++ch) {
- add_bits[ch] = extra_bits * add_bits[ch] / bits;
- }
- }
-
- for (ch = 0; ch < gfc.channels_out; ++ch) {
- targ_bits[ch] += add_bits[ch];
- extra_bits -= add_bits[ch];
- }
-
- for (bits = 0, ch = 0; ch < gfc.channels_out; ++ch) {
- bits += targ_bits[ch];
- }
- if (bits > LameInternalFlags.MAX_BITS_PER_GRANULE) {
- var sum = 0;
- for (ch = 0; ch < gfc.channels_out; ++ch) {
- targ_bits[ch] *= LameInternalFlags.MAX_BITS_PER_GRANULE;
- targ_bits[ch] /= bits;
- sum += targ_bits[ch];
- }
- }
-
- return max_bits;
- }
-
- this.reduce_side = function (targ_bits, ms_ener_ratio, mean_bits, max_bits) {
-
- /*
- * ms_ener_ratio = 0: allocate 66/33 mid/side fac=.33 ms_ener_ratio =.5:
- * allocate 50/50 mid/side fac= 0
- */
- /* 75/25 split is fac=.5 */
- var fac = .33 * (.5 - ms_ener_ratio) / .5;
- if (fac < 0)
- fac = 0;
- if (fac > .5)
- fac = .5;
-
- /* number of bits to move from side channel to mid channel */
- /* move_bits = fac*targ_bits[1]; */
- var move_bits = 0 | (fac * .5 * (targ_bits[0] + targ_bits[1]));
-
- if (move_bits > LameInternalFlags.MAX_BITS_PER_CHANNEL - targ_bits[0]) {
- move_bits = LameInternalFlags.MAX_BITS_PER_CHANNEL - targ_bits[0];
- }
- if (move_bits < 0)
- move_bits = 0;
-
- if (targ_bits[1] >= 125) {
- /* dont reduce side channel below 125 bits */
- if (targ_bits[1] - move_bits > 125) {
-
- /* if mid channel already has 2x more than average, dont bother */
- /* mean_bits = bits per granule (for both channels) */
- if (targ_bits[0] < mean_bits)
- targ_bits[0] += move_bits;
- targ_bits[1] -= move_bits;
- } else {
- targ_bits[0] += targ_bits[1] - 125;
- targ_bits[1] = 125;
- }
- }
-
- move_bits = targ_bits[0] + targ_bits[1];
- if (move_bits > max_bits) {
- targ_bits[0] = (max_bits * targ_bits[0]) / move_bits;
- targ_bits[1] = (max_bits * targ_bits[1]) / move_bits;
- }
- };
-
- /**
- * Robert Hegemann 2001-04-27:
- * this adjusts the ATH, keeping the original noise floor
- * affects the higher frequencies more than the lower ones
- */
- this.athAdjust = function (a, x, athFloor) {
- /*
- * work in progress
- */
- var o = 90.30873362;
- var p = 94.82444863;
- var u = Util.FAST_LOG10_X(x, 10.0);
- var v = a * a;
- var w = 0.0;
- u -= athFloor;
- /* undo scaling */
- if (v > 1E-20)
- w = 1. + Util.FAST_LOG10_X(v, 10.0 / o);
- if (w < 0)
- w = 0.;
- u *= w;
- u += athFloor + o - p;
- /* redo scaling */
-
- return Math.pow(10., 0.1 * u);
- };
-
- /**
- * Calculate the allowed distortion for each scalefactor band, as determined
- * by the psychoacoustic model. xmin(sb) = ratio(sb) * en(sb) / bw(sb)
- *
- * returns number of sfb's with energy > ATH
- */
- this.calc_xmin = function (gfp, ratio, cod_info, pxmin) {
- var pxminPos = 0;
- var gfc = gfp.internal_flags;
- var gsfb, j = 0, ath_over = 0;
- var ATH = gfc.ATH;
- var xr = cod_info.xr;
- var enable_athaa_fix = (gfp.VBR == VbrMode.vbr_mtrh) ? 1 : 0;
- var masking_lower = gfc.masking_lower;
-
- if (gfp.VBR == VbrMode.vbr_mtrh || gfp.VBR == VbrMode.vbr_mt) {
- /* was already done in PSY-Model */
- masking_lower = 1.0;
- }
-
- for (gsfb = 0; gsfb < cod_info.psy_lmax; gsfb++) {
- var en0, xmin;
- var rh1, rh2;
- var width, l;
-
- if (gfp.VBR == VbrMode.vbr_rh || gfp.VBR == VbrMode.vbr_mtrh)
- xmin = athAdjust(ATH.adjust, ATH.l[gsfb], ATH.floor);
- else
- xmin = ATH.adjust * ATH.l[gsfb];
-
- width = cod_info.width[gsfb];
- rh1 = xmin / width;
- rh2 = DBL_EPSILON;
- l = width >> 1;
- en0 = 0.0;
- do {
- var xa, xb;
- xa = xr[j] * xr[j];
- en0 += xa;
- rh2 += (xa < rh1) ? xa : rh1;
- j++;
- xb = xr[j] * xr[j];
- en0 += xb;
- rh2 += (xb < rh1) ? xb : rh1;
- j++;
- } while (--l > 0);
- if (en0 > xmin)
- ath_over++;
-
- if (gsfb == Encoder.SBPSY_l) {
- var x = xmin * gfc.nsPsy.longfact[gsfb];
- if (rh2 < x) {
- rh2 = x;
- }
- }
- if (enable_athaa_fix != 0) {
- xmin = rh2;
- }
- if (!gfp.ATHonly) {
- var e = ratio.en.l[gsfb];
- if (e > 0.0) {
- var x;
- x = en0 * ratio.thm.l[gsfb] * masking_lower / e;
- if (enable_athaa_fix != 0)
- x *= gfc.nsPsy.longfact[gsfb];
- if (xmin < x)
- xmin = x;
- }
- }
- if (enable_athaa_fix != 0)
- pxmin[pxminPos++] = xmin;
- else
- pxmin[pxminPos++] = xmin * gfc.nsPsy.longfact[gsfb];
- }
- /* end of long block loop */
-
- /* use this function to determine the highest non-zero coeff */
- var max_nonzero = 575;
- if (cod_info.block_type != Encoder.SHORT_TYPE) {
- // NORM, START or STOP type, but not SHORT
- var k = 576;
- while (k-- != 0 && BitStream.EQ(xr[k], 0)) {
- max_nonzero = k;
- }
- }
- cod_info.max_nonzero_coeff = max_nonzero;
-
- for (var sfb = cod_info.sfb_smin; gsfb < cod_info.psymax; sfb++, gsfb += 3) {
- var width, b;
- var tmpATH;
- if (gfp.VBR == VbrMode.vbr_rh || gfp.VBR == VbrMode.vbr_mtrh)
- tmpATH = athAdjust(ATH.adjust, ATH.s[sfb], ATH.floor);
- else
- tmpATH = ATH.adjust * ATH.s[sfb];
-
- width = cod_info.width[gsfb];
- for (b = 0; b < 3; b++) {
- var en0 = 0.0, xmin;
- var rh1, rh2;
- var l = width >> 1;
-
- rh1 = tmpATH / width;
- rh2 = DBL_EPSILON;
- do {
- var xa, xb;
- xa = xr[j] * xr[j];
- en0 += xa;
- rh2 += (xa < rh1) ? xa : rh1;
- j++;
- xb = xr[j] * xr[j];
- en0 += xb;
- rh2 += (xb < rh1) ? xb : rh1;
- j++;
- } while (--l > 0);
- if (en0 > tmpATH)
- ath_over++;
- if (sfb == Encoder.SBPSY_s) {
- var x = tmpATH * gfc.nsPsy.shortfact[sfb];
- if (rh2 < x) {
- rh2 = x;
- }
- }
- if (enable_athaa_fix != 0)
- xmin = rh2;
- else
- xmin = tmpATH;
-
- if (!gfp.ATHonly && !gfp.ATHshort) {
- var e = ratio.en.s[sfb][b];
- if (e > 0.0) {
- var x;
- x = en0 * ratio.thm.s[sfb][b] * masking_lower / e;
- if (enable_athaa_fix != 0)
- x *= gfc.nsPsy.shortfact[sfb];
- if (xmin < x)
- xmin = x;
- }
- }
- if (enable_athaa_fix != 0)
- pxmin[pxminPos++] = xmin;
- else
- pxmin[pxminPos++] = xmin * gfc.nsPsy.shortfact[sfb];
- }
- /* b */
- if (gfp.useTemporal) {
- if (pxmin[pxminPos - 3] > pxmin[pxminPos - 3 + 1])
- pxmin[pxminPos - 3 + 1] += (pxmin[pxminPos - 3] - pxmin[pxminPos - 3 + 1])
- * gfc.decay;
- if (pxmin[pxminPos - 3 + 1] > pxmin[pxminPos - 3 + 2])
- pxmin[pxminPos - 3 + 2] += (pxmin[pxminPos - 3 + 1] - pxmin[pxminPos - 3 + 2])
- * gfc.decay;
- }
- }
- /* end of short block sfb loop */
-
- return ath_over;
- };
-
- function StartLine(j) {
- this.s = j;
- }
-
- this.calc_noise_core = function (cod_info, startline, l, step) {
- var noise = 0;
- var j = startline.s;
- var ix = cod_info.l3_enc;
-
- if (j > cod_info.count1) {
- while ((l--) != 0) {
- var temp;
- temp = cod_info.xr[j];
- j++;
- noise += temp * temp;
- temp = cod_info.xr[j];
- j++;
- noise += temp * temp;
- }
- } else if (j > cod_info.big_values) {
- var ix01 = new_float(2);
- ix01[0] = 0;
- ix01[1] = step;
- while ((l--) != 0) {
- var temp;
- temp = Math.abs(cod_info.xr[j]) - ix01[ix[j]];
- j++;
- noise += temp * temp;
- temp = Math.abs(cod_info.xr[j]) - ix01[ix[j]];
- j++;
- noise += temp * temp;
- }
- } else {
- while ((l--) != 0) {
- var temp;
- temp = Math.abs(cod_info.xr[j]) - pow43[ix[j]] * step;
- j++;
- noise += temp * temp;
- temp = Math.abs(cod_info.xr[j]) - pow43[ix[j]] * step;
- j++;
- noise += temp * temp;
- }
- }
-
- startline.s = j;
- return noise;
- }
-
- /**
- *
- * -oo dB => -1.00
- * - 6 dB => -0.97
- * - 3 dB => -0.80
- * - 2 dB => -0.64
- * - 1 dB => -0.38
- * 0 dB => 0.00
- * + 1 dB => +0.49
- * + 2 dB => +1.06
- * + 3 dB => +1.68
- * + 6 dB => +3.69
- * +10 dB => +6.45
- *
- */
- this.calc_noise = function (cod_info, l3_xmin, distort, res, prev_noise) {
- var distortPos = 0;
- var l3_xminPos = 0;
- var sfb, l, over = 0;
- var over_noise_db = 0;
- /* 0 dB relative to masking */
- var tot_noise_db = 0;
- /* -200 dB relative to masking */
- var max_noise = -20.0;
- var j = 0;
- var scalefac = cod_info.scalefac;
- var scalefacPos = 0;
-
- res.over_SSD = 0;
-
- for (sfb = 0; sfb < cod_info.psymax; sfb++) {
- var s = cod_info.global_gain
- - (((scalefac[scalefacPos++]) + (cod_info.preflag != 0 ? pretab[sfb]
- : 0)) << (cod_info.scalefac_scale + 1))
- - cod_info.subblock_gain[cod_info.window[sfb]] * 8;
- var noise = 0.0;
-
- if (prev_noise != null && (prev_noise.step[sfb] == s)) {
-
- /* use previously computed values */
- noise = prev_noise.noise[sfb];
- j += cod_info.width[sfb];
- distort[distortPos++] = noise / l3_xmin[l3_xminPos++];
-
- noise = prev_noise.noise_log[sfb];
-
- } else {
- var step = POW20(s);
- l = cod_info.width[sfb] >> 1;
-
- if ((j + cod_info.width[sfb]) > cod_info.max_nonzero_coeff) {
- var usefullsize;
- usefullsize = cod_info.max_nonzero_coeff - j + 1;
-
- if (usefullsize > 0)
- l = usefullsize >> 1;
- else
- l = 0;
- }
-
- var sl = new StartLine(j);
- noise = this.calc_noise_core(cod_info, sl, l, step);
- j = sl.s;
-
- if (prev_noise != null) {
- /* save noise values */
- prev_noise.step[sfb] = s;
- prev_noise.noise[sfb] = noise;
- }
-
- noise = distort[distortPos++] = noise / l3_xmin[l3_xminPos++];
-
- /* multiplying here is adding in dB, but can overflow */
- noise = Util.FAST_LOG10(Math.max(noise, 1E-20));
-
- if (prev_noise != null) {
- /* save noise values */
- prev_noise.noise_log[sfb] = noise;
- }
- }
-
- if (prev_noise != null) {
- /* save noise values */
- prev_noise.global_gain = cod_info.global_gain;
- }
-
- tot_noise_db += noise;
-
- if (noise > 0.0) {
- var tmp;
-
- tmp = Math.max(0 | (noise * 10 + .5), 1);
- res.over_SSD += tmp * tmp;
-
- over++;
- /* multiplying here is adding in dB -but can overflow */
- /* over_noise *= noise; */
- over_noise_db += noise;
- }
- max_noise = Math.max(max_noise, noise);
-
- }
-
- res.over_count = over;
- res.tot_noise = tot_noise_db;
- res.over_noise = over_noise_db;
- res.max_noise = max_noise;
-
- return over;
- }
-
- /**
- * updates plotting data
- *
- * Mark Taylor 2000-??-??
- *
- * Robert Hegemann: moved noise/distortion calc into it
- */
- this.set_pinfo = function (gfp, cod_info, ratio, gr, ch) {
- var gfc = gfp.internal_flags;
- var sfb, sfb2;
- var l;
- var en0, en1;
- var ifqstep = (cod_info.scalefac_scale == 0) ? .5 : 1.0;
- var scalefac = cod_info.scalefac;
-
- var l3_xmin = new_float(L3Side.SFBMAX);
- var xfsf = new_float(L3Side.SFBMAX);
- var noise = new CalcNoiseResult();
-
- calc_xmin(gfp, ratio, cod_info, l3_xmin);
- calc_noise(cod_info, l3_xmin, xfsf, noise, null);
-
- var j = 0;
- sfb2 = cod_info.sfb_lmax;
- if (cod_info.block_type != Encoder.SHORT_TYPE
- && 0 == cod_info.mixed_block_flag)
- sfb2 = 22;
- for (sfb = 0; sfb < sfb2; sfb++) {
- var start = gfc.scalefac_band.l[sfb];
- var end = gfc.scalefac_band.l[sfb + 1];
- var bw = end - start;
- for (en0 = 0.0; j < end; j++)
- en0 += cod_info.xr[j] * cod_info.xr[j];
- en0 /= bw;
- /* convert to MDCT units */
- /* scaling so it shows up on FFT plot */
- en1 = 1e15;
- gfc.pinfo.en[gr][ch][sfb] = en1 * en0;
- gfc.pinfo.xfsf[gr][ch][sfb] = en1 * l3_xmin[sfb] * xfsf[sfb] / bw;
-
- if (ratio.en.l[sfb] > 0 && !gfp.ATHonly)
- en0 = en0 / ratio.en.l[sfb];
- else
- en0 = 0.0;
-
- gfc.pinfo.thr[gr][ch][sfb] = en1
- * Math.max(en0 * ratio.thm.l[sfb], gfc.ATH.l[sfb]);
-
- /* there is no scalefactor bands >= SBPSY_l */
- gfc.pinfo.LAMEsfb[gr][ch][sfb] = 0;
- if (cod_info.preflag != 0 && sfb >= 11)
- gfc.pinfo.LAMEsfb[gr][ch][sfb] = -ifqstep * pretab[sfb];
-
- if (sfb < Encoder.SBPSY_l) {
- /* scfsi should be decoded by caller side */
- gfc.pinfo.LAMEsfb[gr][ch][sfb] -= ifqstep * scalefac[sfb];
- }
- }
- /* for sfb */
-
- if (cod_info.block_type == Encoder.SHORT_TYPE) {
- sfb2 = sfb;
- for (sfb = cod_info.sfb_smin; sfb < Encoder.SBMAX_s; sfb++) {
- var start = gfc.scalefac_band.s[sfb];
- var end = gfc.scalefac_band.s[sfb + 1];
- var bw = end - start;
- for (var i = 0; i < 3; i++) {
- for (en0 = 0.0, l = start; l < end; l++) {
- en0 += cod_info.xr[j] * cod_info.xr[j];
- j++;
- }
- en0 = Math.max(en0 / bw, 1e-20);
- /* convert to MDCT units */
- /* scaling so it shows up on FFT plot */
- en1 = 1e15;
-
- gfc.pinfo.en_s[gr][ch][3 * sfb + i] = en1 * en0;
- gfc.pinfo.xfsf_s[gr][ch][3 * sfb + i] = en1 * l3_xmin[sfb2]
- * xfsf[sfb2] / bw;
- if (ratio.en.s[sfb][i] > 0)
- en0 = en0 / ratio.en.s[sfb][i];
- else
- en0 = 0.0;
- if (gfp.ATHonly || gfp.ATHshort)
- en0 = 0;
-
- gfc.pinfo.thr_s[gr][ch][3 * sfb + i] = en1
- * Math.max(en0 * ratio.thm.s[sfb][i],
- gfc.ATH.s[sfb]);
-
- /* there is no scalefactor bands >= SBPSY_s */
- gfc.pinfo.LAMEsfb_s[gr][ch][3 * sfb + i] = -2.0
- * cod_info.subblock_gain[i];
- if (sfb < Encoder.SBPSY_s) {
- gfc.pinfo.LAMEsfb_s[gr][ch][3 * sfb + i] -= ifqstep
- * scalefac[sfb2];
- }
- sfb2++;
- }
- }
- }
- /* block type short */
- gfc.pinfo.LAMEqss[gr][ch] = cod_info.global_gain;
- gfc.pinfo.LAMEmainbits[gr][ch] = cod_info.part2_3_length
- + cod_info.part2_length;
- gfc.pinfo.LAMEsfbits[gr][ch] = cod_info.part2_length;
-
- gfc.pinfo.over[gr][ch] = noise.over_count;
- gfc.pinfo.max_noise[gr][ch] = noise.max_noise * 10.0;
- gfc.pinfo.over_noise[gr][ch] = noise.over_noise * 10.0;
- gfc.pinfo.tot_noise[gr][ch] = noise.tot_noise * 10.0;
- gfc.pinfo.over_SSD[gr][ch] = noise.over_SSD;
- }
-
- /**
- * updates plotting data for a whole frame
- *
- * Robert Hegemann 2000-10-21
- */
- function set_frame_pinfo(gfp, ratio) {
- var gfc = gfp.internal_flags;
-
- gfc.masking_lower = 1.0;
-
- /*
- * for every granule and channel patch l3_enc and set info
- */
- for (var gr = 0; gr < gfc.mode_gr; gr++) {
- for (var ch = 0; ch < gfc.channels_out; ch++) {
- var cod_info = gfc.l3_side.tt[gr][ch];
- var scalefac_sav = new_int(L3Side.SFBMAX);
- System.arraycopy(cod_info.scalefac, 0, scalefac_sav, 0,
- scalefac_sav.length);
-
- /*
- * reconstruct the scalefactors in case SCFSI was used
- */
- if (gr == 1) {
- var sfb;
- for (sfb = 0; sfb < cod_info.sfb_lmax; sfb++) {
- if (cod_info.scalefac[sfb] < 0) /* scfsi */
- cod_info.scalefac[sfb] = gfc.l3_side.tt[0][ch].scalefac[sfb];
- }
- }
-
- set_pinfo(gfp, cod_info, ratio[gr][ch], gr, ch);
- System.arraycopy(scalefac_sav, 0, cod_info.scalefac, 0,
- scalefac_sav.length);
- }
- /* for ch */
- }
- /* for gr */
- }
-
-}
-
-
-function CalcNoiseData() {
- this.global_gain = 0;
- this.sfb_count1 = 0;
- this.step = new_int(39);
- this.noise = new_float(39);
- this.noise_log = new_float(39);
-}
-
-//package mp3;
-
-
-function GrInfo() {
- //float xr[] = new float[576];
- this.xr = new_float(576);
- //int l3_enc[] = new int[576];
- this.l3_enc = new_int(576);
- //int scalefac[] = new int[L3Side.SFBMAX];
- this.scalefac = new_int(L3Side.SFBMAX);
- this.xrpow_max = 0.;
-
- this.part2_3_length = 0;
- this.big_values = 0;
- this.count1 = 0;
- this.global_gain = 0;
- this.scalefac_compress = 0;
- this.block_type = 0;
- this.mixed_block_flag = 0;
- this.table_select = new_int(3);
- this.subblock_gain = new_int(3 + 1);
- this.region0_count = 0;
- this.region1_count = 0;
- this.preflag = 0;
- this.scalefac_scale = 0;
- this.count1table_select = 0;
-
- this.part2_length = 0;
- this.sfb_lmax = 0;
- this.sfb_smin = 0;
- this.psy_lmax = 0;
- this.sfbmax = 0;
- this.psymax = 0;
- this.sfbdivide = 0;
- this.width = new_int(L3Side.SFBMAX);
- this.window = new_int(L3Side.SFBMAX);
- this.count1bits = 0;
- /**
- * added for LSF
- */
- this.sfb_partition_table = null;
- this.slen = new_int(4);
-
- this.max_nonzero_coeff = 0;
-
- var self = this;
- function clone_int(array) {
- return new Int32Array(array);
- }
- function clone_float(array) {
- return new Float32Array(array);
- }
- this.assign = function (other) {
- self.xr = clone_float(other.xr); //.slice(0); //clone();
- self.l3_enc = clone_int(other.l3_enc); //.slice(0); //clone();
- self.scalefac = clone_int(other.scalefac);//.slice(0); //clone();
- self.xrpow_max = other.xrpow_max;
-
- self.part2_3_length = other.part2_3_length;
- self.big_values = other.big_values;
- self.count1 = other.count1;
- self.global_gain = other.global_gain;
- self.scalefac_compress = other.scalefac_compress;
- self.block_type = other.block_type;
- self.mixed_block_flag = other.mixed_block_flag;
- self.table_select = clone_int(other.table_select);//.slice(0); //clone();
- self.subblock_gain = clone_int(other.subblock_gain); //.slice(0); //.clone();
- self.region0_count = other.region0_count;
- self.region1_count = other.region1_count;
- self.preflag = other.preflag;
- self.scalefac_scale = other.scalefac_scale;
- self.count1table_select = other.count1table_select;
-
- self.part2_length = other.part2_length;
- self.sfb_lmax = other.sfb_lmax;
- self.sfb_smin = other.sfb_smin;
- self.psy_lmax = other.psy_lmax;
- self.sfbmax = other.sfbmax;
- self.psymax = other.psymax;
- self.sfbdivide = other.sfbdivide;
- self.width = clone_int(other.width); //.slice(0); //.clone();
- self.window = clone_int(other.window); //.slice(0); //.clone();
- self.count1bits = other.count1bits;
-
- self.sfb_partition_table = other.sfb_partition_table.slice(0); //.clone();
- self.slen = clone_int(other.slen); //.slice(0); //.clone();
- self.max_nonzero_coeff = other.max_nonzero_coeff;
- }
-}
-
-
-var L3Side = {};
-
-
- /**
- * max scalefactor band, max(SBMAX_l, SBMAX_s*3, (SBMAX_s-3)*3+8)
- */
-L3Side.SFBMAX = (Encoder.SBMAX_s * 3);
-
-/*
- * MP3 quantization
- *
- * Copyright (c) 1999-2000 Mark Taylor
- * Copyright (c) 1999-2003 Takehiro Tominaga
- * Copyright (c) 2000-2007 Robert Hegemann
- * Copyright (c) 2001-2005 Gabriel Bouvigne
- *
- * This library is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2 of the License, or (at your option) any later version.
- *
- * This library is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Library General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with this library; if not, write to the
- * Free Software Foundation, Inc., 59 Temple Place - Suite 330,
- * Boston, MA 02111-1307, USA.
- */
-
-/* $Id: Quantize.java,v 1.24 2011/05/24 20:48:06 kenchis Exp $ */
-
-//package mp3;
-
-//import java.util.Arrays;
-
-
-function Quantize() {
- var bs;
- this.rv = null;
- var rv;
- this.qupvt = null;
- var qupvt;
-
- var vbr = new VBRQuantize();
- var tk;
-
- this.setModules = function (_bs, _rv, _qupvt, _tk) {
- bs = _bs;
- rv = _rv;
- this.rv = _rv;
- qupvt = _qupvt;
- this.qupvt = _qupvt;
- tk = _tk;
- vbr.setModules(qupvt, tk);
- }
-
- /**
- * convert from L/R <. Mid/Side
- */
- this.ms_convert = function (l3_side, gr) {
- for (var i = 0; i < 576; ++i) {
- var l = l3_side.tt[gr][0].xr[i];
- var r = l3_side.tt[gr][1].xr[i];
- l3_side.tt[gr][0].xr[i] = (l + r) * (Util.SQRT2 * 0.5);
- l3_side.tt[gr][1].xr[i] = (l - r) * (Util.SQRT2 * 0.5);
- }
- };
-
- /**
- * mt 6/99
- *
- * initializes cod_info, scalefac and xrpow
- *
- * returns 0 if all energies in xr are zero, else 1
- */
- function init_xrpow_core(cod_info, xrpow, upper, sum) {
- sum = 0;
- for (var i = 0; i <= upper; ++i) {
- var tmp = Math.abs(cod_info.xr[i]);
- sum += tmp;
- xrpow[i] = Math.sqrt(tmp * Math.sqrt(tmp));
-
- if (xrpow[i] > cod_info.xrpow_max)
- cod_info.xrpow_max = xrpow[i];
- }
- return sum;
- }
-
- this.init_xrpow = function (gfc, cod_info, xrpow) {
- var sum = 0;
- var upper = 0 | cod_info.max_nonzero_coeff;
-
- cod_info.xrpow_max = 0;
-
- /*
- * check if there is some energy we have to quantize and calculate xrpow
- * matching our fresh scalefactors
- */
-
- Arrays.fill(xrpow, upper, 576, 0);
-
- sum = init_xrpow_core(cod_info, xrpow, upper, sum);
-
- /*
- * return 1 if we have something to quantize, else 0
- */
- if (sum > 1E-20) {
- var j = 0;
- if ((gfc.substep_shaping & 2) != 0)
- j = 1;
-
- for (var i = 0; i < cod_info.psymax; i++)
- gfc.pseudohalf[i] = j;
-
- return true;
- }
-
- Arrays.fill(cod_info.l3_enc, 0, 576, 0);
- return false;
- }
-
- /**
- * Gabriel Bouvigne feb/apr 2003
- * Analog silence detection in partitionned sfb21 or sfb12 for short blocks
- *
- * From top to bottom of sfb, changes to 0 coeffs which are below ath. It
- * stops on the first coeff higher than ath.
- */
- function psfb21_analogsilence(gfc, cod_info) {
- var ath = gfc.ATH;
- var xr = cod_info.xr;
-
- if (cod_info.block_type != Encoder.SHORT_TYPE) {
- /* NORM, START or STOP type, but not SHORT blocks */
- var stop = false;
- for (var gsfb = Encoder.PSFB21 - 1; gsfb >= 0 && !stop; gsfb--) {
- var start = gfc.scalefac_band.psfb21[gsfb];
- var end = gfc.scalefac_band.psfb21[gsfb + 1];
- var ath21 = qupvt.athAdjust(ath.adjust, ath.psfb21[gsfb],
- ath.floor);
-
- if (gfc.nsPsy.longfact[21] > 1e-12)
- ath21 *= gfc.nsPsy.longfact[21];
-
- for (var j = end - 1; j >= start; j--) {
- if (Math.abs(xr[j]) < ath21)
- xr[j] = 0;
- else {
- stop = true;
- break;
- }
- }
- }
- } else {
- /* note: short blocks coeffs are reordered */
- for (var block = 0; block < 3; block++) {
- var stop = false;
- for (var gsfb = Encoder.PSFB12 - 1; gsfb >= 0 && !stop; gsfb--) {
- var start = gfc.scalefac_band.s[12]
- * 3
- + (gfc.scalefac_band.s[13] - gfc.scalefac_band.s[12])
- * block
- + (gfc.scalefac_band.psfb12[gsfb] - gfc.scalefac_band.psfb12[0]);
- var end = start
- + (gfc.scalefac_band.psfb12[gsfb + 1] - gfc.scalefac_band.psfb12[gsfb]);
- var ath12 = qupvt.athAdjust(ath.adjust, ath.psfb12[gsfb],
- ath.floor);
-
- if (gfc.nsPsy.shortfact[12] > 1e-12)
- ath12 *= gfc.nsPsy.shortfact[12];
-
- for (var j = end - 1; j >= start; j--) {
- if (Math.abs(xr[j]) < ath12)
- xr[j] = 0;
- else {
- stop = true;
- break;
- }
- }
- }
- }
- }
-
- }
-
- this.init_outer_loop = function (gfc, cod_info) {
- /*
- * initialize fresh cod_info
- */
- cod_info.part2_3_length = 0;
- cod_info.big_values = 0;
- cod_info.count1 = 0;
- cod_info.global_gain = 210;
- cod_info.scalefac_compress = 0;
- /* mixed_block_flag, block_type was set in psymodel.c */
- cod_info.table_select[0] = 0;
- cod_info.table_select[1] = 0;
- cod_info.table_select[2] = 0;
- cod_info.subblock_gain[0] = 0;
- cod_info.subblock_gain[1] = 0;
- cod_info.subblock_gain[2] = 0;
- cod_info.subblock_gain[3] = 0;
- /* this one is always 0 */
- cod_info.region0_count = 0;
- cod_info.region1_count = 0;
- cod_info.preflag = 0;
- cod_info.scalefac_scale = 0;
- cod_info.count1table_select = 0;
- cod_info.part2_length = 0;
- cod_info.sfb_lmax = Encoder.SBPSY_l;
- cod_info.sfb_smin = Encoder.SBPSY_s;
- cod_info.psy_lmax = gfc.sfb21_extra ? Encoder.SBMAX_l : Encoder.SBPSY_l;
- cod_info.psymax = cod_info.psy_lmax;
- cod_info.sfbmax = cod_info.sfb_lmax;
- cod_info.sfbdivide = 11;
- for (var sfb = 0; sfb < Encoder.SBMAX_l; sfb++) {
- cod_info.width[sfb] = gfc.scalefac_band.l[sfb + 1]
- - gfc.scalefac_band.l[sfb];
- /* which is always 0. */
- cod_info.window[sfb] = 3;
- }
- if (cod_info.block_type == Encoder.SHORT_TYPE) {
- var ixwork = new_float(576);
-
- cod_info.sfb_smin = 0;
- cod_info.sfb_lmax = 0;
- if (cod_info.mixed_block_flag != 0) {
- /*
- * MPEG-1: sfbs 0-7 long block, 3-12 short blocks MPEG-2(.5):
- * sfbs 0-5 long block, 3-12 short blocks
- */
- cod_info.sfb_smin = 3;
- cod_info.sfb_lmax = gfc.mode_gr * 2 + 4;
- }
- cod_info.psymax = cod_info.sfb_lmax
- + 3
- * ((gfc.sfb21_extra ? Encoder.SBMAX_s : Encoder.SBPSY_s) - cod_info.sfb_smin);
- cod_info.sfbmax = cod_info.sfb_lmax + 3
- * (Encoder.SBPSY_s - cod_info.sfb_smin);
- cod_info.sfbdivide = cod_info.sfbmax - 18;
- cod_info.psy_lmax = cod_info.sfb_lmax;
- /* re-order the short blocks, for more efficient encoding below */
- /* By Takehiro TOMINAGA */
- /*
- * Within each scalefactor band, data is given for successive time
- * windows, beginning with window 0 and ending with window 2. Within
- * each window, the quantized values are then arranged in order of
- * increasing frequency...
- */
- var ix = gfc.scalefac_band.l[cod_info.sfb_lmax];
- System.arraycopy(cod_info.xr, 0, ixwork, 0, 576);
- for (var sfb = cod_info.sfb_smin; sfb < Encoder.SBMAX_s; sfb++) {
- var start = gfc.scalefac_band.s[sfb];
- var end = gfc.scalefac_band.s[sfb + 1];
- for (var window = 0; window < 3; window++) {
- for (var l = start; l < end; l++) {
- cod_info.xr[ix++] = ixwork[3 * l + window];
- }
- }
- }
-
- var j = cod_info.sfb_lmax;
- for (var sfb = cod_info.sfb_smin; sfb < Encoder.SBMAX_s; sfb++) {
- cod_info.width[j] = cod_info.width[j + 1] = cod_info.width[j + 2] = gfc.scalefac_band.s[sfb + 1]
- - gfc.scalefac_band.s[sfb];
- cod_info.window[j] = 0;
- cod_info.window[j + 1] = 1;
- cod_info.window[j + 2] = 2;
- j += 3;
- }
- }
-
- cod_info.count1bits = 0;
- cod_info.sfb_partition_table = qupvt.nr_of_sfb_block[0][0];
- cod_info.slen[0] = 0;
- cod_info.slen[1] = 0;
- cod_info.slen[2] = 0;
- cod_info.slen[3] = 0;
-
- cod_info.max_nonzero_coeff = 575;
-
- /*
- * fresh scalefactors are all zero
- */
- Arrays.fill(cod_info.scalefac, 0);
-
- psfb21_analogsilence(gfc, cod_info);
- };
-
- function BinSearchDirection(ordinal) {
- this.ordinal = ordinal;
- }
-
- BinSearchDirection.BINSEARCH_NONE = new BinSearchDirection(0);
- BinSearchDirection.BINSEARCH_UP = new BinSearchDirection(1);
- BinSearchDirection.BINSEARCH_DOWN = new BinSearchDirection(2);
-
- /**
- * author/date??
- *
- * binary step size search used by outer_loop to get a quantizer step size
- * to start with
- */
- function bin_search_StepSize(gfc, cod_info, desired_rate, ch, xrpow) {
- var nBits;
- var CurrentStep = gfc.CurrentStep[ch];
- var flagGoneOver = false;
- var start = gfc.OldValue[ch];
- var Direction = BinSearchDirection.BINSEARCH_NONE;
- cod_info.global_gain = start;
- desired_rate -= cod_info.part2_length;
-
- for (; ;) {
- var step;
- nBits = tk.count_bits(gfc, xrpow, cod_info, null);
-
- if (CurrentStep == 1 || nBits == desired_rate)
- break;
- /* nothing to adjust anymore */
-
- if (nBits > desired_rate) {
- /* increase Quantize_StepSize */
- if (Direction == BinSearchDirection.BINSEARCH_DOWN)
- flagGoneOver = true;
-
- if (flagGoneOver)
- CurrentStep /= 2;
- Direction = BinSearchDirection.BINSEARCH_UP;
- step = CurrentStep;
- } else {
- /* decrease Quantize_StepSize */
- if (Direction == BinSearchDirection.BINSEARCH_UP)
- flagGoneOver = true;
-
- if (flagGoneOver)
- CurrentStep /= 2;
- Direction = BinSearchDirection.BINSEARCH_DOWN;
- step = -CurrentStep;
- }
- cod_info.global_gain += step;
- if (cod_info.global_gain < 0) {
- cod_info.global_gain = 0;
- flagGoneOver = true;
- }
- if (cod_info.global_gain > 255) {
- cod_info.global_gain = 255;
- flagGoneOver = true;
- }
- }
-
-
- while (nBits > desired_rate && cod_info.global_gain < 255) {
- cod_info.global_gain++;
- nBits = tk.count_bits(gfc, xrpow, cod_info, null);
- }
- gfc.CurrentStep[ch] = (start - cod_info.global_gain >= 4) ? 4 : 2;
- gfc.OldValue[ch] = cod_info.global_gain;
- cod_info.part2_3_length = nBits;
- return nBits;
- }
-
- this.trancate_smallspectrums = function (gfc, gi, l3_xmin, work) {
- var distort = new_float(L3Side.SFBMAX);
-
- if ((0 == (gfc.substep_shaping & 4) && gi.block_type == Encoder.SHORT_TYPE)
- || (gfc.substep_shaping & 0x80) != 0)
- return;
- qupvt.calc_noise(gi, l3_xmin, distort, new CalcNoiseResult(), null);
- for (var j = 0; j < 576; j++) {
- var xr = 0.0;
- if (gi.l3_enc[j] != 0)
- xr = Math.abs(gi.xr[j]);
- work[j] = xr;
- }
-
- var j = 0;
- var sfb = 8;
- if (gi.block_type == Encoder.SHORT_TYPE)
- sfb = 6;
- do {
- var allowedNoise, trancateThreshold;
- var nsame, start;
-
- var width = gi.width[sfb];
- j += width;
- if (distort[sfb] >= 1.0)
- continue;
-
- Arrays.sort(work, j - width, width);
- if (BitStream.EQ(work[j - 1], 0.0))
- continue;
- /* all zero sfb */
-
- allowedNoise = (1.0 - distort[sfb]) * l3_xmin[sfb];
- trancateThreshold = 0.0;
- start = 0;
- do {
- var noise;
- for (nsame = 1; start + nsame < width; nsame++)
- if (BitStream.NEQ(work[start + j - width], work[start + j
- + nsame - width]))
- break;
-
- noise = work[start + j - width] * work[start + j - width]
- * nsame;
- if (allowedNoise < noise) {
- if (start != 0)
- trancateThreshold = work[start + j - width - 1];
- break;
- }
- allowedNoise -= noise;
- start += nsame;
- } while (start < width);
- if (BitStream.EQ(trancateThreshold, 0.0))
- continue;
-
- do {
- if (Math.abs(gi.xr[j - width]) <= trancateThreshold)
- gi.l3_enc[j - width] = 0;
- } while (--width > 0);
- } while (++sfb < gi.psymax);
-
- gi.part2_3_length = tk.noquant_count_bits(gfc, gi, null);
- };
-
- /**
- * author/date??
- *
- * Function: Returns zero if there is a scalefac which has not been
- * amplified. Otherwise it returns one.
- */
- function loop_break(cod_info) {
- for (var sfb = 0; sfb < cod_info.sfbmax; sfb++)
- if (cod_info.scalefac[sfb]
- + cod_info.subblock_gain[cod_info.window[sfb]] == 0)
- return false;
-
- return true;
- }
-
- /* mt 5/99: Function: Improved calc_noise for a single channel */
-
- function penalties(noise) {
- return Util.FAST_LOG10((0.368 + 0.632 * noise * noise * noise));
- }
-
- /**
- * author/date??
- *
- * several different codes to decide which quantization is better
- */
- function get_klemm_noise(distort, gi) {
- var klemm_noise = 1E-37;
- for (var sfb = 0; sfb < gi.psymax; sfb++)
- klemm_noise += penalties(distort[sfb]);
-
- return Math.max(1e-20, klemm_noise);
- }
-
- function quant_compare(quant_comp, best, calc, gi, distort) {
- /**
- * noise is given in decibels (dB) relative to masking thesholds.
- *
- * over_noise: ??? (the previous comment is fully wrong)
- * tot_noise: ??? (the previous comment is fully wrong)
- * max_noise: max quantization noise
- */
- var better;
-
- switch (quant_comp) {
- default:
- case 9:
- {
- if (best.over_count > 0) {
- /* there are distorted sfb */
- better = calc.over_SSD <= best.over_SSD;
- if (calc.over_SSD == best.over_SSD)
- better = calc.bits < best.bits;
- } else {
- /* no distorted sfb */
- better = ((calc.max_noise < 0) && ((calc.max_noise * 10 + calc.bits) <= (best.max_noise * 10 + best.bits)));
- }
- break;
- }
-
- case 0:
- better = calc.over_count < best.over_count
- || (calc.over_count == best.over_count && calc.over_noise < best.over_noise)
- || (calc.over_count == best.over_count
- && BitStream.EQ(calc.over_noise, best.over_noise) && calc.tot_noise < best.tot_noise);
- break;
-
- case 8:
- calc.max_noise = get_klemm_noise(distort, gi);
- //$FALL-THROUGH$
- case 1:
- better = calc.max_noise < best.max_noise;
- break;
- case 2:
- better = calc.tot_noise < best.tot_noise;
- break;
- case 3:
- better = (calc.tot_noise < best.tot_noise)
- && (calc.max_noise < best.max_noise);
- break;
- case 4:
- better = (calc.max_noise <= 0.0 && best.max_noise > 0.2)
- || (calc.max_noise <= 0.0 && best.max_noise < 0.0
- && best.max_noise > calc.max_noise - 0.2 && calc.tot_noise < best.tot_noise)
- || (calc.max_noise <= 0.0 && best.max_noise > 0.0
- && best.max_noise > calc.max_noise - 0.2 && calc.tot_noise < best.tot_noise
- + best.over_noise)
- || (calc.max_noise > 0.0 && best.max_noise > -0.05
- && best.max_noise > calc.max_noise - 0.1 && calc.tot_noise
- + calc.over_noise < best.tot_noise
- + best.over_noise)
- || (calc.max_noise > 0.0 && best.max_noise > -0.1
- && best.max_noise > calc.max_noise - 0.15 && calc.tot_noise
- + calc.over_noise + calc.over_noise < best.tot_noise
- + best.over_noise + best.over_noise);
- break;
- case 5:
- better = calc.over_noise < best.over_noise
- || (BitStream.EQ(calc.over_noise, best.over_noise) && calc.tot_noise < best.tot_noise);
- break;
- case 6:
- better = calc.over_noise < best.over_noise
- || (BitStream.EQ(calc.over_noise, best.over_noise) && (calc.max_noise < best.max_noise || (BitStream
- .EQ(calc.max_noise, best.max_noise) && calc.tot_noise <= best.tot_noise)));
- break;
- case 7:
- better = calc.over_count < best.over_count
- || calc.over_noise < best.over_noise;
- break;
- }
-
- if (best.over_count == 0) {
- /*
- * If no distorted bands, only use this quantization if it is
- * better, and if it uses less bits. Unfortunately, part2_3_length
- * is sometimes a poor estimator of the final size at low bitrates.
- */
- better = better && calc.bits < best.bits;
- }
-
- return better;
- }
-
- /**
- * author/date??
- *
- *
- * Amplify the scalefactor bands that violate the masking threshold.
- * See ISO 11172-3 Section C.1.5.4.3.5
- *
- * distort[] = noise/masking
- * distort[] > 1 ==> noise is not masked
- * distort[] < 1 ==> noise is masked
- * max_dist = maximum value of distort[]
- *
- * Three algorithms:
- * noise_shaping_amp
- * 0 Amplify all bands with distort[]>1.
- *
- * 1 Amplify all bands with distort[] >= max_dist^(.5);
- * ( 50% in the db scale)
- *
- * 2 Amplify first band with distort[] >= max_dist;
- *
- *
- * For algorithms 0 and 1, if max_dist < 1, then amplify all bands
- * with distort[] >= .95*max_dist. This is to make sure we always
- * amplify at least one band.
- *
- */
- function amp_scalefac_bands(gfp, cod_info, distort, xrpow, bRefine) {
- var gfc = gfp.internal_flags;
- var ifqstep34;
-
- if (cod_info.scalefac_scale == 0) {
- ifqstep34 = 1.29683955465100964055;
- /* 2**(.75*.5) */
- } else {
- ifqstep34 = 1.68179283050742922612;
- /* 2**(.75*1) */
- }
-
- /* compute maximum value of distort[] */
- var trigger = 0;
- for (var sfb = 0; sfb < cod_info.sfbmax; sfb++) {
- if (trigger < distort[sfb])
- trigger = distort[sfb];
- }
-
- var noise_shaping_amp = gfc.noise_shaping_amp;
- if (noise_shaping_amp == 3) {
- if (bRefine)
- noise_shaping_amp = 2;
- else
- noise_shaping_amp = 1;
- }
- switch (noise_shaping_amp) {
- case 2:
- /* amplify exactly 1 band */
- break;
-
- case 1:
- /* amplify bands within 50% of max (on db scale) */
- if (trigger > 1.0)
- trigger = Math.pow(trigger, .5);
- else
- trigger *= .95;
- break;
-
- case 0:
- default:
- /* ISO algorithm. amplify all bands with distort>1 */
- if (trigger > 1.0)
- trigger = 1.0;
- else
- trigger *= .95;
- break;
- }
-
- var j = 0;
- for (var sfb = 0; sfb < cod_info.sfbmax; sfb++) {
- var width = cod_info.width[sfb];
- var l;
- j += width;
- if (distort[sfb] < trigger)
- continue;
-
- if ((gfc.substep_shaping & 2) != 0) {
- gfc.pseudohalf[sfb] = (0 == gfc.pseudohalf[sfb]) ? 1 : 0;
- if (0 == gfc.pseudohalf[sfb] && gfc.noise_shaping_amp == 2)
- return;
- }
- cod_info.scalefac[sfb]++;
- for (l = -width; l < 0; l++) {
- xrpow[j + l] *= ifqstep34;
- if (xrpow[j + l] > cod_info.xrpow_max)
- cod_info.xrpow_max = xrpow[j + l];
- }
-
- if (gfc.noise_shaping_amp == 2)
- return;
- }
- }
-
- /**
- * Takehiro Tominaga 2000-xx-xx
- *
- * turns on scalefac scale and adjusts scalefactors
- */
- function inc_scalefac_scale(cod_info, xrpow) {
- var ifqstep34 = 1.29683955465100964055;
-
- var j = 0;
- for (var sfb = 0; sfb < cod_info.sfbmax; sfb++) {
- var width = cod_info.width[sfb];
- var s = cod_info.scalefac[sfb];
- if (cod_info.preflag != 0)
- s += qupvt.pretab[sfb];
- j += width;
- if ((s & 1) != 0) {
- s++;
- for (var l = -width; l < 0; l++) {
- xrpow[j + l] *= ifqstep34;
- if (xrpow[j + l] > cod_info.xrpow_max)
- cod_info.xrpow_max = xrpow[j + l];
- }
- }
- cod_info.scalefac[sfb] = s >> 1;
- }
- cod_info.preflag = 0;
- cod_info.scalefac_scale = 1;
- }
-
- /**
- * Takehiro Tominaga 2000-xx-xx
- *
- * increases the subblock gain and adjusts scalefactors
- */
- function inc_subblock_gain(gfc, cod_info, xrpow) {
- var sfb;
- var scalefac = cod_info.scalefac;
-
- /* subbloc_gain can't do anything in the long block region */
- for (sfb = 0; sfb < cod_info.sfb_lmax; sfb++) {
- if (scalefac[sfb] >= 16)
- return true;
- }
-
- for (var window = 0; window < 3; window++) {
- var s1 = 0;
- var s2 = 0;
-
- for (sfb = cod_info.sfb_lmax + window; sfb < cod_info.sfbdivide; sfb += 3) {
- if (s1 < scalefac[sfb])
- s1 = scalefac[sfb];
- }
- for (; sfb < cod_info.sfbmax; sfb += 3) {
- if (s2 < scalefac[sfb])
- s2 = scalefac[sfb];
- }
-
- if (s1 < 16 && s2 < 8)
- continue;
-
- if (cod_info.subblock_gain[window] >= 7)
- return true;
-
- /*
- * even though there is no scalefactor for sfb12 subblock gain
- * affects upper frequencies too, that's why we have to go up to
- * SBMAX_s
- */
- cod_info.subblock_gain[window]++;
- var j = gfc.scalefac_band.l[cod_info.sfb_lmax];
- for (sfb = cod_info.sfb_lmax + window; sfb < cod_info.sfbmax; sfb += 3) {
- var amp;
- var width = cod_info.width[sfb];
- var s = scalefac[sfb];
- s = s - (4 >> cod_info.scalefac_scale);
- if (s >= 0) {
- scalefac[sfb] = s;
- j += width * 3;
- continue;
- }
-
- scalefac[sfb] = 0;
- {
- var gain = 210 + (s << (cod_info.scalefac_scale + 1));
- amp = qupvt.IPOW20(gain);
- }
- j += width * (window + 1);
- for (var l = -width; l < 0; l++) {
- xrpow[j + l] *= amp;
- if (xrpow[j + l] > cod_info.xrpow_max)
- cod_info.xrpow_max = xrpow[j + l];
- }
- j += width * (3 - window - 1);
- }
-
- {
- var amp = qupvt.IPOW20(202);
- j += cod_info.width[sfb] * (window + 1);
- for (var l = -cod_info.width[sfb]; l < 0; l++) {
- xrpow[j + l] *= amp;
- if (xrpow[j + l] > cod_info.xrpow_max)
- cod_info.xrpow_max = xrpow[j + l];
- }
- }
- }
- return false;
- }
-
- /**
- *
- * Takehiro Tominaga /date??
- * Robert Hegemann 2000-09-06: made a function of it
- *
- * amplifies scalefactor bands,
- * - if all are already amplified returns 0
- * - if some bands are amplified too much:
- * * try to increase scalefac_scale
- * * if already scalefac_scale was set
- * try on short blocks to increase subblock gain
- *
- */
- function balance_noise(gfp, cod_info, distort, xrpow, bRefine) {
- var gfc = gfp.internal_flags;
-
- amp_scalefac_bands(gfp, cod_info, distort, xrpow, bRefine);
-
- /*
- * check to make sure we have not amplified too much loop_break returns
- * 0 if there is an unamplified scalefac scale_bitcount returns 0 if no
- * scalefactors are too large
- */
-
- var status = loop_break(cod_info);
-
- if (status)
- return false;
- /* all bands amplified */
-
- /*
- * not all scalefactors have been amplified. so these scalefacs are
- * possibly valid. encode them:
- */
- if (gfc.mode_gr == 2)
- status = tk.scale_bitcount(cod_info);
- else
- status = tk.scale_bitcount_lsf(gfc, cod_info);
-
- if (!status)
- return true;
- /* amplified some bands not exceeding limits */
-
- /*
- * some scalefactors are too large. lets try setting scalefac_scale=1
- */
- if (gfc.noise_shaping > 1) {
- Arrays.fill(gfc.pseudohalf, 0);
- if (0 == cod_info.scalefac_scale) {
- inc_scalefac_scale(cod_info, xrpow);
- status = false;
- } else {
- if (cod_info.block_type == Encoder.SHORT_TYPE
- && gfc.subblock_gain > 0) {
- status = (inc_subblock_gain(gfc, cod_info, xrpow) || loop_break(cod_info));
- }
- }
- }
-
- if (!status) {
- if (gfc.mode_gr == 2)
- status = tk.scale_bitcount(cod_info);
- else
- status = tk.scale_bitcount_lsf(gfc, cod_info);
- }
- return !status;
- }
-
- /**
- *
- * Function: The outer iteration loop controls the masking conditions
- * of all scalefactorbands. It computes the best scalefac and
- * global gain. This module calls the inner iteration loop
- *
- * mt 5/99 completely rewritten to allow for bit reservoir control,
- * mid/side channels with L/R or mid/side masking thresholds,
- * and chooses best quantization instead of last quantization when
- * no distortion free quantization can be found.
- *
- * added VBR support mt 5/99
-
- *
- * some code shuffle rh 9/00
- *
- *
- * @param l3_xmin
- * allowed distortion
- * @param xrpow
- * coloured magnitudes of spectral
- * @param targ_bits
- * maximum allowed bits
- */
- this.outer_loop = function (gfp, cod_info, l3_xmin, xrpow, ch, targ_bits) {
- var gfc = gfp.internal_flags;
- var cod_info_w = new GrInfo();
- var save_xrpow = new_float(576);
- var distort = new_float(L3Side.SFBMAX);
- var best_noise_info = new CalcNoiseResult();
- var better;
- var prev_noise = new CalcNoiseData();
- var best_part2_3_length = 9999999;
- var bEndOfSearch = false;
- var bRefine = false;
- var best_ggain_pass1 = 0;
-
- bin_search_StepSize(gfc, cod_info, targ_bits, ch, xrpow);
-
- if (0 == gfc.noise_shaping)
- /* fast mode, no noise shaping, we are ready */
- return 100;
- /* default noise_info.over_count */
-
- /* compute the distortion in this quantization */
- /* coefficients and thresholds both l/r (or both mid/side) */
- qupvt.calc_noise(cod_info, l3_xmin, distort, best_noise_info,
- prev_noise);
- best_noise_info.bits = cod_info.part2_3_length;
-
- cod_info_w.assign(cod_info);
- var age = 0;
- System.arraycopy(xrpow, 0, save_xrpow, 0, 576);
-
- while (!bEndOfSearch) {
- /* BEGIN MAIN LOOP */
- do {
- var noise_info = new CalcNoiseResult();
- var search_limit;
- var maxggain = 255;
-
- /*
- * When quantization with no distorted bands is found, allow up
- * to X new unsuccesful tries in serial. This gives us more
- * possibilities for different quant_compare modes. Much more
- * than 3 makes not a big difference, it is only slower.
- */
-
- if ((gfc.substep_shaping & 2) != 0) {
- search_limit = 20;
- } else {
- search_limit = 3;
- }
-
- /*
- * Check if the last scalefactor band is distorted. in VBR mode
- * we can't get rid of the distortion, so quit now and VBR mode
- * will try again with more bits. (makes a 10% speed increase,
- * the files I tested were binary identical, 2000/05/20 Robert
- * Hegemann) distort[] > 1 means noise > allowed noise
- */
- if (gfc.sfb21_extra) {
- if (distort[cod_info_w.sfbmax] > 1.0)
- break;
- if (cod_info_w.block_type == Encoder.SHORT_TYPE
- && (distort[cod_info_w.sfbmax + 1] > 1.0 || distort[cod_info_w.sfbmax + 2] > 1.0))
- break;
- }
-
- /* try a new scalefactor conbination on cod_info_w */
- if (!balance_noise(gfp, cod_info_w, distort, xrpow, bRefine))
- break;
- if (cod_info_w.scalefac_scale != 0)
- maxggain = 254;
-
- /*
- * inner_loop starts with the initial quantization step computed
- * above and slowly increases until the bits < huff_bits. Thus
- * it is important not to start with too large of an inital
- * quantization step. Too small is ok, but inner_loop will take
- * longer
- */
- var huff_bits = targ_bits - cod_info_w.part2_length;
- if (huff_bits <= 0)
- break;
-
- /*
- * increase quantizer stepsize until needed bits are below
- * maximum
- */
- while ((cod_info_w.part2_3_length = tk.count_bits(gfc, xrpow,
- cod_info_w, prev_noise)) > huff_bits
- && cod_info_w.global_gain <= maxggain)
- cod_info_w.global_gain++;
-
- if (cod_info_w.global_gain > maxggain)
- break;
-
- if (best_noise_info.over_count == 0) {
-
- while ((cod_info_w.part2_3_length = tk.count_bits(gfc,
- xrpow, cod_info_w, prev_noise)) > best_part2_3_length
- && cod_info_w.global_gain <= maxggain)
- cod_info_w.global_gain++;
-
- if (cod_info_w.global_gain > maxggain)
- break;
- }
-
- /* compute the distortion in this quantization */
- qupvt.calc_noise(cod_info_w, l3_xmin, distort, noise_info,
- prev_noise);
- noise_info.bits = cod_info_w.part2_3_length;
-
- /*
- * check if this quantization is better than our saved
- * quantization
- */
- if (cod_info.block_type != Encoder.SHORT_TYPE) {
- // NORM, START or STOP type
- better = gfp.quant_comp;
- } else
- better = gfp.quant_comp_short;
-
- better = quant_compare(better, best_noise_info, noise_info,
- cod_info_w, distort) ? 1 : 0;
-
- /* save data so we can restore this quantization later */
- if (better != 0) {
- best_part2_3_length = cod_info.part2_3_length;
- best_noise_info = noise_info;
- cod_info.assign(cod_info_w);
- age = 0;
- /* save data so we can restore this quantization later */
- /* store for later reuse */
- System.arraycopy(xrpow, 0, save_xrpow, 0, 576);
- } else {
- /* early stop? */
- if (gfc.full_outer_loop == 0) {
- if (++age > search_limit
- && best_noise_info.over_count == 0)
- break;
- if ((gfc.noise_shaping_amp == 3) && bRefine && age > 30)
- break;
- if ((gfc.noise_shaping_amp == 3)
- && bRefine
- && (cod_info_w.global_gain - best_ggain_pass1) > 15)
- break;
- }
- }
- } while ((cod_info_w.global_gain + cod_info_w.scalefac_scale) < 255);
-
- if (gfc.noise_shaping_amp == 3) {
- if (!bRefine) {
- /* refine search */
- cod_info_w.assign(cod_info);
- System.arraycopy(save_xrpow, 0, xrpow, 0, 576);
- age = 0;
- best_ggain_pass1 = cod_info_w.global_gain;
-
- bRefine = true;
- } else {
- /* search already refined, stop */
- bEndOfSearch = true;
- }
-
- } else {
- bEndOfSearch = true;
- }
- }
-
- /*
- * finish up
- */
- if (gfp.VBR == VbrMode.vbr_rh || gfp.VBR == VbrMode.vbr_mtrh)
- /* restore for reuse on next try */
- System.arraycopy(save_xrpow, 0, xrpow, 0, 576);
- /*
- * do the 'substep shaping'
- */
- else if ((gfc.substep_shaping & 1) != 0)
- trancate_smallspectrums(gfc, cod_info, l3_xmin, xrpow);
-
- return best_noise_info.over_count;
- }
-
- /**
- * Robert Hegemann 2000-09-06
- *
- * update reservoir status after FINAL quantization/bitrate
- */
- this.iteration_finish_one = function (gfc, gr, ch) {
- var l3_side = gfc.l3_side;
- var cod_info = l3_side.tt[gr][ch];
-
- /*
- * try some better scalefac storage
- */
- tk.best_scalefac_store(gfc, gr, ch, l3_side);
-
- /*
- * best huffman_divide may save some bits too
- */
- if (gfc.use_best_huffman == 1)
- tk.best_huffman_divide(gfc, cod_info);
-
- /*
- * update reservoir status after FINAL quantization/bitrate
- */
- rv.ResvAdjust(gfc, cod_info);
- };
-
- /**
- *
- * 2000-09-04 Robert Hegemann
- *
- * @param l3_xmin
- * allowed distortion of the scalefactor
- * @param xrpow
- * coloured magnitudes of spectral values
- */
- this.VBR_encode_granule = function (gfp, cod_info, l3_xmin, xrpow, ch, min_bits, max_bits) {
- var gfc = gfp.internal_flags;
- var bst_cod_info = new GrInfo();
- var bst_xrpow = new_float(576);
- var Max_bits = max_bits;
- var real_bits = max_bits + 1;
- var this_bits = (max_bits + min_bits) / 2;
- var dbits, over, found = 0;
- var sfb21_extra = gfc.sfb21_extra;
-
- Arrays.fill(bst_cod_info.l3_enc, 0);
-
- /*
- * search within round about 40 bits of optimal
- */
- do {
-
- if (this_bits > Max_bits - 42)
- gfc.sfb21_extra = false;
- else
- gfc.sfb21_extra = sfb21_extra;
-
- over = outer_loop(gfp, cod_info, l3_xmin, xrpow, ch, this_bits);
-
- /*
- * is quantization as good as we are looking for ? in this case: is
- * no scalefactor band distorted?
- */
- if (over <= 0) {
- found = 1;
- /*
- * now we know it can be done with "real_bits" and maybe we can
- * skip some iterations
- */
- real_bits = cod_info.part2_3_length;
-
- /*
- * store best quantization so far
- */
- bst_cod_info.assign(cod_info);
- System.arraycopy(xrpow, 0, bst_xrpow, 0, 576);
-
- /*
- * try with fewer bits
- */
- max_bits = real_bits - 32;
- dbits = max_bits - min_bits;
- this_bits = (max_bits + min_bits) / 2;
- } else {
- /*
- * try with more bits
- */
- min_bits = this_bits + 32;
- dbits = max_bits - min_bits;
- this_bits = (max_bits + min_bits) / 2;
-
- if (found != 0) {
- found = 2;
- /*
- * start again with best quantization so far
- */
- cod_info.assign(bst_cod_info);
- System.arraycopy(bst_xrpow, 0, xrpow, 0, 576);
- }
- }
- } while (dbits > 12);
-
- gfc.sfb21_extra = sfb21_extra;
-
- /*
- * found=0 => nothing found, use last one found=1 => we just found the
- * best and left the loop found=2 => we restored a good one and have now
- * l3_enc to restore too
- */
- if (found == 2) {
- System.arraycopy(bst_cod_info.l3_enc, 0, cod_info.l3_enc, 0, 576);
- }
- }
-
- /**
- * Robert Hegemann 2000-09-05
- *
- * calculates * how many bits are available for analog silent granules * how
- * many bits to use for the lowest allowed bitrate * how many bits each
- * bitrate would provide
- */
- this.get_framebits = function (gfp, frameBits) {
- var gfc = gfp.internal_flags;
-
- /*
- * always use at least this many bits per granule per channel unless we
- * detect analog silence, see below
- */
- gfc.bitrate_index = gfc.VBR_min_bitrate;
- var bitsPerFrame = bs.getframebits(gfp);
-
- /*
- * bits for analog silence
- */
- gfc.bitrate_index = 1;
- bitsPerFrame = bs.getframebits(gfp);
-
- for (var i = 1; i <= gfc.VBR_max_bitrate; i++) {
- gfc.bitrate_index = i;
- var mb = new MeanBits(bitsPerFrame);
- frameBits[i] = rv.ResvFrameBegin(gfp, mb);
- bitsPerFrame = mb.bits;
- }
- };
-
- /* RH: this one needs to be overhauled sometime */
-
- /**
- *
- * 2000-09-04 Robert Hegemann
- *
- * * converts LR to MS coding when necessary
- * * calculates allowed/adjusted quantization noise amounts
- * * detects analog silent frames
- *
- * some remarks:
- * - lower masking depending on Quality setting
- * - quality control together with adjusted ATH MDCT scaling
- * on lower quality setting allocate more noise from
- * ATH masking, and on higher quality setting allocate
- * less noise from ATH masking.
- * - experiments show that going more than 2dB over GPSYCHO's
- * limits ends up in very annoying artefacts
- *
- * res_factor is the percentage of the target bitrate that should
- * be used on average. the remaining bits are added to the
- * bitreservoir and used for difficult to encode frames.
- *
- * Since we are tracking the average bitrate, we should adjust
- * res_factor "on the fly", increasing it if the average bitrate
- * is greater than the requested bitrate, and decreasing it
- * otherwise. Reasonable ranges are from .9 to 1.0
- *
- * Until we get the above suggestion working, we use the following
- * tuning:
- * compression ratio res_factor
- * 5.5 (256kbps) 1.0 no need for bitreservoir
- * 11 (128kbps) .93 7% held for reservoir
- *
- * with linear interpolation for other values.
- *
- * Mode Extention:
- * When we are in stereo mode, there are 4 possible methods to store these
- * two channels. The stereo modes -m? are using a subset of them.
- *
- * -ms: MPG_MD_LR_LR
- * -mj: MPG_MD_LR_LR and MPG_MD_MS_LR
- * -mf: MPG_MD_MS_LR
- * -mi: all
- *
- * layer III enc->dec delay: 1056 (1057?) (observed)
- * layer II enc->dec delay: 480 (481?) (observed)
- *
- * polyphase 256-16 (dec or enc) = 240
- * mdct 256+32 (9*32) (dec or enc) = 288
- * total: 512+16
- *
- * My guess is that delay of polyphase filterbank is actualy 240.5
- * (there are technical reasons for this, see postings in mp3encoder).
- * So total Encode+Decode delay = ENCDELAY + 528 + 1
- *
- */
-
-
- /**
- * auto-adjust of ATH, useful for low volume Gabriel Bouvigne 3 feb 2001
- *
- * modifies some values in gfp.internal_flags.ATH (gfc.ATH)
- */
-//private void adjust_ATH(final LameInternalFlags gfc) {
- function adjust_ATH(gfc) {
- var gr2_max, max_pow;
-
- if (gfc.ATH.useAdjust == 0) {
- gfc.ATH.adjust = 1.0;
- /* no adjustment */
- return;
- }
-
- /* jd - 2001 mar 12, 27, jun 30 */
- /* loudness based on equal loudness curve; */
- /* use granule with maximum combined loudness */
- max_pow = gfc.loudness_sq[0][0];
- gr2_max = gfc.loudness_sq[1][0];
- if (gfc.channels_out == 2) {
- max_pow += gfc.loudness_sq[0][1];
- gr2_max += gfc.loudness_sq[1][1];
- } else {
- max_pow += max_pow;
- gr2_max += gr2_max;
- }
- if (gfc.mode_gr == 2) {
- max_pow = Math.max(max_pow, gr2_max);
- }
- max_pow *= 0.5;
- /* max_pow approaches 1.0 for full band noise */
-
- /* jd - 2001 mar 31, jun 30 */
- /* user tuning of ATH adjustment region */
- max_pow *= gfc.ATH.aaSensitivityP;
-
- /*
- * adjust ATH depending on range of maximum value
- */
-
- /* jd - 2001 feb27, mar12,20, jun30, jul22 */
- /* continuous curves based on approximation */
- /* to GB's original values. */
- /* For an increase in approximate loudness, */
- /* set ATH adjust to adjust_limit immediately */
- /* after a delay of one frame. */
- /* For a loudness decrease, reduce ATH adjust */
- /* towards adjust_limit gradually. */
- /* max_pow is a loudness squared or a power. */
- if (max_pow > 0.03125) { /* ((1 - 0.000625)/ 31.98) from curve below */
- if (gfc.ATH.adjust >= 1.0) {
- gfc.ATH.adjust = 1.0;
- } else {
- /* preceding frame has lower ATH adjust; */
- /* ascend only to the preceding adjust_limit */
- /* in case there is leading low volume */
- if (gfc.ATH.adjust < gfc.ATH.adjustLimit) {
- gfc.ATH.adjust = gfc.ATH.adjustLimit;
- }
- }
- gfc.ATH.adjustLimit = 1.0;
- } else { /* adjustment curve */
- /* about 32 dB maximum adjust (0.000625) */
- var adj_lim_new = 31.98 * max_pow + 0.000625;
- if (gfc.ATH.adjust >= adj_lim_new) { /* descend gradually */
- gfc.ATH.adjust *= adj_lim_new * 0.075 + 0.925;
- if (gfc.ATH.adjust < adj_lim_new) { /* stop descent */
- gfc.ATH.adjust = adj_lim_new;
- }
- } else { /* ascend */
- if (gfc.ATH.adjustLimit >= adj_lim_new) {
- gfc.ATH.adjust = adj_lim_new;
- } else {
- /* preceding frame has lower ATH adjust; */
- /* ascend only to the preceding adjust_limit */
- if (gfc.ATH.adjust < gfc.ATH.adjustLimit) {
- gfc.ATH.adjust = gfc.ATH.adjustLimit;
- }
- }
- }
- gfc.ATH.adjustLimit = adj_lim_new;
- }
- }
-
- /**
- *
- * some simple statistics
- *
- * bitrate index 0: free bitrate . not allowed in VBR mode
- * : bitrates, kbps depending on MPEG version
- * bitrate index 15: forbidden
- *
- * mode_ext:
- * 0: LR
- * 1: LR-i
- * 2: MS
- * 3: MS-i
- *
- */
- function updateStats(gfc) {
- var gr, ch;
-
- /* count bitrate indices */
- gfc.bitrate_stereoMode_Hist[gfc.bitrate_index][4]++;
- gfc.bitrate_stereoMode_Hist[15][4]++;
-
- /* count 'em for every mode extension in case of 2 channel encoding */
- if (gfc.channels_out == 2) {
- gfc.bitrate_stereoMode_Hist[gfc.bitrate_index][gfc.mode_ext]++;
- gfc.bitrate_stereoMode_Hist[15][gfc.mode_ext]++;
- }
- for (gr = 0; gr < gfc.mode_gr; ++gr) {
- for (ch = 0; ch < gfc.channels_out; ++ch) {
- var bt = gfc.l3_side.tt[gr][ch].block_type | 0;
- if (gfc.l3_side.tt[gr][ch].mixed_block_flag != 0)
- bt = 4;
- gfc.bitrate_blockType_Hist[gfc.bitrate_index][bt]++;
- gfc.bitrate_blockType_Hist[gfc.bitrate_index][5]++;
- gfc.bitrate_blockType_Hist[15][bt]++;
- gfc.bitrate_blockType_Hist[15][5]++;
- }
- }
- }
-
- function lame_encode_frame_init(gfp, inbuf) {
- var gfc = gfp.internal_flags;
-
- var ch, gr;
-
- if (gfc.lame_encode_frame_init == 0) {
- /* prime the MDCT/polyphase filterbank with a short block */
- var i, j;
- var primebuff0 = new_float(286 + 1152 + 576);
- var primebuff1 = new_float(286 + 1152 + 576);
- gfc.lame_encode_frame_init = 1;
- for (i = 0, j = 0; i < 286 + 576 * (1 + gfc.mode_gr); ++i) {
- if (i < 576 * gfc.mode_gr) {
- primebuff0[i] = 0;
- if (gfc.channels_out == 2)
- primebuff1[i] = 0;
- } else {
- primebuff0[i] = inbuf[0][j];
- if (gfc.channels_out == 2)
- primebuff1[i] = inbuf[1][j];
- ++j;
- }
- }
- /* polyphase filtering / mdct */
- for (gr = 0; gr < gfc.mode_gr; gr++) {
- for (ch = 0; ch < gfc.channels_out; ch++) {
- gfc.l3_side.tt[gr][ch].block_type = Encoder.SHORT_TYPE;
- }
- }
- newMDCT.mdct_sub48(gfc, primebuff0, primebuff1);
-
- /* check FFT will not use a negative starting offset */
- /* check if we have enough data for FFT */
- /* check if we have enough data for polyphase filterbank */
- }
-
- }
-
- /**
- *
- * encodeframe() Layer 3
- *
- * encode a single frame
- *
- *
- * lame_encode_frame()
- *
- *
- * gr 0 gr 1
- * inbuf: |--------------|--------------|--------------|
- *
- *
- * Polyphase (18 windows, each shifted 32)
- * gr 0:
- * window1 <----512---.
- * window18 <----512---.
- *
- * gr 1:
- * window1 <----512---.
- * window18 <----512---.
- *
- *
- *
- * MDCT output: |--------------|--------------|--------------|
- *
- * FFT's <---------1024---------.
- * <---------1024-------.
- *
- *
- *
- * inbuf = buffer of PCM data size=MP3 framesize
- * encoder acts on inbuf[ch][0], but output is delayed by MDCTDELAY
- * so the MDCT coefficints are from inbuf[ch][-MDCTDELAY]
- *
- * psy-model FFT has a 1 granule delay, so we feed it data for the
- * next granule.
- * FFT is centered over granule: 224+576+224
- * So FFT starts at: 576-224-MDCTDELAY
- *
- * MPEG2: FFT ends at: BLKSIZE+576-224-MDCTDELAY (1328)
- * MPEG1: FFT ends at: BLKSIZE+2*576-224-MDCTDELAY (1904)
- *
- * MPEG2: polyphase first window: [0..511]
- * 18th window: [544..1055] (1056)
- * MPEG1: 36th window: [1120..1631] (1632)
- * data needed: 512+framesize-32
- *
- * A close look newmdct.c shows that the polyphase filterbank
- * only uses data from [0..510] for each window. Perhaps because the window
- * used by the filterbank is zero for the last point, so Takehiro's
- * code doesn't bother to compute with it.
- *
- * FFT starts at 576-224-MDCTDELAY (304) = 576-FFTOFFSET
- *
- *
- */
-
-
- this.lame_encode_mp3_frame = function (gfp, inbuf_l, inbuf_r, mp3buf, mp3bufPos, mp3buf_size) {
- var mp3count;
- var masking_LR = new_array_n([2, 2]);
- /*
- * LR masking &
- * energy
- */
- masking_LR[0][0] = new III_psy_ratio();
- masking_LR[0][1] = new III_psy_ratio();
- masking_LR[1][0] = new III_psy_ratio();
- masking_LR[1][1] = new III_psy_ratio();
- var masking_MS = new_array_n([2, 2]);
- /* MS masking & energy */
- masking_MS[0][0] = new III_psy_ratio();
- masking_MS[0][1] = new III_psy_ratio();
- masking_MS[1][0] = new III_psy_ratio();
- masking_MS[1][1] = new III_psy_ratio();
- //III_psy_ratio masking[][];
- var masking;
- /* pointer to selected maskings */
- var inbuf = [null, null];
- var gfc = gfp.internal_flags;
-
- var tot_ener = new_float_n([2, 4]);
- var ms_ener_ratio = [.5, .5];
- var pe = [[0., 0.], [0., 0.]];
- var pe_MS = [[0., 0.], [0., 0.]];
-
-//float[][] pe_use;
- var pe_use;
-
- var ch, gr;
-
- inbuf[0] = inbuf_l;
- inbuf[1] = inbuf_r;
-
- if (gfc.lame_encode_frame_init == 0) {
- /* first run? */
- lame_encode_frame_init(gfp, inbuf);
-
- }
-
- /********************** padding *****************************/
- /**
- *
- * padding method as described in
- * "MPEG-Layer3 / Bitstream Syntax and Decoding"
- * by Martin Sieler, Ralph Sperschneider
- *
- * note: there is no padding for the very first frame
- *
- * Robert Hegemann 2000-06-22
- *
- */
- gfc.padding = 0;
- if ((gfc.slot_lag -= gfc.frac_SpF) < 0) {
- gfc.slot_lag += gfp.out_samplerate;
- gfc.padding = 1;
- }
-
- /****************************************
- * Stage 1: psychoacoustic model *
- ****************************************/
-
- if (gfc.psymodel != 0) {
- /*
- * psychoacoustic model psy model has a 1 granule (576) delay that
- * we must compensate for (mt 6/99).
- */
- var ret;
- var bufp = [null, null];
- /* address of beginning of left & right granule */
- var bufpPos = 0;
- /* address of beginning of left & right granule */
- var blocktype = new_int(2);
-
- for (gr = 0; gr < gfc.mode_gr; gr++) {
-
- for (ch = 0; ch < gfc.channels_out; ch++) {
- bufp[ch] = inbuf[ch];
- bufpPos = 576 + gr * 576 - Encoder.FFTOFFSET;
- }
- if (gfp.VBR == VbrMode.vbr_mtrh || gfp.VBR == VbrMode.vbr_mt) {
- ret = psy.L3psycho_anal_vbr(gfp, bufp, bufpPos, gr,
- masking_LR, masking_MS, pe[gr], pe_MS[gr],
- tot_ener[gr], blocktype);
- } else {
- ret = psy.L3psycho_anal_ns(gfp, bufp, bufpPos, gr,
- masking_LR, masking_MS, pe[gr], pe_MS[gr],
- tot_ener[gr], blocktype);
- }
- if (ret != 0)
- return -4;
-
- if (gfp.mode == MPEGMode.JOINT_STEREO) {
- ms_ener_ratio[gr] = tot_ener[gr][2] + tot_ener[gr][3];
- if (ms_ener_ratio[gr] > 0)
- ms_ener_ratio[gr] = tot_ener[gr][3] / ms_ener_ratio[gr];
- }
-
- /* block type flags */
- for (ch = 0; ch < gfc.channels_out; ch++) {
- var cod_info = gfc.l3_side.tt[gr][ch];
- cod_info.block_type = blocktype[ch];
- cod_info.mixed_block_flag = 0;
- }
- }
- } else {
- /* no psy model */
- for (gr = 0; gr < gfc.mode_gr; gr++)
- for (ch = 0; ch < gfc.channels_out; ch++) {
- gfc.l3_side.tt[gr][ch].block_type = Encoder.NORM_TYPE;
- gfc.l3_side.tt[gr][ch].mixed_block_flag = 0;
- pe_MS[gr][ch] = pe[gr][ch] = 700;
- }
- }
-
- /* auto-adjust of ATH, useful for low volume */
- adjust_ATH(gfc);
-
- /****************************************
- * Stage 2: MDCT *
- ****************************************/
-
- /* polyphase filtering / mdct */
- newMDCT.mdct_sub48(gfc, inbuf[0], inbuf[1]);
-
- /****************************************
- * Stage 3: MS/LR decision *
- ****************************************/
-
- /* Here will be selected MS or LR coding of the 2 stereo channels */
- gfc.mode_ext = Encoder.MPG_MD_LR_LR;
-
- if (gfp.force_ms) {
- gfc.mode_ext = Encoder.MPG_MD_MS_LR;
- } else if (gfp.mode == MPEGMode.JOINT_STEREO) {
- /*
- * ms_ratio = is scaled, for historical reasons, to look like a
- * ratio of side_channel / total. 0 = signal is 100% mono .5 = L & R
- * uncorrelated
- */
-
- /**
- *
- * [0] and [1] are the results for the two granules in MPEG-1,
- * in MPEG-2 it's only a faked averaging of the same value
- * _prev is the value of the last granule of the previous frame
- * _next is the value of the first granule of the next frame
- *
- */
-
- var sum_pe_MS = 0.;
- var sum_pe_LR = 0.;
- for (gr = 0; gr < gfc.mode_gr; gr++) {
- for (ch = 0; ch < gfc.channels_out; ch++) {
- sum_pe_MS += pe_MS[gr][ch];
- sum_pe_LR += pe[gr][ch];
- }
- }
-
- /* based on PE: M/S coding would not use much more bits than L/R */
- if (sum_pe_MS <= 1.00 * sum_pe_LR) {
-
- var gi0 = gfc.l3_side.tt[0];
- var gi1 = gfc.l3_side.tt[gfc.mode_gr - 1];
-
- if (gi0[0].block_type == gi0[1].block_type
- && gi1[0].block_type == gi1[1].block_type) {
-
- gfc.mode_ext = Encoder.MPG_MD_MS_LR;
- }
- }
- }
-
- /* bit and noise allocation */
- if (gfc.mode_ext == MPG_MD_MS_LR) {
- masking = masking_MS;
- /* use MS masking */
- pe_use = pe_MS;
- } else {
- masking = masking_LR;
- /* use LR masking */
- pe_use = pe;
- }
-
- /* copy data for MP3 frame analyzer */
- if (gfp.analysis && gfc.pinfo != null) {
- for (gr = 0; gr < gfc.mode_gr; gr++) {
- for (ch = 0; ch < gfc.channels_out; ch++) {
- gfc.pinfo.ms_ratio[gr] = gfc.ms_ratio[gr];
- gfc.pinfo.ms_ener_ratio[gr] = ms_ener_ratio[gr];
- gfc.pinfo.blocktype[gr][ch] = gfc.l3_side.tt[gr][ch].block_type;
- gfc.pinfo.pe[gr][ch] = pe_use[gr][ch];
- System.arraycopy(gfc.l3_side.tt[gr][ch].xr, 0,
- gfc.pinfo.xr[gr][ch], 0, 576);
- /*
- * in psymodel, LR and MS data was stored in pinfo. switch
- * to MS data:
- */
- if (gfc.mode_ext == MPG_MD_MS_LR) {
- gfc.pinfo.ers[gr][ch] = gfc.pinfo.ers[gr][ch + 2];
- System.arraycopy(gfc.pinfo.energy[gr][ch + 2], 0,
- gfc.pinfo.energy[gr][ch], 0,
- gfc.pinfo.energy[gr][ch].length);
- }
- }
- }
- }
-
- /****************************************
- * Stage 4: quantization loop *
- ****************************************/
-
- if (gfp.VBR == VbrMode.vbr_off || gfp.VBR == VbrMode.vbr_abr) {
-
- var i;
- var f;
-
- for (i = 0; i < 18; i++)
- gfc.nsPsy.pefirbuf[i] = gfc.nsPsy.pefirbuf[i + 1];
-
- f = 0.0;
- for (gr = 0; gr < gfc.mode_gr; gr++)
- for (ch = 0; ch < gfc.channels_out; ch++)
- f += pe_use[gr][ch];
- gfc.nsPsy.pefirbuf[18] = f;
-
- f = gfc.nsPsy.pefirbuf[9];
- for (i = 0; i < 9; i++)
- f += (gfc.nsPsy.pefirbuf[i] + gfc.nsPsy.pefirbuf[18 - i])
- * Encoder.fircoef[i];
-
- f = (670 * 5 * gfc.mode_gr * gfc.channels_out) / f;
- for (gr = 0; gr < gfc.mode_gr; gr++) {
- for (ch = 0; ch < gfc.channels_out; ch++) {
- pe_use[gr][ch] *= f;
- }
- }
- }
- gfc.iteration_loop.iteration_loop(gfp, pe_use, ms_ener_ratio, masking);
-
- /****************************************
- * Stage 5: bitstream formatting *
- ****************************************/
-
- /* write the frame to the bitstream */
- bs.format_bitstream(gfp);
-
- /* copy mp3 bit buffer into array */
- mp3count = bs.copy_buffer(gfc, mp3buf, mp3bufPos, mp3buf_size, 1);
-
- if (gfp.bWriteVbrTag)
- vbr.addVbrFrame(gfp);
-
- if (gfp.analysis && gfc.pinfo != null) {
- for (ch = 0; ch < gfc.channels_out; ch++) {
- var j;
- for (j = 0; j < FFTOFFSET; j++)
- gfc.pinfo.pcmdata[ch][j] = gfc.pinfo.pcmdata[ch][j
- + gfp.framesize];
- for (j = FFTOFFSET; j < 1600; j++) {
- gfc.pinfo.pcmdata[ch][j] = inbuf[ch][j - FFTOFFSET];
- }
- }
- qupvt.set_frame_pinfo(gfp, masking);
- }
-
- updateStats(gfc);
-
- return mp3count;
- }
-}
-
-
-//package mp3;
-
-function VBRSeekInfo() {
- /**
- * What we have seen so far.
- */
- this.sum = 0;
- /**
- * How many frames we have seen in this chunk.
- */
- this.seen = 0;
- /**
- * How many frames we want to collect into one chunk.
- */
- this.want = 0;
- /**
- * Actual position in our bag.
- */
- this.pos = 0;
- /**
- * Size of our bag.
- */
- this.size = 0;
- /**
- * Pointer to our bag.
- */
- this.bag = null;
- this.nVbrNumFrames = 0;
- this.nBytesWritten = 0;
- /* VBR tag data */
- this.TotalFrameSize = 0;
-}
-
-
-
-function IIISideInfo() {
- this.tt = [[null, null], [null, null]];
- this.main_data_begin = 0;
- this.private_bits = 0;
- this.resvDrain_pre = 0;
- this.resvDrain_post = 0;
- this.scfsi = [new_int(4), new_int(4)];
-
- for (var gr = 0; gr < 2; gr++) {
- for (var ch = 0; ch < 2; ch++) {
- this.tt[gr][ch] = new GrInfo();
- }
- }
-}
-
-
-function III_psy_xmin() {
- this.l = new_float(Encoder.SBMAX_l);
- this.s = new_float_n([Encoder.SBMAX_s, 3]);
-
- var self = this;
- this.assign = function (iii_psy_xmin) {
- System.arraycopy(iii_psy_xmin.l, 0, self.l, 0, Encoder.SBMAX_l);
- for (var i = 0; i < Encoder.SBMAX_s; i++) {
- for (var j = 0; j < 3; j++) {
- self.s[i][j] = iii_psy_xmin.s[i][j];
- }
- }
- }
-}
-
-
-
-//package mp3;
-
-/**
- * Variables used for --nspsytune
- *
- * @author Ken
- *
- */
-function NsPsy() {
- this.last_en_subshort = new_float_n([4, 9]);
- this.lastAttacks = new_int(4);
- this.pefirbuf = new_float(19);
- this.longfact = new_float(Encoder.SBMAX_l);
- this.shortfact = new_float(Encoder.SBMAX_s);
-
- /**
- * short block tuning
- */
- this.attackthre = 0.;
- this.attackthre_s = 0.;
-}
-
-
-
-
-LameInternalFlags.MFSIZE = (3 * 1152 + Encoder.ENCDELAY - Encoder.MDCTDELAY);
-LameInternalFlags.MAX_HEADER_BUF = 256;
-LameInternalFlags.MAX_BITS_PER_CHANNEL = 4095;
-LameInternalFlags.MAX_BITS_PER_GRANULE = 7680;
-LameInternalFlags.BPC = 320;
-
-function LameInternalFlags() {
- var MAX_HEADER_LEN = 40;
-
-
- /********************************************************************
- * internal variables NOT set by calling program, and should not be *
- * modified by the calling program *
- ********************************************************************/
-
- /**
- * Some remarks to the Class_ID field: The Class ID is an Identifier for a
- * pointer to this struct. It is very unlikely that a pointer to
- * lame_global_flags has the same 32 bits in it's structure (large and other
- * special properties, for instance prime).
- *
- * To test that the structure is right and initialized, use: if ( gfc .
- * Class_ID == LAME_ID ) ... Other remark: If you set a flag to 0 for uninit
- * data and 1 for init data, the right test should be "if (flag == 1)" and
- * NOT "if (flag)". Unintended modification of this element will be
- * otherwise misinterpreted as an init.
- */
- this.Class_ID = 0;
-
- this.lame_encode_frame_init = 0;
- this.iteration_init_init = 0;
- this.fill_buffer_resample_init = 0;
-
- //public float mfbuf[][] = new float[2][MFSIZE];
- this.mfbuf = new_float_n([2, LameInternalFlags.MFSIZE]);
-
- /**
- * granules per frame
- */
- this.mode_gr = 0;
- /**
- * number of channels in the input data stream (PCM or decoded PCM)
- */
- this.channels_in = 0;
- /**
- * number of channels in the output data stream (not used for decoding)
- */
- this.channels_out = 0;
- /**
- * input_samp_rate/output_samp_rate
- */
- //public double resample_ratio;
- this.resample_ratio = 0.;
-
- this.mf_samples_to_encode = 0;
- this.mf_size = 0;
- /**
- * min bitrate index
- */
- this.VBR_min_bitrate = 0;
- /**
- * max bitrate index
- */
- this.VBR_max_bitrate = 0;
- this.bitrate_index = 0;
- this.samplerate_index = 0;
- this.mode_ext = 0;
-
- /* lowpass and highpass filter control */
- /**
- * normalized frequency bounds of passband
- */
- this.lowpass1 = 0.;
- this.lowpass2 = 0.;
- /**
- * normalized frequency bounds of passband
- */
- this.highpass1 = 0.;
- this.highpass2 = 0.;
-
- /**
- * 0 = none 1 = ISO AAC model 2 = allow scalefac_select=1
- */
- this.noise_shaping = 0;
-
- /**
- * 0 = ISO model: amplify all distorted bands
- * 1 = amplify within 50% of max (on db scale)
- * 2 = amplify only most distorted band
- * 3 = method 1 and refine with method 2
- */
- this.noise_shaping_amp = 0;
- /**
- * 0 = no substep
- * 1 = use substep shaping at last step(VBR only)
- * (not implemented yet)
- * 2 = use substep inside loop
- * 3 = use substep inside loop and last step
- */
- this.substep_shaping = 0;
-
- /**
- * 1 = gpsycho. 0 = none
- */
- this.psymodel = 0;
- /**
- * 0 = stop at over=0, all scalefacs amplified or
- * a scalefac has reached max value
- * 1 = stop when all scalefacs amplified or a scalefac has reached max value
- * 2 = stop when all scalefacs amplified
- */
- this.noise_shaping_stop = 0;
-
- /**
- * 0 = no, 1 = yes
- */
- this.subblock_gain = 0;
- /**
- * 0 = no. 1=outside loop 2=inside loop(slow)
- */
- this.use_best_huffman = 0;
-
- /**
- * 0 = stop early after 0 distortion found. 1 = full search
- */
- this.full_outer_loop = 0;
-
- //public IIISideInfo l3_side = new IIISideInfo();
- this.l3_side = new IIISideInfo();
- this.ms_ratio = new_float(2);
-
- /* used for padding */
- /**
- * padding for the current frame?
- */
- this.padding = 0;
- this.frac_SpF = 0;
- this.slot_lag = 0;
-
- /**
- * optional ID3 tags
- */
- //public ID3TagSpec tag_spec;
- this.tag_spec = null;
- this.nMusicCRC = 0;
-
- /* variables used by Quantize */
- //public int OldValue[] = new int[2];
- this.OldValue = new_int(2);
- //public int CurrentStep[] = new int[2];
- this.CurrentStep = new_int(2);
-
- this.masking_lower = 0.;
- //public int bv_scf[] = new int[576];
- this.bv_scf = new_int(576);
- //public int pseudohalf[] = new int[L3Side.SFBMAX];
- this.pseudohalf = new_int(L3Side.SFBMAX);
-
- /**
- * will be set in lame_init_params
- */
- this.sfb21_extra = false;
-
- /* BPC = maximum number of filter convolution windows to precompute */
- //public float[][] inbuf_old = new float[2][];
- this.inbuf_old = new Array(2);
- //public float[][] blackfilt = new float[2 * BPC + 1][];
- this.blackfilt = new Array(2 * LameInternalFlags.BPC + 1);
- //public double itime[] = new double[2];
- this.itime = new_double(2);
- this.sideinfo_len = 0;
-
- /* variables for newmdct.c */
- //public float sb_sample[][][][] = new float[2][2][18][Encoder.SBLIMIT];
- this.sb_sample = new_float_n([2, 2, 18, Encoder.SBLIMIT]);
- this.amp_filter = new_float(32);
-
- /* variables for BitStream */
-
- /**
- *
- * mpeg1: buffer=511 bytes smallest frame: 96-38(sideinfo)=58
- * max number of frames in reservoir: 8
- * mpeg2: buffer=255 bytes. smallest frame: 24-23bytes=1
- * with VBR, if you are encoding all silence, it is possible to
- * have 8kbs/24khz frames with 1byte of data each, which means we need
- * to buffer up to 255 headers!
- *
- */
- /**
- * also, max_header_buf has to be a power of two
- */
- /**
- * max size of header is 38
- */
-
- function Header() {
- this.write_timing = 0;
- this.ptr = 0;
- //public byte buf[] = new byte[MAX_HEADER_LEN];
- this.buf = new_byte(MAX_HEADER_LEN);
- }
-
- this.header = new Array(LameInternalFlags.MAX_HEADER_BUF);
-
- this.h_ptr = 0;
- this.w_ptr = 0;
- this.ancillary_flag = 0;
-
- /* variables for Reservoir */
- /**
- * in bits
- */
- this.ResvSize = 0;
- /**
- * in bits
- */
- this.ResvMax = 0;
-
- //public ScaleFac scalefac_band = new ScaleFac();
- this.scalefac_band = new ScaleFac();
-
- /* daa from PsyModel */
- /* The static variables "r", "phi_sav", "new", "old" and "oldest" have */
- /* to be remembered for the unpredictability measure. For "r" and */
- /* "phi_sav", the first index from the left is the channel select and */
- /* the second index is the "age" of the data. */
- this.minval_l = new_float(Encoder.CBANDS);
- this.minval_s = new_float(Encoder.CBANDS);
- this.nb_1 = new_float_n([4, Encoder.CBANDS]);
- this.nb_2 = new_float_n([4, Encoder.CBANDS]);
- this.nb_s1 = new_float_n([4, Encoder.CBANDS]);
- this.nb_s2 = new_float_n([4, Encoder.CBANDS]);
- this.s3_ss = null;
- this.s3_ll = null;
- this.decay = 0.;
-
- //public III_psy_xmin[] thm = new III_psy_xmin[4];
- //public III_psy_xmin[] en = new III_psy_xmin[4];
- this.thm = new Array(4);
- this.en = new Array(4);
-
- /**
- * fft and energy calculation
- */
- this.tot_ener = new_float(4);
-
- /* loudness calculation (for adaptive threshold of hearing) */
- /**
- * loudness^2 approx. per granule and channel
- */
- this.loudness_sq = new_float_n([2, 2]);
- /**
- * account for granule delay of L3psycho_anal
- */
- this.loudness_sq_save = new_float(2);
-
- /**
- * Scale Factor Bands
- */
- this.mld_l = new_float(Encoder.SBMAX_l);
- this.mld_s = new_float(Encoder.SBMAX_s);
- this.bm_l = new_int(Encoder.SBMAX_l);
- this.bo_l = new_int(Encoder.SBMAX_l);
- this.bm_s = new_int(Encoder.SBMAX_s);
- this.bo_s = new_int(Encoder.SBMAX_s);
- this.npart_l = 0;
- this.npart_s = 0;
-
- this.s3ind = new_int_n([Encoder.CBANDS, 2]);
- this.s3ind_s = new_int_n([Encoder.CBANDS, 2]);
-
- this.numlines_s = new_int(Encoder.CBANDS);
- this.numlines_l = new_int(Encoder.CBANDS);
- this.rnumlines_l = new_float(Encoder.CBANDS);
- this.mld_cb_l = new_float(Encoder.CBANDS);
- this.mld_cb_s = new_float(Encoder.CBANDS);
- this.numlines_s_num1 = 0;
- this.numlines_l_num1 = 0;
-
- /* ratios */
- this.pe = new_float(4);
- this.ms_ratio_s_old = 0.;
- this.ms_ratio_l_old = 0.;
- this.ms_ener_ratio_old = 0.;
-
- /**
- * block type
- */
- this.blocktype_old = new_int(2);
-
- /**
- * variables used for --nspsytune
- */
- this.nsPsy = new NsPsy();
-
- /**
- * used for Xing VBR header
- */
- this.VBR_seek_table = new VBRSeekInfo();
-
- /**
- * all ATH related stuff
- */
- //public ATH ATH;
- this.ATH = null;
-
- this.PSY = null;
-
- this.nogap_total = 0;
- this.nogap_current = 0;
-
- /* ReplayGain */
- this.decode_on_the_fly = true;
- this.findReplayGain = true;
- this.findPeakSample = true;
- this.PeakSample = 0.;
- this.RadioGain = 0;
- this.AudiophileGain = 0;
- //public ReplayGain rgdata;
- this.rgdata = null;
-
- /**
- * gain change required for preventing clipping
- */
- this.noclipGainChange = 0;
- /**
- * user-specified scale factor required for preventing clipping
- */
- this.noclipScale = 0.;
-
- /* simple statistics */
- this.bitrate_stereoMode_Hist = new_int_n([16, 4 + 1]);
- /**
- * norm/start/short/stop/mixed(short)/sum
- */
- this.bitrate_blockType_Hist = new_int_n([16, 4 + 1 + 1]);
-
- //public PlottingData pinfo;
- //public MPGLib.mpstr_tag hip;
- this.pinfo = null;
- this.hip = null;
-
- this.in_buffer_nsamples = 0;
- //public float[] in_buffer_0;
- //public float[] in_buffer_1;
- this.in_buffer_0 = null;
- this.in_buffer_1 = null;
-
- //public IIterationLoop iteration_loop;
- this.iteration_loop = null;
-
- for (var i = 0; i < this.en.length; i++) {
- this.en[i] = new III_psy_xmin();
- }
- for (var i = 0; i < this.thm.length; i++) {
- this.thm[i] = new III_psy_xmin();
- }
- for (var i = 0; i < this.header.length; i++) {
- this.header[i] = new Header();
- }
-
-}
-
-
-
-function FFT() {
-
- var window = new_float(Encoder.BLKSIZE);
- var window_s = new_float(Encoder.BLKSIZE_s / 2);
-
- var costab = [
- 9.238795325112867e-01, 3.826834323650898e-01,
- 9.951847266721969e-01, 9.801714032956060e-02,
- 9.996988186962042e-01, 2.454122852291229e-02,
- 9.999811752826011e-01, 6.135884649154475e-03
- ];
-
- function fht(fz, fzPos, n) {
- var tri = 0;
- var k4;
- var fi;
- var gi;
-
- n <<= 1;
- /* to get BLKSIZE, because of 3DNow! ASM routine */
- var fn = fzPos + n;
- k4 = 4;
- do {
- var s1, c1;
- var i, k1, k2, k3, kx;
- kx = k4 >> 1;
- k1 = k4;
- k2 = k4 << 1;
- k3 = k2 + k1;
- k4 = k2 << 1;
- fi = fzPos;
- gi = fi + kx;
- do {
- var f0, f1, f2, f3;
- f1 = fz[fi + 0] - fz[fi + k1];
- f0 = fz[fi + 0] + fz[fi + k1];
- f3 = fz[fi + k2] - fz[fi + k3];
- f2 = fz[fi + k2] + fz[fi + k3];
- fz[fi + k2] = f0 - f2;
- fz[fi + 0] = f0 + f2;
- fz[fi + k3] = f1 - f3;
- fz[fi + k1] = f1 + f3;
- f1 = fz[gi + 0] - fz[gi + k1];
- f0 = fz[gi + 0] + fz[gi + k1];
- f3 = (Util.SQRT2 * fz[gi + k3]);
- f2 = (Util.SQRT2 * fz[gi + k2]);
- fz[gi + k2] = f0 - f2;
- fz[gi + 0] = f0 + f2;
- fz[gi + k3] = f1 - f3;
- fz[gi + k1] = f1 + f3;
- gi += k4;
- fi += k4;
- } while (fi < fn);
- c1 = costab[tri + 0];
- s1 = costab[tri + 1];
- for (i = 1; i < kx; i++) {
- var c2, s2;
- c2 = 1 - (2 * s1) * s1;
- s2 = (2 * s1) * c1;
- fi = fzPos + i;
- gi = fzPos + k1 - i;
- do {
- var a, b, g0, f0, f1, g1, f2, g2, f3, g3;
- b = s2 * fz[fi + k1] - c2 * fz[gi + k1];
- a = c2 * fz[fi + k1] + s2 * fz[gi + k1];
- f1 = fz[fi + 0] - a;
- f0 = fz[fi + 0] + a;
- g1 = fz[gi + 0] - b;
- g0 = fz[gi + 0] + b;
- b = s2 * fz[fi + k3] - c2 * fz[gi + k3];
- a = c2 * fz[fi + k3] + s2 * fz[gi + k3];
- f3 = fz[fi + k2] - a;
- f2 = fz[fi + k2] + a;
- g3 = fz[gi + k2] - b;
- g2 = fz[gi + k2] + b;
- b = s1 * f2 - c1 * g3;
- a = c1 * f2 + s1 * g3;
- fz[fi + k2] = f0 - a;
- fz[fi + 0] = f0 + a;
- fz[gi + k3] = g1 - b;
- fz[gi + k1] = g1 + b;
- b = c1 * g2 - s1 * f3;
- a = s1 * g2 + c1 * f3;
- fz[gi + k2] = g0 - a;
- fz[gi + 0] = g0 + a;
- fz[fi + k3] = f1 - b;
- fz[fi + k1] = f1 + b;
- gi += k4;
- fi += k4;
- } while (fi < fn);
- c2 = c1;
- c1 = c2 * costab[tri + 0] - s1 * costab[tri + 1];
- s1 = c2 * costab[tri + 1] + s1 * costab[tri + 0];
- }
- tri += 2;
- } while (k4 < n);
- }
-
- var rv_tbl = [0x00, 0x80, 0x40,
- 0xc0, 0x20, 0xa0, 0x60, 0xe0, 0x10,
- 0x90, 0x50, 0xd0, 0x30, 0xb0, 0x70,
- 0xf0, 0x08, 0x88, 0x48, 0xc8, 0x28,
- 0xa8, 0x68, 0xe8, 0x18, 0x98, 0x58,
- 0xd8, 0x38, 0xb8, 0x78, 0xf8, 0x04,
- 0x84, 0x44, 0xc4, 0x24, 0xa4, 0x64,
- 0xe4, 0x14, 0x94, 0x54, 0xd4, 0x34,
- 0xb4, 0x74, 0xf4, 0x0c, 0x8c, 0x4c,
- 0xcc, 0x2c, 0xac, 0x6c, 0xec, 0x1c,
- 0x9c, 0x5c, 0xdc, 0x3c, 0xbc, 0x7c,
- 0xfc, 0x02, 0x82, 0x42, 0xc2, 0x22,
- 0xa2, 0x62, 0xe2, 0x12, 0x92, 0x52,
- 0xd2, 0x32, 0xb2, 0x72, 0xf2, 0x0a,
- 0x8a, 0x4a, 0xca, 0x2a, 0xaa, 0x6a,
- 0xea, 0x1a, 0x9a, 0x5a, 0xda, 0x3a,
- 0xba, 0x7a, 0xfa, 0x06, 0x86, 0x46,
- 0xc6, 0x26, 0xa6, 0x66, 0xe6, 0x16,
- 0x96, 0x56, 0xd6, 0x36, 0xb6, 0x76,
- 0xf6, 0x0e, 0x8e, 0x4e, 0xce, 0x2e,
- 0xae, 0x6e, 0xee, 0x1e, 0x9e, 0x5e,
- 0xde, 0x3e, 0xbe, 0x7e, 0xfe];
-
- this.fft_short = function (gfc, x_real, chn, buffer, bufPos) {
- for (var b = 0; b < 3; b++) {
- var x = Encoder.BLKSIZE_s / 2;
- var k = 0xffff & ((576 / 3) * (b + 1));
- var j = Encoder.BLKSIZE_s / 8 - 1;
- do {
- var f0, f1, f2, f3, w;
- var i = rv_tbl[j << 2] & 0xff;
-
- f0 = window_s[i] * buffer[chn][bufPos + i + k];
- w = window_s[0x7f - i] * buffer[chn][bufPos + i + k + 0x80];
- f1 = f0 - w;
- f0 = f0 + w;
- f2 = window_s[i + 0x40] * buffer[chn][bufPos + i + k + 0x40];
- w = window_s[0x3f - i] * buffer[chn][bufPos + i + k + 0xc0];
- f3 = f2 - w;
- f2 = f2 + w;
-
- x -= 4;
- x_real[b][x + 0] = f0 + f2;
- x_real[b][x + 2] = f0 - f2;
- x_real[b][x + 1] = f1 + f3;
- x_real[b][x + 3] = f1 - f3;
-
- f0 = window_s[i + 0x01] * buffer[chn][bufPos + i + k + 0x01];
- w = window_s[0x7e - i] * buffer[chn][bufPos + i + k + 0x81];
- f1 = f0 - w;
- f0 = f0 + w;
- f2 = window_s[i + 0x41] * buffer[chn][bufPos + i + k + 0x41];
- w = window_s[0x3e - i] * buffer[chn][bufPos + i + k + 0xc1];
- f3 = f2 - w;
- f2 = f2 + w;
-
- x_real[b][x + Encoder.BLKSIZE_s / 2 + 0] = f0 + f2;
- x_real[b][x + Encoder.BLKSIZE_s / 2 + 2] = f0 - f2;
- x_real[b][x + Encoder.BLKSIZE_s / 2 + 1] = f1 + f3;
- x_real[b][x + Encoder.BLKSIZE_s / 2 + 3] = f1 - f3;
- } while (--j >= 0);
-
- fht(x_real[b], x, Encoder.BLKSIZE_s / 2);
- /* BLKSIZE_s/2 because of 3DNow! ASM routine */
- /* BLKSIZE/2 because of 3DNow! ASM routine */
- }
- }
-
- this.fft_long = function (gfc, y, chn, buffer, bufPos) {
- var jj = Encoder.BLKSIZE / 8 - 1;
- var x = Encoder.BLKSIZE / 2;
-
- do {
- var f0, f1, f2, f3, w;
- var i = rv_tbl[jj] & 0xff;
- f0 = window[i] * buffer[chn][bufPos + i];
- w = window[i + 0x200] * buffer[chn][bufPos + i + 0x200];
- f1 = f0 - w;
- f0 = f0 + w;
- f2 = window[i + 0x100] * buffer[chn][bufPos + i + 0x100];
- w = window[i + 0x300] * buffer[chn][bufPos + i + 0x300];
- f3 = f2 - w;
- f2 = f2 + w;
-
- x -= 4;
- y[x + 0] = f0 + f2;
- y[x + 2] = f0 - f2;
- y[x + 1] = f1 + f3;
- y[x + 3] = f1 - f3;
-
- f0 = window[i + 0x001] * buffer[chn][bufPos + i + 0x001];
- w = window[i + 0x201] * buffer[chn][bufPos + i + 0x201];
- f1 = f0 - w;
- f0 = f0 + w;
- f2 = window[i + 0x101] * buffer[chn][bufPos + i + 0x101];
- w = window[i + 0x301] * buffer[chn][bufPos + i + 0x301];
- f3 = f2 - w;
- f2 = f2 + w;
-
- y[x + Encoder.BLKSIZE / 2 + 0] = f0 + f2;
- y[x + Encoder.BLKSIZE / 2 + 2] = f0 - f2;
- y[x + Encoder.BLKSIZE / 2 + 1] = f1 + f3;
- y[x + Encoder.BLKSIZE / 2 + 3] = f1 - f3;
- } while (--jj >= 0);
-
- fht(y, x, Encoder.BLKSIZE / 2);
- /* BLKSIZE/2 because of 3DNow! ASM routine */
- }
-
- this.init_fft = function (gfc) {
- /* The type of window used here will make no real difference, but */
- /*
- * in the interest of merging nspsytune stuff - switch to blackman
- * window
- */
- for (var i = 0; i < Encoder.BLKSIZE; i++)
- /* blackman window */
- window[i] = (0.42 - 0.5 * Math.cos(2 * Math.PI * (i + .5)
- / Encoder.BLKSIZE) + 0.08 * Math.cos(4 * Math.PI * (i + .5)
- / Encoder.BLKSIZE));
-
- for (var i = 0; i < Encoder.BLKSIZE_s / 2; i++)
- window_s[i] = (0.5 * (1.0 - Math.cos(2.0 * Math.PI
- * (i + 0.5) / Encoder.BLKSIZE_s)));
-
- }
-
-}
-
-/*
- * psymodel.c
- *
- * Copyright (c) 1999-2000 Mark Taylor
- * Copyright (c) 2001-2002 Naoki Shibata
- * Copyright (c) 2000-2003 Takehiro Tominaga
- * Copyright (c) 2000-2008 Robert Hegemann
- * Copyright (c) 2000-2005 Gabriel Bouvigne
- * Copyright (c) 2000-2005 Alexander Leidinger
- *
- * This library is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2 of the License, or (at your option) any later version.
- *
- * This library is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Library General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with this library; if not, write to the
- * Free Software Foundation, Inc., 59 Temple Place - Suite 330,
- * Boston, MA 02111-1307, USA.
- */
-
-/* $Id: PsyModel.java,v 1.27 2011/05/24 20:48:06 kenchis Exp $ */
-
-
-/*
- PSYCHO ACOUSTICS
-
-
- This routine computes the psycho acoustics, delayed by one granule.
-
- Input: buffer of PCM data (1024 samples).
-
- This window should be centered over the 576 sample granule window.
- The routine will compute the psycho acoustics for
- this granule, but return the psycho acoustics computed
- for the *previous* granule. This is because the block
- type of the previous granule can only be determined
- after we have computed the psycho acoustics for the following
- granule.
-
- Output: maskings and energies for each scalefactor band.
- block type, PE, and some correlation measures.
- The PE is used by CBR modes to determine if extra bits
- from the bit reservoir should be used. The correlation
- measures are used to determine mid/side or regular stereo.
- */
-/*
- Notation:
-
- barks: a non-linear frequency scale. Mapping from frequency to
- barks is given by freq2bark()
-
- scalefactor bands: The spectrum (frequencies) are broken into
- SBMAX "scalefactor bands". Thes bands
- are determined by the MPEG ISO spec. In
- the noise shaping/quantization code, we allocate
- bits among the partition bands to achieve the
- best possible quality
-
- partition bands: The spectrum is also broken into about
- 64 "partition bands". Each partition
- band is about .34 barks wide. There are about 2-5
- partition bands for each scalefactor band.
-
- LAME computes all psycho acoustic information for each partition
- band. Then at the end of the computations, this information
- is mapped to scalefactor bands. The energy in each scalefactor
- band is taken as the sum of the energy in all partition bands
- which overlap the scalefactor band. The maskings can be computed
- in the same way (and thus represent the average masking in that band)
- or by taking the minmum value multiplied by the number of
- partition bands used (which represents a minimum masking in that band).
- */
-/*
- The general outline is as follows:
-
- 1. compute the energy in each partition band
- 2. compute the tonality in each partition band
- 3. compute the strength of each partion band "masker"
- 4. compute the masking (via the spreading function applied to each masker)
- 5. Modifications for mid/side masking.
-
- Each partition band is considiered a "masker". The strength
- of the i'th masker in band j is given by:
-
- s3(bark(i)-bark(j))*strength(i)
-
- The strength of the masker is a function of the energy and tonality.
- The more tonal, the less masking. LAME uses a simple linear formula
- (controlled by NMT and TMN) which says the strength is given by the
- energy divided by a linear function of the tonality.
- */
-/*
- s3() is the "spreading function". It is given by a formula
- determined via listening tests.
-
- The total masking in the j'th partition band is the sum over
- all maskings i. It is thus given by the convolution of
- the strength with s3(), the "spreading function."
-
- masking(j) = sum_over_i s3(i-j)*strength(i) = s3 o strength
-
- where "o" = convolution operator. s3 is given by a formula determined
- via listening tests. It is normalized so that s3 o 1 = 1.
-
- Note: instead of a simple convolution, LAME also has the
- option of using "additive masking"
-
- The most critical part is step 2, computing the tonality of each
- partition band. LAME has two tonality estimators. The first
- is based on the ISO spec, and measures how predictiable the
- signal is over time. The more predictable, the more tonal.
- The second measure is based on looking at the spectrum of
- a single granule. The more peaky the spectrum, the more
- tonal. By most indications, the latter approach is better.
-
- Finally, in step 5, the maskings for the mid and side
- channel are possibly increased. Under certain circumstances,
- noise in the mid & side channels is assumed to also
- be masked by strong maskers in the L or R channels.
-
-
- Other data computed by the psy-model:
-
- ms_ratio side-channel / mid-channel masking ratio (for previous granule)
- ms_ratio_next side-channel / mid-channel masking ratio for this granule
-
- percep_entropy[2] L and R values (prev granule) of PE - A measure of how
- much pre-echo is in the previous granule
- percep_entropy_MS[2] mid and side channel values (prev granule) of percep_entropy
- energy[4] L,R,M,S energy in each channel, prev granule
- blocktype_d[2] block type to use for previous granule
- */
-//package mp3;
-
-//import java.util.Arrays;
-
-
-function PsyModel() {
-
- var fft = new FFT();
-
- var LOG10 = 2.30258509299404568402;
-
- var rpelev = 2;
- var rpelev2 = 16;
- var rpelev_s = 2;
- var rpelev2_s = 16;
-
- /* size of each partition band, in barks: */
- var DELBARK = .34;
-
- /* tuned for output level (sensitive to energy scale) */
- var VO_SCALE = (1. / (14752 * 14752) / (Encoder.BLKSIZE / 2));
-
- var temporalmask_sustain_sec = 0.01;
-
- var NS_PREECHO_ATT0 = 0.8;
- var NS_PREECHO_ATT1 = 0.6;
- var NS_PREECHO_ATT2 = 0.3;
-
- var NS_MSFIX = 3.5;
-
- var NSATTACKTHRE = 4.4;
- var NSATTACKTHRE_S = 25;
-
- var NSFIRLEN = 21;
-
- /* size of each partition band, in barks: */
- var LN_TO_LOG10 = 0.2302585093;
-
- function NON_LINEAR_SCALE_ENERGY(x) {
- return x;
- }
-
- /**
- *
- * L3psycho_anal. Compute psycho acoustics.
- *
- * Data returned to the calling program must be delayed by one
- * granule.
- *
- * This is done in two places.
- * If we do not need to know the blocktype, the copying
- * can be done here at the top of the program: we copy the data for
- * the last granule (computed during the last call) before it is
- * overwritten with the new data. It looks like this:
- *
- * 0. static psymodel_data
- * 1. calling_program_data = psymodel_data
- * 2. compute psymodel_data
- *
- * For data which needs to know the blocktype, the copying must be
- * done at the end of this loop, and the old values must be saved:
- *
- * 0. static psymodel_data_old
- * 1. compute psymodel_data
- * 2. compute possible block type of this granule
- * 3. compute final block type of previous granule based on #2.
- * 4. calling_program_data = psymodel_data_old
- * 5. psymodel_data_old = psymodel_data
- * psycho_loudness_approx
- * jd - 2001 mar 12
- * in: energy - BLKSIZE/2 elements of frequency magnitudes ^ 2
- * gfp - uses out_samplerate, ATHtype (also needed for ATHformula)
- * returns: loudness^2 approximation, a positive value roughly tuned for a value
- * of 1.0 for signals near clipping.
- * notes: When calibrated, feeding this function binary white noise at sample
- * values +32767 or -32768 should return values that approach 3.
- * ATHformula is used to approximate an equal loudness curve.
- * future: Data indicates that the shape of the equal loudness curve varies
- * with intensity. This function might be improved by using an equal
- * loudness curve shaped for typical playback levels (instead of the
- * ATH, that is shaped for the threshold). A flexible realization might
- * simply bend the existing ATH curve to achieve the desired shape.
- * However, the potential gain may not be enough to justify an effort.
- *
- */
- function psycho_loudness_approx(energy, gfc) {
- var loudness_power = 0.0;
- /* apply weights to power in freq. bands */
- for (var i = 0; i < Encoder.BLKSIZE / 2; ++i)
- loudness_power += energy[i] * gfc.ATH.eql_w[i];
- loudness_power *= VO_SCALE;
-
- return loudness_power;
- }
-
- function compute_ffts(gfp, fftenergy, fftenergy_s, wsamp_l, wsamp_lPos, wsamp_s, wsamp_sPos, gr_out, chn, buffer, bufPos) {
- var gfc = gfp.internal_flags;
- if (chn < 2) {
- fft.fft_long(gfc, wsamp_l[wsamp_lPos], chn, buffer, bufPos);
- fft.fft_short(gfc, wsamp_s[wsamp_sPos], chn, buffer, bufPos);
- }
- /* FFT data for mid and side channel is derived from L & R */
- else if (chn == 2) {
- for (var j = Encoder.BLKSIZE - 1; j >= 0; --j) {
- var l = wsamp_l[wsamp_lPos + 0][j];
- var r = wsamp_l[wsamp_lPos + 1][j];
- wsamp_l[wsamp_lPos + 0][j] = (l + r) * Util.SQRT2 * 0.5;
- wsamp_l[wsamp_lPos + 1][j] = (l - r) * Util.SQRT2 * 0.5;
- }
- for (var b = 2; b >= 0; --b) {
- for (var j = Encoder.BLKSIZE_s - 1; j >= 0; --j) {
- var l = wsamp_s[wsamp_sPos + 0][b][j];
- var r = wsamp_s[wsamp_sPos + 1][b][j];
- wsamp_s[wsamp_sPos + 0][b][j] = (l + r) * Util.SQRT2 * 0.5;
- wsamp_s[wsamp_sPos + 1][b][j] = (l - r) * Util.SQRT2 * 0.5;
- }
- }
- }
-
- /*********************************************************************
- * compute energies
- *********************************************************************/
- fftenergy[0] = NON_LINEAR_SCALE_ENERGY(wsamp_l[wsamp_lPos + 0][0]);
- fftenergy[0] *= fftenergy[0];
-
- for (var j = Encoder.BLKSIZE / 2 - 1; j >= 0; --j) {
- var re = (wsamp_l[wsamp_lPos + 0])[Encoder.BLKSIZE / 2 - j];
- var im = (wsamp_l[wsamp_lPos + 0])[Encoder.BLKSIZE / 2 + j];
- fftenergy[Encoder.BLKSIZE / 2 - j] = NON_LINEAR_SCALE_ENERGY((re
- * re + im * im) * 0.5);
- }
- for (var b = 2; b >= 0; --b) {
- fftenergy_s[b][0] = (wsamp_s[wsamp_sPos + 0])[b][0];
- fftenergy_s[b][0] *= fftenergy_s[b][0];
- for (var j = Encoder.BLKSIZE_s / 2 - 1; j >= 0; --j) {
- var re = (wsamp_s[wsamp_sPos + 0])[b][Encoder.BLKSIZE_s
- / 2 - j];
- var im = (wsamp_s[wsamp_sPos + 0])[b][Encoder.BLKSIZE_s
- / 2 + j];
- fftenergy_s[b][Encoder.BLKSIZE_s / 2 - j] = NON_LINEAR_SCALE_ENERGY((re
- * re + im * im) * 0.5);
- }
- }
- /* total energy */
- {
- var totalenergy = 0.0;
- for (var j = 11; j < Encoder.HBLKSIZE; j++)
- totalenergy += fftenergy[j];
-
- gfc.tot_ener[chn] = totalenergy;
- }
-
- if (gfp.analysis) {
- for (var j = 0; j < Encoder.HBLKSIZE; j++) {
- gfc.pinfo.energy[gr_out][chn][j] = gfc.pinfo.energy_save[chn][j];
- gfc.pinfo.energy_save[chn][j] = fftenergy[j];
- }
- gfc.pinfo.pe[gr_out][chn] = gfc.pe[chn];
- }
-
- /*********************************************************************
- * compute loudness approximation (used for ATH auto-level adjustment)
- *********************************************************************/
- if (gfp.athaa_loudapprox == 2 && chn < 2) {
- // no loudness for mid/side ch
- gfc.loudness_sq[gr_out][chn] = gfc.loudness_sq_save[chn];
- gfc.loudness_sq_save[chn] = psycho_loudness_approx(fftenergy, gfc);
- }
- }
-
- /* mask_add optimization */
- /* init the limit values used to avoid computing log in mask_add when it is not necessary */
-
- /**
- *
- * For example, with i = 10*log10(m2/m1)/10*16 (= log10(m2/m1)*16)
- *
- * abs(i)>8 is equivalent (as i is an integer) to
- * abs(i)>=9
- * i>=9 || i<=-9
- * equivalent to (as i is the biggest integer smaller than log10(m2/m1)*16
- * or the smallest integer bigger than log10(m2/m1)*16 depending on the sign of log10(m2/m1)*16)
- * log10(m2/m1)>=9/16 || log10(m2/m1)<=-9/16
- * exp10 is strictly increasing thus this is equivalent to
- * m2/m1 >= 10^(9/16) || m2/m1<=10^(-9/16) which are comparisons to constants
- *
- */
-
- /**
- * as in if(i>8)
- */
- var I1LIMIT = 8;
- /**
- * as in if(i>24) . changed 23
- */
- var I2LIMIT = 23;
- /**
- * as in if(m<15)
- */
- var MLIMIT = 15;
-
- var ma_max_i1;
- var ma_max_i2;
- var ma_max_m;
-
- /**
- * This is the masking table:
- * According to tonality, values are going from 0dB (TMN) to 9.3dB (NMT).
- * After additive masking computation, 8dB are added, so final values are
- * going from 8dB to 17.3dB
- *
- * pow(10, -0.0..-0.6)
- */
- var tab = [1.0, 0.79433, 0.63096, 0.63096,
- 0.63096, 0.63096, 0.63096, 0.25119, 0.11749];
-
- function init_mask_add_max_values() {
- ma_max_i1 = Math.pow(10, (I1LIMIT + 1) / 16.0);
- ma_max_i2 = Math.pow(10, (I2LIMIT + 1) / 16.0);
- ma_max_m = Math.pow(10, (MLIMIT) / 10.0);
- }
-
- var table1 = [3.3246 * 3.3246,
- 3.23837 * 3.23837, 3.15437 * 3.15437, 3.00412 * 3.00412,
- 2.86103 * 2.86103, 2.65407 * 2.65407, 2.46209 * 2.46209,
- 2.284 * 2.284, 2.11879 * 2.11879, 1.96552 * 1.96552,
- 1.82335 * 1.82335, 1.69146 * 1.69146, 1.56911 * 1.56911,
- 1.46658 * 1.46658, 1.37074 * 1.37074, 1.31036 * 1.31036,
- 1.25264 * 1.25264, 1.20648 * 1.20648, 1.16203 * 1.16203,
- 1.12765 * 1.12765, 1.09428 * 1.09428, 1.0659 * 1.0659,
- 1.03826 * 1.03826, 1.01895 * 1.01895, 1];
-
- var table2 = [1.33352 * 1.33352,
- 1.35879 * 1.35879, 1.38454 * 1.38454, 1.39497 * 1.39497,
- 1.40548 * 1.40548, 1.3537 * 1.3537, 1.30382 * 1.30382,
- 1.22321 * 1.22321, 1.14758 * 1.14758, 1];
-
- var table3 = [2.35364 * 2.35364,
- 2.29259 * 2.29259, 2.23313 * 2.23313, 2.12675 * 2.12675,
- 2.02545 * 2.02545, 1.87894 * 1.87894, 1.74303 * 1.74303,
- 1.61695 * 1.61695, 1.49999 * 1.49999, 1.39148 * 1.39148,
- 1.29083 * 1.29083, 1.19746 * 1.19746, 1.11084 * 1.11084,
- 1.03826 * 1.03826];
-
- /**
- * addition of simultaneous masking Naoki Shibata 2000/7
- */
- function mask_add(m1, m2, kk, b, gfc, shortblock) {
- var ratio;
-
- if (m2 > m1) {
- if (m2 < (m1 * ma_max_i2))
- ratio = m2 / m1;
- else
- return (m1 + m2);
- } else {
- if (m1 >= (m2 * ma_max_i2))
- return (m1 + m2);
- ratio = m1 / m2;
- }
-
- /* Should always be true, just checking */
-
- m1 += m2;
- //if (((long)(b + 3) & 0xffffffff) <= 3 + 3) {
- if ((b + 3) <= 3 + 3) {
- /* approximately, 1 bark = 3 partitions */
- /* 65% of the cases */
- /* originally 'if(i > 8)' */
- if (ratio >= ma_max_i1) {
- /* 43% of the total */
- return m1;
- }
-
- /* 22% of the total */
- var i = 0 | (Util.FAST_LOG10_X(ratio, 16.0));
- return m1 * table2[i];
- }
-
- /**
- *
- */
- var i = 0 | Util.FAST_LOG10_X(ratio, 16.0);
- if (shortblock != 0) {
- m2 = gfc.ATH.cb_s[kk] * gfc.ATH.adjust;
- } else {
- m2 = gfc.ATH.cb_l[kk] * gfc.ATH.adjust;
- }
- if (m1 < ma_max_m * m2) {
- /* 3% of the total */
- /* Originally if (m > 0) { */
- if (m1 > m2) {
- var f, r;
-
- f = 1.0;
- if (i <= 13)
- f = table3[i];
-
- r = Util.FAST_LOG10_X(m1 / m2, 10.0 / 15.0);
- return m1 * ((table1[i] - f) * r + f);
- }
-
- if (i > 13)
- return m1;
-
- return m1 * table3[i];
- }
-
- /* 10% of total */
- return m1 * table1[i];
- }
-
- var table2_ = [1.33352 * 1.33352,
- 1.35879 * 1.35879, 1.38454 * 1.38454, 1.39497 * 1.39497,
- 1.40548 * 1.40548, 1.3537 * 1.3537, 1.30382 * 1.30382,
- 1.22321 * 1.22321, 1.14758 * 1.14758, 1];
-
- /**
- * addition of simultaneous masking Naoki Shibata 2000/7
- */
- function vbrpsy_mask_add(m1, m2, b) {
- var ratio;
-
- if (m1 < 0) {
- m1 = 0;
- }
- if (m2 < 0) {
- m2 = 0;
- }
- if (m1 <= 0) {
- return m2;
- }
- if (m2 <= 0) {
- return m1;
- }
- if (m2 > m1) {
- ratio = m2 / m1;
- } else {
- ratio = m1 / m2;
- }
- if (-2 <= b && b <= 2) {
- /* approximately, 1 bark = 3 partitions */
- /* originally 'if(i > 8)' */
- if (ratio >= ma_max_i1) {
- return m1 + m2;
- } else {
- var i = 0 | (Util.FAST_LOG10_X(ratio, 16.0));
- return (m1 + m2) * table2_[i];
- }
- }
- if (ratio < ma_max_i2) {
- return m1 + m2;
- }
- if (m1 < m2) {
- m1 = m2;
- }
- return m1;
- }
-
- /**
- * compute interchannel masking effects
- */
- function calc_interchannel_masking(gfp, ratio) {
- var gfc = gfp.internal_flags;
- if (gfc.channels_out > 1) {
- for (var sb = 0; sb < Encoder.SBMAX_l; sb++) {
- var l = gfc.thm[0].l[sb];
- var r = gfc.thm[1].l[sb];
- gfc.thm[0].l[sb] += r * ratio;
- gfc.thm[1].l[sb] += l * ratio;
- }
- for (var sb = 0; sb < Encoder.SBMAX_s; sb++) {
- for (var sblock = 0; sblock < 3; sblock++) {
- var l = gfc.thm[0].s[sb][sblock];
- var r = gfc.thm[1].s[sb][sblock];
- gfc.thm[0].s[sb][sblock] += r * ratio;
- gfc.thm[1].s[sb][sblock] += l * ratio;
- }
- }
- }
- }
-
- /**
- * compute M/S thresholds from Johnston & Ferreira 1992 ICASSP paper
- */
- function msfix1(gfc) {
- for (var sb = 0; sb < Encoder.SBMAX_l; sb++) {
- /* use this fix if L & R masking differs by 2db or less */
- /* if db = 10*log10(x2/x1) < 2 */
- /* if (x2 < 1.58*x1) { */
- if (gfc.thm[0].l[sb] > 1.58 * gfc.thm[1].l[sb]
- || gfc.thm[1].l[sb] > 1.58 * gfc.thm[0].l[sb])
- continue;
- var mld = gfc.mld_l[sb] * gfc.en[3].l[sb];
- var rmid = Math.max(gfc.thm[2].l[sb],
- Math.min(gfc.thm[3].l[sb], mld));
-
- mld = gfc.mld_l[sb] * gfc.en[2].l[sb];
- var rside = Math.max(gfc.thm[3].l[sb],
- Math.min(gfc.thm[2].l[sb], mld));
- gfc.thm[2].l[sb] = rmid;
- gfc.thm[3].l[sb] = rside;
- }
-
- for (var sb = 0; sb < Encoder.SBMAX_s; sb++) {
- for (var sblock = 0; sblock < 3; sblock++) {
- if (gfc.thm[0].s[sb][sblock] > 1.58 * gfc.thm[1].s[sb][sblock]
- || gfc.thm[1].s[sb][sblock] > 1.58 * gfc.thm[0].s[sb][sblock])
- continue;
- var mld = gfc.mld_s[sb] * gfc.en[3].s[sb][sblock];
- var rmid = Math.max(gfc.thm[2].s[sb][sblock],
- Math.min(gfc.thm[3].s[sb][sblock], mld));
-
- mld = gfc.mld_s[sb] * gfc.en[2].s[sb][sblock];
- var rside = Math.max(gfc.thm[3].s[sb][sblock],
- Math.min(gfc.thm[2].s[sb][sblock], mld));
-
- gfc.thm[2].s[sb][sblock] = rmid;
- gfc.thm[3].s[sb][sblock] = rside;
- }
- }
- }
-
- /**
- * Adjust M/S maskings if user set "msfix"
- *
- * Naoki Shibata 2000
- */
- function ns_msfix(gfc, msfix, athadjust) {
- var msfix2 = msfix;
- var athlower = Math.pow(10, athadjust);
-
- msfix *= 2.0;
- msfix2 *= 2.0;
- for (var sb = 0; sb < Encoder.SBMAX_l; sb++) {
- var thmLR, thmM, thmS, ath;
- ath = (gfc.ATH.cb_l[gfc.bm_l[sb]]) * athlower;
- thmLR = Math.min(Math.max(gfc.thm[0].l[sb], ath),
- Math.max(gfc.thm[1].l[sb], ath));
- thmM = Math.max(gfc.thm[2].l[sb], ath);
- thmS = Math.max(gfc.thm[3].l[sb], ath);
- if (thmLR * msfix < thmM + thmS) {
- var f = thmLR * msfix2 / (thmM + thmS);
- thmM *= f;
- thmS *= f;
- }
- gfc.thm[2].l[sb] = Math.min(thmM, gfc.thm[2].l[sb]);
- gfc.thm[3].l[sb] = Math.min(thmS, gfc.thm[3].l[sb]);
- }
-
- athlower *= ( Encoder.BLKSIZE_s / Encoder.BLKSIZE);
- for (var sb = 0; sb < Encoder.SBMAX_s; sb++) {
- for (var sblock = 0; sblock < 3; sblock++) {
- var thmLR, thmM, thmS, ath;
- ath = (gfc.ATH.cb_s[gfc.bm_s[sb]]) * athlower;
- thmLR = Math.min(Math.max(gfc.thm[0].s[sb][sblock], ath),
- Math.max(gfc.thm[1].s[sb][sblock], ath));
- thmM = Math.max(gfc.thm[2].s[sb][sblock], ath);
- thmS = Math.max(gfc.thm[3].s[sb][sblock], ath);
-
- if (thmLR * msfix < thmM + thmS) {
- var f = thmLR * msfix / (thmM + thmS);
- thmM *= f;
- thmS *= f;
- }
- gfc.thm[2].s[sb][sblock] = Math.min(gfc.thm[2].s[sb][sblock],
- thmM);
- gfc.thm[3].s[sb][sblock] = Math.min(gfc.thm[3].s[sb][sblock],
- thmS);
- }
- }
- }
-
- /**
- * short block threshold calculation (part 2)
- *
- * partition band bo_s[sfb] is at the transition from scalefactor band sfb
- * to the next one sfb+1; enn and thmm have to be split between them
- */
- function convert_partition2scalefac_s(gfc, eb, thr, chn, sblock) {
- var sb, b;
- var enn = 0.0;
- var thmm = 0.0;
- for (sb = b = 0; sb < Encoder.SBMAX_s; ++b, ++sb) {
- var bo_s_sb = gfc.bo_s[sb];
- var npart_s = gfc.npart_s;
- var b_lim = bo_s_sb < npart_s ? bo_s_sb : npart_s;
- while (b < b_lim) {
- // iff failed, it may indicate some index error elsewhere
- enn += eb[b];
- thmm += thr[b];
- b++;
- }
- gfc.en[chn].s[sb][sblock] = enn;
- gfc.thm[chn].s[sb][sblock] = thmm;
-
- if (b >= npart_s) {
- ++sb;
- break;
- }
- // iff failed, it may indicate some index error elsewhere
- {
- /* at transition sfb . sfb+1 */
- var w_curr = gfc.PSY.bo_s_weight[sb];
- var w_next = 1.0 - w_curr;
- enn = w_curr * eb[b];
- thmm = w_curr * thr[b];
- gfc.en[chn].s[sb][sblock] += enn;
- gfc.thm[chn].s[sb][sblock] += thmm;
- enn = w_next * eb[b];
- thmm = w_next * thr[b];
- }
- }
- /* zero initialize the rest */
- for (; sb < Encoder.SBMAX_s; ++sb) {
- gfc.en[chn].s[sb][sblock] = 0;
- gfc.thm[chn].s[sb][sblock] = 0;
- }
- }
-
- /**
- * longblock threshold calculation (part 2)
- */
- function convert_partition2scalefac_l(gfc, eb, thr, chn) {
- var sb, b;
- var enn = 0.0;
- var thmm = 0.0;
- for (sb = b = 0; sb < Encoder.SBMAX_l; ++b, ++sb) {
- var bo_l_sb = gfc.bo_l[sb];
- var npart_l = gfc.npart_l;
- var b_lim = bo_l_sb < npart_l ? bo_l_sb : npart_l;
- while (b < b_lim) {
- // iff failed, it may indicate some index error elsewhere
- enn += eb[b];
- thmm += thr[b];
- b++;
- }
- gfc.en[chn].l[sb] = enn;
- gfc.thm[chn].l[sb] = thmm;
-
- if (b >= npart_l) {
- ++sb;
- break;
- }
- {
- /* at transition sfb . sfb+1 */
- var w_curr = gfc.PSY.bo_l_weight[sb];
- var w_next = 1.0 - w_curr;
- enn = w_curr * eb[b];
- thmm = w_curr * thr[b];
- gfc.en[chn].l[sb] += enn;
- gfc.thm[chn].l[sb] += thmm;
- enn = w_next * eb[b];
- thmm = w_next * thr[b];
- }
- }
- /* zero initialize the rest */
- for (; sb < Encoder.SBMAX_l; ++sb) {
- gfc.en[chn].l[sb] = 0;
- gfc.thm[chn].l[sb] = 0;
- }
- }
-
- function compute_masking_s(gfp, fftenergy_s, eb, thr, chn, sblock) {
- var gfc = gfp.internal_flags;
- var j, b;
-
- for (b = j = 0; b < gfc.npart_s; ++b) {
- var ebb = 0, m = 0;
- var n = gfc.numlines_s[b];
- for (var i = 0; i < n; ++i, ++j) {
- var el = fftenergy_s[sblock][j];
- ebb += el;
- if (m < el)
- m = el;
- }
- eb[b] = ebb;
- }
- for (j = b = 0; b < gfc.npart_s; b++) {
- var kk = gfc.s3ind_s[b][0];
- var ecb = gfc.s3_ss[j++] * eb[kk];
- ++kk;
- while (kk <= gfc.s3ind_s[b][1]) {
- ecb += gfc.s3_ss[j] * eb[kk];
- ++j;
- ++kk;
- }
-
- { /* limit calculated threshold by previous granule */
- var x = rpelev_s * gfc.nb_s1[chn][b];
- thr[b] = Math.min(ecb, x);
- }
- if (gfc.blocktype_old[chn & 1] == Encoder.SHORT_TYPE) {
- /* limit calculated threshold by even older granule */
- var x = rpelev2_s * gfc.nb_s2[chn][b];
- var y = thr[b];
- thr[b] = Math.min(x, y);
- }
-
- gfc.nb_s2[chn][b] = gfc.nb_s1[chn][b];
- gfc.nb_s1[chn][b] = ecb;
- }
- for (; b <= Encoder.CBANDS; ++b) {
- eb[b] = 0;
- thr[b] = 0;
- }
- }
-
- function block_type_set(gfp, uselongblock, blocktype_d, blocktype) {
- var gfc = gfp.internal_flags;
-
- if (gfp.short_blocks == ShortBlock.short_block_coupled
- /* force both channels to use the same block type */
- /* this is necessary if the frame is to be encoded in ms_stereo. */
- /* But even without ms_stereo, FhG does this */
- && !(uselongblock[0] != 0 && uselongblock[1] != 0))
- uselongblock[0] = uselongblock[1] = 0;
-
- /*
- * update the blocktype of the previous granule, since it depends on
- * what happend in this granule
- */
- for (var chn = 0; chn < gfc.channels_out; chn++) {
- blocktype[chn] = Encoder.NORM_TYPE;
- /* disable short blocks */
- if (gfp.short_blocks == ShortBlock.short_block_dispensed)
- uselongblock[chn] = 1;
- if (gfp.short_blocks == ShortBlock.short_block_forced)
- uselongblock[chn] = 0;
-
- if (uselongblock[chn] != 0) {
- /* no attack : use long blocks */
- if (gfc.blocktype_old[chn] == Encoder.SHORT_TYPE)
- blocktype[chn] = Encoder.STOP_TYPE;
- } else {
- /* attack : use short blocks */
- blocktype[chn] = Encoder.SHORT_TYPE;
- if (gfc.blocktype_old[chn] == Encoder.NORM_TYPE) {
- gfc.blocktype_old[chn] = Encoder.START_TYPE;
- }
- if (gfc.blocktype_old[chn] == Encoder.STOP_TYPE)
- gfc.blocktype_old[chn] = Encoder.SHORT_TYPE;
- }
-
- blocktype_d[chn] = gfc.blocktype_old[chn];
- // value returned to calling program
- gfc.blocktype_old[chn] = blocktype[chn];
- // save for next call to l3psy_anal
- }
- }
-
- function NS_INTERP(x, y, r) {
- /* was pow((x),(r))*pow((y),1-(r)) */
- if (r >= 1.0) {
- /* 99.7% of the time */
- return x;
- }
- if (r <= 0.0)
- return y;
- if (y > 0.0) {
- /* rest of the time */
- return (Math.pow(x / y, r) * y);
- }
- /* never happens */
- return 0.0;
- }
-
- /**
- * these values are tuned only for 44.1kHz...
- */
- var regcoef_s = [11.8, 13.6, 17.2, 32, 46.5,
- 51.3, 57.5, 67.1, 71.5, 84.6, 97.6, 130,
- /* 255.8 */
- ];
-
- function pecalc_s(mr, masking_lower) {
- var pe_s = 1236.28 / 4;
- for (var sb = 0; sb < Encoder.SBMAX_s - 1; sb++) {
- for (var sblock = 0; sblock < 3; sblock++) {
- var thm = mr.thm.s[sb][sblock];
- if (thm > 0.0) {
- var x = thm * masking_lower;
- var en = mr.en.s[sb][sblock];
- if (en > x) {
- if (en > x * 1e10) {
- pe_s += regcoef_s[sb] * (10.0 * LOG10);
- } else {
- pe_s += regcoef_s[sb] * Util.FAST_LOG10(en / x);
- }
- }
- }
- }
- }
-
- return pe_s;
- }
-
- /**
- * these values are tuned only for 44.1kHz...
- */
- var regcoef_l = [6.8, 5.8, 5.8, 6.4, 6.5, 9.9,
- 12.1, 14.4, 15, 18.9, 21.6, 26.9, 34.2, 40.2, 46.8, 56.5,
- 60.7, 73.9, 85.7, 93.4, 126.1,
- /* 241.3 */
- ];
-
- function pecalc_l(mr, masking_lower) {
- var pe_l = 1124.23 / 4;
- for (var sb = 0; sb < Encoder.SBMAX_l - 1; sb++) {
- var thm = mr.thm.l[sb];
- if (thm > 0.0) {
- var x = thm * masking_lower;
- var en = mr.en.l[sb];
- if (en > x) {
- if (en > x * 1e10) {
- pe_l += regcoef_l[sb] * (10.0 * LOG10);
- } else {
- pe_l += regcoef_l[sb] * Util.FAST_LOG10(en / x);
- }
- }
- }
- }
- return pe_l;
- }
-
- function calc_energy(gfc, fftenergy, eb, max, avg) {
- var b, j;
-
- for (b = j = 0; b < gfc.npart_l; ++b) {
- var ebb = 0, m = 0;
- var i;
- for (i = 0; i < gfc.numlines_l[b]; ++i, ++j) {
- var el = fftenergy[j];
- ebb += el;
- if (m < el)
- m = el;
- }
- eb[b] = ebb;
- max[b] = m;
- avg[b] = ebb * gfc.rnumlines_l[b];
- }
- }
-
- function calc_mask_index_l(gfc, max, avg, mask_idx) {
- var last_tab_entry = tab.length - 1;
- var b = 0;
- var a = avg[b] + avg[b + 1];
- if (a > 0.0) {
- var m = max[b];
- if (m < max[b + 1])
- m = max[b + 1];
- a = 20.0 * (m * 2.0 - a)
- / (a * (gfc.numlines_l[b] + gfc.numlines_l[b + 1] - 1));
- var k = 0 | a;
- if (k > last_tab_entry)
- k = last_tab_entry;
- mask_idx[b] = k;
- } else {
- mask_idx[b] = 0;
- }
-
- for (b = 1; b < gfc.npart_l - 1; b++) {
- a = avg[b - 1] + avg[b] + avg[b + 1];
- if (a > 0.0) {
- var m = max[b - 1];
- if (m < max[b])
- m = max[b];
- if (m < max[b + 1])
- m = max[b + 1];
- a = 20.0
- * (m * 3.0 - a)
- / (a * (gfc.numlines_l[b - 1] + gfc.numlines_l[b]
- + gfc.numlines_l[b + 1] - 1));
- var k = 0 | a;
- if (k > last_tab_entry)
- k = last_tab_entry;
- mask_idx[b] = k;
- } else {
- mask_idx[b] = 0;
- }
- }
-
- a = avg[b - 1] + avg[b];
- if (a > 0.0) {
- var m = max[b - 1];
- if (m < max[b])
- m = max[b];
- a = 20.0 * (m * 2.0 - a)
- / (a * (gfc.numlines_l[b - 1] + gfc.numlines_l[b] - 1));
- var k = 0 | a;
- if (k > last_tab_entry)
- k = last_tab_entry;
- mask_idx[b] = k;
- } else {
- mask_idx[b] = 0;
- }
- }
-
- var fircoef = [
- -8.65163e-18 * 2, -0.00851586 * 2, -6.74764e-18 * 2, 0.0209036 * 2,
- -3.36639e-17 * 2, -0.0438162 * 2, -1.54175e-17 * 2, 0.0931738 * 2,
- -5.52212e-17 * 2, -0.313819 * 2
- ];
-
- this.L3psycho_anal_ns = function (gfp, buffer, bufPos, gr_out, masking_ratio, masking_MS_ratio, percep_entropy, percep_MS_entropy, energy, blocktype_d) {
- /*
- * to get a good cache performance, one has to think about the sequence,
- * in which the variables are used.
- */
- var gfc = gfp.internal_flags;
-
- /* fft and energy calculation */
- var wsamp_L = new_float_n([2, Encoder.BLKSIZE]);
- var wsamp_S = new_float_n([2, 3, Encoder.BLKSIZE_s]);
-
- /* convolution */
- var eb_l = new_float(Encoder.CBANDS + 1);
- var eb_s = new_float(Encoder.CBANDS + 1);
- var thr = new_float(Encoder.CBANDS + 2);
-
- /* block type */
- var blocktype = new_int(2), uselongblock = new_int(2);
-
- /* usual variables like loop indices, etc.. */
- var numchn, chn;
- var b, i, j, k;
- var sb, sblock;
-
- /* variables used for --nspsytune */
- var ns_hpfsmpl = new_float_n([2, 576]);
- var pcfact;
- var mask_idx_l = new_int(Encoder.CBANDS + 2), mask_idx_s = new_int(Encoder.CBANDS + 2);
-
- Arrays.fill(mask_idx_s, 0);
-
- numchn = gfc.channels_out;
- /* chn=2 and 3 = Mid and Side channels */
- if (gfp.mode == MPEGMode.JOINT_STEREO)
- numchn = 4;
-
- if (gfp.VBR == VbrMode.vbr_off)
- pcfact = gfc.ResvMax == 0 ? 0 : ( gfc.ResvSize)
- / gfc.ResvMax * 0.5;
- else if (gfp.VBR == VbrMode.vbr_rh || gfp.VBR == VbrMode.vbr_mtrh
- || gfp.VBR == VbrMode.vbr_mt) {
- pcfact = 0.6;
- } else
- pcfact = 1.0;
-
- /**********************************************************************
- * Apply HPF of fs/4 to the input signal. This is used for attack
- * detection / handling.
- **********************************************************************/
- /* Don't copy the input buffer into a temporary buffer */
- /* unroll the loop 2 times */
- for (chn = 0; chn < gfc.channels_out; chn++) {
- /* apply high pass filter of fs/4 */
- var firbuf = buffer[chn];
- var firbufPos = bufPos + 576 - 350 - NSFIRLEN + 192;
- for (i = 0; i < 576; i++) {
- var sum1, sum2;
- sum1 = firbuf[firbufPos + i + 10];
- sum2 = 0.0;
- for (j = 0; j < ((NSFIRLEN - 1) / 2) - 1; j += 2) {
- sum1 += fircoef[j]
- * (firbuf[firbufPos + i + j] + firbuf[firbufPos + i
- + NSFIRLEN - j]);
- sum2 += fircoef[j + 1]
- * (firbuf[firbufPos + i + j + 1] + firbuf[firbufPos
- + i + NSFIRLEN - j - 1]);
- }
- ns_hpfsmpl[chn][i] = sum1 + sum2;
- }
- masking_ratio[gr_out][chn].en.assign(gfc.en[chn]);
- masking_ratio[gr_out][chn].thm.assign(gfc.thm[chn]);
- if (numchn > 2) {
- /* MS maskings */
- /* percep_MS_entropy [chn-2] = gfc . pe [chn]; */
- masking_MS_ratio[gr_out][chn].en.assign(gfc.en[chn + 2]);
- masking_MS_ratio[gr_out][chn].thm.assign(gfc.thm[chn + 2]);
- }
- }
-
- for (chn = 0; chn < numchn; chn++) {
- var wsamp_l;
- var wsamp_s;
- var en_subshort = new_float(12);
- var en_short = [0, 0, 0, 0];
- var attack_intensity = new_float(12);
- var ns_uselongblock = 1;
- var attackThreshold;
- var max = new_float(Encoder.CBANDS), avg = new_float(Encoder.CBANDS);
- var ns_attacks = [0, 0, 0, 0];
- var fftenergy = new_float(Encoder.HBLKSIZE);
- var fftenergy_s = new_float_n([3, Encoder.HBLKSIZE_s]);
-
- /*
- * rh 20040301: the following loops do access one off the limits so
- * I increase the array dimensions by one and initialize the
- * accessed values to zero
- */
-
- /***************************************************************
- * determine the block type (window type)
- ***************************************************************/
- /* calculate energies of each sub-shortblocks */
- for (i = 0; i < 3; i++) {
- en_subshort[i] = gfc.nsPsy.last_en_subshort[chn][i + 6];
- attack_intensity[i] = en_subshort[i]
- / gfc.nsPsy.last_en_subshort[chn][i + 4];
- en_short[0] += en_subshort[i];
- }
-
- if (chn == 2) {
- for (i = 0; i < 576; i++) {
- var l, r;
- l = ns_hpfsmpl[0][i];
- r = ns_hpfsmpl[1][i];
- ns_hpfsmpl[0][i] = l + r;
- ns_hpfsmpl[1][i] = l - r;
- }
- }
- {
- var pf = ns_hpfsmpl[chn & 1];
- var pfPos = 0;
- for (i = 0; i < 9; i++) {
- var pfe = pfPos + 576 / 9;
- var p = 1.;
- for (; pfPos < pfe; pfPos++)
- if (p < Math.abs(pf[pfPos]))
- p = Math.abs(pf[pfPos]);
-
- gfc.nsPsy.last_en_subshort[chn][i] = en_subshort[i + 3] = p;
- en_short[1 + i / 3] += p;
- if (p > en_subshort[i + 3 - 2]) {
- p = p / en_subshort[i + 3 - 2];
- } else if (en_subshort[i + 3 - 2] > p * 10.0) {
- p = en_subshort[i + 3 - 2] / (p * 10.0);
- } else
- p = 0.0;
- attack_intensity[i + 3] = p;
- }
- }
-
- if (gfp.analysis) {
- var x = attack_intensity[0];
- for (i = 1; i < 12; i++)
- if (x < attack_intensity[i])
- x = attack_intensity[i];
- gfc.pinfo.ers[gr_out][chn] = gfc.pinfo.ers_save[chn];
- gfc.pinfo.ers_save[chn] = x;
- }
-
- /* compare energies between sub-shortblocks */
- attackThreshold = (chn == 3) ? gfc.nsPsy.attackthre_s
- : gfc.nsPsy.attackthre;
- for (i = 0; i < 12; i++)
- if (0 == ns_attacks[i / 3]
- && attack_intensity[i] > attackThreshold)
- ns_attacks[i / 3] = (i % 3) + 1;
-
- /*
- * should have energy change between short blocks, in order to avoid
- * periodic signals
- */
- for (i = 1; i < 4; i++) {
- var ratio;
- if (en_short[i - 1] > en_short[i]) {
- ratio = en_short[i - 1] / en_short[i];
- } else {
- ratio = en_short[i] / en_short[i - 1];
- }
- if (ratio < 1.7) {
- ns_attacks[i] = 0;
- if (i == 1)
- ns_attacks[0] = 0;
- }
- }
-
- if (ns_attacks[0] != 0 && gfc.nsPsy.lastAttacks[chn] != 0)
- ns_attacks[0] = 0;
-
- if (gfc.nsPsy.lastAttacks[chn] == 3
- || (ns_attacks[0] + ns_attacks[1] + ns_attacks[2] + ns_attacks[3]) != 0) {
- ns_uselongblock = 0;
-
- if (ns_attacks[1] != 0 && ns_attacks[0] != 0)
- ns_attacks[1] = 0;
- if (ns_attacks[2] != 0 && ns_attacks[1] != 0)
- ns_attacks[2] = 0;
- if (ns_attacks[3] != 0 && ns_attacks[2] != 0)
- ns_attacks[3] = 0;
- }
-
- if (chn < 2) {
- uselongblock[chn] = ns_uselongblock;
- } else {
- if (ns_uselongblock == 0) {
- uselongblock[0] = uselongblock[1] = 0;
- }
- }
-
- /*
- * there is a one granule delay. Copy maskings computed last call
- * into masking_ratio to return to calling program.
- */
- energy[chn] = gfc.tot_ener[chn];
-
- /*********************************************************************
- * compute FFTs
- *********************************************************************/
- wsamp_s = wsamp_S;
- wsamp_l = wsamp_L;
- compute_ffts(gfp, fftenergy, fftenergy_s, wsamp_l, (chn & 1),
- wsamp_s, (chn & 1), gr_out, chn, buffer, bufPos);
-
- /*********************************************************************
- * Calculate the energy and the tonality of each partition.
- *********************************************************************/
- calc_energy(gfc, fftenergy, eb_l, max, avg);
- calc_mask_index_l(gfc, max, avg, mask_idx_l);
- /* compute masking thresholds for short blocks */
- for (sblock = 0; sblock < 3; sblock++) {
- var enn, thmm;
- compute_masking_s(gfp, fftenergy_s, eb_s, thr, chn, sblock);
- convert_partition2scalefac_s(gfc, eb_s, thr, chn, sblock);
- /**** short block pre-echo control ****/
- for (sb = 0; sb < Encoder.SBMAX_s; sb++) {
- thmm = gfc.thm[chn].s[sb][sblock];
-
- thmm *= NS_PREECHO_ATT0;
- if (ns_attacks[sblock] >= 2 || ns_attacks[sblock + 1] == 1) {
- var idx = (sblock != 0) ? sblock - 1 : 2;
- var p = NS_INTERP(gfc.thm[chn].s[sb][idx], thmm,
- NS_PREECHO_ATT1 * pcfact);
- thmm = Math.min(thmm, p);
- }
-
- if (ns_attacks[sblock] == 1) {
- var idx = (sblock != 0) ? sblock - 1 : 2;
- var p = NS_INTERP(gfc.thm[chn].s[sb][idx], thmm,
- NS_PREECHO_ATT2 * pcfact);
- thmm = Math.min(thmm, p);
- } else if ((sblock != 0 && ns_attacks[sblock - 1] == 3)
- || (sblock == 0 && gfc.nsPsy.lastAttacks[chn] == 3)) {
- var idx = (sblock != 2) ? sblock + 1 : 0;
- var p = NS_INTERP(gfc.thm[chn].s[sb][idx], thmm,
- NS_PREECHO_ATT2 * pcfact);
- thmm = Math.min(thmm, p);
- }
-
- /* pulse like signal detection for fatboy.wav and so on */
- enn = en_subshort[sblock * 3 + 3]
- + en_subshort[sblock * 3 + 4]
- + en_subshort[sblock * 3 + 5];
- if (en_subshort[sblock * 3 + 5] * 6 < enn) {
- thmm *= 0.5;
- if (en_subshort[sblock * 3 + 4] * 6 < enn)
- thmm *= 0.5;
- }
-
- gfc.thm[chn].s[sb][sblock] = thmm;
- }
- }
- gfc.nsPsy.lastAttacks[chn] = ns_attacks[2];
-
- /*********************************************************************
- * convolve the partitioned energy and unpredictability with the
- * spreading function, s3_l[b][k]
- ********************************************************************/
- k = 0;
- {
- for (b = 0; b < gfc.npart_l; b++) {
- /*
- * convolve the partitioned energy with the spreading
- * function
- */
- var kk = gfc.s3ind[b][0];
- var eb2 = eb_l[kk] * tab[mask_idx_l[kk]];
- var ecb = gfc.s3_ll[k++] * eb2;
- while (++kk <= gfc.s3ind[b][1]) {
- eb2 = eb_l[kk] * tab[mask_idx_l[kk]];
- ecb = mask_add(ecb, gfc.s3_ll[k++] * eb2, kk, kk - b,
- gfc, 0);
- }
- ecb *= 0.158489319246111;
- /* pow(10,-0.8) */
-
- /**** long block pre-echo control ****/
- /**
- *
- * dont use long block pre-echo control if previous granule was
- * a short block. This is to avoid the situation:
- * frame0: quiet (very low masking)
- * frame1: surge (triggers short blocks)
- * frame2: regular frame. looks like pre-echo when compared to
- * frame0, but all pre-echo was in frame1.
- *
- */
- /*
- * chn=0,1 L and R channels
- *
- * chn=2,3 S and M channels.
- */
-
- if (gfc.blocktype_old[chn & 1] == Encoder.SHORT_TYPE)
- thr[b] = ecb;
- else
- thr[b] = NS_INTERP(
- Math.min(ecb, Math.min(rpelev
- * gfc.nb_1[chn][b], rpelev2
- * gfc.nb_2[chn][b])), ecb, pcfact);
-
- gfc.nb_2[chn][b] = gfc.nb_1[chn][b];
- gfc.nb_1[chn][b] = ecb;
- }
- }
- for (; b <= Encoder.CBANDS; ++b) {
- eb_l[b] = 0;
- thr[b] = 0;
- }
- /* compute masking thresholds for long blocks */
- convert_partition2scalefac_l(gfc, eb_l, thr, chn);
- }
- /* end loop over chn */
-
- if (gfp.mode == MPEGMode.STEREO || gfp.mode == MPEGMode.JOINT_STEREO) {
- if (gfp.interChRatio > 0.0) {
- calc_interchannel_masking(gfp, gfp.interChRatio);
- }
- }
-
- if (gfp.mode == MPEGMode.JOINT_STEREO) {
- var msfix;
- msfix1(gfc);
- msfix = gfp.msfix;
- if (Math.abs(msfix) > 0.0)
- ns_msfix(gfc, msfix, gfp.ATHlower * gfc.ATH.adjust);
- }
-
- /***************************************************************
- * determine final block type
- ***************************************************************/
- block_type_set(gfp, uselongblock, blocktype_d, blocktype);
-
- /*********************************************************************
- * compute the value of PE to return ... no delay and advance
- *********************************************************************/
- for (chn = 0; chn < numchn; chn++) {
- var ppe;
- var ppePos = 0;
- var type;
- var mr;
-
- if (chn > 1) {
- ppe = percep_MS_entropy;
- ppePos = -2;
- type = Encoder.NORM_TYPE;
- if (blocktype_d[0] == Encoder.SHORT_TYPE
- || blocktype_d[1] == Encoder.SHORT_TYPE)
- type = Encoder.SHORT_TYPE;
- mr = masking_MS_ratio[gr_out][chn - 2];
- } else {
- ppe = percep_entropy;
- ppePos = 0;
- type = blocktype_d[chn];
- mr = masking_ratio[gr_out][chn];
- }
-
- if (type == Encoder.SHORT_TYPE)
- ppe[ppePos + chn] = pecalc_s(mr, gfc.masking_lower);
- else
- ppe[ppePos + chn] = pecalc_l(mr, gfc.masking_lower);
-
- if (gfp.analysis)
- gfc.pinfo.pe[gr_out][chn] = ppe[ppePos + chn];
-
- }
- return 0;
- }
-
- function vbrpsy_compute_fft_l(gfp, buffer, bufPos, chn, gr_out, fftenergy, wsamp_l, wsamp_lPos) {
- var gfc = gfp.internal_flags;
- if (chn < 2) {
- fft.fft_long(gfc, wsamp_l[wsamp_lPos], chn, buffer, bufPos);
- } else if (chn == 2) {
- /* FFT data for mid and side channel is derived from L & R */
- for (var j = Encoder.BLKSIZE - 1; j >= 0; --j) {
- var l = wsamp_l[wsamp_lPos + 0][j];
- var r = wsamp_l[wsamp_lPos + 1][j];
- wsamp_l[wsamp_lPos + 0][j] = (l + r) * Util.SQRT2 * 0.5;
- wsamp_l[wsamp_lPos + 1][j] = (l - r) * Util.SQRT2 * 0.5;
- }
- }
-
- /*********************************************************************
- * compute energies
- *********************************************************************/
- fftenergy[0] = NON_LINEAR_SCALE_ENERGY(wsamp_l[wsamp_lPos + 0][0]);
- fftenergy[0] *= fftenergy[0];
-
- for (var j = Encoder.BLKSIZE / 2 - 1; j >= 0; --j) {
- var re = wsamp_l[wsamp_lPos + 0][Encoder.BLKSIZE / 2 - j];
- var im = wsamp_l[wsamp_lPos + 0][Encoder.BLKSIZE / 2 + j];
- fftenergy[Encoder.BLKSIZE / 2 - j] = NON_LINEAR_SCALE_ENERGY((re
- * re + im * im) * 0.5);
- }
- /* total energy */
- {
- var totalenergy = 0.0;
- for (var j = 11; j < Encoder.HBLKSIZE; j++)
- totalenergy += fftenergy[j];
-
- gfc.tot_ener[chn] = totalenergy;
- }
-
- if (gfp.analysis) {
- for (var j = 0; j < Encoder.HBLKSIZE; j++) {
- gfc.pinfo.energy[gr_out][chn][j] = gfc.pinfo.energy_save[chn][j];
- gfc.pinfo.energy_save[chn][j] = fftenergy[j];
- }
- gfc.pinfo.pe[gr_out][chn] = gfc.pe[chn];
- }
- }
-
- function vbrpsy_compute_fft_s(gfp, buffer, bufPos, chn, sblock, fftenergy_s, wsamp_s, wsamp_sPos) {
- var gfc = gfp.internal_flags;
-
- if (sblock == 0 && chn < 2) {
- fft.fft_short(gfc, wsamp_s[wsamp_sPos], chn, buffer, bufPos);
- }
- if (chn == 2) {
- /* FFT data for mid and side channel is derived from L & R */
- for (var j = Encoder.BLKSIZE_s - 1; j >= 0; --j) {
- var l = wsamp_s[wsamp_sPos + 0][sblock][j];
- var r = wsamp_s[wsamp_sPos + 1][sblock][j];
- wsamp_s[wsamp_sPos + 0][sblock][j] = (l + r) * Util.SQRT2 * 0.5;
- wsamp_s[wsamp_sPos + 1][sblock][j] = (l - r) * Util.SQRT2 * 0.5;
- }
- }
-
- /*********************************************************************
- * compute energies
- *********************************************************************/
- fftenergy_s[sblock][0] = wsamp_s[wsamp_sPos + 0][sblock][0];
- fftenergy_s[sblock][0] *= fftenergy_s[sblock][0];
- for (var j = Encoder.BLKSIZE_s / 2 - 1; j >= 0; --j) {
- var re = wsamp_s[wsamp_sPos + 0][sblock][Encoder.BLKSIZE_s / 2 - j];
- var im = wsamp_s[wsamp_sPos + 0][sblock][Encoder.BLKSIZE_s / 2 + j];
- fftenergy_s[sblock][Encoder.BLKSIZE_s / 2 - j] = NON_LINEAR_SCALE_ENERGY((re
- * re + im * im) * 0.5);
- }
- }
-
- /**
- * compute loudness approximation (used for ATH auto-level adjustment)
- */
- function vbrpsy_compute_loudness_approximation_l(gfp, gr_out, chn, fftenergy) {
- var gfc = gfp.internal_flags;
- if (gfp.athaa_loudapprox == 2 && chn < 2) {
- // no loudness for mid/side ch
- gfc.loudness_sq[gr_out][chn] = gfc.loudness_sq_save[chn];
- gfc.loudness_sq_save[chn] = psycho_loudness_approx(fftenergy, gfc);
- }
- }
-
- var fircoef_ = [-8.65163e-18 * 2,
- -0.00851586 * 2, -6.74764e-18 * 2, 0.0209036 * 2,
- -3.36639e-17 * 2, -0.0438162 * 2, -1.54175e-17 * 2,
- 0.0931738 * 2, -5.52212e-17 * 2, -0.313819 * 2];
-
- /**
- * Apply HPF of fs/4 to the input signal. This is used for attack detection
- * / handling.
- */
- function vbrpsy_attack_detection(gfp, buffer, bufPos, gr_out, masking_ratio, masking_MS_ratio, energy, sub_short_factor, ns_attacks, uselongblock) {
- var ns_hpfsmpl = new_float_n([2, 576]);
- var gfc = gfp.internal_flags;
- var n_chn_out = gfc.channels_out;
- /* chn=2 and 3 = Mid and Side channels */
- var n_chn_psy = (gfp.mode == MPEGMode.JOINT_STEREO) ? 4 : n_chn_out;
- /* Don't copy the input buffer into a temporary buffer */
- /* unroll the loop 2 times */
- for (var chn = 0; chn < n_chn_out; chn++) {
- /* apply high pass filter of fs/4 */
- firbuf = buffer[chn];
- var firbufPos = bufPos + 576 - 350 - NSFIRLEN + 192;
- for (var i = 0; i < 576; i++) {
- var sum1, sum2;
- sum1 = firbuf[firbufPos + i + 10];
- sum2 = 0.0;
- for (var j = 0; j < ((NSFIRLEN - 1) / 2) - 1; j += 2) {
- sum1 += fircoef_[j]
- * (firbuf[firbufPos + i + j] + firbuf[firbufPos + i
- + NSFIRLEN - j]);
- sum2 += fircoef_[j + 1]
- * (firbuf[firbufPos + i + j + 1] + firbuf[firbufPos
- + i + NSFIRLEN - j - 1]);
- }
- ns_hpfsmpl[chn][i] = sum1 + sum2;
- }
- masking_ratio[gr_out][chn].en.assign(gfc.en[chn]);
- masking_ratio[gr_out][chn].thm.assign(gfc.thm[chn]);
- if (n_chn_psy > 2) {
- /* MS maskings */
- /* percep_MS_entropy [chn-2] = gfc . pe [chn]; */
- masking_MS_ratio[gr_out][chn].en.assign(gfc.en[chn + 2]);
- masking_MS_ratio[gr_out][chn].thm.assign(gfc.thm[chn + 2]);
- }
- }
- for (var chn = 0; chn < n_chn_psy; chn++) {
- var attack_intensity = new_float(12);
- var en_subshort = new_float(12);
- var en_short = [0, 0, 0, 0];
- var pf = ns_hpfsmpl[chn & 1];
- var pfPos = 0;
- var attackThreshold = (chn == 3) ? gfc.nsPsy.attackthre_s
- : gfc.nsPsy.attackthre;
- var ns_uselongblock = 1;
-
- if (chn == 2) {
- for (var i = 0, j = 576; j > 0; ++i, --j) {
- var l = ns_hpfsmpl[0][i];
- var r = ns_hpfsmpl[1][i];
- ns_hpfsmpl[0][i] = l + r;
- ns_hpfsmpl[1][i] = l - r;
- }
- }
- /***************************************************************
- * determine the block type (window type)
- ***************************************************************/
- /* calculate energies of each sub-shortblocks */
- for (var i = 0; i < 3; i++) {
- en_subshort[i] = gfc.nsPsy.last_en_subshort[chn][i + 6];
- attack_intensity[i] = en_subshort[i]
- / gfc.nsPsy.last_en_subshort[chn][i + 4];
- en_short[0] += en_subshort[i];
- }
-
- for (var i = 0; i < 9; i++) {
- var pfe = pfPos + 576 / 9;
- var p = 1.;
- for (; pfPos < pfe; pfPos++)
- if (p < Math.abs(pf[pfPos]))
- p = Math.abs(pf[pfPos]);
-
- gfc.nsPsy.last_en_subshort[chn][i] = en_subshort[i + 3] = p;
- en_short[1 + i / 3] += p;
- if (p > en_subshort[i + 3 - 2]) {
- p = p / en_subshort[i + 3 - 2];
- } else if (en_subshort[i + 3 - 2] > p * 10.0) {
- p = en_subshort[i + 3 - 2] / (p * 10.0);
- } else {
- p = 0.0;
- }
- attack_intensity[i + 3] = p;
- }
- /* pulse like signal detection for fatboy.wav and so on */
- for (var i = 0; i < 3; ++i) {
- var enn = en_subshort[i * 3 + 3]
- + en_subshort[i * 3 + 4] + en_subshort[i * 3 + 5];
- var factor = 1.;
- if (en_subshort[i * 3 + 5] * 6 < enn) {
- factor *= 0.5;
- if (en_subshort[i * 3 + 4] * 6 < enn) {
- factor *= 0.5;
- }
- }
- sub_short_factor[chn][i] = factor;
- }
-
- if (gfp.analysis) {
- var x = attack_intensity[0];
- for (var i = 1; i < 12; i++) {
- if (x < attack_intensity[i]) {
- x = attack_intensity[i];
- }
- }
- gfc.pinfo.ers[gr_out][chn] = gfc.pinfo.ers_save[chn];
- gfc.pinfo.ers_save[chn] = x;
- }
-
- /* compare energies between sub-shortblocks */
- for (var i = 0; i < 12; i++) {
- if (0 == ns_attacks[chn][i / 3]
- && attack_intensity[i] > attackThreshold) {
- ns_attacks[chn][i / 3] = (i % 3) + 1;
- }
- }
-
- /*
- * should have energy change between short blocks, in order to avoid
- * periodic signals
- */
- /* Good samples to show the effect are Trumpet test songs */
- /*
- * GB: tuned (1) to avoid too many short blocks for test sample
- * TRUMPET
- */
- /*
- * RH: tuned (2) to let enough short blocks through for test sample
- * FSOL and SNAPS
- */
- for (var i = 1; i < 4; i++) {
- var u = en_short[i - 1];
- var v = en_short[i];
- var m = Math.max(u, v);
- if (m < 40000) { /* (2) */
- if (u < 1.7 * v && v < 1.7 * u) { /* (1) */
- if (i == 1 && ns_attacks[chn][0] <= ns_attacks[chn][i]) {
- ns_attacks[chn][0] = 0;
- }
- ns_attacks[chn][i] = 0;
- }
- }
- }
-
- if (ns_attacks[chn][0] <= gfc.nsPsy.lastAttacks[chn]) {
- ns_attacks[chn][0] = 0;
- }
-
- if (gfc.nsPsy.lastAttacks[chn] == 3
- || (ns_attacks[chn][0] + ns_attacks[chn][1]
- + ns_attacks[chn][2] + ns_attacks[chn][3]) != 0) {
- ns_uselongblock = 0;
-
- if (ns_attacks[chn][1] != 0 && ns_attacks[chn][0] != 0) {
- ns_attacks[chn][1] = 0;
- }
- if (ns_attacks[chn][2] != 0 && ns_attacks[chn][1] != 0) {
- ns_attacks[chn][2] = 0;
- }
- if (ns_attacks[chn][3] != 0 && ns_attacks[chn][2] != 0) {
- ns_attacks[chn][3] = 0;
- }
- }
- if (chn < 2) {
- uselongblock[chn] = ns_uselongblock;
- } else {
- if (ns_uselongblock == 0) {
- uselongblock[0] = uselongblock[1] = 0;
- }
- }
-
- /*
- * there is a one granule delay. Copy maskings computed last call
- * into masking_ratio to return to calling program.
- */
- energy[chn] = gfc.tot_ener[chn];
- }
- }
-
- function vbrpsy_skip_masking_s(gfc, chn, sblock) {
- if (sblock == 0) {
- for (var b = 0; b < gfc.npart_s; b++) {
- gfc.nb_s2[chn][b] = gfc.nb_s1[chn][b];
- gfc.nb_s1[chn][b] = 0;
- }
- }
- }
-
- function vbrpsy_skip_masking_l(gfc, chn) {
- for (var b = 0; b < gfc.npart_l; b++) {
- gfc.nb_2[chn][b] = gfc.nb_1[chn][b];
- gfc.nb_1[chn][b] = 0;
- }
- }
-
- function psyvbr_calc_mask_index_s(gfc, max, avg, mask_idx) {
- var last_tab_entry = tab.length - 1;
- var b = 0;
- var a = avg[b] + avg[b + 1];
- if (a > 0.0) {
- var m = max[b];
- if (m < max[b + 1])
- m = max[b + 1];
- a = 20.0 * (m * 2.0 - a)
- / (a * (gfc.numlines_s[b] + gfc.numlines_s[b + 1] - 1));
- var k = 0 | a;
- if (k > last_tab_entry)
- k = last_tab_entry;
- mask_idx[b] = k;
- } else {
- mask_idx[b] = 0;
- }
-
- for (b = 1; b < gfc.npart_s - 1; b++) {
- a = avg[b - 1] + avg[b] + avg[b + 1];
- if (a > 0.0) {
- var m = max[b - 1];
- if (m < max[b])
- m = max[b];
- if (m < max[b + 1])
- m = max[b + 1];
- a = 20.0
- * (m * 3.0 - a)
- / (a * (gfc.numlines_s[b - 1] + gfc.numlines_s[b]
- + gfc.numlines_s[b + 1] - 1));
- var k = 0 | a;
- if (k > last_tab_entry)
- k = last_tab_entry;
- mask_idx[b] = k;
- } else {
- mask_idx[b] = 0;
- }
- }
-
- a = avg[b - 1] + avg[b];
- if (a > 0.0) {
- var m = max[b - 1];
- if (m < max[b])
- m = max[b];
- a = 20.0 * (m * 2.0 - a)
- / (a * (gfc.numlines_s[b - 1] + gfc.numlines_s[b] - 1));
- var k = 0 | a;
- if (k > last_tab_entry)
- k = last_tab_entry;
- mask_idx[b] = k;
- } else {
- mask_idx[b] = 0;
- }
- }
-
- function vbrpsy_compute_masking_s(gfp, fftenergy_s, eb, thr, chn, sblock) {
- var gfc = gfp.internal_flags;
- var max = new float[Encoder.CBANDS], avg = new_float(Encoder.CBANDS);
- var i, j, b;
- var mask_idx_s = new int[Encoder.CBANDS];
-
- for (b = j = 0; b < gfc.npart_s; ++b) {
- var ebb = 0, m = 0;
- var n = gfc.numlines_s[b];
- for (i = 0; i < n; ++i, ++j) {
- var el = fftenergy_s[sblock][j];
- ebb += el;
- if (m < el)
- m = el;
- }
- eb[b] = ebb;
- max[b] = m;
- avg[b] = ebb / n;
- }
- for (; b < Encoder.CBANDS; ++b) {
- max[b] = 0;
- avg[b] = 0;
- }
- psyvbr_calc_mask_index_s(gfc, max, avg, mask_idx_s);
- for (j = b = 0; b < gfc.npart_s; b++) {
- var kk = gfc.s3ind_s[b][0];
- var last = gfc.s3ind_s[b][1];
- var dd, dd_n;
- var x, ecb, avg_mask;
- dd = mask_idx_s[kk];
- dd_n = 1;
- ecb = gfc.s3_ss[j] * eb[kk] * tab[mask_idx_s[kk]];
- ++j;
- ++kk;
- while (kk <= last) {
- dd += mask_idx_s[kk];
- dd_n += 1;
- x = gfc.s3_ss[j] * eb[kk] * tab[mask_idx_s[kk]];
- ecb = vbrpsy_mask_add(ecb, x, kk - b);
- ++j;
- ++kk;
- }
- dd = (1 + 2 * dd) / (2 * dd_n);
- avg_mask = tab[dd] * 0.5;
- ecb *= avg_mask;
- thr[b] = ecb;
- gfc.nb_s2[chn][b] = gfc.nb_s1[chn][b];
- gfc.nb_s1[chn][b] = ecb;
- {
- /*
- * if THR exceeds EB, the quantization routines will take the
- * difference from other bands. in case of strong tonal samples
- * (tonaltest.wav) this leads to heavy distortions. that's why
- * we limit THR here.
- */
- x = max[b];
- x *= gfc.minval_s[b];
- x *= avg_mask;
- if (thr[b] > x) {
- thr[b] = x;
- }
- }
- if (gfc.masking_lower > 1) {
- thr[b] *= gfc.masking_lower;
- }
- if (thr[b] > eb[b]) {
- thr[b] = eb[b];
- }
- if (gfc.masking_lower < 1) {
- thr[b] *= gfc.masking_lower;
- }
-
- }
- for (; b < Encoder.CBANDS; ++b) {
- eb[b] = 0;
- thr[b] = 0;
- }
- }
-
- function vbrpsy_compute_masking_l(gfc, fftenergy, eb_l, thr, chn) {
- var max = new_float(Encoder.CBANDS), avg = new_float(Encoder.CBANDS);
- var mask_idx_l = new_int(Encoder.CBANDS + 2);
- var b;
-
- /*********************************************************************
- * Calculate the energy and the tonality of each partition.
- *********************************************************************/
- calc_energy(gfc, fftenergy, eb_l, max, avg);
- calc_mask_index_l(gfc, max, avg, mask_idx_l);
-
- /*********************************************************************
- * convolve the partitioned energy and unpredictability with the
- * spreading function, s3_l[b][k]
- ********************************************************************/
- var k = 0;
- for (b = 0; b < gfc.npart_l; b++) {
- var x, ecb, avg_mask, t;
- /* convolve the partitioned energy with the spreading function */
- var kk = gfc.s3ind[b][0];
- var last = gfc.s3ind[b][1];
- var dd = 0, dd_n = 0;
- dd = mask_idx_l[kk];
- dd_n += 1;
- ecb = gfc.s3_ll[k] * eb_l[kk] * tab[mask_idx_l[kk]];
- ++k;
- ++kk;
- while (kk <= last) {
- dd += mask_idx_l[kk];
- dd_n += 1;
- x = gfc.s3_ll[k] * eb_l[kk] * tab[mask_idx_l[kk]];
- t = vbrpsy_mask_add(ecb, x, kk - b);
- ecb = t;
- ++k;
- ++kk;
- }
- dd = (1 + 2 * dd) / (2 * dd_n);
- avg_mask = tab[dd] * 0.5;
- ecb *= avg_mask;
-
- /**** long block pre-echo control ****/
- /**
- *
- * dont use long block pre-echo control if previous granule was
- * a short block. This is to avoid the situation:
- * frame0: quiet (very low masking)
- * frame1: surge (triggers short blocks)
- * frame2: regular frame. looks like pre-echo when compared to
- * frame0, but all pre-echo was in frame1.
- *
- */
- /*
- * chn=0,1 L and R channels chn=2,3 S and M channels.
- */
- if (gfc.blocktype_old[chn & 0x01] == Encoder.SHORT_TYPE) {
- var ecb_limit = rpelev * gfc.nb_1[chn][b];
- if (ecb_limit > 0) {
- thr[b] = Math.min(ecb, ecb_limit);
- } else {
- /**
- *
- * Robert 071209:
- * Because we don't calculate long block psy when we know a granule
- * should be of short blocks, we don't have any clue how the granule
- * before would have looked like as a long block. So we have to guess
- * a little bit for this END_TYPE block.
- * Most of the time we get away with this sloppyness. (fingers crossed :)
- * The speed increase is worth it.
- *
- */
- thr[b] = Math.min(ecb, eb_l[b] * NS_PREECHO_ATT2);
- }
- } else {
- var ecb_limit_2 = rpelev2 * gfc.nb_2[chn][b];
- var ecb_limit_1 = rpelev * gfc.nb_1[chn][b];
- var ecb_limit;
- if (ecb_limit_2 <= 0) {
- ecb_limit_2 = ecb;
- }
- if (ecb_limit_1 <= 0) {
- ecb_limit_1 = ecb;
- }
- if (gfc.blocktype_old[chn & 0x01] == Encoder.NORM_TYPE) {
- ecb_limit = Math.min(ecb_limit_1, ecb_limit_2);
- } else {
- ecb_limit = ecb_limit_1;
- }
- thr[b] = Math.min(ecb, ecb_limit);
- }
- gfc.nb_2[chn][b] = gfc.nb_1[chn][b];
- gfc.nb_1[chn][b] = ecb;
- {
- /*
- * if THR exceeds EB, the quantization routines will take the
- * difference from other bands. in case of strong tonal samples
- * (tonaltest.wav) this leads to heavy distortions. that's why
- * we limit THR here.
- */
- x = max[b];
- x *= gfc.minval_l[b];
- x *= avg_mask;
- if (thr[b] > x) {
- thr[b] = x;
- }
- }
- if (gfc.masking_lower > 1) {
- thr[b] *= gfc.masking_lower;
- }
- if (thr[b] > eb_l[b]) {
- thr[b] = eb_l[b];
- }
- if (gfc.masking_lower < 1) {
- thr[b] *= gfc.masking_lower;
- }
- }
- for (; b < Encoder.CBANDS; ++b) {
- eb_l[b] = 0;
- thr[b] = 0;
- }
- }
-
- function vbrpsy_compute_block_type(gfp, uselongblock) {
- var gfc = gfp.internal_flags;
-
- if (gfp.short_blocks == ShortBlock.short_block_coupled
- /* force both channels to use the same block type */
- /* this is necessary if the frame is to be encoded in ms_stereo. */
- /* But even without ms_stereo, FhG does this */
- && !(uselongblock[0] != 0 && uselongblock[1] != 0))
- uselongblock[0] = uselongblock[1] = 0;
-
- for (var chn = 0; chn < gfc.channels_out; chn++) {
- /* disable short blocks */
- if (gfp.short_blocks == ShortBlock.short_block_dispensed) {
- uselongblock[chn] = 1;
- }
- if (gfp.short_blocks == ShortBlock.short_block_forced) {
- uselongblock[chn] = 0;
- }
- }
- }
-
- function vbrpsy_apply_block_type(gfp, uselongblock, blocktype_d) {
- var gfc = gfp.internal_flags;
-
- /*
- * update the blocktype of the previous granule, since it depends on
- * what happend in this granule
- */
- for (var chn = 0; chn < gfc.channels_out; chn++) {
- var blocktype = Encoder.NORM_TYPE;
- /* disable short blocks */
-
- if (uselongblock[chn] != 0) {
- /* no attack : use long blocks */
- if (gfc.blocktype_old[chn] == Encoder.SHORT_TYPE)
- blocktype = Encoder.STOP_TYPE;
- } else {
- /* attack : use short blocks */
- blocktype = Encoder.SHORT_TYPE;
- if (gfc.blocktype_old[chn] == Encoder.NORM_TYPE) {
- gfc.blocktype_old[chn] = Encoder.START_TYPE;
- }
- if (gfc.blocktype_old[chn] == Encoder.STOP_TYPE)
- gfc.blocktype_old[chn] = Encoder.SHORT_TYPE;
- }
-
- blocktype_d[chn] = gfc.blocktype_old[chn];
- // value returned to calling program
- gfc.blocktype_old[chn] = blocktype;
- // save for next call to l3psy_anal
- }
- }
-
- /**
- * compute M/S thresholds from Johnston & Ferreira 1992 ICASSP paper
- */
- function vbrpsy_compute_MS_thresholds(eb, thr, cb_mld, ath_cb, athadjust, msfix, n) {
- var msfix2 = msfix * 2;
- var athlower = msfix > 0 ? Math.pow(10, athadjust) : 1;
- var rside, rmid;
- for (var b = 0; b < n; ++b) {
- var ebM = eb[2][b];
- var ebS = eb[3][b];
- var thmL = thr[0][b];
- var thmR = thr[1][b];
- var thmM = thr[2][b];
- var thmS = thr[3][b];
-
- /* use this fix if L & R masking differs by 2db or less */
- if (thmL <= 1.58 * thmR && thmR <= 1.58 * thmL) {
- var mld_m = cb_mld[b] * ebS;
- var mld_s = cb_mld[b] * ebM;
- rmid = Math.max(thmM, Math.min(thmS, mld_m));
- rside = Math.max(thmS, Math.min(thmM, mld_s));
- } else {
- rmid = thmM;
- rside = thmS;
- }
- if (msfix > 0) {
- /***************************************************************/
- /* Adjust M/S maskings if user set "msfix" */
- /***************************************************************/
- /* Naoki Shibata 2000 */
- var thmLR, thmMS;
- var ath = ath_cb[b] * athlower;
- thmLR = Math.min(Math.max(thmL, ath), Math.max(thmR, ath));
- thmM = Math.max(rmid, ath);
- thmS = Math.max(rside, ath);
- thmMS = thmM + thmS;
- if (thmMS > 0 && (thmLR * msfix2) < thmMS) {
- var f = thmLR * msfix2 / thmMS;
- thmM *= f;
- thmS *= f;
- }
- rmid = Math.min(thmM, rmid);
- rside = Math.min(thmS, rside);
- }
- if (rmid > ebM) {
- rmid = ebM;
- }
- if (rside > ebS) {
- rside = ebS;
- }
- thr[2][b] = rmid;
- thr[3][b] = rside;
- }
- }
-
- this.L3psycho_anal_vbr = function (gfp, buffer, bufPos, gr_out, masking_ratio, masking_MS_ratio, percep_entropy, percep_MS_entropy, energy, blocktype_d) {
- var gfc = gfp.internal_flags;
-
- /* fft and energy calculation */
- var wsamp_l;
- var wsamp_s;
- var fftenergy = new_float(Encoder.HBLKSIZE);
- var fftenergy_s = new_float_n([3, Encoder.HBLKSIZE_s]);
- var wsamp_L = new_float_n([2, Encoder.BLKSIZE]);
- var wsamp_S = new_float_n([2, 3, Encoder.BLKSIZE_s]);
- var eb = new_float_n([4, Encoder.CBANDS]), thr = new_float_n([4, Encoder.CBANDS]);
- var sub_short_factor = new_float_n([4, 3]);
- var pcfact = 0.6;
-
- /* block type */
- var ns_attacks = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
- [0, 0, 0, 0]];
- var uselongblock = new_int(2);
-
- /* usual variables like loop indices, etc.. */
-
- /* chn=2 and 3 = Mid and Side channels */
- var n_chn_psy = (gfp.mode == MPEGMode.JOINT_STEREO) ? 4
- : gfc.channels_out;
-
- vbrpsy_attack_detection(gfp, buffer, bufPos, gr_out, masking_ratio,
- masking_MS_ratio, energy, sub_short_factor, ns_attacks,
- uselongblock);
-
- vbrpsy_compute_block_type(gfp, uselongblock);
-
- /* LONG BLOCK CASE */
- {
- for (var chn = 0; chn < n_chn_psy; chn++) {
- var ch01 = chn & 0x01;
- wsamp_l = wsamp_L;
- vbrpsy_compute_fft_l(gfp, buffer, bufPos, chn, gr_out,
- fftenergy, wsamp_l, ch01);
-
- vbrpsy_compute_loudness_approximation_l(gfp, gr_out, chn,
- fftenergy);
-
- if (uselongblock[ch01] != 0) {
- vbrpsy_compute_masking_l(gfc, fftenergy, eb[chn], thr[chn],
- chn);
- } else {
- vbrpsy_skip_masking_l(gfc, chn);
- }
- }
- if ((uselongblock[0] + uselongblock[1]) == 2) {
- /* M/S channel */
- if (gfp.mode == MPEGMode.JOINT_STEREO) {
- vbrpsy_compute_MS_thresholds(eb, thr, gfc.mld_cb_l,
- gfc.ATH.cb_l, gfp.ATHlower * gfc.ATH.adjust,
- gfp.msfix, gfc.npart_l);
- }
- }
- /* TODO: apply adaptive ATH masking here ?? */
- for (var chn = 0; chn < n_chn_psy; chn++) {
- var ch01 = chn & 0x01;
- if (uselongblock[ch01] != 0) {
- convert_partition2scalefac_l(gfc, eb[chn], thr[chn], chn);
- }
- }
- }
-
- /* SHORT BLOCKS CASE */
- {
- for (var sblock = 0; sblock < 3; sblock++) {
- for (var chn = 0; chn < n_chn_psy; ++chn) {
- var ch01 = chn & 0x01;
-
- if (uselongblock[ch01] != 0) {
- vbrpsy_skip_masking_s(gfc, chn, sblock);
- } else {
- /* compute masking thresholds for short blocks */
- wsamp_s = wsamp_S;
- vbrpsy_compute_fft_s(gfp, buffer, bufPos, chn, sblock,
- fftenergy_s, wsamp_s, ch01);
- vbrpsy_compute_masking_s(gfp, fftenergy_s, eb[chn],
- thr[chn], chn, sblock);
- }
- }
- if ((uselongblock[0] + uselongblock[1]) == 0) {
- /* M/S channel */
- if (gfp.mode == MPEGMode.JOINT_STEREO) {
- vbrpsy_compute_MS_thresholds(eb, thr, gfc.mld_cb_s,
- gfc.ATH.cb_s, gfp.ATHlower * gfc.ATH.adjust,
- gfp.msfix, gfc.npart_s);
- }
- /* L/R channel */
- }
- /* TODO: apply adaptive ATH masking here ?? */
- for (var chn = 0; chn < n_chn_psy; ++chn) {
- var ch01 = chn & 0x01;
- if (0 == uselongblock[ch01]) {
- convert_partition2scalefac_s(gfc, eb[chn], thr[chn],
- chn, sblock);
- }
- }
- }
-
- /**** short block pre-echo control ****/
- for (var chn = 0; chn < n_chn_psy; chn++) {
- var ch01 = chn & 0x01;
-
- if (uselongblock[ch01] != 0) {
- continue;
- }
- for (var sb = 0; sb < Encoder.SBMAX_s; sb++) {
- var new_thmm = new_float(3);
- for (var sblock = 0; sblock < 3; sblock++) {
- var thmm = gfc.thm[chn].s[sb][sblock];
- thmm *= NS_PREECHO_ATT0;
-
- if (ns_attacks[chn][sblock] >= 2
- || ns_attacks[chn][sblock + 1] == 1) {
- var idx = (sblock != 0) ? sblock - 1 : 2;
- var p = NS_INTERP(gfc.thm[chn].s[sb][idx], thmm,
- NS_PREECHO_ATT1 * pcfact);
- thmm = Math.min(thmm, p);
- } else if (ns_attacks[chn][sblock] == 1) {
- var idx = (sblock != 0) ? sblock - 1 : 2;
- var p = NS_INTERP(gfc.thm[chn].s[sb][idx], thmm,
- NS_PREECHO_ATT2 * pcfact);
- thmm = Math.min(thmm, p);
- } else if ((sblock != 0 && ns_attacks[chn][sblock - 1] == 3)
- || (sblock == 0 && gfc.nsPsy.lastAttacks[chn] == 3)) {
- var idx = (sblock != 2) ? sblock + 1 : 0;
- var p = NS_INTERP(gfc.thm[chn].s[sb][idx], thmm,
- NS_PREECHO_ATT2 * pcfact);
- thmm = Math.min(thmm, p);
- }
-
- /* pulse like signal detection for fatboy.wav and so on */
- thmm *= sub_short_factor[chn][sblock];
-
- new_thmm[sblock] = thmm;
- }
- for (var sblock = 0; sblock < 3; sblock++) {
- gfc.thm[chn].s[sb][sblock] = new_thmm[sblock];
- }
- }
- }
- }
- for (var chn = 0; chn < n_chn_psy; chn++) {
- gfc.nsPsy.lastAttacks[chn] = ns_attacks[chn][2];
- }
-
- /***************************************************************
- * determine final block type
- ***************************************************************/
- vbrpsy_apply_block_type(gfp, uselongblock, blocktype_d);
-
- /*********************************************************************
- * compute the value of PE to return ... no delay and advance
- *********************************************************************/
- for (var chn = 0; chn < n_chn_psy; chn++) {
- var ppe;
- var ppePos;
- var type;
- var mr;
-
- if (chn > 1) {
- ppe = percep_MS_entropy;
- ppePos = -2;
- type = Encoder.NORM_TYPE;
- if (blocktype_d[0] == Encoder.SHORT_TYPE
- || blocktype_d[1] == Encoder.SHORT_TYPE)
- type = Encoder.SHORT_TYPE;
- mr = masking_MS_ratio[gr_out][chn - 2];
- } else {
- ppe = percep_entropy;
- ppePos = 0;
- type = blocktype_d[chn];
- mr = masking_ratio[gr_out][chn];
- }
-
- if (type == Encoder.SHORT_TYPE) {
- ppe[ppePos + chn] = pecalc_s(mr, gfc.masking_lower);
- } else {
- ppe[ppePos + chn] = pecalc_l(mr, gfc.masking_lower);
- }
-
- if (gfp.analysis) {
- gfc.pinfo.pe[gr_out][chn] = ppe[ppePos + chn];
- }
- }
- return 0;
- }
-
- function s3_func_x(bark, hf_slope) {
- var tempx = bark, tempy;
-
- if (tempx >= 0) {
- tempy = -tempx * 27;
- } else {
- tempy = tempx * hf_slope;
- }
- if (tempy <= -72.0) {
- return 0;
- }
- return Math.exp(tempy * LN_TO_LOG10);
- }
-
- function norm_s3_func_x(hf_slope) {
- var lim_a = 0, lim_b = 0;
- {
- var x = 0, l, h;
- for (x = 0; s3_func_x(x, hf_slope) > 1e-20; x -= 1)
- ;
- l = x;
- h = 0;
- while (Math.abs(h - l) > 1e-12) {
- x = (h + l) / 2;
- if (s3_func_x(x, hf_slope) > 0) {
- h = x;
- } else {
- l = x;
- }
- }
- lim_a = l;
- }
- {
- var x = 0, l, h;
- for (x = 0; s3_func_x(x, hf_slope) > 1e-20; x += 1)
- ;
- l = 0;
- h = x;
- while (Math.abs(h - l) > 1e-12) {
- x = (h + l) / 2;
- if (s3_func_x(x, hf_slope) > 0) {
- l = x;
- } else {
- h = x;
- }
- }
- lim_b = h;
- }
- {
- var sum = 0;
- var m = 1000;
- var i;
- for (i = 0; i <= m; ++i) {
- var x = lim_a + i * (lim_b - lim_a) / m;
- var y = s3_func_x(x, hf_slope);
- sum += y;
- }
- {
- var norm = (m + 1) / (sum * (lim_b - lim_a));
- /* printf( "norm = %lf\n",norm); */
- return norm;
- }
- }
- }
-
- /**
- * The spreading function. Values returned in units of energy
- */
- function s3_func(bark) {
- var tempx, x, tempy, temp;
- tempx = bark;
- if (tempx >= 0)
- tempx *= 3;
- else
- tempx *= 1.5;
-
- if (tempx >= 0.5 && tempx <= 2.5) {
- temp = tempx - 0.5;
- x = 8.0 * (temp * temp - 2.0 * temp);
- } else
- x = 0.0;
- tempx += 0.474;
- tempy = 15.811389 + 7.5 * tempx - 17.5
- * Math.sqrt(1.0 + tempx * tempx);
-
- if (tempy <= -60.0)
- return 0.0;
-
- tempx = Math.exp((x + tempy) * LN_TO_LOG10);
-
- /**
- *
- * Normalization. The spreading function should be normalized so that:
- * +inf
- * /
- * | s3 [ bark ] d(bark) = 1
- * /
- * -inf
- *
- */
- tempx /= .6609193;
- return tempx;
- }
-
- /**
- * see for example "Zwicker: Psychoakustik, 1982; ISBN 3-540-11401-7
- */
- function freq2bark(freq) {
- /* input: freq in hz output: barks */
- if (freq < 0)
- freq = 0;
- freq = freq * 0.001;
- return 13.0 * Math.atan(.76 * freq) + 3.5
- * Math.atan(freq * freq / (7.5 * 7.5));
- }
-
- function init_numline(numlines, bo, bm, bval, bval_width, mld, bo_w, sfreq, blksize, scalepos, deltafreq, sbmax) {
- var b_frq = new_float(Encoder.CBANDS + 1);
- var sample_freq_frac = sfreq / (sbmax > 15 ? 2 * 576 : 2 * 192);
- var partition = new_int(Encoder.HBLKSIZE);
- var i;
- sfreq /= blksize;
- var j = 0;
- var ni = 0;
- /* compute numlines, the number of spectral lines in each partition band */
- /* each partition band should be about DELBARK wide. */
- for (i = 0; i < Encoder.CBANDS; i++) {
- var bark1;
- var j2;
- bark1 = freq2bark(sfreq * j);
-
- b_frq[i] = sfreq * j;
-
- for (j2 = j; freq2bark(sfreq * j2) - bark1 < DELBARK
- && j2 <= blksize / 2; j2++)
- ;
-
- numlines[i] = j2 - j;
- ni = i + 1;
-
- while (j < j2) {
- partition[j++] = i;
- }
- if (j > blksize / 2) {
- j = blksize / 2;
- ++i;
- break;
- }
- }
- b_frq[i] = sfreq * j;
-
- for (var sfb = 0; sfb < sbmax; sfb++) {
- var i1, i2, start, end;
- var arg;
- start = scalepos[sfb];
- end = scalepos[sfb + 1];
-
- i1 = 0 | Math.floor(.5 + deltafreq * (start - .5));
- if (i1 < 0)
- i1 = 0;
- i2 = 0 | Math.floor(.5 + deltafreq * (end - .5));
-
- if (i2 > blksize / 2)
- i2 = blksize / 2;
-
- bm[sfb] = (partition[i1] + partition[i2]) / 2;
- bo[sfb] = partition[i2];
- var f_tmp = sample_freq_frac * end;
- /*
- * calculate how much of this band belongs to current scalefactor
- * band
- */
- bo_w[sfb] = (f_tmp - b_frq[bo[sfb]])
- / (b_frq[bo[sfb] + 1] - b_frq[bo[sfb]]);
- if (bo_w[sfb] < 0) {
- bo_w[sfb] = 0;
- } else {
- if (bo_w[sfb] > 1) {
- bo_w[sfb] = 1;
- }
- }
- /* setup stereo demasking thresholds */
- /* formula reverse enginerred from plot in paper */
- arg = freq2bark(sfreq * scalepos[sfb] * deltafreq);
- arg = ( Math.min(arg, 15.5) / 15.5);
-
- mld[sfb] = Math.pow(10.0,
- 1.25 * (1 - Math.cos(Math.PI * arg)) - 2.5);
- }
-
- /* compute bark values of each critical band */
- j = 0;
- for (var k = 0; k < ni; k++) {
- var w = numlines[k];
- var bark1, bark2;
-
- bark1 = freq2bark(sfreq * (j));
- bark2 = freq2bark(sfreq * (j + w - 1));
- bval[k] = .5 * (bark1 + bark2);
-
- bark1 = freq2bark(sfreq * (j - .5));
- bark2 = freq2bark(sfreq * (j + w - .5));
- bval_width[k] = bark2 - bark1;
- j += w;
- }
-
- return ni;
- }
-
- function init_s3_values(s3ind, npart, bval, bval_width, norm, use_old_s3) {
- var s3 = new_float_n([Encoder.CBANDS, Encoder.CBANDS]);
- /*
- * The s3 array is not linear in the bark scale.
- *
- * bval[x] should be used to get the bark value.
- */
- var j;
- var numberOfNoneZero = 0;
-
- /**
- *
- * s[i][j], the value of the spreading function,
- * centered at band j (masker), for band i (maskee)
- *
- * i.e.: sum over j to spread into signal barkval=i
- * NOTE: i and j are used opposite as in the ISO docs
- *
- */
- if (use_old_s3) {
- for (var i = 0; i < npart; i++) {
- for (j = 0; j < npart; j++) {
- var v = s3_func(bval[i] - bval[j]) * bval_width[j];
- s3[i][j] = v * norm[i];
- }
- }
- } else {
- for (j = 0; j < npart; j++) {
- var hf_slope = 15 + Math.min(21 / bval[j], 12);
- var s3_x_norm = norm_s3_func_x(hf_slope);
- for (var i = 0; i < npart; i++) {
- var v = s3_x_norm
- * s3_func_x(bval[i] - bval[j], hf_slope)
- * bval_width[j];
- s3[i][j] = v * norm[i];
- }
- }
- }
- for (var i = 0; i < npart; i++) {
- for (j = 0; j < npart; j++) {
- if (s3[i][j] > 0.0)
- break;
- }
- s3ind[i][0] = j;
-
- for (j = npart - 1; j > 0; j--) {
- if (s3[i][j] > 0.0)
- break;
- }
- s3ind[i][1] = j;
- numberOfNoneZero += (s3ind[i][1] - s3ind[i][0] + 1);
- }
-
- var p = new_float(numberOfNoneZero);
- var k = 0;
- for (var i = 0; i < npart; i++)
- for (j = s3ind[i][0]; j <= s3ind[i][1]; j++)
- p[k++] = s3[i][j];
-
- return p;
- }
-
- function stereo_demask(f) {
- /* setup stereo demasking thresholds */
- /* formula reverse enginerred from plot in paper */
- var arg = freq2bark(f);
- arg = (Math.min(arg, 15.5) / 15.5);
-
- return Math.pow(10.0,
- 1.25 * (1 - Math.cos(Math.PI * arg)) - 2.5);
- }
-
- /**
- * NOTE: the bitrate reduction from the inter-channel masking effect is low
- * compared to the chance of getting annyoing artefacts. L3psycho_anal_vbr
- * does not use this feature. (Robert 071216)
- */
- this.psymodel_init = function (gfp) {
- var gfc = gfp.internal_flags;
- var i;
- var useOldS3 = true;
- var bvl_a = 13, bvl_b = 24;
- var snr_l_a = 0, snr_l_b = 0;
- var snr_s_a = -8.25, snr_s_b = -4.5;
- var bval = new_float(Encoder.CBANDS);
- var bval_width = new_float(Encoder.CBANDS);
- var norm = new_float(Encoder.CBANDS);
- var sfreq = gfp.out_samplerate;
-
- switch (gfp.experimentalZ) {
- default:
- case 0:
- useOldS3 = true;
- break;
- case 1:
- useOldS3 = (gfp.VBR == VbrMode.vbr_mtrh || gfp.VBR == VbrMode.vbr_mt) ? false
- : true;
- break;
- case 2:
- useOldS3 = false;
- break;
- case 3:
- bvl_a = 8;
- snr_l_a = -1.75;
- snr_l_b = -0.0125;
- snr_s_a = -8.25;
- snr_s_b = -2.25;
- break;
- }
- gfc.ms_ener_ratio_old = .25;
- gfc.blocktype_old[0] = gfc.blocktype_old[1] = Encoder.NORM_TYPE;
- // the vbr header is long blocks
-
- for (i = 0; i < 4; ++i) {
- for (var j = 0; j < Encoder.CBANDS; ++j) {
- gfc.nb_1[i][j] = 1e20;
- gfc.nb_2[i][j] = 1e20;
- gfc.nb_s1[i][j] = gfc.nb_s2[i][j] = 1.0;
- }
- for (var sb = 0; sb < Encoder.SBMAX_l; sb++) {
- gfc.en[i].l[sb] = 1e20;
- gfc.thm[i].l[sb] = 1e20;
- }
- for (var j = 0; j < 3; ++j) {
- for (var sb = 0; sb < Encoder.SBMAX_s; sb++) {
- gfc.en[i].s[sb][j] = 1e20;
- gfc.thm[i].s[sb][j] = 1e20;
- }
- gfc.nsPsy.lastAttacks[i] = 0;
- }
- for (var j = 0; j < 9; j++)
- gfc.nsPsy.last_en_subshort[i][j] = 10.;
- }
-
- /* init. for loudness approx. -jd 2001 mar 27 */
- gfc.loudness_sq_save[0] = gfc.loudness_sq_save[1] = 0.0;
-
- /*************************************************************************
- * now compute the psychoacoustic model specific constants
- ************************************************************************/
- /* compute numlines, bo, bm, bval, bval_width, mld */
-
- gfc.npart_l = init_numline(gfc.numlines_l, gfc.bo_l, gfc.bm_l, bval,
- bval_width, gfc.mld_l, gfc.PSY.bo_l_weight, sfreq,
- Encoder.BLKSIZE, gfc.scalefac_band.l, Encoder.BLKSIZE
- / (2.0 * 576), Encoder.SBMAX_l);
- /* compute the spreading function */
- for (i = 0; i < gfc.npart_l; i++) {
- var snr = snr_l_a;
- if (bval[i] >= bvl_a) {
- snr = snr_l_b * (bval[i] - bvl_a) / (bvl_b - bvl_a) + snr_l_a
- * (bvl_b - bval[i]) / (bvl_b - bvl_a);
- }
- norm[i] = Math.pow(10.0, snr / 10.0);
- if (gfc.numlines_l[i] > 0) {
- gfc.rnumlines_l[i] = 1.0 / gfc.numlines_l[i];
- } else {
- gfc.rnumlines_l[i] = 0;
- }
- }
- gfc.s3_ll = init_s3_values(gfc.s3ind, gfc.npart_l, bval, bval_width,
- norm, useOldS3);
-
- /* compute long block specific values, ATH and MINVAL */
- var j = 0;
- for (i = 0; i < gfc.npart_l; i++) {
- var x;
-
- /* ATH */
- x = Float.MAX_VALUE;
- for (var k = 0; k < gfc.numlines_l[i]; k++, j++) {
- var freq = sfreq * j / (1000.0 * Encoder.BLKSIZE);
- var level;
- /*
- * ATH below 100 Hz constant, not further climbing
- */
- level = this.ATHformula(freq * 1000, gfp) - 20;
- // scale to FFT units; returned value is in dB
- level = Math.pow(10., 0.1 * level);
- // convert from dB . energy
- level *= gfc.numlines_l[i];
- if (x > level)
- x = level;
- }
- gfc.ATH.cb_l[i] = x;
-
- /*
- * MINVAL. For low freq, the strength of the masking is limited by
- * minval this is an ISO MPEG1 thing, dont know if it is really
- * needed
- */
- /*
- * FIXME: it does work to reduce low-freq problems in S53-Wind-Sax
- * and lead-voice samples, but introduces some 3 kbps bit bloat too.
- * TODO: Further refinement of the shape of this hack.
- */
- x = -20 + bval[i] * 20 / 10;
- if (x > 6) {
- x = 100;
- }
- if (x < -15) {
- x = -15;
- }
- x -= 8.;
- gfc.minval_l[i] = (Math.pow(10.0, x / 10.) * gfc.numlines_l[i]);
- }
-
- /************************************************************************
- * do the same things for short blocks
- ************************************************************************/
- gfc.npart_s = init_numline(gfc.numlines_s, gfc.bo_s, gfc.bm_s, bval,
- bval_width, gfc.mld_s, gfc.PSY.bo_s_weight, sfreq,
- Encoder.BLKSIZE_s, gfc.scalefac_band.s, Encoder.BLKSIZE_s
- / (2.0 * 192), Encoder.SBMAX_s);
-
- /* SNR formula. short block is normalized by SNR. is it still right ? */
- j = 0;
- for (i = 0; i < gfc.npart_s; i++) {
- var x;
- var snr = snr_s_a;
- if (bval[i] >= bvl_a) {
- snr = snr_s_b * (bval[i] - bvl_a) / (bvl_b - bvl_a) + snr_s_a
- * (bvl_b - bval[i]) / (bvl_b - bvl_a);
- }
- norm[i] = Math.pow(10.0, snr / 10.0);
-
- /* ATH */
- x = Float.MAX_VALUE;
- for (var k = 0; k < gfc.numlines_s[i]; k++, j++) {
- var freq = sfreq * j / (1000.0 * Encoder.BLKSIZE_s);
- var level;
- /* freq = Min(.1,freq); */
- /*
- * ATH below 100 Hz constant, not
- * further climbing
- */
- level = this.ATHformula(freq * 1000, gfp) - 20;
- // scale to FFT units; returned value is in dB
- level = Math.pow(10., 0.1 * level);
- // convert from dB . energy
- level *= gfc.numlines_s[i];
- if (x > level)
- x = level;
- }
- gfc.ATH.cb_s[i] = x;
-
- /*
- * MINVAL. For low freq, the strength of the masking is limited by
- * minval this is an ISO MPEG1 thing, dont know if it is really
- * needed
- */
- x = (-7.0 + bval[i] * 7.0 / 12.0);
- if (bval[i] > 12) {
- x *= 1 + Math.log(1 + x) * 3.1;
- }
- if (bval[i] < 12) {
- x *= 1 + Math.log(1 - x) * 2.3;
- }
- if (x < -15) {
- x = -15;
- }
- x -= 8;
- gfc.minval_s[i] = Math.pow(10.0, x / 10)
- * gfc.numlines_s[i];
- }
-
- gfc.s3_ss = init_s3_values(gfc.s3ind_s, gfc.npart_s, bval, bval_width,
- norm, useOldS3);
-
- init_mask_add_max_values();
- fft.init_fft(gfc);
-
- /* setup temporal masking */
- gfc.decay = Math.exp(-1.0 * LOG10
- / (temporalmask_sustain_sec * sfreq / 192.0));
-
- {
- var msfix;
- msfix = NS_MSFIX;
- if ((gfp.exp_nspsytune & 2) != 0)
- msfix = 1.0;
- if (Math.abs(gfp.msfix) > 0.0)
- msfix = gfp.msfix;
- gfp.msfix = msfix;
-
- /*
- * spread only from npart_l bands. Normally, we use the spreading
- * function to convolve from npart_l down to npart_l bands
- */
- for (var b = 0; b < gfc.npart_l; b++)
- if (gfc.s3ind[b][1] > gfc.npart_l - 1)
- gfc.s3ind[b][1] = gfc.npart_l - 1;
- }
-
- /*
- * prepare for ATH auto adjustment: we want to decrease the ATH by 12 dB
- * per second
- */
- var frame_duration = (576. * gfc.mode_gr / sfreq);
- gfc.ATH.decay = Math.pow(10., -12. / 10. * frame_duration);
- gfc.ATH.adjust = 0.01;
- /* minimum, for leading low loudness */
- gfc.ATH.adjustLimit = 1.0;
- /* on lead, allow adjust up to maximum */
-
-
- if (gfp.ATHtype != -1) {
- /* compute equal loudness weights (eql_w) */
- var freq;
- var freq_inc = gfp.out_samplerate
- / (Encoder.BLKSIZE);
- var eql_balance = 0.0;
- freq = 0.0;
- for (i = 0; i < Encoder.BLKSIZE / 2; ++i) {
- /* convert ATH dB to relative power (not dB) */
- /* to determine eql_w */
- freq += freq_inc;
- gfc.ATH.eql_w[i] = 1. / Math.pow(10, this.ATHformula(freq, gfp) / 10);
- eql_balance += gfc.ATH.eql_w[i];
- }
- eql_balance = 1.0 / eql_balance;
- for (i = Encoder.BLKSIZE / 2; --i >= 0;) { /* scale weights */
- gfc.ATH.eql_w[i] *= eql_balance;
- }
- }
- {
- for (var b = j = 0; b < gfc.npart_s; ++b) {
- for (i = 0; i < gfc.numlines_s[b]; ++i) {
- ++j;
- }
- }
- for (var b = j = 0; b < gfc.npart_l; ++b) {
- for (i = 0; i < gfc.numlines_l[b]; ++i) {
- ++j;
- }
- }
- }
- j = 0;
- for (i = 0; i < gfc.npart_l; i++) {
- var freq = sfreq * (j + gfc.numlines_l[i] / 2) / (1.0 * Encoder.BLKSIZE);
- gfc.mld_cb_l[i] = stereo_demask(freq);
- j += gfc.numlines_l[i];
- }
- for (; i < Encoder.CBANDS; ++i) {
- gfc.mld_cb_l[i] = 1;
- }
- j = 0;
- for (i = 0; i < gfc.npart_s; i++) {
- var freq = sfreq * (j + gfc.numlines_s[i] / 2) / (1.0 * Encoder.BLKSIZE_s);
- gfc.mld_cb_s[i] = stereo_demask(freq);
- j += gfc.numlines_s[i];
- }
- for (; i < Encoder.CBANDS; ++i) {
- gfc.mld_cb_s[i] = 1;
- }
- return 0;
- }
-
- /**
- * Those ATH formulas are returning their minimum value for input = -1
- */
- function ATHformula_GB(f, value) {
- /**
- *
- * from Painter & Spanias
- * modified by Gabriel Bouvigne to better fit the reality
- * ath = 3.640 * pow(f,-0.8)
- * - 6.800 * exp(-0.6*pow(f-3.4,2.0))
- * + 6.000 * exp(-0.15*pow(f-8.7,2.0))
- * + 0.6* 0.001 * pow(f,4.0);
- *
- *
- * In the past LAME was using the Painter &Spanias formula.
- * But we had some recurrent problems with HF content.
- * We measured real ATH values, and found the older formula
- * to be inaccurate in the higher part. So we made this new
- * formula and this solved most of HF problematic test cases.
- * The tradeoff is that in VBR mode it increases a lot the
- * bitrate.
- *
- */
-
- /*
- * This curve can be adjusted according to the VBR scale: it adjusts
- * from something close to Painter & Spanias on V9 up to Bouvigne's
- * formula for V0. This way the VBR bitrate is more balanced according
- * to the -V value.
- */
-
- // the following Hack allows to ask for the lowest value
- if (f < -.3)
- f = 3410;
-
- // convert to khz
- f /= 1000;
- f = Math.max(0.1, f);
- var ath = 3.640 * Math.pow(f, -0.8) - 6.800
- * Math.exp(-0.6 * Math.pow(f - 3.4, 2.0)) + 6.000
- * Math.exp(-0.15 * Math.pow(f - 8.7, 2.0))
- + (0.6 + 0.04 * value) * 0.001 * Math.pow(f, 4.0);
- return ath;
- }
-
- this.ATHformula = function (f, gfp) {
- var ath;
- switch (gfp.ATHtype) {
- case 0:
- ath = ATHformula_GB(f, 9);
- break;
- case 1:
- // over sensitive, should probably be removed
- ath = ATHformula_GB(f, -1);
- break;
- case 2:
- ath = ATHformula_GB(f, 0);
- break;
- case 3:
- // modification of GB formula by Roel
- ath = ATHformula_GB(f, 1) + 6;
- break;
- case 4:
- ath = ATHformula_GB(f, gfp.ATHcurve);
- break;
- default:
- ath = ATHformula_GB(f, 0);
- break;
- }
- return ath;
- }
-
-}
-
-
-
-function Lame() {
- var self = this;
- var LAME_MAXALBUMART = (128 * 1024);
-
- Lame.V9 = 410;
- Lame.V8 = 420;
- Lame.V7 = 430;
- Lame.V6 = 440;
- Lame.V5 = 450;
- Lame.V4 = 460;
- Lame.V3 = 470;
- Lame.V2 = 480;
- Lame.V1 = 490;
- Lame.V0 = 500;
-
- /* still there for compatibility */
-
- Lame.R3MIX = 1000;
- Lame.STANDARD = 1001;
- Lame.EXTREME = 1002;
- Lame.INSANE = 1003;
- Lame.STANDARD_FAST = 1004;
- Lame.EXTREME_FAST = 1005;
- Lame.MEDIUM = 1006;
- Lame.MEDIUM_FAST = 1007;
-
- /**
- * maximum size of mp3buffer needed if you encode at most 1152 samples for
- * each call to lame_encode_buffer. see lame_encode_buffer() below
- * (LAME_MAXMP3BUFFER is now obsolete)
- */
- var LAME_MAXMP3BUFFER = (16384 + LAME_MAXALBUMART);
- Lame.LAME_MAXMP3BUFFER = LAME_MAXMP3BUFFER;
-
- var ga;
- var bs;
- var p;
- var qupvt;
- var qu;
- var psy = new PsyModel();
- var vbr;
- var ver;
- var id3;
- var mpglib;
- this.enc = new Encoder();
-
- this.setModules = function (_ga, _bs, _p, _qupvt, _qu, _vbr, _ver, _id3, _mpglib) {
- ga = _ga;
- bs = _bs;
- p = _p;
- qupvt = _qupvt;
- qu = _qu;
- vbr = _vbr;
- ver = _ver;
- id3 = _id3;
- mpglib = _mpglib;
- this.enc.setModules(bs, psy, qupvt, vbr);
- }
-
- /**
- * PSY Model related stuff
- */
- function PSY() {
- /**
- * The dbQ stuff.
- */
- this.mask_adjust = 0.;
- /**
- * The dbQ stuff.
- */
- this.mask_adjust_short = 0.;
- /* at transition from one scalefactor band to next */
- /**
- * Band weight long scalefactor bands.
- */
- this.bo_l_weight = new_float(Encoder.SBMAX_l);
- /**
- * Band weight short scalefactor bands.
- */
- this.bo_s_weight = new_float(Encoder.SBMAX_s);
- }
-
- function LowPassHighPass() {
- this.lowerlimit = 0.;
- }
-
- function BandPass(bitrate, lPass) {
- this.lowpass = lPass;
- }
-
- var LAME_ID = 0xFFF88E3B;
-
- function lame_init_old(gfp) {
- var gfc;
-
- gfp.class_id = LAME_ID;
-
- gfc = gfp.internal_flags = new LameInternalFlags();
-
- /* Global flags. set defaults here for non-zero values */
- /* see lame.h for description */
- /*
- * set integer values to -1 to mean that LAME will compute the best
- * value, UNLESS the calling program as set it (and the value is no
- * longer -1)
- */
-
- gfp.mode = MPEGMode.NOT_SET;
- gfp.original = 1;
- gfp.in_samplerate = 44100;
- gfp.num_channels = 2;
- gfp.num_samples = -1;
-
- gfp.bWriteVbrTag = true;
- gfp.quality = -1;
- gfp.short_blocks = null;
- gfc.subblock_gain = -1;
-
- gfp.lowpassfreq = 0;
- gfp.highpassfreq = 0;
- gfp.lowpasswidth = -1;
- gfp.highpasswidth = -1;
-
- gfp.VBR = VbrMode.vbr_off;
- gfp.VBR_q = 4;
- gfp.ATHcurve = -1;
- gfp.VBR_mean_bitrate_kbps = 128;
- gfp.VBR_min_bitrate_kbps = 0;
- gfp.VBR_max_bitrate_kbps = 0;
- gfp.VBR_hard_min = 0;
- gfc.VBR_min_bitrate = 1;
- /* not 0 ????? */
- gfc.VBR_max_bitrate = 13;
- /* not 14 ????? */
-
- gfp.quant_comp = -1;
- gfp.quant_comp_short = -1;
-
- gfp.msfix = -1;
-
- gfc.resample_ratio = 1;
-
- gfc.OldValue[0] = 180;
- gfc.OldValue[1] = 180;
- gfc.CurrentStep[0] = 4;
- gfc.CurrentStep[1] = 4;
- gfc.masking_lower = 1;
- gfc.nsPsy.attackthre = -1;
- gfc.nsPsy.attackthre_s = -1;
-
- gfp.scale = -1;
-
- gfp.athaa_type = -1;
- gfp.ATHtype = -1;
- /* default = -1 = set in lame_init_params */
- gfp.athaa_loudapprox = -1;
- /* 1 = flat loudness approx. (total energy) */
- /* 2 = equal loudness curve */
- gfp.athaa_sensitivity = 0.0;
- /* no offset */
- gfp.useTemporal = null;
- gfp.interChRatio = -1;
-
- /*
- * The reason for int mf_samples_to_encode = ENCDELAY + POSTDELAY;
- * ENCDELAY = internal encoder delay. And then we have to add
- * POSTDELAY=288 because of the 50% MDCT overlap. A 576 MDCT granule
- * decodes to 1152 samples. To synthesize the 576 samples centered under
- * this granule we need the previous granule for the first 288 samples
- * (no problem), and the next granule for the next 288 samples (not
- * possible if this is last granule). So we need to pad with 288 samples
- * to make sure we can encode the 576 samples we are interested in.
- */
- gfc.mf_samples_to_encode = Encoder.ENCDELAY + Encoder.POSTDELAY;
- gfp.encoder_padding = 0;
- gfc.mf_size = Encoder.ENCDELAY - Encoder.MDCTDELAY;
- /*
- * we pad input with this many 0's
- */
-
- gfp.findReplayGain = false;
- gfp.decode_on_the_fly = false;
-
- gfc.decode_on_the_fly = false;
- gfc.findReplayGain = false;
- gfc.findPeakSample = false;
-
- gfc.RadioGain = 0;
- gfc.AudiophileGain = 0;
- gfc.noclipGainChange = 0;
- gfc.noclipScale = -1.0;
-
- gfp.preset = 0;
-
- gfp.write_id3tag_automatic = true;
- return 0;
- }
-
- this.lame_init = function () {
- var gfp = new LameGlobalFlags();
-
- var ret = lame_init_old(gfp);
- if (ret != 0) {
- return null;
- }
-
- gfp.lame_allocated_gfp = 1;
- return gfp;
- }
-
- function filter_coef(x) {
- if (x > 1.0)
- return 0.0;
- if (x <= 0.0)
- return 1.0;
-
- return Math.cos(Math.PI / 2 * x);
- }
-
- this.nearestBitrateFullIndex = function (bitrate) {
- /* borrowed from DM abr presets */
-
- var full_bitrate_table = [8, 16, 24, 32, 40, 48, 56, 64, 80,
- 96, 112, 128, 160, 192, 224, 256, 320];
-
- var lower_range = 0, lower_range_kbps = 0, upper_range = 0, upper_range_kbps = 0;
-
- /* We assume specified bitrate will be 320kbps */
- upper_range_kbps = full_bitrate_table[16];
- upper_range = 16;
- lower_range_kbps = full_bitrate_table[16];
- lower_range = 16;
-
- /*
- * Determine which significant bitrates the value specified falls
- * between, if loop ends without breaking then we were correct above
- * that the value was 320
- */
- for (var b = 0; b < 16; b++) {
- if ((Math.max(bitrate, full_bitrate_table[b + 1])) != bitrate) {
- upper_range_kbps = full_bitrate_table[b + 1];
- upper_range = b + 1;
- lower_range_kbps = full_bitrate_table[b];
- lower_range = (b);
- break;
- /* We found upper range */
- }
- }
-
- /* Determine which range the value specified is closer to */
- if ((upper_range_kbps - bitrate) > (bitrate - lower_range_kbps)) {
- return lower_range;
- }
- return upper_range;
- }
-
- function optimum_samplefreq(lowpassfreq, input_samplefreq) {
- /*
- * Rules:
- *
- * - if possible, sfb21 should NOT be used
- */
- var suggested_samplefreq = 44100;
-
- if (input_samplefreq >= 48000)
- suggested_samplefreq = 48000;
- else if (input_samplefreq >= 44100)
- suggested_samplefreq = 44100;
- else if (input_samplefreq >= 32000)
- suggested_samplefreq = 32000;
- else if (input_samplefreq >= 24000)
- suggested_samplefreq = 24000;
- else if (input_samplefreq >= 22050)
- suggested_samplefreq = 22050;
- else if (input_samplefreq >= 16000)
- suggested_samplefreq = 16000;
- else if (input_samplefreq >= 12000)
- suggested_samplefreq = 12000;
- else if (input_samplefreq >= 11025)
- suggested_samplefreq = 11025;
- else if (input_samplefreq >= 8000)
- suggested_samplefreq = 8000;
-
- if (lowpassfreq == -1)
- return suggested_samplefreq;
-
- if (lowpassfreq <= 15960)
- suggested_samplefreq = 44100;
- if (lowpassfreq <= 15250)
- suggested_samplefreq = 32000;
- if (lowpassfreq <= 11220)
- suggested_samplefreq = 24000;
- if (lowpassfreq <= 9970)
- suggested_samplefreq = 22050;
- if (lowpassfreq <= 7230)
- suggested_samplefreq = 16000;
- if (lowpassfreq <= 5420)
- suggested_samplefreq = 12000;
- if (lowpassfreq <= 4510)
- suggested_samplefreq = 11025;
- if (lowpassfreq <= 3970)
- suggested_samplefreq = 8000;
-
- if (input_samplefreq < suggested_samplefreq) {
- /*
- * choose a valid MPEG sample frequency above the input sample
- * frequency to avoid SFB21/12 bitrate bloat rh 061115
- */
- if (input_samplefreq > 44100) {
- return 48000;
- }
- if (input_samplefreq > 32000) {
- return 44100;
- }
- if (input_samplefreq > 24000) {
- return 32000;
- }
- if (input_samplefreq > 22050) {
- return 24000;
- }
- if (input_samplefreq > 16000) {
- return 22050;
- }
- if (input_samplefreq > 12000) {
- return 16000;
- }
- if (input_samplefreq > 11025) {
- return 12000;
- }
- if (input_samplefreq > 8000) {
- return 11025;
- }
- return 8000;
- }
- return suggested_samplefreq;
- }
-
- /**
- * convert samp freq in Hz to index
- */
- function SmpFrqIndex(sample_freq, gpf) {
- switch (sample_freq) {
- case 44100:
- gpf.version = 1;
- return 0;
- case 48000:
- gpf.version = 1;
- return 1;
- case 32000:
- gpf.version = 1;
- return 2;
- case 22050:
- gpf.version = 0;
- return 0;
- case 24000:
- gpf.version = 0;
- return 1;
- case 16000:
- gpf.version = 0;
- return 2;
- case 11025:
- gpf.version = 0;
- return 0;
- case 12000:
- gpf.version = 0;
- return 1;
- case 8000:
- gpf.version = 0;
- return 2;
- default:
- gpf.version = 0;
- return -1;
- }
- }
-
- /**
- * @param bRate
- * legal rates from 8 to 320
- */
- function FindNearestBitrate(bRate, version, samplerate) {
- /* MPEG-1 or MPEG-2 LSF */
- if (samplerate < 16000)
- version = 2;
-
- var bitrate = Tables.bitrate_table[version][1];
-
- for (var i = 2; i <= 14; i++) {
- if (Tables.bitrate_table[version][i] > 0) {
- if (Math.abs(Tables.bitrate_table[version][i] - bRate) < Math
- .abs(bitrate - bRate))
- bitrate = Tables.bitrate_table[version][i];
- }
- }
- return bitrate;
- }
-
- /**
- * @param bRate
- * legal rates from 32 to 448 kbps
- * @param version
- * MPEG-1 or MPEG-2/2.5 LSF
- */
- function BitrateIndex(bRate, version, samplerate) {
- /* convert bitrate in kbps to index */
- if (samplerate < 16000)
- version = 2;
- for (var i = 0; i <= 14; i++) {
- if (Tables.bitrate_table[version][i] > 0) {
- if (Tables.bitrate_table[version][i] == bRate) {
- return i;
- }
- }
- }
- return -1;
- }
-
- function optimum_bandwidth(lh, bitrate) {
- /**
- *
- * Input:
- * bitrate total bitrate in kbps
- *
- * Output:
- * lowerlimit: best lowpass frequency limit for input filter in Hz
- * upperlimit: best highpass frequency limit for input filter in Hz
- *
- */
- var freq_map = [new BandPass(8, 2000),
- new BandPass(16, 3700), new BandPass(24, 3900),
- new BandPass(32, 5500), new BandPass(40, 7000),
- new BandPass(48, 7500), new BandPass(56, 10000),
- new BandPass(64, 11000), new BandPass(80, 13500),
- new BandPass(96, 15100), new BandPass(112, 15600),
- new BandPass(128, 17000), new BandPass(160, 17500),
- new BandPass(192, 18600), new BandPass(224, 19400),
- new BandPass(256, 19700), new BandPass(320, 20500)];
-
- var table_index = self.nearestBitrateFullIndex(bitrate);
- lh.lowerlimit = freq_map[table_index].lowpass;
- }
-
- function lame_init_params_ppflt(gfp) {
- var gfc = gfp.internal_flags;
- /***************************************************************/
- /* compute info needed for polyphase filter (filter type==0, default) */
- /***************************************************************/
-
- var lowpass_band = 32;
- var highpass_band = -1;
-
- if (gfc.lowpass1 > 0) {
- var minband = 999;
- for (var band = 0; band <= 31; band++) {
- var freq = (band / 31.0);
- /* this band and above will be zeroed: */
- if (freq >= gfc.lowpass2) {
- lowpass_band = Math.min(lowpass_band, band);
- }
- if (gfc.lowpass1 < freq && freq < gfc.lowpass2) {
- minband = Math.min(minband, band);
- }
- }
-
- /*
- * compute the *actual* transition band implemented by the polyphase
- * filter
- */
- if (minband == 999) {
- gfc.lowpass1 = (lowpass_band - .75) / 31.0;
- } else {
- gfc.lowpass1 = (minband - .75) / 31.0;
- }
- gfc.lowpass2 = lowpass_band / 31.0;
- }
-
- /*
- * make sure highpass filter is within 90% of what the effective
- * highpass frequency will be
- */
- if (gfc.highpass2 > 0) {
- if (gfc.highpass2 < .9 * (.75 / 31.0)) {
- gfc.highpass1 = 0;
- gfc.highpass2 = 0;
- System.err.println("Warning: highpass filter disabled. "
- + "highpass frequency too small\n");
- }
- }
-
- if (gfc.highpass2 > 0) {
- var maxband = -1;
- for (var band = 0; band <= 31; band++) {
- var freq = band / 31.0;
- /* this band and below will be zereod */
- if (freq <= gfc.highpass1) {
- highpass_band = Math.max(highpass_band, band);
- }
- if (gfc.highpass1 < freq && freq < gfc.highpass2) {
- maxband = Math.max(maxband, band);
- }
- }
- /*
- * compute the *actual* transition band implemented by the polyphase
- * filter
- */
- gfc.highpass1 = highpass_band / 31.0;
- if (maxband == -1) {
- gfc.highpass2 = (highpass_band + .75) / 31.0;
- } else {
- gfc.highpass2 = (maxband + .75) / 31.0;
- }
- }
-
- for (var band = 0; band < 32; band++) {
- var fc1, fc2;
- var freq = band / 31.0;
- if (gfc.highpass2 > gfc.highpass1) {
- fc1 = filter_coef((gfc.highpass2 - freq)
- / (gfc.highpass2 - gfc.highpass1 + 1e-20));
- } else {
- fc1 = 1.0;
- }
- if (gfc.lowpass2 > gfc.lowpass1) {
- fc2 = filter_coef((freq - gfc.lowpass1)
- / (gfc.lowpass2 - gfc.lowpass1 + 1e-20));
- } else {
- fc2 = 1.0;
- }
- gfc.amp_filter[band] = (fc1 * fc2);
- }
- }
-
- function lame_init_qval(gfp) {
- var gfc = gfp.internal_flags;
-
- switch (gfp.quality) {
- default:
- case 9: /* no psymodel, no noise shaping */
- gfc.psymodel = 0;
- gfc.noise_shaping = 0;
- gfc.noise_shaping_amp = 0;
- gfc.noise_shaping_stop = 0;
- gfc.use_best_huffman = 0;
- gfc.full_outer_loop = 0;
- break;
-
- case 8:
- gfp.quality = 7;
- //$FALL-THROUGH$
- case 7:
- /*
- * use psymodel (for short block and m/s switching), but no noise
- * shapping
- */
- gfc.psymodel = 1;
- gfc.noise_shaping = 0;
- gfc.noise_shaping_amp = 0;
- gfc.noise_shaping_stop = 0;
- gfc.use_best_huffman = 0;
- gfc.full_outer_loop = 0;
- break;
-
- case 6:
- gfc.psymodel = 1;
- if (gfc.noise_shaping == 0)
- gfc.noise_shaping = 1;
- gfc.noise_shaping_amp = 0;
- gfc.noise_shaping_stop = 0;
- if (gfc.subblock_gain == -1)
- gfc.subblock_gain = 1;
- gfc.use_best_huffman = 0;
- gfc.full_outer_loop = 0;
- break;
-
- case 5:
- gfc.psymodel = 1;
- if (gfc.noise_shaping == 0)
- gfc.noise_shaping = 1;
- gfc.noise_shaping_amp = 0;
- gfc.noise_shaping_stop = 0;
- if (gfc.subblock_gain == -1)
- gfc.subblock_gain = 1;
- gfc.use_best_huffman = 0;
- gfc.full_outer_loop = 0;
- break;
-
- case 4:
- gfc.psymodel = 1;
- if (gfc.noise_shaping == 0)
- gfc.noise_shaping = 1;
- gfc.noise_shaping_amp = 0;
- gfc.noise_shaping_stop = 0;
- if (gfc.subblock_gain == -1)
- gfc.subblock_gain = 1;
- gfc.use_best_huffman = 1;
- gfc.full_outer_loop = 0;
- break;
-
- case 3:
- gfc.psymodel = 1;
- if (gfc.noise_shaping == 0)
- gfc.noise_shaping = 1;
- gfc.noise_shaping_amp = 1;
- gfc.noise_shaping_stop = 1;
- if (gfc.subblock_gain == -1)
- gfc.subblock_gain = 1;
- gfc.use_best_huffman = 1;
- gfc.full_outer_loop = 0;
- break;
-
- case 2:
- gfc.psymodel = 1;
- if (gfc.noise_shaping == 0)
- gfc.noise_shaping = 1;
- if (gfc.substep_shaping == 0)
- gfc.substep_shaping = 2;
- gfc.noise_shaping_amp = 1;
- gfc.noise_shaping_stop = 1;
- if (gfc.subblock_gain == -1)
- gfc.subblock_gain = 1;
- gfc.use_best_huffman = 1;
- /* inner loop */
- gfc.full_outer_loop = 0;
- break;
-
- case 1:
- gfc.psymodel = 1;
- if (gfc.noise_shaping == 0)
- gfc.noise_shaping = 1;
- if (gfc.substep_shaping == 0)
- gfc.substep_shaping = 2;
- gfc.noise_shaping_amp = 2;
- gfc.noise_shaping_stop = 1;
- if (gfc.subblock_gain == -1)
- gfc.subblock_gain = 1;
- gfc.use_best_huffman = 1;
- gfc.full_outer_loop = 0;
- break;
-
- case 0:
- gfc.psymodel = 1;
- if (gfc.noise_shaping == 0)
- gfc.noise_shaping = 1;
- if (gfc.substep_shaping == 0)
- gfc.substep_shaping = 2;
- gfc.noise_shaping_amp = 2;
- gfc.noise_shaping_stop = 1;
- if (gfc.subblock_gain == -1)
- gfc.subblock_gain = 1;
- gfc.use_best_huffman = 1;
- /*
- * type 2 disabled because of it slowness, in favor of full outer
- * loop search
- */
- gfc.full_outer_loop = 0;
- /*
- * full outer loop search disabled because of audible distortions it
- * may generate rh 060629
- */
- break;
- }
-
- }
-
- function lame_init_bitstream(gfp) {
- var gfc = gfp.internal_flags;
- gfp.frameNum = 0;
-
- if (gfp.write_id3tag_automatic) {
- id3.id3tag_write_v2(gfp);
- }
- /* initialize histogram data optionally used by frontend */
-
- gfc.bitrate_stereoMode_Hist = new_int_n([16, 4 + 1]);
- gfc.bitrate_blockType_Hist = new_int_n([16, 4 + 1 + 1]);
-
- gfc.PeakSample = 0.0;
-
- /* Write initial VBR Header to bitstream and init VBR data */
- if (gfp.bWriteVbrTag)
- vbr.InitVbrTag(gfp);
- }
-
- /********************************************************************
- * initialize internal params based on data in gf (globalflags struct filled
- * in by calling program)
- *
- * OUTLINE:
- *
- * We first have some complex code to determine bitrate, output samplerate
- * and mode. It is complicated by the fact that we allow the user to set
- * some or all of these parameters, and need to determine best possible
- * values for the rest of them:
- *
- * 1. set some CPU related flags 2. check if we are mono.mono, stereo.mono
- * or stereo.stereo 3. compute bitrate and output samplerate: user may have
- * set compression ratio user may have set a bitrate user may have set a
- * output samplerate 4. set some options which depend on output samplerate
- * 5. compute the actual compression ratio 6. set mode based on compression
- * ratio
- *
- * The remaining code is much simpler - it just sets options based on the
- * mode & compression ratio:
- *
- * set allow_diff_short based on mode select lowpass filter based on
- * compression ratio & mode set the bitrate index, and min/max bitrates for
- * VBR modes disable VBR tag if it is not appropriate initialize the
- * bitstream initialize scalefac_band data set sideinfo_len (based on
- * channels, CRC, out_samplerate) write an id3v2 tag into the bitstream
- * write VBR tag into the bitstream set mpeg1/2 flag estimate the number of
- * frames (based on a lot of data)
- *
- * now we set more flags: nspsytune: see code VBR modes see code CBR/ABR see
- * code
- *
- * Finally, we set the algorithm flags based on the gfp.quality value
- * lame_init_qval(gfp);
- *
- ********************************************************************/
- this.lame_init_params = function (gfp) {
- var gfc = gfp.internal_flags;
-
- gfc.Class_ID = 0;
- if (gfc.ATH == null)
- gfc.ATH = new ATH();
- if (gfc.PSY == null)
- gfc.PSY = new PSY();
- if (gfc.rgdata == null)
- gfc.rgdata = new ReplayGain();
-
- gfc.channels_in = gfp.num_channels;
- if (gfc.channels_in == 1)
- gfp.mode = MPEGMode.MONO;
- gfc.channels_out = (gfp.mode == MPEGMode.MONO) ? 1 : 2;
- gfc.mode_ext = Encoder.MPG_MD_MS_LR;
- if (gfp.mode == MPEGMode.MONO)
- gfp.force_ms = false;
- /*
- * don't allow forced mid/side stereo for mono output
- */
-
- if (gfp.VBR == VbrMode.vbr_off && gfp.VBR_mean_bitrate_kbps != 128
- && gfp.brate == 0)
- gfp.brate = gfp.VBR_mean_bitrate_kbps;
-
- if (gfp.VBR == VbrMode.vbr_off || gfp.VBR == VbrMode.vbr_mtrh
- || gfp.VBR == VbrMode.vbr_mt) {
- /* these modes can handle free format condition */
- } else {
- gfp.free_format = false;
- /* mode can't be mixed with free format */
- }
-
- if (gfp.VBR == VbrMode.vbr_off && gfp.brate == 0) {
- /* no bitrate or compression ratio specified, use 11.025 */
- if (BitStream.EQ(gfp.compression_ratio, 0))
- gfp.compression_ratio = 11.025;
- /*
- * rate to compress a CD down to exactly 128000 bps
- */
- }
-
- /* find bitrate if user specify a compression ratio */
- if (gfp.VBR == VbrMode.vbr_off && gfp.compression_ratio > 0) {
-
- if (gfp.out_samplerate == 0)
- gfp.out_samplerate = map2MP3Frequency((int)(0.97 * gfp.in_samplerate));
- /*
- * round up with a margin of 3 %
- */
-
- /*
- * choose a bitrate for the output samplerate which achieves
- * specified compression ratio
- */
- gfp.brate = 0 | (gfp.out_samplerate * 16 * gfc.channels_out / (1.e3 * gfp.compression_ratio));
-
- /* we need the version for the bitrate table look up */
- gfc.samplerate_index = SmpFrqIndex(gfp.out_samplerate, gfp);
-
- if (!gfp.free_format) /*
- * for non Free Format find the nearest allowed
- * bitrate
- */
- gfp.brate = FindNearestBitrate(gfp.brate, gfp.version,
- gfp.out_samplerate);
- }
-
- if (gfp.out_samplerate != 0) {
- if (gfp.out_samplerate < 16000) {
- gfp.VBR_mean_bitrate_kbps = Math.max(gfp.VBR_mean_bitrate_kbps,
- 8);
- gfp.VBR_mean_bitrate_kbps = Math.min(gfp.VBR_mean_bitrate_kbps,
- 64);
- } else if (gfp.out_samplerate < 32000) {
- gfp.VBR_mean_bitrate_kbps = Math.max(gfp.VBR_mean_bitrate_kbps,
- 8);
- gfp.VBR_mean_bitrate_kbps = Math.min(gfp.VBR_mean_bitrate_kbps,
- 160);
- } else {
- gfp.VBR_mean_bitrate_kbps = Math.max(gfp.VBR_mean_bitrate_kbps,
- 32);
- gfp.VBR_mean_bitrate_kbps = Math.min(gfp.VBR_mean_bitrate_kbps,
- 320);
- }
- }
-
- /****************************************************************/
- /* if a filter has not been enabled, see if we should add one: */
- /****************************************************************/
- if (gfp.lowpassfreq == 0) {
- var lowpass = 16000.;
-
- switch (gfp.VBR) {
- case VbrMode.vbr_off:
- {
- var lh = new LowPassHighPass();
- optimum_bandwidth(lh, gfp.brate);
- lowpass = lh.lowerlimit;
- break;
- }
- case VbrMode.vbr_abr:
- {
- var lh = new LowPassHighPass();
- optimum_bandwidth(lh, gfp.VBR_mean_bitrate_kbps);
- lowpass = lh.lowerlimit;
- break;
- }
- case VbrMode.vbr_rh:
- {
- var x = [19500, 19000, 18600, 18000, 17500, 16000,
- 15600, 14900, 12500, 10000, 3950];
- if (0 <= gfp.VBR_q && gfp.VBR_q <= 9) {
- var a = x[gfp.VBR_q], b = x[gfp.VBR_q + 1], m = gfp.VBR_q_frac;
- lowpass = linear_int(a, b, m);
- } else {
- lowpass = 19500;
- }
- break;
- }
- default:
- {
- var x = [19500, 19000, 18500, 18000, 17500, 16500,
- 15500, 14500, 12500, 9500, 3950];
- if (0 <= gfp.VBR_q && gfp.VBR_q <= 9) {
- var a = x[gfp.VBR_q], b = x[gfp.VBR_q + 1], m = gfp.VBR_q_frac;
- lowpass = linear_int(a, b, m);
- } else {
- lowpass = 19500;
- }
- }
- }
- if (gfp.mode == MPEGMode.MONO
- && (gfp.VBR == VbrMode.vbr_off || gfp.VBR == VbrMode.vbr_abr))
- lowpass *= 1.5;
-
- gfp.lowpassfreq = lowpass | 0;
- }
-
- if (gfp.out_samplerate == 0) {
- if (2 * gfp.lowpassfreq > gfp.in_samplerate) {
- gfp.lowpassfreq = gfp.in_samplerate / 2;
- }
- gfp.out_samplerate = optimum_samplefreq(gfp.lowpassfreq | 0,
- gfp.in_samplerate);
- }
-
- gfp.lowpassfreq = Math.min(20500, gfp.lowpassfreq);
- gfp.lowpassfreq = Math.min(gfp.out_samplerate / 2, gfp.lowpassfreq);
-
- if (gfp.VBR == VbrMode.vbr_off) {
- gfp.compression_ratio = gfp.out_samplerate * 16 * gfc.channels_out
- / (1.e3 * gfp.brate);
- }
- if (gfp.VBR == VbrMode.vbr_abr) {
- gfp.compression_ratio = gfp.out_samplerate * 16 * gfc.channels_out
- / (1.e3 * gfp.VBR_mean_bitrate_kbps);
- }
-
- /*
- * do not compute ReplayGain values and do not find the peak sample if
- * we can't store them
- */
- if (!gfp.bWriteVbrTag) {
- gfp.findReplayGain = false;
- gfp.decode_on_the_fly = false;
- gfc.findPeakSample = false;
- }
- gfc.findReplayGain = gfp.findReplayGain;
- gfc.decode_on_the_fly = gfp.decode_on_the_fly;
-
- if (gfc.decode_on_the_fly)
- gfc.findPeakSample = true;
-
- if (gfc.findReplayGain) {
- if (ga.InitGainAnalysis(gfc.rgdata, gfp.out_samplerate) == GainAnalysis.INIT_GAIN_ANALYSIS_ERROR) {
- gfp.internal_flags = null;
- return -6;
- }
- }
-
- if (gfc.decode_on_the_fly && !gfp.decode_only) {
- if (gfc.hip != null) {
- mpglib.hip_decode_exit(gfc.hip);
- }
- gfc.hip = mpglib.hip_decode_init();
- }
-
- gfc.mode_gr = gfp.out_samplerate <= 24000 ? 1 : 2;
- /*
- * Number of granules per frame
- */
- gfp.framesize = 576 * gfc.mode_gr;
- gfp.encoder_delay = Encoder.ENCDELAY;
-
- gfc.resample_ratio = gfp.in_samplerate / gfp.out_samplerate;
-
- /**
- *
- * For VBR, take a guess at the compression_ratio.
- * For example:
- *
- * VBR_q compression like
- * - 4.4 320 kbps/44 kHz
- * 0...1 5.5 256 kbps/44 kHz
- * 2 7.3 192 kbps/44 kHz
- * 4 8.8 160 kbps/44 kHz
- * 6 11 128 kbps/44 kHz
- * 9 14.7 96 kbps
- *
- * for lower bitrates, downsample with --resample
- *
- */
- switch (gfp.VBR) {
- case VbrMode.vbr_mt:
- case VbrMode.vbr_rh:
- case VbrMode.vbr_mtrh:
- {
- /* numbers are a bit strange, but they determine the lowpass value */
- var cmp = [5.7, 6.5, 7.3, 8.2, 10, 11.9, 13, 14,
- 15, 16.5];
- gfp.compression_ratio = cmp[gfp.VBR_q];
- }
- break;
- case VbrMode.vbr_abr:
- gfp.compression_ratio = gfp.out_samplerate * 16 * gfc.channels_out
- / (1.e3 * gfp.VBR_mean_bitrate_kbps);
- break;
- default:
- gfp.compression_ratio = gfp.out_samplerate * 16 * gfc.channels_out
- / (1.e3 * gfp.brate);
- break;
- }
-
- /*
- * mode = -1 (not set by user) or mode = MONO (because of only 1 input
- * channel). If mode has not been set, then select J-STEREO
- */
- if (gfp.mode == MPEGMode.NOT_SET) {
- gfp.mode = MPEGMode.JOINT_STEREO;
- }
-
- /* apply user driven high pass filter */
- if (gfp.highpassfreq > 0) {
- gfc.highpass1 = 2. * gfp.highpassfreq;
-
- if (gfp.highpasswidth >= 0)
- gfc.highpass2 = 2. * (gfp.highpassfreq + gfp.highpasswidth);
- else
- /* 0% above on default */
- gfc.highpass2 = (1 + 0.00) * 2. * gfp.highpassfreq;
-
- gfc.highpass1 /= gfp.out_samplerate;
- gfc.highpass2 /= gfp.out_samplerate;
- } else {
- gfc.highpass1 = 0;
- gfc.highpass2 = 0;
- }
- /* apply user driven low pass filter */
- if (gfp.lowpassfreq > 0) {
- gfc.lowpass2 = 2. * gfp.lowpassfreq;
- if (gfp.lowpasswidth >= 0) {
- gfc.lowpass1 = 2. * (gfp.lowpassfreq - gfp.lowpasswidth);
- if (gfc.lowpass1 < 0) /* has to be >= 0 */
- gfc.lowpass1 = 0;
- } else { /* 0% below on default */
- gfc.lowpass1 = (1 - 0.00) * 2. * gfp.lowpassfreq;
- }
- gfc.lowpass1 /= gfp.out_samplerate;
- gfc.lowpass2 /= gfp.out_samplerate;
- } else {
- gfc.lowpass1 = 0;
- gfc.lowpass2 = 0;
- }
-
- /**********************************************************************/
- /* compute info needed for polyphase filter (filter type==0, default) */
- /**********************************************************************/
- lame_init_params_ppflt(gfp);
- /*******************************************************
- * samplerate and bitrate index
- *******************************************************/
- gfc.samplerate_index = SmpFrqIndex(gfp.out_samplerate, gfp);
- if (gfc.samplerate_index < 0) {
- gfp.internal_flags = null;
- return -1;
- }
-
- if (gfp.VBR == VbrMode.vbr_off) {
- if (gfp.free_format) {
- gfc.bitrate_index = 0;
- } else {
- gfp.brate = FindNearestBitrate(gfp.brate, gfp.version,
- gfp.out_samplerate);
- gfc.bitrate_index = BitrateIndex(gfp.brate, gfp.version,
- gfp.out_samplerate);
- if (gfc.bitrate_index <= 0) {
- gfp.internal_flags = null;
- return -1;
- }
- }
- } else {
- gfc.bitrate_index = 1;
- }
-
- /* for CBR, we will write an "info" tag. */
-
- if (gfp.analysis)
- gfp.bWriteVbrTag = false;
-
- /* some file options not allowed if output is: not specified or stdout */
- if (gfc.pinfo != null)
- gfp.bWriteVbrTag = false;
- /* disable Xing VBR tag */
-
- bs.init_bit_stream_w(gfc);
-
- var j = gfc.samplerate_index + (3 * gfp.version) + 6
- * (gfp.out_samplerate < 16000 ? 1 : 0);
- for (var i = 0; i < Encoder.SBMAX_l + 1; i++)
- gfc.scalefac_band.l[i] = qupvt.sfBandIndex[j].l[i];
-
- for (var i = 0; i < Encoder.PSFB21 + 1; i++) {
- var size = (gfc.scalefac_band.l[22] - gfc.scalefac_band.l[21])
- / Encoder.PSFB21;
- var start = gfc.scalefac_band.l[21] + i * size;
- gfc.scalefac_band.psfb21[i] = start;
- }
- gfc.scalefac_band.psfb21[Encoder.PSFB21] = 576;
-
- for (var i = 0; i < Encoder.SBMAX_s + 1; i++)
- gfc.scalefac_band.s[i] = qupvt.sfBandIndex[j].s[i];
-
- for (var i = 0; i < Encoder.PSFB12 + 1; i++) {
- var size = (gfc.scalefac_band.s[13] - gfc.scalefac_band.s[12])
- / Encoder.PSFB12;
- var start = gfc.scalefac_band.s[12] + i * size;
- gfc.scalefac_band.psfb12[i] = start;
- }
- gfc.scalefac_band.psfb12[Encoder.PSFB12] = 192;
- /* determine the mean bitrate for main data */
- if (gfp.version == 1) /* MPEG 1 */
- gfc.sideinfo_len = (gfc.channels_out == 1) ? 4 + 17 : 4 + 32;
- else
- /* MPEG 2 */
- gfc.sideinfo_len = (gfc.channels_out == 1) ? 4 + 9 : 4 + 17;
-
- if (gfp.error_protection)
- gfc.sideinfo_len += 2;
-
- lame_init_bitstream(gfp);
-
- gfc.Class_ID = LAME_ID;
-
- {
- var k;
-
- for (k = 0; k < 19; k++)
- gfc.nsPsy.pefirbuf[k] = 700 * gfc.mode_gr * gfc.channels_out;
-
- if (gfp.ATHtype == -1)
- gfp.ATHtype = 4;
- }
-
- switch (gfp.VBR) {
-
- case VbrMode.vbr_mt:
- gfp.VBR = VbrMode.vbr_mtrh;
- //$FALL-THROUGH$
- case VbrMode.vbr_mtrh:
- {
- if (gfp.useTemporal == null) {
- gfp.useTemporal = false;
- /* off by default for this VBR mode */
- }
-
- p.apply_preset(gfp, 500 - (gfp.VBR_q * 10), 0);
- /**
- *
- * The newer VBR code supports only a limited
- * subset of quality levels:
- * 9-5=5 are the same, uses x^3/4 quantization
- * 4-0=0 are the same 5 plus best huffman divide code
- *
- */
- if (gfp.quality < 0)
- gfp.quality = LAME_DEFAULT_QUALITY;
- if (gfp.quality < 5)
- gfp.quality = 0;
- if (gfp.quality > 5)
- gfp.quality = 5;
-
- gfc.PSY.mask_adjust = gfp.maskingadjust;
- gfc.PSY.mask_adjust_short = gfp.maskingadjust_short;
-
- /*
- * sfb21 extra only with MPEG-1 at higher sampling rates
- */
- if (gfp.experimentalY)
- gfc.sfb21_extra = false;
- else
- gfc.sfb21_extra = (gfp.out_samplerate > 44000);
-
- gfc.iteration_loop = new VBRNewIterationLoop(qu);
- break;
-
- }
- case VbrMode.vbr_rh:
- {
-
- p.apply_preset(gfp, 500 - (gfp.VBR_q * 10), 0);
-
- gfc.PSY.mask_adjust = gfp.maskingadjust;
- gfc.PSY.mask_adjust_short = gfp.maskingadjust_short;
-
- /*
- * sfb21 extra only with MPEG-1 at higher sampling rates
- */
- if (gfp.experimentalY)
- gfc.sfb21_extra = false;
- else
- gfc.sfb21_extra = (gfp.out_samplerate > 44000);
-
- /*
- * VBR needs at least the output of GPSYCHO, so we have to garantee
- * that by setting a minimum quality level, actually level 6 does
- * it. down to level 6
- */
- if (gfp.quality > 6)
- gfp.quality = 6;
-
- if (gfp.quality < 0)
- gfp.quality = LAME_DEFAULT_QUALITY;
-
- gfc.iteration_loop = new VBROldIterationLoop(qu);
- break;
- }
-
- default: /* cbr/abr */
- {
- var vbrmode;
-
- /*
- * no sfb21 extra with CBR code
- */
- gfc.sfb21_extra = false;
-
- if (gfp.quality < 0)
- gfp.quality = LAME_DEFAULT_QUALITY;
-
- vbrmode = gfp.VBR;
- if (vbrmode == VbrMode.vbr_off)
- gfp.VBR_mean_bitrate_kbps = gfp.brate;
- /* second, set parameters depending on bitrate */
- p.apply_preset(gfp, gfp.VBR_mean_bitrate_kbps, 0);
- gfp.VBR = vbrmode;
-
- gfc.PSY.mask_adjust = gfp.maskingadjust;
- gfc.PSY.mask_adjust_short = gfp.maskingadjust_short;
-
- if (vbrmode == VbrMode.vbr_off) {
- gfc.iteration_loop = new CBRNewIterationLoop(qu);
- } else {
- gfc.iteration_loop = new ABRIterationLoop(qu);
- }
- break;
- }
- }
- /* initialize default values common for all modes */
-
- if (gfp.VBR != VbrMode.vbr_off) { /* choose a min/max bitrate for VBR */
- /* if the user didn't specify VBR_max_bitrate: */
- gfc.VBR_min_bitrate = 1;
- /*
- * default: allow 8 kbps (MPEG-2) or 32 kbps (MPEG-1)
- */
- gfc.VBR_max_bitrate = 14;
- /*
- * default: allow 160 kbps (MPEG-2) or 320 kbps (MPEG-1)
- */
- if (gfp.out_samplerate < 16000)
- gfc.VBR_max_bitrate = 8;
- /* default: allow 64 kbps (MPEG-2.5) */
- if (gfp.VBR_min_bitrate_kbps != 0) {
- gfp.VBR_min_bitrate_kbps = FindNearestBitrate(
- gfp.VBR_min_bitrate_kbps, gfp.version,
- gfp.out_samplerate);
- gfc.VBR_min_bitrate = BitrateIndex(gfp.VBR_min_bitrate_kbps,
- gfp.version, gfp.out_samplerate);
- if (gfc.VBR_min_bitrate < 0)
- return -1;
- }
- if (gfp.VBR_max_bitrate_kbps != 0) {
- gfp.VBR_max_bitrate_kbps = FindNearestBitrate(
- gfp.VBR_max_bitrate_kbps, gfp.version,
- gfp.out_samplerate);
- gfc.VBR_max_bitrate = BitrateIndex(gfp.VBR_max_bitrate_kbps,
- gfp.version, gfp.out_samplerate);
- if (gfc.VBR_max_bitrate < 0)
- return -1;
- }
- gfp.VBR_min_bitrate_kbps = Tables.bitrate_table[gfp.version][gfc.VBR_min_bitrate];
- gfp.VBR_max_bitrate_kbps = Tables.bitrate_table[gfp.version][gfc.VBR_max_bitrate];
- gfp.VBR_mean_bitrate_kbps = Math.min(
- Tables.bitrate_table[gfp.version][gfc.VBR_max_bitrate],
- gfp.VBR_mean_bitrate_kbps);
- gfp.VBR_mean_bitrate_kbps = Math.max(
- Tables.bitrate_table[gfp.version][gfc.VBR_min_bitrate],
- gfp.VBR_mean_bitrate_kbps);
- }
-
- /* just another daily changing developer switch */
- if (gfp.tune) {
- gfc.PSY.mask_adjust += gfp.tune_value_a;
- gfc.PSY.mask_adjust_short += gfp.tune_value_a;
- }
-
- /* initialize internal qval settings */
- lame_init_qval(gfp);
- /*
- * automatic ATH adjustment on
- */
- if (gfp.athaa_type < 0)
- gfc.ATH.useAdjust = 3;
- else
- gfc.ATH.useAdjust = gfp.athaa_type;
-
- /* initialize internal adaptive ATH settings -jd */
- gfc.ATH.aaSensitivityP = Math.pow(10.0, gfp.athaa_sensitivity
- / -10.0);
-
- if (gfp.short_blocks == null) {
- gfp.short_blocks = ShortBlock.short_block_allowed;
- }
-
- /*
- * Note Jan/2003: Many hardware decoders cannot handle short blocks in
- * regular stereo mode unless they are coupled (same type in both
- * channels) it is a rare event (1 frame per min. or so) that LAME would
- * use uncoupled short blocks, so lets turn them off until we decide how
- * to handle this. No other encoders allow uncoupled short blocks, even
- * though it is in the standard.
- */
- /*
- * rh 20040217: coupling makes no sense for mono and dual-mono streams
- */
- if (gfp.short_blocks == ShortBlock.short_block_allowed
- && (gfp.mode == MPEGMode.JOINT_STEREO || gfp.mode == MPEGMode.STEREO)) {
- gfp.short_blocks = ShortBlock.short_block_coupled;
- }
-
- if (gfp.quant_comp < 0)
- gfp.quant_comp = 1;
- if (gfp.quant_comp_short < 0)
- gfp.quant_comp_short = 0;
-
- if (gfp.msfix < 0)
- gfp.msfix = 0;
-
- /* select psychoacoustic model */
- gfp.exp_nspsytune = gfp.exp_nspsytune | 1;
-
- if (gfp.internal_flags.nsPsy.attackthre < 0)
- gfp.internal_flags.nsPsy.attackthre = PsyModel.NSATTACKTHRE;
- if (gfp.internal_flags.nsPsy.attackthre_s < 0)
- gfp.internal_flags.nsPsy.attackthre_s = PsyModel.NSATTACKTHRE_S;
-
-
- if (gfp.scale < 0)
- gfp.scale = 1;
-
- if (gfp.ATHtype < 0)
- gfp.ATHtype = 4;
-
- if (gfp.ATHcurve < 0)
- gfp.ATHcurve = 4;
-
- if (gfp.athaa_loudapprox < 0)
- gfp.athaa_loudapprox = 2;
-
- if (gfp.interChRatio < 0)
- gfp.interChRatio = 0;
-
- if (gfp.useTemporal == null)
- gfp.useTemporal = true;
- /* on by default */
-
- /*
- * padding method as described in
- * "MPEG-Layer3 / Bitstream Syntax and Decoding" by Martin Sieler, Ralph
- * Sperschneider
- *
- * note: there is no padding for the very first frame
- *
- * Robert Hegemann 2000-06-22
- */
- gfc.slot_lag = gfc.frac_SpF = 0;
- if (gfp.VBR == VbrMode.vbr_off)
- gfc.slot_lag = gfc.frac_SpF = (((gfp.version + 1) * 72000 * gfp.brate) % gfp.out_samplerate) | 0;
-
- qupvt.iteration_init(gfp);
- psy.psymodel_init(gfp);
- return 0;
- }
-
- function update_inbuffer_size(gfc, nsamples) {
- if (gfc.in_buffer_0 == null || gfc.in_buffer_nsamples < nsamples) {
- gfc.in_buffer_0 = new_float(nsamples);
- gfc.in_buffer_1 = new_float(nsamples);
- gfc.in_buffer_nsamples = nsamples;
- }
- }
-
- this.lame_encode_flush = function (gfp, mp3buffer, mp3bufferPos, mp3buffer_size) {
- var gfc = gfp.internal_flags;
- var buffer = new_short_n([2, 1152]);
- var imp3 = 0, mp3count, mp3buffer_size_remaining;
-
- /*
- * we always add POSTDELAY=288 padding to make sure granule with real
- * data can be complety decoded (because of 50% overlap with next
- * granule
- */
- var end_padding;
- var frames_left;
- var samples_to_encode = gfc.mf_samples_to_encode - Encoder.POSTDELAY;
- var mf_needed = calcNeeded(gfp);
-
- /* Was flush already called? */
- if (gfc.mf_samples_to_encode < 1) {
- return 0;
- }
- mp3count = 0;
-
- if (gfp.in_samplerate != gfp.out_samplerate) {
- /*
- * delay due to resampling; needs to be fixed, if resampling code
- * gets changed
- */
- samples_to_encode += 16. * gfp.out_samplerate / gfp.in_samplerate;
- }
- end_padding = gfp.framesize - (samples_to_encode % gfp.framesize);
- if (end_padding < 576)
- end_padding += gfp.framesize;
- gfp.encoder_padding = end_padding;
-
- frames_left = (samples_to_encode + end_padding) / gfp.framesize;
-
- /*
- * send in a frame of 0 padding until all internal sample buffers are
- * flushed
- */
- while (frames_left > 0 && imp3 >= 0) {
- var bunch = mf_needed - gfc.mf_size;
- var frame_num = gfp.frameNum;
-
- bunch *= gfp.in_samplerate;
- bunch /= gfp.out_samplerate;
- if (bunch > 1152)
- bunch = 1152;
- if (bunch < 1)
- bunch = 1;
-
- mp3buffer_size_remaining = mp3buffer_size - mp3count;
-
- /* if user specifed buffer size = 0, dont check size */
- if (mp3buffer_size == 0)
- mp3buffer_size_remaining = 0;
-
- imp3 = this.lame_encode_buffer(gfp, buffer[0], buffer[1], bunch,
- mp3buffer, mp3bufferPos, mp3buffer_size_remaining);
-
- mp3bufferPos += imp3;
- mp3count += imp3;
- frames_left -= (frame_num != gfp.frameNum) ? 1 : 0;
- }
- /*
- * Set gfc.mf_samples_to_encode to 0, so we may detect and break loops
- * calling it more than once in a row.
- */
- gfc.mf_samples_to_encode = 0;
-
- if (imp3 < 0) {
- /* some type of fatal error */
- return imp3;
- }
-
- mp3buffer_size_remaining = mp3buffer_size - mp3count;
- /* if user specifed buffer size = 0, dont check size */
- if (mp3buffer_size == 0)
- mp3buffer_size_remaining = 0;
-
- /* mp3 related stuff. bit buffer might still contain some mp3 data */
- bs.flush_bitstream(gfp);
- imp3 = bs.copy_buffer(gfc, mp3buffer, mp3bufferPos,
- mp3buffer_size_remaining, 1);
- if (imp3 < 0) {
- /* some type of fatal error */
- return imp3;
- }
- mp3bufferPos += imp3;
- mp3count += imp3;
- mp3buffer_size_remaining = mp3buffer_size - mp3count;
- /* if user specifed buffer size = 0, dont check size */
- if (mp3buffer_size == 0)
- mp3buffer_size_remaining = 0;
-
- if (gfp.write_id3tag_automatic) {
- /* write a id3 tag to the bitstream */
- id3.id3tag_write_v1(gfp);
-
- imp3 = bs.copy_buffer(gfc, mp3buffer, mp3bufferPos,
- mp3buffer_size_remaining, 0);
-
- if (imp3 < 0) {
- return imp3;
- }
- mp3count += imp3;
- }
- return mp3count;
- };
-
- this.lame_encode_buffer = function (gfp, buffer_l, buffer_r, nsamples, mp3buf, mp3bufPos, mp3buf_size) {
- var gfc = gfp.internal_flags;
- var in_buffer = [null, null];
-
- if (gfc.Class_ID != LAME_ID)
- return -3;
-
- if (nsamples == 0)
- return 0;
-
- update_inbuffer_size(gfc, nsamples);
-
- in_buffer[0] = gfc.in_buffer_0;
- in_buffer[1] = gfc.in_buffer_1;
-
- /* make a copy of input buffer, changing type to sample_t */
- for (var i = 0; i < nsamples; i++) {
- in_buffer[0][i] = buffer_l[i];
- if (gfc.channels_in > 1)
- in_buffer[1][i] = buffer_r[i];
- }
-
- return lame_encode_buffer_sample(gfp, in_buffer[0], in_buffer[1],
- nsamples, mp3buf, mp3bufPos, mp3buf_size);
- }
-
- function calcNeeded(gfp) {
- var mf_needed = Encoder.BLKSIZE + gfp.framesize - Encoder.FFTOFFSET;
- /*
- * amount needed for FFT
- */
- mf_needed = Math.max(mf_needed, 512 + gfp.framesize - 32);
-
- return mf_needed;
- }
-
- function lame_encode_buffer_sample(gfp, buffer_l, buffer_r, nsamples, mp3buf, mp3bufPos, mp3buf_size) {
- var gfc = gfp.internal_flags;
- var mp3size = 0, ret, i, ch, mf_needed;
- var mp3out;
- var mfbuf = [null, null];
- var in_buffer = [null, null];
-
- if (gfc.Class_ID != LAME_ID)
- return -3;
-
- if (nsamples == 0)
- return 0;
-
- /* copy out any tags that may have been written into bitstream */
- mp3out = bs.copy_buffer(gfc, mp3buf, mp3bufPos, mp3buf_size, 0);
- if (mp3out < 0)
- return mp3out;
- /* not enough buffer space */
- mp3bufPos += mp3out;
- mp3size += mp3out;
-
- in_buffer[0] = buffer_l;
- in_buffer[1] = buffer_r;
-
- /* Apply user defined re-scaling */
-
- /* user selected scaling of the samples */
- if (BitStream.NEQ(gfp.scale, 0) && BitStream.NEQ(gfp.scale, 1.0)) {
- for (i = 0; i < nsamples; ++i) {
- in_buffer[0][i] *= gfp.scale;
- if (gfc.channels_out == 2)
- in_buffer[1][i] *= gfp.scale;
- }
- }
-
- /* user selected scaling of the channel 0 (left) samples */
- if (BitStream.NEQ(gfp.scale_left, 0)
- && BitStream.NEQ(gfp.scale_left, 1.0)) {
- for (i = 0; i < nsamples; ++i) {
- in_buffer[0][i] *= gfp.scale_left;
- }
- }
-
- /* user selected scaling of the channel 1 (right) samples */
- if (BitStream.NEQ(gfp.scale_right, 0)
- && BitStream.NEQ(gfp.scale_right, 1.0)) {
- for (i = 0; i < nsamples; ++i) {
- in_buffer[1][i] *= gfp.scale_right;
- }
- }
-
- /* Downsample to Mono if 2 channels in and 1 channel out */
- if (gfp.num_channels == 2 && gfc.channels_out == 1) {
- for (i = 0; i < nsamples; ++i) {
- in_buffer[0][i] = 0.5 * ( in_buffer[0][i] + in_buffer[1][i]);
- in_buffer[1][i] = 0.0;
- }
- }
-
- mf_needed = calcNeeded(gfp);
-
- mfbuf[0] = gfc.mfbuf[0];
- mfbuf[1] = gfc.mfbuf[1];
-
- var in_bufferPos = 0;
- while (nsamples > 0) {
- var in_buffer_ptr = [null, null];
- var n_in = 0;
- /* number of input samples processed with fill_buffer */
- var n_out = 0;
- /* number of samples output with fill_buffer */
- /* n_in <> n_out if we are resampling */
-
- in_buffer_ptr[0] = in_buffer[0];
- in_buffer_ptr[1] = in_buffer[1];
- /* copy in new samples into mfbuf, with resampling */
- var inOut = new InOut();
- fill_buffer(gfp, mfbuf, in_buffer_ptr, in_bufferPos, nsamples,
- inOut);
- n_in = inOut.n_in;
- n_out = inOut.n_out;
-
- /* compute ReplayGain of resampled input if requested */
- if (gfc.findReplayGain && !gfc.decode_on_the_fly)
- if (ga.AnalyzeSamples(gfc.rgdata, mfbuf[0], gfc.mf_size,
- mfbuf[1], gfc.mf_size, n_out, gfc.channels_out) == GainAnalysis.GAIN_ANALYSIS_ERROR)
- return -6;
-
- /* update in_buffer counters */
- nsamples -= n_in;
- in_bufferPos += n_in;
- if (gfc.channels_out == 2)
- ;// in_bufferPos += n_in;
-
- /* update mfbuf[] counters */
- gfc.mf_size += n_out;
-
- /*
- * lame_encode_flush may have set gfc.mf_sample_to_encode to 0 so we
- * have to reinitialize it here when that happened.
- */
- if (gfc.mf_samples_to_encode < 1) {
- gfc.mf_samples_to_encode = Encoder.ENCDELAY + Encoder.POSTDELAY;
- }
- gfc.mf_samples_to_encode += n_out;
-
- if (gfc.mf_size >= mf_needed) {
- /* encode the frame. */
- /* mp3buf = pointer to current location in buffer */
- /* mp3buf_size = size of original mp3 output buffer */
- /* = 0 if we should not worry about the */
- /* buffer size because calling program is */
- /* to lazy to compute it */
- /* mp3size = size of data written to buffer so far */
- /* mp3buf_size-mp3size = amount of space avalable */
-
- var buf_size = mp3buf_size - mp3size;
- if (mp3buf_size == 0)
- buf_size = 0;
-
- ret = lame_encode_frame(gfp, mfbuf[0], mfbuf[1], mp3buf,
- mp3bufPos, buf_size);
-
- if (ret < 0)
- return ret;
- mp3bufPos += ret;
- mp3size += ret;
-
- /* shift out old samples */
- gfc.mf_size -= gfp.framesize;
- gfc.mf_samples_to_encode -= gfp.framesize;
- for (ch = 0; ch < gfc.channels_out; ch++)
- for (i = 0; i < gfc.mf_size; i++)
- mfbuf[ch][i] = mfbuf[ch][i + gfp.framesize];
- }
- }
-
- return mp3size;
- }
-
- function lame_encode_frame(gfp, inbuf_l, inbuf_r, mp3buf, mp3bufPos, mp3buf_size) {
- var ret = self.enc.lame_encode_mp3_frame(gfp, inbuf_l, inbuf_r, mp3buf,
- mp3bufPos, mp3buf_size);
- gfp.frameNum++;
- return ret;
- }
-
- function InOut() {
- this.n_in = 0;
- this.n_out = 0;
- }
-
-
- function NumUsed() {
- this.num_used = 0;
- }
-
- /**
- * Greatest common divisor.
- *
- * Joint work of Euclid and M. Hendry
- */
- function gcd(i, j) {
- return j != 0 ? gcd(j, i % j) : i;
- }
-
- /**
- * Resampling via FIR filter, blackman window.
- */
- function blackman(x, fcn, l) {
- /*
- * This algorithm from: SIGNAL PROCESSING ALGORITHMS IN FORTRAN AND C
- * S.D. Stearns and R.A. David, Prentice-Hall, 1992
- */
- var wcn = (Math.PI * fcn);
-
- x /= l;
- if (x < 0)
- x = 0;
- if (x > 1)
- x = 1;
- var x2 = x - .5;
-
- var bkwn = 0.42 - 0.5 * Math.cos(2 * x * Math.PI) + 0.08 * Math.cos(4 * x * Math.PI);
- if (Math.abs(x2) < 1e-9)
- return (wcn / Math.PI);
- else
- return (bkwn * Math.sin(l * wcn * x2) / (Math.PI * l * x2));
- }
-
- function fill_buffer_resample(gfp, outbuf, outbufPos, desired_len, inbuf, in_bufferPos, len, num_used, ch) {
- var gfc = gfp.internal_flags;
- var i, j = 0, k;
- /* number of convolution functions to pre-compute */
- var bpc = gfp.out_samplerate
- / gcd(gfp.out_samplerate, gfp.in_samplerate);
- if (bpc > LameInternalFlags.BPC)
- bpc = LameInternalFlags.BPC;
-
- var intratio = (Math.abs(gfc.resample_ratio
- - Math.floor(.5 + gfc.resample_ratio)) < .0001) ? 1 : 0;
- var fcn = 1.00 / gfc.resample_ratio;
- if (fcn > 1.00)
- fcn = 1.00;
- var filter_l = 31;
- if (0 == filter_l % 2)
- --filter_l;
- /* must be odd */
- filter_l += intratio;
- /* unless resample_ratio=int, it must be even */
-
- var BLACKSIZE = filter_l + 1;
- /* size of data needed for FIR */
-
- if (gfc.fill_buffer_resample_init == 0) {
- gfc.inbuf_old[0] = new_float(BLACKSIZE);
- gfc.inbuf_old[1] = new_float(BLACKSIZE);
- for (i = 0; i <= 2 * bpc; ++i)
- gfc.blackfilt[i] = new_float(BLACKSIZE);
-
- gfc.itime[0] = 0;
- gfc.itime[1] = 0;
-
- /* precompute blackman filter coefficients */
- for (j = 0; j <= 2 * bpc; j++) {
- var sum = 0.;
- var offset = (j - bpc) / (2. * bpc);
- for (i = 0; i <= filter_l; i++)
- sum += gfc.blackfilt[j][i] = blackman(i - offset, fcn,
- filter_l);
- for (i = 0; i <= filter_l; i++)
- gfc.blackfilt[j][i] /= sum;
- }
- gfc.fill_buffer_resample_init = 1;
- }
-
- var inbuf_old = gfc.inbuf_old[ch];
-
- /* time of j'th element in inbuf = itime + j/ifreq; */
- /* time of k'th element in outbuf = j/ofreq */
- for (k = 0; k < desired_len; k++) {
- var time0;
- var joff;
-
- time0 = k * gfc.resample_ratio;
- /* time of k'th output sample */
- j = 0 | Math.floor(time0 - gfc.itime[ch]);
-
- /* check if we need more input data */
- if ((filter_l + j - filter_l / 2) >= len)
- break;
-
- /* blackman filter. by default, window centered at j+.5(filter_l%2) */
- /* but we want a window centered at time0. */
- var offset = (time0 - gfc.itime[ch] - (j + .5 * (filter_l % 2)));
-
- /* find the closest precomputed window for this offset: */
- joff = 0 | Math.floor((offset * 2 * bpc) + bpc + .5);
- var xvalue = 0.;
- for (i = 0; i <= filter_l; ++i) {
- var j2 = i + j - filter_l / 2;
- var y;
- y = (j2 < 0) ? inbuf_old[BLACKSIZE + j2] : inbuf[in_bufferPos
- + j2];
- xvalue += y * gfc.blackfilt[joff][i];
- }
- outbuf[outbufPos + k] = xvalue;
- }
-
- /* k = number of samples added to outbuf */
- /* last k sample used data from [j-filter_l/2,j+filter_l-filter_l/2] */
-
- /* how many samples of input data were used: */
- num_used.num_used = Math.min(len, filter_l + j - filter_l / 2);
-
- /*
- * adjust our input time counter. Incriment by the number of samples
- * used, then normalize so that next output sample is at time 0, next
- * input buffer is at time itime[ch]
- */
- gfc.itime[ch] += num_used.num_used - k * gfc.resample_ratio;
-
- /* save the last BLACKSIZE samples into the inbuf_old buffer */
- if (num_used.num_used >= BLACKSIZE) {
- for (i = 0; i < BLACKSIZE; i++)
- inbuf_old[i] = inbuf[in_bufferPos + num_used.num_used + i
- - BLACKSIZE];
- } else {
- /* shift in num_used.num_used samples into inbuf_old */
- var n_shift = BLACKSIZE - num_used.num_used;
- /*
- * number of samples to
- * shift
- */
-
- /*
- * shift n_shift samples by num_used.num_used, to make room for the
- * num_used new samples
- */
- for (i = 0; i < n_shift; ++i)
- inbuf_old[i] = inbuf_old[i + num_used.num_used];
-
- /* shift in the num_used.num_used samples */
- for (j = 0; i < BLACKSIZE; ++i, ++j)
- inbuf_old[i] = inbuf[in_bufferPos + j];
-
- }
- return k;
- /* return the number samples created at the new samplerate */
- }
-
- function fill_buffer(gfp, mfbuf, in_buffer, in_bufferPos, nsamples, io) {
- var gfc = gfp.internal_flags;
-
- /* copy in new samples into mfbuf, with resampling if necessary */
- if ((gfc.resample_ratio < .9999) || (gfc.resample_ratio > 1.0001)) {
- for (var ch = 0; ch < gfc.channels_out; ch++) {
- var numUsed = new NumUsed();
- io.n_out = fill_buffer_resample(gfp, mfbuf[ch], gfc.mf_size,
- gfp.framesize, in_buffer[ch], in_bufferPos, nsamples,
- numUsed, ch);
- io.n_in = numUsed.num_used;
- }
- } else {
- io.n_out = Math.min(gfp.framesize, nsamples);
- io.n_in = io.n_out;
- for (var i = 0; i < io.n_out; ++i) {
- mfbuf[0][gfc.mf_size + i] = in_buffer[0][in_bufferPos + i];
- if (gfc.channels_out == 2)
- mfbuf[1][gfc.mf_size + i] = in_buffer[1][in_bufferPos + i];
- }
- }
- }
-
-}
-
-
-
-function GetAudio() {
- var parse;
- var mpg;
-
- this.setModules = function (parse2, mpg2) {
- parse = parse2;
- mpg = mpg2;
- }
-}
-
-
-function Parse() {
- var ver;
- var id3;
- var pre;
-
- this.setModules = function (ver2, id32, pre2) {
- ver = ver2;
- id3 = id32;
- pre = pre2;
- }
-}
-
-function MPGLib() {
-}
-
-function ID3Tag() {
- var bits;
- var ver;
-
- this.setModules = function (_bits, _ver) {
- bits = _bits;
- ver = _ver;
- }
-}
-
-function Mp3Encoder(channels, samplerate, kbps) {
- if (arguments.length != 3) {
- console.error('WARN: Mp3Encoder(channels, samplerate, kbps) not specified');
- channels = 1;
- samplerate = 44100;
- kbps = 128;
- }
- var lame = new Lame();
- var gaud = new GetAudio();
- var ga = new GainAnalysis();
- var bs = new BitStream();
- var p = new Presets();
- var qupvt = new QuantizePVT();
- var qu = new Quantize();
- var vbr = new VBRTag();
- var ver = new Version();
- var id3 = new ID3Tag();
- var rv = new Reservoir();
- var tak = new Takehiro();
- var parse = new Parse();
- var mpg = new MPGLib();
-
- lame.setModules(ga, bs, p, qupvt, qu, vbr, ver, id3, mpg);
- bs.setModules(ga, mpg, ver, vbr);
- id3.setModules(bs, ver);
- p.setModules(lame);
- qu.setModules(bs, rv, qupvt, tak);
- qupvt.setModules(tak, rv, lame.enc.psy);
- rv.setModules(bs);
- tak.setModules(qupvt);
- vbr.setModules(lame, bs, ver);
- gaud.setModules(parse, mpg);
- parse.setModules(ver, id3, p);
-
- var gfp = lame.lame_init();
-
- gfp.num_channels = channels;
- gfp.in_samplerate = samplerate;
- gfp.out_samplerate = samplerate;//fix by xiangyuecn 2018-12-6 01:48:12 64kbps以下可能无声音，手动控制输出码率
- gfp.brate = kbps;
- gfp.mode = MPEGMode.STEREO;
- gfp.quality = 3;
- gfp.bWriteVbrTag = false;
- gfp.disable_reservoir = true;
- gfp.write_id3tag_automatic = false;
-
- var retcode = lame.lame_init_params(gfp);
- var maxSamples = 1152;
- var mp3buf_size = 0 | (1.25 * maxSamples + 7200);
- var mp3buf = new_byte(mp3buf_size);
-
- this.encodeBuffer = function (left, right) {
- if (channels == 1) {
- right = left;
- }
- if (left.length > maxSamples) {
- maxSamples = left.length;
- mp3buf_size = 0 | (1.25 * maxSamples + 7200);
- mp3buf = new_byte(mp3buf_size);
- }
-
- var _sz = lame.lame_encode_buffer(gfp, left, right, left.length, mp3buf, 0, mp3buf_size);
- return new Int8Array(mp3buf.subarray(0, _sz));
- };
-
- this.flush = function () {
- var _sz = lame.lame_encode_flush(gfp, mp3buf, 0, mp3buf_size);
- return new Int8Array(mp3buf.subarray(0, _sz));
- };
-}
-
-//fix 精简
-L3Side.SFBMAX = (Encoder.SBMAX_s * 3);
-//testFullLength();
-lamejs.Mp3Encoder = Mp3Encoder;
-}
-//fs=require('fs');
-lamejs();
-
-
-Recorder.lamejs=lamejs;
-
-//end3 ****结束copy lamejs*****
-})();
\ No newline at end of file
diff --git a/spaces/KevinGeng/Laronix_voice_quality_checking_system_FILEIO/app.py b/spaces/KevinGeng/Laronix_voice_quality_checking_system_FILEIO/app.py
deleted file mode 100644
index a6e3c1b62cd3dce90ccfc05204a86fa411bc55be..0000000000000000000000000000000000000000
--- a/spaces/KevinGeng/Laronix_voice_quality_checking_system_FILEIO/app.py
+++ /dev/null
@@ -1,112 +0,0 @@
-from random import sample
-import gradio as gr
-import torchaudio
-import torch
-import torch.nn as nn
-import lightning_module
-import pdb
-import jiwer
-from local.convert_metrics import nat2avaMOS, WER2INTELI
-
-# ASR part
-from transformers import pipeline
-# p = pipeline("automatic-speech-recognition")
-p = pipeline(
- "automatic-speech-recognition",
- model="KevinGeng/whipser_medium_en_PAL300_step25",
-)
-# WER part
-transformation = jiwer.Compose([
- jiwer.ToLowerCase(),
- jiwer.RemoveWhiteSpace(replace_by_space=True),
- jiwer.RemoveMultipleSpaces(),
- jiwer.ReduceToListOfListOfWords(word_delimiter=" ")
-])
-
-# WPM part
-from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
-processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-phoneme_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-# phoneme_model = pipeline(model="facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-class ChangeSampleRate(nn.Module):
- def __init__(self, input_rate: int, output_rate: int):
- super().__init__()
- self.output_rate = output_rate
- self.input_rate = input_rate
-
- def forward(self, wav: torch.tensor) -> torch.tensor:
- # Only accepts 1-channel waveform input
- wav = wav.view(wav.size(0), -1)
- new_length = wav.size(-1) * self.output_rate // self.input_rate
- indices = (torch.arange(new_length) * (self.input_rate / self.output_rate))
- round_down = wav[:, indices.long()]
- round_up = wav[:, (indices.long() + 1).clamp(max=wav.size(-1) - 1)]
- output = round_down * (1. - indices.fmod(1.)).unsqueeze(0) + round_up * indices.fmod(1.).unsqueeze(0)
- return output
-
-model = lightning_module.BaselineLightningModule.load_from_checkpoint("epoch=3-step=7459.ckpt").eval()
-
-def calc_mos(audio_path, ref):
- wav, sr = torchaudio.load(audio_path, channels_first=True)
- if wav.shape[0] > 1:
- wav = wav.mean(dim=0, keepdim=True) # Mono channel
- osr = 16_000
- batch = wav.unsqueeze(0).repeat(10, 1, 1)
- csr = ChangeSampleRate(sr, osr)
- out_wavs = csr(wav)
- # ASR
- trans = p(audio_path)["text"]
- # WER
- wer = jiwer.wer(ref, trans, truth_transform=transformation, hypothesis_transform=transformation)
-
- # WER convert to Intellibility score
- INTELI_score = WER2INTELI(wer*100)
-
- # MOS
- batch = {
- 'wav': out_wavs,
- 'domains': torch.tensor([0]),
- 'judge_id': torch.tensor([288])
- }
- with torch.no_grad():
- output = model(batch)
- predic_mos = output.mean(dim=1).squeeze().detach().numpy()*2 + 3
- # MOS to AVA MOS
- AVA_MOS = nat2avaMOS(predic_mos)
- # Phonemes per minute (PPM)
- with torch.no_grad():
- logits = phoneme_model(out_wavs).logits
- phone_predicted_ids = torch.argmax(logits, dim=-1)
- phone_transcription = processor.batch_decode(phone_predicted_ids)
- lst_phonemes = phone_transcription[0].split(" ")
- wav_vad = torchaudio.functional.vad(wav, sample_rate=sr)
- ppm = len(lst_phonemes) / (wav_vad.shape[-1] / sr) * 60
-
- return AVA_MOS, INTELI_score, trans, phone_transcription, ppm
-
-
-description ="""
-MOS prediction demo using UTMOS-strong w/o phoneme encoder model, which is trained on the main track dataset.
-This demo only accepts .wav format. Best at 16 kHz sampling rate.
-
-Paper is available [here](https://arxiv.org/abs/2204.02152)
-
-Add ASR based on wav2vec-960, currently only English available.
-Add WER interface.
-"""
-
-
-iface = gr.Interface(
- fn=calc_mos,
- inputs=[gr.Audio(type='filepath', label="Audio to evaluate"),
- gr.Textbox(placeholder="Input reference here (Don't keep this empty)", label="Reference")],
- outputs=[gr.Textbox(placeholder="Naturalness Score", label="Naturalness Score, ranged from 0 to 5, the higher the better."),
- gr.Textbox(placeholder="Intelligibility Score", label = "Intelligibility Score, range from 0 to 100, the higher the better"),
- gr.Textbox(placeholder="Hypothesis", label="Hypothesis"),
- gr.Textbox(placeholder="Predicted Phonemes", label="Predicted Phonemes"),
- gr.Textbox(placeholder="Speaking Rate, Phonemes per minutes", label="PPM")],
- title="Laronix's Voice Quality Checking System Demo",
- description=description,
- allow_flagging="auto",
-)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/seg_heads/panoptic_fpn_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/seg_heads/panoptic_fpn_head.py
deleted file mode 100644
index 8d8b901360922f6cdb9f8d15b60dac8d7514ee75..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/seg_heads/panoptic_fpn_head.py
+++ /dev/null
@@ -1,174 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmengine.model import ModuleList
-from torch import Tensor
-
-from mmdet.registry import MODELS
-from mmdet.structures import SampleList
-from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
-from ..layers import ConvUpsample
-from ..utils import interpolate_as
-from .base_semantic_head import BaseSemanticHead
-
-
-@MODELS.register_module()
-class PanopticFPNHead(BaseSemanticHead):
- """PanopticFPNHead used in Panoptic FPN.
-
- In this head, the number of output channels is ``num_stuff_classes
- + 1``, including all stuff classes and one thing class. The stuff
- classes will be reset from ``0`` to ``num_stuff_classes - 1``, the
- thing classes will be merged to ``num_stuff_classes``-th channel.
-
- Arg:
- num_things_classes (int): Number of thing classes. Default: 80.
- num_stuff_classes (int): Number of stuff classes. Default: 53.
- in_channels (int): Number of channels in the input feature
- map.
- inner_channels (int): Number of channels in inner features.
- start_level (int): The start level of the input features
- used in PanopticFPN.
- end_level (int): The end level of the used features, the
- ``end_level``-th layer will not be used.
- conv_cfg (Optional[Union[ConfigDict, dict]]): Dictionary to construct
- and config conv layer.
- norm_cfg (Union[ConfigDict, dict]): Dictionary to construct and config
- norm layer. Use ``GN`` by default.
- init_cfg (Optional[Union[ConfigDict, dict]]): Initialization config
- dict.
- loss_seg (Union[ConfigDict, dict]): the loss of the semantic head.
- """
-
- def __init__(self,
- num_things_classes: int = 80,
- num_stuff_classes: int = 53,
- in_channels: int = 256,
- inner_channels: int = 128,
- start_level: int = 0,
- end_level: int = 4,
- conv_cfg: OptConfigType = None,
- norm_cfg: ConfigType = dict(
- type='GN', num_groups=32, requires_grad=True),
- loss_seg: ConfigType = dict(
- type='CrossEntropyLoss', ignore_index=-1,
- loss_weight=1.0),
- init_cfg: OptMultiConfig = None) -> None:
- seg_rescale_factor = 1 / 2**(start_level + 2)
- super().__init__(
- num_classes=num_stuff_classes + 1,
- seg_rescale_factor=seg_rescale_factor,
- loss_seg=loss_seg,
- init_cfg=init_cfg)
- self.num_things_classes = num_things_classes
- self.num_stuff_classes = num_stuff_classes
- # Used feature layers are [start_level, end_level)
- self.start_level = start_level
- self.end_level = end_level
- self.num_stages = end_level - start_level
- self.inner_channels = inner_channels
-
- self.conv_upsample_layers = ModuleList()
- for i in range(start_level, end_level):
- self.conv_upsample_layers.append(
- ConvUpsample(
- in_channels,
- inner_channels,
- num_layers=i if i > 0 else 1,
- num_upsample=i if i > 0 else 0,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- ))
- self.conv_logits = nn.Conv2d(inner_channels, self.num_classes, 1)
-
- def _set_things_to_void(self, gt_semantic_seg: Tensor) -> Tensor:
- """Merge thing classes to one class.
-
- In PanopticFPN, the background labels will be reset from `0` to
- `self.num_stuff_classes-1`, the foreground labels will be merged to
- `self.num_stuff_classes`-th channel.
- """
- gt_semantic_seg = gt_semantic_seg.int()
- fg_mask = gt_semantic_seg < self.num_things_classes
- bg_mask = (gt_semantic_seg >= self.num_things_classes) * (
- gt_semantic_seg < self.num_things_classes + self.num_stuff_classes)
-
- new_gt_seg = torch.clone(gt_semantic_seg)
- new_gt_seg = torch.where(bg_mask,
- gt_semantic_seg - self.num_things_classes,
- new_gt_seg)
- new_gt_seg = torch.where(fg_mask,
- fg_mask.int() * self.num_stuff_classes,
- new_gt_seg)
- return new_gt_seg
-
- def loss(self, x: Union[Tensor, Tuple[Tensor]],
- batch_data_samples: SampleList) -> Dict[str, Tensor]:
- """
- Args:
- x (Union[Tensor, Tuple[Tensor]]): Feature maps.
- batch_data_samples (list[:obj:`DetDataSample`]): The batch
- data samples. It usually includes information such
- as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
-
- Returns:
- Dict[str, Tensor]: The loss of semantic head.
- """
- seg_preds = self(x)['seg_preds']
- gt_semantic_segs = [
- data_sample.gt_sem_seg.sem_seg
- for data_sample in batch_data_samples
- ]
-
- gt_semantic_segs = torch.stack(gt_semantic_segs)
- if self.seg_rescale_factor != 1.0:
- gt_semantic_segs = F.interpolate(
- gt_semantic_segs.float(),
- scale_factor=self.seg_rescale_factor,
- mode='nearest').squeeze(1)
-
- # Things classes will be merged to one class in PanopticFPN.
- gt_semantic_segs = self._set_things_to_void(gt_semantic_segs)
-
- if seg_preds.shape[-2:] != gt_semantic_segs.shape[-2:]:
- seg_preds = interpolate_as(seg_preds, gt_semantic_segs)
- seg_preds = seg_preds.permute((0, 2, 3, 1))
-
- loss_seg = self.loss_seg(
- seg_preds.reshape(-1, self.num_classes), # => [NxHxW, C]
- gt_semantic_segs.reshape(-1).long())
-
- return dict(loss_seg=loss_seg)
-
- def init_weights(self) -> None:
- """Initialize weights."""
- super().init_weights()
- nn.init.normal_(self.conv_logits.weight.data, 0, 0.01)
- self.conv_logits.bias.data.zero_()
-
- def forward(self, x: Tuple[Tensor]) -> Dict[str, Tensor]:
- """Forward.
-
- Args:
- x (Tuple[Tensor]): Multi scale Feature maps.
-
- Returns:
- dict[str, Tensor]: semantic segmentation predictions and
- feature maps.
- """
- # the number of subnets must be not more than
- # the length of features.
- assert self.num_stages <= len(x)
-
- feats = []
- for i, layer in enumerate(self.conv_upsample_layers):
- f = layer(x[self.start_level + i])
- feats.append(f)
-
- seg_feats = torch.sum(torch.stack(feats, dim=0), dim=0)
- seg_preds = self.conv_logits(seg_feats)
- out = dict(seg_preds=seg_preds, seg_feats=seg_feats)
- return out
diff --git a/spaces/KyanChen/RSPrompter/mmpretrain/datasets/base_dataset.py b/spaces/KyanChen/RSPrompter/mmpretrain/datasets/base_dataset.py
deleted file mode 100644
index dffdf04772163b5fa55afabc8e15ac8c118aadd2..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpretrain/datasets/base_dataset.py
+++ /dev/null
@@ -1,219 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-from os import PathLike
-from typing import List, Optional, Sequence, Union
-
-import mmengine
-import numpy as np
-from mmengine.dataset import BaseDataset as _BaseDataset
-
-from mmpretrain.registry import DATASETS, TRANSFORMS
-
-
-def expanduser(path):
- """Expand ~ and ~user constructions.
-
- If user or $HOME is unknown, do nothing.
- """
- if isinstance(path, (str, PathLike)):
- return osp.expanduser(path)
- else:
- return path
-
-
-@DATASETS.register_module()
-class BaseDataset(_BaseDataset):
- """Base dataset for image classification task.
-
- This dataset support annotation file in `OpenMMLab 2.0 style annotation
- format`.
-
- .. _OpenMMLab 2.0 style annotation format:
- https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/tutorials/basedataset.md
-
- Comparing with the :class:`mmengine.BaseDataset`, this class implemented
- several useful methods.
-
- Args:
- ann_file (str): Annotation file path.
- metainfo (dict, optional): Meta information for dataset, such as class
- information. Defaults to None.
- data_root (str): The root directory for ``data_prefix`` and
- ``ann_file``. Defaults to ''.
- data_prefix (str | dict): Prefix for training data. Defaults to ''.
- filter_cfg (dict, optional): Config for filter data. Defaults to None.
- indices (int or Sequence[int], optional): Support using first few
- data in annotation file to facilitate training/testing on a smaller
- dataset. Defaults to None, which means using all ``data_infos``.
- serialize_data (bool): Whether to hold memory using serialized objects,
- when enabled, data loader workers can use shared RAM from master
- process instead of making a copy. Defaults to True.
- pipeline (Sequence): Processing pipeline. Defaults to an empty tuple.
- test_mode (bool, optional): ``test_mode=True`` means in test phase,
- an error will be raised when getting an item fails, ``test_mode=False``
- means in training phase, another item will be returned randomly.
- Defaults to False.
- lazy_init (bool): Whether to load annotation during instantiation.
- In some cases, such as visualization, only the meta information of
- the dataset is needed, which is not necessary to load annotation
- file. ``Basedataset`` can skip load annotations to save time by set
- ``lazy_init=False``. Defaults to False.
- max_refetch (int): If ``Basedataset.prepare_data`` get a None img.
- The maximum extra number of cycles to get a valid image.
- Defaults to 1000.
- classes (str | Sequence[str], optional): Specify names of classes.
-
- - If is string, it should be a file path, and the every line of
- the file is a name of a class.
- - If is a sequence of string, every item is a name of class.
- - If is None, use categories information in ``metainfo`` argument,
- annotation file or the class attribute ``METAINFO``.
-
- Defaults to None.
- """ # noqa: E501
-
- def __init__(self,
- ann_file: str,
- metainfo: Optional[dict] = None,
- data_root: str = '',
- data_prefix: Union[str, dict] = '',
- filter_cfg: Optional[dict] = None,
- indices: Optional[Union[int, Sequence[int]]] = None,
- serialize_data: bool = True,
- pipeline: Sequence = (),
- test_mode: bool = False,
- lazy_init: bool = False,
- max_refetch: int = 1000,
- classes: Union[str, Sequence[str], None] = None):
- if isinstance(data_prefix, str):
- data_prefix = dict(img_path=expanduser(data_prefix))
-
- ann_file = expanduser(ann_file)
- metainfo = self._compat_classes(metainfo, classes)
-
- transforms = []
- for transform in pipeline:
- if isinstance(transform, dict):
- transforms.append(TRANSFORMS.build(transform))
- else:
- transforms.append(transform)
-
- super().__init__(
- ann_file=ann_file,
- metainfo=metainfo,
- data_root=data_root,
- data_prefix=data_prefix,
- filter_cfg=filter_cfg,
- indices=indices,
- serialize_data=serialize_data,
- pipeline=transforms,
- test_mode=test_mode,
- lazy_init=lazy_init,
- max_refetch=max_refetch)
-
- @property
- def img_prefix(self):
- """The prefix of images."""
- return self.data_prefix['img_path']
-
- @property
- def CLASSES(self):
- """Return all categories names."""
- return self._metainfo.get('classes', None)
-
- @property
- def class_to_idx(self):
- """Map mapping class name to class index.
-
- Returns:
- dict: mapping from class name to class index.
- """
-
- return {cat: i for i, cat in enumerate(self.CLASSES)}
-
- def get_gt_labels(self):
- """Get all ground-truth labels (categories).
-
- Returns:
- np.ndarray: categories for all images.
- """
-
- gt_labels = np.array(
- [self.get_data_info(i)['gt_label'] for i in range(len(self))])
- return gt_labels
-
- def get_cat_ids(self, idx: int) -> List[int]:
- """Get category id by index.
-
- Args:
- idx (int): Index of data.
-
- Returns:
- cat_ids (List[int]): Image category of specified index.
- """
-
- return [int(self.get_data_info(idx)['gt_label'])]
-
- def _compat_classes(self, metainfo, classes):
- """Merge the old style ``classes`` arguments to ``metainfo``."""
- if isinstance(classes, str):
- # take it as a file path
- class_names = mmengine.list_from_file(expanduser(classes))
- elif isinstance(classes, (tuple, list)):
- class_names = classes
- elif classes is not None:
- raise ValueError(f'Unsupported type {type(classes)} of classes.')
-
- if metainfo is None:
- metainfo = {}
-
- if classes is not None:
- metainfo = {'classes': tuple(class_names), **metainfo}
-
- return metainfo
-
- def full_init(self):
- """Load annotation file and set ``BaseDataset._fully_initialized`` to
- True."""
- super().full_init()
-
- # To support the standard OpenMMLab 2.0 annotation format. Generate
- # metainfo in internal format from standard metainfo format.
- if 'categories' in self._metainfo and 'classes' not in self._metainfo:
- categories = sorted(
- self._metainfo['categories'], key=lambda x: x['id'])
- self._metainfo['classes'] = tuple(
- [cat['category_name'] for cat in categories])
-
- def __repr__(self):
- """Print the basic information of the dataset.
-
- Returns:
- str: Formatted string.
- """
- head = 'Dataset ' + self.__class__.__name__
- body = []
- if self._fully_initialized:
- body.append(f'Number of samples: \t{self.__len__()}')
- else:
- body.append("Haven't been initialized")
-
- if self.CLASSES is not None:
- body.append(f'Number of categories: \t{len(self.CLASSES)}')
-
- body.extend(self.extra_repr())
-
- if len(self.pipeline.transforms) > 0:
- body.append('With transforms:')
- for t in self.pipeline.transforms:
- body.append(f' {t}')
-
- lines = [head] + [' ' * 4 + line for line in body]
- return '\n'.join(lines)
-
- def extra_repr(self) -> List[str]:
- """The extra repr information of the dataset."""
- body = []
- body.append(f'Annotation file: \t{self.ann_file}')
- body.append(f'Prefix of images: \t{self.img_prefix}')
- return body
diff --git a/spaces/Laihiujin/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp b/spaces/Laihiujin/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp
deleted file mode 100644
index 48757e2b0156b2c1513b615d2a17e5aee5172ae7..0000000000000000000000000000000000000000
--- a/spaces/Laihiujin/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp
+++ /dev/null
@@ -1,46 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-/*!
-* Copyright (c) Facebook, Inc. and its affiliates.
-* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-*/
-
-#include
-
-#include
-#include
-
-
-at::Tensor
-ms_deform_attn_cpu_forward(
- const at::Tensor &value,
- const at::Tensor &spatial_shapes,
- const at::Tensor &level_start_index,
- const at::Tensor &sampling_loc,
- const at::Tensor &attn_weight,
- const int im2col_step)
-{
- AT_ERROR("Not implement on cpu");
-}
-
-std::vector
-ms_deform_attn_cpu_backward(
- const at::Tensor &value,
- const at::Tensor &spatial_shapes,
- const at::Tensor &level_start_index,
- const at::Tensor &sampling_loc,
- const at::Tensor &attn_weight,
- const at::Tensor &grad_output,
- const int im2col_step)
-{
- AT_ERROR("Not implement on cpu");
-}
-
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/infer_pack/attentions.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/infer_pack/attentions.py
deleted file mode 100644
index 679d8511efc2afd7352670ed48f86072809520be..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/infer_pack/attentions.py
+++ /dev/null
@@ -1,414 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from lib.infer.infer_libs.infer_pack import commons
-from lib.infer.infer_libs.infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- window_size=10,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
-
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- window_size=window_size,
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- proximal_bias=False,
- proximal_init=True,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.ModuleList()
- self.norm_layers_0 = nn.ModuleList()
- self.encdec_attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- proximal_bias=proximal_bias,
- proximal_init=proximal_init,
- )
- )
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(
- MultiHeadAttention(
- hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- causal=True,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
- device=x.device, dtype=x.dtype
- )
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class MultiHeadAttention(nn.Module):
- def __init__(
- self,
- channels,
- out_channels,
- n_heads,
- p_dropout=0.0,
- window_size=None,
- heads_share=True,
- block_length=None,
- proximal_bias=False,
- proximal_init=False,
- ):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
- self.conv_q = nn.Conv1d(channels, channels, 1)
- self.conv_k = nn.Conv1d(channels, channels, 1)
- self.conv_v = nn.Conv1d(channels, channels, 1)
- self.conv_o = nn.Conv1d(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
- self.emb_rel_k = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
- self.emb_rel_v = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
-
- nn.init.xavier_uniform_(self.conv_q.weight)
- nn.init.xavier_uniform_(self.conv_k.weight)
- nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with torch.no_grad():
- self.conv_k.weight.copy_(self.conv_q.weight)
- self.conv_k.bias.copy_(self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.size(), query.size(2))
- query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
- key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
- value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
- scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
- if self.window_size is not None:
- assert (
- t_s == t_t
- ), "Relative attention is only available for self-attention."
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(
- query / math.sqrt(self.k_channels), key_relative_embeddings
- )
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).to(
- device=scores.device, dtype=scores.dtype
- )
- if mask is not None:
- scores = scores.masked_fill(mask == 0, -1e4)
- if self.block_length is not None:
- assert (
- t_s == t_t
- ), "Local attention is only available for self-attention."
- block_mask = (
- torch.ones_like(scores)
- .triu(-self.block_length)
- .tril(self.block_length)
- )
- scores = scores.masked_fill(block_mask == 0, -1e4)
- p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = torch.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(
- self.emb_rel_v, t_s
- )
- output = output + self._matmul_with_relative_values(
- relative_weights, value_relative_embeddings
- )
- output = (
- output.transpose(2, 3).contiguous().view(b, d, t_t)
- ) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = torch.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- max_relative_position = 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padded_relative_embeddings = F.pad(
- relative_embeddings,
- commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
- )
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[
- :, slice_start_position:slice_end_position
- ]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.size()
- # Concat columns of pad to shift from relative to absolute indexing.
- x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.view([batch, heads, length * 2 * length])
- x_flat = F.pad(
- x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
- )
-
- # Reshape and slice out the padded elements.
- x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
- :, :, :length, length - 1 :
- ]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.size()
- # padd along column
- x = F.pad(
- x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
- )
- x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
- # add 0's in the beginning that will skew the elements after reshape
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
- x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = torch.arange(length, dtype=torch.float32)
- diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
- return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- filter_channels,
- kernel_size,
- p_dropout=0.0,
- activation=None,
- causal=False,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = self.conv_1(self.padding(x * x_mask))
- if self.activation == "gelu":
- x = x * torch.sigmoid(1.702 * x)
- else:
- x = torch.relu(x)
- x = self.drop(x)
- x = self.conv_2(self.padding(x * x_mask))
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_pack/modules/F0Predictor/__init__.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_pack/modules/F0Predictor/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/LaynzKunz/Model-RCV/lib/infer_pack/commons.py b/spaces/LaynzKunz/Model-RCV/lib/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Model-RCV/lib/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
- """KL(P||Q)"""
- kl = (logs_q - logs_p) - 0.5
- kl += (
- 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
- )
- return kl
-
-
-def rand_gumbel(shape):
- """Sample from the Gumbel distribution, protect from overflows."""
- uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
- return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
- g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
- return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def slice_segments2(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
- position = torch.arange(length, dtype=torch.float)
- num_timescales = channels // 2
- log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
- num_timescales - 1
- )
- inv_timescales = min_timescale * torch.exp(
- torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
- )
- scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
- signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
- signal = F.pad(signal, [0, 0, 0, channels % 2])
- signal = signal.view(1, channels, length)
- return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def shift_1d(x):
- x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
- return x
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2, 3) * mask
- return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = list(filter(lambda p: p.grad is not None, parameters))
- norm_type = float(norm_type)
- if clip_value is not None:
- clip_value = float(clip_value)
-
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item() ** norm_type
- if clip_value is not None:
- p.grad.data.clamp_(min=-clip_value, max=clip_value)
- total_norm = total_norm ** (1.0 / norm_type)
- return total_norm
diff --git a/spaces/LightChen2333/OpenSLU/model/decoder/__init__.py b/spaces/LightChen2333/OpenSLU/model/decoder/__init__.py
deleted file mode 100644
index 06a2ee86998009c2ce9105c5ecbab26b0fbb8425..0000000000000000000000000000000000000000
--- a/spaces/LightChen2333/OpenSLU/model/decoder/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from model.decoder.agif_decoder import AGIFDecoder
-from model.decoder.base_decoder import StackPropagationDecoder, BaseDecoder, DCANetDecoder
-from model.decoder.gl_gin_decoder import GLGINDecoder
-
-__all__ = ["StackPropagationDecoder", "BaseDecoder", "DCANetDecoder", "AGIFDecoder", "GLGINDecoder"]
diff --git a/spaces/MarBeanInc/MarBeanInc/app.py b/spaces/MarBeanInc/MarBeanInc/app.py
deleted file mode 100644
index 618008db094b4154299e286aee489b5481faa1b8..0000000000000000000000000000000000000000
--- a/spaces/MarBeanInc/MarBeanInc/app.py
+++ /dev/null
@@ -1,155 +0,0 @@
-from pathlib import Path
-from typing import List, Dict, Tuple
-import matplotlib.colors as mpl_colors
-
-import pandas as pd
-import seaborn as sns
-import shinyswatch
-
-import shiny.experimental as x
-from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
-
-sns.set_theme()
-
-www_dir = Path(__file__).parent.resolve() / "www"
-
-df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
-numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()
-species: List[str] = df["Species"].unique().tolist()
-species.sort()
-
-app_ui = x.ui.page_fillable(
- shinyswatch.theme.minty(),
- ui.layout_sidebar(
- ui.panel_sidebar(
- # Artwork by @allison_horst
- ui.input_selectize(
- "xvar",
- "X variable",
- numeric_cols,
- selected="Bill Length (mm)",
- ),
- ui.input_selectize(
- "yvar",
- "Y variable",
- numeric_cols,
- selected="Bill Depth (mm)",
- ),
- ui.input_checkbox_group(
- "species", "Filter by species", species, selected=species
- ),
- ui.hr(),
- ui.input_switch("by_species", "Show species", value=True),
- ui.input_switch("show_margins", "Show marginal plots", value=True),
- width=2,
- ),
- ui.panel_main(
- ui.output_ui("value_boxes"),
- x.ui.output_plot("scatter", fill=True),
- ui.help_text(
- "Artwork by ",
- ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
- class_="text-end",
- ),
- ),
- ),
-)
-
-
-def server(input: Inputs, output: Outputs, session: Session):
- @reactive.Calc
- def filtered_df() -> pd.DataFrame:
- """Returns a Pandas data frame that includes only the desired rows"""
-
- # This calculation "req"uires that at least one species is selected
- req(len(input.species()) > 0)
-
- # Filter the rows so we only include the desired species
- return df[df["Species"].isin(input.species())]
-
- @output
- @render.plot
- def scatter():
- """Generates a plot for Shiny to display to the user"""
-
- # The plotting function to use depends on whether margins are desired
- plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot
-
- plotfunc(
- data=filtered_df(),
- x=input.xvar(),
- y=input.yvar(),
- palette=palette,
- hue="Species" if input.by_species() else None,
- hue_order=species,
- legend=False,
- )
-
- @output
- @render.ui
- def value_boxes():
- df = filtered_df()
-
- def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
- return x.ui.value_box(
- title,
- count,
- {"class_": "pt-1 pb-0"},
- showcase=x.ui.as_fill_item(
- ui.tags.img(
- {"style": "object-fit:contain;"},
- src=showcase_img,
- )
- ),
- theme_color=None,
- style=f"background-color: {bgcol};",
- )
-
- if not input.by_species():
- return penguin_value_box(
- "Penguins",
- len(df.index),
- bg_palette["default"],
- # Artwork by @allison_horst
- showcase_img="penguins.png",
- )
-
- value_boxes = [
- penguin_value_box(
- name,
- len(df[df["Species"] == name]),
- bg_palette[name],
- # Artwork by @allison_horst
- showcase_img=f"{name}.png",
- )
- for name in species
- # Only include boxes for _selected_ species
- if name in input.species()
- ]
-
- return x.ui.layout_column_wrap(1 / len(value_boxes), *value_boxes)
-
-
-# "darkorange", "purple", "cyan4"
-colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
-colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]
-
-palette: Dict[str, Tuple[float, float, float]] = {
- "Adelie": colors[0],
- "Chinstrap": colors[1],
- "Gentoo": colors[2],
- "default": sns.color_palette()[0], # type: ignore
-}
-
-bg_palette = {}
-# Use `sns.set_style("whitegrid")` to help find approx alpha value
-for name, col in palette.items():
- # Adjusted n_colors until `axe` accessibility did not complain about color contrast
- bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1]) # type: ignore
-
-
-app = App(
- app_ui,
- server,
- static_assets=str(www_dir),
-)
diff --git a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/modeling/roi_heads/detic_fast_rcnn.py b/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/modeling/roi_heads/detic_fast_rcnn.py
deleted file mode 100644
index 186822dd8f67ef9d991ee79101b3bf1243a722a5..0000000000000000000000000000000000000000
--- a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/modeling/roi_heads/detic_fast_rcnn.py
+++ /dev/null
@@ -1,595 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import math
-import json
-import numpy as np
-from typing import Dict, Union
-import torch
-from fvcore.nn import giou_loss, smooth_l1_loss
-from torch import nn
-from torch.nn import functional as F
-import fvcore.nn.weight_init as weight_init
-import detectron2.utils.comm as comm
-from detectron2.config import configurable
-from detectron2.layers import ShapeSpec, batched_nms, cat, cross_entropy, nonzero_tuple
-from detectron2.structures import Boxes, Instances
-from detectron2.utils.events import get_event_storage
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
-from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference
-from detectron2.modeling.roi_heads.fast_rcnn import _log_classification_stats
-
-from torch.cuda.amp import autocast
-from ..utils import load_class_freq, get_fed_loss_inds
-from .zero_shot_classifier import ZeroShotClassifier
-
-__all__ = ["DeticFastRCNNOutputLayers"]
-
-
-class DeticFastRCNNOutputLayers(FastRCNNOutputLayers):
- @configurable
- def __init__(
- self,
- input_shape: ShapeSpec,
- *,
- mult_proposal_score=False,
- cls_score=None,
- sync_caption_batch = False,
- use_sigmoid_ce = False,
- use_fed_loss = False,
- ignore_zero_cats = False,
- fed_loss_num_cat = 50,
- dynamic_classifier = False,
- image_label_loss = '',
- use_zeroshot_cls = False,
- image_loss_weight = 0.1,
- with_softmax_prop = False,
- caption_weight = 1.0,
- neg_cap_weight = 1.0,
- add_image_box = False,
- debug = False,
- prior_prob = 0.01,
- cat_freq_path = '',
- fed_loss_freq_weight = 0.5,
- softmax_weak_loss = False,
- **kwargs,
- ):
- super().__init__(
- input_shape=input_shape,
- **kwargs,
- )
- self.mult_proposal_score = mult_proposal_score
- self.sync_caption_batch = sync_caption_batch
- self.use_sigmoid_ce = use_sigmoid_ce
- self.use_fed_loss = use_fed_loss
- self.ignore_zero_cats = ignore_zero_cats
- self.fed_loss_num_cat = fed_loss_num_cat
- self.dynamic_classifier = dynamic_classifier
- self.image_label_loss = image_label_loss
- self.use_zeroshot_cls = use_zeroshot_cls
- self.image_loss_weight = image_loss_weight
- self.with_softmax_prop = with_softmax_prop
- self.caption_weight = caption_weight
- self.neg_cap_weight = neg_cap_weight
- self.add_image_box = add_image_box
- self.softmax_weak_loss = softmax_weak_loss
- self.debug = debug
-
- if softmax_weak_loss:
- assert image_label_loss in ['max_size']
-
- if self.use_sigmoid_ce:
- bias_value = -math.log((1 - prior_prob) / prior_prob)
- nn.init.constant_(self.cls_score.bias, bias_value)
-
- if self.use_fed_loss or self.ignore_zero_cats:
- freq_weight = load_class_freq(cat_freq_path, fed_loss_freq_weight)
- self.register_buffer('freq_weight', freq_weight)
- else:
- self.freq_weight = None
-
- if self.use_fed_loss and len(self.freq_weight) < self.num_classes:
- # assert self.num_classes == 11493
- print('Extending federated loss weight')
- self.freq_weight = torch.cat(
- [self.freq_weight,
- self.freq_weight.new_zeros(
- self.num_classes - len(self.freq_weight))]
- )
-
- assert (not self.dynamic_classifier) or (not self.use_fed_loss)
- input_size = input_shape.channels * \
- (input_shape.width or 1) * (input_shape.height or 1)
-
- if self.use_zeroshot_cls:
- del self.cls_score
- del self.bbox_pred
- assert cls_score is not None
- self.cls_score = cls_score
- self.bbox_pred = nn.Sequential(
- nn.Linear(input_size, input_size),
- nn.ReLU(inplace=True),
- nn.Linear(input_size, 4)
- )
- weight_init.c2_xavier_fill(self.bbox_pred[0])
- nn.init.normal_(self.bbox_pred[-1].weight, std=0.001)
- nn.init.constant_(self.bbox_pred[-1].bias, 0)
-
- if self.with_softmax_prop:
- self.prop_score = nn.Sequential(
- nn.Linear(input_size, input_size),
- nn.ReLU(inplace=True),
- nn.Linear(input_size, self.num_classes + 1),
- )
- weight_init.c2_xavier_fill(self.prop_score[0])
- nn.init.normal_(self.prop_score[-1].weight, mean=0, std=0.001)
- nn.init.constant_(self.prop_score[-1].bias, 0)
-
-
- @classmethod
- def from_config(cls, cfg, input_shape):
- ret = super().from_config(cfg, input_shape)
- ret.update({
- 'mult_proposal_score': cfg.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE,
- 'sync_caption_batch': cfg.MODEL.SYNC_CAPTION_BATCH,
- 'use_sigmoid_ce': cfg.MODEL.ROI_BOX_HEAD.USE_SIGMOID_CE,
- 'use_fed_loss': cfg.MODEL.ROI_BOX_HEAD.USE_FED_LOSS,
- 'ignore_zero_cats': cfg.MODEL.ROI_BOX_HEAD.IGNORE_ZERO_CATS,
- 'fed_loss_num_cat': cfg.MODEL.ROI_BOX_HEAD.FED_LOSS_NUM_CAT,
- 'dynamic_classifier': cfg.MODEL.DYNAMIC_CLASSIFIER,
- 'image_label_loss': cfg.MODEL.ROI_BOX_HEAD.IMAGE_LABEL_LOSS,
- 'use_zeroshot_cls': cfg.MODEL.ROI_BOX_HEAD.USE_ZEROSHOT_CLS,
- 'image_loss_weight': cfg.MODEL.ROI_BOX_HEAD.IMAGE_LOSS_WEIGHT,
- 'with_softmax_prop': cfg.MODEL.ROI_BOX_HEAD.WITH_SOFTMAX_PROP,
- 'caption_weight': cfg.MODEL.ROI_BOX_HEAD.CAPTION_WEIGHT,
- 'neg_cap_weight': cfg.MODEL.ROI_BOX_HEAD.NEG_CAP_WEIGHT,
- 'add_image_box': cfg.MODEL.ROI_BOX_HEAD.ADD_IMAGE_BOX,
- 'debug': cfg.DEBUG or cfg.SAVE_DEBUG or cfg.IS_DEBUG,
- 'prior_prob': cfg.MODEL.ROI_BOX_HEAD.PRIOR_PROB,
- 'cat_freq_path': cfg.MODEL.ROI_BOX_HEAD.CAT_FREQ_PATH,
- 'fed_loss_freq_weight': cfg.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT,
- 'softmax_weak_loss': cfg.MODEL.ROI_BOX_HEAD.SOFTMAX_WEAK_LOSS,
- })
- if ret['use_zeroshot_cls']:
- ret['cls_score'] = ZeroShotClassifier(cfg, input_shape)
- return ret
-
- def losses(self, predictions, proposals, \
- use_advanced_loss=True,
- classifier_info=(None,None,None)):
- """
- enable advanced loss
- """
- scores, proposal_deltas = predictions
- gt_classes = (
- cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0)
- )
- num_classes = self.num_classes
- if self.dynamic_classifier:
- _, cls_id_map = classifier_info[1]
- gt_classes = cls_id_map[gt_classes]
- num_classes = scores.shape[1] - 1
- assert cls_id_map[self.num_classes] == num_classes
- _log_classification_stats(scores, gt_classes)
-
- if len(proposals):
- proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) # Nx4
- assert not proposal_boxes.requires_grad, "Proposals should not require gradients!"
- gt_boxes = cat(
- [(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals],
- dim=0,
- )
- else:
- proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device)
-
- if self.use_sigmoid_ce:
- loss_cls = self.sigmoid_cross_entropy_loss(scores, gt_classes)
- else:
- loss_cls = self.softmax_cross_entropy_loss(scores, gt_classes)
- return {
- "loss_cls": loss_cls,
- "loss_box_reg": self.box_reg_loss(
- proposal_boxes, gt_boxes, proposal_deltas, gt_classes,
- num_classes=num_classes)
- }
-
-
- def sigmoid_cross_entropy_loss(self, pred_class_logits, gt_classes):
- if pred_class_logits.numel() == 0:
- return pred_class_logits.new_zeros([1])[0] # This is more robust than .sum() * 0.
-
- B = pred_class_logits.shape[0]
- C = pred_class_logits.shape[1] - 1
-
- target = pred_class_logits.new_zeros(B, C + 1)
- target[range(len(gt_classes)), gt_classes] = 1 # B x (C + 1)
- target = target[:, :C] # B x C
-
- weight = 1
-
- if self.use_fed_loss and (self.freq_weight is not None): # fedloss
- appeared = get_fed_loss_inds(
- gt_classes,
- num_sample_cats=self.fed_loss_num_cat,
- C=C,
- weight=self.freq_weight)
- appeared_mask = appeared.new_zeros(C + 1)
- appeared_mask[appeared] = 1 # C + 1
- appeared_mask = appeared_mask[:C]
- fed_w = appeared_mask.view(1, C).expand(B, C)
- weight = weight * fed_w.float()
- if self.ignore_zero_cats and (self.freq_weight is not None):
- w = (self.freq_weight.view(-1) > 1e-4).float()
- weight = weight * w.view(1, C).expand(B, C)
- # import pdb; pdb.set_trace()
-
- cls_loss = F.binary_cross_entropy_with_logits(
- pred_class_logits[:, :-1], target, reduction='none') # B x C
- loss = torch.sum(cls_loss * weight) / B
- return loss
-
-
- def softmax_cross_entropy_loss(self, pred_class_logits, gt_classes):
- """
- change _no_instance handling
- """
- if pred_class_logits.numel() == 0:
- return pred_class_logits.new_zeros([1])[0]
-
- if self.ignore_zero_cats and (self.freq_weight is not None):
- zero_weight = torch.cat([
- (self.freq_weight.view(-1) > 1e-4).float(),
- self.freq_weight.new_ones(1)]) # C + 1
- loss = F.cross_entropy(
- pred_class_logits, gt_classes,
- weight=zero_weight, reduction="mean")
- elif self.use_fed_loss and (self.freq_weight is not None): # fedloss
- C = pred_class_logits.shape[1] - 1
- appeared = get_fed_loss_inds(
- gt_classes,
- num_sample_cats=self.fed_loss_num_cat,
- C=C,
- weight=self.freq_weight)
- appeared_mask = appeared.new_zeros(C + 1).float()
- appeared_mask[appeared] = 1. # C + 1
- appeared_mask[C] = 1.
- loss = F.cross_entropy(
- pred_class_logits, gt_classes,
- weight=appeared_mask, reduction="mean")
- else:
- loss = F.cross_entropy(
- pred_class_logits, gt_classes, reduction="mean")
- return loss
-
-
- def box_reg_loss(
- self, proposal_boxes, gt_boxes, pred_deltas, gt_classes,
- num_classes=-1):
- """
- Allow custom background index
- """
- num_classes = num_classes if num_classes > 0 else self.num_classes
- box_dim = proposal_boxes.shape[1] # 4 or 5
- fg_inds = nonzero_tuple((gt_classes >= 0) & (gt_classes < num_classes))[0]
- if pred_deltas.shape[1] == box_dim: # cls-agnostic regression
- fg_pred_deltas = pred_deltas[fg_inds]
- else:
- fg_pred_deltas = pred_deltas.view(-1, self.num_classes, box_dim)[
- fg_inds, gt_classes[fg_inds]
- ]
-
- if self.box_reg_loss_type == "smooth_l1":
- gt_pred_deltas = self.box2box_transform.get_deltas(
- proposal_boxes[fg_inds],
- gt_boxes[fg_inds],
- )
- loss_box_reg = smooth_l1_loss(
- fg_pred_deltas, gt_pred_deltas, self.smooth_l1_beta, reduction="sum"
- )
- elif self.box_reg_loss_type == "giou":
- fg_pred_boxes = self.box2box_transform.apply_deltas(
- fg_pred_deltas, proposal_boxes[fg_inds]
- )
- loss_box_reg = giou_loss(fg_pred_boxes, gt_boxes[fg_inds], reduction="sum")
- else:
- raise ValueError(f"Invalid bbox reg loss type '{self.box_reg_loss_type}'")
- return loss_box_reg / max(gt_classes.numel(), 1.0)
-
- def inference(self, predictions, proposals):
- """
- enable use proposal boxes
- """
- predictions = (predictions[0], predictions[1])
- boxes = self.predict_boxes(predictions, proposals)
- scores = self.predict_probs(predictions, proposals)
- if self.mult_proposal_score:
- proposal_scores = [p.get('objectness_logits') for p in proposals]
- scores = [(s * ps[:, None]) ** 0.5 \
- for s, ps in zip(scores, proposal_scores)]
- image_shapes = [x.image_size for x in proposals]
- return fast_rcnn_inference(
- boxes,
- scores,
- image_shapes,
- self.test_score_thresh,
- self.test_nms_thresh,
- self.test_topk_per_image,
- )
-
-
- def predict_probs(self, predictions, proposals):
- """
- support sigmoid
- """
- # scores, _ = predictions
- scores = predictions[0]
- num_inst_per_image = [len(p) for p in proposals]
- if self.use_sigmoid_ce:
- probs = scores.sigmoid()
- else:
- probs = F.softmax(scores, dim=-1)
- return probs.split(num_inst_per_image, dim=0)
-
-
- def image_label_losses(self, predictions, proposals, image_labels, \
- classifier_info=(None,None,None), ann_type='image'):
- '''
- Inputs:
- scores: N x (C + 1)
- image_labels B x 1
- '''
- num_inst_per_image = [len(p) for p in proposals]
- scores = predictions[0]
- scores = scores.split(num_inst_per_image, dim=0) # B x n x (C + 1)
- if self.with_softmax_prop:
- prop_scores = predictions[2].split(num_inst_per_image, dim=0)
- else:
- prop_scores = [None for _ in num_inst_per_image]
- B = len(scores)
- img_box_count = 0
- select_size_count = 0
- select_x_count = 0
- select_y_count = 0
- max_score_count = 0
- storage = get_event_storage()
- loss = scores[0].new_zeros([1])[0]
- caption_loss = scores[0].new_zeros([1])[0]
- for idx, (score, labels, prop_score, p) in enumerate(zip(
- scores, image_labels, prop_scores, proposals)):
- if score.shape[0] == 0:
- loss += score.new_zeros([1])[0]
- continue
- if 'caption' in ann_type:
- score, caption_loss_img = self._caption_loss(
- score, classifier_info, idx, B)
- caption_loss += self.caption_weight * caption_loss_img
- if ann_type == 'caption':
- continue
-
- if self.debug:
- p.selected = score.new_zeros(
- (len(p),), dtype=torch.long) - 1
- for i_l, label in enumerate(labels):
- if self.dynamic_classifier:
- if idx == 0 and i_l == 0 and comm.is_main_process():
- storage.put_scalar('stats_label', label)
- label = classifier_info[1][1][label]
- assert label < score.shape[1]
- if self.image_label_loss in ['wsod', 'wsddn']:
- loss_i, ind = self._wsddn_loss(score, prop_score, label)
- elif self.image_label_loss == 'max_score':
- loss_i, ind = self._max_score_loss(score, label)
- elif self.image_label_loss == 'max_size':
- loss_i, ind = self._max_size_loss(score, label, p)
- elif self.image_label_loss == 'first':
- loss_i, ind = self._first_loss(score, label)
- elif self.image_label_loss == 'image':
- loss_i, ind = self._image_loss(score, label)
- elif self.image_label_loss == 'min_loss':
- loss_i, ind = self._min_loss_loss(score, label)
- else:
- assert 0
- loss += loss_i / len(labels)
- if type(ind) == type([]):
- img_box_count = sum(ind) / len(ind)
- if self.debug:
- for ind_i in ind:
- p.selected[ind_i] = label
- else:
- img_box_count = ind
- select_size_count = p[ind].proposal_boxes.area() / \
- (p.image_size[0] * p.image_size[1])
- max_score_count = score[ind, label].sigmoid()
- select_x_count = (p.proposal_boxes.tensor[ind, 0] + \
- p.proposal_boxes.tensor[ind, 2]) / 2 / p.image_size[1]
- select_y_count = (p.proposal_boxes.tensor[ind, 1] + \
- p.proposal_boxes.tensor[ind, 3]) / 2 / p.image_size[0]
- if self.debug:
- p.selected[ind] = label
-
- loss = loss / B
- storage.put_scalar('stats_l_image', loss.item())
- if 'caption' in ann_type:
- caption_loss = caption_loss / B
- loss = loss + caption_loss
- storage.put_scalar('stats_l_caption', caption_loss.item())
- if comm.is_main_process():
- storage.put_scalar('pool_stats', img_box_count)
- storage.put_scalar('stats_select_size', select_size_count)
- storage.put_scalar('stats_select_x', select_x_count)
- storage.put_scalar('stats_select_y', select_y_count)
- storage.put_scalar('stats_max_label_score', max_score_count)
-
- return {
- 'image_loss': loss * self.image_loss_weight,
- 'loss_cls': score.new_zeros([1])[0],
- 'loss_box_reg': score.new_zeros([1])[0]}
-
-
- def forward(self, x, classifier_info=(None,None,None)):
- """
- enable classifier_info
- """
- if x.dim() > 2:
- x = torch.flatten(x, start_dim=1)
- scores = []
-
- if classifier_info[0] is not None:
- cls_scores = self.cls_score(x, classifier=classifier_info[0])
- scores.append(cls_scores)
- else:
- cls_scores = self.cls_score(x)
- scores.append(cls_scores)
-
- if classifier_info[2] is not None:
- cap_cls = classifier_info[2]
- if self.sync_caption_batch:
- caption_scores = self.cls_score(x, classifier=cap_cls[:, :-1])
- else:
- caption_scores = self.cls_score(x, classifier=cap_cls)
- scores.append(caption_scores)
- scores = torch.cat(scores, dim=1) # B x C' or B x N or B x (C'+N)
-
- proposal_deltas = self.bbox_pred(x)
- if self.with_softmax_prop:
- prop_score = self.prop_score(x)
- return scores, proposal_deltas, prop_score
- else:
- return scores, proposal_deltas
-
-
- def _caption_loss(self, score, classifier_info, idx, B):
- assert (classifier_info[2] is not None)
- assert self.add_image_box
- cls_and_cap_num = score.shape[1]
- cap_num = classifier_info[2].shape[0]
- score, caption_score = score.split(
- [cls_and_cap_num - cap_num, cap_num], dim=1)
- # n x (C + 1), n x B
- caption_score = caption_score[-1:] # 1 x B # -1: image level box
- caption_target = caption_score.new_zeros(
- caption_score.shape) # 1 x B or 1 x MB, M: num machines
- if self.sync_caption_batch:
- # caption_target: 1 x MB
- rank = comm.get_rank()
- global_idx = B * rank + idx
- assert (classifier_info[2][
- global_idx, -1] - rank) ** 2 < 1e-8, \
- '{} {} {} {} {}'.format(
- rank, global_idx,
- classifier_info[2][global_idx, -1],
- classifier_info[2].shape,
- classifier_info[2][:, -1])
- caption_target[:, global_idx] = 1.
- else:
- assert caption_score.shape[1] == B
- caption_target[:, idx] = 1.
- caption_loss_img = F.binary_cross_entropy_with_logits(
- caption_score, caption_target, reduction='none')
- if self.sync_caption_batch:
- fg_mask = (caption_target > 0.5).float()
- assert (fg_mask.sum().item() - 1.) ** 2 < 1e-8, '{} {}'.format(
- fg_mask.shape, fg_mask)
- pos_loss = (caption_loss_img * fg_mask).sum()
- neg_loss = (caption_loss_img * (1. - fg_mask)).sum()
- caption_loss_img = pos_loss + self.neg_cap_weight * neg_loss
- else:
- caption_loss_img = caption_loss_img.sum()
- return score, caption_loss_img
-
-
- def _wsddn_loss(self, score, prop_score, label):
- assert prop_score is not None
- loss = 0
- final_score = score.sigmoid() * \
- F.softmax(prop_score, dim=0) # B x (C + 1)
- img_score = torch.clamp(
- torch.sum(final_score, dim=0),
- min=1e-10, max=1-1e-10) # (C + 1)
- target = img_score.new_zeros(img_score.shape) # (C + 1)
- target[label] = 1.
- loss += F.binary_cross_entropy(img_score, target)
- ind = final_score[:, label].argmax()
- return loss, ind
-
-
- def _max_score_loss(self, score, label):
- loss = 0
- target = score.new_zeros(score.shape[1])
- target[label] = 1.
- ind = score[:, label].argmax().item()
- loss += F.binary_cross_entropy_with_logits(
- score[ind], target, reduction='sum')
- return loss, ind
-
-
- def _min_loss_loss(self, score, label):
- loss = 0
- target = score.new_zeros(score.shape)
- target[:, label] = 1.
- with torch.no_grad():
- x = F.binary_cross_entropy_with_logits(
- score, target, reduction='none').sum(dim=1) # n
- ind = x.argmin().item()
- loss += F.binary_cross_entropy_with_logits(
- score[ind], target[0], reduction='sum')
- return loss, ind
-
-
- def _first_loss(self, score, label):
- loss = 0
- target = score.new_zeros(score.shape[1])
- target[label] = 1.
- ind = 0
- loss += F.binary_cross_entropy_with_logits(
- score[ind], target, reduction='sum')
- return loss, ind
-
-
- def _image_loss(self, score, label):
- assert self.add_image_box
- target = score.new_zeros(score.shape[1])
- target[label] = 1.
- ind = score.shape[0] - 1
- loss = F.binary_cross_entropy_with_logits(
- score[ind], target, reduction='sum')
- return loss, ind
-
-
- def _max_size_loss(self, score, label, p):
- loss = 0
- target = score.new_zeros(score.shape[1])
- target[label] = 1.
- sizes = p.proposal_boxes.area()
- ind = sizes[:-1].argmax().item() if len(sizes) > 1 else 0
- if self.softmax_weak_loss:
- loss += F.cross_entropy(
- score[ind:ind+1],
- score.new_tensor(label, dtype=torch.long).view(1),
- reduction='sum')
- else:
- loss += F.binary_cross_entropy_with_logits(
- score[ind], target, reduction='sum')
- return loss, ind
-
-
-
-def put_label_distribution(storage, hist_name, hist_counts, num_classes):
- """
- """
- ht_min, ht_max = 0, num_classes
- hist_edges = torch.linspace(
- start=ht_min, end=ht_max, steps=num_classes + 1, dtype=torch.float32)
-
- hist_params = dict(
- tag=hist_name,
- min=ht_min,
- max=ht_max,
- num=float(hist_counts.sum()),
- sum=float((hist_counts * torch.arange(len(hist_counts))).sum()),
- sum_squares=float(((hist_counts * torch.arange(len(hist_counts))) ** 2).sum()),
- bucket_limits=hist_edges[1:].tolist(),
- bucket_counts=hist_counts.tolist(),
- global_step=storage._iter,
- )
- storage._histograms.append(hist_params)
\ No newline at end of file
diff --git a/spaces/MesutUnutur/germanToEnglishTextToImage/README.md b/spaces/MesutUnutur/germanToEnglishTextToImage/README.md
deleted file mode 100644
index d2ffaf63baaba7778e934a291fc8202f81c78fee..0000000000000000000000000000000000000000
--- a/spaces/MesutUnutur/germanToEnglishTextToImage/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: GermanToEnglishTextToImage
-emoji: 👁
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mohamed90/Geoappfolium/Dockerfile b/spaces/Mohamed90/Geoappfolium/Dockerfile
deleted file mode 100644
index f6990bc632cc24121ee7df3b97b931a858db1742..0000000000000000000000000000000000000000
--- a/spaces/Mohamed90/Geoappfolium/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM python:3.9
-
-WORKDIR /test1
-
-COPY . /test1
-
-RUN pip install --no-cache-dir --upgrade -r /test1/requirements.txt
-
-COPY . .
-
-CMD ["streamlit", "run", "/test1/app.py", "--address", "0.0.0.0", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/NeuralInternet/Text-Generation_Playground/convert-to-flexgen.py b/spaces/NeuralInternet/Text-Generation_Playground/convert-to-flexgen.py
deleted file mode 100644
index 917f023c3fe395c2e3cbcad11c9cdc6b85ef1e7e..0000000000000000000000000000000000000000
--- a/spaces/NeuralInternet/Text-Generation_Playground/convert-to-flexgen.py
+++ /dev/null
@@ -1,60 +0,0 @@
-'''
-
-Converts a transformers model to a format compatible with flexgen.
-
-'''
-
-import argparse
-import os
-from pathlib import Path
-
-import numpy as np
-import torch
-from tqdm import tqdm
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54))
-parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.")
-args = parser.parse_args()
-
-def disable_torch_init():
- """
- Disable the redundant torch default initialization to accelerate model creation.
- """
- import torch
- global torch_linear_init_backup
- global torch_layer_norm_init_backup
-
- torch_linear_init_backup = torch.nn.Linear.reset_parameters
- setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
-
- torch_layer_norm_init_backup = torch.nn.LayerNorm.reset_parameters
- setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
-
-def restore_torch_init():
- """Rollback the change made by disable_torch_init."""
- import torch
- setattr(torch.nn.Linear, "reset_parameters", torch_linear_init_backup)
- setattr(torch.nn.LayerNorm, "reset_parameters", torch_layer_norm_init_backup)
-
-if __name__ == '__main__':
- path = Path(args.MODEL)
- model_name = path.name
-
- print(f"Loading {model_name}...")
- #disable_torch_init()
- model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
- #restore_torch_init()
-
- tokenizer = AutoTokenizer.from_pretrained(path)
-
- out_folder = Path(f"models/{model_name}-np")
- if not Path(out_folder).exists():
- os.mkdir(out_folder)
-
- print(f"Saving the converted model to {out_folder}...")
- for name, param in tqdm(list(model.model.named_parameters())):
- name = name.replace("decoder.final_layer_norm", "decoder.layer_norm")
- param_path = os.path.join(out_folder, name)
- with open(param_path, "wb") as f:
- np.save(f, param.cpu().detach().numpy())
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/prepend_token_dataset.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/prepend_token_dataset.py
deleted file mode 100644
index fd1331f4c44c1595eb9bb78baa0cf5cf3bcce9ad..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/prepend_token_dataset.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import torch
-
-from . import BaseWrapperDataset
-
-
-class PrependTokenDataset(BaseWrapperDataset):
- def __init__(self, dataset, token=None):
- super().__init__(dataset)
- self.token = token
- if token is not None:
- self._sizes = np.array(dataset.sizes) + 1
- else:
- self._sizes = dataset.sizes
-
- def __getitem__(self, idx):
- item = self.dataset[idx]
- if self.token is not None:
- item = torch.cat([item.new([self.token]), item])
- return item
-
- @property
- def sizes(self):
- return self._sizes
-
- def num_tokens(self, index):
- n = self.dataset.num_tokens(index)
- if self.token is not None:
- n += 1
- return n
-
- def size(self, index):
- n = self.dataset.size(index)
- if self.token is not None:
- n += 1
- return n
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/raw_label_dataset.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/raw_label_dataset.py
deleted file mode 100644
index d054904f419bd64855d33a2a770b43f671c7c8d8..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/raw_label_dataset.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from . import FairseqDataset
-
-
-class RawLabelDataset(FairseqDataset):
- def __init__(self, labels):
- super().__init__()
- self.labels = labels
-
- def __getitem__(self, index):
- return self.labels[index]
-
- def __len__(self):
- return len(self.labels)
-
- def collater(self, samples):
- return torch.tensor(samples)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/models/ofa/ofa.py b/spaces/OFA-Sys/OFA-Image_Caption/models/ofa/ofa.py
deleted file mode 100644
index 01abdf64706d9555a42fa4cd7a7f38fb6649c53e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/models/ofa/ofa.py
+++ /dev/null
@@ -1,410 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-OFA
-"""
-from typing import Optional
-
-import logging
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.models import register_model, register_model_architecture
-from fairseq.modules.transformer_sentence_encoder import init_bert_params
-
-from .unify_transformer import TransformerModel
-
-logger = logging.getLogger(__name__)
-
-
-@register_model("ofa")
-class OFAModel(TransformerModel):
- __jit_unused_properties__ = ["supported_targets"]
-
- def __init__(self, args, encoder, decoder):
- super().__init__(args, encoder, decoder)
-
- # We follow BERT's random weight initialization
- self.apply(init_bert_params)
-
- self.classification_heads = nn.ModuleDict()
- if hasattr(self.encoder, "dictionary"):
- self.eos: int = self.encoder.dictionary.eos()
-
- @staticmethod
- def add_args(parser):
- super(OFAModel, OFAModel).add_args(parser)
- parser.add_argument(
- "--pooler-dropout",
- type=float,
- metavar="D",
- help="dropout probability in the masked_lm pooler layers",
- )
- parser.add_argument(
- "--pooler-classifier",
- type=str,
- choices=['mlp', 'linear'],
- help="type of pooler classifier",
- )
- parser.add_argument(
- "--pooler-activation-fn",
- choices=utils.get_available_activation_fns(),
- help="activation function to use for pooler layer",
- )
- parser.add_argument(
- "--spectral-norm-classification-head",
- action="store_true",
- help="Apply spectral normalization on the classification head",
- )
-
- @property
- def supported_targets(self):
- return {"self"}
-
- def forward(
- self,
- src_tokens,
- src_lengths,
- prev_output_tokens,
- patch_images: Optional[torch.Tensor] = None,
- patch_images_2: Optional[torch.Tensor] = None,
- patch_masks: Optional[torch.Tensor] = None,
- code_masks: Optional[torch.Tensor] = None,
- sample_patch_num: Optional[int] = None,
- features_only: bool = False,
- classification_head_name: Optional[str] = None,
- token_embeddings: Optional[torch.Tensor] = None,
- return_all_hiddens: bool = False,
- alignment_layer: Optional[int] = None,
- alignment_heads: Optional[int] = None,
- ):
- if classification_head_name is not None:
- features_only = True
-
- encoder_out = self.encoder(
- src_tokens,
- src_lengths=src_lengths,
- patch_images=patch_images,
- patch_masks=patch_masks,
- patch_images_2=patch_images_2,
- token_embeddings=token_embeddings,
- return_all_hiddens=return_all_hiddens,
- sample_patch_num=sample_patch_num
- )
- x, extra = self.decoder(
- prev_output_tokens,
- code_masks=code_masks,
- encoder_out=encoder_out,
- features_only=features_only,
- alignment_layer=alignment_layer,
- alignment_heads=alignment_heads,
- src_lengths=src_lengths,
- return_all_hiddens=return_all_hiddens,
- )
-
- pad = self.encoder.padding_idx
- if classification_head_name is not None:
- prev_lengths = prev_output_tokens.ne(pad).sum(1)
- gather_index = prev_lengths[:, None, None].expand(x.size(0), 1, x.size(2)) - 1
- sentence_representation = x.gather(1, gather_index).squeeze()
- if self.classification_heads[classification_head_name].use_two_images:
- hidden_size = sentence_representation.size(1)
- sentence_representation = sentence_representation.view(-1, hidden_size * 2)
- for k, head in self.classification_heads.items():
- # for torch script only supports iteration
- if k == classification_head_name:
- x = head(sentence_representation)
- break
-
- return x, extra
-
- def register_embedding_tokens(self, ans2label_dict, src_dict, bpe):
- """Register embedding tokens"""
- logger.info("Registering embedding tokens")
- self.ans_tensor_list = []
- for i in range(len(ans2label_dict)):
- ans = src_dict[-len(ans2label_dict)+i]
- ans = ans[5:-1].replace('_', ' ')
- ans_tensor = src_dict.encode_line(
- line=bpe.encode(' {}'.format(ans.lower())),
- add_if_not_exist=False,
- append_eos=False
- ).long()
- self.ans_tensor_list.append(ans_tensor)
-
- def register_classification_head(
- self, name, num_classes=None, inner_dim=None, use_two_images=False, **kwargs
- ):
- """Register a classification head."""
- logger.info("Registering classification head: {0}".format(name))
- if name in self.classification_heads:
- prev_num_classes = self.classification_heads[name].out_proj.out_features
- prev_inner_dim = self.classification_heads[name].dense.out_features
- if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
- logger.warning(
- 're-registering head "{}" with num_classes {} (prev: {}) '
- "and inner_dim {} (prev: {})".format(
- name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
- )
- )
- self.classification_heads[name] = OFAClassificationHead(
- input_dim=self.args.encoder_embed_dim,
- inner_dim=inner_dim or self.args.encoder_embed_dim,
- num_classes=num_classes,
- activation_fn=self.args.pooler_activation_fn,
- pooler_dropout=self.args.pooler_dropout,
- pooler_classifier=self.args.pooler_classifier,
- use_two_images=use_two_images,
- do_spectral_norm=getattr(
- self.args, "spectral_norm_classification_head", False
- ),
- )
-
- def upgrade_state_dict_named(self, state_dict, name):
- super().upgrade_state_dict_named(state_dict, name)
-
- prefix = name + "." if name != "" else ""
- current_head_names = (
- []
- if not hasattr(self, "classification_heads")
- else self.classification_heads.keys()
- )
-
- # Handle new classification heads present in the state dict.
- keys_to_delete = []
- for k in state_dict.keys():
- if not k.startswith(prefix + "classification_heads."):
- continue
-
- head_name = k[len(prefix + "classification_heads.") :].split(".")[0]
- num_classes = state_dict[
- prefix + "classification_heads." + head_name + ".out_proj.weight"
- ].size(0)
- inner_dim = state_dict[
- prefix + "classification_heads." + head_name + ".dense.weight"
- ].size(0)
-
- if getattr(self.args, "load_checkpoint_heads", False):
- if head_name not in current_head_names:
- self.register_classification_head(head_name, num_classes, inner_dim)
- else:
- if head_name not in current_head_names:
- logger.warning(
- "deleting classification head ({}) from checkpoint "
- "not present in current model: {}".format(head_name, k)
- )
- keys_to_delete.append(k)
- elif (
- num_classes
- != self.classification_heads[head_name].out_proj.out_features
- or inner_dim
- != self.classification_heads[head_name].dense.out_features
- ):
- logger.warning(
- "deleting classification head ({}) from checkpoint "
- "with different dimensions than current model: {}".format(
- head_name, k
- )
- )
- keys_to_delete.append(k)
- for k in keys_to_delete:
- del state_dict[k]
-
- def truncate_emb(key):
- if key in state_dict:
- state_dict[key] = state_dict[key][:-1, :]
-
- # When finetuning on translation task, remove last row of
- # embedding matrix that corresponds to mask_idx token.
- loaded_dict_size = state_dict["encoder.embed_tokens.weight"].size(0)
- if (
- loaded_dict_size == len(self.encoder.dictionary) + 1
- and "" not in self.encoder.dictionary
- ):
- truncate_emb("encoder.embed_tokens.weight")
- truncate_emb("decoder.embed_tokens.weight")
- truncate_emb("encoder.output_projection.weight")
- truncate_emb("decoder.output_projection.weight")
-
- if loaded_dict_size < len(self.encoder.dictionary):
- num_langids_to_add = len(self.encoder.dictionary) - loaded_dict_size
- embed_dim = state_dict["encoder.embed_tokens.weight"].size(1)
-
- new_lang_embed_to_add = torch.zeros(num_langids_to_add, embed_dim)
- if getattr(self, "ans_tensor_list", None):
- assert len(new_lang_embed_to_add) == len(self.ans_tensor_list)
- for i, ans_tensor in enumerate(self.ans_tensor_list):
- ans_embed = F.embedding(ans_tensor, state_dict["encoder.embed_tokens.weight"])
- ans_embed = ans_embed.sum(0) / ans_embed.size(0)
- new_lang_embed_to_add[i] = ans_embed
- else:
- nn.init.normal_(new_lang_embed_to_add, mean=0, std=embed_dim ** -0.5)
- new_lang_embed_to_add = new_lang_embed_to_add.to(
- dtype=state_dict["encoder.embed_tokens.weight"].dtype,
- )
-
- state_dict["encoder.embed_tokens.weight"] = torch.cat(
- [state_dict["encoder.embed_tokens.weight"], new_lang_embed_to_add]
- )
- state_dict["decoder.embed_tokens.weight"] = torch.cat(
- [state_dict["decoder.embed_tokens.weight"], new_lang_embed_to_add]
- )
- state_dict["decoder.output_projection.weight"] = torch.cat(
- [state_dict["decoder.output_projection.weight"], new_lang_embed_to_add]
- )
-
- # Copy any newly-added classification heads into the state dict
- # with their current weights.
- if hasattr(self, "classification_heads"):
- cur_state = self.classification_heads.state_dict()
- for k, v in cur_state.items():
- if prefix + "classification_heads." + k not in state_dict:
- logger.info("Overwriting " + prefix + "classification_heads." + k)
- state_dict[prefix + "classification_heads." + k] = v
-
-
-class OFAClassificationHead(nn.Module):
- """Head for sentence-level classification tasks."""
-
- def __init__(
- self,
- input_dim,
- inner_dim,
- num_classes,
- activation_fn,
- pooler_dropout,
- pooler_classifier,
- use_two_images=False,
- do_spectral_norm=False,
- ):
- super().__init__()
- self.pooler_classifier = pooler_classifier
- self.use_two_images = use_two_images
- input_dim = input_dim * 2 if use_two_images else input_dim
- if pooler_classifier == "mlp":
- self.dense = nn.Linear(input_dim, inner_dim)
- self.activation_fn = utils.get_activation_fn(activation_fn)
- self.dropout = nn.Dropout(p=pooler_dropout)
- self.out_proj = nn.Linear(inner_dim, num_classes)
- elif pooler_classifier == "linear":
- self.dropout = nn.Dropout(p=pooler_dropout)
- self.out_proj = nn.Linear(input_dim, num_classes)
- else:
- raise NotImplementedError
-
- if do_spectral_norm:
- self.out_proj = torch.nn.utils.spectral_norm(self.out_proj)
-
- def forward(self, features, **kwargs):
- if self.pooler_classifier == 'mlp':
- x = features
- x = self.dropout(x)
- x = self.dense(x)
- x = self.activation_fn(x)
- x = self.dropout(x)
- x = self.out_proj(x)
- elif self.pooler_classifier == 'linear':
- x = features
- x = self.dropout(x)
- x = self.out_proj(x)
- else:
- raise NotImplementedError
- return x
-
-
-@register_model_architecture("ofa", "ofa_large")
-def ofa_large_architecture(args):
- args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
- args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024)
- args.encoder_layers = getattr(args, "encoder_layers", 12)
- args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
- args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
- args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
- args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
- args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
- args.decoder_ffn_embed_dim = getattr(
- args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
- )
- args.decoder_layers = getattr(args, "decoder_layers", 12)
- args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
- args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
- args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True)
- args.attention_dropout = getattr(args, "attention_dropout", 0.0)
- args.relu_dropout = getattr(args, "relu_dropout", 0.0)
- args.dropout = getattr(args, "dropout", 0.0)
- args.max_target_positions = getattr(args, "max_target_positions", 1024)
- args.max_source_positions = getattr(args, "max_source_positions", 1024)
- args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
- args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
- args.share_decoder_input_output_embed = getattr(
- args, "share_decoder_input_output_embed", True
- )
- args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
-
- args.decoder_output_dim = getattr(
- args, "decoder_output_dim", args.decoder_embed_dim
- )
- args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
- args.no_scale_embedding = getattr(args, "no_scale_embedding", True)
- args.layernorm_embedding = getattr(args, "layernorm_embedding", True)
-
- args.activation_fn = getattr(args, "activation_fn", "gelu")
- args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
- args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
- args.pooler_classifier = getattr(args, "pooler_classifier", "mlp")
-
- args.resnet_drop_path_rate = getattr(args, "resnet_drop_path_rate", 0.0)
- args.encoder_drop_path_rate = getattr(args, "encoder_drop_path_rate", 0.0)
- args.decoder_drop_path_rate = getattr(args, "decoder_drop_path_rate", 0.0)
-
- args.resnet_type = getattr(args, "resnet_type", "resnet152")
- args.token_bucket_size = getattr(args, "token_bucket_size", 256)
- args.image_bucket_size = getattr(args, "image_bucket_size", 42)
-
- args.freeze_encoder_embedding = getattr(args, "freeze_encoder_embedding", False)
- args.freeze_decoder_embedding = getattr(args, "freeze_decoder_embedding", False)
- args.add_type_embedding = getattr(args, "add_type_embedding", True)
- args.attn_scale_factor = getattr(args, "attn_scale_factor", 2)
-
- args.code_image_size = getattr(args, "code_image_size", 128)
- args.patch_layernorm_embedding = getattr(args, "patch_layernorm_embedding", True)
- args.code_layernorm_embedding = getattr(args, "code_layernorm_embedding", True)
- args.entangle_position_embedding = getattr(args, "entangle_position_embedding", False)
- args.disable_entangle = getattr(args, "disable_entangle", False)
- args.sync_bn = getattr(args, "sync_bn", False)
-
- args.scale_attn = getattr(args, "scale_attn", False)
- args.scale_fc = getattr(args, "scale_fc", False)
- args.scale_heads = getattr(args, "scale_heads", False)
- args.scale_resids = getattr(args, "scale_resids", False)
-
-
-@register_model_architecture("ofa", "ofa_base")
-def ofa_base_architecture(args):
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
- args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 768)
- args.encoder_layers = getattr(args, "encoder_layers", 6)
- args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12)
- args.decoder_layers = getattr(args, "decoder_layers", 6)
- args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12)
- args.resnet_type = getattr(args, "resnet_type", "resnet101")
- ofa_large_architecture(args)
-
-
-@register_model_architecture("ofa", "ofa_huge")
-def ofa_huge_architecture(args):
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1280)
- args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1280)
- args.encoder_layers = getattr(args, "encoder_layers", 24)
- args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
- args.decoder_layers = getattr(args, "decoder_layers", 12)
- args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
- args.resnet_type = getattr(args, "resnet_type", "resnet152")
- ofa_large_architecture(args)
-
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/text_to_speech/vocoder.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/text_to_speech/vocoder.py
deleted file mode 100644
index 65d9f9f06bfe7ffa3ed332bb41c4cdd65ac2b916..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/text_to_speech/vocoder.py
+++ /dev/null
@@ -1,197 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import json
-from typing import Dict
-
-import numpy as np
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from fairseq.data.audio.audio_utils import (
- get_window, get_fourier_basis, get_mel_filters, TTSSpectrogram
-)
-from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig
-from fairseq.models.text_to_speech.hifigan import Generator as HiFiGANModel
-
-logger = logging.getLogger(__name__)
-
-
-class PseudoInverseMelScale(torch.nn.Module):
- def __init__(self, n_stft, n_mels, sample_rate, f_min, f_max) -> None:
- super(PseudoInverseMelScale, self).__init__()
- self.n_mels = n_mels
- basis = get_mel_filters(
- sample_rate, (n_stft - 1) * 2, n_mels, f_min, f_max
- )
- basis = torch.pinverse(basis) # F x F_mel
- self.register_buffer('basis', basis)
-
- def forward(self, melspec: torch.Tensor) -> torch.Tensor:
- # pack batch
- shape = melspec.shape # B_1 x ... x B_K x F_mel x T
- n_mels, time = shape[-2], shape[-1]
- melspec = melspec.view(-1, n_mels, time)
-
- freq, _ = self.basis.size() # F x F_mel
- assert self.n_mels == n_mels, (self.n_mels, n_mels)
- specgram = self.basis.matmul(melspec).clamp(min=0)
-
- # unpack batch
- specgram = specgram.view(shape[:-2] + (freq, time))
- return specgram
-
-
-class GriffinLim(torch.nn.Module):
- def __init__(
- self, n_fft: int, win_length: int, hop_length: int, n_iter: int,
- window_fn=torch.hann_window
- ):
- super(GriffinLim, self).__init__()
- self.transform = TTSSpectrogram(
- n_fft, win_length, hop_length, return_phase=True
- )
-
- basis = get_fourier_basis(n_fft)
- basis = torch.pinverse(n_fft / hop_length * basis).T[:, None, :]
- basis *= get_window(window_fn, n_fft, win_length)
- self.register_buffer('basis', basis)
-
- self.n_fft = n_fft
- self.win_length = win_length
- self.hop_length = hop_length
- self.n_iter = n_iter
-
- self.tiny = 1.1754944e-38
-
- @classmethod
- def get_window_sum_square(
- cls, n_frames, hop_length, win_length, n_fft,
- window_fn=torch.hann_window
- ) -> torch.Tensor:
- w_sq = get_window(window_fn, n_fft, win_length) ** 2
- n = n_fft + hop_length * (n_frames - 1)
- x = torch.zeros(n, dtype=torch.float32)
- for i in range(n_frames):
- ofst = i * hop_length
- x[ofst: min(n, ofst + n_fft)] += w_sq[:max(0, min(n_fft, n - ofst))]
- return x
-
- def inverse(self, magnitude: torch.Tensor, phase) -> torch.Tensor:
- x = torch.cat(
- [magnitude * torch.cos(phase), magnitude * torch.sin(phase)],
- dim=1
- )
- x = F.conv_transpose1d(x, self.basis, stride=self.hop_length)
- win_sum_sq = self.get_window_sum_square(
- magnitude.shape[-1], hop_length=self.hop_length,
- win_length=self.win_length, n_fft=self.n_fft
- ).to(magnitude.device)
- # remove modulation effects
- approx_nonzero_indices = win_sum_sq > self.tiny
- x[:, :, approx_nonzero_indices] /= win_sum_sq[approx_nonzero_indices]
- x *= self.n_fft / self.hop_length
- x = x[:, :, self.n_fft // 2:]
- x = x[:, :, :-self.n_fft // 2:]
- return x
-
- def forward(self, specgram: torch.Tensor) -> torch.Tensor:
- angles = np.angle(np.exp(2j * np.pi * np.random.rand(*specgram.shape)))
- angles = torch.from_numpy(angles).to(specgram)
- _specgram = specgram.view(-1, specgram.shape[-2], specgram.shape[-1])
- waveform = self.inverse(_specgram, angles).squeeze(1)
- for _ in range(self.n_iter):
- _, angles = self.transform(waveform)
- waveform = self.inverse(_specgram, angles).squeeze(1)
- return waveform.squeeze(0)
-
-
-class GriffinLimVocoder(nn.Module):
- def __init__(self, sample_rate, win_size, hop_size, n_fft,
- n_mels, f_min, f_max, window_fn,
- spec_bwd_max_iter=32,
- fp16=False):
- super().__init__()
- self.inv_mel_transform = PseudoInverseMelScale(
- n_stft=n_fft // 2 + 1, n_mels=n_mels, sample_rate=sample_rate,
- f_min=f_min, f_max=f_max
- )
- self.gl_transform = GriffinLim(
- n_fft=n_fft, win_length=win_size, hop_length=hop_size,
- window_fn=window_fn, n_iter=spec_bwd_max_iter
- )
- if fp16:
- self.half()
- self.inv_mel_transform.half()
- self.gl_transform.half()
- else:
- self.float()
- self.inv_mel_transform.float()
- self.gl_transform.float()
-
- def forward(self, x):
- # x: (B x) T x D -> (B x) 1 x T
- # NOTE: batched forward produces noisier waveform. recommend running
- # one utterance at a time
- self.eval()
- x = x.exp().transpose(-1, -2)
- x = self.inv_mel_transform(x)
- x = self.gl_transform(x)
- return x
-
- @classmethod
- def from_data_cfg(cls, args, data_cfg: S2TDataConfig):
- feat_cfg = data_cfg.config["features"]
- window_fn = getattr(torch, feat_cfg["window_fn"] + "_window")
- return cls(
- sample_rate=feat_cfg["sample_rate"],
- win_size=int(feat_cfg["win_len_t"] * feat_cfg["sample_rate"]),
- hop_size=int(feat_cfg["hop_len_t"] * feat_cfg["sample_rate"]),
- n_fft=feat_cfg["n_fft"], n_mels=feat_cfg["n_mels"],
- f_min=feat_cfg["f_min"], f_max=feat_cfg["f_max"],
- window_fn=window_fn, spec_bwd_max_iter=args.spec_bwd_max_iter,
- fp16=args.fp16
- )
-
-
-class HiFiGANVocoder(nn.Module):
- def __init__(
- self, checkpoint_path: str, model_cfg: Dict[str, str],
- fp16: bool = False
- ) -> None:
- super().__init__()
- self.model = HiFiGANModel(model_cfg)
- state_dict = torch.load(checkpoint_path)
- self.model.load_state_dict(state_dict["generator"])
- if fp16:
- self.model.half()
- logger.info(f"loaded HiFiGAN checkpoint from {checkpoint_path}")
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- # (B x) T x D -> (B x) 1 x T
- model = self.model.eval()
- if len(x.shape) == 2:
- return model(x.unsqueeze(0).transpose(1, 2)).detach().squeeze(0)
- else:
- return model(x.transpose(-1, -2)).detach()
-
- @classmethod
- def from_data_cfg(cls, args, data_cfg: S2TDataConfig):
- vocoder_cfg = data_cfg.vocoder
- assert vocoder_cfg.get("type", "griffin_lim") == "hifigan"
- with open(vocoder_cfg["config"]) as f:
- model_cfg = json.load(f)
- return cls(vocoder_cfg["checkpoint"], model_cfg, fp16=args.fp16)
-
-
-def get_vocoder(args, data_cfg: S2TDataConfig):
- if args.vocoder == "griffin_lim":
- return GriffinLimVocoder.from_data_cfg(args, data_cfg)
- elif args.vocoder == "hifigan":
- return HiFiGANVocoder.from_data_cfg(args, data_cfg)
- else:
- raise ValueError("Unknown vocoder")
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/peel-loops.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/peel-loops.go
deleted file mode 100644
index 6df9bbdcbd362ed89df1bc41de091c8076fa5fdd..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/peel-loops.go and /dev/null differ
diff --git a/spaces/PickleYard/stable-diffusion-webui-cpu/app.py b/spaces/PickleYard/stable-diffusion-webui-cpu/app.py
deleted file mode 100644
index 723fab1dcee0b8cade7795de3440be792b536048..0000000000000000000000000000000000000000
--- a/spaces/PickleYard/stable-diffusion-webui-cpu/app.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import os
-from sys import executable as pyexecutable
-import subprocess
-import pathlib
-import gc
-
-def Gitclone(URI:str,ClonePath:str = "") -> int :
- if(ClonePath == "") :
- while True:
- i=subprocess.run([r"git",r"clone",URI])
- if(i.returncode == 0 ):
- del i
- gc.collect()
- return 0
- else :
- del i
- else:
- while True:
- i=subprocess.run([r"git",r"clone",URI,ClonePath])
- if(i.returncode == 0 ):
- del i
- gc.collect()
- return 0
- else :
- del i
-def DownLoad(URI:str,DownloadPath:str,DownLoadFileName:str ) -> int:
- while (True):
- i=subprocess.run([r"aria2c",r"-c",r"-x" ,r"16", r"-s",r"16", r"-k" ,r"1M" ,r"-m",r"0",r"--enable-mmap=false",r"--console-log-level=error",r"-d",DownloadPath,r"-o",DownLoadFileName,URI]);
- if(i.returncode == 0 ):
- del i
- gc.collect()
- return 0
- else :
- del i
-user_home =pathlib.Path.home().resolve()
-os.chdir(str(user_home))
-#clone stable-diffusion-webui repo
-print("cloning stable-diffusion-webui repo")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui.git",str(user_home / r"stable-diffusion-webui"))
-os.chdir(str(user_home / r"stable-diffusion-webui"))
-os.system("git reset --hard 89f9faa63388756314e8a1d96cf86bf5e0663045")
-#
-
-#install extensions
-print("installing extensions")
-Gitclone(r"https://huggingface.co/embed/negative",str(user_home / r"stable-diffusion-webui" / r"embeddings" / r"negative"))
-Gitclone(r"https://huggingface.co/embed/lora",str(user_home / r"stable-diffusion-webui" / r"models" / r"Lora" / r"positive"))
-DownLoad(r"https://huggingface.co/embed/upscale/resolve/main/4x-UltraSharp.pth",str(user_home / r"stable-diffusion-webui" / r"models" / r"ESRGAN") ,r"4x-UltraSharp.pth")
-while True:
- if(subprocess.run([r"wget",r"https://raw.githubusercontent.com/camenduru/stable-diffusion-webui-scripts/main/run_n_times.py",r"-O",str(user_home / r"stable-diffusion-webui" / r"scripts" / r"run_n_times.py")]).returncode == 0):
- break
-Gitclone(r"https://github.com/deforum-art/deforum-for-automatic1111-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"deforum-for-automatic1111-webui" ))
-Gitclone(r"https://github.com/AlUlkesh/stable-diffusion-webui-images-browser",str(user_home / r"stable-diffusion-webui" / r"extensions"/ r"stable-diffusion-webui-images-browser"))
-Gitclone(r"https://github.com/camenduru/stable-diffusion-webui-huggingface",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-huggingface"))
-Gitclone(r"https://github.com/camenduru/sd-civitai-browser",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-civitai-browser"))
-Gitclone(r"https://github.com/kohya-ss/sd-webui-additional-networks",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks"))
-Gitclone(r"https://github.com/Mikubill/sd-webui-controlnet",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-controlnet"))
-Gitclone(r"https://github.com/fkunn1326/openpose-editor",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"openpose-editor"))
-Gitclone(r"https://github.com/jexom/sd-webui-depth-lib",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-depth-lib"))
-Gitclone(r"https://github.com/hnmr293/posex",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"posex"))
-Gitclone(r"https://github.com/nonnonstop/sd-webui-3d-open-pose-editor",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-3d-open-pose-editor"))
-#中文本地化的请解除下一行的注释
-#Gitclone(r"https://github.com/dtlnor/stable-diffusion-webui-localization-zh_CN.git",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-localization-zh_CN"))
-Gitclone(r"https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git" , str(user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-tagcomplete"))
-Gitclone(r"https://github.com/camenduru/sd-webui-tunnels",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-tunnels"))
-Gitclone(r"https://github.com/etherealxx/batchlinks-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"batchlinks-webui"))
-Gitclone(r"https://github.com/catppuccin/stable-diffusion-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-catppuccin"))
-
-#Gitclone(r"https://github.com/KohakuBueleaf/a1111-sd-webui-locon",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-locon" ))
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-rembg"))
-Gitclone(r"https://github.com/ashen-sensored/stable-diffusion-webui-two-shot",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-two-shot"))
-Gitclone(r"https://github.com/camenduru/sd_webui_stealth_pnginfo",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_webui_stealth_pnginfo"))
-
-os.chdir(user_home / r"stable-diffusion-webui")
-
-#download ControlNet models
-print("extensions dolwnload done .\ndownloading ControlNet models")
-dList =[r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_scribble_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_seg_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_ip2p_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_shuffle_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_canny_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1p_sd15_depth_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_inpaint_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_lineart_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_mlsd_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_normalbae_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_openpose_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_scribble_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_seg_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_softedge_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15s2_lineart_anime_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1e_sd15_tile_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_style_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_seg_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_openpose_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_keypose_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd15v2.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd15v2.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd15v2.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_zoedepth_sd15v1.pth"]
-for i in range(0,len(dList)): DownLoad(dList[i],str(user_home / "stable-diffusion-webui" / "extensions" / "sd-webui-controlnet" / "models"),pathlib.Path(dList[i]).name)
-del dList
-
-#download model
-#you can change model download address here
-print("ControlNet models download done.\ndownloading model")
-DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.5-pruned.ckpt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"anything-v4.5-pruned.ckpt")
-DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.0.vae.pt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"anything-v4.0.vae.pt")
-DownLoad(r"https://huggingface.co/gsdf/Counterfeit-V3.0/resolve/main/Counterfeit-V3.0_fp16.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"Counterfeit-V3.0_fp16.safetensors")
-DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1B_orangemixs.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"AOM3A1B_orangemixs.safetensors")
-DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"orangemix.vae.pt")
-DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Baked%20VAE.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"MeinaPastelV5_BakedVAE.safetensors")
-DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Without%20VAE.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"MeinaPastelV5_WithoutVAE.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/9474",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"chilloutmix_NiPrunedFp16.safetensors")
-
-DownLoad(r"https://civitai.com/api/download/models/39885",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"Better_light.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/21065",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"LAS.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/39164",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"backlighting.safetensors")
-#strt webui
-
-print("Done\nStarting Webui...")
-os.chdir(user_home / r"stable-diffusion-webui")
-while True:
- ret=subprocess.run([r"python3" ,r"launch.py",r"--precision",r"full",r"--no-half",r"--no-half-vae",r"--enable-insecure-extension-access",r"--medvram",r"--skip-torch-cuda-test",r"--enable-console-prompts",r"--ui-settings-file="+str(pathlib.Path(__file__).parent /r"config.json")])
- if(ret.returncode == 0 ):
- del ret
- gc.collect()
- else :
- del ret
-
-del os ,user_home ,pyexecutable ,subprocess
\ No newline at end of file
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/backbone/__init__.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/backbone/__init__.py
deleted file mode 100644
index 3979686562c6cd9f91eaa391b57a5c03347f40c9..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/backbone/__init__.py
+++ /dev/null
@@ -1,239 +0,0 @@
-from collections import OrderedDict
-
-from torch import nn
-
-from maskrcnn_benchmark.modeling import registry
-from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform
-from maskrcnn_benchmark.layers import DropBlock2D, DyHead
-from . import fpn as fpn_module
-from . import bifpn
-from . import resnet
-from . import efficientnet
-from . import efficientdet
-from . import swint
-from . import swint_v2
-from . import swint_vl
-from . import swint_v2_vl
-
-
-@registry.BACKBONES.register("R-50-C4")
-@registry.BACKBONES.register("R-50-C5")
-@registry.BACKBONES.register("R-101-C4")
-@registry.BACKBONES.register("R-101-C5")
-def build_resnet_backbone(cfg):
- body = resnet.ResNet(cfg)
- model = nn.Sequential(OrderedDict([("body", body)]))
- return model
-
-
-@registry.BACKBONES.register("R-50-RETINANET")
-@registry.BACKBONES.register("R-101-RETINANET")
-def build_resnet_c5_backbone(cfg):
- body = resnet.ResNet(cfg)
- model = nn.Sequential(OrderedDict([("body", body)]))
- return model
-
-
-@registry.BACKBONES.register("SWINT-FPN-RETINANET")
-def build_retinanet_swint_fpn_backbone(cfg):
- """
- Args:
- cfg: a detectron2 CfgNode
-
- Returns:
- backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
- """
- if cfg.MODEL.SWINT.VERSION == "v1":
- body = swint.build_swint_backbone(cfg)
- elif cfg.MODEL.SWINT.VERSION == "v2":
- body = swint_v2.build_swint_backbone(cfg)
- elif cfg.MODEL.SWINT.VERSION == "vl":
- body = swint_vl.build_swint_backbone(cfg)
- elif cfg.MODEL.SWINT.VERSION == "v2_vl":
- body = swint_v2_vl.build_swint_backbone(cfg)
-
- in_channels_stages = cfg.MODEL.SWINT.OUT_CHANNELS
- out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
- in_channels_p6p7 = out_channels
- fpn = fpn_module.FPN(
- in_channels_list=[
- 0,
- in_channels_stages[-3],
- in_channels_stages[-2],
- in_channels_stages[-1],
- ],
- out_channels=out_channels,
- conv_block=conv_with_kaiming_uniform(
- cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU
- ),
- top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels),
- drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None,
- use_spp=cfg.MODEL.FPN.USE_SPP,
- use_pan=cfg.MODEL.FPN.USE_PAN,
- return_swint_feature_before_fusion=cfg.MODEL.FPN.RETURN_SWINT_FEATURE_BEFORE_FUSION
- )
- if cfg.MODEL.FPN.USE_DYHEAD:
- dyhead = DyHead(cfg, out_channels)
- model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn), ("dyhead", dyhead)]))
- else:
- model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
- return model
-
-
-@registry.BACKBONES.register("SWINT-FPN")
-def build_swint_fpn_backbone(cfg):
- """
- Args:
- cfg: a detectron2 CfgNode
-
- Returns:
- backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
- """
- if cfg.MODEL.SWINT.VERSION == "v1":
- body = swint.build_swint_backbone(cfg)
- elif cfg.MODEL.SWINT.VERSION == "v2":
- body = swint_v2.build_swint_backbone(cfg)
- elif cfg.MODEL.SWINT.VERSION == "vl":
- body = swint_vl.build_swint_backbone(cfg)
- elif cfg.MODEL.SWINT.VERSION == "v2_vl":
- body = swint_v2_vl.build_swint_backbone(cfg)
-
- in_channels_stages = cfg.MODEL.SWINT.OUT_CHANNELS
- out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
- fpn = fpn_module.FPN(
- in_channels_list=[
- in_channels_stages[-4],
- in_channels_stages[-3],
- in_channels_stages[-2],
- in_channels_stages[-1],
- ],
- out_channels=out_channels,
- conv_block=conv_with_kaiming_uniform(
- cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU
- ),
- top_blocks=fpn_module.LastLevelMaxPool(),
- drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None,
- use_spp=cfg.MODEL.FPN.USE_SPP,
- use_pan=cfg.MODEL.FPN.USE_PAN
- )
- if cfg.MODEL.FPN.USE_DYHEAD:
- dyhead = DyHead(cfg, out_channels)
- model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn), ("dyhead", dyhead)]))
- else:
- model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
- return model
-
-
-@registry.BACKBONES.register("CVT-FPN-RETINANET")
-def build_retinanet_cvt_fpn_backbone(cfg):
- """
- Args:
- cfg: a detectron2 CfgNode
-
- Returns:
- backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
- """
- body = cvt.build_cvt_backbone(cfg)
- in_channels_stages = cfg.MODEL.SPEC.DIM_EMBED
- out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
- in_channels_p6p7 = out_channels
- fpn = fpn_module.FPN(
- in_channels_list=[
- 0,
- in_channels_stages[-3],
- in_channels_stages[-2],
- in_channels_stages[-1],
- ],
- out_channels=out_channels,
- conv_block=conv_with_kaiming_uniform(
- cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU
- ),
- top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels),
- drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None,
- use_spp=cfg.MODEL.FPN.USE_SPP,
- use_pan=cfg.MODEL.FPN.USE_PAN
- )
- if cfg.MODEL.FPN.USE_DYHEAD:
- dyhead = DyHead(cfg, out_channels)
- model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn), ("dyhead", dyhead)]))
- else:
- model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
- return model
-
-
-@registry.BACKBONES.register("EFFICIENT7-FPN-RETINANET")
-@registry.BACKBONES.register("EFFICIENT7-FPN-FCOS")
-@registry.BACKBONES.register("EFFICIENT5-FPN-RETINANET")
-@registry.BACKBONES.register("EFFICIENT5-FPN-FCOS")
-@registry.BACKBONES.register("EFFICIENT3-FPN-RETINANET")
-@registry.BACKBONES.register("EFFICIENT3-FPN-FCOS")
-def build_eff_fpn_p6p7_backbone(cfg):
- version = cfg.MODEL.BACKBONE.CONV_BODY.split('-')[0]
- version = version.replace('EFFICIENT', 'b')
- body = efficientnet.get_efficientnet(cfg, version)
- in_channels_stage = body.out_channels
- out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
- in_channels_p6p7 = out_channels
- in_channels_stage[0] = 0
- fpn = fpn_module.FPN(
- in_channels_list=in_channels_stage,
- out_channels=out_channels,
- conv_block=conv_with_kaiming_uniform(
- cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU
- ),
- top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels),
- drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None,
- use_spp=cfg.MODEL.FPN.USE_SPP,
- use_pan=cfg.MODEL.FPN.USE_PAN
- )
- model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
- return model
-
-
-@registry.BACKBONES.register("EFFICIENT7-BIFPN-RETINANET")
-@registry.BACKBONES.register("EFFICIENT7-BIFPN-FCOS")
-@registry.BACKBONES.register("EFFICIENT5-BIFPN-RETINANET")
-@registry.BACKBONES.register("EFFICIENT5-BIFPN-FCOS")
-@registry.BACKBONES.register("EFFICIENT3-BIFPN-RETINANET")
-@registry.BACKBONES.register("EFFICIENT3-BIFPN-FCOS")
-def build_eff_fpn_p6p7_backbone(cfg):
- version = cfg.MODEL.BACKBONE.CONV_BODY.split('-')[0]
- version = version.replace('EFFICIENT', 'b')
- body = efficientnet.get_efficientnet(cfg, version)
- in_channels_stage = body.out_channels
- out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
- bifpns = nn.ModuleList()
- for i in range(cfg.MODEL.BIFPN.NUM_REPEATS):
- first_time = (i==0)
- fpn = bifpn.BiFPN(
- in_channels_list=in_channels_stage[1:],
- out_channels=out_channels,
- first_time=first_time,
- attention=cfg.MODEL.BIFPN.USE_ATTENTION
- )
- bifpns.append(fpn)
- model = nn.Sequential(OrderedDict([("body", body), ("bifpn", bifpns)]))
- return model
-
-
-@registry.BACKBONES.register("EFFICIENT-DET")
-def build_efficientdet_backbone(cfg):
- efficientdet.g_simple_padding = True
- compound = cfg.MODEL.BACKBONE.EFFICIENT_DET_COMPOUND
- start_from = cfg.MODEL.BACKBONE.EFFICIENT_DET_START_FROM
- model = efficientdet.EffNetFPN(
- compound_coef=compound,
- start_from=start_from,
- )
- if cfg.MODEL.BACKBONE.USE_SYNCBN:
- import torch
- model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
- return model
-
-
-def build_backbone(cfg):
- assert cfg.MODEL.BACKBONE.CONV_BODY in registry.BACKBONES, \
- "cfg.MODEL.BACKBONE.CONV_BODY: {} are not registered in registry".format(
- cfg.MODEL.BACKBONE.CONV_BODY
- )
- return registry.BACKBONES[cfg.MODEL.BACKBONE.CONV_BODY](cfg)
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py
deleted file mode 100644
index 8fe17110246725b70c32cf2e3951f0b10e9c9923..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py
+++ /dev/null
@@ -1,75 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-from torch import nn
-
-from .roi_box_feature_extractors import make_roi_box_feature_extractor
-from .roi_box_predictors import make_roi_box_predictor
-from .inference import make_roi_box_post_processor
-from .loss import make_roi_box_loss_evaluator
-from maskrcnn_benchmark.utils.amp import custom_fwd, custom_bwd
-
-class ROIBoxHead(torch.nn.Module):
- """
- Generic Box Head class.
- """
-
- def __init__(self, cfg):
- super(ROIBoxHead, self).__init__()
- self.feature_extractor = make_roi_box_feature_extractor(cfg)
- self.predictor = make_roi_box_predictor(cfg)
- self.post_processor = make_roi_box_post_processor(cfg)
- self.loss_evaluator = make_roi_box_loss_evaluator(cfg)
- self.onnx = cfg.MODEL.ONNX
-
- @custom_fwd(cast_inputs=torch.float32)
- def forward(self, features, proposals, targets=None):
- """
- Arguments:
- features (list[Tensor]): feature-maps from possibly several levels
- proposals (list[BoxList]): proposal boxes
- targets (list[BoxList], optional): the ground-truth targets.
-
- Returns:
- x (Tensor): the result of the feature extractor
- proposals (list[BoxList]): during training, the subsampled proposals
- are returned. During testing, the predicted boxlists are returned
- losses (dict[Tensor]): During training, returns the losses for the
- head. During testing, returns an empty dict.
- """
-
- if self.training:
- # Faster R-CNN subsamples during training the proposals with a fixed
- # positive / negative ratio
- with torch.no_grad():
- proposals = self.loss_evaluator.subsample(proposals, targets)
-
- # extract features that will be fed to the final classifier. The
- # feature_extractor generally corresponds to the pooler + heads
- x = self.feature_extractor(features, proposals)
- # final classifier that converts the features into predictions
- class_logits, box_regression = self.predictor(x)
-
- if self.onnx:
- return x, (class_logits, box_regression, [box.bbox for box in proposals]), {}
-
- if not self.training:
- result = self.post_processor((class_logits, box_regression), proposals)
- return x, result, {}
-
- loss_classifier, loss_box_reg = self.loss_evaluator(
- [class_logits], [box_regression]
- )
- return (
- x,
- proposals,
- dict(loss_classifier=loss_classifier, loss_box_reg=loss_box_reg),
- )
-
-
-def build_roi_box_head(cfg):
- """
- Constructs a new box head.
- By default, uses ROIBoxHead, but if it turns out not to be enough, just register a new class
- and make it a parameter in the config
- """
- return ROIBoxHead(cfg)
diff --git a/spaces/Plurigrid/LifeSim/src/components/ui/alert.tsx b/spaces/Plurigrid/LifeSim/src/components/ui/alert.tsx
deleted file mode 100644
index f589783193a6cfe14032a77b89055cb3e920fe8c..0000000000000000000000000000000000000000
--- a/spaces/Plurigrid/LifeSim/src/components/ui/alert.tsx
+++ /dev/null
@@ -1,59 +0,0 @@
-import * as React from "react"
-import { cva, type VariantProps } from "class-variance-authority"
-
-import { cn } from "@/lib/utils"
-
-const alertVariants = cva(
- "relative w-full rounded-lg border border-stone-200 p-4 [&:has(svg)]:pl-11 [&>svg+div]:translate-y-[-3px] [&>svg]:absolute [&>svg]:left-4 [&>svg]:top-4 [&>svg]:text-stone-950 dark:border-stone-800 dark:[&>svg]:text-stone-50",
- {
- variants: {
- variant: {
- default: "bg-white text-stone-950 dark:bg-stone-950 dark:text-stone-50",
- destructive:
- "border-red-500/50 text-red-500 dark:border-red-500 [&>svg]:text-red-500 dark:border-red-900/50 dark:text-red-900 dark:dark:border-red-900 dark:[&>svg]:text-red-900",
- },
- },
- defaultVariants: {
- variant: "default",
- },
- }
-)
-
-const Alert = React.forwardRef<
- HTMLDivElement,
- React.HTMLAttributes & VariantProps
->(({ className, variant, ...props }, ref) => (
-
-))
-Alert.displayName = "Alert"
-
-const AlertTitle = React.forwardRef<
- HTMLParagraphElement,
- React.HTMLAttributes
->(({ className, ...props }, ref) => (
-
-))
-AlertTitle.displayName = "AlertTitle"
-
-const AlertDescription = React.forwardRef<
- HTMLParagraphElement,
- React.HTMLAttributes
->(({ className, ...props }, ref) => (
-
-))
-AlertDescription.displayName = "AlertDescription"
-
-export { Alert, AlertTitle, AlertDescription }
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/optim/linear_warmup_lr_scheduler.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/optim/linear_warmup_lr_scheduler.py
deleted file mode 100644
index 03274a1ae52b6f20473973b77619f34b2bddd6a1..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/optim/linear_warmup_lr_scheduler.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing as tp
-
-from torch.optim import Optimizer
-from torch.optim.lr_scheduler import _LRScheduler
-
-
-class LinearWarmupLRScheduler(_LRScheduler):
- """Inverse square root LR scheduler.
-
- Args:
- optimizer (Optimizer): Torch optimizer.
- warmup_steps (int): Number of warmup steps.
- warmup_init_lr (tp.Optional[float]): Initial learning rate
- during warmup phase. When not set, use the provided learning rate.
- """
- def __init__(self, optimizer: Optimizer, warmup_steps: int, warmup_init_lr: tp.Optional[float] = 0):
- self.warmup_steps = warmup_steps
- self.warmup_init_lr = warmup_init_lr
- super().__init__(optimizer)
-
- def _get_sched_lr(self, lr: float, step: int):
- if step < self.warmup_steps:
- warmup_init_lr = self.warmup_init_lr or 0
- lr_step = (lr - warmup_init_lr) / self.warmup_steps
- lr = warmup_init_lr + step * lr_step
- return lr
-
- def get_lr(self):
- return [self._get_sched_lr(base_lr, self.last_epoch) for base_lr in self.base_lrs]
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/tests/common_utils/__init__.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/tests/common_utils/__init__.py
deleted file mode 100644
index 74ffcfef96fec35c99b2a1a053a61f44f7a8bbe9..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/tests/common_utils/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from .temp_utils import TempDirMixin
-from .wav_utils import get_batch_white_noise, get_white_noise, save_wav
diff --git a/spaces/Raaniel/Search_Engine2.0/README.md b/spaces/Raaniel/Search_Engine2.0/README.md
deleted file mode 100644
index e7bca8efa427929ddffc9f64e72d37556f9c68e6..0000000000000000000000000000000000000000
--- a/spaces/Raaniel/Search_Engine2.0/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Search Engine2.0
-emoji: 🏢
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RamAnanth1/roomGPT/README.md b/spaces/RamAnanth1/roomGPT/README.md
deleted file mode 100644
index 2821b3a20618f8581bee62f8b2f5781baae71eb9..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/roomGPT/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: RoomGPT
-emoji: 🌖
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.20.1
-python_version: 3.10.9
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: hysts/ControlNet
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ramse/TTS_Hindi/modules/hifigan/hifigan.py b/spaces/Ramse/TTS_Hindi/modules/hifigan/hifigan.py
deleted file mode 100644
index ae7e61f56b00d60bcc49a18ece3edbe54746f7ea..0000000000000000000000000000000000000000
--- a/spaces/Ramse/TTS_Hindi/modules/hifigan/hifigan.py
+++ /dev/null
@@ -1,365 +0,0 @@
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-from modules.parallel_wavegan.layers import UpsampleNetwork, ConvInUpsampleNetwork
-from modules.parallel_wavegan.models.source import SourceModuleHnNSF
-import numpy as np
-
-LRELU_SLOPE = 0.1
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def apply_weight_norm(m):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size * dilation - dilation) / 2)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.h = h
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- xt = c2(xt)
- x = xt + x
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.h = h
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- xt = c(xt)
- x = xt + x
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Conv1d1x1(Conv1d):
- """1x1 Conv1d with customized initialization."""
-
- def __init__(self, in_channels, out_channels, bias):
- """Initialize 1x1 Conv1d module."""
- super(Conv1d1x1, self).__init__(in_channels, out_channels,
- kernel_size=1, padding=0,
- dilation=1, bias=bias)
-
-
-class HifiGanGenerator(torch.nn.Module):
- def __init__(self, h, c_out=1):
- super(HifiGanGenerator, self).__init__()
- self.h = h
- self.num_kernels = len(h['resblock_kernel_sizes'])
- self.num_upsamples = len(h['upsample_rates'])
-
- if h['use_pitch_embed']:
- self.harmonic_num = 8
- self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h['upsample_rates']))
- self.m_source = SourceModuleHnNSF(
- sampling_rate=h['audio_sample_rate'],
- harmonic_num=self.harmonic_num)
- self.noise_convs = nn.ModuleList()
- self.conv_pre = weight_norm(Conv1d(80, h['upsample_initial_channel'], 7, 1, padding=3))
- resblock = ResBlock1 if h['resblock'] == '1' else ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(h['upsample_rates'], h['upsample_kernel_sizes'])):
- c_cur = h['upsample_initial_channel'] // (2 ** (i + 1))
- self.ups.append(weight_norm(
- ConvTranspose1d(c_cur * 2, c_cur, k, u, padding=(k - u) // 2)))
- if h['use_pitch_embed']:
- if i + 1 < len(h['upsample_rates']):
- stride_f0 = np.prod(h['upsample_rates'][i + 1:])
- self.noise_convs.append(Conv1d(
- 1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
- else:
- self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = h['upsample_initial_channel'] // (2 ** (i + 1))
- for j, (k, d) in enumerate(zip(h['resblock_kernel_sizes'], h['resblock_dilation_sizes'])):
- self.resblocks.append(resblock(h, ch, k, d))
-
- self.conv_post = weight_norm(Conv1d(ch, c_out, 7, 1, padding=3))
- self.ups.apply(init_weights)
- self.conv_post.apply(init_weights)
-
- def forward(self, x, f0=None):
- if f0 is not None:
- # harmonic-source signal, noise-source signal, uv flag
- f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)
- har_source, noi_source, uv = self.m_source(f0)
- har_source = har_source.transpose(1, 2)
-
- x = self.conv_pre(x)
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, LRELU_SLOPE)
- x = self.ups[i](x)
- if f0 is not None:
- x_source = self.noise_convs[i](har_source)
- x = x + x_source
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
- remove_weight_norm(self.conv_pre)
- remove_weight_norm(self.conv_post)
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False, use_cond=False, c_in=1):
- super(DiscriminatorP, self).__init__()
- self.use_cond = use_cond
- if use_cond:
- from utils.hparams import hparams
- t = hparams['hop_size']
- self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2)
- c_in = 2
-
- self.period = period
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(c_in, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x, mel):
- fmap = []
- if self.use_cond:
- x_mel = self.cond_net(mel)
- x = torch.cat([x_mel, x], 1)
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_cond=False, c_in=1):
- super(MultiPeriodDiscriminator, self).__init__()
- self.discriminators = nn.ModuleList([
- DiscriminatorP(2, use_cond=use_cond, c_in=c_in),
- DiscriminatorP(3, use_cond=use_cond, c_in=c_in),
- DiscriminatorP(5, use_cond=use_cond, c_in=c_in),
- DiscriminatorP(7, use_cond=use_cond, c_in=c_in),
- DiscriminatorP(11, use_cond=use_cond, c_in=c_in),
- ])
-
- def forward(self, y, y_hat, mel=None):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y, mel)
- y_d_g, fmap_g = d(y_hat, mel)
- y_d_rs.append(y_d_r)
- fmap_rs.append(fmap_r)
- y_d_gs.append(y_d_g)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False, use_cond=False, upsample_rates=None, c_in=1):
- super(DiscriminatorS, self).__init__()
- self.use_cond = use_cond
- if use_cond:
- t = np.prod(upsample_rates)
- self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2)
- c_in = 2
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(c_in, 128, 15, 1, padding=7)),
- norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
- norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
- norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x, mel):
- if self.use_cond:
- x_mel = self.cond_net(mel)
- x = torch.cat([x_mel, x], 1)
- fmap = []
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
- def __init__(self, use_cond=False, c_in=1):
- super(MultiScaleDiscriminator, self).__init__()
- from utils.hparams import hparams
- self.discriminators = nn.ModuleList([
- DiscriminatorS(use_spectral_norm=True, use_cond=use_cond,
- upsample_rates=[4, 4, hparams['hop_size'] // 16],
- c_in=c_in),
- DiscriminatorS(use_cond=use_cond,
- upsample_rates=[4, 4, hparams['hop_size'] // 32],
- c_in=c_in),
- DiscriminatorS(use_cond=use_cond,
- upsample_rates=[4, 4, hparams['hop_size'] // 64],
- c_in=c_in),
- ])
- self.meanpools = nn.ModuleList([
- AvgPool1d(4, 2, padding=1),
- AvgPool1d(4, 2, padding=1)
- ])
-
- def forward(self, y, y_hat, mel=None):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- if i != 0:
- y = self.meanpools[i - 1](y)
- y_hat = self.meanpools[i - 1](y_hat)
- y_d_r, fmap_r = d(y, mel)
- y_d_g, fmap_g = d(y_hat, mel)
- y_d_rs.append(y_d_r)
- fmap_rs.append(fmap_r)
- y_d_gs.append(y_d_g)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
- loss = 0
- for dr, dg in zip(fmap_r, fmap_g):
- for rl, gl in zip(dr, dg):
- loss += torch.mean(torch.abs(rl - gl))
-
- return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
- r_losses = 0
- g_losses = 0
- for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
- r_loss = torch.mean((1 - dr) ** 2)
- g_loss = torch.mean(dg ** 2)
- r_losses += r_loss
- g_losses += g_loss
- r_losses = r_losses / len(disc_real_outputs)
- g_losses = g_losses / len(disc_real_outputs)
- return r_losses, g_losses
-
-
-def cond_discriminator_loss(outputs):
- loss = 0
- for dg in outputs:
- g_loss = torch.mean(dg ** 2)
- loss += g_loss
- loss = loss / len(outputs)
- return loss
-
-
-def generator_loss(disc_outputs):
- loss = 0
- for dg in disc_outputs:
- l = torch.mean((1 - dg) ** 2)
- loss += l
- loss = loss / len(disc_outputs)
- return loss
diff --git a/spaces/Redgon/bingo/tests/parse.ts b/spaces/Redgon/bingo/tests/parse.ts
deleted file mode 100644
index 92940fe6315f1d7cb2b267ba5e5a7e26460a1de3..0000000000000000000000000000000000000000
--- a/spaces/Redgon/bingo/tests/parse.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-import { promises as fs } from 'fs'
-import { join } from 'path'
-import { parseHeadersFromCurl } from '@/lib/utils'
-
-(async () => {
- const content = await fs.readFile(join(__dirname, './fixtures/curl.txt'), 'utf-8')
- const headers = parseHeadersFromCurl(content)
- console.log(headers)
-
- const cmdContent = await fs.readFile(join(__dirname, './fixtures/cmd.txt'), 'utf-8')
- const cmdHeaders = parseHeadersFromCurl(cmdContent)
- console.log(cmdHeaders)
-})()
diff --git a/spaces/RegalHyperus/rvc-lovelive-genshin/infer_pack/attentions.py b/spaces/RegalHyperus/rvc-lovelive-genshin/infer_pack/attentions.py
deleted file mode 100644
index 77cb63ffccf3e33badf22d50862a64ba517b487f..0000000000000000000000000000000000000000
--- a/spaces/RegalHyperus/rvc-lovelive-genshin/infer_pack/attentions.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from infer_pack import commons
-from infer_pack import modules
-from infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- window_size=10,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
-
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- window_size=window_size,
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- proximal_bias=False,
- proximal_init=True,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.ModuleList()
- self.norm_layers_0 = nn.ModuleList()
- self.encdec_attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- proximal_bias=proximal_bias,
- proximal_init=proximal_init,
- )
- )
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(
- MultiHeadAttention(
- hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- causal=True,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
- device=x.device, dtype=x.dtype
- )
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class MultiHeadAttention(nn.Module):
- def __init__(
- self,
- channels,
- out_channels,
- n_heads,
- p_dropout=0.0,
- window_size=None,
- heads_share=True,
- block_length=None,
- proximal_bias=False,
- proximal_init=False,
- ):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
- self.conv_q = nn.Conv1d(channels, channels, 1)
- self.conv_k = nn.Conv1d(channels, channels, 1)
- self.conv_v = nn.Conv1d(channels, channels, 1)
- self.conv_o = nn.Conv1d(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
- self.emb_rel_k = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
- self.emb_rel_v = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
-
- nn.init.xavier_uniform_(self.conv_q.weight)
- nn.init.xavier_uniform_(self.conv_k.weight)
- nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with torch.no_grad():
- self.conv_k.weight.copy_(self.conv_q.weight)
- self.conv_k.bias.copy_(self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.size(), query.size(2))
- query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
- key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
- value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
- scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
- if self.window_size is not None:
- assert (
- t_s == t_t
- ), "Relative attention is only available for self-attention."
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(
- query / math.sqrt(self.k_channels), key_relative_embeddings
- )
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).to(
- device=scores.device, dtype=scores.dtype
- )
- if mask is not None:
- scores = scores.masked_fill(mask == 0, -1e4)
- if self.block_length is not None:
- assert (
- t_s == t_t
- ), "Local attention is only available for self-attention."
- block_mask = (
- torch.ones_like(scores)
- .triu(-self.block_length)
- .tril(self.block_length)
- )
- scores = scores.masked_fill(block_mask == 0, -1e4)
- p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = torch.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(
- self.emb_rel_v, t_s
- )
- output = output + self._matmul_with_relative_values(
- relative_weights, value_relative_embeddings
- )
- output = (
- output.transpose(2, 3).contiguous().view(b, d, t_t)
- ) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = torch.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- max_relative_position = 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padded_relative_embeddings = F.pad(
- relative_embeddings,
- commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
- )
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[
- :, slice_start_position:slice_end_position
- ]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.size()
- # Concat columns of pad to shift from relative to absolute indexing.
- x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.view([batch, heads, length * 2 * length])
- x_flat = F.pad(
- x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
- )
-
- # Reshape and slice out the padded elements.
- x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
- :, :, :length, length - 1 :
- ]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.size()
- # padd along column
- x = F.pad(
- x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
- )
- x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
- # add 0's in the beginning that will skew the elements after reshape
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
- x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = torch.arange(length, dtype=torch.float32)
- diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
- return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- filter_channels,
- kernel_size,
- p_dropout=0.0,
- activation=None,
- causal=False,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = self.conv_1(self.padding(x * x_mask))
- if self.activation == "gelu":
- x = x * torch.sigmoid(1.702 * x)
- else:
- x = torch.relu(x)
- x = self.drop(x)
- x = self.conv_2(self.padding(x * x_mask))
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
diff --git a/spaces/RichardMB1217/blip2/app.py b/spaces/RichardMB1217/blip2/app.py
deleted file mode 100644
index a3ff1efe7a21840e156455a717a48980434a62ed..0000000000000000000000000000000000000000
--- a/spaces/RichardMB1217/blip2/app.py
+++ /dev/null
@@ -1,285 +0,0 @@
-from io import BytesIO
-
-import string
-import gradio as gr
-import requests
-from utils import Endpoint, get_token
-
-
-def encode_image(image):
- buffered = BytesIO()
- image.save(buffered, format="JPEG")
- buffered.seek(0)
-
- return buffered
-
-
-def query_chat_api(
- image, prompt, decoding_method, temperature, len_penalty, repetition_penalty
-):
-
- url = endpoint.url
- url = url + "/api/generate"
-
- headers = {
- "User-Agent": "BLIP-2 HuggingFace Space",
- "Auth-Token": get_token(),
- }
-
- data = {
- "prompt": prompt,
- "use_nucleus_sampling": decoding_method == "Nucleus sampling",
- "temperature": temperature,
- "length_penalty": len_penalty,
- "repetition_penalty": repetition_penalty,
- }
-
- image = encode_image(image)
- files = {"image": image}
-
- response = requests.post(url, data=data, files=files, headers=headers)
-
- if response.status_code == 200:
- return response.json()
- else:
- return "Error: " + response.text
-
-
-def query_caption_api(
- image, decoding_method, temperature, len_penalty, repetition_penalty
-):
-
- url = endpoint.url
- url = url + "/api/caption"
-
- headers = {
- "User-Agent": "BLIP-2 HuggingFace Space",
- "Auth-Token": get_token(),
- }
-
- data = {
- "use_nucleus_sampling": decoding_method == "Nucleus sampling",
- "temperature": temperature,
- "length_penalty": len_penalty,
- "repetition_penalty": repetition_penalty,
- }
-
- image = encode_image(image)
- files = {"image": image}
-
- response = requests.post(url, data=data, files=files, headers=headers)
-
- if response.status_code == 200:
- return response.json()
- else:
- return "Error: " + response.text
-
-
-def postprocess_output(output):
- # if last character is not a punctuation, add a full stop
- if not output[0][-1] in string.punctuation:
- output[0] += "."
-
- return output
-
-
-def inference_chat(
- image,
- text_input,
- decoding_method,
- temperature,
- length_penalty,
- repetition_penalty,
- history=[],
-):
- text_input = text_input
- history.append(text_input)
-
- prompt = " ".join(history)
-
- output = query_chat_api(
- image, prompt, decoding_method, temperature, length_penalty, repetition_penalty
- )
- output = postprocess_output(output)
- history += output
-
- chat = [
- (history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)
- ] # convert to tuples of list
-
- return {chatbot: chat, state: history}
-
-
-def inference_caption(
- image,
- decoding_method,
- temperature,
- length_penalty,
- repetition_penalty,
-):
- output = query_caption_api(
- image, decoding_method, temperature, length_penalty, repetition_penalty
- )
-
- return output[0]
-
-
-title = """
BLIP-2
"""
-description = """Gradio demo for BLIP-2, image-to-text generation from Salesforce Research. To use it, simply upload your image, or click one of the examples to load them.
- Disclaimer: This is a research prototype and is not intended for production use. No data including but not restricted to text and images is collected."""
-article = """Paper: BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
- Code: BLIP2 is now integrated into GitHub repo: LAVIS: a One-stop Library for Language and Vision
- 🤗 `transformers` integration: You can now use `transformers` to use our BLIP-2 models! Check out the official docs
-
Project Page: BLIP2 on LAVIS
- Description: Captioning results from BLIP2_OPT_6.7B. Chat results from BLIP2_FlanT5xxl.
-
-
We have now suspended the official BLIP2 demo from March 23. 2023.
-
For example usage, see notebooks https://github.com/salesforce/LAVIS/tree/main/examples.
-"""
-
-endpoint = Endpoint()
-
-examples = [
- ["house.png", "How could someone get out of the house?"],
- ["flower.jpg", "Question: What is this flower and where is it's origin? Answer:"],
- ["pizza.jpg", "What are steps to cook it?"],
- ["sunset.jpg", "Here is a romantic message going along the photo:"],
- ["forbidden_city.webp", "In what dynasties was this place built?"],
-]
-
-with gr.Blocks(
- css="""
- .message.svelte-w6rprc.svelte-w6rprc.svelte-w6rprc {font-size: 20px; margin-top: 20px}
- #component-21 > div.wrap.svelte-w6rprc {height: 600px;}
- """
-) as iface:
- state = gr.State([])
-
- gr.Markdown(title)
- gr.Markdown(description)
- gr.Markdown(article)
-
- with gr.Row():
- with gr.Column(scale=1):
- image_input = gr.Image(type="pil", interactive=True)
-
- # with gr.Row():
- sampling = gr.Radio(
- choices=["Beam search", "Nucleus sampling"],
- value="Beam search",
- label="Text Decoding Method",
- interactive=True,
- )
-
- temperature = gr.Slider(
- minimum=0.5,
- maximum=1.0,
- value=1.0,
- step=0.1,
- interactive=True,
- label="Temperature (used with nucleus sampling)",
- )
-
- len_penalty = gr.Slider(
- minimum=-1.0,
- maximum=2.0,
- value=1.0,
- step=0.2,
- interactive=True,
- label="Length Penalty (set to larger for longer sequence, used with beam search)",
- )
-
- rep_penalty = gr.Slider(
- minimum=1.0,
- maximum=5.0,
- value=1.5,
- step=0.5,
- interactive=True,
- label="Repeat Penalty (larger value prevents repetition)",
- )
-
- with gr.Column(scale=1.8):
-
- with gr.Column():
- caption_output = gr.Textbox(lines=1, label="Caption Output")
- caption_button = gr.Button(
- value="Caption it!", interactive=True, variant="primary"
- )
- caption_button.click(
- inference_caption,
- [
- image_input,
- sampling,
- temperature,
- len_penalty,
- rep_penalty,
- ],
- [caption_output],
- )
-
- gr.Markdown("""Trying prompting your input for chat; e.g. example prompt for QA, \"Question: {} Answer:\" Use proper punctuation (e.g., question mark).""")
- with gr.Row():
- with gr.Column(
- scale=1.5,
- ):
- chatbot = gr.Chatbot(
- label="Chat Output (from FlanT5)",
- )
-
- # with gr.Row():
- with gr.Column(scale=1):
- chat_input = gr.Textbox(lines=1, label="Chat Input")
- chat_input.submit(
- inference_chat,
- [
- image_input,
- chat_input,
- sampling,
- temperature,
- len_penalty,
- rep_penalty,
- state,
- ],
- [chatbot, state],
- )
-
- with gr.Row():
- clear_button = gr.Button(value="Clear", interactive=True)
- clear_button.click(
- lambda: ("", [], []),
- [],
- [chat_input, chatbot, state],
- queue=False,
- )
-
- submit_button = gr.Button(
- value="Submit", interactive=True, variant="primary"
- )
- submit_button.click(
- inference_chat,
- [
- image_input,
- chat_input,
- sampling,
- temperature,
- len_penalty,
- rep_penalty,
- state,
- ],
- [chatbot, state],
- )
-
- image_input.change(
- lambda: ("", "", []),
- [],
- [chatbot, caption_output, state],
- queue=False,
- )
-
- examples = gr.Examples(
- examples=examples,
- inputs=[image_input, chat_input],
- )
-
-iface.queue(concurrency_count=1, api_open=False, max_size=10)
-iface.launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/Rifd/Gxtaucok/header_patch.py b/spaces/Rifd/Gxtaucok/header_patch.py
deleted file mode 100644
index f2e66b0e35adb99f5b84015453bd9f8863c417a8..0000000000000000000000000000000000000000
--- a/spaces/Rifd/Gxtaucok/header_patch.py
+++ /dev/null
@@ -1,17 +0,0 @@
- with gr.Box(visible=is_spaces):
- if(is_spaces and is_shared_ui):
- gr.HTML(f'''
-
-
🚨 using CPU
-
🚧 (WIP) Automatic1111 Stable Diffusion Web UI on 🤗 Hugging Face Spaces | Running model: WarriorMama777/AOM3A3.safetensors
-
You can duplicate this Space to run it privately without a queue and load additional checkpoints.
-
- ''')
- elif(is_spaces):
- gr.HTML(f'''
-
-
🚧 (WIP) Private Automatic1111 Stable Diffusion Web UI on 🤗 Hugging Face Spaces
-
This Space is currently running on CPU
-
- ''')
-
\ No newline at end of file
diff --git a/spaces/Robo2000/ClinicalTerminologyAISearch-GR/app.py b/spaces/Robo2000/ClinicalTerminologyAISearch-GR/app.py
deleted file mode 100644
index 1ae7ef51442697baea8353ece414883958cebd7a..0000000000000000000000000000000000000000
--- a/spaces/Robo2000/ClinicalTerminologyAISearch-GR/app.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import os
-import pandas as pd
-import gradio as gr
-# SNOMEDCT Download https://www.nlm.nih.gov/healthit/snomedct/us_edition.html
-# LOINC Download https://loinc.org/downloads/
-# ECQM for Value Set Measures and Quality Reporting: https://vsac.nlm.nih.gov/download/ecqm?rel=20220505&res=eh_only.unique_vs.20220505.txt
-# SNOMED Nurse Subset https://www.nlm.nih.gov/healthit/snomedct/index.html?_gl=1*36x5pi*_ga*MTI0ODMyNjkxOS4xNjY1NTY3Mjcz*_ga_P1FPTH9PL4*MTY2Nzk4OTI1My41LjEuMTY2Nzk4OTY5Ni4wLjAuMA..
-
-def MatchLOINC(name):
- basedir = os.path.dirname(__file__)
- pd.set_option("display.max_rows", None)
- data = pd.read_csv(f'LoincTableCore.csv')
- swith=data.loc[data['COMPONENT'].str.contains(name, case=False, na=False)]
- return swith
-
-def MatchLOINCPanelsandForms(name):
- basedir = os.path.dirname(__file__)
- data = pd.read_csv(f'PanelsAndForms.csv')
- swith=data.loc[data['ParentName'].str.contains(name, case=False, na=False)]
- return swith
-
-def MatchSNOMED(name):
- basedir = os.path.dirname(__file__)
- data = pd.read_csv(f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')
- swith=data.loc[data['term'].str.contains(name, case=False, na=False)]
- #swith = data[data['term'].str.match(name)]
- return swith
-
-def MatchOMS(name):
- basedir = os.path.dirname(__file__)
- data = pd.read_csv(f'SnomedOMS.csv')
- swith=data.loc[data['SNOMED CT'].str.contains(name, case=False, na=False)]
- #swith = data[data['SNOMED CT'].str.match(name)]
- return swith
-
-
-
-with gr.Blocks() as demo:
- name = gr.Textbox(label="Enter a term or word to match and find LOINC, SNOMED and OMS clinical terminologies.")
-
- output1 = gr.DataFrame(label="LOINC Terminology")
- output2 = gr.DataFrame(label="LOINC Assessment Panels")
- output3 = gr.DataFrame(label="SNOMED Terminology")
- output4 = gr.DataFrame(label="SNOMED and OMS Terminology")
-
- #output1 = gr.TextArea(label="Output Match LOINC", max_lines=10, interactive=True, )
- #output2 = gr.TextArea(label="Output Match LOINC Panels and Forms", max_lines=10, interactive=True,)
- #output3 = gr.TextArea(label="Output Match SNOMED", max_lines=10, interactive=True,)
- #output4 = gr.TextArea(label="Output Match SNOMED", max_lines=10, interactive=True,)
-
- button1 = gr.Button("Match LOINC Clinical Terminology")
- button1.click(fn=MatchLOINC, inputs=name, outputs=output1)
-
- button2 = gr.Button("Match LOINC Panels and Forms")
- button2.click(fn=MatchLOINCPanelsandForms, inputs=name, outputs=output2)
-
- button3 = gr.Button("Match SNOMED Clinical Terminology")
- button3.click(fn=MatchSNOMED, inputs=name, outputs=output3)
-
- button3 = gr.Button("Match SNOMED and OMS Clinical Terminology")
- button3.click(fn=MatchOMS, inputs=name, outputs=output4)
-
-
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Rules99/YouRadiologist/README.md b/spaces/Rules99/YouRadiologist/README.md
deleted file mode 100644
index 7d3a208f6d3afa100f4a7b722ea1be06b12942f2..0000000000000000000000000000000000000000
--- a/spaces/Rules99/YouRadiologist/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: YouRadiologist
-emoji: 👨⚕️🦴
-colorFrom: black
-colorTo: white
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Sakil/A_cover_letter_generator_for_jobs/README.md b/spaces/Sakil/A_cover_letter_generator_for_jobs/README.md
deleted file mode 100644
index 6626216931344003cc8e252011e965aa43832238..0000000000000000000000000000000000000000
--- a/spaces/Sakil/A_cover_letter_generator_for_jobs/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: A_cover_letter_generator_for_jobs
-emoji: 👀
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Salesforce/EDICT/my_half_diffusers/training_utils.py b/spaces/Salesforce/EDICT/my_half_diffusers/training_utils.py
deleted file mode 100644
index fa1694161fc54c7fd097abf3bcbf44c498daad4b..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_half_diffusers/training_utils.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import copy
-import os
-import random
-
-import numpy as np
-import torch
-
-
-def enable_full_determinism(seed: int):
- """
- Helper function for reproducible behavior during distributed training. See
- - https://pytorch.org/docs/stable/notes/randomness.html for pytorch
- """
- # set seed first
- set_seed(seed)
-
- # Enable PyTorch deterministic mode. This potentially requires either the environment
- # variable 'CUDA_LAUNCH_BLOCKING' or 'CUBLAS_WORKSPACE_CONFIG' to be set,
- # depending on the CUDA version, so we set them both here
- os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
- os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
- torch.use_deterministic_algorithms(True)
-
- # Enable CUDNN deterministic mode
- torch.backends.cudnn.deterministic = True
- torch.backends.cudnn.benchmark = False
-
-
-def set_seed(seed: int):
- """
- Args:
- Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.
- seed (`int`): The seed to set.
- """
- random.seed(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed_all(seed)
- # ^^ safe to call this function even if cuda is not available
-
-
-class EMAModel:
- """
- Exponential Moving Average of models weights
- """
-
- def __init__(
- self,
- model,
- update_after_step=0,
- inv_gamma=1.0,
- power=2 / 3,
- min_value=0.0,
- max_value=0.9999,
- device=None,
- ):
- """
- @crowsonkb's notes on EMA Warmup:
- If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
- to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
- gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
- at 215.4k steps).
- Args:
- inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
- power (float): Exponential factor of EMA warmup. Default: 2/3.
- min_value (float): The minimum EMA decay rate. Default: 0.
- """
-
- self.averaged_model = copy.deepcopy(model).eval()
- self.averaged_model.requires_grad_(False)
-
- self.update_after_step = update_after_step
- self.inv_gamma = inv_gamma
- self.power = power
- self.min_value = min_value
- self.max_value = max_value
-
- if device is not None:
- self.averaged_model = self.averaged_model.to(device=device)
-
- self.decay = 0.0
- self.optimization_step = 0
-
- def get_decay(self, optimization_step):
- """
- Compute the decay factor for the exponential moving average.
- """
- step = max(0, optimization_step - self.update_after_step - 1)
- value = 1 - (1 + step / self.inv_gamma) ** -self.power
-
- if step <= 0:
- return 0.0
-
- return max(self.min_value, min(value, self.max_value))
-
- @torch.no_grad()
- def step(self, new_model):
- ema_state_dict = {}
- ema_params = self.averaged_model.state_dict()
-
- self.decay = self.get_decay(self.optimization_step)
-
- for key, param in new_model.named_parameters():
- if isinstance(param, dict):
- continue
- try:
- ema_param = ema_params[key]
- except KeyError:
- ema_param = param.float().clone() if param.ndim == 1 else copy.deepcopy(param)
- ema_params[key] = ema_param
-
- if not param.requires_grad:
- ema_params[key].copy_(param.to(dtype=ema_param.dtype).data)
- ema_param = ema_params[key]
- else:
- ema_param.mul_(self.decay)
- ema_param.add_(param.data.to(dtype=ema_param.dtype), alpha=1 - self.decay)
-
- ema_state_dict[key] = ema_param
-
- for key, param in new_model.named_buffers():
- ema_state_dict[key] = param
-
- self.averaged_model.load_state_dict(ema_state_dict, strict=False)
- self.optimization_step += 1
diff --git a/spaces/Samhita/geolocator/README.md b/spaces/Samhita/geolocator/README.md
deleted file mode 100644
index 22cb16bf2e557e7704b93e88de1ef4b852bc78a4..0000000000000000000000000000000000000000
--- a/spaces/Samhita/geolocator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Geolocator
-emoji: 📍
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SankarSrin/image-matting-app/ppmatting/models/backbone/hrnet.py b/spaces/SankarSrin/image-matting-app/ppmatting/models/backbone/hrnet.py
deleted file mode 100644
index 96e23a77e656142a97c573feb501f983aecebbef..0000000000000000000000000000000000000000
--- a/spaces/SankarSrin/image-matting-app/ppmatting/models/backbone/hrnet.py
+++ /dev/null
@@ -1,835 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-
-import paddle
-import paddle.nn as nn
-import paddle.nn.functional as F
-
-from paddleseg.cvlibs import manager, param_init
-from paddleseg.models import layers
-from paddleseg.utils import utils
-
-__all__ = [
- "HRNet_W18_Small_V1", "HRNet_W18_Small_V2", "HRNet_W18", "HRNet_W30",
- "HRNet_W32", "HRNet_W40", "HRNet_W44", "HRNet_W48", "HRNet_W60", "HRNet_W64"
-]
-
-
-class HRNet(nn.Layer):
- """
- The HRNet implementation based on PaddlePaddle.
-
- The original article refers to
- Jingdong Wang, et, al. "HRNet：Deep High-Resolution Representation Learning for Visual Recognition"
- (https://arxiv.org/pdf/1908.07919.pdf).
-
- Args:
- pretrained (str, optional): The path of pretrained model.
- stage1_num_modules (int, optional): Number of modules for stage1. Default 1.
- stage1_num_blocks (list, optional): Number of blocks per module for stage1. Default (4).
- stage1_num_channels (list, optional): Number of channels per branch for stage1. Default (64).
- stage2_num_modules (int, optional): Number of modules for stage2. Default 1.
- stage2_num_blocks (list, optional): Number of blocks per module for stage2. Default (4, 4).
- stage2_num_channels (list, optional): Number of channels per branch for stage2. Default (18, 36).
- stage3_num_modules (int, optional): Number of modules for stage3. Default 4.
- stage3_num_blocks (list, optional): Number of blocks per module for stage3. Default (4, 4, 4).
- stage3_num_channels (list, optional): Number of channels per branch for stage3. Default [18, 36, 72).
- stage4_num_modules (int, optional): Number of modules for stage4. Default 3.
- stage4_num_blocks (list, optional): Number of blocks per module for stage4. Default (4, 4, 4, 4).
- stage4_num_channels (list, optional): Number of channels per branch for stage4. Default (18, 36, 72. 144).
- has_se (bool, optional): Whether to use Squeeze-and-Excitation module. Default False.
- align_corners (bool, optional): An argument of F.interpolate. It should be set to False when the feature size is even,
- e.g. 1024x512, otherwise it is True, e.g. 769x769. Default: False.
- """
-
- def __init__(self,
- input_channels=3,
- pretrained=None,
- stage1_num_modules=1,
- stage1_num_blocks=(4, ),
- stage1_num_channels=(64, ),
- stage2_num_modules=1,
- stage2_num_blocks=(4, 4),
- stage2_num_channels=(18, 36),
- stage3_num_modules=4,
- stage3_num_blocks=(4, 4, 4),
- stage3_num_channels=(18, 36, 72),
- stage4_num_modules=3,
- stage4_num_blocks=(4, 4, 4, 4),
- stage4_num_channels=(18, 36, 72, 144),
- has_se=False,
- align_corners=False,
- padding_same=True):
- super(HRNet, self).__init__()
- self.pretrained = pretrained
- self.stage1_num_modules = stage1_num_modules
- self.stage1_num_blocks = stage1_num_blocks
- self.stage1_num_channels = stage1_num_channels
- self.stage2_num_modules = stage2_num_modules
- self.stage2_num_blocks = stage2_num_blocks
- self.stage2_num_channels = stage2_num_channels
- self.stage3_num_modules = stage3_num_modules
- self.stage3_num_blocks = stage3_num_blocks
- self.stage3_num_channels = stage3_num_channels
- self.stage4_num_modules = stage4_num_modules
- self.stage4_num_blocks = stage4_num_blocks
- self.stage4_num_channels = stage4_num_channels
- self.has_se = has_se
- self.align_corners = align_corners
-
- self.feat_channels = [i for i in stage4_num_channels]
- self.feat_channels = [64] + self.feat_channels
-
- self.conv_layer1_1 = layers.ConvBNReLU(
- in_channels=input_channels,
- out_channels=64,
- kernel_size=3,
- stride=2,
- padding=1 if not padding_same else 'same',
- bias_attr=False)
-
- self.conv_layer1_2 = layers.ConvBNReLU(
- in_channels=64,
- out_channels=64,
- kernel_size=3,
- stride=2,
- padding=1 if not padding_same else 'same',
- bias_attr=False)
-
- self.la1 = Layer1(
- num_channels=64,
- num_blocks=self.stage1_num_blocks[0],
- num_filters=self.stage1_num_channels[0],
- has_se=has_se,
- name="layer2",
- padding_same=padding_same)
-
- self.tr1 = TransitionLayer(
- in_channels=[self.stage1_num_channels[0] * 4],
- out_channels=self.stage2_num_channels,
- name="tr1",
- padding_same=padding_same)
-
- self.st2 = Stage(
- num_channels=self.stage2_num_channels,
- num_modules=self.stage2_num_modules,
- num_blocks=self.stage2_num_blocks,
- num_filters=self.stage2_num_channels,
- has_se=self.has_se,
- name="st2",
- align_corners=align_corners,
- padding_same=padding_same)
-
- self.tr2 = TransitionLayer(
- in_channels=self.stage2_num_channels,
- out_channels=self.stage3_num_channels,
- name="tr2",
- padding_same=padding_same)
- self.st3 = Stage(
- num_channels=self.stage3_num_channels,
- num_modules=self.stage3_num_modules,
- num_blocks=self.stage3_num_blocks,
- num_filters=self.stage3_num_channels,
- has_se=self.has_se,
- name="st3",
- align_corners=align_corners,
- padding_same=padding_same)
-
- self.tr3 = TransitionLayer(
- in_channels=self.stage3_num_channels,
- out_channels=self.stage4_num_channels,
- name="tr3",
- padding_same=padding_same)
- self.st4 = Stage(
- num_channels=self.stage4_num_channels,
- num_modules=self.stage4_num_modules,
- num_blocks=self.stage4_num_blocks,
- num_filters=self.stage4_num_channels,
- has_se=self.has_se,
- name="st4",
- align_corners=align_corners,
- padding_same=padding_same)
-
- self.init_weight()
-
- def forward(self, x):
- feat_list = []
- conv1 = self.conv_layer1_1(x)
- feat_list.append(conv1)
- conv2 = self.conv_layer1_2(conv1)
-
- la1 = self.la1(conv2)
-
- tr1 = self.tr1([la1])
- st2 = self.st2(tr1)
-
- tr2 = self.tr2(st2)
- st3 = self.st3(tr2)
-
- tr3 = self.tr3(st3)
- st4 = self.st4(tr3)
-
- feat_list = feat_list + st4
-
- return feat_list
-
- def init_weight(self):
- for layer in self.sublayers():
- if isinstance(layer, nn.Conv2D):
- param_init.normal_init(layer.weight, std=0.001)
- elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)):
- param_init.constant_init(layer.weight, value=1.0)
- param_init.constant_init(layer.bias, value=0.0)
- if self.pretrained is not None:
- utils.load_pretrained_model(self, self.pretrained)
-
-
-class Layer1(nn.Layer):
- def __init__(self,
- num_channels,
- num_filters,
- num_blocks,
- has_se=False,
- name=None,
- padding_same=True):
- super(Layer1, self).__init__()
-
- self.bottleneck_block_list = []
-
- for i in range(num_blocks):
- bottleneck_block = self.add_sublayer(
- "bb_{}_{}".format(name, i + 1),
- BottleneckBlock(
- num_channels=num_channels if i == 0 else num_filters * 4,
- num_filters=num_filters,
- has_se=has_se,
- stride=1,
- downsample=True if i == 0 else False,
- name=name + '_' + str(i + 1),
- padding_same=padding_same))
- self.bottleneck_block_list.append(bottleneck_block)
-
- def forward(self, x):
- conv = x
- for block_func in self.bottleneck_block_list:
- conv = block_func(conv)
- return conv
-
-
-class TransitionLayer(nn.Layer):
- def __init__(self, in_channels, out_channels, name=None, padding_same=True):
- super(TransitionLayer, self).__init__()
-
- num_in = len(in_channels)
- num_out = len(out_channels)
- self.conv_bn_func_list = []
- for i in range(num_out):
- residual = None
- if i < num_in:
- if in_channels[i] != out_channels[i]:
- residual = self.add_sublayer(
- "transition_{}_layer_{}".format(name, i + 1),
- layers.ConvBNReLU(
- in_channels=in_channels[i],
- out_channels=out_channels[i],
- kernel_size=3,
- padding=1 if not padding_same else 'same',
- bias_attr=False))
- else:
- residual = self.add_sublayer(
- "transition_{}_layer_{}".format(name, i + 1),
- layers.ConvBNReLU(
- in_channels=in_channels[-1],
- out_channels=out_channels[i],
- kernel_size=3,
- stride=2,
- padding=1 if not padding_same else 'same',
- bias_attr=False))
- self.conv_bn_func_list.append(residual)
-
- def forward(self, x):
- outs = []
- for idx, conv_bn_func in enumerate(self.conv_bn_func_list):
- if conv_bn_func is None:
- outs.append(x[idx])
- else:
- if idx < len(x):
- outs.append(conv_bn_func(x[idx]))
- else:
- outs.append(conv_bn_func(x[-1]))
- return outs
-
-
-class Branches(nn.Layer):
- def __init__(self,
- num_blocks,
- in_channels,
- out_channels,
- has_se=False,
- name=None,
- padding_same=True):
- super(Branches, self).__init__()
-
- self.basic_block_list = []
-
- for i in range(len(out_channels)):
- self.basic_block_list.append([])
- for j in range(num_blocks[i]):
- in_ch = in_channels[i] if j == 0 else out_channels[i]
- basic_block_func = self.add_sublayer(
- "bb_{}_branch_layer_{}_{}".format(name, i + 1, j + 1),
- BasicBlock(
- num_channels=in_ch,
- num_filters=out_channels[i],
- has_se=has_se,
- name=name + '_branch_layer_' + str(i + 1) + '_' +
- str(j + 1),
- padding_same=padding_same))
- self.basic_block_list[i].append(basic_block_func)
-
- def forward(self, x):
- outs = []
- for idx, input in enumerate(x):
- conv = input
- for basic_block_func in self.basic_block_list[idx]:
- conv = basic_block_func(conv)
- outs.append(conv)
- return outs
-
-
-class BottleneckBlock(nn.Layer):
- def __init__(self,
- num_channels,
- num_filters,
- has_se,
- stride=1,
- downsample=False,
- name=None,
- padding_same=True):
- super(BottleneckBlock, self).__init__()
-
- self.has_se = has_se
- self.downsample = downsample
-
- self.conv1 = layers.ConvBNReLU(
- in_channels=num_channels,
- out_channels=num_filters,
- kernel_size=1,
- bias_attr=False)
-
- self.conv2 = layers.ConvBNReLU(
- in_channels=num_filters,
- out_channels=num_filters,
- kernel_size=3,
- stride=stride,
- padding=1 if not padding_same else 'same',
- bias_attr=False)
-
- self.conv3 = layers.ConvBN(
- in_channels=num_filters,
- out_channels=num_filters * 4,
- kernel_size=1,
- bias_attr=False)
-
- if self.downsample:
- self.conv_down = layers.ConvBN(
- in_channels=num_channels,
- out_channels=num_filters * 4,
- kernel_size=1,
- bias_attr=False)
-
- if self.has_se:
- self.se = SELayer(
- num_channels=num_filters * 4,
- num_filters=num_filters * 4,
- reduction_ratio=16,
- name=name + '_fc')
-
- self.add = layers.Add()
- self.relu = layers.Activation("relu")
-
- def forward(self, x):
- residual = x
- conv1 = self.conv1(x)
- conv2 = self.conv2(conv1)
- conv3 = self.conv3(conv2)
-
- if self.downsample:
- residual = self.conv_down(x)
-
- if self.has_se:
- conv3 = self.se(conv3)
-
- y = self.add(conv3, residual)
- y = self.relu(y)
- return y
-
-
-class BasicBlock(nn.Layer):
- def __init__(self,
- num_channels,
- num_filters,
- stride=1,
- has_se=False,
- downsample=False,
- name=None,
- padding_same=True):
- super(BasicBlock, self).__init__()
-
- self.has_se = has_se
- self.downsample = downsample
-
- self.conv1 = layers.ConvBNReLU(
- in_channels=num_channels,
- out_channels=num_filters,
- kernel_size=3,
- stride=stride,
- padding=1 if not padding_same else 'same',
- bias_attr=False)
- self.conv2 = layers.ConvBN(
- in_channels=num_filters,
- out_channels=num_filters,
- kernel_size=3,
- padding=1 if not padding_same else 'same',
- bias_attr=False)
-
- if self.downsample:
- self.conv_down = layers.ConvBNReLU(
- in_channels=num_channels,
- out_channels=num_filters,
- kernel_size=1,
- bias_attr=False)
-
- if self.has_se:
- self.se = SELayer(
- num_channels=num_filters,
- num_filters=num_filters,
- reduction_ratio=16,
- name=name + '_fc')
-
- self.add = layers.Add()
- self.relu = layers.Activation("relu")
-
- def forward(self, x):
- residual = x
- conv1 = self.conv1(x)
- conv2 = self.conv2(conv1)
-
- if self.downsample:
- residual = self.conv_down(x)
-
- if self.has_se:
- conv2 = self.se(conv2)
-
- y = self.add(conv2, residual)
- y = self.relu(y)
- return y
-
-
-class SELayer(nn.Layer):
- def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
- super(SELayer, self).__init__()
-
- self.pool2d_gap = nn.AdaptiveAvgPool2D(1)
-
- self._num_channels = num_channels
-
- med_ch = int(num_channels / reduction_ratio)
- stdv = 1.0 / math.sqrt(num_channels * 1.0)
- self.squeeze = nn.Linear(
- num_channels,
- med_ch,
- weight_attr=paddle.ParamAttr(
- initializer=nn.initializer.Uniform(-stdv, stdv)))
-
- stdv = 1.0 / math.sqrt(med_ch * 1.0)
- self.excitation = nn.Linear(
- med_ch,
- num_filters,
- weight_attr=paddle.ParamAttr(
- initializer=nn.initializer.Uniform(-stdv, stdv)))
-
- def forward(self, x):
- pool = self.pool2d_gap(x)
- pool = paddle.reshape(pool, shape=[-1, self._num_channels])
- squeeze = self.squeeze(pool)
- squeeze = F.relu(squeeze)
- excitation = self.excitation(squeeze)
- excitation = F.sigmoid(excitation)
- excitation = paddle.reshape(
- excitation, shape=[-1, self._num_channels, 1, 1])
- out = x * excitation
- return out
-
-
-class Stage(nn.Layer):
- def __init__(self,
- num_channels,
- num_modules,
- num_blocks,
- num_filters,
- has_se=False,
- multi_scale_output=True,
- name=None,
- align_corners=False,
- padding_same=True):
- super(Stage, self).__init__()
-
- self._num_modules = num_modules
-
- self.stage_func_list = []
- for i in range(num_modules):
- if i == num_modules - 1 and not multi_scale_output:
- stage_func = self.add_sublayer(
- "stage_{}_{}".format(name, i + 1),
- HighResolutionModule(
- num_channels=num_channels,
- num_blocks=num_blocks,
- num_filters=num_filters,
- has_se=has_se,
- multi_scale_output=False,
- name=name + '_' + str(i + 1),
- align_corners=align_corners,
- padding_same=padding_same))
- else:
- stage_func = self.add_sublayer(
- "stage_{}_{}".format(name, i + 1),
- HighResolutionModule(
- num_channels=num_channels,
- num_blocks=num_blocks,
- num_filters=num_filters,
- has_se=has_se,
- name=name + '_' + str(i + 1),
- align_corners=align_corners,
- padding_same=padding_same))
-
- self.stage_func_list.append(stage_func)
-
- def forward(self, x):
- out = x
- for idx in range(self._num_modules):
- out = self.stage_func_list[idx](out)
- return out
-
-
-class HighResolutionModule(nn.Layer):
- def __init__(self,
- num_channels,
- num_blocks,
- num_filters,
- has_se=False,
- multi_scale_output=True,
- name=None,
- align_corners=False,
- padding_same=True):
- super(HighResolutionModule, self).__init__()
-
- self.branches_func = Branches(
- num_blocks=num_blocks,
- in_channels=num_channels,
- out_channels=num_filters,
- has_se=has_se,
- name=name,
- padding_same=padding_same)
-
- self.fuse_func = FuseLayers(
- in_channels=num_filters,
- out_channels=num_filters,
- multi_scale_output=multi_scale_output,
- name=name,
- align_corners=align_corners,
- padding_same=padding_same)
-
- def forward(self, x):
- out = self.branches_func(x)
- out = self.fuse_func(out)
- return out
-
-
-class FuseLayers(nn.Layer):
- def __init__(self,
- in_channels,
- out_channels,
- multi_scale_output=True,
- name=None,
- align_corners=False,
- padding_same=True):
- super(FuseLayers, self).__init__()
-
- self._actual_ch = len(in_channels) if multi_scale_output else 1
- self._in_channels = in_channels
- self.align_corners = align_corners
-
- self.residual_func_list = []
- for i in range(self._actual_ch):
- for j in range(len(in_channels)):
- if j > i:
- residual_func = self.add_sublayer(
- "residual_{}_layer_{}_{}".format(name, i + 1, j + 1),
- layers.ConvBN(
- in_channels=in_channels[j],
- out_channels=out_channels[i],
- kernel_size=1,
- bias_attr=False))
- self.residual_func_list.append(residual_func)
- elif j < i:
- pre_num_filters = in_channels[j]
- for k in range(i - j):
- if k == i - j - 1:
- residual_func = self.add_sublayer(
- "residual_{}_layer_{}_{}_{}".format(
- name, i + 1, j + 1, k + 1),
- layers.ConvBN(
- in_channels=pre_num_filters,
- out_channels=out_channels[i],
- kernel_size=3,
- stride=2,
- padding=1 if not padding_same else 'same',
- bias_attr=False))
- pre_num_filters = out_channels[i]
- else:
- residual_func = self.add_sublayer(
- "residual_{}_layer_{}_{}_{}".format(
- name, i + 1, j + 1, k + 1),
- layers.ConvBNReLU(
- in_channels=pre_num_filters,
- out_channels=out_channels[j],
- kernel_size=3,
- stride=2,
- padding=1 if not padding_same else 'same',
- bias_attr=False))
- pre_num_filters = out_channels[j]
- self.residual_func_list.append(residual_func)
-
- def forward(self, x):
- outs = []
- residual_func_idx = 0
- for i in range(self._actual_ch):
- residual = x[i]
- residual_shape = paddle.shape(residual)[-2:]
- for j in range(len(self._in_channels)):
- if j > i:
- y = self.residual_func_list[residual_func_idx](x[j])
- residual_func_idx += 1
-
- y = F.interpolate(
- y,
- residual_shape,
- mode='bilinear',
- align_corners=self.align_corners)
- residual = residual + y
- elif j < i:
- y = x[j]
- for k in range(i - j):
- y = self.residual_func_list[residual_func_idx](y)
- residual_func_idx += 1
-
- residual = residual + y
-
- residual = F.relu(residual)
- outs.append(residual)
-
- return outs
-
-
-@manager.BACKBONES.add_component
-def HRNet_W18_Small_V1(**kwargs):
- model = HRNet(
- stage1_num_modules=1,
- stage1_num_blocks=[1],
- stage1_num_channels=[32],
- stage2_num_modules=1,
- stage2_num_blocks=[2, 2],
- stage2_num_channels=[16, 32],
- stage3_num_modules=1,
- stage3_num_blocks=[2, 2, 2],
- stage3_num_channels=[16, 32, 64],
- stage4_num_modules=1,
- stage4_num_blocks=[2, 2, 2, 2],
- stage4_num_channels=[16, 32, 64, 128],
- **kwargs)
- return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W18_Small_V2(**kwargs):
- model = HRNet(
- stage1_num_modules=1,
- stage1_num_blocks=[2],
- stage1_num_channels=[64],
- stage2_num_modules=1,
- stage2_num_blocks=[2, 2],
- stage2_num_channels=[18, 36],
- stage3_num_modules=3,
- stage3_num_blocks=[2, 2, 2],
- stage3_num_channels=[18, 36, 72],
- stage4_num_modules=2,
- stage4_num_blocks=[2, 2, 2, 2],
- stage4_num_channels=[18, 36, 72, 144],
- **kwargs)
- return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W18(**kwargs):
- model = HRNet(
- stage1_num_modules=1,
- stage1_num_blocks=[4],
- stage1_num_channels=[64],
- stage2_num_modules=1,
- stage2_num_blocks=[4, 4],
- stage2_num_channels=[18, 36],
- stage3_num_modules=4,
- stage3_num_blocks=[4, 4, 4],
- stage3_num_channels=[18, 36, 72],
- stage4_num_modules=3,
- stage4_num_blocks=[4, 4, 4, 4],
- stage4_num_channels=[18, 36, 72, 144],
- **kwargs)
- return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W30(**kwargs):
- model = HRNet(
- stage1_num_modules=1,
- stage1_num_blocks=[4],
- stage1_num_channels=[64],
- stage2_num_modules=1,
- stage2_num_blocks=[4, 4],
- stage2_num_channels=[30, 60],
- stage3_num_modules=4,
- stage3_num_blocks=[4, 4, 4],
- stage3_num_channels=[30, 60, 120],
- stage4_num_modules=3,
- stage4_num_blocks=[4, 4, 4, 4],
- stage4_num_channels=[30, 60, 120, 240],
- **kwargs)
- return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W32(**kwargs):
- model = HRNet(
- stage1_num_modules=1,
- stage1_num_blocks=[4],
- stage1_num_channels=[64],
- stage2_num_modules=1,
- stage2_num_blocks=[4, 4],
- stage2_num_channels=[32, 64],
- stage3_num_modules=4,
- stage3_num_blocks=[4, 4, 4],
- stage3_num_channels=[32, 64, 128],
- stage4_num_modules=3,
- stage4_num_blocks=[4, 4, 4, 4],
- stage4_num_channels=[32, 64, 128, 256],
- **kwargs)
- return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W40(**kwargs):
- model = HRNet(
- stage1_num_modules=1,
- stage1_num_blocks=[4],
- stage1_num_channels=[64],
- stage2_num_modules=1,
- stage2_num_blocks=[4, 4],
- stage2_num_channels=[40, 80],
- stage3_num_modules=4,
- stage3_num_blocks=[4, 4, 4],
- stage3_num_channels=[40, 80, 160],
- stage4_num_modules=3,
- stage4_num_blocks=[4, 4, 4, 4],
- stage4_num_channels=[40, 80, 160, 320],
- **kwargs)
- return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W44(**kwargs):
- model = HRNet(
- stage1_num_modules=1,
- stage1_num_blocks=[4],
- stage1_num_channels=[64],
- stage2_num_modules=1,
- stage2_num_blocks=[4, 4],
- stage2_num_channels=[44, 88],
- stage3_num_modules=4,
- stage3_num_blocks=[4, 4, 4],
- stage3_num_channels=[44, 88, 176],
- stage4_num_modules=3,
- stage4_num_blocks=[4, 4, 4, 4],
- stage4_num_channels=[44, 88, 176, 352],
- **kwargs)
- return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W48(**kwargs):
- model = HRNet(
- stage1_num_modules=1,
- stage1_num_blocks=[4],
- stage1_num_channels=[64],
- stage2_num_modules=1,
- stage2_num_blocks=[4, 4],
- stage2_num_channels=[48, 96],
- stage3_num_modules=4,
- stage3_num_blocks=[4, 4, 4],
- stage3_num_channels=[48, 96, 192],
- stage4_num_modules=3,
- stage4_num_blocks=[4, 4, 4, 4],
- stage4_num_channels=[48, 96, 192, 384],
- **kwargs)
- return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W60(**kwargs):
- model = HRNet(
- stage1_num_modules=1,
- stage1_num_blocks=[4],
- stage1_num_channels=[64],
- stage2_num_modules=1,
- stage2_num_blocks=[4, 4],
- stage2_num_channels=[60, 120],
- stage3_num_modules=4,
- stage3_num_blocks=[4, 4, 4],
- stage3_num_channels=[60, 120, 240],
- stage4_num_modules=3,
- stage4_num_blocks=[4, 4, 4, 4],
- stage4_num_channels=[60, 120, 240, 480],
- **kwargs)
- return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W64(**kwargs):
- model = HRNet(
- stage1_num_modules=1,
- stage1_num_blocks=[4],
- stage1_num_channels=[64],
- stage2_num_modules=1,
- stage2_num_blocks=[4, 4],
- stage2_num_channels=[64, 128],
- stage3_num_modules=4,
- stage3_num_blocks=[4, 4, 4],
- stage3_num_channels=[64, 128, 256],
- stage4_num_modules=3,
- stage4_num_blocks=[4, 4, 4, 4],
- stage4_num_channels=[64, 128, 256, 512],
- **kwargs)
- return model
diff --git a/spaces/SarthakSidhant/Go-Cattle/diseases/grass tetany (hypomagnesemia).md b/spaces/SarthakSidhant/Go-Cattle/diseases/grass tetany (hypomagnesemia).md
deleted file mode 100644
index d80fa7ef9065deccc1af8c781c2ebfd047622f4e..0000000000000000000000000000000000000000
--- a/spaces/SarthakSidhant/Go-Cattle/diseases/grass tetany (hypomagnesemia).md
+++ /dev/null
@@ -1,32 +0,0 @@
-## Grass tetany (hypomagnesemia)
-
-**Information:** Grass tetany, also known as **summer pasture disease** or **grass staggers**, is a metabolic disorder that affects cattle. It is caused by a low level of magnesium in the blood.
-
-**Symptoms:**
-
-* Muscle tremors
-* Stiffness
-* Difficulty walking
-* Convulsions
-* Coma
-
-**Remedies:**
-
-* There is no specific cure for grass tetany.
-* Treatment is usually supportive and may include:
- * Administering magnesium sulfate intravenously
- * Treating other underlying conditions
-
-**Causes:**
-
-* Grass tetany is caused by a low level of magnesium in the blood.
-* Magnesium is important for muscle function, and a low level of magnesium can lead to muscle tremors and seizures.
-* Grass tetany is more common in spring and early summer, when cattle are grazing on new growth grass.
-* New growth grass is often low in magnesium.
-* Grass tetany is also more common in pregnant cattle and in cattle that are stressed.
-
-**Prevention:**
-
-* The best way to prevent grass tetany is to feed cattle a diet that is high in magnesium.
-* Magnesium supplements are also available.
-* Cattle that are at risk of grass tetany, such as those that are grazing on new growth grass, should be supplemented with magnesium.
diff --git a/spaces/SeViLA/SeViLA/app/multipage.py b/spaces/SeViLA/SeViLA/app/multipage.py
deleted file mode 100644
index 040f76ebd2f86d7ded9e8a224a20ce779862c607..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/app/multipage.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""
- # Copyright (c) 2022, salesforce.com, inc.
- # All rights reserved.
- # SPDX-License-Identifier: BSD-3-Clause
- # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-"""
-This file is the framework for generating multiple Streamlit applications
-through an object oriented framework.
-"""
-
-# Import necessary libraries
-import streamlit as st
-
-# Define the multipage class to manage the multiple apps in our program
-class MultiPage:
- """Framework for combining multiple streamlit applications."""
-
- def __init__(self) -> None:
- """Constructor class to generate a list which will store all our applications as an instance variable."""
- self.pages = []
-
- def add_page(self, title, func) -> None:
- """Class Method to Add pages to the project
- Args:
- title ([str]): The title of page which we are adding to the list of apps
-
- func: Python function to render this page in Streamlit
- """
-
- self.pages.append({"title": title, "function": func})
-
- def run(self):
- # Drodown to select the page to run
- page = st.sidebar.selectbox(
- "Navigation", self.pages, format_func=lambda page: page["title"]
- )
-
- # run the app function
- page["function"]()
diff --git a/spaces/SuCicada/Lain-TTS/app.py b/spaces/SuCicada/Lain-TTS/app.py
deleted file mode 100644
index 4b13c2f9c738f0ffa168dfc07efd94b94739492e..0000000000000000000000000000000000000000
--- a/spaces/SuCicada/Lain-TTS/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import os
-import subprocess
-import sys
-
-
-# Run shell command and capture output in real-time
-def init():
- process = subprocess.Popen("""
- bash ./run.sh
- """, stdout=subprocess.PIPE, shell=True)
- while True:
- output = process.stdout.readline().decode()
- if output == '' and process.poll() is not None:
- break
- if output:
- print(output.strip())
-
- # Wait for the command to finish and get the return code
- return_code = process.poll()
- print(f"Command exited with return code {return_code}")
-
-
-is_space = os.getenv("SYSTEM") == "spaces"
-if is_space:
- init()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydev_ipython/version.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydev_ipython/version.py
deleted file mode 100644
index 1de0047e6b4e79162a21167ec5a99f465b1e51c2..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydev_ipython/version.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# encoding: utf-8
-"""
-Utilities for version comparison
-
-It is a bit ridiculous that we need these.
-"""
-
-#-----------------------------------------------------------------------------
-# Copyright (C) 2013 The IPython Development Team
-#
-# Distributed under the terms of the BSD License. The full license is in
-# the file COPYING, distributed as part of this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
-
-from distutils.version import LooseVersion
-
-#-----------------------------------------------------------------------------
-# Code
-#-----------------------------------------------------------------------------
-
-def check_version(v, check):
- """check version string v >= check
-
- If dev/prerelease tags result in TypeError for string-number comparison,
- it is assumed that the dependency is satisfied.
- Users on dev branches are responsible for keeping their own packages up to date.
- """
- try:
- return LooseVersion(v) >= LooseVersion(check)
- except TypeError:
- return True
-
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/util.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/util.py
deleted file mode 100644
index 4a9a9842ac96e8f422cbd64b38278227294b6297..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/util.py
+++ /dev/null
@@ -1,1038 +0,0 @@
-#!~/.wine/drive_c/Python25/python.exe
-# -*- coding: utf-8 -*-
-
-# Copyright (c) 2009-2014, Mario Vilas
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-# * Redistributions of source code must retain the above copyright notice,
-# this list of conditions and the following disclaimer.
-# * Redistributions in binary form must reproduce the above copyright
-# notice,this list of conditions and the following disclaimer in the
-# documentation and/or other materials provided with the distribution.
-# * Neither the name of the copyright holder nor the names of its
-# contributors may be used to endorse or promote products derived from
-# this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-
-"""
-Miscellaneous utility classes and functions.
-
-@group Helpers:
- PathOperations,
- MemoryAddresses,
- CustomAddressIterator,
- DataAddressIterator,
- ImageAddressIterator,
- MappedAddressIterator,
- ExecutableAddressIterator,
- ReadableAddressIterator,
- WriteableAddressIterator,
- ExecutableAndWriteableAddressIterator,
- DebugRegister,
- Regenerator,
- BannerHelpFormatter,
- StaticClass,
- classproperty
-"""
-
-__revision__ = "$Id$"
-
-__all__ = [
-
- # Filename and pathname manipulation
- 'PathOperations',
-
- # Memory address operations
- 'MemoryAddresses',
- 'CustomAddressIterator',
- 'DataAddressIterator',
- 'ImageAddressIterator',
- 'MappedAddressIterator',
- 'ExecutableAddressIterator',
- 'ReadableAddressIterator',
- 'WriteableAddressIterator',
- 'ExecutableAndWriteableAddressIterator',
-
- # Debug registers manipulation
- 'DebugRegister',
-
- # Miscellaneous
- 'Regenerator',
- ]
-
-import sys
-import os
-import ctypes
-import optparse
-
-from winappdbg import win32
-from winappdbg import compat
-
-#==============================================================================
-
-class classproperty(property):
- """
- Class property method.
-
- Only works for getting properties, if you set them
- the symbol gets overwritten in the class namespace.
-
- Inspired on: U{http://stackoverflow.com/a/7864317/426293}
- """
- def __init__(self, fget=None, fset=None, fdel=None, doc=""):
- if fset is not None or fdel is not None:
- raise NotImplementedError()
- super(classproperty, self).__init__(fget=classmethod(fget), doc=doc)
- def __get__(self, cls, owner):
- return self.fget.__get__(None, owner)()
-
-class BannerHelpFormatter(optparse.IndentedHelpFormatter):
- "Just a small tweak to optparse to be able to print a banner."
- def __init__(self, banner, *argv, **argd):
- self.banner = banner
- optparse.IndentedHelpFormatter.__init__(self, *argv, **argd)
- def format_usage(self, usage):
- msg = optparse.IndentedHelpFormatter.format_usage(self, usage)
- return '%s\n%s' % (self.banner, msg)
-
-# See Process.generate_memory_snapshot()
-class Regenerator(object):
- """
- Calls a generator and iterates it. When it's finished iterating, the
- generator is called again. This allows you to iterate a generator more
- than once (well, sort of).
- """
-
- def __init__(self, g_function, *v_args, **d_args):
- """
- @type g_function: function
- @param g_function: Function that when called returns a generator.
-
- @type v_args: tuple
- @param v_args: Variable arguments to pass to the generator function.
-
- @type d_args: dict
- @param d_args: Variable arguments to pass to the generator function.
- """
- self.__g_function = g_function
- self.__v_args = v_args
- self.__d_args = d_args
- self.__g_object = None
-
- def __iter__(self):
- 'x.__iter__() <==> iter(x)'
- return self
-
- def next(self):
- 'x.next() -> the next value, or raise StopIteration'
- if self.__g_object is None:
- self.__g_object = self.__g_function( *self.__v_args, **self.__d_args )
- try:
- return self.__g_object.next()
- except StopIteration:
- self.__g_object = None
- raise
-
-class StaticClass (object):
- def __new__(cls, *argv, **argd):
- "Don't try to instance this class, just use the static methods."
- raise NotImplementedError(
- "Cannot instance static class %s" % cls.__name__)
-
-#==============================================================================
-
-class PathOperations (StaticClass):
- """
- Static methods for filename and pathname manipulation.
- """
-
- @staticmethod
- def path_is_relative(path):
- """
- @see: L{path_is_absolute}
-
- @type path: str
- @param path: Absolute or relative path.
-
- @rtype: bool
- @return: C{True} if the path is relative, C{False} if it's absolute.
- """
- return win32.PathIsRelative(path)
-
- @staticmethod
- def path_is_absolute(path):
- """
- @see: L{path_is_relative}
-
- @type path: str
- @param path: Absolute or relative path.
-
- @rtype: bool
- @return: C{True} if the path is absolute, C{False} if it's relative.
- """
- return not win32.PathIsRelative(path)
-
- @staticmethod
- def make_relative(path, current = None):
- """
- @type path: str
- @param path: Absolute path.
-
- @type current: str
- @param current: (Optional) Path to the current directory.
-
- @rtype: str
- @return: Relative path.
-
- @raise WindowsError: It's impossible to make the path relative.
- This happens when the path and the current path are not on the
- same disk drive or network share.
- """
- return win32.PathRelativePathTo(pszFrom = current, pszTo = path)
-
- @staticmethod
- def make_absolute(path):
- """
- @type path: str
- @param path: Relative path.
-
- @rtype: str
- @return: Absolute path.
- """
- return win32.GetFullPathName(path)[0]
-
- @staticmethod
- def split_extension(pathname):
- """
- @type pathname: str
- @param pathname: Absolute path.
-
- @rtype: tuple( str, str )
- @return:
- Tuple containing the file and extension components of the filename.
- """
- filepart = win32.PathRemoveExtension(pathname)
- extpart = win32.PathFindExtension(pathname)
- return (filepart, extpart)
-
- @staticmethod
- def split_filename(pathname):
- """
- @type pathname: str
- @param pathname: Absolute path.
-
- @rtype: tuple( str, str )
- @return: Tuple containing the path to the file and the base filename.
- """
- filepart = win32.PathFindFileName(pathname)
- pathpart = win32.PathRemoveFileSpec(pathname)
- return (pathpart, filepart)
-
- @staticmethod
- def split_path(path):
- """
- @see: L{join_path}
-
- @type path: str
- @param path: Absolute or relative path.
-
- @rtype: list( str... )
- @return: List of path components.
- """
- components = list()
- while path:
- next = win32.PathFindNextComponent(path)
- if next:
- prev = path[ : -len(next) ]
- components.append(prev)
- path = next
- return components
-
- @staticmethod
- def join_path(*components):
- """
- @see: L{split_path}
-
- @type components: tuple( str... )
- @param components: Path components.
-
- @rtype: str
- @return: Absolute or relative path.
- """
- if components:
- path = components[0]
- for next in components[1:]:
- path = win32.PathAppend(path, next)
- else:
- path = ""
- return path
-
- @staticmethod
- def native_to_win32_pathname(name):
- """
- @type name: str
- @param name: Native (NT) absolute pathname.
-
- @rtype: str
- @return: Win32 absolute pathname.
- """
- # XXX TODO
- # There are probably some native paths that
- # won't be converted by this naive approach.
- if name.startswith(compat.b("\\")):
- if name.startswith(compat.b("\\??\\")):
- name = name[4:]
- elif name.startswith(compat.b("\\SystemRoot\\")):
- system_root_path = os.environ['SYSTEMROOT']
- if system_root_path.endswith('\\'):
- system_root_path = system_root_path[:-1]
- name = system_root_path + name[11:]
- else:
- for drive_number in compat.xrange(ord('A'), ord('Z') + 1):
- drive_letter = '%c:' % drive_number
- try:
- device_native_path = win32.QueryDosDevice(drive_letter)
- except WindowsError:
- e = sys.exc_info()[1]
- if e.winerror in (win32.ERROR_FILE_NOT_FOUND, \
- win32.ERROR_PATH_NOT_FOUND):
- continue
- raise
- if not device_native_path.endswith(compat.b('\\')):
- device_native_path += compat.b('\\')
- if name.startswith(device_native_path):
- name = drive_letter + compat.b('\\') + \
- name[ len(device_native_path) : ]
- break
- return name
-
- @staticmethod
- def pathname_to_filename(pathname):
- """
- Equivalent to: C{PathOperations.split_filename(pathname)[0]}
-
- @note: This function is preserved for backwards compatibility with
- WinAppDbg 1.4 and earlier. It may be removed in future versions.
-
- @type pathname: str
- @param pathname: Absolute path to a file.
-
- @rtype: str
- @return: Filename component of the path.
- """
- return win32.PathFindFileName(pathname)
-
-#==============================================================================
-
-class MemoryAddresses (StaticClass):
- """
- Class to manipulate memory addresses.
-
- @type pageSize: int
- @cvar pageSize: Page size in bytes. Defaults to 0x1000 but it's
- automatically updated on runtime when importing the module.
- """
-
- @classproperty
- def pageSize(cls):
- """
- Try to get the pageSize value on runtime.
- """
- try:
- try:
- pageSize = win32.GetSystemInfo().dwPageSize
- except WindowsError:
- pageSize = 0x1000
- except NameError:
- pageSize = 0x1000
- cls.pageSize = pageSize # now this function won't be called again
- return pageSize
-
- @classmethod
- def align_address_to_page_start(cls, address):
- """
- Align the given address to the start of the page it occupies.
-
- @type address: int
- @param address: Memory address.
-
- @rtype: int
- @return: Aligned memory address.
- """
- return address - ( address % cls.pageSize )
-
- @classmethod
- def align_address_to_page_end(cls, address):
- """
- Align the given address to the end of the page it occupies.
- That is, to point to the start of the next page.
-
- @type address: int
- @param address: Memory address.
-
- @rtype: int
- @return: Aligned memory address.
- """
- return address + cls.pageSize - ( address % cls.pageSize )
-
- @classmethod
- def align_address_range(cls, begin, end):
- """
- Align the given address range to the start and end of the page(s) it occupies.
-
- @type begin: int
- @param begin: Memory address of the beginning of the buffer.
- Use C{None} for the first legal address in the address space.
-
- @type end: int
- @param end: Memory address of the end of the buffer.
- Use C{None} for the last legal address in the address space.
-
- @rtype: tuple( int, int )
- @return: Aligned memory addresses.
- """
- if begin is None:
- begin = 0
- if end is None:
- end = win32.LPVOID(-1).value # XXX HACK
- if end < begin:
- begin, end = end, begin
- begin = cls.align_address_to_page_start(begin)
- if end != cls.align_address_to_page_start(end):
- end = cls.align_address_to_page_end(end)
- return (begin, end)
-
- @classmethod
- def get_buffer_size_in_pages(cls, address, size):
- """
- Get the number of pages in use by the given buffer.
-
- @type address: int
- @param address: Aligned memory address.
-
- @type size: int
- @param size: Buffer size.
-
- @rtype: int
- @return: Buffer size in number of pages.
- """
- if size < 0:
- size = -size
- address = address - size
- begin, end = cls.align_address_range(address, address + size)
- # XXX FIXME
- # I think this rounding fails at least for address 0xFFFFFFFF size 1
- return int(float(end - begin) / float(cls.pageSize))
-
- @staticmethod
- def do_ranges_intersect(begin, end, old_begin, old_end):
- """
- Determine if the two given memory address ranges intersect.
-
- @type begin: int
- @param begin: Start address of the first range.
-
- @type end: int
- @param end: End address of the first range.
-
- @type old_begin: int
- @param old_begin: Start address of the second range.
-
- @type old_end: int
- @param old_end: End address of the second range.
-
- @rtype: bool
- @return: C{True} if the two ranges intersect, C{False} otherwise.
- """
- return (old_begin <= begin < old_end) or \
- (old_begin < end <= old_end) or \
- (begin <= old_begin < end) or \
- (begin < old_end <= end)
-
-#==============================================================================
-
-def CustomAddressIterator(memory_map, condition):
- """
- Generator function that iterates through a memory map, filtering memory
- region blocks by any given condition.
-
- @type memory_map: list( L{win32.MemoryBasicInformation} )
- @param memory_map: List of memory region information objects.
- Returned by L{Process.get_memory_map}.
-
- @type condition: function
- @param condition: Callback function that returns C{True} if the memory
- block should be returned, or C{False} if it should be filtered.
-
- @rtype: generator of L{win32.MemoryBasicInformation}
- @return: Generator object to iterate memory blocks.
- """
- for mbi in memory_map:
- if condition(mbi):
- address = mbi.BaseAddress
- max_addr = address + mbi.RegionSize
- while address < max_addr:
- yield address
- address = address + 1
-
-def DataAddressIterator(memory_map):
- """
- Generator function that iterates through a memory map, returning only those
- memory blocks that contain data.
-
- @type memory_map: list( L{win32.MemoryBasicInformation} )
- @param memory_map: List of memory region information objects.
- Returned by L{Process.get_memory_map}.
-
- @rtype: generator of L{win32.MemoryBasicInformation}
- @return: Generator object to iterate memory blocks.
- """
- return CustomAddressIterator(memory_map,
- win32.MemoryBasicInformation.has_content)
-
-def ImageAddressIterator(memory_map):
- """
- Generator function that iterates through a memory map, returning only those
- memory blocks that belong to executable images.
-
- @type memory_map: list( L{win32.MemoryBasicInformation} )
- @param memory_map: List of memory region information objects.
- Returned by L{Process.get_memory_map}.
-
- @rtype: generator of L{win32.MemoryBasicInformation}
- @return: Generator object to iterate memory blocks.
- """
- return CustomAddressIterator(memory_map,
- win32.MemoryBasicInformation.is_image)
-
-def MappedAddressIterator(memory_map):
- """
- Generator function that iterates through a memory map, returning only those
- memory blocks that belong to memory mapped files.
-
- @type memory_map: list( L{win32.MemoryBasicInformation} )
- @param memory_map: List of memory region information objects.
- Returned by L{Process.get_memory_map}.
-
- @rtype: generator of L{win32.MemoryBasicInformation}
- @return: Generator object to iterate memory blocks.
- """
- return CustomAddressIterator(memory_map,
- win32.MemoryBasicInformation.is_mapped)
-
-def ReadableAddressIterator(memory_map):
- """
- Generator function that iterates through a memory map, returning only those
- memory blocks that are readable.
-
- @type memory_map: list( L{win32.MemoryBasicInformation} )
- @param memory_map: List of memory region information objects.
- Returned by L{Process.get_memory_map}.
-
- @rtype: generator of L{win32.MemoryBasicInformation}
- @return: Generator object to iterate memory blocks.
- """
- return CustomAddressIterator(memory_map,
- win32.MemoryBasicInformation.is_readable)
-
-def WriteableAddressIterator(memory_map):
- """
- Generator function that iterates through a memory map, returning only those
- memory blocks that are writeable.
-
- @note: Writeable memory is always readable too.
-
- @type memory_map: list( L{win32.MemoryBasicInformation} )
- @param memory_map: List of memory region information objects.
- Returned by L{Process.get_memory_map}.
-
- @rtype: generator of L{win32.MemoryBasicInformation}
- @return: Generator object to iterate memory blocks.
- """
- return CustomAddressIterator(memory_map,
- win32.MemoryBasicInformation.is_writeable)
-
-def ExecutableAddressIterator(memory_map):
- """
- Generator function that iterates through a memory map, returning only those
- memory blocks that are executable.
-
- @note: Executable memory is always readable too.
-
- @type memory_map: list( L{win32.MemoryBasicInformation} )
- @param memory_map: List of memory region information objects.
- Returned by L{Process.get_memory_map}.
-
- @rtype: generator of L{win32.MemoryBasicInformation}
- @return: Generator object to iterate memory blocks.
- """
- return CustomAddressIterator(memory_map,
- win32.MemoryBasicInformation.is_executable)
-
-def ExecutableAndWriteableAddressIterator(memory_map):
- """
- Generator function that iterates through a memory map, returning only those
- memory blocks that are executable and writeable.
-
- @note: The presence of such pages make memory corruption vulnerabilities
- much easier to exploit.
-
- @type memory_map: list( L{win32.MemoryBasicInformation} )
- @param memory_map: List of memory region information objects.
- Returned by L{Process.get_memory_map}.
-
- @rtype: generator of L{win32.MemoryBasicInformation}
- @return: Generator object to iterate memory blocks.
- """
- return CustomAddressIterator(memory_map,
- win32.MemoryBasicInformation.is_executable_and_writeable)
-
-#==============================================================================
-try:
- _registerMask = win32.SIZE_T(-1).value
-except TypeError:
- if win32.SIZEOF(win32.SIZE_T) == 4:
- _registerMask = 0xFFFFFFFF
- elif win32.SIZEOF(win32.SIZE_T) == 8:
- _registerMask = 0xFFFFFFFFFFFFFFFF
- else:
- raise
-
-class DebugRegister (StaticClass):
- """
- Class to manipulate debug registers.
- Used by L{HardwareBreakpoint}.
-
- @group Trigger flags used by HardwareBreakpoint:
- BREAK_ON_EXECUTION, BREAK_ON_WRITE, BREAK_ON_ACCESS, BREAK_ON_IO_ACCESS
- @group Size flags used by HardwareBreakpoint:
- WATCH_BYTE, WATCH_WORD, WATCH_DWORD, WATCH_QWORD
- @group Bitwise masks for Dr7:
- enableMask, disableMask, triggerMask, watchMask, clearMask,
- generalDetectMask
- @group Bitwise masks for Dr6:
- hitMask, hitMaskAll, debugAccessMask, singleStepMask, taskSwitchMask,
- clearDr6Mask, clearHitMask
- @group Debug control MSR definitions:
- DebugCtlMSR, LastBranchRecord, BranchTrapFlag, PinControl,
- LastBranchToIP, LastBranchFromIP,
- LastExceptionToIP, LastExceptionFromIP
-
- @type BREAK_ON_EXECUTION: int
- @cvar BREAK_ON_EXECUTION: Break on execution.
-
- @type BREAK_ON_WRITE: int
- @cvar BREAK_ON_WRITE: Break on write.
-
- @type BREAK_ON_ACCESS: int
- @cvar BREAK_ON_ACCESS: Break on read or write.
-
- @type BREAK_ON_IO_ACCESS: int
- @cvar BREAK_ON_IO_ACCESS: Break on I/O port access.
- Not supported by any hardware.
-
- @type WATCH_BYTE: int
- @cvar WATCH_BYTE: Watch a byte.
-
- @type WATCH_WORD: int
- @cvar WATCH_WORD: Watch a word.
-
- @type WATCH_DWORD: int
- @cvar WATCH_DWORD: Watch a double word.
-
- @type WATCH_QWORD: int
- @cvar WATCH_QWORD: Watch one quad word.
-
- @type enableMask: 4-tuple of integers
- @cvar enableMask:
- Enable bit on C{Dr7} for each slot.
- Works as a bitwise-OR mask.
-
- @type disableMask: 4-tuple of integers
- @cvar disableMask:
- Mask of the enable bit on C{Dr7} for each slot.
- Works as a bitwise-AND mask.
-
- @type triggerMask: 4-tuple of 2-tuples of integers
- @cvar triggerMask:
- Trigger bits on C{Dr7} for each trigger flag value.
- Each 2-tuple has the bitwise-OR mask and the bitwise-AND mask.
-
- @type watchMask: 4-tuple of 2-tuples of integers
- @cvar watchMask:
- Watch bits on C{Dr7} for each watch flag value.
- Each 2-tuple has the bitwise-OR mask and the bitwise-AND mask.
-
- @type clearMask: 4-tuple of integers
- @cvar clearMask:
- Mask of all important bits on C{Dr7} for each slot.
- Works as a bitwise-AND mask.
-
- @type generalDetectMask: integer
- @cvar generalDetectMask:
- General detect mode bit. It enables the processor to notify the
- debugger when the debugee is trying to access one of the debug
- registers.
-
- @type hitMask: 4-tuple of integers
- @cvar hitMask:
- Hit bit on C{Dr6} for each slot.
- Works as a bitwise-AND mask.
-
- @type hitMaskAll: integer
- @cvar hitMaskAll:
- Bitmask for all hit bits in C{Dr6}. Useful to know if at least one
- hardware breakpoint was hit, or to clear the hit bits only.
-
- @type clearHitMask: integer
- @cvar clearHitMask:
- Bitmask to clear all the hit bits in C{Dr6}.
-
- @type debugAccessMask: integer
- @cvar debugAccessMask:
- The debugee tried to access a debug register. Needs bit
- L{generalDetectMask} enabled in C{Dr7}.
-
- @type singleStepMask: integer
- @cvar singleStepMask:
- A single step exception was raised. Needs the trap flag enabled.
-
- @type taskSwitchMask: integer
- @cvar taskSwitchMask:
- A task switch has occurred. Needs the TSS T-bit set to 1.
-
- @type clearDr6Mask: integer
- @cvar clearDr6Mask:
- Bitmask to clear all meaningful bits in C{Dr6}.
- """
-
- BREAK_ON_EXECUTION = 0
- BREAK_ON_WRITE = 1
- BREAK_ON_ACCESS = 3
- BREAK_ON_IO_ACCESS = 2
-
- WATCH_BYTE = 0
- WATCH_WORD = 1
- WATCH_DWORD = 3
- WATCH_QWORD = 2
-
- registerMask = _registerMask
-
-#------------------------------------------------------------------------------
-
- ###########################################################################
- # http://en.wikipedia.org/wiki/Debug_register
- #
- # DR7 - Debug control
- #
- # The low-order eight bits of DR7 (0,2,4,6 and 1,3,5,7) selectively enable
- # the four address breakpoint conditions. There are two levels of enabling:
- # the local (0,2,4,6) and global (1,3,5,7) levels. The local enable bits
- # are automatically reset by the processor at every task switch to avoid
- # unwanted breakpoint conditions in the new task. The global enable bits
- # are not reset by a task switch; therefore, they can be used for
- # conditions that are global to all tasks.
- #
- # Bits 16-17 (DR0), 20-21 (DR1), 24-25 (DR2), 28-29 (DR3), define when
- # breakpoints trigger. Each breakpoint has a two-bit entry that specifies
- # whether they break on execution (00b), data write (01b), data read or
- # write (11b). 10b is defined to mean break on IO read or write but no
- # hardware supports it. Bits 18-19 (DR0), 22-23 (DR1), 26-27 (DR2), 30-31
- # (DR3), define how large area of memory is watched by breakpoints. Again
- # each breakpoint has a two-bit entry that specifies whether they watch
- # one (00b), two (01b), eight (10b) or four (11b) bytes.
- ###########################################################################
-
- # Dr7 |= enableMask[register]
- enableMask = (
- 1 << 0, # Dr0 (bit 0)
- 1 << 2, # Dr1 (bit 2)
- 1 << 4, # Dr2 (bit 4)
- 1 << 6, # Dr3 (bit 6)
- )
-
- # Dr7 &= disableMask[register]
- disableMask = tuple( [_registerMask ^ x for x in enableMask] ) # The registerMask from the class is not there in py3
- try:
- del x # It's not there in py3
- except:
- pass
-
- # orMask, andMask = triggerMask[register][trigger]
- # Dr7 = (Dr7 & andMask) | orMask # to set
- # Dr7 = Dr7 & andMask # to remove
- triggerMask = (
- # Dr0 (bits 16-17)
- (
- ((0 << 16), (3 << 16) ^ registerMask), # execute
- ((1 << 16), (3 << 16) ^ registerMask), # write
- ((2 << 16), (3 << 16) ^ registerMask), # io read
- ((3 << 16), (3 << 16) ^ registerMask), # access
- ),
- # Dr1 (bits 20-21)
- (
- ((0 << 20), (3 << 20) ^ registerMask), # execute
- ((1 << 20), (3 << 20) ^ registerMask), # write
- ((2 << 20), (3 << 20) ^ registerMask), # io read
- ((3 << 20), (3 << 20) ^ registerMask), # access
- ),
- # Dr2 (bits 24-25)
- (
- ((0 << 24), (3 << 24) ^ registerMask), # execute
- ((1 << 24), (3 << 24) ^ registerMask), # write
- ((2 << 24), (3 << 24) ^ registerMask), # io read
- ((3 << 24), (3 << 24) ^ registerMask), # access
- ),
- # Dr3 (bits 28-29)
- (
- ((0 << 28), (3 << 28) ^ registerMask), # execute
- ((1 << 28), (3 << 28) ^ registerMask), # write
- ((2 << 28), (3 << 28) ^ registerMask), # io read
- ((3 << 28), (3 << 28) ^ registerMask), # access
- ),
- )
-
- # orMask, andMask = watchMask[register][watch]
- # Dr7 = (Dr7 & andMask) | orMask # to set
- # Dr7 = Dr7 & andMask # to remove
- watchMask = (
- # Dr0 (bits 18-19)
- (
- ((0 << 18), (3 << 18) ^ registerMask), # byte
- ((1 << 18), (3 << 18) ^ registerMask), # word
- ((2 << 18), (3 << 18) ^ registerMask), # qword
- ((3 << 18), (3 << 18) ^ registerMask), # dword
- ),
- # Dr1 (bits 22-23)
- (
- ((0 << 23), (3 << 23) ^ registerMask), # byte
- ((1 << 23), (3 << 23) ^ registerMask), # word
- ((2 << 23), (3 << 23) ^ registerMask), # qword
- ((3 << 23), (3 << 23) ^ registerMask), # dword
- ),
- # Dr2 (bits 26-27)
- (
- ((0 << 26), (3 << 26) ^ registerMask), # byte
- ((1 << 26), (3 << 26) ^ registerMask), # word
- ((2 << 26), (3 << 26) ^ registerMask), # qword
- ((3 << 26), (3 << 26) ^ registerMask), # dword
- ),
- # Dr3 (bits 30-31)
- (
- ((0 << 30), (3 << 31) ^ registerMask), # byte
- ((1 << 30), (3 << 31) ^ registerMask), # word
- ((2 << 30), (3 << 31) ^ registerMask), # qword
- ((3 << 30), (3 << 31) ^ registerMask), # dword
- ),
- )
-
- # Dr7 = Dr7 & clearMask[register]
- clearMask = (
- registerMask ^ ( (1 << 0) + (3 << 16) + (3 << 18) ), # Dr0
- registerMask ^ ( (1 << 2) + (3 << 20) + (3 << 22) ), # Dr1
- registerMask ^ ( (1 << 4) + (3 << 24) + (3 << 26) ), # Dr2
- registerMask ^ ( (1 << 6) + (3 << 28) + (3 << 30) ), # Dr3
- )
-
- # Dr7 = Dr7 | generalDetectMask
- generalDetectMask = (1 << 13)
-
- ###########################################################################
- # http://en.wikipedia.org/wiki/Debug_register
- #
- # DR6 - Debug status
- #
- # The debug status register permits the debugger to determine which debug
- # conditions have occurred. When the processor detects an enabled debug
- # exception, it sets the low-order bits of this register (0,1,2,3) before
- # entering the debug exception handler.
- #
- # Note that the bits of DR6 are never cleared by the processor. To avoid
- # any confusion in identifying the next debug exception, the debug handler
- # should move zeros to DR6 immediately before returning.
- ###########################################################################
-
- # bool(Dr6 & hitMask[register])
- hitMask = (
- (1 << 0), # Dr0
- (1 << 1), # Dr1
- (1 << 2), # Dr2
- (1 << 3), # Dr3
- )
-
- # bool(Dr6 & anyHitMask)
- hitMaskAll = hitMask[0] | hitMask[1] | hitMask[2] | hitMask[3]
-
- # Dr6 = Dr6 & clearHitMask
- clearHitMask = registerMask ^ hitMaskAll
-
- # bool(Dr6 & debugAccessMask)
- debugAccessMask = (1 << 13)
-
- # bool(Dr6 & singleStepMask)
- singleStepMask = (1 << 14)
-
- # bool(Dr6 & taskSwitchMask)
- taskSwitchMask = (1 << 15)
-
- # Dr6 = Dr6 & clearDr6Mask
- clearDr6Mask = registerMask ^ (hitMaskAll | \
- debugAccessMask | singleStepMask | taskSwitchMask)
-
-#------------------------------------------------------------------------------
-
-###############################################################################
-#
-# (from the AMD64 manuals)
-#
-# The fields within the DebugCtlMSR register are:
-#
-# Last-Branch Record (LBR) - Bit 0, read/write. Software sets this bit to 1
-# to cause the processor to record the source and target addresses of the
-# last control transfer taken before a debug exception occurs. The recorded
-# control transfers include branch instructions, interrupts, and exceptions.
-#
-# Branch Single Step (BTF) - Bit 1, read/write. Software uses this bit to
-# change the behavior of the rFLAGS.TF bit. When this bit is cleared to 0,
-# the rFLAGS.TF bit controls instruction single stepping, (normal behavior).
-# When this bit is set to 1, the rFLAGS.TF bit controls single stepping on
-# control transfers. The single-stepped control transfers include branch
-# instructions, interrupts, and exceptions. Control-transfer single stepping
-# requires both BTF=1 and rFLAGS.TF=1.
-#
-# Performance-Monitoring/Breakpoint Pin-Control (PBi) - Bits 5-2, read/write.
-# Software uses these bits to control the type of information reported by
-# the four external performance-monitoring/breakpoint pins on the processor.
-# When a PBi bit is cleared to 0, the corresponding external pin (BPi)
-# reports performance-monitor information. When a PBi bit is set to 1, the
-# corresponding external pin (BPi) reports breakpoint information.
-#
-# All remaining bits in the DebugCtlMSR register are reserved.
-#
-# Software can enable control-transfer single stepping by setting
-# DebugCtlMSR.BTF to 1 and rFLAGS.TF to 1. The processor automatically
-# disables control-transfer single stepping when a debug exception (#DB)
-# occurs by clearing DebugCtlMSR.BTF to 0. rFLAGS.TF is also cleared when a
-# #DB exception occurs. Before exiting the debug-exception handler, software
-# must set both DebugCtlMSR.BTF and rFLAGS.TF to 1 to restart single
-# stepping.
-#
-###############################################################################
-
- DebugCtlMSR = 0x1D9
- LastBranchRecord = (1 << 0)
- BranchTrapFlag = (1 << 1)
- PinControl = (
- (1 << 2), # PB1
- (1 << 3), # PB2
- (1 << 4), # PB3
- (1 << 5), # PB4
- )
-
-###############################################################################
-#
-# (from the AMD64 manuals)
-#
-# Control-transfer recording MSRs: LastBranchToIP, LastBranchFromIP,
-# LastExceptionToIP, and LastExceptionFromIP. These registers are loaded
-# automatically by the processor when the DebugCtlMSR.LBR bit is set to 1.
-# These MSRs are read-only.
-#
-# The processor automatically disables control-transfer recording when a
-# debug exception (#DB) occurs by clearing DebugCtlMSR.LBR to 0. The
-# contents of the control-transfer recording MSRs are not altered by the
-# processor when the #DB occurs. Before exiting the debug-exception handler,
-# software can set DebugCtlMSR.LBR to 1 to re-enable the recording mechanism.
-#
-###############################################################################
-
- LastBranchToIP = 0x1DC
- LastBranchFromIP = 0x1DB
- LastExceptionToIP = 0x1DE
- LastExceptionFromIP = 0x1DD
-
-#------------------------------------------------------------------------------
-
- @classmethod
- def clear_bp(cls, ctx, register):
- """
- Clears a hardware breakpoint.
-
- @see: find_slot, set_bp
-
- @type ctx: dict( str S{->} int )
- @param ctx: Thread context dictionary.
-
- @type register: int
- @param register: Slot (debug register) for hardware breakpoint.
- """
- ctx['Dr7'] &= cls.clearMask[register]
- ctx['Dr%d' % register] = 0
-
- @classmethod
- def set_bp(cls, ctx, register, address, trigger, watch):
- """
- Sets a hardware breakpoint.
-
- @see: clear_bp, find_slot
-
- @type ctx: dict( str S{->} int )
- @param ctx: Thread context dictionary.
-
- @type register: int
- @param register: Slot (debug register).
-
- @type address: int
- @param address: Memory address.
-
- @type trigger: int
- @param trigger: Trigger flag. See L{HardwareBreakpoint.validTriggers}.
-
- @type watch: int
- @param watch: Watch flag. See L{HardwareBreakpoint.validWatchSizes}.
- """
- Dr7 = ctx['Dr7']
- Dr7 |= cls.enableMask[register]
- orMask, andMask = cls.triggerMask[register][trigger]
- Dr7 &= andMask
- Dr7 |= orMask
- orMask, andMask = cls.watchMask[register][watch]
- Dr7 &= andMask
- Dr7 |= orMask
- ctx['Dr7'] = Dr7
- ctx['Dr%d' % register] = address
-
- @classmethod
- def find_slot(cls, ctx):
- """
- Finds an empty slot to set a hardware breakpoint.
-
- @see: clear_bp, set_bp
-
- @type ctx: dict( str S{->} int )
- @param ctx: Thread context dictionary.
-
- @rtype: int
- @return: Slot (debug register) for hardware breakpoint.
- """
- Dr7 = ctx['Dr7']
- slot = 0
- for m in cls.enableMask:
- if (Dr7 & m) == 0:
- return slot
- slot += 1
- return None
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/oneformer/data/build.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/oneformer/data/build.py
deleted file mode 100644
index 2611644589d6a5978c257a4e349a1b466f366c0c..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/oneformer/data/build.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from typing import Any, Callable, Dict, List, Optional, Union
-import torch.utils.data as torchdata
-
-from annotator.oneformer.detectron2.config import configurable
-
-
-from annotator.oneformer.detectron2.data.common import DatasetFromList, MapDataset
-from annotator.oneformer.detectron2.data.dataset_mapper import DatasetMapper
-from annotator.oneformer.detectron2.data.samplers import (
- InferenceSampler,
-)
-from annotator.oneformer.detectron2.data.build import (
- get_detection_dataset_dicts,
- trivial_batch_collator
-)
-"""
-This file contains the default logic to build a dataloader for training or testing.
-"""
-
-__all__ = [
- "build_detection_test_loader",
-]
-
-
-def _test_loader_from_config(cfg, dataset_name, mapper=None):
- """
- Uses the given `dataset_name` argument (instead of the names in cfg), because the
- standard practice is to evaluate each test set individually (not combining them).
- """
- if isinstance(dataset_name, str):
- dataset_name = [dataset_name]
-
- dataset = get_detection_dataset_dicts(
- dataset_name,
- filter_empty=False,
- proposal_files=[
- cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name
- ]
- if cfg.MODEL.LOAD_PROPOSALS
- else None,
- )
- if mapper is None:
- mapper = DatasetMapper(cfg, False)
- return {
- "dataset": dataset,
- "mapper": mapper,
- "num_workers": cfg.DATALOADER.NUM_WORKERS,
- "sampler": InferenceSampler(len(dataset))
- if not isinstance(dataset, torchdata.IterableDataset)
- else None,
- }
-
-
-@configurable(from_config=_test_loader_from_config)
-def build_detection_test_loader(
- dataset: Union[List[Any], torchdata.Dataset],
- *,
- mapper: Callable[[Dict[str, Any]], Any],
- sampler: Optional[torchdata.Sampler] = None,
- batch_size: int = 1,
- num_workers: int = 0,
- collate_fn: Optional[Callable[[List[Any]], Any]] = None,
-) -> torchdata.DataLoader:
- """
- Similar to `build_detection_train_loader`, with default batch size = 1,
- and sampler = :class:`InferenceSampler`. This sampler coordinates all workers
- to produce the exact set of all samples.
-
- Args:
- dataset: a list of dataset dicts,
- or a pytorch dataset (either map-style or iterable). They can be obtained
- by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.
- mapper: a callable which takes a sample (dict) from dataset
- and returns the format to be consumed by the model.
- When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``.
- sampler: a sampler that produces
- indices to be applied on ``dataset``. Default to :class:`InferenceSampler`,
- which splits the dataset across all workers. Sampler must be None
- if `dataset` is iterable.
- batch_size: the batch size of the data loader to be created.
- Default to 1 image per worker since this is the standard when reporting
- inference time in papers.
- num_workers: number of parallel data loading workers
- collate_fn: same as the argument of `torch.utils.data.DataLoader`.
- Defaults to do no collation and return a list of data.
-
- Returns:
- DataLoader: a torch DataLoader, that loads the given detection
- dataset, with test-time transformation and batching.
-
- Examples:
- ::
- data_loader = build_detection_test_loader(
- DatasetRegistry.get("my_test"),
- mapper=DatasetMapper(...))
-
- # or, instantiate with a CfgNode:
- data_loader = build_detection_test_loader(cfg, "my_test")
- """
- if isinstance(dataset, list):
- dataset = DatasetFromList(dataset, copy=False)
- if mapper is not None:
- dataset = MapDataset(dataset, mapper)
- if isinstance(dataset, torchdata.IterableDataset):
- assert sampler is None, "sampler must be None if dataset is IterableDataset"
- else:
- if sampler is None:
- sampler = InferenceSampler(len(dataset))
- return torchdata.DataLoader(
- dataset,
- batch_size=batch_size,
- sampler=sampler,
- drop_last=False,
- num_workers=num_workers,
- collate_fn=trivial_batch_collator if collate_fn is None else collate_fn,
- )
\ No newline at end of file
diff --git a/spaces/TTT-9552/Y7cLhT3pE9gV4xW2nQ5/Dockerfile b/spaces/TTT-9552/Y7cLhT3pE9gV4xW2nQ5/Dockerfile
deleted file mode 100644
index 4cb0ce42128d9a2ad33a395883f5e5455a38c707..0000000000000000000000000000000000000000
--- a/spaces/TTT-9552/Y7cLhT3pE9gV4xW2nQ5/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM node:18-bullseye-slim
-RUN apt-get update && \
- apt-get install -y git
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-WORKDIR /app
-RUN npm install
-COPY Dockerfile greeting.md* .env* ./
-RUN npm run build
-EXPOSE 7860
-ENV NODE_ENV=production
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/resolvelib/resolvers.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/resolvelib/resolvers.py
deleted file mode 100644
index 2c3d0e306f91f9dfac1843b40babd223766bbf50..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/resolvelib/resolvers.py
+++ /dev/null
@@ -1,547 +0,0 @@
-import collections
-import itertools
-import operator
-
-from .providers import AbstractResolver
-from .structs import DirectedGraph, IteratorMapping, build_iter_view
-
-RequirementInformation = collections.namedtuple(
- "RequirementInformation", ["requirement", "parent"]
-)
-
-
-class ResolverException(Exception):
- """A base class for all exceptions raised by this module.
-
- Exceptions derived by this class should all be handled in this module. Any
- bubbling pass the resolver should be treated as a bug.
- """
-
-
-class RequirementsConflicted(ResolverException):
- def __init__(self, criterion):
- super(RequirementsConflicted, self).__init__(criterion)
- self.criterion = criterion
-
- def __str__(self):
- return "Requirements conflict: {}".format(
- ", ".join(repr(r) for r in self.criterion.iter_requirement()),
- )
-
-
-class InconsistentCandidate(ResolverException):
- def __init__(self, candidate, criterion):
- super(InconsistentCandidate, self).__init__(candidate, criterion)
- self.candidate = candidate
- self.criterion = criterion
-
- def __str__(self):
- return "Provided candidate {!r} does not satisfy {}".format(
- self.candidate,
- ", ".join(repr(r) for r in self.criterion.iter_requirement()),
- )
-
-
-class Criterion(object):
- """Representation of possible resolution results of a package.
-
- This holds three attributes:
-
- * `information` is a collection of `RequirementInformation` pairs.
- Each pair is a requirement contributing to this criterion, and the
- candidate that provides the requirement.
- * `incompatibilities` is a collection of all known not-to-work candidates
- to exclude from consideration.
- * `candidates` is a collection containing all possible candidates deducted
- from the union of contributing requirements and known incompatibilities.
- It should never be empty, except when the criterion is an attribute of a
- raised `RequirementsConflicted` (in which case it is always empty).
-
- .. note::
- This class is intended to be externally immutable. **Do not** mutate
- any of its attribute containers.
- """
-
- def __init__(self, candidates, information, incompatibilities):
- self.candidates = candidates
- self.information = information
- self.incompatibilities = incompatibilities
-
- def __repr__(self):
- requirements = ", ".join(
- "({!r}, via={!r})".format(req, parent)
- for req, parent in self.information
- )
- return "Criterion({})".format(requirements)
-
- def iter_requirement(self):
- return (i.requirement for i in self.information)
-
- def iter_parent(self):
- return (i.parent for i in self.information)
-
-
-class ResolutionError(ResolverException):
- pass
-
-
-class ResolutionImpossible(ResolutionError):
- def __init__(self, causes):
- super(ResolutionImpossible, self).__init__(causes)
- # causes is a list of RequirementInformation objects
- self.causes = causes
-
-
-class ResolutionTooDeep(ResolutionError):
- def __init__(self, round_count):
- super(ResolutionTooDeep, self).__init__(round_count)
- self.round_count = round_count
-
-
-# Resolution state in a round.
-State = collections.namedtuple("State", "mapping criteria backtrack_causes")
-
-
-class Resolution(object):
- """Stateful resolution object.
-
- This is designed as a one-off object that holds information to kick start
- the resolution process, and holds the results afterwards.
- """
-
- def __init__(self, provider, reporter):
- self._p = provider
- self._r = reporter
- self._states = []
-
- @property
- def state(self):
- try:
- return self._states[-1]
- except IndexError:
- raise AttributeError("state")
-
- def _push_new_state(self):
- """Push a new state into history.
-
- This new state will be used to hold resolution results of the next
- coming round.
- """
- base = self._states[-1]
- state = State(
- mapping=base.mapping.copy(),
- criteria=base.criteria.copy(),
- backtrack_causes=base.backtrack_causes[:],
- )
- self._states.append(state)
-
- def _add_to_criteria(self, criteria, requirement, parent):
- self._r.adding_requirement(requirement=requirement, parent=parent)
-
- identifier = self._p.identify(requirement_or_candidate=requirement)
- criterion = criteria.get(identifier)
- if criterion:
- incompatibilities = list(criterion.incompatibilities)
- else:
- incompatibilities = []
-
- matches = self._p.find_matches(
- identifier=identifier,
- requirements=IteratorMapping(
- criteria,
- operator.methodcaller("iter_requirement"),
- {identifier: [requirement]},
- ),
- incompatibilities=IteratorMapping(
- criteria,
- operator.attrgetter("incompatibilities"),
- {identifier: incompatibilities},
- ),
- )
-
- if criterion:
- information = list(criterion.information)
- information.append(RequirementInformation(requirement, parent))
- else:
- information = [RequirementInformation(requirement, parent)]
-
- criterion = Criterion(
- candidates=build_iter_view(matches),
- information=information,
- incompatibilities=incompatibilities,
- )
- if not criterion.candidates:
- raise RequirementsConflicted(criterion)
- criteria[identifier] = criterion
-
- def _remove_information_from_criteria(self, criteria, parents):
- """Remove information from parents of criteria.
-
- Concretely, removes all values from each criterion's ``information``
- field that have one of ``parents`` as provider of the requirement.
-
- :param criteria: The criteria to update.
- :param parents: Identifiers for which to remove information from all criteria.
- """
- if not parents:
- return
- for key, criterion in criteria.items():
- criteria[key] = Criterion(
- criterion.candidates,
- [
- information
- for information in criterion.information
- if (
- information.parent is None
- or self._p.identify(information.parent) not in parents
- )
- ],
- criterion.incompatibilities,
- )
-
- def _get_preference(self, name):
- return self._p.get_preference(
- identifier=name,
- resolutions=self.state.mapping,
- candidates=IteratorMapping(
- self.state.criteria,
- operator.attrgetter("candidates"),
- ),
- information=IteratorMapping(
- self.state.criteria,
- operator.attrgetter("information"),
- ),
- backtrack_causes=self.state.backtrack_causes,
- )
-
- def _is_current_pin_satisfying(self, name, criterion):
- try:
- current_pin = self.state.mapping[name]
- except KeyError:
- return False
- return all(
- self._p.is_satisfied_by(requirement=r, candidate=current_pin)
- for r in criterion.iter_requirement()
- )
-
- def _get_updated_criteria(self, candidate):
- criteria = self.state.criteria.copy()
- for requirement in self._p.get_dependencies(candidate=candidate):
- self._add_to_criteria(criteria, requirement, parent=candidate)
- return criteria
-
- def _attempt_to_pin_criterion(self, name):
- criterion = self.state.criteria[name]
-
- causes = []
- for candidate in criterion.candidates:
- try:
- criteria = self._get_updated_criteria(candidate)
- except RequirementsConflicted as e:
- self._r.rejecting_candidate(e.criterion, candidate)
- causes.append(e.criterion)
- continue
-
- # Check the newly-pinned candidate actually works. This should
- # always pass under normal circumstances, but in the case of a
- # faulty provider, we will raise an error to notify the implementer
- # to fix find_matches() and/or is_satisfied_by().
- satisfied = all(
- self._p.is_satisfied_by(requirement=r, candidate=candidate)
- for r in criterion.iter_requirement()
- )
- if not satisfied:
- raise InconsistentCandidate(candidate, criterion)
-
- self._r.pinning(candidate=candidate)
- self.state.criteria.update(criteria)
-
- # Put newly-pinned candidate at the end. This is essential because
- # backtracking looks at this mapping to get the last pin.
- self.state.mapping.pop(name, None)
- self.state.mapping[name] = candidate
-
- return []
-
- # All candidates tried, nothing works. This criterion is a dead
- # end, signal for backtracking.
- return causes
-
- def _backjump(self, causes):
- """Perform backjumping.
-
- When we enter here, the stack is like this::
-
- [ state Z ]
- [ state Y ]
- [ state X ]
- .... earlier states are irrelevant.
-
- 1. No pins worked for Z, so it does not have a pin.
- 2. We want to reset state Y to unpinned, and pin another candidate.
- 3. State X holds what state Y was before the pin, but does not
- have the incompatibility information gathered in state Y.
-
- Each iteration of the loop will:
-
- 1. Identify Z. The incompatibility is not always caused by the latest
- state. For example, given three requirements A, B and C, with
- dependencies A1, B1 and C1, where A1 and B1 are incompatible: the
- last state might be related to C, so we want to discard the
- previous state.
- 2. Discard Z.
- 3. Discard Y but remember its incompatibility information gathered
- previously, and the failure we're dealing with right now.
- 4. Push a new state Y' based on X, and apply the incompatibility
- information from Y to Y'.
- 5a. If this causes Y' to conflict, we need to backtrack again. Make Y'
- the new Z and go back to step 2.
- 5b. If the incompatibilities apply cleanly, end backtracking.
- """
- incompatible_reqs = itertools.chain(
- (c.parent for c in causes if c.parent is not None),
- (c.requirement for c in causes),
- )
- incompatible_deps = {self._p.identify(r) for r in incompatible_reqs}
- while len(self._states) >= 3:
- # Remove the state that triggered backtracking.
- del self._states[-1]
-
- # Ensure to backtrack to a state that caused the incompatibility
- incompatible_state = False
- while not incompatible_state:
- # Retrieve the last candidate pin and known incompatibilities.
- try:
- broken_state = self._states.pop()
- name, candidate = broken_state.mapping.popitem()
- except (IndexError, KeyError):
- raise ResolutionImpossible(causes)
- current_dependencies = {
- self._p.identify(d)
- for d in self._p.get_dependencies(candidate)
- }
- incompatible_state = not current_dependencies.isdisjoint(
- incompatible_deps
- )
-
- incompatibilities_from_broken = [
- (k, list(v.incompatibilities))
- for k, v in broken_state.criteria.items()
- ]
-
- # Also mark the newly known incompatibility.
- incompatibilities_from_broken.append((name, [candidate]))
-
- # Create a new state from the last known-to-work one, and apply
- # the previously gathered incompatibility information.
- def _patch_criteria():
- for k, incompatibilities in incompatibilities_from_broken:
- if not incompatibilities:
- continue
- try:
- criterion = self.state.criteria[k]
- except KeyError:
- continue
- matches = self._p.find_matches(
- identifier=k,
- requirements=IteratorMapping(
- self.state.criteria,
- operator.methodcaller("iter_requirement"),
- ),
- incompatibilities=IteratorMapping(
- self.state.criteria,
- operator.attrgetter("incompatibilities"),
- {k: incompatibilities},
- ),
- )
- candidates = build_iter_view(matches)
- if not candidates:
- return False
- incompatibilities.extend(criterion.incompatibilities)
- self.state.criteria[k] = Criterion(
- candidates=candidates,
- information=list(criterion.information),
- incompatibilities=incompatibilities,
- )
- return True
-
- self._push_new_state()
- success = _patch_criteria()
-
- # It works! Let's work on this new state.
- if success:
- return True
-
- # State does not work after applying known incompatibilities.
- # Try the still previous state.
-
- # No way to backtrack anymore.
- return False
-
- def resolve(self, requirements, max_rounds):
- if self._states:
- raise RuntimeError("already resolved")
-
- self._r.starting()
-
- # Initialize the root state.
- self._states = [
- State(
- mapping=collections.OrderedDict(),
- criteria={},
- backtrack_causes=[],
- )
- ]
- for r in requirements:
- try:
- self._add_to_criteria(self.state.criteria, r, parent=None)
- except RequirementsConflicted as e:
- raise ResolutionImpossible(e.criterion.information)
-
- # The root state is saved as a sentinel so the first ever pin can have
- # something to backtrack to if it fails. The root state is basically
- # pinning the virtual "root" package in the graph.
- self._push_new_state()
-
- for round_index in range(max_rounds):
- self._r.starting_round(index=round_index)
-
- unsatisfied_names = [
- key
- for key, criterion in self.state.criteria.items()
- if not self._is_current_pin_satisfying(key, criterion)
- ]
-
- # All criteria are accounted for. Nothing more to pin, we are done!
- if not unsatisfied_names:
- self._r.ending(state=self.state)
- return self.state
-
- # keep track of satisfied names to calculate diff after pinning
- satisfied_names = set(self.state.criteria.keys()) - set(
- unsatisfied_names
- )
-
- # Choose the most preferred unpinned criterion to try.
- name = min(unsatisfied_names, key=self._get_preference)
- failure_causes = self._attempt_to_pin_criterion(name)
-
- if failure_causes:
- causes = [i for c in failure_causes for i in c.information]
- # Backjump if pinning fails. The backjump process puts us in
- # an unpinned state, so we can work on it in the next round.
- self._r.resolving_conflicts(causes=causes)
- success = self._backjump(causes)
- self.state.backtrack_causes[:] = causes
-
- # Dead ends everywhere. Give up.
- if not success:
- raise ResolutionImpossible(self.state.backtrack_causes)
- else:
- # discard as information sources any invalidated names
- # (unsatisfied names that were previously satisfied)
- newly_unsatisfied_names = {
- key
- for key, criterion in self.state.criteria.items()
- if key in satisfied_names
- and not self._is_current_pin_satisfying(key, criterion)
- }
- self._remove_information_from_criteria(
- self.state.criteria, newly_unsatisfied_names
- )
- # Pinning was successful. Push a new state to do another pin.
- self._push_new_state()
-
- self._r.ending_round(index=round_index, state=self.state)
-
- raise ResolutionTooDeep(max_rounds)
-
-
-def _has_route_to_root(criteria, key, all_keys, connected):
- if key in connected:
- return True
- if key not in criteria:
- return False
- for p in criteria[key].iter_parent():
- try:
- pkey = all_keys[id(p)]
- except KeyError:
- continue
- if pkey in connected:
- connected.add(key)
- return True
- if _has_route_to_root(criteria, pkey, all_keys, connected):
- connected.add(key)
- return True
- return False
-
-
-Result = collections.namedtuple("Result", "mapping graph criteria")
-
-
-def _build_result(state):
- mapping = state.mapping
- all_keys = {id(v): k for k, v in mapping.items()}
- all_keys[id(None)] = None
-
- graph = DirectedGraph()
- graph.add(None) # Sentinel as root dependencies' parent.
-
- connected = {None}
- for key, criterion in state.criteria.items():
- if not _has_route_to_root(state.criteria, key, all_keys, connected):
- continue
- if key not in graph:
- graph.add(key)
- for p in criterion.iter_parent():
- try:
- pkey = all_keys[id(p)]
- except KeyError:
- continue
- if pkey not in graph:
- graph.add(pkey)
- graph.connect(pkey, key)
-
- return Result(
- mapping={k: v for k, v in mapping.items() if k in connected},
- graph=graph,
- criteria=state.criteria,
- )
-
-
-class Resolver(AbstractResolver):
- """The thing that performs the actual resolution work."""
-
- base_exception = ResolverException
-
- def resolve(self, requirements, max_rounds=100):
- """Take a collection of constraints, spit out the resolution result.
-
- The return value is a representation to the final resolution result. It
- is a tuple subclass with three public members:
-
- * `mapping`: A dict of resolved candidates. Each key is an identifier
- of a requirement (as returned by the provider's `identify` method),
- and the value is the resolved candidate.
- * `graph`: A `DirectedGraph` instance representing the dependency tree.
- The vertices are keys of `mapping`, and each edge represents *why*
- a particular package is included. A special vertex `None` is
- included to represent parents of user-supplied requirements.
- * `criteria`: A dict of "criteria" that hold detailed information on
- how edges in the graph are derived. Each key is an identifier of a
- requirement, and the value is a `Criterion` instance.
-
- The following exceptions may be raised if a resolution cannot be found:
-
- * `ResolutionImpossible`: A resolution cannot be found for the given
- combination of requirements. The `causes` attribute of the
- exception is a list of (requirement, parent), giving the
- requirements that could not be satisfied.
- * `ResolutionTooDeep`: The dependency tree is too deeply nested and
- the resolver gave up. This is usually caused by a circular
- dependency, but you can try to resolve this by increasing the
- `max_rounds` argument.
- """
- resolution = Resolution(self.provider, self.reporter)
- state = resolution.resolve(requirements, max_rounds=max_rounds)
- return _build_result(state)
diff --git a/spaces/TeamHaltmannSusanaHWCEO/Fire-DiffusionV0.1Beta/README.md b/spaces/TeamHaltmannSusanaHWCEO/Fire-DiffusionV0.1Beta/README.md
deleted file mode 100644
index 9c27903a99985b8aa17384459d1e195890531a68..0000000000000000000000000000000000000000
--- a/spaces/TeamHaltmannSusanaHWCEO/Fire-DiffusionV0.1Beta/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Fire DiffusionV0.1Beta
-emoji: 📚
-colorFrom: green
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Terminus0501/vits-uma-genshin-honkai/README.md b/spaces/Terminus0501/vits-uma-genshin-honkai/README.md
deleted file mode 100644
index 1c0aa069bfd980b6b45bb2bf62ff74bd9b0b61c2..0000000000000000000000000000000000000000
--- a/spaces/Terminus0501/vits-uma-genshin-honkai/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-license: apache-2.0
-title: ' vits-uma-genshin-honkai'
-sdk: gradio
-sdk_version: 3.7
-emoji: 🐨
-colorTo: yellow
-pinned: false
-app_file: app.py
-duplicated_from: ikechan8370/vits-uma-genshin-honkai
----
diff --git a/spaces/Terminus0501/vits-uma-genshin-honkai/transforms.py b/spaces/Terminus0501/vits-uma-genshin-honkai/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/Terminus0501/vits-uma-genshin-honkai/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/TexR6/AttentionMaps/capture_weights.py b/spaces/TexR6/AttentionMaps/capture_weights.py
deleted file mode 100644
index bd91d92c4f6bb75b93e42b106fa46ac5b77ea681..0000000000000000000000000000000000000000
--- a/spaces/TexR6/AttentionMaps/capture_weights.py
+++ /dev/null
@@ -1,326 +0,0 @@
-import torch
-import numpy as np
-import torch.nn as nn
-import torch.nn.functional as F
-
-from torch.cuda.amp import autocast
-
-from utils import (get_width_and_height_from_size, load_pretrained_weights, get_model_params)
-
-VALID_MODELS = ('ViT-B_16', 'ViT-B_32', 'ViT-L_16', 'ViT-L_32')
-
-class PositionEmbs(nn.Module):
- def __init__(self, num_patches, emb_dim, dropout_rate=0.1):
- super(PositionEmbs, self).__init__()
- self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, emb_dim))
- if dropout_rate > 0:
- self.dropout = nn.Dropout(dropout_rate)
- else:
- self.dropout = None
-
- @autocast()
- def forward(self, x):
- out = x + self.pos_embedding
-
- if self.dropout:
- out = self.dropout(out)
-
- return out
-
-class MlpBlock(nn.Module):
- """ Transformer Feed-Forward Block """
- def __init__(self, in_dim, mlp_dim, out_dim, dropout_rate=0.1):
- super(MlpBlock, self).__init__()
-
- # init layers
- self.fc1 = nn.Linear(in_dim, mlp_dim)
- self.fc2 = nn.Linear(mlp_dim, out_dim)
- self.act = nn.GELU()
- if dropout_rate > 0.0:
- self.dropout1 = nn.Dropout(dropout_rate)
- self.dropout2 = nn.Dropout(dropout_rate)
- else:
- self.dropout1 = None
- self.dropout2 = None
-
- @autocast()
- def forward(self, x):
-
- out = self.fc1(x)
- out = self.act(out)
- if self.dropout1:
- out = self.dropout1(out)
-
- out = self.fc2(out)
- out = self.dropout2(out)
- return out
-
-
-class LinearGeneral(nn.Module):
- def __init__(self, in_dim=(768, ), feat_dim=(12, 64)):
- super(LinearGeneral, self).__init__()
-
- self.weight = nn.Parameter(torch.randn(*in_dim, *feat_dim))
- self.bias = nn.Parameter(torch.zeros(*feat_dim))
-
- @autocast()
- def forward(self, x, dims):
- a = torch.tensordot(x, self.weight, dims=dims) + self.bias
- return a
-
-
-class SelfAttention(nn.Module):
- def __init__(self, in_dim, heads=8, dropout_rate=0.1):
- super(SelfAttention, self).__init__()
- self.heads = heads
- self.head_dim = in_dim // heads
- self.scale = self.head_dim**0.5
-
- self.query = LinearGeneral((in_dim, ), (self.heads, self.head_dim))
- self.key = LinearGeneral((in_dim, ), (self.heads, self.head_dim))
- self.value = LinearGeneral((in_dim, ), (self.heads, self.head_dim))
- self.out = LinearGeneral((self.heads, self.head_dim), (in_dim, ))
-
- if dropout_rate > 0:
- self.dropout = nn.Dropout(dropout_rate)
- else:
- self.dropout = None
-
- @autocast()
- def forward(self, x):
- b, n, _ = x.shape
-
- q = self.query(x, dims=([2], [0]))
- k = self.key(x, dims=([2], [0]))
- v = self.value(x, dims=([2], [0]))
-
- q = q.permute(0, 2, 1, 3)
- k = k.permute(0, 2, 1, 3)
- v = v.permute(0, 2, 1, 3)
-
- attn_weights = torch.matmul(q, k.transpose(-2, -1)) / self.scale
- attn_weights = F.softmax(attn_weights, dim=-1)
- out = torch.matmul(attn_weights, v)
- out = out.permute(0, 2, 1, 3)
-
- out = self.out(out, dims=([2, 3], [0, 1]))
-
- return out, attn_weights
-
-
-class EncoderBlock(nn.Module):
- def __init__(self, in_dim, mlp_dim, num_heads, dropout_rate=0.1, attn_dropout_rate=0.1):
- super(EncoderBlock, self).__init__()
-
- self.norm1 = nn.LayerNorm(in_dim)
- self.attn = SelfAttention(in_dim, heads=num_heads, dropout_rate=attn_dropout_rate)
- if dropout_rate > 0:
- self.dropout = nn.Dropout(dropout_rate)
- else:
- self.dropout = None
- self.norm2 = nn.LayerNorm(in_dim)
- self.mlp = MlpBlock(in_dim, mlp_dim, in_dim, dropout_rate)
-
- @autocast()
- def forward(self, x):
- residual = x
- out = self.norm1(x)
- out, attn_weights = self.attn(out)
- if self.dropout:
- out = self.dropout(out)
- out += residual
- residual = out
-
- out = self.norm2(out)
- out = self.mlp(out)
- out += residual
- return out, attn_weights
-
-
-class Encoder(nn.Module):
- def __init__(self,
- num_patches,
- emb_dim,
- mlp_dim,
- num_layers=12,
- num_heads=12,
- dropout_rate=0.1,
- attn_dropout_rate=0.0):
- super(Encoder, self).__init__()
-
- # positional embedding
- self.pos_embedding = PositionEmbs(num_patches, emb_dim, dropout_rate)
-
- # encoder blocks
- in_dim = emb_dim
- self.encoder_layers = nn.ModuleList()
- for i in range(num_layers):
- layer = EncoderBlock(in_dim, mlp_dim, num_heads, dropout_rate, attn_dropout_rate)
- self.encoder_layers.append(layer)
- self.norm = nn.LayerNorm(in_dim)
-
- @autocast()
- def forward(self, x):
- attn_weights = []
- out = self.pos_embedding(x)
-
- for layer in self.encoder_layers:
- out, weights = layer(out)
- attn_weights.append(weights)
-
- out = self.norm(out)
- return out, attn_weights
-
-
-class VisionTransformer(nn.Module):
- """ Vision Transformer.
- Most easily loaded with the .from_name or .from_pretrained methods.
- Args:
- params (namedtuple): A set of Params.
- References:
- [1] https://arxiv.org/abs/2010.11929 (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)
- Example:
-
-
- import torch
- >>> from vision_transformer_pytorch import VisionTransformer
- >>> inputs = torch.rand(1, 3, 256, 256)
- >>> model = VisionTransformer.from_pretrained('ViT-B_16')
- >>> model.eval()
- >>> outputs = model(inputs)
- """
- def __init__(self, params=None):
- super(VisionTransformer, self).__init__()
- self._params = params
-
- self.embedding = nn.Conv2d(3, self._params.emb_dim, kernel_size=self.patch_size, stride=self.patch_size)
- # class token
- self.cls_token = nn.Parameter(torch.zeros(1, 1, self._params.emb_dim))
-
- # transformer
- self.transformer = Encoder(num_patches=self.num_patches,
- emb_dim=self._params.emb_dim,
- mlp_dim=self._params.mlp_dim,
- num_layers=self._params.num_layers,
- num_heads=self._params.num_heads,
- dropout_rate=self._params.dropout_rate,
- attn_dropout_rate=self._params.attn_dropout_rate)
-
- # classfier
- self.classifier = nn.Linear(self._params.emb_dim, self._params.num_classes)
-
- @property
- def image_size(self):
- return get_width_and_height_from_size(self._params.image_size)
-
- @property
- def patch_size(self):
- return get_width_and_height_from_size(self._params.patch_size)
-
- @property
- def num_patches(self):
- h, w = self.image_size
- fh, fw = self.patch_size
- gh, gw = h // fh, w // fw
- return gh * gw
-
- @autocast()
- def extract_features(self, x):
- emb = self.embedding(x) # (n, c, gh, gw)
- emb = emb.permute(0, 2, 3, 1) # (n, gh, hw, c)
- b, h, w, c = emb.shape
- emb = emb.reshape(b, h * w, c)
-
- # prepend class token
- cls_token = self.cls_token.repeat(b, 1, 1)
- emb = torch.cat([cls_token, emb], dim=1)
-
- # transformer
- feat, attn_weights = self.transformer(emb)
- return feat, attn_weights
-
- @autocast()
- def forward(self, x):
- feat, attn_weights = self.extract_features(x)
-
- # classifier
- logits = self.classifier(feat[:, 0])
- return logits, attn_weights
-
- @classmethod
- def from_name(cls, model_name, in_channels=3, **override_params):
- """create an vision transformer model according to name.
- Args:
- model_name (str): Name for vision transformer.
- in_channels (int): Input data's channel number.
- override_params (other key word params):
- Params to override model's global_params.
- Optional key:
- 'image_size', 'patch_size',
- 'emb_dim', 'mlp_dim',
- 'num_heads', 'num_layers',
- 'num_classes', 'attn_dropout_rate',
- 'dropout_rate'
- Returns:
- An vision transformer model.
- """
- cls._check_model_name_is_valid(model_name)
- params = get_model_params(model_name, override_params)
- model = cls(params)
- model._change_in_channels(in_channels)
- return model
-
- @classmethod
- def from_pretrained(cls, model_name, weights_path=None, in_channels=3, num_classes=1000, **override_params):
- """create an vision transformer model according to name.
- Args:
- model_name (str): Name for vision transformer.
- weights_path (None or str):
- str: path to pretrained weights file on the local disk.
- None: use pretrained weights downloaded from the Internet.
- in_channels (int): Input data's channel number.
- num_classes (int):
- Number of categories for classification.
- It controls the output size for final linear layer.
- override_params (other key word params):
- Params to override model's global_params.
- Optional key:
- 'image_size', 'patch_size',
- 'emb_dim', 'mlp_dim',
- 'num_heads', 'num_layers',
- 'num_classes', 'attn_dropout_rate',
- 'dropout_rate'
- Returns:
- A pretrained vision transformer model.
- """
- model = cls.from_name(model_name, num_classes=num_classes, **override_params)
- load_pretrained_weights(model, model_name, weights_path=weights_path, load_fc=(num_classes == 1000))
- model._change_in_channels(in_channels)
- return model
-
- @classmethod
- def _check_model_name_is_valid(cls, model_name):
- """Validates model name.
- Args:
- model_name (str): Name for vision transformer.
- Returns:
- bool: Is a valid name or not.
- """
- if model_name not in VALID_MODELS:
- raise ValueError('model_name should be one of: ' + ', '.join(VALID_MODELS))
-
- def _change_in_channels(self, in_channels):
- """Adjust model's first convolution layer to in_channels, if in_channels not equals 3.
- Args:
- in_channels (int): Input data's channel number.
- """
- if in_channels != 3:
- self.embedding = nn.Conv2d(in_channels,
- self._params.emb_dim,
- kernel_size=self.patch_size,
- stride=self.patch_size)
-
-vit_weights = VisionTransformer.from_name('ViT-B_16', num_classes=1000)
-model_weights = torch.load('pretrained_weights/ViT-B_16_imagenet21k_imagenet2012.pth',
- map_location=torch.device('cpu'))
-vit_weights.load_state_dict(model_weights)
diff --git a/spaces/Thafx/sdpp/app.py b/spaces/Thafx/sdpp/app.py
deleted file mode 100644
index 249d1d25f16a16a1c34a003e56813387a4305dc0..0000000000000000000000000000000000000000
--- a/spaces/Thafx/sdpp/app.py
+++ /dev/null
@@ -1,181 +0,0 @@
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-
-model_id = 'wavymulder/portraitplus'
-prefix = 'portrait+ style,'
-
-scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
-
-pipe = StableDiffusionPipeline.from_pretrained(
- model_id,
- torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
- scheduler=scheduler)
-
-pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
- model_id,
- torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
- scheduler=scheduler)
-
-if torch.cuda.is_available():
- pipe = pipe.to("cuda")
- pipe_i2i = pipe_i2i.to("cuda")
-
-def error_str(error, title="Error"):
- return f"""#### {title}
- {error}""" if error else ""
-
-
-def _parse_args(prompt, generator):
- parser = argparse.ArgumentParser(
- description="making it work."
- )
- parser.add_argument(
- "--no-half-vae", help="no half vae"
- )
-
- cmdline_args = parser.parse_args()
- command = cmdline_args.command
- conf_file = cmdline_args.conf_file
- conf_args = Arguments(conf_file)
- opt = conf_args.readArguments()
-
- if cmdline_args.config_overrides:
- for config_override in cmdline_args.config_overrides.split(";"):
- config_override = config_override.strip()
- if config_override:
- var_val = config_override.split("=")
- assert (
- len(var_val) == 2
- ), f"Config override '{var_val}' does not have the form 'VAR=val'"
- conf_args.add_opt(opt, var_val[0], var_val[1], force_override=True)
-
-def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
- generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
- prompt = f"{prefix} {prompt}" if auto_prefix else prompt
-
- try:
- if img is not None:
- return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
- else:
- return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
- except Exception as e:
- return None, error_str(e)
-
-
-
-def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
-
- result = pipe(
- prompt,
- negative_prompt = neg_prompt,
- num_inference_steps = int(steps),
- guidance_scale = guidance,
- width = width,
- height = height,
- generator = generator)
-
- return result.images[0]
-
-def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
- ratio = min(height / img.height, width / img.width)
- img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
- result = pipe_i2i(
- prompt,
- negative_prompt = neg_prompt,
- init_image = img,
- num_inference_steps = int(steps),
- strength = strength,
- guidance_scale = guidance,
- width = width,
- height = height,
- generator = generator)
-
- return result.images[0]
-
- def fake_safety_checker(images, **kwargs):
- return result.images[0], [False] * len(images)
-
- pipe.safety_checker = fake_safety_checker
-
-css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
- gr.HTML(
- f"""
-
-
-
📸 Portrait Plus 📸
-
-
- Demo for Portrait+
- Stable Diffusion model by Wavymulder. {"" if prefix else ""}
- Running on {"GPU 🔥" if torch.cuda.is_available() else f"CPU ⚡"}.
-
-
Please use the prompt template below to achieve the desired result:
-
-
-Prompt:
-
-portrait+ style photograph of * subject * , (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, realistic, photo-realistic, full length frame, High detail RAW color art, piercing, diffused soft lighting, shallow depth of field, sharp focus, hyperrealism, cinematic lighting
-
-
-Example: portrait+ style photograph of Heath Ledger as Batman
-
-Important note: Portrait+ works best at a 1:1 aspect ratio, it is also successful using tall aspect ratios as well.
-
-Negative Prompt:
-
-blender illustration hdr lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature
-
-
-Have Fun & Enjoy ⚡ //THAFX
-
-
- """
- )
- with gr.Row():
-
- with gr.Column(scale=55):
- with gr.Group():
- with gr.Row():
- prompt = gr.Textbox(label="Prompt", show_label=False,max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
- generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
- image_out = gr.Image(height=512)
- error_output = gr.Markdown()
-
- with gr.Column(scale=45):
- with gr.Tab("Options"):
- with gr.Group():
- neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
- auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (portrait+ style,)", value=prefix, visible=prefix)
-
- with gr.Row():
- guidance = gr.Slider(label="Guidance scale", value=7, maximum=15)
- steps = gr.Slider(label="Steps", value=20, minimum=2, maximum=75, step=1)
-
- with gr.Row():
- width = gr.Slider(label="Width", value=768, minimum=64, maximum=1024, step=8)
- height = gr.Slider(label="Height", value=768, minimum=64, maximum=1024, step=8)
-
- seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
- with gr.Tab("Image to image"):
- with gr.Group():
- image = gr.Image(label="Image", height=256, tool="editor", type="pil")
- strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
- auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
- inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
- outputs = [image_out, error_output]
- prompt.submit(inference, inputs=inputs, outputs=outputs)
- generate.click(inference, inputs=inputs, outputs=outputs)
-
-
-
-demo.queue(concurrency_count=1)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Trangluna2002/AI_Cover_Gen/README.md b/spaces/Trangluna2002/AI_Cover_Gen/README.md
deleted file mode 100644
index 9b50beed4778c8d4f86c294a3b272edff8ef44f9..0000000000000000000000000000000000000000
--- a/spaces/Trangluna2002/AI_Cover_Gen/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AICoverGen
-emoji: 🚀
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.44.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Tuana/what-would-mother-say/utils/ui.py b/spaces/Tuana/what-would-mother-say/utils/ui.py
deleted file mode 100644
index b82d444510e86102a6de472dbed8051e9418eec9..0000000000000000000000000000000000000000
--- a/spaces/Tuana/what-would-mother-say/utils/ui.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import streamlit as st
-from PIL import Image
-
-def set_state_if_absent(key, value):
- if key not in st.session_state:
- st.session_state[key] = value
-
-def set_initial_state():
- set_state_if_absent("question", "Provide a Twitter username")
- set_state_if_absent("result", None)
- set_state_if_absent("haystack_started", False)
-
-def reset_results(*args):
- st.session_state.result = None
-
-# def set_openai_api_key(api_key: str):
-# st.session_state["OPENAI_API_KEY"] = api_key
-
-# def set_serper_dev_key(api_key: str):
-# st.session_state["SERPER_KEY"] = api_key
-
-def sidebar():
- with st.sidebar:
- image = Image.open('logo/haystack-logo-colored.png')
- st.markdown("Thanks for coming to this 🤗 Space.\n\n"
- "This is a project for fun and is a sister to the [should-i-follow](https://huggingface.co/spaces/deepset/should-i-follow) Space."
- " There's a lot that can be improved to make this app better.\n\n"
- "**Take results with a grain of** 🧂\n\n"
- "")
-
- st.markdown(
- "## How to use\n"
- # "1. Enter your [OpenAI API](https://platform.openai.com/account/api-keys) and [SerperDev API](https://serper.dev/) keys below\n"
- "1. Enter a query that includes a Mastodon username and be descriptive about wanting a post as a result.\n"
- "2. Enjoy 🤗\n"
- )
-
- # openai_api_key_input = st.text_input(
- # "OpenAI API Key",
- # type="password",
- # placeholder="Paste your OpenAI API key here (sk-...)",
- # help="You can get your API key from https://platform.openai.com/account/api-keys.",
- # value=st.session_state.get("OPENAI_API_KEY", ""),
- # )
-
- # serper_api_key_input = st.text_input(
- # "SerperDev API Key",
- # type="password",
- # placeholder="Paste your SerperDev API key here (sk-...)",
- # help="You can get your API key from https://serper.dev.",
- # value=st.session_state.get("SERPER_KEY", ""),
- # )
-
- # if openai_api_key_input:
- # set_openai_api_key(openai_api_key_input)
-
- # if serper_api_key_input:
- # set_serper_dev_key(serper_api_key_input)
-
- st.markdown("---")
- st.markdown(
- "## How this works\n"
- "This app was built with [Haystack](https://haystack.deepset.ai) using the"
- " [`Agent`](https://docs.haystack.deepset.ai/docs/agent) custom [`PromptTemplates`](https://docs.haystack.deepset.ai/docs/prompt_node#templates)\n\n"
- "as well as a custom `MastodonFetcher` node\n"
- " The source code is also on [GitHub](https://github.com/TuanaCelik/what-would-mother-say)"
- " with instructions to run locally.\n"
- "You can see how the `Agent` was set up [here](https://github.com/TuanaCelik/what-would-mother-say/blob/main/utils/haystack.py)")
- st.markdown("---")
- st.markdown("Made by [tuanacelik](https://twitter.com/tuanacelik)")
- st.markdown("---")
- st.markdown("""Thanks to [mmz_001](https://twitter.com/mm_sasmitha)
- for open sourcing [KnowledgeGPT](https://knowledgegpt.streamlit.app/) which helped me with this sidebar 🙏🏽""")
- st.image(image, width=250)
\ No newline at end of file
diff --git a/spaces/Violetmae14/images-to-audio/README.md b/spaces/Violetmae14/images-to-audio/README.md
deleted file mode 100644
index ae3677770021f8618981957785ceff38f59fe32b..0000000000000000000000000000000000000000
--- a/spaces/Violetmae14/images-to-audio/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Image To Audio Using Facebook Images
-emoji: 🚀
-colorFrom: yellow
-colorTo: gray
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/VishalF5/Text_Similarity/app.py b/spaces/VishalF5/Text_Similarity/app.py
deleted file mode 100644
index 82a80a599fa60f1ed7fc56b8aaf557d8186648ba..0000000000000000000000000000000000000000
--- a/spaces/VishalF5/Text_Similarity/app.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import streamlit as st
-import tensorflow_hub as hub
-from sklearn.metrics.pairwise import cosine_similarity
-
-
-# Encoder
-encoder_url = 'https://tfhub.dev/google/universal-sentence-encoder/4'
-
-encoder = hub.load(encoder_url)
-
-
-
-# Calculating Cosine Similarity between two sentences
-def get_similarity(sentence_a, sentence_b):
- embed_a = encoder([sentence_a])
- embed_b = encoder([sentence_b])
- similarity = cosine_similarity(embed_a, embed_b)[0][0]
- return f'The similarity score is : "{similarity:.2f}"'
-
-# Interface
-st.title("Text Similarity")
-
-input_text1 = st.text_input("Enter First Sentence : " )
-input_text2 = st.text_input("Enter Second Sentence : ")
-
-
-
-if st.button('Predict'):
- st.write("Sentence 1 : " ,input_text1)
- st.write("Sentence 2 : " ,input_text2)
-
- res = get_similarity(input_text1 ,input_text2)
-
- st.success(res)
-
diff --git a/spaces/Vision-CAIR/MiniGPT-v2/README.md b/spaces/Vision-CAIR/MiniGPT-v2/README.md
deleted file mode 100644
index d89b908eb713b104b9c58416b2b7ea5f29704e87..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/MiniGPT-v2/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: MiniGPT-v2
-emoji: 🚀
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: other
----
\ No newline at end of file
diff --git a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/datasets/datasets/caption_datasets.py b/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/datasets/datasets/caption_datasets.py
deleted file mode 100644
index 78bab668d34c8a28917af171700d43dbb20f3926..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/datasets/datasets/caption_datasets.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import os
-from collections import OrderedDict
-
-from minigpt4.datasets.datasets.base_dataset import BaseDataset
-from PIL import Image
-
-
-class __DisplMixin:
- def displ_item(self, index):
- sample, ann = self.__getitem__(index), self.annotation[index]
-
- return OrderedDict(
- {
- "file": ann["image"],
- "caption": ann["caption"],
- "image": sample["image"],
- }
- )
-
-
-class CaptionDataset(BaseDataset, __DisplMixin):
- def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
- """
- vis_root (string): Root directory of images (e.g. coco/images/)
- ann_root (string): directory to store the annotation file
- """
- super().__init__(vis_processor, text_processor, vis_root, ann_paths)
-
- self.img_ids = {}
- n = 0
- for ann in self.annotation:
- img_id = ann["image_id"]
- if img_id not in self.img_ids.keys():
- self.img_ids[img_id] = n
- n += 1
-
- def __getitem__(self, index):
-
- # TODO this assumes image input, not general enough
- ann = self.annotation[index]
-
- img_file = '{:0>12}.jpg'.format(ann["image_id"])
- image_path = os.path.join(self.vis_root, img_file)
- image = Image.open(image_path).convert("RGB")
-
- image = self.vis_processor(image)
- caption = self.text_processor(ann["caption"])
-
- return {
- "image": image,
- "text_input": caption,
- "image_id": self.img_ids[ann["image_id"]],
- }
-
-
-class CaptionEvalDataset(BaseDataset, __DisplMixin):
- def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
- """
- vis_root (string): Root directory of images (e.g. coco/images/)
- ann_root (string): directory to store the annotation file
- split (string): val or test
- """
- super().__init__(vis_processor, text_processor, vis_root, ann_paths)
-
- def __getitem__(self, index):
-
- ann = self.annotation[index]
-
- image_path = os.path.join(self.vis_root, ann["image"])
- image = Image.open(image_path).convert("RGB")
-
- image = self.vis_processor(image)
-
- return {
- "image": image,
- "image_id": ann["image_id"],
- "instance_id": ann["instance_id"],
- }
diff --git a/spaces/Wassim/public-custom-search/README.md b/spaces/Wassim/public-custom-search/README.md
deleted file mode 100644
index fc26f01d50bd0fc6a425c5651f9e63ba04877d4b..0000000000000000000000000000000000000000
--- a/spaces/Wassim/public-custom-search/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Public Custom Search
-emoji: 📈
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.43.2
-app_file: app.py
-pinned: false
-license: gpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/HowtoBuildColorVideoFromNumpyImages.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/HowtoBuildColorVideoFromNumpyImages.py
deleted file mode 100644
index 3064d1a924ad781920413eb6f4981f521e67fbfc..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/HowtoBuildColorVideoFromNumpyImages.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# out numpy array of images is in the charile temp directory
-#
-import numpy
-from numpy import load
-import cv2
-colorImagesNumpyArray = load('C:/temp/charilie/colorImages.npy')
-print(colorImagesNumpyArray.shape)
-
-# we can see the Deoldify conda environment is not compitable
-# I will a diffrent enviroment that has numpy and open cv. You can do the same
-
-#(2499, 226, 400, 3)
-
-# so we have 2499 images . each image is has a resoltion of 226X400 with 3 color channles
-
-# extract the dimentions of the first images (all of them are the same)
-height , width , channels = colorImagesNumpyArray[0].shape
-size = (width, height)
-newVideoOut = cv2.VideoWriter('C:/temp/charilie/myVideo.avi',cv2.VideoWriter_fourcc(*'DIVX'),15,size)
-
-for image in colorImagesNumpyArray:
- newVideoOut.write(image)
-
-newVideoOut.release()
-cv2.destroyAllWindows()
-
diff --git a/spaces/XzJosh/otto-Bert-VITS2/data_utils.py b/spaces/XzJosh/otto-Bert-VITS2/data_utils.py
deleted file mode 100644
index be3a29a93188c5b3386f22e5db29e5e96d78109a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/otto-Bert-VITS2/data_utils.py
+++ /dev/null
@@ -1,321 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-import commons
-from mel_processing import spectrogram_torch, mel_spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import cleaned_text_to_sequence, get_bert
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, speaker_id, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
-
- def __init__(self, audiopaths_sid_text, hparams):
- self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
- self.spk_map = hparams.spk2id
- self.hparams = hparams
-
- self.use_mel_spec_posterior = getattr(hparams, "use_mel_posterior_encoder", False)
- if self.use_mel_spec_posterior:
- self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
-
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
- self.add_blank = hparams.add_blank
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 300)
-
- random.seed(1234)
- random.shuffle(self.audiopaths_sid_text)
- self._filter()
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
-
- audiopaths_sid_text_new = []
- lengths = []
- skipped = 0
- for _id, spk, language, text, phones, tone, word2ph in self.audiopaths_sid_text:
- audiopath = f'{_id}'
- if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
- phones = phones.split(" ")
- tone = [int(i) for i in tone.split(" ")]
- word2ph = [int(i) for i in word2ph.split(" ")]
- audiopaths_sid_text_new.append([audiopath, spk, language, text, phones, tone, word2ph])
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
- else:
- skipped += 1
- print("skipped: ", skipped, ", total: ", len(self.audiopaths_sid_text))
- self.audiopaths_sid_text = audiopaths_sid_text_new
- self.lengths = lengths
-
- def get_audio_text_speaker_pair(self, audiopath_sid_text):
- # separate filename, speaker_id and text
- audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
-
- bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language, audiopath)
-
- spec, wav = self.get_audio(audiopath)
- sid = torch.LongTensor([int(self.spk_map[sid])])
- return (phones, spec, wav, sid, tone, language, bert)
-
- def get_audio(self, filename):
- audio, sampling_rate = load_wav_to_torch(filename)
- if sampling_rate != self.sampling_rate:
- raise ValueError("{} {} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate))
- audio_norm = audio / self.max_wav_value
- audio_norm = audio_norm.unsqueeze(0)
- spec_filename = filename.replace(".wav", ".spec.pt")
- if self.use_mel_spec_posterior:
- spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
- try:
- spec = torch.load(spec_filename)
- except:
- if self.use_mel_spec_posterior:
- spec = mel_spectrogram_torch(audio_norm, self.filter_length,
- self.n_mel_channels, self.sampling_rate, self.hop_length,
- self.win_length, self.hparams.mel_fmin, self.hparams.mel_fmax, center=False)
- else:
- spec = spectrogram_torch(audio_norm, self.filter_length,
- self.sampling_rate, self.hop_length, self.win_length,
- center=False)
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename)
- return spec, audio_norm
-
- def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
- pold = phone
- w2pho = [i for i in word2ph]
- word2ph = [i for i in word2ph]
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
- pold2 = phone
-
- if self.add_blank:
- p1 = len(phone)
- phone = commons.intersperse(phone, 0)
- p2 = len(phone)
- t1 = len(tone)
- tone = commons.intersperse(tone, 0)
- t2 = len(tone)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert_path = wav_path.replace(".wav", ".bert.pt")
- try:
- bert = torch.load(bert_path)
- assert bert.shape[-1] == len(phone)
- except:
- bert = get_bert(text, word2ph, language_str)
- torch.save(bert, bert_path)
- #print(bert.shape[-1], bert_path, text, pold)
- assert bert.shape[-1] == len(phone)
-
- assert bert.shape[-1] == len(phone), (
- bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho)
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
- return bert, phone, tone, language
-
- def get_sid(self, sid):
- sid = torch.LongTensor([int(sid)])
- return sid
-
- def __getitem__(self, index):
- return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
- def __len__(self):
- return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
- """ Zero-pads model inputs and targets
- """
-
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text, audio and speaker identities
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized, sid]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(
- torch.LongTensor([x[1].size(1) for x in batch]),
- dim=0, descending=True)
-
- max_text_len = max([len(x[0]) for x in batch])
- max_spec_len = max([x[1].size(1) for x in batch])
- max_wav_len = max([x[2].size(1) for x in batch])
-
- text_lengths = torch.LongTensor(len(batch))
- spec_lengths = torch.LongTensor(len(batch))
- wav_lengths = torch.LongTensor(len(batch))
- sid = torch.LongTensor(len(batch))
-
- text_padded = torch.LongTensor(len(batch), max_text_len)
- tone_padded = torch.LongTensor(len(batch), max_text_len)
- language_padded = torch.LongTensor(len(batch), max_text_len)
- bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
-
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- text_padded.zero_()
- tone_padded.zero_()
- language_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- bert_padded.zero_()
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- text = row[0]
- text_padded[i, :text.size(0)] = text
- text_lengths[i] = text.size(0)
-
- spec = row[1]
- spec_padded[i, :, :spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wav = row[2]
- wav_padded[i, :, :wav.size(1)] = wav
- wav_lengths[i] = wav.size(1)
-
- sid[i] = row[3]
-
- tone = row[4]
- tone_padded[i, :tone.size(0)] = tone
-
- language = row[5]
- language_padded[i, :language.size(0)] = language
-
- bert = row[6]
- bert_padded[i, :, :bert.size(1)] = bert
-
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, tone_padded, language_padded, bert_padded
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
- """
- Maintain similar input lengths in a batch.
- Length groups are specified by boundaries.
- Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
- It removes samples which are not included in the boundaries.
- Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
- """
-
- def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
- super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
- self.lengths = dataset.lengths
- self.batch_size = batch_size
- self.boundaries = boundaries
-
- self.buckets, self.num_samples_per_bucket = self._create_buckets()
- self.total_size = sum(self.num_samples_per_bucket)
- self.num_samples = self.total_size // self.num_replicas
-
- def _create_buckets(self):
- buckets = [[] for _ in range(len(self.boundaries) - 1)]
- for i in range(len(self.lengths)):
- length = self.lengths[i]
- idx_bucket = self._bisect(length)
- if idx_bucket != -1:
- buckets[idx_bucket].append(i)
-
- for i in range(len(buckets) - 1, 0, -1):
- if len(buckets[i]) == 0:
- buckets.pop(i)
- self.boundaries.pop(i + 1)
-
- num_samples_per_bucket = []
- for i in range(len(buckets)):
- len_bucket = len(buckets[i])
- total_batch_size = self.num_replicas * self.batch_size
- rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
- num_samples_per_bucket.append(len_bucket + rem)
- return buckets, num_samples_per_bucket
-
- def __iter__(self):
- # deterministically shuffle based on epoch
- g = torch.Generator()
- g.manual_seed(self.epoch)
-
- indices = []
- if self.shuffle:
- for bucket in self.buckets:
- indices.append(torch.randperm(len(bucket), generator=g).tolist())
- else:
- for bucket in self.buckets:
- indices.append(list(range(len(bucket))))
-
- batches = []
- for i in range(len(self.buckets)):
- bucket = self.buckets[i]
- len_bucket = len(bucket)
- if (len_bucket == 0):
- continue
- ids_bucket = indices[i]
- num_samples_bucket = self.num_samples_per_bucket[i]
-
- # add extra samples to make it evenly divisible
- rem = num_samples_bucket - len_bucket
- ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
- # subsample
- ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
- # batching
- for j in range(len(ids_bucket) // self.batch_size):
- batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]]
- batches.append(batch)
-
- if self.shuffle:
- batch_ids = torch.randperm(len(batches), generator=g).tolist()
- batches = [batches[i] for i in batch_ids]
- self.batches = batches
-
- assert len(self.batches) * self.batch_size == self.num_samples
- return iter(self.batches)
-
- def _bisect(self, x, lo=0, hi=None):
- if hi is None:
- hi = len(self.boundaries) - 1
-
- if hi > lo:
- mid = (hi + lo) // 2
- if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
- return mid
- elif x <= self.boundaries[mid]:
- return self._bisect(x, lo, mid)
- else:
- return self._bisect(x, mid + 1, hi)
- else:
- return -1
-
- def __len__(self):
- return self.num_samples // self.batch_size
diff --git a/spaces/YE01/saya-vits/app.py b/spaces/YE01/saya-vits/app.py
deleted file mode 100644
index abd74c43e6c59fb43cdf5ae465a85b0383065e5a..0000000000000000000000000000000000000000
--- a/spaces/YE01/saya-vits/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-
-import torch
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import text_to_sequence
-
-hps = utils.get_hparams_from_file("configs/saya.json")
-
-net_g = SynthesizerTrn(
- len(symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- **hps.model)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("saya_2000.pth", net_g, None)
-
-
-def get_text(text, _hps):
- text_norm = text_to_sequence(text, _hps.data.text_cleaners)
- if hps.data.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = torch.LongTensor(text_norm)
- return text_norm
-
-
-def tss(text, noise_scale, noise_scale_w, length_scale):
- stn_tst = get_text(text, hps)
- with torch.no_grad():
- x_tst = stn_tst.unsqueeze(0)
- x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
- audio = net_g.infer(x_tst, x_tst_lengths,
- noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][
- 0, 0].data.cpu().float().numpy()
-
- return hps.data.sampling_rate, audio
-
-
-with gr.Blocks() as app:
- with gr.Tabs():
- with gr.TabItem('Basic'):
- tts_input1 = gr.TextArea(label='日语文本', value='わたしは沙耶，パパのこと探しに来たの。')
- tts_input2 = gr.Number(label='噪声比例', value=.667)
- tts_input3 = gr.Number(label='噪声偏差', value=.8)
- tts_input4 = gr.Number(label='时长比例', value=.9)
- tts_submit = gr.Button('合成', variant='primary')
- tts_output = gr.Audio(label='Output')
- tts_submit.click(fn=tss, inputs=[tts_input1, tts_input2, tts_input3, tts_input4], outputs=tts_output)
- app.launch()
diff --git a/spaces/YONG627/456123/yolov5-code-main/utils/loggers/wandb/wandb_utils.py b/spaces/YONG627/456123/yolov5-code-main/utils/loggers/wandb/wandb_utils.py
deleted file mode 100644
index c8ab3819738111557909b858073a1af0bff47463..0000000000000000000000000000000000000000
--- a/spaces/YONG627/456123/yolov5-code-main/utils/loggers/wandb/wandb_utils.py
+++ /dev/null
@@ -1,193 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# WARNING ⚠️ wandb is deprecated and will be removed in future release.
-# See supported integrations at https://github.com/ultralytics/yolov5#integrations
-
-import logging
-import os
-import sys
-from contextlib import contextmanager
-from pathlib import Path
-
-from utils.general import LOGGER, colorstr
-
-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
-RANK = int(os.getenv('RANK', -1))
-DEPRECATION_WARNING = f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " \
- f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.'
-
-try:
- import wandb
-
- assert hasattr(wandb, '__version__') # verify package import not local dir
- LOGGER.warning(DEPRECATION_WARNING)
-except (ImportError, AssertionError):
- wandb = None
-
-
-class WandbLogger():
- """Log training runs, datasets, models, and predictions to Weights & Biases.
-
- This logger sends information to W&B at wandb.ai. By default, this information
- includes hyperparameters, system configuration and metrics, model metrics,
- and basic data metrics and analyses.
-
- By providing additional command line arguments to train.py, datasets,
- models and predictions can also be logged.
-
- For more on how this logger is used, see the Weights & Biases documentation:
- https://docs.wandb.com/guides/integrations/yolov5
- """
-
- def __init__(self, opt, run_id=None, job_type='Training'):
- """
- - Initialize WandbLogger instance
- - Upload dataset if opt.upload_dataset is True
- - Setup training processes if job_type is 'Training'
-
- arguments:
- opt (namespace) -- Commandline arguments for this run
- run_id (str) -- Run ID of W&B run to be resumed
- job_type (str) -- To set the job_type for this run
-
- """
- # Pre-training routine --
- self.job_type = job_type
- self.wandb, self.wandb_run = wandb, wandb.run if wandb else None
- self.val_artifact, self.train_artifact = None, None
- self.train_artifact_path, self.val_artifact_path = None, None
- self.result_artifact = None
- self.val_table, self.result_table = None, None
- self.max_imgs_to_log = 16
- self.data_dict = None
- if self.wandb:
- self.wandb_run = wandb.init(config=opt,
- resume='allow',
- project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
- entity=opt.entity,
- name=opt.name if opt.name != 'exp' else None,
- job_type=job_type,
- id=run_id,
- allow_val_change=True) if not wandb.run else wandb.run
-
- if self.wandb_run:
- if self.job_type == 'Training':
- if isinstance(opt.data, dict):
- # This means another dataset manager has already processed the dataset info (e.g. ClearML)
- # and they will have stored the already processed dict in opt.data
- self.data_dict = opt.data
- self.setup_training(opt)
-
- def setup_training(self, opt):
- """
- Setup the necessary processes for training YOLO models:
- - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
- - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
- - Setup log_dict, initialize bbox_interval
-
- arguments:
- opt (namespace) -- commandline arguments for this run
-
- """
- self.log_dict, self.current_epoch = {}, 0
- self.bbox_interval = opt.bbox_interval
- if isinstance(opt.resume, str):
- model_dir, _ = self.download_model_artifact(opt)
- if model_dir:
- self.weights = Path(model_dir) / 'last.pt'
- config = self.wandb_run.config
- opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str(
- self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \
- config.hyp, config.imgsz
-
- if opt.bbox_interval == -1:
- self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
- if opt.evolve or opt.noplots:
- self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval
-
- def log_model(self, path, opt, epoch, fitness_score, best_model=False):
- """
- Log the model checkpoint as W&B artifact
-
- arguments:
- path (Path) -- Path of directory containing the checkpoints
- opt (namespace) -- Command line arguments for this run
- epoch (int) -- Current epoch number
- fitness_score (float) -- fitness score for current epoch
- best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
- """
- model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model',
- type='model',
- metadata={
- 'original_url': str(path),
- 'epochs_trained': epoch + 1,
- 'save period': opt.save_period,
- 'project': opt.project,
- 'total_epochs': opt.epochs,
- 'fitness_score': fitness_score})
- model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
- wandb.log_artifact(model_artifact,
- aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
- LOGGER.info(f'Saving model artifact on epoch {epoch + 1}')
-
- def val_one_image(self, pred, predn, path, names, im):
- pass
-
- def log(self, log_dict):
- """
- save the metrics to the logging dictionary
-
- arguments:
- log_dict (Dict) -- metrics/media to be logged in current step
- """
- if self.wandb_run:
- for key, value in log_dict.items():
- self.log_dict[key] = value
-
- def end_epoch(self):
- """
- commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
-
- arguments:
- best_result (boolean): Boolean representing if the result of this evaluation is best or not
- """
- if self.wandb_run:
- with all_logging_disabled():
- try:
- wandb.log(self.log_dict)
- except BaseException as e:
- LOGGER.info(
- f'An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}'
- )
- self.wandb_run.finish()
- self.wandb_run = None
- self.log_dict = {}
-
- def finish_run(self):
- """
- Log metrics if any and finish the current W&B run
- """
- if self.wandb_run:
- if self.log_dict:
- with all_logging_disabled():
- wandb.log(self.log_dict)
- wandb.run.finish()
- LOGGER.warning(DEPRECATION_WARNING)
-
-
-@contextmanager
-def all_logging_disabled(highest_level=logging.CRITICAL):
- """ source - https://gist.github.com/simon-weber/7853144
- A context manager that will prevent any logging messages triggered during the body from being processed.
- :param highest_level: the maximum logging level in use.
- This would only need to be changed if a custom level greater than CRITICAL is defined.
- """
- previous_level = logging.root.manager.disable
- logging.disable(highest_level)
- try:
- yield
- finally:
- logging.disable(previous_level)
diff --git a/spaces/YanzBotz/YanzBotz-Models/app-full.py b/spaces/YanzBotz/YanzBotz-Models/app-full.py
deleted file mode 100644
index b1c03a17f034da7e81513a3e7bff88441cf51bf2..0000000000000000000000000000000000000000
--- a/spaces/YanzBotz/YanzBotz-Models/app-full.py
+++ /dev/null
@@ -1,503 +0,0 @@
-import os
-import glob
-import json
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-import yt_dlp
-import ffmpeg
-import subprocess
-import sys
-import io
-import wave
-from datetime import datetime
-from fairseq import checkpoint_utils
-from lib.infer_pack.models import (
- SynthesizerTrnMs256NSFsid,
- SynthesizerTrnMs256NSFsid_nono,
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono,
-)
-from vc_infer_pipeline import VC
-from config import Config
-config = Config()
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"
-
-audio_mode = []
-f0method_mode = []
-f0method_info = ""
-
-if limitation is True:
- audio_mode = ["Upload audio", "TTS Audio"]
- f0method_mode = ["pm", "harvest"]
- f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better). (Default: PM)"
-else:
- audio_mode = ["Input path", "Upload audio", "Youtube", "TTS Audio"]
- f0method_mode = ["pm", "harvest", "crepe"]
- f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better), and Crepe effect is good but requires GPU (Default: PM)"
-
-if os.path.isfile("rmvpe.pt"):
- f0method_mode.insert(2, "rmvpe")
-
-def create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, file_index):
- def vc_fn(
- vc_audio_mode,
- vc_input,
- vc_upload,
- tts_text,
- tts_voice,
- f0_up_key,
- f0_method,
- index_rate,
- filter_radius,
- resample_sr,
- rms_mix_rate,
- protect,
- ):
- try:
- print(f"Converting using {model_name}...")
- if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
- audio, sr = librosa.load(vc_input, sr=16000, mono=True)
- elif vc_audio_mode == "Upload audio":
- if vc_upload is None:
- return "You need to upload an audio", None
- sampling_rate, audio = vc_upload
- duration = audio.shape[0] / sampling_rate
- if duration > 20 and limitation:
- return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- elif vc_audio_mode == "TTS Audio":
- if len(tts_text) > 100 and limitation:
- return "Text is too long", None
- if tts_text is None or tts_voice is None:
- return "You need to enter text and select a voice", None
- asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
- audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
- vc_input = "tts.mp3"
- times = [0, 0, 0]
- f0_up_key = int(f0_up_key)
- audio_opt = vc.pipeline(
- hubert_model,
- net_g,
- 0,
- audio,
- vc_input,
- times,
- f0_up_key,
- f0_method,
- file_index,
- # file_big_npy,
- index_rate,
- if_f0,
- filter_radius,
- tgt_sr,
- resample_sr,
- rms_mix_rate,
- version,
- protect,
- f0_file=None,
- )
- info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
- print(f"{model_name} | {info}")
- return info, (tgt_sr, audio_opt)
- except:
- info = traceback.format_exc()
- print(info)
- return info, None
- return vc_fn
-
-def load_model():
- models = []
- with open(f"weights/model_info.json", "r", encoding="utf-8") as f:
- models_info = json.load(f)
- for character_name, info in models_info.items():
- if not info['enable']:
- continue
- model_title = info['title']
- model_name = info['model_path']
- model_author = info.get("author", None)
- model_cover = f"weights/{character_name}/{info['cover']}"
- model_index = f"weights/{character_name}/{info['feature_retrieval_library']}"
- cpt = torch.load(f"weights/{character_name}/{model_name}", map_location="cpu")
- tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- if_f0 = cpt.get("f0", 1)
- version = cpt.get("version", "v1")
- if version == "v1":
- if if_f0 == 1:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
- else:
- net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- model_version = "V1"
- elif version == "v2":
- if if_f0 == 1:
- net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
- model_version = "V2"
- del net_g.enc_q
- print(net_g.load_state_dict(cpt["weight"], strict=False))
- net_g.eval().to(config.device)
- if config.is_half:
- net_g = net_g.half()
- else:
- net_g = net_g.float()
- vc = VC(tgt_sr, config)
- print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
- models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, model_index)))
- return models
-
-def cut_vocal_and_inst(url, audio_provider, split_model):
- if url != "":
- if not os.path.exists("dl_audio"):
- os.mkdir("dl_audio")
- if audio_provider == "Youtube":
- ydl_opts = {
- 'noplaylist': True,
- 'format': 'bestaudio/best',
- 'postprocessors': [{
- 'key': 'FFmpegExtractAudio',
- 'preferredcodec': 'wav',
- }],
- "outtmpl": 'dl_audio/youtube_audio',
- }
- with yt_dlp.YoutubeDL(ydl_opts) as ydl:
- ydl.download([url])
- audio_path = "dl_audio/youtube_audio.wav"
- if split_model == "htdemucs":
- command = f"demucs --two-stems=vocals {audio_path} -o output"
- result = subprocess.run(command.split(), stdout=subprocess.PIPE)
- print(result.stdout.decode())
- return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
- else:
- command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
- result = subprocess.run(command.split(), stdout=subprocess.PIPE)
- print(result.stdout.decode())
- return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
- else:
- raise gr.Error("URL Required!")
- return None, None, None, None
-
-def combine_vocal_and_inst(audio_data, audio_volume, split_model):
- if not os.path.exists("output/result"):
- os.mkdir("output/result")
- vocal_path = "output/result/output.wav"
- output_path = "output/result/combine.mp3"
- if split_model == "htdemucs":
- inst_path = "output/htdemucs/youtube_audio/no_vocals.wav"
- else:
- inst_path = "output/mdx_extra_q/youtube_audio/no_vocals.wav"
- with wave.open(vocal_path, "w") as wave_file:
- wave_file.setnchannels(1)
- wave_file.setsampwidth(2)
- wave_file.setframerate(audio_data[0])
- wave_file.writeframes(audio_data[1].tobytes())
- command = f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}'
- result = subprocess.run(command.split(), stdout=subprocess.PIPE)
- print(result.stdout.decode())
- return output_path
-
-def load_hubert():
- global hubert_model
- models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
- ["hubert_base.pt"],
- suffix="",
- )
- hubert_model = models[0]
- hubert_model = hubert_model.to(config.device)
- if config.is_half:
- hubert_model = hubert_model.half()
- else:
- hubert_model = hubert_model.float()
- hubert_model.eval()
-
-def change_audio_mode(vc_audio_mode):
- if vc_audio_mode == "Input path":
- return (
- # Input & Upload
- gr.Textbox.update(visible=True),
- gr.Checkbox.update(visible=False),
- gr.Audio.update(visible=False),
- # Youtube
- gr.Dropdown.update(visible=False),
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False),
- gr.Button.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Slider.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Button.update(visible=False),
- # TTS
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False)
- )
- elif vc_audio_mode == "Upload audio":
- return (
- # Input & Upload
- gr.Textbox.update(visible=False),
- gr.Checkbox.update(visible=True),
- gr.Audio.update(visible=True),
- # Youtube
- gr.Dropdown.update(visible=False),
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False),
- gr.Button.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Slider.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Button.update(visible=False),
- # TTS
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False)
- )
- elif vc_audio_mode == "Youtube":
- return (
- # Input & Upload
- gr.Textbox.update(visible=False),
- gr.Checkbox.update(visible=False),
- gr.Audio.update(visible=False),
- # Youtube
- gr.Dropdown.update(visible=True),
- gr.Textbox.update(visible=True),
- gr.Dropdown.update(visible=True),
- gr.Button.update(visible=True),
- gr.Audio.update(visible=True),
- gr.Audio.update(visible=True),
- gr.Audio.update(visible=True),
- gr.Slider.update(visible=True),
- gr.Audio.update(visible=True),
- gr.Button.update(visible=True),
- # TTS
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False)
- )
- elif vc_audio_mode == "TTS Audio":
- return (
- # Input & Upload
- gr.Textbox.update(visible=False),
- gr.Checkbox.update(visible=False),
- gr.Audio.update(visible=False),
- # Youtube
- gr.Dropdown.update(visible=False),
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False),
- gr.Button.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Slider.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Button.update(visible=False),
- # TTS
- gr.Textbox.update(visible=True),
- gr.Dropdown.update(visible=True)
- )
- else:
- return (
- # Input & Upload
- gr.Textbox.update(visible=False),
- gr.Checkbox.update(visible=True),
- gr.Audio.update(visible=True),
- # Youtube
- gr.Dropdown.update(visible=False),
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False),
- gr.Button.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Slider.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Button.update(visible=False),
- # TTS
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False)
- )
-
-def use_microphone(microphone):
- if microphone == True:
- return gr.Audio.update(source="microphone")
- else:
- return gr.Audio.update(source="upload")
-
-if __name__ == '__main__':
- load_hubert()
- models = load_model()
- tts_voice_list = asyncio.new_event_loop().run_until_complete(edge_tts.list_voices())
- voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
- with gr.Blocks() as app:
- gr.Markdown(
- "#
Combined Genshin Impact RVC Models\n"
- "##
The input audio should be clean and pure voice without background music.\n"
- "###
It is recommended to use google colab for more features. \n"
- "[](https://colab.research.google.com/drive/1Tgr6q9kKiB5P37rUitrB3CsNl8JP9iQZ?usp=sharing)\n\n"
- "[](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
- )
- with gr.Tabs():
- for (name, title, author, cover, model_version, vc_fn) in models:
- with gr.TabItem(name):
- with gr.Row():
- gr.Markdown(
- '
'
- f'
{title}
\n'+
- f'
RVC {model_version} Model
\n'+
- (f'
Model author: {author}
' if author else "")+
- (f'' if cover else "")+
- '
'
- )
- with gr.Row():
- with gr.Column():
- vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
- # Input
- vc_input = gr.Textbox(label="Input audio path", visible=False)
- # Upload
- vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True)
- vc_upload = gr.Audio(label="Upload audio file", source="upload", visible=True, interactive=True)
- # Youtube
- vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
- vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
- vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
- vc_split = gr.Button("Split Audio", variant="primary", visible=False)
- vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
- vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
- vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
- # TTS
- tts_text = gr.Textbox(visible=False, label="TTS text", info="Text to speech input")
- tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
- with gr.Column():
- vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
- f0method0 = gr.Radio(
- label="Pitch extraction algorithm",
- info=f0method_info,
- choices=f0method_mode,
- value="pm",
- interactive=True
- )
- index_rate1 = gr.Slider(
- minimum=0,
- maximum=1,
- label="Retrieval feature ratio",
- info="(Default: 0.7)",
- value=0.7,
- interactive=True,
- )
- filter_radius0 = gr.Slider(
- minimum=0,
- maximum=7,
- label="Apply Median Filtering",
- info="The value represents the filter radius and can reduce breathiness.",
- value=3,
- step=1,
- interactive=True,
- )
- resample_sr0 = gr.Slider(
- minimum=0,
- maximum=48000,
- label="Resample the output audio",
- info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
- value=0,
- step=1,
- interactive=True,
- )
- rms_mix_rate0 = gr.Slider(
- minimum=0,
- maximum=1,
- label="Volume Envelope",
- info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
- value=1,
- interactive=True,
- )
- protect0 = gr.Slider(
- minimum=0,
- maximum=0.5,
- label="Voice Protection",
- info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
- value=0.5,
- step=0.01,
- interactive=True,
- )
- with gr.Column():
- vc_log = gr.Textbox(label="Output Information", interactive=False)
- vc_output = gr.Audio(label="Output Audio", interactive=False)
- vc_convert = gr.Button("Convert", variant="primary")
- vc_volume = gr.Slider(
- minimum=0,
- maximum=10,
- label="Vocal volume",
- value=4,
- interactive=True,
- step=1,
- info="Adjust vocal volume (Default: 4}",
- visible=False
- )
- vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
- vc_combine = gr.Button("Combine",variant="primary", visible=False)
- vc_convert.click(
- fn=vc_fn,
- inputs=[
- vc_audio_mode,
- vc_input,
- vc_upload,
- tts_text,
- tts_voice,
- vc_transform0,
- f0method0,
- index_rate1,
- filter_radius0,
- resample_sr0,
- rms_mix_rate0,
- protect0,
- ],
- outputs=[vc_log ,vc_output]
- )
- vc_split.click(
- fn=cut_vocal_and_inst,
- inputs=[vc_link, vc_download_audio, vc_split_model],
- outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
- )
- vc_combine.click(
- fn=combine_vocal_and_inst,
- inputs=[vc_output, vc_volume, vc_split_model],
- outputs=[vc_combined_output]
- )
- vc_microphone_mode.change(
- fn=use_microphone,
- inputs=vc_microphone_mode,
- outputs=vc_upload
- )
- vc_audio_mode.change(
- fn=change_audio_mode,
- inputs=[vc_audio_mode],
- outputs=[
- vc_input,
- vc_microphone_mode,
- vc_upload,
- vc_download_audio,
- vc_link,
- vc_split_model,
- vc_split,
- vc_vocal_preview,
- vc_inst_preview,
- vc_audio_preview,
- vc_volume,
- vc_combined_output,
- vc_combine,
- tts_text,
- tts_voice
- ]
- )
- app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)
\ No newline at end of file
diff --git a/spaces/YazawaSunrise/so-vits-svc-LoveLive/preprocess_hubert_f0.py b/spaces/YazawaSunrise/so-vits-svc-LoveLive/preprocess_hubert_f0.py
deleted file mode 100644
index 4fe7f21541acb01537797f430d53b3c0e63279e1..0000000000000000000000000000000000000000
--- a/spaces/YazawaSunrise/so-vits-svc-LoveLive/preprocess_hubert_f0.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import os
-import argparse
-
-import torch
-import json
-from glob import glob
-
-from pyworld import pyworld
-from tqdm import tqdm
-from scipy.io import wavfile
-
-import utils
-from mel_processing import mel_spectrogram_torch
-#import h5py
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-
-import parselmouth
-import librosa
-import numpy as np
-
-
-def get_f0(path,p_len=None, f0_up_key=0):
- x, _ = librosa.load(path, 32000)
- if p_len is None:
- p_len = x.shape[0]//320
- else:
- assert abs(p_len-x.shape[0]//320) < 3, (path, p_len, x.shape)
- time_step = 320 / 32000 * 1000
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
- f0 = parselmouth.Sound(x, 32000).to_pitch_ac(
- time_step=time_step / 1000, voicing_threshold=0.6,
- pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
-
- pad_size=(p_len - len(f0) + 1) // 2
- if(pad_size>0 or p_len - len(f0) - pad_size>0):
- f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
-
- f0bak = f0.copy()
- f0 *= pow(2, f0_up_key / 12)
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak
-
-def resize2d(x, target_len):
- source = np.array(x)
- source[source<0.001] = np.nan
- target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
- res = np.nan_to_num(target)
- return res
-
-def compute_f0(path, c_len):
- x, sr = librosa.load(path, sr=32000)
- f0, t = pyworld.dio(
- x.astype(np.double),
- fs=sr,
- f0_ceil=800,
- frame_period=1000 * 320 / sr,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, 32000)
- for index, pitch in enumerate(f0):
- f0[index] = round(pitch, 1)
- assert abs(c_len - x.shape[0]//320) < 3, (c_len, f0.shape)
-
- return None, resize2d(f0, c_len)
-
-
-def process(filename):
- print(filename)
- save_name = filename+".soft.pt"
- if not os.path.exists(save_name):
- devive = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- wav, _ = librosa.load(filename, sr=16000)
- wav = torch.from_numpy(wav).unsqueeze(0).to(devive)
- c = utils.get_hubert_content(hmodel, wav)
- torch.save(c.cpu(), save_name)
- else:
- c = torch.load(save_name)
- f0path = filename+".f0.npy"
- if not os.path.exists(f0path):
- cf0, f0 = compute_f0(filename, c.shape[-1] * 2)
- np.save(f0path, f0)
-
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--in_dir", type=str, default="dataset/32k", help="path to input dir")
- args = parser.parse_args()
-
- print("Loading hubert for content...")
- hmodel = utils.get_hubert_model(0 if torch.cuda.is_available() else None)
- print("Loaded hubert.")
-
- filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True)#[:10]
-
- for filename in tqdm(filenames):
- process(filename)
-
\ No newline at end of file
diff --git a/spaces/YouLiXiya/Mobile-SAM/GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py b/spaces/YouLiXiya/Mobile-SAM/GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py
deleted file mode 100644
index 9158d5f6260ec74bded95377d382387430d7cd70..0000000000000000000000000000000000000000
--- a/spaces/YouLiXiya/Mobile-SAM/GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py
+++ /dev/null
@@ -1,43 +0,0 @@
-batch_size = 1
-modelname = "groundingdino"
-backbone = "swin_T_224_1k"
-position_embedding = "sine"
-pe_temperatureH = 20
-pe_temperatureW = 20
-return_interm_indices = [1, 2, 3]
-backbone_freeze_keywords = None
-enc_layers = 6
-dec_layers = 6
-pre_norm = False
-dim_feedforward = 2048
-hidden_dim = 256
-dropout = 0.0
-nheads = 8
-num_queries = 900
-query_dim = 4
-num_patterns = 0
-num_feature_levels = 4
-enc_n_points = 4
-dec_n_points = 4
-two_stage_type = "standard"
-two_stage_bbox_embed_share = False
-two_stage_class_embed_share = False
-transformer_activation = "relu"
-dec_pred_bbox_embed_share = True
-dn_box_noise_scale = 1.0
-dn_label_noise_ratio = 0.5
-dn_label_coef = 1.0
-dn_bbox_coef = 1.0
-embed_init_tgt = True
-dn_labelbook_size = 2000
-max_text_len = 256
-text_encoder_type = "bert-base-uncased"
-use_text_enhancer = True
-use_fusion_layer = True
-use_checkpoint = True
-use_transformer_ckpt = True
-use_text_cross_attention = True
-text_dropout = 0.0
-fusion_dropout = 0.0
-fusion_droppath = 0.1
-sub_sentence_present = True
diff --git a/spaces/Zpwang-AI/InsultingLanguageDetection/display_v3/2023-04-14_17-06-18/epoch3-f1score0.12.ckpt/zero_to_fp32.py b/spaces/Zpwang-AI/InsultingLanguageDetection/display_v3/2023-04-14_17-06-18/epoch3-f1score0.12.ckpt/zero_to_fp32.py
deleted file mode 100644
index f00e256bb7879727ab1d785173f4aac6967876da..0000000000000000000000000000000000000000
--- a/spaces/Zpwang-AI/InsultingLanguageDetection/display_v3/2023-04-14_17-06-18/epoch3-f1score0.12.ckpt/zero_to_fp32.py
+++ /dev/null
@@ -1,483 +0,0 @@
-#!/usr/bin/env python
-'''Copyright The Microsoft DeepSpeed Team'''
-
-# This script extracts fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints. It gets
-# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
-# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
-# application.
-#
-# example: python zero_to_fp32.py . pytorch_model.bin
-
-import argparse
-import torch
-import glob
-import math
-import os
-import re
-from collections import OrderedDict
-
-# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
-# DeepSpeed data structures it has to be available in the current python environment.
-from deepspeed.utils import logger
-from deepspeed.checkpoint.constants import (DS_VERSION,
- OPTIMIZER_STATE_DICT,
- SINGLE_PARTITION_OF_FP32_GROUPS,
- FP32_FLAT_GROUPS,
- ZERO_STAGE,
- PARTITION_COUNT,
- PARAM_SHAPES,
- BUFFER_NAMES)
-
-debug = 0
-
-# load to cpu
-device = torch.device('cpu')
-
-
-def atoi(text):
- return int(text) if text.isdigit() else text
-
-
-def natural_keys(text):
- '''
- alist.sort(key=natural_keys) sorts in human order
- http://nedbatchelder.com/blog/200712/human_sorting.html
- (See Toothy's implementation in the comments)
- '''
- return [atoi(c) for c in re.split(r'(\d+)', text)]
-
-
-def get_model_state_file(checkpoint_dir, zero_stage):
- if not os.path.isdir(checkpoint_dir):
- raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
-
- # there should be only one file
- if zero_stage == 2:
- file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
- elif zero_stage == 3:
- file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
-
- if not os.path.exists(file):
- raise FileNotFoundError(f"can't find model states file at '{file}'")
-
- return file
-
-
-def get_optim_files(checkpoint_dir):
- # XXX: need to test that this simple glob rule works for multi-node setup too
- optim_files = sorted(glob.glob(os.path.join(checkpoint_dir,
- "*_optim_states.pt")),
- key=natural_keys)
-
- if len(optim_files) == 0:
- raise FileNotFoundError(
- f"can't find '*_optim_states.pt' files in directory '{checkpoint_dir}'")
-
- return optim_files
-
-
-def parse_model_state(file):
- state_dict = torch.load(file, map_location=device)
-
- if BUFFER_NAMES not in state_dict:
- raise ValueError(f"{file} is not a model state checkpoint")
- buffer_names = state_dict[BUFFER_NAMES]
- if debug:
- print("Found buffers:", buffer_names)
-
- # recover just the buffers while restoring them to fp32 if they were saved in fp16
- buffers = {
- k: v.float()
- for k,
- v in state_dict["module"].items() if k in buffer_names
- }
- param_shapes = state_dict[PARAM_SHAPES]
-
- ds_version = state_dict.get(DS_VERSION, None)
-
- return buffers, param_shapes, ds_version
-
-
-def parse_optim_states(files, ds_checkpoint_dir):
-
- total_files = len(files)
- state_dicts = []
- for f in files:
- state_dicts.append(torch.load(f, map_location=device))
-
- if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
- raise ValueError(f"{files[0]} is not a zero checkpoint")
- zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
- world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
-
- # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
- # parameters can be different from data parallelism for non-expert parameters. So we can just
- # use the max of the partition_count to get the dp world_size.
-
- if type(world_size) is list:
- world_size = max(world_size)
-
- if world_size != total_files:
- raise ValueError(
- f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
- "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
- )
-
- # the groups are named differently in each stage
- if zero_stage == 2:
- fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
- elif zero_stage == 3:
- fp32_groups_key = FP32_FLAT_GROUPS
- else:
- raise ValueError(f"unknown zero stage {zero_stage}")
-
- if zero_stage == 2:
- fp32_flat_groups = [
- state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key]
- for i in range(len(state_dicts))
- ]
- elif zero_stage == 3:
- # if there is more than one param group, there will be multiple flattened tensors - one
- # flattened tensor per group - for simplicity merge them into a single tensor
- #
- # XXX: could make the script more memory efficient for when there are multiple groups - it
- # will require matching the sub-lists of param_shapes for each param group flattened tensor
-
- fp32_flat_groups = [
- torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key],
- 0) for i in range(len(state_dicts))
- ]
-
- return zero_stage, world_size, fp32_flat_groups
-
-
-def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
- """
- Returns fp32 state_dict reconstructed from ds checkpoint
-
- Args:
- - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
-
- """
- print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
-
- optim_files = get_optim_files(ds_checkpoint_dir)
- zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
- print(
- f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
-
- model_file = get_model_state_file(ds_checkpoint_dir, zero_stage)
- buffers, param_shapes, ds_version = parse_model_state(model_file)
- print(f'Parsing checkpoint created by deepspeed=={ds_version}')
-
- if zero_stage == 2:
- return _get_fp32_state_dict_from_zero2_checkpoint(world_size,
- param_shapes,
- fp32_flat_groups,
- buffers)
- elif zero_stage == 3:
- return _get_fp32_state_dict_from_zero3_checkpoint(world_size,
- param_shapes,
- fp32_flat_groups,
- buffers)
-
-
-def _get_fp32_state_dict_from_zero2_checkpoint(world_size,
- param_shapes,
- fp32_flat_groups,
- buffers):
-
- # Reconstruction protocol:
- #
- # XXX: document this
-
- if debug:
- for i in range(world_size):
- for j in range(len(fp32_flat_groups[0])):
- print(
- f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
-
- # XXX: memory usage doubles here (zero2)
- num_param_groups = len(fp32_flat_groups[0])
- merged_single_partition_of_fp32_groups = []
- for i in range(num_param_groups):
- merged_partitions = [sd[i] for sd in fp32_flat_groups]
- full_single_fp32_vector = torch.cat(merged_partitions, 0)
- merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
- avail_numel = sum([
- full_single_fp32_vector.numel()
- for full_single_fp32_vector in merged_single_partition_of_fp32_groups
- ])
-
- if debug:
- wanted_params = sum([len(shapes) for shapes in param_shapes])
- wanted_numel = sum(
- [sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
- # not asserting if there is a mismatch due to possible padding
- print(f"Have {avail_numel} numels to process.")
- print(f"Need {wanted_numel} numels in {wanted_params} params.")
-
- state_dict = OrderedDict()
-
- # buffers
- state_dict.update(buffers)
- if debug:
- print(f"added {len(buffers)} buffers")
-
- # params
- # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
- # out-of-core computing solution
- total_numel = 0
- total_params = 0
- for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
- offset = 0
- avail_numel = full_single_fp32_vector.numel()
- for name, shape in shapes.items():
-
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
- total_params += 1
-
- if debug:
- print(
- f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} "
- )
- state_dict[name] = full_single_fp32_vector.narrow(
- 0,
- offset,
- unpartitioned_numel).view(shape)
- offset += unpartitioned_numel
-
- # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
- # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
- # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
- # live optimizer object, so we are checking that the numbers are within the right range
- align_to = 2 * world_size
-
- def zero2_align(x):
- return align_to * math.ceil(x / align_to)
-
- if debug:
- print(f"original offset={offset}, avail_numel={avail_numel}")
-
- offset = zero2_align(offset)
- avail_numel = zero2_align(avail_numel)
-
- if debug:
- print(f"aligned offset={offset}, avail_numel={avail_numel}")
-
- # Sanity check
- if offset != avail_numel:
- raise ValueError(
- f"consumed {offset} numels out of {avail_numel} - something is wrong")
-
- print(
- f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements"
- )
-
- return state_dict
-
-
-def zero3_partitioned_param_info(unpartitioned_numel, world_size):
- remainder = unpartitioned_numel % world_size
- padding_numel = (world_size - remainder) if remainder else 0
- partitioned_numel = math.ceil(unpartitioned_numel / world_size)
- return partitioned_numel, padding_numel
-
-
-def _get_fp32_state_dict_from_zero3_checkpoint(world_size,
- param_shapes,
- fp32_flat_groups,
- buffers):
-
- # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
- # param, re-consolidating each param, while dealing with padding if any
-
- avail_numel = fp32_flat_groups[0].numel() * world_size
- # merge list of dicts, preserving order
- param_shapes = {k: v for d in param_shapes for k, v in d.items()}
-
- if debug:
- for i in range(world_size):
- print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
-
- wanted_params = len(param_shapes)
- wanted_numel = sum(shape.numel() for shape in param_shapes.values())
- # not asserting if there is a mismatch due to possible padding
- print(f"Have {avail_numel} numels to process.")
- print(f"Need {wanted_numel} numels in {wanted_params} params.")
-
- state_dict = OrderedDict()
-
- # buffers
- state_dict.update(buffers)
- if debug:
- print(f"added {len(buffers)} buffers")
-
- # params
- # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
- # out-of-core computing solution
- offset = 0
- total_numel = 0
- total_params = 0
- for name, shape in param_shapes.items():
-
- unpartitioned_numel = shape.numel()
- total_numel += unpartitioned_numel
- total_params += 1
-
- partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
- if debug:
- print(
- f"{total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
- )
-
- # XXX: memory usage doubles here
- state_dict[name] = torch.cat(
- tuple(fp32_flat_groups[i].narrow(0,
- offset,
- partitioned_numel)
- for i in range(world_size)),
- 0).narrow(0,
- 0,
- unpartitioned_numel).view(shape)
- offset += partitioned_numel
-
- offset *= world_size
-
- # Sanity check
- if offset != avail_numel:
- raise ValueError(
- f"consumed {offset} numels out of {avail_numel} - something is wrong")
-
- print(
- f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements"
- )
-
- return state_dict
-
-
-def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
- """
- Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
- ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
- via a model hub.
-
- Args:
- - ``checkpoint_dir``: path to the desired checkpoint folder
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
-
- Returns:
- - pytorch ``state_dict``
-
- Note: this approach may not work if your application doesn't have sufficient free CPU memory and
- you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
- the checkpoint.
-
- A typical usage might be ::
-
- from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
- # do the training and checkpoint saving
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
- model = model.cpu() # move to cpu
- model.load_state_dict(state_dict)
- # submit to model hub or save the model to share with others
-
- In this example the ``model`` will no longer be usable in the deepspeed context of the same
- application. i.e. you will need to re-initialize the deepspeed engine, since
- ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
- If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
-
- """
- if tag is None:
- latest_path = os.path.join(checkpoint_dir, 'latest')
- if os.path.isfile(latest_path):
- with open(latest_path, 'r') as fd:
- tag = fd.read().strip()
- else:
- raise ValueError(f"Unable to find 'latest' file at {latest_path}")
-
- ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
-
- if not os.path.isdir(ds_checkpoint_dir):
- raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
-
- return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
-
-
-def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
- """
- Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
- loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
-
- Args:
- - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
- - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
- """
-
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
- print(f"Saving fp32 state dict to {output_file}")
- torch.save(state_dict, output_file)
-
-
-def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
- """
- 1. Put the provided model to cpu
- 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
- 3. Load it into the provided model
-
- Args:
- - ``model``: the model object to update
- - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
- - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
-
- Returns:
- - ``model`: modified model
-
- Make sure you have plenty of CPU memory available before you call this function. If you don't
- have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
- conveniently placed for you in the checkpoint folder.
-
- A typical usage might be ::
-
- from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
- model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
- # submit to model hub or save the model to share with others
-
- Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
- of the same application. i.e. you will need to re-initialize the deepspeed engine, since
- ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
- """
- logger.info(f"Extracting fp32 weights")
- state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-
- logger.info(f"Overwriting model with fp32 weights")
- model = model.cpu()
- model.load_state_dict(state_dict, strict=False)
-
- return model
-
-
-if __name__ == "__main__":
-
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "checkpoint_dir",
- type=str,
- help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
- parser.add_argument(
- "output_file",
- type=str,
- help=
- "path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)"
- )
- parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
- args = parser.parse_args()
-
- debug = args.debug
-
- convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file)
diff --git a/spaces/aaboutblankk/digiplay-CamelliaMix_NSFW_diffusers_v1.1/README.md b/spaces/aaboutblankk/digiplay-CamelliaMix_NSFW_diffusers_v1.1/README.md
deleted file mode 100644
index e224f1339d28f52dc1135cf79b9944984ad02ab4..0000000000000000000000000000000000000000
--- a/spaces/aaboutblankk/digiplay-CamelliaMix_NSFW_diffusers_v1.1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Digiplay-CamelliaMix NSFW Diffusers V1.1
-emoji: 📚
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aaronb/DragGAN/stylegan2/op/fused_act.py b/spaces/aaronb/DragGAN/stylegan2/op/fused_act.py
deleted file mode 100644
index bf89097172081631fc9ffa5119646560465756a1..0000000000000000000000000000000000000000
--- a/spaces/aaronb/DragGAN/stylegan2/op/fused_act.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import os
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-import warnings
-
-module_path = os.path.dirname(os.path.abspath(__file__))
-
-try:
- fused = load(
- "fused",
- sources=[
- os.path.join(module_path, "fused_bias_act.cpp"),
- os.path.join(module_path, "fused_bias_act_kernel.cu"),
- ],
- )
-except:
- warnings.warn(
- f"(This is not error) Switch to native implementation"
- )
-
- fused = None
-
-
-class FusedLeakyReLUFunctionBackward(Function):
- @staticmethod
- def forward(ctx, grad_output, out, bias, negative_slope, scale):
- ctx.save_for_backward(out)
- ctx.negative_slope = negative_slope
- ctx.scale = scale
-
- empty = grad_output.new_empty(0)
-
- grad_input = fused.fused_bias_act(
- grad_output.contiguous(), empty, out, 3, 1, negative_slope, scale
- )
-
- dim = [0]
-
- if grad_input.ndim > 2:
- dim += list(range(2, grad_input.ndim))
-
- if bias:
- grad_bias = grad_input.sum(dim).detach()
-
- else:
- grad_bias = empty
-
- return grad_input, grad_bias
-
- @staticmethod
- def backward(ctx, gradgrad_input, gradgrad_bias):
- out, = ctx.saved_tensors
- gradgrad_out = fused.fused_bias_act(
- gradgrad_input.contiguous(),
- gradgrad_bias,
- out,
- 3,
- 1,
- ctx.negative_slope,
- ctx.scale,
- )
-
- return gradgrad_out, None, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
- @staticmethod
- def forward(ctx, input, bias, negative_slope, scale):
- empty = input.new_empty(0)
-
- ctx.bias = bias is not None
-
- if bias is None:
- bias = empty
-
- out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
- ctx.save_for_backward(out)
- ctx.negative_slope = negative_slope
- ctx.scale = scale
-
- return out
-
- @staticmethod
- def backward(ctx, grad_output):
- out, = ctx.saved_tensors
-
- grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
- grad_output, out, ctx.bias, ctx.negative_slope, ctx.scale
- )
-
- if not ctx.bias:
- grad_bias = None
-
- return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
- def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5):
- super().__init__()
-
- if bias:
- self.bias = nn.Parameter(torch.zeros(channel))
-
- else:
- self.bias = None
-
- self.negative_slope = negative_slope
- self.scale = scale
-
- def forward(self, input):
- return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
- if input.device.type == "cpu":
- if bias is not None:
- rest_dim = [1] * (input.ndim - bias.ndim - 1)
- return (
- F.leaky_relu(
- input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
- )
- * scale
- )
-
- else:
- return F.leaky_relu(input, negative_slope=0.2) * scale
-
- else:
- return FusedLeakyReLUFunction.apply(
- input.contiguous(), bias, negative_slope, scale
- )
-
-
-class FusedLeakyReLU_Native(nn.Module):
- def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5):
- super().__init__()
-
- if bias:
- self.bias = nn.Parameter(torch.zeros(channel))
-
- else:
- self.bias = None
-
- self.negative_slope = negative_slope
- self.scale = scale
-
- def forward(self, input):
- return fused_leaky_relu_native(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu_native(input, bias, negative_slope=0.2, scale=2 ** 0.5):
- return scale * F.leaky_relu(input + bias.view((1, -1) + (1,) * (len(input.shape) - 2)), negative_slope=negative_slope)
diff --git a/spaces/abhilashb/NLP-Test/app.py b/spaces/abhilashb/NLP-Test/app.py
deleted file mode 100644
index ebe8500e5f871271c2b9f7d0b570936b33dbc530..0000000000000000000000000000000000000000
--- a/spaces/abhilashb/NLP-Test/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-title = 'NLP Context QA with Transformers and Roberta Base Squad2'
-
-question1T = "What pressures do teens face?"
-question2T = "What do teens deal with?"
-question3T = "What persistent fears might teens face?"
-
-question1A = "What do half of American adults suffer from?"
-question2A = "What cognitive issues do adults face after COVID?"
-question3A = "What anxiety and changes are faced by adults?"
-
-question1E = "What problems do elderly have due to medical issues?"
-question2E = "What helps mental health for elderly?"
-question3E = "How many older adultsexperience mental disorders?"
-
-context1 = "Pressures teens face: Youth mental health expert have raised concerns about the extreme pressures on children and teens throughout the COVID-19 pandemic. Lingering effects of school closures and COVID-related stressors are key factors in teen stress. Many young people are also dealing with overwhelming pressure to achieve good grades in school or gain admission to elite colleges and universities. The need to be superstars in sports, the performing arts or other extracurricular activities. Tough schedules that don't allow enough time for rest, relaxation and unstructured fun. They deal with Bullying whether in person, via social media or both. They face persistent fears about climate change, global conflict and other weighty issues. They may face discrimination based on race, gender, sexual orientation, weight, religion, disability or other factors. Teens also face problems related to a poverty or lack of money for safe, stable housing and enough nutritious food."
-context2 = "Pressures adults face: Nearly half of Americans surveyed reported recent symptoms of an anxiety or depressive disorder, and 10% feel their mental health needs are not being met. Rates of anxiety, depression, and substance use disorder have increased since the beginning of the pandemic. People who have mental illnesses or disorders and then get COVID-19 are more likely to die than those who don’t have mental illnesses or disorders. Adults face a number of symptoms related to brain and mental health including cognitive and attention deficits like brain fog, anxiety and depression, seizures, and suicidal behavior. Stressors caused by the COVID-19 pandemic is not yet fully understood but include changes to daily routines, virtual office and schooling, mask wearing, caregiver absence, loss and grief, and financial instability. People more likely to experience difficulties include people from racial and ethnic minority groups, mothers and pregnant women, people with finanical or housing insecurity, children, people with disabilities, people with pre-existing mental illnesses or substance use problems and health care workers."
-context3 = "Pressures facing elderly: Anxiety and depression have increased for older adults since the start of the pandemic. Elders cope with uncertainty better than younger generations, however depression and anxiety have negative impacts on quality of life, function and general health. Due to medical vulnerability elders face isolation with sacrifices and pain to endure including loneliness. At least one in four older adults experience mental disorders such as depression, anxiety and dementia. Number of seniors is expected to double by 2030. Isolation, affective and anxiety disorders, dementia, and psychosis are common as well as sleep disorders. Behavioral disorders, cognitive deterioration or confusion states as a result of physical disorders and surgical interventions occur for elderly. Health care providers including those in primary care can play a key role in promoting mental health by working with mental health professionals, local governments, civil society organizations, families and communities to provide comprehensive mental health care and supportive environments. Elderly should be encouraged to participate in communities and society while policy makers should ensure health concerns are addressed in national health planning and policies."
-
-# Model (autotrain compatible) https://huggingface.co/deepset/roberta-base-squad2/tree/main
-# Model Card: https://huggingface.co/deepset/roberta-base-squad2
-model_name = "deepset/roberta-base-squad2"
-question_answerer = pipeline("question-answering", model=model_name, tokenizer=model_name)
-
-interface = gr.Interface.from_pipeline(question_answerer,
- title = title,
- theme = "peach",
- examples = [
- [context1, question1T],[context1, question2T],[context1, question3T],
- [context2, question1A],[context2, question2A],[context2, question3A],
- [context3, question1E],[context3, question2E],[context3, question3E]
- ]).launch()
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/bbox_heads/double_bbox_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/bbox_heads/double_bbox_head.py
deleted file mode 100644
index 6c154cb3c0d9d7639c3d4a2a1272406d3fab8acd..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/bbox_heads/double_bbox_head.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, normal_init, xavier_init
-
-from mmdet.models.backbones.resnet import Bottleneck
-from mmdet.models.builder import HEADS
-from .bbox_head import BBoxHead
-
-
-class BasicResBlock(nn.Module):
- """Basic residual block.
-
- This block is a little different from the block in the ResNet backbone.
- The kernel size of conv1 is 1 in this block while 3 in ResNet BasicBlock.
-
- Args:
- in_channels (int): Channels of the input feature map.
- out_channels (int): Channels of the output feature map.
- conv_cfg (dict): The config dict for convolution layers.
- norm_cfg (dict): The config dict for normalization layers.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- conv_cfg=None,
- norm_cfg=dict(type='BN')):
- super(BasicResBlock, self).__init__()
-
- # main path
- self.conv1 = ConvModule(
- in_channels,
- in_channels,
- kernel_size=3,
- padding=1,
- bias=False,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg)
- self.conv2 = ConvModule(
- in_channels,
- out_channels,
- kernel_size=1,
- bias=False,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=None)
-
- # identity path
- self.conv_identity = ConvModule(
- in_channels,
- out_channels,
- kernel_size=1,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=None)
-
- self.relu = nn.ReLU(inplace=True)
-
- def forward(self, x):
- identity = x
-
- x = self.conv1(x)
- x = self.conv2(x)
-
- identity = self.conv_identity(identity)
- out = x + identity
-
- out = self.relu(out)
- return out
-
-
-@HEADS.register_module()
-class DoubleConvFCBBoxHead(BBoxHead):
- r"""Bbox head used in Double-Head R-CNN
-
- .. code-block:: none
-
- /-> cls
- /-> shared convs ->
- \-> reg
- roi features
- /-> cls
- \-> shared fc ->
- \-> reg
- """ # noqa: W605
-
- def __init__(self,
- num_convs=0,
- num_fcs=0,
- conv_out_channels=1024,
- fc_out_channels=1024,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- **kwargs):
- kwargs.setdefault('with_avg_pool', True)
- super(DoubleConvFCBBoxHead, self).__init__(**kwargs)
- assert self.with_avg_pool
- assert num_convs > 0
- assert num_fcs > 0
- self.num_convs = num_convs
- self.num_fcs = num_fcs
- self.conv_out_channels = conv_out_channels
- self.fc_out_channels = fc_out_channels
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
-
- # increase the channel of input features
- self.res_block = BasicResBlock(self.in_channels,
- self.conv_out_channels)
-
- # add conv heads
- self.conv_branch = self._add_conv_branch()
- # add fc heads
- self.fc_branch = self._add_fc_branch()
-
- out_dim_reg = 4 if self.reg_class_agnostic else 4 * self.num_classes
- self.fc_reg = nn.Linear(self.conv_out_channels, out_dim_reg)
-
- self.fc_cls = nn.Linear(self.fc_out_channels, self.num_classes + 1)
- self.relu = nn.ReLU(inplace=True)
-
- def _add_conv_branch(self):
- """Add the fc branch which consists of a sequential of conv layers."""
- branch_convs = nn.ModuleList()
- for i in range(self.num_convs):
- branch_convs.append(
- Bottleneck(
- inplanes=self.conv_out_channels,
- planes=self.conv_out_channels // 4,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg))
- return branch_convs
-
- def _add_fc_branch(self):
- """Add the fc branch which consists of a sequential of fc layers."""
- branch_fcs = nn.ModuleList()
- for i in range(self.num_fcs):
- fc_in_channels = (
- self.in_channels *
- self.roi_feat_area if i == 0 else self.fc_out_channels)
- branch_fcs.append(nn.Linear(fc_in_channels, self.fc_out_channels))
- return branch_fcs
-
- def init_weights(self):
- # conv layers are already initialized by ConvModule
- normal_init(self.fc_cls, std=0.01)
- normal_init(self.fc_reg, std=0.001)
-
- for m in self.fc_branch.modules():
- if isinstance(m, nn.Linear):
- xavier_init(m, distribution='uniform')
-
- def forward(self, x_cls, x_reg):
- # conv head
- x_conv = self.res_block(x_reg)
-
- for conv in self.conv_branch:
- x_conv = conv(x_conv)
-
- if self.with_avg_pool:
- x_conv = self.avg_pool(x_conv)
-
- x_conv = x_conv.view(x_conv.size(0), -1)
- bbox_pred = self.fc_reg(x_conv)
-
- # fc head
- x_fc = x_cls.view(x_cls.size(0), -1)
- for fc in self.fc_branch:
- x_fc = self.relu(fc(x_fc))
-
- cls_score = self.fc_cls(x_fc)
-
- return cls_score, bbox_pred
diff --git a/spaces/aditi2222/updated_t5/app.py b/spaces/aditi2222/updated_t5/app.py
deleted file mode 100644
index ef14c2443c74cd2c428922f7cc91a4b7f38e307d..0000000000000000000000000000000000000000
--- a/spaces/aditi2222/updated_t5/app.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import torch
-from transformers import (T5ForConditionalGeneration,T5Tokenizer)
-import gradio as gr
-
-best_model_path = "aditi2222/t5_paraphrase_updated"
-model = T5ForConditionalGeneration.from_pretrained(best_model_path)
-tokenizer = T5Tokenizer.from_pretrained("aditi2222/t5_paraphrase_updated")
-
-def tokenize_data(text):
- # Tokenize the review body
- input_ = str(text) + ' '
- max_len = 64
- # tokenize inputs
- tokenized_inputs = tokenizer(input_, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='pt')
-
- inputs={"input_ids": tokenized_inputs['input_ids'],
- "attention_mask": tokenized_inputs['attention_mask']}
- return inputs
-
-def generate_answers(text):
- inputs = tokenize_data(text)
- results= model.generate(input_ids= inputs['input_ids'], attention_mask=inputs['attention_mask'], do_sample=True,
- max_length=64,
- top_k=120,
- top_p=0.98,
- early_stopping=True,
- num_return_sequences=1)
- answer = tokenizer.decode(results[0], skip_special_tokens=True)
- return answer
-
-iface = gr.Interface(fn=generate_answers, inputs=['text'], outputs=["text"])
-iface.launch(inline=False, share=True)
\ No newline at end of file
diff --git a/spaces/akhaliq/Real-Time-Voice-Cloning/demo_cli.py b/spaces/akhaliq/Real-Time-Voice-Cloning/demo_cli.py
deleted file mode 100644
index 0c5f2adf8f129792f9edb071b4b6b610fd2bfd34..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Real-Time-Voice-Cloning/demo_cli.py
+++ /dev/null
@@ -1,206 +0,0 @@
-from encoder.params_model import model_embedding_size as speaker_embedding_size
-from utils.argutils import print_args
-from utils.modelutils import check_model_paths
-from synthesizer.inference import Synthesizer
-from encoder import inference as encoder
-from vocoder import inference as vocoder
-from pathlib import Path
-import numpy as np
-import soundfile as sf
-import librosa
-import argparse
-import torch
-import sys
-import os
-from audioread.exceptions import NoBackendError
-
-
-if __name__ == '__main__':
- ## Info & args
- parser = argparse.ArgumentParser(
- formatter_class=argparse.ArgumentDefaultsHelpFormatter
- )
- parser.add_argument("-e", "--enc_model_fpath", type=Path,
- default="encpretrained.pt",
- help="Path to a saved encoder")
- parser.add_argument("-s", "--syn_model_fpath", type=Path,
- default="synpretrained.pt",
- help="Path to a saved synthesizer")
- parser.add_argument("-v", "--voc_model_fpath", type=Path,
- default="vocpretrained.pt",
- help="Path to a saved vocoder")
- parser.add_argument("--cpu", action="store_true", help="If True, processing is done on CPU, even when a GPU is available.")
- parser.add_argument("--no_sound", action="store_true", help="If True, audio won't be played.")
- parser.add_argument("--seed", type=int, default=None, help="Optional random number seed value to make toolbox deterministic.")
- parser.add_argument("--no_mp3_support", action="store_true", help="If True, disallows loading mp3 files to prevent audioread errors when ffmpeg is not installed.")
- parser.add_argument("-audio", "--audio_path", type=Path, required = True,
- help="Path to a audio file")
- parser.add_argument("--text", type=str, required = True, help="Text Input")
- parser.add_argument("--output_path", type=str, required = True, help="output file path")
-
- args = parser.parse_args()
- print_args(args, parser)
- if not args.no_sound:
- import sounddevice as sd
-
- if args.cpu:
- # Hide GPUs from Pytorch to force CPU processing
- os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-
- if not args.no_mp3_support:
- try:
- librosa.load("samples/1320_00000.mp3")
- except NoBackendError:
- print("Librosa will be unable to open mp3 files if additional software is not installed.\n"
- "Please install ffmpeg or add the '--no_mp3_support' option to proceed without support for mp3 files.")
- exit(-1)
-
- print("Running a test of your configuration...\n")
-
- if torch.cuda.is_available():
- device_id = torch.cuda.current_device()
- gpu_properties = torch.cuda.get_device_properties(device_id)
- ## Print some environment information (for debugging purposes)
- print("Found %d GPUs available. Using GPU %d (%s) of compute capability %d.%d with "
- "%.1fGb total memory.\n" %
- (torch.cuda.device_count(),
- device_id,
- gpu_properties.name,
- gpu_properties.major,
- gpu_properties.minor,
- gpu_properties.total_memory / 1e9))
- else:
- print("Using CPU for inference.\n")
-
- ## Remind the user to download pretrained models if needed
- check_model_paths(encoder_path=args.enc_model_fpath,
- synthesizer_path=args.syn_model_fpath,
- vocoder_path=args.voc_model_fpath)
-
- ## Load the models one by one.
- print("Preparing the encoder, the synthesizer and the vocoder...")
- encoder.load_model(args.enc_model_fpath)
- synthesizer = Synthesizer(args.syn_model_fpath)
- vocoder.load_model(args.voc_model_fpath)
-
-
- ## Run a test
- # print("Testing your configuration with small inputs.")
- # # Forward an audio waveform of zeroes that lasts 1 second. Notice how we can get the encoder's
- # # sampling rate, which may differ.
- # # If you're unfamiliar with digital audio, know that it is encoded as an array of floats
- # # (or sometimes integers, but mostly floats in this projects) ranging from -1 to 1.
- # # The sampling rate is the number of values (samples) recorded per second, it is set to
- # # 16000 for the encoder. Creating an array of length will always correspond
- # # to an audio of 1 second.
- # print(" Testing the encoder...")
- # encoder.embed_utterance(np.zeros(encoder.sampling_rate))
-
- # # Create a dummy embedding. You would normally use the embedding that encoder.embed_utterance
- # # returns, but here we're going to make one ourselves just for the sake of showing that it's
- # # possible.
- # embed = np.random.rand(speaker_embedding_size)
- # # Embeddings are L2-normalized (this isn't important here, but if you want to make your own
- # # embeddings it will be).
- # embed /= np.linalg.norm(embed)
- # # The synthesizer can handle multiple inputs with batching. Let's create another embedding to
- # # illustrate that
- # embeds = [embed, np.zeros(speaker_embedding_size)]
- # texts = ["test 1", "test 2"]
- # print(" Testing the synthesizer... (loading the model will output a lot of text)")
- # mels = synthesizer.synthesize_spectrograms(texts, embeds)
-
- # # The vocoder synthesizes one waveform at a time, but it's more efficient for long ones. We
- # # can concatenate the mel spectrograms to a single one.
- # mel = np.concatenate(mels, axis=1)
- # # The vocoder can take a callback function to display the generation. More on that later. For
- # # now we'll simply hide it like this:
- # no_action = lambda *args: None
- # print(" Testing the vocoder...")
- # # For the sake of making this test short, we'll pass a short target length. The target length
- # # is the length of the wav segments that are processed in parallel. E.g. for audio sampled
- # # at 16000 Hertz, a target length of 8000 means that the target audio will be cut in chunks of
- # # 0.5 seconds which will all be generated together. The parameters here are absurdly short, and
- # # that has a detrimental effect on the quality of the audio. The default parameters are
- # # recommended in general.
- # vocoder.infer_waveform(mel, target=200, overlap=50, progress_callback=no_action)
-
- print("All test passed! You can now synthesize speech.\n\n")
-
-
- ## Interactive speech generation
- print("This is a GUI-less example of interface to SV2TTS. The purpose of this script is to "
- "show how you can interface this project easily with your own. See the source code for "
- "an explanation of what is happening.\n")
-
- print("Interactive generation loop")
- # while True:
- # Get the reference audio filepath
- message = "Reference voice: enter an audio filepath of a voice to be cloned (mp3, " "wav, m4a, flac, ...):\n"
- in_fpath = args.audio_path
-
- if in_fpath.suffix.lower() == ".mp3" and args.no_mp3_support:
- print("Can't Use mp3 files please try again:")
- ## Computing the embedding
- # First, we load the wav using the function that the speaker encoder provides. This is
- # important: there is preprocessing that must be applied.
-
- # The following two methods are equivalent:
- # - Directly load from the filepath:
- preprocessed_wav = encoder.preprocess_wav(in_fpath)
- # - If the wav is already loaded:
- original_wav, sampling_rate = librosa.load(str(in_fpath))
- preprocessed_wav = encoder.preprocess_wav(original_wav, sampling_rate)
- print("Loaded file succesfully")
-
- # Then we derive the embedding. There are many functions and parameters that the
- # speaker encoder interfaces. These are mostly for in-depth research. You will typically
- # only use this function (with its default parameters):
- embed = encoder.embed_utterance(preprocessed_wav)
- print("Created the embedding")
-
-
- ## Generating the spectrogram
- text = args.text
-
- # If seed is specified, reset torch seed and force synthesizer reload
- if args.seed is not None:
- torch.manual_seed(args.seed)
- synthesizer = Synthesizer(args.syn_model_fpath)
-
- # The synthesizer works in batch, so you need to put your data in a list or numpy array
- texts = [text]
- embeds = [embed]
- # If you know what the attention layer alignments are, you can retrieve them here by
- # passing return_alignments=True
- specs = synthesizer.synthesize_spectrograms(texts, embeds)
- spec = specs[0]
- print("Created the mel spectrogram")
-
-
- ## Generating the waveform
- print("Synthesizing the waveform:")
-
- # If seed is specified, reset torch seed and reload vocoder
- if args.seed is not None:
- torch.manual_seed(args.seed)
- vocoder.load_model(args.voc_model_fpath)
-
- # Synthesizing the waveform is fairly straightforward. Remember that the longer the
- # spectrogram, the more time-efficient the vocoder.
- generated_wav = vocoder.infer_waveform(spec)
-
-
- ## Post-generation
- # There's a bug with sounddevice that makes the audio cut one second earlier, so we
- # pad it.
- generated_wav = np.pad(generated_wav, (0, synthesizer.sample_rate), mode="constant")
-
- # Trim excess silences to compensate for gaps in spectrograms (issue #53)
- generated_wav = encoder.preprocess_wav(generated_wav)
-
- # Save it on the disk
- filename = args.output_path
- print(generated_wav.dtype)
- sf.write(filename, generated_wav.astype(np.float32), synthesizer.sample_rate)
- print("\nSaved output as %s\n\n" % filename)
diff --git a/spaces/akhaliq/anything-v4.0/app.py b/spaces/akhaliq/anything-v4.0/app.py
deleted file mode 100644
index 146d4144fcc64ad8a5b69e399e22ae65a0a85c4f..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/anything-v4.0/app.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-
-model_id = 'andite/anything-v4.0'
-prefix = ''
-
-scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
-
-pipe = StableDiffusionPipeline.from_pretrained(
- model_id,
- torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
- scheduler=scheduler)
-
-pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
- model_id,
- torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
- scheduler=scheduler)
-
-if torch.cuda.is_available():
- pipe = pipe.to("cuda")
- pipe_i2i = pipe_i2i.to("cuda")
-
-def error_str(error, title="Error"):
- return f"""#### {title}
- {error}""" if error else ""
-
-def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
-
- generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
- prompt = f"{prefix} {prompt}" if auto_prefix else prompt
-
- try:
- if img is not None:
- return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
- else:
- return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
- except Exception as e:
- return None, error_str(e)
-
-def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
-
- result = pipe(
- prompt,
- negative_prompt = neg_prompt,
- num_inference_steps = int(steps),
- guidance_scale = guidance,
- width = width,
- height = height,
- generator = generator)
-
- return result.images[0]
-
-def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
- ratio = min(height / img.height, width / img.width)
- img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
- result = pipe_i2i(
- prompt,
- negative_prompt = neg_prompt,
- init_image = img,
- num_inference_steps = int(steps),
- strength = strength,
- guidance_scale = guidance,
- width = width,
- height = height,
- generator = generator)
-
- return result.images[0]
-
-css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
- gr.HTML(
- f"""
-
-
-
Anything V4.0
-
-
- Demo for Anything V4.0 Stable Diffusion model.
- {"Add the following tokens to your prompts for the model to work properly: prefix" if prefix else ""}
-
- Running on {"GPU 🔥" if torch.cuda.is_available() else f"CPU 🥶. For faster inference it is recommended to upgrade to GPU in Settings"} after duplicating the space
- """)
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/akhaliq/deeplab2/tensorflow_ops/kernels/merge_semantic_and_instance_maps_op_kernel.cc b/spaces/akhaliq/deeplab2/tensorflow_ops/kernels/merge_semantic_and_instance_maps_op_kernel.cc
deleted file mode 100644
index 2a5071bb21e0b06a472be9efaba2f7438e6e9f35..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/tensorflow_ops/kernels/merge_semantic_and_instance_maps_op_kernel.cc
+++ /dev/null
@@ -1,279 +0,0 @@
-// Copyright 2021 The Deeplab2 Authors.
-//
-// 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.
-
-#include
-#define EIGEN_USE_THREADS
-
-#define _USE_MATH_DEFINES
-
-#include
-#include
-#include
-#include
-#include
-#include
-
-#include /*third_party*/"tensorflow/core/framework/op_kernel.h"
-#include /*third_party*/"tensorflow/core/framework/register_types.h"
-#include /*third_party*/"tensorflow/core/framework/tensor.h"
-#include /*third_party*/"tensorflow/core/framework/tensor_shape.h"
-#include /*third_party*/"tensorflow/core/framework/types.h"
-#include /*third_party*/"tensorflow/core/lib/core/errors.h"
-#include /*third_party*/"tensorflow/core/lib/core/status.h"
-#include /*third_party*/"tensorflow/core/platform/logging.h"
-#include /*third_party*/"merge_semantic_and_instance_maps_op_kernel.h" // local headers
-
-namespace tensorflow_models {
-namespace deeplab {
-namespace deeplab2 {
-
-namespace {
-
-using tensorflow::Tensor;
-using tensorflow::TensorShape;
-using tensorflow::TTypes;
-using tensorflow::errors::InvalidArgument;
-
-} // namespace
-
-namespace functor {
-
-// This function merges the semantic segmentation and class-agnostic
-// instance segmentation to form the panoptic segmentation. In particular,
-// the class label of each instance mask is inferred from the majority
-// votes from the corresponding pixels in the semantic segmentation. This
-// operation is first poposed in the DeeperLab paper and adopted by the
-// Panoptic-DeepLab.
-// - DeeperLab: Single-Shot Image Parser, T-J Yang, et al. arXiv:1902.05093.
-// - Panoptic-DeepLab, B. Cheng, et al. In CVPR, 2020.
-// Specialization of MergeSemanticAndInstanceMaps< for CPU.
-template <>
-void MergeSemanticAndInstanceMaps::operator()(
- const Eigen::ThreadPoolDevice& d,
- typename TTypes::ConstTensor semantic_maps,
- typename TTypes::ConstTensor instance_maps,
- const std::unordered_set& thing_ids_set, int label_divisor,
- int stuff_area_limit, int void_label,
- typename TTypes::Tensor parsing_maps) {
- const int num_batches = semantic_maps.dimension(0);
- const int height = semantic_maps.dimension(1);
- const int width = semantic_maps.dimension(2);
-
- for (int b = 0; b < num_batches; ++b) {
- // A vector to keep track of which pixels are predicted as `thing` or
- // `stuff` class.
- std::vector is_thing(height * width, true);
-
- // For each instance, find its corresponding histogram of semantic labels.
- // Suppose car label = 2 and road label = 5, and predicted instance 3 has
- // 5 pixels predicted as car and 20 pixels predicted as road. Then,
- // instance_id_to_semantic_histogram[3][2] = 5 and
- // instance_id_to_semantic_histogram[3][5] = 20.
- using InstanceIdType = int32_t;
- using SemanticLabelType = int32_t;
- using CountsType = int32_t;
- std::unordered_map>
- instance_id_to_semantic_histogram;
- // A map from stuff label to area.
- std::unordered_map stuff_label_to_area;
- for (int h = 0; h < height; ++h) {
- for (int w = 0; w < width; ++w) {
- const int semantic_val = semantic_maps(b, h, w);
- if (thing_ids_set.find(semantic_val) == thing_ids_set.end()) {
- // Skip if it is `stuff`.
- is_thing[w + width * h] = false;
- ++stuff_label_to_area[semantic_val];
- continue;
- }
- const int instance_val = instance_maps(b, h, w);
- ++instance_id_to_semantic_histogram[instance_val][semantic_val];
- }
- }
- // Keep track of how many instances for each semantic_label.
- std::unordered_map
- semantic_label_to_instance_counts;
- // Find the new semantic label and instance id for each instance. We use
- // majority vote to find the new semantic label while reorder the instance
- // id in the following way. In the original instance map, every instance
- // has a different instance id. In the new instance map, every instance
- // `in the same semantic class` should have a different id, but instances
- // `in different semantic classes` can have the same instance id. This
- // reduces the maximum instance label value and avoids the problem of
- // combining the two maps with the label_divisor.
- std::unordered_map>
- instance_id_to_new_semantic_label_and_instance_id;
- for (const auto& instance_to_histogram :
- instance_id_to_semantic_histogram) {
- const int instance_val = instance_to_histogram.first;
- const std::unordered_map
- semantic_histogram = instance_to_histogram.second;
- int semantic_label = -1;
- int max_count = 0;
- // Find the majority semantic label.
- for (const auto& semantic_to_count : semantic_histogram) {
- // Break ties deterministically by select the smaller semantic label.
- if (semantic_to_count.second > max_count ||
- (semantic_to_count.second == max_count &&
- semantic_to_count.first < semantic_label)) {
- max_count = semantic_to_count.second;
- semantic_label = semantic_to_count.first;
- }
- }
- ++semantic_label_to_instance_counts[semantic_label];
- // For `thing` class, we set instance id starting from 1, while for
- // `stuff` class, we use instance id 0.
- instance_id_to_new_semantic_label_and_instance_id[instance_val] = {
- semantic_label, semantic_label_to_instance_counts[semantic_label]};
- }
- // Create a new semantic map by assigning the majority semantic label for
- // each instance.
- std::vector semantic_map(height * width);
- // Create a new instance map by assigning ordered instance id's.
- std::vector instance_map(height * width);
- for (int h = 0; h < height; ++h) {
- for (int w = 0; w < width; ++w) {
- const int pixel = w + width * h;
- if (is_thing[pixel]) {
- const int instance_val = instance_maps(b, h, w);
- // Assign the majority semantic vote in the new semantic map, and
- // reorder the instance id in the new instance map.
- std::tie(semantic_map[pixel], instance_map[pixel]) =
- instance_id_to_new_semantic_label_and_instance_id[instance_val];
- } else {
- // If current pixel belongs to `stuff` class, keep the same semantic
- // label in the new semantic map. We also check if its area is
- // smaller than the stuff_area_limit_ or not. If true, we re-assign
- // the segment with void_label_.
- const int semantic_val = semantic_maps(b, h, w);
- if (stuff_area_limit > 0 &&
- stuff_label_to_area[semantic_val] <= stuff_area_limit) {
- semantic_map[pixel] = void_label;
- } else {
- semantic_map[pixel] = semantic_val;
- }
- // If current pixel belongs to `stuff` class, assign 0 in the new
- // instance map.
- instance_map[pixel] = 0;
- }
- }
- }
- // Merge those semantic map and instance map.
- for (int h = 0; h < height; ++h) {
- for (int w = 0; w < width; ++w) {
- const int pixel = w + width * h;
- parsing_maps(b, h, w) =
- semantic_map[pixel] * label_divisor + instance_map[pixel];
- }
- }
- }
-}
-
-template <>
-std::unordered_set Convert1DInt32TensorToSet(
- const Eigen::ThreadPoolDevice& d, const Tensor& tensor) {
- std::unordered_set target_set;
- const int n_vals = tensor.dim_size(0);
- typename TTypes::ConstTensor tensor_data =
- tensor.tensor();
- for (int i = 0; i < n_vals; i++) {
- target_set.insert(tensor_data(i));
- }
-
- return target_set;
-}
-
-} // namespace functor
-
-template
-class MergeSemanticAndInstanceMapsOp : public tensorflow::OpKernel {
- public:
- explicit MergeSemanticAndInstanceMapsOp(
- tensorflow::OpKernelConstruction* context)
- : OpKernel(context) {
- OP_REQUIRES_OK(context, context->GetAttr("label_divisor", &label_divisor_));
- OP_REQUIRES(context, label_divisor_ > 0,
- InvalidArgument("Label divisor must be positive."));
- OP_REQUIRES_OK(context,
- context->GetAttr("stuff_area_limit", &stuff_area_limit_));
- OP_REQUIRES(context, stuff_area_limit_ >= 0,
- InvalidArgument("Stuff area limit must be non-negative."));
- OP_REQUIRES_OK(context, context->GetAttr("void_label", &void_label_));
- OP_REQUIRES(context, void_label_ >= 0,
- InvalidArgument("Void label must be non-negative."));
- }
-
- void Compute(tensorflow::OpKernelContext* context) override {
- // Extract the inputs.
- const Tensor& semantic_maps = context->input(0);
- const Tensor& instance_maps = context->input(1);
- const Tensor& thing_ids_tensor = context->input(2);
-
- // Convert thing_ids_tensor into a set.
- std::unordered_set thing_ids_set =
- functor::Convert1DInt32TensorToSet(context->eigen_device(),
- thing_ids_tensor);
-
- // Extract the constants.
- const int batch = semantic_maps.dim_size(0);
- const int height = semantic_maps.dim_size(1);
- const int width = semantic_maps.dim_size(2);
-
- // Check input shapes.
- OP_REQUIRES(context,
- instance_maps.dim_size(0) == batch &&
- instance_maps.dim_size(1) == height &&
- instance_maps.dim_size(2) == width,
- InvalidArgument(
- "Expect semantic and instance maps have the same shape.",
- instance_maps.shape().DebugString()));
-
- Tensor* parsing_maps = nullptr;
- OP_REQUIRES_OK(context,
- context->allocate_output(
- 0, TensorShape({batch, height, width}), &parsing_maps));
-
- functor::MergeSemanticAndInstanceMaps()(
- context->eigen_device(), semantic_maps.tensor(),
- instance_maps.tensor(), thing_ids_set, label_divisor_,
- stuff_area_limit_, void_label_, parsing_maps->tensor());
- }
-
- private:
- // Label divisor, the value used to combine the semantic and instance map to
- // generate the parsing map.
- int label_divisor_;
-
- // Stuff area limit is used to remove predicted stuff segments whose area are
- // smaller than it.
- int stuff_area_limit_;
-
- // Removed predicted stuff segments are re-assigned with void label.
- int void_label_;
-};
-
-REGISTER_KERNEL_BUILDER(
- Name("MergeSemanticAndInstanceMaps").Device(tensorflow::DEVICE_CPU),
- MergeSemanticAndInstanceMapsOp);
-
-#ifdef GOOGLE_CUDA
-REGISTER_KERNEL_BUILDER(
- Name("MergeSemanticAndInstanceMaps").Device(tensorflow::DEVICE_GPU),
- MergeSemanticAndInstanceMapsOp)
-#endif // GOOGLE_CUDA
-
-} // namespace deeplab2
-} // namespace deeplab
-} // namespace tensorflow_models
diff --git a/spaces/akhaliq/paint-by-example/header.html b/spaces/akhaliq/paint-by-example/header.html
deleted file mode 100644
index cef7f42cdec0e8fc54d8f86578da1b142da7e946..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/paint-by-example/header.html
+++ /dev/null
@@ -1,18 +0,0 @@
-
-
-
- Paint by Example 🎨
-
-
-
-
- Paint by Example, upload a source image and draw a mask for what you want to replace with a example image.
-
-
-
\ No newline at end of file
diff --git a/spaces/akiyamasho/AnimeBackgroundGAN/network/__init__.py b/spaces/akiyamasho/AnimeBackgroundGAN/network/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/alex-mindspace/gpt-agents/swarmai/utils/task_queue/PandasQueue.py b/spaces/alex-mindspace/gpt-agents/swarmai/utils/task_queue/PandasQueue.py
deleted file mode 100644
index 0daef833448df84b4945f7e18f753b706f17ccf8..0000000000000000000000000000000000000000
--- a/spaces/alex-mindspace/gpt-agents/swarmai/utils/task_queue/PandasQueue.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import uuid
-import pandas as pd
-from datetime import datetime
-
-from swarmai.utils.task_queue.TaskQueueBase import TaskQueueBase
-from swarmai.utils.task_queue.Task import Task
-from swarmai.agents.AgentBase import AgentBase
-
-class PandasQueue(TaskQueueBase):
- """Super simple implementatin of the versatile task queue using pandas DataFrame.
- Pretty slow, but allows for easy manipulation of tasks, filtering, etc.
- Thread-safeness is handeled by the TaskQueueBase class.
-
- In the current swarm architecture the taks should have following attributes:
- - task_id: unique identifier of the task
- - priority: priority of the task. Task queue will first return high priority tasks.
- - task_type: type of the task, so that specific agents can filter tasks
- - task_description: description of the task
- - status: status of the task, e.g. "pending", "in progress", "completed", "failed", 'cancelled'
- """
-
- def __init__(self, task_types: list, agent_types: list, task_association: dict):
- """
- Task association is a dictionary that returns a list of task_types for a given agent_type.
-
- Attributes:
- - task_types (list[str]): list of task types that are supported by the task queue
- - agent_types (list[str]): list of agent types that are supported by the task queue
- - task_association (dict): dictionary that returns a list of task_types for a given agent_type
- """
- super().__init__()
- self.columns = ["task_id", "priority", "task_type", "task_description", "status", "add_time", "claim_time", "complete_time", "claim_agent_id"]
- self.tasks = pd.DataFrame(columns=self.columns)
- self.task_types = task_types
- self.agent_types = agent_types
- self.task_association = task_association
-
- def add_task(self, task: Task) -> bool:
- """Adds a task to the queue.
-
- Task attr = (task_id, priority, task_type, task_description, status)
- """
- if task.task_type not in self.task_types:
- raise ValueError(f"Task type {task.task_type} is not supported.")
-
- if task.task_description is None:
- raise ValueError(f"Task description {task.task_description} is not valid.")
-
- if isinstance(task.task_description, str) == False:
- raise ValueError(f"Task description {task.task_description} is not valid.")
-
- if task.task_description == "":
- raise ValueError(f"Task description {task.task_description} is not valid.")
-
- priority = task.priority
- task_type = task.task_type
- task_description = task.task_description
- status = "pending"
- add_time = datetime.now()
-
- task_i = pd.DataFrame([[uuid.uuid4(), priority, task_type, task_description, status, add_time, None, None, None]], columns=self.columns)
- self.tasks = pd.concat([self.tasks, task_i], ignore_index=True)
-
- def get_task(self, agent: AgentBase) -> Task:
- """Gets the next task from the queue, based on the agent type
- """
- supported_tasks = self._get_supported_tasks(agent.agent_type)
-
- df_clone = self.tasks.copy()
-
- # get only pending tasks
- df_clone = df_clone[df_clone["status"] == "pending"]
-
- # get only supported tasks
- df_clone = df_clone[df_clone["task_type"].isin(supported_tasks)]
-
- if len(df_clone) == 0:
- return None
-
- # sort by priority
- df_clone = df_clone.sort_values(by="priority", ascending=False)
-
- # get the first task
- task = df_clone.iloc[0]
-
- # claim the task
- status = "in progress"
- claim_time = datetime.now()
- claim_agent_id = agent.agent_id
- task_obj = Task(task_id=task["task_id"], priority=task["priority"], task_type=task["task_type"], task_description=task["task_description"], status=status)
-
- # update the task in the queue
- df_i = pd.DataFrame([[task["task_id"], task["priority"], task["task_type"], task["task_description"], status, task["add_time"], claim_time, None, claim_agent_id]], columns=self.columns)
- self.tasks = self.tasks[self.tasks["task_id"] != task["task_id"]]
- self.tasks = pd.concat([self.tasks, df_i], ignore_index=True)
-
- return task_obj
-
- def complete_task(self, task_id):
- """Completes the task with the given task_id.
- """
- task = self.tasks[self.tasks["task_id"] == task_id]
- if len(task) == 0:
- """In case task was deleted from the queue"""
- return False
-
- task = task.iloc[0]
-
- if task["status"] != "in progress":
- return False
-
- status = "completed"
- complete_time = datetime.now()
- df_i = pd.DataFrame([[task["task_id"], task["priority"], task["task_type"], task["task_description"], status, task["add_time"], task["claim_time"], complete_time, task["claim_agent_id"]]], columns=self.columns)
- self.tasks = self.tasks[self.tasks["task_id"] != task["task_id"]]
- self.tasks = pd.concat([self.tasks, df_i], ignore_index=True)
- return True
-
- def reset_task(self, task_id: str):
- task = self.tasks[self.tasks["task_id"] == task_id]
- if len(task) == 0:
- """In case task was deleted from the queue"""
- return False
-
- task = task.iloc[0]
- status = "pending"
- df_i = pd.DataFrame([[task["task_id"], task["priority"], task["task_type"], task["task_description"], status, task["add_time"], None, None, None]], columns=self.columns)
- self.tasks = self.tasks[self.tasks["task_id"] != task["task_id"]]
- self.tasks = pd.concat([self.tasks, df_i], ignore_index=True)
- return True
-
- def _get_supported_tasks(self, agent_type):
- """Returns a list of supported tasks for a given agent type.
- """
- if agent_type not in self.agent_types:
- raise ValueError(f"Agent type {agent_type} is not supported.")
-
- if self.task_association is None:
- # get all present task types
- return self.task_types
-
- return self.task_association[agent_type]
-
- def get_all_tasks(self):
- """Returns all tasks in the queue.
- Allows the manager model to bush up the tasks list to delete duplicates or unnecessary tasks.
- """
- raise NotImplementedError
\ No newline at end of file
diff --git a/spaces/algomuffin/jojo_fork/e4e/utils/model_utils.py b/spaces/algomuffin/jojo_fork/e4e/utils/model_utils.py
deleted file mode 100644
index e51e95578f72b3218d6d832e3b604193cb68c1d7..0000000000000000000000000000000000000000
--- a/spaces/algomuffin/jojo_fork/e4e/utils/model_utils.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import torch
-import argparse
-from models.psp import pSp
-from models.encoders.psp_encoders import Encoder4Editing
-
-
-def setup_model(checkpoint_path, device='cuda'):
- ckpt = torch.load(checkpoint_path, map_location='cpu')
- opts = ckpt['opts']
-
- opts['checkpoint_path'] = checkpoint_path
- opts['device'] = device
- opts = argparse.Namespace(**opts)
-
- net = pSp(opts)
- net.eval()
- net = net.to(device)
- return net, opts
-
-
-def load_e4e_standalone(checkpoint_path, device='cuda'):
- ckpt = torch.load(checkpoint_path, map_location='cpu')
- opts = argparse.Namespace(**ckpt['opts'])
- e4e = Encoder4Editing(50, 'ir_se', opts)
- e4e_dict = {k.replace('encoder.', ''): v for k, v in ckpt['state_dict'].items() if k.startswith('encoder.')}
- e4e.load_state_dict(e4e_dict)
- e4e.eval()
- e4e = e4e.to(device)
- latent_avg = ckpt['latent_avg'].to(device)
-
- def add_latent_avg(model, inputs, outputs):
- return outputs + latent_avg.repeat(outputs.shape[0], 1, 1)
-
- e4e.register_forward_hook(add_latent_avg)
- return e4e
diff --git a/spaces/ali-ghamdan/deoldify/fastai/gen_doc/core.py b/spaces/ali-ghamdan/deoldify/fastai/gen_doc/core.py
deleted file mode 100644
index daf8679f86447b7aecb6a7523540fd0b10e97798..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/deoldify/fastai/gen_doc/core.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from ..core import *
-import re
-
-def strip_fastai(s): return re.sub(r'^fastai\.', '', s)
-
diff --git a/spaces/ali-ghamdan/deoldify/fastai/metrics.py b/spaces/ali-ghamdan/deoldify/fastai/metrics.py
deleted file mode 100644
index 46fdddf3de2cf8d987ecb4c7d7cb3503afa995ad..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/deoldify/fastai/metrics.py
+++ /dev/null
@@ -1,361 +0,0 @@
-"Implements various metrics to measure training accuracy"
-from .torch_core import *
-from .callback import *
-from .layers import *
-from .basic_train import LearnerCallback
-
-__all__ = ['error_rate', 'accuracy', 'accuracy_thresh', 'dice', 'exp_rmspe', 'fbeta','FBeta', 'mse', 'mean_squared_error',
- 'mae', 'mean_absolute_error', 'rmse', 'root_mean_squared_error', 'msle', 'mean_squared_logarithmic_error',
- 'explained_variance', 'r2_score', 'top_k_accuracy', 'KappaScore', 'ConfusionMatrix', 'MatthewsCorreff',
- 'Precision', 'Recall', 'R2Score', 'ExplainedVariance', 'ExpRMSPE', 'RMSE', 'Perplexity', 'AUROC', 'auc_roc_score',
- 'roc_curve', 'MultiLabelFbeta', 'foreground_acc']
-
-def fbeta(y_pred:Tensor, y_true:Tensor, thresh:float=0.2, beta:float=2, eps:float=1e-9, sigmoid:bool=True)->Rank0Tensor:
- "Computes the f_beta between `preds` and `targets`"
- beta2 = beta ** 2
- if sigmoid: y_pred = y_pred.sigmoid()
- y_pred = (y_pred>thresh).float()
- y_true = y_true.float()
- TP = (y_pred*y_true).sum(dim=1)
- prec = TP/(y_pred.sum(dim=1)+eps)
- rec = TP/(y_true.sum(dim=1)+eps)
- res = (prec*rec)/(prec*beta2+rec+eps)*(1+beta2)
- return res.mean()
-
-def accuracy(input:Tensor, targs:Tensor)->Rank0Tensor:
- "Computes accuracy with `targs` when `input` is bs * n_classes."
- n = targs.shape[0]
- input = input.argmax(dim=-1).view(n,-1)
- targs = targs.view(n,-1)
- return (input==targs).float().mean()
-
-def accuracy_thresh(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True)->Rank0Tensor:
- "Computes accuracy when `y_pred` and `y_true` are the same size."
- if sigmoid: y_pred = y_pred.sigmoid()
- return ((y_pred>thresh)==y_true.byte()).float().mean()
-
-def top_k_accuracy(input:Tensor, targs:Tensor, k:int=5)->Rank0Tensor:
- "Computes the Top-k accuracy (target is in the top k predictions)."
- input = input.topk(k=k, dim=-1)[1]
- targs = targs.unsqueeze(dim=-1).expand_as(input)
- return (input == targs).max(dim=-1)[0].float().mean()
-
-def foreground_acc(input, target, void_code):
- "Computes non-background accuracy, e.g. camvid for multiclass segmentation"
- target = target.squeeze(1)
- mask = target != void_code
- return (input.argmax(dim=1)[mask]==target[mask]).float().mean()
-
-def error_rate(input:Tensor, targs:Tensor)->Rank0Tensor:
- "1 - `accuracy`"
- return 1 - accuracy(input, targs)
-
-def dice(input:Tensor, targs:Tensor, iou:bool=False, eps:float=1e-8)->Rank0Tensor:
- "Dice coefficient metric for binary target. If iou=True, returns iou metric, classic for segmentation problems."
- n = targs.shape[0]
- input = input.argmax(dim=1).view(n,-1)
- targs = targs.view(n,-1)
- intersect = (input * targs).sum().float()
- union = (input+targs).sum().float()
- if not iou: return (2. * intersect / union if union > 0 else union.new([1.]).squeeze())
- else: return (intersect / (union-intersect+eps) if union > 0 else union.new([1.]).squeeze())
-
-def psnr(input:Tensor, targs:Tensor)->Rank0Tensor:
- return 10 * (1. / mean_squared_error(input, targs)).log10()
-
-def exp_rmspe(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Exp RMSE between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- pred, targ = torch.exp(pred), torch.exp(targ)
- pct_var = (targ - pred)/targ
- return torch.sqrt((pct_var**2).mean())
-
-def mean_absolute_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean absolute error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return torch.abs(targ - pred).mean()
-
-def mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean squared error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return F.mse_loss(pred, targ)
-
-def root_mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Root mean squared error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return torch.sqrt(F.mse_loss(pred, targ))
-
-def mean_squared_logarithmic_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean squared logarithmic error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return F.mse_loss(torch.log(1 + pred), torch.log(1 + targ))
-
-def explained_variance(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Explained variance between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- var_pct = torch.var(targ - pred) / torch.var(targ)
- return 1 - var_pct
-
-def r2_score(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "R2 score (coefficient of determination) between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- u = torch.sum((targ - pred) ** 2)
- d = torch.sum((targ - targ.mean()) ** 2)
- return 1 - u / d
-
-class RegMetrics(Callback):
- "Stores predictions and targets to perform calculations on epoch end."
- def on_epoch_begin(self, **kwargs):
- self.targs, self.preds = Tensor([]), Tensor([])
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- assert last_output.numel() == last_target.numel(), "Expected same numbers of elements in pred & targ"
- self.preds = torch.cat((self.preds, last_output.cpu()))
- self.targs = torch.cat((self.targs, last_target.cpu()))
-
-class R2Score(RegMetrics):
- "Computes the R2 score (coefficient of determination)."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, r2_score(self.preds, self.targs))
-
-class ExplainedVariance(RegMetrics):
- "Computes the explained variance."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, explained_variance(self.preds, self.targs))
-
-class RMSE(RegMetrics):
- "Computes the root mean squared error."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, root_mean_squared_error(self.preds, self.targs))
-
-class ExpRMSPE(RegMetrics):
- "Computes the exponential of the root mean square error."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, exp_rmspe(self.preds, self.targs))
-
-# Aliases
-mse = mean_squared_error
-mae = mean_absolute_error
-msle = mean_squared_logarithmic_error
-rmse = root_mean_squared_error
-
-class ConfusionMatrix(Callback):
- "Computes the confusion matrix."
-
- def on_train_begin(self, **kwargs):
- self.n_classes = 0
-
- def on_epoch_begin(self, **kwargs):
- self.cm = None
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- preds = last_output.argmax(-1).view(-1).cpu()
- targs = last_target.cpu()
- if self.n_classes == 0:
- self.n_classes = last_output.shape[-1]
- self.x = torch.arange(0, self.n_classes)
- cm = ((preds==self.x[:, None]) & (targs==self.x[:, None, None])).sum(dim=2, dtype=torch.float32)
- if self.cm is None: self.cm = cm
- else: self.cm += cm
-
- def on_epoch_end(self, **kwargs):
- self.metric = self.cm
-
-@dataclass
-class CMScores(ConfusionMatrix):
- "Base class for metrics which rely on the calculation of the precision and/or recall score."
- average:Optional[str]="binary" # `binary`, `micro`, `macro`, `weigthed` or None
- pos_label:int=1 # 0 or 1
- eps:float=1e-9
-
- def _recall(self):
- rec = torch.diag(self.cm) / self.cm.sum(dim=1)
- if self.average is None: return rec
- else:
- if self.average == "micro": weights = self._weights(avg="weighted")
- else: weights = self._weights(avg=self.average)
- return (rec * weights).sum()
-
- def _precision(self):
- prec = torch.diag(self.cm) / self.cm.sum(dim=0)
- if self.average is None: return prec
- else:
- weights = self._weights(avg=self.average)
- return (prec * weights).sum()
-
- def _weights(self, avg:str):
- if self.n_classes != 2 and avg == "binary":
- avg = self.average = "macro"
- warn("average=`binary` was selected for a non binary case. Value for average has now been set to `macro` instead.")
- if avg == "binary":
- if self.pos_label not in (0, 1):
- self.pos_label = 1
- warn("Invalid value for pos_label. It has now been set to 1.")
- if self.pos_label == 1: return Tensor([0,1])
- else: return Tensor([1,0])
- elif avg == "micro": return self.cm.sum(dim=0) / self.cm.sum()
- elif avg == "macro": return torch.ones((self.n_classes,)) / self.n_classes
- elif avg == "weighted": return self.cm.sum(dim=1) / self.cm.sum()
-
-
-class Recall(CMScores):
- "Computes the Recall."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, self._recall())
-
-class Precision(CMScores):
- "Computes the Precision."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, self._precision())
-
-@dataclass
-class FBeta(CMScores):
- "Computes the F`beta` score."
- beta:float=2
-
- def on_train_begin(self, **kwargs):
- self.n_classes = 0
- self.beta2 = self.beta ** 2
- self.avg = self.average
- if self.average != "micro": self.average = None
-
- def on_epoch_end(self, last_metrics, **kwargs):
- prec = self._precision()
- rec = self._recall()
- metric = (1 + self.beta2) * prec * rec / (prec * self.beta2 + rec + self.eps)
- metric[metric != metric] = 0 # removing potential "nan"s
- if self.avg: metric = (self._weights(avg=self.avg) * metric).sum()
- return add_metrics(last_metrics, metric)
-
- def on_train_end(self, **kwargs): self.average = self.avg
-
-@dataclass
-class KappaScore(ConfusionMatrix):
- "Computes the rate of agreement (Cohens Kappa)."
- weights:Optional[str]=None # None, `linear`, or `quadratic`
-
- def on_epoch_end(self, last_metrics, **kwargs):
- sum0 = self.cm.sum(dim=0)
- sum1 = self.cm.sum(dim=1)
- expected = torch.einsum('i,j->ij', (sum0, sum1)) / sum0.sum()
- if self.weights is None:
- w = torch.ones((self.n_classes, self.n_classes))
- w[self.x, self.x] = 0
- elif self.weights == "linear" or self.weights == "quadratic":
- w = torch.zeros((self.n_classes, self.n_classes))
- w += torch.arange(self.n_classes, dtype=torch.float)
- w = torch.abs(w - torch.t(w)) if self.weights == "linear" else (w - torch.t(w)) ** 2
- else: raise ValueError('Unknown weights. Expected None, "linear", or "quadratic".')
- k = torch.sum(w * self.cm) / torch.sum(w * expected)
- return add_metrics(last_metrics, 1-k)
-
-@dataclass
-class MatthewsCorreff(ConfusionMatrix):
- "Computes the Matthews correlation coefficient."
- def on_epoch_end(self, last_metrics, **kwargs):
- t_sum = self.cm.sum(dim=1)
- p_sum = self.cm.sum(dim=0)
- n_correct = torch.trace(self.cm)
- n_samples = p_sum.sum()
- cov_ytyp = n_correct * n_samples - torch.dot(t_sum, p_sum)
- cov_ypyp = n_samples ** 2 - torch.dot(p_sum, p_sum)
- cov_ytyt = n_samples ** 2 - torch.dot(t_sum, t_sum)
- return add_metrics(last_metrics, cov_ytyp / torch.sqrt(cov_ytyt * cov_ypyp))
-
-class Perplexity(Callback):
- "Perplexity metric for language models."
- def on_epoch_begin(self, **kwargs): self.loss,self.len = 0.,0
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- self.loss += last_target.size(1) * CrossEntropyFlat()(last_output, last_target)
- self.len += last_target.size(1)
-
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, torch.exp(self.loss / self.len))
-
-def auc_roc_score(input:Tensor, targ:Tensor):
- "Computes the area under the receiver operator characteristic (ROC) curve using the trapezoid method. Restricted binary classification tasks."
- fpr, tpr = roc_curve(input, targ)
- d = fpr[1:] - fpr[:-1]
- sl1, sl2 = [slice(None)], [slice(None)]
- sl1[-1], sl2[-1] = slice(1, None), slice(None, -1)
- return (d * (tpr[tuple(sl1)] + tpr[tuple(sl2)]) / 2.).sum(-1)
-
-def roc_curve(input:Tensor, targ:Tensor):
- "Computes the receiver operator characteristic (ROC) curve by determining the true positive ratio (TPR) and false positive ratio (FPR) for various classification thresholds. Restricted binary classification tasks."
- targ = (targ == 1)
- desc_score_indices = torch.flip(input.argsort(-1), [-1])
- input = input[desc_score_indices]
- targ = targ[desc_score_indices]
- d = input[1:] - input[:-1]
- distinct_value_indices = torch.nonzero(d).transpose(0,1)[0]
- threshold_idxs = torch.cat((distinct_value_indices, LongTensor([len(targ) - 1]).to(targ.device)))
- tps = torch.cumsum(targ * 1, dim=-1)[threshold_idxs]
- fps = (1 + threshold_idxs - tps)
- if tps[0] != 0 or fps[0] != 0:
- fps = torch.cat((LongTensor([0]), fps))
- tps = torch.cat((LongTensor([0]), tps))
- fpr, tpr = fps.float() / fps[-1], tps.float() / tps[-1]
- return fpr, tpr
-
-@dataclass
-class AUROC(Callback):
- "Computes the area under the curve (AUC) score based on the receiver operator characteristic (ROC) curve. Restricted to binary classification tasks."
- def on_epoch_begin(self, **kwargs):
- self.targs, self.preds = LongTensor([]), Tensor([])
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- last_output = F.softmax(last_output, dim=1)[:,-1]
- self.preds = torch.cat((self.preds, last_output.cpu()))
- self.targs = torch.cat((self.targs, last_target.cpu().long()))
-
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, auc_roc_score(self.preds, self.targs))
-
-class MultiLabelFbeta(LearnerCallback):
- "Computes the fbeta score for multilabel classification"
- # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
- _order = -20
- def __init__(self, learn, beta=2, eps=1e-15, thresh=0.3, sigmoid=True, average="micro"):
- super().__init__(learn)
- self.eps, self.thresh, self.sigmoid, self.average, self.beta2 = \
- eps, thresh, sigmoid, average, beta**2
-
- def on_train_begin(self, **kwargs):
- self.c = self.learn.data.c
- if self.average != "none": self.learn.recorder.add_metric_names([f'{self.average}_fbeta'])
- else: self.learn.recorder.add_metric_names([f"fbeta_{c}" for c in self.learn.data.classes])
-
- def on_epoch_begin(self, **kwargs):
- dvc = self.learn.data.device
- self.tp = torch.zeros(self.c).to(dvc)
- self.total_pred = torch.zeros(self.c).to(dvc)
- self.total_targ = torch.zeros(self.c).to(dvc)
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- pred, targ = (last_output.sigmoid() if self.sigmoid else last_output) > self.thresh, last_target.byte()
- m = pred*targ
- self.tp += m.sum(0).float()
- self.total_pred += pred.sum(0).float()
- self.total_targ += targ.sum(0).float()
-
- def fbeta_score(self, precision, recall):
- return (1 + self.beta2)*(precision*recall)/((self.beta2*precision + recall) + self.eps)
-
- def on_epoch_end(self, last_metrics, **kwargs):
- self.total_pred += self.eps
- self.total_targ += self.eps
- if self.average == "micro":
- precision, recall = self.tp.sum() / self.total_pred.sum(), self.tp.sum() / self.total_targ.sum()
- res = self.fbeta_score(precision, recall)
- elif self.average == "macro":
- res = self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ)).mean()
- elif self.average == "weighted":
- scores = self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ))
- res = (scores*self.total_targ).sum() / self.total_targ.sum()
- elif self.average == "none":
- res = listify(self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ)))
- else:
- raise Exception("Choose one of the average types: [micro, macro, weighted, none]")
-
- return add_metrics(last_metrics, res)
diff --git a/spaces/aliabd/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/DOMException.pm b/spaces/aliabd/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/DOMException.pm
deleted file mode 100644
index d49c69859a45b10b93fe1720f2264f211da21dc3..0000000000000000000000000000000000000000
--- a/spaces/aliabd/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/DOMException.pm
+++ /dev/null
@@ -1,88 +0,0 @@
-######################################################################
-package XML::DOM::DOMException;
-######################################################################
-
-use Exporter;
-
-use overload '""' => \&stringify;
-use vars qw ( @ISA @EXPORT @ErrorNames );
-
-BEGIN
-{
- @ISA = qw( Exporter );
- @EXPORT = qw( INDEX_SIZE_ERR
- DOMSTRING_SIZE_ERR
- HIERARCHY_REQUEST_ERR
- WRONG_DOCUMENT_ERR
- INVALID_CHARACTER_ERR
- NO_DATA_ALLOWED_ERR
- NO_MODIFICATION_ALLOWED_ERR
- NOT_FOUND_ERR
- NOT_SUPPORTED_ERR
- INUSE_ATTRIBUTE_ERR
- );
-}
-
-sub UNKNOWN_ERR () {0;} # not in the DOM Spec!
-sub INDEX_SIZE_ERR () {1;}
-sub DOMSTRING_SIZE_ERR () {2;}
-sub HIERARCHY_REQUEST_ERR () {3;}
-sub WRONG_DOCUMENT_ERR () {4;}
-sub INVALID_CHARACTER_ERR () {5;}
-sub NO_DATA_ALLOWED_ERR () {6;}
-sub NO_MODIFICATION_ALLOWED_ERR () {7;}
-sub NOT_FOUND_ERR () {8;}
-sub NOT_SUPPORTED_ERR () {9;}
-sub INUSE_ATTRIBUTE_ERR () {10;}
-
-@ErrorNames = (
- "UNKNOWN_ERR",
- "INDEX_SIZE_ERR",
- "DOMSTRING_SIZE_ERR",
- "HIERARCHY_REQUEST_ERR",
- "WRONG_DOCUMENT_ERR",
- "INVALID_CHARACTER_ERR",
- "NO_DATA_ALLOWED_ERR",
- "NO_MODIFICATION_ALLOWED_ERR",
- "NOT_FOUND_ERR",
- "NOT_SUPPORTED_ERR",
- "INUSE_ATTRIBUTE_ERR"
- );
-sub new
-{
- my ($type, $code, $msg) = @_;
- my $self = bless {Code => $code}, $type;
-
- $self->{Message} = $msg if defined $msg;
-
-# print "=> Exception: " . $self->stringify . "\n";
- $self;
-}
-
-sub getCode
-{
- $_[0]->{Code};
-}
-
-#------------------------------------------------------------
-# Extra method implementations
-
-sub getName
-{
- $ErrorNames[$_[0]->{Code}];
-}
-
-sub getMessage
-{
- $_[0]->{Message};
-}
-
-sub stringify
-{
- my $self = shift;
-
- "XML::DOM::DOMException(Code=" . $self->getCode . ", Name=" .
- $self->getName . ", Message=" . $self->getMessage . ")";
-}
-
-1; # package return code
diff --git a/spaces/aliabd/SummerTime/model/third_party/HMNet/Utils/Constants.py b/spaces/aliabd/SummerTime/model/third_party/HMNet/Utils/Constants.py
deleted file mode 100644
index 144064098eff3014e5c6894d0ab55beb8717b1d2..0000000000000000000000000000000000000000
--- a/spaces/aliabd/SummerTime/model/third_party/HMNet/Utils/Constants.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-PAD_WORD_ID = 0
-UNK_WORD_ID = 1
-END_WORD_ID = 2
-
-PAD_CHAR = 261
-BOW_CHAR = 259
-EOW_CHAR = 260
-
-ALM_MAX_VOCAB_SIZE = 20000
-
-
-class bcolors:
- HEADER = "\033[95m"
- OKBLUE = "\033[94m"
- OKGREEN = "\033[92m"
- WARNING = "\033[93m"
- FAIL = "\033[91m"
- ENDC = "\033[0m"
- BOLD = "\033[1m"
- UNDERLINE = "\033[4m"
diff --git a/spaces/allknowingroger/Image-Models-Test150/app.py b/spaces/allknowingroger/Image-Models-Test150/app.py
deleted file mode 100644
index 52ea120cb7ec3df9b0691de62cc395124782ef0d..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test150/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
- "digiplay/CleanLinearMix",
- "wzneric/df_m_id94",
- "Yntec/LehinaModel",
- "wzneric/df_m_id143",
- "CiroN2022/tape-people",
- "wzneric/df_wm_id4",
- "ayoubkirouane/Stable-Cats-Generator",
- "CiroN2022/wake-up",
- "milaidy/christinaa",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
- try:
- model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
- except Exception as error:
- def the_fn(txt):
- return None
- model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
- model_idx+=1
-
-
-def send_it_idx(idx):
- def send_it_fn(prompt):
- output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
- return output
- return send_it_fn
-
-def get_prompts(prompt_text):
- return prompt_text
-
-def clear_it(val):
- if int(val) != 0:
- val = 0
- else:
- val = 0
- pass
- return val
-
-def all_task_end(cnt,t_stamp):
- to = t_stamp + 60
- et = time.time()
- if et > to and t_stamp != 0:
- d = gr.update(value=0)
- tog = gr.update(value=1)
- #print(f'to: {to} et: {et}')
- else:
- if cnt != 0:
- d = gr.update(value=et)
- else:
- d = gr.update(value=0)
- tog = gr.update(value=0)
- #print (f'passing: to: {to} et: {et}')
- pass
- return d, tog
-
-def all_task_start():
- print("\n\n\n\n\n\n\n")
- t = time.gmtime()
- t_stamp = time.time()
- current_time = time.strftime("%H:%M:%S", t)
- return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
- nn = len(models)
- return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
- with gr.Column(scale=12):
- # with gr.Row():
- # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
- with gr.Row():
- with gr.Row(scale=6):
- primary_prompt=gr.Textbox(label="Prompt", value="")
- # real_prompt=gr.Textbox(label="Real prompt")
- with gr.Row(scale=6):
- # improve_prompts_btn=gr.Button("Improve")
- with gr.Row():
- run=gr.Button("Run",variant="primary")
- clear_btn=gr.Button("Clear")
- with gr.Row():
- sd_outputs = {}
- model_idx = 1
- for model_path in models:
- with gr.Column(scale=3, min_width=320):
- with gr.Box():
- sd_outputs[model_idx] = gr.Image(label=model_path)
- pass
- model_idx += 1
- pass
- pass
-
- with gr.Row(visible=False):
- start_box=gr.Number(interactive=False)
- end_box=gr.Number(interactive=False)
- tog_box=gr.Textbox(value=0,interactive=False)
-
- start_box.change(
- all_task_end,
- [start_box, end_box],
- [start_box, tog_box],
- every=1,
- show_progress=False)
-
- primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
- run.click(all_task_start, None, [start_box, end_box, tog_box])
- runs_dict = {}
- model_idx = 1
- for model_path in models:
- runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
- model_idx += 1
- pass
- pass
-
- # improve_prompts_btn_clicked=improve_prompts_btn.click(
- # get_prompts,
- # inputs=[primary_prompt],
- # outputs=[primary_prompt],
- # cancels=list(runs_dict.values()))
- clear_btn.click(
- clear_fn,
- None,
- [primary_prompt, *list(sd_outputs.values())],
- cancels=[*list(runs_dict.values())])
- tog_box.change(
- clear_it,
- tog_box,
- tog_box,
- cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
-
\ No newline at end of file
diff --git a/spaces/angelasnpang/segment-anything-ui/service.py b/spaces/angelasnpang/segment-anything-ui/service.py
deleted file mode 100644
index e16d3fe19fa975e666c9984b488943051d743035..0000000000000000000000000000000000000000
--- a/spaces/angelasnpang/segment-anything-ui/service.py
+++ /dev/null
@@ -1,112 +0,0 @@
-from typing import IO, List
-import cv2
-import torch
-from segment_anything import SamPredictor, sam_model_registry, SamAutomaticMaskGenerator
-from PIL import Image
-import numpy as np
-import io
-
-def to_file(item) -> IO[bytes]:
- # Create a BytesIO object
- file_obj = io.BytesIO()
- if isinstance(item, Image.Image):
- item.save(file_obj, format='PNG')
- if isinstance(item, np.ndarray):
- np.save(file_obj, item)
- # Reset the file object's position to the beginning
- file_obj.seek(0)
- # Return the file object
- return file_obj
-
-def get_sam(model_type, checkpoint_path, device=None):
- if device is None:
- device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
- sam = sam_model_registry[model_type](checkpoint=checkpoint_path)
- sam.to(device=device)
- return sam
-
-def draw_mask(img: Image.Image, boolean_mask: np.ndarray, color: tuple, mask_alpha: float) -> Image.Image:
- int_alpha = int(mask_alpha*255)
- color_mask = Image.new('RGBA', img.size, color=color)
- color_mask.putalpha(Image.fromarray(boolean_mask.astype(np.uint8)*int_alpha, mode='L'))
- result = Image.alpha_composite(img, color_mask)
-
- return result
-def random_color():
- return tuple(np.random.randint(0,255, 3))
-
-def draw_masks(img: Image.Image, boolean_masks: np.ndarray) -> Image.Image:
- img = img.copy()
- for boolean_mask in boolean_masks:
- img = draw_mask(img, boolean_mask, random_color(), 0.2)
- return img
-
-def cutout(img: Image.Image, boolean_mask: np.ndarray):
- rgba_img = img.convert('RGBA')
- mask = Image.fromarray(boolean_mask).convert("L")
- rgba_img.putalpha(mask)
- return rgba_img
-
-
-def predict_conditioned(sam, pil_img, **kwargs):
- rgb_arr = pil_image_to_rgb_array(pil_img)
- predictor = SamPredictor(sam)
- predictor.set_image(rgb_arr)
- masks, quality, _ = predictor.predict(**kwargs)
- return masks, quality
-
-def predict_all(sam, pil_img):
- rgb_arr = pil_image_to_rgb_array(pil_img)
- mask_generator = SamAutomaticMaskGenerator(sam)
- results = mask_generator.generate(rgb_arr)
- masks = []
- quality = []
- for result in results:
- masks.append(result['segmentation'])
- quality.append(result['stability_score'])
- masks = np.array(masks)
- quality = np.array(quality)
- return masks, quality
-
-def pil_image_to_rgb_array(image):
- if image.mode == "RGBA":
- rgb_image = Image.new("RGB", image.size, (255, 255, 255))
- rgb_image.paste(image, mask=image.split()[3]) # Apply alpha channel as the mask
- rgb_array = np.array(rgb_image)
- else:
- rgb_array = np.array(image.convert("RGB"))
- return rgb_array
-
-def box_pts_to_xyxy(pt1, pt2):
- """convert box from pts format to XYXY
- Args:
- pt1 : (x1, y1) first corner of a box
- pt2 : (x2, y2) second corner, diagonal to pt1
-
- Returns:
- xyxy: (x_min, y_min, x_max, y_max)
- """
- x1, y1 = pt1
- x2, y2 = pt2
- return (min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2))
-
-def crop_empty(image:Image.Image):
- # Convert image to numpy array
- np_image = np.array(image)
-
- # Find non-transparent pixels
- non_transparent_pixels = np_image[:, :, 3] > 0
-
- # Calculate bounding box coordinates
- rows = np.any(non_transparent_pixels, axis=1)
- cols = np.any(non_transparent_pixels, axis=0)
- ymin, ymax = np.where(rows)[0][[0, -1]]
- xmin, xmax = np.where(cols)[0][[0, -1]]
-
- # Crop the image
- cropped_image = np_image[ymin:ymax+1, xmin:xmax+1, :]
-
- # Convert cropped image back to PIL image
- pil_image = Image.fromarray(cropped_image)
-
- return pil_image
\ No newline at end of file
diff --git a/spaces/anirbans403/wikisummarizer/README.md b/spaces/anirbans403/wikisummarizer/README.md
deleted file mode 100644
index 914a31129496a8361168e7de96d03cbe0a9164a6..0000000000000000000000000000000000000000
--- a/spaces/anirbans403/wikisummarizer/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Wikisummarizer
-emoji: 👁
-colorFrom: pink
-colorTo: blue
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: `Wikisummarizer`
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: `indigo`
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: `pink`
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: `streamlit`
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: `app.py`
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: `True`
-Whether the Space stays on top of your list.
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/colorama/ansitowin32.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/colorama/ansitowin32.py
deleted file mode 100644
index abf209e60c7c4a9b1ae57452e36b383969848c2e..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/colorama/ansitowin32.py
+++ /dev/null
@@ -1,277 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-import re
-import sys
-import os
-
-from .ansi import AnsiFore, AnsiBack, AnsiStyle, Style, BEL
-from .winterm import enable_vt_processing, WinTerm, WinColor, WinStyle
-from .win32 import windll, winapi_test
-
-
-winterm = None
-if windll is not None:
- winterm = WinTerm()
-
-
-class StreamWrapper(object):
- '''
- Wraps a stream (such as stdout), acting as a transparent proxy for all
- attribute access apart from method 'write()', which is delegated to our
- Converter instance.
- '''
- def __init__(self, wrapped, converter):
- # double-underscore everything to prevent clashes with names of
- # attributes on the wrapped stream object.
- self.__wrapped = wrapped
- self.__convertor = converter
-
- def __getattr__(self, name):
- return getattr(self.__wrapped, name)
-
- def __enter__(self, *args, **kwargs):
- # special method lookup bypasses __getattr__/__getattribute__, see
- # https://stackoverflow.com/questions/12632894/why-doesnt-getattr-work-with-exit
- # thus, contextlib magic methods are not proxied via __getattr__
- return self.__wrapped.__enter__(*args, **kwargs)
-
- def __exit__(self, *args, **kwargs):
- return self.__wrapped.__exit__(*args, **kwargs)
-
- def __setstate__(self, state):
- self.__dict__ = state
-
- def __getstate__(self):
- return self.__dict__
-
- def write(self, text):
- self.__convertor.write(text)
-
- def isatty(self):
- stream = self.__wrapped
- if 'PYCHARM_HOSTED' in os.environ:
- if stream is not None and (stream is sys.__stdout__ or stream is sys.__stderr__):
- return True
- try:
- stream_isatty = stream.isatty
- except AttributeError:
- return False
- else:
- return stream_isatty()
-
- @property
- def closed(self):
- stream = self.__wrapped
- try:
- return stream.closed
- # AttributeError in the case that the stream doesn't support being closed
- # ValueError for the case that the stream has already been detached when atexit runs
- except (AttributeError, ValueError):
- return True
-
-
-class AnsiToWin32(object):
- '''
- Implements a 'write()' method which, on Windows, will strip ANSI character
- sequences from the text, and if outputting to a tty, will convert them into
- win32 function calls.
- '''
- ANSI_CSI_RE = re.compile('\001?\033\\[((?:\\d|;)*)([a-zA-Z])\002?') # Control Sequence Introducer
- ANSI_OSC_RE = re.compile('\001?\033\\]([^\a]*)(\a)\002?') # Operating System Command
-
- def __init__(self, wrapped, convert=None, strip=None, autoreset=False):
- # The wrapped stream (normally sys.stdout or sys.stderr)
- self.wrapped = wrapped
-
- # should we reset colors to defaults after every .write()
- self.autoreset = autoreset
-
- # create the proxy wrapping our output stream
- self.stream = StreamWrapper(wrapped, self)
-
- on_windows = os.name == 'nt'
- # We test if the WinAPI works, because even if we are on Windows
- # we may be using a terminal that doesn't support the WinAPI
- # (e.g. Cygwin Terminal). In this case it's up to the terminal
- # to support the ANSI codes.
- conversion_supported = on_windows and winapi_test()
- try:
- fd = wrapped.fileno()
- except Exception:
- fd = -1
- system_has_native_ansi = not on_windows or enable_vt_processing(fd)
- have_tty = not self.stream.closed and self.stream.isatty()
- need_conversion = conversion_supported and not system_has_native_ansi
-
- # should we strip ANSI sequences from our output?
- if strip is None:
- strip = need_conversion or not have_tty
- self.strip = strip
-
- # should we should convert ANSI sequences into win32 calls?
- if convert is None:
- convert = need_conversion and have_tty
- self.convert = convert
-
- # dict of ansi codes to win32 functions and parameters
- self.win32_calls = self.get_win32_calls()
-
- # are we wrapping stderr?
- self.on_stderr = self.wrapped is sys.stderr
-
- def should_wrap(self):
- '''
- True if this class is actually needed. If false, then the output
- stream will not be affected, nor will win32 calls be issued, so
- wrapping stdout is not actually required. This will generally be
- False on non-Windows platforms, unless optional functionality like
- autoreset has been requested using kwargs to init()
- '''
- return self.convert or self.strip or self.autoreset
-
- def get_win32_calls(self):
- if self.convert and winterm:
- return {
- AnsiStyle.RESET_ALL: (winterm.reset_all, ),
- AnsiStyle.BRIGHT: (winterm.style, WinStyle.BRIGHT),
- AnsiStyle.DIM: (winterm.style, WinStyle.NORMAL),
- AnsiStyle.NORMAL: (winterm.style, WinStyle.NORMAL),
- AnsiFore.BLACK: (winterm.fore, WinColor.BLACK),
- AnsiFore.RED: (winterm.fore, WinColor.RED),
- AnsiFore.GREEN: (winterm.fore, WinColor.GREEN),
- AnsiFore.YELLOW: (winterm.fore, WinColor.YELLOW),
- AnsiFore.BLUE: (winterm.fore, WinColor.BLUE),
- AnsiFore.MAGENTA: (winterm.fore, WinColor.MAGENTA),
- AnsiFore.CYAN: (winterm.fore, WinColor.CYAN),
- AnsiFore.WHITE: (winterm.fore, WinColor.GREY),
- AnsiFore.RESET: (winterm.fore, ),
- AnsiFore.LIGHTBLACK_EX: (winterm.fore, WinColor.BLACK, True),
- AnsiFore.LIGHTRED_EX: (winterm.fore, WinColor.RED, True),
- AnsiFore.LIGHTGREEN_EX: (winterm.fore, WinColor.GREEN, True),
- AnsiFore.LIGHTYELLOW_EX: (winterm.fore, WinColor.YELLOW, True),
- AnsiFore.LIGHTBLUE_EX: (winterm.fore, WinColor.BLUE, True),
- AnsiFore.LIGHTMAGENTA_EX: (winterm.fore, WinColor.MAGENTA, True),
- AnsiFore.LIGHTCYAN_EX: (winterm.fore, WinColor.CYAN, True),
- AnsiFore.LIGHTWHITE_EX: (winterm.fore, WinColor.GREY, True),
- AnsiBack.BLACK: (winterm.back, WinColor.BLACK),
- AnsiBack.RED: (winterm.back, WinColor.RED),
- AnsiBack.GREEN: (winterm.back, WinColor.GREEN),
- AnsiBack.YELLOW: (winterm.back, WinColor.YELLOW),
- AnsiBack.BLUE: (winterm.back, WinColor.BLUE),
- AnsiBack.MAGENTA: (winterm.back, WinColor.MAGENTA),
- AnsiBack.CYAN: (winterm.back, WinColor.CYAN),
- AnsiBack.WHITE: (winterm.back, WinColor.GREY),
- AnsiBack.RESET: (winterm.back, ),
- AnsiBack.LIGHTBLACK_EX: (winterm.back, WinColor.BLACK, True),
- AnsiBack.LIGHTRED_EX: (winterm.back, WinColor.RED, True),
- AnsiBack.LIGHTGREEN_EX: (winterm.back, WinColor.GREEN, True),
- AnsiBack.LIGHTYELLOW_EX: (winterm.back, WinColor.YELLOW, True),
- AnsiBack.LIGHTBLUE_EX: (winterm.back, WinColor.BLUE, True),
- AnsiBack.LIGHTMAGENTA_EX: (winterm.back, WinColor.MAGENTA, True),
- AnsiBack.LIGHTCYAN_EX: (winterm.back, WinColor.CYAN, True),
- AnsiBack.LIGHTWHITE_EX: (winterm.back, WinColor.GREY, True),
- }
- return dict()
-
- def write(self, text):
- if self.strip or self.convert:
- self.write_and_convert(text)
- else:
- self.wrapped.write(text)
- self.wrapped.flush()
- if self.autoreset:
- self.reset_all()
-
-
- def reset_all(self):
- if self.convert:
- self.call_win32('m', (0,))
- elif not self.strip and not self.stream.closed:
- self.wrapped.write(Style.RESET_ALL)
-
-
- def write_and_convert(self, text):
- '''
- Write the given text to our wrapped stream, stripping any ANSI
- sequences from the text, and optionally converting them into win32
- calls.
- '''
- cursor = 0
- text = self.convert_osc(text)
- for match in self.ANSI_CSI_RE.finditer(text):
- start, end = match.span()
- self.write_plain_text(text, cursor, start)
- self.convert_ansi(*match.groups())
- cursor = end
- self.write_plain_text(text, cursor, len(text))
-
-
- def write_plain_text(self, text, start, end):
- if start < end:
- self.wrapped.write(text[start:end])
- self.wrapped.flush()
-
-
- def convert_ansi(self, paramstring, command):
- if self.convert:
- params = self.extract_params(command, paramstring)
- self.call_win32(command, params)
-
-
- def extract_params(self, command, paramstring):
- if command in 'Hf':
- params = tuple(int(p) if len(p) != 0 else 1 for p in paramstring.split(';'))
- while len(params) < 2:
- # defaults:
- params = params + (1,)
- else:
- params = tuple(int(p) for p in paramstring.split(';') if len(p) != 0)
- if len(params) == 0:
- # defaults:
- if command in 'JKm':
- params = (0,)
- elif command in 'ABCD':
- params = (1,)
-
- return params
-
-
- def call_win32(self, command, params):
- if command == 'm':
- for param in params:
- if param in self.win32_calls:
- func_args = self.win32_calls[param]
- func = func_args[0]
- args = func_args[1:]
- kwargs = dict(on_stderr=self.on_stderr)
- func(*args, **kwargs)
- elif command in 'J':
- winterm.erase_screen(params[0], on_stderr=self.on_stderr)
- elif command in 'K':
- winterm.erase_line(params[0], on_stderr=self.on_stderr)
- elif command in 'Hf': # cursor position - absolute
- winterm.set_cursor_position(params, on_stderr=self.on_stderr)
- elif command in 'ABCD': # cursor position - relative
- n = params[0]
- # A - up, B - down, C - forward, D - back
- x, y = {'A': (0, -n), 'B': (0, n), 'C': (n, 0), 'D': (-n, 0)}[command]
- winterm.cursor_adjust(x, y, on_stderr=self.on_stderr)
-
-
- def convert_osc(self, text):
- for match in self.ANSI_OSC_RE.finditer(text):
- start, end = match.span()
- text = text[:start] + text[end:]
- paramstring, command = match.groups()
- if command == BEL:
- if paramstring.count(";") == 1:
- params = paramstring.split(";")
- # 0 - change title and icon (we will only change title)
- # 1 - change icon (we don't support this)
- # 2 - change title
- if params[0] in '02':
- winterm.set_title(params[1])
- return text
-
-
- def flush(self):
- self.wrapped.flush()
diff --git a/spaces/ashercn97/AsherTesting/extensions/api/util.py b/spaces/ashercn97/AsherTesting/extensions/api/util.py
deleted file mode 100644
index a9d581eb03c0d56e628f6ed21d7f19c85deb9bce..0000000000000000000000000000000000000000
--- a/spaces/ashercn97/AsherTesting/extensions/api/util.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import asyncio
-import functools
-import threading
-import time
-import traceback
-from threading import Thread
-from typing import Callable, Optional
-
-from modules import shared
-from modules.chat import load_character_memoized
-from modules.presets import load_preset_memoized
-
-
-# We use a thread local to store the asyncio lock, so that each thread
-# has its own lock. This isn't strictly necessary, but it makes it
-# such that if we can support multiple worker threads in the future,
-# thus handling multiple requests in parallel.
-api_tls = threading.local()
-
-
-def build_parameters(body, chat=False):
-
- generate_params = {
- 'max_new_tokens': int(body.get('max_new_tokens', body.get('max_length', 200))),
- 'do_sample': bool(body.get('do_sample', True)),
- 'temperature': float(body.get('temperature', 0.5)),
- 'top_p': float(body.get('top_p', 1)),
- 'typical_p': float(body.get('typical_p', body.get('typical', 1))),
- 'epsilon_cutoff': float(body.get('epsilon_cutoff', 0)),
- 'eta_cutoff': float(body.get('eta_cutoff', 0)),
- 'tfs': float(body.get('tfs', 1)),
- 'top_a': float(body.get('top_a', 0)),
- 'repetition_penalty': float(body.get('repetition_penalty', body.get('rep_pen', 1.1))),
- 'repetition_penalty_range': int(body.get('repetition_penalty_range', 0)),
- 'encoder_repetition_penalty': float(body.get('encoder_repetition_penalty', 1.0)),
- 'top_k': int(body.get('top_k', 0)),
- 'min_length': int(body.get('min_length', 0)),
- 'no_repeat_ngram_size': int(body.get('no_repeat_ngram_size', 0)),
- 'num_beams': int(body.get('num_beams', 1)),
- 'penalty_alpha': float(body.get('penalty_alpha', 0)),
- 'length_penalty': float(body.get('length_penalty', 1)),
- 'early_stopping': bool(body.get('early_stopping', False)),
- 'mirostat_mode': int(body.get('mirostat_mode', 0)),
- 'mirostat_tau': float(body.get('mirostat_tau', 5)),
- 'mirostat_eta': float(body.get('mirostat_eta', 0.1)),
- 'seed': int(body.get('seed', -1)),
- 'add_bos_token': bool(body.get('add_bos_token', True)),
- 'truncation_length': int(body.get('truncation_length', body.get('max_context_length', 2048))),
- 'ban_eos_token': bool(body.get('ban_eos_token', False)),
- 'skip_special_tokens': bool(body.get('skip_special_tokens', True)),
- 'custom_stopping_strings': '', # leave this blank
- 'stopping_strings': body.get('stopping_strings', []),
- }
-
- preset_name = body.get('preset', 'None')
- if preset_name not in ['None', None, '']:
- preset = load_preset_memoized(preset_name)
- generate_params.update(preset)
-
- if chat:
- character = body.get('character')
- instruction_template = body.get('instruction_template', shared.settings['instruction_template'])
- if str(instruction_template) == "None":
- instruction_template = "Vicuna-v1.1"
-
- name1, name2, _, greeting, context, _ = load_character_memoized(character, str(body.get('your_name', shared.settings['name1'])), shared.settings['name2'], instruct=False)
- name1_instruct, name2_instruct, _, _, context_instruct, turn_template = load_character_memoized(instruction_template, '', '', instruct=True)
- generate_params.update({
- 'stop_at_newline': bool(body.get('stop_at_newline', shared.settings['stop_at_newline'])),
- 'chat_generation_attempts': int(body.get('chat_generation_attempts', shared.settings['chat_generation_attempts'])),
- 'mode': str(body.get('mode', 'chat')),
- 'name1': name1,
- 'name2': name2,
- 'context': context,
- 'greeting': greeting,
- 'name1_instruct': name1_instruct,
- 'name2_instruct': name2_instruct,
- 'context_instruct': body.get('context_instruct', context_instruct),
- 'turn_template': turn_template,
- 'chat-instruct_command': str(body.get('chat-instruct_command', shared.settings['chat-instruct_command'])),
- 'history': body.get('history', {'internal': [], 'visible': []})
- })
-
- return generate_params
-
-
-def try_start_cloudflared(port: int, max_attempts: int = 3, on_start: Optional[Callable[[str], None]] = None):
- Thread(target=_start_cloudflared, args=[
- port, max_attempts, on_start], daemon=True).start()
-
-
-def _start_cloudflared(port: int, max_attempts: int = 3, on_start: Optional[Callable[[str], None]] = None):
- try:
- from flask_cloudflared import _run_cloudflared
- except ImportError:
- print('You should install flask_cloudflared manually')
- raise Exception(
- 'flask_cloudflared not installed. Make sure you installed the requirements.txt for this extension.')
-
- for _ in range(max_attempts):
- try:
- public_url = _run_cloudflared(port, port + 1)
-
- if on_start:
- on_start(public_url)
-
- return
- except Exception:
- traceback.print_exc()
- time.sleep(3)
-
- raise Exception('Could not start cloudflared.')
-
-
-def _get_api_lock(tls) -> asyncio.Lock:
- """
- The streaming and blocking API implementations each run on their own
- thread, and multiplex requests using asyncio. If multiple outstanding
- requests are received at once, we will try to acquire the shared lock
- shared.generation_lock multiple times in succession in the same thread,
- which will cause a deadlock.
-
- To avoid this, we use this wrapper function to block on an asyncio
- lock, and then try and grab the shared lock only while holding
- the asyncio lock.
- """
- if not hasattr(tls, "asyncio_lock"):
- tls.asyncio_lock = asyncio.Lock()
-
- return tls.asyncio_lock
-
-
-def with_api_lock(func):
- """
- This decorator should be added to all streaming API methods which
- require access to the shared.generation_lock. It ensures that the
- tls.asyncio_lock is acquired before the method is called, and
- released afterwards.
- """
- @functools.wraps(func)
- async def api_wrapper(*args, **kwargs):
- async with _get_api_lock(api_tls):
- return await func(*args, **kwargs)
- return api_wrapper
diff --git a/spaces/ashercn97/AsherTesting/modules/monkey_patch_gptq_lora.py b/spaces/ashercn97/AsherTesting/modules/monkey_patch_gptq_lora.py
deleted file mode 100644
index bf8d478d8b76eae296e1fb80a4266b0475b7d0c2..0000000000000000000000000000000000000000
--- a/spaces/ashercn97/AsherTesting/modules/monkey_patch_gptq_lora.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copied from https://github.com/johnsmith0031/alpaca_lora_4bit
-
-import sys
-from pathlib import Path
-
-sys.path.insert(0, str(Path("repositories/alpaca_lora_4bit")))
-
-import autograd_4bit
-from amp_wrapper import AMPWrapper
-from autograd_4bit import (
- Autograd4bitQuantLinear,
- load_llama_model_4bit_low_ram
-)
-from monkeypatch.peft_tuners_lora_monkey_patch import (
- Linear4bitLt,
- replace_peft_model_with_gptq_lora_model
-)
-
-from modules import shared
-from modules.GPTQ_loader import find_quantized_model_file
-
-replace_peft_model_with_gptq_lora_model()
-
-
-def load_model_llama(model_name):
- config_path = str(Path(f'{shared.args.model_dir}/{model_name}'))
- model_path = str(find_quantized_model_file(model_name))
- model, tokenizer = load_llama_model_4bit_low_ram(config_path, model_path, groupsize=shared.args.groupsize, is_v1_model=False)
- for n, m in model.named_modules():
- if isinstance(m, Autograd4bitQuantLinear) or isinstance(m, Linear4bitLt):
- if m.is_v1_model:
- m.zeros = m.zeros.half()
- m.scales = m.scales.half()
- m.bias = m.bias.half()
-
- autograd_4bit.use_new = True
- autograd_4bit.auto_switch = True
-
- model.half()
- wrapper = AMPWrapper(model)
- wrapper.apply_generate()
-
- return model, tokenizer
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Akshey Singhal.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Akshey Singhal.html
deleted file mode 100644
index 0d5b27fac4057330cb0ac90d0528468baad36abb..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Akshey Singhal.html
+++ /dev/null
@@ -1,132 +0,0 @@
-
-
-
- Akshey Singhal
-
-
-
-
-
-
Akshey Singhal
-
-
-
1. How did you hear about SM and why SM?
Through LinkedIn.
enthusiastic & curious to help others- wants to help others learn and grow, has a passion for machine learning and a desire to share that experience with others, and a desire to contribute to the field by helping to educate the next generation of machine learning practitioners.
Being a mentor is a fulfilling experience, as it allows me to share my knowledge and expertise with others and potentially make a positive impact on their lives and careers. It is a great opportunity to stay up-to-date on the latest developments and trends in machine learning, as mentoring others may require you to continually learn and stay current in the field.
2. Give me a brief overview of your career journey.
Moved from Engineer to DS field. First job was as an associate engineer at a finance firm. - Currently, working ML engineer with a VPN Company Windscribe worked in the DS field for ~6 years Like working with a startup - building solutions
Moved to Canada in 2020. master - computing & DA Started DS career in the energy domain worked more with startups worked with RBC
worked in different domains in fields and across DS. want to bring a change using these skillsets.
3. Do you have any experience mentoring, either formally or informally? How was your experience mentoring with Great Learning been?
- Been a mentor at Great Learning. ( Great Ratings ) Worked with them for over a year (16 months)
4. According to you What are some common mistakes beginners make? What do they need the most help with?
- people don't know how to showcase their skills. not able to present work in their resume. Language in resume. Candidates face a lot of challenges in getting first interviews.
- Follow up: How can you help with these as a mentor?
- Identifying the skillsets of mentees & challenges they are facing - background understanding session - Resume building and editing - Step-by-step process to apply for jobs Once interview calls are received - mock interviews.
5. Do you have any questions about SM? - How the structure works? - how does it work? - a scenario where mentees don't select mentors? - payg mentoring? - how does community work? - range for ISA % - How many mentees are on the platform? - Wasn't aware of the exact number so we skipped this.
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Suhas Shekar.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Suhas Shekar.html
deleted file mode 100644
index bc0fad74fc92bb8d5ee4e26ea616986069105ccc..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Suhas Shekar.html
+++ /dev/null
@@ -1,134 +0,0 @@
-
-
-
- Suhas Shekar
-
-
-
-
-
-
Suhas Shekar
-
-
-
How did you hear about SM?
Was thinking about mentorship, and this popped up
Brief background
UCLA - econ major minor in CS
2.5 years as DS at Accenture
at Meta for 5.5 years (Ads, WhatsApp. crypto, now VR Team, managing a team of 5)
product analytics - influencing product strategy
FB data scientists are basically product analysts
Mentorship exp
Was part of Purple Squirrel (mentorship program, hourly rate), but they closed
leading teams
started coaching in 7th grade (tennis)
career dev chair in college
I got a ton of great mentorship
FB has an internal mentorship program
The role of a mentor
Zoom out - what do you really want - do some soul searching (lifestyle-centric planning)
be realistic, intermingle life stuff (e.g. startups vs big corps)
startups can be very top down
What do beginners need and how can you help?
now managing ppl older than me,
communicating data insights into actual business outcomes
writing a TLDR, creating slides, next steps, and context
"What's the so what?"
Good visualizations
folks get stuck in academic type thinking on writing for biz - number everything, clear and concise sentences, making good visuals
The tech stuff, you can learn online and with books
But learning business communication is not so straightforward
every person is different
give examples (I have some examples of my own examples that were not good - what's missing)
sometimes handhold a bit before they take-off (e.g. do 3/30 slides for them)
On resumes - not a JD, what business impact?
interview prepping
understanding what the day-to-day of different roles look like
-
-
Questions about SM:
What should I expect?
It's a higher commitment from the mentees.
What % are finding mentees?
How does the ISA work?
Is mostly job seekers - what about mid-career folks?
What kind of jobs are people looking for?
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/avans06/whisper-webui-translate/app-network.py b/spaces/avans06/whisper-webui-translate/app-network.py
deleted file mode 100644
index 4f0e565b9029761d4b995fe32a65c58d1de55f53..0000000000000000000000000000000000000000
--- a/spaces/avans06/whisper-webui-translate/app-network.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Run the app with no audio file restrictions, and make it available on the network
-from app import create_ui
-from src.config import ApplicationConfig
-
-create_ui(ApplicationConfig.create_default(input_audio_max_duration=-1, server_name="0.0.0.0"))
\ No newline at end of file
diff --git a/spaces/awacke1/HTML5-3D-Map-Hospitals/index.html b/spaces/awacke1/HTML5-3D-Map-Hospitals/index.html
deleted file mode 100644
index 6c2dac4bcfb17bca7f3b7701db2071c634641336..0000000000000000000000000000000000000000
--- a/spaces/awacke1/HTML5-3D-Map-Hospitals/index.html
+++ /dev/null
@@ -1,50 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/awacke1/MistralCoder/app.py b/spaces/awacke1/MistralCoder/app.py
deleted file mode 100644
index 8e3d707e98e2344a99aba6cc587460ae085ea391..0000000000000000000000000000000000000000
--- a/spaces/awacke1/MistralCoder/app.py
+++ /dev/null
@@ -1,206 +0,0 @@
-from huggingface_hub import InferenceClient
-import gradio as gr
-
-client = InferenceClient(
- "mistralai/Mistral-7B-Instruct-v0.1"
-)
-
-
-def format_prompt(message, history):
- prompt = ""
- for user_prompt, bot_response in history:
- prompt += f"[INST] {user_prompt} [/INST]"
- prompt += f" {bot_response} "
- prompt += f"[INST] {message} [/INST]"
- return prompt
-
-def generate(
- prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
-):
- temperature = float(temperature)
- if temperature < 1e-2:
- temperature = 1e-2
- top_p = float(top_p)
-
- generate_kwargs = dict(
- temperature=temperature,
- max_new_tokens=max_new_tokens,
- top_p=top_p,
- repetition_penalty=repetition_penalty,
- do_sample=True,
- seed=42,
- )
-
- formatted_prompt = format_prompt(prompt, history)
-
- stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
- output = ""
-
- for response in stream:
- output += response.token.text
- yield output
- return output
-
-
-additional_inputs=[
- gr.Slider(
- label="Temperature",
- value=0.9,
- minimum=0.0,
- maximum=1.0,
- step=0.05,
- interactive=True,
- info="Higher values produce more diverse outputs",
- ),
- gr.Slider(
- label="Max new tokens",
- value=256,
- minimum=0,
- maximum=1048,
- step=64,
- interactive=True,
- info="The maximum numbers of new tokens",
- ),
- gr.Slider(
- label="Top-p (nucleus sampling)",
- value=0.90,
- minimum=0.0,
- maximum=1,
- step=0.05,
- interactive=True,
- info="Higher values sample more low-probability tokens",
- ),
- gr.Slider(
- label="Repetition penalty",
- value=1.2,
- minimum=1.0,
- maximum=2.0,
- step=0.05,
- interactive=True,
- info="Penalize repeated tokens",
- )
-]
-
-css = """
- #mkd {
- height: 200px;
- overflow: auto;
- border: 1px solid #ccc;
- }
-"""
-
-with gr.Blocks(css=css) as demo:
-
- gr.ChatInterface(
- generate,
- additional_inputs=additional_inputs,
- examples = [
- ["🐍 Write a Python Streamlit program that shows a thumbs up and thumbs down button for scoring an evaluation. When the user clicks, maintain a saved text file that tracks and shows the number of clicks with a refresh and sorts responses by the number of clicks."],
- ["📊 Create a Pandas DataFrame and display it using Streamlit. Use emojis to indicate the status of each row (e.g., ✅ for good, ❌ for bad)."],
- ["🗂 Using Gradio, create a simple interface where users can upload a CSV file and filter the data based on selected columns."],
- ["😃 Implement emoji reactions in a Streamlit app. When a user clicks on an emoji, record the click count in a Pandas DataFrame and display the DataFrame."],
- ["🔗 Create a program that fetches a dataset from Huggingface Hub and shows basic statistics about it using Pandas in a Streamlit app."],
- ["🤖 Use Gradio to create a user interface for a text summarizer model from Huggingface Hub."],
- ["📈 Create a Streamlit app to visualize time series data. Use Pandas to manipulate the data and plot it using Streamlit’s native plotting options."],
- ["🎙 Implement a voice-activated feature in a Gradio interface. Use a pre-trained model from Huggingface Hub for speech recognition."],
- ["🔍 Create a search function in a Streamlit app that filters through a Pandas DataFrame and displays the results."],
- ["🤗 Write a Python script that uploads a model to Huggingface Hub and then uses it in a Streamlit app."],
- ["👏 Create a Gradio interface for a clapping hands emoji (👏) counter. When a user inputs a text, the interface should return the number of clapping hands emojis in the text."],
- ["📜 Use Pandas to read an Excel sheet in a Streamlit app. Allow the user to select which sheet they want to view."],
- ["🔒 Implement a login screen in a Streamlit app using Python. Secure the login by hashing the password."],
- ["🤩 Create a Gradio interface that uses a model from Huggingface Hub to generate creative text based on a user’s input. Add sliders for controlling temperature and other hyperparameters."]
- ]
- )
- gr.HTML("""
🤖 Mistral Chat - Gradio 🤖
- In this demo, you can chat with Mistral-7B-Instruct model. 💬
- Learn more about the model here. 📚
-
🛠 Model Features 🛠
-
-
🪟 Sliding Window Attention with 128K tokens span
-
🚀 GQA for faster inference
-
📝 Byte-fallback BPE tokenizer
-
-
📜 License 📜 Released under Apache 2.0 License
-
📦 Usage 📦
-
-
📚 Available on Huggingface Hub
-
🐍 Python code snippets for easy setup
-
📈 Expected speedups with Flash Attention 2
-
- """)
-
- markdown="""
- | Feature | Description | Byline |
- |---------|-------------|--------|
- | 🪟 Sliding Window Attention with 128K tokens span | Enables the model to have a larger context for each token. | Increases model's understanding of context, resulting in more coherent and contextually relevant outputs. |
- | 🚀 GQA for faster inference | Graph Query Attention allows faster computation during inference. | Speeds up the model inference time without sacrificing too much on accuracy. |
- | 📝 Byte-fallback BPE tokenizer | Uses Byte Pair Encoding but can fall back to byte-level encoding. | Allows the tokenizer to handle a wider variety of input text while keeping token size manageable. |
- | 📜 License | Released under Apache 2.0 License | Gives you a permissive free software license, allowing you freedom to use, modify, and distribute the code. |
- | 📦 Usage | | |
- | 📚 Available on Huggingface Hub | The model can be easily downloaded and set up from Huggingface. | Makes it easier to integrate the model into various projects. |
- | 🐍 Python code snippets for easy setup | Provides Python code snippets for quick and easy model setup. | Facilitates rapid development and deployment, especially useful for prototyping. |
- | 📈 Expected speedups with Flash Attention 2 | Upcoming update expected to bring speed improvements. | Keep an eye out for this update to benefit from performance gains. |
-
-# 🛠 Model Features and More 🛠
-
-## Features
-
-- 🪟 Sliding Window Attention with 128K tokens span
- - **Byline**: Increases model's understanding of context, resulting in more coherent and contextually relevant outputs.
-
-- 🚀 GQA for faster inference
- - **Byline**: Speeds up the model inference time without sacrificing too much on accuracy.
-
-- 📝 Byte-fallback BPE tokenizer
- - **Byline**: Allows the tokenizer to handle a wider variety of input text while keeping token size manageable.
-
-- 📜 License: Released under Apache 2.0 License
- - **Byline**: Gives you a permissive free software license, allowing you freedom to use, modify, and distribute the code.
-
-## Usage 📦
-
-- 📚 Available on Huggingface Hub
- - **Byline**: Makes it easier to integrate the model into various projects.
-
-- 🐍 Python code snippets for easy setup
- - **Byline**: Facilitates rapid development and deployment, especially useful for prototyping.
-
-- 📈 Expected speedups with Flash Attention 2
- - **Byline**: Keep an eye out for this update to benefit from performance gains.
- """
- gr.Markdown(markdown)
-
-
- def SpeechSynthesis(result):
- documentHTML5='''
-
-
-
- Read It Aloud
-
-
-
-
🔊 Read It Aloud
-
-
-
-
-
- '''
- gr.HTML(documentHTML5)
- # components.html(documentHTML5, width=1280, height=1024)
- #return result
- SpeechSynthesis(markdown)
-
-
-demo.queue().launch(debug=True)
\ No newline at end of file
diff --git a/spaces/awacke1/Topic-modeling/app.py b/spaces/awacke1/Topic-modeling/app.py
deleted file mode 100644
index b7611bc447c1cb437cedc3e84f3fb5b58953508c..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Topic-modeling/app.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import streamlit as st
-import transformers
-import numpy as np
-
-# Load the pre-trained model
-model1 = transformers.pipeline("text2text-generation", model="bigscience/T0pp")
-model2 = transformers.pipeline("text2text-generation", model="google/flan-t5-xxl")
-model3 = transformers.pipeline("text2text-generation", model="google/flan-t5-xl")
-model4 = transformers.pipeline("text2text-generation", model="tuner007/pegasus_paraphrase")
-model5 = transformers.pipeline("text2text-generation", model="tuner007/pegasus_paraphrase")
-
-# Define the Streamlit app
-def main():
- st.title("Topic Modeling with Hugging Face")
- text = st.text_area("Enter some text to generate topics", height=200)
-
- if st.button("Generate Topics"):
- # Generate topics
- topics1 = model1(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7)
- topics2 = model2(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7)
- topics3 = model3(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7)
- topics4 = model4(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7)
- topics5 = model5(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7)
-
- # Print topics
- st.write("Top 5 topics:")
- for i in range(5):
- st.write(f"{i+1}. {topics1[i]['generated_text']}")
- st.write(f"{i+1}. {topics2[i]['generated_text']}")
- st.write(f"{i+1}. {topics3[i]['generated_text']}")
- st.write(f"{i+1}. {topics4[i]['generated_text']}")
- st.write(f"{i+1}. {topics5[i]['generated_text']}")
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/awacke1/VizLib-Matplotlib/README.md b/spaces/awacke1/VizLib-Matplotlib/README.md
deleted file mode 100644
index 88179dfd97c0dea0864a0e29913ff5725dfa2acd..0000000000000000000000000000000000000000
--- a/spaces/awacke1/VizLib-Matplotlib/README.md
+++ /dev/null
@@ -1,25 +0,0 @@
----
-title: 📈VizLib Matplotlib🎨
-emoji: 🚀💻🎓
-colorFrom: purple
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-📈 Discover the Power of Matplotlib: A Tutorial to Create Stunning Visualizations in Python 🐍
-Python enthusiasts and data scientists, rejoice! Our new Matplotlib tutorial will teach you how to create professional-quality visualizations to take your data analysis to the next level.
-
-🎨 Versatile Library for Creating Charts and Graphs
-Matplotlib is a powerful and versatile library that enables you to create a wide range of charts and graphs with ease. From heatmaps to 3D visualizations, our tutorial covers 10 different types of plots, allowing you to choose the perfect one for your data.
-
-🚀 Interactive Visualizations with Streamlit
-In this tutorial, you'll learn how to use Matplotlib with Streamlit to interactively display your visualizations, making it easy to share your work with others. Our step-by-step guide is designed to be accessible to beginners, while also providing advanced techniques for more experienced users.
-
-💻 Lots of Code Examples and Images
-With lots of code examples and images, our tutorial will guide you through creating heatmaps, contour plots, quiver plots, and many more. You'll also learn how to customize your visualizations with color maps and labels, and how to create 3D plots that showcase your data in a whole new dimension.
-
-🎓 For Everyone, from Beginners to Experts
-Whether you're a data analyst, a data scientist, or simply looking to add data visualization skills to your repertoire, our Matplotlib tutorial has something for everyone. So don't wait any longer to unleash the power of Matplotlib and create stunning visualizations that bring your data to life.
\ No newline at end of file
diff --git a/spaces/b1sheng/kg_llm_leaderboard_test/app.py b/spaces/b1sheng/kg_llm_leaderboard_test/app.py
deleted file mode 100644
index 4ea2c738a8a92cf48a11b7f0ea971abf7a4f230a..0000000000000000000000000000000000000000
--- a/spaces/b1sheng/kg_llm_leaderboard_test/app.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import gradio as gr
-import pandas as pd
-
-from src.assets.text_content import *
-from src.assets.css_html_js import custom_css
-
-
-def get_leaderboard_df():
- data = {
- 'Datasets': ['metrics','SOTA(FT)', 'SOTA(ZS)', 'FLAN-T5-XXL', 'text-davinci-001', 'text-davinci-002', 'text-davinci-003', 'ChatGPT', 'GPT-4'],
- 'KQApro': ['Acc','93.85', '94.20', '37.27', '38.28', '38.01', '40.35', '47.93', '57.20'],
- 'LC-quad2': ['F1','33.10', '-', '30.14', '33.04', '33.77', '39.04', '42.76', '54.95'],
- 'WQSP': ['Acc','73.10', '62.98', '59.87', '67.68', '72.34', '79.60', '83.70', '90.45'],
- 'CWQ': ['Acc','72.20', '-', '46.69', '51.77', '53.96', '57.54', '64.02', '71.00'],
- 'GrailQA': ['Acc','76.31', '-', '29.02', '27.58', '30.50', '35.43', '46.77', '51.40'],
- 'GraphQ': ['Acc','41.30', '-', '32.27', '38.32', '40.85', '47.95', '53.10', '63.20'],
- 'QALD-9': ['F1','67.82', '-', '30.17', '38.54', '44.96', '46.19', '45.71', '57.20'],
- 'MKQA': ['Acc','46.00', '-', '20.17', '26.97', '30.14', '39.05', '44.30', '59.20']
- }
-
- df = pd.DataFrame(data)
- return df
-
-
-def search_table(df, query):
- return df[df.apply(lambda row: row.astype(str).str.lower().str.contains(query.lower()).any(), axis=1)]
-
-
-original_df = get_leaderboard_df()
-leaderboard_df = original_df.copy()
-
-demo = gr.Blocks(css=custom_css)
-with demo:
- gr.HTML(TITLE)
- gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
- with gr.Row():
- with gr.Box(elem_id="search-bar-table-box"):
- search_bar = gr.Textbox(
- placeholder="🔍 Search your model and press ENTER...",
- show_label=False,
- elem_id="search-bar",
- )
-
- with gr.Tabs(elem_classes="tab-buttons") as tabs:
- with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=1):
- leaderboard_table = gr.components.Dataframe(
- value=leaderboard_df,
- max_rows=None,
- elem_id="leaderboard-table",
- )
-
- # Dummy leaderboard for handling the case when the user uses backspace key
- hidden_leaderboard_table_for_search = gr.components.Dataframe(
- value=original_df,
- max_rows=None,
- visible=False,
- )
- search_bar.submit(
- search_table,
- [hidden_leaderboard_table_for_search, search_bar],
- leaderboard_table,
- )
- with gr.TabItem("About", elem_id="llm-benchmark-tab-table", id=2):
- gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
-
- with gr.Row():
- with gr.Accordion("📙 Citation", open=False):
- citation_button = gr.Textbox(
- value=CITATION_BUTTON_TEXT,
- label=CITATION_BUTTON_LABEL,
- elem_id="citation-button",
- ).style(show_copy_button=True)
-
-demo.queue(concurrency_count=40).launch()
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/utils/BypassNode.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/utils/BypassNode.js
deleted file mode 100644
index bc671933c32935a1a99f5305a110b2a4cbaab865..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/utils/BypassNode.js
+++ /dev/null
@@ -1,85 +0,0 @@
-/**
- * @author sunag / http://www.sunag.com.br/
- */
-
-import { Node } from '../core/Node.js';
-
-function BypassNode( code, value ) {
-
- Node.call( this );
-
- this.code = code;
- this.value = value;
-
-}
-
-BypassNode.prototype = Object.create( Node.prototype );
-BypassNode.prototype.constructor = BypassNode;
-BypassNode.prototype.nodeType = "Bypass";
-
-BypassNode.prototype.getType = function ( builder ) {
-
- if ( this.value ) {
-
- return this.value.getType( builder );
-
- } else if ( builder.isShader( 'fragment' ) ) {
-
- return 'f';
-
- }
-
- return 'void';
-
-};
-
-BypassNode.prototype.generate = function ( builder, output ) {
-
- var code = this.code.build( builder, output ) + ';';
-
- builder.addNodeCode( code );
-
- if ( builder.isShader( 'vertex' ) ) {
-
- if ( this.value ) {
-
- return this.value.build( builder, output );
-
- }
-
- } else {
-
- return this.value ? this.value.build( builder, output ) : builder.format( '0.0', 'f', output );
-
- }
-
-};
-
-BypassNode.prototype.copy = function ( source ) {
-
- Node.prototype.copy.call( this, source );
-
- this.code = source.code;
- this.value = source.value;
-
-};
-
-BypassNode.prototype.toJSON = function ( meta ) {
-
- var data = this.getJSONNode( meta );
-
- if ( ! data ) {
-
- data = this.createJSONNode( meta );
-
- data.code = this.code.toJSON( meta ).uuid;
-
- if ( this.value ) data.value = this.value.toJSON( meta ).uuid;
-
- }
-
- return data;
-
-};
-
-export { BypassNode };
diff --git a/spaces/bioriAsaeru/text-to-voice/Adobe Acrobat Pro DC 19.021.20061 Crack With Keygen UPD 2020 Download.md b/spaces/bioriAsaeru/text-to-voice/Adobe Acrobat Pro DC 19.021.20061 Crack With Keygen UPD 2020 Download.md
deleted file mode 100644
index 078204fb1ebd87d8adc6349e893990105cb97435..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Adobe Acrobat Pro DC 19.021.20061 Crack With Keygen UPD 2020 Download.md
+++ /dev/null
@@ -1,27 +0,0 @@
-
-```html
-
Adobe Acrobat Pro DC 19.021.20061 Crack With Keygen 2020 Download
-
Adobe Acrobat Pro DC is the leading PDF editing software that allows you to create, edit, convert, and sign PDF documents. With Adobe Acrobat Pro DC, you can access your PDF files from anywhere, on any device, and share them with anyone. You can also use Adobe Acrobat Pro DC to create interactive forms, collect feedback, and collaborate with others.
-
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However, Adobe Acrobat Pro DC is not a free software. You need to purchase a subscription or a license to use all its features. If you don't want to pay for Adobe Acrobat Pro DC, you might be tempted to download a cracked version from the internet. But this is not a good idea. Here are some reasons why you should avoid Adobe Acrobat Pro DC 19.021.20061 Crack With Keygen 2020 Download:
-
-
It is illegal. Downloading and using a cracked version of Adobe Acrobat Pro DC is a violation of the software's license agreement and copyright laws. You could face legal consequences if you are caught.
-
It is unsafe. Downloading and installing a cracked version of Adobe Acrobat Pro DC could expose your computer to malware, viruses, spyware, and other threats. You could lose your data, compromise your privacy, or damage your system.
-
It is unreliable. A cracked version of Adobe Acrobat Pro DC might not work properly or have all the features of the original software. You could experience errors, crashes, glitches, or compatibility issues.
-
It is unethical. Downloading and using a cracked version of Adobe Acrobat Pro DC is unfair to the developers who worked hard to create the software and provide updates and support. You are also depriving yourself of the benefits of using a genuine and authorized version of Adobe Acrobat Pro DC.
-
-
Therefore, we recommend that you do not download or use Adobe Acrobat Pro DC 19.021.20061 Crack With Keygen 2020 Download. Instead, you should purchase a legitimate copy of Adobe Acrobat Pro DC from the official website or an authorized reseller. You will get the best performance, security, and customer service from Adobe Acrobat Pro DC.
-```
-
-```html
-
If you are looking for a PDF editing software that is affordable, easy to use, and reliable, you might want to consider some alternatives to Adobe Acrobat Pro DC. Here are some of the best PDF editors that you can download or use online for free or at a low cost:
-
-
PDFescape: This is a web-based PDF editor that allows you to view, edit, annotate, fill out forms, and create PDFs online. You can also download a desktop version for Windows. PDFescape has a free plan that lets you edit up to 100 pages and 10 MB of PDF files. You can also upgrade to a premium plan for more features and storage.
-
Foxit PhantomPDF: This is a powerful and versatile PDF editor that works on Windows, Mac, Linux, iOS, and Android. You can create, edit, convert, sign, protect, and share PDF files with Foxit PhantomPDF. You can also use advanced features such as OCR, redaction, collaboration, and cloud integration. Foxit PhantomPDF offers a free trial for 14 days and a subscription plan starting from $9.99 per month.
-
Nitro Pro: This is a fast and easy PDF editor that works on Windows. You can create, edit, convert, sign, review, and share PDF files with Nitro Pro. You can also use features such as OCR, batch processing, digital signatures, and cloud integration. Nitro Pro offers a free trial for 14 days and a license plan starting from $159 per user.
-
-
These are some of the best alternatives to Adobe Acrobat Pro DC that you can try today. They are legal, safe, reliable, and ethical. They will help you work with PDF files without breaking the bank or risking your security.
-
-```
d5da3c52bf
-
-
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Guide du routard rome epub download Experience Rome like a local with this guide.md b/spaces/bioriAsaeru/text-to-voice/Guide du routard rome epub download Experience Rome like a local with this guide.md
deleted file mode 100644
index 0becec89c3dfa720bc73a5ed5762c4c03d5b521a..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Guide du routard rome epub download Experience Rome like a local with this guide.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/blanchon/gaussian-splatting-kit/services/colmap.py b/spaces/blanchon/gaussian-splatting-kit/services/colmap.py
deleted file mode 100644
index f8f10df4597d008bf0fcf1e3f8bd4e41cecb0f88..0000000000000000000000000000000000000000
--- a/spaces/blanchon/gaussian-splatting-kit/services/colmap.py
+++ /dev/null
@@ -1,244 +0,0 @@
-from typing import Literal, Optional
-from io import IOBase
-import os
-from pathlib import Path
-import shutil
-import subprocess
-from rich.progress import Progress
-from rich.console import Console
-
-console = Console()
-
-class FailedProcess(Exception):
- pass
-
-def colmap_feature_extraction(
- database_path: Path,
- image_path: Path,
- camera: Literal["OPENCV"],
- colmap_command: str = "colmap",
- use_gpu: bool = True,
- stream_file: Optional[IOBase] = None
- ):
- total = len(list(image_path.glob("*.jpg")))
- with Progress(console=console) as progress:
- task = progress.add_task("Feature Extraction", total=total)
-
- database_path.parent.mkdir(parents=True, exist_ok=True)
- cmd = [
- colmap_command,
- "feature_extractor",
- "--database_path", database_path.as_posix(),
- "--image_path", image_path.as_posix(),
- "--ImageReader.single_camera", "1",
- "--ImageReader.camera_model", camera,
- "--SiftExtraction.use_gpu", "1" if use_gpu else "0",
- # "--SiftExtraction.domain_size_pooling", "1",
- # "--SiftExtraction.estimate_affine_shape", "1"
- ]
- console.log(f"💻 Executing command: {' '.join(cmd)}")
-
- _stdout = stream_file if stream_file else subprocess.PIPE
- with subprocess.Popen(cmd, stdout=_stdout, stderr=subprocess.STDOUT, text=True) as process:
- if process.stdout:
- for line in process.stdout:
- if line.startswith("Processed file "):
- line_process = line\
- .replace("Processed file [", "")\
- .replace("]", "")\
- .replace("\n", "")
- current, total = line_process.split("/")
- progress.update(task, completed=int(current), total=int(total), refresh=True)
-
- progress.update(task, completed=int(total), refresh=True)
-
- return_code = process.returncode
-
- if return_code == 0:
-
- console.log(f'Feature stored in {database_path.as_posix()}.')
- console.log('✅ Feature extraction completed.')
- else:
- raise FailedProcess("Feature extraction failed.")
-
-def colmap_feature_matching(
- database_path: Path,
- image_path: Path,
- colmap_command: str = "colmap",
- use_gpu: bool = True,
- stream_file: Optional[IOBase] = None
- ):
- total = len(list(image_path.glob("*.jpg")))
- with Progress(console=console) as progress:
- task = progress.add_task("Feature Matching", total=total)
-
- database_path
- cmd = [
- colmap_command,
- "exhaustive_matcher",
- "--database_path", database_path.as_posix(),
- "--SiftMatching.use_gpu", "1" if use_gpu else "0"
- ]
- console.log(f"💻 Executing command: {' '.join(cmd)}")
-
- _stdout = stream_file if stream_file else subprocess.PIPE
- with subprocess.Popen(cmd, stdout=_stdout, stderr=subprocess.STDOUT, text=True) as process:
- if process.stdout:
- for line in process.stdout:
- pass
-
- progress.update(task, completed=int(total), refresh=True)
-
- return_code = process.returncode
-
- if return_code == 0:
-
- console.log('✅ Feature matching completed.')
- else:
- raise FailedProcess("Feature matching failed.")
-
-def colmap_bundle_adjustment(
- database_path: Path,
- image_path: Path,
- sparse_path: Path,
- colmap_command: str = "colmap",
- stream_file: Optional[IOBase] = None
- ):
- total = len(list(image_path.glob("*.jpg")))
- with Progress(console=console) as progress:
- task = progress.add_task("Bundle Adjustment", total=total)
-
- cmd = [
- colmap_command,
- "mapper",
- "--database_path", database_path.as_posix(),
- "--image_path", image_path.as_posix(),
- "--output_path", sparse_path.as_posix(),
- "--Mapper.ba_global_function_tolerance=0.000001"
- # "--Mapper.ba_local_max_num_iterations", "40",
- # "--Mapper.ba_global_max_num_iterations", "100",
- # "--Mapper.ba_local_max_refinements", "3",
- # "--Mapper.ba_global_max_refinements", "5"
- ]
- console.log(f"💻 Executing command: {' '.join(cmd)}")
-
- sparse_path.mkdir(parents=True, exist_ok=True)
-
- _stdout = stream_file if stream_file else subprocess.PIPE
- with subprocess.Popen(cmd, stdout=_stdout, stderr=subprocess.STDOUT, text=True) as process:
- if process.stdout:
- for line in process.stdout:
- print(line)
- if line.startswith("Registering image #"):
- line_process = line\
- .replace("Registering image #", "")\
- .replace("\n", "")
- *_, current = line_process.split("(")
- current, *_ = current.split(")")
- progress.update(task, completed=int(current), refresh=True)
-
- progress.update(task, completed=int(total), refresh=True)
-
- return_code = process.returncode
-
- if return_code == 0:
- console.log('✅ Bundle adjustment completed.')
- else:
- raise FailedProcess("Bundle adjustment failed.")
-
-def colmap_image_undistortion(
- image_path: Path,
- sparse0_path: Path,
- source_path: Path,
- colmap_command: str = "colmap",
- stream_file: Optional[IOBase] = None
- ):
- total = len(list(image_path.glob("*.jpg")))
- with Progress(console=console) as progress:
- task = progress.add_task("Image Undistortion", total=total)
- cmd = [
- colmap_command,
- "image_undistorter",
- "--image_path", image_path.as_posix(),
- "--input_path", sparse0_path.as_posix(),
- "--output_path", source_path.as_posix(),
- "--output_type", "COLMAP"
- ]
- console.log(f"💻 Executing command: {' '.join(cmd)}")
-
- _stdout = stream_file if stream_file else subprocess.PIPE
- with subprocess.Popen(cmd, stdout=_stdout, stderr=subprocess.STDOUT, text=True) as process:
- if process.stdout:
- for line in process.stdout:
- if line.startswith("Undistorting image ["):
- line_process = line\
- .replace("Undistorting image [", "")\
- .replace("]", "")\
- .replace("\n", "")
- current, total = line_process.split("/")
- progress.update(task, completed=int(current), total=int(total), refresh=True)
-
- progress.update(task, completed=int(total), refresh=True)
-
- return_code = process.returncode
-
- if return_code == 0:
- console.log('✅ Image undistortion completed.')
- else:
- raise FailedProcess("Image undistortion failed.")
-
-def colmap(
- source_path: Path,
- camera: Literal["OPENCV"] = "OPENCV",
- colmap_command: str = "colmap",
- use_gpu: bool = True,
- skip_matching: bool = False,
- stream_file: Optional[IOBase] = None
-):
- image_path = source_path / "input"
- if not image_path.exists():
- raise Exception(f"Image path {image_path} does not exist. Exiting.")
-
- total = len(list(image_path.glob("*.jpg")))
- if total == 0:
- raise Exception(f"No images found in {image_path}. Exiting.")
-
- database_path = source_path / "distorted" / "database.db"
-
- sparse_path = source_path / "distorted" / "sparse"
-
- if not skip_matching:
- colmap_feature_extraction(database_path, image_path, camera, colmap_command, use_gpu, stream_file)
- colmap_feature_matching(database_path, image_path, colmap_command, use_gpu, stream_file)
- colmap_bundle_adjustment(database_path, image_path, sparse_path, colmap_command, stream_file)
-
- colmap_image_undistortion(image_path, sparse_path / "0", source_path, colmap_command, stream_file)
-
- origin_path = source_path / "sparse"
- destination_path = source_path / "sparse" / "0"
- destination_path.mkdir(exist_ok=True)
- console.log(f"🌟 Moving files from {origin_path} to {destination_path}")
- for file in os.listdir(origin_path):
- if file == '0':
- continue
- source_file = os.path.join(origin_path, file)
- destination_file = os.path.join(destination_path, file)
- shutil.copy(source_file, destination_file)
-
-if __name__ == "__main__":
- import tempfile
- with tempfile.NamedTemporaryFile(mode='w+t') as temp_file:
- print(f"Using temp file: {temp_file.name}")
- try:
- colmap(
- source_path = Path("/home/europe/Desktop/gaussian-splatting-kit/test/"),
- camera = "OPENCV",
- colmap_command = "colmap",
- use_gpu = True,
- skip_matching = False,
- stream_file = open("/home/europe/Desktop/gaussian-splatting-kit/test.log", "w+t")
- )
- except FailedProcess:
- console.log("🚨 Error executing colmap.")
- temp_file.seek(0)
- print(temp_file.read())
\ No newline at end of file
diff --git a/spaces/bofenghuang/whisper-demo-german/README.md b/spaces/bofenghuang/whisper-demo-german/README.md
deleted file mode 100644
index e0f03247249211a34b0183037a5a2901051c50a6..0000000000000000000000000000000000000000
--- a/spaces/bofenghuang/whisper-demo-german/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Whisper German Demo
-emoji: 🤫
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.9.1
-app_file: app.py
-pinned: false
-tags:
-- whisper-event
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/point_rend/point_head.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/point_rend/point_head.py
deleted file mode 100644
index 1786fad5c54841faf86b1fbef83d909e3bf2b1f9..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/point_rend/point_head.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.layers import ShapeSpec, cat
-from detectron2.utils.events import get_event_storage
-from detectron2.utils.registry import Registry
-
-POINT_HEAD_REGISTRY = Registry("POINT_HEAD")
-POINT_HEAD_REGISTRY.__doc__ = """
-Registry for point heads, which makes prediction for a given set of per-point features.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-"""
-
-
-def roi_mask_point_loss(mask_logits, instances, point_labels):
- """
- Compute the point-based loss for instance segmentation mask predictions
- given point-wise mask prediction and its corresponding point-wise labels.
- Args:
- mask_logits (Tensor): A tensor of shape (R, C, P) or (R, 1, P) for class-specific or
- class-agnostic, where R is the total number of predicted masks in all images, C is the
- number of foreground classes, and P is the number of points sampled for each mask.
- The values are logits.
- instances (list[Instances]): A list of N Instances, where N is the number of images
- in the batch. These instances are in 1:1 correspondence with the `mask_logits`. So, i_th
- elememt of the list contains R_i objects and R_1 + ... + R_N is equal to R.
- The ground-truth labels (class, box, mask, ...) associated with each instance are stored
- in fields.
- point_labels (Tensor): A tensor of shape (R, P), where R is the total number of
- predicted masks and P is the number of points for each mask.
- Labels with value of -1 will be ignored.
- Returns:
- point_loss (Tensor): A scalar tensor containing the loss.
- """
- with torch.no_grad():
- cls_agnostic_mask = mask_logits.size(1) == 1
- total_num_masks = mask_logits.size(0)
-
- gt_classes = []
- for instances_per_image in instances:
- if len(instances_per_image) == 0:
- continue
-
- if not cls_agnostic_mask:
- gt_classes_per_image = instances_per_image.gt_classes.to(dtype=torch.int64)
- gt_classes.append(gt_classes_per_image)
-
- gt_mask_logits = point_labels
- point_ignores = point_labels == -1
- if gt_mask_logits.shape[0] == 0:
- return mask_logits.sum() * 0
-
- assert gt_mask_logits.numel() > 0, gt_mask_logits.shape
-
- if cls_agnostic_mask:
- mask_logits = mask_logits[:, 0]
- else:
- indices = torch.arange(total_num_masks)
- gt_classes = cat(gt_classes, dim=0)
- mask_logits = mask_logits[indices, gt_classes]
-
- # Log the training accuracy (using gt classes and 0.0 threshold for the logits)
- mask_accurate = (mask_logits > 0.0) == gt_mask_logits.to(dtype=torch.uint8)
- mask_accurate = mask_accurate[~point_ignores]
- mask_accuracy = mask_accurate.nonzero().size(0) / max(mask_accurate.numel(), 1.0)
- get_event_storage().put_scalar("point/accuracy", mask_accuracy)
-
- point_loss = F.binary_cross_entropy_with_logits(
- mask_logits, gt_mask_logits.to(dtype=torch.float32), weight=~point_ignores, reduction="mean"
- )
- return point_loss
-
-
-@POINT_HEAD_REGISTRY.register()
-class StandardPointHead(nn.Module):
- """
- A point head multi-layer perceptron which we model with conv1d layers with kernel 1. The head
- takes both fine-grained and coarse prediction features as its input.
- """
-
- def __init__(self, cfg, input_shape: ShapeSpec):
- """
- The following attributes are parsed from config:
- fc_dim: the output dimension of each FC layers
- num_fc: the number of FC layers
- coarse_pred_each_layer: if True, coarse prediction features are concatenated to each
- layer's input
- """
- super(StandardPointHead, self).__init__()
- # fmt: off
- num_classes = cfg.MODEL.POINT_HEAD.NUM_CLASSES
- fc_dim = cfg.MODEL.POINT_HEAD.FC_DIM
- num_fc = cfg.MODEL.POINT_HEAD.NUM_FC
- cls_agnostic_mask = cfg.MODEL.POINT_HEAD.CLS_AGNOSTIC_MASK
- self.coarse_pred_each_layer = cfg.MODEL.POINT_HEAD.COARSE_PRED_EACH_LAYER
- input_channels = input_shape.channels
- # fmt: on
-
- fc_dim_in = input_channels + num_classes
- self.fc_layers = []
- for k in range(num_fc):
- fc = nn.Conv1d(fc_dim_in, fc_dim, kernel_size=1, stride=1, padding=0, bias=True)
- self.add_module("fc{}".format(k + 1), fc)
- self.fc_layers.append(fc)
- fc_dim_in = fc_dim
- fc_dim_in += num_classes if self.coarse_pred_each_layer else 0
-
- num_mask_classes = 1 if cls_agnostic_mask else num_classes
- self.predictor = nn.Conv1d(fc_dim_in, num_mask_classes, kernel_size=1, stride=1, padding=0)
-
- for layer in self.fc_layers:
- weight_init.c2_msra_fill(layer)
- # use normal distribution initialization for mask prediction layer
- nn.init.normal_(self.predictor.weight, std=0.001)
- if self.predictor.bias is not None:
- nn.init.constant_(self.predictor.bias, 0)
-
- def forward(self, fine_grained_features, coarse_features):
- x = torch.cat((fine_grained_features, coarse_features), dim=1)
- for layer in self.fc_layers:
- x = F.relu(layer(x))
- if self.coarse_pred_each_layer:
- x = cat((x, coarse_features), dim=1)
- return self.predictor(x)
-
-
-@POINT_HEAD_REGISTRY.register()
-class ImplicitPointHead(nn.Module):
- """
- A point head multi-layer perceptron which we model with conv1d layers with kernel 1. The head
- takes both fine-grained features and instance-wise MLP parameters as its input.
- """
-
- def __init__(self, cfg, input_shape: ShapeSpec):
- """
- The following attributes are parsed from config:
- channels: the output dimension of each FC layers
- num_layers: the number of FC layers (including the final prediction layer)
- image_feature_enabled: if True, fine-grained image-level features are used
- positional_encoding_enabled: if True, positional encoding is used
- """
- super(ImplicitPointHead, self).__init__()
- # fmt: off
- self.num_layers = cfg.MODEL.POINT_HEAD.NUM_FC + 1
- self.channels = cfg.MODEL.POINT_HEAD.FC_DIM
- self.image_feature_enabled = cfg.MODEL.IMPLICIT_POINTREND.IMAGE_FEATURE_ENABLED
- self.positional_encoding_enabled = cfg.MODEL.IMPLICIT_POINTREND.POS_ENC_ENABLED
- self.num_classes = (
- cfg.MODEL.POINT_HEAD.NUM_CLASSES if not cfg.MODEL.POINT_HEAD.CLS_AGNOSTIC_MASK else 1
- )
- self.in_channels = input_shape.channels
- # fmt: on
-
- if not self.image_feature_enabled:
- self.in_channels = 0
- if self.positional_encoding_enabled:
- self.in_channels += 256
- self.register_buffer("positional_encoding_gaussian_matrix", torch.randn((2, 128)))
-
- assert self.in_channels > 0
-
- num_weight_params, num_bias_params = [], []
- assert self.num_layers >= 2
- for l in range(self.num_layers):
- if l == 0:
- # input layer
- num_weight_params.append(self.in_channels * self.channels)
- num_bias_params.append(self.channels)
- elif l == self.num_layers - 1:
- # output layer
- num_weight_params.append(self.channels * self.num_classes)
- num_bias_params.append(self.num_classes)
- else:
- # intermediate layer
- num_weight_params.append(self.channels * self.channels)
- num_bias_params.append(self.channels)
-
- self.num_weight_params = num_weight_params
- self.num_bias_params = num_bias_params
- self.num_params = sum(num_weight_params) + sum(num_bias_params)
-
- def forward(self, fine_grained_features, point_coords, parameters):
- # features: [R, channels, K]
- # point_coords: [R, K, 2]
- num_instances = fine_grained_features.size(0)
- num_points = fine_grained_features.size(2)
-
- if num_instances == 0:
- return torch.zeros((0, 1, num_points), device=fine_grained_features.device)
-
- if self.positional_encoding_enabled:
- # locations: [R*K, 2]
- locations = 2 * point_coords.reshape(num_instances * num_points, 2) - 1
- locations = locations @ self.positional_encoding_gaussian_matrix.to(locations.device)
- locations = 2 * np.pi * locations
- locations = torch.cat([torch.sin(locations), torch.cos(locations)], dim=1)
- # locations: [R, C, K]
- locations = locations.reshape(num_instances, num_points, 256).permute(0, 2, 1)
- if not self.image_feature_enabled:
- fine_grained_features = locations
- else:
- fine_grained_features = torch.cat([locations, fine_grained_features], dim=1)
-
- # features [R, C, K]
- mask_feat = fine_grained_features.reshape(num_instances, self.in_channels, num_points)
-
- weights, biases = self._parse_params(
- parameters,
- self.in_channels,
- self.channels,
- self.num_classes,
- self.num_weight_params,
- self.num_bias_params,
- )
-
- point_logits = self._dynamic_mlp(mask_feat, weights, biases, num_instances)
- point_logits = point_logits.reshape(-1, self.num_classes, num_points)
-
- return point_logits
-
- @staticmethod
- def _dynamic_mlp(features, weights, biases, num_instances):
- assert features.dim() == 3, features.dim()
- n_layers = len(weights)
- x = features
- for i, (w, b) in enumerate(zip(weights, biases)):
- x = torch.einsum("nck,ndc->ndk", x, w) + b
- if i < n_layers - 1:
- x = F.relu(x)
- return x
-
- @staticmethod
- def _parse_params(
- pred_params,
- in_channels,
- channels,
- num_classes,
- num_weight_params,
- num_bias_params,
- ):
- assert pred_params.dim() == 2
- assert len(num_weight_params) == len(num_bias_params)
- assert pred_params.size(1) == sum(num_weight_params) + sum(num_bias_params)
-
- num_instances = pred_params.size(0)
- num_layers = len(num_weight_params)
-
- params_splits = list(
- torch.split_with_sizes(pred_params, num_weight_params + num_bias_params, dim=1)
- )
-
- weight_splits = params_splits[:num_layers]
- bias_splits = params_splits[num_layers:]
-
- for l in range(num_layers):
- if l == 0:
- # input layer
- weight_splits[l] = weight_splits[l].reshape(num_instances, channels, in_channels)
- bias_splits[l] = bias_splits[l].reshape(num_instances, channels, 1)
- elif l < num_layers - 1:
- # intermediate layer
- weight_splits[l] = weight_splits[l].reshape(num_instances, channels, channels)
- bias_splits[l] = bias_splits[l].reshape(num_instances, channels, 1)
- else:
- # output layer
- weight_splits[l] = weight_splits[l].reshape(num_instances, num_classes, channels)
- bias_splits[l] = bias_splits[l].reshape(num_instances, num_classes, 1)
-
- return weight_splits, bias_splits
-
-
-def build_point_head(cfg, input_channels):
- """
- Build a point head defined by `cfg.MODEL.POINT_HEAD.NAME`.
- """
- head_name = cfg.MODEL.POINT_HEAD.NAME
- return POINT_HEAD_REGISTRY.get(head_name)(cfg, input_channels)
diff --git a/spaces/caffeinum/VToonify/vtoonify/model/stylegan/distributed.py b/spaces/caffeinum/VToonify/vtoonify/model/stylegan/distributed.py
deleted file mode 100644
index 51fa243257ef302e2015d5ff36ac531b86a9a0ce..0000000000000000000000000000000000000000
--- a/spaces/caffeinum/VToonify/vtoonify/model/stylegan/distributed.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import math
-import pickle
-
-import torch
-from torch import distributed as dist
-from torch.utils.data.sampler import Sampler
-
-
-def get_rank():
- if not dist.is_available():
- return 0
-
- if not dist.is_initialized():
- return 0
-
- return dist.get_rank()
-
-
-def synchronize():
- if not dist.is_available():
- return
-
- if not dist.is_initialized():
- return
-
- world_size = dist.get_world_size()
-
- if world_size == 1:
- return
-
- dist.barrier()
-
-
-def get_world_size():
- if not dist.is_available():
- return 1
-
- if not dist.is_initialized():
- return 1
-
- return dist.get_world_size()
-
-
-def reduce_sum(tensor):
- if not dist.is_available():
- return tensor
-
- if not dist.is_initialized():
- return tensor
-
- tensor = tensor.clone()
- dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
-
- return tensor
-
-
-def gather_grad(params):
- world_size = get_world_size()
-
- if world_size == 1:
- return
-
- for param in params:
- if param.grad is not None:
- dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
- param.grad.data.div_(world_size)
-
-
-def all_gather(data):
- world_size = get_world_size()
-
- if world_size == 1:
- return [data]
-
- buffer = pickle.dumps(data)
- storage = torch.ByteStorage.from_buffer(buffer)
- tensor = torch.ByteTensor(storage).to('cuda')
-
- local_size = torch.IntTensor([tensor.numel()]).to('cuda')
- size_list = [torch.IntTensor([0]).to('cuda') for _ in range(world_size)]
- dist.all_gather(size_list, local_size)
- size_list = [int(size.item()) for size in size_list]
- max_size = max(size_list)
-
- tensor_list = []
- for _ in size_list:
- tensor_list.append(torch.ByteTensor(size=(max_size,)).to('cuda'))
-
- if local_size != max_size:
- padding = torch.ByteTensor(size=(max_size - local_size,)).to('cuda')
- tensor = torch.cat((tensor, padding), 0)
-
- dist.all_gather(tensor_list, tensor)
-
- data_list = []
-
- for size, tensor in zip(size_list, tensor_list):
- buffer = tensor.cpu().numpy().tobytes()[:size]
- data_list.append(pickle.loads(buffer))
-
- return data_list
-
-
-def reduce_loss_dict(loss_dict):
- world_size = get_world_size()
-
- if world_size < 2:
- return loss_dict
-
- with torch.no_grad():
- keys = []
- losses = []
-
- for k in sorted(loss_dict.keys()):
- keys.append(k)
- losses.append(loss_dict[k])
-
- losses = torch.stack(losses, 0)
- dist.reduce(losses, dst=0)
-
- if dist.get_rank() == 0:
- losses /= world_size
-
- reduced_losses = {k: v for k, v in zip(keys, losses)}
-
- return reduced_losses
diff --git a/spaces/calvin/MuseGAN/README.md b/spaces/calvin/MuseGAN/README.md
deleted file mode 100644
index f0d52387cbb786cc3f915fd539f474bdac06ed04..0000000000000000000000000000000000000000
--- a/spaces/calvin/MuseGAN/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: MuseGAN
-emoji: 📊
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-license: wtfpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/tracking/test_hungarian_tracker.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/tracking/test_hungarian_tracker.py
deleted file mode 100644
index 660c635990a3370945e7f14422dcd978320e4782..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/tracking/test_hungarian_tracker.py
+++ /dev/null
@@ -1,102 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-import unittest
-from typing import Dict
-import torch
-
-from detectron2.config import instantiate
-from detectron2.structures import Boxes, Instances
-
-
-class TestBaseHungarianTracker(unittest.TestCase):
- def setUp(self):
- self._img_size = np.array([600, 800])
- self._prev_boxes = np.array(
- [
- [101, 101, 200, 200],
- [301, 301, 450, 450],
- ]
- ).astype(np.float32)
- self._prev_scores = np.array([0.9, 0.9])
- self._prev_classes = np.array([1, 1])
- self._prev_masks = np.ones((2, 600, 800)).astype("uint8")
- self._curr_boxes = np.array(
- [
- [302, 303, 451, 452],
- [101, 102, 201, 203],
- ]
- ).astype(np.float32)
- self._curr_scores = np.array([0.95, 0.85])
- self._curr_classes = np.array([1, 1])
- self._curr_masks = np.ones((2, 600, 800)).astype("uint8")
-
- self._prev_instances = {
- "image_size": self._img_size,
- "pred_boxes": self._prev_boxes,
- "scores": self._prev_scores,
- "pred_classes": self._prev_classes,
- "pred_masks": self._prev_masks,
- }
- self._prev_instances = self._convertDictPredictionToInstance(self._prev_instances)
- self._curr_instances = {
- "image_size": self._img_size,
- "pred_boxes": self._curr_boxes,
- "scores": self._curr_scores,
- "pred_classes": self._curr_classes,
- "pred_masks": self._curr_masks,
- }
- self._curr_instances = self._convertDictPredictionToInstance(self._curr_instances)
-
- self._max_num_instances = 200
- self._max_lost_frame_count = 0
- self._min_box_rel_dim = 0.02
- self._min_instance_period = 1
- self._track_iou_threshold = 0.5
-
- def _convertDictPredictionToInstance(self, prediction: Dict) -> Instances:
- """
- convert prediction from Dict to D2 Instances format
- """
- res = Instances(
- image_size=torch.IntTensor(prediction["image_size"]),
- pred_boxes=Boxes(torch.FloatTensor(prediction["pred_boxes"])),
- pred_masks=torch.IntTensor(prediction["pred_masks"]),
- pred_classes=torch.IntTensor(prediction["pred_classes"]),
- scores=torch.FloatTensor(prediction["scores"]),
- )
- return res
-
- def test_init(self):
- cfg = {
- "_target_": "detectron2.tracking.hungarian_tracker.BaseHungarianTracker",
- "video_height": self._img_size[0],
- "video_width": self._img_size[1],
- "max_num_instances": self._max_num_instances,
- "max_lost_frame_count": self._max_lost_frame_count,
- "min_box_rel_dim": self._min_box_rel_dim,
- "min_instance_period": self._min_instance_period,
- "track_iou_threshold": self._track_iou_threshold,
- }
- tracker = instantiate(cfg)
- self.assertTrue(tracker._video_height == self._img_size[0])
-
- def test_initialize_extra_fields(self):
- cfg = {
- "_target_": "detectron2.tracking.hungarian_tracker.BaseHungarianTracker",
- "video_height": self._img_size[0],
- "video_width": self._img_size[1],
- "max_num_instances": self._max_num_instances,
- "max_lost_frame_count": self._max_lost_frame_count,
- "min_box_rel_dim": self._min_box_rel_dim,
- "min_instance_period": self._min_instance_period,
- "track_iou_threshold": self._track_iou_threshold,
- }
- tracker = instantiate(cfg)
- instances = tracker._initialize_extra_fields(self._curr_instances)
- self.assertTrue(instances.has("ID"))
- self.assertTrue(instances.has("ID_period"))
- self.assertTrue(instances.has("lost_frame_count"))
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/chaozn/face_emotion_classifier/README.md b/spaces/chaozn/face_emotion_classifier/README.md
deleted file mode 100644
index d44d3eb02613c5c4c859732eae474df52e3c4074..0000000000000000000000000000000000000000
--- a/spaces/chaozn/face_emotion_classifier/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Face Emotion Classifier
-emoji: 🏢
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/docs/demo/openvino_cpp_readme.md b/spaces/chendl/compositional_test/multimodal/YOLOX/docs/demo/openvino_cpp_readme.md
deleted file mode 100644
index 3f455940a26a0cc4ce6b12fee4bb97725055458a..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/docs/demo/openvino_cpp_readme.md
+++ /dev/null
@@ -1 +0,0 @@
-../../demo/OpenVINO/cpp/README.md
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/hubconf.py b/spaces/chendl/compositional_test/multimodal/YOLOX/hubconf.py
deleted file mode 100644
index 6ff7f37fdd7efdd126e04f7ede3a9d066e74dde6..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/hubconf.py
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-
-"""
-Usage example:
- import torch
- model = torch.hub.load("Megvii-BaseDetection/YOLOX", "yolox_s")
- model = torch.hub.load("Megvii-BaseDetection/YOLOX", "yolox_custom",
- exp_path="exp.py", ckpt_path="ckpt.pth")
-"""
-dependencies = ["torch"]
-
-from yolox.models import ( # isort:skip # noqa: F401, E402
- yolox_tiny,
- yolox_nano,
- yolox_s,
- yolox_m,
- yolox_l,
- yolox_x,
- yolov3,
- yolox_custom
-)
diff --git a/spaces/chendl/compositional_test/transformers/examples/pytorch/summarization/run_summarization.py b/spaces/chendl/compositional_test/transformers/examples/pytorch/summarization/run_summarization.py
deleted file mode 100644
index 42c1bef72702f381db0e1a85f8d8136714b93285..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/pytorch/summarization/run_summarization.py
+++ /dev/null
@@ -1,753 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 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.
-"""
-Fine-tuning the library models for sequence to sequence.
-"""
-# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
-
-import logging
-import os
-import sys
-from dataclasses import dataclass, field
-from typing import Optional
-
-import datasets
-import evaluate
-import nltk # Here to have a nice missing dependency error message early on
-import numpy as np
-from datasets import load_dataset
-from filelock import FileLock
-
-import transformers
-from transformers import (
- AutoConfig,
- AutoModelForSeq2SeqLM,
- AutoTokenizer,
- DataCollatorForSeq2Seq,
- HfArgumentParser,
- MBart50Tokenizer,
- MBart50TokenizerFast,
- MBartTokenizer,
- MBartTokenizerFast,
- Seq2SeqTrainer,
- Seq2SeqTrainingArguments,
- set_seed,
-)
-from transformers.trainer_utils import get_last_checkpoint
-from transformers.utils import check_min_version, is_offline_mode, send_example_telemetry
-from transformers.utils.versions import require_version
-
-
-# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.28.0")
-
-require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
-
-logger = logging.getLogger(__name__)
-
-try:
- nltk.data.find("tokenizers/punkt")
-except (LookupError, OSError):
- if is_offline_mode():
- raise LookupError(
- "Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
- )
- with FileLock(".lock") as lock:
- nltk.download("punkt", quiet=True)
-
-# A list of all multilingual tokenizer which require lang attribute.
-MULTILINGUAL_TOKENIZERS = [MBartTokenizer, MBartTokenizerFast, MBart50Tokenizer, MBart50TokenizerFast]
-
-
-@dataclass
-class ModelArguments:
- """
- Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
- """
-
- model_name_or_path: str = field(
- metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
- )
- config_name: Optional[str] = field(
- default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
- )
- tokenizer_name: Optional[str] = field(
- default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
- )
- cache_dir: Optional[str] = field(
- default=None,
- metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
- )
- use_fast_tokenizer: bool = field(
- default=True,
- metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
- )
- model_revision: str = field(
- default="main",
- metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
- )
- use_auth_token: bool = field(
- default=False,
- metadata={
- "help": (
- "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
- "with private models)."
- )
- },
- )
- resize_position_embeddings: Optional[bool] = field(
- default=None,
- metadata={
- "help": (
- "Whether to automatically resize the position embeddings if `max_source_length` exceeds "
- "the model's position embeddings."
- )
- },
- )
-
-
-@dataclass
-class DataTrainingArguments:
- """
- Arguments pertaining to what data we are going to input our model for training and eval.
- """
-
- lang: Optional[str] = field(default=None, metadata={"help": "Language id for summarization."})
-
- dataset_name: Optional[str] = field(
- default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
- )
- dataset_config_name: Optional[str] = field(
- default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
- )
- text_column: Optional[str] = field(
- default=None,
- metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
- )
- summary_column: Optional[str] = field(
- default=None,
- metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
- )
- train_file: Optional[str] = field(
- default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."}
- )
- validation_file: Optional[str] = field(
- default=None,
- metadata={
- "help": (
- "An optional input evaluation data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
- )
- },
- )
- test_file: Optional[str] = field(
- default=None,
- metadata={
- "help": "An optional input test data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
- },
- )
- overwrite_cache: bool = field(
- default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
- )
- preprocessing_num_workers: Optional[int] = field(
- default=None,
- metadata={"help": "The number of processes to use for the preprocessing."},
- )
- max_source_length: Optional[int] = field(
- default=1024,
- metadata={
- "help": (
- "The maximum total input sequence length after tokenization. Sequences longer "
- "than this will be truncated, sequences shorter will be padded."
- )
- },
- )
- max_target_length: Optional[int] = field(
- default=128,
- metadata={
- "help": (
- "The maximum total sequence length for target text after tokenization. Sequences longer "
- "than this will be truncated, sequences shorter will be padded."
- )
- },
- )
- val_max_target_length: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "The maximum total sequence length for validation target text after tokenization. Sequences longer "
- "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
- "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
- "during ``evaluate`` and ``predict``."
- )
- },
- )
- pad_to_max_length: bool = field(
- default=False,
- metadata={
- "help": (
- "Whether to pad all samples to model maximum sentence length. "
- "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
- "efficient on GPU but very bad for TPU."
- )
- },
- )
- max_train_samples: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "For debugging purposes or quicker training, truncate the number of training examples to this "
- "value if set."
- )
- },
- )
- max_eval_samples: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
- "value if set."
- )
- },
- )
- max_predict_samples: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "For debugging purposes or quicker training, truncate the number of prediction examples to this "
- "value if set."
- )
- },
- )
- num_beams: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
- "which is used during ``evaluate`` and ``predict``."
- )
- },
- )
- ignore_pad_token_for_loss: bool = field(
- default=True,
- metadata={
- "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
- },
- )
- source_prefix: Optional[str] = field(
- default="", metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
- )
-
- forced_bos_token: Optional[str] = field(
- default=None,
- metadata={
- "help": (
- "The token to force as the first generated token after the decoder_start_token_id."
- "Useful for multilingual models like mBART where the first generated token"
- "needs to be the target language token (Usually it is the target language token)"
- )
- },
- )
-
- def __post_init__(self):
- if (
- self.dataset_name is None
- and self.train_file is None
- and self.validation_file is None
- and self.test_file is None
- ):
- raise ValueError("Need either a dataset name or a training, validation, or test file.")
- else:
- if self.train_file is not None:
- extension = self.train_file.split(".")[-1]
- assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
- if self.validation_file is not None:
- extension = self.validation_file.split(".")[-1]
- assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
- if self.test_file is not None:
- extension = self.test_file.split(".")[-1]
- assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
- if self.val_max_target_length is None:
- self.val_max_target_length = self.max_target_length
-
-
-summarization_name_mapping = {
- "amazon_reviews_multi": ("review_body", "review_title"),
- "big_patent": ("description", "abstract"),
- "cnn_dailymail": ("article", "highlights"),
- "orange_sum": ("text", "summary"),
- "pn_summary": ("article", "summary"),
- "psc": ("extract_text", "summary_text"),
- "samsum": ("dialogue", "summary"),
- "thaisum": ("body", "summary"),
- "xglue": ("news_body", "news_title"),
- "xsum": ("document", "summary"),
- "wiki_summary": ("article", "highlights"),
- "multi_news": ("document", "summary"),
-}
-
-
-def main():
- # See all possible arguments in src/transformers/training_args.py
- # or by passing the --help flag to this script.
- # We now keep distinct sets of args, for a cleaner separation of concerns.
-
- parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
- if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
- # If we pass only one argument to the script and it's the path to a json file,
- # let's parse it to get our arguments.
- model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
- else:
- model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
- # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
- # information sent is the one passed as arguments along with your Python/PyTorch versions.
- send_example_telemetry("run_summarization", model_args, data_args)
-
- # Setup logging
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- handlers=[logging.StreamHandler(sys.stdout)],
- )
-
- if training_args.should_log:
- # The default of training_args.log_level is passive, so we set log level at info here to have that default.
- transformers.utils.logging.set_verbosity_info()
-
- log_level = training_args.get_process_log_level()
- logger.setLevel(log_level)
- datasets.utils.logging.set_verbosity(log_level)
- transformers.utils.logging.set_verbosity(log_level)
- transformers.utils.logging.enable_default_handler()
- transformers.utils.logging.enable_explicit_format()
-
- # Log on each process the small summary:
- logger.warning(
- f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
- + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
- )
- logger.info(f"Training/evaluation parameters {training_args}")
-
- if data_args.source_prefix is None and model_args.model_name_or_path in [
- "t5-small",
- "t5-base",
- "t5-large",
- "t5-3b",
- "t5-11b",
- ]:
- logger.warning(
- "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
- "`--source_prefix 'summarize: ' `"
- )
-
- # Detecting last checkpoint.
- last_checkpoint = None
- if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
- last_checkpoint = get_last_checkpoint(training_args.output_dir)
- if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
- raise ValueError(
- f"Output directory ({training_args.output_dir}) already exists and is not empty. "
- "Use --overwrite_output_dir to overcome."
- )
- elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
- logger.info(
- f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
- "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
- )
-
- # Set seed before initializing model.
- set_seed(training_args.seed)
-
- # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
- # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
- # (the dataset will be downloaded automatically from the datasets Hub).
- #
- # For CSV/JSON files this script will use the first column for the full texts and the second column for the
- # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
- #
- # In distributed training, the load_dataset function guarantee that only one local process can concurrently
- # download the dataset.
- if data_args.dataset_name is not None:
- # Downloading and loading a dataset from the hub.
- raw_datasets = load_dataset(
- data_args.dataset_name,
- data_args.dataset_config_name,
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- else:
- data_files = {}
- if data_args.train_file is not None:
- data_files["train"] = data_args.train_file
- extension = data_args.train_file.split(".")[-1]
- if data_args.validation_file is not None:
- data_files["validation"] = data_args.validation_file
- extension = data_args.validation_file.split(".")[-1]
- if data_args.test_file is not None:
- data_files["test"] = data_args.test_file
- extension = data_args.test_file.split(".")[-1]
- raw_datasets = load_dataset(
- extension,
- data_files=data_files,
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
- # https://huggingface.co/docs/datasets/loading_datasets.html.
-
- # Load pretrained model and tokenizer
- #
- # Distributed training:
- # The .from_pretrained methods guarantee that only one local process can concurrently
- # download model & vocab.
- config = AutoConfig.from_pretrained(
- model_args.config_name if model_args.config_name else model_args.model_name_or_path,
- cache_dir=model_args.cache_dir,
- revision=model_args.model_revision,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- tokenizer = AutoTokenizer.from_pretrained(
- model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
- cache_dir=model_args.cache_dir,
- use_fast=model_args.use_fast_tokenizer,
- revision=model_args.model_revision,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- model = AutoModelForSeq2SeqLM.from_pretrained(
- model_args.model_name_or_path,
- from_tf=bool(".ckpt" in model_args.model_name_or_path),
- config=config,
- cache_dir=model_args.cache_dir,
- revision=model_args.model_revision,
- use_auth_token=True if model_args.use_auth_token else None,
- )
-
- # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
- # on a small vocab and want a smaller embedding size, remove this test.
- embedding_size = model.get_input_embeddings().weight.shape[0]
- if len(tokenizer) > embedding_size:
- model.resize_token_embeddings(len(tokenizer))
-
- if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
- if isinstance(tokenizer, MBartTokenizer):
- model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.lang]
- else:
- model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.lang)
-
- if model.config.decoder_start_token_id is None:
- raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
-
- if (
- hasattr(model.config, "max_position_embeddings")
- and model.config.max_position_embeddings < data_args.max_source_length
- ):
- if model_args.resize_position_embeddings is None:
- logger.warning(
- "Increasing the model's number of position embedding vectors from"
- f" {model.config.max_position_embeddings} to {data_args.max_source_length}."
- )
- model.resize_position_embeddings(data_args.max_source_length)
- elif model_args.resize_position_embeddings:
- model.resize_position_embeddings(data_args.max_source_length)
- else:
- raise ValueError(
- f"`--max_source_length` is set to {data_args.max_source_length}, but the model only has"
- f" {model.config.max_position_embeddings} position encodings. Consider either reducing"
- f" `--max_source_length` to {model.config.max_position_embeddings} or to automatically resize the"
- " model's position encodings by passing `--resize_position_embeddings`."
- )
-
- prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
-
- # Preprocessing the datasets.
- # We need to tokenize inputs and targets.
- if training_args.do_train:
- if "train" not in raw_datasets:
- raise ValueError("--do_train requires a train dataset")
- column_names = raw_datasets["train"].column_names
- elif training_args.do_eval:
- if "validation" not in raw_datasets:
- raise ValueError("--do_eval requires a validation dataset")
- column_names = raw_datasets["validation"].column_names
- elif training_args.do_predict:
- if "test" not in raw_datasets:
- raise ValueError("--do_predict requires a test dataset")
- column_names = raw_datasets["test"].column_names
- else:
- logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
- return
-
- if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)):
- assert (
- data_args.lang is not None
- ), f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --lang argument"
-
- tokenizer.src_lang = data_args.lang
- tokenizer.tgt_lang = data_args.lang
-
- # For multilingual translation models like mBART-50 and M2M100 we need to force the target language token
- # as the first generated token. We ask the user to explicitly provide this as --forced_bos_token argument.
- forced_bos_token_id = (
- tokenizer.lang_code_to_id[data_args.forced_bos_token] if data_args.forced_bos_token is not None else None
- )
- model.config.forced_bos_token_id = forced_bos_token_id
-
- # Get the column names for input/target.
- dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None)
- if data_args.text_column is None:
- text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
- else:
- text_column = data_args.text_column
- if text_column not in column_names:
- raise ValueError(
- f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}"
- )
- if data_args.summary_column is None:
- summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
- else:
- summary_column = data_args.summary_column
- if summary_column not in column_names:
- raise ValueError(
- f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}"
- )
-
- # Temporarily set max_target_length for training.
- max_target_length = data_args.max_target_length
- padding = "max_length" if data_args.pad_to_max_length else False
-
- if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"):
- logger.warning(
- "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for"
- f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
- )
-
- def preprocess_function(examples):
- # remove pairs where at least one record is None
-
- inputs, targets = [], []
- for i in range(len(examples[text_column])):
- if examples[text_column][i] and examples[summary_column][i]:
- inputs.append(examples[text_column][i])
- targets.append(examples[summary_column][i])
-
- inputs = [prefix + inp for inp in inputs]
- model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)
-
- # Tokenize targets with the `text_target` keyword argument
- labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True)
-
- # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
- # padding in the loss.
- if padding == "max_length" and data_args.ignore_pad_token_for_loss:
- labels["input_ids"] = [
- [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
- ]
-
- model_inputs["labels"] = labels["input_ids"]
- return model_inputs
-
- if training_args.do_train:
- train_dataset = raw_datasets["train"]
- if data_args.max_train_samples is not None:
- max_train_samples = min(len(train_dataset), data_args.max_train_samples)
- train_dataset = train_dataset.select(range(max_train_samples))
- with training_args.main_process_first(desc="train dataset map pre-processing"):
- train_dataset = train_dataset.map(
- preprocess_function,
- batched=True,
- num_proc=data_args.preprocessing_num_workers,
- remove_columns=column_names,
- load_from_cache_file=not data_args.overwrite_cache,
- desc="Running tokenizer on train dataset",
- )
-
- if training_args.do_eval:
- max_target_length = data_args.val_max_target_length
- eval_dataset = raw_datasets["validation"]
- if data_args.max_eval_samples is not None:
- max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
- eval_dataset = eval_dataset.select(range(max_eval_samples))
- with training_args.main_process_first(desc="validation dataset map pre-processing"):
- eval_dataset = eval_dataset.map(
- preprocess_function,
- batched=True,
- num_proc=data_args.preprocessing_num_workers,
- remove_columns=column_names,
- load_from_cache_file=not data_args.overwrite_cache,
- desc="Running tokenizer on validation dataset",
- )
-
- if training_args.do_predict:
- max_target_length = data_args.val_max_target_length
- predict_dataset = raw_datasets["test"]
- if data_args.max_predict_samples is not None:
- max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
- predict_dataset = predict_dataset.select(range(max_predict_samples))
- with training_args.main_process_first(desc="prediction dataset map pre-processing"):
- predict_dataset = predict_dataset.map(
- preprocess_function,
- batched=True,
- num_proc=data_args.preprocessing_num_workers,
- remove_columns=column_names,
- load_from_cache_file=not data_args.overwrite_cache,
- desc="Running tokenizer on prediction dataset",
- )
-
- # Data collator
- label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
- data_collator = DataCollatorForSeq2Seq(
- tokenizer,
- model=model,
- label_pad_token_id=label_pad_token_id,
- pad_to_multiple_of=8 if training_args.fp16 else None,
- )
-
- # Metric
- metric = evaluate.load("rouge")
-
- def postprocess_text(preds, labels):
- preds = [pred.strip() for pred in preds]
- labels = [label.strip() for label in labels]
-
- # rougeLSum expects newline after each sentence
- preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
- labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
-
- return preds, labels
-
- def compute_metrics(eval_preds):
- preds, labels = eval_preds
- if isinstance(preds, tuple):
- preds = preds[0]
- # Replace -100s used for padding as we can't decode them
- preds = np.where(preds != -100, preds, tokenizer.pad_token_id)
- decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
- labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
- decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
-
- # Some simple post-processing
- decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
-
- result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
- result = {k: round(v * 100, 4) for k, v in result.items()}
- prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
- result["gen_len"] = np.mean(prediction_lens)
- return result
-
- # Override the decoding parameters of Seq2SeqTrainer
- training_args.generation_max_length = (
- training_args.generation_max_length
- if training_args.generation_max_length is not None
- else data_args.val_max_target_length
- )
- training_args.generation_num_beams = (
- data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
- )
-
- # Initialize our Trainer
- trainer = Seq2SeqTrainer(
- model=model,
- args=training_args,
- train_dataset=train_dataset if training_args.do_train else None,
- eval_dataset=eval_dataset if training_args.do_eval else None,
- tokenizer=tokenizer,
- data_collator=data_collator,
- compute_metrics=compute_metrics if training_args.predict_with_generate else None,
- )
-
- # Training
- if training_args.do_train:
- checkpoint = None
- if training_args.resume_from_checkpoint is not None:
- checkpoint = training_args.resume_from_checkpoint
- elif last_checkpoint is not None:
- checkpoint = last_checkpoint
- train_result = trainer.train(resume_from_checkpoint=checkpoint)
- trainer.save_model() # Saves the tokenizer too for easy upload
-
- metrics = train_result.metrics
- max_train_samples = (
- data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
- )
- metrics["train_samples"] = min(max_train_samples, len(train_dataset))
-
- trainer.log_metrics("train", metrics)
- trainer.save_metrics("train", metrics)
- trainer.save_state()
-
- # Evaluation
- results = {}
- if training_args.do_eval:
- logger.info("*** Evaluate ***")
- metrics = trainer.evaluate(metric_key_prefix="eval")
- max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
- metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
-
- trainer.log_metrics("eval", metrics)
- trainer.save_metrics("eval", metrics)
-
- if training_args.do_predict:
- logger.info("*** Predict ***")
-
- predict_results = trainer.predict(predict_dataset, metric_key_prefix="predict")
- metrics = predict_results.metrics
- max_predict_samples = (
- data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
- )
- metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
-
- trainer.log_metrics("predict", metrics)
- trainer.save_metrics("predict", metrics)
-
- if trainer.is_world_process_zero():
- if training_args.predict_with_generate:
- predictions = predict_results.predictions
- predictions = np.where(predictions != -100, predictions, tokenizer.pad_token_id)
- predictions = tokenizer.batch_decode(
- predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
- )
- predictions = [pred.strip() for pred in predictions]
- output_prediction_file = os.path.join(training_args.output_dir, "generated_predictions.txt")
- with open(output_prediction_file, "w") as writer:
- writer.write("\n".join(predictions))
-
- kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "summarization"}
- if data_args.dataset_name is not None:
- kwargs["dataset_tags"] = data_args.dataset_name
- if data_args.dataset_config_name is not None:
- kwargs["dataset_args"] = data_args.dataset_config_name
- kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
- else:
- kwargs["dataset"] = data_args.dataset_name
-
- if data_args.lang is not None:
- kwargs["language"] = data_args.lang
-
- if training_args.push_to_hub:
- trainer.push_to_hub(**kwargs)
- else:
- trainer.create_model_card(**kwargs)
-
- return results
-
-
-def _mp_fn(index):
- # For xla_spawn (TPUs)
- main()
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/chendl/compositional_test/transformers/examples/pytorch/text-classification/README.md b/spaces/chendl/compositional_test/transformers/examples/pytorch/text-classification/README.md
deleted file mode 100644
index 1bc01b416b74c285ff6c93e049d9aa417bffa80f..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/pytorch/text-classification/README.md
+++ /dev/null
@@ -1,203 +0,0 @@
-
-
-# Text classification examples
-
-## GLUE tasks
-
-Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py).
-
-Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding
-Evaluation](https://gluebenchmark.com/). This script can fine-tune any of the models on the [hub](https://huggingface.co/models)
-and can also be used for a dataset hosted on our [hub](https://huggingface.co/datasets) or your own data in a csv or a JSON file
-(the script might need some tweaks in that case, refer to the comments inside for help).
-
-GLUE is made up of a total of 9 different tasks. Here is how to run the script on one of them:
-
-```bash
-export TASK_NAME=mrpc
-
-python run_glue.py \
- --model_name_or_path bert-base-cased \
- --task_name $TASK_NAME \
- --do_train \
- --do_eval \
- --max_seq_length 128 \
- --per_device_train_batch_size 32 \
- --learning_rate 2e-5 \
- --num_train_epochs 3 \
- --output_dir /tmp/$TASK_NAME/
-```
-
-where task name can be one of cola, sst2, mrpc, stsb, qqp, mnli, qnli, rte, wnli.
-
-We get the following results on the dev set of the benchmark with the previous commands (with an exception for MRPC and
-WNLI which are tiny and where we used 5 epochs instead of 3). Trainings are seeded so you should obtain the same
-results with PyTorch 1.6.0 (and close results with different versions), training times are given for information (a
-single Titan RTX was used):
-
-| Task | Metric | Result | Training time |
-|-------|------------------------------|-------------|---------------|
-| CoLA | Matthews corr | 56.53 | 3:17 |
-| SST-2 | Accuracy | 92.32 | 26:06 |
-| MRPC | F1/Accuracy | 88.85/84.07 | 2:21 |
-| STS-B | Pearson/Spearman corr. | 88.64/88.48 | 2:13 |
-| QQP | Accuracy/F1 | 90.71/87.49 | 2:22:26 |
-| MNLI | Matched acc./Mismatched acc. | 83.91/84.10 | 2:35:23 |
-| QNLI | Accuracy | 90.66 | 40:57 |
-| RTE | Accuracy | 65.70 | 57 |
-| WNLI | Accuracy | 56.34 | 24 |
-
-Some of these results are significantly different from the ones reported on the test set of GLUE benchmark on the
-website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the website.
-
-The following example fine-tunes BERT on the `imdb` dataset hosted on our [hub](https://huggingface.co/datasets):
-
-```bash
-python run_glue.py \
- --model_name_or_path bert-base-cased \
- --dataset_name imdb \
- --do_train \
- --do_predict \
- --max_seq_length 128 \
- --per_device_train_batch_size 32 \
- --learning_rate 2e-5 \
- --num_train_epochs 3 \
- --output_dir /tmp/imdb/
-```
-
-> If your model classification head dimensions do not fit the number of labels in the dataset, you can specify `--ignore_mismatched_sizes` to adapt it.
-
-
-### Mixed precision training
-
-If you have a GPU with mixed precision capabilities (architecture Pascal or more recent), you can use mixed precision
-training with PyTorch 1.6.0 or latest, or by installing the [Apex](https://github.com/NVIDIA/apex) library for previous
-versions. Just add the flag `--fp16` to your command launching one of the scripts mentioned above!
-
-Using mixed precision training usually results in 2x-speedup for training with the same final results:
-
-| Task | Metric | Result | Training time | Result (FP16) | Training time (FP16) |
-|-------|------------------------------|-------------|---------------|---------------|----------------------|
-| CoLA | Matthews corr | 56.53 | 3:17 | 56.78 | 1:41 |
-| SST-2 | Accuracy | 92.32 | 26:06 | 91.74 | 13:11 |
-| MRPC | F1/Accuracy | 88.85/84.07 | 2:21 | 88.12/83.58 | 1:10 |
-| STS-B | Pearson/Spearman corr. | 88.64/88.48 | 2:13 | 88.71/88.55 | 1:08 |
-| QQP | Accuracy/F1 | 90.71/87.49 | 2:22:26 | 90.67/87.43 | 1:11:54 |
-| MNLI | Matched acc./Mismatched acc. | 83.91/84.10 | 2:35:23 | 84.04/84.06 | 1:17:06 |
-| QNLI | Accuracy | 90.66 | 40:57 | 90.96 | 20:16 |
-| RTE | Accuracy | 65.70 | 57 | 65.34 | 29 |
-| WNLI | Accuracy | 56.34 | 24 | 56.34 | 12 |
-
-
-## PyTorch version, no Trainer
-
-Based on the script [`run_glue_no_trainer.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue_no_trainer.py).
-
-Like `run_glue.py`, this script allows you to fine-tune any of the models on the [hub](https://huggingface.co/models) on a
-text classification task, either a GLUE task or your own data in a csv or a JSON file. The main difference is that this
-script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like.
-
-It offers less options than the script with `Trainer` (for instance you can easily change the options for the optimizer
-or the dataloaders directly in the script) but still run in a distributed setup, on TPU and supports mixed precision by
-the mean of the [🤗 `Accelerate`](https://github.com/huggingface/accelerate) library. You can use the script normally
-after installing it:
-
-```bash
-pip install git+https://github.com/huggingface/accelerate
-```
-
-then
-
-```bash
-export TASK_NAME=mrpc
-
-python run_glue_no_trainer.py \
- --model_name_or_path bert-base-cased \
- --task_name $TASK_NAME \
- --max_length 128 \
- --per_device_train_batch_size 32 \
- --learning_rate 2e-5 \
- --num_train_epochs 3 \
- --output_dir /tmp/$TASK_NAME/
-```
-
-You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run
-
-```bash
-accelerate config
-```
-
-and reply to the questions asked. Then
-
-```bash
-accelerate test
-```
-
-that will check everything is ready for training. Finally, you can launch training with
-
-```bash
-export TASK_NAME=mrpc
-
-accelerate launch run_glue_no_trainer.py \
- --model_name_or_path bert-base-cased \
- --task_name $TASK_NAME \
- --max_length 128 \
- --per_device_train_batch_size 32 \
- --learning_rate 2e-5 \
- --num_train_epochs 3 \
- --output_dir /tmp/$TASK_NAME/
-```
-
-This command is the same and will work for:
-
-- a CPU-only setup
-- a setup with one GPU
-- a distributed training with several GPUs (single or multi node)
-- a training on TPUs
-
-Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.
-
-## XNLI
-
-Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_xnli.py).
-
-[XNLI](https://cims.nyu.edu/~sbowman/xnli/) is a crowd-sourced dataset based on [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
-
-#### Fine-tuning on XNLI
-
-This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in 106 mins on a single tesla V100 16GB.
-
-```bash
-python run_xnli.py \
- --model_name_or_path bert-base-multilingual-cased \
- --language de \
- --train_language en \
- --do_train \
- --do_eval \
- --per_device_train_batch_size 32 \
- --learning_rate 5e-5 \
- --num_train_epochs 2.0 \
- --max_seq_length 128 \
- --output_dir /tmp/debug_xnli/ \
- --save_steps -1
-```
-
-Training with the previously defined hyper-parameters yields the following results on the **test** set:
-
-```bash
-acc = 0.7093812375249501
-```
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/generation/logits_process.py b/spaces/chendl/compositional_test/transformers/src/transformers/generation/logits_process.py
deleted file mode 100644
index 95c8064ee40445ebc3209d88c30bab7e78bee56d..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/generation/logits_process.py
+++ /dev/null
@@ -1,982 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team
-#
-# 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.
-
-import inspect
-import math
-from typing import Callable, Iterable, List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from ..utils import add_start_docstrings
-from ..utils.logging import get_logger
-
-
-logger = get_logger(__name__)
-
-
-LOGITS_PROCESSOR_INPUTS_DOCSTRING = r"""
- Args:
- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
- Indices of input sequence tokens in the vocabulary.
-
- Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and
- [`PreTrainedTokenizer.__call__`] for details.
-
- [What are input IDs?](../glossary#input-ids)
- scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
- Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
- search or log softmax for each vocabulary token when using beam search
- kwargs:
- Additional logits processor specific kwargs.
-
- Return:
- `torch.FloatTensor` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
-
-"""
-
-
-class LogitsProcessor:
- """Abstract base class for all logit processors that can be applied during generation."""
-
- @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- """Torch method for processing logits."""
- raise NotImplementedError(
- f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
- )
-
-
-class LogitsWarper:
- """Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."""
-
- @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- """Torch method for warping logits."""
- raise NotImplementedError(
- f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
- )
-
-
-class LogitsProcessorList(list):
- """
- This class can be used to create a list of [`LogitsProcessor`] or [`LogitsWarper`] to subsequently process a
- `scores` input tensor. This class inherits from list and adds a specific *__call__* method to apply each
- [`LogitsProcessor`] or [`LogitsWarper`] to the inputs.
- """
-
- @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.FloatTensor:
- for processor in self:
- function_args = inspect.signature(processor.__call__).parameters
- if len(function_args) > 2:
- if not all(arg in kwargs for arg in list(function_args.keys())[2:]):
- raise ValueError(
- f"Make sure that all the required parameters: {list(function_args.keys())} for "
- f"{processor.__class__} are passed to the logits processor."
- )
- scores = processor(input_ids, scores, **kwargs)
- else:
- scores = processor(input_ids, scores)
- return scores
-
-
-class MinLengthLogitsProcessor(LogitsProcessor):
- r"""
- [`LogitsProcessor`] enforcing a min-length by setting EOS probability to 0.
-
- Args:
- min_length (`int`):
- The minimum length below which the score of `eos_token_id` is set to `-float("Inf")`.
- eos_token_id (`Union[int, List[int]]`):
- The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
- """
-
- def __init__(self, min_length: int, eos_token_id: Union[int, List[int]]):
- if not isinstance(min_length, int) or min_length < 0:
- raise ValueError(f"`min_length` has to be a non-negative integer, but is {min_length}")
-
- if isinstance(eos_token_id, int):
- eos_token_id = [eos_token_id]
- if not all([isinstance(i, int) for i in eos_token_id]) or any([i < 0 for i in eos_token_id]):
- logger.warning(f"`eos_token_id` has to be a list of positive integers, but is {eos_token_id}")
-
- self.min_length = min_length
- self.eos_token_id = eos_token_id
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- cur_len = input_ids.shape[-1]
- if cur_len < self.min_length:
- for i in self.eos_token_id:
- scores[:, i] = -float("inf")
- return scores
-
-
-class MinNewTokensLengthLogitsProcessor(LogitsProcessor):
- r"""
- [`LogitsProcessor`] enforcing a min-length of new tokens by setting EOS (End-Of-Sequence) token probability to 0.
-
- Args:
- prompt_length_to_skip (`int`):
- The input tokens length.
- min_new_tokens (`int`):
- The minimum *new* tokens length below which the score of `eos_token_id` is set to `-float("Inf")`.
- eos_token_id (`Union[int, List[int]]`):
- The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
- """
-
- def __init__(self, prompt_length_to_skip: int, min_new_tokens: int, eos_token_id: Union[int, List[int]]):
- for arg_name, arg_value in [
- ("prompt_length_to_skip", prompt_length_to_skip),
- ("min_new_tokens", min_new_tokens),
- ]:
- if not isinstance(arg_value, int) or arg_value < 0:
- raise ValueError(f"`{arg_name}` has to be a positive integer, but is {arg_value}")
-
- if isinstance(eos_token_id, int):
- eos_token_id = [eos_token_id]
- if not all([isinstance(i, int) for i in eos_token_id]) or any([i < 0 for i in eos_token_id]):
- logger.warning(f"`eos_token_id` has to be a list of positive integers, but is {eos_token_id}")
-
- self.prompt_length_to_skip = prompt_length_to_skip
- self.min_new_tokens = min_new_tokens
- self.eos_token_id = eos_token_id
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- new_tokens_length = input_ids.shape[-1] - self.prompt_length_to_skip
- if new_tokens_length < self.min_new_tokens:
- for i in self.eos_token_id:
- scores[:, i] = -float("inf")
-
- return scores
-
-
-class TemperatureLogitsWarper(LogitsWarper):
- r"""
- [`LogitsWarper`] for temperature (exponential scaling output probability distribution).
-
- Args:
- temperature (`float`):
- The value used to module the logits distribution.
- """
-
- def __init__(self, temperature: float):
- if not isinstance(temperature, float) or not (temperature > 0):
- raise ValueError(f"`temperature` has to be a strictly positive float, but is {temperature}")
-
- self.temperature = temperature
-
- def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.FloatTensor:
- scores = scores / self.temperature
- return scores
-
-
-class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
- r"""
- [`LogitsProcessor`] enforcing an exponential penalty on repeated sequences.
-
- Args:
- repetition_penalty (`float`):
- The parameter for repetition penalty. 1.0 means no penalty. See [this
- paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
- """
-
- def __init__(self, penalty: float):
- if not isinstance(penalty, float) or not (penalty > 0):
- raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
-
- self.penalty = penalty
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- score = torch.gather(scores, 1, input_ids)
-
- # if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
- score = torch.where(score < 0, score * self.penalty, score / self.penalty)
-
- scores.scatter_(1, input_ids, score)
- return scores
-
-
-class EncoderRepetitionPenaltyLogitsProcessor(LogitsProcessor):
- r"""
- [`LogitsProcessor`] enforcing an exponential penalty on tokens that are not in the original input.
-
- Args:
- hallucination_penalty (`float`):
- The parameter for hallucination penalty. 1.0 means no penalty.
- encoder_input_ids (`torch.LongTensor`):
- The encoder_input_ids that should not be repeated within the decoder ids.
- """
-
- def __init__(self, penalty: float, encoder_input_ids: torch.LongTensor):
- if not isinstance(penalty, float) or not (penalty > 0):
- raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
-
- self.penalty = 1 / penalty
- self.encoder_input_ids = encoder_input_ids
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- score = torch.gather(scores, 1, self.encoder_input_ids)
-
- # if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
- score = torch.where(score < 0, score * self.penalty, score / self.penalty)
-
- scores.scatter_(1, self.encoder_input_ids, score)
- return scores
-
-
-class TopPLogitsWarper(LogitsWarper):
- """
- [`LogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.
-
- Args:
- top_p (`float`):
- If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
- higher are kept for generation.
- filter_value (`float`, *optional*, defaults to `-float("Inf")`):
- All filtered values will be set to this float value.
- min_tokens_to_keep (`int`, *optional*, defaults to 1):
- Minimum number of tokens that cannot be filtered.
- """
-
- def __init__(self, top_p: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
- top_p = float(top_p)
- if top_p < 0 or top_p > 1.0:
- raise ValueError(f"`top_p` has to be a float > 0 and < 1, but is {top_p}")
-
- self.top_p = top_p
- self.filter_value = filter_value
- self.min_tokens_to_keep = min_tokens_to_keep
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- sorted_logits, sorted_indices = torch.sort(scores, descending=False)
- cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
-
- # Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
- sorted_indices_to_remove = cumulative_probs <= (1 - self.top_p)
- if self.min_tokens_to_keep > 1:
- # Keep at least min_tokens_to_keep
- sorted_indices_to_remove[..., -self.min_tokens_to_keep :] = 0
-
- # scatter sorted tensors to original indexing
- indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
- scores = scores.masked_fill(indices_to_remove, self.filter_value)
- return scores
-
-
-class TopKLogitsWarper(LogitsWarper):
- r"""
- [`LogitsWarper`] that performs top-k, i.e. restricting to the k highest probability elements.
-
- Args:
- top_k (`int`):
- The number of highest probability vocabulary tokens to keep for top-k-filtering.
- filter_value (`float`, *optional*, defaults to `-float("Inf")`):
- All filtered values will be set to this float value.
- min_tokens_to_keep (`int`, *optional*, defaults to 1):
- Minimum number of tokens that cannot be filtered.
- """
-
- def __init__(self, top_k: int, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
- if not isinstance(top_k, int) or top_k <= 0:
- raise ValueError(f"`top_k` has to be a strictly positive integer, but is {top_k}")
-
- self.top_k = max(top_k, min_tokens_to_keep)
- self.filter_value = filter_value
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- top_k = min(self.top_k, scores.size(-1)) # Safety check
- # Remove all tokens with a probability less than the last token of the top-k
- indices_to_remove = scores < torch.topk(scores, top_k)[0][..., -1, None]
- scores = scores.masked_fill(indices_to_remove, self.filter_value)
- return scores
-
-
-class TypicalLogitsWarper(LogitsWarper):
- r"""
- [`LogitsWarper`] that performs typical decoding. See [Typical Decoding for Natural Language
- Generation](https://arxiv.org/abs/2202.00666) for more information.
-
- Args:
- mass (`float`):
- Value of typical_p between 0 and 1 inclusive, defaults to 0.9.
- filter_value (`float`, *optional*, defaults to `-float("Inf")`):
- All filtered values will be set to this float value.
- min_tokens_to_keep (`int`, *optional*, defaults to 1):
- Minimum number of tokens that cannot be filtered.
- """
-
- def __init__(self, mass: float = 0.9, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
- mass = float(mass)
- if not (mass > 0 and mass < 1):
- raise ValueError(f"`typical_p` has to be a float > 0 and < 1, but is {mass}")
-
- self.filter_value = filter_value
- self.mass = mass
- self.min_tokens_to_keep = min_tokens_to_keep
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- # calculate entropy
- normalized = torch.nn.functional.log_softmax(scores, dim=-1)
- p = torch.exp(normalized)
- ent = -(normalized * p).nansum(-1, keepdim=True)
-
- # shift and sort
- shifted_scores = torch.abs((-normalized) - ent)
- sorted_scores, sorted_indices = torch.sort(shifted_scores, descending=False)
- sorted_logits = scores.gather(-1, sorted_indices)
- cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
-
- # Remove tokens with cumulative mass above the threshold
- last_ind = (cumulative_probs < self.mass).sum(dim=1)
- last_ind[last_ind < 0] = 0
- sorted_indices_to_remove = sorted_scores > sorted_scores.gather(1, last_ind.view(-1, 1))
- if self.min_tokens_to_keep > 1:
- # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
- sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
- indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
-
- scores = scores.masked_fill(indices_to_remove, self.filter_value)
- return scores
-
-
-class EpsilonLogitsWarper(LogitsWarper):
- r"""
- [`LogitsWarper`] that performs epsilon-sampling, i.e. restricting to tokens with `prob >= epsilon`. Takes the
- largest min_tokens_to_keep tokens if no tokens satisfy this constraint. See [Truncation Sampling as Language Model
- Desmoothing](https://arxiv.org/abs/2210.15191) for more information.
-
- Args:
- epsilon (`float`):
- If set to > 0, only the most tokens with probabilities `epsilon` or higher are kept for generation.
- filter_value (`float`, *optional*, defaults to `-float("Inf")`):
- All filtered values will be set to this float value.
- min_tokens_to_keep (`int`, *optional*, defaults to 1):
- Minimum number of tokens that cannot be filtered.
- """
-
- def __init__(self, epsilon: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
- epsilon = float(epsilon)
- if epsilon <= 0 or epsilon >= 1:
- raise ValueError(f"`epsilon_cutoff` has to be a float > 0 and < 1, but is {epsilon}")
-
- min_tokens_to_keep = int(min_tokens_to_keep)
- if min_tokens_to_keep < 1:
- raise ValueError(
- f"`min_tokens_to_keep` has to be a strictly positive integer, but is {min_tokens_to_keep}"
- )
-
- self.epsilon = epsilon
- self.filter_value = filter_value
- self.min_tokens_to_keep = min_tokens_to_keep
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- # Determine which indices to remove
- probabilities = scores.softmax(dim=-1)
- indices_to_remove = probabilities < self.epsilon
-
- # Keep the words with the 'min_tokens_to_keep'-highest probabilities
- top_k = min(self.min_tokens_to_keep, scores.size(-1)) # Safety check
- indices_to_remove = indices_to_remove & (scores < torch.topk(scores, top_k)[0][..., -1, None])
-
- scores = scores.masked_fill(indices_to_remove, self.filter_value)
- return scores
-
-
-class EtaLogitsWarper(LogitsWarper):
- r"""
- [`LogitsWarper`] that performs eta-sampling, i.e. calculates a dynamic cutoff `eta := min(epsilon, sqrt(epsilon,
- e^-entropy(probabilities)))` and restricts to tokens with `prob >= eta`. Takes the largest min_tokens_to_keep
- tokens if no tokens satisfy this constraint. See [Truncation Sampling as Language Model
- Desmoothing](https://arxiv.org/abs/2210.15191) for more information.
-
- Args:
- min_tokens_to_keep (`int`, *optional*, defaults to 1):
- Minimum number of tokens that cannot be filtered."""
-
- def __init__(self, epsilon: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
- epsilon = float(epsilon)
- if epsilon <= 0 or epsilon >= 1:
- raise ValueError(f"`eta_cutoff` has to be a float > 0 and < 1, but is {epsilon}")
-
- min_tokens_to_keep = int(min_tokens_to_keep)
- if min_tokens_to_keep < 1:
- raise ValueError(
- f"`min_tokens_to_keep` has to be a strictly positive integer, but is {min_tokens_to_keep}"
- )
-
- self.epsilon = torch.tensor(epsilon)
- self.filter_value = filter_value
- self.min_tokens_to_keep = min_tokens_to_keep
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- # Calculate the adaptive cutoff
- probabilities = scores.softmax(dim=-1)
- entropy = torch.distributions.Categorical(logits=scores).entropy()
- eta = torch.min(self.epsilon, torch.sqrt(self.epsilon) * torch.exp(-entropy))[..., None]
- indices_to_remove = probabilities < eta
-
- # Keep the words with the 'min_tokens_to_keep'-highest probabilities
- top_k = min(self.min_tokens_to_keep, scores.size(-1)) # Safety check
- indices_to_remove = indices_to_remove & (scores < torch.topk(scores, top_k)[0][..., -1, None])
-
- scores = scores.masked_fill(indices_to_remove, self.filter_value)
- return scores
-
-
-def _get_ngrams(ngram_size: int, prev_input_ids: torch.Tensor, num_hypos: int):
- generated_ngrams = [{} for _ in range(num_hypos)]
- for idx in range(num_hypos):
- gen_tokens = prev_input_ids[idx].tolist()
- generated_ngram = generated_ngrams[idx]
- for ngram in zip(*[gen_tokens[i:] for i in range(ngram_size)]):
- prev_ngram_tuple = tuple(ngram[:-1])
- generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
- return generated_ngrams
-
-
-def _get_generated_ngrams(banned_ngrams, prev_input_ids, ngram_size, cur_len):
- # Before decoding the next token, prevent decoding of ngrams that have already appeared
- start_idx = cur_len + 1 - ngram_size
- ngram_idx = tuple(prev_input_ids[start_idx:cur_len].tolist())
- return banned_ngrams.get(ngram_idx, [])
-
-
-def _calc_banned_ngram_tokens(
- ngram_size: int, prev_input_ids: torch.Tensor, num_hypos: int, cur_len: int
-) -> List[Iterable[int]]:
- """Copied from fairseq for no_repeat_ngram in beam_search"""
- if cur_len + 1 < ngram_size:
- # return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
- return [[] for _ in range(num_hypos)]
-
- generated_ngrams = _get_ngrams(ngram_size, prev_input_ids, num_hypos)
-
- banned_tokens = [
- _get_generated_ngrams(generated_ngrams[hypo_idx], prev_input_ids[hypo_idx], ngram_size, cur_len)
- for hypo_idx in range(num_hypos)
- ]
- return banned_tokens
-
-
-class NoRepeatNGramLogitsProcessor(LogitsProcessor):
- r"""
- [`LogitsProcessor`] that enforces no repetition of n-grams. See
- [Fairseq](https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345).
-
- Args:
- ngram_size (`int`):
- All ngrams of size `ngram_size` can only occur once.
- """
-
- def __init__(self, ngram_size: int):
- if not isinstance(ngram_size, int) or ngram_size <= 0:
- raise ValueError(f"`ngram_size` has to be a strictly positive integer, but is {ngram_size}")
- self.ngram_size = ngram_size
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- num_batch_hypotheses = scores.shape[0]
- cur_len = input_ids.shape[-1]
- banned_batch_tokens = _calc_banned_ngram_tokens(self.ngram_size, input_ids, num_batch_hypotheses, cur_len)
-
- for i, banned_tokens in enumerate(banned_batch_tokens):
- scores[i, banned_tokens] = -float("inf")
-
- return scores
-
-
-class EncoderNoRepeatNGramLogitsProcessor(LogitsProcessor):
- r"""
- [`LogitsProcessor`] that enforces no repetition of encoder input ids n-grams for the decoder ids. See
- [ParlAI](https://github.com/facebookresearch/ParlAI/blob/master/parlai/core/torch_generator_agent.py#L1350).
-
- Args:
- encoder_ngram_size (`int`):
- All ngrams of size `ngram_size` can only occur within the encoder input ids.
- encoder_input_ids (`int`):
- The encoder_input_ids that should not be repeated within the decoder ids.
- """
-
- def __init__(self, encoder_ngram_size: int, encoder_input_ids: torch.LongTensor):
- if not isinstance(encoder_ngram_size, int) or encoder_ngram_size <= 0:
- raise ValueError(
- f"`encoder_ngram_size` has to be a strictly positive integer, but is {encoder_ngram_size}"
- )
- self.ngram_size = encoder_ngram_size
- if len(encoder_input_ids.shape) == 1:
- encoder_input_ids = encoder_input_ids.unsqueeze(0)
- self.batch_size = encoder_input_ids.shape[0]
- self.generated_ngrams = _get_ngrams(encoder_ngram_size, encoder_input_ids, self.batch_size)
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- # B x num_beams
- num_hypos = scores.shape[0]
- num_beams = num_hypos // self.batch_size
- cur_len = input_ids.shape[-1]
- banned_batch_tokens = [
- _get_generated_ngrams(
- self.generated_ngrams[hypo_idx // num_beams], input_ids[hypo_idx], self.ngram_size, cur_len
- )
- for hypo_idx in range(num_hypos)
- ]
-
- for i, banned_tokens in enumerate(banned_batch_tokens):
- scores[i, banned_tokens] = -float("inf")
-
- return scores
-
-
-class NoBadWordsLogitsProcessor(LogitsProcessor):
- """
- [`LogitsProcessor`] that enforces that specified sequences will never be sampled.
-
- Args:
- bad_words_ids (`List[List[int]]`):
- List of list of token ids that are not allowed to be generated. In order to get the token ids of the words
- that should not appear in the generated text, use `tokenizer(bad_words, add_prefix_space=True,
- add_special_tokens=False).input_ids`.
- eos_token_id (`Union[int, List[int]]`):
- The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
- """
-
- def __init__(self, bad_words_ids: List[List[int]], eos_token_id: Union[int, List[int]]):
- if not isinstance(bad_words_ids, List) or len(bad_words_ids) == 0:
- raise ValueError(f"`bad_words_ids` has to be a non-empty list, but is {bad_words_ids}.")
- if any(not isinstance(bad_word_ids, list) for bad_word_ids in bad_words_ids):
- raise ValueError(f"`bad_words_ids` has to be a list of lists, but is {bad_words_ids}.")
- if any(
- any((not isinstance(token_id, (int, np.integer)) or token_id < 0) for token_id in bad_word_ids)
- for bad_word_ids in bad_words_ids
- ):
- raise ValueError(
- f"Each list in `bad_words_ids` has to be a list of positive integers, but is {bad_words_ids}."
- )
-
- if eos_token_id is None:
- eos_token_id = []
- if isinstance(eos_token_id, int):
- eos_token_id = [eos_token_id]
-
- bad_words_ids = list(
- filter(lambda bad_token_seq: all([bad_token_seq != [i] for i in eos_token_id]), bad_words_ids)
- )
- self.bad_words_id_length_1 = []
- self.bad_words_id_length_greater_than_1 = []
- for word in bad_words_ids:
- if len(word) == 1:
- self.bad_words_id_length_1.append(word[0])
- else:
- self.bad_words_id_length_greater_than_1.append(word)
-
- self.static_bad_words_mask: Optional[torch.LongTensor] = None
-
- for banned_token_seq in self.bad_words_id_length_greater_than_1:
- if len(banned_token_seq) == 0:
- raise ValueError(f"Banned words token sequences {bad_words_ids} cannot have an empty list")
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- if self.static_bad_words_mask is None and len(self.bad_words_id_length_1) > 0:
- self.static_bad_words_mask = self._calc_static_bad_word_mask(scores)
-
- dynamic_banned_tokens = self._calc_banned_bad_words_ids(input_ids.tolist())
- scores = self._set_scores_to_inf_for_banned_tokens(scores, dynamic_banned_tokens)
-
- return scores
-
- def _calc_static_bad_word_mask(self, scores: torch.FloatTensor) -> torch.BoolTensor:
- static_bad_words_mask = torch.zeros(scores.shape[1])
- static_bad_words_mask[self.bad_words_id_length_1] = 1
- return static_bad_words_mask.unsqueeze(0).to(scores.device).bool()
-
- def _tokens_match(self, prev_tokens: List[int], tokens: List[int]) -> bool:
- if len(tokens) == 0:
- # if bad word tokens is just one token always ban it
- return True
- elif len(tokens) > len(prev_tokens):
- # if bad word tokens are longer then prev input_ids they can't be equal
- return False
- else:
- return prev_tokens[-len(tokens) :] == tokens
-
- def _calc_banned_bad_words_ids(self, prev_input_ids: List[List[int]]) -> Iterable[int]:
- banned_tokens = []
- for prev_input_ids_slice in prev_input_ids:
- banned_tokens_slice = []
- for banned_token_seq in self.bad_words_id_length_greater_than_1:
- if self._tokens_match(prev_input_ids_slice, banned_token_seq[:-1]):
- banned_tokens_slice.append(banned_token_seq[-1])
-
- banned_tokens.append(banned_tokens_slice)
-
- return banned_tokens
-
- def _set_scores_to_inf_for_banned_tokens(
- self, scores: torch.Tensor, banned_tokens: List[List[int]]
- ) -> torch.Tensor:
- """
- Modifies the scores in place by setting the banned token positions to `-inf`. Banned token is expected to be a
- list of list of banned tokens to ban in the format [[batch index, vocabulary position],...
-
- Args:
- scores: logits distribution of shape (batch size, vocabulary size)
- banned_tokens: list of list of tokens to ban of length (batch_size)
- """
- banned_mask_list = []
- for idx, batch_banned_tokens in enumerate(banned_tokens):
- for token in batch_banned_tokens:
- # Eliminates invalid bad word IDs that are over the vocabulary size.
- if token <= scores.shape[1]:
- banned_mask_list.append([idx, token])
- else:
- logger.error(
- f"An invalid bad word ID is defined: {token}. This ID is not contained in the "
- "vocabulary, and is therefore ignored."
- )
- if not banned_mask_list and self.static_bad_words_mask is None:
- return scores
-
- else:
- if banned_mask_list:
- banned_mask = torch.LongTensor(banned_mask_list)
- indices = torch.ones(len(banned_mask))
- # A sparse tensor is generated from a list of coordinates: [[0, 1], [0, 2], [2, 0]]. A conversion to dense tensor generates:
- # [ 0 1 1 ]
- # [ 0 0 0 ]
- # [ 1 0 0 ]
-
- banned_mask = (
- torch.sparse.LongTensor(banned_mask.t(), indices, scores.size())
- .to(scores.device)
- .to_dense()
- .bool()
- )
-
- if self.static_bad_words_mask is not None:
- banned_mask = torch.bitwise_or(banned_mask, self.static_bad_words_mask)
- else:
- banned_mask = self.static_bad_words_mask
-
- scores = scores.masked_fill(banned_mask, -float("inf"))
- return scores
-
-
-class PrefixConstrainedLogitsProcessor(LogitsProcessor):
- r"""
- [`LogitsProcessor`] that enforces constrained generation and is useful for prefix-conditioned constrained
- generation. See [Autoregressive Entity Retrieval](https://arxiv.org/abs/2010.00904) for more information.
-
- Args:
- prefix_allowed_tokens_fn: (`Callable[[int, torch.Tensor], List[int]]`):
- This function constraints the beam search to allowed tokens only at each step. This function takes 2
- arguments `inputs_ids` and the batch ID `batch_id`. It has to return a list with the allowed tokens for the
- next generation step conditioned on the previously generated tokens `inputs_ids` and the batch ID
- `batch_id`.
- """
-
- def __init__(self, prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]], num_beams: int):
- self._prefix_allowed_tokens_fn = prefix_allowed_tokens_fn
- self._num_beams = num_beams
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- mask = torch.full_like(scores, -math.inf)
- for batch_id, beam_sent in enumerate(input_ids.view(-1, self._num_beams, input_ids.shape[-1])):
- for beam_id, sent in enumerate(beam_sent):
- mask[batch_id * self._num_beams + beam_id, self._prefix_allowed_tokens_fn(batch_id, sent)] = 0
-
- return scores + mask
-
-
-class HammingDiversityLogitsProcessor(LogitsProcessor):
- r"""
- [`LogitsProcessor`] that enforces diverse beam search. Note that this logits processor is only effective for
- [`PreTrainedModel.group_beam_search`]. See [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence
- Models](https://arxiv.org/pdf/1610.02424.pdf) for more details.
-
- Args:
- diversity_penalty (`float`):
- This value is subtracted from a beam's score if it generates a token same as any beam from other group at a
- particular time. Note that `diversity_penalty` is only effective if `group beam search` is enabled.
- num_beams (`int`):
- Number of beams used for group beam search. See [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more
- details.
- num_beam_groups (`int`):
- Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
- See [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
- """
-
- def __init__(self, diversity_penalty: float, num_beams: int, num_beam_groups: int):
- if not isinstance(diversity_penalty, float) or (not diversity_penalty > 0.0):
- raise ValueError("`diversity_penalty` should be a float strictly larger than 0.")
- self._diversity_penalty = diversity_penalty
- if not isinstance(num_beams, int) or num_beams < 2:
- raise ValueError("`num_beams` should be an integer strictly larger than 1.")
- self._num_beams = num_beams
- if not isinstance(num_beam_groups, int) or num_beam_groups < 2:
- raise ValueError("`num_beam_groups` should be an integer strictly larger than 1.")
- if num_beam_groups > num_beams:
- raise ValueError("`beam_groups` has to be smaller or equal to `num_beams`.")
- self._num_sub_beams = num_beams // num_beam_groups
-
- def __call__(
- self,
- input_ids: torch.LongTensor,
- scores: torch.FloatTensor,
- current_tokens: torch.LongTensor,
- beam_group_idx: int,
- ) -> torch.FloatTensor:
- # hamming diversity: penalise using same token in current group which was used in previous groups at
- # the same time step
- batch_size = current_tokens.shape[0] // self._num_beams
- group_start_idx = beam_group_idx * self._num_sub_beams
- group_end_idx = min(group_start_idx + self._num_sub_beams, self._num_beams)
- group_size = group_end_idx - group_start_idx
- vocab_size = scores.shape[-1]
-
- if group_start_idx == 0:
- return scores
-
- for batch_idx in range(batch_size):
- # predicted tokens of last time step of previous groups
- previous_group_tokens = current_tokens[
- batch_idx * self._num_beams : batch_idx * self._num_beams + group_start_idx
- ]
- token_frequency = torch.bincount(previous_group_tokens, minlength=vocab_size).to(scores.device)
- scores[batch_idx * group_size : (batch_idx + 1) * group_size] -= self._diversity_penalty * token_frequency
-
- return scores
-
-
-class ForcedBOSTokenLogitsProcessor(LogitsProcessor):
- r"""
- [`LogitsProcessor`] that enforces the specified token as the first generated token.
-
- Args:
- bos_token_id (`int`):
- The id of the token to force as the first generated token.
- """
-
- def __init__(self, bos_token_id: int):
- self.bos_token_id = bos_token_id
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- cur_len = input_ids.shape[-1]
- if cur_len == 1:
- num_tokens = scores.shape[1]
- scores[:, [i for i in range(num_tokens) if i != self.bos_token_id]] = -float("inf")
- scores[:, self.bos_token_id] = 0
- return scores
-
-
-class ForcedEOSTokenLogitsProcessor(LogitsProcessor):
- r"""
- [`LogitsProcessor`] that enforces the specified token as the last generated token when `max_length` is reached.
-
- Args:
- max_length (`int`):
- The maximum length of the sequence to be generated.
- eos_token_id (`Union[int, List[int]]`):
- The id of the token to force as the last generated token when `max_length` is reached. Optionally, use a
- list to set multiple *end-of-sequence* tokens.
- """
-
- def __init__(self, max_length: int, eos_token_id: Union[int, List[int]]):
- self.max_length = max_length
- if isinstance(eos_token_id, int):
- eos_token_id = [eos_token_id]
- self.eos_token_id = eos_token_id
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- cur_len = input_ids.shape[-1]
- if cur_len == self.max_length - 1:
- num_tokens = scores.shape[1]
- scores[:, [i for i in range(num_tokens) if i not in self.eos_token_id]] = -float("inf")
- for i in self.eos_token_id:
- scores[:, i] = 0
- return scores
-
-
-class InfNanRemoveLogitsProcessor(LogitsProcessor):
- r"""
- [`LogitsProcessor`] that removes all `nan` and `inf` values to avoid the generation method to fail. Note that using
- the logits processor should only be used if necessary since it can slow down the generation method.
- """
-
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
- # set all nan values to 0.0
- scores[scores != scores] = 0.0
-
- # set all inf values to max possible value
- scores[scores == float("inf")] = torch.finfo(scores.dtype).max
-
- return scores
-
-
-class ExponentialDecayLengthPenalty(LogitsProcessor):
- r"""
- [`LogitsProcessor`] that exponentially increases the score of the eos_token_id after regulation_start has been
- reached.
-
- Args:
- exponential_decay_length_penalty (`tuple(int, float)`):
- This tuple shall consist of: `(start_index, decay_factor)` where `start_index` indicates where penalty
- starts and `decay_factor` represents the factor of exponential decay
- eos_token_id (`Union[int, List[int]]`):
- The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
- input_ids_seq_length (`int`):
- The length of the input sequence.
- """
-
- def __init__(
- self,
- exponential_decay_length_penalty: Tuple[int, float],
- eos_token_id: Union[int, List[int]],
- input_ids_seq_length: int,
- ):
- self.regulation_start = exponential_decay_length_penalty[0] + input_ids_seq_length
- self.regulation_factor = exponential_decay_length_penalty[1]
- if isinstance(eos_token_id, int):
- eos_token_id = [eos_token_id]
- self.eos_token_id = eos_token_id
-
- def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.FloatTensor:
- cur_len = input_ids.shape[-1]
- if cur_len > self.regulation_start:
- for i in self.eos_token_id:
- scores[:, i] = scores[:, i] * pow(self.regulation_factor, cur_len - self.regulation_start)
- return scores
-
-
-class LogitNormalization(LogitsProcessor, LogitsWarper):
- r"""
- [`LogitsWarper`] and [`LogitsProcessor`] for normalizing the scores using log-softmax. It's important to normalize
- the scores during beam search, after applying the logits processors or warpers, since the search algorithm used in
- this library doesn't do it (it only does it before, but they may need re-normalization) but it still supposes that
- the scores are normalized when comparing the hypotheses.
- """
-
- def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
- scores = scores.log_softmax(dim=-1)
- return scores
-
-
-class SuppressTokensAtBeginLogitsProcessor(LogitsProcessor):
- r"""
- [`SuppressTokensAtBeginLogitsProcessor`] supresses a list of tokens as soon as the `generate` function starts
- generating using `begin_index` tokens. This should ensure that the tokens defined by `begin_suppress_tokens` at not
- sampled at the begining of the generation.
- """
-
- def __init__(self, begin_suppress_tokens, begin_index):
- self.begin_suppress_tokens = list(begin_suppress_tokens)
- self.begin_index = begin_index
-
- def __call__(self, input_ids, scores):
- if input_ids.shape[1] == self.begin_index:
- scores[:, self.begin_suppress_tokens] = -float("inf")
-
- return scores
-
-
-class SuppressTokensLogitsProcessor(LogitsProcessor):
- r"""This processor can be used to suppress a list of tokens. The processor will set their log probs to `-inf` so that they
- are not sampled."""
-
- def __init__(self, suppress_tokens):
- self.suppress_tokens = list(suppress_tokens)
-
- def __call__(self, input_ids, scores):
- scores[:, self.suppress_tokens] = -float("inf")
- return scores
-
-
-class ForceTokensLogitsProcessor(LogitsProcessor):
- r"""This processor takes a list of pairs of integers which indicates a mapping from generation indices to token
- indices that will be forced before sampling. The processor will set their log probs to `inf` so that they are
- sampled at their corresponding index."""
-
- def __init__(self, force_token_map: List[List[int]]):
- self.force_token_map = dict(force_token_map)
-
- def __call__(self, input_ids, scores):
- generation_idx = input_ids.shape[-1]
- current_token = self.force_token_map.get(generation_idx, None)
- if current_token is not None:
- scores[:, :] = -float("inf")
- scores[:, current_token] = 0
- return scores
-
-
-class WhisperTimeStampLogitsProcessor(LogitsProcessor):
- r"""
- Whisper specific Processor. This processor can be used to force a list of tokens. The processor will set their log
- probs to `inf` so that they are sampled at their corresponding index.
-
- Args:
- generate_config (`GenerateConfig`):
- The generate config used to generate the output. The following parameters are required:
- eos_token_id (`int`, *optional*, defaults to 50257):
- The id of the *end-of-sequence* token.
- no_timestamps_token_id (`int`, *optional*, defaults to 50363):
- The id of the `"<|notimestamps|>"` token.
- max_initial_timestamp_index (`int`, *optional*, defaults to 1):
- Used to set the maximum value of the initial timestamp. This is used to prevent the model from
- predicting timestamps that are too far in the future.
- """
-
- def __init__(self, generate_config): # support for the kwargs
- self.eos_token_id = generate_config.eos_token_id
- self.no_timestamps_token_id = generate_config.no_timestamps_token_id
- self.timestamp_begin = generate_config.no_timestamps_token_id + 1
-
- self.begin_index = len(generate_config.forced_decoder_ids) + 2
- if generate_config.forced_decoder_ids[-1][1] == self.no_timestamps_token_id:
- self.begin_index -= 1
- self.max_initial_timestamp_index = generate_config.max_initial_timestamp_index
-
- def __call__(self, input_ids, scores):
- # suppress <|notimestamps|> which is handled by without_timestamps
- scores[:, self.no_timestamps_token_id] = -float("inf")
-
- if input_ids.shape[1] == self.begin_index - 1:
- scores[:, :] = -float("inf")
- scores[:, self.timestamp_begin] = 0
- return scores
-
- # timestamps have to appear in pairs, except directly before eos_token; mask logits accordingly
- for k in range(input_ids.shape[0]):
- seq = list(input_ids[k, self.begin_index :].tolist())
- last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.timestamp_begin
- penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.timestamp_begin
-
- if last_was_timestamp:
- if penultimate_was_timestamp: # has to be non-timestamp
- scores[k, self.timestamp_begin :] = -float("inf")
- else: # cannot be normal text tokens
- scores[k, : self.eos_token_id] = -float("inf")
-
- # apply the `max_initial_timestamp` option
- if input_ids.shape[1] == self.begin_index and self.max_initial_timestamp_index is not None:
- last_allowed = self.timestamp_begin + self.max_initial_timestamp_index
- scores[:, last_allowed + 1 :] = -float("inf")
-
- # if sum of probability over timestamps is above any other token, sample timestamp
- logprobs = torch.nn.functional.log_softmax(scores.float(), dim=-1)
- for k in range(input_ids.shape[0]):
- timestamp_logprob = logprobs[k, self.timestamp_begin :].logsumexp(dim=-1)
- max_text_token_logprob = logprobs[k, : self.timestamp_begin].max()
- if timestamp_logprob > max_text_token_logprob:
- scores[k, : self.timestamp_begin] = -float("inf")
-
- return scores
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cffi/cffi_opcode.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cffi/cffi_opcode.py
deleted file mode 100644
index a0df98d1c743790f4047672abcae0d00f993a2ce..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cffi/cffi_opcode.py
+++ /dev/null
@@ -1,187 +0,0 @@
-from .error import VerificationError
-
-class CffiOp(object):
- def __init__(self, op, arg):
- self.op = op
- self.arg = arg
-
- def as_c_expr(self):
- if self.op is None:
- assert isinstance(self.arg, str)
- return '(_cffi_opcode_t)(%s)' % (self.arg,)
- classname = CLASS_NAME[self.op]
- return '_CFFI_OP(_CFFI_OP_%s, %s)' % (classname, self.arg)
-
- def as_python_bytes(self):
- if self.op is None and self.arg.isdigit():
- value = int(self.arg) # non-negative: '-' not in self.arg
- if value >= 2**31:
- raise OverflowError("cannot emit %r: limited to 2**31-1"
- % (self.arg,))
- return format_four_bytes(value)
- if isinstance(self.arg, str):
- raise VerificationError("cannot emit to Python: %r" % (self.arg,))
- return format_four_bytes((self.arg << 8) | self.op)
-
- def __str__(self):
- classname = CLASS_NAME.get(self.op, self.op)
- return '(%s %s)' % (classname, self.arg)
-
-def format_four_bytes(num):
- return '\\x%02X\\x%02X\\x%02X\\x%02X' % (
- (num >> 24) & 0xFF,
- (num >> 16) & 0xFF,
- (num >> 8) & 0xFF,
- (num ) & 0xFF)
-
-OP_PRIMITIVE = 1
-OP_POINTER = 3
-OP_ARRAY = 5
-OP_OPEN_ARRAY = 7
-OP_STRUCT_UNION = 9
-OP_ENUM = 11
-OP_FUNCTION = 13
-OP_FUNCTION_END = 15
-OP_NOOP = 17
-OP_BITFIELD = 19
-OP_TYPENAME = 21
-OP_CPYTHON_BLTN_V = 23 # varargs
-OP_CPYTHON_BLTN_N = 25 # noargs
-OP_CPYTHON_BLTN_O = 27 # O (i.e. a single arg)
-OP_CONSTANT = 29
-OP_CONSTANT_INT = 31
-OP_GLOBAL_VAR = 33
-OP_DLOPEN_FUNC = 35
-OP_DLOPEN_CONST = 37
-OP_GLOBAL_VAR_F = 39
-OP_EXTERN_PYTHON = 41
-
-PRIM_VOID = 0
-PRIM_BOOL = 1
-PRIM_CHAR = 2
-PRIM_SCHAR = 3
-PRIM_UCHAR = 4
-PRIM_SHORT = 5
-PRIM_USHORT = 6
-PRIM_INT = 7
-PRIM_UINT = 8
-PRIM_LONG = 9
-PRIM_ULONG = 10
-PRIM_LONGLONG = 11
-PRIM_ULONGLONG = 12
-PRIM_FLOAT = 13
-PRIM_DOUBLE = 14
-PRIM_LONGDOUBLE = 15
-
-PRIM_WCHAR = 16
-PRIM_INT8 = 17
-PRIM_UINT8 = 18
-PRIM_INT16 = 19
-PRIM_UINT16 = 20
-PRIM_INT32 = 21
-PRIM_UINT32 = 22
-PRIM_INT64 = 23
-PRIM_UINT64 = 24
-PRIM_INTPTR = 25
-PRIM_UINTPTR = 26
-PRIM_PTRDIFF = 27
-PRIM_SIZE = 28
-PRIM_SSIZE = 29
-PRIM_INT_LEAST8 = 30
-PRIM_UINT_LEAST8 = 31
-PRIM_INT_LEAST16 = 32
-PRIM_UINT_LEAST16 = 33
-PRIM_INT_LEAST32 = 34
-PRIM_UINT_LEAST32 = 35
-PRIM_INT_LEAST64 = 36
-PRIM_UINT_LEAST64 = 37
-PRIM_INT_FAST8 = 38
-PRIM_UINT_FAST8 = 39
-PRIM_INT_FAST16 = 40
-PRIM_UINT_FAST16 = 41
-PRIM_INT_FAST32 = 42
-PRIM_UINT_FAST32 = 43
-PRIM_INT_FAST64 = 44
-PRIM_UINT_FAST64 = 45
-PRIM_INTMAX = 46
-PRIM_UINTMAX = 47
-PRIM_FLOATCOMPLEX = 48
-PRIM_DOUBLECOMPLEX = 49
-PRIM_CHAR16 = 50
-PRIM_CHAR32 = 51
-
-_NUM_PRIM = 52
-_UNKNOWN_PRIM = -1
-_UNKNOWN_FLOAT_PRIM = -2
-_UNKNOWN_LONG_DOUBLE = -3
-
-_IO_FILE_STRUCT = -1
-
-PRIMITIVE_TO_INDEX = {
- 'char': PRIM_CHAR,
- 'short': PRIM_SHORT,
- 'int': PRIM_INT,
- 'long': PRIM_LONG,
- 'long long': PRIM_LONGLONG,
- 'signed char': PRIM_SCHAR,
- 'unsigned char': PRIM_UCHAR,
- 'unsigned short': PRIM_USHORT,
- 'unsigned int': PRIM_UINT,
- 'unsigned long': PRIM_ULONG,
- 'unsigned long long': PRIM_ULONGLONG,
- 'float': PRIM_FLOAT,
- 'double': PRIM_DOUBLE,
- 'long double': PRIM_LONGDOUBLE,
- 'float _Complex': PRIM_FLOATCOMPLEX,
- 'double _Complex': PRIM_DOUBLECOMPLEX,
- '_Bool': PRIM_BOOL,
- 'wchar_t': PRIM_WCHAR,
- 'char16_t': PRIM_CHAR16,
- 'char32_t': PRIM_CHAR32,
- 'int8_t': PRIM_INT8,
- 'uint8_t': PRIM_UINT8,
- 'int16_t': PRIM_INT16,
- 'uint16_t': PRIM_UINT16,
- 'int32_t': PRIM_INT32,
- 'uint32_t': PRIM_UINT32,
- 'int64_t': PRIM_INT64,
- 'uint64_t': PRIM_UINT64,
- 'intptr_t': PRIM_INTPTR,
- 'uintptr_t': PRIM_UINTPTR,
- 'ptrdiff_t': PRIM_PTRDIFF,
- 'size_t': PRIM_SIZE,
- 'ssize_t': PRIM_SSIZE,
- 'int_least8_t': PRIM_INT_LEAST8,
- 'uint_least8_t': PRIM_UINT_LEAST8,
- 'int_least16_t': PRIM_INT_LEAST16,
- 'uint_least16_t': PRIM_UINT_LEAST16,
- 'int_least32_t': PRIM_INT_LEAST32,
- 'uint_least32_t': PRIM_UINT_LEAST32,
- 'int_least64_t': PRIM_INT_LEAST64,
- 'uint_least64_t': PRIM_UINT_LEAST64,
- 'int_fast8_t': PRIM_INT_FAST8,
- 'uint_fast8_t': PRIM_UINT_FAST8,
- 'int_fast16_t': PRIM_INT_FAST16,
- 'uint_fast16_t': PRIM_UINT_FAST16,
- 'int_fast32_t': PRIM_INT_FAST32,
- 'uint_fast32_t': PRIM_UINT_FAST32,
- 'int_fast64_t': PRIM_INT_FAST64,
- 'uint_fast64_t': PRIM_UINT_FAST64,
- 'intmax_t': PRIM_INTMAX,
- 'uintmax_t': PRIM_UINTMAX,
- }
-
-F_UNION = 0x01
-F_CHECK_FIELDS = 0x02
-F_PACKED = 0x04
-F_EXTERNAL = 0x08
-F_OPAQUE = 0x10
-
-G_FLAGS = dict([('_CFFI_' + _key, globals()[_key])
- for _key in ['F_UNION', 'F_CHECK_FIELDS', 'F_PACKED',
- 'F_EXTERNAL', 'F_OPAQUE']])
-
-CLASS_NAME = {}
-for _name, _value in list(globals().items()):
- if _name.startswith('OP_') and isinstance(_value, int):
- CLASS_NAME[_value] = _name[3:]
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/johabprober.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/johabprober.py
deleted file mode 100644
index d7364ba61eca930aa1c868abe3b322cceb995a6b..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/johabprober.py
+++ /dev/null
@@ -1,47 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-# Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301 USA
-######################### END LICENSE BLOCK #########################
-
-from .chardistribution import JOHABDistributionAnalysis
-from .codingstatemachine import CodingStateMachine
-from .mbcharsetprober import MultiByteCharSetProber
-from .mbcssm import JOHAB_SM_MODEL
-
-
-class JOHABProber(MultiByteCharSetProber):
- def __init__(self) -> None:
- super().__init__()
- self.coding_sm = CodingStateMachine(JOHAB_SM_MODEL)
- self.distribution_analyzer = JOHABDistributionAnalysis()
- self.reset()
-
- @property
- def charset_name(self) -> str:
- return "Johab"
-
- @property
- def language(self) -> str:
- return "Korean"
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/dml/color.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/dml/color.py
deleted file mode 100644
index 2f2f25cb275336c3f2d2b4c3580448252d62ad6a..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/dml/color.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# encoding: utf-8
-
-"""
-DrawingML objects related to color, ColorFormat being the most prominent.
-"""
-
-from __future__ import (
- absolute_import, division, print_function, unicode_literals
-)
-
-from ..enum.dml import MSO_COLOR_TYPE
-from ..oxml.simpletypes import ST_HexColorAuto
-from ..shared import ElementProxy
-
-
-class ColorFormat(ElementProxy):
- """
- Provides access to color settings such as RGB color, theme color, and
- luminance adjustments.
- """
-
- __slots__ = ()
-
- def __init__(self, rPr_parent):
- super(ColorFormat, self).__init__(rPr_parent)
-
- @property
- def rgb(self):
- """
- An |RGBColor| value or |None| if no RGB color is specified.
-
- When :attr:`type` is `MSO_COLOR_TYPE.RGB`, the value of this property
- will always be an |RGBColor| value. It may also be an |RGBColor|
- value if :attr:`type` is `MSO_COLOR_TYPE.THEME`, as Word writes the
- current value of a theme color when one is assigned. In that case,
- the RGB value should be interpreted as no more than a good guess
- however, as the theme color takes precedence at rendering time. Its
- value is |None| whenever :attr:`type` is either |None| or
- `MSO_COLOR_TYPE.AUTO`.
-
- Assigning an |RGBColor| value causes :attr:`type` to become
- `MSO_COLOR_TYPE.RGB` and any theme color is removed. Assigning |None|
- causes any color to be removed such that the effective color is
- inherited from the style hierarchy.
- """
- color = self._color
- if color is None:
- return None
- if color.val == ST_HexColorAuto.AUTO:
- return None
- return color.val
-
- @rgb.setter
- def rgb(self, value):
- if value is None and self._color is None:
- return
- rPr = self._element.get_or_add_rPr()
- rPr._remove_color()
- if value is not None:
- rPr.get_or_add_color().val = value
-
- @property
- def theme_color(self):
- """
- A member of :ref:`MsoThemeColorIndex` or |None| if no theme color is
- specified. When :attr:`type` is `MSO_COLOR_TYPE.THEME`, the value of
- this property will always be a member of :ref:`MsoThemeColorIndex`.
- When :attr:`type` has any other value, the value of this property is
- |None|.
-
- Assigning a member of :ref:`MsoThemeColorIndex` causes :attr:`type`
- to become `MSO_COLOR_TYPE.THEME`. Any existing RGB value is retained
- but ignored by Word. Assigning |None| causes any color specification
- to be removed such that the effective color is inherited from the
- style hierarchy.
- """
- color = self._color
- if color is None or color.themeColor is None:
- return None
- return color.themeColor
-
- @theme_color.setter
- def theme_color(self, value):
- if value is None:
- if self._color is not None:
- self._element.rPr._remove_color()
- return
- self._element.get_or_add_rPr().get_or_add_color().themeColor = value
-
- @property
- def type(self):
- """
- Read-only. A member of :ref:`MsoColorType`, one of RGB, THEME, or
- AUTO, corresponding to the way this color is defined. Its value is
- |None| if no color is applied at this level, which causes the
- effective color to be inherited from the style hierarchy.
- """
- color = self._color
- if color is None:
- return None
- if color.themeColor is not None:
- return MSO_COLOR_TYPE.THEME
- if color.val == ST_HexColorAuto.AUTO:
- return MSO_COLOR_TYPE.AUTO
- return MSO_COLOR_TYPE.RGB
-
- @property
- def _color(self):
- """
- Return `w:rPr/w:color` or |None| if not present. Helper to factor out
- repetitive element access.
- """
- rPr = self._element.rPr
- if rPr is None:
- return None
- return rPr.color
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/treeTools.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/treeTools.py
deleted file mode 100644
index 24e10ba5b19ef41d56a552527680a4c73503cc3c..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/treeTools.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Generic tools for working with trees."""
-
-from math import ceil, log
-
-
-def build_n_ary_tree(leaves, n):
- """Build N-ary tree from sequence of leaf nodes.
-
- Return a list of lists where each non-leaf node is a list containing
- max n nodes.
- """
- if not leaves:
- return []
-
- assert n > 1
-
- depth = ceil(log(len(leaves), n))
-
- if depth <= 1:
- return list(leaves)
-
- # Fully populate complete subtrees of root until we have enough leaves left
- root = []
- unassigned = None
- full_step = n ** (depth - 1)
- for i in range(0, len(leaves), full_step):
- subtree = leaves[i : i + full_step]
- if len(subtree) < full_step:
- unassigned = subtree
- break
- while len(subtree) > n:
- subtree = [subtree[k : k + n] for k in range(0, len(subtree), n)]
- root.append(subtree)
-
- if unassigned:
- # Recurse to fill the last subtree, which is the only partially populated one
- subtree = build_n_ary_tree(unassigned, n)
- if len(subtree) <= n - len(root):
- # replace last subtree with its children if they can still fit
- root.extend(subtree)
- else:
- root.append(subtree)
- assert len(root) <= n
-
- return root
diff --git a/spaces/cihyFjudo/fairness-paper-search/Dove trovare Operazione Rosebud 1 il film con Richard Attenborough e Kim Cattrall.md b/spaces/cihyFjudo/fairness-paper-search/Dove trovare Operazione Rosebud 1 il film con Richard Attenborough e Kim Cattrall.md
deleted file mode 100644
index 5620aae51ed5a478e462844e34719f02ef733710..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Dove trovare Operazione Rosebud 1 il film con Richard Attenborough e Kim Cattrall.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/Miracle Box 3.04 Crack With Serial Number The Easiest Way to Repair Update and Customize Your Phone [Torrent]!.md b/spaces/cihyFjudo/fairness-paper-search/Miracle Box 3.04 Crack With Serial Number The Easiest Way to Repair Update and Customize Your Phone [Torrent]!.md
deleted file mode 100644
index 83c4c86b854861075a82c9ba4a6ae77b368b1e8c..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Miracle Box 3.04 Crack With Serial Number The Easiest Way to Repair Update and Customize Your Phone [Torrent]!.md
+++ /dev/null
@@ -1,8 +0,0 @@
-
-
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vw gamma code calculator v2 0 Terminator Genisys (English) hindi dubbed movie download Newton movie in hindi download 720p hd alvin and the chipmunks 1 full movie free download in hindi The Twilight Saga Eclipse 2010 BRRip x264 [Dual Audio] [Eng Hindi] [375MB] [CooL GuY] a2zRG marathi vahini nagade sexy photo transformers 1 2007 full movie in hindi flippingbook publisher 2.6 keygen crack catia v5 r19 64 bit crack free download IPTV with AutoUpdateOption - over 800 chanels utorrent
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\ No newline at end of file
diff --git a/spaces/codellama/codellama-playground/app.py b/spaces/codellama/codellama-playground/app.py
deleted file mode 100644
index 3c097e27dd394b7701f05f41fdcf144f5e1c015c..0000000000000000000000000000000000000000
--- a/spaces/codellama/codellama-playground/app.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import json
-import os
-import shutil
-import requests
-
-import gradio as gr
-from huggingface_hub import Repository
-from text_generation import Client
-
-from share_btn import community_icon_html, loading_icon_html, share_js, share_btn_css
-
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-
-API_URL = "https://api-inference.huggingface.co/models/codellama/CodeLlama-13b-hf"
-
-FIM_PREFIX = "
"
-FIM_MIDDLE = " "
-FIM_SUFFIX = " "
-
-FIM_INDICATOR = ""
-
-EOS_STRING = ""
-EOT_STRING = ""
-
-theme = gr.themes.Monochrome(
- primary_hue="indigo",
- secondary_hue="blue",
- neutral_hue="slate",
- radius_size=gr.themes.sizes.radius_sm,
- font=[
- gr.themes.GoogleFont("Open Sans"),
- "ui-sans-serif",
- "system-ui",
- "sans-serif",
- ],
-)
-
-client = Client(
- API_URL,
- headers={"Authorization": f"Bearer {HF_TOKEN}"},
-)
-
-
-def generate(
- prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
-):
-
- temperature = float(temperature)
- if temperature < 1e-2:
- temperature = 1e-2
- top_p = float(top_p)
- fim_mode = False
-
- generate_kwargs = dict(
- temperature=temperature,
- max_new_tokens=max_new_tokens,
- top_p=top_p,
- repetition_penalty=repetition_penalty,
- do_sample=True,
- seed=42,
- )
-
- if FIM_INDICATOR in prompt:
- fim_mode = True
- try:
- prefix, suffix = prompt.split(FIM_INDICATOR)
- except:
- raise ValueError(f"Only one {FIM_INDICATOR} allowed in prompt!")
- prompt = f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}"
-
-
- stream = client.generate_stream(prompt, **generate_kwargs)
-
-
- if fim_mode:
- output = prefix
- else:
- output = prompt
-
- previous_token = ""
- for response in stream:
- if any([end_token in response.token.text for end_token in [EOS_STRING, EOT_STRING]]):
- if fim_mode:
- output += suffix
- yield output
- return output
- print("output", output)
- else:
- return output
- else:
- output += response.token.text
- previous_token = response.token.text
- yield output
- return output
-
-
-examples = [
- "X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1)\n\n# Train a logistic regression model, predict the labels on the test set and compute the accuracy score",
- "// Returns every other value in the array as a new array.\nfunction everyOther(arr) {",
- "Poor English: She no went to the market. Corrected English:",
- "def alternating(list1, list2):\n results = []\n for i in range(min(len(list1), len(list2))):\n results.append(list1[i])\n results.append(list2[i])\n if len(list1) > len(list2):\n \n else:\n results.extend(list2[i+1:])\n return results",
- "def remove_non_ascii(s: str) -> str:\n \"\"\" \nprint(remove_non_ascii('afkdj$$('))",
-]
-
-
-def process_example(args):
- for x in generate(args):
- pass
- return x
-
-
-css = ".generating {visibility: hidden}"
-
-monospace_css = """
-#q-input textarea {
- font-family: monospace, 'Consolas', Courier, monospace;
-}
-"""
-
-
-css += share_btn_css + monospace_css + ".gradio-container {color: black}"
-
-description = """
-
-
🦙 Code Llama Playground
-
-
-
This is a demo to generate text and code with the following Code Llama model (13B). Please note that this model is not designed for instruction purposes but for code completion. If you're looking for instruction or want to chat with a fine-tuned model, you can use this demo instead. You can learn more about the model in the blog post or paper
-"""
-
-with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
- with gr.Column():
- gr.Markdown(description)
- with gr.Row():
- with gr.Column():
- instruction = gr.Textbox(
- placeholder="Enter your code here",
- lines=5,
- label="Input",
- elem_id="q-input",
- )
- submit = gr.Button("Generate", variant="primary")
- output = gr.Code(elem_id="q-output", lines=30, label="Output")
- with gr.Row():
- with gr.Column():
- with gr.Accordion("Advanced settings", open=False):
- with gr.Row():
- column_1, column_2 = gr.Column(), gr.Column()
- with column_1:
- temperature = gr.Slider(
- label="Temperature",
- value=0.1,
- minimum=0.0,
- maximum=1.0,
- step=0.05,
- interactive=True,
- info="Higher values produce more diverse outputs",
- )
- max_new_tokens = gr.Slider(
- label="Max new tokens",
- value=256,
- minimum=0,
- maximum=8192,
- step=64,
- interactive=True,
- info="The maximum numbers of new tokens",
- )
- with column_2:
- top_p = gr.Slider(
- label="Top-p (nucleus sampling)",
- value=0.90,
- minimum=0.0,
- maximum=1,
- step=0.05,
- interactive=True,
- info="Higher values sample more low-probability tokens",
- )
- repetition_penalty = gr.Slider(
- label="Repetition penalty",
- value=1.05,
- minimum=1.0,
- maximum=2.0,
- step=0.05,
- interactive=True,
- info="Penalize repeated tokens",
- )
-
- gr.Examples(
- examples=examples,
- inputs=[instruction],
- cache_examples=False,
- fn=process_example,
- outputs=[output],
- )
-
- submit.click(
- generate,
- inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty],
- outputs=[output],
- )
-demo.queue(concurrency_count=16).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/mpegaudiodsp_init_arm.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/mpegaudiodsp_init_arm.c
deleted file mode 100644
index d87bd27ad8dd53c0a2d88a2b9bd4298656878415..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/mpegaudiodsp_init_arm.c
+++ /dev/null
@@ -1,38 +0,0 @@
-/*
- * Copyright (c) 2011 Mans Rullgard
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include
-
-#include "libavutil/attributes.h"
-#include "libavutil/arm/cpu.h"
-#include "libavcodec/mpegaudiodsp.h"
-#include "config.h"
-
-void ff_mpadsp_apply_window_fixed_armv6(int32_t *synth_buf, int32_t *window,
- int *dither, int16_t *out, ptrdiff_t incr);
-
-av_cold void ff_mpadsp_init_arm(MPADSPContext *s)
-{
- int cpu_flags = av_get_cpu_flags();
-
- if (have_armv6(cpu_flags)) {
- s->apply_window_fixed = ff_mpadsp_apply_window_fixed_armv6;
- }
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_lbr.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_lbr.c
deleted file mode 100644
index bef0054dbed74a83234335acdca4939d2896a5d0..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_lbr.c
+++ /dev/null
@@ -1,1840 +0,0 @@
-/*
- * Copyright (C) 2016 foo86
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#define BITSTREAM_READER_LE
-
-#include "libavutil/channel_layout.h"
-#include "libavutil/mem_internal.h"
-
-#include "dcadec.h"
-#include "dcadata.h"
-#include "dcahuff.h"
-#include "dca_syncwords.h"
-#include "bytestream.h"
-#include "decode.h"
-
-#define AMP_MAX 56
-
-enum LBRFlags {
- LBR_FLAG_24_BIT = 0x01,
- LBR_FLAG_LFE_PRESENT = 0x02,
- LBR_FLAG_BAND_LIMIT_2_3 = 0x04,
- LBR_FLAG_BAND_LIMIT_1_2 = 0x08,
- LBR_FLAG_BAND_LIMIT_1_3 = 0x0c,
- LBR_FLAG_BAND_LIMIT_1_4 = 0x10,
- LBR_FLAG_BAND_LIMIT_1_8 = 0x18,
- LBR_FLAG_BAND_LIMIT_NONE = 0x14,
- LBR_FLAG_BAND_LIMIT_MASK = 0x1c,
- LBR_FLAG_DMIX_STEREO = 0x20,
- LBR_FLAG_DMIX_MULTI_CH = 0x40
-};
-
-enum LBRChunkTypes {
- LBR_CHUNK_NULL = 0x00,
- LBR_CHUNK_PAD = 0x01,
- LBR_CHUNK_FRAME = 0x04,
- LBR_CHUNK_FRAME_NO_CSUM = 0x06,
- LBR_CHUNK_LFE = 0x0a,
- LBR_CHUNK_ECS = 0x0b,
- LBR_CHUNK_RESERVED_1 = 0x0c,
- LBR_CHUNK_RESERVED_2 = 0x0d,
- LBR_CHUNK_SCF = 0x0e,
- LBR_CHUNK_TONAL = 0x10,
- LBR_CHUNK_TONAL_GRP_1 = 0x11,
- LBR_CHUNK_TONAL_GRP_2 = 0x12,
- LBR_CHUNK_TONAL_GRP_3 = 0x13,
- LBR_CHUNK_TONAL_GRP_4 = 0x14,
- LBR_CHUNK_TONAL_GRP_5 = 0x15,
- LBR_CHUNK_TONAL_SCF = 0x16,
- LBR_CHUNK_TONAL_SCF_GRP_1 = 0x17,
- LBR_CHUNK_TONAL_SCF_GRP_2 = 0x18,
- LBR_CHUNK_TONAL_SCF_GRP_3 = 0x19,
- LBR_CHUNK_TONAL_SCF_GRP_4 = 0x1a,
- LBR_CHUNK_TONAL_SCF_GRP_5 = 0x1b,
- LBR_CHUNK_RES_GRID_LR = 0x30,
- LBR_CHUNK_RES_GRID_LR_LAST = 0x3f,
- LBR_CHUNK_RES_GRID_HR = 0x40,
- LBR_CHUNK_RES_GRID_HR_LAST = 0x4f,
- LBR_CHUNK_RES_TS_1 = 0x50,
- LBR_CHUNK_RES_TS_1_LAST = 0x5f,
- LBR_CHUNK_RES_TS_2 = 0x60,
- LBR_CHUNK_RES_TS_2_LAST = 0x6f,
- LBR_CHUNK_EXTENSION = 0x7f
-};
-
-typedef struct LBRChunk {
- int id, len;
- const uint8_t *data;
-} LBRChunk;
-
-static const int8_t channel_reorder_nolfe[7][5] = {
- { 0, -1, -1, -1, -1 }, // C
- { 0, 1, -1, -1, -1 }, // LR
- { 0, 1, 2, -1, -1 }, // LR C
- { 0, 1, -1, -1, -1 }, // LsRs
- { 1, 2, 0, -1, -1 }, // LsRs C
- { 0, 1, 2, 3, -1 }, // LR LsRs
- { 0, 1, 3, 4, 2 }, // LR LsRs C
-};
-
-static const int8_t channel_reorder_lfe[7][5] = {
- { 0, -1, -1, -1, -1 }, // C
- { 0, 1, -1, -1, -1 }, // LR
- { 0, 1, 2, -1, -1 }, // LR C
- { 1, 2, -1, -1, -1 }, // LsRs
- { 2, 3, 0, -1, -1 }, // LsRs C
- { 0, 1, 3, 4, -1 }, // LR LsRs
- { 0, 1, 4, 5, 2 }, // LR LsRs C
-};
-
-static const uint8_t lfe_index[7] = {
- 1, 2, 3, 0, 1, 2, 3
-};
-
-static const uint16_t channel_layouts[7] = {
- AV_CH_LAYOUT_MONO,
- AV_CH_LAYOUT_STEREO,
- AV_CH_LAYOUT_SURROUND,
- AV_CH_SIDE_LEFT | AV_CH_SIDE_RIGHT,
- AV_CH_FRONT_CENTER | AV_CH_SIDE_LEFT | AV_CH_SIDE_RIGHT,
- AV_CH_LAYOUT_2_2,
- AV_CH_LAYOUT_5POINT0
-};
-
-static float cos_tab[256];
-static const float lpc_tab[16] = {
- /* lpc_tab[i] = sin((i - 8) * (M_PI / ((i < 8) ? 17 : 15))) */
- -0.995734176295034521871191178905, -0.961825643172819070408796290732,
- -0.895163291355062322067016499754, -0.798017227280239503332805112796,
- -0.673695643646557211712691912426, -0.526432162877355800244607799141,
- -0.361241666187152948744714596184, -0.183749517816570331574408839621,
- 0.0, 0.207911690817759337101742284405,
- 0.406736643075800207753985990341, 0.587785252292473129168705954639,
- 0.743144825477394235014697048974, 0.866025403784438646763723170753,
- 0.951056516295153572116439333379, 0.994521895368273336922691944981
-};
-
-av_cold void ff_dca_lbr_init_tables(void)
-{
- int i;
-
- for (i = 0; i < 256; i++)
- cos_tab[i] = cos(M_PI * i / 128);
-}
-
-static int parse_lfe_24(DCALbrDecoder *s)
-{
- int step_max = FF_ARRAY_ELEMS(ff_dca_lfe_step_size_24) - 1;
- int i, ps, si, code, step_i;
- float step, value, delta;
-
- ps = get_bits(&s->gb, 24);
- si = ps >> 23;
-
- value = (((ps & 0x7fffff) ^ -si) + si) * (1.0f / 0x7fffff);
-
- step_i = get_bits(&s->gb, 8);
- if (step_i > step_max) {
- av_log(s->avctx, AV_LOG_ERROR, "Invalid LFE step size index\n");
- return AVERROR_INVALIDDATA;
- }
-
- step = ff_dca_lfe_step_size_24[step_i];
-
- for (i = 0; i < 64; i++) {
- code = get_bits(&s->gb, 6);
-
- delta = step * 0.03125f;
- if (code & 16)
- delta += step;
- if (code & 8)
- delta += step * 0.5f;
- if (code & 4)
- delta += step * 0.25f;
- if (code & 2)
- delta += step * 0.125f;
- if (code & 1)
- delta += step * 0.0625f;
-
- if (code & 32) {
- value -= delta;
- if (value < -3.0f)
- value = -3.0f;
- } else {
- value += delta;
- if (value > 3.0f)
- value = 3.0f;
- }
-
- step_i += ff_dca_lfe_delta_index_24[code & 31];
- step_i = av_clip(step_i, 0, step_max);
-
- step = ff_dca_lfe_step_size_24[step_i];
- s->lfe_data[i] = value * s->lfe_scale;
- }
-
- return 0;
-}
-
-static int parse_lfe_16(DCALbrDecoder *s)
-{
- int step_max = FF_ARRAY_ELEMS(ff_dca_lfe_step_size_16) - 1;
- int i, ps, si, code, step_i;
- float step, value, delta;
-
- ps = get_bits(&s->gb, 16);
- si = ps >> 15;
-
- value = (((ps & 0x7fff) ^ -si) + si) * (1.0f / 0x7fff);
-
- step_i = get_bits(&s->gb, 8);
- if (step_i > step_max) {
- av_log(s->avctx, AV_LOG_ERROR, "Invalid LFE step size index\n");
- return AVERROR_INVALIDDATA;
- }
-
- step = ff_dca_lfe_step_size_16[step_i];
-
- for (i = 0; i < 64; i++) {
- code = get_bits(&s->gb, 4);
-
- delta = step * 0.125f;
- if (code & 4)
- delta += step;
- if (code & 2)
- delta += step * 0.5f;
- if (code & 1)
- delta += step * 0.25f;
-
- if (code & 8) {
- value -= delta;
- if (value < -3.0f)
- value = -3.0f;
- } else {
- value += delta;
- if (value > 3.0f)
- value = 3.0f;
- }
-
- step_i += ff_dca_lfe_delta_index_16[code & 7];
- step_i = av_clip(step_i, 0, step_max);
-
- step = ff_dca_lfe_step_size_16[step_i];
- s->lfe_data[i] = value * s->lfe_scale;
- }
-
- return 0;
-}
-
-static int parse_lfe_chunk(DCALbrDecoder *s, LBRChunk *chunk)
-{
- int ret;
-
- if (!(s->flags & LBR_FLAG_LFE_PRESENT))
- return 0;
-
- if (!chunk->len)
- return 0;
-
- ret = init_get_bits8(&s->gb, chunk->data, chunk->len);
- if (ret < 0)
- return ret;
-
- // Determine bit depth from chunk size
- if (chunk->len >= 52)
- return parse_lfe_24(s);
- if (chunk->len >= 35)
- return parse_lfe_16(s);
-
- av_log(s->avctx, AV_LOG_ERROR, "LFE chunk too short\n");
- return AVERROR_INVALIDDATA;
-}
-
-static inline int parse_vlc(GetBitContext *s, const VLC *vlc,
- int nb_bits, int max_depth)
-{
- int v = get_vlc2(s, vlc->table, nb_bits, max_depth);
- if (v >= 0)
- return v;
- // Rare value
- return get_bits(s, get_bits(s, 3) + 1);
-}
-
-static int parse_tonal(DCALbrDecoder *s, int group)
-{
- unsigned int amp[DCA_LBR_CHANNELS_TOTAL];
- unsigned int phs[DCA_LBR_CHANNELS_TOTAL];
- unsigned int diff, main_amp, shift;
- int sf, sf_idx, ch, main_ch, freq;
- int ch_nbits = av_ceil_log2(s->nchannels_total);
-
- // Parse subframes for this group
- for (sf = 0; sf < 1 << group; sf += diff ? 8 : 1) {
- sf_idx = ((s->framenum << group) + sf) & 31;
- s->tonal_bounds[group][sf_idx][0] = s->ntones;
-
- // Parse tones for this subframe
- for (freq = 1;; freq++) {
- if (get_bits_left(&s->gb) < 1) {
- av_log(s->avctx, AV_LOG_ERROR, "Tonal group chunk too short\n");
- return AVERROR_INVALIDDATA;
- }
-
- diff = parse_vlc(&s->gb, &ff_dca_vlc_tnl_grp[group], DCA_TNL_GRP_VLC_BITS, 2);
- if (diff >= FF_ARRAY_ELEMS(ff_dca_fst_amp)) {
- av_log(s->avctx, AV_LOG_ERROR, "Invalid tonal frequency diff\n");
- return AVERROR_INVALIDDATA;
- }
-
- diff = get_bitsz(&s->gb, diff >> 2) + ff_dca_fst_amp[diff];
- if (diff <= 1)
- break; // End of subframe
-
- freq += diff - 2;
- if (freq >> (5 - group) > s->nsubbands * 4 - 6) {
- av_log(s->avctx, AV_LOG_ERROR, "Invalid spectral line offset\n");
- return AVERROR_INVALIDDATA;
- }
-
- // Main channel
- main_ch = get_bitsz(&s->gb, ch_nbits);
- main_amp = parse_vlc(&s->gb, &ff_dca_vlc_tnl_scf, DCA_TNL_SCF_VLC_BITS, 2)
- + s->tonal_scf[ff_dca_freq_to_sb[freq >> (7 - group)]]
- + s->limited_range - 2;
- amp[main_ch] = main_amp < AMP_MAX ? main_amp : 0;
- phs[main_ch] = get_bits(&s->gb, 3);
-
- // Secondary channels
- for (ch = 0; ch < s->nchannels_total; ch++) {
- if (ch == main_ch)
- continue;
- if (get_bits1(&s->gb)) {
- amp[ch] = amp[main_ch] - parse_vlc(&s->gb, &ff_dca_vlc_damp, DCA_DAMP_VLC_BITS, 1);
- phs[ch] = phs[main_ch] - parse_vlc(&s->gb, &ff_dca_vlc_dph, DCA_DPH_VLC_BITS, 1);
- } else {
- amp[ch] = 0;
- phs[ch] = 0;
- }
- }
-
- if (amp[main_ch]) {
- // Allocate new tone
- DCALbrTone *t = &s->tones[s->ntones];
- s->ntones = (s->ntones + 1) & (DCA_LBR_TONES - 1);
-
- t->x_freq = freq >> (5 - group);
- t->f_delt = (freq & ((1 << (5 - group)) - 1)) << group;
- t->ph_rot = 256 - (t->x_freq & 1) * 128 - t->f_delt * 4;
-
- shift = ff_dca_ph0_shift[(t->x_freq & 3) * 2 + (freq & 1)]
- - ((t->ph_rot << (5 - group)) - t->ph_rot);
-
- for (ch = 0; ch < s->nchannels; ch++) {
- t->amp[ch] = amp[ch] < AMP_MAX ? amp[ch] : 0;
- t->phs[ch] = 128 - phs[ch] * 32 + shift;
- }
- }
- }
-
- s->tonal_bounds[group][sf_idx][1] = s->ntones;
- }
-
- return 0;
-}
-
-static int parse_tonal_chunk(DCALbrDecoder *s, LBRChunk *chunk)
-{
- int sb, group, ret;
-
- if (!chunk->len)
- return 0;
-
- ret = init_get_bits8(&s->gb, chunk->data, chunk->len);
-
- if (ret < 0)
- return ret;
-
- // Scale factors
- if (chunk->id == LBR_CHUNK_SCF || chunk->id == LBR_CHUNK_TONAL_SCF) {
- if (get_bits_left(&s->gb) < 36) {
- av_log(s->avctx, AV_LOG_ERROR, "Tonal scale factor chunk too short\n");
- return AVERROR_INVALIDDATA;
- }
- for (sb = 0; sb < 6; sb++)
- s->tonal_scf[sb] = get_bits(&s->gb, 6);
- }
-
- // Tonal groups
- if (chunk->id == LBR_CHUNK_TONAL || chunk->id == LBR_CHUNK_TONAL_SCF)
- for (group = 0; group < 5; group++) {
- ret = parse_tonal(s, group);
- if (ret < 0)
- return ret;
- }
-
- return 0;
-}
-
-static int parse_tonal_group(DCALbrDecoder *s, LBRChunk *chunk)
-{
- int ret;
-
- if (!chunk->len)
- return 0;
-
- ret = init_get_bits8(&s->gb, chunk->data, chunk->len);
- if (ret < 0)
- return ret;
-
- return parse_tonal(s, chunk->id);
-}
-
-/**
- * Check point to ensure that enough bits are left. Aborts decoding
- * by skipping to the end of chunk otherwise.
- */
-static int ensure_bits(GetBitContext *s, int n)
-{
- int left = get_bits_left(s);
- if (left < 0)
- return AVERROR_INVALIDDATA;
- if (left < n) {
- skip_bits_long(s, left);
- return 1;
- }
- return 0;
-}
-
-static int parse_scale_factors(DCALbrDecoder *s, uint8_t *scf)
-{
- int i, sf, prev, next, dist;
-
- // Truncated scale factors remain zero
- if (ensure_bits(&s->gb, 20))
- return 0;
-
- // Initial scale factor
- prev = parse_vlc(&s->gb, &ff_dca_vlc_fst_rsd_amp, DCA_FST_RSD_VLC_BITS, 2);
-
- for (sf = 0; sf < 7; sf += dist) {
- scf[sf] = prev; // Store previous value
-
- if (ensure_bits(&s->gb, 20))
- return 0;
-
- // Interpolation distance
- dist = parse_vlc(&s->gb, &ff_dca_vlc_rsd_apprx, DCA_RSD_APPRX_VLC_BITS, 1) + 1;
- if (dist > 7 - sf) {
- av_log(s->avctx, AV_LOG_ERROR, "Invalid scale factor distance\n");
- return AVERROR_INVALIDDATA;
- }
-
- if (ensure_bits(&s->gb, 20))
- return 0;
-
- // Final interpolation point
- next = parse_vlc(&s->gb, &ff_dca_vlc_rsd_amp, DCA_RSD_AMP_VLC_BITS, 2);
-
- if (next & 1)
- next = prev + ((next + 1) >> 1);
- else
- next = prev - ( next >> 1);
-
- // Interpolate
- switch (dist) {
- case 2:
- if (next > prev)
- scf[sf + 1] = prev + ((next - prev) >> 1);
- else
- scf[sf + 1] = prev - ((prev - next) >> 1);
- break;
-
- case 4:
- if (next > prev) {
- scf[sf + 1] = prev + ( (next - prev) >> 2);
- scf[sf + 2] = prev + ( (next - prev) >> 1);
- scf[sf + 3] = prev + (((next - prev) * 3) >> 2);
- } else {
- scf[sf + 1] = prev - ( (prev - next) >> 2);
- scf[sf + 2] = prev - ( (prev - next) >> 1);
- scf[sf + 3] = prev - (((prev - next) * 3) >> 2);
- }
- break;
-
- default:
- for (i = 1; i < dist; i++)
- scf[sf + i] = prev + (next - prev) * i / dist;
- break;
- }
-
- prev = next;
- }
-
- scf[sf] = next; // Store final value
-
- return 0;
-}
-
-static int parse_st_code(GetBitContext *s, int min_v)
-{
- unsigned int v = parse_vlc(s, &ff_dca_vlc_st_grid, DCA_ST_GRID_VLC_BITS, 2) + min_v;
-
- if (v & 1)
- v = 16 + (v >> 1);
- else
- v = 16 - (v >> 1);
-
- if (v >= FF_ARRAY_ELEMS(ff_dca_st_coeff))
- v = 16;
- return v;
-}
-
-static int parse_grid_1_chunk(DCALbrDecoder *s, LBRChunk *chunk, int ch1, int ch2)
-{
- int ch, sb, sf, nsubbands, ret;
-
- if (!chunk->len)
- return 0;
-
- ret = init_get_bits8(&s->gb, chunk->data, chunk->len);
- if (ret < 0)
- return ret;
-
- // Scale factors
- nsubbands = ff_dca_scf_to_grid_1[s->nsubbands - 1] + 1;
- for (sb = 2; sb < nsubbands; sb++) {
- ret = parse_scale_factors(s, s->grid_1_scf[ch1][sb]);
- if (ret < 0)
- return ret;
- if (ch1 != ch2 && ff_dca_grid_1_to_scf[sb] < s->min_mono_subband) {
- ret = parse_scale_factors(s, s->grid_1_scf[ch2][sb]);
- if (ret < 0)
- return ret;
- }
- }
-
- if (get_bits_left(&s->gb) < 1)
- return 0; // Should not happen, but a sample exists that proves otherwise
-
- // Average values for third grid
- for (sb = 0; sb < s->nsubbands - 4; sb++) {
- s->grid_3_avg[ch1][sb] = parse_vlc(&s->gb, &ff_dca_vlc_avg_g3, DCA_AVG_G3_VLC_BITS, 2) - 16;
- if (ch1 != ch2) {
- if (sb + 4 < s->min_mono_subband)
- s->grid_3_avg[ch2][sb] = parse_vlc(&s->gb, &ff_dca_vlc_avg_g3, DCA_AVG_G3_VLC_BITS, 2) - 16;
- else
- s->grid_3_avg[ch2][sb] = s->grid_3_avg[ch1][sb];
- }
- }
-
- if (get_bits_left(&s->gb) < 0) {
- av_log(s->avctx, AV_LOG_ERROR, "First grid chunk too short\n");
- return AVERROR_INVALIDDATA;
- }
-
- // Stereo image for partial mono mode
- if (ch1 != ch2) {
- int min_v[2];
-
- if (ensure_bits(&s->gb, 8))
- return 0;
-
- min_v[0] = get_bits(&s->gb, 4);
- min_v[1] = get_bits(&s->gb, 4);
-
- nsubbands = (s->nsubbands - s->min_mono_subband + 3) / 4;
- for (sb = 0; sb < nsubbands; sb++)
- for (ch = ch1; ch <= ch2; ch++)
- for (sf = 1; sf <= 4; sf++)
- s->part_stereo[ch][sb][sf] = parse_st_code(&s->gb, min_v[ch - ch1]);
-
- if (get_bits_left(&s->gb) >= 0)
- s->part_stereo_pres |= 1 << ch1;
- }
-
- // Low resolution spatial information is not decoded
-
- return 0;
-}
-
-static int parse_grid_1_sec_ch(DCALbrDecoder *s, int ch2)
-{
- int sb, nsubbands, ret;
-
- // Scale factors
- nsubbands = ff_dca_scf_to_grid_1[s->nsubbands - 1] + 1;
- for (sb = 2; sb < nsubbands; sb++) {
- if (ff_dca_grid_1_to_scf[sb] >= s->min_mono_subband) {
- ret = parse_scale_factors(s, s->grid_1_scf[ch2][sb]);
- if (ret < 0)
- return ret;
- }
- }
-
- // Average values for third grid
- for (sb = 0; sb < s->nsubbands - 4; sb++) {
- if (sb + 4 >= s->min_mono_subband) {
- if (ensure_bits(&s->gb, 20))
- return 0;
- s->grid_3_avg[ch2][sb] = parse_vlc(&s->gb, &ff_dca_vlc_avg_g3, DCA_AVG_G3_VLC_BITS, 2) - 16;
- }
- }
-
- return 0;
-}
-
-static void parse_grid_3(DCALbrDecoder *s, int ch1, int ch2, int sb, int flag)
-{
- int i, ch;
-
- for (ch = ch1; ch <= ch2; ch++) {
- if ((ch != ch1 && sb + 4 >= s->min_mono_subband) != flag)
- continue;
-
- if (s->grid_3_pres[ch] & (1U << sb))
- continue; // Already parsed
-
- for (i = 0; i < 8; i++) {
- if (ensure_bits(&s->gb, 20))
- return;
- s->grid_3_scf[ch][sb][i] = parse_vlc(&s->gb, &ff_dca_vlc_grid_3, DCA_GRID_VLC_BITS, 2) - 16;
- }
-
- // Flag scale factors for this subband parsed
- s->grid_3_pres[ch] |= 1U << sb;
- }
-}
-
-static float lbr_rand(DCALbrDecoder *s, int sb)
-{
- s->lbr_rand = 1103515245U * s->lbr_rand + 12345U;
- return s->lbr_rand * s->sb_scf[sb];
-}
-
-/**
- * Parse time samples for one subband, filling truncated samples with randomness
- */
-static void parse_ch(DCALbrDecoder *s, int ch, int sb, int quant_level, int flag)
-{
- float *samples = s->time_samples[ch][sb];
- int i, j, code, nblocks, coding_method;
-
- if (ensure_bits(&s->gb, 20))
- return; // Too few bits left
-
- coding_method = get_bits1(&s->gb);
-
- switch (quant_level) {
- case 1:
- nblocks = FFMIN(get_bits_left(&s->gb) / 8, DCA_LBR_TIME_SAMPLES / 8);
- for (i = 0; i < nblocks; i++, samples += 8) {
- code = get_bits(&s->gb, 8);
- for (j = 0; j < 8; j++)
- samples[j] = ff_dca_rsd_level_2a[(code >> j) & 1];
- }
- i = nblocks * 8;
- break;
-
- case 2:
- if (coding_method) {
- for (i = 0; i < DCA_LBR_TIME_SAMPLES && get_bits_left(&s->gb) >= 2; i++) {
- if (get_bits1(&s->gb))
- samples[i] = ff_dca_rsd_level_2b[get_bits1(&s->gb)];
- else
- samples[i] = 0;
- }
- } else {
- nblocks = FFMIN(get_bits_left(&s->gb) / 8, (DCA_LBR_TIME_SAMPLES + 4) / 5);
- for (i = 0; i < nblocks; i++, samples += 5) {
- code = ff_dca_rsd_pack_5_in_8[get_bits(&s->gb, 8)];
- for (j = 0; j < 5; j++)
- samples[j] = ff_dca_rsd_level_3[(code >> j * 2) & 3];
- }
- i = nblocks * 5;
- }
- break;
-
- case 3:
- nblocks = FFMIN(get_bits_left(&s->gb) / 7, (DCA_LBR_TIME_SAMPLES + 2) / 3);
- for (i = 0; i < nblocks; i++, samples += 3) {
- code = get_bits(&s->gb, 7);
- for (j = 0; j < 3; j++)
- samples[j] = ff_dca_rsd_level_5[ff_dca_rsd_pack_3_in_7[code][j]];
- }
- i = nblocks * 3;
- break;
-
- case 4:
- for (i = 0; i < DCA_LBR_TIME_SAMPLES && get_bits_left(&s->gb) >= 6; i++)
- samples[i] = ff_dca_rsd_level_8[get_vlc2(&s->gb, ff_dca_vlc_rsd.table, 6, 1)];
- break;
-
- case 5:
- nblocks = FFMIN(get_bits_left(&s->gb) / 4, DCA_LBR_TIME_SAMPLES);
- for (i = 0; i < nblocks; i++)
- samples[i] = ff_dca_rsd_level_16[get_bits(&s->gb, 4)];
- break;
-
- default:
- av_assert0(0);
- }
-
- if (flag && get_bits_left(&s->gb) < 20)
- return; // Skip incomplete mono subband
-
- for (; i < DCA_LBR_TIME_SAMPLES; i++)
- s->time_samples[ch][sb][i] = lbr_rand(s, sb);
-
- s->ch_pres[ch] |= 1U << sb;
-}
-
-static int parse_ts(DCALbrDecoder *s, int ch1, int ch2,
- int start_sb, int end_sb, int flag)
-{
- int sb, sb_g3, sb_reorder, quant_level;
-
- for (sb = start_sb; sb < end_sb; sb++) {
- // Subband number before reordering
- if (sb < 6) {
- sb_reorder = sb;
- } else if (flag && sb < s->max_mono_subband) {
- sb_reorder = s->sb_indices[sb];
- } else {
- if (ensure_bits(&s->gb, 28))
- break;
- sb_reorder = get_bits(&s->gb, s->limited_range + 3);
- if (sb_reorder < 6)
- sb_reorder = 6;
- s->sb_indices[sb] = sb_reorder;
- }
- if (sb_reorder >= s->nsubbands)
- return AVERROR_INVALIDDATA;
-
- // Third grid scale factors
- if (sb == 12) {
- for (sb_g3 = 0; sb_g3 < s->g3_avg_only_start_sb - 4; sb_g3++)
- parse_grid_3(s, ch1, ch2, sb_g3, flag);
- } else if (sb < 12 && sb_reorder >= 4) {
- parse_grid_3(s, ch1, ch2, sb_reorder - 4, flag);
- }
-
- // Secondary channel flags
- if (ch1 != ch2) {
- if (ensure_bits(&s->gb, 20))
- break;
- if (!flag || sb_reorder >= s->max_mono_subband)
- s->sec_ch_sbms[ch1 / 2][sb_reorder] = get_bits(&s->gb, 8);
- if (flag && sb_reorder >= s->min_mono_subband)
- s->sec_ch_lrms[ch1 / 2][sb_reorder] = get_bits(&s->gb, 8);
- }
-
- quant_level = s->quant_levels[ch1 / 2][sb];
- if (!quant_level)
- return AVERROR_INVALIDDATA;
-
- // Time samples for one or both channels
- if (sb < s->max_mono_subband && sb_reorder >= s->min_mono_subband) {
- if (!flag)
- parse_ch(s, ch1, sb_reorder, quant_level, 0);
- else if (ch1 != ch2)
- parse_ch(s, ch2, sb_reorder, quant_level, 1);
- } else {
- parse_ch(s, ch1, sb_reorder, quant_level, 0);
- if (ch1 != ch2)
- parse_ch(s, ch2, sb_reorder, quant_level, 0);
- }
- }
-
- return 0;
-}
-
-/**
- * Convert from reflection coefficients to direct form coefficients
- */
-static void convert_lpc(float *coeff, const int *codes)
-{
- int i, j;
-
- for (i = 0; i < 8; i++) {
- float rc = lpc_tab[codes[i]];
- for (j = 0; j < (i + 1) / 2; j++) {
- float tmp1 = coeff[ j ];
- float tmp2 = coeff[i - j - 1];
- coeff[ j ] = tmp1 + rc * tmp2;
- coeff[i - j - 1] = tmp2 + rc * tmp1;
- }
- coeff[i] = rc;
- }
-}
-
-static int parse_lpc(DCALbrDecoder *s, int ch1, int ch2, int start_sb, int end_sb)
-{
- int f = s->framenum & 1;
- int i, sb, ch, codes[16];
-
- // First two subbands have two sets of coefficients, third subband has one
- for (sb = start_sb; sb < end_sb; sb++) {
- int ncodes = 8 * (1 + (sb < 2));
- for (ch = ch1; ch <= ch2; ch++) {
- if (ensure_bits(&s->gb, 4 * ncodes))
- return 0;
- for (i = 0; i < ncodes; i++)
- codes[i] = get_bits(&s->gb, 4);
- for (i = 0; i < ncodes / 8; i++)
- convert_lpc(s->lpc_coeff[f][ch][sb][i], &codes[i * 8]);
- }
- }
-
- return 0;
-}
-
-static int parse_high_res_grid(DCALbrDecoder *s, LBRChunk *chunk, int ch1, int ch2)
-{
- int quant_levels[DCA_LBR_SUBBANDS];
- int sb, ch, ol, st, max_sb, profile, ret;
-
- if (!chunk->len)
- return 0;
-
- ret = init_get_bits8(&s->gb, chunk->data, chunk->len);
- if (ret < 0)
- return ret;
-
- // Quantizer profile
- profile = get_bits(&s->gb, 8);
- // Overall level
- ol = (profile >> 3) & 7;
- // Steepness
- st = profile >> 6;
- // Max energy subband
- max_sb = profile & 7;
-
- // Calculate quantization levels
- for (sb = 0; sb < s->nsubbands; sb++) {
- int f = sb * s->limited_rate / s->nsubbands;
- int a = 18000 / (12 * f / 1000 + 100 + 40 * st) + 20 * ol;
- if (a <= 95)
- quant_levels[sb] = 1;
- else if (a <= 140)
- quant_levels[sb] = 2;
- else if (a <= 180)
- quant_levels[sb] = 3;
- else if (a <= 230)
- quant_levels[sb] = 4;
- else
- quant_levels[sb] = 5;
- }
-
- // Reorder quantization levels for lower subbands
- for (sb = 0; sb < 8; sb++)
- s->quant_levels[ch1 / 2][sb] = quant_levels[ff_dca_sb_reorder[max_sb][sb]];
- for (; sb < s->nsubbands; sb++)
- s->quant_levels[ch1 / 2][sb] = quant_levels[sb];
-
- // LPC for the first two subbands
- ret = parse_lpc(s, ch1, ch2, 0, 2);
- if (ret < 0)
- return ret;
-
- // Time-samples for the first two subbands of main channel
- ret = parse_ts(s, ch1, ch2, 0, 2, 0);
- if (ret < 0)
- return ret;
-
- // First two bands of the first grid
- for (sb = 0; sb < 2; sb++)
- for (ch = ch1; ch <= ch2; ch++)
- if ((ret = parse_scale_factors(s, s->grid_1_scf[ch][sb])) < 0)
- return ret;
-
- return 0;
-}
-
-static int parse_grid_2(DCALbrDecoder *s, int ch1, int ch2,
- int start_sb, int end_sb, int flag)
-{
- int i, j, sb, ch, nsubbands;
-
- nsubbands = ff_dca_scf_to_grid_2[s->nsubbands - 1] + 1;
- if (end_sb > nsubbands)
- end_sb = nsubbands;
-
- for (sb = start_sb; sb < end_sb; sb++) {
- for (ch = ch1; ch <= ch2; ch++) {
- uint8_t *g2_scf = s->grid_2_scf[ch][sb];
-
- if ((ch != ch1 && ff_dca_grid_2_to_scf[sb] >= s->min_mono_subband) != flag) {
- if (!flag)
- memcpy(g2_scf, s->grid_2_scf[ch1][sb], 64);
- continue;
- }
-
- // Scale factors in groups of 8
- for (i = 0; i < 8; i++, g2_scf += 8) {
- if (get_bits_left(&s->gb) < 1) {
- memset(g2_scf, 0, 64 - i * 8);
- break;
- }
- // Bit indicating if whole group has zero values
- if (get_bits1(&s->gb)) {
- for (j = 0; j < 8; j++) {
- if (ensure_bits(&s->gb, 20))
- break;
- g2_scf[j] = parse_vlc(&s->gb, &ff_dca_vlc_grid_2, DCA_GRID_VLC_BITS, 2);
- }
- } else {
- memset(g2_scf, 0, 8);
- }
- }
- }
- }
-
- return 0;
-}
-
-static int parse_ts1_chunk(DCALbrDecoder *s, LBRChunk *chunk, int ch1, int ch2)
-{
- int ret;
- if (!chunk->len)
- return 0;
- if ((ret = init_get_bits8(&s->gb, chunk->data, chunk->len)) < 0)
- return ret;
- if ((ret = parse_lpc(s, ch1, ch2, 2, 3)) < 0)
- return ret;
- if ((ret = parse_ts(s, ch1, ch2, 2, 4, 0)) < 0)
- return ret;
- if ((ret = parse_grid_2(s, ch1, ch2, 0, 1, 0)) < 0)
- return ret;
- if ((ret = parse_ts(s, ch1, ch2, 4, 6, 0)) < 0)
- return ret;
- return 0;
-}
-
-static int parse_ts2_chunk(DCALbrDecoder *s, LBRChunk *chunk, int ch1, int ch2)
-{
- int ret;
-
- if (!chunk->len)
- return 0;
- if ((ret = init_get_bits8(&s->gb, chunk->data, chunk->len)) < 0)
- return ret;
- if ((ret = parse_grid_2(s, ch1, ch2, 1, 3, 0)) < 0)
- return ret;
- if ((ret = parse_ts(s, ch1, ch2, 6, s->max_mono_subband, 0)) < 0)
- return ret;
- if (ch1 != ch2) {
- if ((ret = parse_grid_1_sec_ch(s, ch2)) < 0)
- return ret;
- if ((ret = parse_grid_2(s, ch1, ch2, 0, 3, 1)) < 0)
- return ret;
- }
- if ((ret = parse_ts(s, ch1, ch2, s->min_mono_subband, s->nsubbands, 1)) < 0)
- return ret;
- return 0;
-}
-
-static int init_sample_rate(DCALbrDecoder *s)
-{
- double scale = (-1.0 / (1 << 17)) * sqrt(1 << (2 - s->limited_range));
- float scale_t = scale;
- int i, br_per_ch = s->bit_rate_scaled / s->nchannels_total;
- int ret;
-
- av_tx_uninit(&s->imdct);
-
- ret = av_tx_init(&s->imdct, &s->imdct_fn, AV_TX_FLOAT_MDCT, 1,
- 1 << (s->freq_range + 5), &scale_t, AV_TX_FULL_IMDCT);
- if (ret < 0)
- return ret;
-
- for (i = 0; i < 32 << s->freq_range; i++)
- s->window[i] = ff_dca_long_window[i << (2 - s->freq_range)];
-
- if (br_per_ch < 14000)
- scale = 0.85;
- else if (br_per_ch < 32000)
- scale = (br_per_ch - 14000) * (1.0 / 120000) + 0.85;
- else
- scale = 1.0;
-
- scale *= 1.0 / INT_MAX;
-
- for (i = 0; i < s->nsubbands; i++) {
- if (i < 2)
- s->sb_scf[i] = 0; // The first two subbands are always zero
- else if (i < 5)
- s->sb_scf[i] = (i - 1) * 0.25 * 0.785 * scale;
- else
- s->sb_scf[i] = 0.785 * scale;
- }
-
- s->lfe_scale = (16 << s->freq_range) * 0.0000078265894;
-
- return 0;
-}
-
-static int alloc_sample_buffer(DCALbrDecoder *s)
-{
- // Reserve space for history and padding
- int nchsamples = DCA_LBR_TIME_SAMPLES + DCA_LBR_TIME_HISTORY * 2;
- int nsamples = nchsamples * s->nchannels * s->nsubbands;
- int ch, sb;
- float *ptr;
-
- // Reallocate time sample buffer
- av_fast_mallocz(&s->ts_buffer, &s->ts_size, nsamples * sizeof(float));
- if (!s->ts_buffer)
- return AVERROR(ENOMEM);
-
- ptr = s->ts_buffer + DCA_LBR_TIME_HISTORY;
- for (ch = 0; ch < s->nchannels; ch++) {
- for (sb = 0; sb < s->nsubbands; sb++) {
- s->time_samples[ch][sb] = ptr;
- ptr += nchsamples;
- }
- }
-
- return 0;
-}
-
-static int parse_decoder_init(DCALbrDecoder *s, GetByteContext *gb)
-{
- int old_rate = s->sample_rate;
- int old_band_limit = s->band_limit;
- int old_nchannels = s->nchannels;
- int version, bit_rate_hi;
- unsigned int sr_code;
-
- // Sample rate of LBR audio
- sr_code = bytestream2_get_byte(gb);
- if (sr_code >= FF_ARRAY_ELEMS(ff_dca_sampling_freqs)) {
- av_log(s->avctx, AV_LOG_ERROR, "Invalid LBR sample rate\n");
- return AVERROR_INVALIDDATA;
- }
- s->sample_rate = ff_dca_sampling_freqs[sr_code];
- if (s->sample_rate > 48000) {
- avpriv_report_missing_feature(s->avctx, "%d Hz LBR sample rate", s->sample_rate);
- return AVERROR_PATCHWELCOME;
- }
-
- // LBR speaker mask
- s->ch_mask = bytestream2_get_le16(gb);
- if (!(s->ch_mask & 0x7)) {
- avpriv_report_missing_feature(s->avctx, "LBR channel mask %#x", s->ch_mask);
- return AVERROR_PATCHWELCOME;
- }
- if ((s->ch_mask & 0xfff0) && !(s->warned & 1)) {
- avpriv_report_missing_feature(s->avctx, "LBR channel mask %#x", s->ch_mask);
- s->warned |= 1;
- }
-
- // LBR bitstream version
- version = bytestream2_get_le16(gb);
- if ((version & 0xff00) != 0x0800) {
- avpriv_report_missing_feature(s->avctx, "LBR stream version %#x", version);
- return AVERROR_PATCHWELCOME;
- }
-
- // Flags for LBR decoder initialization
- s->flags = bytestream2_get_byte(gb);
- if (s->flags & LBR_FLAG_DMIX_MULTI_CH) {
- avpriv_report_missing_feature(s->avctx, "LBR multi-channel downmix");
- return AVERROR_PATCHWELCOME;
- }
- if ((s->flags & LBR_FLAG_LFE_PRESENT) && s->sample_rate != 48000) {
- if (!(s->warned & 2)) {
- avpriv_report_missing_feature(s->avctx, "%d Hz LFE interpolation", s->sample_rate);
- s->warned |= 2;
- }
- s->flags &= ~LBR_FLAG_LFE_PRESENT;
- }
-
- // Most significant bit rate nibbles
- bit_rate_hi = bytestream2_get_byte(gb);
-
- // Least significant original bit rate word
- s->bit_rate_orig = bytestream2_get_le16(gb) | ((bit_rate_hi & 0x0F) << 16);
-
- // Least significant scaled bit rate word
- s->bit_rate_scaled = bytestream2_get_le16(gb) | ((bit_rate_hi & 0xF0) << 12);
-
- // Setup number of fullband channels
- s->nchannels_total = ff_dca_count_chs_for_mask(s->ch_mask & ~DCA_SPEAKER_PAIR_LFE1);
- s->nchannels = FFMIN(s->nchannels_total, DCA_LBR_CHANNELS);
-
- // Setup band limit
- switch (s->flags & LBR_FLAG_BAND_LIMIT_MASK) {
- case LBR_FLAG_BAND_LIMIT_NONE:
- s->band_limit = 0;
- break;
- case LBR_FLAG_BAND_LIMIT_1_2:
- s->band_limit = 1;
- break;
- case LBR_FLAG_BAND_LIMIT_1_4:
- s->band_limit = 2;
- break;
- default:
- avpriv_report_missing_feature(s->avctx, "LBR band limit %#x", s->flags & LBR_FLAG_BAND_LIMIT_MASK);
- return AVERROR_PATCHWELCOME;
- }
-
- // Setup frequency range
- s->freq_range = ff_dca_freq_ranges[sr_code];
-
- // Setup resolution profile
- if (s->bit_rate_orig >= 44000 * (s->nchannels_total + 2))
- s->res_profile = 2;
- else if (s->bit_rate_orig >= 25000 * (s->nchannels_total + 2))
- s->res_profile = 1;
- else
- s->res_profile = 0;
-
- // Setup limited sample rate, number of subbands, etc
- s->limited_rate = s->sample_rate >> s->band_limit;
- s->limited_range = s->freq_range - s->band_limit;
- if (s->limited_range < 0) {
- av_log(s->avctx, AV_LOG_ERROR, "Invalid LBR band limit for frequency range\n");
- return AVERROR_INVALIDDATA;
- }
-
- s->nsubbands = 8 << s->limited_range;
-
- s->g3_avg_only_start_sb = s->nsubbands * ff_dca_avg_g3_freqs[s->res_profile] / (s->limited_rate / 2);
- if (s->g3_avg_only_start_sb > s->nsubbands)
- s->g3_avg_only_start_sb = s->nsubbands;
-
- s->min_mono_subband = s->nsubbands * 2000 / (s->limited_rate / 2);
- if (s->min_mono_subband > s->nsubbands)
- s->min_mono_subband = s->nsubbands;
-
- s->max_mono_subband = s->nsubbands * 14000 / (s->limited_rate / 2);
- if (s->max_mono_subband > s->nsubbands)
- s->max_mono_subband = s->nsubbands;
-
- // Handle change of sample rate
- if ((old_rate != s->sample_rate || old_band_limit != s->band_limit) && init_sample_rate(s) < 0)
- return AVERROR(ENOMEM);
-
- // Setup stereo downmix
- if (s->flags & LBR_FLAG_DMIX_STEREO) {
- DCAContext *dca = s->avctx->priv_data;
-
- if (s->nchannels_total < 3 || s->nchannels_total > DCA_LBR_CHANNELS_TOTAL - 2) {
- av_log(s->avctx, AV_LOG_ERROR, "Invalid number of channels for LBR stereo downmix\n");
- return AVERROR_INVALIDDATA;
- }
-
- // This decoder doesn't support ECS chunk
- if (dca->request_channel_layout != DCA_SPEAKER_LAYOUT_STEREO && !(s->warned & 4)) {
- avpriv_report_missing_feature(s->avctx, "Embedded LBR stereo downmix");
- s->warned |= 4;
- }
-
- // Account for extra downmixed channel pair
- s->nchannels_total += 2;
- s->nchannels = 2;
- s->ch_mask = DCA_SPEAKER_PAIR_LR;
- s->flags &= ~LBR_FLAG_LFE_PRESENT;
- }
-
- // Handle change of sample rate or number of channels
- if (old_rate != s->sample_rate
- || old_band_limit != s->band_limit
- || old_nchannels != s->nchannels) {
- if (alloc_sample_buffer(s) < 0)
- return AVERROR(ENOMEM);
- ff_dca_lbr_flush(s);
- }
-
- return 0;
-}
-
-int ff_dca_lbr_parse(DCALbrDecoder *s, const uint8_t *data, DCAExssAsset *asset)
-{
- struct {
- LBRChunk lfe;
- LBRChunk tonal;
- LBRChunk tonal_grp[5];
- LBRChunk grid1[DCA_LBR_CHANNELS / 2];
- LBRChunk hr_grid[DCA_LBR_CHANNELS / 2];
- LBRChunk ts1[DCA_LBR_CHANNELS / 2];
- LBRChunk ts2[DCA_LBR_CHANNELS / 2];
- } chunk = { {0} };
-
- GetByteContext gb;
-
- int i, ch, sb, sf, ret, group, chunk_id, chunk_len;
-
- bytestream2_init(&gb, data + asset->lbr_offset, asset->lbr_size);
-
- // LBR sync word
- if (bytestream2_get_be32(&gb) != DCA_SYNCWORD_LBR) {
- av_log(s->avctx, AV_LOG_ERROR, "Invalid LBR sync word\n");
- return AVERROR_INVALIDDATA;
- }
-
- // LBR header type
- switch (bytestream2_get_byte(&gb)) {
- case DCA_LBR_HEADER_SYNC_ONLY:
- if (!s->sample_rate) {
- av_log(s->avctx, AV_LOG_ERROR, "LBR decoder not initialized\n");
- return AVERROR_INVALIDDATA;
- }
- break;
- case DCA_LBR_HEADER_DECODER_INIT:
- if ((ret = parse_decoder_init(s, &gb)) < 0) {
- s->sample_rate = 0;
- return ret;
- }
- break;
- default:
- av_log(s->avctx, AV_LOG_ERROR, "Invalid LBR header type\n");
- return AVERROR_INVALIDDATA;
- }
-
- // LBR frame chunk header
- chunk_id = bytestream2_get_byte(&gb);
- chunk_len = (chunk_id & 0x80) ? bytestream2_get_be16(&gb) : bytestream2_get_byte(&gb);
-
- if (chunk_len > bytestream2_get_bytes_left(&gb)) {
- chunk_len = bytestream2_get_bytes_left(&gb);
- av_log(s->avctx, AV_LOG_WARNING, "LBR frame chunk was truncated\n");
- if (s->avctx->err_recognition & AV_EF_EXPLODE)
- return AVERROR_INVALIDDATA;
- }
-
- bytestream2_init(&gb, gb.buffer, chunk_len);
-
- switch (chunk_id & 0x7f) {
- case LBR_CHUNK_FRAME:
- if (s->avctx->err_recognition & (AV_EF_CRCCHECK | AV_EF_CAREFUL)) {
- int checksum = bytestream2_get_be16(&gb);
- uint16_t res = chunk_id;
- res += (chunk_len >> 8) & 0xff;
- res += chunk_len & 0xff;
- for (i = 0; i < chunk_len - 2; i++)
- res += gb.buffer[i];
- if (checksum != res) {
- av_log(s->avctx, AV_LOG_WARNING, "Invalid LBR checksum\n");
- if (s->avctx->err_recognition & AV_EF_EXPLODE)
- return AVERROR_INVALIDDATA;
- }
- } else {
- bytestream2_skip(&gb, 2);
- }
- break;
- case LBR_CHUNK_FRAME_NO_CSUM:
- break;
- default:
- av_log(s->avctx, AV_LOG_ERROR, "Invalid LBR frame chunk ID\n");
- return AVERROR_INVALIDDATA;
- }
-
- // Clear current frame
- memset(s->quant_levels, 0, sizeof(s->quant_levels));
- memset(s->sb_indices, 0xff, sizeof(s->sb_indices));
- memset(s->sec_ch_sbms, 0, sizeof(s->sec_ch_sbms));
- memset(s->sec_ch_lrms, 0, sizeof(s->sec_ch_lrms));
- memset(s->ch_pres, 0, sizeof(s->ch_pres));
- memset(s->grid_1_scf, 0, sizeof(s->grid_1_scf));
- memset(s->grid_2_scf, 0, sizeof(s->grid_2_scf));
- memset(s->grid_3_avg, 0, sizeof(s->grid_3_avg));
- memset(s->grid_3_scf, 0, sizeof(s->grid_3_scf));
- memset(s->grid_3_pres, 0, sizeof(s->grid_3_pres));
- memset(s->tonal_scf, 0, sizeof(s->tonal_scf));
- memset(s->lfe_data, 0, sizeof(s->lfe_data));
- s->part_stereo_pres = 0;
- s->framenum = (s->framenum + 1) & 31;
-
- for (ch = 0; ch < s->nchannels; ch++) {
- for (sb = 0; sb < s->nsubbands / 4; sb++) {
- s->part_stereo[ch][sb][0] = s->part_stereo[ch][sb][4];
- s->part_stereo[ch][sb][4] = 16;
- }
- }
-
- memset(s->lpc_coeff[s->framenum & 1], 0, sizeof(s->lpc_coeff[0]));
-
- for (group = 0; group < 5; group++) {
- for (sf = 0; sf < 1 << group; sf++) {
- int sf_idx = ((s->framenum << group) + sf) & 31;
- s->tonal_bounds[group][sf_idx][0] =
- s->tonal_bounds[group][sf_idx][1] = s->ntones;
- }
- }
-
- // Parse chunk headers
- while (bytestream2_get_bytes_left(&gb) > 0) {
- chunk_id = bytestream2_get_byte(&gb);
- chunk_len = (chunk_id & 0x80) ? bytestream2_get_be16(&gb) : bytestream2_get_byte(&gb);
- chunk_id &= 0x7f;
-
- if (chunk_len > bytestream2_get_bytes_left(&gb)) {
- chunk_len = bytestream2_get_bytes_left(&gb);
- av_log(s->avctx, AV_LOG_WARNING, "LBR chunk %#x was truncated\n", chunk_id);
- if (s->avctx->err_recognition & AV_EF_EXPLODE)
- return AVERROR_INVALIDDATA;
- }
-
- switch (chunk_id) {
- case LBR_CHUNK_LFE:
- chunk.lfe.len = chunk_len;
- chunk.lfe.data = gb.buffer;
- break;
-
- case LBR_CHUNK_SCF:
- case LBR_CHUNK_TONAL:
- case LBR_CHUNK_TONAL_SCF:
- chunk.tonal.id = chunk_id;
- chunk.tonal.len = chunk_len;
- chunk.tonal.data = gb.buffer;
- break;
-
- case LBR_CHUNK_TONAL_GRP_1:
- case LBR_CHUNK_TONAL_GRP_2:
- case LBR_CHUNK_TONAL_GRP_3:
- case LBR_CHUNK_TONAL_GRP_4:
- case LBR_CHUNK_TONAL_GRP_5:
- i = LBR_CHUNK_TONAL_GRP_5 - chunk_id;
- chunk.tonal_grp[i].id = i;
- chunk.tonal_grp[i].len = chunk_len;
- chunk.tonal_grp[i].data = gb.buffer;
- break;
-
- case LBR_CHUNK_TONAL_SCF_GRP_1:
- case LBR_CHUNK_TONAL_SCF_GRP_2:
- case LBR_CHUNK_TONAL_SCF_GRP_3:
- case LBR_CHUNK_TONAL_SCF_GRP_4:
- case LBR_CHUNK_TONAL_SCF_GRP_5:
- i = LBR_CHUNK_TONAL_SCF_GRP_5 - chunk_id;
- chunk.tonal_grp[i].id = i;
- chunk.tonal_grp[i].len = chunk_len;
- chunk.tonal_grp[i].data = gb.buffer;
- break;
-
- case LBR_CHUNK_RES_GRID_LR:
- case LBR_CHUNK_RES_GRID_LR + 1:
- case LBR_CHUNK_RES_GRID_LR + 2:
- i = chunk_id - LBR_CHUNK_RES_GRID_LR;
- chunk.grid1[i].len = chunk_len;
- chunk.grid1[i].data = gb.buffer;
- break;
-
- case LBR_CHUNK_RES_GRID_HR:
- case LBR_CHUNK_RES_GRID_HR + 1:
- case LBR_CHUNK_RES_GRID_HR + 2:
- i = chunk_id - LBR_CHUNK_RES_GRID_HR;
- chunk.hr_grid[i].len = chunk_len;
- chunk.hr_grid[i].data = gb.buffer;
- break;
-
- case LBR_CHUNK_RES_TS_1:
- case LBR_CHUNK_RES_TS_1 + 1:
- case LBR_CHUNK_RES_TS_1 + 2:
- i = chunk_id - LBR_CHUNK_RES_TS_1;
- chunk.ts1[i].len = chunk_len;
- chunk.ts1[i].data = gb.buffer;
- break;
-
- case LBR_CHUNK_RES_TS_2:
- case LBR_CHUNK_RES_TS_2 + 1:
- case LBR_CHUNK_RES_TS_2 + 2:
- i = chunk_id - LBR_CHUNK_RES_TS_2;
- chunk.ts2[i].len = chunk_len;
- chunk.ts2[i].data = gb.buffer;
- break;
- }
-
- bytestream2_skip(&gb, chunk_len);
- }
-
- // Parse the chunks
- ret = parse_lfe_chunk(s, &chunk.lfe);
-
- ret |= parse_tonal_chunk(s, &chunk.tonal);
-
- for (i = 0; i < 5; i++)
- ret |= parse_tonal_group(s, &chunk.tonal_grp[i]);
-
- for (i = 0; i < (s->nchannels + 1) / 2; i++) {
- int ch1 = i * 2;
- int ch2 = FFMIN(ch1 + 1, s->nchannels - 1);
-
- if (parse_grid_1_chunk (s, &chunk.grid1 [i], ch1, ch2) < 0 ||
- parse_high_res_grid(s, &chunk.hr_grid[i], ch1, ch2) < 0) {
- ret = -1;
- continue;
- }
-
- // TS chunks depend on both grids. TS_2 depends on TS_1.
- if (!chunk.grid1[i].len || !chunk.hr_grid[i].len || !chunk.ts1[i].len)
- continue;
-
- if (parse_ts1_chunk(s, &chunk.ts1[i], ch1, ch2) < 0 ||
- parse_ts2_chunk(s, &chunk.ts2[i], ch1, ch2) < 0) {
- ret = -1;
- continue;
- }
- }
-
- if (ret < 0 && (s->avctx->err_recognition & AV_EF_EXPLODE))
- return AVERROR_INVALIDDATA;
-
- return 0;
-}
-
-/**
- * Reconstruct high-frequency resolution grid from first and third grids
- */
-static void decode_grid(DCALbrDecoder *s, int ch1, int ch2)
-{
- int i, ch, sb;
-
- for (ch = ch1; ch <= ch2; ch++) {
- for (sb = 0; sb < s->nsubbands; sb++) {
- int g1_sb = ff_dca_scf_to_grid_1[sb];
-
- uint8_t *g1_scf_a = s->grid_1_scf[ch][g1_sb ];
- uint8_t *g1_scf_b = s->grid_1_scf[ch][g1_sb + 1];
-
- int w1 = ff_dca_grid_1_weights[g1_sb ][sb];
- int w2 = ff_dca_grid_1_weights[g1_sb + 1][sb];
-
- uint8_t *hr_scf = s->high_res_scf[ch][sb];
-
- if (sb < 4) {
- for (i = 0; i < 8; i++) {
- int scf = w1 * g1_scf_a[i] + w2 * g1_scf_b[i];
- hr_scf[i] = scf >> 7;
- }
- } else {
- int8_t *g3_scf = s->grid_3_scf[ch][sb - 4];
- int g3_avg = s->grid_3_avg[ch][sb - 4];
-
- for (i = 0; i < 8; i++) {
- int scf = w1 * g1_scf_a[i] + w2 * g1_scf_b[i];
- hr_scf[i] = (scf >> 7) - g3_avg - g3_scf[i];
- }
- }
- }
- }
-}
-
-/**
- * Fill unallocated subbands with randomness
- */
-static void random_ts(DCALbrDecoder *s, int ch1, int ch2)
-{
- int i, j, k, ch, sb;
-
- for (ch = ch1; ch <= ch2; ch++) {
- for (sb = 0; sb < s->nsubbands; sb++) {
- float *samples = s->time_samples[ch][sb];
-
- if (s->ch_pres[ch] & (1U << sb))
- continue; // Skip allocated subband
-
- if (sb < 2) {
- // The first two subbands are always zero
- memset(samples, 0, DCA_LBR_TIME_SAMPLES * sizeof(float));
- } else if (sb < 10) {
- for (i = 0; i < DCA_LBR_TIME_SAMPLES; i++)
- samples[i] = lbr_rand(s, sb);
- } else {
- for (i = 0; i < DCA_LBR_TIME_SAMPLES / 8; i++, samples += 8) {
- float accum[8] = { 0 };
-
- // Modulate by subbands 2-5 in blocks of 8
- for (k = 2; k < 6; k++) {
- float *other = &s->time_samples[ch][k][i * 8];
- for (j = 0; j < 8; j++)
- accum[j] += fabs(other[j]);
- }
-
- for (j = 0; j < 8; j++)
- samples[j] = (accum[j] * 0.25f + 0.5f) * lbr_rand(s, sb);
- }
- }
- }
- }
-}
-
-static void predict(float *samples, const float *coeff, int nsamples)
-{
- int i, j;
-
- for (i = 0; i < nsamples; i++) {
- float res = 0;
- for (j = 0; j < 8; j++)
- res += coeff[j] * samples[i - j - 1];
- samples[i] -= res;
- }
-}
-
-static void synth_lpc(DCALbrDecoder *s, int ch1, int ch2, int sb)
-{
- int f = s->framenum & 1;
- int ch;
-
- for (ch = ch1; ch <= ch2; ch++) {
- float *samples = s->time_samples[ch][sb];
-
- if (!(s->ch_pres[ch] & (1U << sb)))
- continue;
-
- if (sb < 2) {
- predict(samples, s->lpc_coeff[f^1][ch][sb][1], 16);
- predict(samples + 16, s->lpc_coeff[f ][ch][sb][0], 64);
- predict(samples + 80, s->lpc_coeff[f ][ch][sb][1], 48);
- } else {
- predict(samples, s->lpc_coeff[f^1][ch][sb][0], 16);
- predict(samples + 16, s->lpc_coeff[f ][ch][sb][0], 112);
- }
- }
-}
-
-static void filter_ts(DCALbrDecoder *s, int ch1, int ch2)
-{
- int i, j, sb, ch;
-
- for (sb = 0; sb < s->nsubbands; sb++) {
- // Scale factors
- for (ch = ch1; ch <= ch2; ch++) {
- float *samples = s->time_samples[ch][sb];
- uint8_t *hr_scf = s->high_res_scf[ch][sb];
- if (sb < 4) {
- for (i = 0; i < DCA_LBR_TIME_SAMPLES / 16; i++, samples += 16) {
- unsigned int scf = hr_scf[i];
- if (scf > AMP_MAX)
- scf = AMP_MAX;
- for (j = 0; j < 16; j++)
- samples[j] *= ff_dca_quant_amp[scf];
- }
- } else {
- uint8_t *g2_scf = s->grid_2_scf[ch][ff_dca_scf_to_grid_2[sb]];
- for (i = 0; i < DCA_LBR_TIME_SAMPLES / 2; i++, samples += 2) {
- unsigned int scf = hr_scf[i / 8] - g2_scf[i];
- if (scf > AMP_MAX)
- scf = AMP_MAX;
- samples[0] *= ff_dca_quant_amp[scf];
- samples[1] *= ff_dca_quant_amp[scf];
- }
- }
- }
-
- // Mid-side stereo
- if (ch1 != ch2) {
- float *samples_l = s->time_samples[ch1][sb];
- float *samples_r = s->time_samples[ch2][sb];
- int ch2_pres = s->ch_pres[ch2] & (1U << sb);
-
- for (i = 0; i < DCA_LBR_TIME_SAMPLES / 16; i++) {
- int sbms = (s->sec_ch_sbms[ch1 / 2][sb] >> i) & 1;
- int lrms = (s->sec_ch_lrms[ch1 / 2][sb] >> i) & 1;
-
- if (sb >= s->min_mono_subband) {
- if (lrms && ch2_pres) {
- if (sbms) {
- for (j = 0; j < 16; j++) {
- float tmp = samples_l[j];
- samples_l[j] = samples_r[j];
- samples_r[j] = -tmp;
- }
- } else {
- for (j = 0; j < 16; j++) {
- float tmp = samples_l[j];
- samples_l[j] = samples_r[j];
- samples_r[j] = tmp;
- }
- }
- } else if (!ch2_pres) {
- if (sbms && (s->part_stereo_pres & (1 << ch1))) {
- for (j = 0; j < 16; j++)
- samples_r[j] = -samples_l[j];
- } else {
- for (j = 0; j < 16; j++)
- samples_r[j] = samples_l[j];
- }
- }
- } else if (sbms && ch2_pres) {
- for (j = 0; j < 16; j++) {
- float tmp = samples_l[j];
- samples_l[j] = (tmp + samples_r[j]) * 0.5f;
- samples_r[j] = (tmp - samples_r[j]) * 0.5f;
- }
- }
-
- samples_l += 16;
- samples_r += 16;
- }
- }
-
- // Inverse prediction
- if (sb < 3)
- synth_lpc(s, ch1, ch2, sb);
- }
-}
-
-/**
- * Modulate by interpolated partial stereo coefficients
- */
-static void decode_part_stereo(DCALbrDecoder *s, int ch1, int ch2)
-{
- int i, ch, sb, sf;
-
- for (ch = ch1; ch <= ch2; ch++) {
- for (sb = s->min_mono_subband; sb < s->nsubbands; sb++) {
- uint8_t *pt_st = s->part_stereo[ch][(sb - s->min_mono_subband) / 4];
- float *samples = s->time_samples[ch][sb];
-
- if (s->ch_pres[ch2] & (1U << sb))
- continue;
-
- for (sf = 1; sf <= 4; sf++, samples += 32) {
- float prev = ff_dca_st_coeff[pt_st[sf - 1]];
- float next = ff_dca_st_coeff[pt_st[sf ]];
-
- for (i = 0; i < 32; i++)
- samples[i] *= (32 - i) * prev + i * next;
- }
- }
- }
-}
-
-/**
- * Synthesise tones in the given group for the given tonal subframe
- */
-static void synth_tones(DCALbrDecoder *s, int ch, float *values,
- int group, int group_sf, int synth_idx)
-{
- int i, start, count;
-
- if (synth_idx < 0)
- return;
-
- start = s->tonal_bounds[group][group_sf][0];
- count = (s->tonal_bounds[group][group_sf][1] - start) & (DCA_LBR_TONES - 1);
-
- for (i = 0; i < count; i++) {
- DCALbrTone *t = &s->tones[(start + i) & (DCA_LBR_TONES - 1)];
-
- if (t->amp[ch]) {
- float amp = ff_dca_synth_env[synth_idx] * ff_dca_quant_amp[t->amp[ch]];
- float c = amp * cos_tab[(t->phs[ch] ) & 255];
- float s = amp * cos_tab[(t->phs[ch] + 64) & 255];
- const float *cf = ff_dca_corr_cf[t->f_delt];
- int x_freq = t->x_freq;
-
- switch (x_freq) {
- case 0:
- goto p0;
- case 1:
- values[3] += cf[0] * -s;
- values[2] += cf[1] * c;
- values[1] += cf[2] * s;
- values[0] += cf[3] * -c;
- goto p1;
- case 2:
- values[2] += cf[0] * -s;
- values[1] += cf[1] * c;
- values[0] += cf[2] * s;
- goto p2;
- case 3:
- values[1] += cf[0] * -s;
- values[0] += cf[1] * c;
- goto p3;
- case 4:
- values[0] += cf[0] * -s;
- goto p4;
- }
-
- values[x_freq - 5] += cf[ 0] * -s;
- p4: values[x_freq - 4] += cf[ 1] * c;
- p3: values[x_freq - 3] += cf[ 2] * s;
- p2: values[x_freq - 2] += cf[ 3] * -c;
- p1: values[x_freq - 1] += cf[ 4] * -s;
- p0: values[x_freq ] += cf[ 5] * c;
- values[x_freq + 1] += cf[ 6] * s;
- values[x_freq + 2] += cf[ 7] * -c;
- values[x_freq + 3] += cf[ 8] * -s;
- values[x_freq + 4] += cf[ 9] * c;
- values[x_freq + 5] += cf[10] * s;
- }
-
- t->phs[ch] += t->ph_rot;
- }
-}
-
-/**
- * Synthesise all tones in all groups for the given residual subframe
- */
-static void base_func_synth(DCALbrDecoder *s, int ch, float *values, int sf)
-{
- int group;
-
- // Tonal vs residual shift is 22 subframes
- for (group = 0; group < 5; group++) {
- int group_sf = (s->framenum << group) + ((sf - 22) >> (5 - group));
- int synth_idx = ((((sf - 22) & 31) << group) & 31) + (1 << group) - 1;
-
- synth_tones(s, ch, values, group, (group_sf - 1) & 31, 30 - synth_idx);
- synth_tones(s, ch, values, group, (group_sf ) & 31, synth_idx);
- }
-}
-
-static void transform_channel(DCALbrDecoder *s, int ch, float *output)
-{
- LOCAL_ALIGNED_32(float, values, [DCA_LBR_SUBBANDS ], [4]);
- LOCAL_ALIGNED_32(float, result, [DCA_LBR_SUBBANDS * 2], [4]);
- int sf, sb, nsubbands = s->nsubbands, noutsubbands = 8 << s->freq_range;
-
- // Clear inactive subbands
- if (nsubbands < noutsubbands)
- memset(values[nsubbands], 0, (noutsubbands - nsubbands) * sizeof(values[0]));
-
- for (sf = 0; sf < DCA_LBR_TIME_SAMPLES / 4; sf++) {
- // Hybrid filterbank
- s->dcadsp->lbr_bank(values, s->time_samples[ch],
- ff_dca_bank_coeff, sf * 4, nsubbands);
-
- base_func_synth(s, ch, values[0], sf);
-
- s->imdct_fn(s->imdct, result[0], values[0], sizeof(float));
-
- // Long window and overlap-add
- s->fdsp->vector_fmul_add(output, result[0], s->window,
- s->history[ch], noutsubbands * 4);
- s->fdsp->vector_fmul_reverse(s->history[ch], result[noutsubbands],
- s->window, noutsubbands * 4);
- output += noutsubbands * 4;
- }
-
- // Update history for LPC and forward MDCT
- for (sb = 0; sb < nsubbands; sb++) {
- float *samples = s->time_samples[ch][sb] - DCA_LBR_TIME_HISTORY;
- memcpy(samples, samples + DCA_LBR_TIME_SAMPLES, DCA_LBR_TIME_HISTORY * sizeof(float));
- }
-}
-
-int ff_dca_lbr_filter_frame(DCALbrDecoder *s, AVFrame *frame)
-{
- AVCodecContext *avctx = s->avctx;
- int i, ret, nchannels, ch_conf = (s->ch_mask & 0x7) - 1;
- const int8_t *reorder;
- uint64_t channel_mask = channel_layouts[ch_conf];
-
- nchannels = av_popcount64(channel_mask);
- avctx->sample_rate = s->sample_rate;
- avctx->sample_fmt = AV_SAMPLE_FMT_FLTP;
- avctx->bits_per_raw_sample = 0;
- avctx->profile = FF_PROFILE_DTS_EXPRESS;
- avctx->bit_rate = s->bit_rate_scaled;
-
- if (s->flags & LBR_FLAG_LFE_PRESENT) {
- channel_mask |= AV_CH_LOW_FREQUENCY;
- reorder = channel_reorder_lfe[ch_conf];
- } else {
- reorder = channel_reorder_nolfe[ch_conf];
- }
-
- av_channel_layout_uninit(&avctx->ch_layout);
- av_channel_layout_from_mask(&avctx->ch_layout, channel_mask);
-
- frame->nb_samples = 1024 << s->freq_range;
- if ((ret = ff_get_buffer(avctx, frame, 0)) < 0)
- return ret;
-
- // Filter fullband channels
- for (i = 0; i < (s->nchannels + 1) / 2; i++) {
- int ch1 = i * 2;
- int ch2 = FFMIN(ch1 + 1, s->nchannels - 1);
-
- decode_grid(s, ch1, ch2);
-
- random_ts(s, ch1, ch2);
-
- filter_ts(s, ch1, ch2);
-
- if (ch1 != ch2 && (s->part_stereo_pres & (1 << ch1)))
- decode_part_stereo(s, ch1, ch2);
-
- if (ch1 < nchannels)
- transform_channel(s, ch1, (float *)frame->extended_data[reorder[ch1]]);
-
- if (ch1 != ch2 && ch2 < nchannels)
- transform_channel(s, ch2, (float *)frame->extended_data[reorder[ch2]]);
- }
-
- // Interpolate LFE channel
- if (s->flags & LBR_FLAG_LFE_PRESENT) {
- s->dcadsp->lfe_iir((float *)frame->extended_data[lfe_index[ch_conf]],
- s->lfe_data, ff_dca_lfe_iir,
- s->lfe_history, 16 << s->freq_range);
- }
-
- if ((ret = ff_side_data_update_matrix_encoding(frame, AV_MATRIX_ENCODING_NONE)) < 0)
- return ret;
-
- return 0;
-}
-
-av_cold void ff_dca_lbr_flush(DCALbrDecoder *s)
-{
- int ch, sb;
-
- if (!s->sample_rate)
- return;
-
- // Clear history
- memset(s->part_stereo, 16, sizeof(s->part_stereo));
- memset(s->lpc_coeff, 0, sizeof(s->lpc_coeff));
- memset(s->history, 0, sizeof(s->history));
- memset(s->tonal_bounds, 0, sizeof(s->tonal_bounds));
- memset(s->lfe_history, 0, sizeof(s->lfe_history));
- s->framenum = 0;
- s->ntones = 0;
-
- for (ch = 0; ch < s->nchannels; ch++) {
- for (sb = 0; sb < s->nsubbands; sb++) {
- float *samples = s->time_samples[ch][sb] - DCA_LBR_TIME_HISTORY;
- memset(samples, 0, DCA_LBR_TIME_HISTORY * sizeof(float));
- }
- }
-}
-
-av_cold int ff_dca_lbr_init(DCALbrDecoder *s)
-{
- if (!(s->fdsp = avpriv_float_dsp_alloc(0)))
- return AVERROR(ENOMEM);
-
- s->lbr_rand = 1;
- return 0;
-}
-
-av_cold void ff_dca_lbr_close(DCALbrDecoder *s)
-{
- s->sample_rate = 0;
-
- av_freep(&s->ts_buffer);
- s->ts_size = 0;
-
- av_freep(&s->fdsp);
- av_tx_uninit(&s->imdct);
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/flacdata.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/flacdata.h
deleted file mode 100644
index ef218407772f8f318721316c2254c8f6fd00a1e9..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/flacdata.h
+++ /dev/null
@@ -1,31 +0,0 @@
-/*
- * FLAC data header
- * Copyright (c) 2003 Alex Beregszaszi
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_FLACDATA_H
-#define AVCODEC_FLACDATA_H
-
-#include
-
-extern const int ff_flac_sample_rate_table[16];
-
-extern const int32_t ff_flac_blocksize_table[16];
-
-#endif /* AVCODEC_FLACDATA_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/intrax8.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/intrax8.c
deleted file mode 100644
index e4c8b96c9c096fb56de227bcc3fc3f435bd5d59e..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/intrax8.c
+++ /dev/null
@@ -1,810 +0,0 @@
-/*
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * @brief IntraX8 (J-Frame) subdecoder, used by WMV2 and VC-1
- */
-
-#include "libavutil/avassert.h"
-#include "libavutil/thread.h"
-#include "avcodec.h"
-#include "get_bits.h"
-#include "idctdsp.h"
-#include "msmpeg4_vc1_data.h"
-#include "intrax8huf.h"
-#include "intrax8.h"
-#include "intrax8dsp.h"
-#include "mpegutils.h"
-
-#define VLC_BUFFER_SIZE 28150
-
-#define MAX_TABLE_DEPTH(table_bits, max_bits) \
- ((max_bits + table_bits - 1) / table_bits)
-
-#define DC_VLC_BITS 9
-#define AC_VLC_BITS 9
-#define OR_VLC_BITS 7
-
-#define DC_VLC_MTD MAX_TABLE_DEPTH(DC_VLC_BITS, MAX_DC_VLC_BITS)
-#define AC_VLC_MTD MAX_TABLE_DEPTH(AC_VLC_BITS, MAX_AC_VLC_BITS)
-#define OR_VLC_MTD MAX_TABLE_DEPTH(OR_VLC_BITS, MAX_OR_VLC_BITS)
-
-static VLC j_ac_vlc[2][2][8]; // [quant < 13], [intra / inter], [select]
-static VLC j_dc_vlc[2][8]; // [quant], [select]
-static VLC j_orient_vlc[2][4]; // [quant], [select]
-
-static av_cold void x8_init_vlc(VLC *vlc, int nb_bits, int nb_codes,
- int *offset, const uint8_t table[][2])
-{
- static VLCElem vlc_buf[VLC_BUFFER_SIZE];
-
- vlc->table = &vlc_buf[*offset];
- vlc->table_allocated = VLC_BUFFER_SIZE - *offset;
- ff_init_vlc_from_lengths(vlc, nb_bits, nb_codes, &table[0][1], 2,
- &table[0][0], 2, 1, 0, INIT_VLC_STATIC_OVERLONG, NULL);
- *offset += vlc->table_size;
-}
-
-static av_cold void x8_vlc_init(void)
-{
- int i;
- int offset = 0;
-
-// set ac tables
- for (int i = 0; i < 2; i++)
- for (int j = 0; j < 2; j++)
- for (int k = 0; k < 8; k++)
- x8_init_vlc(&j_ac_vlc[i][j][k], AC_VLC_BITS, 77,
- &offset, x8_ac_quant_table[i][j][k]);
-
-// set dc tables
- for (int i = 0; i < 2; i++)
- for (int j = 0; j < 8; j++)
- x8_init_vlc(&j_dc_vlc[i][j], DC_VLC_BITS, 34, &offset,
- x8_dc_quant_table[i][j]);
-
-// set orient tables
- for (i = 0; i < 2; i++)
- x8_init_vlc(&j_orient_vlc[0][i], OR_VLC_BITS, 12,
- &offset, x8_orient_highquant_table[i]);
- for (i = 0; i < 4; i++)
- x8_init_vlc(&j_orient_vlc[1][i], OR_VLC_BITS, 12,
- &offset, x8_orient_lowquant_table[i]);
-
- av_assert2(offset == VLC_BUFFER_SIZE);
-}
-
-static void x8_reset_vlc_tables(IntraX8Context *w)
-{
- memset(w->j_dc_vlc_table, 0, sizeof(w->j_dc_vlc_table));
- memset(w->j_ac_vlc_table, 0, sizeof(w->j_ac_vlc_table));
- w->j_orient_vlc_table = NULL;
-}
-
-static inline void x8_select_ac_table(IntraX8Context *const w, int mode)
-{
- int table_index;
-
- av_assert2(mode < 4);
-
- if (w->j_ac_vlc_table[mode])
- return;
-
- table_index = get_bits(w->gb, 3);
- // 2 modes use same tables
- w->j_ac_vlc_table[mode] = j_ac_vlc[w->quant < 13][mode >> 1][table_index].table;
- av_assert2(w->j_ac_vlc[mode]);
-}
-
-static inline int x8_get_orient_vlc(IntraX8Context *w)
-{
- if (!w->j_orient_vlc_table) {
- int table_index = get_bits(w->gb, 1 + (w->quant < 13));
- w->j_orient_vlc_table = j_orient_vlc[w->quant < 13][table_index].table;
- }
-
- return get_vlc2(w->gb, w->j_orient_vlc_table, OR_VLC_BITS, OR_VLC_MTD);
-}
-
-#define extra_bits(eb) (eb) // 3 bits
-#define extra_run (0xFF << 8) // 1 bit
-#define extra_level (0x00 << 8) // 1 bit
-#define run_offset(r) ((r) << 16) // 6 bits
-#define level_offset(l) ((l) << 24) // 5 bits
-static const uint32_t ac_decode_table[] = {
- /* 46 */ extra_bits(3) | extra_run | run_offset(16) | level_offset(0),
- /* 47 */ extra_bits(3) | extra_run | run_offset(24) | level_offset(0),
- /* 48 */ extra_bits(2) | extra_run | run_offset(4) | level_offset(1),
- /* 49 */ extra_bits(3) | extra_run | run_offset(8) | level_offset(1),
-
- /* 50 */ extra_bits(5) | extra_run | run_offset(32) | level_offset(0),
- /* 51 */ extra_bits(4) | extra_run | run_offset(16) | level_offset(1),
-
- /* 52 */ extra_bits(2) | extra_level | run_offset(0) | level_offset(4),
- /* 53 */ extra_bits(2) | extra_level | run_offset(0) | level_offset(8),
- /* 54 */ extra_bits(2) | extra_level | run_offset(0) | level_offset(12),
- /* 55 */ extra_bits(3) | extra_level | run_offset(0) | level_offset(16),
- /* 56 */ extra_bits(3) | extra_level | run_offset(0) | level_offset(24),
-
- /* 57 */ extra_bits(2) | extra_level | run_offset(1) | level_offset(3),
- /* 58 */ extra_bits(3) | extra_level | run_offset(1) | level_offset(7),
-
- /* 59 */ extra_bits(2) | extra_run | run_offset(16) | level_offset(0),
- /* 60 */ extra_bits(2) | extra_run | run_offset(20) | level_offset(0),
- /* 61 */ extra_bits(2) | extra_run | run_offset(24) | level_offset(0),
- /* 62 */ extra_bits(2) | extra_run | run_offset(28) | level_offset(0),
- /* 63 */ extra_bits(4) | extra_run | run_offset(32) | level_offset(0),
- /* 64 */ extra_bits(4) | extra_run | run_offset(48) | level_offset(0),
-
- /* 65 */ extra_bits(2) | extra_run | run_offset(4) | level_offset(1),
- /* 66 */ extra_bits(3) | extra_run | run_offset(8) | level_offset(1),
- /* 67 */ extra_bits(4) | extra_run | run_offset(16) | level_offset(1),
-
- /* 68 */ extra_bits(2) | extra_level | run_offset(0) | level_offset(4),
- /* 69 */ extra_bits(3) | extra_level | run_offset(0) | level_offset(8),
- /* 70 */ extra_bits(4) | extra_level | run_offset(0) | level_offset(16),
-
- /* 71 */ extra_bits(2) | extra_level | run_offset(1) | level_offset(3),
- /* 72 */ extra_bits(3) | extra_level | run_offset(1) | level_offset(7),
-};
-#undef extra_bits
-#undef extra_run
-#undef extra_level
-#undef run_offset
-#undef level_offset
-
-static void x8_get_ac_rlf(IntraX8Context *const w, const int mode,
- int *const run, int *const level, int *const final)
-{
- int i, e;
-
-// x8_select_ac_table(w, mode);
- i = get_vlc2(w->gb, w->j_ac_vlc_table[mode], AC_VLC_BITS, AC_VLC_MTD);
-
- if (i < 46) { // [0-45]
- int t, l;
- if (i < 0) {
- *level =
- *final = // prevent 'may be used uninitialized'
- *run = 64; // this would cause error exit in the ac loop
- return;
- }
-
- /*
- * i == 0-15 r = 0-15 l = 0; r = i & %01111
- * i == 16-19 r = 0-3 l = 1; r = i & %00011
- * i == 20-21 r = 0-1 l = 2; r = i & %00001
- * i == 22 r = 0 l = 3; r = i & %00000
- */
-
- *final =
- t = i > 22;
- i -= 23 * t;
-
- /* l = lut_l[i / 2] = { 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 3 }[i >> 1];
- * 11 10'01 01'00 00'00 00'00 00'00 00 => 0xE50000 */
- l = (0xE50000 >> (i & 0x1E)) & 3; // 0x1E or ~1 or (i >> 1 << 1)
-
- /* t = lut_mask[l] = { 0x0f, 0x03, 0x01, 0x00 }[l];
- * as i < 256 the higher bits do not matter */
- t = 0x01030F >> (l << 3);
-
- *run = i & t;
- *level = l;
- } else if (i < 73) { // [46-72]
- uint32_t sm;
- uint32_t mask;
-
- i -= 46;
- sm = ac_decode_table[i];
-
- e = get_bits(w->gb, sm & 0xF);
- sm >>= 8; // 3 bits
- mask = sm & 0xff;
- sm >>= 8; // 1 bit
-
- *run = (sm & 0xff) + (e & mask); // 6 bits
- *level = (sm >> 8) + (e & ~mask); // 5 bits
- *final = i > (58 - 46);
- } else if (i < 75) { // [73-74]
- static const uint8_t crazy_mix_runlevel[32] = {
- 0x22, 0x32, 0x33, 0x53, 0x23, 0x42, 0x43, 0x63,
- 0x24, 0x52, 0x34, 0x73, 0x25, 0x62, 0x44, 0x83,
- 0x26, 0x72, 0x35, 0x54, 0x27, 0x82, 0x45, 0x64,
- 0x28, 0x92, 0x36, 0x74, 0x29, 0xa2, 0x46, 0x84,
- };
-
- *final = !(i & 1);
- e = get_bits(w->gb, 5); // get the extra bits
- *run = crazy_mix_runlevel[e] >> 4;
- *level = crazy_mix_runlevel[e] & 0x0F;
- } else {
- *level = get_bits(w->gb, 7 - 3 * (i & 1));
- *run = get_bits(w->gb, 6);
- *final = get_bits1(w->gb);
- }
- return;
-}
-
-/* static const uint8_t dc_extra_sbits[] = {
- * 0, 1, 1, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7,
- * }; */
-static const uint8_t dc_index_offset[] = {
- 0, 1, 2, 3, 4, 5, 7, 9, 13, 17, 25, 33, 49, 65, 97, 129, 193,
-};
-
-static int x8_get_dc_rlf(IntraX8Context *const w, const int mode,
- int *const level, int *const final)
-{
- int i, e, c;
-
- av_assert2(mode < 3);
- if (!w->j_dc_vlc_table[mode]) {
- int table_index = get_bits(w->gb, 3);
- // 4 modes, same table
- w->j_dc_vlc_table[mode] = j_dc_vlc[w->quant < 13][table_index].table;
- }
-
- i = get_vlc2(w->gb, w->j_dc_vlc_table[mode], DC_VLC_BITS, DC_VLC_MTD);
-
- /* (i >= 17) { i -= 17; final =1; } */
- c = i > 16;
- *final = c;
- i -= 17 * c;
-
- if (i <= 0) {
- *level = 0;
- return -i;
- }
- c = (i + 1) >> 1; // hackish way to calculate dc_extra_sbits[]
- c -= c > 1;
-
- e = get_bits(w->gb, c); // get the extra bits
- i = dc_index_offset[i] + (e >> 1);
-
- e = -(e & 1); // 0, 0xffffff
- *level = (i ^ e) - e; // (i ^ 0) - 0, (i ^ 0xff) - (-1)
- return 0;
-}
-
-// end of huffman
-
-static int x8_setup_spatial_predictor(IntraX8Context *const w, const int chroma)
-{
- int range;
- int sum;
- int quant;
-
- w->dsp.setup_spatial_compensation(w->dest[chroma], w->scratchpad,
- w->frame->linesize[chroma > 0],
- &range, &sum, w->edges);
- if (chroma) {
- w->orient = w->chroma_orient;
- quant = w->quant_dc_chroma;
- } else {
- quant = w->quant;
- }
-
- w->flat_dc = 0;
- if (range < quant || range < 3) {
- w->orient = 0;
-
- // yep you read right, a +-1 idct error may break decoding!
- if (range < 3) {
- w->flat_dc = 1;
- sum += 9;
- // ((1 << 17) + 9) / (8 + 8 + 1 + 2) = 6899
- w->predicted_dc = sum * 6899 >> 17;
- }
- }
- if (chroma)
- return 0;
-
- av_assert2(w->orient < 3);
- if (range < 2 * w->quant) {
- if ((w->edges & 3) == 0) {
- if (w->orient == 1)
- w->orient = 11;
- if (w->orient == 2)
- w->orient = 10;
- } else {
- w->orient = 0;
- }
- w->raw_orient = 0;
- } else {
- static const uint8_t prediction_table[3][12] = {
- { 0, 8, 4, 10, 11, 2, 6, 9, 1, 3, 5, 7 },
- { 4, 0, 8, 11, 10, 3, 5, 2, 6, 9, 1, 7 },
- { 8, 0, 4, 10, 11, 1, 7, 2, 6, 9, 3, 5 },
- };
- w->raw_orient = x8_get_orient_vlc(w);
- if (w->raw_orient < 0)
- return -1;
- av_assert2(w->raw_orient < 12);
- av_assert2(w->orient < 3);
- w->orient=prediction_table[w->orient][w->raw_orient];
- }
- return 0;
-}
-
-static void x8_update_predictions(IntraX8Context *const w, const int orient,
- const int est_run)
-{
- w->prediction_table[w->mb_x * 2 + (w->mb_y & 1)] = (est_run << 2) + 1 * (orient == 4) + 2 * (orient == 8);
-/*
- * y = 2n + 0 -> // 0 2 4
- * y = 2n + 1 -> // 1 3 5
- */
-}
-
-static void x8_get_prediction_chroma(IntraX8Context *const w)
-{
- w->edges = 1 * !(w->mb_x >> 1);
- w->edges |= 2 * !(w->mb_y >> 1);
- w->edges |= 4 * (w->mb_x >= (2 * w->mb_width - 1)); // mb_x for chroma would always be odd
-
- w->raw_orient = 0;
- // lut_co[8] = {inv,4,8,8, inv,4,8,8} <- => {1,1,0,0;1,1,0,0} => 0xCC
- if (w->edges & 3) {
- w->chroma_orient = 4 << ((0xCC >> w->edges) & 1);
- return;
- }
- // block[x - 1][y | 1 - 1)]
- w->chroma_orient = (w->prediction_table[2 * w->mb_x - 2] & 0x03) << 2;
-}
-
-static void x8_get_prediction(IntraX8Context *const w)
-{
- int a, b, c, i;
-
- w->edges = 1 * !w->mb_x;
- w->edges |= 2 * !w->mb_y;
- w->edges |= 4 * (w->mb_x >= (2 * w->mb_width - 1));
-
- switch (w->edges & 3) {
- case 0:
- break;
- case 1:
- // take the one from the above block[0][y - 1]
- w->est_run = w->prediction_table[!(w->mb_y & 1)] >> 2;
- w->orient = 1;
- return;
- case 2:
- // take the one from the previous block[x - 1][0]
- w->est_run = w->prediction_table[2 * w->mb_x - 2] >> 2;
- w->orient = 2;
- return;
- case 3:
- w->est_run = 16;
- w->orient = 0;
- return;
- }
- // no edge cases
- b = w->prediction_table[2 * w->mb_x + !(w->mb_y & 1)]; // block[x ][y - 1]
- a = w->prediction_table[2 * w->mb_x - 2 + (w->mb_y & 1)]; // block[x - 1][y ]
- c = w->prediction_table[2 * w->mb_x - 2 + !(w->mb_y & 1)]; // block[x - 1][y - 1]
-
- w->est_run = FFMIN(b, a);
- /* This condition has nothing to do with w->edges, even if it looks
- * similar it would trigger if e.g. x = 3; y = 2;
- * I guess somebody wrote something wrong and it became standard. */
- if ((w->mb_x & w->mb_y) != 0)
- w->est_run = FFMIN(c, w->est_run);
- w->est_run >>= 2;
-
- a &= 3;
- b &= 3;
- c &= 3;
-
- i = (0xFFEAF4C4 >> (2 * b + 8 * a)) & 3;
- if (i != 3)
- w->orient = i;
- else
- w->orient = (0xFFEAD8 >> (2 * c + 8 * (w->quant > 12))) & 3;
-/*
- * lut1[b][a] = {
- * ->{ 0, 1, 0, pad },
- * { 0, 1, X, pad },
- * { 2, 2, 2, pad }
- * }
- * pad 2 2 2;
- * pad X 1 0;
- * pad 0 1 0 <-
- * -> 11 10 '10 10 '11 11'01 00 '11 00'01 00 => 0xEAF4C4
- *
- * lut2[q>12][c] = {
- * ->{ 0, 2, 1, pad},
- * { 2, 2, 2, pad}
- * }
- * pad 2 2 2;
- * pad 1 2 0 <-
- * -> 11 10'10 10 '11 01'10 00 => 0xEAD8
- */
-}
-
-static void x8_ac_compensation(IntraX8Context *const w, const int direction,
- const int dc_level)
-{
- int t;
-#define B(x,y) w->block[0][w->idct_permutation[(x) + (y) * 8]]
-#define T(x) ((x) * dc_level + 0x8000) >> 16;
- switch (direction) {
- case 0:
- t = T(3811); // h
- B(1, 0) -= t;
- B(0, 1) -= t;
-
- t = T(487); // e
- B(2, 0) -= t;
- B(0, 2) -= t;
-
- t = T(506); // f
- B(3, 0) -= t;
- B(0, 3) -= t;
-
- t = T(135); // c
- B(4, 0) -= t;
- B(0, 4) -= t;
- B(2, 1) += t;
- B(1, 2) += t;
- B(3, 1) += t;
- B(1, 3) += t;
-
- t = T(173); // d
- B(5, 0) -= t;
- B(0, 5) -= t;
-
- t = T(61); // b
- B(6, 0) -= t;
- B(0, 6) -= t;
- B(5, 1) += t;
- B(1, 5) += t;
-
- t = T(42); // a
- B(7, 0) -= t;
- B(0, 7) -= t;
- B(4, 1) += t;
- B(1, 4) += t;
- B(4, 4) += t;
-
- t = T(1084); // g
- B(1, 1) += t;
-
- w->block_last_index[0] = FFMAX(w->block_last_index[0], 7 * 8);
- break;
- case 1:
- B(0, 1) -= T(6269);
- B(0, 3) -= T(708);
- B(0, 5) -= T(172);
- B(0, 7) -= T(73);
-
- w->block_last_index[0] = FFMAX(w->block_last_index[0], 7 * 8);
- break;
- case 2:
- B(1, 0) -= T(6269);
- B(3, 0) -= T(708);
- B(5, 0) -= T(172);
- B(7, 0) -= T(73);
-
- w->block_last_index[0] = FFMAX(w->block_last_index[0], 7);
- break;
- }
-#undef B
-#undef T
-}
-
-static void dsp_x8_put_solidcolor(const uint8_t pix, uint8_t *dst,
- const ptrdiff_t linesize)
-{
- int k;
- for (k = 0; k < 8; k++) {
- memset(dst, pix, 8);
- dst += linesize;
- }
-}
-
-static const int16_t quant_table[64] = {
- 256, 256, 256, 256, 256, 256, 259, 262,
- 265, 269, 272, 275, 278, 282, 285, 288,
- 292, 295, 299, 303, 306, 310, 314, 317,
- 321, 325, 329, 333, 337, 341, 345, 349,
- 353, 358, 362, 366, 371, 375, 379, 384,
- 389, 393, 398, 403, 408, 413, 417, 422,
- 428, 433, 438, 443, 448, 454, 459, 465,
- 470, 476, 482, 488, 493, 499, 505, 511,
-};
-
-static int x8_decode_intra_mb(IntraX8Context *const w, const int chroma)
-{
- uint8_t *scantable;
- int final, run, level;
- int ac_mode, dc_mode, est_run, dc_level;
- int pos, n;
- int zeros_only;
- int use_quant_matrix;
- int sign;
-
- av_assert2(w->orient < 12);
- w->bdsp.clear_block(w->block[0]);
-
- if (chroma)
- dc_mode = 2;
- else
- dc_mode = !!w->est_run; // 0, 1
-
- if (x8_get_dc_rlf(w, dc_mode, &dc_level, &final))
- return -1;
- n = 0;
- zeros_only = 0;
- if (!final) { // decode ac
- use_quant_matrix = w->use_quant_matrix;
- if (chroma) {
- ac_mode = 1;
- est_run = 64; // not used
- } else {
- if (w->raw_orient < 3)
- use_quant_matrix = 0;
-
- if (w->raw_orient > 4) {
- ac_mode = 0;
- est_run = 64;
- } else {
- if (w->est_run > 1) {
- ac_mode = 2;
- est_run = w->est_run;
- } else {
- ac_mode = 3;
- est_run = 64;
- }
- }
- }
- x8_select_ac_table(w, ac_mode);
- /* scantable_selector[12] = { 0, 2, 0, 1, 1, 1, 0, 2, 2, 0, 1, 2 }; <-
- * -> 10'01' 00'10' 10'00' 01'01' 01'00' 10'00 => 0x928548 */
- scantable = w->permutated_scantable[(0x928548 >> (2 * w->orient)) & 3];
- pos = 0;
- do {
- n++;
- if (n >= est_run) {
- ac_mode = 3;
- x8_select_ac_table(w, 3);
- }
-
- x8_get_ac_rlf(w, ac_mode, &run, &level, &final);
-
- pos += run + 1;
- if (pos > 63) {
- // this also handles vlc error in x8_get_ac_rlf
- return -1;
- }
- level = (level + 1) * w->dquant;
- level += w->qsum;
-
- sign = -get_bits1(w->gb);
- level = (level ^ sign) - sign;
-
- if (use_quant_matrix)
- level = (level * quant_table[pos]) >> 8;
-
- w->block[0][scantable[pos]] = level;
- } while (!final);
-
- w->block_last_index[0] = pos;
- } else { // DC only
- w->block_last_index[0] = 0;
- if (w->flat_dc && ((unsigned) (dc_level + 1)) < 3) { // [-1; 1]
- int32_t divide_quant = !chroma ? w->divide_quant_dc_luma
- : w->divide_quant_dc_chroma;
- int32_t dc_quant = !chroma ? w->quant
- : w->quant_dc_chroma;
-
- // original intent dc_level += predicted_dc/quant;
- // but it got lost somewhere in the rounding
- dc_level += (w->predicted_dc * divide_quant + (1 << 12)) >> 13;
-
- dsp_x8_put_solidcolor(av_clip_uint8((dc_level * dc_quant + 4) >> 3),
- w->dest[chroma],
- w->frame->linesize[!!chroma]);
-
- goto block_placed;
- }
- zeros_only = dc_level == 0;
- }
- if (!chroma)
- w->block[0][0] = dc_level * w->quant;
- else
- w->block[0][0] = dc_level * w->quant_dc_chroma;
-
- // there is !zero_only check in the original, but dc_level check is enough
- if ((unsigned int) (dc_level + 1) >= 3 && (w->edges & 3) != 3) {
- int direction;
- /* ac_comp_direction[orient] = { 0, 3, 3, 1, 1, 0, 0, 0, 2, 2, 2, 1 }; <-
- * -> 01'10' 10'10' 00'00' 00'01' 01'11' 11'00 => 0x6A017C */
- direction = (0x6A017C >> (w->orient * 2)) & 3;
- if (direction != 3) {
- // modify block_last[]
- x8_ac_compensation(w, direction, w->block[0][0]);
- }
- }
-
- if (w->flat_dc) {
- dsp_x8_put_solidcolor(w->predicted_dc, w->dest[chroma],
- w->frame->linesize[!!chroma]);
- } else {
- w->dsp.spatial_compensation[w->orient](w->scratchpad,
- w->dest[chroma],
- w->frame->linesize[!!chroma]);
- }
- if (!zeros_only)
- w->wdsp.idct_add(w->dest[chroma],
- w->frame->linesize[!!chroma],
- w->block[0]);
-
-block_placed:
- if (!chroma)
- x8_update_predictions(w, w->orient, n);
-
- if (w->loopfilter) {
- uint8_t *ptr = w->dest[chroma];
- ptrdiff_t linesize = w->frame->linesize[!!chroma];
-
- if (!((w->edges & 2) || (zeros_only && (w->orient | 4) == 4)))
- w->dsp.h_loop_filter(ptr, linesize, w->quant);
-
- if (!((w->edges & 1) || (zeros_only && (w->orient | 8) == 8)))
- w->dsp.v_loop_filter(ptr, linesize, w->quant);
- }
- return 0;
-}
-
-// FIXME maybe merge with ff_*
-static void x8_init_block_index(IntraX8Context *w, AVFrame *frame)
-{
- // not parent codec linesize as this would be wrong for field pics
- // not that IntraX8 has interlacing support ;)
- const ptrdiff_t linesize = frame->linesize[0];
- const ptrdiff_t uvlinesize = frame->linesize[1];
-
- w->dest[0] = frame->data[0];
- w->dest[1] = frame->data[1];
- w->dest[2] = frame->data[2];
-
- w->dest[0] += w->mb_y * linesize << 3;
- // chroma blocks are on add rows
- w->dest[1] += (w->mb_y & ~1) * uvlinesize << 2;
- w->dest[2] += (w->mb_y & ~1) * uvlinesize << 2;
-}
-
-av_cold int ff_intrax8_common_init(AVCodecContext *avctx,
- IntraX8Context *w,
- int16_t (*block)[64],
- int block_last_index[12],
- int mb_width, int mb_height)
-{
- static AVOnce init_static_once = AV_ONCE_INIT;
-
- w->avctx = avctx;
- w->mb_width = mb_width;
- w->mb_height = mb_height;
- w->block = block;
- w->block_last_index = block_last_index;
-
- // two rows, 2 blocks per cannon mb
- w->prediction_table = av_mallocz(w->mb_width * 2 * 2);
- if (!w->prediction_table)
- return AVERROR(ENOMEM);
-
- ff_wmv2dsp_init(&w->wdsp);
-
- ff_init_scantable_permutation(w->idct_permutation,
- w->wdsp.idct_perm);
-
- ff_permute_scantable(w->permutated_scantable[0], ff_wmv1_scantable[0],
- w->idct_permutation);
- ff_permute_scantable(w->permutated_scantable[1], ff_wmv1_scantable[2],
- w->idct_permutation);
- ff_permute_scantable(w->permutated_scantable[2], ff_wmv1_scantable[3],
- w->idct_permutation);
-
- ff_intrax8dsp_init(&w->dsp);
- ff_blockdsp_init(&w->bdsp);
-
- ff_thread_once(&init_static_once, x8_vlc_init);
-
- return 0;
-}
-
-av_cold void ff_intrax8_common_end(IntraX8Context *w)
-{
- av_freep(&w->prediction_table);
-}
-
-int ff_intrax8_decode_picture(IntraX8Context *w, Picture *pict,
- GetBitContext *gb, int *mb_x, int *mb_y,
- int dquant, int quant_offset,
- int loopfilter, int lowdelay)
-{
- int mb_xy;
-
- w->gb = gb;
- w->dquant = dquant;
- w->quant = dquant >> 1;
- w->qsum = quant_offset;
- w->frame = pict->f;
- w->loopfilter = loopfilter;
- w->use_quant_matrix = get_bits1(w->gb);
-
- w->mb_x = *mb_x;
- w->mb_y = *mb_y;
-
- w->divide_quant_dc_luma = ((1 << 16) + (w->quant >> 1)) / w->quant;
- if (w->quant < 5) {
- w->quant_dc_chroma = w->quant;
- w->divide_quant_dc_chroma = w->divide_quant_dc_luma;
- } else {
- w->quant_dc_chroma = w->quant + ((w->quant + 3) >> 3);
- w->divide_quant_dc_chroma = ((1 << 16) + (w->quant_dc_chroma >> 1)) / w->quant_dc_chroma;
- }
- x8_reset_vlc_tables(w);
-
- for (w->mb_y = 0; w->mb_y < w->mb_height * 2; w->mb_y++) {
- x8_init_block_index(w, w->frame);
- mb_xy = (w->mb_y >> 1) * (w->mb_width + 1);
- if (get_bits_left(gb) < 1)
- goto error;
- for (w->mb_x = 0; w->mb_x < w->mb_width * 2; w->mb_x++) {
- x8_get_prediction(w);
- if (x8_setup_spatial_predictor(w, 0))
- goto error;
- if (x8_decode_intra_mb(w, 0))
- goto error;
-
- if (w->mb_x & w->mb_y & 1) {
- x8_get_prediction_chroma(w);
-
- /* when setting up chroma, no vlc is read,
- * so no error condition can be reached */
- x8_setup_spatial_predictor(w, 1);
- if (x8_decode_intra_mb(w, 1))
- goto error;
-
- x8_setup_spatial_predictor(w, 2);
- if (x8_decode_intra_mb(w, 2))
- goto error;
-
- w->dest[1] += 8;
- w->dest[2] += 8;
-
- pict->qscale_table[mb_xy] = w->quant;
- mb_xy++;
- }
- w->dest[0] += 8;
- }
- if (w->mb_y & 1)
- ff_draw_horiz_band(w->avctx, w->frame, w->frame,
- (w->mb_y - 1) * 8, 16,
- PICT_FRAME, 0, lowdelay);
- }
-
-error:
- *mb_x = w->mb_x;
- *mb_y = w->mb_y;
-
- return 0;
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/vc1dsp_init_mips.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/vc1dsp_init_mips.c
deleted file mode 100644
index 94126f3a9d359b77783c4ed2c0455e1ce8bdf5cd..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/vc1dsp_init_mips.c
+++ /dev/null
@@ -1,120 +0,0 @@
-/*
- * Copyright (c) 2016 Zhou Xiaoyong
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/mips/cpu.h"
-#include "libavutil/attributes.h"
-#include "libavcodec/vc1dsp.h"
-#include "vc1dsp_mips.h"
-#include "config.h"
-
-#define FN_ASSIGN(OP, X, Y, INSN) \
- dsp->OP##vc1_mspel_pixels_tab[1][X+4*Y] = ff_##OP##vc1_mspel_mc##X##Y##INSN; \
- dsp->OP##vc1_mspel_pixels_tab[0][X+4*Y] = ff_##OP##vc1_mspel_mc##X##Y##_16##INSN
-
-av_cold void ff_vc1dsp_init_mips(VC1DSPContext *dsp)
-{
- int cpu_flags = av_get_cpu_flags();
-
- if (have_mmi(cpu_flags)) {
- #if _MIPS_SIM != _ABIO32
- dsp->vc1_inv_trans_8x8 = ff_vc1_inv_trans_8x8_mmi;
- dsp->vc1_inv_trans_4x8 = ff_vc1_inv_trans_4x8_mmi;
- dsp->vc1_inv_trans_8x4 = ff_vc1_inv_trans_8x4_mmi;
-#endif
- dsp->vc1_inv_trans_4x4 = ff_vc1_inv_trans_4x4_mmi;
- dsp->vc1_inv_trans_8x8_dc = ff_vc1_inv_trans_8x8_dc_mmi;
- dsp->vc1_inv_trans_4x8_dc = ff_vc1_inv_trans_4x8_dc_mmi;
- dsp->vc1_inv_trans_8x4_dc = ff_vc1_inv_trans_8x4_dc_mmi;
- dsp->vc1_inv_trans_4x4_dc = ff_vc1_inv_trans_4x4_dc_mmi;
-
- dsp->vc1_h_overlap = ff_vc1_h_overlap_mmi;
- dsp->vc1_v_overlap = ff_vc1_v_overlap_mmi;
- dsp->vc1_h_s_overlap = ff_vc1_h_s_overlap_mmi;
- dsp->vc1_v_s_overlap = ff_vc1_v_s_overlap_mmi;
-
- dsp->vc1_v_loop_filter4 = ff_vc1_v_loop_filter4_mmi;
- dsp->vc1_h_loop_filter4 = ff_vc1_h_loop_filter4_mmi;
- dsp->vc1_v_loop_filter8 = ff_vc1_v_loop_filter8_mmi;
- dsp->vc1_h_loop_filter8 = ff_vc1_h_loop_filter8_mmi;
- dsp->vc1_v_loop_filter16 = ff_vc1_v_loop_filter16_mmi;
- dsp->vc1_h_loop_filter16 = ff_vc1_h_loop_filter16_mmi;
-
- FN_ASSIGN(put_, 0, 0, _mmi);
- FN_ASSIGN(put_, 0, 1, _mmi);
- FN_ASSIGN(put_, 0, 2, _mmi);
- FN_ASSIGN(put_, 0, 3, _mmi);
-
- FN_ASSIGN(put_, 1, 0, _mmi);
- //FN_ASSIGN(put_, 1, 1, _mmi);//FIXME
- //FN_ASSIGN(put_, 1, 2, _mmi);//FIXME
- //FN_ASSIGN(put_, 1, 3, _mmi);//FIXME
-
- FN_ASSIGN(put_, 2, 0, _mmi);
- //FN_ASSIGN(put_, 2, 1, _mmi);//FIXME
- //FN_ASSIGN(put_, 2, 2, _mmi);//FIXME
- //FN_ASSIGN(put_, 2, 3, _mmi);//FIXME
-
- FN_ASSIGN(put_, 3, 0, _mmi);
- //FN_ASSIGN(put_, 3, 1, _mmi);//FIXME
- //FN_ASSIGN(put_, 3, 2, _mmi);//FIXME
- //FN_ASSIGN(put_, 3, 3, _mmi);//FIXME
-
- FN_ASSIGN(avg_, 0, 0, _mmi);
- FN_ASSIGN(avg_, 0, 1, _mmi);
- FN_ASSIGN(avg_, 0, 2, _mmi);
- FN_ASSIGN(avg_, 0, 3, _mmi);
-
- FN_ASSIGN(avg_, 1, 0, _mmi);
- //FN_ASSIGN(avg_, 1, 1, _mmi);//FIXME
- //FN_ASSIGN(avg_, 1, 2, _mmi);//FIXME
- //FN_ASSIGN(avg_, 1, 3, _mmi);//FIXME
-
- FN_ASSIGN(avg_, 2, 0, _mmi);
- //FN_ASSIGN(avg_, 2, 1, _mmi);//FIXME
- //FN_ASSIGN(avg_, 2, 2, _mmi);//FIXME
- //FN_ASSIGN(avg_, 2, 3, _mmi);//FIXME
-
- FN_ASSIGN(avg_, 3, 0, _mmi);
- //FN_ASSIGN(avg_, 3, 1, _mmi);//FIXME
- //FN_ASSIGN(avg_, 3, 2, _mmi);//FIXME
- //FN_ASSIGN(avg_, 3, 3, _mmi);//FIXME
-
- dsp->put_no_rnd_vc1_chroma_pixels_tab[0] = ff_put_no_rnd_vc1_chroma_mc8_mmi;
- dsp->avg_no_rnd_vc1_chroma_pixels_tab[0] = ff_avg_no_rnd_vc1_chroma_mc8_mmi;
- dsp->put_no_rnd_vc1_chroma_pixels_tab[1] = ff_put_no_rnd_vc1_chroma_mc4_mmi;
- dsp->avg_no_rnd_vc1_chroma_pixels_tab[1] = ff_avg_no_rnd_vc1_chroma_mc4_mmi;
- }
-
- if (have_msa(cpu_flags)) {
- dsp->vc1_inv_trans_8x8 = ff_vc1_inv_trans_8x8_msa;
- dsp->vc1_inv_trans_4x8 = ff_vc1_inv_trans_4x8_msa;
- dsp->vc1_inv_trans_8x4 = ff_vc1_inv_trans_8x4_msa;
-
- FN_ASSIGN(put_, 1, 1, _msa);
- FN_ASSIGN(put_, 1, 2, _msa);
- FN_ASSIGN(put_, 1, 3, _msa);
- FN_ASSIGN(put_, 2, 1, _msa);
- FN_ASSIGN(put_, 2, 2, _msa);
- FN_ASSIGN(put_, 2, 3, _msa);
- FN_ASSIGN(put_, 3, 1, _msa);
- FN_ASSIGN(put_, 3, 2, _msa);
- FN_ASSIGN(put_, 3, 3, _msa);
- }
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/AR.FreeFlight APK The Best Way to Enjoy Your Parrot Drone.md b/spaces/congsaPfin/Manga-OCR/logs/AR.FreeFlight APK The Best Way to Enjoy Your Parrot Drone.md
deleted file mode 100644
index bab6c9554ae86abb01e8041cd0211482ec765597..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/AR.FreeFlight APK The Best Way to Enjoy Your Parrot Drone.md
+++ /dev/null
@@ -1,167 +0,0 @@
-
-
AR FreeFlight APK: How to Download and Use the App for Parrot Drones
-
Do you own a Parrot drone and want to have more fun and control over it? If yes, then you should try the AR FreeFlight APK, a free app that lets you pilot your Parrot drone from your Android device. In this article, we will show you what is AR FreeFlight APK, how to download and install it, how to use it, and some tips and tricks for using it. Let's get started!
AR FreeFlight APK is an application that allows you to discover and pilot the Parrot AR.Drone and AR.Drone 2.0 from your Android device. It is developed by Parrot, a French company that specializes in wireless devices for mobile phones, automobiles, and drones. The app is compatible with most Android devices running Android 2.2 or higher.
-
Features of AR FreeFlight APK
-
Some of the features of AR FreeFlight APK are:
-
-
It lets you control your Parrot drone using the accelerometer or touch screen of your device.
-
It lets you view the live video stream from your Parrot drone's camera on your device's screen.
-
It lets you record and save your Parrot drone's videos and photos on your device or on a USB flash drive.
-
It lets you share your Parrot drone's videos and photos on social media platforms like YouTube, Facebook, Twitter, etc.
-
It lets you access various settings and options for your Parrot drone, such as speed, altitude, tilt, battery level, etc.
-
It lets you update the firmware of your Parrot drone wirelessly.
-
It lets you play games and challenges with your Parrot drone, such as racing, acrobatics, etc.
-
-
Benefits of AR FreeFlight APK
-
Some of the benefits of using AR FreeFlight APK are:
-
-
It is free to download and use.
-
It is easy to use and has a user-friendly interface.
-
It enhances the performance and functionality of your Parrot drone.
-
It adds more fun and excitement to your Parrot drone experience.
-
-
How to Download and Install AR FreeFlight APK
-
Requirements for AR FreeFlight APK
-
Before you download and install AR FreeFlight APK, make sure you have the following requirements:
-
-
A compatible Android device running Android 2.2 or higher.
-
A stable internet connection.
-
A compatible Parrot drone (AR.Drone or AR.Drone 2.0).
-
A fully charged battery for your Parrot drone.
-
-
Steps to Download and Install AR FreeFlight APK
-
To download and install AR FreeFlight APK on your Android device, follow these steps:
-
-
Go to the Google Play Store on your device and search for "AR.FreeFlight". Alternatively, you can use this link to go directly to the app page.
-
Tap on the "Install" button and wait for the app to download and install on your device.
Once the app is installed, open it and grant the necessary permissions for the app to access your device's camera, microphone, storage, etc.
-
That's it! You have successfully downloaded and installed AR FreeFlight APK on your Android device. You can now use it to pilot your Parrot drone.
-
-
How to Use AR FreeFlight APK
-
How to Connect Your Parrot Drone to AR FreeFlight APK
-
To connect your Parrot drone to AR FreeFlight APK, follow these steps:
-
-
Turn on your Parrot drone and wait for the lights to turn green.
-
Go to the settings of your Android device and turn on the Wi-Fi.
-
Scan for the available Wi-Fi networks and select the one that matches your Parrot drone's name (e.g. "ardrone2_123456").
-
Enter the password for your Parrot drone's Wi-Fi network (the default password is "1234567890").
-
Wait for the connection to be established. You should see a blue icon on the top right corner of your device's screen indicating that you are connected to your Parrot drone.
-
Go back to the AR FreeFlight APK app and tap on the "Pilot" button. You should see the live video feed from your Parrot drone's camera on your device's screen.
-
Congratulations! You have successfully connected your Parrot drone to AR FreeFlight APK. You can now control your Parrot drone with your device.
-
-
How to Control Your Parrot Drone with AR FreeFlight APK
-
To control your Parrot drone with AR FreeFlight APK, follow these steps:
-
-
Make sure you have enough space around you and your Parrot drone for a safe flight.
-
Tap on the "Take Off" button on the app to launch your Parrot drone in the air. It will hover at a fixed height and wait for your commands.
-
To move your Parrot drone forward, backward, left, or right, tilt your device in the corresponding direction. The more you tilt, the faster your Parrot drone will move.
-
To rotate your Parrot drone clockwise or counterclockwise, swipe your finger on the right side of the screen. The more you swipe, the faster your Parrot drone will rotate.
-
To make your Parrot drone ascend or descend, swipe your finger on the left side of the screen. The more you swipe, the faster your Parrot drone will change its altitude.
-
To perform flips and rolls with your Parrot drone, double tap on the screen and then tilt your device in the direction you want to flip or roll.
-
To land your Parrot drone, tap on the "Land" button on the app. Your Parrot drone will gently descend and touch down on the ground.
-
That's it! You have successfully controlled your Parrot drone with AR FreeFlight APK. You can now enjoy flying your Parrot drone with ease and fun.
-
-
How to Record and Share Your Parrot Drone Videos with AR FreeFlight APK
-
To record and share your Parrot drone videos with AR FreeFlight APK, follow these steps:
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-
Before you start flying your Parrot drone, make sure you have enough storage space on your device or on a USB flash drive connected to your device.
-
To start recording a video of your Parrot drone flight, tap on the "Record" button on the app. You should see a red dot on the top left corner of the screen indicating that you are recording.
-
To stop recording a video of your Parrot drone flight, tap on the "Record" button again. You should see a green dot on the top left corner of the screen indicating that you have stopped recording.
-
To view the recorded videos of your Parrot drone flights, tap on the "Media" button on the app. You should see a list of thumbnails of all the videos you have recorded.
-
To play a video of your Parrot drone flight, tap on the thumbnail of the video you want to watch. You should see a full-screen view of the video with playback controls.
-
To delete a video of your Parrot drone flight, tap and hold on the thumbnail of the video you want to delete. You should see a pop-up menu with an option to delete the video. Tap on "Delete" and confirm.
-
To share a video of your Parrot drone flight, tap and hold on the thumbnail of the video you want to share. You should see a pop-up menu with various options to share the video via email, Bluetooth , Facebook, Twitter, YouTube, etc. Tap on the option you want and follow the instructions to share the video.
-
That's it! You have successfully recorded and shared your Parrot drone videos with AR FreeFlight APK. You can now show off your Parrot drone skills and adventures to your friends and family.
-
-
Tips and Tricks for Using AR FreeFlight APK
-
How to Calibrate Your Parrot Drone with AR FreeFlight APK
-
To calibrate your Parrot drone with AR FreeFlight APK, follow these steps:
-
-
Make sure your Parrot drone is on a flat and stable surface.
-
Go to the settings of the AR FreeFlight APK app and tap on the "Flat Trim" button. You should see a message saying "Flat trim done".
-
That's it! You have successfully calibrated your Parrot drone with AR FreeFlight APK. This will improve the stability and accuracy of your Parrot drone's flight.
-
-
How to Adjust the Settings of Your Parrot Drone with AR FreeFlight APK
-
To adjust the settings of your Parrot drone with AR FreeFlight APK, follow these steps:
-
-
Go to the settings of the AR FreeFlight APK app and tap on the "Settings" button. You should see a list of various settings and options for your Parrot drone.
-
To change the speed of your Parrot drone, tap on the "Speed" slider and drag it to the left or right. The lower the speed, the easier it is to control your Parrot drone. The higher the speed, the more agile and responsive your Parrot drone is.
-
To change the altitude of your Parrot drone, tap on the "Altitude" slider and drag it to the left or right. The lower the altitude, the closer your Parrot drone is to the ground. The higher the altitude, the farther your Parrot drone is from the ground.
-
To change the tilt of your Parrot drone, tap on the "Tilt" slider and drag it to the left or right. The lower the tilt, the less your Parrot drone leans forward or backward when you tilt your device. The higher the tilt, the more your Parrot drone leans forward or backward when you tilt your device.
-
To change the mode of your Parrot drone, tap on the "Mode" button and select either "Normal" or "Absolute Control". In normal mode, your Parrot drone moves in relation to its own orientation. In absolute control mode, your Parrot drone moves in relation to your device's orientation.
-
To change other settings and options of your Parrot drone, such as video quality, sound effects, firmware update, etc., tap on the corresponding buttons and follow the instructions.
-
That's it! You have successfully adjusted the settings of your Parrot drone with AR FreeFlight APK. This will customize your Parrot drone's flight according to your preferences and needs.
-
-
How to Troubleshoot Common Issues with AR FreeFlight APK
-
If you encounter any issues or problems with AR FreeFlight APK, here are some possible solutions:
-
-
If you cannot connect your Parrot drone to AR FreeFlight APK, make sure you have turned on both your device's Wi-Fi and your Parrot drone's Wi-Fi. Also, make sure you have entered the correct password for your Parrot drone's Wi-Fi network.
-
If you experience lag or delay in the video stream from your Parrot drone's camera, make sure you have a stable internet connection and a good Wi-Fi signal. Also, try lowering the video quality in the app settings.
-
If you experience poor performance or stability of your Parrot drone's flight, make sure you have calibrated your Parrot drone with AR FreeFlight APK. Also, try adjusting the speed, altitude, tilt, and mode settings in the app settings.
-
If you experience any other issues or problems with AR FreeFlight APK, try restarting both your device and your Parrot drone. Also, try updating both your device's software and your Parrot drone's firmware.
-
-
Conclusion
-
In conclusion, AR FreeFlight APK is a great app that lets you discover and pilot the Parrot AR.Drone and AR.Drone 2.0 from your Android device. It has many features and benefits that enhance your Parrot drone experience. It is also easy to download, install, use, and troubleshoot. If you own a Parrot drone and want to have more fun and control over it, you should definitely try AR FreeFlight APK. You won't regret it!
-
FAQs
-
Here are some FAQs that you might have about AR FreeFlight APK:
-
-
Is AR FreeFlight APK safe to use?
-
Yes, AR FreeFlight APK is safe to use. It is developed by Parrot, a reputable company that has been in the wireless devices industry for over 20 years. It is also verified by Google Play Protect, which scans apps for malware and other threats. However, as with any app, you should always be careful about what permissions you grant and what data you share.
-
Is AR FreeFlight APK compatible with other drones?
-
No, AR FreeFlight APK is only compatible with the Parrot AR.Drone and AR.Drone 2.0. If you have a different drone, you should look for another app that is compatible with your drone model and brand.
-
How much does AR FreeFlight APK cost?
-
AR FreeFlight APK is free to download and use. However, some features and options may require in-app purchases or subscriptions. For example, if you want to access more games and challenges, you may need to pay a fee or subscribe to a plan.
-
How can I contact the developers of AR FreeFlight APK?
-
If you have any questions, feedback, or issues with AR FreeFlight APK, you can contact the developers of the app by emailing them at customer.service@parrot.com. You can also visit their website or follow them on social media platforms like Facebook, Twitter, Instagram, etc.
-
Where can I find more information about AR FreeFlight APK?
-
If you want to learn more about AR FreeFlight APK, you can check out the app's description on the Google Play Store, the app's user manual, or the app's FAQ page. You can also watch some tutorials and reviews of the app on YouTube or other video platforms.
- 197e85843d
-
-
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Create Stunning AI Art with PicSo Mod APK - Customize Your AI Girl in Minutes.md b/spaces/congsaPfin/Manga-OCR/logs/Create Stunning AI Art with PicSo Mod APK - Customize Your AI Girl in Minutes.md
deleted file mode 100644
index f99e713c7197a42bbb905df36a44d45dd4f6fe65..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Create Stunning AI Art with PicSo Mod APK - Customize Your AI Girl in Minutes.md
+++ /dev/null
@@ -1,78 +0,0 @@
-
-
PicSo – Customize Your AI Girl Mod APK: A Review
-
Have you ever dreamed of creating your own anime or human character, turning your photos or videos into cartoons, or generating art from any text prompt? If yes, then you might want to check out PicSo – Customize Your AI Girl Mod APK, a powerful and fun app that lets you do all these things and more. In this article, we will review this app and tell you everything you need to know about it, including how to download and install it, what are its features and benefits, and some FAQs.
-
What is PicSo – Customize Your AI Girl Mod APK?
-
PicSo – Customize Your AI Girl Mod APK is a modified version of PicSo – Customize Your AI Girl, an app that is described as an AI art generator for everyone. It allows you to create amazing digital art using different tools and features, such as:
PicSo is named after Picasso, which expresses that everyone can be a great artist like Picasso. It is an AI image generator that turns your imagination into digital art, and disparate ideas to synthesize objects. And allow you to effortlessly create art that’s based on fantasy and dystopia sci-fi scenes deep within our super-charged painting creator.
-
A powerful AI girl creator
-
With PicSo, you can create your own anime or human character with just a few keywords. You can customize every aspect of your character's appearance, from hair color and eye shape to clothing and accessories. You can also choose from different styles and backgrounds to make your character unique and personalized.
-
A fun image and video to cartoon converter
-
PicSo also lets you turn any photo or video into a cartoon in seconds. You can upload your plain photos or videos, and watch them transform into animated pictures with different effects and filters. You can also convert yourself and your pets into cartoons in a flash.
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A creative text to art maker
-
Another feature of PicSo is the text to art maker, which allows you to generate art from any text prompt. You can enter any words or sentences, and PicSo will create an AI picture art based on your input. You can also choose from different styles and themes to match your mood and preference.
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How to download and install PicSo – Customize Your AI Girl Mod APK?
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If you want to enjoy all the features and benefits of PicSo without any limitations, you can download and install the mod apk version of the app. Here are the steps to do so:
-
Download the mod apk file from a trusted source
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The first step is to find a reliable source that offers the mod apk file of PicSo – Customize Your AI Girl. You can search online for websites that provide this file, but make sure to check the reviews and ratings before downloading anything. Alternatively, you can use this link to download the mod apk file directly.
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Enable unknown sources on your device
-
Before you install the mod apk file, you need to enable unknown sources on your device. This is because the mod apk file is not from the official Google Play Store, and your device might block the installation by default. To enable unknown sources, go to your device settings, then security, then toggle on the option that says "allow installation of apps from unknown sources".
-
Install the mod apk file and enjoy
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Once you have enabled unknown sources, you can install the mod apk file by tapping on it and following the instructions. After the installation is complete, you can open the app and enjoy all the features and benefits of PicSo – Customize Your AI Girl Mod APK.
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What are the features of PicSo – Customize Your AI Girl Mod APK?
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PicSo – Customize Your AI Girl Mod APK has many features that make it different from the original app. Some of these features are:
-
Unlimited access to all styles and features
-
With the mod apk version, you can access all the styles and features of PicSo without any restrictions. You can create as many AI girls, cartoons, and art as you want, and use any style or theme that you like. You don't have to worry about running out of coins or credits, as you have unlimited resources in the mod apk version.
-
No ads or watermarks
-
Another feature of the mod apk version is that it removes all the ads and watermarks that might annoy you in the original app. You can enjoy a smooth and uninterrupted experience with PicSo, without any distractions or interruptions. You can also save and share your creations without any watermarks or logos.
-
High-quality output and fast processing
-
The mod apk version also improves the quality and speed of the output and processing of PicSo. You can get high-resolution and realistic results with PicSo, and export them in various formats. You can also enjoy a fast and stable performance with PicSo, as it uses advanced AI technology and optimization techniques.
-
What are the benefits of using PicSo – Customize Your AI Girl Mod APK?
-
Besides the features, there are also many benefits of using PicSo – Customize Your AI Girl Mod APK. Some of these benefits are:
-
Express your creativity and imagination
-
PicSo is a great app for expressing your creativity and imagination. You can create anything you want with PicSo, from anime characters and cartoons to art and text. You can also mix and match different styles and elements to create unique and original works. PicSo is a fun and easy way to unleash your artistic potential.
-
Make your own anime or human character
-
PicSo is also a great app for making your own anime or human character. You can customize every detail of your character's appearance, personality, and background. You can also create different scenarios and stories for your character, and share them with others. PicSo is a perfect app for anime fans and role-players.
-
Turn any photo or video into a cartoon
-
PicSo is also a great app for turning any photo or video into a cartoon. You can upload any photo or video from your gallery or camera, and watch it transform into a cartoon with different effects and filters. You can also turn yourself and your pets into cartoons, and have fun with them. PicSo is a great app for making memories more lively and colorful.
-
Generate art from any text prompt
-
PicSo is also a great app for generating art from any text prompt. You can enter any words or sentences, and PicSo will create an AI picture art based on your input. You can also choose from different styles and themes to match your mood and preference. PicSo is a great app for exploring new ideas and inspirations.
-
Conclusion
-
PicSo – Customize Your AI Girl Mod APK is an amazing app that lets you create stunning digital art using different tools and features. It allows you to create your own anime or human character, turn any photo or video into a cartoon, generate art from any text prompt, and more. It also gives you unlimited access to all styles and features, removes ads and watermarks, improves quality and speed, and offers many other benefits. If you want to download and install this app, you can follow the steps mentioned above, or use this link to get it directly.
-
FAQs
-
-
Question
Answer
-
Is PicSo – Customize Your AI Girl Mod APK safe to use?
Yes, PicSo – Customize Your AI Girl Mod APK is safe to use, as long as you download it from a trusted source. However, you should always be careful when installing apps from unknown sources, and scan them for viruses or malware before using them.
-
What are the requirements for using PicSo – Customize Your AI Girl Mod APK?
To use PicSo – Customize Your AI Girl Mod APK, you need to have an Android device that runs on Android 5.0 or higher, and has at least 100 MB of free storage space. You also need to have a stable internet connection to use the app's features.
-
Can I use PicSo – Customize Your AI Girl Mod APK offline?
No, PicSo – Customize Your AI Girl Mod APK requires an internet connection to work. This is because the app uses cloud-based AI technology to generate and process your digital art. If you want to use the app offline, you can save your creations to your device and view them later.
-
Can I share my creations with others using PicSo – Customize Your AI Girl Mod APK?
Yes, you can share your creations with others using PicSo – Customize Your AI Girl Mod APK. You can save your creations to your device, and then share them via social media, email, or other apps. You can also export your creations in different formats, such as JPG, PNG, GIF, MP4, etc.
-
Can I request new styles or features for PicSo – Customize Your AI Girl Mod APK?
Yes, you can request new styles or features for PicSo – Customize Your AI Girl Mod APK. You can contact the developers of the app via their email address: picso@picso.ai. You can also leave feedback or suggestions on their website: https://picso.ai/.
-
197e85843d
-
-
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/N.O.V.A. Legacy Craft and Upgrade Your Weapons in this Action-Packed Sci-Fi Adventure.md b/spaces/congsaPfin/Manga-OCR/logs/N.O.V.A. Legacy Craft and Upgrade Your Weapons in this Action-Packed Sci-Fi Adventure.md
deleted file mode 100644
index 234ddcfbf7f12fb43e2367d81f0bb420f014b6ae..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/N.O.V.A. Legacy Craft and Upgrade Your Weapons in this Action-Packed Sci-Fi Adventure.md
+++ /dev/null
@@ -1,166 +0,0 @@
-
-
N.O.V.A. Legacy Download: How to Play the Epic Sci-Fi FPS on Your Device
-
If you are looking for a thrilling and immersive sci-fi shooter game that you can play on your device, you should definitely check out N.O.V.A. Legacy. This game is a remake of the first episode of the critically acclaimed N.O.V.A. saga, which has been praised for its stunning graphics, engaging gameplay, and captivating story.
In this article, we will tell you everything you need to know about N.O.V.A. Legacy, including its story, features, download and installation process, and tips and tricks for playing it. By the end of this article, you will be ready to join the fight against the alien invaders and uncover the mystery behind their sudden assault.
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The Story of N.O.V.A. Legacy
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N.O.V.A. Legacy is set in a futuristic world where humanity is facing a threat from an unknown alien force that has attacked Earth and its colonies. You play as Kal Wardin, a veteran N.O.V.A. marine who has retired from active duty but is summoned back to don his Mobile Armored Suit and strike against the enemies of the Colonial Administration forces.
-
You are not alone in this mission, as you are helped by Yelena, your personal AI agent who guides you through the battlefield and provides you with useful information. Together, you must protect humanity's destiny by engaging in combat against the alien invaders while uncovering the mystery behind their sudden assault.
-
The Features of N.O.V.A. Legacy
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N.O.V.A. Legacy offers a variety of features that make it one of the best sci-fi shooter games available for mobile devices. Here are some of them:
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Story Mode
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This is the offline campaign mode where you can delve into the story of N.O.V.A. Legacy and fight to uncover the truth about the alien invaders. You will face 19 action-packed FPS levels that will test your skills and reflexes as you encounter different enemies, environments, and challenges.
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Shadow Missions
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These are limited-time battlefields where you can assault the alien Special Ops Force and earn rewards for completing them. You will need to use your best strategies and tactics to survive these missions as they are more difficult than the regular ones.
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Special Ops
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These are unique alien formations that require a critical strike from you to destroy them. You will need to use your most powerful weapons and suit cores to deal with these enemies as they are more resilient than the others.
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Deathmatch
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This is the online multiplayer mode where you can compete with up to 8 players from around the world in a free-for-all battle. You will need to be the last shooter standing on the battlefield by eliminating your opponents and avoiding getting caught in the crossfire. You can also customize your character with 3D models and skins to stand out from the crowd.
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Team Deathmatch
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This is another online multiplayer mode where you can team up with up to 3 other players and face another team of 4 players in a 4v4 battle. You will need to cooperate with your teammates and use your skills and weapons to defeat the enemy team and score more points than them.
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Customization
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N.O.V.A. Legacy allows you to customize your character and your weapons with 3D models and skins that you can unlock by playing the game or by purchasing them with real money. You can choose from a variety of options to create your own unique look and style.
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Leaderboards and Leagues
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You can also track your progress and performance in N.O.V.A. Legacy by checking the leaderboards and leagues that rank you according to your score, kills, deaths, and other statistics. You can also compare yourself with other players from around the world and see how you stack up against them.
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Suit Cores
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These are special items that you can equip on your Mobile Armored Suit to enhance its abilities and stats. You can collect different types of suit cores that have different effects, such as increasing your health, damage, speed, accuracy, and more. You can also upgrade your suit cores to make them more powerful.
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Weapons
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N.O.V.A. Legacy offers a wide range of weapons that you can use to fight against the alien invaders. You can choose from sci-fi guns such as plasma rifles, laser guns, railguns, and more, or modern weaponry such as assault rifles, shotguns, sniper rifles, and more. You can also upgrade your weapons to improve their performance and add attachments such as scopes, silencers, and more.
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How to Download and Install N.O.V.A. Legacy on Your Device
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N.O.V.A. Legacy is available for free on various platforms and devices. Here is how you can download and install it on yours:
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For Android devices
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If you have an Android device, you can download N.O.V.A. Legacy from the Google Play Store by following these steps:
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Open the Google Play Store app on your device.
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Search for N.O.V.A. Legacy in the search bar.
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Select the game from the list of results and tap on Install.
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Wait for the game to download and install on your device.
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Launch the game and enjoy playing it.
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For iOS devices
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If you have an iOS device, you can download N.O.V.A. Legacy from the App Store by following these steps:
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Open the App Store app on your device.
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Search for N.O.V.A. Legacy in the search bar.
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Select the game from the list of results and tap on Get.
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Wait for the game to download and install on your device.
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Launch the game and enjoy playing it.
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For PC and Mac devices
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If you have a PC or Mac device, you can download N.O.V.A. Legacy from the BlueStacks emulator by following these steps:
Launch BlueStacks and sign in with your Google account.
-
Search for N.O.V.A. Legacy in the search bar.
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Select the game from the list of results and click on Install.
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Wait for the game to download and install on your device.
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Launch the game and enjoy playing it.
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Tips and Tricks for Playing N.O.V.A. Legacy
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N.O.V.A. Legacy is a fun and challenging game that requires skill and strategy to master. Here are some tips and tricks that can help you improve your gameplay and win more battles:
-
How to aim and shoot effectively
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Aiming and shooting are essential skills in any shooter game, especially in N.O.V.A. Legacy where you face fast-paced combat scenarios. Here are some tips on how to aim and shoot effectively:
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Use the auto-aim feature to lock on your target automatically. This will help you save time and ammo while shooting accurately.
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Use the zoom feature to aim more precisely at long-range targets. This will help you deal more damage and headshots to your enemies.
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Use the fire button to shoot manually or the auto-fire feature to shoot automatically when your crosshair is on an enemy. This will help you control your fire rate and accuracy better.
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Use the reload button to reload your weapon when it is low on ammo or when you have a break in the action. This will help you avoid running out of ammo in the middle of a fight.
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How to use cover and movement wisely
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Cover and movement are also important skills in N.O.V.A. Legacy, as they can help you avoid getting hit by enemy fire and surprise your opponents with your tactics. Here are some tips on how to use cover and movement wisely:
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-
Use the cover button to crouch behind obstacles and walls that can protect you from enemy fire. This will help you reduce the damage you take and heal your health faster.
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Use the movement joystick to move around the battlefield and change your position frequently. This will help you dodge enemy fire and flank your enemies from different angles.
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Use the jump button to jump over obstacles and gaps that can help you reach higher or lower areas. This will help you access new vantage points and escape from dangerous situations.
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Use the sprint button to run faster and cover more distance in a short time. This will help you chase or retreat from your enemies quickly and efficiently.
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How to manage your ammo and health efficiently
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Ammo and health are vital resources in N.O.V.A. Legacy, as they determine how long you can survive and fight in the game. Here are some tips on how to manage your ammo and health efficiently:
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Pick up ammo boxes and health packs that are scattered around the battlefield. These will help you replenish your ammo and health when they are low.
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Switch between different weapons that have different ammo types and capacities. This will help you conserve your ammo and use the best weapon for each situation.
-
Use suit cores that can boost your ammo and health stats, such as the Ammo Core, the Health Core, the Regen Core, and more. These will help you increase your ammo and health limits and regeneration rates.
-
Avoid wasting your ammo and health by shooting blindly or recklessly. This will help you save your resources for when you really need them.
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How to choose the best weapons and suit cores for your playstyle
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N.O.V.A. Legacy offers a variety of weapons and suit cores that you can use to customize your character and enhance your performance in the game. However, not all weapons and suit cores are suitable for every playstyle, so you need to choose wisely. Here are some tips on how to choose the best weapons and suit cores for your playstyle:
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Consider the range, damage, fire rate, accuracy, stability, magazine size, reload time, and attachments of each weapon. These factors affect how well each weapon performs in different scenarios and situations.
-
Consider the type, effect, level, rarity, cost, and compatibility of each suit core. These factors affect how much each suit core improves your abilities and stats in the game.
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Experiment with different combinations of weapons and suit cores that match your playstyle and preferences. You can test them out in the training mode or in different game modes to see how they work for you.
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Upgrade your weapons and suit cores regularly to make them more powerful and effective. You can use credits and cards that you earn by playing the game or by purchasing them with real money.
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How to earn more credits and cards easily
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Credits and cards are the main currencies in N.O.V.A. Legacy that you can use to buy new weapons, suit cores, skins, models, upgrades, and more. Here are some tips on how to earn more credits and cards easily:
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Play the game regularly and complete the daily and weekly missions that reward you with credits and cards. These missions are simple and fun to do and can help you improve your skills and progress in the game.
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Play the online multiplayer modes and win more matches that reward you with credits and cards. These modes are competitive and challenging and can help you test your skills and strategies against other players.
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Play the shadow missions and special ops that reward you with credits and cards. These missions are difficult and rare and can help you face the toughest enemies and challenges in the game.
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Watch ads and videos that reward you with credits and cards. These ads and videos are short and optional and can help you earn some extra resources without spending any money.
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Buy credits and cards with real money if you want to support the game developers and get more resources faster. You can choose from different packages and offers that suit your budget and needs.
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Conclusion
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N.O.V.A. Legacy is an epic sci-fi shooter game that you can play on your device for free. It has a captivating story, stunning graphics, engaging gameplay, and various features that make it one of the best games of its genre. You can download and install it easily from the Google Play Store, the App Store, or the BlueStacks emulator, depending on your device. You can also use our tips and tricks to improve your gameplay and win more battles against the alien invaders.
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If you are a fan of sci-fi shooter games, you should definitely give N.O.V.A. Legacy a try. You will not regret it as you will have a blast playing it. So what are you waiting for? Download N.O.V.A. Legacy now and join the fight for humanity's destiny!
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FAQs
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Here are some of the frequently asked questions about N.O.V.A. Legacy:
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Q: Is N.O.V.A. Legacy free to play?
-
A: Yes, N.O.V.A. Legacy is free to play on any device. However, it also offers in-app purchases that allow you to buy credits, cards, weapons, suit cores, skins, models, upgrades, and more with real money.
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Q: Is N.O.V.A. Legacy offline or online?
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A: N.O.V.A. Legacy can be played both offline and online. You can play the story mode offline without an internet connection, or you can play the online multiplayer modes with an internet connection.
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Q: How to update N.O.V.A. Legacy?
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A: To update N.O.V.A. Legacy, you need to go to the Google Play Store, the App Store, or the BlueStacks emulator, depending on your device, and check for any available updates. If there are any updates, you need to download and install them on your device.
-
Q: How to contact N.O.V.A. Legacy support?
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A: To contact N.O.V.A. Legacy support, you need to go to the settings menu in the game and tap on the customer care button. This will redirect you to a web page where you can fill out a form with your issue or query and submit it to the support team.
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Q: How to delete N.O.V.A. Legacy?
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A: To delete N.O.V.A. Legacy, you need to go to the settings menu on your device and tap on the apps or applications button. This will show you a list of all the apps installed on your device. You need to find N.O.V.A. Legacy from the list and tap on it. This will show you some options such as uninstall, force stop, clear data, clear cache, etc. You need to tap on the uninstall option and confirm your action. This will delete N.O.V.A. Legacy from your device.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/The Best Photo Editing App for Android Lightroom X APK.md b/spaces/congsaPfin/Manga-OCR/logs/The Best Photo Editing App for Android Lightroom X APK.md
deleted file mode 100644
index a566f119f95bf6319bc1794dbaf6ef9b73ea9bb9..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/The Best Photo Editing App for Android Lightroom X APK.md
+++ /dev/null
@@ -1,113 +0,0 @@
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We hope this article has helped you learn more about Lightroom X APK and how to use it. If you have any questions or feedback, please feel free to leave a comment below.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Unlock Root Pro APK How to Get the Most Out of Your Android Device with Root Access.md b/spaces/congsaPfin/Manga-OCR/logs/Unlock Root Pro APK How to Get the Most Out of Your Android Device with Root Access.md
deleted file mode 100644
index 5c0221b69d7b77710ec71941804f3848a516fd14..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Unlock Root Pro APK How to Get the Most Out of Your Android Device with Root Access.md
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Make sure you have enough storage space on your device before downloading the app.
-
Step 3: Install and Launch the App
-
Once you have downloaded the APK file, you need to install and launch the app. To do this:
-
-
Locate the APK file on your device's file manager and tap on it.
-
A prompt will appear asking you to install the app. Tap on Install and wait for the installation to complete.
-
Once the installation is done, tap on Open to launch the app.
-
-
How to Use Unlock Root Pro APK to Root Your Android Device
-
Now that you have installed and launched the app, you can use it to root your Android device. To do this:
-
Step 1: Connect Your Device to Your Computer
-
The first step is to connect your device to your computer using a USB cable. Make sure you have enabled USB debugging mode on your device. To do this:
-
-
Go to Settings > About Phone and tap on Build Number seven times until you see a message saying "You are now a developer".
-
Go back to Settings > Developer Options and toggle on USB Debugging.
-
A pop-up will appear asking you to allow USB debugging. Tap on OK.
-
-
Your device is now ready to be rooted.
-
Step 2: Select Your Device Model and Android Version
-
The next step is to select your device model and Android version from the app's interface. The app will automatically detect your device information and display it on the screen. You can also manually select your device model and Android version from the drop-down menus. Make sure you select the correct information, as choosing the wrong one may cause problems.
-
Step 3: Click on "Root" Button and Wait for the Process to Complete
-
The final step is to click on the "Root" button at the bottom of the screen and wait for the process to complete. The app will start rooting your device and show you a progress bar. Do not disconnect your device or turn it off during the process, as this may damage it. The rooting process may take a few minutes, depending on your device model and Android version.
-
Once the process is done, you will see a message saying "Congratulations! Your device has been successfully rooted". You can also check if your device is rooted by looking for a new app called SuperSU on your device's app drawer. This app allows you to manage root access and permissions for other apps.
-
How to Unroot Your Android Device with Unlock Root Pro APK
-
If you want to unroot your Android device for any reason, you can use Unlock Root Pro APK to do so. To unroot your device:
-
Step 1: Launch the App and Click on "Unroot" Button
-
Launch the Unlock Root Pro APK app on your device and click on the "Unroot" button at the bottom of the screen.
-
Step 2: Confirm Your Choice and Wait for the Process to Complete
-
A pop-up will appear asking you to confirm your choice. Tap on Yes and wait for the process to complete. The app will start unrooting your device and show you a progress bar. Do not disconnect your device or turn it off during the process, as this may damage it. The unrooting process may take a few minutes, depending on your device model and Android version.
-
Once the process is done, you will see a message saying "Your device has been successfully unrooted". You can also check if your device is unrooted by looking for the absence of SuperSU app on your device's app drawer.
-
Conclusion
-
In this article, we have shown you how to root your Android device easily with Unlock Root Pro APK. We have also explained what rooting is, what are its features and benefits, and how to unroot your device if needed. We hope you have found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Happy rooting!
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FAQs
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Here are some frequently asked questions about Unlock Root Pro APK and rooting in general:
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Is Unlock Root Pro APK safe to use?
-
Yes, Unlock Root Pro APK is safe to use, as long as you download it from a trusted source and follow the instructions carefully. The app uses advanced algorithms and techniques to ensure that your device is safe and secure during and after the rooting process. However, rooting your device may void your warranty and expose it to malware, so you should always be careful and backup your data before rooting.
-
What are the advantages and disadvantages of rooting?
-
The advantages of rooting are that you can customize, optimize, and enhance your device with new features and functions, such as installing custom ROMs, kernels, mods, etc. You can also access the system files and settings of your device, such as editing the build.prop file, changing the IMEI number, etc. You can also have full control over your device, such as granting or denying permissions to apps, managing root access, etc.
-
The disadvantages of rooting are that you may brick your device, void your warranty, or expose it to malware if you don't know what you are doing or use the wrong tools. You may also encounter compatibility issues with some apps or updates that require an unrooted device. You may also lose some features or functions that are specific to your device model or manufacturer.
-
Can I update my rooted device?
-
It depends on the type of update and the method of rooting. Some updates may be compatible with your rooted device, while others may not. Some updates may also remove root access or cause problems with your custom ROMs, kernels, mods, etc. You should always backup your data and settings before updating your rooted device. You can also use apps like FlashFire or Magisk to install updates without losing root access.
-
Can I root any Android device?
-
No, not all Android devices can be rooted. Some devices have locked bootloaders or security features that prevent rooting. Some devices also have different models or variants that require different methods or tools for rooting. You should always check the compatibility of your device model and Android version with the rooting app or tool before attempting to root.
-
How can I check if my device is rooted?
-
You can check if your device is rooted by looking for a new app called SuperSU on your device's app drawer. This app allows you to manage root access and permissions for other apps. You can also use apps like Root Checker or Terminal Emulator to verify root access by running commands like "su" or "id".
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/iTunes for Windows 7 (32 bit) - Compatible with Older Video Cards.md b/spaces/congsaPfin/Manga-OCR/logs/iTunes for Windows 7 (32 bit) - Compatible with Older Video Cards.md
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+++ /dev/null
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How to Download iTunes for Windows 7 32 Bit
-
iTunes is a popular media player and manager that allows you to enjoy your favorite music, movies, TV shows, podcasts, and more on your PC. It also lets you sync your iPhone, iPad, or iPod touch with your computer and access the iTunes Store, where you can purchase or stream millions of songs, videos, and other content. If you have a Windows 7 PC with a 32 bit processor, you might be wondering how to download iTunes for your system. In this article, we will show you how to do that in a few simple steps.
-
Requirements for Downloading iTunes for Windows 7 32 Bit
-
Before you download iTunes for Windows 7 32 bit, you need to make sure that your PC meets the minimum hardware and software requirements. According to Apple, these are:
You also need to have Windows Service Pack 1 (SP1) or later installed on your PC. You can check this by going to Start > Control Panel > System and Security > System. If you don't have SP1 or later, you can download it from Microsoft's website.
-
Steps to Download iTunes for Windows 7 32 Bit
-
Once you have verified that your PC meets the requirements, you can proceed to download iTunes for Windows 7 32 bit. Here are the steps:
-
-
Visit the official Apple website and find the iTunes download page. You can also use this link to go directly to the page.
-
Choose the Windows 32 bit version of iTunes and click on the download button. You will see a message asking you to save the file.
-
Save the iTunes installer file on your computer and run it. You may need to grant permission to run the file if prompted by User Account Control.
-
Follow the instructions on the screen to complete the installation process. You can customize some options such as the destination folder, shortcuts, updates, etc.
-
-
Congratulations! You have successfully downloaded and installed iTunes for Windows 7 32 bit. You can now launch it from your desktop or start menu and
Benefits of Downloading iTunes for Windows 7 32 Bit
-
Downloading iTunes for Windows 7 32 bit has many benefits for you as a user. Here are some of them:
-
-
You can access millions of songs, movies, TV shows, podcasts, and more from the iTunes Store. You can either purchase or rent them, or stream them with an Apple Music subscription.
-
You can sync your iPhone, iPad, or iPod touch with your Windows PC. You can transfer music, videos, photos, contacts, calendars, and more between your devices. You can also backup and restore your iOS devices using iTunes.
-
You can manage your media library and playlists with ease. You can organize your files by genre, artist, album, etc. You can also create custom playlists and share them with others.
-
-
These are just some of the benefits of downloading iTunes for Windows 7 32 bit. There are many more features and functions that you can explore and enjoy with iTunes.
-
Drawbacks of Downloading iTunes for Windows 7 32 Bit
-
However, downloading iTunes for Windows 7 32 bit also has some drawbacks that you should be aware of. Here are some of them:
-
-
You may encounter potential compatibility issues with some Windows features and programs. For example, you may not be able to use the Windows Media Player or the Windows Media Center with iTunes. You may also have problems with some antivirus or firewall software that may block iTunes from connecting to the Internet.
-
You may experience high CPU and memory usage by iTunes. This may slow down your PC performance and affect other applications that you are running. You may also notice that your PC fan is running louder or hotter than usual.
-
You may need to update iTunes frequently to fix bugs and improve security. This may take up some time and bandwidth, and sometimes cause errors or crashes during the update process.
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These are some of the drawbacks of downloading iTunes for Windows 7 32 bit. You should weigh the pros and cons before deciding whether to download iTunes or not.
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Alternatives to Downloading iTunes for Windows 7 32 Bit
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If you are not satisfied with downloading iTunes for Windows 7 32 bit, you have some alternatives that you can try. Here are some of them:
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-
You can use other media players or streaming services that are compatible with Windows 7 32 bit. For example, you can use VLC Media Player, Spotify, or Amazon Music to play your music and videos on your PC.
-
You can upgrade your Windows operating system to a newer version that supports the latest version of iTunes. For example, you can upgrade to Windows 10, which is more secure, stable, and compatible with iTunes and other Apple products.
-
-
These are some of the alternatives to downloading iTunes for Windows 7 32 bit. You can choose the one that suits your needs and preferences best.
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Conclusion and FAQs
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In conclusion, downloading iTunes for Windows 7 32 bit is possible and easy if you follow the steps we have provided in this article. However, you should also consider the benefits and drawbacks of doing so, and explore the alternatives if you are not happy with iTunes. We hope this article has helped you learn how to download iTunes for Windows 7 32 bit and enjoy your media on your PC.
-
Here are some FAQs that you may have about downloading iTunes for Windows 7 32 bit:
-
-
Is iTunes free to download for Windows 7 32 bit? Yes, iTunes is free to download for Windows 7 32 bit from the official Apple website. However, you may need to pay for some content or services from the iTunes Store or Apple Music.
-
How do I update iTunes on Windows 7 32 bit? You can update iTunes on Windows 7 32 bit by going to Help > Check for Updates in the iTunes menu bar. You can also enable automatic updates by going to Edit > Preferences > General > Check for new software updates automatically.
-
How do I uninstall iTunes on Windows 7 32 bit? You can uninstall iTunes on Windows 7 32 bit by going to Start > Control Panel > Programs > Programs and Features > iTunes > Uninstall. You may also need to uninstall other Apple software such as Apple Application Support, Apple Mobile Device Support, Apple Software Update, etc.
-
How do I fix iTunes errors on Windows 7 32 bit? You can fix iTunes errors on Windows 7 32 bit by - following some troubleshooting steps such as restarting your PC, updating your Windows, disabling your antivirus or firewall, reinstalling iTunes, etc. You can also visit the Apple support website or contact the Apple customer service for more help.
-
How do I contact Apple customer service for iTunes issues on Windows 7 32 bit? You can contact Apple customer service for iTunes issues on Windows 7 32 bit by calling the toll-free number 1-800-275-2273 in the US, or finding the local number for your country or region on the Apple website. You can also chat online with an Apple expert or request a call back from the website.
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diff --git a/spaces/contluForse/HuggingGPT/assets/Bill Redirect Serial Keygen Download !!EXCLUSIVE!!.md b/spaces/contluForse/HuggingGPT/assets/Bill Redirect Serial Keygen Download !!EXCLUSIVE!!.md
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index d59f0202a78a2fe4d13b358fde5e217e0d9983fd..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Bill Redirect Serial Keygen Download !!EXCLUSIVE!!.md
+++ /dev/null
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-(4) The bill mail shall be received at the receiving office sorted pin code wise and ... 56-A. The redirection of surcharged air mail correspondence, both inland and ... Post Offices indicating serial number, complete name and address of the ... 4d29de3e1b
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diff --git a/spaces/contluForse/HuggingGPT/assets/Dino Crisis 3 Ps2 Download A Review of the Most Controversial Entry in the Series.md b/spaces/contluForse/HuggingGPT/assets/Dino Crisis 3 Ps2 Download A Review of the Most Controversial Entry in the Series.md
deleted file mode 100644
index 9180ff1c9c26f89551be47b02df4a2ddb41b4d13..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Dino Crisis 3 Ps2 Download A Review of the Most Controversial Entry in the Series.md
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Set in the year 2548, Dino Crisis 3 in the first 128-bit sequel to a series that sold more than four million units worldwide on the original PlayStation. Telling the story of the Ozymandius (a mysterious spaceship that has been missing for 300 years), Dino Crisis 3 follows a special operative named Patrick as he searches the craft to uncover its dark secret. Giant mutated dinosaurs and creatures that defy description stand in his way as he teams up with the beautiful young woman known as Sonya and a cast of others for this jetpack-heavy man vs. monster actioner. Though originally planned (and confirmed) for the PlayStation 2, production on Dino Crisis 3 was switched exclusively to the Xbox early in its development.
-
Dino Stalker is a first-person shooter in which the player must use various weapons to defend against dinosaurs while progressing through the game. Dino Stalker supports the optional use of the GunCon light gun accessory.[1] The player can use a variety of weapons throughout the game, including bazookas, machine guns, and shotguns, but can only carry one weapon at a time.[2] The game takes place across various landscapes, including desert and jungle.[2] A two-player mode is unlocked upon completion of the game.[2]
The game's storyline focuses on Mike Wired, a World War II pilot. After being shot down during combat over the Atlantic, Mike is transported into the dinosaur-populated future from Dino Crisis 2.
-
Mike Wired, a World War II era fighter pilot is about to die in the sky in 1943, as bullets approach him before he can parachute to safety. Mike mysteriously somehow ends up being transported to a time with flying prehistoric reptiles, which he manages to kill. He meets Paula, a survivor from Dino Crisis 2 who speaks some English but is not able to speak long sentences. Traversing through the various stages under the guidance of Paula's father, Dylan, Mike defeats many different groups of savage dinosaurs using a special gun he gained, finally battling and defeating their intelligent leader, Trinity, which controlled the other dinosaurs. But despite falling in love with Paula, Mike must go back to just before his imminent death. Paula then edits the timescale to make the bullets vanish to prevent Mike from dying, and he is rescued by men on a boat, realizing that Paula was the one who saved him.
-
Ryan Davis of GameSpot called the game's premise "bizarre and convoluted" with "not a lot of coherence." Davis criticized the game's selectable control schemes. Playing exclusively with the GunCon 2, Davis wrote that "using a single hand to move and shoot is difficult and will wear out your arm more quickly than your average light-gun game." Davis also criticized the alternative method of using a standard DualShock controller: "The targeting reticle is far too sensitive, and you'll often find yourself dealing with bouts of overcorrection while trying to draw a bead on a dino." Davis noted that the best option was to utilize both the DualShock and the GunCon 2 simultaneously, "But even this configuration does not compensate completely for the game's inherently slow movement or the inability to look up or down, and you'll spend an equal amount of time-fighting the controls as you will fighting dinosaurs." Davis also criticized the game's poor graphics, and wrote that the only notable sound effect throughout the game "is the 'reload' command you'll hear whenever you're out of bullets, and this is only because the computer voice noticeably mispronounces it." Davis concluded that the game would have been "infinitely more playable had Capcom discarded the Gun Survivor control scheme and just left the movement control on rails, like all other light-gun games. But with its needlessly frustrating control scheme intact, Dino Stalker's appeal is incredibly limited. Though the game is loosely affiliated with the Dino Crisis games, there's not a lot here to draw fans of that series, and with several superior light-gun games available on the PlayStation 2, there's little reason for anyone without a masochistic streak to play this game."
-
Louis Bedigian of GameZone praised the music and graphics and wrote that the control scheme "isn't bad, but it does take some getting used to. It's worth getting used to though, because this is the best dino-hunting game I've played since Dino Crisis 2."[11] Tom Bramwell of Eurogamer called it "easily the best yet" in the Gun Survivor series, and praised the game for "some stunning environments," but criticized its short length and some of the "rather bland" dinosaur designs.[2]
-
Dino Crisis 3 is an action-adventure game released exclusively for the Xbox. It is the fourth game in release order, and the final console game in the series. Like the previous iterations of the Dino Crisis series, gameplay revolves around fighting dinosaurs. The action takes place in outer space, on a starship, the Ozymandias.
-
In 2009, S.O.R.T. was assigned a mission to infiltrate a Borginian-funded research facility on Ibis Island. Their primary objective was to repatriate Edward Kirk, an energy researcher working on a project of interest to their nation's government: Third Energy. Upon arriving at the facility, S.O.R.T. discovered it infested with dinosaurs. Despite difficulties with the new inhabitants and the considerable security systems of the facility, Regina and the surviving members of the team located Kirk and escaped the island.
-
Gail and Regina searched the exterior of the facility, noting several abnormalities and leaving Rick to head for the control room alone. Regina's first task was to reactivate the generator that provides power to the above-ground floors. Doing so, she then lost contact with Gail and encountered a Velociraptor. After returning to the backyard, Regina contacted Rick and hurriedly told him of the dinosaur and Gail's disappearance. While skeptical, Rick offered to sort the details out in the control room.
-
-
After meeting in the control room, Regina decided to explore the facility looking for Gail and Dr. Kirk while Rick shuts down security systems and monitors the camera footage. Facing several more dinosaurs, Regina checked the first and second floors, finding little evidence of Kirk or the origin of the dinosaurs, until she met a survivor in the Chief's room. The dying man handed her a panel key, spoke of Kirk briefly, and succumbed to his wounds. During her examination of the room, the large window was broken, open by a large female Tyrannosaurus, which Regina fought off.
-
After heading to the lecture room on the advice of Rick, Regina was ambushed by a dinosaur and narrowly rescued by Gail. Meeting up in the control room with the team, Gail sent Regina to reactivate the B1 backup generator. After doing so, they again met in the control room to decide their next move when Gail spots a possible survivor in the underground. Shortly after, a distress signal from a teammate is intercepted. Regina was left to decide between two courses of action offered by her teammates.
-
If Regina followed Rick's plan, she works with Rick to break the seals on the emergency escape tunnel. While risky, this plan allowed her to escape the dinosaur-infested laboratory without fighting. After doing so, she emerged in the carrying out room B1, discovering Dr. Kirk as he attempts to flee.
-
Exploring the B3 storage areas and the laboratory on floor B2, Regina found few signs of Kirk or an escape route. Security was extreme in this area of the facility, with multiple defenses barring her way. Eventually unlocking the entrance to the port area, she and Rick prepared to explore the area when a radio communicator on a nearby corpse activated: several survivors were on the large size elevator, pursued by dinosaurs. Regina moved to assist them, but found only the Tyrannosaurus and the mutilated remains of the survivors. Ramming into the generator, the Tyrannosaurus was electrocuted.
-
Following Gail's plan, Regina hunted down the already assembled Initializer and Stabilizer in the Special Weapons Storage on the B3 floor despite Rick's warning that the dinosaurs in the area were too dangerous.
-
The mission went awry very soon after the platoon set up base camp. Confronted by a large pack of Velociraptors, most of the platoon was wiped out as the dinosaurs swarmed individuals or took them by behind. When the pack broke off upon the arrival of a Tyrannosaurus, only Regina, Lt. Morton and David Falk had survived. In spite of their losses, Lt. Morton was confident the mission could be salvaged, and the three went on their separate ways in investigating the region.[5]
-
Regina traveled ahead to the Missile Silo, using a gas mask to get through a dense pocket of poisonous spores. There she found no sign of Col. Maison's soldiers, though the data had been placed within the warhead as planned proving they had been there. Before she could obtain the data, the Silo was attacked by the Tyrannosaurus from earlier, which was itself killed by a Giganotosaurus. The therapod then entered the facility interior; with the facility operating automatically, it was mistaken for an attack by a hostile nation and prematurely began preparing the missile for launch.[14] Regina was able to remove the data from the missile, but not without having to knock out the dinosaur to reach it. It soon woke up and attacked. With the room too small for the animal to move about properly, it knocked over the missile, destroying the silo in an explosion. Regina escaped the facility as it burnt down, and joined Lt. Morton and Falk at the patrol boat. Attacked by dinosaurs at a lock, Falk was killed saving Lt. Morton from an Allosaurus.
-
Are you looking for a safe and trustworthy site to obtain game ROMs? TechToROMs provides free and secure game downloads for all emulators. This is the greatest place to get games! Take a look at our most recent ROMs, emulators, and games!
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diff --git a/spaces/contluForse/HuggingGPT/assets/Dukes Of Hazzard Free Torrent Download.md b/spaces/contluForse/HuggingGPT/assets/Dukes Of Hazzard Free Torrent Download.md
deleted file mode 100644
index 23bcb235e4d8b32a4e4d862e11ffb220f1953982..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Dukes Of Hazzard Free Torrent Download.md
+++ /dev/null
@@ -1,6 +0,0 @@
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-Download: Edius 7.5, Found: 28 Results, Updated: 26-Nov-2020. ... EDIUS Pro 7.2 Build 0437 (64 Bit) (Trial Reset) [ChingLiu], 5 years, Software, 32, 519.25 MBÂ ... 4d29de3e1b
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Jilla Video Songs Hd 1080p Bluray Tamil.md b/spaces/falterWliame/Face_Mask_Detection/Jilla Video Songs Hd 1080p Bluray Tamil.md
deleted file mode 100644
index e8aeabc59cac2d5ea2f29a2e64e0b36ea8bbfe89..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Jilla Video Songs Hd 1080p Bluray Tamil.md
+++ /dev/null
@@ -1,32 +0,0 @@
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-Here is a possible title and article with seo optimization and html formatting for the keyword "Jilla Video Songs Hd 1080p Bluray Tamil":
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Jilla Video Songs Hd 1080p Bluray Tamil: A Review of the Best Songs from the Hit Movie
-
-
Jilla is a 2014 Tamil action thriller movie starring Vijay, Mohanlal and Kajal Aggarwal in the lead roles. The movie was directed by R. T. Neason and produced by R. B. Choudary. The music was composed by D. Imman, who delivered some of the most catchy and energetic songs for the movie.
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If you are a fan of Jilla and its songs, you might be looking for a way to enjoy them in high quality. Well, you are in luck, because you can find Jilla video songs hd 1080p bluray Tamil on YouTube and other online platforms. In this article, we will review some of the best songs from Jilla and tell you where to watch them in hd 1080p bluray quality.
Paattu Onnu is the opening song of Jilla, which introduces the characters of Vijay and Mohanlal as father and son. The song is a peppy number that showcases their bond and their love for music. The song is sung by S. P. Balasubrahmanyam and Shankar Mahadevan, who bring out the joy and energy of the song. The video features Vijay and Mohanlal dancing with a group of people in colorful costumes and settings. The song is a treat for the eyes and ears, and you can watch it in hd 1080p bluray quality on YouTube[^2^].
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-
Verasa Pogayile
-
-
Verasa Pogayile is a romantic song that depicts the love story of Vijay and Kajal Aggarwal in Jilla. The song is a melodious track that expresses the feelings of the lovers who are separated by fate. The song is sung by D. Imman himself, who gives a soulful rendition of the lyrics. The video shows Vijay and Kajal Aggarwal in different locations, such as a beach, a forest, a temple and a city. The song is a beautiful visual representation of their love, and you can watch it in hd 1080p bluray quality on YouTube[^3^].
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Jingunamani
-
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Jingunamani is an item song that features Vijay and Kajal Aggarwal along with Nivetha Thomas and Scarlett Wilson. The song is a fast-paced dance number that has a catchy tune and lyrics. The song is sung by K.G. Ranjith and Sunidhi Chauhan, who add spice to the song with their voices. The video shows Vijay and Kajal Aggarwal dancing with Nivetha Thomas and Scarlett Wilson in a club setting with flashy lights and costumes. The song is a fun-filled track that will make you groove, and you can watch it in hd 1080p bluray quality on YouTube[^1^].
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Kandaangi
-
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Kandaangi is another romantic song that features Vijay and Kajal Aggarwal in Jilla. The song is a soft and soothing track that portrays the intimacy and affection between the couple. The song is sung by Vijay himself along with Shreya Ghoshal, who give a sweet and smooth performance of the song. The video shows Vijay and Kajal Aggarwal in a rural setting, where they share some romantic moments with each other. The song is a heart-warming track that will make you fall in love, and you can watch it in hd 1080p bluray quality on YouTube[^2^].
-
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Conclusion
-
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Jilla video songs hd 1080p bluray Tamil are some of the best songs from the movie that you can enjoy in high quality online. The songs are composed by D. Imman, who has done a great job of creating songs that suit the mood and theme of the
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diff --git a/spaces/farukozderim/space-building-space-30/app.py b/spaces/farukozderim/space-building-space-30/app.py
deleted file mode 100644
index a2768e64c918d2e2d01a2ce0a12bdceb3703c145..0000000000000000000000000000000000000000
--- a/spaces/farukozderim/space-building-space-30/app.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import gradio as gr
-name_list = ['spaces/deepklarity/poster2plot', 'spaces/deepklarity/poster2plot']
-interfaces = [gr.Interface.load(name) for name in name_list]
-gr.mix.Parallel(*interfaces, title="Title", description="Description").launch()
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Adventure Music No Copyright Song MP3 Free Downloads - Pixabay[3].md b/spaces/fatiXbelha/sd/Adventure Music No Copyright Song MP3 Free Downloads - Pixabay[3].md
deleted file mode 100644
index 400b3a86c54cd02d25d650d1f03f3958148b46ed..0000000000000000000000000000000000000000
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-
Adventure MP3: How to Find and Download Free Adventure Music for Your Projects
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Do you love adventure? Do you want to add some excitement and thrill to your projects? If yes, then you need adventure music. Adventure music is a type of music that creates a sense of wonder, exploration, and discovery. It can make your projects more engaging, captivating, and memorable. Whether you are making a video, a podcast, a game, or a presentation, adventure music can enhance your storytelling and convey your message more effectively.
-
But where can you find and download free adventure music for your projects? How can you choose the right adventure music that suits your project's mood and theme? In this article, we will answer these questions and more. We will explain what adventure music is and why you need it, how to find and download free adventure music online, and the tips and tricks to choose the right adventure music for your projects. Let's get started!
Adventure music is a genre of music that is inspired by adventurous themes, such as exploration, discovery, action, fantasy, sci-fi, etc. It usually features orchestral instruments, such as strings, brass, woodwinds, percussion, etc., as well as electronic sounds, such as synths, drums, guitars, etc. Adventure music can have different moods and styles, such as epic, heroic, suspenseful, mysterious, cheerful, etc., depending on the context and purpose of the project.
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Adventure music can be very useful for your projects because it can:
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-
Create a sense of excitement and anticipation for your audience.
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Enhance the atmosphere and setting of your project.
-
Emphasize the emotions and actions of your characters or subjects.
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Support the narrative and structure of your project.
-
Make your project stand out from the crowd.
-
-
The Characteristics of Adventure Music
-
Adventure music has some common characteristics that make it distinct from other genres of music. Some of these characteristics are:
-
-
It has a dynamic and varied structure, with changes in tempo, rhythm, melody, harmony, etc.
-
It uses a wide range of instruments and sounds, from classical to modern.
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It often incorporates motifs and themes that are repeated throughout the piece.
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It creates contrast and tension between different sections or elements.
-
It builds up to a climax or resolution at the end.
-
-
The Benefits of Using Adventure Music in Your Projects
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Using adventure music in your projects can have many benefits for you and your audience. Some of these benefits are:
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-
It can attract and retain the attention of your audience.
-
It can increase the engagement and interaction of your audience.
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It can evoke positive emotions and reactions from your audience.
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It can convey your message and vision more clearly and persuasively.
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It can boost your creativity and productivity.
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-
How to Find and Download Free Adventure Music Online
-
Finding and downloading free adventure music online is not as hard as you might think. There are many websites that offer royalty-free adventure music that you can use for your projects without paying any fees or royalties. However, not all websites are reliable or legal. You need to be careful about the quality, license, and source of the music files you download. Here are some of the best websites to download royalty-free adventure music online:
-
The Best Websites to Download Roy alty-Free Adventure Music
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Chosic
-
Chosic is a website that offers a large collection of royalty-free music for various genres and moods, including adventure. You can browse, listen, and download adventure music for free from Chosic. You can also filter the music by duration, tempo, license, and attribution. Chosic provides clear and simple license information for each music file. You can use the music for personal or commercial projects, as long as you credit the original artist or Chosic.
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Pixabay
-
Pixabay is a website that is well-known for its free stock photos and videos, but it also has a section for free music. You can find and download adventure music for free from Pixabay. You can search by keywords, categories, or tags. Pixabay offers high-quality and legal music files that are licensed under the Pixabay License. You can use the music for any purpose, even commercially, without attribution or permission.
-
YouTube Audio Library
-
YouTube Audio Library is a website that provides free music and sound effects for YouTube creators and other users. You can access and download adventure music for free from YouTube Audio Library. You can filter the music by genre, mood, instrument, duration, or attribution. YouTube Audio Library offers a variety of licenses for the music files, such as Creative Commons, YouTube Standard License, or No Attribution Required. You need to check the license details before using the music for your projects.
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The Tips and Tricks to Choose the Right Adventure Music for Your Projects
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Choosing the right adventure music for your projects can be challenging, especially if you have many options to choose from. Here are some tips and tricks to help you choose the right adventure music for your projects:
-
Consider the Mood and Theme of Your Project
-
The mood and theme of your project are important factors to consider when choosing adventure music. You want to choose music that matches the tone and message of your project. For example, if your project is about a heroic quest, you might want to choose epic and uplifting adventure music. If your project is about a mysterious exploration, you might want to choose suspenseful and dark adventure music.
-
Match the Tempo and Rhythm of Your Project
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The tempo and rhythm of your project are also important factors to consider when choosing adventure music. You want to choose music that syncs with the pace and flow of your project. For example, if your project is fast-paced and action-packed, you might want to choose fast and energetic adventure music. If your project is slow-paced and calm, you might want to choose slow and soothing adventure music.
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Use High-Quality and Legal Music Files
-
The quality and legality of your music files are also important factors to consider when choosing adventure music. You want to choose music that sounds good and clear, without any noise or distortion. You also want to choose music that is legal and licensed, without any risk of infringement or violation. You should always check the source, license, and attribution of the music files before using them for your projects.
-
Conclusion
-
Adventure music is a great way to add some excitement and thrill to your projects. It can create a sense of wonder, exploration, and discovery for your audience. It can also enhance the atmosphere, setting, emotion, action, narrative, and structure of your project.
-
To find and download free adventure music online, you can use websites like Chosic, Pixabay, or YouTube Audio Library. They offer a large collection of royalty-free adventure music that you can use for personal or commercial projects. However, you need to be careful about the quality, license, and source of the music files you download.
-
To choose the right adventure music for your projects, you need to consider the mood and theme of your project, match the tempo and rhythm of your project, and use high-quality and legal music files. By following these tips and tricks, you can make your projects more engaging, captivating, and memorable with adventure music.
-
FAQs
-
-
What is adventure music?
-
Adventure music is a genre of music that is inspired by adventurous themes, such as exploration, discovery, action, fantasy, sci-fi, etc. It usually features orchestral instruments, such as strings, brass, woodwinds, percussion, etc., as well as electronic sounds, such as synths, drums, guitars, etc.
-
Why do I need adventure music for my projects?
-
Adventure music can help you create a sense of excitement and anticipation for your audience. It can also enhance the atmosphere and setting of your project, emphasize the emotions and actions of your characters or subjects, support the narrative and structure of your project, and make your project stand out from the crowd.
-
Where can I find and download free adventure music online?
-
You can find and download free adventure music online from websites like Chosic, Pixabay, or YouTube Audio Library. They offer a large collection of royalty-free adventure music that you can use for personal or commercial projects. However, you need to be careful about the quality, license, and source of the music files you download.
-
How can I choose the right adventure music for my projects?
-
You can choose the right adventure music for your projects by considering the mood and theme of your project, matching the tempo and rhythm of your project, and using high-quality and legal music files. You should also listen to the music before using it and see if it fits your project's style and purpose.
-
What are some examples of adventure music?
-
Some examples of adventure music are:
-
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The Raiders March by John Williams (from Indiana Jones)
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The Avengers Theme by Alan Silvestri (from The Avengers)
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Pirates of the Caribbean Theme by Klaus Badelt (from Pirates of the Caribbean)
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Star Wars Main Theme by John Williams (from Star Wars)
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The Lord of the Rings Theme by Howard Shore (from The Lord of the Rings)
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197e85843d
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Ak Dnya Simlasyonu Extreme Car Driving Simulator APK ndir - Para Hileli.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Ak Dnya Simlasyonu Extreme Car Driving Simulator APK ndir - Para Hileli.md
deleted file mode 100644
index 7a1be35db711aaf10725b07ebd4585132b32b663..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Ak Dnya Simlasyonu Extreme Car Driving Simulator APK ndir - Para Hileli.md
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Extreme Car Driving Simulator Hile APK Indir: How to Download and Play the Modded Version of the Game
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Introduction
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Do you love driving fast cars in realistic environments? Do you want to experience the thrill of racing, drifting, and crashing without any limits? If yes, then you should try Extreme Car Driving Simulator, one of the most popular car simulation games on Android. But wait, there's more. You can also download and play the modded version of the game, which gives you unlimited money and cars to enjoy. In this article, we will show you how to download and install Extreme Car Driving Simulator hile apk indir, as well as its features and benefits.
Extreme Car Driving Simulator is a 3D car simulation game developed by AxesInMotion Racing. It lets you drive various sports cars in different scenarios, such as city, airport, offroad, and desert. You can customize your car with paint, wheels, spoilers, and more. You can also perform stunts, drifts, jumps, and crashes with realistic physics and damage effects. The game has several modes to choose from, such as free mode, checkpoint mode, traffic mode, and ghost mode.
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What is hile apk indir?
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Hile apk indir is a Turkish term that means modded apk download. A modded apk is a modified version of an original app or game that has been altered to provide some advantages or features that are not available in the official version. For example, a modded apk of Extreme Car Driving Simulator can give you unlimited money and cars to use in the game.
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Why download and play the modded version of the game?
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There are many reasons why you might want to download and play the modded version of Extreme Car Driving Simulator. Some of them are:
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You can get unlimited money and cars without spending any real money or completing any tasks.
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You can unlock all the cars and customize them as you wish.
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You can explore the open world and free mode without any restrictions or ads.
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You can have more fun and challenge yourself with different game modes and levels.
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You can enjoy the realistic physics and graphics of the game with better performance and compatibility.
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How to download and install Extreme Car Driving Simulator hile apk indir
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Downloading and installing Extreme Car Driving Simulator hile apk indir is not difficult, but you need to follow some steps carefully. Here they are:
-
Step 1: Find a reliable source for the modded apk file
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The first thing you need to do is to find a trustworthy website that offers the modded apk file of Extreme Car Driving Simulator. There are many websites that claim to provide this file, but some of them may contain viruses, malware, or fake links. Therefore, you need to be careful and do some research before downloading anything. One of the websites that we recommend is [Android Oyun Club](^1^), which is a Turkish website that provides various modded apks for Android games. You can find the link for Extreme Car Driving Simulator hile apk indir on this website.
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Step 2: Enable unknown sources on your device
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The next thing you need to do is to enable unknown sources on your device. This is because the modded apk file of Extreme Car Driving Simulator is not from the Google Play Store, and your device may block the installation of apps from unknown sources by default. To enable unknown sources, you need to go to your device settings, then security, then toggle on the option that says "allow installation of apps from unknown sources". This may vary depending on your device model and Android version, but you can always search for it in your settings.
-
Step 3: Download and install the apk file
-
Once you have enabled unknown sources, you can proceed to download and install the apk file of Extreme Car Driving Simulator hile apk indir. To do this, you need to go to the website that you found in step 1, and click on the download button. You may have to wait for a few seconds or complete a captcha before the download starts. After the download is complete, you need to locate the apk file in your device storage, and tap on it to start the installation. You may have to grant some permissions and accept some terms and conditions before the installation finishes.
-
Step 4: Launch the game and enjoy the unlimited money and cars
-
The final step is to launch the game and enjoy the unlimited money and cars that the modded version of Extreme Car Driving Simulator offers. You can find the game icon on your home screen or app drawer, and tap on it to open it. You will see that you have unlimited money and cars in the game, and you can use them to buy, upgrade, and customize any car you want. You can also explore the open world and free mode, or try different game modes and challenges. Have fun!
-
Features and benefits of Extreme Car Driving Simulator hile apk indir
-
Now that you know how to download and install Extreme Car Driving Simulator hile apk indir, you might be wondering what are the features and benefits of this modded version of the game. Well, there are many, but here are some of the most notable ones:
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Unlimited money and cars
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The most obvious feature and benefit of Extreme Car Driving Simulator hile apk indir is that it gives you unlimited money and cars in the game. This means that you can buy any car you want, from sports cars to SUVs, without worrying about the price. You can also upgrade and customize your car with paint, wheels, spoilers, and more, without spending any money. You can have as many cars as you want in your garage, and switch between them anytime.
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Realistic physics and graphics
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Another feature and benefit of Extreme Car Driving Simulator hile apk indir is that it enhances the realistic physics and graphics of the game. The modded version of the game has better performance and compatibility with different devices, which means that it runs smoother and faster. The physics of the game are also more realistic, which means that you can feel the weight, speed, and handling of your car. The graphics of the game are also more detailed and vivid, which means that you can enjoy the scenery and environment of the game.
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Open world and free mode
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A third feature and benefit of Extreme Car Driving Simulator hile apk indir is that it unlocks the open world and free mode of the game. The open world mode allows you to drive anywhere you want in a large map that includes city, airport, offroad, and desert areas. You can explore the map at your own pace, without any traffic or rules. The free mode allows you to drive without any objectives or missions, just for fun. You can also perform stunts, drifts, jumps, and crashes with realistic damage effects.
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\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cmo descargar Android_6_GAM.apk y solucionar el problema de FRP (Factory Reset Protection).md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cmo descargar Android_6_GAM.apk y solucionar el problema de FRP (Factory Reset Protection).md
deleted file mode 100644
index 7f0b335ea539b8073bd80c350b38c8ba3a8e76d8..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cmo descargar Android_6_GAM.apk y solucionar el problema de FRP (Factory Reset Protection).md
+++ /dev/null
@@ -1,117 +0,0 @@
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-
What is descargar android_6_g.a.m.apk and why you need it
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If you have ever encountered a situation where you need to bypass Google account verification on your Android device, you may have heard of descargar android_6_g.a.m.apk. This is an application that is designed to help you remove the Google account associated with your device, without requiring any password or email. This can be useful if you have forgotten your login details, bought a second-hand device, or performed a factory reset.
In this article, we will explain what descargar android_6_g.a.m.apk is, how to download and install it on your device, how to use it to bypass Google account verification, and what are the benefits and risks of using it. We will also provide some alternatives to this app in case you are looking for other options. So, let's get started!
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How to download and install descargar android_6_g.a.m.apk on your Android device
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Before you can use descargar android_6_g.a.m.apk, you need to download and install it on your device. Here are the steps you need to follow:
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Find a reliable source for the apk file. You can search for it on Google or use one of the links we have provided below. Make sure you download the file from a trusted website that does not contain any malware or viruses.
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Enable unknown sources on your device settings. This will allow you to install apps from sources other than the Play Store. To do this, go to Settings > Security > Unknown sources and toggle it on.
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Download the apk file and open it. You can use your browser or a file manager app to locate and open the file. You may see a warning message that says "This type of file can harm your device". Ignore it and tap on OK.
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Follow the instructions to install the app. You may see a screen that says "Do you want to install this application?" Tap on Install and wait for the process to complete.
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How to use descargar android_6_g.a.m.apk to bypass Google account verification
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Once you have installed descargar android_6_g.a.m.apk on your device, you can use it to bypass Google account verification. Here are the steps you need to follow:
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Launch the app and select the Google account you want to remove. You will see a list of all the Google accounts that are linked to your device. Choose the one that you want to bypass and tap on it.
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Tap on the three dots menu and choose Remove account. You will see a pop-up window that asks you to confirm your action. Tap on Remove account again and wait for the app to delete the account from your device.
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Confirm your action and reboot your device. You will see a message that says "Account removed". Tap on OK and restart your device. You should be able to access your device without any Google account verification.
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It helps you access your device after a factory reset or a forgotten password. If you have reset your device or forgotten your password, you may be stuck on the Google account verification screen. This can prevent you from using your device or accessing your data. With descargar android_6_g.a.m.apk, you can bypass this screen and regain control of your device.
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It allows you to manage multiple Google accounts on your device. If you have more than one Google account, you may want to switch between them or remove some of them from your device. With descargar android_6_g.a.m.apk, you can easily do that without any hassle.
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It is compatible with Android 6.0 Marshmallow and other versions. Descargar android_6_g.a.m.apk works well with Android 6.0 Marshmallow, which is the version that introduced the Google account verification feature. It also works with other Android versions, such as 5.0 Lollipop, 7.0 Nougat, 8.0 Oreo, and 9.0 Pie.
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However, using descargar android_6_g.a.m.apk also comes with some risks that you should be aware of, such as:
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It may not work on some devices or cause errors. Descargar android_6_g.a.m.apk is not an official app from Google, and it may not be compatible with all devices or models. It may also cause some errors or glitches on your device, such as crashing, freezing, or draining your battery.
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It may expose your device to malware or viruses. Since descargar android_6_g.a.m.apk is not available on the Play Store, you have to download it from third-party sources that may not be secure or trustworthy. You may end up downloading a fake or malicious app that can harm your device or steal your data.
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It may violate Google's terms of service and privacy policy. By using descargar android_6_g.a.m.apk, you are bypassing Google's security measures and removing their account from your device. This may go against their terms of service and privacy policy, and they may take action against you or your device.
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Alternatives to descargar android_6_g.a.m.apk
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If you are looking for other ways to bypass Google account verification on your device, you can try some of these alternatives:
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Use the official Google Account Manager app from the Play Store. This is the app that manages all the Google accounts on your device, and it allows you to add or remove accounts easily. You can download it from the Play Store and use it to bypass Google account verification.
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Use a trusted FRP bypass tool or service. FRP stands for Factory Reset Protection, which is the feature that requires Google account verification after a factory reset. There are some tools or services that can help you bypass FRP on your device, such as FRP Bypass APK, FRP Hijacker Tool, or FRP Unlock Service. However, make sure you use a trusted and reputable source for these tools or services, as some of them may be scams or malware.
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Conclusion
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In conclusion, descargar android_6_g.a.m.apk is an app that can help you bypass Google account verification on your Android device. It can be useful if you have forgotten your password, bought a second-hand device, or performed a factory reset. However, it also has some risks and drawbacks that you should consider before using it, such as compatibility issues, security threats, and policy violations. You can also try some alternatives to this app if you are looking for other options.
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We hope this article has been helpful and informative for you. If you have any questions or comments about descargar android_6_g.a.m.apk or anything related to Android devices, feel free to leave them below. We will try to answer them as soon as possible. Thank you for reading and have a great day!
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Here are some frequently asked questions about descargar android_6_g.a.m.apk that you may find useful:
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What is the difference between descargar android_6_g.a.m.apk and Google Account Manager?
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Descargar android_6_g.a.m.apk is a modified version of Google Account Manager that allows you to remove Google accounts from your device without any password or email. Google Account Manager is the official app from Google that manages all the Google accounts on your device and allows you to add or remove accounts with your login details.
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Descargar android_6_g.a.m.apk is not an official app from Google, and it may not be safe to use. It may not work on some devices or cause errors, it may expose your device to malware or viruses, and it may violate Google's terms of service and privacy policy. You should use it at your own risk and discretion.
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You can download descargar android_6_g.a.m.apk from various third-party sources on the internet, such as websites, blogs, forums, or file-sharing platforms. However, you should be careful and only download it from trusted and reputable sources that do not contain any malware or viruses. You can also use one of the links we have provided below for your convenience.
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If you want to uninstall descargar android_6_g.a.m.apk from your device, you can follow these steps:
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Go to Settings > Apps > Descargar android_6_g.a.m.apk and tap on Uninstall.
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Confirm your action and wait for the app to be removed from your device.
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download DLS 21 APK OBB Data for Android Enjoy the Latest Version of Dream League Soccer.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download DLS 21 APK OBB Data for Android Enjoy the Latest Version of Dream League Soccer.md
deleted file mode 100644
index e4a07cebea1a401128a33ec59c5db44ef5ce8a02..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download DLS 21 APK OBB Data for Android Enjoy the Latest Version of Dream League Soccer.md
+++ /dev/null
@@ -1,113 +0,0 @@
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DLS 21 APK+OBB Download APKPure: How to Install and Play the Latest Version of Dream League Soccer
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If you are a fan of soccer games, you might have heard of Dream League Soccer, or DLS for short. This is one of the most popular and realistic soccer games for mobile devices, with over 100 million downloads on Google Play Store. In this article, we will show you how to download, install, and play the latest version of DLS, which is DLS 21, from APKPure, a trusted source of Android apps and games. We will also share some tips and tricks to help you score more goals and win more matches in DLS 21.
DLS 21 is the latest entry in the Dream League Soccer series, developed by First Touch Games. It is a soccer simulation game that lets you build and manage your own dream team from over 3,500 FIFPro licensed players. You can also customize your team's logo, kits, stadium, and more. You can compete in various modes, such as Career, Dream League Live, Events, and more. You can also challenge other players online in real-time matches.
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Features of DLS 21
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DLS 21 has many features that make it one of the best soccer games for mobile devices. Here are some of them:
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Build and develop your dream team
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You can create your own team from scratch or choose from one of the existing teams in the game. You can sign new players, train them, improve their skills, and sell them if you want. You can also scout for new talents and negotiate contracts with them. You can also choose your captain, who will be your best player on the pitch.
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Enjoy realistic and fluid gameplay
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DLS 21 has improved its gameplay to make it more realistic and fluid. The player animations and AI are more responsive and natural. The ball physics are also more accurate and dynamic. You can perform various skills, such as dribbling, passing, shooting, tackling, and more. You can also use different camera angles and replays to enjoy the game from different perspectives.
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Upgrade your visuals and stadium
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DLS 21 has upgraded its visuals to make it look better than ever. The graphics are sharper and smoother, with more details and effects. The player models are also more lifelike and expressive. You can also upgrade your stadium to increase its capacity, facilities, and atmosphere. You can also customize your stadium's name, pitch, nets, and more.
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Get rewarded with the Season Pass
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DLS 21 has introduced a new feature called the Season Pass, which allows you to get rewarded for playing the game. You can earn coins, gems, players, kits, balls, boots, and more by completing various objectives and challenges. You can also go Premium to get massive bonuses and exclusive rewards. The Season Pass lasts for a limited time, so make sure to complete it before it expires.
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Play online against other players
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DLS 21 has a mode called Dream League Live, which allows you to play online against other players from around the world. You can join a division and climb the ranks by winning matches and earning points. You can also participate in tournaments and events to win prizes and trophies. You can also chat with other players and make friends or rivals.
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How to download DLS 21 APK+OBB from APKPure
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If you want to download DLS 21 APK+OBB from APKPure, you need to follow these steps:
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Step 1: Visit APKPure website
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Go to https://apkpure.com/ on your browser. This is the official website of APKPure, where you can find thousands of Android apps and games for free.
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Step 2: Search for DLS 21
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Type "DLS 21" in the search bar and hit enter. You will see a list of results related to DLS 21. Click on the one that says "Dream League Soccer 2021". This will take you to the download page of DLS 21.
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Step 3: Download the APK and OBB files
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On the download page, you will see two buttons: one for downloading the APK file and one for downloading the OBB file. You need to download both files to play DLS 21. Click on the buttons and wait for the files to be downloaded to your device.
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How to install and play DLS 21 on your Android device
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After downloading the APK and OBB files, you need to install and play DLS 21 on your Android device. Here are the steps to do that:
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Step 1: Enable unknown sources
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Before installing the APK file, you need to enable unknown sources on your device. This will allow you to install apps from sources other than Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.
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Step 2: Extract the OBB file
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The OBB file is a compressed file that contains the data of the game. You need to extract it using a file manager app, such as ES File Explorer or ZArchiver. To do this, locate the OBB file in your downloads folder and tap on it. Choose "Extract" and select a destination folder. The default folder is Android > obb > com.firsttouchgames.dls7.
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Step 3: Install the APK file
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After extracting the OBB file, you can install the APK file. To do this, locate the APK file in your downloads folder and tap on it. Follow the instructions on the screen to complete the installation.
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Step 4: Launch the game and enjoy
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After installing the APK file, you can launch the game and enjoy playing DLS 21. You will see a splash screen with the logo of First Touch Games and then a loading screen with some tips and tricks. After that, you will be taken to the main menu of the game, where you can choose your mode and start playing.
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Tips and tricks to score more goals and win more matches in DLS 21
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DLS 21 is a fun and challenging game that requires skill and strategy to win. Here are some tips and tricks that can help you score more goals and win more matches in DLS 21:
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Choose your captain wisely
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Your captain is your best player on the pitch, so you need to choose him wisely. You can choose from one of the existing players in the game or create your own custom player. You can also change your captain anytime in the game settings. Your captain should have high ratings in attributes such as shooting, passing, dribbling, speed, stamina, etc.
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Train your players regularly
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Your players need to train regularly to improve their skills and performance. You can train your players in various aspects, such as shooting, passing, dribbling, defending, etc. You can also use coins or gems to boost their training progress. Training your players will increase their ratings and make them more effective on the pitch.
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Use the right formation and tactics
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Your formation and tactics are crucial for your success in DLS 21. You can choose from different formations, such as 4-4-2, 3-5-2, 4-3-3, etc. You can also customize your tactics, such as attacking style, defensive style, pressing, width, etc. You should choose the formation and tactics that suit your playstyle and your players' strengths and weaknesses. You can also change your formation and tactics during the match if needed.
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Master the controls and skills
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DLS 21 has simple and intuitive controls that allow you to perform various actions, such as passing, shooting, dribbling, tackling, etc. You can also use the virtual joystick and buttons to move your players and execute skills. You should master the controls and skills to have more control over the game and outsmart your opponents. You can also customize your controls in the game settings.
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Be smart with your transfers and upgrades
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DLS 21 allows you to buy and sell players in the transfer market. You can also upgrade your players' attributes using coins or gems. You should be smart with your transfers and upgrades to improve your team's quality and balance. You should look for players that fit your formation and tactics, have high potential, and are affordable. You should also upgrade your players' attributes that are most relevant for their positions and roles.
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Conclusion
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DLS 21 is a fantastic soccer game that will keep you entertained for hours. You can download it from APKPure, a reliable source of Android apps and games. You can also install it easily on your Android device and start playing right away. You can also follow our tips and tricks to score more goals and win more matches in DLS 21. We hope you enjoy playing DLS 21 and have fun!
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FAQs
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Here are some frequently asked questions about DLS 21:
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Is DLS 21 free to play?
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Yes, DLS 21 is free to play, but it contains in-app purchases that allow you to buy coins, gems, players, kits, balls, boots, etc.
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Is DLS 21 offline or online?
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DLS 21 can be played both offline and online. You can play offline in Career mode, where you can compete in various leagues and cups. You can also play online in Dream League Live mode, where you can play against other players from around the world.
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How to update DLS 21?
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You can update DLS 21 by visiting APKPure website and downloading the latest version of the APK and OBB files. You can also enable auto-update in the game settings to get notified when a new update is available.
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How to get unlimited coins and gems in DLS 21?
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There is no official way to get unlimited coins and gems in DLS 21. However, you can earn coins and gems by playing the game regularly, completing objectives and challenges, watching ads, etc. You can also buy coins and gems using real money if you want.
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How to hack DLS 21?
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We do not recommend hacking DLS 21 as it may harm your device or account. Hacking DLS 21 may also ruin the fun and challenge of the game. If you want to enjoy DLS 21, you should play it fair and square.
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Tag After School on Apkvision and Experience the Horror of a Haunted High School.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Tag After School on Apkvision and Experience the Horror of a Haunted High School.md
deleted file mode 100644
index 2593d7b948e6f86e18720b277fc7c36345a45522..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Tag After School on Apkvision and Experience the Horror of a Haunted High School.md
+++ /dev/null
@@ -1,170 +0,0 @@
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Tag After School Apkvision: A Fun and Interactive High School Simulation Game
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Do you miss your high school days? Do you want to relive your memories of friendship, romance, drama, and adventure? Do you want to experience a different kind of high school life in a virtual world? If you answered yes to any of these questions, then you should try Tag After School Apkvision, a fun and interactive high school simulation game that will make you feel like you are back in school.
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What is Tag After School Apkvision?
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Tag After School Apkvision is a mobile game that takes place in a virtual high school, where you can create your own character, interact with other students, join clubs, participate in events, and explore various locations. You can also choose your own story path, make decisions that affect your relationships, and discover secrets that will change your life.
The game begins with a brief introduction to the school and its various characters, followed by a character creation screen where you can customize the appearance and name of your character. You can also choose your personality type from four options: friendly, cool, shy, or rebellious. Your personality type will influence how other characters react to you and what kind of events you can encounter.
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After creating your character, you can start your first day at school. You will meet your classmates, teachers, club members, and potential love interests. You can talk to them, ask them questions, compliment them, tease them, or flirt with them. You can also join one of the four clubs available: music club, art club, sports club, or drama club. Each club has its own activities, events, and stories that you can participate in.
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The game is divided into chapters that correspond to different days at school. Each chapter has multiple scenes that you can play through. Some scenes are mandatory for the main story progression, while others are optional for side stories or extra content. You can also replay any scene that you have already completed if you want to change your choices or outcomes.
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The features and the graphics
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Tag After School Apkvision has many features that make it an enjoyable and immersive game. Some of these features are:
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A rich and diverse cast of characters that have their own personalities, backgrounds, preferences, and stories.
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A dynamic and responsive dialogue system that allows you to choose from multiple options and see how they affect your relationships and events.
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A variety of locations that you can visit and explore, such as classrooms, cafeteria, library, gym, auditorium, park, mall, and more.
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A stunning and colorful graphics that create a realistic and lively atmosphere for the game.
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A catchy and upbeat soundtrack that matches the mood and tone of the game.
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A simple and intuitive user interface that makes the game easy to navigate and play.
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The pros and cons
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Like any other game, Tag After School Apkvision has its pros and cons. Some of the pros are:
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It is free to download and play, with optional in-app purchases for extra content and features.
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It is compatible with most Android devices, with a minimum requirement of Android 4.4 and up.
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It is updated regularly with new chapters, events, characters, and improvements.
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It has a friendly and supportive community of players and developers that you can interact with on social media platforms.
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It may contain some ads that can interrupt your gameplay or consume your data.
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It may have some bugs or glitches that can affect your performance or progress.
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It may have some mature or sensitive themes that may not be suitable for younger audiences.
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It may require a stable internet connection to play smoothly and access all the features.
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How to download and install Tag After School Apkvision?
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If you are interested in playing Tag After School Apkvision, you may wonder how to download and install it on your device. Here are the steps that you need to follow:
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The requirements and the steps
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The first thing that you need to do is to check if your device meets the minimum requirements for the game. As mentioned earlier, you need to have an Android device with Android 4.4 or higher, and at least 100 MB of free storage space. You also need to have a good internet connection to download and play the game.
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The next thing that you need to do is to download the game from a reliable source. You can either download it from the official website of Apkvision, or from the Google Play Store. Both sources are safe and secure, and will provide you with the latest version of the game.
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After downloading the game, you need to install it on your device. If you downloaded it from Apkvision, you need to enable the installation of apps from unknown sources in your device settings. Then, you need to locate the downloaded file in your file manager, and tap on it to start the installation process. If you downloaded it from Google Play Store, you just need to tap on the install button after downloading it, and wait for it to finish installing.
-
The tips and the tricks
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Once you have installed the game on your device, you are ready to play it. However, before you start playing, you may want to know some tips and tricks that can help you enjoy the game more. Here are some of them:
-
-
Save your progress frequently by tapping on the menu button on the top right corner of the screen, and then tapping on the save button. You can also load your previous progress by tapping on the load button.
-
Use your diamonds wisely by spending them on important choices or scenes that can affect your story or relationships. You can earn more diamonds by watching ads, completing tasks, or buying them with real money.
-
Try different choices and outcomes by replaying scenes or chapters that you have already completed. You can also switch between different clubs or love interests by creating multiple profiles or accounts.
-
Follow the official social media accounts of Tag After School Apkvision on Facebook, Instagram, Twitter, or YouTube for more updates, news, spoilers, fan art, contests, and more.
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The warnings and the precautions
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While playing Tag After School Apkvision can be fun and entertaining, you should also be aware of some warnings and precautions that can prevent you from having any problems or issues with the game. Here are some of them:
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-
Do not download or install Tag After School Apkvision from any other sources than Apkvision or Google Play Store. Other sources may contain viruses, malware, spyware, or other harmful elements that can damage your device or steal your personal information.
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Do not use any hacks, cheats, mods, or tools that can alter or manipulate the game in any way. These may cause errors, crashes, bans, or other consequences that can ruin your gameplay or account.
-
Do not share your personal information, such as your name, address, phone number, email, password, or credit card details, with anyone online or in the game. This may expose you to scams, frauds, phishing, or identity theft.
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Do not engage in any inappropriate or offensive behavior, such as bullying, harassment, discrimination, spamming, trolling, or cheating, with other players or developers in the game or on social media platforms. This may result in reports, complaints, warnings, or bans.
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-
Why should you play Tag After School Apkvision?
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Now that you know what Tag After School Apkvision is and how to play it, you may wonder why you should play it. Here are some reasons why you should play Tag After School Apkvision:
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The benefits and the challenges
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Playing Tag After School Apkvision can bring you many benefits and challenges that can enhance your gaming experience and skills. Some of these are:
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It can stimulate your imagination and creativity by allowing you to create your own character and story.
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It can improve your communication and social skills by enabling you to interact with other characters and players.
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It can increase your knowledge and awareness by exposing you to different topics and issues that are relevant to high school life.
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It can test your decision-making and problem-solving skills by presenting you with various choices and outcomes that have consequences.
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It can entertain and amuse you by providing you with fun and interactive gameplay and content.
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The reviews and the ratings
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Playing Tag After School Apkvision can also give you an idea of how other players and critics think about the game. You can read their reviews and ratings on Apkvision, Google Play Store, or other websites that feature the game. Here are some examples of what they say about the game:
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-
-
Name
-
Review
-
Rating
-
-
-
Alice
-
I love this game so much! It's so fun and addictive. The characters are so cute and interesting. The story is so engaging and unpredictable. The graphics are so beautiful and colorful. The music is so catchy and upbeat. I highly recommend this game to anyone who loves high school simulation games.
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5 stars
-
-
-
Bob
-
This game is okay, but it has some flaws. The game is too short and repetitive. The choices are too limited and obvious. The ads are too annoying and frequent. The game is too easy and boring. I wish the game had more content and features.
-
3 stars
-
-
-
Charlie
-
This game is terrible. It's a waste of time and money. The game is full of bugs and glitches. The game is too slow and laggy. The game is too unrealistic and childish. The game is too inappropriate and offensive. I hate this game so much.
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1 star
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The alternatives and the comparisons
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If you are looking for other games that are similar to Tag After School Apkvision, you can also check out some of the alternatives that are available on Apkvision or Google Play Store. Here are some of them:
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-
High School Story Apkvision: A high school simulation game where you can build your own school, customize your character, date your crush, throw parties, make friends, and more.
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Campus: Date Sim Apkvision: A dating simulation game where you can meet three beautiful girls at a college campus, flirt with them, impress them, and win their hearts.
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Episode - Choose Your Story Apkvision: A story-based game where you can choose from thousands of stories in different genres, such as romance, drama, comedy, fantasy, horror, etc., and make choices that shape your story.
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Choices: Stories You Play Apkvision: Another story-based game where you can choose from hundreds of stories in different categories, such as adventure, mystery, action, etc., and make choices that affect your story.
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My Candy Love - Episode / Otome game Apkvision: A romance simulation game where you can create your own character, meet different boys at a high school, chat with them, date them, and find your true love.
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-
You can compare these games with Tag After School Apkvision based on various criteria, such as the genre, the theme, the gameplay, the graphics, the sound, the features, the reviews, the ratings, etc. You can also try them out and see which one suits your preferences and tastes better.
-
Conclusion
-
In conclusion, Tag After School Apkvision is a fun and interactive high school simulation game that will make you feel like you are back in school. You can create your own character, interact with other students, join clubs, participate in events, and explore various locations. You can also choose your own story path, make decisions that affect your relationships, and discover secrets that will change your life. The game has many features that make it an enjoyable and immersive game, such as a rich and diverse cast of characters, a dynamic and responsive dialogue system, a variety of locations, a stunning and colorful graphics, a catchy and upbeat soundtrack, and a simple and intuitive user interface. The game is free to download and play, with optional in-app purchases for extra content and features. The game is compatible with most Android devices, with a minimum requirement of Android 4.4 and up. The game is updated regularly with new chapters, events, characters, and improvements. The game has a friendly and supportive community of players and developers that you can interact with on social media platforms.
-
However, the game also has some flaws that you should be aware of. The game may contain some ads that can interrupt your gameplay or consume your data. The game may have some bugs or glitches that can affect your performance or progress. The game may have some mature or sensitive themes that may not be suitable for younger audiences. The game may require a stable internet connection to play smoothly and access all the features. You should also be careful not to download or install the game from any other sources than Apkvision or Google Play Store. You should also avoid using any hacks, cheats, mods, or tools that can alter or manipulate the game in any way. You should also not share your personal information with anyone online or in the game. You should also not engage in any inappropriate or offensive behavior with other players or developers in the game or on social media platforms.
-
If you are looking for a fun and interactive high school simulation game that will make you feel like you are back in school, then you should try Tag After School Apkvision. You will surely enjoy the game and its content. You will also learn a lot from the game and its characters. You will also have a lot of fun playing the game and making your own story.
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FAQs
-
Here are some frequently asked questions about Tag After School Apkvision:
-
-
Q: How many chapters are there in Tag After School Apkvision?
-
A: There are currently 10 chapters in Tag After School Apkvision, with more chapters coming soon.
-
Q: How many love interests are there in Tag After School Apkvision?
-
A: There are currently 8 love interests in Tag After School Apkvision, with 2 love interests for each club.
-
Q: How can I get more diamonds in Tag After School Apkvision?
-
A: You can get more diamonds by watching ads, completing tasks, or buying them with real money.
-
Q: How can I contact the developers of Tag After School Apkvision?
-
A: You can contact the developers of Tag After School Apkvision by sending them an email at tagafterschool@gmail.com or by visiting their website at https://tagafterschool.com/.
-
Q: How can I support the developers of Tag After School Apkvision?
-
A: You can support the developers of Tag After School Apkvision by giving them a positive review and rating on Apkvision or Google Play Store, by sharing the game with your friends and family, by following their social media accounts, by joining their fan club or discord server, or by making a donation to them.
- 401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/fffiloni/Image-to-Story/README.md b/spaces/fffiloni/Image-to-Story/README.md
deleted file mode 100644
index 8e4e3f5537b177809902321125a54c9dfb863f8d..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Image-to-Story/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Image To Story
-emoji: 👁
-colorFrom: pink
-colorTo: red
-python_version: 3.10.12
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/flax-community/roberta-hindi/About/model_description.md b/spaces/flax-community/roberta-hindi/About/model_description.md
deleted file mode 100644
index 7bc562a64b6be145cc4baf44512324c295c0c895..0000000000000000000000000000000000000000
--- a/spaces/flax-community/roberta-hindi/About/model_description.md
+++ /dev/null
@@ -1,3 +0,0 @@
-## Model description
-
-It is a monolingual transformers model pretrained on a large corpus of Hindi data (100GB+) in a self-supervised fashion.
\ No newline at end of file
diff --git a/spaces/flax-community/spanish-image-captioning/app.py b/spaces/flax-community/spanish-image-captioning/app.py
deleted file mode 100644
index cafdd54e173fa18af6bad844e61d0aec978c4335..0000000000000000000000000000000000000000
--- a/spaces/flax-community/spanish-image-captioning/app.py
+++ /dev/null
@@ -1,140 +0,0 @@
-from io import BytesIO
-import streamlit as st
-import pandas as pd
-import os
-import numpy as np
-from streamlit import caching
-from PIL import Image
-from model.flax_clip_vision_marian.modeling_clip_vision_marian import (
- FlaxCLIPVisionMarianMT,
-)
-from transformers import MarianTokenizer
-from utils import (
- get_transformed_image,
-)
-import matplotlib.pyplot as plt
-from mtranslate import translate
-
-
-from session import _get_state
-
-state = _get_state()
-
-
-@st.cache
-def load_model(ckpt):
- return FlaxCLIPVisionMarianMT.from_pretrained(ckpt)
-
-
-tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-es")
-
-@st.cache
-def generate_sequence(pixel_values, num_beams, temperature, top_p, do_sample, top_k, max_length):
- output_ids = state.model.generate(input_ids=pixel_values, max_length=max_length, num_beams=num_beams, temperature=temperature, top_p = top_p, top_k=top_k, do_sample=do_sample)
- print(output_ids)
- output_sequence = tokenizer.batch_decode(output_ids[0], skip_special_tokens=True, max_length=max_length)
- return output_sequence
-
-def read_markdown(path, parent="./sections/"):
- with open(os.path.join(parent, path)) as f:
- return f.read()
-
-
-checkpoints = ["./ckpt/ckpt-23999"] # TODO: Maybe add more checkpoints?
-dummy_data = pd.read_csv("references.tsv", sep="\t")
-
-st.set_page_config(
- page_title="Spanish Image Captioning",
- layout="wide",
- initial_sidebar_state="collapsed",
- page_icon="./misc/csi-logo.png",
-)
-
-st.title("Spanish Image Captioning")
-st.write(
- "[Bhavitvya Malik](https://huggingface.co/bhavitvyamalik), [Gunjan Chhablani](https://huggingface.co/gchhablani)"
-)
-
-st.sidebar.title("Generation Parameters")
-max_length = st.sidebar.number_input("Max Length", min_value=16, max_value=128, value=64, step=1, help="The maximum length of sequence to be generated.")
-do_sample = st.sidebar.checkbox("Sample", value=False, help="Sample from the model instead of using beam search.")
-top_k = st.sidebar.number_input("Top K", min_value=10, max_value=200, value=50, step=1, help="The number of highest probability vocabulary tokens to keep for top-k-filtering.")
-num_beams = st.sidebar.number_input("Number of Beams", min_value=2, max_value=10, value=4, step=1, help="Number of beams to be used in beam search.")
-temperature = st.sidebar.select_slider("Temperature", options = list(np.arange(0.0,1.1, step=0.1)), value=1.0, help ="The value used to module the next token probabilities.", format_func=lambda x: f"{x:.2f}")
-top_p = st.sidebar.select_slider("Top-P", options = list(np.arange(0.0,1.1, step=0.1)),value=1.0, help="Nucleus Sampling : If set to float < 1, only the most probable tokens with probabilities that add up to :obj:`top_p` or higher are kept for generation.", format_func=lambda x: f"{x:.2f}")
-if st.sidebar.button("Clear All Cache"):
- caching.clear_cache()
-
-image_col, intro_col = st.beta_columns([3, 8])
-image_col.image("./misc/sic-logo.png", use_column_width="always")
-intro_col.write(read_markdown("intro.md"))
-
-with st.beta_expander("Usage"):
- st.markdown(read_markdown("usage.md"))
-
-with st.beta_expander("Article"):
- st.write(read_markdown("abstract.md"))
- st.write(read_markdown("caveats.md"))
- st.write("## Methodology")
- st.image(
- "./misc/Spanish-IC.png"
- )
- st.markdown(read_markdown("pretraining.md"))
- st.write(read_markdown("challenges.md"))
- st.write(read_markdown("social_impact.md"))
- st.write(read_markdown("references.md"))
- # st.write(read_markdown("checkpoints.md"))
- st.write(read_markdown("acknowledgements.md"))
-
-
-if state.model is None:
- with st.spinner("Loading model..."):
- state.model = load_model(checkpoints[0])
-
-first_index = 40
-# Init Session State
-if state.image_file is None:
- state.image_file = dummy_data.loc[first_index, "image_file"]
- state.caption = dummy_data.loc[first_index, "caption"].strip("- ")
-
- image_path = os.path.join("images", state.image_file)
- image = plt.imread(image_path)
- state.image = image
-
-new_col1, new_col2 = st.beta_columns([5,5])
-
-if new_col1.button("Get a random example", help="Get a random example from one of the seeded examples."):
- sample = dummy_data.sample(1).reset_index()
- state.image_file = sample.loc[0, "image_file"]
- state.caption = sample.loc[0, "caption"].strip("- ")
-
- image_path = os.path.join("images", state.image_file)
- image = plt.imread(image_path)
- state.image = image
-
-transformed_image = get_transformed_image(state.image)
-# Display Image
-new_col1.image(state.image, use_column_width="always")
-
-# Display Reference Caption
-with new_col1.beta_expander("Reference Caption"):
- st.write("**Reference Caption**: " + state.caption)
- st.markdown(
- f"""**English Translation**: {translate(state.caption, 'en')}"""
- )
-
-
-sequence = ['']
-if new_col2.button("Generate Caption", help="Generate a caption in the Spanish."):
- with st.spinner("Generating Sequence..."):
- sequence = generate_sequence(transformed_image, num_beams, temperature, top_p, do_sample, top_k, max_length)
-# print(sequence)
-
-if sequence!=['']:
- new_col2.write(
- "**Generated Caption**: "+sequence[0]
- )
-
- new_col2.write(
- "**English Translation**: "+ translate(sequence[0])
- )
diff --git a/spaces/flowers-team/Interactive_DeepRL_Demo/js/ui_state/components/global_elements.js b/spaces/flowers-team/Interactive_DeepRL_Demo/js/ui_state/components/global_elements.js
deleted file mode 100644
index 4981d77acddb21fb08cd498587a1207997df3656..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/Interactive_DeepRL_Demo/js/ui_state/components/global_elements.js
+++ /dev/null
@@ -1,47 +0,0 @@
-import Component from '../lib/component.js';
-import store from '../store/index.js';
-
-/**
- * @classdesc UI component for global elements.
- */
-export default class GlobalElements extends Component{
-
- /**
- * @constructor
- */
- constructor() {
- super({
- store,
- element: document.body,
- eventName: 'globalElementsChange'
- });
- }
-
- /**
- * Renders the global UI elements.
- */
- render() {
- let dict = window.lang_dict[store.state.language]['globalElements'];
-
- // Title
- this.element.querySelector('#demoTitle').innerText = dict['demoTitle'];
-
- // Tabs buttons
- this.element.querySelector('#getting-started-btn').innerText = dict['gettingStarted'];
- this.element.querySelector('#parkour-custom-btn').innerText = dict['parkourCustomization'];
- this.element.querySelector('#advanced-options-btn').innerText = dict['advancedOptions'];
- this.element.querySelector('#about-btn').innerHTML = ` ${dict['about']}`;
-
- // Changes the selected index of the language dropdown
- this.element.querySelector('#langSelect').selectedIndex = store.state.language == 'EN' ? 0 : 1;
-
- // Save env modal
- let modal = this.element.querySelector('#saveEnvModal');
- modal.querySelector('#save-modal-title').innerHTML = dict['saveEnvModal']['title'];
- modal.querySelector('#save-modal-text').innerText = dict['saveEnvModal']['text'];
- modal.querySelector('#env-name-label').innerText = dict['saveEnvModal']['nameLabel'];
- modal.querySelector('#env-description-label').innerText = dict['saveEnvModal']['descriptionLabel'];
- modal.querySelector('#save-cancel-btn').innerText = dict['saveEnvModal']['cancelBtn'];
- modal.querySelector('#save-confirm-btn').innerText = dict['saveEnvModal']['confirmBtn'];
- }
-};
\ No newline at end of file
diff --git a/spaces/freddyaboulton/gradio_foliumtest/README.md b/spaces/freddyaboulton/gradio_foliumtest/README.md
deleted file mode 100644
index ed7002c19363577d761f4635e171b4934e3ca96e..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/gradio_foliumtest/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
-
----
-tags: [gradio-custom-component, gradio-template-Fallback]
-title: gradio_foliumtest V0.0.2
-colorFrom: purple
-colorTo: indigo
-sdk: docker
-pinned: false
-license: apache-2.0
----
diff --git a/spaces/fxmikau/o4gpt/app.py b/spaces/fxmikau/o4gpt/app.py
deleted file mode 100644
index a7919b786de5a3adf91f6037739671325babb4dd..0000000000000000000000000000000000000000
--- a/spaces/fxmikau/o4gpt/app.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import time
-
-from theme_dropdown import create_theme_dropdown # noqa: F401
-
-import gradio as gr
-
-dropdown, js = create_theme_dropdown()
-
-with gr.Blocks(theme='fxmikau/HaleyCH_Theme@0.0.2') as demo:
- with gr.Row().style(equal_height=True):
- with gr.Column(scale=10):
- gr.Markdown(
- """
- # Theme preview: `HaleyCH_Theme`
- To use this theme, set `theme='fxmikau/HaleyCH_Theme'` in `gr.Blocks()` or `gr.Interface()`.
- You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version
- of this theme.
- """
- )
- with gr.Column(scale=3):
- with gr.Box():
- dropdown.render()
- toggle_dark = gr.Button(value="Toggle Dark").style(full_width=True)
-
- dropdown.change(None, dropdown, None, _js=js)
- toggle_dark.click(
- None,
- _js="""
- () => {
- document.body.classList.toggle('dark');
- document.querySelector('gradio-app').style.backgroundColor = 'var(--color-background-primary)'
- }
- """,
- )
-
- name = gr.Textbox(
- label="Name",
- info="Full name, including middle name. No special characters.",
- placeholder="John Doe",
- value="John Doe",
- interactive=True,
- )
-
- with gr.Row():
- slider1 = gr.Slider(label="Slider 1")
- slider2 = gr.Slider(label="Slider 2")
- gr.CheckboxGroup(["A", "B", "C"], label="Checkbox Group")
-
- with gr.Row():
- with gr.Column(variant="panel", scale=1):
- gr.Markdown("## Panel 1")
- radio = gr.Radio(
- ["A", "B", "C"],
- label="Radio",
- info="Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.",
- )
- drop = gr.Dropdown(["Option 1", "Option 2", "Option 3"], show_label=False)
- drop_2 = gr.Dropdown(
- ["Option A", "Option B", "Option C"],
- multiselect=True,
- value=["Option A"],
- label="Dropdown",
- interactive=True,
- )
- check = gr.Checkbox(label="Go")
- with gr.Column(variant="panel", scale=2):
- img = gr.Image(
- "https://gradio.app/assets/img/header-image.jpg", label="Image"
- ).style(height=320)
- with gr.Row():
- go_btn = gr.Button("Go", label="Primary Button", variant="primary")
- clear_btn = gr.Button(
- "Clear", label="Secondary Button", variant="secondary"
- )
-
- def go(*args):
- time.sleep(3)
- return "https://gradio.app/assets/img/header-image.jpg"
-
- go_btn.click(go, [radio, drop, drop_2, check, name], img, api_name="go")
-
- def clear():
- time.sleep(0.2)
- return None
-
- clear_btn.click(clear, None, img)
-
- with gr.Row():
- btn1 = gr.Button("Button 1").style(size="sm")
- btn2 = gr.UploadButton().style(size="sm")
- stop_btn = gr.Button("Stop", label="Stop Button", variant="stop").style(
- size="sm"
- )
-
- with gr.Row():
- gr.Dataframe(value=[[1, 2, 3], [4, 5, 6], [7, 8, 9]], label="Dataframe")
- gr.JSON(
- value={"a": 1, "b": 2, "c": {"test": "a", "test2": [1, 2, 3]}}, label="JSON"
- )
- gr.Label(value={"cat": 0.7, "dog": 0.2, "fish": 0.1})
- gr.File()
- with gr.Row():
- gr.ColorPicker()
- gr.Video("https://gradio-static-files.s3.us-west-2.amazonaws.com/world.mp4")
- gr.Gallery(
- [
- (
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/lion.jpg",
- "lion",
- ),
- (
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/logo.png",
- "logo",
- ),
- (
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/tower.jpg",
- "tower",
- ),
- ]
- ).style(height="200px", grid=2)
-
- with gr.Row():
- with gr.Column(scale=2):
- chatbot = gr.Chatbot([("Hello", "Hi")], label="Chatbot")
- chat_btn = gr.Button("Add messages")
-
- def chat(history):
- time.sleep(2)
- yield [["How are you?", "I am good."]]
-
- chat_btn.click(
- lambda history: history
- + [["How are you?", "I am good."]]
- + (time.sleep(2) or []),
- chatbot,
- chatbot,
- )
- with gr.Column(scale=1):
- with gr.Accordion("Advanced Settings"):
- gr.Markdown("Hello")
- gr.Number(label="Chatbot control 1")
- gr.Number(label="Chatbot control 2")
- gr.Number(label="Chatbot control 3")
-
-
-if __name__ == "__main__":
- demo.queue().launch()
diff --git a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/Mishalsgpt.py b/spaces/g4f/freegpt-webui/g4f/Provider/Providers/Mishalsgpt.py
deleted file mode 100644
index 63080c674900a181f66380bcfe6c185b7469cebd..0000000000000000000000000000000000000000
--- a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/Mishalsgpt.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import os, requests, uuid
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://mishalsgpt.vercel.app'
-model = ['gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- headers = {
- 'Content-Type': 'application/json',
- }
- data = {
- 'model': model,
- 'temperature': 0.7,
- 'messages': messages
- }
- response = requests.post(url + '/api/openai/v1/chat/completions',
- headers=headers, json=data, stream=True)
- yield response.json()['choices'][0]['message']['content']
-
-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]])
diff --git a/spaces/ggwvits/vits-uma-genshin-honkai/modules.py b/spaces/ggwvits/vits-uma-genshin-honkai/modules.py
deleted file mode 100644
index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000
--- a/spaces/ggwvits/vits-uma-genshin-honkai/modules.py
+++ /dev/null
@@ -1,388 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
-
-class ConvFlow(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
- self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/utils/__init__.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/utils/__init__.py
deleted file mode 100644
index d32952997e45feaa06fa407908000e6e1a9b7b9c..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/utils/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import warnings
-
-from . import train
-from . import losses
-from . import metrics
-
-warnings.warn(
- "`smp.utils` module is deprecated and will be removed in future releases.",
- DeprecationWarning,
-)
diff --git a/spaces/golem4300/RVC-TTS/lib/infer_pack/models_onnx.py b/spaces/golem4300/RVC-TTS/lib/infer_pack/models_onnx.py
deleted file mode 100644
index 1bbfb69e458245dc7215dfce1daa38827a4a3069..0000000000000000000000000000000000000000
--- a/spaces/golem4300/RVC-TTS/lib/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,582 +0,0 @@
-import math
-import torch
-import numpy as np
-from torch import nn
-from torch.nn import functional as F
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from lib.infer_pack import modules, attentions, commons
-from torch.nn.utils import weight_norm, remove_weight_norm
-from lib.infer_pack.commons import init_weights, get_padding
-from lib.infer_pack.commons import init_weights, sequence_mask
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.modules import ResidualCouplingLayer, WN, ResBlock1, ResBlock2, LRELU_SLOPE
-
-class TextEncoder(nn.Module):
- def __init__(
- self,
- input_dim,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(input_dim, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-
- if f0:self.emb_pitch = nn.Embedding(256, hidden_channels)
- self.encoder = attentions.Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, phone, pitch, lengths):
- x = self.emb_phone(phone) + self.emb_pitch(pitch) if pitch is not None else self.emb_phone(phone)
- x *= math.sqrt(self.hidden_channels)
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1)
- x_mask = torch.unsqueeze(sequence_mask(lengths, x.size(2)), 1).to(x.dtype)
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return m, logs, x_mask
-
-class TextEncoder768(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(768, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:self.emb_pitch = nn.Embedding(256, hidden_channels)
- self.encoder = attentions.Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch is None: x = self.emb_phone(phone)
- else: x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels)
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1)
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(x.dtype)
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return m, logs, x_mask
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(
- self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0,
- ):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for _ in range(n_flows):
- self.flows.append(
- modules.ResidualCouplingLayer(
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- mean_only=True,
- )
- )
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows: x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows): x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
- def remove_weight_norm(self):
- for i in range(self.n_flows):
- self.flows[i * 2].remove_weight_norm()
-
-class PosteriorEncoder(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-class Generator(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=0,
- ):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes): self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None: x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None: xs = self.resblocks[i * self.num_kernels + j](x)
- else: xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- for l in self.ups: remove_weight_norm(l)
- for l in self.resblocks: l.remove_weight_norm()
-
-class SineGen(torch.nn.Module):
- def __init__(
- self,
- samp_rate,
- harmonic_num=0,
- sine_amp=0.1,
- noise_std=0.003,
- voiced_threshold=0,
- ):
- super(SineGen, self).__init__()
- self.sine_amp = sine_amp
- self.noise_std = noise_std
- self.harmonic_num = harmonic_num
- self.dim = self.harmonic_num + 1
- self.sampling_rate = samp_rate
- self.voiced_threshold = voiced_threshold
-
- def _f02uv(self, f0):
- uv = torch.ones_like(f0)
- uv = uv * (f0 > self.voiced_threshold)
- return uv
-
- def forward(self, f0, upp):
- with torch.no_grad():
- f0 = f0[:, None].transpose(1, 2)
- f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
- f0_buf[:, :, 0] = f0[:, :, 0]
- for idx in np.arange(self.harmonic_num): f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)
- rad_values = (f0_buf / self.sampling_rate) % 1
- rand_ini = torch.rand(f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device)
- rand_ini[:, 0] = 0
- rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
- tmp_over_one = torch.cumsum(rad_values, 1)
- tmp_over_one *= upp
- tmp_over_one = F.interpolate(tmp_over_one.transpose(2, 1), scale_factor=upp, mode="linear", align_corners=True).transpose(2, 1)
- rad_values = F.interpolate(rad_values.transpose(2, 1), scale_factor=upp, mode="nearest").transpose(2, 1)
- tmp_over_one %= 1
- tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
- cumsum_shift = torch.zeros_like(rad_values)
- cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
- sine_waves = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi)
- sine_waves = sine_waves * self.sine_amp
- uv = self._f02uv(f0)
- uv = F.interpolate(uv.transpose(2, 1), scale_factor=upp, mode="nearest").transpose(2, 1)
- noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
- noise = noise_amp * torch.randn_like(sine_waves)
- sine_waves = sine_waves * uv + noise
- return sine_waves, uv, noise
-
-class SourceModuleHnNSF(torch.nn.Module):
- def __init__(
- self,
- sampling_rate,
- harmonic_num=0,
- sine_amp=0.1,
- add_noise_std=0.003,
- voiced_threshod=0,
- is_half=True,
- ):
- super(SourceModuleHnNSF, self).__init__()
-
- self.sine_amp = sine_amp
- self.noise_std = add_noise_std
- self.is_half = is_half
- self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod)
- self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
- self.l_tanh = torch.nn.Tanh()
-
- def forward(self, x, upp=None):
- sine_wavs, uv, _ = self.l_sin_gen(x, upp)
- if self.is_half: sine_wavs = sine_wavs.half()
- sine_merge = self.l_tanh(self.l_linear(sine_wavs))
- return sine_merge, None, None
-
-
-class GeneratorNSF(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- sr,
- is_half=False,
- ):
- super(GeneratorNSF, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
- self.m_source = SourceModuleHnNSF(sampling_rate=sr, harmonic_num=0, is_half=is_half)
- self.noise_convs = nn.ModuleList()
- self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- c_cur = upsample_initial_channel // (2 ** (i + 1))
- self.ups.append(weight_norm(ConvTranspose1d(upsample_initial_channel // (2**i), upsample_initial_channel // (2 ** (i + 1)), k, u, padding=(k - u) // 2,)))
- if i + 1 < len(upsample_rates):
- stride_f0 = np.prod(upsample_rates[i + 1 :])
- self.noise_convs.append(Conv1d(1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2,))
- else: self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes): self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- self.upp = np.prod(upsample_rates)
-
- def forward(self, x, f0, g=None):
- har_source, noi_source, uv = self.m_source(f0, self.upp)
- har_source = har_source.transpose(1, 2)
- x = self.conv_pre(x)
- if g is not None: x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- x_source = self.noise_convs[i](har_source)
- x = x + x_source
- xs = None
- for j in range(self.num_kernels):
- if xs is None: xs = self.resblocks[i * self.num_kernels + j](x)
- else: xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-sr2sr = {"32k": 32000,"40k": 40000,"48k": 48000,}
-
-class SynthesizerTrnMsNSFsidM(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr,
- version,
- **kwargs
- ):
- super().__init__()
- if type(sr) == type("strr"): sr = sr2sr[sr]
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- self.spk_embed_dim = spk_embed_dim
- if version == "v1": self.enc_p = TextEncoder(inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout)
- else: self.enc_p = TextEncoder768(inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout)
- self.dec = GeneratorNSF(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels, sr=sr, is_half=kwargs["is_half"])
- self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels)
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- self.speaker_map = None
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def construct_spkmixmap(self, n_speaker):
- self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels))
- for i in range(n_speaker): self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]))
- self.speaker_map = self.speaker_map.unsqueeze(0)
-
- def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
- if self.speaker_map is not None:
- g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1))
- g = g * self.speaker_map
- g = torch.sum(g, dim=1)
- g = g.transpose(0, -1).transpose(0, -2).squeeze(0)
- else:
- g = g.unsqueeze(0)
- g = self.emb_g(g).transpose(1, 2)
-
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- return self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2, 3, 5, 7, 11, 17]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs += [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for d in self.discriminators:
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminatorV2, self).__init__()
- periods = [2, 3, 5, 7, 11, 17, 23, 37]
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs += [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for d in self.discriminators:
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ]
- )
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0),)),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0),)),
- norm_f(Conv2d( 128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0),)),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0),)),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0),)),
- ]
- )
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
- b, c, t = x.shape
- if t % self.period != 0:
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
\ No newline at end of file
diff --git a/spaces/gotiQspiryo/whisper-ui/Super-Recorder-Cydia-Cracked-Ios.md b/spaces/gotiQspiryo/whisper-ui/Super-Recorder-Cydia-Cracked-Ios.md
deleted file mode 100644
index ea12f02c96b10b27e6f37cc4c45ebc3925945932..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/Super-Recorder-Cydia-Cracked-Ios.md
+++ /dev/null
@@ -1,66 +0,0 @@
-## Super Recorder Cydia Cracked Ios
-
-
-
-
-
- 
-
-
-
-
-
-**LINK [https://miimms.com/2txSTg](https://miimms.com/2txSTg)**
-
-
-
-
-
-
-
-
-
-
-
-
-
-# How to Record Phone Calls on iOS with Super Recorder Cydia Tweak
-
-
-
-If you are looking for a way to record phone calls on your iPhone, you might be interested in Super Recorder Cydia tweak. Super Recorder is a powerful and versatile tweak that lets you record phone calls with the press of a button, transcript recordings, adjust the volume and quality of the audio, and more.
-
-
-
-Super Recorder is compatible with iOS 9.3.3 and later versions, including iOS 12. You can download it from the hAcx Repo[^1^] or from other sources[^2^], but be aware that some cracked versions may not work properly or may contain malware. The tweak costs $3.99 and comes with a free trial period.
-
-
-
-To use Super Recorder, you need to enable it from the Settings app and configure its options according to your preferences. You can choose to auto-record certain phone calls, play a beep noise when recording, display a status bar icon or an app badge when recording, and more. You can also set the transcript language and the audio format (MP3 or WAV).
-
-
-
-When you make or receive a phone call, you will see a red record button on the call screen. You can tap it to start or stop recording at any time. You can also use an Activator gesture or a Flipswitch toggle to control the recording. The recorded calls will be saved in the Super Recorder app, where you can play them back, share them, delete them, or transcript them.
-
-
-
-Super Recorder is a handy tweak for anyone who needs to record phone calls on their iPhone for personal or professional reasons. It offers a lot of features and customization options that make it one of the best call recorder tweaks for iOS. However, before you use it, make sure you are aware of the legal implications of recording phone calls in your country or region, as some jurisdictions may require consent from both parties.
-
-
-
-One of the advantages of Super Recorder Cydia tweak is that it allows you to transcript your recorded calls into text. This can be useful for taking notes, creating summaries, or converting speech to text. To transcript a recording, you need to open the Super Recorder app and tap on the transcript icon next to the recording. You will see a progress bar and then the transcript will appear below the recording. You can copy, edit, or share the transcript as you wish.
-
-
-
-Another feature of Super Recorder Cydia tweak is that it lets you adjust the volume and quality of the recorded audio. You can do this from the Settings app or from the Super Recorder app. You can increase or decrease the volume of the microphone or the speaker, and you can choose between high, medium, or low quality for the audio. You can also enable noise cancellation and voice enhancement options to improve the sound quality.
-
-
-
-Super Recorder Cydia tweak is not only for phone calls, but also for other apps that use audio. You can use it to record voice memos, FaceTime calls, Skype calls, WhatsApp calls, and more. You can also use it to record system sounds or any sound that comes out of your device. You can access all your recordings from the Super Recorder app or from the Files app.
-
- dfd1c89656
-
-
-
-
-
diff --git a/spaces/gptbase/GPTBase/utils.py b/spaces/gptbase/GPTBase/utils.py
deleted file mode 100644
index dfa68c645ad7973be3cd8eda13e3d9b6af17bbf6..0000000000000000000000000000000000000000
--- a/spaces/gptbase/GPTBase/utils.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import requests
-import json
-import streamlit as st
-
-@st.experimental_memo()
-def upload_file(file, api_key, ai_id):
- file_data = {"file": file}
- url = f'https://gptbase.ai/api/v1/ais/{ai_id}/files'
- headers = {"Authorization": f"Bearer {api_key}"}
- try:
- response = requests.post(url, headers=headers, files=file_data)
- print("upload")
- return True
- except requests.exceptions.HTTPError as err:
- return False
\ No newline at end of file
diff --git a/spaces/gradio-discord-bots/gpt-35-turbo/README.md b/spaces/gradio-discord-bots/gpt-35-turbo/README.md
deleted file mode 100644
index dfeb51af18a44bfa805aa50a37eb58d07406b418..0000000000000000000000000000000000000000
--- a/spaces/gradio-discord-bots/gpt-35-turbo/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: gpt-35-turbo
-emoji: 🌍
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: freddyaboulton/ChatinterfaceTests
-tags:
-- discord-source
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gradio/leaderboard_main/DESCRIPTION.md b/spaces/gradio/leaderboard_main/DESCRIPTION.md
deleted file mode 100644
index 39267b584fec09a88b94170039959ec3ea2f3f58..0000000000000000000000000000000000000000
--- a/spaces/gradio/leaderboard_main/DESCRIPTION.md
+++ /dev/null
@@ -1 +0,0 @@
-A simple dashboard ranking spaces by number of likes.
\ No newline at end of file
diff --git a/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/renderer/gl/__init__.py b/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/renderer/gl/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/gulabpatel/Real-ESRGAN/realesrgan/archs/__init__.py b/spaces/gulabpatel/Real-ESRGAN/realesrgan/archs/__init__.py
deleted file mode 100644
index f3fbbf3b78e33b61fd4c33a564a9a617010d90de..0000000000000000000000000000000000000000
--- a/spaces/gulabpatel/Real-ESRGAN/realesrgan/archs/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import arch modules for registry
-# scan all the files that end with '_arch.py' under the archs folder
-arch_folder = osp.dirname(osp.abspath(__file__))
-arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
-# import all the arch modules
-_arch_modules = [importlib.import_module(f'realesrgan.archs.{file_name}') for file_name in arch_filenames]
diff --git a/spaces/gwang-kim/DATID-3D/eg3d/metrics/metric_main.py b/spaces/gwang-kim/DATID-3D/eg3d/metrics/metric_main.py
deleted file mode 100644
index 52318ee48a523f30e7eace0b62b936c7826ffc56..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/eg3d/metrics/metric_main.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
-# property and proprietary rights in and to this material, related
-# documentation and any modifications thereto. Any use, reproduction,
-# disclosure or distribution of this material and related documentation
-# without an express license agreement from NVIDIA CORPORATION or
-# its affiliates is strictly prohibited.
-
-"""Main API for computing and reporting quality metrics."""
-
-import os
-import time
-import json
-import torch
-import dnnlib
-
-from . import metric_utils
-from . import frechet_inception_distance
-from . import kernel_inception_distance
-from . import precision_recall
-from . import perceptual_path_length
-from . import inception_score
-from . import equivariance
-
-#----------------------------------------------------------------------------
-
-_metric_dict = dict() # name => fn
-
-def register_metric(fn):
- assert callable(fn)
- _metric_dict[fn.__name__] = fn
- return fn
-
-def is_valid_metric(metric):
- return metric in _metric_dict
-
-def list_valid_metrics():
- return list(_metric_dict.keys())
-
-#----------------------------------------------------------------------------
-
-def calc_metric(metric, **kwargs): # See metric_utils.MetricOptions for the full list of arguments.
- assert is_valid_metric(metric)
- opts = metric_utils.MetricOptions(**kwargs)
-
- # Calculate.
- start_time = time.time()
- results = _metric_dict[metric](opts)
- total_time = time.time() - start_time
-
- # Broadcast results.
- for key, value in list(results.items()):
- if opts.num_gpus > 1:
- value = torch.as_tensor(value, dtype=torch.float64, device=opts.device)
- torch.distributed.broadcast(tensor=value, src=0)
- value = float(value.cpu())
- results[key] = value
-
- # Decorate with metadata.
- return dnnlib.EasyDict(
- results = dnnlib.EasyDict(results),
- metric = metric,
- total_time = total_time,
- total_time_str = dnnlib.util.format_time(total_time),
- num_gpus = opts.num_gpus,
- )
-
-#----------------------------------------------------------------------------
-
-def report_metric(result_dict, run_dir=None, snapshot_pkl=None):
- metric = result_dict['metric']
- assert is_valid_metric(metric)
- if run_dir is not None and snapshot_pkl is not None:
- snapshot_pkl = os.path.relpath(snapshot_pkl, run_dir)
-
- jsonl_line = json.dumps(dict(result_dict, snapshot_pkl=snapshot_pkl, timestamp=time.time()))
- print(jsonl_line)
- if run_dir is not None and os.path.isdir(run_dir):
- with open(os.path.join(run_dir, f'metric-{metric}.jsonl'), 'at') as f:
- f.write(jsonl_line + '\n')
-
-#----------------------------------------------------------------------------
-# Recommended metrics.
-
-@register_metric
-def fid50k_full(opts):
- opts.dataset_kwargs.update(max_size=None, xflip=False)
- fid = frechet_inception_distance.compute_fid(opts, max_real=None, num_gen=50000)
- return dict(fid50k_full=fid)
-
-@register_metric
-def kid50k_full(opts):
- opts.dataset_kwargs.update(max_size=None, xflip=False)
- kid = kernel_inception_distance.compute_kid(opts, max_real=1000000, num_gen=50000, num_subsets=100, max_subset_size=1000)
- return dict(kid50k_full=kid)
-
-@register_metric
-def pr50k3_full(opts):
- opts.dataset_kwargs.update(max_size=None, xflip=False)
- precision, recall = precision_recall.compute_pr(opts, max_real=200000, num_gen=50000, nhood_size=3, row_batch_size=10000, col_batch_size=10000)
- return dict(pr50k3_full_precision=precision, pr50k3_full_recall=recall)
-
-@register_metric
-def ppl2_wend(opts):
- ppl = perceptual_path_length.compute_ppl(opts, num_samples=50000, epsilon=1e-4, space='w', sampling='end', crop=False, batch_size=2)
- return dict(ppl2_wend=ppl)
-
-@register_metric
-def eqt50k_int(opts):
- opts.G_kwargs.update(force_fp32=True)
- psnr = equivariance.compute_equivariance_metrics(opts, num_samples=50000, batch_size=4, compute_eqt_int=True)
- return dict(eqt50k_int=psnr)
-
-@register_metric
-def eqt50k_frac(opts):
- opts.G_kwargs.update(force_fp32=True)
- psnr = equivariance.compute_equivariance_metrics(opts, num_samples=50000, batch_size=4, compute_eqt_frac=True)
- return dict(eqt50k_frac=psnr)
-
-@register_metric
-def eqr50k(opts):
- opts.G_kwargs.update(force_fp32=True)
- psnr = equivariance.compute_equivariance_metrics(opts, num_samples=50000, batch_size=4, compute_eqr=True)
- return dict(eqr50k=psnr)
-
-#----------------------------------------------------------------------------
-# Legacy metrics.
-
-@register_metric
-def fid50k(opts):
- opts.dataset_kwargs.update(max_size=None)
- fid = frechet_inception_distance.compute_fid(opts, max_real=50000, num_gen=50000)
- return dict(fid50k=fid)
-
-@register_metric
-def kid50k(opts):
- opts.dataset_kwargs.update(max_size=None)
- kid = kernel_inception_distance.compute_kid(opts, max_real=50000, num_gen=50000, num_subsets=100, max_subset_size=1000)
- return dict(kid50k=kid)
-
-@register_metric
-def pr50k3(opts):
- opts.dataset_kwargs.update(max_size=None)
- precision, recall = precision_recall.compute_pr(opts, max_real=50000, num_gen=50000, nhood_size=3, row_batch_size=10000, col_batch_size=10000)
- return dict(pr50k3_precision=precision, pr50k3_recall=recall)
-
-@register_metric
-def is50k(opts):
- opts.dataset_kwargs.update(max_size=None, xflip=False)
- mean, std = inception_score.compute_is(opts, num_gen=50000, num_splits=10)
- return dict(is50k_mean=mean, is50k_std=std)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/haakohu/deep_privacy2/dp2/generator/dummy_generators.py b/spaces/haakohu/deep_privacy2/dp2/generator/dummy_generators.py
deleted file mode 100644
index c81b4d4f70bd84fb42bfe8ab3d6bd06f918533c4..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2/dp2/generator/dummy_generators.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import torch
-from .base import BaseGenerator
-from torchvision.transforms.functional import gaussian_blur
-import torch.nn.functional as F
-
-
-class PixelationGenerator(BaseGenerator):
-
- def __init__(self, pixelation_size, **kwargs):
- super().__init__(z_channels=0)
- self.pixelation_size = pixelation_size
- self.z_channels = 0
- self.latent_space = None
-
- def forward(self, img, condition, mask, **kwargs):
- old_shape = img.shape[-2:]
- img = F.interpolate(img, size=(
- self.pixelation_size, self.pixelation_size), mode="bilinear", align_corners=True)
- img = F.interpolate(img, size=old_shape, mode="bilinear", align_corners=True)
- out = img*(1-mask) + condition*mask
- return {"img": out}
-
-
-class MaskOutGenerator(BaseGenerator):
-
- def __init__(self, noise: str, **kwargs):
- super().__init__(z_channels=0)
- self.noise = noise
- self.z_channels = 0
- assert self.noise in ["rand", "constant"]
- self.latent_space = None
-
- def forward(self, img, condition, mask, **kwargs):
-
- if self.noise == "constant":
- img = torch.zeros_like(img)
- elif self.noise == "rand":
- img = torch.rand_like(img)
- out = img*(1-mask) + condition*mask
- return {"img": out}
-
-
-class IdentityGenerator(BaseGenerator):
-
- def __init__(self):
- super().__init__(z_channels=0)
-
- def forward(self, img, condition, mask, **kwargs):
- return dict(img=img)
-
-
-class GaussianBlurGenerator(BaseGenerator):
-
- def __init__(self):
- super().__init__(z_channels=0)
- self.sigma = 7
-
- def forward(self, img, condition, mask, **kwargs):
- img_blur = gaussian_blur(img, kernel_size=min(self.sigma*3, img.shape[-1]), sigma=self.sigma)
- return dict(img=img * mask + (1-mask) * img_blur)
diff --git a/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/bias_act.cpp b/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/bias_act.cpp
deleted file mode 100644
index 3adaeee2ae44e96655d354c2bdfb81de8ebfe6c6..0000000000000000000000000000000000000000
--- a/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/bias_act.cpp
+++ /dev/null
@@ -1,99 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto. Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include
-#include
-#include
-#include "bias_act.h"
-
-//------------------------------------------------------------------------
-
-static bool has_same_layout(torch::Tensor x, torch::Tensor y)
-{
- if (x.dim() != y.dim())
- return false;
- for (int64_t i = 0; i < x.dim(); i++)
- {
- if (x.size(i) != y.size(i))
- return false;
- if (x.size(i) >= 2 && x.stride(i) != y.stride(i))
- return false;
- }
- return true;
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp)
-{
- // Validate arguments.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x");
- TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x");
- TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x");
- TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x");
- TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
- TORCH_CHECK(b.dim() == 1, "b must have rank 1");
- TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds");
- TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements");
- TORCH_CHECK(grad >= 0, "grad must be non-negative");
-
- // Validate layout.
- TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense");
- TORCH_CHECK(b.is_contiguous(), "b must be contiguous");
- TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x");
- TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x");
- TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x");
-
- // Create output tensor.
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
- torch::Tensor y = torch::empty_like(x);
- TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x");
-
- // Initialize CUDA kernel parameters.
- bias_act_kernel_params p;
- p.x = x.data_ptr();
- p.b = (b.numel()) ? b.data_ptr() : NULL;
- p.xref = (xref.numel()) ? xref.data_ptr() : NULL;
- p.yref = (yref.numel()) ? yref.data_ptr() : NULL;
- p.dy = (dy.numel()) ? dy.data_ptr() : NULL;
- p.y = y.data_ptr();
- p.grad = grad;
- p.act = act;
- p.alpha = alpha;
- p.gain = gain;
- p.clamp = clamp;
- p.sizeX = (int)x.numel();
- p.sizeB = (int)b.numel();
- p.stepB = (b.numel()) ? (int)x.stride(dim) : 1;
-
- // Choose CUDA kernel.
- void* kernel;
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
- {
- kernel = choose_bias_act_kernel(p);
- });
- TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func");
-
- // Launch CUDA kernel.
- p.loopX = 4;
- int blockSize = 4 * 32;
- int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
- void* args[] = {&p};
- AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
- return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
- m.def("bias_act", &bias_act);
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/heiyubili/bingo/src/components/ui/textarea.tsx b/spaces/heiyubili/bingo/src/components/ui/textarea.tsx
deleted file mode 100644
index e25af722c7a5dc1121a9ab58d6716952f9f76081..0000000000000000000000000000000000000000
--- a/spaces/heiyubili/bingo/src/components/ui/textarea.tsx
+++ /dev/null
@@ -1,24 +0,0 @@
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-export interface TextareaProps
- extends React.TextareaHTMLAttributes {}
-
-const Textarea = React.forwardRef(
- ({ className, ...props }, ref) => {
- return (
-
- )
- }
-)
-Textarea.displayName = 'Textarea'
-
-export { Textarea }
diff --git a/spaces/hf4all/web-ui/_next/static/chunks/757de1a6.cd4299fbf5be8e3c.js b/spaces/hf4all/web-ui/_next/static/chunks/757de1a6.cd4299fbf5be8e3c.js
deleted file mode 100644
index c755934c21396fa0e8c7a365d438a544aa8b1592..0000000000000000000000000000000000000000
--- a/spaces/hf4all/web-ui/_next/static/chunks/757de1a6.cd4299fbf5be8e3c.js
+++ /dev/null
@@ -1 +0,0 @@
-"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[121],{25372:function(t,n,r){r.d(n,{VQF:function(){return i},mcF:function(){return o}});var e=r(83270);function i(t){return(0,e.w_)({tag:"svg",attr:{viewBox:"0 0 512 512"},child:[{tag:"path",attr:{fill:"none",strokeLinecap:"square",strokeMiterlimit:"10",strokeWidth:"44",d:"M416 128L192 384l-96-96"}}]})(t)}function o(t){return(0,e.w_)({tag:"svg",attr:{viewBox:"0 0 512 512"},child:[{tag:"rect",attr:{width:"336",height:"336",x:"128",y:"128",fill:"none",strokeLinejoin:"round",strokeWidth:"32",rx:"57",ry:"57"}},{tag:"path",attr:{fill:"none",strokeLinecap:"round",strokeLinejoin:"round",strokeWidth:"32",d:"M383.5 128l.5-24a56.16 56.16 0 00-56-56H112a64.19 64.19 0 00-64 64v216a56.16 56.16 0 0056 56h24"}}]})(t)}}}]);
\ No newline at end of file
diff --git a/spaces/hgrif/rhyme-with-ai/rhyme_with_ai/token_weighter.py b/spaces/hgrif/rhyme-with-ai/rhyme_with_ai/token_weighter.py
deleted file mode 100644
index d7a7cecc5227ba083bdee829bed86c8427a06376..0000000000000000000000000000000000000000
--- a/spaces/hgrif/rhyme-with-ai/rhyme_with_ai/token_weighter.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import numpy as np
-
-
-class TokenWeighter:
- def __init__(self, tokenizer):
- self.tokenizer_ = tokenizer
- self.proba = self.get_token_proba()
-
- def get_token_proba(self):
- valid_token_mask = self._filter_short_partial(self.tokenizer_.vocab)
- return valid_token_mask
-
- def _filter_short_partial(self, vocab):
- valid_token_ids = [v for k, v in vocab.items() if len(k) > 1 and "#" not in k]
- is_valid = np.zeros(len(vocab.keys()))
- is_valid[valid_token_ids] = 1
- return is_valid
diff --git a/spaces/hhhhardman/VITS/commons.py b/spaces/hhhhardman/VITS/commons.py
deleted file mode 100644
index 2153153f527d94e2abb641ea00c80b518ff6c5bd..0000000000000000000000000000000000000000
--- a/spaces/hhhhardman/VITS/commons.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
- return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
- return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size*dilation - dilation)/2)
-
-
-def intersperse(lst, item):
- result = [item] * (len(lst) * 2 + 1)
- result[1::2] = lst
- return result
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2,3) * mask
- return path
diff --git a/spaces/hlydecker/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/customized_langchain/indexes/graph.py b/spaces/hlydecker/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/customized_langchain/indexes/graph.py
deleted file mode 100644
index 0eddbb1de178f1d9df0335e7cd02c0eecd493a00..0000000000000000000000000000000000000000
--- a/spaces/hlydecker/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/customized_langchain/indexes/graph.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from typing import List
-
-from langchain.indexes.graph import *
-from langchain.indexes.graph import GraphIndexCreator as OriginalGraphIndexCreator
-
-
-class GraphIndexCreator(OriginalGraphIndexCreator):
- def from_texts(self, texts: List[str]) -> NetworkxEntityGraph:
- """Create graph index from text."""
- if self.llm is None:
- raise ValueError("llm should not be None")
- graph = self.graph_type()
- chain = LLMChain(llm=self.llm, prompt=KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT)
-
- for text in texts:
- output = chain.predict(text=text)
- knowledge = parse_triples(output)
- for triple in knowledge:
- graph.add_triple(triple)
- return graph
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/loss_functions/__init__.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/loss_functions/__init__.py
deleted file mode 100644
index 72b8078b9dddddf22182fec2555d8d118ea72622..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/loss_functions/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from __future__ import absolute_import
-from . import *
\ No newline at end of file
diff --git a/spaces/hongtu/DeepDanbooru_string/README.md b/spaces/hongtu/DeepDanbooru_string/README.md
deleted file mode 100644
index 4330b6f969246dc764a34ea254d2e807159f1c55..0000000000000000000000000000000000000000
--- a/spaces/hongtu/DeepDanbooru_string/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
----
-title: DeepDanbooru String
-emoji: 💬
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-duplicated_from: NoCrypt/DeepDanbooru_string
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/huggingface/Model_Cards_Writing_Tool/specific_extraction.py b/spaces/huggingface/Model_Cards_Writing_Tool/specific_extraction.py
deleted file mode 100644
index 6be7b29fe557288b89449ab5a28052ce40e43727..0000000000000000000000000000000000000000
--- a/spaces/huggingface/Model_Cards_Writing_Tool/specific_extraction.py
+++ /dev/null
@@ -1,528 +0,0 @@
-import re
-import streamlit as st
-from modelcards import CardData, ModelCard
-from markdownTagExtract import tag_checker,listToString,to_markdown
-#from specific_extraction import extract_it
-
-
-# from persist import persist
-#global bytes_data
-
-
-################################################################
-#### Markdown parser logic #################################
-################################################################
-
-def file_upload():
- bytes_data = st.session_state.markdown_upload
- return bytes_data
-
-
-# Sets up the basics
-model_card_md = file_upload() # this is where the new model card will be read in from
-model_card_md = model_card_md#.decode("utf-8")
-# Does metadata appear in any other format than this?
-metadata_re = re.compile("^---(.*?)---", re.DOTALL)
-header_re = re.compile("^\s*# (.*)", re.MULTILINE)
-subheader_re = re.compile("^\s*## (.*)", re.MULTILINE)
-subsubheader_re = re.compile("^\s*### (.*)", re.MULTILINE)
-subsubsubheader_re = re.compile("^\s*#### (.*)", re.MULTILINE)
-# We could be a lot more flexible on this re.
-# We require keys to be bold-faced here.
-# We don't have to require bold, as long as it's key:value
-# **License:**
-# Bold terms use ** or __
-# Allows the mixing of ** and __ for bold but eh whatev
-key_value_re = re.compile("^\s*([*_]{2}[^*_]+[*_]{2})([^\n]*)", re.MULTILINE)
-# Hyphens or stars mark list items.
-# Unordered list
-list_item_re = re.compile("^\s*[-*+]\s+.*", re.MULTILINE)
-# This is the ordered list
-enum_re = re.compile("^\s*[0-9].*", re.MULTILINE)
-table_re = re.compile("^\s*\|.*", re.MULTILINE)
-text_item_re = re.compile("^\s*[A-Za-z(](.*)", re.MULTILINE)
-# text_item_re = re.compile("^\s*#\s*.*", re.MULTILINE)
-# Allows the mixing of -* and *- for italics but eh whatev
-italicized_text_item_re = re.compile(
- "^[_*][^_*\s].*\n?.*[^_*][_*]$", flags=re.MULTILINE
-)
-tag_re = re.compile("^\s*<.*", re.MULTILINE)
-image_re = re.compile("!\[.*\]\(.*\)", re.MULTILINE)
-
-
-subheader_re_dict = {}
-subheader_re_dict[header_re] = subheader_re
-subheader_re_dict[subheader_re] = subsubheader_re
-subheader_re_dict[subsubheader_re] = subsubsubheader_re
-
-
-def get_metadata(section_text):
- return list(metadata_re.finditer(section_text))
-
-
-def find_images(section_text):
- return list(image_re.finditer(section_text))
-
-
-def find_tags(section_text):
- return list(tag_re.finditer(section_text))
-
-
-def find_tables(section_text):
- return list(table_re.finditer(section_text))
-
-
-def find_enums(section_text):
- return list(enum_re.finditer(section_text))
-
-
-# Extracts the stuff from the .md file
-def find_key_values(section_text):
- return list(key_value_re.finditer(section_text))
-
-
-def find_lists(section_text):
- # Find lists: Those lines starting with either '-' or '*'
- return list(list_item_re.finditer(section_text))
-
-
-def find_texts(section_text):
- # Find texts: Free writing within a section
- basic_text = list(text_item_re.finditer(section_text))
- ital_text = list(italicized_text_item_re.finditer(section_text))
- free_text = basic_text + ital_text
- return free_text
-
-
-def find_headers(full_text):
- headers = list(header_re.finditer(full_text))
- subheaders = list(subheader_re.finditer(full_text))
- subsubheaders = list(subsubheader_re.finditer(full_text))
- subsubsubheaders = list(subsubsubheader_re.finditer(full_text))
- return (headers, subheaders, subsubheaders, subsubsubheaders)
-
-
-metadata_list = get_metadata(model_card_md)
-if metadata_list != []:
- metadata_end = metadata_list[-1].span()[-1]
- print("Metadata extracted")
- # Metadata processing can happen here.
- # For now I'm just ignoring it.
- model_card_md = model_card_md[metadata_end:]
-else:
- print("No metadata found")
-
-# Matches of all header types
-headers_list = find_headers(model_card_md)
-print("Headers extracted")
-# This type of header (one #)
-headers = headers_list[0]
-## This type of header (two ##)
-subheaders = headers_list[1]
-### This type of header
-subsubheaders = headers_list[2]
-#### This type of header
-subsubsubheaders = headers_list[3]
-
-# Matches of bulleted lists
-lists_list = find_lists(model_card_md)
-print("Bulleted lists extracted")
-
-enums_list = find_enums(model_card_md)
-print("Enumerated lists extracted")
-
-key_value_list = find_key_values(model_card_md)
-print("Key values extracted")
-
-tables_list = find_tables(model_card_md)
-print("Tables extracted")
-
-tags_list = find_tags(model_card_md)
-print("Markup tags extracted")
-
-images_list = find_images(model_card_md)
-print("Images extracted")
-
-# Matches of free text within a section
-texts_list = find_texts(model_card_md)
-print("Free text extracted")
-
-
-# List items have the attribute: value;
-# This provides for special handling of those strings,
-# allowing us to check if it's a list item in order to split/print ok.
-LIST_ITEM = "List item"
-KEY_VALUE = "Key: Value"
-FREE_TEXT = "Free text"
-ENUM_LIST_ITEM = "Enum item"
-TABLE_ITEM = "Table item"
-TAG_ITEM = "Markup tag"
-IMAGE_ITEM = "Image"
-
-
-def create_span_dict(match_list, match_type):
- """
- Creates a dictionary made out of all the spans.
- This is useful for knowing which types to fill out with what in the app.
- Also useful for checking if there are spans in the .md file that we've missed.
- """
- span_dict = {}
- for match in match_list:
- if len(match.group().strip()) > 0:
- span_dict[(match.span())] = (match.group(), match_type)
- return span_dict
-
-
-metadata_span_dict = create_span_dict(metadata_list, "Metadata")
-# Makes a little dict for each span type
-header_span_dict = create_span_dict(headers, "# Header")
-subheader_span_dict = create_span_dict(subheaders, "## Subheader")
-subsubheader_span_dict = create_span_dict(subsubheaders, "### Subsubheader")
-subsubsubheader_span_dict = create_span_dict(subsubsubheaders, "#### Subsubsubheader")
-key_value_span_dict = create_span_dict(key_value_list, KEY_VALUE)
-lists_span_dict = create_span_dict(lists_list, LIST_ITEM)
-enums_span_dict = create_span_dict(enums_list, ENUM_LIST_ITEM)
-tables_span_dict = create_span_dict(tables_list, TABLE_ITEM)
-tags_span_dict = create_span_dict(tags_list, TAG_ITEM)
-images_span_dict = create_span_dict(images_list, IMAGE_ITEM)
-texts_span_dict = create_span_dict(texts_list, FREE_TEXT)
-
-# We don't have to have these organized by type necessarily.
-# Doing it here for clarity.
-all_spans_dict = {}
-all_spans_dict["headers"] = header_span_dict
-all_spans_dict["subheaders"] = subheader_span_dict
-all_spans_dict["subsubheaders"] = subsubheader_span_dict
-all_spans_dict["subsubsubheaders"] = subsubsubheader_span_dict
-all_spans_dict[LIST_ITEM] = lists_span_dict
-all_spans_dict[KEY_VALUE] = key_value_span_dict
-all_spans_dict[TABLE_ITEM] = tables_span_dict
-all_spans_dict[ENUM_LIST_ITEM] = enums_span_dict
-all_spans_dict[TAG_ITEM] = tags_span_dict
-all_spans_dict[IMAGE_ITEM] = images_span_dict
-all_spans_dict[FREE_TEXT] = texts_span_dict
-
-
-def get_sorted_spans(spans_dict):
- merged_spans = {}
- for span_dict in spans_dict.values():
- merged_spans.update(span_dict)
- sorted_spans = sorted(merged_spans)
- return sorted_spans, merged_spans
-
-
-sorted_spans, merged_spans = get_sorted_spans(all_spans_dict)
-
-# Sanity/Parse check. Have we captured all spans in the .md file?
-if sorted_spans[0][0] != 0:
- print("FYI, our spans don't start at the start of the file.")
- print("We did not catch this start:")
- print(model_card_md[: sorted_spans[0][0]])
-
-for idx in range(len(sorted_spans) - 1):
- last_span_end = sorted_spans[idx][1]
- new_span_start = sorted_spans[idx + 1][0]
- if new_span_start > last_span_end + 1:
- start_nonparse = sorted_spans[idx]
- end_nonparse = sorted_spans[idx + 1]
- text = model_card_md[start_nonparse[1] : end_nonparse[0]]
- if text.strip():
- print("Found an unparsed span in the file:")
- print(start_nonparse)
- print(" ---> ")
- print(end_nonparse)
- print(text)
-
-# print(header_span_dict)
-def section_map_to_help_text(text_retrieved):
-
- presit_states = {
- "## Model Details": "Give an overview of your model, the relevant research paper, who trained it, etc.",
- "## How to Get Started with the Model": "Give an overview of how to get started with the model",
- "## Limitations and Biases": "Provide an overview of the possible Limitations and Risks that may be associated with this model",
- "## Uses": "Detail the potential uses, intended use and out-of-scope uses for this model",
- "## Training": "Provide an overview of the Training Data and Training Procedure for this model",
- "## Evaluation Results": "Detail the Evaluation Results for this model",
- "## Environmental Impact": "Provide an estimate for the carbon emissions: Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here.",
- "## Citation Information": "How to best cite the model authors",
- "## Glossary": "If relevant, include terms and calculations in this section that can help readers understand the model or model card.",
- "## More Information": "Any additional information",
- "## Model Card Authors": "This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc.",
- "Model Card Contact": "Mediums to use, in order to contact the model creators",
- "## Technical Specifications": " Additional technical information",
- '## Model Examination': " Examining the model",
- }
-
- for key in presit_states:
- if key == text_retrieved:
- return presit_states(key)
-
-
-def section_map_to_persist(text_retrieved):
-
- presit_states = {
- "Model_details_text": "## Model Details",
- "Model_how_to": "## How to Get Started with the Model",
- "Model_Limits_n_Risks": "## Limitations and Biases",
- "Model_uses": "## Uses",
- "Model_training": "## Training",
- "Model_Eval": "## Evaluation Results",
- "Model_carbon": "## Environmental Impact",
- "Model_cite": "## Citation Information",
- "Glossary": "## Glossary",
- "More_info": "## More Information",
- "Model_card_authors": "## Model Card Authors",
- "Model_card_contact": "## Model Card Contact",
- "Technical_specs": "## Technical specifications",
- "Model_examin": "## Model Examination",
- }
-
- for key in presit_states:
- if presit_states[key] == text_retrieved:
- return key
-
-
-def main():
- # st.write('here')
- print(extract_it("Model_details_text"))
-
-
-def extract_headers():
- headers = {}
- subheaders = {}
- subsubheaders = {}
- subsubsubheaders = {}
- previous = (None, None, None, None)
-
- for s in sorted_spans:
- if merged_spans[s][1] == "# Header":
- headers[s] = (sorted_spans.index(s), previous[0])
- previous = (sorted_spans.index(s), previous[1], previous[2], previous[3])
- if merged_spans[s][1] == "## Subheader":
- subheaders[s] = (sorted_spans.index(s), previous[1])
- previous = (previous[0], sorted_spans.index(s), previous[2], previous[3])
- if merged_spans[s][1] == "### Subsubheader":
- subsubheaders[s] = (sorted_spans.index(s), previous[2])
- previous = (previous[0], previous[1], sorted_spans.index(s), previous[3])
- if merged_spans[s][1] == "#### Subsubsubheader":
- subsubsubheaders[s] = (sorted_spans.index(s), previous[3])
- previous = (previous[0], previous[1], previous[2], sorted_spans.index(s))
-
- return headers, subheaders, subsubheaders, subsubsubheaders
-
-
-def stringify():
- headers, subheaders, subsubheaders, subsubsubheaders = extract_headers()
- headers_strings = {}
- subheaders_strings = {}
- subsubheaders_strings = {}
- subsubsubheaders_strings = {}
-
- first = None
- for i in headers:
- if headers[i][1] == None:
- continue
- sub_spans = sorted_spans[headers[i][1] : headers[i][0]]
- lines = []
- for x in sub_spans:
- lines.append(merged_spans[x][0])
- try:
- name = lines[0]
- except:
- name = "Model Details"
- lines = "".join(lines)
- # print(merged_spans[i][0] + "-------------------")
- # print(lines)
- headers_strings[
- name.replace("\n# ", "")
- .replace(" ", "")
- .replace(" ", "")
- .replace("\n", "")
- .replace("{{", "")
- .replace("}}", "")
- ] = lines
- first = i
-
- first = None
- for i in subheaders:
- if subheaders[i][1] == None:
- continue
- sub_spans = sorted_spans[subheaders[i][1] : subheaders[i][0]]
- lines = []
- for x in sub_spans:
- if merged_spans[x][1] == "## Subheader" and first == None:
- break
- elif merged_spans[x][1] == "# Header":
- break
- else:
- lines.append(merged_spans[x][0])
- try:
- name = lines[0]
- except:
- name = "Model Details"
- lines = "".join(lines)
- # print(merged_spans[i][0] + "-------------------")
- # print(lines)
- subheaders_strings[
- name.replace("\n# ", "").replace(" ", "").replace(" ", "")
- ] = lines
- first = i
-
- first = None
- for i in subsubheaders:
- if subsubheaders[i][1] == None:
- continue
- sub_spans = sorted_spans[subsubheaders[i][1] : subsubheaders[i][0]]
- lines = []
- for x in sub_spans:
- if merged_spans[x][1] == "## Subheader" or (
- merged_spans[x][1] == "### Subsubheader" and first == None
- ):
- break
- else:
- lines.append(merged_spans[x][0])
- lines = "".join(lines)
-
- subsubheaders_strings[
- merged_spans[i][0].replace("\n", "").replace("### ", "").replace(" ", "")
- ] = lines
- first = i
-
- for i in subsubsubheaders:
- if subsubsubheaders[i][1] == None:
- continue
- sub_spans = sorted_spans[subsubsubheaders[i][1] : subsubsubheaders[i][0]]
- lines = []
- for x in sub_spans:
- if (
- merged_spans[x][1] == "## Subheader"
- or merged_spans[x][1] == "### Subsubheader"
- ):
- break
- else:
- lines.append(merged_spans[x][0])
- lines = "".join(lines)
-
- subsubsubheaders_strings[
- merged_spans[i][0].replace("#### ", "").replace("**", "").replace("\n", "")
- ] = lines
-
- return (
- headers_strings,
- subheaders_strings,
- subsubheaders_strings,
- subsubsubheaders_strings,
- )
-
-
-def extract_it(text_to_retrieve):
- print("Span\t\tType\t\tText")
- print("-------------------------------------")
- found_subheader = False
- current_subheader = " "
- page_state = " "
- help_text = " "
- #st.write("in cs- body here")
-
- (
- headers_strings,
- subheaders_strings,
- subsubheaders_strings,
- subsubsubheaders_strings,
- ) = stringify()
-
- h_keys = list(headers_strings.keys())
- sh_keys = list(subheaders_strings.keys())
- ssh_keys = list(subsubheaders_strings.keys())
- sssh_keys = list(subsubsubheaders_strings.keys())
-
- needed = [
- "model details",
- "howto",
- "limitations",
- "uses",
- "training",
- "evaluation",
- "environmental",
- "citation",
- "glossary",
- "more information",
- "authors",
- "contact",
- ] # not sure what keyword should be used for citation, howto, and contact
- # info_strings = {
- # "details": "## Model Details",
- # "howto": "## How to Get Started with the Model",
- # "limitations": "## Limitations and Biases",
- # "uses": "## Uses",
- # "training": "## Training",
- # "evaluation": "## Evaluation Results",
- # "environmental": "## Environmental Impact",
- # "citation": "## Citation Information",
- # "glossary": "## Glossary",
- # "more information": "## More Information",
- # "authors": "## Model Card Authors",
- # "contact": "## Model Card Contact",
- # }
- info_strings = {
- "model details": "",
- "howto": "",
- "limitations": "",
- "uses": "",
- "training": "",
- "evaluation": "",
- "environmental": "",
- "citation": "",
- "glossary": "",
- "more information": "",
- "authors": "",
- "contact": "",
- }
-
- for x in needed:
- for l in h_keys:
- if x in l.lower():
- info_strings[x] = info_strings[x] + headers_strings[l]
- for i in sh_keys:
- if x in i.lower():
- info_strings[x] = info_strings[x] + subheaders_strings[i]
- for z in ssh_keys:
- try:
- if x in z.lower():
- info_strings[x] = info_strings[x] + subsubheaders_strings[z]
- except:
- continue
- for y in sssh_keys:
- try:
- if x in y.lower():
- info_strings[x] = info_strings[x] + subsubsubheaders_strings[y]
- except:
- continue
-
- extracted_info = {
- "Model_details_text": info_strings["model details"],
- "Model_how_to": info_strings["howto"],
- "Model_Limits_n_Risks": info_strings["limitations"],
- "Model_uses": info_strings["uses"],
- "Model_training": info_strings["training"],
- "Model_Eval": info_strings["evaluation"],
- "Model_carbon": info_strings["environmental"],
- "Model_cite": info_strings["citation"],
- "Glossary": info_strings["glossary"],
- "More_info": info_strings["more information"],
- "Model_card_authors": info_strings["authors"],
- "Model_card_contact": info_strings["contact"],
- "Technical_specs": "## Technical specifications",
- "Model_examin": "## Model Examination",
- }
-
- #text_to_retrieve = "Model_details_text"
-
- new_t = extracted_info[text_to_retrieve] + " "
-
- return(new_t)
-
-
-if __name__ == "__main__":
-
- main()
diff --git a/spaces/huggingface/rlhf-interface/utils.py b/spaces/huggingface/rlhf-interface/utils.py
deleted file mode 100644
index 69fd9fed9b3f4072caecfe7c90a4cb57af959201..0000000000000000000000000000000000000000
--- a/spaces/huggingface/rlhf-interface/utils.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import subprocess
-from huggingface_hub.repository import _lfs_log_progress
-
-def force_git_push(
- repo,
- ):
- """
- force a simple git push
- Blocking. Will return url to commit on remote
- repo.
- """
- command = "git push --force"
-
- try:
- with _lfs_log_progress():
- process = subprocess.Popen(
- command.split(),
- stderr=subprocess.PIPE,
- stdout=subprocess.PIPE,
- encoding="utf-8",
- cwd=repo.local_dir,
- )
-
- stdout, stderr = process.communicate()
- return_code = process.poll()
- process.kill()
-
- if len(stderr):
- print(stderr)
-
- if return_code:
- raise subprocess.CalledProcessError(
- return_code, process.args, output=stdout, stderr=stderr
- )
-
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- return repo.git_head_commit_url()
\ No newline at end of file
diff --git a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/docs/eval.md b/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/docs/eval.md
deleted file mode 100644
index 9ce1621357c03ee8a25c004e5f01850990df1628..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/docs/eval.md
+++ /dev/null
@@ -1,43 +0,0 @@
-## Eval on ICCV2021-MFR
-
-coming soon.
-
-
-## Eval IJBC
-You can eval ijbc with pytorch or onnx.
-
-
-1. Eval IJBC With Onnx
-```shell
-CUDA_VISIBLE_DEVICES=0 python onnx_ijbc.py --model-root ms1mv3_arcface_r50 --image-path IJB_release/IJBC --result-dir ms1mv3_arcface_r50
-```
-
-2. Eval IJBC With Pytorch
-```shell
-CUDA_VISIBLE_DEVICES=0,1 python eval_ijbc.py \
---model-prefix ms1mv3_arcface_r50/backbone.pth \
---image-path IJB_release/IJBC \
---result-dir ms1mv3_arcface_r50 \
---batch-size 128 \
---job ms1mv3_arcface_r50 \
---target IJBC \
---network iresnet50
-```
-
-
-## Inference
-
-```shell
-python inference.py --weight ms1mv3_arcface_r50/backbone.pth --network r50
-```
-
-
-## Result
-
-| Datasets | Backbone | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) |
-|:---------------|:--------------------|:------------|:------------|:------------|
-| WF12M-PFC-0.05 | r100 | 94.05 | 97.51 | 95.75 |
-| WF12M-PFC-0.1 | r100 | 94.49 | 97.56 | 95.92 |
-| WF12M-PFC-0.2 | r100 | 94.75 | 97.60 | 95.90 |
-| WF12M-PFC-0.3 | r100 | 94.71 | 97.64 | 96.01 |
-| WF12M | r100 | 94.69 | 97.59 | 95.97 |
\ No newline at end of file
diff --git a/spaces/hzwluoye/gpt4/client/css/checkbox.css b/spaces/hzwluoye/gpt4/client/css/checkbox.css
deleted file mode 100644
index 94955b604ea3fab493a50d740fb29be1a8ef6cd3..0000000000000000000000000000000000000000
--- a/spaces/hzwluoye/gpt4/client/css/checkbox.css
+++ /dev/null
@@ -1,55 +0,0 @@
-.checkbox input {
- height: 0;
- width: 0;
- display: none;
-}
-
-.checkbox span {
- font-size: 0.875rem;
- color: var(--colour-2);
- margin-left: 4px;
-}
-
-.checkbox label:after {
- content: "";
- position: absolute;
- top: 50%;
- transform: translateY(-50%);
- left: 5px;
- width: 20px;
- height: 20px;
- background: var(--blur-border);
- border-radius: 90px;
- transition: 0.33s;
-}
-
-.checkbox input + label:after,
-.checkbox input:checked + label {
- background: var(--colour-3);
-}
-
-.checkbox input + label,
-.checkbox input:checked + label:after {
- background: var(--blur-border);
-}
-
-.checkbox input:checked + label:after {
- left: calc(100% - 5px - 20px);
-}
-
-@media screen and (max-width: 990px) {
- .checkbox label {
- width: 25px;
- height: 15px;
- }
-
- .checkbox label:after {
- left: 2px;
- width: 10px;
- height: 10px;
- }
-
- .checkbox input:checked + label:after {
- left: calc(100% - 2px - 10px);
- }
-}
diff --git a/spaces/ibaiGorordo/Lane-Shape-Prediction-with-Transformers/app.py b/spaces/ibaiGorordo/Lane-Shape-Prediction-with-Transformers/app.py
deleted file mode 100644
index 67fa9173dd72cf702c1500079d2b1208ae4e3b85..0000000000000000000000000000000000000000
--- a/spaces/ibaiGorordo/Lane-Shape-Prediction-with-Transformers/app.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import gradio as gr
-import cv2
-import numpy as np
-from PIL import Image
-from lstr import LSTR
-model_path = "models/model_float32.onnx"
-
-title = "Lane Shape Prediction with Transformers (LSTR)"
-description = "Demo for performing lane detection using the LSTR model. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "